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Socioeconomically deprived children are at increased risk of ill-health associated with sedentary behavior , malnutrition , and helminth infection . The resulting reduced physical fitness , growth retardation , and impaired cognitive abilities may impede children’s capacity to pay attention . The present study examines how socioeconomic status ( SES ) , parasitic worm infections , stunting , food insecurity , and physical fitness are associated with selective attention and academic achievement in school-aged children . The study cohort included 835 children , aged 8–12 years , from eight primary schools in socioeconomically disadvantaged neighborhoods of Port Elizabeth , South Africa . The d2-test was utilized to assess selective attention . This is a paper and pencil letter-cancellation test consisting of randomly mixed letters d and p with one to four single and/or double quotation marks either over and/or under each letter . Children were invited to mark only the letters d that have double quotation marks . Cardiorespiratory fitness was assessed via the 20 m shuttle run test and muscle strength using the grip strength test . The Kato-Katz thick smear technique was employed to detect helminth eggs in stool samples . SES and food insecurity were determined with a pre-tested questionnaire , while end of year school results were used as an indicator of academic achievement . Children infected with soil-transmitted helminths had lower selective attention , lower school grades ( academic achievement scores ) , and lower grip strength ( all p<0 . 05 ) . In a multiple regression model , low selective attention was associated with soil-transmitted helminth infection ( p<0 . 05 ) and low shuttle run performance ( p<0 . 001 ) , whereas higher academic achievement was observed in children without soil-transmitted helminth infection ( p<0 . 001 ) and with higher shuttle run performance ( p<0 . 05 ) . Soil-transmitted helminth infections and low physical fitness appear to hamper children’s capacity to pay attention and thereby impede their academic performance . Poor academic achievement will make it difficult for children to realize their full potential , perpetuating a vicious cycle of poverty and poor health . ClinicalTrials . gov ISRCTN68411960
Attention skills are relevant for academic foundations and are important for learning [1] . Selective attention is the ability to select and focus on a particular task , while simultaneously suppressing irrelevant or distracting information . Competing information can occur both externally and internally due to visual or auditory distractions or distracting thoughts [2] . Selective attention has been associated with important domains in education , such as language processing [3] , literacy [4] , and numeracy [5] , and hence , plays an important role in academic achievement . A growing body of literature documents that children from low-income households exhibit more attention deficits compared to their higher-income peers [6 , 7] . Of note , academic achievement depends on multiple factors such as educational opportunity , socioeconomic status ( SES ) , health and nutritional status , family environment [8 , 9] , social competence [10] , cognitive skills , and the ability to pay attention [2] . Children growing up in socioeconomically deprived environments face multiple challenges . Essential services , such as health care , sanitation , physical security , electricity , and high quality academic and physical education are often lacking , with serious consequences for children’s psychological and physiological development and wellbeing [11] . Poverty also limits the parents’ ability to provide a responsive , supportive , and safe learning environment [12] , and lessens the probability that children will have access to cognitively stimulating materials ( e . g . , books and toys ) [13] . Families with low income often invest most of their resources into covering their basic needs , such as food and housing , and have therefore limited means to invest in the future of their children [14] . Poverty also puts children at risk of chronic malnutrition [14] . Chronic malnutrition causes stunting and has been found to be associated with poor cognitive development resulting in low IQ , and problems with motor development [15] . This , in turn , can impede children’s ability to concentrate , process information , and focus on academic work [16] . Poor living conditions with a lack of clean water , inadequate sanitation , and insufficient hygiene also favor parasitic worm and intestinal protozoa infections [17 , 18] , which may lead to symptoms such as abdominal pain , diarrhea , anemia , growth retardation , reduced physical fitness , cognitive impairment , and poor academic achievement [19 , 20] . Recent systematic reviews suggest associations between parasitic worm infection and children’s cognitive function and academic performance , but positive effects of mass treatment on cognition or school performance remain elusive [21–24] . A study by Ezeamama et al . [25] found that roundworm ( Ascaris lumbricoides ) infection was associated with poor performance on tests of memory , whereas whipworm ( Trichuris trichiura ) infection was associated with poor performance on tests of verbal fluency among Filipino children . To our knowledge , there is a paucity of studies investigating whether soil-transmitted helminth infections are associated with selective attention . Children from families with low SES are also less likely to have access to health care or health insurance , resulting in a greater risk of illness and school absenteeism and consequently a lack of academic input compared to better-off peers [16] . Recent reviews and meta-analyses have shown that physical activity elicits short- and long-term benefits for children’s executive function [26] , attention [27] , and other academic outcomes [28] . Yet , physical activity levels are often low among poor children and youth , also in South Africa [29] . For instance , a study by Walter [30] , which focused on primary school children in disadvantaged schools , observed that most children do not achieve the recommended 60 min of daily moderate-to-vigorous physical activity ( MVPA ) . These results are not surprising given that sport and recreation facilities are often inadequate , inaccessible , or in poor condition , while qualified teachers are scarce and physical education and extramural sport programs are rare [31] . The purpose of the present study was to find out how children’s selective attention and academic achievement relate to age , sex , SES , helminth infection status , stunting , food security , and physical fitness . In a first step , we looked at bivariate associations and compared children with or without helminth infection , and stunted or non-stunted children . In a second step , we examined multivariate associations to find out how age , sex , SES , helminth infection status , stunting , food security , and physical fitness relate to selective attention and academic achievement if all these variables are considered simultaneously .
The “Disease , Activity and School children’s Health” ( DASH ) cohort study was approved by the ethical review board of Northwestern and Central Switzerland ( EKNZ; reference no . 2014–179 , approval date: 17 June 2014 ) , the Nelson Mandela Metropolitan University ( NMMU ) Human Ethics Committee ( study number H14-HEA-HMS002 , approval date: 4 July 2014 ) , the Eastern Cape Department of Education ( approval date: 3 August 2014 ) , and the Eastern Cape Department of Health ( approval date: 7 November 2014 ) in Port Elizabeth , South Africa . The study is registered at ISRCTN registry under controlled-trials . com ( unique identifier: ISRCTN68411960 , registration date: 1 October 2014 ) . Details regarding the information of potential study participants , exclusions due to medical reasons , management of helminth infections , and referrals , are provided in a previously published study protocol [32] . In brief , oral assent from each participating child was sought and individual written informed consent was obtained from parents/guardians . Participation was voluntary and children could withdraw from the study at any time without further obligations . Children were eligible for this study if they met the following inclusion criteria: ( i ) are willing to participate in the study; ( ii ) have a written informed consent by a parent/guardian; ( iii ) are not participating in other clinical trials during the study period; and ( iv ) do not suffer from medical conditions , which will prevent participation in the study , as determined by qualified medical personnel . To ensure confidentiality , each study participant was given a unique identification number . All tests were available in English , Xhosa , and Afrikaans . To ensure optimal translation of the tests , we collaborated with independent professional translators and followed the procedure set out by Brislin [33] . Thus , test instructions and items were translated from English into Xhosa and Afrikaans , and pilot-tested with a small sample of Xhosa and Afrikaans speaking students and school children of the same age as the study cohort . Schools were recruited from 2014 to 2015 . Data assessment took place between February 2015 and March 2015 . The study involved 8- to 12-year-old children attending grade 4 from eight schools located in socioeconomically disadvantaged neighborhoods in Port Elizabeth , South Africa . South African public schools are classified into five groups , with quintile one standing for the poorest and quintile five for the least poor [34] . Study schools belonged to quintile three . The sample size calculation for the study was based on achieving sufficient precision in estimating the prevalence of soil-transmitted helminth infections , with a targeted sample size of approximately 1000 grade 4 school children ( for more details regarding power calculation see Yap et al . [32] ) . All children were asked to remove their shoes and jerseys/jackets before standing on a digital weighing scale ( Micro T7E electronic platform scale , Optima Electronics; George , South Africa ) . Body weight was measured once and recorded to the nearest 0 . 1 kg . With the shoes removed , each child then stood against a Seca stadiometer ( Surgical SA; Johannesburg , South Africa ) with their back erect and shoulders relaxed . Body height was measured once and recorded to the nearest 0 . 1 cm . Upper body strength was determined by the grip strength test [35] . The Saehan hydraulic hand dynamometer ( MSD Europe BVBA; Tisselt , Belgium ) was employed . The field investigator demonstrated how to hold the hand dynamometer and instructed the child to sit relaxed , spine erect , and arm position at a 90° angle . Each child had six trials , alternating between the right and left hand with a 30 sec resting period between trials , griping the hand dynamometer as hard as possible . All six trials were recorded to the nearest 1 kg and averaged . To measure children’s aerobic fitness , the 20 m shuttle run test was utilized , following the test protocol described by Léger et al . [36] . A premeasured running course was laid out on a flat terrain and marked with color-coded cones . Children who felt sick or voiced discomfort were excluded . The test procedures were explained and a researcher demonstrated two trial runs . Once children were familiar with the test procedures , they started with a running speed of 8 . 5 km/h , following a researcher who set the pace according to the acoustic signal . The frequency of the sound signal gradually increased every min by 0 . 5 km/h . If a child was unable to cross the marked 2 m line before each end of the course at the moment of the sound signal for two successive intervals , the individual maximum was reached . Children were then asked to stop running and the fully completed laps were noted . To estimate children’s SES , they were asked to answer nine items , covering household-level living standards , such as infrastructure and housing characteristics ( house type , number of bedrooms , type of toilet and access to indoor water , indoor toilet/bathroom , and electricity ) and questions related to ownership of three durable assets ( presence of a working refrigerator , washing machine , and car ) . The dichotomized items ( 0 = poor quality , not available; 1 = higher quality , available ) were summed up to build an overall SES index , with higher scores reflecting higher SES . The validity of similar measures has been established in previous research [37] . Food insecurity was measured with four questions about hunger , portion size , and meal frequency ( e . g . , “did you go to bed hungry last night ? ” ) . The items were adapted from the Household Hunger Scale [38] . Response options were summed up to obtain a score for each participant ranging from 0 ( food insecure/hungry ) to 4 ( food secure/not hungry ) . This score was used to obtain an overall index of food security , with higher scores reflecting higher food security . To diagnose helminth infections , stool containers with unique identifiers were handed out to school children with the instruction to return them with a small portion of their own morning stool . The diagnostic work-up was done on the same day . Duplicate 41 . 7 mg Kato-Katz thick smears were prepared from each stool sample [39] . Slides were independently read under a microscope by experienced laboratory technicians who counted the number of helminth eggs and recorded them for each species separately . For quality control , a random sample of 10% of all Kato-Katz thick smears was re-examined by a senior technician . In case of discordant results , the slides were re-read a third time and results discussed among the technicians until agreement was reached . Soil-transmitted helminth egg counts were multiplied by a factor of 24 to obtain a proxy for helminth infection intensity , as expressed by the number of eggs per 1 g of stool ( EPG ) [40] . Subsequently , a single 400 mg oral dose of albendazole ( INRESA; Bartenheim , France ) was administered to all children participating in the study , according to WHO and national treatment guidelines . Otherwise , to our knowledge , no further helminthiasis control interventions took place in recent years in the study community where the cohort group stems from . Children’s selective attention capacity was measured with the d2 attention test , developed by Brickenkamp et al . [41] . The d2 test determines the capacity to focus on one stimulus/fact , while suppressing awareness to competing distractors . The d2 attention test is a paper and pencil letter-cancellation test that consists of 14 lines of 47 randomly mixed letters d and p . Participants were instructed to identify and mark all d letters with two dashes arranged either as single dashes ( i . e . , one above and one below the ds ) , or in pairs above or below the ds . After 20 sec , the researcher signaled to continue on the next line . Altogether , the test lasted 4 min and 40 sec . The test was performed in groups of 20–25 students and conducted during the first school lesson in a quiet room , with an average room temperature of 24°C . Pencils were distributed and the test procedure was explained to the children in their native language . Additionally , a practice line was provided on the blackboard to ensure that all participants understood the test procedures . Furthermore , children were encouraged to practice on the test line prior to launching the test . As shown in Table 1 , several different parameters can be calculated after completion of the d2 test . For instance , the total number of items processed is a measure of processing speed ( TN ) , while the number of all errors relative to the total number of items processed is a measure of precision and thoroughness , referred to as accuracy in the present text ( E% ) . By contrast , the number of correctly marked characters minus the number of incorrectly marked characters is a measure of concentration ability and performance ( CP ) . E% and CP are not inflated by excessive skipping as they are based on the number of target and non-target characters cancelled as opposed to processing speed which can be influenced by test strategies [42] . In our study , we therefore used E% and CP as dependent variables due to their resistance to falsification . Processes of selective attention are required for successful completion since not only the letter d is orthographically similar to the letter p , but there are many distracting letters with more or less than two dashes [41] . As an indicator of academic achievement , we collected from each school the children’s end-of-year results which are based on the mean of four subjects: ( i ) home language ( Xhosa or Afrikaans , in this case ) ; ( ii ) first additional language ( English , in this case ) ; ( iii ) mathematics; and ( iv ) life skills . Learner achievement in each subject is graded on a scale of 1 to 7 , whereby a rating of 1 ( 0–29% ) indicates “not achieved” and one of 7 ( 80–100% ) indicates “outstanding achievement” . A rating of 4 ( 50–59% ) indicates “adequate achievement” . Data were double-entered , validated using EpiData version 3 . 1 ( EpiData Association; Odense , Denmark ) , and merged into a single database . Statistical analyses were performed with SPSS version 23 ( IBM Corporation; Armonk , United States of America ) for Windows and STATA version 13 . 0 ( STATA; College Station , United States of America ) . Anthropometric indicators and fitness performance scores were expressed as means ( M ) and standard deviations ( SD ) . To describe the anthropometry of the children , body weight and height values were utilized to calculate the body mass index ( BMI ) , defined as weight ( in kg ) /height2 ( in m2 ) . BMI-for-age and height-for-age ( HAZ ) were thus available for every participant [43] . The BMI and height-for-age z-scores ( HAZ ) were calculated using the World Health Organization ( WHO ) growth reference [43] . The sex-adjusted HAZ z-scores were used as an indicator for stunting [44] . The level at which the child stopped running during the 20 m shuttle run test was used to calculate an estimate of maximal oxygen uptake ( VO2 max ) , readily adjusted for age [36] . The parasitological status was expressed in terms of prevalence of helminth infection . Selective attention was expressed as raw values . Statistical significance was set at p<0 . 05 . In a first step , separate mixed linear and mixed logistic regression models with random intercepts for school classes were calculated to compare selective attention and physical fitness among ( i ) stunted and normally grown children; and ( ii ) soil-transmitted helminth infected and non-infected children . In a second step , SES , age , sex , soil-transmitted helminth infection status , stunting , food insecurity , grip strength , and VO2 max were analyzed simultaneously in multiple linear regression models , with random intercepts for school classes , in order to determine the simultaneous impact of these variables on selective attention and academic achievement . To interpret the findings , the following statistical coefficients were displayed: for mixed linear and mixed logistic regression models the means and 95% confidence interval ( CI ) , and for multiple linear regression models the unstandardized B coefficients in combination with the 95% CI .
As shown in the participant flow chart diagram ( Fig 1 ) , after receiving written informed consent from a parent or legal guardian , a total of 1 , 009 students agreed to take part in the study . Data of 970 children were available for further analyses . Complete data records were available for 835 children; 61 . 8% ( n = 516 ) were black African ( mostly Xhosa speaking ) , while the remaining 38 . 2% ( n = 319 ) were colored African ( mostly Afrikaans speaking ) . All analyses presented in this article refer to this final cohort , including 417 girls ( 49 . 9% ) and 418 boys ( 50 . 1% ) . An overview of the descriptive statistics and sex differences for all study variables is provided in Table 2 . Boys were , on average , slightly older than girls and had a lower BMI . Overall , 31 . 0% of the children were infected with T . trichiura and/or A . lumbricoides , yet no hookworm infections were found . Stunting was observed in 12 . 3% of the children and the mean food insecurity score was 3 . 1 . No significant sex differences were identified for height , weight , helminth infection status , stunting , food insecurity , and SES . Stratification by age ( Table 3 ) revealed that older children were significantly taller , heavier , and more stunted , and had a higher prevalence of helminth infection . As shown in Table 2 , boys achieved significantly higher mean grip strength and had a higher mean VO2 max estimate than girls . As shown in Table 3 , older children ( aged 10–12 years ) achieved higher mean grip strength scores than their younger peers ( 8- to 9-years old ) . The stratification by age also revealed that the younger group ( 8–9 years ) reached a higher estimated VO2 max than the older group ( 10–12 years ) . As displayed in Table 2 , stratification by sex revealed that girls and boys did not differ with regard to their selective attention capacity . Younger children had a significantly lower percentage of errors ( see Table 3 for mean scores ) . Girls reached statistically significantly higher academic scores than boys ( Table 2 ) . Stratification by age revealed that older children’s academic achievement was lower than younger children’s academic achievement ( see Table 3 for mean scores ) . A higher percentage of errors in the attention test was associated with poorer academic achievement ( r = -0 . 33 , p<0 . 05 ) , whereas a positive association was observed between concentration performance ( CP ) and academic achievement ( r = 0 . 33 , p<0 . 05 ) , as assessed by students’ academic achievement scores . As shown in Table 4 , children with no soil-transmitted helminth infection had higher mean grip strength test results compared to their infected counterparts . The comparison between stunted and non-stunted children revealed that children not classified as being stunted achieved significantly higher mean grip strength test results . The mean VO2 max results did not differ between the two groups . Fig 2 shows the univariate comparisons between infected versus non-infected and stunted versus non-stunted children in selective attention and academic performance . As illustrated , in these uncontrolled analyses , children infected with soil-transmitted helminths performed weaker on the d2 test of attention , compared to their non-infected counterparts . Stunted children had a lower mean concentration performance and a higher mean percentage of errors , but only the latter was statistically significant . Children without a soil-transmitted helminth infection and non-stunted children achieved statistically significantly higher academic achievement scores compared to their infected and stunted peers . Additional analyses showed that infected children had a significantly higher risk of being stunted , and vice versa ( infected children: 25% stunted; non-infected children: 7% stunted; stunted children: 62% infected; non-stunted children: 27% infected χ2[1 , 835] = 53 . 2 , p < . 001 ) . Accordingly , multivariate analyses were performed in the next step to avoid problems associated with multi-collinearity . In the multiple linear regression model presented in Table 5 , soil-transmitted helminth infection was statistically significantly and negatively associated with the mean CP score . The mean CP score of children with soil-transmitted helminth infection was 7 . 99 points lower compared to their non-infected peers . Grip strength and the estimated mean VO2 max were statistically significantly and positively associated with the mean CP score . The mean CP score increased by 0 . 98 points per ml kg-1 min-1 VO2 max , whereas the mean CP score increased by 0 . 92 points per kg grip strength . Age and soil-transmitted helminth infection were negatively associated with the error percentage in the d2 test of attention . The mean error percentage increased by 1 . 6% per year of age whereas a soil-transmitted helminth infection was associated with a 3 . 3% higher error percentage compared to non-infected children . The mean VO2 max was statistically significantly and positively associated with the mean E% score . The mean E% score decreased by 0 . 24% per ml kg-1 min-1 VO2 max . In the multiple linear regression model , lower SES , male sex , higher age , being infected with soil-transmitted helminths , and a lower cardiorespiratory fitness were statistically significantly and negatively associated with academic achievement . The mean academic achievement score increased by 0 . 06 per point in the SES score . By contrast , children’s academic achievement score decreased by 0 . 43 per additional year of age and was 0 . 45 lower among children classified as being infected with soil-transmitted helminths compared to non-infected peers . Boys had 0 . 42 lower academic achievement scores than girls and a higher VO2 max was associated with higher academic achievement , yet only with a marginal increase of 0 . 02 per ml kg-1 min-1 VO2 max . No significant associations were observed for stunting , food insecurity , and grip strength .
The most important findings of the present study are that , in the multivariate analyses , soil-transmitted helminth infections and lower physical fitness were negatively associated with selective attention , while lower SES , positive soil-transmitted helminth infection status , lower cardiorespiratory fitness , and higher age were associated with poorer academic achievement . Without implying causality , our data suggest that an infection with T . trichiura , A . lumbricoides , or both , is associated with lower selective attention capacity ( in terms of attention capacity and accuracy ) and reduced physical fitness among school-aged children in terms of muscular strength measured as grip strength . Moreover , children infected with soil-transmitted helminths had significantly lower academic achievement scores . It is conceivable that the general well-being of infected children , as expressed in abdominal pain , fatigue , and listlessness , negatively affects their cognitive performance [21 , 25] . In a study by Liu et al . [22] carried out in South-western China , children infected with either T . trichiura or A . lumbricoides were also lagging behind their non-infected peers . In the same study from China , infection with one or multiple species of soil-transmitted helminths was associated with reduced speed of processing and working memory performance and worse school performance ( in terms of standardized mathematics test scores ) . Heavy A . lumbricoides and T . trichiura infections have been associated with cognitive impairment and were both linked with significantly increased disability weight ( DW ) in the Global Burden of Disease ( GBD ) study [45] . Our finding that soil-transmitted helminths are associated with reduced attention capacity and accuracy is novel and warrants further investigation . Yet , to our knowledge , there is no conclusive evidence whether reduced physical fitness and strength are a direct consequence of soil-transmitted helminth infection . Our analyses did not reveal any associations between VO2 max and single or double species helminth infections . Müller et al . [46] found that 9-year-old boys infected with T . trichiura had a lower mean VO2 max estimate in a slightly different sample of children from the same cohort . Of note , another cross-sectional study by Müller et al . [47] did not find any correlation between VO2 max results and soil-transmitted helminth infections among school-aged children from Côte d’Ivoire , which is at odds with findings from China by Yap et al . [48] who reported reduced VO2 max estimates of school-aged children infected with T . trichiura . In our study , irrespective of age , children infected with A . lumbricoides , T . trichiura , or both species concurrently had a lower mean grip strength compared to non-infected children . Yap et al . [20] reported increased grip strength one month after albendazole treatment . Given these findings , further research is needed to deepen the understanding of whether and how soil-transmitted helminth infections are related to VO2 max and grip strength among school-aged children . The univariate analyses also suggested that stunted children have deficits in selective attention and achieve lower academic performance compared to non-stunted children . However , these associations disappeared in the multiple regression analyses . Thus , while previous research suggested that the main causes of stunting include intrauterine growth retardation , inadequate nutrition , and poor dietary diversity to support the rapid growth and development of infants and young children [49] , and that stunting can result in cognitive impairments [49 , 50] , the association between stunting and the outcomes was no longer significant after all possible influences were taken into account . In the present study , multivariate analyses are warranted as some of the independent variables were associated . For instance , our findings confirmed that stunted children had a significantly higher risk of being infected with soil-transmitted helminth , which is in line with prior research showing that chronic soil-transmitted helminthiasis is a cause of stunting [49] . The univariate analyses further showed that stunted and non-stunted children differed significantly in grip strength , whereas they had similar mean VO2 max values . Our findings align with a study of Malina et al . [51] reporting that stunted children had lower grip strength than their non-stunted peers . Grip strength was shown to be a valid indicator for total muscle strength in children [52] , and was associated with physical health outcomes in previous studies with children and adolescents [53] . While we did not observe a statistically significant difference , a recent study by Armstrong et al . [54] found that stunted South African primary school children also performed poorer in a 20 m shuttle run test as well as in other physical fitness tests , a finding corroborated by Yap et al . [48] who reported a lower mean VO2 max estimate of stunted school children in China . With regard to selective attention and academic achievement and how they might be associated with soil-transmitted helminth infection status , stunting , food insecurity , and physical fitness , we found that attention capacity is associated with infection status and physical fitness . This confirms the notion of a negative relationship between T . trichiura and A . lumbricoides on the one hand and cognition on the other , as reported in prior research [21 , 22 , 25 , 55] . Furthermore , our findings suggest that after controlling for confounding factors , academic achievement is negatively associated with age and soil-transmitted helminth infection , and positively associated with SES . Only few studies have looked at the relationship between children’s physical fitness and their selective attention in low socioeconomic settings . A study by Tine and Butler [56] reported improvements in selective attention after a 12 min session of aerobic exercise in both lower- and higher-income children . Lower-income children exhibited greater improvements in selective attention compared to their higher income peers . The fact that aerobic fitness was associated with selective attention in our sample of disadvantaged school children , combined with the finding of Tine et al . [56] is highly encouraging since ( i ) primary school children’s aerobic fitness can be improved through regular training [57] , and ( ii ) selective attention is associated with academic and cognitive outcomes [2] . As highlighted by Armstrong et al . [58] , there is a particularly pronounced need for encouraging fitness in South African primary schools . However , the multifactorial nature of physical fitness and attention capacity of children growing up in socioeconomically deprived environments requires that health conditions such as asthma , fetal alcohol syndrome , and human immunodeficiency virus ( HIV ) infection status , which were not assessed in the present study , must also be considered [59] . Stratification by age revealed that 8- and 9-year-old children achieved better academic achievement scores than their 10- to 12-year-old peers . This may be explained by the fact that disadvantaged communities do not have the financial means to promote children with special needs or learning disabilities [59] . Children suffering from reading difficulties , attention deficit hyperactivity disorder ( ADHD ) , fetal alcohol syndrome or neglect do not get the required academic support and as a consequence are not able to keep up with their peers . Students failing to achieve adequate grades are retained up to 3 years until they get too old and automatically progress to the next grade [59 , 60] , which explains the wide age range of the participants in the current study . Girls seemed to achieve better academic results compared to boys , while there was no statistically significant difference between sex in the test of attention . A meta-analysis by Voyer and Voyer [61] found a consistent female advantage in school marks for all course content areas . The present study expands previous research in several important ways; to our knowledge , associations between selective attention and soil-transmitted helminth infection status as well as stunting has not previously been investigated . It also contributes to the finding that chronic soil-transmitted helminth infections and cognitive impairment are associated [62] . Furthermore , this study provides new evidence that physical fitness might be associated with increased selective attention in children from a low socioeconomic environment , even after controlling for major covariates . Our study has several limitations . First , our results are derived from a cross-sectional study and causal inferences cannot be drawn . Second , academic achievement was measured with the average end-of-year mark ( achieved at the end of grade 3 ) , which corresponds to the summary of four subjects ( mathematics , home language , additional language , and life skills ) . While the objectivity of school grades can be questioned as a reliable outcome in empirical research ( e . g . , due to attributions or stereotypes of the teachers , different standards between classes/schools ) , this measure has a high ecological validity because sufficiently high grades are needed for academic promotion . Moreover , the influence of class was controlled for , and our study showed that selective attention and the academic achievement scores were moderately correlated ( r>0 . 30 ) . Third , we used an indirect measurement of VO2 max to assess aerobic fitness and it is still debated whether the maximal oxygen uptake is receptive enough for change [63] due to varying personal living conditions . However , this test was chosen because it seemed well suited for a resource-constrained setting due to its ease of application [36] . Furthermore , the 20 m shuttle run test proved to be a valid measure of children’s physical fitness in previous studies [64] , and could be related to various health outcomes in school-aged children [65] . Fourth , anthropometric measurements were taken only once , which could be a source of increased measurement error . Fifth , only a single stool sample was obtained from each child . Hence , some of the helminth infections , particularly those of light intensity , were missed . Finally , we acknowledge that our study took place in disadvantaged communities ( quintile three schools ) . As a consequence , variation in SES was limited , which might have resulted in an underestimation of SES as a predictor of selective attention and academic achievement . In conclusion , our study provides new insights into the relative importance of different determinants of school children’s selective attention in a disadvantaged setting of South Africa . We found that soil-transmitted helminth infection and lower physical fitness may hamper children’s capacity to pay attention during cognitive tasks , and directly or indirectly impede their academic performance . It is conceivable that poor academic achievement will hinder children from realizing their full potential and disrupt the vicious cycle of poverty and ill health .
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Children growing up in challenging environments , such as townships in South Africa , are at an increased risk of ill-health associated with sedentary behavior , poor nutrition , growth retardation , and infections with parasitic worms . Negative factors such as limited educational resources , insufficient health care and safety are exacerbating the effects of poverty and , taken together , might cause developmental delays and school failure . A total of 835 school children aged 8–12 years were examined for soil-transmitted helminth infection , physical fitness , selective attention , stunting , household socioeconomic conditions , and food security . Furthermore , children’s academic achievement scores were utilized as a proxy for academic achievement . The multivariate analyses showed that low selective attention was associated with soil-transmitted helminth infection and low shuttle run performance , whereas higher academic achievement was observed in children without soil-transmitted helminth infection and with higher shuttle run performance . Our study suggests that soil-transmitted helminths and low physical fitness hinder children from realizing their full potential .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
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] |
2017
|
Associations between selective attention and soil-transmitted helminth infections, socioeconomic status, and physical fitness in disadvantaged children in Port Elizabeth, South Africa: An observational study
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Nuage are amorphous ultrastructural granules in the cytoplasm of male germ cells as divergent as Drosophila , Xenopus , and Homo sapiens . Most nuage are cytoplasmic ribonucleoprotein structures implicated in diverse RNA metabolism including the regulation of PIWI-interacting RNA ( piRNA ) synthesis by the PIWI family ( i . e . , MILI , MIWI2 , and MIWI ) . MILI is prominent in embryonic and early post-natal germ cells in nuage also called germinal granules that are often associated with mitochondria and called intermitochondrial cement . We find that GASZ ( Germ cell protein with Ankyrin repeats , Sterile alpha motif , and leucine Zipper ) co-localizes with MILI in intermitochondrial cement . Knockout of Gasz in mice results in a dramatic downregulation of MILI , and phenocopies the zygotene–pachytene spermatocyte block and male sterility defect observed in MILI null mice . In Gasz null testes , we observe increased hypomethylation and expression of retrotransposons similar to MILI null testes . We also find global shifts in the small RNAome , including down-regulation of repeat-associated , known , and novel piRNAs . These studies provide the first evidence for an essential structural role for GASZ in male fertility and epigenetic and post-transcriptional silencing of retrotransposons by stabilizing MILI in nuage .
The differentiation program of the germline is distinct from somatic cells in that resetting of the epigenome by demethylation of DNA and histones must take place for proper post-fertilization development of the embryo [1] . DNA demethylation occurs during primordial germ cell ( PGC ) migration as part of their normal development [2] . Different elements within the genome are remethylated at distinct time windows in a sex-specific fashion . Remethylation of retrotransposons occurs in the male germline at embryonic day 17 . 5 ( E17 . 5 ) and in the female germline during postnatal oocyte maturation [1] . The resetting of the epigenetic state of the germline followed by the acquisition of male-specific methylation imprints , while a necessary component for post-fertilization development , exposes the germline to potential risk from retrotransposon mobilization [3] . Insects and mammals resolve this problem through the action of several classes of small RNAs including piRNAs ( ∼27 nt PIWI family-interacting RNAs ) [4]–[9] . Two classes of piRNAs , repeat-associated piRNAs and non-repeat-associated piRNAs based on their similarity to retrotransposons , are present in the germline of animals as primitive as sponges [10] . Repeat-associated piRNAs limit expression of retrotransposons at the post-transcriptional level and through epigenetic silencing by the recruitment of DNA methyltransferases including DNMT3A and DNMT3L [11]–[15] . In the absence of these small RNAs , retrotransposon expression is dramatically increased in the germline , leading to DNA damage and cell death . Regulation of retrotransposon repression is coordinated by proteins in spatially specialized compartments of ribonucleoprotein-rich structures called nuage . According to the nomenclature proposed by Chuma et al . [16] , embryonic prospermatogonia , postnatal spermatogonia and spermatocytes possess a form of nuage appearing as perinuclear granules transiently associated with mitochondria and thus termed intermitochondrial cement . In contrast , a single large granule of nuage , present in post-meiotic spermatids is called the chromatoid body [17] , [18] . Multiple proteins have been localized by electron microscopy to both intermitochondrial cement and the chromatoid body including mouse VASA homolog ( MVH; also called DDX4 or DEAD-box polypeptide 4 ) , tudor-domain containing 1 ( TDRD1 ) , tudor-domain containing 6 ( TDRD6 ) , and tudor-domain containing 7 ( TDRD7 ) [19]–[21] . The chromatoid body is not believed to arise merely by coalescence of intermitochondrial cement granules; however , to date there are no examples of proteins localized by electron microscopy specifically to the intermitochondrial cement but absent from the chromatoid body . Nuage are proposed sites for multiple RNA processing events including translational repression , RNA-mediated gene silencing , mRNA degradation , and nonsense-mediated mRNA decay [22] . A number of germ cell-specific mRNAs display translational repression with a lag of up to a week between their transcription and translation [23] , [24] . The evidence for nuage regulation of mRNA is strongest for the chromatoid body . Translationally regulated mRNAs such as transition protein 2 ( Tnp2 ) have been localized to the chromatoid body [25] . DEAD box helicases , including MVH/DDX4 and DDX25 , which can unwind RNA in vitro , are localized to the chromatoid body [26]–[30] . MicroRNAs ( ∼22 nt non-coding RNAs ) and components of the RNA-Induced Silencing Complex ( RISC ) machinery including Dicer , Argonaute 2 ( AGO2 ) , and Argonaute 3 ( AGO3 ) with demonstrated in vitro endonuclease activity , localize to the chromatoid body where they may function in translational control and mRNA stability , but their potential association with the intermitochondrial cement is not described [22] , [31] . The chromatoid body also contains MILI and MIWI RNA endonucleases that generate piRNAs [32] , [33] . Most MIWI-associated mRNAs in spermatids are associated with the RNP fraction with a smaller number associated with polysomes suggestive of a function in translational control [34] . Prior to the meiotic divisions , the role of nuage in mRNA metabolism in primordial germ cells , spermatogonia , and spermatocytes is unknown . Whereas MILI is present throughout this period , MIWI2 is restricted to nuage granules in embryonic testes , and MIWI is present in those of pachytene spermatocytes [13] , [35]–[37] . The localization of PIWI family proteins to the intermitochondrial cement has not been clearly defined , although MILI interacts with the intermitochondrial cement protein TDRD1 , MVH and other nuage proteins [33] , [38] . Maelstrom ( MAEL ) , the putative 3′–5′ endonuclease for piRNA 3′ end formation , is also associated with nuage in mammals [39]–[41] . Thus , multiple components necessary for piRNA generation are connected physically to nuage . Genetic evidence supports a conserved requirement for proper nuage assembly in retrotransposon control in the germline . In Drosophila , mutants of PIWI , aubergine ( AUB ) , AGO3 , the RNA helicases VASA and armitage ( ARMI ) , and TUDOR-domain containing proteins krimper ( KRIMP ) and spindle-E ( SPN-E ) have defects in piRNA synthesis and retrotransposon control . In Drosophila , the interaction of two PIWI family members with opposite strand polarity during nuage assembly has also been proposed to facilitate the “ping-pong” mechanism of amplification necessary for retrotransposon inhibition [42] . piRNA defects and derepression of retrotransposons in germ cells also occurs in zebrafish ZILI and ZIWI mutants [43] , [44] . Consistent with a key role of these nuage-associated proteins in the mammalian male germline , knockouts of Mvh , Mael , Mili , and Miwi2 block at the spermatocyte stage , while knockouts of TDRD1 , TDRD6 , and MIWI disrupt spermatogenesis at the spermatid stage . With the exception of MVH , which has not been assessed for these defects , all other nuage mutants with a spermatocyte arrest have defects in retrotransposon regulation and piRNA production [7] , [20] , [35] , [38] , [45]–[48] . GASZ is a 475 amino acid Germ cell-specific protein with four Ankyrin repeats , a Sterile alpha motif , and a basic leucine Zipper domain [49] that is conserved across vertebrate evolution in amphibians , fish , birds , and mammals [50] . Our previous studies have shown that GASZ localizes to the Balbiani body , a nuage structure in Xenopus laevis oocytes . The high degree of evolutionary conservation of GASZ in vertebrates and the potential localization of GASZ to a conserved germline-specific structure stimulated our interest to determine the expression and essential roles of GASZ in nuage and its germline function in mammals .
To define the roles of GASZ in mammals , a null mutation in Gasz was generated ( Figure 1 ) . Gasz+/− mice were viable and produced pups ( 1 . 00±0 . 01 litters/month; 8 . 25±0 . 29 pups/litter , n = 10 ) whose genotypes were consistent with Mendelian ratios ( 25 . 9% WT , 48 . 5% Gasz+/− , 25 . 7% Gasz−/−; n = 495 ) indicating that GASZ is not essential for embryogenesis . Whereas Gasz−/− females were fertile ( 0 . 95±0 . 02 litters/month; 6 . 16±0 . 32 pups/litter ) , Gasz−/− males were sterile . Furthermore , although GASZ is a maternal effect protein [49] , observed in early preimplantation embryos , the viability of offspring from Gasz null females ( i . e . , oocytes lacking Gasz mRNA ) indicates that maternal GASZ is also not required . To define the cause of the infertility in Gasz−/− males , postnatal testes were analyzed grossly and histologically ( Figure 2 ) . Testes from Gasz−/− males were significantly smaller ( P<0 . 0001 ) than Gasz+/− or WT littermates ( Figure 1A ) . Six-week-old Gasz−/− testes ( 19 . 2±1 . 07 mg; n = 20 ) were ∼20% of the weight of WT ( 89 . 9±5 . 68 mg; n = 20 ) and Gasz+/− ( 85 . 8±5 . 50 mg; n = 20 ) testes . Whereas WT and Gasz+/− testes from adult males demonstrated robust spermatogenesis ( Figure 2H ) , the seminiferous tubules of 6-week-old Gasz−/− testes showed markedly reduced spermatocytes , no post-meiotic spermatids or spermatozoa , and significant vacuolization ( Figure 2I–2K ) . The most mature meiotic cells in seminiferous tubules from stages VII–XII were early meiotic germ cells while those from stages I–VI showed a mixture of degenerating spermatocytes . Gasz−/− and Gasz+/− testes at postnatal day 5 ( P5 ) were similar at the gross and histologic levels ( Figure S1 ) . At postnatal day 10 ( P10 ) , the composition of Gasz−/− testes ( Figure 2C ) had not changed substantially in composition , whereas Gasz+/− testes ( Figure 2B ) had advanced to contain preleptotene and leptotene spermatocytes . At P12 , Gasz−/− testes ( Figure 2E ) contained predominantly spermatogonia with only 25% of the tubules containing preleptotene spermatocytes but no leptotene spermatocytes as compared with Gasz+/− testes where more advanced zygotene spermatocytes were present ( Figure 2D ) . By P14 , Gasz−/− testes displayed early pachytene germ cell loss due to apoptosis ( Figure S3 ) , with the most advanced germ cells being zygotene spermatocytes ( Figure 2G ) , while Gasz+/− testes ( Figure 2F ) had advanced to the mid-pachytene stage . Thus , histological analysis supports a consistent delay in Gasz−/− spermatogenesis beginning at meiotic prophase . Gasz−/− spermatogenesis fails at an identical point in juveniles and young adults . However , by 6 months of age , there are few germ cells in the Gasz−/− testes with many tubules displaying a Sertoli cell only phenotype ( Figure 2L and 2M ) . To further confirm our histological findings , we analyzed several genes that are expressed in spermatocytes ( Figure S2 and Figure S3 ) . γH2AX is expressed from late spermatogonia through pachytene spermatocytes [51] . γH2AX shifts from staining autosomes to the XY body in pachytene spermatocytes . XY body staining by γH2AX was absent from Gasz−/− testes , in contrast to Gasz+/− testes ( Figure S2 ) . Instead , γH2AX labeled autosomes similar to Gasz+/− early spermatocytes or dying spermatocytes with increased intensity . Our findings are reminiscent of MILI , MIWI2 , and MVH knockout mouse models that demonstrate male sterility due to a zygotene:pachytene block but normal female fertility [7] , [38] , [45] . GASZ is present at low levels in the cytoplasm of type A and B spermatogonia and pre-leptotene spermatocytes , showing peak intensity in middle to late pachytene spermatocytes , and localizing to finer granules in the cytoplasm of round spermatids ( Figure S4 and Figure S5 ) . Using antibodies against mitochondrial cytochrome c , GASZ was confirmed to localize to the interstices of spermatocyte mitochondrial clusters ( i . e . , intermitochondrial cement ) ( Figure 3A and inset ) , consistent with our previous findings that GASZ localizes to nuage in frog oocytes [50] . GASZ partially co-localizes with TDRD1 and MVH in the intermitochondrial cement ( Figure 3B–3I ) and displays a substantial overlap with MILI in spermatocytes ( Figure 3J–3L ) . Whereas TDRD1 and MVH relocalize to the chromatoid body in spermatids ( Figure 3E and 3F ) , GASZ fails to do so and also shows no relationship with unclustered mitochondria ( Figure 3D ) . In late pachytene spermatocytes MIWI and GASZ also overlap ( Figure S6 ) . MVH shows intense granular distribution in Gasz+/− testes but is dramatically reduced in the Gasz−/− spermatocytes ( Figure S7 ) . We also analyzed the relationship of GASZ and MILI at earlier time points to assess their interaction in immature germ cells . MILI was present in perinuclear granules in cell cycle arrested gonocytes of newborn mice ( Figure 3N ) in a distribution similar to that described in embryonic male germ cells [13] . There is a significant overlap between GASZ and MILI in newborn gonocytes ( Figure 3M–3O ) . We compared the immunostaining of nuage proteins in the newborn Gasz−/− testis versus controls . TDRD1 was reduced but also diffusely cytoplasmic , failing to localize in a perinuclear granular pattern ( Figure 4B ) . MILI was strikingly absent from the same GASZ null gonocytes ( Figure 4F ) . MVH immunostaining also showed reduced granular localization ( Figure 4J ) . Analysis of TDRD1 ( Figure 4D ) and MVH ( Figure 4L ) at E16 . 5 revealed a similar delocalization of these proteins in GASZ null gonocytes . However , MILI staining of E16 . 5 gonocytes ( Figure 4H ) was variable; in most germ cells , MILI staining was absent , whereas in only 2 . 25% ( 5 out of 222 gonocytes ) granular MILI staining remained . To study GASZ protein:protein interactions , we screened a P17 testis library by yeast two-hybrid analysis using full-length GASZ as bait . We found GASZ interacts with itself and RANBP9 ( Figure 5A ) , a known MVH interactor that also localizes to nuage and the chromatoid body [52] . MIWI , but not MILI , or MVH , also directly interacts with GASZ ( Figure 5B ) . Antibodies to GASZ could co-immunoprecipitate MIWI , TDRD1 , and MVH , but not MILI or MAEL from P21 testes ( Figure 5C ) . Conversely , antibodies to MIWI could also co-immunoprecipitate GASZ ( Figure 5D ) . We observed a striking reduction in the amount of multiple intermitochondrial cement proteins in Gasz−/− testis lysates at P21 as well as at times prior to the observed spermatocyte loss ( Figure 5E ) . Multiple interactions between these nuage proteins suggest that GASZ is a component of this network . Quantitative RT-PCR showed relatively modest reduction ( roughly 2-fold ) in the intermitochondrial cement mRNAs in contrast to their change in protein abundance ( Figure S8 and Table S1 ) . Our findings by Western blot analysis were consistent with reduced MVH immunostaining of spermatocytes ( Figure S7 ) and absence of MILI in gonocytes ( Figure 4 ) . We also examined newborn testes by electron microscopy for the presence of intermitochondrial cement . Although we could find nuage material associated with clustered mitochondria in most neonatal gonocytes in controls , we failed to detect this structure in Gasz−/− germ cells ( Figure S9 ) . Our results indicate that GASZ plays a key role in the formation and/or maintenance of this network of RNA processing proteins . There is increased retrotransposon transcription in MILI and MIWI2 null testes [7] , [38] , and dying Gasz−/− spermatocytes have a characteristic chromatin pattern similar to these knockouts . Because of these similarities and GASZ association with MIWI , we measured levels of the retrotransposons intracisternal A particle ( IAP ) and long interspersed nuclear element 1 ( Line L1 ) in the testes of P14 Gasz−/− mice . Quantitative RT-PCR demonstrated up to a 15-fold increase ( p<0 . 05 ) in Line L1 and up to a 4-fold increase in IAP mRNA ( p<0 . 05 ) in the postnatal Gasz−/− testis as well as similar increases in embryonic testes compared to controls ( Figure 6A and 6B ) . The largest increases were seen in the Line L1 ORF2 ( encoding the reverse transcriptase and endonuclease domains ) and IAP GAG mRNAs at P14 . We saw even more dramatic increases in IAP GAG protein and Line L1 ORF1p . At P14 , levels of IAP GAG and Line L1 ORF1p proteins are essentially undetectable in the WT samples but significantly up-regulated in the Gasz−/− testes ( Figure 6C ) . Likewise , in gonocytes of control newborn ( P0 ) mice , retrotransposon proteins IAP GAG and Line L1 ORF1p were undetectable but were dramatically elevated in the cytoplasm of the Gasz−/− gonocytes ( Figure 6D–6G ) . Consistent with compromised transposable element DNA methylation seen in MILI and MIWI2 null testes , we found significant hypomethylation of presumed germ cell-derived Line L1 and IAP sequences in Gasz−/− testes compared to controls at P14 ( Figure 6H ) . Sequences demonstrating appropriate methylation in the Gasz knockout may reflect the presence of admixed somatic cell DNA . Thus , the delayed meiotic initiation and spermatocyte apoptosis in the Gasz−/− testes are likely secondary to abnormal derepression of retrotransposons in the male germline at the transcriptional and post-transcriptional levels . Since increased retrotransposon expression in MILI and MIWI2 null germ cells has been ascribed to the loss of repression by repeat-associated RNAs , we evaluated their abundance by small RNA sequencing of P7 , P10 and P14 Gasz+/− and Gasz−/− testes using the Illumina Next Generation Sequencing platform that yields over 2 million sequence reads per sample . Control testes showed an increase in pachytene piRNAs at P14 , resulting in a proportional reduction in miRNA reads contributing to the overall small RNA pool . By contrast , Gasz−/− testes failed to induce piRNAs , and the small RNAome ( 17–40 nt ) was dominated by miRNAs in Gasz−/− testes compared to controls ( 71% vs . 44% , Figure 7A ) . However , the overall miRNA profiles ( the subset of miRNAs expressed and relative abundance ) were strikingly similar in both genotypes suggesting that miRNA biogenesis and function are likely intact in the absence of GASZ; the relative increase in miRNA read abundance is a consequence of reduced piRNA sequences within a fixed sample of small RNA reads . MicroRNAs have been shown to be present in the chromatoid body , but based on our analysis , they are clearly present prior to the appearance of this structure . This observation suggests that the formation of the intermitochondrial cement and chromatoid body are not required for microRNA biogenesis , but might be important for their action on target mRNAs . After excluding potential contributions to the Gasz null phenotype by miRNAs , we analyzed the remaining small RNAs in greater detail . This analysis revealed that most non-repeat-associated piRNAs showed a skewed distribution with 6% contributing 95–99% of the reads . There was a similar low number of known non-repeat-associated piRNAs in the control and null samples at P10 ( Figure 7A ) , consistent with previous studies demonstrating a robust increase at P14 pachytene stage in wild-type testes [6] . Non-repeat-associated piRNAs contributed only 14% of the piRNA reads in control testes at this age . However , 99% of the 6500 non-repeat-associated piRNAs detected in control P10 testes were reduced in Gasz−/− testes , 87% to undetectable levels . The decline was even more dramatic in P14 Gasz−/− testes when many more piRNAs are produced in the control testes ( Figure 7B and Table S2 ) . Although the delayed spermatogenic development and apoptotic loss of spermatocytes could preclude the expression of these piRNAs in Gasz−/− testes , the concurrent increase in miRNAs and expression of prepachytene piRNAs , normally abundant by P8 [6] , indicate that these two effects are unlikely to be the cause . Over 1500 of 1700 distinct non-repeat-associated RNAs ( 92% ) with greater abundance at P7 versus P14 were substantially reduced , and 1418 of these ( 90% ) displayed a sustained reduction at subsequent time-points ( Table S2 ) . Twenty-seven of the 100 small RNAs displaying a relative increase in Gasz−/− testes appeared to be variants of miRNAs or potential passenger strands , many mapping to a miRNA cluster on the X chromosome ( Table S3 ) . Two additional 22–23 nt sequences , DQ712837 mapping to Small Cajal body specific RNA 15 ( Scarna15 ) and DQ688886 mapping to an intron of 1700041C02Rik , contributed 40% of the “piRNA” reads remaining in Gasz−/− testes . The 127 nt Scarna15 is predicted to form two stem-loops and DQ712837 derives from the stem of the second stem-loop . If we exclude these two likely misannotated Dicer-dependent small RNAs , the non-repeat-associated piRNAs reads are 99% reduced . Control testes express an abundance of repeat-associated small RNAs compared to the Gasz−/− testes ( Figure 7A and Table S4 ) , which fall into two size categories that peak at 22 and 27 nt ( Figure S10A ) . While there is a small peak at 22 nt in Gasz−/− testes , the 27 nt peak is essentially absent . To analyze the repeat-associated piRNAs , we excluded all sequences whose length was not 25–29 nt . Many of the 19–23 nt sequences map to SINEs ( Figure S10D and Figure S10E and Table S3 ) and typically show less than a two-fold change . These SINE-associated small RNAs belong to a novel small RNA class that are cleaved from their precursor RNA by a Dicer-dependent but DGCR8-independent mechanism [53] . We also excluded 19–23 nt sequences that did not map to SINEs as potential Dicer-dependent endo-siRNAs ( Figure S10F and Figure S10G ) . All 25–29 nt repeat-associated RNAs were classified as repeat-associated piRNAs ( Figure 7C ) . These repeat-associated piRNAs map to LTRs ( e . g . , ERV-K IAPLTR1a_I_MM and MaLR MTA_Mm_LTR ) or LINEs ( L1_MM ) , and are 10 to 100-fold less abundant in Gasz−/− testes ( Figure S10B and Figure S10C and Table S4 ) . The majority of the 25–29 nt repeat-associated piRNAs contained a U at position 1 and an A at position 10 ( Figure S10H ) , characteristic of participation in the “ping-pong” synthesis reaction [54] . In contrast , the 19–23 nt repeat-associated small RNAs had an initial U but a variable position 10 ( Figure S10I ) , suggesting a lack of amplification by the “ping-pong” mechanism and confirming that these sequences are unlikely to be synthesized by the piRNA machinery . Subsequent analysis showed that the biggest difference in repeat-associated piRNAs occurred at P7 when many more elements were affected . Thus , the defect in repeat-associated piRNA production preceding delayed meiotic prophase initiation in Gasz−/− testes is a cause rather than an effect of this process . Initially , 25% of the small RNAs sequences ( >1 million reads ) in control testes were unclassified . The majority of the unclassified sequences were 25–29 nt ( Figure 7D and Figure S10J ) , but a 19–23 nt class was also present ( Figure S10K ) . The majority of these unclassified RNAs possess a 5′ U but a variable 10th position ( Figure S10L ) . Because of their similarity to piRNAs , we term the 25–29 nt category of unclassified sequences as “putative piRNAs” while the smaller size category called “unknown small RNAs” may contain novel miRNAs or other Dicer-dependent small RNAs . “Putative piRNA” reads were reduced compared to controls at P7 ( 7 . 9% versus 0 . 29% ) , P10 ( 1 . 6% versus 16 . 9% ) and P14 ( 5 . 10% versus 0 . 91% ) ( Figure 7D ) . These findings indicate that GASZ plays a major role prior to the pachytene stage in facilitating the production of multiple types of piRNAs , including those associated with repeats involved in regulation of retrotransposons .
GASZ was initially identified by our group as a male and female germ cell and maternal effect gene product [49] . Herein , we show that GASZ is not essential for fertility in the female germline . In contrast , absence of GASZ leads to male sterility due to a block at the zygotene-pachytene transition , reminiscent of the defect that is observed in knockouts of two PIWI family members , MILI and MIWI2 [7] , [11] , [38] . In frog oocytes , GASZ is expressed in the Balbiani body , a nuage structure [50] . Similarly , we show here that GASZ localizes to nuage in testicular primordial germ cells , gonocytes , spermatogonia , and spermatocytes . Using various markers , GASZ appears to be a component of the nuage termed intermitochondrial cement . We show that many nuage proteins depend upon GASZ for their normal levels . Aside from MIWI , which is expressed in germ cell types absent in the Gasz knockout , the reduction of MILI , MAEL , MVH , TDRD1 , TDRD6 , and TDRD7 in Gasz−/− testicular lysates likely reflects destabilization of this entire ultrastructural feature . TDRD1 and MVH become mislocalized in gonocytes and they become subsequently lost during postnatal spermatogenesis , suggesting that the loss of nuage proteins may result secondary to degradation following persistent failure to localize to germinal granules . The lesser change in MVH protein in GASZ null testes at P10 may indicate that MVH is less dependent upon GASZ than are the remaining nuage proteins . Perhaps this effect is due to additional proteins functionally redundant with GASZ or that MVH has a greater intrinsic stability in the absence of GASZ than other nuage proteins . Protein-protein interaction motifs in GASZ , including ankyrin domains , a sterile alpha motif , and a leucine zipper , may serve to direct the association and/or stabilization of nuage proteins . GASZ self-interaction may suggest the protein forms multimeric complexes as part of this function . A model depicting direct interactions within the nuage and proposed GASZ placement within this protein complex is provided ( Figure S11 ) . The Gasz null mice phenocopy the loss of MILI . Since the TDRD1:MILI interaction is critical [33] , [48] and absence of GASZ leads to disruption of the TDRD1 distribution in the cytoplasm ( Figure 4 ) , MILI could be physically dislodged from its granule position in Gasz null germ cells leading to a subsequent destabilization of MILI . Although formation of intermitochondrial cement is disrupted in both Tdrd1−/− and Gasz−/− germ cells , MILI is only unstable in Gasz null cells indicating a distinct role for GASZ in stabilization of MILI . As we have shown , that all but 2 . 2% of germ cells at E16 . 5 contain MILI , and at P0 , MILI is absent . Similar to the Mili null mice [6] , [11] , [38] , the absence of MILI in the Gasz knockout , is believed to mechanistically disrupt piRNA synthesis , resulting in an increase in retrotransposons and subsequent catastrophe for the male germ line . The current model for maintenance of transposable element repression in the germline involves the production of piRNAs by PIWI family members MILI and MIWI2 in embryonic and postnatal germ cells [6] , [7] . Repeat-associated piRNAs are abundant in embryonic germ cells coincident with DNA remethylation of retrotransposons in the male germline at E17 . 5 . GASZ is expressed in embryonic testes and co-localizes with MILI in fetal and newborn gonocytes . Transcriptional regulation of retrotransposons by antisense repeat-associated piRNAs , bound to MIWI2 , whose nuclear localization depends upon MILI , has been proposed [13] . Consistent with the proposed mechanism , we observe a similar reduction of promoter methylation of IAP and Line L1 in Gasz−/− testes as observed in Mili−/− and Miwi2−/− testes . Mechanistically , absence of Gasz and the consequent loss of MILI and MILI-mediated retrotransposon repression affects fetal gonocytes during the interval that retrotransposon remethylation normally occurs . Although there is a continuity of the cell cycle arrest in male gonocytes between E16 . 5 and P0 , the shift in Gasz null testes at E16 . 5 from slightly detectable MILI to its absence suggests that nuage is changing during this interval despite a lack of cell cycle progression . However , both MVH and TDRD1 displayed similar behavior in Gasz null testes at E16 . 5 and P0 . Although we can find a direct interaction between GASZ and MIWI , we could not find evidence for direct interaction between GASZ and MILI . We speculate that a GASZ-MILI interaction occurs indirectly though other mediators . MVH can interact with MILI and MIWI [38] , as can TDRD1 [33] , [48] both of which co-immunoprecipitate with GASZ . As shown in Figure 5A , GASZ interacts with RANBP9 and MIWI , known MVH interactors . Thus a large complex of proteins may be required for co-localization of GASZ with MILI and its support of MILI function in retrotransposon control . The inability to co-immunoprecipitate GASZ and MILI may suggest that MILI is more weakly “linked” to GASZ in this complex . Most of the nuage protein mutants that block in meiotic prophase have defects in repeat-associated piRNAs . Nuage protein knockouts cause two types of spermatogenic defects – those that block during meiotic prophase ( including MVH , MILI , MIWI2 and MAEL ) and those that block during haploid differentiation ( including DDX25 , MIWI , TDRD1 , and TDRD6 ) . In the latter class , Tdrd1−/− and Miwi−/− testes do not have altered repeat-associated piRNAs , both of which block at the spermatid stage and do not appear to have defects in stem cell maintenance . Nearly all of the former class possesses some defect in piRNA biosynthesis , but repeat-associated piRNAs are not altered in all mutants of this class . Like GASZ , disruption of MILI and MIWI2 result in an absence of repeat-associated piRNAs and a meiotic block prior to the loss of all germ cells from seminiferous tubules by 6 months of age [6] , [11] , [38] . The Mael−/− testis phenotype is unique in showing isolated pachytene piRNA defects but blocking during meiotic prophase with elevated retrotransposons [46] . An important gap in our knowledge is whether Mvh1098/1098 testes display repeat-associated piRNA defects and elevated retrotransposons similar to vasa mutants in Drosophila [55] and the majority of piRNA pathway mutants blocking during meiotic prophase . Germ cell apoptosis in Gasz null mice may depend upon retrotransposon expression combined with meiotic defects , representing distinct MILI-dependent functions . The GAG , POL , and PRT proteins are all required for efficient retrotransposition of IAP [56] , but their individual contribution to cellular toxicity has not been described . Line L1 proteins are toxic to cells by several mechanisms including activation of BAX and caspase 3 [57] . Expression of the reverse transcriptase of ORF2 alone causes cellular pathology in cell lines and could inhibit completion of meiotic recombination in nuage protein knockouts through its ability to bind random breaks in DNA [58] , [59] . Alternatively , the cause of apoptosis in Gasz−/− spermatocytes and nuage knockouts in the meiotic class may be retrotransposon-independent . Two observations suggest MILI regulates meiosis independent of retrotransposons . TDRD1 , by preventing the processing of mRNAs into piRNAs , confers specificity in non-repeat piRNA generation by MILI [48] . Line L1 dysregulation in the Tdrd1−/− mutant must depend upon the decreased mRNAs or increased unannotated RNAs that results from the shift of the substrate RNA used by MILI to generate piRNAs . Since TDRD1 deficient testes do not block during meiotic prophase despite elevated Line L1 expression , spermatocyte death is unlikely to be due to isolated retrotransposon dysfunction . Missense mutants of the zebrafish MILI ortholog ZILI have isolated meiotic defects but no alterations in retrotransposon expression [43] , indicating MILI's contribution to meiosis and retrotransposon control are separable . Knockout models suggest that homologous chromosome synapsis depends upon functional MILI [11] , [38] , MIWI2 [7] , [11] and MAEL [46] . Mvh null testes block at the zygotene-pachytene transition but their ability to complete synapsis has not been assessed [45] . Therefore , the apoptotic loss of Gasz−/− spermatocytes may not result from retrotransposon-induced DNA damage alone , but could also result from failed MILI-dependent meiotic functions including homologous chromosome synapsis or meiotic recombination . MILI has been proposed to cause general effects on germ cell mRNA translation not limited to control of retrotransposon mRNAs [37] . It is possible that this aspect of MILI function might contribute to the reduction of nuage protein levels in Gasz null testes . Translational control of mRNAs necessary for meiotic prophase by MILI may indirectly affect retrotransposon promoter methylation or post-transcriptional processing of retrotransposon mRNAs in addition to MILI's actions mediated by repeat-associated piRNAs . The piRNA pathway in mammals is specialized to support male germ cell development . Zebrafish and Drosophila share the requirement for PIWI family-dependent piRNA production for fertility of both sexes [42]–[44]; whereas , it is required only for male fertility in the mouse . GASZ is not correlated with this shift because GASZ orthologs are present in many vertebrates including zebrafish [50] . In the mammalian male germline , GASZ supports piRNA biosynthesis required for initiation of retrotransposon repression during the embryonic period and maintenance during meiotic prophase when alterations to chromatin and transcriptional increase would otherwise be favorable toward their expression . GASZ is the first mammalian germline- and intermitochondrial cement-specific protein lacking domains for RNA modification which impacts piRNA processing by localizing or stabilizing multiple proteins in the nuage including PIWI family members .
We electroporated the linearized Gasz targeting vector ( Figure S1A ) into the HPRT-negative AB2 . 2 ES cell line; selected clones in hypoxanthine , aminopterine , thymidine , and 1- ( 2 -deoxy-2 fluoro-D-arabinofuranosyl ) -5-iodouracil; and screened the ES cell DNA by Southern blot as described [60] to identify the mutant Gasz allele , Gasztm1Zuk ( herein called Gasz− ) . Correctly targeted clones were identified by using 5′ and 3′ probes as shown . Targeted ES cell clones were injected into blastocysts to produce chimeric male mice [61] , which were bred to produce C57BL6/J×129 hybrid F1 Gasz−/− offspring . Ten homozygous mutant and heterozygous sires were bred with WT females over a 6-month mating period . Similar mating trials were performed with homozygous mutant and heterozygous dams . Except for testis defects , Gasz−/− mice were grossly indistinguishable from their littermates and lived to become adults . Full-length His-tagged GASZ was injected into guinea pigs to produce polyclonal antibodies ( Cocalico Biologicals , Reamstown , PA ) . Membranes with 20 or 50 µg of total testis lysate per lane were probed with guinea pig anti-GASZ ( 1∶1000 ) , rabbit anti-IAP GAG ( 1∶1000 ) , rabbit anti-TDRD1 ( 1∶1000 ) , rabbit anti-TDRD6 ( 1∶1000 ) , rabbit anti-TDRD7 ( 1∶500 ) , rabbit anti-MVH ( 1∶500 ) , rabbit anti-MAELSTROM ( 1∶250 Abcam ab28661 ) , or rabbit anti-MILI ( 1∶250 Abcam ab36764 ) polyclonal antibodies . After developing , the membrane was stripped and re-probed with anti-β-actin clone AC-15 ( Sigma ) at 1∶5 , 000 . Secondary anti-guinea pig and anti-mouse horseradish peroxidase-conjugated antibodies ( Jackson ImmunoResearch , West Grove , PA ) were used at 1∶10 , 000 . Testes were fixed in Bouin's fixative ( for histology ) , 4% paraformaldehyde ( immunofluorescence ) or 4% paraformaldehyde/6 . 6% acetic acid ( for immunohistochemistry ) , and embedded in paraffin . Antigen retrieval was performed on 5 µm sections by boiling for 20 minutes in citrate buffer pH 6 . 0 . Samples for immunofluorescence were incubated with rabbit anti-γH2AX ( 1∶5 , 000; 05–636 Upstate , now Millipore , Billerica , MA ) , guinea pig anti-GASZ ( 1∶300 ) , rabbit anti-MVH ( 1∶500 ) , rabbit anti-TDRD1 ( 1∶100 ) , rabbit anti-MILI ( 1∶500 ) , rabbit anti-IAP GAG ( 1∶500 ) , rabbit anti-ORF1 ( 1∶500 ) , and mouse anti-cytochrome c ( 1∶300 , 556433 BD Biosciences San Jose , CA ) . Alexa594-conjugated anti-guinea pig , Alexa488-conjugated anti-rabbit , and Alexa488-conjugated anti-mouse antibodies ( Jackson ImmunoResearch ) were used at 1∶500 . Sections were mounted with Vectashield mounting medium with DAPI or propidium iodide ( Vector Laboratories ) . Representative images for immunofluorescence were selected and captured on a Zeiss Axiovert s100 2TV . When required , deconvolution was performed with softWoRx v3 . 3 . 6 ( Applied Precision , Issaquah , WA ) . One mg of testicular lysates from WT mice were prepared with 0 . 5% NP-40 lysis buffer as described previously [62] . Pre-cleared lysates were incubated overnight at 4°C with anti-GASZ , anti-MIWI , or no primary antibody , followed by incubation with protein G beads in 5% BSA to precipitate immune complexes . Co-immunoprecipitating proteins were detected by western blotting with anti-MVH ( 1∶500; ab13840 Abcam Inc , Cambridge , MA ) , anti-MIWI ( 1∶1000 ) , anti-TDRD1 ( 1∶1000 ) , anti-MVH ( 1∶500; Abcam Cambridge , MA ) , anti-GASZ ( 1∶500 ) , or β actin as above . Secondary antibodies used were horseradish peroxidase-labeled donkey anti-rabbit , goat anti-mouse , or goat anti-guinea pig ( Jackson ImmunoResearch , West Grove , PA ) . Ten µg of WT and age-matched Gasz−/− lysates were used as loading controls . To evaluate meiotic progression , slides were incubated with guinea pig anti-GASZ ( 1∶500 ) , rabbit anti-SYCP3 ( 1∶500 ) , or rabbit anti-H1 . T ( 1∶1000 ) . Staining was visualized using biotinylated goat anti-rabbit or goat anti-mouse secondary antibodies at 1∶200 and the Vectastain ABC Kit according to the manufacturer ( Vector Laboratories , Burlingame , CA ) . TUNEL analysis was performed with three sections from five mice of each genotype for 10- , 21-day-old and 6-week-old mice by using the Chemicon ApopTag Fluorescein In Situ Apoptosis Detection Kit ( S7110 ) . Representative images were captured on a Ziess Axioskop ( Carl Zeiss MicroImaging , Thornwood , NY ) . Yeast two-hybrid screening was performed by using CLONTECH Matchmaker Two-Hybrid Library Construction & Screening Kit . A yeast cDNA library was constructed from 17-day-old mouse testis cDNA . Full-length mouse Gasz cDNA , was subcloned into pGBKT7 vector , and was used as the bait construct for screening a yeast cDNA library constructed from 17-day-old mouse testis cDNA . After yeast mating , clones that grew on ( SD Leu- Trp- Ade- His- X-α-Gal ) selection plates were isolated , and candidate pGADT7-Rec-cDNAs were sequenced . Interactions were confirmed by mating mouse Gasz bait with prey constructs . To analyze how GASZ interacts with itself and other interacting proteins we generated prey constructs , by using pGADT7 , expressing full-length GASZ , MILI , MIWI , and MVH constructs , as well as a RANBP9 construct lacking its proline-rich amino terminus ( aa 1–50 ) . Using the CLONTECH Matchmaker BioSensor Kit All constructs were confirmed by DNA sequencing . We made the mating culture by using full-length mouse Gasz bait mated with the prey constructs . Protein-protein interactions were quantified from the resultant fluorescent signal of an equal number of mated cells on a 96 well oxygen biosensor plate post culture for 24 hours using the CLONTECH Matchmaker BioSensor Kit . We added an equal number of mated cells to a 96 well oxygen biosensor plate , cultured the cells at 30°C for 24 hours , and quantified the resultant fluorescent signal . Using the Superscript III Reverse Transcriptase Kit ( Invitrogen , Rockville , MD ) cDNA was synthesized from total mouse testis RNA from mice on embryonic day 18 . 5 and postnatal days 0 , 7 , and 14 primed with random hexamers following treatment with Turbo DNA-free ( Ambion , Austin , TX ) . Gasz was PCR amplified using ACCGGTCCTCTCAGAAATTAAAA ( forward , 111–133 of NM_023729 . 2 ) and ATTGGCGTCATAAGTCTCCTACA ( reverse , 455–477 of NM_023729 . 2 ) primers . QPCR was performed in duplicate with primers specific to IAP and LINE1 [7] on six 14-day-old animals of each genotype using SYBR Green PCR Master Mix on a 7500 Real Time PCR System machine ( Applied Biosystems ) . Nuage protein mRNAs were similarly quantified using primers designed in Primer Express ( Applied Biosystems , Foster City , CA ) with the exception of those for Mvh [63] and are described in Table S1 . Relative quantification was performed using Actb or Gapdh as appropriate . Cycle conditions were as follows: one cycle at 50°C for 2 min , followed by 1 cycle at 95°C for 10 min , followed by 40 cycles at 95°C for 15 s and 60°C for 1 min . The relative amount of transcripts was calculated by the ΔΔCT method . SEM was calculated for the duplicate measurements , and the relative amount of target gene transcripts was plotted . Significance was determined using Student t-test . Testes were removed from newborn mice and drop fixed in a modified Karnovsky's mixture of 2 . 5% glutaraldehyde , 2% paraformaldehyde plus 2 mM CaCl2 in 0 . 1 M cacodylate buffer pH 7 . 4 overnight at 4°C . After primary fixation , the tissue was rinsed 3 times for 5 minutes in 0 . 1 M cacodylate buffer pH 7 . 4 . The tissue was then post-fixed in 1% OsO4 in pH 7 . 4 . The tissue was dehydrated in a gradient series from 30% to 100% ethanol . After ethanol dehydration , the tissue was given 3 changes of fresh propylene oxide for 20 minutes each . The tissue was infiltrated up to 1∶1 propylene oxide plus Spurr's Low Viscosity embedding resin . The tissue was placed in individual 00 BEEM capsules and polymerized at 70°C overnight . Thick sections were cut with a HistoDiatome knife on an RCM MT-6000XL ultra-microtome , stained with toluidine blue and examined on a light microscope for orientation . Thin sections were cut at 80 nm using a Diatome Ultra knife on the same ultra-microtome and picked up on 200 mesh copper grids . Thin sections were stained for 15 minutes in a saturated aqueous solution of uranyl acetate , counterstained for 6 minutes with Reynold's lead citrate and examined on a Hitachi H7500 transmission electron microscope . Digital images were captured using Gatan Digital Micrograph software and a Gatan US1000 camera . Total testicular genomic DNA was recovered from 3 pooled 14-day-old mice by phenol:chloroform extraction and ethanol precipitation . Aliquots of DNA were denatured for 15 minutes at 50°C in 0 . 3 M NaOH and bisulfite treatment was performed as described in [64] . Briefly , samples were incubated at 50°C overnight with sodium metabisulfite and hydroxyquinone and purified using a modified procedure for a Viral RNA Miniprep kit ( Qiagen ) . Samples were desulfonated in NaOH for 15 minutes , neutralized with HCl , and purified as before . PCRs were performed using Platinum Taq Supermix ( Invitrogen ) or ExTaq ( Takara ) using primers described below from [11] to amplify twelve CG dinucleotides in IAP LTR1_Mm ERVK and from [7] to amplify eight CG dinucleotides in L1MD-A2 . Each PCR was performed in 4 replicates , which were pooled and purified using a DNA clean-up and concentrator kit ( Zymo Research ) . Amplicons were ligated into pGEM-T Easy vector ( Promega ) and sequenced . Capital letters in the reference sequences below denote sequences that are complementary to primer binding sites . These do not contain CG dinucleotides , and all cytosine residues are expected to convert during bisulfite treatment . Conversion of essentially all cytosines not part of CG dinucleotides confirmed the efficacy of bisulfite treament . IAP LTR1_Mm ERVK ( prebisulfite conversion ) : chr10:83255039–83255316 ( all IAP sequence ) CTGTGTTCTAAGTGGTAAACAAATAATCTGcgcatgtgccaagggtatcttatgactacttgtgctctgccttccccgtgacgtcaactcggccgatgggctgcagccaatcaaggagtgacacgtccgaggcgaaggagaatgctccttaagagggacggggttttcgtttttctctctctcttgcttcgctctctcttgcttcttgctctcttttcctgaagatgtaagaataaagctttgccgcagaagATTCTGGTCTGTGGTGTTCTTCCTG Forward primer IAP-bisF2 from: TTGTGTTTTAAGTGGTAAATAAATAATTTG Reverse primer IAP-bisR2 from: CAAAAAAAACACACAAACCAAAAT L1Md-A2 ( prebisulfite conversion ) : chrX:154580101-1544585723 ( L1 beginning at 154580138 ) AAGTTACAAATAATTTTCTGGGGCCcggatctggggcacaagtcccttccgctcgactcgtgactcgagccccgggctaccttgccagcagagtcttgcccaacacctgcaagggtccacacaggactccccgcgggaccctaagacctctGGTGAGTGGATCACAGTGCCTGCCC Forward primer methyl L1-F: AAGTTATAAATAATTTTTTGGGGTT Reverse primer methyl L1-R: AAACAAACACTATAATCCACTCACC Fifteen µg of total RNA from WT and Gasz−/− mouse testes were gel-fractionated to isolate 18–40 nt small RNAs , followed by 3′ and 5′ adapter ligation , and product amplification by RT-PCR as per the small RNA kit ( FC-102-1009 , Illumina ) protocol . Finally , the small RNA library was sequenced using a Solexa/Illumina GA-1 Genome analyzer . Small RNA sequences were analyzed through a high-throughput computational pipeline . For each sample , all sequence reads were aligned to a reference set of miRNAs ( miRNA pipeline ) and all currently identified piRNAs . The reads are also mapped to the reference mouse genome ( NCBI Build 37 , UCSC mm9 ) using the Pash software package [65] , [66] , and uploaded to Genboree platform ( www . genboree . com ) to identify snoRNAs , scRNAs and repeat-associated small RNAs . We performed a local Smith-Waterman alignment of each unique sequence read against each of the mature microRNAs in miRBase version 11 . 0 , allowing for a 3 base overhang on the 5′ end and a 6 base overhang on the 3′ end . The alignments were scored such that a matching or overhanging base counts as 2 points and mismatches as −1 . Each unique sequence read which achieves a per-base alignment score of 2 ( i . e . , a perfect match ) was associated with each mature microRNA for which it achieved that score . The read counts of all redundantly aligning reads to multiple hairpins in the genome were equally apportioned to each mature microRNA to which they align . For repeat-associated small RNAs , each mapping is associated with LINEs , SINEs , DNA and RNA repeat subtypes . Reads were also mapped to consensus mouse and mammalian repeats from Repbase [67] , and rasiRNAs [11] using Blat [68] with a sensitive setting only requiring one 8mer seed filtered such that≥90% of the reads mapped . All sequences corresponding to repeat-associated piRNAs and putative novel piRNAs have been deposited at piRNABank ( http://pirnabank . ibab . ac . in/ ) [69] .
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Many aspects of RNA processing are essential for or prominent in the differentiation of germ cells . Some RNA metabolism in animal germ cells is associated with physical structures surrounding the cell nucleus called nuage . Nuage has a distinct granular appearance prior to the meiotic divisions with unclear functions . We have identified a protein called GASZ , which plays a structural role in this early nuage . In mice lacking GASZ , retrotransposons—endogenous viral-like particles—become released from their typical repressed state in the germline by the loss of small RNAs called piRNAs , resulting in DNA damage and delayed germ cell maturation . Protection of the germline from genetic intruders may require the association of piRNA-synthesizing enzymes and other components of this nuage structure through direct or indirect associations with GASZ . Mutations in GASZ and other nuage components may contribute to infertility in men who do not produce spermatozoa .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology/germ",
"cells",
"genetics",
"and",
"genomics/animal",
"genetics",
"molecular",
"biology/rna-protein",
"interactions",
"cell",
"biology/developmental",
"molecular",
"mechanisms",
"genetics",
"and",
"genomics/epigenetics",
"developmental",
"biology/developmental",
"molecular",
"mechanisms"
] |
2009
|
GASZ Is Essential for Male Meiosis and Suppression of Retrotransposon Expression in the Male Germline
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Evolutionary game dynamics in structured populations has been extensively explored in past decades . However , most previous studies assume that payoffs of individuals are fully determined by the strategic behaviors of interacting parties , and social ties between them only serve as the indicator of the existence of interactions . This assumption neglects important information carried by inter-personal social ties such as genetic similarity , geographic proximity , and social closeness , which may crucially affect the outcome of interactions . To model these situations , we present a framework of evolutionary multiplayer games on graphs with edge diversity , where different types of edges describe diverse social ties . Strategic behaviors together with social ties determine the resulting payoffs of interactants . Under weak selection , we provide a general formula to predict the success of one behavior over the other . We apply this formula to various examples which cannot be dealt with using previous models , including the division of labor and relationship- or edge-dependent games . We find that labor division can promote collective cooperation markedly . The evolutionary process based on relationship-dependent games can be approximated by interactions under a transformed and unified game . Our work stresses the importance of social ties and provides effective methods to reduce the calculating complexity in analyzing the evolution of realistic systems .
Understanding the emergence and persistence of cooperation in the population of egoists is an enduring challenge that has inspired a myriad of studies from biology to sociology [1] . Evolutionary game theory has been widely employed to investigate this cooperation conundrum at different levels of living systems [2] . Typically , social dilemmas are depicted by two-player two-strategy games where each player can choose either to cooperate or to defect [3] . In these games , mutual cooperation brings each player a reward R while mutual defection a punishment P; when a cooperator encounters a defector , the cooperator obtains a sucker’s payoff S and the defector gets the temptation T . Different rankings of payoff entries R , S , T , P represent different social dilemmas [3] . Despite the simplicity of this representation , in the real world , many interactions occur beyond the dyadic scenarios and often involve more than two individuals . For examples , in a S . cerevisiae population , a cooperative yeast produces an enzyme to hydrolyze sucrose into monosaccharides while the most of them diffuse away and are exploited by nearby yeasts [4] ( see Ref . [5 , 6] for more examples in microbes and Ref . [7 , 8] in human societies ) . Interactions in these examples are better modeled by multiplayer games [9] . Generally , multiplayer games cannot be represented by a collection of two-player games [10] whereas the latter can always be regarded as the simplest case of the former [9] , making the study of multiplayer games of great importance for the evolution of cooperation [11 , 12] . One particular example is the threshold public goods game [13] . It captures the strategic interactions of individuals when the provision of public goods needs a threshold surpassed . Such a threshold can be a minimum amount of funding for building national defense , a minimum height of a dam for securing the public safety , etc [8] . In this game , each individual has two options—to contribute an amount of investment to the goods pool or not to contribute . The benefit is provided only when the total investment exceeds a threshold [13] . Recent advance in exploring interaction patterns of living agents shows that populations often exhibit structural characteristics , which expands our research interests in evolutionary dynamics from traditional well-mixed to structured populations [14–25] . Graphs serve as a good tool to model such a system , where vertices of graphs represent individuals and edges specify one’s interaction and dispersal neighborhoods . In the case of weak selection where individuals’ payoffs obtained from games slightly affect their fitness or reproductive rates , evolutionary outcomes on graphs , especially the conditions for one strategy to be favored over the other , can be tackled analytically . For example , Tarnita et al . derive a simple condition to predict the evolutionary outcome for two-player two-strategy games [26] . This condition relies on all the payoff entries R , S , T , P and one “structure coefficient” . As shown in their work , the structure coefficient summarizes all the effects of a population structure on the condition for the success of strategies and it is independent of payoff entries . Due to the generality of the above results , calculating structure coefficients provides a convenient way to quantify the effect of population structures on the evolutionary outcome [10 , 23 , 27–31] . Nonetheless , the closed-form expressions of the structure coefficients are often hard to calculate under multiplayer games , even in the simplest well-mixed populations [9] . This becomes even more challenging when the population structure is taken into account . Even so , there are still a few seminal work about evolutionary multiplayer games on graphs [10 , 31–35] . For example , Peña et al . derive the structure coefficients for evolutionary multiplayer games on finite ring graphs and infinite regular graphs [33] . Based on competition between territorial animals , Broom et al . develop a new modelling framework to investigate collective interactions , which is capable and flexible to compare and analyze various spatial structures [34] . McAvoy et al . study when a multiplayer game can be broken down into a sequence of interactions with fewer individuals and show that a simple population structure can greatly complicate the reduction [10] . Prior studies about games on graphs usually assume that social ties between individuals only indicate the presence of interactions [10 , 20 , 31–37] . The other relevant information associated with social ties , such as the genetic and physical relationships between interactants , is often ignored . In such cases , individuals’ strategic behaviors are the only determinant of the outcome of an interaction . Typically , in two-player interactions , if two distinct individuals take the same strategy , their common opponent obtains the same payoff when encountering each of them separately [14 , 15] . When engaging in group interactions , one’s payoff relies on the number of opposing cooperators but is independent of which one is the cooperator [32 , 33] . Indeed , this assumption significantly reduces the calculation complexity and thus makes it possible for many well-known results [38 , 39] . However , recent studies show that overlooking the information of social ties could make theoretical predictions deviate greatly from empirical observations [40–43] . For example , people possess strong and weak social ties , such as intimate interpersonal relationships with relatives and tenuous relationships with acquaintance [43 , 44]; failing to account for the tie strengths leads to a globally accelerated information diffusion and a remarkably distinct diffusion direction from that in actual networks [41 , 42] . In well-mixed populations , when distinct frequencies of interactions between pairs are considered , altruistic traits can flourish whereas neglecting such information on social ties leads to the extinction of altruism [23] . Here , the second example clearly conveys that the information associated with social ties can affect the evolution of a certain behavioral trait ( strategy ) in a nontrivial way . Besides , we offer two other representative cases . In the example of the division of labor in colonies of eusocial insects and human societies , the production of collective benefits needs different individuals to cooperatively perform different subtasks [45–48] . When many individuals assigned one subtask cooperate , cooperation from an individual assigned another subtask is more crucial to the colony productivity than cooperation from individuals assigned the same subtask . The other situation is that the payoff structure of an interaction may be relationship-dependent [49] . It means that an individual may concurrently play various types of games with its neighbors , depending on the social tie they are connected with [50 , 51] . For instance , individuals can play coordinations games ( or even harmony games ) with its friends and prisoner’s dilemma with strangers . To better understand the role of social ties in the evolution of strategic behaviors , we present a comprehensive framework of evolutionary multiplayer games on graphs with edge diversity . Each type of edges describes one kind of relationship between two connected individuals , such as having the same or different task skills [46–48 , 52] , owning close or distinct consanguinity or geographical distance and so on . We investigate both finite and infinite regular graphs with n types of edges . We provide a simple condition to predict when natural selection favors one strategic behavior over the other . The condition is validated by Monte Carlo simulations . Applying it to the case of division of labor where cooperation from individuals performing different subtasks is required for producing benefits ( see the example of army ants retrieving prey items [46] ) , we find labor division significantly lowers the barrier to establish cooperative society . Then we explore the scenario where each individual simultaneously participates in many multiplayer games and these games can differ in payoff entries or metaphors . We find evolutionary dynamics for such diverse interactions can be approximated by an evolutionary process with a unified payoff structure . This result provides us insights into simplifying complex and diverse interactions in real-world systems as simple and unified interactions in theoretical calculations . Our work also covers the evolutionary games on weighted graphs ( see the example of bacterium Escherichia coli [53] ) . Intriguingly , in our framework , strong edges do not act as a promoter of cooperation .
Here we briefly introduce the model of evolutionary multiplayer games on graphs with edge diversity . We first consider the stochastic evolutionary dynamics on a graph-structured population with a finite size N and later investigate the dynamics in infinite populations . Each individual occupies a node of a random regular graph with degree k . Note that this graph is determined randomly and fixed during the process of evolution . On the graph , each node is linked to k other nodes and each edge is assigned an edge type selected from n types ( 1 ≤ n ≤ k ) . Among the k edges connected to a node , the number of type i edges is gi , which means ∑ i = 1 n g i = k . During the evolution , in each generation , every individual obtains a payoff by interacting with k adjacent individuals in a single game , analogous to the setting of spatial multiplayer game in prior studies [33 , 54] . In the game , each individual chooses either strategy A or strategy B . We use ( s1 , s2 , ⋯ , sn ) denote a neighborhood state: among gi neighbors connected by edges of type i , the number of individuals using strategy A is si while the number of those using strategy B is gi − si . In such a neighborhood , a focal A-player gets a payoff a s 1 s 2 ⋯ s n while a focal B-player gets a payoff b s 1 s 2 ⋯ s n . Fig 1 illustrates an example of the spatial structure and Table 1 presents the payoff structure for n = 2 . Our model can recover the traditional setting by taking n = 1 . After the interaction , individual i’s payoff πi is transformed to its reproductive rate or fitness by Fi = 1 − ω + ωπi , where ω represents the intensity of selection , i . e . , the extent to which the payoff from games influences the reproductive success . Here we consider the weak selection ( ω ≪ 1 ) . The population evolves according to the death-birth rule [38] . This update rule can be interpreted in the context of genetic evolution or cultural evolution [16 , 23] . In the genetic context , a random individual such as i is selected to die . After that , one of i’s neighbors is chosen to reproduce an offspring with probability proportional to its reproductive rate . Then this offspring occupies the vacant site . When the death-birth rule is interpreted as a genetic process , different evolutionary outcomes may occur , depending on the meaning of the edge type . For instance , if the edge type represents genetic similarity , during the evolution , the edge type may be changed due to the gene replication and dispersal . But if the edge type represents geographic proximity , the above genetic process does not change the edge types . To avoid the contingency of evolutionary outcomes on the specific meaning of the edge types , in this paper , we thus interpret the the death-birth rule as a kind of behavior imitation in the cultural evolution . That is , a random individual i resolves to update its strategy , and it adopts neighbor j’s strategy proportionally to j’s fitness , i . e . , with probability F j / ∑ l ∈ Ω i F l , where Ωi is the set of i’s neighbors . With this interpretation , for all possible meanings of edge types we considered in this model , the process of behavior imitation does not change the edge types .
In finite populations , the fixation probability is a well-established measure to quantify the evolutionary success of different traits or strategies [55] . The fixation probability ρA denotes the probability that a single A-player starting in a random position propagates and takes over the whole population of B-players . Analogously , ρB is the probability that a single B-player starting in a random position propagates and takes over the whole population of A-players . Natural selection favors strategy A over B if ρ A > ρ B . Using weak selection , in large random regular graphs with n edge types ( k ≥ 3 and 1 ≤ gi ≤ k ) , we obtain the condition under which A-players are selected over B-players ( see S1 File , Section 1 ) , given by ∑ s 1 = 0 g 1 ∑ s 2 = 0 g 2 ⋯ ∑ s n = 0 g n σ s 1 s 2 ⋯ s n ( a s 1 s 2 ⋯ s n - b ( g 1 - s 1 ) ( g 2 - s 2 ) ⋯ ( g n - s n ) ) > 0 , ( 1 ) where σ s 1 s 2 ⋯ s n ( 0 ≤ s1 ≤ g1 , 0 ≤ s2 ≤ g2 , ⋯ , 0 ≤ sn ≤ gn ) is the structure coefficient that relies on population structures and update rules but is independent of payoff values a s 1 s 2 ⋯ s n and b s 1 s 2 ⋯ s n . There are totally Π i = 1 n ( g i + 1 ) structure coefficients for Eq ( 1 ) . All structure coefficients here are positive and we can eliminate an extra structure coefficient through dividing the sigma rule [see Eq ( 1 ) ] by any one of them . σ s 1 s 2 ⋯ s n can be approximated by σ s 1 s 2 ⋯ s n = ( k - 2 ) ( k - ∑ j = 1 n s j ) k 2 ( k + 1 ) ( k + 2 ) Π j = 1 n ( g j s j ) ( k ∑ j = 1 n s j ) ∑ l = 0 k ( k - l ) { [ 2 k + ( k - 2 ) l ] Ψ ( k , ∑ j = 1 n s j , l ) + [ k 2 - ( k - 2 ) l ] Φ ( k , ∑ j = 1 n s j , l ) } , where Ψ ( k , i , l ) = ( l k − 1 − i ) 1 ( k − 2 ) ( k − 1 ) l + ( k − 1 − l k − i ) 1 ( k − 1 ) k − 1 − l , Φ ( k , i , l ) = ( l k − i ) 1 ( k − 1 ) l + ( k − 1 − l k − 1 − i ) 1 ( k − 2 ) ( k − 1 ) k − 1 − l . a s 1 s 2 ⋯ s n - b ( g 1 - s 1 ) ( g 2 - s 2 ) ⋯ ( g n - s n ) in Eq ( 1 ) indicates the “gains from flipping” [33 , 35] , the change in payoffs for a focal A-player who interacts with si A-players of type i ( 1 ≤ i ≤ n ) in a group when all individuals change their strategies ( from strategy A to strategy B or B to A ) simultaneously . Considering ∑ s 1 = 0 g 1 ∑ s 2 = 0 g 2 ⋯ ∑ s n = 0 g n σ s 1 s 2 ⋯ s n = 1 , σ s 1 s 2 ⋯ s n can be viewed as a probability corresponding to term a s 1 s 2 ⋯ s n - b ( g 1 - s 1 ) ( g 2 - s 2 ) ⋯ ( g n - s n ) . Eq ( 1 ) thus indicates that strategy A is favored over B if the expected gain in payoffs from flipping is positive . When n = 1 , our analytical prediction is in line with a previous study about evolutionary multiplayer games on graphs [33] ( see S1 File , Section 2 ) . To understand the structure coefficient for the case with n > 1 , we set the sum of the number of opposing A-players to be S , i . e . , ∑ j = 1 n s j = S . We find that σ s 1 s 2 ⋯ s n is the product of the structure coefficient corresponding to n = 1 ( denoted σS ) and an additional term ∏ j = 1 n ( g j s j ) / ( k ∑ j = 1 n s j ) . This term represents the probability of the configuration s1 s2⋯sn to occur under a given S . Intuitively , with edge diversity , we distinguish A-players in the neighborhood by their types . For a given number of A-players S , the probability of a specific configuration ( si A-players within gi individuals of type i ) indeed follows the multivariate hypergeometric distribution ∏ j = 1 n ( g j s j ) / ( k S ) . Our result shows that the structure coefficient associated with a specific configuration for diverse edges is simply a product of the probability for this configuration to occur and the corresponding structure coefficient without distinguishing edges . Infinite populations usually serve as a baseline model to investigate the evolutionary dynamics of a system . Therefore we conduct a consistent investigation in infinite populations . The evolutionary dynamics of multiplayer games on graphs with edge diversity can be described in terms of replicator equation [56] ( see S1 File , Section 4 ) , given by x ˙ = ω ( k - 2 ) x ( 1 - x ) k 2 f ( x ) , ( 2 ) where f ( x ) = ∑ s 1 = 0 g 1 ∑ s 2 = 0 g 2 ⋯ ∑ s n = 0 g n [ ∏ j = 1 n ( g j s j ) x s j ( 1 − x ) g j − s j ] ( Λ a − Λ b ) , Λ a = ∑ r 1 = 0 g 1 - s 1 ∑ r 2 = 0 g 2 - s 2 ⋯ ∑ r n = 0 g n - s n [ ∏ j = 1 n ( g j − s j r j ) z r j ( 1 - z ) g j - s j - r j ] ∑ j = 1 n [ ( s j + r j ) a ( s 1 + r 1 ) ( s 2 + r 2 ) ⋯ ( s n + r n ) + ( z s j + r j z ) a ( s 1 + r 1 - δ 1 j ) ( s 2 + r 2 - δ 2 j ) ⋯ ( s n + r n - δ n j ) ] , Λ b = ∑ r 1 = 0 s 1 ∑ r 2 = 0 s 2 ⋯ ∑ r n = 0 s n [ ∏ j = 1 n ( s j r j ) z r j ( 1 - z ) s j - r j ] ∑ j = 1 n [ ( g j - s j + r j ) b ( s 1 - r 1 ) ( s 2 - r 2 ) ⋯ ( s n - r n ) + ( z ( g j - s j ) + r j z ) b ( s 1 - r 1 + δ 1 j ) ( s 2 - r 2 + δ 2 j ) ⋯ ( s n - r n + δ n j ) ] , z = 1/ ( k − 1 ) . δij equals to 1 if j = i and 0 otherwise . This seemingly complicated Eq ( 2 ) could be greatly simplified when applied to specific examples , from pairwise games [56] to traditional multiplayer games such as volunteer’s dilemmas [57] , multiplayer stag-hunt game [13] , and multiplayer snowdrift game [58] . When strategy A represents cooperation and B defection , Eq ( 1 ) can effectively predict the success of cooperation over defection . In the following , we apply Eqs ( 1 ) and ( 2 ) to four representative examples and organize them as follows . Example 1 describes two-player games . Different from previous studies , here each type of edges are endowed with an independent payoff matrix . Example 2 presents the scenario where every individual participates in different multiplayer games concurrently . Example 3 discusses the evolutionary multiplayer games on weighted graphs , where edge weights represent interaction rates . In example 4 , we study the prevailing collective activity in social insects and human societies—division of labor .
Due to variations in environment or gene , individuals own distinct social status or play different roles in colonies [65 , 66] . Typically , individuals with geographic proximity and genetic similarity tend to establish closer social ties than those separated by remote geographic space or distinguished by large genetic difference . Encountering different types of individuals , one may be affected differently . Here we model the heterogeneous influence by different types of edges and develop a framework of evolutionary multiplayer games on graphs with edge diversity . Since the two-player game is the simplest multiplayer game , our findings are applicable to pairwise interactions . We make a thorough investigation in both finite and infinite populations . We provide the analytical formula of structure coefficients for random regular graphs with n types of edges , which effectively predicts when natural selection favors one strategic behavior over the other . Our framework is able to address the situation where individuals concurrently face diverse social dilemmas . This is in stark contrast with the ideal assumption in most previous studies where all interactions are described by a unified game metaphor [19–22 , 31–33] . In the real word , an individual may be caught in a volunteer’s dilemma with its colleagues and meanwhile it engages in public goods games with its neighbors . The tragedy of commons suggests that cooperation is often hard to persist in the public goods game . Fortunately , the public goods game is merely one of the many types of social dilemmas individuals encounter . Our work reveals that leveraging the distinct nature of diverse social dilemmas can entail an evolutionary outcome where cooperators are rescued and are able to coexist with defectors . In addition , a seminal work by McAvoy et . al . tells that under asymmetric two-player games the evolutionary processes behave macroscopically like that governed by symmetric games [59] . Here we confirm that irrespective of two-player or multiplayer games , the evolutionary dynamics with diverse interactions can be approximated by that governed by a single game . For more complicated cases where sizes of group interactions are different , we also provide an efficient method to simplify it . Our work greatly reduces the complexity when investigating the evolutionary dynamics in real-world systems . Besides , multiplayer games on weighted graphs can be considered . We find that the presence of strong social ties does not always provide an evolutionary advantage to cooperators , which seems to coincide with recent findings under aspiration dynamics [63] . This contrasts with the conclusion in Ref . [23] where they show that strong ties boost cooperation most . The main difference between our work and theirs is that we do not couple the strength of interactions and the probability of replacement along an edge . In their work , a strong social tie indicates not only a higher frequency of interactions but also a more probable path for strategy dispersal . Simultaneously enhancing the strength of interactions and the likelihood of dispersal leads to a strong strategy reciprocity between individuals and thus facilitates the clustering of cooperators . However , if strong ties merely indicate frequent interactions as in our work , we show that they fail to promote cooperation , irrespective of group or pairwise interactions . Note that in our model , individuals derive payoffs only from interactions with their nearest neighbors [33 , 54] . When individuals can interact with both the nearest and second-nearest neighbors , the impact of social ties on the evolution of cooperation are more complicated [64] . A further investigation along this direction may generate new insights . A prior study has considered that players’ social influence affects the strategy dispersal , which in turn modifies players’ social influence [67] . Such a coevolution actually corresponds to a dynamic structure for strategy dispersal , different from the static structure in this paper . Within this framework , we consider how the division of labor affects the evolution of cooperation . As well known , the division of labor prevails in colonies of social insects , hunting groups of lions , and human societies [45–48 , 52] , where individuals are born or trained to perform specialized subtasks . Such specialization not only makes them more productive on their own subtasks but also results in synergistic effects on the overall productivity when they cooperate with each other . We here model the strategic interactions under the division of labor as a multi-threshold public goods game . The public goods are provided only when individuals of distinct types cooperate . We find that the division of labor could promote the evolution of cooperation . The reason lies in that task specialization transforms a many-player interaction into several coupled fewer-player interactions . Such a transformation helps to reduce the free-riding behaviors . Our work also extends the research scope about the interplay between the evolution of a population and the diversity . The two basic elements of a population are individuals and social ties . Most prior studies about diversity focus on individuals’ attributes , such as the number of social ties they have , the ability to influence their opponents , etc [68 , 69] . Such diversity highlights that two individuals are different when possessing different attributes . Here we stress the diversity of social ties . Social ties not just establish the connections between separated individuals . They carry a massive amount of information about two connected individuals , such as the intimacy of the interpersonal relationships , the frequency of physical contact , and even the history about previous interactions . All these are unlikely to be captured by individuals’ attributes . The example of division of labor also proved that the diversity of social ties ( or edge diversity ) could catalyze cooperation . Our recent work about interactive diversity is pertinent to this topic [24 , 70] . Interactive diversity describes that each individual adopts independent strategies in different interactions . Thus even facing an identical strategy by two different opponents , the focal individual could be influenced differently due to its own behavior . Nevertheless , the influence difference fully depends on strategies between interactants and is unrelated to other information like genetic similarity or geographic proximity . Thus , interactive diversity does not essentially capture diverse social ties explored in this paper [37] . We wish our work could attract more work into the evolutionary dynamics along edges . Here we stress that edge diversity proposed in this paper is different from edge multiplexity , an important terminology in social networks [71] . Although both edge multiplexity and edge diversity describe the association between individuals rather than individuals’ attributes , they have different implications . Edge multiplexity means that the relationship between two individuals is multiplex when they interact in multiple social contexts . For example , two individuals can be both friends and colleagues . But edge diversity , simply speaking , means that edges are different . Specifically , in our work , it means that different edges may carry different social , physical or genetic information between individuals , such as the interaction frequency ( or rate ) , geographic distance or genetic similarity . It highlights the differences between edges rather than the multiplexity of the associated relationship . For example , even if an individual shares the same multiplex relationship with two other partners , due to the distinct interaction rates , the two social ties are still interpreted as different edges in terms of edge diversity . In this paper we constrain that each social tie has symmetric effects on connected individuals . For example , if Alice is close to Bob in consanguinity or geographic sites , Bob is close to Alice . Thus the benefit that cooperative Alice brings to Bob is identical to that of cooperative Bob to Alice . A promising and challenging extension is the interactions with asymmetric social ties , such as the relationship between leaders and followers . In such case , each individual should be endowed with an independent payoff function [10 , 72] . Despite much complicity in analytical calculations , we expect a further research into this realistic situation , which is bound to provide fruitful insights . We point out that our theoretical results are based on assumption of weak selection , as used by most previous theoretical studies [19–22 , 32 , 33 , 35] . Although the assumption of weak selection is reasonable in many cases and also make this conundrum accessible to analytical calculation [73] , other situations routinely encountered in social or natural science are better captured by strong selection . Thus , a further investigation with strong selection is necessary to enrich our understanding to the collective behavior in complex systems [36 , 74] . Finally , in this paper , we assume that the types of edges remain unchanged throughout the evolution . This is natural in many cases , like when types of edges indicate the geographic proximity . Nevertheless , when edges’ types represent the genetic difference between linked individuals and the population evolve based on individuals’ reproduction , edges’ types evolve as well [59] . A study into the coevolution of individuals’ traits and edge types is expected .
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The outcome of an interaction often relies on not only interactants’ strategic behaviors but also genetic and physical relationships between interactants , such as genetic similarity and geographic proximity . Thus when encountering different opponents who use the same strategy , an individual may derive different payoffs . Social ties , acting as carriers of such information , are crucial to biological interactions . However , most prior studies simplify social ties as binary states ( i . e . , either present or absent ) and ignore the information carried . Here we study evolutionary multiplayer games on graphs and introduce different types of edges to describe diverse social ties . We derive a simple rule to predict when a strategic behavior is more successful than the other . Based on this rule , we find that the labor division in eusocial insects could promote prosocial behavior . In addition , when payoff structures in different interactions are relationship-dependent , the condition for the success of one behavior can be obtained by studying interactions described by a unified payoff structure . Our work not only extends established results on the evolution of cooperation on graphs , but also shows the possibility to simplify complex and diverse interactions in real-world systems as simple and unified interactions in theoretical calculations .
|
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"Abstract",
"Introduction",
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"Results",
"Discussion"
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2019
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Evolutionary multiplayer games on graphs with edge diversity
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Invasive aspergillosis ( IA ) , primarily caused by Aspergillus fumigatus , is an opportunistic fungal infection predominantly affecting immunocompromised and neutropenic patients that is difficult to treat and results in high mortality . Investigations of neutrophil-hypha interaction in vitro and in animal models of IA are limited by lack of temporal and spatial control over interactions . This study presents a new approach for studying neutrophil-hypha interaction at single cell resolution over time , which revealed an evasive fungal behavior triggered by interaction with neutrophils: Interacting hyphae performed de novo tip formation to generate new hyphal branches , allowing the fungi to avoid the interaction point and continue invasive growth . Induction of this mechanism was independent of neutrophil NADPH oxidase activity and neutrophil extracellular trap ( NET ) formation , but could be phenocopied by iron chelation and mechanical or physiological stalling of hyphal tip extension . The consequence of branch induction upon interaction outcome depends on the number and activity of neutrophils available: In the presence of sufficient neutrophils branching makes hyphae more vulnerable to destruction , while in the presence of limited neutrophils the interaction increases the number of hyphal tips , potentially making the infection more aggressive . This has direct implications for infections in neutrophil-deficient patients and opens new avenues for treatments targeting fungal branching .
Aspergillus fumigatus is a filamentous , conidiating fungus ubiquitous to the environment . Airborne conidia are inhaled in the hundreds by individuals every day [1] and are quickly cleared by macrophages [2–4] . If not cleared , conidia grow into elongated hyphal structures , which present a unique challenge for the cellular immune system: Hyphae cannot be phagocytosed by immune cells , requiring deployment of extracellular antifungal mechanisms such as degranulation [5] , production of extracellular reactive oxygen species ( ROS ) [6] , secretion of ion-sequestering proteins such as Calprotectin [7] and Lactoferrin [8] , and production of neutrophil extracellular traps ( NETs ) [9–11] . However , the fungus often overcomes these immune mechanisms and infections ( aspergillosis ) are frequent in particular immunosuppressed patients [12] and those suffering from chronic granulomatous disease ( CGD ) [13] or chronic obstructive pulmonary disease ( COPD ) [14] . The mechanisms by which Aspergillus can avoid both cellular and extracellular antifungal mechanisms are not well understood , hampering our ability to develop effective treatments to avoid aspergillosis in these patients [15] . The presence of dichotomously branched hyphae has long been used as a diagnostic indicator for invasive A . fumigatus infection [16] . It has been generally assumed that controlled branching enhances fungal invasion , as strains that exhibit perturbed branching behavior and irregular growth polarity exhibit reduced virulence in vivo [17–19] . Perturbed branching in these mutant strains is driven by altered ROS production or localization [20 , 21] , calcium signalling [18 , 22] , hypoxic stress responses [19] , or cell wall synthesis pathways [23 , 24] . However , the study of host-hyphae interactions in vitro and in animal models of IA is limited by lack of control over the interactions and lack of methods to study these interactions at single cell resolution . Microscale technologies , particularly microfluidic devices , are emerging as an important tool for ex vivo analysis of neutrophil biology [25] . Recent work has begun to explore the applicability of these devices to investigating host-pathogen interaction [26 , 27] . For example , work by our group recently demonstrated that migration along chemotactic gradients acts to prime neutrophils for antifungal activity against A . fumigatus [27] , while others have used microfluidic techniques to identify immunosuppressive fungal metabolites [28] . For microbiologists , microfluidics has provided a useful platform for imaging active Candida hyphal growth at high resolution , revealing that internal polarity protein complexes are asymmetrically skewed toward softer substratum [29] . Here we present an in vitro microfluidic assay that enables us to study interactions between neutrophils and hyphae at cellular resolution over time . We designed microfluidic devices to confine the growing hyphae in linear channels , enabling high spatial and temporal resolution monitoring of growing hyphal tips . These new microfluidic devices facilitate live study of interactions in unprecedented detail . Key features of the microfluidic device include spatial control of hypha growth , allowing alignment of multiple hyphae in parallel lanes , as well as temporal and spatial control of interactions between hyphae and neutrophils , facilitating detailed imaging studies . Using the new tools , we systematically explored the mechanisms by which neutrophils trigger hyphal branching . Furthermore , we designed microfluidic devices to decouple branching from neutrophil interaction , using mechanical obstacles . This enabled us to quantify and compare the effect of neutrophils on hyphae at different stages of branching , from thick , primary hyphae through to thin , highly branched structures . We find that A . fumigatus branching after interaction with human neutrophils is independent of neutrophil NADPH oxidase activity and NETosis , but can be phenocopied by iron chelation and mechanical or physiological stalling of hyphal tip extension . Evasive branching and the consequential increased number of hyphal tips can enable the fungus to continue growing aggressively in the presence of small numbers of neutrophils . However , in the presence of sufficient numbers of neutrophils , branching increases fungus vulnerability to neutrophil destruction . Our findings suggest that new approaches to controlling A . fumigatus infections in patients are possible , and provide a new set of tools to further explore these possibilities for drug discovery .
To allow measurement of reproducible and controllable leukocyte-hypha interactions , we designed a microfluidic device to confine the growing hyphae along a single axis . The device consists of parallel channels with circular “swarming chambers” separating a fungal loading chamber from a neutrophil loading chamber ( Fig 1A ) . The device is multiplexed to allow 12 fields of view per device . Moreover , the use of microfluidic devices for microscopy enabled high throughput and high-resolution imaging in a single plane ( Fig 1A ) . Each viewing field accommodates 9 parallel interaction channels , allowing the potential for simultaneous capture of over 100 individual , interacting hyphae per condition , with 12 conditions tested per experiment . Conidia were seeded into the central chamber of the device ( Fig 1Aii ) and hyphae cultured for 18 hours , until they began entering the interaction channels ( Fig 1Aiii ) . Hypha entered 4–5 of the 9 interaction channels on average ( S1A Fig ) , with a slight bias observed toward the far viewing field , as might be expected for hyphae exhibiting primarily radial growth ( S1A Fig ) . Chemical treatments could be administered throughout the entire device by loading through the central chamber , or selectively to the growing tips of the hypha by administering into the outer channel via the neutrophil loading port ( Fig 1Ai ) . Delivery of neutrophils into the outer channel was followed by migration down the interaction channels towards the growing hyphae ( S1 Movie ) . Upon interaction of neutrophils with the hyphae , particularly when close to the growing tip , we observed an unexpected hyphal response: The hyphae formed new growth tips and branched , a phenotype we hypothesized to represent damage-induced “evasive branching” ( Fig 1Ciii–1Civ , S2 Movie ) . Neutrophils engaged new hyphal tips and inhibited their growth , resulting in a transient decrease in leading tip velocity ( Fig 2A ) . However , at least one hypha was almost always observed to avoid confrontation and continue penetrative growth ( Fig 2Evi , S2 Movie ) . The frequency of hyphal branching was proportional to the number of interacting neutrophils ( Fig 2B ) , occurred proximally to sites of interaction ( Fig 2C ) , and began shortly following initiation of the interaction ( Fig 2D ) . During induction of apical branching , neutrophil interaction decreased the growth velocity of the leading hyphal tip , relative to the number of neutrophils involved ( Fig 2A and 2E ) . To test the specificity of branch induction to neutrophil interaction , we repeated this assay using a range of other primary human immune cells: eosinophils; monocytes; monocyte-derived macrophages , and lymphocytes ( Fig 2F and 2G ) . Although comparable numbers of each cell type were delivered , fewer interactions with hyphae were observed for these cells compared to neutrophils , as they exhibited little recruitment to fungal hyphae ( Fig 2F ) . When they were observed to interact , apical branch induction occurred at less than half the rate for interaction with these cell types compared to neutrophils ( Fig 2G ) , while induction of lateral branching was not observed at all . Together , these correlative observations strongly support a model in which hyphae form new branches in response to neutrophil interactions . Following delivery of neutrophils , robust antifungal responses were observed , including attachment of neutrophils both to the growing tip and along the length of hypha ( Fig 3 ) , formation of neutrophil swarms ( Fig 3Aii ) , NETosis ( Fig 3Cii ) and generation of ROS ( Fig 3Aiii and 3Ciii ) . Given that neutrophil interaction induced formation of new hyphal tips , we hypothesized that this induction is dependent on neutrophil antimicrobial mechanisms . To test this hypothesis , we performed a series of experiments aimed at inhibiting or enhancing specific neutrophil functions . Our previous studies demonstrated that neutrophils primed by an N-formyl-methionine-leucyl-phenylalanine ( fMLP ) gradient exhibited enhanced suppression of A . fumigatus growth [27] . Introduction of an fMLP gradient in the current study prior to delivery of neutrophils resulted in increased recruitment of neutrophils to sites of interaction ( S1B Fig ) , an increase in hyphal exposure to NETs ( Fig 4A ) , and increased fungal branching ( Fig 4B ) . We therefore hypothesized that NETosis might be central to inducing hyphal branching . Partial inhibition of NETosis has been demonstrated by reducing the activity of Neutrophil Elastase or Myeloperoxidase by pre-treatment Neutrophil Elastase inhibitor ( NEi ) or 4-aminobenzoic acid hydrazide ( ABAH ) respectively [8 , 30] . Pre-treatment of neutrophils with these chemicals did not significantly reduce NETosis or effect branching in this system ( Fig 4A and 4B ) . Inhibition of NADPH oxidase using diphenyleneiodonium chloride ( DPI ) successfully abrogated the enhanced NETosis observed for fMLP-primed neutrophils ( Fig 4A ) , but reducing NETosis in this manner appeared to have no effect on hyphal branch induction ( Fig 4B ) . Degrading NETs using DNAse , one of the most robust methods of reducing NETs from healthy neutrophils [31 , 32] , also failed to effect hyphal branch induction ( Fig 4B ) , suggesting that branch induction occurs independently on NETosis . In a physiological setting , platelets have been demonstrated to coordinate with neutrophils to enhance antifungal activity [33] . To test whether this phenotype could be recapitulated in our system , autologous platelet poor plasma ( PPP ) , platelet rich plasma ( PRP ) , or washed platelets ( WP ) were added alone or immediately preceding neutrophil delivery . Strikingly , we observed robust stimulation of hyphal hyperbranching in response to exposure to PPP and PRP , but not WP , even in the absence of neutrophils ( Fig 5A and 5B ) . Lack of hyperbranching in the acid citrate dextran ( ACD ) -containing media control and the WP group ruled out a role for ACD in induction of this response . Induction of the hyperbranching response appeared to coincide with formation of cross-linked fibrin clots in the plasma groups ( S3 Movie ) . To test whether clot formation was sufficient to induce hyphal branching , we spiked WP with fluorescently labelled Fibrinogen , which formed robust clots upon platelet activation by the hyphae . This treatment increased hyphal branching relative to the ACD control ( Fig 5C ) . However , the non-clotting control treatment of Fibrinogen alone induced additional branching compared to the clotting treatment group , demonstrating that Fibrinogen alone is sufficient to induce branching and that formation of cross-linked fibrin clots was dispensable for this response ( Fig 5C ) . Plasma is a complex mix of proteins , peptides and various chemicals , some of which are stable at 56°C , such as Fibrinogen , and some of which are degraded , such as Complement factors . Treatment of hyphae with heat-treated ( HT ) -plasma did not induce hyperbranching ( Fig 5C ) , ruling out endogenous Fibrinogen as the inducing factor present in plasma , and instead implicating a heat-labile component in this pathway . Importantly , plasma-induced hyperbranching significantly attenuated leading tip velocity , relative to the number of branches produced ( Fig 5C and 5D ) , suggesting a strong link between increased branching and decreased hyphal tip growth speed . The ability of non-commensal fungi to sense and avoid physical obstacles ( thigmotropism ) plays an important role in their hyphal growth in their primary environment , and is also implicated in virulence [34] . Since induction of branching was observed following decreased hyphal growth speed in response to increased neutrophil recruitment ( Fig 2A , S2 Movie ) , we hypothesized that hypha might be responding to mechanical impedance of leading tip growth . To test this hypothesis , we developed a new device that incorporated a range of physical obstacles into our microfluidic channels , designed to apply variable mechanical impedance to the growing tip upon contact . During interaction with the hypha tip , these obstacles induced apical branching similar to that observed during neutrophil interaction ( Fig 6A and 6B ) , at a frequency relative to the ability of the obstacle to slow tip extension ( Fig 6C and 6D ) . A crescent-shaped “tip-trap” obstacle , which efficiently slowed tip extension ( Fig 6D and 6E ) induced branching at a rate of more than 80% ( Fig 6B and 6C , S4 Movie ) , demonstrating that physical stalling of tip extension is sufficient to induce hyphal branching . Since single neutrophils were observed to induce apical branching ( Fig 6A ) and seemed unlikely to be physically blocking hyphal extension , we hypothesized that they might instead induce branching by physiologically stalling tip extension . To test whether physiologically stalling growth was sufficient to induce apical branching , we compared branching rates between actively growing hyphae and hyphae whose growth had been arrested by reducing the temperature to 4°C for 6 hours prior to imaging at 37°C ( Fig 7A ) . To synchronize growth of control hyphae with the cold-paused group , control hyphae were also initially arrested at 4°C for 6 hours and then grown continuously . Upon resuming growth , a 3-fold increase in branching was observed for growth-arrested hypha ( Fig 7B ) , confirming that stalling tip extension in the absence of mechanical impedance is sufficient to induce branching at the hypha tip . Neutrophil-derived lactoferrin has been shown to inhibit hyphal growth by chelation of iron [8] . To test whether iron chelation could induce branching , we exposed actively growing hyphae to non-specific ( EDTA ) and specific ( hydroxybenzyl ethylenediamine—HBED , Lactoferrin ) iron chelators ( Fig 8 ) . The addition of HBED resulted in a statistically significant increase in hyphal branching ( Fig 8A ) , while treatment with EDTA or active Lactoferrin also significantly slowed hyphal tip growth and appeared to double average branching ( from 0 . 26 to 0 . 56 and 0 . 48 branches/hypha/hour respectively , Fig 8 ) . Importantly , Lactoferrin-induced growth inhibition was not observed for iron-saturated Lactoferrin ( “Lactoferrin ( Fe ) ” , Fig 8B ) , demonstrating that the growth inhibition effect of Lactoferrin treatment was dependent on its capacity to chelate iron . The robust branching frequency induced by the novel crescent-shaped “tip-trap” obstacle ( Fig 6C , S4 Movie ) facilitated measurement of the effect of branch induction on hyphal growth speed . By comparing hyphae during induction of sequential branching , we observed that tip velocity was reduced post-branch , relative to the number of times branches were induced ( Fig 9Ai and 9Aii , S5 Movie ) . This approach also enabled us to test the relationship between the degree of hyphal branching and the susceptibility of hypha to destruction by neutrophils . Addition of neutrophils to the device following induction of sequential branching demonstrated that neutrophils were better able to compromise the cell wall integrity of highly branched hyphae , as indicated by reduced time to loss of hyphal cytoplasmic EGFP fluorescence ( Fig 9B and 9D , S6 Movie ) , likely due to their smaller diameter ( Fig 9C and 9E ) . Importantly , neutrophils with reduced capacity to produce ROS ( DPI treated ) and neutrophils isolated from at-risk patients ( kidney transplant recipients receiving immunosuppressive therapy , S1 Table ) were less efficient at killing primary hyphae , exhibiting 42% and 59% killing respectively , compared to 86–87% in control groups . The time required for killing was also increased , even for thin , highly branched hyphae generated using sequential mechanical branch induction ( Fig 9F ) . These observations support a model in which induction of branching may inhibit hyphal infection by slowing hyphal penetration , and increase hyphal vulnerability to destruction by healthy neutrophils , so long as sufficient numbers of active neutrophils are present . However , if neutrophil numbers are insufficient , or their activity suppressed , branch induction provides an evasive mechanism by which hyphae can avoid neutrophil interaction to continue , and in some cases amplify , hyphal growth .
Here we demonstrate that neutrophil-hypha interactions correlate with branching of Aspergillus fumigatus hyphae . The ability of neutrophils to pause hyphal tip extension is key to this fungal response . The response could be phenocopied by stalling of hyphal tip growth mechanically , thermally , and via chelation of iron . Branching is associated with lower hyphal tip extension velocity , and results in thinner hyphae with increased surface area-to-volume ratios , although total fungal mass may not be affected . In the presence of excess neutrophils , thinner hyphae exhibit increased vulnerability to destruction . Mutations that induce constitutive hypha hyperbranching in A . fumigatus are associated with severe growth inhibition and decreased virulence in infection models [17–19] , likely due to the slower speeds at which they are able to penetrate the tissue , and potentially an increased vulnerability to destruction . Induction of hyperbranching by plasma may provide a mechanism that sensitizes hypha to destruction by innate immune cells , but may also have negative implications for clot formation and blockage of vessels in infection [35–37] . Clot formation was robustly induced by hyphae in the presence of platelets and Fibrinogen , although formation of cross-linked Fibrin was dispensable for induction of branching . In the context of our iron chelation experiments , the ability of Fibrinogen alone to induce branching is likely linked to its activity as a iron-binding protein [38] . Identification of specific plasma factors that induce hyphal hyperbranching remains an interesting avenue for further research . Our study expands upon the current understanding of the activity of neutrophils in aspergillosis . Neutrophils are assumed to be ineffective at killing phagocytosed conidia in vitro due to the tough outer coating [39] and because conidia appear to remain immunologically silent to neutrophils until they begin to germinate [40 , 41] . Observations are emerging suggesting that the primary role of neutrophils is to kill hyphae [42 , 43] . Working in concert with non-cellular immune components , such as platelets [33] , defensins [44] and complement [45 , 46] , extracellular neutrophil mechanisms act to restrict invasive growth and destroy hyphal structures . This study has demonstrated that interaction with neutrophils induces loss of hyphal tip polarity and induction of de novo hyphal tip formation . Given that this effect could be phenocopied by chelation of iron , neutrophil-derived Lactoferrin is a good candidate to mediate this interaction . Neutrophil-derived Lactoferrin has previously been shown to inhibit conidial growth [8 , 47] , but was not found to significantly affect hyphal viability [8] . However , hyphae exposed to human neutrophils show increased expression of ion-transporter catalysis mediators , including the 4-fold up-regulation of the ferric-chelate reductase Afu6g13750 [48] . Fungal ferric-chelate reductases are functionally homologous to components of the human NADPH oxidase complex [49] , and are important for both iron acquisition and production of ROS . Given the central role of intracellular ROS in maintenance of hyphal tip polarity [20 , 21] and the role of ion sequestration in neutrophil suppression of fungal growth [7 , 50] , the link between neutrophils , iron-sequestration , and loss of tip polarity is well supported . Furthermore , localized extracellular ROS generated by neutrophils at sites of interaction along the length of the hyphae appeared to coincide with de novo tip formation in some instances ( Fig 3C ) suggesting a parallel pathway by which neutrophil interaction might induce localized re-polarization and lateral hyphal branch formation . The current study was enabled by innovative microfluidic devices that allowed us to study interactions between neutrophils and fungal hyphae in unprecedented detail by: 1 ) Guiding the growth of individual hypha tips down parallel channels , allowing us to track hypha growth speed over defined distances; 2 ) Initiating interaction and branching with highly controlled temporal and spatial constrains; 3 ) Integration of imaging and biochemical assays; and 4 ) Providing a high-throughput system to trigger branching with utmost control over location , number , and timing of branching . Although predominantly associated with diseases that suppress the immune system , opportunistic fungal infections are becoming an increasing problem in the clinic [51] . Modern medical treatments involving organ and bone marrow transplant rely heavily on immunosuppression of the patient to avoid tissue rejection and graft-versus-host disease . Although a direct effect of immunosuppressant therapy on neutrophil activity remains controversial , our previous studies indicate that neutrophils from these patients exhibit reduced activity against A . fumigatus [27] . Prophylactic treatment of immunosuppressed patients with broad-spectrum antibiotics , aimed at reducing risk of bacterial infection , is standard practice [52] . This not only encourages development of multidrug-resistant bacterial strains [53] , but also affects commensal bacteria niches [54] . In combination with host immunosuppression , this scenario creates an environment permissive for opportunistic fungal infection [55–57] . Our studies indicate that , if insufficient neutrophil numbers are present , hyphal branching acts as an evasive maneuver , allowing the hyphae to continue penetrative growth past the interaction point . Given the susceptibility of neutropenic patients to fungal infection , these observations suggest that interacting with low neutrophil numbers may actually make the infection more aggressive by increasing the number of tissue-penetrating hyphal tips . Although still relatively rare , rates of opportunistic fungal infection are steadily rising [51] , and once established , infections are very difficult to treat [56] . Shared eukaryotic biology underpins the toxicity of many current antifungal medications to the host [58] , while few attempts at development of new therapies have been made in the last decades [59] . The ability to modulate either neutrophil or hyphal behavior in the context of this interaction has direct relevance to the outcome of infection . Correction of neutrophil numbers or perhaps even stimulation of existing neutrophil functions in neutropenic patients is likely to improve outcome by increasing hyphae destruction . Similarly , blockage of de novo hyphal tip formation by targeting key fungal proteins such as WetA [60] may reduce hyphal virulence by reducing the number of hyphae for neutrophils to target . Conversely , induction of hyperbranching , such as can be achieved by targeting regulatory factors such as Calcineurin [61 , 62] , may slow hyphal growth enough to allow destruction by neutrophils . The assay we describe here may provide a high-throughput screening platform for identification of drugs affecting these important pathways . In particular , the novel “tip-trap” devices we present allow exceptional control over induction of hyphal branching , would be well suited to identification of compounds that inhibit de novo tip formation , and could be applied to a wide range of fungal species that exhibit hyphal growth .
The microfluidic device was designed with three key elements , ( 1 ) the neutrophil loading ports and outer channel ( Fig 1Ai , in purple ) , ( 2 ) the central conidia loading port and inner chambers ( Fig 1Aii , in green ) and ( 3 ) the neutrophil-hypha interaction channels ( Fig 1Aiii ) . The twelve imaging fields on each device contain nine 10 x 675 μm interaction channels spaced 65 μm apart , each with seven 50 μm diameter swarming chambers spaced at 50 μm intervals . Both the outer neutrophil loading channel and inner conidia port were fabricated at 50 μm depth , while the interaction channels at 10 μm depth to facilitate single-plane imaging . The master wafer was fabricated using standard photolithographic technologies with Mylar photomasks ( FineLine Imaging , Colorado Springs , CO ) . Polydimethylsiloxane ( PDMS ) ( Sylgard 184 , Elsworth Adhesives , Wilmington , MA ) microfluidic devices were made by replica molding from the master wafer . Briefly , PDMS and curing agent were combined at a 10:1 ratio , mixed thoroughly , and poured over the master wafer . PDMS was then degassed for at least 30 min , then baked at 65°C for at least 3 hours . The PDMS was then peeled from the master wafer , and holes punched . The two outer neutrophil loading holes were punched with a 0 . 75 mm puncher , while the inner conidia loading port was punched using a 1 . 2 mm puncher . Each device was then cut out using an 8 mm puncher ( Harris Uni-Core , Ted Pella Inc . , Redding , CA ) . Following oxygen plasma treatment , devices and 12-well glass-bottom plates ( MatTek Corp . Ashland , MA ) were bonded at 85°C on a hotplate for 10 mins , and allowed to cool slowly . One day prior to the experiment , devices were primed with Iscove’s Modified Dulbeco’s Medium ( IMDM ) with 20% fetal bovine serum ( FBS ) by loading the device with a gel-loading tip , inner port first followed by outer ports , such that media formed a dome on top of the device covering all three loading ports . Devices were subjected to vacuum for 10 mins , then allowed to re-pressurize for at least 20 mins , or until all air bubbles within the device were gone . The entire device was then submerged in media ( 2 mL per well in 12 well plate ) , and 0 . 5 μL of conidia ( at 5 x 107 conidia/mL ) loaded into the central port using a gel-loading tip , with care taken not to pipette into the surrounding media . Loaded devices were then incubated overnight , allowing germination of conidia and growth of hyphae into the outer chambers and interaction channels . Once hyphae began to enter the interaction channels , neutrophils were delivered through the loading port and into the outer channels . For N-formyl-Met-Leu-Phe ( fMLP ) experiments , fMLP ( Sigma ) at 100nM was delivered through the central loading port . Immune cells were isolated from human peripheral blood samples from healthy donors aged 21 years and older ( Research Blood Components , LLC Allston , MA ) drawn into 10 mL sodium heparin vacuum tubes ( Vacutainer , Becton Dickinson ) . Nucleated cells were isolated by HetaSep gradient , then immune subsets were separated using EasySep kits from STEMCELL Technologies ( Vancouver , Canada ) . Neutrophils were isolated using a Human Neutrophil Enrichment Kit , eosinophils with a Human Eosinophil Enrichment Kit , monocytes using a Human Monocyte Enrichment Kit , and lymphocytes using a Human Lymphocyte Enrichment Kit . Human macrophages were differentiated from monocytes using standard techniques . Briefly: Monocytes were suspended in 1640 Roswell Park Memorial Institute medium ( RPMI ) and incubated for 2 hours at 37°C with 5% CO2 to allow monocyte adherence . After 2 hours , the non-adherent population was removed , and the remaining cells washed with ice cold Phosphate Buffered Saline ( PBS ) and trypsinized for 5 min at 37°C . Cells were then suspended in 1640 RPMI + 10% FBS medium containing Granulocyte macrophage colony-stimulating factor ( GM-CSF ) , and incubated at 37°C with 5% CO2 for 24 hours . The GM-CSF medium was then replaced with 1640 RPMI with 1% Pen/Strep and 10% human serum ( Sigma ) . Cells were maintained in 1640 RPMI with Pen/Strep and 10% human serum for 7 days with the medium changed every 3 days . Prior to loading into the device , cells were typically stained with Hoechst 33342 ( Life Technologies , Carlsbad , CA ) according to the manufacturers protocol and re-suspended at 6 x 107 cells/mL in media . For imaging ROS , CellROX DeepRed reagent ( ThermoFisher ) was added at 12 . 5 μM to both to the media within the device and used to pre-stain neutrophils for 30 min prior to co-delivery with cells . For all drug treatments , neutrophils were pre-treated for at least 30 mins with drug ( diphenyleneiodonium chloride ( DPI—Sigma ) at 200 μM , 4-aminobenzoic hydrazide ( ABAH—Sigma ) at 500 μM , N-Methoxysuccinyl-Ala-Pro-Val-chloromethyl ketone ( NEi—ThermoFisher ) at 100 μM ) and washed prior to delivery . A . fumigatus conidia of strain 293 expressing cytosolic EGFP of RFP were a kind gift from Jatin Vyas , prepared by Nida Khan using standard methods . Upon receipt , conidia were filtered using spin columns with 5 μm pores ( Thermo Scientific ) to isolate un-germinated spores and re-suspended at 5 x 107 conidia/mL in dH2O . To prepare plasma fractions , including WP , PRP , and PPP , blood was collected from healthy volunteers in accordance with IRB protocols ( 2009-P-000295 ) . Platelets were prepared from blood collected into 2 . 9 mL trisodium citrate Vacutainer tubes ( Sarstedt , Nümbrecht , Germany ) . PRP was prepared by centrifugation of the whole blood at 210 g , 22°C , for 20 minutes . The PRP supernatant was gently pipetted into two tubes . In one tube , 20% volume acid citrate dextrose solution ( ACD ) ( Boston Bioproducts Inc . , Ashland , MA ) was added and the PRP was incubated at 37°C until the start of the experiment . The second tube was centrifuged at 1900 g , 22°C , for 10 minutes . The PPP was removed , leaving a platelet pellet . IMDM with 20% FBS was used to re-suspend the platelet pellet , and ACD was added to the PPP and the platelet suspension . Platelets were stained with Calcein ( Thermo Fisher Scientific Inc , Grand Island , NY ) . HT-plasma was prepared by heating PPP to 56°C for 30 mins . All plasma preparations were kept at 37°C until the start of the experiment . Iron chelation was achieved by delivering chelating treatments throughout the entire device , via the central port . EDTA ( Sigma-Aldrich ) was delivered at 10 μM , HBED ( Santa Cruz Biotechnology ) at 100 μM , and Lactoferrin groups at 100 μM . Human Lactoferrin derived from human breast milk was purchased from Sigma in both active and inactive ( iron-saturated ) forms . All time-lapse imaging experiments were performed at 37°C with 5% carbon dioxide on a fully-automated Nikon TiE microscope , using a Plan Fluor 10x Ph1 DLL ( NA = 0 . 3 ) lens . Image capture was performed using NIS-elements ( Nikon Inc , Melville , NY ) and analysis performed using FIJI ( FIJI is just ImageJ , NIH ) and Python scripts . NIS-elements and FIJI software were used to normalize the background color , and convert the file to an AVI . Python scripts used OpenCV ( opencv . org ) and TrackPy ( http://soft-matter . github . io/trackpy/v0 . 3 . 0/ ) toolkits . OpenCV was used for background subtraction , automated cell counts and NET identification . A 5-frame rolling background filter was applied to remove cells that remained stationary for more than 10 mins from the tracking analysis . Tracks were based on relative velocity and position and informed by the inherent physical limitations imposed by the microfluidic channels . The TrackPy toolkit was used for cell tracking using the cells positions identified previously . Automated cell identification was based on cell size and shape , NET production was based on color and size , and hyphal tip velocity was measured by manually tracking tips in FIJI . Graphing and statistical analysis was performed using Prism ( v6 . 0c , GraphPad Software Inc . La Jolla , CA ) . Statistical analysis included two-tailed Student’s t-tests for comparing pairs of normally distributed data , and one-way ANOVA with Tukey’s post hoc test for comparing datasets containing multiple treatment groups . Samples from kidney transplant patients ( S1 Table ) were collected at the Massachusetts General Hospital ( MGH ) with informed consent and IRB approval under protocol number 2009-P-002826 .
|
For patients who have compromised immune systems , infection with yeasts and moulds are often deadly . To learn more about how immune cells fight fungal infection , we developed an “infection-on-a-chip” device , which allowed us to directly image interactions in real time . To our surprise , when immune cells attacked the fungi and stalled its growth , the fungi formed new branches , allowing it to avoid the interaction and continue growing . Our hypothesis is that in people with healthy immune cells , forcing the fungi to branch may make it more vulnerable to further attack . However , in patients with impaired immune systems , increasing the number of fungal branches may make the infection more aggressive . By developing new drugs that affect fungal branching , we may be able to give the immune cells of sick patients an advantage in their uphill battle against fungal infection .
|
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2017
|
Neutrophil Interactions Stimulate Evasive Hyphal Branching by Aspergillus fumigatus
|
Ler , a member of the H-NS protein family , is the master regulator of the LEE pathogenicity island in virulent Escherichia coli strains . Here , we determined the structure of a complex between the DNA-binding domain of Ler ( CT-Ler ) and a 15-mer DNA duplex . CT-Ler recognizes a preexisting structural pattern in the DNA minor groove formed by two consecutive regions which are narrower and wider , respectively , compared with standard B-DNA . The compressed region , associated with an AT-tract , is sensed by the side chain of Arg90 , whose mutation abolishes the capacity of Ler to bind DNA . The expanded groove allows the approach of the loop in which Arg90 is located . This is the first report of an experimental structure of a DNA complex that includes a protein belonging to the H-NS family . The indirect readout mechanism not only explains the capacity of H-NS and other H-NS family members to modulate the expression of a large number of genes but also the origin of the specificity displayed by Ler . Our results point to a general mechanism by which horizontally acquired genes may be specifically recognized by members of the H-NS family .
Enteropathogenic Escherichia coli ( EPEC ) and enterohaemorrhagic E . coli ( EHEC ) are causal agents of infectious diarrhea . While the former is responsible mainly for infantile diarrhea , EHEC infections are associated with hemorrhagic colitis and may produce a life-threatening complication known as hemolytic uremic syndrome . EPEC and EHEC are non-invasive pathogens that produce characteristic attaching and effacing ( A/E ) intestinal lesions [1] . The genes required for the formation of A/E lesions are clustered on a pathogenicity island known as the locus of enterocyte effacement ( LEE ) . LEE genes are organized in five major operons ( LEE1 to LEE5 ) and several smaller transcriptional units and they encode the components of a type III secretion system ( TTSS ) , an adhesin ( intimin ) and its receptor ( Tir ) , effector proteins secreted by the TTSS , chaperones , and several transcription regulators [2] . The first gene of the LEE1 operon encodes the LEE-encoded regulator Ler , which is essential for the formation of A/E lesions in infected cells [3] , [4] and for the in vivo virulence of A/E pathogenic E . coli strains [5] . Ler ( 123 amino acids , 14 . 3 kDa ) is the master regulator of LEE expression and is required to activate LEE genes that are otherwise repressed by the histone-like nucleoid structuring protein H-NS [2] . The H-NS protein , best characterized in E . coli and Salmonella , is a member of a family of transcriptional regulators with affinity for AT-rich DNA sequences that mediate the adaptive response of bacterial cells to changes in multiple environmental factors associated with colonization of different ecological niches , including human hosts . H-NS is usually an environmentally-dependent transcriptional repressor . H-NS-mediated repression ( usually termed silencing ) is alleviated either by alterations in physicochemical parameters ( i . e . , a transition from low ( 25°C ) to high ( 37°C ) temperature ) , by the activity of proteins that displace H-NS from its target DNA sequences , such as Ler , or by a combination of both . H-NS regulation is strongly associated with pathogenicity , thus understanding the basis of the selective regulation of virulence genes could lead to sustainable antimicrobial strategies that are less susceptible to acquiring resistance . In addition to the LEE genes , Ler is also involved in the regulation of other horizontally acquired virulence genes located outside the LEE loci and scattered throughout the chromosome of A/E pathogenic strains [3] , [6] , [7] . However , Ler does not regulate other H-NS-silenced operons such as bgl [8] and proU [3] . This observation shows that Ler is not a general antagonist of H-NS , but a specific activator of virulence operons acquired by horizontal transfer ( HT ) . Selective regulation of HT genes has been demonstrated in the plasmid R27 encoded H-NS paralogue ( H-NSR27 ) and in chromosomal H-NS in the presence of a co-regulator of the Hha/YmoA family [9] . The mechanism of Ler-mediated activation has been extensively studied in operons located both within the LEE loci , such as LEE2/LEE3 [10] , grlRA [11] , [12] and LEE5 [8] , and outside , including nleA ( for non-LEE-encoded effector A ) [13] and the lpf1 fimbrial operon [6] , [14] . These studies suggest that Ler counteracts the silencing activity of H-NS by directly binding to DNA and displacing H-NS from specific promoter regions . Ler does not exert dominant negative effects on H-NS function and there is no evidence of a direct interaction between Ler and H-NS [8] . Despite the wealth of biochemical/biophysical data , including the proposal of a DNA sequence consensus motif for H-NS [15] , the lack of structural data on the complexes formed between H-NS or H-NS family members and DNA has until now prevented a detailed understanding of the mechanism of DNA recognition and the basis of the selectivity within H-NS family proteins . All H-NS-related proteins identified to date are predicted to be organized in two structurally different domains . While the oligomerization domains of Ler and H-NS differ greatly , their DNA binding domains are very similar , thereby suggesting that they account for the similar recognition properties of both proteins , and possibly also for their distinct selectivity . While a possible interplay between protein oligomerization and DNA binding cannot be ruled out , a detailed understanding of the recognition mechanism by individual DNA-binding domains is a prerequisite for further studies . The C-terminal domain of Ler ( CT-Ler ) , exhibits significant amino acid homology with the C-terminal H-NS DNA-binding domain ( CT-H-NS; 36 . 0% identity , 63 . 8% similarity ) and its deletion abolishes DNA binding [16] . CT-Ler contains a sequence ( TWSGVGRQP ) similar to the consensus core DNA-binding motif found in H-NS-like proteins ( TWTGXGRXP ) [17] . Here we present the solution structure of a complex formed by CT-Ler bound to a natural occurring DNA sequence of the LEE2/LEE3 regulatory region . This is the first report of a DNA complex that includes a member of the H-NS family characterized at atomic detail . Our results reveal that CT-Ler does not participate in base-specific contacts but recognizes specific structural features in the DNA minor groove . The indirect readout mechanism can be extended to H-NS and other H-NS family members and explains their capacity to modulate the expression of a large number of genes . The CT-Ler/DNA structure provides clues for the mechanism by which HT genes may be specifically recognized by members of the H-NS family and illustrates the general features of DNA minor groove readout .
We used a CT-Ler construct encompassing residues 70–116 ( Figure 1A ) . This construct gave rise to a folded and functional domain ( Figure S1 ) with excellent solubility and long-term stability . Residues 117–123 are part of an extension that is dispensable to counteract H-NS repression [18] . NMR spectra of a construct including these residues showed that they are disordered and have no effect on the structure of the folded domain , as seen by the exact coincidence of the cross-peak position of most residues in HSQC NMR spectra of different constructs ( Figure S2 ) . The sequence of the short DNA fragment used to form the complex was based on the regulatory region of the LEE2/LEE3 operons spanning positions -221 to -101 . This region was protected by Ler in footprinting experiments [10] . Seven 30 bp long dsDNA , LeeA-LeeG , with a 15 bp overlap between consecutive fragments ( Figure 1B , Table S1 ) were tested for binding to CT-Ler using fluorescence anisotropy . As positive and negative controls , we used two 30-mer duplexes: an adenine tract that was previously employed to study the DNA-binding properties of CT-H-NS , ( GGCAAAAAAC ) 3 [19] and ( GTG ) 10 ( Figure S3 ) . CT-Ler showed the highest affinities for LeeF and LeeG ( Figure 1B ) and we further analyzed its binding to the 15 bp overlapping region of theses two fragments , namely LeeFG ( AAATAATTGATAATA ) . Fluorescence anisotropy titrations showed small but systematic deviations from the 1∶1 model , suggesting simultaneous multiple binding to this DNA sequence ( Figure 1C ) . Since the consensus binding motif proposed for H-NS is only 10 bp long [15] we designed a new 15 bp DNA , LeeH ( GCGATAATTGATAGG ) , containing the central 10 bp of LeeFG flanked by GC base pairs for thermal stability . LeeH partially matches the proposed H-NS consensus sequence ( tCG ( t/a ) T ( a/t ) AATT ) [15] . A good fit to a 1∶1 model with apparent Kd 1 . 10±0 . 05 µM was observed for this duplex ( Figure 1C ) . The complex of CT-Ler with LeeH was solved by a combination of NMR and small-angle X-ray scattering ( SAXS ) . The structure determination protocol consisted of the independent calculation of the structure of bound CT-Ler and DNA , followed by intermolecular NOE ( iNOEs ) driven docking and a final scoring including SAXS data . CT-Ler structures were calculated based on 1302 NOE distance restraints , together with torsion angle and experimentally determined hydrogen bonds . The restraint and structural statistics of the 20 lowest energy structures are shown in Table S2 . None of the structures contained distance or dihedral angle violations >0 . 5 Å or 5° , respectively . The pattern and intensities of bound DNA NOEs were typical of a B-form . The DNA structure was optimized in explicit solvent using experimental restrains determined in the bound form , starting from canonical B-DNA as described in the Materials and Methods section . The absence of major distortions in the DNA structure caused by CT-Ler binding was confirmed by the good agreement between the experimental SAXS curve of free LeeH and the prediction based on the DNA model extracted from the final complex ( Figure S4 ) . The DNA region most affected by CT-Ler binding , identified by the combined chemical shift perturbations of nucleotide protons , is centered in the symmetrical 4 bp AT-tract , AATT ( Figure 2A ) . The largest chemical shift perturbations of CT-Ler ( Figure 2B ) were observed for residues Val88 to Arg93 . The 30 assigned iNOEs involve protein residues located in the region where the chemical shift perturbations were observed . On the basis of these iNOE restraints and the mapped interfaces , 400 CT-Ler/LeeH complex structures were generated as described in Materials and Methods and ranked by energy and NMR intermolecular restraint ( irestraint ) violations . The quality of the structures was confirmed by comparing the predicted and experimentally determined SAXS curves of the complex . The SAXS profile predicted for the best NMR-derived complex structure is in good agreement with the experimental curve ( Figure 3A ) . The scatter plot in Figure 3B shows that , in general , the best NMR structures also fit SAXS data well . The final ensemble of 20 structures was selected using a scoring function that combined docking energy and measures of the agreement with experimental NMR and SAXS data ( red circles ) . The ensemble is well defined ( Figure 3C ) , with a pairwise RMSD ( heavy atoms ) of 1 . 30±0 . 38 Å and all conformers exhibited good geometry , no violations of iNOE distance restraints >0 . 5 Å and correctly explained the SAXS data . Most of the protein residues are in the core region of the Ramachandran plot . The small irestraint deviations illustrate that the protein-DNA interface is well defined , allowing us to elucidate a molecular basis for CT-Ler/LeeH recognition . The structure of DNA-bound CT-Ler contains a central helix ( residues 93–101 ) and a triple-stranded antiparallel β-sheet ( β1:76–78 , β2:84–85 , β3:109–110 ) . The β1-β2-hairpin is connected to the α-helix by a loop ( Loop2:86–92 ) . A turn and a short 310-helix ( 105–108 ) link the helix to the β3 strand . The similarity between the Cα and Cβ secondary chemical shifts of the free and bound forms indicate that the secondary structure is retained upon binding ( Figure S5 ) . The overall protein fold is analogous to that previously described for CT-H-NS in the absence of DNA [19] . CT-Ler binds as a monomer inserting Loop2 and the N-terminal end of the α-helix into the DNA minor groove and contacting the central 6 bp region ( A6A7T8T9G10A11 ) ( Figure 4 ) . The complex buries 953±55 . 64 Å2 of surface area and is stabilized by non-specific hydrophobic and polar contacts , involving mainly the sugar-phosphates backbone and residues of the consensus DNA-binding motif found in H-NS-like proteins . Residues Trp85 , Gly89 , Arg90 and Pro92 ( Figure 1A ) , highly conserved among H-NS-like proteins , are located in the complex interface ( Figure 4B ) , and all gave rise to iNOE restraints with DNA . A summary of the observed intermolecular contacts is shown in Figure 4D . The interaction surface of CT-Ler is positively charged and the Arg90 side chain is deeply inserted inside a narrow minor groove ( Figure 4B and C ) . In addition , Arg93 at the N-terminus of the α-helix and the helix-dipole moment itself create a positively charged region that points into the negatively charged minor groove . The width of the LeeH minor groove varies along the sequence and deviates significantly from the average value of canonical B-DNA ( Figure 5 ) . The groove progressively narrows towards the A7pT8 base step , and widens at the T9pG10 base step . The DNA electrostatic potential is modulated by the width of the minor groove . The guanidinium group of Arg90 interacts with the narrowest region of the groove where the electrostatic potential is most negative ( Figure 5A and B ) . The approach of Loop2 , where Arg90 is located , is enabled by the adjacent widening of the minor groove . Sequence-dependent variations of DNA structure can be described in terms of helical parameters , such as roll and helix twist ( Figure 5C and D ) . The roll angle is most negative ( −4 . 64°±1 . 38 ) at the A7pT8 base step and is small or negative for most of the steps in LeeH except for the pyrimidine-purine base steps , which show large positive values . A series of consecutive small/negative roll angles leads to the narrowing of the minor groove [20] . The groove widening at T9pG10 can be traced to a combination of positive roll and a small helix twist of 33 . 8°±0 . 8 , indicating that the segment is slightly unwound with respect to the standard B-form . The region including the A6A7T8T9 stretch is slightly overwound , with an average helix twist of 37 . 4°±1 . 6 . To verify the relevance of Arg90 in the interaction , we replaced this residue by glycine ( R90G ) , glutamine ( R90Q ) or lysine ( R90K ) and tested their effects on the affinity of CT-Ler to LeeH . All CT-Ler variants were properly folded , as determined from NMR , and their interaction with LeeH was measured by fluorescence anisotropy ( Figure 6A ) . The mutated domains showed no affinity to LeeH or highly reduced affinity ( R90K ) , thereby confirming that Arg90 is an essential residue . The effect of these mutations on the binding of Ler ( 3–116 ) , including the oligomerization domain , to the LEE2 regulatory region ( positions −225 to +121 ) was determined using electrophoretic mobility shift assays ( EMSA ) ( Figure 6B ) . In agreement with the results obtained with the isolated CT-domain , DNA binding by Ler is abolished by R90Q and R90G mutations and strongly reduced in the case of the R90K variant . These experiments confirm the essential role of Arg90 in the context of the oligomeric Ler protein and for the range of binding sequences present in one of its natural targets . The structure of the CT-Ler/LeeH complex does not show base specific contacts . On the contrary , the structure of the complex suggests that CT-Ler recognizes local structural features of the minor groove that may be associated with distinct DNA sequences . In order to gain some insight into the range of DNA sequences that can be recognized by CT-Ler , we measured the dissociation constants of complexes formed by two series of short DNA duplexes related to the LeeH sequence . In the first series we introduced a single base pair replacement in each of the ten central positions of LeeH . Adenines and thymines were replaced by guanines and cytosines , respectively , and guanine in position 10 was mutated to adenine , to preserve the purine-pyrimidine sequence . In the second series , we compared the binding of CT-Ler to several 10-mer duplexes . One of these contained the AT-tract ( AATT ) that interacts with CT-Ler in the LeeH complex flanked by GC base pairs to ensure thermal stability . Variants were designed to test the effect of interrupting the AT-tract by TpA steps at a number of positions . Affinity to CT-Ler was measured by fluorescence anisotropy . The results are shown in Figure 7 and the DNA sequences and dissociation constants are listed in Table S3 . Figure 7A shows the relative Kd values of the single base-pair replacements of LeeH . The largest effects were observed when the base pairs of A6 or A7 were replaced . The base pair of G10 resulted to be similarly relevant . A smaller effect was observed at the position of T8 . Small non-specific effects were observed in all the remaining sites except that of A4 . The most affected base pairs were at the sites where the minor groove width in LeeH is more different from the standard B-DNA and define the features that we hypothesize to be recognized by CT-Ler: the narrow groove where the Arg90 side chain is inserted and the wide adjacent region that enables the approach of Loop2 . Figure 7B show the relative dissociation constants of the complexes formed by the 10-mer duplexes . The presence of TpA steps in CGCAATAGCG , CGCTATAGCG and CGCTTAAGCG results in a decrease in the stability of the complexes . The remaining three sequences ( CGCAATTGCG , CGCAAATGCG , and CGCAAAAGCG ) show AT-tracts of the same length but their affinity for CT-Ler differs . The complex with the A4 stretch is 2-fold less stable than that containing the AATT motif . The AT-tract in LeeH is terminated by a TpG pyrimidine-purine step . Replacing it by a TpC pyrimidine-pyrimidine step in a 10 bp duplex had only a minor effect on the affinity for CT-Ler ( cf . AATT and AATTC in Table S3 ) . Interestingly , replacement of the T9pG10 step in LeeH by the alternative pyrimidine-purine step , TpA , resulted in a major loss of stability of the complex . The DNA binding domains of Ler and H-NS share a high degree of similarity both in sequence and in structure . We carried out experiments to specifically test two key points that are apparent from the analysis of the Ler/LeeH complex , namely the role of the conserved arginine residue ( Arg90 in Ler , Arg114 in H-NS ) in Loop2 and the requirement for an AT-tract and the effect of interrupting TpA steps . H-NS Arg114 , corresponding to Arg90 in Ler , was mutated to glycine and the affinity towards the −225 to +121 LEE2 region was compared with that of the wild type form by EMSA . As in the case of Ler , replacing the arginine residue in Loop2 results in a substantial loss of affinity ( Figure 8A ) . However , H-NS retains some residual activity even when arginine was replaced by glycine while this drastic mutation caused a complete loss of activity in the case of Ler . The requirement for a narrow minor groove in the case of Ler can be assessed by the relative affinities towards the AATT and TATA 10-mer duplexes . Titrations of CT-H-NS with both oligonucleotides ( Figure 8 ) provided dissociation constants of circa 41 μM for the AATT complex and 102 μM , 2–3-fold larger , for the TATA complex . CT-Ler showed similar relative affinities for the same oligonucleotides ( Table S3 ) , thereby suggesting that these two domains have similar requirements for a narrow minor groove . As many H-NS and Ler target sequences may overlap , the relative affinity of the DNA-binding domains of these two proteins is relevant . As the CT-Ler complex studied included only the structured domain , we compared CT-Ler with the CT-domain of H-NS including only residues 95 to 137 , excluding linker residues . This H-NS construct is properly folded as shown by the observation of well resolved NMR spectra ( Figure 8 ) . The same natural DNA fragment ( LEE2 positions −225 to +121 ) used in EMSA assays with Ler ( Figure 6B ) and H-NS ( Figure 8A ) was selected to compare the affinities of the CT-domains of these two proteins . The large number of binding sites for Ler and H-NS in this extended DNA fragment , as shown by footprinting experiments , allows the assessment of the relative overall affinities of the two domains for the whole range of sequences present in one of their common natural targets . The affinity of CT-Ler is larger than that of CT-H-NS , which under the conditions of the experiment hardly caused any retardation ( Figure 8C ) . This observation contrasts with the similar affinity towards the same DNA fragment shown by longer constructs of Ler and H-NS that include the oligomerization and linker domains ( cf . Figure 6B and 8C ) and highlights varying relevance of interactions outside the folded CT-domains of these two proteins . The contribution of residues outside of the structured H-NS DNA-binding domain has been previously described [21] , [22] .
The structure of the complex between CT-Ler and LeeH shows that DNA shape and electrostatics , rather than base specific contacts , form the basis for the recognition of the CT-Ler binding site . This mechanism is referred to as indirect readout . Arg90 is a key residue for the CT-Ler interaction with DNA . Its side chain is inserted deep into a narrow minor groove . The requirement for Arg90 is strict in the case of CT-Ler and the R90G and R90Q mutants of Ler are totally inactive . The R90K mutant shows some residual binding suggesting that a positive charge is required . Arginine interactions with the DNA minor groove have been described in eukaryote nucleosomes [23] , [24] and in DNA interactions by a nucleoid-associated protein of Mycobacterium tuberculosis [25] . These observations suggest that this mechanism may be universal for indirect DNA recognition of AT-rich sequences . A correlation between minor groove width and the electrostatic potential has been demonstrated as well as the preference for arginine binding to the narrowest regions where the electrostatic potential is more negative [23] . For CT-Ler , the narrow minor groove may be provided by a relatively short AT-tract as only the Arg90 side chain has to be inserted . The minimum width in the AATT motif is observed at the ApT step , matching the site where the guanidinium group is inserted . Continuous polyA tracts of 4 ( Figure 7 ) and 6 nucleotides ( Figure S3 ) of length give less stable complexes than sequences combining A and T . However , the presence of highly dynamic TpA steps [26] interrupting the AT-tracts decreases the affinity for CT-Ler . The presence of guanine , with its 2-amino group extending into the minor groove and increasing its width is also predicted to destabilize the insertion of the arginine side chain . We explored the effect of introducing TpG or TpA steps in the sequence recognized by CT-Ler . Figure 7 clearly shows that an uninterrupted AT-tract is needed for an efficient interaction with CT-Ler . However , a narrow AT-tract is not the only requirement for CT-Ler interaction . The lower affinity of the G10A variant of LeeH shows that , next to the narrow region , a rigid wide minor groove is also required to enable the access of Loop2 delivering the side chain of Arg90 into the narrowest region of the minor groove . Both sequences , T9pG10 in LeeH and T9pA10 in the mutated duplex , could adopt wide minor grooves . However , while the former is expected to provide a permanently wide groove , the flexible TpA step may switch between expanded and compressed forms , interfering with the approach of Loop2 directly or indirectly through the entropic penalty associated to stiffening of the DNA in the complex . The structure of the complex as well as the affinity data with DNA sequence variants show that CT-Ler recognizes a pattern in the minor groove of DNA formed by two consecutive regions that are narrower and wider , respectively , with respect to standard B-DNA and show the optimal shape and electrostatic potential distribution for binding . This structural pattern is present in the free LeeH DNA fragment as shown by the observation of diagnostic inter-strand NOES between AdeH2 and ThyH1' protons of A7/A23 and T25/T9 , respectively supporting minor groove narrowing both in the free and bound forms of LeeH . Moreover , the SAXS data of free LeeH is better explained by the structure of LeeH in the complex than the structure of a canonical B-DNA LeeH ( Figure S4 ) . Therefore , at least in the case of LeeH , CT-Ler recognizes pre-existing DNA structural features following an indirect readout mechanism . The molecular basis of the preference that H-NS displays for some promoter regions has been extensively studied . AT-tracts were initially postulated to be high affinity sites for H-NS and related to the presence of a narrow minor groove [27] . More recently , two short high affinity H-NS sites with an identical sequence , 5'-TCGATATATT-3' were identified in the E . coli proU promoter [28] . Lang et al . proposed that a 10 bp long consensus sequence ( tCG ( t/a ) T ( a/t ) AATT ) [15] acts as a nucleation site for cooperative binding to more extensive regions . In a recent study , a shorter segment of 5–6 nucleotides comprising only A/T nucleotides was found to be over-represented in genomic loci bound by H-NS in E . coli [29] . The interaction of the H-NS CT-domain , including a few residues from the linker region , with a short oligonucleotide was studied by NMR [22] . The authors concluded that a structural anomaly in the DNA associated with a TpA step was crucial for H-NS recognition . Our results suggest that AT-tracts and wide TpA steps may be simultaneously required by H-NS family proteins . The correct positioning of a compressed and widened minor groove is the specific recognition signal for CT-Ler . Pyrimidine-purine steps tend to widen the minor groove and TpA steps may contribute to its widening , which is required after the AT-tract . However , in the case of Ler , a TpG step was preferred to the TpA step , suggesting that a wide narrow groove after the AT-tract is the true structural requirement . CT-Ler and CT-H-NS showed similar structural requirements: mutation of Arg114 reduced the affinity of the complex , and introduction of TpA steps in the AT-tract caused a similar decrease in stability . This result is consistent with the fact that Ler targets can also be occupied by H-NS . Ler and H-NS bind to multiple sites . An indirect readout mechanism allows recognition of multiple sequences , if they adopt similar minor groove patterns . The absence of structural changes between the free and bound forms of CT-Ler ( Figure S5 ) supports a lock and key model for interactions involving the structured CT-domain and may account for the relatively high specificity of Ler , as compared with H-NS where additional interactions outside the CT-domain are comparatively more important . Comparison of constructs containing exclusively the structured region of the CT-domains of Ler and H-NS show that the former has higher affinity for the range of sequences present in a natural segment where both proteins bind . Several features , not present in CT-H-NS , may contribute to the higher stability of the CT-Ler complex . An additional arginine residue ( Arg93 ) combined with the helix dipole provides additional electrostatic interactions , thus stabilizing the CT-Ler complex . While both Ler and H-NS have a conserved tryptophan residue that , in the case of Ler , forms hydrophobic interactions with DNA , CT-Ler presents an additional tryptophan residue in close contact with DNA . The dipoles of both indole rings are oriented with their positive end towards the negatively charged DNA backbone and the side chain NH of Trp94 forms a hydrogen bond with the DNA backbone . We have determined for the first time the structure of a complex formed by the DNA-binding domain of a member of the H-NS family . Our results highlight the similarities in the DNA recognition mechanisms used by CT-Ler and CT-H-NS but also evidence some differences that may contribute to the differential recognition of some genes by Ler and H-NS .
DNA fragments containing the coding sequence of Ler residues 65–123 , 70–116 ( CT-Ler ) and 3–116 fused to an N-terminal His6-tag were amplified by PCR from EHEC strain 0157:H7 and subcloned into the pHAT2 vector . To overexpress CT-H-NS , DNA encoding this fragment ( amino acids 95–137 ) with six histidine residues tagged at its N terminus was amplified by PCR using the full length H-NS construction [30] as template and then subcloned into the pHAT2 vector . Point mutations were generated using the QuikChange site-directed mutagenesis kit ( Stratagene ) . Ler fragments 65–123 , 70–116 and 3–116 and CT-H-NS were overexpressed in BL21 ( DE3 ) cells with overnight incubation at 15°C by induction with 0 . 5 mM IPTG when an O . D . 600 of 0 . 7 was reached . For 15N and/or 13C isotopic labeling , cells were grown in M9 minimal media containing 15NH4Cl and/or 13C-glucose . For 10% 13C enrichment we used a carbon source consisting of a 1∶10 mixture of 12C-glucose/13C-glucose [31] , [32] . Cells were harvested by centrifugation , frozen and resuspended in 20 mM HEPES ( pH 8 . 0 ) , 1 M NaCl , 5 mM imidazol , 5% ( v/v ) glycerol , treated for 30 min with lysozyme and DNAse and sonicated ( 6×10 s on ice ) . After centrifugation , the His-tagged fusion proteins were isolated with Ni-NTA beads ( Qiagen ) and further purified by size exclusion chromatography on a Superdex 75 column in 20 mM sodium phosphate , 150 mM NaCl , 0 . 2 mM EDTA , 0 . 01% ( w/v ) NaN3 pH 5 . 7 or 20 mM sodium phosphate , 300 mM NaCl , 0 . 01% ( w/v ) NaN3 pH 7 . 5 . The expression and purification procedure for full length H-NS has been previously described [30] . DNA samples were prepared by hybridization of complementary oligonucleotides purchased from Sigma-Aldrich . Quality control was assessed by MALDI-TOF mass spectrometry . Oligonucleotides were mixed in equimolar amounts and annealed by heating to 92°C for 4 min and slowly cooled to room temperature . Changes in CT-Ler intrinsic fluorescence anisotropy were monitored upon DNA addition . All measurements were recorded on a PTI QuantaMaster spectrophotometer equipped with a peltier cell , using an excitation wavelength of 295 nm to selectively excite CT-Ler tryptophans and emission detection at 344 nm . Fluorescence measurements were performed in 40 mM HEPES ( pH 7 . 5 ) , 60 mM potassium glutamate , 0 . 01% ( w/v ) NaN3 at 20°C . More details on data acquisition and equipment settings were previously described [33] . For the initial screening of the -221 to -101 regulatory region of LEE2 , the apparent fraction saturation of CT-Ler was used to infer about DNA binding preferences . To measure the affinity of CT-Ler for 15 bp and 10 bp DNA fragments , titrations were performed at least in duplicate . The fitting was performed assuming a 1∶1 binding using the following equations [34]: ( 1 ) ( 2 ) where A is the observed anisotropy , Af and Ab are the anisotropies of free CT-Ler and the complex respectively , fb is the fraction of bound CT-Ler and Q is the ratio of quantum yields of bound and free forms . Equations 1 and 2 were solved iteratively until the theoretical binding isotherm matched the experimental data . Kd and Ab were considered to be adjustable parameters . All spectra were acquired at 25°C on 600 , 700 , 800 or 900 MHz Bruker spectrometers . Data processing and analysis were carried out with NMRPipe [35] , NMRViewJ [36] , and CARA [37] . NMR spectra for structure determination were recorded on a ∼1 mM sample containing a 1∶1 complex of uniformly 13C- and 15N-labeled CT-Ler and unlabeled DNA in 20 mM sodium phosphate ( pH 5 . 7 ) , 150 mM NaCl , 0 . 2 mM EDTA and 0 . 01% ( w/v ) NaN3 . Backbone and aliphatic assignments of free and DNA-bound CT-Ler were obtained by standard methods . Aromatic resonances were assigned using 2D 1H-13C-edited-NOESY optimized for aromatic resonances . Stereospecific assignments of Val and Leu methyl groups were obtained from a constant time 1H-13C-HSQC on a 10% 13C-labeled protein sample [31] . Non-exchangeable protons of the LeeH duplex bound to CT-Ler were assigned using 2D F1 , F2-13C-filtered TOCSY and NOESY spectra in D2O [38] . Exchangeable protons and H2 protons were assigned from 2D F1 , F2-15N/13C-filtered NOESY spectrum in H2O [39] . Free DNA resonances were assigned using 2D DQF-COSY , TOCSY and 2D NOESY spectra . Proton chemical shifts were referenced using 4 , 4-dimethyl-4-silapentane-1-sulfonic acid ( DSS ) as an internal standard , whereas 15N and 13C chemical shifts were indirectly referenced . Chemical shift assignments have been deposited in the BioMagResBank database under BMRB accession number 17729 . Protein distance restraints were obtained from 2D 1H-13C-edited NOESY ( aromatic optimized in D2O ) , 3D 1H-15N-edited NOESY-HSQC and two 3D 1H-13C-edited NOESY-HSQC ( in H2O and in D2O ) experiments with a mixing time of 120 ms . Data were automatically assigned and the NOE distance restraints were obtained iteratively using the Unio'08/CYANA 2 . 1 suite program [40] , [41] and manually inspected . The distance restraints for the DNA in complex with CT-Ler were obtained measuring initial NOE build-up rates from 2D F1 , F2-15N/13C-filtered NOESY spectra recorded with mixing time of 50 , 75 , 100 and 150 ms . Intermolecular NOEs were detected using a combination of 2D NOESY , 2D F1 , F2-13C-filtered NOESY and 2D F2-13C-filtered NOESY experiments , together with 3D F1-13C , 15N-filtered , [F2] 13C-edited 3D NOESY spectrum [42] . Additional intermolecular NOEs were obtained by analyzing the 3D 15N-edited and 13C-edited NOESY spectra . Protein backbone dihedral angle restraints were derived using a combination of TALOS [43] and quantitative analysis of 3JHNHα obtained from a 3D HNHA spectrum [44] . Restraints on side chain angle and stereospecific assignments of Hβ proton resonances were based on 3JNHβ couplings , obtained from a 3D HNHB spectrum , in combination with observed intraresidual NOEs using the HABAS routine of the CYANA 2 . 1 program [45] . 1H-15N HSQC spectra for analysis of the interaction of 15N-labeled CT-H-NS ( 100 µM ) with dsDNA were obtained at 25°C in 20 mM sodium phosphate ( pH 5 . 7 ) , 150 mM NaCl , 0 . 2 mM EDTA and 0 . 01% ( w/v ) NaN3 . The structure of CT-Ler was determined by simulated annealing using the torsion angle dynamic simulation program CYANA 2 . 1 [45] and further water refinement with CNS 1 . 2 . 1 [46] , [47] . Protein structure calculation was based on Unio'08/CYANA-generated upper distances , 3JHNHα/3JNHHβ couplings , and TALOS-driven dihedral angle restraints . Based on H/D exchange experiments , backbone NOE pattern and 13Cα/13Cβ chemical shifts , hydrogen bond restraints were also used in the structure calculation . An ensemble of 100 protein structures was generated and the 20 lowest energy conformers were docked onto a B-DNA . The observed overlap and broadening of DNA resonances hampered the complete quantitative analysis of NOESY spectra for bound DNA . Only a set of 282 well resolved cross-peaks were converted into distances using initial build-up rates and reference to the cytosine H5-H6 cross-peaks . Upper and lower limits were defined as ± 20% of the calculated distances . The structure of LeeH was fixed as B-DNA and further energy-refined using miniCarlo [48] followed by a 20 ps molecular dynamics refinement in explicit solvent using the Amber force field [49] and including NOE-derived distance restraints . To preserve the helical conformation of DNA , weak planarity restraints were also introduced . The DNA backbone was constrained to a range typical of B-form and all glycosidic angles were restrained as anti . Hydrogen bond restraints were used for all base pairs in which the imino proton was observed . The complex structure was generated employing 30 iNOEs , supplemented with highly ambiguous intermolecular restraints ( AIRs ) that were driven from the mapped binding interfaces . A total of 22 intermolecular NOE restraints were simultaneously assigned to the two symmetry-related protons in the AATT central region of the DNA and used as ambiguous restraints . HADDOCK 2 . 0 [50] was used to generated 2000 structures by rigid docking energy minimization , and 400 structures with the lowest energy were selected for semi-flexible refinement process . These 400 structures were finally refined in explicit water including all experimental restraints . Structures were then ranked using the energy-based HADDOCK scoring function ( sum of intermolecular electrostatic , van der Waals , desolvation and AIR energies ) and NOE energy term . The quality of these structures was evaluated in terms of the violations to the NOE data and the value defining the agreement to SAXS curve . A final ensemble of 20 structures was obtained by re-scoring the pool of 400 structures using the following scoring function . ( 3 ) ( 4 ) where and correspond to the root mean squared deviations with respect to the best possible value in and Ei respectively . Coordinates of the final ensemble were deposited in the Brookhaven Protein Data Bank under the accession number 2lev . Minor groove geometry and helical parameters were analyzed using w3DNA [51] . Electrostatic potentials were obtained at physiological ionic strength using DelPhi [52] . SAXS data for LeeH and the CT-Ler/LeeH complex were collected on a MAR345 image plate detector at the X33 European Molecular Biology Laboratory ( DESY , Hamburg , Germany ) [53] . The scattering patterns were measured at 25°C for 2 min at sample concentrations of 4 . 6 and 2 . 7 mg/ml and 6 . 6 and 3 . 3 mg/ml for LeeH and CT-Ler/LeeH , respectively . A momentum transfer range of 0 . 018< s <0 . 62 Å−1 was measured . Repetitive measurements indicated that samples did not present radiation damage . Buffer subtraction and the estimation of the radius of gyration , Rg , and the forward scattering , I ( 0 ) , through Guinier's approach were performed with PRIMUS [54] . The scattering profile of LeeH was obtained from merging curves at both concentrations . For CT-Ler/LeeH , SAXS profiles at both concentrations were virtually equivalent and only data from the highest concentrated sample were used for further analysis . Using Guinier's approach , the radii of gyration of LeeH and CT-Ler/LeeH were estimated to be 15 . 6±0 . 1 and 18 . 2±0 . 1 Å , respectively . All data manipulations were performed with the program PRIMUS . Using a bovine serum albumin sample ( 3 . 3 mg/ml ) , an estimated molecular weight of 18 kDa was obtained for CT-Ler/LeeH ( theoretical MW of 16 . 3 kDa ) , thereby indicating the presence of a monomeric particle in solution . The agreement of the SAXS curve to various three-dimensional models was quantified with the program CRYSOL [55] using a momentum transfer range of 0 . 018< s <0 . 40 Å−1 . The DNA fragment used in this assay ( LEE2 positions −225 to +121 ) was obtained by PCR amplification from EHEC strain 0157:H7 . The indicated concentrations of PCR-generated DNA and H-NS or Ler proteins were mixed in a total volume of 20 μl of 15 mM sodium phosphate , 100 mM NaCl , 0 . 01% ( w/v ) NaN3 pH 7 . 5 . 1 mM tris ( 2-carboxyethyl ) -phosphine ( TCEP ) was included for samples containing full length H-NS . After 20 min of incubation at room temperature , glycerol was added to 10% ( w/v ) final concentration and the reaction mixtures were electrophoresed on either 1 . 5% agarose or 7% polyacrylamide gels in 0 . 5x Tris-borate-EDTA buffer . The DNA bands were stained with ethidium bromide .
|
Pathogenic Escherichia coli strains and other enterobacteria carry genes acquired from other bacteria by a process known as horizontal gene transfer . Proper regulation of the genes that are expressed in a given moment is crucial for the success of the bacteria . The protein H-NS is a global regulator that binds DNA and maintains a large number of genes silent until they are required , for example , to sustain the bacteria's colonization of a new host . Ler is a member of the H-NS family that competes with H-NS to activate the expression of a group of horizontally acquired genes that encode for a molecular machine used by E . coli to infect human cells . Ler and H-NS share a similar DNA-binding domain and can bind to different DNA sequences . Here , we present the structure of a complex between the DNA-binding domain of Ler and a natural DNA fragment . This structure reveals that Ler recognizes specific DNA shapes , explaining its capacity to regulate genes with different sequences . A single arginine residue is key for the recognition of a DNA narrow minor groove , which is one of , though not the only , hallmarks of the DNA shapes that are recognized by H-NS and Ler .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"gene",
"expression",
"genetics",
"biology",
"genetics",
"and",
"genomics",
"dna",
"transcription"
] |
2011
|
Indirect DNA Readout by an H-NS Related Protein: Structure of the DNA Complex of the C-Terminal Domain of Ler
|
Dopaminergic neurons ( DAs ) of the rodent substantia nigra pars compacta ( SNc ) display varied electrophysiological properties in vitro . Despite this , projection patterns and functional inputs from DAs to other structures are conserved , so in vivo delivery of consistent , well-timed dopamine modulation to downstream circuits must be coordinated . Here we show robust coordination by linear parameter controllers , discovered through powerful mathematical analyses of data and models , and from which consistent control of DA subthreshold oscillations ( STOs ) and spontaneous firing emerges . These units of control represent coordinated intracellular variables , sufficient to regulate complex cellular properties with radical simplicity . Using an evolutionary algorithm and dimensionality reduction , we discovered metaparameters , which when regressed against STO features , revealed a 2-dimensional control plane for the neuron’s 22-dimensional parameter space that fully maps the natural range of DA subthreshold electrophysiology . This plane provided a basis for spiking currents to reproduce a large range of the naturally occurring spontaneous firing characteristics of SNc DAs . From it we easily produced a unique population of models , derived using unbiased parameter search , that show good generalization to channel blockade and compensatory intracellular mechanisms . From this population of models , we then discovered low-dimensional controllers for regulating spontaneous firing properties , and gain insight into how currents active in different voltage regimes interact to produce the emergent activity of SNc DAs . Our methods therefore reveal simple regulators of neuronal function lurking in the complexity of combined ion channel dynamics .
Midbrain dopaminergic neuron ( DA ) activity influences many brain functions . Timing of spikes and bursts in DAs signal reward prediction error [1] , and DA activity is implicated in schizophrenia and depression [2] . Modeling the origins of DA activity is vital for research into the neural basis of behavior and neuropsychiatric therapeutic design . Correlations exist between DA ion channel properties and electrophysiology . A-type potassium ( KA ) channels regulate spike timing , with negative correlations between current amplitude and interspike interval voltage slope [3] , and between the inactivation time constant and rebound voltage slope [4] . Hyperpolarization-activated ( HCN ) channel density regulates rebound delay [5] . Modulating calcium-dependent potassium ( SK ) channels can alter firing rate ( FR ) [6] , FR adaptation ratio [7] , or modulate firing from regular spiking to bursting [8] . KA density modulates spontaneous FR [9] as does blockade of ERG potassium [10] . Blockade of T-type calcium modulates rebound spiking [11] . Subthreshold membrane potential dynamics ultimately help determine the impact of a presynaptic spike and whether DAs fire , making the interplay between these channels crucial for controlling DAs and their role in brain function . Analysis of populations of electrophysiological models has previously been used to uncover the influence of ion channel parameters on output features [12 , 13] . Here , we generate and analyze a population of DA models , discovering powerful , simultaneous controllers of multiple functions , and thereby providing a means to formulate quantitative hypotheses about polychannel regulatory targets in the real neurons . Characterization of subthreshold activity in substantia nigra pars compacta ( SNc ) DAs has elucidated how channels activated in the subthreshold regime combine to regulate responses . Here , a subthreshold oscillation ( STO ) emerges in the presence of tetrodotoxin ( TTX ) , with increased amplitude under tetraethylammonium ( TEA ) [14 , 15] . STOs depend on L-type calcium activation [16] resulting in depolarization terminated by SK . The characteristics of these STOs reveal the balance of subthreshold currents present . Prior work had hypothesized that the spontaneous pacemaker firing observed in DAs in vitro was entrained by the STOs [17] , but recent evidence has indicated that persistent sodium current is a likely driver of pacemaker firing [18 , 19] , rather than STO frequency . Computational models of DAs have successfully reproduced STOs and elucidated their dependence on interactions between calcium and SK [17 , 20] , but no previous studies reproduced the full range of STOs or their functional regulation over this range . Furthermore , prior computational models that may provide insight into the interactions between DA activity modes have used single parameter sets producing single activity patterns [4 , 20 , 21] , whereas a range of spontaneous oscillation and spiking features have been observed . We wondered if a parsimonious means to modulate STOs over their full range was discoverable in a population of models . First we reasoned that electrophysiological measures from neuronal populations reveal feature variability across cells of the same type [22 , 23] . Varied features derive from varied ion channel expression , current densities [9] , kinetic gating [24 , 25] , morphology [26–28] . Differences in these properties somehow balance within each cell , resulting in similar function for neurons with different underlying properties [29–31] . Pharmacological manipulations across cells of the same type can have variable effects , seen in the variable responses of DAs to apamin [32] , but how variability observed in ion channel properties contributes to variability in electrophysiological properties has yet to be fully characterized [33] . Here we apply a novel method for population parameter search and dimensionality reduction to address these issues . Our analysis of a population of algorithmically generated DA models produces low-dimensional mappings of ion channel properties onto STOs . Our technique identifies a region of model parameter space in which linear operators vary STO features and regenerate the complete DA population activity by coregulating ion channels and controlling the models’ emergent electrophysiology . From this subthreshold-regulating plane , we produce a population of models with the full range of spontaneous firing characteristics and further show how discovery of this population enables identification of the ion channel coregulations that promote variety of DA function . We demonstrate how this model population generalizes to drug perturbations , contains known compensatory mechanisms , and reveals intracellular properties responsible for susceptibility to activity mode state transitions . Finally , we demonstrate a complex relationship between subthreshold and spiking modes , thus providing theoretical insight into the various interpretations of numerous empirical results by showing how the membrane potential history constrains phase shift and frequency modulation of pacemaking , indicating distinct ion channel state profiles during either STO or spontaneous firing activity .
The neuron model used a multi-compartment dendritic morphology based on a previously published DA model morphology [21] , with an axon initial segment ( AIS ) and axonal compartment added . Into this , we incorporated a set of equations determining membrane potential , intracellular calcium , and ion channel gating . The included channels were transient sodium ( NaT ) , hyperpolarization-activated cation ( HCN ) , T-type calcium , Cav3 ( CaT ) , low-threshold L-type calcium , Cav1 . 3 ( CaL ) , delayed rectifier potassium ( Kv2 ) , large-conductance potassium ( BK ) , small-conductance , calcium-dependent potassium ( SK ) , transient A-type potassium , Kv4 . 3 ( KA ) , and ether-a-go-go-related-gene potassium ( KERG ) , along with the leak conductance . A full description of the channel models used and tuning process applied to those channels to improve their fit to SNc DA observations is provided in Ion channel tuning procedures , below . Briefly , for each ion channel we found an appropriate existing model that captured fundamental properties of the channel , adjusted baseline kinetic parameters to approximate available observations from recordings of that channel type in SNc DAs , then inserted parameters into those equations for coherently modulating time constants and voltage dependence within the ranges found in those empirical observations . For the optimization and for each channel model as required , we incorporated parameters g ¯ , maximal conductance , Vhalf for modifying voltage-dependence of the default channel models , τmod for modifying time constants for activation and inactivation , e , the reversal potential , and k , the Ca2+-dependence of a channel gating variable , along with intracellular calcium parameters Pmax and β ( see Membrane potential equation , below ) . Additional scaling factor parameters were incorporated to allow the optimization to adjust NaT and Kv2 conductances and kinetics in the AIS . Tuning procedures for the equations underlying gating of each ion channel model are described in detail in S1 Methods and were conducted prior to algorithmic model population optimization . This pretuning was intended to discover the range of channel parameters necessary to represent the range of single channel patch clamp recordings measured from SNC DAs . As a basis for each channel model we used existing channel models that have been incorporated into previously published single cell models of SNc DAs . In general , parameter tuning was performed for the ion channel models that had been derived from neuron types other than SNc DAs , and aimed to ensure that activity of the channels resembled recordings from SNc DAs . Section S1 Methods also provides a detailed summary of the parameter values of the model used for each channel type . We used the non-dominated sorting ( NS ) differential evolution ( DE ) algorithm ( NSDE ) . The DE implementation followed the ‘classic DE’ algorithm , or ‘DE/ran/1/bin’ , with uniform jitter , d = 0 . 1 , in the DE standard nomenclature [37] . We used a modified version of the BluePyOpt [38] python framework for single neuron optimization to run the algorithm . Parameter optimizations for neuron models usually hold single mean measures or voltage traces as ideal targets , and allow for ‘acceptable’ models within 2–3 standard deviations . Here we introduced a ‘soft thresholding’ of the error function coupled with a neighborhood penalty to prevent systematic bias due to targeting exact feature values ( Fig 1A and 1B ) . To calculate an error for each model feature , we subtracted the mean and divided by the standard deviation of experimental measures . Next , we subtracted a soft threshold from the normalized error equal to 2 standard deviations , setting negative values to 0 , such that feature values within 2 standard deviations of the mean had error values of 0 . We used this as the error of the non-dominated sorting differential evolution ( NSDE ) framework [37] . Finally , we imposed a penalty based on a measure of ‘crowdedness’ in feature space of previously selected 0 error models ( Fig 1C ) , thus biasing the algorithm to evenly cover feature space in the 0 error region . We verified the optimization could fully sample a target feature space , and studied how mappings from parameters to features influence the distribution of models selected by the algorithm . To do this , we simulated a simple system comprising two sigmoidal transfer functions that transformed two parameters in the range 0–100 to features in the range 0–1 ( Fig 1D ) and optimized using a 100 member population for 100 generations . The target feature ranges were each 0–1 , so selection was driven entirely by crowdedness penalty . We performed one optimization with independent parameter to feature mappings and sigmoid functions centered at P1 = 50 and P2 = 50 ( orange in Fig 1D ) . We performed a second optimization in which the first feature calculated from a sigmoid centered at P1 = 70 and with the second feature calculated from a sigmoid parameterized by a function of P1 and P2: ( P1 − P2 ) × 2 ( green in Fig 1D ) . Finally , we perfomed a third optimization with the first feature calculated from a sigmoid centered at P1 = 30 and with the second feature calculated from a sigmoid parameterized by another function of P1 and P2: ( P1 + P2 ) /2 ( purple in Fig 1D ) . The optimization found a near uniform distribution of both feature values simultaneously ( Fig 1E ) despite the narrow intervals and interactions in parameter space that map to feature space . Such a mapping from parameter space to feature space intervals is one definition of ‘parameter sensitivity , ’ and parameter values chosen by this test optimization form a Gaussian distribution ( Fig 1F ) whose mean falls at the center of the transfer functions ( i . e . , the most sensitive regions of parameter space ) . Thus this algorithm naturally samples parameter space proportional to parameter sensitivity , and provides a direct estimate of multidimensional sensitivity , including parameter interactions , using the distribution of models selected ( for a discussion of why mapping a Gaussian parameter distribution into a uniform feature distribution requires a sigmoidal transfer function , and optimal information encoding consequences , see [39] ) . We have used this approach previously to successfully generate populations of models reflecting the range of in vitro excitability present in recordings from striatal medium spiny neurons [40] . Five trial runs of the evolutionary algorithm , starting from different parameter values and using different random number seeds were performed for the subthreshold optimization to ameliorate problems of local minima in the parameter search . One trial was performed of the suprathreshold optimization due to the additional computational resources required for this run . Trial-and-error was used to assess optimization metaparameters such as population size and number of generations . A smaller population and/or fewer generations than those reported were found to inadequately sample the desired feature space . Correlations were calculated with Pearson’s linear correlation coefficient , using the Matlab function ‘corr’ . Neuron model error was calculated by extracting features from the voltage response in various protocols and comparing those feature values with target values . For the subthreshold optimization a total of 7 feature values were extracted: oscillation amplitude , oscillation amplitude variance , oscillation frequency , oscillation frequency variance , input resistance and sag amplitude during a small hyperpolarization , and sag amplitude during a large hyperpolarization . Oscillation amplitude was calculated as mean peak to trough voltage difference , and frequency was calculated using mean peak to peak time interval . These were captured during a 5 s recording with no injected current , and under simulated TTX and TEA application by removing the NaT , Kv2 and BK currents . Due to the high-dimensional parameter space of the non-linear model being explored , an oscillation with mean amplitude or frequency in the target range could potentially be generated by a membrane potential fluctuating chaotically due to a combination of outlier ion channel parameters . To penalize these models with mean feature values in the correct range but with highly variable voltage fluctuations , we included variance of both amplitude and frequency in the error calculation , thereby biasing the optimization to find those models with stable ( low variance ) ongoing oscillations . Input resistance was calculated from response to a −10 pA hyperpolarizing current of 500 ms duration . We calculated sag amplitude in both this small hyperpolarizing current protocol , and during a 1s injection of −200 pA , by subtracting the voltage at the trough of the response from the steady state voltage during the final 5% of current injection ( see S5 Fig ) . For spontaneous firing optimization , we added features recorded during spontaneous pacemaking activity , with no current injection , and with the AP generating currents enabled ( NaT , Kv2 , and BK ) . From AP times during 5 s recording we measured firing rate ( FR ) as the mean of the inverse of interspike intervals ( ISIs ) and coefficient of variation ( CV ) of the ISIs . From the voltage waveform of each AP , we calculated AP threshold ( upward crossing of 5 mV ms−1 in dV/dt ) , AP amplitude ( voltage difference between AP peak and AP threshold ) , AP width ( duration with V above half amplitude from AP threshold ) , afterhyperpolarization potential ( AHP ) depth ( V difference between AP threshold and first V minimum after the AP peak ) , AHP time ( time from AP peak to AHP minimum ) , AP rise rate ( mean mV ms−1 between crossing of AP threshold +10% of AP amplitude and AP peak −10% of AP amplitude on the rising side of the AP ) , and AP fall rate ( mean mV ms−1 between crossing of AP peak −10% of AP amplitude and AP threshold + 10% of AP amplitude on the falling side of the AP ) . These features are illustrated schematically in S5 Fig . From the spontaneous firing recording we also calculated time differences between AP threshold crossing in the AIS relative to the soma , and in the soma relative to a non-axon-bearing proximal dendrite , to ensure AP initiation in the AIS [34 , 41 , 42] . Finally , we measured first AP time after the large hyperpolarization protocol , to assess rebound delay . Error scores were calculated for each parameter set according to: Err x = { T x , if T x ≥ 0 0 , otherwise ( 8 ) where Tx = ( ( |Modelx − TargetMeanx| ) /TargetSDx ) − SoftThreshold , Modelx is the calculated value for feature x in the model , TargetMeanx and TargetSDx are the target mean and standard deviation for feature x based on empirical observation , SoftThreshold is an offset indicating the range of feature values considered ‘acceptable’ , and Errx is the error score for feature x used by NSDE to determine model fitness . Total error was used as the decision maker between models with equal dominance rank , and a crowdedness function in feature space was used for selection when there was a full population of 0 error models and further 0 errors models were found in the offspring . Among the pool of 0 error models combined from parents and offspring , the crowdedness function sorted models by finding those with minimum Euclidean distance to nearest neighbor in feature space , selecting one of those models to be removed from the pool and placed as the ‘next most crowded model’ in a sorted list , recalculating distance to nearest neighbours for the remaining models in the pool , and so on until the pool is sorted . STO amplitude and frequency variance , sag amplitude during the small hyperpolarization , relative AP times between AIS , soma , and proximal dendrite , and AHP time from peak were not included in the crowdedness calculation , as these features were intended only to penalize models breaking these conditions . The target means and standard deviations used for all features are shown in Table 1 .
Our aim was to produce populations of neuron models that represented the range of electrophysiological properties seen in the real DA population , and investigate relationships between ion channels involved in the characteristic subthreshold activity patterns and pacemaker firing found in these cells . Ensembles of electrophysiological models have been used for this purpose in prior approaches that vary parameters around a known seed [12 , 13] . In previous work where an established parameter set for a particular model is not known , evolutionary algorithms have been shown to be highly effective at searching the high-dimensional space of ion channel parameters [44–46] . Notable studies have produced populations of parameter sets with single electrophysiological responses used as the search targets , with each parameter set representing either an individual neuron [47–50] or an average response [51–55] . Extending the framework of fitting to recordings from individual neurons , multiple optimization trials can be performed to match parameter sets to many neurons of a particular cell type , producing multiple ensembles each representing multiple fits to a different empirical recording from a specific neuron [56] . Here we use an unbiased evolutionary search of high-dimensional parameter space to find parameter sets for a new , previously unparameterized model representing a specific cell type , SNc DAs . However , during this search we incorporate a novel soft-thresholding of the error function in conjunction with a crowdedness penalty function to an otherwise standard evolutionary algorithm , differential evolution . This approach automatically accessed and sampled parameter sets from across the range of empirically observed DA electrophysiological features . Our method therefore instantiates a single optimization procedure that can find parameter sets representing models of a particular neuronal class , and then produce a large population of models , each member of which representing an instance of the class based on its empirical features , and the entire population spread across the range of all possible features . Further details are presented in the Optimization algorithm section of Materials and methods . We first used the algorithm to construct an unbiased population of multicompartment SNc DA models ( described in detail in Materials and methods ) with activity matching only the subthreshold characteristics of these cells , observed in the presence of TTX and TEA . TTX application is sufficient to induce an STO , and TEA application is reported to increase STO amplitude . To recreate this , we omitted currents blocked by TTX ( NaT ) and TEA ( Kv2 and BK ) , and used high-voltage STO as the target [17 , 57] , with amplitudes between 13 . 4 and 21 . 4 mV , and frequencies between 1 . 6 and 5 . 6 Hz , according to the ranges in [57] ( although STO frequency range was not explicitly specified in this study , we made an approximation from pacemaking frequencies described there ) . Further constraints on subthreshold activity were input resistance and sag response ( see Materials and methods for full details of optimization targets ) . We ran our optimization algorithm on 165 CPU cores on the IBM Cloud using a population size of 165 for 5000 generations and generated 825 , 000 unique models in 50 hours . A wide range of STO features were generated , with amplitudes from 0–132 . 3 mV and frequencies from 0–18 . 0 Hz ( Fig 2A ) . Within the target feature range , 18 , 783 models were found , distributed approximately evenly ( although with fewer high frequency oscillation models found ) across the full target range ( Fig 2B ) , such that different parameter sets producing every different combination of the observed subthreshold characteristics were found ( Fig 2C ) . To test the reliability of our optimization approach using these relatively unconstrained target features , we performed 4 additional optimization runs with different random seeds , and refer also to those results in the following analyses . To describe the region of parameter space in which valid models of the adult WT DA subthreshold population were successfully found , we performed PCA on all good parameter sets from our initial optimization run , shown in S2 Fig . The distribution of each PC was well fit by a Gaussian ( e . g . , S2B Fig ) , indicating that the population of models approximates a multidimensional Gaussian in parameter space , despite STO features being more uniform in feature space ( Fig 2B ) . Densely sampled regions were those over which features changed rapidly when metaparameters changed slowly ( i . e . were more sensitive ) . A Gaussian distribution in metaparameter space suggests a sigmoidal transfer function from model metaparameters to model features ( Fig 1 ) . We next investigated the sensitivity of STO features to multi-dimensional parameter changes . Two PCs , 19 and 22 , accounting for relatively low amounts of variance among parameter sets ( see S2A Fig ) , were most highly correlated with STO features ( Fig 2D ) . Their unique coefficients ( Fig 2E ) represent specific combinations of parameter changes that are correlated with variation in STO features ( Fig 2F ) . We next asked whether constraining parameters to covary along PCs 19 and 22 would reproduce the range of STO features , demonstrating the sufficiency of linear parameter transformations to control STO features throughout the target range . We repeated the optimization while restricting the 22 free parameters to vary only according to the ratios of coefficients plotted in Fig 2E , enforcing covariation of all parameters with a single metaparameter . This optimization found STO amplitudes from 0–26 . 4 mV and STO frequencies from 0–14 . 6 Hz . The target region of feature space contained 38 , 270 models , and when STO features were mapped into this plane of control ( Fig 2G ) , their variation fell along orthogonal axes . This analysis shows how control of electrophysiological features can be achieved within low dimensional projections of a high dimensional parameter space . Having shown that two PCs can be used to control STO over a broad range of features by constraining ion channel parameters to vary in specific ratios , we next aimed to find optimal controllers of these features . We employed partial least squares regression ( PLSR ) to define these controllers . PLSR is a multivariate regression method , which finds coefficients ( ratios ) of independent variables that optimally predict dependent variables , and has been used previously to understand the relationship between ionic conductances and action potential features in cardiac models [12 , 58–60] . We applied PLSR to the parameter sets identified by our algorithm and determined the linear transformations ( controllers ) of parameter sets that were most reliable at predicting changes in STO features . We performed PLSR independently on the 5 model populations generated from the 5 optimization runs . PLSR found controllers ( linear operators ) that most consistently mapped parameter sets to either STO amplitude ( Fig 3A , left ) or frequency ( Fig 3A , right ) . Fig 3A shows optimization trials sorted by similarity of PLSR coefficients , revealing consistent controllers of function across trials . Mean coefficients for the amplitude-predicting component ( APC ) had large magnitudes for g ¯ Leak , βCai , and km , SK . These coefficients are similar to those found for PC22 in Fig 2E , but without the large coefficients for g ¯ HCN and Vh , HCN , indicating that some parameter coefficients found in the PC do not enhance reliability of prediction of the resulting model activity ( of course , reliability of prediction was not a constraint of PCA ) . The mean frequency-predicting component ( FPC ) had large coefficients for g ¯ KA and Vh , KA , g ¯ KERG and Vh , KERG , g ¯ CaL and Vh , CaL , βCai , and km , SK . Remapping STO feature values from our initial optimization into the control plane defined by the APC and FPC revealed reliable prediction of STO frequency ( Fig 3C and 3E left ) , but less reliable prediction of STO amplitude ( Fig 3D and 3E right ) . This finding was reinforced by an R2 statistic value of only 0 . 063 for the APC , but a larger R2 statistic value of 0 . 653 for the FPC ( S2A and S2B Fig ) . To investigate the reliability of these axes of control further , we divided models into 10 bins from across the range of predicted values for each STO feature , and the actual feature values of models from each bin are shown in the boxplots of Fig 3E . The progression of median values for actual features within each bin very closely follows a sigmoid for STO frequency , as shown by the sigmoid fit to the bin medians ( Fig 3E left ) . For STO amplitude , this fit stretched the sigmoid to a near linear controller , and the larger variances in boxplots for STO amplitude ( Fig 3E right ) indicate weaker control of this feature using the PLSR coefficients than for STO frequency . The vertical histograms in Fig 3E show the distributions of predicted feature values for STO frequency and amplitude , and the horizontal histograms show a Gaussian distributions of models within parameter space ( as in S2E Fig along PCs ) . A Gaussian distribution in parameter space and a near uniform distribution of STO frequency reinforces that the model transfer function within the control plane was sigmoidal , suggesting that we indeed have uncovered an axis close to the optimal controller of STO frequency . The parameter space around the steepest slope of this sigmoid identifies a region of highest sensitivity , and our optimization technique therefore performed an implicit sensitivity analysis by centering our model population around this region , while identifying viable parameter sets that fill the target feature range . The low prediction precision of STO amplitude highlights a limitation of our linear model of parameter changes required to modulate amplitude . Because our model consists of many non-linear components , non-linear metaparameters could act as controllers and potentially facilitate greater prediction accuracy ( indeed , performing second-order polynomial regressions of parameters against STO features obtained increased R2 statistic measures of 0 . 907 for the FPC and 0 . 397 for the APC ( see S2C and S2D Fig ) ) . Our linear controllers , while not fully able to predict the features , were nevertheless strikingly sufficient for recovery of the full range of activity of that feature . Our intention in this first stage of optimization was to identify a lower dimensional region of parameter space that could linearly produce accurate subthreshold activity , suggesting that parameters for subthreshold ion channels could be meaningfully constrained by controlling metaparameters . We next aimed to use this dimensionally-reduced control plane as a basis for generating a population of spiking SNc DA models . We therefore used the controlling metaparameters of STO features derived from linear regression as a plane from which to extend the parameter search for spiking activity . The precise relationship between STO and the spontaneous , pacemaker firing observed in SNc DAs is yet to be fully characterized . It has been hypothesized that the STO might underlie the depolarization leading to pacemaker firing , with the STO driving the voltage until the spiking currents activate [17] , but this idea has been challenged by observations that the calcium current , which forms the basis of the STO depolarization , does not necessarily dictate pacemaker frequency . Instead , this frequency may be more reliant on subthreshold sodium channel activation [19] . Multiple sources of depolarization must interact during the spontaneous interspike interval , resulting in a complex and potentially compensatory framework dictating pacemaker frequency [18 , 61] . The regularity of pacemaker firing also varies in SNc DAs with synaptic inputs [41] , current injection [41 , 62] , ion channel blockade [63 , 64] , and juvenile development [36] , and each has been demonstrated to modulate regularity of firing . Again , multiple interacting currents may lead to particular regimes that make a neuron more or less susceptible to irregular or burst spiking despite normal pacemaker function . A major aim of this work was to facilitate precise predictions concerning specific ratios of ion channel properties that can regulate features within each of the SNc DA activity modes . As such , we next aimed to utilize our novel optimization framework to generate a population of models within the full empirical range of SNc DA spontaneous electrophysiological characteristics . This would allow investigation of the relationship between subthreshold and suprathreshold regimes , and their currents . We performed an optimization with 3 channels added to the model , NaT , Kv2 and BK , each of which was absent in the previous subthreshold condition ( simulating blockade by TTX and TEA ) . Channel parameters were g ¯ , Vhalf and τmod , and control parameters were added for scaling the g ¯ of NaT and Kv2 in the AIS , shifting Vhalf for NaT in the AIS , as well as for shifting the Vhalf of fast and slow inactivation relative to activation for NaT , in order to allow the optimization to adjust the NaT window current ( see Materials and methods and S1 Methods ) . In our analysis of the subthreshold response of the model , we uncovered a 2D linear plane capable of reproducing the full range of STO frequencies and amplitudes . Here we used this dimensionality reduction of 22 parameters to 2 metaparameters as a basis for the addition of the AP generating currents , substantially reducing the complexity of the parameter space to be explored for spiking models . This approach enabled the AP firing models to have STO features from potentially any point in the frequency and amplitude ranges , and ensured that they had reasonable input resistance and sag amplitudes , as these features were also constrained in the subthreshold search . We simulated a spontaneous firing protocol , and small and large hyperpolarization protocols with all currents , as well as a spontaneous subthreshold activity protocol maintaining simulated TTX and TEA , to guarantee that appropriate subthreshold response would not be lost by the addition of spiking currents . While many measurements of rodent SNc DA AP features have been made in prior publications , [36] reported AP features such as threshold , amplitude , width , rise slope , decay slope , AHP depth and rebound delay from a consistent set of experimental protocols , so we chose to use this single source for target feature values for the spontaneous firing protocol . Although mean ISIs were reported in [36] as 0 . 92±0 . 54 s , we chose to allow AP firing rates between 0 . 5 and 8 . 0 Hz , approximating the range reported elsewhere [65 , 66] under varying experimental conditions . Additional features included constraints on relative timing of AP onset in the AIS , soma , and proximal , non-axon-bearing dendrite , to ensure AP initiation occurred in the AIS [34 , 41 , 42] . This optimization had 16 free parameters and 23 target features ( see Materials and methods for full details of parameter and feature ranges ) . We ran the optimization with a population size of 165 across 165 CPU cores on the IBM Cloud for 10 , 000 generations , testing 1 , 650 , 000 parameter sets in 600 hours . This search found 2 , 047 models matching all target features simultaneously . A second optimization , performed for comparison , used the full original ranges of all parameters from from Tables 2 and 3 ( excluding of course the APC and FPC metaparameters ) , resulting in 36 free parameters . When similarly run for 10 , 000 generations , this optimization was unable to find any models matching all target features simultaneously , highlighting the utility of building spiking models outward from a subspace of subthreshold parameters . We then applied additional constraints to check the validity of discovered models using constraints approximated from observations in the SNc DA literature and experimental protocols that models were previously not tested on . Applying these constraints during the optimization would have increased simulation time excessively , so we based the optimization on features extracted from a minimal set of protocols and applied these contraints post-hoc . We tested the response of each model to a 100 pA current injection , and eliminated models from the final population that fired at above 30 Hz in that condition , constraining the population to a reasonable range for SNc DA neurons [41] . We further restricted the final population by removing models that produced unusual responses under simulated channel blockade experiments from which we were unable to automatically calculate feature values . This resulted in a final population of 727 models meeting all criteria . Parameter values for this final population are shown in S3 Fig . The relationship between frequencies of STO and spontaneous firing in SNc DAs is unknown . One observation [19] suggests a positive linear correlation , such that neurons with a fast STO have a fast spontaneous firing rate and vice versa , albeit with potential outliers . Feature values of the model population are shown in Fig 4A for 12 of the target features . Among our model population , we found a correlation of 0 . 34 ( p < 0 . 001 ) between STO and spontaneous firing frequencies ( Fig 4B ) , but we also found outliers across the full feature range . Other feature values were spread across the majority of the target ranges , with one exception: rebound spike times were constrained to a narrow , low range . Delay to first spike after a hyperpolarization is known to depend on the kinetics of the KA current [4] , which were constrained to covary completely within the 2D plane of STO frequency and amplitude controllers , such that the optimization did not have the opportunity to fully explore parameters contributing to this feature . The population therefore only represents the portion of SNc DAs with a fast rebound response , and leaves open a possible mechanism to regulate this important timing property among SNc DAs [67] . Albeit , this is a common property of SNc DAs , which have generally faster rebound from hyperpolarization than DAs in the ventral tegmental area ( VTA ) . Limiting the population to fast rebound demonstrates that the dimensionality reduction approach used here may constrain parameter sets to covary in a specific way , and potentially restrict feature value variation in subsequent optimizations of the population . We explored how parameters related to the features of the spontaneous firing by performing PLSR on the final population ( shown for all spiking features in S4 Fig ) . Parameter coefficients for predicting spontaneous FR are shown in Fig 4C . The largest coefficient corresponds to Vhalf , NaT and is negative , indicating that active sodium current at lower membrane potentials was the most reliable way to increase FR . A prominent positive coefficient for g ¯ NaT further indicates the crucial role of sodium current in determining FR . Another prominent positive coefficient corresponded to the subthreshold oscillation’s FPC , revealing that modulating subthreshold currents along this metaparameter both increases STO frequency and reliably increases spontaneous FR . Note that because only the FPC and APC parameters affect the STO , the remaining coefficients also indicate the parameters that cause the divergence between STO frequency and spontaneous FR seen in Fig 5B . The remaining two prominent coefficients corresponded to g ¯ BK and Vhalf , BK , while very low coefficients were found for all Kv2 parameters , implicating BK current more strongly in dictating FR than Kv2 current . Blockade of either of these currents has been found to significantly elevate spontaneous FR in SNc DAs [64] , whereas only one of these currents was found to reliably modulate spontaneous FR within our population of models . Correlation coefficients above 0 . 25 were found for only a handful of additional feature pairs ( indicated by red lines in Fig 4A ) with the strongest correlations ( above 0 . 7 ) appearing only between features already expected to covary , such as AP rise rate and AP amplitude , or AP fall rate and AP width . Most feature pairs had correlations under 0 . 25 , indicating that the crowdedness function used in this optimization approach had encouraged spread across combinations of features , limiting correlations among feature pairs where possible . AHP depth is an example of a feature found to be uncorrelated with FR , and the PLSR coefficients predicting AHP depth are shown in Fig 4D . The population of models can be arranged in terms of their predicted FR and AHP values , which represents a view through parameter space , shown in Fig 4E and 4F . The colors in Fig 4E and 4F show mean feature values from models within a particular part of the predicted feature space ( FR and AHP ) . The consistent variation of these features across this projection of parameter space is another example of our analysis of a population-based parameter search discovering parsimonious dimensions of control of electrophysiological features within the high-dimensional parameter space of a non-linear model . Next , we examined the generalizability and robustness of the discovered models by performing blocking experiments on individual channels and observing the effects on spiking activity . We simulated spontaneous activity under conditions in which each current was blocked by reducing g ¯ to 0 . Each blockade experiment was recorded for 10 s , and features such as FR in the final 5 s were extracted for all 727 models in the final population . First , we observed that spontaneous firing did not emerge reliably when blocking Kv2 in these models ( not shown ) , indicating that the optimization had found a region of parameter space in which the Kv2 current was essential for generating spontaneous firing . Examples of spontaneous FR and STO voltage traces are shown in Fig 5A , from four models from different points of the feature space ( corresponding to the colored circles in Fig 4B ) . Blockade of the BK , KA and KERG potassium currents ( Fig 5B ) produced a reliable increase in spontaneous firing rate across the model population , consistent with empirical observations [10 , 24 , 64] . The traces in Fig 5B show the effects of channel blockade on the orange and green example models shown in Fig 5A , indicating modulations of AP shape and the robustness of the spontaneous firing . Because blockade of NaT eliminated spiking , we aimed to more thoroughly examine the effects of perturbations to depolarizing currents . We scaled g ¯ NaT , g ¯ CaL and g ¯ HCN between 0 and 2 times their values for each good model , at intervals of 0 . 25 . This led to 9 scaling factor values for each of these 3 parameters , giving 729 combinations . We simulated spontaneous activity for all 727 models in the final population , for each of the 729 scaling factor combinations , calculating 529 , 983 sets of features . We measured the firing rate change as a multiplier from baseline for each individual , and calculated mean change for each scaling factor combination across all individuals . Circle colors in Fig 5C shows mean firing rate change , where an empty space indicates spontaneous firing was eliminated in all 727 individuals . Across the 9 squares , g ¯ NaT is scaled from 0 to 2 , with g ¯ CaL scaled horizontally from 0 to 2 and g ¯ HCN scaled vertically from 0 to 2 within each square . The left square , with full blockade of NaT , shows no activity among any individual regardless of any change to g ¯ CaL or g ¯ HCN . The bottom left corner of the center square , with default NaT values , shows that full blockade of both CaL and HCN was also sufficient to eliminate AP generation in all individuals . A common feature across squares is that change to spontaneous firing rate by perturbation of one depolarizing current can be compensated for by an opposing perturbation of other depolarizing currents , indicating degeneracy among sources of depolarization . This type of degeneracy between currents has been shown to be useful for robust activity generation in neurons [61] . For example , firing rates can be maintained during gradual increase of g ¯ CaL ( moving right within a square ) by decreasing g ¯ HCN ( moving down within a square ) , mirroring SNc DA juvenile development , during which a compensatory reduction in HCN current accompanies a developmental increase in CaL current [68] . We found that the mean effect of blockade of CaL was a reduction in spontaneous FR ( baseline: 4 . 3527± 1 . 40474 Hz; CaL block: 0 . 0549± 0 . 3389 Hz ) , consistent with some experimental results [16 , 68 , 69] , but in contrast to [19] , where downregulation of CaL sufficient to eliminate [Ca2+] transients during APs was found to have no mean effect on spontaneous FR . The resilience of DA models to CaL blockade has previously been shown to require specific balance between NaT and CaL parameters [18 , 70] , with models with low CaL to NaT conductance ratios entering the regime found among most of the final population here . While the final population of models generalized to the effects of multiple experimental perturbations , future optimizations with additional constraints may allow construction of a model population that reflects a finer balance among parameters , much as prior experimental and modeling results have indicated are likely present in the real SNc DA population . The final channel blockade experiment targeted SK current and did not produce a reliable effect on spontaneous FR ( not shown ) . Instead , an increase in CV ISI ( Fig 6A ) appeared inconsistently , thus replicating empirical observations that SK modulation can , but does not necessarily , induce CV ISI increases in SNc DAs [32 , 63 , 71] . Our models were deterministic , so modulation of firing regularity must result from fundamental alterations to mechanisms responsible for the pattern of spontaneous activity . Large increases in CV ISI manifested as prolonged bursts with multiple spikes ( Fig 6B , green ) , while small increases in CV ISI manifested as doublet or triplet bursting activity ( Fig 6B , orange ) . Most individuals in the final population did not show an increase in CV ISI under SK block ( Fig 6B , black ) , despite the presence of characteristic plateau potentials emerging in some models under simulated TTX , TEA and apamin application ( Fig 6B , black ) . Note that plateau potentials under SK block in subthreshold simulations was neither necessary nor sufficient for inducing bursting activity , leading us to next examine the conditions leading to increased bursting susceptibility under SK blockade . A low value of g ¯ Kv2 was a necessary condition for burst generation under SK block ( Fig 6C ) , with CV ISI only increasing under SK block in models with g ¯ Kv2 < 0 . 005Scm−2 ( termed low-Kv2 models ) . This condition was not sufficient for burst generation , however , with some low-Kv2 model CV ISI values remaining low ( Fig 6C , inset ) . We performed PLSR on all low-Kv2 model parameter sets , regressing against CV ISI under SK block to determine the directions in parameter space that most reliably lead to bursting activity . The largest coefficients , shown in Fig 6D , corresponded to Vhalf , BK and Vhalf , Kv2 ( both negative ) and indicate that higher voltage thresholds for activation of repolarizing potassium currents leads to a higher likelihood that a model is susceptible to bursting under SK block . The next most prominent parameter coefficient was for the conductance scaling parameter for NaT in the AIS , meaning that models with strong NaT in the AIS relative to the soma are more likely to burst . This was followed by Vhalf , NaT , further highlighting the voltage threshold of spike-generating currents as a strong regulator of burst susceptibility under SK . We next calculated feature values for burst characteristics: mean number of APs in a burst , interburst interval , and burst duration . Of the 31 models with CV ISI above 0 . 2 under SK block , all burst feature values could be calculated for 22 ( burst features could not be calculated for 9 models with irregular spiking , resembling that of the orange trace in Fig 6B ) . Fig 6E–6G shows these characteristics . The parameters with the highest correlation with each feature were all related to the NaT channel . Number of APs per burst was most highly correlated with Vh axNaT ( 0 . 501 , Fig 6E ) , and interburst interval and burst duration were each most highly correlated with Vh hsshift , NaT ( −0 . 519 and −0 . 671 , shown in Fig 6F and 6G , respectively ) . This indicates that sodium channel slow inactivation plays a significant role in terminating and resuming bursts , resembling the empirical finding that slow inactivation of sodium determines depolarization block during depolarization-induced bursts in SNc DAs [72 , 73] . These observations represent a set of experimentally testable predictions surrounding burst susceptibility under SK block: first , that bursting only emerges in SNc DAs with a lower magnitude of Kv2 current; second , that among those cells with low Kv2 current , half activation voltage of rectifying potassium channels such as Kv2 and BK will indicate the degree of spiking irregularity generated under SK block; and third that , in this condition , burst properties are correlated with the voltage at which NaT current activates and inactivates . Above , we explored the relation between SNc DA spontaneous activity in the sub- and supra-threshold regimes , finding a model population with a positive correlation between oscillation and spiking frequencies ( Fig 4B ) . Regression analysis indicated that both subthreshold and suprathreshold current parameters influence spontaneous firing rate ( Fig 4C ) . However , the interaction between these two electrophysiological phenomena remained obscure , even with complete access to model parameters and feature measurements . To further elucidate this relationship , we investigated the voltage trajectories of both activity patterns in the models . In the phase plane of V and dV/dt , both subthreshold oscillation and spontaneous APs form loops ( Fig 7A shows one model with similar frequencies for both regimes ) . A smaller amplitude and slower subthreshold oscillation ( Fig 7A , orange ) is naturally nested within the larger and faster AP ( Fig 7A , blue ) . We noticed that these trajectories drew nearest each other on their rising phases while the membrane potential accelerated into the AP within the spiking regime and the subthreshold oscillation decelerated towards its peak , crossing in some cases , indicated by the arrow in Fig 7B . The two regimes therefore had identical voltage and rate of voltage change at certain phases . Given this observation that ongoing activity was most similar at these points , we first asked if inserting spiking currents through simulated instantaneous TTX and TEA washout at this precise phase in the subthreshold regime would cause the model to transition immediately into its normal spontaneous firing mode ? We performed this experiment by simulating the model shown in Fig 7A and 7B for 5 s in the subthreshold regime , with g ¯ NaT , g ¯ Kv2 and g ¯ BK set to 0 . At the detected crossing of the voltage indicated by the arrow in Fig 7B , we simulated instantaneous washout of TTX and TEA by setting the spiking conductances to the default values for that model then continued the simulation . The resulting membrane potentials , shown in green in Fig 7C , diverged from both the normal subthreshold oscillation ( orange ) and spontaneous firing ( blue ) trajectories , reaching a lower subthreshold peak than the normal oscillation and remaining hyperpolarized for over 500 ms before generating an AP . Due to the ongoing maintenance of the voltage in the subthreshold regime prior to spiking current activation , gating variables for the activation and inactivation of sodium and potassium channels were not in a configuration to immediately enter an AP , as they would have been at that phase had an ongoing spontaneous firing regime been present . Of course , all internal variables of the model are different during the subthreshold and spiking regimes . We next examined the impact of modulating this state difference on spike timing after spiking current activation . The difference in internal state between the two regimes at the point of crossover in the phase plane was calculated by the difference between every variable in the model , from all channels across all compartments , including voltage and calcium variables , leading to a 336-dimensional ‘state variable difference vector’ . Simultaneous activation of the spiking currents and subtraction of this difference vector across all variables in all compartments of the model readily shifted the model from the subthreshold regime to the spiking regime , resulting in a near perfect match of the remaining simulation to the default spiking regime voltage trajectory ( compare blue and red traces in Fig 7D and 7E ) . Next , we performed a sequence of similar experiments by sequentially restoring fractions of the difference vector , at 9 intervals from 0 . 1 to 0 . 9 . Fig 7D shows the resulting voltage trajectories with Fig 7E showing a zoom in on the black boxed region of Fig 7D . In Fig 7E , the hyperpolarization and delayed AP found previously with no state variable adjustment ( green in Fig 7C ) can be seen in the green trace as the additional loop prior to AP onset ( AP onset occurs when the trajectory heads upwards and to the right into the AP ) . This loop , and subsequent delayed AP , also occurred when subtracting 10% of the difference vector ( lightest red trace ) , but at 20% a transition occurred , and the trajectory immediately proceeded into the AP with no prolonged hyperpolarization . Subsequent subtraction of increasingly large fractions of the difference vector ( lighter to darker red traces in Fig 7D and 7E ) moved the resulting trajectory towards the normal spiking trajectory ( blue trace in Fig 7D and 7E ) . We found increased ISIs immediately after spiking current onset ( Fig 7F ) , which gradually decreased over the first 6 APs , increasing the firing rate towards the normal frequency . However , with either 0% or 10% of the difference vector subtracted , the ISIs were actually closer to the normal values than with 20% of the difference vector subtracted , which resulted in the strongest frequency modulation and increased the first ISI to nearly double its normal value . Further increasing the fraction of state variable difference vector subtracted gradually decreased ISIs towards normal values . ISIs decay to within 5% of the original ISI baseline after 5 ISIs , a process taking around 2 s in the default case ( green in Fig 7F ) , which also includes approximately 500 ms before AP onset . The instantaneous transition from spontaneous firing to subthreshold oscillation through simulated TTX and TEA application can also be performed in silico . Here , we simulated the model for 5 s with spiking currents , then set spiking conductances to 0 , at the time indicated by the arrow in Fig 7G . The green trace in Fig 7G shows that the model immediately enters the subthreshold oscillation regime , but with a frequency modulation relative to the normal ongoing subthreshold oscillation , shown in orange . Again , we subtracted incremental proportions of the state variable difference vector , this time reflecting the transition from the spontaneous firing regime to the subthreshold oscillation regime , and plotted the interpeak intervals ( IPIs ) after spiking current block ( Fig 7H ) . In the case without difference vector subtraction , we observed a smaller first IPI , which became still smaller for the second IPI , then began relaxation towards the normal frequency , indicating a transitional period in which the neuron adapted to the new regime . IPIs remained 5% below baseline for 5 IPIs , taking approximately 860 ms to increase to baseline . Incrementally subtracting increasing fractions of the difference vector led to incremental increases in IPIs , until the activity was indistinguishable from the normal STO with 100% of the state variable difference vector subtracted , instantaneously transitioning the neuron from the spiking regime to the subthreshold oscillation regime . These results indicate that the alternate configuration of state variables attained during subthreshold oscillation compared with spontaneous firing leads to a temporal lag of spiking activity caused by a prolonged hyperpolarization upon activation of spiking currents for this parameter set . This hyperpolarization did not occur if state variables were adjusted by subtracting 20% of the difference vector , and doing so instead led to a modulated ( slower ) frequency of spontaneous firing , despite this parameter set having ongoing oscillations at a similar frequency to the spontaneous firing . Experimental disambiguation of these dynamical system states has therefore been challenging .
Our optimization discovered parameter space regions in which models spread across the range of acceptable outputs , from a naive starting point . Prior optimization approaches used a single ideal feature set as a target [48–51 , 53 , 55 , 74] , and database approaches targeted uniformity in parameter space but not feature space [31 , 75] . Related work in cardiac myocyte [12 , 59 , 76] and neuron modeling [13] achieved parameter sensitivity analysis by varying parameters around an existing , already established seed . These approaches did not utilize unbiased search to discover the model population as reported here . Recent advances in cardiac modeling have focused on how to find parameter sets for models that reflect the diversity present across cellular populations [77] , including the use of distribution fitting to adapt model databases to experimental feature distributions , demonstrating enhanced prediction of perturbation response in the realistically distributed population [78] . Here we fit to feature ranges and encourage an even sampling of a uniform feature distribution , but the resulting model population could be further sampled to achieve a fit to specific data sets by applying distribution fitting in future . It is known that ion channel properties are intrinsically variable , and yet correlated to preserve function [22] . Studies have showed that overexpression of one channel can lead to a compensatory enhancement of an opposing current [25 , 30] , and demonstrated computationally that a single intracellular property ( e . g . calcium concentration ) governing expression of multiple ion channels can result in functional homeostasis , maintaining activity in response to a perturbation by simultaneously adapting several currents [79] . Additionally , similar activity patterns can result from variable conductance levels [80] , and pharmacological blockade of one current can produce different activity from neurons that were at different balance points [57] . Examples of this principle can be found in the results of our parameter search , with neuron models within the target range of features produced by different combinations of parameters , leading to different responses to simulated channel blockade ( Figs 5 and 6 ) . Our study accounts for feature variability by linearly varying combinations of kinetic parameters , mirroring evidence from weakly electric fish electrocytes , which showed coordination between the control of electric organ discharge frequency and ion channel kinetics [29] . Within the model population we observed a relatively weak correlation of 0 . 34 between STO and firing frequencies ( Fig 4B ) . Despite our then constraining parameters for channels not blocked by TTX and TEA to the plane of STO control , the STO frequency-predicting coefficient had only a moderate influence on the firing frequency ( Fig 4C ) . We then observed , through simulated instantaneous TTX and TEA washout , that ion channels occupy fundamentally different states in the two regimes even when frequencies align ( Fig 7 ) . We therefore conclude that although the same channels play a role in generating and influencing both STO and pacemaking frequencies , these phenomena are not trivially connected , aligning our study with prior modeling work [18] . We arrived at this model population using unbiased evolutionary search conducted from a naive starting point within empirically observed parameter ranges , using features of the STO as clues that the search could follow to achieve realistic balance of ion channel parameters . This confirmation of prior modeling conclusions , and the generalizations of the models to channel blockade ( Figs 5 and 6 ) , were not a priori constraints on the search , and therefore indicate the potential of the resulting model population to generate new predictions of currently unknown properties of SNc DAs , such as the precise influence of intracellular parameters upon electrophysiological features described herein ( Figs 3B , 4C , 4D and 6D and S4 Fig ) . Modeling has previously demonstrated that different plausible firing patterns that relate to different DA subpopulations can be generated by varying SK conductance , with lower activation of SK co-occurring with increased bursting activity [8] . Furthermore , [36] showed that burst firing is a hallmark of juvenile SNc DAs and the transition from P2 to P8 is likely mediated by increased SK current . Here we imposed a constraint on spontaneous firing regularity and prevented spontaneously bursting models from emerging . A subset of our final population nevertheless entered bursting regimes under SK blockade ( Fig 6 ) . As such , this SNc DA model is capable of producing bursting activity , and we expect that given the correct target features during optimization , multiple DA model populations with various activity modes could be attained , mirroring developmental stages or adult subpopulations . Our results point to a naive , optimal , and unbiased means to identify which channel currents can be targeted by drugs or intracellular processes to efficiently regulate neuronal properties around the population mode . If , for example , a DA experiences a persistent change in input activity that reduces the frequency of dendritic calcium influx and [Ca2+] , feedback modulation targeting those ion channels along the FPC’s parameters ( Fig 3 ) could efficiently restore prior function . Previously , [79] implemented a feedback model of such functional homeostasis within a crab stomatogastric ganglion neuron model , using a single , activity-dependent intracellular process ( [Ca2+] ) that governed expression of multiple ion channels . Our method discovers optimal ratios of channel properties and their transformations required to implement this feedback control . The parameter coefficients found here align with prior experimental and modeling work on the SNc DA STO . In the influential model of [17] , dendritic diameter sets STO frequency because diameter dictates the rate of change of [Ca2+] . In our results diameter was fixed but g ¯ CaL and Vhalf , CaL consistently modulated STO frequency ( Figs 2G and 3B ) . Other parameters with strong influence on STO frequency were g ¯ KERG and Vhalf , KERG , and previous models showed that blockade of KERG increases STO frequency [81] . Our method uncovered this relationship and the specific ratio between KERG and CaL currents controlling frequency is predicted . Our final population of SNc DA models had a significant correlation coefficient of 0 . 34 between STO and firing frequencies . All parameter sets found produced outputs matching SNc DA electrophysiology , and proved robust to ion channel perturbations , suggesting that all are viable SNc DA models . As such , we can consider the population a potential surrogate for the real SNc DA population and therefore capable of yielding new insights into this important neuron type . The positive correlation predicted between STO and firing frequencies is supported by one piece of evidence [19] , but further data could confirm whether the relationship and distributions of our model population are accurate . Our population approach allowed us to produce variable responses to drug-like , channel blockade perturbations across hundreds of parameter sets for the same model formulation ( Figs 5 and 6 ) . Only a subset of models responded to SK blockade with CV ISI increase , replicating experimental observations [32] and leading to the prediction that only SNc DAs with lower Kv2 conductance are susceptible to bursting under SK blockade . This type of prediction is impossible to formulate from a single parameter set representing an average neuron . In such a case , we may have blocked SK and reduced Kv2 conductance , only to find that CV ISI remained low . Of course , the model would still be plausible because both low and high CV ISI under SK block are realistic responses among SNc DAs . Specifically , we predict for the entire population of SNc DAs: 1 . the ratios for ion channel modulation which control STO frequency and amplitude ( e . g . , the ratio of KERG and CaL currents capable of controlling STO frequency; Fig 3B ) ; 2 . a correlation of 0 . 34 between STO and firing frequencies ( Fig 4B ) ; 3 . the ratios of ion channel modulation which control pacemaking frequency ( Fig 4C ) 4 . a relatively low Kv2 conductance for burst induction under SK block ( Fig 6C ) ; and 5 . the ratios of NaT , Kv2 and BK currents which best predict ISI CV under SK block ( Fig 6D ) . Our novel method discovered how a high-dimensional , experimentally observed system of interacting ion channels might be controlled . Subthreshold oscillatory activity was captured thousands of times by varying parameters independently over a broad range . By asking how simpler modulations of these parameter sets might reproduce the full range of natural oscillations , we discovered a two dimensional projection of the full parameter space sufficient to control the system . The PCs that were found to be most correlated with subthreshold features across multiple trials were always those accounting for a low amount of the variance among parameter sets , and the precise reason for this remains an open question . One limitation of the current study derives from the lack of detailed experimental characterization of SNc DA STO . While high voltage STO amplitude was characterized in [57] , frequency was not . We assumed a frequency similar to the spontaneous FR reported in [57] , which may be incorrect , despite [82] reporting a similar though slower frequency of STO under TTX compared with pacemaking . Furthermore , under blockade of potassium channels with TEA , some SNc DAs enter a mode of high-threshold calcium spiking that can be resilient to apamin [15 , 57] . With more precise feature values our optimization approach might be used to identify parameter combinations necessary to regulate each of these modes . When utilizing this low-dimensional projection of subthreshold parameter space as a foundation from which to extend the parameter search into the space of parameters for spiking currents , our optimization was unable to sample the full range of some features , most notably the delay to first spike after hyperpolarization , which was at the low end of the empirically observed range for all models found ( Fig 4A ) . In the Results section , we suggested that this is possibly due to the low-dimensional subspace overly constraining KA channel kinetic parameters , which are known to be correlated with this feature in the empirical population [4] . To test such constraints , the optimization could be continued from the current population of models , but with only the most constrained feature included in the crowdedness function , encouraging the development of a model population by the algorithm that spreads maximally in this feature . Note that all other constraints would still be included in the error function , ensuring the quality of newly generated models . If regions of the feature space were still inaccessible , then the optimization could be continued with all parameters free . Although the high-dimensional optimization was too complex to find any good models from a naive start , starting from a population of known good models and observing the spread in parameter space that maintains all feature constraints would be a method of testing if the low-dimensional subspace overly constrains parameters . If models with longer delays to first spike could be found using this approach , then a linear subspace precludes discovery of those good models . We gained greater prediction accuracy using a 296-parameter , second order non-linear fit ( see S3 Fig ) , and the possibility that certain non-linear fitting may facilitate dimensionality reduction during parameter search that facilitates recovery of a larger region of feature space than strictly linear reductions represents an important direction for future work . This study has produced hundreds of potential neuron models , each with a unique combination of intracellular parameters , and each equally valid according to our empirically derived criteria . The model database resulting from our parameter search provides a unique resource for exploring the relationship between ion channel properties and activity patterns among SNc DAs . Future work can take several directions . These optimization techniques can be used with alternate feature ranges to generate model subpopulations representative of , for example , the developmental stages described in [36] , or subpopulations defined by anatomy or function within the midbrain , potentially extending to the striking differences between mesoaccumbal and nigrostriatal DAs [4] . Exploration of the role of additional sources in feature variation , such as morphology [52] , can be addressed by future optimizations using the same algorithm , possible couped to parametric models of neurogensis [83] . This database also offers the opportunity to explore population responses to drugs or drug combinations within this neuron type . We discovered low-dimensional controllers that provide a simple description of high-dimensional parameter changes that consistently modulate electrophysiological features . Our combination of optimization and analysis provides a powerful tool for uncovering key regulatory constraints and relationships among many intracellular mechanisms [33] , and presents numerous testable hypotheses while suggesting the possibility that our low dimensional projections can discover nature’s real canvas upon which functional regulation of neurons and neural systems is composed .
|
Electrophysiological activity of the neuronal membrane and concomitant ion channel properties are highly variable within groups of neurons of the same type from the same brain region . Reconciliation of the mechanisms generating neuronal activity is challenging due to the complexity of the interactions between the channel currents involved . Here we present a set of mathematical analyses that uncover the low-dimensional intracellular parameter combinations capable of regulating features of subthreshold oscillations and spontaneous firing in empirically constrained models of nigral dopaminergic neurons . This method generates , from a naive starting point , linear combinations of ion channel properties that are surprisingly capable of reliably controlling a wide variety of emergent electrophysiological activity , thereby predicting drug effects and shedding light on unsuspected compensatory mechanisms that contribute to neuronal function .
|
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2019
|
Dimensions of control for subthreshold oscillations and spontaneous firing in dopamine neurons
|
Paramyxoviruses represent a family of RNA viruses causing significant human diseases . These include measles virus , the most infectious virus ever reported , in addition to parainfluenza virus , and other emerging viruses . Paramyxoviruses likely share common replication machinery but their mechanisms of RNA biosynthesis activities and details of their complex polymerase structures are unknown . Mechanistic and functional details of a paramyxovirus polymerase would have sweeping implications for understanding RNA virus replication and for the development of new antiviral medicines . To study paramyxovirus polymerase structure and function , we expressed an active recombinant Nipah virus ( NiV ) polymerase complex assembled from the multifunctional NiV L protein bound to its phosphoprotein cofactor . NiV is an emerging highly pathogenic virus that causes severe encephalitis and has been declared a global public health concern due to its high mortality rate . Using negative-stain electron microscopy , we demonstrated NiV polymerase forms ring-like particles resembling related RNA polymerases . We identified conserved sequence elements driving recognition of the 3′-terminal genomic promoter by NiV polymerase , and leading to initiation of RNA synthesis , primer extension , and transition to elongation mode . Polyadenylation resulting from NiV polymerase stuttering provides a mechanistic basis for transcription termination . It also suggests a divergent adaptation in promoter recognition between pneumo- and paramyxoviruses . The lack of available antiviral therapy for NiV prompted us to identify the triphosphate forms of R1479 and GS-5734 , two clinically relevant nucleotide analogs , as substrates and inhibitors of NiV polymerase activity by delayed chain termination . Overall , these findings provide low-resolution structural details and the mechanism of an RNA polymerase from a previously uncharacterized virus family . This work illustrates important functional differences yet remarkable similarities between the polymerases of nonsegmented negative-strand RNA viruses .
Paramyxoviruses describe a family of viruses responsible for significant disease , ranging from lower respiratory disease due to parainfluenza virus and measles virus as well as emerging pathogens such as Nipah and Hendra viruses causing severe encephalitis . The replication machinery of these viruses , the polymerase complex , is largely unstudied due to their complexity and size and to date there has been no report of a recombinant paramyxovirus polymerase . The biochemistry and importantly , the potential inhibition of these protein complexes represents a significant opportunity for understanding and preventing disease . Nipah virus ( NiV ) is an emerging pathogenic paramyxovirus part of the Henipavirus genus within the Paramyxoviridae family [1 , 2] . NiV infection in humans is characterized by systemic vasculitis , ultimately resulting in fatal encephalitis [3 , 4] . Due to its highly pathogenic nature and the lack of approved therapeutics or vaccines , NiV has been classified as a category C priority pathogen by the Centers for Disease Control and Prevention and the National Institute of Allergy and Infectious Diseases [5] . There are no approved antiviral drugs or vaccines for NiV infection [6] . NiV has a nonsegmented negative-strand ( NNS ) RNA genome of approximatively 18 kilobases [7] . It contains the classic non-coding regions such as the 3′ leader ( Le ) and 5′ trailer ( Tr ) sequences , gene-start ( GS ) , gene-end ( GE ) , RNA-editing , and intergenic sequences in addition to coding regions for six proteins: nucleoprotein ( N ) , large ( L ) protein , phosphoprotein ( P ) , matrix ( M ) protein , fusion ( F ) protein , and glycoprotein ( G ) [7–10] . The genomes of NNS RNA viruses are transcribed and replicated by the RNA-dependent RNA polymerase ( RdRp ) . The RdRp is a multiprotein complex composed of the L protein and the P protein [11] . The Le region is a bipartite promoter that is highly conserved among Paramyxoviruses [12] . In NiV , mutations within the two conserved promoter elements ( nt 1–12 and 79–91 ) result in loss of minigenome function [13] . The L protein of the NNS RNA viruses serves the two main enzymatic functions responsible for RdRp and mRNA cap formation . The L protein ranges in size within NSS viruses but is approximately 250 kDa [11] . Six highly conserved amino acid sequences , known as conserved regions ( CR ) I to VI , are found in the L protein of all NNS RNA viruses [14 , 15] . The L protein exists in a complex with the P protein , which acts as a molecular chaperone for the polymerase and enhances processivity [16] . Structural characterization of the L-P complex remains challenging due to the large size of the L protein and the difficulty of obtaining adequate quantities of highly purified proteins . However , the cryo-electron microscopy ( EM ) structure of the L protein from vesicular stomatitis virus ( VSV ) , a negative-sense RNA virus of the Rhabdoviridae family , has recently revealed how the domain organization and enzymatic function are organized in a three-dimensional structure [17] . Negative-stain EM of VSV L protein showed the RdRp domain containing the CRIII adopts a ring- or doughnutlike architecture decorated with multiple appendages in various orientations [18] . CRIV and V , mapped to the capping domain , appear as a single , globular appendage covering the hole of the doughnut . Additionally , there are three small , globular appendages corresponding to the connector domain , the methyl-transferase domain , and the C-terminal domain ( CTD ) . This CTD was described as the +domain in the structure of a HMPV L protein fragment [19] . The +domain is critical for HMPV MTase activity . The addition of VSV P protein causes a molecular rearrangement that results in the appendages forming a condensed tail on one side of the core structure . Conserved sequences within the RdRp and capping domains suggest that the molecular organization of L proteins from other viruses may be similar . The X-ray crystal structure of the NiV P multimerization domain and the N protein have recently been solved [20 , 21] . The P protein acts as a link between the L protein and the nucleocapsid template , and acts as a chaperone for an RNA-free form of the N protein [20] . The NiV P protein is composed of three domains , known as the N-terminal domain , the P multimerization domain , and the X domain . The P multimerization domain has been reported as a tetrameric coiled coil while others have reported it forms an elongated coiled-coil trimer[20 , 22] . The structure of P in relationship to the L protein is not known . The P protein may undergo structural re-organization in the context of the L-P or L-P-N complexes . Details of this structural re-organization , combined with the known structure of the NiV N protein , will provide insights into the NiV enzymatic machinery [21] . It is thought that NiV employs the same or similar mechanism as other NNS RNA viruses such as VSV ( Rhabdoviridae ) and respiratory syncytial virus ( RSV; Pneumoviridae ) . Current mechanistic knowledge of paramyxoviral L proteins has been limited to bioinformatic studies . There has been no report of the purification or biochemical characterization of an active paramyxoviral polymerase , which presents an exciting opportunity to probe the enzymatic properties of the polymerase of an emerging pathogen . Here , we present the first report of the expression , purification and biophysical and biochemical characterization of a paramyxovirus polymerase complex . Visualization of purified NiV polymerase by negative stain-EM shows ring-like particles . Using a combination of enzymatic approaches , we demonstrate that NiV polymerase is active in de novo and primer-dependent RNA synthesis . We identified sequence requirements for promoter recognition and for polyadenylation by NiV polymerase . We discovered that NiV polymerase is much more prone to stuttering than RSV polymerase , which could explain differences in promoter sequences between pneumo- and paramyxoviruses . Considering NiV’s status as a priority pathogen , we also characterized clinically-relevant nucleotide analogs that inhibit NiV replication by a mechanism of delayed chain termination . The purified enzyme represents a powerful tool for understanding the enzymatic function of the polymerase from a previously uncharacterized family of viruses and has significant implications for the development of novel inhibitors of NiV and other related viruses .
The paramyxoviral polymerases comprise a large ( L ) protein and a phosphoprotein ( P ) . We hypothesized that the NiV L protein adopts a structure similar to the VSV L protein given their moderate sequence similarity ( 35% ) . Using homology modeling to generate a three-dimensional view of the NiV L protein , our model indicates the RdRp domain adopts a classic right-handed configuration ( Fig 1A ) . The sequences of NNS RNA virus L proteins contain conserved residues that are in close spatial proximity to one another and correspond to catalytically active sites within the protein . The NiV L protein contains a 831-GDNE-834 motif within the RdRp domain in contrast to the GDNQ sequence in most other NNS RNA virus L proteins ( Fig 1B ) . In addition , highly conserved histidine and arginine residues are found within the capping domain . The histidine of the HR motif forms a covalent intermediate during polyribonucleotidyltransferase ( PRNTase ) reaction in VSV polymerase , although this enzymatic activity has not been reported for NiV ( Fig 1B ) . Importantly , the close spatial proximity of these conserved catalytic sites suggests a potential functional cooperativity among multiple L protein domains . We then produced NiV polymerase through the co-expression of the N-terminal FLAG-tag L and P proteins of NiV within insect cells infected with a single recombinant baculovirus clone . The protein was purified by FLAG-tag and size exclusion-chromatography ( SEC ) . Fractions corresponding to a peak near the void volume were pooled , concentrated , and analyzed by SDS-PAGE revealing bands consistent with the expected molecular weights of the L protein ( 260 kDa ) and P protein ( 80 kDa ) ( Figs 1C and S1 ) . In order to understand and compare their effects in enzymatic assays , we expressed and purified NiV L ( D832A-N833A ) -P and NiV L ( H1347A ) -P containing mutations in the catalytic sites of the RdRp and capping domains , respectively . Fractions from a predominant peak were pooled for each protein , concentrated , and analyzed by SDS-PAGE to reveal bands consistent with the expected molecular weights of the L and P proteins ( S2 Fig ) . The L proteins from NiV L ( wild-type [wt] ) -P , NiV L ( H1347A ) -P , and NiV L ( D832A-N833A ) -P were excised from a denaturing gel and subjected to proteolysis followed by liquid chromatograph-tandem mass spectroscopy ( LC-MS/MS ) to verify the sequences were complete and to confirm amino acid substitutions . Results from these assays showed ≥95% sequence coverage for all three L proteins ( example coverage map shown in S3 Fig ) . Similar results were obtained for the NiV P protein ( coverage map in S3 Fig ) . A minor host contaminant protein co-purified with the NiV L-P complex ( Fig 1C ) . This protein was also excised from a denaturing gel and positively identified using LC-MS/MS as a heat shock cognate ( HSC ) 70 protein . Previous work identified that heat shock protein 70 and/or HSC70 co-purifies with RSV polymerase , a closely related polymerase to NiV polymerase [23] . Typical protein concentrations ranged from 0 . 7–1 . 7 mg/mL , with an average yield of about 0 . 5 mg per liter of insect cell culture . We probed the molecular architecture of the NiV L ( wt ) -P complex through visualization by negative-stain transmission EM . The images revealed small , 5–10 nm globular particles and 20–70 nm larger particles that appeared to be clumped or aggregated protein ( S1 Fig ) . The smaller particles were selected for alignment and two-dimensional ( 2D ) classification . Class averages showed both smaller , 5–8 nm globular particles with even density ( Fig 1D , top left ) and larger 8–11-nm globular or ring-like particles ( Fig 1D , top right and bottom row ) that appear to have an indentation or cavity in the center . Our data do not show globular appendages that could map to other regions of the L or P proteins , suggesting the NiV L ( wt ) -P complex adopts a unique molecular arrangement . Using these insights , we explored the activity of the protein in functional enzymatic assays . The RdRp activity of NiV L ( wt ) -P was measured in a radiometric assay using α33P-labeled guanosine triphosphate ( GTP ) as nucleotide substrate for the reaction . The leader ( Le ) promoter region at the 3′-end of the NiV genome was chosen to design the 12-mer synthetic RNA template for the RdRp reaction because it contains the authentic sequence recognized by the polymerase during virus replication ( Fig 2A ) . This uridine-rich template contains three consecutive cytidines at its 5′-terminus that are required for product detection through α33P-GTP incorporation into the nascent complementary RNA strand . In the presence of NiV L ( wt ) -P , α33P-GTP was not sufficient to promote RNA synthesis without other nucleotides ( Fig 2A , lane 1 ) . The addition of cytosine triphosphate ( CTP ) + adenosine triphosphate ( ATP ) was required for the formation of 10- and 11-mer products ( Fig 2A , lane 2 ) . In the presence of the NiV L ( D832A-N833A ) -P mutant , nearly 100% of product formation was inhibited ( Fig 2A , lanes 3 and 4 ) . In comparison , the H1347A mutation resulted in only ~50% reduction in de novo RNA synthesis relative to the wild-type enzyme ( Fig 2A , lanes 5 and 6 ) . In the cell-based minigenome assay , the adjacent H1347A and R1348A mutations completely blocked the luciferase reporter activity ( Fig 2B ) . The more pronounced detrimental effect of H1347A in the minigenome assay compared with the RdRp assay is most likely due to RNA capping impairment in cells . In the de novo RdRp assay , ATP and CTP were both required to support RNA synthesis ( S4 Fig ) , with a Km value for CTP and ATP of 79±21 μM and 18±2 μM , respectively ( Figs 2C and S4 ) . The Le bipartite promoter region is highly conserved among Paramyxoviruses [12] . In NiV , its first element ( nt 1–12 ) contains a uridine-rich sequence also found in other NNS RNA viruses . In the NiV L-P enzymatic assay , swapping the RNA template from NiV to RSV-derived promoter resulted in comparable RdRp activity ( S5 Fig ) . To better understand the specificity of recognition of the NiV Le region nt 1–12 , we systematically introduced an adenine at each of the first nine bases on the promoter sequence . Positions 5 , 8 , and 9 were minimally affected by changes in promoter sequence ( Fig 2D , black bars ) . An adenine at position 1 , 2 , 6 , or 7 resulted in a 50–75% reduction in RNA synthesis ( Fig 2D , dark gray bars ) . Positions 3 and 4 were not only the most sensitive to nucleobase change with ~80% loss in activity ( Fig 2D , light gray bars ) , changes at these positions also resulted in unexpected RNA product sizes that were both shorter and longer than the reference promoter sequence ( S6 Fig ) . The longer aberrant products might result from enzyme stuttering . Taken together , the result of this adenine scan indicates that nucleotides 3 and 4 on the Le region are the most important for promoter recognition by NiV L-P . Elongation of RNA synthesis by NiV L-P was mimicked by using a short 4-nucleotide ( nt ) primer complementary to the 3′-end of the 12-mer Le template . Since the nucleotide sequence at position 5 on the RNA template is not important ( Fig 2D ) , it was changed from uridine ( U ) to C to enable enzymatic primer labeling and 5-mer product formation by adding α33P-GTP to the NiV L ( wt ) -P RNA complex ( Fig 3A , Fig 3B lane 1 ) . The primer extension activity profile of NiV L ( wt ) -P was also compared with the RNA product from the D832A-N833A and H1347A mutants . As expected , the D832A-N833A mutation in the RdRp active site completely abrogated primer extension activity ( Figs 3C and S7 ) . In contrast , the H1347A mutation in the capping domain had little effect on primer extension compared with the wt enzyme ( Fig 3C ) . In the primer extension assay , the 4-mer primer was fully converted by NiV L ( wt ) -P to 6-mer product in the presence of ATP ( Fig 3B , lane 2 ) . Compared with de novo RNA synthesis , the Km requirement for ATP by NiV L ( wt ) -P was reduced by ~1 , 000-fold to 0 . 015±0 . 001μM ( S8 Fig ) . The NiV RNA template only allows for one CTP addition event at position +7 . Adding ATP + CTP to the reaction resulted in 10- and 11-mer products predominantly , with minor products at positions +9 , +12 , and +13 ( Fig 3B , lane 3 ) . R1479 is a cytidine analog recently found to inhibit NiV replication [24] . Replacing CTP with R1479 triphosphate ( TP ) did not inhibit 10- and 11-mer product formation ( Fig 3B , lane 4 ) . This indicates that in the NiV L-P assay , single incorporation of R1479-TP into the nascent RNA does not result in immediate chain termination . In comparison , the same reaction conducted with the obligate chain terminator 3′dCTP instead of CTP resulted in the expected 7-mer product ( green arrow in S9 Fig ) . GS-5734 , an adenosine analog , is also known to inhibit NiV replication [25] . Replacing ATP with GS-5734-TP inhibited 10- and 11-mer product formation , and resulted in accumulation of 9-mer product ( Fig 3B , lane 5 ) . This requires GS-5734-TP to be incorporated three times , at positions +6 , +8 , and +9 . We conclude that GS-5734-TP is a delayed ( or leaky ) terminator of RNA chain synthesis , which is consistent with prior observations using RSV polymerase [26] . In comparison , use of 3′dATP instead of ATP resulted in the expected 6-mer RNA product ( blue arrow in S9 Fig ) . In paramyxoviruses and other NNS RNA viruses , the precise molecular mechanism for recognizing the non-coding GE signal as template for termination of gene transcription and polyadenylation is not well understood . Toward this aim , a new RNA template was designed to measure NiV L-P slippage leading to mRNA polyadenylation . This oligonucleotide sequence contains the first four bases complementary to the 4-mer primer , immediately followed with the M-F GE signal containing a AAUG block , followed by a poly ( U ) 6 tract and a GAA intergenic region ( Fig 4A ) . The 4-mer primer was converted into a 5-mer product with the addition of α33P-GTP ( Fig 4A , lane 1 ) . As expected , adding α33P-GTP + UTP resulted in predominantly a 7-mer product and adding α33P-GTP + UTP + ATP resulted in largely the 8-mer product ( Fig 4A , lanes 2 and 3 ) . With the addition of α33P-GTP + UTP + ATP + CTP , the polymerase produces longer RNA than the expected length ( 18 nt ) indicative of polyadenylation ( Fig 4A , lane 4 ) . The polyadenylation results from the NiV polymerase stuttering along the poly ( U ) tract as part of the GE signal . A poly ( U ) 6 tract template was designed without the AAUG part of the GE signal to understand the effects of this upstream sequence on polyadenylation . In this new template , the 4-mer primer was converted into a 5-mer product with the addition of α33P-GTP ( Fig 4B , lane 1 ) . Three other conditions were analyzed: α33P-GTP + ATP , α33P-GTP + ATP + CTP , and α33P-GTP + ATP + CTP + UTP . These three conditions yielded products significantly longer than expected , 11 , 12 , and 18 nt , respectively ( Fig 4B , lanes 2 , 3 , and 4 ) . Our results show that the sequence upstream of the poly ( U ) tract is not critical for polyadenylation and instead the efficiency of polyadenylation increases in the absence of the AAUG sequence . The same experiments were repeated with the RNA polymerase of RSV , a member of the Pneumoviridae family . This time , the four standard conditions ( α33P-GTP alone , α33P-GTP + ATP , α33P-GTP + ATP + CTP , and α33P-GTP + ATP + CTP + UTP ) yielded products mainly with the expected size of 5 , 11 , 12 , and 18 nt , respectively ( Fig 4B , lanes 5 , 6 , 7 , and 8 ) . Compared with NiV , the amount of polyadenylation resulting from RSV L-P stuttering was negligible . To confirm that RSV L-P is intrinsically less prone to polyadenylation , similar experiments were repeated with the authentic RSV Le promoter sequence containing a poly ( U ) 6 tract . There again , RSV L-P generated products of the expected size , without any strong evidence of polyadenylation ( S10 Fig ) . These results could explain why the genomic promoters in pneumovirus RNA contain a polyU tract that is not found in the corresponding paramyxovirus sequences ( Fig 4C ) . The minimum number of uridines within a template sequence required to trigger polyadenylation is not known for NiV polymerase . Our results showed that , within the natural M-F GE sequence six uridines signaled polyadenylation and that enzyme stuttering was more pronounced in the absence of the AAUG sequence . Based on this observation , we varied the number of uridines on the poly ( U ) tract template to understand the minimum number of uridines required for polyadenylation , while keeping the rest of the sequence unchanged ( Fig 5A ) . For all templates , the 4-mer primer was converted into a 5-mer product with the addition of α33P-GTP ( Fig 5B , lanes 1–6 ) . Upon the addition of α33P-GTP + ATP , only templates containing between four and six uridines triggered polyadenylation ( Fig 5B , lanes 7–9 ) . These results indicate at least four consecutive uridines are required for polyadenylation . Templates containing one , two , or three uridines yielded predominantly products of expected size , 6 , 7 , and 8 nt , respectively ( Fig 5B , lanes 10–12 ) . All six templates contained a GA sequence directly downstream of the poly ( U ) tract . To determine the contribution to enzymatic stuttering of the next correct nucleotides , the same experiments were repeated with CTP + UTP ( opposite GA on template ) in addition to α33P-GTP + ATP . The main effect of adding the next correct nucleotides was a marked reduction in polyadenylation for the poly ( U ) 4 and poly ( U ) 5 templates ( Fig 5C ) . In addition to the upstream and downstream sequences , we also found that ATP concentration was critical for polyadenylation . At 100 nM ATP , the efficiency of polyadenylation with the pol ( U ) 6 template was minimal ( Fig 6A , lane 1 ) . The efficiency of polyadenylation and the size of the products gradually increased with increasing concentrations of ATP up to 1 mM ( Fig 6A , lanes 2–6 ) . With requirements for such high ATP concentrations for enzymatic stuttering , we hypothesized that ATP analogs acting as chain terminators might block polyadenylation by competing with natural ATP . This was confirmed by adding GS-5734-TP to the reaction at a physiologically relevant concentration of 100 μM , which resulted in significant reduction in polyadenylation even in the presence of 1 mM ATP ( Fig 6B ) . This result suggests that chain-terminating adenosine analogs might also inhibit transcription termination by blocking polymerase stuttering .
This study is the first report of the expression , purification , and biophysical and biochemical characterization of an active recombinant paramyxovirus polymerase . Using an insect cell expression system with an affinity tag on the L protein , we purified the NiV L ( wt ) -P , NiV L ( H1347A ) -P , and NiV L ( D832A-N833A ) -P by FLAG purification followed by SEC . The SEC profile revealed a heterogenous preparation with a significant peak near the void volume for all three L-P complexes ( S1 Fig ) . Despite employing multiple chromatographic techniques , we were unable to separate this aggregated population from a more monodisperse population . We verified the sequence identity of the L and P proteins by mass spectrometry and subjected the purified protein to analysis by SDS-PAGE , which revealed bands of nearly equal intensity for L and P proteins ( Figs 1C and S2 ) . Preparations of NiV L ( wt ) -P , NiV L ( H1347A ) -P and NiV L ( D832A-N833A ) -P showed minor bands likely attributed to HSP90 and HSC70 proteins ( S2 Fig ) . Previous work has shown that HSP90 may work as a chaperone [27] for the polymerase complex; future studies must critically evaluate the role of chaperone proteins with recombinant NiV L-P . Bioinformatic , biochemical , and biophysical analyses indicate that paramyxoviral P proteins contain disordered and folded regions [28 , 29] . It has also been reported that the multimerization domain of NiV P protein alone adopts a long , parallel , tetrameric , coiled coil structure with an additional alpha-helical cap while others have reported it forms an elongated coiled-coil trimer [20 , 22] . Structural studies on the P protein fragment were completed on P protein expressed in Escherichia coli cells without L or N proteins , which contrasts with the work presented here [20 , 22] . It has been shown that fragments of NNS RNA virus P proteins oligomerize with a range of stoichiometries [20 , 28 , 30–32] . When complexed with NiV L protein , the NiV P protein may adopt a different oligomeric state than when in solution alone . An understanding of the L-P interaction for NiV and other related viruses will not only provide insights into viral replication but will also inform the design of therapeutic compounds to interfere with replication by disrupting this interaction . To further probe molecular arrangement and monodispersity of the NiV L-P complex , we visualized the purified NiV L-P complex using negative-stain EM followed by 2D class averaging . Our data revealed two main populations of globular particles , ranging in size from 5–11 nm; four representative class averages are displayed ( Fig 1D ) . An additional subset of aggregated particles not included in class averages , was also observed ( S1 Fig ) . The globular particles adopted a core ring-like structure with some class averages showing an interior density ( Fig 1D ) . These structures resemble those of the VSV L RdRp when visualized by negative-stain EM [17 , 18] . We also clearly saw particles smaller than expected for an L-P complex of this molecular weight . Our data are not of high enough resolution to provide insights into the secondary structure of the P protein or the role , if any , of the NiV L-P quaternary structure , which remains an important question for NiV and other paramyxoviral polymerases . Our negative-stain EM data do not clearly show the P protein , which may be due to its overall intrinsic disorder , size , and the low resolution of our micrographs . This absence of any defined P protein structure may account for the smaller particle sizes . Overall , our interpretation is the NiV L-P complex exhibits a range of molecular arrangements with similarities to related polymerases . Given the molecular architectures we observed by negative-stain EM , we wanted to use recombinant NiV L-P complex to understand the molecular interactions between the polymerase and its RNA substrate . More specifically , we present here how NiV L-P recognizes the 3′-end of the viral genome , and how the polymerase transitions from initiation of RNA synthesis to elongation mode . The de novo RNA synthesis assay showed that high concentrations of CTP and ATP are needed for the initiation step ( Fig 2C ) , which is consistent with the role of these two nucleotides in initial primer bond formation given the Le promoter sequence at the 3′-end of the template ( Fig 2A ) . The 1 , 000-fold reduction in Km for ATP in the primer extension assay confirmed that the enzyme had transitioned to elongation mode , with faster and more efficient RNA synthesis ( S8 Fig ) . The change in enzyme kinetics associated with transition from initiation to elongation is reminiscent of other viral RNA polymerases [33] . The formation of 10- and 11-mer RNA products by NiV L-P in the de novo RdRp assay using a 12-mer template was not completely unexpected since a +3-initiation site had previously been reported for the related RSV polymerase [23] . Since the majority products in the primer extension assay were also 10- and 11-mer ( Fig 3 ) , we concluded that these shorter RNAs were not generated by +3-initiation , but instead mainly by RNA synthesis starting at the +1-position and premature dissociation at the 5′-end of the template . The lack of full length 12-mer product is most likely due to the enzyme dissociating from the 5′-end of the template . The adenine scanning experiment showed that , except for position +5 , all other first six bases or positions on the template are important for efficient de novo initiation of RNA synthesis . The observation that NiV L-P can also accommodate an RSV template indicates that , rather than recognizing a specific sequence involving each nucleobase , the polymerase complex may be sensitive to a certain sequence environment that remains to be further explored . Likewise , it has recently been shown that recombinant rabies virus polymerase also recognizes the VSV promoter sequence [34] . As a follow-up to this work , measuring the direct binding interaction between NiV polymerase and various RNA promoter sequences would help to better define the specificity of promoter recognition . Interestingly , the H1347A mutation in the HR motif of the capping domain conferred a 2-fold loss in RdRp activity ( Fig 2A ) , and this effect was almost completely reverted in the primer extension assay ( Fig 3B ) . This finding suggests some level of cooperativity between RdRp and the capping domain during the early stages of RNA synthesis , which could be a shared function with other NNS RNA viruses [35] . The high degree of sequence conservation in the CRV capping motif among NNS RNA virus L proteins suggests that NiV L-P should function as a capping enzyme ( Fig 1B ) . Since PRNTase activity in L proteins from viruses other than rhabdoviruses has never been established [36–38] , an extension to the work present here would be to study the molecular requirements in NiV L-P for mRNA capping . Another aim of our experiments was to understand which sequence elements in NiV gene junctions control the balance between replication and transcription , given that both events are initiated by the same genomic RNA template sequence but lead to two different RNA products: full-length antigenomic RNA and shorter mRNAs . It is already known from other NNS RNA viruses that intracellular levels of N protein favor polymerase replication through nucleocapsid assembly , but do not directly inhibit transcription [39 , 40] . Another important mechanism regulating the efficiency of transcription of NNS RNA virus genomes is polyadenylation by polymerase stuttering . In NiV , GE sequences located within gene junctions contain five or six consecutive uridine repeats [10] . We found that NiV L-P recognizes the M-F GE sequence containing six uridines to produce polyadenylated RNA , and that the efficiency of stuttering is significantly increased when the sequence upstream of the poly ( U ) 6 tract is removed ( Fig 4 ) . Four consecutive uridines were sufficient to trigger polymerase slippage along the template when ATP was provided at a high concentration as the first correct nucleotide in the enzymatic reaction ( Fig 5 ) . Adding the next correct nucleotides increased the amount of read-through products relative to poly ( A ) RNAs . This observation is important to understand how NiV and probably other NNS RNA viruses have evolved by optimizing polymerase slippage along template strands containing uridine-rich regions to regulate transcriptional termination and prevent aberrant polyadenylation . VSV requires a poly ( U ) 7 tract for polyadenylation , whereas RSV polymerase only needs a four-uridine repeat in its cis-acting GE region [41] . In VSV , the spacing between the GS and GE sequences affects mRNA synthesis by modulating transcription termination [42] . Although we cannot exclude that the GS signal also regulates transcription termination in NiV , it is tempting to hypothesize that polymerases from different NNS RNA virus families respond differently to intergenic sequences . Even within the same polymerase , the response to intergenic sequences is different if transcribing or replicating . Uridine-rich regions are not unique to GE signals . The 3′-terminal leader and trailer promoter regions in pneumovirus genomes also contain a poly ( U ) tract , which has been shown to trigger enzyme stuttering [43] . However , we found that NIV L-P is much more prone to polyadenylation than RSV L-P ( Fig 4B ) . Furthermore , RSV L-P reads through its leader promoter RNA without significant polyadenylation ( S10 Fig ) . We think these results explain why pneumoviruses contain a polyU-tract sequence in their genomic and antigenomic promoters , which are not found in paramyxoviruses , further suggesting different mechanisms for controlling polyadenylation between proteins from these two virus families . Since poly ( U ) tracts are also found upstream of gene-start sequences and in editing sites [7 , 44–48] , it is likely that the data presented here could also help to study how polymerase stuttering in coding and non-coding uridine-rich regions affect transcription initiation and mRNA editing . Ultimately , more work will be needed to further elucidate the role of polyadenylation signals in the regulation of NNS RNA virus polymerase activities . Nucleoside analogs are the backbone of most antiviral therapies . Recently , two nucleoside analogs have been reported as potent paramyxovirus inhibitors . R1479 , a 4′azido-cytidine analog previously developed for the treatment of hepatitis C virus infection , also inhibits NiV replication with an in vitro potency of 1–5 μM [24] . The anti-Ebola molecule GS-5734 is a broad-spectrum clinical-stage adenosine analog that also targets NiV , with an in vitro antiviral potency of 0 . 05 μM [25 , 26] . In our study , we showed that the respective TP forms of both nucleoside analogs are efficient substrates incorporated into viral RNA by NiV L-P ( Fig 3 ) . In our assay conditions , a single incorporation of R1479-TP was not sufficient to block RNA synthesis , which indicates that this cytidine analog is not an immediate chain terminator . This contrasts with the immediate chain termination effect of R1479-TP against hepatitis C virus polymerase [49] . In the case of GS-5734-TP , three incorporation events were required to prevent full-length RNA synthesis ( Fig 3 ) . This delayed or leaky chain termination effect on NiV L-P has also been described for RSV polymerase [26] . In addition , we observed that GS-5734-TP also inhibits transcription termination by competing with natural ATP and preventing polyadenylation ( Fig 6 ) . This additional mechanism of action might explain why GS-5734 is more potent than R1479 in infected cells , and sheds light on the potential use of adenosine analogs as dual replication and transcription termination inhibitors . Studies such as this one not only provide valuable tools to evaluate the mechanism of action of existing clinically relevant RNA polymerase inhibitors , but could also be used to develop novel biochemical assays to discover inhibitors of NiV and related paramyxoviruses of public health importance .
R1479-TP and GS-5734-TP were synthesized at Alios BioPharma ( South San Francisco , CA ) . Oligonucleotides were synthesized at Dharmacon , Inc ( Lafayette , CO ) . Their sequences with analysis of secondary structures can be found in S11 Fig . The sequence of the oligonucleotides used as product size markers in sequencing urea PAGE is as follows: 5'-ACCA-3' ( 4-mer ) , 5'-ACCAG-3' ( 5-mer ) , 5'-ACCAGA-3' ( 6-mer ) , and 5'-ACCAGACAAGGG-3' ( 12-mer ) . A homology-based three-dimensional structure of the NiV L protein was generated using RaptorX [50] . To generate an accurate model , approximately 100 residues were removed from the sequence . The molecular graphics of the resultant structure ( blue ) were generated using the University of California at San Francisco Chimera package [51] . The codon-optimized ORFs for the NiV L and P proteins ( Bangladesh genotype , Genbank accession AY988601 . 1 ) ( Genscript , Piscataway , NJ ) were a gift from Michael Lo , cloned separately into pFastBac Dual expression vector . The 3XFLAG tag was added to the N-terminus of the NiV L protein by nested polymerase chain reaction ( PCR ) ( two rounds of PCR with overlapping oligos containing the 3XFLAG tag; Eton Bioscience , San Diego , CA ) with PrimeSTAR DNA polymerase ( Takara Bio , Shiga , Japan ) . The 5′ SacI / HindIII region from the untagged NiV L was then replaced by the 3XFLAG-L PCR fragment . The SacI / XhoI fragment containing NiV P-His was cloned into SacI / XhoI cut pFBD-3xFlag-NiV L to make pFBD-3xFlag NiV L-P-His . The L protein , under the control of the polyhedron promoter , was cloned with an N-terminal 3X FLAG tag . The P protein , under the control of the P10 promoter , was cloned with a C-terminal hexahistidine tag . E . coli DH10Bac was transformed with the pFastBac Dual vector to yield bacmid DNA . A high-titer baculovirus stock was generated after transfection of bacmid DNA into Sf9 cells using Cellfectin . Mutagenesis of the L gene to generate NiV L ( H1347A ) and NiV L ( D832A-N833A ) was completed by amplifying the whole template DNA with complementary pairs of mutagenic oligonucleotides ( Eton Bioscience ) using Kapa HiFi HotStart DNA polymerase ( Kapa Biosystems , Wilmington , MA ) , followed by full-length insert sequencing confirmation . Two liters of Sf9 insect cells were infected with the baculovirus stock at a multiplicity of infection of 1 and harvested 72 hours post-infection by centrifugation for 10 minutes at 1000 × g The cells were resuspended in lysis buffer ( 50 mM Tris pH 7 . 4 , 150 mM NaCl , 10% glycerol , 0 . 1% octyl β-D-glucopyranoside , 0 . 1% n-dodecyl β-D-maltoside , EDTA-free protease inhibitor , 10 U/mL benzonase ) , lysed by microfluidization , and clarified through centrifugation for 30 minutes at 14 , 000 × g . The clarified lysate was incubated with EZview Red ANTI-FLAG M2 Affinity gel , washed twice with lysis buffer , and the NiV polymerase complex was eluted using 3X FLAG peptide , and concentrated using Amicon Ultra Centrifugal Filters . The sample was further purified by size-exclusion chromatography using a Superdex 200 Increase 10/300 GL at a flow rate of 0 . 3 mL/min with a running buffer of 50 mM Tris pH 7 . 4 , 150 mM NaCl , 10% glycerol , 0 . 1% octyl β-D-glucopyranoside , 0 . 1% n-dodecyl β-D-maltoside , 1 mM DTT . The protein was then concentrated using an Amicon Ultra Centrifugal Filter , subjected to SDS-PAGE , and quantified using a Bradford Assay . The bands associated with the L , P , and HSC70 proteins were digested with trypsin , chymotrypsin , and elastase followed by analysis by nano LC-MS/MS with a Waters NanoAcquity HPLC system interfaced to a ThermoFisher Q Exactive . The peptides were then loaded on a trapping column and eluted over a 75-μm analytical column at 350 nL/min . Both columns used were packed with Luna C18 resin ( Phenomenex , Torrance , CA ) . The mass spectrometer was operated in data-dependent mode , with MS and MS/MS performed in the Orbitrap at 70 , 000 full-width at half-maximum ( FWHM ) resolution and 17 , 500 FWHM resolution , respectively . The fifteen most abundant ions were selected for MS/MS . Samples were prepared on continuous carbon films supported by nitrocellulose-coated 400-mesh copper grids ( Ted Pella ) . A 3 μl drop of NiV L ( wt ) -P , diluted 60-fold in buffer ( 20 mM Tris pH 7 . 5 , 10 mM KCl , 2 mM DTT , 6 mM MgCl2 ) , was applied to a freshly plasma-cleaned grid , blotted to a thin film with filter paper and immediately stained with 1% ( wt/v ) uranyl formate . Electron microscopy was performed using an FEI Tecnai T12 electron microscope operating at 120keV equipped with an FEI Eagle 4k x 4k CCD camera . Images were collected at nominal magnifications of 110 , 000× ( 0 . 10 nm/pixel ) and 67 , 000× ( 0 . 16 nm/pixel ) using the automated image acquisition software package Leginon [52] . The images were acquired at a nominal underfocus of -0 . 9 μm to -1 . 8 μm and electron doses of ~25 e-/Å2 . Image processing was performed using the Appion software package[53] . Contrast transfer functions of the images were corrected using ctffind4 [54] . Individual particles in the 67 , 000× images were selected using automated picking protocols followed by several rounds of reference-free alignment and classification based on the XMIPP processing package to sort them into self-similar groups [55] . BSRT7/5 cells were a gift from K . Conzelmann [56] , and were propagated in DMEM supplemented with 5% FBS and 1 mg/mL G418 antibiotic . A Nanoluciferase-based NiV minigenome assay was adapted from a previously developed NiV minigenome assay [9] . Briefly , a bacteriophage T7 polymerase-based NiV minigenome was synthesized ( Genscript , Piscataway , NJ ) expressing a reporter fusion construct of Nanoluciferase ( Promega , Madison , WI ) and mNeonGreen fluorescent protein [57] . The open-reading frame encoding the reporter fusion protein was flanked by a T7 promoter , hammerhead ribozyme , NiV leader and N gene untranslated region at the 3′-end , with the NiV L gene untranslated region and genomic trailer , and a hepatitis delta virus ribozyme at the 5′-end . BSRT7/5 cells ( 1×104 ) were seeded in 96-well plates overnight . The next day , NiV support plasmids consisting of 50 , 32 , and 50 ng/well of N , P , and L plasmids , respectively , and NiV minigenome ( 120 ng ) prepared in RNase-free TE buffer , were mixed with 0 . 6 μL/well LT-1 transfection reagent ( Mirus Bio , Madison , WI ) and 10 μL Opti-MEM/well . Complexes were mixed and incubated for 30 minutes at room temperature before being added to cells . For negative controls , the L plasmid was substituted with an equivalent amount of pcDNA 3 . 1 plasmid expressing the red fluorescent protein mCherry ( Clontech ) . At 48 hours post-transfection of minigenome and support plasmids , 50 μL of Nanoluciferase assay buffer solution ( Promega ) was added directly to each well . Well contents were transferred to 96-well opaque white plates , and after 3 minutes , luminescence was read on a plate reader using 0 . 1 msec integration time ( HT-Synergy , Biotek , Winooski , VT ) . Unless otherwise specified , all NiV and RSV polymerase reactions consisted of 0 . 2 μM oligonucleotide template derived the NiV leader promoter , 0 . 2 μM recombinant L-P complex , with a buffer containing 20 mM Tris pH 7 . 5 , 10 mM KCl , 2 mM DTT , 0 . 5% triton , 10% DMSO , 6mM MgCl2 . Recombinant RSV L-P was produced through the co-expression of RSV L and P proteins in a baculovirus expression system , according to previously described procedures [23] . In the primer-dependent reaction , this was then combined with 200 μM primer . Reactions were initiated through addition of specific nucleoside TP for the template sequence to final volume of 10 μL and incubated at 30°C for 30 minutes . The radioisotope tracer in these reactions was α33P-GTP . Reactions were stopped with the addition of an equal volume of gel loading buffer ( Ambion , Austin , TX ) denatured at 95°C for 5 minutes , and run on a 22 . 5% polyacrylamide urea sequencing gel for 2 hours at 80W . The migration products were exposed to a phosphor-screen , scanned on Typhoon phosphorimager ( GE Healthcare , Chicago , IL ) and quantified using ImageQuant ( GE Healthcare ) .
|
RNA viruses replicate and transcribe their genomes using complex enzymatic machines known as RNA-dependent RNA polymerases . The chemical reactions driving nucleotide addition are shared among nucleic acid polymerases but the underlying mechanisms of RNA biosynthesis and the complex polymerase structures are diverse . Of these RNA viruses is the paramyxovirus family , which includes major human pathogens . Paramyxoviruses have common biological and genetic properties but little is known about their replication machinery . Insights into the structure , function , and mechanisms of RNA synthesis of one paramyxovirus polymerase will likely extend to the entire virus family . An emerging , highly pathogenic paramyxovirus is Nipah virus ( NiV ) , which causes encephalitis in humans . We have purified NiV polymerase , probed its enzymatic and biophysical properties and developed it as a model paramyxovirus polymerase . We investigated template strand sequence elements driving RNA biosynthesis for NiV polymerase and obtained a snapshot of NiV polymerase molecular organization using electron microscopy to provide the first structural information on a paramyxovirus polymerase . This work extends previous knowledge by producing the first recombinant paramyxovirus polymerase and using this protein in enzymatic assays to highlight key functional and structural characteristics for the design of new medicines .
|
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2018
|
Initiation, extension, and termination of RNA synthesis by a paramyxovirus polymerase
|
Mycolactone , the macrolide exotoxin produced by Mycobacterium ulcerans , causes extensive tissue destruction by inducing apoptosis of host cells . In this study , we aimed at the production of antibodies that could neutralize the cytotoxic activities of mycolactone . Using the B cell hybridoma technology , we generated a series of monoclonal antibodies with specificity for mycolactone from spleen cells of mice immunized with the protein conjugate of a truncated synthetic mycolactone derivative . L929 fibroblasts were used as a model system to investigate whether these antibodies can inhibit the biological effects of mycolactone . By measuring the metabolic activity of the fibroblasts , we found that anti-mycolactone mAbs can completely neutralize the cytotoxic activity of mycolactone . The toxin neutralizing capacity of anti-mycolactone mAbs supports the concept of evaluating the macrolide toxin as vaccine target .
Mycobacterium ulcerans , the causative agent of the neglected tropical skin disease Buruli ulcer ( BU ) , produces the exotoxin mycolactone , which is responsible for the formation of chronic necrotizing skin lesions [1 , 2] . Early diagnosis followed by rapid initiation of the currently recommended treatment with rifampicin and streptomycin [3] is crucial to avoid massive tissue destruction and long-term disabilities . Mycolactones consist of a 12-membered macrolide core structure , a short C-linked upper side chain ( comprising C12–C20 ) and a longer C5-O-linked lower polyunsaturated acyl side chain . Mycolactones produced by different M . ulcerans lineages differ in the structure of the lower side chain , but are otherwise conserved [4] . For the lower side chain variations in length , the number of double bonds and the number and localization of hydroxyl groups have been described . While M . ulcerans strains may produce mixtures of several mycolactone species , the composition of these pools seems to be highly conserved for a particular M . ulcerans lineage [5] . Strains belonging to the genomically monomorphic classical African lineage produce the most toxic variant , mycolactone A/B [6] . This lineage is responsible for > 95% of the BU cases reported worldwide [7] . Mycolactone-deficient M . ulcerans mutants are less virulent and intradermal injection of the toxin in animal models is sufficient to induce the formation of BU-like lesions [4 , 8] . Since mycolactones play such a central role in the pathogenesis of BU [1 , 2 , 9] , they may represent a suitable target for both vaccine development and passive immunotherapy . However , due to their lipid-like nature , as well as their cytotoxic and immunosuppressive [10–12] activities , attempts to raise antibody responses against mycolactones have failed so far . While extracts from M . ulcerans cultures have been the only source of mycolactones for a long time , highly defined synthetic mycolactones have become available more recently [6 , 13 , 14] , greatly facilitating experimental work with these compounds . Here , we generated mycolactone specific immune-sera and monoclonal antibodies ( mAbs ) by immunizing mice with a protein conjugate of a non-toxic synthetic truncated mycolactone derivative .
Animal experiments performed were approved by the animal welfare committee of the Canton of Basel ( authorization number 2375 ) and were conducted in compliance with the Swiss Animal Welfare Act ( TSchG ) , Animal Welfare Ordinance ( TSchV ) , and the Animal Experimentation Ordinance ( TVV ) . Natural mycolactones A/B , C and F as well as variants PG-157 , PG-165 , PG-182 and core PG-119 were produced as described previously [6 , 14] . The synthesis of biotin-conjugate PG-204 and the immunogen PG-203 will be described elsewhere . All compounds were HPLC-purified . For biological testing 0 . 5 mg/ml mycolactone stock solutions were made by adding cell culture grade DMSO ( Sigma ) . Extracts of mycolactone were prepared using mycobacterial pellets from cultures of strains isolated from lesions of Cameroonian BU patients ( S1013 , S1019 , S1047 ) . Briefly , 1 ml of a chloroform-methanol solution ( 2:1 , v/v ) was added to the individual pellets . Bacteria were resuspended by vortexing and lipids were extracted by incubating the samples shaking at RT for 2 h . Then , 200 μL ddH2O were added to induce a phase separation . After vigorous vortexing , the samples were centrifuged for 10 min and the lower organic phase was transferred to a fresh tube . Samples were placed in a Speedvac ( Thermo Scientific ) for complete drying . 50 μL acetone were added and after an additional spin for 10 min at maximal speed , the acetone-soluble lipid fraction containing mycolactones was separated from the precipitate , collected in fresh tubes and again dried using the Speedvac device . For biological testing , the acetone-soluble lipid fractions were resuspended in cell culture grade DMSO ( Sigma ) . 2 mg of mycolactone PG-203 were coupled to 2 mg of BSA using the Imject EDC BSA spin kit ( Pierce ) . NMRI mice ( Harlan Laboratories ) were immunized thrice in 3 week intervals by subcutaneous injection of 40 μg of the coupled product emulsified in Sigma adjuvant . Serum antibody titers against the biotinylated mycolactone derivative PG-204 were tested by ELISA . Based on the ELISA results , one NMRI mouse was selected to receive a final intravenous injection of 40 μg of the PG-203-BSA conjugate without adjuvant . Three days after this last booster dose , hybridoma cell lines were generated as described below . Mice were euthanized and the spleen was aseptically removed . Spleen tissue was mashed and the cells were fused with PAI myeloma cells in the presence of PEG 1500 ( Roche ) [15] . Mother cell line culture supernatants were tested by ELISA for the presence of anti-mycolactone antibodies and positive lines were cloned by limiting dilution . mAbs were purified from hybridoma culture supernatants by affinity chromatography using a HiTrap Protein A HP column ( GE Healthcare ) . Isotypes were determined by ELISA with isotype specific reagents ( SouthernBiotech ) . NeutrAvidin Coated High Capacity plates ( Thermo Scientific ) were incubated with 2 μg/ml biotinylated mycolactone PG-204 in PBS for 2 h at 37°C in the dark . The PG-204 solution was then replaced by SuperBlock T20 ( TBS ) blocking buffer ( Thermo Scientific ) . Plates were incubated for 1 h at 37°C in the dark , then washed twice with PBS 0 . 05% Tween . Hybridoma supernatants were applied to the plates for 2 h at 37°C in the dark . Plates were washed four times with PBS 0 . 05% Tween . Bound anti-mycolactone antibodies were detected using an Alkaline Phosphatase ( AP ) conjugated goat anti-mouse IgG antibody ( Sigma ) diluted 1:20 , 000 in blocking buffer . The plate was incubated for 1 h at 37°C in the dark , and then washed four times with PBS 0 . 05% Tween . Development was done using the Alkaline Phosphatase Yellow ( pNPP ) Liquid Substrate System ( Sigma ) . The absorbance was measured at 405 nm with a microplate reader ( Tecan Sunrise ) and the values were plotted against the concentration . For competition experiments Maxisorp plates were coated with mAbs ( 10 μg/ml ) o/n and then blocked with SuperBlock T20 ( TBS ) blocking buffer ( Thermo Scientific ) for 1 h at 37°C . After washing twice with PBS 0 . 05% Tween , serial dilutions of synthetic mycolactone compounds were added and incubated for 2 h at 37°C in the dark . DMSO served as negative control . Subsequently biotinylated PG-204 was added ( 100 pg/ml ) without washing steps and incubated for an additional 30 min . Plates were washed four times with PBS 0 . 05% Tween . Bound PG-204 was detected with alkaline phosphatase coupled streptavidin ( SouthernBiotech ) after 45 min incubation at 37°C . The development was performed as described above . Murine L929 fibroblasts were cultivated at 37°C and 5% CO2 in RPMI medium ( Gibco ) supplemented with 10% FCS ( Sigma ) , 2 mM glutamine ( Gibco ) and 0 . 05 mM β-mercaptoethanol ( Gibco ) . For the assay , L929 cells ( 24 , 000 per well ) were seeded in 24-well plates ( Falcon ) and incubated o/n at 37°C . Medium was aliquoted and mixed with 15 ng/ml synthetic mycolactone A/B , mycolactone core PG-119 or varying amounts of mycolactone extracts . Dependent on the assay format , fixed or increasing concentrations of anti-mycolactone antibody or isotype-matching control antibody ( JD4 . 1 ) were added and incubated for 10 min . The medium in the 24-well plates was aspirated and replaced by 500 μl medium containing the mycolactone-antibody mixes . After an incubation period of 48 h , Resazurin solution ( Sigma ) was added to the wells ( 10% v/v ) , and the cells were further incubated for 2 hours at 37°C and 5% CO2 . In order to quantitatively assess metabolic activities , the fluorescence intensities were measured using a SpectraMax Gemini XS ( Molecular Devices ) , and the obtained values were normalized for the DMSO control . The experiments were set up in duplicates and performed at least three times . Fluorescence intensities were plotted against the log concentration of neutralizing antibody . SPR experiments were performed on Biacore 3000 or T200 instruments ( GE Healthcare , Uppsala , Sweden ) at 25°C . HEPES buffer ( 10 mM HEPES , 150 mM NaCl , 0 . 05% ( w/v ) Tween 20 , pH 7 . 4 ) was used as running buffer and flow rates of 5 or 50 μl min-1 were used for the immobilization and binding assays , respectively . Anti-mycolactone mAbs were captured on the chip surface by primary antibodies . First , the primary antibody was immobilized covalently on the carboxymethyldextran surface of a CM5 sensor ( GE Healthcare , Uppsala , Sweden ) . The carboxymethyl groups of the dextran sensor surface were activated for 600 s with a mixture of 0 . 2 M ( 3-dimethylaminopropyl ) carbodiimide ( EDC ) and 0 . 05 M N-hydroxysuccinimide ( NHS ) . Next , the primary antibody , a rat anti-mouse IgG1 mAb ( SouthernBiotech , AL , USA ) diluted in 10 mM borate buffer ( pH 8 . 2 ) to a concentration of 20 μg/ml was contacted for 120 s with the activated sensor surface to achieve antibody densities of 8000–12000 RUs . Subsequently , remaining active ester molecules on the sensor were deactivated by applying 1M ethanolamine ( pH 8 . 0 ) for 420 s . Anti-mycolactone mAbs were diluted in running buffer to a concentration of 200 nM and captured by the primary antibodies to achieve antibody densities in the range of 2500–4000 RUs . Mycolactone derivative PG-204 was titrated over the surfaces with the immobilized anti-mycolactone mAbs . Association and dissociation phases were monitored for 240 and 3600 s , respectively . Bound mycolactone was washed out from the surface in regeneration steps ( injection of 10 mM glycine , pH 2 . 2 ) .
For the generation of mycolactone specific antibody responses we have immunized mice with PG-203 , a truncated and non-cytotoxic mycolactone derivative coupled to BSA via a diethylene glycol-based linker replacing the C5-O-linked polyunsaturated acyl side chain ( Fig 1A ) . Mice had developed strong anti-mycolactone IgG responses after two immunizations with the adjuvanted protein carrier conjugate and a third immunization boosted the immune response further ( Fig 2 ) . Spleen cells of mice immunized with the PG-203-BSA conjugate were used for the generation of hybridomas producing anti-mycolactone mAbs by conventional B cell hybridoma technology . Hybridoma cell culture supernatants were screened by ELISA using NeutrAvidin plates coated with PG-204 ( Fig 1C ) , a biotinylated derivative of PG-203 . Screening led to the identification of 12 hybridoma clones producing the anti-mycolactone mAbs JD5 . 1 to JD5 . 12 . All twelve mAbs were of the IgG1 isotype and showed binding to PG-204 , but not to PG-183 ( Fig 3 ) , a mycolactone derivative biotinylated at the C-linked short upper side chain ( Fig 1D ) . The fine specificity of mAbs was further characterized by competition ELISAs . Plates were coated with anti-mycolactone mAbs and the biotinylated mycolactone derivative PG-204 was added as reporter molecule . By titrating in increasing amounts of synthetic natural variants of mycolactone ( mycolactone A/B , C and F ) , a truncated synthetic derivative ( PG-119 ) , or derivatives with modifications in the C-linked upper side chain ( PG-157 , PG-165 and PG-182 ) we were able to assess the fine specificity of the binding . All mAbs showed very similar fine specificity patterns ( depicted for the prototype mAb JD5 . 1 in Fig 4 ) . As predicted from the structure of the immunizing truncated mycolactone derivative PG-203 , the presence/absence or detailed structure of the C5-O-linked polyunsaturated lower acyl side chain had no significant effect on binding to the mAbs . PG-119 , a truncated mycolactone lacking the lower fatty acid acyl side chain was thus as efficient as the complete mycolactones A/B , C and F in competing with the reporter molecule PG-204 ( Fig 4 ) . In contrast , modifications in the C-linked upper side chain had major effects on the efficiency as competitor . The more complex the modifications were , the less competition was observed ( Fig 4 ) , suggesting that the upper side chain along with the mycolactone core constitutes part of the epitope of the mAbs . For all mAbs , addition of a terminal hydroxyl group in PG-165 reduced binding and a major extension of the upper side chain in PG-182 abrogated competition completely . Only for PG-157 , a derivative with a slight extension of the upper side chain , a difference in the competition pattern was observed , with only mAbs 5 . 9 and 5 . 11 showing slight inhibition ( the difference is depicted for the prototype mAbs JD5 . 1 and JD5 . 11 in Fig 5 ) . In Surface Plasmon Resonance ( SPR ) analyses the anti-mycolactone mAbs immobilized on the surface of the sensor chip showed very tight binding to the biotinylated mycolactone derivative PG-204 . The association and dissociation rate constants could not be precisely determined as the dissociation rate constants were outside the limitation of the measuring technology ( Fig 6 ) . For some of the mAbs ( JD5 . 1 , JD5 . 2 , JD5 . 5 , JD5 . 8 and JD5 . 10 ) dissociation rates were extremely slow with the monitored dissociation rate constant outside the limitation of the instrument resolution ( koff < 10−6 s-1 ) . Also the other mAbs showed slow dissociation rates with an estimated koff approximating the resolution limit ( koff between 10−5 s-1 and 10−6 s-1 ) . Measurements with mycolactone A/B failed , most likely due to aggregation of the more hydrophobic complete toxin molecule . To investigate whether mycolactone specific antibodies can neutralize the cytotoxic activity of the macrolide toxin , L929 fibroblasts were incubated with synthetic mycolactone A/B at the cytotoxic concentration of 15 ng/ml ( 20 nM ) in the presence of serially diluted anti-mycolactone mAbs . After 48 h of incubation , the metabolic activity of the L929 cells was assessed by performing Resazurin-based assays . While all anti-mycolactone mAbs showed toxin-neutralizing activity , the molar ratio of antibody versus mycolactone required for neutralization varied ( Fig 7 ) . While several mAbs neutralized the toxin completely already at a molar ratio of 2 . 5 , only partial inhibition was observed at a ratio of 12 . 5 for the least active mAb JD5 . 2 ( Fig 7 ) . The morphology of L929 cells treated with mycolactone and sufficient amounts of toxin-neutralizing antibody was indistinguishable from that of DMSO-treated control cells . In contrast , cells treated with no or insufficient amounts of antibody showed characteristic signs of apoptosis . The anti-mycolactone mAbs also neutralized the mycolactones produced by cultivated M . ulcerans bacteria ( S1 Fig ) . Competition with the truncated non-toxic mycolactone derivative PG-119 lacking the lower acyl side chain ( Fig 1 ) reconfirmed specificity of the neutralizing activity . At a concentration of 40 ng/ml ( 86 nM ) , PG-119 completely abrogated the toxin neutralizing activity of mAb JD5 . 1 ( Fig 8 ) .
Here we describe for the first time the production of antibodies against mycolactone , the main virulence factor of M . ulcerans . So far , no antibodies against mycolactone were detected in mice or humans infected with M . ulcerans [16] and attempts by several groups to generate mycolactone-specific antisera or mAbs by immunization with protein carrier conjugates of chemically modified mycolactone extracted from M . ulcerans cultures have failed before . This may be related to residual cytotoxicity of the conjugates and killing of B cells that incorporated them after binding to their mycolactone specific surface immunoglobulins . With the availability of synthetic mycolactone derivatives it has become possible to both generate highly defined protein conjugates for immunization and to develop reliable assays for the detection of mycolactone specific antibodies . For immunization we have used here a carrier conjugate of a mycolactone derivative in which the C5-O-linked polyunsaturated acyl side chain was replaced by a diethylene glycol-based linker . Since the structure of the lower side chain is crucially important for the cytotoxic activity of mycolactone [6] , this compound ( PG-203 ) was expected to be non-cytotoxic , even if it were released from the carrier protein after massive uptake by mycolactone specific B cells . Coupling of the synthetic mycolactone derivative to the carrier protein BSA ensured T cell help for the mycolactone specific B cells , leading to clonal expansion , affinity maturation and isotype switching . As a result high anti-mycolactone IgG titers were elicited in mice already by two immunizations with the adjuvanted PG-203 carrier conjugate . All mAbs generated from the immunized mice were of the IgG1 subclass and exhibited high affinity and specificity for mycolactone . Competition assays indicated that the epitope recognized includes elements of the upper short side chain that is C-linked to the core structure . As expected [6] , structural variation or absence of the lower C5-O-linked lower polyunsaturated acyl side chain had no effect on antibody binding . All mAbs exhibited high affinity binding with very slow dissociation rates which did not permit exact determination of the affinity constant by SPR analyses . Currently , there is no highly effective vaccine against the major mycobacterial diseases tuberculosis , leprosy and BU available . BCG , originally developed against tuberculosis , may offer only partial and short-lasting [17] or no [18] protection against BU . Attempts to develop a subunit vaccine [16 , 19 , 20] or a live vaccine based on mycolactone-deficient M . ulcerans [21] had limited success . All twelve mycolactone-specific mAbs generated here showed , albeit to a varying degree , the capacity to neutralize mycolactone and to rescue mammalian cells from apoptosis in an in vitro assay . This supports the concept to target mycolactone in BU vaccine design . Both prophylaxis and therapy with toxin-neutralizing antibodies and active immunization with toxoids are highly successful strategies for protection against pathogens such as diphtheria or tetanus bacteria that produce a toxin as key virulence factor . M . ulcerans has evolved from a common ancestor with M . marinum by acquisition of a plasmid designated pMUM , which encodes the polyketide synthases required for mycolactone biosynthesis [22–24] . None of the other pathogenic mycobacteria produce a macrolide toxin , making mycolactone an excellent target for the development of a species specific diagnostic test . Such an assay would also have potential for the monitoring of treatment success by measuring mycolactone levels in fine needle aspirates from closed BU lesions or swab samples from ulcerative lesions . The availability of the mAbs described here is enabling the development of a competition assay for the quantification of mycolactone . If non-competing mAbs specific for the lower part of the core and the lower side chain can be generated , development of an antigen capture assay may become possible . Taken together , the first successful generation of mycolactone specific antibodies described in this report will stimulate development of new tools for research and control of BU .
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Mycolactones A/B ( also simply referred to as mycolactone ) are macrolide exotoxins produced by Mycobacterium ulcerans , the causative agent of the neglected tropical skin disease Buruli ulcer ( BU ) . The potent cytotoxic and anti-inflammatory activities of mycolactones lead to severe destruction of the subcutaneous fat tissue with minimal inflammation in the core of the lesion . Due to the lipid-like nature of mycolactones , the production of antibodies against these molecules has so far been unsuccessful . By using the classical approach of fusing immortal cells with spleen cells of mice immunized with a protein conjugate of a truncated non-toxic synthetic mycolactone , we were able to generate hybridoma cells producing mycolactone-specific monoclonal antibodies . Mammalian cell-based cytotoxicity assays demonstrated that these antibodies have neutralizing activity and can fully block the cytotoxic activity of mycolactone , resulting in survival of the target cells . These findings support the concept to target mycolactone by a vaccine . Furthermore , the anti-mycolactone antibodies may represent useful tools for BU diagnostics development .
|
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2016
|
Antibody-Mediated Neutralization of the Exotoxin Mycolactone, the Main Virulence Factor Produced by Mycobacterium ulcerans
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We recently reported the medicinal chemistry re-optimization of a series of compounds derived from the human tyrosine kinase inhibitor , lapatinib , for activity against Plasmodium falciparum . From this same library of compounds , we now report potent compounds against Trypanosoma brucei brucei ( which causes human African trypanosomiasis ) , T . cruzi ( the pathogen that causes Chagas disease ) , and Leishmania spp . ( which cause leishmaniasis ) . In addition , sub-micromolar compounds were identified that inhibit proliferation of the parasites that cause African animal trypanosomiasis , T . congolense and T . vivax . We have found that this set of compounds display acceptable physicochemical properties and represent progress towards identification of lead compounds to combat several neglected tropical diseases .
Neglected tropical diseases ( NTDs ) are a collection of 20 communicable diseases [1] . Though mostly treatable and/or preventable , NTDs remain a leading cause of morbidity and mortality affecting over 1 billion people worldwide . Many of the current treatments have significant side effects , poor efficacy and there are increasing reports of resistance in the literature [2 , 3] highlighting the need for novel chemotherapeutics . Collectively , 17 of these NTDs account for 26 million disability-adjusted life years ( DALY ) [4] , which is a sum of years of life lost due to premature mortality and those lost due to ill health or disability . In addition , livestock are also susceptible to various parasitic infections , and can lead to the development of diseases such as African animal trypanosomiasis ( AAT ) , which has devastating economic effects on communities . AAT is caused by infection with T . congolense or T . vivax [5] , and of the three main chemotherapeutics currently listed for treatment , diminazene aceturate and isometamidium chloride are the most widely used [6] , though reports of resistance are increasing to both of these [7–8] . As part of our efforts to identify new drugs for human African trypanosomiasis ( HAT ) , we undertook a target class repurposing project [9] , wherein we screened known human tyrosine kinase inhibitors against a number of pathogenic protozoan parasites ( Fig 1 ) . Historically , we have cross-screened our compounds against P . falciparum , T . b . brucei , T . cruzi , and L . major to fully leverage chemical space covered by our optimization campaign [10] . This cross-screening led to the identification of 1 [10] as a potent inhibitor of proliferation of P . falciparum , T . b . brucei and T . cruzi . Herein , we report the results of a screening campaign using a set of previously reported , structurally related compounds previously optimized for their activity against P . falciparum [11] . We screened these compounds for activity against T . b . brucei , T . cruzi and L . major and we also report activity data for a subset of these compounds against T . congolense and T . vivax . The previously reported P . falciparum data for these compounds , along with additional screening data that is not discussed directly in this study , is provided in the Supplementary Information , S1 Table . Established target product profiles for Chagas disease , leishmaniasis and HAT , were used to define hit and lead candidate criteria ( summarized in the Supplementary Information , S2 Table ) [14–15] . We note that , according to a committee coordinated by the Global Health Innovative Technology Fund ( GHIT ) , a hit compound for Chagas disease or leishmaniasis should have an EC50 < 10 μM [15] .
When tested against intracellular T . cruzi parasites , several compounds displayed low micromolar inhibition of T . cruzi , and these included truncated head groups 3-chloro-4-methoxyphenyl ( 6 , EC50: 3 . 8 μM ) and para-methoxyphenyl ( 9 , EC50: 1 . 9 μM ) , as well as the para-trifluoromethoxyphenyl ( 10 , EC50: 1 . 4 μM ) analog ( Fig 2 ) . Removal of the phenylsulfonamide and replacement with the para-substituted pyrimidine ( 14 , EC50: 0 . 090 μM ) ( Fig 3 ) led to the most potent compound against T . cruzi identified in this series and afforded a significant boost in selectivity index ( SI ) versus 3T3 cells ( SI: 170 ) . Other submicromolar compounds were identified , such as the pyrazole analog ( 16 , EC50: 0 . 92 μM ) and the t-butyl carbamate ( 21 , EC50: 0 . 60 μM ) , though selectivity versus 3T3 cells was an issue for 16 in particular ( SI: 2 . 5 ) . An additional compound , 23 ( EC50: 0 . 62 μM ) ( Fig 4 ) , had an improved lipophilic ligand efficiency [13] ( LLE; 1 . 8 compared to -0 . 29 for 1 , see Supplementary Information , S3 Table for a complete list of values ) and maintained potent inhibition of T . cruzi , though the aqueous solubility was poor ( <1 μM , see Supplementary Information , S4 Table for complete list of ADME values ) and the human liver microsome intrinsic clearance ( HLM Clint: 170 μL/min/mg ) , and rat hepatocyte intrinsic clearance ( RH Clint: 45 μL/min/106 cells ) were both high . Importantly , host cell toxicity ( 3T3 cells ) was generally low for the series , and the selectivity index ( SI ) was above the targeted 10×EC50 threshold . The general SAR trends observed for this series against T . cruzi are summarized in Fig 5 . Some of the SAR trends observed with T . cruzi were mirrored in L . major . Truncation of the head group to the 3-chloro-4-methoxyphenyl ( 6 , EC50: 1 . 4 μM ) , 3-chlorophenyl ( 7 , EC50: 3 . 4 μM ) , para-methoxyphenyl ( 9 , EC50: 4 . 4 μM ) , and the para-trifluoromethoxyphenyl ( 10 , EC50: 0 . 78 μM ) all led to potent analogs ( Fig 2 ) . In addition , several pyridyl ( 5 , and 11 ) , and pyrimidine ( 2 ) derivatives exhibited potent inhibition ( EC50 values ranging from 2 . 1–3 . 9 μM ) . The meta- substituted sulfonamide analog ( 13 ) was the most potent compound identified ( EC50: 0 . 20 μM ) and suggested that the change in vector may be favorable ( Fig 3 ) . This idea was supported by the thiadiazole analog 15 ( EC50: 0 . 96 μM ) which possesses a more acute vector than 13 yet maintains potency . In addition , compound activity remained upon removal of the tail group ( red in Fig 1 ) and replacement with the t-butyl carbamate ( 21 , EC50: 1 . 1 μM ) or pyrazole ( 16 , EC50: 0 . 83 μM ) . Finally , removal of the sulfonamide and replacement with the pyrimidine ( 14; EC50: 1 . 5 μM ) , as well as incorporation of an -NH linker ( 17–20 ) , led to several compounds with low-micromolar inhibition ( EC50 values ranging from 0 . 87–2 . 5 μM ) of L . major . Importantly , host cell toxicity ( HepG2 ) was found to be low for these analogs and the SI was greater than the 10×EC50 threshold that was targeted . The general SAR trends around this series for activity against L . major are summarized in Fig 6 . With a reduction of the lipophilicity of the head group several sub-micromolar inhibitors of T . b . brucei , including para substituted pyridines ( 5 , 11 and 12; EC50 values ranging from 0 . 27–0 . 70 μM ) , pyrazines ( 3 and 4; EC50: 0 . 62 and 0 . 91 μM respectively ) and several substituted anilines ( 6 , 8 and 10; EC50 values ranging from 0 . 22–0 . 65 μM ) were identified ( Fig 2 ) . Replacement of the phenylsulfonamide with the pyrimidine ( 14; EC50: 0 . 0060 μM ) ( Fig 3 ) led to a 10-fold improvement in potency over 1 ( EC50: 0 . 079 μM ) . Additionally , incorporation of an -NH linker led to compounds with an improvement in potency ( 17–20; EC50 values ranging from 0 . 098–0 . 16 μM ) . A combination of the pyrazine head group with the pyrimidine tail ( 22; EC50: 0 . 43 μM ) ( Fig 4 ) produced a sub-micromolar inhibitor of T . b . brucei proliferation . This compound had a significantly improved LLE above the desired range ( 4 . 3 ) , and improved aqueous solubility ( 44 μM ) , though HLM ( Clint: 180 μL/min/mg ) and RH clearance ( Clint: 130 μL/min/106 cells ) are still of concern and require further optimization . The general SAR trends around this series for activity against T . b . brucei are summarized in Fig 7 . Given the activity observed against T . b . brucei , a selection of compounds that demonstrated sub-micromolar inhibition of T . b . brucei were further profiled against T . congolense and T . vivax , which are the main causative agents for African animal trypanosomiasis ( AAT ) ( Table 1 ) , to determine how well the activity translated to these closely related kinetoplastids [5] . All of the analogs screened were observed to be sub-micromolar against both T . congolense and T . vivax . Compounds 22 and 23 were observed to be more potent than diminazene for T . congolense ( EC50: 0 . 050 μM ) , and T . vivax ( EC50: 0 . 040 μM ) , respectively . None of the compounds tested were more potent than isometamidium against either species . We selected the most promising T . brucei inhibitor ( 14 ) for pharmacokinetic ( PK ) analysis on the basis of its high potency ( EC50: 0 . 0060 μM ) . PK parameters were measured for both brain and plasma following a single intraperitoneal ( i . p . ) dose of 10 mg/kg ( see Supplementary information , S5 and S6 Tables ) . To achieve efficacy in the in vivo acute HAT model , we were aiming to exceed 10×EC50 for 6–8 h , though at this dose we were only able to exceed these levels for 4 h in plasma ( see Supplementary information , S1 Fig ) . Further , 14 had an excellent brain to plasma exposure ratio of 7 . 4 , which was encouraging given CNS exposure is required to treat stage 2 HAT infections , when parasites have crossed the blood-brain barrier [16] . Given that the PK results of 14 indicated a higher dose would be required to achieve 10×EC50 , we administered a single i . p . dose of 25 mg/kg in a murine model of acute HAT . Control mice received an i . p . dose of drug vehicle ( dimethylsulfoxide , 3 . 4 mL/kg ) . There was no evidence of toxicity and a 10-fold reduction in parasitemia was observed in 75% of mice dosed with 14 while one had no detectable parasitemia ( see Supplementary information , S2 Fig ) . Given the improvements in the overall ADME profile of 22 , coupled with its sub-micromolar inhibition of T . b . brucei we sought to progress it into an efficacy study . Mice were administered orally ( p . o . ) once daily 60 mg/kg for the first three days post-infection . Observing no signs of toxicity , the dose was increased to 70 mg/kg for the remaining three days of treatment . Control mice received a p . o . dose of drug vehicle ( 10% NMP and 90% PEG , 10 mL/kg ) . By day 5 , there was no statistically significant difference between the control group and the group treated with 22 ( Fig 8 ) . In summary , by screening a series of anti-P . falciparum proliferation inhibitors against kinetoplastids , we have identified several potent compounds against T . cruzi , L . major , and T . b . brucei . When taken in its entirety , this set of compounds generally shows low host cell toxicity and the analogs had good to excellent selectivity for the parasite of interest . Of note was the identification of 22 which exhibited potent inhibition of T . b . brucei , and an improved ADME profile but , when it was progressed into an in vivo model of acute HAT infection , failed to affect parasitemia . We are working to ascertain the reasons for the lack of translation from in vitro to in vivo and we continue to pursue further optimization of this series as anti-trypanosomal and anti-leishmanial lead compounds , the results of which will be reported in due course .
The assay was performed following a previously reported procedure [17] . Briefly , in a 96-well plate , compounds were added in triplicates at 50 μM and in serial dilutions 1:2 in HMI-9 medium . To each well , 100 μL of 2 . 5×103 T . b . brucei ( strain 427 , received as a gift from Dr . C . C . Wang at UCSF ) in HMI-9 medium was were added and incubated at 37°C , 5% CO2 for 48 h . Following incubation , 20 μL of PrestoBlue were added to each well and incubated for additional 4 h . Fluorescence was read at 530 nm excitation and 590 nm emission . Suramin at 100 μM was used as positive control and reference for calculation of IC50 . This study was carried out in strict accordance with the USA Public Health Service Policy on Humane Care and Use of Laboratory Animals and Association for Assessment and Accreditation of Laboratory Animal Care accreditation guidelines . All mice were maintained in the University of Georgia Animal Facility under pathogen-free conditions . The protocol ( AUP # A2013 06-011-A10 ) was approved by the University of Georgia Institutional Animal Care and Use Committee . Bloodstream form ( BSF ) T . brucei brucei CA427 parasites were maintained at densities below 1×106 cells/mL in HMI-9 media supplemented with 10% fetal bovine serum ( Atlanta Biologicals ) , 10% SERUM PLUS ( Sigma ) , and 1% Antibiotic-Antimycotic Solution ( Corning cellgro ) at 37°C , 5% CO2 [18] . Parasites were centrifuged at 5000 xg for 3 min at room temperature and resuspended in cold 1xPBS containing 1% glucose to yield a solution of 2 . 5×106 cells/mL . Correct cell density following resuspension was confirmed using a Z2 Coulter Counter ( Beckman ) . Parasite viability was observed by motility with a Neubauer Bright-line hemocytometer . Cells were kept on ice until infection . Compound 14 –on day 0 , female Swiss-Webster mice ( 8–10 weeks old , 20–25 g , n = 4 per group ) ( Harlan ) were infected i . p . with 2 . 5×105 trypanosomes by injecting 100 μL of resuspension using 26G needles . Day 1 to 3 post-infection , mice received a single i . p . dose of either drug vehicle ( dimethylsulfoxide , 3 . 4 mL/kg ) ( Fisher Scientific ) or drug ( 14 , 25 mg/kg ) . Parasitemia was determined on days 2 and 3 post-infection by collecting 3 μL of blood from the tail vein . Compound 22 –on day 0 , female Swiss-Webster mice ( 8–10 weeks old , 20–25 g , n = 4 per group ) were infected i . p . with 1×105 trypanosomes by injecting 100 μL of resuspension using 26G needles . Day 1 to 3 post-infection , mice received a single p . o . dose of either drug vehicle ( 10% NMP and 90% PEG , 10 mL/kg ) ( Fisher Scientific ) or drug ( 22 , 60 mg/kg ) . From days 4 to 6 , the dose was increased to 70 mg/kg for the mice in the treatment group . Parasitemia was determined on days 3 to 6 post-infection by collecting 3 μL of blood from the tail vein . Blood samples were mixed with 21 μL of RBC Lysis Solution ( Qiagen ) and incubated at room temperature for 15–45 min prior to observing for parasites by hemocytometer . Humane euthanasia by CO2 overdose followed by incision to form a bilateral pneumothorax was conducted on mice at study termination . All animal experimental protocols were approved and performed in accordance with the guidelines of the Institutional Animal Care and Use Committee ( IACUC ) at the University of Georgia . In a 96-well plate , 5×104 3T3 cells in DMEM without phenol red supplemented with 2% FBS were added to each well . Cells were incubated for 2 h to allow for attachment . Compounds were added in triplicates at 50 μM and in serial dilutions 1:2 . To each well , 5×104 T . cruzi trypomastigotes ( Tulahuen strain expressing β-galactosidase ) were added and incubated at 37°C , 5% CO2 for 96 h . Following incubation , 50 μL of 500 μM chlorophenol Red-β-D-galactopyranoside ( CPRG ) in phosphate-buffered saline with 0 . 5% NP40 was added to each well and incubated at 37°C , 5% CO2 for 4 h . Absorbance was read at 590–595 nm . Amphotericin B at 4 μM was used as positive control and reference for calculation of EC50 . In vitro drug sensitivity assay for T . congolense ( 72 h ) : Compounds were tested in vitro for efficacy against the IL3000 T . congolense ( drug sensitive ) strain which was originally derived from the Trans Mara I strain , isolated from a bovine in 1966 in Kenya [19] . In brief , test compounds were prepared as 10 mg/mL DMSO stocks for each assay run , data points were averaged and EC50 values were determined using Softmax Pro 5 . 2 ( Molecular Devices , Inc . ) . Compounds were assayed in at least three separate , independent experiments and an 11-point dilution curve was used to determine the EC50 values . Bloodstream form trypanosomes were supported in HMI-9 medium containing 20% bovine serum and were incubated with test compounds for 69 h at 34°C in a humidified atmosphere containing 5% CO2 . Thereafter , 10 μL of Resazurin dye ( 12 . 5 mg in 100 mL of phosphate-buffered saline , Sigma-Aldrich , Buchs , Switzerland ) was added for an additional 3 h . Plates were read using a fluorescence plate reader ( Spectramax , Gemini XS , Bucher Biotec , Basel , Switzerland ) using an excitation wavelength of 536 nm and an emission wavelength of 588 nm . Ex vivo drug sensitivity assay for T . vivax ( 48 h ) : Compounds were tested ex vivo against the STIB 719 / ILRAD 560 T . vivax ( drug sensitive ) strain which originated from the Y486 strain , isolated from a naturally infected bovine in 1976 in Zaria , Nigeria [19] . Compounds were assayed in at least three separate , independent experiments and an 11-point dilution curve was used to determine the EC50 values . Bloodstream form trypanosomes were harvested from a highly parasitaemic mouse ( via cardiac puncture ) and were incubated with test compounds for 45 h at 37°C in a humidified atmosphere containing 5% CO2 , supported in HMI-9 medium containing 20% bovine serum . Thereafter , Resazurin dye was added to monitor trypanosome viability as described in the preceding section . Amastigote assays were performed using pLEXSY-hyg2-luciferase L . major transfected promastigotes and the murine RAW 264 . 7 macrophage cell line ( ATCC TIB-71 , Manassas , VA ) . Briefly , luciferase-expressing promastigotes of each Leishmania species were generated by adding a luciferase encoding region to the pLEXSY-hyg2 vector ( Jena Biosciences , Germany ) as described [20] . One μg of the linearized pLEXSY-hyg2-luciferase vector were electroporated ( 480 V , 13 Ω , and 500 μF ) into 4×107 Leishmania promastigotes . Selection for stable promastigote transfectants was carried out using hygromycin B ( 100 μg/mL ) . RAW 264 . 7 macrophages were maintained in growth medium ( Dulbecco’s Modified Eagle’s Medium supplemented with heat-inactivated 10% FBS ( Life Technologies ) ) . Cells were harvested and resuspended in growth medium at 2 . 0×105 cells/mL , and 1×104 cells/well were dispensed ( final volume 50 μL ) in each well of a 384 well tissue-culture treated white plate using a EVO Freedom liquid handling system ( Tecan , Durham , NC ) . Plates were incubated at 37°C in 5% CO2 for 24 h after which the culture medium was removed from each well using the EVO Freedom liquid handler . Metacyclic phase pLEXSY-hyg2-luciferase-Leishmania promastigotes ( MOI = 1:10 L . major ) were added to each assay plate well and allowed to infect the RAW 264 . 7 macrophages . After an overnight incubation , the growth medium was aspirated and each well was washed three times with 40 μL of fresh growth medium to remove non-internalized promastigotes . After the third wash , 69 . 2 μL of growth medium was added to each well . Compound dilutions ( final concentration ranges 0 . 5–10 , 000 ng/mL ) were generated and dispensed using the liquid handling system . Compound treated plates were incubated at 37°C in 5% CO2 for 96 h . After incubation , 7 . 5 μL of a luciferin solution ( Caliper Life Science , Waltham , MA ) diluted to 150 μg/mL was added to each well , and the plates were incubated for 30 min at 37°C in the dark . Data were captured using an Infinite M200 plate reader ( Tecan ) . EC50s were generated for each concentration response test using GraphPad Prism software 6 . 0 . In a 96-well plate , 5×104 NIH/3T3 ( ATCC CRL-1658 ) cells ( purchased from ATCC ) in DMEM without phenol red supplemented with 2% FBS were added to each well . Cells were incubated for 2 h to allow for attachment . Compounds were added in triplicates at 200 μM and in serial dilutions 1:2 and incubated at 37°C , 5% CO2 for 96 h . Ionomycin at 100 μM was used as positive control and reference for calculation of EC50 . Following incubation , 10 μL of Alamar Blue ( ThermoFisher ) were added to each well and cells incubated at 37°C , 5% CO2 for 4 h . Absorbance was read at 590–595 nm . HepG2 cells were cultured in complete Minimal Essential Medium prepared by supplementing MEM with 0 . 19% sodium bicarbonate , 10% heat inactivated FBS , 2 mM L-glutamine , 0 . 1 mM MEM non-essential amino , 0 . 009 mg/mL insulin , 1 . 76 mg/mL bovine serum albumin , 20 units/mL penicillin–streptomycin , and 0 . 05 mg/mL gentamycin . HepG2 cells cultured in complete MEM were first washed with 1× Hank’s Balanced Salt Solution ( Invitrogen #14175095 ) , trypsinized using a 0 . 25% trypsin/EDTA solution , assessed for viability using trypan blue , and resuspended at 250 , 000 cells/mL . Using a Tecan EVO Freedom robot , 38 . 3 μL of cell suspension were added to each well of clear , cell culture-treated 384-well microtiter plates for a final concentration of 9570 liver cells per well , and plated cells were incubated overnight in 5% CO2 at 37°C . Drug plates were prepared with the Tecan EVO Freedom using sterile 96 well plates containing twelve duplicate 1 . 6-fold serial dilutions of each test compound suspended in DMSO . 4 . 25 μL of diluted test compound was then added to the 38 . 3 μL of media in each well providing a 10%-fold final dilution of compound . Compounds were tested from a range of 57 ng/mL to 10 , 000 ng/mL for all assays . Mefloquine was used as a plate control for all assays with a concentration ranging from 113 ng/mL to 20 , 000 ng/mL . After a 48 h incubation period , 8 μL of a 1 . 5 mg/mL solution of MTT diluted in complete MEM media was added to each well . All plates were subsequently incubated in the dark for 1 h at room temperature . After incubation , the media and drugs in each well was removed by shaking the plate over sink , and the plates were left to dry in a fume hood for 15 mins . Next , 30 μL of isopropanol acidified by addition of HCl at a final concentration of 0 . 36% was added to dissolve the formazan dye crystals created by reduction of MTT . Plates are put on a 3-D rotator for 15–30 mins . Absorbance was determined in all wells using a Tecan iControl 1 . 6 Infinite plate reader . The 50% toxic concentrations ( TC50 ) were then generated for each toxicity dose response test using GraphPad Prism ( GraphPad Software Inc . , San Diego , CA ) using the nonlinear regression ( sigmoidal dose-response/variable slope ) equation .
|
As part of our efforts to identify compounds that are active against the parasite that causes malaria ( P . falciparum ) , we employed a “parasite hopping” approach in our drug discovery efforts . This involved screening a library of demonstrated antiparasitic agents against other parasites responsible for a host of neglected tropical diseases ( NTDs ) including Chagas disease ( T . cruzi ) , human African trypanosomiasis ( T . brucei ) and cutaneous leishmaniasis ( L . major ) . The compounds we identified generally show improved selectivity for the parasite of interest over the mammalian cell lines tested and , from this work , we have made progress towards the identification of lead compounds against multiple NTDs .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
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"laboratory",
"medicine",
"biological",
"cultures",
"tropical",
"diseases",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"parasitology",
"toxicology",
"parasitemia",
"trypanosoma",
"brucei",
"toxicity",
"protozoans",
"neglected",
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"diseases",
"quantitative",
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"cell",
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"trypanosoma",
"cruzi",
"nih",
"3t3",
"cells",
"trypanosoma",
"eukaryota",
"biology",
"and",
"life",
"sciences",
"trypanosoma",
"brucei",
"gambiense",
"organisms"
] |
2018
|
Anilinoquinoline based inhibitors of trypanosomatid proliferation
|
We address the issue of reliably detecting and quantifying cross-frequency coupling ( CFC ) in neural time series . Based on non-linear auto-regressive models , the proposed method provides a generative and parametric model of the time-varying spectral content of the signals . As this method models the entire spectrum simultaneously , it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals . As the model is probabilistic , it also provides a score of the model “goodness of fit” via the likelihood , enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach . Using three datasets obtained with invasive neurophysiological recordings in humans and rodents , we demonstrate that these models are able to replicate previous results obtained with other metrics , but also reveal new insights such as the influence of the amplitude of the slow oscillation . Using simulations , we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods . We also show how the likelihood can be used to find optimal filtering parameters , suggesting new properties on the spectrum of the driving signal , but also to estimate the optimal delay between the coupled signals , enabling a directionality estimation in the coupling .
We note y the signal containing the high-frequency activity , and x the signal with slow frequency oscillations , also called the exogenous driver . When a signal x results from a band-pass filtering step , we note the central frequency of the filter fx and the bandwidth Δfx . The value of the signal x at time t is denoted x ( t ) . To estimate PAC , the typical pipeline reported in the literature consists in four main processing steps: The Modulation Index ( MI ) described in the pioneering work of [6] is the mean over time of the composite signal z = a y e ϕ x . The stronger the coupling between ϕx and ay , the more the MI deviates from zero . This index has been further improved by Ozkurt et al . with a simple normalization [26] . Another approach [23 , 44] has been to partition [0 , 2π] into smaller intervals to get the time points t when ϕx ( t ) is within each interval , and to compute the mean of ay ( t ) on these time points . PAC was then quantified by looking at how much the distribution of ay differs from uniformity with respect to ϕx . For instance , a simple height ratio [44] , or a Kullback-Leibler divergence as proposed by Tort et al . [23] , can be computed between the estimated distribution and the uniform distribution . Alternatively , it was proposed in [11] to use direct correlation between x and ay . As this method yielded artificially weaker coupling values when the maximum amplitude ay was not exactly on the peaks or troughs of x , this method was later extended to generalized linear models ( GLM ) using both cos ( ϕx ) and sin ( ϕx ) by Penny et al . [22] . Other approaches employed a measure of coherence [45] or the phase-locking value [46] . All these last three approaches offer metrics which are independent of the phase at which the maximum amplitude occurs . The methods of Tort et al . [23] , Ozkurt et al . [26] , and Penny et al . [22] will be considered for comparison in our experiments . As one can see , there is a long list of methods to quantify CFC in neural time series . Yet , a number of limitations which can significantly affect the outcomes and interpretations of neuroscientific findings exist with these approaches . For example , in typical PAC analysis , a systematic bias rises where one constructs the so-called comodulogram . A comodulogram is obtained by evaluating the chosen metric over a grid of frequency fx and fy . This bias emerges from the choice of the bandpass filter , which involves the critical choice of the bandwidth Δfy . It has been reported several times that to observe any amplitude modulation , the bandwidth of the fast oscillation Δfy has to be at least twice as high as the frequency of the slow oscillations fx: Δfy > 2fx [27 , 47] . As a comodulogram uses different values for fy , many studies have used a variable bandwidth , by taking a fixed number of cycles in the filters . The bandwidth is thus proportional to the center frequency: Δfy ∝ fy . This choice leads to a systematic bias , as it hides any possible coupling below the diagonal fy = 2fx/α , where α = Δfy/fy is the proportionality factor . Other studies have used a constant bandwidth Δfy; yet this also biases the results towards the low driver frequency fx , considering that it hides any coupling with fx > Δfy/2 . A proper way to build a comodulogram would be to take a variable bandwidth Δfy ∝ fx , with Δfy > 2fx . However , this is not common practice as it is computationally very demanding , because it implies to bandpass filter y again for each value of fx . Another common issue arises with the use of the Hilbert transform to estimate the amplitude and the phase of real-valued signals . Such estimations rely on the hypothesis that the signals x and y are narrow-band , i . e . almost sinusoidal . However , numerous studies have used this technique on very wide-band signals such as the entire gamma band ( 80-150 Hz ) [6] ( see other examples in [28] ) . The narrow-band assumption is debatable for high frequency activity and , consequently , using the Hilbert transform may yield non-meaningful amplitude estimations , and potentially poor estimations of PAC [27 , 28] . Note also that , in this context , wavelet-based filtering is equivalent to the Hilbert transform [48 , 49] , and therefore does not provide a more valid alternative option . Besides these issues of filtering and inappropriate use of Hilbert transforms , Hyafil [29] also warned that certain choices of bandwidth Δfy might mistake phase-frequency coupling for PAC , or create spurious amplitude-amplitude coupling; see also the more recent work in [24] for discussion and more practical recommendations for PAC analysis . Here we advocate that the DAR models detailed in the next sections address a number of the limitations just mentioned . They do not use bandpass filter or Hilbert transform on the high frequencies y . They introduce a measure of goodness of fit , through the use of a probabilistic signal model whose quality can be assessed by evaluating the likelihood of the data under the model . In practice , the likelihood quantifies how much variance of the signal can be explained by the model , and is similar to the R2 coefficient in generalized linear models ( GLM ) . To the best of our knowledge , the only related model-based approach to measure PAC used GLM [22] . With GLM , however , the modeling part is done independently on each signal yf , which is the band-pass filtered version of y around frequency f . For each of these frequencies f a different model is fitted . By doing so , a GLM approach cannot model the wide-band signal y as it is limited to multiple estimations frequency by frequency bin . This largely limits the use of the likelihood to compare models or parameters . On the contrary , we propose to model y globally , without filtering it in different frequency bands . To conclude this section , and to position this work in the broader context of modeling approaches for neuroscience data , we would like to stress that our proposed method can be considered as an encoding model for CFC , as opposed to a decoding model [50–52] . Indeed , our model reports how much empirical data can be explained and by doing so enables us to test neuroscience hypotheses in a principled manner [51] . The literature on the use of non-linear auto-regressive ( AR ) models is quite large and covers fields such as audio signal processing and econometrics . For instance , AR models with conditional heteroskedasticity ( ARCH [53] , GARCH [54] ) are extremely popular in econometrics where they are used to model signals whose overall amplitude varies as a function of time . Here , however , in the context of CFC and PAC , one would like to model variations in the spectrum itself , such as shifts in peak frequencies ( a . k . a . frequency modulations ) or changes in amplitude only within certain frequency bands ( a . k . a . amplitude modulations ) . To achieve this , one idea is to define a linear AR model , whose coefficients are a function of time and change slowly depending on a non-linear function of the signal . The first models based on this idea are SETAR models [41] , which switch between several AR models depending on the amplitude of the signal with respect to some thresholds . To get a smoother transition between regimes , SETAR models have inspired other models like EXPAR [55] or STAR [42] , in which the AR coefficients change continuously depending on a non-linear function of the past of the signal . These models share the same underlying motivation as the DAR models described below but , crucially , DAR models can be designed and parametrized to capture PAC phenomena independently of the phase in the driving signal at which the high frequency content is the strongest . In other words , DAR models can work equivalently well if the high frequency peaks are in the troughs , the rising phase , the decreasing phase or the peaks of the low frequency driving signal . Moreover , as DAR models do not require to infer the driving behavior from the signal itself and rather rely on the prior knowledge of the slow oscillation , the inference is significantly faster and more robust .
In this section , we present the outcome of using the model selection procedure to estimate the best filtering frequency fx and bandwidth Δfx to extract the driver x . We first describe the outcome on simulated signals ( ground truth ) and then on empirical datasets . Given that DAR models are parametric with a limited number of parameters to estimate , less time samples may be needed to estimate PAC as compared to non-parametric methods . We tested this assumption using simulated signals of varying duration . We computed their comodulograms ( as in Fig 7 ) and selected the frequencies of maximum coupling . For each duration , we simulated 200 signals , and plotted the 2D histogram showing the fraction of time each frequency pairs corresponded to a maximum . We then compared the same four methods: DAR models with ( p , m ) = ( 10 , 1 ) , the GLM-based model [22] , and two non-parametric methods [23 , 26] . Results shown in Fig 9 show that parametric approaches provided a more robust estimation of PAC frequencies with short signals ( T = 2 sec ) than non-parametric methods . The robustness to small sample size is a key feature of parametric models , as it significantly improves PAC analysis during shorter experiments . When undertaking a PAC analysis across time using a sliding time window , parametric models should therefore provide more robust PAC estimates . Note that the specific time values in these simulations should not be taken as general guidelines as they depend on the simulation parameters such as the signal-to-noise ratio . However , across all tests , parametric methods consistently provided more accurate results than non-parametric ones . One can note that in DAR models , the driver contains not only the phase of the slow oscillation , but also its amplitude . As the driver is not a perfect sinusoid , its amplitude fluctuates with time . On the contrary , most PAC metrics discard the amplitude fluctuations of the slow oscillation and only consider its phase . To evaluate these two options , we compared two drivers using DAR models: the original ( complex ) driver x ( t ) , and the normalized driver x ˜ ( t ) = x ( t ) / | x ( t ) | . This normalized driver only contains the phase information , as in most traditional PAC metrics . Using cross-validation , we compared the log-likelihood of four fitted models , and found a difference always in favor of the non-normalized driver x ( t ) , as it can be visualized in S3 Fig . This result shows that the coupling phenomenon is associated with amplitude fluctuations , a kind of phase/amplitude-amplitude coupling , as it was previously observed in [25] . Indeed , the GLM parametric method [22] was improved when taking into account the amplitude of the slow oscillation . Here , we use our generative model framework to provide an easy comparison tool through the likelihood , to validate this neuroscientific insight from the signals . In this section , we report the results of the directionality estimates using both simulations and neurophysiological signals . It is noteworthy that in DAR models , we arbitrarily call driver the slow oscillation although the model makes no assumption on the directionality of the coupling .
Cross-frequency coupling ( CFC ) and phase-amplitude coupling ( PAC ) more specifically have been proposed to play a fundamental role in neural processes ranging from the encoding , maintenance and retrieval of information [3–5 , 8 , 17 , 81 , 82] , to large-scale communication across neural ensembles [7 , 19 , 83 , 84] . While a steady increase in observations of PAC in neural data has been seen , how to best detect and quantify such phenomena remains difficult to settle . We argue that a method using DAR models , as described here , is rich enough to capture the time-varying statistics of brain signals in addition to provide efficient inference algorithms . These non-linear statistical models are probabilistic , allowing the estimation of their goodness of fit to the data , and allowing for an easy and fully controlled comparison across models and parameters . In other words , they offer a unique principled data-driven model selection approach , an estimation strategy of phase/amplitude-amplitude coupling based on the approximation of the actual signals , a better temporal resolution of dynamic PAC and the estimation of coupling directionality . One of the main features of PAC estimation through our method is the ability to compare models or parameters on non-synthetic data . On the contrary , traditional PAC metrics cannot be compared on non-synthetic data , and two different choices of parameters can lead to different interpretations . There is no legitimate way to decide which parameter shall be used with empirical data using traditional metrics . The likelihood of the DAR model that can be estimated on left-out data offers a rigorous solution to this problem . We presented here results on both simulated signals and empirical neurophysiological signals . The simulations gave us an illustration of the phenomenon we want to model , and helped us understand how to visualize a fitted DAR model . They also served a validation purpose for the bandwidth selection approach that we performed on real data . Using the data-driven parameter selection on non-synthetic signals , we showed how to choose sensible parameters for the filtering of the slow oscillation . All empirical signals are different , and it was for example reported in the neuroscience literature that peak frequencies vary between individuals [85] and that this should not be overlooked in the analysis of the data . The parameter selection based on fitted DAR models makes it possible to fit parameters on individual datasets . Our results also shed light on the asymmetrical and wide-band properties of the slow oscillation , which could denote crucial features involved in cognition [32] . The second novelty of our method stands in considering the amplitude fluctuations of the slow oscillation in the PAC measure and not only its phase . Using the rodent and human data , we showed that the instantaneous amplitude of the slow oscillation influences the coupling in PAC , as it was previously suggested in [25] . The amplitude information should therefore not be discarded as it is done by existing PAC metrics . For instance , the measure of alpha/gamma coupling reported during rest [86 , 87] should incorporate alpha fluctuations when studied in the context of visual tasks [88] , as an increase of alpha power is often concomitant with a decrease of gamma power [89] . The comparison between DAR models considering or not these low-frequency power fluctuations would inform on the nature of the coupling: purely phase-amplitude , or rather phase/amplitude-amplitude . In Tort et al . [14] , both theta power changes and modulation of theta/gamma PAC were reported in rats having to make a left or right decision to find a reward in a maze . The use of our method could decipher whether the changes in coupling were related to the changes in power , informing on the underlying mechanisms of decision-making . Moreover , as our method models the entire spectrum simultaneously , a phase-frequency coupling could potentially be captured in our models . Therefore , our method is not limited to purely phase-amplitude coupling , and extends the traditional CFC analysis . Furthermore , in those types of experiments , changes in PAC can be very fast depending on the cognitive state of the subject . Therefore , the need for dynamic PAC estimates is growing [14] . We showed with simulations that DAR models are more robust than non-parametric methods when estimating PAC on small time samples . This robustness is critical for time-limited experiments and also when analyzing PAC across time in a fine manner , typically when dynamic processes are at play . Last but not least , likelihood comparison can also be used to estimate the delay between the coupled components , which would give new insights on highly debated questions on the role of oscillations in neuronal communication [90 , 91] . For example , a delay close to zero could suggest that the low and high frequency components of the coupling might be generated in the same area , whereas a large delay would suggest they might come from different areas . As an alternative interpretation , the two components may come from the same area , but the coupling mechanism itself might be lagged . In this case , a negative delay would suggest that the low frequency oscillation is driven by the high frequency oscillations , whereas a positive delay would suggest that the low frequency oscillation drives the high frequency amplitude modulation . In any case , this type of analysis will provide valuable information to guide further experimental questions . A recent concern in PAC analysis is that all PAC metrics may detect a coupling even though the signal is not composed of two cross-frequency coupled oscillators [30 , 92–95] . It may happen for instance with sharp slow oscillations , described in humans intracranial recordings [68] . Sharp edges are known not to be well described by a Fourier analysis , which decomposes the signal in a linear combination of sinusoids . Indeed , such sharp slow oscillations create artificial high frequency activity at each sharp edge , and these high frequencies are thus artificially coupled with the slow oscillations . This false positive detection is commonly referred to as “spurious” coupling [96] . Fig 12 shows a comodulogram computed on a simulated spurious PAC dataset , using a spike train at 10 Hz , as described in [94] . The figure shows that all four methods , including the proposed one , detect some significant PAC , even though there is no nested oscillations in the signal . Even though our method does not use filtering in the high frequencies , it does not solve this issue and is affected in the same way as other traditional PAC metrics . Indeed , our work shed light on the wide-band property of the slow oscillations , but DAR models cannot cope with full-band slow oscillations , which contain strong harmonic components in the high frequencies . However , we consider that such “spurious” PAC can also be a relevant feature of a signal , as stated in [68] . In their study , they show that abnormal beta oscillations ( 13-30 Hz ) in the basal ganglia and motor cortex underlie some “spurious” PAC , but are actually a strong feature associated with Parkinson’s disease . A robust way to disentangle the different mechanisms that lead to similar PAC results remains to be developed . The method we presented in this paper uses univariate signals obtained invasively in rodents or humans . As a lot of neurophysiological research uses non-invasive MEG or EEG recordings containing multiple channels , a multivariate analysis could be of high interest . One way to use data from multiple channels is to estimate a single signal using a spatial filter such as in [97] . Such a method is therefore complementary to univariate PAC metrics like ours which can be applied to the output of the spatial filter . The method from [97] builds spatial filters that maximize the difference between , say , high-frequency activity that appears during peaks of a low-frequency oscillation versus high-frequency activity that is unrelated to the low-frequency oscillation . Again , from the signal obtained with the spatial filter , it is straightforward to adapt most PAC metrics such as our method . Neurophysiological signals have all the statistical properties to make them a challenge from a signal processing perspective . They contain non-linearities , non-stationarities , they are noisy and they can be long , hence posing important computational challenges . Our method based on DAR models offer novel and more robust possibilities to analyse neurophysiological signals , paving the way for new insights on how our brain functions via spectral interactions using local or distant coupling mechanisms . Inline with the open science philosophy of this journal , our method is fully available as an open source package that comes with documentation , tests , and examples: https://pactools . github . io .
|
Neural oscillations synchronize information across brain areas at various anatomical and temporal scales . Of particular relevance , slow fluctuations of brain activity have been shown to affect high frequency neural activity , by regulating the excitability level of neural populations . Such cross-frequency-coupling can take several forms . In the most frequently observed type , the power of high frequency activity is time-locked to a specific phase of slow frequency oscillations , yielding phase-amplitude-coupling ( PAC ) . Even when readily observed in neural recordings , such non-linear coupling is particularly challenging to formally characterize . Typically , neuroscientists use band-pass filtering and Hilbert transforms with ad-hoc correlations . Here , we explicitly address current limitations and propose an alternative probabilistic signal modeling approach , for which statistical inference is fast and well-posed . To statistically model PAC , we propose to use non-linear auto-regressive models which estimate the spectral modulation of a signal conditionally to a driving signal . This conditional spectral analysis enables easy model selection and clear hypothesis-testing by using the likelihood of a given model . We demonstrate the advantage of the model-based approach on three datasets acquired in rats and in humans . We further provide novel neuroscientific insights on previously reported PAC phenomena , capturing two mechanisms in PAC: influence of amplitude and directionality estimation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion"
] |
[
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] |
2017
|
Non-linear auto-regressive models for cross-frequency coupling in neural time series
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Schistosoma mansoni is a parasite of major public health importance in developing countries , where it causes a neglected tropical disease known as intestinal schistosomiasis . However , the distribution of the parasite within many endemic regions is currently unknown , which hinders effective control . The purpose of this study was to characterize the prevalence and intensity of infection of S . mansoni in a remote area of western Tanzania . Stool samples were collected from 192 children and 147 adults residing in Gombe National Park and four nearby villages . Children were actively sampled in local schools , and adults were sampled passively by voluntary presentation at the local health clinics . The two datasets were therefore analysed separately . Faecal worm egg count ( FWEC ) data were analysed using negative binomial and zero-inflated negative binomial ( ZINB ) models with explanatory variables of site , sex , and age . The ZINB models indicated that a substantial proportion of the observed zero FWEC reflected a failure to detect eggs in truly infected individuals , meaning that the estimated true prevalence was much higher than the apparent prevalence as calculated based on the simple proportion of non-zero FWEC . For the passively sampled data from adults , the data were consistent with close to 100% true prevalence of infection . Both the prevalence and intensity of infection differed significantly between sites , but there were no significant associations with sex or age . Overall , our data suggest a more widespread distribution of S . mansoni in this part of Tanzania than was previously thought . The apparent prevalence estimates substantially under-estimated the true prevalence as determined by the ZINB models , and the two types of sampling strategies also resulted in differing conclusions regarding prevalence of infection . We therefore recommend that future surveillance programmes designed to assess risk factors should use active sampling whenever possible , in order to avoid the self-selection bias associated with passive sampling .
Schistosomiasis , which is caused by trematode parasites in the genus Schistosoma , is a typically chronic disease that can result in debilitation and severe pathology in infected patients [1] . Infection with the parasite can also be asymptomatic and can remain undetected for a long period of time [2 , 3] , particularly when presenting intestinal schistosomiasis . This can lead to complacency and tolerance of the disease by both patients and the community . Thus , the disease does not receive as much treatment or financial support as malaria , tuberculosis and HIV/AIDS [4] . This has led the World Health Organisation to categorise schistosomiasis as a Neglected Tropical Disease [5] . Both S . mansoni ( which typically causes intestinal schistosomiasis ) and S . haematobium ( now termed urogenital schistosomiasis ) are endemic throughout Tanzania , with a prevalence of up to 80% in some areas [6–8] . The disease constitutes a major public health problem [9] , but the control efforts have been limited by a lack of reliable data on the distribution and prevalence of the parasite across different parts of the country . Recent estimates indicate that S . haematobium infection is distributed mainly along the coast of the Indian Ocean and in inland villages around Lake Victoria [10] , while S . mansoni infections have been reported in most parts of the country except the eastern coastal areas and Zanzibar and Pemba islands [3 , 10 , 11] . However , reliable data on schistosomiasis infection in Tanzania are mostly limited to the northeast and Lake Victoria areas , which have been more extensively studied due to their accessibility and more advanced infrastructure compared to other parts of the country [10] . Similar information is currently lacking for the southern and western areas , including the Kigoma District . In these areas , information on schistosomiasis comes mostly from hospital records , but as a consequence of poor recording and non-random sampling [12] , this information gives biased estimates of population health attributes such as prevalence and infection intensity [13] . Furthermore , a general limitation of assessing the impact of parasitic infections is the typically highly aggregated nature of the data , and the statistical models that these characteristics demand . Schistosomes tend to show over-dispersion in abundance , with some host individuals having very high observed faecal egg or adult worm counts and others having few or zero counts , which is typical of many parasite species [14] . For some such data , a zero-inflated negative binomial ( ZINB ) distribution has also been used [15 , 16] , with a proportion of the ‘zero’ count observations described by a latent class of individuals that are not infected with the parasite , and the remainder of the ‘zero’ count observations obtained from the negative binomial distribution describing the infected individuals . Standard analytical approaches have often assumed a Poisson or negative binomial distribution for count data but have tended to separately estimate the prevalence based on the proportion of observed counts above zero . This under-estimates the true prevalence because of the imperfect sensitivity of egg detection in infected individuals [17 , 18] . It may therefore be preferable to use a ZINB distribution to allow simultaneous consideration of both the skewed underlying distribution , including zero counts from infected individuals , and the proportion of truly uninfected individuals [19] . This allows a less biased estimation of prevalence because it does not assume that all ‘zero’ count samples represent uninfected individuals; rather , it allows for some parasite infections to be present but not detected . This is reflected in the zeros expected under a negative binomial distribution: in a theoretical population where all individuals are infected with equal numbers of parasites and a standard Kato-Katz test is applied , a distribution of counts would arise due to the non-random distribution of parasite eggs in faeces . This distribution may well contain zeros arising from the imperfect sensitivity of the diagnostic method , but these would not reflect uninfected individuals: they are in fact ‘false negative’ counts . Conversely , a ZINB model reflects two underlying processes: an infected/uninfected status for each individual whereby each uninfected individual must have a count of zero ( the ‘extra’ zeros as estimated by the zero-inflation part of the model ) , and a distribution of observed counts from the infected individuals , which may take any positive discrete value including zero ( the negative binomial part of the model ) . Therefore , each of the zero observations may actually be derived from either uninfected ( zero-inflated ) or infected ( negative binomial ) individuals . Extending this principle , such models can use the model’s zero-inflation and negative binomial terms to separate the factors affecting the presence/absence of infection in the host ( or more correctly , infection with adult female parasites ) from the factors affecting the distribution of egg shedding intensity between infected hosts . This is done by estimating the effects of a set of potential risk factors for the degree of zero-inflation using a logistic regression model ( binomial response with logit link ) , and separately estimating the effects of a set of potential risk factors describing the intensity of observed counts from infected individuals using a negative binomial regression ( typically with a log link ) . Either the same set of risk factors can be used for these two parts of the model , or different sets of risk factors can be used where there is an a priori justification for excluding a risk factor used in either the zero-inflation or negative binomial term from the other term . Another factor to consider in parasite control measures is whether there are other reservoirs of infection that might maintain the disease even if all humans were treated [20] . For schistosomiasis , there is some evidence that non-human primates ( mostly baboons and vervet monkeys ) can harbour the same species of schistosomes as humans [21–24] . The prevalence of schistosomes in the wild animals is not clearly known , but neither is the prevalence in humans in major areas of contact such as at Gombe National Park in western Tanzania [22 , 24] . Given the economic importance of primate ecotourism in Tanzania [25] , an important knowledge gap to address is the potential schistosomiasis risk humans might pose to animals and the potential risk posed to tourists . The park was made famous by Jane Goodall for its chimpanzees and is now a popular tourist destination [26] . Although very little is known about schistosome prevalence or intensity in humans in this area [10 , 22 , 27] , in an accompanying study we confirmed the presence of S . mansoni in the main vector of disease in the region , Biomphalaria pfeifferi [24] . The purpose of this study was to establish the distribution and prevalence of S . mansoni in humans residing within Gombe National Park and its surrounding villages , using appropriate sampling and analytical approaches .
Ethical Clearance for this study ( No . NIMR/HQ/R . 8a/Vol . IX/892 ) was issued by the Tanzania’s National Institute for Medical Research ( NIMR ) . The permission to survey schistosomiasis in villages and schools in the study area was obtained from the Tanzania Commission for Science and Technology ( COSTECH ) through the Executive Director of Kigoma District ( Ref . No . KDC/G1/6/70 ) . Before collecting stool samples full consent was obtained from each participant . A meeting was held with prospective participants who were informed in Kiswahili ( the official language used in the study area ) about the goals of the study , their voluntary participation and its implications . Those willing to participate in the study were asked to give consent through writing ( signature ) and oral for those who could not read or write . Parents , guardians and teachers gave consent on behalf of the children involved in the study . The WHO recommended dose of anthelmintics was given to all consenting individuals infected with schistosomes and other helminths . This study was conducted in Gombe National Park and the neighbouring villages to the north ( Kiziba , Bugamba , Mwamgongo ) and south ( Mtanga ) , along the eastern shores of Lake Tanganyika in the Kigoma District ( Fig 1 ) . Gombe National Park ( 4◦53′ S , 29◦38′ E ) is a narrow strip of rugged terrain and hills along the shores of Lake Tanganyika [24 , 28] . The top of the hills forms the park’s eastern boundary while the lakeshore forms its western boundary . The park is directly bordered by Mtanga and Mwamgongo villages , each of which are inhabited by approximately 5000 people , with most of them engaged in fishing activities in Lake Tanganyika [29] . The more northern villages ( Bugamba and Kiziba ) each harbour about 10 , 000 residents , the majority of whom are farmers [30] . Each of the studied villages has at least one stream that runs through it , where B . pfeifferi snails known to transmit schistosomes in the area have been identified [24 , 30] . Two separate sampling strategies were employed for this study . The first dataset was obtained by active sampling of a target of 60 school children selected from "standard three" classes at a single school per village . This class was chosen because the pupils represent the median age of primary school children in Tanzania ( 9–12 years ) , and also so that the results obtained would be comparable with other studies that have been used to inform control programmes [31] . In schools where standard three class had fewer than 60 children , additional pupils were recruited from standard four class . There was no primary school in Gombe National Park at the time of study , so this site was not included in the statistical modelling for the active sampling dataset . The second dataset was obtained by passive sampling of adult individuals presenting voluntarily at the village clinics , which is therefore potentially subject to self-selection bias . Village sub-divisions were used as selection units for adults , with a cut-off point of at most ten individuals from each sub-division to ensure equal village representation . At Gombe , sampling was conducted in the main residence areas of Kalande near the park’s southern boundary , Kasekela in the centre of the park and Mitumba near its northern boundary . At each site , any dependent children accompanying adults to the clinic ( “accompanying children” ) were also sampled . These were not included in the statistical modelling due to small sample sizes and potentially different self-selection bias , so egg counts are reported for qualitative comparison only . This included all nine children above 12 months of age who were resident in Gombe at the time of sampling , because there were no schools to be sampled . Sampling was conducted in 2010 during the wet season ( January to April ) . For adults , consenting participants were registered , weighed and their age and sex recorded . For school children , age was obtained from the school register while for accompanying children , their medical clinic cards were used to estimate their age . Sampling kits and instructions on the collection protocol were distributed in the morning of the first sampling day and the samples collected back in the morning of the following day . Each sampling kit was comprised of a wooden spatula for picking up a stool sample , a pre-labeled plastic vial ( 120 ml ) for depositing the stools and a locally made polythene plastic bag for keeping the samples . Infection status was based on a single sample from each individual and a single Kato-Katz slide per sample . The WHO recommends that samples are obtained over three days and multiple slides counted per sample [31] to avoid underestimating prevalence and over-estimating intensity of infections [32–34] , so our approach is conservative as a first assessment of whether schistosomiasis is present at a substantial level in the region . This approach has been used by previous epidemiological studies [35] and we followed recommendations to scan entire Kato-Katz slides rather than extrapolating from sampling a subset of slides , to increase accuracy [36] . We have also employed appropriate distributions in our statistical analyses to fully account for the imperfect sensitivity of egg detection in faecal samples . Stools were examined for S . mansoni and other helminths using the Kato-Katz kit , following the manufacturer’s guidelines ( Bio-Manguinhos , Rio de Janeiro , Brazil ) and descriptions in the literature [31 , 36 , 37] . The faecal material was first pressed through a mesh screen filter to remove large particles . The filtrate was then transferred onto a microscope slide through a template hole that holds 41 . 7 mg of faecal material , which is a recommended standard [37 , 38] . The template was then removed and a hydrophilic cellophane paper previously soaked in glycerol-malachite green solution placed on the faecal material . A second slide was placed onto the cellophane strip and pressed to spread out the sample for easy observation of parasite eggs . The bottom slide was left to clear for 30–60 minutes . The slide was then placed under a compound microscope and the entire preparation examined using a 10x objective and the parasite identified using 40 x objectives . All schistosomes and other helminth eggs observed on each slide were identified and counted based on standard guidelines [24 , 37 , 39] . The eggs of S . mansoni were easily identified based on their distinguishing lateral spines , while other helminth eggs were identified based on their morphology , size and appearance of eggs/larvae . The observed number of schistosome eggs observed per Kato Katz slide ( holding 41 . 7 mg of faeces ) was recorded as raw faecal worm egg counts ( FWEC ) , and also converted to the standardised eggs per gram ( EPG ) , as typically used in the literature by simply multiplying the FWEC by 24 . EPG is used as a proxy for estimating the intensity of parasitic infections since it is related to total worm count but can be readily estimated from live patients [31 , 40–44] . We refer to the proportion of samples with EPG greater than zero as the “apparent prevalence” to distinguish it from the prevalence as estimated by the statistical models , which take into account uncertainty about which zero counts reflect true absence of eggs and which represent lack of detection of eggs . The observed FWEC were used for the statistical models instead of the EPG in order to fulfil the requirement that the response for the statistical model is distributed according to a count [45] , and where appropriate , model estimates were transformed to the equivalent scale as EPG by multiplying by the same constant of 24 . Because of the fundamental difference in the sampling procedures between the actively sampled ( school children ) and passively sampled ( self-selected adults ) data , the two datasets were analysed separately , but using the same procedure . A negative binomial model was first fit to the data , using a stepwise algorithm to select the best-fitting model from the four possible explanatory variables of site , sex , linear effect of age , and quadratic effect of age . Sex and site were fitted as categorical variables with 'Male' as the reference category for sex , and the site with the highest number of observations for each dataset chosen as the reference category ( Kiziba for the actively sampled dataset and Mwamgongo for the passively sampled dataset ) . Once the best fitting negative binomial ( NB ) model had been found , two zero-inflated generalisations of this selected negative binomial model were tested: first using only an intercept term in the zero-inflation part of the model ( ZINB1 ) , and secondly using the same predictors in the zero-inflation part of the model as were used in the negative binomial part of the model ( ZINB2 ) . Both ZINB models allow the underlying distribution of FWEC to be conceptually split into two groups: those individuals belonging to the egg shedding group , and those individuals belonging to the ‘zero’ group . This assumes that all samples containing one or more observed eggs originated from an individual in the infected group , whereas samples containing no eggs could have originated from either an infected individual , from whom a positive sample may have been obtained on another day , or from an individual in the ‘zero’ group , from whom it is not theoretically possible to obtain a sample containing eggs . Using this distribution effectively allows separate generalized linear models to be fit simultaneously to the intensity of egg shedding in infected individuals , using a negative binomial distribution with log link , and to the prevalence of egg shedding between individuals , using a binomial distribution with logit link . The ZINB1 model allows for a set of extra zeros that do not belong to the distribution of 'infected' individuals , with an equal probability of each individual being in this extra zero set independent of the predictors in the model . The ZINB2 model allows the probability of each individual being in the extra zero set to depend on the explanatory variables also used for the NB part of the model . Both ZINB1 and ZINB2 models collapse to the NB model in the special case that the extra-zero component is estimated to be negligible ( i . e . prevalence is estimated to be close to 100% ) . All statistical analyses were performed in R Version 3 . 2 . 2 [46] . The NB models were fit using the glm . nb function of the MASS package [47] , and the step-wise selection algorithm was based on the Akaike Information Criterion ( AIC ) [48] . Models including zero-inflation terms were fit using the zeroinfl function in the pscl package [49] . Assessment of model fit for zero-inflated models is not valid using AIC , so two alternative approaches were used based on: ( a ) the Vuong statistic [49]; and ( b ) a distribution of 1000 likelihood ratio test statistics obtained from data generated under the NB model [50] . The ZINB models were only considered preferable to the NB model in the case that both fit statistics indicated that this was the case .
A total of 198 and 149 FWEC observations were made for the actively and passively sampled datasets respectively , of which 105 and 54 were counts of greater than zero , corresponding to apparent prevalence of 53% and 36% ( Table 1 ) . For accompanying children , 35 individuals were sampled , of which 15 had egg counts greater than zero , corresponding to an apparent prevalence of 43% . The number of participants and ratio of adults to children varied substantially between sites , with Mwamgongo showing the highest number of people who participated in the study and the highest apparent prevalence compared to the other villages ( Table 1 and S1 Table ) . For accompanying children , although sample sizes were small , Mwamgongo also showed higher apparent prevalence than the other sites ( Table 1 ) . Qualitatively , there was substantial variation in observed FWEC between sites , and variation between age groups for some sites ( S1 Table , Figs 2 and 3 ) . There were also differences between male and female adults at some sites . For example , at Bugamba , adult females showed lower apparent prevalence and lower FWEC than adult males or children . This was not the case at Mwamgongo , where females with non-zero counts showed higher loads than males . Overall , the qualitative data suggest high variance among individuals and sites .
This study represents the first large-scale attempt to quantify the prevalence and intensity of schistosome infection in the Gombe ecosystem in western Tanzania . Overall , the results suggest that schistosomiasis could pose a substantial threat to human health in this under-sampled region . Despite the previous impression that the disease is rare in this region [10] , Mwamgongo village , for instance , showed an apparent prevalence of more than 89% in children and an estimated true prevalence of approximately 100% for both adults and children , which is far higher than the estimated national average of 51 . 5% [51] . Although observed FWEC suggested extensive variation among villages , estimation of true prevalence of infections using zero-inflated models suggested that all of the sampled villages had a high proportion of individuals with schistosome infections , although with a low mean egg shedding rate in passively sampled adults relative to actively sampled children . Encouragingly , the data suggest that prevalence of schistosomiasis is lower for the residents of Gombe National Park , although the estimated true prevalence still indicates that at least two thirds of adults are infected . This is despite the finding that the closest villages to the north ( Mwamgongo and Bugamba ) showed high prevalence and intensity of infection , both in terms of observed FWEC and parameters estimated from the models ( Table 1 and S1 Table ) . Within the park , low infection levels could be due to the transient nature of residents ( who might originate from other regions where schistosomiasis prevalence is lower or where treatment is more readily available ) but there does not seem to be spill-over from the neighbouring villages . This also suggests that interactions between humans and potential primate reservoirs ( the baboons ) are not posing a major risk factor either to humans or to the wild animals in these regions . However , active sampling of school children was not possible in Gombe so these conclusions are based only on the passively sampled dataset . So , a difference in the magnitude of self-selection bias within Gombe relative to the other sites cannot be ruled out . The apparent discordance in prevalence and intensity between the actively and passively sampled datasets within the same sites is also illustrative of the potential disadvantages of over-reliance on passively sampled data . Although school surveys using stool samples collected over three consecutive days is the WHO recommended protocol for prevalence and intensity of infection mapping [31] , prevalence estimated for these remote areas tend to rely on passively sampled data from hospital records . The actively sampled data can be assumed to be a representative and random sample of the target population of school children within each site , which therefore gives an unbiased estimate of the prevalence and intensity of infection within this population . In contrast , the passively sampled data is subject to self-selection bias of unknown and inestimable magnitude , which is a problem that is well known within the field of surveillance [52] . Despite this , passively sampled data is frequently used because it is typically cheaper and easier to collect , particularly when the dataset has already been collected for other purposes [53] . Although the magnitude of the bias is variable , passive surveillance systems typically under-estimate prevalence of infections [54 , 55] . In contrast , we found higher estimates of prevalence in the passively sampled data relative to the actively sampled child data , which could be explained by individuals that suspect themselves to be infected being more likely to present for sampling and treatment . It is also possible that the actively sampled data may itself be subject to some bias; for example , due to school absenteeism or clinically sick children not attending school . Since the target populations for active and passively sampled data are not identical ( school children vs . the adult population ) , we cannot directly assess the consequences of relying on passive sampling alone for robust assessment of prevalence . It is also not possible to directly compare the relative prevalence in the two age groups . Application of models that can separately estimate variables explaining variation in prevalence and intensity of parasite loads emphasised two important limitations of traditional approaches: 1 ) assessing prevalence based on the proportion of non-zero FWEC; and 2 ) confounding differences due to age with sampling strategies ( active sampling of all individuals vs self-selected volunteers ) . The estimated prevalence based on the ZINB2 model for the child data was higher than the apparent prevalence , because a proportion of the zero counts were due to a low counting sensitivity . Note that the converse is not possible ( observed eggs are never assumed to be false positives ) , so the apparent prevalence is an estimate of the true prevalence that is necessarily biased downwards . The ZINB2 model provided a significantly better fit to the data than the NB model for the actively sampled data , and there is evidence to suggest that the true infection prevalence amongst children is less than 100% in some villages . There was no evidence that the zero-inflated models produced a better fit to the passively sampled data than the simpler NB model , which means that all of the observed zero egg counts in this data were consistent with a failure to detect eggs in truly infected individuals due to imperfect sensitivity of egg detection . This could be interpreted as the data being consistent with a true infection prevalence of 100% . However , it is impossible to exclude the possibility that a zero-inflated model would provide a better fit to a larger dataset , and when the ZINB2 model is used , there is some evidence that the prevalence is less than 90% in Gombe . Since we did not have a large enough sample size of accompanying children they were excluded from the statistical analyses , but for a future study it would be worth exploring the consequences of including different sampling schemes when age is not a confounding factor . Age has often been suggested as a risk factor for parasite infections , in relation to development of immunity and behaviours that increase risk of infection [56–60] . If we had considered only the raw FWEC counts or combined the two datasets , our conclusions might also have been that children tended to show higher infection levels than adults . However , the ZINB modeling suggested that true prevalence in adults was actually higher than in children at some sites , which is likely due to self-selection bias in the passively sampled data . The significantly better fit of the ZINB2 model compared to the NB model for the actively sampled individuals also suggests that a single over-dispersed Poisson distribution is not able to adequately explain all of the zero count observations . Zero-inflated distributions have been used in the fields of parasitology [16 , 61] , bovine mastitis [62] , forest science [63] , and medical epidemiology [64] , although their use has also been criticized in modeling road traffic accident analysis data [65] . They are conceptually useful when zero observations can arise from either count data ( such as a Poisson , negative binomial or other distribution ) or from a truly zero individual [19] , and may be of value in other similar applications . Correct identification of the degree of zero-inflation depends on the correct choice of distribution for the infected group , so that the number of ‘expected’ zeros is accurate . The negative binomial distribution is widely used , but other distributions such as the lognormal-Poisson may be more correct for some datasets [66] and may influence the estimates for zero-inflation [67] . Considerable care should therefore always be put into selection of the most biologically sensible distribution model for analysis of count data , and if appropriate some consideration towards the possibility of using a zero-inflated model should be made . In this case , there is a biologically plausible explanation for modeling a process where some individuals are uninfected , with a distribution of FWEC between the infected individuals , so the choice of ZINB model is justified . A single Kato Katz slide was used to estimate the infection intensity , combined with rigorous statistical analysis to overcome the differences in sensitivity for prevalence and infection intensity of S . mansoni in epidemiological studies . A larger number of Kato Katz slides ( as recommended by WHO guidelines [31] ) would have decreased the relative size of the 95% confidence intervals by adding more information to the data . However , the coefficient estimates obtained should not be biased by the reduced sample size , except for the estimate of the over-dispersion parameter k , which partly reflects the variability between samples and would therefore be affected by the increased precision associated with more slides . In contrast , the bias in the apparent prevalence estimates would be expected to decrease as the number of slides was increased due to the increased probability of detecting eggs , and therefore decreased false negative rate . This reflects the difficulties in interpreting apparent prevalence , and emphasises the value of an unbiased estimate of the prevalence such as that given by the ZINB model . Based on the ZINB and negative binomial models respectively , there was no significant effect of sex on FWEC within either the actively or passively sampled datasets . Previous studies have also not found any effect of sex on parasitic infections and predicted that other factors may be determining the infection levels [68–70] . However , since schistosomiasis transmission is highly related to occupational activities and water contact , in areas where fishing or farming are mostly done by women , such as among the Mende people in the Sierra-Leone , higher prevalence of schistosomiasis has been reported among females than males [71] . It is therefore possible that both men and women in the Gombe ecosystem , their social and cultural duties notwithstanding , are equally exposed to schistosome transmission in the local streams . As there is an insufficient supply of running tap water in the studied villages , both women and men have to use the streams or lake , albeit for different purposes . While men often come in contact with the stream water for bathing and ablution before prayers for Muslims , women use the stream water mostly for performing domestic chores as well as bathing . Nevertheless , interpretation of results could be confounded by self-selection and a gender bias in the willingness of individuals to participate in voluntary studies for the passively sampled data . In our study , for example , there were 82 male and 67 female volunteers . However , such forms of bias should not be present in the actively sampled data . We also found that , while there was a trend for decreasing raw FWEC in both children and adults ( Figs 2 and 3 ) , statistical models suggested a weak ( although non-significant ) effect of age in adults . Age-related differences in infection distributions have been found in other studies [56–60] , where the prevalence and intensity of S . mansoni were found to rise slowly in children and then slowly decline in older individuals . It has been suggested that young people tend to be more susceptible to infection due to being in contact with water more often than adults [58 , 72] and that the decrease in schistosome infection with age may be due to acquired immunity after repeated exposure [58 , 72 , 73] . Sampling of a broader range of age classes would be required to test these effects but with consistent sampling strategies across age groups . While difficult to implement , active sampling of adults would be required to fully test for age-specific differences in susceptibility . The most pronounced differences in FWEC in our study appeared to be due to site . Although statistical modeling suggested that there were no dramatic differences among the villages , both raw egg counts and prevalence and intensity estimated from the ZINB models suggested differences between sites in children , with Bugamba and Mwamgongo standing out as the highest risk areas . In other communities living along the shores of Lake Victoria in northwest Tanzania , local variation in schistosome infections has been attributed to patchy distribution of snails [7 , 74] . It is possible , therefore , that differences among the study villages in the present study could be due to differences in the resident snail populations . In a recent survey , Bakuza [24] did not find any infected snails in the streams sampled within the Gombe National Park boundaries and no snails at all were found in the stream running through Mtanga . This could help to explain the lower prevalence and intensity of infections at these sites . Further work is required to quantify whether differences in infection levels in humans are related to differences in snail densities in the various villages . Variation in schistosomiasis infection among villages could arise due to possible ecological risk factors , such as contact rates with infested water sources , human population density , socio-economic levels and differences in local snail ecology . Our findings offer some guidance on how to optimally distribute the limited resources for schistosomiasis control in areas along the shores of Lake Tanganyika , Tanzania and similar resource-poor settings in other endemic countries . Some differences between sites were found , which could be of relevance to designing future studies to improve understanding of the social and occupational risk factors for transmission of the disease . However , consistent with WHO guidelines , we recommend that surveillance should be conducted using active sampling wherever possible , to enable accurate estimation of prevalence and intensity . Moreover , we recommend that statistical models are applied based on the most appropriate distributions explaining the data . Compared to other studies , we found little influence of age or sex , which could reflect either differences between sampling procedures or real cultural or biological differences in the geographic region sampled for this study . Finally , qualitatively different prevalence estimates were obtained using the ZINB model compared to the observed prevalence based on the simple proportion of observed egg counts above zero , which demonstrates the potential for erroneous inference when ignoring the biasing effect of imperfect egg detection methods when estimating prevalence .
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Schistosomiasis constitutes a major public health problem in Tanzania , with up to 80% prevalence of infection in some areas . Infection with the disease causes abdominal pain , diarrhoea , stunted growth and impaired cognitive abilities in children . Accurate information on the distribution of schistosomiasis within Tanzania is not available for most rural areas . To establish the prevalence of the intestinal form of the disease among communities residing along the shores of Lake Tanganyika , we quantified the presence of the causal agent ( Schistosoma mansoni ) by searching for their eggs in faecal samples . Children were actively sampled from schools but adult sampling relied on volunteers , so the data from the two age groups were analysed separately . The number of positive samples was high in both but significant differences in prevalence and intensity were found between the sampled villages only for the children . Statistical models accounting for false negative stool samples indicated that the apparent prevalence was a gross under-estimate of the true prevalence of infection . Our results emphasise the importance of considering the type of sampling when assessing risk factors associated with parasitic disease . The information obtained from this study will help to guide the optimal distribution of schistosomiasis control resources in the region , such as through targeted allocation of drugs and personnel to those areas with higher estimated prevalence .
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2017
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Estimating the prevalence and intensity of Schistosoma mansoni infection among rural communities in Western Tanzania: The influence of sampling strategy and statistical approach
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Understanding the epidemiology of seasonal influenza is critical for healthcare resource allocation and early detection of anomalous seasons . It can be challenging to obtain high-quality data of influenza cases specifically , as clinical presentations with influenza-like symptoms may instead be cases of one of a number of alternate respiratory viruses . We use a new dataset of confirmed influenza virological data from 2011-2016 , along with high-quality denominators informing a hierarchical observation process , to model seasonal influenza dynamics in New South Wales , Australia . We use approximate Bayesian computation to estimate parameters in a climate-driven stochastic epidemic model , including the basic reproduction number R0 , the proportion of the population susceptible to the circulating strain at the beginning of the season , and the probability an infected individual seeks treatment . We conclude that R0 and initial population susceptibility were strongly related , emphasising the challenges of identifying these parameters . Relatively high R0 values alongside low initial population susceptibility were among the results most consistent with these data . Our results reinforce the importance of distinguishing between R0 and the effective reproduction number ( Re ) in modelling studies .
Influenza is a highly contagious , rapidly evolving respiratory virus that circulates globally on a seasonal basis [1 , 2] . It can cause death in at-risk groups , and places a high burden on health systems ( e . g . , emergency room ( ER ) and general practitioner ( GP ) services ) . In some cases , antigenic shift creates circumstances where influenza can be particularly infectious or dangerous , resulting in influenza pandemics such as those that occurred in 1918 and 2009 . However , understanding normal seasonal circulation is critical for effective healthcare resource allocation , forecasting future seasonal dynamics , and early detection of anomalous seasons . Influenza has been widely studied , through both experimental studies on animal models such as ferrets ( e . g . [3 , 4] ) , and modelling studies based on data from human populations [5 , 6] . There remains substantial uncertainty surrounding the way influenza spreads within populations . Model-based analyses depend on the underlying assumptions and available data , and knowledge gaps exist that may be difficult or impossible to test using animal models ( e . g . , for seasonal influenza , long-term patterns of immunity through and across many seasons ) . Parameter estimation can also be challenging . For example , a key epidemic parameter is the basic reproduction number , R0 , which is defined using a completely susceptible population . However , in practice few populations are likely to be completely susceptible , and so estimates are instead of Re , the effective reproduction number [7 , 8] . To determine R0 itself , it is necessary to also have an estimate of the proportion of individuals that are susceptible at the beginning of an outbreak . Unfortunately , estimating the initial susceptible proportion is close to impossible in practice , given the complexity of immunity and interactions between strains . Immunity to a strain may vary substantially between individuals with similar infection or vaccination histories [9] , and the life course of antibodies and interactions between antibodies to different strains is complex [10] . The rapid evolution and multi-strain nature of influenza means that individuals may have variable immune responses to different strains , particularly in the presence of complex phenomena such as “antigenic seniority” [11 , 12 , 13] , whereby individuals have increased long-term immunity to the first strain they were exposed to during their lifetime . Recent evidence suggests that there may be some level of immunity existing in populations even to novel influenza strains with pandemic potential [14 , 15] . The presence of antibodies in an individual , particularly at low levels , may not prevent reinfection but may reduce the chance of infection when challenged or lead to a milder or asymptomatic illness [16 , 17] . Further , many historical studies have relied on serological data as a means of determining if influenza infection occurred ( e . g . [18 , 19 , 20] ) . However , the results of serological studies can vary substantially based on the criteria used [21] , and produce estimates that are vastly different from active surveillance of symptomatic cases ( e . g . , [22] ) , and the relationships between antibodies and immunity is not completely understood [23] . Estimates for Re are often between 1 . 0–2 . 0 ( e . g . , see [24] ) , however in rare cases estimates of R0 have been as high as 22 . 7 [25 , 26] . While these most extreme values may be highly unlikely , estimates in the intermediate range are plausible when associated with reduced population susceptibility . Assumptions around parameter values can propagate through a modelling analysis , and impact on how we understand and characterise epidemic dynamics , e . g . , when estimating the final size of a seasonal epidemic , or when calculating optimal vaccination policy . As such , careful analysis of high-quality data , estimating both population susceptibility and R0 , is critical to inform present and future modeling efforts . Effective modelling of influenza dynamics is further complicated by the paucity and quality of available data . Influenza is difficult to identify clinically; patients presenting with influenza-like symptoms may have influenza ( Fig 1 ) , but they may also have some other respiratory virus , such as human rhinovirus , or respiratory syncytial virus [27] . To determine the actual cause of these symptoms requires a ( relatively expensive ) test , which may be appropriate for an at-risk individual presenting serious symptoms at a hospital but is unlikely to be necessary for an otherwise healthy individual in the 15–45 age group ( for example ) . In addition , patients may be asymptomatic ( while potentially still able to infect others ) , or those with mild symptoms may choose not to seek medical treatment at all . So , confirmed cases are likely to be only a subset of actual cases , with poor “denominator data” . The hierarchical observation process that links population level epidemic dynamics to observed confirmed cases ( Fig 2 ) may make identifiability challenging and can in some cases introduce bias to estimates [28 , 29 , 30] . In this study , we used a new , high-quality dataset along with modern Bayesian parameter estimation methods to investigate seasonal influenza dynamics in New South Wales , Australia , from 2011–2016 . We used Polymerase Chain Reaction ( PCR ) confirmed influenza infections from the Australian Sentinel Practices Research Network ( ASPREN; [31] ) to fit models . Within these data , approximately 0 . 5% of general practitioners ( GPs; known as family physicians in North America ) within Australia reported each Influenza-like illness ( ILI ) case that they observed , along with the number of consultations they performed , and submitted swab samples for c . 20% of these ILI cases for virological testing via PCR . These data were available weekly . Knowing the numbers of doctors , consultations , ILI observed , and samples tested provides critical and unparalleled denominator data against which to assess the hierarchical observation process between population-level influenza dynamics and the observed confirmed infections . We fit model parameters using approximate Bayesian computation ( ABC ) , assuming the same strain-specific parameters for seasons in which the same predominant strain circulated . By performing this analysis using high-quality confirmed influenza data ( including denominators ) , state of the art parameter estimation techniques , and minimal assumptions , we are able to characterize the epidemiology of influenza within populations . In doing so , we aim to both progress the science of disease modeling , and produce new epidemiological knowledge around population-level immunity to seasonal influenza .
We chose to use an SEIRS-type model , with an Erlang-2 infectious period and exponential exposed and immune times , to model seasonal influenza ( Fig 3 ) , which is consistent with known influenza biology . In this form of model , we track the number of individuals in the population who are susceptible ( S ) , exposed but not yet infectious ( E ) , infectious ( I ) , or recovered and immune from reinfection ( R ) at any time . We include an observed class ( O ) to allow individuals to choose to seek treatment , or not . Note we allow immunity to wane , so that individuals return from the recovered class to the susceptible class , and include external importation of cases at a low rate . Transition events are stochastic , with rates that depend on the number of individuals in each class , and fitted parameters that relate to the epidemiology of the disease . We fit the initial proportion of individuals that are susceptible to the strain circulating in the given season as a parameter , and model seasons that had the same predominant circulating strain together , i . e . , 2011 and 2013 both had predominantly H1N1pdm09 circulating , so we fit these seasons to have the same epidemic parameters , but each with a separate initial population susceptibility . The presence of an exposed class in the chosen model indicates that there is some lag between when an individual is exposed to the disease , and when they become infectious . We assume that they are not symptomatic during this exposed period . This is consistent with many existing models and known influenza epidemiology [32] . By using models aggregated at a state level , without any age- or spatial- structuring , we make a number of simplifying assumptions . Specifically , we lose the ability to model heterogeneity in mixing and thus infection rates , vaccination coverage , immunity , or case ascertainment . We know that these factors should , in reality , be heterogeneous , as there is strong evidence for variations based on ( in particular ) age structure [33 , 34] . However , the data we have access to are unfortunately too sparse to effectively parametrize models at this fine level of detail . In some cases , this may introduce some degree of bias . For example , newborn children entering the population are necessarily susceptible , and by not tracking demographics we may overestimate the rate at which immunity wanes ( i . e . , underestimate the duration of immunity ) , which could potentially have some impact on estimates of other parameters , including R0 .
Having chosen the SEIRS-type model , we used ABC to estimate posterior distributions for model parameters , and to evaluate the impact of these fitted model parameters on underlying epidemic dynamics at the population level . We parameterized transmission based on physical quantities , including the latent , infectious , and immune durations , the probability of an infectious person seeking treatment over their infectious period , the basic reproduction number , R0 , and the proportion of individuals susceptible to the circulating strain at the beginning of each season . R0 was assumed to have seasonal forcing , for which we used variations in temperature ( T ) from the annual mean ( T ¯ ) , i . e . , R 0 = R ¯ 0 + a ( T - T ¯ ) , with R ¯ 0 and a being estimated parameters . Alternate seasonal forcing terms were tested ( e . g . , based on specific humidity with the same form as Yang et al . [35] ) , however these produced similar quality results while requiring more parameters , and were harder to interpret . We therefore chose to use temperature-based forcing for simplicity . Results were broadly similar between strains , so in the text we report primarily the H1N1pdm09 results ( circulating in 2011 & 2013 ) ; a summary of H3N2 ( 2014 & 2016 ) appears in Table 1 , and figures appear in S1 Text . Initial susceptibility values in seasons of the same strain were highly correlated ( e . g . correlation coefficient 0 . 90 between initial susceptibility in 2011 and 2013 ) . The posterior densities had similar forms between strains , however the H3N2 seasons ( 2014 & 2016 ) had slightly lower median R ¯ 0 , associated with slightly higher initial susceptibility ( Tables 1 & 2 ) . For H3N2 seasons , the maximum bivariate posterior density occurred at R ¯ 0 = 4 . 71 and initial susceptibility 0 . 22 ( in 2014 ) ( S1 Text ) .
Seasonal influenza can be a challenging process to characterize given the complex nature of immunity and the inability to discriminate influenza from other respiratory viruses without testing . In this study , we used confirmed virological influenza data , with known denominators , to ensure that the model is specifically based on true influenza cases and the hierarchical observation process is accurate , and modern Bayesian inference techniques , with uninformative , unbounded priors , to ensure that assumptions around these priors did not impact on the resulting parameters estimates . We identified strong posterior relationships between R0 , population susceptibility , and the probability an infected individual seeks treatment . The bivariate posterior distribution had maximum density where relatively high R0 values correspond with low levels of population susceptibility ( while the effective reproduction number Re remains within the expected range ) . This was in sharp contrast to the values that would be obtained by considering only the marginal posterior distribution of each parameter . This highlights the importance of carefully considering the challenges of identifiability in parameter estimates , and in particular the importance of considering immunity alongside transmissibility in modelling studies . While there remains uncertainty around parameter estimates , we encourage researchers to consider carefully the challenges of identifying related parameters , and the impact that strong prior and modeling assumptions can have on parameter estimates .
The critical concern when using these data is an understanding of how underlying population-wide influenza dynamics translate to observed , confirmed influenza cases in the available dataset . We propose that filtering occurs at three levels: There may well be variation from these fixed parameters in practice . However , if all parameters were allowed to vary freely then it would not be possible to identify them , as only the result of all three levels of filtering is observed . We considered a continuous-time stochastic epidemic model , with an SEIRS structure , with an observation class and Erlang-2 sojurn times in the infectious class ( modelled by splitting these classes into two consecutive compartments ) ( Fig 3 ) . This process was approximated in discrete time with 8 timesteps per day . So as to avoid diurnal dynamics with transmission rates varying within days and being maximised in the early hours of the morning , we used the daily mean temperature at each timestep . We also allow external importation of cases , at a per-susceptible rate ξ ( constant through time ) which we fit as part of the model . Note that because of the structure of the model , the mean infectious time of an individual is 2/ ( 2γ + λ ) ( see S1 Text ) . The model was parameterized based on physical epidemiological quantities , which were then transformed into model-based rates , which appear in Table 3 . Given these transformed parameters , transitions for the SEIR model are as appears in Table 4 , using the increments that appear in Table 5 . We chose this parameterisation so as to put priors on the physical quantities of interest . That is , priors were defined on R0 , mean latent , infectious and immune durations , and the probability of observation over the infectious period , and then transformed to obtain epidemic model parameters such as β and γ . Initial conditions were fit for each season , so that susceptibility to the circulating strain in that season could be determined . Prior distributions for epidemic parameters appear in Table 1 . Per-susceptible external importation of cases at a low rate was used to allow for the possibility of epidemic fade-out and reintroduction from elsewhere , and was assigned an exponential prior distribution with rate 109 ( approximately 3 individual case importations per year per million susceptible individuals ) . We did not reject simulation runs in which fade-out occurred , except to require circulation of influenza in any week in which there were influenza cases observed . We chose to use exponential prior distributions so as to avoid putting maximum limits on the values these parameters may take , i . e . , in the same sense that you would choose uniform priors to be uninformative , but with infinite positive support . Rates for the infectious and latent period exponential priors were chosen so that the means were within a reasonable range of influenza epidemiological parameters . The prior for R ¯ 0 was initially chosen as Exponential with large mean so as to have a relatively flat density; we truncated this prior at the highest published estimate of R0 for influenza , 22 . 7 [25] . While arguments could be made for a more informative prior with further reduced density at higher R0 values , we chose to retain this relatively uninformative prior so as to minimise modelling assumptions . The probability that an individual chooses to seek treatment was assumed to be constant , both within and between seasons . In practice this may not be the case , particularly when a circulating strain is unusually transmissible , or produces stronger symptoms ( in either case , potentially leading to greater media attention ) . However , the hierarchical observation structure means that we are able to capture some of this effect in the variation in testing probabilities , as we explicitly use the actual testing proportions given in our dataset , which varied each week . Note that we incorporate vaccination into this model by removing , deterministically , a proportion of susceptibles to a separate vaccinated class ( i . e . , they are still counted as part of N but otherwise do not interact with the model dynamics ) . Specifically we remove 21% of the population , based on published vaccination rates and efficacy statistics in Australia [46 , 47] . We chose to do this rather than take vaccination rate as a fitted parameter so as to minimize the number of parameters to be fit . Consequently , we set the prior for initial season susceptibility to be uniformly distributed from 0 to 0 . 75 . We used temperature as the covariate with which to enable seasonal forcing , following the relationship R 0 = R ¯ 0 + a ( T - T ¯ ) ( with T the daily mean temperature and T ¯ the mean temperature over the study period ) . A number of studies have considered a variety of climate covariates and their interaction with influenza transmission both in populations and experimental models ( e . g . , [48 , 49] ) ; with indications that relationships with and between climate factors are complex and may vary globally [50] . We noted similar quality model fits when using the specific humidity formulation presented by Shaman et al . [51] , however we chose to use the simplest seasonal link , temperature , as this form produced similar quality fits while requiring fewer parameters and enabling greater ease of interpretation . We used approximate Bayesian computation [52 , 53] to calculate posterior distributions for the model parameters . Specifically , we generated candidate parameter sets from the prior distributions listed above . For a given candidate parameter set , we simulated a realisation of the model , and observed the sample path ( i . e . , the number of infected individuals attending ASPREN doctors each week in the simulated realisation ) for 2011 and 2013 ( H1 seasons ) , or 2014 and 2016 ( H3 seasons ) . We compared this simulated realisation to the true sample path , using the square root of mean squared error as score function D : = 1 # weeks ∑ i = 1 # weeks ( true i - candidate i ) 2 , with truei the observed number of cases in the ith week from the ASPREN dataset and candidatei the number in the simulated realisation from the candidate parameter set . There were 104 weeks in each of the study periods ( # weeks ) . The parameter set is accepted if D is less than some tolerance level , in this case set to 4 . 5 . We observe that this choice of score function and threshold produces reasonable model fits , while still being high enough to accept parameter sets regularly . Choice of score function in ABC is problem-specific [54] , and it is likely that a variety of other metrics could reasonably be used to produce reasonable fits . Note that a candidate parameter set can produce a range of scores given that realisations are stochastic , and the accepted realisations are generally among the best possible realisations from the given parameter set ( i . e . , those that most closely fit the data ) , however , the average realisation from these parameter sets still fit the data relatively well ( S1 Text ) . Kernel density estimates were constructed from posterior samples using the algorithm of Botev et al . [55] .
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When patients present to their doctor with influenza-like symptoms , they may have influenza , or some other respiratory virus . The only way to discriminate between these viruses is with an expensive test , which is not performed in many cases . Additionally , results other than influenza may not be reported . This means that it can be difficult to determine how much influenza is circulating in the population each season . We used a unique dataset of confirmed influenza with denominators to fit models for seasonal influenza in New South Wales , Australia . Knowing the denominators allowed us to estimate population level trends . We found that the relationship between influenza transmission rates and immunity due to previous infections was critical , with relatively high transmission corresponding to substantial preexisting immunity likely . This existing immunity is critical to understanding and effectively modeling influenza dynamics .
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2018
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Characterising seasonal influenza epidemiology using primary care surveillance data
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The unfolded protein response ( UPR ) , which is activated by perturbations of the endoplasmic reticulum homeostasis , has been shown to play an important role in innate immunity and inflammation . However , little is known about the molecular mechanisms underlying activation of the UPR during immune responses . Using small RNA deep sequencing and reverse genetic analysis , we show that the microRNA mir-233 is required for activation of the UPR in Caenorhabditis elegans exposed to Pseudomonas aeruginosa PA14 . P . aeruginosa infection up-regulates the expression of mir-233 in a p38 MAPK-dependent manner . Quantitative proteomic analysis identifies SCA-1 , a C . elegans homologue of the sarco/endoplasmic reticulum Ca2+-ATPase , as a target of mir-233 . During P . aeruginosa PA14 infection , mir-233 represses the protein levels of SCA-1 , which in turn leads to activation of the UPR . Whereas mir-233 mutants are more sensitive to P . aeruginosa infection , knockdown of sca-1 leads to enhanced resistance to the killing by P . aeruginosa . Our study indicates that microRNA-dependent pathways may have an impact on innate immunity by activating the UPR .
The innate immune system , the first line of defense against microbial infection , is evolutionarily conserved in both vertebrate and invertebrate animals . Activation of the innate immune system upon pathogen infection results in a definitive anti-microbial response to invading microbes . The genetically tractable model organism Caenorhabditis elegans has contributed greatly to advancing our understanding of innate immunity in animals [1] , [2] . During the last decade , C . elegans-based studies have identified a variety of signaling pathways involved in innate immunity , including the p38 mitogen activated protein kinase ( MAPK ) PMK-1 signaling pathway [2] , [3] . The p38 MAPK pathway plays a major role in the immune responses to pathogens , mainly through regulating the expression of secreted antimicrobials , including C-type lectins , ShK toxins , and CUB-like genes [4] . In eukaryotic cells , the endoplasmic reticulum ( ER ) is a principal site for the folding and maturation of most secreted and transmembrane proteins [5] , [6] . Increased load of misfolded proteins that enter the ER can lead to ER stress . To cope with this stress , cells trigger a signal transduction system collectively termed the unfolded protein response ( UPR ) . The UPR is conserved from yeast to human and is an integrated intracellular signaling pathway that links the ER lumen with the cytoplasm and nucleus . Accumulating evidence has revealed that innate immunity is a physiologically relevant source of ER stress in C . elegans [7] , [8] . The PMK-1 signaling pathway is required for activation of the UPR induced by Pseudomonas aeruginosa infection [7] or pore-forming toxins produced by human pathogens , such as Staphylococcus aureus , Streptococcus pyogenes , and Aeromonas hydrophilia [8] . However , the molecular mechanism underlying activation of the UPR by PMK-1 in these processes remains unclear . MicroRNAs ( miRNAs ) , a class of RNA molecules of 22 nucleotides ( nt ) long , are pivotal regulators of gene expression in metazoa [9] . In animals , miRNAs mainly target specific mRNAs through imperfect base pairing with the 3′-untranslated region ( 3′ UTR ) of these mRNAs [10] . miRNAs are involved in a wide variety of biological processes , including patterning of the nervous system , inflammation and immunity , cell death and proliferation , and development [11] . Since the first two miRNAs , lin-4 and let-7 , were identified as regulators of developmental timing [12] , [13] , more than 175 of miRNA genes have been confirmed in C . elegans [14] . Previous studies have revealed that a reduction in total miRISC activity or mutations in dcr-1 , drsh-1 and alg-1 genes enhances worm resistance to pathogenic bacteria P . aeruginosa PA14 or Bacillus thuringiensis DB27 [15] , [16] . As these genes are required for miRNA processing , these results imply that miRNAs are probably involved in innate immune responses to pathogenic bacteria . Furthermore , Liu et al . [17] have demonstrated that the mir-84 ( n4037 ) and mir-241 ( n4316 ) mutants exhibit enhanced resistance , whereas the mir-48 ( n4097 ) mutant worms exhibit decreased resistance to P . aeruginosa infection . Thus , different let-7 miRNA homologs play distinct roles in innate immune responses to bacterial infection . To better understand the role of miRNAs in innate immunity , we used RNA deep sequencing to carry out a comprehensive survey of miRNA expression in wild type ( WT ) animals grown on live P . aeruginosa PA14 . We screened the up-regulated miRNAs and discovered that mir-233 was required for resistance to P . aeruginosa PA14 infection . Using a proteomic approach , we identified that sca-1 , which encodes a C . elegans homologue of the sarco/endoplasmic reticulum Ca2+-ATPase ( SERCA ) [18] , was the target of mir-233 . Down-regulation of SCA-1 protein levels by mir-233 resulted in activation of the UPR , which in turn conferred resistance to P . aeruginosa PA14 infection . Finally , our data demonstrate that the UPR pathway functions in the intestine , the major site of pathogen exposure .
To explore whether miRNAs are involved in innate immunity in C . elegans , we determined the miRNA expression profiles in worms exposed to P . aeruginosa PA14 using small RNA deep sequencing . We found that 40 miRNAs at 4 h , 68 miRNAs at 8 h , and 64 miRNAs at 12 h post-infection were up-regulated , respectively ( S1 Table ) . We hypothesized that some of the miRNAs up-regulated in response to bacterial infection play a role in C . elegans innate immunity . Thus , we focused on the 88 miRNAs and miRNA families that were up-regulated after PA14 infection . To identify individual miRNAs that play prominent roles in innate immunity , we tested 47 available mutant strains of these 88 miRNAs . Whereas mutations in most of the tested miRNAs did not influence the immune phenotype , mir-232 ( ndf56 ) ;F13H10 . 5 and mir-233 ( n4761 ) mutants exhibited enhanced susceptibility to the killing by PA14 ( Fig . 1A and S2 Table ) . Using quantitative RT-PCR ( qRT-PCR ) , we confirmed that the expression of mir-232 was markedly elevated in worms at 4 h , 8 h , and 12 h after exposure to P . aeruginosa PA14 , compared with worms grown in the standard laboratory food Escherichia coli OP50 ( Fig . 1B ) . Meanwhile , up-regulation of mir-233 was observed in worms at 4 h post-infection . Furthermore , using transgenic animals that express mir-232p::gfp or mir-233p::gfp , we observed that PA14 infection significantly increased expression of mir-232p::gfp ( Fig . 1C ) and mir-233p::gfp ( Fig . 1D ) . The mir-233 ( n4761 ) mutant is a deletion that removed not only mir-233 , but also the gene W03G11 . 4 . However , knockdown of W03G11 . 4 by RNAi had no impact on survival of WT worms after P . aeruginosa PA14 infection ( S1A–S1C Fig . ) . Thus , the immune-deficient phenotype of the mir-233 ( n4761 ) mutant was not due to the removal of W03G11 . 4 . Likewise , the mir-232 ( ndf56 ) ;F13H10 . 5 mutant animals also have two mutations . We found that knockdown of F13H10 . 5 by RNAi led to enhanced resistance to PA14 infection ( S1D Fig . ) . These results suggest that the mutations in mir-232 ( ndf56 ) and F13H10 . 5 display a mixed effect on innate immunity . Thus , we focused on the role for mir-233 in innate immunity . In addition to its defect in a response to P . aeruginosa PA14 infection , the mir-233 ( n4761 ) worms were more sensitive to killings by the Gram-positive bacteria S . aureus ATCC 25923 , Enterococcus faecalis ATCC 29212 , and the Gram-negative bacterium Salmonella typhimurium 468 than WT animals ( S2A–S2C Fig . ) . Furthermore , we found that the mir-233 ( n4761 ) mutant exhibited a comparable lifespan to WT worms ( S3 Fig . ) , suggesting that the effect of mir-233 on the defense against P . aeruginosa is not due to changes in aging . Animal miRNAs usually bind to their target mRNAs , thus resulting in down-regulation of protein translation . It is reasoned that in response to bacterial infection , mir-233 functions to silence genes that have a negative impact on C . elegans innate immunity . Three different miRNA target prediction algorithms ( MICRORNA , TargetScan , and DIANA-microT ) suggested 38 putative target genes of mir-233 ( S4 Fig . ) . To confirm the target genes of mir-233 that are involved in innate immunity , we used isobaric tags for relative and absolute quantitation ( iTRAQ ) -based quantitative proteomic approach to analyze the early changes in protein levels in worms exposed to P . aeruginosa PA14 . As a commonly used quantitative proteomics method , iTRAQ allows for simultaneous comparative quantification of multiple samples within a single run , leading to a reduction of the experimental errors produced from individual experiments [19] . We found that the expression of 51 , 45 , and 85 proteins were up-regulated , while 19 , 115 , and 133 proteins were down-regulated at 4 h , 8 h , and 12 h post-infection , respectively ( Fig . 2A and S3 , S4 , S5 Tables ) . However , when comparing our protein data set to two microarray data sets from the studies published by Shapira et al [20] and Troemel et al [4] , we found very little overlap between our protein data set and the two microarray data sets . These results suggest that these gene expression changes are not reflected in our proteomic analysis . Many factors such as posttranscriptional regulation , variable rates of protein turnover , and post-translational modification , may account for the observed discordance between transcript and protein levels [21] . We found only one gene ( sca-1 ) overlapped between the down-regulated-protein encoding genes and the target genes of mir-233 ( Fig . 2B ) . The protein levels of SCA-1 were markedly reduced at 8 h post-infection ( Fig . 2A and S4 Table ) . miRNA target prediction algorithms revealed that the sca-1 gene contained one putative binding site for mir-233 in the 3′UTR region ( Fig . 3A ) . SCA-1 is the C . elegans homologue of the mammalian SERCAs , which shows about 70% amino acid sequence identity and 80% similarity to the three human SERCA proteins [18] . Using Western blotting , we found that the protein levels of SCA-1 were decreased in WT worms at 8 h after P . aeruginosa PA14 infection ( Fig . 3B ) . However , the protein levels of SCA-1 in the mir-233 ( n4761 ) mutant were significantly higher than those in WT worms after P . aeruginosa PA14 infection ( Fig . 3B ) . In contrast , the mRNA levels of sca-1 in WT and the mir-233 ( n4761 ) animals were similar after PA14 infection ( Fig . 3C ) , indicating that transcriptional regulation is unlikely to be involved in the reduced SCA-1 protein levels . To address whether mir-233 regulated the protein levels of SCA-1 through 3′UTR , we constructed a 4×NLS::GFP vector driven by the rpl-28 promoter , which contained the 3′UTR of sca-1 ( Prpl-28::gfp:sca-1 3′UTR ) [22] . Meanwhile , a sca-1-3′UTR mutant reporter construct was generated by replacing the putative mir-233 binding site with an oligonucleotide containing the exact identical sequence of mir-233 . Specially , an Prpl-28::histone-24::mCherry:let-858 3′UTR construct that drives constitutive and mCherry expression was used as an internal control . We found that the GFP expression was markedly reduced in WT worms at 8 h after P . aeruginosa PA14 infection ( Fig . 3D ) . However , mutagenesis of the putative binding site for mir-233 in the sca-1-3′UTR abolished the inhibition of the GFP expression in WT worms . Furthermore , we found that the GFP expression was much higher in the mir-233 ( n4761 ) mutant than that in WT worms after PA14 infection ( Fig . 3D ) . These results suggest that mir-233 suppresses the protein levels of SCA-1 through binding to its 3′UTR and inhibiting its translation after PA14 infection . A mutation in sca-1 ( mca-4 ) has shown to result in embryonic and larval lethality [18] , [23] . To test the role of sca-1 in pathogen susceptibility , we reduced the expression of the gene by RNAi . However , as demonstrated previously , the majority of sca-1 ( RNAi ) worms died or were arrested at the L1 stage [18] , [23] , [24] . To overcome this problem , we diluted the sca-1 RNAi bacteria with an empty vector control at a ratio of 1 to 4 . This dilution modified the severity of the phenotype enough to enable development . qRT-PCR analysis demonstrated that knockdown of sca-1 by RNAi in 1/4 dilution reduced approximately 50% of sca-1 mRNA levels ( S5 Fig . ) . The 1/4 sca-1 RNAi exhibited enhanced resistance to P . aeruginosa PA14 infection in WT worms , compared to those subjected to the vector control RNAi ( Fig . 4 ) . Next , we determined whether up-regulated expression of SCA-1 protein contributed to the immune-deficient phenotype of the mir-233 ( n4761 ) mutant . The mir-233 ( n4761 ) mutant subjected to sca-1 RNAi showed markedly increased survival relative to the mir-233 ( n4761 ) mutant exposed to the empty vector , to a degree that was comparable to the survival of WT worms subjected to sca-1 RNAi ( Fig . 4 ) . These results suggest that mir-233 is involved in innate immunity by suppressing SCA-1 expression . A previous study has demonstrated that P . aeruginosa PA14 infection activates the UPR [7] . The UPR is comprised of three branches , the ribonuclease inositol-requiring protein–1 ( IRE-1 ) , the PERK kinase homologue PEK-1 , and the transcription factor ATF6 [5] , [6] . In C . elegans , the IRE-1-XBP-1 pathway is required for resistance to P . aeruginosa infection or to the treatment of pore-forming toxins [7] , [8] . IRE-1 splices an intron from XBP1 mRNA , producing the activated ‘spliced form’ of XBP-1 ( XBP-1s ) [5] , [6] . As sca-1 RNAi leads to the UPR [25] , we tested the role of mir-233 in the UPR . First , the levels of xbp-1s transcript were increased approximately 4-fold in WT worms , but not in the mir-233 ( n4761 ) mutant , at 8 h after P . aeruginosa PA14 infection ( Fig . 5A ) . However , sca-1 RNAi restored the expression of xbp-1s in the mir-233 ( n4761 ) mutant exposed to PA14 . The induction of BiP/GRP78 , a molecular chaperone , reflects activation of the IRE-1-XBP-1 branch of the UPR [5] , [6] . In C . elegans , hsp-4 gene encodes a homologue of mammalian BiP/GRP78 , which is a target of XBP-1 . Second , we detected the UPR activation using transgenic worms carrying Phsp-4::gfp [26] , and found that P . aeruginosa PA14 infection induced the expression of Phsp-4::gfp in WT worms , but not in the mir-233 ( n4761 ) mutant ( Fig . 5B ) . Furthermore , the expression of Phsp-4::gfp in the mir-233 ( n4761 ) mutant was significantly restored by sca-1 RNAi after PA14 infection ( Fig . 5C ) . Similar results were obtained using qRT-PCR analysis for the mRNA levels of hsp-4 ( Fig . 5D and 5E ) . Taken together , these results indicate that mir-233 is critically involved in silencing the sca-1 transcript to activate the IRE-1/XBP-1 pathway . A previous study has suggested that the effect of the IRE-1/XBP-1 signaling on worm survival upon P . aeruginosa infection is due to increased tolerance and not increased resistance [7] . Here we tested P . aeruginosa accumulation in the intestine of the mir-233 ( n4761 ) mutant or worms subjected to sca-1 RNAi . We found that , like a mutation in xbp-1 ( zc12 ) , the mutation in mir-233 or knockdown of sca-1 had no impact on the colony forming units ( CFU ) of PA14 in worms ( S6A Fig . ) . Moreover , the accumulation of P . aeruginosa expressing GFP in the mir-233 ( n4761 ) or sca-1 ( RNAi ) worms was comparable to that observed for WT worms with empty vector ( S6B Fig . ) . In contrast , a mutation in pmk-1 ( km25 ) , a conserved p38 MAPK pathway that plays a crucial role in innate immunity , resulted in a significant increase in accumulation of P . aeruginosa [7] ( S6A–S6B Fig . ) . These results implicate that innate immune pathways may play distinct roles in the pathogenesis of infectious diseases . A previous study has shown that activation of the UPR in response to P . aeruginosa infection or to the pore-forming toxins produced by many human pathogens is mediated by the p38 MAPK orthologue PMK-1 [3] , [8] . These results raised the possibility that PMK-1 could regulate the UPR through the mir-233/SCA-1 pathway . To test this hypothesis , we first examined the effect of PMK-1 on mir-233 expression . qRT-PCR analysis indicated that a mutation in pmk-1 ( km25 ) led to a significant decrease in both the levels of mir-233 and the expression of mir-233p::gfp at 4 h after PA14 infection ( Fig . 6A and 6B ) . Second , the protein levels of SCA-1 in the pmk-1 ( km25 ) mutant were markedly higher than those in WT worms during PA14 infection ( Fig . 6C ) . Finally , the expressions of xbp-1s and hsp-4 were reduced in the pmk-1 ( km25 ) mutant , compared with WT worms after PA14 infection ( Fig . 6D and 6E ) , consistent with the observation reported by Richardson et al [7] . However , sca-1 RNAi restored the expressions of xbp-1s and hsp-4 in the pmk-1 ( km25 ) mutant to the same levels as WT worms ( Fig . 6D and 6E ) . These results suggest that activation of the IRE-1/XBP-1 signaling by PMK-1 is through the mir-233/SCA-1 pathway . It has been shown that the transcription factor ATF-7 functions as a regulator of PMK-1-mediated innate immunity via repression of immune gene expression [27] , raising a possibility that the PMK-1-induced expression of mir-233 is mediated by ATF-7 . To test this hypothesis , we tested the role of ATF-7 on mir-233 expression . However , we found that the expression of mir-233 in the atf-7 ( gk715 ) mutant was comparable to that in WT worms after PA14 infection ( S7A Fig . ) . Meanwhile , atf-7 RNAi did not influence the expression of mir-233p::gfp in either WT or pmk-1 ( km25 ) worms after PA14 infection ( S7B Fig . ) . Thus , ATF-7 is not involved in the induction of mir-233 mediated by PMK-1 . The IRE-1-XBP-1 signaling has shown to be required for resistance to P . aeruginosa infection during larval development [7] . We found that , like WT worms , all the sca-1 ( RNAi ) worm eggs grown on plates with P . aeruginosa PA14 as the only food source could develop to the fourth larval stage ( L4 ) , comparable to their development on E . coli OP50 ( S8 Fig . ) . In contrast , unlike WT or sca-1 ( RNAi ) worms , the mir-233 ( n4761 ) mutant on P . aeruginosa PA14 plates exhibited severely attenuated larval development , and approximately 60% larva could not reach the L4 stage by 72 h ( S8 Fig . ) . Next , we asked if XBP-1 was required for resistance to P . aeruginosa PA14 in adult worms . We found that the xbp-1 ( zc12 ) mutant exhibited enhanced sensitivity of young adult worms to the killing by P . aeruginosa PA14 ( Fig . 7A ) . It should be noted that the lifespan of the xbp-1 ( zc12 ) mutant was similar to that of WT worms ( S9 Fig . ) , suggesting that the impact of XBP-1 on the defense against P . aeruginosa is not due to changes in aging . It has been reported that PEK-1 , a branch of the UPR that is distinct from the IRE-1-XBP-1 pathway , is important for the worm resistance to P . aeruginosa infection under lower temperatures [28] . However , we found that the survival of the pek-1 ( ok275 ) mutant was comparable to that of WT animals after PA14 infection at standard temperature ( 25°C ) ( S10 Fig . ) . The survival of the xbp-1 ( zc12 ) ;mir-233 ( n4761 ) double mutants was comparable to that of the xbp-1 ( zc12 ) mutant worms after PA14 infection ( Fig . 7A ) . Although sca-1 ( RNAi ) worms were more resistant to PA14 infection than WT worms , the survival of xbp-1 ( zc12 ) ; sca-1 RNAi was comparable to that of the xbp-1 ( zc12 ) mutant after P . aeruginosa PA14 infection , suggesting that the immune-resistant phenotype of sca-1 ( RNAi ) worms is XBP-1-dependent ( Fig . 7B ) . Unlike the mir-233 ( n4761 ) mutant , the immune-deficient phenotype of the pmk-1 ( km25 ) mutant was only partially rescued by knockdown of sca-1 ( Fig . 7C ) . These results implicate that the UPR signaling is one of the downstream effectors in PMK-1-mediated innate immunity . Indeed , the survival of the xbp-1;pmk-1 double mutants was comparable to that of the pmk-1 ( km25 ) mutant ( S11 Fig . ) . We noticed that mir-233 was highly expressed in the intestine of C . elegans ( S12 Fig . ) . We found that intestinal-specific sca-1 RNAi resulted in enhanced resistance to PA14 infection ( Fig . 7D ) , whereas epidermal- and muscular-specific sca-1 RNAi did not affect the survival of worms ( S13A–S13B Fig . ) . Likewise , intestinal-specific RNAi of xbp-1 enhanced sensitivity to PA14 infection ( Fig . 7D ) , whereas epidermal- and muscular specific RNAi of xbp-1 failed to affect the survival of worms ( S14A–S14B Fig . ) . These data suggest that the UPR in the intestine probably is required for innate immunity .
The accumulation of unfolded proteins beyond the levels that ER can cope with leads to ER stress , which in turn activates ER stress signaling , the UPR [6] . Many of the molecular chaperones within the ER , such as GRP78 , calnexin and calreticulin , are Ca2+ binding proteins [29] . Thus , the luminal Ca2+ is crucial for proper folding and maturation of proteins in the ER [30] . As a Ca2+-ATPase , the SERCA pump is responsible for transferring Ca2+ from the cytosol to the ER lumen via ATP hydrolysis [31] . In mammalian cells , inhibition of SERCA activity or down-regulation of SERCA expression is among the main mechanisms that evoke the UPR under a variety of pathophysiological conditions [30] , [32] . For example , the oxidized LDL can induce oxidative stress in endothelial cells , which in turn inhibits SERCA by enhancing oxidation of the Cys674 residue of these ATPases [32] . Disturbed ER calcium stores leads to the UPR . In C . elegans , the UPR is induced in response to P . aeruginosa infection or to pore-forming toxins produced by a variety of human pathogens , suggesting that the innate immune responses represent a physiologically relevant inducer of the UPR [7] , [8] . Our study reveals two novel regulatory components of the UPR: mir-233 and SCA-1 . During P . aeruginosa infection , mir-233 negatively regulates the protein expression of SCA-1 through direct base pairing to the 3′ UTR of its mRNA . Suppression of SCA-1 , in turn , promotes the activation of the UPR , which confers resistance to the pathogen . In the current study , mir-233 was identified by screening 47 up-regulated miRNAs for susceptibility to P . aeruginosa infection . The mir-233 ( n4761 ) mutant animals were found to be susceptible to several other bacterial pathogens . These results suggest that mir-233 is required for immune responses to pathogenic bacteria in general . As demonstrated previously [33] , [34] , the mir-233 ( n4761 ) mutant animals showed normal phenotypes including locomotion , egg laying , pumping , and defecation . In addition , we found that a mutation in mir-233 ( n4761 ) did not influence the lifespan of worms . Thus , the pathogen killing assays reflect the effect of mir-233 on host defense against attacking pathogens , not mir-233-dependent changes in aging . Previously , Horvitz and colleagues have revealed that a single mutation of the majority of miRNAs does not lead to obvious developmental or growth defects of worms [33] , [34] . These observations lead them to hypothesize that there is significant functional redundancy among miRNAs . Our results reinforce the idea that most miRNAs act redundantly with other miRNAs or other pathways . Although 47 miRNA genes were up-regulated after P . aeruginosa PA14 infection , single mutations in most of these miRNAs did not influence innate immune responses to PA14 infection . However , we could not exclude the role of these miRNAs in innate immune responses to other microbes . It is possible that innate immunity is regulated by multiple miRNAs and a large number of target genes , which together consist of the miRNA-target network . Thus , a single mutation in these miRNAs does not de-repress the expression of immune-related genes as the other miRNAs that might regulate in parallel the target genes . Our results demonstrate that mir-233 down-regulates the protein levels of its target gene SCA-1 during P . aeruginosa PA14 infection . SCA-1 is identified by iTRAQ analysis and miRNA target prediction algorithms . It should be noted that there was little overlap between the expressions of proteins at each of the time-points . The data from the proteomic analysis probably varied greatly , contributing to the statistically no significant difference between the expressions of proteins at each of the time-points . SCA-1 is the only C . elegans homolog of mammalian SERCA [18] , which is required for the maintenance of intracellular Ca2+ homeostasis . In C . elegans , down-regulation of sca-1 by RNAi leads to Ca2+ depletion , thus inducing activation of the UPR [25] . Thus , these results imply a role for mir-233 in regulating the UPR during the immune response . Indeed , an increase in expression of xbp-1s and Phsp-4::gfp in response to PA14 infection are only observed in WT worms , but not in the mir-233 ( n4761 ) mutant . It has been shown that innate immunity constitutes a physiologically relevant source of ER stress in C . elegans [7] , [8] . Although the molecular mechanism by which innate immunity-induced UPR is not clear , PMK-1 , which plays a principal role in C . elegans defense against pathogens , is involved in the activation of the UPR . Our data demonstrate that PMK-1 mediates up-regulation of mir-233 expression after P . aeruginosa PA14 infection . In contrast , a mutation in pmk-1 leads to an increase in the protein expression of SCA-1 . Meanwhile , knockdown of sca-1 by RNAi restores the induction of xbp-1s in the pmk-1 ( km25 ) mutant . Thus , PMK-1 promotes the activation of the UPR by regulating the mir-233/SCA-1 signaling during the immune response . Of the three canonical braches of the UPR , the IRE-1-XBP-1 signaling is involved in resistance to P . aeruginosa and to pore-forming toxins in C . elegans [7] , [8] . Our results demonstrate that knockdown of sca-1 by RNAi enhances immune resistance to P . aeruginosa infection in adult WT worms , but not in the xbp-1 ( zc12 ) mutant . Moreover , intestinal-specific knockdown of xbp-1 leads to enhanced susceptibility to PA14 infection . These results indicate that mir-233-mediated activation of the UPR in the intestine , which is in direct contact with pathogens , confers defense against P . aeruginosa infection . The protective role of the UPR in innate immunity is not fully understood . During larval development , P . aeruginosa infection induces the PMK-1-dependent innate immune response , which in turn causes a disruption of ER homeostasis in the absence of the functional IRE-1–XBP-1 pathway [7] . Thus , Richardson et al [7] suggest that the principal mechanism by which XBP-1 promotes survival of worms during P . aeruginosa infection is to alleviate the detrimental effect induced by the immune response . These authors demonstrate that the UPR is also induced in response to collateral damage caused by activation of the p38 pathway , which is not associated with bacterial infection . These results , however , raises the possibility that the UPR regulated by mir-233 contributes to a more general stress resistance , but not innate immunity per se . Indeed , the UPR , which is activated by hypoxia , is required for protecting worms against hypoxic injury [35] , [36] . As organisms are faced with a variety of stresses that results in protein misfolding and aggregation , the maintenance of the ER proteome may be a common mechanism to coordinate stress resistance . In summary , we report that mir-233 is up-regulated by PMK-1 during P . aeruginosa PA14 infection . mir-233 down-regulates the protein expression of its target SCA-1 , resulting in activation of the UPR . The UPR in turn confers C . elegans defense against P . aeruginosa PA14 infection . Thus , mir-233 is a key miRNA that modulates the UPR during the immune response .
Mutated and transgenic strains used in this study include: xbp-1 ( zc12 ) , pmk-1 ( km25 ) , ZcIs[hsp-4::gfp] , wwEx33[mir-232p::gfp+unc-119 ( + ) ] , NR222 ( rde-1 ( ne219 ) ; kzIs9[pKK1260 ( plin-12::nls::gfp ) , pKK1253 ( plin-26::rde-1 ) , rol-6] ) ; NR350 ( rde-1 ( ne219 ) ; kzIs20[pDM#715 ( phlh-1::rde-1 ) , pek-1 ( ok275 ) , and atf-7 ( gk715 ) were kindly provided by the Caenorhabditis Genetics Center ( CGC ) . The strain for intestinal-specific RNAi ( sid-1 ( qt9 ) ; Is[vha-6pr::sid-1]; Is[sur-5pr:: gfp::nls] ) were kindly provided by Dr . Gary Ruvkun ( Massachusetts General Hospital , Harvard Medical School ) . miRNA mutants ( from CGC ) used in this study were listed in S6 Table . Mutants and transgenic strains were backcrossed three times into the N2 strain used in the laboratory . All strains were maintained on nematode growth media ( NGM ) and fed with E . coli OP50 . Standard conditions were used for C . elegans growth at 20°C [37] . Synchronized populations of worms were cultivated at 20°C until the young adult stage . For all pathogen assays , 75 µg/ml of fivefluoro-2′-deoxyuridine ( FUdR ) was added to the assay plates to abolish the growth of progeny . P . aeruginosa PA14 ( a gift from Dr . K Zhu , Institute of Microbiology , CAS ) or E . faecalis ATCC 29212 , or S . typhimurium 468 , or S . aureus ATCC 25923 ( gifts from Dr . WH Lee , Kunming Institute of Zoology , CAS ) was grown in different medium [4] , [38] , [39] , then seeded on slow-killing plates , which contained modified NGM ( 0 . 35% instead of 0 . 25% peptone ) as described [4] , [39] . Three plates of about 50–60 animals per plate were tested per assay and all experiments were performed three times independently at 25°C . After infected with P . aeruginosa PA14 infection for 4 h , 8 h , and 12 h , worms were washed with M9 buffer for several times in order to remove bacteria . Then , worms were collected and RNAs were extracted using the mirVana miRNA Isolation Kit ( Ambion ) , according to the manufacturer's instructions . Approximately 20 µg of RNAs per sample were submitted to Beijing Genomics Institute ( BGI ) -Shenzhen ( Shenzhen , China ) for sequencing . In brief , the sequencing was performed as follows: RNAs corresponding to 15–30 nt in size were purified by polyacrylamide gel electrophoresis ( PAGE ) , and ligated with adapters to the 5′ and 3′ termini of the RNA . Then the RNAs were amplified by RT-PCR . cDNA libraries were sequenced using an Illumina Genome Analyzer . Illumina data can be found in the Gene Expression Omnibus ( GEO ) of NCBI under the accession number GSE17095742 . The clones of genes for RNAi were from the Ahringer library [40] . RNAi feeding experiments were performed on synchronized L1 larvae at 20°C for 40 h . The development assays were performed as described previously [7] . Strains ( mir-233 ( n4761 ) mutant , sca-1 ( RNAi ) , and mir-233 ( n4761 ) ; sca-1 ( RNAi ) worms ) were egg laid on plates of PA14 ( at least 80 eggs for each strain ) , and the fraction of worms growing to at least the L4 larval stage between the plates was averaged . Development was monitored daily for three days for experiments conducted at 25°C . To generate a clone directed against W03G11 . 4 , a 1420 bp fragment was amplified from genomic DNA by PCR using the primers 5′-GAC ATT ATG GTT GCT TCG-3′ ( F ) and 5′-GAG ATG CTG AGG TGA GAG-3′ ( R ) . The fragment was TA-cloned into a PstI and KpnI-linearized L4440 feeding vector , as in the RNAi library [41] . The vector mir-233p::gfp was generated by subcloning a 2 . 5 kb promoter fragment of mir-233 into an expression vector ( pPD95 . 75 ) . The construct was co-injected with the marker plasmid pRF4 containing rol-6 ( su1006 ) into gonads of WT worms by standard techniques [42] , respectively . The transgenic worms were confirmed prior to assay . The expression of GFP was observed under a Zeiss Axioskop 2 plus fluorescence microscope ( Carl Zeiss , Jena , Germany ) . Three plates of about 30 animals per plate were tested per assay and all experiments were performed three times independently . After worms were homogenized in liquid nitrogen , the homogenate was lysed on ice for 30 min in lysis buffer ( BioTeKe , Beijing , China ) . The proteins of lysates ( 50 µg per well ) were separated on a 7% SDS-polyacrylamide gel . Proteins were then transferred to immobilon-PSQ transfer PVDF membrane ( Millipore , Bedford , MA ) . Primary antibodies were anti-ATP2A2/SERCA2 antibodies ( 1∶1000 dilution; Cell Signaling , Beverly , MA ) , and anti-actin antibodies ( 1∶1000 dilution; Santa Cruz Biotech . , Santa Cruz , CA ) . The secondary antibody was a peroxidase-coupled anti-rabbit IgG ( 1∶8000 dilution; Santa Cruz Biotech . ) . The membrane was exposed to Kodak X-OMAT film ( Kodak , Xiamen , China ) , and the film was developed . The ppD129 . 57 plasmid ( a gift from Dr . Min Han , University of Colorado , Boulder , USA ) , which contains rpl-28 promoter:4NLS::gfp:let-858 3′UTR , was used as the vector for 3′UTR reporter constructs . The 3′UTRs of sca-1 were PCR amplified from genomic DNA and cloned into ppD129 . 57 to replace the let-858 3′UTR and make a sca-1-3′UTR ( wt ) reporter construct . A sca-1-3′UTR ( mut ) reporter construct was generated by replacing the putative mir-233 binding site with an oligonucleotide containing the exact complementary sequence of mir-233 . The 3′UTR reporter constructs and the mCherry internal control plasmid ( also a gift from Dr . M . Han ) were coinjected into the gonad of WT and mir-233 ( n4761 ) worms following standard protocols . The transgenic worms were confirmed prior to assay . The expression of GFP and mCherry was monitored using a Zeiss Axioskop 2 plus fluorescence microscope . ImageJ program ( NIH ) was used to quantify the fluorescence intensity of GFP or mCherry fluorescence . Equal regions of the worm were selected , and the intensities of fluorescence within the selected regions were measured with a standard size of 516×564 pixels . The mean pixelintensity ( total pixel intensity/area ) for each frame was then calculated . We expressed the fluorescence signal ( F ) as a ratio with the baseline fluorescence ( F0 ) . More than 30 worms per plate were observed to calculate the mean fluorescence intensity . Three plates were tested per assay and all experiments were performed three times independently . Total RNA was isolated from worms with the mirVana miRNA Isolation Kit ( Ambion ) . Random-primed cDNAs were generated by reverse transcription of the total RNA samples with SuperScript II ( Invitrogen ) . A real time-PCR analysis was conducted using SYBR Premix-Ex TagTM ( Takara , Dalian , China ) on an Applied Biosystems Prism 7000 Sequence Detection System ( Applied Biosystems , Foster City , CA ) . act-1 was used for an internal control . The primers used for PCR were as follows: hsp-4: 5′- TCA ATG ACG ACG ACA CGC -3′ ( F ) , 5′- CTC CAG AAC TTC GAG ACG G -3′ ( R ) ; xbp-1 splicing: 5′-ACC GTC TGC TCC TTC CTC AATG-3′ ( F ) , 5′- ACC GTC TGC TCC TTC CTC AAT G-3′ ( R ) ; act-1: 5′-GGG CGA AGA AGG AAA TGG TC-3′ ( F ) , 5′- CAG GTG GCG TAG GTG GAG AA -3′ ( R ) . After infected with P . aeruginosa PA14 infection for 4 h , 8 h , and 12 h , worms were washed with M9 buffer for several times . After worms were homogenized in liquid nitrogen , the homogenate was lysed on ice for 30 minutes in lysis buffer ( BioTeKe , Beijing , China ) . Following centrifugation at 10 , 000× g for 10 min , the supernatant was collected . Approximately 100 µg of total proteins per sample were submitted to Beijing Genomics Institute ( BGI ) -Shenzhen for proteomic analysis . Briefly , after trypsin digestion of the samples , the peptides were labeled with 4-Plex iTRAQ reagents ( Applied Biosystems ) . Then the mixtures of iTRAQ-labeled peptides were fractionated into 10 portions using SCX chromatography ( Shimadzu , Japan ) , and were subjected to nanoelectrospray ionization followed by tandem mass spectrometry ( MS/MS ) using the TripleTOF 5600 System ( AB SCIEX , USA ) . Candidate proteins were quantified using ProteinPilot Software 4 . 0 . 8085 ( Applied Biosystems-MDS SCIEX Ins ) . We set a 1 . 5-fold change as the threshold and a two-tailed P-value <0 . 05 to identify significant changes . Lifespan assays were conducted on NGM agar plates of E . coli OP50 at 20°C , starting with day 1 adults [43] . Animals were transferred to new plates during each day of their reproductive period and after that were transferred every third day . Survival of animals was monitored every day . Worms that did not move when gently prodded and displayed no pharyngeal pumping were marked as dead . Approximately 100 animals were tested on each plate with three replicates , and three independent assays were performed for each result . These results are mean ± SD of three independent experiments performed in triplicate . Differences in survival rates were analyzed using the log-rank test . The statistical significance of differences in gene expression and fluorescence intensity was assessed by performing a one-way ANOVA followed by a Student-Newman-Keuls test . Data were analyzed using SPSS11 . 0 software ( SPSS Inc . ) .
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In the model organism Caenorhabditis elegans , the IRE1–XBP1 pathway , a major branch of the unfolded protein response ( UPR ) , is required for host defense against pathogens . However , how innate immune responses activate the UPR is not fully understood . In this report , we find that Pseudomonas aeruginosa PA14 infection up-regulates the expression of the microRNA mir-233 in C . elegans . The response of mir-233 to P . aeruginosa PA14 infection is dependent on a major pathway of innate immunity , the p38 MAPK signaling cascade . The up-regulation of mir-233 is functionally important since a mutation in mir-233 leads to hypersensitivity of the nematode to the killing by P . aeruginosa PA14 . Furthermore , we demonstrate that mir-233 contributes to the activation of the UPR by repressing the protein levels of its target SCA-1 , a C . elegans homologue of the sarco/endoplasmic reticulum Ca2+-ATPase . Thus , mir-233 is an important regulator of the UPR during the innate immune response .
|
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"Abstract",
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2015
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mir-233 Modulates the Unfolded Protein Response in C. elegans during Pseudomonas aeruginosa Infection
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The parasite Onchocerca volvulus has , until recently , been regarded as the cause of a chronic yet non-fatal condition . Recent analyses , however , have indicated that in addition to blindness , the parasite can also be directly associated with human mortality . Such analyses also suggested that the relationship between microfilarial load and excess mortality might be non-linear . Determining the functional form of such relationship would contribute to quantify the population impact of mass microfilaricidal treatment . Data from the Onchocerciasis Control Programme in West Africa ( OCP ) collected from 1974 through 2001 were used to determine functional relationships between microfilarial load and excess mortality of the human host . The goodness-of-fit of three candidate functional forms ( a ( log- ) linear model and two saturating functions ) were explored and a saturating ( log- ) sigmoid function was deemed to be statistically the best fit . The excess mortality associated with microfilarial load was also found to be greater in younger hosts . The attributable mortality risk due to onchocerciasis was estimated to be 5 . 9% . Incorporation of this non-linear functional relationship between microfilarial load and excess mortality into mathematical models for the transmission and control of onchocerciasis will have important implications for our understanding of the population biology of O . volvulus , its impact on human populations , the global burden of disease due to onchocerciasis , and the projected benefits of control programmes in both human and economic terms .
Human onchocerciasis , also known as ‘river blindness’ , is the parasitic infection caused by the filarial nematode Onchocerca volvulus . This neglected tropical disease [1] is the second most common cause of infectious blindness worldwide , after trachoma [2] , [3] . The parasite is transmitted solely by black fly ( Simulium ) vectors , which breed in fast flowing rivers . Irreversible unilateral or bilateral blindness is the worst disease sequela of a chronic , cumulative process that was deemed to result from repeated host's inflammatory reactions against degenerating microfilariae in the cornea and triggered by filarial products [4] , [5] . More recently , however , it has been proposed that this process is largely due to the endosymbiotic Wolbachia bacteria released by dying microfilariae in the anterior chamber of the eye . These bacteria can elicit much of the inflammatory host response that culminates in the lesions characteristic of ocular and dermal onchocercal disease [6] by stimulating the recruitment of neutrophils , the production of chemokines and cytokines , and the release of cytotoxic mediators by neutrophils , which lead to increased corneal opacity , and a range of skin complaints [6] , [7] , [8] . By contrast , the pathogenesis of retinal lesions may partly arise from autoimmune processes related to cross-reactivity between the O . volvulus antigen Ov39 and the human retinal antigen hr44 [9] . Blindness incidence has recently been shown to be associated with past microfilarial load in individuals followed up within the Onchocerciasis Control Programme in West Africa ( OCP ) cohort [10] , confirming the progressive worsening of onchocercal eye disease with parasite exposure . Although excess human mortality due to onchocercal blindness has been well documented [11] , [12] , [13] , conclusive demonstration of a more direct relationship between parasite load and increased death rate of the human host has proved elusive [14] . New analyses , however , have indicated that , in addition to causing blindness , the parasite can also be directly associated with human mortality [15] . It is possible that this could arise as a result of the parasite exerting immunosuppressive effects on the host both to autologous [16] , [17] and heterologous antigens [18] , [19] ( thus rendering hosts more susceptible to other infections ) , as well as from systemic effects such as those culminating in epilepsy [20] , [21] , [22] , [23] , and growth retardation syndromes [24] , [25] , among others . Poor nutritional status as a result of loss of visual acuity and blindness has also been implicated in excess mortality [13] , [14] . The OCP was launched in West Africa in 1974–1975 . Control initially took the form of weekly larviciding of Simulium damnosum sensu lato breeding sites ( vector control ) and surveillance activities in Benin , Burkina Faso , Côte d'Ivoire , Ghana , Mali , Niger and Togo . The programme started at slightly different times in different areas [26] . In 1986 the OCP expanded to include Guinea , Guinea-Bissau , Senegal and Sierra Leone with the aim of protecting the original area from invasion by infected savannah black flies migrating from western and southern locations not covered by the programme . The OCP began mass treatment with the microfilaricidal drug ivermectin ( Mectizan® ) in selected areas ( as a sole measure or in combination with vector control ) in 1988 , as the main bulk of insecticidal operations in the OCP core area were scaled down 14 years after their commencement [27] , [28] . Epidemiological surveillance in the OCP area comprised surveys of vital and clinical status undertaken in sentinel villages , together with an assessment of microfilarial load via skin snips . In many such villages there were repeated surveys . Since the start of the programme until its closure in December 2002 , more than 2 , 000 villages were surveyed in the 11 West African countries finally included in the OCP [15] . In this article we examine the functional relationship between human mortality rate and skin microfilarial density , using the whole of the OCP dataset ( spanning from 1974 to 2001 , the latter being the year final epidemiological surveys were conducted ) . Only recently , and using the full OCP dataset , was an association demonstrated between microfilarial load and excess mortality ( an increased mortality rate incurred by individuals infected with O . volvulus microfilariae compared to uninfected individuals ) for both sexes [15] . A positive association between microfilarial load and mortality of the human host had been demonstrated prior to this for males ( with more than 100 microfilariae per skin snip ) after adjusting for visual acuity but not for females [14] . Crucial to the estimation of the effect of infection is the shape of the relationship between microfilarial load and excess mortality . It is well recognized that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates [29] . Regression calibration is an approximate method of adjusting for such errors in non-linear relationships which gives reasonably adjusted point estimates of model parameters [30] . This method has been much applied in ionizing radiation epidemiology , as for example in analyses of the Japanese A-bomb survivor data and other radiation-exposed groups [31] , [32] , [33] , [34] . Knowledge of the precise shape of the relationship between parasite load and excess human mortality will be important for its incorporation into mathematical models of transmission dynamics and control [35] , [36] . Not only will this have implications for our understanding of the mechanisms regulating parasite abundance in human populations , and our ability to quantify the population impact of mass microfilaricidal treatment , but also for informing policy , as the benefits of onchocerciasis control programmes may be measured not only in terms of blindness cases averted , but also in terms of number of deaths prevented [37] . This will be crucial for the ongoing reassessment of the global burden of disease due to neglected tropical diseases in general [38] , [39] and onchocerciasis in particular .
The methods used in the epidemiological surveys of the OCP have been previously described [14] , [40] . At each survey a complete census of the village was conducted , and approximately 84% of persons enumerated in the census were examined [41] . The countries participating in the OCP signed a memorandum of agreement that covered all issues pertaining to the operations and covered clearance for epidemiological , parasitological , and ophthalmological surveys , etc . Our study satisfied the requirements for ethical clearance within the memorandum . Additionally , a committee consisting of the Chief of Units of OCP ensured that the plans and the methodology of work were correctly followed by the technicians in the field . Communities were free to participate in the taking of skin snip samples . Individuals were only included in the analysis cohort if they satisfied a number of consistency checks ( such as consistency of ages between surveys; known sex; consistency of blindness and vital status codes; and correct temporal sequence of registration , examination , and blindness codes ) , and had been included in at least two surveys ( in the last of which they could have been declared dead ) . Parasitological assessment comprised taking bloodless skin snips ( with a 2 mm Holth-type corneoscleral punch ) , one from the right and one from the left iliac crests . The skin snips were placed in distilled water for 30 minutes and any microfilariae that emerged were counted under a dissection microscope . Negative snips were re-incubated up to a total of 24 hours in saline solution . Biopsies taken with this punch are relatively similar in size , weighing between 1 and 3 mg [42] . The microfilarial load of each person was measured as the arithmetic mean of the two skin snip counts . To estimate microfilarial load at any time linear interpolation was performed between point measurements following the methodology of Little et al . [10] , [15] . Microfilarial load was assumed to increase linearly with age , from 0 at age 0; and to vary linearly between measurements but to be constant after the last measurement in a person ( Figure S1 ) . Microfilarial load was lagged under the assumption that any association between parasite load and host mortality would probably be due to an individual's previous microfilarial load rather than their current burden . Two years was the chosen lag because our previous analysis had found the regression coefficient for the relation between host mortality and blindness prevalence to be greatest when a latency of two years was assumed . However , changing lag periods within a 0–4 year range had little impact on the results [10] , [15] . Because the OCP used both antivectorial and antimicrofilarial measures , it would be desirable in the analyses to take account of the number of ivermectin treatments received by each person . Unfortunately , the OCP did not keep patient-specific records of drug administration; however , the ( therapeutic ) coverage of eligible people in villages where treatment with the microfilaricidal drug ivermectin was provided ranged between 85 and 95% . When a separate analysis was conducted of the relationship between microfilarial load and mortality in the period before ivermectin was administered , the results of these analyses were very similar to those for the full dataset [15] . Table 1 summarizes the data used in the current analysis . Person-years were calculated for strata defined by age group ( 16 groups in total: [0–5 ) , [5–10 ) , … , [70–75 ) , [75+] ) , sex ( 2 groups , males and females ) , country of residence ( 11 groups , each corresponding to an OCP country ) , calendar year of follow up ( 27 groups in total: 1974 , 1975 , … , 2001 ) , calendar year of first survey ( 6 groups in total: 1973 , 1974–1976 , 1977–1979 , 1980–1983 , 1984–1989 , 1990–2001 ) , and microfilarial load ( 12 groups in total: [0–1 ) , [1–2 ) , [2–3 ) , [3–5 ) , [5–10 ) , [10–20 ) , [20–50 ) , [50–100 ) , [100–200 ) , [200–300 ) , [300–400 ) , [400+] ) . To acquaint the reader with the data , Table S1 presents the number of deaths and person-years of follow-up by ( to facilitate visual inspection ) , a somewhat coarser stratification of microfilarial load and host age . Because Bayesian Markov Chain Monte Carlo ( MCMC ) model fitting ( the method of choice for our purposes ) is computationally intensive , it was necessary to reduce the dataset from the 135 , 138 ( data-containing ) cells present in the full person-year/case table . Preliminary analyses had suggested that collapsing over calendar year of follow-up made very little difference to parameter estimates although it did increase the degree of extra-Poisson variability ( overdispersion ) in the number of deaths per stratum and thus increased parameter uncertainties , making our statistical inference more conservative ( Protocol S1 ) . A version of the dataset was prepared collapsing over calendar year of follow-up , containing 11 , 386 cells , and this was used for all further analyses . Table S2 compares the relative risk of mortality associated with each microfilarial load stratum for the full and collapsed datasets . It was assumed that the expected number of deaths in stratum i , was given by , ( 1 ) Here PYi denotes the number of person-years in stratum i; α = ( α0 , α1 , … , α33 ) , is a vector of regression coefficients; zi is a vector of covariates ( for the strata as defined above plus the average year of follow-up and prevalence of blindness in each stratum ) ; represents the microfilarial dose response , and is an interaction term with host age as described below . Following the model presented in Little et al . [15] , zi comprises age group , sex , country of residence , calendar year of first survey ( as categorical variables ) , and year of follow-up and prevalence of blindness ( as continuous variables ) . The continuous variables year of follow-up and prevalence of blindness were standardized by subtracting their mean and dividing by their standard deviation . This is standard practice for improving the efficiency of fitting statistical models to data . The covariate in Equation ( 1 ) denotes the average microfilarial load in stratum i . The function is referred to as the dose-response model ( or interchangeably the dose-response function ) and , depending on its functional form , it effects either a ( log- ) linear or non-linear adjustment on the expected number of deaths in stratum i . The term in Equation ( 1 ) permits an interaction between microfilarial load and the average age of individuals within a stratum on the expected number of deaths . Average age is denoted and the parameter γ determines the direction and magnitude of the interaction . That is , for a negative value of γ and fixed , declines with increasing and conversely , for a positive value of γ and a fixed , increases with increasing . If γ = 0 then there is no interaction between age and microfilarial load . The term is the relative risk of mortality associated with a mean microfilarial load and mean age . Both and were rescaled by dividing by their respective standard deviations , again to improve the efficiency of fitting the model to the data . The model presented in Little et al . [15] assumed that the dose-response function was log-linear ( and interactions with age were not considered ) . In this paper , different functional forms were explored , each nested within a sigmoidal-type relationship given by , ( 2 ) Setting β3 = 1 yields a hyperbolic functional form which can be linearized under the constraints β3 = 1 and β2 = 0 . The null model ( no relationship between a stratum's mean microfilarial load and the expected number of deaths , corresponding to density independence ) is obtained by setting β1 = 0 . It is noteworthy that although we refer to linear , hyperbolic and sigmoid dose-response models , strictly speaking , these are actually log-linear , log-hyperbolic and log-sigmoid dose-responses ( since appears in the exponent of Equation ( 1 ) ) but for the sake of brevity we have dropped this prefix in what follows . This approach ( in contrast with models fitted with different forms of as a multiplicative term outside the exponent of Equation ( 1 ) ) , considerably aided MCMC convergence , yielding much more reliable results , and without compromising the flexibility of the functional form to provide an adequate description of the data . For the reader's convenience , Table 2 lists the definitions of all parameters and variables referred to throughout the text . A regression calibration technique [30] , similar to that used in analyses of the effects of ionizing radiation on human mortality [31] , [32] , [33] , [34] , was used to explore the effect of measurement error in microfilarial loads on the functional form of the dose-response model . Of particular interest was whether measurement error could alter the most parsimonious yet adequate choice of dose-response . The essence of regression calibration is to replace the observed value of a covariate measured with error ( here the average microfilarial load per stratum ) with its expected value ( also referred to as the adjusted value ) . The regression of the number of deaths per stratum on the expected microfilarial loads per stratum then produces approximately unbiased point estimates of the dose-response parameters ( i . e . the βs in Equation ( 2 ) ) . A detailed description of the calculation of measurement error-adjusted microfilarial loads is given in Protocol S2 and Figure S2 . The most conceptually important stage of this procedure is the formulation of a measurement error model . Measurement error fundamentally occurs at the individual host level; it describes the distribution of microfilarial counts if counts were hypothetically measured repeatedly from the same person . Consequently , it was assumed that the two microfilarial counts measured per individual ( a skin snip was taken from the left and right iliac crests , see section Microfilarial Load ) were negatively binomially distributed with mean x and ‘overdispersion’ parameter k . Parameter k is an inverse measure of the degree of extra-Poisson variability in microfilarial counts such that as k→∞ the distribution becomes Poisson . In this case , it can also be thought of as an inverse measure of the degree of measurement error . Since each individual in the OCP dataset was skin snipped only twice at each follow-up , measurement error could not be reliably estimated from the available data . Consequently , regression calibration was performed by imputing into the statistical model ( Equation ( 1 ) ) measurement error-adjusted microfilarial loads that were calculated assuming the following degrees of measurement error: Models were fitted with a linear , a hyperbolic or a sigmoid dose-response function ( Equation ( 2 ) ) and with or without an age interaction ( i . e . either letting γ be estimated or setting it equal to 0 ) . The null model ( parasite density-independent human mortality ) was also fitted to the data . Bayesian MCMC techniques [44] were used to fit the models in OpenBUGS [45] ( http://www . openbugs . info/w/ ) , the currently maintained and updated version of WinBUGS [46] . Negative binomial errors were assumed for the numbers of deaths in each cell . This form of error structure was used because of evidence of overdispersion ( extra-Poisson variability , see Protocol S1 ) . Each parameter was assigned a vague prior , e . g . , a normal distribution with mean = 0 . 0 and variance = 1000 or for the overdispersion parameter of the negative binomial distribution ( which must be positive ) , a gamma distribution with shape and scale parameters = 0 . 001 . Following techniques suggested by Gelman and Rubin [47] , three starting values for the Gibbs sampling algorithm were assigned in order to assess convergence on the parameter posterior distributions and to check that our conclusions were not sensitive to the choice of starting values . In general , the first 2 , 000 samples from each chain were discarded as ‘burn-in’ and a further 4 , 000 samples were used to estimate the marginal posterior distributions . For models fitted to the unadjusted microfilarial loads , goodness of fit was assessed using the deviance information criterion ( DIC ) [48] . The DIC is a Bayesian generalization of Akaike's information criterion [49] , based on a trade-off between the fit of the model to the data and its complexity ( number of parameters ) . The DIC is used to choose between competing models in an analogous way to the AIC [50]; a more complex albeit ‘less parsimonious’ model ( a model with more estimated parameters ) may be chosen as the most adequate model if it has a lower DIC value compared to a simpler model [48] . The DIC was not an appropriate tool to select among models fitted to measurement error-adjusted microfilarial loads . While the point parameters estimates obtained from models fitted to measurement error-adjusted data are approximately ( or sometimes exactly ) unbiased , they do not take into account the uncertainty introduced by the adjustment procedure ( i . e . the adjusted values are estimates with associated uncertainties ) [30] , [51] . Consequently , the uncertainty in the dose-response parameters estimated using adjusted microfilarial loads will be underestimated which also renders the DIC invalid . This is because a component of the DIC evaluates the mean deviance ( −2×log-likelihood ) over the joint posterior distribution of the parameters and , since the dose-response parameters' contribution to the posterior will be inaccurate , potentially spurious DIC values may arise . As a solution to this , log-likelihoods were calculated at the posterior means of the fitted parameters . Since vague priors were used , parameter posterior means are equivalent to frequentist maximum likelihood estimates ( MLEs ) [44] and therefore the log-likelihood at these values is the model's maximum log-likelihood ( MLL ) . The MLLs were used to conduct pairwise likelihood ratio tests ( LRTs ) [52] in order to select the most parsimonious yet adequate model . The risk of mortality attributable to infection with O . volvulus ( as assessed by the presence of skin microfilariae ) over and above that due to blindness was calculated from each fitted model ( Equation ( 1 ) ) by first calculating for each ( microfilaria positive or microfilaria negative ) stratum the expected number of deaths not due to infection with O . volvulus , i . e . , assuming a microfilarial load of zero , ( 3 ) Summing these values over all strata and subtracting from the total number of recorded deaths , N , yields the number of deaths attributable to infection with O . volvulus ( after having adjusted for the prevalence of blindness as this already is a covariate in the models of Equation ( 1 ) ) . The attributable risk of mortality is this value expressed as a percentage , ( 4 )
Table 3 presents values of parameter estimates ( posterior means ) , DIC , MLL and attributable risk ( AR ) of mortality due to infection with O . volvulus microfilariae for different dose-response models fitted assuming microfilarial loads were measured without error . These are referred to as ‘naïve’ fits . Table 3 also contains the DIC and MLL values for the null model of no association between microfilarial load and human mortality ( density independence ) . Bearing in mind that the lower the DIC the better the fit , the results presented in Table 3 indicate that: a ) infection with O . volvulus is associated with human mortality , confirming previously published results [15]; b ) microfilarial load and age are interactively associated with human excess mortality such that for a given microfilarial load the relative risk of mortality declines with increasing age ( indicated by the statistically significantly negative value of parameter γ ) , and c ) the sigmoid dose-response relationship was the best fit of those considered suggesting that the relative risk of mortality increases non-linearly initially and saturates at high microfilarial loads . Pairwise comparisons of model MLLs using LRTs confirm the above results with each LRT giving a P value<0 . 001 . The fitted relationship between the relative risk of mortality and microfilarial load derived from the sigmoid dose-response with age interaction is depicted in Figure 1 . To aid visual inspection of the fit , model-derived ( Protocol S1 ) point estimates of mortality relative risks are also displayed . To illustrate graphically how the age interaction modifies the relative risk in different age groups , Figure 2 depicts the fitted relative risks ( again from the sigmoid dose-response model ) for individuals <20 years and individuals ≥20 years . From this figure it is clear that for a given microfilarial load the relative risk of mortality is much greater in the younger age group . The AR of 5 . 0% calculated from the linear dose-response model without an age interaction ( Table 3 ) is similar to the 5 . 2% previously calculated from the OCP data [15] . This is the lowest value of AR , calculated from the poorest fitting model . From Table 3 it is clear that non-linearity in the dose-response leads to higher estimates of AR as does the interaction with host age ( with the exception of the sigmoid dose-response model ) . The AR calculated from the best fit sigmoid dose-response which includes an interaction with host age is 5 . 9% . Therefore , of the total number of 23 , 333 ( any cause ) deaths recorded in the OCP cohort ( Table 1 ) , 1 , 377 would be attributable to infection with O . volvulus ( in addition to those due by ( any cause ) blindness ) . From Figure 3 it can be seen that adjusting microfilarial loads for measurement error decreases the magnitude of large observations and , to a lesser extent , increases the magnitude of small observations . The severity of this effect increases with an increasing magnitude of measurement error . How this affects the fitted dose-response models and the corresponding estimates of AR is shown by the results presented in Table 4 , Table 5 and Figure 4 . Table 4 presents parameter estimates , MLLs and ARs calculated from dose-response models fitted to measurement error-adjusted microfilarial loads . As with the naïve fits , inclusion of an interaction between the dose-response and age improved the fit of all models and consequently , the results presented in Table 4 are from models which included this interaction . Within Table 5 are the results of pairwise LRTs comparing the fit of the dose-response relationships fitted assuming the same magnitude of measurement error . The results in Tables 4 and 5 indicate that: a ) the sigmoid dose-response ( interacting with host age ) is the best fit even at high degrees of measurement error , and b ) the parameters of the dose-response are only appreciably altered at high degrees of measurement error ( i . e . for k = 1 ) . This latter effect is depicted graphically in Figure 4 which shows the best fit sigmoid dose-response models for each assumed magnitude of measurement error . Also included in the figure are the fitted linear dose-response models which illustrate the so-called ‘gradient attenuation’ effect of measurement error; the measurement error-adjusted gradient becomes increasingly steep for increasing assumed magnitudes of measurement error ( compare Figure 4A , which corresponds to adjustment for the minimum Poisson measurement errors , k→∞ , with Figure 4C , which corresponds to adjustment for a high degree of measurement error , k = 1 ) .
The analyses presented here demonstrate that excess human mortality is non-linearly associated with microfilarial load in the area covered by the OCP in West Africa . This is an important finding since , in the past , excess mortality caused by onchocerciasis has generally not been considered to be significant ( but see [15] ) , nor has its functional relationship with O . volvulus microfilarial load been statistically ascertained . To our knowledge , this is the first time that the functional form of the relationship between microfilarial load and excess mortality of the human host has been explored in relation to the OCP data . In the ‘typical’ generalized infection with O . volvulus , immunosuppression of Th1 and Th2 responses is thought to occur via a specific T regulatory-1 response and the non-specific involvement of IL-10 [53] . The sigmoid ( S-shaped ) dose-response model is initially increasing slowly ( small changes at the lowest end of microfilarial loads ) , then more rapidly , and subsequently saturating , so it may be that low microfilarial loads exert a smaller effect which increases with infection intensity but beyond a certain microfilarial load there is little increase in immunosuppression and hence in excess mortality . A model with facilitated parasite establishment due to immunosuppression has been presented to explain age-profiles of worm burden in onchocerciasis , which for savannah settings such as those in the OCP area , tend to saturate or decrease beyond 35–40 years of age [54] . This immunocompromised state of individuals infected with O . volvulus could leave them more vulnerable to other , possibly fatal infections [17] , [18] , [19] , [55] . Onchocerciasis may also be involved in neuropathology ( in the form of epilepsy ) , growth retardation , and general debilitation of the host , which may also be dependent on parasite burden such that pathology increases with increasing microfilarial load up to a certain point [20] , [21] , [22] , [23] , [24] , [25] . More generally in helminthiases , the worms not only cause contemporaneous pathology and disability during the period of active infection , but infection typically also poses the risk of later , irreversible chronic sequelae or even early mortality , arising partly from anti-parasite immune responses that can often cause permanent damage through direct or systemic post-inflammatory effects [56] . The decreasing relative risk of mortality with increasing host age indicated by all of our fitted models is an intriguing result and suggests that the cumulative damage caused by chronic infection with O . volvulus may have a lesser effect on mortality than on other sequelae . For instance , infection-induced immuno-suppression may leave children more susceptible to potentially lethal infections compared to adults . Alternatively , interactions between the parasite and the immune system may change over time/age . In schistosomiasis for instance , evidence has been presented for an association between a pubertal hormone and reduced intensities of Schistosoma japonicum infection and re-infection [57] which lends support to the hypothesis that developmental changes occurring during adolescence are necessary to build resistance to schistosomiasis [58] . It is possible that similar age-related immunological changes occur in onchocerciasis which could potentially reduce levels or impact of immunosuppression in adults , leaving children relatively more vulnerable to serious infections . It has been shown that children from O . volvulus-infected mothers ( and these will be the majority in highly endemic areas ) have not only a substantially higher risk of becoming infected; but also acquire patent infection earlier in life , and tend to develop higher infection levels . When longitudinally followed up during the OCP vector control activities , their infection also persisted at higher levels [59] . A limitation of the present work is that while the analysis was based on individual data , the start and duration of control measures were recorded mainly on a community or regional basis . The OCP recorded the geographical ( percentage of communities ) and therapeutic ( percentage of eligible individuals in a community ) coverage of ivermectin treatments but not usually the number of treatments received by each individual in a cohort . Knowledge of an individual's ivermectin treatments would help to explain microfilarial load measurements . However , one would not expect ivermectin to have a major effect on the functional relationship between microfilarial load and host mortality ( the immunity-facilitating effects of treatment reverse immunosuppression only temporarily [60] , [61] ) . Indeed a more likely consequence is that the statistical power to discern the functional form of the dose-response would have been reduced due to ivermectin suppressing and somewhat homogenizing microfilarial loads over the course of the OCP ( see Figure 5 in [10] ) . Additionally , in our previous work , the relationship between microfilarial load and mortality was separately assessed in the period before ivermectin was distributed and the results of these analyses were very similar to those for the full dataset [15] . Another possible weakness is that in order to determine the microfilarial load at any time , we linearly interpolated between measurements , and the microfilarial load was assumed to be constant after the last measurement in an individual . This assumption might not fully reflect the true situation . In the early stages of vector control and follow-up ( before ivermectin was introduced ) microfilarial loads might have increased after the last survey point , whereas later , microfilarial loads might have decreased as a result of ivermectin treatment . Moreover , this effect may depend on host age ( and sex ) since microfilarial loads tend to increase throughout childhood and early adulthood before saturating or decreasing ( or increasing ) from middle age onwards for West African savannah settings [36] , [41] , [54] . If this is the case then it is possible that our assumption of a constant microfilarial load after the last measurement has underestimated microfilarial loads in children ( and women [36] ) , particularly in pre-ivermectin stages of vector control . Consequently , it is possible that an alternative explanation for the observed interaction between the dose-response and host age is that this may be an artefact arising from this assumption rather than an indicator of an underlying biological mechanism . However , since the average length of follow-up was fairly short ( 8 . 30 years , Table 1 ) and the period of latency between exposure and mortality was assumed to be 2 years , it seems unlikely that our assumptions would have introduced such manifest bias . The use of mid-interval estimates for mortality may have incurred some inaccuracies in event times . Since the mortality endpoint was only ascertained during surveys , if a person died between surveys , the death was deemed to have occurred midway between them . Because intervals greater than 10 years could elapse between surveys , the times imputed for the mortality events may be significantly in error . However , since such events were relatively infrequent , we do not expect that significant bias would be introduced by the use of such mid-interval estimates . Measurement error or discrepancy between the actual average skin microfilarial load of an individual and the value recorded can arise in two main ways . First , assuming microfilariae are randomly distributed throughout the skin , one would expect counts from the same individual to be Poisson distributed . However , there is uneven dispersion of microfilariae in the body [43] and typically some clustering is seen [62] . Such aggregation leads to extra-Poisson variation which motivated our choice of the negative binomial measurement error model . Second , the skin snip examination and microfilarial counting procedure is subject to observer variation and the sensitivity of the method , the precise form of which is not known . Also , the OCP protocol , by which snips were further incubated for 24 hours only if negative at 30 minutes , leads to underestimation of microfilarial counts in those snips positive after the first half hour as microfilarial emergence increases with time in the incubation medium [63] . Different degrees of measurement error were explored and our conclusion that the non-linear , saturating ( S-shaped ) function is the best fit to the data was unaffected . However , the exact relationship between microfilarial load and relative risk of mortality is somewhat blurred by the consideration of measurement error , particularly because measurement errors in microfilarial loads have not been previously estimated and remain largely unknown . In general , increasing magnitudes of measurement error increasingly homogenize the data; large microfilarial loads are reduced , small microfilarial loads are increased . To understand intuitively why this occurs , consider that the variability in the observed data comprises both variability introduced by measurement error and underlying variability in individuals' true microfilarial loads . As one assigns an increasing proportion of this variability to measurement error ( by decreasing k in our model ) the residual variability left for the true microfilarial loads is reduced . The degrees of measurement error assumed in this analysis were chosen for the following reasons . Poisson measurement error ( corresponding to k→∞ ) was deemed a good basic model corresponding to a random distribution of microfilariae within the skin and no other sources of error . The value of k = 15 was estimated ( Protocol S3 ) from available data previously published [43] comprising 20 repeated microfilarial counts , measured from the left and right iliac crests by skin biopsy in the same way as the OCP data were collected , from each of 15 individuals over a 24-hour period ( Figure S3 ) . To the best of our knowledge this is the most comprehensive data on repeated measurements of microfilarial counts and thus an appropriate dataset from which to guide our choice of measurement error . Although there is no forthcoming reason why measurement error arising from the random sampling of microfilariae within the skin ( i . e . discounting error introduced by observers examining the medium in which skin snips are incubated for microfilarial enumeration ) will vary significantly among individuals and populations , the small study from which the auxiliary measurement error data were derived clearly does not account for the measurement error which may have been introduced into the OCP dataset by the various technicians analysing the skin snips . This is why the robustness of our results was also assessed using an arbitrarily greater magnitude of measurement error ( k = 1 ) . Methods of adjusting for the effects of covariate measurement error are well developed for linear [51] and generalized linear models [30] . By applying such methods , previous studies have adjusted for the effects of measurement error in microfilarial load when exploring how microfilarial load relates to: a ) the uptake of microfilariae by the black fly ( Simulium ) vector [64] , [65] , and b ) the development of larvae within the vector [66] . Standard methods of adjustment were not valid for the analysis presented here because of the overdispersion evident in the response variable ( the number of deaths per stratum ) and the non-linearity of the hyperbolic and sigmoid dose-response functions . Consequently , and motivated by methods used in ionizing radiation epidemiology to address similar problems [31] , [32] , [33] , [34] , regression calibration was used to obtain approximately adjusted point estimates of the dose-response parameters . A major limitation of this method is that it does not accurately reflect parameter uncertainties . The Bayesian MCMC techniques used to fit the models in this analysis were employed because MCMC offers a powerful tool for fitting complex non-linear models to data . Indeed it may seem unusual to fit models using Bayesian methods but adjust for measurement error using frequentist regression calibration . A Bayesian approach to measurement error [67] , [68] , [69] is an attractive alternative to regression calibration because variability in the posterior distribution of the ( dose-response ) model parameters reflects all the uncertainty introduced by measurement error . However , applying such methods to dose-response models fitted to stratum-level data presents considerable complications . This is because variability introduced by measurement error on the individual dose ( microfilarial load ) measurements must be passed to uncertainty of the mean dose in the stratum . A two-stage Bayesian method of achieving this has been developed and applied to dose-response models of the effects of ionizing radiation on human mortality [32] , [70] but the development of a similar method for the models presented here is beyond the scope of this paper . Parasite density-dependent host mortality would remove individuals with heavy microfilarial burdens from their community , which could significantly impact on transmission dynamics . Mortality of the human host has already been somewhat incorporated as a function of microfilarial load in models for the transmission of O . volvulus [35] , [71] but not using functional forms such as described in this article . Previously , a minimum worm burden was required for eye lesions to occur , and the rate of going blind was related to the number of eye lesions , blind individuals experiencing a differential death rate compared to the non-blind [71] . Our model assumes , instead , that excess mortality depends upon ( suitably lagged ) microfilarial load ( in this paper the lag is two years ) , rather than upon the number of ocular lesions or the rate of becoming blind . However , it is possible , and biologically more plausible , that mortality at a given age may be related to cumulative microfilarial load , or more generally to a weighted sum of microfilarial load rather than just lagged load . The computer simulation ONCHOSIM model [72] incorporates the probability of going blind as a cumulative function of microfilarial load . When a person becomes blind , their remaining lifespan is projected to decrease [71] . This means that while the model includes excess mortality of the blind , it ignores excess mortality among sighted individuals with heavy microfilarial loads . The economic analysis of the impact of the OCP has been based on predictions based on ONCHOSIM , and therefore on prevention of blindness [73] , [74] . The effect of incorporating excess human mortality in the form of the sigmoid model described here ( the preferred non-linear model ) , would imply that most of the benefits ( reductions in morbidity and mortality ) would accrue once the infection intensity is very much reduced and maintained at low levels in advanced stages of the control programme , making the economic assessment of longer horizons more cost-effective [75] . It would also imply that controlling the infection in the younger sections of the population might be of greatest public health importance [76] . With a parasite whose life expectancy may exceed 10 years [35] , [72] , and the prospects of onchocerciasis control/elimination in the post-OCP era mainly relying on the mass distribution of ivermectin [77] , [78] , the required duration , sustainability , and impact of the intervention is of interest to scientists and policy-makers alike [37] . Mathematical models relating infection intensity and ocular morbidity in terms of loss of visual acuity and blindness incidence will be important to help impact assessment in relevant ( savannah ) areas of the African Programme for Onchocerciasis Control ( APOC ) . The ongoing assessment of the global burden of the neglected tropical diseases [38] , [39] is revising previous estimates based on updated data and methodologies , a better understanding of the relationships between infection , morbidity and mortality , and the impact of large-scale control programmes . For onchocerciasis , and in addition to visual impairment , blindness and troublesome itching ( included in the previous disease models [79] , [80] ) , it will be important to consider the relationships here described between microfilarial load and excess human mortality . In addition , the interplay between density-dependent processes ( parasite-associated human mortality being one of them ) and worm distribution among hosts will influence the regulation of parasite population abundance , the stability of the host-parasite system , the rates of reinfection following cessation of control operations [81] , [82] , and the rates of spread of any drug resistance that may emerge in large-scale ivermectin-based interventions [83] , [84] .
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Human onchocerciasis ( River Blindness ) is a parasitic disease leading to visual impairment including blindness . Blindness may lead to premature death , but infection with the parasite itself ( Onchocerca volvulus ) may also cause excess mortality in sighted individuals . The excess risk of mortality may not be directly ( linearly ) proportional to the intensity of infection ( a measure of how many parasites an individual harbours ) . We analyze cohort data from the Onchocerciasis Control Programme in West Africa , collected between 1974 and 2001 , by fitting a suite of quantitative models ( including a ‘null’ model of no relationship between infection intensity and mortality , a ( log- ) linear function , and two plateauing curves ) , and choosing the one that is the most statistically adequate . The risk of human mortality initially increases with parasite density but saturates at high densities ( following an S-shape curve ) , and such risk is greater in younger individuals for a given infection intensity . Our results have important repercussions for programmes aiming to control onchocerciasis ( in terms of how the benefits of the programme are calculated ) , for measuring the burden of disease and mortality caused by the infection , and for a better understanding of the processes that govern the density of parasite populations among human hosts .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology",
"epidemiology",
"global",
"health",
"neglected",
"tropical",
"diseases",
"public",
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] |
2012
|
Density-Dependent Mortality of the Human Host in Onchocerciasis: Relationships between Microfilarial Load and Excess Mortality
|
The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science . Though retrospective studies have examined the fidelity of mouse models to their respective human conditions , approaches for prospective translation of insights from mouse models to patients remain relatively unexplored . Here , we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments . We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets . We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly . Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance . Our study shows that when prospectively evaluating biological associations in mouse studies , semi-supervised learning approaches , combining mouse and human data for biological inference , provide the most accurate assessment of human in vivo disease processes . Finally , we proffer a delineation of four categories of model system-to-human “Translation Problems” defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches .
Generalization of insights from disease model systems to the human in vivo context remains a persistent challenge in biomedical science . The association of molecular features with a phenotype in a model system often does not hold true in the corresponding human indication , due to some combination of the fidelity of the experimental system to human in vivo biology and the inherent complexity of human disorders [1–7] . Though it is now routine to collect clinical samples from patients and associate molecular features with clinical phenotypes , there are discrepancies between the phenotypes measurable in patients and those investigable by use of model systems . Outside of a clinical trial , novel perturbations to the disease system cannot be directly investigated in the patient in vivo context , whereas model systems can be used to study the impact of innumerable perturbations to the disease system and to associate molecular features with these responses . As a consequence of this discrepancy , murine and other model systems of disease are likely to remain an important part of biomedical research . Therefore , methods for improving generalizability of mouse-derived molecular signatures to human in vivo contexts are needed for more impactful translational research . The utility of mouse models for studying inflammatory pathologies was recently assessed by a pair of studies examining the correspondence between gene expression in murine models of inflammatory pathologies and human contexts [1 , 2] . In these studies , mouse molecular and phenotype data were matched to human in vivo molecular and phenotype data , enabling direct comparison of genomic responses between mice and humans . These studies analyzed the same datasets and came to conflicting conclusions about the relevance of mouse models for inflammatory disease research , with Seok et al . concluding that mouse models poorly mimic human pathologies and Takao et al . concluding that mouse models usefully mimic human pathologies [1 , 2] . A key methodological difference between the two studies was that Takao et al . examined genes significantly changed in both contexts [1 , 2] . However , in prospective translational studies , the corresponding mouse and human in vivo datasets and perturbations are rarely available making accurate pre-selection of genes that change in both human and mouse contexts unlikely . Therefore , prospective studies will often need to proceed on the basis of molecular changes in the model system alone . The aim of our study is to develop a machine learning approach to address the challenge of prospective inference of human biology from model systems . Here , we consider a machine learning approach successful if it correctly predicts a higher proportion of human differentially expressed genes ( DEG ) and enriched signaling pathways than implicated by the corresponding mouse model . The essence of our approach is to apply a machine learning classifier to assign predicted phenotypes , derived from a mouse dataset , to molecular datasets of disease-context human samples and to infer human DEGs and enriched pathways downstream of the machine learning model using these inferred phenotypes . We assessed our approach by testing it on the datasets from the Seok and Takao studies , where mouse phenotypes and gene expression data were matched to patient clinical phenotypes and gene expression data [1 , 2 , 8–20] . While mouse experiments alone failed to capture a large portion of human in vivo biology , using these datasets to train computational models produced more precise and comprehensive predictions of human in vivo biology . In particular , semi-supervised training of a neural network identified significantly more human in vivo DEGs and pathways than mouse models alone or other machine learning approaches examined here . We identify aspects of model system study design that influence the performance of our neural network and show that the added benefit of our method is driven by recovery of biological processes not present in the mouse disease models . Our results suggest that computational generalization of insights from mouse model systems better predicts human in vivo disease biology and that such approaches may facilitate more clinically impactful translation of model system insights .
We assembled a cohort of mouse-to-human translation case studies from the datasets analyzed in Seok et al . and Takao et al . ( Table 1 ) [1] [2] . We defined case studies as all pairs of mouse ( training dataset ) and human ( test dataset ) datasets for the same disease condition . By constructing case studies in this manner , multiple mouse strains and experimental protocols could be compared to different presentations of that same disease in independent human cohorts . The final cohort consisted of 36 mouse-to-human translation case studies in which mouse-to-human biological correspondence and machine learning translation approaches could be assessed ( Table 2 ) . Baseline correspondence between each mouse model and human dataset was assessed by differential expression analysis and Gene Ontology ( GO ) pathway enrichment analysis of differentially expressed , homologous mouse and human transcripts . We computed the precision and recall of the DEGs and pathways with respect to correspondence between mouse and human datasets and summarized these quantities using two F-scores . The F-score gave an equal weighting on the correctness of DEG and pathway predictions ( precision ) and how comprehensive ( recall ) the predictions were relative to the human-predicted associations . The F-scores of the machine learning model predictions were calculated by comparing the algorithm-predicted human DEGs and pathways to those derived using the true human phenotypes . The mouse predicted DEGs and enriched pathways constituted the baseline performance against which our machine-learning approaches were compared . We implemented supervised and semi-supervised versions of k-nearest neighbors ( KNN ) , support vector machine ( SVM ) , random forest ( RF ) , and neural network ( NN ) algorithms using Lasso or elastic net ( EN ) regularization as a feature selection method . By exploring a range of machine learning models with different model structure and varying the regularization parameter α , we were able to assess the effect of model structure and feature selection stringency on performance . In supervised models , a machine learning classifier was trained on the mouse dataset and applied to the human test dataset to infer predicted phenotypes from which we inferred human DEGs and enriched pathways . In semi-supervised models , a supervised classifier was initially trained on the mouse data alone to predict the human samples . Following this first step , the predicted human samples with the highest classification confidence were selected to create an augmented mouse-human training set ( Fig 1 ) . Retraining with the predicted human samples allowed us to humanize the new classifier using unsupervised information from the human test dataset . The new classifier was then used to reclassify the human samples . This procedure of retraining , prediction , merging predicted human samples with the training set , and dropping the confidence threshold each iteration terminated when the lowered confidence threshold resulted in merging all human samples with the training set . The phenotypes associated with the human samples at this step were taken as the final semi-supervised model prediction from which predicted human DEGs and enriched pathways were inferred . Model DEG and pathway F-scores were computed by comparing the algorithm-predicted DEGs and pathways , using computationally inferred human phenotypes on the human test data , to those identified when using the true phenotypes on the human test data . We compared the performance of 1 , 728 machine learning classifiers to the mouse-predicted DEG and pathway associations . Classifier performance was summarized by the area under the receiver operator characteristic curve ( AUC ) for the accuracy of the predicted human phenotypes and the F-score of predicted human DEGs and pathways . A generalized linear model ( GLM ) was trained to assess the impact of Lasso/EN regularization α values and the type of machine learning classifier on the AUC and DEG F-score performance metrics . Neither the value of α ( p = 0 . 374 ) , nor the type of machine learning approach ( p = 0 . 874 ) significantly impacted the AUC ( S1 Table ) . However , both α ( p = 0 . 0000215 ) and the type of machine learning method ( p = 0 . 000902 ) significantly impacted the F-score ( S2 Table ) . The significance of the regularization parameter and classifier type for F-score and not AUC suggests that though each model had comparable accuracy , the biological relevance of the predicted phenotypes was significantly influenced by feature selection stringency and machine learning model structure . Since the F-score directly measured the biological relevance of the predictions made by a particular algorithm , we focused on it as the relevant performance metric , emphasizing gaining biological insights over mere numerical predictive capacity . We computed the 95% confidence intervals of the F-scores for each machine learning approach and mouse model across all case studies and regularization parameters ( Fig 2A ) . The overall performance of mouse-derived DEGs for predicting human DEGs was low ( F-score 95% CI [0 . 082 , 0 . 158] ) and though many models significantly outperformed the mouse , the F-scores were still somewhat low indicating an imbalance in precision and recall in some case studies . We investigated the role that the experimental design of the mouse cohorts may be contributing to this imbalance using a GLM and found that smaller sample sizes and larger class imbalances in the mouse datasets resulted in significantly lower model F-scores ( S3 Table ) . Though most machine learning models balanced precision and recall , we noted a cluster of models with precision < 0 . 2 and recall > 0 . 3 ( S1 Fig ) . All of these could be attributed to case studies in which human dataset GSE9960 was the test dataset ( Table 2 ) . Here , the mouse training datasets were comprised of mouse leukocytes and the poor performance of the models suggests that mouse leukocytes are not reflective of human peripheral blood mononuclear cell ( PBMC ) biology . We retained case studies with GSE9960 to examine whether our models could add translational value despite this inter-tissue mouse and human discrepancy . The semi-supervised NN ( ssNN ) , semi-supervised RF ( ssRF ) , KNN , SVM , and RF outperformed the mouse model , with similar behavior found for the precision and recall ( Fig 2A , S4 and S5 Tables ) . We found that ssNN F-scores were significantly higher than all other models indicating it was the most successful model ( 95% CI [0 . 253 , 0 . 342] , p < 0 . 05 ) . Finally , we examined the performance of the ssNN across all case studies for each setting of the regularization parameter and found Lasso regularization ( α = 1 . 0 ) had the highest F-score across all case studies ( median F-score = 0 . 281 ) ( S6 Table ) . Based upon the GLM , F-scores , and performance at each value of α , we concluded that the ssNN with Lasso regularization was the most broadly effective approach for prediction of human DEGs . Having identified the ssNN as the most broadly effective model , we examined the genes selected in the semi-supervised training procedure ( Fig 2B and 2C , S7 Table ) . Most of the genes selected by the ssNN were not concordantly differentially expressed in mouse and human contexts ( Fig 2B ) . The genes most frequently included in the ssNN models tended to have either strong differential regulation in the human context alone ( e . g LCN2 ) or be among those genes that exhibit concordant differential expression in both mouse and human contexts ( e . g . ARG1 ) ( Fig 2C ) . Recall that the semi-supervised training procedure begins with a model and features informed only by the mouse training dataset , demonstrated by the cluster of genes exhibiting large mouse fold changes . That these genes have correspondingly small human fold changes suggests that the neural network is responsive to the addition of predicted human samples in the training procedure and is able to prioritize those genes that are relevant to the human context and ignore those relevant only in the mouse context ( Fig 2B and 2C ) . We next compared the DEGs and pathways predicted by the ssNN and mouse models in each case study ( Fig 2D , S8 Table ) . In most cases , the mouse pathway F-score is higher than the DEG F-score indicating that the mouse models considered here are more predictive of human pathway function than differential expression events ( Fig 2D ) . The correspondence between the enriched pathways identified by mouse models and human in vivo contexts was relatively consistent across disease indications , ( Fig 2D ) , suggesting that mouse models of inflammatory pathologies recapitulate similar proportions of human in vivo molecular biology across indications independent of disease etiology complexity . Notable exceptions to this pattern of mouse-human pathway correspondence were the endotoxemia and cecal ligation and puncture ( CLP ) mouse models , none of which , had any corresponding human DEGs at permissive statistical thresholds ( WMW p < 0 . 05 , FDR q < 0 . 25 ) ( Fig 2D ) . Despite this , in 9 of 14 endotoxemia or CLP mouse cases , the ssNN characterized a large proportion of human sepsis biology despite being trained on nonrepresentative mouse models ( Fig 2D ) . Similarly , in 5 of 6 cases where the human PBMC dataset was the test dataset and mouse leukocyte gene expression was the training set , the ssNN equaled or surpassed the mouse . These results indicate that the semi-supervised approach provided substantial benefit when mouse models , such as CLP-driven sepsis and LPS stimulated endotoxemia , did not recapitulate molecular features of human disease biology . In total , the ssNN predicted an equal or greater proportion of human enriched pathways in 29 of 36 case studies ( Fig 2D ) . In the other cases , the mouse models of Streptococcus Pneumoniae Serotype 2 ( SPS2 ) and Staphylococcus Aureus ( SA ) driven sepsis outperformed the ssNN in particular human cohorts . A single human sepsis dataset , GSE13015 , where many of the patients had other infections , was implicated in 3 of these 7 case studies [9] . This suggests that the C57 strain mouse with an SA or SPS2-driven sepsis is an unusually satisfactory direct model for human sepsis with other infectious complications . The ssNN may have failed to outperform the mouse in these cases due to the heterogeneity of infections in the human cohort , an interpretation supported by the fact that the ssNN outperforms the combined mouse cohort by a wide margin when the AJ and C57 mouse models are combined into a single training cohort ( Fig 2D ) . Therefore , when predicting biological associations in a heterogeneous human cohort , the ssNN performs better when trained on a heterogeneous mouse cohort . This diversity of sepsis mouse models in our cohort made it possible to assess the correspondence of different protocols for generating sepsis mouse models to the human disease context . While CLP mouse models failed to identify any DEGs , the SPS2 and SA sepsis mouse models were both partially predictive of DEGs and pathways in human sepsis cohorts . The SA mouse sepsis cohort was comprised of two mouse strains , the highly susceptible A/J mouse strain and the somewhat resistant C57BL/6J strain [8] . We were therefore able to compare four cohorts of sepsis models ( SPS2- C57BL/6J , SA-A/J , SA-C57BL6J , and SA-mixed ( A/J and C57BL6J ) ) in order to identify the most representative mouse models of clinical sepsis . Since pathway predictions had a greater correspondence to human sepsis than DEGs alone , we compared the pathway associations derived from each sepsis mouse model to one another to identify common and distinguishing features of each model ( Fig 3A ) . In total , 442 pathways and processes were enriched across all human sepsis cohorts and multiple mouse sepsis models correctly predicted subsets of these pathways . All mouse models and strains correctly identified a set of 112 pathways including signaling by FGFR1 , FGFR2 , FGFR3 , and FGFR4 , and MAPK1 signaling ( S9 Table ) . This pathway signature of human sepsis appears to be highly reproducible in multiple mouse sepsis models , rendering it a stable signature for assessing therapeutic interventions and benchmarking mouse sepsis models against human data . Examining mouse sepsis model F-scores by component precision and recall revealed that while aggregating predictions across multiple mouse models improves the coverage of human sepsis pathway predicted , it simultaneously degrades the precision of these pathway signatures and ultimately only accounts for half of the totality of human in vivo sepsis signaling ( Fig 3B ) . This contrasts with our finding that increasing the heterogeneity of the mouse cohort improved the predictive power of the ssNN suggesting that a heterogeneous mouse cohort contains latent features that the ssNN detects and incorporates into its predictions of human in vivo pathways . Therefore , a key limitation of these sepsis mouse models appears to be that they lack in depth and correspondence of biological functions to the processes of human in vivo sepsis and that the ssNN is able to recover this missing information through integration with human datasets . We then compared the combined pathway predictions of all mouse sepsis models to the predictions of the ssNN across all sepsis cases to assess correspondence with human in vivo sepsis pathway signatures ( Fig 3C ) . The mouse sepsis models confirmed two pathways that the ssNN missed: the CD28-dependent VAV1 pathway and the oxidative stress induced senescence pathway . The oxidative stress senescence pathway was implicated by both of the SA mouse models in isolation , but not the mixed cohort , while the CD28-dependent VAV1 pathway was specifically implicated in the C57 strain . Use of a CD28 mimetic peptide has been shown to increase survival in gram-negative and polymicrobial models of mouse sepsis and has been explored as a therapeutic option for human sepsis [21] . Though the mouse model identified two pathways missed by the ssNN , the ssNN performed with comparable precision to the mouse models overall ( precision = 0 . 72 ) and recovered a strikingly higher proportion of in vivo human sepsis pathways ( recall = 0 . 96 ) ( Fig 3C ) . Furthermore , the ssNN recovered a set of 163 pathways enriched in human sepsis in vivo that were not identified in any mouse models of sepsis ( S10 Table ) . These pathways included thrombin signaling , TGFβ signaling , as well as several RNA transcriptional and post-translational modification-based pathways ( S10 Table ) that all mouse models of sepsis lacked . Both thrombin and TGFβ signaling have been shown to play key roles in the pathology of sepsis and have been investigated for therapeutic and prognostic applications in sepsis [22 , 23] [24] . This result suggests that combining context-associated human data with mouse disease model data recovers important aspects of human in vivo signaling .
The lack of fidelity of mouse models for representing complex human biology is one of the most pressing challenges in biomedical science . Failures of inter-species translation are likely driven by a combination of evolutionary factors , experimental design limitations , and the challenges of comparing biological function between species and tissues [25–27] . It is well known that particular features exist that translate well between model systems and humans , particularly at the level of pathway function [28 , 29] . However , a key methodological issue in inter-species translation is to consider what will be knowable in prospective translation of a model system experiment , that pre-selection of translatable features is often not possible . In this study we demonstrate that semi-supervised training of a neural network is a powerful approach to inter-species translation and show that successful translation is dependent upon the computational method , the model system-to-human tissue pairings , and the experimental design of the model systems studies . The low pathway recall in the sepsis mouse models demonstrates that there are human disease-associated biological functions simply not present in mouse disease biology . Despite this intrinsic limitation of the mouse , our semi-supervised learning approach prospectively discovers mouse features predictive of human biology , offering a valuable tool for inter-species molecular translation . The ideal case for characterizing the biology of human disorders would be the availability of comprehensive human phenotype and molecular data from clinical cohorts . However , since novel perturbations to the disease system cannot be studied in the human in vivo context outside of a clinical trial , mouse disease model systems and emerging human in vitro model systems will continue to play an important role in biomedical research . It is in this context that we propose a delineation of four categories of Translation Problems , those of generalizing insights from model systems to human in vivo contexts ( Fig 4 ) . The most challenging case is when only model system molecular and phenotype data are available ( Category 4 ) , where a large proportion of biomedical research falls . If human-based prior knowledge , such as candidate genes or clinical observations , is available to integrate with model system data , then generalization can be characterized as a Category 3 problem . In Category 2 problems , condition-specific human molecular data is available to combine with model system molecular and outcome data to characterize human biology . Inferences from solving Category 2 problems can be further refined with human-based prior knowledge in a Category 1 problem . Within this framework , our efforts here are best viewed as an approach to Category 2 translation problems in which we show that ssNN modeling provides a framework for integration of high throughput , high-coverage datasets from model system and human contexts for molecular translation . However , different categories of translation problems have datasets with different properties and will likely require alternative computational methods . In a recent crowd-sourced competition , a series of challenges were posed for translating molecular and pathway responses between rat and human in vitro models . No computational methods were broadly effective in across challenge events and it appears that none of the competitors employed semi-supervised machine learning approaches [30 , 31] . This finding supports our delineation of Translation Problems into different categories defined by the coverage and resolution of the data available for model training . Other computational translation efforts often use information about how genes change between experimental groups in both model system and human contexts [32 , 33] . A key advantage to our approach to inter-species translation is that information about gene regulation in the human context is not required for successful modeling . The driving principle of semi-supervised learning ( transfer learning ) is that combining information from multiple domains can enhance model performance . In these applications , a set of training data ( Xtrain and Ytrain ) are integrated with a context-related dataset ( Xcontext ) to improve the performance of the algorithm in an approach known as inductive transfer learning . Our approach is an example of transductive transfer learning , where Xcontext = Xtest , and the test dataset is incorporated into the algorithm training procedure in an unsupervised manner . Examining machine learning models with different structures allowed us to assess whether different model structures resulted in better performance and how responsive different models structures were to a semi-supervised training procedure . In the case of KNN and SVM models , the human samples were classified by distances in mouse gene expression feature space , a model structure we found did not gain in performance with semi-supervised training . By contrast , the NN and RF improved in performance with semi-supervised training suggesting that these approaches are more responsive to reweighting model features by incorporating unsupervised human information . Although the NN ended up being the most biologically successful model , direct interpretation of NN model weights and neurons remains challenging . Here , we use the NN as a prediction-only model and derive biological insights in downstream analyses , though as NN interpretability methods advance it may be possible to gain additional biological insights by direct interpretation of the NN model structure . Despite advances in the fidelity of model system biology to human contexts , generalizability of findings of model system experiments will continue to be a key issue in both basic biology and translational science research [34 , 35] . Whenever the model system data alone forms the basis of inference , whether through direct interpretation or indirectly through a computational description of the model system’s biology , key aspects of human biology are likely to be overlooked or misrepresented . Semi-supervised learning approaches that neither aim for a generalizable computational model nor rely on the model system training data alone , recover more relevant human in vivo biology as a downstream consequence of creating good predictions of human phenotype for a specific patient cohort . This conceptual shift from direct interpretations of model system data to the indirect generalization of model system biology through integration with human data in semi-supervised learning framework has the potential to aid in successful translation of preclinical insights to patients .
Datasets were obtained from Gene Expression Omnibus [36] and selected based on their inclusion in two studies comparing mouse and human genomic responses [1 , 2] . Since we used the human datasets as test datasets and the mouse datasets as training datasets for machine learning applications , we applied the additional criteria that phenotypes and tissues of origin were comparable between mouse model and human in vivo datasets to ensure comparable training and test cases for algorithm performance comparison . Based on these criteria , we excluded the acute respiratory distress syndrome and acute infection datasets , and mouse splenocyte samples from GSE7404 , GSE5663 antibiotic treated sepsis mice spleen samples , and GSE26472 mouse liver and lung samples . The final cohort consisted of 6 mouse cohorts and 7 human cohorts ( Table 1 ) . Mouse array probe identifiers were converted to gene symbols and mapped to homologous human genes using the mouse genome informatics database [37 , 38] . If multiple diseases or microarray platforms were used in a dataset , the dataset was partitioned by disease type and array platform to create multiple case studies , resulting in 36 case studies ( Table 2 ) . Duplicate genes in each dataset in each case study were removed by retaining those genes with the maximum average expression across all samples . Datasets were z-scored by gene . We implemented supervised and semi-supervised versions of the k-nearest neighbors ( KNN ) , support vector machine ( SVM ) , random forest ( RF ) , and neural network ( NN ) algorithms . Simulations showed that three neighbors were sufficient for training the KNN models ( data not shown ) . Simulations from 10 to 1000 decision trees showed that 50 decision trees were sufficient for training the RF ( data not shown ) . The NN was a feed-forward neural network with three layers . The input layer consisted of one node for each feature , the output layer consisted of two nodes , one for each class , and the hidden layer consisted of the average of the number of input and output nodes rounded up to the nearest integer . NN synapse weights were computed using scaled conjugate gradient backpropagation . Prior to model training , we performed feature selection with either Lasso or elastic net ( EN ) regularization . Different values of the regularization parameter α were examined to assess the impact of varying the number of features selected for training the supervised and semi-supervised classifiers ( α = 1 . 0 , 0 . 9 , 0 . 7 , 0 . 5 , 0 . 3 , 0 . 1 ) . In the case of supervised classification models , Lasso and EN regularization underwent 10-fold cross validation ( leave one out cross validation for mouse endotoxemia dataset GSE5663 ) to learn a set of features . These features were then used to train a supervised classifier ( KNN , SVM , RF , or NN ) on the mouse dataset . The supervised classification model was then applied to the human dataset for that particular case study to infer predicted human phenotypes . In the case of semi-supervised models , feature selection was performed on the mouse dataset in the same manner as supervised models . These features were then used to train an initial supervised classification model on the mouse data alone to predict the human samples’ phenotypes . Following this initial training and prediction step , the human samples with the highest 10% of confidence scores on their predicted phenotypes were combined with the mouse dataset to create a new augmented training set . In the second iteration , feature selection and model training proceeded using this training set of mouse and human samples . All human samples in the test set were re-classified and the confidence score threshold of inclusion was dropped by 10% . Feature selection , model retraining , classification , and training set augmentation continued until all human samples were incorporated into the training set . Since NN training is inherently stochastic , we specified that the semi-supervised NN would proceed to the second iteration only if more than one human sample was classified into each class . If this condition as not met after 50 training iterations , the semi-supervised NN proceeded with further training and prediction iterations on the human dataset using an initial model that did not have human predicted phenotypes in both classes . Classification models were evaluated by their ability to discriminate between human phenotypes and by the extent to which analyzing the human molecular data using the predicted human phenotypes implicated the same genes as using the true human phenotypes . Classification performance was assessed by the area under the receiver operating characteristic curve ( AUC ) for the test set of human samples . Differential expression analysis was performed on the homologous mouse and human genes using the phenotypes from the original datasets to identify differentially expressed mouse and human genes . Following model prediction , differential expression analysis was then performed on the human dataset using the predicted phenotypes . Differential expression was assessed by the Wilcoxon-Mann-Whitney ( WMW ) test with Benjamini Hochberg False Discovery Rate ( FDR ) correction ( significance: WMW p < 0 . 05 and FDR q < 0 . 25 ) . GO enrichment was performed on all DEGs in each case study , for the human data , mouse data , and human data with predicted phenotypes using the Reactome pathway database annotation option in GO [39] [40 , 41] . A DEG or enriched pathway identified in the mouse model was considered a true positive ( TP ) if that gene or pathway was also implicated in the human data analyzed using the true phenotypes . False negatives ( FN ) were DEGs or enriched pathways implicated in the human data , but not implicated by the mouse model . False positives ( FP ) were DEGs or pathways implicated in the mouse but not in the human data . DEGs and pathways identified using the predicted human phenotypes generated machine learning approaches were considered TP , FP , and FN by their correspondence to the DEGs and pathways implicated in human data analyzed using the true phenotypes . We computed the precision and recall for the DEGs predicted by the mouse model and machine learning classifiers and aggregated these into an F-score for each prediction modality . Enriched pathway precision , recall , and F-scores were analogously computed for TP , FP , and FN predicted pathways from the mouse model and machine learning classifiers . All analyses were implemented in MATLAB 2016b . KNN , SVM , and RF functions were implemented using the fitcknn , fitcsvm , and TreeBagger functions respectively . Neural networks were implemented using the MATLAB Neural Network Toolbox . Semi-supervised functions are deposited at: https://www . mathworks . com/matlabcentral/fileexchange/69718-semi-supervised-learning-functions
|
Empirical comparison of genomic responses in mouse models and human disease contexts is not sufficient for addressing the challenge of prospective translation from mouse models to human disease contexts . We address this challenge by developing a semi-supervised machine learning approach that combines supervised modeling of mouse datasets with unsupervised modeling of human disease-context datasets to predict human in vivo differentially expressed genes and enriched pathways . Semi-supervised training of a feed forward neural network was the most efficacious model for translating experimentally derived mouse biological associations to the human in vivo disease context . We find that computational generalization of signaling insights substantially improves upon direct generalization of mouse experimental insights and argue that such approaches can facilitate more clinically impactful translation of insights from preclinical studies in model systems to patients .
|
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2019
|
Computational translation of genomic responses from experimental model systems to humans
|
Infections of stratified epithelia contribute to a large group of common diseases , such as dermatological conditions and sexually transmitted diseases . To investigate how epithelial structure affects infection dynamics , we develop a general ecology-inspired model for stratified epithelia . Our model allows us to simulate infections , explore new hypotheses and estimate parameters that are difficult to measure with tissue cell cultures . We focus on two contrasting pathogens: Chlamydia trachomatis and Human papillomaviruses ( HPV ) . Using cervicovaginal parameter estimates , we find that key infection symptoms can be explained by differential interactions with the layers , while clearance and pathogen burden appear to be bottom-up processes . Cell protective responses to infections ( e . g . mucus trapping ) generally lowered pathogen load but there were specific effects based on infection strategies . Our modeling approach opens new perspectives for 3D tissue culture experimental systems of infections and , more generally , for developing and testing hypotheses related to infections of stratified epithelia .
Stratified epithelia cover most of the human body’s exterior and line the inner cavities , such as the mouth and vagina . Localized ( non-systemic ) infections of these epithelia can cause a wide range of conditions that collectively represent a major burden on global public health systems . For instance , skin conditions are ranked 4th in global years lost due to disability ( YLDs ) and are in the top 10 most prevalent diseases globally [1] . Infections ( viral , fungal , bacterial , etc . ) are either the etiological agents or are secondary opportunistic infections ( e . g . scabies , eczema ) of many skin conditions and thus play a major role in their burden and outcomes . While stratified epithelia are often the first line of defense against infections [2] , their cells are the primary target for many viruses or bacteria . This is why understanding epithelial life-cycles , signaling , and dynamics is an active line of research [3] . Epithelial infections are very heterogeneous in their outcomes , ranging from short sub-clinical acute infections to chronic pathologies [1] . Our hypothesis is that the stratified structure is one of the keys to understanding these patterns . Though experimental and clinical methods used for studying these infections are increasingly quantitative ( e . g . flow cytometry or -omics technologies ) , theoretical frameworks for understanding infection properties and dynamics in stratified epithelia are lacking since most models consider infections of monolayers or blood . Here , we build on the analogy between a host and an ecological system [4 , 5] to investigate how the stratification of the epithelium drives infection dynamics . We focus on keratinocyte epithelia as an example as it is a well-studied stratified system with important public health implications . Localized infections of stratified epithelia such as the cervicovaginal mucosa are involved in a range of health concerns , such as decreasing fertility [6–9] or carcinogenesis [10] . Studying the cervical epithelium has greatly helped improve women’s health [11] and histological studies of cervical infections have characterized both healthy and diseased cells . The ectocervix is a non-keratinized stratified epithelium that acts as an important barrier to prevent infections from entering the upper part of the female genital tract and affecting fertility . The tight packing of the epithelial cells and their migration to the surface are believed to prevent bacteria and viruses from reaching the dermis [12] . Furthermore , the continual production of surface mucus is thought to aid in trapping and removing invaders [13] . Studying these processes using tractable experimental systems has been a challenge given the complexity of recreating stratified epithelia with realistic features , but this is changing rapidly [14] . Mathematical modeling can aid this experimental work by helping to estimate parameters such as changes in cell migration or mucus production rate . The vast majority of mathematical models of within-host dynamics focus on virulent viruses causing systemic infections , such as HIV ( for a review , see [15] ) , but some investigate pathogens that only ( or mainly ) target epithelia such as Chlamydia [16–20] , HPV [21–24] , Epstein-Barr Virus ( EBV ) [25 , 26] or HSV [27] . A common feature of these models is that they focus on the pathogen and the associated immune response , while largely overlooking the epithelium itself . As a consequence , with few exceptions ( e . g . [23] ) , they assume that the population of cells infected by the pathogen is homogeneous and not structured . We take an ecological approach to model the stratified epithelium to investigate the effect of the structure of the life cycle of the host cells on infection dynamics . The analogy between ecological systems and within-host interactions is not new ( e . g . [4] ) , but it is becoming increasingly common and has underlaid successful quantitative tools for understanding viral kinetics [15 , 28] and drug resistance [29] . From an ecological perspective , the stratified epithelial structure can be viewed as having stages or age structure ( herein called ‘stage-structure’ ) , meaning the full life-cycle of an individual cell is divided up into stages ( or ages ) . Therefore , populations of one stage give rise to another in a successive fashion . Ecological populations with stage-structure have been shown to have rich dynamics [30] . If resource populations ( species low in a food chain ) are stage-structured , then the resulting dynamics can impact the entire ecological system [30–32] . Generally , oversimplifying ( e . g . not considering the stage-structure of the resource ) or not considering the resources is known to potentially lead to incorrect predictions about the behavior of the system [33] . Similar importance has been shown in host-pathogen systems . For instance , by combining mathematical models with experimental data Mideo et al . showed that differences between Plasmodium chabaudi strains could be most parsimoniously explained by their different affinity for erythrocytes of different ages , as well as differences in erythropoiesis , i . e . in how red blood cells are made [34] . Target cell heterogeneity has also been put forward to explain the HIV co-receptor switch [35] . While we pursue this analogy , we insist that stratified epithelia exhibit features that differ from traditional populations . For instance , differentiated keratinocytes ( or ‘adults’ ) do not reproduce to make stem cells ( or ‘juveniles’ ) the way free-living species do . Additionally , the epithelium self-regulates its dynamics as a means to maintain homeostasis , which involves the maintenance of constant numbers of cells by physiological processes , such as states of dormancy , proliferation and signaling [3] . Together , this calls for a system-specific approach . Having a framework for epithelial dynamics allows us to simulate infections . For this , we chose two prevalent stratified epithelium infections with very different biological features: Human papillomaviruses ( HPVs ) and Chlamydia trachomatis bacteria . In the United States alone , more than 1 . 5 million cases of C . trachomatis were reported to the Center for Disease Control ( CDC ) in 2017 and HPVs are the most common sexually transmitted infection in the country [36] . Most HPVs are , in fact , not sexually transmitted and are part of a large family of viruses that infect stratified epithelia throughout the body ( of the mucosal and or cutaneous tissues ) and are considered part of our virome [37 , 38] . While both Chlamydia and HPVs replicate intra-cellularly , these two infections exhibit contrasting strategies for infecting the squamous epithelium: HPVs cause non-lytic basal-up infections , whereas chlamydia infections are from the surface-down and are lytic . As mentioned , there are some previous mathematical models of both HPV and chlamydia [16–24] and , importantly , the biology of these two pathogens have been considerably studied , with well characterized life-histories ( HPVs [37] and Chlamydia [39] ) . Consequently , this provided us with peer-reviewed parameter estimates , biologically grounded assumptions and previous results from mathematical models without epithelium stage-structure with which to compare our results . Finally , to maintain focus on the epithelium , we used a simple model for the immune response , as in earlier studies ( e . g . on HSV , [27] ) . We address to what extent epithelium dynamics affect infection dynamics and as a result determine infection outcomes . First , we introduce a general epithelium model , which we calibrate using existing data , as well as original cell culture data from a spontaneously immortalized human cell line ( NIKS ) [40] . With this data we infer parameters that are difficult to measure , such as the fraction of symmetric cell divisions . We then ‘infect’ this epithelial model with chlamydia , wart-associated HPVs and oncogenic ( high-risk , HR ) HPVs to investigate how protective measures by the epithelium affect infection load and duration , while identifying the parameters that control key infection traits . We find that epithelium stratification plays a key role in the dynamics and outcomes of these infections .
Our model abstracts the stratified epithelium into four phenotypically distinct populations , relevant to clinical and experimental models of the epithelium: stem-like cells in the basal and parabasal layers , and differentiated cells in the mid and surface layers ( Fig 1 and equation system 1 ) . These phenotypes can be identified experimentally using immunofluorescence techniques that target genes or proteins expressed differentially as the cells mature and move up the epithelial column . The model is sufficiently generic that it can represent any stratified squamous epithelium , keratinized or not . We considered the cervicovaginal mucosa as an example to parametrize and infect . The model includes 7 parameters , of which 4 are inferred from cervical autoradiographic experiments done in 1970 [41] and one , Nb , is a scaling parameter describing the surface of the basal monolayer considered . The two remaining capture the difference in symmetric divisions probabilities by the basal and parabasal cells ( Δp is fixed at zero and Δq is free and calibrated ) . All the parameters are listed in Table 1 . The parameters for which we have less information are related to the fraction of cells dividing symmetrically ( e . g . a parabasal cell produces two daughter parabasal cells or two differentiated cells ) . Existing data suggests symmetric divisions are expected to be low [42 , 43] . This is further reinforced by our estimate of epithelium thickness . Histological studies calculate 26 to 28 cell layers in the vaginal epithelium depending on the stage of the menstrual cycle [44] and in vivo studies of the cervical epithelium count 16 to 17 layers [45] . To achieve comparable values , and assuming that the ranges of the other parameters are biologically plausible , we find that symmetric divisions must be rare . Calibrating Δq ≈ −0 . 012 gives an epithelium ‘thickness’ of 17 layers , i . e . 17Nb . Analytical results shown in S1 Text revealed the need for some degree of symmetric division biased towards producing differentiated cells ( Δq < 0 ) . Furthermore , if we assume each layer of parabasal cells has the same number of basal cells and that the differentiated cells are half the number of cells per layer ( because they are twice the size [46] ) , then 17Nb corresponds to 26 layers . Finally , we found that the mid layers , that is the differentiated , Ud , and parabasal layers , Up , are larger than the basal and superficial , Us , layers . To obtain experimentally relevant parameter estimates , we used our model and the known parameters as priors to estimate values using original data from raft cultures of NIKS ( Normal Immortal Keratinocytes ) cells . The NIKS cell-line grows into a 3D epithelium structure and is commonly used as a model of cervicovaginal tissue and HPV infections , though they are known to differ from in vivo tissue [40] . Fig 2A and 2B show an example of NIKS cell growth into stratified form . Fig 2C shows the dynamics of the number of basal and suprabasal ( non-keratinized and keratinized ) cells , along with the inferred dynamics from the model . From this data ( of growth from single layer to stratified ) the symmetric divisions were inferred to be negligible in the basal layer but important in the parabasal layers ( Table 1 ) . This implies then that the constant basal layer assumption , and thus model 1 , is appropriate for fitting organotypic culture datasets . The data constrained the replication rate of the parabasal cells , ρp , to be low and the Δq was estimated to be close to −1 , suggesting that while the replication rate is low , nearly all parabasal divisions produce two differentiated cells which move up the column ( Table 1 ) . This , along with the higher than in vivo estimates for the basal replication rate , ρb , is consistent with a growth phase of an epithelium growing up to homeostasis . We performed a sensitivity analysis to explore the general behavior of the model and identify the parameters that have the largest effect on homeostasis , i . e . at a dynamic equilibrium without infection ( Table 2 ) . This showed that the total number of cells in the layers above the basal layer is mostly governed by the basal cell proliferation rate , ρb . Additionally , the time for the system to reach homeostasis ( which is important for repairing damaged tissues ) depends on the proliferation rate of the parabasal cells ( ρp; S1 Text ) . Indeed , homeostasis is reached faster when the replication rate , ρp , or the symmetric divisions of the parabasal cells , Δq , are significantly higher , as found from fitting the data and the model simulations ( Table 1 and not shown ) . Having generated and calibrated a model for epithelial dynamics , we could then simulate infections to investigate how stratification affects important properties of the infection . Epithelial infections by both chlamydia and HPVs are heterogeneous in their clinical manifestations . Chlamydia infections can be asymptomatic or with clinical manifestations such as cervicitis [47] . The lytic nature of chlamydia infections reduces the epithelium to lower cell numbers than homeostasis , therefore affecting the integrity of the layers ( Fig 3 ) . This is consistent with the cervical erosion observed in chlamydia-driven cervicitis or in infections by other lytic pathogens such as HSV [48] . Several HPVs have been found to be associated in wart-like lesions , which are substantive cell overgrowth above homeostasis levels . Among the mucosal α-genus HPVs , HPV6 and HPV11 often ( though not always ) generate papillary lesions or warts . In cutaneous stratified squamous epithelia several HPVs are associated to warts ( e . g . species 2 and 4 of the α-genus and the types of the μ- and ν-papillomaviruses ) in various locations , such as the feet and hands [49] . Conversely , HR-HPV types cause flat lesions ( yet with a thickening of the epithelium ) [37] . How differences between HPV types translate into this observed diversity of clinical manifestations in the epithelium is not always clear . What is clear is that HR-HPVs have stronger cell transforming properties than low-risk ( LR ) and wart-associated HPVs [37] . The epithelium model allowed us to identify conditions that lead to wart-like manifestations . When assuming that there can be rare events of new virions entering the basal layer ( e . g . due to immunosuppression and cytokines loosening epithelial junctions ) and that wart-associated types do not drive cell proliferation in lower layers [37] , we find that they must either have higher burst sizes ( produce more virions per cell ) than HR-HPV types or be better at driving differentiated cells back into S-phase in the upper layers ( ρa and θ control the peak of infected cells in Table 2 ) . Burst size , θ , also controls how quickly the number of infected cells increases , as does the infection rate , β . This explains why simulations of wart-associated HPVs with higher burst sizes are more effective at reaching basal cells , as illustrated by the differences in shading of basal layers between Fig 4A and 4B . Epidemiological studies that directly compare viral loads of LR vs . HR genital HPVs are needed , however , wart-associated HPVs ( mucosal or cutaneous ) have higher viral loads in warts than other HPVs [49] . HR-HPVs have enhanced E6 and E7 oncoprotein effects in the lower layers [37] . In spite of this increase in epithelial cell division rate their infections are flat , slow growing , and are often clinically indistinguishable from a normal epithelium for many months . For this to occur , we find that the extra proliferation in the basal , ab , and upper layers , ρa , and the type’s burst size , θ , must be kept low ( Table 2 and Fig 4B ) . This implies HR-HPVs would be less ‘productive’ ( shed less virions ) than wart-associated HPV during an infection of the same duration ( Fig 4C ) . If HR-HPVs were to have low burst sizes but high oncoprotein-driven proliferation in the lower layers , then their infections would be wart-like ( S1 Text , S2 Fig ) . Thus , to maintain flat lesions the strong HR oncogenes need to be down-regulated . A simulated representation of silent , productive HR-HPV infection is shown in Fig 4B . For an infection to be sufficiently disruptive to generate a visible manifestation , both the size of inoculum ( number of cells infected initially ) and how quickly the microabrasion closes from repair appear to matter . For instance , the wart-like overgrowth of cells in Fig 4A can be created either by a small inoculum with slow repair or by a large inoculum and fast repair . When microabrasions close quickly ( within a few days ) and only a small number of cells are infected initially , both HR and wart-associated types do not cause any visible disruption to homeostasis Fig 4D . Clinically , these infections would be asymptomatic with normal cytology and would likely only be detectable using PCR methods . Finally , we compared our HPV results with a non-stratified model of HPV infection ( see S1 Text ) and find that it is unable to reproduce the features associated with HR and non-HR HPV infections if using the same biologically constrained parameter ranges ( see S4 and S5 Figs ) . For some parameter combinations the kinetics of chlamydia infections had an acute phase only ( Fig 3A ) , as have been observed in guinea pigs and other animal models [18] . We obtain this qualitative pattern most readily when the infection rates are the same for all layers or when the lower layers are difficult to infect ( for instance due to the reduced permeability down the epithelium column [12] ) and the population of immune effectors grows rapidly . From the sensitivity analysis , duration is longer when the EBs can infect the lower layers , ηu , and shortened when the cell recovery rate is high ( Table 2 ) . We also found an acute phase can be followed by a chronic phase , where a pathogen load stabilizes to a set point value ( Fig 3B ) . How quickly a chronic phase is reached depended on chlamydia’s infection rates of the various layers . Generally , infection rates had to be low to achieve the chronic phase ( because if too high then the bacteria burn through the epithelium and its population crashes ) . Additionally , if the layers are differentially infected by chlamydia ( i . e . βb < βp < βu ) , then the chronic phase is reached earlier ( see S3 Fig ) . In contrast , long-lasting wart-associated and HR HPV infections did not exhibit an acute and a chronic phase in our model . Instead , they persisted by monotonically reaching an equilibrium ( e . g . Fig 4Di and 4Dii ) . Also , for both HPVs , the immunity killing rate , κ , was the most important factor in determining infection duration ( Table 2 ) . With more antigen in the lower layers to detect , the efficiency of immune killing ( κ ) becomes important for determining duration of infection , speed of growth and size of infected cell accumulation ( Table 2 ) . Finally , while Chlamydia and HPVs can cause either acute or chronic infections [51] , our model showed that a clinically detected chronic state is achieved through different underlying dynamic patterns for each pathogen . Upon infection , epithelia exhibit defense mechanisms such as increasing mucus flow , tightening the packing of cells , migration to the surface [52] and increasing proliferation ( promoted by Interleukin-22 cytokines [53–55] ) . We varied epithelial parameters from their homeostasis value to investigate in detail the effect of such mechanisms on various measures of infection using our infection models for HPV and chlamydia ( models 3 , 4 and sensitivity analyses in Table 2 ) . We found some mechanisms had similar effect on both HPVs and chlamydia . First , increasing upward migration of epithelial cells , ν , reduced the maximum pathogen load reached during the infection ( Table 2 ) . Second , mucus trapping , ζ , delayed the peak and the duration ( although it played a bigger role in decreasing the peak of infection for chlamydia than for HPV ) . And finally , for all infections , increasing basal or parabasal cell proliferation , ρb and ρp , scored high in affecting all the infection measures , e . g . size of peak or duration ( ‘effects of epithelial parameters’ in Table 2 ) . However , a pathogen-specific effect was that increasing basal proliferation , ρb , of uninfected cells decreases the time to clear HPVs but not chlamydia . Together , this suggests that epithelial cell features themselves play an important role in infection dynamics and outcomes .
The rate of basal cell proliferation had a strong effect on the homeostasis of both uninfected and infected epithelia , which suggests an ecological ‘bottom-up controlled’ system [57 , 58] , analogous to those found in free-living food webs . These bottom-up effects are more apparent if we consider that basal cell replication is strongly determined by the resources that are available in the basal lamina , such as growth factor . While we did not explicitly model the resources of the basal layer ( it is implicit in the basal proliferation rates ) , the growth of the cells in the experimental set-up does depend on concentration and temporo-spatial distribution of growth factors , impacting epithelial thickness and proliferation rates . Therefore , this ecological insight of bottom-up driven systems , could be tested more formally in experimental systems by monitoring resource concentrations . The key role of bottom-up control is further supported by our finding that accelerating basal cell proliferation , as a response to infection [53 , 54] , affected all infection measures ( e . g . time of peak , total load , duration ) . This infection response , then , can have a strong effect on the severity and duration of infections . However , using the same response mechanism might be differentially effective depending on the infection strategy of the pathogen . For instance , we found that increasing cell proliferation did not shorten the infection of chlamydia . This is probably because proliferation increases the number of uninfected epithelial cells in the upper layers which , for chlamydia , means more ‘resources’ . Pathogens can have different tropisms for the various cell phenotypes of the stratified epithelium . For instance , EBV more readily infects and replicates in differentiated cells of the upper/mid layers [59] , whereas HPV infects the basal layer to establish an infection [37] . We hypothesized that this should impact how effective protective processes ( e . g . increased mucus production ) of the epithelium are against them . In chlamydia , where the pathogen infects all cell types equally well , we found that tight packing ( i . e . epithelial permeability ) mattered to the pathology at the site . The speed at which the epithelium shrank and the stability of the infection system ( how quickly it can reach chronic phase ) depended on how well the bacteria could access cells down the column . If the bacteria were able to infect the bottom of the column quickly , that led to a population crash due to the lack of resources . On the contrary , and somehow unexpectedly , less epithelial permeability stabilized the infection that then lasted much longer and exhibited a clear chronic phase . This stabilizing effect is also observed in ecological systems when one stage is invulnerable to attack , i . e . a stage refugia [32 , 60] . For instance , a parasitic wasp was introduced as a biological control of red scales ( a common plant pest ) . It successfully controlled the red scales because one of the mature stage of the red scales was not vulnerable to attack [32] . Such effects from decreasing permeability ( protecting the basal replicative stage ) would have implications in the context of treatments that bolster cell adhesion and require testing experimentally . Considering pathogens with contrasted life-histories allowed us to identify how similar infection outcomes arise . In the case of chlamydia , the interaction between free-form chlamydia and its infection rates of the various stages drove the chronic phase , but although the activation of the immune response through the same feedback ultimately led to clearance , this feedback affected several infection features . In contrast , HR-HPV persistence was achieved via a slow growth strategy that delays clearance by decreasing the negative dynamical feedback involving the immune system ( i . e . faster growth implies faster immune detection and clearance ) . Indeed , HPV types appear to evade , or counteract , these immune responses differently . In particular , viral protein E6 of various HPV types differ in their many cellular binding partners resulting in a variety of effects on host processes [61] . We found that the difference between HPV-induced genital warts and epithelial lesions depended most on the number of virions an infected cell releases upon death ( or ‘burst size’ ) and the initial size of inoculum; suggesting that more productive viruses are better colonizers . A ‘colonization’ strategy ( in ecology ‘r strategy’ ) appeared to have a cost for the virus because infecting the basal layer of the epithelium triggers the immune response . While more sites are colonized , each site is exploited less optimally . Another feature that was mediated through the immune response feedback was that mucus trapping delayed the peak of the infection ( i . e . the decreased progeny of bacteria and viruses meant less antigen and thus slower immunity detection ) . To compare our results to HPV epidemiological studies of acute HPV infections , we see that the model creates underlying patterns ( e . g . viral load Fig 4C ) that could be looked for using prospective studies of HPV infections with normal cytology . Study designs with dense sampling ( with visits every 3 or less months ) are best for capturing the dynamics of these infections , particularly for the exponential increase and decay of viral loads . The majority of HPV prospective studies are of persistent infections and with advancing cytological abnormality but there are exceptions . For instance , Marks et al . sampled young women with HPV16 infections every 3 months and found that a greater than 2 log decrease in viral load was associated to clearance and a single viral load measure could not predict clearance [62] . The HR-HPV viral load dynamics from our model ( Fig 4C ) can provide possible underlying explanations and our exponential decrease is consistent with the decrease found by this epidemiological study . Though , sampling once would not give enough information as to whether the infection is increasing or decreasing at a given point . Consecutive viral load measures , then , are more appropriate to estimate clearance or persistence [63] . The effect of stage-structure on infection dynamics can be interpreted in light of earlier results from ecology or epidemiology . For instance , in epidemiology , it is known that the more a general population of infected host is subdivided into classes , the more rapid the growth rate of the epidemic is and the shorter it lasts [64] . Our model bears even more parallels with age structured models in epidemiology where the age groups of the host population are explicitly considered . In many of these models , children tend to be key to the spread of epidemics [64] , a result that echoes the bottom-up effects we identify . However , the driving forces in the two models are different: in our model it is due to the fact that basal cells are the ones replicating , whereas in epidemiology it is usually driven by longer lasting acquired immunity at higher ages . Spatial structure is a natural extension of our model that could be investigated further . Here , the different cell populations partly capture the vertical structure . A specific consequence of not including space is that the immune system effects are more homogeneous than in reality , where more resident immune cells are present in the lower layers . The assumption of well-mixed populations holds best when the model represents a portion of the squamous epithelium ( rather than , for instance , the whole cervix ) . In the case of patchy infections like HPV , a metapopulation modeling approach may be more appropriate ( e . g . [22 , 38] ) or a full spatial model [21] . We chose not to include space since much of the experimental data available on these systems is not spatial . Instead most are cell population counts from immunofluorescence or flow cytometry techniques . Several mathematical modeling methods , such as agent-based models , are available to study spatial aspects of infections , particularly cell-to-cell spread [65] . These should be of interest to those studying chlaymdia infections . Even though HPVs have not been found to spread cell-to-cell like other viruses [66] , studying the spatial aspects of their infections should most certainly still be considered in future studies . Introducing stochastic aspects in epithelial dynamics have recently refueled the discussion on the determinants of HPV clearance [23] . In general , considering stochastic dynamics could matter most when pathogen populations approach low-levels ( i . e . very few infected cells or small loads ) . For instance , our finding that mucus trapping can delay the peak and the duration of infections could interact with stochasticity . This is similarly true for infections started with a small inoculum , very rapid abrasion closure , and rapid repair with small inoculum . These processes keep the pathogen populations sizes down and thus , as seen in ecological systems , stochasticity should play a larger role in extinction . As for the spatial structure , it is important to stress that there often is little data on the initial stage of the infections , when the pathogen is rare . Many previous works have used simplified descriptions of the immune response in a similar fashion as we have chosen to model here [15 , 27] . Models with simplified immunity usually ask conceptual questions or are used to infer parameter values from data with few measured cell types ( e . g . only counting CD8+ and CD4+ T cells ) . Future work interested in specific questions that are immune related , for instance comparing the respective roles of innate and adaptive immunity in clearance , could benefit from more detailed descriptions of immune effectors . In particular , expression of cytokines are interesting as they are important in the epithelium’s part in innate immunity [52] . Our model does not attempt to capture the progression stages that HPVs can cause in persisting infections . To appropriately model these changes would require several adjustments , including that cell proliferation of infected cells and probabilities of symmetric divisions become time variant . Indeed , our model can be adapted to study other oncoviruses that infect the epithelium , where future studies can consider the contexts of immune evasion and cellular transformation driven by oncogenes [37] . In addition , there is increased interest in how epithelial cell dynamics ( e . g . cell competition , mechanisms to maintain homeostasis and repair ) interact with our knowledge of how tumor viruses alter cellular programing , in particular changing balanced cell fate ratios , skewing squamous differentiation toward a proliferative phenotype [67] . New modeling methods will require possible evolutionary approaches of cell phenotypes emerging over time . In many ways , the simultaneous infection of a host by different pathogen strains or even species is the rule rather than the exception [68] . Of particular interest is how different pathogens or strains interact inside a host and how this affects the course of the infection . For instance , HPV infections are often of multiple HPV types and as lesions progress to cancer there is clonal-selection , usually leading to a single type as the main driver of the tumor [69] . One straightforward extension of this model would be to investigate coinfections between pathogens with similar cell tropisms ( e . g . chlamydia and EBV ) or pathogens that differ in their life-cycles . Our model could consider both infections at once or be adapted to study organotypic models that include multiple pathogen infections ( e . g . HSV , EBV and HPV coinfecting the same tissues and cells [70] ) or the effects of the resident microbiota . Finally , opening a dialogue between mathematical modeling and experimental data generates new hypotheses to test . One of the clearest illustrations of this is our result that burst size differences appear as the most parsimonious explanation to explain symptom differences between wart-causing and lesion-causing HPV infections . Technological improvements in clinical and experimental techniques also allow us to test more subtle predictions . Testing hypotheses generated by the model will allow us to move forward by validating the model assumptions that are consistent with the data and rejecting the others . This will allow us to increase the model complexity and test more elaborate predictions . We hope to inspire experimental studies on infections of stratified epithelia to focus more on dynamics and time series approaches ( including mathematics ) to better understand these varied and broadly impacting pathogens .
The Thunder Bay Regional Health Research Institute’s Biosafety Committee approved all research involving NIKS cell line cultures . The NIKS cell line [40] was obtained from Dr . Paul Lambert , McArdle Laboratory for Cancer Research , University of Wisconsin . Organotypic culture growing techniques used here have already been described in detail elsewhere [71 , 72] . Original experiments were performed to obtain time series data with sufficient replicates for model fitting . Three independent experiments were performed , with rafts harvested at one-week intervals ( 0 , 1 , 2 , and 3 weeks ) starting the day after lifting them to an air-liquid interface . From a total of 12 formalin-fixed , paraffin-embedded ( FFPE ) rafts , 48 tissue slices were imaged using fluorescence microscopy ( DAPI staining for cell nuclei ) and resulted in 3 Fields of View ( FOV ) per slice ( n = 144 ) . Counts in each FOV were done semi-automatized using ImageJ cell counting software . The uninfected epithelial model consists of 4 cell populations of distinct phenotypes to capture epithelial structure ( Fig 1 ) : basal cells ( assumed to have a constant population size , Ub = Nb , as cells that move up are replaced immediately ) , parabasal cells ( with population size Up ) , differentiated cells of the mid and upper layers ( with population size Ud ) and of the surface layer ( with population size Us ) . Since we are interested in cervicovaginal infections of non-keratinized squamous epithelia , we assume the top layer of keratinocytes are close to death and are shed from the surface as they die . The cell population dynamics are captured by three ordinary differential equations ( ODE ) : U s . = ν U d - μ U s U d . = ρ p ( 1 - Δ q ) U p - ν U d U p . = ρ b ( 1 - Δ p ) U b + ρ p Δ q U p ( 1 ) Dots above the variables indicate time derivatives . Basal cells proliferate at a rate ρb , giving rise to parabasal cells which in turn proliferate at a rate ρp , while entering the mid and upper layers of the squamous column ( Eq 1 ) . These cells are differentiated and migrate up to the surface layer at a rate ν . Mature keratinocytes die at the surface of the epithelium at a rate μ . There is an overlap between cell phenotype and spatial structure since an epithelial cell moves up the stages as it ages ( Fig 1 ) . When modeling stem cell divisions , we follow earlier studies [23 , 73] and introduce the fraction of basal cell divisions that are symmetric and give rise to two basal cells , p1 , and the fraction that creates two parabasal daughter cells is p2 . Note that q1 and q2 are the parabasal equivalent terms ( see Fig 1 ) . The generation of parabasal cells from basal cells is found by 2 p2 + ( 1 − p1 − p2 ) which we simplify to 1 − Δp by assuming Δp = p1 − p2 and the equivalent of this for the generation of differentiated cells is Δq = q1 − q2 in equation system 1 . We considered distinct probabilities of divisions for the two layers ( ps and qs ) , even though both the basal and parabasal layers are mostly made up of the same transit-amplifying cells , because the basal layer also contains stem cells which can divide in an unlimited fashion [74] . Thus , the two layers should have distinct properties . Finally , the assumption that the basal layer is constant implies that we must assume Δp = 0 in order for the basal layer to neither grow nor shrink . However , we maintain this structure of the model because Δp would be needed if one were to either relax the assumption of a constant basal layers ( e . g . when studying a growing epithelium , as in organotypic cultures ) or when it is infected ( e . g . HPV infections might alter this parameter and make p1 divisions more frequent [67]; though we do not address this feature of HPV infection directly ) . We chose to not include the stochastic nature of these cell divisions , as it has been considered previously [23 , 73] , and we were interested in understanding deterministic behaviors of the system , such as active repair or active changes to cell ratios . All the variables and parameters used are summarized in Fig 1 and Table 1 . Finally , the model is sufficiently general that it can represent different kinds of stratified epithelia , including keratinized and non-keratinized squamous epithelia . To calibrate parameters ( Table 1 ) , we initially relied on a study from 1970 that used in vivo autoradiography techniques to calculate the mean cell cycle time for epithelial cells in cervical and vaginal tissues [41] . They found that basal cells have a relatively slow cycle of approximately 33 days and that 1 . 14% of these cells are synthesizing DNA at a given time point . Parabasal cells have a much shorter cell cycle ( 2 . 6 days ) and 14 . 25% of these cells are synthesizing DNA . Differentiated cells do not divide and have a life expectancy of 4 days ( Table 1 ) . A detailed analytical analysis of this uninfected model can be found in the S1 Text . For fitting raft cell culture data , we did not want to assume a priori that the basal layer starts off as a constant , especially since in the experiments the tissue is grown-up from a single layer cells . So we used a variation of our model by assuming the basal layer was not constant but rather followed this equation: U b . = ρ b U b Δ p ( 1 - U b N b ) . ( 2 ) Here we assume the basal layer ( cells that are touching the basal lamina ) are growing until they reach a maximum capacity , Nb , and Δp is not assumed to be zero . There are other changes from the previous model: Us now represents the surface cells that are keratinized , and since the Up and Ud cells cannot be distinguished experimentally we summed these two variables for fitting the ‘suprabasal’ cells counted in the experiment . Modeling infections of the stratified epithelium requires adding populations of free-forms of the pathogens , infected cells and immune cells . See Fig 5 for the schematics of the models and Table 3 for the parameter estimates . Nearly all the parameter values could be set using data from the literature , which mostly lay in narrow ranges ( Tables 1 and 3 ) . Parameters for which we had little information were either kept free or calibrated . For instance we used Δq to scale all equilibrium population sizes ( see Results ) . To test the robustness of our results , we performed uncertainty and sensitivity analyses using Latin Hypercube Sampling and Partial Rank Correlation Coefficients ( PRCC ) via the pse package in R [77] , which is popular for disease models [78] , and numerical integration was done using deSolve package . We generated 1 , 000 parameter sets by Latin Hypercube sampling from uniformly distributed parameter values within a specified biologically realistic range . PRCCs were calculated between the rank-transformed samples and the resulting output matrix of the response variables ( e . g . duration of infection , maximum pathogen load ) . 100 bootstraps were performed to generate 95% confidence intervals . The magnitude of the PRCCs determines the effect strength of a given parameter on a specific response variable ( 0 for no effect and 1 for very strong ) and the sign indicates whether the response grows or shrinks with increasing the parameter value . Monotonicity for each parameter was checked for each response variable , and the parameter ranges were shortened when monotonicity was not obeyed . This was not common and was usually for values very close to zero . We inferred parameter values from the data we collected over 3 weeks from a 3D raft culture of NIKS cells . Note that cells attached to the basal membrane were considered basal and those above them were counted as suprabasal cells . This was done ( rather than use differentiation markers ) in order to differentiate between true basal cells and parabasals and to estimate a carrying capacity , Nb . Model parameters were inferred using maximum likelihood estimation and trajectory matching , assuming measurement error follows a Poisson distribution . Fitting and model predictions were performed in R software [79] , using packages bbmle [80] , deSolve [81] , and pomp [82] . Note that the parameter values estimated experimentally were not used for the infection models since the experiments had the tissues growing up into full stratified form while infections usually start with the epithelium already at homeostasis , thus the epithelium parameters from the literature were more appropriate .
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Many epithelia are stratified in layers of cells and their infection can result in many pathologies , from rashes to cancer . It is important to understand to what extent the epithelial structure determines infection dynamics and outcomes . To aid experimental and clinical studies , we develop a mathematical model that recreates epithelial and infection dynamics . By applying it to a virus , human papillomavirus ( HPV ) , and a bacteria , chlamydia , we show that considering stratification improves our general understanding of disease patterns . For instance , the duration of infection can be driven by the rate at which the stem cells of the epithelium divide . Having a general model also allows us to investigate and compare hypotheses . This ecological framework can be modified to study specific pathogens or to estimate parameters from data generated in 3D skin cell culture experiments .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"urology",
"medicine",
"and",
"health",
"sciences",
"keratinocytes",
"pathology",
"and",
"laboratory",
"medicine",
"cell",
"cycle",
"and",
"cell",
"division",
"pathogens",
"cell",
"processes",
"opportunistic",
"infections",
"cell",
"differentiation",
"epithelial",
"cells",
"developmental",
"biology",
"sexually",
"transmitted",
"diseases",
"basal",
"cells",
"infectious",
"diseases",
"chlamydia",
"infection",
"human",
"papillomavirus",
"infection",
"animal",
"cells",
"biological",
"tissue",
"anatomy",
"cell",
"biology",
"genitourinary",
"infections",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"viral",
"diseases"
] |
2019
|
Epithelial stratification shapes infection dynamics
|
Burkholderia pseudomallei , the causative agent for melioidosis , has become a public health problem in India and across the world . Melioidosis can be difficult to diagnose because of the inconsistent clinical presentations of the disease . This study aims to determine the genetic diversity among the clinical isolates of B . pseudomaelli from India in order to establish a molecular epidemiology and elucidate the Southeast Asian association . Molecular typing using multi locus sequence typing was performed on thirty one archived B . pseudomallei clinical isolates , previously characterised from specimens obtained from patients admitted to the Christian Medical College & Hospital , Vellore from 2015 to 2016 . Further investigations into the genetic heterogeneity and evolution at a regional and global level were performed using insilico tools . Multi locus sequence typing ( MLST ) of the isolates from systemic and localized forms of melioidosis , including blood , pus , tissue , and urine specimens , revealed twenty isolates with novel sequence types and eleven with previously reported sequence types . High genetic diversity was observed using MLST with a strong association within the Southeast Asian region . Molecular typing of B . pseudomallei clinical isolates using MLST revealed high genetic diversity and provided a baseline molecular epidemiology of the disease in India with a strong Southeast Asian association of the strains . Future studies should focus on whole genome based Single-Nucleotide-Polymorphism ( SNP ) which has the advantage of a high discriminatory power , to further understand the novel sequence types reported in this study .
Burkholderia pseudomallei , the causative agent of the infectious disease melioidosis , is estimated to cause 165 , 000 cases of human melioidosis per year worldwide [1] . B . pseudomallei , an environmental saprophyte is commonly found in wet soil and stagnant water throughout endemic regions . The mode of infection is by inhalation , through cuts in the skin , and occasionally through ingestion [2] . The most severe clinical manifestation is melioidosis septic shock , which is often associated with pneumonia and bacterial dissemination to distant sites [3] . Melioidosis often affects individuals with one or more pre-existing conditions associated with an altered immune response , the most common being diabetes mellitus [4 , 5] . Melioidosis is endemic to Southeast Asia and Northern Australia , but still can be recognized in other countries worldwide [6] . Regional variations in clinical presentation of melioidosis are widely observed such as the predominance of pivotal swelling seen in Australia but not in Thailand . The contributing factors for this diversity are still unclear whether bacterial , host or environmental . There is no correlation between the clinical presentations and genotypes to date , even though environmental partitioning between Australian and Asian population of B . pseudomallei have been reported previously [7] . Melioidosis has become a public health problem in India , due to the steady rise in case detection rates from various parts of the country . Moreover , no consistency has been observed in the forms of melioidosis ( clinical presentations ) reported among the sporadic cases across the country in the last two decades . A recent report reveals genetic diversity among clinical isolates of B . pseudomallei from South India [8] . This study focuses on the clinical manifestations and genetic diversity of B . pseudomallei isolated from patients across India using the multi locus sequence typing ( MLST ) scheme for B . pseudomallei as described by PubMLST , with an attempt to establish the molecular epidemiology in Southeast Asian region .
The B . pseudomallei are from clinical specimens ( blood , pus , tissue and urine ) from patients admitted to the Christian Medical College and Hospital , Vellore . This study was approved with ethical clearance to use the clinical isolates by the Institutional Review Board with IRB MIN 16044 . The clinical samples are anonymized .
Within the thirty one clinical isolates obtained during 2015 to 2016 , Systemic forms of melioidosis ( blood ) contributed to 51 . 6% ( n = 16 ) and localized to 45% ( n = 14 ) with one urinary tract infection ( 3 . 2% ) . In the case of the localised infections , the number of isolates from pus and the urinary tract infection were 29% ( n = 9 ) , tissue 16% ( n = 5 ) respectively . Infected patients were from the states of Tamilnadu ( n = 11; 35 . 5% ) , West Bengal ( n = 5; 16 . 1% ) , Andhra Pradesh ( n = 6; 19 . 5% ) , Jharkhand ( n = 5; 16 . 1% ) , Kerala ( n = 1; 3 . 2% ) and Tripura ( n = 1; 32% ) . Twenty isolates had distinct allelic profiles from the existing database and were assigned new sequence types ( ST ) ( 13 new STs , ST1630-ST1642 ) . Eleven isolates in this study were found to be previously reported STs ( ST51 , ST1364 , ST1099 , ST300 , ST1552 , ST375 , ST56 , ST71 , ST228 and ST99 ) . The genetic diversity among the identified STs was low as four isolates had ST1630 ( 12 . 9% ) , three had ST1639 ( 9 . 7% ) , two had ST51 , ST1637 , 1641 ( 6 . 5% ) and the remaining 19 isolates having different STs . Isolate identifiers with geographical location , type of specimen and the corresponding STs are represented in Table 1 . Interestingly in two patients with both systemic and localized infections ( VBBP002 –Systemic–ST1630 and VBBP006 –localized—ST1631; VBBP011—Systemic–ST51 and VBBP023—localized–ST375 ) , different STs were observed with respect to the site of isolation—they are single loci variants . goeBURSTanalysis clustered 18 of the study STs into a single major clonal complex of founder ST300 . Four STs ( 1634 , 1632 , 1636 , and 1639 ) were found to be singletons and are outliers ( Fig 1 ) . Thirty five percent ( n = 11 ) of the identified STs in this study have been previously reported and were found to be associated with Singapore ( ST51 ) , China ( ST51 , ST1099 ) , Thailand ( ST51 , ST99 , ST375 , ST228 , ST300 ) , Malaysia ( ST51 , ST99 ) , Burma ( ST51 ) , Bangladesh ( ST56 ) , Cambodia ( ST56 ) , Vietnam ( ST56 ) , Philippines ( ST99 ) and Sri Lanka ( ST1364 ) of Southeast Asia ( Fig 2 ) [9 , 10] . Except for the isolates with the ST51 ( 6 . 5% ) and ST 56 ( 3% ) , no association was found between the epidemiological year and the prevalence of the isolates . ST51 and ST 56 were first observed in 1935 and 1947 and are still seen in clinical cases ( Table 2 ) . Nucleotide diversity among the study isolates as calculated by DNA SP6 was 0 . 00212 and within the Indian isolates was 0 . 00182 ( Table 3 ) . Splits tree analysis depicts 80% of the isolates associated with Southeast Asia into one group wherein the rest of the study isolates are grouped differently ( Fig 3 ) .
The burden of B . pseudomallei in India and across the world is of a great concern due to its wide distribution in the community as an environmental etiological agent . Molecular typing by MLST method serves as a powerful epidemiological tool to determine the source of infection ( local epidemiology ) and understand the diversity and evolution of the pathogen population . Though this study involved a small number of isolates ( n = 31 ) , the identified STs in this study provide information on regional and country wide sequence diversity . The different types of melioidosis in the Southeast Asian region include bacteraemia , skin/soft tissue infections , localized abscesses ( splenic , prostatic , liver , prostatic , parotid ) , pneumonia and genitourinary tract infections [11] . There was no association found between the different types of melioidosis and the sequence types among the study isolates , which showed high diversity . Thirty five percent ( n = 11 ) of the study isolates are confined to STs of South East Asia conferring a regional association and the remaining are novel STs . Though there is high diversity among B . pseudomallei across India and South East Asia , this study provides insights into the regional STs corresponding to both systemic and localized infections being consistent over a long period of time . The correlation between a few of the identified STs ( 51 , 56 ) and the epidemiological years denote persistent strains causing infection across the continent . The variation among isolates ( VBBP002:VBBP006 and VBBP011:VBBP023 ) in both systemic and localized infections from the same patient with a single loci variation shows possible evolution over a short period of time; however genome wide studies are needed to provide valid information . Nucleotide diversity and splits network analysis within the house keeping genes shows the least differences for the 6 housekeeping genes , reducing the possibilities of recombination events but more of single nucleotide polymorphisms . The nucleotide diversity of the gmhD gene was found to be ( 0 . 00306 ) and has the maximum number of allelic profiles within the study population . The existence of the parallelogram type of split tree network of all the south Indian isolates signifies the possibility of recombination events , but the Southeast Asian study isolates did not show a typical parallelogram and lie on the same group conferring the absence of recombination events [12] . Though Multi locus sequence typing ( MLST ) is one of the most commonly used Single-Nucleotide-Polymorphism ( SNP ) based phylogeny with the use of seven housekeeping genes , it represents only 0 . 05% ( 3401 nucleotides ) of the total bacterial genome of 7 . 4 million nucleotides , with less discriminatory potential being the main limitation in a closely related group or within the sequence type . However , whole genome based SNP typing provides phylogeny with high discriminatory power , which could further type the isolates belonging to same sequence types and/or clonal group . This was substantiated by the studies done by Price et al . , 2015 , to show the differences between same sequence types but polyclonal by Whole Genome Sequencing in a patient with chronic melioidosis across years unveiling the genome plasticity [13] , but the study did not indicate the non synonymous nucleotide polymorphisms . Additionally , Chapple et al 2016 describe the differences in B . pseudomallei by Whole Genome Sequences within the same sequence types being persistent across years and different regions , but the mutations not correlating to the environment factors [14] . This evidence gives a glimpse of the high evolution in B . pseudomallei , with conserving the core genome having strong ancestral relationships as derived in this study . Prospective studies based on whole genome phylogeny would provide higher resolution over the genome plasticity of B . pseudomallei in India and the regional association through the conserved regions on this pathogen . Continent wide large scale genomic studies would enable us to establish a regional association of the strains [15] . To conclude , future studies must focus on whole genome based SNP typing in order to understand the phylogeny and evolution of this bacterium .
|
Burkholderia pseudomallei , a gram negative bacteria , is the causative agent for melioidosis . Annually , around 165 , 000 people suffer from melioidosis worldwide . B . pseudomallei is present in wet soil and stagnant water . It enters the human body via percutaneous inoculation , inhalation , aspiration , and occasionally ingestion . Clinical presentations of B . pseudomallei vary by geographical region . Melioidosis occurs predominantly in Southeast Asia , northern Australia , South Asia ( including India ) , and China . Occasional cases occur in other countries around the world . Melioidosis has become a public health problem in India , due to the increasing numbers of people affected in various parts of the country . This study provides baseline data on the genetic diversity among B . pseudomallei isolates from different clinical samples ( blood , pus , tissue and urine ) of patients admitted to a tertiary care hospital using signature nucleotide sequences via multi locus sequence typing ( MLST ) . Further , this study shows a relationship among B . pseudomallei previously reported in various Southeast Asian countries over the years from 1935 and 1947 with those seen in current clinical cases .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"sequencing",
"techniques",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"melioidosis",
"geographical",
"locations",
"india",
"bacterial",
"diseases",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"infectious",
"diseases",
"bioinformatics",
"biological",
"databases",
"molecular",
"biology",
"genetic",
"loci",
"nucleotide",
"sequencing",
"people",
"and",
"places",
"sequence",
"databases",
"blood",
"anatomy",
"asia",
"physiology",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"thailand"
] |
2018
|
Multi locus sequence typing of Burkholderia pseudomallei isolates from India unveils molecular diversity and confers regional association in Southeast Asia
|
Models of competitive template replication , although basic for replicator dynamics and primordial evolution , have not yet taken different sequences explicitly into account , neither have they analyzed the effect of resource partitioning ( feeding on different resources ) on coexistence . Here we show by analytical and numerical calculations that Gause's principle of competitive exclusion holds for template replicators if resources ( nucleotides ) affect growth linearly and coexistence is at fixed point attractors . Cases of complementary or homologous pairing between building blocks with parallel or antiparallel strands show no deviation from the rule that the nucleotide compositions of stably coexisting species must be different and there cannot be more coexisting replicator species than nucleotide types . Besides this overlooked mechanism of template coexistence we show also that interesting sequence effects prevail as parts of sequences that are copied earlier affect coexistence more strongly due to the higher concentration of the corresponding replication intermediates . Template and copy always count as one species due their constraint of strict stoichiometric coupling . Stability of fixed-point coexistence tends to decrease with the length of sequences , although this effect is unlikely to be detrimental for sequences below 100 nucleotides . In sum , resource partitioning ( niche differentiation ) is the default form of competitive coexistence for replicating templates feeding on a cocktail of different nucleotides , as it may have been the case in the RNA world . Our analysis of different pairing and strand orientation schemes is relevant for artificial and potentially astrobiological genetics .
Gause ( 1934 ) in the Golden Age of theoretical ecology formulated the principle of competitive exclusion , proposing in effect what usually is being referred to as “complete competitors cannot coexist” [1] . Later investigations have confirmed that in stable steady state the number of coexisting species cannot be larger than the number of resources , provided that growth rates depend linearly on resource concentrations and that we look for coexistence at fixed densities [2]–[4] . For maximal coexistence to occur , the competitors must consume the resources in different proportions . Since the seminal experiments of Spiegelman [5] and the deep theoretical insights of Eigen [6] , nucleic acid replication kinetics has been under repeated scrutiny . In the “default” model of Eigen with constant total population concentration the fastest replicator ( and its associated mutant cloud ) wins , consonant with “survival of the fittest”; the tacit assumption being that the competing sequences are complete competitors in the sense of Gause . More detailed investigations of RNA replication kinetics have greatly improved these models , taking into account saturation of the replicase enzyme , asymmetry of plus and minus RNA strands , and replicationally inert double-strand formation [7]–[9]; the latter phenomenon yielding coexistence due to the self-limitation of growth . Von Kiedrowski [10] , [11] discovered a somewhat similar phenomenon for his artificial non-enzymatic chemical self-replicators growing parabolically , where self-limitation of growth arises from reversible double-strand formation . Szathmáry and Gladkih [12] showed that the consequential parabolic growth leads to stable dynamical coexistence . Yet none of these models included a detailed analysis of base composition and sequence effects on coexistence . In this paper we remedy this deficiency . We explicitly take into consideration the concentration of up to four different building blocks ( “nucleotides” , with the aim that the model should be general enough to deal with different number of bases and base-pairing modes [13]–[15] ) and a large number of competing different sequences , in order ( i ) to present , at least in part , the missing theory of competing template replicators having different sequences and ( ii ) to answer the question whether Gause's principle holds for such replicators . During the forthcoming analysis we deliberately introduce some simplifications . We assume that template replication rates depend on nucleotide concentration linearly ( there are no cooperative effects ) and that the dynamics of these abiotic resources are not periodically forced , for example . We neglect replicase enzymes and assume that template and replica separate irreversibly upon completion of elongation . The kinetic effects are simplified to the extent that the elongation rate of template polymerization depends only on the identity of the inserted nucleotide and nothing else . We know that this is a crucial simplification but already with this rule different sequences may assume very different kinetic phenotypes . In agreement with this , we neglect secondary and tertiary structures . The raison d'être for these assumptions is that we would like to demonstrate the effect of competition for resources of competing template sequences as simply and clearly as possible . ( We note that as mentioned above , irreversible or reversible pair formation can lead to coexistence , and that enzyme saturation leads to linear growth instead of exponential . ) We deliberately want to see the dynamics of coexistence under irreversible exponential growth tendency , as a kind of worst case . The effect of the sequence diversity of templates on dynamical coexistence is not trivial . If there are two resources A and B , then it is trivial that sequences of and may coexist . But what about and ? Are these sufficiently similar for competitive exclusion or sufficiently different for competitive coexistence ? And have and got the same features in competition , or not ? This question is relevant since a recent study [16] in the ecological literature indicated that life-history traits of organisms can promote dynamical coexistence on limiting resources beyond the effect of simple resource partitioning . Thus two templates with the same nucleotide composition but substantially different sequences may be regarded as adopting two different life history strategies . As replication proceeds sequentially , templates might be regarded as consuming different resources during different stages of their life histories . What is the effect ( if any ) of this stage-structure on template coexistence ? Some of the effects that we show in this paper are far from trivial . Our calculations show the effect of resource partitioning on template coexistence and shed more light on early molecular evolution , which surely was affected by sequence effects of template replicators .
The following model of template replication models RNA replication , dealing with 4 monomers , complementary pairing and antiparallel strand polarity . We have investigated the coexistence of groups of sequence pairs of length . As the sequence space is huge ( ) , consequently , the Cartesian product of the sequence space yielding all possible combinations of sequence pairs is even more huge ( ) . Therefore it is usually impossible to investigate the coexistence of all possible combinations of sequences . Instead , we used a reasonably large sample of the full combined space to estimate the probability and stability of coexisting sequences over four different monomers . We have investigated sequences of length , as this is the maximum length for which the space could be reasonably analyzed . Our analysis was performed using the following two methods ( for parameters , see Text S1 ) : The results of the analysis of coexistence of complementary pairs of sequences of length 4 can be seen in Tables 1 and 2 ( methods M1 and M2 , respectively ) . Accordingly , we can state that the increasing number of complementary pairs reduces the probability and stability of coexistence and that on four different monomers a maximum of four different sequence pairs could possibly coexist . Thus Gause's principle ( against first intuition ) limits the number of sequence pairs instead of individual sequences because of the dynamical coupling between the template and its complement ( the plus-minus ensamble behaves like a single replicator , see [6] , [17] ) . In the intermediate case of non-complementary pairing with antiparallel polarity , it is still the number of sequence pairs that limits coexistence ( and not individual sequences ) , though due to a better partitioning of resources coexistence is more probable on average ( for details , see Text S1 ) . Coexistence can be visualized in case of two coexisting sequence pairs of length in two dimensions: each cell of a matrix represents a certain combination of two pairs , and is labeled by the first sequence of the first pair ( rows ) and the first sequence of the second pair ( columns ) . Sequences are ordered according to standard lexicographic ordering along the horizontal and vertical axes ( see Fig . 2 ) . The higher probability and mean stability of coexistence in the bottom corner correspond to the “niche partitioning”: first sequence consumes more , while the second sequence consumes more . Our model is applicable for any sequence length , predicting whether there is coexistence for any combination of sequences . However , for larger values of the full combined sequence space is enormous therefore it is impossible to perform exhaustive search for all coexisting cases . By brute force search we have found some illustrative examples , according to Method M2 ( for details see Text S1 ) . Our results ( tested up to ) show that 8 sequences ( 4 sequence pairs ) can stably coexist ( linear asymptotic stability was explicitly tested ) . There is no theoretical ( but computational ) objection to applying this model for even longer sequences . In this section we deal with a simplified system: we restrict our attention to homologous base pairing ignoring the polarity of strands ( see Fig . 1 ) , allowing only two monomer types ( see the extended model of four monomers in the Text S1 ) . Due to homologous pairing of monomers and the lack of different polarities of the strands , template and copy are identical , thus a pair of pairs consists of a total of two different sequences . Intermediates of the first sequence ( pair ) are denoted as , intermediates of the second are denoted by , their concentrations by and ( as were introduced previously ) , monomers are denoted by and their concentrations by and , ( ) . Note that results in this section hold for all cases when template and copy are identical: this happens not only in the case of homologous pairing with parallel orientation , but also for some exotic cases of antiparallel strands , like palindromic sequences of homologous pairing and reverse-palindromic sequences of complementary pairing ( Fig . 1 ) . We briefly note that such palindromes are not likely to coexist any more than non-palindromic sequence pairs .
In this paper we have provided the foundations of the hitherto missing theory of template replication where replication intermediates and different sequences are explicitly taken into account . Under the assumption of fixed stable steady state densities for resources and competitors Gause's principle [1] fully rules over replicator dynamics: coexistence of more replicators than the number of limiting resources ( nucleotides ) is not asymptotically stable . We have found , however , that template and copy ( or plus and minus strands ) count as one replicator , since they are stoichiometrically coupled . We have found cases of coexistence where Gause's principle seems to be violated in that two sequences can coexist with exactly the same nucleotide composition but adequately different sequences: this is a version of the stage-structure effect on coexistence found in theoretical ecology [16] . The part of a sequence that is replicated earlier has a stronger effect than that replicated later , since replication intermediates corresponding to positions in the front are more abundant , hence they influence competitive dynamics more strongly . We have demonstrated the trend that the stability of coexistence in terms of the leading eigenvalue decreases with sequence length . This may be considered bad news; however we should not forget that a good share of ribozymes [18] and aptamers [19] is smaller than 100 nucleotides , for which one still would get acceptable local stability values . ( Note that the smallest known ribozyme consists of 5 nucleotides [20] . ) The relevance of our findings can be questioned on the grounds that ribozyme replicators should have been longer than considered in this paper . This objection partially loses force if one considers known ribozyme sizes and the early constraints on replication . We discuss these issues in turn . The smallest ribozymes known are: ( 1 ) the trinculeotide that catalyses metal ion-dependent cleavage of RNA [21] , ( 2 ) the pentanucleotide promoting peptide bond formation [22] , and ( 3 ) the 19 nucleotides long minimalized hammerhead ribozyme for RNA cleavage [23] . Note that these ribozymes are devoid of complex secondary structures that would significantly alter their potential replication kinetics . Nevertheless , Yarus notes that the stable conformation of a cavity formed by the single-stranded overhang beyond the three base pair formed between enzyme and substrates seems to be essential for catalysis [22] . Regarding the replication issue , it is generally believed that small RNA replicators preceded long ones , partly supported by the non-enzymatic replication in the von Kiedrowski experiments [10] . In fact , the production of a generalized replicase ribozyme that could replicate long RNA-s is an unsolved problem . This prompted Ellington [24] to suggest a collectively autocatalytic set consisting of a modest replicase and a ligase . In such a system only small fragments would be replicated , followed by ligation to yield the longer ribozyme structures . Noteworthy in this regard is the case of the collectively autocatalytic , ligating set of Lehman [25] , in which fragments of lenghts 43 , 65 , 55 and 52 are used as pieces in the assembly . It remains to be seen whether these fragments would stably coexist when replicated , using the right combination of the resource and structure fitness landscapes . Of course , we might find in the future ribozymes that could be assembled from even smaller pieces; for such cases our theory would almost immediately apply . In any case , we predict that dynamical coexistence of small , functionally important RNA replicators will be demonstrated in the near future . Mechanisms for template coexistence have been in the focus of models of primordial replicator evolution ( cf . [6] , [26] ) . Here we have shown that up to four replicator pairs ( plus and minus sequences ) can stably coexist in the same environment without any special coupling . Thus we argue that for any special theory showing that different template replicators can coexist one might find that in effect up to different replicators may coexist without explicit representation of the four nucleotides as resources . This calls for further investigations . Recently there has been an upsurge in interest in exo/astrobiology . It is in this context that we have deliberately presented results for homologous pairing also , even with parallel orientation of the strands . Although such configurations are not unheard of even in our world , we wanted to see how such features would in general affect dynamical coexistence of template replicators . We have obtained the fitness landscapes through a distribution of elongation and degradation rates . The main reason behind this is tractability: although the 2D structures as phenotypes of RNA molecules can be calculated for most cases , this does not automatically yield phenotypes in terms of replication rates . We are temporarily satisfied with the phenotype richness that our local rules provided ( see Fig . S3 ) . What is more , we predict that the main finding that Gause rules over competitive coexistence of template replicators in stable steady state would not be violated even with more complex fitness landscapes .
In each experiment , we integrated the system of sequence pairs ( Eqs . ( 5 ) – ( 9 ) extended with the dynamics of the rest pairs ) until convergence ( when the difference of the concentrations of any two intermediates at two successive time steps is less than ) or until extinction ( if the concentration of an intermediate is less than the corresponding sequences pair is treated as extinct ) . We are interested in how many sequence pairs can coexist maximally on four different monomers . According to Gause's principle , one would expect a maximum of two sequence pairs to coexist , as that yields four different sequences . Since members of the pairs are stoichiometrically coupled , this should affect the dynamics , allowing different mechanisms of coexistence . Let us introduce two complementary sequences:where is the is the type of the monomer at position . Since is the complement of , the overbar denotes the complementing monomer pair ( and , thus , etc . ) . Replication of the sequences happens as builds up stepwise along . Using the notation above , the intermediate complexes during replication are:When the new copy is completed along the other template , the two strands separate instantaneously yielding and . The schema of the reactions is as follows . Equations ( 2 ) , ( 3 ) and ( 4 ) correspond to the replication of and strands and the generation/degradation of components , respectively . ( 2 ) ( 3 ) ( 4 ) where is the elongation rate constant for the given monomer at position , and denote degradation rates for the type of monomer and for the species . The corresponding dynamics of the intermediates is as follows ( , denotes concentration of and , respectively , denotes the concentration of the monomer in the sequence , overbar denotes the complementing pair ) : ( 5 ) ( 6 ) ( 7 ) ( 8 ) and the dynamics of the monomer , where denotes the concentration of monomer , etc . is: ( 9 ) where if , otherwise 0 ( Kronecker delta ) . The extension of the dynamics for more sequence pairs ( i . e . , to more than one copy and template ) is straightforward . The dynamics of the intermediates is independent for each pair and the dynamics of the monomers provides the coupling between the equations of different pairs of sequences . Because of the cross-coupling of equations , no analytical solution was found ( some analytical results will be presented for simplified cases ) . For the numerical integration of the ODE system to find steady-state solutions we have used the CVODE code from the SUNDIALS project of the Lawrence Livermore National Laboratory [27] . Uniform degradation rates of sequence intermediates allow for a completely analytic approach . For the positivity test of concentrations , we introduce the following notation for the constants of the power sum of : ( 10 ) ( 11 ) The solution of the dynamics of the non-complementary pairing system with uniform degradation rates for intermediates and monomers ( and , respectively , for all ) provides the concentrations of the last intermediates of and : ( 12 ) ( 13 ) where and are the stable steady state monomer concentrations ( for detailed derivation , see Text S1 ) . Let us assume that influx can counter degradation . In this case the condition of coexistence ( ) : ( 14 ) To sum up , coexistence is possible if and are of different signs . If the two elongation rate constants of the two monomers are identical ( ) the parameter equals 1 , thus the simple criteria of coexistence are the following: ( 15 ) For example , sequences = and = are not coexisting ( and ) , according to Gause's principle , as for both sequences is the limiting resource for which they compete . On the other hand , = and = are coexisting ( and ) . Sequences = and = demonstrate an example of irregular coexistence seemingly violating Gause's principle , as both have -majority . Though , according to Eq . ( 15 ) , ( and ) , and thus behaves as having -majority .
|
The dynamical theory of competing templates has not yet taken the effect of sequences explicitly into account . One might think that complementary sequences have very limited competition only . We show that , despite interesting sequence effects , competing template replicators yield to Gause's principle of competitive exclusion so that the number of stably coexisting template species cannot exceed the number of nucleotide species on which they grow , although one of the findings is that plus and minus strands together count as one species . Thus up to four different templates/ribozymes can constitute the first steps to an early , segmented genome: we suggest that other mechanisms build on this baseline mechanism .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Models"
] |
[
"systems",
"biology",
"systems",
"ecology",
"ecology",
"theoretical",
"ecology",
"chemistry",
"biology",
"computational",
"chemistry",
"theoretical",
"chemistry"
] |
2013
|
Gause's Principle and the Effect of Resource Partitioning on the Dynamical Coexistence of Replicating Templates
|
Schistosomiasis is one of the most important neglected tropical diseases . Despite effective chemotherapeutic treatments , this disease continues to afflict hundreds of millions of people . Understanding the natural intermediate snail hosts of schistosome parasites is vital to the suppression of this disease . A recently identified genomic region in Caribbean Biomphalaria glabrata snails strongly influences their resistance to infection by Schistosoma mansoni . This region contains novel genes having structural similarity to known pathogen recognition proteins . Here we elaborate on the probable structure and role of one of these genes , grctm6 . We characterised the expression of Grctm6 in a population of Caribbean snails , and performed a siRNA knockdown of Grctm6 . We show that this protein is not only expressed in B . glabrata hemolymph , but that it also has a role in modulating the number of S . mansoni cercariae released by infected snails , making it a possible target for the biological control of schistosomiasis .
The World Health Organization ( WHO ) has estimated that schistosomiasis , a detrimental parasitic helminth disease , affects approximately 258 million people , making it one of the most important parasitic diseases in the world [1 , 2] . Millions of people are chemotherapeutically treated for schistosomiasis , but in areas where this parasite is endemic there are high rates of reinfection and persistent debilitating illness . Schistosomiasis-attributed mortality in sub-Saharan Africa alone exceeds 250 , 000 per year and , with no effective human vaccine , alternative control methods are vital for reducing the burden of this neglected tropical disease [3] . Schistosome miracidia must infect a compatible aquatic snail host in order to produce the cercarial stage that is capable of infecting human hosts . Controlling this intermediate snail host is a primary method for the alternative control of schistosomiasis [4] . Biomphalaria glabrata is the intermediate freshwater snail host of Schistosoma mansoni in the Americas , and has been a target for the successful control of schistosome transmission since the 1950s [5 , 6] . Though the initial success of this biological control strategy was limited to Puerto Rico [5] , contemporary attempts have expanded to other Caribbean island systems , East Africa ( on another snail host ) , and South America [4–10] . Efforts to control B . glabrata populations have commonly employed the introduction of carnivorous or competitive snail species , but molluscicides have also been heavily exploited [4–12] . Both of these measures can have negative ecological impacts [11] . Despite these consequences , snail control has been shown to be the most successful approach to reduce the prevalence of schistosomiasis , particularly if it is paired with human pharmacological treatment [12] . Recent efforts have begun to focus on determining the relative importance of individual B . glabrata genes on schistosome-infection resistance , with the goal of characterizing snail immune responses to infection , and eventually manipulating snail populations so that they are more naturally resistant to schistosome infection [13–15] . Allelic variation in the Guadeloupe Resistance Complex ( GRC ) , a recently discovered novel gene region in B . glabrata , has been shown to strongly influence Guadeloupean B . glabrata ( BgGUA ) resistance to Guadeloupean S . mansoni ( SmGUA ) infection [13] . Allelic variation in this genomic region has an 8-fold effect of infection odds , greater than for any other known snail locus [13 , 16 , 17] . Resistance is dominant , suggesting a mechanism of parasite recognition and/or clearance by the host , rather than host recognition by the parasite [13] . There are three distinct haplotypes in the GRC region ( with 15 coding genes ) , which we designate R ( for the dominant allele that confers increased resistance ) , S1 and S2 ( for the two alleles that confer increased susceptibility; S1 and S2 are equivalent in their effects ) . The GRC region contains several genes having structural similarity to membrane-bound , pathogen recognition molecules and receptors such as Toll-like receptors and Fc receptors . The region also appears to be under balancing selection , again consistent with a role in pathogen recognition [13] . Determining the functions of these genes , and their potential immunological roles during schistosome infection , is vital for understanding schistosome-infection resistance by this snail species . Given that Biomphalaria species are major intermediate hosts for human schistosomiasis , understanding how schistosome infections can be controlled in these snails may provide insights into ways to proactively limit schistosomiasis transmission . In the present study , we chose one of the GRC genes for in-depth functional analysis: grctm6 , which encodes the Guadeloupe Resistance Complex Transmembrane 6 ( Grctm6 ) protein . grctm6 is a particularly compelling candidate locus because the resistant allele at this locus shows high non-synonymous substitution relative to the two susceptibility alleles ( particularly in the predicted extracellular domain ) , susceptibility is not correlated with mRNA levels , and because bioinformatic structural analyses confirms that Grctm6 is a potential candidate for immunological activity due to its predicted transmembrane structure . We report that this gene is expressed at the protein level in hemolymph , and demonstrate that a short interfering RNA ( siRNA ) knockdown of Grctm6 increased the number of cercariae released into the environment by treated snails .
B . glabrata ( BgGUA: “snails” ) and S . mansoni ( SmGUA: all miracidia or cercariae described ) were collected in 2005 in Guadeloupe and maintained as previously described [13 , 18] . The SmGUA strain of S . mansoni was cycled through BgGUA and hamsters , and parasite eggs were isolated from rodent livers . BgGUA snails were genotyped based on their GRC locus as previously described [13] . From the outbred BgGUA population we isolated 6 independent , partially-inbred lines that were homozygous at the GRC locus ( 2 RR , 2 S1S1 , and 2 S2S2 lines ) . We used these lines to verify the baseline resistance ( percentage infected ) and levels of constitutive expression of grctm6 in each of the three genotypes . All RNAi studies were done on a single RR line . Snails for all experiments were size matched ( ~7 mm ) and housed identically . The Oregon State University Institutional Animal Care and Use Committee , which adheres to Public Health Service Domestic Assurance for humane care and use of laboratory animals ( PHS Animal Welfare Assurance Number A3229-01 ) , approved this research as Animal Care and Use Proposal 4360 . Alignment of the protein products of the three alleles of Grctm6 found in BgGUA were calculated previously from RNA-sequencing [13] . We calculated protein molecular weights using Science Gateway ( http://www . sciencegateway . org/tools/proteinmw . htm ) . We examined secondary structure using PSIPRED ( http://bioinf . cs . ucl . ac . uk/psipred/ ) . In addition , the signal peptide ( http://www . cbs . dtu . dk/services/SignalP/ ) , transmembrane domain ( http://www . cbs . dtu . dk/services/TMHMM/ ) , and asparagine glycosylation ( http://www . cbs . dtu . dk/services/NetNGlyc/ ) were predicted using the Center for Biological Sequence Analysis’ prediction servers . Homology searches were performed with DELTA-BLAST ( https://blast . ncbi . nlm . nih . gov/ ) and Pfam ( pfam . xfam . org/ ) . Parasite challenges were carried out as previously described [13] . In brief , snails were placed in 2 ml of dechlorinated water in individual wells of a 24 well dish containing 10 or 20 miracidia for 24 h , and subsequently transferred into tubs containing 10 snails each to be monitored for infection . All infections were conducted at 1 pm in the afternoon following animal sacrifice at 11 am . Two independent lines of each RR , S1S1 , and S2S2 snails were challenged and pooled by genotype for the verification of GRC locus susceptibility . This was done on two separate occasions using a minimum of 30 snails each time ( n = 64 S1S1 , 68 S2S2 , 86 RR ) . These snails were examined for cercarial shedding , and scored as either infected or uninfected . Starting 5 weeks post challenge , and for 5 subsequent weeks , snails were placed in 2 ml of dechlorinated water in individual wells of a 24 well dish and exposed to light for 3 h beginning at 9 am . For the siRNA experiments , RR snails were treated with enhanced green fluorescent protein , ( GFP , Sham injected ) or grctm6 oligonucleotides ( oligos ) and then challenged with miracidia . We challenged using 10 or 20 miracidia , and did two independent trials for each number of miracidia . We used a minimum of 30 snails per trial ( Snails that survived for analysis: n = 55 snails using GFP oligos , and n = 37 using grctm6 oligos for 20 miracidial challenges; n = 65 using GFP oligos , and n = 63 using grctm6 oligos for 10 miracidial challenges ) . These were examined for cercarial shedding as described above [14 , 19 , 20] . siRNA treated snails were challenged at the beginning of the third day post siRNA injection . Cercariae were enumerated by taking three equal aliquots from a 2 ml sample ( or counting all cercariae in a well if density was low ) . All shedding snails were individually marked with nail polish so that a cumulative count over the 5 week scoring period could be achieved for each snail . Specific siRNA oligos for grctm6 were designed and produced by Integrated DNA technologies ( IDT ) . Oligos aligned to the more conserved 3′ intracellular domain . Three oligos ( GUUAGGACACCGUCAAUU , CACUGCUGACAUUGGCAG , UUUCAUUUGCAUUGCUUG ) were suspended according to the manufacturer’s instructions and injected into live ~7 mm RR snails at 2 μg/μl as previously described [14 , 19] . In brief , either enhanced GFP IDT control oligos ( GFP /Sham ) or the grctm6 oligo mixture was suspended in Xfect transfection reagent with nanoparticles ( Clontech ) according to the manufacturer’s instructions and each snail received a single 10 μl injection distal to the heart [14 , 15] . The siRNA-mediated knockdown was assessed at the mRNA level over 4 days ( the end of day 0–4 ) , and additionally confirmed by Western blot analysis of protein levels 2–4 days post-injection . Mortality was also compared , within each single day , between GFP control oligos and the grctm6 oligo mixture to ensure that the grctm6 oligo mixture was not inducing additional mortality . Quantitative RT-PCR ( qPCR ) was used to quantify mRNA transcripts of grctm6 in whole snail lysates of BgGUA snails , and used to detect the extent of siRNA knockdown of grctm6 mRNA in RR snails . Constitutive levels of grctm6 mRNA were assessed in 2 independent homozygous lines of each RR , S1S1 , and S2S2 snails and pooled within a genotype ( 2 RR lines pooled , 2 S1S1 lines pooled , and 2 S2S2 lines pooled ) . RR snails were also assessed for their grctm6 mRNA levels 0 , 1 , 2 , 3 , and 4 days post injection of GFP or grctm6 oligos . In brief , whole snails were snap-frozen in liquid nitrogen , total RNA extracted using the Direct-Zol RNA miniprep kit ( Zymo Research ) and cDNA synthesized using iScript Reverse Transcriptase Supermix for RT-qPCR ( BioRad ) . Additionally , the head-foot , albumen gland , or hemolymph were removed and snap frozen for tissue analysis of R snails . Hemolymph was collected by head-foot retraction as previously described [14] . qPCR was performed , as previously described [21–23] . All primers were prepared at 300 nM , had a single melt curve , had efficiencies between 90–100% , and were designed or verified using Primer 3 ( National Center for Biotechnology Information ) . grctm6 primers aligned to the more conserved 3′ intracellular domain and showed 100% identity to all three alleles . B-actin ( F: 5’-GCTTCCACCTCTTCATCTCTTG -3’; R: 5’-GAACGTAGCTTCTGGACATCTG-3’ ) was used as an internal control , and did not vary across treatments . grctm6 ( F , 5′-TGTTGAGTACGCTGCTGTCAATAAG -3′; R , 5′- ATTCATATCCTTGTTGCTTGGGTCC-3′ ) was used with the following PCR conditions ( in a Applied Biosystems 7500 fast qPCR thermocycler ) : 95°C for 5 min; 40 cycles of 95°C for 15s and 60°C for 15s . All mRNA levels of GRC lines were normalized to β-actin expression and presented relative to S2 snails . All mRNA levels of oligo injected RR snails were normalized to β-actin expression and presented relative to GFP control samples . Rabbit polyclonal Grctm6 antibodies were produced against a peptide epitope ( within RR Grctm6 , isolated , purified , and validated by Genscript custom antibody services ( Genscript ) . Re-validation of the antibody was performed in house and it was found to be effective at a concentration 1:2000 for western blot detection of synthetically generated Grctm6 peptides and native Grctm6 isolated from hemolymph . Genscript Anti-rabbit IgG secondary HRP conjugate ( 1:2000 ) was used for detection . Western blots were used to detect the presence of Grctm6 protein in RR snail hemolymph following siRNA knockdown as previously described with the modifications described below [15] . Snail tissue preps ( whole snail , albumen gland , head-foot , hemolymph +/- hemocytes , and hemocytes ) were examined from pooled untreated samples from RR snails . Hemolymph preparations were obtained using the head-foot retraction method and either directly added to lysis buffer ( Bolt LDS sample and reducing buffer ( Thermo ) ) , or cells were removed by centrifugation at 1200g before protein extraction was performed . Unmodified hemolymph produced a consistently detectable band when interrogated with a Grctm6 polyclonal antibody , so this tissue was extracted and used for knockdown analysis . Snail hemolymph was collected , pooled ( 4–10 snails per sample ) , and mixed with Bolt LDS sample and reducing buffer ( Thermo ) , and homogenised using a 25G needle before being heated to 95°C for 7 minutes . Tissue samples ( whole snail removed from the shell , albumen gland , and head-foot ) were homogenized in BOLT LDS sample and reducing buffer using a 18G needle followed by a 25G needle and then treated identically to hemolymph samples . Total protein levels in each sample were quantified using absorbance at 280 nm ( Nanodrop , Thermo ) , additional loading buffer was added to more concentrated samples to ensure all samples had equivalent concentrations of total protein , and 750 ng of total protein was used for each sample per well ( 500 ng for Fig 3B ) [15] . This was the maximum protein concentration that could be used across all samples . Electrophoresis was performed in a 10% pre-cast Bolt Bis-Tris gel for 20 minutes at 200 V . Samples were blotted using a Pierce power blotter cassette ( Thermo ) , and western blot detection was done using an iBind Solutions kit and iBind western device ( Thermo ) . Detection was achieved via Supersignal West Pico luminol solution ( Thermo ) and chemiluminescence was acquired using a MyECL imager ( Thermo ) . Densitometry was performed post image acquisition using ImageJ software ( NIH ) , and was calculated on the only clear band , which coincided with the predicted 68 kDa size of Grctm6 . Three independent blots were run , each inclusive of all 3–5 samples/treatment ( 5 on Day 3 ) . Relative density normalized to BgActin was calculated independently for each western blot and then averaged as a triplicate for each sample ( comparisons within a day only ) . Statistical analyses were completed as indicated , and were generally completed by one-way ANOVA ( or unpaired Student’s t-test ) with a Tukey post-test unless otherwise specified ( p<0 . 05 ) . If a Barlett’s test ( or F-test ) for equal variance failed , then data underwent a natural log transformation ( ln ) before reanalysis . Analyses of the susceptibility of snail populations were done by calculating the Z score ( standard score ) of the population . Analyses were completed using GraphPad Prism software ( La Jolla , CA , USA ) .
In the GRC region there are seven genes coding for putative membrane spanning proteins that have structural similarities [13] . We examined one of the two genes that were identified by Tennessen et al . [13] as most likely to be responsible for schistosome resistance . Our further analysis of Grctm6 indicates that it is likely a single-pass transmembrane protein of ~68 kDa ( Fig 1 ) . The predicted extracellular sequence of the R allele protein differs substantially from those of the two S alleles , but the signal sequence and the transmembrane and cytosolic domains are far less variable between the R and S alleles ( Fig 1 ) . This highly variable extracellular domain also has the potential to be glycosylated , which is common in B . glabrata [24] . Interestingly , both the suspected extracellular and intracellular stretches of this protein are long ( >200aa ) and are likely to support substantial secondary structure ( extracellular: 9–11% α helices , 30–39% β strands; intracellular: 6–13% α helices , 7–13% β strands ) . Therefore , it is likely that both contain unidentified functional domains ( Fig 1 ) . However , consistent with previous results [13] we found no significant homology to any known protein domains [13] . Among our inbred lines , RR snails were ~25% as likely to be infected by SmGUA as either S1S1 or S2S2 snails ( p <0 . 01; Fig 2A ~0 . 2 vs ~0 . 8 ) . These same snail lines were examined for mRNA expression of grctm6 . RR snails showed no consistent corresponding increase or decrease relative to susceptible snails , although S2S2 snails had ~2–3 fold higher mRNA levels of grctm6 than S1S1 or RR snails ( p<0 . 01; Fig 2B ) . These findings verify , for our inbred lines , that the constitutive mRNA expression of grctm6 in whole snails does not explain the resistance of the different genotypes at the GRC locus , and that amino acid sequence divergence may be biologically important for this gene’s function [13] . grctm6 mRNA transcripts were detected in all of the snail tissues that were isolated . Transcript levels appear to be slightly elevated in hemolymph , although a statistical difference was only found between hemolymph and the head-foot ( p = 0 . 03; Fig 3A ) . When Grctm6 protein levels were examined , the only preparations that had consistently detectable Grctm6 protein were from whole hemolymph lysates ( Fig 3B ) . Grctm6 protein was possibly present in isolated hemocytes and cell free hemolymph , but only unmodified hemolymph ( hemolymph that received no manipulations prior to protein extraction ) produced a consistently detectable band at ~68kDa at various total protein concentrations ( Fig 3B ) . A more sensitive antibody and immunohistological analysis would be required to definitively determine the tissue specific/cellular location of Grctm6 . It is puzzling that we were only able to consistently detect Grctm6 in unmodified hemolymph . Perhaps some part of the cell separation protocol modifies or destroys the epitope our antibody binds . It is possible that spinning the cells triggers an intracellular trimming of Grctm6 and loss of the epitope our antibody recognises . It is also possible that , because we are using a novel polyclonal antibody , we were unable to detect smaller amounts of Grctm6 in other tissues because of low sensitivity . Regardless , this is the first evidence that Grctm6 exists at a protein level in any tissue or species of Biomphalaria . Using RR snails , we knocked down grctm6 mRNA via siRNA . Injections of oligos caused approximately 25–30% mortality over the first days post-injection ( Fig 4A ) . However , our siRNA knockdown of Grctm6 did not increase mortality beyond that of the control ( GFP ) , so the initial dip in survival is likely the result of physical damage to the snail from the injection procedure . grctm6 mRNA was significantly reduced by up to ~60% during the first 3 days post siRNA injection in whole snail lysates ( p = 0 . 02; Fig 4B ) . Given that grctm6 mRNA levels were reduced after 3 days , but normalize by the end of day 4 , we examined extent of the protein knockdown surrounding the third day . The amount of Grctm6 protein was significantly reduced by ~30% in the hemolymph 3 days post siRNA injection , but was unmodified on any other day ( p = 0 . 03; Fig 4C ) . Since we hypothesise that Grctm6 may have some recognition function , we chose to infect snails at the beginning of day 3 , given that the protein levels of Grctm6 are reduced from the control on that day , and may be sub-physiological [13] . Two independent doses of miracidia were used so that potential changes to schistosome susceptibility were not overlooked by using a single dose of miracidia [18] . Resistant snails treated with grctm6 oligos or GFP oligos exhibited equivalent susceptibility to infection by SmGUA ( Fig 5A and 5B ) . However , grctm6 injected snails had significantly higher ( ~3–4 fold ) cercarial shedding of infected individuals that shed at least one cercariae ( p = 0 . 04 , 0 . 03; Fig 5C and 5D ) . This pattern was apparent when snails were challenged with either 10 or 20 miracidia , although overall cercarial shedding was ~150-fold lower in snails challenged with 10 . This is the first evidence that this protein is directly involved in snail host defense to any pathogen , and specifically to schistosomes .
In the last decade , there has been a flurry of breakthroughs elucidating snail-schistosome interactions that have exploited a pharmacological , or an RNAi knockdown of a protein of interest in either the snail host or the schistosome [14 , 15 , 19 , 20 , 25–36] . Generally , these RNAi knockdowns have targeted proteins that have been relatively well described in other species , or are homologous to another group of defined-immunologic targets . We used this technique to assess the importance of a completely uncharacterized gene/protein , with no known homologs in other species , on snail-schistosome compatibility following its discovery by linkage mapping . Tennessen et al . [13] described a novel genomic region ( the GRC ) with alleles that are strongly associated with snail resistance to schistosome infection . Seven transcriptionally expressed , coding GRC genes , designated the grctm loci , are particularly promising candidates in this genomic region . Though these transcripts ( including grctm6 ) bear little resemblance to any known proteins , they share some characteristics common to immunologic membrane bound receptors ( single-pass transmembrane proteins with a highly variable extracellular domain , and sequence divergence between disease relevant alleles ) [13] . Grctm6 is of particular interest because of its putative structure and because the R allele at the grctm6 locus has high non-synonymous substitution relative to the S alleles . In this study , we verified that GRC genotypes are strongly correlated with resistance and that constitutive transcript variation does not explain resistance differences between the various alleles in BgGUA snails [13] . Previous RNA-Seq data from outbred snails suggested that both S alleles show approximately 2-fold higher expression of grctm6 than R [13] . Our qPCR results on inbred lines indicate that S2 , but not S1 , has at least 2-fold higher expression than R , supporting the notion that mRNA levels of grctm6 are not a likely explanation for GRC locus-associated resistance in BgGUA ( Fig 2B ) . We also show that Grctm6 is expressed at the protein level in hemolymph ( but were unable to determine if it is specific to hemolymph ) . Although this partial and transient knockdown of Grctm6 did not significantly change the proportion of hosts infected , it did increase cercarial shedding , indicating that Grctm6 has a role in modulating the extent/burden of the schistosome infection in BgGUA . Increasing the number of miracidia used to challenge snails from 10 to 20 has little effect on the proportion of snails infected , but a huge effect on the number of cercariae shed by infected snails ( Fig 5 ) . Thus , it is plausible that grctm6 has a role in controlling the number of miracidia that successfully infect the snail , but that we only observed an effect on cercarial shedding because this trait may be more sensitive , than proportion infected , to the number of parasites that successfully established . Alternately , grctm6 may help to regulate some subsequent larval stage leading to cercariogenesis or cercarial release into the environment . Regardless , we have demonstrated that the Grctm6 protein has an important effect on the extent of schistosome infection . We speculate , based on amino acid sequences , that Grctm6 may be a membrane bound receptor . R and S alleles exhibit substantive sequence divergences ( 15–45% amino acid differences , Fig 1 ) in the extracellular region of this protein , which could indicate variation in extracellular domain stabilities , target ligands , and/or binding affinities [24 , 37] . The more conserved region of Grctm6 is located in the putative transmembrane and cytoplasmic regions , which could serve to preserve potential outside-in signaling functions of this protein as in other immune receptor proteins [38] . The high expression of Grctm6 in hemolymph , relative to other tissues , ( Fig 3 ) is noteworthy because that is the location of crucial snail-schistosome interactions [39–41] . A rigorous immunological analysis would be required to determine if any of these speculations regarding the mechanistic or immunological role of Grctm6 on parasite infection or immunity are accurate . Although we have clearly shown that this protein influences the numbers of cercariae shed , whether Grctm6 actually functions as an immune receptor , and at what stage in the infection process it acts , remain to be shown . Grctm6 could have negative impacts on any stage of sporocyst growth or development , and further immunohistological and functional analysis are required to determine how Grctm6 is mechanistically involved in schistosome infection . It is interesting that knocking down Grctm6 expression affected cercarial shedding but not susceptibility ( percentage infected ) . However , this effect was achieved with just a 30% protein knockdown . Perhaps a full knockdown would yield a much stronger phenotype , including an increased susceptibility . A CRISPR/Cas9 knockout would be one way to conclusively test the role of this locus [42] . It would also be pertinent to examine the cellular location of this protein by immunohistochemistry using monoclonal antibodies from recombinant Grctm6 . This technique could provide vital future information pertaining to the biological mechanism of Grctm6 . We also note that there are six other transmembrane loci in this region , and can’t rule out the possibility that other loci act in concert with grctm6 to produce the observed susceptibility phenotypes . Here we have shown that a modest reduction in Grctm6 protein levels affects cercarial output , which suggests that this protein may be involved in a pathway which is important during a challenge by S . mansoni . More importantly , this study provides a potentially new type of target for controlling transmission of schistosomes at the snail stage . If , in the future , the resistant allele of genes like grctm6 could be gene-driven into a population of B . glabrata[43] , then it is possible that their resistance to schistosomes could be improved without completely immunocompromising the snail . Using these types of modern molecular methods , we would anticipate fewer deleterious ecological consequences than those following contemporary snail control methods , which involve introduced predators/competitors or molluscicides [44 , 45] . Even a genetic manipulation that only reduces the number of cercariae shed into the environment could have epidemiological consequences . Further exploration of these genes , their physiological functions , and potential roles for the control of schistosomiasis will be important for combatting this widespread and destructive disease .
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Schistosomiasis is one of the most prevalent parasitic diseases in the world . Though treatments for schistosomiasis infection exist , there is no vaccine , and reinfection is common in areas where the parasite occurs . One possible way to mitigate schistosomiasis is by controlling the transmission of the parasite larvae from the snails that carry them . Understanding the snail-parasite relationship is essential for the development of new means to interrupt transmission of the parasite from snails to humans . Snails possess immune mechanisms for fighting infection , most of which are based in hemolymph tissue . Here we characterize a novel protein , Grctm6 , in a snail host of schistosome parasites . Grctm6 is structurally similar to certain other immune proteins and is present in snail hemolymph . Importantly , we demonstrate that the release of schistosomes by infected snails is exacerbated when this protein is experimentally suppressed in live snails . These results support the suitability of Grctm6 as a possible target for reducing the transmission of this human disease .
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2017
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Schistosome infectivity in the snail, Biomphalaria glabrata, is partially dependent on the expression of Grctm6, a Guadeloupe Resistance Complex protein.
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Bow-tie or hourglass structure is a common architectural feature found in many biological systems . A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers . Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components , and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes . Little is known , however , about how bow-tie architectures evolve . Here , we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal . We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed . Mathematically speaking , bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient . The maximal compression possible ( the rank of the goal ) determines the size of the narrowest part of the network—that is the bow-tie . A further requirement is that a process is active to reduce the number of links in the network , such as product-rule mutations , otherwise a non-bow-tie solution is found in the evolutionary simulations . This offers a mechanism to understand a common architectural principle of biological systems , and a way to quantitate the effective rank of the goals under which they evolved .
Many natural and engineered systems show a bow-tie architecture [1 , 2] . A bow-tie ( also termed hourglass ) architecture is a feature of multi-layered networks in which the intermediate layer has significantly fewer components than the input and output layers . The intermediate layer is called the “waist” [3] , “knot” [1] or “core” [4] of the bow-tie and in gene-regulatory networks the ‘input-output’ [5] or ‘selector’ gene [6] . Bow-ties mean that the network is capable of processing a variety of inputs , converting them into a small set of universal intermediates and then reusing these intermediates to construct a wide range of outputs ( see Fig . 1 ) . A bow-tie architecture is found for example in metabolic networks [1 , 7–9] , where the large range of nutrients consumed by the organism is decomposed into 12 universal precursors ( including pyruvate , G6P , F6P , PEP , AKG , ACCOA [1 , 10] ) from which the organism builds again all of its biomass including carbohydrates , nucleic acids and proteins . In mammalian signal transduction , a set of less than 10 pathways mediates information transfer between hundreds of possible input signals and the resulting expression changes in thousands of genes [10–13]—the same pathways are co-opted in different cell types to connect different inputs and outputs . The human visual system consists of multiple layers of signal processing , where hundreds of millions of photoreceptors in the retina fan in to only about one million ganglion cells [14] whose axons form the optic nerve . These in turn fan out to parallel processing pathways in the visual cortex that detect pattern , color , depth and movement [15] . Many developmental gene regulatory networks have bow-tie structures in which a single intermediate gene ( ‘input-output’ or ‘selector’ gene ) combines information from multiple patterning genes ( the input layers ) and then initiates a self-contained developmental program by regulating an array of output genes [5 , 6] that can produce a large variety of morphologies [17–20] . Studies of other biological signaling networks such as epidermal receptor signaling [21] , GPCR signaling [22] , signaling in both the innate [23 , 24] and the adaptive immune system also documented bow-tie organizations [4 , 25] . Objects manufactured by humans do not evolve in the biological sense , however the ongoing process of technological innovation is thought to have shared features a with biological evolution [26 , 27] . Many non-biological networks show bow-tie architectures as well . This includes the world wide web [28] , internet protocols [3] , production pipelines and some economic systems—see Table 1 . Bow-ties in technology have , in a sense , evolved . For example , whereas in the past each machine had its own energy source ( river for mill , fire for stove ) , in today’s power grid a universal intermediate—220V 50Hz AC electricity—connects multiple input energy sources ( coal , oil , solar etc . ) to multiple output appliances [1] . Bow-ties have been suggested to have functional implications . Bow-ties allow evolvability , because new inputs can be readily converted to new outputs , using the same well-tested intermediate processes [2] . On the other hand , bow-ties are vulnerable to damage in the intermediate processes [1 , 25] . In developmental gene regulatory networks , modulated expression of the ‘waist’ ( ‘input-output’ or ‘selector’ ) gene can result in markedly different phenotypes . Thus it is thought that these ‘waist’ genes are hotspots for the evolution of novel phenotypes [5 , 6] . Once a bow-tie is established , it is hard to change its core components because changes to the bow-tie affect many processes at once [2 , 3] . Recently , Polouliakh et al . hypothesized that the narrow intermediate layer in signaling networks may serve to distinguish between different sets of inputs and assigns the correct set of outputs for each . Since this intermediate layer is narrow compared to the number of inputs , different inputs are grouped together and share a common output response [22] . The prevalence of bow-tie architectures in biology raises the question of how they evolved . In particular , one may ask whether there are evolutionary mechanisms that spontaneously give rise to bow-ties . This question is significant when considering the fact that most evolutionary simulations of multi-layered networks do not automatically give rise to bow-ties [3 , 29] . Generically , in fields as diverse as artificial neural networks [30] and evolution of biological networks , simulations result in highly connected networks with no bow-tie [31–37] . Bow-tie evolution in the context of internet protocol networks was studied by Akhshabi and Dovrolis [3] . Their model assumed that the node connectivity monotonously decreases between layers , namely protocols at the input layer are general in terms of their function ( have many connections each ) , and become more and more specific towards the output layer ( in which they often have only a single connection ) . Bow-tie structures are then a direct outcome of this inhomogeneity in properties between layers . Such assumptions are relevant for technological applications , but are not relevant in the biological context . We thus sought a biologically plausible mechanism . We were inspired by recent advances in understanding the evolution of a different feature that is common to various biological networks—modularity . Using simulations , several studies showed that evolution under modular goals with rules that tend to eliminate connections spontaneously lead to modular structure [29 , 31 , 38–40] . This led us to ask whether one can find situations in which evolution spontaneously leads to bow-tie architectures . Here , we study the evolution of bow-tie architecture using several simple models of multi-layered networks and biologically plausible evolutionary scenarios . We find that bow-ties evolve when two conditions are met: ( i ) the evolutionary goal has deficient rank; ( ii ) The effects of mutations on interaction intensities between components are described by a product rule—namely the mutated element is multiplied by a random number . Product-rule mutations are more biologically realistic than the commonly used sum-rule mutations which add ( rather than multiply ) a random number to the mutated element [41–47] . For a detailed discussion of product-mutations , their biological relevance and their evolutionary effect , the reader is referred to an earlier work [29] . We further show that the narrowest possible waist in the bow-tie is equal to the rank of the goal . We demonstrate this in simulations of evolution in linear and nonlinear model systems .
We begin with a simple linear model of a multi-layered network and later extend this framework to nonlinear models as well . The network in our model is a system that receives an input vector s→=u→1 , performs L consecutive stages of processing; each stage produces an intermediate u→2 , u→3 , … u→L where the product of the final processing layer is the network output vector u→L+1=v→ . Here , we assume each of these processing layers can be described by a linear transformation A1 , A2 , …AL , such that the system output is AL⋯A2A1s→=v→ . Each matrix contains all possible interaction intensities between network nodes at two consecutive layers . For example the matrix entry A ( l ) ij represents the effect of the j-th node in layer l on the i-th node in layer l + 1 . In this model , connections are only possible between a node and any node at the next layer . Connections within layers or backward connections are not allowed . For illustration see Fig . 1 . As a concrete biological example , one may think of a metabolic network . The input vector is the number of nutrient molecules of different types that are consumed by the organism . Taking the example of carbohydrate metabolism: elements of the input vector s→=u→1 represent the amounts of the various sugars consumed: s1 could be number of glucose molecules consumed , s2 number of lactose , etc . Sugar metabolism applies a series of enzymatic reactions which first break complex sugars into simpler ones ( monosaccharides ) and then converts them to either of several possible output products: ATP ( energy source for short term usage ) , glycogen ( carbohydrate storage ) or other sugars , for example 5-carbon sugars ( pentose ) used for the synthesis of nucleotides , nucleic acids and aromatic amino acids . Intermediate nodes in the model represent intermediate sugar metabolism products , such as glucose 6-phosphate , fructose 6-phosphate , pyruvate etc . [10] . The output vector represents the numbers of molecules produced using the consumed carbohydrates: v1 could be number of ATP molecules , v2 number of glycogen molecules , v3 number of ribose-5-phosphate , etc . This is admittedly a simplified description of sugar metabolism . For example , it does not take into account the hierarchical order of different sugars uptake , or metabolic cycles . Yet , it captures the degeneracy which enables replacing one sugar by another and still obtaining the very same output products . Related models have been useful for understanding multi-layered biological networks in diverse contexts , such as metabolic , gene regulatory and signal transduction networks [33 , 38 , 46 , 48–57] . In the linear model , the total input-output relationship of the network is given by the product of the matrices A1 , A2 , … AL , which represents the overall transfer of signals from the first ( input ) to the last ( output ) layer . Employing this formalism , we evolve these networks to match a desired goal—given by a matrix G . The goal matrix describes the desired output for any possible vector of inputs , and thus defines the entire input-output function . The dimension of the goal matrix G corresponds to the number of system inputs and outputs Doutput × Dinput . We also extended this model to describe a gradually growing network for which the goal dimensions also change ( see S1 Text ) . The fitness of a network equals the distance between the total effect of the network ( the product of the matrices ) and the desired goal G , namely F = − || A ( L ) A ( L−1 ) … A ( 1 ) – G || . Note that each goal can be satisfied by an infinite number of matrix combinations that are all equally fit . For example , if G is the unit matrix G = I and L = 2 , all pairs of matrices that are inverse to each other A ( 2 ) = [A ( 1 ) ]−1 satisfy the goal , because A ( 2 ) × A ( 1 ) = I . To evolve the networks , we use a standard evolutionary simulation framework [37 , 58–60] . Briefly , the simulation starts with a population of N networks , each described by a set of L matrices . At each generation the networks are duplicated and mutated with some probability resulting in modified interaction intensities . A mutation means a change to an element of one of the matrices . Fitness is then evaluated for each structure in comparison to a goal . N individuals are then selected to form the next generation of the population , such that fitter individuals are more likely to be selected . This process is repeated , until high fitness evolves ( see Methods section for more details ) . Guided by studies on the evolution of modularity , we tested evolutionary situations which are biased to reduce or eliminate interactions . Such a mechanism is a product-rule mutation scheme in which elements of the matrices are multiplied by a random number drawn from a normal distribution N ( 1 , σ ) ( as opposed to a sum-rule mutation where a random number is added instead of multiplied ) [29] . Product-rule mutations are a more realistic description of the way that DNA mutations affect biochemical parameters than sum-rule mutations [41–47] . Biological mutations are more likely to decrease existing interactions than to create novel ones that did not exist before [61–63] . This property is captured by product-rule mutations ( but not by sum-rule ) [29] . With this mechanism , evolution finds networks satisfying the goal which are highly sparse—that is , networks with a small number of significant interactions [29 , 46] . As controls , we also simulated evolution with sum-rule mutations ( in which a random number is added to matrix elements ) . In the example of carbohydrate metabolism , different input sugars ( glucose , lactose etc . ) can be used to produce any of the final products ( ATP , glycogen , ribose-6P ) . If one only examines the output molecules , one cannot tell which original sugar was their source . This degeneracy can be mathematically represented as a goal matrix with linearly dependent rows . To study the effect of these dependencies on the network structures that can evolve , we tested evolution towards goals described by matrices with different ranks . The rank r is the number of linearly independent rows in the matrix . The rank of the goal matrix is full , if all rows of the matrix are independent . If some of the rows are dependent , the matrix has deficient rank—a rank smaller than the full rank . Deficient rank means that the input-output transformation maps inputs to a limited subspace of outputs , of dimension r . Below , we discuss the implications of this concept also for nonlinear systems . As an example of a 3 × 3 matrix with rank r = 1 consider the following matrix whose two last rows are given by a constant multiplying the first row: Rank-deficient matrices can be decomposed into a product of ( generally non-square ) matrices A ( L ) Doutput×DL−1×⋯×A ( 2 ) D2×D1×A ( 1 ) D1×Dinput=GDoutput×Dinput , whose smallest dimension equals the rank of the goal , namely mini ( Di ) = r . Because the rank of the goal matrix is smaller than its dimension r < D , this decomposition is equivalent to a narrow waist , whose width is equal to the goal matrix rank r . As a simple example consider the 3 × 3 goal above . It is decomposable into a product of a column vector by a row vector: This decomposition represents a 3-layer network , whose intermediate layer has only one active node—namely has a bow-tie structure ( see left scheme in Fig . 1B ) . Importantly , however rank-deficient matrices can also be decomposed into products of full matrices . For any choice of A ( 1 ) , one can find A ( 2 ) = [A ( 1 ) ]−1 G , as long as A ( 1 ) is invertible . In fact most random choices of A ( 1 ) will yield full-matrix decompositions which represent a non-bow-tie architecture ( right scheme in Fig . 1B ) . More generally , let GDoutput×Dinput be a goal matrix with rank r = rank ( G ) . Let there be a decomposition of GDoutput×Dinput into a product of L matrices G = A ( L ) A ( L−1 ) …A ( 1 ) . This L matrix decomposition is a representation of a network that has L + 1 layers of nodes , whereas each matrix represents the interaction intensities between all nodes in two adjacent layers . If and only if G has deficient rank , it can be decomposed into a product of matrices having dimensions smaller than the goal dimensions . This means that the matrices represent a network with intermediate layers that consist of fewer active nodes than the number of inputs and outputs to the network . Otherwise , if the matrix has full rank , each layer must have a number of active nodes which is at least as large as the rank , making a bow-tie impossible in a case of full rank . This argument follows from the fact that matrix multiplication cannot increase rank , i . e . rank ( A B ) ≤ min ( rank ( A ) , rank ( B ) ) for any two matrices A and B [64] . The narrowest layer in the network is termed the waist [3] . The waist can be narrow because of the low rank that allows compressing the inputs down to fewer nodes , and then computing the outputs based on those nodes . While in principle such parsimonious bow-tie decompositions exist for every rank-deficient matrix , they constitute only a small fraction of all possible decompositions . Thus , the question remains whether evolutionary dynamics can find the solution with a bow-tie out of the infinite number of non-bow-tie solutions . To address this question , we evolved networks towards rank-deficient goals , with and without the product-rule mutation scheme described above . We studied goals of dimension D = 6–8 consisting of L = 4–6 matrices , tested 4–8 different goals for each dimension , and evolved networks towards each goal in 100–3000 repeated simulations , each starting from different random initial conditions . We found that a rank-deficient goal together with product-rule mutations gave rise to networks that satisfy the goal and show bow-tie architectures . Full rank goals always led to evolved networks that satisfied the goal but had no bow-tie architecture at all , namely all layers had exactly the same number of nodes as the input and output layers . Rank-deficient goals with a different mutational scheme ( sum rule mutations ) could sometimes lead to architectures in which intermediate layers had fewer nodes than the input and output layers , but these were mostly not as narrow as the goal rank . When noise was introduced to the simulations ( see below ) , the sum-mutation scheme led to full ( non bow-tie ) structures even under low noise levels . We conclude that a bow-tie evolves when ( i ) the goal has deficient rank , and ( ii ) mutation rule is product and not sum . Bow-tie width equals the rank of the goal . For example , consider a network with 5 layers of nodes ( L = 4 matrices of interaction layers ) , each consisting of 6 nodes ( Dinput = Doutput = D = 6 ) . We simulated their evolution towards goals of different ranks between 1 and full rank ( r = 1–6 ) . We repeated the simulation 700 times for each goal starting from different random matrix initial conditions . We then analyzed the number of active nodes at each layer . Since in a numerical simulation we do not obtain exact zeroes , but rather very small values , we defined active nodes as nodes who , if removed ( incoming and outgoing interactions of the node set to zero ) have a larger than 0 . 1% relative effect on fitness ( see Methods ) . We find that the number of active nodes is smallest on average at the middle layer . The number of active nodes at this waist is most often equal to the rank of the goal ( Fig . 2 ) , and never lower than this rank . The first and last layers are constrained to have exactly D active nodes by the definition of the problem ( Fig . 3 ) . In a minority of cases ( ~20% , see Table 1 in S1 Text ) the waist showed more active nodes than the rank of the goal . For comparison , if the mutational mechanism is not biased to decrease interactions ( i . e . sum-mutations ) the vast majority ( 94%-97% ) of the runs ended with mid-layer which had more elements than the rank of the goal ( Table 1 in S1 Text ) . We show a representative example of a network configuration obtained in simulation in Fig . 1C . We tested the sensitivity of this mechanism for bow-tie evolution to model parameters . A bow-tie was obtained under a wide range of values of selection intensity , mutation size , mutation rate and population size that spanned 1 . 3 decades ( mutation rate ) to 2 decades ( mutation size ) ( Fig . 4; see S1 Text for more details ) . We also tested the sensitivity of the structure obtained to the evolutionary goal by comparing simulation results with different goals having the same rank . We find that the location and width of the waist are insensitive to the choice of the goal ( see Fig . 9 in S1 Text ) . We tested the location of the waist in simulations of multi-layered networks with equal number of inputs and outputs ( L = 4 , D = 6; L = 6 , D = 8 ) . While in principle the waist could reside at any layer between the input and output layers , in practice , it falls most often in the middle layer . Intuitively , this can be explained by symmetry considerations: The mutational mechanism works uniformly on all layers to eliminate connections . While the dimensions of the goal matrix constrain the number of active nodes at the network boundary layers ( input and output ) , connections near the middle layer are least “protected” and thus mostly prone to removal , resulting in the network waist being on average in the middle layer . Biological networks are often prone to fluctuations , for example due to temporal variations in internal molecule numbers or environmental fluctuations . We thus tested the robustness of the suggested evolutionary mechanism to fluctuations in either the goal or the interaction intensities . We started by testing the sensitivity of the evolutionary mechanism to rank accuracy by perturbing the rank-deficient goal matrix , yet keeping the goal constant throughout every simulation run . This produced goal matrices that are ‘almost rank deficient’: full rank , but with some of the eigenvalues close to zero . The noise strength is given by the difference between the norms of the noisy and clean goals divided by the norm of the clean goal ( see Methods ) . We find that for noise strength up to about 1% , bow-tie architecture with middle layers whose width equals the goal rank were reached in most simulation runs , just as in the absence of noise . Thus , our evolutionary simulation is robust to small perturbations to exactly rank-deficient goals—see Fig . 5 for illustration and compare to Fig . 2 with no noise . The median waist size increased above the clean rank when noise intensity increased above 1% ( see Figs . 14–15 in S1 Text for the dependence of bow-tie on the noise level ) . For estimation of the noise magnitude in biological networks , see S1 Text . While in the previous scenario the goal rank was noisy , but remained constant throughout every simulation run , temporal fluctuations are also ubiquitous in biological networks . To test the effect of temporal fluctuations , we added statistically independent noise realizations ( white noise ) to all matrix entries ( and also to the goal ) at each generation . The fitness evaluation then reads: F = − || ( A ( L ) + εL ) ( A ( L−1 ) + εL−1 ) … ( A ( 1 ) + ε1 ) – ( G + εG ) || , where εi are independent noise realizations . Since this noise changes at a higher frequency than the typical evolutionary timescale ( the mutation rate ) , we expected that the system will be able to filter it out to some extent . Since here the noise affects all network components and not only the goal , we refer to the induced fluctuations in fitness as a global measure of the noise intensity . We compared the ability of evolution with either product or sum mutations to cope with this temporal noise . We find that product-mutations filter out the noise much more efficiently than sum-mutations . When the clean goal rank was 1 , the network structure evolved by product-rule evolutionary scheme was unaffected until the relative magnitude of induced fluctuations in fitness reached values of 0 . 3 ( std/mean ) . Sum mutations , in contrast , led to bow-tie of width 3 ( compared to the minimal width in this case , 1 ) even in the absence of noise; bow-tie width sharply increased to 5 when temporal noise was added . Since complete absence of noise is a non-realistic scenario in biological systems , we conclude that sum-mutations cannot account for bow-tie evolution . See Fig . 16 in S1 Text for illustration . Finally , we asked whether the present mechanism would apply in a nonlinear network model . While goal rank is a straightforward measure of dimensionality in linear systems , the concept of rank is more elusive when it comes to nonlinear systems . Yet , one can intuitively think that a similar concept could exist there too . To test this hypothesis we employed a well-studied problem of image analysis using perceptron nonlinear neural networks [65 , 66] . In this problem , each node integrates over weighted inputs and produces an output which is passed through a non-linear transfer function , u ( l+1 ) = f ( A ( l ) u ( l ) – T ( l+1 ) ) , where A ( l ) and T ( l ) are the weight matrix and corresponding set of thresholds in the l-th layer , and u ( l ) is the set of inputs propagated from the previous layer ( see Methods ) . We evolved the networks towards a goal of identifying features in a 2 × 2 retina with Boolean pixel values ( D2 = 4 inputs ) ( Fig . 6 ) . Low dimensionality was achieved by defining as a goal four outputs that depend only on two features of the image . The four required Boolean outputs were: ( a ) at least one pixel in the left retina column , ( b ) at least one pixel in the right column , ( c ) pixels in both left and right columns , ( d ) pixel ( s ) in the left or in the right columns . These four outputs can be fully represented by only two features: ( a ) and ( b ) , making the 4-dimensional input space redundant . Thus , the effective “rank” here is r = 2 . Simulations evolving the weights {Aij ( l ) } and thresholds {Ti ( l ) } values using product-rule mutations that led to nearly perfect solutions ( fitness of less than 10−4 from the optimum ) mostly had a narrow waist: one of the intermediate layers had only two active nodes in 75% of the runs ( see Table 1 in S1 Text ) . For comparison , simulations with a mutation rule that was not biased to eliminate interactions ( sum-rule ) were much less likely to lead to networks with a narrow waist ( this was observed in only 45% of runs ) . Detailed statistics over 500 runs of network structures obtained with either mutational scheme is presented in Fig . 12 in S1 Text .
We studied the evolution of bow-ties in layered networks . We find that bow-ties evolve spontaneously when two conditions are met: the goal has deficient rank and the effect of mutations on interactions is well-approximated by a product-rule . The size of the narrowest layer—the waist of the bow-tie—is bounded from below by the rank of the goal . We find the evolution of narrow waists in a wide range of evolutionary parameters , in both linear and nonlinear multi-layered network models . We find that bow-tie structures can also evolve under temporal noise , if the mutational scheme is approximated by a product-rule . An alternative mutational scheme—sum-rule—proved much more vulnerable to noise and did not lead to bow-tie structures . The concept of rank is defined clearly in the case of matrix-like goals and linear transfer functions . In more complex situations , such as the nonlinear retina problem and gene regulatory networks , the rank corresponds to the minimal number of input features on which the outputs depend . One may hypothesize that in the case of probabilistic time dependent signaling in cells and nervous systems , rank may be related to the information theory measure of information source entropy . This is the minimal number of bits which is sufficient to encode the source [67] . A natural information source ( “input” ) —such as biological signals—is often redundant . Its compression ( source coding ) can shorten the description length while still preserving all the necessary information ( “waist” ) . In analogy to the goal rank , the shortest possible description equals the source entropy . The present results can supply an operational definition of the goal rank in a layered nonlinear system—the minimal evolved waist under the present assumptions [68–74] . Here we considered the input-output relation as the sole force guiding the evolution of the network , however there may be other constraints or processes affecting network structures . For example , in the visual system , the fan-in of ganglion cells into the optic nerve was suggested to be partially due to space limitation [16] . A recent study , suggested that bow-ties in developmental gene regulatory network can evolve due to hierarchy in specificity [79] . Cross-talk between networks as well as addition and deletion of network nodes can also influence network structure . A previous study [53] focused on the contribution of robustness to evolving network topologies . It suggested that connectivity can vary between genes in a network , such that genes that buffer genetic variation are highly connected , although the overall network is sparse . Bow-tie structures are also common in multi-layered artificial neural networks used for classification and dimensionality reduction problems . While there are parallels in the functional role of bow-ties there with the biological bow-ties which are the focus of this study , these artificial neural networks are designed a priori to have this bow-tie structure . Multi-layered neural networks often use an intermediate ( hidden ) layer whose number of nodes is smaller than the number of input and output nodes [30 , 75] . There , the role of the hidden layer is to capture the significant features of the inputs . The favorable usage of bow-tie structures in neural networks suggests that often the number of important features is lower than the number of inputs [65] . The transformation between input and hidden layer was shown to map the data into a space in which discrimination is easier [76 , 77] . A similar functional principle was observed in several signaling networks—where a large number of input signals funnel through a narrow intermediate layer to produce a limited number of output programs [22 , 23 , 78] . Kitano and colleagues [22 , 23] highlighted the structural similarity between these biological bow-tie networks and neural network classifiers . Previous studies debated over the role of noise in shaping complex network architecture . Some argue that there is a trade-off between compression and noise mitigation relying on information theory arguments [73 , 80]; others suggest that thermal noise can aid funneling evolutionary dynamics , and avoiding local extrema when the fitness landscape is rugged [81] . The evolutionary process in our model can filter out temporal noise to some extent and still produce bow-tie structure manifested by an intermediate layer whose number of active nodes equals the goal rank . This results from the separation of timescales between the evolutionary process which is driven by the mutation rate ( slow ) and the temporal noise ( fast ) . The evolutionary process can then average out the rapid temporal fluctuations . Evolution under product-mutations characteristically has dynamical attractor states ( such as zero rate constants , which remain zero upon multiplication by a number ) , in contrast to evolution under sum-mutations [29] . This dynamical stability renders the product-mutation landscape more noise-proof than the sum-mutation one . These results call for further research to better understand the multiple roles played by noise in the evolution of complex networks . When a biological network expands gradually such that the goal rank remains intact , the bow-tie is usually “ossified” ( see S1 Text and Fig . 17 in S1 Text ) . Namely , a bow-tie node ( s ) established when the network is small is very likely to remain a bow-tie rather than being replaced by another node . Thus bow-tie nodes end up as among the most ancient in the network . This induces a correlation between node connectivity and its evolutionary age . It would be interesting to validate this prediction by testing whether bow-tie network elements are indeed the most ancient ones . One may speculate whether the relation of bow-tie width to goal rank may be instructive in fields outside of biology . As an example of compression by bow ties , consider alphabets . The entire vocabulary of a language can be transmitted between people using a bow-tie of 20–30 characters . This is not the only possible design: syllabaries such as Japanese Kana represent syllables instead of the vowels/consonants of alphabets , and logographies such as Chinese represent words . The size of the bow-tie in each case may be hypothesized to be close to the minimum required for capturing each level: many tens of syllables , and many thousands of words . Efficiency considerations are probably at play as a ‘selective force’: comparing number systems such as Arabic numerals to Roman numerals shows a progression from a cumbersome to a more efficient bow-tie description . Taken together , our results suggest a mechanism for the evolution of bow-tie architectures in biology and a way to quantitate the rank of the evolutionary goals under which they evolved .
The evolutionary simulation was written in Matlab using standard framework [58–60] . The source codes and analysis scripts are available as supporting materials . We initialized the population of matrices by drawing their N ⋅ LD 2 terms from a uniform distribution . Population size was set to N = 100 . Each “individual” consists of a set of L matrices . In each generation the population was duplicated . One of the copies was kept intact , and elements of the other copy had a probability p to be mutated—as we explain below . Fitness of each of the 2N individuals was evaluated by F = − || A ( L ) A ( L – 1 ) … A ( 1 ) – G || , where || ⋅ || denotes the sum of squares of elements [82] . The best possible fitness is zero , achieved if A ( L ) A ( L − 1 ) …A ( 1 ) = G exactly . Otherwise , fitness values are negative . We constructed the goal matrices from combinations of ‘0’ and ‘10’ terms . We tested goals of different ranks and different internal structures and found no sensitivity for goal details other than its rank ( see S1 Text ) . N individuals are selected out of the 2N population of original and mutated ones , based on their fitness ( see below ) . This mutation–selection process was repeated until the simulation stopping condition was satisfied ( either a preset number of generations or when mean population fitness was within 0 . 01 of the optimum ) . Repeated simulations were run using the same parameters , where at every single run the Matlab random seed was initialized to a different value . Consequently , each run starts from different initial conditions and uses different mutational realizations . In the analysis , we checked whether the runs converged . Only runs that gave results within 0 . 01 from the optimum were considered in the analysis . We then analyzed in each run the number of active nodes in the layer ( see below ) . In the figures we show either the median number or histogram of active nodes per layer , when applicable . We tested the evolution of bow-tie networks in this non-linear problem which resembles standard neural network studies [39 , 65 , 84] . We defined a problem with 4 inputs and 4 outputs and 2 internal processing layers consisting of 4 nodes each . The inputs represent a 4-pixel retina , where each pixel could be either black or white , as described in the results section . The evolutionary simulation followed a similar procedure to the linear problem described above . Mutation , selection , and data analysis methods were similar to the ones used in the linear problem as described above . The main difference is that the output of each layer was not a linear function of the inputs as before , but rather a non-linear function u ( l + 1 ) = f ( A ( l ) u ( l ) – T ( l + 1 ) ) , where A ( l ) and T ( l ) are the weights matrix and corresponding set of thresholds in the l-th layer correspondingly , and u ( l ) is the set of inputs propagated from the previous layer . The non-linear transfer function f was rescaled to range between 0 and 1 , f ( x ) = ( 1 + tanh ( x ) ) / 2 . The result of this computation is fed to the next layer until the last ( output ) layer is reached . In non-linear systems the evolutionary goal cannot be described by a single goal matrix as in the linear case . Rather , it is defined by pairs of input /output relations . The evolutionary simulation tested all possible inputs simultaneously , and evolved the network parameters to provide the correct output in each case . The fitness was defined as the difference between the network output and the desired output , in similarity to the linear model and then averaged over all possible input/output pairs . Inputs and outputs were encoded by Boolean vectors . Internal layer calculation used continuous values , but simulations could reach very high precision ( ≤ 10 −10 from the optimum ) . Simulations were run for 104 generations . Only runs that reached fitness within 10−4 of the optimum were considered in the analysis . The retina simulation was written in Wolfram Mathematica . We initialized the population of matrices and corresponding thresholds by drawing their N ⋅ LD ( D + 1 ) terms from a uniform distribution in the range [-2 , +2] . Population size was set as N = 100 . In each generation the population was duplicated . One of the copies was kept intact , and elements of the other copy had a per-term probability p = 0 . 2 to be mutated . The mutation was implemented through multiplying the mutated term by a random number drawn from a normal distribution with mean 1 and std 0 . 5 ( thus a probability of about q = 0 . 02 to change sign ) . To determine active nodes in this case , we begin by setting each weight to zero in its turn A ( l ) ij = 0 leaving all other terms intact . This procedure was not applied to the threshold values Ti ( l ) because a node may be left in the network , even if no inputs are propagated through it from an upper layer . In these cases the role of such a node is to introduce a constant bias set by its threshold . We then calculate the fitness value of the modified network F˜ and define the difference compared to the original fitness value: ΔF=|F−F˜| . We compare ΔF / F between all weights located at the same layer . A network interaction whose relative effect on fitness is less than 10−4 was set to zero . A node whose entire set of outgoing weights was set to zero was considered inactive .
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Many biological systems show bow-tie ( also called hourglass ) architecture . A bow-tie means that a large number of inputs are converted to a small number of intermediates , which then fan out to generate a large number of outputs . For example , cells use a wide variety of nutrients; process them into 12 metabolic precursors , which are then used to make all of the cells biomass . Similar principles exist in biological signaling and in the information processing in the visual system . Despite the ubiquity of bow-tie structures in biology , there is no explanation of how they evolved . Here , we find that bow-ties spontaneously evolve when the information in the evolutionary goal they evolved to satisfy can be compressed . Mathematically , this means that the matrix representing the goal has deficient rank . The maximal compression possible determines the width of the bow-tie—the narrowest part in the network ( equal to the rank of the goal matrix ) . This offers a mechanism to understand a common architectural principle of biological systems , and a way to quantitate the rank of the goals under which they evolved .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
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Evolution of Bow-Tie Architectures in Biology
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Translation elongation factor P ( EF-P ) alleviates ribosome pausing at a subset of motifs encoding consecutive proline residues , and is required for growth in many organisms . Here we show that Bacillus subtilis EF-P also alleviates ribosome pausing at sequences encoding tandem prolines and ribosomes paused within several essential genes without a corresponding growth defect in an efp mutant . The B . subtilis efp mutant is instead impaired for flagellar biosynthesis which results in the abrogation of a form of motility called swarming . We isolate swarming suppressors of efp and identify mutations in 8 genes that suppressed the efp mutant swarming defect , many of which encode conserved ribosomal proteins or ribosome-associated factors . One mutation abolished a translational pause site within the flagellar C-ring component FliY to increase flagellar number and restore swarming motility in the absence of EF-P . Our data support a model wherein EF-P-alleviation of ribosome pausing may be particularly important for macromolecular assemblies like the flagellum that require precise protein stoichiometries .
Translation elongation factor P ( EF-P ) has been shown to alleviate ribosome pausing at consecutive proline residues ( XPPX motifs ) in Bacteria and Eukaryotes [1–3] . While EF-P is widely distributed and often required for rapid growth , the reason it is highly conserved is unknown [4–6] . In Escherichia coli , the manner in which EF-P promotes growth is thought to be pleiotropic by enhancing translation of multiple proteins that contain XPPX motifs [7] . Systems-level approaches , however , show that not all XPPX motifs induce ribosome pausing in the absence of EF-P , and that even fewer of those pauses result in decreased protein expression [8–10] . Thus , EF-P pleiotropy may be limited . Consistent with limited pleiotropy , the phenotypes of an efp mutant in E . coli are conditional , and are suppressed when translation rates are reduced [11] . Finally , apparent pleiotropy is organism-specific as growth defects in Bacillus subtilis efp mutants are negligible , even under conditions of high translation [12] . Instead , EF-P in B . subtilis is specifically required for swarming motility [12 , 13] . Swarming motility is a flagellar-mediated form of movement on surfaces and often requires an increase in flagellar number relative to swimming in liquid [14–18] . Increasing flagellar number is complicated as flagella are trans-envelope nanomachines that require hierarchical assembly of dozens of subunits in precise stoichiometry [19 , 20] . At the core of each flagellum is a type III secretion apparatus and early-class secretion is activated after the flagellar base plate and C-ring rotor are fully assembled [21–24] . Early class secreted products span the cell envelope to form an axle-like rod and universal joint-like hook [25–27] . Once the hook is polymerized to a certain length , the secretion specificity changes , the late-class sigma factor σD is activated , and late-class flagellar proteins are exported to assemble the filament [28–30] . Mutants that decrease the efficiency of flagellar expression or assembly abolish swarming motility and can do so at any step in the hierarchy [24 , 31] . The mechanism by which EF-P specifically activates swarming motility in B . subtilis is unknown . Here we show that B . subtilis EF-P functions in a manner similar to that reported in other organisms and alleviates ribosome pausing at a subset of XPPX motifs . Flagellar assembly requires translation of a large number of proteins , and efp mutants were found to have a reduced number of flagella . Cells lacking EF-P were defective in hook completion due to translational pausing at one particular XPPX motif within the basal body component FliY . FliY in turn was necessary to activate early-class secretion . EF-P structurally resembles a tRNA and while it is thought to promote translation entropically , the mechanism is poorly understood [32] . Genetic analysis reported here further indicates that mutations in a wide variety of conserved genes related to the ribosome suppress the absence of EF-P , which may aid in the understanding of the EF-P mechanism .
The reason that EF-P is required for swarming motility is unknown . Cells mutated for the master activator of flagellar biosynthesis SwrA lack swarming motility due to reduced transcription of the fla/che flagellar operon and a proportional reduction in flagellar number ( Fig 1A ) [31 , 33] . To determine if cells mutated for efp also had reduced flagellar number , a functional variant of the filament protein Hag that could be labeled with a fluorescent dye ( hagT209C ) was introduced into various genetic backgrounds [34] . Qualitatively , the efp mutant appeared to have fewer filaments than wild type and more closely resembled cells mutated for the master activator of flagellar biosynthesis SwrA ( Fig 2A ) . As swarming motility requires an elevated number of flagella per cell , we infer that the decrease in flagellar number likely accounts for the swarming defect observed upon mutation of efp . Flagella are assembled in a stepwise fashion , with the basal body assembled first , followed by the rod-hook , and finally the filament . Thus , a decrease in flagellar filament number could result from a decrease in either the number of hooks or basal bodies . To determine if mutation of efp affected hook and/or basal body number , functional variants of the hook protein FlgE ( FlgET123C ) or basal body C-ring subunit FliM ( FliM-GFP ) were introduced into various genetic backgrounds , and fluorescent puncta were quantified with 3D structured illumination microscopy [31 , 35] . Mutation of efp resulted in a decrease in hook number compared to wild type , but the number of basal bodies remained the same ( Fig 2A–2C ) . By contrast and consistent with previous reports , mutation of swrA resulted in a decrease in both basal body and hook numbers [24 , 31] ( Fig 2A–2C ) . We conclude that EF-P is required for completion of a step in flagellar assembly between incorporation of FliM into the C-ring and completion of the hook . A defect in hook completion prevents export of the anti-sigma factor FlgM , resulting in a decrease in expression directed by RNA polymerase and the sigma factor σD ( Fig 1A ) [30 , 36] . To determine whether the efp mutant was defective in σD-dependent gene expression , a σD-dependent transcriptional reporter in which the promoter of flagellin Phag fused to β-galactosidase ( Phag-lacZ ) was inserted at an ectopic location in various genetic backgrounds [33 , 37] . Cells mutated for either swrA or efp showed a decrease in expression from the Phag promoter , and expression was partially restored to either swrA or efp mutants by mutation of flgM ( Fig 3 ) . SwrA and EF-P appeared to enhance Phag-lacZ expression by different pathways , however , as an efp swrA double mutant synergized to decrease promoter activity ( Fig 3 ) . Further , expression in an efp swrA flgM triple mutant remained low relative to either the swrA flgM or the efp flgM double mutants ( Fig 3 ) . We conclude that EF-P promotes hook completion and σD-dependent gene expression by a mechanism unrelated to SwrA activation of the Pfla/che promoter . One way that EF-P could promote flagellar biosynthesis is if it alleviated ribosome pausing as it does in a number of other organisms [1–3] . To determine whether EF-P in B . subtilis alleviates ribosome pausing , the ribosome pause sites of wild type and an efp mutant were compared . In brief , mRNA fragments protected by ribosome footprinting were purified , subjected to Illumina sequencing , and the codons in the ribosomal P-site were identified using the 3’ end assignment method [10] . Each codon in the genome was then assigned a pause score , defined as the number of reads that mapped to a particular position divided by the average read density for the corresponding gene ( S3 Table ) . Approximately 250 codons in 180 genes had a pause score that was at least 10-fold higher in the efp mutant compared to WT ( S4 Table ) . In the absence of EF-P , proline codons were enriched in both the ribosome P-site and E-site , and the tripeptides encompassing the “-2” , “E” , “P” , and “A” sites showed an enrichment of pausing at PPX and XPP motifs ( Fig 4A–4C , S5 Table ) . We conclude that in the absence of EF-P , B . subtilis ribosomes paused more frequently at XPPX motifs , consistent with that reported in other organisms . The 180 genes that experienced increased ribosome pausing in the absence of EF-P were predicted to be diverse in function ( S6 Table ) . Five genes with EF-P-alleviated pause sites appeared to be directly related to the flagellum: fliI ( encoding the secretion accessory protein FliI ) , fliF ( encoding the basal body base plate FliF ) , motB ( encoding the stator component MotB ) , sigD ( encoding the alternative sigma factor σD ) , and fliY ( encoding the C-ring component FliY ) , ( Fig 1B , S4 Table ) [38] . FliF , σD , and FliY are all required for hook completion and their translational impairment might be consistent with the efp mutant phenotype but it wasn’t clear which , or how many , of the sites were directly responsible [24 , 35] . Moreover , EF-P-alleviated pause sites were observed in several essential genes , even though mutation of efp did not substantially reduce growth rate ( S4 Table ) [12 , 39] . Thus , as ribosome pauses were found in at least 5 genes known to be involved in motility ( not including genes of unknown function ) , and the location of EF-P-alleviated translational pausing was not necessarily predictive of phenotype , we concluded that ribosome profiling alone was insufficient to identify the hook-promoting EF-P target . As an alternative approach to determine how EF-P increased hook number , spontaneous suppressors were isolated that restored swarming motility to an efp mutant . Cells mutated for efp were initially incapable of migrating from the site of inoculation on swarming motility agar ( Fig 5A and 5B ) , but after prolonged incubation , second-site mutations that suppressed the need for EF-P emerged from the central colony as motile flares . Twenty-four suppressors of efp ( soe ) mutations were independently isolated and each suppressor resulted in a partial restoration of swarming motility . The location of each suppressor mutation in the genome was identified using a combination of SPP1-mediated transduction linkage mapping and whole genome sequencing . The mutations were organized into 6 different classes based on their chromosomal location ( Table 1 ) .
Translation elongation factor P ( EF-P ) is conserved in all domains of life and has been shown to alleviate ribosome pausing at a subset of sequences encoding tandem proline residues ( XPPX motifs ) [8 , 10 , 58] . In many organisms , EF-P is required for growth , presumably because it enhances translation of one or more XPPX-containing essential proteins , the ValS aminoacyl-tRNA synthetase in particular [4–6 , 59] . In B . subtilis however , efp mutants have negligible growth defects and instead are specifically incapable of a flagellar-mediated surface motility called swarming [12 , 13 , 39] . We show here that B . subtilis EF-P alleviates ribosome pausing at XPPX motifs in a manner nearly indistinguishable from other organisms . We further attribute the efp mutant swarming defect to a decrease in flagellar number at the level of flagellar hook biosynthesis , and we analyze spontaneous suppressor mutants that restore swarming motility . Many of the suppressors were in ribosome subunits or ribosome-associated factors and were likely compensatory . One suppressor mutant , however , was in the motility target of EF-P and changed a ribosome pause-inducing SPP motif to APP in the flagellar C-ring component , FliY . FliY is homologous to the protein FliN found in the flagellar C-ring of other bacteria , and the efp mutant flagellar assembly defect is consistent with FliY being a motility-related EF-P target [24 , 60] . A fliY deletion does not perfectly phenocopy mutation of efp , as FliY is necessary for flagellar C-ring assembly and all forms of flagellar motility , whereas the efp mutant has wild type basal body numbers and can swim but not swarm ( Fig 2A ) [13] . Moreover , the fliY mutant lacks flagellar filaments whereas the efp mutant does not , perhaps because FliY , like FliN , may be a docking point for the late class flagellar secretion protein FliH ( Fig 2 ) [61 , 62] . The absence of EF-P instead increases ribosome pausing and decreases FliY copy number , thereby reducing the frequency of flagella that complete basal body assembly and activate early-class type III secretion [24] . Why EF-P is needed to specifically relieve translational pausing of FliY is unclear . The need for EF-P may be unavoidable as the SPP motif falls within a highly conserved sequence of residues that are nearly invariant . The FliYsoe allele in otherwise wild type cells , however , exhibited nearly wild type levels of swarming motility suggesting that an EF-P-independent variant is indeed tolerated ( Fig 1C , S1F Fig ) . Alternatively , EF-P pausing relief may play a regulatory role . While EF-P in B . subtilis is constitutively expressed , it is post-translationally modified by 5-aminopentanolylation which is predicted to be built through the sequential maturation of at least 3 EF-P modification intermediates [63] . Moreover , previous work has shown that the modification state of B . subtilis EF-P alters its activity and therefore may represent a method of regulating EF-P function [39 , 63] . We note that while FliN of E . coli does not encode an XPPX motif , translational pausing in the absence of EF-P is nonetheless conserved at a series of four consecutive valine residues , perhaps indirectly due to increased ribosome pausing in ValS and a concomitant decrease in tRNAs charged with valine ( S4 Fig ) [10] . Suppression of the efp mutant swarming defect could be achieved through mutation of 7 additional loci , many of which are broadly conserved and could be readily related to the translational machinery ( S2 Fig , S7 Table ) . The location of these additional suppressors may provide insight into the mechanism by which EF-P promotes translation in diverse organisms . Homologs of YeeI are highly conserved and poorly studied , but one YeeI homolog in humans , TACO1 , has been implicated in activating the translation of Cox1 , which contains 4 XPPX motifs [41] . YdiF is a broadly conserved member of the ABC-F family of ATPases which comprises many proteins known to interact with the ribosome such as EF-3 in Eukaryotes and EttA in E . coli [43] . YacO is homologous to RlmB in E . coli , a highly conserved protein that methylates the 23S rRNA guanosine G2251 within the ribosomal peptidyltransferase domain [64] . Rae1 has been recently shown to act as a ribosomal A-site endoribonuclease , and it was hypothesized that ribosome stalling may increase its access to its mRNA substrate and thereby increase its activity [42] . Finally , S3 and S10 are components of the small subunit of the ribosome itself: S3 is involved in mRNA processivity and S10 is involved in binding to the P-site tRNA [48 , 50] . Further , the residue altered by soe24 ( S10M88R ) has been implicated in the direct interaction with the last protein identified by efp suppressor analysis , NusG [49] . NusG couples transcription and translation in E . coli by binding both RNA polymerase and the leading ribosome on the transcript to promote transcriptional elongation [47 , 49] . In B . subtilis , however , NusG is thought to do the opposite and promote transcriptional pausing [65 , 66] . In E . coli , NusG also binds to the ribosome but whether it does so in B . subtilis and how the nusGsoe allele suppresses the efp swarming defect is unclear [47 , 49] . NusGsoe appears to be a gain-of-function allele that does not increase FliY protein levels but rather increases the expression of σD-dependent late-class flagellar genes , including the flagellar filament ( Fig 3 , Fig 8 ) . The increase in σD -dependent gene expression , however , is likely an indirect effect of suppression as artificial activation of σD was insufficient to restore swarming to the efp mutant ( S1E Fig ) . While the mechanism by which NusGN21S suppresses the efp mutant swarming defect is unknown , it appears to operate in parallel to the alleviation of ribosome pausing in FliY , as the nusGsoe and fliYsoe alleles synergized to enhance swarming in the efp mutant background ( Fig 5H ) . The majority of flagellar genes including both fliY and sigD are encoded on what is thought to be a single transcript from the 27kb 32 gene fla/che operon ( Fig 1 ) [67–70] . Perhaps NusG is somehow involved in the expression of long transcripts . Ultimately , EF-P alleviates ribosome pausing at some but not all XPPX motifs , and the context that causes a particular primary sequence to trigger stalling is unclear . For example , cells fail to swarm when ribosomes pause at an SPP motif in the fliY transcript and swarming is restored by substitution to a APP motif , another site that also experiences strong pausing elsewhere in the genome . Moreover , even in situations where ribosome pausing is severe , there may or may not be phenotypic consequences . For example , ribosomes also pause at and accumulate upstream of a PPP motif in the valS transcript but little to no growth defect is observed , and unlike the case in E . coli , pauses at valine residues are not enriched in B . subtilis ( S5 Fig , Compare Fig 3B to S4D Fig ) . Thus , one cannot predict whether ribosomes pause at particular motifs by bioinformatics , and it may be difficult to predict the phenotypes of efp mutants simply from ribosome profiling data sets . Our work supports previous observations in E . coli that the phenotypic effect of EF-P may be most significant for pauses in proteins for which relative stoichiometry is important . For example , EF-P-alleviated pausing has been shown to be important for the maintenance of subunit ratio for the F1F0 ATPase [8 , 71] . Moreover , EF-P relieves translational pausing within CadC , a transcriptional activator that is antagonized by direct interaction with LysP [3] . Thus translational pausing creates a stoichiometric imbalance and results in constitutive antagonism of CadC and deactivation of the CadC transcriptional target [3] . Here we provide evidence that EF-P supports synthesis of the protein FliY , which when in stoichiometric deficiency limits the cells ability to complete flagellar basal body biosynthesis , increase flagellar number , and perform swarming motility . We broadly speculate that biological systems which depend on stoichiometry may be particularly sensitive to translational pausing and thus display enhanced phenotypic dependency on EF-P .
B . subtilis and E . coli strains were grown in lysogeny broth ( LB ) ( 10 g tryptone , 5 g yeast extract , 5 g NaCl per L ) or on LB plates fortified with 1 . 5% Bacto agar at 37°C . When appropriate , antibiotics were included at the following concentrations: 100 μg/ml ampicillin , 10 μg/ml tetracycline , 100 μg/ml spectinomycin , 5 μg/ml chloramphenicol , 5 μg/ml kanamycin , and 1 μg/ml erythromycin plus 25 μg/ml lincomycin ( mls ) . Isopropyl β-D-thiogalactopyranoside ( IPTG , Sigma ) was added to the medium at the indicated concentration when appropriate . Strain construction and suppressor isolation details are described in the S1 Text . Strains used in this study are listed in Table 2 , plasmids are listed in S9 Table , and primers are listed in S10 Table . For quantitative swarm assays , strains were grown to mid log phase ( OD600 0 . 3–1 . 0 ) concentrated to an OD600 of 10 in PBS pH 7 . 4 ( 0 . 8% NaCl , 0 . 02% KCl , 100 mM Na2HPO4 , and 17 . 5 mM KH2PO4 ) plus 0 . 5% India ink . LB plates fortified with 0 . 65% agar were dried for 10 min open-faced in a laminar flow hood and subsequently inoculated by spotting 10 uL cell resuspensions onto the center of the plate . Plates were dried an additional 10 min open-faced in a laminar flow hood and then incubated at 37°C in a humid chamber . Swarm radius was measured along the same axis every 30 minutes . Images of swarm plates were obtained by toothpick-inoculating a colony into the center of an LB plate fortified with 0 . 65% agar . Plates were dried open-faced in a laminar flow hood for 12 min and incubated at 37°C in a humid chamber for 16 hrs . Images were taken using a BioRad Gel Doc . Fluorescence micrographs were generated with a Nikon 80i microscope along with a phase contrast objective Nikon Plan Apo 100X and an Excite 120 metal halide lamp . FM4-64 was visualized with a C-FL HYQ Texas Red Filter Cube ( excitation filter 532–587 nm , barrier filter >590 nm ) . GFP and Alexa Fluor 488 were visualized using a C-FL HYQ FITC Filter Cube ( FITC , excitation filter 460–500 nm , barrier filter 515–550 nm ) . Images were captured with a Photometrics Coolsnap HQ2 camera in black and white and subsequently false colored and superimposed using Metamorph image software . For fluorescent microscopy of flagellar filaments and hooks , 1 . 0 ml of broth culture was harvested at mid-log phase , resuspended in 50 μl of PBS buffer containing 5μg/ml Alexa Fluor 488 C5 maleimide ( Molecular Probes ) , incubated for 2 min at room temperature , and washed once in 1 . 0 ml of PBS buffer . The suspension was pelleted , resuspended in 30 μl of PBS buffer containing 5 μg/ml FM 4–64 ( Invitrogen T13320 ) , and incubated for 2 min at room temperature . The cells were pelleted , resuspeneded in 30 μl PBS buffer , and were observed by spotting 5 μl of suspension on a microscope slide and immobilized with a poly-L-lysine-treated glass coverslip . For fluorescent microscopy of flagellar basal bodies , 1 . 0 ml of broth culture was harvested at mid-log phase , resuspended in 30 μl of PBS buffer containing 5 μg/ml FM 4–64 , and incubated for 2 min at room temperature . The cells were pelleted , resuspeneded in 30 μl PBS buffer , and were observed by spotting 5 μl of suspension on a microscope slide and immobilized with a poly-L-lysine-treated glass coverslip . For super-resolution microscopy , the OMX 3D-SIM Super-Resolution system with a 1 . 42-numerical-aperture ( NA ) Olympus 60X oil objective was used . FM4-64 was observed using laser line 561 and emission filter 609 nm to 654 nm , and GFP ( along with Alexa Fluor 488 ) was observed using laser line 488 nm and emission filter 500 nm to 550 nm . Images were captured using PCO Edge 5 . 5 sCMOS cameras , processed using SoftWorx imaging software , and analyzed using Imaris software . Strains were grown in LB at 37°C to OD600 0 . 7–1 . 0 and 1 mL was harvested by centrifugation at 18 , 000 xg . The pellet was resuspended in 1 mL Z-buffer ( 40 mM NaH2PO4 , 60 mM Na2HPO4 , 10 mM KCl , 1 mM MgSO4 , and 38 mM 2-mercaptoethanol ) , 200 μg lysozyme was added , and cells were lysed at 30°C for 15 min . To obtain optical density readings within the linear range , each lysate was appropriately diluted to a final volume of 500 μL in Z-buffer . The reaction was started by the addition of 100 μL start buffer ( 4 mg/mL ortho-Nitrophenyl-β-galactoside in Z-buffer ) , and incubated at 30°C . The reaction was stopped by the addition of 250 μL 1M Na2CO3 and the OD420 of the mixture was measured . The β-galactosidase-specific activity was calculated according to the equation ( OD420 * Dilution factor * 1000 ) / ( time * OD600 ) . Average β-galactosidase activity and the standard deviations for all experiments can be found in S10 Table . The expression constructs for His-SUMO-FliY ( pDP288 ) and His-SUMO-FliG ( pKB43 ) were introduced into E . coli Rosetta gami II cells and grown at 37°C in Terrific broth ( 12 g tryptone , 24 g yeast extract , 4 ml glycerol , 2 . 31 g monobasic potassium phosphate and 12 . 54 g dibasic potassium phosphate per liter ) to mid-log phase . 1 mM IPTG was then added and the culture was grown overnight at 16°C . Cells were pelleted , resuspended in lysis buffer ( 50 mM Na2HPO4 and 300 mM NaCl ) and lysed using an Avestin EmulsiFlex-C3 at approximately 15 , 000 psi . Cell debris was pelleted by centrifugation at 31 , 000 xg for 30 min and Ni-nitrolotriacetic acid resin ( Novagen ) was added to the clarified supernatant . The resin-lysate mixture was incubated at 4°C for 3 hrs . The resin was applied to a 1-cm separation column ( Bio-Rad ) , washed twice with 10 mL lysis buffer , and once with 10 mL wash buffer ( 50 mM Na2HPO4 , 300 mM NaCl , and 30 mM imidazole ) Protein was eluted with lysis buffer containing 100 mM imidazole . To cleave the His-SUMO tag from the purified protein , ubiquitin ligase/protease was added and the reaction was incubated at room temperature for 3 hrs . To remove remaining uncleaved protein or free His-SUMO from the cleavage reaction , Ni-nitrolotriacetic acid resin ( Novagen ) was added and incubated at 4°C for 1 h . The resin was pelleted by centrifugation and the supernatant , containing untagged FliY or FliG , was dialyzed into PBS pH 7 . 4 plus 10% glycerol and stored at -20°C . One milligram of purified FliY protein was sent to Cocalico Biologicals Inc . for serial injection into a rabbit host for antibody generation . Anti-FliY serum was mixed with FliY-conjugated Affi-Gel-10 resin ( Bio-Rad 1536099 ) and incubated overnight at 4°C . The resin was packed onto a 1-cm column ( Bio-Rad ) and then washed with 100 mM glycine ( pH 2 . 5 ) to release the antibody and immediately neutralized with 2M Tris base . The purification of the antibody was verified by SDS-PAGE . Purified anti-FliY antibody was dialyzed into PBS–50% glycerol and stored at -20°C . Ribosome profiling libraries were prepared as described previously with minor modifications [75] . 300 mL LB exponential phase cultures ( OD600 0 . 3–0 . 4 ) grown at 37°C were subjected to rapid filtration and subsequently flash frozen in liquid nitrogen . Cells were lysed in 650 μL lysis buffer ( 10 mM MgCl2 , 100 mM NH4Cl , 5 mM CaCl2 , 20 mM Tris pH 8 . 0 , 0 . 1% NP-40 , 0 . 4% Triton X-100 , 0 . 1 units/μL RNase free DNase I ( Invitrogen AM2222 ) , 0 . 5 units/μL Superase-In ( Invitrogen AM2696 ) ) using a Spex 6875 freezer mill set to 10 cycles of 2 min runs at 15 cps separated by 2 min rests . Following lysis , 25 A260 units of lysate were digested with 1500 units of S7 micrococcal nuclease ( Roche 10107921001 ) for 1 hr at room temp after which the reaction was quenched by the addition of EGTA to a final concentration of 6 mM . The digested lysate was then applied to a 10%-50% sucrose gradient and centrifuged in a Ti-40 rotor at 35 , 000 rpm for 2 . 5 hrs at 4°C . 700 μL of fractions containing 70S ribosomes were denatured in 1% SDS and extracted once with an equal volume of 75°C acid phenol , once with an equal volume of room temp acid phenol , and RNA was precipitated with isopropanol . The precipitant was resuspended in 12 μL H2O and 25 μg RNA was mixed with 2X loading dye ( 10 mM EDTA , 30 μg/mL bromophenol blue , and 98% formamide ) and resolved on a 15% polyacrylamide TBE Urea gel . After staining the gel in SYBR Gold ( Sigma S11494 ) for 3 min , products between ~15 and 40 bp were excised using the 10 bp O’range ladder as the standard ( Thermo Scientific SM1313 ) and subsequently gel extracted . RNA was resuspended in H2O and the 3’ ends were dephosphorylated with T4 poly-nucleotide kinase ( Lucigen 30061–1 ) at 37°C for 1 hr . RNA was precipitated in isopropanol and ligated to 1 μL 1 μg/μL Linker 1 ( IDT /5rApp/CTGTAGGCACCATCAAT/3ddC/ ) with T4 RNA Ligase 2 , truncated ( NEB M0242S ) in a 50 μL reaction at 25°C for 2 . 5 hrs . Products were precipitated , mixed in 2X loading dye and resolved on a 15% polyacrylamide TBE Urea gel . After staining the gel in SYBR Gold for 3 min , products between 30 and 100 bp were excised using the 10 bp O’range ladder as the standard and subsequently gel extracted . Isolated RNA was then reverse transcribed using Superscript III ( Invitrogen 18080044 ) and 2 μL 1 . 25 μM reverse transcription primer ( IDT 5′- ( Phos ) -AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGC- ( SpC18 ) -CACTCA- ( SpC18 ) -TTCAGACGTGTGCTCTTCCGATCTATTGATGGTGC CTACAG-3′ ) in a 20 μL reaction for 30 min at 48C . RNA was subsequently hydrolyzed by the addition of 2 . 2 μL 1N NaOH and incubation at 98C for 20 min . Reverse transcription products were resolved on a 10% polyacrylamide TBE urea gel , the gel was stained in SYBR Gold for 3 min , and cDNA products were gel extracted . After resuspension in H2O , cDNA products were circularized using CircLigase ( Epicentre CL4111K ) in a 20 μL reaction volume at 60°C for 1 hr and subsequently heat-inactivated at 80°C for 10 min . Circularized products were precipitated in isopropanol , resuspended in H2O , and used as a template for 20 μL PCR reactions using Phusion Polymerase ( NEB M0530S ) with forward library primer ( IDT 5′-AATGATACGGCGACCACCGAGATCTACAC-3′ ) and Indexed reverse library primer ( IDT 5′-CAAGCAGAAGACGGCATACGAGATNNNNNNNNGACTGGAGTTCAGACGTGTGCTCTTCCG-3′ ) where NNNNNNNN represents the barcode sequence unique to each library . After 6–10 cycles , two PCR reactions per sample with no apparent duplexed products after resolution of 2 μL on an 8% polyacrylamide TBE urea gel were pooled and DNA was purified with a QIAquick kit ( Qiagen 28106 ) and eluted in 20 μL H2O . Libraries were sequenced using the Illumina NextSeq 500 platform in a single-end flow cell at the Indiana University Center for Genomics and Bioinformatics . Total RNA was extracted from the same cell lysates used to create ribosome profiling libraries . Following lysis , 2 . 5 A260 units were diluted in 700 μL H2O and denatured in 1% SDS . RNA was extracted once with an equal volume of hot acid phenol , once with an equal volume of room temp acid phenol , and RNA was precipitated with isopropanol . Precipitant was resuspended in H2O and 10 μg RNA was DNAse treated using 4 units of RNase free DNase I ( Invitrogen AM2222 ) at 37°C for 30 min in a 20 μL reaction volume . RNA was precipitated in isopropanol , resuspended in H2O and libraries were prepared by the Indiana University Center for Genomics and Bioinformatics using the ScriptSeq RNA-library kit ( Illumina SSV21124 ) . Libraries were sequenced using the Illumina NextSeq 500 platform in a single-end flow cell at the Indiana University Center for Genomics and Bioinformatics . NGSutils v 0 . 5 . 9 was used to remove sequencing adapters ( CTGTAGGCACCATCAAT ) and filter out any reads shorter than 25 bp . Fastx v 0 . 0 . 13 was subsequently used to remove the first base from each read and resulting reads were aligned to the NCIB 3610 genome ( NZ_CP020102 . 1 ) using Bowtie v 1 . 1 . 2 . Using the 3primeassignment . pl script ( S1 File ) , 1 , 750 , 000–3 , 550 , 000 reads per sample that uniquely aligned to the genome were assigned to a single position corresponding to the 15th nucleotide from the 3’ end according to the 3’ assignment method described previously [10] . Only genes with an average read density greater than 0 . 1 ( defined as the number of mapped reads divided by the number of codons ) in all samples were analyzed further ( S11 Table ) . For each sample , the pausescore . pl script ( S1 File ) was used to determine the pause score for each codon in the filtered list of genes defined as the number of reads assigned to that position divided by the average read density of that gene . The first and last 6 codons of each gene were excluded from this analysis . The pause scores for all codons calculated in this way can be found in S3 Table . Average pause scores for all 8 , 000 potential tripeptides were determined in one of two ways–either with the tripeptide centered on the P-site or the E-site . In both methods only the pause score for the P-site codon was used to determine the average . The average pause scores for all tripeptides calculated in this way can be found in S5 Table . Weighted sequence logos were generated by compiling all sequences in which the P-site codon had a pause score of 10 or greater and visualized using the WebLogo 3 online tool at http://weblogo . threeplusone . com/ . Clustered orthologous group assignment was performed with the DIAMOND mapping mode of eggNOG version 4 . 5 [76] . E . coli ribosome profiling datasets ( SRX823699 , SRX823700 , SRX823701 , and SRX823703 ) published by Woolstenhulme et al . , 2015 were downloaded from the Sequence Read Archive and analyzed as described above using the MG1655 genome ( NC_000913 . 3 ) as a reference . RNA-sequencing analysis was performed using the default parameters of the RSEM ( v 1 . 3 . 0 ) calculate expression function and the NCIB 3610 genome ( NZ_CP020102 . 1 ) as a reference [77] . The transcripts per kilobase million ( TPM ) reported in the RSEM output were used to generate Fig 8 and S8 Table . A local database of 2554 genomes were annotated with the Pfam library using the software hmmer v 3 . 1b2 and an E-value threshold of 1e-10 [78 , 79] . Proteins that contained both a CheC and FliMN_C domain were considered to be FliY homologs . The resulting 282 sequences were aligned using the default parameters of muscle version 3 . 8 . 31 [80] and visualized using the WebLogo 3 online tool at http://weblogo . threeplusone . com/ . The list of FliY homolog accession numbers can be found in S1 Table . Strains were grown to mid-log phase , concentrated to an OD600 of 10 in lysis buffer ( 17 . 2 mM Tris pH 7 . 0 , 8 . 6 mM EDTA pH 8 . 0 , 1 mg/ml Lysozyme , 0 . 1 mg/ml RNaseA , 20 mg/ml DNase I and 50 mg/ml phenylmethane sulfonyl fluoride ) and incubated at 37°C for 30 min . 6X SDS sample buffer ( 500 mM Tris pH 6 . 8 , 22% glycerol , 10% SDS and 0 . 12% bromophenol blue ) was added , and samples were boiled for 5 min . 12 μL boiled samples containing 2 . 13x107 cells ( calculated according to the equation: 2 . 13x108 cells/mL/OD600 ) were loaded onto 12% polyacrylamide gels . To generate the standard curves , the following amounts of protein were mixed with 12 uL of the corresponding deletion mutant’s lysate and the entire mixtures were loaded onto each appropriate gel: FliY– 0 . 184 pmol , 0 . 092 pmol , 0 . 037 pmol , 0 . 018 pmol , or 0 . 0092 pmol . FliG– 0 . 22 pmol , 0 . 144 pmol , 0 . 072 pmol , 0 . 029 pmol , or 0 . 014 pmol . Lysates were resolved at 150 V for 1 . 25 h and transferred onto nitrocellulose membranes . For blots in which FliY concentration was analyzed , a 1:2 , 000 dilution of affinity purified anti-FliY antisera and a 1:2 , 000 dilution of affinity purified anti-FliG antisera ( serving as a loading control ) were used as primary antibodies . For blots in which FliG concentration was analyzed , a 1:2 , 000 dilution of affinity purified anti-FliG antisera and a 1:80 , 000 dilution of crude anti-σA antisera ( serving as a loading control ) were used as primary antibodies . Following incubation with the primary antibodies , nitrocellulose membranes were probed with Alexa Fluor 750-conjugated goat anti-rabbit immunoglobulin G ( Life technologies A21039 ) and blots were imaged using a FluorChem R system . Images were analyzed using Image Studio Lite version 5 . 2 . To determine the conservation of S3 , S10 , NusG , YeeI , YacO , and Rae1 across all domains of life , the genomes of 191 organisms identified by Ciccarelli et al . were annotated with the Pfam library using the software hmmer v 3 . 1b2 and an E-value threshold of 1e-5 [78 , 79 , 81] . The presence of S10 was established by the annotation of a Ribosomal_S10 domain . The presence of S3 was established by the annotation of a protein that contained both a Ribosomal_S3_C domain as well as a KH_2 domain . The presence of NusG was established by the annotation of a protein that contained both a KOW domain as well as a NusG domain . The presence of YeeI was established by the annotation of a Transcrip_reg domain . The presence of YacO was established by the annotation of a protein that contained both a SpoU_methylase domain as well as a SpoU_sub_bind domain . Finally , the presence of Rae1 was established by the annotation of a NYN_YacP domain . To determine the conservation of YdiF , a blastp database consisting of all YdiF-subfamily sequences identified previously was constructed [43] . The genomes of all 191 organisms were then analyzed for sequences that aligned to at least one YdiF homolog with an E-value threshold of 1e-10 and greater than 75% identity using BLAST+ version 2 . 2 . 31 [82] . The data are presented using the Interactive Tree of Life visualization software [83] . All accession numbers for the identified homologs can be found in S7 Table . Ribosome profiling and RNA-sequencing data are available at the Gene Expression Omnibus under accession number GSE126235 . Data were analyzed using custom Perl scripts .
|
Translation elongation factor P ( EF-P ) is a highly conserved protein that alleviates ribosome pausing at consecutive proline residues . Unlike most organisms , EF-P in the bacterium Bacillus subtilis is not required for growth but is instead required for a flagellar-mediated form of motility called swarming . By mapping spontaneous suppressors , we identify 7 broadly distributed ribosome-associated factors that , when mutated , allow swarming in the absence of EF-P , the location of which may provide mechanistic insight . Moreover , we show that EF-P enhances flagellar biosynthesis by alleviating ribosome pausing within a single flagellar structural component FliY and we implicate the RNA polymerase pausing factor NusG in long operon expression . Finally , we extend ribosome profiling analysis in the absence of EF-P to gram-positive bacteria .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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"pathogens",
"bacillus",
"microbiology",
"flagellar",
"motility",
"mutation",
"prokaryotic",
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"frameshift",
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] |
2019
|
Suppressor mutations in ribosomal proteins and FliY restore Bacillus subtilis swarming motility in the absence of EF-P
|
Topographic neuronal maps arise as a consequence of axon trajectory choice correlated with the localisation of neuronal soma , but the identity of the pathways coordinating these processes is unknown . We addressed this question in the context of the myotopic map formed by limb muscles innervated by spinal lateral motor column ( LMC ) motor axons where the Eph receptor signals specifying growth cone trajectory are restricted by Foxp1 and Lhx1 transcription factors . We show that the localisation of LMC neuron cell bodies can be dissociated from axon trajectory choice by either the loss or gain of function of the Reelin signalling pathway . The response of LMC motor neurons to Reelin is gated by Foxp1- and Lhx1-mediated regulation of expression of the critical Reelin signalling intermediate Dab1 . Together , these observations point to identical transcription factors that control motor axon guidance and soma migration and reveal the molecular hierarchy of myotopic organisation .
Neural circuits are frequently organised in a topographic manner such that the position of a neuronal cell body is correlated with the location of the post-synaptic target and therefore its axon trajectory . Since the inference of such organisational principles [1] , the molecular identity of many neuronal migration and axon guidance cues has been uncovered [2] , [3] . Recent studies have also begun to identify the transcription factors that control neuronal identity and deploy the repertoire of neuronal migration and axon guidance receptors and signals employed in neural circuit assembly [4] , [5] , [6] . These observations raise the possibility that correlated neuronal soma localisation and axon trajectory of topographically ordered neural circuits arise as a consequence of specific transcription factors directing both axon guidance and cell body migration effector expression . Vertebrate spinal motor neurons are organised myotopically in longitudinal columns such that the location of their soma in the ventral spinal cord corresponds to the position of their muscle targets in the periphery [7] . In mouse and chick , motor neurons innervating axial and body wall muscles are located in medially positioned columns , whereas motor neurons innervating limb muscles are located in the lateral motor column ( LMC ) present only at spinal cord levels in register with limbs . LMC neurons are further subdivided according to their axon trajectory within the limb: lateral LMC ( LMCl ) neurons innervate dorsal limb muscles , whereas medial LMC ( LMCm ) neurons innervate ventral limb muscles [8] , [9] , [10] . Motor pools are also organised myotopically such that , in general , the anterio-posterior location of a pool within the LMC correlates with the proximo-distal location of its limb muscle target [7] , [9] , [11] , [12] . A motor axon guidance decision point is at the base of the limb where LMC axons interact with mesenchymal cells resulting in the selection of a dorsal or a ventral limb nerve trajectory [10] , [13] . Concomitant with this process , LMC somata migrate from the progenitor-rich ventricular zone to the ventral horn of the spinal cord [14] , [15] , with the later-born LMCl neurons migrating past the earlier-born LMCm neurons in a manner reminiscent of the inside-out lamination of the developing cerebral cortex [16] , [17] , [18] . Recent studies also describe a topographic relationship between motor neuron soma and dendrite localisation in Drosophila and the patterns of motor neuron recruitment during swimming in fish [19] , [20] . The molecular signals controlling the trajectory of LMC axons are characterised , but those controlling LMC soma position in the spinal cord are poorly understood . The LIM homeodomain proteins Isl1 and Lhx1 , expressed by LMCm and LMCl neurons respectively , act in conjunction with the pan-LMC forkhead domain transcription factor Foxp1 to specify the dorsoventral axon trajectory in the limb by regulating the expression of axonal Eph tyrosine kinase receptors that enable LMC growth cones to respond to ephrin ligands in the limb mesenchyme . Genetic evidence argues that ephrin-A ligands in the ventral limb repulse EphA-expressing LMCl axons into the dorsal limb nerve , while ephrin-B ligands in the dorsal limb repulse EphB-expressing LMCm axons into the ventral limb nerve [21] , [22] , [23] , [24] , [25] , [26] . The clustering of some motor pools relies on EphA4 , type II cadherins , and the ETS transcription factor Pea3 [27] , [28] , [29] , while migration of LMCl and LMCm neurons into their appropriate columnar location can be biased by Lhx1 and Isl1 and requires Foxp1 [21] , [22] , [23] . These observations raise the possibility that Foxp1 , Lhx1 , and Isl1 control the migration of LMC cell bodies within the ventral horn by restricting the expression of specific effectors of neuronal migration . The extracellular matrix protein Reelin is a crucial neuronal migration signal that acts through the lipoprotein receptors VLDLR or ApoER2 to induce the phosphorylation of the intracellular adaptor protein Dab1 leading to remodelling of the actin cytoskeleton [30] . Loss of Reelin or its signalling effectors disrupts the layering of the neuronal somata within the cerebral cortex [31] , [32] , [33] but the role of Reelin in neuronal migration remains controversial . Reelin has been proposed to act as a neuronal migration stop signal [34]; however , since Reelin expression in the ventricular zone can partially rescue the pre-plate splitting defects in Reelin-deficient mice , Reelin has also been proposed to act as a permissive signal enabling neurons to interpret distinct migration cues [35] . Similar to cortical neurons , spinal neuron progenitor clones migrate away from the ventricular zone in radial spoke-like trajectories [14] and the migration of preganglionic ( PG ) motor neurons and the layering of the dorsal horn laminae is controlled by Reelin [36] , [37] . These studies raise the possibility that Reelin may also regulate the localisation of LMC neurons and is thus a general migration cue specifying the position of many different classes of spinal neurons including LMC motor neurons . Using gain and loss of function experiments in chick and mouse , we provide evidence that Reelin directs LMC neuron migration but not the selection of limb axon trajectory . We also show that Foxp1 and Lhx1 , the transcription factors specifying LMC axon trajectory choice , gate Reelin signalling through the restriction of Dab1 , a key signalling intermediate . Thus , the same transcription factors are directing neuronal soma migration and axon trajectory selection revealing the molecular hierarchy controlling the establishment of a somatotopic map .
To explore the possibility that Reelin signalling might control LMC soma migration , we monitored the expression of Reelin , its receptors , and their adaptor protein Dab1 in mouse embryos between embryonic day of development ( e ) 11 . 5 and e12 . 5 and in chick embryos between Hamburger and Hamilton ( HH ) stages ( St ) 23 and 30 [38] in limb-level spinal cord . These stages correspond to the times at which LMCl neurons are migrating out of the ventricular zone and reach their final position lateral to LMCm neurons [17] , [22] . We used the transcription factor Foxp1 as a pan-LMC marker and subdivided the LMC based on the presence of Isl1 and Lhx1 transcription factors [21] , [23] , [25] . Reelin has previously been detected in the thoracic spinal cord adjacent to PG neurons [36] . At limb levels Reelin is expressed from e10 . 5 ( Figure S1 ) and in e11 . 5 mouse embryos we observed Reelin expression in cells medio-dorsal to LMC neurons , and by e12 . 5 this domain expanded ventrally , resulting in a Reelin-rich band intercalated between the ventricular zone and the LMC ( Figure 1A–H ) . We also observed a similar Reelin mRNA and protein distribution in chick embryos ( Figure S1 ) . We next monitored the expression of Reelin receptors VLDLR and ApoER2 and their intracellular adaptor protein Dab1 in mouse and chick spinal cords . In e11 . 5 mouse embryos at both limb levels , VLDLR protein and mRNA were apparently expressed in all LMC neurons ( Figure 1I–L; unpublished data ) . However , VLDLR protein levels appeared higher in LMCl neurons relative to LMCm neurons ( Figure 1K ) . By e12 . 5 VLDLR mRNA and protein levels appeared uniform throughout the LMC ( Figure 1M–P; unpublished data ) . In chick embryos , VLDLR mRNA was present in apparently all lumbar LMC neurons at both HH St 24 and HH St 30 ( Figure S1 ) . At the stages examined , ApoER2 mRNA was expressed in the ventricular zone adjacent to the floor plate of both mouse and chick embryos; however , its expression in LMC neurons was only apparent in mouse embryos ( Figure 1Q–T; Figure S1; unpublished data ) . In mouse , Dab1 mRNA and protein were present throughout the LMC from e10 . 5 , at both limb levels; however , at later ages examined , an LMC subpopulation expressed Dab1 mRNA and protein at noticeably higher levels ( Figure 1U–AF; Figure S1 , Figure S4; unpublished data ) . At e11 . 5 , this expression domain ( Dab1high ) was confined to the medio-ventral aspect of the LMC corresponding to Foxp1+Isl1− LMCl neurons while the low-level Dab1 expression domain ( Dab1low ) was confined to the dorsally positioned Isl1+Foxp1+ LMCm neurons ( Figure 1U–X ) . By e12 . 5 , Dab1high and Dab1low LMC neurons were found in , respectively , lateral and medial aspect of the LMC , and corresponded to LMCl and LMCm neurons ( Figure 1Y–AB ) . Similar Dab1 mRNA distribution was observed in chick embryos ( Figure S1 ) . Together , our expression data raise the possibility that Reelin signalling directs LMC soma migration and the disparate Dab1 expression levels in LMCl and LMCm neurons suggest that these neuronal populations may differ in their responsiveness to Reelin . To determine whether Reelin signalling influences LMC neuron migration , we examined the spinal cord of Dab1 and Reelin ( Reln ) mutant mice ( Figure 2 ) [31] , [32] . Since Reelin signalling is required for the appropriate positioning of PG neurons which share a part of their migration trajectory with LMC neurons [36] , [39] , we focused our analysis on caudal lumbar-sacral ( LS ) levels , which contain no PG neurons , as assessed by phospho-Smad1 expression [23] . During LMC migration , the total number of LMC neurons , LMCl and LMCm subtype specification , and radial glia development was unaffected by Dab1 and Reln loss of function ( Figure S2 , Figure S3; unpublished data ) . Additionally , most likely because of its impaired degradation [40] , Dab1 protein levels in LMC neurons were increased in Reln mutants , suggesting that all LMC neurons are responsive to Reelin ( Figure S4 ) . We next analysed the localisation of lumbar LMC neurons in Dab1 and Reln mutants at e12 . 5 , the time at which , in control embryos , the majority of wild type LMCl neurons have terminated their migration and are positioned lateral to LMCm neurons ( Figure 2A–D ) . In Dab1 mutants , LMCl neurons settled ventral to LMCm neurons , which were abnormally shifted to a lateral position in the ventral horn , and many LMCl and LMCm neurons were intermingled ( Figure 2E–H ) . This neuronal displacement was more evident when we superimposed the position of LMCl and LMCm neurons in images of adjacent wild type ( wt ) and Dab1 mutant spinal cords sections ( Figure 2D , H ) . To assess the expressivity of this phenotype and to account for LMC neuron displacement along mediolateral ( ML ) and dorsoventral ( DV ) axes simultaneously , we performed a two-dimensional position analysis of LMC neuron position using the bivariate statistical Hotelling's T2 test . We measured the mean ML and DV coordinates of wild type and Dab1 mutant LMC neurons within the ventral spinal cord . To compensate for sectioning artefacts , we normalised the ML coordinates to the distance from the ventricular zone to the lateral edge of the Foxp1+ expression domain and the DV coordinates to the dorsoventral extent of the Foxp1+ expression domain , two standard measurements that are not different between Dab1 mutants and wild type littermates ( see Experimental procedures for details; unpublished data ) . Thus , with the lateral-most edge of the LMC defined as ML: 100% , and with the dorsal-most domain of the LMC defined as DV: 100% , in wild type embryos , the mean position of LMCm neurons was not changed significantly by Dab1 mutation; however , these neurons were spread over a larger mediolateral zone compared to wild type littermates ( Figure 2I; Table S2 ) . In contrast , by visual inspection of at least six spinal cord sections per embryo , we noted that in six out of six embryos analysed , LMCl neurons were positioned aberrantly . Quantification revealed that LMCl neuron position was significantly shifted in a medio-ventral direction in Dab1 mutants relative to wild type littermates ( ( ML: 73%; DV: 33% ) versus ( ML: 79%; DV: 39% ) ; p<0 . 0035 , Hotelling's T2 test; Table S2 ) , which could be observed at least until e15 . 5 ( Figure 2S–U , W–Y; unpublished data ) . A similar LMC migration phenotype was also observed in the cervical spinal cord as well ( unpublished data ) , and in chick LMC neurons expressing a Dab1 protein in which the five tyrosines essential for Reelin signalling have been mutated ( Dab15YF; Figure S5 , Table S3; [41] ) . We also noted that in four out of four embryos , the position within the ventral spinal cord of a Pea3-expressing motor neuron pool was shifted medio-ventrally at e15 . 5 ( Figure 2V , Z ) . Together , these results demonstrate that in the limb-level spinal cord , Dab1 is essential for the normal migration of LMC neurons and motor pool position . We next examined the position of lumbar LMC neurons in Reln mutant embryos at e12 . 5: Reln mutation did not alter the mean position of LMCm neurons ( Figure 2J–Q; Table S2 ) , although as in Dab1−/− embryos , these neurons were spread over a larger area of the LMC when compared to controls ( Figure 2R ) . In contrast , in three out of four embryos , we observed that LMCl neurons were positioned abnormally , with quantification revealing that the mean LMCl neuron position in Reln mutants was significantly shifted in the medio-ventral direction relative to wild type , with many LMCl neurons found intermingled with LMCm neurons ( ( ML: 75%; DV: 35% ) versus ( ML: 80%; DV: 41% ) ; p<0 . 0473 , Hotelling's T2 test; Figure 2J–R; Table S2 ) . Migration defects observed in Reln mutants mirrored those observed in Dab1 mutants , thus implicating Reelin signalling in the specification of LMC soma position in the ventral spinal cord . Based on the differential expression and the requirement for its function in LMCm and LMCl neurons , we reasoned that the levels of Dab1 expression , rather than simply its presence or absence , might influence the migration of LMC neurons . We therefore asked whether increasing Dab1 expression would shift the position of LMC soma laterally . To do this , we used in ovo electroporation to introduce a Dab1::GFP fusion protein or GFP expression plasmids into the lumbar spinal cord of HH St 17/19 embryos and monitored the position of GFP+ LMC neurons at HH St 29 [22] . Dab1::GFP was expressed with equal efficiency in LMCl and LMCm neurons and did not change their identity nor affect their axon trajectory in the limb ( Figure S6; unpublished data ) . The mean position of LMCl neurons with elevated Dab1 levels was the same as that of LMCl neurons expressing GFP ( Figure 3A–G , I; Table S3 ) . However , in four out of five embryos , we observed that LMCm neurons with elevated Dab1 expression were observed in a more ventro-lateral position ( Figure 3E–I; ( ML: 70%; DV: 49% ) ) compared to LMCm neurons expressing GFP ( Figure 3A–D , I; ( ML: 67%; DV: 59% ) , p = 0 . 0165 , Hotelling's T2 test; Table S3 ) , demonstrating that increasing Dab1 expression levels in LMC neurons is sufficient to shift their position laterally . The myotopic relationship between LMC soma position and axon trajectory within the limb raises the possibility that changes in LMC soma position in Dab1 or Reln mutants could result in the selection of inappropriate limb trajectory by LMC axons . To examine the LMCl axon limb trajectory in Dab1 mutants , we used the Lhx1tlz marker line [42] and quantified the proportion of LacZ+ LMCl axons projecting into e11 . 5 forelimb dorsal and ventral limb nerves in Dab1−/−; Lhx1tlz/+ , and Lhx1tlz/+ littermate embryos [24] . In Lhx1tlz/+ embryos we observed ∼99% of LacZ+ axons within the dorsal limb nerves and ∼1% of LacZ+ axons within the ventral limb nerves ( Figure 4A , B , E ) . The proportions of LacZ+ in dorsal and ventral limb nerves of littermate Dab1−/−; Lhx1tlz/+ embryos were not significantly different ( Figure 4C–E; 98% and 2% , respectively , p>0 . 5 , Student's t test ) . Additionally , in whole mount e12 . 5 Dab1−/−; Lhx1tlz/+ embryos , we did not detect any aberrantly projecting LMCl axons at either limb level ( unpublished data ) . To trace LMCm axons we used the hcrest/Isl1-PLAP reporter line in which the Isl1 enhancer-promoter drives the expression of placental alkaline phosphatase ( PLAP ) in LMCm neurons at forelimb levels [43] . PLAP enzymatic reaction was used to detect LMCm axons in Dab1−/−; hcrest/Isl1-PLAP+ and control hcrest/Isl1-PLAP+ e11 . 5 forelimbs , followed by axonal signal quantification . In hcrest/Isl1-PLAP+ embryos , ∼99% of PLAP+ axons were found in the ventral limb nerve , while ∼1% of PLAP+ axons were found in the dorsal limb nerve ( Figure 4F , G , J ) , proportions not significantly different from Dab1−/−; hcrest/Isl1-PLAP+ embryos ( Figure 4H–J; 99% and 1% , respectively; p = 0 . 335 , Student's t test ) . LMCm limb trajectory in Reln mutants was also apparently normal ( unpublished data ) , indicating that neither Dab1 nor Reelin are required for the selection of limb trajectory by LMC axons and demonstrating that the LMC soma position can be dissociated from axon trajectory selection . Since our results indicated that the Dab1 protein level determines the position of LMC neuron somata but not their axon trajectory , we next evaluated whether the deployment of effector pathways governing these processes might be coordinated by a common set of transcriptional inputs . To determine whether Foxp1 , a transcription factor specifying LMC cell fate , participates in the control of Dab1 expression in LMC neurons , we analyzed the embryonic spinal cords in which Foxp1 is expressed in all motor neurons ( Hb9::Foxp1 transgenic ) as well as in those lacking Foxp1 function [21] , [23] . We first focused our analysis on upper cervical levels , where Foxp1 and Dab1 expression levels are normally low or undetectable ( Figure 5A–C; Figure S7; unpublished data ) . In e12 . 5 Hb9::Foxp1+ spinal cords , compared to control embryos , we observed a significant increase in Dab1 mRNA levels ( 30 arbitrary ( arb . ) units versus 16 in controls; p = 0 . 002 , Student's t test; Figure 5A , C , D , F , M ) as well as protein expression levels associated with ectopic Foxp1+ neurons , without any obvious changes in Reelin expression ( Figure 5A , B , D , E , M; Figure S7; 30 arb . units versus 16 in controls; p<0 . 001 , Student's t test ) . To determine whether Foxp1 is required for Dab1 expression , we examined the lower cervical spinal cord of Foxp1 mutant mice at e12 . 5 . When compared to controls , Foxp1 mutant spinal cords exhibited a significant decrease in Dab1 mRNA levels ( 15 arb . units versus 33 in control littermates; p<0 . 001 , Student's t test; Figure 5G , I , J , L , M ) as well as Dab1 protein levels ( Figure 5G , H , J , K , M; Figure S7; 12 arb . units versus 37 in control littermates; p<0 . 001 , Student's t test ) , demonstrating that Foxp1 is both sufficient and required for Dab1 expression in migrating LMC neurons . Although Foxp1 controls Dab1 expression , because of its uniform expression throughout the LMC , it appeared to us an unlikely determinant of the differential level of Dab1 expression in LMCl and LMCm neurons . LIM homeodomain proteins Isl1 and Lhx1 are determinants of , respectively , LMCm and LMCl neuronal fate , can influence their migration , and can control their axon trajectory by modulating Eph receptor expression ( Figure S8 and Text S1; [22] , [24] , [25] , [42] ) . We thus hypothesized that while Foxp1 activates Dab1 expression in all LMC neurons , Isl1 and Lhx1 have opposing effects on Dab1: ( 1 ) Isl1 lowers Dab1 expression in LMCm neurons while ( 2 ) Lhx1 elevates Dab1 expression in LMCl neurons . We tested the first of these hypotheses by electroporating Isl1 and LacZ expression plasmids , or a control LacZ expression plasmid alone into HH St 17/19 chick lumbar spinal cords and measuring changes in Dab1 mRNA levels relative to the unelectroporated control side at HH St 29 [22] . Expression of LacZ did not affect Isl1 or Dab1 mRNA expression while overexpression of Isl1 significantly reduced Dab1 mRNA expression levels in LMC neurons ( Figure S9; e/u values: 1 . 4 for LacZ versus 0 . 7 for Isl1 , p<0 . 001 , Student's t test ) indicating that Isl1 can suppress Dab1 mRNA expression . To test whether Isl1 is required to control Dab1 expression , we examined the effects of siRNAs directed against Isl1 in LMC neurons but observed no significant difference in Dab1 expression when compared to controls ( Figure S9 and Text S1 ) . Together , these data suggest that Isl1 is sufficient but might be dispensable for the modulation of Dab1 expression in LMC neurons . We next tested whether Lhx1 is required to specify the position of LMCl neurons by examining embryos with a conditional loss of Lhx1 function in LMC neurons , obtained by crossing Lhx1flox homozygotes with Isl1Cre/+; Lhx1tlz/+ mice , in which Isl1Cre drives Cre recombinase expression in all LMC neurons . We focused our analysis on e12 . 5 lumbosacral levels in two groups of embryos obtained from these crosses: Lhx1tlz/flox; Isl1Cre/+ , designated as Lhx1COND , and control Lhx1tlz/+ , designated as Lhx1+/− . Lhx1 loss of function did not affect the total number of LMC or LMCm neurons but resulted in ∼60% of LMCl neurons ( Foxp1+Isl1− ) losing their Lhx1 expression ( Isl1−Lhx1/5+Foxp1+: 37 . 3% versus 95 . 2% in controls; p<0 . 001 , Student's t test , Figure 6I , unpublished data ) . We determined the soma position of three LMC neuronal populations: LMCm , LMCl , and LMCl neurons lacking Lhx1 expression , which were defined as Isl1−Foxp1+Lhx1/5− ( LMCl* ) . As in control embryos , in which the majority of LMCl neurons settled in the most lateral part of the LMC , in Lhx1COND embryos , a significant proportion of LMCl* neurons settled laterally and the mean position of LMCm , LMCl , or LMCl* neurons was not changed when compared to controls ( Figure 6A–J; Table S4 ) . However , in Lhx1COND embryos , many LMCl* neurons were found in medial locations , intermingled with LMCm neurons ( Figure 6A–H ) , and these neuronal displacements were more evident when we superimposed the positions of LMCl* , LMCl , and LMCm neurons in images of adjacent control and Lhx1COND spinal cords sections ( Figure S10 ) . To further characterise the medially displaced population of LMCl* neurons , we counted the number of LMC neurons in four equal quadrants of the LMC ( Figure 6J , K , unpublished data ) . In both Lhx1 mutant and control embryos the majority of LMCm neurons were in the medial half of the LMC ( unpublished data ) . In control embryos , 60% of LMCl neurons were in the lateral half of the LMC , compared to 42% of LMCl* neurons in Lhx1 mutants , representing a significant change ( p = 0 . 003 , Student's t test , Figure 6K ) , indicating that Lhx1 is required for LMCl position specification . To determine whether Lhx1 directs LMCl migration by controlling Dab1 expression , we compared Dab1 protein levels in the lumbar spinal cord of e12 . 5 Lhx1 mutants in which at least 50% of LMCl neurons lost their Lhx1 expression and littermate controls [22] . Our analysis revealed that in Lhx1 mutants , Dab1 protein expression in LMC neurons was decreased by ∼20% when compared to control embryos ( Figure 7A–H , O; p = 0 . 038 , Student's t test ) . We also quantified Dab1 mRNA and protein levels in the LMCm , defined as containing >90% of Isl1+Foxp1+ neurons and LMCl defined as Isl1−Foxp1+ . Within the LMCm , Dab1 mRNA and protein levels were not significantly different from controls , while in LMCl of Lhx1 mutants , relative to controls , Dab1 mRNA was decreased significantly by approximately 40% ( p = 0 . 01 , Student's t test ) and Dab1 protein was decreased significantly by ∼14% ( p = 0 . 017 , Student's t test , Figure 7O ) , indicating that Lhx1 is required for the differential expression of Dab1 in LMC neurons . Together , our results reveal that Foxp1 and Lhx1 coordinate LMC myotopy through their modulation of expression of neuronal migration and axon guidance effectors .
Following their birth near the ventricular zone , spinal neurons first migrate radially by perikaryal translocation , then tangentially , either in dorsal or ventral direction [14] . Reelin has been proposed as a radial migration signal; however , our observations argue that the initial , apparently radial trajectory of LMC motor neurons is Reelin signalling independent as is the case of PG and hindbrain motor neurons [36] , [39] . Thus , in general , the radial migration trajectory of motor neurons might not require Reelin signalling , but once it is terminated , Reelin becomes an important guidance signal , suggesting that unlike cortical neurons that rely on Reelin for their localisation in the radial plane , motor neurons at different rostrocaudal levels of the spinal cord depend on Reelin for the tangential aspect of their migration . How does Reelin act in motor neuron migration ? The initial model where Reelin is a migration stop signal has been challenged by observations that Reelin overexpression in the cortical ventricular zone can rescue , at least in part , pre-plate splitting defects associated with Reelin loss of function [34] , [35] . Likewise , overexpression of Reelin in the ventricular zone of the spinal cord rescues Reln mutant PG neuron migration defects but does not cause an overt phenotype in a wild type background [44] . In the context of LMC neurons , the Reelin expression domain is intercalated between the emerging postmitotic neurons and their final lateral position , thus precluding a function as a migration stop signal , unless at the time of their early migration LMC motor neurons are insensitive to Reelin . Our functional Reelin fragment overexpression in the ventral spinal cord resulted in LMCl motor neurons moving beyond their normal lateral position ( E . P . , T . -J . K . , and A . K . , unpublished observations ) ; thus , in the context of motor neurons , Reelin is unlikely to function as a migration stop signal , rather , it likely promotes migration or enables LMC neurons to respond to a cue that provides spatial information . What is the relationship of the Reelin-mediated LMC position specification to that mediated by cadherins , Eph receptors , and the transcription factor Pea3 [27] , [28] , [29] ? Because of their restricted expression patterns and functional analysis phenotypes , these are thought to operate at the level of motor pools , in contrast to Reelin signalling which appears to specify the position of the entire LMCl division . Cadherins have been shown to be involved in the clustering of specific motor pools via their combinatorial expression imparting different adhesion properties on specific motor pools . Similarly , although the early migration of LMC motor neurons in EphA4 mutants appears to be normal , eventually the position of the tibialis motor pool is shifted . Because of these observations , it is likely that Cadherins , EphA4 , and Pea3 act at a step following Reelin-mediated migration of LMCl neurons . Unfortunately , since ETS genes , arguably the earliest molecular markers of motor pools , begin to be expressed at the time when LMCl somata attain their lateral position [45] , it is technically difficult to ascertain experimentally whether motor pool clustering precedes or coincides with LMCl lateral migration . The differences between the LMC position phenotypes in Dab1 and Lhx1COND mutants might shed some light on this hierarchy . In Dab1 mutants , although shifted medio-ventrally , LMCl neurons remain clustered , in contrast to Lhx1 mutant LMCl motor neurons that can be found intermingled with LMCm neurons . These observations suggest that while the Dab1 mutation probably only leads to the absence of sensitivity to Reelin , the loss of the transcription factor Lhx1 might have consequences beyond the loss of Dab1 , resulting , for example , in a change in expression of cell surface adhesion molecules allowing LMCl and LMCm neurons to intermingle . Our findings demonstrate that migration of LMC neurons within the ventral spinal cord requires Reelin signalling through the intracellular adaptor protein Dab1 . This requirement is principally evident in LMCl neurons and corresponds to the high level of Dab1 protein and mRNA expressed in this population when compared to LMCm neurons . Other studies have also implicated Dab1 protein levels controlled by Cullin5 and Notch signalling as a determinant of neuronal migration [46] , [47] , raising the question of how might differential Dab1 expression specify LMC soma position in the ventral spinal cord . Upon activation of the Reelin pathway , Dab1 is phosphorylated and rapidly degraded [30] , [34] . Therefore , in the presence of Reelin , the low Dab1 protein levels in LMCm neurons might be depleted faster than the higher Dab1 protein levels in LMCl neurons , resulting in the termination of Reelin signalling and thus a migration stop occurring sooner in LMCm neurons than in LMCl neurons . This mode of Dab1 function assumes that Reelin promotes migration of LMC neurons , or is a factor enabling their reception of a migration cue and is consistent with our observation that both LMCl and LMCm neurons can respond to Reelin . Thus similar to the Toll-like receptor ( TLR ) [48] and chemokine [49] signalling pathways regulated by the level of expression of a signalling intermediate , Reelin signal is differentially gated in two neuronal populations through opposing levels of Dab1 expression . In such a model , we would favour the idea that Dab1 concentration , in the presence of Reelin , is an instructive determinant of LMC neuron position , although the formal demonstration of this through , for example , the change of LMCm Dab1 levels to match exactly those in LMCl neurons is technically challenging . Following its phosphorylation , Dab1 is targeted for polyubiquitination and degradation by Cullin5 [47] , raising the possibility that in LMC neurons , Dab1 protein stability might contribute to the differences in Dab1 protein in LMC neurons . However , since in LMC neurons Cullin5 is apparently expressed at equal levels by LMCl and LMCm neurons ( E . P . and A . K . , unpublished observations ) , and because of the selective enrichment of Dab1 mRNA in LMCl neurons , compared to LMCm neurons , we favour the hypothesis that differential transcriptional regulation of the Dab1 gene or its mRNA stability is an important factor contributing to Dab1 protein levels in LMC neurons . Our results demonstrate that Dab1 expression levels in LMC neurons are set by Foxp1 and Lhx1 , two transcription factors that are essential for the specification of LMC soma position [21] , [22] , [23] . Our data suggest the following model of Dab1 expression control in LMC neurons: a basal level of Dab1 expression in LMC neurons is induced or maintained by Foxp1 , while Lhx1 , a transcription factor selectively expressed in LMCl neurons , could act to elevate Dab1 expression in LMCl neurons . Additionally , based on its ability to suppress Lhx1 [22] and Dab1 mRNA expression in LMC neurons , Isl1 might function to diminish Dab1 expression in LMCm neurons . Thus , although we cannot exclude the influence of other transcription factors or distinguish whether the control of Dab1 expression by Foxp1 and Lhx1 occurs at the level of the Dab1 promoter , through intermediary transcription factors or regulation of Dab1 mRNA stability , we propose that the concerted action of Foxp1 and Lhx1 leads to differential Dab1 expression levels in LMC neurons . Could transcription factor control of Dab1 expression be a general mechanism gating Reelin signalling in the CNS ? In the cortex , examples of control of migration effectors by transcription factors include the coupling of neurogenesis to migration by bHLH control of doublecortin and p35 , Tbx20 control of the planar cell-polarity pathway , and Nkx2 . 1 control of Neuropilin2 expression [6] , but to our knowledge , a general link between a specific transcription factor and Dab1 expression has so far only been established for CREB/CREM [50] . Intriguingly , in the spinal cord , like LMC neurons , PG neurons migrate in response to Reelin and also require Foxp1 for their specification [21] , [23] , [36] , yet although their initial lateral migration path is shared , they eventually occupy two distinct locations in the spinal cord , raising the question of the identity of the divergent migration cues that act on these two motor neuron populations . The myotopic organisation of spinal motor neurons is the consequence of the selection of a specific axon trajectory in the limb mesenchyme and of a particular soma location within the spinal cord . The two processes can be uncoupled by loss of Reelin , Eph signalling , or mutation of Lmx1b , a LIM homeodomain transcription factor that controls ephrin ligand expression in the limb [24] , [26] , [42] , raising the question of the molecular hierarchy controlling myotopy . Foxp1 and Lhx1 determine the selection of a dorsal or ventral LMC axon trajectory through restriction of Eph receptor expression [21] , [22] , [23] , and our data suggest that they gate LMC neuron sensitivity to Reelin signals , thereby specifying the position of LMC soma in the ventral spinal cord . These observations imply that the selection of an LMC axon trajectory in the limb and soma position within the ventral horn are normally controlled coordinately by Foxp1 and LIM homeodomain transcription factors . Based on these observations , we propose a simple hierarchy for motor axon trajectory and soma position selection coordination ( Figure 8 ) . Foxp1 together with Lhx1 and Isl1 transcription factors are required for the expression of Eph receptors in LMC axons , and thus their repulsion from ephrin ligands in the limb mesenchyme , leading to their selection of a dorsal or a ventral limb trajectory . Foxp1 , Lhx1 , and possibly Isl1 also establish disparate Dab1 protein levels in LMC neurons , thus enabling their cell bodies to segregate into distinct mediolateral positions . A number of transcription factors regulating reception of specific axon guidance receptors has already been described [4] , [5] , implying that some of them may also direct neuronal migration , thus coordinating topographic organisation of neuronal circuits . Moreover , topographical organisation also extends to dendrite arborisation and synaptic activity [19] , [51] , and since Foxp1 regulates the position of motor neuron dendrites [21] , it remains plausible that the transcription factors controlling migration and axon projections may be used to control other facets of topographic organisation . Why should neuronal migrations and axon trajectories be controlled coordinately ? LMC neurons within a specific motor pool , i . e . those innervating a particular muscle , are electrically coupled through gap junctions , possibly to consolidate their electrical activity patterns during the time of spinal motor circuit assembly [52] . Aberrant soma position could result in the inability of LMC neurons to form electrically coupled motor pools even though neuromuscular junctions with appropriate muscle targets in the limb might be maintained . Thus , a motor neuron might receive appropriate signals from its muscle target but is unable to synchronise its electrophysiological maturation , such as calcium transient waves [53] , with other motor neurons in its pool because of their dispersed position . The emergence of functional motor circuitry also depends on the formation of specific sensory-motor contacts achieved by sensory axons synapsing on the dendrites of homonymous motor neurons within the ventral spinal cord [54] . Motor neurons in distinct pools have stereotypic dendritic arbor shapes which in principle could be dictated by the position of the motor neuron soma [28] , although it remains to be determined whether motor neuron soma displacement , without any effects on molecular markers of cell fate , results in dendritic arborisation defects and whether such defects alter the sensory-motor connectivity . Reelin signalling has also been implicated in cortical dendrite formation , raising the possibility that Reln mutation might lead to LMC dendritic arbour defects independently of its effect on soma localisation . Moreover , in Reln mutant mice , although retrograde and electrophysiological analysis reveals relatively normal cortico-thalamic connectivity , retinal circuit connectivity is perturbed possibly due to defects in neuronal layer formation [55] , [56] . Because of the involvement of Reelin in synapse function [57] , it is difficult to dissociate the functional consequences of altered topography in Reelin signalling loss of function from altered synaptic function . However , examples of severe functional deficits caused by neural circuit topography disruption apparently independent of Reelin signalling [58] highlight the importance of topographic organisation of the nervous system .
All mice were maintained and genotyped by PCR as previously described [21] , [31] , [43] , [59] , [60] , [61] , [62]; Reln allele was Relnrl/J ( Jackson Laboratory , USA ) . Fertilised chick eggs ( Couvoir Simentin , Canada ) were staged according to Hamburger and Hamilton [38] . Chicken Dab1L isoform ( NM_204238 ) [63] was cloned by RT-PCR ( Invitrogen , USA ) and fused in frame to GFP at the C-terminus in pN2-eGFP ( Invitrogen , USA ) . Chick spinal cord electroporation was performed using a Ovodyne TSS20 square pulse generator ( Intracell , UK ) as described [24] , [64] . Immunofluorescence stainings were carried out on 12 µm cryosections as described [22] , [24] . For antisera used and dilutions , see Table S1 . In situ mRNA detection was performed as previously described [65] , [66] . Probe sequence details are available upon request . Images were acquired using a Zeiss LSM confocal microscope or a Leica DM6000 microscope with Improvision Volocity software . Quantification of protein and mRNA expression , GFP- and β-gal-labelled axon projections was as described [24] , [65] . To quantify axon projections in hCrest/Isl1-PLAP embryos , 12 µm cryosections were immunostained ( see Table S1 ) , post-fixed , washed , and incubated at 65°C . Phosphatase activity was revealed simultaneously in sections containing mutant and control tissue . The signal was quantified in sections sampled at 30–50 µm rostrocaudal intervals at the cervical level with at least six sections analysed per embryo . All quantifications were done between lumbosacral ( LS ) 4 and LS6 levels as assessed by vertebra counts and absence of pSmad1+ PG neurons [23] . Neurons were imaged in 12 µm cryosections sampled at 100 µm intervals using a Zeiss LSM confocal or Leica DM6000 fluorescent light microscope; ML and DV values were calculated using ImageJ software measurements of distance ( D ) and angle ( α ) of motor neuron soma from the ventral edge of the ventricular zone ( see Text S1 for details ) and then plotted using Matlab software running the “dscatter” function , which creates a scatter plot with contour lines linking data points with similar frequency and colour intensities that increase with data point frequency . In all cases , to compare the vectors of means between experimental and control groups , we used a two-sample Hotelling's T2 , which is a two-dimensional generalization of the Student's t test , combined with a randomization test under the assumption of unequal variances , which does not rely on the stringent assumptions of the parametric Hotelling's T2 , to circumvent the difficulty of having moderately sized samples . The analysis was implemented using the NCSS software package ( Hitze J . ( 2007 ) ; Kaysville , Utah , www . ncss . com ) .
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Many areas of our nervous system are organized in a topographic manner , such that the location of a neuron relative to its neighbors is often spatially correlated with its axonal trajectory and therefore target identity . In this study , we focus on the spinal myotopic map , which is characterized by the stereotyped organization of motor neuron cell bodies that is correlated with the trajectory of their axons to limb muscles . An open question for how this map forms is the identity of the molecules that coordinate the expression of effectors of neuronal migration and axonal guidance . Here , we first show that Dab1 , a key protein that relays signals directing neuronal migration , is expressed at different concentrations in specific populations of limb-innervating motor neurons and determines the position of their cell bodies in the spinal cord . We then demonstrate that Foxp1 and Lhx1 , the same transcription factors that regulate the expression of receptors for motor axon guidance signals , also modulate Dab1 expression . The significance of our findings is that we identify a molecular hierarchy linking effectors of both neuronal migration and axonal projections , and therefore coordinating neuronal soma position with choice of axon trajectory . In general , our findings provide a framework in which to address the general question of how the nervous system is organized .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience/motor",
"systems",
"neuroscience",
"neuroscience/neurodevelopment",
"neuroscience/neuronal",
"signaling",
"mechanisms"
] |
2010
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Foxp1 and Lhx1 Coordinate Motor Neuron Migration with Axon Trajectory Choice by Gating Reelin Signalling
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Somatosensory information from the periphery is routed to the spinal cord through centrally-projecting sensory axons that cross into the central nervous system ( CNS ) via the dorsal root entry zone ( DREZ ) . The glial cells that ensheath these axons ensure rapid propagation of this information . Despite the importance of this glial-axon arrangement , how this afferent nerve is assembled during development is unknown . Using in vivo , time-lapse imaging we show that as centrally-projecting pioneer axons from dorsal root ganglia ( DRG ) enter the spinal cord , they initiate expression of the cytokine TNFalpha . This induction coincides with ensheathment of these axons by associated glia via a TNF receptor 2 ( TNFR2 ) -mediated process . This work identifies a signaling cascade that mediates peripheral glial-axon interactions and it functions to ensure that DRG afferent projections are ensheathed after pioneer axons complete their navigation , which promotes efficient somatosensory neural function .
Efficient somatosensory perception requires that neurons propagate sensory stimuli from the periphery to the central nervous system ( CNS ) . Cutaneous somatosensory stimuli are first detected by excitable neurites in the skin , transduced past the dorsal root ganglion ( DRG ) cell soma and then conveyed to the CNS via a centrally-projecting afferent axon [1–3] . Glial cells that ensheath these components ensure that this somatosensory information is efficiently and rapidly propagated [4–6] . During development , the sensory neurons and glia that ensheath them are specified from neural crest cells that migrate from their dorsal neuroepithelial origin [7 , 8] . These neural crest cells eventually coalesce to form DRG , which continue to add neurons throughout embryonic development [7 , 8] . Each sensory neuron then produces a pseudobipolar axon that innervates its peripheral target and a central projection that crosses into the spinal cord at the dorsal root entry zone ( DREZ ) [1–3] . In mice at late embryonic stages , glial cells located at the DREZ are thought to act as positive substrates for axonal entry into the spinal cord [9] . However , our understanding of how DRG pioneer axons enter the spinal cord and how they influence development of associated glia is unknown . In addition , although our understanding of molecular pathways that mediate DRG neuronal differentiation and specification are abundant [10] , we know very little about the molecular pathways that are required for the development of sensory glia within the DRG [11] . In fact , although there is a growing body of literature of known signaling pathways that mediate glial association and ensheathment of axons in the periphery [11–17] , it is still limited compared to our understanding of neuronal development [5 , 18] . The most well characterized molecular mechanism that controls peripheral axon-glial communication is the Neuregulin 1 Type III ( Nrg1-III ) ligand and the ErbB family of receptors [5 , 19 , 20] . Interestingly , however , genetic knockouts that disrupt Nrg1-III/ErbB signaling have peripheral axons with associated glia , suggesting that additional mechanisms likely function to ensure glia ensheath peripheral axons [21 , 22] . To identify signaling pathways that mediate glial ensheathment of DRG pioneer axons , we took a multipronged approach , merging the elegance of zebrafish in vivo , time-lapse imaging with the power of mouse genetics . Live imaging in zebrafish revealed that pioneer DRG axons navigate to the DREZ with glial cells closely associated with the growth cone . We demonstrate that the cytokine tumor necrosis factor alpha ( TNFa ) is upregulated in pioneer axons upon spinal cord entry and that a TNFa/TNF receptor 2 ( TNFR2 ) pathway directs ensheathment of the central projection , as genetic disruption of either TNFa or TNFR2 lead to a failure of glial ensheathment of pioneer axons in both mice and zebrafish . Together , these data identify critical cellular determinants of sensory , central projection formation and a mechanism underlying peripheral axon-glial communication .
Sensory afferent nerves are ensheathed by peripheral glia from the DRG to the edge of the spinal cord . However , the precise spatiotemporal dynamics of this cellular arrangement at the DREZ are not understood . Therefore , we characterized early stages of DREZ development to understand how the primary afferent circuit is established . We started by investigating how DRG afferent axons enter the spinal cord . To achieve this , we directly visualized the navigation of pioneer axons to the DREZ . Using Tg ( ngn1:egfp ) zebrafish , which have regulatory sequences of neurogenin1 ( ngn1 ) driving expression of GFP in developing DRG neurons [23] , we began imaging embryos at 48 hours post fertilization ( hpf ) , prior to central and peripheral axon formation ( Fig 1A , S1 Movie ) . Beginning at this stage , newly specified DRG neurons had transient , filopodia-like structures that projected ventrally initially , followed by similar projections that extended dorsally toward the presumptive DREZ ( Fig 1A , S1 Movie ) . These filopodia-like structures transitioned into single axons , and between 48 and 56 hpf , the dorsal processes transitioned into a growth cone that extended dorsally away from the cell soma ( Fig 1A , S1 Movie ) . The growth cone eventually ceased its dorsal migration and then entered the spinal cord at a position that corresponded with the location of the dorsolateral fasciculus ( DLF ) between 50 and 72 hpf ( Fig 1A , S1 Movie ) . By 72 hpf , each somite along the anterior-posterior ( AP ) axis of the larva contained a centrally-projecting DRG axon that had entered the DREZ . To characterize the velocity and trajectory of these pioneer axons as they migrated toward the DREZ , we traced growth cones in four individual movies similar to S1 Movie . Tracing the most dorsal location of the growth cone at each time point revealed that DRG pioneer axons navigated with constant velocity toward the DREZ ( Fig 1B , S1 Movie ) . When this tracing was plotted to represent the path of the growth cone , we saw that axons navigated directly dorsally from the cell body to the DLF ( Fig 1C , S1 Movie ) . Based on these data , we conclude that the DRG pioneer axon has a striking ability to navigate directly to the DREZ . We next sought to determine when glial cells associated with nascent sensory afferents during navigation to the DREZ . To do this , we used Tg ( sox10:eos ) ;Tg ( ngn1:egfp ) embryos , which label DRG precursors with Eos , and DRG sensory neurons with GFP [23 , 24] . We imaged these animals from 48 to 72 hpf to capture glial dynamics in relation to the DRG pioneer axon . Early in development , sensory neurons are derived from sox10+ neural crest cells and begin expressing ngn1 when they become post-mitotic neurons [24] . Therefore , in our imaging , sensory neurons were ngn1+ ( green ) and also transiently expressed Eos ( red ) and appeared yellow . All other non-neuronal cells or glia were marked with Eos only and appeared red ( Fig 2 , S2 Movie ) . Time-lapse imaging of these embryos , beginning at 48 hpf , revealed that as the pioneer growth cone navigated dorsally toward the DREZ , sox10+ glial cells lagged slightly behind the growth cone ( Fig 2 , S2 Movie ) . Once the pioneer axon reached the level of the DLF , the growth cone entered the spinal cord while associated sox10+ glia remained in the periphery , associated with the central afferent projection from the DRG to the edge of the spinal cord at the DREZ ( Fig 2 , S2 Movie ) . In these images , the axon and associated glial cell can be distinguished based on the distribution of fluorescent protein which shows an axon with a concentrated fluorescent projection and a glial cell with fan-like morphology and more diffuse distribution of fluorescent protein . Based on these data , we conclude that glial cells navigate with the pioneer growth cone to the DREZ before they ensheath the peripheral arm of the afferent axon . We next sought to identify the molecular mechanism that mediates glial ensheathment of DRG pioneer axons . In our search for transgenic lines that labeled DRG neurons , we identified a new transgenic line , Tg ( tnfa:gfp ) , which uses tumor necrosis factor α ( tnfa ) regulatory sequences to drive expression of GFP in zebrafish DRG neurons ( Fig 3A ) [25] . This expression pattern is consistent with the expression of TNFa in mouse DRG neurons during development [26] . To confirm that Tg ( tnfa:gfp ) is a faithful reporter of endogenous tnfa mRNA expression in DRG neurons , we used multiplex fluorescent in situ hybridization ( FISH ) probes , which tile the tnfa transcript with 37 , 20-nucleotide RNA probes directly conjugated to a fluorescent reporter [27] . We performed this analysis in Tg ( ngn1:gfp ) larvae at 72 hpf and visualized that tnfa expression co-localized with GFP+ sensory neurons ( S1A Fig ) , consistent with the hypothesis that Tg ( tnfa:gfp ) recapitulates endogenous tnfa expression . Interestingly , we noticed that in Tg ( tnfa:gfp ) embryos , GFP expression was only detected in DRG neurons at 72 hpf , but not at 48 hpf ( Fig 3A and 3B , 48 hpf-18% , 72 hpf-97% , n = 30 DRG ) . When Tg ( tnfa:gfp ) expression was scored at 52 hpf , we observed GFP+ neurons predominantly in anterior DRG that were first to send pioneer axons into the spinal cord and an absence of GFP expression in posterior DRG that had yet to send pioneer axons to the DREZ ( Fig 3B , 52 hpf-75% , n = 30 DRG ) . We therefore hypothesized that tnfa may be selectively expressed only in DRG neurons that had formed a pioneer axon that had entered the CNS . To test this hypothesis , we used time-lapse imaging between 48 and 72 hpf in Tg ( tnfa:gfp ) ;Tg ( sox10:mrfp ) embryos , when pioneer DRG axons navigate into the spinal cord . Using this double transgenic line , we were able to visualize entry of the growth cone with the Tg ( sox10:mrfp ) line because DRG neurons transiently express sox10 before upregulating tnfa expression . When we quantified the intensity of Tg ( tnfa:gfp ) from the movie represented in Fig 3C , we noted that GFP was not detectable in DRG neurons before the pioneer axon entered the spinal cord , but increased after entry ( Fig 3C and 3D , S3 Movie ) . By 72 hpf , GFP intensity was brightly expressed in nearly all DRG neurons along the trunk of the zebrafish and this profile was consistently visualized ( Fig 3B and 3C , S3 Movie ) . Based on these data , we demonstrate that tnfa is upregulated in DRG neurons as pioneer axons enter the spinal cord . To test if Tg ( tnfa:gfp ) expression in DRG neurons required pioneer axon navigation into the spinal cord , we performed axon transections using a pulsed nitrogen dye laser to perturb pioneer axon entry into the CNS [28] . Using Tg ( tnfa:gfp ) ;Tg ( sox10:mrfp ) embryos , we visualized and axotomized DRG pioneer axons that could be identified as RFP+ and GFP− at 48 hpf . We scored successful axotomy by an aberration of the filopodia-like projections at the growth cone of axotomized axons ( S2 Fig ) . In these ablations we targeted the growth cone and did not see any debris that corresponded with RFP+ glia . At 72 hpf , 24 hours post axotomy ( hpa ) , we detected a reduction in GFP+ DRG neurons ( 20% GFP+ n = 5 DRG ) ( Fig 3E and 3G ) . To confirm that this lack of expression was not a response to axotomy , we axotomized GFP+ axons that had already entered the spinal cord at 48 hpf . 24 hpa , we did not visualize an elimination or reduction of GFP expression in these DRG neurons ( Fig 3F and 3G ) . These data demonstrate that axons that have entered the spinal cord continue to express Tg ( tnfa:gfp ) even after axotomy . However , we acknowledge that post-transcriptional delay in protein detection may mask underlying changes in gene expression . We therefore also axotomized the peripheral projection of the DRG neuron before the afferent axon had projected into the spinal cord and scored GFP expression at 72 hpf . Similarly , peripheral axotomy also did not reduce the number of GFP+ neurons at 72 hpf ( Fig 3G ) . Taken together , these data are consistent with the hypothesis that spinal cord entry is required for Tg ( tnfa:gfp ) expression in DRG pioneer axons . Given the upregulation of TNFa after the pioneer axon enters the spinal cord , we hypothesized that TNFa-mediated signaling would be important in the maintenance of pioneer axons and/or the glia that ensheath them . To investigate this hypothesis , we injected a tnfa translation blocking morpholino oligonucleotide ( MO ) [29] into single-cell Tg ( sox10:eos ) embryos and characterized the outcome of perturbed TNFa signaling on DRG pioneer axon-associated glia . In Tg ( sox10:eos ) tnfa morphants at 72 hpf , we visualized sox10+ axonal projections , but did not detect any sox10+ glia ensheathing pioneer axons ( 26% contained sox10+ glia , n = 54 DRG ) , which is in contrast to the full ensheathment we observed in wildtype control larvae ( 100% contained sox10+ glia , n = 40 DRG ) ( Fig 4A and 4B ) . We extended our analysis of these experiments to determine if this phenotype was a delay in glial ensheathment by scoring the ensheathment phenotype at 96 hpf . Similarly , at 96 hpf , we visualized defective ensheathment in tnfa morphants when compared to wildtype larvae ( 36% contained sox10+ glia , n = 61 DRG ) . To confirm these MO-injected phenotypes were specific to perturbation of tnfa , we utilized the CRISPR/cas9 system to generate F0 tnfa mutants [30 , 31] ( S2 Table ) . To do this , we injected Tg ( sox10:eos ) embryos with guide RNAs ( gRNA ) specific to tnfa and scored glial ensheathment of DRG pioneer axons at the DREZ at 72 hpf . Consistent with our MO data , glial ensheathment was disrupted in F0 tnfa gRNA-injected larvae ( wildtype– 99% contained sox10+ glia , n = 160 DRG , tnfa– 67% contained sox10+ glia , n = 160 DRG , p<0 . 0001 students t-test ) ( Fig 4A , S3C Fig ) . To confirm that these injections induced mutations that disrupted the genomic region of tnfa , we performed a T7 endonuclease assay ( S3A Fig ) [30 , 31] and detected that 7 out of the 13 selected larvae that were assayed showed potential mutations . We then confirmed this analysis by sequencing tnfa in the specific larvae that showed a potential mutation in the T7 assay and had a glial ensheathment phenotype ( S3D Fig ) [30 , 31] . We next asked if this role for TNFa in glial ensheathment was evolutionarily conserved in mice . To test this hypothesis , we measured ensheathment of the DRG afferent nerve in wildtype and Tnfsf1a-/- ( TNF-/- ) pups at P0 using electron microscopy . We first quantified this by calculating the percentage of an axon bundle that was ensheathed by glial membrane . In wildtype pups at P0 , on average , 1 . 7% of individual axonal bundles were unensheathed ( Fig 4C and 4D ) . Of these wildtype axonal bundles , only 11 nerve bundles of the 110 that we measured contained axons that were more than 3% unensheathed , indicating nearly every axon of the afferent nerve is ensheathed by birth ( Fig 4C and 4E , n = 3 animals ) . In contrast , in TNF-/- pups , we observed that 27 . 6% of individual axon bundles within the DRG afferent sensory nerve were unensheathed ( Fig 4C–4E , n = 3 animals ) . We also observed several small caliber axons that lacked glial membrane around the entirety of the bundle mixed with large caliber axons that were also incompletely ensheathed , which caused mixing between these axonal bundles ( Fig 4C ) . We therefore also quantified the number of axons in each small caliber bundle . In wildtype pups , small caliber bundles had on average , 32 . 0 axons , whereas TNF-/- pups contained 84 . 8 axons ( Fig 4F , n = 3 animals ) . When binning the axons bundles into ones that were ensheathed versus unensheathed , TNF-/- pups contained 45 . 6 that were ensheathed compared to 91 . 8 that were unensheathed ( Fig 4G , n = 3 animals ) . This is compared to wildtype that had 32 . 0 ensheathed and 24 . 3 unensheathed ( Fig 4G , n = 3 animals ) . Based on these data and our analysis from zebrafish , we propose that tnfa is required for glial ensheathment of DRG pioneer axons . To further test this model , we collected trunk tissue from P0 wildtype and TNF-/- pups at P0 , sectioned through the L3-L6 spinal column and stained tissue with the glial marker S100 and neuronal marker βIII Tubulin [26 , 32] . In wildtype pups , we observed S100+ cells associated with DRG Tubulin+ axons ( Fig 4H ) . In contrast , we observed significantly reduced S100+ labeling along the DRG central projection in TNF-/- pups ( Fig 4H ) . To confirm this phenotype , we labeled TNF-/- and wildtype tissue with additional glial markers , including ErbB3 , a marker for glial precursors/immature glia in the PNS and Isl1 , which is expressed in sensory neurons within the DRG [4 , 5] . Consistent with the hypothesis that glial ensheathment was disrupted in TNF-/- pups , ErbB3 was reduced along Isl+ sensory afferent axons compared to wildtype pups ( S4 Fig ) . To test the possibility that pioneer axon TNFa was signaling directly to associated glia , we investigated if a canonical downstream signaling component of TNFa , nuclear factor kappa-light-chain-enhancer of activated B cells ( NFkB ) [33] , was expressed in glia along DRG sensory afferent axons . We first scored the expression of GFP in Tg ( NFkB:egfp ) ;Tg ( sox10:mrfp ) larvae , which express GFP under control of nf-kb binding domains , reliably reporting NFkB activity [34] , from 48 to 72 hpf . In these embryos between 48 and 56 hpf , we detected GFP expression in glial cells ensheathing the DRG central projection . Importantly , GFP expression between 48 and 72 hpf was only detected in DRG that had formed a central projection that extended into the spinal cord ( 48 hpf- 7 . 5% , 72 hpf-87 . 5% , n = 30 DRG ) ( Fig 5A and 5B ) . By 72 hpf , 87 . 5% of DRG examined along the length of the zebrafish trunk had GFP+ glial cells , suggesting robust activation of NFkB was correlated with the entry of the pioneer axon into the spinal cord ( Fig 5B ) . To confirm this hypothesis , we used time-lapse imaging in Tg ( NFkB:egfp ) ;Tg ( sox10:mrfp ) embryos from 48 to 72 hpf , the period when pioneer DRG axons navigate into the spinal cord . With Tg ( sox10:mrfp ) , we could visualize the entry of the growth cone into the spinal cord . In these movies , we did not detect GFP in pioneer axon-associated glia when the central axon formed . However , we observed GFP expression in associated glia soon after the centrally-projecting axon entered the spinal cord and by 72 hpf , GFP was expressed in nearly all pioneer axon-associated glia ( Fig 5A and 5B ) . Based on this data , we conclude that NFkB is active in DRG glia after pioneer axons have entered the spinal cord . To test if Tg ( NFkB:gfp ) expression in DREZ glia is dependent on the pioneer axon entering the spinal cord , we perturbed the navigation of pioneer axons into the CNS by transecting pioneer axons after they formed , but before they successfully navigated to the DREZ in Tg ( NFkB:gfp ) ;Tg ( sox10:mrfp ) embryos [28] . At 72 hpf , 24 hpa , we did not detect GFP in afferent axon-associated glia when pioneer axons were successfully axotomized before they entered the spinal cord ( 25% GFP+ n = 8 DRG ) ( Fig 5C ) . To confirm that this lack of expression was not a response to axotomy , we axotomized sox10+ axons that had already entered the spinal cord at 48 hpf . At 24 hpa , we did not visualize an elimination or reduction of GFP expression in these DRG glia ( 87 . 5% GFP+ n = 8 DRG ) ( Fig 5C ) . As a control , we also axotomized the DRG peripheral projection between 48 and 56 hpf , before afferent projections had navigated into the spinal cord , and then scored GFP expression at 72 hpf . In these studies , we always saw GFP+ glia along the afferent projection ( Fig 5C ) . Therefore , these data are consistent with the hypothesis that axon entry is required for NFkB activation in DRG glia . Because the upregulation of TNFa in DRG neurons correlated with the entry of the pioneer axon into the spinal cord , we sought to understand the temporal dynamics of TNFa upregulation in DRG neurons compared to NFkB activation in associated glia . To do this , we compared intensity profiles from our Tg ( tnfa:gfp ) and Tg ( NFkB:egfp ) time-lapse movies and normalized the time for the point of axon entry into the spinal cord . These data showed that Tg ( NFkB:egfp ) intensity increased at similar time points compared to the time of axon entry ( Figs 3D and 5D ) . Based on this analysis , we conclude that NFkB is upregulated in pioneer axon-associated glia after the pioneer axon enters the spinal cord . If this model is correct and TNFa-mediated signaling is active in sensory glia as the pioneer axon enters the spinal cord , then perturbation of TNFa should disrupt NFkB expression in associated glia . To test this hypothesis , we injected a tnfa MO into Tg ( NFkB:egfp ) zebrafish embryos and assayed GFP expression in DRG glia at 72 hpf ( Fig 5E and 5F ) . Consistent with our hypothesis , GFP expression in pioneer axon-associated glia was completely abolished in tnfa morphants ( tnfa MO = 0% GFP+ DRG , n = 30 DRG ) ( Fig 5E and 5F ) . Based on these data , we conclude that upregulation of TNFa in DRG neurons induces TNFa-mediated expression of NFkB in associated glia . To dissect this pathway further , we sought to determine if TNFa acted as a cleaved soluble peptide or in its transmembrane form . To test the requirement of TNFa cleavage as a function of glial ensheathment of the DRG central projection , we treated Tg ( sox10:eos ) zebrafish embryos with TNFa Protease Inhibitor-1 ( TAPI-1 ) , an inhibitor of the metalloprotease adam17 , which is required for TNFa cleavage [35] . In larvae treated with TAPI-1 from 24 to 72 hpf , we observed that glial ensheathment of DRG pioneer axons was identical to DMSO-treated larvae ( S5 Fig ) . As confirmation that TAPI-1 disrupted metalloprotease activity in our paradigm , we visualized that differentiation of peripheral glia was expedited along the mixed portion of spinal peripheral nerves , a phenotype that is consistent with the lack of MMP activity that has previously been reported ( S5 Fig ) [36] . From these data , we conclude that TNFa cleavage is not required for proper ensheathment of DRG central axons . TNFa-mediated signaling is modulated through two receptors , tumor necrosis factor receptor 1 ( TNFR1 ) and TNFR2 . Previous studies demonstrate that TNFR2 , rather than TNFR1 , is more likely to signal via transmembrane TNFa [33] . TNFR2 is also exclusively expressed in non-neuronal cells of the DRG [37] . To confirm that tnfr2 is expressed in glia in the DRG , we designed multiplex fluorescent in situ hybridization probes specific to tnfr2 . We performed in situ hybridization analysis with these probes in Tg ( ngn1:gfp ) zebrafish embryos and observed that tnfr2 mRNA was localized around GFP+ sensory neurons , consistent with the expression of tnfr2 in DRG glia at 72 hpf ( S1 Fig ) . We extended this analysis by visualizing both tnfa and tnfr2 expression in Tg ( ngn1:gfp ) animals and saw that tnfa was exclusively expressed in GFP+ sensory neurons while tnfr2 was expressed in cells surrounding GFP+/tnfa+ sensory neurons ( S1 Fig ) . This data led us to conclude that tnfr2 is expressed in pioneer axon-associated glia . Therefore , we investigated the role of TNFa-TNFR2 mediated signaling in pioneer axon glia ensheathment . We first tested if NFkB upregulation in associated glia was dependent on TNFR2 . To investigate this , we injected a tnfr2 MO [29] into Tg ( NFkB:egfp ) zebrafish and observed that GFP expression was absent in DRG glia at 72 hpf ( tnfr2 MO = 0% GFP+ DRG , n = 30 DRG ) ( Fig 5E and 5F ) . These data are consistent with the model that NFkB activity is driven by TNFR2 signaling . We next investigated the role of TNFR2 in glial ensheathment of DRG afferent axons . To do this , we injected a translation blocking MO to tnfr2 into single-cell Tg ( ngn1:egfp ) and Tg ( sox10:eos ) embryos [29] . Consistent with the hypothesis that TNFa-TNFR2 signaling is required for glial ensheathment of the central projection , we observed a significant reduction in the number of pioneer axons that had associated sox10+ glia at 72 hpf , which phenocopied tnfa morphants ( 52% contained sox10+ glia , n = 31 DRG ) and was significantly different from wildtype controls ( 100% contained sox10+ glia , n = 40 DRG ) ( Fig 6A and 6B ) . To confirm that this phenotype was not due to a developmental delay , we scored the ensheathment phenotype at 96 hpf and again visualized defective ensheathment in tnfr2 morphant larvae when compared to wildtype controls ( 45% contained sox10+ glia , n = 44 DRG ) . To confirm these MO-injected phenotypes were specific to perturbation of tnfr2 , we injected Tg ( sox10:eos ) embryos with gRNAs specific to tnfr2 and scored glial ensheathment at 72 hpf [30 , 31] . In these F0 gRNA-injected animals , DRG pioneer axons lacked sox10+ glial cells . ( wildtype 99% contained sox10+ glia , n = 160 DRG , tnfr2 60% contained sox10+ glia , n = 170 DRG , p<0 . 0001 students t-test ) ( Figs 6A and S3C ) . We confirmed that these injections induced mutations that disrupted the genomic region of tnfr2 with a T7 endonuclease assay that detected 10 out of the 13 selected larvae were potential mutants ( S3B Fig ) [30 , 31] . We then sequenced the genomic region of tnfr2 in a subset of these individual fish that were scored for defective glial ensheathment to confirm the gRNA induced genomic aberrations at the PAM site ( S3E Fig ) [30 , 31] . We also similarly tested the requirement of tnfr1 in pioneer axon glial ensheathment but did not visualize a phenotype ( 100% contained sox10+ glia , n = 24 DRG ) ( Fig 6B ) . Based on these data , we propose that tnfr2 is required for glial ensheathment of DRG afferent axons . As we did with TNFa , we next asked if TNFR2 played a role in glial ensheathment in mice . To do this , we collected trunk tissue from P0 wildtype and Tnfrsf1b-/- ( TNFR2-/- ) P0 pups , sectioned through the L3-L6 spinal column , and stained the tissue for the glial marker S100 and neuronal marker βIII Tubulin . In wildtype pups , S100 staining labeled cells that associated with Tubulin+ axons along the central projection ( Fig 6C ) . However , this labeling was absent or reduced in TNFR2-/- pups ( Fig 6C ) . Similar to TNFa-/- pups , TNFR2-/- pups also had reduced staining of ErbB3 along Isl+ sensory axons ( S4 Fig ) . We also quantified this ensheathment phenotype via electron microscopy . In TNFR2-/- pups , we observed a significant lack of glial ensheathment and organization in the afferent nerve and on average , 21 . 7% of individual axon bundles were unsheathed , compared to less than 2% of disrupted ensheathment observed in wildtype pups ( Fig 6D and 6E , n = 3 animals ) . Overall the percentage of axonal bundles that were unensheathed was significantly increased in TNFR2-/- pups when compared to wildtype ( Fig 6E and 6F , n = 3 animals ) ( p<0 . 0001 ) . In addition , we quantified the number of axons in each small caliber bundle . In wildtype pups , there were on average , 31 . 5 axons per small caliber bundle , whereas is TNFR2-/- pups , there 30 . 9 axons ( Fig 6G ) . When binning the axons bundles into ones that were ensheathed vs unensheathed , the TNFR2-/- pups had on average , 24 . 3 that were ensheathed compared to 30 . 9 that were unensheathed ( Fig 6H , n = 3 animals ) . This is compared to wildtype that had 32 . 0 ensheathed and 24 . 3 unensheathed ( Fig 6H , n = 3 animals ) . Based on these data , we propose TNFa-TNFR2 signaling is required for glial ensheathment of the afferent DRG nerve and that TNFa also has a non-TNFR2 function in nerve development ( S6C Fig ) [26] . Expression analyses of tnfa and tnfr2 support the hypothesis that TNFa and TNFR2 function within DRG to drive glial ensheathment . To directly test this model , we created mosaic zebrafish larvae by injecting tnfa or tnfr2 MOs into a single cell of 16-cell Tg ( sox10:eos ) or Tg ( ngn1:gfp ) embryos with a rhodamine-dextran tracer , and then raised those embryos to 72 hpf , when we analyzed glial ensheathment along pioneer axons and scored the cells that contained the rhodamine-dextran marker , and therefore , were MO+ . In these larvae , we observed normal glial ensheathment of pioneer axons in all larvae that did not have rhodamine-dextran+ DRG cells ( tnfa = 510 nerves , tnfr2 = 296 nerves ) ( S3 Table ) . In contrast , we did observe significant glial ensheathment defects along pioneer axons when cells within the DRG were either rhodamine-dextran+/tnfa MO+ or rhodamine-dextran+/tnfr2 MO+ ( tnfa = 33 DRG , tnfr2 = 12 DRG ) ( S3 Table ) . From these data , we conclude that tnfa and tnfr2 function autonomously within DRG neurons and glia to mediate pioneer axons glial ensheathment . To gain a deeper understanding of the role of TNFa/TNFR2 signaling in glial ensheathment , we used time-lapse imaging between 48 to 72 hpf in Tg ( sox10:eos ) embryos that were injected with a tnfr2 MO ( Fig 6I ) . In these morphants , we observed that while DRG glia still associated with the single pioneer growth cone as it entered the spinal cord , the glia collapsed back towards the neuronal cell body soon after spinal cord entry , resulting in a naked pioneer axon ( Fig 6I ) . In these movies , we also observed ectopic axonal projections from the DRG in a subset of larvae . Therefore , we scored ectopic projections at 72 hpf in Tg ( ngn1:egfp ) larvae and visualized that 44% of tnfa morphants and 33% of tnfr2 morphants had ectopic DRG projections ( 0% of wildtype animals contained these projections ) ( S6A and S6B Fig ) . However , these ectopic axons also lacked glial ensheathment . Based on these data , we conclude that entry of the pioneer axon into the spinal cord induces glial ensheathment via a TNFa/TNFR2-mediated pathway .
To achieve the precise timing of glial ensheathment during development , axons must coordinate their development with neighboring glia [6 , 18] . The Neuregulin 1 Type III ( Nrg1-III ) ligand and ErbB receptors are the most studied example of axon-glial communication that controls the spatiotemporal timing of ensheathment [4 , 5 , 19 , 20] . Nrg1-III is a transmembrane ligand , which until cleaved , ensures only glia in close contact with the axon can bind via ErbB and drive ensheathment and ultimately , myelination [4 , 19 , 20] . In this manuscript , we identify another communication mechanism that spatiotemporally controls ensheathment , which requires TNFa and TNFR2 . In our model , by controlling the expression of TNFa , ensheathment of the DRG pioneer axon is precisely timed . Spatially , ensheathment is restricted by requiring glia to contact via transmembrane TNFa . It will be interesting to examine in future studies whether axonally-derived tnfa is the only source that contributes to tnfr2-mediated glial ensheathment . In addition , whether TNFa-TNFR2 signaling also has a role , like NrgI-III/ErbB3 , in myelination of these ensheathed axons , remains to be seen , but is an intriguing next step in these studies given that vast literature on NFkB in glial development [13–15 , 38] . Another interesting and unanswered question of both of these pathways is what induces upregulation of the ligand . Given the temporal upregulation of TNFa that we see as the pioneer axon enters the spinal cord , it is possible that Nrg1-III could be similarly upregulated along axons as they enter nascent tissue . This hypothesis is supported by evidence that muscle-derived neurotrophic factors BDNF and GDNF upregulate Nrg1-III in motor axons [39] . Although our understanding of how axons control their ensheathment is limited , the work we present here implicates an additional signaling cascade that functions in this manner [12] . TNFa-mediated signaling is also required for the differentiation of DRG neurons in late stages of embryogenesis [26] . In the absence of TNFa , excess DRG neurons are present in the ganglia of P14 mice , a phenotype that is likely instigated from the failure of those neurons to undergo normal programmed cell death [26] . This neuronal pathway is mediated through TNFR1 and is likely to function when pioneer DRG axons have already entered the spinal cord . In our analysis , TNFa is also required for proper glial ensheathment , but functions through TNFR2 . Our results are consistent with the hypothesis that TNFa signaling functions multiple times within development of the sensory circuit , but perhaps distinctly through TNFR1 and TNFR2 . For example , we scored an excess of axons in small caliber bundles in TNFa-/- but not in TNFR2-/- pups . One potential explanation for this is that TNFa functions in both axonal and glial development whereas TNFR2 has a role only in glial ensheathment , and this may explain the sensory defects previously described in TNFa-/- pups [26] . Complicating this hypothesis , however , is evidence that DREZ-associated glia are progenitors for DRG cells , which may be defective based on our observation that glial ensheathment is defective . Our data does not distinguish the core cause of our phenotype as loss of glial ensheathment could cause neuronal phenotypes or a phenotype in neurons could drive glial ensheathment defects . Either way , these results introduce a paradigm where TNFa could be used in two distinct pathways with two different cellular outcomes , glial versus neuronal . The developmental timing of these distinct pathways may contribute to their outcomes , shifting TNFa-mediated signaling from a TNFR2 cell arrangement and proliferation cue to a TNFR1-mediated cell death pathway . It is possible that this shift could be controlled by differential expression of the receptors or by variance in cleavage of the TNFa ligand from the membrane . This utility of TNFa during development is further amplified by the fact that the receptors , TNFR1 and TNFR2 , can also act as ligands and induce reverse signaling [40] . Identifying candidate downstream components and effectors of NFkB [41] , may shed light on this hypothesis . Although TNFa signaling has been primarily studied in the immune system , it has also recently been shown to be essential for other developmental programs , including hematopoeisis [42] . We believe our discovery that TNFa likely functions as a transmembrane cue to modulate behavior of two contacting cells during neurodevelopment likely has broad implications . For example , this mechanism could be deployed in multiple developmental paradigms such as neuronal migration , cellular tiling or synaptic specificity , where contacting cells must communicate during their development . This possibility significantly expands the potential roles of TNFa during development .
All animal studies were approved by the University of Virginia Institutional Animal Care and Use Committee . Zebrafish strains used in this study were: AB* , Tg ( sox10 ( 4 . 9 ) :eos ) w9 [24] , Tg ( sox10:mrfp ) vu234 [43] , Tg ( neurod:gfp ) nl1 [23] , Tg ( ngn1:egfp ) w61 [23] , Tg ( tnfa:gfp ) pd1028 [44] and Tg ( NFkB:egfp ) nc1 [34] . Abbreviations used for each line are denoted in S1 Table . Embryos were produced from pairwise matings and raised at 28 . 5°C in egg water in constant darkness and staged by hours or days post fertilization ( hpf and dpf ) . Embryos of either sex were used for all experiments [45] . Pigmentation was inhibited for immunohistochemistry and live imaging with phenylthiourea ( PTU ) ( 0 . 004% ) in egg water . Stable , germline transgenic lines were used in all experiments . All experiments were carried out in compliance with the Association for Assessment of Laboratory Animal Care policies and approved by the University of Virginia Animal Care and Use Committee . Wildtype mice are on a B6;129 mixed background . TNF-/- and TNFR2-/- mice were maintained as homozygotes , purchased from The Jackson Laboratory , and were backcrossed to a B6;129 mixed background for four or more generations [26] . Animals of either sex were used for all experiments . PTU-treated embryos were manually dechorionated at 24 hpf and anesthetized with 3-aminobenzoic acid ester ( Tricaine ) , immersed in 0 . 8% low-melting point agarose and mounted on their right side in glass-bottomed 35 mm Petri dishes ( Electron Microscopy Sciences ) [46] . A Quorum WaveFX-XI spinning disc confocal system ( Quorum Technologies Inc . ) equipped with a 25X multi-immersion objective ( NA = 0 . 8 ) , a 40X oil objective ( NA = 1 . 4 ) , a 40X water objective ( NA = 1 . 1 ) and a 63X water objective ( NA = 1 . 2 ) mounted on a motorized Zeiss AxioObserver ZI microscope was used to capture images . MetaMorph and Photoshop were used to process images and enhance brightness and contrast of images . Supplementary videos were annotated using ImageJ trackM plugin and formatted using ImageJ . Zebrafish embryos and larvae were fixed in 4% PFA for 3 hours at room temperature ( 25°C ) or overnight at 4°C , mounted in sectioning agar and incubated in 30% sucrose overnight at 4°C . For immunohistochemistry , we collected 20 μm transverse sections of the trunk using a cryostat microtome . For mouse tissue , animals were euthanized at P0 and the L3/L6 region of the spinal column was dissected and placed in PFA overnight at 4°C and then 30% sucrose for at least 2 days at 4°C . Tissue was mounted with OCT and then cryosectioned into 20 μm sections . Animals were fixed and stained as previously described [26 , 46] . The primary antibodies used in this study include: Sox10—1:5 , 000 [47] , Acetylated Tubulin 1:10 , 000 ( Sigma ) , βIII Tubulin 1:1 , 000 ( Covance ) , S100 1:1 , 000 ( Dako ) [48] , ErbB3 1:200 ( Santa Cruz ) and Isl1 1:100 ( Developmental Studies Hybridoma Bank ) . The secondary antibodies used include Alexa antibodies ( Invitrogen ) ( 1:600 ) ; goat anti-rabbit 568 , goat anti-mouse 568 , goat anti-rabbit 647 and goat anti-mouse 647 . After staining , zebrafish animals were stored in 50% glycerol/50% 1X PBS until imaged when they were mounted under a bridged coverslip . FISH probes were produced by Biosearch Technologies . Custom Stellaris RNA FISH probes specific to tnfa and tnfr2 probes were made by conjugating 37 different 20 nucleotide oligos specific to tnfa mRNA to fluor red 590 dye and by conjugating 40 different 20 nucleotide oligos specific to tnfr2 to quasar 670 dye . Tg ( ngn1:gfp ) zebrafish at 3 dpf were fixed with 4% PFA overnight and then stored in 100% methanol at 4°C . Embryos were mounted and sectioned into 20 μM sections . The BioSearch Technologies protocol for FISH on frozen tissue was used as a guide . Briefly , after sectioning , slides were incubated in PBS for 5 min , then in hybridization buffer ( 1 ml formamide , 2 . 5 ml 20X SSC , 10 μl 50 mg/ml Heparin , 500 μl 10 mg/ml tRNA , 10 μl Tween 20 , 92 μl 1M Citric Acid , 7 . 29 ml DEPC Water ) for 30 min at 37°C . Probe solution , diluted in 200 μl of hybridization buffer ( 2 μl of 12 . 5 μM probe ) was then incubated overnight at 37°C . Slides were then washed for 2 hours with PBS , mounted with DAPI slide mount and imaged . To image glial dynamics with the pioneer axon , we used Tg ( sox10:eos ) ;Tg ( ngn1:egfp ) embryos treated with PTU , to label DRG precursors with a photoconvertible protein , Eos , that when exposed to UV light , transitions from a green to red emission state , and DRG sensory neurons with GFP [24] . During development , sensory neurons are derived from sox10+ neural crest cells and then turn on expression of ngn when they become post-mitotic neurons [24] . So throughout this imaging , animals were exposed to 450 nm laser for the entire z-stack every 20 minutes to photoconvert the Eos protein . Therefore , in our imaging , sensory neurons were ngn1+ ( green ) and also transiently expressed Eos ( red ) and appeared yellow . All other non-neuronal cells or glia were marked with Eos only and appeared red . Axotomy was completed on 48 hpf Tg ( tnfa:gfp ) ; Tg ( sox10:mrfp ) and Tg ( NFkB:gfp ) ;Tg ( sox10:mrfp ) embryos . A ROI was defined and using a high energy pulse with a MicroPoint laser , as previously described , axotomy was performed [28] . After axotomy , a subset of embryos were imaged for 24 hours every 10 min at 40X . tnfa , tnfr1 , and tnfr2 MOs [29] were diluted from a stock solution of 1 mM in injection buffer to create a working concentration of 0 . 5 mM ( tnfa ) , 0 . 6 mM ( tnfr1 ) and 0 . 20 mM ( tnfr2 ) , as previously described [29] . Each MO was injected into single-cell embryos ( S4 Table ) . Depending on the experiment , embryos/larvae were then mounted or fixed as described above at the appropriate age for the experiment . Morphants that were selected for analysis did not display gross morphological abnormalities , contained normal blood flow , and showed an intact spinal cord with normal peripheral motor nerves exiting the spinal cord within each somite . For quantification of DRG neuronal projections , an abnormal projection was defined as a DRG that had more than one axonal projection that extended to the DREZ . An absence of sox10+ ensheathment was scored if the afferent nerve contained a thin sox10+ axonal projection without surrounding sox10+ glia . gRNAs specific to tnfa ( NM_212859 ) and tnfr2 ( NM_001089510 ) were injected in a cocktail with cas9 RNA ( 75 ng/ul gRNA , 150 ng/ul cas9 ) into single cell-embryos [30 , 31] ( S4 Table ) . At 72 hpf , 16 animals were randomly selected , mounted and imaged . 10 nerves per animal were scored for presence or absence of Sox10+ glia at the DREZ . Animals that showed gross morphological phenotypes or spinal cord patterning abnormalities were discarded from analysis . To confirm that gRNAs were generating mutants within tnfa or tnfr2 coding regions , we isolated DNA from 16 randomly selected animals , and performed T7 endonuclease assay as previously described [30 , 31] . Uninjected animals were used as controls . To confirm that the gRNA was lesioning at the PAM site , we sequenced a subset of these that were imaged to confirm a glial ensheathment defect and compared their sequence to uninjected controls [30 , 31] . gRNA target sites and PCR genotyping primers are provided in S2 Table . Tg ( sox10:eos ) embryos at 24 hpf were exposed to TNFa Protease Inhibitor-1 ( TAPI-1 ) [35] that was diluted 1:1 , 000 in egg water . At 72 hpf , drug-treated and DMSO-treated control larvae were scored for disruption of DREZ glia and the pioneer axon . Using MTrackJ plugin of ImageJ , a ROI was identified in the Tg ( sox10:mrfp ) channel on the developing DRG neuron and pixel intensity was measured for every time point in the movie . This track was then loaded onto the Tg ( tnfa:gfp ) channel and an intensity measurement was similarly identified . The intensity value for GFP and RFP channels at these specific ROIs were determined by ImageJ . These intensity values were then compared to a background ROI in the background black space of the GFP channel . Plotted on the graph is the average of 3 movies for each 5 minute time point in the movie . The values for each time point in each time-lapse movie were calculated by subtracting the background ROI intensity from the ROI intensity of the fluorescent+ cell . The ROI used for this measurement can be seen in S3 Movie . At P0 , pups were euthanized and the spinal column with DRG intact was removed by microdissection . Afferent DRG nerves were removed from the spinal cord and DRG and fixed in 4% formaldehyde , processed for electron microscopy and then imaged with an electron microscope . Four nerves from three different animals were processed and imaged per genotype . MetaMorph software was used to generate a composite Z-image for zebrafish cell and nerve counts . Individual z-images were sequentially observed to confirm composite accuracy . All graphically presented data represent the mean of the analyzed data . For mouse quantification , DRG and DREZ from L3-L6 spinal cord nerve regions from 3 individual animals were scored . 20 μm sections , divided into triplicates that could be stained individually for ErbB3 and S100 were quantified . For zebrafish analysis , at least 6 nerves from 5 individual animals were used for quantification . GraphPad Prism software was used to determine statistical analysis . A Fisher’s exact test using a confidence interval of 95% was used in Figs 3G , 4D , 5C and 6B , or a student’s T-test was used in Figs 4F , 4G , 6F and 6G , to determine the level of significance .
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To survive , animals must receive sensory information from their environment and relay it to the central nervous system . The efficient transfer of this environmental information to the brain and spinal cord requires both neuronal and glial populations found along peripheral sensory nerves . In this work , we describe the early steps of sensory nerve assembly . We identify that a molecule most well described in inflammation , tumor necrosis factor alpha ( TNFa ) , and its obligate receptor are utilized to control glial ensheathment of sensory axons . This ensheathment occurs shortly after the first sensory axons enter the spinal cord . Together , our work uses time-lapse imaging and genetic manipulation to demonstrate the orchestrated coordination of glial and neuronal cells during nerve formation , providing insight into how sensory information from the periphery is integrated into the CNS during development .
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2017
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TNFa/TNFR2 signaling is required for glial ensheathment at the dorsal root entry zone
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Chemical signals are prevalent in sexual communication systems . Mate recognition has been extensively studied within the Lepidoptera , where the production and recognition of species-specific sex pheromone signals are typically the defining character . While the specific blend of compounds that makes up the sex pheromones of many species has been characterized , the molecular mechanisms underpinning the evolution of pheromone-based mate recognition systems remain largely unknown . We have focused on two sets of sibling species within the leafroller moth genera Ctenopseustis and Planotortrix that have rapidly evolved the use of distinct sex pheromone blends . The compounds within these blends differ almost exclusively in the relative position of double bonds that are introduced by desaturase enzymes . Of the six desaturase orthologs isolated from all four species , functional analyses in yeast and gene expression in pheromone glands implicate three in pheromone biosynthesis , two Δ9-desaturases , and a Δ10-desaturase , while the remaining three desaturases include a Δ6-desaturase , a terminal desaturase , and a non-functional desaturase . Comparative quantitative real-time PCR reveals that the Δ10-desaturase is differentially expressed in the pheromone glands of the two sets of sibling species , consistent with differences in the pheromone blend in both species pairs . In the pheromone glands of species that utilize ( Z ) -8-tetradecenyl acetate as sex pheromone component ( Ctenopseustis obliquana and Planotortrix octo ) , the expression levels of the Δ10-desaturase are significantly higher than in the pheromone glands of their respective sibling species ( C . herana and P . excessana ) . Our results demonstrate that interspecific sex pheromone differences are associated with differential regulation of the same desaturase gene in two genera of moths . We suggest that differential gene regulation among members of a multigene family may be an important mechanism of molecular innovation in sex pheromone evolution and speciation .
Variation is the raw material of evolution; however the nature of this variation remains a topic of much discussion amongst evolutionary biologists [1] , [2] , [3] . The potential role in evolution of mutations that impact gene regulation rather than the amino acid sequence of a protein was initially proposed in the 1970s . King & Wilson [4] noticed that orthologous proteins between humans and chimpanzees were very similar at the amino acid level compared to the phenotypic differences between the two species and suggested that gene regulation could play an important role in explaining some of the phenotypic differences between the two primates . Since then the relative importance in evolution of regulatory mutations ( those affecting gene expression ) versus structural mutations ( those resulting in amino acid substitutions within the coding region of a protein ) has been debated ( e . g . , Hoekstra and Coyne [1] and references therein ) . While structural mutations between orthologous proteins are easy to identify , mutations that affect the regulation of a gene are more difficult to recognize . Regulatory mutations can fall close to the gene in its promoter region ( cis-regulatory mutations ) or act at a distance from the gene ( trans-regulatory mutations ) . Cis-regulatory mutations usually result in the gain or loss of a site involved in binding a regulatory factor , such as a transcription factor , whereas trans-acting regulatory mutations typically affect the transcription factors themselves . Interestingly , trans-acting regulatory mutations can involve both regulatory and structural mutations of the transcription factor . The relative importance of cis- versus trans-regulatory mutations in the course of evolution is also predicted to be influenced by the degree of pleiotropy [5] . Since transcription factors generally influence the expression of multiple genes , trans-regulatory mutations are more likely to affect a number of traits . On the other hand , cis-regulatory mutations are more likely to impact the target gene alone [6] . In spite of this , both types of regulatory mutations have been identified in the evolution of distinct cases of beneficial traits within species [1] and in the evolution of major morphological innovations at higher taxonomic levels [7] . Despite these examples , there remains little evidence regarding the nature of molecular innovations that underpin the evolution of new species . Few genes involved in speciation have been identified to date [8] , and many causal mutations associated with species differences remain unknown . This makes the topic of great interest to further investigate the role of structural mutations and gene regulation in traits that can lead to speciation . Finally , whether these mutations are present as standing variation in the ancestral species or if the process of speciation is limited by the rate of acquisition of newly arising mutations has only recently attracted attention , with most discussion restricted to the acquisition of beneficial traits within species [9] , [10] . Speciation is often associated with changes in mate recognition systems [11] . Mate recognition has been extensively studied within the Lepidoptera where the production of long-range species-specific sex pheromone signals by females and their recognition by conspecific males are critical steps . Furthermore , for many species within the Lepidoptera , sex pheromones are often the defining character for biological species [12] . The sex pheromones of many species of moths have been identified [13] , and in some systems , enzymes involved in the biosynthesis of pheromone components have been characterized [14] , [15] . An important structural characteristic of moth sex pheromone components is the position of double bond ( s ) along the fatty acid backbone of the molecule . These double bonds are introduced at specific carbon positions by distinct members of the fatty-acyl desaturase family that have evolved a role in pheromone biosynthesis from an ancestral function in essential fatty-acid biosynthesis [16] . A core set of desaturase transcripts is typically found in the pheromone glands of female moths of a given species [16] , including two Δ9-desaturases , one with a preference for 16 carbon ( 16C ) fatty acids over 18 carbon ( 18C ) fatty acids and a second with the opposite preference , together with one or several members of the so-called Δ11-desaturase clade that includes enzymes that are increasingly being shown to possess a wide range of desaturation abilities ( e . g . Liénard et al . [17] and references therein ) . Changes in enzymes involved in pheromone biosynthesis , including desaturases , have been implicated in pheromone differences in both moths and flies . In the Lepidoptera , these include examples of gene neofunctionalisation , where new desaturases have arisen by gene duplication and then diverged to evolve a new function [18] , [19] . In contrast , there is a single example where differential expression of desaturase genes in the Asian corn borer is responsible for producing distinct pheromone blends [20] . In Drosophila differential regulation of desaturase F is implicated in the production of distinct cuticular hydrocarbon pheromones between species [21] . With only a few cases to draw from , the importance of structural versus regulatory changes involved in mate recognition and speciation is an open question and more examples are required to form a consensus of their relative contribution . To investigate the role of desaturases in changes in sex pheromone blends , we have studied the mode of evolution in these enzymes within two genera of leafroller moths , Ctenopseustis ( brown-headed leafrollers ) and Planotortrix ( green-headed leafrollers ) . Both genera are endemic to New Zealand , with species within the two genera widely distributed across the two main islands [22] . Sequence divergence at the COI locus averages 10% , suggesting that the two genera diverged around 5 million years ago [23] . Although some species develop on specific host plants , such as P . aviciennae on mangroves ( Avicienna marina ) or C . fraterna on silver fern ( Cyathea dealbata ) , most of the species within the genera are polyphagous , and can develop on angiosperms or gymnosperms , including a number of horticultural and silvicultural crops . Many of the sibling species are difficult to differentiate using classical morphology [22] or mitochondrial bar-coding DNA markers [23] , suggesting that they have diverged in the last 500 , 000 years . Despite this recent divergence , the different sibling species use distinct sex pheromones . Their sex pheromone compounds are tetradecenyl acetates that differ primarily in the position of a single double bond within a fourteen carbon fatty acid backbone . They are biosynthesized from fatty acids ( myristic , palmitic or stearic acid ) , which are desaturated at specific positions , chain-shortened via β-oxidation , reduced to fatty alcohols , and acetylated to give the final products . Within the genera sex pheromone blends contain up to three components in specific ratios ( Table S1 ) that are desaturated at the Δ5 , Δ7 , Δ8 , Δ9 or Δ10 positions , all in the Z configuration [24] . Each genus contains a pair of sibling species that utilize distinct sex pheromone blends . In Planotortrix , P . excessana uses a blend of ( Z ) -5-tetradecenyl acetate ( Z5-14:OAc ) and ( Z ) -7-tetradecenyl acetate ( Z7-14:OAc ) [25] , [26] , while its sibling species P . octo , utilizes ( Z ) -8-tetradecenyl acetate ( Z8-14:OAc ) and trace amounts ( 2% ) of ( Z ) -10-tetradecenyl acetate ( Z10-14:OAc ) [25] . In Ctenopseustis , C . obliquana uses a blend of Z5-14:OAc and Z8-14:OAc [27] , while its sibling species C . herana utilizes a sex pheromone consisting solely of Z5-14:OAc [28] . Thus each species pair is characterized by a gain or loss of a particular pheromone component that differs only in the position of a double bond . Sex pheromones are central to mate recognition in moths and form barriers to gene flow among the Lepidoptera . Species within Ctenopseustis and Planotortrix are no exception . Males of these species are specifically attracted to the sex pheromone blend of their conspecific females , but not to that produced by the respective sibling species , restricting gene flow between sibling species ( see Foster et al . [24] for a review ) . Briefly , Clearwater et al . [29] investigated the cross responses of C . obliquana and C . herana males to conspecific and sibling species sex pheromone blends , as well as females , in wind tunnel and field cage experiments . They found strong preferences for the conspecific pheromone in both experimental formats . Furthermore , Foster et al . [30] tested the attractiveness of a range of ratios of Z5-14:OAc and Z8-14:OAc to C . obliquana and C . herana in a wind tunnel . C . obliquana never landed on lures containing only Z5-14:OAc , while C . herana males never landed on lures containing Z5-14:OAc and Z8-14:OAc . Similarly , field cage cross attraction experiments using P . excessana and P . octo showed high species specificity in male mating behaviour [25] . The lack of interbreeding in the wild is also supported by isozyme-based population genetics , with at least one fixed difference identified for each sibling species pair [31] , [32] . The biosynthesis of the compounds found in the pheromone blends of the Ctenopseustis and Planotortrix species have been studied by Fatty Acid Methyl Ester ( FAME ) analysis of pheromone glands [33] and by monitoring the incorporation of labelled precursors [34] , [35] , [36] . The Z8-14:OAc used by P . octo is the product of Δ10-desaturation of palmitic acid followed by chain shortening , reduction and acetylation [35] , while the Z5-14:OAc and Z7-14:OAc used by P . excessana are the products of Δ9-desaturase activity [36] . The Δ10- and Δ9-desaturases responsible for these activities have been isolated and characterized from P . octo [37] . The biosynthesis of Z5-14:OAc used by Ctenopseustis species was investigated by labelling experiments in C . herana , where unlike in Planotortrix , FAME analysis implicated the action of a specific Δ5-desaturase [34] . Therefore at least four desaturases are thought to potentially contribute to pheromone biosynthesis in these two genera: two Δ9-desaturases , a Δ10-desaturase and a Δ5-desaturase ( Figure 1 ) . We set out to investigate whether structural mutations within coding regions of these desaturases or differential regulation of a standing set of desaturase genes are responsible for the diversity in sex pheromone components used by species within the genera Ctenopseustis and Planotortrix . To obtain a general picture of the set of genes present in these leafroller moths , we first isolated and characterized desaturases from genomic DNA and cDNA from the pheromone glands of C . obliquana , C . herana , P . octo and P . excessana . We then performed functional assays and quantitative real-time PCR to identify the desaturases likely to have a role in sex pheromone biosynthesis in these species . Finally , we compared coding region sequences and the expression of the genes in the pheromone glands of the sibling species to address our question .
Initially we isolated as many desaturase-encoding sequences as possible from two sets of sibling species in the genera Ctenopseustis and Planotortrix . By Polymerase Chain Reaction ( PCR ) using degenerate primers designed to target conserved regions of lepidopteran fatty acyl desaturases , we isolated 29 desaturase-like sequences from genomic DNA and/or pheromone gland cDNA of C . obliquana , C . herana , P . excessana , P . octo and in some instances , also from the more distantly related Planotortrix species , P . notophaea . For many of these genes , sequences were recovered from two strains of P . excessana ( the North Island and South Island strains ) . The sequences fall into six groups of desaturase-like genes ( desat1-6 ) . Rapid Amplification of cDNA Ends ( RACE ) PCR was used together with genome walking and analysis of preliminary whole genome sequencing assemblies of C . obliquana and P . octo ( unpublished data ) to construct predicted coding regions for each ortholog of each desaturase-like gene . The resulting gene contigs were confirmed by PCR and sequencing from pheromone gland cDNA wherever possible . Coding regions were obtained for all genes from all species except for desat3 and desat5 from C . herana , and the final 3′ ∼100 bp of desat5 from C . obliquana . Desat1 and desat5 of P . octo were isolated previously by Hao et al . [37] . All sequences isolated during this study have been deposited on GenBank ( accession numbers JN022472–JN022498 ) . An amino acid alignment derived from the 29 sequences grouped into each of the six desaturase-like genes is presented in Figure S1 . The predicted desaturases for which we obtained full length coding regions ranged in size from 331 to 358 amino acids ( Table 1 , Text S1 , S2 , S3 , S4 , S5 , S6 ) . Between species ( excluding P . notophaea ) , amino acid sequence identities were highest for desat6 ( 98 . 6%–100% ) followed by desat1 ( 97 . 4%–99 . 4% ) and desat2 ( 96 . 1%–99 . 4% ) , with the lowest displayed by desat3 ( 90 . 4%–95 . 5% ) and desat4 ( 90 . 2%–96 . 8% ) . The full length sequence of the desat5 was only obtained for Planotortrix species , where the amino acid identity between sibling species was 99 . 4% . Intron positions were inferred by PCR from genomic DNA or in the case of C . obliquana and P . octo by direct observation from genome scaffolds . Where determined , intron positions were conserved among orthologous genes . Desat2 contains no introns , desat4 contains two introns and the remaining desaturase-like genes , desat1 , desat3 , desat5 and desat6 each contain three introns . The relative positions and phase of the introns are indicated on the alignments presented in Text S1 , S2 , S3 , S4 , S5 , S6 . Phylogenetic analysis was conducted on all predicted desaturases from Ctenopseustis and Planotortrix reported above or previously [37] , as well as a set of currently available lepidopteran desaturase sequences ( Figure 2 ) . Three well-supported clades were observed including a Δ9-desaturase ( 16C>18C ) clade into which the predicted protein desat1 falls , a Δ9-desaturase ( 18C>16C ) clade into which desat6 falls and a so-called Δ11-desaturase clade into which all the remaining Ctenopseustis and Planotortrix desaturases ( desat2 , desat3 , desat4 and desat5 ) fall . In each case , the Ctenopseustis and Planotortrix orthologs group together , well supported by high bootstrap values . Moreover , some Ctenopseustis and Planotortrix desaturases group closely with previously characterized desaturases from other species . First , the Ctenopseustis and Planotortrix desat2 orthologs group closely with a non-functional desaturase from Choristoneura rosaceana [38]; second , the desat6 orthologs group with a Δ9-desaturase from Epiphyas postvittana [39]; and third , even though less closely related , desat3 groups with a terminal desaturase from Operophtera brumata [40] . Finally , the six predicted desaturases from Ctenopseustis and Planotortrix are all well separated from each other in the phylogenetic tree , with many other lepidopteran desaturases inter-dispersed between them . We looked for evidence of nonsense or missense mutations in the sequence of the desaturase-like genes that might impact function and explain differences in the pheromone components used by the different species . No amino acid substitutions were found in the active site regions , such as the histidine-rich regions involved in ion coordination , and no premature stops or frame-shift mutations could be identified . We then undertook likelihood ratio tests using PAML to look for evidence of selection acting on the coding regions of the six desaturases [41] . There was some evidence for positive selection in one of three model comparisons for desat3 and desat5 ( Table 2 ) , together with some significantly selected sites identified in desat3 ( 7 ) , desat4 ( 2 ) , desat5 ( 1 ) and desat6 ( 1 ) ( Table 3 ) . However , the ratio of non-synonymous to synonymous ( dN/dS ) nucleotide substitutions ( M0 ) were less than one for all genes , indicative of overall purifying selection and suggestive of conserved function ( Table 1 ) . We then examined the function of the predicted desaturases , or more specifically substrate preference and desaturation specificity within the fatty acid precursors . We investigated sufficient desaturases so that at least one ortholog of each of the six predicted desaturases was characterized . The open reading frames of predicted desaturases were subcloned into the YEpOLEX or pYEX-expression vectors and functional expression was conducted in desaturase-deficient yeast strains . FAME extracts from transformed yeast were analysed to infer the ability of each desaturase to introduce double bonds to pheromone precursors at specific positions and DiMethyl-DiSulphide ( DMDS ) derivatization was performed to verify the structural identity of the unsaturated products . Since functional analyses from P . octo have shown that desat1 has Δ9-desaturase activity with a preference for 16C over 18C precursors , and desat5 is a Δ10-desaturase [37] , we focused on orthologs of desat2 , desat3 , desat4 and desat6 . Desat2 from C . obliquana or C . herana had the same profile as Cu2+-induced yeast transformed with the pYEX-CHT-only vector ( Figure 3A ) , indicating that they were unable to utilize typical sex pheromone precursors as substrates . FAME analyses from yeast transformed with desat3 from P . excessana and desat4 from C . herana revealed the presence of three and two additional mono-unsaturated products , respectively . In the case of desat3 , all DMDS adducts exhibited a diagnostic ion at m/z 61 , corresponding to FAMEs with a terminal double bond . These were subsequently identified as the Δ13-14:Me ( m/z 334 [M+] , 61 and 273 ) , the Δ15-16:Me ( m/z 362 [M+] , 61 and 301 ) ( Figure 3B–3E ) and the Δ17-18:Me ( m/z 390 [M+] , 61 and 329 ) ( Figure 3B ) . The DMDS adducts for desat4 exhibited a diagnostic ion at m/z 175 , which is characteristic of FAMEs with a double bond at the sixth carbon position ( Δ6 ) and was absent in control samples ( Figure 3C ) . The DMDS adducts corresponded to the Δ6-14:Me ( m/z 334 [M+] , 175 and 159 ) ( Figure 3D ) and Δ6-16:Me ( m/z 362 [M+] , 175 and 187 ) ( Figure 3D , 3F ) . No other characteristic ions for mono-unsaturated compounds were detected . YEpOLEX-Cob-desat6 yeast transformants were able to grow on media lacking Unsaturated Fatty-Acids ( UFAs ) , indicating that desat6 from C . obliquana encodes a functional desaturase that could complement the UFA auxotrophic ole1 strain . Methylated fatty-acid extracts from yeast bearing desat6 transformants showed two major peaks with retention times corresponding to Z9-16:Me and Z9-18:Me ( Figure 4A ) . DMDS analyses revealed a diagnostic ion at m/z 217 , confirming the identity of unsaturated FAMEs with a double bond at the Δ9 position . In addition , more Z9-18:Me than Z9-16:Me was produced , with small amounts of Z9-14:Me ( m/z 334 [M+] , 217 and 117 ) , Z9-15:Me ( m/z 348 [M+] , 217 and 131 ) and Z9-17:Me ( m/z 376 [M+] , 217 and 159 ) also detected ( Figure 4B ) . We are therefore able to summarise the activities of the six predicted desaturase groups , assuming that their function is conserved among orthologs . Desat1 encodes a Δ9-desaturase , with a preference for 16C over 18C precursors , desat2 encodes an enzyme for which an activity has not yet been detected , desat3 encodes a terminal desaturase , desat4 encodes a Δ6-desaturase , desat5 a Δ10 desaturase and desat6 a Δ9-desaturase with a preference for 18C over 16C precursors . Based on these functional analyses we concluded that desat1 , desat5 and desat6 are likely to play a role in sex pheromone biosynthesis in these species . Initial gene expression analysis using quantitative real-time PCR conducted from cDNA from pooled tissue samples ( approx . 100 pheromone glands ) by species was undertaken on all six desaturases to examine relative gene expression in larval fat bodies , adult male and female abdomens and adult female pheromone glands . The apparently non-functional desat2 was highly expressed only in larval fat bodies , while all other desaturases had detectable levels of expression across all tissues with desat1 , desat5 and desat6 , showing high levels of expression in the pheromone gland ( data not shown ) . Because of these results and the unlikely involvement of desat2 , desat3 and desat4 in pheromone biosynthesis based on their desaturase activities , we focused on the analysis of the expression levels of desat1 , desat5 and desat6 , comparing gene expression from pheromone glands of individual female moths . Our hypothesis was that expression differences among the desaturases are responsible for the observed pattern of sex pheromone component differences between the sibling species . The most striking expression differences in the pheromone glands compared with adult abdomens of the different species among the three desaturases were observed for desat5 ( Figure 5 ) . Levels of gene expression were higher in C . obliquana ( 34-fold ) and P . octo ( 273-fold ) pheromone glands compared with abdomens than in C . herana and P . excessana , respectively , where no significant differences in expression were observed between both tissues . Differences in expression of desat1 and desat6 between species and tissues were also observed , but were far less striking than for desat5 . Desat1 was more highly expressed in the pheromone glands than in abdomens of P . excessana ( 3 . 7-fold ) and P . octo ( 3 . 3-fold ) , while for desat6 P . octo showed higher levels of expression than P . excessana both in the gland and the abdomen ( 1 . 8-fold ) . No significant differences in desat1 and desat6 expression were found in the two Ctenopseustis species . To verify that the primers used for quantitative RT-PCR were able to amplify the appropriate desaturase gene with low or barely detectable levels of expression in a particular species ( for example desat5 from C . herana and P . excessana ) , PCRs were conducted using genomic DNA or plasmids containing the relevant desaturase cDNA . In all cases , melting-curve analysis confirmed the presence of a single product of the expected size and/or sequence and negative controls contained no product ( data not shown ) .
The molecular mechanisms involved in the production of the variants that underpin the evolution of new mate recognition systems and new species remain poorly understood . Toward addressing this , we have investigated the molecular differences in desaturase genes involved in pheromone biosynthesis in sibling species within the Ctenopseustis and Planotortrix genera of leafroller moths . We were particularly interested in whether differential regulation of a standing set of desaturase genes within a multigene family is involved in the observed differences in sex pheromone composition between the sibling species , C . obliquana vs . C herana and P . octo vs P . excessana . Initially , we set out to identify the sequences encoding the four desaturases previously identified biochemically within the pheromone glands of these species . In total we isolated 27 new lepidopteran desaturase-like sequences from five species within the leafroller moth genera Ctenopseustis and Planotortrix ( C . obliquana , C . herana , P . octo , P . excessana and P . notophaea ) . Together with the two desaturases previously isolated and characterized from P . octo [37] , these 29 sequences represent six orthologous desaturases . We examined the ability of a representative subset of these predicted desaturases to introduce double bonds in sex pheromone precursors such that at least one member of each set of six orthologous desaturases has now been functionally characterized . Two of the six desaturases from Ctenopseustis and Planotortrix encode Δ9-desaturases ( desat1 and desat6 ) that display highly conserved functions across all lepidopteran insects investigated to date [17] , [19] , [37] , [38] , [39] , [42] . For example , desat6 from C . obliquana and its ortholog from E . postvittana ( Epo-Z9 ) encode a Δ9-desaturase with a preference for 18 carbon fatty acid precursors over 16 carbon precursors [39] . The remaining four desaturases all fall into the so-called Δ11 clade . While desat2 from C . obliquana and C . herana have no activity on typical sex pheromone fatty acid precursors , similar to desaturases identified previously from Choristoneura rosaceana and Ch . parallela [38] , [43] , we identified two desaturases showing interesting activities: desat3 and desat4 . Desat3 from P . excessana encodes a desaturase with terminal desaturase activity , an activity that has recently been implicated in pheromone biosynthesis in the winter moth , Operophtera brumata [40] . In contrast with Ctenopseustis and Planotortrix , however , the sex pheromone of the winter moth is not derived from saturated fatty acids , but from linolenic acid . Desat4 from C . herana on the other hand encoded an enzyme with Δ6-desaturase activity . Like the terminal desaturase activity , this desaturation ability has only recently been observed in pheromone biosynthesis within the Lepidoptera . Wang et al . [18] described a Δ6-desaturase from the Chinese tussah silkworm , Antheraea pernyi , involved in the biosynthesis of the ( E ) -6-hexadecenoic acid as an intermediate in the pathway to producing the dienoic sex pheromone composed of ( E , Z ) -6 , 11-hexadecadienal , ( E , Z ) -6 , 11-hexadecadienyl acetate and ( E , Z ) -4 , 9-tetradecadienyl acetate . Despite these interesting associations with pheromone biosynthesis in other Lepidoptera , it is unlikely that desat3 and desat4 , as well as desat2 , are involved in pheromone biosynthesis in Ctenopseustis and Planotortrix because of their inability to produce desaturated precursors of the sex pheromone components found in the respective species , and their low level of gene expression levels in pheromone glands of all four species . There are a number of examples of desaturase orthologs within the Lepidoptera that share a conserved biological function , especially within the two highly conserved Δ9-desaturase subfamilies ( desat1 and desat6 ) ( e . g . , Roelofs et al . [19] , Hao et al . [37] ) . Within the Δ11-like clade , in which the desat2 , desat3 , desat4 and desat5 orthologs all fall , the most widespread function is Δ11-desaturase activity . Members of this gene subfamily have been shown to evolve more rapidly than the Δ9-clades [19] , which results in Δ11-like orthologs from distantly related species being less conserved ( 50–60% ) and displaying more variable activities ( i . e . , Δ10 , Z/E11 , terminal desaturase activity , bifunctional Δ10/12 or Δ11/13 ) . However , in closely related species the function of orthologs is usually conserved . This is the case for the Z/E11-desaturases from Ostrinia spp . [19] , non-functional desaturases ( e . g . desat2 orthologs ) from Choristoneura spp . [38] , [43] or Z11-desaturases from Helicoverpa spp . [42] , [44] . Only in the case of Choristoneura parallela and Ch . rosaceana did two Δ11-orthologs that shared 92% protein identity differed slightly in activity , producing E11-14:acid or a mixture of Z/E11-14:acids , respectively [38] , [43] . Still both enzymes introduced double bonds at the 11th carbon position , only differing in their isomeric preference . Of course one cannot rule out that some of the amino acid differences identified between the different orthologs might encode functional differences . However , we provide circumstantial evidence this is not the case from high sequence conservation ( 90–99% identity ) , from phylogenetic relationships within groups ( desat2 , desat3 , desat4 and desat5 , respectively ) , from tests of selection and overall evidence of purifying selection on coding region sequences ( Table 1 , Table 2 , Table 3 ) , and from the position of the amino acid differences being outside the active site regions . We also have tested some orthologous desaturases and do not find differences in activity . Altogether , evidence from previous desaturase studies together with our sequence , phylogenetic and functional analyses provide strong support that significant functional differences between desat3 , desat4 and especially desat5 orthologs are unlikely . Desat1 , desat5 and desat6 have biological activities that are sufficient to explain all but one of the observed sex pheromone components and their routes of biosynthesis ( Figure 1 ) . Foster [36] provided evidence for the role of two distinct Δ9-desaturases in the biosynthesis of the Z5-14:OAc ( desat6 ) and Z7-14:OAc ( desat1 ) components of the P . excessana sex pheromone blend and a Δ10-desaturase ( desat5 ) in the biosynthesis of Z8-14:OAc in P . octo [35] and C . obliquana [33] . The exception is the biosynthesis of Z5-14:OAc in C . herana and C . obliquana that is thought to be produced directly from myristic acid by a Δ5-desaturase [34] , rather than from palmitic or stearic acid via desat1 or desat6 , respectively , followed by rounds of chain shortening as found in P . excessana [35] . Despite intensive efforts , we are yet to identify another member of the fatty acyl desaturase family displaying Δ5-desaturase activity from any of the four species . We then examined whether differential gene regulation of desat1 , desat5 and desat6 could be responsible for the different sex pheromone components produced by the sibling species pairs C . obliquana vs C . herana and P . octo vs P . excessana . Interestingly , whereas desat1 and desat6 were expressed at largely similar levels in the four species , desat5 showed substantial differences in gene expression in the pheromone gland of female moths between C . obliquana vs C . herana , and P . octo vs P . excessana ( Figure 5 ) . C . obliquana had higher levels of expression of desat5 in female pheromone glands compared with C . herana and similarly , P . octo had higher levels of expression of desat5 compared with P . excessana , which can be associated with the presence ( in C . obliquana and P . octo ) or absence ( in C . herana and P . excessana ) of the Z8-14:OAc sex pheromone component . While we have found evidence for the role of differential gene regulation in the evolution of new pheromone blends , structural mutations have been implicated in this process in genes immediately downstream of the desaturation step within the Lepidoptera . In the European corn borer , Ostrinia nubilalis , a fatty-acyl reductase has been identified that alters the specificity for the desaturated precursors , leading to the production of distinct sex pheromone blends between the so-called Z and E races [45] . Since only a single reductase allele has been identified that is active in pheromone biosynthesis in these moths , with the two alleles diverging at more than 30 amino acid positions ( 8% ) together with several coding regions being under positive selection , only structural mutations are postulated to be responsible for sex pheromone diversity at the reduction step in this species [45] . In contrast , there was no strong evidence for structural mutations being responsible for the production of different sex pheromone components by the sibling species pairs P . octo vs P . excessana , since the desat5 genes are highly conserved ( 99% identity ) and neither candidate loss of function mutations nor any evidence for selection acting on the coding regions of the orthologous desaturases were identified ( Table 1 , Table 2 , Table 3 ) . Although we miss the comparison between complete desat5 cDNA sequences for C . obliquana and C . herana , P . octo and P . excessana differ by only two amino acid differences at the desat5 locus . The first difference is a single amino acid deletion that occurs only in P . octo with respect to other Planotortrix desat5 orthologs , and still confers this species with an intact Δ10-desaturase activity [37] . The second difference is a glutamic acid to lysine substitution two amino acids towards the C terminus from the indel . Despite the possibility that one or both of these substitutions might impact the desaturase activity of desat5 in P . excessana , the fact that they lie outside active site regions suggests this is unlikely . Together , these changes in gene expression strongly suggest that a differential gene regulation of desat5 controls the presence/absence of the Z8-14:OAc in the sex pheromone of these sibling species pairs . These results also suggest that the same molecular mechanism may have been involved independently in pheromone evolution in the two genera . In both genera a major change in expression of desat5 in the pheromone gland has occurred . The evolutionary event may have been either a loss of expression event from an ancestor expressing high levels of desat5 in their pheromone glands , or a gain event with the ancestor expressing little desat5 in their pheromone gland . We can reasonably infer the order of these evolutionary events by looking at sex pheromone composition in basal species in the two genera ( see Newcomb and Gleeson [46] for discussion ) . Both Z8-14:OAc and Z10-16:OAc are synthesised from palmitic acid by a Δ10-desaturase ( desat5 ) , with Z8-14:OAc being produced following a round of β-oxidation , before reduction and acetylation ( see Figure 1 ) . Within Ctenopseustis , the basal species , C . servana , does not use any pheromone components that contain a double bond in an even position , suggesting that there has been a gain of the use of Z8-14:OAc or Z10-16:OAc early in the evolution of the genus after the split from C . servana . The use of these components presumably through the gain of expression of desat5 in their pheromone glands , then become widespread , used by both C . obliquana ( Z8-14:OAc ) and C . filicis ( Z10-16:OAc ) . Subsequently South Island populations of a C . obliquana-like ancestor lost the expression of desat5 in the pheromone gland to give rise to C . herana , which only produces Z5-14:OAc . A similar loss of desat5 expression may also have occurred in the formation of the C . obliquana type II that occurs in a highly restricted North Island population and also only uses Z5-14:OAc as its sex pheromone [29] , [47] . The most parsimonious scenario that explains sex pheromone evolution within Ctenopseustis thus suggests that desat5 pheromone gland expression was lost in C . herana following divergence from its C . obliquana-like ancestor . In contrast , the evolution of Δ10- and Δ8-unsaturated sex pheromone components within Planotortrix likely derives from an evolutionary scenario involving a gain of expression of desat5 in the pheromone glands of P . octo . Hence in this genus , components such as Z5-14:OAc and Z7-14:OAc that are derived from the action of Δ9-desaturases are widespread within the group and thus probably represent the ancestral pheromone blend . The use of Z8-14:OAc is restricted to just two species within the genera , P . octo and its geographically isolated relative P . octoides , the latter of which is only found on the Chathman Islands . Therefore , the presence of Z8-14:OAc is a derived condition within the genus and indicates that desat5 expression in the pheromone glands of P . octo is likely a gain of function event . The alternative scenario that all species but two in the Planotortrix genus have lost expression of desat5 independently is a less parsimonious explanation . Simultaneously or subsequently to producing Z8-14:OAc P . octo must have also lost the ability to produce Z5-14:OAc and Z7-14:OAc . This may have come about through a change in one or both of the Δ9-desaturases . Apart from this study , sex pheromone evolution through regulatory changes in desaturases has been investigated only in the corn borer moths , Ostrinia furnacalis and O . scapulalis [20] . In this example a Δ11- and Δ14-desaturase show alternate expression in the pheromone gland of the two species , with the Δ11-desaturase being expressed in the pheromone gland of O . scapulalis but not in O . furnacalis and vice versa for the Δ14-desaturase . Together these examples point to differential gene regulation among a standing set of desaturase genes as a mechanism involved in producing novel sex pheromone components and blends within the Lepidoptera . A further example comes from Drosophila melanogaster where differences in mating ability between African and cosmopolitan populations are caused by sex pheromone differences and have been suggested as a case of incipient speciation [48] . Here , a Δ9-desaturase is differentially regulated in the population through a cis-regulatory deletion within the promoter of the gene resulting in cuticular hydrocarbon sex pheromone differences that may ultimately promote the speciation of the two D . melanogaster races . Differential gene regulation can result from two classes of mutation; either changes in the regulatory region of the differentially expressed gene ( cis-regulatory mutation ) or changes in transcriptions factors that bind to the promoter ( trans-regulatory mutation ) . Trans-regulatory mutations are often associated with pleiotropic impacts on the regulation of other genes due to transcription factors typically acting on several promoters . Therefore it is perhaps more likely that cis-regulatory mutations are responsible for the differential regulation of desat5 in Ctenopseustis and Planotortrix , an hypothesis which we are currently in the process of testing . In conclusion we show that interspecific pheromone differences between sibling species are determined by parallel changes in desaturase gene expression in two sister genera . This case study suggests that differential regulation within large multigene families may be an important process in speciation , with changes in gene expression underpinning the evolution of novel mating systems .
Ctenopseustis herana , C . obliquana , Planotortrix excessana , P . octo and P . notophaea were obtained from the Plant & Food Research insect rearing facility at the Mt Albert Research Centre , Auckland , New Zealand . The history of these strains is reported in Newcomb and Gleeson [46] , except that an additional strain of P . excessana derived from material caught in the South Island of New Zealand was used to generate a P . excessana South Island strain and the original P . excessana strain is now known as P . excessana North Island . Insects were reared on a 16∶8 light cycle , with larvae reared at 20°C and pupae and adults at 18°C . Genomic DNA was extracted using the DNeasy Blood & Tissue Kit ( Qiagen ) . Total RNA was extracted from two distinct regions of the abdomen of 2–3 day old virgin adult females and from fat bodies of 5th instar larvae . From the adult females the pheromone gland , located within the dorsal region of the 8th and 9th abdominal segments ( denoted ‘pheromone gland’ ) was dissected . As a control , a lateral region of the 4th to 6th abdominal segments of the same adult females ( denoted ‘abdomen’ ) was also dissected . RNA was isolated from dissected tissue using 800 µl of Trizol ( Invitrogen , Carlsbad , CA , USA ) following the manufacturer's instructions . The expression of desaturase genes was initially characterized from RNA generated from pools of 100 pheromone glands , while RNA from single pheromone glands was used for subsequent Quantitative Real-Time PCR experiments . After DNase treatment ( DNaseI amplification grade , Invitrogen ) , the cDNA synthesis was carried out using the iScript cDNA Synthesis Kit ( Bio-Rad ) from 1 µg of total RNA or approximately 100 ng of total RNA for single samples , and incubated at 50°C for 1 hr , followed by 70°C for 15 mins . In order to identify the different desaturases involved in sex pheromone biosynthesis , a progressive approach using successive and complementary methods was used for each species . Initially , degenerate primers were applied to genomic and pheromone gland cDNA using primers designed to conserved amino acid motifs found in lepidopteran desaturases . Sequences of the coding region of desaturase genes were then extended by means of 5′ and 3′ Rapid Amplification of cDNA ends ( RACE ) , genome walking , or Inverse PCR . All the primers used are listed in Table S2 . All PCR amplifications were performed on a GeneAmp 9700 ( Applied Biosystems ) . The fragments of interest were cloned into pGEM-T Easy Vector System ( Promega ) and transformed into JM109 competent E . coli cells , according to the manufacturer's instructions . Sequencing was performed at the Allan Wilson Centre Genome Service ( AWCGS ) at Massey University , Palmerston North , New Zealand or Macrogen in South Korea . Degenerate PCR was performed for each species on the genomic DNA or pheromone gland cDNA using 0 . 2 µl of Platinum Taq DNA polymerase ( 5 units/µl , Invitrogen ) , 1 . 5 mM Mg2+ , 0 . 2 mM of each dNTP and 2 µM of each degenerate PCR primer ( Table S2 ) . Cycling conditions were 2 min at 94°C , 35 cycles of 94°C for 10 s , 50°C for 10 s and 72°C for 1 min , and a final extension of 72°C for 10 min . The 3′ ends of the coding regions were obtained using a modified version of the 3′ RACE System for Rapid Amplification of cDNA ends ( Invitrogen ) . First strand cDNA synthesis was carried out in a final volume of 14 µl using 1–2 µg total RNA , 1 µl 3′AP ( or RoRidT16 ) primer ( 10 µM ) , 1 µl of 10 mM dNTPs , and incubated at 65°C for 5 min , and placed on ice for 1 min . Then , 4 µl of 5× first strand buffer , 1 µl of 0 . 1 M DTT and 1 µl of Superscript III ( 200units/µl , Invitrogen ) were added to the mixture . The reactions were incubated at 50°C for 1 hr , followed by 70°C for 15 min . The 3′-tagged products were detected by PCR amplification using forward desaturase group-specific primers ( 3′ RACE-F primers ) and the 3′AUAP ( or Ri ) primer , with 0 . 2 µl of Platinum Taq DNA polymerase ( 5units/µl , Invitrogen ) , 1 . 5 mM Mg2+ , 0 . 2 mM of each dNTP and 0 . 2 µM of each primer . Cycling conditions were 2 min at 94°C , 30 cycles of 94°C for 10 s , 55°C for 30 s and 72°C for 1 min , and a final extension of 72°C for 10 min . The 5′ ends of cDNAs were amplified using the 5′ RACE System for Rapid Amplification of cDNA ends kit ( Invitrogen ) . Oligo-dC tails were added to purified 3′ RACE cDNA in a final volume of 20 µl with 4 µl of 5× first tailing buffer , 2 µl of 2 mM dCTP , 3 µl of 5 mM CoCl2 . Reactions were incubated at 94°C for 3 min and placed on ice for 1 min . Then , 1 µl of TdT ( 400units/µl ) was added , and the reaction was incubated at 37°C for 10 min and stopped at 65°C for 10 min . Oligo-dC-tailed products were amplified by normal PCR ( 30 cycles ) , and 1 µl of the later reaction was used for a nested-PCR ( 25 cycles ) , using the 5′ RACE-F and 5′ RACE-R primers ( Table S2 ) . Inverse PCR was performed by digesting genomic DNA overnight at 37°C with NdeI or SalI , BSA ( 100 µg/ml ) and Spermidine ( 2 mM ) . Classical phenol/chloroform extraction and ethanol precipitation were used to purify the digested products . These were then circularized by ligation overnight at 16°C with T4 DNA ligase ( 400 units/µl , New England Biolabs ) , and purified as previously described . The 3′ ends of two distinct fragments were amplified in C . herana using the Inverse PCR primers ( Table S2 ) under the same conditions as described for the 3′ RACE . In some cases , genome walking was used to extend the coding sequences of desaturase genes . For this approach genomic DNA was digested overnight at 37°C with DraI , EcoRV , PvuII and StuI , separately , and the products purified with the DNA Isolation Kit for Cells and Tissues ( Roche ) . GenomeWalker adapters were then ligated to both ends of the digests by incubating 3 µl of template overnight at 16°C with 2 µl of adapter primers ( 100 µM ) and 1 µl of T4 DNA ligase ( 400 units/µl , New England Biolabs ) in a final volume of 20 µl . Specific tagged-products were amplified by normal and nested-PCR ( see 5′ RACE ) , using the GW1-F and GW1-R primers ( Table S2 ) . Gene-specific PCR was conducted using highly specific PCRs ( Gene-specific primers , Table S2 ) to verify all contigs of the coding regions . Sequences were analysed using Geneious Pro v5 . 3 . 4 ( Biomatters ) . Sequences were aligned using ClustalX [49] and codon aligned nucleotide alignments were produced using RevTrans version 1 . 4 . 1 ( www . cbs . dtu . dk/services/RevTrans/ ) . For phylogenetic analyses , explicit models of evolution were determined using Modeltest [50] and GTR+I+Γ was implemented for likelihood and Bayesian analyses . Parsimony and maximum likelihood analyses were performed in PAUP v4 . 0b ( Sinauer Associates , Sunderland , Massachusetts ) , and Bayesian inference was implemented in MrBayes v3 . 0b4 [51] . Evidence for selection was tested by looking for deviations from neutral expectations using the CODEML program in the PAML package [41] . Multiple models were run ( M0 , M3 , M7 , M8 and M8a ) to assess selection pressures . Comparisons of nested models were used to assess heterogeneous selective pressure amongst sites ( M0–M3 ) or positive selection ( M7–M8 ) . Nested models were compared by the implementation of a likelihood-ratio test ( LRT ) , where the LRT is twice the log-likelihood difference of the nested models . Significance was tested using a χ2 test with degrees of freedom equal to the difference in the number of parameters between the two models . Pheromone glands were dissected from two-day-old virgin adult females of C . herana , C . obliquana , P . excessana and P . octo . Pheromone glands were pooled in lots of 100 or used singly in RNA extractions . Fat bodies were dissected from 5th instar larvae , and pooled in lots of five , while abdomens were extracted singly . RNA and cDNA were extracted as described in the gene isolation section . The expression of the desaturases together with the housekeeper genes actin , α-tubulin and elongation factor 1 α were determined using primers described in Table S2 . All quantitative real-time PCRs were performed for the pheromone gland and the abdomen , for each specimen , in duplicate . Experiments were performed on the LightCycler480 Real-Time Instrument ( Roche Diagnostics , Basel , Switzerland ) , in a final reaction volume of 10 µL , with 80 ng of cDNA , 5 µL 2× SYBR Green Mix ( Bio-Rad ) , and 0 . 5 µM of each primer . The PCR cycling conditions were set to 2 min at 95°C followed by 45 cycles of 15 s at 95°C , 30 s at 60°C and 30 s at 72°C . A final dissociation curve analysis was added ( 15 s at 95°C , 15 s at 60°C , and a gradual heating to 95°C at 0 . 01°C/s ) to confirm the presence of a single amplicon . Relative expression levels were calculated according to the ΔΔCp method [52] , [53] . The amplification efficiency was calculated for each PCR using the LinRegPCR software [54] . For each sample , the average Threshold Cycle values were extracted , and a normalization factor , based on the geometric averaging of the reference gene expression levels , was determined using geNorm [55] . Normalization factors allowed correction for PCR efficiency and normalization of the gene expression levels . To test for differences in levels of normalised relative expression between species and tissues one way ANOVAs were conducted using GraphPad Prism 5 , with individual comparisons made using Bonferroni-corrected t-tests at the 95% significance level . Functional assays were performed with at least one ortholog of each of the desaturases . The Cob-desat2 , Cher-desat2 , Pex-desat3 and Cher-desat4 ORFs were cloned into pYEX-CHT vectors and transformed into the desaturase- and elongase-deficient ( elo1 ole1 ) strain of Saccharomyces cerevisiae ( MATa elo1::HIS3 ole1::LEU2 ade2 his3 leu2 ura3 ) [52] . In the same way , the Cob-desat6 ORF was cloned into the YEpOLEX vector and transformed in the desaturase-deficient ( ole1 ) strain of S . cerevisiae ( MATα ole1Δ::LEU2 leu2-3 leu2-112 trp1-1 ura3-52 his4 ) [56] , [57] . Functional assays were performed with the S . c . Easy Transformation kit ( Invitrogen AB , Lidingö , Sweden ) . pYEX-CHT and YEpOLEX vectors only were used as negative control . Transformed ole1 elo1 or ole1 yeast cells were incubated for 4 days at 30°C on selective medium plates containing 0 . 7% YNB ( w/o amino acids , with ammonium sulphate ) and a complete drop-out medium lacking uracil and leucine ( ForMedium LTD , Norwich , England ) , 2% glucose , 0 . 01% adenine , 1% tergitol ( type Nonidet NP-40 , Sigma-Aldrich Sweden AB , Stockholm , Sweden ) and 0 . 5 mM unsaturated oleic acid ( Larodan Fine Chemicals , Malmö , Sweden ) . Note that in addition to Z9-18:Acid , ole1 elo1 yeast contained residual traces of Z9-16:Acid because of supplementation during the earlier procedure of making cells competent . Individual ole1 elo1 colonies were selected and incubated in 10 ml fresh selective medium and cultures were incubated in inclined position at 30°C for 48 hr and 250 rpm ( Innova 42 , New Brunswick Scientific ) . Yeast cultures were diluted to an OD600 = 0 . 4 in 10 ml fresh SC-U medium and supplemented with 25 µl CuSO4 1 M in water ( final concentration: 2 . 5 mM ) . After 48 hr of incubation at 250 rpm in presence of copper , yeast cells were collected by centrifugation at 2 , 000× g ( Labofuge 200 , Heraeus Instruments ) and washed with sterile water . The total yeast lipid fraction was extracted with chloroform∶methanol ( 2∶1 , v∶v ) and the extracts were base-methanolyzed according to standard protocols [17] , [58] . Double bond localization in methyl esters was determined by dimethyl disulfide ( DMDS ) derivatization [59] before GC-MS analysis . To test for genetic complementation of the ole1 auxotrophy by the YEpOLEX-Cob-desat6 , individual SC-U-Leu yeast colonies were selected and patched onto YPAD plates lacking Fatty Acids ( FA ) and incubated for 4 days at 30°C . Positive transformants were subsequently grown for 48 hr at 30°C and 300 rpm in 10 ml SC medium without FA , recovered by centrifugation and washed with water , followed by base methanolysis and DMDS derivatization . Before analysis by GC-MS analyses , samples were concentrated under a gentle flow of pure nitrogen to a final volume of approx . 50 µl . For analysis of fatty acid methyl esters ( FAMEs ) , 3 µl was injected on a gas chromatograph ( Hewlett Packard HP 5890II GC system ) coupled to a mass selective detector ( HP 5972 ) and equipped with a polar INNOWAX column ( 100% polyethylene glycol , 30 m×0 . 25 mm×0 . 25 µm , Agilent Technologies ) . The GC-MS was operated in electron impact mode ( 70 eV ) and the injector was configured in splitless mode at 220°C with helium used as carrier gas ( velocity: 30 cm/s ) . The oven temperature was maintained for 2 min at 50°C and increased at a rate of 10°C/min up to 220°C , held for 20 min . For analysis of DMDS adducts , 2 µl was injected on a GC ( Hewlett Packard HP 6890 , Agilent Technologies ) equipped with an HP-5MS capillary column ( 5% Phenyl Methyl Siloxane; 30 m×250 µm: df = 0 . 25 µm; carrier gas: helium; velocity: 30 cm/s ) , an automatic injector ( HP-7683 ) , and coupled to a HP 5973 mass selective detector . The injector was configured in splitless mode at 250°C . The oven temperature was maintained for 2 min at 80°C , increased at a rate of 15°C/min up to 140°C , increased at a rate of 5°C/min up to 280°C , and held for 10 min .
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Chemical signals are prevalent in sexual communication systems , especially within the Lepidoptera where sex pheromones are typically one of the defining characteristics of species . We have isolated six desaturases from two groups of sibling species of leafroller moths belonging to the genera Ctenopseustis and Planotortrix . Functional analyses in yeast and quantitative RT–PCR indicate that three of the desaturases are involved in the biosynthesis of sex pheromone components in these species . One of three enzymes is a Δ10-desaturase that is differentially expressed in the pheromone glands of the two sets of sibling species , consistent with differences in the pheromone blend in both species pairs . In the pheromone glands of species that utilize ( Z ) -8-tetradecenyl acetate as sex pheromone component ( C . obliquana and P . octo ) , the expression levels of the Δ10-desaturase are significantly higher than pheromone gland expression levels in their sibling species ( C . herana and P . excessana ) . Our results demonstrate that interspecific sex pheromone differences are associated with differential regulation of the same desaturase gene in these two genera of moths . Based on these findings differential gene regulation among members of a multigene family may be an important mechanism of molecular innovation in sex pheromone evolution and speciation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"evolutionary",
"biology"
] |
2012
|
Sex Pheromone Evolution Is Associated with Differential Regulation of the Same Desaturase Gene in Two Genera of Leafroller Moths
|
Spinal motoneurones ( Mns ) constitute the final output for the execution of motor tasks . In addition to innervating muscles , Mns project excitatory collateral connections to Renshaw cells ( RCs ) and other Mns , but the latter have received little attention . We show that Mns receive strong synaptic input from other Mns throughout development and into maturity , with fast-type Mns systematically receiving greater recurrent excitation than slow-type Mns . Optical recordings show that activation of Mns in one spinal segment can propagate to adjacent segments even in the presence of intact recurrent inhibition . While it is known that transmission at the neuromuscular junction is purely cholinergic and RCs are excited through both acetylcholine and glutamate receptors , here we show that neurotransmission between Mns is purely glutamatergic , indicating that synaptic transmission systems are differentiated at different postsynaptic targets of Mns .
Motoneurones ( Mns ) are the ultimate neural targets of effector commands issued from the central nervous system . Their activity is modulated by an intricate network of interneurones [1] that affect the spatial and temporal distribution of excitation to different motor pools [2] . Mns also receive direct inputs from supraspinal tracts and sensory afferents , and their outputs are not confined to the peripheral muscles but also include excitatory collateral terminals to Renshaw cells ( RCs ) . Early anatomical studies [3] have shown that Mn axon collaterals have large ramifications that invade the motor nucleus and may form synaptic contacts with other Mns . An early evidence of functional connectivity between Mns was found in the adult cat [4] but was attributed to the presence of gap junctions . The first proof of the existance of chemical synapses between Mns was found in tadpoles [5] . A simlar observation in juvenile rats was attributed to the presence of afferent fibers in the ventral roots ( VRs ) [6] , a possibility that was subsequently ruled out [7 , 8] . Recurrent excitation was also described in neonatal mice [9] , in which VR stimulation elicited a small postsynaptic response in Mns . None of the previous studies provided a comprehensive analysis of the extent of recurrent excitation , and they only illustrated a few recordings of small evoked currents . Furthermore , there are contrasting reports on the type of receptors mediating recurrent excitation , with evidence showing sensitivity to either glutamatergic antagonists [6] , cholinergic antagonists [7] , or both [9] . Here , we perform a systematic study of recurrent excitatory circuitry and demonstrate that recurrent excitation between Mns is strong and it is maintained throughout development into maturity . Our data show that while recurrent excitation between intrasegmental and intersegmental Mns is comparable in size , fast-type Mns receive a 10-fold greater amount of recurrent excitation compared to slow Mns . Under normal physiological conditions , recurrent excitation can override recurrent inhibition , and Mn firing in one spinal segment propagates to neighbouring segments . Remarkably , while acetylcholine and a mixture of acetylcholine and glutamate act at the neuromuscular junction and RC synapses , respectively [8 , 9] , neurotransmission between Mns is purely glutamatergic .
We performed paired recordings to measure the efficacy of unitary connections in fluorescently labelled Mns innervating gastrocnemius . Simultaneous infrared and confocal imaging was used to identify and patch fluorescent Mns in a dorsal horn–ablated spinal cord ( Fig 1A ) . Strychnine ( 0 . 5 μM ) and gabazine ( 3 μM ) were applied to block recurrent inhibition . Mns were patched in whole-cell voltage clamp while putative presynaptic cells were stimulated in loose-cell attached configuration ( see Materials and methods ) until an evoked response was detected in the postsynaptic cell . Fig 1B shows an example of a paired recording with an average evoked current of −34 pA . A location map constructed from 14 out of the 18 recorded pairs ( Fig 1C ) shows that connected Mns tended to be within 150 μM from one another but with no systematic relationship between distance and size of response ( range 11–125 pA ) . The rise and decay times of evoked currents ( Fig 1D , response size colour coded ) were fast , with a median rise time of 0 . 59 ms and decay time of 3 . 75 ms . There was , however , no correlation between either of the kinetic parameters and the size of response . In oblique slice preparations ( see Materials and methods ) , we assessed the pharmacology of evoked responses ( Fig 1E ) . The postsynaptic current was fully abolished by bath application of 50 μM D- ( - ) -2-Amino-5-phosphonopentanoic acid ( APV ) and 2 μM 2 , 3-Dioxo-6-nitro-1 , 2 , 3 , 4-tetrahydrobenzo[f]quinoxaline-7-sulfonamide disodium salt ( NBQX ) , to block N-Methyl-D-aspartate ( NMDA ) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ( AMPA ) receptors , respectively ( Fig 1F , top ) . Identical results were obtained from all 4 pairs tested ( Fig 1F , bottom ) . Because the tested unitary connections might have represented a specific local subset of the entire population of Mn–Mn synapses , we investigated the pharmacology of currents evoked by VR stimulation , thus pooling responses to all inputs from a given segment ( Fig 2A ) . In the example of Fig 2A–2C , we simultaneously recorded from an RC ( Fig 2B , top , red ) and an Mn ( Fig 2B , bottom , blue ) . The RC response shown includes a second component originating from a gap junction [10] , contacting a neighbouring RC in which VR stimulation evoked an action potential . Whereas bath application of glutamate antagonists resulted in a reduction of the RC response to approximately 50% , the response in the Mn is completely abolished . The remaining cholinergic component of the response in the RC was blocked by further application of 10 nM methyllycaconitine ( MLA ) and 5 μM dihydro-β-erythroidine ( DHβE ) to block α7 and αβ receptors , respectively ( Fig 2C ) . Group data from 16 Mn recordings are illustrated in Fig 2D . The mean latency ( ±SEM ) of responses of 1 . 60 ± 0 . 13 ms and the corresponding response jitter , quantified using the standard deviation of the latencies , was 0 . 08 ± 0 . 01 ms , consistent with monosynaptic responses to VR stimulation . In all cases , application of glutamate antagonists resulted in complete suppression of evoked currents ( Fig 2D ) . While the data from Fig 2D were obtained from juvenile mice ( P7–14 ) , we performed similar recordings from more mature animals ( P15–25 ) to determine whether pure glutamatergic transmission is preserved throughout development . Fig 2E and 2F shows that the response is fully suppressed by glutamatergic blockade . In 20 Mns recorded in voltage clamp ( black , Fig 2G ) or in current clamp ( blue , to reduce the duration of the stimulus artefact ) , glutamatergic antagonists entirely suppressed responses , whereas prior cholinergic blockade had no significant effect ( n = 6 , Wilcoxon sign-rank z = −0 . 53 , P = 0 . 600 ) . The mean latency of the responses was 1 . 54 ± 0 . 22 ms , with a jitter of 0 . 13 ± 0 . 03 ms . We next investigated whether recurrent excitation could propagate across segments in coronal preparations ( see Materials and methods ) from juvenile mice ( P7–14 ) in which Mns innervating gastrocnemius were labelled . Fig 3A illustrates recurrent excitatory postsynaptic currents ( rEPSCs ) recorded in L5 ( left ) and L4 ( right ) Mns while stimulating the L5 ( upper , blue trace ) or L4 ( lower , red trace ) VR . The rEPSC size from 43 recordings from L4 and L5 Mns is plotted against the distance from the L4/L5 border , colour coded to represent responses evoked by L4 ( red circles ) or L5 ( blue circles ) VR stimulation ( Fig 3B ) . There were no obvious differences in rEPSC size between the 2 stimulated roots or between L4 and L5 Mns . Comparison of rEPSCs from responses to VR stimulation from the same segment or neighbouring segment showed no significant differences ( Fig 3B right , Wilcoxon rank-sum z = 0 . 61 , P = 0 . 541 ) . Despite the lack of correlation between the size of the current and the position of the recorded Mn with respect to the stimulated VR , we observed a very broad distribution of amplitudes of rEPSCs , with sizes ranging from 60 to more than 5 , 000 pA ( see S1 Fig , showing the rEPSCs recorded in all Mns in which the position was registered . ) In the experiments above , the rEPSCs were pharmacologically isolated by blocking fast inhibitory transmission with strychnine and gabazine , in order to avoid potential bias in the current measurements , due to the opening of the large inhibitory conductances associated with the activation of RCs following VR stimulation . However , because the cord might become hyperexcitable following full block of synaptic inhibition , we tested whether propagation of recurrent excitation across segments was preserved even with intact inhibition . In 3 different preparations , we stimulated either L4 or L5 VRs and recorded the rEPSCs from 21 Mns located in the adjacent L5 or L4 segment ( see individual responses in S2 Fig ) . Amplitudes from experiments performed without block of synaptic inhibition are shown as red ( descending ) or blue ( ascending ) crosses in Fig 3B ( left ) . The box and whisker plot ( Fig 3B , right ) shows that a comparison of intersegmental responses in the presence and the absence of antagonists of inhibition shows no significant differences in size ( Wilcoxon rank-sum z = 0 . 48 , P = 0 . 635 ) . The mean latency ( ±SEM ) of responses of 1 . 64 ± 0 . 09 ms was consistent with monosynaptic activation and not significantly different from those from oblique slices ( 1 . 60 ± 0 . 13 ms , Wilcoxon rank-sum z = −0 . 90 , P = 0 . 384 ) . Latencies of responses from Mns in neighbouring segments ( 1 . 77 ± 0 . 13 ms ) were longer compared to those recorded from within the same segment that was stimulated ( 1 . 43 ± 0 . 09 ms , Wilcoxon rank-sum z = −2 . 43 , P = 0 . 015 ) . The corresponding response jitters were very small both within ( 0 . 08 ± 0 . 03 ms ) and across ( 0 . 08 ± 0 . 01 ms ) segments , and a comparison between the two showed no significant difference ( Wilcoxon rank-sum z=−0 . 58 , P = 0 . 559 ) . These results demonstrate that , while the latency of synaptic responses may be greater across segments compared to within segments , they are both mediated by monosynaptic connections between Mns within and across segments . It has been recently demonstrated that in zebrafish , Mns are electrically coupled with V2a interneurones that in turn project back to motor nuclei with glutamatergic synapses [11] . It is therefore possible that VR-evoked , synchronized antidromic spikes could elicit firing in V2a interneurones , which in turn could give rise to the observed short-latency , low-jitter EPSCs . In order to test this possibility , we used a dorsal horn–ablated preparation taken from mice selectively expressing enhanced green fluorescent protein ( EGFP ) in V2a interneurones . While stimulating a VR , we performed simultaneous whole-cell recordings from an Mn and cell-attached recordings from a V2a interneurone . At a stimulation intensity that elicited a maximal rEPSC in the Mn , no spikes could be evoked in the V2a interneurones in 3 different preparations ( n = 46 , V2a interneurones tested ) . We also performed simultaneous whole-cell recordings of Mns and V2a interneurones , and in n = 9 cells , we could not elicit any synaptic or electrically mediated current from the interneurone ( one example of a double recording in each preparation is shown in Fig 4B ) throughout the stimulated or adjacent segment of the spinal cord . The position of all recorded cells is overlayed with a picture of a cord in Fig 4A . These results therefore exclude any involvement of V2a interneurones in mediating the rEPSCs . We next assessed whether the magnitude of recurrent excitation was related to the intrinsic properties of postsynaptic Mns . Two types of Mns were identified according to their firing pattern at rheobase in current clamp recordings [12] . The first type ( Fig 3C , left , purple ) has high rheobase , produces delayed firing with a pronounced increase in firing rate during positive current application , and is associated with fast-type units . By contrast , the second type ( Fig 3C , right , green ) has a lower rheobase and immediate firing with little change in spike frequency , characteristic of slow-type units [12] . High rheobase ( Fig 3D , left ) and accelerating initial firing ( Fig 3D , middle ) were correlated with the size of rEPSCs , with median values of 1 , 814 in the delayed-firing cells and 267 in the immediate-firing cells . Comparison between the 2 groups confirmed a significant difference ( Fig 3D right , Wilcoxon rank-sum z = 3 . 72 , P<0 . 001 ) . Differences between the 2 cell types are also associated with their passive properties , with delayed-firing Mns showing lower resistances ( median 22 MΩ ) and higher capacitances ( median 237 pF ) than their immediate-firing counterparts ( median resistance 44 MΩ , median capacitance 125 pF ) , both at statistically significant levels ( Wilcoxon rank-sum |z|≥2 . 94 , P≤0 . 003 ) . Recurrent excitatory responses were recorded from delayed-firing ( Fig 3E , purple ) and immediate-firing ( Fig 3E , green ) cells in both voltage clamp ( top ) and current clamp ( bottom ) . Pooling all cell types together , correlations were observed between the size of response and resistance or capacitance ( Spearman |r|≥0 . 516 , P<0 . 001 , Fig 3F ) . The presence of strychnine and gabazine during electrophysiological recordings precluded evaluation of whether recurrent excitation could override recurrent inhibition . We therefore conducted calcium imaging experiments in mice selectively expressing GCaMP6s in Mns to evaluate the propagation of recurrent excitation across different segments with recurrent inhibition intact . Fig 5A–5C illustrates a coronal preparation with a suction electrode applied to the L5 VR ( Fig 5A , left ) . Calcium signals were acquired throughout the dorsal motor column of L4 and L5 ( Fig 5A , middle ) , with 146 ms frame interval before , during , and following a train of 3 VR stimulations at 30 Hz . The signal from regions of interest , defined within the outline of Mn somata , was evaluated for the period of acquisition under control conditions , in the presence of 0 . 5 μM strychnine and 3 μM gabazine , and following application of 50 μM APV and 2 μM NBQX ( Fig 5A , right ) . While the latency of the rEPSCs recorded electrophysiologically was short ( 1 . 64–1 . 77 ms ) and exhibited a very low jitter ( 0 . 08 ms ) , a greater variability in the apparent onset of the calcium signals was observed ( Fig 5A , middle ) . However , analysis of each individual trace confirmed that the latency of responses were always within 150 ms to 300 ms . Because the responses were evoked by a single trial of a train of 3 stimuli applied over a 100-ms period , and images were acquired at a frame interval of 146 ms using a calcium indicator with slow kinetics ( approximately 200 ms [13] ) , a variable shift of 1 to 2 frames was expected . In control , recurrent excitation evoked spikes in Mns from both L4 and L5 segments , as shown by the running medians and interquartile ranges of the relative fluorescence signal ( Fig 5B , red ) throughout both segments . Bath application of strychnine and gabazine resulted in substantial amplification of responses throughout the motor column ( Fig 5B , green ) , whereas additional application of glutamatergic antagonists abolished responses from the L4 segment and substantially attenuated those from L5 Mns ( Fig 5B , blue ) . The residual response in L5 Mns reflects antidromic activation . Scattergrams , colour-coded by regions , comparing control responses to those during application of inhibitory antagonists ( Fig 5C , left ) and additional glutamatergic blockade ( Fig 5C , right ) , confirm that while responses were greater in the lumbar regions closer to the stimulated VR , the relative effects of block of recurrent inhibition—or excitation—were similar throughout L4 and L5 . Group data from 461 Mns from 7 preparations are shown in Fig 5D , comparing responses within and across segments for the 3 conditions . In control , responses were significantly greater within the stimulated segment compared to outside ( Fig 5D , left , Wilcoxon rank-sum z = 8 . 50 , P<0 . 001 ) , and these difference were maintained after block of inhibition ( Fig 5D , middle ) and excitation ( Fig 5D , right ) ( z≥7 . 23 , P<0 . 001 ) . Pooling Mns from both segments , blockade of inhibition consistently increased the signal ( Wilcoxon sign-rank z−18 . 59 , P<0 . 001 ) , whereas a significant reduction in signal was observed following additional application of glutamatergic antagonists ( z = 18 . 20 , P<0 . 001 ) . Residual firing was mostly confined to Mns within the stimulated segment through antidromic activation , thus confirming the purely glutamatergic nature of recurrent excitation .
Our experiments show that strong recurrent excitation between Mns is maintained throughout development , and fast-type Mns receive greater recurrent excitation than slow ones . We demonstrate that synaptic transmission between Mns is purely glutamatergic . While it could be argued that the observed small unitary postsynaptic responses ( approximately 100 pA ) would have little effect on the excitability of Mns whose somata are very large , VR stimulation evoked responses usually exceeding 1 nA , indicating extensive convergence of segmental Mn populations . Very few studies have examined , in any detail , recurrent excitation between Mns . In both available electrophysiological studies [7 , 9] , the size of the recurrent EPSCs is not reported , even though the examples shown suggest a small size—of the order of 100 pA—corresponding to the lower bound of our observations . The previous studies were performed on young neonatal animals ( P0–P4 ) , while we obtained all our recordings starting from the second week of age . Therefore , our estimates of the size of recurrent excitation is not directly comparable to that reported by [9] and [7] because most of the increase in input conductance and dendritic arborization occurs between P2–P4 and P10–P13 [14] . In the only previous study performed on more mature animals ( P10–P20 rats [6] ) , recurrent EPSPs were reported in the range of 3 to 15 mV , similar to our observations in current clamp . While the conduction velocity reported in this study ( 0 . 35–0 . 96 m/s ) would point towards stimulation of unmyelinated fibers , the consistent observation of anti-dromically induced firing strongly suggests that Mn axons were indeed stimulated , and the slow conduction thus most likely reflects incomplete myelination at this developmental stage . The variation in the magnitude of rEPSCs is associated with Mn classification into delayed and immediate-firing types , with larger responses systematically observed in the delayed-firing , low-resistance , and high-capacitance cells . Our results are therefore consistent with a structural connectivity in which the fast-type larger Mns receive stronger recurrent excitation compared to slow-type smaller cells . This pattern of connectivity suggests that recurrent excitation could play a role in sequential recruitment of fast-type units during motor tasks in which progressively increasing muscular forces are needed . Alternatively , recurrent excitation might represent a closed-loop amplification circuit that reinforces and increases the firing rate preferentially in fast-type Mns and thus rapidly increases muscle contraction strength when required . Distinguishing between these two possibilities would require a full characterization of presynaptic Mns in order to determine whether it is slow or fast Mns that are preferentially connected to fast Mns . In neonatal animals , VR stimulation can induce fictive locomotion [8] and entrain the spontaneous rhythmic bursting induced by block of inhibition [15] . Furthermore , optogenetic activation or silencing of motor pools alters the frequency and phase of chemically induced fictive locomotion [16] . These effects cannot be explained solely by recurrent excitation and may provide evidence for Mn collaterals contacting unidentified interneurones [17] . A similar finding has been recently reported in zebrafish , for which gap junctions between Mns and V2a interneurones can alter the swimming pattern [11] . In our experiments however , an involvement of gap junctions between Mns and V2a interneurones seems unlikely because in none of our recordings could we detect spikes in identified V2a interneurones following VR stimulation . It is possible , however , that Mns synapse onto other , so far unidentified interneurones that , in turn , could project back to Mns . While the existence of such a disynaptic pathway is possible , it would not account for the large , constant latency responses observed both within and across segments . Indeed , in our electrophysiological recordings during pharmacological blockade of recurrent inhibition , we often observed a late disynaptic component . At present , we cannot ascertain whether the disynaptic current results from premotor interneurons or from orthodromic activation of Mn pools that were not antidromically activated by VR stimulation . In either case , such recruitment implies the existence of a positive-feedback amplifying circuit whose tendency to reverberate may be suppressed by recurrent inhibition . The recurrent excitation characterised in the present study includes predominantly a monosynaptic component , and this is evidenced by three observations . First , the connectivity between Mn pairs must have been monosynaptic . Second , the latency of responses within ( 1 . 43 ± 0 . 09 ms ) and between ( 1 . 77 ± 0 . 13 ms ) lumbar segments were within the time-scale of neurotransmission through only a single synapse . Finally , the response jitters within ( 0 . 08 ± 0 . 03 ms ) and between ( 0 . 08 ± 0 . 01 ms ) segments were very small and virtually identical . These observations are only consistent with a monosynaptic connectivity between Mns both within the same segment and across neighbouring segments . While the occurrence of synaptic projections between Mns crossing spinal segments may be regarded as unusual , it is perfectly compatible with the known rostrocaudal distribution of Mn dendritic trees , which may span over 1 millimetre in juvenile mice with little or no change into adulthood [14] . A glutamate receptor–dependent effect on Mn EPSPs evoked by VR stimulation has been reported previously [6] , but it was attributed to afferent fibres within the root [18] , a possibility now excluded by subsequent labelling studies [8] . A previous study has reported a purely cholinergic response to VR stimulation in a small proportion ( 2/9 ) of Mns [9] . Across all electrophysiological recordings of the present study , however , there was not a single instance of a cholinergic component . The origin of such a discrepancy may result from differences in maturity because in the previous study [9] neonatal mice ( P0-P4 ) were used , while our experiments were performed on mice of at least partially weight-bearing age ( P7–P25 ) . Neurotransmission between Mns is purely glutamatergic , yet following normal maturation , the neuromuscular junction is solely cholinergic [19] and synaptic transmission of recurrent collaterals onto RCs is mixed with both cholinergic and glutamatergic components [20] . This remarkable dissociation demonstrates a differentiation of neurotransmission systems on the basis of the different postsynaptic targets of Mns . However , the presence of vesicular glutamate transporters in Mn collateral terminals is still controversial . Immunohistochemistry and in situ hybridization studies have reported the expression of the vesicular glutamate transporter 2 ( VGlut2 ) in some Mn terminals onto RCs that are either positive [9] or negative [21] for the vesicular acetylcholine transporter . These respective findings indicate either coexistence or segregration of cholinergic and glutamatergic transmission of Mns onto RCs . Others , however , have not detected the presence of VGlut2—or any other vesicular glutamate transporter—in Mn terminals [8 , 22] . It is possible that such discrepencies arise from undetectable albeit functional expression levels of VGlut2 . Another possibility is the existence of an unidentified vesicular glutamate transporter [8 , 22] . This hypothesis is supported by the presence of glutamate-releasing C-fibres in the dorsal horn that are nevertheless negative for all known vesicular glutamate transporters [23 , 24] . Because many Mn terminals may contain more aspartate than glutamate [25] , it has been proposed that the released neurotransmitter could be aspartate . However , aspartate alone cannot activate AMPA receptors that mediate responses of RCs [26] or of the Mns characterised in the present study . Glutamate therefore remains the most likely candidate .
All experiments were carried out in accordance with the Animal ( Scientific Procedures ) Act ( Home Office , UK , 1986 ) and were approved by the UCL Ethical Committee , under project licence number 70/7621 . Intramuscular injections were performed under inhaled isofluorane anaesthesia and by a surgical procedure supervised and approved by the Veterinary Surgeon named by the Home Office , UK . Before being euthanized , animals were administered terminal anaesthesia via intraperitoneal injection of a mixture of ketamine and xylazine ( 80 mg/Kg and 10 mg/Kg , respectively ) . Experiments were performed on preparations obtained from male or female mice bred using a C57BL/6J background . For electrophysiological experiments with simultaneous recordings from Mns and RCs , a transgenic strain—in which the EGFP is expressed under the control of the promotor of the neuronal glycine transporter GlyT-2 [27]—was used to label glycinergic interneurones . Simultaneous recordings from Mns and V2a interneurones were performed on mice expressing EGFP under the control of the Chx10 transcription factor [28] . Following anaesthesia by intraperitoneal injection of a mixture of ketamine/xylazine ( 80 mg/kg and 10 mg/kg , respectively ) , both juvenile and mature mice were decapitated and the spinal cord dissected in normal icecold aCSF containing ( in mM ) 113 NaCl , 3 KCl , 25 NaHCO3 , 1 NaH2PO4 , 2 CaCl2 , 2 MgCl2 , and 11 D-glucose ( same solution was used for recording ) . The spinal cord was then glued onto an agar block and affixed to the chamber of a vibrating slicer ( HM 650V , Microm , ThermoFisher Scientific , UK ) . We used a slicing solution containing ( in mM ) 130 K-gluconate , 15 KCl , 0 . 05 EGTA , 20 HEPES , 25 D-glucose , 3 kynurenic acid , and ph 7 . 4 with NaOH [29] . For cutting oblique slices , the cord was glued to an agar block cut at a 45-degree angle , with the ventral side facing the direction of the blade [20] . For coronally sliced preparations in which the dorsal horns were ablated , the cord was glued horizontally with the ventral surface facing upwards . A blade was used to transect the cord at the L1–L2 boundary at an angle that allowed visualization of the exact position of the central canal under a dissection microscope . The vibratome blade was then aligned to the central canal , and the ventral portion of the cord was sliced away from the dorsal part . Alignment with the central canal was essential to ensure a consistent dorsoventral level of the ablation across different preparations and to retain the dorsal motor nuclei near the cut surface of the tissue . Identical procedures were used for juvenile ( P7–14 ) and mature ( P15–25 ) animals . For older animals , we routinely cut the first slice within 8 minutes following decapitation . Because spinal cord preparations are extremely sensitive to anoxia , especially prior to slicing , we found that minimizing the time to obtain the first slice consistently resulted in viable preparations with healthy Mns [20] . All recordings from postsynaptic Mns were performed with a Molecular Devices Axopatch 200B amplifier , filtered at 5 kHz and digitized at 50 kHz . Patch pipettes were pulled to resistances in the range of 0 . 8–2 MΩ when filled with ( in mM ) 125 K-gluconate , 6 KCl , 10 HEPES , 0 . 1 EGTA , 2 Mg-ATP , pH 7 . 3 with KOH , and osmolarity of 290 to 310 mOsm . During voltage clamp recordings , Mns were clamped at −60 mV with series resistances in the range of 2 to 10 MΩ compensated by 60% to 80% . During paired recordings , loose cell–attached stimulation was used to evoke spikes in putative presynaptic Mns using an ELC-03X ( NPI Instruments , Bauhofring , Germany ) amplifier and a 4–5 MΩ pipette filled with normal aCSF [30] . Within each field of view ( 240 μm × 240 μm ) , typically up to 40 Mns could be visualized , but in order to avoid excessive mechanical disturbances of the tissue while recording from the postsynaptic target , only those located within the first 150 from the surface could be tested for connections . Typically , 1 Mn for every 40 tested was connected to the recorded cell . VR stimulation was delivered to evoke rEPSCs in Mns using a glass suction electrode whose tip was cut to correspond with the size of the VR [31] . The stimulation intensity was increased until the size of the rEPSC remained constant , typically at 5× threshold . In order to exclude direct stimulation of the ventral white matter , VRs were only used if they were of sufficient length to afford no possible physical contact between the slice and suction pipette . This was tested before and after each recording by confirming that side-to-side movement of the suction pipette resulted only in movement of the root and not the slice . The position of the stimulating electrode for all the dorsal horn–ablated preparations is shown for the experiments performed in the presence and absence of block of synaptic inhibition in Supplementary S1 Fig and S2 Fig , respectively , and ranged between 200 μm and 1500 μm from the edge of the glass pipette to the point of entry of the VR . For measuring the size of the excitatory response in some Mns , for which it was necessary to prevent action potentials , cells were hyperpolarised below their resting membrane potential . Measurements of synaptic current and potentials from these recordings were adjusted to their predicted value at −60 mV , assuming a reversal potential of 0 mV for excitatory conductances . All electrophysiological experiments were performed in the presence of 0 . 5 μM strychnine and 3 μM gabazine except where stated otherwise . Where indicated , excitatory receptors were blocked using APV , NBQX , MLA , or DHβE . In order to label Mns innervating the ankle flexor gastrocnemius muscle , intramuscular injections were performed 2 to 5 days prior to recording . Inhalant isofluorane was used for the induction and maintenance of anaesthesia . Traction was applied to the lower limb , and an incision was made through the skin and deep fascia overlying the muscle . A Hamilton syringe loaded with a glass needle was used to inject 1 μl of CTB-Alexa-Fluor-555 ( 0 . 2% in 1× phosphate buffer saline ) into the middle of the muscle belly over a period of at least 1 minute . The skin was closed by suture using a buried stitch before cessation of anaesthesia and recovery . Calcium imaging experiments were performed on animals selectively expressing the genetically encoded calcium indicator GCaMP6s in Mns . These mice were generated by crossing mice expressing Cre under the control of choline-acetyltransferase ( ChAT-Cre; JAX mouse line number 006410 ) with animals with the gene expressing GCaMP6 flanked by a flox-Stop cassette ( JAX mouse line number 028866 [32] ) . Upon recombination with Cre , GCaMP6 is selectively expressed in Mns and other cholinergic cells of the offspring . Because the only other population of ChAT-positive lumbar spinal cells are the cholinergic partition neurones located around the central canal , there was no ambiguity in the identification of Mns from their basal GCaMP6 fluorescence and position within the motor nuclei . Dorsal horn–ablated coronal slice preparations ( P9–12 ) were used for imaging experiments to visualise the dorsal motor nuclei in the L4 and L5 segments containing Mns innervating tibialis anterior , gastrocnemius , and peroneus longus close to the cut surface . A laser-scanning confocal unit ( D-Eclipse C1 , Nikon , UK ) with a diode laser ( λ=488 nm , power output from optic fibre 3–5 mW ) was used to locate and record calcium signals from Mns reaching a depth of approximately 100 μm from the surface . Fields of 128 × 64 pixels ( pixel size 1 . 38 μm and dwell 7 . 2 μs ) were scanned with a frame interval of 146 ms over different regions throughout the dorsal motor column . Trains of 3 stimulii at 30 Hz were delivered to the VR ( L4 or L5 ) while images were being acquired from at least 1 s before the onset of the first stimulus pulse . For each field , calcium signals were acquired for a total of 35 frames corresponding to approximately 5 s , and the position of each field was recorded . Posthoc analysis was performed to quantify Mn responses . Within each field , single Mns were identified by their fluorescence , and regions of interests were defined by the contour profiles of their somata . The time course of excitation was measured using the change in mean fluoresence following stimulation divided by the baseline average . In some cases , slow drifts in fluorescence was corrected by fitting an exponential to the initial trace before the stimulus . Changes in fluorescence exceeding 2 standard deviations of the baseline noise were measured over a 1 s window following VR stimulation .
|
Motoneurones ( Mns ) are the last elements of the networks that command , coordinate , and actuate the most essential of behaviours: movement . Their activation triggers muscle contractions , and the many diseases affecting Mns cause progressive and fatal paralysis . We show here that Mns themselves form an interconnected network and that activity in one segment of the spinal cord can propagate reciprocally to neighbouring segments , thus constituting a positive feedback loop that can amplify the strength of motor output . Remarkably , while Mns excite muscles through release of the neurotransmitter acetylcholine , our data show that the synapses between Mns operate through glutamate , providing a rare example of differentiation of transmitter systems according to the postsynaptic targets .
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2018
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Recurrent excitation between motoneurones propagates across segments and is purely glutamatergic
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Despite that over 90 million pregnancies are at risk of Plasmodium vivax infection annually , little is known about the epidemiology and impact of the infection in pregnancy . We undertook a health facility-based prospective observational study in pregnant women from Guatemala ( GT ) , Colombia ( CO ) , Brazil ( BR ) , India ( IN ) and Papua New Guinea PNG ) . Malaria and anemia were determined during pregnancy and fetal outcomes assessed at delivery . A total of 9388 women were enrolled at antennal care ( ANC ) , of whom 53% ( 4957 ) were followed until delivery . Prevalence of P . vivax monoinfection in maternal blood at delivery was 0 . 4% ( 20/4461 ) by microscopy [GT 0 . 1% , CO 0 . 5% , BR 0 . 1% , IN 0 . 2% , PNG 1 . 2%] and 7% ( 104/1488 ) by PCR . P . falciparum monoinfection was found in 0 . 5% ( 22/4463 ) of women by microscopy [GT 0% , CO 0 . 5% , BR 0% , IN 0% , PNG 2%] . P . vivax infection was observed in 0 . 4% ( 14/3725 ) of placentas examined by microscopy and in 3 . 7% ( 19/508 ) by PCR . P . vivax in newborn blood was detected in 0 . 02% ( 1/4302 ) of samples examined by microscopy [in cord blood; 0 . 05% ( 2/4040 ) by microscopy , and 2 . 6% ( 13/497 ) by PCR] . Clinical P . vivax infection was associated with increased risk of maternal anemia ( Odds Ratio-OR , 5 . 48 , [95% CI 1 . 83–16 . 41]; p = 0 . 009 ) , while submicroscopic vivax infection was not associated with increased risk of moderate-severe anemia ( Hb<8g/dL ) ( OR , 1 . 16 , [95% CI 0 . 52–2 . 59]; p = 0 . 717 ) , or low birth weight ( <2500g ) ( OR , 0 . 52 , [95% CI , 0 . 23–1 . 16]; p = 0 . 110 ) . In this multicenter study , the prevalence of P . vivax infection in pregnancy by microscopy was overall low across all endemic study sites; however , molecular methods revealed a significant number of submicroscopic infections . Clinical vivax infection in pregnancy was associated with maternal anemia , which may be deleterious for infant’s health . These results may help to guide maternal health programs in settings where vivax malaria is endemic; they also highlight the need of addressing a vulnerable population such as pregnant women while embracing malaria elimination in endemic countries .
More than a third of the world′s population is at risk of Plasmodium vivax infection , 90% of whom live in the Asia and the Pacific regions , while the rest live in the American ( 6% ) and African ( 3% ) regions . [1–3] In comparison with P . falciparum , P . vivax infection has received little attention mostly because it has been considered a relatively benign disease . [3 , 4] However , in recent years due to the increasing evidence of the severity of P . vivax infection and the recognition of its economic impact in endemic countries , a greater interest has been devoted to this neglected and complex species . [3 , 5] This renewed interest is also enhanced by the recognition that with intensified control efforts P . vivax has become the predominant malaria species and the major challenge to malaria elimination outside Africa . It is well recognized that pregnant women have an increased risk of P . falciparum infection and disease , and its effects on maternal and infant health have been well documented in sub-Saharan Africa . [6] Less is known about the burden of P . vivax in pregnancy and its impact on maternal and infant health . In 2007 , it was estimated that the number of pregnancies at risk of P . vivax was at least 92 million globally . [7] Some studies on the effect of P . vivax infection in pregnancy have suggested that the infection is associated with maternal anemia , miscarriage , congenital malaria and preterm delivery , as well as with severe disease . [8–17] These studies were mostly done in Asia , and with some exceptions , [12 , 18–20] were based on limited numbers or case reports , thus providing an incomplete picture of the overall burden and impact of P . vivax in pregnancy . In particular , there were limited data from Latin America . A comprehensive description of the burden and impact of P . vivax infection in pregnancy across different malaria transmission settings is needed to guide control strategies . We present here the results of a prospective observational study in pregnant women exposed to vivax malaria that aimed to determine the burden of P . vivax malaria and its impact on pregnancy outcomes . The study followed a multicenter approach including different epidemiological settings broadly representing of P . vivax endemic areas; one Asian country , one country from the Pacific region and three countries from the Americas . The objectives of the study were to estimate the prevalence of P . vivax infection at different time points overall and for each of the sites , and the incidence of P . vivax malaria during pregnancy . It also aimed to assess the clinical impact of P . vivax infection during pregnancy on maternal health and fetal outcomes .
The study protocol was reviewed and approved by the national and/or local ethics review boards in each of the study sites , the Institutional Review Board ( IRB ) of the Centers for Disease Control and Prevention ( CDC ) , and the Hospital Clinic of Barcelona Ethics Review Committee . The study was conducted in accordance with the Good Clinical Practice Guidelines , the Declaration of Helsinki , and local rules and regulations of each partner country . This was a health facility-based prospective observational study of pregnant women attending routine antenatal care ( ANC ) clinics undertaken between June 2008 and October 2011 in five P . vivax endemic countries—Colombia ( CO ) , Guatemala ( GT ) , Brazil ( BR ) , India ( IN ) and Papua New Guinea ( PNG ) –of different malaria endemicity characteristics ( Table 1 ) . The historic levels of malaria transmission varied across study sites from hypoendemic in Guatemala , India and Colombia , mesoendemic in Brazil , to hyperendemic in PNG . P . vivax was the predominant species in all sites except for PNG where P . vivax co-existed with P . falciparum ( 50% ) and other species ( P . malariae and P . ovale , 5% ) . Unselected pregnant women of any age , gestational age , and parity , attending the ANC clinic at each study site , independently of parasitological or disease status , were invited to participate , and after signing a written informed consent were recruited into the study . There were four study visits: recruitment visit , coinciding with an ANC visit; two subsequent scheduled ANC visits one month apart; and at delivery . Project personnel were trained on study procedures that were standardized across all study sites . At each study visit and regardless of the presence of symptoms suggestive of malaria , a capillary blood sample was collected to prepare two thick and thin blood smears and two filter papers ( Whatmann 3MM ) to determine Plasmodium parasitemia . From the same samples the hemoglobin ( Hb ) concentration was measured at enrolment , at scheduled ANC study visits , at delivery and at any other time the woman was suspected to have malaria . In addition , demographic , obstetric and clinical information were recorded on standardized questionnaires . Pregnant women were encouraged to deliver at the study health facility . In case of home delivery , they were advised to come to the study health facility within the first week of giving birth . A passive surveillance system to identify study women presenting with clinical malaria was set up at each study health facility . If the women reported any signs and/or symptoms suggestive of clinical malaria , a capillary blood sample was collected to prepare two thick and thin blood smears and two filter papers to determine Plasmodium parasitemia , and for determination of Hb concentration . At delivery , the pregnancy outcome and the clinical information on the mother and the neonate were collected . Placental blood from all women delivering at the health facility was collected onto filter paper for PCR molecular analysis , and two impression smears were stained with Giemsa and read following standard procedures for parasitemia determination . [21] For the preparation of the impression smears a 2 . 5x2 . 5 cm3 sample from the placenta , that should include the full thickness of the tissue from the maternal to the fetal side , was cut and put in contact with the slide after being dried with a piece of filter paper . In a randomly selected subsample of women ( 10% ) a placental biopsy was collected , following a similar procedure ( 2 . 5x2 . 5 cm3 sample ) to that for the preparation of the impression smear . The biopsy was kept at 4°C in 50 mL of 10% neutral buffer formalin , processed for histological examination , and stained with haematoxylin and eosin as previously described . [22] The histological examination of placental biopsies for malaria infection was performed by local pathologists trained for the purposes of the study ( S1 File ) . Cord blood samples were collected after birth for parasitemia determination . As soon as the umbilical cord was clamped , cut , and separated from the newborn study trained staff collected 5mL cord blood sample taken from the cord with a syringe and needle . Cord blood was used for preparation of two thick and thin blood smears to determine Plasmodium parasitemia , and two filter papers to perform PCR molecular analysis . Newborn samples were collected by heel prick after medical assessment was complete and within the first 12 hours of life also for parasitemia determination . In GT , BR and PNG gestational age was assessed by the Ballard’s method for deliveries that occurred at the health facility within the first 72 hours after birth [23] . In CO and IN gestational age was assessed by ultrasound assessment performed at enrolment . All newborns were weighed on a digital scale , accurate to the nearest gram , within the first 2 hours of life and examined for any clinical abnormalities . For deliveries occurred at home , the study personnel obtained information on the pregnancy outcome through home visits . All women and newborns with malaria infection or anemia were treated according to the national guidelines in each country . Strategies for malaria control in pregnancy differed across study sites . While these relied on active detection of infection with microscopy at each ANC visit in some countries , in other countries passive detection of cases , or weekly prophylaxis with chloroquine ( CQ ) until delivery were carried out ( Table 1 ) . At enrolment a subsample of 1500 women ( 300 per study site ) were randomly selected for the determination of the prevalence of P . vivax and P . falciparum infection by PCR methods . Likewise , a subsample of 1500 women ( 300 per study site ) was randomly selected at delivery for the same purpose . The 1500 women from whom samples were obtained at delivery were different from those selected at enrolment . A total of 500 placental samples ( 100 per site ) and 500 cord blood samples ( 100 per site ) were also analysed following same methodology . A simple random sampling method was used for the selection of women in each country , and for each time point and compartment . The sample size for prevalence by PCR was agreed among study investigators according to preliminary results of the first 100 selected samples in each site that showed a prevalence of P . vivax monoinfection of 7 . 5% by PCR , and on the availability of resources for molecular analyses in the study . PCR assays were not performed on newborn blood samples . To estimate the impact of submicroscopic P . vivax and P . falciparum infections on maternal anemia and low birth weight ( LBW ) , a nested case-control study was conducted . All countries contributed to this pooled analysis . A definition for moderate-severe anemia as Hb less than 8g/dL was agreed for the purpose of the analysis . All moderate-severe anemia cases ( n = 342 ) , and LBW cases ( <2500g ) ( n = 327 ) existing across countries for which a blood smear and a filter paper were available , were included in the case control analysis . A total of 414 controls to anemia cases , and a total of 410 controls to LBW cases , were randomly selected . Logistic regression models used to evaluate the impact of P . vivax submicroscopic infections on anemia and LBW were adjusted by site and P . falciparum infections . Similarly , for the evaluation of the impact of P . falciparum submicrocopic infections , models were adjusted by site and P . vivax infections . Giemsa-stained thick and thin blood slides were read onsite in all countries following WHO standard quality-controlled procedures to establish parasite presence and density of Plasmodium asexual stages . [24] Two independent expert malaria microscopists read all slides and results were registered in two separate forms . Discrepant results ( positive vs . negative ) were resolved by a third reading done by a different microscopist . A blood slide was declared as negative only when no parasites were found after reading 200 fields . Results were expressed in parasites/μL after counting the number of parasites per 500 white blood cells or reaching 500 parasites; counting was normalized using estimated leukocyte counts of 8000/μL . External validation of a blood slides subsample ( 100 slides per country ) was done at the Hospital Clinic and at the Hospital Sant Joan de Deu , in Barcelona , Spain . Hb was measured by Coulter Counter ( except in PNG where it was done by Hemocue , HemoCue , Ltd , Angelhom , Sweden; accuracy of 0 . 1 g/dL ) using 50–100 μL collected in a 0 . 5mL EDTA tube ( microtainer ) . Molecular detection of Plasmodium species in samples from CO , GT , BR and PNG was performed by Real Time PCR at the Istituto Superiore di Sanità ( ISS ) in Rome , Italy . Samples from India were analysed , due to local regulatory requirements , at the International Center for Genetic Engineering and Biotechnology ( ICGEB ) , in New Delhi using the same protocol as that at ISS but adapted for the sake of instrument sensitivity ( 3rd step at amplification was 72°C for 25 sec instead of 72°C for 5 sec ) . DNA was extracted from whole blood-spot filter paper from maternal peripheral blood collected at ANC , at delivery and during passive case detection , and from placental and cord blood using Purelink Genomic DNA Kit ( Invitrogen ) . P . vivax and P . falciparum infections were detected with a LightCycler 480 system ( Roche ) . Species-specific primers and Taqman probes were selected from the sequence of the small 18S rRNA subunit as previously described by Veron et al 2009 . [25] Briefly , pre-incubation was at 95°C for 10 min; amplification at 95°C for 10 sec , 50°C for 20 sec and 72°C for 5 sec for 50 cycles . All reactions were in duplicate in a final volume of 20 μL . The sensitivity of the PCR assay performed at the ISS for detection of P . falciparum infections was between 10–100 times higher in comparison to the microscopy . An external validation of the PCR methods used by the ISS and the ICGEB , for detection of P . vivax and P . falciparum species , was performed by the Malaria in Pregnancy Consortium ( MiPc ) ( http://www . mip-consortium . org ) in a subset of 20 samples . Internal validation between ISS and ICGEB was also done . A standardised system for data entry , data management , and statistical analysis was established . All clinical and laboratory data were collected using standardised questionnaires . The data collection and management was performed using the OpenClinica open source software , version 2 . 0 . Copyright OpenClinica LLC and collaborators , Waltham , MA , USA , www . OpenClinica . com . All data were double entered by two independent data clerks at each of the sites . There was a specific URL link to access the data entry software . In PNG data were doubled-entered into form-specific databases ( FoxPro 9·0 , Microsoft , USA ) . Validation and cleaning were done using the same software , and statistical analysis was performed using Stata 13 ( Stata Corporation , College Station , TX , USA ) . Differences between proportions were compared using the Pearson′s chi-squared test or Fisher's exact test depending on type of variables . For continuous variables , Student’s T-tests were used to compare the groups . Incidence rates were calculated as the number of new episodes/person-year at risk using the Poisson distribution in the exposed and unexposed groups , with primigravid women as the comparator group . The impact of P . vivax infection on maternal and newborn health was determined through a multicenter-pooled analysis . The case control study for submicroscopic infections with and anemia was analyzed using logistic regression models . We adjusted all regression models for possible operational confounding variables such as country and previous malaria episodes . In the analyses of P . falciparum infections , they were included in the models as being free of P . vivax ( see definitions section ) . Likewise , in the analyses of P . vivax infections , they were included as being free of P . falciparum . Multivariate analyses were performed by a forward-stepwise procedure , using p<0 . 05 and p>0 . 10 from the likelihood ratio test , as enter and remove criteria respectively . Results from the estimated models were expressed as OR and 95% CI . Missing values were coded as such and excluded from analysis . P . vivax microscopic monoinfection was defined as the presence of asexual P . vivax parasites of any density and absence of other Plasmodium species on the blood smear . P . vivax clinical malaria episode was defined as the latter plus any signs or symptoms suggestive of malaria ( axillary temperature ≥37 . 5°C or history of fever in the last 24 hours , headache , arthromyalgias , and/or pallor ) . P . vivax submicroscopic infection was defined as a PCR that was positive for P . vivax and negative for P . falciparum , with a concomitant blood film negative by microscopy . P . falciparum microscopic monoinfection was defined as the presence of asexual P . falciparum of any density and absence of other Plasmodium species on the blood smear . P . falciparum clinical malaria episode was defined as the latter plus any signs or symptoms suggestive of malaria . P . falciparum submicroscopic infection was defined as a PCR that was positive for P . falciparum and negative for P . vivax , with a concomitant blood film negative by microscopy . The duration of any single malaria episode was estimated as 28 days . Congenital malaria was defined as presence of asexual Plasmodium parasites of any species in the cord blood or in the newborn′s peripheral blood at delivery , regardless of clinical symptoms or signs in the neonate . Placental infection was classified according to a previously established definition . [21] Briefly , acute infection was defined as the presence of parasites , with absent or minimal pigment deposition within fibrin or cells within fibrin , chronic infection as the presence of parasites and a significant amount of pigment deposition , and past infection as the presence of pigment with absence of parasites . Prematurity was defined as gestational age < 37 weeks . LBW was defined as birth weight <2500g .
A total of 9388 pregnant women were recruited across all sites ( 2043 in CO; 2009 in GT; 1657 in BR; 1982 in IN; 1697 in PNG ) , and 4957 ( 53% ) of them were followed up until delivery ( Fig 1 ) . Table 2 the baseline characteristics of the study participants by site ( S1 Table ) . Mean age of study women was 23 . 6 years [standard deviation ( SD ) = 5 . 6] , mean gestational age 23 weeks ( SD = 8 ) ; nearly 40% ( 3759/9468 ) of the women were primigravidae . At enrolment , overall mean Hb level was 10 . 5 g/dL ( SD 1 . 8 ) , and 57% ( 5201/9105 ) of women were anemic ( Hb<11g/dL ) . Around 70% of women ( 6471 ) were enrolled at their first ANC visit versus at later ANC visit . The overall prevalence of P . vivax mono-infection detected by microscopy was 0 . 8% ( 73/9299 ) at enrolment and 0 . 4% ( 20/4461 ) at delivery . Of those women who were parasitemic for P . vivax 57 . 6% ( 42/73 ) were symptomatic at enrolment and 20% ( 4/20 ) at delivery . The prevalence of P . vivax mono-infection by PCR assessed in a subsample of women was 8 . 5% ( 124/1455 ) at enrolment and 6 . 9% ( 104/1488 ) at delivery , and of those 9 . 7% ( 12/124 ) and 4 . 8% ( 5/104 ) reported symptoms , respectively ( Fig 2 ) . The overall prevalence of P . falciparum mono-infection detected by microscopy was 1 . 3% ( 124/9305 ) at enrolment and 0 . 5% ( 22/4463 ) at delivery . Of those women who were parasitemic for P . falciparum 16 . 1% ( 20/124 ) were symptomatic at enrolment and 27 . 3% ( 6/22 ) at delivery . No P . falciparum infection was detected by microscopy in GT and BR neither at enrolment or delivery , nor in India at delivery . The prevalence of P . falciparum mono-infection by PCR assessed in a subsample of women was 6 . 9% ( 80/1157 ) at enrolment and 2% ( 24/1191 ) at delivery , and of those 10% ( 8/80 ) and 12 . 5% ( 3/24 ) reported symptoms , respectively ( Fig 3 ) . There were no mixed infections ( P . vivax plus P . falciparum ) detected by microscopy during pregnancy or at delivery in CO , GT , BR and IN . In PNG mixed infections were observed in 9/818 ( 1 . 1% ) of placental impression smears only . The prevalence of mixed infections detected by PCR was 2 . 2% ( 26/1165 ) , 0 . 3% ( 4/1191 ) , 0 . 3% ( 1/379 ) at enrolment , delivery , and in cord blood , respectively . No mixed infections were detected in the placental blood . Data on malaria infection in the placenta are shown in Table 3 . Data on congenital malaria can be found in Table 4 . Data on incidence of malaria is shown in Table 5 . The incidence rate of P . vivax infection detected by microscopy was not associated with parity ( Incidence Rate Ratio-IRR for multigravidae-women with 4 or more pregnancies- , 1 . 14 [95% CI 0 . 70–1 . 87]; p = 0 . 619 ) . For P . falciparum the IRR for multigravidae was 0 . 39 , [95% CI 0 . 25–0 . 62]; p<0 . 001 ) . A total of 898 passive case detection visits ( 528 in PNG , 183 in CO , 75 in BR , 62 in GT and 50 in IN ) were registered among study women who reported symptoms or had signs suggestive of malaria infection . Of those , 823 women had a blood smear collected , and in 141 ( 17% ) of them malaria infection was confirmed by microscopy; 68 cases were due to P . vivax , 72 cases were due to P . falciparum , and one case was due to P . malariae . In 85% ( 117/137 ) malaria episodes women were found to be anemic ( Hb<11g/dL ) and 14% ( 19/137 ) severely anemic ( Hb<7g/dL ) . Only 8 women with clinical malaria ( 2 with P . vivax and 6 with P . falciparum infection , and all from CO ) required hospital admission ( data from admitted women in PNG not available ) . On average , 2% ( 86/4227 ) of study women reported at delivery a history of having had a malaria episode during the current pregnancy , and 2 . 3% ( 90/3830 ) having taken antimalarial drugs during the same pregnancy . See S2 Table for incidence data disaggregated by PCD or ADI , overall and by country . Overall , parasite densities in pregnant women throughout the study were low in most cases . The geometric mean density ( GMD ) for P . vivax infections on maternal peripheral blood at any time during the study was 1275 parasites/μL ( 95% CI 906 . 5–1643 . 5 ) ; being 385 parasites/μL ( 95% CI 226 . 9–543 . 1 ) and 2396 parasites/μL ( 95% CI 1579 . 5–3212 . 5 ) the GMDs for P . vivax asymptomatic and symptomatic infections , respectively . The GMD for P . falciparum infections on maternal peripheral blood was 1678 parasites/μL ( 95% CI 1268 . 6–2087 . 4 ) ; being 918 parasites/μL ( 95% CI 681 . 7–1154 . 3 ) and 4087 parasites/μL ( 95% CI 2362 . 1–5811 . 8 ) the GMDs for P . falciparum asymptomatic and symptomatic infections , respectively . Parasite densities on placental blood ranged for P . vivax from 0 . 6 parasites/μL in the positive case detected in Brazil to 25 . 5 parasites/μL arithmetic mean ( SD 31 . 8 ) in PNG . For P . falciparum densities in the placenta ranged from 21 . 4 parasites/μL arithmetic mean ( SD 25 . 5 ) in CO up to 6255 parasites/μL arithmetic mean ( SD 7845 ) in placentas from PNG . Overall , 10 . 2% ( 505/4943 ) of babies born to women enrolled in the study had LBW ( range 2 . 8% in CO– 16 . 5% in PNG ) , and the prevalence of pre-term birth was 12 . 6% ( 455/3590 ) across countries ( Table 6 ) . At delivery , on average 53 . 4% ( 2285/4278 ) of study women were anemic ( range 17 . 8% in BR– 88 . 3% in IN ) , and 4 . 5% of them ( 193/4278 ) were severely anemic . Most of the documented deliveries ( 96% ) took place at a health facility ( hospital/health center ) attended by skilled staff . During the study follow up there were three maternal deaths and their clinical diagnoses at the time of death were recorded; one in CO due to P . falciparum malaria , one in PNG due to post-partum hemorrhage with no evidence of malaria at any time during pregnancy , and one in IN associated with P . vivax malaria and anemia . At enrolment women with P . vivax or P . falciparum clinical malaria were more than 5-times as likely to be anemic compared to non-infected women ( adjusted OR , 5 . 48 , [95% CI 1 . 82–16 . 41]; p = 0 . 009 ) , and adjusted OR , 5 . 57 , [95% CI 1 . 22–25 . 34]; p = 0 . 012 , respectively ) ( Table 7 ) . In contrast , those women with clinical malaria at delivery , whether infected with P . vivax ( p = 0 . 754 ) or P . falciparum ( p = 0 . 122 ) , did not show an increased risk of anemia at the same time point compared to non-infected or infected-asymptomatic women . No association was found between P . vivax or P . falciparum clinical malaria at enrolment ( p = 0 . 100 , and p = 0 . 760 , respectively ) or at delivery ( p = 0 . 120 , and p = 0 . 060 , respectively ) and an increased risk of LBW . Women with P . vivax malaria at delivery detected by microscopy did not have an increased risk of anemia ( adjusted OR , 0 . 62 , [95% CI 0 . 22–1 . 81]; p = 0 . 384 ) or LBW ( adjusted OR , 0 . 90 , [95% CI 0 . 20–4 . 10]; p = 0 . 895 ) , compared to uninfected women . However , those women with P . falciparum malaria showed an increased risk for both anemia ( adjusted OR , 4 . 07 , [95% CI 1 . 93–8 . 58]; p = 0 . 0002 ) and LBW ( adjusted OR , 4 . 28 , [95% CI 1 . 75–10 . 44]; p = 0 . 0014 ) . In the nested case-control sub-analysis , women with P . vivax submicroscopic infection at delivery did not show an increased risk of moderate-severe anemia ( adjusted OR , 1 . 16 , [95% CI 0 . 52–2 . 59]; p = 0 . 717 ) or LBW ( adjusted OR , 0 . 52 , [95% CI 0 . 23–1 . 16]; p = 0 . 110 ) compared to uninfected women ( negative by microscopy and by PCR ) . Likewise , P . falciparum submicroscopic infections at delivery were not associated with moderate-severe anemia ( adjusted OR , 2 . 93 , [95% CI 0 . 24–36 . 23]; p = 0 . 402 ) or LBW ( adjusted OR , 1 . 97 , [95% CI 0 . 46–8 . 46]; p = 0 . 359 ) . Malaria infection in the placenta ( whether detected by impression smear or PCR ) was not associated with an increased risk of LBW ( adjusted OR , 0 . 67 , [95% CI 0 . 07–6 . 18]; p = 0 . 725 for P . vivax , and adjusted OR , 8 . 61 , [95% CI 0 . 79–94 . 09]; p = 0 . 077 , for P . falciparum ) or anemia ( adjusted OR , 2 . 04 , [95% CI 0 . 70–5 . 92]; p = 0 . 189 for P . vivax , and adjusted OR , 0 . 77 , [95% CI 0 . 16–3 . 67]; p = 0 . 746 , for P . falciparum ) . In contrast , a statistically significant association was found for placental infections detected by histology with LBW ( adjusted OR , 2 . 36 , [95% CI 1 . 48–3 . 76]; p = <0 . 001 ) and anemia ( adjusted OR , 1 . 66 , [95% CI 1 . 09–2 . 51]; p = 0 . 017 ) . Histology did not allow distinguishing between species , but of the infected placentas according to histology a higher proportion were positive for P . falciparum ( 11 . 4% ) compared to P . vivax ( 4 . 8% ) by impression smear or PCR .
This multicenter study in representative areas endemic for vivax malaria shows that the prevalence of P . vivax malaria was overall low across study sites . However , the prevalence of submicroscopic infections was significant in some areas , which may have implications for detection of malaria infections with the currently used diagnostic tools . Clinical episodes due to P . vivax infection were associated with maternal anemia , which may be also detrimental for infant’s health . These findings can be relevant for guiding maternal health programs in settings where vivax malaria is endemic , as well as for monitoring , evaluation and surveillance activities in countries that are in the pathway towards malaria elimination .
|
More than 90 million pregnancies are exposed to P . vivax infection every year . While it is well known that pregnant women have an increased risk of P . falciparum infection and disease , much less is known on the epidemiology and the impact of P . vivax in pregnancy . A health-facility based observational study was conducted in pregnant women living in five vivax endemic countries aimed to determine the burden of the infection in pregnancy and its impact on the mother and the newborn health . We found that the prevalence of P . vivax malaria in pregnant women attending the routine antenatal clinic visits was overall low across all sites , however submicroscopic infections were unexpectedly high in some areas . Pregnant women with clinical malaria experienced an increased risk of anemia , which may have a deleterious impact on infant health . These findings may be useful for guiding maternal health programs in vivax endemic settings , as well as for malaria elimination activities .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"maternal",
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"plasmodium",
"obstetrics",
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"gynecology",
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] |
2017
|
Burden and impact of Plasmodium vivax in pregnancy: A multi-centre prospective observational study
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The creation of protein from DNA is a dynamic process consisting of numerous reactions , such as transcription , translation and protein folding . Each of these reactions is further comprised of hundreds or thousands of sub-steps that must be completed before a protein is fully mature . Consequently , the time it takes to create a single protein depends on the number of steps in the reaction chain and the nature of each step . One way to account for these reactions in models of gene regulatory networks is to incorporate dynamical delay . However , the stochastic nature of the reactions necessary to produce protein leads to a waiting time that is randomly distributed . Here , we use queueing theory to examine the effects of such distributed delay on the propagation of information through transcriptionally regulated genetic networks . In an analytically tractable model we find that increasing the randomness in protein production delay can increase signaling speed in transcriptional networks . The effect is confirmed in stochastic simulations , and we demonstrate its impact in several common transcriptional motifs . In particular , we show that in feedforward loops signaling time and magnitude are significantly affected by distributed delay . In addition , delay has previously been shown to cause stable oscillations in circuits with negative feedback . We show that the period and the amplitude of the oscillations monotonically decrease as the variability of the delay time increases .
Gene regulation forms a basis for cellular decision-making processes and transcriptional signaling is one way in which cells can modulate gene expression patterns [1] . The intricate networks of transcription factors and their targets are of intense interest to theorists because it is hoped that topological similarities between networks will reveal functional parallels [2] . Models of gene regulatory networks have taken many forms , ranging from simplified Boolean networks [3] , [4] , to full-scale , stochastic descriptions simulated using Gillespie's algorithm [5] . The majority of models , however , are systems of nonlinear ordinary differential equations ( ODEs ) . Yet , because of the complexity of protein production , ODE models of transcriptional networks are at best heuristic reductions of the true system , and often fail to capture many aspects of network dynamics . Many ignored reactions , like oligomerization of transcription factors or enzyme-substrate binding , occur at much faster timescales than reactions such as transcription and degradation of proteins . Reduced models are frequently obtained by eliminating these fast reactions [6]–[9] . Unfortunately , even when such reductions are done correctly , problems might still exist . For instance , if within the reaction network there exists a linear ( or approximately linear ) sequence of reactions , the resulting dynamics can appear to be delayed . This type of behavior has long been known to exist in gene regulatory networks [10] . Delay differential equations ( DDEs ) have been used as an alternative to ODE models to address this problem . In protein production , one can think of delay as resulting from the sequential assembly of first mRNA and then protein [10]–[12] . Delay can qualitatively alter the local stability of genetic regulatory network models [13] as well as their dynamics , especially in those containing feedback . For instance , delay can lead to oscillations in models of transcriptional negative feedback [11] , [14]–[18] , and experimental evidence suggests that robust oscillations in simple synthetic networks are due to transcriptional delay [19] , [20] . Protein production delay times are difficult to measure in live cells , though recent work has shown that the time it takes for transcription to occur in yeast can be on the order of minutes and is highly variable [21] . Still , transcriptional delay is thought to be important in a host of naturally occurring gene networks . For instance , mathematical models suggest that circadian oscillations are governed by delayed negative feedback systems [22] , [23] , and this was experimentally shown to be true in mammalian cells [24] . Delay appears to play a role in cell cycle control [25] , [26] , apoptosis induction by the p53 network [27] , and the response of the network [15] . Delay can also affect the stochastic nature of gene expression , and the relation between the two can be subtle and complex [28]–[31] . In this study , we examine the consequences of randomly distributed delay on simple gene regulatory networks: We assume that the delay time for protein production , , is not constant but instead a random variable . If denotes the probability density function ( PDF ) of , this situation can be described deterministically by an integro-delay differential equation [32] of the form ( 1 ) where is a positive definite state vector of protein concentrations , and is a vector function representing the production and degradation rates of the proteins . Note that processes that do not require protein synthesis ( like dilution and degradation ) will depend on the instantaneous , rather than the delayed , state of the system . Therefore is in general a function of both the present and past state of the system . Equation ( 1 ) only holds in the limit of large protein numbers [32] . As protein numbers approach zero , the stochasticity associated with chemical interactions becomes non-negligible . Here , we address this issue by expanding on Eq . ( 1 ) using an exact stochastic algorithm that takes into account variability within the delay time [32] . We further use a queueing theory approach to examine how this variability affects timing in signaling cascades . We find that when the mean of the delay time is fixed , increased delay variability accelerates downstream signaling . Noise can thus increase signaling speed in gene networks . In addition , we find that in simple transcriptional networks containing feed-forward or feedback loops the variability in the delay time nontrivially affects network dynamics . Queueing theory has recently been used to understand the behavior of genetic networks [33]–[35] . Here we are mainly interested in dynamical phenomena to which the theory of queues in equilibrium used in previous studies cannot be applied . As we explain below , gene networks can be modeled as thresholded queueing systems: Proteins exiting one queue do not enter another queue , as would be the case in typical queueing networks . Rather , they modulate the rate at which transcription is initiated , and thus affect the rate at which proteins enter other queues .
The transcription of genetic material into mRNA and its subsequent translation into protein involves potentially hundreds or thousands of biochemical reactions . Hence , detailed models of these processes are prohibitively complex . When simulating genetic circuits it is frequently assumed that gene expression instantaneously results in fully formed proteins . However , each step in the chain of reactions leading from transcription initiation to a folded protein takes time ( Figure 1 ) . Models that do not incorporate the resulting delay may not accurately capture the dynamical behavior of genetic circuits [17] . While earlier models have included either fixed or distributed delay [32] , [36] , [37] , here we examine specifically the effects of delay variability on transcriptional signaling . In one recent study , Bel et al . studied completion time distributions associated with Markov chains modeling linear chemical reaction pathways [38] . Using rigorous analysis and numerical simulations they show that , if the number of reactions is large , completion time distributions for an idealized class of models exhibit a sharp transition in the coefficient of variation ( CV , defined as the standard deviation divided by the mean of the distribution ) , going from near ( indicating a nearly deterministic completion time ) to near ( indicating an exponentially distributed completion time ) as system bias moves from forward to reverse . However , it is possible , and perhaps likely , that the limiting distributions described by Bel et al . do not provide good approximations for protein production . For instance , when the number of rate limiting reactions is small , but greater than one , the distribution of delay times can be more complex . Moreover , linear reaction pathways only represent one possible and necessarily simplified reaction scheme . Protein production involves many reaction types that are nonlinear and/or reversible , each of which is influenced by intrinsic and extrinsic noise [39] , and these reactions may impact the delay time distribution in complicated ways . Therefore , we do not try to derive the actual shape of , but examine the effects its statistical properties have on transcriptional signaling . To do this , we represent protein production as a delayed reaction of the form ( 2 ) where is the gene , and transcription is initiated at rate , which can depend explicitly on both time and protein number , . After initiation , it takes a random time , , for a protein to be formed . Note that the presence of time delay implies that scheme ( 2 ) defines a non-Markovian process . Such processes can be simulated exactly using an extension of the Gillespie algorithm ( See Methods and [28] , [32] ) . If the biochemical reaction pathway that leads to functional protein is known and relatively simple , direct stochastic simulation of every step in the network is preferable to simulation based on scheme ( 2 ) . From the point of view of multi-scale modeling , however , paradigm ( 2 ) is useful when the biochemical reaction network is either extremely complex or poorly mapped , since one needs to know only the statistical properties of . In the setting of scheme ( 2 ) , first assume that does not depend on , and protein formation is initiated according to a memoryless process with rate . A fully formed protein enters the population a random time after the initiation of protein formation . We assume that the molecules do not interact while forming; that is , the formation of one protein does not affect that of another . Each protein therefore emerges from an independent reaction channel after a random time . This process is equivalent to an queue [40] , where indicates a memoryless source ( transcription initiation ) , a general service time distribution ( delay time distribution ) , and refers to the number of service channels . In our model , the order in which initiation events enter a queue is not necessarily preserved . As Figure 1 ( B ) illustrates , it is possible for the initiation order to be permuted upon exit [32] . The assumption that proteins can “skip ahead” complicates the analysis of transient dynamics of such queues , and is essential in much of the following . While there are steps where such skipping can occur ( such as protein folding ) , there are others for which it cannot . For instance , it is unlikely that one RNA polymerase can skip ahead of another – and similarly for ribosomes during translation off of the same transcript . Therefore , protein skipping may be more relevant in eukaryotes , where transcription and translation must occur separately , than prokaryotes , where they may occur simultaneously . However , if there is more than one copy of the gene ( which is common for plasmid-based synthetic gene networks in E . coli ) , or more than one transcript , some skipping is likely occur . Therefore it is likely that the full results that follow are more relevant for genes of copy number greater than one . One purpose of transcription factors is to propagate signals to downstream target genes . Determining the dynamics and stochasticity of these signaling cascades is of both theoretical and experimental interest [41] , [42] . Therefore , we first examine the impact that distributed delay has on simple downstream signaling . Consider the situation depicted in Figure 1 ( C ) , in which the product of the first gene regulates the transcription of a second gene . Using the same nomenclature as in scheme ( 2 ) we write ( 3a ) ( 3b ) where and are the copy numbers of the upstream and downstream genes , and are the number of functional proteins of each type , and is the random delay time of gene . The transcription rate of gene 2 depends on and is given by a Hill function . We consider the case in which activates ( depicted in Figure 1 ) and the case in which represses . We now ask: If starts at zero and gene 1 is suddenly turned on , how long does it take until the signal is detected by gene 2 ? In other words , assume , where is the Heaviside step function . At what time does reach a level that is detectable by gene 2 ? In order to make the problem tractable , we assume that the Hill function is steep and switch-like , so that we can make the approximation ( 4a ) ( 4b ) Here is the maximum transcriptional initiation rate of and is the threshold value of the Hill function , i . e . the number of molecules of needed for half repression ( or half activation ) of gene 2 . The second gene therefore becomes repressed ( or activated ) at the time at which copies of protein have been fully formed . We first examine reaction ( 3a ) . Assume that at time there are no proteins in the system . Let denote the number of transcription initiation events that have occurred by time ( the arrival process of the queueing system ) , the number of proteins being formed at time ( the size of the queue at time ) , and the number of functional proteins that have been completed by time ( the exit process of the queueing system ) . Since the arrival process is memoryless , is a Poisson process with constant rate for . Hence , the expected value of is . The exit process , i . e . the number of fully functional proteins that have emerged from the queue , , is a nonhomogenous Poisson process with time-dependent rate , where is the cumulative distribution function ( CDF ) of the delay time . It then follows that Inactivation ( or activation ) of gene 2 occurs when enough protein has accumulated to trigger a transcriptional change , according to Eq . ( 4a ) or ( 4b ) . In other words , the random time it takes for the signal to propagate , , is given by . Trivially , changes by an amount identical to a change in the mean of the delay distribution . To examine the effects of randomness in delay on the signaling time , we therefore keep the mean of the delay distribution fixed , , and vary . The probability density function of is given by ( See Methods ) ( 5 ) Consequently , the mean and variance of the time it takes for the original signal to propagate to the downstream gene can be written as: ( 6 ) ( 7 ) To gain insight into the behaviors of Eqs . ( 6 ) and ( 7 ) , we first examine a representative , analytically tractable example . Assume that the delay time can take on discrete values , and with equal probability . In this case , ( 8 ) where is the upper incomplete gamma function . Expanding for small , we obtain ( See Methods ) ( 9 ) which is the deterministic limit . The first term is the mean delay time and the second is the average time to initiate proteins at rate . A similar expansion for fixed and large gives ( see panel ( c ) in Figure 2 ) ( 10 ) It follows that for larger delay variability , the mean signaling time decreases with delay variability ( See Figure 2 ( A ) ) . Indeed , Eqs . ( 9 ) and ( 10 ) form the asymptotic boundaries for the mean signaling time . The intersection of the two asymptotes at , gives an estimate of when the behavior of the system changes from the deterministic limit ( for ) to a regime in which increasing the variability decreases the mean signaling time ( for ) . It follows that the deterministic approximation given by Eq . ( 9 ) is valid in an increasing range , as grows ( See Figure 2 ( C ) ) . Indeed , an asymptotic analysis of Eq . ( 8 ) shows that the corrections to Eq . ( 9 ) are approximately of size , and therefore rapidly decrease with ( See Methods ) . The bottom row of Figure 2 shows that these observations hold more generally: When is gamma distributed the mean time to produce proteins , , is very sensitive to randomness in delay time , but only when is small to intermediate . As expected , the densities of the times to produce proteins , , are approximately normal and independent of the delay distribution when is large ( Middle panels of Figure 2 ) . We therefore expect that for each fixed threshold , is a decreasing function of the standard deviation of the delay . We have proved this to be true for symmetric delay distributions ( See Methods ) . Intuitively , this is due to the fact that the order in which proteins enter the queue is not the same as the order in which they exit . Proteins that enter the queue before the protein , but exit after the protein increase , while the opposite is true for proteins that enter the queue after the protein , and exit before it . Since only finitely many proteins enter the queue before the protein , while infinitely many enter after it , the balance favors a decrease in the mean signaling time . Moreover , as delay variability increases , interchanges in exit order become more likely , and this effect becomes more pronounced . We outline the analytical argument: For each fixed time , is an increasing function of , hence is decreasing function of for all . Referring to Eq . ( 6 ) , this implies that is a decreasing function of in the symmetric case . In sum , mean signaling times decrease as delay variability increases ( with fixed mean delay ) . This effect is most significant for small to moderate thresholds . We note that the decrease in mean signaling time phenomenon depends on a sufficient number of proteins entering the queue . If transcription is only active long enough for less than proteins to be initiated , then mean signaling time will actually increase as delay variability increases . This phenomenon is explained in the subsection of the Methods section that analyzes repressor switches . Using the above results , we now examine more complicated transcriptional signaling networks . In particular , we turn to two common feedforward loops - the type 1 coherent and the type 1 incoherent feedforward loops ( FFL ) [43] , shown in Figure 3 . Each of these networks is a transcriptional cascade resulting in the specific response of the output , gene 3 . The coherent FFL generally acts as a delayed response network , while the incoherent FFL has various possible responses , such as pulsatile response [43] , response time acceleration [44] , and fold-change detection [45] . To examine the effect of distributed delay on these networks we assume that at gene starts transcription of protein at rate , i . e . The second gene , , starts transcription after reaches the threshold , so that . For the coherent FFL , we assume that the promoter of gene acts as an AND gate so that . We further assume that the promoter of in the incoherent FFL is active only in the presence of and absence of , so that we may write . The signaling time between any two nodes and within the network , i . e . the random time between the initiation of transcription of gene to the formation of a total of proteins is denoted . For each of the three pathways , the PDF of the signaling time is given by Eq . ( 5 ) . In addition , because the random times and are additive ( as are their variances ) , we can directly calculate the time at which reaches the threshold of gene as . Therefore , the random time at which the coherent FFL turns on is simply given by . Because and are decreasing functions of the delay variability , it can be expected that so is . In contrast to the coherent FFL , the dynamics of the pulse generating incoherent FFL are less trivial . Since the repressor ( ) overrides the activator ( ) , assuming transcription of turns on at time and turns off at time , generating a pulse of duration . Note that can increase or decrease as a function of the standard deviation of the delay ( see Figure 4 where was equal for all pathways ) . To see this , write as follows: ( 11 ) Each of the terms on the right side of Eq . ( 11 ) is the expected signaling time of a single gene ( , , and , respectively ) . Consequently , depends on as a linear combination of expected signaling time curves of the type pictured in Figure 2 . The shapes of these signaling time curves determine the behavior of as a function of . Figure 4 shows that the behavior of the duration of the transcriptional pulse as a function of the delay variability depends on the values of each threshold within the network . These observations can also be extended to networks with recurrent architectures . For instance , consider the transcriptional delayed negative feedback circuit [17] , which can be described using an extension of scheme ( 2 ) : ( 12a ) ( 12b ) where is a decreasing Hill function ( i . e . represses its own production ) and is the degradation rate due to dilution and proteolysis . Mather et al . examined the oscillations produced by systems of the type described by scheme ( 12 ) when the delay is nonrandom ( degrade and fire oscillators ) [17] . Starting with no proteins , is produced at a rate governed by the Hill function . When the level of exceeds the midpoint of the Hill function , gene effectively shuts down . The proteins remaining in the queue exit , producing a spike , after which degradation diminishes . When the protein level drops sufficiently , reaction ( 12a ) reactivates and production of resumes , commencing another oscillation cycle . Note that this circuit will not oscillate without delay . As a result during each oscillation the gene is turned on until its own signal reaches itself , at which time the gene is turned off [17] . Therefore , the peak height of one oscillation is determined by the length of time the gene was in the “ON” state . Since that time is determined by the gene's signaling time , our theory predicts that the mean peak height of the oscillations will decrease as the variability in the delay time increases . Indeed , this is exactly what our stochastic simulations show in Figure 5 . This is consistent with the fact that the negative feedback circuit is dynamically similar to the sub-circuit within the incoherent FFL . Here we explicitly used a gamma-distributed delay time with mean , and . We can use our theory to predict the change in the peak height of the oscillator as a function of . For a delay that is gamma-distributed , the change in signaling time as a function of can be written as ( 13 ) where is given by Eq . ( 6 ) and is the reduction in the expected signaling time . If we assume that the amount of time that protein is produced during a burst in the delayed negative feedback oscillator is also reduced by this amount , then it is possible to predict the change in the peak height accordingly . To a first approximation , if the promoter is in the “ON” state for a time that is less , then a total of less protein will be produced . Therefore we can write the expected peak height of the oscillator as ( 14 ) However , due to degradation , Eq . ( 14 ) overestimates the correction to the peak height . Due to exponential degradation , only a fraction of the lost protein would have made it through to the peak . Also , the duration of enzymatic decay is also reduced by a time . Therefore , if we assume that the enzymatic decay reaction is saturated , we need to add an amount to Eq . ( 14 ) . This gives us a more accurate prediction of the mean peak height as ( 15 ) Figure 5 shows that this approximation works well , even for a Hill coefficient as low as .
The existence of delay in the production of protein has been known of for some time . For many systems its presence does not seriously impact performance . For example , the existence of fixed points in simple downstream regulatory networks without feedback is unaffected by delay . Delay is important if the timing of signal propagation impacts the function of the network . Delay can also change a network's dynamics . In networks with feedback , for instance , delay can result in bifurcations that are not present in the corresponding non-delayed system . The delayed negative feedback oscillator is a prime example [17] . Moreover , while the effect of delay in a single reaction may be small , it is cumulative and linearly additive in directed lines . The intrinsic stochasticity of the reactions that create mature protein make some variation in delay time inevitable . However , we do not yet know the exact nature of this variability or the functional form of the probability density function . To further complicate matters , there may exist a substantial amount of extrinsic variability in the delay time – the statistics of the PDF may vary from cell to cell . We focused on the transient dynamics of queues in order to demonstrate the effects of distributed delay in a tractable setting . However , as mentioned earlier , queues may not always be a good model for protein production . For genes with low copy number or few available transcripts queues with service channels ( queues ) may provide a better description . For eukaryotic systems models in which transcription and translation are decoupled into separate queues may also be relevant . In addition , as protein production rates are often coupled with extrinsic factors such as growth rate and cell cycle phase , may depend on time and on the state of the system . The complexity of biochemical reaction networks suggests the use of networks of queues [46] , and sources could be toggled on and off by other components of a reaction network . Even protein production from a single transcript may be more accurately described by a sequence of queues with each codon as one in a chain of service stations . In such a model ribosomes move from one codon station to the next , and are not able to skip ahead . Such models will be considered in future studies . One further complication occurs if the burstiness of the promoter is large [47] . In the above analysis , we assumed that the initiation events of proteins were exponentially distributed in time . Since this is not necessarily the case due to the burstiness of promoters , some limits need to be put on the usefulness of the above results . Equations ( 9 ) and ( 10 ) suggest that the transition to accelerated behavior occurs when ( 16 ) One can think of as the average time , , it takes to initiate proteins , and rewrite the boundary as . One can then assume that if the burstiness of the initiation events is not large , i . e . that the mean burst size is less than the signal threshold , then it does not matter what the distribution of initiation events is . In other words , as long as approximately proteins are initiated in the time , and the variance of that number is not large , then Eq . ( 16 ) still holds .
Gillespie's stochastic simulation algorithm generates an exact stochastic realization for a system of species interacting through reactions . The state of the system is stored in the vector , and each reaction is characterized by a state change vector and its propensity function . If the system is in state and reaction occurs then the system state changes to [5] . The idea behind extending Gillespie's SSA to model distributed delay is that if a reaction is to be delayed by some amount of time then we temporarily store this reaction along with the time at which the event will occur and we only apply this reaction at the given time . We used a version of the algorithm equivalent to those described in [32] , [48] . Note that [48] also describes a more efficient version of the algorithm .
|
Delay in gene regulatory networks often arises from the numerous sequential reactions necessary to create fully functional protein from DNA . While the molecular mechanisms behind protein production and maturation are known , it is still unknown to what extent the resulting delay affects signaling in transcriptional networks . In contrast to previous studies that have examined the consequences of fixed delay in gene networks , here we investigate how the variability of the delay time influences the resulting dynamics . The exact distribution of “transcriptional delay” is still unknown , and most likely greatly depends on both intrinsic and extrinsic factors . Nevertheless , we are able to deduce specific effects of distributed delay on transcriptional signaling that are independent of the underlying distribution . We find that the time it takes for a gene encoding a transcription factor to signal its downstream target decreases as the delay variability increases . We use queueing theory to derive a simple relationship describing this result , and use stochastic simulations to confirm it . The consequences of distributed delay for several common transcriptional motifs are also discussed .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"systems",
"biology",
"stochastic",
"processes",
"mathematics",
"theoretical",
"biology",
"regulatory",
"networks",
"synthetic",
"biology",
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"computational",
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"signaling",
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"molecular",
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2011
|
Stochastic Delay Accelerates Signaling in Gene Networks
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Plants and algae adapt to fluctuating light conditions to optimize photosynthesis , minimize photodamage , and prioritize energy investments . Changes in the translation of chloroplast mRNAs are known to contribute to these adaptations , but the scope and magnitude of these responses are unclear . To clarify the phenomenology , we used ribosome profiling to analyze chloroplast translation in maize seedlings following dark-to-light and light-to-dark shifts . The results resolved several layers of regulation . ( i ) The psbA mRNA exhibits a dramatic gain of ribosomes within minutes after shifting plants to the light and reverts to low ribosome occupancy within one hour in the dark , correlating with the need to replace damaged PsbA in Photosystem II . ( ii ) Ribosome occupancy on all other chloroplast mRNAs remains similar to that at midday even after 12 hours in the dark . ( iii ) Analysis of ribosome dynamics in the presence of lincomycin revealed a global decrease in the translation elongation rate shortly after shifting plants to the dark . The pausing of chloroplast ribosomes at specific sites changed very little during these light-shift regimes . A similar but less comprehensive analysis in Arabidopsis gave similar results excepting a trend toward reduced ribosome occupancy at the end of the night . Our results show that all chloroplast mRNAs except psbA maintain similar ribosome occupancy following short-term light shifts , but are nonetheless translated at higher rates in the light due to a plastome-wide increase in elongation rate . A light-induced recruitment of ribosomes to psbA mRNA is superimposed on this global response , producing a rapid and massive increase in PsbA synthesis . These findings highlight the unique translational response of psbA in mature chloroplasts , clarify which steps in psbA translation are light-regulated in the context of Photosystem II repair , and provide a foundation on which to explore mechanisms underlying the psbA-specific and global effects of light on chloroplast translation .
Energy from sunlight fuels life on earth through the process of photosynthesis . Light is both an essential resource and a source of stress for photosynthetic organisms , as it damages cellular structures through photo-oxidative processes and the production of reactive oxygen species . Photosynthesis is compromised when excess light damages the Photosystem II ( PSII ) reaction center or when excitation of Photosystem I ( PSI ) and PSII is unbalanced [1 , 2] . A key target of photodamage is the D1 reaction center protein of PSII , which is encoded by the chloroplast psbA gene . In an elaborate repair cycle , damaged D1 is degraded and then replaced with newly synthesized D1 [3 , 4] . To compensate for its high rate of turnover , D1 is the most rapidly synthesized protein in photosynthesizing cells . In plants and algae , light exerts rapid changes in D1 synthesis at the level of translation [5] . The phenomenon of light-regulated psbA translation has been intensively studied due to its central role in maintaining photosynthetic homeostasis and the ease of monitoring D1 synthesis . However , light-regulated translation in chloroplasts is not limited to the psbA mRNA [6] . This has been documented most thoroughly for the rbcL mRNA , whose translation initiation and elongation rates have been shown to change in response to light [7–12] . Despite a large body of literature on light-regulated chloroplast translation , major gaps remain in the characterization of the basic phenomenology: i . e . which genes respond to light at the translational level , with what kinetics , and at which step in translation . Studies have been limited to the few most rapidly synthesized proteins , so no information is available for the majority of the chloroplast’s ~80 protein-coding genes . In addition , many studies examined light responses in plants that had been grown for extended periods in the absence of light , at which point effects on chlorophyll synthesis and energy supply confound data interpretation . For example , an influential series of reports employed a de-etiolation regime in which barley seedlings were germinated and grown in the absence of light for many days . Illumination of these plants triggered the incorporation of radiolabeled amino acids into chlorophyll binding proteins in isolated chloroplasts , a result that was initially interpreted as light-induced translation [13] . However , subsequent experiments showed that much of this effect was due to stabilization of nascent apoproteins by chlorophyll , whose synthesis is induced by light [14 , 15] . The degree to which regulated translation contributed to that phenomenon remains unclear . Further ambiguities arise from the fact that many studies assayed translation in isolated chloroplasts or etioplasts [5] , whose energy and redox status may differ from those in vivo . In this study , we revisit the phenomenon of light-regulated chloroplast translation using ribosome profiling [16] , a method that was not available at the time of the work summarized above . Ribosome profiling provides a genome-wide , quantitative and high-resolution snapshot of ribosome occupancy in an intact organism at the time of harvest . To minimize effects of light on energy supply , we used young maize seedlings grown in diurnal cycles prior to depletion of their seed reserves , and we monitored ribosome occupancy shortly after shifting plants from the light to the dark , or vice versa . We distinguished effects of light on translation initiation and elongation rates by following the kinetics of ribosome occupancy after introducing lincomycin , which specifically inhibits ribosomes at the first few codons in an open reading frame ( ORF ) . Our results show that transition from dark to light is accompanied by a global increase in the rate of translation elongation in chloroplasts , and that the rapid recruitment of ribosomes to the psbA mRNA is superimposed on this global response . Surprisingly , the abundance and positions of ribosomes on all chloroplast mRNAs other than psbA are maintained largely unchanged even after 12 hours in the dark , implying a balanced decrease in rates of initiation and elongation . This comprehensive analysis clarifies the phenomenology of light-regulated chloroplast translation and provides a basis for mechanistic hypotheses to be tested in future studies .
We grew maize seedlings for 8 days in light-dark cycles . Plants at this stage are photosynthetically competent but have not exhausted seed reserves . We subjected these plants to one of three light-shift regimes ( Fig 1A ) : ( i ) Plants that experienced 15 minutes of light at dawn were compared with plants that were maintained in the dark and harvested at the same time; ( ii ) Plants that experienced one hour of dark at midday were compared with plants that were maintained in the light and harvested at the same time; ( iii ) Plants that were reilluminated for 15 minutes after one hour of dark at midday were compared with plants harvested prior to reillumination . We used a moderate light intensity ( ~300 μmol m-2s-1 ) to minimize photodamage . Three biological replicates were performed for each comparison . Leaf tissue was processed for ribosome profiling ( Ribo-seq ) and RNA-seq analysis as described previously [17] . Ribo-seq reads mapping to the nuclear and chloroplast genomes exhibited the expected size distributions and three-nucleotide periodicity , and mapped primarily to protein-coding sequences ( S1A–S1D Fig ) , demonstrating that they derive primarily from ribosome footprints . Read counts from chloroplast genes were normalized to the length of the ORF and sequencing depth by expressing them as reads per kilobase ( in the ORF ) per million reads mapped to nuclear protein coding sequences ( RPKM ) . The Ribo-seq and RNA-seq RPKM values were highly reproducible across the replicates ( Pearson Correlation ~ 0 . 99 , S1E Fig ) . Every chloroplast gene was represented by at least 47 Ribo-seq reads and 1000 RNA-seq reads in each replicate , and the majority were represented much more deeply than this ( S1F Fig ) . The results are presented in Fig 1B as the ratio of values in light to dark for each of the three comparisons described above . The abundance of ribosome footprints on psbA RNA is highly dynamic in response to light , increasing approximately 6-fold after 15 minutes of light at dawn , decreasing approximately 8-fold after 1 hour in the dark at midday , and increasing approximately 8-fold after 15 minutes of reillumination at midday ( see Ribo-seq , top panel ) . Strikingly , the psbA mRNA was the only chloroplast mRNA to behave in this manner: ribosome footprint abundance on all other chloroplast ORFs changed less than two-fold in each comparison . The abundance of psbA mRNA did not vary ( see RNA-seq , middle panel ) , indicating that the changes in ribosome footprint abundance are due solely to effects on translation . Several other mRNAs showed small changes in ribosome density ( ratio of Ribo-seq to RNA-seq reads ) in response to light , but the change exceeded 2-fold only for ndhJ at midday ( Fig 1B bottom ) . To assess whether additional differences become apparent at longer times after shifting from one condition to the other , ribosome density after 7 hours in the light ( midday ) is compared to that after 12 hours in the dark ( dawn ) in Fig 1C . This comparison shows that ribosome density on all chloroplast ORFs excepting psbA changes very little even after extended times in light or dark . Normalized read counts for each condition are plotted separately in Fig 2 and S2 Fig . This view of the data shows that mRNA abundance across all five conditions exhibited only small variations , although mRNAs from several genes were approximately 2-fold more abundant at dawn than at midday regardless of light condition . Furthermore , all chloroplast mRNAs other than psbA maintained similar ribosome density ( Ribo-seq/RNA-seq ) at all time points . The Ribo-seq RPKM from psbA after 1 or 12 hours in the dark was similar to that from other genes encoding photosystem subunits . Illuminating plants for 15 minutes restored psbA ribosome occupancy to the same level as that in plants that had been maintained in the light for 7 hours . These findings were corroborated by results from traditional polysome assays in which the association of mRNAs with ribosomes was assessed by their rate of sedimentation through a sucrose gradient . Consistent with the ribosome profiling data , the distribution of psbA mRNA shifted toward lower molecular weight fractions shortly after shifting to the dark ( and vice versa ) , whereas the sedimentation profiles of the rbcL and atpB/E mRNAs were indistinguishable even when comparing plants that had been in the dark or light for many hours ( Fig 3 and S3 Fig ) . Although rapid sedimentation could result from the association of mRNAs with large non-ribosomal ribonucleoprotein particles , the ribosome profiling data argue against this possibility: the ribosome footprints exhibited similar size , 3-nucleotide periodicity and confinement to ORFs among all assayed conditions ( S1 Fig ) . Together , these findings strongly support the interpretation that light has minimal impact on the ribosome-association of the vast majority of chloroplast mRNAs , whether comparing plants that have been shifted from one condition to the other for short ( 15 minutes ) or long ( many hours ) periods of time . By contrast , cytosolic ribosomes shift toward smaller polysomes after 1-hour in the dark ( Fig 3 bottom , 25S and 18S rRNAs ) , as reported previously for Arabidopsis [18] . Pioneering studies detected paused ribosomes at several positions in the psbA RNA in isolated barley etioplasts , and showed that these pauses increase in magnitude after many hours of illumination [19 , 20] . To assess the effects of light on ribosome pausing in developed chloroplasts , we examined the distribution of ribosome footprints along specific chloroplast ORFs; peaks in the distribution can be used to infer positions at which ribosomes dwell for an unusually long time [21] . Ribosome distribution along psbA was remarkably similar in material harvested at each assayed time point following a shift from one condition to the other ( Fig 4 ) , arguing against a role for regulated ribosome pausing in light-regulated psbA expression . Analogous plots for six other chloroplast ORFs are shown in Fig 4 , and likewise showed very similar ribosome distributions in all conditions; that said , a site near the petA start codon appeared to be more highly occupied in the dark than in the light . A quantitative , plastome-wide comparison of ribosome distributions detected several locations at which ribosome dwell-time may differ to a small degree in dark and light ( S4 Fig ) . Although several of these were reproducible in our experiments , their statistical significance is unclear . These possible exceptions aside , our results demonstrate that light does not cause wide-spread or high-magnitude changes in the pausing of ribosomes at specific locations on chloroplast mRNAs . To examine the kinetics with which ribosomes are lost from psbA mRNA upon a shift to dark , plants were processed for ribosome profiling over a time course after shifting plants to the dark at midday ( Fig 5A ) . A small reduction in ribosome footprints was apparent after 10 minutes in the dark , whereas 30 minutes was sufficient ( or nearly so ) to reduce ribosome occupancy to that after 1 hour in the dark . These data , together with those shown in Fig 2 , show that new steady-state ribosome occupancies on psbA RNA are established within approximately 15 minutes after shifting plants to the light and 30 minutes after shifting plants to the dark . Although ribosome occupancy is typically a good proxy for relative rates of protein synthesis within a cell ( or organelle ) under any single condition [22 , 23] , the relationship between ribosome occupancy and protein synthesis becomes unpredictable when comparing different conditions due to possible differences in rates of translation elongation . In fact , classic studies reported a decrease in the translation elongation rate on the psbA and rbcL mRNAs after shifting isolated chloroplasts ( psbA ) or intact plants ( rbcL ) to the dark [8 , 24–27] . To address this possibility in maize seedlings , we used in vivo pulse-labelling assays to examine how the rates of RbcL and PsbA synthesis change over time after shifting plants to the dark or light ( Fig 5B ) . Pulse-labeling was performed during four consecutive 15-minute windows following shifts to dark and back to light at midday . The results show that PsbA synthesis drops rapidly after the shift to the dark and increases rapidly after reillumination , correlating in a general sense with the ribosome profiling data . Notably , however , the decrease in PsbA synthesis was apparent in the first 15 minutes after the shift to the dark ( lane “0” in Fig 5B ) , preceding the clearance of ribosomes from its mRNA . Although labeled RbcL was poorly resolved in these experiments , the results suggest that RbcL synthesis decreased after the shift to dark and increased again after the shift to light , despite the unchanged association of its mRNA with ribosomes . These results suggest that the rate of ribosome movement along the psbA and rbcL mRNAs slows shortly after the shift to dark , and is restored shortly after the shift back to the light . Results above show that ribosome occupancy on all chloroplast ORFs other than psbA changes very little following shifts from light-to-dark or dark-to-light , even after many hours in the new condition . This is consistent with two scenarios: either light has minimal effect on their translation or it triggers concerted changes in rates of initiation and elongation such that the average number of ribosomes associated with each mRNA is maintained . The data shown in Fig 5 together with the prior studies discussed above support the view that the rate of ribosome movement along the psbA and rbcL mRNAs does decrease soon after a shift to the dark . However , it is not known whether other chloroplast mRNAs are similarly affected . To provide a plastome-wide view of the effects of light on translation elongation , we performed ribosome profiling over a time course following treatment of seedlings with the peptidyl-transferase inhibitor lincomycin ( Fig 6 ) . Lincomycin does not inhibit ribosomes harboring nascent peptides longer than approximately five amino acids , so ribosomes that have passed the first few codons continue to elongate in its presence [28] . Thus , changes in the translation elongation rate will be reflected by changes in the rate of ribosome clearance from ORF bodies after treatment with lincomycin ( see Fig 6A ) . Lincomycin does not inhibit cytosolic ribosomes , so we normalized chloroplast read counts to cytosolic read counts . These experiments required that lincomycin be introduced into the chloroplasts of intact seedlings as rapidly as possible . Of the approaches we explored ( see Materials and Methods ) , we found the introduction of lincomycin via thread wicks sewn through the stem to be most effective . Pilot experiments demonstrated that chloroplast protein synthesis in the leaf is inhibited starting approximately 10 minutes after initiating this treatment . Therefore , we harvested leaf tissue for ribosome profiling immediately prior to lincomycin treatment , and 12 and 30 min after initiating treatment ( Fig 6A , bottom ) . For analysis of elongation rate in the dark , plants were dark-adapted for 30 minutes prior to lincomycin treatment , and were maintained in the dark throughout the treatment . During the 30-minutes of dark-adaptation , ribosome occupancy on psbA mRNA is reduced to its dark steady-state level ( see Fig 5A ) ; therefore , this experiment monitored the elongation rate only of those ribosomes that remained bound to psbA RNA after that time . We observed that the abundance and size distribution of ribosome footprints mapping to chloroplast start codons changed over time following lincomycin treatment ( S5 Fig ) . The change in footprint size likely results from the fact that lincomycin traps ribosomes in a “rotated” conformation [29] . This effect was similar in the light and dark ( S5 Fig ) , demonstrating that lincomycin inhibited chloroplast ribosomes in both conditions . The distribution of ribosome footprints along the rbcL and psbA mRNAs at each time point following lincomycin treatment in the dark or light is displayed in Fig 6B ( top ) . The rate of ribosome clearance is much slower in the dark than in the light , consistent with the prior evidence that light increases the rate of translation elongation on the psbA and rbcL mRNAs [8 , 24–27] . Analogous plots for other genes show similar effects ( Fig 6B bottom , S6A Fig ) , indicating that a reduction in translation elongation rate upon a shift to the dark is not specific for psbA and rbcL . We did not observe an obvious shift in ribosome footprints toward the 3’-end of ORFs over the lincomycin time course . This may be due to the fact that treatment of intact plants results in highly asynchronous exposure of chloroplasts to the antibiotic . To provide a plastome-wide accounting of rates of translation elongation rate in light versus dark , ribosome footprint abundance on each ORF at each time point following lincomycin treatment is plotted as a percentage of the value just prior to lincomycin treatment in Fig 6C . The results show that the rate of ribosome clearance is , in general , considerably slower in the dark than in the light . A correlation plot of the ratio of ribosome footprints on each gene in light versus dark after 30 minutes of lincomycin treatment in the two replicates ( S6C Fig ) supports this conclusion , and suggests further that ORFs may differ in the degree to which the translation elongation rate drops following a shift to the dark . However , systematic differences between replicates suggest that varying transport of lincomycin to the apical region of the leaf accounts for some or all of this variation ( see S6C and S6D Fig ) . Thus , additional experiments will be necessary to make firm conclusions about gene-specific differences in translation elongation rates . We attempted to monitor the effects of light on translation initiation rates by following the buildup of ribosomes at start codons following lincomycin treatment . Ribosomes build up at the psbA start codon much more rapidly in the light than in the dark following lincomycin treatment ( Fig 6B ) , consistent with the ribosome profiling and polysome data indicating that light stimulates the recruitment of ribosomes to the psbA mRNA . Furthermore , a metagene analysis showed that , on average , ribosomes build up at start codons more rapidly in the light than in the dark ( S5 Fig ) . However , our ability to make inferences about initiation rates was complicated by several considerations . First , many chloroplast start codons did not accumulate ribosomes following lincomycin treatment either in the light or in the dark . Furthermore , the rate of ribosome build-up at start codons will be influenced by the reservoir of mRNAs with a vacant translation initiation region , a parameter that we cannot assess . That said , results above showing that ( i ) ribosome occupancy and distribution on most chloroplast ORFs is similar in the light and dark , ( ii ) the rate of ribosome movement along these ORFs is slower in the dark , and ( iii ) the average rate of ribosome buildup at start codons is greater in the light than the dark in lincomycin-treated plants , imply that the rate of translation initiation for most chloroplast mRNAs changes roughly in concert with changes in the elongation rate following light shifts . Unexpectedly , we observed that ribosome footprints accumulated to high levels at a number of sites outside of translation initiation regions over the lincomycin time course , even as bulk ribosomes cleared these genes as expected ( see Fig 7A and S6B Fig for examples ) . The positions and magnitude of this feature were reproducible between replicate experiments ( Fig 7B ) . The size-distribution of the footprints that build up at these sites was similar to that of lincomycin-bound ribosomes at start codons and distinct from that of elongating ribosomes ( S5 Fig bottom ) , suggesting that these are footprints of ribosomes in the initiation mode . To examine that possibility , we compared the sequences around these “ribosome build-up” sites to those at sites from which ribosomes cleared following lincomycin treatment ( Fig 7C ) . The sites of ribosome build-up ( Fig 7C top ) show a strong enrichment for sequences resembling ribosome binding sites: a start codon preceded by a predicted Shine-Dalgarno element . Several observations argue against the possibility that these are stalled elongating ribosomes: ( i ) the size distribution of their footprints resembles that of initiating ribosomes and not elongating ribosomes; ( ii ) many of them are out of frame with the ORF in which they reside and several are in UTRs; ( iii ) the abundance of ribosomes that accumulate at many of these sites exceeds that of the ribosomes found upstream prior to lincomycin treatment ( see e . g . psbL in S6B Fig ) . These observations strongly suggest that translation initiation complexes formed anew at ectopic sites following lincomycin treatment . This may be a consequence of the clearing of ribosomes from ORFs following lincomycin treatment , which will increase the accessibility of sequences resembling translation initiation regions while also increasing the concentration of free ribosomes available for initiation . Regardless of the mechanism , these footprints obscured calculations of ribosome run-off rates , and were therefore excluded from calculations of ribosome occupancy for the purpose of comparing elongation rates in light and dark ( Fig 6C ) . We repeated a subset of experiments with Arabidopsis to determine whether light affects chloroplast translation similarly in a C4 monocot ( maize ) and a C3 dicot ( Arabidopsis ) ( Fig 8 ) . Similar to maize , the psbA ORF in Arabidopsis showed a roughly 7-fold decrease in ribosome footprint abundance ( Ribo-seq ) and ribosome density ( Ribo-seq/RNA-seq ) following one hour in the dark at midday , and restoration to the original level after 15 minutes of reillumination ( Fig 8A ) . Also as in maize , this highly dynamic response was unique to psbA and was not accompanied by a change in ribosome distribution ( Fig 8B ) , implying that light did not have a substantive effect on the dwell time of ribosomes at specific sites . Several other genes showed a similar pattern of ribosome loss and gain but over a much smaller dynamic range ( e . g . ndhJ and ndhK ) . In general , RNA levels were more dynamic in Arabidopsis than in maize over the conditions sampled . In particular , RNAs encoding many ribosomal proteins and NDH subunits increased after one hour in the dark and even more at the end of the night . For most ribosomal genes , this increase in RNA was reflected by an increase in ribosome footprint abundance such that ribosome density ( Ribo-seq/RNA-seq ) remained fairly constant . However , for most ndh genes , ribosome footprint abundance did not increase proportionally to RNA abundance in the dark , resulting in reduced ribosome density ( see ndhG , ndhI , ndhJ , ndhK ) . The only striking difference between the data for Arabidopsis and maize involved ribosome density at the end of the night ( dark blue bars in Fig 8A ) . Whereas in maize , ribosome density was maintained or even increased through the night for most chloroplast ORFs ( Fig 1C , Fig 2 ) , ribosome density was considerably lower at the end of the night than at midday for many genes in Arabidosis ( e . g . psbB , psbH , psbK , ndhC , rps11 ) . Thus , although ribosomes remained associated with all RNAs through the night in both species , there was generally more clearing of ribosomes over the night in Arabidopsis . To complement the ribosome profiling data , we used in vivo pulse-labeling to assay rates of chloroplast protein synthesis in Arabidopsis seedlings after shifting to the dark and following reillumination at midday ( Fig 8C ) . These experiments were performed in the presence of the cytosolic translation inhibitor cycloheximide to facilitate the detection of chloroplast-encoded proteins . D1 synthesis decreased dramatically after ~40 minutes in the dark and was restored within ~15 minutes of reillumination , correlating with the change in ribosome footprint abundance on psbA RNA . The synthesis of all of the other identifiable proteins ( RbcL , PsbB , PsbC , PsaA/B , AtpA , AtpB ) also decreased following the dark shift and increased after reillumination , albeit with less dynamic range than D1 synthesis . Decreased synthesis of these proteins in the dark was not reflected by reduced ribosome footprint abundance ( Fig 8A ) , implying that it results , at least in part , from a reduced rate of translation elongation . Together , these results suggest that many of the themes established with the more comprehensive analyses in maize hold true also in Arabidopsis . That said , the data also suggest some differences that will be interesting to explore in the future . It remains to be determined whether these are meaningful differences or simply reflect differences in physiological status: Arabidopsis plants were grown on synthetic sucrose-containing medium , whereas maize seedlings were grown in soil and had not yet exhausted seed reserves .
It is well known that light stimulates psbA translation in chloroplasts [5 , 38 , 39] , so our finding that psbA ribosome occupancy changes rapidly when green plants are shifted between dark and light may appear to be nothing more than confirmatory . However , prior experiments involved illumination of undeveloped chloroplasts ( de-etiolation ) and/or failed to address whether light increases the rate of psbA translation initiation over and above any global stimulation . To our knowledge , the only prior studies to examine the effect of light on psbA ribosome association in plants involved the illumination of etiolated barley seedlings [9 , 36 , 40 , 41]; in these experiments the psbA and rbcL mRNAs were recruited to polysomes following illumination , but this was mirrored by a global increase in chloroplast polysome content . A second set of experiments cited as evidence for light-induced psbA translation initiation employed a reporter gene fused to the psbA 5’UTR in tobacco plastids [42 , 43] . However , these experiments compared reporter expression in etiolated seedlings to that after 24 hours ( or more ) of illumination , during which time the activation of photomorphogenetic programs might impact chloroplast gene expression . Furthermore , the possibility that increased reporter expression reflected a global increase in initiation and/or elongation rate was not addressed . The regulation of psbA translation by light has been intensively studied in the single-celled alga Chlamydomonas reinhardtii . The prevailing view from this body of work is that psbA translation is regulated at different steps in the contexts of PSII biogenesis and repair; the former is believed to involve regulated initiation and the latter regulated elongation [5 , 38 , 44] . In our experiments , we exposed leaves with assembled photosystems to moderate light intensities . It is clear that the large and specific induction of psbA translation initiation we observed is a homeostatic repair mechanism: the response takes place in the context of developed chloroplasts , it is triggered within minutes of the shift to light , and it results in a substantial over-production of D1 with respect to other PSII subunits . Thus , our results add an important piece to this understanding by showing that light induces a rapid increase in the rate of translation initiation on the psbA RNA over and above any global increase in developed plant chloroplasts , and that this does not require excessive light intensities . The physiological and environmental contexts of light-regulated psbA translation are entirely different in land plants and Chlamydomonas: the former are sessile , multicellular , and land-dwelling whereas the latter are motile , single-celled , and aquatic . It would not be surprising if distinct mechanisms have evolved to maintain PSII homeostasis in these different contexts . That said , psbA mRNA in Chlamyomonas is lost from polysomes after one hour in the dark and regained within ~15 minutes of illumination at a moderate light intensity [45] , much as we observed in maize and Arabidopsis . Although those experiments did not address whether this response was specific to psbA , they do suggest that light may regulate psbA translation at the initiation step in the context of PSII repair ( and not just de novo biogenesis ) in Chlamydomonas , as in plants . Classic studies demonstrated that the rate of translation elongation on several chloroplast mRNAs in plants changes in response to light . The two primary examples focused on ( i ) psbA , whose translation elongation rate decreased and increased when isolated chloroplasts were shifted to the dark and light , respectively [24–27 , 46] , and ( ii ) rbcL , which maintained polysome association after shifting Amaranth seedlings to the dark despite reduced RbcL synthesis [8] . By using ribosome profiling to follow the rate of ribosome run-off after treating maize seedlings with lincomycin , we were able to extend these conclusions in several ways . First , we showed that the effects on psbA translation elongation previously detected in isolated chloroplasts occurs also in vivo . Second , we showed that the effects on both rbcL and psbA are not gene-specific , but rather reflect a plastome-wide change in translation elongation rate . Our results leave open the possibility that the magnitude of this effect varies among ORFs , but additional experiments would be required to make firm conclusions in this regard . Our results also clarified the effects of light on ribosome pausing . Pioneering ribosome toe-printing assays had revealed that ribosomes pause at specific sites on the psbA RNA in barley chloroplasts [19] , leading to speculation that light may regulate psbA translation by altering pausing at specific sites . A subsequent study showed that toe-print patterns change many hours after illuminating etiolated barley seedlings [20] . By contrast , our data showed that light had no apparent impact on the distribution of ribosomes along the psbA mRNA in mature maize and Arabidopsis chloroplasts over the time scales we examined . Similar results were obtained for all other chloroplast ORFs . Thus , our results provide strong evidence that light does not have any major effects on ribosome pausing on chloroplast mRNAs . Although ribosome dwell time can be influenced by RNA structure , the sequence of the nascent peptide , and other features [21 , 47] , these behaviors do not appear to be modulated in the context of light-regulated protein synthesis in chloroplasts . Our finding that psbA was the only ORF to experience a substantial increase in ribosome occupancy following a transfer to light was unexpected because light has been reported to activate translation of several other chloroplast mRNAs via effects on proteins that bind their 5’-UTRs [38] . That ribosome occupancy on maize chloroplast mRNAs other than psbA was maintained almost unchanged even after twelve hours in the dark was also unexpected . Although there is prior evidence that ribosomes remain bound to several chloroplast mRNAs in the dark [8 , 36] , it has commonly been assumed that many chloroplast ORFs lose ribosome association over the course of the night; this is illustrated by proposals that special mechanisms are required to stabilize chloroplast RNAs at night due to their lack of ribosome association [e . g . 48] . Our results show that this is not the case in maize ( excepting psbA ) , and that it applies to no more than a handful of RNAs in Arabidopsis . Although we did not directly measure initiation rates , our measurements of ribosome occupancy and elongation rates suggest that ribosome occupancy is maintained through a concerted reduction in the translation elongation and initiation rates . The maintenance of ribosome association even after many hours in the dark may have evolved as a means to protect mRNAs from degradation , and to allow a rapid resumption of translation upon exposure to light . Our results provide a foundation on which to delve into the mechanisms underlying each layer of light regulation , including the nature of the light-induced signal ( s ) , the proteins whose activities are modulated by those signals , and the mechanisms by which these stimulate or inhibit translation in either a global or psbA-specific manner . The light-induced signals that trigger psbA-specific ribosome recruitment , the plastome-wide increase in elongation rate , and the plastome-wide increase in initiation rate may be shared or distinct . Studies involving isolated chloroplasts treated with inhibitors of photosynthetic electron transport [26 , 34 , 35 , 37] have shown that light impacts chloroplast translation via its effect on photosynthesis . Photosynthesis-dependent changes in ATP , the ATP/ADP ratio , NADPH , reduced thioredoxin , reduced plastoquinone , stromal pH , and a trans-thylakoid proton gradient have all been invoked as potential triggers of light-induced effects on chloroplast translation [5] . Experiments that resolve the psbA-specific response from the global responses , and that assess the effects of disrupting specific steps in photosynthesis in intact plants will be required to pinpoint the photosynthesis-dependent signals that regulate chloroplast translation . Changes in translation can be observed within minutes of shifting plants from light to dark ( and vice versa ) , strongly suggesting that the effects are mediated by post-translational modifications of pre-existing proteins . Candidates for proteins that regulate psbA-specific ribosome recruitment include HCF173 and HCF244 , which are required specifically for psbA translation initiation in Arabidopsis [49 , 50] . Plastome-wide effects may involve modifications of general elongation and initiation factors or other global translation modulators . For example , PSRP1 , the chloroplast ortholog of a bacterial ribosome hibernation factor , has been proposed to repress chloroplast translation in the dark [51] . Future experiments can be directed toward testing these hypotheses , as well as assessing whether changes in light intensity and quality trigger behaviors similar to those described here . Given that the majority of chloroplast-encoded proteins reside in complexes that include nucleus-encoded subunits , our findings also raise the question of whether fluctuations in rates of chloroplast protein synthesis in response to light are reflected by corresponding changes in the synthesis of the nucleus-encoded proteins with which they partner .
Zea mays inbred line B73 was used for all experiments involving maize . Plants were grown in soil under light/dark cycles of 12 h/12 h ( for experiments in Fig 1 ) or 16 h/8 h ( for other experiments ) at a temperature of 28° and 26°C for the light and dark periods , respectively . Plants were illuminated using a light intensity of 200–300 μmolm-2s-1 , which is on the low end of typical “growth light” intensities used for maize and much lower than high-light treatments which typically exceed 1000 μmol m-2s-1 . [e . g . 52 , 53] . Light-shift experiments were performed on the ninth day after planting , at which time the third leaf was starting to emerge . The apical half of leaves two and three were used for ribosome profiling experiments . This tissue was flash-frozen in liquid N2 immediately after the indicated light treatment and stored at -80°C . Each Ribo-seq replicate pooled tissue from three seedlings . A single seedling was used for each pulse-labeling assay . Arabidopsis ( Arabidopsis thaliana Col-0 ) seedlings were grown on agar plates ( 1x Murashige and Skoog Basal Medium ( Sigma ) , 0 . 3% ( w/v ) Phytogel , 1% ( w/v ) sucrose , pH 5 . 8 ) . Seeds were planted with 1-cm spacing to minimize shading . Plants were grown at 22°C under light/dark cycles of 16 h/8 h for 14 days for ribosome profiling , or for 10 days for pulse labeling . Light intensity was approximately 100 μmol m-2s-1 , consistent with typical Arabidopsis growth light intensities and much lower than high-light treatments [54] . For sequencing , aerial parts were harvested from approximately 25 seedlings per replicate , flash-frozen in liquid N2 and stored at -80°C . The treatment and harvest of maize and Arabidopsis in the dark were performed under a dim green light . We explored multiple methods to introduce lincomycin into maize . Watering intact seedlings with a lincomycin solution resulted in an unacceptably slow response time . Vacuum infiltration of detached leaves had secondary effects on chloroplast ribosome behavior , as revealed by Ribo-seq analysis of a mock-treated sample . Addition of lincomycin to maize protoplasts completely inhibited D1 synthesis within 10–15 min , but protoplast preparation is time consuming and is unsuitable for studies of dark to light transitions . We ultimately chose to introduce lincomycin into maize seedlings through thread wicks , because chloroplast protein synthesis in leaves was inhibited in 10–15 min ( as assayed by pulse-labeling ) and seedlings remained intact . Cotton threads ( DMC crochet thread size 5 ) soaked in 1 mg/ml lincomycin ( Sigma ) were sewn four times through the stem of each seedling beneath the first ( lowest ) leaf . With 2 threads in each sewing , each wick consisted of 8 threads . The threads were cut at ~5 cm and the ends were placed in a 1 . 5 ml tube containing lincomycin . The apical half of leaves two and three were processed for ribosome profiling . For maize , the labeling was performed as in [55] . In brief , an emery board was used to create two parallel scratches ( 1 cm apart ) across the upper surface of leaf two , approximately 3-cm from the leaf tip . About 50 μCi of EasyTag Express 35S Protein Labeling Mix ( PerkinElmer: 35S-methionine and cysteine; >1000 Ci/mmol , 11 mCi/mL ) in 10 μL was added to the wounds . After 15-min of labeling , a 3-cm tissue section spanning the wounds was harvested and frozen in liquid N2 . Plants were illuminated with a light intensity of 250 μmol m-2s-1 or maintained in the dark , as indicated . The tissue was homogenized in protein homogenization buffer ( 10 mM Tris-Cl pH 7 . 5 , 10% glycerol , 5 mM EDTA , 2 mM EGTA , 40 mM β-mercaptoethanol , 2 μg/mL pepstatin , 2 μg/mL leupeptin , 2 mM phenylmethylsulfonyl fluoride ) . Lysates were fractionated by SDS-PAGE and transferred to nitrocellulose . Radiolabeled proteins were then detected with a Storm phosphorimager . Because the labeling efficiency of the leaf scratch method is variable , we used radiolabeled cytosolic proteins to normalize sample loading . This precluded the use of cycloheximide , which limited the number of chloroplast gene products we could resolve . For each Arabidopsis sample , the first two rosette leaves from three seedlings were pooled and placed in a clear 24-well plastic plate , with care taken to avoid overlap of leaves . Leaves were pre-incubated for 30 min in 135 μl of labeling buffer containing cycloheximide ( 20 μg/mL cycloheximide , 1x Murashige and Skoog Basal Medium ( Sigma ) , 1% ( w/v ) sucrose , pH 5 . 8 ) . Labeling was then initiated by the addition of 15 μl of EasyTag Express 35S Protein Labeling Mix . Labeling was performed for 20 min under a light intensity of approximately 100 μmol m-2s-1 ( or in the dark , as indicated ) . After labeling , leaves were washed once in the labeling buffer ( lacking 35S ) and then frozen in liquid N2 . The tissue was homogenized as for maize . The membrane fraction was collected by centrifugation at 13 , 000 x g for 5 min and washed once with the homogenization buffer . Samples were fractionated in SDS-PAGE gels containing 6M urea ( 11% polyacrylamide ) , with loading normalized on the basis of equal chlorophyll ( assayed as in [56] ) . Proteins were transferred to nitrocellulose and imaged with a Storm phosphorimager . With the exception of the first replicate of the experiment in Fig 1 , we prepared ribosome footprints and total RNA according to the small-scale protocol described in [57] , and purified footprints between approximately 20 and 40 nucleotides . For the first replicate of the experiment in Fig 1 , we used the protocol described in [17] , and purified footprints between approximately 20 and 35 nucleotides . Ribo-seq libraries were prepared using the NEXTflex Small RNA Sequencing Kit v2 or v3 ( Bioo Scientific ) with additional steps described previously [17] . rDNA was depleted after first strand synthesis using biotinylated DNA oligonucleotides together with Dynabeads M-270 Streptavidin or MyOne Streptavidin C1 ( ThermoFisher ) . Replicate 1 of the experiments in Fig 1 used the set of 47 biotinylated DNA oligonucleotides described in [17] . All other experiments used the oligonucleotides described in [57] . For RNA-seq , total RNA samples extracted from aliquots of the same lysates used for Ribo-seq were treated with TURBO DNase ( ThermoFisher ) followed by treatment with the Ribo-Zero rRNA Removal Kit ( Plant Leaf ) ( Illumina ) . One hundred ng of the rRNA-depleted RNA was used for library construction using the NEXTflex Rapid Directional qRNA-Seq Kit ( Bioo Scientific ) . The libraries were sequenced on a HiSeq 4000 , HiSeq 2500 or NextSeq 500 instrument ( Illumina ) , with read lengths of 50 to 100 nucleotides for Ribo-seq and 100 nucleotides for RNA-seq . Read processing , alignment and analysis were performed according to the procedures described previously [17] . In brief , adapter sequences were trimmed using cutadapt [58] . Ribo-seq analyses used reads with lengths between 18 and 40 nucleotides . Read alignments were performed using Bowtie 2 with default parameters [59] . Reads were aligned sequentially to the following gene sets , with unaligned reads from each step used as input for the next alignment: ( i ) chloroplast tRNA and rRNA; ( ii ) chloroplast genome; ( iii ) mitochondrial tRNA and rRNA; ( iv ) mitochondrial genome; ( v ) nuclear tRNA and rRNA; ( vi ) nuclear genome . For metagene analysis , all protein coding sequence ( CDS ) coordinates from all transcript variants were combined to make a union CDS coordinate . Custom Perl scripts extracted mapping information using SAMtools [60] . The distribution of ribosome footprint lengths and RPKM for both the Ribo-seq and RNA-seq data were calculated based only on reads mapping to CDS regions . For chloroplast RPKM calculations , the reads mapping to the first 10 and the last 30 nucleotides of the CDS , which arise from initiating and terminating ribosomes , respectively , were excluded , and we defined the total number of mapped reads as the number mapping to nuclear CDS . For intron-containing chloroplast genes , Ribo-seq RPKM was calculated only from the CDS in the last exon , with the exception of rps12 , where exon 2 was used . The rationale for these choices was discussed previously [17] . For the lincomycin assays , the ribosome run-off time course was determined from the RPKM values in the ORF body ( from codon 8 to the stop codon ) . Because maize chloroplast ribosome footprints translating the same codon share a similar 3’-end position regardless of footprint size [17] , the normalized abundance of footprint 3’-ends was used to determine sites at which ribosomes accumulate during lincomycin treatment . The 3’-end positions were extracted using SAMtools and normalized to million reads mapped to nuclear protein coding sequences . Sites at which the normalized 3’-end coverage increased more than 5-fold after 30-min of lincomycin treatment in the light ( see e . g . S6B Fig ) were removed from calculations used for the ribosome run-off analysis shown in Fig 6C and S6C and S6D Fig . Evidence discussed in the text strongly suggests that these result from internal binding of ribosomes in the initiation mode rather than stalled elongating ribosomes . Ribosome build-up sites located outside annotated translation initiation regions that had >50 reads per million after 30 min of lincomycin treatment in the light were reported in Fig 7 . Read mapping statistics and chloroplast RPKM values from all the experiments are provided in S1 Table . Polysome analyses were performed as described previously [55] . The psbA , atpB/E , rbcL and ndhJ probes used for RNA gel blots correspond to maize chloroplast genome nucleotide positions 295–1074 , 54590–55790 , 57036–57607 and 50535–51014 , respectively . Illumina read sequences were deposited at the NCBI Sequence Read Archive with accession number SRP133508 . Alignments of reads to the maize chloroplast genome used Genbank accession X86563 . B73 RefGen_v3 assembly ( maizegdb . org ) was used for other genomes . The gene set from maize genome annotation 6a ( phytozome . jgi . doe . gov ) was reduced to the gene set annotated in 5b+ ( 60 , 211 transcripts ) ( gramene . org ) . For Arabidopsis , we used TAIR10 genome and annotation ( arabidopsis . org ) .
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Our experiments address the effects of light on protein synthesis within chloroplasts , whose ~80 genes are essential for photosynthesis and account for a large fraction of the protein synthesis in leaf tissue . Light is necessary for photosynthesis but it also triggers photo-oxidative damage . It is known that light-induced changes in chloroplast translation aid photosystem repair and prioritize energy consumption in the dark . However , prior studies have been limited in scope and offer conflicting views of the nature of these responses . In this study , we used new methods to generate a comprehensive description of chloroplast translation in maize at various time scales after shifting plants from dark to light and vice versa . We discovered that psbA mRNA , which encodes a protein that is particularly prone to photodamage , exhibits dynamic changes in ribosome occupancy in response to light and that it is unique in this regard . Ribosome occupancy on other chloroplast mRNAs is static even after many hours in the dark; however , these mRNAs are nonetheless translated at reduced rates in the dark due to a reduced rate of ribosome movement . Resolution of these superimposed effects clarifies the phenomenology of light-regulated chloroplast translation and provides a basis for exploring underlying mechanisms .
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2018
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Multilevel effects of light on ribosome dynamics in chloroplasts program genome-wide and psbA-specific changes in translation
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5’ ends are important for determining the fate of RNA molecules . BCDIN3D is an RNA phospho-methyltransferase that methylates the 5’ monophosphate of specific RNAs . In order to gain new insights into the molecular function of BCDIN3D , we performed an unbiased analysis of its interacting RNAs by Thermostable Group II Intron Reverse Transcriptase coupled to next generation sequencing ( TGIRT-seq ) . Our analyses showed that BCDIN3D interacts with full-length phospho-methylated tRNAHis and miR-4454 . Interestingly , we found that miR-4454 is not synthesized from its annotated genomic locus , which is a primer-binding site for an endogenous retrovirus , but rather by Dicer cleavage of mature tRNAHis . Sequence analysis revealed that miR-4454 is identical to the 3’ end of tRNAHis . Moreover , we were able to generate this ‘miRNA’ in vitro through incubation of mature tRNAHis with Dicer . As found previously for several pre-miRNAs , a 5’P-tRNAHis appears to be a better substrate for Dicer cleavage than a phospho-methylated tRNAHis . Moreover , tRNAHis 3’-fragment/‘miR-4454’ levels increase in cells depleted for BCDIN3D . Altogether , our results show that in addition to microRNAs , BCDIN3D regulates tRNAHis 3’-fragment processing without negatively affecting tRNAHis’s canonical function of aminoacylation .
The 5’ ends of eukaryotic RNAs are vital for determining their processing and function [1] . The most well-known 5’ end RNA modification is the m7G cap , which has multiple functions , including of protecting messenger RNAs from exonucleolytic degradation , and of marking them for nucleo-cytoplasmic export and translation [2] . Additionally , a chemically simpler 5’ end modification by O-methylation occurs directly on 5’ phosphate ends , either on the γ-phosphate of nascent tri-phosphorylated snRNAs , or on the α-phosphate of processed monophosphate RNAs [3] . These methylations are carried out by the Bicoid Interacting 3 ( BIN3 ) [4] family of methyltransferases , which are conserved from fission yeast to humans [5] . In humans , two BIN3 related enzymes , MePCE and BCDIN3D , have been identified . Jeronimo et al . , uncovered that MePCE methylates the γ-phosphate of the 7SK snRNA [6] , while we discovered that BCDIN3D methylates the 5’ monophosphate of two specific microRNA precursors , pre-miR-145 and pre-miR-23b , both in vitro and in cells , to inhibit their processing into mature miRNA by Dicer [5] . Our initial analysis in MCF-7 cells also suggested that other microRNAs could be methylated by BCDIN3D [5] . However , due to lack of methods to specifically enrich 5’ phospho-methylated RNAs , the methylation of other types of RNAs with 5’ monophosphates was not analyzed in our initial study . MePCE forms a stable ribonucleoprotein complex with its 7SK small nuclear RNA target [6] . Based on these findings , we hypothesized that the other member of the BIN3 family , BCDIN3D , may interact with at least a subset of its RNA targets in a similar manner . Purification and sequencing of RNAs interacting with BCDIN3D revealed that BCDIN3D specifically interacts with mature tRNAHis and miR-4454 , which our results suggest to be a tRNAHis 3’ fragment derived from cleavage of mature tRNAHis . Importantly , BCDIN3D downregulation results in increased levels of tRNAHis 3’ fragments in cells . Overall , our results indicate that tRNAHis interaction with BCDIN3D plays non-canonical roles , one of which is to regulate the generation of tRNAHis 3’ fragments . This may have important biological implications as miR-4454 has been identified as a potential biomarker in several human diseases [7–9] . Moreover , given that tRNA 3’ fragments limit the mobility of transposable elements in mammalian cells [10] , BCDIN3D may impact genomic stability .
In order to purify RNAs interacting with BCDIN3D , we used HeLa-S3-FlpIn cells containing a single insertion at a FRT locus of BCDIN3D tagged with a FLAG tag at its C-terminus ( denoted BCDIN3Df or B3Df ) . We first purified BCDIN3Df with a protocol that combines FLAG antibody co-immunoprecipitation , followed by FLAG peptide elution . We previously used this same protocol to show an RNase A-sensitive interaction of BCDIN3Df with Dicer [5] . We then extracted RNAs from FLAG eluates of Control and BCDIN3Df cells with a protocol that allows recovery of RNAs of all sizes ( see Methods ) . Analysis of the extracted RNAs on a denaturing 15% polyacrylamide/urea gel stained with silver showed a prominent band of 70 to 90 nucleotides in the FLAG eluates from BCDIN3Df cells , along with lighter non-specific bands present in the FLAG eluates from both Control and BCDIN3Df cells ( Fig 1A ) . In order to unbiasedly uncover the identity of the small RNAs interacting with BCDIN3D , we sought to use next generation sequencing ( NGS ) . Commonly used small RNA-Seq protocols use RNA ligases to add RNA adaptors to each end of the RNA prior to reverse transcription into cDNA and NGS library amplification . However , RNAs methylated by BCDIN3D cannot be amplified by these protocols because the dimethyl-phosphate ( 5’Pme2 ) is resistant to ligation by RNA ligases [5] . In an attempt to circumvent the 5’Pme2 end ligation problem , we first used a small RNA-Seq protocol that employs T4 Rnl2tr ( T4 RNA ligase 2 , truncated ) to add an adaptor only on the 3’ end , followed by reverse transcription and cDNA circularization with CircLigase prior to amplification [11] , but our attempts were unsuccessful . We then reasoned that RNA secondary structure could also prevent efficient amplification of BCDIN3D-interacting RNAs . Therefore , we used a TGIRT-seq protocol that combines template switching to the RNA 3’ end and a highly processive Thermostable Group II Intron Reverse Transcriptase [12] for cDNA synthesis ( Fig 1B ) . The use of this method resulted in successful library amplification . Because the visible BCDIN3D-specific RNA band is 70 to 90 nt , we first focused on paired end reads longer than 50 nt ( Fig 1C and S1 Table ) . The reads were dominated by mature tRNAHis , containing both the non-templated 5’ G-1 characteristic of tRNAHis , and the non-templated 3’ CCA tail [13] ( see WebLogo and IGV plot of obtained sequences in Fig 1C ) . In addition to tRNAHis , the BCDIN3D-interacting RNA libraries also included other reads corresponding to RNAs >50 nt , but these were found at the same level in the Control libraries ( Fig 1C and S1 Table ) , and were consequently deemed background reads . We confirmed our TGIRT-seq results by northern blotting with a specific tRNAHis probe ( probe #1 ) that is complementary to the TΨC arm ( Fig 1C and 1D and S4 Table ) . Notably , this probe detected full-length tRNAHis molecules present exclusively in the BCDIN3Df FLAG eluates , while a U6 probe detected a background U6 RNA in both samples ( Fig 1D ) . As mentioned above , our TGIRT-seq results show that BCDIN3D exclusively interacts with mature tRNAHis containing the non-templated 5’ G-1 ( Fig 1C ) . Consistent with this observation , our in vitro RNA methyltransferase assays with recombinant BCDIN3D show that BCDIN3D has a marked preference for mature tRNAHis containing the 5’ G-1 , when compared to a pre-tRNAHis without 5’ G-1 ( Fig 1E ) , which as expected , is the preferred substrate of tRNAHis Guanylyltransferase 1 Like ( THG1L ) ( Fig 1F ) , the human homolog of yeast Thg1 that adds the non-templated 5’ G-1 on pre-tRNAHis [14] . We next sought to determine whether the BCDIN3Df-interacting tRNAHis molecules are methylated on their 5’ ends . Treatment of a synthetic pre-miRNA harboring different 5’ end modifications with an alkaline phosphatase originating from the Antarctic strain TAB5 , also called Antarctic Phosphatase [15] , can remove 5’P and 5’Pme1 but not 5’Pme2 from the 5’ ends of RNAs , as judged by a distinct electrophoretic mobility shift on the denaturing 15% polyacrylamide/urea gel shown on Fig 2A . When we performed in vitro RNA methyltransferase assays with pre-miR-145-5’P using the radioactively labeled methyl group donor S-Adenosyl-Methionine [3H]-S-SAM [5] , the small fraction of C[3H]3-methylated pre-miRNA-145 detected by autoradiogram neither disappeared , nor changed its mobility upon treatment with Antarctic Phosphatase ( Fig 2B ) , consistent with our previous results that BCDIN3D dimethylates pre-miRNAs [5] . While this manuscript was in preparation , tRNAHis was reported to be monomethylated by BCDIN3D in 293T cells and in vitro , based on analysis of RNase A-digested fragments detected by mass spectrometry [16] . Our in vitro RNA methyltransferase assay differed from the assay used in [16] in that ours omitted MgCl2 and included 5 mM DTT as a reducing agent [5] . As also reported by Blazer et al . , our conditions ensure optimal BCDIN3D activity in vitro [17] . Under our conditions , tRNAHis methylated by BCDIN3D in vitro becomes fully resistant to Antarctic Phosphatase ( Fig 2D ) . Thus , our data suggest that tRNAHis methylated by BCDIN3D is not monomethylated , and is most likely phospho-dimethylated . Furthermore , the migration of tRNAHis that co-purified with BCDIN3Df in the FLAG eluates likewise did not change upon treatment with Antarctic Phosphatase , suggesting that tRNAHis associated with BCDIN3Df is largely dimethylated in HeLa-S3 cells ( Fig 2E ) . Our TGIRT-seq analysis revealed a significant number of reads with a length < 50 nt and specific to the BCDIN3Df FLAG eluates ( Fig 3A and S3 Table ) . Some of these reads could be due to a reverse transcription roadblock at modified bases of tRNAHis ( S2 Fig ) . For example , while a very large number of reads <50 nt stop just before the G37 residue , no faster migrating band of the expected signal intensity was detected by northern blot with a tRNAHis probe #1 ( Fig 1D ) . Although not detected by mass spectrometry [16] , tRNAHis interacting with BCDIN3D appears to be partially methylated at G37 , as evidenced by the observed nucleotide misincorporation introduced by TGIRT at the G37 position ( Fig 1C ) . This is also consistent with the fact that BCDIN3D interacts with TRMT5 , which is the enzyme responsible for m1G methylation at that tRNA position [18] ( S2 Table , S2C Fig ) . As shown in Fig 3A and 3B , a significant number of < 50nt reads specific to BCDIN3Df FLAG eluates map to hsa-miR-4454 . We validated this result with Reverse Transcription coupled to quantitative PCR ( RTqPCR ) with a custom Taqman probe for hsa-miR-4454 ( S3 Fig ) . Moreover , a probe complementary to the sequence of this miRNA showed fast migrating bands in northern blots ( Fig 3C , tRNAHis probe #2 ) , but also a strong signal corresponding to the full-length tRNAHis . The latter is simply due to the fact that except for a G residue at the position of the m1A modification in the tRNA , the annotated sequence of hsa-miR-4454 is identical to that of the 3’ end of mature tRNAHis that includes the 3’ CCA sequence ( Fig 3D ) . Given the observed interaction between BCDIN3D and tRNAHis , we hypothesized that hsa-miR-4454 may be a tRNAHis 3’ fragment . This hypothesis is particularly appealing for three major reasons: 1 ) the annotated hsa-miR-4454 does not have an optimal miRNA stem-loop as defined by [19] ( S4 Fig ) ; 2 ) the annotated hsa-miR-4454 genomic locus does not display any feature of transcriptionally active chromatin in HeLa-S3-FlpIn cells ( S4B Fig ) ; and 3 ) the annotated hsa-miR-4454 sequence is the reverse complement of the primer binding site of a HERVH-int LTR-retrotransposon ( chr4:163093605–163096486 in hg38 genome assembly ) , which as its name indicates uses a tRNAHis for reverse transcription ( S4C Fig ) . Close analysis of the hsa-miR-4454 reads from the BCDIN3Df FLAG eluates strongly supports our hypothesis that they originate from tRNAHis rather than the annotated genomic locus . Indeed , some hsa-miR-4454 reads from the BCDIN3Df FLAG eluates display heterogenous 5’ ends that differ from the sequence at the annotated MIR4454 genomic locus , but match the sequence of tRNAHis ( Fig 3D , IGV plots of reads mapping to hsa-miR-4454 from two biological repeats ) . About 48% of miR-4454 reads are ~18 nt long , and perfectly match tRNAHis with a CCA tail ( Fig 3D ) . The rest of the reads are longer and show a high level of polymorphism at what would be the “G4” residue of the annotated hsa-miR-4454 stem loop ( Fig 3D and S4D Fig ) . This residue aligns to the A57 residue of tRNAHis , which is m1A methylated [16 , 20] , leading to high levels of polymorphism introduced by TGIRT at that position of tRNAHis ( Fig 1C ) [21 , 22] . The spectrum of incorporated nucleotides , dominated by T and G , is characteristic of TGIRT-III mis-incorporation at m1A [23] and is similar in both miR-4454 and full-length tRNAHis reads ( Fig 3D and S4D Fig ) . Moreover , as mentioned above , a smaller fraction of reads go beyond the annotated hsa-miR-4454 stem loop , and the mismatched nucleotides perfectly align to tRNAHis ( Fig 3D ) . Altogether , these observations indicate that the hsa-miR-4454 reads from BCDIN3Df FLAG eluates derive from tRNAHis . Next , we sought to assess whether hsa-miR-4454 could be generated by Dicer cleavage of tRNAHis as previously reported for CCA ending tRNA fragments [24] . In order to test our hypothesis , we performed in vitro Dicer processing assays with synthetic 5’P and 5’Pme2 tRNAHis in the presence of 1 mM MgCl2 , as previously described [5] ( Fig 4A ) . These experiments produced several noteworthy observations . The first observation was that incubation with Dicer reduces the amount of visible full-length 5’P-tRNAHis ( Fig 4A , SYBR Gold stained gel ) . Interestingly , in contrast to what is observed with Dicer processing of pre-miR-145 into a miRNA duplex [5] , no clear RNAs of smaller size were visible in our denaturing polyacrylamide/urea gels stained with SYBR Gold ( Fig 4A , left panel ) in Dicer- vs mock-treated lanes , suggesting that tRNAHis fragments generated by Dicer may be more heterogenous than the miRNA duplex products . Indeed , northern blot with tRNAHis northern blot probe #2 showed that Dicer produces a series of discrete 3’ fragments ( Fig 4A , tRNAHis northern blot ) of similar size to the tRNAHis 3’ fragments interacting with BCDIN3Df ( Fig 3C ) . Quantification of the faster migrating band by ImageQuant clearly showed an inhibitory effect of 5’Pme2 on the processing of tRNAHis 3’ fragments by Dicer ( Fig 4A , graph at the right ) . In order to examine in detail these effects , we performed TGIRT-seq of RNAs purified from two independent in vitro Dicer assays using synthetic tRNAHis-5’P and -5’Pme2 and 2 μl of Dicer at 1 μg/μl . This setup corresponds to conditions where Dicer reduces the amount of visible full-length 5’P-tRNAHis by ~50% . We first quantified the proportion of full-length tRNAHis reads present in Dicer-treated vs mock-treated samples ( Fig 4B ) . As expected from the northern blot results , this analysis showed that Dicer treatment reduces the levels of full-length tRNAHis-5’P by approximately 54% . Most importantly , this same analysis showed that tRNAHis-5Pme2 is significantly more resistant to Dicer cleavage than tRNAHis-5’P ( ** , p-value of ~0 . 0095 ) . This parallels our previous finding that 5’Pme2 inhibits the processing of pre-miR-145 by Dicer [5] . We then analyzed the TGIRT-seq data to quantify the effect of 5’Pme2 modification on the generation of Dicer cleavage products . To avoid noise from reverse transcription stops and/or incomplete 3’->5’ chemical synthesis of tRNAHis , we analyzed at which nucleotide position in tRNAHis the reads end in mock- and Dicer-treated samples ( Fig 4C ) . As expected , above 90% of reads in the mock-treated samples have 3’ ends corresponding to the last nucleotide of tRNAHis ( dotted lines in Fig 4C ) , and this proportion decreases significantly in the Dicer treated samples ( solid lines in Fig 4C ) . Concomitantly , new read ends corresponding to Dicer cuts appear ( Fig 4C ) . These Dicer-specific read ends form several discrete peaks that are shown on the tRNAHis sequence and diagram ( Fig 4C ) . Interestingly , two of the major cut sites were significantly decreased in the Dicer-treated tRNAHis-5Pme2 compared to tRNAHis-5’P . These two sites are located on the double stranded part of the TΨC arm of tRNAHis . However , 5’Pme2 only marginally inhibited the generation of a minor cut site in the middle of the loop of the TΨC arm . Thus , 5’ phospho-methylation of tRNAHis significantly affects some Dicer cuts , but not others ( Fig 4C ) . As seen in Fig 3D , the reads of tRNAHis 3’ fragments interacting with BCDIN3D are also heterogenous and very similar in size to the tRNAHis 3’ fragments generated in vitro by Dicer . Overall our results suggest that Dicer can generate tRNAHis 3’ fragments in a way that is sensitive to the methylation status of the 5’P . In order to determine how BCDIN3D knockdown affects the levels of miR-4454/tRNAHis 3’ fragments , we analyzed microRNA-Seq data from MDA-MB-231shNC and shBCDIN3D cells . The MDA-MB-231shBCDIN3D cells reduce BCDIN3D mRNA and protein levels by approximately 70% compared to shNC cells ( Fig 4D ) . The dataset was produced from small RNAs < 34 nt extracted from a denaturing 15% polyacrylamide/urea gel ( see Methods ) . When analyzed with a microRNA-Seq bioinformatic pipeline , very few reads mapped to hsa-miR-4454 . However , most microRNA-Seq bioinformatic pipelines pre-filter reads mapping to rRNAs and tRNAs . Therefore , we re-analyzed our data with our TGIRT-seq pipeline that does not pre-filter any small RNA reads ( S1 Fig ) . Our analysis revealed a significant number of miR-4454/tRNAHis 3’ fragment reads in MDA-MB-231 cells , going up to 473 reads per million in the shBCDIN3D condition , which is comparable to miRNAs well-expressed in MDA-MB-231 cells , such as miR-23a . Importantly , this analysis showed that BCDIN3D depletion significantly increased the levels of tRNAHis 3’ fragments ( Fig 4E ) , without affecting either the tRNAHis levels , or the steady-state level of tRNAHis aminoacylation detected by acidic gel and northern blot ( Fig 4F , quantified in Fig 4G ) in these cells . Thus , our results suggest that BCDIN3D activity on tRNAHis could protect this tRNA from digestion by Dicer in cells , which is consistent with our observation that 5’Pme2 inhibits the generation of tRNAHis 3’ fragments by Dicer in vitro ( Fig 4C ) . To determine how miR-4454/tRNAHis 3’ fragments are produced in cells , we performed Drosha and Dicer knock downs experiments in MDA-MB-231 cells ( S5A Fig ) . Our results showed that miR-4454/tRNAHis 3’ fragments behave differently from canonical miRNAs in that their levels were unaffected by Drosha knock down ( S5A Fig ) . Unfortunately , we were unable to formally show that Dicer is responsible for the generation of tRNAHis 3’ fragments in MDA-MB-231 cells , because we were unable to functionally knock-down Dicer in these cells ( S5A Fig ) . However , while the tRNAHis 3’ fragment is not incorporated into Ago2 ( S5B Fig ) , analysis of tRNAHis 3’ fragments in the relational database of Transfer RNA related Fragments tRFdb [25] showed that the tRF with ID: 3013b , which corresponds to the tRNAHis 3’ fragment described here , is highly enriched in Ago3 and Ago4 PARCLIP ( S5C Fig ) . These findings suggest that tRNAHis 3’ fragments may have regulatory function ( s ) in cells . Our result showing increased levels of tRNAHis 3’ fragments in MDA-MB-231 cells depleted for BCDIN3D is reproduced in Hap1 cells that have a BCDIN3D knock-out and complete loss of tRNAHis methylation ( S6 Fig ) . Thus , BCDIN3D regulates the levels of tRNAHis 3’ fragments in at least two different cell lines . In the future , it will be of upmost importance to investigate the biological function of tRNAHis 3’ fragments , including in contexts where BCDIN3D function may be of clinical importance , such as cancer and metabolic disease [5 , 26 , 27] .
Based on our results , we hypothesize that BCDIN3D forms an RNP with mature tRNAHis that is phospho-methylated ( Figs 1 and 2 ) . This could result from relatively slow product release as recently shown for the MePCE phosphomethyltransferase [28] . Stable interaction with tRNAHis may regulate BCDIN3D activity towards its other targets , including precursor miRNAs [5] or other yet to be uncovered phospho-methylated RNAs , by affecting BCDIN3D structure and/or RNA target selection . In this context , it will be of particular interest to investigate which cellular conditions disrupt BCDIN3D interaction with tRNAHis , and how in turn those conditions affect BCDIN3D methyltransferase activity towards its other RNA targets . The gel shift assay for probing tRNAHis phospho-methylation status developed here ( Fig 2 ) provides a fast tool for testing tRNAHis methylation in cells ( S6 Fig ) , and iterations of this assay could be used for other BCDIN3D targets as well . Our results also suggest that one of the molecular consequences of BCDIN3D-mediated methylation of tRNAHis is to inhibit its cleavage by Dicer ( Fig 4A–4E and S6 Fig ) . In this context , it is intriguing that BCDIN3D downregulation or knock-out does not decrease the steady-state levels of full-length tRNAHis in cells ( Fig 4F , S6A Fig and [16] ) . This may be due to compensation by higher levels of transcription at tRNAHis loci in shBCDIN3D cells . Our discovery that BCDIN3D partially protects tRNAHis from Dicer cleavage may have evolutionary relevance . We discovered BCDIN3D in our screen for previously uncharacterized methyltransferases with human homologs conserved in fission yeast but not budding yeast [5] . Indeed , BCDIN3D and MePCE have a single homolog in fission yeast [5] . Among the known RNAs targeted by BCDIN3D and MePCE ( pre-miRNA , tRNAHis and 7SK ) , only tRNAHis exists in fission yeast . Interestingly , Dicer is also conserved in fission yeast but not budding yeast . Dcr1 , the fission yeast Dicer homolog , does not process microRNAs , but has a role in generating small interfering RNAs [29] . Thus , the function of tRNAHis protection from Dicer processing may be the most ancient function of the BIN3 family of 5’ phospho-methyltransferases . BCDIN3D depletion significantly increases the levels of tRNAHis 3’ fragments in cells ( Fig 4E and S6B Fig ) . It was recently shown that specific tRNA 3’ fragments limit the reverse transcription of LTR-retrotransposons in mammalian cells [10] . In this context , it is plausible that the high levels of BCDIN3D observed in aggressive breast cancers [5 , 27] could decrease the levels of tRNAHis 3’ fragments to promote the mobility of these transposable elements and to enhance genomic evolution , which is a driving force promoting metastasis and drug resistance of cancer cells [30] . A recent paper by Hasler et al . showed that in La knockdown that shifts the pre-tRNA-Ile2-TTA-2-3 towards a hairpin structure , the pre-tRNA is cleaved by Dicer to generate a small RNA that acts as a microRNA ( miR-1983 ) [31] . Thus , analogously to the mode of action of La , BCDIN3D-mediated phospho-methylation may stabilize a tRNAHis three-dimensional structure that counteracts Dicer processing . Finally , hsa-miR-4454 has been detected in a number of studies aiming to identify cancer and disease biomarkers including bladder cancer [7] , inflammatory bowel disease [9] , and osteoarthritis [8] . Our results indicate that miR-4454 actually corresponds to a tRNAHis 3’ fragment . Thus it will be of high interest to re-evaluate these translational studies in the light of hsa-miR-4454 being a tRNA 3’ fragment regulated by BCDIN3D and to explore its physiological functions .
HeLa-S3-FlpIn Parental and BCDIN3Df were previously described [5] . These cells were grown in spinner flasks at 75 rpm in RPMI containing 10% fetal bovine serum ( FBS ) , 100 U/ml penicillin , 100 μg/ml streptomycin and 2 mM L-glutamine ( RPMI+10%FBS+PSQ ) and supplemented with 200 μg/ml of Zeocin ( parental ) or 400 μg/ml hygromycin ( BCDIN3Df ) . MDA-MB-231shBCDIN3D were generated by transfection of MDA-MB-231 cells with pRS-shBCDIN3D TR317908C/TI368844 plasmid from Origene , while the matched MDA-MB-231shNC were generated by transfection with pRS-Scrambled TR30012 . These cells were grown in DMEM+10%FBS+PSQ+1 μg/ml of puromycin . The Hap1 Parental and BCDIN3D KO cells were produced by Horizon and were grown in IMDM+10%FBS+PSQ . 2×107 Hela-S3-Flp-In and Hela-S3-Flp-In-BCDIN3Df cells grown to a density of 4–6×105 cells per ml were used per Co-IP . The cells were washed twice with 25 ml of cold PBS , extracted with 0 . 6 ml of cold co-IP buffer ( 20 mM HEPES pH7 . 5 , 150 mM NaCl , 20% glycerol , 0 . 1% NP40 , 1 mM EDTA , 0 . 1 mM PMSF supplemented with EDTA-free Complete Protease Inhibitor cocktail from Roche ) for 1 h at 4°C and cleared by centrifugation for 10 min at 15 , 000 × g at 4°C . The supernatant was incubated for 4 h with 40 μl of pre-washed anti FLAG M2 conjugated beads ( Sigma ) at 4°C . The beads were washed 3 times with 0 . 6 ml of co-IP buffer , once with 0 . 6 ml of TBS , and eluted with 100 μl of TBS containing 150 ng/μl of 3xFLAG peptide for 30 min at 4°C . Half of the FLAG eluates were used for protein analysis and quantification , and half for RNA purification and analysis . RNA was purified using the Qiagen RNeasy MinElute Cleanup Kit with a modified protocol that allows recovery of RNAs of all sizes . 50 μl of FLAG eluates was mixed with 50 μl of water , 350 μl of RLT buffer and 675 μl of 100% molecular grade Ethanol . The mixture was passed through the Qiagen RNeasy MinElute column . The column was successively washed with 500 μl of RPE buffer and 750 μl of 80% ethanol , dried by centrifugation and the RNA was eluted twice with 17 μl of water . Proteins were migrated on a NuPAGE 4–12% Bis-Tris gel and stained with a Colloidal Blue Staining kit . Specific bands present in BCDIN3Df but not in the Control FLAG eluates were cut out using a Gene Catcher tip and sent for LC-MS/MS analysis at the Taplin Mass Spectrometry Facility at Harvard Medical School . RNA samples were separated on a denaturing 15% polyacrylamide/urea gel and stained using the FASTsilver Gel Staining Kit ( #341298 ) . TGIRT-seq libraries were prepared with a modification of the Total RNA-seq method [32] . Reverse transcription with TGIRT-III ( InGex ) was initiated from a DNA primer ( 5'-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTN-3' ) encoding the reverse complement of the Illumina Read2 sequencing primer binding site ( R2R ) annealed to a complementary RNA oligonucleotide ( R2 ) such that there is a single nucleotide 3’ DNA overhang composed of an equimolar mixture of A , G , C and T . The RNA oligonucleotide is blocked at its 3’ end with C3Sp ( IDT ) to inhibit template switching to itself . Reactions contained purified RNAs , reaction medium ( 20 mM Tris-HCl , pH7 . 5 , 450 mM NaCl , 5 mM MgCl2 ) , 5 mM DTT , 100 nM starting annealed molecule and 1 μM TGIRT-III . Reactions were pre-incubated at room temperature for 30 min and cDNA synthesis was initiated by addition of 1 mM dNTPs ( an equimolar mix of dATP , dGTP , dCTP and dTTP ) . Reactions were incubated at 60°C for 15 min and were terminated by adding 5 N NaOH to a final concentration of 0 . 25 N and incubated at 95°C for 3 min to degrade RNAs and denature protein . The reactions were then cooled to room temperature and neutralized with 5 N HCl . cDNAs were purified by using a Qiagen MinElute Reaction Cleanup Kit and then ligated at their 3’ ends to a DNA oligonucleotide encoding the reverse complement of the Illumina Read1 primer binding site ( R1R ) using Thermostable 5’ AppDNA/RNA Ligase ( New England Biolabs ) . Ligated cDNAs were re-purified with MinElute Reaction Cleanup Kit and amplified by PCR for 12 cycles using Phusion DNA polymerase ( Thermo Fisher Scientific ) with overlapping multiplex and barcode primers that add sequences necessary for Illumina sequencing . PCR reactions were cleaned up with AMPure XP beads ( Beckman Coulter ) to remove adapter dimers . Libraries were sequenced on a NextSeq 500 instrument ( 75-nt , paired-end reads ) at the Next Generation Sequencing Facility at MD Anderson Science Park . 10 μg of total RNA was separated on a denaturing 15% polyacrylamide/urea gel and small RNAs < 34 nt were extracted from the gel . The rest of the library preparation was performed as in Ingolia et al . [11] with minor modifications . RNA methyltransferase assays with BCDIN3D were performed in a total volume of 100 μl in 50 mM Tris-HCl , pH8; 150 mM NaCl; 1 mM EDTA; 5 mM DTT; 20% glycerol; 4 μl of 3H-SAM ( PerkinElmer NET155250UC ) ; 1X EDTA-free Complete Protease Inhibitor cocktail from Roche , 80 U of RNaseOUT from Invitrogen with 1 μg of recombinant BCDIN3D and 1 μl of 100 μM synthetic RNA for 2h at 37°C . tRNAHis guanylation assays with THG1L were performed in a total volume of 40 μl in 25 mM HEPES , pH7 . 4; 125 mM NaCl; 10 mM MgCl2 , 3 mM DTT; 3 mM ATP , 0 . 8 μM tRNAHis , 0 . 4 μM 32Pα-GTP and 0 . 4 μM recombinant THG1L for 1h30 at 25°C . Treatments with Antarctic Phosphatase or T4 Polynucleotide Kinase ( New England Biolabs ) were performed on 100 pmol of synthetic RNAs or on 100 ng of RNA purified from FLAG eluates . The reactions were performed in a total volume of 20 μl of 1X Antarctic Phosphatase Reaction Buffer ( 50 mM Bis-Tris-Propane-HCl , 1 mM MgCl2 , 0 . 1 mM ZnCl2 , pH 6 @ 25°C ) with 5 units of Antarctic Phosphatase or 20 units of T4 Polynucleotide Kinase or mock for 30 min at 37°C . Total RNA and protein extraction was typically performed on ~ 5x105 cells using the RNA/Protein Plus purification kit from Norgen ( Product # 48000 ) . Cells grown on 3 cm diameter dishes were washed with 2 ml of PBS and lysed with 300 μl of Lysis Buffer supplemented with 10 μl of β-mercaptoethanol per ml for 5 min on a rocking table . RNA extraction was performed according to the manufacturer’s instructions . The whole flow-through after the RNA binding step was used for protein purification . The RNA and protein concentrations were measured with a Denovix device . For the purification of charged tRNAs , 10^6 cells were resuspended in 300 μl of cold Lysis Buffer solution ( 0 . 3 M Sodium Acetate pH 5 . 2 , 10 mM EDTA ) and extracted with 300 μl of cold acetate-saturated Phenol/Chloroform ( pH 4 . 5 ) . 250 μl of the aqueous upper layer was mixed with 675 μl of cold ethanol and centrifuged for 1 h at 18 , 600 rcf at 4°C . The RNA pellet was resuspended in 50 μl of RNA Resuspension Solution ( 10 mM Sodium Acetate pH 5 . 2 , 1 mM EDTA ) . To deacylate a portion of the tRNA , 1 μg of RNA was treated with 0 . 1M Tris-HCl , pH 9 . 5 for 30 min at 37°C . Taqman RTqPCR was performed with the Taqman MicroRNA Reverse Transcription Kit from Applied Biosystems . 500 ng of total RNA was used for reverse transcription of mRNAs with the SuperScript III First-Strand Synthesis System from invitrogen . Real-time PCR analysis was performed on a StepOne Plus system . The Northern Blots were performed as previously described [33] . Small DNA ladders were used as markers in our denaturing 15% polyacrylamide/urea gels , either the 10 bp DNA Ladder ( #10821–015 ) from Invitrogen , or the ss20 ssDNA Ladder from Simplex sciences . Please note that the ssDNA ladder bands are offset by ~10–20 nt compared to RNA . ChIP-Seq experiments and bioinformatic analysis was performed as previously described [34] . 20 pmol of synthetic RNA was incubated with the indicated volumes of human recombinant Dicer ( Invitrogen # K3600-01 or Origene TP319214 ) in a total volume of 15 μl in 100 mM KCl , 10 mM Tris-HCl , pH8 , 0 . 1 mM EDTA , 1 mM MgCl2 , 0 . 5 mM dTT supplemented with 0 . 5 U/μl RNaseOUT for 2h at 37°C . The samples were mixed with 15 μl of Gel Loading Buffer II ( Ambion ) , heated for 15 min at 70°C and separated on a denaturing 15% polyacrylamide/urea gel . The gels were stained for 5 min with 1x SYBR Gold and the signal was detected and analyzed with the G:Box gel doc system from Syngene . X-RIP was performed as previously described [5] .
|
We previously identified an important modification of human microRNAs , written by BCDIN3D , an RNA phospho-methyltransferase linked to triple negative breast cancer , obesity and type II diabetes . Here , we employed a powerful sequencing method that overcomes RNA secondary structure and RNA modifications , to unbiasedly identify RNAs stably bound to BCDIN3D . This analysis showed that BCDIN3D interacts with full-length phospho-methylated tRNAHis and with miR-4454 . Close inspection of miR-4454 sequence revealed that it is identical to the tRNAHis 3’ end , and we present evidence that miR-4454 is in fact a tRNA fragment generated through Dicer cleavage . Overall , our results indicate that tRNAHis interaction with BCDIN3D plays non-canonical roles , one of which is to regulate the generation of tRNAHis 3’ fragments . This may have important biological implications as ‘miR-4454’ has been identified as a potential biomarker in several human diseases . Our work also provides a compelling example of how RNA modifiers and RNA processing enzymes multi-task using different species of small RNAs , such as miRNAs and tRNAs .
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2019
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BCDIN3D regulates tRNAHis 3’ fragment processing
|
The obligate intracellular parasite Toxoplasma gondii secretes effector proteins into the host cell that manipulate the immune response allowing it to establish a chronic infection . Crosses between the types I , II and III strains , which are prevalent in North America and Europe , have identified several secreted effectors that determine strain differences in mouse virulence . The polymorphic rhoptry protein kinase ROP18 was recently shown to determine the difference in virulence between type I and III strains by phosphorylating and inactivating the interferon-γ ( IFNγ ) -induced immunity-related GTPases ( IRGs ) that promote killing by disrupting the parasitophorous vacuole membrane ( PVM ) in murine cells . The polymorphic pseudokinase ROP5 determines strain differences in virulence through an unknown mechanism . Here we report that ROP18 can only inhibit accumulation of the IRGs on the PVM of strains that also express virulent ROP5 alleles . In contrast , specific ROP5 alleles can reduce IRG coating even in the absence of ROP18 expression and can directly interact with one or more IRGs . We further show that the allelic combination of ROP18 and ROP5 also determines IRG evasion and virulence of strains belonging to other lineages besides types I , II and III . However , neither ROP18 nor ROP5 markedly affect survival in IFNγ-activated human cells , which lack the multitude of IRGs present in murine cells . These findings suggest that ROP18 and ROP5 have specifically evolved to block the IRGs and are unlikely to have effects in species that do not have the IRG system , such as humans .
Toxoplasma gondii is a widespread intracellular parasite capable of infecting most warm-blooded animals and is an important opportunistic pathogen for immunocompromised individuals and unborn fetuses . Toxoplasma resides within a non-fusogenic parasitophorous vacuole and has three apical secretory organelles , the micronemes , rhoptries and dense granules , which secrete proteins into the host cell during invasion that mediate important host-pathogen interactions [1] . In general , an asymptomatic but chronic infection is established in immunocompetent humans . However , in rare cases Toxoplasma can cause severe disease even in immunocompetent people . Diverse disease outcomes may be due to genetic differences between infecting strains [2] . Toxoplasma has a partially clonal population structure of 12–15 [3] , [4] haplogroups with the majority of North American and European isolates belonging to the canonical types I , II and III strains [5] , [6] , although haplogroup 12 has been recently shown to be prevalent in wild animals in North America [6] . In mice , these strains differ in virulence , with type I strains having an LD100 of just one parasite , compared to the LD50 of ∼103 or ∼105 parasites for types II and III strains , respectively [7] , [8] . Type I strains may also be more virulent in humans , as they are more frequently isolated from cases of congenital or severe ocular toxoplasmosis than from animals [5] , [9] . Interestingly , in South America , more genetically diverse strains are isolated , while the canonical strains are rarely found [10] . Some of these strains are associated with high mortality rates in mice [11] . Additionally , there are high rates of ocular toxoplasmosis in humans in South America [12] , [13] , and some strains isolated from French Guiana have been reported to cause severe disseminated toxoplasmosis even in healthy individuals [14] . The determinants of canonical strain-specific differences in murine virulence are well studied , but the same determinants for non-canonical strains or for human infection remain unknown . Mice and humans use divergent immune mechanisms to resist Toxoplasma . Interferon-γ ( IFNγ ) is essential to murine Toxoplasma resistance , and IFNγ-deficient mice die after infection even with avirulent strains [15] . Some of the important downstream effectors of this immune activation are the IFNγ-inducible immunity-related GTPases ( IRGs ) , which belong to the dynamin family of GTPases and can cooperatively oligomerize to vesiculate membranes . Mice deficient in individual members of the IRG family die of toxoplasmosis , but at different stages of infection , and expression of the IRGs is required even in non-hematopoietic cells , suggesting IRGs have non-redundant , crucial roles in the innate immune response against Toxoplasma [16]–[18] . Different IRGs are sequentially and cooperatively loaded onto the parasitophorous vacuole membrane ( PVM ) with Irgb6 and Irgb10 initiating and stabilizing the loading of the other members [19] . The IRGs are able to disrupt the PVM and kill the parasite [20] , [21] . While mice have 23 IRG genes , humans have only two IRG genes: IRGC which is expressed only in the testis and IRGM which is expressed independently of IFNγ induction and has a truncation in the nucleotide-binding G-domain [22] . Despite these differences , IRGM plays a role in autophagy-mediated destruction of Mycobacterium tuberculosis and Salmonella typhimurium in human cells , and some variants are associated with increased risk for Crohn's disease [23] , [24] . Thus , IRGM may have an immune role , but its lack of GTPase activity suggests a distinct mechanism of action in humans . Humans do have other known IFNγ-mediated mechanisms of resistance to Toxoplasma . For instance , IFNγ-induced indoleamine 2 , 3-dioxygenase ( IDO1 ) degrades cellular tryptophan for which Toxoplasma is auxotrophic , thereby inhibiting Toxoplasma growth [25] , [26] . The NALP1 inflammasome also mediates the innate immune response to Toxoplasma , and NALP1 was recently identified as a susceptibility locus for human congenital toxoplasmosis [27] . Toxoplasma strain differences in evasion of murine immune responses exist . For instance , type I strains are able to prevent the accumulation of IRGs on the PVM , while types II and III strains are susceptible to killing by the IRGs even when co-infecting the same cell as a type I parasite [28] . Because strain-specific evasion of the IRGs is correlated with increased virulence in the mouse , it is likely that the genetic determinants of IRG evasion will also be associated with virulence . Quantitative Trait Locus ( QTL ) mapping analyses of the virulence of F1 progeny derived from type I×II , I×III and II×III crosses have identified the genetic loci associated with virulence , and subsequent experiments have identified the causative genes within these loci . ROP18 , a highly polymorphic rhoptry protein kinase , was identified as a virulence locus in the II×III QTL study and the only virulence locus in the I×III cross [7] , [29] . ROP18 is highly expressed in types I and II strains but an insertion in the promoter prevents expression in type III strains . Addition of a type I or II copy of ROP18 into an avirulent type III strain makes that strain become virulent [7] , [11] . Recently , it was shown that type I ROP18 can phosphorylate a conserved threonine in the G-domain of Irga6 and Irgb6 , disrupting their accumulation on the PVM [30] , [31] . However , type II strains have the highest percentage of vacuoles coated with IRGs [19] , [28] despite the fact that a type II copy of ROP18 is also able to make a type III strain virulent , suggesting that other polymorphic proteins are involved in IRG evasion [7] . ROP18 was also shown to promote the degradation of the endoplasmic reticulum-associated transcription factor ATF6-β , compromising CD8 T cell-mediated adaptive immune responses [32] . Importantly , ROP18-mediated ATF6-β degradation occurs in human as well as murine cells . The ROP5 locus , which consists of a family of 4–10 tandem duplicates of highly polymorphic genes encoding for rhoptry pseudokinases that localize to the PVM , is another important virulence determinant in mice [33] , [34] . Deletion of ROP5 in a type I strain significantly attenuates virulence . Furthermore , ROP5 was the only significant virulence locus identified in the recent I×II QTL analysis and was the main virulence locus in the II×III QTL study [7] , [34] . Both types I and III strains have a virulent ROP5 locus , but the mechanism by which ROP5 affects virulence and which of the three major ROP5 isoforms , A , B or C , [33] are necessary to complement the virulence of type II are not known . A third virulence locus , identified in the II×III QTL study , contains the rhoptry protein kinase ROP16 , which in types I and III strains leads to sustained phosphorylation and activation of STAT3/6 [35] . It was recently shown that ROP16 and the dense granule protein GRA15 , suggested to be the fourth virulence locus in the II×III QTL study [36] , affect the accumulation of p65 guanylate binding proteins ( GBPs ) on the PVM in infected murine cells [37] . Because GBPs are also dynamin family members and were found on the same vacuoles as the IRGs , ROP16 and GRA15 might also affect the accumulation of the IRGs on the PVM . Furthermore , since the GBPs are present in humans , ROP16 and GRA15 could possibly affect survival in IFNγ-stimulated human cells . Because the murine and human immune responses to Toxoplasma are so different , it cannot be assumed that ROP18 , ROP5 , ROP16 and GRA15 , which determine Toxoplasma virulence in mice , similarly affect survival in human cells . Furthermore , it is currently unknown for most of these proteins what effects they have outside the clonal lineages from which they were identified . Many of the exotic strains are highly virulent in mice , but because they are so divergent from the canonical strains and the exotic strains have not been used in QTL or gene manipulation studies , it is not known what factors drive virulence in these strains . For example , IRG evasion has not been measured for the exotic strains , and it may be that this is strictly a type I phenotype . In this study , we find that ROP18 can only inhibit accumulation of the IRGs on the PVM of strains that also express virulent ROP5 alleles . Expression of ROP18 in strains that do not express virulent ROP5 alleles does not affect IRG accumulation or in vivo virulence . In contrast , specific ROP5 alleles can reduce IRG coating even in the absence of ROP18 expression and directly interact with Irga6 to inhibit its oligomerization . Non-canonical strains exhibit differences in evasion of IRG-mediated killing as well , and the allelic combination of ROP18 and ROP5 also correlates with strain differences in IRG evasion and virulence for these strains . However , neither ROP18 nor ROP5 markedly affect parasite survival in IFNγ-activated human cells .
Type II strains have the highest percentage of IRG-coated vacuoles compared to types I and III strains [19] , [28] even though they possess a ROP18 allele capable of conferring virulence to a type III strain [7] . To determine if , like ROP18I [30] , [31] , the increased virulence due to ROP18II is correlated with reduced IRG coating in a type III background , we measured the percentage of vacuoles coated with Irgb6 by immunofluorescence in IFNγ-stimulated mouse embryonic fibroblasts ( MEFs ) infected with type I , II , III , III + ROP18I , or III + ROP18II ( Figure 1A ) . Indeed , transgenic expression in the type III strain CEP of either ROP18I or ROP18II decreased the average number of vacuoles coated with Irgb6 from 45% to 23% ( P = 0 . 001 ) for ROP18I or 29% ( P = 0 . 003 ) for ROP18II ( Figure 1B ) . Although it is generally assumed that once the PVM is coated , it will eventually lead to killing of the parasite inside , it has also been shown that Toxoplasma can escape a coated vacuole and invade a new cell [37] , [38] . Therefore , to measure killing of Toxoplasma , 100 parasites were seeded on a monolayer of MEFs , either previously stimulated for 24 hours with IFNγ or left untreated , and the number of plaques that form after 4–7 days of growth was determined . Type III had an average of 45% plaque loss when comparing plaques formed on IFNγ-stimulated MEFs to unstimulated MEFs . This percentage plaque loss was similar to the percentage of vacuoles coated with Irgb6 , suggesting that coated vacuoles are eventually destroyed . Furthermore , plaque loss is drastically reduced in Atg7 deficient MEFs ( Figure S1 ) in which the IRGs are misregulated as previously reported for Atg5 deficient MEFs [19] , [39] , suggesting the killing observed is indeed due to the IRGs . Similar to the decrease in Irgb6 coating , the plaque loss of type III + ROP18I or ROP18II was significantly decreased to 18% ( P = 0 . 0002 ) and 21% ( P = 0 . 0004 ) , respectively ( Figure 1B ) . The 23% PVM coating and 18% killing of type III + ROP18I is similar to the 25% coating and 35% plaque loss of the type I strain GT1 . Thus , ROP18 expression can likely explain most of the difference in IRG coating and killing between type I and type III strains . Despite the ability of ROP18II to reduce IRG coating of type III strain vacuoles and subsequent killing of the parasite , type II strains are still very susceptible to the IRGs , with 70% Irgb6 coating and 73% plaque loss for Pru ( type II ) ( Figure 1B ) . Thus , there must be at least one other gene involved in IRG evasion that is shared between types I and III but different in type II . It was recently demonstrated that the ROP5 cluster of pseudokinases accounts for most of the variation in virulence between types I and II strains and between types II and III strains , with types I and III strains possessing a virulent ROP5 locus [33] , [34] . Therefore , the ROP5 locus is an excellent candidate for explaining strain differences in IRG evasion . We tested a potential role of ROP5 in mediating ROP18-independent strain differences in IRG evasion by using the S22 strain , an avirulent F1 progeny from a II×III cross [40] which possesses the avirulent ROP18III and ROP5II alleles . We compared the percentage plaque loss and percentage of Irgb6 coated vacuoles between S22 and an S22 transgenic strain carrying the cosmid LC37 , which contains the ROP5 locus from the RH ( type I ) genome and was previously shown to have significantly increased virulence [33] . Expression of ROP5I significantly reduced the Irgb6 coating from 48% to 28% ( P<0 . 001 ) , and the plaque loss from 38% to 27% ( n . s . ) ( Figure 2A ) . Thus , ROP5I can function independently of ROP18I/II to prevent IRG accumulation on the PVM and subsequent killing of the parasite . While ROP5 can function independently of ROP18 in reducing IRG accumulation on the PVM of S22 + LC37 vacuoles , type II strains , which have a virulent allele of ROP18 and an avirulent ROP5 locus , have a high percentage of IRG-coated vacuoles . This suggests that either ROP18 cannot function independently of ROP5 , or that ROP18 is inhibited in the type II background . We expressed ROP18II in S22 and in S22 + LC37 to determine if ROP18II can function in the absence of virulent ROP5 alleles . ROP18II only slightly reduced Irgb6 coating in S22 from 47% to 41% ( n . s . ) and plaque loss from 39% to 24% ( n . s . ) . However , ROP18II significantly reduced Irgb6 coating from 31% to 7% ( P<0 . 001 ) and plaque loss from 27% to 9% ( P<0 . 01 ) when expressed in S22 + LC37 ( Figure 2A ) . Together , this suggests that ROP18 needs the virulent ROP5 locus for its function . That the Irgb6 coating and plaque loss in S22 + LC37 + ROP18II are similar to those in RH ( type I ) signifies that these two genes are sufficient to complement IRG evasion and plaque loss in the S22 background . To determine if the interactive effect of ROP18 and ROP5 on parasite survival also occurs in vivo , we infected outbred CD-1 mice by intraperitoneal injection with S22 , S22 + ROP18II , S22 + LC37 or S22 + LC37 + ROP18II tachyzoites expressing firefly luciferase and followed parasite growth and dissemination using in vivo imaging . On the third day after infection , the parasite burden in S22 + LC37 and S22 + LC37 + ROP18II-infected mice was 10-fold higher than in S22 or S22 + ROP18II-infected mice . By day six , both strains containing the LC37 cosmid had disseminated throughout the peritoneal cavity , but S22 + LC37 + ROP18II-infected mice had 35-fold higher luciferase activity than S22 + LC37-infected mice ( P = 0 . 03 ) , which in turn had 10-fold higher activity than S22 + ROP18II-infected mice ( P = 0 . 1 ) and 30-fold higher activity than S22-infected mice ( P = 0 . 06 ) . While S22 + ROP18II had a greater parasite burden than S22 , this was not significant ( P = 0 . 27 ) . Indeed , S22 + LC37 + ROP18II killed 100% of the mice in the acute stage of infection at both a low and high dose ( Figure 2B and C ) . Likewise , in keeping with the increased IRG evasion of S22 + LC37 but not S22 + ROP18II , S22 + LC37 showed increased virulence compared to S22 , but S22 + ROP18II-infected mice survived the infection and did not show significant differences compared to S22 infected mice ( Figure 2D ) . Thus , overall these results suggest that ROP18 only affects virulence in the context of a virulent ROP5 locus . Although mouse virulence has been determined for many non-canonical strains [11] , it is unknown what factors determine virulence in these strains . We wondered if virulent non-canonical strains could also evade IRG-mediated killing , or if IRG evasion is specific to type I strains . We measured the percentage plaque loss in IFNγ-stimulated MEFs as well as percentage Irgb6-coated vacuoles for strains from haplogroups 1–11 [6] , [41] . In general , IRG evasion correlates with virulence as strains that have a mortality rate of greater than 90% in CD-1 mice also have 25% or less Irgb6-coated vacuoles and plaque loss ( Figure 3A ) . However , some exceptions are CASTELLS and COUGAR , which exhibit greater than 50% Irgb6 coating and plaque loss in IFNγ-stimulated MEFs , despite a high mortality rate in mice [11] . These strains may have a different mechanism underlying their virulence in mice besides IRG evasion . For most strains , the Irgb6 coating and plaque loss correlates with their ROP18 allele ( Figures 3A and S2 ) [11] . For example , CASTELLS and P89 , as well as the type III strains CEP and VEG , have between 40% and 50% Irgb6 coating , and all of these strains do not express ROP18 because they have a ROP18III-like allele that contains an insertion in the promoter [11] . The strains that express a type I-like allele of ROP18 , with the exception of BOF , display 25% or less Irgb6 coating . Type II strains and COUGAR are highly susceptible to the IRGs with 70% and 53% Irgb6 coating respectively , despite having the virulent ROP18II allele . For type II strains , the avirulent ROP5 locus likely explains the high degree of Irgb6 coating , but it is unknown what versions of ROP5 are present in the non-canonical strains . For most of the strains mentioned above , Irgb6 coating correlates with their ROP18 allele , suggesting that they also have a virulent ROP5 locus , as this is necessary for ROP18 to function ( Figure 2 ) . It is currently unknown what determines the virulence and IRG evasion properties of the ROP5I/III locus because both copy number and amino acid sequence of the individual copies differ between the canonical strains [33] . To identify differences that may be associated with virulence or IRG evasion , we sequenced the different ROP5 isoforms of strains from haplogroups 1–11 ( GenBank JQ743705-JQ743783 ) . Based on the Toxoplasma genome sequence ( www . ToxoDb . org ) and our own genome sequencing of seven non-canonical Toxoplasma strains ( Minot et al . , submitted ) , we identified four distinct ROP5 open reading frames that we amplified and sequenced separately using isoform specific primers . Sequence chromatograms indicated that two or more alleles were present for the second ROP5 reading frame . We therefore cloned the PCR product from this ROP5 gene and sequenced multiple clones to obtain sequences from the different alleles , but some alleles may still be missing . Sequences from this expanded paralog matched what has previously been called both ROP5-B ( minor ) and C ( major ) genes ( Figure 3B ) [33] , [34] . We could not differentiate B and C alleles for all strains if they were not similar to the canonical strains , so we refer to them here as B copies . We determined that besides the three major ROP5 copies that were previously described , 2 other highly divergent ROP5 isoforms exist that we call ROP5L-A and ROP5L-B ( Figures S3 and S4 ) . Interestingly , ROP5L-A and ROP5L-B are highly conserved between strains , but we find that these isoforms are not expressed in tachyzoites ( Figure S3 ) so they will not be discussed further . The previously described ROP5 genes ( A , B and C ) [33] are highly divergent with strong evidence for diversifying selection ( Figure 3C ) . , especially in surface exposed residues in the kinase domain [42] In general , for ROP5-A and for ROP5-B and C , which cluster together , alleles can be divided into distinct groups with the BOF , P89 , CAST and GPHT strains grouping with the virulent types I and III alleles ( Figure 3B ) . A second allelic group consists of the strains VAND , RUB , GUY-KOE , GUY-DOS and GUY-MAT . The ability to confer virulence of this allelic group is unknown but because these strains are all highly virulent [11] and able to evade the IRGs , these alleles are likely virulent . A third very divergent group of alleles contains the strains MAS , CASTELLS and TgCatBr5 , but there is less diversity in the ROP5-A , B and C isoforms present in these strains . The COUGAR allele is most similar to but divergent from the second group , but interestingly , COUGAR has only one B/C allele . The avirulent ROP5 locus from type II is also divergent , and a phylogenetic analysis of all ROP5 alleles indicates that the type II ROP5-B and C genes are more closely related to ROP5-A than to ROP5-B or C of the other strains . These results suggest that ROP5-B and/or C could be important for IRG evasion and virulence since type II strains and COUGAR have high levels of IRG coating ( Figure 3A ) and seem to have either ROP5 alleles that are all ROP5-A-like ( type II ) or are missing ROP5-C ( COUGAR ) ( Figure 3B ) . Next , we tested whether differences in ROP5 expression or copy number could account for strain differences in IRG evasion . For example , BOF has virulent ROP18 and ROP5 alleles but is highly coated by Irgb6 ( Figure 3A–B ) . To estimate copy number differences between the strains we have sequenced , we plotted the sequencing coverage of the ROP5 locus versus the average genome coverage , as this was previously shown to be a good estimate for copy number [43] . Most of the strains had about twice as many reads at ROP5-A and B as the rest of the genome , while MAS and TgCatBr5 have 3–5 copies of each gene ( Figure 3D ) . However , coincident with our inability to amplify ROP5-A , we found that BOF is missing ROP5-A and has only one copy of ROP5-B . We also looked at ROP5 expression levels determined using RNA-Seq data from 24 hour infections of murine bone marrow derived macrophages with each strain ( Figure 3D ) . BOF has barely detectable expression of ROP5-B and no expression of ROP5-A , likely explaining its high Irgb6 coating despite having a similar ROP5-B/C amino acid sequence to types I and III . Indeed BOF + LC37 has virtually no Irgb6 coating ( 0 . 33% ) compared to BOF ( 40% Irgb6 coating , P = 0 . 001 ) ( Figure 4A ) . ROP5 expression levels can also likely explain many intra-haplogroup strain differences where ROP18 and ROP5 coding sequence are the same; for example , VEG has higher ROP5 expression levels compared to the other type III strain CEP , and VEG has slightly reduced IRG coating compared to CEP . Thus , higher ROP5 expression is correlated with reduced IRG coating , suggesting a non-enzymatic , dose-dependent role for ROP5 in IRG evasion . Because the LC37 cosmid that reduced Irgb6 coating and plaque loss in S22 and BOF contains ROP5-A , B and C it is unknown which of these isoforms ( or which combination ) is important for IRG evasion . However , the fact that type II ROP5 alleles are less divergent and more similar to ROP5-A suggests type II is missing ROP5-B and C . Additionally , ROP5-C was previously described as the major allele with A and B as minor alleles when trace reads were assembled for the ROP5 coding region of types I , II and III [34] . Therefore , we tested if ROP5-AIII , ROP5-CIII or LC37 , which contains the entire ROP5 locus , could complement IRG evasion in the type II background . Although some of the effects we see in the type II background will be due to an interaction with ROP18 , because ROP18 is present in all backgrounds , we can still compare the effects of individual ROP5 genes . We find , as expected , that expression of ROP5-AIII in the type II strain Pru led to only a slight but significant reduction in Irgb6 coating ( 51% , P<0 . 05 ) , but expression of ROP5-CIII in Pru led to a significant reduction of IRG coating ( 36% , P<0 . 001 ) similar to that of Pru + LC37 ( 38% , P<0 . 001 ) compared to a heterologous control ( 62% ) ( Figure 4B ) . The 36% IRG coated vacuoles in Pru + ROP5-CIII is comparable to the 25% IRG coated vacuoles for GT1 , suggesting that the lack of ROP5-C may account for the excessive IRG accumulation on type II vacuoles . To see if ROP5-CIII can also increase the survival of type II parasites in vivo , we infected CD-1 mice with Pru , Pru + ROP5-AIII , Pru + ROP5-CIII or Pru + LC37 . The growth and dissemination of Pru and Pru + LC37 was determined by in vivo imaging of luciferase activity . On the third day post infection , Pru + LC37-infected mice had twice the parasite burden of Pru-infected mice ( Figure 4C and D ) . By day six , there was 50 fold higher luciferase activity in Pru + LC37-infected mice ( P<0 . 0001 ) , and the parasites had disseminated throughout the peritoneal cavity . Indeed , 100% of Pru + LC37-infected mice died within 11 days of infection even at the lowest dose ( Figure 4E ) . Mice infected with Pru parasites expressing only ROP5-AIII or ROP5-CIII survived the infection ( Figure 4E ) but Pru + ROP5-CIII-infected mice had more ruffled fur and lost significantly more weight ( Figure 4F ) than Pru-infected mice throughout the course of infection ( P = 0 . 01 at 15 days post infection ) while Pru + ROP5-AIII-infected mice continued to gain weight . Together , these results suggest that while expression of ROP5-CIII can reduce Irgb6 coating of type II parasites , ROP5-CIII only partially enhances the survival of type II parasites in vivo , and the whole ROP5 locus is required to significantly increase virulence in mice . It is not clear how ROP5 inhibits IRG accumulation at the PVM , but other pseudokinases have been shown to serve as protein scaffolds or to regulate the activity of kinases [44] . Since ROP18 requires ROP5 for fully efficient IRG evasion , and there is an interactive effect of adding ROP18 and ROP5 to the S22 strain , it is possible that ROP5 and ROP18 interact directly . To test this hypothesis , we immunoprecipitated ROP5 and ROP18II-HA from MEFs infected with CEP or CEP + ROP18II-HA for one hour with or without previous IFNγ stimulation . We were unable to detect by western blot co-immunoprecipitation of ROP18 and ROP5 ( Figure 5A ) . Furthermore , when recombinant , tagged ROP18 kinase domain ( Lim et al . , submitted ) is added to cell lysates from IFNγ-stimulated or unstimulated MEFs infected for one hour with Pru + ROP5-CIIIHA , and ROP5 is immunoprecipitated with anti-HA , we do not co-immunoprecipitate ROP18 ( Figure S5A ) indicating that there is no direct interaction between ROP5-CIII and the ROP18 kinase domain . Next we tested the hypothesis that ROP18 is only active in the presence of virulent ROP5 alleles by immunoprecipitating ROP18II-HA from MEFs infected with S22 , S22 + ROP18IIHA , and S22 + LC37 + ROP18IIHA for one hour with or without previous IFNγ stimulation for use in an in vitro kinase assay . We found that there was no difference in the activity of ROP18 immunoprecipitated from parasites with or without a virulent ROP5 , as measured by the phosphorylation of an optimized substrate ( Lim et al . , submitted ) in vitro , ( Figures 5B and S5B ) . This established that ROP18 was active in all backgrounds and indicated that there are no irreversible effects of ROP5 on ROP18 kinase activity . Because ROP5 does not directly interact with or irreversibly affect ROP18 kinase activity , we next tested the hypothesis that ROP5 directly interacts with one or more IRGs . We immunoprecipitated HA-tagged proteins from IFNγ-stimulated or untreated MEFs infected for one hour with Pru , Pru + ROP5-AIII-HA , Pru + ROP5-CIII-HA , or RH + GRA15II-HA and lysed in the presence or absence of GTPγS ( a non-hydrolyzable form of GTP ) . Co-immunoprecipitated proteins were separated by SDS-PAGE and identified by mass-spectrometry . We did not recover any ROP18 peptides , again suggesting that ROP5 does not directly interact with ROP18 . We did , however , recover 13 peptides ( 38% sequence coverage ) from Irga6 only in the Pru + ROP5-CIII-HA infected samples lysed in the presence of GTPγS ( Figure 5C ) suggesting a specific interaction between ROP5-C and Irga6 because the other HA-tagged , PVM associated proteins did not co-immunoprecipitate Irga6 under these conditions . Under different buffer conditions and in the absence of GTPγS , we also recovered 4 peptides of Irga6 and 2 peptides ( 9 . 8% sequence coverage ) of Irgb10 only in the Pru + ROP5-CIII-HA infected samples ( data not shown ) . Because ROP5 lacks kinase activity [42] but reduces IRG localization to the PVM , we wondered if Irga6 binding by ROP5 could inhibit Irga6 oligomerization , which is necessary for its activity . To test this hypothesis , we measured the GTP-mediated oligomerization of recombinant Irga6 by dynamic light scattering in the presence of recombinant maltose binding protein ( MBP ) -tagged ROP5 or MBP alone . We found the predicted hydrodynamic radius of Irga6 to be reduced in the presence of ROP5 but not MBP ( Figure 5D ) . Thus , we find that ROP5-CIII binds and inhibits the oligomerization of at least one IRG . It was recently reported that p65 guanylate-binding proteins ( GBPs ) , members of the dynamin superfamily that includes the IRGs , also accumulate on the Toxoplasma PVM alongside the IRGs [37] . Because ROP16 and GRA15 were shown to affect GBP coating , we were interested to see if ROP16 and GRA15 also affect IRG coating . We measured the effect of ROP16 and GRA15 on IRG coating and IRG-mediated killing in types I , II and III genetic backgrounds . In a type I background , the deletion of ROP16 , the transgenic expression of GRA15II , or both in combination did not significantly alter IRG coating or killing ( Figure 6A and not shown ) . Likewise , type IIΔgra15 , type II transgenically expressing ROP16I , and type III transgenically expressing GRA15II showed no statistical differences in Irgb6 coating or plaque loss compared to their parental strains . Thus , while these genes may affect GBP coating , they do not significantly alter Irgb6 accumulation . Not all of the F1 progeny in the I×II cross that have the type I ROP5 are as virulent as type I in mice [34] indicating that there are other genes besides ROP5 and ROP18 that affect virulence . While the genetic location of the dense granule protein GRA2 has not been verified as a QTL affecting virulence , an RHΔgra2 strain is one of the few type I knockouts that have reduced mouse virulence [45] . While the reason for this reduced virulence is unknown , it is known that GRA2 functions in the formation of the tubulovesicular network in the Toxoplasma PVM [46] , which creates negative curvature in the PVM that might help to attract Toxoplasma proteins secreted into the host cell back to the PVM [47] . Indeed , it has been shown that the RHΔgra2 strain has reduced ROP18 localization to the tubulovesicular network in the Toxoplasma PVM [47] . We therefore hypothesized that this GRA2-dependent ROP18 and ROP5 localization and/or localization of other proteins , would be important for IRG evasion . Indeed , the RHΔgra2 strain has significantly increased IRG coating to 36% ( P<0 . 001 ) and increased plaque loss on IFNγ-stimulated MEFs to 24% ( P = 0 . 08 ) ( Figure 6B ) . Therefore , a protein required for the formation of the PVM structure also affects IRG accumulation . We wondered if there are strain differences in the survival of Toxoplasma in IFNγ-stimulated human cells since strain differences in virulence have been primarily studied in mice , and human cells lack the multitude of IRGs present in murine cells . We measured the percentage plaque loss of different types I , II and III strains as well as of non-canonical strains in human foreskin fibroblasts ( HFFs ) pre-stimulated for 24 hours with IFNγ ( Figure 7A ) . In general , the percentage plaque loss in IFNγ-stimulated HFFs is higher than in IFNγ-stimulated MEFs . The type I strains RH and GT1 have plaque losses of 54% and 63% , respectively , while the type II strains ME49 and Pru have plaque losses of 73% and 96% , respectively and the type III strains CEP and VEG have plaque losses of 90% and 67% , respectively . The non-canonical strains range in plaque loss from 47% ( GUY-DOS ) to 67% ( CASTELLS ) . Thus , strain susceptibility to IFNγ-mediated killing in human cells does not correlate with that of murine cells . As we have shown , ROP18 and ROP5 are responsible for most of the strain differences in IFNγ-susceptibility in murine cells , but Toxoplasma IFNγ-susceptibility in murine cells does not correlate with IFNγ-susceptibility in human cells . To test if ROP18 affects IFNγ-mediated killing in human cells , we first examined type III strains transgenically expressing a virulent copy of ROP18 . Neither ROP18I nor ROP18II expression in type III decreases the percentage plaque loss compared to the parental strain ( Figure 7B ) , suggesting that ROP18 is not responsible for strain differences in IFNγ-mediated killing in human cells . To see if ROP5 affects survival in IFNγ-activated human cells , we compared the percent plaque loss in IFNγ-stimulated HFFs between S22 and S22 + LC37 and between Pru and Pru + LC37 . The plaque loss decreases from 87% for S22 to 76% for S22 + LC37 ( P = 0 . 03 ) and from 96% in Pru to 88% for Pru + LC37 ( P = 0 . 01 ) ( Figure 7C ) . Although the differences in plaque loss due to expression of ROP5 are significant , the differences are minimal ( ±10% ) . Thus , virulent alleles of ROP18 and ROP5 do not largely affect parasite survival in IFNγ-stimulated human cells .
We report that the precise allelic combination of the Toxoplasma polymorphic ROP18 and ROP5 genes determines Toxoplasma strain differences in susceptibility to killing by IFNγ-stimulated MEFs , even for non-canonical strains . We also show that ROP18 and ROP5 function by inhibiting the accumulation of and subsequent killing by the IRGs . Toxoplasma strains also differ in their susceptibility to killing by IFNγ-stimulated HFFs , but this is not determined by ROP18 or ROP5 . Previous studies on the role of ROP18 in mediating strain differences in IRG accumulation on the PVM have produced inconsistent results . Initial studies of in vivo primed macrophages infected with the type III strain CTG expressing ROP18I and L929 cells expressing ROP18I infected with the type II strain ME49 showed minimal effects of ROP18 on Irgb6 and Irga6 coating [19] , [28] . More recently , ROP18I was shown to phosphorylate a conserved threonine in the switch 1 loop of the GTPase domain of Irga6 and Irgb6 leading to their subsequent inactivation [30] , [31] . Here , we report that both ROP18I and ROP18II can prevent the accumulation of IRGs on the PVM but only when expressed in a genetic background that contains the virulent ROP5 locus . The lack of virulent ROP5 in type II strains therefore likely explains why L929 expression of ROP18I did not affect IRG accumulation on type II vacuoles in those cells [19] . Previously it was shown that the avirulent strain S22 transgenic for the cosmid LC37 , containing ROP5 , had slightly fewer Irgb6 coated vacuoles ( ∼72% ) than wild type S22 ( 87% ) in IFNγ-stimulated MEFs , but growth inhibition as measured by uracil uptake was not affected [19] . In contrast , we see a significant decrease in the percentage of vacuoles coated with Irgb6 and increased parasite survival when comparing S22 + LC37 with S22 . This could be due to the concentration of IFNγ , the exact assay used or the genotype of the host cells used , as the IRGs are divergent between mouse strains . We find that LC37 also reduces Irgb6 coating and promotes parasite survival in Pru and BOF , and that ROP5-C can explain most of the reduction in IRG coating in vitro . However , Pru + LC37 was significantly more virulent in mice than Pru + ROP5-CIII suggesting the other ROP5 genes may have additional roles besides IRG evasion , in mouse virulence . Currently , all Toxoplasma genes that determine strain differences in virulence were identified using pairwise crosses between types I , II and III . Because types I and III are progeny from a cross ( es ) between type II and a strain named alpha ( similar to type VI ) and beta ( similar to type IX ) , respectively , these three strains are closely related to each other [48] . In recent years it has become appreciated that in South America , many other highly divergent strains exist , and types I , II and III are rarely isolated . To date , no studies have been done to determine the virulence determinants of these strains . Here we report that for these strains the allelic combination and/or expression level of ROP18 and ROP5 also determine how well these strains evade the accumulation of the IRGs and their virulence in mice . Surprisingly , even though the North American/European and South American strains diverged an estimated one million years ago [41] , they all use the same two genes to evade the murine IFNγ response . This suggests that evasion of host IRGs is crucial for Toxoplasma . However , most strains do not completely evade the IRGs as this would be an unsuccessful strategy to ensure transmission in mice as the host would be killed before infectious cysts are formed . This could mean that ROP18/ROP5 allelic combinations of highly virulent strains might have evolved to evade the IRGs of species that are more resistant to Toxoplasma , for example rats [49] , and that mice are just an accidental host or it could be an artifact of the mouse lab strains commonly used . Strains such as type II , type III , BOF ( VI ) , P89 ( IX ) and CASTELLS ( IV ) that either lack ( type II ) or do not express ( BOF ) virulent ROP5 alleles or do not express ROP18 ( type III , P89 and CASTELLS ) and are therefore less virulent in mice seem better adapted to cause chronic infections in mice . Indeed , the large majority of Toxoplasma isolates in North America and Europe belong to type II [2] . ROP5 reduces IRG coating of the Toxoplasma PVM independently of ROP18 despite a lack of kinase activity [42] . Many pseudokinases have been shown to act as scaffolds or regulators of active kinases [44] . We find that ROP5 is not necessary for ROP18 kinase activity in vitro nor did we find evidence for any direct interactions between ROP5 and ROP18 ( Figure 5A ) . We find instead that ROP5 directly interacts with and inhibits the oligomerization of Irga6 ( Figure 5C and D ) . Expression levels of ROP5 seem to correlate with the intra-haplotype differences in IRG coating between CEP and VEG , supporting a non-enzymatic , dose-dependent inhibition of the IRGs by ROP5 . Importantly , both the IRGs and the ROP5 locus have expanded , perhaps due to an evolutionary arms race whereby new host IRG genes required new ROP5 genes so Toxoplasma could continue to evade IFNγ-mediated killing . Although ROP5 can inhibit IRG oligomerization , we see an interactive effect with ROP18 on IRG-coating and virulence . Perhaps the reduced oligomerization of IRGs in the presence of virulent ROP5 alleles is reversible , but this de-oligomerization might provide access for ROP18 to bind and phosphorylate the IRGs on the threonines in their switch I loop , to prevent re-activation . If this model is correct than the interaction of ROP18 with the IRGs [30] , [31] should only occur in the presence of virulent ROP5 alleles . To defend itself against the IRGs Toxoplasma must have evolved a mechanism to ensure appropriate trafficking of ROP18 and ROP5 to the PVM upon their secretion into the host cytoplasm . The N-terminal amphipathic helices ( RAH domains ) of both proteins are required for efficient localization to the PVM , and it was speculated that their specificity for the PVM versus other membranes might be because of a preference for negative curvature [47] . Indeed , we found that RHΔgra2 parasites that have a disrupted tubulovesicular network [46] , which provides much of the negative curvature of the PVM , have increased IRG accumulation . This indicates that the attraction of ROP5 , ROP18 and possibly other secreted proteins to the PVM , which is attenuated in RHΔgra2 [47] , outweighs the possible attraction the IRGs may have for the negative curvature of the PVM [50] . It is likely that the increased IRG accumulation on the PVM of RHΔgra2 accounts for its decrease in virulence [45] . Because all Toxoplasma strains seem to rely on ROP5 and ROP18 for evasion of the murine IFNγ response , these proteins could be attractive drug targets if they are also involved in evasion of the human IFNγ response . However , we find that although there are significant strain differences in susceptibility to IFNγ-mediated killing by HFFs , ROP5 and ROP18 do not markedly affect survival in those cells . This might not be surprising because humans do not possess the large variety of IRGs of murine cells ( 23 members ) but only a single member ( IRGM ) that is not regulated by IFNγ [22] . The effector mechanisms induced by IFNγ in human cells that are effective against Toxoplasma include tryptophan degradation [25] , iron depletion [51] , P2X7-mediated death of the host cell [52] and activation of the NALP1 inflammasome [27] . While the IRGs do not mediate vacuolar destruction in human cells , we wondered if another group of dynamin-related large GTPase , the GBPs , could be involved in IFNγ-mediated killing by HFFs , but we failed to see GBP1 at the PVM in HFFs ( data not shown ) . The Toxoplasma strains that were most resistant to IFNγ-mediated killing by HFFs have also been shown to be able to cause severe disease even in immunocompetent humans . In future studies , strain differences in survival in IFNγ-activated HFFs may provide insight into that mechanism .
A rat monoclonal antibody against HA ( 3F10 , Roche , 1∶500 dilution ) , a goat polyclonal antibody against mouse TGTP ( A-20 , Santa Cruz Biotechnology , 1∶100 dilution ) , a mouse monoclonal antibody against Toxoplasma surface antigen ( SAG ) -1 ( DG52 ) [53] , and a mouse polyclonal antibody against the N-terminus of ROP5 [54] were used in immunofluorescence assays or immunoprecipitations . Secondary antibodies for immunofluorescence were coupled with Alexa Fluor 488 or Alexa Fluor 594 ( Molecular Probes ) . Secondary antibodies used in Western blotting were conjugated to peroxidase ( Kirkegaard & Perry Laboratories ) . Mouse IFNγ from Peprotech and human IFNγ from AbD serotec were dissolved in DMEM with 10% FBS . Parasites were maintained in vitro by serial passage on monolayers of human foreskin fibroblasts ( HFFs ) at 37°C in 5% CO2 . The following representatives for each haplotype were used: RH and GT1 for type I , ME49 and Pru for type II , CEP and VEG for type III , MAS and CASTELLS for type IV , GUY-KOE and GUY-MAT for type V , GPHT and BOF for type VI , CAST for type VII , TgCatBr5 for type VIII , P89 for type IX , GUY-DOS and VAND for type X and COUGAR for type XI . A Pru strain engineered to express firefly luciferase and GFP ( PruΔHXGPRT A7 ) [55] , a CEP and RH strain engineered to express clickbeetle luciferase and GFP ( CEPΔHXGPRT C22 and RH 1-1 ) [56] , CEP + ROP18II , Pru + ROP16I [7] , RHΔgra2 [45] , RHΔrop16 [57] , RH + GRA15II and CEP + GRA15II [36] have been described previously . HFFs were grown as described previously [36] . WT C57BL6/J MEFs were a gift from A . Sinai ( University of Kentucky College of Medicine , Lexington , KY ) , Atg7+/− and Atg7−/− MEFs [58] from Masaaki Komatsu ( The Tokyo Metropolitan Institute Medical Science ) and all MEFs were grown in HFF media supplemented with 10 mM Hepes . All parasite strains and cell lines were routinely checked for Mycoplasma contamination and it was never detected . Monolayers of MEF cells grown on coverslips and incubated for 24 hours with or without 1000 U/ml IFNγ . Parasites were allowed to invade for 20 minutes , non-invading parasites were then washed away with PBS 3 times , and the infection proceeded for 1 hour . The cells were then fixed with 3% ( v/v ) formaldehyde in PBS for 20 minutes at room temperature , permeabilized with 0 . 2% saponin and blocked in PBS with 3% ( w/v ) BSA and 5% ( v/v ) FBS . Percent Irgb6 coating was determined in a blind fashion by finding intracellular parasites and then scoring Irgb6 coating as positive or negative . The coding sequence for ROP5 from types I ( GT1 ) , II ( ME49 ) , and III ( VEG ) was predicted from ToxoDB genomic sequence using ORF Finder ( NCBI ) . ROP5 genomic DNA from additional strains was amplified by PCR with isoform specific primers confirmed by sequence chromatograms . ROP5 was amplified with the following primers forward 5′CGATTCACGCTTTCCATGT′3 , reverse 5′TCCTTCAGCGGAAAACAGAT′3 for ROP5-A , forward 5′CATTTCATGCCTTCCCAGTT′3 , reverse 5′GCGCTCGAGTACTTGTCCTG′3 for ROP5-B/C , forward 5′GTCCCTGGAAAACTGTTTCG′3 , reverse 5′GTGAACAGAGAGCGTCCAA′3 for ROP5-D , forward 5′ATTCTGCAATGCCCAAAAGA′3 , reverse 5′TTCATGTTGGATACGGCAAC′3 for ROP5-E and 5′AAAAGGCGCGGCGAGCTAGCGTC′3 as an alternate forward primer for ROP5-A for MAS and CASTELLS . The ROP5-B/C PCR products produced mixed sequence and therefore the PCR product was cloned and multiple clones were sequenced . The following primers were used to sequence ROP5-A and ROP5-B 5′ATAGGTAACCGGGACGCTTG′3 , 5′CCACTTCGGAAGAGACTTGC′3 , 5′GGACAGACGCAGGCTTTTAC′3 The following primers were used to sequence ROP5-D and ROP5-E 5′TGAGCTGAAAACCGACTTCAC′3 , 5′GGTGACTGGAACACTCGACA′3 , 5′TTTTCCGGACCTTGTCTTTG′3 , 5′TTCGGGAGAGACTTGCTCAG′3 , 5′GCTGTGACAGTTCCGACTCA′3 Sequences were aligned using ClustalX and Neighbor-Joining phylogenetic trees were made with Molecular Evolutionary Genetic Analysis ( MEGA ) software version 4 . 1 with 1000 bootstraps and default settings [59] . The Non-synonymous Analysis Program ( SNAP ) was used to calculate the proportion of synonymous and non-synonymous changes in coding regions [60] . The coding region and putative promoter ( 766 bp upstream of the start codon for ROP5-A and 681 bp upstream for ROP5-B ) of ROP5-A and ROP5-B was amplified from type III Toxoplasma genomic DNA by PCR ( A forward , 5′-CCACGCATTCTTCCACTCAGTACCG-3′; B forward , 5′-CCACAATGGCTACCAGGTCCTGCG-3′; A/B reverse , 5′-CTACGCGTAGTCCGGGACGTCGTACGGGTAAGCGACTGAGGGCGC-3′ ) . The coding region of ROP18 , along with putative promoter ( 742 bp upstream of the ATG start codon ) , from type I Toxoplasma genomic DNA was amplified by PCR . ( Forward 5′-CACCAGATTCGAAACGCGGAAGTA-3′; Reverse 5′-TTACGCGTAGTCCGGGACGTCGTACGGGTATTCTGTGTGGAGATGTTCCTGCTGTTC -3′ ) . These primers amplified these genes specifically as confirmed by sequencing and the sequence matched the previously published data [30]–[34] . Sequence coding for an HA tag was included in the reverse primer ( denoted with italics ) to C-terminally tag the protein . ROP5-AIIIHA , ROP5-CIIIHA and ROP18I were then cloned into pENTR/D-TOPO ( Invitrogen ) , and then cloned into pTKO-att ( described in [58] ) by LR recombination ( Invitrogen ) . The pTKO-att-ROP5IIIHA vectors were then linearized by digestion with HindIII ( NEB ) , which does not cut inside either gene . Linearized vector was transfected into PruΔHXGPRT parasites by electroporation as described previously [58] . The pTKO-att-ROP18 vector was linearized by digestion with NdeI ( NEB ) and transfected into CEPΔHXGPRT C22 parasites by electroporation . Stable integrants were selected in media with 25 mg/ml mycophenolic acid ( Axxora ) and 25 mg/ml xanthine ( Alfa Aesar ) and cloned by limiting dilution . To express ROP18II in the S22 and S22 LC37 parasite strains , 35 µg of pTKO-att-ROP18IIHA [1] was linearized by HindIII ( NEB ) and 1 µg of pTUB-CAT were co-transfected by electroporation . Stable integrants were selected by passage of 106 parasites every 2 days in 2 µM chloramphenicol . Expression of ROP18 and ROP5III was confirmed by IF and Western blot for HA staining ( Figure 6A–D ) . The LC37 cosmid from the pSC/Ble library ( gift of M . J . Gubbels , Boston College , Boston , MA ) was expressed in PruΔHXGPRT A7 and BOF by transfecting 50 µg cosmid and selecting twice extracellularly for 1 . 5 hours with 5 µg/ml phleomycin . Integration was confirmed by PCR with the Type I ROP5 specific forward primer ( 5′-TTTTCCGCAGGCCGTGGC-3′ ) and ROP5A/B reverse for Pru and amplification of ROP5-A for BOF . For the plaque assays , 100–300 parasites per well were added to monolayers of MEFs seeded the day before or HFFs seeded two days before and either previously stimulated with 1000 U/ml mouse IFNγ , 100 U/ml human IFNγ or left unstimulated for 24 hours before infection in a 24 well plate in either MEF media or DMEM with 1% FBS for HFFs . Infections were then incubated for 4–7 days at 37°C and the number of plaques was counted using a microscope . CD-1 ( Charles River Laboratories ) mice were intraperitoneally ( i . p . ) infected with 500 or 5000 syringe-lysed tachyzoites in 300 µl PBS using a 28 gauge needle . On days 3 , 6 and 12 post infection , parasite burden and dissemination was measured by bioluminescence emission imaging . Mice were injected i . p . with 3 mg firefly D-luciferin ( Gold Biotechnology ) dissolved in PBS , anesthetized with isofluorane , and imaged with an IVIS Spectrum-bioluminescent and fluorescent imaging system ( Xenogen Corporation ) . Images were processed and analyzed with Living Image software . The MIT Committee on Animal Care approved all protocols . All mice were maintained in specific pathogen-free conditions , in accordance with institutional and federal regulations . For genomic sequencing , DNA was isolated from freshly lysed Toxoplasma parasites using a Trizol-based extraction ( Invitrogen ) . This DNA was subsequently prepared for high-throughput sequencing according to the Illumina single-end genomic DNA kit protocol ( COUGAR , CASTELLS and MAS ) and 36 nucleotides of each library was sequenced on an Illumina GAII and processed using the standard Illumina pipeline . Paired-end sequencing Illumina libraries were constructed for the genomic DNA of P89 , GUY-KOE , TgCatBr5 , BOF using the Nextera Illumina compatible DNA sample prep kit ( Epicenter ) and amplified with the modified PCR protocols described previously [61] . Sequence reads were aligned to the Toxoplasma and human genomes using the Maq software package [62] . Reference Toxoplasma genomes from a type II ( Me49 ) , a type I ( GT1 ) and a type III ( VEG ) strain were obtained from http://toxodb . org ( release 6 . 3 ) . For RNA sequencing , murine bone-marrow derived macrophages ( BMDM ) were seeded in 6 well plates at 70% confluency and infected with different strains of Toxoplasma at three multiplicity of infections ( MOIs ) : 15 , 10 and 7 . 5 . After 24 hours total RNA was extracted from all infected cells using the Qiagen RNeasy Plus kit . Integrity , size and concentration of RNA was then checked using the Agilent 2100 Bioanalyzer . The RNA was processed for high-throughput sequencing according to standard Illumina protocols . Briefly , after mRNA pull down from total RNA using Dynabeads mRNA Purification Kit ( Invitrogen ) , mRNA was fragmented into 200-400 base pair-long fragments and reverse transcribed to into cDNA , before Illumina sequencing adapters were added to each end . Libraries were barcoded and subject to paired end sequencing on the Illumina HiSeq2000 ( 40+40 nucleotides ) and processed using the standard Illumina pipeline . All libraries were spiked with trace amounts of the phiX bacteriophage for quality control purposes . After sequencing , the samples were de-barcoded to separate reads from the multiplexed samples using a custom Perl script . Reads were assembled into full sequences by mapping to exons and across exon junctions using the organism's genomes as a template . Maq was used to estimate Toxoplasma transcript abundance for ROP5 and ROP18 based on our sequenced alleles . A more detailed analysis of the genome and RNA-seq data will be described elsewhere . Immunoprecipitations were each performed with 5 µg of rat anti-HA ( 3F10 , Roche ) or mouse anti-ROP5 [54] conjugated to 25 µl of protein G dynabead slurry ( Life Technologies ) . The HA antibodies were crosslinked at room temperature with 5 mM Bis ( Sulfosuccinimidyl ) suberate ( BS3 ) ( Pierce ) prepared in conjugation buffer ( 20 mM sodium phosophate , 150 mM sodium chloride , pH 7 . 5 ) for 30 minutes and quenched by adding 50 mM Tris-Cl , pH 7 . 5 for 15 minutes and finally washed with conjugation buffer . For each immunoprecipitation ( IP ) condition , 4 . 2×106 MEFs were infected at an MOI of ∼5–10 , with the strain and condition indicated . After 1 hour , uninvaded parasites were washed away with PBS and the infected cells were treated with 0 . 25% trypsin for 5 minutes at 37°C . The cells were quenched and harvested with growth media and subsequently washed with PBS + 1 mM PMSF and lysed for 15 minutes at 4°C with light agitation in 1 ml of IP lysis buffer [50 mM HEPES-KOH ( pH 7 . 5 ) , 300 mM NaCl , 10 mM β-glycerophosphate , 1 mM NaF , 0 . 1 mM NaVO4 , 1 mM PMSF , 1% NP-40 , and protease inhibitor cocktail ( Roche ) ] . The lysate was then centrifuged at 16 , 000 g for 30 minutes at 4°C and the supernatant was collected . For ROP18 binding assays , 1 µg of ROP18 recombinant kinase domain [residues 187–554 , fused to a series of N-terminal fusion tags consisting of: ( His6 ) - ( glutathione S-transferase ) - ( maltose binding protein ) - ( Streptococcus protein B1 domain ) - ( TEV cleavage site ) , ( Lim , D et al . , submitted ) ] were incubated for 30 minutes before adding conjugated and crosslinked antibody beads described above and agitating them for 3 hours at 4°C . The beads were washed 3 times with IP wash buffer [10 mM HEPES-KOH ( pH 7 . 5 ) , 150 mM NaCl , 20 mM β-glycerophosphate , and 0 . 5% NP-40] , washed 3× with HEPES-buffered saline ( HBS ) and boiled in sample buffer . The samples were western blotted with anti-GST HRP conjugate ( GE Healthcare Life Sciences ) and anti-HA ( 3F10 , Roche ) antibodies . Immunoprecipitations for kinase assays were performed as above but with several changes . The cleared lysates were incubated with 10 µg of rat anti-HA ( 3F10 , Roche ) per IP reaction and incubated for 90 minutes , washed 5 times with the IP lysis buffer and 3 times with IP wash buffers and HBS ( all buffers contained 300 mM NaCl ) . Half of the beads were boiled in sample buffer for western blotting with anti-HA and the other half used for the kinase assay . Kinase assays using a ROP18 model peptide substrate ( NH3-KKKKKWISEHTRYFF-CONH2 ) ( Lim , D . et al . , submitted ) were conducted at room temperature with a reaction buffer consisting of 50 mM HEPES pH 7 . 5 , 300 mM NaCl , 10 mM DTT , 10 mM MgSO4 and 60 µM cold ATP . Each reaction contained 0 . 5 mM of peptide substrate and 2 to 10 µCi of 32P-γ-ATP . Reactions were stopped after 30 minutes by spotting on Whatman P81 phospho-cellulose paper , which were then dried and washed with 0 . 425% phosphoric acid until no significant radioactivity remained in the washes . Radioactivity captured on P81 filters was then quantified by phosphorimage analysis with ImageQuant 5 . 2 software ( Molecular Dynamics ) . The radioactivity detected was normalized to the amount of protein immunoprecipitated as determined by the above Western blot . Immunoprecipitations were performed as above with a monolayer of confluent MEFs in a T175 lysed in the presence or absence of 0 . 5 mM GTPγS ( Sigma ) and precipitated using 30 µg of HA antibody . The washed beads were boiled in sample buffer and samples were subjected to SDS–PAGE and colloidal coomassie ( Invitrogen ) staining . For mass spectrometry analysis , proteins were excised from each lane of a coomassie-stained SDS-PAGE gel encompassing the entire molecular weight range . Trypsin digested extracts were analyzed by reversed phase HPLC and a ThermoFisher LTQ linear ion trap mass spectrometer . Peptides were identified from the MS data using SEQUEST algorithms44 that searched a species-specific database generated from NCBI's non-redundant ( nr . fasta ) database . Recombinant Irga6 [residues 1–413 , fused to a series of N-terminal fusion tags consisting of: ( His6 ) - ( glutathione S-transferase ) - ( maltose binding protein ) - ( Streptococcus protein B1 domain ) - ( TEV cleavage site ) , ( Lim et al , submitted ) ] oligomerization was monitored in 50 mM Tris/5 mM MgCl2/2 mM DTT by dynamic light scattering ( DLS ) . Oligomerization was initiated by the addition of 10 mM GTP ( Sigma ) to 20 µM Irga6 in the presence or absence of 40 µM recombinant ( His6 ) -MBP-tagged ROP5-CIII or ( His6 ) -MBP . The reaction was mixed by pipetting and immediately transferred to a quartz cuvette and equilibrated to 37°C . DLS was performed using a DynaPro NanoStar Light Scatterer ( Wyatt Technologies ) with an acquisition time of 10 sec over 35 minutes and analyzed using the DYNAMICS software version 7 . 1 . 4 . The mean hydrodynamic radius of the population was estimated using the standard curve of molecular weight for globular proteins and is not equal to the actual size of the oligomer . This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The MIT Committee on Animal Care ( assurance number A-3125-01 ) approved all protocols . All mice were maintained in specific pathogen-free conditions , and all efforts were made to minimize suffering . Sequences can be accessed in GenBank: ROP5-BL sequences JQ743705–JQ743719 , ROP5-AL JQ743720–JQ743735 , ROP5A–C sequences JQ743736–JQ743783 .
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Toxoplasma gondii can infect any warm-blooded animal and is transmitted orally by consumption of tissue cysts . To facilitate transmission , the parasite must balance induction and evasion of host immune responses to allow parasite growth and persistence , while avoiding excessive parasite burden , which can kill the host before infectious cysts are formed . Different strains of Toxoplasma have likely evolved specific effector molecules to modulate the immune responses of different hosts . In many mammals , including mice but not humans , the cytokine interferon gamma ( IFNγ ) induces the immunity-related GTPases ( IRGs ) , which are essential to the murine immune response to Toxoplasma . They function by binding to and disrupting the parasite-containing vacuole . However , some Toxoplasma strains prevent the IRGs from disrupting the parasitophorous vacuole . It was previously shown that the secreted Toxoplasma kinase ROP18 promotes virulence in mice by phosphorylating the IRGs , leading to their inactivation . We report that ROP18 requires another virulence factor , the secreted pseudokinase ROP5 , to prevent IRG accumulation , and these two proteins determine the majority of strain differences in IRG evasion , even for divergent strains for which virulence determinants have not been studied . Additionally , we show that ROP18 and ROP5 do not affect Toxoplasma survival in IFNγ-stimulated human cells . Thus , ROP18 and ROP5 are strain- and host-specific determinants of Toxoplasma immune evasion .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"immunology",
"biology",
"microbiology",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] |
2012
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The Rhoptry Proteins ROP18 and ROP5 Mediate Toxoplasma gondii Evasion of the Murine, But Not the Human, Interferon-Gamma Response
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Organ size control is of particular importance for developmental biology and agriculture , but the mechanisms underlying organ size regulation remain elusive in plants . Meristemoids , which possess stem cell-like properties , have been recognized to play important roles in leaf growth . We have recently reported that the Arabidopsis F-box protein STERILE APETALA ( SAP ) /SUPPRESSOR OF DA1 ( SOD3 ) promotes meristemoid proliferation and regulates organ size by influencing the stability of the transcriptional regulators PEAPODs ( PPDs ) . Here we demonstrate that KIX8 and KIX9 , which function as adaptors for the corepressor TOPLESS and PPD , are novel substrates of SAP . SAP interacts with KIX8/9 and modulates their protein stability . Further results show that SAP acts in a common pathway with KIX8/9 and PPD to control organ growth by regulating meristemoid cell proliferation . Thus , these findings reveal a molecular mechanism by which SAP targets the KIX-PPD repressor complex for degradation to regulate meristemoid cell proliferation and organ size .
How plants control final organ size is an intriguing question in developmental biology . Organ size is also important for plant yield and biomass . Previous studies suggest that the developing organs possess intrinsic signals to control their final size , although plant growth is affected by various environmental factors[1–5] . However , how plants determine their organ size is still unclear . Cell proliferation and cell expansion play a predominant role in determining plant organ growth . Leaf development in Arabidopsis provides a good model system for analyzing the coordination of these two important processes[6 , 7] . After the leaf primordium is initiated , cells in the primordium divide continuously to generate new cells with small size . In the tip region of the leaf , cell division gradually ceases and cells begin to differentiate and expand . Then this cell differentiation domain spreads down , forming a cell-cycle arrest front that moves toward the leaf base[8 , 9] . While most cells behind this cell-cycle arrest front exit cell division , the meristemoid cells that possess stem cell-like properties divide a few rounds and then form stomata or epidermal pavement cells [10 , 11] . This proliferation of meristemoid cells is specific for dicot plants [12] . In Arabidopsis , meristemoid cells generate about 48% of all pavement cells in leaves[13] , indicating that the amplifying division of these meristemoid cells contributes significantly to leaf size . Several key factors have been revealed to influence leaf size by regulating cell division rate[14–16] , the duration of cell division[17–29] , or cell expansion[30–37] . However , how plants determine organ growth through meristemoid cell proliferation is largely unknown . PEAPOD1 ( PPD1 ) and PPD2 were the first two genes identified to regulate leaf size by limiting meristemoid cell proliferation[8] . The tandemly repeated PPD1 and PPD2 genes encode two plant specific transcriptional regulators . Knock-out or down-regulation of PPD genes results in large and dome-shape leaves due to the prolonged proliferation of meristemoids[8 , 12] . A recent study shows that PPD proteins interact with KIX8 and KIX9 , which act as adaptors to recruit the transcription repressor TOPLESS ( TPL ) [12] . Thus , PPD , KIX and TPL may function as a repressor complex to control meristemoid proliferation and leaf growth[12] . We have recently reported that the F-box protein STERILE APETALA ( SAP ) /SUPPRESSOR OF DA1 ( SOD3 ) positively regulates leaf growth by promoting meristemoid cell proliferation[38] . SAP promotes organ growth by targeting PPD proteins for degradation . The ppd mutant partially suppresses the organ growth phenotypes of sod3-1 , suggesting that SAP may also target other proteins for degradation to control organ growth . Here we report that KIX8 and KIX9 are two novel targets of SAP . SAP interacts with KIX8 and KIX9 and modulates their protein stability . We further demonstrate that SAP acts with KIX and PPD in a common genetic pathway to control meristemoid cell proliferation and organ growth . These results reveal a novel genetic and molecular mechanism in which SAP targets the KIX-PPD repressor complex for degradation to control organ growth .
To identify novel components involved in SAP-mediated organ size control , we carried out a yeast two-hybrid screen for SAP-interacting proteins . KIX8 and KIX9 were found to interact with SAP in this screen . KIX8 and KIX9 have been shown to form a repressor complex with TOPLESS and control meristemoid cell proliferation[12] , suggesting that KIX8 and KIX9 are good candidates for SAP-interacting proteins . We confirmed that SAP can interact with full-length KIX8 and KIX9 in yeast cells ( Fig 1A ) . To analyze which domain of KIX proteins is responsible for the interaction with SAP , we used different truncations of KIX proteins in the yeast two-hybrid assays . However , none of these truncations showed an interaction with SAP in yeast cells , indicating that full length of KIX proteins is required for the interaction ( S1 Fig ) . To examine their interactions in vitro , we performed pull-down assays using His-tagged KIX8 and KIX9 and GST-tagged SAP proteins expressed in E . coli . As shown in Fig 1B , His-KIX8 and His-KIX9 bound to GST-SAP but not the GST-GUS control , indicating that SAP directly interacts with KIX8 and KIX9 in vitro . We then tested whether SAP could interact with KIX8 and KIX9 in planta using split luciferase complementation assays . Nicotiana benthamiana leaves cotransformed with SAP-nLUC and cLUC-KIX8 or cLUC-KIX9 constructs showed luciferase activity , whereas the negative control did not have luciferase activity , indicating that SAP associates with KIX8 and KIX9 in vivo ( Fig 1C ) . To confirm the interaction of SAP with KIX8/9 , we generated Arabidopsis transgenic lines expressing 35S:Myc-KIX8 and 35S:Myc-KIX9 , and crossed them with 35S:GFP-SAP or 35S:GFP to obtain 35S:Myc-KIX8/9 in 35S:GFP-SAP background and the 35S:GFP background , respectively . Co-immunoprecipitation analysis showed that GFP-SAP associated with Myc-KIX8/9 in Arabidopsis ( Fig 1D ) . Taken together , these data demonstrate that SAP can form a protein complex with KIX8/9 in Arabidopsis . As SAP functions in an SKP1/Cullin/F-box ( SCF ) E3 ubiquitin ligase complex to mediate proteasome-dependent degradation of substrate proteins[38] , we further investigated whether SAP could influence the stability of KIX8 and KIX9 . We first tested whether the levels of KIX8 and KIX9 proteins could be affected by the ubiquitin-proteasome system . 35S:Myc-KIX8 or 35S:Myc-KIX9 transgenic plants were treated with the proteasome inhibitor MG132 , and Myc-KIX8 and Myc-KIX9 proteins were then detected by immunoblot analysis . The amounts of Myc-KIX8 and Myc-KIX9 proteins were accumulated during MG132 treatment ( Fig 2A and 2B ) , suggesting that the stability of KIX8 and KIX9 proteins is influenced by the ubiquitin-proteasome pathway . Next , we checked whether overexpression of SAP could affect the protein levels of KIX8 and KIX9 . Two independent lines of 35S:Myc-KIX8 or 35S:Myc-KIX9 were crossed with 35S:GFP-SAP ( SAP-OX ) or 35S:GFP ( control ) to generate 35S:Myc-KIX8/9 in SAP-OX background and 35S:GFP ( control ) background , respectively . Protein extracts of ten-day-old seedlings were subjected to immunoblot analysis . As shown in Fig 2C and 2D , the protein levels of Myc-KIX8 and Myc-KIX9 were obviously decreased in 35S:GFP-SAP lines ( SAP-OX ) compared with those in 35S:GFP lines ( Control ) , while the transcription levels of KIX8 and KIX9 were not affected by overexpression of SAP , as shown by quantitative real-time PCR analysis ( Fig 2C and 2D ) . These results indicate that overexpression of SAP causes the degradation of KIX8 and KIX9 proteins in Arabidopsis . To analyze whether mutations in SAP could cause the accumulation of KIX8 and KIX9 in Arabidopsis , we crossed two independent lines of 35S:Myc-KIX8 and 35S:Myc-KIX9 with the sod3-1 mutant that has a loss-of-function mutation in SAP and obtained 35S:Myc-KIX8/9 in sod3-1 background . The sod3-1 mutant had higher levels of Myc-KIX8 and Myc-KIX9 proteins than the wild type , whereas the transcription levels of KIX8 and KIX9 were not affected by the sod3-1 mutation ( Fig 2E and 2F ) . These data reveal that SAP regulates KIX8/9 protein stability in Arabidopsis . Previously we showed that SAP modulates the protein stability of PPD proteins to regulate organ growth [38] . Therefore , we further asked whether SAP-mediated degradation of PPD is dependent of KIX8/9 . We then transiently expressed Myc-PPD and GFP-SAP in the mesophyll protoplasts of either the wild type or kix8-1 kix9-1 . In the wild type , the protein stability of PPD was decreased by overexpression of SAP , whereas in kix8-1 kix9-1 the protein stability of PPD was not affected by SAP ( Fig 2G ) . These results suggest that KIX8/9 is required for SAP-mediated degradation of PPD . By contrast , SAP promotes KIX degradation in either wild type or ppd2 ( Fig 2H ) . As KIX , PPD and TPL form a protein complex [12] , and SAP modulates the protein stability of both KIX and PPD , we further asked whether SAP also affects the protein stability of TPL . We then transiently expressed Myc-TPL and GFP-SAP in protoplasts . However , we did not detect an obvious decrease of the protein level of Myc-TPL when overexpressing GFP-SAP , suggesting that SAP may not promote TPL degradation ( S2 Fig ) . To investigate whether KIX8 and KIX9 affects the expression of PPD genes , we analyzed the transcript levels of PPD genes in the kix mutants . The expression of PPD2 was slightly increased in the kix8-1 kix9-1 mutant ( S3 Fig ) , indicating a feed back regulation of this protein complex in transcription level . As SAP associates with KIX8 and KIX9 and regulates their stability , we investigated whether KIX8 and KIX9 could function with SAP in a common pathway to control organ size . The kix8-1 kix9-1 double mutant showed large and dome-shaped leaves[12] , whereas the sod3-1 mutant had decreased organ size [38] . We crossed sod3-1 with kix8-1 kix9-1 to generate sod3-1 kix8-1 kix9-1 triple mutant . As shown in Fig 3 , the small leaf and flower phenotypes of sod3-1 were partially suppressed by kix8-1 kix9-1 . The area of cells in sod3-1 , kix8-1 kix9-1 and sod3-1 kix8-1 kix9-1 leaves and petals was comparable to that in wild-type leaves and petals , suggesting that SAP and KIX control organ growth by influencing cell proliferation ( Fig 3F and 3H ) . Furthermore , the silique length of triple mutant sod3-1 kix8-1 kix9-1 was significantly increased in comparison with that of sod3-1 , although kix8-1 kix9-1 siliques showed similar length to wild-type siliques ( Fig 3C and 3I ) . These genetic data indicate that kix8-1 kix9-1 is partially epistatic to sod3-1 with respect to organ size , suggesting that SAP functions with KIX8/9 in a common genetic pathway to control organ growth . We have previously reported that 35S:GFP-SAP plants have large flowers , dome-shaped leaves and short siliques [38] . By contrast , 35S:Myc-KIX8 and 35S:Myc-KIX9 plants showed decreased organ size ( S4 Fig ) . The phenotypes of 35S:GFP-SAP were partially rescued by 35S:Myc-KIX ( S5 Fig ) , further suggesting that SAP and KIX8/9 may function in a common genetic pathway to control organ size . We have previously shown that SAP associates with PPD proteins and regulates their stability to control organ growth[38] . SAP acts genetically with PPD to regulate organ growth[38] . It has been reported that PPD proteins physically interact with KIX8 and KIX9 [12] , although their genetic interactions remain unknown so far . We therefore asked whether SAP , KIX8 , KIX9 and PPD could act in a common genetic pathway to control organ size . The Δppd mutant ( Ler ecotype ) showed large and dome-shape leaves due to the deletion of both PPD1 and PPD2 genes [8] . In Columbia ecotype ( Col-0 ) , ppd2-1 and ami-ppd showed large and dome-shape leaves , like those observed in the Δppd mutant [8 , 12 , 38] . As sod3-1 and kix8-1 kix9-1 mutants are in Col-0 , we conducted a cross between sod3-1 kix8-1 kix9-1 and ppd2-1 to generate the quadruple mutant sod3-1 kix8-1 kix9-1 ppd2-1 . As shown in Fig 4 , the leaf , petal and silique phenotypes of sod3-1 kix8-1 kix9-1 triple mutant were partially suppressed by ppd2-1 ( Fig 4 ) . It was shown that KIX8 and KIX9 interact with PPD[12] , and SAP modulates the stability of both PPD and KIX8/9 . Thus , it is possible that SAP may act upstream of the KIX-PPD complex to control organ growth . To analyze how the SAP-KIX-PPD module regulates meristemoid cell proliferation , we performed dental resin imprints of the leaf epidermis to follow the fate of meristemoid cells from 12 to 14 days after germination ( DAG ) . Meristemoids are small triangular cells originating from asymmetric division of meristemoid mother cells . A meristemoid undergoes limited rounds of asymmetric divisions before it becomes a guard mother cell or a stoma[11 , 13 , 39] . We investigated how many meristemoid cells become guard mother cells and stomas or undergo asymmetric division and still maintain the meristemoid functions over time . In the sod3-1 mutant , the proportion of asymmetric dividing meristemoids was decreased compared with that in the wild type between 12 and 13 DAG ( 3% versus 13% ) and between 13 and 14 DAG ( 6%versus 15% ) ( Figs 5A and S6 ) . By contrast , more meristemoids in sod3-1 became guard mother cells or stomata than those in the wild type ( Fig 5A ) . These results indicate that meristemoid division in sod3-1 arrests earlier than that in the wild type . In contrast , the kix8-1 kix9-1 mutant and the ppd2-1 mutant showed more amplifying division of meristemoid cells than the wild type ( Fig 5A ) , which is consistent with previous studies[8 , 12] . The sod3-1 kix8-1 kix9-1 triple mutant showed an increased proportion of asymmetric dividing cells and a decreased proportion of meristemoids becoming guard mother cells or stomata compared with the sod3-1 single mutant , indicating that kix8 kix9 partially suppresses the arrested proliferation of meristemoids in sod3-1 ( Fig 5A ) . Furthermore , more meristemoids in the sod3-1 kix8-1 kix9-1 ppd2-1 quadruple mutant underwent asymmetric division than those in the sod3-1 kix8-1 kix9-1 triple mutant . These results suggest that SAP functions genetically with KIX8 , KIX9 and PPD2 to control meristemoid cell proliferation . The PPD proteins have been reported to associate with the promoter of their target genes such as CYCD3;2 and CYCD3;3 and repress their expression[12] . KIX8 and KIX9 form the complex with PPD and are required for the repression of PPD target genes[12] . As SAP controls meristemoid proliferation and organ growth by targeting PPD and KIX for degradation , we investigated whether SAP could influence the expression of PPD target genes . We analyzed the mRNA levels of CYCD3;2 and CYCD3;3 in the first pair of leaves at 12 DAG . As shown in Fig 5B , expression levels of CYCD3;2 and CYCD3;3 were higher in the kix8-1 kix9-1 and the ppd2-1 mutants than those in the wild type , which is consistent with the previous study[12] ( Fig 5B ) . By contrast , expression of CYCD3;2 and CYCD3;3 was down-regulated in the sod3-1 mutant , suggesting that SAP positively influences the expression of these two genes ( Fig 5B ) . In accordance with the organ size phenotypes , kix8-1 kix9-1 partially suppressed the reduced expression levels of CYCD3;2 and CYCD3;3 in sod3-1 . Similarly , expression levels of CYCD3;2 and CYCD3;3 in sod3-1 kix8-1 kix9-1 ppd2-1 were higher than those in sod3-1 kix8-1 kix9-1 . Thus , these results suggest that SAP acts upstream of the KIX-PPD complex to regulate the expression of PPD target genes . In addition , expression levels of several other PPD-regulated genes related to cell proliferation and organ growth were also suppressed in the sod3-1 mutant background , and the suppression was partially released by kix8-1 kix9-1 and ppd2-1 ( S7 Fig ) . These data further support that the SAP-KIX-PPD module regulates the expression of common downstream genes to regulate organ growth .
How plants determine their organ size is an interesting part of developmental biology . Meristemoids , which possess stem cell-like activity , have been recognized to regulate organ growth in Arabidopsis . However , only a few factors have been described to regulate organ growth through meristemoid cell proliferation . We have previously demonstrated that SAP promotes organ growth by increasing meristemoid cell proliferation . SAP mediates the degradation of PPD1 and PPD2 [38] , two negative regulators of meristemoid cell proliferation[8] . In this study , we identify KIX8 and KIX9 as two novel targets of SAP . SAP directly interacts with KIX8 and KIX9 , and modulates their protein stability . Genetic analyses suggest that SAP functions with KIX8 and KIX9 in a common pathway to regulate organ growth through meristemoid cell proliferation . Our results reveal a novel molecular mechanism that SAP targets the KIX-PPD complex for degradation to control meristemoid cell proliferation and organ growth . In Arabidopsis leaves , meristemoid cell division gives rise to almost half of the total number of pavement cells , thereby contributing significantly to the final leaf size[9 , 10] . The PPD proteins were the first two identified factors that control organ size by restricting meristemoid cell proliferation in Arabidopsis[8] . The ppd mutants showed large and dome-shaped leaves due to increased meristemoid cell proliferation . The KIX8 and KIX9 have been recently shown to recruit the transcription repressor TPL and form a repressor complex with PPD[12] . The kix8-1 kix9-1 mutant exhibited similar organ growth phenotypes to ppd mutants , although their genetic interactions remain unknown . We have recently revealed that SAP/SOD3 , an F-box protein , modulates the stability of PPD to control organ growth by influencing meristemoid cell proliferation[38] . Considering that the ppd mutant only partially suppressed the organ growth phenotypes of sod3-1[38] , it is possible that SAP may target other substrates for degradation as well . Supporting this idea , we demonstrate that SAP interacts with KIX8 and KIX9 and targets them for degradation . Overexpression of SAP resulted in the destabilization of KIX8 and KIX9 proteins , while KIX8 and KIX9 proteins were accumulated in the sod3-1 mutant compared with those in the wild type . Interestingly , SAP-mediated degradation of PPD proteins is dependent of KIX8 and KIX9 , indicating that SAP may target PPD-KIX complex for degradation . Genetic analyses showed that kix8-1 kix9-1 partially suppressed the organ growth and meristemoid proliferation phenotypes of sod3-1 , suggesting that SAP may act in a common pathway with KIX8 and KIX9 to control organ growth by regulating meristemoid cell proliferation . In addition , overexpression of KIX8 or KIX9 decreases organ size , and 35S:Myc-KIX8 or 35S:Myc-KIX9 can partially rescue the organ size phenotypes of 35S:GFP-SAP , reinforcing the genetic interaction of SAP and KIX . These results support that KIX8 and KIX9 are two novel substrates of SAP , and SAP targets the KIX-PPD complex for degradation in Arabidopsis . Consistent with this conclusion , the simultaneous disruptions of both KIX8/9 and PPD2 suppressed the organ growth and meristemoid proliferation phenotypes of sod3-1 better than the disruption of either KIX8/9 or PPD2 . It has been shown that the PPD-KIX complex represses the expression of D-type cyclins and other target genes . Loss-of-function of PPD or KIX results in up-regulation of PPD target genes . Interestingly , we found that PPD target genes , such as CYCD3;2 and CYCD3;3 , were down-regulated in the sod3-1 mutant ( Fig 5B ) , suggesting that SAP positively influences the expression of PPD target genes . The expression levels of CYCD3;2 and CYCD3;3 in sod3-1 were partially rescued by kix8-1 kix9-1 , and further restored by kix8-1 kix9-1 ppd2-1 ( Fig 5B ) , suggesting that SAP functions with the KIX-PPD complex in a common pathway to regulate the expression of PPD target genes . Thus , it is possible that SAP may release the transcriptional repression of PPD target genes by targeting the KIX-PPD repressor complex for degradation ( Fig 5C ) . Notably , in sod3-1 ppd2-1 kix8 kix9 , the expression levels of CYCD3;2 and CYCD3;3 are still decreased compared to the wild type , which is consistent with the observation that the organ size of sod3-1 ppd2-1 kix8 kix9 is decreased compared to the wild type . This indicates that SAP may target other substrates which also regulate the expression of CYCD3;2 and CYCD3;3 . We have shown that SAP medicates the degradation of PPD1 , which functions redundantly with PPD2 in organ size control [38] . Besides , SAP may target other unidentified substrates , including KIX8/9 homologues , for degradation . In sod3-1 ppd2-1 kix8 kix9 , PPD1 and other SAP targets may accumulate and repress the expression of CYCD3;2 and CYCD3;3 . Organ size is an important agronomic trait that influences biomass and yield . Leaves or seeds are usually harvested as the main products in crops . Thus , the increased production of plant organs would be valuable for crop producers . The ppd and kix mutants produced large organs , and overexpression of SAP increased organ size in Arabidopsis . Interestingly , the mtbs-1 mutant , which contains a mutation in the PPD homolog , has been recently reported to produce large leaves , seed pods and seeds in Medicago truncatula[40] . Down-regulation of the PPD homolog can also increase seed size and quality in soybean[40] . In addition , a recent study showed that allelic variation in the intron of SAP homolog contributes to flower size in Capsella[41] . These studies suggest that the SAP-KIX-PPD module have conserved functions in different plant species . As homologs of SAP , KIX and PPD are found in eudicot genera[8 , 12 , 38] , SAP , KIX and PPD homologs in dicots ( e . g soybean and oilseed rape ) could be manipulated to increase seed and organ size in crops . During breeding programs , breeders have already selected important yield related traits , such as seed and organ size and seed shape . Thus , it will be interesting to investigate whether natural allelic variations of SAP , KIX and PPD homologs have been selected by crop breeders in the future .
The suppressor of da1-1 ( sod3-1 ) , ppd2-1 ( SALK_142698 ) , 35S:GFP-SAP , 35S:GFP , and kix8-1 kix9-1 plants were described previously[12 , 38] . All transgenic plants and mutants were in the Arabidopsis thaliana Col-0 ecotype . Plants were grown in greenhouse under the long-day conditions ( 16 hrs light/8 hrs dark ) at 22°C . The primers used for all the constructs were listed in S1 Table . The coding sequences ( CDS ) of KIX8 and KIX9 were cloned into pCAMBIA1300-221-Myc to construct 35S:Myc-KIX8/9 . The plasmids were transformed into Arabidopsis plants using Agrobacterium tumefaciens GV3101 . MS medium supplemented with hygromycin ( 30 μg ml−1 ) was used to screen transformants . T2 seeds with a typical 3:1 segregation ratio for hygromycin-resistant versus hygromycin-sensitive were used for protein stability analysis . To measure leaf area , petal area and silique length , we photographed leaves , petals ( stage 14 ) and siliques and used ImageJ software to analyze the images . To measure cell size , leaves and petals were treated with the clearing solution [38] and then photographed under a differential interference contrast ( DIC ) microscope ( Leica DM2500 ) . The middle region of adaxial side of petals and the palisade parenchyma cells in the middle region of the leaves were used for cell size measurement . Dental resin imprints were performed as described before[13] . The dental resin imprints were taken daily from the abaxial surface of the first leaves from DAG ( day after germination ) 12 to DAG 14 . The surface of the epidermis was copied with Vinyl Polysiloxane impression material . The Vinyl Polysiloxane impression surface was further copied by covering with clear nail polish , and the nail polish copies were observed by scanning electron microscopy . The primers used for quantitative RT-PCR analysis were listed in S1 Table . Total RNA extraction and quantitative RT-PCR analysis were performed as described before [38] . ACTIN2 was used as a control for normalization . Relative amounts of mRNA were calculated using the Cycle threshold ( Ct ) method as described previously[38] . For yeast two-hybrid analysis , the bait construct pGBKT7-SAP described before[38] was used to screen for SAP interacting proteins using the Matchmaker Gold Yeast Two-Hybrid system ( Clontech ) . The CDS of KIX8 and KIX9 were cloned into pGADT7 to confirm the interaction of KIX8/9 with SAP . The primers used to construct pGADT7-KIX8/9 were listed in S1 Table . Transformation of yeast cells was performed according to the user manual ( Clontech ) . Transformation of the bait vector pGBKT7 with KIX8-AD and KIX9-AD and the prey vector pGADT7 with SAP-BD was used as the negative control . Protoplasts were isolated from Arabidopsis leaves and the transformation was performed as described before [42] . EOD1-FLAG was used as a control for protoplast transformation to indicate that transformation efficiency was comparable between different transformations . After transformation , protoplasts were cultured for 16 hours at 22°C in the dark and then total proteins were isolated for Western blot analysis . The primers used to construct His-KIX8/9 were listed in S1 Table . The coding sequences of KIX8 and KIX9 were cloned into pET-28a ( + ) . GST-SAP and GST-GUS were described before[38] . Pull-down assay was performed as described previously[27] , and the precipitates were analyzed by immunoblot with anti-GST ( Abmart ) and anti-His ( Abmart ) antibodies . The primers used to construct cLUC-KIX8/9 and SAP-nLUC were listed in S1 Table . The CDS of KIX8 and KIX9 were cloned into the vector pCAMBIA-split_cLUC , and the CDS of SAP was cloned into the vector pCAMBIA-split_nLUC . The plasmids were transformed into A . tumefaciens GV3101 and transiently expressed in N . benthamiana leaves as described previously[27] . The luciferin ( 0 . 5 mM ) was sprayed on leaves and incubated 3 min before luminescence detection by NightOWL II LB983 imaging apparatus . To prevent protein degradation , seedlings were pre-treated with MG132 before co-immunoprecipitation experiments . Co-immunoprecipitation was performed as described before [27] . The immunoprecipitates were detected by immunoblot analysis with anti-Myc ( Abmart ) and anti-GFP ( Abmart ) antibodies , respectively . Ten-day-old seedlings were incubated in liquid MS medium with 50 μM MG132 or DMSO control for 16 h . Total protein were extracted and analyzed by immunoblot using anti-RPN6 ( Enzo ) and anti-Myc ( Abmart ) antibodies , respectively . Myc-KIX8 and Myc-KIX9 protein levels were quantified by ImageJ software . Sequence data from this article can be found in the EMBL/GenBank data libraries under accession numbers: AT5G35770 ( STERILE APETALA [SAP] ) , AT4G14713 ( PEAPOD1 [PPD1] ) , AT4G14720 ( PEAPOD2 [PPD2] ) , AT3G24150 ( KIX8 ) , and AT4G32295 ( KIX9 ) .
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Organ size is coordinately regulated by cell proliferation and cell expansion; however , the mechanisms of organ size control are still poorly understood . We have previously demonstrated that the Arabidopsis F-box protein STERILE APETALA ( SAP ) /SUPPRESSOR OF DA1 ( SOD3 ) controls organ size by promoting meristemoid proliferation . SAP functions as part of a SKP1/Cullin/F-box ( SCF ) E3 ubiquitin ligase complex and modulates the stability of the transcriptional regulators PEAPODs ( PPDs ) to control organ growth . Here we show that KIX8 and KIX9 are novel substrates of SAP . KIX8 and KIX9 have been shown to form a transcriptional repressor complex with PPD and TOPLESS ( TPL ) to regulate leaf growth . We found that SAP interacts with KIX8/9 in vitro and in vivo , and modulates their protein stability . Further analyses indicate that SAP acts in a common pathway with KIX8/9 and PPD to control meristemoid proliferation and organ growth . These findings reveal that SAP regulates organ size by targeting the KIX-PPD repressor complex for degradation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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2018
|
STERILE APETALA modulates the stability of a repressor protein complex to control organ size in Arabidopsis thaliana
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Extrinsic apoptosis is a programmed cell death triggered by external ligands , such as the TNF-related apoptosis inducing ligand ( TRAIL ) . Depending on the cell line , the specific molecular mechanisms leading to cell death may significantly differ . Precise characterization of these differences is crucial for understanding and exploiting extrinsic apoptosis . Cells show distinct behaviors on several aspects of apoptosis , including ( i ) the relative order of caspases activation , ( ii ) the necessity of mitochondria outer membrane permeabilization ( MOMP ) for effector caspase activation , and ( iii ) the survival of cell lines overexpressing Bcl2 . These differences are attributed to the activation of one of two pathways , leading to classification of cell lines into two groups: type I and type II . In this work we challenge this type I/type II cell line classification . We encode the three aforementioned distinguishing behaviors in a formal language , called signal temporal logic ( STL ) , and use it to extensively test the validity of a previously-proposed model of TRAIL-induced apoptosis with respect to experimental observations made on different cell lines . After having solved a few inconsistencies using STL-guided parameter search , we show that these three criteria do not define consistent cell line classifications in type I or type II , and suggest mutants that are predicted to exhibit ambivalent behaviors . In particular , this finding sheds light on the role of a feedback loop between caspases , and reconciliates two apparently-conflicting views regarding the importance of either upstream or downstream processes for cell-type determination . More generally , our work suggests that these three distinguishing behaviors should be merely considered as type I/II features rather than cell-type defining criteria . On the methodological side , this work illustrates the biological relevance of STL-diagrams , STL population data , and STL-guided parameter search implemented in the tool Breach . Such tools are well-adapted to the ever-increasing availability of heterogeneous knowledge on complex signal transduction pathways .
Apoptosis , a major form of programmed cell death , plays a crucial role in shaping organs during development and controls homeostasis and tissue integrity throughout life [1] , [2] . Moreover defective apoptosis is often involved in cancer development and progression [3] . Apoptosis can be triggered by intrinsic or extrinsic stimuli . Intrinsic apoptosis is triggered in case of cell damage ( e . g . stress , UV radiation ) or cell malfunction ( e . g . oncogene activation ) . Extrinsic apoptosis is initiated by the presence of extracellular death ligands , such as Fas ligand ( FasL ) , Tumor Necrosis Factor ( TNF ) , or TRAIL [2] . Because the latter has a unique ability to trigger apoptosis in various cancer cell lines without significant toxicity toward normal cells , TRAIL-induced apoptosis has been the focus of extensive studies [1] . The effects of TRAIL application can be significantly different from one cell line to another [4]–[6] . The current understanding is that cell death results from the activation of one of two parallel pathways , leading to the classification of cell lines into two distinct cell types . In type I cells , effector caspases are directly activated by initiator caspases . Mitochondria outer membrane permeabilization ( MOMP ) is not required to generate lethal levels of caspase activity . In type II cells , the activation of initiator caspases triggers MOMP that in turn triggers effector caspases activation . MOMP is required for cell death . This necessity of mitochondrial pathway activation to undergo apoptosis is often referred as type II phenotype , in contrast to type I phenotype where MOMP is a side effect of apoptosis . Many models of apoptosis , based on different mathematical formalisms , ranging from logical models to differential equation systems , have been proposed so far [2] , [6]–[21] . To investigate the molecular origins of the two above-mentioned distinct phenotypes , Aldridge and colleagues developed a model describing key biochemical steps in TRAIL-induced apoptosis: extrinsic apoptosis reaction model ( EARM1 . 4 ) [6] . EARM1 . 4 is an extension of a model developed to capture cell-to-cell variability in apopotosis of HeLa cells [15] , [16] . In [6] , the authors tested the hypothesis that the distinct cell behaviors can be explained solely by measured differences in protein concentrations before stimulation among different cell lines . Cell line models share the same set of ordinary differential equations and kinetic parameters , but possess specific protein contents at the initial state ( i . e . before TRAIL application ) . These differences in the initial concentrations of a dozen of key apoptotic proteins are consistent with quantitative immunoblotting measurements . Then the authors use an abstract criterion that measures the influence of changes in initial protein concentrations on the future states of the system ( i . e . divergence of trajectories ) : the direct finite-time Lyapunov exponent ( DLE ) . They show that this criterion defines a partition of the state space that preserves known differences between phenotypes: type I and type II cells are associated to distinct regions in the state space [6] . The DLE-induced partition can be graphically represented as 2D slices of the high dimensional state space called DLE diagrams [6] , [22] . As shown in [6] , DLE diagrams are intuitive tools to predict the effect of mutations on cell type . However , the connection between the abstract DLE notion and cell phenotypes remains elusive: why type I and type II cells correspond to two different regions separated by a third one having high DLE values ? Understanding this relationship is important to evaluate the general applicability of the proposed approach . Moreover in [6] , the authors also probed the functioning of the apoptotic pathways in different cell lines and for different mutants using three different experimental methods: clonogenic assays , microscopy imaging and flow cytometry measurements of immunostained cells . These experiments probe subtly different aspects of the interplay of different pathway components , and most notably on the role of MOMP in the apoptotic response: death/survival following TRAIL stimulation of derived cell lines overexpressing Bcl2 ( Property 1 ) , synchronous/sequential activation of initiator and effector caspases ( Property 2 ) , and effector caspase activation prior/posterior to MOMP ( Property 3 ) . However , the authors do not test the consistency of EARM predictions with the detailed experimental information they provide . In this work we address the two above-mentioned problems by using a formal language , signal temporal logic ( STL ) . STL was originally developed for monitoring purposes to specify the expected behavior of physical systems , including notably the order of physical events as well as the temporal distance between them [23] . Like other temporal logics and formal verification frameworks [24]–[31] , it has been applied to the analysis of biomolecular networks [32] , [33] . In particular , because it allows expressing in a rigorous manner transient behaviors of dynamical systems , one can encode as STL properties various cellular responses observed with different experimental methods and associated to type I/II phenotypes . Because STL properties have a quantitative interpretation , describing how robustly behaviors of the system satisfy or violate the property , STL diagrams can be constructed analogously to DLE diagrams . However , since STL diagrams are each associated to a specific STL property their interpretations do not suffer from ambiguities . Moreover , one can benefit from the expressive power of the STL language to encode detailed experimental information and thoroughly test the consistency of EARM with the various observations ( Figure 1 ) . We report three findings . Firstly , our results highlighted that the three experimental methods proposed in [6] to investigate the importance of MOMP for cell death from three different perspectives , each suggesting a type I/II distinguishing criterion , do not lead to consistent cell line classifications . For example the ΔXIAP HCT116 cell line should be classified as type II based on Properties 1 and 2 , and as type I based on Property 3 . This challenges the well-posedness of the type I/II notion . Secondly , using our systematic approach , we found several inconsistencies between model predictions and actual observations . Taking again advantage of the quantitative interpretation of STL properties , we searched for valid parameters using a cost function that is minimal when all properties are consistent with experimental data and state-of-the-art global optimization tools . Inconsistencies have been resolved simply by modifying a few parameters , thus showing that there is no need for structural changes in the model . Thirdly , our findings reconciliate the apparently contradictory views expressed by Scaffidi and colleagues [5] and Aldridge and colleagues [6] about the origins of type I and II phenotypes . Indeed , Scaffidi , Barnhart and colleagues suggest that the initiator caspase activation capabilities are the main determinants of the type I/II phenotype of a cell line [5] , [34] , whereas Eissing and colleagues , Jost and colleagues , and Aldridge and colleagues suggest that the latter is mainly controlled by the relative abundance of downstream proteins , most notably XIAP and caspase-3 [4] , [6] , [7] . Our results suggest that , unlike downstream proteins , the modification of the concentration of upstream proteins within physiological range has a negligible effect on cellular responses . However , the critical effects of downstream protein concentration changes are fed back to upstream processes and are amplified via a positive feedback loop involving caspases 3 , 6 , and 8 , leading to the activation of initiator caspases . Finally , the comparison of the STL and DLE diagrams showed that the DLE criterion essentially captures the notion of cell survival or cell death , like Property 1 . This lead us to better understand why the fairly abstract DLE criterion induced biologically-relevant partitions in the work of Aldridge and colleagues [6] . A last contribution is that we extended the functionalities of the Breach tool [33] so that phase diagrams can be automatically computed given any differential equation model and STL property . Therefore , the methodology presented here can be applied to other complex biomolecular networks . The first three sections of the Results part deal with the detailed analysis of three different observed phenotypes associated with type I/II behaviors , encoded in STL , and confronted with model predictions . In the last two sections , we study whether the EARM model can be reconciled with all the considered observations on all cell lines and search for the origins of cell type differences .
In summary , we found that EARM1 . 4 satisfies the majority of the observed behaviors encoded in STL ( Figure 7 ) . This is commendable for a model of this size and complexity , given that EARM1 . 4 has not been tuned with respect to these properties , even if the model and the specific observations we used to state our STL properties have been published in the same paper [6] . However , few discrepancies were identified . It is important to test whether the proposed model is structurally not capable of accounting for all the observed properties . If not , this would call for significant model revision . We first tried to resolve inconsistencies by minor adjustments of the thresholds we used in formulae . However , property satisfaction values proved robust to threshold changes ( Figure S1 ) . We therefore resorted to search for better parameter values using global optimization methods [41] . We defined a cost function that indicates for any given parameter how far the model is from satisfying all its constraints . More precisely , the cost function aggregates three measures: how many properties are consistent with the observations , how robustly satisfied or falsified they are , and how large are the deviations of the parameters with respect to their reference values . Then , we used the global optimization tool CMA-ES [42] to search automatically for parameters minimizing this cost function ( see Methods ) . Here , one should note that the real-valued semantics of STL properties is critical: continuous optimization tools take advantage of the graded interpretation of STL properties , whose values indicate their “distances from satisfaction” . The sole use of traditional Boolean-valued interpretations of temporal logic formulae would have made this search impractical . Because of their ambiguous phenotypes , T47D cell data were not used for parameter search . We started with 43 parameters , that is , all catalytic and forward reaction rates ( see Method section ) . After applying our optimization procedure we found that the modification of only 2 parameters was sufficient to achieve full agreement with experimental data . The parameters found by the search procedure are a parameter regulating the strength of the caspases feedback loop ( 2 . 71 fold increase ) and a parameter regulating the kinetics of PARP cleavage ( 55 . 6 fold decrease ) ( Table S1 ) . Given the usually large uncertainties in actual parameter values , such changes can still be considered as acceptable . New parameter values lead to satisfaction of Property 1–3 in nominal cells corresponding to all HCT116 and SKW6 . 4 normal and derived cell lines . To test whether property values are corrected at the cell population level , we recomputed the population data with these new parameter values . As shown in Figure S5 , all inconsistencies were indeed resolved at the cell population level for all cell types ( again with the exception of T47D cells ) . It is important to note that the significantly different phenotypic responses of the different cell lines are in the model solely explained by observed differences in the initial concentrations of a dozen of key proteins . Therefore one can use EARM1 . 4 with new parameter values ( Table S1 ) and STL diagrams to investigate the origins of the different behaviors shown by cell lines . One important question is to distinguish whether the different behaviors can be explained exclusively by differences in upstream protein concentrations or exclusively by differences in downstream protein concentrations , or whether a combination of upstream and downstream changes is needed [6] . Indeed , it has been proposed that the main differences between type I and II behaviors are essentially due to differences in the efficiency of initiator caspase activation by the DISC [5] , [34] , [43] . It has also been proposed that the main determinant is the concentration of XIAP relative to caspase-3 [4] , [6] . These questions can easily be answered using STL diagrams . Figure 8 shows the XIAP/caspase-3 and FLIP/caspase-8 diagrams for Property 2 , computed with respect to the HCT116 cell line . It is apparent that the sole change of the concentrations of XIAP and capsase-3 from their original values to values corresponding to SKW6 . 4 cells is sufficient to alter the behavior of those cells from a type II to a type I phenotype . A similar change but for FLIP and caspase-8 proteins has no effect: cells remains with a type II phenotype . As illustrated in Figure S6 and S7 , this is true for all properties and both directions ( i . e . , modifying protein concentrations from HCT116 to SKW6 . 4 values and vice-versa ) . This lack of influence of any upstream protein concentration is in apparent contradiction with the markedly different profiles for caspase-8 activation observed experimentally in [5] between type I and type II cells , and in EARM1 . 4 between HCT116 and SKW6 . 4 cell lines , and even more between normal and ΔXIAP cells that differ only in XIAP concentration ( Figure 8C and 8D ) . The latter comparison suggests that differences in downstream protein concentrations feed back on upstream protein activities . To test this , we created feedback mutants ( denoted ΔFB cells ) by zeroing the cleaveage rate of caspase-8 by caspase-6 . Similar activation profiles for caspase-8 in HCT116 and SKW6 . 4 cell lines were then obtained showing that in normal conditions differential activation of upstream processes is the consequence of differential downstream processes activation ( Figure 8C and 8D ) . So , one can reconciliate the different views expressed by Scaffidi and colleagues and by Aldridge and colleagues [5] , [6] . There are indeed functionally significant differences in upstream protein activities ( e . g . caspase-8 ) in type I and II cells . However , according to EARM1 . 4 model , these differences do not result from differences in upstream protein levels but rather from downstream differences that feed their influence back on upstream processes . The feedback loop is required to preserve synchronous initiator and effector caspase activation in type I cells . Note that using STL was instrumental here . Indeed , because all cells die in these simulations ( no Bcl2 overexpression ) , DLE diagrams were not offering relevant information . Interestingly , the analysis of the FLIP/caspase-8 STL diagrams for Property 2 and 3 reveals that moderate inhibition of caspase-8 levels ( e . g . , by one third ) in SKW6 . 4 cells would transform them into cells showing mixed type behaviors ( Figure S7 D and F ) . Indeed the model predicts that these cells would present a sequential activation of caspases ( Property 2 satisfied; a type II feature ) and a MOMP-independent death ( Property 3 violated; a type I feature ) . This mutant would show exactly the opposite behavior of ΔXIAP HCT116 cells , a combination of behaviors that has not yet been observed . Therefore , the detailed analysis of this cell line could possibly provide valuable information on the interplay between the two apoptotic pathways . Similarly , the partial inhibition of caspase-3 levels in SKW6 . 4 cells would also lead to cells showing mixed type behaviors ( Property 1 remains false whereas Property 2 and 3 change to true; Figure S6 B , D , and F ) .
In this work , we expressed in a formal language , STL , a number of observed properties on molecular details of extrinsic apoptosis in several mammalian cell lines , and systematically tested their consistency with a previously-proposed model developed to capture the same process in the same cell lines , EARM1 . 4 . It is important to note that even if model and experimental data have been published in the same article [6] , the model has not been tuned to comply with the various observed properties we tested on the different cell lines . Indeed , we found several inconsistencies between model predictions and experimental observations . These inconsistencies can be resolved by model reparametrization involving a limited number of parameter changes . However , these needed changes were affecting key processes , namely the PARP cleavage rate and the strength of the caspases-3 , -6 and -8 feedback loop . It is remarkable that the model was able to explain a number of experiments probing different aspect of apoptosis made on different cell lines and mutants , simply by taking into account observed differences in protein concentrations but keeping the same model structure and reaction rates for all cell lines . This makes it a valuable tool to investigate the origins of the two different cell responses . Unlike in in vivo experiments , the number of factors that could explain these differences is limited in EARM1 . 4 . Using STL diagrams , we showed that observed differences in the concentrations of upstream proteins in different cell lines could not account for the observed cell type changes . This finding is consistent with the observation based on in vivo and in silico works that downstream proteins , most notably XIAP and caspase-3 , play a key role [6] , but is in apparent contradiction with the observation that upstream protein activities are markedly different in type I and II cell lines [5] . Detailed analysis showed that the effects of downstream protein concentration differences are in fact fed back to upstream processes and amplified via the positive feedback loop involving caspases 3 , 6 , and 8 . This finding reconciliates the views expressed by Scaffidi and colleagues and by Aldridge and colleagues [5] , [6] . Based on experimental observations , we defined three properties associated with type II behaviors: ( 1 ) the cell survives if Bcl2 is over-expressed , ( 2 ) the activations of initiator and effector caspases are sequential , and ( 3 ) MOMP precedes caspase-3 activation . They all assess the role of mitochondria for cell death and differ only in subtle means . However , they are not always equivalent . For example , ΔXIAP HCT116 cells satisfy Property 3 but not Property 2 , leading to interpretations like ΔXIAP HCT116 being type I cells while exhibiting a type II phenotype . Based on our work , there is no evidence that one property could be considered as a defining criterion , excepted maybe for historical or practical reasons ( cell types were originally defined based on caspase activation kinetics whereas Bcl2 overexpression is considered as the standard method for cell type classification ) . This challenges the consensual understanding that there exists ( implicitly ) well-defined type I and type II phenotypes . It should be noted that here we go beyond the notion of mixed cell type introduced by Aldridge and colleagues for describing T47D cells . The authors implicitly assume that cell types are well defined but that within a population of cells a mixture of both phenotypes can be observed , coming from cell-to-cell variability [16] , [44] , [45] . Here we propose that these three properties are considered as type II features . Then the ΔXIAP HCT116 cells would be more consistently qualified as possessing some type I and some type II features . With the accumulation of more detailed characterizations of apoptosis in more cell lines , it is likely that the use of the loosely-defined notion of cell types will otherwise become more and more problematic . Like the DLE diagrams introduced by Aldridge and Haller [22] , STL diagrams are a convenient and intuitive way to represent the influence of various factors on complex dynamical properties . However , STL diagrams are superior on several counts . Firstly , one can benefit from the expressive power of temporal logics to express different observed properties of the dynamics of the cell response . It allows us to test in which respect are the cell lines different . Secondly , although the evaluation of STL properties and of the DLE returns continuous values , the fact that STL values are signed – positive values indicate satisfaction and negative values indicate falsity – allows for a more direct interpretation of the diagrams . Moreover , it allows defining statistics over populations of cells . Thirdly , DLE generates well-defined partitions if in some regions a small change in the initial state has a mild effect on the future system's state , thus generating low DLE values , and in other regions , similar changes have drastic effects , thus generating high DLE values . Although this is clearly the case in cell lines overexpressing Bcl2 since some cells die , whereas others survive ( Figure 3 ) , this is not generally true . DLE and STL diagrams are particularly useful to have a rapid view of the consequences of changing a few factors , initial concentrations in our case . This feature allows us to foresee the consequences of mutations ( e . g . ΔXIAP mutants in XIAP/caspase-3 diagrams ) , to investigate the ( lack of ) influence of given factors ( e . g . FLIP changes in FLIP/caspase-8 diagrams ) , and to assess the influence of cell-to-cell heterogeneity by representing graphically the means and standard values of populations in diagrams . However , heterogeneity in diagrams is limited to two dimensions . Moreover , since the cell lines differ in more than two dimensions , only one cell line can be correctly mapped in the state space slice of the diagram . Other cell lines are projected onto it , making their interpretations subject to caution . To solve this issue , we introduced population property values for describing the behavior of cell populations . These values and their statistics , notably means , standard deviations , and percentage of satisfaction , offer a more accurate view than phase diagrams . Indeed , even if we found that the rapid picture offered by STL diagrams are often consistent with population property values , a few cases illustrated the need to compute these statistics as well ( e . g . T47D cells manifesting in silico a clear mixed-type behavior with respect to Property 1 , that is not present in the phase diagram in Figure 3 ) . In addition to computing diagram and population statistics , STL properties also enable model revision based on experimental observations . Observed properties are encoded in STL and the continuous semantics of STL is used to search for valid parameter values . Traditional model revision methods based on curve fitting could not be adapted here by lack of well-defined time series data . The non-standard use of continuous semantics for temporal logic formula interpretation is essential to allow for an effective search [46]–[48] . Using global optimization methods , we found that the few discrepancies we had identified in earlier steps can be resolved by modifying only a restricted set of model parameters . Remarkably , one of the two selected parameters is regulating the strength of the caspases feedback loop , a process that is predicted to play an important role in the genesis of type I or type II behaviors . The development of experimental methods to probe quantitatively subtle aspects of the dynamics of biological processes has spurred the development of large and complex quantitative models [49] , [50] . However the available experimental data is seldom in the form of time series data directly usable by standard model validation and model calibration techniques . Therefore tools allowing for the exploration of model properties , the comparison between predictions and observations and the revision of models that are adapted to the available experimental data are increasingly needed . Temporal logics offer a flexible means to encode for a broad range of experimentally-observed properties . Moreover they are also formal languages that allow automating model analysis . Because it supports STL and uses by default distributions for parameter and initial concentrations , Breach naturally allows the exploration of properties of cell populations . We expect that Breach will become a valuable tool for the computational biologists to explore model properties , and more importantly , to get tight connections between experimental data and model predictions [51] .
We used the model of extrinsic apoptosis proposed by Aldridge and colleagues [6] named EARM1 . 4 . This model is an extension and adaptation of a previous model , EARM1 . 0 , proposed in [15] . EARM1 . 0 has been calibrated on HeLa cells using live and fixed cell imaging , flow cytometry of caspases substrates and biochemical analysis . EARM1 . 4 has been adapted to HCT116 , SKW6 . 4 and T47D cells , and has been shown to capture their capacity to die or survive in OE-Bcl2 clonogenic experiments . It is a mass-action ODE model based on nearly 70 reactions and involving 17 native proteins , 40 modified proteins or protein complexes , and TRAIL . For each cell line , the model assumes different nominal initial protein concentrations . Nominal concentrations refer here to concentrations found in a hypothetical mean cell within the cell population . More precisely , out of the 17 native proteins , 12 have been quantified by immunoblotting and the relative differences between cell lines have been used to set nominal initial protein concentrations for HCT116 , SKW6 . 4 and T47D cells ( see Table 1 ) . Besides initial concentrations , the 3 models are identical . ΔXIAP and OE-Bcl2 mutant cell lines are defined with respect to their parent cell line . In ΔXIAP cells , the XIAP concentration is set to 0 . In OE-Bcl2 cells , the initial Bcl2 concentration is 10 times higher than in the parent cell line . For cells with modified feedback ( ΔFB cells ) we set the cleavage rate of caspase 8 by caspase 6 , k7 , to 0 . To represent cell-to-cell variability within cell lines , we assumed that protein concentrations are log-normally distributed . The means of protein concentrations were the nominal values . The coefficient of variation were either measured , for caspase-3 and XIAP [6] , or assumed to be 25% as in [16] . The complete model together with Breach is available in Supplementary Materials as MATLAB files ( S9 ) . The names of the variables , constants and reactions used in the model are the same as in [6] . STL is an intuitive yet formal language for specifying the properties of continuous dynamical systems . It allows us to express in a ( pseudo- ) natural language hypothesis on the mechanistic functioning of the system taken from available biological knowledge in a formal way so that model consistency can be precisely and systematically tested . Given a model of the system , expected properties are expressed using predicates describing constraints on protein concentrations , like cPARP<105 , traditional logical operators , like and , or and implies , and temporal operators , like eventually[a , b] , always[a , b] , and until[a , b] . Time intervals [a , b] limit the scope of temporal operators . These operators can be combined to create properties of arbitrary complexities . For example , always[0–6h] ( XIAP>103 and cPARP<105 ) is a valid STL formula . The formal syntax is given in Table S2 ( top ) . STL properties are then interpreted with respect to so-called signals . In this context , signals are functions from time to the reals representing the evolution of the different concentrations in the system . Computationaly speaking , they often come from ( discrete ) time-series data obtained by numerical simulation of the ODE model . The semantics is defined such that it captures a notion of distance from satisfaction . For example , the interpretation at time t of the predicate XIAP>103 is simply the value of XIAP ( t ) - 103 . Trivially it is positive if XIAP>103 , and negative if XIAP<103 . The interpretation of XIAP>103 and cPARP<105 at time t is the minimum of the value of the two operands at time t . Note that it is positive if and only if both operands have positive values . Similarly , the interpretation of always ( XIAP>103 ) is the minimum of the value of XIAP>103 at all future time instants . It is positive if XIAP is always greater than 103 . The interpretation of STL formulas is also illustrated on Figure 9 . More generally , the continuous interpretation of STL properties ensures that if the value of a property is positive ( resp . negative ) , then the property holds ( resp . is violated ) in a more usual Boolean interpretation . Moreover , it captures a notion of “distance from satisfaction”: a large positive value indicates a robustly satisfied property , whereas a large negative value indicates a property that is far from satisfaction . The semantics is formally defined in Table S2 ( bottom ) . Note that property values are relative to the formula , in the sense that values obtained for different STL formulas are not directly comparable between each other . Given an STL property , the associated STL phase diagram is a representation of the value of the property as a function of the system's initial configuration . More precisely diagrams represent property values in 2D slices of a high dimensional state space . Each point in the diagram is associated to the value of the STL formula evaluated on the system's trajectory starting at this point . Boundaries were set so as to enclose the variability observed between cell lines . Diagrams are defined with respect to a particular cell line: with the exception of the two variables of the diagram , all other variables assume their nominal values for the given cell line . Other cell lines are placed on the diagram based on the initial concentrations of the selected proteins . Unless mentioned otherwise , the HCT116 cell line is used as a reference . In practice , a 50×50 grid of linearly-spaced points is used for the computation of each diagram . For each point on the grid , we computed the cell behavior predicted by the model and then the value of the STL property associated with this behavior ( see Program S1 ) . The ranges for caspase-3 , XIAP , caspase-8 and FLIP are [0 , 106] , [0 , 6*104] , [1200 , 4500] and [0 , 800] , respectively . Similarly , DLE diagrams represent the direct finite-time Lyapunov exponent for all points in ( a 2D slice of ) the state space . This value captures the sensitivity to initial conditions: , where denotes the maximum eigenvalue of the matrix M and t is some future time instant ( here 6 or 4 hours ) . Again , a 50×50 grid of linearly-spaced points is used for the computation of DLE diagrams . Given an STL property , the STL population data correspond to the evaluation of this property on all the simulated individual cell behaviors among a population of cells of a given cell line . Based on these property values , statistics are computed . For STL population data , 5000 different initial conditions are obtained for each cell line by sampling around its nominal initial conditions from lognormal distributions . Mean values , value distributions and percentages of satisfaction of the property ( i . e . the percentage of cells in the population satisfying a given property ) are then computed . The search procedure has two phases . In the first phase we search for new parameters for EARM1 . 4 that lead to full agreement with experimental data ( Figure 7 ) . In the second phase , when a solution is found , we minimize the number of modified parameters . We use a cost function composed of three different components: continuous , Boolean , and parameter penalties . The continuous penalties correspond to the ( negation of ) the continuous values of STL properties , and the Boolean penalties correspond to their Boolean value multiplied by a ( negative ) constant . These costs decrease when more properties are consistent with observations ( Bpenalty ) , and when they are more robustly consistent with observations ( Cpenalty ) . In the continuous component , weights are used to balance the importance of all properties , given their typical range . The last component penalizes parameter deviations from their original values ( Ppenalty ) . The overall cost is the weighted sum of these three components . In the first phase , we selected 43 parameters ( 14 catalytic rates of enzymatic reactions and 29 forward rates ) out of approximately 80 parameters in EARM1 . 4 . Parameter modifications were limited to a 100-fold change . We set weights so that the Boolean , continuous , and parameter penalties contributed to approximately 50% , 30% , and 20% of the cost , respectively . After 10 hours of computations ( 2 . 2 GHz processor , 8GB RAM ) , the search converged to a state in which all expected properties were satisfied by the model ( T47D cells excluded ) . In the second phase , we selected the parameters that changed by more than 5 folds ( there were 5 such parameters: kc9 , kc25 , kc20 , k7 and k24 ) and run the search again for each pair of these parameters . The cost function was modified by setting the Cpenalty parameter to 0 , and the beta parameter such as the Boolean penalty was responsible for approximately 90% of the cost . As a result , parameter deviations were minimized while preserving the agreement with the experimental data . We found that reparametrization of only one pair of parameters allowed for satisfaction of all properties for all cell lines . All the computations have been made using Breach [33] , [48] . This MATLAB/C++ toolbox allows for efficient numerical simulation , for sensitivity computation , and for STL property and DLE evaluation . In particular , DLEs can efficiently be computed via forward sensitivity analysis [52] . Breach is particularly oriented towards the analysis of parametric systems , in the sense that it offers efficient routines for global sensitivity analysis and parameter search , and that the graphical user interface facilitates the modification of parameters and initial conditions , and the exploration of parameter spaces .
|
Apoptosis , a major form of programmed cell death , plays a crucial role in shaping organs during development and controls homeostasis and tissue integrity throughout life . Defective apoptosis is often involved in cancer development and progression . Current understanding of externally triggered apoptosis is that death results from the activation of one out of two parallel signal transduction pathways . This leads to a classification of cell lines in two main types: type I and II . In the context of chemotherapy , understanding the cell-line-specific molecular mechanisms of apoptosis is important since this could guide drug usage . Biologists investigate the details of signal transduction pathways often at the single cell level and construct models to assess their current understanding . However , no systematic approach is employed to check the consistency of model predictions and experimental observations on various cell lines . Here we propose to use a formal specification language to encode the observed properties and a systematic approach to test whether model predictions are consistent with expected properties . Such property-guided model development and model revision approaches should guarantee an optimal use of the often heterogeneous experimental data .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"systems",
"biology",
"biochemical",
"simulations",
"signaling",
"networks",
"biology",
"computational",
"biology"
] |
2013
|
STL-based Analysis of TRAIL-induced Apoptosis Challenges the Notion of Type I/Type II Cell Line Classification
|
Neurotrophins are key regulators of neuronal survival and differentiation during development . Activation of their cognate receptors , Trk receptors , a family of receptor tyrosine kinases ( RTKs ) , is pivotal for mediating the downstream functions of neurotrophins . Recent studies reveal that cyclin-dependent kinase 5 ( Cdk5 ) , a serine/threonine kinase , may modulate RTK signaling through phosphorylation of the receptor . Given the abundant expression of both Cdk5 and Trk receptors in the nervous system , and their mutual involvement in the regulation of neuronal architecture and synaptic functions , it is of interest to investigate if Cdk5 may also modulate Trk signaling . In the current study , we report the identification of TrkB as a Cdk5 substrate . Cdk5 phosphorylates TrkB at Ser478 at the intracellular juxtamembrane region of TrkB . Interestingly , attenuation of Cdk5 activity or overexpression of a TrkB mutant lacking the Cdk5 phosphorylation site essentially abolishes brain-derived neurotrophic factor ( BDNF ) –triggered dendritic growth in primary hippocampal neurons . In addition , we found that Cdk5 is involved in BDNF-induced activation of Rho GTPase Cdc42 , which is essential for BDNF-triggered dendritic growth . Our observations therefore reveal an unanticipated role of Cdk5 in TrkB-mediated regulation of dendritic growth through modulation of BDNF-induced Cdc42 activation .
Neurotrophins are indispensable for multiple aspects of neuronal development , such as the maintenance of neuronal survival , regulation of neuronal architecture , and synaptic plasticity . Members of the neurotrophins include the prototypic member nerve growth factor ( NGF ) , brain-derived neurotrophic factor ( BDNF ) , neurotrophin ( NT ) –3 , and NT-4/5 . Downstream responses of neurotrophins are transduced by a family of receptor tyrosine kinases ( RTKs ) known as Trks , and also the low-affinity neurotrophin receptor p75 . Although all neurotrophins bind p75 , they associate with different Trk receptors with rather remarkable selectivity . NGF interacts selectively with TrkA , while BDNF and NT-4/5 bind preferentially to TrkB . NT-3 , on the other hand , associates with TrkC with high affinity , although it also binds TrkA and TrkB with low affinity . Similar to other RTKs , activation of Trks leads to dimerization and autophosphorylation of the receptors , followed by the recruitment and initiation of a myriad of signaling pathways including the Ras/MAPK , PI3K , and PLCγ pathways [1 , 2] . Interestingly , recent studies have demonstrated that activity of cyclin-dependent kinase 5 ( Cdk5 ) , a serine/threonine kinase , is required for the downstream actions of a RTK , ErbB . Cdk5 was found to phosphorylate ErbB2/3 , a phosphorylation that is essential for the activation of the receptors [3 , 4] . Cdk5 is a member of the cyclin-dependent kinase family , but it is unique in several aspects . First of all , it is activated by the neural-specific non-cyclin activators p35 and p39 . Secondly , Cdk5 is not involved in the regulation of cell cycle control , but is implicated in neuronal migration , synapse functions/maintenance , and neuronal survival [5 , 6] . The importance of Cdk5 in neuronal development and migration is underscored by the aberrant phenotypes exhibited by mice lacking Cdk5 and its activators . Cdk5 knockout mice and p35/p39 double knockout mice both exhibit perinatal death with severe cortical lamination defects [7 , 8] . Furthermore , swollen soma and nuclear margination is evident in Cdk5-deficient neurons , implicating Cdk5 as an essential regulator of neuronal survival [7] . Interestingly , truncation of the Cdk5 activator p35 into p25 has also been associated with prolonged Cdk5 activation in a number of neurodegenerative diseases [9] , thus revealing that precise regulation of Cdk5 activity is essential for maintenance of neuronal survival [10] . Furthermore , an increasing number of studies are pointing to an essential role of Cdk5 at the synapse , where it is not only involved in the formation and maintenance of synapses , but is also indispensable for the regulation of synaptic transmission and synaptic plasticity [5] . While the mechanisms by which Cdk5 regulates such diverse functions remain to be unraveled , the identification of ErbB receptors as Cdk5 substrates suggests that Cdk5 may exert its biological effects by modulating signaling pathways downstream of RTK activation . This piece of evidence , together with the abundant expression of Cdk5 and Trk receptors in the nervous system and their shared implication in a number of biological functions , prompted us to further examine if Cdk5 also regulates the signaling of Trk receptors . In the current study , we report the identification of TrkB as a substrate of Cdk5 . More importantly , we found that Cdk5-mediated phosphorylation of TrkB is essential for BDNF-induced dendritic growth through the modulation of Cdc42 activity . Our findings provide evidence for a crosstalk between the Cdk5 and neurotrophin signaling pathways , and lend further support to the idea that Cdk5 is a modulator of RTK signaling .
Given the increasing evidence implicating Cdk5 in the modulation of RTK signaling , we sought to examine if Cdk5 may also play a role in Trk signaling . Literature search revealed that TrkA , TrkB , and TrkC all contain serine- or threonine-directed proline residues at the intracellular juxtamembrane region of the receptors , but only TrkB and TrkC contain Cdk5 consensus sites S/TPXK/H/R ( Figure 1A ) . To explore the potential interplay between Trk receptors and Cdk5 , we first examined if Trk receptors associated with Cdk5 or p35 . TrkA , TrkB , or TrkC was overexpressed together with Cdk5 or p35 in COS7 cells , and immunoprecipitation was performed with Cdk5 , p35 , or pan-Trk antibody . Interestingly , all three Trk receptors were observed to associate with Cdk5 ( Figure 1B ) and p35 ( Figure 1C ) , while no association was observed when immunoprecipitation was performed with IgG control . Since both TrkB and its ligand BDNF are abundantly expressed in the brain throughout development , we next proceeded to verify the interaction between TrkB and Cdk5/p35 in postnatal brains . We found that TrkB associated with both p35 and Cdk5 in postnatal day 7 ( P7 ) rat brain lysates ( Figure 1D ) . Furthermore , Flag-tagged Cdk5 pulled down TrkB from the membrane fraction of adult brain lysates ( Figure 1E ) . These observations collectively suggest that TrkB interacted with Cdk5/p35 in both postnatal and adult brains . Since both p35 and Cdk5 are present in brain lysates and likely exist as a complex , the observed interaction between TrkB and Cdk5/p35 did not provide specific information on whether TrkB associated specifically with Cdk5 or p35 . To delineate between these two possibilities , the interaction between TrkB , p35 , and Cdk5 was examined in p35+/+ and p35−/− brain lysates ( Figure 1F ) . Interestingly , we found that in the absence of p35 , the association between Cdk5 and TrkB was essentially abolished , indicating that p35 was required for the association between Cdk5 and TrkB in vivo . We next proceeded to examine if Trk receptors , TrkB in particular , served as Cdk5 substrates using in vitro kinase assay . TrkA , TrkB , and TrkC were overexpressed in COS7 cells and immunoprecipitated by pan-Trk antibody . Incubation with Cdk5/p25 revealed that TrkB and TrkC , but not TrkA , were phosphorylated by Cdk5/p25 in vitro ( Figure 2A ) . This is in agreement with the lack of Cdk5 consensus sites in TrkA , and points to the possibility that Cdk5 may phosphorylate TrkB and TrkC at the Cdk5 consensus sites at the juxtamembrane region ( Figure 1A ) . To examine this possibility , a GST fusion protein containing only the juxtamembrane region of TrkB was prepared . In vitro kinase assay verified that Cdk5/p35 phosphorylated TrkB at the juxtamembrane region ( Figure 2B ) . It has previously been proposed that p25 and p35 may confer different substrate specificities . Results from our in vitro kinase assay suggested that Cdk5 phosphorylated TrkB regardless of whether it was activated by p25 or p35 , although further studies will be required to delineate the relative contributions of p25 and p35 to endogenous phosphorylation of TrkB by Cdk5 . We were next interested in identifying the Cdk5 phosphorylation site ( s ) on TrkB . Three TrkB-juxtamembrane region mutants were generated: TrkB M1 , where Ser478 was mutated to alanine; TrkB M2 , where Thr489 was mutated to alanine; and TrkB DM , where both Ser478 and Thr489 were mutated to alanine . Interestingly , phosphorylation of the TrkB-juxtamembrane region was almost completely abolished when Cdk5/p25 was incubated with TrkB M1 or TrkB DM ( Figure 2C ) , thus revealing that Ser478 was required for Cdk5-mediated phosphorylation of the TrkB-juxtamembrane region . We further verified the importance of this site for Cdk5-mediated phosphorylation of TrkB by generating a phospho-specific TrkB antibody against Ser478 . Preincubation of the antibody with blocking peptide prevented detection of Ser478-phosphorylated TrkB , indicating that the antibody was sufficiently specific ( Figure 2D ) . Full-length TrkB mutants lacking the potential Cdk5 phosphorylation sites were overexpressed with or without Cdk5/p35 in HEK293T cells . Interestingly , Ser478-phosphorylated TrkB was not observed in the absence of Cdk5/p35 , indicating that Cdk5 was essential for the phosphorylation of TrkB at Ser478 in HEK293T cells . More importantly , when TrkB mutants lacking Ser478 were expressed ( TrkB M1 and TrkB DM ) , phosphorylation of TrkB at Ser478 was essentially abolished ( Figure 2E ) . Taken together , our observations indicate that Cdk5 phosphorylated TrkB at Ser478 at the juxtamembrane region of TrkB . To further examine if Cdk5 is essential for phosphorylation of TrkB at Ser478 in vivo , we examined the effect of inhibiting Cdk5 activity on phospho-Ser478 ( p-Ser478 ) TrkB levels in cortical neurons . We found that at basal level , TrkB was weakly phosphorylated at Ser478 . Interestingly , stimulation with BDNF led to a marked increase in p-Ser478 TrkB levels , indicating that phosphorylation of TrkB at Ser478 was at least in part ligand dependent . Remarkably , treatment with Cdk5 selective inhibitor roscovitine ( Ros ) almost abrogated the BDNF-triggered increase in p-Ser478 TrkB levels ( Figure 3A ) , suggesting that Cdk5 was involved in the BDNF-stimulated component of TrkB Ser478 phosphorylation . To further establish the involvement of Cdk5 in Ser478 phosphorylation of TrkB in vivo , the levels of p-Ser478 TrkB in cdk5+/+ and cdk5−/− brain lysates were examined . Importantly , we found that Ser478-phosphorylated TrkB was basically undetectable in Cdk5−/− brain lysates ( Figure 3B ) . Similarly , cortical neurons prepared from Cdk5−/− brains exhibited undetectable levels of p-Ser478 TrkB . In addition , BDNF stimulation failed to trigger an increase in p-Ser478 TrkB levels ( Figure 3C ) . These observations strongly suggest that Cdk5 is essential for phosphorylation of TrkB at Ser478 in vivo , and that BDNF-stimulated increase in Ser478 phosphorylation of TrkB requires Cdk5 activity . Since BDNF stimulation was observed to increase Ser478 phosphorylation of TrkB , and Cdk5 was required for phosphorylating TrkB at Ser478 , we were interested to examine if BDNF stimulation affects Cdk5 activity . BDNF has previously been observed to increase Cdk5 activity after 3 d of BDNF stimulation in cortical neurons [11] . In agreement with this observation , we found that BDNF treatment led to an increase in Cdk5 activity within 15 min of BDNF stimulation ( Figure 4A ) . More importantly , addition of Trk inhibitor K252a essentially abolished BDNF-triggered increase in Cdk5 activity , indicating that the increase in Cdk5 activity was dependent on TrkB activation ( Figure 4B ) . It has previously been demonstrated that Cdk5 activity is enhanced by phosphorylation at Tyr15 [12] . Given the activation of tyrosine kinase activity of TrkB upon ligand stimulation , we were interested to investigate if BDNF treatment leads to phosphorylation of Cdk5 at Tyr15 , thereby enhancing its activity . We found that BDNF stimulation enhanced association between Cdk5 and TrkB in cortical neurons ( Figure 4C ) . More importantly , in vitro kinase assay using purified TrkB and Cdk5 revealed that TrkB phosphorylated Cdk5 at Tyr15 ( Figure 4D and 4E ) . TrkB-mediated phosphorylation of Cdk5 was abolished with the addition of Trk inhibitor K252a , further verifying that Tyr15 phosphorylation of Cdk5 was TrkB dependent ( Figure 4E ) . These observations collectively indicate that upon BDNF stimulation , Cdk5 was recruited to TrkB and phosphorylated by TrkB at Tyr15 , thus leading to enhanced Cdk5 activity to promote phosphorylation of TrkB at Ser478 . Given the neural-specific nature of Cdk5 , its abundant expression throughout development , and its essential role in the phosphorylation of TrkB at Ser478 , we were interested in examining the biological significance of this phosphorylation on the downstream functions of BDNF/TrkB signaling . As a first step , we examined if Cdk5-mediated phosphorylation of TrkB affects TrkB activation and downstream signaling cascades . Interestingly , we found that inhibition of Cdk5 activity by Cdk5 selective inhibitor Ros only marginally affected tyrosine phosphorylation of TrkB and initiation of downstream signaling pathways including phosphorylation of Erk1/2 , Akt , and CREB ( data not shown ) . Indeed , BDNF-stimulated increase in TrkB tyrosine phosphorylation was weakly affected in cdk5−/− cortical neurons ( Figure 3C ) . Furthermore , activation of Akt and Erk1/2 following BDNF stimulation was also comparable in cdk5+/+ and cdk5−/− cortical neurons ( data not shown ) . Our observations thus revealed that Cdk5-mediated phosphorylation of TrkB did not significantly affect activation of the receptor , nor its initiation and recruitment of downstream signaling pathways . Although Cdk5-mediated phosphorylation of TrkB had negligible effect on the downstream signaling of TrkB , it cannot be ruled out that Ser478 phosphorylation of TrkB is essential for the downstream functions of BDNF/TrkB signaling . We thus sought to examine if Cdk5-mediated phosphorylation of TrkB affects its downstream functions . BDNF has been observed to stimulate dendrite growth and development in hippocampal neurons [13 , 14] . In accordance with earlier observations , BDNF treatment led to a marked increase in the number of primary dendrites in hippocampal neurons ( Figure 5A ) , although the length and branching of dendrites were not affected ( data not shown ) . Interestingly , treatment with Cdk5 selective inhibitor Ros almost completely abolished the BDNF-stimulated dendritic growth , without affecting the basal number of dendrites ( Figure 5A ) . Furthermore , overexpression of dominant negative ( DN ) Cdk5 ( Figure 5B ) and transfection with Cdk5 short interfering RNA ( siRNA ) ( Figure 5C ) both abrogated BDNF-induced increase in primary dendrites . More importantly , BDNF similarly failed to induce an increase in primary dendrites in cdk5−/− hippocampal neurons ( Figure 5D ) . These observations collectively reveal that Cdk5 activity was required for BDNF-induced increase in primary dendrites in hippocampal neurons . To verify the importance of Ser478 phosphorylation of TrkB in BDNF-triggered dendritic growth , TrkB wild-type ( WT ) or TrkB M1 was overexpressed in hippocampal neurons . Remarkably , overexpression of TrkB M1 similarly abolished the BDNF-induced increase in primary dendrites ( Figure 5E ) . Taken together , our data indicate that Cdk5-mediated phosphorylation of TrkB at Ser478 was required for BDNF-triggered dendritic growth in hippocampal neurons . Rho GTPases , including RhoA , Rac1 , and Cdc42 , are key regulators of actin cytoskeleton dynamics . Since BDNF stimulation has been observed to activate Rac1 and Cdc42 in neurons [15] , we were interested to delineate if Rho GTPases contribute to BDNF-stimulated dendritic growth . To investigate if Rho GTPases are involved , and to identify the Rho GTPase ( s ) implicated , hippocampal neurons were transfected with WT or DN Rac1 , Cdc42 , or RhoA . We found that while overexpression of WT and DN Rac1 increased the basal number of dendrites in the absence of BDNF treatment , overexpression of both forms of Rac1 abolished BDNF-stimulated dendritic growth . On the other hand , while overexpression of DN RhoA slightly enhanced primary dendrites irrespective of BDNF stimulation , overexpression of both WT and DN forms of RhoA inhibited BDNF-stimulated dendritic growth . Remarkably , in contrast to the inhibition of BDNF-stimulated dendritic growth in cells overexpressing WT Rac1 and RhoA , BDNF stimulation of hippocampal neurons overexpressing WT Cdc42 resulted in an increase in primary dendrites , which was nearly abolished by overexpression of DN Cdc42 ( Figure 6A ) . Our observations therefore suggest that while Rac1 and RhoA may also modulate BDNF-stimulated dendritic growth , it is the activation of Cdc42 following BDNF stimulation that most likely mediates the increase in primary dendrites by BDNF . To examine if phosphorylation of TrkB by Cdk5 affects dendritic growth through modulating BDNF-triggered activation of Cdc42 , we first examined if the BDNF-induced increase in Cdc42 activity was affected by treatment with Cdk5 selective inhibitor Ros . In agreement with earlier findings , BDNF treatment resulted in an increase in Cdc42 activity . Interestingly , treatment with Ros significantly reduced BDNF-induced Cdc42 activity in cortical neurons ( Figure 6B ) , suggesting that Cdk5 activity was involved in BDNF-triggered activation of Cdc42 . To investigate if the reduction in Cdc42 activity contributes to the abrogation of BDNF-induced dendritic growth following attenuation of Cdk5 activity , the effect of overexpressing constitutively active ( CA ) Cdc42 with TrkB M1 on BDNF-induced dendritic growth was examined . Remarkably , overexpression of CA Cdc42 reversed the abrogation of BDNF-induced dendritic growth by TrkB M1 ( Figure 6C ) . More importantly , while overexpression of CA Cdc42 had negligible effect on BDNF-stimulated increase in primary dendrites in Cdk5+/+ neurons , overexpression of CA Cdc42 similarly rescued the lack of dendritic growth in cdk5−/− neurons following BDNF stimulation ( Figure 6D ) . These observations strongly suggest that Cdk5-mediated phosphorylation of TrkB at Ser478 was essential for the BDNF-triggered increase in primary dendrites through modulating BDNF-induced Cdc42 activity .
In the current study , we report the identification of TrkB as a novel Cdk5 substrate by providing evidence that Cdk5 phosphorylates TrkB at Ser478 , located at the intracellular juxtamembrane region of the receptor . The near absence of Ser478-phosphorylated TrkB in cdk5−/− brain underscores the importance of Cdk5 in this phosphorylation in vivo . More importantly , we found that Cdk5-mediated phosphorylation of TrkB is required for BDNF-stimulated increase in primary dendrites . Furthermore , we demonstrated that Cdk5 activity is involved in BDNF-induced increase in Cdc42 activity , which underlies BDNF-induced dendritic growth in hippocampal neurons . Overexpression of CA Cdc42 restored BDNF-stimulated increase in primary dendrites in cdk5−/− neurons , lending further support that Cdk5-mediated phosphorylation of TrkB at Ser478 is essential for BDNF-induced Cdc42 activation and increase in primary dendrites . Our findings therefore reveal an unanticipated role of Cdk5 in mediating downstream functions of Trk signaling . Activation of Rho GTPases has been implicated in a number of functions downstream of neurotrophin stimulation . For example , a recent study reported that synaptic maturation involves BDNF-stimulated increase in Cdc42 activity [16] . In addition , activation of Cdc42 is involved in the regulation of retinal growth cone filopodia by BDNF [17] . Activation of Rac1 following neurotrophin stimulation has also been observed to mediate neuronal migration triggered by neurotrophin treatment [18] . Our observation that BDNF-stimulated increase in Cdc42 activity contributes to the increase in primary dendrites corroborates these studies . It is interesting to note that overexpression of WT and DN Rac1 and RhoA also inhibited BDNF-induced increase in primary dendrites . While it is rather intriguing to observe similar actions by the WT and DN forms of these two Rho GTPases , our observation nonetheless suggests that Rac1 and RhoA may also play a role in BDNF-stimulated dendritic growth . Further studies will be required to delineate their involvements in BDNF-dependent regulation of dendritic development . Although different Rho GTPases have been identified as essential downstream mediators of neurotrophin functions , much less is known about the mechanisms by which neurotrophin treatment results in Rho GTPase activation , and how this process is regulated . The activity of Rho GTPases is controlled by a number of factors . Conversion from the GDP-bound , inactive state to the GTP-bound , active state is facilitated by guanine nucleotide exchange factors ( GEFs ) . The activated Rho GTPases then translocate to the plasma membrane , where they activate other downstream effectors such as PAK1 to modulate actin dynamics [19] . Indeed , neurotrophins have recently been observed to induce Rho GTPase activity through recruitment of a number of GEFs . TrkA was demonstrated to bind to Kalirin , an association that is essential for NGF-induced Rac1 activation and neurite outgrowth [20] . Furthermore , NGF treatment induces plasma membrane translocation of the GEFs Vav2 and Vav3 , an event that is required for activation of Rac1 and Cdc42 and the induction of neurite outgrowth following NGF treatment in PC12 cells [21] . NGF also stimulates activation of the Rac-specific GEF p-Rex1 in PC12 cells [18] . Two recent studies reveal that neurotrophin stimulation in Schwann cells also leads to Rho GTPase activation through activation of GEFs . TrkC activation results in activation of the Cdc42-specific GEF Dbs [22] and Rac-specific GEF Tiam1 [23] , both of which are required for NT-3-stimulated Schwann cell migration . Finally , TrkB was also recently demonstrated to bind and phosphorylate Tiam1 to mediate a BDNF-triggered change in cell shape [24] . On the other hand , recent studies accentuate the importance of membrane recruitment of Rho GTPase to lipid rafts for the function of these Rho GTPases . Lipid rafts are microdomains in plasma membrane rich in cholesterol and sphingolipids . Targeting of activated Rac1 to lipid rafts is required for activation of downstream effector Pak1 [25] . More importantly , neurotrophin-triggered Rac1 activation and morphological changes in hippocampal neurons have also been observed to require localization of Rac1 to lipid rafts [26] . Finally , BDNF has also been observed to increase Cdc42 activity in cerebellar granule neurons through enhancing calcium influx following the activation of PLCγ and PI3K pathways , a series of events that are essential for BDNF-mediated growth cone turning [27] . While a number of mechanisms have been postulated to underlie neurotrophin-mediated activation of Rho GTPases , it appears that the mechanisms implicated may vary with different downstream functions of Trk activation and the GEF involved . In the current study , we demonstrated that Ser478 phosphorylation of TrkB by Cdk5 is essential for the Cdc42-dependent increase in primary dendrites triggered by BDNF , thus adding a new regulatory component to the mechanisms involved in Rho GTPase activation by neurotrophin . Although the precise downstream pathways by which this phosphorylation affects Cdc42 activation remains to be determined , our observations provide some interesting insights . First of all , while inhibition of Cdk5-mediated TrkB phosphorylation at Ser478 essentially abolished BDNF-induced increase in primary dendrites , it was surprising to observe that Cdk5 activity had a negligible effect on TrkB activation and initiation of downstream signaling pathways . This suggests that Cdk5 activity probably did not affect BDNF-dependent activation of Cdc42 and the induction of primary dendrites through modulating activation of downstream signaling . This is unexpected because BDNF-stimulated increase in primary dendrites was previously observed to depend on PI3K/Akt pathways in cortical neurons [28] . Nonetheless , accumulating evidence reveals that the location at which Trk receptors are activated may play a pivotal role in determining the precise downstream significance of Trk activation . For example , BDNF-induced increase in primary dendrites was recently demonstrated to involve TrkB activation in the lipid rafts [13] . In addition , retrograde transport of activated Trk receptors as signaling endosomes is emerging as a key regulator of neuronal survival [29] . Since we examined changes in TrkB downstream signaling cascades only in total lysates , it remains possible that Cdk5 activity may specifically affect TrkB signaling only at certain subcellular/plasma membrane compartments . Secondly , overexpression of CA Cdc42 restored BDNF-induced dendritic growth in cdk5−/− neurons and in neurons overexpressing TrkB M1 ( Figure 6 ) , suggesting that maintenance of Cdc42 activation was sufficient to overcome the lack of BDNF-stimulated dendritic growth when Cdk5-mediated TrkB phosphorylation was absent . It thus appears that Cdk5 may impair BDNF-induced Cdc42 activation by affecting activation of the Rho GTPase . On the other hand , it should also be noted that overexpression of both the DN and CA forms of Cdc42 had a negligible effect on the basal number of primary dendrites in both cdk5+/+ and cdk5−/− neurons ( Figure 6 ) . Our observation is in agreement with an earlier study demonstrating that overexpression of DN or CA Cdc42 had no effect on the number of primary dendrites in chick spinal neurons [30] . In addition , it is consistent with the observation that modulation of Cdk5 activity or overexpression of TrkB M1 affected only BDNF-induced dendritic growth , without affecting the basal number of dendrites . Nonetheless , the inability of CA Cdc42 to mimic BDNF in the induction of primary dendrites suggests that activation of Cdc42 per se was insufficient to trigger dendritic growth in the absence of BDNF , and that additional , BDNF-dependent event ( s ) are required for the induction of dendritic growth by BDNF . Although the precise pathways implicated remain to be identified , it is tempting , in light of the emerging importance of lipid rafts in the activation of Rho GTPase , to speculate that BDNF may be required to stimulate translocation of activated Cdc42 to lipid rafts . In support of this hypothesis , it was observed that activation of Rac1 depends on the translocation of the activated Rho GTPase to lipid rafts [25 , 26] . In addition , in the absence of cholesterol , CA Rac1 failed to translocate to plasma membrane in fibroblasts [25] . More importantly , depletion of cholesterol similarly abolished BDNF-induced increase in primary dendrites in hippocampal neurons [13] . These observations collectively suggest that the inability of CA Cdc42 to increase dendritic growth in the absence of BDNF treatment may be related to the lack of CA Cdc42 translocation to lipid rafts , which may potentially be induced by BDNF treatment . A thorough investigation of the importance of lipid rafts in Cdc42 activation and primary dendrite induction by BDNF will shed light on the mechanisms by which BDNF-triggered dendritic growth is regulated . Given the near absence of Ser478-phosphorylated TrkB in cdk5−/− brain , we believe that Cdk5 functions as the predominant kinase for this phosphorylation in vivo . Nonetheless , it was interesting to note that prior to BDNF stimulation , a basal level of Ser478-phosphorylated TrkB was detected in cortical neurons that was not inhibited by pretreatment with the Cdk5 inhibitor Ros . This may suggest that other serine kinases are present to phosphorylate TrkB at Ser478 in the absence of BDNF stimulation . Nonetheless , given the marked inhibition of BDNF-stimulated increase in TrkB phosphorylation by Ros , we believe that Cdk5 is essential for the BDNF-dependent component of TrkB phosphorylation at Ser478 . Given the abundant expression of Cdk5 and TrkB in neurons throughout development , and their respective concentration at the synapse , it would be interesting to examine if Cdk5 activity is also involved in other downstream functions of TrkB signaling , such as the regulation of neuronal survival and synaptic plasticity . Preliminary findings from our laboratory reveal that Cdk5 activity is also required for BDNF-stimulated neuronal survival in cortical neurons ( unpublished data ) . In addition , the juxtamembrane region of Trk receptors has been associated with the regulation of Trk receptor internalization [31] and degradation [32] . Further investigation of whether this phosphorylation also affects the internalization and degradation of the receptor would provide further insights into the biological significance of this phosphorylation . In addition , since Cdk5 was observed to associate with TrkA without phosphorylating the receptor , further delineation of the consequences of this interaction would be essential for thoroughly understanding the crosstalk between Trk receptors and Cdk5 . A preliminary study revealed that , similar to TrkB , TrkA phosphorylates Cdk5 at Tyr15 ( unpublished data ) . The differential interaction of TrkA and TrkB with Cdk5 , together with the differential localization of TrkA and TrkB in different neuronal populations , may provide a novel mechanism by which Cdk5 can regulate the signaling of different neuronal populations . In conclusion , our findings have provided evidence for a regulatory role of Cdk5 in Trk-induced dendritic growth , and lend support for an emerging role of Cdk5 as a regulator of RTK signaling . Given the importance of neurotrophin/Trk signaling in almost all aspects of neuronal development and function , our findings will likely have far-reaching implications for further elucidating the signaling mechanisms involved in the regulation of neuronal survival , synapse formation , and synaptic plasticity .
The antibodies against Trk ( C-14 ) , Cdk5 ( DC-17 ) , p35 , and Shc were purchased from Santa Cruz Biotechnology ( http://www . scbt . com ) . The antibodies against TrkB and SH2B were from BD Biosciences ( http://www . bdbiosciences . com ) . The polyclonal antibodies recognizing phospho-TrkA ( Tyr490 ) , p44/42 mitogen-activated protein kinase ( Erk1/2 ) , phospho-p44/42 mitogen-activated protein kinase , AKT , phospho-AKT ( Ser473 ) , CREB , and phospho-Ser133 CREB were obtained from Cell Signaling Technology ( http://www . cellsignal . com ) . Antibodies specific for actin and β-tubulin type III were from Sigma-Aldrich ( http://www . sigmaaldrich . com ) . Antibody against the p-Ser478 of TrkB was raised by synthetic peptide ( CISNDDDSApSPLHHIS; Bio-Synthesis , http://www . biosyn . com ) and purified using AminoLink Kit ( Pierce , http://www . piercenet . com ) . Expression vectors of p35 , Cdk5 , and DN Cdk5 were prepared as previously described [3] . Flag-tagged and GST-tagged Cdk5 were generated by PCR , and subcloned into the mammalian expression vectors pcDNA3 ( Invitrogen , http://www . invitrogen . com ) and pGEX-6P-1 ( Amersham Biosciences , http://www5 . amershambiosciences . com ) , respectively . HA-tagged and GST-tagged Rac1 , Cdc42 , and RhoA constructs were gifts from Yung-Hou Wong ( Hong Kong University of Science and Technology , Hong Kong ) . The expression vectors of TrkA , TrkB , and TrkC were constructed as described [33] . Three TrkB mutants lacking the potential Cdk5 phosphorylation sites were constructed by mutating Ser478 ( TrkB M1 ) , Thr489 ( TrkB M2 ) , or both Ser478 and Thr489 ( TrkB DM ) to alanine using the overlapping PCR technique , followed by subcloning into pcDNA3 . GST-TrkB-Juxta construct was generated by PCR and subcloned into pGEX-6P-1 . Protein purification was performed according to the manufacturer's protocol . Stealth RNAi molecules for Cdk5 were prepared as previously described [34] . The sequences used were: Cdk5 siRNA , CCUCCGGGAGAUCUGUCUACUCAAA; and control siRNA ( Cdk5 ) , CCUAGGGCUAGCUGUUCAUCCCAAA . Cdk5 and p35 knockout mice were kindly provided by A . B . Kulkarni ( National Institutes of Health , Bethesda , Maryland ) and T . Curran ( St . Jude Children's Research Hospital , Memphis , Tennessee ) , and L . H . Tsai ( Harvard Medical School , Boston , Massachusetts ) , respectively . Mice from different stages were collected and genotyped as described [7 , 35] . Rat cortical and hippocampal neuron cultures were prepared as previously described [33 , 34] . Subsequent to digestion with 0 . 25% trypsin in Hank's Balanced Salt Solution without Ca2+ and Mg2+ at 37 °C for 5 min , the reaction was stopped by 2 . 5% heat-inactivated horse serum . The dissociated neurons were seeded in culture dishes coated with 10 μg/ml poly-D-lysine . Two hours later the medium was replaced by neurobasal medium supplemented with 2 mM L-glutamine and 2% B27 supplement . Selective Cdk5 inhibitor Ros ( Calbiochem , http://www . merckbiosciences . com/html/CBC/home . html ) was used to inhibit Cdk5 activity in primary neuron cultures . Primary cultures at 3 d in vitro ( DIV3 ) were treated with or without BDNF ( 50 ng/ml ) in the presence of Ros ( 10 or 25 μM ) or DMSO for 3 d before harvesting or fixation . For transfection of primary cultures , cortical and hippocampal neurons were seeded on coverslips in 12-well dishes at a cell density of 2 × 105 per coverslip . Neurons were transfected using calcium phosphate precipitation at DIV3 . Twenty-four hours after transfection , the cultures were treated with BDNF for 3 d . Primary hippocampal neuron cultures on coverslips in 12-well dishes were seeded at a cell density of 5 × 104 per coverslip for siRNA transfection . Cultures were transfected at DIV3 with Lipofectamine 2000 transfection reagent following the manufacturer's protocols ( Invitrogen ) . The transfected cells were incubated at 37 °C with 5% CO2 for 24 h before treatment , and were then treated with BDNF for 3 d . COS7 cells and HEK293T cells were obtained from American Type Culture Collection ( http://www . atcc . org ) . Both cells were maintained in DMEM supplemented with 10% heat-inactivated fetal bovine serum , penicillin ( 50 units/ml ) , and streptomycin ( 100 μg/ml ) at 37 °C with 5% CO2 . COS7 cells and HEK293T cells were transfected using Lipofectamine Plus transfection reagents following the supplier's instructions ( Invitrogen ) . The cells were treated and harvested 24 h after transfection . Cells were lysed at 4 °C for 30 min in lysis buffer ( RIPA: 1× PBS , 1% NP40 , 0 . 1% SDS , and 0 . 5% sodium deoxycholate ) with various protease inhibitors ( 1 mM phenylmethylsulfonyl fluoride [PMSF] , 1 mM sodium orthovanadate [NaOV] , 2 μg/ml antipain , 10 μg/ml leupeptin , 30 nM okadaic acid , 5 mM benzamidine , and 10 μg/ml aprotinin ) . Brain tissues were homogenized in lysis buffer ( 0 . 5% NP-40 , 20 mM Tris-HCl , 150 mM NaCl , 1 mM EDTA , 1 mM EGTA , and 1 mM NaF [pH 7 . 5] ) supplemented with various protease inhibitors ( 1 mM PMSF , 1 mM NaOV , 2 μg/ml antipain , 10 μg/ml leupeptin , 30 nM okadaic acid , 5 mM benzamidine , and 10 μg/ml aprotinin ) . Proteins were resolved by SDS-PAGE and subsequently electro-transferred onto a nitrocellulose membrane . Immmunoblots were probed with the desired primary antibodies at 4 °C overnight . After washing with TBS-T , the corresponding HRP-conjugated secondary antibody was added and incubated for 2 h at room temperature . Proteins were then visualized using enhanced chemiluminescence Western blotting detection reagents with reference to the supplier's instructions ( Amersham Biosciences ) . For immunoprecipitation , 1–2 mg of protein lysates was incubated with 1 μg of the corresponding antibody at 4 °C overnight with rotation . Forty microliters of protein G Sepharose ( Amersham Biosciences ) pre-washed with 1× PBS was added and rotated at 4 °C for 1 h . After intense washing with the lysis buffer , the immunoprecipitated protein and its associated proteins were analyzed by SDS-PAGE and Western blotting . Flag-tagged protein was overexpressed in COS7 cells and the cell lysate was obtained as described above . The cell lysate obtained was incubated with anti-Flag M2 affinity gel ( Sigma-Aldrich ) at 4 °C overnight with rotation . The Flag-tagged protein was pulled down by the affinity gel , and the affinity gel was washed twice with lysis buffer ( 0 . 5% NP-40 , 20 mM Tris-HCl , 150 mM NaCl , 1 mM EDTA , 1 mM EGTA , and 1 mM NaF [pH 7 . 5] ) with various protease inhibitors ( 1 mM PMSF , 1 mM NaOV , 2 μg/ml antipain , 10 μg/ml leupeptin , 30 nM okadaic acid , 5 mM benzamidine , and 10 μg/ml aprotinin ) . One to two milligrams of proteins prepared from brain tissues was incubated with the affinity gel , and the Flag-tagged protein pulled down by the affinity gel for 1 h . The affinity gel was washed twice with lysis buffer supplemented with protease inhibitors . The proteins pulled down by the Flag-tagged protein were subjected to Western blot analysis . Recombinant Cdk5/p35 and Cdk5/p25 were kindly provided by Shin-Ichi Hisanaga ( Tokyo Metropolitan University , Tokyo ) . TrkA , TrkB , and TrkC were immunoprecipitated from transfected HEK293T cells , and used as substrates for reconstituted Cdk5/p35 or Cdk5/p25 in the in vitro kinase assay . The kinase assay was performed at 30 °C for 30 min in kinase buffer containing 100 μM [γ-32P] ATP as described [36] . To examine if TrkB phosphorylated Cdk5 , recombinant TrkB kinase domain ( Upstate Biotechnology , http://www . upstate . com ) was incubated with GST-Cdk5 for 30 min at 30 °C , with or without Trk inhibitor K252a pretreatment ( 100 nM ) for 10 min , in the presence of 100 μM [γ-32P] ATP or cold ATP . To examine if BDNF stimulated Cdk5 activity , primary cortical neurons were treated with BDNF with or without 30 min of K252a pretreatment ( 100 nM ) . The immunoprecipitated Cdk5/p35 complexes from the lysates were washed three times with lysis buffer and twice with kinase buffer . The in vitro kinase reaction was performed at 30 °C for 30 min with kinase buffer containing 100 μM histone H1 peptide and 100 μM [γ-32P] ATP as described [37] . The phosphorylated proteins were resolved by SDS-PAGE . After the gel was dried , the phosphorylated proteins were visualized by autoradiography . For TrkB-mediated Cdk5 phosphorylation , the phosphorylated protein was resolved by SDS-PAGE , and blotted with phospho-Cdk2 ( Tyr15; Santa Cruz Biotechnology ) or phosphotyrosine antibody ( 4G10; Upstate Biotechnology ) . GTPase activity was measured as described [38] . Briefly , cultured cortical neurons at DIV7 were pretreated with DMSO or Ros for 30 min , followed by treatment with BDNF for another 5 min . Cells were lysed at 4 °C and incubated with Pak1-PBD agarose with constant rocking at 4 °C for 1 h . The proteins bound to the beads were washed three times with lysis buffer at 4 °C , eluted in SDS sample buffer , and analyzed for bound Cdc42 by Western blotting using monoclonal antibody against Cdc42 ( Upstate Biotechnology ) . GTPase activity was quantified by densitometry analysis of the blots . Following fixation in 4% paraformaldehyde and 5% sucrose in PBS with Ca2+ and Mg2+ for 30 min , the cells were washed three times with PBS , and were blocked with 1% bovine serum albumin and 10% goat serum for 20 min . The cells were then incubated with the corresponding primary antibody ( 1:150–500 ) at 4 °C overnight , and were subsequently washed with PBS three times . Following incubation with FITC or rhodamine conjugated secondary antibody ( 1:1 , 000 ) for 1 h at room temperature , the cells were washed again , stained with DAPI , and mounted with coverslips and MOWIOL ( Calbiochem ) . Mounted cells were visualized under fluorescent microscope ( Leica , http://www . leica . com ) . All data were expressed as mean ± standard deviation . Statistical significance was determined by one-way analysis of variance followed by Bonferroni's post hoc test with 95% confidence . A p-value of smaller than 0 . 05 was considered as statistically significant .
|
Accurate transmission of information in the nervous system requires the precise formation of contact points between neurons . Regulation of these contact sites involves fine tuning the number and branching of dendritic processes on neurons . Throughout development , several secreted factors act to regulate dendrite number and branching . One important family of these factors is neurotrophins , which are indispensable for the survival and development of neurons . For example , stimulation of hippocampal neurons with one neurotrophin , brain-derived neurotrophic factor ( BDNF ) , increases the number of dendrites directly extending from the cell body . Here , we report that BDNF-stimulated dendritic growth requires phosphorylation of the BDNF receptor , TrkB , by a kinase known as cyclin-dependent kinase 5 ( Cdk5 ) . Inhibiting phosphorylation of TrkB by Cdk5 essentially abolishes the induction of dendrites by BDNF . Our observations reveal that Cdk5 serves as a regulator of neurotrophin function . Since Cdk5 and neurotrophins both play essential roles in neuronal development , our findings suggest that the interplay between Cdk5 and TrkB may also be implicated in the regulation of other biological processes during development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology",
"arthropods",
"vertebrates",
"mus",
"(mouse)",
"neuroscience",
"mammals"
] |
2007
|
Cdk5 Is Involved in BDNF-Stimulated Dendritic Growth in Hippocampal Neurons
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The influenza A virus RNA polymerase is a heterotrimeric complex responsible for viral genome transcription and replication in the nucleus of infected cells . We recently carried out a proteomic analysis of purified polymerase expressed in human cells and identified a number of polymerase-associated cellular proteins . Here we characterise the role of one such host factors , SFPQ/PSF , during virus infection . Down-regulation of SFPQ/PSF by silencing with two independent siRNAs reduced the virus yield by 2–5 log in low-multiplicity infections , while the replication of unrelated viruses as VSV or Adenovirus was almost unaffected . As the SFPQ/PSF protein is frequently associated to NonO/p54 , we tested the potential implication of the latter in influenza virus replication . However , down-regulation of NonO/p54 by silencing with two independent siRNAs did not affect virus yields . Down-regulation of SFPQ/PSF by siRNA silencing led to a reduction and delay of influenza virus gene expression . Immunofluorescence analyses showed a good correlation between SFPQ/PSF and NP levels in infected cells . Analysis of virus RNA accumulation in silenced cells showed that production of mRNA , cRNA and vRNA is reduced by more than 5-fold but splicing is not affected . Likewise , the accumulation of viral mRNA in cicloheximide-treated cells was reduced by 3-fold . In contrast , down-regulation of SFPQ/PSF in a recombinant virus replicon system indicated that , while the accumulation of viral mRNA is reduced by 5-fold , vRNA levels are slightly increased . In vitro transcription of recombinant RNPs generated in SFPQ/PSF-silenced cells indicated a 4–5-fold reduction in polyadenylation but no alteration in cap snatching . These results indicate that SFPQ/PSF is a host factor essential for influenza virus transcription that increases the efficiency of viral mRNA polyadenylation and open the possibility to develop new antivirals targeting the accumulation of primary transcripts , a very early step during infection .
The influenza A viruses belong to the family Orthomyxoviridae and contain a segmented , single-stranded RNA genome of negative polarity ( for a review see [1] . Each of the genomic RNA segments is encapsidated in a ribonucleoprotein particle ( RNP ) containing the polymerase complex and a number of nucleoprotein ( NP ) monomers , depending on their size [2] , [3] . Contrary to many other RNA viruses , the influenza virus RNPs are transcribed and replicated in the nucleus of infected cells . The enzyme responsible for these activities is the viral polymerase , a heterotrimer that comprises the PB1 , PB2 and PA subunits [4]–[6] . The PB1 subunit acts as polymerase [7] , [8] while PB2 and PA are responsible for cap-binding and cap-snatching , respectively [9]–[12] . The heterotrimer has a compact structure [2] , [13]–[15] and is required for both transcription and replication [7] , [16]–[19] . The polymerase complex can be found associated to the RNP structure or in a soluble form [20] , the latter being able to oligomerise in vivo [21] , [22] . Along the years , a number of human cell factors have been described as interactors of influenza virus polymerase and in some specific cases their role in virus replication has been studied [23]–[36] . In one such studies , we identified the human SFPQ/PSF factor as associated in vivo to influenza virus polymerase by proteomic analysis of purified complexes [34] . Human SFPQ/PSF is a nuclear multifunctional protein that has been implicated in a series of steps in the human gene expression pathway ( for a review , see [37] . It was first described as associated to the polypyrimidine tract-binding protein ( PTB ) [38] and contains regions rich in arginine/glycine and proline/glutamine close to its N-terminus as well as two RRMs located more C-terminal . SFPQ/PSF can be found as a heterodimer with p54nrb/NonO , a protein that is highly homologous to the SFPQ/PSF C-terminal half . The SFPQ/PSF-p54nrb/NonO heterodimer co-purifies with DNA topoisomerase and interacts with RAD1 recombinase , leading to the stimulation of nucleic acid strand transfer and the cleavage/religation steps [39]–[43] . In addition , several reports have shown that SFPQ/PSF and/or p54nrb/NonO can regulate cellular transcription in a variety of genes ( reviewed in [37] . In agreement with the SFPQ/PSF association with PTB , its binding has been reported to elements in the splicing machinery , like the U4/U6-U5 tri-snRNP and many other splicing factors [44]–[46] and the RNA pol II CTD [47] , probably in a RRM-dependent manner [48] . Consistent with these interactions , SFPQ/PSF has been shown to stimulate the splicing of mRNAs transcribed by strong transcriptional activators and to control alternative splicing [49] , [50] . In spite of the above mentioned associations , most of SFPQ/PSF-p54nrb/NonO can be found in the “nuclear matrix” fraction [51] , consistent with the proposed role for the heterodimer in the specific retention in the nuclear matrix of RNA which has been A>I hyperedited [37] , [52] . In addition to these many potential functions assigned to SFPQ/PSF or the SFPQ/PSF-p54nrb/NonO heterodimer , the former has been shown to bind specifically to a defined stem-loop in hepatitis delta RNA [53] and the 3′-end of HCV genome [54] , while the latter inhibits the transport and expression of HIV mRNAs containing the instability region ( INS ) [55] . Here we have analysed the role of SFPQ/PSF in the influenza virus infection . Silencing the SFPQ/PSF gene , but not p54nrb/NonO , strongly reduced virus multiplication . The accumulation of viral genomic vRNA and mRNA as well as viral proteins was reduced , probably as a consequence of the inhibition of primary and secondary transcription , but normal splicing of virus mRNA was observed . In vitro transcription of recombinant RNPs generated in SFPQ-silenced cells resulted in reduced levels of viral poly A+ RNA . These results are consistent with a role for SFPQ/PSF during the polyadenylation step in the synthesis of viral mRNAs from the parental RNP templates , the earliest nuclear step in virus replication cycle , as well as during secondary transcription .
In previous studies we analysed the localisation of SFPQ/PSF and NP by confocal immunofluorescence of cells infected with VIC influenza virus . A clear co-localisation was observed at 4–6 hours post-infection ( hpi ) , particularly prominent at the periphery of the cell nucleus [34] . To further analyse this association in infected cells we carried out co-immunoprecipitation experiments . Cultures of A549 cells were infected at high moi with the VIC strain of influenza virus and at various times after infection cell extracts were prepared and used for immunoprecipitation with anti-SFPQ/PSF or control antibodies . The immunoprecipitates were analysed by Western-blotting with antibodies specific for SFPQ/PSF and NP . The results are presented in Figure 2 and a clear co-immunoprecipitation of NP was observed with the SFPQ/PSF-specific antibodies . In view of these results , the relevance of SFPQ/PSF for influenza virus multiplication was then studied by gene silencing . Cultures of A549 cells were transfected with SFPQ/PSF-specific siRNA , or a scrambled unspecific siRNA as a control , and then infected with VIC influenza virus at low multiplicity . Samples were withdrawn from the supernatant medium at various times after infection and the virus titre was determined by plaque-assay on MDCK cells . The results of kinetics experiments run in triplicate are presented in Figure 3A . A protracted virus growth kinetics was apparent in the SFPQ/PSF–silenced cultures as compared to cultures transfected with control siRNA and the final titre was reduced by 2–3 log units in various experiments similar to that presented in Figure 3A . The level of SFPQ/PSF down-regulation was verified at various times after siRNA transfection and virus infection and is presented in Figure 3D . These results suggested that SFPQ/PSF plays an important role during influenza virus infection . To verify that the virus growth inhibition is really due to SFPQ/PSF down-regulation and not to an spurious off-target effect of the particular siRNA used , similar experiments were performed with an independent SFPQ/PSF-specific siRNA and the results are presented in Figure 3B and 3C . Furthermore , two additional virus strains were used in these experiments , WSN and a VIC/WSN recombinant virus . Again , a strong reduction in the yield of virus production was obtained , thereby confirming that SFPQ/PSF in an important human host factor for the multiplication of influenza virus . As SFPQ/PSF is a multifunctional protein involved in many steps of cellular transcription and post-transcriptional RNA processing [37] , it is conceivable that its down-regulation indirectly leads to reductions in influenza virus multiplication . For instance , it would be conceivable that SFPQ/PSF down-regulation inhibits cellular transcription and/or splicing to a level that makes influenza virus unable to replicate , as it depends on these processes for its own transcription and gene expression . Such a possibility would seem unlikely , as the general pattern of cellular protein synthesis is not altered by SFPQ/PSF silencing ( see below ) , but we carried out controls to ascertain the specificity of SFPQ/PSF requirement for influenza virus multiplication . The multiplication of two additional viruses was studied in SFPQ/PSF-silenced cells: Vesicular stomatitis virus ( VSV ) , as an additional example of negative-stranded RNA virus , and Adenovirus 5 ( Ad5 ) , as a nuclear virus that is strongly dependent on the cellular transcriptional and splicing machineries . Cultures of A549 cells were SFPQ/PSF- or control-silenced and infected with either VSV or Ad5 and the virus accumulated in the culture supernatant ( VSV ) or the infected cells ( Ad5 ) was determined by plaque-assay on BHK21 ( VSV ) or HEK293T cells ( Ad5 ) . As presented in Figure 4A and 4B , the multiplication of neither virus was affected by the down-regulation of SFPQ/PSF , indicating that this human protein is a host factor specifically important for influenza virus multiplication . Since it has been shown that SFPQ/PSF associates to p54nrb/NonO ( see above ) , it was important to ascertain whether influenza virus requires SFPQ/PSF or the SFPQ/PSF-p54nrb/NonO heterodimer for proper multiplication . Hence , we analysed the multiplication of influenza virus in A549 cells after silencing p54nrb/NonO by transfection of a p54nrb/NonO-specific siRNA . Importantly , silencing of p54nrb/NonO did not alter the accumulation of SFPQ/PSF or vice versa ( data not shown ) . As indicated in Figure 5 , no reduction in virus yield was observed when using the VIC virus strain ( Figure 5A ) and a small reduction in WSN virus amplification was observed by down-regulation of p54nrb/NonO , much more limited than that observed when silencing SFPQ/PSF ( Figure 5B ) , in spite of an almost complete block of p54nrb/NonO expression ( Figure 5C ) . Therefore , we conclude that it is SFPQ/PSF by itself what influenza virus requires carrying out a normal infection cycle . Once the relevance of SFPQ/PSF for influenza virus infection was verified , we analysed the role of this host factor in the virus cycle . First , the synthesis of viral proteins was studied in SFPQ/PSF- and control-silenced A549 cells . Cultures of SFPQ/PSF- or control-silenced A549 cells were infected at high multiplicity and pulse-labelled with 35S-met-cys at various times after infection . The labelled proteins were analysed by polyacrylamide gel electrophoresis and autoradiography and the results are presented in Figure 6 . The synthesis of the major virus proteins was reduced and delayed in SFPQ/PSF-silenced cells , indicating that SFPQ/PSF down-regulation leads to a general reduction and delay in virus gene expression . However , it is worth mentioning that only a slight change was observed in the level and pattern of cellular protein synthesis upon SFPQ/PSF silencing ( compare lanes 0 in siCRTL and S1 panels in Figure 6 ) . Similar results were obtained when the accumulation of viral proteins was determined by Western-blot ( Figure S1 ) . To analyse further the phenotype of the infection cycle in SFPQ/PSF-silenced cells , the localisation of progeny RNPs was studied by confocal immunofluorescence with anti-NP antibodies . The results are presented in Figure 7 . As expected , the level of SFPQ/PSF was decreased in SFPQ/PSF-silenced cells and a general reduction in NP signal was observed , as compared with infected , control-silenced cells ( Figure 7A ) . Only cells with a level of SFPQ/PSF similar to that of control-silenced cells showed an accumulation of NP comparable to control infected cells , although the localisation was not normal ( Figure 7A; arrow ) . Significantly , a linear correlation was observed between the immunofluorescence signals of SFPQ/PSF and NP in random fields of control- or SFPQ/PSF-silenced cells ( Figure 7B ) . Thus , the low levels of virus protein synthesis ( Figure 6 ) and the small virus production ( Figure 3 ) observed in SFPQ/PSF-silenced cultures could be simply the consequence of the infection of a small number of cells not completely silenced upon transfection of SFPQ/PSF-specific siRNA . The reduction of virus protein synthesis under conditions of SFPQ/PSF down-regulation might be due to defects in genome amplification , virus transcription , splicing or translation of viral mRNA . As SFPQ/PSF has been described as a splicing factor , we first analysed whether its down-regulation would alter the splicing of virus mRNAs . The amounts of NS1 and NS2 mRNAs were determined in control and SFPQ/PSF silenced infected cells by a RT-qPCR procedure using TaqMan probes ( Table S1 ) . The proportion of NS1 versus total NS mRNA was indistinguishable in both experimental conditions ( ratio control/silenced cells 1 . 016+/−0 . 035; n = 7 experiments ) . Next , the levels of accumulation of vRNA , cRNA and mRNA were determined in SFPQ/PSF-silenced and control-silenced cells . Total cell RNA was isolated at various times after high-multiplicity infections and the amounts of virus-specific RNAs corresponding to the NP , NS and M virus segments were determined by hybridisation with specific probes . The results of a representative experiment are presented in Figure 8 . In control-silenced cells , the kinetics of accumulation of virus RNAs showed a pattern analogous to that previously described [56] , [57] , while in SFPQ/PSF-silenced cells a protracted kinetics was observed and 4-5 fold reductions in maximal accumulations of vRNA , cRNA and mRNA were determined . These results indicated that SFPQ/PSF is required for virus RNA replication but could not tell whether this was a direct effect and whether SFPQ/PSF also played a role in virus transcription . To clarify these questions , the accumulations of primary virus transcripts were determined after infection of SFPQ/PSF-silenced and control-silenced cells . The cells were infected at high multiplicity in the presence of cycloheximide to avoid the synthesis of viral proteins and hence the replication of viral RNA [58] . The accumulation of total NS transcripts was determined by RT-qPCR and demonstrated that silencing SFPQ/PSF leads to a 3-fold reduction in primary transcription ( Figure 9A ) . To verify the inhibition of virus multiplication , virus mRNA was determined in infected cells treated or not with cycloheximide . The results are presented in Figure 9B and document around 50-fold reduction upon treatment with the drug . These results suggest that SFPQ/PSF might simply be required for virus primary transcription and the observed reduction in vRNA accumulation would be an indirect consequence , since viral protein expression is essential for viral RNA replication [58] . To test whether virus RNA replication is directly inhibited by SFPQ/PSF down-regulation , in addition to the observed inhibition of primary transcription , we made use of a recombinant replicon system to analyse RNA replication and secondary transcription . In this system , no primary transcription is required for RNA replication to take place , as the virus proteins are provided by plasmid expression in trans . Human HEK293T cells were transfected with SFPQ-specific or control siRNAs and later transfected with plasmids encoding the virus polymerase subunits , NP and a virus genomic plasmid encoding the cat gene in negative polarity . The down-regulation of SFPQ/PSF was ascertained by Western-blot ( Figure 10D ) and the CAT protein accumulation was determined as a reporter of total replicon activity . The results indicated that silencing SFPQ/PSF lead to a 5-fold reduction in CAT expression ( Figure 10A ) . To determine whether this reduction was due to alterations in viral RNA replication , transcription or both , total cell RNA was isolated and used to determine negative-polarity and positive-polarity RNA accumulations by hybridisation with specific probes . As shown in Figure 10B , a 5-fold reduction in viral transcription was apparent , whereas a two-fold increase was observed between the accumulations of vRNA in control or SFPQ/PSF-silenced cells ( Figure 10C ) . These results indicated that SFPQ/PSF is required for viral transcription , but not for virus RNA replication and suggest that , in the absence of SFPQ/PSF , the viral RNPs are preferentially devoted to RNA replication instead of transcribing their template . One possible mechanism to explain the effects of SFPQ/PSF down-regulation on influenza virus transcription would imply that the interaction of SFPQ/PSF with the viral polymerase present in the RNP increases the affinity of the enzyme for the cap structure . The apparent Kd for the interaction of the isolated PB2 cap-binding domain with 7mGTP is around 170 µM [10] , in good agreement with the inhibition data reported for cap-RNA crosslinking to influenza virus RNPs [59] , whereas the affinity of binding of eIF4E or CBC to cap-analogues is much higher [60] , [61] . This is in contradistinction to the elution profiles of eIF4F and influenza polymerase-template complexes on a 7mGTP-Sepharose resin [14] which show a stronger binding of the latter . Hence , it is conceivable that some cellular factor ( s ) , for instance SFPQ/PSF , interact with viral polymerase and enhances its affinity for binding to cap . To test such possibility we generated recombinant RNPs by transfection of HEK293T cells , which had previously been either control- or SFPQ/PSF-silenced . Total cell extracts of these cells were used for in vitro transcription using ß-globin mRNA as cap-donor . A wide concentration range of cap-donor was employed to test for a potential difference in cap-donor dose response when the reaction was performed in the presence or absence of SFPQ/PSF protein . The results are presented in Figure 11 and no change in the profile of virus RNA synthesis was apparent when SFPQ/PSF was silenced ( Figure 11B ) . However , a clear change in the size distribution of RNA products was observed depending on the downregulation of SFPQ/PSF and irrespective of the cap-donor concentration ( Figure 11A ) . In the presence of SFPQ/PSF the RNA profile was reminiscent of a polyadenylated virus mRNA , as the transcript size was slightly larger than the template ( used in the gel as a marker ) while downregulation of SFPQ/PSF led to a variety of RNA sizes , always smaller than the template . To further characterise the transcripts generated with SFPQ/PSF-silenced samples they were separated into poly A+ and poly A− fractions and analysed by denaturing polyacrylamide gel electrophoresis . The results are presented in Figure 12 . A consistent reduction in the amount of polyadenylated RNA was observed when SFPQ/PSF was downregulated , with a corresponding increased in the poly A− fraction ( Figure 12A ) . Quantification of 5 experiments indicated that the total amount of transcript was not affected by SFPQ/PSF downregulation but the fraction of poly A+ viral mRNA was reduced about 4–5 fold ( Figure 12 B ) . Similar results were obtained when other SFPQ/PSF-specific siRNA was used ( see Figure S2 ) . A considerable fraction of the viral poly A− transcrips showed sizes smaller than the template , consistent with RNA degradation or premature termination but , interestingly , the profile of these poly A− transcripts was identical for control- or SFPQ/PSF-silenced samples , suggesting that the reduction in poly A+ transcripts is not due to a defect in transcript elongation but most probably to a deficiency in the polyadenylation step . The results presented here show that SFPQ/PSF is specifically required for influenza virus multiplication and indicate that this cellular factor is essential for the transcription of viral RNPs during both primary and secondary mRNA synthesis . Furthermore , the results shown suggest that SFPQ/PSF plays a role during the polyadenylation step in virus transcription . With the evidence presented , the following picture could be envisaged upon SFPQ down-regulation in infected cells: The primary viral mRNAs lacking poly A would be unstable and hence their accumulation diminished as compared to infections performed in normal cells . In addition , normal recycling of the transcribing polymerase could be affected . As a consequence of these effects , viral RNA replication would be indirectly inhibited , since little polymerase and NP would be synthesised . Moreover , a similar inhibition could be predicted for secondary transcription of the small amount of viral progeny RNA , with the final result of very low viral gene expression and virus production . At present it is not clear how SFPQ/PSF participates in the polyadenylation step of viral transcription . The available evidence indicates that polyadenylation of viral mRNAs is carried out by the polymerase by reiterative copy of the oligo U signal located close to the 5′-terminus of the vRNA template [62]–[65] and is mechanistically distinct from the cellular cleavage and polyadenylation process . The SFPQ/PSF protein is an RNA-binding protein that has been described as essential for the formation of the spliceosome and can be cross-linked to the pre-mRNA in the spliceosome [46] . Furthermore , purified SFPQ/PSF can be specifically cross-linked to poly U , but not to poly C , A or G , showing the same sequence specificity than PTB [38] . Therefore , it is tempting to speculate that SFPQ/PSF could interact both with the viral polymerase [34] and with the viral polyadenylation signal within the RNP to promote polymerase stuttering at the site . Further experiments will be required to test this and other possible alternatives . Down-regulation of SFPQ/PSF leads to a dramatic decrease in the yield of virus infection and hence it could be considered as a new target for antiviral design , a particularly interesting one as it is involved in a very early stage of the infection . Silencing of mouse SFPQ/PSF leads to chromosome instability [66] and we have verified that down-regulation of SFPQ/PSF in some human cells strongly reduces their growth kinetics ( unpublished results ) . Therefore , one should aim at blocking the association of SFPQ/PSF with virus polymerase/RNP for the design of potential new antivirals .
The HEK 293T cell line [67] was obtained from J . C . de la Torre and the A549 human cell line [68] was obtained from J . A . Melero . The MDCK and BHK21 cell lines were purchased from ATCC . Cell culture was carried out as described [69] . The influenza virus strains A/Victoria/3/75 ( H3N2 ) ( VIC ) , WSN ( H1N1 ) and a recombinant of both strains ( VIC/WSN ) was used in these experiments . Titrations were done in MDCK cells as described [70] . Vesicular stomatitis virus ( VSV ) was provided by R . Alfonso and titrated in BHK21 cells . Adenovirus 5 was provided by P . Fortes . Virus stocks were prepared and titrated in HEK 293T cells as described [71] . Plasmids pCMVPB1 , pCMVPB2 , pCMVPA and pCMVNP , expressing the influenza virus polymerase subunits and the NP have been described [57] . Plasmid pHHCAT , that transcribes a virus-like cat gene in negative polarity , was provided by A . Rodríguez . The monoclonal antibodies specific for PA have been described [72] , [73] . A monoclonal antibody specific for the N-terminal region of M1 and M2 proteins [74] was provided by A . García-Sastre . Antisera specific for PB1 and NP were generated by immunisation of rabbits with recombinant proteins [2] , [14] , [31] . A monoclonal antibody specific for SFPQ/PSF ( ab11825 ) and polyclonal sera specific for GAPDH ( ab9485 ) and actin ( ab8226 ) were purchased to Abcam . The secondary antibodies used for Western-blot and immunofluorescence was purchased to Sigma and Invitrogen , respectively . Total mouse IgG , with no known specificity , was used as immunoprecipitation control . Protein samples were separated by polyacrylamide-SDS gel electrophoresis and transferred to Immobilon filters . Western-blotting was carried out essentially as described [75]: The membranes were saturated with 3% BSA for 1 h and then incubated with the primary antibodies for 1 h at room temperature . The filters were washed with PBS containing 0 . 25% Tween 20 and incubated with the appropriate secondary antibody conjugated to horseradish peroxidase . After further washing as above the filters were developed by enhanced chemiluminiscence . For immunoprecipitations , extracts from mock-infected or infected cells were incubated with goat-antimouse-agarose beads ( A6531 , Sigma ) loaded with a monoclonal antibody specific for SFPQ/PSF or an equal amount of total mouse IgG as negative control . After extensive washing with a buffer containing 50 mM Tris HCl pH 7 , 5 , 150 mM NaCl , 0 , 5% NP-40 , 1 , 5 mM MgCl2 , 1 mM DTT , 1 u/µl HPRI and protease inhibitors containing EDTA , the beads were extracted by boiling with Laemmli loading buffer and the samples were analysed by Western-blot with antibodies specific for SFPQ/PSF and NP . For immunofluorescence , cells were fixed with 3% paraformaldehyde . The cultures were permeabilised with 0 . 5% Triton X100 and processed for indirect immunofluorescence as described before [31] . Images were collected on a Leica SP5 confocal microscope ( Leica Microsystems ) and processed with the LAS AF Software ( Leica Microsystems ) . For quantisation of cellular staining with anti-SFPQ/PSF and NP antibodies , the average intensities of 50 random images ( 1024×1024 pixels ) of each preparation were determined using the LAS AF Software . The procedures for protein labelling in vivo have been described [76] . Cultures were washed , incubated for 1 h in a DMEM medium lacking met-cys and labelled with 35S-met-cys to a final concentration of 200 µCi/ml . After incubation for 1 h , total extracts were prepared in Laemmli sample buffer and processed by polyacrylamide gel electrophoresis and autoradiography . Quantisation of CAT protein in total cell extracts was done by ELISA ( GE Healthcare ) . The amplification in vivo of recombinant RNPs was performed essentially as described [77] . In brief , cultures of HEK293T cells ( 2 . 5 106 cells ) were transfected with a mixture of plasmids expressing the polymerase subunits ( pCMVPB1 , 12 . 5 ng; pCMVPB2 , 12 . 5 ng; pCMVPA , 2 . 5 ng ) , NP ( pCMVNP , 500 ng ) and a genomic plasmid expressing a vRNA-like cat gene ( pHHCAT , 500 ng ) , using the calcium phosphate technique [78] . At 24 hours post-transfection , total cell extracts were prepared for CAT determination or total cell RNA was extracted . For RNA extraction cell pellets were resuspended in 1 ml of TRIZOL reagent ( Invitrogen ) and the RNA was purified as recommended by the manufacturer . The RNA was digested with RNAse-free DNAse ( 1 u/µg ) for 1 h at 37°C , extracted with phenol-chloroform-isoamylalcohol and precipitated with ethanol . For alternative splicing studies , poly A+ RNA was isolated by two rounds of chromatography on oligo-dT-cellulose as described previously [79] . The purified RNA was resuspended in nuclease-free water and the absorbance was measured at 260 nm ( NanoDrop ND-1000 ) . For dot-blot hybridisation , aliquots of purified RNAs were denatured for 15 min at 55°C in 10SSC , 7 . 5% formaldehyde and fixed on nylon filters by UV cross-linking . As controls , total yeast RNA or various amounts of plasmids containing cDNAs of the full-length virus genomic segments or the corresponding viral mRNAs , were fixed on the hybridisation filters . Hybridisation was carried out overnight at 40°C in 6xSSC , 0 . 5% SDS , 5× Denhardt's mixture 26–47% formamide , depending on the probe , and 100 µg/ml single-stranded DNA . After washing with 0 . 5xSSC-0 . 5% SDS at 40°C , the filters were quantified in a phosphorimager . As probes , synthetic oligonucleotides specific to detect the various RNA species of each RNA segment analysed were used ( Table S1 ) . They were labelled with gamma-32P-ATP and polynucleotide kinase . Additionally , specific riboprobes were used to specifically detect cRNAs . They were transcribed by T3 RNA polymerase using as templates synthetic DNAs containing T3 promoters fused to the viral sequences ( Table S1 ) . For siRNA transfection , cultured A549 cells were incubated independently with 5 nM of siSFPQ 1 ( 107613 ) , siSFPQ 2 ( 15923 ) , specific for SFPQ , or siNonO 1 ( s9614 ) , siNonO 2 ( s9612 ) , specific for NonO , or an irrelevant siRNA ( AM4611 ) from Ambion , using Lipofectamine ( Invitrogen ) as recommended by the manufacturer . Transfection was carried out twice on consecutive days to increase the silencing efficiency before infection . Quantification of virus-specific RNAs for splicing and primary transcription analyses was carried out by RT-qPCR as follows: The RT reaction was performed by addition of 100 ng of RNA resuspended in 10 µl of nuclease-free water and 10 µl of Reaction Mix 2x ( Applied Biosystems ) as recommended by the manufacturer . From each 20-µl reaction , 2 µl of cDNA was transferred directly to 96-well PCR plates and 12 , 5 µl of TaqMan universal master mixture ( Applied Biosystems ) and 1 , 25 µl of Custom TaqMan assay ( designed by Applied BioSystems ) were added . PCR was carried out in a PRISM 7000 Sequence detection system ( Applied Biosystems ) , with 1 cycle of 50°C for 2 min followed by 1 cycle of 95°C for 10 min , 40 cycles of 95°C for 15 s and 60°C for 1 min . The cycle threshold ( Ct ) was determined with analytical software ( SDS; Applied Biosystems ) . Serial dilutions of cDNA were used to ensure amplification in the lineal range . To construct calibration curves for quantification , we generated PCR products whose sequences were identical to the spliced or unspliced mRNAs of NS segment . The sequences of TaqMan probes and primers are presented in Table S1 . For in vitro transcription , extracts from control- or SFPQ/PSF-silenced HEK293T cells in which a mini-RNP was reconstituted [2] were incubated for 60 min at 30°C in 20 µl reactions containing 50 mM Tris . HCl , 100 mM KCl , 2 mM MgCl2 , 1 mM DTT , 1 mM each of ATP , CTP and UTP , 1 µM alpha-32P-GTP ( 0 . 5 mCi/µmol ) , 10 µg/ml actinomycin D , 1 u/µl HPRI , pH 8 . 0 and µg/ml ( or various amounts , depending on the experiment ) ß-globin mRNA . The reaction products were phenol-extracted , ethanol-precipitated and analysed by electrophoresis on 6% polyacrylamide-7 M urea gels . The purified in vitro transcripts were separated into poly A+ and poly A− fractions by chromatography on oligo dT-cellulose as described [80] . To monitor the recovery of RNAs during extraction and fractionation two labelled synthetic oligonucleotides were added , a 50 nt oligonucleotide lacking poly T sequences and a 70 nt long containing a 39T tract at its 3′-terminus .
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The influenza A viruses cause annual epidemics and occasional pandemics of respiratory infections that may be life threatening . The viral genome contains 8 RNA molecules forming ribonucleoproteins that replicate and transcribe in the nucleus of infected cells . Influenza viruses are intracellular parasites that need the host cell machinery to replicate . To better understand this virus-cell interplay we purified the viral RNA polymerase expressed in human cells and identified several specifically associated cellular proteins . Here we characterise the role of one of them , the proline-glutamine rich splicing factor ( SFPQ/PSF ) . Down-regulation of SFPQ/PSF indicated that it is essential for virus multiplication . Specifically , the accumulation of messenger and genomic virus-specific RNAs was reduced by SFPQ/PSF silencing in infected cells . Furthermore , transcription of parental ribonucleoproteins was affected by SFPQ/PSF down-regulation . The consequences of silencing SFPQ/PSF on the transcription and replication of a viral recombinant replicon indicated that it is required for virus transcription but not for virus RNA replication . In vitro transcription experiments indicated that SFPQ/PSF increases the efficiency of virus mRNA polyadenylation . This is the first description of a cellular factor essential for influenza virus transcription and opens the possibility to identify inhibitors that target this host-virus interaction and block virus gene expression .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"genetics",
"biology",
"microbiology",
"genetics",
"and",
"genomics"
] |
2011
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The Splicing Factor Proline-Glutamine Rich (SFPQ/PSF) Is Involved in Influenza Virus Transcription
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The contribution of epigenetic variation to phenotypic variation is unclear . Imprinted genes , because of their strong association with epigenetic modifications , represent an opportunity for the discovery of such phenomena . In mammals and flowering plants , a subset of genes are expressed from only one parental allele in a process called gene imprinting . Imprinting is associated with differential DNA methylation and chromatin modifications between parental alleles . In flowering plants imprinting occurs in a seed tissue - endosperm . Proper endosperm development is essential for the production of viable seeds . We previously showed that in Arabidopsis thaliana intraspecific imprinting variation is correlated with naturally occurring DNA methylation polymorphisms . Here , we investigated the mechanisms and function of allele-specific imprinting of the class IV homeodomain leucine zipper ( HD-ZIP ) transcription factor HDG3 . In imprinted strains , HDG3 is expressed primarily from the methylated paternally inherited allele . We manipulated the methylation state of endogenous HDG3 in a non-imprinted strain and demonstrated that methylation of a proximal transposable element is sufficient to promote HDG3 expression and imprinting . Gain of HDG3 imprinting was associated with earlier endosperm cellularization and changes in seed weight . These results indicate that epigenetic variation alone is sufficient to explain imprinting variation and demonstrate that epialleles can underlie variation in seed development phenotypes .
DNA methylation is a heritable epigenetic mark that can , on occasion , effect gene transcription and influence development . DNA methylation is a particularly influential regulator of gene expression in endosperm , a triploid extraembryonic seed tissue that supports embryo development . In endosperm , developmentally programmed DNA demethylation causes maternally inherited endosperm genomes to be hypomethylated compared to the paternally inherited endosperm genome [1–3] . Methylation differences between maternal and paternal alleles identify their parent-of-origin and establish imprinting , an epigenetic phenomenon in which a gene is expressed primarily from one parental allele [4] . Imprinting is theorized to have evolved over conflict between maternally and paternally inherited alleles in offspring over the extent of maternal investment [5 , 6] . Under the kinship theory , over evolutionary time silencing of the maternally inherited allele and expression of the paternally inherited allele is predicted to result for genes where the paternally inherited allele’s optimum expression level in offspring is higher than the maternally inherited allele’s [7] . Comparison of imprinting between species in the Arabidopsis genus has provided empirical support for this hypothesis [8 , 9] . Recent genomic approaches have revealed extensive natural DNA methylation variation within Arabidopsis thaliana [10 , 11] . Whereas the contribution of genetic variation to phenotypic diversity is well-established , the impact of epigenetic variation , or epialleles , on phenotype is only beginning to be understood [12 , 13] . Processes affected by epialleles include patterns of floral development , sex determination , fruit ripening , fruit nutritional content , and senescence , among others [14–19] . We previously demonstrated that natural variation in DNA methylation is associated with imprinting variation , with as many as 10% of imprinted genes estimated to be variably imprinted within A . thaliana and maize [20 , 21] . Seed development varies extensively among Arabidopsis accessions and has previously been shown to be influenced by parent-of-origin effects [20 , 22] , thus raising the possibility that variation in imprinting could influence seed phenotypes . One of these variably imprinted genes , HOMEDOMAIN GLABROUS3 ( HDG3 ) , is a member of the class IV homeodomain leucine zipper transcription factor ( HD-ZIP ) family , which regulates diverse aspects of plant patterning and development [23 , 24] . Studies on the function of class IV HD-ZIP genes in trichome differentiation , sepal giant cell formation , and suppression of somatic embryogenesis , among others , have led to the conclusion that class IV HD-ZIP family genes promote endoreduplication and cell differentiation [24–27] . Several members of the class IV HD-ZIPs are primarily expressed in endosperm and exhibit imprinted expression patterns , including FWA/HDG6 , HDG8 , HDG9 and HDG3 [2 , 23 , 28] . FWA , HDG8 , and HDG9 are maternally expressed imprinted genes ( MEGs ) , whereas HDG3 is a paternally expressed imprinted gene ( PEG ) [2 , 28] . The function of the imprinted class IV HD-ZIP genes during seed development , if any , is unknown . The activity of HDG3 alleles is correlated with DNA methylation . In endosperm of imprinted strains , the highly expressed paternal HDG3 allele is methylated and the lowly expressed maternal allele is hypomethylated over a Helitron TE sequence 5’ of the transcriptional start site [2] . Maternally inherited endosperm alleles are demethylated by the 5-methylcytosine DNA glycosylase gene DME; in dme mutants , maternal alleles retain their methylation and are also expressed [2 , 29] . Of 927 Arabidopsis accessions with sufficient methylation data [11] , 32 ( 3 . 5% ) have no methylation in the HDG3 5’ region and 871 ( 94% ) have greater than 50% methylation . When strains where HDG3 methylation is low , such as Cvi or Kz_9 , are the paternal parent in crosses with Col , where the maternal allele is demethylated , there is no methylation difference between maternal and paternal alleles in endosperm and HDG3 is biallelically expressed [20] . Together , these data suggest that ( 1 ) DNA demethylation promotes repression of the maternally-inherited HDG3 allele whereas DNA methylation promotes expression ( or inhibits repression ) of the paternal HDG3 allele and that ( 2 ) imprinting variation is due to cis epigenetic variation at HDG3 [20] . However , a cis or trans genetic contribution to imprinting variation cannot be excluded because of DNA sequence polymorphisms between the strains and alleles that do and do not exhibit imprinting . Here , we show that a naturally occurring epiallele can contribute to variation in seed phenotypes in Arabidopsis . We tested whether cis epigenetic variation is sufficient to explain imprinting variation by generating a methylated HDG3 Cvi allele that mimicked a methylated HDG3 Col allele . We found that the HDG3 Cvi allele switched from a hypomethylated , non-imprinted , repressed state to an imprinted , paternally biased , expressed state . Additionally , gain of HDG3 imprinting altered endosperm development and final seed size . These data indicate that naturally occurring epialleles can have phenotypic consequences in endosperm , a tissue where methylation is dynamic as a programmed part of development .
We previously showed that several genes that are imprinted in endosperm when Col is the paternal parent are not imprinted when Cvi is the paternal parent [20] . To further examine naturally occurring endosperm gene expression variation , we sequenced the transcriptomes of endosperm from Col x Col and Col x Cvi F1 seeds . Comparison of these transcriptomes identified 957 genes that were expressed two-fold or higher in Col x Col and 1187 that were expressed two-fold or higher in Col x Cvi endosperm ( Fig 1A; S1 Table ) . The gene with the lowest expression in Col x Cvi relative to Col is HDG3 , which is expressed 64-fold lower in Col x Cvi endosperm ( Fig 1A ) . We previously reported that HDG3 is a PEG in Cvi x Col crosses but is biallelically expressed in Col x Cvi [20] . To further explore the expression variation of HDG3 , we performed in situ hybridization on developing seeds ( Fig 1B–1C; S1 Fig ) . In Col x Col seeds , HDG3 is expressed specifically in the micropylar , peripheral , and chalazal endosperm , with the highest expression at the heart stage of development ( Fig 1C ) . The same pattern was observed in Cvi x Col ( Fig 1C ) . Whereas HDG3 expression was detected by in situ hybridization in F1 endosperm when Col was the paternal parent , it was not detected in endosperm when Cvi was the paternal parent ( Fig 1C ) . Additionally , we performed RT-qPCR on biological triplicates of Col , Cvi , and Col-Cvi F1 endosperm . Expression in Col x Col and Cvi x Col was approximately 10-fold higher than in Cvi x Cvi or Col x Cvi , indicating that HDG3 expression is higher when it is imprinted ( Fig 1D ) , consistent with the mRNA-seq ( Fig 1A ) and in situ data ( Fig 1C ) . Thus , although expression is from both maternally and paternally inherited alleles in Col x Cvi crosses ( and presumably Cvi x Cvi crosses ) as detected by mRNA-seq [20] , the total expression in those crosses is lower than when HDG3 is imprinted . As we previously showed that the Cvi allele is naturally hypomethylated [20] , together these results suggest that DNA methylation of the HDG3 5’ region promotes HDG3 expression ( Fig 1E ) . There is also evidence for imprinting variation of HDG3 in other species . In Arabidopsis lyrata , expression of HDG3 is specific to the endosperm but levels differ between two accessions , MN47 and Kar , and their reciprocal crosses ( S2 Fig ) . In endosperm with high HDG3 expression ( Kar x MN47 ) , expression is strongly paternally biased ( 76% paternal instead of the expected 33% ) , whereas in the reciprocal cross expression of HDG3 is much lower and more reflective of the 2:1 maternal:paternal ratio in the endosperm ( 79% maternal ) ( S2 Fig ) ( 8 ) . The correlation between high expression of HDG3 and paternal allele bias in A . lyrata thus mirrors A . thaliana . To examine if HDG3 influenced endosperm development , we compared seeds from hdg3 mutant plants and segregating wild-type siblings in the Col background . We confirmed predominantly paternal expression of HDG3 [2 , 20] by reciprocal crosses between wild type and hdg3-1 mutants ( Fig 2A ) . When hdg3 was crossed as a female to a wild-type sibling male , expression of HDG3 was detected in endosperm in a similar manner as in Col x Col ( Fig 2A ) . In contrast , when wild-type females were crossed to hdg3 mutant males , the accumulation of HDG3 transcript in endosperm was dramatically affected , with no transcript detected in most cases , despite the presence of a wild-type maternally inherited allele ( Fig 2A ) . We assessed embryo stage and the extent of endosperm cellularization for sectioned wild-type and hdg3 seeds at 5 days after pollination . Embryo development was more variable in hdg3 , although this difference was not statistically significant , and endosperm cellularization was significantly delayed compared to wild-type seeds ( Fig 2B , S2 and S3 Table ) . Reciprocal crosses between wild-type and hdg3 mutant plants indicated that the endosperm cellularization phenotype was dependent on paternal genotype , consistent with HDG3 function being primarily supplied from the paternally-inherited allele ( S2 and S3 Table ) . Additionally , the weight and area of hdg3 seeds was slightly reduced compared to Col , suggesting that in the Col background HDG3 promotes seed growth or filling ( Fig 2C and 2D ) . Several PEGs have been shown to influence seed abortion phenotypes in interploidy crosses [30 , 31] , but we found no effect of hdg3 on this process ( S3 Fig ) . To understand the potential molecular consequences of the loss of hdg3 , we profiled endosperm gene expression in wild-type Col and hdg3-1 by RNA-seq at 7 days after pollination ( DAP ) ( Fig 3 ) . 150 genes had at least two-fold higher expression upon loss of hdg3 , while 238 genes had at least two-fold lower expression in hdg3 mutant endosperm ( Fig 3 , S4 Table ) . Differentially expressed genes included developmental regulators such as HOMEOBOX 3 ( WOX9 ) and gibberellin oxidases , which effect the level of a key phytohormone necessary for typical seed development [32] ( Fig 3 ) . The loss of hdg3 also impacted the expression of ten imprinted genes , including the MEG HDG9 ( Fig 3 ) . We hypothesized that the endosperm gene expression phenotypes associated with low expression of HDG3 from Cvi paternal alleles might in some respects mimic hdg3 mutants . Indeed , of the 238 genes that are down-regulated in hdg3 mutants , 100 are also down regulated in Col x Cvi crosses , where HDG3 expression is also low ( Fig 3 ) . This is a highly significant overlap ( hypergeometric test in R , p = 6 . 079e-69 ) ( Fig 3 ) . These data suggest that the Cvi HDG3 allele , in its hypomethylated and relatively transcriptionally repressed state , could be important for some of the accession-specific developmental traits imparted by Cvi [20 , 22 , 33] . Thus , to test both the imprinting mechanism and function of HDG3 further , we introduced methylation at the HDG3 locus in Cvi . To distinguish the importance of genetic variation from epigenetic variation for HDG3 expression and imprinting , we generated transgenic lines in which the endogenous HDG3 Cvi allele gained methylation in the same region that is methylated in Col . Cvi was transformed with a transgene consisting of an inverted repeat ( HDG3 IR ) of the 450 bp HDG3 5’ region from Cvi under the control of the constitutive 35S promoter . Processing of the expressed hairpin RNA into small RNAs is expected to direct methylation to the endogenous HDG3 Cvi locus . We identified multiple independent transgenic lines in which the HDG3 5’ region gained methylation in leaves ( S4 Fig ) . DNA methylation was present in the same region as in Col , although non-CG methylation was considerably higher ( S4 Fig ) . To determine whether the Cvi allele remained methylated when paternally inherited in endosperm , Cvi HDG3 IR plants from three independent transgenic lines were crossed as males to wild type Col females and DNA methylation was evaluated in F1 endosperm by locus-specific bisulfite-PCR . Although the 35S promoter has been reported to have no activity in syncytial endosperm [34] , we detected transcripts from the hairpin RNA in endosperm at 7 DAP ( S5 Fig ) . Bisulfite sequencing showed that the paternally inherited HDG3 Cvi allele from the IR line was hypermethylated relative to the paternally inherited HDG3 Cvi allele in Col x Cvi endosperm ( Fig 4; S6 Fig ) . The HDG3 Cvi allele from Col x Cvi HDG3 IR endosperm was methylated in both CG and non-CG contexts , indicative of RNA-directed DNA methylation , although at a lower level than in leaves or in F1 embryos ( S6 Fig ) . Examination of the bisulfite clones indicated some variation in paternal allele methylation , with clones with 0% methylation detected , unlike naturally methylated paternal alleles from Cvi x Col crosses ( S6 Fig ) . This could be due to stochastic silencing of the IR transgene in individual siliques/seeds or ineffective RNA-directed DNA methylation . The maternally inherited Col allele was unaffected in Col x Cvi HDG3 IR endosperm , remaining hypomethylated like in Col x Cvi endosperm . Thus , we successfully established an alternate epigenetic state specifically for the Cvi HDG3 allele in endosperm . Having established a methylated Cvi HDG3 allele , we tested whether paternal allele methylation was sufficient to switch HDG3 from a non-imprinted , repressed state to an imprinted , more active state . In two independent lines , in situ hybridization of F1 seeds from Col x Cvi HDG3 IR crosses indicated the presence of HDG3 transcript in endosperm , in contrast to Col x Cvi endosperm ( Fig 5A ) . Hybridization signal was primarily detected in uncellularized endosperm on the chalazal side of the peripheral endosperm ( Fig 5A ) . However , the penetrance of Cvi HDG3 expression was variable , with about half of the seeds exhibiting HDG3 expression detectable by in situ ( Fig 5A ) . This might be related to the variation in methylation of the HDG3 Cvi allele in Col x Cvi HDG3 IR seeds ( S6 Fig ) . Analysis of total HDG3 transcript abundance by RT-qPCR at 6–7 days after pollination showed that HDG3 expression was 2-3-fold higher in Col x Cvi HDG3 IR endosperm compared to Col x Cvi endosperm ( Fig 5B ) . Higher expression of HDG3 in Col x Cvi HDG3 IR endosperm is consistent with HDG3 being more highly expressed when imprinted ( Fig 1 ) . Thus , to measure allele-specific expression of HDG3 , Col and Cvi alleles were distinguished using TaqMan probes in an RT-qPCR assay . In crosses between Col females and three independent Cvi HDG3 IR lines , the fraction of transcript derived from the Cvi allele increased compared to control crosses between Col females and Cvi males . In Col x Cvi , the Cvi allele accounts for 23% of the transcripts by this assay , in good agreement with prior allele-specific mRNA-seq results [20] . In Col x Cvi HDG3 IR lines , the Cvi fraction was between 50–60% , indicating paternal allele bias ( the expectation for non-imprinted genes is 33% paternal ) ( Fig 5C ) . This is slightly less than the fraction of paternal allele expression in Cvi x Col crosses by mRNA-seq ( 79% ) [20] . Together , these data indicate that the naturally occurring methylation variation at HDG3 is sufficient to explain imprinting variation . We conclude that the methylated Cvi HDG3 allele in Cvi HDG3 IR plants is active and the gene is imprinted . Does the change of HDG3 expression and imprinting in Cvi affect seed development ? To test the phenotypic consequences of expressing HDG3 from previously repressed Cvi alleles , we compared the phenotypes of seeds from Col x Cvi ( low HDG3 expression ) and Col x Cvi HDG3 IR ( 2-4-fold increased HDG3 expression ) seeds by sectioning and staining ( Fig 6 ) . In crosses with the HDG3 IR lines , endosperm cellularization occurred at a significantly earlier stage of embryo development , where it was observed as early as the globular stage of embryogenesis ( Fig 6A and 6B , S2 and S3 Table ) . Whereas endosperm development appeared accelerated , embryo development was significantly delayed ( Fig 6B , S2 and S3 Table ) . The effect on endosperm cellularization was also observed in Cvi x Cvi HDG3 IR F1 seeds , although to a lesser extent ( S2 , S3 Table ) . Mature selfed seeds from Cvi HDG3 IR plants weighed significantly less than selfed seeds from Cvi and had reduced area ( Fig 6C and 6D ) . This is consistent with known correlations between early endosperm cellularization and the production of smaller seeds [35–37] . These observations support the hypothesis that hypomethylation and repression of the Cvi HDG3 allele is important for Cvi-directed developmental programs and that epiallelic variation contributes to the natural variation in seed development in Arabidopsis .
The establishment , maintenance , and inheritance of DNA methylation are fairly well understood processes . Disruption of methylation patterns by mutations in DNA methyltransferase enzymes have clear gene expression consequences . However , whether or not methylation is regulatory during development–meaning that dynamic loss or gain of methylation is a normal aspect of gene regulation–is less well understood . An exception to this is in the endosperm , where active DNA demethylation in the female gamete before fertilization establishes differential DNA methylation after fertilization , a step that is essential for normal seed development [38] . We thus hypothesized that the phenotypic impact of naturally occurring epialleles might be particularly evident in the endosperm , because the differential methylation between maternal and paternal alleles that is required for gene imprinting could be variable across accessions [20] . We have shown that HDG3 represents a case study of this proposed phenomenon . By placing a methylation trigger in Cvi ( the HDG3 IR transgene ) , we were able to convert the Cvi HDG3 allele from a hypomethylated to a methylated state . This switch in methylation was sufficient to promote expression of the paternally inherited Cvi HDG3 allele in endosperm to 3-fold higher levels . Because we altered methylation at the endogenous HDG3 Cvi locus , which retains all DNA sequence polymorphisms , we have shown that methylation variation alone is sufficient to cause expression , and thus imprinting , variation . However , our results also show that it is unlikely that methylation of the proximal TE accounts for all of the expression differences between paternal Col and Cvi HDG3 alleles in endosperm . The paternally inherited methylated Cvi allele , while more highly expressed than paternally inherited naturally hypomethylated Cvi allele , was not as highly expressed as paternally inherited methylated Col alleles in endosperm ( Figs 1 and 5 ) . Additional cis genetic or trans genetic or epigenetic variation likely also affects HDG3 expression levels . Finally , it is not possible to determine from the experiments presented here whether the original difference in methylation between naturally methylated and non-methylated alleles lacks any genetic basis . Cvi lacks the small RNAs associated with the 5’ TE that are found in many other accessions , but the ultimate cause of this difference remains unknown ( S4 Fig ) . Our experiments also shed light on the relative receptiveness of maternal and paternal endosperm genomes to de novo methylation . The HDG3 inverted repeat transgene should create endosperm small RNAs that are homologous to both Col and Cvi alleles ( there are only 4 SNPs and a 3 bp indel between Col and Cvi in the IR target region ) . Yet , in endosperm from Col x Cvi HDG3 IR crosses , the paternally inherited Cvi allele had high levels of non-CG methylation , whereas the maternally inherited Col alleles remained hypomethylated despite the presence of the IR transcript ( Fig 4 , S5 Fig , S6 Fig ) . In contrast , F1 embryos from the same crosses were indeed more highly methylated in the non-CG context on maternal Col alleles compared to maternal Col alleles from Col x Cvi crosses ( S6 Fig ) . Thus , maternally inherited HDG3 alleles in endosperm are refractory to de novo methylation even when a methylation trigger is present , in contrast to maternally inherited HDG3 alleles in embryos . These results further support findings that once a region is actively demethylated on the maternally inherited endosperm genome , it is “protected” from de novo methylation even when triggering small RNAs are present [31] . Finally , although the direct targets of the HDG3 transcription factor are still unknown , we have shown that natural variation in HDG3 expression ( expressed in Col , low expression in Cvi ) has consequences for seed gene expression programs and development ( Figs 2 , 3 , 6 and 7 ) . Expression of HDG3 in seeds fathered by Cvi caused dramatically earlier endosperm cellularization and the seeds were smaller and lighter at maturity ( Fig 6 ) . These findings are consistent with class IV HD-ZIP genes inhibiting the cell cycle and promoting cellular differentiation [24 , 27] . However , mutation of hdg3 in Col , while displaying the predicted opposite effect on endosperm cellularization timing , also resulted in smaller seeds weighing slightly less than wild-type ( Figs 2 , 6 and 7 ) . Although the effects on final seed size are seemingly contradictory and the physiological basis remains incompletely understood , these results are predicted under the aegis of the kinship theory [7] . The theory predicts that PEGs promote maternal investment in offspring , which is consistent with the effects of the hdg3 mutation in Col ( i . e . less maternal investment results in smaller seeds ) . Our results suggest that this effect is specific to a Col seed developmental program . In Cvi endosperm , expression of HDG3 is seemingly maladaptive , leading to the production of smaller seeds . Cvi naturally produces much larger seeds than Col or Ler , although fewer in number [20 , 22 , 33] ( Figs 2 and 6 ) . Our results suggest that the loss of HDG3 expression in Cvi was an important part of the evolutionary process that resulted in these phenotypic differences . In summary , we have demonstrated that seed phenotypic differences can be caused by methylation differences at single genes . This study provides further evidence that epigenetic differences underlie developmental adaptations in plants . We have previously shown that the imprinting status of many genes varies between accessions; our current study argues that intraspecific variation in imprinting is an important determinant of seed developmental variation .
The SALK insertion mutant was obtained from the Arabidopsis Biological Resource Center [39] . hdg3-1 ( SALK_033462 ) was backcrossed to Col-0 three times before experimentation . For experiments comparing or crossing wild-type and hdg3 mutant plants , plants were F3 segregants from selfed progeny of HDG3/hdg3-1 . Plants were grown in a growth chamber or greenhouse with 16-hour days at 22° C . For crosses , flowers were emasculated and then pollinated after 2 days . Controlled floral pollinations were performed for each specified cross . At least two independent in situ experiments were performed for each genotype . Siliques were harvested 5 or 6 days after pollination ( DAP ) and fixed in FAA overnight at 4°C . Following dehydration and clearing ( HistoClear , National Diagnostics ) , samples were embedded in Paraplast Plus ( McCormick Scientific ) , and sectioned at 9 μM ( Leica RM 2065 rotary microtome ) . Ribbons were mounted with DEPC water on ProbeOn Plus slides ( Fisher ) at 42°C and dried overnight at 37°C . For probes , a 278 bp region of HDG3 ( S5 Table ) and previously published 602 bp probe for PDF1 [40] were amplified from endosperm cDNA and cloned into P-GEM T vectors ( Promega ) . Plasmids containing sense and antisense oriented fragments were identified and linear templates were amplified using M13 forward and reverse primers for probe synthesis . Antisense and sense RNA probes were synthesized in vitro with digoxigenin-UTPs using T7 or SP6 polymerase ( DIG RNA labeling kit , Roche/Sigma Aldrich ) . Probes were subsequently hydrolyzed and dot blots were performed to estimate probe concentration . Pre-hybridization steps were preformed according to [41] except Pronase digestion occurred for 15 minutes at 37°C . Hybridization and post-hybridizations were performed according to [42] , with minor modifications . For higher confidence in directly comparing expression patterns , slides corresponding to the cross and its reciprocal were processed face to face in the same pairs for hybridization , antibody , and detection steps . Negative controls consisted of hybridizing sense probes to wild-type tissue and antisense probes to hdg3 tissue . The sense probe lacked signal ( S1 Fig ) . A probe to PDF1 , which is expressed in the L1 embryo layer [43] , served as a positive control for successful in situ hybridization ( S1 Fig ) . Hybridization was performed overnight at 55°C , slides were then washed twice in 0 . 2X SSC for 60 mins each at 55°C , then twice in NTE for 5 min at 37°C and RNaseA treated for 20 min at 37°C , followed by two more 5 min NTE washes . Slides were incubated at room temperature for 1 hour with Anti-DIG antibody ( Roche/Sigma Aldrich ) diluted 1:1250 in buffer A [42] . Slides were then washed four times for 20 min each at room temperature with buffer A and once for 5 min with detection buffer [42] . Colorimetric detections were performed using NBT/BCIP Ready-To-Use Tablets ( Roche/Sigma Aldrich ) dissolved in water . Slides were allowed to develop 16–24 hours before stopping . Slides were dehydrated , mounted in Permount ( Electron Microscopy Sciences ) and imaged using a Zeiss Axio Imager M2 . Minor level adjustments and smart sharpen were applied to images to compensate for image transfer from live to digital ( Adobe Photoshop ) . Plant material was fixed and embedded as previously described and sectioned at 9 μm . Slides were dewaxed twice in xylenes for 5 minutes , rehydrated through a graded ethanol/0 . 85% salt series from 100%-30% , 1 minute each , stained in 0 . 6% Safranin O Solution ( Cat# 2016–03 , Sigma Aldrich ) for 5 minutes , washed with water , stained with a saturated 2 . 5% Aniline blue ( Harleco–EMD Millipore , #128–12 ) in 2% glacial acetic acid aqueous solution for 3 minutes , washed with water , rapidly dehydrated though graded ethanol/salt series to 100% , 5 seconds for each step , and then twice in xylenes for 5 minutes each . Slides were briefly drained , cover slipped and mounted with Cytoseal™ 60 ( Thermo Scientific ) and imaged using a Zeiss Axio Imager M2 . Previously processed slides from double staining and in situ hybridization experiments were re-examined and used for embryo and endosperm developmental analyses . Using previously published endosperm cellularization and embryogenesis stages [34 , 44] , individual seeds at 5 DAP were scored first for embryo stage and then for respective endosperm stage . Endosperm stage was given a numerical score ( -3 to +5 ) depending on the relative stage of endosperm cellularization compared to the expected endosperm cellularization stage given the embryo stage . Individual seeds with matching embryogenesis and endosperm cellularization stages were scored “normal” and ranged from 0–1; seeds that were scored “early” were defined as being +1 . 5 to +5 stages further along in the cellularization process compared to normal . Seeds that were scored “delayed” were defined as being -1 to -3 stages behind in the cellularization process compared to normal . To determine whether any developmental differences in endosperm cellularization or embryogenesis were statistically significant , we implemented the asymptotic generalized Pearson chi-squared test from the coin package [45] in R with default scoring weights ( S3 Table ) . Developmental stage was treated as an ordinal variable , while cross genotype was treated as a non-ordinal , nominal variable . Pairwise comparisons were carried out with the R function pairwiseOrdinalIndependence from the rcompanion package . For all tests , embryo development data was collapsed into three categories young ( pre-globular to globular ) , middle ( late globular to early heart ) , and older ( heart to torpedo ) and detailed endosperm cellularization data was collapsed into the categories delayed , normal , and early . The 450 bp sequence 5' of HDG3 corresponding to a fragment of AT2TE60490 from Chr2: 13740010–13740460 was amplified from Cvi ( S5 Table ) and cloned into the directional entry vector pENTR-TOPO-D ( Invitrogen ) . The sequence was then inserted twice in an inverted repeat conformation into the vector pFGCGW [46] with a LR clonase reaction ( Invitrogen ) . Cvi plants were transformed with the inverted repeat transgene by floral dipping and T1 lines were screened for DNA methylation using bisulfite sequencing . T3 plants homozygous for the IR transgene and with a methylated HDG3 5’ region in leaves , or their T4 progeny , were identified and used for subsequent experiments . RNA was isolated from endosperm dissected from seeds at 6 or 7 DAP as described [47] using the RNAqueous Micro Kit ( Ambion , Life Technologies Corporation ) . DNAse I-treated RNA ( Invitrogen , Life Technologies Corporation ) was used for cDNA synthesis with oligo-dT primer using Superscript II reverse transcriptase ( Invitrogen ) according to manufacturer’s instructions . Quantitative RT-PCR ( RT-qPCR ) was performed using Fast SYBR Green Master Mix or TaqMan Gene Expression Master Mix ( Applied Biosystems ) . All reactions were performed in three or four technical replicates using a StepOne Plus Real-Time PCR system ( Applied Biosystems ) . For SYBR Green based assays , relative expression was calculated using the ddCt method as described [48] . The reference gene was AT1G58050 [49] . For allele-specific expression in Col-Cvi crosses , a multiplex TaqMan assay was developed by designing primers and PrimeTime® Double-quenched Custom Probes with online tool http://www . idtdna . com/pages/products/gene-expression/custom-qpcr-probes . Cycling conditions were 15 cycles: 95°C for 15 seconds , 63°C for 30 seconds , 72°C for 30 seconds followed by 25 cycles: 95°C for 15 seconds , 63°C for 30 seconds with touchdown 0 . 05°C/cycle , and 72°C for 30 seconds . The relative expression of each allele within each genotype was calculated using a standard curve ( R2 value >0 . 99 ) as reference . Primer and probe sequences are in S5 Table . Genomic DNA was isolated from leaves , endosperm , and embryo at 6 or 7 days after pollination using a CTAB procedure . Bisulfite treatment was performed using the MethylCode Bisulfite Conversion Kit ( Invitrogen , Life Technologies Corporation ) or BisulFlash DNA Bisulfite Conversion Easy Kit ( Epigentek Group Inc . ) following the manufacturer’s protocols . 2 μl bisulfite treated DNA was used in PCR reactions with 2 . 5 U ExTaq DNA polymerase ( Takara ) and 0 . 4 μM primers using the following cycling conditions ( 95°C 3 minutes , 40 cycles of [95°C for 15 seconds , 50°C for 30 seconds , 72°C for 45 seconds] , 72°C for 5 minutes ) . PCR products were gel purified , cloned using a TOPO-TA ( Invitrogen ) or CloneJet ( Life Technologies ) PCR cloning kit and individual colonies were sequenced . Sequences were aligned using SeqMan and methylation was quantified using CyMate [50] . RNA was isolated from endosperm of Col-0 , hdg3-1 and Col-0 x Cvi seeds at 7 DAP as described above . Three replicates for each cross were obtained . DNAse treated RNA was used as input for the SmartSeq Clontech Ultralow RNA-Seq kit . Libraries were constructed by the Genome Technology Core at Whitehead Institute . Six libraries were multiplexed per lane in a Hi-Seq 2500 Standard mode , 40 base , single read run . Each replicate was sequenced to a depth of between 33 and 41 million reads . Reads were processed with Trim_galore using the command “trim_galore -q 25—phred64—fastqc—stringency 5—length 18” . Processed reads were aligned to the TAIR10 genome with Tophat2 [51] using the command “tophat -i 30 -I 3000 —b2-very-sensitive—solexa1 . 3-quals -p 5—segment-mismatches 1—segment-length 18” . Differential gene expression was detected with Cuffdiff2 [52] and the ARAPORT11 annotation ( S1 and S2 Tables ) . Reads are deposited in GEO GSE118371 .
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The contribution of genetic variation to phenotypic variation is well-established . By contrast , it is unknown how frequently epigenetic variation causes differences in organismal phenotypes . Epigenetic information is closely associated with but not encoded in the DNA sequence . In practice , it is challenging to disentangle genetic variation from epigenetic variation , as what appears to be epigenetic variation might have an underlying genetic basis . DNA methylation is one form of epigenetic information . HDG3 encodes an endosperm specific transcription factor that exists in two states in A . thaliana natural populations: methylated and expressed and hypomethylated and repressed . We show that pure epigenetic variation is sufficient to explain expression variation of HDG3 - a naturally lowly expressed allele can be switched to a higher expressed state by adding DNA methylation . We also show that expression of HDG3 in strains where it is normally hypomethylated and relatively repressed causes a seed development phenotype . These data indicate that naturally circulating epialleles have consequences for seed phenotypic variation .
|
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"acids",
"phenotypes",
"genetics",
"biology",
"and",
"life",
"sciences",
"organisms",
"fruit",
"and",
"seed",
"anatomy"
] |
2018
|
A variably imprinted epiallele impacts seed development
|
CLAVATA signaling restricts stem cell identity in the shoot apical meristem ( SAM ) in Arabidopsis thaliana . In rice ( Oryza sativa ) , FLORAL ORGAN NUMBER2 ( FON2 ) , closely related to CLV3 , is involved as a signaling molecule in a similar pathway to negatively regulate stem cell proliferation in the floral meristem ( FM ) . Here we show that the FON2 SPARE1 ( FOS1 ) gene encoding a CLE protein functions along with FON2 in maintenance of the FM . In addition , FOS1 appears to be involved in maintenance of the SAM in the vegetative phase , because constitutive expression of FOS1 caused termination of the vegetative SAM . Genetic analysis revealed that FOS1 does not need FON1 , the putative receptor of FON2 , for its action , suggesting that FOS1 and FON2 may function in meristem maintenance as signaling molecules in independent pathways . Initially , we identified FOS1 as a suppressor that originates from O . sativa indica and suppresses the fon2 mutation in O . sativa japonica . FOS1 function in japonica appears to be compromised by a functional nucleotide polymorphism ( FNP ) at the putative processing site of the signal peptide . Sequence comparison of FOS1 in about 150 domesticated rice and wild rice species indicates that this FNP is present only in japonica , suggesting that redundant regulation by FOS1 and FON2 is commonplace in species in the Oryza genus . Distribution of the FNP also suggests that this mutation may have occurred during the divergence of japonica from its wild ancestor . Stem cell maintenance may be regulated by at least three negative pathways in rice , and each pathway may contribute differently to this regulation depending on the type of the meristem . This situation contrasts with that in Arabidopsis , where CLV signaling is the major single pathway in all meristems .
Intercellular communication plays a crucial role in the determination of cell fate in plant development . Cell fate is determined by positional information emanating from neighboring or distant cells . Recent molecular genetic studies have revealed that peptide signaling molecules are involved in intercellular communication to regulate various aspects of plant development , such as stem cell maintenance , vascular differentiation , stomata patterning , and leaf size control [1]–[5] . The CLE genes encode small secreted proteins with a plant-specific domain called the CLE domain [6] . The CLAVATA3 ( CLV3 ) gene of Arabidopsis thaliana , and the FLORAL ORGAN NUMBER2 ( FON2 ) and FON2-LIKE CLE PROTEIN1 ( FCP1 ) genes of rice ( Oryza sativa ) are involved in stem cell maintenance in the shoot apical meristem ( SAM ) and the floral meristem ( FM ) [1] , [7] , [8] . Tracheary element differentiation inhibitory factor ( TDIF ) has a role in suppressing the differentiation of tracheary elements in Zinnia elegans [3] . Recent biochemical studies have revealed that functional peptides of CLV3 and TDIF in vivo are dodeca peptides derived from the conserved CLE domains [3] , [4] . In Arabidopsis , stem cell identity in the SAM is maintained by a regulatory feedback loop comprising the CLV and WUSCHEL ( WUS ) genes [9] , [10] . CLV3 acts as a negative regulator of stem cell maintenance by repressing WUS , which encodes a novel homeodomain transcription factor that is expressed in the organizing center and promotes the identity of the stem cells overlying its expression domain [9]–[11] . Conversely , WUS positively regulates CLV3 expression in the stem cell region . CLV3 peptide secreted from the stem cell appears to act through putative receptor complexes , consisting of CLV1 , CLV2 or CORYNE/SOL2 [1] , [12]–[15] . A recent biochemical study has revealed that CLV3 peptide binds directly to the extracellular domain of CLV1 [16] . When negative regulation of CLV signaling is removed by severe mutations of the CLV1 and CLV3 genes , enlargement of the SAM and the FM occurs , resulting in a fasciated stem and an increase in the number of flowers and floral organs [17] , [18] . A similar genetic mechanism to regulate stem cell maintenance seems to be conserved in monocots . Mutations in the FON1 and FON2 genes in rice cause enlargement of the FM , resulting in an increase in the number of floral organs such as stamens and carpels [7] , [8] , [19] , [20] . A double mutant of fon1 and fon2 shows no additive phenotype , suggesting that the two genes act in the same genetic pathway [8] . FON1 encodes a receptor-like kinase with a leucine-rich repeat ( LRR ) structure in the extracellular domain that is closely related to Arabidopsis CLV1 [7] . FON2 is a member of the CLE gene family , and the CLE domain of FON2 is similar to that of CLV3 [8] , [21] . Likewise , in maize ( Zea mays ) , the thick tassel dwarf1 ( td1 ) gene encodes a CLV1-like receptor kinase , and the fasciated ear2 ( fea2 ) gene , like Arabidopsis CLV2 , encodes an LRR protein that lacks a cytoplasmic domain [22] , [23] . Loss of function of these genes results in enlargement of the inflorescence meristem ( IM ) and the FM , causing fasciation of the inflorescences and an increase in floral organ number in maize . Constitutive expression of the FON2 gene results in a severe decrease in the number of flowers and floral organs , probably because of a reduction in the size of the IM and the FM in rice , resulting in a phenotype similar to the wus flower [8] , [11] . The effect of FON2 overexpression is not observed in the fon1 mutant , suggesting that FON1 is a putative receptor of FON2 . Thus , CLV-related genes negatively regulate stem cell proliferation in the reproductive meristems in both rice and maize , as they do in Arabidopsis . Despite this conservation , meristems in the vegetative phase are not affected by mutations in these CLV-related genes in the grasses , unlike in Arabidopsis [7] , [8] , [22] , [23] . In rice , constitutive expression of FON2 does not affect meristem maintenance in the vegetative phase [8] , [24] . We previously showed that FCP1 is probably involved in stem cell maintenance in the vegetative SAM because constitutive expression of FCP1 causes consumption of the SAM , similar to overexpression of CLV3 in Arabidopsis [24] . This action of FCP1 is also observed in fon1 mutants , suggesting that FCP1 requires a receptor other than FON1 . Thus , it is likely that , depending on the type of meristem , two independent pathways negatively regulate stem cell maintenance in rice . In maize , expression of td1 is excluded from the vegetative SAM [23] . Thus , meristem maintenance in the vegetative phase is regulated differently from that in the reproductive phase in the grasses . During the positional cloning of FON2 , we found that expressivity of the fon2 mutation is markedly reduced in F2 plants from a cross between the fon2 mutant ( O . sativa japonica ) and Kasalath ( O . sativa indica ) [8] . To explain this difference , we hypothesized that the indica genome might contain genes that suppress the fon2 mutation . In this paper , we describe the isolation and characterization of a gene , named FON2 SPARE1 ( FOS1 ) , that suppresses the fon2 mutation . FOS1 encodes a secreted protein with a CLE domain , and is expressed in the SAM , IM and FM . Genetic and molecular analyses indicate that FOS1 , together with FON2 , is likely to be involved in stem cell maintenance in the FM , in rice species in the Oryza genus including O . sativa indica; by contrast , FOS1 function seems to be severely compromised in O . sativa japonica . In addition , FOS1 is likely to be involved in maintenance of the SAM in the vegetative phase , because overexpression of FOS1 caused the formation of abnormal shoots with a terminated meristem . Analysis of the FOS1 sequence from a large number of domesticated and wild rice species reveals that a nucleotide substitution related to the function of FOS1 may have occurred during divergence of the domesticated rice O . sativa japonica from its wild ancestor .
In wild-type rice , a single pistil derived from congenitally fused carpels develops into a floret , and a single ovule is formed in the pistil . After fertilization , a single seed is formed within the husks , which are derived from the palea and lemma in a floret ( Figure 1A ) . In fon1 and fon2 mutants , by contrast , the number of floral organs such as pistils increases due to an enlargement of the FM ( Figure 2B ) [8] , [19] . Therefore , “twin seeds” are formed within the husks in these fon mutants ( Figure 1B ) when two or more pistils are produced in a floret ( the third and fourth seeds cannot develop to maturity ) . Here we used the twin seed phenotype as an indication of fon2 mutation . In a previous screen of the fon2 phenotype for positional cloning [8] , we found that expressivity of the fon2 mutation was reduced in F2 plants from a cross between the fon2-1 mutant ( japonica ) and Kasalath ( indica ) . To estimate quantitatively the frequency of the appearance of the fon2 phenotype , we assessed the numbers of twin-seed phenotypes in this study . First , we counted the number of plants producing the twin-seed phenotype among F2 plants from the fon2-1 and Kasalath cross . As a result , we found that the number of F2 plants showing a fon2 phenotype was reduced markedly in the F2 plants: only 4 . 5% of F2 plants showed a fon2 phenotype ( Table 1 ) . Second , we found that the number of the twin-seed phenotypes per panicle was also reduced markedly ( Figure 1C ) . The median frequency of the appearance of the twin-seed phenotype per panicle was 71% in the fon2-1 mutant ( japonica ) ; by contrast , it was reduced to 17% in the F2 plants showing a fon2 phenotype . These results suggest that the indica ( Kasalath ) genome causes a reduction in the expressivity of the fon2 mutation in the F2 progenies . In other words , it is likely that the indica genome has one or more genes that suppress the phenotype caused by the fon2 mutation . To address this possibility , we performed quantitative trait locus ( QTL ) analysis ( see Materials and Methods ) . For QTL analysis , we checked the genotypes of the F2 plants showing the fon2 phenotype to confirm that the fon2 locus was homozygous for the mutation . As a result , a major QTL that suppressed the fon2 mutation was detected in the region between 40 and 80 cM on chromosome 2 . In a group of F2 plants showing high suppressor activity , the frequency of genotypes homozygous for the indica genome was very high , whereas that of genotypes homozygous for the japonica genome was very low ( Figure 1D ) . In a group of F2 plants showing low suppressor activity , by contrast , the opposite result was obtained ( Figure 1E ) . Thus , in this region of chromosome 2 , high or low suppressor activity was closely associated with a genotype homozygous for indica or japonica , respectively . The above results indicated that a putative gene that suppresses the fon2 mutation is located in the 40–80 cM region of chromosome 2 in the indica genome . We assumed that if indica has a gene that is functionally redundant to FON2 in this region , this gene should behave as a suppressor-like gene function in genetic analyses . A strong candidate for such a gene would be a CLE gene , like FON2 . A survey of rice genomic sequences identified a candidate CLE gene located at about 54 cM on chromosome 2 . Next , we examined whether the CLE gene on chromosome 2 in indica ( tentatively named CLE-C2 ) could suppress the fon2 mutation by conducting the following two experiments . First , we introduced a 3 . 3-kb genomic fragment from Kasalath containing the CLE gene into the fon2-1 mutant by Agrobacterium-mediated transformation . We found that the defect in the fon2 flowers was completely rescued in the transgenic plants , suggesting that the CLE-C2 gene suppressed the fon2 mutation ( Figure 2C , 2G , and 2H ) . Second , we applied a genetic approach using a Nipponbare/Kasalath chromosomal segment substitution line ( N/K CSSL#9 ) , in which the chromosomal segment of Nipponbare ( japonica ) encompassing the CLE-C2 gene was replaced by that of the Kasalath ( indica ) genome . We crossed fon2-1 ( japonica ) with N/K CSSL#9 , and then screened for F2 plants that were homozygous for both fon2-1 and the indica CLE-C2 allele by determining the genotype with molecular markers . The results indicated that the flower phenotype of the plants screened was identical to that of wild type , suggesting that the fon2 mutation was also suppressed ( Figure 2D , 2G , and 2H ) . Taken together , these results clearly indicate that the CLE-C2 gene located at about 54 cM on chromosome 2 functions as a fon2 suppressor . Thus , we designated this CLE gene as FON2 SPARE1 ( FOS1 ) because this gene can substitute for FON2 . Sequence analysis revealed that FOS1 consists of one exon with a single open reading frame and encodes a putative small protein of 131 amino acids ( Figure S1 ) . FOS1 has a signal peptide that is rich in hydrophobic amino acids at its N-terminus and a CLE domain at its C-terminus ( Figure 3A ) . Among the CLE proteins in rice and Arabidopsis , the CLE domain of FOS1 is more similar to those of CLE8 and CLE13 than to those of FON2 , FCP1 and CLV3 ( Figure 3B ) . Notably , the nucleotide sequence of FOS1 in the fon2-1 mutant ( background , Fukei71 ) was identical to that of standard japonica wild-type strains such as Nipponbare and Taichung65 ( T65 ) . By contrast , we found an amino acid difference at the putative cleavage site of the signal peptide between japonica ( all three stains; AB455109 ) and indica ( Kasalath; AB455108 ) ( Figure 3A; Figure S1 ) . It is possible that this amino acid substitution in japonica FOS1 causes a defect in the processing of FOS1 and a reduction in the amount of active CLE peptide in japonica . Except for this mutation , no nucleotide change causing indels of amino acids was detected in the FOS1 gene between indica and japonica . We analyzed the spatial and temporal expression patterns of FOS1 by in situ hybridization . In indica , FOS1 was expressed in all aerial apical meristems , not only in the FM and the IM in the reproductive phase , but also in the SAM in the vegetative phase ( Figure 4A–4C ) . In japonica , FOS1 transcripts were also detected in all apical meristems in a spatial distribution pattern similar to that observed in indica ( Figure 4E–4G ) . These spatial expression patterns suggest that FOS1 may be involved in meristem maintenance in rice . In addition to the meristems , FOS1 transcripts were also detected in the primordia of lateral organs such as the leaf and the floral organs . No significant differences were observed at the transcriptional level in the expression patterns of FOS1 between indica and japonica , suggesting that the functional difference between indica FOS1 and japonica FOS1 is not due to differences at the transcriptional level . No signals were detected in the SAM and FM when sense probes were used ( Figure 4D and 4H ) . Next , we expressed constitutively indica FOS1 by using the rice Actin1 promoter . Unlike shoots transformed with a control vector , Actin1:indica-FOS1 shoots stopped growing at the seedling stage after a few abnormal and malformed leaves were produced ( Figure 4I and 4P ) . A longitudinal section of the shoot apex revealed that a dome-shaped shoot apical meristem ( SAM ) was strongly compromised in Actin1:indica-FOS1 plants , as compared with transgenic seedlings carrying a control vector ( Figure 4K and 4L ) . Next , we examined the expression pattern of rice OSH1 , an ortholog of Arabidopsis SHOOT MERISTEMLESS ( STM ) and maize knotted1 ( kn1 ) , which marks undifferentiated cells in the meristem [25] . OSH1 was expressed uniformly in the meristem except for the site of leaf initiation ( P0 ) ( Figure 4M ) . By contrast , OSH1 expression was not observed in the meristem of Actin1:indica-FOS1 shoots ( Figure 4N ) . These results indicate that constitutive expression of indica-FOS1 terminated meristem function , suggesting that FOS1 is involved in the maintenance of stem cells in the vegetative SAM as well as in the FM . To address whether , FON1 , a putative receptor of FON2 , is required for FOS1 function , we introduced an indica genomic fragment containing FOS1 into the fon1-5 mutant , which has a severe mutation and is thought to be a null allele of fon1 . The numbers of floral organs such as stamens and pistils in fon1-5 transformed with the indica FOS1 gene were identical to those in wild type , suggesting that FOS1 functions normally in this fon1 mutant ( Figure 2F–2H ) . Next , we expressed indica-FOS1 constitutively in fon1-5 . The resulting shoot showed a phenotype identical to that of wild type constitutively expressing indica FOS1 ( Figure 4J ) . These results suggest that the FON1 receptor is not required for the function of FOS1 and that FOS1 is likely to function in an independent signaling pathway . To elucidate when the mutation observed in japonica FOS1 occurred during rice evolution , we compared the FOS1 sequence of a number of varieties/species of domesticated rice and wild rice species such as O . rufipogon . To encompass genetic diversity , we examined a core collection of domesticated rice from around the world ( WRC , 67 accessions ) and from Japan ( JRC , 50 accessions ) ( Table S1; Table S2 ) [26] , [27] . As described above , the one-base change that causes an amino acid substitution at the putative cleavage site of the signal peptide in FOS1 and is associated with its function was found in three japonica strains ( fon2-1 , Nipponbare and T65 ) . Hereafter , we call this mutation a functional nucleotide polymorphism ( FNP ) without reference to the japonica or indica type . Sequence analysis showed that 66 out of 68 accessions of japonica had the FNP ( Haplotype B , see below ) , whereas 59 out of 60 accessions of indica did not ( Haplotype A ) ( Table S1; Table S2; Figure S2 ) . Thus , the FNP was closely associated with japonica except for three accessions ( Calotoc , Pinulupot 1 , Padi Perak ) . Although the accessions in the WRC have been designated indica or japonica by phenotypic analysis , it seemed likely that the genome of two subspecies might have been introgressed into each other during recent breeding programs . Thus , we examined the type of genome around the FOS1 locus in the three exceptional accessions by using molecular markers . The results clearly indicated that the FNP is consistent with the japonica genome , but not with the indica genome ( Table 2 ) . Next , we compared the FOS1 sequence from five wild rice species ( 22 accessions ) and the African domesticated rice O . glaberrima ( 2 accessions ) , all of which have an AA genome ( Table S3 ) . Nucleotide polymorphisms were found in FOS1 among the wild and domesticated rice accessions ( Figure S2 ) . We classified the FOS1 sequences into 13 haplotypes , and generated a network of these haplotypes ( Figure 5 ) . The network indicated that the prototype of FOS1 is haplotype C , which is shared by two wild rice species , O . rufipogon and O . glumaepatula . The FOS1 sequence in wild rice species and domesticated rice may have been derived from this haplotype . None of the accessions of wild rice species showed the FNP at the processing site . Therefore , the FNP in FOS1 is specific to the genome of O . sativa japonica . Because Asian domesticated rice species , namely japonica and indica , are thought to have derived independently from O . rufipogon [28]–[30] , this FNP may have occurred during the diversification of japonica from O . rufipogon .
The presence of a factor that suppresses fon mutations in indica was initially assumed from the low expressivity of the fon phenotype in F2 plants from a cross between japonica and indica . Although there are two possible explanations for this low expressivity – namely , differences in the genetic background of japonica and indica , or the presence of a major gene in the genome of indica – QTL analysis provided evidence in support of the latter possibility . Several lines of evidence suggest that the function of FOS1 is likely to be compromised in japonica . As a result , mutations at the FON2 locus result in enlargement of the FM and an increase in the floral organ number in japonica [8] . In indica , by contrast , functional FOS1 probably masks fon2 mutations by substituting for FON2 function in regulating maintenance of the FM ( Figure S3 ) . Likewise , in F2 plants from a cross between japonica and indica , FOS1 derived from indica is likely to mask the fon2 mutation . The frequency ( 4 . 5% ) of the appearance of the fon2 phenotype , which is also confirmed by the genotype , in those F2 plants is roughly consistent with that expected for the appearance of double mutants . Overexpression of japonica FOS1 produced an abnormal shoot , as did overexpression of indica FOS1 , suggesting that japonica FOS1 is not a complete loss-of-function mutant . In wild-type japonica , however , FOS1 CLE peptide , even if produced in part , would be insufficient to restrict stem cells in the FM . Because our rice research is principally based on japonica , indica FOS1 appears to behave as though it is a suppressor of the fon2 mutation . A more likely interpretation is , however , that FOS1 regulates maintenance of the FM redundantly with FON2 in a wide range of species in the genus Oryza ( see below ) and that japonica is a mutant for the FOS1 locus . In plant development , it is well known that genes that encode closely related proteins have redundant functions . APETALA1 ( AP1 ) and CAULIFLOWER ( CAL ) , which that encode MADS-box transcription factors , regulate floral meristem identity together with LEAFY [31] , [32] . The ap1 cal double mutant has a striking phenotype , showing excessive proliferation of the inflorescence meristem , which resembles a cauliflower . This phenotype differs from that of the ap1 single mutant . Because CAL has less effect on floral meristem identity , its single mutation shows no phenotype . CAL was identified as an enhancer of the ap1 phenotype in F2 plants from a cross between the ap1 mutant on a Landsberg electa background and wild-type Wassilewskija [31] . Thus , the identification of FOS1 in this study resembles the discovery of CAL in Arabidopsis , although FOS1 has the opposite effect; that is , it appears to be a suppressor of fon2 . In the case of AP1 and CAL , functional redundancy is due to the factors themselves; by contrast , signaling pathways comprising a different signaling molecule and its receptor might be redundant in the case of meristem maintenance in rice , as discussed below . Our previous study demonstrated that FON2 is a negative regulator of stem cell maintenance in the FM [8] . In this study , introduction of indica FOS1 into fon2-1 by genetic methods using a chromosomal segment substitution line or by Agrobacterium-mediated transformation completely suppressed the fon2 mutation . This finding suggests that FOS1 can substitute for the function of FON2 . Thus , FOS1 is likely to play an important role in maintenance of the FM in indica and either one of FOS1 and FON2 appears to be sufficient to restrict stem cell proliferation in the FM . There are two possible explanations for the redundancy of FOS1 and FON2 . Both CLE peptides may be involved in the same pathway and may share their receptors . Alternatively , there may exist two independent pathways: one involving FOS1 and one involving FON2 as signaling molecules . Two experiments in this study supported the latter possibility . First , a genomic fragment containing indica FOS1 was able to rescue a severe mutant of fon1 , in which the putative receptor of FON2 is defective [7] . Second , constitutive expression of indica FOS1 in fon1 mutant showed abnormal shoots , a phenotype that is similar to that of wild type overexpressing indica FOS1 . These results suggest that FON1 is not required for FOS1 function and that the signaling pathways involving FON2 and FOS1 are independent of each other . Because wild species in the Oryza genus have no mutation in the functional region of FOS1 , these two pathways may function in the FM in all Oryza species except for japonica ( Figure 6 ) . We found that FOS1 is expressed in the vegetative phase , and constitutive expression of FOS1 generates abnormal shoots with malformed leaves . It is , therefore , likely that FOS1 may be involved in maintenance of the vegetative SAM . Constitutive expression of FON2 , by contrast , does not cause abnormalities in the shoot [24] . In this respect , FOS1 and FON2 may have diversified functionally ( Figure 6 ) . In contrast to the FM , the vegetative SAM seems to be unaffected by loss of both FOS1 and FON2 because shoot morphology is normal in fon2 mutants in japonica [8] . Therefore , stem cell maintenance in the vegetative SAM may be regulated by an as yet unidentified negative pathway . FCP1 is likely to be involved in this pathway because its constitutive expression consumes stem cells in the vegetative SAM [24] . Thus , stem cell maintenance may be regulated by at least three negative pathways in rice , and each pathway may contribute differently to this regulation depending on the type of the meristem ( Figure 6 ) . This situation contrasts with that in Arabidopsis , where CLV signaling is the major single pathway in all meristems . Recent genetic and phylogenetic analyses have revealed that indica and japonica arose independently from a genetically distinct population in a wild ancestor , O . rufipogon [28]–[30] , [33] . Our haplotype network of FOS1 is also consistent with an independent origin of the two subspecies . In our network , haplotype C would have been the prototype of FOS1 for all domesticated and wild rice species . Haplotype A , associated with indica , and haplotype B , associated with japonica , would have been produced by the occurrence of a single nucleotide change in haplotype C during rice evolution . Indica may have been derived from an O . rufipogon species with haplotype A . The FNP at the cleavage site of the signal peptide is responsible for the generation of haplotype B . Although there is no O . rufipogon accession with haplotype B , it is possible that japonica might have been domesticated from an unidentified ancestor with this FNP . Many types of mutation are found in FOS1 of wild rice species , including not only amino acid substitutions but also insertions or deletions ( Figure S2 ) . There are , however , no mutations that affect the function of FOS1 in the coding region , such as an amino acid change in the CLE domain or a frameshift mutation . This observation suggests that defects in FOS1 may not be neutral and that FOS1 may be essential for the growth and survival of wild rice species under natural conditions . In line with this hypothesis , it is unlikely that an O . rufipogon species that has the FNP in FOS1 will be found in the natural population at present .
Taichung 65 ( T65 ) and Kasalath were used as representative strains of wild-type japonica and indica , respectively , in molecular genetic and histochemical analyses . Nipponbare/Kasalath chromosomal segment substitution line #9 ( N/K CSSL#9 ) was obtained from the Rice Genome Resource Center , Japan . Core collections of O . sativa ( World Rice Collection ( WRC ) and Japanese Rice Collection ( JRC ) ) were obtained from the Genebank of National Institute of Agrobiological Sciences , Japan ( Table S1; Table S2 ) [26] , [27] . Wild rice species were obtained from the National Institute of Genetics , Japan ( Table S3 ) . F2 plants from a cross between fon2-1 and Kasalath were used to search for a gene that suppresses the fon2 mutation . We obtained 154 F2 plants showing a fon2 phenotype from about 2 , 000 F2 plants and checked their genotypes to confirm that the fon2 locus has the mutant allele . For QTL analysis , the suppressor activity in each F2 plant that had the fon2 mutation was estimated by calculating the frequency of the twin seeds , an indication of the fon2 mutation . Next , the genotypes of about 90 loci in the 89 F2 plants were determined by using molecular markers [34] . As a result , a major QTL was found at around 40–80 cM on chromosome 2 ( LOD score: 6 . 3 ) . A gene ( FOS1 ) encoding a protein with a CLE domain was then identified at around 40 and 80 cM on chromosome 2 by searching the rice genomic sequence database using the amino acid sequence of the FON2 CLE domain as a query . FOS1 cDNA was amplified with the appropriate primers ( Table S4 ) from total RNA isolated from young panicles of T65 ( japonica ) and Kasalath ( indica ) . After sequencing of the RT-PCR product , the open reading frame was predicted . To introduce indica FOS1 into the fon2 mutant , a 3 . 3-kb FOS1 genomic fragment , including 2 . 6 kb of sequence directly upstream of the initiation codon of FOS1 , from the Kasalath genome was used . For constitutive expression of FOS1 , a FOS1 cDNA derived from T65 or Kasalath was placed under the rice Actin1 promoter [35] . The resulting plasmids , designated Actin1:japonica-FOS1 ( T65 ) and Actin1:indica-FOS1 ( Kasalath ) , were introduced into Agrobacterium tumefaciens strain EHA101 and transformed into rice as described previously [36] . For the in situ hybridization probe for FOS1 , a 646-bp fragment consisting of the entire coding region , the 5′ UTR ( 137 bp ) and the 3′ UTR ( 113 bp ) was amplified with the appropriate primers ( Table S4 ) . The fragment was cloned into a T-vector by TA-cloning ( Novagen , Madison ) . The OSH1 probe was prepared as described in the original paper [25] . Probe synthesis , preparation of sections , in situ hybridization , and microscopic observation were performed as described previously [7] , [24] . The genomic region of FOS1 was amplified with the appropriate primers ( Table S4 ) . The amplified fragments were purified with Montage PCR Filter Units ( Millipore , Billerica ) and sequenced with the same primers used for amplification . The haplotype network was constructed by using the program TCS1 [37] .
|
The body plan of plants is regulated by the function of apical meristems that are generated in the embryo . Leaves and floral organs are derived from cells supplied by stem cells in the vegetative shoot apical meristem ( SAM ) and the floral meristem ( FM ) , respectively . Thus , genetic regulation of stem cell maintenance is a central issue in plant development . In the model plant Arabidopsis thaliana , CLAVATA3 ( CLV3 ) functions as a key signaling molecule to restrict the size of the stem cell population in both the SAM and the FM . In rice , however , we show here that two CLV3-like genes , FLORAL ORGAN NUMBER2 ( FON2 ) and FON2 SPARE1 ( FOS1 ) , redundantly regulate maintenance of the FM . We also show that FOS1 is likely to be involved in maintenance of the vegetative SAM , whereas FON2 plays no role in regulation in this meristem . FOS1 appears to act via a putative receptor that differs from the FON2 receptor , suggesting that these two signaling molecules function in independent pathways to restrict stem cells in different ways depending on the type of meristem . In addition , we show that the FOS1 gene was compromised in the standard rice , Oryza sativa spp . japonica , during the evolution of rice .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"biology/plant",
"genomes",
"and",
"evolution",
"developmental",
"biology/plant",
"growth",
"and",
"development",
"genetics",
"and",
"genomics/gene",
"function",
"plant",
"biology/plant",
"growth",
"and",
"development",
"plant",
"biology/plant",
"genetics",
"and",
"gene",
"expression"
] |
2009
|
FON2 SPARE1 Redundantly Regulates Floral Meristem Maintenance with FLORAL ORGAN NUMBER2 in Rice
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Animal African Trypanosomosis ( AAT ) presents a severe problem for agricultural development in sub-Saharan Africa . It is caused by several trypanosome species and current means of diagnosis are expensive and impractical for field use . Our aim was to discover antigens for the detection of antibodies to Trypanosoma congolense , one of the main causative agents of AAT . We took a proteomic approach to identify potential immunodiagnostic parasite protein antigens . One hundred and thirteen proteins were identified which were selectively recognized by infected cattle sera . These were assessed for likelihood of recombinant protein expression in E . coli and fifteen were successfully expressed and assessed for their immunodiagnostic potential by ELISA using pooled pre- and post-infection cattle sera . Three proteins , members of the invariant surface glycoprotein ( ISG ) family , performed favorably and were then assessed using individual cattle sera . One antigen , Tc38630 , evaluated blind with 77 randomized cattle sera in an ELISA assay gave sensitivity and specificity performances of 87 . 2% and 97 . 4% , respectively . Cattle immunoreactivity to this antigen diminished significantly following drug-cure , a feature helpful for monitoring the efficacy of drug treatment .
Animal African Trypanosomosis ( AAT ) presents a severe problem for agricultural development in sub-Saharan Africa . The economic loss in cattle production is estimated to be between US$ 1 billion per annum [1]–[3] , increasing to US$ 5 billion when secondary costs are taken into consideration [1] , [3] , [4] . It is estimated that around 60 million cattle are at risk in endemic regions [1] , [4] . In addition , AAT affects many other domestic livestock such as pigs , camels , goats , sheep and horses . There are no vaccines and treatment is usually via intramuscular administration using trypanocides , either diminazene ( therapeutically ) or isometamidium ( prophylactically ) . Current diagnostics methods are laborious ( microscopy ) , expensive ( PCR ) and generally impractical for use in the field , at the point of treatment . AAT is caused by several species of protozoan parasites of the genus trypanosoma , including T . congolense , T . brucei , T . vivax and T . evansi . [1] . T . congolense is the most pathogenic and main causative agent of cattle AAT and is transmitted in Africa by tsetse flies of the genus Glossina [1] , [5] . Symptoms of T . congolense AAT include anaemia , weakness , weight loss and , in most untreated cases , death [5] , [6] . These symptoms are often used to clinically diagnose AAT , although they are congruent with many other anaemia causing diseases prevalent in the endemic regions including babesiosis , anaplasmosis , hemonchosis , and theileriosis . Wrong diagnosis is costly and counter productive to efficient treatment . Currently , specific diagnostics rely on microscopic detection of the parasites , the laboratory detection of specific antibodies or the detection of parasite DNA by PCR [5] , [6] . A card agglutination test ( CATT ) , such as that used to detect human African trypanosomosis in the field , is available for T . evansi infections [5] but is not applicable to T . congolense and T . vivax infections . Diagnosis in the field for these pathogens currently relies on whole cell lysate tests that suffer from antigen instability , reproducibility and specificity problems [5] . With this in mind , we set out to discover new diagnostic antigens for T . congolense that might be compatible with ELISA assays and subsequent development into lateral flow devices . In order to identify antigens for recombinant production and test development , we utilised a proteomic approach similar to that recently used to identify invariant surface glycoprotein ( ISG ) 65 as a potential diagnostic antigen for human African trypanosomosis [7] . Briefly , this method involves loading identical amounts of parasite whole-cell detergent lysates onto identical amounts of immobilized IgG isolated from the same animals before and after experimental infection and then comparing , by label-free quantitative proteomics , the proteins subsequently eluted from the immobilized IgGs and selecting those >100-fold more highly represented in the eluate from the immobilized infection IgG . This is a systematic approach to antigen identification , as was the identification of T . vivax GM6 from a cDNA expression library screen with infected bovine sera [8] , [9] . Both are alternatives to candidate-based approaches , whereby potential diagnostic antigens are selected based on literature precedent . A successful example of the latter for AAT is the selection of HSP70/BiP [10] because , despite substantial conservation of sequence between species , HSP70/BiP is known to be highly immunogenic . Here , using the systematic proteomics approach , we report the identification , production and evaluation of several new potential diagnostic antigens . The two best performing antigens were both ISG family members , one of which ( Tc38630 ) has been recently reported as a potential diagnostic antigen for T . congolense infections using mouse sera [11] . In this study , we extensively characterise a recombinant Tc38630 construct against diagnostically relevant cattle sera and demonstrated its potential for the diagnosis of T . congolense infections in bovines . We also demonstrate its ability to distinguish , to some degree , between successful and failed drug treatments .
Rodents were used to propagate sufficient T . congolense parasites to make the detergent lysates for immunoaffinity chromatography and proteomics . The animal procedures were carried out according the United Kingdom Animals ( Scientific Procedures ) Act 1986 and according to specific protocols approved by The University of Dundee Ethics Committee and as defined and approved in the UK Home Office Project License PPL 60/3836 held by MAJF . All cattle studies were reviewed by the ClinVet Animal Ethics Committee ( CAEC ) , who authorised the clinical research organization to conduct the studies ( approval numbers CV12/884; CV12/928 and CV12/885 ) . The study protocols were designed to allow the use of the study animals in compliance with the ClinVet Policy on the ethical use of animals and the South African National Standard “SANS 10386:2008 The care and use of animals for scientific purposes” . Permission to do research was granted by the South African Department of Agriculture , Forestry and Fisheries ( DAFF ) under Section 20 of the South African Animal Diseases Act , 1984 ( ACT NO . 35 of 1984 ) . Cattle sera were obtained from experimental infections being conducted as part of exploratory clinical efficacy studies , using preliminary formulations of novel trypanocides , at ClinVet International ( Pty ) Ltd and provided by GALVmed . All animals were obtained from tsetse free areas and shown to below the limits of trypanosomosis detection by polymerase chain reaction–restriction fragment length polymorphism ( PCR-RFLP ) assay [12] , performed by the Molecular Diagnostic Services laboratory , Department of Veterinary Tropical Diseases , Pretoria . Animals were maintained in fly-proof facilities in a tsetse free area ( ClinVet International ( Pty ) Ltd , Bloemfontein , South Africa ) . Serum samples for IgG purification were collected at either 7 days pre-infection or 28 days post infection with T . congolense strains 02J and 31J from Centro de Biotecnologia , Faculdade de Veterinária , Universidade Eduardo Mondlane , Maputo , Mozambique and strains KONT2/133 and KONT2/151 , isolated in 2004 from naturally infected cattle in Kontcha , Cameroon and obtained from the Institute of Tropical Medicine , Antwerp , Belgium . Each animal was only infected with one strain . Test sera were obtained from a total of forty calves from Clinvet studies that were collected at weekly intervals until 98 days post-infection ( dpi ) . These animals were subjected to drug treatments on dpi 10 or 35 . Sera from −1 , 0 , 7 , 14 , 28 , 35 , 42 , 49 , 63 , 77 , 91 and 98 dpi were evaluated by ELISA . A panel of 77 randomised and blinded ( at source ) T . congolense sera were also evaluated against the best candidate antigen from this study . Parasitaemia levels were monitored by microscopy and packed cell volume ( PCV ) by standard methods [13] . Aliquots ( 50 µl ) of sera each from 13 post-infected ( day +28 ) and the same 13 pre-infected ( day −7 ) calves were pooled separately . Each pool was applied to a 1 ml protein G column ( GE Healthcare ) equilibrated in phosphate buffered saline ( PBS ) . The columns were washed with 10 ml of PBS and the bound IgG antibodies were eluted with 50 mM sodium citrate pH 2 . 8 , and collected in 1 ml fractions into tubes containing 200 µl of 1 M Tris-HCl , buffer pH 8 . 5 . Peak fractions containing IgG were combined and dialysed for 16 h against coupling buffer ( 0 . 1 M NaHCO3 , 0 . 5 M NaCl , pH 8 . 3 ) . The purified IgGs were then coupled at 4 mgmL−1 of packed gel to CNBr-activated Sepharose 4B beads following the procedures detailed in [7] . Three BalbC mice were injected with one stabilate of T . congolense strain IL 3000 . After five days , mouse blood was harvested with citrate anticoagulant , adjusted to 5×104 parasites per ml with PBS and aliquots of 0 . 2 ml ( 1×104 mL−1 parasites ) were injected into the peritoneal cavity of 44 NMR1 mice . The mouse blood was harvested after 7 days with citrate anticoagulant and centrifuged at 1000× g for 10 min at 4°C . Plasma was removed and the buffy layer was resuspended in separation buffer plus glucose ( SB+glucose; 57 mM Na2HPO4 , 3 mM KH2PO4 , 44 mM NaCl , 10 gL−1 glucose ) and applied to a DE52 DEAE-cellulose ( Whatman ) column that had been pre-equilibrated with SB+glucose . The trypanosomes were washed through the column with SB+glucose , counted , centrifuged ( 900 g , 15 min , 4°C ) , resuspended in 1 ml PBS and then adjusted to 3 . 7×107 parasites mL−1 in 20 ml ice-cold lysis buffer ( 50 mM Na2PO4 , pH 7 . 2 , 2% n-octyl β-D-glucopyranoside ( nOG ) detergent , 1 mM PMSF , 1 mM TLCK , 1 µg mL−1 aprotinin , 1 µg mL−1 leupeptin and 1× Roche protease cocktail minus EDTA ) . The lysate was incubated for 30 min on ice and then centrifuged at 100 , 000 g for 1 h at 4°C . Identical aliquots of T . congolense lysate ( 7×108 cell equivalents ) were incubated in parallel with 0 . 5 ml packed volume of each of the CNBr Sepharose-IgG ( post-infection and pre-infection ) gels , rotating for 3 h at 4°C . The gels were then packed into disposable 10 ml columns and washed with 10 ml of 10 mM Na2PO4 , pH 7 . 2 , 200 mM NaCl , 1% nOG , followed by 10 ml of 5 mM Na2PO4 pH 7 . 2 , 1% nOG . The trypanosome proteins were eluted with 500 µl of 50 mM sodium citrate , pH 2 . 8 , 1% nOG into tubes containing 100 µl of 1 M Tris pH 8 . 5 for neutralization . The eluates were further concentrated to 270 µl using a centrifugal concentrator ( Millipore , 0 . 5 ml capacity with 10 kDa MW cut off membrane ) . The concentrates containing the trypanosome proteins were then transferred to low binding Eppendorf tubes and the proteins precipitated by adding 1 ml ice-cold ethanol and incubation for 24 h at −20°C . Following ethanol precipitation , the proteins eluted from the post-infection IgG and pre-infection IgG columns were dissolved in SDS sample buffer , reduced with DTT and run on a precast 4–12% Bis-Tris gradient SDS-PAGE ( Invitrogen ) using the MES running system . The gel was stained with colloidal Coomassie blue and equivalent regions of the infection and control lanes were cut out , reduced and alkylated with iodoacetamide and digested in-gel with trypsin . The tryptic peptides were analysed by LC-MS/MS on a Thermo Orbitrap XL system and MaxQuant software was used to match peptides to the predicted trypanosome protein databases ( combined GeneDB and UniProt predicted protein sequences ) . The program MaxQuant was used to obtain relative intensity data of the peptides recovered from the post-infection and pre-infection ( control ) IgG columns . This produced a list of 98 proteins found uniquely in the eluate of the infection IgG column ranked by relative intensity ( Table S1 in Text S1 ) . A second list of proteins with a >100-fold higher abundance in the post-infection eluate than the pre-infection eluate ( 15 proteins ) was also selected ( Table S1 in Text S1 ) . Proteins with peptide coverage under 10% were discarded . TriTrypDB [14] was used to obtain the predicted protein sequences and names of the selected proteins . Most were annotated as “hypothetical proteins” , in these cases if a trypanosomatid homologue was annotated this name was used . The list was visually inspected and ubiquitin , ribosomes and large structural proteins such as those from the paraflagellar rod and clathrin were removed . The sequences were further examined against different NCBI databases in order to prioritise targets for recombinant production . The closest homologues ( from any organism , trypanosomatid , bovine and humans ) were noted . Any close homologues that had produced crystal structures and/or had domains identified by Conserved Domain Database ( CDD ) [15] were also noted . A shortlist of proteins was chosen for expression and purification trials based on uniqueness to T . congolense ( <33% identity to human or bovine homologs ) and perceived ease of expression in E . coli [16] . Several of these proteins appear to be unique to T . congolense , with no close homologues in T . brucei or other trypanosomatids . Proteins with homologues in the PDB were considered favourable for expression , as were proteins less than 50 kDa . These were checked using XtalPred [16] for major areas of disorder , coiled coils , signal peptides and transmembrane domains . This produced a list of 15 potential diagnostic protein antigens for expression trials ( Table S1 in Text S1 ) . A pilot IP was initially preformed ( data not shown ) which produced a shortlist of 14 proteins uniquely recognised by the post-infection IgGs . Twelve of these were selectively recognised by the post-infected IgG eluant in the second IP described here . However , from this list only two proteins , TcIL3000 . 0 . 38630 and TcIL3000 . 2 . 1660 passed the infection∶control ratio cutoff ( >100-fold higher abundance in the post-infection eluate than the pre-infection eluate ) . Three additional proteins were produced solubly and are described here . However , their infection∶control ratios were low with only 2–4 times enrichment in the infected IgG eluant . In total , seven proteins could be produced as soluble proteins in sufficient quantities for assessment . Most of the genes listed in Table 1 were amplified by PCR , using the primers described , from T . congolense genomic DNA ( kindly provided by Mark Carrington , University of Cambridge ) and ligated into pCR2 . 1-TOPO using the TOPO TA Cloning Kit ( Invitrogen ) . The exception was the gene encoding Tc38630 , which was made as a synthetic gene by GenScript in a pUC vector with restriction sites ( Nde1 and Xho1 ) in place for downstream cloning . The coding sequence was optimized to avoid rare codon combinations in E . coli and disfavoured mRNA structures and cis elements for protein expression . All of the coding sequences were inserted into a pET15b-derived plasmid ( Novagen ) modified to include a tobacco etch virus ( TEV ) protease cleavage site between the His6 tag and the coding sequence . Genes for Tc38630 , Tc29290 and Tc51750 were also inserted into a modified pET15b-derived plasmid encoding for a TEV cleavable histidine tagged maltose binding protein ( kind gift of Dr . Thomas Eadsforth ) . Recombinant protein expression was achieved with E . coli BL21-CodonPlus ( DE3 ) RIPL cells ( Stratagene ) in autoinduction medium containing 50 µg mL−1 ampicillin and 12 µg mL−1 chloramphenicol , with the exception of Tc38630 constructs which were expressed in BL21 Gold ( DE3 ) cells in the presence of 50 µg mL−1 ampicillin . Cells were grown for 24 h at room temperature , harvested by centrifugation ( 3 , 500× g , 30 min , 4°C ) and lysed with a French press in buffer A ( 50 mM Tris-HCl , pH 7 . 5 , 250 mM NaCl ) . Cell debris was removed by centrifugation at 25 , 000× g ( 30 min , 4°C ) . The proteins were captured using nickel affinity chromatography on a 5 ml HisTrap column ( GE Healthcare ) and eluted using an imidazole gradient . This was followed by dialysis into buffer A and proteolytic cleavage of the histidine tag with 1 mg TEV protease per 20 mg protein ( 4°C , 16 h ) . The TEV protease carries a histidine tag and so the application of the sample to another HisTrap column removed the protease , any uncleaved protein and the histidine tag itself from the cleaved product , which passed through the column . Size exclusion chromatography on a Superdex 200 26/60 column provided the final stage in purification . With the exception of Tc3710 , a final polishing step was performed on a Superose 12 10/30 column . All proteins were >95% pure , as judged by sodium dodecyl sulphate polyacrylamide gel electrophoresis ( SDS PAGE ) and Coomassie blue staining ( Figure S1 in Text S1 ) . The amino acid sequences of the successfully expressed protein domains are shown in ( Table S2 in Text S1 ) . White polystyrene Costar untreated plates were coated with 50 µl per well of target protein at a concentration of 2 µg mL−1 in plating buffer ( 0 . 05 M NaHCO3 , pH 9 . 6 ) for 3 h at room temperature . The plates were then treated overnight at 4°C with PBS containing 5% bovine serum albumin ( BSA ) and 0 . 1% Tween-20 to block non-specific adsorption sites . Calf sera were serially diluted from 1∶500 to 1∶16000 and transferred in triplicate by a liquid handling device ( Bio-Tek , Precision ) to the ELISA plates and incubated for 1 h at room temperature after which they were aspirated and the wells washed with PBS containing 0 . 1% BSA using the liquid handling device . This wash cycle was repeated 5 times . Biotinylated anti-bovine-IgG ( Jackson labs ) was added at a dilution of 1∶4000 and incubated for 1 h . Excess anti-bovine-IgG antibody was washed away ( as described before ) and 50 µl per well of ExtrAvidin-Horse Radish Peroxidase ( HRP ) at a dilution of 1∶4000 was added to the plates and incubated for 1 h . The solution was aspirated and the wash steps were repeated . Finally , chemiluminescent Femto substrate ( Pierce ) diluted 1∶5 ( i . e . , 0 . 5 ml solution A , 0 . 5 ml solution B with 4 ml PBS ) was applied to the wells at 50 µl per well and plates were read using an Envision plate reader after 2 . 5 min incubation at 22°C . Bar graphs were generated using Microsoft Excel . Box plots and Receiver Operator Characteristic ( ROC ) curves were generated using IBM SPSS software . Wilcoxon's signed rank test was used to establish the statistical significance of the antibody response on 28 days post infection versus that on each of the post treatment values .
A proteomics approach was used to identify proteins selectively recognised by the IgG fraction of pooled sera from T . congolense infected calves , with the IgG fraction of pooled sera obtained from the same calves prior to infection used as the control . This was achieved by following previously established methods [7] . Briefly , IgG from pooled pre- and 28 day post-infected cattle sera were immobilised onto Sepharose beads and incubated with T . congolense lysate . The bound and subsequently eluted proteins were separated by SDS-PAGE . Gel slices were subjected to S-alkylation and tryptic digestion and the tryptic peptides were analysed by LC-MS/MS . These peptides were matched to proteins in the T . congolense database and relative intensities assigned using MaxQuant software . This process identified 113 proteins that were either unique or >100-fold more abundant in the eluate from the infection IgGs compared to the eluate from the pre-infection IgGs ( Table S1 in Text S1 ) . A large percentage of the proteins thus identified were ‘hypothetical’ . The proteins were therefore tentatively assigned to protein family groups based on homology to other annotated trypanosomatid sequences and/or protein fold identification from the CCD [16] . The identified proteins ( Table S1 in Text S1 ) are mostly surface glycoproteins such as variant surface glycoprotein B ( VSG_B ) , expression site associated genes ( ESAGs ) , invariant surface glycoproteins ( ISGs ) , trans-sialidases , unknown proteins that have signal peptides and/or transmembrane domains and proteins involved in trafficking such as soluble NSF attachment protein receptors ( SNAREs ) . These surface and secretory pathway glycoproteins are similar to the proteins identified in a comparable study using human Trypanosoma brucei gambiense infection and control IgGs [7] . Fifteen of the identified T . congolense proteins were selected for expression trials based on assessment for predicted ease of expression in E . coli . This assessment utilized prior knowledge of the features of “difficult to express” protein families , such as the presence of multiple transmembrane domains , regions of protein disorder and regions of low complexity [16] . The selected proteins were put into expression trials as full-length constructs or as truncated constructs when single transmembrane domains and/or signal peptides needed to be removed to assist soluble protein expression ( Table 1 ) . Constructs encoding all or part of the fifteen selected proteins were cloned into a modified pET15bTEV vector which allowed for the production of histidine tagged proteins which could be purified by immobilised metal affinity chromatography followed by tag removal and further purification by size exclusion chromatography . Seven proteins were successfully purified in monomeric form to >95% purity ( Figure S1 in Text S1 ) . The remainder were expressed in inclusion bodies and were not pursued further . The successfully expressed and purified monomeric recombinant proteins ( Table S1 in Text S1 ) were tested in ELISA assays for their reactivity to cattle sera . In the initial experiments , we used pooled pre-infection ( day −7 ) and post-infection ( day +28 ) sera ( Figure 1 ) . All antigens tested showed significant significant differences in immunoreactivity between pre- and posrt-infection sera ( i . e , paired T-test p<0 . 05 ) . From this initial assessment , three proteins , all members of the ISG family ( Tc38630 , Tc29290 and Tc51750 ) , were selected for further investigation as they had post- to pre- infection signal ratios >3 . The four other proteins , a member of the PNP_UDP1 superfamily ( Tc2530 ) , a tyrosine specific protein phosphatase ( Tc5750 ) , an adenosine 5′-monophosphoramidase ( Tc3710 ) and a transferrin receptor-like/PAG-like protein ( Tc03060 ) had relatively poor immunoselectivity ratios and were not progressed further . We found that the addition of an N-terminal maltose binding protein ( MBP ) affinity tag to the T . congolense protein constructs generally increased the expression and overall yield of the recombinant proteins by about 20-fold . However , as MBP is an E . coli-derived protein it was thought prudent to remove it by proteolytic cleavage before assessing the immunodiagnostic potential of the antigen . This reduced the final yield improvement for the monomeric recombinant protein to about 3-fold . MBP-fusion expressed ( with tag subsequently removed ) typical yields for Tc38630 , Tc29290 and Tc51750 were 2 mg/L , 0 . 3 mg/L and 4 mg/L respectively . Viable quantities of Tc29290 can only be obtained using the fusion protein . Purified , MBP-tag cleaved and monomeric ( by gel filtration ) forms of Tc38630 , Tc29290 and Tc51750 were used to create ELISA plates for testing with 40 individual pre- ( day −7 ) and post-infection ( day +28 ) calf sera . To judge the sensitivity and specificity of each of the three ELISA assays , receiver operating characteristic ( ROC ) curve analysis was performed . The recombinant Tc38630 protein , with a ROC curve area of 0 . 954 was clearly superior to Tc29290 and Tc51750 for diagnostic potential ( Figure S2 in Text S1 ) . Tc38630 was taken forward to be tested with 77 randomised and blinded pre- and post-infection calf sera . Based on the previous data , we selected an initial cut-off of 650000 relative light units ( RLU ) to predict infected ( >650000 RLU ) and uninfected ( <650000 RLU ) calves . Following decoding of the data and ROC curve analysis ( Figure 2 ) , we found that Tc38630 ELISA gave a sensitivity of 87 . 2% and specificity of 97 . 4% . Exploring alternative cutoff values ( Table 2 ) we can see that the optimal sensitivity of the assay is achieved with a cutoff of 418686 RLU which gave a sensitivity and specificity of 94 . 9% and 89 . 5% , respectively . Scatter and box plots indicating the ranges of ELISA readings obtained with pre- and post-infection sera are shown in ( Figure 2B and 2C ) . It is worth noting that it has been previously reported that this antigen does not cross react with the common non-pathogenic cattle trypanosome T . theileri [11] . In order to determine if immunoreactivity to Tc38630 might distinguish successful from failed drug-treatment , we first tested sera taken at several time points from 40 calves subjected to experimental infection and drug treatment 35 days later . When the cattle infection status was taken into account ( assessed by the presence or absence of parasitemia at day 98 ) two distinct antibody responses were noted . Thus , relative to immunoreactivity at day 28 , persistent and higher immunoreactivity to Tc38630 correlated with presence of parasitaemia at day 98 , i . e . , failed drug treatment ( Figure 3B ) , while lower immunoreactivity to Tc38630 correlated with aparasitaemia at day 98 , i . e . , successful drug treatment ( Figure 3A ) . When analysed at the level of individual animal responses , we noticed that the sera from ‘drug-cured’ animals , i . e . , those aparasitaemic at the end of the experiment , typically showed a two-wave antibody response to Tc38630 ( Figure 4 , A and B ) , whereas animals that remained infected at the end of the study show a constant rise in immunoresponsivenss to Tc38630 ( Figure 4 , C and D ) . Taken together , these data suggest that Tc38630 may have the potential to distinguish successful and failed drug treatments by comparing ELISA titres shortly before treatment with those >50 days later .
In this study , for practical reasons , we have used T . congolense cells propagated in mice to generate bloodstream form parasite lysates for affinity chromatography on immobilized bovine IgG matrices . Since it is conceivable that host animal environment may influence parasite gene expression , we must acknowledge that there may consequently be limitations in the list of specifically-recognized antigens ( Table S1 in Text S1 ) . Nevertheless , three proteins belonging to the invariant surface glycoprotein ( ISG ) family ( Tc38630 , Tc45510 and Tc29290 ) were successfully identified as potential diagnostic antigens by proteomic selection followed by assessment of likely ease of expression in E . coli followed by protein expression trials . The best performing of these , Tc38630 , was also recently reported as a potential diagnostic antigen for T . congolense infections using experimental mouse sera [11] . Although annotated as hypothetical in the genome database , Tc38630 has been previously identified by quantitative proteomics as being most abundant on the metacyclic and bloodstream trypomastigote forms of T . congolense [17] . The ISGs have , thus far , only been characterised in T . brucei , where ISG65 and ISG75 have been shown to be moderately abundant ( 50000 to 70000 copies per cell ) type-1 integral membrane cell surface glycoproteins [18] . While the physiological function of the ISGs is unclear , they are known to be endocytosed and recycled [19] and ISG75 has been implicated as the principal receptor for the uptake of the drug suramin [20] . The surface location , reasonable abundance and lack of similarity with any mammalian proteins most likely all contribute to the immunogenicity of ISGs in animal infections . Indeed , ISGs have been proposed and/or used as diagnostic antigens for human or animal infections with T . brucei gambiense [7] , [21] , T . evansi [22] and T . congolense [11] . Nevertheless , choosing which of the many ( 49 in the case of T . congolense ) ISG protein sequences in each trypanosome species [23] to select for protein expression and ELISA trials is a daunting task without the prioritisation provided by the proteomic approach taken in [7] for T . brucei and in this paper for T . congolense . Other selection criteria can lead to disappointing results; for example , the T . congolense ISG with the greatest similarity to that selected for the diagnosis of human T . brucei gambiense infections proved to be poor for the diagnosis of T . congolense infections in cattle ( unpublished data ) . Further , although there is a lack of ISG sequence similarity between trypanosome species ( typically <10% sequence identity and <20% sequence similarity ) [23] it would be premature to assume that immunoreactivity to Tc38630 is specific for T . congolense bovine infections over T . brucei and T . vivax infections , and this issue awaits analysis . In summary , we have identified a member of the invariant surface glycoprotein family ( Tc38630 ) as a diagnostic antigen for the detection of T . congolense infection in cattle . It is known that serum antibody persistence in Trypanosome infected cattle after curative treatment , or self-cure , is on average 3–4 months [24] and can be up to 13 months [25] . At the point of care , an ideal diagnostic antigen would be able to indicate active cattle infections with high sensitivity and specificity and be able to accurately track drug efficacy . The latter requires that immunoreactivity to the antigen is maintained in continuing infections and significantly reduces following cure . The data provided here suggests that this may well be the case for the recombinant Tc38630 protein construct . Therefore , further development of this antigen into lateral flow devices may be warranted . Nevertheless , it must be acknowledged that antigen , as opposed to antibody , detection tests are undoubtedly better for monitoring pathogen burden and drug efficacy . Unfortunately , antigen detection systems for AAT are currently lacking .
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Animal African Trypanosomosis ( AAT ) is a set of diseases whereby animals are infected with single-cell parasites that replicate in their bloodstream . The disease in cattle results in weight-loss and death , and AAT is a significant veterinary problem for sub-Saharan Africa . One of the principal trypanosome species responsible for AAT in cattle is Trypanosoma congolense and , although there are drug-treatments for these infections , current diagnostic methods are impractical for field use . Our aim was to discover protein molecules from the parasite to which infected animals make antibodies , to then make these proteins in bacteria and to subsequently demonstrate that they can be used to detect antibodies in cattle serum , thus diagnosing AAT . To discover the diagnostic proteins , we dissolved parasites in a detergent solution and applied them to beads coated with antibodies from infected cattle and to beads coated with antibodies from un-infected cattle . We then compared the proteins bound to each and selected those proteins that were at least 100-fold enriched by the infected cattle antibodies . We refined this list , according to practical and performance considerations , and settled on one protein , called Tc38630 . Testing Tc38630 with cattle sera showed that it can detect about nine out of ten AAT infections .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"biochemistry",
"veterinary",
"diseases",
"veterinary",
"parasitology",
"proteins",
"biology",
"and",
"life",
"sciences",
"proteomics",
"microbiology",
"veterinary",
"science",
"parasitology"
] |
2014
|
Proteomic Selection of Immunodiagnostic Antigens for Trypanosoma congolense
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The helminth Ascaris causes ascariasis in both humans and pigs . Humans , especially children , experience significant morbidity including respiratory complications , growth deficits and intestinal obstruction . Given that 800 million people worldwide are infected by Ascaris , this represents a significant global public health concern . The severity of the symptoms and associated morbidity are related to the parasite burden and not all hosts are infected equally . While the pathology of the disease has been extensively examined , our understanding of the molecular mechanisms underlying resistance and susceptibility to this nematode infection is poor . In order to investigate host differences associated with heavy and light parasite burden , an experimental murine model was developed utilising Ascaris-susceptible and -resistant mice strains , C57BL/6J and CBA/Ca , respectively , which experience differential burdens of migratory Ascaris larvae in the host lungs . Previous studies identified the liver as the site where this difference in susceptibility occurs . Using a label free quantitative proteomic approach , we analysed the hepatic proteomes of day four post infection C57BL/6J and CBA/Ca mice with and without Ascaris infection to identify proteins changes potentially linked to both resistance and susceptibility amongst the two strains , respectively . Over 3000 proteins were identified in total and clear intrinsic differences were elucidated between the two strains . These included a higher abundance of mitochondrial proteins , particularly those associated with the oxidative phosphorylation pathway and reactive oxygen species ( ROS ) production in the relatively resistant CBA/Ca mice . We hypothesise that the increased ROS levels associated with higher levels of mitochondrial activity results in a highly oxidative cellular environment that has a dramatic effect on the nematode’s ability to successfully sustain a parasitic association with its resistant host . Under infection , both strains had increased abundances in proteins associated with the oxidative phosphorylation pathway , as well as the tricarboxylic acid cycle , with respect to their controls , indicating a general stress response to Ascaris infection . Despite the early stage of infection , some immune-associated proteins were identified to be differentially abundant , providing a novel insight into the host response to Ascaris . In general , the susceptible C57BL/6J mice displayed higher abundances in immune-associated proteins , most likely signifying a more active nematode cohort with respect to their CBA/Ca counterparts . The complement component C8a and S100 proteins , S100a8 and S100a9 , were highly differentially abundant in both infected strains , signifying a potential innate immune response and the importance of the complement pathway in defence against macroparasite infection . In addition , the signatures of an early adaptive immune response were observed through the presence of proteins , such as plastin-2 and dipeptidyl peptidase 1 . A marked decrease in proteins associated with translation was also observed in both C57BL/6J and CBA/Ca mice under infection , indicative of either a general response to Ascaris or a modulatory effect by the nematode itself . Our research provides novel insights into the in vivo host-Ascaris relationship on the molecular level and provides new research perspectives in the development of Ascaris control and treatment strategies .
Ascariasis is an important , widespread geohelminth disease of humans and pigs [1] . Over 800 million people are estimated to be infected with the causative agent , the human roundworm , Ascaris lumbricoides [2] and the equivalent in pigs , Ascaris suum , is equally ubiquitious [3] . The impact on children is particularly severe , with chronic morbidity such as growth retardation and diminished cognitive development being exhibited [4] . Transmission is linked to poor disposal of human waste leading to extensive contamination of the environment with long-lived , resistant eggs that embryonate under appropriate conditions of temperature and moisture [4] . The intensity of worm burden in the intestine of the host is a major determinant of the severity of disease [5] . Furthermore , the number of macroparasites a host carries is fundamental to our understanding of helminth parasite epidemiology [5] . Since the seminal work of Crofton [6] , that characterized the frequency distribution of macroparasites as clumped or overdispersed , the ubiquity of what is now known as aggregation , has been demonstrated for a wide range of host-macroparasite systems [7] . Longitudinal field-based studies that measured the patterns of helminth re-infection in individual patients after the provision of anthelmintic treatment identified the phenomenon known as predisposition [8] , whereby individuals demonstrated a degree of consistency in their patterns of re-infection and this was demonstrated from a range of endemic regions and geohelminth species [5] . Predisposition was also maintained over multiple rounds of chemotherapeutic treatment [9] and many years after a single round of treatment [10] . Aggregation and predisposition were also successfully modelled in outbred pigs infected with A . suum and were described as analogous to those of A . lumbricoides in humans [11] . The basis of this predisposition remains unknown [12] although it has been predicted that both exposure and host susceptibility are likely to influence the observed epidemiological patterns [5] . However , unravelling the relative contributions to aggregation and predisposition and hence susceptibility/resistance remains challenging for both ethical and logistical reasons [13] . As outlined by Keymer and Pagal [14] , experimental manipulation utilising appropriate animal models is desirable , in tandem with human studies under field conditions , in order to study the multiple factors likely to be involved in predisposition . Ascaris is a parasite that not only exists as an adult worm in the host intestine but also has a migratory pathway undertaken by its larvae , known as the hepato-trachaeal migration [15] . Symptoms occur during larval migration due to tissue damage [16] and the resultant pathology has been documented in the liver of both humans [17–19] and pigs [20–24] . Loeffler [25] described a transient or seasonal syndrome of pulmonary infiltrates , mild to marked respiratory symptoms and peripheral eosinophilia that he subsequently attributed to larval Ascaris in the lungs , termed Loeffler’s syndrome [26] . Pulmonary symptoms can be severe and life-threatening [27] and have also been documented in pigs [28] . Despite its global prevalence and sheer numbers of individuals it infects , ascariasis remains a classic neglected disease [1] and part of the explanation for this neglect , is because pigs as animal models are costly and laborious and we lack the versatility of inbred strains . The majority of model organisms that have been infected with Ascaris are so-called abnormal hosts , whereby the parasite does not complete its life-cycle , but manifests itself as the early migratory phase [29] . Furthermore , in the vast majority of such experimental systems , the basis of resistance or susceptibility to infection has not been clearly established [29] . In contrast , a convenient and repeatable mouse model for exploring the susceptibility to Ascaris during the early phase of infection has been developed [30–32] . C57BL/6J are uniquely susceptible to infection with the porcine ascarid , A . suum , and larval burdens recovered from the lungs of this strain , markedly exceed those recovered from similarly infected strains of which the most resistant is the CBA/Ca mouse [30] . These two strains therefore represent opposite response phenotypes [5] , mimicking the extremes of predisposition detected in humans and pigs . Subsequently , this model was utilised to assess the significance of inflammatory processes within the murine lung . Such responses mirrored larval intensity and it was concluded that the pulmonary inflammatory immune response was not prominently involved in primary protection of mice to Ascaris infection in the lungs [31] . The lack of support for a pulmonary mechanism led to the suggestion that a hepatic/post-hepatic factor , which varies between C57BL/6J and CBA/Ca mice , may play a critical role in the successful migration through host tissues [31] . Evidence in the form of a differential histopathological change in the liver between the two strains was observed , whereby the resistant strain , CBA/Ca demonstrated an earlier intense inflammatory response coupled with a more rapid tissue repair in the liver [32] . C57BL/6 and CBA/Ca mice have also been used as model organisms to study susceptibility and resistance to other helminth and protist parasites . For example , in the case of Brugia malayi , the parasites were cleared more rapidly from the blood stream in CBA/Ca mice compared to C57BL/6J mice [33] . Studies involving Leishmania , demonstrated that CBA mice were able to control infection with Leishmania major , but not an infection with Leishmania amazonensis [34] . C57BL/6 mice conversely were found to be resistant to L . major infection , but were , as was the case for the CBA mice , susceptible to L . amazonensis [35] . Schistosoma mansoni caused low grade pathology in C57BL/6 mice , but a more aggravated pathology in their CBA counterparts [36] . In the case of Plasmodium berghei ANKA , both CBA and C57BL/6 were susceptible to the development of cerebral malaria [37] . In short , it is clear that both strains respond differently when challenged by a diversity of parasitic infections . However , depending on the parasite species , the mouse strains will exhibit a difference in resistance and susceptibility . The aims of the present study were to investigate the differences in the liver proteomes between the two mouse strains , one deemed to be susceptible , the other resistant , with both control and infected groups within each strain . To address these aims we employed high throughput quantitative mass spectrometry ( MS ) which is routinely used to identify and quantify thousands of proteins from highly complex samples . We specifically used label-free quantitative ( LFQ ) mass spectrometry [38] on total protein extracts from the left liver lobes of C57BL/6J and CBA/Ca mice with and without Ascaris infection sampled on day 4 post-infection ( p . i . ) . The hepatic lobe and time-point choice were based on the study of Dold et al . [32] , who demonstrated relatively high and equivalent larval numbers in C57BL/6J and CBA/Ca left lobes at day 4 p . i . , a time point at which they also observed the onset of a differential inflammatory response to infection between the two mouse strains . LFQ-based proteomics is increasingly being used to investigate human pathogens and parasites including Chlamydia trachomatis [39] , Acinetobacter baumannii [40] and Plasmodium vivax- infected and uninfected erythrocytes [41] and is providing unprecedented insight into both specific and broad level interactions between the infectious agent and its host .
The samples obtained for this study were part of a project authorised by the Health Products Regulatory Authority ( HPRA ) , the competent authority that regulates scientific animal research in Ireland in accordance with Directive 2010/63/EU and its Irish transposition , SI No 543 of 2012 ( Project Authorisation ID AE19136/P008 ID; Case Reference 7015826 ) . In addition , this project was ethically approved by the TCD Animal Research Ethics Committee ( AREC ) prior to HPRA submission and approval . Both C57BL/6J and CBA/Ca mice ( Harlan laboratories ) were infected with 1000 embryonated ova of A . suum and euthanized using cervical dislocation on day four post infection ( p . i . ) . At this time point , five animals were euthanised from all four groups: C57BL/6J infected , C57BL/6J control , CBA/Ca infected and CBA/Ca control [42] . The livers were extracted and each lobe was snap frozen separately using liquid nitrogen and stored at -80°C until required . To confirm a differential larval burdens in susceptible and resistant strains , post-mortems of 5 infected mice from each strain were conducted on day 7 post-infection . Living Ascaris larvae were recovered from the lungs of each mouse by means of the modified Baermann technique [30] . A pellet of the isolated viable larvae was suspended in a 5 ml solution of 0 . 9% saline and 6% formalin . Prior to larval counts the 5 ml solutions were agitated to ensure a homogeneous distribution of larvae within the sample . Larval counts were recorded from lung samples by means of pipetting 2 mls of solution into the chamber of a nematode counting slide ( Chalex corporation ) [32] . The number of larvae in the gridded area , which represented 1 ml , was counted under X40 magnification . The number of larvae in a 1 ml solution was multiplied by the total volume in order to estimate the number of larvae in each lung sample . The left lobes of day four p . i . were homogenized in 6M urea , 2M thiourea , supplemented with a protease inhibitor cocktail ( Roche , Complete Mini ) . Samples were centrifuged for 5 min at 11 , 200 × g to pellet any cellular debris . The supernatant was then removed and quantified using the Qubit™ protein quantification system ( Invitrogen ) , following the manufacturer’s instructions . Three independent biological replicates for each group were analysed in this study . 75 μg of each sample was precipitated using the 2D Clean-Up Kit ( GE HealthCare ) , following the manufacturer’s instructions . The resulting protein pellet was resuspended in 6M urea , 2M thiourea , 0 . 1 M Tris-HCl , pH 8 . 0 . 50mM ammonium bicarbonate was added to each sample and proteins were reduced with 0 . 5M dithiothreitol ( DTT ) at 56°C for 20 min and alkylated with 0 . 55M iodoacetamide ( IAA ) at room temperature for 15 min , in the dark . 1 μl of a 1% w/v solution of Protease Max Surfactant Trypsin Enhancer ( Promega ) and 1 μg of Sequence Grade Trypsin ( Promega ) was added to give a protein:trypsin ratio of 75:1 . The protein/trypsin mixture was incubated at 37°C for 18 hours . Digestion was terminated by adding 1 μl of 100% trifluoroacetic acid ( Sigma Aldrich ) and incubation at room temperature for 5 min . Samples were centrifuged for 10 min at 13 , 000 × g and a volume equivalent to 40 μg of pre-digested protein was removed and purified for mass spectrometry using C18 Spin Columns ( Pierce ) , following the manufacturer’s instructions . The eluted peptides were dried using a SpeedyVac concentrator ( Thermo Scientific Savant DNA120 ) and resuspended in 2% v/v acetonitrile and 0 . 05% v/v trifluoroacetic acid ( TFA ) . Samples were sonicated for 5 min to aid peptide resuspension followed by centrifugation for 5 min at 16 , 000 × g . The supernatant was removed and used for mass spectrometry . 1 μg of each digested sample was loaded onto a QExactive ( ThermoFisher Scientific ) high-resolution accurate mass spectrometer connected to a Dionex Ultimate 3000 ( RSLCnano ) chromatography system . The peptides were separated by a 2% to 40% gradient of acetonitrile on a Biobasic C18 Picofrit column ( 100mm length , 75mm ID ) , using a 120 minute reverse-phase gradient at a flow rate of 250nL min-1 . All data were acquired with the mass spectrometer operating in automatic data dependent switching mode . A full MS scan at 140 , 000 resolution and a range of 300–1700 m/z was followed by an MS/MS scan , resolution 17 , 500 and a range of 200–2000 m/z , selecting the 15 most intense ions prior to MS/MS . Protein identification and LFQ normalisation of MS/MS data was performed using MaxQuant v1 . 5 . 0 . 8 ( http://www . maxquant . org ) following the general procedures and settings outlined in [43] . The Andromeda search algorithm [44] incorporated in the MaxQuant software was used to correlate MS/MS data against the SWISS-PROT database for Mus musculus [45] ( 16 , 773 entries , downloaded May 2015 ) and a contaminant sequence set provided by MaxQuant . The following search parameters were used: first search peptide tolerance of 20 ppm , second search peptide tolerance 4 . 5 ppm with cysteine carbamidomethylation as a fixed modification and N-acetylation of protein and oxidation of methionine as variable modifications and a maximum of two missed cleavage sites allowed . False Discovery Rates ( FDR ) were set to 1% for both peptides and proteins and the FDR was estimated following searches against a target-decoy database . LFQ intensities were calculated using the MaxLFQ algorithm [46] from razor and unique peptides with a minimum ratio count of two peptides across samples . Peptides with minimum length of seven amino acids were considered for identification and proteins were only considered identified when more than one unique peptide for each protein was observed . Perseus v . 1 . 5 . 0 . 8 ( www . maxquant . org/ ) was used for data analysis , processing and visualisation . Normalised LFQ intensity values were used as the quantitative measurement of protein abundance for subsequent analysis . The data matrix was first filtered for the removal of contaminants and peptides identified by site . LFQ intensity values were log2 transformed [47] and each sample was assigned to its corresponding group ( C57BL/6J control and infected; CBA/Ca control and infected ) . Proteins not found in two out of three replicates in at least one group were omitted from the analysis . A data-imputation step was conducted to replace missing values with values that simulate signals of low abundant proteins [48] chosen randomly from a distribution specified by a downshift of 2 . 6 times the mean standard deviation ( SD ) of all measured values and a width of 0 . 37 times this SD . Two sample t-tests were performed for all relevant comparisons using a cut-off of p<0 . 05 on the post imputated dataset to identify statistically significant differentially abundant ( SSDA ) proteins . Volcano plots were generated in Perseus by plotting negative log p-values on the y-axis and log2 fold-change values on the x-axis for each pair-wise comparison to visualise changes in protein expression . The ‘categories’ function in Perseus was utilized to highlight and visualise the distribution of various pathways and processes on selected volcano plots . Normalised intensity values were used for a principal component analysis ( PCA ) . Exclusively expressed proteins ( those that were uniquely expressed or completely absent in one group ) were identified from the pre-imputation dataset and included in subsequent analyses . Hierarchical clustering was performed on Z-score normalised intensity values for all differentially abundant proteins by clustering both samples and proteins using Euclidean distance and complete linkage . Gene ontology ( GO ) mapping was also performed in Perseus using the UniProt gene ID for all identified proteins to query the Perseus annotation file ( downloaded January 2015 ) and extract terms for biological process , molecular function , Kyoto Encyclopaedia of Genes and Genomes ( KEGG ) name , KEGG pathway , protein family ( pfam ) and InterPro . GO and KEGG term enrichment analysis was performed on the major protein clusters identified by hierarchical clustering using a Fisher’s exact test ( a Benjamini-Hochberg corrected FDR of2% ) for enrichment in Uniprot Keywords , gene ontology biological process ( GOBP ) , gene ontology cellular component ( GOCC ) and KEGG ( FDR <2% ) . The Search Tool for the Retrieval of INteracting Genes/Proteins ( STRING ) [49] v10 ( http://string-db . org/ ) was used to map known and predicted protein:protein interactions . UniProt gene lists ( extracted from Perseus ) were inputted and analysed in STRING using the medium to high confidence ( 0 . 5–0 . 7 ) setting to produce interactive protein networks for each group in all comparisons . GO term enrichment analyses for biological process , molecular function and cellular compartment were then conducted to identify potential pathways and processes that warranted further analysis . Such pathways were examined using the KEGG pathway analysis ( http://www . kegg . jp/kegg/tool/map_pathway2 . html ) [50 , 51] , using the ‘KEGG Mapper—Search&Color Pathway’ tool . The equivalent KEGG identifiers were obtained using the UniProt ‘Retrieve/ID mapping’ function ( http://www . uniprot . org/uploadlists/ ) with the organism set to M . musculus ( mmu ) . Retrieved KEGG IDs were used to identify the most represented pathways . The MS proteomics data and MaxQuant search output files have been deposited to the ProteomeXchange Consortium [52] via the PRIDE partner repository with the dataset identifier PXD003555 .
Performing two sample t-tests resulted in the identification of SSDA proteins both between and within strains ( S2 Dataset ) . These differentially abundant proteins , together with proteins uniquely expressed in each group , were analysed using the interaction network analysis software , STRING , to identify biological pathways and processes over-represented in a particular group . Biological processes and pathways identified by STRING were investigated further for their representation within the differentially abundant protein dataset and displayed on volcano plots using the ‘categories’ function in Perseus to highlight proteins involved in selected biological processes . Two sample t-tests ( p<0 . 05 ) on the post-imputation dataset identified 479 SSDA proteins between C57BL/6J infected and control with the log2 fold change ranging from -8 . 2 to 7 . 2 ( Table 1 ) . The 20 most differentially abundant proteins for both groups are displayed in Fig 2A . The most abundant proteins in C57BL/6J controls were histone H1 . 4 , tyrosine aminotransferase , and histone H1 . 1 . In comparison , for C57BL/6J infected samples , the most abundant proteins consisted of two S100 proteins ( protein S100-A8 and protein S100-A9 ) and cytochrome c oxidase subunit 7C protein . STRING analysis identified enrichment of terms associated with the proteasome , the mitochondrion , RNA splicing and actin cytoskeleton in C57BL/6J infected individuals ( Fig 3A ) whereas enriched terms were identified for translation in the C57BL/6J controls ( Fig 3B ) . Two sample t-tests on the post-imputation dataset identified 193 SSDA proteins ( log2 fold change range: -6 . 1 to 5 . 9; Table 1 ) with the 20 most differentially abundant proteins displayed in Fig 2B . The most abundant proteins in CBA/Ca infected mice were: protein S100-A8 , seminal vesicle secretory protein 4 , and dipeptidyl peptidase 1 . For CBA/Ca control mice the most SSDA proteins included: 7-alpha-hydroxycholest-4-en-3-one 12-alpha-hydroxylase , costars family protein ABRACL , and collagen alpha-1 ( I ) chain . STRING analysis revealed that CBA/Ca infected mice had an overrepresentation of spliceosome and actin cytoskeleton proteins in comparison to control samples ( Fig 3C ) . As observed in the C57BL/6J mice , terms associated with translational proteins were enriched in addition to RNA processing and glutathione metabolism in CBA/Ca control mice with respect to their infected counterparts ( Fig 3D ) . Two sample t-tests ( p<0 . 05 ) on the post-imputation dataset identified 354 SSDA proteins respectively and the Log2 fold changes ranged from -6 . 7 to 13 . 6 ( Table 1 ) . The top 20 most SSDA proteins are shown in Fig 2C . The most abundantly expressed proteins observed in CBA/Ca control samples are haemoglobin subunit beta-2 , S-methylmethionine , and putative hydroxypyruvate isomerase . The log2 fold change for haemoglobin subunit beta-2 was the highest observed in the dataset , being 13 . 6 . For C57BL/6J controls , the most abundantly expressed proteins are aldehyde oxidase 3 , ADP-ribosylation factor 5 , and H-2 class I histocompatibility antigen K-B alpha chain . Two clusters were observed in the CBA/Ca control samples representing ribosomal and oxidative phosphorylation biological processes ( S1A Fig ) . However STRING analysis failed to identify any well populated clusters in C57BL/6J compared to CBA/Ca control ( S1B Fig ) . Two sample t-tests ( p<0 . 05 ) on the post-imputation dataset identified 410 SSDA proteins , respectively and the log2 fold changes ranged from -8 . 1 to 9 . 2 ( Table 1 ) . The 20 most SSDA proteins are displayed in Fig 2D . The most abundant proteins in CBA/Ca infected compared to C57BL/6J were found to be: putative hydroxypyruvate isomerase , S-methylmethionine , haemoglobin subunit beta-2 . The most abundant proteins present in C57BL/6J infected compared to their CBA/Ca counter parts were: murinoglobulin-2 , aldehyde oxidase 3 , and chitinase-3-like protein 3 . STRING analysis resolved clusters with enriched terms for oxidative phosphorylation and translation in CBA/Ca infected mice ( S1C Fig ) . Terms associated with the glutathione metabolism are enriched in C57BL/6J infected mice with respect to CBA/Ca infected mice ( S1D Fig ) . Proteins involved in the processes and pathways of interest ( identified in Perseus and STRING ) were displayed on volcano plots arising from the 2-way t-tests to give an indication of expression profile for all significant and non-significant representatives . The five processes that were consistently overrepresented and/or differentially abundant and selected for further analysis were mitochondria , electron transfer chain , immune system , ribosome and iron ion binding ( Table 1 ) . KEGG analysis was also used to provide a protein centric view for selected pathways ( S2–S4 Figs ) . Mitochondrial associated proteins were consistently differentially abundant among all samples . Of the 2 , 307 proteins identified and reported here , 324 were associated with the mitochondrion . A higher abundance of mitochondrial proteins was generally observed in CBA/Ca mice both with and without infection ( Table 1; Fig 1C ) in comparison to C57BL/6J mice , although an increase in mitochondrial protein abundances was observed for both strains under infection ( Fig 2 ) . GO analysis in Perseus resolved 80 oxidative phosphorylation ( OXPHOS ) terms , including electron transfer chain ( ETC ) complex I to V proteins , the most differentially abundant of which were cytochrome c oxidases ( cox7c and cox7a2 ) . CBA/Ca control and infected mice had a higher abundance of proteins involved in the OXPHOS pathway , when compared to C57BL/6J ( S2A Fig ) . Although there was a significant upregulation of OXPHOS proteins in C57BL/6J infected mice when compared to their controls , the extent of increase was less than that for CBA/Ca . KEGG analysis also indicated that there was a generally lower abundance of ETC proteins in C57BL/6J infected mice , compared to their CBA/Ca counterparts . However Complex I proteins , were less abundant in C57BL/6J infected mice , when compared to their controls ( S2A Fig ) . Proteins of the tricarboxylic acid ( TCA ) cycle were intrinsically more abundant in CBA/Ca control mice , when compared to their C57BL/6J counterparts . This difference between the two strains was observed also under infection . When infected , both mouse strains had increased abundance of these proteins . However , C57BL/6J infected mice had higher abundances for the entire TCA cycle ( S3 Fig ) . A clear change in the abundance of proteins involved in the translational process during infection was observed . Ribosomal proteins were more abundant in both CBA/Ca controls and infected with respect to their C57BL/6J counterparts ( S1A and S1C Fig ) . Under infection however , both CBA/Ca and C57BL/6J mice had significantly lower abundances for many ribosomal proteins ( Fig 2 ) . KEGG analysis further confirmed this finding demonstrating that CBA/Ca had more intrinsically abundant ribosomal proteins than C57BL/6J , a difference that became more pronounced under infection ( S2B Fig ) . GO and KEGG term enrichment analysis indicated that there were very few immune associated pathways respondent to infection . Of these , 20 immune associated proteins were differentially abundant ( relative fold difference > 1 . 5 ) between infected and control samples ( Table 2 ) . Eight proteins: s100-A8; s100-A9; dipeptidyl peptidase 1; coronin-1A; galectin; vitronectin; moesin and plasminogen were more abundantly expressed in both C57BL/6J and CBA/Ca infected mice with respect to their uninfected counterparts indicating a potential conservation of response to Ascaris in both mice strains . The majority of SSDA immune proteins were observed in C57BL/6J Ascaris-infected mice ( Table 2 ) . A number of immune proteins demonstrated higher intrinsic abundances in C57BL/6J compared to CBA/Ca ( in both infected and uninfected comparisons ) and included dipeptidyl peptidase 1 , complement C4-B , galectin-3 and the MHC class I molecules H2-D1 and H2-K1 ( Fig 2C and 2D ) . 16 proteins associated with the biological process term “response to stress” were enriched in the shared “response to infection” cluster ( Cluster D on Fig 1B ) . Many of these proteins are also associated with the cytoskeleton and cytoskeletal organisation , terms that were also shown to be significantly enriched within this “infected” cluster ( S2 Table; Fig 1C ) . Glutathione is known to reduce the reactive oxygen species ( ROS ) , H2O2 to H2O . Because of its ROS reducing capacities , it was investigated in this study . KEGG analysis identified an increase in glutathione metabolic proteins of C57BL/6J control samples , compared to their CBA/Ca counterparts ( S4 Fig ) . This inherent difference was also observed when comparing both strains under infection . Additionally , there was an increase in abundance of glutathione metabolism proteins in C57BL/6J mice when infected , compared to their controls . This increase was less pronounced in CBA/Ca mice .
Parasite infection can place additional stresses on the physiological processes of the host . One of the major findings of this study was the higher abundance of proteins involved in mitochondrial processes in the CBA/Ca mouse strain , both with and without infection . These processes include OXPHOS and the TCA cycle , which were significantly enriched in the CBA/Ca proteome in comparison to their C57BL/6J counterparts . These intrinsic differences may represent a significant difference between both strains in terms of the susceptibility to Ascaris infection . The mitochondria are the main source of ROS , which are produced as by-products in the OXPHOS pathway as part of cellular respiration [56 , 57] . ROS have a vast range of biological functions and play a role in defence against parasites and pathogens , apoptosis , cell survival , cell growth , proliferation , differentiation and many other signalling pathways [57 , 58] . Higher ROS production in CBA/Ca mice may generate a prohibitive oxidative environment for Ascaris within the murine liver and explain the decrease in observable migration to the lungs by Ascaris from the CBA/Ca strain [32] . Our results support previous findings that CBA/Ca mice have higher superoxide production and have a higher tolerance to ROS than their C57BL/6 counterparts [59 , 60] . Staecker et al . [60] found that the mouse cochlea of the C57BL/6J strain showed an early-onset hearing loss pathology compared to CBA/Ca mice , potentially linked to increased levels of free radicals present in the cochlea . This increase was attributed to the adult hearing loss ( Ahl ) gene , first identified by Johnson et al . [61] , which is thought to cause a decrease in protective antioxidant enzymes in C57BL/6J mice . Taken together , these studies suggest that CBA/Ca mice not only produce higher endogenous levels of ROS and protective antioxidants but can tolerate ROS levels that could potentially affect an invading parasite . Under infection , both strains exhibited higher abundances of OXPHOS and TCA cycle proteins indicating a general stress response to Ascaris infection . Although the increase in abundance was generally more pronounced in C57BL/6J mice , when compared to their corresponding controls , infected CBA/Ca mice had higher overall abundances representing the higher endogenous levels of mitochondrial proteins in CBA/Ca control mice . Interestingly , C57BL/6J infected mice had lower abundances of complex I proteins specifically in comparison to their controls . Complex I is an important site of superoxide production ( together with complex III ) [62] and has been linked with a number of diseases spanning early childhood ( Leigh disease ) [63] to adulthood ( Parkinson’s and Alzheimer’s disease ) [62] . Additionally , quinone-binding inhibitors , which inhibit complex I , were shown to increase ROS production [62] . TCA cycle proteins showed a similar pattern of abundance changes amongst samples , with higher intrinsic abundances observed in CBA/Ca mice and increased abundances observed under infection . However , C57BL/6J infected mice displayed higher abundances of TCA-associated proteins under infection than CBA/Ca infected mice , compared to their controls . The TCA cycle takes place in the mitochondria [64] and results in the production of the reduced co-enzyme nicotinamide adenine dinucleotide hydride ( NADH ) , which contributes electrons to the OXPHOS pathway . Thus , it seems that in response to infection both mice strains increase OXPHOS activity . The differences in mitochondrial protein abundance and presumed ROS levels between the susceptible and resistant strains can be explained not only by intrinsic basal differences but also the extent of parasitic interaction within each strain . Based on the significant reduction in nematode numbers that reach the lungs in CBA/Ca mice [31] one can assume that the CBA/Ca-Ascaris interaction represents a far less compatible host-parasite interaction in comparison to the C57BL/6J-Ascaris interaction . Nematodes in C57BL/6J livers seem capable of successful establishment within their hosts and in doing so , modulate host defence pathways and responses to allow for completion of the parasite life-cycle . Ascaris secrete a plethora of proteins in their excretory/secretory fluid with potential immunomodulatory and antioxidant defence functions [65–67] . The first complete predicted secretome was generated during the Ascaris suum genome project identifying 775 predicted secretory proteins with a rich number of peptidases for the migration through host tissues , as well as products potentially involved in the evasion or modulation of host defences [68] . In addition , proteomic analyses of the excretory/secretory products of larval and adult Ascaris have characterised heat shock proteins , ABA-1 , proteases , serpins , chitinases and a suite of individual proteins that have been implicated in immune evasion and modulation and have been suggested to play a role in parasite survival [69] . It is not unreasonable to postulate that Ascaris also secrete proteins that reduce host mitochondrial processes and ROS production specifically . Modulation of host ROS production has been widely reported for a number of taxonomically diverse parasites , including the liver fluke , Fasciola hepatica and the causative agent of Chagas disease , Trypanosoma cruzi [70–73] . In addition , antioxidant production in parasites has been linked to improved parasite survival [74 , 75] and helminths are known to possess an array of ROS-reducing products , such as superoxide dismutase ( SOD ) [76] with A . suum also possessing catalase ( CAT ) [77] , and peroxiredoxin ( Prx ) [76 , 78] , which offer the nematode mechanisms for ROS contention . An intrinsic difference between the two mouse strains was also observed for glutathione metabolism , with C57BL/6J mice displaying a higher abundance in proteins involved in this pathway . Under infection , both strains displayed an increase in abundance for these proteins , with C57BL/6J mice presenting a more pronounced upregulation . Most mitochondria lack CAT , therefore , H2O2 conversion is maintained by glutathione ( GSH ) within these organelles [79] . H2O2 is reduced to H2O by glutathione peroxidase ( GPx ) , using GSH [80] and this reaction produces oxidised glutathione ( GSSG ) , which is then reduced back to GSH by glutathione reductase ( GR ) , consuming one molecule of NADPH . This increase would need to be countered by increased antioxidants , such as GSH . Our findings clearly indicate that mitochondrial- and ROS-associated proteins are more abundantly expressed in both strains under infection , indicative of the host liver being within a state of stress . In C57BL/6J mice , these increases are less pronounced than in CBA/Ca mice indicating that although a response to Ascaris is mounted , the levels achieved in C57BL/6Js are not equivalent to those in CBA/Ca mice . In addition , because ROS levels are presumably reduced in C57BL/6J mice , the nematodes entering the liver have a less cytotoxic environment to contend with and can potentially mount immuno-modulatory and antioxidative perturbations on their host . The host immune system represents an important obstacle to the completion of the parasite life-cycle . To circumvent this threat , parasites have evolved strategies to evade or modulate aspects of the host immune response . In relation to Ascaris , previous studies examining the host immune response within the liver have focussed on the role of pro-inflammatory cytokines in co-ordinating defences against the parasite during its migratory phase [31 , 81 , 82] . Within the present study , gene ontology and KEGG pathway analyses failed to identify any clear process level immune responses to Ascaris infection and few differentially abundant immune proteins were identified either between strains or in response to the presence of Ascaris . The low number of immune-responsive proteins may be explained in two ways . Firstly , the liver is an immune tolerant organ and the hepatic system circumvents the lymphatic system . This ensures that no unwanted immune activation occurs in response to commensal pathogens present in the bowel , which can sometimes enter the blood stream after endothelial damage [83] . This system is thought to be exploited by other parasites , such as malaria-causing Plasmodium species , where the parasite enters the liver in the pre-erythrocytic stage , potentially to evade the host immune system [84] . Secondly , the experimental time point examined within the present study may be too early in the infection to observe an adaptive immune response , as the adaptive immune response takes 4–7 days to engage fully [85] . Of the identified differentially abundant immune proteins , broadly three groups can be defined ( see Table 2 ) : proteins observed in both mouse strains , protein abundance only observed in C57BL/6J mice , and proteins observed in CBA/Ca mice only . Overall , there appeared to be more immune-associated proteins that had increased abundance in response to infection , which was to be expected , and C57BL/6J mice displayed a higher abundance in some immune-associated proteins . The proteins observed to be more abundant in both strains signify a potential innate immune response through the identification of proteins , such as complement component C8a and S100 proteins , S100a8 and S100a9 . The alpha polypeptide of complement 8 interacts with other complement proteins to form the membrane attack complex , a cell-killing structure [86] . The complex binds to the cell membrane of target cells , forming transmembrane channels resulting in cell lysis and subsequent death [86] . Although , complement recruitment can occur in response to tissue damage potentially associated with parasite infection , complement components have previously been implicated as factors in mediating the adherence of myeloid cells to nematode parasites , resulting in parasite death although the susceptibility amongst different nematode species is variable [87] . Secretory products released by nematodes , such as Brugia malayi and Trichinella spiralis , have been identified to inhibit the chemotaxis activities of complement component 5a , identifying the complement component as a target for immunomodulation by parasitic nematodes [88] . Proteins S100a8 and S100a9 were among the most abundantly expressed under infection in both mouse strains , and completely absent in the control samples ( Fig 1C and 1D ) . The S100 group is involved in multiple cellular functions , including cellular contraction , motility , cell differentiation and calcium regulation [89] . S100a8 and S100a9 can exist as homodimers , but in the presence of calcium , they will form a heterodimer called calprotectin [90 , 91] which is expressed mainly by granulocytes , monocytes , and macrophages [90] . These proteins were recently identified as danger-associated molecular patterns ( DAMPs ) [92] which have been shown to interact with toll-like receptors ( TLRs ) , such as TLR4 [93] . It is thought that S100a8/S100a9 are actively released by cells sensing danger , rather than passively [90] . Calprotectin has been found to be a chemotactic factor for neutrophils and other mononuclear cells , with the exception of lymphocytes , in mice [94] , further confirming its role in the immune system . S100a8 and S100a9 heterodimers are known to be important in Leishmania infection . S100a8 and S100a9 recruitment results in the elimination of Leishmania and knockout mice experiencing a more severe infection . Such proteins probably have a role in the recruitment of neutrophils to the site of infection [91] . Moreover , S100a8 and S100a9 primed macrophages were better at killing Leishmania than non-primed macrophages . In addition , Edgeworth et al . [95] , using human biopsies , found that S100a8/S100a9 heterodimer secretion , by macrophages , onto adults of the filarial nematode , Onchocerca volvulus , was part of an early immune response—demonstrating a putative immune role against nematode parasites . A similarly early response may be signified by the presence of these proteins in infected C57BL/6J and CBA/Ca mice suggesting a conserved role in defence against parasitic nematodes . In short , our results suggest the presence of an innate immune response ( see Table 2 ) through the presence of a complement protein ( C8a ) and several proteins involved in leukocyte chemotaxis ( such as S100a8 , S100a9 , galactin-3 ) . Additionally , there is evidence of an early adaptive immune response through the presence of proteins such as plastin-2 ( Lcp1 ) and dipeptidyl peptidase 1 ( Ctsc ) . Given that C57BL/6J mice carry more active Ascaris than CBA/Ca mice , it was unsurprising to find that a higher number of immune-associated proteins were observed in the susceptible strain . Of the nineteen proteins with known immune function ( Table 2 ) fifteen had higher fold changes in C57BL/6J compared to CBA/Ca mice or were exclusively more statistically abundant in C57BL/6J mice . The two most abundantly expressed proteins in the ‘C57BL/6J’ only group , were annexin A1 and neutrophil gelatinase-associated lipocalin , both of which are expressed as a cellular response to interleukin-1 ( IL-1 ) . It seems therefore that the restrictions encountered by the nematode larva in the resistant strain results in the reduced immune response in comparison to their susceptible counterparts . Very little is known about how extracellular macroparasites , such as Ascaris , successfully modulate their host . The results of this study suggest that under infection , a down regulation of translational proteins occurs for both strains , in particular S6 ribosomal protein ( part of the mammalian/mechanistic target of rapamycin ( mTOR ) pathway ) . Additionally , CBA/Ca control mice had , both with and without infection , higher ribosomal protein abundances compared to C57BL/6J mice . Under infection this intrinsic difference , however , became more pronounced . Pathogens are known to interact with translational proteins in their host [96] . Viruses , for example , are dependent on the host translational machinery for their own protein synthesis . Cells in turn can promote gene expression in response to the environmental situation , e . g . hypoxia , glucose deprivation [96] and pathogens , such as certain bacteria , are known to secrete effectors into the cytoplasm which in turn can reduce translation itself [97] . Inactivation of translation machinery is a documented strategy of certain parasites , such as the trypansome , Leishmania major [98] . Once the parasite has invaded a macrophage , the surface protease GP63 cleaves mTOR and mTOR complex 1 ( mTORC1 ) , which inhibits translational initiation [98] . It is reasonable to postulate that the widespread lower abundance of translation proteins may indicate direct modulation by Ascaris which is known to secrete a wide range of effector-like molecules [69] . However it must be acknowledged that the down regulation of translation may in fact signify a defensive strategy employed by the host , in response to damage caused by Ascaris , as is seen in bacteria-induced epithelial damage , which results in the triggering of signals that suppress host translation [96] . Aged erythrocytes are broken down by macrophages in the spleen and liver [99]; the liver is therefore an important organ in haemoglobin metabolism . Haemoglobin beta-2 ( Hbb2 ) is the most abundantly expressed protein in CBA/Ca mice , and displayed relative fold changes of over 12 , 500 between CBA/CA and C57BL/6J controls and a fold chance of over 500 between CBA/CA and C57BL/6J infected mice . The haemoglobin of mice consists of two haemoglobin alpha ( Hbba ) , one haemoglobin beta-1 ( Hbb-1 , major chain ) and one Hbb-2 ( minor chain ) subunits . There are three different haplotypes for haemoglobin beta ( Hbb ) : HbbS ( single , βS ) , HbbD ( diffuse , βD ) and HbbP . C57BL/6J mice are homozygous for HbbS [100] , whereas CBA mice are heterozygous for HbbD [100–102] . The βS haplotype has one reactive cysteine residue on position 93 ( βCys93 ) , whereas βD and βP have an extra reactive cysteine residue: βCys13 [100] . Interestingly , both βCys93 and βCys13 can be modified by GSSG , with βCys13 being more susceptible than βCys93 [100] . Furthermore , GSSG may be reduced to GSH at βCys13 , without the need of GR nor NADPH [100] . Hempe et al . [100] postulate that the concentration of these cysteine sulfhydryl groups determines the availability of GSH for enzymatic reactions . Having a higher concentration of haemoglobin could therefore be a mechanism of CBA/Ca mice to establish their ROS tolerance ( as observed in the previously mentioned radiation studies ) , as a higher haemoglobin concentration would coincide with more βCys93 , and thus more chances for glutathione to be reduced . The allelic differences in haemoglobin are also thought to confer a differential ability of various mouse strains to contend with different parasites . For example , C57 mice ( HbbS ) are relatively resistant to Plasmodium infection compared to BALB/c mice ( HbbD ) [103] . Ascariasis is a debilitating disease affecting an estimated 800 million individuals globally . While the pathology of the disease has been extensively examined , our understanding of the molecular mechanisms underlying resistance and susceptibility to nematode infection is poorly understood . Here we provide a novel insight into the changes in a host liver proteome in response to Ascaris infection in vivo within two murine strains varying in their resistance and susceptibility to infection . Our results provide evidence for significant intrinsic differences in the hepatic proteomes of both mouse strains , potentially associated with resistance to Ascaris infection . Given the higher levels of proteins associated with ROS producing and processing and the general increased tolerance to ROS in CBA/Ca mice [59] in comparison to C57BL/6J mice , in particular , we hypothesise that higher ROS levels and the associated oxidative environment could be involved in the inhibition of Ascaris larval in CBA/Ca mice . Whether the intrinsic differences in mitochondrial protein abundances are due to different levels of mitochondrial biogenesis and number between the two strains has yet to be determined . Our research provides new insights into the intricacies and complexities of the host-parasite relationship of Ascaris . In addition , potential parasite modulation of translational processes by Ascaris were clearly evident in both strains . Our findings also provide a new understanding of previous studies that utilised these two mouse strains for experiments involving early-onset hearing loss , radiation exposure , and several other micro and macroparasite infections . Given our findings and the central role of the liver in the Ascaris migratory pathway , we suggest a potentially novel research direction to develop alternative preventative control strategies for Ascaris . It seems that the key determinant in murine resistance to Ascaris lies in highly oxidative conditions that presumably restricts and arrests successful larval migration within the CBA/Ca hepatic environment . Larval nematodes that enter the C57BL/6J liver seem free to continue their onward progression and through the secretion of their excretory/secretory compounds further sustain their parasitic lifecycle through manipulation and modulation of the host liver . So although defence responses are mounted in C57BL/6J mice it seems that Ascaris is already well-established in its attempts to contend with the host response . However , through the manipulation of hepatic ROS levels in the susceptible mouse strain , we may now be able to determine the importance of intrinsic ROS in conferring resistance to Ascaris . Although significant research is required to fully understand the determinants of resistance to Ascaris in our murine model , it does seem that we have at least been presented with new options in our pursuit of strategies to control a disease that affects an estimated one eighth of our planet’s population . 7-alpha-hydroxycholest-4-en-3-one 12-alpha-hydroxylase: O88962; ADP-ribosylation factor 5: P84084; Aldehyde oxidase 3: G3X982; Annexin A1: P10107; Chitinase-3-like protein 3: O35744; Collagen alpha-1 ( I ) chain: P11087; Complement C4-B: P01029; Complement component C8 alpha chain: Q8K182; Coronin-1A: O89053; Costars family protein ABRACL: Q4KML4; Cytochrome c oxidase subunit 7A2 , mitochondrial: P48771; Cytochrome c oxidase subunit 7C , mitochondrial: P17665; Dipeptidyl peptidase 1: P97821; Galectin-3: P16110; H-2 class I histocompatibility antigen K-B alpha chain: P01901; H-2 class I histocompatibility antigen , D-B alpha chain: P01899; H-2 class I histocompatibility antigen , K-B alpha chain: P01901; Haemoglobin alpha: P01942; Haemoglobin beta-1: P02088; Haemoglobin subunit beta-2: P02089; Histone H1 . 1: P43275; Histone H1 . 4: P43274; Interleukin-1 beta: P10749; Mitochondrial antiviral-signalling: Q8VCF0; Moesin: P26041; Murinoglobulin-2: P28666; Neutrophil gelatinase-associated lipocalin: P11672; Plasminogen: P20918; Plastin-2: Q61233; Protein S100-A8: P27005; Protein S100-A9: P31725; Putative hydroxypyruvate isomerase: Q8R1F5; S6 ribosomal protein: P62754; Seminal vesicle secretory protein 4: P18419; S-methylmethionine: Q91WS4; Tyrosine aminotransferase: Q8QZR1; Vitronectin: P29788 . UniProt accession numbers are provided for all identified proteins in S1 and S2 Datasets .
|
Ascaris infection is a significant burden on the people who live in developing countries with infection being linked to poor hygiene and low socio-economic status . The parasite causes a range of symptoms , especially in children , which include both chronic morbidity , such as growth retardation , and acute outcomes , such as intestinal obstruction . Certain people tend to be more heavily infected than others , with those individuals experiencing worse morbidity . The understanding of the difference between susceptible and resistant people is an essential first step in the development of new therapies in order to eliminate this neglected parasitic disease . Using an established mouse model involving a susceptible and resistant strain , we aimed to gain insight into the host-Ascaris interaction at the hepatic interface and elucidate some of the molecular mechanisms potentially involved in resistance . A number of key intrinsic differences were determined between both strains including major differences in mitochondrial and ROS associated processes which may present the nematodes with differing oxidative conditions and explain the failure of the nematode to establish a successful parasitism in the resistant strain . In addition , we resolved signatures of the innate and early adaptive immune response and a major reduction in the proteins associated with translation in both strains under infection . Our findings need to be further explored , but could be the foundation for a better understanding of the mechanisms behind the differential parasite burden and in the future , potential new therapies for control .
|
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2016
|
A Proteomic Investigation of Hepatic Resistance to Ascaris in a Murine Model
|
CFTR modulators have revolutionized the treatment of individuals with cystic fibrosis ( CF ) by improving the function of existing protein . Unfortunately , almost half of the disease-causing variants in CFTR are predicted to introduce premature termination codons ( PTC ) thereby causing absence of full-length CFTR protein . We hypothesized that a subset of nonsense and frameshift variants in CFTR allow expression of truncated protein that might respond to FDA-approved CFTR modulators . To address this concept , we selected 26 PTC-generating variants from four regions of CFTR and determined their consequences on CFTR mRNA , protein and function using intron-containing minigenes expressed in 3 cell lines ( HEK293 , MDCK and CFBE41o- ) and patient-derived conditionally reprogrammed primary nasal epithelial cells . The PTC-generating variants fell into five groups based on RNA and protein effects . Group A ( reduced mRNA , immature ( core glycosylated ) protein , function <1% ( n = 5 ) ) and Group B ( normal mRNA , immature protein , function <1% ( n = 10 ) ) variants were unresponsive to modulator treatment . However , Group C ( normal mRNA , mature ( fully glycosylated ) protein , function >1% ( n = 5 ) ) , Group D ( reduced mRNA , mature protein , function >1% ( n = 5 ) ) and Group E ( aberrant RNA splicing , mature protein , function > 1% ( n = 1 ) ) variants responded to modulators . Increasing mRNA level by inhibition of NMD led to a significant amplification of modulator effect upon a Group D variant while response of a Group A variant was unaltered . Our work shows that PTC-generating variants should not be generalized as genetic ‘nulls’ as some may allow generation of protein that can be targeted to achieve clinical benefit .
The development of variant-specific modulators that correct dysfunctional cystic fibrosis transmembrane conductance regulator ( CFTR ) protein is an excellent model for precision medicine [1–4] . Cystic fibrosis ( CF ) is a progressive , multi-organ , life-threating , autosomal recessive disease caused by variants in CFTR gene leading to reduced or no protein function in approximately 70 , 000 individuals worldwide [5–7] . Two classes of compounds have been approved by the US Food and Drug Administration ( FDA ) . Ivacaftor ( VX-770; Kalydeco ) potentiates function by increasing the probability of channel opening to enhance chloride ion conductance of CFTR gating variants [8 , 9] . Lumacaftor ( VX-809 ) corrects the processing and trafficking of the most common CF-causing variant ( F508del ) to increase the quantity of CFTR channels at the cell surface [10] . A potentiator-corrector combination ( ivacaftor and lumacaftor; Orkambi ) has been approved for individuals with CF who carry two copies of the F508del variant [11] . More recently , a new CFTR corrector , tezacaftor ( VX-661 ) in combination with ivacaftor ( Symdeko ) has demonstrated clinical efficacy in individuals who carry two copies of F508del , or one copy of F508del and a variant from a select set of ‘residual function variants’ [12–14] . While these break-through treatments dramatically alter outcomes in CF , they require the presence of targetable CFTR protein . However , approximately 28% of individuals with CF carry one or two variants that introduce premature termination codons ( PTCs ) resulting in loss of CFTR protein ( https://cftr2 . org ) . The challenge of treating variants that cause premature termination is not unique to CF . It has been estimated that one-third of inherited and acquired human diseases are caused by nonsense , frameshift or splice-site variants that lead to generation of PTCs [15] . RNA transcripts bearing a PTC are generally targeted for elimination by a cellular quality-control mechanism called nonsense-mediated mRNA decay ( NMD ) [16] . Our current mechanistic understanding of NMD leads to the prediction that transcripts containing PTCs greater than 50 nucleotides upstream of the last exon-exon junction should undergo NMD [17] . In cells derived from individuals carrying PTC-generating variants , NMD reduces the level of mRNA transcripts to 5–25% of normal ( i . e . PTC-free ) level and substantially reduces synthesis of the encoded truncated protein [18] . Moreover , truncated proteins that derive from any residual nonsense transcripts typically lack function . However , there are circumstances where PTC-generating variants could produce transcript resulting in functional protein . Nonsense or frameshift variants within the last exon generally do not activate NMD thereby allowing synthesis of C-terminally truncated polypeptides [17 , 19] . Furthermore , the efficiency of NMD can vary among cell types and individuals [20 , 21] . Consequently , transcripts containing PTCs , even those that are targeted by NMD , can be maintained at low steady-state levels that may allow production of truncated protein [22–26] . Under both scenarios , protein may be present in cells that can be targeted with small molecules to generate sufficient levels of function to ameliorate disease . However , for many genes , including CFTR , it is unknown which , if any , PTC-generating variants permit production of protein that is targetable . At least one intron , and pre-mRNA splicing are required for NMD of mammalian mRNAs that harbor or acquire PTCs [27 , 28] . Inclusion of at least 200 bp intron sequences each from the 5' and 3' splice sites ensures that majority of the regulatory signals necessary for constitutive and alternative splicing are present [29 , 30] . We and others have demonstrated that expression minigenes ( EMGs ) containing at least 200 bp of flanking intron splices heteronuclear pre-mRNA in precisely the same fashion , and with the same fidelity as observed in primary cells [31 , 32] . Furthermore , we have shown that disease-associated variants alter splicing patterns of EMGs that replicate patterns found in primary nasal cells of affected individuals [33] . Here , we have performed a systematic study of nonsense and frameshift variants located in four regions of CFTR that were postulated to have varying effects on mRNA stability , protein production , and/or function . Using primary nasal cells and three different cell line models stably expressing CFTR-EMGs , we report molecular consequences of 26 PTC-generating variants in CFTR , and identify which variants allow generation of CFTR responsive to currently available modulator therapies and those that require alternative therapeutic approaches .
Variants that introduce premature termination codons ( PTCs ) located in the last exon or less than 50–55 nucleotides upstream of the 3’-most exon-exon junction ( E-EJ ) generally do not elicit nonsense-mediated mRNA decay ( NMD ) [17] . Consequently , individuals carrying such variants have stable RNA transcripts that can synthesize C-terminal truncated protein [34] . As we know that certain 3’ nonsense variants in CFTR generate stable protein [35–37] , we wanted to determine if the function of these truncated proteins could be augmented by CFTR modulators [13] . To test this concept , we used expression mini-genes ( EMG ) to evaluate the transcriptional and translational effects of eleven variants ( nine nonsense and two frameshift variants ) that introduce a PTC into the most distal 3’ region of CFTR ( Fig 1A , top ) . EMGs contain full-length CFTR cDNA and introns flanking the variants under study , and they faithfully reproduce splicing patterns observed in affected tissues ( S1 Fig ) [32 , 33] . A key advantage of EMGs is the inclusion of intron sequences which allow formation of E-EJs upon splicing that are required to engage NMD ( Fig 1A , bottom ) [27] . EMGs containing each of the 11 variants and wild-type ( WT ) were individually integrated into a single genomic site in Human Embryonic Kidney ( HEK ) 293-Flpin cells . Six nonsense variants ( E1401X to Q1476X ) , and two frameshift variants ( p . E1418RfsX14 and p . S1435Gfs14X ) predicted to evade NMD based on their location ( Fig 1A ) demonstrated no significant difference in mRNA levels compared to WT CFTR ( Fig 1B ) [38] . Conversely , the three variants located in regions predicted to be subjected to NMD ( E1371X , Q1382X and Q1390X ) had significantly reduced levels of CFTR mRNA transcript consistent with degradation . The presence of detectable amounts of PTC-bearing transcript is expected as NMD is not completely effective in cell line models that utilize potent constitutive promoters ( as employed here ) . Other investigators have reported that ~5–25% of PTC bearing mRNA can escape NMD under these circumstances [18 , 39] . EMGs offer the advantage that protein synthesis can be studied simultaneously with RNA synthesis and splicing [32] . The WT-CFTR-EMG produced abundant mature , complex-glycosylated CFTR protein ( band C ) and minor amounts of immature , core-glycosylated CFTR protein ( band B ) as determined by immunoblot ( IB ) analysis ( Fig 1C ) . Variants at or between codons 1371 and 1412 generated only immature truncated protein ( Fig 1C , S2 Fig ) . In contrast , variants located more 3’ including nonsense ( E1418X ) , and two frameshifts ( E1418Rfs14X and S1435Gfs14X ) that truncate protein at residue 1442 and 1459 respectively generated minimal to moderate amounts of mature truncated protein . The final two variants S1455X and Q1476X , showed no apparent effect on the steady-state amounts of the immature or mature truncated forms of CFTR when compared with WT , as shown previously ( Fig 1C , S2 Fig ) [40 , 41] . The nonsense and frameshift variants in the 3’region fell into three groups ( A-C ) based on effects on RNA and protein levels ( Fig 1D ) . We used this molecular characterization to select variants for testing with FDA-approved CFTR corrector compounds . Variant Q1390X was chosen to determine if any protein synthesized from the severely reduced levels of RNA transcript could be stabilized . However , treatment of cells expressing the Q1390X-CFTR-EMG with correctors either alone or in combination ( lumacaftor/tezacaftor or both ) did not result in the appearance of CFTR protein ( left panel , lanes 3–5 vs lane 2 ) ( Fig 1E ) . Conversely , the correctors ( lumacaftor/tezacaftor or both ) increased the abundance of mature and immature CFTR in the cell line expressing E1418X-EMG ( right panel , lanes 3–5 vs lane 2; Fig 1E ) . To evaluate the function of C-terminal truncated forms of CFTR , EMGs were integrated into the genomes of CF bronchial epithelial cells ( CFBE41o- ) and/or Madin-Darby Canine Kidney ( MDCK ) cells . These two cell lines retain the ability to polarize and each has been used previously to assess CFTR chloride channel function [42–50] . Chloride channel activity of CFTR was measured in Ussing chambers by activation with forskolin followed by inhibition with the CFTR-specific compound Inh-172 . A representative tracing from CFBE cells expressing E1418X-EMG ( Fig 1F , dashed blue line ) from group C demonstrates that CFTR chloride channel function is present ( ΔIsc = 4 . 1 ± 0 . 6 μA/cm2 representing ~ 3 . 5% of the chloride channel current generated in cell lines expressing WT-CFTR , Fig 1G ) . Acute exposure to the potentiator ( ivacaftor ) resulted in a minimal increase in function ( Fig 1F , blue solid line ) . However , CFTR correctors ( lumacaftor , tezacaftor or both ) in combination dramatically increased E1418X-CFTR current by ~ 4 . 6 fold equivalent to ~ 16% of WT-CFTR function , consistent with the increased steady state levels of mature protein upon treatment with these correctors ( Fig 1E; green , red , and purple lines ) . Addition of ivacaftor to the corrector-treated cells further increased current achieving about 23% of the function of cells expressing WT-CFTR ( Fig 1F and 1G ) . Likewise , two frameshift variants in group C ( E1418RfsX14 and E1435GfsX14 ) exhibited improvement in CFTR function upon modulator treatments ( ~ 2 . 3% to ~ 13 . 4% and ~ 10 . 4% to ~ 30 . 3% of WT-CFTR respectively ) ( Fig 1G ) . A variant in group B ( Q1412X ) generated minimal CFTR function , and exhibited no improvement with modulators ( Fig 1G ) . Similarly , Q1390X from group A displayed negligible function and no response to modulators , as expected for a variant that induced NMD leading to severe reduction of RNA and no mature CFTR protein ( Fig 1G ) . To address whether cell-type specific factors affected processing , function or response of truncated forms of CFTR , we created MDCK cells expressing 9 of the 11 variants . Group C variants E1418X and E1418Rfs14X exhibited residual channel activity ( Isc = 8 . 8 ± 0 . 6 μA/cm2 and 5 . 5 ± 0 . 3 μA/cm2 representing ~ 9% and ~ 6% of WT-CFTR function respectively ) ( Fig 1H ) . This level of function is consistent with that reported from studies of primary nasal cells bearing E1418X variant ( ~ 10% of WT ) [51] and is also consistent with minimal amount of mature CFTR protein generated by this variant ( Fig 1C ) . Lumacaftor , tezacaftor or both followed by acute treatment with ivacaftor increased CFTR current in the MDCK cell lines expressing E1418X and E1418Rfs14X to ~ 35% and ~ 18% of WT-CFTR function , respectively . Likewise , modulator treatments increased CFTR function to near WT levels in the cells expressing three downstream variants from group C ( E1435Gfs14X , S1455X and Q1476X ) ( Fig 1H ) . As expected , three upstream nonsense variants in group A ( Q1382X , Q1390X ) and group B ( E1401X and Q1412X ) displayed negligible function ( <1% of WT-CFTR ) , and no improvement with modulators . These results indicate that Group C nonsense and frameshift variants downstream of codon 1417 allow synthesis of stable truncated CFTR that responds to CFTR modulators . Prior studies of synthetically truncated forms of CFTR revealed that protein sequence following intracellular loop ( ICL6 ) is not required for conformational maturation of CFTR [52 , 53] . To test whether naturally occurring CF-causing nonsense variants in exon 22 encoding ICL6 ( codons 1150 to 1218 ) allow production of stable and potentially drug-targetable forms of truncated CFTR , we utilized EMG-i21-i22 that contains abridged introns 21 and 22 incorporated into full-length CFTR cDNA to evaluate variant effects ( Fig 2A , top , S3 Fig ) . Each of the seven nonsense variants are predicted to engage NMD because they are located >50 nt from the 3’-most E-EJ within EMG-i21-i22 ( Fig 2A , bottom ) . Indeed , each of the seven nonsense variants produced lower steady state levels of CFTR mRNA compared to WT ( Fig 2B ) . Transient expression in HEK293 cells was utilized to determine if any translated products were processed to mature forms of CFTR; conditions that could lead to therapeutic benefit if NMD could be counteracted . IB revealed two apparent patterns: core glycosylated truncated CFTR only ( R1158X and R1162X ) or complex glycosylated and core glycosylated truncated forms of CFTR ( 7 variants; Fig 2C ) . To evaluate glycosylation status , proteins were subjected to endoglycosidase H which removes sugar moieties from immature core glycosylated protein ( see EMG-WT , lane 2; Fig 2D ) and PNGase F that removes sugars from mature complex and immature core glycosylated protein ( EMG-WT , lane 3; Fig 2D ) . As expected , endoglycosidase endo H and PNGase F digestion altered truncated protein generated by EMGs bearing R1158X or R1162X ( Fig 2D ) . Thus , only core glycosylated truncated protein was generated by these two variants . Conversely , susceptibility to digestion by PNGase F but not by endo H confirmed that the higher molecular mass CFTR protein generated by S1196X was complex glycosylated ( Fig 2D ) . Complex glycosylated truncated CFTR generated by the remaining nonsense variants in this cluster showed the same susceptibility to PNGase F but not Endo H ( S4 Fig ) . These results indicate that the nine naturally occurring nonsense variants in exon 22 fall into the previously described group A or group D based on mRNA abundance , and whether they permit synthesis of mature truncated CFTR ( Fig 2E ) . We next tested whether the mature truncated CFTR protein generated by group D nonsense variants were functional . CFBE cells stably expressing EMG-S1196X generated baseline chloride channel activity upon application of forskolin that was inhibited by inh-172 ( Fig 2F; dashed blue line; Isc = 1 . 03 ± 0 . 1 μA/cm2 representing ~1 . 3% of the chloride current generated by WT-CFTR in the same cell line ) . Acute exposure to ivacaftor generated a 4 . 5 fold increase in CFTR function from baseline levels ( Fig 2F , blue solid line ) . There was only a marginal increase in forskolin stimulated CFTR function upon treatment of cells with correctors ( lumacaftor , tezacaftor or both; Fig 2F; green , red , and purple lines ) . However , application of ivacaftor substantially increased CFTR function ( ~11 fold compared to baseline levels; Fig 2F and 2G ) . The level of combined modulator response of EMG-S1196X exceeded 10% of the chloride currents generated by cells expressing WT-CFTR . A similar profile of response was observed for cells expressing additional group D variants ( W1204X and S1206X ) ; whereas group A variants R1158X and R1162X failed to generate current in response to forskolin or after treatment with any of the CFTR modulators ( Fig 2G ) , as observed for the Group A variants in the 3’ region . To verify studies in CFBE cell lines , MDCK cell lines were created that stably expressed CFTR-EMGs bearing 6 of the exon 22 nonsense variants . EMG-S1196X generated functional CFTR protein with activity similar to that observed in CFBE cells ( 1 . 8 ± 0 . 3 μA/cm2 representing 1 . 7% of current observed in MDCK cells expressing WT-CFTR; Fig 2G and 2H ) . Furthermore , ivacaftor increased S1196X-CFTR function ( ~ 6 . 8 fold representing ~ 7 . 8% WT-CFTR ) ; and application of correctors ( lumacaftor , tezacaftor or both ) resulted in dramatic increases in function ( ~ 12 . 5 fold representing ~ 21 . 5% WT-CFTR function; Fig 2H ) . Interestingly , group D variants W1204X and S1206X also exhibited similar robust responses when ivacaftor was combined with correctors ( lumacaftor , tezacaftor or both; Fig 2H ) . Notably , Y1182X variant showed even greater response to the modulators when compared to other variants in group D ( Fig 2H ) . Finally , as noted in CFBE cell lines , MDCK cells bearing EMGs with group A variants ( R1158X and R1162X ) did not generate forskolin-activated CFTR current or respond to any of the CFTR modulators ( Fig 2H ) . Together , these results indicate that corrector and potentiator treatment , especially in combination , elicits substantial CFTR function for exon 22 nonsense variants that generate mature truncated CFTR . Antagonism of NMD caused by nonsense variants that produce modulator responsive CFTR could provide substantial therapeutic benefit . To address this issue , we evaluated whether NMD inhibition increases the function of exon 22 nonsense variants expressed in primary nasal epithelial cells . As predicted by EMGs , CFTR transcript bearing two exon 22 nonsense variants ( R1158X and S1196X ) was reduced in the primary cells . Quantification by pyrosequencing revealed that CFTR transcript bearing R1158X was much less abundant ( 8 . 5% ) compared to CFTR transcript with the F508del variant ( Fig 3A , left bar graph ) . F508del transcript is expressed at approximately 83% of WT- CFTR transcript [31 , 54] , suggesting that R1158X levels were ~ 7 . 0% of WT level . Likewise , the level of CFTR transcript with S1196X was significantly lower ( 22 . 2% ) compared to transcript bearing G85E ( Fig 3A , right bar graph ) . Expression levels of S1196X relative to WT could not be drawn since expression of G85E relative to WT has not been established so far . To evaluate the time scale of NMD inhibition , a mRNA stability assay was performed on the HEK293 cell lines stably expressing R1158X and S1196X . While WT CFTR transcript level was stable over 120 minutes , transcripts bearing either nonsense variant were degraded to ~50% of WT levels by 30 min ( R1158X ) or 90 min ( S1196X ) ( Fig 3B ) . To verify that the reduction in transcript abundance was due to NMD , we used siRNA mediated knockdown of UPF1 , a gene that mediates nonsense transcript degradation [55] . Western blot analysis showed efficient siRNA mediated downregulation of UPF1 expression in HEK293 cells stably expressing R1158X ( ~29 . 4% ) , and S1196X ( ~29 . 8% ) ( Fig 3C ) . Transfection of S1196X expressing cells with Non-Targeted ( NT ) and GAPDH-targeted siRNA had no effect on UPF1 level ( Fig 3C ) . UPF1 knockdown resulted in significant increases in CFTR transcript abundance in HEK293 cells stably expressing R1158X ( 1 . 9±0 . 5 fold ) and S1196X ( 2 . 1±0 . 7 fold ) compared to untreated cells or cells transfected with non-target ( NT ) or GAPDH siRNA ( Fig 3D ) . We next determined whether inhibition of NMD augments modulator treatment of CFTR bearing exon 22 nonsense variants . Cells transfected with UPF1 siRNA exhibited significant potentiation of S1196X-CFTR function by ivacaftor ( red solid line , 13 . 81 ± 0 . 6 μA/cm2 representing ~ 18% WT-CFTR function Fig 3E and 3F ) ; that was further increased upon corrector ( lumacaftor ) treatment ( green solid line , 23 . 9 ± 1 . 1 μA/cm2 representing 30% WT-CFTR function Fig 3E and 3F ) . However , UPF1 inhibition could not increase the function of R1158X-CFTR consistent with prior evidence that this form of CFTR is severely misfolded and non-responsive to modulators , even after improvement of transcript abundance by NMD inhibition ( Fig 3F ) . Finally , transfection with GAPDH siRNAs did not alter the effect of ivacaftor alone or ivacaftor/lumacaftor combination compared to non-targeted ( NT ) siRNA transfected cells ( Fig 3E and 3F ) . Collectively , these results indicate that suppression of NMD should be able to amplify modulator response of CFTR bearing exon 22 nonsense variants that generate complex glycosylated truncated CFTR . Nonsense variant E831X is caused by a change in the first nucleotide of exon 15 of CFTR . This change alters the 3’ splice site of intron 14 leading to the generation of aberrantly spliced CFTR transcripts in primary airway epithelial cells [56] . Of the resulting three proteins , only CFTR missing glutamate at codon 831 ( CFTR-del831 ) generates mature glycosylated protein that functions similarly to WT [56] . However , response to CFTR modulators in cell-based system has not been reported for this ‘nonsense’ variant . To this end , we introduced E831X into an EMG containing flanking sequences from introns 14 to 18 ( EMG-i14-18 ) ( Fig 4A , top ) . WT-EMG-i14-i18 was found to splice normally in HEK293 stable cells ( S5 Fig ) , as previously shown [32] . HEK293 stable cells expressing E831X-EMG generated 3 CFTR splice isoforms: Isoform 1 ( CFTR-E831X i . e . truncation at 831 ) , Isoform 2 ( CFTR-del831-873 i . e . in frame deletion of exon 15 ) , and Isoform 3 ( CFTRdelE831 , single amino acid deletion ) , ( Fig 4A , bottom , and S6 Fig ) , as previously reported [56] . Furthermore , IB analysis identified three CFTR specific protein bands , each generated from their respective splice isoform ( Fig 4B ) . Thus , the effect of E831X on mRNA splicing and protein production constitutes fifth group , E . To assess modulator response , CFBE cells stably expressing E831X-EMG were created to measure CFTR function . CFTR function was evident upon addition of forskolin and inhibition using inh-172 ( 14 . 7 ± 0 . 65 μA/cm2 representing ~8% of current in CFBE cells expressing WT-EMG ) ( Fig 4C , graph ) . Acute treatment with ivacaftor alone did not result in significant improvement of E831X-EMG function . However , correctors ( lumacaftor , tezacaftor or both ) increased E831X-CFTR function ( ~13 . 6% WT-CFTR ) and subsequent acute addition of ivacaftor increased function further ( ~15 . 0% of WT-CFTR ) ( Fig 4C , graph ) . Furthermore , primary nasal epithelial cells harvested from an individual with CF harboring E831X ( in trans with F508del ) exhibited residual CFTR function ( Fig 4D , black tracing ) that was increased by ivacaftor and further augmented by correctors ( lumacaftor and tezacaftor ) ( Fig 4D , green and red tracings ) . CFTR specific current that drops below baseline after addition of Inh-172 is due to the constitutive activation of CFTR in primary nasal cell culture . Of note , modulator responses were greater in F508del/E831X primary cells compared to F508del/Indel ( 2184InsA , 2183delAA>G , and 3659delC ) primary cells ( Fig 4D , graph ) . Since the indel variant does not generate functional CFTR , the increased CFTR function in the E831X/F508del cells compared to the indel/F508del cells can be attributed to CFTR generated by the E831X variant . Thus , modulator combinations demonstrate consistent evidence of functional improvement in primary and CFBE cells expressing E831X-CFTR . Lastly , we determined what treatment options are appropriate for nonsense variants occurring in the 5’ region of CFTR . This area was reasonable to study as it has been reported that variants that introduce PTCs in the 5’ of processed mRNA may evade NMD [57] . Under these circumstances , translation may initiate at downstream Met codons leading to the synthesis of N-terminal truncated protein . To establish whether 5’ nonsense variants in CFTR evade NMD , we quantified RNA transcripts from primary nasal epithelial cells of a CF individual harboring L88X and the F508del variant . Three methods established that CFTR transcript bearing the L88X variant was stable and at quantities similar to RNA transcripts containing F508del ( Fig 5A and 5B ) . Sanger sequencing revealed that transcript bearing the G nucleotide at nt 263 ( corresponding to L88X ) was almost as abundant as T nucleotide present in transcripts bearing F508del ( Fig 5A , left panel ) . Fragment size analysis capitalizing on the 3bp deletion caused by the F508del variant revealed that 233 bp fragments amplified from F508del transcript were of near equal abundance to 236 bp fragments derived from L88X transcript ( F508del ( 52% ) and L88X ( 48% ) ; Fig 5A , right panel ) . As the prior two methods use PCR that may not linearly amplify transcript [58] , we performed RNA sequencing of the L88X bearing primary cells . Sequencing depth distribution of all transcribed genes were similar in the F508del/L88X and healthy control nasal cells ( Fig 5B , left graph ) . Expression levels of the target gene ( CFTR ) , three housekeeping genes ( TBP , GAPDH , and B2M ) , and NMD regulator genes in F508del/L88X sample were in the same range as in healthy control , indicating that NMD machinery was not compromised in the affected individual ( S7A Fig ) . The counts of L88X transcripts ( n = 7 ) sequenced from L88X/F508del primary cells were similar to F508del ( n = 8; Fig 5B , right graph ) . Exon skipping was not observed in the nasal cells of the individual harboring L88X/F508del ( S7B and S7C Fig ) . Since F508del transcript is found at 83% of WT levels [31 , 54] , we conclude that L88X does not elicit NMD . Absence of NMD was also detected in primary nasal cells of CF individual harboring another 5’ nonsense variant G27X in trans with F508del ( S8 Fig ) . Next , we investigated whether other nonsense variants in the 5’ region of CFTR evade NMD . Seven different naturally occurring nonsense variants including G27X and L88X were introduced into WT-CFTR EMG i1-i5 ( Fig 5C ) . WT-EMG i1-i5 resulted in normal splicing when expressed in HEK293 cells ( S9 Fig ) . Each EMG including WT was stably integrated into HEK293 cells and CFTR mRNA abundance was quantified using qRT-PCR . The levels of CFTR RNA transcripts in the cell line bearing the G27X and L88X EMGs were not different from cells with the WT EMG ( Fig 5D ) . This result suggested that G27X and L88X transcripts expressed from the CFTR EMG in the HEK293 cells evaded NMD , as observed in the primary cells . Furthermore , CFTR transcript levels in HEK293 stable cell lines expressing the five other 5’ nonsense variants were no different than WT-EMG . The ability of the N-terminus nonsense variants to bypass NMD is likely due to re-initiation of translation downstream start codon ( s ) [57 , 59] . Methionines at codon positions 150 , 152 and 265 in exons 3 , 4 , and 7 of CFTR have been shown to be able to operate as alternative start sites in CFTR [60 , 61] . IB analysis showed that each of the seven cell lines expressing 5 ‘nonsense variants ( lanes 2–6 , Fig 5E , and S10A Fig ) generated two CFTR-specific products ( ( ~135 kDa and ~130 kDa; indicated with stars ) consistent with downstream translation initiation . Deglycosylation assay revealed that shortened protein fragments generated from 5’ nonsense variant , e . g . G27X , are immature core glycosylated ( S10B Fig ) . Of note , these proteins are distinct from the molecular mass of immature core-glycosylated protein generated from wild-type-CFTR-EMG ( lane 1 ) , Phe508del cDNA ( lane 7 ) , and wild-type-CFTR cDNA ( lane 9 , Fig 5E ) . Thus , 5’ nonsense variants were classified into group B based on CFTR mRNA and protein characteristics . Similar sized molecular mass bands were previously reported in association with nonsense variant Y122X , and 5’ PTC caused by a frameshift variant ( c . 120del23 ) when expressed using intronless constructs ( i . e . cDNA ) [61 , 62] . Since mRNA transcripts bearing 5’ nonsense variants were stable , we evaluated the feasibility of readthrough therapy . CFBE stable cells expressing CFTR EMGi1-i5 with L88X were created to test whether readthrough compound ( G418 ) is effective in improving CFTR function . L88X-CFTR generated minimal chloride current ( 1 . 18 ± 0 . 1 μA/cm2; Fig 5F ) . Ivacaftor either alone or in combination with lumacaftor was not effective in restoring L88X-CFTR function ( Fig 5F ) . Additionally , treatment with G418 at low ( 5 μM ) and high ( 25 μM ) concentrations followed by acute treatment with ivacaftor did not improve L88X-CFTR function . However , G418 in combination with lumacaftor increased activity of L88X-CFTR by ~ 4 fold ( 3 . 5% of WT-CFTR function ) at 5 μM and by ~ 9 fold ( 8 . 5% of WT-CFTR function ) at 25 μM concentration ( Fig 5F , graph ) .
Genetic variants that generate premature termination codons ( PTCs ) usually cause severe reduction in protein quantity , either due to nonsense mediated RNA decay ( NMD ) and/or degradation of any truncated protein that is synthesized [15 , 63–65] . We show here that exceptions exist to both paradigms which leads to a reconsideration of variants that might be amenable to protein-targeted therapies . Systematic analysis of variants clustered in four regions of CFTR provides compelling evidence that a fraction of PTC-generating variants allow production of protein which can be processed to a stable mature glycosylated form . Importantly , chloride channel function of these mature forms of CFTR can be augmented by FDA approved modulators . Additionally , our results inform where evolving therapeutic approaches might be most effectively employed . For example , compounds that modestly inhibit NMD could be utilized at non-toxic doses to increase the amount of CFTR truncated beyond ICL6 and modulators could be used to achieve therapeutic level of chloride transport . Conversely , CFTR transcripts bearing 5’ PTC-generating variants that allow normal levels of RNA transcript would be ideal targets for read-through strategies . Our studies emphasize that the consequences of PTC-generating variants upon RNA and protein can be assembled into groups ( Table 1 ) , each necessitating different strategies to achieve optimal precision therapy . Our rationale for the selection of nonsense variants from the C-terminal and ICL-6 regions was based on prior evidence that CFTR is stable after truncation in these regions [52 , 53] . However , these studies employed complementary DNA ( cDNA ) constructs that lacked introns and did not undergo pre-mRNA splicing , a requirement for engagement of NMD [27 , 28 , 65] . Consequently , the cDNA-based studies did not model the in vivo effects of the nonsense variants in each region . Assessing the clinical consequences of the PTC-generating variants necessitated that the mRNA be derived from intron-containing constructs . The EMG system employed here faithfully replicates in vivo splicing events and engages NMD [33] . A key additional advantage of the EMGs is that variant effect upon mRNA stability and protein synthesis can be evaluated simultaneously [31 , 32] . Furthermore , use of the CMV , a potent constitutively active promoter , enables detection and characterization of proteins that are present at low levels in vivo . Such studies can provide justification for therapeutic strategies to augment the level and function of truncated proteins generated from genes bearing PTC-generating variants . Furthermore , EMGs provide a viable alternative to primary cell analysis when affected tissues are difficult to procure . This includes variants that are carried by small numbers of geographically dispersed individuals or cell types that cannot be easily accessed ( i . e . lung or pancreas ) . EMGs also allow interrogation of the effects of variants upon CFTR processing and function individually , rather than in a primary cell context where contributions of variants in both CFTR genes usually have to be taken into account . Finally , EMGs can be expressed in different cell lines to address the issue of cell-type specific factors . In this study , we employed three cell lines , of which two were of human origin . CFBE cells provide a native human context for CFTR expression [47] whereas MDCK cells are of mammalian non-human origin but retain epithelial cell machinery for protein trafficking and polarization [42–48] . HEK293 stable cell lines are useful for rapid evaluation of mRNA stability and protein processing in a human-derived cell model system that does not polarize [66 , 67] . Although heterologous expression systems may not capture features of airway cells from primary nasal or bronchial cultures such as assessment of nasal mucociliary clearance [68] , they have been deemed sufficient to approve clinical expansion of drug labels by the FDA [69 , 70] . Consistent results among the different cell lines verified by observations in available primary cells increase confidence that the observed functional effects are due to heterologous expression of mutant forms of CFTR . Investigation of nonsense or frameshift variants located in the 3’ region provided an excellent opportunity to test the feasibility of targeting truncated CFTR , as PTC-generating variants located either in the last exon or < 50 nt from the EJC in the penultimate exon are not subject to NMD [17 , 34 , 65] . As expected , CFTR transcripts bearing 8 PTC ( 6 nonsense and 2 frameshift ) variants located in the aforementioned regions were stable , whereas those bearing 3 nonsense variants located elsewhere ( Q1390X , Q1382X , and E1371X ) were unstable . Synthetic truncations of CFTR established that the C-terminal domain modulates the biogenesis and maturation of CFTR and suggested that variants that introduced PTCs upstream of codon 1390 or downstream of codon 1440 may result in truncated but stable forms of CFTR protein [36 , 71] . However , by using EMGs we show that nonsense variants at or upstream of codon 1390 do not generate stable truncated protein due to NMD of mRNA transcript . Consequently , therapeutic targeting of PTC-generating variants upstream of codon 1390 should focus on abrogation of NMD and targeting of truncated CFTR . Conversely , we found that truncations caused by naturally-occurring PTC-generating variants up to codon 1418 were associated with low to wild-type amounts of mature truncated CFTR protein that were partially functional . Consistent with our observation , CFTR bearing E1418X has been reported to be functional in primary nasal epithelial cells derived from an individual with CF [72] . CFTR bearing each of the 5 naturally-occurring PTC variants from 1418 to 1476 were responsive to lumacaftor , tezacaftor , or both in combination , consistent with our observation that correctors increase the level of mature truncated forms of CFTR . Together , our results indicate that PTC-generating variants in the C-terminus of proteins should be carefully evaluated as they may allow generation of stable mRNA and mature truncated protein with residual function . In the case of CF , individuals carrying PTC-generating variants at or downstream of codon 1418 may benefit from protein modulator treatments . A cluster of 7 naturally occurring CF-causing nonsense variants within exon 22 encoding the ICL6 region were studied as it has been shown that CFTR missing the second nucleotide binding domain ( NBD2 ) and thereafter matures well to form a functional chloride channel at the cell surface [52 , 53] . These studies demonstrated that CFTR truncated at codon 1218 ( just prior to NBD2 ) generated protein kinase A ( PKA ) stimulated halide conductance when expressed in in BHK cells and chloride conductance by single channel recording when embedded in planar lipid bilayers [52 , 53] . Our studies revealed that CFTR truncated up to codon 1182 is glycosylated and partially functional whereas truncation 20 residues further upstream ( to codon 1162 ) results in immature protein with no CFTR function . More recently , truncations studied in ATP-binding cassette transporter , Ste6 , of yeast demonstrated distinct metabolic stabilities [73] . L1240X and R1268X truncations exhibited similar stabilities as the wild-type protein . In contrast , truncations in between these two locations destabilized the protein emphasizing the fidelity with which Endoplasmic-Reticulum-associated degradation substrates are selected [73] . The truncated forms of CFTR that retained residual chloride channel function , especially Y1182X , were remarkably responsive to ivacaftor when combined with a corrector . We emphasize that modulator mediated improvements in CFTR function observed here in stable cell lines expressing nonsense variants cannot be extrapolated to improvements expected in individuals harboring nonsense variants unless measures are taken to antagonize NMD [74] . Indeed , primary cells demonstrated that CFTR mRNA bearing R1158X and S1196X undergo NMD , and it is reasonable to predict that transcripts in vivo bearing each of the remaining 5 nonsense variants in this region would be similarly degraded . We show in cell lines that increasing the stability of transcripts bearing S1196X expressed in CFBE cells by NMD disruption resulted in higher forskolin-activated CFTR chloride currents that were substantially augmented by modulators . However , it remains to be determined whether NMD can be effectively inhibited in vivo to stabilize disease-causing PTC transcripts with minimal deleterious impact on the normal transcriptome . In addition to its role in RNA surveillance , NMD is a post-transcriptional regulatory pathway that keeps transcriptome under control from ‘noisy’ expression of faulty transcripts across various mammalian species [75–78] . Therefore , therapeutic strategies based on interference of this pathway are limited . Recently , antisense oligonucleotide mediated reduction of NMD factors have been proposed to be effective and safe to stabilize nonsense transcripts [79] . From a therapeutic perspective , the risks inherent in antagonizing NMD may be better justified when the target RNA transcript encodes a protein that is partially functional and responsive to modulators ( e . g . exon 22 nonsense variants ) . The importance of understanding pathologic mechanism for treatment of individuals bearing PTC variants is further illustrated by the E831X variant . Hinzpeter and colleagues demonstrated that the nucleotide change that predicted a nonsense variant at codon 831 actually altered RNA splicing , leading to production of minimally functional CFTR missing a single amino acid at codon 831 ( del831 ) [80] . We now show that ivacaftor and correctors augmented current generated by a CFBE cell line stably expressing the E831X-EMG . The most likely target for the modulators is the CFTR isoform missing the amino acid at 831 as it achieves mature glycosylation and is partially functional [80] . We also show that correctors augment potentiator response in primary nasal cells with genotype E831X/F508del . Based on drug response data from stable cells that had hemizygous expression of F508del-CFTR , we inferred that increased response of modulators in primary nasal cells was due to their action on E831X allele rather than F508del alone . In support of this supposition , a recent study has confirmed that corrector-potentiator combination therapy is beneficial to CF individuals with the F508del/E831X genotype [13] and the FDA has now approved use of ivacaftor for E831X based on analysis of in vitro data [81] . CFTR mRNA transcripts bearing nonsense variants in the 5’ exons did not elicit NMD thereby providing an opportunity for protein synthesis . Our observation is consistent with studies of other genes where mRNAs with nonsense and frameshift variants in the first exon do not engage NMD due to internal methionine usage [57 , 61 , 82–85] , even though mechanistic models predict that variants should activate NMD [17 , 86] . It is postulated that translation from downstream Met codons removes complexes that occur at exon-exon junctions from pioneer transcripts and in doing so , eliminates the trigger for NMD [59] . Here , we show in both primary and stable cells that five disease-causing nonsense variants located up to exon 4 of CFTR resist NMD . We propose that NMD was not engaged due to removal of complexes since each of the five nonsense variants in 5’ exons produced protein of a molecular weight consistent with translation initiation from internal methionine at codon 254 . RNA sequencing provided evidence that NMD machinery was not compromised in the primary nasal cells of an individual with one of the 5’ nonsense variants . The proteins generated from internal methionines had residual CFTR chloride channel activity , as previously reported [60] , but were poorly responsive to CFTR modulators . From a treatment perspective , 5’ nonsense variants that do not affect mRNA stability due to internal translation initiation are attractive targets for readthrough therapeutics . However , incorporation of a foreign amino acid at a premature-termination codon may not be sufficient to restore protein function , if the intended residue at this position is critical [87] . Therefore , readthrough therapeutics in combination with treatment aimed to increase protein stability and function , e . g . CFTR directed modulators ( correctors and/or potentiators ) , should be efficacious for treating PTCs with stable mRNA . G418 , a neomycin analog , is the most widely used compound for readthrough of PTCs [88–90] . Our detailed studies of L88X in its native CFTR context in primary cells and in CFTR expression minigene in CFBE cells showed that G418 in combination with lumacaftor significantly increased CFTR function of L88X . Similarly , other investigators have shown improvement in CFTR function by G418-CFTR corrector and potentiator co-treatment in intestinal organoids from CF individuals harboring E60X ( exon 3 ) [91]; and Fisher Rat Thyroid ( FRT ) or HEK293 cells expressing Y122X ( exon 4 ) [62 , 92] . Although the former study did not measure RNA levels in intestinal organoids , and the latter studies utilized hybrid minigenes or cDNA constructs that could not evoke NMD , it can be predicted from our EMG results that E60X and Y122X transcripts are stable in vivo and therefore likely to be responsive to readthrough agents . Interestingly , there is mounting evidence that efficacy of PTC suppression by readthrough compounds is affected by the sequence context of nonsense variants [93 , 94] . Of note , the three 5’ nonsense variants ( E60X , L88X , and Y122X ) that show improvement in CFTR function by G418-modulator co-therapy have different sequences coding for nonsense variants ( UAG , UGA , and UAA ) , different flanking amino acids ( ArgXGlu , PheXTyr , and IleXLeu ) , and different nucleotide sequences at -1 ( A , U , and U ) , and +4 positions ( C , U , and C ) [62 , 91] . There could be numerous factors that affect efficiency of readthrough , but data presented here and by others [62 , 91 , 92 , 94] show that favorable clinical outcome is possible using readthrough and protein modulator co-therapies for those 5’- nonsense variants that unequivocally produce normal mRNA levels . In summary , our systematic approach reveals that PTC-generating variants have a variety of consequences that can be exploited for therapeutic purposes . We show that the location of PTC-generating variants can help predict whether NMD may be engaged but that effects on protein stability and residual function are less obvious . Three scenarios are evident that necessitate different strategies . First , individuals harboring disease-causing PTC-generating variants that produce stable RNA and mature protein ( e . g . 3’ end ) are eligible for currently available protein modulators without a need for NMD inhibitor although translational read-through drugs might be beneficial . Second , individuals harboring variants generating unstable mRNA but mature protein ( e . g . exon22/ICL6 ) should be considered for NMD inhibitor and protein modulator therapy . Finally , individuals with nonsense variants ( e . g . 5’ end ) where mRNA abundance is not affected due to use of alternative start sites should be amenable to read-through and protein modulator treatment without the need for NMD inhibitor . These results show that nonsense and frameshift variants that introduce PTCs can have markedly different effects on CFTR protein synthesis and eligibility for modulator treatments .
This study was approved by the Institutional Review Boards at Johns Hopkins Medicine , Baltimore , and Case Western Reserve University/University Hospitals Case Medical Center , Cleveland ( approval numbers IRB00116966 and UHCMC#10-14-14 ) . Written informed consent was obtained from all subjects . The purpose of this study was to systematically evaluate mRNA stability , protein production , and/or function of PTC-generating variants in CFTR to identify which variants allow generation of CFTR responsive to currently available modulator therapies and those that require alternative therapeutic approaches . Twenty six PTC generating variants were selected . To explore mRNA and protein using a single platform we generated four WT-EMGs . Each variant EMG was created by site directed mutagenesis of WT-EMG . Variant and WT-EMGs were expressed in three different stable cell lines . Primary nasal cells obtained from CF individuals harboring PTC generating variants were conditionally reprogrammed . CFTR mRNA abundances , mRNA stability , and splicing were assessed by qRT-PCR , Sanger sequencing , fragment analysis , pyrosequencing , and RNA sequencing . CFTR protein processing was evaluated by immunoblotting and glycosidase digestion . CFTR function was determined by short-circuit current ( Isc ) measurements on Ussing chambers . To evaluate whether modulators were effective in improving CFTR function of cells expressing PTC generating variants , following FDA approved small molecules were selected; ( i ) Correctors ( lumacaftor and tezacaftor ) , and ( ii ) Potentiator ( ivacaftor ) . CFTR specific function in the cells was calculated as change in Isc ( ΔIsc ) defined as the difference between the sustained phase of the current response after stimulation with forskolin and the baseline achieved after adding Inh-172 . UPF1 siRNA was used to determine effect of NMD inhibtion on CFTR function in cells expressing EMG harboring nonsense variants that produced unstable mRNA . G418 was used to evaluate whether translational readthough resulted in improvement of CFTR function in cells expressing EMG harboring nonsense variants that produced stable mRNA . Each experiment was repeated at least 3 times . Four EMGs were created as described previously [7 , 32 , 33] . CFTR-EMG-i1-i5 contained: abridged intron 1 ( 216 bp of 5' and 212 bp of 3' ) , abridged intron 2 ( 311 bp of 5' and 264 bp of 3' ) , abridged intron 3 ( 374 bp of 5' and 456 bp of 3' ) , abridged intron 4 ( 307 bp of 5' and 333 bp of 3' ) , and full-length intron 5 ( 882 bp ) . CFTR-EMG-i14-i18 contained: full-length intron 14 ( 2272 bp ) , abridged intron 15 ( 259 bp of 5' and 359 bp of 3' ) , full-length intron 16 ( 668 bp ) , abridged intron 17 ( 330 bp of 5' and 302 bp of 3' ) , and abridged intron 18 ( 333 bp of 5' and 339 bp of 3' ) . CFTR-EMG-i21-i22 contained abridged intron 21 ( 227 bp of 5' and 222 bp of 3' ) , and abridged intron 22 ( 191 bp of 5' and 256 bp of 3' ) . CFTR-EMG-i25-i26 contained full-length intron 25 ( 598 bp ) and full-length intron 26 ( 1343 bp ) . A single nucleotide alteration c . 3519T>G ( p . Gly1173Gly ) was introduced to avoid missplicing of EMG-i21-22 . Human embryonic Kidney ( HEK293 ) , CF bronchial epithelial ( CFBE41o- ) , and Madin Darby Canine Kidney ( MDCK II ) cells each containing a Flp Recombinase Target ( FRT ) integration site , which facilitates site-specific recombination , were used to create stable cell lines expressing WT-CFTR-EMG or variant CFTR-EMG , as described previously [32 , 46 , 47 , 49 , 50] . Nasal cells were collected from CF and healthy individuals following IRB protocols at Johns Hopkins University , Baltimore ( IRB# 00116966 ) and Case Western Reserve University , Cleveland ( IRB# UHCMC#10-14-14 ) . An experienced physician performed endoscopic procedures to harvest nasal cells from individuals after informed consent was obtained . Nasal epithelial cells were collected from the mid-part of the inferior turbinate of healthy/CF individuals by brushing with interdental brushes , after spraying a topical anesthetic on the nasal mucosa . Primary human nasal epithelial ( HNE ) cells were harvested from CF individuals and healthy volunteers . Expansion and culture of nasal epithelia were performed as previously described [95 , 96] . Briefly , nasal cells were expanded by culturing in DMEM/F-12 media in the presence of 10 μM Y-27632 , a ROCK inhibitor , and irradiated fibroblast feeder cells . After 2 passages of expansion , cells were seeded ( 5x105 cells/cm2 ) onto snap-well inserts ( Costar #3801 ) . On confluence ( day 5–7 ) , propagation media was replaced with differentiation media containing Ultroser G serum substitute ( Pall; Port Washington , NY ) without reagent Y . The following day , cells were maintained at an air-liquid interface ( ALI ) by removing media from the apical compartment and providing media to the basal compartment only . The apical surface was washed with phosphate-buffered saline ( PBS ) to remove any mucus accumulation , and the medium was replaced in the basal compartment every 48 h . Cells were maintained at 37°C and 5% CO2 . CFTR mRNA abundance in stable cells was determined by real-time , quantitative reverse transcriptase polymerase chain reaction ( qRT-PCR ) . Briefly , cDNAs were synthesized using iscript cDNA synthesis kit ( Biorad#170–8890 ) . PCRs for target gene ( CFTR ) and housekeeping gene ( B2M ) were performed using SsoAdanced Universal SYBR Green mix ( Biorad#172–5271 ) Sequence of CFTR primer pair was: CFTR , forward 5’-TGACCTTCTGCCTCTTACCA-3’ , reverse 5’-CACTATCACTGGCACTGTTGC-3’ . B2M primer pairs are commercially available from Biorad ( #qHsaCID0015347 ) . Real time qRT-PCR data were obtained on CFX connect Real time system ( BioRad ) . Expression levels were calculated by subtracting housekeeping control ( B2M ) cycle threshold ( Ct ) values from target ( CFTR ) Ct values to normalize for total input , resulting in ΔCt levels . Relative transcript abundance was computed as 2^−ΔCt . Each sample was run in triplicate . Since CF individuals harboring nonsense variant were in compound heterozygosity with a different CFTR variant , we were able to quantify relative abundance of each allele . Reverse transcription ( RT ) was carried out using 50–250 ng total RNA using i-Script cDNA synthesis kit ( BioRad#170–8890 ) . The reaction mix was incubated for 5 min at 25°C , 30 min at 42°C and 5 min at 85°C . Undiluted cDNA product was used to perform following assays . Statistical analysis was performed , and graphs were generated using GraphPad Prism6 ( GraphPad Software Inc . ) . Results are presented as means ± SEM , with the number of experiments indicated . One-way ANOVA followed by Dunnett's multiple comparisons test was performed . P values ≤ 0 . 05 were considered significant . Individual-level data underlying each graph and exact P values are provided in S1–S5 Data .
|
The development of variant specific modulators that correct dysfunctional cystic fibrosis transmembrane conductance regulator ( CFTR ) protein is an excellent example of precision medicine . Currently there is no molecular treatment available for individuals with cystic fibrosis ( CF ) carrying nonsense or frameshift variants because such variants introduce a premature termination codon ( PTC ) , and are not expected to produce CFTR protein . We have performed a systematic study of nonsense and frameshift variants located in four regions of CFTR that we postulated should have varying effects on mRNA stability , protein production , and/or function . Using primary nasal cells and three different cell line models stably expressing CFTR expression mini-genes ( EMGs ) , we report molecular consequences of 26 PTC-generating variants in CFTR , and identify which variants allow generation of CFTR protein that is responsive to currently available modulator therapies and which require alternative therapeutic approaches .
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2018
|
Capitalizing on the heterogeneous effects of CFTR nonsense and frameshift variants to inform therapeutic strategy for cystic fibrosis
|
Correct daily phasing of transcription confers an adaptive advantage to almost all organisms , including higher plants . In this study , we describe a hypothesis-driven network discovery pipeline that identifies biologically relevant patterns in genome-scale data . To demonstrate its utility , we analyzed a comprehensive matrix of time courses interrogating the nuclear transcriptome of Arabidopsis thaliana plants grown under different thermocycles , photocycles , and circadian conditions . We show that 89% of Arabidopsis transcripts cycle in at least one condition and that most genes have peak expression at a particular time of day , which shifts depending on the environment . Thermocycles alone can drive at least half of all transcripts critical for synchronizing internal processes such as cell cycle and protein synthesis . We identified at least three distinct transcription modules controlling phase-specific expression , including a new midnight specific module , PBX/TBX/SBX . We validated the network discovery pipeline , as well as the midnight specific module , by demonstrating that the PBX element was sufficient to drive diurnal and circadian condition-dependent expression . Moreover , we show that the three transcription modules are conserved across Arabidopsis , poplar , and rice . These results confirm the complex interplay between thermocycles , photocycles , and the circadian clock on the daily transcription program , and provide a comprehensive view of the conserved genomic targets for a transcriptional network key to successful adaptation .
The circadian clock functions to optimize physiology and metabolism to the correct time of the day and is crucial for fitness . Organisms experience the external environment as a dynamic relationship between daily changes in temperature ( thermocycles ) and light ( photocycles ) that vary by season and latitude ( Figures S1 and S2 ) . Consequently , most species have evolved an endogenous circadian clock with a period of about 24 h that ensures internal biological processes are appropriately synchronized with the daily changes in the environment [1–3] . Together , environmental cycles and the circadian clock phase gene expression , metabolism , and physiology to the correct time of the day [4] . While much is known about how organisms sense light and integrate photocycles to synchronize the circadian clock , little is known of how ambient thermocycles are sensed and integrated . However , the effect of thermocycles on the circadian clock has been described in multiple model systems [5–7] . Thermocycles of only a few degrees are sufficient to set the phase of the circadian clock in most organisms , and recent data suggest that organisms sense thermocycles directly through the circadian clock [8–10] . In Arabidopsis thaliana , thermocycles of 10 °C difference are dominant over photocycles for setting the phase of gene expression , consistent with the notion that multiple forms of the circadian oscillator may exist that have temperature- and light-specificity [7] . How and to what extent ambient thermocycles influence daily transcription remains unknown . Diurnal conditions and the circadian clock regulate a wide variety of downstream events in higher plants . Microarray time course data predicted that between 6% and 15% of the Arabidopsis transcriptome is regulated by the circadian clock [11 , 12] , while an enhancer trap study estimated that 36% of the transcriptome was regulated by the circadian clock [13] . In comparison , it was estimated that 30% to 50% of the transcriptome cycled under the diurnal conditions of photocycles and continuous temperature [14] . Under both diurnal and circadian conditions , transcript abundance is phased to every hour over the day , and this regulation forms the foundation for time-of-day–specific biological activities [14 , 15] . Previous studies identified several promoter elements involved in phase-specific light or circadian regulation of gene expression in plants [13 , 15–17]; however , it is still unclear how a transcriptional network is constructed that orchestrates multiple modes of phase-specific expression over the day . Oscillatory behaviors/patterns provide multiple levels of redundancy , thereby making analysis of circadian-regulated genes an ideal system to dissect complex networks . Mining biologically relevant information from large datasets is a current focus of major research efforts . Multiple methodologies such as clustering have been developed to organize and infer the patterns emerging from microarray data . Conventional microarray clustering approaches are based on dividing the input data into related subsets based on a distance metric ( Hierarchical , K-Means , Self Organizing Maps , Support Vector Machines ) , or principal-component analysis . Regardless of the clustering method used , groups of co-expressed genes may be co-regulated to form the foundation for analyses of promoter sequences to identify important cis-regulatory elements . Using these methodologies , it has been possible to predict gene regulatory networks from expression profiling in yeast [18 , 19]; however , larger and more complex eukaryotic systems will require new conceptual frameworks to unravel transcriptional networks . In this study we describe a network discovery pipeline , which is a conceptual framework that utilizes predefined hypotheses to search for biologically relevant patterns at multiple levels of genome-scale data . Using this pipeline , we analyzed eleven diurnal and circadian time courses in the reference plant Arabidopsis . We demonstrate that the pipeline is successful at defining conserved cis-regulatory modules involved in phase-specific expression underlying the diurnal and circadian transcriptional network .
In nature , photocycles and thermocycles provide daily cues that set or entrain the circadian clock , regulating a diverse set of biological functions [20] . To understand how photocycles , thermocycles , and the circadian clock interact to control time-of-day–specific transcript abundance in Arabidopsis , we analyzed eleven two-day time courses comprising 132 Affymetrix microarrays ( Figure S3 ) . For seven of the time courses , 7-d-old seedlings were sampled every 4 h over 2 d under thermocycles ( HC , hot/cold ) and/or photocycles ( LD , light/dark ) , or continuous conditions ( LL , continuous light; DD , continuous dark , HH , continuous hot; Table 1 , Figure 1 , and Figure S3 ) . The four remaining time courses LDHH-ST ( Stitt ) , LDHH-SM ( Smith ) , LL_LDHH-SH ( Harmer ) , and LL_LDHH-AM ( Millar ) , were described previously [12 , 14 , 21 , 22] and differ in sampling strategies ( Text S1 ) . In this study , we utilized thermocycles of 12 h at 22 °C ( hot ) and 12 h at 12 °C ( cold ) for three reasons: ( 1 ) the circadian clock is temperature-compensated between 22 °C and 12 °C , which means that the period will remain relatively constant between these temperatures [23]; ( 2 ) these thermocycles drive a temperature-sensitive oscillator [7]; and ( 3 ) these conditions represent the average environmental changes across latitude and season for Arabidopsis thaliana in its natural habitat [24] . The thermocycles and photocycles were not intended to exactly replicate natural conditions . Rather , they were designed to build on widely used conditions utilized by the Arabidopsis community . It has been suggested that in nature thermocycles are shifted 6 h later than photocycles [25] . However , real time temperature and light data recorded over multiple years shows that temperature always increases linearly with light at the beginning of the day and depending on season , microclimate , and latitude , decreases more slowly than light at the end of the day ( Figures S1 and S2 ) . Based on these climatic data , we decided to superimpose thermocycles and photocycles for the LDHC time course , and refer to both the cold/hot and dark/light transition as time zero ( start of the day ) . To determine the quality of the time-course data , we visually inspected the expression patterns of known circadian clock genes [11 , 12 , 14 , 21] and verified the microarray expression pattern for a subset of these genes by qPCR using independent replicates . Figure 1 illustrates the expression pattern of two core circadian clock genes , LATE ELONGATED HYPOCOTYL ( LHY ) and TIMING OF CAB1 ( TOC1 ) , across eight of the conditions . The expression pattern of LHY and TOC1 highlight key trends across conditions . First , the time of peak transcript abundance ( phase ) of LHY and TOC1 relative to our definition of time zero ( dawn ) is the same across all conditions ( Figure 1 ) . This result is important because it demonstrates that defining the cold/hot and dark/light transition as time zero provides an accurate reference point to compare phase between datasets . Second , since we normalized all the time courses together , we were able to compare absolute expression levels across conditions ( Material and Methods ) and found that the light exposure time was almost always positively correlated with expression level ( the highest were under thermocycles alone , which is continuous light with temperature synchronizing the cells ) . Third , photoperiod shifts the phase of some ( e . g . , TOC1 ) , but not all , core circadian clock transcripts ( LHY; Figure 1C versus 1D ) . All diurnal and circadian data can be searched , graphed , and downloaded at the DIURNAL web site ( http://diurnal . cgrb . oregonstate . edu/ ) [26] . We developed a hypothesis-driven bioinformatics platform that can be intuitively applied to all sorts of large-scale data . Our pipeline hinges on two linked concepts: organizing data in a conceptual “series” and defining biologically relevant “patterns” within that series . Serializing data establishes innate contrasts in the data that can then be searched based on predefined hypotheses . Our method is designed to reduce the search space and identify only the biologically relevant information . While our platform may miss unanticipated patterns , both type I and II errors ( false positives and false negatives ) are reduced by applying predefined patterns ( hypotheses ) at multiple steps . The analysis starts ( Figure 2A ) by arranging each microarray dataset into a 48-hour ( 12 time point ) series and searching for specific patterns of expression using HAYSTACK , a model-based pattern-matching algorithm . The resulting lists of co-regulated genes are then used to seed an enumerative promoter-searching tool called ELEMENT , which generates a significance statistic for every potential cis-regulatory word ( 3–8mer ) . Finally , the significance statistics for all potential cis-regulatory words are serialized and searched using HAYSTACK to reveal co-occurring elements that form the basis of transcriptional network modules . The components of the pipeline are available at http://haystack . cgrb . oregonstate . edu/ and http://element . cgrb . oregonstate . edu/ . HAYSTACK is designed to find rare occurrences of very specific patterns in a large dataset and provides an alternative method for clustering microarray data by grouping genes whose expression patterns match the same or similar HAYSTACK patterns . The Web version of HAYSTACK can be used to compare any large-scale dataset representing at least three data points ( e . g . , treatments , genotypes , time points ) against a set of user-supplied model patterns . Here , we have focused on time course data . We developed multiple cycling patterns based on diurnal and circadian time course studies available in the literature: asymmetric , rigid , spike , cosine , sine , and/or box-like patterns ( Figures 2B and S4 ) [11 , 12 , 14 , 21] . In the case of diurnal and circadian time course data , the two most biologically relevant parameters are whether a transcript cycles and the timing or phase of its peak/trough of expression over the day . To capture both cycling and phase information in time course data , the patterns were modeled to 1-h increments over the day . For instance , the modeled spike pattern changes shape reflecting the anticipated peak in the 4-h resolution time course as it is shifted by 1-h increments ( Figures 2C and S5 ) . Using 336 predefined patterns , we used HAYSTACK to interrogate the eleven time courses ( Table 1 , Figure S3 ) . We established significance thresholds by permutation ( Text S1 ) . Two models , cosine and spike , were the most successful ( highest correlation ) at identifying cycling transcripts ( Figure 2D ) . Traditionally , cycling activity has been detected using variations on spectral and sinusoidal analysis , which are based on fitting sine or cosine functions to data [27] . Comparison with one such method , COSOPT [11 , 28] revealed that on average HAYSTACK identified 86% of the cycling transcripts identified by COSOPT and 45% additional cycling transcripts than COSOPT ( Table S1 ) , mainly due to the inclusion of the spike pattern in the analysis . To test whether the enrichment of cycling transcripts identified by HAYSTACK was biologically relevant , we compared the amplitude ( peak to average ) of cycling transcripts to non-cycling transcripts . Despite the fact that HAYSTACK is amplitude-independent ( being based on linear least squares regression ) , cycling transcripts had a higher amplitude change than non-cycling transcripts ( Figure 2E ) . The combination of the eleven diurnal and circadian conditions , with the 24 phases of the day , created 264 independent phase bins , each containing hundreds of co-regulated genes ( Figure 3A ) . The list of genes in each phase bin served as the input for the enumerative promoter-searching tool ELEMENT , which identified overrepresented 3–8mer “words” in 500 bp of the upstream promoter regions [29] . The Web-based version of ELEMENT supports Arabidopsis , poplar , and rice and allows a user to choose various promoter lengths for analysis and to apply statistical filtering including adjusting the false discovery rate ( FDR ) [30 , 31] . ELEMENT was used to assign a significance z-score to each word for each bin . The z-scores were then plotted for each phase bin over the day creating a “z-score profile” for each time course , which in essence represents a serialization of the data ( Figure 3B ) . To adjust for multiple testing , we applied a FDR to the one-tailed p-values corresponding to the observed z-scores . Doing so allowed us to establish a z-score threshold based on the equivalent corrected p-value . Since every 3–8mer word was tested for over-representation , similar z-score profiles often represent overlapping or nested words . For example , multiple words with similar z-score profiles overlap to define the previously characterized Evening Element ( Figure 3B; EE; AATATCT ) [11 , 16] . The z-score profile correctly predicts the EE phase of activity , and in addition provides novel information about flanking sequence due to the use of all 3–8mers . The advantage of the z-score profiles is that they enable all 3–8mers to be evaluated based on specific hypotheses . To identify biologically relevant words across the z-score profile datasets , we applied our different patterns in HAYSTACK . We reasoned that biologically relevant words would have significant z-scores at more than one consecutive phase bin , and would be active at a particular phase of the day . By applying HAYSTACK to the z-score profiles of all 3–8mer words , we identified 2 , 185 unique 3–8 mers representing 200 to 300 words per condition . 75% of these words could be summarized into three groups of “elements , ” which share both z-score profile and sequence similarity ( Figure 3C , Table S2 ) . Two groups , comprising 50% of all the words , could be summarized into four elements , EE , GATA element , G-box , and morning element ( ME ) . All these elements were previously identified in light or circadian-associated studies [13 , 15–17] . The last group which constitutes 25% of the new words were summarized into two related elements , the telo-box ( TBX: AAACCCT ) [32] and a similar unknown element found in the larger maize sbe1 motif , named here the starch box ( SBX: AAGCCC; Figure 3C and 3D ) [33] . Words that comprise the TBX and SBX elements were found at the greatest frequency relative to other motifs across most conditions , only slightly greater than ME/Gbox ( Figure 3C ) , while the EE had the highest z-scores across all conditions ( Table S2 ) . When the six elements making up the three groups are summarized , their predicted activity covers every phase of the day ( Figure 3D ) . Therefore , using the network discovery pipeline we were able to predict cis-regulatory elements with confidence and to define specific aspects of their activity that were previously unknown . Almost all ( 89% ) of the reliably detected Arabidopsis transcripts on the Affymetrix ATH1 Genechip cycle under at least one of the 11 conditions tested ( Figure 4A ) . Between 23% and 35% of gene models on the microarray were not statistically reproducible using the Affymetrix MAS5 present/absent call; these genes were not considered in the cycling analysis ( Figure 4A , grey bars; Table S3 ) . Within individual time courses , 34% to 53% of transcripts were diurnally regulated and between 6% and 31% were circadian regulated . The fewest cycling transcripts ( 6% ) were detected under the continuous dark circadian condition , while the most cycling transcripts were detected under the diurnal conditions of short day photocycles and thermocycles alone ( 53% and 50% , respectively; Figure 4A ) . Most genes typically used as reference genes cycled under at least one condition ( Table S4 ) , leading us to create a new list of reference genes that have consistent expression but do not cycle across any of the 11 conditions tested ( Table S5 ) . 87 transcripts cycled under all 11 conditions ( including continuous dark ) , which included most of the genetically defined circadian clock genes ( Table S6 and S7 ) , and some CONSTANS-like and CCA1/LHY-like genes that are thought to be circadian clock associated . While a diverse group of functions are represented in this gene list , the most highly overrepresented are transcription , energy , metabolism , and cell , organ , and tissue localization . Of particular interest are: CDF3 and PIF5 , which have recently been implicated in controlling growth in Arabidopsis [34] , SIGE , an essential nuclear-encoded chloroplast targeted sigma factor [35] , and the cyclin family protein ( At1g27630 ) , which provides a possible link between the circadian clock and the cell cycle . Most genes cycle under a limited number of conditions , with 50% cycling under one to three conditions and 75% cycling under one to seven conditions ( Figure 4B ) . On average , twice as many genes cycled under diurnal conditions as under circadian conditions ( Figure 4A and 4C ) , consistent with the notion that circadian-regulated transcripts are only a subset of the transcripts that change abundance in response to thermocycles or photocycles . The set of transcripts that cycle under at least one circadian condition overlap with the set of transcripts that cycle under at least one diurnal condition ( Figure 4C ) . However , there was less overlap between individual diurnal and circadian time courses ( Figure 4C , Table S8 ) . This result suggests that there is some specific circadian signaling that may be masked by the superimposed diurnal conditions . Consistent with diurnal cycles driving abundance of most transcripts , the amplitude of 50% of transcripts was higher under diurnal conditions compared to the associated circadian condition ( Figure 4D ) . However , the amplitudes of 25% of the transcripts in the associated time course were higher under circadian conditions than under diurnal conditions , supporting the idea that sometimes superimposed diurnal regulation can reduce the level of circadian signal . In summary , we found that the majority of transcripts in Arabidopsis changed abundance over the day , either regulated by the circadian clock or directly controlled by daily environmental changes , consistent with the adaptive significance of appropriate synchrony with the environmental fluctuations . To understand the global phase relationship between thermocycles , photocycles , and the circadian clock , we looked more closely at the global trends of phase across environmental conditions . Similar to other diurnal and circadian microarray reports in Arabidopsis [12 , 14] , we found transcripts phased to every time over the entire day , and regardless of the condition , the majority of cycling transcripts preceded either dawn or dusk , separated by 12 h ( Figure 5A and 5B ) . These results provide an additional confirmation concerning the phase relationship between external thermocycles or photocycles and transcript abundance . Thermocycles and photocycles set the global phase of the clock to the same reference time; in other words the cold to hot transition is analogous to dawn ( the dark to light transition ) . A previous study has shown that the circadian clock is involved in the control of dawn and dusk anticipation , which improves photosynthetic performance and increases fitness [2] . Our phase clustering results shows the extent of this control since the majority of the cycling genes peaks before dawn or dusk . This suggests that a large part of the gain in fitness conferred by the presence of the clock lies in the proper phasing of those early morning and evening genes . This model fits well with most of our phase data except under two conditions that both involve alternative photoperiods: long day ( 16 h light / 8 h dark ) and short day ( 8 h light / 16 h dark ) photocycles . Under long day photocycles , the large cluster of evening expressed genes preceded dusk by 6 h ( compared to 2–3 h for other conditions ) , and under short day photocycles the large cluster of morning expressed genes preceded dawn by 6 h ( Figure 5B ) . However , under both photoperiod conditions , the 12 h separation of the large clusters was maintained , as seen in all other conditions . This result is striking because it suggests two new aspects of the significance of phase . First , regardless of condition , the dawn/dusk co-regulated gene clusters maintain a 12 h phase difference . Second , photocycles play a dominant role in setting the phase of the dawn/dusk co-regulated gene clusters . We noted transcripts that displayed two peaks over the day under both short and long day photocycles , and generally these transcripts were not called rhythmic by HAYSTACK . We reasoned that either these transcripts reflected overt biological rhythms shorter than 24 h , or circadian regulation that was split by photoperiod as predicted by the “morning and evening oscillator” model [36] . We constructed a 2-peak model , added it to HAYSTACK , and identified many transcripts displaying 2-peak phasing , with long day photocycles having the largest number ( Table S9; Text S1 ) . We found 383 transcripts that displayed 2 peaks under long day photocycles and only 67 of them had originally been called rhythmic by HAYSTACK ( Table S9 , Figure S6 ) . To test if the transcripts detected in the long day time course were circadian regulated in other conditions and at which phase they were expressed , we generated phase over-representation plots that normalized the number of transcripts in a list with a specific phase , by comparing them to the expected number of transcripts at that phase ( Text S1 ) . Of the 383 transcripts that we found with the 2-peak model under long day photocycles , the majority had mid-day specific expression under either short or long day photocycles as called by HAYSTACK without the 2-peak model ( Figure 5C ) . In addition , these transcripts were controlled by the circadian clock and specifically phased to dawn and dusk ( Figure 5D ) . These results suggest that the long photoperiod 2-peak transcripts are in fact circadian regulated , and adds support to the notion that the Arabidopsis circadian network may be composed of photoperiod sensitive morning and evening oscillators [37] . No study to date has addressed the problem of how individual phase relationships of thousands of genes are affected by photocycles , thermocycles , and the circadian clock . To address this question , we constructed “phase topology maps” to compare the phase of individual transcripts between two environmental conditions ( Figure 6 ) . CHLOROPHYLL A/B BINDING protein ( CAB ) gene expression is phased to the middle of the photoperiod irrespective of day length , so a 4-h phase shift relative to dawn is apparent when the photoperiod is extended by 8 h ( Figure 6A ) [38] . Consistent with CAB expression , a higher percentage of transcripts were shifted to a later phase under long day photocycles ( Figure 6B ) . For example , transcripts peaking at 10 h and 22 h after dawn under long day photocycles were shifted by 4 h , since they peaked at 6 h and 18 h after dawn under short day photocycles , respectively . One might predict from CAB gene expression that the entire transcriptome would shift by 4 h; however , this was not the case . Not all transcripts shifted phase between short day and long day photocycles . Transcripts phased to 10 h and 22 h after dawn under long day photocycles displayed the largest shift under short day photocycles . The number of hours the phase of a transcript was shifted correlated with the distance it was phased from either midday or midnight , resulting in a “skewed” linear phase shift topology . A similar but more dramatic skewed linear phase-shift topology was observed between thermocycles alone ( LLHC ) or circadian conditions ( LL_LDHH-SH ) compared to photocycles alone ( LDHH_ST; Figure 6C and 6D ) . We reasoned that this dramatic phase shift might reflect the release into continuous light , either on the initial transfer ( LLHC ) or after entrainment ( LDHH_ST ) , mimicking a phase delay associated with a day length extension . Consistent with this , LDHH_ST and long day photocycles did not have a skewed linear phase-shift topology . Consequently , some genes , but not all , are set by the last dark to light transition . The highest percentage of transcripts that did not shift between LLHC and LDHH_ST had phases at either dawn or dusk . A uniform pattern emerged , similar to short and long photocycles , with the magnitude of the shift equal to the distance from dawn or dusk . In contrast , only 10% to 20% of the transcripts phased to 12 h to 16 h after dawn displayed the skewed linear phase-shift topology between LLHC and LDHC ( Figure 6E ) , suggesting that when photocycles and thermocycles are superimposed , thermocycles dominate to set the phase of the midnight expressed transcripts . Thermocycles phased most transcripts to the same time of day as circadian conditions ( Figure 6F ) , whereas photocycles dramatically shifted specific circadian regulated transcripts from the dawn and dusk set points ( Figure 6D ) . The phase topology results revealed multiple important aspects of phase-specific expression . First , thermocycles and the circadian clock phased transcripts to the same phase of the day , consistent with thermocycles acting through the circadian clock to set phase [8–10] . In contrast , photocycles antagonize the phase of the clock and shift transcripts to a new phase of the day; this photocycle-dependent shift is the same seen in growth rhythms [34 , 39–41] . Second , there are two reference points during the day from which all genes are shifted as predicted by the morning and evening oscillator model [36] . This finding demonstrates the novelty and the importance of comparing environment-specific phase changes across individual transcripts with phase topology graphs . Finally , while the general trend is that thermocycles and photocycles phase transcripts to anticipate dawn and dusk , a closer look at individual transcripts revealed that thermocycles and photocycles play distinct roles in establishing phase . To determine at what time-of-day–specific biological processes occur and how different environmental conditions affect their time of day activity , we queried every phase bin to see whether some gene ontology categories were overrepresented . Using the Classification SuperViewer Tool with Bootstrap at the Bio-Array Resource for Arabidopsis Functional Genomics ( http://bbc . botany . utoronto . ca/ ) , we calculated the normalized frequency for each gene ontology category for each phase bin . Following this procedure , we double plotted the normalized frequency and searched for time-of-day–specific patterns in the gene ontology categories . We found that the gene ontology categories of “cell cycle/dna processing” , “energy” , and “protein synthesis” were phased between midnight and dawn under thermocycles in constant light ( Figure 7A ) . Under conditions with no thermocycles ( any condition with a photocycle ) , these gene ontology categories were phased to midday ( Figure 7B and 7C ) . Even under photocycles and thermocycles together , these gene ontology categories were phased to the same time of day as thermocycles alone ( Figure 7D ) , supporting the idea that thermocycles dominate over photocycles to regulate the genes in these categories . In contrast , photocycles preferentially drive genes from the “energy” category; which are phased before dawn under thermocycles alone , and after dawn when there is a photocycle ( Figure 7A and 7B ) . It should be noted that it is only when thermocycles and photocycles are superimposed that energy is clearly partitioned from cell cycle and protein synthesis . These results suggest that thermocycles synchronize processes such as cell cycle and protein synthesis so that they precede the daily dawn specific growth cycle [34 , 39–41] . In fact , when plants are treated with opposing thermocycles and photocycles ( cold days and warm nights ) , growth is reduced by 75% , despite experiencing the same amount of temperature and light [42 , 43] . This supports the idea that the timing of thermocycles and photocycles plays an essential role in growth regulation . To biologically validate our predictions of cis-regulatory elements from the network discovery pipeline , we tested if a multimer of the unknown element , ATGGGCC , was sufficient to confer diurnal and circadian activity to a luciferase reporter . The ATGGGCC z-score profile corresponded to the phase of protein synthesis transcript abundance under thermocycles and displays sequence , as well as z-score profile similarity , to the TBX and SBX ( Figure 8A and 8F ) . A scan of Arabidopsis promoters ( 500 bp ) revealed 1 , 732 occurrences of ATGGGCC in 1 , 541 genes , which were enriched for protein synthesis gene ontology annotations . Based on these findings , the ATGGGCC was named the protein box ( PBX ) . To validate the PBX , we designed a fusion construct with the PBX in triplicate preceding the minimal nos promoter driving luciferase ( 3xPBX::LUC ) . Multiple independent T2 lines carrying 3xPBX::LUC conferred diurnal and circadian regulation to luciferase activity in vivo under every condition tested ( Figures 8B and 8C and S8 ) . The luciferase activity of the 3xPBX::LUC displayed condition-specific activity consistent with our phase predictions ( Figure 8C ) . Thus , we have identified a new circadian and diurnal response element conferring midnight expression . This element may be considered the plant counterpart to the Rev-ErbA/ROR element ( RRE ) in mammals [44] due to its midnight-specific activity . The TBX , SBX , and PBX are overrepresented at midnight , a time of day lacking predicted light or circadian elements in Arabidopsis , and , in combination with the EE , GATA , ME , G-box elements , these element are predicted to cover every phase of the day ( Figure 3D ) . However , we noted that despite their distinct z-score profiles across conditions , the EE and GATA , and the ME and Gbox shared core sequence while differing at flanking sequence . The GATA box and the EE share the GATA core ( TATC ) , and differ at flanking sequences ( CTtatcC versus AAtatcT ) , while having distinct overrepresentation at 10 h and 13 h after dawn , respectively ( Figures 3D and 8D ) . We also noted a similar situation with the ME and the Gbox ( NccacACN versus GccacGTG ) . The ME and G-box share the CCAC core , yet differ at flanking sequence and were overrepresented before dawn and after dawn , respectively ( Figures 3D and S7 ) . The EE/GATA and ME/Gbox can be thought of as two “phase modules , ” where core sequence specifies time of day and flanking sequence refines the exact phase in a transcription factor or environmental specific fashion . Consistent with this idea , we noted that the EE/GATA displays a condition-dependent phase of overrepresentation between photocycles and thermocycles ( Figure 8E ) . In contrast to the dawn/dusk modules , the elements of the midnight-specific module ( PBX/TBX/SBX ) differ at core sequence and share flanking sequence ( aaaCCC/aagCCC/tggCCC ) . The midnight module displays antiphasic overrepresentation between photocycles and thermocycles ( Figure 8A and 8F ) . Together , the three phase modules cover the entire day , displaying striking similarity to the core circadian network described in mammals [45] . The conservation of network structure between Arabidopsis and mammals led us to speculate that we could extend the network discovery pipeline to distantly related plant species . We reasoned that if specific time-of-day transcriptional networks were conserved between species , then the cis-regulatory elements may also be conserved . To test this , we used all-versus-all reciprocal BLASTP analysis to identify putative Arabidopsis-poplar and Arabidopsis-rice orthologs using the “mutual-best-blast-hit” criteria [46] . To assess the conservation of promoters between orthologs , we used BLAST ( bl2seq ) to directly compare promoters ( 500 bp upstream of the ATG ) of orthologous pairs . We found that only short DNA sequences ( ≤8 mers ) were shared between orthologous promoters , suggesting that cis-elements could be conserved ( Figure S9 ) . To determine if we could detect conserved cis-elements , we assigned the phase of transcript abundance from Arabidopsis to its corresponding ortholog in rice and poplar . In other words , rice and poplar orthologs were organized into phase bins based on cycling in Arabidopsis . We then used this phase information to seed ELEMENT to find cis-regulatory motifs within rice and poplar promoters ( 500 bp upstream of the ATG ) . Using only the orthologous phase information as a predictor in rice and poplar , we found that the timing and overrepresentation of the three cis-regulatory modules were similar across these three species ( Figure 9 ) . These data suggest that both transcriptional networks and time-of-day specific biological processes are well conserved across distantly related plant species .
In this study , we present a framework for de novo prediction of system dynamics and transcriptional circuits in diurnal and circadian biological networks . Our analysis elucidated transcriptional circuitry that regulates phase-specific modules mediating the interaction between external thermocycles , photocycles , and the internal circadian clock . We found that most Arabidopsis transcripts cycle under the diverse diurnal and circadian conditions tested . We confirmed known cis-acting elements and their specific time of activity while expanding both their sequence and phase definitions . We identified and validated a new mid-night cis-element , and found that the predicted activity of these time-of-day–specific cis-elements is conserved across distantly related species . We identified transcriptional circuitry mediated by at least three phase regulatory modules , ME/G-box , EE/GATA , and PBX/TBX/SBX ( Figure S10 ) , which parallel the three mammalian cis elements , Ebox , Dbox , and RRE [45] . The mammalian circadian transcriptional network is controlled by two design principles: “repression precedes activation” and “repression is antiphasic to activation” [45] . The “repression precedes activation” principle predicted in silico that close temporal binding ( 1 h to 3 h ) of activators and repressors to cis-elements results in either moderate phase delays or phase advances in expression . We predicted moderate phase shifts ( 3 h to 6 h ) from dawn and dusk based on the flanking sequence around the ME/Gbox and EE/GATA modules , respectively . Our findings extend this principle by suggesting that evolution of specific flanking sequences provides the promoter context necessary for the phase modularity generated by close temporal binding of activators and repressors . The second principle , “repression is antiphasic to activation” predicts that activators and repressors bind cis elements in antiphase ( 12 h apart ) , resulting in greater amplitude of transcriptional activity . We predicted that the third module , PBX/TBX/SBX , was active at midnight under any condition with thermocycles ( and circadian conditions ) , and 12 h early ( antiphase ) under any condition without thermocycles ( photocycles alone ) . Since plants rarely experience photocycles without thermocycles in nature , the antiphase activity of this module under the condition of photocycles alone suggests that the nature of this module is consistent with the large ( 12 h ) phase shifts predicted by the second principle . AtPurα ( At2g32080 ) , whose homologs were implicated in cell cycle timing in multiple model systems , binds the TBX in vitro [47] , and the phase of its transcript is antiphase ( 12-hr shift ) between photocycles and thermocycles alone as well . The TBX was originally identified in interstitial telomere repeats with overrepresentation in eEF1A genes in Arabidopsis [32] , and in combination with other cis elements , it is involved in cell cycle regulation and sugar signaling [48–50] . The TBX/SBX/PBX module provides a mechanistic link between the circadian clock and cell cycle progression where DNA replication is phased to midnight limiting the coincidence with harmful UV irradiation [51 , 52] . The greater amplitude of transcriptional activity predicted by the second principle is consistent with the TBX/SBX/PBX module ensuring temporal synchrony of the cell cycle and the circadian clock , processes essential for enhanced fitness and adaptation . A three-loop network has been proposed for the Arabidopsis circadian clock [37 , 53] ( Figure S10 ) . Similar to the Drosophila and mammalian network architecture , a three-loop model suggests that the Arabidopsis network may be composed of both morning and evening specific oscillators that are coupled , allowing for photoperiod sensitivity [36 , 37] . In this study , the phase topology maps and 2-peak analysis support morning and evening oscillatory mechanisms ( reference points ) from which all genes are phased . In addition , we show that environmental conditions selectively shift the phase of these two oscillatory mechanisms consistent with them being separable as predicted by analysis of Arabidopsis clock mutant phenotypes . The Arabidopsis circadian clock has a temperature sensitive oscillator that can be distinctly phased from a light sensitive oscillator [7] . Similarly , we found that thermocycles set the phase of the circadian clock , independent of photocycles , creating an internal phase relationship between specific sets of transcripts . The circadian clock governs the coincidence of internal and external cycles controlling important processes such as reproductive timing ( flowering ) in Arabidopsis [54] . Under conditions in which Arabidopsis does not flower , short day photocycles , we found that the morning and evening oscillatory mechanisms establish a unique phase relationship with all other conditions tested . This provides another example of the importance of “external coincidence” in biological timing and the regulation of plant growth and development . About 90% of the Arabidopsis transcriptome cycles under at least one condition of thermocycles , photocycles , or the circadian clock . In retrospect , this result is not surprising since plants must accurately anticipate daily changes in their environment [1–3] . This large number of cycling transcripts may represent multiple levels of regulation and the cycling of rate-limiting protein complexes . While it is possible that the large number of cycling transcripts reflects experimental artifacts , recent re-analysis of old data suggest that most mammalian transcripts cycle [55] . Global transcriptional regulation by the circadian clock in Arabidopsis is reminiscent of the cyanobacterial clock where all transcripts are under the regulation of the circadian clock [56] . Despite global circadian regulation , a two-component regulatory system in cyanobacteria is necessary to mediate the phase-specific expression required for optimized growth under photocycles [57] . Indeed , global diurnal and circadian changes in transcript abundance may reflect underlying rhythms in chromatin structure or modifications as seen in the mammalian and cyanobacterial systems [58–62] . This regulation may be centered on key cis-elements since the Ebox , and its transcriptional activators BMAL1 and CLOCK , are required for chromatin modifications and circadian transcription in the mammalian system [63] . The maximal phase of stem growth in Arabidopsis occurs at dawn under photocycles alone [34 , 40 , 41] , while growth is shifted to dusk under circadian conditions [39] or thermocycles alone ( TPM and JC , unpublished results ) . In fact , when plants are treated with thermocycles and photocycles in antiphase ( cold days and warm nights ) , growth is severely inhibited [42 , 43] , consistent with the specific roles these conditions play in synchronizing growth-related pathways . We found that under any condition with photocycles , energy and protein synthesis genes were overrepresented after the maximal growth phase at dawn , suggesting that photocycles act to partition growth and photosynthesis . However , thermocycles play a distinct and specific role , phasing protein synthesis genes to midnight , preceding the growth phase under this condition , regardless of photocycles . In nature , plants rarely experience photocycles without a corresponding thermocycle , and some developmental processes such as seed germination occur under thermocycles alone [64] . We demonstrated that thermocycles alone controlled more than 50% of the Arabidopsis transcripts , 28% of which cycle only under thermocycles alone , supporting the important role that thermocycles play in setting the internal phase of the plant . Similarly , thermocycles control a large number of transcripts in Drosophila [25] , corroborating the importance of thermocycles across species . It is tempting to predict that thermocycles act to set the phase of the circadian clock , while external photocycles are superimposed , leading to accurate seasonal estimation and appropriate growth/developmental patterns [65] . While the pipeline allowed us to uncover new aspects of the time-of-day–specific transcriptional network , the scale of the dataset provides new insights into how photocycles and thermocycles interact with the circadian clock to govern essential biological functions . Microarray data in Arabidopsis and Drosophila have revealed that the majority of transcripts anticipate dawn and dusk [12 , 14 , 25] . However , our photoperiod data where the 12-h phase difference is maintained over anticipating environmental changes , suggests phase is a fundamental parameter of the oscillator . Underlying the core 12-h phase differences are critical phase nodes , such as the ME/Gbox and EE/GATA . The phase modules are a fundamental aspect of the core clock , always maintaining their relationship despite external conditions . Furthermore , thermocycles act independently of photocycles on processes such as cell cycle , protein synthesis , and DNA replication , possibly through elements such as the PBX/TBX/SBX . Together , thermocycles and photocycles , which are almost always present together in nature , interact to partition biological activities to the correct times over the day . Thus , our pipeline , a conceptual platform that couples new approaches with well-developed methodologies , uncovers novel network topologies and their underlying components .
The time courses are summarized in Table 1 and Figure S3 . Arabidopsis thaliana seedlings , reference accessions Columbia ( Col-0 ) , or Landsberg Erecta ( Ler ) were sterilized , plated on ms agar media plus and minus sucrose , stratified for four days at 4 °C , and released into the specified condition . Temperature and light cycles were monitored every 5 min and recorded using HOBO data recorders ( Onset ) . LDHH_ST , LDHH_SM , and LL_LDHH-AM time courses were previously described [12 , 14 , 21] . The two replicates of the LDHH_ST and LDHH_SM time courses were averaged and double plotted to be parallel to the other time courses . All microarray techniques were per manufacturer-supplied protocols . RNAs were extracted from frozen tissues , and labeled probes were prepared and hybridized to Affymetrix Arabidopsis ATH1 Genechip per Affymetrix protocols ( Affymetrix ) . We checked array quality using standard tools implemented in the Bioconductor packages simpleaffy and affyPLM . All 132 microarrays were normalized together using gcRMA . Present/absent calls were made using the Affymetrix MAS5 program ( Affymetrix ) . HAYSTACK , a model-based pattern-matching algorithm , compares a collection of diurnal/circadian models against microarray time-course data to identify cycling genes ( Figures S4 and S5 ) . HAYSTACK has been implemented in perl , and uses least-square linear regression for each gene against all model cycling patterns with 24 possible phases . A series of statistical tests were used to identify the best-fit model , phase-of-expression , and to estimate a p-value and false-discovery rate ( FDR ) [30 , 31] for each gene . We selected cycling genes using a correlation cutoff of 0 . 8 , which corresponds to a maximum FDR of 3 . 1% to 5 . 8% in different datasets . HAYSTACK can be accessed online at http://haystack . cgrb . oregonstate . edu . We established a cis-regulatory element analysis pipeline to identify the putative promoter sequences upstream of these genes ( Figure 3 ) . This platform comprises databases of putative Arabidopsis , rice , and poplar regulatory DNAs , word statistics for all 3–8mer DNA words occurring in these promoter sequences , software ( http://element . cgrb . oregonstate . edu ) implemented in perl to analyze promoters and apply statistical screening criteria , and a series of accessory scripts to summarize the results of these analyses . The 3xPBX::LUC and UNDER1:LUC constructs were made by ligating two long oligos containing the PBX or UNDER1 into a vector containing the −101/+4 fragment of the NOS minimal promoter and modified firefly luciferase ( luc+ ) as reported previously [15] ( Text S1 ) . Analysis of several T1 plants transformed with the empty plasmids revealed that there was no emitted bioluminescence , suggesting that the plasmid backbone didn't contain a DNA motif that could drive the luciferase reporter ( unpublished data ) . Plasmids were transformed into the Col-0 accession using the floral dip method . Except where indicated , seedlings were grown on MS medium ( Gibco BRL ) with 0 . 8% agar and 3% sucrose . Seedlings of the T1 generation were selected on kanamycin and transferred to soil for propagation . T2 seedlings were grown without selection before imaging . Wild-type seedlings were identified after image collection and removed from the analysis . During the initial week of growth , seedlings were grown under LDHH conditions . Two or three days prior to imaging , seedlings were transferred to the proper entrainment condition ( LDHC or LDHH or LLHC ) on smaller plates without sucrose . Images of seedlings were collected over the course of five days using a cooled CCD camera for 25 min every 2 . 5 h using the Wasabi software ( Hamamatsu Photonics ) in the slice photoncounting mode . The images were quantified using the MetaMorph software ( Universal Imaging ) and graphed using Microsoft Excel ( Microsoft ) . For each independent T2 line , four to six seedlings were analyzed per experiment . To allow comparison with other T2 lines , each value was divided by the median value of the whole time course . The relative bioluminescence values were averaged for the progeny of each T2 line . Three to ten independent T2 lines were used for each experiment . For data display , we generated an average of the average , which combined the values from the four to six seedlings from the three to ten T2 lines analyzed .
All data has been deposited at ArrayExpress under accession number E-MEXP-1304 . These data are also available online at http://diurnal . cgrb . oregonstate . edu .
|
As the earth rotates , environmental conditions oscillate between illuminated warm days and dark cool nights . Plants have adapted to these changes by timing physiological processes to specific times of the day or night . Light and temperature signaling and the circadian clock regulate this adaptive response . To determine the contributions of each of these factors on gene regulation , we analyzed microarray time course experiments interrogating light , temperature , and circadian conditions . We discovered that almost all Arabidopsis genes cycle in at least one condition . From a signaling perspective , this suggests that light , temperature , and circadian clock play an important role in modulating many physiological pathways . To clarify the contribution of transcriptional regulation on this process , we mined the promoters of cycling genes to identify DNA elements associated with expression at specific times of day . This confirmed the importance of several DNA motifs such as the G-box and the evening element in the regulation of gene expression by light and the circadian clock , but also facilitated the discovery of new elements linked to a novel midnight regulatory module . Identification of orthologous promoter elements in rice and poplar revealed a conserved transcriptional regulatory network that allows global adaptation to the ever-changing daily environment .
|
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"Results",
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"Methods",
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2008
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Network Discovery Pipeline Elucidates Conserved Time-of-Day–Specific cis-Regulatory Modules
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During lytic infections , HSV-1 genomes are assembled into unstable nucleosomes . The histones required for HSV-1 chromatin assembly , however , are in the cellular chromatin . We have shown that linker ( H1 ) and core ( H2B and H4 ) histones are mobilized during HSV-1 infection , and proposed that the mobilized histones are available for assembly into viral chromatin . However , the actual relevance of histone mobilization remained unknown . We now show that canonical H3 . 1 and variant H3 . 3 are also mobilized during HSV-1 infection . Mobilization required no HSV-1 protein expression , although immediate early or early proteins enhanced it . We used the previously known differential association of H3 . 3 and H3 . 1 with HSV-1 DNA to test the relevance of histone mobilization . H3 . 3 binds to HSV-1 genomes first , whereas H3 . 1 only binds after HSV-1 DNA replication initiates . Consistently , H3 . 3 and H3 . 1 were differentially mobilized . H3 . 1 mobilization decreased with HSV-1 DNA replication , whereas H3 . 3 mobilization was largely unaffected by it . These results support a model in which previously mobilized H3 . 1 is immobilized by assembly into viral chromatin during HSV-1 DNA replication , whereas H3 . 3 is mobilized and assembled into HSV-1 chromatin throughout infection . The differential mobilizations of H3 . 3 and H3 . 1 are consistent with their differential assembly into viral chromatin . These data therefore relate nuclear histone dynamics to the composition of viral chromatin and provide the first evidence that histone mobilization relates to viral chromatin assembly .
Cellular DNA is wrapped around protein octamers containing two molecules each of histones H2A , H2B , H3 , and H4 , forming the nucleosome [1] . Linker histone H1 binds to DNA at the entry and exit sites of the core nucleosome to promote the formation of higher-order chromatin structures [2] . Nucleosomes are partially or completely disassembled to allow access to the DNA , and are subsequently re-assembled to reform the chromatin structure [3] . Chromatin thus physically modulates access to the DNA , regulating processes that require such access ( e . g . gene expression , DNA replication , and DNA repair ) [3] . The stability of the interactions between the histones within the nucleosome , between nucleosomes , and between nucleosomes and DNA , affects the stability and structure of chromatin , regulating access to the DNA [4]–[6] . The histone variants within the nucleosome affect the stability of the octamer and its associations with DNA [7] , [8] . Canonical core histone H3 . 1 differs from the variant histone H3 . 3 in only four residues . These differences suffice to alter nucleosome interactions , such that nucleosomes containing H3 . 3 are less stable than those containing H3 . 1 [8] . They also dictate specific interactions with histone chaperones , which in turn mediate nucleosome assembly and disassembly . H3 . 1 , which is expressed only during S-phase , specifically interacts with chromatin assembly factor 1 ( CAF-1 ) and is deposited onto DNA primarily during DNA replication [9] . In contrast , H3 . 3 , which is expressed throughout the cell cycle , specifically interacts with histone chaperone complexes containing histone regulator A ( HIRA ) , hDaxx , or DEK [9]–[12] . Of them , HIRA mediates the assembly of H3 . 3 into nucleosomes within the transcription start sites ( TSS ) of active or repressed genes , and within the coding region of active genes , whereas hDaxx mediates its assembly into telomeric chromatin [13] . Gene expression of nuclear replicating dsDNA viruses , such as herpes simplex virus 1 ( HSV-1 ) , is epigenetically regulated ( reviewed in [14] ) . HSV-1 genomes are tightly chromatinized and largely transcriptionally silent during latency , whereas they are assembled into unstable nucleosomes and abundantly transcribed during lytic infections [15]–[17] . Infecting HSV-1 genomes enter the nucleus bound by spermine , which is then replaced with histones [18] , [19] . Later in infection , HSV-1 DNA replication produces additional HSV-1 genomes , which are also assembled into chromatin [17] , [20] . Given that histone synthesis is inhibited during infection [21]–[23] , the histones that are assembled into HSV-1 chromatin are not synthesized de novo . Therefore , the histones assembled into viral chromatin were assembled in , and undergoing exchange with , cellular chromatin before infection . The chromatin exchange of linker histone H1 and core histones H2B and H4 increases during HSV-1 infection [24] , [25] . This mobilization of histones during lytic infections was proposed to provide a source of histones for the assembly of viral chromatin . However , the actual significance of histone mobilization remained unknown . The deposition of specific histone variants onto cellular DNA is governed by histone chaperone complexes , which are coupled to specific chromatin assembly and disassembly pathways [26] . Although the assembly of HSV-1 chromatin has yet to be fully characterized , the differential deposition of H3 . 3 and H3 . 1 into cellular nucleosomes appears to be paralleled for HSV-1 nucleosomes . Whereas H3 . 3 is initially assembled into HSV-1 nucleosomes , H3 . 1 is only assembled into the viral chromatin when HSV-1 DNA replicates [27] . This differential association of H3 . 3 and H3 . 1 with HSV-1 genomes can be used to test whether the mobilization of histones is related to their assembly into HSV-1 chromatin . The HSV-1 transcription activators VP16 and ICP0 induce chromatin remodeling and interact with a variety of cellular proteins that introduce histone post-translational modifications [28]–[37] . Both proteins modulate the mobilization of linker histone H1 and core histones H2B and H4 [24] , [25] . Mutants in VP16 and ICP0 are deficient in HSV-1 gene expression and replication in the vast majority of cell lines , such as Vero cells [38] . However , the activities of these proteins are far less important in a particular cell line , U2OS [39] , in which HSV-1 mutants in VP16 or ICP0 transcribe their genes and replicate with close to wild-type kinetics . To date , the mechanisms whereby U2OS cells complement such mutants remain mostly unknown . U2OS cells therefore provide an excellent model to test whether chromatin dynamics play a major role in cellular antiviral silencing that is counteracted by viral proteins such as ICP0 and VP16 . Here we report that H3 . 3 and H3 . 1 are mobilized during infection . H3 . 3 mobilization does not require HSV-1 proteins and is not drastically affected by HSV-1 DNA replication . H3 . 1 mobilization also requires no HSV-1 proteins . However , H3 . 1 mobilization peaks early in infection and decreases when HSV-1 DNA replicates . The late decrease does not occur when HSV-1 DNA replication is inhibited . These dynamics are consistent with the assembly of previously mobilized H3 . 1 into HSV-1 chromatin during viral DNA replication . The dynamics of H3 . 3 mobilization , in contrast , are consistent with the concomitant mobilization and assembly of H3 . 3 into HSV-1 nucleosomes throughout infection . The mobilizations of H3 . 1 and H3 . 3 are therefore consistent with their differential associations with HSV-1 genomes and provide the first evidence for a role of histone mobilization in the assembly of HSV-1 chromatin . We also show that U2OS cells are defective in mobilizing histone H3 . 1 in response to infection . These cells allow for the replication of HSV-1 mutants in VP16 and ICP0 , indicating that histone mobilization is an important antiviral response that is overcome in part by the activities of viral proteins such as VP16 and ICP0 .
To test whether H3 . 3 or H3 . 1 were mobilized during HSV-1 infection , we evaluated the kinetics of fluorescence recovery after photobleaching ( FRAP ) of GFP-H3 . 3 or -H3 . 1 fusion proteins ( Figure 1 ) . FRAP is the only technique that directly measures histone mobilization ( the mobilized histones are lost during nuclei preparations ) . First , we evaluated the localization of the GFP-tagged histones . Both GFP-H3 fusion proteins were incorporated into cellular chromatin like endogenous histones . Neither extranuclear fluorescence during interphase nor extrachromosomal fluorescence during mitosis were observed in cells expressing either GFP-H3 fusion protein ( Figure S1 A and pre-bleached cells in Figures 1 A and 2 A ) . Moreover , the levels of GFP-H3 . 3 or -H3 . 1 expression did not correlate with their levels in the free pools ( i . e . not bound in chromatin ) , as evaluated by the relative nuclear fluorescence intensity ( Figure S1 B; correlation coefficient r2 = 0 . 002 or 0 . 012 , for GFP-H3 . 3 or -H3 . 1 , respectively ) . We also tested whether expression of GFP-H3 . 3 or -H3 . 1 affected the progression of HSV-1 infection , by evaluating ICP4 expression and accumulation into replication compartments . ICP4 expression , or accumulation into replication compartments , was not inhibited by GFP-H3 . 3 or -H3 . 1 ( Figure S2 A , KOS ) . GFP-H3 . 3 or -H3 . 1 were sometimes enriched in and sometimes depleted from replication compartments , as previously shown for other core and linker histones ( references [24] , [25] , [40] and data not shown ) . The differential binding of H3 . 3 or H3 . 1 to HSV-1 DNA as evaluated by ChIP correlates with HSV-1 DNA replication [27] . We therefore evaluated the FRAP kinetics of GFP-H3 . 3 or GFP-H3 . 1 before ( 4 hpi ) and during ( 7 hpi ) robust HSV-1 DNA replication ( see Figure 6 in reference [41] ) . The relative normalized fluorescence in the photobleached nuclear region recovered faster in infected cells than in mock-infected cells at 4 or 7 h after infection with 30 PFU/cell of strain KOS ( Figure 2 ) . The fluorescence of the photobleached region is normalized to the total nuclear fluorescence at each time , such that recovery is independent of total fluorescence levels . Normalized fluorescence is then expressed relative to the normalized fluorescence before photobleaching ( Figure 1 B ) , again ensuring independence of total fluorescence levels . The relative fluorescence intensity of the photobleached region was greater in HSV-1 infected than in mock-infected cells at all times ( Figure 2 A and B ) . Concomitantly , the fluorescence of the non-bleached region was less intense in HSV-1 infected than in mock-infected cells ( Figure 2 A ) at all times . The decrease in fluorescence intensity of the non-bleached region reflects the movement of GFP-H3 that is outside of the photobleached region prior to photobleaching moving into the photobleached region during photobleaching ( Figure 2 A ) . This subpopulation of the nuclear histone pool is predominantly composed of freely diffusing molecules . In summary , H3 . 3 and H3 . 1 were mobilized during infection . The FRAP kinetics represent the chromatin exchange of all histones , including those that are , at any given time , not bound in chromatin and therefore available in the free pool , those that are weakly associated with chromatin and therefore undergoing fast chromatin exchange , and those that are stably associated with chromatin and therefore undergoing slow chromatin exchange ( Figure 1 B ) [42] . Most core histones in non-infected cells are stably bound in chromatin and therefore undergo slow chromatin exchange ( Figure 1 B ) [42] . This population is not likely available to bind to HSV-1 genomes in timescales relevant to infection . We focused on the histone populations that are most likely available , those in the free pools or undergoing fast chromatin exchange ( Figure 1 B ) . The fluorescence in the photobleached region was normalized to the total nuclear fluorescence , such that the relative levels are independent of any differences in total fluorescence levels . The normalized levels are then expressed as a fraction of the normalized fluorescence of the same nuclear region prior to photobleaching ( Figure 1 B , relative level in the free pool ) , again ensuring independence of total fluorescence levels . As a surrogate measure for the level of histones in the free pools , we assessed the normalized relative level of fluorescence in the photobleached region immediately after photobleaching ( Figure 1 B ) . Only freely diffusing histones equilibrate fast enough to enter the photobleached region in such short times ( approximately 1s ) [42] . As a surrogate measure for the weakly bound histone population , we calculated the initial rate of fast chromatin exchange , the slope between the normalized fluorescence in the photobleached region at the first and second data points after photobleaching ( Figure 1 B , initial rate of fluorescence recovery ) . To account for any potential differences in photobleaching efficiency between experiments , the level of free histones or rates of fast fluorescence recovery in the HSV-1 infected cells was subsequently normalized to those of the mock-infected cells of the same experiment . The mobilization of H3 . 3 or H3 . 1 during HSV-1 infection increased their levels within the free pools . The average relative levels of free H3 . 3 or H3 . 1 increased to 171%±7% or 204%±12% at 4 hpi , respectively , and remained increased to 155%±5% or 151%±7% at 7 hpi , respectively ( Figure 2 C; P<0 . 01 , one-tailed Student's t Test ) . The averages may reflect general increases throughout the cell population , or large increases in only sub-populations of cells . We thus re-evaluated the data by frequency distribution analyses , assessing the levels of free histone per individual cell . The pools of free H3 . 3 or H3 . 1 were increased throughout the population of infected cells , as shown by rightward shifts of the unimodal frequency distributions ( Figure 2 D ) . At either time , greater than 70% of infected cells had increased their free pools of H3 . 3 or H3 . 1 greater than one standard deviation ( S . D . ) above the average level in mock-infected cells ( dashed lines in Figure 2 D ) . Only approximately 16% of cells in a normal population would be expected to have these levels . Moreover , the levels of free GFP-H3 . 3 or -H3 . 1 did not correlate with their expression levels , as evaluated by relative nuclear fluorescence intensity ( Figure S3 A ) . GFP-H3 . 1 levels were similar in mock or infected cells , moreover , although levels of GFP-H3 . 3 tended to be higher in HSV-1 infected cells in some ( but not all ) experiments ( Figure S3 B ) . Mobilization also increased the average relative rates of H3 . 3 or H3 . 1 fast chromatin exchange , to 250%±22% or 342%±43% of mock-infected cells , respectively , at 4 h after infection with 30 PFU/cell of strain KOS ( Figure 2 E , Figure 3 , 4 hpi; P<0 . 01 , one-tailed Student's t Test ) . However , the fast chromatin exchange rates were similar to those in mock-infected cells at 7 h after infection ( Figure 2 E , Figure 3 , 7 hpi; P>0 . 05 , one-tailed Student's t test ) . The rate was increased throughout the cell population at 4 h , shown by rightward shifts of the unimodal frequency distributions ( Figure 2 F , 4 hpi ) . At least 50% of cells had rates of H3 . 3 or H3 . 1 fast chromatin exchange greater than one S . D . above the average rate in mock-infected cells ( dashed lines in Figure 2 F , 4 hpi ) . At 7 hpi , the rates in mock or HSV-1 infected cells had similar frequency distributions ( Figure 2 F , 7 hpi ) . Thus , H3 . 3 and H3 . 1 were mobilized during HSV-1 infections , increasing their free pools and increasing their rates of fast chromatin exchange early during infection . The decrease in H3 . 1 mobilization from 4 to 7 hpi ( P<0 . 01 , one-tailed Student's t Test ) was greater than that of H3 . 3 ( P<0 . 05 , one-tailed Student's t Test; Figure 2 ) . We next evaluated whether mobilization was associated with HSV-1 DNA replication , using phosphonoacetic acid ( PAA ) to inhibit the HSV-1 DNA polymerase [43] . H3 . 3 mobilization did not increase when HSV-1 DNA replication was inhibited ( Figure 4 A , H3 . 3 ) . The normalized relative fluorescence in the photobleached region recovered similarly in cells treated or not with 400 µg/ml of PAA after 7 h of infection with 30 PFU/cell of strain KOS ( Figure 4 A , H3 . 3 ) . Consistently , the increases to the average levels of free H3 . 3 were similar whether HSV-1 DNA replication was inhibited or not ( to 137%±6% or 155%±5% , respectively; Figure 4 B and C , H3 . 3; P>0 . 05 , Tukey's honestly significantly different ( HSD ) test ) , and PAA treatment did not drastically affect the increase in free H3 . 3 throughout the cell population ( Figure 4 C , H3 . 3 ) . The average rates of H3 . 3 fast chromatin exchange were also similar whether or not HSV-1 DNA replicated ( Figure 4 D and E , H3 . 3; P>0 . 05 , Tukey's HSD test ) . Thus , H3 . 3 mobilization does not require , nor is it inhibited by , HSV-1 DNA replication ( or L proteins ) . In contrast , PAA significantly enhanced H3 . 1 mobilization ( Figure 4 A , H3 . 1 ) . The average free H3 . 1 increased to 226%±20% in PAA treated cells , but to only 151%±7% in non-treated cells ( Figure 4 B , H3 . 1; P<0 . 01 , Tukey's HSD test ) . Free H3 . 1 per individual cell displayed a bimodal distribution when HSV-1 DNA replication was inhibited ( Figure 4 C , H3 . 1 ) . One sub-population had similar levels of free H3 . 1 as non-treated cells , whereas another had even higher levels ( Figure 4 C , H3 . 1 ) , resulting in more than 60% of PAA-treated cells with levels greater than one S . D . above the average level in non-treated infected cells ( dashed line in Figure 4 C , H3 . 1 ) . This increase was not due to higher GFP-H3 . 1 levels ( Figure S3 B , H3 . 1 ) . PAA moderately increased the average level of free H3 . 1 in mock-infected cells ( P<0 . 05 , one-tailed Student's t test; data not shown ) , but less so than in infected cells ( 124%±10% versus 226%±20% , respectively; P<0 . 01 , Tukey's HSD test ) . The average rate of H3 . 1 fast chromatin exchange was also increased when HSV-1 DNA replication was inhibited , to 613%±97% ( Figure 4 D , H3 . 1; P<0 . 01 , one-tailed Student's t test ) , a much greater degree than when HSV-1 DNA replicated ( 128±18%; Figure 4 D and E , H3 . 1; P<0 . 01 , Tukey's HSD test ) . The rate per individual cell also displayed a bimodal frequency distribution ( Figure 4 E , H3 . 1 ) . One sub-population of cells had rates of H3 . 1 fast chromatin exchange similar to those in non-treated cells and the other had even greater rates ( Figure 4 E , H3 . 1; P<0 . 01 , Tukey's HSD test ) , resulting in more than 64% of PAA-treated cells with rates greater than one S . D . above the average in non-treated infected cells ( Figure 4 E , H3 . 1 , dashed line; P<0 . 01 , Tukey's HSD test ) . We next evaluated the mobility of H3 . 3 or H3 . 1 during infection with a VP16 and ICP0 HSV-1 mutant strain , KM110 [38] . During KM110 infection of Vero cells , IE protein expression is not efficiently initiated ( Figure S2 A , KM110 ) and , consequently , KM110 DNA is not replicated [38] . H3 . 3 was still mobilized under these conditions of minimal HSV-1 protein expression and no viral DNA replication , albeit to a limited degree . The average relative level of free H3 . 3 was increased to only 111%±3% at 4 ( P<0 . 05 , one-tailed Student's t Test ) , and it remained increased to 121%±5% at 7 ( Figure 5 A; P<0 . 01 , one-tailed Student's t Test ) , hours after infection with 30 PFU/cell of strain KM110 . More than 25% of cells had pools of free H3 . 3 larger than one S . D . above the average level in mock-infected cells at either time , slightly above the expected 16% ( Figure 5 B ) . The average rate of H3 . 3 fast chromatin exchange did not increase during KM110 infection ( Figure 5 C and D ) , similarly to when HSV-1 DNA replication was inhibited with PAA . Mobilization of H3 . 3 without affecting its fast chromatin exchange therefore does not require , nor is it inhibited by , HSV-1 DNA replication ( or L proteins ) . The average relative increase in free H3 . 3 was larger in KOS than in KM110 infected cells at 4 or 7 hpi ( compare Figures 2 C , H3 . 3 and 5 A; P<0 . 01 , Tukey's HSD ) . HSV-1 transcription or protein expression , VP16 , or ICP0 ( or cellular responses to them ) , therefore enhance H3 . 3 mobilization . VP16 and ICP0 interact with many chromatin-modifying proteins [28]–[35] , which modulate histone dynamics [4] , [44] . U2OS cells complement the transcription and replication defects of HSV-1 mutants in VP16 and ICP0 ( Figure S2 B , H3 . 3 KM110 ) [39] . However , they do not directly complement any of the known biochemical activities of either protein . In fact , the mechanisms whereby U2OS cells complement such mutants remain largely unknown . The activities of VP16 and ICP0 , which indirectly modulate the chromatinization of the viral genomes , would be expected to be less important in cells in which histones were not so readily available to chromatinize infecting viral genomes . We therefore tested whether U2OS cells were defective in mobilizing histone H3 . 3 or H3 . 1 ( as discussed later ) . H3 . 3 was mobilized in U2OS cells at 4 and 7 h after infection with 30 PFU/cell of strain KM110 ( Figure 6 , KM110 ) . Mobilization increased the average relative levels of free H3 . 3 , to 126%±6% or 156%±7% at 4 or 7 hpi , respectively ( Figure 6 A , KM110; P<0 . 01 , one-tailed Student's t test ) . However , the average levels of free H3 . 3 were lower than in KOS infected U2OS cells ( even though KOS infections had to be performed at lower multiplicities due to the obvious nuclear morphologic distortion at higher multiplicities ) . Free H3 . 3 was increased to 194%±7% at 4 h after infection with 6 PFU/cell of strain KOS , and remained increased to 164%±5% at 7 h ( Figure 6 A , KOS; P<0 . 01 , one-tailed Student's t test ) . The increases in free H3 . 3 in KM110 or KOS infected cells occurred throughout the population , with 70 or 81% of the cells , respectively , having large increases in their free pool of H3 . 3 at 7 h ( Figure 6 B ) . The apparent defect in H3 . 3 mobilization during KM110 infections may reflect its delayed replication . Only approximately 50% of U2OS cells infected with KM110 had ICP4 accumulated into replication compartments at 7 hpi , in comparison to approximately 80% of the KOS infected cells at 4 hpi ( Figure S2 B , H3 . 3 KM110 and KOS ) . Consistently , the average relative levels of free H3 . 3 at 7 h after KM110 infection were less than those at 4 h ( Figure 6 A; P<0 . 01 , Tukey's HSD ) , but similar to those at 7 h ( Figure 6 A; P>0 . 05 , Tukey's HSD ) , after KOS infection . The defect in the early H3 . 3 mobilization is consistent with VP16 or ICP0 enhancing it . The average rate of H3 . 3 fast chromatin exchange was not altered during KM110 infection , or at 7 h after KOS infection ( Figure 6 C; P>0 . 05 , one-tailed Student's t test ) . It was increased , to 155%±16% , at 4 h after KOS infection ( Figure 6 C , KOS; P<0 . 01 , one-tailed Student's t test ) . These results are also consistent with VP16 or ICP0 increasing the rate of H3 . 3 fast chromatin exchange . VP16 or ICP0 thus enhance the mobilization of H3 . 3 although they are not required for it . The VP16 and ICP0 within infecting virions are not sufficient to fully mobilize H3 . 3 , however , in that infection with UV-inactivated KOS only marginally mobilized it ( it increased the free pool to 121%±5% , but only at 7 h and only in Vero cells , without changes in the cell population distribution or in the rates of histone exchange at any time - data not shown ) . KM110 infection was largely unable to mobilize H3 . 1 in Vero cells ( Figure 7 ) . The average relative level of free , or average rate of fast chromatin exchange of , H3 . 1 were similar to those in mock-infected cells at 4 h after infection with 30 PFU/cell of strain KM110 ( Figure 7 A and C , 4 h; P>0 . 05 , one-tailed Student's t test ) . The average relative level of free H3 . 1 increased to 124%±9% , but only at 7 h after infection ( Figure 7 A , 7 h; P<0 . 05 , one-tailed Student's t test ) while the average rate of fast chromatin exchange tended to increase , but didn't reach statistical significance ( Figure 7 C , 7 h; P>0 . 05 , one-tailed Student's t test ) . The level of free H3 . 1 and its fast chromatin exchange rate per individual cell had bimodal frequency distributions ( Figure 7 B and D ) , with only the far smallest sub-populations having mobilized it ( small solid peaks in Figure 7 B and D ) . Infection with transcription and replication defective HSV-1 did not mobilize H3 . 1 nearly as much as when just HSV-1 DNA replication was inhibited . HSV-1 transcription , VP16 or ICP0 , expression of other IE or E proteins , or initiation of HSV-1 DNA replication ( or cellular responses to them ) , all of which occur in PAA treated cells but not in KM110 infected ones therefore enhance H3 . 1 mobilization . We next evaluated whether U2OS cells mobilized H3 . 1 in response to infection . The average relative level of free H3 . 1 at 4 h after infection of U2OS cells with 30 PFU/cell of strain KM110 was similar to that in mock-infected cells ( Figure 8 A , KM110; P>0 . 05 , one-tailed Student's t test ) . Nevertheless , there was an increased proportion of cells with higher levels of free H3 . 1 ( Figure 8 B , KM110 4 hpi ) , with almost half ( 41% ) of the cells with levels of free H3 . 1 greater than one S . D . above the average level in mock-infected cells ( almost three times the expected percentage if H3 . 1 was not mobilized ) . The average relative level of free H3 . 1 was increased at 7 h after infection , to 126%±5% , with 52% of cells having large increases in their individual pools of free H3 . 1 ( Figure 8 A and B , KM110 7 h; P<0 . 01 , one-tailed Student's t test ) . However , H3 . 1 levels in the free pool were lower than during KOS infection . The average relative level of free H3 . 1 increased to 135%±5% at 4 h and further to 146%±5% at 7 h after infection with 6 PFU/cell of strain KOS ( Figure 8 A , KOS; P<0 . 01 , one-tailed Student's t test ) . Thus , VP16 or ICP0 increase the pool of free H3 . 1 . The rate of H3 . 1 fast chromatin exchange was not altered during KM110 infection ( Figure 8 C and D , KM110; P>0 . 05 , one-tailed Student's t test ) , whereas it tended to decrease at 4 h after KOS infection ( to 81%±8% , Figure 8 C , KOS ) . Although statistical significance was not achieved , 30% of cells had a rate of H3 . 1 fast chromatin exchange lower than one S . D . below that in mock-infected cells ( almost twice as much as expected in a normal distribution; Figure 8 D , KOS ) . The average rate of H3 . 1 fast chromatin exchange decreased to 69%±5% at 7 h after infection ( Figure 8 C and D; P<0 . 05 , one-tailed Student's t test ) . VP16 or ICP0 therefore most likely increase levels of free H3 . 1 by decreasing its rate of fast chromatin exchange . H3 . 1 was differentially mobilized during KOS infection of U2OS or Vero cells ( compare Figure 2 C and E , H3 . 1 to Figure 8 A and C , KOS ) . We next tested whether inhibition of HSV-1 DNA replication also increased H3 . 1 mobilization in U2OS cells . H3 . 1 was mobilized in U2OS cells infected with 6 PFU/cell of strain KOS and treated with 400 µg/ml of PAA ( Figure 9 ) , but the average relative level of free H3 . 1 increased to only 126%±3% ( compared to 146%±5% in untreated cells; Figure 9 A; P<0 . 01 , one-tailed Student's t test ) . The average level of free H3 . 1 at 7 h in the presence of PAA was similar to that at 4 h in the absence of PAA ( 126%±3% vs . 135%±5% , respectively; Figure 9 A; P>0 . 05 , Tukey's HSD ) . The average rates of H3 . 1 fast chromatin exchange at 7 hpi decreased , to 76%±5% or 69%±5% , respectively , whether or not HSV-1 DNA replication was inhibited ( Figure 9 B; P<0 . 01 , one-tailed Student's t test ) . Replication of HSV-1 DNA did not significantly reduce H3 . 1 mobilization in U2OS cells , indicating that H3 . 1 is differentially ( im ) mobilized in Vero and U2OS cells . Pharmacological inhibition of HSV-1 DNA replication enhanced the mobilization of H3 . 1 in Vero cells ( Figure 4 , H3 . 1 ) , whereas infection with transcription and replication defective HSV-1 ( KM110 ) only mobilized it to a limited extent ( Figure 7 ) . To test the requirement for HSV-1 transcription , or expression of IE or E proteins ( or cellular responses to them ) , we evaluated H3 . 1 mobilization in Vero cells infected with the ICP0 mutant strain n212 . This strain has the same ICP0 mutation as KM110 , but wild-type VP16 [45] . n212 proteins are expressed and its DNA is replicated in Vero cells , although with delayed kinetics . Similar populations of cells infected with 30 PFU/cell of strains KOS or n212 had detectable ICP4 expression ( Figure S2 A , KOS versus n212 ) . As expected , however , n212 had delayed replication kinetics ( Figure S2 A , KOS versus n212 ) . H3 . 1 was mobilized in Vero cells infected with 30 PFU/cell of strain n212 , increasing its average relative free levels to 116%±6% or 144%±6% at 4 or 7 h after infection , respectively ( Figure 10 A , H3 . 1; P<0 . 05 , one-tailed Student's t test ) . Sixty-two percent of cells had large increases in free H3 . 1 at 7 h after infection ( Figure 10 B , H3 . 1 ) . The average rate of fast chromatin exchange decreased to 53%±8% at 4 h ( Figure 10 C , H3 . 1; P<0 . 05 , one-tailed Student's t test ) . The rate still tended to be decreased ( to 79%±13% ) at 7 h , although statistical significance was not reached ( Figure 10 C , H3 . 1; P>0 . 05 , one-tailed Student's t test ) . HSV-1 transcription , IE ( other than ICP0 ) or E proteins , or cellular responses to them , are therefore sufficient to induce H3 . 1 mobilization . The average levels of free H3 . 1 were similarly lower during n212 or KM110 infections ( P>0 . 05 at 4 or 7 hpi , Tukey's HSD ) , which are both defective in ICP0 , than during KOS infection . Free H3 . 1 was less increased even at 7 h after n212 infection than at 4 h after KOS infection . Moreover , the decrease in the average rate of H3 . 1 fast chromatin exchange at 4 h after n212 infection contrasts with the increase in KOS infection ( 79%±13% versus 342%±43% , respectively; Figures 2 E , H3 . 1 4 h , and 10 C , H3 . 1 4 h ) . Together , these results indicate that ICP0 enhances H3 . 1 mobilization by increasing its average rate of fast chromatin exchange . To further test the role of ICP0 in H3 . 1 mobilization , we evaluated H3 . 1 mobility in n212 infected U2OS cells , in which n212 replicates with wild-type kinetics ( Figure S2 B , H3 . 1 ) [39] . H3 . 1 was mobilized to at least the same degree during n212 as during KOS infections ( Figure S4 , H3 . 1 compare to Figure 8 , KOS; P>0 . 05 or P<0 . 01 at 4 or 7 hpi , respectively; Tukey's HSD ) . ICP0 is therefore less critical to mobilize H3 . 1 in U2OS than in Vero cells , mirroring its requirements in the HSV-1 replication cycle in each cell type . We next evaluated the effect of ICP0 on H3 . 3 mobilization . H3 . 3 was also mobilized in Vero cells infected with 30 PFU/cell of strain n212 , increasing the average relative level of free H3 . 3 to 131%±6% at 4 h after infection , and 180%±10% at 7 h ( Figure 10 E; P<0 . 01 , one-tailed Student's t test ) . More than 50% of cells had levels of free H3 . 3 above one S . D . over the average level in mock-infected cells at either time ( Figure 10 F ) . Mobilization also tended to increase the average fast chromatin exchange rate , although it did not reach statistical significance ( Figure 10 G; P>0 . 05 , one-tailed Student's t test ) . H3 . 3 was less mobilized at 4 h after n212 than KOS infections ( P<0 . 01 , Tukey's HSD ) . However , free H3 . 3 increased from 4 to 7 h after n212 infection , when its replication delay is compensated ( Figure S2 A , H3 . 3 n212 ) , but not after KOS infections . As a result , the levels of free H3 . 3 were similar at 7 h after n212 or 4 h after KOS infections ( compare Figures 10 E and 2 C , H3 . 3; P>0 . 05 , Tukey's HSD ) . H3 . 3 mobilization thus appears to correlate with the progression of infection . H3 . 3 is fully mobilized during n212 infection of U2OS cells ( Figure S4 , H3 . 3 ) . However , the pool of free H3 . 3 increased independently of changes to its average rate of fast chromatin exchange ( Figure S4 , H3 . 3 compare to Figure 6 C , KOS ) . Considered together , these results further support the model in which ICP0 modulates H3 . 3 mobilization by stimulating its fast chromatin exchange ( Figure S4 , H3 . 3 ) . H3 . 3 was more mobilized during n212 than KM110 infections in Vero ( P<0 . 05 or <0 . 01 at 4 or 7 hpi , respectively , Tukey's HSD ) and U2OS cells ( P<0 . 01 Tukey's HSD; compare Figure S4 E to Figure 6 A ) . However , n212 expresses all proteins and replicates in both cell types whereas KM110 does so only in U2OS cells . Therefore , VP16 contributes to H3 . 3 mobilization in the presence or absence of other viral proteins .
Here , we report that core histones H3 . 3 and H3 . 1 are mobilized during HSV-1 infection . The mobilization of H3 . 3 and H3 . 1 increases their free pools and alters their rates of fast chromatin exchange . Mobilization of H3 . 1 , but not of H3 . 3 decreases with HSV-1 DNA replication , which is consistent with the models in which a population of previously mobilized H3 . 1 is immobilized by assembly into HSV-1 chromatin during HSV-1 DNA replication . Alternatively , H3 . 1 may be promptly displaced from the infecting viral genomes , but not from the replicating ones , by the HSV-1 transcription activators such as ICP0 and VP16 . The differential mobilization of H3 . 3 and H3 . 1 is fully consistent with their known temporal associations with HSV-1 genomes [27] , and provides the first evidence that histone mobilization relates to viral chromatin assembly . Intriguingly , HSV-1 DNA replication does not decrease H3 . 1 mobilization in U2OS cells , which are permissive for ICP0 or VP16 mutants . This defect in the immobilization of histone H3 . 1 in U2OS cells may reflect a defect in assembling silencing chromatin on the viral genomes in these cells . The chromatin-disrupting activities of VP16 and ICP0 would then be less required in these cells , resulting in the observed complementation ( as discussed below ) . The mobilization of histones provides a process whereby histones assembled in ( and exchanging with ) cellular chromatin become available for assembly into HSV-1 chromatin . The differential mobilization of H3 . 3 and H3 . 1 reported herein , which is consistent with their differential assembly into HSV-1 chromatin [27] , provides the first example relating nuclear histone dynamics to the composition of viral chromatin . Moreover , the differential mobilizations of H3 . 3 and H3 . 1 , together with the results from ChIP assays [27] , suggest that there are two mechanisms for HSV-1 chromatin assembly . Mobilization and association of H3 . 3 with HSV-1 DNA is consistent with its assembly into HSV-1 chromatin mainly via DNA replication-independent mechanisms . Mobilization and association of H3 . 1 with HSV-1 DNA is consistent with its assembly into HSV-1 chromatin mainly via DNA replication-dependent mechanisms . H3 . 1 would thus not be expected to significantly associate with HSV-1 genomes in the absence of HSV-1 DNA replication . Appropriately , H3 . 3 is the H3 variant expressed in terminally differentiated neurons [46] , and therefore available to chromatinize non-replicated HSV-1 genomes during the establishment of latency . Alternatively , the association of H3 . 1 with the infecting viral genomes may be specifically disrupted by the viral transactivators such as VP16 and ICP0 , to prevent silencing . The pools of free H3 . 3 and H3 . 1 were both increased early during infection ( to 170% and 200% , respectively; Figure 2 C , 4 hpi ) . However , HSV-1 DNA is primarily associated with H3 . 3 at this time [27] . This association would be consistent with the transcription-associated assembly of H3 . 3 into cellular chromatin . However , H3 . 3 ( and other core histones ) associate with HSV-1 genes of all kinetic classes within the first hour of infection [19] . Most HSV-1 genes are therefore initially assembled into chromatin independently of their individual transcription . The early HSV-1 chromatin assembly may rather be analogous to that of the sperm pronucleus at fertilization , when the protamines bound to the sperm DNA are replaced with H3 . 3-containing nucleosomes by HIRA and chromodomain helicase DNA binding protein 1 ( CHD ) [47] , [48] . Mutations of HIRA or CHD are detrimental to the formation of the male chromatin , and consequently the male DNA remains inaccessible [48] , [49] . Likewise , HSV-1 genomes are first assembled into nucleosomes containing H3 . 3 , and knockdown of HIRA decreases the association of H3 . 3 with the viral genomes and decreases HSV-1 protein expression and DNA replication [27] . The dynamics of viral chromatin are proving important in the regulation of HSV-1 gene expression ( reviewed in [50] ) . Whilst cellular mechanisms promote the establishment of repressive viral chromatin to silence viral gene expression , viral mechanisms counteract silencing and promote the establishment of transcriptionally active viral chromatin . The initial assembly of H3 . 3 , as opposed to H3 . 1 , into HSV-1 nucleosomes may be induced by the virus to facilitate chromatin dynamics to circumvent silencing . The “active” posttranslational modifications of H3 . 3 [51] may recruit the RNA polymerase transcription complex , while unstable H3 . 3-containing nucleosomes [8] may facilitate its access to the viral DNA . The viral transactivators such as VP16 and ICP0 may actively displace H3 . 1 from the viral genomes to allow the assembly of H3 . 3 containing nucleosomes early in infection . Nonetheless , the subsequent assembly of H3 . 1 into HSV-1 nucleosomes is also important . Depletion of Asf1b , the H3/H4 chaperone that interacts with CAF-1 during DNA replication-dependent chromatin assembly , reduces the levels of HSV-1 DNA and L proteins [52] . H3 . 1 is mobilized prior to robust HSV-1 DNA replication ( see Figure 6 in reference [41] ) . However , infection with the transcription and replication defective strain KM110 largely failed to mobilize H3 . 1 , indicating that it is mobilized in response to viral transcription , IE or E proteins , or VP16 . Despite its early mobilization , moreover , H3 . 1 does not significantly associate with HSV-1 genomes in the absence of HSV-1 DNA replication [27] . The mechanisms that mobilize H3 . 1 away from cellular ( or viral ) chromatin therefore differ from those that stably assemble it into the viral chromatin concomitantly with HSV-1 DNA replication . H3 . 1 was differentially mobilized in the two cell lines evaluated , Vero and U2OS ( compare Figure 2 , H3 . 1 and Figure 8 , KOS ) , which are non-permissive or permissive , respectively , for the replication of HSV-1 mutants in two proteins that promote chromatin remodeling , ICP0 and VP16 . Mobilization increased the rate of H3 . 1 fast chromatin exchange in Vero cells ( Figure 2 , H3 . 1 ) but decreased it in U2OS cells ( Figure 8 , KOS ) . Moreover , inhibition of HSV-1 DNA replication increased H3 . 1 mobilization in Vero but not in U2OS cells ( compare Figure 4 , H3 . 1 and Figure 9 ) . The mobilization of H3 . 1 in Vero cells is consistent with promotion of H3 . 1 release from cellular chromatin to increase its levels in the free pool , for later assembly into HSV-1 chromatin . Mobilization in U2OS cells , however , is more consistent with preventing the already free H3 . 1 from re-binding to ( viral ) chromatin . U2OS cells complement the replication defects of HSV-1 mutants in VP16 and ICP0 , proteins which induce chromatin modifications . The different mobilization of H3 . 1 in U2OS and Vero cells provides the first evidence that U2OS cells are defective in a response to infection that is active in Vero cells . Consistently , ICP0 appears to stimulate H3 . 1 mobilization in Vero but not U2OS cells ( compare Figure 10 , H3 . 1 to Figure S5 , H3 . 1 ) , which may well relate to the differences in permissivity of each cell type for the replication of ICP0 mutant strains . The different effects of ICP0 on H3 . 1 mobilization and viral replication in the two cell lines may also reflect H3 . 1 being less efficiently mobilized away from the cellular chromatin in U2OS than in Vero cells . The inability of U2OS cells to efficiently mobilize H3 . 1 would place less reliance on the chromatin remodeling activities of ICP0 in these cells . HSV-1 nucleosomes contain histones with repressive modifications when the viral genes are not expressed ( reviewed in [15] ) . VP16 directly recruits histone acetyltransferases ( KATs ) [30] , [34] , while its associated protein , HCF-1 , recruits the histone methyltransferases ( HMTs ) SET1 and MLL1 and the histone demethylases LSD1 and JMJD [53]–[56] . These enzymes promote the modification of the viral histones with transcription activating marks . Whereas siRNA depletion of KATs does not inhibit IE gene expression , siRNA depletion of LSD1 , or LSD inhibitors , does and also increases the inhibitory modifications on the histones associated with IE promoters [31] , [53] , [55] , [57] . The recruitment of chromatin modifying proteins to HSV-1 genomes may also influence histone mobility . In addition to VP16 recruitment of KATs [30] , [34] , ICP0 disrupts histone deacetylase ( HDAC ) activity [28] , [29] , [32] . Together , VP16 and ICP0 could promote the acetylation of a pool of histones , increasing their chromatin exchange . ICP0 and VP16 also reduce stable ( total ) H3 binding to HSV-1 genomes [30] , [37] , [58] , [59] , further supporting a model in which they promote ( total ) H3 chromatin exchange . ICP0 also stimulates the rate of H3 fast chromatin exchange , possibly by decreasing the propensity of H3 to bind to the viral genomes . However , neither ICP0 nor VP16 is necessary to mobilize ( total ) H3 , indicating that other HSV-1 proteins most likely also induce histone mobilization . We are working to address this model . The mobilization of histones during HSV-1 infection requires chromatin to be assembled and disassembled , processes which involve histone chaperones . Of the four known H3 chaperones [9] , [11] , [12] , three alter HSV-1 gene expression or replication [27] , [52] , [60] . This involvement highlights the importance of viral nucleosome turnover in the regulation of HSV-1 transcription and replication . In summary , the results presented herein strongly support the models in which histones are mobilized away from the cellular genome to form silencing chromatin on the viral genomes , but the viral transcription activators ( such as ICP0 and VP16 ) further mobilize histones away from the viral genomes to prevent silencing . The chromatin dynamics during HSV-1 infection , including the mobilization of histones , are an exciting new area of epigenetic regulation of viral gene expression .
African green monkey ( Vero ) cells were maintained at 37°C in 5% CO2 in Dulbecco's Modified Eagle's Medium ( DMEM ) supplemented with 5% fetal bovine serum ( FBS ) . Osteosarcoma ( U2OS ) cells , a generous gift from Dr . J . Smiley ( University of Alberta ) were maintained at 37°C in 5% CO2 in DMEM supplemented with 10% FBS . Wild-type HSV-1 , strain KOS ( passage 10 ) , and mutant strains n212 ( the late Dr . P . Schaffer; Harvard Medical School ) and KM110 ( Dr . J . Smiley; University of Alberta ) are described [38] , [45] , [61] . Viral stocks were prepared and titrated by standard plaque assay as described [24] , [25] . Phosphonoacetic acid ( PAA; Sigma ) was prepared in DMEM as a 100 mg/ml stock at neutral pH , stored in aliquots at −20°C , and used at 400 µg/ml . The cDNA sequence encoding H3 . 1 , which is fully conserved in mouse ( NP_659531 ) and human ( NP_003522 ) , was obtained from the Riken Mouse cDNA library [62] , [63] . H3 . 1 was PCR amplified with the sense ( 5′- TGGGAGATCTGAGTGG GTTGCTATGG ) and antisense ( 5′- TTTGGTCGACAGCTGGCACGACAGGT ) primers . The amplified sequence was digested with PvuII and BglII for directional in-frame cloning into pEGFP-C1 previously digested with SmaI and BglII . pEGFP-H3 . 3 was a generous gift from Dr . John Th'ng ( Northern Ontario School of Medicine ) . Human H3 . 3 , with flanking 5′ BglII and 3′ EcoR1 restriction sites , was sub-cloned into pEGFP-C1 . Vero and U2OS cells were transfected with Lipofectamine 2000 ( Invitrogen ) as described [24] , [25] . After transfection , cells were incubated at 37°C for at least 12 ( GFP-H3 . 3 ) or 24 ( GFP-H3 . 1 ) h before any other procedure . Transfected cells were seeded onto coverslips for FRAP or immunofluorescence as described [24] , and incubated at 37°C in 5% CO2 for at least 4 h before infection . Infections were done as described [25] . Inoculum was removed 1 h after addition . Cells were then washed and overlayed with fresh 37°C DMEM supplemented with 5% ( Vero ) or 10% ( U2OS ) FBS and incubated in 5% CO2 at 37°C until being subjected to FRAP or any additional procedure . Histone mobilization was evaluated from 4 to 5 or 7 to 8 hours post infection ( hpi ) as described [24] , [25] . A region passing approximately across the middle of the nucleus was photobleached . The photobleached region included nuclear domains containing cellular and viral DNA . Sixty fluorescent and DIC images were collected for each cell at timed intervals from before to after bleaching ( one image per second for the first 20 s , followed by 20 images at 2 s intervals then 20 images at 1 s interval ) . The fluorescence of the photobleached region and of the entire nucleus was measured at each time . The fluorescence of the photobleached region was normalized to the total nuclear fluorescence , ensuring independence of total fluorescence levels . It was next expressed as a percentage of the normalized fluorescence of the same region before photobleaching ( Figure 1 ) , further ensuring independence of total fluorescence levels . Fluorescence in the photobleached region is recovered as bleached GFP-histones exchange for non-bleached GFP-histones . FRAP was only measured for 100 seconds; potential contributions by newly synthesized GFP-core histones are not relevant . The normalized fluorescence intensity of the photobleached nuclear region at the first time after photobleaching was used as a surrogate measure for the levels of histones available in the free pools ( i . e . , not bound in chromatin; Figure 1 ) [42] . The slope between the normalized fluorescence at the first and second data points after photobleaching , representing the initial rate of fluorescence recovery , was used as a surrogate measure for the rate of fast chromatin exchange ( Figure 1 ) . Fluorescent images ( 512 by 512; 12 bit ) were analyzed with Zeiss LSM software . Images were cropped and their contrast and brightness were adjusted for figure preparation using GIMP 2 . For comparisons involving only two samples , we used a one-tailed Student's t Test . For comparisons involving multiple samples , we used ANOVA to identify if any sample was different from the rest . The results for which ANOVA indicated there were differences were further evaluated by post hoc Tukey's Honestly Significant Difference ( HSD ) to identify the samples that differed from each other . To evaluate the association between the level of GFP-histone expression and the level in the free pools of individual cells the square of the correlation coefficient ( r2 ) was calculated .
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H3 . 1 is typically assembled into chromatin during DNA replication-dependent chromatin assembly . However , histones undergo exchange with those not bound in chromatin . During such exchanges , DNA replication-independent chromatin assembly incorporates histone variants , such as H3 . 3 . The HSV-1 genomes are chromatinized , albeit in unstable nucleosomes . The viral genomes initially associate with H3 . 3 , then associate with H3 . 1 only after HSV-1 DNA replication initiates . These differential interactions are consistent with the DNA replication-independent or -dependent assembly of H3 . 3 or H3 . 1 , respectively , in cellular chromatin . We have shown that linker ( H1 ) and core ( H2B and H4 ) histones are mobilized during HSV-1 infection , but the significance of this mobilization remained unknown . We now find that H3 . 3 and H3 . 1 are also mobilized during infection . H3 . 3 is mobilized to a similar extent before or after HSV-1 DNA replication , which is consistent with its DNA replication-independent assembly into HSV-1 chromatin . In contrast , H3 . 1 mobilization decreases during HSV-1 DNA replication , which is consistent with the assembly of previously mobilized H3 . 1 into HSV-1 chromatin concomitant with HSV-1 DNA replication . The mobilizations of H3 . 1 and H3 . 3 are consistent with their kinetics of association with HSV-1 genomes , providing the first indication that histone mobilization relates to the assembly of viral chromatin .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
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The Differential Mobilization of Histones H3.1 and H3.3 by Herpes Simplex Virus 1 Relates Histone Dynamics to the Assembly of Viral Chromatin
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Gene transcription is a noisy process , and cell division cycle is an important source of gene transcription noise . In this work , we develop a mathematical approach by coupling transcription kinetics with cell division cycles to delineate how they are combined to regulate transcription output and noise . In view of gene dosage , a cell cycle is divided into an early stage S 1 and a late stage S 2 . The analytical forms for the mean and the noise of mRNA numbers are given in each stage . The analysis based on these formulas predicts precisely the fold change r* of mRNA numbers from S 1 to S 2 measured in a mouse embryonic stem cell line . When transcription follows similar kinetics in both stages , r* buffers against DNA dosage variation and r* ∈ ( 1 , 2 ) . Numerical simulations suggest that increasing cell cycle durations up-regulates transcription with less noise , whereas rapid stage transitions induce highly noisy transcription . A minimization of the transcription noise is observed when transcription homeostasis is attained by varying a single kinetic rate . When the transcription level scales with cellular volume , either by reducing the transcription burst frequency or by increasing the burst size in S 2 , the noise shows only a minor variation over a wide range of cell cycle stage durations . The reduction level in the burst frequency is nearly a constant , whereas the increase in the burst size is conceivably sensitive , when responding to a large random variation of the cell cycle durations and the gene duplication time .
Single cell studies over last decades have shown that gene transcription is inherently a stochastic process in a bursting fashion [1–5] . The transcriptional bursting , whereby a gene promoter transits randomly between short periods of mRNA production and long periods of no productions , has been widely studied and invoked to explain how the fluctuation of mRNA molecules arises among single cells of identical genes [6–8] . Early studies on the origin of variability in gene expression found that the noise is not solely due to the randomness in reactions intrinsic to gene expression [9] . Recent experiments have suggested that cell division cycle is an important source of gene expression noise [10–13] . In virtually all cells , from bacteria to mammalian cells , a conserved class of genes is involved in cell cycle stage-specific gene expression . For instance , SWI5 and CLB2 are responsible for mitotic progression , whose transcripts are stable during the interphase , but exhibit a 30-fold increase in degradation in the mitosis phase [12] . In budding yeast , acetylation of histone 3 suppresses transcription activity to buffer changes in DNA dose for expression homeostasis of other genes during DNA replication [13] . During cell division processes , genome duplication involves DNA dosage increase at discrete times in S phase , and introduces considerable variations in gene copies [13–15] . Moreover , the time spent between two successive cell-division events [11] , the DNA replication catalyzed by DNA polymerases [16 , 17] , the variation in transcription kinetics between different cell cycle stages [9 , 15 , 18] , and the partition of molecules between two daughter cells [19] , are all observed to be stochastic and may contribute to cell-to-cell variability in transcript counts . It remains largely unexplored how these random events govern mRNA outputs and their fluctuation among individual cells [1] . In this work , we initiate a mathematical approach by coupling the classical two-state model with cell division cycles to delineate the combined contribution of transcription activities and cell divisions in the variability of transcript counts [4 , 6 , 20] . In view of gene dosage , a cell cycle is divided into S 1 and S 2 stages . In each stage , the target gene transits randomly between active and inactive states with constant rates . As usual , we use the mean , the noise , and the noise strength to characterize stochastic gene transcription . For a given random variable N , we denote by E[N] , E[N2] , and Var[N] = E[N − E[N]]2 its mean , the second moment , and variance , respectively . Its noise and the noise strength are defined by η 2 ( N ) = Var [ N ] ( E [ N ] ) 2 = E [ N 2 ] - ( E [ N ] ) 2 ( E [ N ] ) 2 , and Φ ( N ) = Var [ N ] E [ N ] . ( 1 ) We will formulate the master equations for the model and derive the differential equations of the mean and the second moment . The analytical forms of the mean , the noise , and the noise strength at steady-state will be given . We measure the fold change of mRNA copy numbers from S 1 to S 2 by r * = m 2 * / m 1 * , where m 1 * and m 2 * are the mean transcription levels at the two stages . Although r* may take any prescribed value in theory , we find that when the transcription kinetic rates are similar in the two stages , the fold change buffers against the DNA dosage variation and stays within ( 1 , 2 ) , as observed in yeast [12] and mammalian cells [15] . Furthermore , if stage transitions are considerably slower than transcription state transitions and mRNA turnover , then r* ≈ 2 . The accuracy of our theoretical results is tested by numerical examples that generate nearly the same fold change measured in a mouse embryonic stem cell line [15] . Increasing either of the cell cycle durations up-regulates transcription with less noise , and rapid transitions between cell cycle stages are a major source of highly noisy transcription . Our numerical examples also demonstrate that transcription homeostasis does not bring significant changes in transcription noise . If transcription homeostasis is attained by varying a single kinetic rate in the two cell cycle stages , then the homeostasis nearly minimizes transcription noise . Motivated by increasing evidences that many cellular processes depend mainly on the concentration rather than the absolute number of enzymes [18 , 21 , 22] , we continue to study the noise profile when the transcript concentration homeostasis is maintained . Our analysis reveals an interesting phenomenon that the transcription noise is relatively stable when the concentration homeostasis is maintained , either by reducing the transcription burst frequency or by increasing the burst size in late cell cycle phase , over a wide range of cell cycle stage durations . The reduction degree in the burst frequency is nearly a constant , while the increase in the burst size is conceivably sensitive , when responding to a large random variation of the cell cycle durations and the gene duplication time .
In past two decades , the two-state model has been a prevailing tool to characterize stochastic gene transcription in single cells , from bacteria , yeast , to mammalian cells [4–6 , 8 , 20 , 23] . In the model , as depicted in the diagram geneOFF ⇄ γ λ geneON → ν mRNA → δ ∅ , ( 2 ) it is postulated that the gene promoter transits randomly between inactive ( gene OFF ) and active ( gene ON ) states with constant activation rate λ > 0 and inactivation rate γ > 0 . The transcripts are produced only when the gene is active with a synthesis rate ν > 0 , and are turned over with a degradation rate δ > 0 . Apparently , as the four rates are all assumed to be constants , the transcription described by the model is independent of many important cellular processes such as cell growth and cell division . Actively dividing eukaryote cells go through several stages known collectively as the cell division cycle , including Gap 1 phase ( G1 ) for cell growth , the synthesis phase ( S ) for DNA replication , Gap 2 phase ( G2 ) for DNA repairing , and the mitotic phase ( M ) for cell division; see Fig 1 . During S phase , each gene is duplicated into two copies that are transcribed independently in the same cell [15] . During M phase , a cell is divided into two daughter cells and residual mRNA molecules are randomly partitioned . Cell division cycle has global effects on mRNA and protein synthesis , and is also an important source of gene expression noise [10–13] . In recent years , many real-time monitoring methods , such as single molecule fluorescent in situ hybridization ( smFISH ) , have been developed to estimate mRNA copy numbers in different cell cycle stages . In mouse embryonic stem cells , nascent Oct4 and Nanog mRNAs were measured in different phases using smFISH method [15] . It was found that the ratio of the average number of mRNA copies in G2 phase to the average in G1 phase is 1 . 28 ± 0 . 09 for Oct4 mRNA , and 1 . 51 ± 0 . 15 for Nanog mRNA . In yeast cells , CLB2 mRNAs accumulate apace in late S phase and are degraded almost completely before cytokinesis [12] . From the measurements of [12] , we estimated that the median of cytoplasmic CLB2 mRNA copy numbers is ∼10 in G2/M phase , and ∼5 in S phase . It remains an essential and widely open question to quantify how the transition of cell cycle phases , the variation of DNA content and transcription kinetics in different phases , and the random partition of mRNAs in daughter cells affect the dynamics and noise of gene transcription . In this work , we initiate a quantitative approach to this important question by developing a model that couples gene transcription with cell cycles . During DNA replication in S phase , the two complementary strands in each double helix are separated and serve as templates for the production of their counterparts . After the completion of the whole DNA replication process , which takes hours in some cells [24] , each gene copy is doubled with two copies . Normally , the duplication of a single gene takes much shorter time and is completed within seconds to minutes [17 , 25] . For instance , the genome of Escherichia coli K12 has ∼ 4 . 64 million base pairs with ∼ 4375 genes [26] , and is replicated at ∼470 ± 180 bp/s [17] . The average duplication time of each gene takes 1 . 63 ∼ 3 . 66 seconds . In our model , we treat the short duplication process of our target gene as instantaneous , and accordingly , divide a cell cycle into two stages: We make the following assumptions to complete the description of the model: We do not assume constant durations in S 1 and S 2 stages in ( i ) , because the time spent in each cell cycle phase is often not fixed , and the timing for the duplication of the target gene is random . The cell cycle duration in mouse embryonic stem cells measured by flow cytometry varies in 11 ∼ 16 hours [27 , 28] , that are roughly distributed in G1 ( 26% ) , S ( 52% ) , and G2/M ( 22% ) estimated by the percentage of cells in these phases [28] . The times spent in cell cycle phases were also measured by time-lapse microscopy and single cell tracking in T and B lymphocytes from reporter mice , and the total division time data were found to be well approximated by the sum of consecutive independent exponential and Gaussian distributions [11] . We assume that the transcription is turned off at the beginning of each stage , as DNA synthesis is catalyzed by DNA polymerase in nucleosomes , and during late S 2 stage , the chromatin shrinks into chromosome [29–31] . In either case , transcription factors and RNA polymerase II are usually prevented from reaching to gene promoters to initiate transcription [32] . Assumption ( v ) is equivalent to the binomial distribution of mRNA molecules in the two daughter cells , which has been assumed in most theoretical models , and supported by recent experiments . The partition in Escherichia coli measured by the MS2-GFP reporter strongly supports the assumption that each mRNA copy goes to one of the two daughter cells with equal probability [33] . The transcription state of a gene of our interest in a single cell at a time t ≥ 0 can be characterized by the number of active gene copies , the cell cycle stage , and its mRNA copy number . Without loss of generality , we assume that the gene has exactly one copy in S 1 stage , and two copies in S 2 stage , in any single cell of an isogenic cell population . We let I ( t ) denote the number of active genes in a cell . In S 1 stage , I ( t ) = 0 if the gene is OFF , and I ( t ) = 1 if it is ON . In S 2 stage , I ( t ) = 0 if the two gene copies are OFF , I ( t ) = 2 if both are ON , and I ( t ) = 1 in the remaining cases . We let U ( t ) specify the cell cycle stage , with U ( t ) = 1 in S 1 stage , and U ( t ) = 2 in S 2 stage . Let M ( t ) denote the mRNA copy number for the gene in one cell . Then the transcription state can be fully quantified by the following joint probabilities P 1 ( i , m , t ) = Prob { I ( t ) = i , M ( t ) = m , U ( t ) = 1 } , i = 0 , 1 ; m = 0 , 1 , 2 , ⋯ , ( 3 ) P 2 ( i , m , t ) = Prob { I ( t ) = i , M ( t ) = m , U ( t ) = 2 } , i = 0 , 1 , 2 ; m = 0 , 1 , 2 , ⋯ . ( 4 ) For clarity and simplicity in the following calculations , we assume that all cells in the isogenic population are synchronized at the beginning of S 1 stage , and count only newly produced mRNA molecules from time zero . Accordingly , we have the initial condition P 1 ( 0 , 0 , 0 ) = 1 , P 1 ( 0 , m , 0 ) = 0 , m > 0 , P 1 ( 1 , m , 0 ) = P 2 ( 0 , m , 0 ) = P 2 ( 1 , m , 0 ) = P 2 ( 2 , m , 0 ) = 0 , m ≥ 0 . ( 5 ) By using the standard procedure in stochastic process , we calculate the time evolutions of these probabilities based on the basic assumptions ( i ) - ( v ) in our model and derive the master equations: P 1 ′ ( 0 , m , t ) = γ 1 P 1 ( 1 , m , t ) - ( m δ 1 + λ 1 + κ 1 ) P 1 ( 0 , m , t ) + ( m + 1 ) δ 1 P 1 ( 0 , m + 1 , t ) + κ 2 ∑ n = m ∞ ( 1 2 ) n ( n m ) P 2 ( n , t ) , ( 6 ) P 1 ′ ( 1 , m , t ) = λ 1 P 1 ( 0 , m , t ) - ( ν 1 + m δ 1 + γ 1 + κ 1 ) P 1 ( 1 , m , t ) + ν 1 P 1 ( 1 , m - 1 , t ) + ( m + 1 ) δ 1 P 1 ( 1 , m + 1 , t ) , ( 7 ) P 2 ′ ( 0 , m , t ) = κ 1 P 1 ( m , t ) - ( m δ 2 + 2 λ 2 + κ 2 ) P 2 ( 0 , m , t ) + ( m + 1 ) δ 2 P 2 ( 0 , m + 1 , t ) + γ 2 P 2 ( 1 , m , t ) , ( 8 ) P 2 ′ ( 1 , m , t ) = 2 λ 2 P 2 ( 0 , m , t ) + 2 γ 2 P 2 ( 2 , m , t ) + ( m + 1 ) δ 2 P 2 ( 1 , m + 1 , t ) + ν 2 P 2 ( 1 , m - 1 , t ) - ( ν 2 + m δ 2 + λ 2 + γ 2 + κ 2 ) P 2 ( 1 , m , t ) , ( 9 ) P 2 ′ ( 2 , m , t ) = λ 2 P 2 ( 1 , m , t ) - ( 2 ν 2 + m δ 2 + 2 γ 2 + κ 2 ) P 2 ( 2 , m , t ) + 2 ν 2 P 2 ( 2 , m - 1 , t ) + ( m + 1 ) δ 2 P 2 ( 2 , m + 1 , t ) . ( 10 ) The last expression P2 ( n , t ) in ( 6 ) , defined by P 2 ( n , t ) = P 2 ( 0 , n , t ) + P 2 ( 1 , n , t ) + P 2 ( 2 , n , t ) , gives the probability that the cell resides on S 2 stage with n transcripts , and P1 ( m , t ) in ( 8 ) , defined by P1 ( m , t ) = P1 ( 0 , m , t ) + P1 ( 1 , m , t ) , represents the probability that the cell resides on S 1 stage with m copies of mRNA molecules . The technical steps leading to ( 6 ) – ( 10 ) are given in S1 Text . The transcription dynamics of a gene in a cell population is best characterized by the mean value m ( t ) = E[M ( t ) ] of the random process M ( t ) that counts the number of its mRNA copies . The second moment μ ( t ) = E[M2 ( t ) ] is essential in the calculation of its noise that quantifies the fluctuation of mRNA copy numbers among individual cells . More importantly , as the cell division cycle is integrated into our model , we can extend m ( t ) and μ ( t ) to the two cell cycle stages S 1 and S 2 . The comparison of these quantities in the two stages can help us understand how the gene duplication contributes to the variation of transcription levels and noises . To start with , we give the formal definitions of these concepts and present the differential equations that provide a framework from which they can be solved analytically . For this purpose , we need various probabilities by adding the joint probabilities Pj ( i , m , t ) introduced in ( 3 ) – ( 4 ) when i , j , or m runs through all possible values . We use a conventional simplification of notations: If any of i , j and m is removed from Pj ( i , m , t ) , then the new probability is defined by summing Pj ( i , m , t ) over the range of the removed index . For instance , P 1 ( m , t ) = P 1 ( 0 , m , t ) + P 1 ( 1 , m , t ) , P 2 ( m , t ) = P 2 ( 0 , m , t ) + P 2 ( 1 , m , t ) + P 2 ( 2 , m , t ) ( 11 ) are the respective probabilities that the cell resides on S 1 and S 2 stages with m copies of mRNA molecules , without specifying the promoter state . A further summation of the two probabilities in ( 11 ) defines P ( m , t ) = P 1 ( m , t ) + P 2 ( m , t ) ( 12 ) as the probability that there are m copies of mRNA molecules in the cell . Similarly , we can define P1 ( i , t ) and P2 ( i , t ) . To avoid the confusion with these probabilities defined in ( 11 ) , we change them to P1i ( t ) and P2i ( t ) with P 1 i ( t ) = ∑ m = 0 ∞ P 1 ( i , m , t ) , P 2 i ( t ) = ∑ m = 0 ∞ P 2 ( i , m , t ) . ( 13 ) By adding the probabilities in ( 13 ) we have P 1 ( t ) = P 10 ( t ) + P 11 ( t ) , P 2 ( t ) = P 20 ( t ) + P 21 ( t ) + P 22 ( t ) ( 14 ) as the respective probabilities that the cell resides on S 1 and S 2 stages . By adding the master Eqs ( 6 ) – ( 10 ) in m , we obtain a closed system of P1i ( t ) and P2i ( t ) , { P 10 ′ ( t ) = κ 2 P 2 ( t ) + γ 1 P 11 ( t ) - ( λ 1 + κ 1 ) P 10 ( t ) , P 11 ′ ( t ) = λ 1 P 10 ( t ) - ( γ 1 + κ 1 ) P 11 ( t ) , P 20 ′ ( t ) = κ 1 P 1 ( t ) + γ 2 P 21 ( t ) - ( 2 λ 2 + κ 2 ) P 20 ( t ) , P 21 ′ ( t ) = 2 λ 2 P 20 ( t ) - ( λ 2 + γ 2 + κ 2 ) P 21 ( t ) + 2 γ 2 P 22 ( t ) , P 22 ′ ( t ) = λ 2 P 21 ( t ) - ( 2 γ 2 + κ 2 ) P 22 ( t ) . ( 15 ) The initial condition for this system can be derived by a summation of the initial data given in ( 5 ) . This linear system of ordinary differential equations with constant coefficients can be solved analytically , and its solution subject to the corresponding initial condition determines uniquely P1i ( t ) and P2i ( t ) . Due to the technical complexity , we break down the process of determining m ( t ) , μ ( t ) , and their extensions in S 1 and S 2 in several steps , and move most involving calculations to S1 Text . Step 1: The determination of the mean level m ( t ) : With P1 ( m , t ) , P2 ( m , t ) , and P ( m , t ) defined in ( 11 ) and ( 12 ) , we have m ( t ) = E [ M ( t ) ] = ∑ m = 0 ∞ m P ( m , t ) = n 1 ( t ) + n 2 ( t ) , ( 16 ) where n 1 ( t ) = ∑ k = 0 ∞ k P 1 ( k , t ) , and n 2 ( t ) = ∑ k = 0 ∞ k P 2 ( k , t ) . ( 17 ) As we show in S1 Text , n1 ( t ) and n2 ( t ) satisfy the following system of inhomogeneous linear ordinary differential equations with constant coefficients: { n 1 ′ ( t ) = - ( δ 1 + κ 1 ) n 1 ( t ) + κ 2 2 n 2 ( t ) + ν 1 P 11 ( t ) , n 2 ′ ( t ) = κ 1 n 1 ( t ) - ( δ 2 + κ 2 ) n 2 ( t ) + ν 2 [ P 21 ( t ) + 2 P 22 ( t ) ] . ( 18 ) As P11 ( t ) , P21 ( t ) , and P22 ( t ) can be solved uniquely from ( 15 ) , we can find n1 ( t ) and n2 ( t ) by solving ( 18 ) subject to the initial condition n1 ( 0 ) = n2 ( 0 ) = 0 , and find m ( t ) by ( 16 ) . Step 2: The determination of the second moment μ ( t ) : Similar to the definition of m ( t ) in ( 16 ) , we have μ ( t ) = E [ M 2 ( t ) ] = ∑ m = 0 ∞ m 2 P ( m , t ) = ω 1 ( t ) + ω 2 ( t ) , ( 19 ) where ω 1 ( t ) = ∑ k = 0 ∞ k 2 P 1 ( k , t ) , and ω 2 ( t ) = ∑ k = 0 ∞ k 2 P 2 ( k , t ) . ( 20 ) As we show in S1 Text , the time evolutions of ω1 ( t ) and ω2 ( t ) are given by the system { ω 1 ′ ( t ) = - ( 2 δ 1 + κ 1 ) ω 1 ( t ) + κ 2 4 ω 2 ( t ) + δ 1 n 1 ( t ) + κ 2 4 n 2 ( t ) + ν 1 [ 2 n 11 ( t ) + P 11 ( t ) ] , ω 2 ′ ( t ) = κ 1 ω 1 ( t ) - ( 2 δ 2 + κ 2 ) ω 2 ( t ) + δ 2 n 2 ( t ) + ν 2 [ P 21 ( t ) + 2 P 22 ( t ) + 2 n 21 ( t ) + 4 n 22 ( t ) ] , ( 21 ) where n 1 i ( t ) = ∑ m = 0 ∞ m P 1 ( i , m , t ) , i = 0 , 1 , n 2 i ( t ) = ∑ m = 0 ∞ m P 2 ( i , m , t ) , i = 0 , 1 , 2 , ( 22 ) and n 1 ( t ) = n 10 ( t ) + n 11 ( t ) , n 2 ( t ) = n 20 ( t ) + n 21 ( t ) + n 22 ( t ) . Apparently , ( 21 ) is not a closed system , and finding ω1 ( t ) and ω2 ( t ) requires the following system of n1i ( t ) and n2i ( t ) : { n 10 ′ ( t ) = κ 2 2 n 2 ( t ) - ( δ 1 + λ 1 + κ 1 ) n 10 ( t ) + γ 1 n 11 ( t ) , n 11 ′ ( t ) = λ 1 n 10 ( t ) + ν 1 P 11 ( t ) - ( δ 1 + γ 1 + κ 1 ) n 11 ( t ) , n 20 ′ ( t ) = κ 1 n 1 ( t ) + γ 2 n 21 ( t ) - ( δ 2 + 2 λ 2 + κ 2 ) n 20 ( t ) , n 21 ′ ( t ) = 2 λ 2 n 20 ( t ) + 2 γ 2 n 22 ( t ) + ν 2 P 21 ( t ) - ( δ 2 + λ 2 + γ 2 + κ 2 ) n 21 ( t ) , n 22 ′ ( t ) = λ 2 n 21 ( t ) + 2 ν 2 P 22 ( t ) - ( δ 2 + 2 γ 2 + κ 2 ) n 22 ( t ) , ( 23 ) This system is obtained by multiplying ( 6 ) – ( 10 ) with m and then taking sums . As P11 ( t ) , P21 ( t ) , and P22 ( t ) can be solved from ( 15 ) , it is a closed system of n1i ( t ) and n2i ( t ) . By substituting its unique solution subject to the zero initial condition into ( 21 ) , we can determine ω1 ( t ) and ω2 ( t ) , and therefore the second moment μ ( t ) . Step 3: The moment functions on S 1 and S 2 stages: To extend the definitions of m ( t ) and μ ( t ) to the two cell cycle stages S 1 and S 2 , we define the conditional probabilities p 1 ( i , m , t ) = Prob { I ( t ) = i , M ( t ) = m | U ( t ) = 1 } = P 1 ( i , m , t ) P 1 ( t ) , ( 24 ) p 2 ( i , m , t ) = Prob { I ( t ) = i , M ( t ) = m | U ( t ) = 2 } = P 2 ( i , m , t ) P 2 ( t ) , ( 25 ) for the probabilities P1 ( t ) and P2 ( t ) defined in ( 14 ) . Then p 1 ( m , t ) = p 1 ( 0 , m , t ) + p 1 ( 1 , m , t ) , p 2 ( m , t ) = p 2 ( 0 , m , t ) + p 2 ( 1 , m , t ) + p 2 ( 2 , m , t ) are the probabilities that there are m copies of mRNA molecules when the cell resides on S 1 or S 2 stage . The average transcription levels in S 1 and S 2 stages are defined by m 1 ( t ) = ∑ k = 0 ∞ k p 1 ( k , t ) , m 2 ( t ) = ∑ k = 0 ∞ k p 2 ( k , t ) , ( 26 ) and the second moments are defined by μ 1 ( t ) = ∑ k = 0 ∞ k 2 p 1 ( k , t ) , μ 2 ( t ) = ∑ k = 0 ∞ k 2 p 2 ( k , t ) . ( 27 ) By comparing ( 26 ) with the definition of n1 ( t ) and n2 ( t ) in ( 17 ) , and ( 27 ) with the definition of ω1 ( t ) and ω2 ( t ) in ( 20 ) , we find the simple relation m 1 ( t ) = n 1 ( t ) P 1 ( t ) , m 2 ( t ) = n 2 ( t ) P 2 ( t ) , μ 1 ( t ) = ω 1 ( t ) P 1 ( t ) , μ 2 ( t ) = ω 2 ( t ) P 2 ( t ) . ( 28 ) As a cell is either on S 1 or on S 2 stage , we have P1 ( t ) + P2 ( t ) ≡ 1 . From the basic assumption ( i ) , the two stages S 1 and S 2 transit each other by constant rates κ1 and κ2 . It implies that P1 ( t ) and P2 ( t ) are simply related by P 1 ′ ( t ) = κ 2 P 2 ( t ) - κ 1 P 1 ( t ) = κ 2 - ( κ 1 + κ 2 ) P 1 ( t ) . This simple equation can also be derived by adding equations in ( 15 ) . By the assumption that all cells are synchronized on S 1 initially , we have P1 ( 0 ) = 1 . Hence P 1 ( t ) = κ 2 κ 1 + κ 2 + κ 1 κ 1 + κ 2 e - ( κ 1 + κ 2 ) t , P 2 ( t ) = κ 1 κ 1 + κ 2 - κ 1 κ 1 + κ 2 e - ( κ 1 + κ 2 ) t . ( 29 ) Our methods for finding n1 ( t ) and n2 ( t ) in Step 1 , and ω1 ( t ) and ω2 ( t ) in Step 2 , combined with ( 28 ) and ( 29 ) , constitute a complete analytical approach for computing the mean values m1 ( t ) and m2 ( t ) , and the second moments μ1 ( t ) and μ2 ( t ) , in the two cell cycle stages .
Our discussion in the previous section offers a clear analytical approach for finding the mean value m ( t ) and the second moment μ ( t ) of mRNA number M ( t ) , along with their extensions to the two cell cycle stages S 1 and S 2 . However , neither of these functions has a simple analytical expression . For simplicity , we will only present their steady-state values in exact forms , and use their temporal forms in numerical simulations . Although the steady-state values are much simpler than the temporal forms , they are still rather complex and capture the delicate involvement of the system parameters as shown by the next two theorems . For a function f ( t ) that has a finite limit as t → ∞ , we let f* denote its limit . Theorem 1 If the transcription of a gene obeys the model described in Fig 1 , then the mean transcription level of the gene in a population of isogenic cells at steady-state is m * = m 1 * · κ 2 κ 1 + κ 2 + m 2 * · κ 1 κ 1 + κ 2 , ( 30 ) a linear combination of the mean levels m 1 * in S 1 stage and m 2 * in S 2 stage , and m 1 * = 2 ν 1 λ 1 ( δ 2 + κ 2 ) ( λ 2 + γ 2 + κ 2 ) + 2 ν 2 λ 2 κ 1 ( λ 1 + γ 1 + κ 1 ) [ 2 ( δ 1 + κ 1 ) ( δ 2 + κ 2 ) - κ 1 κ 2 ] ( λ 1 + γ 1 + κ 1 ) ( λ 2 + γ 2 + κ 2 ) , ( 31 ) m 2 * = 2 ν 1 λ 1 κ 2 ( λ 2 + γ 2 + κ 2 ) + 4 ν 2 λ 2 ( δ 1 + κ 1 ) ( λ 1 + γ 1 + κ 1 ) [ 2 ( δ 1 + κ 1 ) ( δ 2 + κ 2 ) - κ 1 κ 2 ] ( λ 1 + γ 1 + κ 1 ) ( λ 2 + γ 2 + κ 2 ) . ( 32 ) Theorem 2 If the transcription of a gene obeys the model described in Fig 1 , then the second moment of its mRNA copy number M ( t ) at steady-state is μ * = μ 1 * · κ 2 κ 1 + κ 2 + μ 2 * · κ 1 κ 1 + κ 2 , ( 33 ) where μ 1 * and μ 2 * are the second moments in S 1 and S 2 stages given by μ 1 * = m 1 * + 8 ν 1 ( κ 2 + 2 δ 2 ) · m s 1 * + 2 ν 2 κ 1 · m s 2 * 4 ( κ 1 + 2 δ 1 ) ( κ 2 + 2 δ 2 ) - κ 1 κ 2 , ( 34 ) μ 2 * = m 2 * + 8 ν 1 κ 2 · m s 1 * + 8 ν 2 ( κ 1 + 2 δ 1 ) · m s 2 * 4 ( κ 1 + 2 δ 1 ) ( κ 2 + 2 δ 2 ) - κ 1 κ 2 , ( 35 ) with m s 1 * = ( δ 1 + λ 1 + κ 1 ) m 1 * - κ 1 m 2 * / 2 δ 1 + λ 1 + γ 1 + κ 1 , ( 36 ) m s 2 * = ( δ 2 + κ 2 + 2 λ 2 ) m 2 * - κ 2 m 1 * + 2 ν 2 p 22 * δ 2 + λ 2 + γ 2 + κ 2 , ( 37 ) and p 22 * = 2 λ 2 2 / [ ( κ 2 + λ 2 + γ 2 ) ( κ 2 + 2 λ 2 + 2 γ 2 ) ] . The proofs of Theorems 1 and 2 are given in S1 Text . By using definition ( 1 ) , combined with the analytical expressions ( 31 ) and ( 32 ) of the stationary mean transcription levels , and ( 34 ) and ( 35 ) for the second moments , we derive the noise strengths of mRNA copy numbers in S 1 and S 2 as Φ 1 * = 1 - m 1 * + 1 m 1 * · 8 ν 1 ( κ 2 + 2 δ 2 ) · m s 1 * + 2 ν 2 κ 1 · m s 2 * 4 ( κ 1 + 2 δ 1 ) ( κ 2 + 2 δ 2 ) - κ 1 κ 2 , ( 38 ) Φ 2 * = 1 - m 2 * + 1 m 2 * · 8 ν 1 κ 2 · m s 1 * + 8 ν 2 ( κ 1 + 2 δ 1 ) · m s 2 * 4 ( κ 1 + 2 δ 1 ) ( κ 2 + 2 δ 2 ) - κ 1 κ 2 . ( 39 ) The noises η 1 2 * and η 2 2 * are given by η 1 2 * = 1 m 1 * - 1 + 1 ( m 1 * ) 2 · 8 ν 1 ( κ 2 + 2 δ 2 ) · m s 1 * + 2 ν 2 κ 1 · m s 2 * 4 ( κ 1 + 2 δ 1 ) ( κ 2 + 2 δ 2 ) - κ 1 κ 2 , ( 40 ) η 2 2 * = 1 m 2 * - 1 + 1 ( m 2 * ) 2 · 8 ν 1 κ 2 · m s 1 * + 8 ν 2 ( κ 1 + 2 δ 1 ) · m s 2 * 4 ( κ 1 + 2 δ 1 ) ( κ 2 + 2 δ 2 ) - κ 1 κ 2 . ( 41 )
Our gene transcription model coupling with cell division cycles offers six quantities to characterize the fluctuations of mRNA numbers in single cells: The noise η2* and the noise strength Φ* in cells without referring to cell cycle stages , along with η 1 2 * and Φ 1 * in S 1 stage , and η 2 2 * and Φ 2 * in S 2 stage . Theorems 1 and 2 provide the basic formulas by which these quantities can be computed from the system parameters . The relations between these quantities are far more complicated than our intuition may envisage . We use simple example to demonstrate the delicacy of their relations: Assume ( 43 ) and fix the kinetic rates as in ( 51 ) , and take the stage transition rates κ1 = κ2 = 1/1250 hr−1 . Then applying Theorem 2 , ( 38 ) and ( 39 ) gives Φ 1 * = 43 . 2008 and Φ 2 * = 43 . 8410 . Since Φ 1 * and Φ 2 * are nearly equal , one might expect by intuition that Φ* is about equal to each of Φ 1 * and Φ 2 * , which is in conflict with Φ * = 89 . 3329 > Φ 1 * + Φ 2 * obtained by using Theorems 1 and 2 . Moreover Φ* = 89 . 3329 given here is significantly higher than Φ* ≈ 1 reported in various single cell measurements , including the classical studies by Taniguchi et al . [38] and Yu et al . [39] . In these studies , the genes were active most of time , so that m* and η2* exhibited a strict reciprocal relation , implying Φ* ≈ 1 . The stochastic switching between gene promoter ON and OFF states , combined with transitions between cell cycle stages , may induce much noisier transcriptions . Due to the wide range of the six noise measures and their complex relations , we will discuss their profiles in three special cases below: Gene transcription involves inherently various probabilistic steps that create fluctuations in mRNA and protein counts [1–3] . The random transitions between the active and inactive promoter states have been widely invoked to explain the fluctuation of mRNA numbers among individual cells of identical genes [6 , 7] . Recent experimental studies have revealed that the cell division cycle has global effects on transcriptional outputs , and is thought to be an additional source of transcription noise [10–13] . In this work , we integrated cell division cycles into an extended two-state model to delineate the combined contribution of transcription activities and cell divisions in the variability of transcript counts [4 , 6] . In the model , a cell division cycle is divided into S 1 stage before the duplication of a target gene and the late stage S 2 , on which the durations are independently and exponentially distributed with rates κ1 and κ2 . When a cell divides , each mRNA molecule has an even chance of being partitioned to one of the two daughter cells . We defined two joint probabilities to quantify the system state , and derived the master equations of their time evolutions . From the master equations we obtained the differential equations of the mean and the second moment of mRNA numbers in single cells . By solving these equations we presented in Theorem 1 the steady-state mean transcription level m* in cells , together with the means m 1 * and m 2 * on the two stages . The analytical forms of the second moments are presented in Theorem 2 , which in turn help us determine six noise measures: the noise η2* and the noise strength Φ* without referring to cell cycle stages , along with η 1 2 * and Φ 1 * in S 1 , and η 2 2 * and Φ 2 * in S 2 . The fold change of mRNA counts from S 1 to S 2 is quantified by r * = m 2 * / m 1 * . As a cell contains twice as many copies of each gene in S 2 as that in S 1 , one may envisage by intuition that r* ≈ 2 . However , our Theorem 3 shows that r* can take any prescribed value in theory , although we also found that r* ≈ 2 when the transcription kinetics are unchanged in the two stages , and stage transitions are considerably slower than mRNA turnover and transcription state transitions . The dependance of r* on κ1 and κ2 is examined deeply in Theorem 4 , where the necessary and sufficient conditions for r* < 1 or r* > 2 are identified . In particular , it is proved that r* has an upper bound strictly less than 2 when κ1 ≤ κ2 . We tested the accuracy of our analytical results against various experimental data . For a gene in a mouse embryonic stem cell line , our result predicts r* = 1 . 2791 , which offers a good match with r* = 1 . 28 ± 0 . 09 measured in [15] . The analysis also indicates that if the transcription kinetics do not change considerably in the two cell cycle stages , then the average mRNA counts increase about 1 to 2 folds from S 1 stage to S 2 stage as observed in mouse embryonic cells [15] and yeast [12] . The mean m* increases while the noise η2* decreases in each of the cell cycle durations . Rapid transitions between cell cycle stages were identified as a major source of highly noisy transcription . Eukaryotic cells have a DNA dosage-compensating mechanism to reduce mRNA production in late cell cycle stage , resulting in gene transcription homeostasis that overall transcription remains constant across S 1 and S 2 stages [13 , 18] . Our analysis reveals that transcription homeostasis does not bring significant changes in transcription noise . If transcription homeostasis is attained by varying a single kinetic rate in the two cell cycle stages , then the homeostasis nearly minimizes transcription noise . As many cellular processes depend on the concentration of enzymes rather than their absolute numbers for proper cellular function [18 , 21 , 22] , we also studied the noise profile when the transcript concentration homeostasis is maintained that the mean transcription level scales with the cellular volume in S 1 and S 2 . We found that the transcription noise is relatively stable when the transcript concentration homeostasis is maintained , either by reducing the transcription burst frequency or by increasing the burst size in late cell cycle phase , in the face of a large cell cycle stage duration variation . The reduction degree in the burst frequency is relative robust , while the increase in the burst size is conceivably sensitive , to the large random variation of the cell cycle durations and the gene duplication time . This work provides one of the first theoretical explorations on how the coupling of stochastic promoter state transitions and cell cycle progressions regulates transcription abundance and noise . It presents a core model for further inclusion of more complex transcription kinetics and cell cycle progressions . The kinetic rates may display large variations in different cell cycle phases or within the same phase , or oscillate periodically in the cell cycle progression [44] . With the expansion of the model , motivated and tested by more upcoming experimental data , the approach initiated here is expected to be developed further to help understand the role played by the cell cycle dependent gene expression in cell functions and cell fate decision [45 , 46] .
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Gene transcription in single cells is inherently a stochastic process , resulting in a large variability in the number of transcripts and constituting the phenotypic heterogeneity in cell population . Cell division cycle has global effects on transcriptional outputs , and is thought to be an additional source of transcription noise . In this work , we develop a hybrid model to delineate the combined contribution of transcription activities and cell divisions in the variability of transcript counts . By working with the analytical forms of the mean and the noise of mRNA numbers , we show that if the transcription kinetic rates do not change considerably , then the average mRNA level is increased about 1 to 2 folds from earlier to later cell cycle stages . When transcription homeostasis is attained by varying a single kinetic rate between the two cell cycle stages , we find no significant changes in the transcription noise , and the homeostasis nearly minimizes the noise . In our continuous study on the transcript concentration homeostasis that the transcription level scales with the cellular volume , we find only minor variations of the noise if the homeostasis is maintained either by reducing the transcription burst frequency or by increasing the burst size in late cell cycle phase , in the face of a large cell cycle stage duration variation . The reduction in the burst frequency is relative robust , while the increase in the burst size is conceivably sensitive , to the large random variation of the cell cycle durations and the gene duplication time .
|
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2019
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The nonlinear dynamics and fluctuations of mRNA levels in cell cycle coupled transcription
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The mechanisms underlying human cytomegalovirus ( HCMV ) latency remain incompletely understood . Here , we showed that a HCMV-encoded miRNA , miR-UL148D , robustly accumulates during late stages of experimental latent HCMV infection in host cells and promotes HCMV latency by modulating the immediate early response gene 5 ( IER5 ) -cell division cycle 25B ( CDC25B ) axis in host cells . miR-UL148D inhibited IER5 expression by directly targeting the three-prime untranslated region ( 3’UTR ) of IER5 mRNA and thus rescued CDC25B expression during the establishment of viral latency . Infection with NR-1ΔmiR-UL148D , a derivative of the HCMV clinical strain NR-1 with a miR-UL148D knockout mutation , resulted in sustained induction of IER5 expression but decreased CDC25B expression in host cells . Mechanistically , we further showed that CDC25B plays an important role in suppressing HCMV IE1 and lytic gene transcription by activating cyclin-dependent kinase 1 ( CDK-1 ) . Both gain-of-function and lose-of-function assays demonstrated that miR-UL148D promotes HCMV latency by helping maintain CDC25B activity in host cells . These results provide a novel mechanism through which a HCMV miRNA regulates viral latency .
Human cytomegalovirus ( HCMV ) , a member of the β-herpesvirus subfamily , is a ubiquitous human virus that has infected up to 90% of the adult population worldwide [1] . Although HCMV infection rarely causes clinically symptomatic disease in immunocompetent healthy hosts , HCMV can establish a latent infection in hosts . Reactivation of HCMV from latency in immunocompromised people , such as AIDS patients , solid organ transplant recipients and neonates , can lead to severe morbidity and mortality [2] . The effects of HCMV-mediated disease in such patients have also highlighted the possible role of the virus in the development of cancer and inflammatory diseases such as vascular diseases and autoimmune diseases [3 , 4] . Although previous evidence has suggested that various viral and cellular factors are involved in the establishment of latent HCMV infection [5–10] , the mechanisms underlying this type of infection remain incompletely understood . Latent HCMV infection is initiated by silencing HCMV immediate early ( IE ) genes . HCMV IE gene products , especially the major IE ( MIE ) proteins IE1 and IE2 , initiate the HCMV lytic cycle by activating the expression of a cascade of early and late viral genes [11 , 12] . In latently infected cells , the expression of the MIE gene is blocked , which consequently restricts the expression of most viral genes . Thus , MIE gene silencing is critical for the establishment of viral latency . Although the underlying mechanism remains unclear , recent studies have shown that cellular cyclin-dependent kinase ( CDK ) is involved in modulating the persistence or latency of HCMV infection . CDK1/2 can directly inhibit IE1 and IE2 expression and facilitate viral latency , and pharmaceutical inhibition of CDK activates IE gene expression and thus precludes HCMV latency and contributes to lytic viral replication [13 , 14] . Previous work has demonstrated that HCMV infection elicits cell damage responses and results in the dysregulation of p53 and CDK activity in host cells [15–17] . However , how cellular CDK activity is regulated during latent HCMV infection remains unclear . MicroRNAs ( miRNAs ) , a class of ~22-nt non-coding nucleotides that post-transcriptionally regulate gene expression , constitute a novel gene regulatory network that plays a critical role in almost all fundamental biological processes [18 , 19] . Herpesviruses , including Epstein–Barr virus ( EBV ) , Kaposi’s sarcoma-associated herpesvirus ( KSHV ) , herpes simplex virus 1 ( HSV-1 ) and HCMV , encode the majority of the 250+ reported virally encoded miRNAs [20 , 21] . Herpesvirus miRNAs target both viral and cellular genes to modulate various aspects of virus and cell biology , including viral replication [22 , 23] , cell apoptosis , the cell cycle , host immune responses [24–30] and , most importantly , the establishment and maintenance of viral latency . Various miRNAs encoded by EBV , KSHV and HSV are abundantly expressed during viral latency and may contribute to the establishment or maintenance of this latency by inhibiting viral IE genes or immune surveillance [31–33] . miRNAs may also play a role in HCMV latency . Grey et al . [22] and Murphy et al . [34] reported that hcmv-miR-UL112-1 inhibits the expression of HCMV IE1 , possibly facilitating the establishment and maintenance of viral latency . Moreover , Meshesha et al . [35] tested 20 HCMV miRNAs and found eight miRNAs , including miR-UL112-5p and 3p , miR-UL36-5p and 3p , miR-UL22A-5p and -3p , miR-US29-3p and US22-5p , that may promote viral latency in fresh human PBMCs . Recent studies have also demonstrated that HCMV utilizes cellular miRNAs to promote viral latency and to regulate viral reactivation [36 , 37] . In the present study , we established an experimental HCMV latency model with the HCMV clinical strain NR-1 in Kasumi-3 cells and CD34+ primary hematopoietic progenitor cells ( HPCs ) and then monitored HCMV-encoded miRNAs at various stages of HCMV infection . We found that miR-UL148D abundantly accumulated during the late stage of HCMV infection , and knockout of miR-UL148D impaired the capacity of HCMV to achieve experimental latency . Mechanistically , we showed that miR-UL148D directly targeted IER5 and maintained the expression level of CDC25B , a molecule that can inhibit viral IE1 expression by activating CDK-1 . In contrast , an NR-1 strain with a specific miR-UL148D deletion did not suppress the continuous induction of IER5 , leading to low levels of CDC25B and the eventual failure of latent infection in host cells .
Previous work has demonstrated that HCMV can establish latency in primary hematopoietic cells [6 , 9 , 38–47] as well as in Kasumi-3 cells [48–50] . Here , we first determined whether the NR-1 clinical strain can achieve latent infection in CD34+ hematopoietic progenitor cells ( HPCs ) and Kasumi-3 cells . To accomplish this , primary CD34+ HPCs were isolated from the bone marrow samples of five HCMV-IgG-positive but HCMV-IgM- and HCMV-DNA-negative donors using an anti-CD34 magnetic cell separation kit . Using this method , the percentage of CD34+ HPCs in the total isolated cell population reached approximately 90% ( S2 Fig ) . Then , Kasumi-3 cells and the human CD34+ HPCs were infected with a GFP-expressing version of the HCMV clinical strain NR-1 . As shown in Fig 1A , at 48 hours post-infection with GFP-expressing NR-1 at a multiplicity of 2 PFU/cell , GFP expression was detected in 13 . 2% of the Kasumi-3 cells and 12 . 3% of the CD34+ HPCs . Interestingly , at a multiplicity of 5 PFU/cell , the percentages of infected Kasumi-3 cells and CD34+ HPCs increased significantly to 80 . 0% and 71 . 4% , respectively ( Fig 1A ) . Compared to other BAC-derived virus , including Towne and Ad169 , NR-1 had a much higher infection efficiency at MOI of 5 ( S1 Fig ) . The infection efficiency of NR-1 did not increase significantly when MOI of 10 was used ( Fig 1A ) . We also observed that Kasumi-3 and CD34+ HPCs infected with HCMV virus at MOI of 5 maintained high viability ( S1 Fig ) . Most CD34+ HPCs also maintained a progenitor phenotype at 10 days post-infection of NR-1 at a multiplicity of 5 PFU/cell ( S2 Fig ) . Therefore , a multiplicity of 5 PFU/cell was used for all remaining latent infection experiments . The hallmark of HCMV latency is the maintenance of the viral genome with very limited viral gene transcription . To monitor the status of HCMV transcription in Kasumi-3 cells and CD34+ HPCs , the presence of the viral genome ( IE1 DNA ) and the IE1 transcript ( IE1 RNA ) was assayed in infected cells at four time points along a 10-day time course . The latency-associated UL138 transcript was also measured as a control . We found that viral genomes increased significantly through 4 days post-infection ( dpi ) and then reached a plateau , whereas viral IE1 transcripts peaked at 4 dpi and then decreased to undetectable levels by 10 dpi in both Kasumi-3 cells and CD34+ HPCs ( Fig 1B ) . As a positive control , the latency-associated UL138 transcript was consistently detected along the 10-day time course ( Fig 1B ) . These data suggest that NR-1 virus was able to enter a quiescent state in both Kasumi-3 cells and CD34+ HPCs highlighted by the restriction of viral gene transcription 10 days post-infection . We then induced lytic reactivation in parallel cultures of each cell type following 10 days of latent infection . To accomplish this , infected Kasumi-3 cells were cultured in the presence or absence of 20nM 12-O-tetradecanoylphorbol-13-acetate ( TPA ) , whereas infected primary CD34+ HPCs were cultured in reactivation medium for 2 days prior to the harvest of total cellular RNA [12 , 50 , 51] . Utilizing RT-qPCR , we assessed the expression level of IE1 cDNA relative to that of UL138 . Because UL138 was consistently expressed during both lytic and latent HCMV infections , while IE1 was highly expressed during lytic replication but was silenced during viral latency , the ratio of IE1 to UL138 expression can directly reflect the switch from viral latency to reactivation . As shown in Fig 1C , under latent infection , the ratio of IE1/UL138 was low . However , the IE1/UL138 ratio increased significantly upon HCMV reactivation , suggesting that the virus switched from latency to the lytic replication . To further determine if reactivated virus was infectious , we reactivated HCMV in latently infected cells or cells that underwent a mock infection by co-culturing these cells with HFFs for 2 days . Following the co-culture , the HFFs were isolated and monitored for plaque formation after an additional 5 days by fluorescence microscopy . As shown in Fig 1D , HFFs that were co-cultured with re-activated Kasumi-3 cells or CD34+ HPCs displayed GFP-positive plaques . In contrast , within the same time frame , no GFP-positive plaques were observed in HFFs that were in contact with infected cells not submitted to reactivation treatment , suggesting that induction of reactivation in latently infected cells promotes the release of infectious progeny virions . Taken together , our results demonstrate that the NR-1 strain can achieve latency in both Kasumi-3 cells and primary CD34+ HPCs . We next analyzed the expressional profiles of 14 HCMV-encoded miRNAs in Kasumi-3 cells along a 10-day time course of infection . To accomplish this , a total of 20 , 000 infected cells were harvested for the isolation of total RNA and DNA at each indicted time point along the 10-day time course . We first quantified the viral DNA by qPCR and then calculated the absolute viral genome copies according to the generated standard curve . Next , we assayed the HCMV miRNAs with specific HCMV miRNA probe kit and calculated the absolute amount of each HCMV miRNA based on individual miRNA standard curves . The levels of HCMV miRNAs were then normalized to the viral genome copy number . In the latently infected cells , we found that most of the HCMV miRNAs peaked at 4 dpi but decreased to undetectable levels by 10 dpi , although certain HCMV miRNAs , including miR-UL112 , miR-UL148D and miR-UL22A-5p , were still readily detectable ( Fig 1E ) . Notably , the level of miR-UL148D gradually increased along the 10-day time course of infection and accumulated to the highest level on 10 dpi ( Fig 1E ) . The presence of high levels of miR-UL148D in host cells during the establishment of HCMV latency was further confirmed in CD34+ HPCs . As shown in Fig 1F , when CD34+ HPCs were infected with NR-1 at a multiplicity of 5 PFU/cell , miR-UL148D accumulated at high levels by 10 dpi . These results suggest that miR-UL148D robustly accumulates during HCMV latency in both Kasumi-3 cells and primary CD34+ HPCs . To explore the potential role of miR-UL148D accumulation in modulating HCMV infection latency , we generated NR-1ΔmiR-UL148D , a mutant NR-1 virus with miR-UL148D knockout . NR-1ΔmiR-UL148D displayed a similar infection efficiency of Kasumi-3 cells and CD34+ HPCs ( S1 Fig ) and viral growth curve ( S3A Fig ) to NR-1 . We also extracted total RNA from HFFs infected with NR-1 or NR-1ΔmiR-UL148D 4 days post-infection and assayed the expression level of miR-UL148D . While readily detectable in the NR-1-infected cells , miR-UL148D was non-detectable in the NR-1ΔmiR-UL148D-infected cells ( S3B Fig ) . These results confirmed the efficiency of the miR-UL148D knockout . We then utilized qPCR to compare the levels of HCMV genomes and IE1 transcripts in Kasumi-3 cells and CD34+ HPCs infected with NR-1 or NR-1ΔmiR-UL148D . Compared to the cells infected with NR-1 , the levels of HCMV genomes ( Fig 2A and 2C ) and IE1 transcripts ( Fig 2B and 2D ) were significantly higher in the NR-1ΔmiR-UL148D-infected cells on 10 dpi and 7 dpi . These results suggest that miR-UL148D knockout may impair both the silencing of IE1 transcription and viral replication in Kasumi-3 cells and CD34+ HPCs . To confirm the above results , we measured the levels of viral early and late gene transcripts ( UL54 and UL99 , respectively ) in the infected cells to monitor the status of viral transcription during the 10 days of infection . We observed that the levels of UL54 and UL99 were significantly higher in the cells infected with NR-1ΔmiR-UL148D than in the cells infected with NR-1 , especially on 7 and 10 dpi ( Fig 2B and 2D ) . These results indicated that miR-UL148D knockout possibly induced viral lytic gene transcription . To further validate the role of miR-UL148D in silencing viral IE1 , we utilized a chemically modified miR-UL148D mimic , namely , an miR-UL148D agomir , to increase cellular miR-UL148D levels after infection . The agomir was composed of a synthetic RNA duplex that was chemically modified to achieve greater stability and had been previously validated to provide a gain of function in vivo [52 , 53] . In this experiment , Kasumi-3 cells were incubated with the agomir for 24 h prior to NR-1 or NR-1ΔmiR-UL148D infection . The culture medium was replaced daily , including the addition of fresh agomirs . The viral genome copies and IE1 transcript levels were then measured at four time points along a 10-day time course by qPCR and RT-qPCR , respectively . As shown ( Fig 2E and 2F ) , restoring the expression of miR-UL148D via transfection with the miR-UL148D agomir strongly decreased the HCMV genome copy number and IE1 transcript levels in Kasumi-3 cells infected with NR-1ΔmiR-UL148D . Consistent with the above findings , immunofluorescence staining results indicated that Kasumi-3 cells infected with NR-1ΔmiR-UL148D were positive for viral GFP and IE1 protein expression 10 days post infection , whereas Kasumi-3 cells infected with NR-1 did not show this phenotype ( Fig 2G ) . Moreover , restoring the expression of miR-UL148D via transfection with the miR-UL148D agomir diminished the expression of IE1 protein and viral GFP in the Kasumi-3 cells infected with NR-1ΔmiR-UL148D ( Fig 2G ) . Importantly , these cells could produce infectious virions that were capable of establishing a lytic infection in HFFs in a co-culture experiment ( Fig 2G ) , suggesting that viruses lacking miR-UL148D favor a lytic rather than a latent infection . Taken together , these data suggest that miR-UL148D plays important roles in silencing the expression of HCMV IE1 and establishing viral latency . As CDK1/2 could possibly serve as a suppressor of IE1 transcription [14] , we first explored whether miR-UL148D can target a CDK1/2-associated protein in host cells to characterize the role of miR-UL148D in silencing IE1 . To accomplish this , the computer-aided algorithms TargetScan and RNAhybrid were utilized to identify possible target genes . We identified cellular immediate early response gene 5 ( IER5 ) , a slow-kinetic member of the IER gene family , as a potential target gene of miR-UL148D . As shown in Fig 3A , we identified two potential target sites for miR-UL148D located in the proximal region of the IER5 3’UTR ( base-pairing between the seed sequence , the first 2–8 bases of miR-UL148D , and the 3’UTR of IER5 ) . Binding site 1 is a non-canonical miRNA-binding site because a loop forms in the binding region . The free energy values of the two binding sites , -30 . 1 and -24 . 6 kcal/mol , were well within the range of miRNA-target pairs . Next , we tested whether IER5 serves as a miR-UL148D target . First , we determined the correlation between the expression levels of miR-UL148D , IER5 and CDC25B , a critical CDK-1-regulating phosphatase suppressed by IER5 [54–56] , in Kasumi-3 cells . In this experiment , we transfected Kasumi-3 cells with miR-UL148D mimics . Kasumi-3 cells submitted to mock transfection or transfected with scrambled oligonucleotides served as controls . As shown in Fig 3B , the IER5 protein levels in the Kasumi-3 cells transfected with the miR-UL148D mimics were significantly decreased compared with those in the mock-transfected cells or the cells transfected with scrambled oligonucleotides . Concordantly , miR-UL148D transfection resulted in significant upregulation of CDC25B protein expression ( Fig 3B ) . To determine whether miR-UL148D directly targets the 3’ UTR of IER5 , we inserted the entire 3’UTR of IER5 , containing two predicted miR-UL148D-binding sites , into a luciferase reporter plasmid ( Fig 3C , upper panel ) . The recombinant reporter plasmid and a β-gal control plasmid were then co-transfected into 293T cells . Either the miR-UL148D mimics or a scramble oligonucleotide were simultaneously introduced into the cells . As shown , transfection of the miR-UL148D mimics significantly inhibited firefly luciferase reporter activity ( normalized against β-gal activity ) compared to transfection with the scrambled oligonucleotide control ( Fig 3C , lower panel ) . In contrast , co-transfection of 293T cells with the parental luciferase plasmid ( without the IER5 3’UTR ) and the miR-UL148D mimics did not affect luciferase reporter activity , suggesting that the alteration of luciferase activity caused by miR-UL148D was specific for IER5 ( Fig 3C , lower panel ) . To further validate the binding of miR-UL148D to the two predicted sites on the IER5 3’UTR , we constructed three luciferase reporters in which individual or both miR-UL148D-binding sites were mutated ( Fig 3C , upper panel ) . Luciferase assay results showed that the mutation of individual target sites partially rescued the inhibition caused by miR-UL148D for the WT luciferase construct ( 10% rescue for site 1 and 25% rescue for site 2 ) ( Fig 3C , lower panel ) , while the double mutation resulted in a strong rescue ( 45% rescue compared to the WT construct ) ( Fig 3C , lower panel ) , suggesting that the down-regulation of IER caused by miR-UL148D is mediated by the two target sites acting cooperatively . Taken together , these results demonstrate that miR-UL148D directly targets 2 binding site in the 3’UTR of IER5 . Given that IER5 is able to transcriptionally suppress CDC25B [14] , we next tested whether miR-UL148D could upregulate CDC25B through direct targeting of IER5 in Kasumi-3 cells . In this experiment , IER5 cDNA ( without the 5’UTR ) was synthesized chemically and inserted into a pcDNA3 . 1 plasmid vector ( Fig 3D , upper panel ) . To test the effect of miR-UL148D , a functionally intact IER5-expressing plasmid vector with both miR-UL148D-binding sites mutated ( IER5 Mut ) was also constructed . Either wild-type IER5 ( IER5 WT ) or the IER5 Mut plasmid was then transfected into Kasumi-3 cells together with a β-gal plasmid . Either the miR-UL148D mimics or the scrambled oligonucleotide control were simultaneously introduced into the cells . The cells were harvested at 72 h post-transfection for Western blot analysis . As shown , transfection with either the IER5 WT or IER5 Mut plasmid alone increased IER5 levels , resulting in a reduction of CDC25B expression ( Fig 3D , lower panel ) . However , in cells transfected with IER5 WT , transfection with miR-UL148D largely abolished the increase in IER5 level and restored CDC25B expression to the basal level ( Fig 3D , lower panel ) . In contrast , miR-UL148D did not rescue the CDC25B protein expression in cells transfected with the IER5 Mut plasmid ( Fig 3D , lower panel ) . Taken together , these results show that miR-UL148D can upregulate CDC25B by directly targeting the IER5 3’UTR . As an immediate early response gene , IER5 is constitutively expressed in progenitor cells [57–59] and responds rapidly to external stimulation . Because HCMV infection can lead to the upregulation and activation of p53 [60 , 61] and because IER5 is transcribed in a p53-dependent manner [62 , 63] , HCMV infection may also correspondingly increase the expression of IER5 . Moreover , as IER5 can transcriptionally repress CDC25B [56] , a key protein involved in activating CDK1 via dephosphorylation of CDK1 at Thr14 and Tyr15 [54–56] , we hypothesized that accumulated miR-UL148D modulates IE1 expression by regulating the IER5-CDC25B-CDK1 axis ( Fig 3E ) . According to this model , HCMV infection stimulates IER5 expression , which in turn inhibits CDC25B expression and CDC25B-dependent CDK1 activation , contributing to IE1 transcription and lytic viral replication . Through suppressing IER5 expression , miR-UL148D can serve as a molecular “brake” for this lytic signaling pathway . We next tested whether miR-UL148D affected the expression of IER5 and CDC25B in Kasumi-3 cells and CD34+ HPCs authentically infected with HCMV . In this experiment , we infected Kasumi-3 cells and CD34+ HPCs with NR-1 or NR-1ΔmiR-UL148D at a multiplicity of 5 PFU/cell . The infected cells were harvested at the indicted time points along a 10-day time course . As shown ( Fig 4A ) , possibly in response to HCMV early infection , the levels of IER5 in the Kasumi-3 cells and CD34+ HPCs infected with NR-1 or NR-1ΔmiR-UL148D were increased during the early days ( 1–4 dpi ) of infection . Concordant with this observation , CDC25B levels decreased in Kasumi-3 cells during the early days of infection . However , compared to 4 days post infection , IER5 levels in Kasumi-3 cells and CD34+ HPCs infected with NR-1 significantly decreased at later stages of infection ( 7–10 dpi ) , while levels of CDC25B expression were restored ( Fig 4A ) . In contrast , possibly due to the lack of miR-UL148D , both the Kasumi-3 cells and the CD34+ HPCs that were infected with NR-1ΔmiR-UL148D failed to suppress IER5 expression at later stages of infection , leading to sustained inhibition of CDC25B ( Fig 4A ) . To confirm these results , we also utilized the miR-UL148D agomir to upregulate miR-UL148D levels in Kasumi-3 cells infected with NR-1 or NR-1ΔmiR-UL148D in the same manner as described above . As shown in Fig 4B , in the Kasumi-3 cells infected with NR-1ΔmiR-UL148D , incubation with the miR-UL148D agomir strongly suppressed IER5 expression but restored CDC25B levels at late stages of infection . In the Kasumi-3 cells infected with NR-1 , incubation with the miR-UL148D agomir also dampened the robust increase in IER5 expression observed during the early days ( 1–4 dpi ) of infection ( Fig 4B ) . These results collectively suggest that miR-UL148D plays a role in maintaining CDC25B by suppressing HCMV-induced IER5 expression . Given that accumulated miR-UL148D could possibly maintain CDC25B expression during experimental HCMV latency , we next tested whether IER5 and CDC25B affected IE1 expression and thus viral latency . In this experiment , we first stably overexpressed IER5 in Kasumi-3 cells using lentivirus ( LV-IER5 ) . The lentivirus infection efficiency was approximately 90% at an MOI of 5 ( S4 Fig ) . Forty-eight hours after the lentivirus infection , the Kasumi-3 cells were infected with NR-1 . The transcript levels of IE1 , UL54 and UL99 were then determined at the indicated time points along a 10-day time course . As shown , infection with LV-IER5 resulted in continuous high-level expression of IER5 in the NR-1-infected Kasumi-3 cells , even at late stages of infection , whereas CDC25B expression was significantly decreased along the 10-day time course ( Fig 5A ) . Importantly , we observed that IER5 overexpression significantly increased the transcript levels of IE1 , UL54 and UL99 along the 10-day time course , indicating that IER5 overexpression or inhibition of CDC25B possibly induces viral lytic gene transcription ( Fig 5B ) . To confirm the role of CDC25B in silencing IE1 transcription , we also overexpressed CDC25B in Kasumi-3 cells using lentivirus vector ( LV-CDC25B ) ( Fig 5C ) and measured the transcript levels of IE1 , UL54 and UL99 in Kasumi-3 cells infected with NR-1 or NR-1ΔmiR-UL148D at four time points along a 10-day time course . In the Kasumi-3 cells infected with NR-1 , the transcripts level of IE1 , UL54 and UL99 remained low even at late stages of infection , regardless of the overexpression of CDC25B , although overexpression of CDC25B reduced the increase in IE1 transcription at early stages of infection ( Fig 5D ) . However , in the Kasumi-3 cells infected with NR-1ΔmiR-UL148D , overexpression of CDC25B significantly decreased the transcription of IE1 , UL54 and UL99 along the 10-day time course ( Fig 5D ) , suggesting that CDC25B functions as a suppressor of viral IE1 transcription . We next determined whether direct inhibition of CDC25B impaired the capacity of host cells to silence viral IE1 gene expression . In this experiment , NSC663284 , a specific CDC25B inhibitor , was added to the culture medium of NR-1-infected Kasumi-3 cells to a final concentration of 5 μM , and the cells were then incubated for an additional 48 hours . As shown , treating the infected Kasumi-3 cells with the CDC25B inhibitor increased the percentage of cells in G2/M phase from 15% to 35% after 48 hours of incubation ( Fig 5E ) . In the NR-1-infected Kasumi-3 cells , inhibition of CDC25B with NSC663284 resulted in a rapid increase of the transcription of the viral IE1 , UL54 and UL99 genes , indicating that direct inhibition of CDC25B strongly promoted the transcription of viral lytic genes . The above results collectively suggest that CDC25B expression plays a critical role in silencing viral IE1 and lytic gene transcription . As previous studies have shown that CDC25B can activate CDK-1 by dephosphorylating CDK1 at Thr14 and Tyr15 , which in turn inhibits HCMV IE1 transcription and promotes viral latency [13 , 14 , 55] , we postulated that hcmv-miR-UL148-mediated upregulation of CDC25B suppresses HCMV IE1 expression by activating CDK-1 . In this experiment , we replaced the Thr14 and Tyr15 residues in CDK1 with Ala14 and Phe15 , respectively , to generate a constitutively activated CDK1 mutant ( Fig 6A , upper panel ) . We then stably overexpressed wild-type CDK1 ( CDK1 WT ) and mutated CDK1 ( T14A , Y15F ) ( CDK1 Mut ) in Kasumi-3 cells using lentivirus vectors . The Kasumi-3 cells that were overexpressing CDK1 WT or CDK1 Mut were then infected with NR-1 or NR-1ΔmiR-UL148D . As shown in Fig 6A , overexpression of CDK1 Mut , which was constitutively activated , significantly inhibited IE1 expression in the Kasumi-3 cells infected with NR-1 or NR-1ΔmiR-UL148D , confirming that activated CDK1 can suppress IE1 expression . In contrast , overexpression of CDK1 WT did not suppress IE1 expression in the NR-1ΔmiR-UL148D-infected Kasumi-3 cells ( Fig 6A ) . The failure of CDK1 WT to inhibit IE1 expression at a late stage of HCMV infection likely resulted from the lack of CDC25B activity , correspondingly reducing the activation of CDK1 WT . This result is in agreement with our previous findings that NR-1ΔmiR-UL148D infection results in strong inhibition of CDC25B . We then tested whether direct inhibition of CDK1 activity in infected Kasumi-3 cells could prevent the establishment of viral latency . In this experiment , we added Roscovitine , a CDK1 inhibitor , to the culture medium of NR-1-infected Kasumi-3 cells to a final concentration of 2 μM . After 48 hours in culture , the cells were harvested , and the transcription of IE1 , UL54 and UL99 was assayed at the indicated time points along the infection course ( Fig 6B , upper panel ) . As shown in Fig 6B ( lower panel ) , the transcription of the viral IE1 , UL54 and UL99 genes increased significantly in the Roscovitine-treated cells compared to that in DMSO-treated cells . These results indicate that the activity of CDK1 in host cells plays an important role in silencing viral IE1 transcription . We also tested whether miR-UL148D could activate CDK-1 by modulating CDC25B expression . To accomplish this , we first examined the levels of IER5 , CDC25B and p-CDK1 ( Tyr15 ) in Kasumi-3 cells infected with NR-1 or NR-1ΔmiR-UL148D at 10 dpi . As shown in Fig 6C , p-CDK1 ( Tyr15 ) level was significantly increased and CDC25B level was significantly decreased in the Kasumi-3 cells infected with NR-1ΔmiR-UL148D compared to the mock-infected or NR-1-infected Kasumi-3 cells , suggesting that knockout of miR-UL148D indeed results in inhibition of both CDC25B and CDK1 . In contrast , when the cellular miR-UL148D level in NR-1ΔmiR-UL148D-infected Kasumi-3 cells was increased by transfecting the cells with the miR-UL148D agomir , the CDC25B level increased , while the p-CDK ( Tyr15 ) level decreased ( Fig 6C ) , supporting that hcmv-miR-UL-148D activates CDK1 through upregulating CDC25B .
By screening HCMV-encoded miRNAs at various stages of experimental latent HCMV infection in CD34+ HPCs and Kasumi-3 cells , the present study demonstrated that miR-UL148D robustly accumulates during the establishment of experimental latency . Functional assays further showed that miR-UL148D possibly plays an important role in facilitating HCMV latency by modulating the IER5-CDC25B-CDK1 signaling pathway in progenitor cells . When HCMV enters into latency , the viral IE genes are silenced and the replication cycle of the virus stops . However , recent studies have suggested that HCMV is not totally inactivated during latency; instead , various proteins , including UL138 [64] , vIL-10 [65] , US28 [66] , and ORF94 [67] , as well as a transcript that is antisense to the UL81-UL82 locus called LUNA [68] , can still be detected in latently infected cells and may contribute to viral latency . For example , Humby et al . reported that US28 is important for latent infection of hematopoietic progenitor cells [49] , and Mason et al . found that UL138 and LUNA can elicit immune-suppressive IL-10-producing CD4+ T cells to sustain latent carriage[69] . In agreement with these findings , we reported that certain HCMV miRNAs , especially miR-UL148D , robustly accumulates during the late stages of experimental latent HCMV infection . Knockout of miR-UL148D impaired the silencing of IE1 transcription in the experimental HCMV latency model . However , miR-UL148D agomir treatment effectively suppressed IE1 transcription . It is noteworthy that our mutagenic deletion of miR-UL148D also disrupted the UL150 ORF . The specific physiological function of UL150 remains unknown , although the absence or mutation of UL150 has been observed in laboratory HCMV strains [70] . It seems that the UL150 protein is not directly involved in the inhibition of IE1 because our miR-UL148D agomir treatment restored only the level of miR-UL148D and not that of UL150 in infected cells . Taken together , these results suggest that miR-UL148D possibly contributed to HCMV latency by inhibiting IE1 transcription during latent infection . The target genes for HCMV miRNAs have not been extensively studied to date . However , it has been reported that miR-UL148D targets the chemokine RANTES and IEX-1 during HCMV infection [71 , 72] . According to studies by Kim et al . [71] and Wang et al . [72] , miR-UL148D suppresses the expression of RANTES or the pro-apoptotic IEX-1 in host cells as part of a viral immune evasion or anti-apoptotic strategy . However , RANTES and IEX-1 mainly function during the early stages of HCMV infection , and thus their inhibition may be irrelevant to the establishment of HCMV latency . Additionally , our data showed that miR-UL148D accumulated during late but not early stages of HCMV infection . Therefore , although RANTES and IEX-1 can be targeted by miR-UL148D , neither may play a major role in modulating HCMV latency . In the present study , using in silico analysis and luciferase assays , we identified that miR-UL148D directly targets IER5 by binding to two sites within the IER5 3’UTR . Mutagenesis experiments demonstrated that both sites are required for full miR-UL148D targeting and have a cooperative effect . To assess the functional effect of miR-UL148D on IER5 downregulation , we further tested whether CDC25B expression is affected by miR-UL148D transfection ( Fig 3 ) . In the present study , we focused on the IER5-CDC25B pathway because the inhibitory effect of CDC25B on IE1 transcription through the potential modulation of CDK activity has been previously reported in host cells infected with HCMV [13 , 14] . We found that miR-UL148D transfection alone lead to downregulation of IER5 expression and upregulation of CDC25B expression . Moreover , although transfection with IER5 WT plasmid alone suppressed CDC25B expression , co-transfection of IER5 WT and miR-UL148D rescued CDC25B expression in Kasumi-3 cells . In contrast , miR-UL148D could not rescue CDC25B expression when IER5 was overexpressed using an IER5 Mut plasmid in which the two miR-UL148D-binding sites in the 3’UTR of IER5 mRNA were mutated . These data demonstrate that hcmv-miR-UL148 targets IER5 to modulate the expression of CDC25B . The identification of IER5 as a target of miR-UL148D and its role in the regulation of CDC25B are also supported by the inverse correlation between IER5 and CDC25B expression that was observed during HCMV infection in host cells . As a slow-kinetic member of the cellular immediate early response gene family and a p53 target gene , IER5 expression is significantly induced by the stimulation of DNA damage , such as through irradiation . During the early stages of NR-1 and NR-1ΔmiR-UL148D infection , IER5 expression in host cells rapidly increased , which was correlated with a decrease in CDC25B expression . This result indicates that IER5 also responds to viral DNA as a stimulus . However , during the late stages of NR-1 infection , IER5 expression was significantly downregulated and CDC25B expression was largely restored , possibly due to the high accumulation of miR-UL148D in host cells . On the contrary , during the late stages of NR-1ΔmiR-UL148D infection , IER5 expression was maintained at a high level in infected cells , while CDC25B expression was virtually undetectable . These results argue that accumulated miR-UL148D can dampen the response of IER5 to viral DNA and rescue CDC25B expression during the establishment of experimental HCMV latency . Maintaining CDK1/2 activity has been reported as essential for successfully establishing latent HCMV infection [13 , 14] . In agreement with this , our results suggest that CDC25B contributes to the establishment of HCMV latency through the activation of CDK1 , which in turn inhibits the expression of the viral IE gene . As shown ( Fig 5A ) , overexpression of IER5 in Kasumi-3 cells , which suppressed CDC25B expression , effectively prevented NR-1 virus from achieving latency . On the contrary , overexpression of CDC25B in Kasumi-3 calls restored the ability of NR-1ΔmiR-UL148D to achieve latency ( Fig 5B ) . We directly demonstrated the inhibitory effect of CDK1 on IE1 transcription by overexpressing either a constitutively activated CDK1 mutant or CDK1 WT in host cells infected with NR-1ΔmiR-UL148D . While NR-1ΔmiR-UL148D infection resulted in sustained inhibition of CDC25B , HCMV IE1 expression was suppressed when the constitutively activated CDK1 mutant was overexpressed in host cells . Furthermore , CDK1 WT failed to suppress HCMV IE1 expression during late stages of HCMV infection . These data suggest that the IER5-CDC25B-CDK1 axis has a potential role in the silencing of HCMV IE1 transcription . Moreover , by overexpressing miR-UL148D in host cells infected with NR-1ΔmiR-UL148D and a miR-UL148D agomir , we demonstrated that miR-UL148D could activate CDK1 , possibly by modulating the IER5-CDC25B pathway . Although both CD34+ HPCs and Kasumi-3 cells were utilized to establish latent HCMV infections in the present study and although the results showed a similar accumulation of miR-UL148D in host cells during late-stage HCMV infection , we must acknowledge that our mechanistic study of miR-UL148D’s role in HCMV latency was mainly performed in Kasumi-3 cells . It is noteworthy that the use of Kasumi-3 cells as a viral latency model does not recapitulate all aspects of experimental HCMV latency that have been defined in primary CD34+ HPCs [48] . Therefore , mechanistic studies of HCMV latency in CD34+ HPCs or using an in vivo system are required for better understanding how miR-UL148D functions during latent viral infection . Previously , Meshesha et al . ( 35 ) reported that eight HCMV microRNAs were expressed in latently infected PBMCs by directly screening 20 HCMV miRNAs in human PBMCs . Our in vitro latency model using both primary CD34+ HPCs and Kasumi-3 cells did also suggest several HCMV miRNAs such as miR-UL112-5p , miR-UL36-5p , miR-UL22A-5p etc . , is possibly latency-associated . However , miR-UL148D , suggested as a latency-associated miRNA by our in vitro latency model study , was not among the 20 HCMV miRNAs screened by Meshesha et al . ( 35 ) . Therefore , the expression level and potential function of miR-UL148D during HCMV latency could be further studied using in vivo system in the future . Moreover , while this manuscript was under review , Lau et al also reported that miR-UL148D is expressed during latency and demonstrated that miR-UL148D could limit proinflammatory cytokine secretion of latently infected cells by inhibiting ACVR1B expression[73] , which suggests that miR-UL148D may exert multiple function to facilitate viral latent infection . The future study could further explore the multiple role of miR-UL148D in the viral latency . In conclusion , our study presents the first evidence that HCMV-encoded miR-UL148D robustly accumulates during the establishment of experimental HCMV latency and promotes latent HCMV infection in human progenitor cells by modulating the cellular IER5-CDC25B axis . Identifying the critical role of miR-UL148D during latent viral infection may facilitate the development of novel therapeutic approach for controlling the epidemic infection of HCMV .
Kasumi-3 ( ATCC#CRL-2725 ) cells were maintained in RPMI medium containing 20% fetal bovine serum ( FBS ) and 100 U/ml each of penicillin and streptomycin . Primary human foreskin fibroblasts ( HFF-1; passages 9 to 20 ) ( ATCC #SCRC-1041 ) were cultured in Dulbecco’s modified Eagle’s medium ( DMEM ) containing 10% FBS , 10 mM HEPES , 2 mM L-glutamine , and 100 U/ml each of penicillin and streptomycin . Bone marrow cells were obtained from waste materials used during bone marrow harvest procedures from healthy donors at Jiangsu Province People’s Hospital via a protocol approved by the Institutional Review Board of Nanjing University . All participants provided oral informed consent to study involvement . The participants received written information about the study prior to consent being obtained . As the bone marrow cells were harvested from waste materials used in a standard health examination , written consent was deemed unnecessary by the participants and the Institutional Review Board . Verbal consent was obtained by the interviewers and audited by the Institutional Review Board , and the oral informed consent process was approved by the Institutional Review Board . Primary CD34+ HPCs were isolated via magnetic bead-mediated cell separation ( Miltenyi Biotech , Auburn , CA ) . Cultures of primary CD34+ HPCs were established as described [6 , 51] . Briefly , murine AFT024 cells were plated in 6-well plates coated with 0 . 1% gelatin ( Stem Cell Technologies , Vancouver , CA ) and irradiated . CD34+ cells were cultured in RPMI medium supplemented with 20% FBS , 100 μM 2-mercaptoethanol , and 10 ng/ml each of stem cell factor , Flt-3 ligand , and IL-7 ( R&D Systems , Minneapolis , MN ) and plated in Transwell plates with 0 . 4 μM microporous filters ( Costar , Cambridge , MA ) above the irradiated AFT024 cells . The cells were cultured in an incubator with 5% CO2 at 37°C . Murine AFT024 ( ATCC#SCRC-1007 ) cells were maintained in DMEM supplemented with 10% FBS and 50 μM 2-mercaptoethanol at 32°C . The bacterial artificial chromosome ( BAC ) -derived clinical strain NR-1 was used in this study . For infections , NR-1 expressing enhanced green fluorescent protein ( eGFP ) was used . NR-1ΔmiR-UL148D was generated using a bacterial recombineering method as previously described [11] . In brief , the UL150 region that encoded miR-UL148D was specifically deleted in NR-1 by inserting a Kana cassette . First , Kana cassettes were PCR-amplified using the following primers: forward , 5’-GCCGGTCTCGGAGACCGTGGACGAAAAAGAGAACGCAGCAGCTATCGCTGGC GGAGCGCTCTCGCGTTGCATTTTTGTTC-3’; reverse , 5’-TTCCAGCCCTGCCACGCCCAACG CGGCACTTCCAACAGAGCCACATCCCAGAGGGTATTGGCCCCAATGGGGTCTCGGTG-3’ . The underlined nucleotides correspond to the Kana gene . The sequences that are not underlined represent 54 nucleotides upstream and downstream of pre-miR-UL148D . The amplified DNA fragments were introduced into E . coli EL350 cells containing a wild-type NR-1 bacterial artificial chromosome ( BAC ) for recombination by electroporation ( Bio-Rad , Hercules , CA ) . The miR-UL148D-deleted NR-1 BAC construct containing the Kana cassette was selected on Luria Broth ( LB ) plates containing kanamycin . The NR-1ΔmiR-UL148D BAC vector was then introduced into human foreskin fibroblast cells ( HFF cells ) by electroporation ( Bio-Rad , Hercules , CA ) . Virus particles were harvested from the cells when they showed a complete cytopathic effect , and virus titers were measured with PFU assays of fibroblasts . HCMV was propagated in primary human foreskin fibroblast cells in DMEM supplemented with 10% FBS and 100 U/ml each of penicillin and streptomycin . Virus stocks were stored in DMEM containing 10% FBS and 1 . 5% bovine serum albumin ( BSA ) at -80°C . Kasumi-3 cells and primary CD34+ HPCs were infected with NR-1 or NR-1ΔmiR-UL148D at a multiplicity of 5 PFU/cell . UV-inactivated virus was used for mock infection . One hour before infection , the cell culture medium was replaced with a serum-free medium . The infected cells were incubated for 4 h at 37°C and 5% CO2 and then washed three times with PBS to remove cell-free virus . The infected cells were then harvested at the indicated time points post-infection . The culture medium was changed for all cells every day . For virus reactivation in infected Kasumi-3 cells , 20 nM 12-O-tetradecanoylphorbol acetate ( TPA ) ( Sigma-Aldrich , St Louis , MO ) was added to the latently infected cell culture for 48 hours to induce viral lytic gene reactivation . For virus reactivation in infected primary CD34+ HPCs , the latently infected cells were transferred to reactivation medium [51] containing a modification of Eagle’s medium supplemented with 20% FBS , 1 μM hydrocortisone , 0 . 02 mM folic acid , 0 . 2 mM i-inositol , 0 . 1 mM 2-mercaptoethanol , 2 mM L-glutamine , 100 U/ml each of penicillin and streptomycin , and 15 ng/ml of each of the following cytokines: IL-6 , granulocyte colony-stimulating factor ( G-CSF ) , granulocyte-macrophage colony-stimulating factor ( GM-CSF ) , and IL-3 ( R&D Systems ) . For the co-culture experiments , latently infected or mock-infected Kasumi-3 cells and HPCs were cultured under conditions favoring lytic reactivation ( described above ) or control conditions for 6 days . Cells were then washed with PBS and co-cultured with HFFs for 2 days . Then , the Kasumi-3 cells were removed from the co-cultures , and the HFFs were washed with PBS and cultured for an additional 5 days for fluorescence microscopy analysis of GFP-positive plaques . A miR-UL148D agomir was purchased from RiboBio Co . ( Guangzhou , China ) . The agomir is composed of a synthetic miR-UL148D duplex chemically modified for greater stability and shown to produce gain of function in vivo . The miR-UL148D agomir was added to cell culture medium at a final concentration of 1 mM . Kasumi-3 cells were incubated with the agomir at 37°C for 24 h prior to NR-1ΔmiR-UL148D infection , and the culture medium was changed every day with the addition of fresh miR-UL148D agomir . To generate luciferase reporter plasmids , the full-length IER5 3’UTR with or without mutated binding sites was chemically synthesized and cloned into a pMIR reporter plasmid ( Ambion , Austin , TX ) . Successful insertion was confirmed by sequencing . For luciferase reporter assays , 0 . 2 μg of a firefly luciferase reporter plasmid , 0 . 1 μg of a β-galactosidase expression vector ( Ambion , Austin , TX ) and equal amounts ( 20 pmol ) of HCMV miR-UL148D mimics or scrambled negative control RNA were transfected into 239T cells in 24-well plates using Lipofectamine 3000 ( Life Technology , Carlsbad , CA ) . The β-galactosidase vector was used as a transfection control . At 24 h post-transfection , the transfected cells were analyzed using a luciferase assay kit according to the manufacturer’s instructions ( Promega , Madison , WI ) . To generate IER5 expression plasmids , full-length IER5 cDNA sequences with or without mutated binding sites were chemically synthesized and cloned into a pcDNA3 . 1 ( + ) vector . Successful insertion was confirmed by sequencing . Then , the IER5-expressing plasmid ( 3 μg ) and the β-galactosidase expression vector ( 1 μg ) were co-transfected into Kasumi-3 cells cultured in 6-well plates with 100 pmol of miR-UL148D mimics or scrambled negative control RNA using Lipofectamine 3000 . Cells transfected with the β-galactosidase vector alone served as a transfection control . At 72 h post-transfection , the cells were harvested for western blotting . Lentiviruses encoding IER5 , CDC25B , CDK1 WT , and CDK AF mutant genes were generated and confirmed by the GenePharma Company ( Shanghai , China ) . An empty-backbone lentivirus was used as a control . Cells were incubated with the lentiviruses at a multiplicity of infection ( MOI ) of 5:1 along with 8 μg/ml Polybrene for 48 h before further treatment . The selection marker was GFP . The infected cells were gated for GFP expression during flow cytometry analysis . Cells from three independent experiments were harvested at the indicated time points after transfection or infection . Western blotting was performed using the following primary antibodies: goat polyclonal anti-IER5 antibody , rabbit polyclonal anti-CDC25B antibody , mouse monoclonal anti-GAPDH antibody ( all from Santa Cruz ) , and rabbit monoclonal anti-CDK1 and anti-p-CDK1 ( Tyr15 ) antibodies ( Cell Signaling Technology , Danvers , MA ) . In total , 20 , 000 infected cells were harvested for the isolation of total RNA and DNA at each indicted time point . Fourteen HCMV miRNAs were assayed using a human cytomegalovirus miRNA probe kit according to the manufacturer’s protocol ( ThermoFisher Scientific , Waltham , MA ) . Equal amounts of cDNA were analyzed by qPCR in triplicate using an Applied Biosystems 7500 Real-time PCR machine . The amount of each HCMV miRNA was then calculated with a standard curve . Viral genomic DNA was first quantified by qPCR , and then the absolute viral genomes copy number was calculated with a standard curve . The levels of the HCMV miRNAs were normalized to the viral genome copies to obtain the relative levels of the miRNAs . The infected cells were collected by low-speed centrifugation and washed with PBS three times . Cell-associated viral DNA was isolated as described previously [50] . Viral genome copy number was evaluated by qPCR ( normalized to the cellular gene GAPDH ) and semi-quantitative PCR using the IE1 primer . For the qPCR experiment , all samples were analyzed in triplicate using the SYBR Green Probe and an Applied Biosystems 7500 Real-time PCR machine . All samples were analyzed by PCR for 28 cycles and then submitted to agarose electrophoresis . The absolute HCMV genome copy number was then determined according to a standard curve . Briefly , the IE1 ORF was synthesized and diluted into samples containing a series of different copy numbers . The samples were used as templates for analysis in triplicate with the SYBR Green probe on the Applied Biosystems 7500 Real-time PCR machine to generate a concentration standard curve . Then , the Ct value for cell-associated viral DNA was determined through equal loading of total infected cellular DNA ( 0 . 5 μg ) . Through the standard curve , the copy numbers of cell-associated viral DNA were determined . Intracellular viral RNA was assayed as described previously [50] . In brief , RNA was isolated using Trizol Reagent ( Life Technology , Carlsbad , CA ) according to the manufacturer’s instructions . The RNA samples were treated with DNase using a DNA-free kit ( Ambion , Austin , TX ) according to the manufacturer’s instructions . The concentration of RNA was then determined , and 0 . 5 μg was used for a reverse transcriptase ( RT ) reaction . IE1 cDNA was synthesized using a Reverse Transcription kit with random hexamers according to the manufacturer’s protocol ( Applied Biosystems ) . Equal amounts of cDNA were analyzed by quantitative PCR ( qPCR ) in triplicate using an Applied Biosystems 7500 Real-time PCR machine . RNA was normalized to cellular GAPDH . cDNA quantity was also analyzed by semi-qPCR . For the semi-qPCR , all samples were analyzed using a 28-cycle reaction and subsequent agarose electrophoresis . The primer sets used to amplify DNA and cDNA of IE1 , UL138 , UL54 , UL99 are as follows: IE1 , forward 5’-GCCTTCCCTAAGACCACCAAT-3’ and reverse 5’-ATTTTCTGGGCATAAGCCATAATC-3’; UL138 , forward 5’-TGCGCATGTTTCTGAGCTAC-3’ and reverse 5’-ACGGGTTTCAACAGATC GAC-3’; UL54 , forward 5’-CCCTCGGCTTCTCACAACAAT-3’ , and reverse 5’-CGAGGTAGT CTTGGCCATGCAT-3’; UL99 , forward 5’-GTGTCCCATTCCCGACTCG-3’ and reverse 5’-TTC ACAACGTCCACCCACC-3’ . The primers sets used to amplify DNA and cDNA of GAPDH are as follows: forward 5’-TCAGAAAAAGGGCCCTGACAACT-3’ , reverse 5’-TCCCCTCTTCAAG GGGTCTACA-3’; forward 5’-AGGCTAGGGACGGCCT-3’ , reverse 5’-GCCATGGGTGGAATCAT ATTG-3’ . Infected cells were harvested after the indicated treatments by centrifugation at 300×g for 5 minutes at 4°C and processed for immunofluorescence microscopy . Briefly , the harvested cells were washed using cold phosphate-buffered saline ( PBS ) and fixed with 4% paraformaldehyde for 10 minutes at room temperature . Then , the cells were permeabilized with PBS containing 0 . 2% ( v/v ) Triton X-100 for 5 minutes at room temperature . After being blocked with bovine serum albumin ( BSA ) for 1 hour at room temperature , the cells were incubated with IE1 antibody ( clone 1B12 ) [74] ( 1:100 ) in PBS containing 10 mg/ml BSA overnight at 4°C . The cells were then washed with PBS and incubated with Alexa Flour 594 ( goat anti-rabbit IgG , 1:2000 ) for 1 h at room temperature . The cells were then washed using PBS , mounted and analyzed for GFP ( green ) , IE1 ( red ) , and DAPI ( blue ) using an inverted confocal laser microscope ( Nikon ) . Anti-HCMV IgG and IgM antibodies in plasma were detected by ELISA using an HCMV IgG/IgM kit ( DIA PRO Diagnostic Bioprobes , Milano , Italy ) according to the manufacturer’s instructions . For the IgG-ELISA , an ELISA value of <0 . 5 IU/ml was considered negative and a value of >0 . 5 IU/ml was considered positive , indicating prior exposure to HCMV . For the IgM-ELISA , the test results were calculated using the optical density ( OD ) value at 450 nm , and the cut-off value for positivity was OD > 1 . 2 , indicating acute infection with HCMV . Data derived from at least three independent experiments were presented as the mean ± SEM . Normal distributed variables were compared using Student’s t-test . The reported P values are 2-sided . P < 0 . 05 was considered statistically significant .
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Human cytomegalovirus ( HCMV ) is a herpesvirus that is prevalent around the world . Following primary infection , HCMV can persist for the lifetime of a host by establishing a latent infection . While HCMV infection normally causes no clinical symptoms , reactivation of HCMV from latency can cause deadly disease in immunocompromised individuals . HCMV achieves latent infection in hematopoietic progenitor cells by silencing HCMV immediate early ( IE ) genes , the activation of which serves as the initial step in HCMV replication . HCMV has developed multiple strategies to control the expression of IE genes for latency and reactivation . In the present study , we reported that microRNAs ( miRNAs ) , a class of ~22-nt non-coding nucleotides that post-transcriptionally regulate gene expression , are involved in modulating HCMV latency and reactivation . In particular , we found that HCMV miR-UL148D accumulated in progenitor cells during the establishment of experimental HCMV latency . Furthermore , we identified cellular immediate early response gene 5 ( IER5 ) , a p53 target gene , as a novel target of miR-UL148D . Functionally , miR-UL148D efficiently inhibited the up-regulation of IER5 during latent viral infection , maintaining the activity of CDC25B and CDK1 and thus controlling IE1 transcription . In conclusion , our study provides the first evidence that HCMV miR-UL148D facilitates latent viral infection by modulating the IER5-CDC25B axis in host cells .
|
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"Abstract",
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] |
2016
|
Human Cytomegalovirus miR-UL148D Facilitates Latent Viral Infection by Targeting Host Cell Immediate Early Response Gene 5
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As more regions approach malaria elimination , understanding how different interventions interact to reduce transmission becomes critical . The Lake Kariba area of Southern Province , Zambia , is part of a multi-country elimination effort and presents a particular challenge as it is an interconnected region of variable transmission intensities . In 2012–13 , six rounds of mass test-and-treat drug campaigns were carried out in the Lake Kariba region . A spatial dynamical model of malaria transmission in the Lake Kariba area , with transmission and climate modeled at the village scale , was calibrated to the 2012–13 prevalence survey data , with case management rates , insecticide-treated net usage , and drug campaign coverage informed by surveillance . The model captured the spatio-temporal trends of decline and rebound in malaria prevalence in 2012–13 at the village scale . Various interventions implemented between 2016–22 were simulated to compare their effects on reducing regional transmission and achieving and maintaining elimination through 2030 . Simulations predict that elimination requires sustaining high coverage with vector control over several years . When vector control measures are well-implemented , targeted mass drug campaigns in high-burden areas further increase the likelihood of elimination , although drug campaigns cannot compensate for insufficient vector control . If infections are regularly imported from outside the region into highly receptive areas , vector control must be maintained within the region until importations cease . Elimination in the Lake Kariba region is possible , although human movement both within and from outside the region risk damaging the success of elimination programs .
Malaria is a vector-borne parasitic disease affecting millions of people worldwide , with Plasmodium falciparum still causing over 400 , 000 deaths per year [1] . Recent escalation in vector control has greatly reduced global burden and brought many regions close to elimination [2] . In some settings , mass drug campaigns have been an effective tool for depleting the human infectious reservoir and breaking the cycle of transmission , although the effectiveness of such campaigns has been mixed [3] . The ultimate goal is global malaria eradication , but the extreme heterogeneity in transmission levels , vector bionomics , health systems , and population densities limit the applicability of any single strategy [4] . Elimination at a single country level could be challenging to maintain in the presence of high cross-border movements of infected individuals , depending on the country and its regional context [4] . As a result , the concept and implementation of regional malaria eliminations provides a useful staging for progress towards eventual global eradication [5] . Southern Africa is one region where programs are planning operational strategies for elimination [6] . Elimination in southern Africa requires elimination in the Lake Kariba region of Southern Province , Zambia , where areas of high- and low-intensity transmission are interconnected . Understanding how to achieve elimination in the microcosm of the Lake Kariba area would provide a solution to how to achieve elimination in Southern Africa and possibly in a number of other challenging settings . The Zambia National Malaria Control Centre has successfully scaled up recommended malaria control interventions over the past decade and is pursuing alternative methods to further reduce malaria transmission , including community-targeted parasite reservoir reduction strategies [7 , 8] , with a target elimination date of 2020 . Beginning in 2012 , mass drug campaigns have been carried out annually in the Lake Kariba region of Southern Province , Zambia ( Fig 1 ) , where transmission is seasonal and spatially variable . Understanding the small-scale variation in the interconnected Lake Kariba region through mathematical modeling provides important insights into the critical thresholds for successful outcomes . Previous modeling efforts have provided guidance for conducting successful campaigns in generic settings [10–12] , yet limited work has been done on understanding how a specific geography's individual features can also affect campaign outcomes [13 , 14] or how spatial variation in vectorial capacity can sustain transmission in interconnected areas . In this work , the most detailed spatial model of a specific geography to date is constructed and used to predict how various factors such as variation in transmission intensity and human migration patterns interact to influence the success of control and elimination efforts . Local transmission dynamics were reconstructed within a mechanistic model of malaria transmission using the high-resolution surveillance data collected during the mass test-and-treat ( MTAT ) campaigns of 2012–13 in the Lake Kariba region , which included infection status , age , GPS coordinates of households , recent symptoms , recent treatment , and insecticide-treated net ( ITN ) usage . Village-scale biting rates were selected to calibrate local malaria prevalence and incidence to longitudinal surveillance data and seasonal patterns of clinical case counts reported at health facilities respectively . The simulation framework was then used to assess the outcome of a variety of post-2016 intervention scenarios . Simulations predict that high coverage with vector control is a necessary condition of achieving elimination in this region , and mass drug administrations ( MDAs ) are effective at increasing the likelihood of elimination only if excellent vector control is already in place . Importations of infections into the Lake Kariba area from outside the region present a particular challenge to maintaining elimination if the importations occur in highly receptive areas , and vector control should be sustained in those areas to prevent outbreaks and reestablishment of endemic malaria as long as importations continue .
A high-resolution spatial model of twelve health facility catchment areas ( HFCAs ) in the Lake Kariba region was configured based on village-scale clusters of households and ITN usage , MTAT coverage , case management , and human migration rates derived from the surveys conducted in 2012–13 ( Fig 1 and S1–S7 Figs , Methods ) . Malaria transmission was modeled within each cluster , where vector populations were driven by cluster-specific climate data and local abundance of larval habitats . Preliminary entomological data indicated that both Anopheles arabiensis and Anopheles funestus are present in the study area ( personal communication with Javan Chanda ) , with arabiensis biting rates highest between January and April during the warm rainy season while funestus peaks in September at the beginning of the hot dry season ( Fig 2A ) . Relative abundances of arabiensis and funestus govern the seasonality of malaria transmission , while absolute abundances determine the intensity . For each village-scale cluster , combinations of larval habitat availabilities for arabiensis and funestus were simulated with appropriate patterns of ITN usage , case management rates , and MTAT coverage . The resulting simulated prevalence and clinical case counts were compared with surveillance data to select the combination of habitats yielding the best fit to field observations ( Fig 3 and S8 Fig , Methods ) . Calibration to surveillance data resulted in the expected gradient of higher habitat availability of both vector species closer to Lake Kariba and lower availability in the higher-altitude HFCAs more distant from the lake ( Fig 2B ) . Chiyabi HFCA was predicted to have the largest amount of funestus in this region , suggesting that ITNs may be particularly effective there as funestus is indoor-feeding and highly anthropophilic [15] . The calibrated simulations captured both fine-scale cluster-level variation in malaria prevalence and large-scale spatio-temporal trends of temporary reduction in prevalence observed in the study area following MTAT rounds ( Fig 4 and S9 Fig ) . Surveillance in 2012–13 reported stalled reduction in prevalence by late 2012 and rapid rebound after the following rainy season . This observation was replicated in the spatial model , where significant re-infection between rounds two and three in lakeside clusters drives rebound throughout the study area . The model was able to predict cluster-level prevalence in December 2014 with reasonable accuracy ( S10 Fig , r2 = 0 . 46 ) considering that local variation in 2014 vector control implementation , location-specific migration patterns , and location-specific changes in health-seeking behavior as a result of the community health worker and case investigation programs that began in mid-2014 were not part of our simplified forward-simulation scenarios . The observed seasonality of weekly clinical cases confirmed by rapid diagnostic test ( RDT ) was well-captured by simulations , including a characteristic pattern of high case counts between December and June and a small rise in cases in Chipepo and Sinamalima HFCAs as temperatures rise in October ( Fig 5 ) . Seven HFCAs had strong Spearman’s rank correlation between simulation and observed clinical cases ( rank correlation values between 0 . 62 and 0 . 83 ) , Luumbo and Sinamalima HFCAs had moderate correlation ( values 0 . 45 and 0 . 51 ) , Chiyabi HFCA had weak correlation ( 0 . 23 ) , Gwembe HFCA had very weak correlation ( -0 . 03 ) , and Nyanga Chaamwe HFCA had a weak negative correlation ( -0 . 27 ) likely due to inadequate data . With the exception of Gwembe HFCA , model fits to clinical cases were better for HFCAs with consistent reporting . Individual clusters also occasionally saw discrepancies between observed seasonality of clinical cases at the local health facility and simulated seasonality at the cluster ( Fig 3C ) . On the modeling side , these differences could be due to limited knowledge of how local climate drives mosquito abundances , including the rates at which temporary larval habitats appear and disappear , or how infection , clinical symptoms , and health-seeking are related in this region . Many factors drive uncertainty in both magnitude and seasonality of observed case counts . While simulations estimate a constant proportion of clinical cases seek care at health facilities , several factors could contribute to discrepancies between true and observed clinical incidence in the field . Gwembe HFCA , which otherwise has very low transmission , contains the district hospital . Clinical cases reported from Gwembe HFCA may have traveled from elsewhere within or outside the study area expressly to seek care . Case management rates in simulation were estimated from survey responses during the dry season , while individuals with fever during the wet season may show different health-seeking behavior based on road conditions and personal assessment of whether the fever is malarial . A strong distance-dependence on health-seeking means households closer to the health facility contribute disproportionately more to recorded case counts than to true clinical incidence , yet this data shows significant variation ( S4B Fig ) . Clinics report any RDT-positive individual presenting with fever as a clinical case of malaria . However , a substantial portion of individuals in malaria-endemic areas with non-malarial fevers will be RDT-positive due to low-density infection or recent clearance of malaria [16 , 17] . Finally , the population denominator in this area is unknown , and preliminary analysis from cross-referencing individuals across all surveillance rounds suggests that the true population may be nearly twice as high as the simulation population , which was chosen based on estimates from operations teams working in the region . Incomplete coverage and imperfect diagnostics in MTAT campaigns result in a significant portion of the parasite reservoir being left untreated [18–21] . This untreated reservoir will resume the cycle of transmission during the next rainy season . However , a mass-distribution mode that can treat undetected infections and a drug formulation such as dihydroartemisinin-piperaquine ( DP ) , which has longer prophylactic protection against re-infection than artemether-lumefantrine ( AL ) , the drug used in the MTATs , may be more successful at interrupting transmission [11] . Operations teams in the Lake Kariba region continued mass treatment following the 2012–13 MTAT by administering MDA and focal MDA to a randomized group of HFCAs . Simulations approximated operational activities in 2014–15 by administering a mass distribution of ITNs in June 2014 and MDA with DP to all HFCAs in December 2014 and July 2015 , in line with operational schedules . To compare how case management , ITN usage , and MDA coverage contribute to reducing malaria burden separately and together , a variety of post-2015 intervention scenarios were simulated ( Figs 6–10 ) . For each simulation , the fraction of total study area population living in clusters where no local transmission has occurred over a month-long period was measured for each month between January 2012 and January 2030 . Elimination was counted to have been achieved if no infection events occurred anywhere in the simulated study area over a continuous three-year period . As expected , clusters in high-burden HFCAs were more likely to retain transmission in all non-eliminating scenarios ( Fig 6 ) . Increasing passive case management rates without also distributing more ITNs or implementing any MDA campaigns resulted in nearly all low-burden clusters maintaining very low prevalence through January 2030 and likely interrupting local transmission in many low-burden HFCAs ( scenario 2 ) . Elimination within the Lake Kariba region thus becomes a question of which interventions are effective at reducing transmission in the high-burden clusters . Discontinuing MDAs after the 2015 rounds while maintaining current levels of ITN usage did not result in long-term reduction in regional malaria transmission ( Fig 7A , scenario 1 ) . Increasing passive case management rates ( scenario 2 ) established a new baseline of lower transmission during the decades following the end of MDAs in 2015 but could not achieve region-wide elimination . Combining the 2014–15 MDAs with ramp-ups in case management and increased ITN usage resulted in elimination under highly aggressive ITN distribution campaigns but not if ITN ramp-ups followed historical rates of increase ( Fig 7B and S11 Fig ) . Under the ramp-up scenario ( scenario 3 ) , transmission rebounded after the last ITN distribution in 2022 to a new baseline largely determined by case management rate . In the aggressive scenario ( scenario 4 ) , 87% of simulations resulted in elimination . The aggressive ITN distribution schedule consists of mass distributions with 80% coverage every two years between 2016 and 2022 . As a result , 75–90% of the entire study area population is sleeping under an effective net every night over a 9-year period ( S11F Fig ) . Under the ramp-up schedule , ITN usage with effective nets peaks at only 50–80% . This difference in maximum coverage achieved is enough to prevent elimination from occurring in the simulated scenarios . Can MDA compensate for insufficient vector control ? Extending annual dry-season MDAs through 2020 increased the probability of elimination under the aggressive ITN distribution schedule from 0 . 87 to 0 . 99 ( Fig 8A , scenario 7 ) , but if ITN usage was maintained at current levels or ramped up gradually , elimination was still never observed ( scenarios 5 and 6 ) . Restricting post-2015 MDAs to high-burden HFCAs resulted in outcomes comparable to cases where MDAs were distributed to all HFCAs , with no elimination observed under the ramped ITN distribution and probability of elimination falling slightly from 0 . 99 to 0 . 94 under the aggressive ITN distribution ( Fig 8B , scenarios 8 and 9 ) , although the boosting effect of MDA on top of the aggressive ITN distribution campaigns was not as large as when MDA was implemented in all HFCAs: adding MDA to high-burden areas only increased probability of elimination from 0 . 87 to 0 . 94 while MDA in all areas increased probability of elimination from 0 . 87 to 0 . 99 . In the small fraction of cases where the combination of aggressive ITN distributions and MDAs did not result in elimination , residual transmission remaining in the densely populated Sinamalima and Chiyabi HFCAs was able to reestablish transmission more broadly in the study area ( S12 Fig ) . In all scenarios , MDA coverage is simulated at levels observed during the 2012–13 MTAT campaigns . It is possible that MDA coverage could increase in later rounds as campaign logistics are improved . Scenario 10 in Fig 9 shows results of higher-coverage MDAs given on top of the ramped ITN distributions , similar to scenario 6 but with MDA coverage increased to 70% . Elimination is never observed in these simulations . In contrast , even two years of MDA at 70% coverage in high-burden clusters ( Fig 9 , scenario 11 ) boosts probability of elimination from 0 . 87 to 0 . 99 if high coverage vector control is already present . Human movement within the Lake Kariba region connects areas of high and low transmission . In scenario 12 ( Fig 10 ) , migration rates were increased ten-fold from the base level used in all other scenario projections . Under these conditions , intervention packages that previously resulted in elimination ( scenario 7 ) now have much smaller effects on transmission within the region: elimination is never observed . While the migration rate in scenario 12 is likely to be much higher than the true migration rate , this scenario demonstrates that outcome of an elimination program is highly dependent on the nature and extent of regional human movement . While efforts are beginning to be made to characterize relevant human migration patterns [22–24] , understanding local geographies and conditions will ultimately be crucial for predicting whether an elimination program will succeed . None of scenarios 1–12 include importation from outside the modeled region . While the Lake Kariba districts as a whole represent an isolated area of higher prevalence surrounded by lower-transmission areas [2] , it is possible that travelers from further afield may bring infections into the area . In scenario 13 , a total of 100 infections are imported twice a year into three highly connected , high-population clusters in Gwembe , Munyumbwe , and Sinamalima HFCAs . This importation rate is likely to be higher than the true importation rate , especially during later years of simulation if prevalence follows recent trends and continues to decline throughout southern Africa , and thus represents an upper bound on the effect of imported infections . Including these importations in a simulation with the same intervention mix as the highly successful scenario 7 resulted in no simulations achieving elimination . Maintaining routine vector control operations such as annual spraying with a long-lasting insecticide is sufficient to prevent widespread resurgence , although elimination remains unlikely as long as importations continue ( S13 Fig ) . Elimination programs for a given area should not be designed in isolation but rather in the context of the broader region , as importation pressure from outside the elimination area can potentially disrupt desired outcomes inside the area .
The complex spatial dynamics of malaria transmission in the Lake Kariba region of Southern Zambia were captured in a mathematical model , which was then extended to compare the effects of future interventions . The simulation framework was informed by household surveillance in 2012–13 , clinical case counts reported by local clinics , and preliminary entomological data . This work is the first to examine how village-scale variation in transmission intensity can drive transmission throughout an interconnected region . While the 2012–13 MTAT surveys were a rich source of data for model-building , they presented an incomplete picture of local malaria transmission because measurements were taken during the dry season . The seasonality of transmission was thus difficult to characterize based solely on these measurements . Weekly clinical case counts reported from health facilities were broadly informative of transmission during the remaining months of the year , but ascertaining the proportion of RDT-positive fevers that are true malarial fevers is difficult . The seasonality of cases observed at the health facility may not reflect seasonality of transmission in every village within the HFCA as each HFCA may encompass a range of transmission intensities . Nonetheless , simulations were able to recapture both region- and village-scale spatio-temporal features of transmission observed during the 2012–13 operations . Local entomology drives the seasonality of transmission . Attempting to model the study area with only arabiensis vectors requires non-seasonal year-round transmission in many lakeside areas in order to capture substantial prevalence during the November surveys , which would result in a mismatch with observed seasonality of clinical case counts . Incorporating funestus allowed the model to capture both dry season prevalence and seasonal patterns of clinical cases . More entomological data on vector species abundance and behavior will guide continued model refinements . All simulations assumed full compliance with drug regimens . While perfect adherence to treatment is unlikely [25 , 26] , elimination is possible even when compliance is extremely poor ( S14 Fig ) , and previous modeling work has shown that compliance is not crucial in mass campaigns in settings without drug resistance [11] . Most individuals who harbor dry season infections have low parasite densities [16 , 18] , and a single dose of antimalarial drug is often sufficient to clear those infections . Excellent vector control is the single most important intervention for achieving elimination . Without sustained high coverage with ITNs , elimination is never observed in any of the scenarios tested , and when ITN usage is maintained at high levels over many years , elimination is observed in 87% of simulation runs . In this study , vector control has been modeled exclusively as ITN usage , but indoor residual spraying ( IRS ) would have similar effects if local vectors are susceptible . Combining IRS and ITNs likely represents a more attainable scenario for aggressive vector control given that ITN usage has an unpredictable behavioral component while IRS remains in place unless walls are washed or re-plastered . The simulations of the Lake Kariba area presented here are dominated by arabiensis vectors , which have been modeled with a 50% indoor biting rate . Regions where indoor biting predominates may require slightly less intense vector control interventions to achieve the same elimination outcomes . Unless vector control is also sustained at high coverage , implementing MDA campaigns will not result in elimination and is not a good use of valuable resources . High coverage in drug campaigns is difficult to achieve , and simulations predicted that drug campaigns with coverage as high as 70% could not compensate for insufficient underlying vector control . As MDAs are expensive to administer , we encourage elimination programs to consider whether their operational region meets the preconditions we have discussed here for effective MDA before deciding to implement this intervention . Simulations predict that if MDAs are implemented on top of a very aggressive vector control program , elimination becomes even more likely . As predicted by generic models [27] , however , even in this context transmission can occasionally rebound after vector control ceases ( Fig 8 and S12 Fig ) . Rebound in transmission depends on the extent of human movement as travelers from high-transmission areas re-seed transmission in low-transmission areas . If vector control is well-implemented at high coverage , targeting MDAs to high-burden areas is potentially a reasonable approach to increasing the likelihood of local elimination while saving resources , and could even result in improved outcomes if better coverage with MDA can be achieved in targeted areas . As long as passive case management improves , simulations predict that malaria transmission will die out in low-burden areas . Human movement inside the study area and importation of infection from outside the study area decrease the likelihood of elimination . If certain areas are known to be hot spots of both importation and receptivity , vector control should be implemented as a preventative measure until elimination is achieved in the wider region and imported infections cease . Surveillance must be vigilant in quickly identifying local outbreaks and responding with high-coverage vector control and possibly focal MDA around index cases . If outbreaks are not adequately contained and endemic malaria is reestablished , sustained high coverage with vector control must be re-implemented and MDA should be considered to rapidly deplete the parasite reservoir before other areas also experience resurgence . Elimination in the face of repeated importations is a major challenge and victory should not be declared too soon , as relaxing surveillance prematurely can easily lead to resurgence in areas with a history of high transmission . While generic models have predicted that targeting transmission foci would be effective [28] , this study demonstrates for the first time that elimination strategies in interconnected regions are well-served by focusing directly on reducing transmission in regional hotspots rather than attempting to maintain an “elimination front” and tackling the most difficult areas last . Programs will need to identify where regional hotspots are located , with the understanding that hotspot locations and intensities can change dynamically as interventions are deployed and changes in vector species composition , human movement patterns , housing conditions , and climatic cycles alter the local landscape of transmission . As more regions reduce transmission and approach malaria elimination , it becomes crucial to understand how to set up malaria operations for successful and lasting elimination at local , national , and regional levels . Southern Africa is a particular challenge as vectorial capacity is substantial in some areas and the region has become increasingly interconnected . This study suggests that elimination in the southern Africa region will require several years of sustained high coverage with vector control interventions , and mass drug campaigns are unlikely to be effective if vector control is insufficient . In areas with both high receptivity and high importation rates , vector control must be maintained until importation ceases . Ultimately , it follows that regional malaria elimination in southern Africa is nevertheless within reach with current tools , provided the efficacy and operational efficiency attained in the Lake Kariba operational area can be extended and targeted to other key areas .
During each of the 2012 and 2013 dry seasons ( June-November ) in Southern Province , Zambia , three large-scale MTAT rounds were undertaken [7] . Individuals were visited at their homes in a full community census and , following consent , administered RDTs . Test-positive individuals were treated with the antimalarial drug artemether-lumefantrine ( AL ) , of which the first dose was directly observed . During each MTAT round , RDT results were geo-tagged by household location . Information was collected on household demographics , ITN usage , recent fevers , and recent drug treatments . In this analysis , focus was restricted to a contiguous block of twelve HFCAs in Gwembe and Sinazongwe Districts along Lake Kariba ( Fig 1 ) . These HFCAs cover approximately 80 , 000 people living in a geographic area spanning a range of endemic malaria transmission intensities . Demographics , migration , ITN usage , treatment-seeking , and drug campaign coverage in simulations were informed by survey data collected during the MTAT rounds . Spatial variation in transmission intensity was captured by selecting local vector larval habitat availabilities to match observed seasonal and spatial patterns in RDT prevalence and clinical incidence . Given best-fit habitat parameters for each cluster along with cluster-specific climate , population , historical ITN usage , and drug campaign coverage , the set of 115 clusters was simulated together in a spatial model . Cluster climate was simulated using climate data at cluster’s centroid coordinates , and cluster population was set to the median population size of aggregated households within the cluster across the 2012–13 MTAT rounds . Total population over all 115 clusters was around 52 , 600 individuals , corresponding to the average number of individuals surveilled in each round rather than the total number of distinct individuals surveilled over all rounds . To account for population scaling , each cluster’s calibrated larval habitat parameters were proportionally adjusted to scale the magnitude of vector populations . A cluster of population p in the spatial simulation had larval habitat parameters adjusted to p1000sc* . Importations of malaria from outside the study area were not included unless specifically indicated . Simulations modeled a period of 24 years beginning in 2006 , and climate data after 2013 was inferred from the preceding years . The baseline simulation up to and including the 2012–13 MTAT activities was extended to include the 2014–15 MDA interventions in the Lake Kariba region . Simulated MDA campaigns with dihydroartemisinin-piperaquine ( DP ) were administered to all clusters in two campaigns beginning in December 2014 and July 2015 . Each MDA campaign consisted of two rounds separated by 60 days . During MDA , individuals received treatment regardless of their RDT result . The coverage of the MDA campaigns in each cluster was set to the coverage level of the 2012–13 MTAT campaign for that cluster . DP was administered with age-dependent dosing and full compliance with all treatment courses was assumed unless specifically indicated . Potential post-2015 intervention mixes were simulated to evaluate their ability to reach and maintain malaria elimination in the Lake Kariba region by 2030 ( Figs 6–10 ) , in line with southern Africa regional elimination goals . Combinations of ramp-ups in case management , increases in ITN usage , extending drug campaigns for an additional five years , targeting MDAs at high-burden HFCAs , coverage achieved by drug campaigns , impact of human migration rates , and impact of importation into the region were simulated and compared . Case management ramp-up ( scenarios 2–13 ) was modeled as gradual increase of both the percentage of people with access to malaria treatment and the rate at which symptomatic people seek treatment given treatment is available . Ramp-ups in case management were modeled to begin in January 2012 and plateau in January 2019 ( S4C Fig ) , by which point 93% of malaria clinical cases in children under 5 received treatment , 89% of malaria clinical cases in people over age 5 received treatment and 98% of severe malaria cases across the entire population received treatment . Three ITN coverage ramp-up options over the period 2014–22 were considered ( S3B Fig and S11 Fig ) . Under “maintain current” ( scenarios 1 , 2 , and 5 ) , ITN coverage is maintained at 2015 levels via new ITNs distributed to individuals at birth; under “ramp-up” ( scenarios 3 , 6 , 8 , 10 ) , ITN coverage is gradually increased between 2016 and 2022 , when all distributions cease , extrapolating the historical and present ITN distribution coverage trajectory for each cluster post-2013; and under “aggressive” ( scenarios 4 , 7 , 9 , 11–13 ) , ITN distributions covering 80% of the population administered every other year between 2016 and 2022 . For simulations where MDAs were extended through 2020 ( scenarios 5–10 , 12–13 ) , five annual MDA campaigns with DP were administered beginning each July for the five years from 2016–20 . Each campaign consisted of two rounds separated by 60 days . Campaign coverage at each cluster was set at the cluster’s 2012–13 MTAT coverage and did not vary from year to year . In addition to MDAs over the entire Lake Kariba region , targeted MDA campaigns were simulated ( scenarios 8 , 9 , 11 ) . The twelve HFCAs were divided into high- and low-burden groups where high-burden HFCAs were those with RDT prevalence above 10% in children during surveillance in April 2014 . In simulations of targeted MDAs , clusters in high-burden HFCAs received five additional annual MDAs with DP beginning in July 2016 as described above , while clusters in low-burden HFCAs did not receive MDA after the 2014–15 rounds . All the intervention mix scenarios were simulated in the context of human migration across clusters . Scenarios were simulated at two migration settings . For scenarios 1–11 and 13 , low migration , 7 , 440 round trips and 220 permanent relocations occurred each year . For scenario 12 , high migration , 74 , 400 round trips and 2 , 200 permanent relocations occurred each year . In all scenarios , migration was spread evenly throughout the year and was independent of age . Other than scenario 13 , no intervention scenarios included importation of infections from outside the study area . For scenario 13 , a total of 100 infections were imported annually into the entire study area . Three highly connected , high-population clusters , one from each of Gwembe , Munyumbwe , and Sinamalima HFCAs , were selected to be the loci of importation .
|
What would it take to eliminate malaria in southern Africa ? While the past century has seen many countries eliminate malaria , and many regions have dramatically reduced malaria burden in the last fifteen years , no sub-Saharan African country has yet eliminated malaria . In the Lake Kariba region of Southern Province , Zambia , villages with high and low malaria burden are interconnected through human travel , making elimination potentially very challenging . We used detailed spatial surveillance data—including household locations , climate , clinical malaria incidence , prevalence of malaria infections , and bednet usage rates—to construct a model of interconnected villages in the Lake Kariba region , then tested a variety of intervention scenarios to see which ones could lead to elimination . We found that elimination is indeed possible and requires high , yet realistic , levels of bednet usage among area residents . Mass drug campaigns deployed to kill parasites within the human population can boost the chances of achieving elimination as long as bednet usage is already high .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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"sciences",
"biology",
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"malaria",
"organisms"
] |
2016
|
Malaria Elimination Campaigns in the Lake Kariba Region of Zambia: A Spatial Dynamical Model
|
Early recognition of dengue , particularly patients at risk for plasma leakage , is important to clinical management . The objective of this study was to build predictive models for dengue , dengue hemorrhagic fever ( DHF ) , and dengue shock syndrome ( DSS ) using structural equation modelling ( SEM ) , a statistical method that evaluates mechanistic pathways . We performed SEM using data from 257 Thai children enrolled within 72 h of febrile illness onset , 156 with dengue and 101 with non-dengue febrile illnesses . Models for dengue , DHF , and DSS were developed based on data obtained three and one day ( s ) prior to fever resolution ( fever days -3 and -1 , respectively ) . Models were validated using data from 897 subjects who were not used for model development . Predictors for dengue and DSS included age , tourniquet test , aspartate aminotransferase , and white blood cell , % lymphocytes , and platelet counts . Predictors for DHF included age , aspartate aminotransferase , hematocrit , tourniquet test , and white blood cell and platelet counts . The models showed good predictive performances in the validation set , with area under the receiver operating characteristic curves ( AUC ) at fever day -3 of 0 . 84 , 0 . 67 , and 0 . 70 for prediction of dengue , DHF , and DSS , respectively . Predictive performance was comparable using data based on the timing relative to enrollment or illness onset , and improved closer to the critical phase ( AUC 0 . 73 to 0 . 94 , 0 . 61 to 0 . 93 , and 0 . 70 to 0 . 96 for dengue , DHF , and DSS , respectively ) . Predictive models developed using SEM have potential use in guiding clinical management of suspected dengue prior to the critical phase of illness .
Dengue virus ( DENV ) infection is a major public health issue worldwide particularly in tropical and subtropical regions . An estimated 390 million new DENV infections and 90 million cases of dengue illnesses are estimated to occur in more than 100 endemic countries , resulting in 20 , 000 deaths annually [1 , 2] . In the past 50 years , the incidence of dengue has increased 30-fold [1 , 3 , 4] . DENV infection may result in a wide spectrum of disease severity ranging from asymptomatic infection to dengue fever ( DF ) and dengue hemorrhagic fever ( DHF ) [5] . DHF is characterized by fever , plasma leakage , bleeding diathesis , and thrombocytopenia , that in severe cases leads to shock ( dengue shock syndrome , DSS ) [5] . The mortality rate of DSS , up to 20% [6] , is substantially reduced by timely replacement of intravascular fluid and blood losses , highlighting the importance of timely diagnosis of dengue , DHF , and DSS . Several studies have developed diagnostic tools to predict the severity of an acute dengue illness [7–13] . Potts et al . developed predictive models using logistic regression analysis based on maximum or minimum levels of clinical laboratory variables during the illness [7] and classification and regression tree ( CART ) analysis based on clinical laboratory data on the day of presentation [8] . Chadwick et al . used logistic regression models based on clinical laboratory data within the first 2 days of presentation [9] . Brasier et al . used both logistic regression and a classification and regression tree analyses based on laboratory data on the day of presentation [10] . Recently , Nguyen et al . used logistic regression models based on laboratory data and nonstructural protein 1 rapid antigen testing on the day of presentation within ≤72 hours of fever [12] . These modeling approaches do not consider the underlying mechanisms of illness , are likely to overlook predictors for which opposite , indirect effects may offset one another , and cannot consider the relationship among covariates longitudinally . Predictive models which attempt to capture underlying mechanistic pathways might be a more biologically reliable and robust approach . Progression of acute dengue illness is characterized by interdependent clinical and laboratory factors which change over the course of illness . Predictive models developed using structural equation model ( SEM ) have an advantage over models developed by general regression analysis because SEM can determine interdependent relationships among predictors and how they impact outcomes [14] . No studies have been performed to apply SEM approaches to predict dengue illness severity . The objective of this study was to construct statistical predictive models for dengue , DHF , and DSS by developing a series of SEMs .
Data are from a longitudinal observational investigation at two hospitals in Thailand . Detailed descriptions of this investigation are provided elsewhere [7 , 8] . Briefly , 1 , 384 children between 6 months and 15 years of age who presented with temperature of at least 38 . 5°C for less than 72 hours without any other identified source of infection were enrolled at Queen Sirikit National Institute of Child Health ( QSNICH ) in 1994–1997 ( n = 506 ) , 1999–2002 ( n = 347 ) , and 2004–2007 ( n = 337 ) and the Kamphaeng Phet Provincial Hospital ( KPPPH ) in 1994–1997 ( n = 194 ) . Mean ages of each cohort were 8 . 9 , 6 . 9 , 7 . 8 , and 8 . 9 years , respectively . In each cohort , 67 . 6% ( DHF = 31 . 2% , DSS = 4 . 1% ) , 43 . 0% ( DHF = 18 . 6% , DSS = 3 . 3% ) , 39 . 7% ( DHF = 12 . 4% , DSS = 2 . 7% ) , and 61 . 4% ( DHF = 18 . 3% , DSS = 3 . 8% ) were diagnosed as having dengue , respectively . Most subjects were enrolled from the outpatient clinic . Each child was hospitalized per protocol and monitored in hospital until clinically stable and at least 1 day after defervescence . Children who had signs of shock at the first visit , chronic disease , or an initial alternate non-dengue diagnosis were excluded . All participants’ parents provided written informed consent prior to enrollment . This study followed the Ethical Principles for Medical Research Involving Human Subjects as defined by the Declaration of Helsinki and was approved by the Institutional Review Boards of the Ministry of Public Health ( numbers: 102/2546 , 71/2552 , and 104/2552 ) , Thailand , the US Army ( numbers of Walter Reed Army Institute of Research: 436 , 436b , and 1077/1620 ) , and the University of Massachusetts Medical School ( number: H-2222 ) . Material has been reviewed by the Walter Reed Army Institute of Research . There is no objection to its presentation and/or publication . The opinions or assertions contained herein are the private views of the author , and are not to be construed as official , or as reflecting true views of the Department of the Army , the Department of Defense , or the National Institutes of Health . The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70–25 . Fever day 0 was defined as the day of defervescence , when the temperature was less than 38°C for a consecutive 12 hours; days before and after defervescence were numbered consecutively . Study day 1 was defined as the day a child was enrolled in the study . Illness day 1 was defined as the day of onset of symptoms . Subjects ( or their parent/guardian ) were asked to identify the date of onset of their illness . This usually was the date of onset of fever . Based on review of the medical records including study laboratory tests ( see below ) , a physician who was not involved in patient care assigned a final clinical diagnosis as DF or DHF grade I to IV , guided by the 1997 World Health Organization guidelines [5] . A venous blood specimen was collected daily . Plasma samples were tested for levels of aspartate aminotransferase ( AST ) , alanine aminotransferase ( ALT ) , and albumin using a Clinical System Analyzer ( model 700; Beckmann Instruments , Brea , CA ) . Total white blood cell ( WBC ) count , platelet count , and hematocrit values were determined using a T540 hematologic analyzer ( Coulter Electronics , Hialeah , FL ) . A tourniquet test was performed with the right and left arms alternately each day and the number of petechiae within a 1 sq in template ( up to 20 ) was recorded; daily testing was stopped if the maximum value of 20 petechiae was recorded . Dengue was confirmed by viral isolation by mosquito inoculation and/or detection of viral RNA by reverse transcription polymerase chain reaction in plasma , and/or by serological assays ( immunoglobulin M/G enzyme-linked immunosorbent assay and hemagglutination inhibition assay ) of paired acute-convalescent plasma samples as described [15] . Our statistical analysis focused on the predictive value of clinical and laboratory data that are well validated for testing in typical clinical settings: tourniquet test , AST , ALT , albumin , WBC count , WBC differential , hematocrit , and platelet count . Outcomes of interest ( dengue , DHF , or DSS ) were defined as occurring at fever day +1 , the final day of data collection and the day that a decubitus chest X-ray was performed to detect plasma leakage . To develop the predictive models , we used data from children who had all these variables obtained two and four days earlier ( i . e . , on fever days -3 and -1 ) . There was a large amount of missing data on fever day -3 or -1 ( totally 79 . 3% , ranging from 19 . 1% to 71 . 0% across variables ) in 1 , 244 subjects who had a diagnosis of dengue or other non-dengue febrile illness and any laboratory data between fever day -3 and +1 ( Fig 1 ) . We therefore determined to perform complete analyses to build SEMs using the data from 257 subjects who had the complete data of predictors on fever day -3 or -1 . To assess the sensitivity of our SEMs to missing data , we then employed multiple imputation for the full 1 , 244 subjects . We performed the Markov-chain Monte Carlo method to create 50 imputed data sets with no missing data . The results were pooled across the complete datasets . SEMs were built using Mplus 8 statistical software ( Muthén and Muthén 1998–2017 ) . SEM parameters were estimated using the weighted least squares means and variances adjusted estimator with the theta parameterization . SEMs for dengue , DHF , and DSS were constructed based on the hypothesized mechanisms ( Fig 2A ) . The minimum sample size for our SEMs was estimated as 207 , together with degree of freedom of 66 , significance level of 0 . 05 , and desired statistical power of 0 . 80 , on the basis of model-fitting with the root mean square error of approximation ( RMSEA ) [16 , 17] . Our subjects for developing the predictive models exceeded the minimum required to achieve the desired power level . Significant predictors were retained in the final SEMs . AST , ALT , WBC count , hematocrit , platelet count , and tourniquet test were natural log-transformed after adding integer 1 to improve the distribution and homogeneity of variance . RMSEA below 0 . 07 and the comparative fit index ( CFI ) that exceed 0 . 95 indicate good model fit [18] . However , we did not use the criterion for the χ2 test , because the χ2 test is very sensitive to the large sample size [19] . In the sensitivity analyses using multiple imputation , we used the same criteria of RMSEA and CFI . SEMs were used to develop the predictive models ( see S1 Supporting Information ) . To achieve the earliest prediction , we used total effects of significant predictors at fever day -3 on the outcome of interest diagnosed at fever day +1 . The performance of predictive models was examined by the receiver operating characteristic ( ROC ) curves and the area under the curves ( AUCs ) of the dataset acquired from 897 children who were not used to develop models in this study . The sensitivity ( Se ) , specificity ( Sp ) , positive predictive value ( PPV ) , and negative predictive value ( NPV ) at the Youden index-based optimal cut-offs were calculated . Statistical analysis was performed using SAS 9 . 4 statistical software ( SAS Institute , Cary , NC , USA ) , with exception of SEM . A P value < 0 . 05 was considered to be statistically significant . All data were anonymously analyzed .
Of the 257 children used to develop SEMs; 60 . 7% ( n = 156 ) , 19 . 8% ( n = 51 ) , and 3 . 5% ( n = 9 ) of these children were diagnosed as dengue , DHF , and DSS , respectively . At fever days -3 and -1 , AST , ALT , WBC count , hematocrit , platelet count , and tourniquet test were significantly different between groups ( non-dengue , dengue but non-DHF , DHF but non-DSS , and DSS , Table 1 ) . Pearson correlation coefficients among predictors and outcomes are presented in S2 Supporting Information . Children who were used to develop SEMs ( n = 257 ) were not significantly different from those used for validation of SEM-based predictive models ( n = 897 ) with respect to age ( 8 . 1 vs . 7 . 8 years ) , sex ( 48 . 3 vs . 43 . 6% girls ) , and the proportions of cases with DHF [17 . 3% ( n = 155 ) for the validation set] and DSS [3 . 2% ( n = 29 ) for the validation set] ( P values of t-test or χ2 test >0 . 05 ) . Moreover , the characteristics of 257 children used to develop SEMs were not significantly different from the 987 ( = 1 , 244–257 ) children who had a diagnosis of dengue or non-dengue febrile illness and any laboratory data between fever day -3 and +1 but were not used to develop SEMs , in terms of age , sex , and the proportions of cases with DHF and DSS ( P values of t-test or χ2 test >0 . 05 ) . A higher percentage of subjects in the validation set were enrolled at KPPPH ( 8 . 6 vs . 13 . 5% for developing and validation sets , respectively; P value of χ2 test = 0 . 035 ) . The SEM for any dengue illness included age , AST , WBC count , % lymphocytes , platelet count , and tourniquet test at fever day -3 and AST , WBC count , and tourniquet test at fever day -1 ( Fig 2B ) . AST , WBC count , and tourniquet test at fever day -3 were strongly predictive of their corresponding values at fever day -1 . In addition , WBC and platelet counts were negative predictors of AST at fever day -1 and % lymphocytes was a positive predictor of WBC count at fever day -1 . Taking both direct and indirect effects into account in SEM , the total effects of age , AST , and tourniquet test at fever day -3 were significantly related to an increased risk of any dengue illness , while WBC count , % lymphocytes , and platelet count were inversely related to risk of any dengue illness ( Table 2 ) . The SEM for DHF included age , AST , WBC count , hematocrit , platelet count , and tourniquet test at fever day -3 and AST , hematocrit , platelet count , and tourniquet test at fever day -1 ( Fig 2C ) . AST , hematocrit , platelet count , and tourniquet test at fever day -3 were strongly correlated with their corresponding values at fever day -1 . In addition , WBC count and platelet count were negative predictors of AST at fever day -1 and WBC count was a positive predictor of platelet count at fever day -1 . Taking both direct and indirect effects into account , the total effects of age , AST , hematocrit , and tourniquet test at fever day -3 were related to increased risk of DHF at fever day +1 , while WBC and platelet counts were inversely related to risk of DHF ( Table 2 ) . The SEM for DSS included age , AST , WBC count , % lymphocytes , platelet count , and tourniquet test at fever day -3 and AST , WBC count , and platelet count at fever day -1 ( Fig 2D ) . AST , WBC count , and platelet count at fever day -3 were strongly predictive of their corresponding values at fever day -1 . In addition , WBC and platelet counts were negative predictors of AST at fever day -1 , AST was a positive predictor of WBC count at fever day -1 , and WBC count was a positive predictor of platelet count at fever day -1 . Taking both direct and indirect effects into account , the total effect of AST at fever day -3 was positively significant in diagnosis of DSS at fever day +1 , but that of platelet count was negatively significant ( Table 2 ) . For all three SEMs , fever day -1 parameters were predicted by fever day -3 parameters ( see S3 Supporting Information ) . Therefore , we reasoned that data from a single time point might be sufficient for prediction of outcome . Using the regression coefficients for fever day -1 data , we derived revised SEM equations for dengue ( vs . non-dengue illness ) , DHF ( vs . all other diagnoses ) and DSS ( vs . all other diagnoses ) . The equations are available in S4 Supporting Information . The raw data used for SEM is available in S5 Supporting Information . We tested the predictive model over a range of fever days from -3 to 0; AUCs of the predictive model for dengue were in the range of 0 . 84 to 0 . 94 ( Fig 3A ) . AUCs of the predictive model for DHF were in the range of 0 . 67 to 0 . 93 over fever days -3 to 0 ( Fig 3B ) . Finally , AUCs of the predictive model for DSS were in the range of 0 . 70 to 0 . 96 over fever days -3 to 0 ( Fig 3C ) . The proposed SEMs for dengue and DHF demonstrated good fit of the data , while the model for DSS demonstrated adequate fit of the data ( Fig 2 ) . In the sensitivity analyses using multiple imputations for missing values , our SEMs did not show good fits , but it was acceptable ( RMSEA: 0 . 080 to 0 . 119 , CFI: 0 . 896 to 0 . 953 ) . Although the SEMs performed well over a range of fever day values , this parameter is defined retrospectively; therefore , we sought to further assess the performance of these models under typical clinical scenarios , by testing the models based on study day ( time relative to presentation for medical care ) and illness day ( time relative to illness onset ) . AUCs of the predictive model for dengue were in the range of 0 . 88 to 0 . 92 and 0 . 73 to 0 . 94 for study days 1 to 3 ( Fig 4A ) and illness days 2 to 5 ( Fig 5A ) , respectively . AUCs of the predictive model for DHF were in the range of 0 . 81 to 0 . 91 and 0 . 61 to 0 . 87 for study days 1 to 3 ( Fig 4B ) and illness days 2 to 5 ( Fig 5B ) , respectively . AUCs of the predictive model for DSS were in the range of 0 . 76 to 0 . 92 and 0 . 74 to 0 . 86 for study days 1 to 3 ( Fig 4C ) and illness days 2 to 5 ( Fig 5C ) , respectively .
We sought to develop predictive models to determine the probability of dengue , DHF , or DSS based on clinical and laboratory data available prior to the critical phase of illness which typically occurs within 24 hours of defervescence . We used SEMs to impose a hypothetical structure on the associations between these parameters . This approach allowed us to identify predictors at a single time point as early as fever day -3 which could predict the progression of disease . We further demonstrated that the SEM equations performed well using data from a wide range of time points during illness . The predictive accuracy of the SEMs improved using data collected at later time points , consistent with the progression of clinical and laboratory abnormalities in dengue . Our SEMs identified elevated AST levels and decreased platelet count as significant predictors of all three outcomes of dengue , DHF , and DSS ( Table 2 ) . Previous studies observed that AST and ALT are commonly elevated in dengue and correlate with the severity of illness [15 , 20 , 21] . In our study , because these biomarkers were highly correlated ( e . g . , Pearson r at illness day -3 = 0 . 76 ) , only AST remained in the SEMs . Our findings of an inverse relationship between platelet count and risk of dengue illness are also consistent with previous studies [7 , 8 , 12 , 22 , 23] . Decreased platelet count might be mechanistically linked to plasma leakage through its effects on vascular barrier function [24–26] . In the present study , older age , decreased WBC count , and increased number of petechiae in a tourniquet test were common significant risk factors for dengue and DHF . Although a meta-analysis reported an increased risk of DSS in younger children [23] , which was attributed to their more permeable and fragile microvasculature [27] , other studies reported contrasting findings [28] . These inconsistent results may be due to the differences in approaches to adjustment for potential confounders in statistical models . Indeed , several studies showed a significant association of severe dengue with age in univariable analyses but not in multivariable analyses [7 , 12] . Although decreased WBC count was significantly associated with increased risks for dengue and DHF , this association was not significant for DSS . Both leukopenia and leukocytosis have been described in dengue , depending on the timing during illness [11 , 29 , 30] . Our findings for the tourniquet test were consistent with other studies which showed a crucial role for this clinical parameter in predictive models for acute dengue illnesses [7 , 15 , 31] . A novel aspect of SEM is the ability to identify correlations between factors contributing to prediction of the final outcome , which implies a mechanistic relationship . For example , all three models showed negative associations between WBC and platelet counts at fever day -3 and AST at fever day -1 , and the models for DHF and DSS showed positive associations between WBC count at fever day -3 and platelet count at fever day -1 . The positive association between WBC and platelet counts can potentially be explained by common mechanisms such as bone marrow suppression [30] . The negative association of WBC and platelet counts with AST is less clear , and may point to other mechanistic pathways such as immune activation [32] . Our predictive models showed good performance for identifying children who progressed to dengue , DHF , and DSS . We provided Se , Sp , PPV , and NPV at the Youden index-based optimal cut-offs , however , cut-off points may need to be modified depending on the objectives: for example , to increase Se for detecting a lethal condition . To capture all DSS cases using our SEM model with data from study day 3 , it would be necessary to use a cut-off of 0 . 0172 , with correspondingly lower Sp ( 55 . 3% ) and PPV ( 10 . 6% ) ( Fig 5C ) . Our study followed the case classification of DF , DHF , and DSS in use during the study period ( and still in use in QSNICH and some other countries ) rather than the alternative classification of dengue and severe dengue published in 2009 [33] . Our DSS cases correspond to severe dengue as defined in 2009 , as there were no cases in our study with significant respiratory distress , clinically significant bleeding , or significant organ dysfunction . We did not collect data specifically for identification of cases as dengue with warning signs as defined in the 2009 classification . To our knowledge , this is the first study to apply SEM to address the potential mechanisms as they evolve in acute dengue illnesses and to build predictive models based on clinical and laboratory data at various time points in the illness . Another strength of this study is that causality may be better ascertained through the prospective longitudinal study design . Further , our predictive models were developed based on easily obtained laboratory data and predictive performance of the models were evaluated over a variety of time criteria . Inclusion of biomarkers could further efforts to discern the mechanisms of disease . While we focused on acute dengue illnesses , our approach is likely to be generalizable to other acute illnesses . Our study does have several limitations . First , in model development , selection bias might have arisen from the use of data on only 257 of 1 , 384 participants . Second , the model fit for DSS was borderline . DSS was a rare event ( 3 . 5% ) , and this likely resulted in instability of the SEM model . Third , PPVs in some DSS models were low , but this may also be attributed to the low prevalence of DSS [34] . Fourth , our predictive models were derived from Thai children , which may limit our ability to generalize these findings to patients in other areas . In conclusion , our findings highlight the importance of AST and platelet count early in illness as indicators of dengue , DHF , and DSS . We also identified other early clinical indicators which can be used to predict outcomes . Our approach may serve as a methodological template to investigate the mechanisms of other illnesses using SEM .
|
Dengue virus infection is one of the most critical public health issues , particularly in tropical and subtropical regions . This study developed statistical predictive models using the data obtained from 257 Thai children for dengue , dengue hemorrhagic fever , and dengue shock syndrome using structural equation modelling ( SEM ) . We performed SEM based on clinical and laboratory factors on three and one day ( s ) prior to fever resolution . Our SEM models showed that age , tourniquet test , aspartate aminotransferase , and white blood cell , % lymphocytes , and platelet counts on three days prior to fever resolution were important risk factors for dengue and dengue hemorrhagic fever . Age , aspartate aminotransferase , hematocrit , tourniquet test , and white blood cell and platelet counts were important risk factors for dengue shock syndrome . Our predictive models showed good performances in the validation subjects ( n = 897 ) who were not used for SEM , and thus we concluded that our predictive models can be practically used to guide clinical management of suspected dengue patients . Our study also showed that SEM can be used to predict the developments or severities of other illnesses .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
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2018
|
Use of structural equation models to predict dengue illness phenotype
|
A subset of human cancer cells uses a specialized , aberrant recombination pathway known as ALT to maintain telomeres , which in these cells are characterized by complex aberrations including length heterogeneity , high levels of unpaired C-strand , and accumulation of extra-chromosomal telomere repeats ( ECTR ) . These phenotypes have not been recapitulated in any standard budding or fission yeast mutant . We found that eliminating Ku70 or Ku80 in the yeast-like fungus Ustilago maydis results initially in all the characteristic telomere aberrations of ALT cancer cells , including C-circles , a highly specific marker of ALT . Subsequently the ku mutants experience permanent G2 cell cycle arrest , accompanied by loss of telomere repeats from chromosome ends and even more drastic accumulation of very short ECTRs ( vsECTRs ) . The deletion of atr1 or chk1 rescued the lethality of the ku mutant , and “trapped” the telomere aberrations in the early ALT-like stage . Telomere abnormalities are telomerase-independent , but dramatically suppressed by deletion of mre11 or blm , suggesting major roles for these factors in the induction of the ALT pathway . In contrast , removal of other DNA damage response and repair factors such as Rad51 has disparate effects on the ALT phenotypes , suggesting that these factors process ALT intermediates or products . Notably , the antagonism of Ku and Mre11 in the induction of ALT is reminiscent of their roles in DSB resection , in which Blm is also known to play a key role . We suggest that an aberrant resection reaction may constitute an early trigger for ALT telomeres , and that the outcomes of ALT are distinct from DSB because of the unique telomere nucleoprotein structure .
Eukaryotic chromosome ends , or telomeres , consist of repetitive DNA sequences and a plethora of protective proteins that are crucial for chromosome stability [1 , 2] . Aberrations in the DNA repeat tracts or the protective telomere proteins have been shown to induce chromosome re-arrangements . Owing to the “end-replication” problem , chromosome termini experience progressive telomere loss with each cell division [3] . This process eventually generates critically short telomeres that limit further cell proliferation by triggering a DNA-damage response [4] . Progressive telomere loss can be counter-balanced by telomerase reverse transcriptase , which extends telomeres by using an integral RNA component as the template [5 , 6] . Telomerase expression is repressed in normal somatic cells , but up-regulated in cancer cells , conferring these cells with increased proliferative potential . Telomerase inhibition has been validated as a useful anti-cancer strategy and telomerase inhibitors are in clinical trials for treating a variety of cancers [7] . While the majority of cancer cells utilize telomerase to replenish telomeres , a subset of tumor cells employs an alternative , recombination-based mechanism known as ALT ( alternative lengthening of telomeres ) [8 , 9] . The telomere DNAs in ALT cells are characterized by length heterogeneity , accumulation of unpaired telomere C-strand ( the strand that is rich in C-residue and that carries the 5’ end ) , and a substantial increase in extra-chromosomal telomere repeats ( ECTR ) , which comprise both circular and linear repeats . In particular , extra-chromosomal C-circles are believed to be an especially specific and quantifiable marker of ALT activity [10] . In addition , ALT is associated with extensive genome rearrangements , marked micronucleation , defects in the G2/M checkpoint , and altered double-strand break ( DSB ) repair [11] . While many recombinational repair proteins have been linked to ALT ( including e . g . , the MRN and the 9-1-1 complex ) , this pathway is clearly distinguished from the canonical homology-directed DNA repair ( HDR ) pathway [12 , 13] . For example , RAD51 is the central catalyst of HDR , yet knockdown of RAD51 either fails to suppress or exacerbates ALT phenotypes [14 , 15] . In addition , a key feature of the HDR pathway is the generation of long 3’ overhangs , yet ALT is characterized by the accumulation of telomere 5’ overhangs [15] . Despite intensive efforts , including high throughput screening of driver mutations in ALT , the mechanisms that underlie ALT induction and maintenance remain incompletely understood [11] . One potential contributing factor for ALT is a defect in the chromatin remodeling protein ATRX . ATRX mutations are frequently observed in ALT , and exogenous expression of ATRX in some ALT cells can repress ALT phenotypes [16] . However , knockdown of ATRX is not sufficient to trigger the ALT pathway [11] , implying the existence of other necessary contributing factors . The current lack of knowledge on the ALT pathway hampers the development of mechanism-based inhibitors against this subset of cancer cells [17] . Recombination-mediated telomere extension pathways have also been analyzed in fungi , including S . cerevisiae and S . pombe [18] . Such pathways are typically activated in telomerase null mutants , and have been classified as Type I or Type II according to the sequence amplified at chromosome ends and the factor requirements [19] . A common weakness of existing fungal models of ALT is that they do not recapitulate the characteristic aberrations of ALT cells . For example , none of the S . cerevisiae or S . pombe telomerase mutant survivor has been reported to accumulate excess C-strand overhangs or C-circles . Alternative and more relevant models are therefore desirable . An attractive model organism is the genetically tractable yeast-like fungus Ustilago maydis , which bears a telomere repeat unit that is identical to the mammalian sequence and an average telomere tract of ~300–400 bp [20 , 21] . U . maydis also contains a shelterin-like telomere nucleoprotein complex , and a recombination-repair machinery that is remarkably similar to the mammalian machinery [22] . Two recent observations highlight the striking resemblance of the U . maydis telomere regulatory mechanisms to those in humans . First , as in human cells but different from budding or fission yeast , the two central components of HDR in U . maydis ( i . e . , Rad51 and the BRCA2 ortholog Brh2 ) are each required for normal telomere maintenance in telomerase-positive cells [21 , 23] . Second , just like the human Ku complex , the U . maydis Ku complex is essential for cell viability and loss of Ku expression causes massive telomere aberrations [24 , 25] . In the current study , we further characterized the Ku-deficient U . maydis cells , and found that they initially exhibit telomere abnormalities that are very similar to those observed in ALT cancer cells , including telomere length heterogeneity , the accumulation of C-strands and extrachromosomal telomere repeats ( ECTR ) . The level of C-circles , a highly specific marker of ALT , is dramatically increased in the mutant as well . Moreover , we found that the induction of aberrant telomeres in the Ku-deficient mutant is largely suppressed by deletion of mre11 and fully eliminated by deletion of blm , consistent with previous characterization of ALT cell lines [10 , 13] . By contrast , deletion of rad51 or other DNA damage response and repair factors had disparate effects on various telomere abnormalities , suggesting that these factors process ALT intermediates or products . Interestingly , in the presence of normal checkpoint function , the ku mutants eventually experience permanent cell cycles arrest , which is accompanied by the disappearance of telomere repeats from chromosome ends and a further increase in short ECTRs . Our findings illustrate the complexity of the ALT-like pathway in U . maydis and begin to suggest factors and mechanisms that may mediate the formation of aberrant telomeres in ALT cells .
Previous analysis revealed DNA damage signaling from the telomeres of ku70 and ku80-deficient U . maydis cells that leads to permanent cell cycle arrest . ( To simplify the discussion , we will refer to the Ustilago maydis uku70 and uku80 genes and mutants as ku70 and ku80 . ) The investigation made use of U . maydis strains that conditionally express ku70 or ku80 in nitrate-containing medium ( named ku70nar1 and ku80nar1 ) . Following 18–20 hr of incubation in YPD ( a medium that represses ku expression ) , the ku mutants cease to proliferate and experience complete G2 arrest as judged by cell morphology ( with elongated bud and a single nucleus ) and FACS analysis [24] . Drastic telomere restriction fragment ( TRF ) length heterogeneity and elevated levels of t-circles were also detected [24] . To characterize in greater detail the dynamics of the telomere defects , we further analyzed TRF length distribution at multiple time points following transcriptional repression of the ku70nar1 allele ( Fig 1A ) . Increased total telomere hybridization signal and TRF length heterogeneity became evident ~12 hr post ku70 repression , and these alterations grew more severe over time , reaching a maximum at ~18 hr . This initial TRF length distribution is superseded at ~30 hr by a terminal distribution characterized by the disappearance of extremely long telomere fragments and a further increase ( ~ 3 fold ) in very short telomere fragments ( ~0 . 1 to 0 . 5 kb ) . To determine if these abnormal TRFs are chromosomal or extra-chromosomal , we compared the Southern hybridization patterns of the DNA samples with and without prior PstI treatment ( S1A Fig ) . Interestingly , omitting PstI digestion reduced but did not eliminate the long TRF signal at 18 hr , indicating that only a portion of this signal is due to ECTR . In contrast , the very short telomere fragments at 30 hr are almost entirely due to ECTRs ( and henceforth abbreviated as vsECTRs ) . Prolonged exonuclease treatment eliminated only the faster migrating portion of the vsECTRs , suggesting that the smallest vsECTRs are linear whereas the remaining ones are circular DNAs ( S1B Fig ) . We also quantified the chromosome-associated telomere signals and total telomere DNA signals using the undigested samples , and found the former were reduced by 50% and 90% after 18 hr and 30 hr of repression , whereas the latter were increased by 50% and 3-fold at the same time points ( S1C Fig ) . Hence , the loss of Ku in U . maydis results in a progressive loss of telomere DNAs from the chromosomes , as well as a drastic accumulation of ECTRs , which are initially long and heterogeneous , but subsequently dominated by very short linear and circular fragments . Next , we examined single stranded telomeres in the Ku-deficient mutant at multiple time points using in-gel hybridization assays and observed the disappearance of unpaired G-strand at 6 hr , and the drastic accumulation of unpaired C-strand at 18 hr and thereafter ( Fig 1B ) . We note that while there is a good correlation between the size distribution of TRFs in standard Southern analysis and that of unpaired C-strand , the unpaired G-strand signals are disproportionately concentrated in the high molecular weight region of the gel ( Fig 1B ) . The G-strand signals may therefore arise from a subset of the molecules , perhaps recombination or replication intermediates , which are expected to migrate slower than linear DNAs . More study is required to ascertain the nature of the G-strand signals . Interestingly , despite the dramatic increase in the amount of vsECTRs in the standard telomere Southern analysis , no such increase was detected in either the G-strand or C-strand assays , indicating that vsECTRs are primarily double stranded . The distinct kinetics of the various telomere alterations suggest that the genesis of aberrant telomeres in the ku mutant is a multi-step process that entails the action of many DNA-processing factors . Preferential accumulation of unpaired C-strand has not been reported for other fungal mutants but is characteristic of ALT cancer cells [15] . To characterize this structure in more detail , we subjected 18 hr DNA samples from the ku70nar1 mutant to 2-D gel analysis followed by in-gel hybridization using a G-strand probe ( Fig 1C ) . We found that a substantial portion of the C-strand signals migrate as an arc below the ds linear arc , and are therefore likely due to ss linear DNA ( Fig 1C , marked by red arrows ) . Following denaturation and re-hybridization , signals corresponding to ds linear and ds circular DNAs are detected as well ( Fig 1C , marked by black arrows and asterisks ) . Treatment of the DNA with E . coli RecJf ( a 5’ to 3’ ssDNA exonuclease ) prior to 2D-gel fractionation caused a substantial reduction in the C-strand signals , supporting the presence of 5’ ssDNA overhangs ( as predicted based on the polarity of the C-strand ) ( Fig 1D ) . We also compared the C-strand signals in 1-D native gels before and after subjecting the ku70nar1 DNA to Klenow mediated fill-in synthesis , which is expected to convert 5’ overhangs to duplexes ( S2 Fig ) . Again this treatment eliminated part but not all of the C-strand signals , consistent with the presence of 5’ C-strand overhangs in a fraction of the molecules . Surprisingly , treatment with E . coli Exo I ( a 3’ to 5’ ssDNA exonuclease ) also reduced the C-strand signal , which is not consistent with unpaired C-strand at the ends of chromosomes . Instead , this results indicates that a portion of the signal may come from ss extra-chromosomal linear DNA; both purely single-stranded DNA and single-stranded DNA bearing an internal duplex should be susceptible to both Exo I and RecJf . This result is also consistent with the finding from 2D gel analysis that a significant fraction of the signals is from ss linear DNA . To confirm the existence of ECTRs , we performed standard telomere Southern analysis on samples that had not been treated with restriction enzyme ( Fig 1E ) . Consistent with our hypothesis , the heterogeneous telomere fragments of the ku70nar1 and ku80nar1 mutants are observed in the absence of PstI cleavage , whereas the typical telomere patterns of control cells are only observed with PstI treatment . Hence we conclude that the U . maydis ku mutants are characterized by the possession of abundant ECTRs that bear a high level of unpaired C-strand , a substantial portion of which exists as ss linear DNA . As noted before , unpaired C-strand is believed to be a distinguishing feature of ALT [15] . Other features shared by the U . maydis ku mutants and ALT cells include telomere length heterogeneity and ECTR , suggesting substantial mechanistic resemblances between the two systems . Notably , a recent study identified extra-chromosomal C-circles ( i . e . , circular DNA comprised of continuous C-strand and G-strand nicks/gaps ) as an especially specific and quantifiable marker of ALT activity [10] . We therefore examined the levels of C-circles in the U . maydis mutant and found a dramatic increase of such circles in the ku70nar1 strain upon transcriptional repression of ku70 ( Fig 2A ) . The signals are proportional to the amounts of input DNA , and quantification revealed in the mutant at 30 hr post ku repression ~30 , 000 fold more C-circles than the control cells . Similar to ALT cells , G-circles ( i . e . , circles comprised of continuous G-strand and C-strand nicks/gaps ) are also elevated in the ku mutant , but to a lesser extent ( ~1 , 500 fold ) ( Fig 2A ) . As expected , the C- and G-circle signals require both input DNA and the Φ29 polymerase , and are unaffected by prior exonuclease treatments ( Fig 2B ) . Thus , the U . maydis ku mutant appears to suffer from very similar telomere defects as ALT cancer cells , and may be engaged in the same set of reactions at telomeres . Moreover , these defects exhibit distinct kinetics ( see S3 Fig for a summary of the temporal dynamics of the defects ) and may thus represent different intermediates in a complicated pathway . To assess the reversibility of the disparate abnormalities , we first subjected the ku70nar1 and ku80nar1 strains to transcriptional repression for 18 hr to induce the ALT-like defects , and then re-expressed the ku genes by switching the cells to a nitrate-containing medium , followed by telomere analysis at various time-points . Remarkably , the ku mutants rapidly resumed growth when switched from the restrictive to permissive medium . They underwent the first cell division at ~4 hr after the switch , and continue to divide with a generation time of ~210 minutes thereafter . In addition , the telomere aberrations of the mutants are fully reversible , and similar to the induction process described earlier , different aberrations are reversed with distinct kinetics ( S4 Fig ) . The level of extra-long TRFs ( 5 to >20 kb ) becomes substantially reduced at 24 hr following ku re-expression and is similar to control cells at 48 hr , whereas the level of extremely short fragments bearing telomere repeats ( <0 . 5 kb ) does not return to normal until 72 hr post re-expression ( S4A Fig ) . A difference in kinetics is also observed between the disappearance of unpaired C-strand and the appearance of unpaired G-strand during phenotypic reversal; the former is complete at 24 hr after ku re-expression , but the latter is only complete at 48 hr ( S4B Fig and S4C Fig ) . These results again support the notion that the generation of the telomere defects is likely to be a multi-step process , and that different telomeric structures may represent distinct intermediates along a complicated pathway . In some organisms , Ku has been observed to modulate the activity of telomerase [26 , 27] . To assess the potential contribution of telomerase activity to the ALT-like telomeres in the U . maydis ku mutants , we analyzed several independent trt1 ku70nar1 double mutants , which were constructed by first deleting the trt1 gene ( encoding the telomerase reverse transcriptase in U . maydis [28] ) and then introducing the ku70nar1 allele . Similar to the ku70nar1 single mutant , the double mutant experienced growth arrest when switched to the restrictive condition for ku70 expression ( Fig 3A and 3B ) , and exhibited profound telomere aberrations ( Fig 3C , 3D and 3E ) . Interestingly , while the same aberrations are observed in the double mutant , they occur with a slightly delayed kinetics . For example , while high levels of vsECTRs are evident following 18 hr of ku70 repression in the single mutant , they are not observed until 24 hr after ku70 repression in the double mutant ( Fig 3C ) . Similarly , while unpaired C-strand signals peak at 18 hr after ku70 repression and decline thereafter in the single mutant , the C-strand signals remain near the peak level after 30 hr of repression ( Fig 3D ) . A delay or a mild reduction in C- and G-circle formation is also evident in the double mutant relative to the single mutant ( Fig 3E ) . Thus , while none of the ALT telomere aberrations requires trt1 , the loss of trt1 ( or the shorter initial telomere lengths of the double mutant ( Fig 3C ) ) appears to affect the severity or kinetics of the phenotypes . We conclude that telomerase is not an essential component of the U . maydis ALT pathway , but may act on some of the telomere structures generated during the induction of this pathway . ALT cells invariably exhibit checkpoint defects [11] , and we have previously shown that atr1 or chk1 deletion enables the U . maydis ku70 and ku80-deficient cells to proliferate without suppressing the telomere length heterogeneity in these cells [29] . To determine in greater detail how the combined loss of checkpoint and ku proteins affects telomeres , we measured the levels of chromosome-associated telomere repeats and other aberrant telomere structures in atr1 ku70nar1 and chk1 ku70nar1 double mutants at multiple time points following ku repression . Interestingly , unlike the ku70nar1 single mutant , the double mutants retained substantial amounts of chromosome-associated telomere repeats and accumulated lower levels of ECTRs after 30 hr of repression ( S1C Fig ) . Even after more than ~225 generations ( ~ 9 streaks ) of passage on YPD plates , the double mutants do not progress to the “terminal telomere phenotype” characterized by the predominance of vsECTRs ( Fig 4A ) , implying that the checkpoint proteins are necessary for this progression . Interestingly , we reproducibly detected higher telomere DNA content in the chk1 ku70nar1 mutant in comparison with the atr1 ku70nar1 mutant ( Fig 4A and S1C Fig ) , suggesting some mechanistic differences between the two checkpoint proteins in the ALT pathway . The nature of this difference remains to be determined . With respect to the accumulation of ss telomere DNAs , deleting either atr1 or chk1 reduced the accumulation of unpaired C-strand by ~ 2 fold in the ku70nar1 strain , but had little effect on the disappearance of unpaired G-strand ( Fig 4B and 4C ) . The C- and G-circle levels of the double mutants were also lower than that of the ku70nar1 single mutant ( Fig 4D ) . Thus the checkpoint proteins appear to play a role in exacerbating a subset of the telomere aberrations in the ku mutant . As expected , deleting atr1 or chk1 alone does not induce any obvious telomere defects ( Fig 4B , 4C and 4D ) , suggesting that these checkpoint factors enhance the telomere aberrations only in the context of ku loss . The foregoing analysis hints at the complexity of ALT-like pathway in the U . maydis ku mutants . To begin to identify factors involved in the pathway , we first surveyed a number of factors involved in DDR and repair . These factors , including 9-1-1 subunits , Rad51 , and Mre11 , were previously examined in regard to their roles in the non-viability and telomere length heterogeneity of the ku70nar1 mutant , but not their roles in other ALT-like phenotypes . Specifically , the previous study showed that deleting subunits of the rad9-hus1-rec1 complex or rad51 could not suppress the lethality or telomere length heterogeneity of the ku70nar1 mutant [29] . More detailed and comprehensive characterization of the telomeres of these combination mutants revealed in fact exacerbation of selected telomere phenotypes ( Fig 5 ) . For example , in each of the 9-1-1 ku70nar1 combination mutants , the levels of extremely long telomeres are further elevated relative to the ku70nar1 single mutant ( Fig 5A and 5B ) , whereas the levels of G- and C-strand are comparable to those of the single mutants ( Fig 5C and 5D ) . The effect of deleting rad51 in the ku70nar1 mutant is even more complex ( S5 Fig ) . Both the levels of very long and short telomeres are increased in the double mutant . The level of unpaired C-strand is also increased , but that of C-circle slightly reduced . Previous analysis also revealed strong suppression of the lethality and telomere heterogeneity of the ku70nar1 mutant by mre11 deletion , but not the mre11-H228N ( nuclease deficient ) allele . More detailed characterization of the combination mutants revealed suppression of almost all the telomere aberrations of the ku70nar1 mutant by mre11 deletion; the levels of G-strand , C-strand and C-circles in the combination mutant are all quite similar to the wild type parental strain ( Fig 6A , 6B and 6C ) . In contrast , the mre11-H228N ku70nar1 double mutants have telomeres that closely mimic those of the ku70nar1 single mutant , indicating that a non-nuclease activity of Mre11 confers lethality and is required for the induction of telomere defects upon the loss of ku . The apparently antagonistic effect of the Ku and MRN complexes at the U . maydis telomeres is reminiscent of their relationships in DSB resection in S . cerevisiae [30] . In particular , the resection defect of the S . cerevisiae MRN mutants can be suppressed by concurrent mutations in yKu subunits . However , a significant distinction between S . cerevisiae Ku and U . maydis Ku is that the former suppresses the formation of 3’ overhangs at DSBs , whereas the latter reduces the production of 5’ overhangs at telomeres by MRN . While the basis for this difference is unclear , MRN is known to interact with many DDR and DNA processing factors , and the distinct nucleoprotein structures of broken DNAs and telomeres could play a role in the distinct outcomes of the two systems . To address the roles of factors implicated in resection and other processing events downstream of the resection on the ALT pathway in U . maydis , we analyzed additional combination mutants . For this study , the ku70nar1 allele was introduced into a set of largely isogenic strains ( initially derived from the UCM350 parental strain ) , each harboring a single deletion ( of blm , ctip , dna2 , exo1 , mus81 , top3 ) . These combination mutants , as well as the single mutants , were subjected to growth analysis and the panel of telomere assays ( Fig 7 ) . Remarkably , the deletion of two genes , namely blm and exo1 , significantly restored growth to the ku70nar1 mutant , whereas deletion of others did not ( Fig 7A ) . Interestingly , there is only partial correlation between the suppression of growth and telomere defects ( Fig 7B , 7C and 7D ) . In the case of blm deletion , we observed complete suppression of telomere length heterogeneity , as well as C-strand and C-circle accumulation ( Fig 7B , 7C and 7D ) . Consistent with these findings , the blm ku70nar1 double mutant grows as well as wild type cells in YPD ( Fig 7A ) . In the case of exo1 deletion , we observed only partial reduction of the telomere phenotypes , and accordingly only partial suppression of the growth defect ( compare colony sizes of the blm ku70nar1 and exo1 ku70nar1 mutant , Fig 7A ) . In addition , exo1 deletion could not suppress the lethality of ku70 in a different strain background [29] . Exo1 is thus not necessary for the manifestation of all ku70 phenotypes . On the other hand , the dna2 ku70nar1 and ctip ku70nar1 mutants exhibit telomere aberrations that are similar to or milder than those in the exo1 ku70nar1 mutant , yet the former two grow considerably worse than the latter ( Fig 7B , 7C and 7D ) . Finally , none of the deletions except that of blm substantially reduced the high level of vsECTRs that is characteristic of the terminal stage of ku mutant ( S6 Fig ) . Overall , the results suggest that Blm plays a critical role in the ALT pathways , and some blm-dependent telomere aberration is needed to trigger the signals and damages that fully arrest growth . In addition , many factors implicated in DSB resection play significant roles in the U . maydis ALT pathway . However , it seems likely that not all telomere aberrations contribute equally to the loss of cell viability . The apparently complete suppression of the U . maydis ALT pathway by blm deletion prompted us to examine the role of this helicase further . First , we validated the suppression in two independent clones of blm ku70nar1 double mutants , and showed that these clones exhibit the same degree of ku repression as the ku70nar1 single mutant when grown in YPD ( Fig 8A and 8B ) . Hence the effects of blm deletion cannot be attributed to unrelated genetic mutations or leaky ku70 expression . Second , we analyzed the effect of abolishing just the helicase activity of Blm on the ALT pathway . Based on alignment with well-characterized homologues , we surmise that the K443R substitution in U . maydis Blm should render the protein without helicase activity [31] . Consistent with this hypothesis , we found previously that introducing this point mutation compromised the DNA-damage sensitivity of U . maydis [31] . Notably , repressing ku70 in strains with this point mutation failed to induce any ALT telomere phenotypes ( Fig 8C , 8D , 8E and 8F ) , implying that the blm helicase activity is essential for the ALT pathway . It is worth noting that the blm null and blm-K443R mutations alone caused significant telomere shortening ( Fig 8A and 8C ) , suggesting that the helicase helps to maintain telomeres in telomerase-positive U . maydis . While the mechanistic basis for this phenomenon remains to be worked out , we speculate based on studies with mammalian BLM that the U . maydis Blm may play a role in facilitating telomere replication [32] . Failure to fully replicate the telomere tracts , if not adequately compensated by telomerase-mediated extension , may lead to the shortening of average telomere length .
The telomeres of ALT cancer cells are characterized by complex aberrations including extreme telomere length heterogeneity , accumulation of unpaired telomere C-strand , and substantial increase in extra-chromosomal telomere repeats ( ECTR ) . Recent studies have accentuated the utility of C-circles as a specific and quantifiable marker of ALT [10] . The abundance of C-circles is reported to be 100 times that of the complementary G-circle , highlighting the specific nature of the former abnormality [10] . Notably , all of the ALT telomere features , including the drastic and specific elevation of C-circles , are recapitulated in the U . maydis ku mutants . The resemblance of the telomere phenotypes of the Ustilago mutants to those of ALT cancer cells clearly distinguishes the current model from all previously characterized fungal “survivors” that employ recombination-based telomere maintenance mechanisms . For example , none of the budding or fission yeast survivors have been reported to accumulate preferentially C-strand overhangs or C-circles , even though many do manifest highly heterogeneous telomere lengths [18] . With respect to the factors involved in the generation and processing of ALT telomeres , the U . maydis model likewise exhibits genetic requirements that are consistent with previous studies of ALT cells . For example , a previous report indicates that RAD17 and the 9-1-1 complex are localized to APB and may be involved in ALT telomere metabolism [33] . In support of this notion , we found that deletion of the U . maydis 9-1-1 subunits exacerbated the telomere length heterogeneity of the ku mutant . In addition , despite the central role of RAD51 in standard homologous recombination , previous studies indicate that this protein is not required for ALT [10 , 14] . Interestingly , we found that deletion of U . maydis rad51 reduced the formation of C-circles , but increased the levels of extra-long telomeres , suggesting that while Rad51 is not essential for ALT induction , it may act in intermediate steps of the complex ALT pathway to influence the levels of various products . Finally , analysis of ALT cancer cells strongly suggests that MRN and BLM complex are both required for telomere recombination in these cells [10 , 13 , 34 , 35] . Similarly , we found that U . maydis mre11 and blm deletion dramatically reduced all of the telomere abnormalities associated with the ku mutants , implying a key role for these proteins . Importantly , some residual defects can still be detected in the ku mre11 double mutants , implying the existence of an alternative , albeit minor pathway for generating abnormal telomeres . While we have shown earlier that the U . maydis and human Ku proteins share the property of being essential for viability and for telomere protection , the telomere phenotypes of the human and U . maydis mutants do not appear to be the same . The human KU mutant was reported to suffer massive deletion through excision of t-circles , resulting in signal-free ends ( i . e . , chromosome ends that are free of telomere FISH signals ) [25] . In contrast , we reported previously that the U . maydis ku70nar1 mutant , while possessing numerous t-circles , also accumulates long and heterogeneous telomeres [29] . However , as reported in the current work , these constitute an initial phenotype that is eventually superseded by the terminal phenotype characterized by the loss of telomere repeats from chromosome ends and appearance of abundant vsECTRs ( Fig 9 ) . It is unclear how such high levels of ECTRs can be generated in the mutant . Perhaps ECTRs can be replicated autonomously through an aberrant reaction . Alternatively , long telomere repeat tracts may first be added to chromosome ends ( e . g . , through rolling circle replication of telomere circles ) and then released from the ends through cleavage or recombination . Regardless of the precise mechanisms , our observation indicates that the terminal phenotype of the ku70nar1 mutant exhibits greater resemblance to the human KU mutant than previously realized , further highlighting the relevance of the U . maydis model . A surprising observation is that the terminal telomere phenotypes of the ku70nar1 mutant do not materialize in the absence of chk1 or atr1 . Checkpoint proteins may thus render the ku70nar1 mutant non-viable not only by signaling DNA damage , but also by contributing to the excision of telomere repeats from chromosome ends , and the accumulation of vsECTRs ( Fig 7 ) . Flynn et al . recently showed that an ATR inhibitor selectively reduced the proliferation of ALT cancer cells in comparison with telomerase-positive cells [36] , suggesting that ATR can confer benefits to ALT cells . While this result may reflect a significant difference between the roles of checkpoint proteins in ALT cancer cells and the U . maydis model , it is worth noting that the U . maydis ku70nar1 atr1 mutant also grows substantially slower than the wild type strain and slower than the ku70nar1 chk1 mutant [24] . Perhaps Atr1 in U . maydis can affect ku70 mutant growth both positively and negatively through multiple pathways . More studies will be necessary to determine the detailed mechanisms of Atr1 in Ku-deficient U . maydis and to assess the extent of similarity between the fungal model and ALT cancer cells . Interestingly , the production of ALT phenotypes appears to share many features of DSB resection ( DSBR ) . For example , the antagonism of the Ku and MRN complex in the production of ALT telomeres is quite reminiscent of the interplay between the two complexes in DSBR [30] . Specifically , the radiation sensitivity defect of S . cerevisiae mre11∆ can be suppressed by eliminating Ku , suggesting that Ku antagonizes the function of Mre11 in DSB repair . Another parallel between ALT and DSBR concerns the Mre11 nuclease activity , which is not essential for either pathway [37] . In DBSR , the main function of the MRN complex is to recruit other resection factors such as Exo1 , Dna2 , and Blm . This may also be the function of MRN in ALT [38] . An implication of these similarities is that similar “downstream” factors may be responsible for both DSBR and ALT telomere production . A significant conundrum for this hypothesis is presented by the distinct overhangs produced by ALT and DSBR; the former generates primarily 5’ and the latter 3’ overhangs . One way to rationalize the distinct overhangs is to postulate that the specific nucleoprotein structure of telomeres or telomere factors alters the activity of the resection factors . For example , the intrinsic nuclease activity of Dna2 can act on ssDNA with a free 5’ or 3’ end , but in the presence of RPA , the activity on the free 3’ end is repressed , resulting in the production of 3’ overhangs in DSBR [39 , 40] . It is conceivable that Dna2 and other potential resection factors may act differently on telomere repeats because of the presence of specific telomere DNA-binding proteins . Such a model would have to be addressed by a combination of biochemical assays in vitro and genetic analysis in vivo . In summary , we have shown that elimination of ku in U . maydis leads to an attractive model for ALT . The two special strengths of the model are ( i ) its reversibility and accurate recapitulation of many characteristic features of ALT telomeres , and ( ii ) its ready amenability to molecular genetic manipulations , including the construction of null mutants to access the role of putative ALT factors . In contrast to previous knockdown studies in ALT cell lines or ALT models , the current use of null mutants reveals complete ( or nearly complete ) suppression of this pathway with the loss of Mre11 and Blm , implicating their products as prime targets for inhibitor development . Continued analysis of this model should provide mechanistic insights on the complex reactions that mediate the production of ALT telomeres , and suggest useful strategies for therapeutic intervention .
U . maydis strains were derived from the FB1or UCM350 genetic background [41 , 42] and are listed in S1 Table . Cells were grown in YPD ( restrictive medium ) , which represses the nar1 promoter , or MMD ( permissive medium ) , which induces the nar1 promoter [43] . Additional nutritional supplements ( 0 . 2 μg/ml of pantothenic acid and 50 mM of proline ) were added in MMD ( MMD+ ) for UCM350-drived cells . Controlled expression of ku genes under the nar1 promoter was performed as described previously [21 , 44] . Null mutants were constructed by replacing the entire open reading frames with cassettes expressing resistance to hygromycin ( HygR ) , nourseothricin ( NatR ) , or geneticin ( G418R ) [45 , 46] . Disruption cassettes for ctip and dna2 null strains were freshly generated for this study using Gibson Assembly ( NEB ) according to the manufacturer’s instructions . Briefly , three sets of primers ( S2 Table ) were used to generate three overlapping fragments: ( i ) 5’ UTR of dna2 ( or ctip ) linked to 5’ end of the hygromycin ORF , ( ii ) the complete hygromycin ORF flanked by short segments from the 5’ and 3’UTR of dna2 ( or ctip ) , and ( iii ) 3’ end of the hygromycin ORF linked to 3’ UTR of dna2 ( or ctip ) . Genomic DNAs and a plasmid for hygromycin resistance cassettes were used as the source of template for PCR . The PCR fragments were used as starting substrates for the Gibson Assembly reaction , and the full-length reaction products were cloned into a vector for further application . U . maydis strains were grown for ~ 36 hr in MMD and diluted into fresh YPD ( at OD600 of 0 . 005 ~ 0 . 01 ) to repress the expression of Ku proteins , or into fresh MMD ( at OD600 of 0 . 04 ) as controls . Typically , after growth for 18~20 hrs , the cultures were collected for DNA isolation . For time course experiments , the cultures were grown in YPD for varying durations ( 6 , 12 , 18 , 24 , and 30 hr ) , and then harvested for DNA isolation . For de-repression studies , cells were first grown for 18 hr in YPD to repress Ku expression , and then transferred into fresh MMD medium . After varying durations ( 0 , 24 , 48 , 72 , and 96 hr ) , the cultures were harvested , and their DNAs isolated for telomere analysis . Serial ten-fold dilutions samples of U . maydis cultures ( containing 104 , 103 , 102 , and 10 cells ) were applied to agar plates containing YPD , MMD , or MMD supplemented with pantothenic acid and proline ( MMD+ ) . The YPD plates were incubated at 30°C for 2 days , and the MMD and MMD+ plates for 3 days , and then photographed . Standard telomere Southern analysis was performed as described previously with some modifications [21 , 47] . Briefly , chromosomal DNAs were digested with Pst I ( or without Pst I ) and fractionated in 1 . 2% agarose gels . Following transfer to nylon membranes , the telomere restriction fragments were detected using 32P-labeled UmC8 or UmG8 probe that corresponds to eight copies of the Ustilago C-strand or G-strand telomere repeat . Signals obtained by scanning the Phosphor plates were quantified using ImageQuant software ( Molecular Dynamics Inc . ) . For this analysis as well as for other telomere assays , we examined the telomeres of at least two independent clones of the same genotype , and the results are quite reproducible . The standard in-gel hybridization analysis was performed using a combination of established protocols for U . maydis [21] with minor modification . The labeled UmG4 and UmC4 oligonucleotides ( S2 Table ) , corresponding to four copies of the U . maydis telomeric G-strand and C-strand repeats , were used as the probes , and hybridization was performed in the Church Mix at 45°C . For more detailed analysis of single- and double-stranded telomeric DNA in the ku mutants , two-dimensional gel electrophoresis was performed in a combination with in-gel hybridization assay . The samples applied to the 2-D gels include both PstI-digested genomic DNA and labeled double stranded DNA ladders ( used as markers for ds DNA arc ) . In some experiments , chromosomal DNAs were treated either Exonuclease I ( 2 . 5 U/μg ) or RecJf ( 4 U/μg ) prior to PstI digestion . The first dimension ( 0 . 5% agarose ) was run at 0 . 5 V/cm for 16 h in the absence of ethidium bromide ( EtBr ) . The gel was stained with 0 . 3 μg/ml EtBr to visualize the size standards and bulk chromosomal DNA . Gel strips containing DNA in the 0 . 5 kb to 15 kb size range are excised and impregnated in a 1 . 2% agarose gel containing 0 . 3 μg/ml EtBr . Electrophoresis was then performed in the orthogonal direction at 5 V/cm for 5 h . The gel was further processed according to the standard in-gel hybridization analysis as described above . The C-circle assay was carried out by the rolling circle amplification method as described previously [10] with some modificatons . Genomic DNAs ( 10 ng for C-circles and 20 ng for G-circles ) were combined with 10 μl 0 . 2 mg/ml BSA , 0 . 1% Tween , 1 mM each dATP , dGTP and dTTP , 1× Φ29 Buffer and 5 U Φ29 DNA polymerase ( NEB ) and incubated at 30°C for 16 h . Φ29 DNA polymerase was then inactivated at 65°C for 20 min . For quantification , the reaction products were dot-blotted onto a 2× SSC-soaked nylon membrane and the DNAs were UV-cross-linked onto the membrane . G-strand products of C-circle assays were hybridized at 45°C with 32P-labeled UmC8 probe ( S2 Table ) . The membranes were further processed according to the standard telomere Southern protocol as described above . Signals obtained by scanning the Phosphor plates were quantified using ImageQuant software ( Molecular Dynamics Inc . ) . The G-circle assay was carried out in the same way except that dATP , dCTP and dTTP were used for polymerization and the reaction products were probed with labeled UmG8 ( S2 Table ) . In some experiments , chromosomal DNAs were first treated with exonuclease λ ( 2 . 5 U/μg ) or exonuclease I ( 2 . 5 U/μg ) to eliminate linear DNAs , and then subjected to rolling circle amplification . The linearity of the C-circle and G-circle assays was also confirmed with serial dilutions of genomic DNA . However , we do not know if the two types of circles are amplified with identical efficiency by the Φ29 DNA polymerase .
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The majority of cancer cells use a special enzyme called telomerase to maintain telomeres . However , some cancer cells do not possess telomerase and use instead the so-called ALT mechanism to maintain telomeres . ALT is a complex pathway that entails the action of many factors , and the telomere DNAs of ALT cancer cells are extremely abnormal ( e . g . , they are often detached from the rest of the chromosomes and often exist in single-stranded forms ) . Currently , there are few manipulations that one can use to induce normal cells to engage in the ALT mechanism . The lack of a good “model” system poses a major obstacle to the understanding of this pathway and the development of effective counter-measures against ALT cancer cells . By removing Ku and a checkpoint factor from U . maydis ( a yeast-like fungus ) , we generated clones that exhibit many of the characteristic abnormalities of ALT cancer cells . Moreover , we identified two factors ( i . e . , Mre11 and Blm ) that when deleted , abolished the ALT phenotypes . Further analysis of this model may lead to the development of new strategies for shrinking the telomeres of cancer cells , thereby arresting their proliferation .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
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Mre11 and Blm-Dependent Formation of ALT-Like Telomeres in Ku-Deficient Ustilago maydis
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Fibrinogen is a serum multi-chain protein which , when activated , aggregates to form fibrin , one of the main components of a blood clot . Fibrinolysis controls blood clot dissolution through the action of the enzyme plasmin , which cleaves fibrin at specific locations . Although the main biochemical factors involved in fibrin formation and lysis have been identified , a clear mechanistic picture of how these processes take place is not available yet . This picture would be instrumental , for example , for the design of improved thrombolytic or anti-haemorrhagic strategies , as well as , materials with improved biocompatibility . Here , we present extensive molecular dynamics simulations of fibrinogen which reveal large bending motions centered at a hinge point in the coiled-coil regions of the molecule . This feature , likely conserved across vertebrates according to our analysis , suggests an explanation for the mechanism of exposure to lysis of the plasmin cleavage sites on fibrinogen coiled-coil region . It also explains the conformational variability of fibrinogen observed during its adsorption on inorganic surfaces and it is supposed to play a major role in the determination of the hydrodynamic properties of fibrinogen . In addition the simulations suggest how the dynamics of the D region of fibrinogen may contribute to the allosteric regulation of the blood coagulation cascade through a dynamic coupling between the a- and b-holes , important for fibrin polymerization , and the integrin binding site P1 .
Fibrinogen ( Fg ) is a 340kD multi-chain glyco-protein which can polymerize into fibrin , one of the main components of blood clots . Fibrin formation and lysis ( fibrinolysis ) are tightly controlled processes along the pathway leading to coagulation [1] . Fg , once activated by thrombin , which cleaves the fibrinopeptide A and B ( FpA , FpB ) , exposes specific A- and B-knobs which bind to the corresponding a- and b-holes of neighbor Fg molecules and initiate the fibrin polymerization process . Fibrin is later stabilized by additional non-covalent and covalent interactions . By further interacting with other blood components through its integrin binding sites , fibrin plays an important role in regulating coagulation and immune response . Fibrinolysis on the other hand is effected by plasmin , which cleaves fibrin on specific cleavage points in a well defined temporal sequence [2–4] . The elongated structure of human Fg , as shown by the crystallographic data [5] , is formed by two symmetric units which dimerize through a central globular E region . Each symmetric unit ( protomer ) is constituted by 3 peptide chains Aα , Bβ and γ which depart from their N-terminal region ( E region ) , form an elongated coiled-coil region , and end into two globular domains forming the D region ( Fig 1 ) . The C terminal segment of the Aα chain , i . e . the αC region , as well as the N-terminal parts of chain Aα and Bβ , including FpA and FpB , are mostly disordered ( thus , not resolved in the crystal ) . Although the available crystallographic structures of Fg show a relatively limited variability , atomic force microscopy images of adsorbed Fg on several surfaces reveal a large degree of conformational flexibility . Indeed , the typical tri-nodular structure of Fg , as observed in adsorption studies , where the three nodules correspond to the two D regions and the central E region , is very variable [6] , and the angle formed by the three nodules has a wide distribution [7 , 8] . The source of this conformational flexibility at the molecular level is not well understood . Early sequence analysis [9] and comparison of several crystallographic structures of Fg [5 , 10 , 11] suggested the presence of a hinge point in the middle of the coiled-coil regions connecting the E and D regions . However , the role of this hinge point and the extent of flexibility that it confers to the Fg molecule has not yet been described . Fg is one of the most abundant serum proteins initially adsorbing on foreign surfaces in contact with blood [12 , 13] , and it plays a crucial role in determining the inflammatory response to the material [14 , 15] . In the case of nanoparticles , which have been the subject of intense research for their use in nanomedicine [16 , 17] , Fg contributes with other serum proteins , upon pre-incubation in blood , to form a protein corona surrounding the particle and determining its fate in its clinical use , i . e . , circulation halftime , cell uptake , etc . [18] . Simplified models of Fg have been developed based on its hydrodynamic [19] and adsorption properties [20 , 21] and used to study adsorption on solid surfaces . Similar models have been used to study the competitive adsorption of serum proteins on material surfaces and , in particular , on nanoparticles [22] . In these models , as well as in models of fibrin polymerization [23] , the internal flexibility of Fg is either ignored or treated approximately , although it may play a very important role especially in the characterization of its hydrodynamic properties . Fg , in the polymerised form of fibrin , is a structure subjected to mechanical tension . For this reason early simulation work on Fg focussed on its mechanical properties under external stress [24–27] . Here , instead , we report the results of extensive molecular dynamics ( MD ) simulations performed on Fg in solution . The simulations allow for the identification of large bending motions centered at a hinge point on the coiled-coil region of Fg . We also present an extensive sequence analysis of Fg across vertebrate organisms which suggests that the bending motions associated with the hinge play one or more functional roles . The simulations indicate that one of these roles may consist in the exposure of plasmin cleavage sites on the coiled-coil region . From the simulation results we construct a simplified representation of the internal flexibility of Fg and use it to fit and explain experimental data on conformational distribution of the molecule adsorbed on mica . The results of the fit point to an asymmetry in the adsorption properties of the different sides of Fg , which can be explained by the presence of large charged patches that are unevenly distributed on the surface of the globular domains of the molecule . In addition , the simulation data allow us to characterize the dynamical properties of the D region of Fg involved in fibrin formation and immune response , highlighting the presence of coupled motions between the a- and b-holes and the integrin P1 binding site .
Fg undergoes large bending motions in all the simulations that we have performed . Principal component analysis ( PCA ) is used to quantify these motions . The dominant principal components of motions ( PCA modes , Fig 2 ( a ) –2 ( c ) ) of the Fg protomer are the same in all sampled trajectories as revealed by a large overlap ( see Methods section ) between the three dominant modes ranging from 0 . 6 to 0 . 9 between simulation subsets . In particular , the large overlap observed between the dominant PCA modes in both mono- and di-glycosilated and unglycosilated Fg protomer trajectories shows that the carbohydrate clusters do not affect the large scale dynamics of Fg in solution . Similarly , the dimerization state does not induce any noticeable change in the large scale motion of Fg . Dimer and monomer simulations show consistent hinge bending and the PCA-mode overlap between isolated protomer and dimerised protomer simulations is large . The difference between the dimer and protomer simulations is limited to the dynamics of the dimerization interface , where the absence of the disulphide bridges with the other protomer results in expectedly larger root mean square fluctuations ( RMSF ) localized to the residues α27–44 β 58–75 and γ 14–18 ( see S1 Fig in Supplementary Information ) . Because of the overlap of the largest PCA modes in the different simulation sets , the analysis presented here is done using all the available data merged together in a single set , which improves the statistical significance of the results . The first three PCA modes span the degrees of freedom associated with bending at a hinge point in the coiled-coil region ( Fig 2 ( a ) –2 ( c ) ) , while the 4th PCA mode is related to a pure torsion of the coiled coil along its axis ( not shown ) . The motions are reversible as shown by the time series of the PCA projections ( Fig 2 ( d ) ) Lower ranking PCA modes provide smaller contributions to the overall variance so they will not be analyzed further . The program DynDom [28] , applied to the extremal structures observed along the first PCA mode ( Fig 2 ( a ) ) of the Fg protomer , has been used to identify the regions of the molecule which are more rigid in our simulations , as well as the connecting hinge regions . DynDom reports the presence of two relatively rigid regions , separated by a hinge . The E region and the N-terminal part of the coiled-coil region represent one of the two rigid domains , while the C-terminal part of the coiled-coil region along with the D region represent the second . The hinge region is located approximately in the middle of the coiled-coil region and includes the break in the α-helical structure of the γ chain , which gives rise to a flexible loop ( residues γ70–78 ) , along with the neighbor residues on the Aα and Bβ chains ( Fig 2 ( a ) –2 ( c ) ) . The break in the α-helical structure of the γ-chain is facilitated by two proline residues . The bending around the identified hinge can be described by a bending angle γ and a torsion angle φ defined using groups of atoms from the E region , the hinge region and the D region ( Fig 2 ( a ) ) . The γ and φ angles strongly correlate with the projections along the dominant PCA modes: γ shows a linear correlation coefficient of 0 . 96 with a linear combination of the first 3 PCA modes ( 1st-2nd-3rd/10 , see Methods section ) , while φ has a 0 . 73 linear correlation coefficient with a linear combination of the 1st , 2nd and 4th PCA modes ( 1st/3+2nd/2+4th/2 ) . Our simulation data show a consistent and significant bending occurring at the hinge region and reaching bending angles below 90 degrees ( Fig 2 ( e ) ) . The time it takes for the Fg structure to reach a bending angle below 110 deg from conformations similar to the crystal structure ( bending angle above 150 deg ) is 19±1 ns along the trajectories , averaged over the 12 observed events ( see Fig 2 ( d ) and S1 Movie in Supplementary Information , for examples ) . The reverse process occurs twice in the simulations , taking 20 and 26 ns . The simulations of the full Fg dimer do not show significant correlations between the angle values observed at the two hinges . Comparison of the crystallographic structures of Fg coiled-coil regions from various organisms already hinted at the presence of a flexible hinge [5] . This hypothesis is also supported by hydrogen-deuterium exchange experiments [29] . The latter are in good agreement with our simulations: amino acids from the coiled-coil region with lower helical probability in the simulations ( Fig 3 ( b ) ) correspond to amino acids with low protection factors in the experiments . Our simulations help to clarify the fact that the presence of a flexible hinge is an intrinsic feature of the coiled-coil region , and not an artefact due to sequence or crystal-packing differences in the compared crystal structures . The hinge is positioned on the non-helical segment of the γ chain ( γ70–78 ) , most probably due to the resulting reduction in the stiffness of the coiled coil . This segment is non-helical also in the other crystallized Fg structures [10 , 11] . In addition , this segment has markedly helix-breaking features in most of the available Fg sequences from vertebrates that we have analyzed , showing a large density of proline and glycine residues ( Fig 3 ( a ) ) . As can be seen in Fig 3 ( a ) , the appearance of prolines characteristic for the hinge region happens in three stages during evolution . The lamprey ( Petromyzon marinus ) has no proline , fish ( Danio rerio ) have a single proline and tetrapods have two or more . These steps coincide with major changes in the clotting cascade , namely the appearance of the intrinsic pathway in jawed vertebrates and the addition of the contact pathway ( linking blood clotting and immune response ) in tetrapods [30–32] . We used the program DisEMBL [33] to analyze the Fg sequences and identify disordered or loop segments in the coiled-coil region . The region corresponding to the non-helical segment of the γ chain is marked as a hot-loop with high probability in most of the sequences , with the exception of lamprey Fg which , as mentioned above , is known to have a simpler coagulation mechanisms than the other vertebrates [31] ( Fig 3 ( a ) ) . This analysis supports the idea that the non-helical segment of the γ chain provides a function that is strongly conserved across vertebrates . Our simulations suggest that this conserved function is linked to the bending motion of the coiled-coil region . Besides providing flexibility to the individual Fg molecules as well as the fibrin fibers [5] , the bending at the hinge may help expose the plasmin cleavage sites located nearby on the coiled-coil region , an hypothesis already proposed in ref . [9] . Our simulations strongly support this hypothesis showing that the α-helical structure around the plasmin cleavage sites Aα104–105 and Bβ133–134 is partly disrupted by the bending motions , and the exposure to the solvent of the involved peptide bonds increases ( Fig 3 ( c ) and 3 ( d ) ) . The α-helical structure lost during bending is generally replaced by coil structure in our simulations and not by extended β-sheet structure as observed in experiments [34] and simulations [35] of fibrin subject to tension . The observation of the transition to extended β-sheet may then be linked to the presence of tension along the molecule and/or require significantly larger times than those simulated here to occur spontaneously . It is known that the fibrin molecules straighten when they are integrated into protofibrils [36] . This does not mean that the flexibility of individual molecules is lost . The twisting of fibrin fibers [37] compresses molecules in the center of the fiber and stretches them on the perimeter . If the hinge bending is necessary to accommodate such deformation , it is reasonable to believe that the bending motions at the hinge may actually be reduced by tension applied along the fiber axis . Thus , fibrinolysis assisted by the bending motions at the hinge may selectively take place on fibrin molecules subject to reduced tension . This hypothesis is supported by experimental evidence indicating reduced plasmin fibrinolytic effectiveness on fibrin fibers subject to mechanical tension [38] . Bending may also play an important role in fibrin polymerization . According to the recently proposed Y-ladder model of fibrin polymerization [23] , in the early stages of the process , the protofibril grows as a single strand where each monomer is connected to the next by a single A-knob-a-hole bond between the E domain of one molecule and the D domain of the next in the chain , giving rise to a Y-ladder structure . The latter is later transformed in a double-stranded protofibril by the formation of the other knob-hole bonds and D-D bonds . Hinge bending as observed in the present simulations can provide the necessary flexibility to accommodate new molecules in the growing fiber . In support of this role , Fig . 6C of ref . [8] seems to depict single stranded growth events involving bent fibrin molecules as indicated by arrows 5 and 6 . More in general , an imperfect straightening of Fg during integration into a double-stranded protofibril would provide a mechanism for the formation of branch points in the fibrin network . In the present simulations , several parts of the molecule have been omitted because they are unresolved in the crystal structure , and too long ( and disordered ) to be meaningfully sampled in the course of our simulations . These include the the βN stretch ( residues β1–57 ) and the αC stretch ( residues alpha 201–562 ) . Both parts are mostly disordered ( apart from a tendency to form a compact conformation in the C terminal part of the αC domain ) as revealed by several experimental techniques , including H/D exchange [30] , X-ray crystallography [5 , 10 , 11] and NMR [39] . In fibrinogen , the compact domains of the αC region from the two protomers are believed to interact with each other and with the fibrinopeptide B ( part of the unresolved βB stretch ) in proximity of the E region of the molecule , while in fibrin the alphaC domain are believed to move away from the E domain and take part in inter molecular interactions [40] . Although none of these unresolved stretches are known to interact with the coiled coil region of the protein , in principle they could interfere with the bending movements observed in our simulations . The lack of evidence for interactions with the coiled-coil region and the flexible nature of these stretches suggest that their presence will not directly contribute to a stiffening of the hinges of the coiled-coil region . On the other hand , we cannot exclude that their presence may bias bending along particular directions by hindering hinge movements due to excluded-volume effects . Fg adsorbed on surfaces often forms tri-nodular structures , as reported by AFM experiments [7 , 8] . The three nodules correspond to the E and the two globular D regions of the molecule . The distribution of the α angle formed by the three nodules can be used to quantify the flexibility of Fg [7 , 8] . The model for Fg emerging from our simulations shows that the flexibility is provided by the presence of the two hinges , while the rest of the molecule does not undergo large conformational changes . Assuming that adsorption does not significantly change this picture , the model can be used to fit the conformational distributions from adsorption data and test various hypothesis on the behavior of Fg . In particular we want to test whether adsorption induces correlations in the behavior of the molecule at the two hinges , which could not be observed in the solution simulations . Such correlations could then be related to specific interactions between the protein and the surface . To this end , we proposed two different models for data fitting: on one hand we used a model where the conformation of one hinge does not affect the conformation of the other , so that the resulting distribution of the experimentally observed α angle , because of the symmetry of Fg , can be written as: P ( α ) = ∫ w ( γ 1 , φ 1 ) w ( γ 2 , φ 2 ) P γ 1 , φ 1 , γ 2 , φ 2 ( α ) d γ 1 d φ 1 d γ 2 d φ 2 ( 1 ) where γ1∣2 and φ1∣2 are the bending and torsion angles at the two hinges , respectively ( see Methods section ) , Pγ1 , φ1 , γ2 , φ2 ( α ) is the distribution of the α angle of adsorbed Fg molecules with given hinge angles γ1 , φ1 , γ2 , φ2 , and w ( γ , φ ) is the statistical weight of the hinge angle pairs . On the other hand , we proposed a more general model which includes possible correlations between the two hinges: P ( α ) = ∫ W ( γ 1 , φ 1 , γ 2 , φ 2 ) P γ 1 , φ 1 , γ 2 , φ 2 ( α ) d γ 1 d φ 1 d γ 2 d φ 2 ( 2 ) where W ( γ1 , φ1 , γ2 , φ2 ) is the combined statistical weight of the conformations of the two hinges . The distributions Pγ1 , φ1 , γ2 , φ2 ( α ) were determined using a simple Monte Carlo model ( MC ) for Fg adsorption ( see Methods ) . The statistical weights w ( γ , φ ) and W ( γ1 , φ1 , γ2 , φ2 ) for the two models need to be determined by fitting the experimental data . For simplicity , we transformed the integral in Eqs ( 1 ) and ( 2 ) in a sum over discrete bins defined in the γ − φ space , so that the weight functions w ( γ , φ ) and W ( γ1 , φ1 , γ2 , φ2 ) become discrete arrays where each element is the statistical weight of the corresponding bin . The fit is done using a maximum entropy approach , where the statistical weights from our simulations ( Fig 2 ( e ) ) are used as prior knowledge ( see Methods for details ) . Both models have been used to fit the conformational distribution of Fg adsorbed on mica as observed in AFM experiments [7] . With the independent hinge model , the χ2 of the fit remains above the threshold of 5% confidence level for the given number of degrees of freedom , indicating a poor fit . On the other hand , the general model fits the data very well ( see Fig 4 ( a ) ) . More generally , it is possible to show that the decoupled weight w ( γ1 , φ1 ) w ( γ2 , φ2 ) of the independent hinge model ( Eq 1 ) would give rise to P ( α ) distributions peaked at 180° for any weight function w ( γ , φ ) , which is incompatible with the experimental evidence of a deep trough at 180° [7] ( Fig 4 ( a ) ) . To see this , we can initially fix the φ angles of the two hinges to the same value so that the E and D domains are on the same plane and we can treat the problem as two-dimensional and get a simple analytical expression of α as function of γ1 and γ2 . Then , we can focus on hinges with the same γ angle distribution w ( γ , φ0 ) . A single maximum in this distribution will clearly lead to conformations where both hinges sample the same γ angle , which , thanks to the symmetry of the molecule , results in an α angle of 180° . But also distributions w ( γ , φ0 ) with two ( or more ) equal maxima , will still lead to α-angle distributions with an absolute maximum at 180° , because of the contributions coming from the conformations where the two γ angles sample the same maximum and because the peak at 180° , which has only one tail due to being at the end of the definition interval of the α angle , will grow twice as big as the other peaks due to symmetrical contributions cumulating on the single tail of the peak ( see S3 Fig in Supplementary Information , for an illustrative example ) . Releasing the constraint on φ will not change the situation . The slight drop on the expected counts of the independent hinge model fit at 180° in Fig 4 ( a ) is within the numerical uncertainties introduced by binning the MC data . An analysis of the fitted parameters of the general model , reveals that the largest contributions to the tail of the distribution ( 95° < α < = 110° ) corresponding to the lower gaussian peak identified in ref . [7] ) come from hinge angle pairs with 60° < γ1 ≤ 100° and 100° < γ2 ≤ 140° , that is from fg molecules with one strongly bent and one moderately bent hinge . On the other hand the largest contributions to the 160° peak come from hinge angle pairs with 100° < γ1 ≤ 140° and 140° < γ2 ≤ 180° , that is fg conformations with one moderately bent and one almost unbent hinge . The failure of the independent hinge model and the success of the general model to fit the observed conformational distribution of Fg adsorbed on mica indicates that the conformation of Fg at one hinge affects the conformation at the other hinge upon adsorption on mica . The most probable explanation for this effect is that the propensity to form interactions with mica is not distributed uniformly on the Fg surface . The optimization of the contact surface with mica on both symmetric protomers possibly induces hinge correlations . Since the correlation is observed on a mica surface , which is charged , it is reasonable to assume that the difference between the sides of the Fg protomer has an electrostatic origin . We have verified the presence of asymmetrically distributed charged patches on the Fg surface by calculating the electrostatic potential generated by the molecule along the simulations . As shown in Fig 4 ( b ) , the calculations identify a large negatively charged patch per protomer located on one side of the D region but absent on the other side . This asymmetrical distribution supports our hypothesis regarding the origin of correlations between the two Fg hinges . It has previously been noted that such patches should contribute to the Fg-Fg association during fibrin fibril formation [41] . Here we can identify the D region patch as the a-hole binding site for the fibrinogen A-knob described in reference [42] . Evidently , the involvement of this part of the molecule in interactions with the adsorbing surface may have consequences with respect to fibrin formation . Unlike the data from Fg adsorption on mica , the data from Fg adsorption on highly oriented pyrolytic graphite ( HOPG ) rendered hydrophilic with an amphiphilic carbohydrate-glycine modifier ( GM ) [8] can be fitted with both the general and the independent hinge model at a confidence level larger than 5% ( see S4 Fig in Supplementary Information ) . This may be due to the differences in the conformational distribution induced by a different surface ( for example , the 180° bin on HOPG is significantly more populated than on mica , while the tail of the distribution at small α angles is less populated than on mica ) or due to the lower number of bins/histograms used to report the experimental data on HOPG relative to those on mica in ref . [7] and the overall lower number of data binned , both of which provide less stringent constraints to the fit . Before closing the subsection we would like to note that , the simplified model of Fg flexibility , emerged from the simulations and used in this section to fit experimental data , may help to refine the models used for describing fibrinogen hydrodynamical properties [19] , in the context of adsorption on material surfaces and nanoparticles [20–22] , as well as fibrin polymerization [23] . Fibrinogen has a multitude of binding partners which help the molecule to carry out its functions . In addition to the plasmin cleavage sites on the coiled-coil region , discussed above , the D region of Fg hosts several other functional binding sites . Two important binding sites are the a- and b-holes , on the γC and βC subunit of the D region , respectively , which bind the A- and B-knobs exposed by thrombin upon cleavage of the respective fibrino-peptides . As mentioned already , these binding sites are important for fibrin polymerization as they help to create lateral non-covalent connections between fibrinogen molecules . Surface plasmon resonance experiments showed that binding of fibrino-peptide analogs to the b-hole increases the binding affinity between the a-hole and soluble fibrin fragments containing the A-knob [43] . Hydrogen-deuterium exchange experiments comparing wild-type Fg with the Bβ235Pro/Leu mutant , showed that the mutation , not only leads to a local increase of the flexibility at the βC-γC interface , but also alters the flexibility of the loops surrounding the a-hole on the γC domain [29] . In addition , it has been suggested that engagement of the B knobs into the b-holes produces a subtle domain rearrangement in the D regions , favoring lateral aggregation of protofibrils [40] . All these information , thus , suggest that allosteric effects can take place in the D region . Our simulations , analyzed using principal component analysis restricted separately to the two globular subunits ( γC and βC ) , show that the loops surrounding the a- and b-hole ( residues γ354–363 , γ293–302 and β382–393 , β422–432 ) [44] undergo large correlated fluctuations , as demonstrated by the large weight of those loops in the largest modes of the PCA . To better clarify this aspect , we verified that the dynamics of the a- and b-holes are correlated by the presence of a high correlation pathway ( measured using the linear mutual information ( LMI ) with rcrit ≤ 0 . 85 , see Methods section ) connecting them . At the same critical LMI level , several other distant pairs of residues can be connected on the D region including parts of the P1 [45] and P2 [46] integrin binding sites on the γC domain . All the identified pathways pass through the same bottleneck of residues at Bβ204Pro , γ216Gly , γ217His , γ225Glu . More than 75% of all pathways pass through the residues γ200Gly , γ253Trp and γ348Tyr . The identified narrow pathway is shown in Fig 5 ( a ) . Some of these residues are in contact with the residues locally perturbed upon the Bβ235Pro/Leu mutation , which may explain why the effects of this mutation reach the a-hole . With the exception of γ217His all residues belonging to the bottleneck of the high correlation pathways are absolutely conserved among vertebrates ( sequences discussed above ) . In addition , the same residues appear to be associated with disease-inducing mutations in humans [47] . These data , taken together , reinforce the hypothesis of an allosteric network connecting the a- and b-holes . The integrin binding sites represent another important element of the functionalities of Fg , involved in facilitating immune response . Several residues of the γC domain have been implicated in integrin binding [45 , 46] . Our simulations show that the accessibility of the P1 region of the integrin binding site is affected by the large scale fluctuations of the D region and of the loops around it . Indeed the three largest PCA modes of the D region involve rigid rotations of the βC relative to γC domains , which directly alter the P1 accessibility . In addition , these modes have large components on two loops surrounding the P1 binding site , γ354–363 and β280–285 , whose coordinated fluctuations significantly affect P1 accessibility . The opening/closing mechanism of the P1 cleft described by the largest of the PCA modes is characterized by a change in distance between the integrin binding site P1 and the βC loop B280-B285 from about 1 . 2nm to 1 . 9nm ( Fig 5 ( b ) ) . Extending the PCA analysis to include also the C-terminal segment of the coiled-coil region ( residues α125–189 , β155–458 and γ100–394 ) reveals that the relative rotations of the βC and γC domains described above are associated with movements of the C-terminal segment of the coiled-coil region relative to the D domain . In particular , the second largest PCA mode describes a motion where the distance between the βC domain and the coiled-coil region increases in combination with an opening of the b-hole ( Fig 5 ( c ) ) . This mechanism recalls very closely the one which has been hypothesized to explain how the interaction between B-knob and b-hole induces a slight conformational change which favors the lateral aggregation of protofibrils [40] . The classical atomistic molecular dynamics simulations of fibrinogen in solution reveal the extraordinary flexibility of the molecule resulting in large bending motions of the coiled-coil regions , favored by the presence of two hinges . The hinges are linked to a non-helical segment in the coiled-coil region of the γ chain , a feature conserved across vertebrates . This may indicate a possible functional role for the bending motions . In the simulations , the bending of the coiled-coil region helps to expose the early plasmin cleavage sites . A simplified model of Fg flexibility has been derived from the simulations and used to test hypothesis about Fg adsorption by fitting AFM data . This lead to hypothesize correlations between the two hinges upon adsorption on mica . A probable cause for the correlations is an asymmetric distribution of charged patches on the surface of the molecule and in particular on the D globular regions , which is observed in the simulations . We anticipate that the simplified model presented here could lead to more accurate estimates of the hydrodynamic properties of Fg . Furthermore , an analysis of the pathways joining residues with highly correlated motions in the simulations hints at an allosteric regulation of the binding at the a- and b-hole in the D region of Fg .
The simulations are based on the crystal structure of human Fg ( PDB ID: 3GHG ) [5] . The carbohydrate groups that are only partly resolved in the crystal have been modelled using VMD and introduced in some of the simulations . The unresolved parts of the protein structure ( the αC domain and the N terminal segments of all the chains ) have not been included in the calculations . Several molecular constructs have been prepared to assess the role of the different components of the Fg molecule . The effects of the carbohydrate chains on the dynamics of Fg have been investigated by simulating both the unglycosylated system , the sytem glycosylated at residue β364 ( mono-glycosylated ) and the system glycosylated at residue β364 and γ52 ( di-glycosylated ) . Protomer-protomer interactions were investigated by simulating both the full fibrinogen dimer ( 6 protein chains ) and the protomer system ( 3 chains ) . Although the single or isolated protomer has never been observed experimentally , the reason for studying it are the following: 1 ) the two protomers in the dimer are identical , i . e . they will show a similar behavior , 2 ) since we neglect the unresolved parts of the molecule , the two protomers interact only through the small dimerization interface , i . e . their reciprocal influence is limited , 3 ) A ring of disulphide bridges covalently bonding the three chains in the N-terminal part of the coiled-coil region dramatically reduces the influences of the dynamics of the dimerization domain on the rest of the molecule ( see S1 Fig in Supplementary Information ) . In addition to that , the simulations of the protomer take much less computational time than the dimer i . e . they can be extended to significantly larger time scales . Rectangular simulation boxes with explicit TIP3P water [48] and physiological ion concentration ( 150 mMol [NaCl] ) were prepared using VMD [49] ( Table 1 for box sizes ) . Isobaric-isothermal simulations were set up at a temperature of 310K and pressure of 1atm using NAMD [50] with a Langevin thermostat and a Langevin piston barostat [51 , 52] using 200 ps−1 and 100 ps−1 as decay time , respectively . The covalent bonds involving hydrogen atoms were fixed in length and a 2fs timestep was used . The CHARMM22 force field with CMAP corrections [53] was used with its recent extension to carbohydrates [54] in combination with ParamChem ( http:/www . paramchem . org ) and the CHARMM generalized force field ( CGenFF ) [55] . This force field has been already tested in a large variety of systems and found very reliable in reproducing several biophysical properties , including also the folding process of proteins [56] where it was shown to provide results very similar to other popular force fields ( several versions of AMBER and modifications of CHARMM ) in the characterization of the native state of proteins . The van der Waals forces were cut off at 1 . 2nm while PME was used for long range electrostatic interactions with a grid spacing of 1Å . After energy minimization ( NAMD’s conjugate gradient algorithm , 15000 steps ) of hydrogen atoms and water molecules , the system was heated and equilibrated for 10ns . Production runs statistics are given in Table 1 . We employed collective variable constraints ( distanceXY , directiondir or orient ) to keep the main axis of the molecule aligned to the simulation box and verified that this had no influence on the overall dynamics by comparing to unconstrained simulations . To identify the collective motions of the whole Fg molecule and of its subdomains we performed several principal component analyses ( PCA ) [57] using wordom [58] and GROMACS utilities [59] . DynDom [28] was used to identify rigid domains and hinges of motion . The overlap between spaces spanned by the dominant PCA modes of different simulations was used to quantify the similarity of the observed dynamics [60] . The overlap is defined as: O ( { x _ i } , { y _ i } ) = 1 n ∑ i = 1 n ∑ j = 1 n ( x _ i · y _ j ) 2 , ( 3 ) where { x ¯ i } and { y ¯ j } are the two subspaces spanned by the principle components x ¯ 1 … x ¯ n and y ¯ 1 … y ¯ n , respectively . The linear correlation coefficient between two variables is defined as the ratio between the covariance of the two variables and the product of the two standard deviations , r = ∑ ( x i − x ‾ ) ( y i − y ‾ ) / ∑ ( x i − x ‾ ) 2 ( y i − y ‾ ) 2 . If P1 ( t ) , P2 ( t ) , PN ( t ) are the projections of the trajectory at time t along the 1st , 2nd and Nth PCA components , respectively , a linear combination CP ( t ) of PCA projections is obtained as CP ( t ) = ∑j aj Pj ( t ) , where aj are the coefficients of the combination . As mentioned in the Results section , a very large correlation coefficient ( 0 . 96 ) between CP ( t ) and the γ angle was obtained by using a1 = 1 , a2 = −1 and a3 = −0 . 1 ( and all the other aj = 0 ) . Apart from PCA , as a further measure of correlation between the movement of the residues , the linear mutual information ( LMI ) [61] was used which is defined as follows: L M I ( x i , x j ) = 1 2 ( ln [ det C i ] + ln [ det C j ] - ln [ det C i j ] ) ( 4 ) where xi is the position of the ith Cα atom , C i = ⟨ x i T x i ⟩ and Cij = ⟨ ( xi − ⟨xi⟩ ) ( xj − ⟨xj⟩ ) ⟩ The LMI matrix was calculated for mono-glycosilated fibrinogen simulations . Based on it , a network was built with each node representing a residue . Nodes are connected if their LMI exceeds a threshold rcrit . This parameter was successively reduced until a pathway between the a- and b-holes was identified at rcrit = 0 . 85 . The shortest path connecting the two binding pockets is determined in this network . To save computation time only pathways of a maximum length N where considered such that r c r i t N > 0 . 025 . As a control , pathways between all residues that are at least 6nm apart were identified . The electrostatic potential of the globular domains of fibrinogen was calculated by solving the Poisson-Boltzmann equation with the APBS [62] software over a series of similar aligned structures . Then , the electrostatic potential was averaged over the structures . The time averaged potential for each domain was calculated by averaging the potential at each grid point over all snapshots . Sequences of Fg were identified by searching the UniProt data base [63] . In this search , sequences of 83 different species were identified that contained the γ chain at least up to the hinge region . Complete sequences of all three chains in the coiled-coil region where found for 33 species . Sequence alignments were performed using Chimera [64] , and the analysis of structural disorder was performed using the program DisEMBL [33] . A simplified representation of Fg adsorption has been developed to map Fg conformations represented by the hinge angles to the conformations observed in AFM experiments , characterized by the α angle between the globular domains ( see Fig 6 ) . The model for Fg is built around a central rod , representing the stiff coiled-coil regions , which connects the two hinges . The E region is represented as a sphere placed at the center of the rod . The D regions are also represented as spheres connected to the hinges with rods , that can pivot around the hinge . The dimensions of the model components are extracted from the crystal structure ( Fig 6 ) . An additional point ( x0 in Fig 6 ) on the surface of the E region is used as a reference for the measure of the torsion angles at the two hinges . Within this simplified representation , adsorption occurs if and only if the distance of the E and the two D regions of a Fg molecule from the adsorbing surface ( represented by an ideal plane ) is lower than a threshold hmax and both hinges lie above the surface . We use a simple Monte Carlo ( MC ) algorithm to generate a large set ( 4 ⋅ 107 ) of coarse grained adsorbing Fg conformations . The MC algorithm consists of creating adsorbed conformations by randomly drawing hinge angles for one protomer and placing it such that the first D region as well as the E region contact the surface . The bending angle γ for the second hinge is randomly drawn as well , while the value of φ for the second hinge is calculated to ensure that the second D-region is in contact with the surface . The resulting conformations are accepted in such a way that a uniform distribution of γ and φ is obtained at both hinges . For each accepted conformation we measure the hinge angles , as well as the α angle ( Fig 6 ) between the globular regions , as it would be observed in an AFM experiment , i . e . , we project the regions’ centers on the surface and measure the angle between the three points . To fit the experimentally observed distribution of the α angle we divided the γ − φ-plane characterizing the conformation of one hinge into 12 rectangular bins , indexed accordingly . Each adsorbing Fg conformation can be characterized by the two discrete indexes i and j of the ( γ , φ ) region where its two hinges occur . We then determine the bending distribution Pij ( α ) observed in the conformations sampled with MC from each pair of bins i and j of the ( γ–φ ) plane ( see S2 Fig in the supporting information ) . It is important to note that , not all ij pairs lead to adsorbing structures . We exclude the non-adsorbing ij pairs ( bins ) from the computation . We then assume that the observed experimental distribution P ( α ) is a superposition of the distributions from the adsorbing conformations of Fg in each pair of bins . The most general form of superposition is given by P ( α ) = ∑ij aij Pij ( α ) , which is the discretized version of Eq 2 , where the parameters aij replace the continuous weights W ( γ1 , φ1 , γ2 , φ2 ) . The aij parameters can then be fitted to the experimental distribution . If the two hinges behave independently , then , given the symmetry of the molecule , an independent hinge model P ( α ) = ∑ij ai aj Pij ( α ) should be able to fit the data . This model is the discretized version of Eq 1 , where the parameters ai replace the continuous weights w ( γ , φ ) . In this case , we will have only the ai parameters , i . e . , one parameter for each bin in γ − φ-space . Both the general and the independent hinge model are fitted to the experimental data using the maximum-entropy method [65] , where the Shannon entropy of the parameters is maximized with respect to the a priori knowledge of the system , with the constraint that the reduced χ2 = ∑α ( N ( α ) − Ntot P ( α ) ) 2/Ntot P ( α ) equals 1 , where N ( α ) are the histograms of the α angle from the experiments , and Ntot is the sum of the histograms . The expression that is maximized for the general model is: J = - ∑ i j a i j log ( a i j / m i j ) - λ ( χ 2 - 1 ) - α ( F - 1 ) ( 5 ) where mij is the bias distribution for the parameters , F is the normalization function of the parameters F = ∑ij aij and λ and α are Lagrange multipliers to impose the constraints on χ2 and normalization . A similar expression is used for the independent hinge model with the necessary modifications . The bias distribution mij represents the information already known about the parameters before fitting the data . The statistical weight wi of each γφ bin as measured in the atomistic simulations ( Fig 2 ( e ) ) were used to estimate the mij = wi wj .
|
Fibrinogen , a protein found in the blood of vertebrates , when activated , aggregates and forms fibrin fibers , the basis of a blood clot . Clots are broken down by the enzyme plasmin , which cuts fibrin fibers at specific places , thus helping the regulation of clot persistence . A mechanistic understanding of fibrin degradation by plasmin is still missing . An important determinant of this process might be the flexibility of fibrinogen . The flexible nature of fibrinogen is reported , for example , by the great variety of conformations observed when fibrinogen adsorbs on material surfaces . However , limits in the spatial resolution of these experiments preclude the identification of the atomistic mechanism behind this flexibility . Here , we perform computer simulations that help identifying with atomistic detail large bending motions occurring at a specific hinge on the molecule . We show how these bending motions can explain the variable conformations observed in experiments and how they help exposing sites where plasmin can cut fibrinogen . Furthermore , our simulations let us identify cooperative effects involving several distant parts of fibrinogen that may play a role in the assembly of fibrin fibers . Both the bending and the cooperative effects , thus , represent potential mechanisms for the regulation of blood clotting .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Methods"
] |
[] |
2015
|
The Internal Dynamics of Fibrinogen and Its Implications for Coagulation and Adsorption
|
Human alveolar echinococcocosis ( AE ) is a highly pathogenic zoonotic disease caused by the larval stage of the cestode E . multilocularis . Its life-cycle includes more than 40 species of small mammal intermediate hosts . Therefore , host biodiversity losses could be expected to alter transmission . Climate may also have possible impacts on E . multilocularis egg survival . We examined the distribution of human AE across two spatial scales , ( i ) for continental China and ( ii ) over the eastern edge of the Tibetan plateau . We tested the hypotheses that human disease distribution can be explained by either the biodiversity of small mammal intermediate host species , or by environmental factors such as climate or landscape characteristics . The distributions of 274 small mammal species were mapped to 967 point locations on a grid covering continental China . Land cover , elevation , monthly rainfall and temperature were mapped using remotely sensed imagery and compared to the distribution of human AE disease at continental scale and over the eastern Tibetan plateau . Infection status of 17 , 589 people screened by abdominal ultrasound in 2002–2008 in 94 villages of Tibetan areas of western Sichuan and Qinghai provinces was analyzed using generalized additive mixed models and related to epidemiological and environmental covariates . We found that human AE was not directly correlated with small mammal reservoir host species richness , but rather was spatially correlated with landscape features and climate which could confirm and predict human disease hotspots over a 200 , 000 km2 region . E . multilocularis transmission and resultant human disease risk was better predicted from landscape features that could support increases of small mammal host species prone to population outbreaks , rather than host species richness . We anticipate that our study may be a starting point for further research wherein landscape management could be used to predict human disease risk and for controlling this zoonotic helminthic .
Ecologic systems are nested within one another . This well-known fundamental hierarchical organization [1] is easy to detect in nature but has been generally undervalued as a source of influence on the structure and development of pathogen transmission patterns , and also as a means of understanding the crucial connections between local processes and large-scale distribution patterns . At a community level , Guernier et al . [2] explored the worldwide distribution of human parasitic and infectious diseases ( PID ) and found that , after correcting for cofactors , PID richness ( as for free-living species ) , was strongly correlated with latitude: PID species diversity decreased as one moved from the equator , and the strongly nested pattern of their global distribution was confirmed . They also pointed out how , along such gradient , the maximum range of precipitation and monthly temperature might be intimately connected in generating the observed pattern of disease diversity . This similarity in the diversity patterns of free living organisms and PIDs suggests that common processes are at work which might be explained at large by the climatically based energy hypotheses ( energy availability generates and maintains species richness gradients ) [2] . However , for a given PID species distribution patterns at regional scales may be more complex . Transmission may depend not only on species richness but also on host assemblage composition [3] . For instance , reducing host diversity can increase disease transmission when the lost species are either not hosts or suboptimal for the pathogen , especially when population size of optimal hosts are inversely correlated to species richness . On the other hand , a large number of competent host species may provide a much more stable transmission system for transmission , that is robust against environmental disturbances , anthropogenic or natural , that temporarily decrease the density of some host populations . However , empirical examples of the relationships between host biodiversity and parasite transmission are still relatively rare , in part because suitable datasets that may allow comparisons are deficient [4] . Further complexities are encountered when the distribution range of a pathogen covers a large number of host communities and climatic zones . Such a wide-extent distribution-range consists of a nested hierarchy of transmission systems that can be inter-connected in space and time via dispersion [5] . At these scales , datasets with high resolution and precision , describing hosts , disease distribution and environmental factors ( such as climate and land cover ) are most often heterogeneous . Human alveolar echinococcocosis ( AE ) is a highly pathogenic parasitic disease caused by the larval stage of the cestode Echinococcus . multilocularis , which usually results in a slow-growing multivesicualted tumor-like lesion in the liver of cases . The parasite's life-cycle can exploit a large number of small mammal intermediate hosts ( >40 species known to date ) and several definitive host species ( e . g . foxes , coyotes , wolf , dog , etc . ) . Human infection arises from accidental ingestion of E . multilocularis eggs from direct contact , or via food contaminated by carnivore definitive host faeces . Although patchy , its distribution range covers the Northern hemisphere from the Arctic to the 28th parallel on the Tibetan plateau . In Eurasia , although other carnivores can theoretically sustain the transmission of E . multilocularis , the whole range of the parasite is actually included in the range of its main definitive host , the red fox , Vulpes vulpes , except on the Tibetan plateau where the cestode circulates through the Tibetan fox , Vulpes ferrilata [6] . In contrast , throughout Eurasia , E . multilocularis transmission is sustained by a large variety of different small mammal communities [7] . Within Eurasia , continental China stretches from Siberia in North Xinjiang , Inner Mongolia and Heilongjiang to tropical rain forest in Yunnan , and includes high altitude areas such as the Tibetan plateau , several deserts ( some of them below sea level ) , coastal and agricultural areas ( Figure 1 ) . Based on mammal and plant species comparisons a total of 25 biogeographical regions and 77 sub-regions are defined [8] . Transmission of E . multilocularis is sustained in diverse small mammal communities of western and northern China . While some of these communities have been investigated [5] , [9] many remain unexplored . Here we examine the distribution of human AE disease in China on two spatial scales ( continental and regional ) and test the hypotheses that human disease distribution can be explained by the distribution of intermediate host species richness ( i . e . host biodiversity ) or by environmental factors such as climate and land cover or an interaction between them . Our central hypothesis is that human AE distribution is best explained by considering the impact of landscape on low species-rich communities prone to population outbreaks , rather than host diversity per se . In that case , for endemic areas , basic information on land cover and climate could be used as a proxy to predict high risk transmission systems for this pathogenic helminthic zoonosis .
Continental maps of China for distribution of rainfall , altitude and temperature can be found in Supplementary Material . In summary these abiotics factors did not correlate well to human AE disease distribution . Furthermore , the continental distribution of human AE was not clearly correlated to small mammal species richness , even considering the distribution and number of species that are known to be potential intermediate hosts for E . multilocularis or pest species ( Figure 2 ) . However , the overlap between the spatial distribution of human AE and the spatial distribution of a combination of two Global Land Cover 2000 categories , i . e . ‘meadows’ and ‘alpine and subalpine meadow’ was very clear . The main endemic areas correspond to the central and eastern Tibetan meadows , those of the Tien Shan and Altai Mountains and northern Inner Mongolia ( Figure 3a ) . On the Tibetan plateau , the human AE endemic foci corresponds to the Global land cover category ‘alpine and subalpine meadow’ , characterized by alpine meadows densely covered with thick perennial sedges ( Kobresia spp ) and various forbs , lying generally below 4500 m as described by Schaller [47] . In the western part of the human AE focus , alpine meadow becomes largely riparian with streams , seepages , swamps and lakes . The area is characteristically composed of a 10–40 cm thick moisture retaining sod layer providing a long growing season . Livestock tend to concentrate on grassy habitat ( 28–70 animals/km2 ) and continual grazing and trampling coupled with solifluxion , cause extensive erosion . This anthropogenic activity helps to maintain extensive open habitats favorable to the population surges of the plateau pika ( Ochotona curzoniae ) , a known intermediate hosts of E . multilocularis [48] . By contrast , human AE distribution did not correlate with the Global Land Cover 2000 category ‘alpine and sub-alpine plain grass’ ( Figure 3b ) . This category , lying generally between 4500 and 5000 m , corresponds to an Alpine steppe , dry , cold and windy . Plant coverage is sparse ( Stipa spp , Festuca spp , Poa spp , Carex moorcroftii ) seldom more than 30% , and soil is poor without a sod layer [47] . Despite its great size ( several 100 , 000 km2 ) , the Eastern Tibetan plateau focus of human AE disease correlates well with Alpine meadows . The apparent absence of human AE cases on the western and north western Tibetan plateau may simply be due to the fact that human population density decreases from east to west to low population areas of high altitude desert ( i . e . Chang Tang area ) . From east to west on the Tibetan plateau , climatic conditions get drier and colder , which impacts grass growth and yak/goat/sheep survival on which human populations depend . This aridity can also be expected to affect E . multilocularis egg survival . On the other hand , AE cases may possibly just be undetected in small discrete nomadic Tibetan populations of the western Tibetan plateau , since they are typically isolated from public health facilities and no mass screening has occurred to date west of Naqu in Tibet Autonomous Region . We return to this issue in the following section . Based on this China-wide continental scale analysis , some interesting contrasting results were apparent . Regional areas of south Gansu province ( i . e . Zhang-Puma counties ) , and south Ningxia region ( i . e . Xiji-Guyuan counties , close to the North Liu Pan Mountains ) were not expected to have large number of human AE cases . However , local foci over an area of 400–2500 km2 were confirmed to have significant AE prevalence in humans reaching an average of 4 . 1% and 3 . 0% respectively . These largely montane agricultural zones are considered to be significantly influenced by anthropogenic landscape disturbance caused by deforestation [9] , [49] , [50] . This process led to the regional opening of forest areas to agriculture , with transitional stages of grassland and pastures triggering small mammal population outbreaks , thus potentially fostering transmission of E . multilocularis locally over a relatively short time-span ( 10–20 years ) when optimal small mammal habitats become temporarily available . Thus , in both the provincial Gansu and Ningxia endemic zones , risk of human AE prevalence increased during the 1970s–90s in response to anthropogenic local landscape changes . However these landscape changes cannot be seen on the coarse resolution land use map . A total of 17 , 589 people were screened for hepatic echinococcosis in 94 villages and the overall prevalence of alveolar echinococcosis was 3 . 28% ( 3 . 02–3 . 56 , 95%CI ) . Despite substantial sample sizes , no cases were detected to the west of Qinghai Lake ( Figure 4 ) . People screened in this area ( n = 1 , 975 ) were 95 . 5% Tibetan ( 71 . 7% herdsmen , 25 . 6% children ) . Geographically isolated from the southern hotspot by the lower Qaidam Basin and the Chaka Yanhu depression , this area was not considered for further analysis ( see study area subset Figure 4 ) . Thus , the resulting filtered data set included 15 , 614 people and 81 villages , with a raw prevalence of 3 . 7% ( 3 . 41–4 . 01 , 95%CI ) ( Table 1 ) . Univariate analysis detected significantly higher AE prevalence among females than among males ( X2 = 11 . 2 , df = 1 , p = 0 . 0008 ) and among Tibetan than among Han ethnic groups ( X2 = 6 . 05 , df = 1 , p = 0 . 01 ) . Evidence of occupation differences was found ( X2 = 129 , df = 5 , p<0 . 000001 ) , with AE prevalence greater in herdsmen compared to a pool of other categories ( X2 = 29 . 5 , df = 1 , p<0 . 000001 ) and lower in employee ( X2 = 5 . 57 , df = 1 , p = 0 . 02 ) , student and children ( X2 = 41 . 9 , df = 1 , p<0 . 000001 ) , semi-herdsman ( X2 = 7 . 02 , df = 1 , p = 0 . 008 ) and farmers ( X2 = 32 . 1 , df = 1 , p<0 . 000001 ) . Table 2 shows how different occupations were represented between ethnic groups . The link between age categories and some occupations ( e . g . ‘student and children’ ) is obvious . A non-linear relationship of human AE prevalence to age was found ( Figure 5 ) . The buffer radius maximizing the likelihood of the GAMM was found to be 100 km for all land cover variables , altitude , rainfall and temperature . Figure 5 shows the relationships between AE prevalence in humans and environmental variables . AE prevalence was found to increase exponentially with the ratio of Alpine meadows and to decrease with the ratio of forest ( corresponding to a linear relationship on the linear predictor – data not shown ) . Human AE prevalence increased with the percentage of Alpine steppe to a maximum at 15 . 6% of total land cover , and then decreased to low prevalence . Similar patterns were obtained for rainfall , altitude , and temperature with maximum at 564 mm , 4385 m and −5 . 62°C respectively . This suggests an increased risk for human AE in areas with a larger percentage of alpine grassland at altitudes ranging between 4200–4600 m . In comparison , landscapes dominated by alpine steppe at higher altitude , mostly observed in the western part of the study area , appeared to be clearly sub-optimal . We included in models either altitude as a proxy for other ecological parameters ( such as rainfall , temperature and other unknown factors ) or rainfall and temperature as potential ecological factors impacting directly e . g . Echinococcus egg survival . Models containing non-linear effects on all three variables frequently gave rise to computational difficulties and are not reported here . Models including the spatial random effect consistently returned lower DICs than their non-spatial counterpart ( see Table S1 in Supplementary material for the list of models ) . Evidence for the importance of a non-spatial ( pixel ) random effect was less overwhelming . DIC comparisons show that 5 models among the 33 fitted were quasi-equivalent with a DIC difference lower than 2 . All of them included all variables related to human populations ( ethnicity , gender , age , occupation ) , all landscape variables and at least one variable related to climate or altitude . The mean and 95% credibility intervals of posterior samples for regression coefficients , and the probability that the coefficient be ≤0 ( Tibetan , female , herders , meadows ) or ≥0 ( forest ) , are shown in Table 3 . The proportion of positive coefficient samples were > = 0 . 98 for Tibetan , > = 0 . 99 for female and < = 0 . 03 for forest in all models . 95%CIs for meadow coefficients did not exclude zero or negative values when forest was included , however , >95% of posterior samples for that coefficient were positive . Although the coefficient for herdsmen was greater than zero in less than 90% of samples , dropping this term from models largely increased DIC . For prediction of human AE disease risk , model 5 was selected in order to avoid altitude ( a proxy variable whose importance may change with latitude outside of the study area ) , and in order to maintain the ecologically meaningful variables such as rainfall and temperature which appear not to lead to excessive over-parameterization , even keeping a pixel random effect . Posterior means and standard deviations of the variance parameters for the P-splines and Markov random field were 0 . 0147 ( sd 0 . 02306 ) for age , 0 . 3831 ( sd 0 . 6105 ) for rainfall , 0 . 1468 ( sd 0 . 5690 ) for temperature , 1 . 2036 ( sd 0 . 5164 ) for pixel spatial random effect , and 0 . 0586 ( sd 0 . 0963 ) for pixel random effect . Figure 6a presents the predicted prevalence for a hypothetical 31 . 6 year old male , non-Tibetan , non-herdsman ( corresponding to the mean age of the sampled population and to zero values for co-factors ) . The range of the 95% credibility intervals for each pixel of the spatial random effect is presented in Figure 6b . As a result of this analysis , one large hotspot of human AE disease was indicated in an area at the south-east border of Qinghai Province and at the north-west of Sichuan Province . Over a total area of 290 , 400 km2 , an endemic area of 193 , 600 km2 was calculated to have a human AE prevalence higher than 1 per thousand; for human AE prevalence greater than 1% the predicted transmission zone was 67 , 200 km2 . Predictions were extrapolated across a larger area where rainfall and temperature data remained within the same ranges as the area used to train the model ( Figure 7 ) . Over a total area of 902 , 800 km2 , human AE prevalence was predicted to be higher than 1 per thousand over 664 , 000 km2 , and greater than 1% over 210 , 000 km2 . Three other hotspots of human AE disease were predicted from the landscape model . Two were located in the Tibetan Autonomous Region ( TAR ) , one region at the north-west of Naqu and the other to the south of Dingqing . The south Dingqing putative focus was predicted to be connected to the Qinghai-Sichuan hotspot by a crescent shape corridor of elevated prevalence . This appears to be confirmed because a recent initial screening study ( n = 232 persons ) in Dingqing found a very high AE prevalence of 4 . 7% ( Feng X and Craig PS , unpublished observations ) . Another hotspot was predicted in Gansu at the north-east limit of the transmission area . Actually , the Gansu hotspot was the first confirmed in the late 1980s [51] ( see discussion ) . Furthermore , the area west of Qinghai Lake was predicted as a low prevalence area , which was confirmed by the present study ( this was the subset of 1975 people – 13 villages - where no AE cases could be detected , thus excluded from model fitting ) .
For continental China , we found that the spatial distribution of human AE disease was not clearly correlated to small mammal species richness , even when limiting consideration to the distribution and number of species that are known to be both potential intermediate hosts and/or agricultural/grassland pests . There was however a clear spatial overlap between the distribution of certain grassland types , and the spatial distribution of human AE . The main endemic areas corresponded to the central and eastern Tibetan meadows , those of the Tien Shan and Altai Mountains and of northern Inner Mongolia . Rainfall , altitude and temperature did not directly correlate with human AE disease distribution at continental scale . Human AE prevalence was found to exponentially increase with the ratio of alpine meadows and decrease with the ratio of forests in our regional analysis . The resolution of small mammal atlases in China did not facilitate inclusion of biodiversity indices in regional models . However , the Qinghai-Sichuan human AE disease hotspot lies in one of the areas with the lowest regional biodiversity of small mammals in China ( Figure 1 ) . A comprehensive small mammal survey carried out in Shiqu county ( see location Figure 6 ) , in the middle of the Qinghai-Sichuan human AE hotspot , recorded 6 species only of which Microtus spp and Ochotona curzoniae were classified as pests [48] . By contrast , 15 small mammal species were found in the Rangtang/Maerkang locality of west Sichuan , in the more forested part of the study area , among which a maximum of 10 species were present in some forest habitats [53] . Some of those species potentially outbreaking were shared with the east Tibetan Shiqu area ( i . e . Ochotona cansus , Microtus irene , M . limnophilus ) , but no indications of small mammal population surges were found from the survey and from local farmer interviews . Moreover human AE prevalence in these areas was lower ( 1 . 5% versus 6% in Shiqu county ) [28] . Evidence of interactions between landscape and arvicolid vole population dynamics at various spatial scales has been provided in earlier studies in Western Europe . On the regional scale ( area of about 2500 km2 ) larger variations in vole population densities occur where permanent grassland cover exceeded 50% and 85% of the total land for M . arvalis and A . terrestris respectively [16] , [17] . In complex ecosystems , the population dynamics of small mammals is regulated by both top-down ( predation , parasitism ) and bottom-up ( resources ) forces in a multivariate context [54]–[57] . In general , this means that population surges are less likely in biodiverse communities of small mammals , in regions characterized by heterogeneous landscapes ( e . g . forest mosaics ) . Towards the far west and north of the Tibetan plateau , the increasing aridity of Alpine steppes and semi-deserts and the more patchy distribution of Alpine meadows decrease both primary production and connectivity of optimal habitats , thus the probability of large scale small mammal outbreaks . By contrast extensive grasslands with higher grass production ( Alpine ‘meadows’ ) of the Eastern Tibetan plateau forms optimal conditions on a regional scale for surges of potentially cyclic small mammal species such as the plateau pika ( Ochotona curzoniae ) , several species of Microtus ( M . leucurus , M . irene , M . limnophilus ) , and probably hamsters ( Cricetulus sp . ) . In such contexts , definitive hosts , foxes , take advantage of the most abundant and accessible resources and specialize on them [58]–[60] . Raoul et al . [61] have shown that infection of foxes with E . multilocularis responds quickly and asymptotically to small increases in the densities of favored prey species . This may cause intense environmental contamination and human exposure , directly from fox feces or more likely via dogs infected from preying upon abundant ( infectious ) small mammal intermediate host reservoirs [62] . Such system processes may explain why , on the Tibetan plateau , regional landscape variables were found to help to predict human AE distribution . The greatest difficulty in interpreting these results arises from the fact that in such regional systems , explanatory variables which may determine transmission , directly or not , are not independent . For instance , grass productivity and forest cover are correlated with rainfall and temperature , which are correlated ( locally ) with altitude . This may explain why apparent discrepancies were observed between univariate and multivariate statistics . For instance , most Tibetans were also livestock herders , which may have nullified the occupation variable in the multivariate analysis . Furthermore , human AE disease was found to be correlated with Alpine meadow cover in univariate statistics , but in multivariate statistics effect size of alpine meadow was smaller when the forest term was included in models . . However perfect colinearity between forest and meadow aerial covers was ruled out by examining covariation matrices ( data not shown ) and because discarding one of the two variables in multivariate models led to increased DICs . The spatial random effect may also have nullified other effects suggesting spatial heterogeneity in effect size or the role of other unmeasured factors ( e . g . socio-economics , etc . ) . Large unexplained differences between neighboring villages were observed on the Tibetan plateau . Such differences are commonly reported in most other studies in endemic AE areas , for instance in Gansu [27] , [49] , Ningxia [50] and France [9] . Our study shows that the Zhang county local AE focus ( about 400 km2 ) in south Gansu Province lies in an area where regionally , rainfall and temperature were in the range of those found in the Qinghai-Plateau hotspot , although at a much lower altitude ( 2000–2800 m ) . The Zhang focus , first discovered in the late 1980s [51] was comprehensively described in studies carried out in the late 90s [9] , [49] , [63] . An overall human AE prevalence of 4 . 1% was reported there with some villages reaching more than 10% . Some authors [9] , [27] , [64] had previously provided indications that E . multilocularis transmission in Zhang was the consequence of a transient augmentation in shrub and grass cover in the 1980s , generated by successional growth following deforestation and triggering population outbreaks of the vole M . limnophilus and the hamster C . longicaudatus . Following 20–30 years of deforestation the parasite life-cycle is no longer being maintained in the current farmland landscape due to lack of both suitable intermediate and definitive hosts [5] . This suggest that in areas where climatic conditions are expected to be favorable , but landscape unfavorable , fine grain landscape alteration ( here of anthropogenic origin ) may create transitory local foci of E . multilocularis transmission that are prone to extinction . How the parasite may colonize such areas from larger and more stable regional foci remains open to question ( mainland/island dynamics through fox movements and/or dog trade ? ) [5] . The present study however shows that even those risk areas may be predicted from climate and landscape analysis at medium ( 20 km×20 km ) resolution . In conclusion , our results indicate that the prevalence of the cestdode zoonosis , human alveolar echinococcosis , was higher in areas of relatively low small mammal biodiversity than in more diverse host communities within which they were nested . In low diversity small mammal communities , potential intermediate host species prone to population outbreaks ( e . g . rodents and lagomorphs ) can reach higher densities and enhance transmission . This can be a natural occurrence , as on the homogeneous meadows of the eastern Tibetan plateau where rainfall maintains relatively higher productivity than is possible on the western half of the Tibetan plateau . Conversely , it can also be the result of anthropogenic driven landscape alteration , as in south Gansu where deforestation under favorable climatic conditions led to a transitory decrease of small mammal local biodiversity , and an augmentation of habitats and landscapes favorable to one or several cyclic species of those low diversity communities . Our results support the notion that landscape , small mammal host biodiversity and their population dynamics may protect humans from E . multilocularis transmission by preventing population outbreaks of specific small mammal host species with subsequent consequences on prey/predator relationships and associated host/parasite transmission [65] . This mechanism is clearly different to the dilution and zooprophylaxis effects that have been described and debated elsewhere as potential utilitarian advantages of biodiversity conservation [4]
|
The loss or gain of certain host species may either dilute or amplify the risk of pathogen and parasite infection through direct or indirect effects . The relative contribution of host communities combined with climate and landscape characteristics on non-vector-borne parasite transmission to humans has been a relatively neglected area of investigation . Here we show that zoonotic transmission of the cestode E . multilocularis in China was not directly correlated with small mammal host species richness , but spatially correlated with alpine meadows , forest characteristics and rainfall that confirmed hotspots of human disease in a 200 , 000 km2 region of the eastern Tibetan plateau . Our findings indicate more intensive transmission in homogeneous landscapes with larger areas of optimal habitats for one or some host species in low diversity small mammal communities , making multi-annual population outbreaks more likely . Landscape features that could support large population outbreaks of small mammal host species were better predictors of E . multilocularis transmission to humans than indices of host species richness per se . Our results support the notion that landscape , small mammal host biodiversity and their population dynamics may protect humans from zoonotic parasite transmission where they prevent population outbreaks of a few specific small mammal host species .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion"
] |
[
"medicine",
"public",
"health",
"and",
"epidemiology",
"infectious",
"disease",
"epidemiology",
"spatial",
"epidemiology",
"biodiversity",
"parasitic",
"diseases",
"neglected",
"tropical",
"diseases",
"echinococcosis",
"disease",
"mapping",
"infectious",
"diseases",
"environmental",
"epidemiology",
"epidemiology",
"biology",
"ecology"
] |
2013
|
Drivers of Echinococcus multilocularis Transmission in China: Small Mammal Diversity, Landscape or Climate?
|
If highly pathogenic H5N1 influenza viruses acquire affinity for human rather than avian respiratory epithelium , will their susceptibility to neuraminidase ( NA ) inhibitors ( the likely first line of defense against an influenza pandemic ) change as well ? Adequate pandemic preparedness requires that this question be answered . We generated and tested 31 recombinants of A/Vietnam/1203/04 ( H5N1 ) influenza virus carrying single , double , or triple mutations located within or near the receptor binding site in the hemagglutinin ( HA ) glycoprotein that alter H5 HA binding affinity or specificity . To gain insight into how combinations of HA and NA mutations can affect the sensitivity of H5N1 virus to NA inhibitors , we also rescued viruses carrying the HA changes together with the H274Y NA substitution , which was reported to confer resistance to the NA inhibitor oseltamivir . Twenty viruses were genetically stable . The triple N158S/Q226L/N248D HA mutation ( which eliminates a glycosylation site at position 158 ) caused a switch from avian to human receptor specificity . In cultures of differentiated human airway epithelial ( NHBE ) cells , which provide an ex vivo model that recapitulates the receptors in the human respiratory tract , none of the HA-mutant recombinants showed reduced susceptibility to antiviral drugs ( oseltamivir or zanamivir ) . This finding was consistent with the results of NA enzyme inhibition assay , which appears to predict influenza virus susceptibility in vivo . Therefore , acquisition of human-like receptor specificity does not affect susceptibility to NA inhibitors . Sequence analysis of the NA gene alone , rather than analysis of both the NA and HA genes , and phenotypic assays in NHBE cells are likely to adequately identify drug-resistant H5N1 variants isolated from humans during an outbreak .
The spread of highly pathogenic avian influenza A ( H5N1 ) viruses from Asia to the Middle East , Europe , and Africa raises serious concern about a potential human pandemic [1] , [2] . H5N1 avian influenza virus has been reported in poultry in 63 countries; 359 human cases have been confirmed in 14 countries , with a mortality rate >60% [3] . A poor fit between avian viruses and human cellular receptors is thought to be one of the main barriers to efficient transmission of H5N1 influenza viruses between humans [2] , [4]–[6] . The hemagglutinin ( HA ) glycoproteins of avian influenza viruses bind to avian cell-surface receptors whose saccharides terminate in sialic acid ( SA ) -α2 , 3-galactose ( SAα2 , 3Gal ) , whereas those of human influenza viruses bind to human receptors whose saccharides end in SAα2 , 6Gal . A change in receptor specificity from SAα2 , 3Gal to SAα2 , 6Gal is thought to be necessary before avian influenza viruses can cause a pandemic [4]–[6] . Neuraminidase ( NA ) inhibitors ( oseltamivir and zanamivir ) are anti-influenza drugs that are likely to be the first line of defense in the event of an influenza pandemic , before antigenically matched influenza vaccine is available [1] , [7]–[10] . Although HA mutations that alter viral receptor affinity/specificity can contribute to NA inhibitor resistance in vitro by allowing efficient virus release from infected cells without the need for significant NA activity [9] , [11]–[18] , the importance of HA mutations in the clinical management of influenza in humans remains uncertain [11] , [19]–[23] . One important problem is the lack of a reliable experimental approach ( i . e . , an appropriate cell-culture–based system ) for screening viral isolates for drug sensitivity [9] , [11] , [19] , [20] . HA mutations can either increase or mask NA inhibitor resistance in the available assay systems , which are therefore susceptible to false-positive [24] , [25] and false-negative [21] , [22] results . This problem is likely to reflect a mismatch between human virus receptors and those in available cell-culture systems . The human airway epithelial cells targeted by influenza virus express high concentrations of SAα2 , 6Gal-containing receptors , which are present at low concentrations in the continuous cell lines used to propagate influenza viruses [9] , [11] , [19] , [20] , [26] . To test whether altered receptor-binding properties of the viral HA glycoprotein of highly pathogenic A/Vietnam/1203/04 ( H5N1 ) influenza virus can reduce susceptibility to NA inhibitors in vivo , we generated 31 recombinant viruses carrying amino acid changes within or near the receptor binding site that alter binding affinity or specificity [27] . To evaluate the recombinant viruses' resistance to NA inhibitors , we used , for the first time , a cell-culture–based system that morphologically and functionally recapitulates differentiated human airway epithelial cells ex vivo [28] , [29] . Based on our analysis , we propose that the HA mutations would not be expected to mediate resistance of H5N1 viruses to antiviral drugs , oseltamivir or zanamivir .
To test the hypothesis that substitutions in the viral HA gene can contribute to NA inhibitor resistance , we generated recombinant H5N1 viruses harboring HA point mutations that alter viral receptor specificity or affinity to SA receptors , using two approaches . Our group and others [11]–[18] , [30] , [31] had previously identified a number of HA mutations within and near the receptor binding site that could alter receptor specificity or affinity . However , as a first step in this study , we wished to identify additional HA point mutations that could convert the avian H5 HA to human-type receptor specificity . Previous studies had shown that two HA substitutions ( Q226L and G228S ) are likely to modulate receptor specificity in the H5 serotype [5] . We therefore passaged the wild-type virus ( rgVN1203 ) and two HA mutants ( Q226L and G228S ) in MDCK-SIAT1 cells ( Madin Darby canine kidney cells altered to express predominantly human-type SAα2 , 6 receptors ) . Because of the ability of NA inhibitors to select mutants with altered receptor affinity/specificity during in vitro passage , we also cultured these three H5N1 viruses in MDCK-SIAT1 cells in the presence of 1 µM oseltamivir [12]–[18] . Interestingly , infection with the wild-type virus was undetectable by PCR analysis after two passages with 1 µM of the NA inhibitor in two independent experiments ( data not shown ) . Sequence analysis of the entire HA and NA genes revealed no additional mutations in virus with the G228S substitution after five sequential passages in the presence or absence of the drug . However , virus with the Q226L substitution had acquired two additional HA mutations , N158S ( which eliminates a glycosylation site at position 158 [32] ) and N248D , after five passages with or without compound . The receptor specificity of this triple-mutant ( N158S/Q226L/N248D ) virus was determined by measuring its binding affinity to sialoglycopolymers possessing either SAα2 , 3Gal ( p3′SL ) or SAα2 , 6Gal ( p6′SL ) ( Table S1 ) . This H5N1 variant exhibited enhanced affinity for human-like SAα2 , 6-linked receptor and was unable to bind the avian-like SAα2 , 3-linked receptor ( Figure S1 ) ; therefore , the N158S/Q226L/N248D triple mutation is sufficient to completely switch the host receptor specificity of A/Vietnam/1203/04 ( H5N1 ) virus from avian to human . Our second approach was to use reverse genetics [33] to generate recombinant A/Vietnam/1203/04-like ( H5N1 ) viruses carrying HA mutations previously shown to alter receptor specificity or affinity [11]–[18] , [30] , [31] . This study characterized a total of 15 HA mutants ( Table 1 ) carrying substitutions at a total of 11 positions ( Figure 1A ) . In addition , to gain insight into how combinations of HA and NA mutations can affect the sensitivity of H5N1 virus to NA inhibitors , we rescued viruses carrying the 15 HA changes together with the H274Y NA substitution . This mutation is most frequently associated with the resistance to the NA inhibitor oseltamivir in the N1 NA subtype [11] and was extensively characterized in A/Vietnam/1203/04 ( H5N1 ) -virus background both in vitro and in vivo [34] ( Table S2 ) . The use of the eight-plasmid reverse genetics system allowed us to predict the viability of all 31 recombinant H5N1 viruses in nature . We were able to rescue all of the recombinant viruses from transfected 293T cells as described previously [33] . However , the introduced N158S , T160A , R229S , N158S/N248D , and Q226L/N248D HA mutations could not be stably maintained in A/Vietnam/1203/04 virus after one passage in MDCK cells in two independent experiments because additional HA mutations were observed ( Table S2 ) . Interestingly , A/Vietnam/1203/04 virus simultaneously carrying the Q226L HA mutation and the H274Y NA mutation was genetically unstable , since the stock virus contained a mixture of viruses with Q or L at HA residue 226 as well as a K222I HA substitution . Sequence analysis revealed that the remaining 20 recombinant H5N1 viruses were stably maintained in stock cultures; these viruses grew to comparable titers in the different cell lines used ( Table S2 ) . We measured the affinity of the 21 genetically stable recombinant H5N1 variants , including wild-type virus , for a wide range of high-molecular-weight sialic acid substrates , both natural ( fetuin ) and synthetic ( Table S1 ) . Like most avian influenza strains , wild-type rgVN1203 virus showed a binding preference for avian SAα2 , 3Gal-receptors ( Figure 1B ) . The introduced HA substitutions had various effects on the receptor binding affinity of the H5 hemagglutinin to one or several SAα2 , 3Gal-substrates ( Figure 1B ) . Surprisingly , H5N1 mutants carrying the triple mutation N158S/Q226L/N248D exhibited very weak SAα2 , 6Gal binding , whereas virus with the double mutation N158S/Q226L did not bind to any SAα2 , 3Gal sialosides but showed enhanced binding affinity to SAα2 , 6Gal-substrate ( Figure 1B ) . After observing a discrepancy in the receptor affinity of two N158S/Q226L/N248D HA triple mutants that were independently obtained by passaging in MDCK-SIAT1 cells ( Figure S1 ) or by transfection of 293T-MDCK cells ( Figure 1B ) , we investigated whether the host cell type can determine viral binding properties . We prepared virus stocks by transfecting MDCK-SIAT1 cells , rather than MDCK cells , with the two H5N1 viruses carrying the double N158S/Q226L and triple N158S/Q226L/N248D HA substitutions . Direct sequencing of the double-mutant virus revealed the presence of additional mutations; therefore , we did not assay its receptor specificity . A/Vietnam/1203/04 ( H5N1 ) virus carrying the triple N158S/Q226L/N248D HA mutation and grown in MDCK-SIAT1 cells was genetically stable and demonstrated the switch from avian to human receptor specificity ( Figure 1B ) . We used the fluorometric NA enzyme inhibition assay [35] to test the susceptibility to oseltamivir and zanamivir of the 20 recombinant H5N1 viruses carrying either HA mutations or both HA and NA ( H274Y ) changes . None of the recombinants carrying only HA mutations differed from the wild-type virus in their sensitivity to either NA inhibitor ( mean IC50±SD , 0 . 2±0 . 1 and 1 . 0±0 . 1 nM , respectively , for oseltamivir and zanamivir ) . All double-gene–mutant recombinant viruses were resistant to oseltamivir ( the mean IC50 was ∼2060 times that of wild-type virus ) but remained highly susceptible to zanamivir ( IC50 , 1 . 0±0 . 2 nM ) ( data not shown ) . We next compared the activity of NA inhibitors in four cell-culture systems that differ in the surface distribution of SAα2 , 3- and SAα2 , 6-receptors . We performed plaque reduction assays in MDCK and MDCK-SIAT1 cells and virus reduction assays in human alveolar basal epithelial ( A549 ) and normal human bronchial epithelial ( NHBE ) cells ( Figures 2 and 3 ) . In MDCK cells , which express predominantly SAα2 , 3 ( avian-type ) receptors [26] , all HA mutants except those with the S159N and N158S/Q226L substitutions were significantly more resistant to oseltamivir and to zanamivir than the wild-type strain ( P<0 . 01 ) ( Figure 2A ) . Introduction of the NA H274Y mutation markedly reduced the sensitivity of the recombinant H5N1 viruses to oseltamivir ( mean EC50 , 270 times that of the wild-type strain ) but did not alter their susceptibility to zanamivir . The combined HA and NA ( H274Y ) substitutions therefore reduced oseltamivir sensitivity synergistically in MDCK cells . In MDCK-SIAT1 cells , which have predominantly SAα2 , 6 ( human-type ) receptors [26] , most of the mutants showed resistance to both NA inhibitors ( Figure 2B ) , but fewer viruses were resistant in MDCK-SIAT1 than in MDCK cells . Viruses with Q226L , G228S and Q226L/G228S substitutions , which enhanced binding affinity for SAα2 , 6Gal receptors ( Figure 1B ) , were as sensitive to both NA inhibitors as the wild-type virus in MDCK-SIAT1 cells but not in MDCK cells ( Figure 2 ) . Taken together , our results indicated that the drug sensitivity of the recombinant H5N1 viruses detected in MDCK-SIAT1 cells reflected their affinity for SAα2 , 6Gal rather than for SAα2 , 3Gal receptors . In human lung A549 cells , which have predominantly SAα2 , 6-receptors [36] , the overall sensitivity pattern was similar to that observed in MDCK-SIAT1 cells ( Figure 3A ) . However , we were unable to assay the drug susceptibility of the mutants carrying the double N158S/Q226L and triple N158S/Q226L/N248D HA mutations , because their replication was undetectable . In A549 cells , but not in MDCK and MDCK-SIAT1 cells , only double mutants with the Y161H or K222I HA substitution plus the H274Y NA mutation showed resistance significantly greater ( EC50 increased by a factor of 10-106 ) than that of the H274Y virus to both NA inhibitors ( Figure 3A ) . In differentiated cultures of NHBE cells ( primarily human-type SAα2 , 6-receptors [28] ) , none of the HA mutations resulted in increased resistance to oseltamivir or zanamivir , and there was no difference in susceptibility between viruses carrying only the H274Y NA mutation and those carrying HA mutations as well ( Figure 3B ) . Therefore , the combined HA–NA mutations had a negligible effect on the NA inhibitor sensitivity of H5N1 viruses in NHBE cultures . All recombinant viruses carrying a single HA substitution were slightly less susceptible to zanamivir than to oseltamivir ( the EC50 differed by a factor of ∼5 ) in NHBE cells . This finding was consistent with the data obtained by NA enzyme inhibition assay ( see above ) .
Our results answer several fundamental questions about the effect of HA mutations on the host receptor affinity and NA inhibitor susceptibility of highly pathogenic influenza H5N1 viruses . Importantly , we found that alteration of receptor specificity or affinity does not alter sensitivity to NA inhibitors . In light of global concern about pandemic preparedness , it is crucial not only to understand what mutations might endow H5N1 viruses with human receptor specificity [4]–[6] but also to anticipate the clinical consequences of such adaptation . With increasing clinical use and stockpiling of NA inhibitors for pandemic preparedness , it is also crucial to elucidate molecular mechanisms that contribute to drug resistance . Mutations at specific positions ( 129/134 , 182 , 192 , 227 , and 226/228 ) in the HA gene of H5N1 influenza virus were recently shown to reduce or eliminate binding affinity to avian-type receptors and enhance affinity to human-type receptors [5] , [6] , [37] , [38] . Here , we have identified another possible route of adaptation to human receptors: simultaneous amino acid substitutions at HA positions 158 ( that results in the loss of a glycosylation site ) , 226 and 248 . Our study demonstrated the importance not only of residues near or within the receptor binding site , but also those structural elements that are located in its vicinity that can affect host receptor specificity . H5N1 influenza viruses in H5 clade 1 , such as the virus used here , would be extremely unlikely to acquire all three mutations . However , most members of the clade 2 . 2 family now circulating in Europe , the Middle East and Africa already lack a glycosylation site at HA position 158 [1] . This natural feature reduces the required mutations to only two , thus enhancing the probability of such an occurrence . Taken together , we can conclude that different amino acid substitutions in the H5 HA enable to cause a shift from the avian- to human-type specificity and along with the H5N1 evolution other strain-specific mutations cannot be excluded . HA glycosylation has been reported to affect the specificity or affinity of influenza viruses for cellular receptors [39]–[41] . Our results directly demonstrate that HA glycosylation plays a role in the viability of avian H5N1 influenza viruses in MDCK-SIAT1 cells , which express predominantly human-like SAα2 , 6Gal receptors . We demonstrated that the loss of an MDCK-SIAT1 cell–synthesized oligosaccharide at position 158 of HA , adjacent to the receptor binding site , increased the capacity of the virus to bind to these cells . One possible explanation is that the removal of an oligosaccharide attachment site from the tip of the HA1 subunit eliminates steric hindrance that limits the accessibility of the receptor pocket by this SA-containing oligosaccharide [41] . Further , our results confirm previous reports [39]–[41] that the effect of oligosaccharide removal can differ with the host cell type ( MDCK vs . MDCK-SIAT1 ) in which the H5N1 virus is grown . Therefore , the receptor affinity of the avian H5N1 influenza virus may also be affected by the host cell–determined composition of other oligosaccharides on the two H5 HA subunits . Our results are consistent with what is known about the role of the functional balance between the receptor-binding ( HA ) and receptor-destroying ( NA ) activities of the surface glycoproteins in efficient influenza virus infection [42] , [43] . The genetic and phenotypic instability of 11 of our recombinant H5N1 viruses in vitro may reflect the functional mismatch of their HA and NA glycoproteins . The balance between HA and NA functions could also explain the diverse pattern of influenza virus susceptibility to NA inhibitors observed in different cell-culture systems [11] , [19] , [20] , [42] , [43] . The disparate HA–NA balance required to infect MDCK , MDCK-SIAT1 , and A549 cells , together with the differences in SA receptors between these cell lines and human respiratory epithelial cells , significantly limit the suitability of these commonly used cell lines for phenotypic characterization of NA inhibitor resistance . NHBE cells cultured ex vivo [28] , [29] , [44] offer a new cell-culture–based system that functionally and morphologically recapitulates normal differentiated human airway epithelium; this system allows improved evaluation of the NA inhibitor sensitivity of avian influenza viruses that are potential human pathogens . Taken together , our data demonstrate a parallel between virus susceptibility determined by NA enzyme inhibition assay ( which appears to predict in vivo results [11] , [19] , [20] ) and virus susceptibility in NHBE cells ( an ex vivo model ) . NHBE cells [28] , [44] , which express the sialic acid receptors present in humans , may offer an optimal system for maintaining viral fitness and , as a consequence , for prediction of influenza virus resistance to NA inhibitors in vivo . Our results suggest that the HA mutations that alter the receptor specificity or affinity of highly pathogenic H5N1 viruses are unlikely to mediate concomitant resistance to NA inhibitors in vivo . However , we cannot exclude the possibility that the HA mutations might contribute to the selection of certain NA mutations that lead to drug resistance simply by altering HA–NA balance . Indeed , recent observation that H5N1 viruses from clade 2 isolated in 2005 demonstrated a 25- to 30-fold decrease in sensitivity to oseltamivir carboxylate compared with clade 1 viruses and none of the mutations known to confer NA inhibitor resistance was observed [45] , suggests that the decrease in sensitivities may be due to drift mutations in the NA and HA proteins . Additionally , our finding that A/Vietnam/1203/04 virus carrying the Q226L HA mutation , which is known to switch the receptor specificity in the H3 HA subtype [5] , and the H274Y NA mutation was not genetically stable , could provide evidence that some HA mutations have the potential impact on the acquisition of mutations in NA , including those that can lead to decreased drug susceptibility . One , therefore , can speculate that the identification of oseltamivir-resistant viruses as a significant proportion of influenza H1N1 viruses circulating in Europe [46] could be determined by preceding NA and/or HA mutations . In conclusion , our findings can improve the monitoring of NA inhibitor resistance among viruses with pandemic potential . Further , sequence analysis of the NA gene alone , rather than analysis of both the NA and HA genes , may adequately identify all drug-resistant H5N1 variants . The human airway epithelial cell cultures used in this study could also advance the study of drug resistance mechanisms by serving as a suitable model of the human respiratory cell system for phenotypic characterization of NA inhibitor resistance in clinical testing .
Madin-Darby canine kidney ( MDCK ) , human embryonic kidney ( 293T ) and human alveolar basal epithelial ( A549 ) cells were obtained from the American Type Culture Collection . MDCK cells transfected with cDNA encoding human 2 , 6-sialyltransferase ( MDCK-SIAT1 cells ) were kindly provided by Dr . Mikhail N . Matrosovich . Primary normal human bronchial epithelial ( NHBE ) cells were obtained from Cambrex Bio Science . All cell cultures were maintained as previously described [26] , [28] , [31] , [36] , [44] . Eight plasmids were constructed from the DNA sequences of the 8 gene segments of wild-type A/Vietnam/1203/04 ( H5N1 ) virus for the reverse-genetics generation of recombinant wild-type virus ( rgVN1203 ) . Recombinant virus was generated by DNA transfection of 293T cells [33] , the HA cleavage site was removed , and the point mutations ( Tables 1 and S2 , Figure 1A ) were inserted into the HA and NA genes of rgVN1203 virus by using a Quickchange site-directed mutagenesis kit ( Stratagene ) [32] . Stock viruses were prepared in MDCK cells at 37°C for 72 h and their entire HA and NA genes were sequenced to verify the presence of the mutations . The recombinant viruses were designated according to their HA and NA mutations ( Tables 1 and S2 ) . All experimental work with the H5N1 recombinant viruses was performed in a biosafety level 3+ laboratory approved for use by the U . S . Department of Agriculture and the U . S . Centers for Disease Control and Prevention . The NA inhibitors oseltamivir carboxylate ( oseltamivir ) ( [3R , 4R , 5S]-4-acetamido-5-amino-3-[1-ethylpropoxy]-1-cyclohexene-1-carboxylic acid ) and zanamivir ( 4-guanidino-Neu5Ac2en ) were provided by Hoffmann-La Roche , Ltd . For the first passage , MDCK-SIAT1 cells were infected with influenza H5N1 viruses at a multiplicity of infection ( MOI ) of 0 . 001 PFU/cell and cultivated for 72 h in infection medium [containing 4% bovine serum albumin , sodium bicarbonate , 100 U/ml of penicillin , 100 µg/ml of streptomycin sulfate , 100 µg/ml of kanamycin sulfate , 1 µg/ml of L-1- ( tosyl-amido-2-phenyl ) ethyl chloromethyl ketone ( TPCK ) –treated trypsin ( trypsin ) ( Worthington Diagnostics ) ] with or without 1 μM oseltamivir [31] . Four additional passages identical to the first one were then performed sequentially . The genetic stability of recombinant H5N1 viruses was monitored by plaque assay and by sequencing of the HA and NA genes after transfection of 293T cells and after one passage in MDCK/MDCK-SIAT1 cells . Influenza virus was defined as genetically stable if it was able to replicate efficiently in the cell lines used , maintain a homogeneous plaque phenotype , and did not contain additional subpopulations based on the sequence analysis of the HA and NA genes after one passage in MDCK/MDCK-SIAT1 cells . If different subpopulations were identified , those viruses were designated as unstable ( Tables 1 and S2 ) . The yield of H5N1 viruses in MDCK , MDCK-SIAT1 and A549 cells was defined as log10 of the 50% tissue culture infectious dose ( TCID50 ) as described previously [43] . Briefly , confluent monolayers of cell cultures growing in 96-well microplates were inoculated with serial virus dilutions ( each dilution was added to five wells ) in the presence of trypsin . After 3 days , virus was titrated by HA assay , and virus titers were expressed as log10TCID50/ml by the end-point method of Reed and Muench [47] . NHBE cells were inoculated by exposure of the apical side to recombinant H5N1 viruses at a MOI of 0 . 1 , as determined by TCID50 assay in MDCK cells . After 1 h incubation , the inoculum was removed and the cells were incubated for 24 h . No trypsin was added to the cultures because previous studies in similar cultures demonstrated efficient proteolytic activation of influenza viruses by endogenous proteases [28] . Viruses released into the apical compartment of NHBE were harvested by adding 300 µl of medium to the apical compartment , allowing it to equilibrate for 30 min , and collecting it . Virus titer was determined as log10TCID50/ml in MDCK cells . The binding of human influenza viruses to fetuin was measured in a direct solid-phase assay using the immobilized virus and horseradish peroxidase-conjugated fetuin , as described previously [48] . The affinity of viruses for synthetic 3′- and 6′-substrates ( Table S1 ) was measured in a competitive assay based on the inhibition of binding of the labeled fetuin [49] . The association constants ( Kass ) were determined as sialic acid ( Neu5Ac ) concentration at the point Amax/2 on Scatchard plots . NA activity was determined as described by Potier et al . [35] . Briefly , H5N1 viruses and various concentrations of oseltamivir or zanamivir were preincubated for 30 min at 37°C before addition of the substrate 2′- ( 4-methylumbelliferyl ) -α-D-N-acetylneuraminic acid ( Sigma ) . After 1 h , the reaction was terminated by adding 14 mM NaOH and fluorescence was quantified in a Perkin-Elmer fluorometer . The IC50 was defined as the concentration of NA inhibitor necessary to reduce NA activity by 50% relative to that in a reaction mixture containing virus but no inhibitor . The drug susceptibility of recombinant H5N1 viruses was determined by plaque reduction assay in MDCK and MDCK-SIAT1 cells [50] and by virus reduction assay in A549 and NHBE cells [31] , [51] . Briefly , MDCK or MDCK-SIAT1 cells were inoculated with virus diluted to yield ∼50 plaques per well and were then overlaid with infection medium containing oseltamivir ( 0 . 0001 to 100 µM ) or zanamivir ( 0 . 0001 to 100 µM ) in the presence of trypsin . The results were recorded after 3 days of incubation at 37°C . At least three independent experiments were performed to determine the concentration of compound required to reduce plaque size by 50% , relative to that in untreated wells ( EC50 ) . A549 cells were inoculated with H5N1 viruses at an MOI of 0 . 001 PFU/cell and after 1 h of adsorption were overlaid with infection medium containing oseltamivir ( 0 . 001 to 100 µM ) or zanamivir ( 0 . 001 to 100 µM ) in the presence of trypsin . Virus yield was determined by TCID50 assay of culture supernatants 72 h after inoculation . The drug concentration that caused a 50% decrease in the TCID50 titer in comparison to control wells without drug was defined as the 50% inhibitory concentration ( EC50 ) . The results of three independent experiments were averaged . NHBE cells were inoculated by exposure of the apical side to recombinant H5N1 viruses at an MOI of 0 . 1 in the presence of oseltamivir ( 0 , 0 . 1 , 1 , 10 µM ) or zanamivir ( 0 , 0 . 1 , 1 , or 10 µM ) . These concentrations represent typical plasma minimum and maximum concentrations measured in humans after administration of 75 mg of oseltamivir phosphate or 10 mg of zanamivir , the doses recommended for prophylaxis [10] , [52] . After 1 h incubation , the inoculum was removed and the cells were incubated for another 24 h . Viruses released into the apical compartment of NHBE cells were harvested by the apical addition and collection of 300 µl of medium allowed to equilibrate for 30 min . The virus titer was determined as log10TCID50/ml in MDCK cells .
|
If the avian influenza H5N1 viruses adapt to human hosts , the first step is likely to be a switch in the preference of their viral hemagglutinin ( HA ) glycoprotein to bind to human rather than avian cell receptors . Such a switch may also alter virus susceptibility to neuraminidase ( NA ) inhibitors , which are anti-influenza drugs that are likely to be the first line of defense against a pandemic . We generated recombinant A/Vietnam/1203/04-like ( H5N1 ) viruses carrying HA mutations previously shown to alter receptor specificity or affinity . We also discovered a previously unknown route ( three simultaneous HA amino acid substitutions ) by which highly pathogenic H5N1 viruses can adapt to human receptors . We then used a novel cell-culture–based system ( differentiated human airway epithelial NHBE cells ) to evaluate the recombinant viruses' resistance to NA inhibitors . None of the HA-mutant recombinants showed reduced drug susceptibility . Our results indicate that the tested HA mutations are unlikely to cause resistance to NA inhibitors in vivo . The NHBE system meets the need for an appropriate cell-culture–based system for phenotypic characterization of drug resistance .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/respiratory",
"infections"
] |
2008
|
Human-Like Receptor Specificity Does Not Affect the Neuraminidase-Inhibitor Susceptibility of H5N1 Influenza Viruses
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TORC1 is a central regulator of cell growth in response to amino acids . The role of the evolutionarily conserved Gtr/Rag pathway in the regulation of TORC1 is well-established . Recent genetic studies suggest that an additional regulatory pathway , depending on the activity of Pib2 , plays a role in TORC1 activation independently of the Gtr/Rag pathway . However , the interplay between the Pib2 pathway and the Gtr/Rag pathway remains unclear . In this study , we show that Pib2 and Gtr/Ego form distinct complexes with TORC1 in a mutually exclusive manner , implying dedicated functional relationships between TORC1 and Pib2 or Gtr/Rag in response to specific amino acids . Furthermore , simultaneous depletion of Pib2 and the Gtr/Ego system abolishes TORC1 activity and completely compromises the vacuolar localization of TORC1 . Thus , the amino acid-dependent activation of TORC1 is achieved through the Pib2 and Gtr/Ego pathways alone . Finally , we show that glutamine induces a dose-dependent increase in Pib2-TORC1 complex formation , and that glutamine binds directly to the Pib2 complex . These data provide strong preliminary evidence for Pib2 functioning as a putative glutamine sensor in the regulation of TORC1 .
Cell growth is primarily governed by environmental nutritional conditions [1] . TORC1 , a protein complex that is universally conserved among eukaryotes , plays a pivotal role in the cell’s coordinated response to amino acids [2 , 3] . In the budding yeast , Saccharomyces cerevisiae , TORC1 consists of a central protein kinase , Tor1 or Tor2 , along with Kog1 , Lst8 and Tco89 [2] . When amino acids are available , TORC1 activates anabolic processes such as protein synthesis and suppresses catabolic processes such as autophagy , and these effects are reversed under amino acid or nitrogen starvation conditions [4 , 5] . The progress of each anabolic or catabolic process is controlled by the phosphorylation status of corresponding TORC1 substrates , such as Sch9 and Atg13 [6 , 7] . A fundamental but poorly addressed question in the study of TORC1 concerns the mechanism by which amino acid availability is interpreted and results in the activation or deactivation of TORC1 . In yeast , the most well-established regulator of TORC1 is the heterodimeric small GTPase complex Gtr1–Gtr2 , the orthologue of which is RagA/B–RagC/D in mammals [8 , 9] . Both Gtr1 and Gtr2 bind to a guanine nucleotide , GTP or GDP , with the GTP/GDP-bound state of each subunit distinct at any given moment [10] . Gtr1–Gtr2 is anchored to the vacuolar membrane via a scaffold known as the Ego protein complex , consisting of Ego1 , Ego2 , and Ego3 [11–13] . When Gtr1 binds to GTP , it binds to and activates TORC1 [10] . In mammals , the GTP form of RagA/B recruits mTORC1 to the lysosomal membrane , where it encounters the small GTPase , Rheb , which results in the activation of mTORC1 [8] . In contrast , the existence of a similar TORC1 activator corresponding to Rheb remains ambiguous in budding yeast . Recently , multiple amino acid sensor proteins were identified in mammals , namely Sestrin and CASTOR , which are leucine and arginine sensor proteins , respectively [14 , 15] . However , it remains unclear whether similar amino acid sensors exist in yeast . Recent work has demonstrated that Gtr2 is regulated by its own GTPase-activating protein ( GAP ) , Lst4–Lst7 , which is an orthologue of mammalian FNIP–Folliculin and appears to be controlled by amino acid availability [16–18] , suggesting that a similar system may exist for Gtr1 . Despite these advances in our understanding of Gtr/Rag-dependent TORC1 regulation , one intriguing observation in particular remains to be accounted for: although TORC1 is essential for cell growth , Δgtr1 or Δgtr2 mutants show only a very slight defect in growth . Recently , Stracka et al . provided an important insight into this apparent paradox , finding that there are two types of TORC1-activating responses determined by the availability of certain amino acids [19] . One of these is the Gtr-dependent response , in which cells exhibit a transient increase in TORC1 activity in response to poor nitrogen sources , exemplified by leucine . Cells also show a rapid and sustained activation of TORC1 that is independent of Gtr in response to preferred nitrogen sources such as glutamine . Thus , two independent mechanisms are thought to activate TORC1 in response to amino acids . Although the specific proteins responsible for the glutamine-responsive system remain unknown , genetic experiments suggest that it could involve the vacuolar membrane protein Pib2 . Pib2 was originally identified as a protein with phosphatidylinositol ( PtdIns ) 3P-binding activity mediated by its own FYVE domain [20] . Kim et al . showed that Pib2 is also involved in TORC1 regulation: the Δpib2 mutant exhibits synthetic lethality with Δgtr1 and lysosomal membrane permeabilization in response to endoplasmic reticulum membrane stress [21] . Two more recent studies suggested that Pib2 might transduce glutamine signals to TORC1 in parallel to the Gtr/Ego system [22 , 23] . However , these studies were unable to address several important questions surrounding such a role for Pib2 , including whether the amino acid-dependent activation of TORC1 is achieved through the Pib2 and Gtr/Ego pathways alone ( i . e . , the effect of the simultaneous absence of Pib2 and the Gtr/Ego system on the activity and localization of TORC1 ) ; the nature of the molecular mechanism by which Pib2 modulates TORC1 activity; the identity of what senses glutamine; and how glutamine regulates TORC1 activity . In this study , we provide further characterization of the role of Pib2 in the glutamine-responsive pathway for TORC1 activation independently of the Gtr/Ego system . Our detailed analyses provide three important findings that clarify the function of Pib2 . First , we find that Pib2 and Gtr/Ego form distinct complexes with TORC1 in a mutually exclusive manner . Second , our data indicate that simultaneous depletion of Pib2 and the Gtr/Ego system abolishes TORC1 activity and completely compromises the vacuolar localization of TORC1 . Thus , the data strongly suggest that amino acid-dependent activation of TORC1 is achieved through the Pib2 and Gtr/Ego pathways alone . Finally , we show that glutamine induces a dose-dependent increase in TORC1 complex formation and that glutamine binds directly to the Pib2 complex . These data suggest that Pib2 plays a role as an integral component of a putative glutamine sensor .
Three recent studies have suggested that Pib2 might transduce glutamine signals to TORC1 in parallel to the Gtr/Ego system [21–23] . We first attempted to validate these findings by adopting an alternative , detailed approach . Genome-wide synthetic genetic array ( SGA ) analysis showed that the mutation of PIB2 results in a synthetic growth defect with the TORC1 subunits TOR1 and TCO89 , and SLM4/EGO3 , a component of the Ego complex [24] . Consistent with these previous findings , we also observed synthetic lethality of Δpib2 with Δgtr1 and Δego1 ( S1A Fig ) . We next investigated the response of Δpib2 and Δgtr1 cells to supplementation of individual amino acids using an amino-acid prototrophic strain maintained on YMM , a synthetic , minimal medium that allows for the examination of the effect of individual amino acids [19 , 25] . Cells were grown in YMM medium containing ammonium sulfate as the sole nitrogen source ( i . e . , in the absence of any amino acids ) , before being subjected to nitrogen starvation in nitrogen-free YMM for 30 minutes . Under these conditions , the phosphorylation of Sch9 is reduced to a basal level . Next , various amino acids were added , and the phosphorylation of Sch9 was monitored at each time point . In wild-type cells , addition of glutamine immediately activated TORC1 within 1 minute and sustained activation for 30 minutes ( S1B Fig ) [19] . In Δgtr1 cells , activation was delayed by 5 min , and was marginally weaker , but marked phosphorylation was sustained for at least 30 minutes ( S1B Fig ) . The degree of TORC1 activation was strikingly lower in Δpib2 cells than in wild-type cells , although minimal activation was observed at 1 minute ( S1B Fig ) . The low level of phosphorylation continued for 15 minutes before a small increase was observed , although this increase was attenuated in comparison to wild-type cells . We also evaluated the effect of glutamine supplementation on the phosphorylation state of Atg13 , another TORC1 substrate that is involved in autophagy . Upon amino acid treatment , Atg13 was phosphorylated with slower kinetics than Sch9 in wild type cells ( S1C Fig ) . As we observed for Sch9 phosphorylation , TORC1 stimulation by glutamine was compromised severely in Δpib2 and partially in Δgtr1 strains ( S1C Fig ) . These results indicate that Pib2 is crucial for the response to glutamine . This conclusion is also complemented by biochemical analyses of TORC1 activity presented in a recent study [22] . On the other hand , our findings with regard to the phosphorylation response to leucine were in marked contrast to the response to glutamine ( S1D Fig ) . First , wild-type cells exhibited a relatively gradual response to the addition of leucine , starting at 1 minute before reaching a peak at 5–7 minutes . Phosphorylation of Sch9 started to decrease at 9 minutes , and was almost completely absent after 30 minutes ( S1D Fig ) . This confirms that leucine-dependent activation of TORC1 is transient , as described previously [19] . Interestingly , this leucine-dependent transient TORC1 activation was abolished in both Δgtr1 cells and Δpib2 cells ( S1D Fig ) . However , Δgtr1 or Δpib2 cells grew in YMM medium supplemented only with leucine as the nitrogen source , albeit to a lesser extent than wild type cells ( S1E Fig ) . This finding may be explained by the metabolic derivation of other amino acids from leucine supporting cell growth under these conditions . Thus , both the Pib2- and Gtr/Ego-dependent pathways are critical in the early phase of leucine-dependent TORC1 activation . Δgtr1 cells exhibit only a limited decrease in TORC1 activity , as determined by the phosphorylation status of the target Sch9 following chemical cleavage with NTCB ( Fig 1A ) [6] . Likewise , Δpib2 cells exhibited only slight decrease in TORC1 activity ( Fig 1A ) . To examine TORC1 activity in the simultaneous absence of Gtr1 and Pib2 proteins , we employed a system that combines the tetracycline-repressible promoter and an N-end rule-based degron that destabilizes the target protein , resulting in complete depletion of the protein [26] . In Δgtr1 tetO7-Ubi-Leu-3HA-PIB2 ( PIB2depletion ) cells , the repressible Pib2 mutant protein was mostly depleted from cells 90 minutes after addition of doxycycline to shut off PIB2 expression ( Fig 1B ) . Even after shutoff , PIB2depletion cells grew similarly to wild-type cells , like Δgtr1 cells ( Fig 1C bottom ) . However , Δgtr1 PIB2depletion double-mutant cells exhibited minimal proliferation following doxycycline treatment , indicating that Gtr1 and Pib2 play redundant yet essential roles in cell growth ( Fig 1C bottom ) . Remarkably , TORC1 activity was completely abolished in Δgtr1 PIB2depletion double-mutant cells after shutoff , in contrast to the small decrease in TORC1 activity in Δgtr1 and PIB2depletion single-mutant cells ( Fig 1D ) . Even before doxycycline addition , the level of Pib2 was reduced due to destabilization by the degron , leading to a defect in TORC1 activity and slower growth in combination with Δgtr1 ( Fig 1C and 1D ) . Collectively , these data indicate that simultaneous depletion of Pib2 and Gtr1 leads to complete abolition of TORC1 activity , providing the first direct evidence that the amino acid-dependent activation of TORC1 is achieved through the Pib2 and Gtr/Ego pathways alone . Next , we sought to determine whether Pib2 is physically associated with TORC1 . To this end , we constructed a strain expressing genomically-integrated GFP-Pib2 and Gtr1-TAP under the control of their endogenous promoters in order to ensure expression at physiologically relevant levels . These tags do not have any effect on Pib2 and Gtr1 function , as determined by rapamycin sensitivity ( S2A Fig ) [13] . Pull-down experiments using magnetic beads conjugated to GFP-binding protein revealed that endogenous Tor1 protein was co-precipitated with GFP-Pib2 , indicating that Pib2 is physically associated with TORC1 ( Fig 2A left panel ) . We next investigated Pib2’s relationship with the Gtr complex . However , Gtr1-TAP was not detected in the GFP-Pib2-precipitated fraction ( Fig 2A left panel ) . Conversely , when Gtr1-TAP was pulled down , GFP-Pib2 could not be detected in the precipitated fraction , whereas Gtr1-TAP co-precipitates with Tor1 , albeit to a lesser extent than GFP-Pib2 ( Fig 2A right panel and 2B . During the process of optimizing conditions to enhance the interaction between Gtr1-TAP and Tor1 , we noted that provision of an additional copy of GTR1-TAP distinctly increases the protein amount of Tor1 co-precipitated with Gtr1-TAP ( S2B Fig right panel , and S2C Fig ) . Even in this situation , GFP-Pib2 was not detected in the Gtr1-TAP-precipitated fraction ( S2B Fig right panel , S2C Fig ) . In parallel , GFP-Pib2 was co-precipitated with Tor1 , but not with Gtr1-TAP ( Fig 2B left panel and 2C ) . We also immunopurified TAP-tagged Pib2 from yeast cells , and co-precipitating proteins were processed for identification using liquid chromatography tandem mass spectrometry ( LC–MS/MS ) . Notably , we found that with the exclusion of common contaminants of proteomic analysis [27] , TAP-Pib2 co-immunoprecipitated with all the known components of TORC1 , namely Tor1 , Tor2 , Kog1 , Lst8 and Tco89 , but not any specific component of the TORC2 , Gtr1–Gtr2 or Ego complexes ( Fig 2C , S2 Table ) . These results indicate that Pib2 and Gtr1 independently associate with distinct TORC1 complexes . As TORC1 is known to localize to the vacuolar membrane , we next sought to determine the site at which Pib2 fulfills its function . Microscopy of chromosomally tagged GFP-Pib2 expressed from its own promoter revealed that the GFP signal closely co-localizes with Vph1-mCherry , a marker of the vacuolar membrane ( Fig 3A ) . This vacuolar signal was completely abolished in the Δvps34 mutant ( Fig 3A ) , in which the sole PtdIns3-kinase , Vps34 , is absent , as reported previously [28] . Two Vps34-containing complexes have been experimentally identified in yeast: class I , which consists of Vps34 , Vps15 , Atg6/Vps30 , Atg14 , and an Atg38 dimer , functions specifically in autophagy , whereas class II , which consists of Vps34 , Vps15 , Atg6/Vps30 , and Vps38 , functions in vacuolar protein sorting [29 , 30] . However , significant signal persisted on the vacuolar membrane in the absence of the class I complex in Δatg14 or Δatg38 mutant cells , as well as in the absence of the class II complex in Δvps38 mutant cells , and in the absence of both classes in the Δatg6/vps30 mutant , indicating that PtdIns3P is generated by a third Vps34 complex distinct from class I or II ( Fig 3A ) . These results are consistent with a recent study that employed an in vitro approach to assess TORC1 kinase assay , which found that Atg6 is dispensable for Pib2-dependent TORC1 activation [22] . Pib2 contains a FYVE domain , which is known to bind PtdIns3P [21] . As the localization of Pib2 on the vacuole is totally dependent on PtdIns3P ( Fig 3A ) , we investigated whether the FYVE domain of Pib2 dictates the vacuolar localization of Pib2 . We examined this possibility by assessing the subcellular localization of two Pib2 variants , one completely lacking the FYVE domain ( ΔFYVE , lacking residues 452–525 ) , whereas the Pib2-R475A C523S variant was mutated at two residues ( R475A and C523S ) , which results in an inability to bind both zinc ions and PtdIns3P [31 , 32] . Microscopy of these GFP-Pib2 mutants expressed under the control of the endogenous Pib2 promoter revealed that a considerable portion of both GFP-Pib2 mutants remain on the vacuole , although the remainder was observed to disperse throughout the cytoplasm ( Fig 3B ) . This indicates that the vacuolar localization of Pib2 is only partially dependent on its FYVE domain , and that another mechanism may exist for its vacuolar localization . These findings are in contrast with a previous study that used a heterogeneous MET promoter to over-express Pib2 proteins , finding that vacuole localization was entirely dependent upon the FYVE domain [21] . However , cells expressing FYVE mutants were much more sensitive to rapamycin than cells expressing wild-type Pib2 , suggesting that TORC1 activity is severely impaired by the appreciable mislocalization of Pib2 ( Fig 3C ) . By constitutively localizing GFP-Pib2 to vacuolar membranes , we were able to further investigate the significance of Pib2 localization in the activation of TORC1 . To this end , we devised an experiment employing a fusion protein , GFP-binding protein ( GBP ) -Pho8 ( Vacuolar alkaline phosphatase ) , which localizes to the vacuolar membrane and exposes GBP to the cytoplasm where it is able to bind GFP [13] . Therefore , if the function of Pib2 requires its vacuolar localization , it should be possible to restore TORC1 activity in cells harboring the GFP-Pib2 FYVE mutants by expressing GBP-Pho8 . Enhancer GBP was employed in this experiment , binding to which enhances the fluorescence of bound GFP [33] . However , this means that the recruitment of Pib2 to the vacuole results in an increased fluorescence signal , preventing quantitative determination . We therefore followed the change in cytosolic GFP signal , which decreases as Pib2 localizes to the vacuole , to determine the localization of GFP-Pib2 FYVE mutant proteins . As expected , the stable expression of GBP-Pho8 led to a reduced level of cytoplasmic GFP-Pib2 , reflecting an increased fraction of GFP-Pib2 localization to the vacuolar membrane ( Fig 3B ) . Importantly , protein levels of the Pib2 FYVE mutants are comparable–indeed marginally lower–than that of wild type Pib2 , and are not influenced by the presence of GBP-Pho8 ( S3 Fig ) . In addition , mobility shift of FYVE mutants was observed only in the presence of GBP-Pho8 , hinting at a role for activated TORC1 in Pib2 modification at the vacuolar membrane ( S3 Fig , left panel ) . The constitutive localization of Pib2 FYVE mutants further resulted in the reactivation of TORC1 , as indicated by rapamycin sensitivity of GBP-Pho8-expressing strains in comparison to cells lacking GBP-Pho8 ( Fig 3C ) . Thus , we conclude that the vacuolar localization of Pib2 is necessary for the execution of its function in TORC1 activation . In addition to the constitutive vacuolar localization of GFP-Pib2 , we also observed the natural formation of vacuole-associated GFP-Pib2 puncta under nitrogen starvation . After addition of either glutamine or leucine , these puncta disappeared ( Fig 4A , S4A Fig ) . Therefore , we investigated the relationship between GFP-Pib2 and GFP-Tor1 puncta [34] . Because tagging of both Pib2 and Tor1 with mCherry provided insufficient signal , we used Ego3-mCherry as a reference , a protein that co-localizes with GFP-Tor1 , as previously reported ( Fig 4B ) [13] . GFP-Pib2 exhibited clear co-localization with Ego3-mCherry both on the vacuolar membrane and in puncta ( Fig 4B ) . These observations provide strong evidence that TORC1-containing Pib2 and Gtr/Ego complexes are localized in close proximity to each other under nitrogen starvation conditions . In wild-type cells subjected to nitrogen starvation , TORC1 shifts between the vacuolar membrane and vacuole-associated puncta . Following the addition of glutamine to cells starved for nitrogen for 30 minutes , GFP-Tor1 puncta disappeared within 5 minutes , with the protein reassuming its localization at the vacuolar membrane ( Fig 4C , S4B Fig , S1 Video ) . When leucine was added in place of glutamine , an essentially identical shift in localization was observed , although puncta disappeared more slowly ( Fig 4C , S4B Fig ) . The translocation of Tor1 is evidently highly regulated by amino acids . In Δgtr1 cells , GFP-Tor1 constitutively formed puncta , even after the addition of glutamine ( Fig 4C , S4B Fig ) , which is consistent with a previous report [13] . Interestingly , GFP-Pib2 was also observed predominantly as puncta in Δgtr1 cells even after the addition of either glutamine or leucine , although limited vacuolar signal persisted ( S4C and S4D Fig ) . This was not because the distribution of PtdIns3P became punctate , as determined by monitoring of GFP-FYVE , a general probe for PtdIns3P ( S4E Fig ) . Therefore , these data suggest that the Gtr system causes TORC1 and Pib2 to spread over the vacuolar membrane in response to amino acids . By contrast , Δpib2 cells exhibited a change in GFP-Tor1 , as in wild-type cells , albeit to a slightly reduced extent ( Fig 4C , S4B Fig ) . We next investigated the effect of simultaneous depletion of Pib2 and Gtr1 on the localization of Tor1 . Using the same Δgtr1 PIB2depletion ( tetO7-Ubi-Leu-3HA-PIB2 ) double-mutant strain described above , we observed the formation of GFP-Tor1 puncta associated with the vacuole ( Fig 4D ) . Upon treatment with doxycycline , GFP-Tor1 dissociated from puncta but did not localize to the vacuolar membrane irrespective of the presence of supplemented amino acid , with GFP-Tor1 remaining diffused throughout the cytoplasm ( Fig 4D ) . Western blotting indicated that the disappearance of GFP-Tor1 puncta was not due to changes in protein abundance ( Fig 4E ) . Taken together , we conclude that , at least under the conditions assessed , the TORC1 complex is tethered to the vacuolar membrane exclusively through the Pib2 and Gtr/Ego pathways . To investigate the molecular mechanism underlying the amino acid dependent activation of TORC1 by Pib2 , we examined the effect of nutrient conditions on the association between Pib2 and TORC1 . As GFP-Pib2 could not be immunoprecipitated under nitrogen starvation conditions , we instead conducted a pull-down of GFP-Tor1 expressed under the control of its own promoter ( Fig 5A ) . Amino acid deprivation led to a decrease in the amount of Pib2 bound to Tor1 ( Fig 5A , S5A Fig ) . However , this reduction was suppressed by the addition of glutamine in a dose-dependent manner ( Fig 5A , S5A Fig ) . We also observed a mobility shift of GFP-Pib2 in response to glutamine addition ( Fig 5A ) . This shift is dependent on phosphorylation by TORC1 because treatment with either phosphatase or rapamycin led to the collapse of the slower migrating GFP-Pib2 species into a single , faster-migrating protein band ( S5B Fig ) . However , we have not yet assessed the physiological relevance of Pib2 phosphorylation . This immunoprecipitation result prompted us to test the possibility that glutamine might act directly on the interaction between Pib2 and TORC1 . Consistent with this possibility , addition of glutamine to all buffers used in the pull-down experiment efficiently improved the co-precipitation of Tor1 by GFP-Pib2 in a dose-dependent manner ( Fig 5B , S5C Fig ) . This effect was specific to glutamine: the presence of leucine did not enhance the interaction ( S5D and S5E Fig ) . Semi-quantitative LC-MS/MS analyses of co-purified materials with TAP-Pib2 using glutamine-supplemented buffers provided further evidence to support this model ( Fig 5C ) . The abundance of each co-purified protein was quantified by the exponentially modified Protein Abundance Index ( emPAI ) [35] . This approach again showed that glutamine addition strengthened the interaction between Pib2 with all known components of TORC1 in vitro , with the exception of Tco89 ( Fig 5C ) . The lack of significance observed for Tco89 was due to variation between replicate measurements . In spite of this inconsistent result for Tco89 , a strong trend of increased co-precipitation in the presence of glutamine is evident . The increase apparently represents specific binding because the amount of Kar2 , a common contaminant of proteomic analyses , co-precipitating with Pib2 was not affected by the supplementation of glutamine ( Fig 5C ) . Although the two stereoisomers of glutamine , l- and d-glutamine , have the same physical and chemical properties , only l-glutamine is synthesized and incorporated into proteins in cells . Re-addition of d-glutamine to amino acid starved cells failed to sustain TORC1 activity as assessed by Sch9 and Atg13 phosphorylation ( Fig 5D ) . Crucially , in clear contrast to l-glutamine , d-glutamine did not effect a dose-dependent increase in Pib2-TORC1 interaction when added to immunoprecipitation buffers , providing further evidence for the physiological significance of Pib2-mediated TORC1 activation ( Fig 5E , S5F Fig ) . Pib2 is a protein comprising 635 amino acids that specify two well-conserved motifs , a FYVE domain and a tail motif , and five weakly conserved motifs that are designated A-E motifs ( Fig 6A ) [21] . We next examined which domain of Pib2 is responsible for binding to TORC1 by expressing truncated versions of Pib2 fused to GFP in Δpib2 cells . Association with Tor1 was subsequently assessed by immunoprecipitation using buffers containing glutamine . All Pib2 mutants containing Motif E interacted with Tor1 , although the amount of Tor1 co-precipitated with Pib2304-635 was less than the others ( Fig 6B , S6 Fig ) . On the other hand , Pib2440-635 , which lacks motif E , almost completely lost the ability to associate with Tor1 , suggesting that motif E is required for the interaction between Pib2 and TORC1 ( Fig 6B , S6 Fig ) . We further investigated this possibility by expressing the truncated Pib2 variants in Δpib2 cells and assessing their effect on TORC1 activation and rapamycin sensitivity . Two mutants ( Pib2165-635 and Pib2304-635 ) , which interact with Tor1 , restored the ability of glutamine to activate TORC1 and rendered cells resistant to rapamycin ( Fig 6C and 6D ) . As reported in a recent paper by Michel et al . , cells expressing N-terminally truncated variants of Pib2 were more resistant to rapamycin than wild-type cells , and the extent of this resistance was comparable to that of cells expressing a Pib250-635 truncation mutant , indicating that only the first 50 N-terminal residues of Pib2 may have a negative impact on TORC1 activity ( Fig 6C ) [23] . In contrast , two other variants were not able to rectify TORC1 activation in Δpib2 cells ( Fig 6C and 6D ) . The first of these , Pib2440-635 , lacks motif E and does not interact with Tor1 . The second , Pib21-620 includes motif E but lacks the tail motif of the protein . This is consistent with previous data suggesting that removal of the tail motif completely diminishes complementation in Δpib2 cells [21] . Furthermore , we isolated pib2-2 , in which we identified a proline to serine mutation at the 337th residue of Pib2 , which is within motif E of the protein . This point mutation confers temperature sensitivity on Δgtr1 Δego1 cells ( Fig 7A and 7B ) . The 337th proline residue is highly conserved among diverse fungal species , suggesting its functional importance in the Pib2 protein . TORC1 activity in pib2-2 cells at restrictive temperature was examined by monitoring phosphorylation of Sch9 ( Fig 7C ) . Sch9 phosphorylation was nearly completely abolished when considering a pib2-2 mutant in a Δgtr1 Δego1 background at 37°C , reproducing our findings following rapamycin treatment ( Fig 7C ) . To confirm the reliability of this approach , we also assessed the stability of the GFP-Pib2P337S proteins at 37°C . Western blot analysis revealed that steady levels of GFP-Pib2P337S and GFP-Pib2wt are comparable ( S7A and S7B Fig ) . Moreover , the interaction between Pib2P337S and Tor1 is compromised even at 4°C , as shown in Fig 7D and 7E , demonstrating that the failure of GFP-Pib2P337S protein to interact with Tor1 leads to the inactivation of TORC1 in Δgtr1 Δego1 cells . Taken together , these data strongly suggest that motif E of Pib2 is required for glutamine-induced TORC1 activation through its interaction with TORC1 . Based on the above results , we hypothesized that Pib2 itself is a glutamine sensor . To test this , we first developed a glutamine equilibrium binding assay using the glutamine-binding protein ( GlnBP ) from Escherichia coli as a positive control ( S8A Fig ) [36] . However , we were not able to detect any significant binding of Pib2 recombinant proteins purified from E . coli with glutamine ( Fig 8 ) . We therefore tested if a yet-unidentified Pib2-binding protein is able to sense glutamine directly or facilitate the binding of Pib2 to glutamine . We incubated purified 6His-Pib2 or 6His-MBP immobilized on Ni-beads with cell extracts prepared from Δpib2 cells , followed by extensive washing of beads to elute unbound protein . Beads were then incubated in the presence of radiolabeled glutamine , and the level of glutamine binding was quantified after washing of the beads [14] . When incubated with cell extracts , we found that radioactive glutamine binds 6His-Pib2 but not 6His-MBP in a manner that is almost fully competitively inhibited when excess unlabeled glutamine is supplied ( Fig 8 ) . In contrast to glutamine , tritium-labeled leucine binds 6His-Sestrin2 but not 6His-Pib2 nor 6His-MBP , in spite of the treatment of yeast cell extracts , further underscoring the specificity of Pib2 binding with glutamine ( S8B Fig ) . Thus , we conclude that Pib2 acts as a part of a putative glutamine sensor in cooperation with a yet unidentified protein that is able to interact with Pib2 .
In this study , we have addressed the question of how the dual Pib2- and Gtr-dependent pathways are dynamically engaged in TORC1 activation in response to specific amino acids . The most obvious and important question concerns what signal these pathways respond to . We recapitulated the pioneering results of Stracka et al . , who showed that TORC1 activity exhibits different responses to glutamine and other amino acids ( as exemplified by leucine ) and proposed the existence of a Gtr-independent mechanism of TORC1 activation by glutamine [19] . More recent studies have suggested that Pib2 is involved in the glutamine-dependent activation of TORC1 independently of the Gtr/Ego system [21–23] . This notion was confirmed and extended further by the following findings presented in this work . First , through the use of a double depletion experiment , we found that in the absence of both Gtr1 and Pib2 TORC1 activity is completely blocked ( Fig 1D ) . This finding is underscored by our localization studies , which indicate that the double depletion of Gtr1 and Pib2 causes a complete defect in TORC1 localization to the vacuole ( Fig 4D ) . Together , these data therefore provide a strong indication that the Gtr/Ego system and Pib2 function as the sole regulators of TORC1 activation , although we cannot exclude the possibility that other proteins may be involved in TORC1 regulation under specific conditions other than nitrogen starvation . Second , we comprehensively demonstrate that while both Pib2 and Gtr1 interact with TORC1 , they do not interact with each other ( Fig 2A–2C , S2B and S2C Fig ) . This direct evidence for the existence of distinct TORC1 pools explains the specialized roles of the two pathways in amino acid response . Third , our protein analyses of Pib2 functional domains provide conclusive evidence that the E domain ( Fig 6B , S6 Fig ) , and in particular the 337th proline residue ( Fig 7D and 7E ) , is essential for binding with TORC1 , and that interaction between the E domain and TORC1 results in TORC1 activation and thereby rapamycin resistance , which is an important characterization of the Pib2 protein ( Fig 6C and 6D , Fig 7C ) . Forth , we very clearly show that Pib2-mediated TORC1 regulation is dependent on the concentration of glutamine by immunoprecipitation studies ( Fig 5A–5C ) . This result draws a direct link between glutamine availability and Pib2 function . Finally , we provide evidence that Pib2 plays a role as a part of a glutamine sensor , although the molecular context of this function remains to be established ( Fig 8 ) . In sharp contrast to ours and the previous observation by Stracka et al . , Varlakhanova et al . claim that Gtr/Ego and Pib2 work together to activate TORC1 , rather than independently , based on the fact that both Pib2 and Gtr/Ego are required for glutamine and leucine-induced TORC1 activation [37] . In this paper , the authors use the Δgtr1 Δgtr2 double knockout strain as a Gtr/Ego mutant instead of the Δgtr1 single knockout strain , as we used in our analyses . The Δgtr1 Δgtr2 double knockout may be a reason for this discrepancy . Stracka et al . previously noted that Δgtr2 cell growth is poor in comparison to Δgtr1 cells due to a more prominent role of Gtr2 in permease sorting [19] . This is consistent with the strong phenotype of the Δgtr1 Δgtr2 double knockout strain in terms of the amino-acid response . Furthermore , there are two remarkable differences between our experiments and the approach of Varlakhanova et al . First , their parental strain is auxotrophic for amino acids ( W303 ) , but we and Stracka et al . used a prototrophic strain ( FY3 ) . By employing a prototrophic strain , we were able to avoid the potential confounding effects arising from the requirement of additional amino acids for growth . Second , we assessed the phosphorylation state of Sch9 to monitor changes in TORC1 activity . Sch9 , which is a direct target of TORC1 phosphorylation , is a well-established and widely employed assay in the TORC1 field [6] . On the other hand , Varlakhanova et al . monitored TORC1 activity based on the phosphorylation state of the 40S ribosomal subunit Rps6 , which is not a direct target of TORC1 , at residues Ser232/Ser233 . Recently , it has been reported that TORC1 , via Ypk3 , regulates Ser-232 and Ser-233 phosphorylation , whereas TORC2 also regulates Ser-232 phosphorylation via Ypk1 and Ypk2 [38] . Thus , the interrogation of Rps6 phosphorylation alone as a measure of TORC1 activity must be interpreted with caution . In addition to the different genetic backgrounds used in these experiments , these additional technical factors may contribute to the observed discrepancies . Although beyond the scope of this present study , it would be interesting to further investigate the cause of these contradictory findings in a future study . The vacuolar localization of Pib2 is only partially dependent on its FYVE domain , which is responsible for the association of proteins with PtdIns3P [21] , suggesting that another mechanism mediated by PtdIns3P may exist for its vacuolar localization ( Fig 3B ) . It is unlikely that residual Pib2 observed on the vacuole membrane is caused by binding to Gtr/Ego–anchored TORC1 , as Tor1 is localized to the vacuole in Δvps34 cells [22] . Despite only a partial localization defect , mutations in the FYVE domain lead to the impairment of TORC1 activity , indicative of the direct involvement of PtdIns3P in Pib2-mediated TORC1 activation ( Fig 3C ) . This enabled us to assess the significance of the vacuolar localization of Pib2 in TORC1 activation . Forced tethering of the Pib2 FYVE mutants on the vacuolar membrane bypassed the requirement for the FYVE domain ( Fig 3B and 3C ) . Although PtdIns3P metabolism is implicated in mTORC1 regulation in mammalian cells [39–41] , and the yeast Δvps34 mutant is also rapamycin-sensitive [42] , our data now demonstrate that the vacuolar localization of Pib2 alone is required for its function in TORC1 activation and that the binding of the FYVE domain to PtdIns3P plays no role other than to determine Pib2 localization . Our immunoprecipitation results provide further evidence that Pib2 and Gtr/Ego are present in distinct TORC1-containing complexes ( Fig 2A–2C , S2B and S2C Fig ) . Both Pib2 and Gtr/Ego co-localize on the vacuolar membrane and in associated puncta at the same time , migrating comparably between these localizations in response to amino acid supplementation ( Fig 4B ) . Moreover , the Gtr/Ego system to a degree modulates the vacuolar membrane localization of Pib2 ( S4C and S4D Fig ) . Therefore , we cannot rule out the possibility that the Pib2- and Gtr-containing TORC1 complexes are associated with each other in a weak manner that is not detectable by conventional immunoprecipitation methods . Like mTORC1 , yeast TORC1 may form a homodimer [43] , and if so , it could contribute to potential hetero-association between Pib2-Gtr complexes and homo-association of the Pib2 complex . Such a higher-order relationship between the Pib2 and Gtr pathways may underlie the simultaneous requirement for Pib2 and Gtr in the response to leucine . In fact , a genome-wide in vivo screen for protein-protein interactions revealed that Pib2 is localized in close proximity to Gtr1 and Gtr2 [44] . However , this system is based on protein-fragment complementation . A problem with the approach as used in this investigation of Pib2 is that an mDHFR fragment was fused to the C-terminal region of Pib2 . We and other groups have observed that the introduction of a C-terminal tagging sequence to the PIB2 gene impairs the function of the encoded Pib2 protein , although it is not yet clear why . The physiological significance of this reported interaction therefore remains dubious . Given that Pib2 , but not PtdIns3P , forms puncta in the absence of the Gtr system ( S4C–S4E Fig ) , it is possible that multiple Pib2-containing TORC1 complexes could undergo some type of self-oligomerization ( Fig 4C ) . During revision , Prouteau et al . reported that in Δgtr1 Δgtr2 cells , TORC1 oligomerizes into a higher-level helical assembly that has been named a TOROID [45] . Gtr1 and Gtr2 play a role in the disassembly of the TOROID in response to nutrients , although the mechanistic details remain uncharacterized . We speculate that vacuole-associated GFP-Pib2 puncta in Δgtr1 cells could be Pib2 proteins that have become stuck in TOROIDs . This is consistent with the fact that we were unable to immunoprecipitate GFP-Pib2 under nitrogen starvation conditions in which GFP-Tor1 exhibits punctate structures ( Fig 5A ) . In this scenario , a Gtr/Ego-independent localization of Pib2 on the vacuolar membrane could be possible , but currently it is difficult to address this problem experimentally in the absence of mechanistic details of Gtr1 and Gtr2 function in TOROID formation . The question of whether amino acid sensors exist remains controversial in yeast , although in mammals Sestrin and CASTOR have been identified as leucine and arginine sensor proteins , respectively [14 , 15] . In this work , we demonstrated the following: ( 1 ) l-glutamine but neither d-glutamine nor l-leucine facilitates the interaction between Pib2 and TORC1 in vivo and in vitro ( Fig 5A–5C and 5E , S5C Fig ) , and ( 2 ) glutamine directly binds the Pib2 complex but not Pib2 alone ( Fig 8 ) . These data strongly suggest that Pib2 is a part of a glutamine sensor . We propose a model describing how a glutamine signal is transduced through the vacuolar protein Pib2 . Without glutamine , TORC1 cannot associate with the vacuole . On the other hand , the Pib2 complex , which is tethered to the vacuolar membrane through the interaction between its FYVE domain and PtdIns3P generated by Vps34 , binds to glutamine directly in the presence of cytoplasmic glutamine . The binding of the Pib2 complex to glutamine facilitates Pib2–TORC1 complex formation , allowing TORC1 to associate with the vacuolar surface , where it is subsequently activated through an unknown mechanism . We attempted to identify glutamine sensor molecules that interact with Pib2 by mass spectrometry , and while all components of TORC1 were identified using this approach ( Fig 2C , Fig 5C ) , our search for glutamine-sensing molecules yielded no obvious candidates . Identification of such a molecule will provide a deeper understanding of the molecular details of glutamine’s function in TORC1 activation , as well as Rag-independent mTORC1 activation by glutamine in mammals [46] .
The yeast strains used in this study are listed in S1 Table . Yeast cells were grown in YPD ( 1% yeast extract ( BD Biosciences ) ; 2% peptone ( BD Biosciences ) ; 2% glucose ( Wako ) ) , SCD ( 0 . 17% yeast nitrogen base without amino acids and ammonium sulfate ( BD Biosciences ) , 0 . 5% ( NH4 ) 2SO4 ( nacalai tesque ) , 0 . 5% casamino acid ( BD Biosciences ) , 2% glucose ( Wako ) ) , or YMM . This detailed composition of YMM is as follows ( for 1 liter ) : 3 . 0 g of KH2PO4 ( Wako ) , 0 . 5 g of MgSO4•7H2O ( Wako ) , 5 . 0 g of K2SO4 ( Wako ) , 15 mg of EDTA ( Sigma ) , 4 . 5 mg of ZnSO4•7H2O ( Wako ) , 0 . 3 mg of CoCl2•6H2O ( Wako ) , 1 . 0 mg of MnCl2•4H2O ( Wako ) , 0 . 3 mg of CuSO4•5H2O ( Wako ) , 4 . 5 mg of CaCl2•2H2O ( Wako ) , 3 . 0 mg of FeSO4•7H2O ( Wako ) , 0 . 4 mg of NaMoO4•2H2O ( Wako ) , 1 . 0 mg of H3BO3 ( Wako ) , 0 . 1 mg of KI ( nacalai tesque ) , 0 . 05 mg of biotin ( TCI ) , 1 mg of calcium pantothenate ( nacalai tesque ) , 1 mg of nicotinic acid ( nacalai tesque ) , 25 mg of inositol ( nacalai tesque ) , 1 mg of pyridoxine ( nacalai tesque ) , 0 . 2 mg of p-aminobenzoic acid ( nacalai tesque ) , 1 mg of thiamine ( Wako ) , 0 . 002 mg of Folic acid ( nacalai tesque ) , 0 . 2mg of riboflavin ( nacalai tesque ) , 2% glucose ( Wako ) ; volume adjusted to 1 . 0 liter with 10 mM potassium hydrogen phthalate•H2O ( pH 5 . 0 ) ( Wako ) [25] . Ammonium sulfate ( final conc . 0 . 5 g/liter ) ( nacalai tesque ) , the auxotrophic supplement uracil ( final conc . 20 mg /liter ) ( Wako ) , or various amounts of l-glutamine ( Wako ) , d-glutamine ( Wako ) or l-leucine ( Sigma ) were added where indicated . Rapamycin ( 53123-88-8; LKT Laboratories ) in stock solution ( 1 mg/ml ethanol and Triton X-100 ( Wako ) at a ratio of 9:1 ( v/v ) ) was added to YPD to achieve a final concentration of 0 . 2 μg/ml . Cells were grown in SCD media , and genetic depletion was initiated by addition of doxycycline to growth media at a final concentration 4 μg/ml . For non-depleted controls , the same volume of ethanol ( solvent ) was added . Cells were grown in SCD or YMM medium containing ammonium sulfate as the sole nitrogen source , and then shifted to nitrogen-free YMM for 30 minutes , after which the indicated amino acids were added . Cells were collected by centrifugation ( 600 x g , 2 min ) and subjected to microscopy . The cells were observed on a Leica AF6500 fluorescence imaging system ( Leica Microsystems ) mounted on a DMI6000B microscope ( HCX PL APO 100/1 . 40–0 . 70 oil-immersion objective lens , xenon lamp ( Leica Microsystems ) ) under the control of the LAS-AF software ( Leica Microsystems ) . For time-lapse imaging , the cells were grown in YMM medium containing ammonium sulfate as the sole nitrogen source , and then shifted to nitrogen-free YMM on a glass bottom dish ( Matsunami Glass ) mounted with 2 mg/ml of concanavalin A ( Sigma ) . Cells were then subjected to time-lapse imaging after addition of glutamine ( final concentration of 0 . 5 mg/ml ) . Images were recorded using a DeltaVision Personal system ( Applied Precision ) mounted on a IX71 microscope ( UPlanSApo 100x/1 . 40 oil-immersion objective lens , LED lamp ( OLYMPUS ) ) . ImageJ software ( National Institutes of Health ) was used to process and produce merged images . NTCB treatments were performed as previously reported with slight modifications [6] . Briefly , 8 OD units of cells were treated with 6% trichloroacetic acid ( Wako ) for at least 15 minutes on ice , washed twice with ice-cold acetone , and dried using a SpeedVac . The pellets were re-dissolved in 200 μl of urea buffer ( 50 mM Tris-Cl ( pH 7 . 5 ) ( Sigma ) , 5 mM EDTA ( Wako ) , 6 M urea ( Wako ) , 1% SDS ( nacalai tesque ) , Complete EDTA-free protease inhibitor cocktail ( Roche ) , 1 mM PMSF ( Wako ) , 1 μM microcystin-LR ( Wako ) , and PhosSTOP ( Roche ) ) and lysed with the FastPrep instrument ( MP-Biomedicals ) and 0 . 6-mm-diameter zirconia beads ( Biomedical Science ) . After centrifugation at 20 , 000 × g for 10 min at 4°C , 100 μl of supernatant were transferred to a new 1 . 5 ml reaction tube . The lysates were mixed with 30 μl of 1 M 2- ( cyclohexylamino ) ethanesulfonic acid ( pH 10 . 5 ) ( Wako ) and 20 μl of 7 . 5 M NTCB ( Sigma ) and incubated overnight at room temperature . Each sample was mixed with 50 μl of 4×loading buffer ( 800 mM Tris-Cl ( pH 6 . 8 ) ( Sigma ) , 6% SDS ( nacalai tesque ) , 400 mM dithiothreitol ( Wako ) , 8 M urea ( Wako ) , 0 . 04% bromophenol blue ( Sigma ) ) and subjected to SDS-PAGE and western blot analysis . Anti-protein A ( P-3775; Sigma-Aldrich ) , anti-GFP ( 11814460001; Roche ) , anti-Tor1 ( sc-11900; Santa Cruz Biotechnology ) , anti-PGK ( 459250; Thermo Fisher Scientific ) , rabbit anti-Goat IgG HRP ( anti-TAP ) ( ab6741; abcam ) , goat anti-mouse IgG , human ads-HRP ( 1030–05; Southern Bio Tech ) , anti-rabbit IgG HRP-linked antibody ( 7074S; Cell signaling ) , anti-Atg13 ( a gift from Dr . Yoshinori Ohsumi , Tokyo Institute of Technology ) , and anti-HA ( 901501; BioLegend ) antibodies were used for western blotting . Cells were resuspended in TAP-A buffer ( 50 mM Tris-HCl ( pH 8 . 0 ) ( Wako ) , 150 mM NaCl ( Wako ) , 10% glycerol ( Wako ) , 1 mM DTT ( Wako ) , 1 mM EDTA ( Sigma ) supplemented with Complete EDTA-free protease inhibitor cocktail ( Roche ) , 1 mM PMSF ( Wako ) , 1 μM microcystin-LR ( Wako ) and PhosSTOP ( Roche ) , and lysed using a FastPrep instrument ( MP-Biomedicals ) and zirconia beads . After lysis , cell lysates were incubated for 10 min at 4°C following addition of Triton X-100 ( 0 . 2% final concentration ) ( Wako ) , and then clarified by centrifugation at 20 , 000 × g for 10 min at 4°C . Gtr1-TAP proteins were precipitated with magnetic beads covalently coupled to rabbit IgG ( Dynabeads M-270 Epoxy beads: Invitrogen ) . GFP-tagged Pib2 proteins were precipitated with magnetic GFP-Trap-M beads ( Chromotek ) . The beads were washed three times with TAP-A buffer containing 0 . 2% of Triton X-100 . In S2B Fig , Rag buffer ( 40 mM Na-HEPES ( pH 7 . 4 ) ( DOJINDO ) , 5 mM MgCl2 ( WAKO ) , 150 mM NaCl2 ( WAKO ) ) was used instead of TAP-A buffer . In Fig 5 and S5 Fig , all buffers were added with the indicated final concentration of l-glutamine , d-glutamine or l-leucine . Bound proteins were eluted in SDS sample buffer by heating for 5 min at 95°C . Proteins were resolved by SDS-PAGE and analyzed by standard western blotting techniques . GFP-PIB2 cells grown in YPDA without or with rapamycin were lysed and GFP-PIB2 was immunoprecipitated as described in the previous section . Reactions both with and without lambda phosphatase ( New England Biolabs ) were set up and incubated at 30°C for 30 min . Prior to removal from the beads , the beads were washed once with lysis buffer . HU buffer was then added to each sample , which were then incubated at 65°C for 15 min to elute the bound proteins . Phos-tag ( Wako ) was used to detect the mobility shift by phosphorylation . Cells expressing TAP-tagged proteins were resuspended in Lysis150 buffer ( 50 mM Tris-HCl pH 8 , 150 mM NaCl , 10% glycerol , 1 mM dithiothreitol ( DTT ) , 0 . 2% Triton X-100 ) supplemented with 1 mM PMSF , Complete EDTA-free protease inhibitor cocktail ( Roche ) , and PhosSTOP ( Roche ) , and were then lysed using a glass bead homogenizer . TAP-tagged proteins were immunoprecipitated using rabbit IgG ( Sigma ) conjugated to M270 epoxy Dynabeads . Bound proteins were eluted by Laemmli buffer , followed by SDS-PAGE . The proteins were excised from each gel , destained and digested in the gels with 12 . 5 ng/μl trypsin ( Wako ) in 50 mM ammonium bicarbonate overnight at 37°C . The peptides were desalted with 3 M Empore C18 Solid Phase Extraction Disks ( Sigma ) . NanoLC-MS/MS analysis was conducted using a Q Exacitve hybrid quadrupole-orbitrap mass spectrometer ( Thermo Fisher Scientific ) , with Xcalibur software , and coupled to an EASY-nLC 1000 ( Thermo Fisher Scientific ) . The data were processed , searched and quantified using Proteome Discoverer ( version 2 . 1 . 0 . 81 , Thermo Fisher Scientific ) , employing the S . cerevisiae UniProt database ( version Feb . 21 , 2016 ) containing 6749 entries . The search parameters were as follows: trypsin digestion with two missed cleavage permitted; variable modifications , protein N-terminal acetylation , oxidation of methionine , propionamidation of cysteine and phosphorylation of serine , threonine and tyrosine; peptide charge ( 2+ , 3+ and 4+ ) ; peptide mass tolerance for MS data , ±10 p . p . m . ; and fragment mass tolerance , ±0 . 02 Da . The abundance of each protein was quantified by the exponentially modified Protein Abundance Index ( emPAI ) , which provides an estimate of absolute abundance of proteins by quantitating the number of peptides identified by MS [35] . To compare results from multiple purifications , relative abundances of proteins defined as emPAIprey/emPAIbait were calculated . 6His-fused Pib2 , 6His-MBP , 6His-GlnBP-MBP and 6His-Sestrin2 proteins were expressed in E . coli Rosetta2 ( DE3 ) by adding 0 . 25 mM IPTG for 3 h at 30°C and purified using Ni-NTA beads ( QIAGEN ) according to the manufacture’s protocol . A portion of the purified proteins were visualized by CBB to confirm purity . Δpib2 cells were resuspended in lysis buffer ( 50 mM Tris-Cl pH 8 , 150 mM NaCl , 10% glycerol , 1 mM dithiothreitol ( DTT ) , 0 . 2% Triton X-100 , 1 mM EDTA ) supplemented with 1 mM PMSF , Complete protease inhibitor cocktail EDTA free ( Roche ) , PhoSTOP ( Roche ) and 1 μM microcysteine-LR and were lysed with a glass bead homogenizer . The cleared cell extract was then incubated with Ni-NTA beads containing 6His-Pib2 or 6His-MBP for 60 min at 4°C . The beads were washed thoroughly three times in lysis buffer and resuspended in 200μl of the lysis buffer . The beads were incubated with 2 μCi of l-[3 , 4-3H ( N ) ]-glutamine or l-[4 , 5-3H ( N ) ]-leucine on ice for 30 min ( with shaking every five minutes ) in the presence or absence of 10 mM unlabeled glutamine or leucine . After incubation , the beads were spun down ( 3 , 000g for 1 min ) , and washed three times with lysis buffer . The beads were then resuspended in 900 μl lysis buffer and equally split into three separate scintillation tubes containing 5 ml of Ultima Gold ( PerkinElmer ) for quantification by a scintillation counter ( PerkinElmer ) . The pib2-2 allele was obtained by random in vitro mutagenesis of PIB2-kanMX4 with a primer upstream of the promoter and one downstream of the antibiotics marker . The Mutagenized DNA of PIB2 was integrated by homologs recombination into PIB2 locus in the Δgtr1 Δego1 strain ( YAY2531 ) and cells were screened for temperature sensitive alleles that could grow on YPD plates supplemented with G418 at 30°C but not at 37°C . Significance of differences was determined using an unpaired two-tailed Student’s t test or Mann-Whitney U-test . ns stands for “not significant” . All materials will be made freely available upon request .
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TORC1 is a central regulator of cell growth in response to amino acids . The evolutionarily conserved Gtr/Rag pathway is a well-established TORC1 regulatory pathway . In this study , we show that two molecular machineries , Pib2 and Gtr/Ego , form distinct complexes with TORC1 in a mutually exclusive manner , implying an exclusive functional relationship between TORC1 and Pib2 or Gtr/Rag in response to various amino acids . We also show that the amino acid-dependent activation of TORC1 is achieved through the Pib2 and Gtr/Ego pathways by anchoring them to the vacuolar membrane . Finally , we show that glutamine binds directly to the Pib2 complex and that glutamine enhances Pib2-TORC1 complex formation . Collectively we provide evidence supporting a role for Pib2 as an element of a putative glutamine sensor .
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"methods"
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2018
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Gtr/Ego-independent TORC1 activation is achieved through a glutamine-sensitive interaction with Pib2 on the vacuolar membrane
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Pavlovian influences are important in guiding decision-making across health and psychopathology . There is an increasing interest in using concise computational tasks to parametrise such influences in large populations , and especially to track their evolution during development and changes in mental health . However , the developmental course of Pavlovian influences is uncertain , a problem compounded by the unclear psychometric properties of the relevant measurements . We assessed Pavlovian influences in a longitudinal sample using a well characterised and widely used Go-NoGo task . We hypothesized that the strength of Pavlovian influences and other ‘psychomarkers’ guiding decision-making would behave like traits . As reliance on Pavlovian influence is not as profitable as precise instrumental decision-making in this Go-NoGo task , we expected this influence to decrease with higher IQ and age . Additionally , we hypothesized it would correlate with expressions of psychopathology . We found that Pavlovian effects had weak temporal stability , while model-fit was more stable . In terms of external validity , Pavlovian effects decreased with increasing IQ and experience within the task , in line with normative expectations . However , Pavlovian effects were poorly correlated with age or psychopathology . Thus , although this computational construct did correlate with important aspects of development , it does not meet conventional requirements for tracking individual development . We suggest measures that might improve psychometric properties of task-derived Pavlovian measures for future studies .
A leitmotif in the nascent field of computational psychiatry [1–4] is that carefully curated cognitive tasks can be used to identify latent dimensions of decision-making . These parametrize process accounting for how the tasks are solved , and are identified according to the models that best fit behaviour . Individuals are characterized according to their coordinates in these dimensions and it is by this means that dysfunction is delineated . A number of such dimensions , quantifying features such as reward and punishment sensitivity [5 , 6] , uncertainty [7 , 8] , exploration [9] , metacognition [10] , interpersonal modelling [11] have been extensively investigated in laboratory tasks . However , to characterize individuals in a psychometrically competent manner , it does not suffice to have external validity in terms of indices of development and pathology . Temporal stability is also crucial [12 , 13] . Stability and related psychometric properties are increasingly important as computational psychiatry moves from describing differences between selected groups of individuals , for example well vs . ill groups , to describing individual change attributable to development , vulnerability to psychopathology , and recovery from psychiatric disorder . Stability is also crucial if computational parameters are to guide diagnosis and personalized psychiatry . Unstable measures may have predictive value [14 , 15] , especially if their variability can be understood [16] , but cannot easily characterise individual trajectories . It is unclear whether computational tasks that have been well validated in the laboratory , and which are starting to be used in epidemiological samples studies [17 , 18] , have psychometric properties sufficient to pinpoint individual dispositions . In particular , while learning tasks are amongst the most popular in computational psychiatry , it is not clear if they bear repetition , for instance whether the identity of the best fitting process model remains the same when they are applied again . Furthermore , we often do not know if parameters inferred by using these best models are psychometrically reliable , covarying with traits , or change with the individual’s state and experience . Here , we study the population distribution and psychometrics of a paradigmatic computational measure , namely the extent to which an individual’s decision-making is guided by Pavlovian influences [19] . This is the direct predisposition to prefer particular actions in response to features of a stimulus , such as the appetitive or aversive consequences that it predicts . This predisposition can help or hinder instrumental behaviour , which is defined in terms of the contingency between action and outcome . Pavlovian biases have often been studied because of their translational relevance for anxiety , post-traumatic stress , and other disorders [20–23] . The incidence of psychiatric symptoms where Pavlovian influences have been implicated rises in adolescence . For example , we recently described a peak in mood symptoms in a large non-clinical sample around the age of 16 in females [13] . It is thus important to examine how Pavlovian influences vary with age as well as characteristics such as mood , sex and IQ [1] . This in turn makes psychometric questions related to tasks assessing Pavlovian phenomena particularly pressing . Pavlovian influences elicited by predictions of reward and punishment have been extensively studied through variants of a Go-NoGo task [19 , 24–26] . Here , subjects prefer to execute , rather than withhold , actions in proportion to their expectations of winning money . This Pavlovian ‘bias’ is quantified as a perturbation of a standard reinforcement learning model , using a form of bonus that is proportional to the predicted value associated with the stimuli concerned [25] . This rewards ‘Go’ actions in the face of ‘potential win’ stimuli and ‘No-Go’ actions in the face of ‘potential loss’ stimuli . To assess Pavlovian bias and associated decision-making characteristics , we used an orthogonalised Go-NoGo task , wherein optimal decisions ( Go vs . NoGo ) are independent of the goal outcome ( winning vs . avoiding loss ) . This has been extensively validated to assess Pavlovian bias , while much is known about neural function in this task [19 , 24 , 25 , 27] . We administered it on two occasions ( termed ‘baseline’ and ‘long follow-up’ ) in a large , naturalistic , epidemiologically informed sample of 14 to 24 year olds [28] . We first validated the class of reinforcement-learning models developed in the laboratory in this population . In the process , we asked which model best described behaviour and ascertained that estimates of Pavlovian bias were robust with respect to secondary modelling details . We considered a model with differential sensitivity to wins and losses , which Guitart-Masip et al , 2014 , found to fit behaviour best ( ‘valenced-sensitivity’ model ) . We also considered variants , particularly equal sensitivity to wins and losses , but differential learning to these outcomes ( ‘valenced-learning model’ ) . We then compared the psychometric properties of the best models using real but also simulated data . We examined the external validity of the best models by assessing correlations between parameter values and the variables of age , IQ and mood . Calendar age is a key variable in development , albeit not the same as developmental time [29–31] . IQ is also an important yardstick , as theoretical [32] and experimental [33] findings motivate further examination of its relationship with Pavlovian tendencies . Specifically , we argued that in the orthogonalized Go-NoGo task used here , efficient instrumental learning rather than reliance on non-instrumental Pavlovian biases is most profitable . Therefore , participants with higher IQ might be expected to rely less on Pavlovian guidance . Finally , we used the ‘Mood and Feelings Questionnaire’ , or MFQ for external validation . This was motivated by two considerations: first , research has suggested links between Pavlovian bias and so-called internalizing disorders [20 , 21 , 26] . Second , we have found that MFQ is a good simple proxy for the ‘general psychopathology factor’ ( see S1 Fig and [13] ) . It may thus shed light into how Pavlovian bias contributes to psychiatric vulnerability or resilience in general , although further research should address associations with other , specific dimensions of psychopathology . We examined longitudinal changes and correlations , particularly concerning the best fitting model and the trajectory of Pavlovian parameters . First , we performed a ‘short follow-up’ study over an interval of 6 months . This is short in developmental but not in test-retest terms . It helped us to subsequently interpret the results of our main , ‘long follow-up’ study , about 18 months post-baseline . Over and above model parameters , model-fit was of over-arching importance , as it assessed how well a specific cognitive model captures individual behaviour . We then explored the dependence of model-fit on age , task repetition and IQ . We tested three hypotheses for the trajectory of the Pavlovian parameter . One is that the bias characterising an individual was a trait that remained stable over the time of our study . Alternatively , it might reflect a slowly changing developmental disposition , specifically one where younger participants were more strongly guided by Pavlovian biases . A third possibility is that Pavlovian biases reflected prior beliefs not only dependent on the context of ‘opportunity’ ( appetitive trials ) or ‘threat’ ( aversive trials ) , but also dependent on other features of the context , such as ‘task taking place in a particular laboratory’ . In the latter case , participants could update their prior beliefs about a link between appetitiveness and the appropriate action across test sessions .
We tested 61 participants at baseline , and then after an interval of 6 months ( Fig 1 ) , using the task briefly described above ( and fully in Methods; very similar to [26] ) . We thus first explored temporal individual stability and group-level change over a time scale which was short in developmental terms . We report uncorrected Pearson r for approximately Gaussian quantities and Spearman ρ for non-Gaussian ones , using parameters inferred from the preferred model variant that emerged from model-comparisons . This was the ‘valenced learning’ variant , quite similar but not identical to the established one [19 , 24] . Please see the Methods section for details . For the overall propensity to choose action over inaction , parametrized by the ‘Go Bias’ , baseline estimates were significantly correlated with short follow-up estimates , as hypothesized ( r = 0 . 30 , p = 0 . 018; Fig 2 ) . However , this was not true for the other parameters . The Pavlovian bias , parametrizing the propensity to action in the context of opportunity and inaction in a context of loss , had p = 0 . 54 . The motivational exchange rate , which measures how strongly likelihood of a choice depends on its value , had p = 0 . 55 . For the learning rates for the appetitive and aversive contexts , p was 0 . 13 , and 0 . 52 respectively . The irreducible noise parameter , quantifying decision variability that could not be reduced by learning motivating actions , had r = 0 . 24 , p = 0 . 052 . The extent to which the model accounted for behaviour , the integrated likelihood measure , was the most inter-correlated variable between baseline and short follow-up , r = 0 . 43 , p = 0 . 00047 . As we shall see below , similar results obtained in the larger , long follow-up study , suggesting that developmentally , individuals largely maintain their rank within the cohort with respect to this measure . Next , we tested whether each of the parameters increased or decreased with task repetition . As we had no a priori hypotheses , we applied a Bonferroni correction for 6 tests , so that a corrected threshold of p = 0 . 05 corresponded to uncorrected p = 0 . 0083 . We found that outcome sensitivity clearly increased ( uncorrected Wilcoxon p = 5 . 5e-7 ) over the 6-month interval . There was weak evidence that Pavlovian bias decreased ( from a median on 0 . 20 to 0 . 12; uncorr . t-test p = 0 . 024 ) . There was no significant change in the other parameters over the short follow-up , but there was good evidence that the median integrated likelihood increased ( from -66 . 4 to -53 . 7 , uncor . Wilc . p = 0 . 0038 ) . 54 of the short-interval participants were also included in the long follow-up sample ( Fig 1 ) . They are included in the long follow-up analyses below , but their exclusion results in minimal change . For example , the baseline vs . long follow-up correlation ρ of Pavlovian bias does not change , while its p-value would drop slightly from 0 . 017 to 0 . 020 . Key results from the short-follow up study thus were that model fit was longitudinally the most stable measure , while the group shifted its outcome sensitivity and Pavlovian bias in the direction of benefitting performance , and it improved its model-fit . In the large naturalistic study , we first collected data from N1 = 817 participants ( ‘baseline’ sample ) . Of these , N2 = 556 ( 68% ) also provided valid data at a follow-up session , on average 18 months later ( ‘long follow up’ sample ) . We first analysed performance simply in terms of the proportion of correct responses that participants achieved in each task condition , time during the task and session ( Fig 3 ) . The characteristic ‘Pavlovian bias’ interaction pattern was seen , with Pavlovian-incongruent conditions showing worse performance than the corresponding congruent ones ( Go to Win > NoGo to Win , NoGo to Avoid Loss > Go to Avoid Loss ) at all stages . As shown in Fig 3A ( ‘early trials’ panel , second vs . third pair of boxes ) , G2AL showed a clearly better level starting level of performance than the other pavlovian-incongruent condition , NG2W ( baseline difference: 18% , pcor < 1e-10; long follow-up difference: 16% , pcor < 1e-10 ) . However , for the ‘late’ trials ( Fig 3B ) the improvement in median fraction of correct responses in G2AL was modest compared to those of NG2W . Hence , median performance in the latter now matched the former ( baseline difference: -0 . 6% , pcor < 0 . 05; long follow-up difference: -0 . 6% , pcor = NS ) . This pattern suggests that not only Pavlovian congruency , but action and/or valence biases in learning and decision making need to be considered . As descriptive statistics do not distinguish clearly the roles of Pavlovian bias and other processes of interest , we fitted a range of computational models capable of these distinctions to the data . We used the integrated likelihood ( iL ) and Bayesian Information Criterion ( iBIC ) to quantify complexity-corrected accuracy ( Fig 4 ) and thereby compare models [24] . We assessed whether the identity of the best-fitting model remained the same over testing sessions , and whether estimates of Pavlovian bias were robust to secondary modelling considerations . We expected correlations for each task parameter across time to be positive . Accordingly , we report uncorrected p’s for Spearman correlations and Wilcoxon paired t-tests . We were also interested in the direction of any systematic change . Here , in the absence of a priori hypotheses , we report Wilcoxon tests , applying a Bonferroni correction for as many comparisons as there were parameters . In the baseline sample , the valenced-learning model performed best ( Fig 4 , leftmost ) , with the valenced-sensitivity model 255 . 7 BIC units behind . Importantly , these two models produced very similar estimates for the parameters of interest here ( r = 0 . 88 between models for the Pavlovian parameters , r = 0 . 93 for Go-bias , p <<1e-05 ) . A variant of the valenced-senstivity model , the ‘sensitivity ratio’ model , also furnished highly similar Pavlovian bias estimates ( e . g . r = 0 . 93 , p <<1e-05 with valenced-learning ) . As a quality check , the ‘irreducible noise’ parameters , which quantify lack of attention and motivation-independent lapses , were reassuringly low ( 5–7% ) . In the long follow-up sample the valenced-learning model again obtained an advantage , here of 275 . 6 BIC units over the second-best , valenced-sensitivity model . However , the mean advantage of the winning model per participant was only 0 . 15 BIC units at baseline , while the typical individual uncertainty in iBIC , estimated as the SD of BIC scores refitted to data generated using the exact mean population value of each parameter for the winning model , was 7 . 9 units . Thus , while evidence of >250 BIC units is considered overwhelming by conventional standards [34] , we asked if in studies with N>500 it might arise by chance and , as importantly , what difference in predictive power it signifies . Using a paired Wilcoxon test to compare fits for the two models for the data obtained in the first ( baseline ) testing session gave p = 0 . 09 , while for the long follow-up Wilcoxon p was 1 . 7e-4 , overall providing evidence against a false positive finding . Similarly , using integrated likelihood estimates at the individual level yielded values of approximate protected exceedance probabilities of 0 . 569 for baseline and 0 . 974 for long follow-up in favour of valenced-learning [35] . However , when we asked if having a greater likelihood for the one model at baseline implied a similar ordering at long follow-up , a chi-square test showed no evidence ( p = 0 . 38 ) . Numerical studies later showed that even if individuals’ employment of a particular model [valenced learning or sensitivity] remained fixed , and so constituted a ‘type’ , our relatively brief experiment would be under-powered to allocate individuals to their type reliably ( see S1 Appendix , ‘In silico simulated agents’ reliability and biases’ ) . We then estimated predictive power . First , we expressed the difference in BIC in terms of the probability of the model better predicting a participant’s decision per trial . Even at long follow-up , this difference was very small , mean ΔPpt = 0 . 0011 , compared to a grand mean prediction probability per trial , Ppt = 0 . 64 . We also introduced a new out-of-sample , or ‘left out likelihood’ ( LOL ) comparison method , suitable for tasks involving learning which present challenges for predictive tests . This is important , as predictive tests do not rely on the approximations inherent in the BIC and become more and more powerful as datasets become larger ( interested readers are referred to the Methods section for validation and details ) . LOL testing confirmed in an unbiased manner that the likely difference in predictive power between the two best models was very small , interquartile range of ΔPpt -0 . 012 to 0 . 0026 . Here the mean predictability per trial was 0 . 73 ( see Fig 5 and Methods ) . To further assess model quality , we examined correlations between parameters within each of the best models . The ones remaining significant after correction for multiple comparisons for the valenced-learning model are shown in Table 2 ( valenced-sensitivity is similar ) . Four within-model significant correlations were consistently found at baseline and long follow-up . The Pavlovian bias was anticorrelated with both learning rates , while the motivational exchange rate correlated with the Go-bias and anticorrelated with irreducible noise . The motivational exchange rate is also known as reward sensitivity or inverse decision temperature . It can be seen as the power that a unit of additional reward ( or loss ) has to shift behaviour off indifference between choices . The correlations were also significant in the long follow-up sample ( Pav . bias vs . appetitive and aversive learning rates: ρ = -0 . 14 , -0 . 19 , p = 0 . 016 , 0 . 00011; motiv . Exchange rate vs . irreducible noise and Go bias: ρ = -0 . 13 , 0 . 14 , p = 0 . 043 , 0 . 020 ) . S2 Table shows similar results for the valenced sensitivity model . We then examined stability in the quality and nature of the performance , which is the main focus of the study . We started with descriptive measures . As suggested in Fig 3 , while there was no overall change in G2W performance between baseline and follow-up ( uncorr . Wilcoxon p = 0 . 31 ) the other three conditions did improve ( G2AL by a median of 5 . 5% , p< 1e-5 corrected for 4 comparisons; NG2W by 2 . 8% , p = 5 . 4e-4; No-Go to avoid loss ( NG2AL ) by 2 . 8% , p = 1 . 78e-4 ) . Next , we used these changes to compare an estimate of Pavlovian bias in follow-up vs . baseline . This estimate showed modest stability across time ( Spearman ρ = 0 . 146 , p = 5 . 4e-4; Fig 6A ) but there was a significant reduction in its mean ( p = 0 . 0019 ) , largely attributable to a closing of the gap between G2W-NG2W ( Fig 6B ) . Cross-sectionally , at baseline the first three conditions showed no significant linear or quadratic dependence on age ( uncorr . regression p: G2W , 0 . 22; G2AL , 0 . 16; NG2W , 0 . 65 ) . For NG2AL , the linear regression explained 1 . 0% of the variance , p = 0 . 021 corrected for 8 comparisons , with positive linear dependency on age . At long-follow-up , again the first three conditions showed no significant dependency at the uncorrected level ( regression p: 0 . 98 , 0 . 096 , 0 . 73 ) . However , at this time there was trend evidence for a positive linear and negative quadratic dependence of NG2AL performance with age ( regression p = 0 . 093 corr . for 8 comparisons , adj . r2 = 0 . 012; S2 Fig ) . To explore the evolution of the component cognitive processes , we examined the stability of the parameter values extracted from the fits of the winning computational model . To put the results that follow in perspective , the Pearson correlation for IQ between sessions was 0 . 77 , pcor << 1e-05 while for the mood measure it was 0 . 61 , pcor << 1e-05 . A modest correlation across time points was detected for the Pavlovian bias ( r = 0 . 10 , p = 0 . 017; Fig 6A ) . A more salient result was an overall reduction in Pavlovian influences with session ( from a median of 0 . 205 at baseline to 0 . 142 at long follow-up , pcor < 1e-5 ) . We found evidence that the short-follow-up participants changed their Pavlovian bias as much in the 6-month interval as the rest did in the mean-18-month interval . The scaled-information Bayes factor in favour of no difference was 4 . 01 ( JZS scaled Bayes factor = 7 . 15 , t = 0 . 76 , p = 0 . 45 ) . Decreases in Pavlovian bias between baseline and long follow-up were strongly correlated with improvements in performance in the Pavlovian-incongruent conditions , and anticorrelated with improvements in the Pavlovian-congruent conditions ( S3 Table ) . We then performed a set of latent change score ( LCS;S3 Appendix ) analyses , useful for describing longitudinal change [36] . The most complex multivariate-normal model that can be fitted to the data , known as the ‘just-identifiable model’ , showed a significant dependence of change on baseline ( regr . beta = -0 . 878 , p<1e-3 ) , with higher-bias individuals at baseline reducing their bias more at long follow up ( visible in Fig 5A ) . This model was superior to one assuming that change only represented regression-to-mean ( χ2 = 7 . 63 , df = 1 , p = 0 . 0057 ) and also to one assuming the same mean and variance at long follow-up vs baseline ( χ2 = 6 . 37 , df = 2 , p = 0 . 041 ) . See S3 Appendix for illustration and more details . We found a substantial temporal correlation for the motivational exchange rate ( ln ( beta ) ; r = 0 . 253 , p = < 1e-5; Fig 7B ) and especially for the model fit measure , the integrated likelihood , ( r = 0 . 37 , p < 1e-15; Fig 8A ) . The latter increased at long follow-up , from a median of -68 . 9 to -64 . 4 , pcor = 0 . 023 . There was evidence for a temporal correlation in learning rates , in both the appetitive and aversive domains ( r = 0 . 09 , p = 0 . 045 and r = 0 . 11 , p = 0 . 011 respectively ) . Both increased significantly from baseline to long follow up ( median differences of 0 . 028 and 0 . 027 respectively; both pcor< 1e-05 ) . There was trend evidence of temporal correlation for the bias parameter favouring action over all trials ( ‘Go bias’: r = 0 . 082 , p = 0 . 054 ) and the lapse rate parameter ( r = 0 . 077 , p = 0 . 068 ) . Go-bias decreased ( median 0 . 73 to 0 . 57 , pcor = 0 . 014 ) , but the most significant change was a decrease in lapse rate ( median 0 . 069 to 0 . 055 , pcor < 1e-05 ) . For none of the parameters , or their change , did we find evidence of correlation with participant age at baseline ( correcting for seven comparisons ) . We also examined whether the baseline parameters depended on gender , mood ( the ‘Mood and Feelings Questionnaire’ ) or IQ ( WASI total IQ ) . Correcting for multiple comparisons , we found no significant dependency of baseline parameters on gender or mood and consequently did not analyse for such dependencies further . IQ was significantly related to the parameters , decreasing with increasing Pavlovian bias at baseline ( r = -0 . 13 , pcor = 0 . 007 ) as hypothesized . Other parameters also related significantly to IQ , notably motivational exchange rate ( r = 0 . 27 , pcor< 1e-10 ) , appetitive learning rate ( r = 0 . 11 , pcor = 0 . 044 ) and aversive learning rate ( r = 0 . 19 , pcor < 1e-05 ) . IQ strongly correlated with overall model fit ( r = 0 . 28 , pcor < 1e-10 ) . In the long follow-up sample the model fit ( r = 0 . 31 , pcor < 1e-10 ) , motivational exchange rate ( r = 0 . 24 , pcor < 1e-05 ) , and lapse rate ( r = -0 . 14 , pcor = 0 . 014 ) , but no other parameter , correlated with IQ . Regressing IQ on the ( log ) appetitive and aversive sensitivities of the valenced-sensitivity model obtained a multiple r-squared of 6 . 1% , whereas the single beta of the valenced-learning model absorbed 7 . 6% of the variance in IQ . We then examined the distributions of parameters and of model fit for evidence of sub-grouping . We used the baseline data for this screening , as it was epidemiologically most representative . There was no evidence for sub-grouping in the parameters , but there was for the fit measures , where a bimodal distribution was evident ( Fig 8A ) . This motivated analysis of the joint distribution of baseline and long follow-up model-fit measures . This was best described by a mixture of two major , approximately equipopulous , Gaussian components and two somewhat less prominent ones ( Fig 8B ) . This means that the behaviour of the high-likelihood clusters is much more predictable ( less random ) according to our models . We performed a mixed-effects analysis ( here , controlling for re-test ) to draw out differences between the clusters in terms of Mood , IQ and task parameters . As expected , we found differences between sub-groups in integrated likelihood ( by construction ) and motivational exchange rates and irreducible noise , which are closely related . More interesting , we found a consistent pattern across parameters and IQ , where the cluster which fit worse at both time points ( blue or 4 in Fig 8 ) did significantly worse than most in all performance-sensitive measures , and had increased Pavlovian bias compared to clusters 2 and 3 ( 2 and 3 had the better fit at follow-up , Fig 8B ) . Pavlovian bias did not differ amongst the other clusters , while cluster 4 had greater IQ than the others . See ‘S2 Appendix: Clustering analyses’ , for statistical details and illustration . Finally , we performed in silico analyses to determine whether the fitting procedure could reliably recover known parameters , and whether it might introduce spurious correlations between them ( see S1 Appendix , ‘In silico simulated agents’ reliability and biases’ for details ) . Recoverability in silico was much better than stability in vivo , suggesting that the former is not a limiting factor in our study . Similarly , no spurious correlation was observed between the Pavlovian bias and learning rates , which means that the associations between them seen in the real data are likely to reflect a true feature of the study population rather than a modelling artefact .
We report the first longitudinal assessment of the psychometric properties of a key computational parameter , namely the Pavlovian bias . We did this in a large , epidemiologically-based study of young people . The Go-NoGo task that we used yielded informative results in terms of the cognitive process likely to operate , the evolution over time of the parameters of that process , the construct validity of the model parameters in question and also useful methodological considerations . We found behaviour was well fit by two models , the winning one having two different learning rates for reward and loss but a single sensitivity to returns , and the other having a single learning rate , but two different sensitivities . This is consistent with , but also finesses , recent results stressing the dependence of learning rates on outcome valence , especially in young people [37 , 38] . We compared models not only on the basis of approximations to the statistical evidence for each , but also on their ability to predict left-out decisions . The dual-learning model fit better , but there was no evidence for a clear allocation of model type to individuals . Model-fit improved with practice and was greater for subjects with higher IQ . Estimates of the Pavlovian bias were robust to model type , but their test-retest stability was weak , limiting inferences about individual development . At the group level , Pavlovian bias decreased when participants were re-tested 1–2 years post-baseline . Pavlovian bias changed over test sessions , but not over age , in a characteristic pattern . In terms of hypotheses we set out to test , we interpret this as strong evidence against this parameter , as measured by this task , being a fixed stable trait . Second , we interpret a longer follow-up resulting in the same reduction in Pavlovian bias as the short one as modest evidence against this bias being a disposition slowly changing with development . The pattern is most strongly supportive of a hypothesis that Pavlovian bias approximates an experience-dependent prior expectation . Of course , our models do not directly compute the ‘probability that in an appetitive context , the correct decision is to act’ , which would formally be a belief . However , an agent using such a belief would prefer Pavlovian-congruent actions , and a weakening of such a belief with experience would lead to weakening of this preference , so the evolution of Pavlovian parameters approximates beliefs or expectancies about contingency . The fact that model-fit was good and improved with time , yet parameters changed , argues against a fixed disposition . The virtual absence of cross-sectional age dependency of the parameters , and shifts being as pronounced after 6 months as after 1–2 years , argues against spontaneous slow development and for an effect of practice . Lack of strong age dependency of performance has been observed in other reinforcement learning tasks ( e . g . [37] in adolescents vs . adults ) , but both performance and cognitive parameter age dependencies very much depend on the specific details of the task at hand [39 , 40] . Practice effects may have affected the improvement in the extent to which people were described by our models . Selective attrition may also have affected our data , though a follow-up rate of 68% is reassuring here . However , attrition may have rendered the long follow-up sample epidemiologically less representative than the baseline one . In this task , the Pavlovian bias aids performance in two conditions , and harms it in the other two . If one interprets the bias as a heuristic that is generally useful , the fact that it reduces with repeated testing is consistent with its approximating a prior belief which can be at least partially overwhelmed by evidence , rather than being rigidly hard-wired by genetics and the early-childhood environment . This may apply more generally to other cognitive biases , and may happen over multiple timescales . Our study only employed a limited number of follow-up occasions , making it difficult to discern longitudinal changes not due to practice effects . In this study , any age , gender or mood dependency of Pavlovian bias , if present , was too subtle to resolve . Psychometrically , the orthogonalized Go-NoGo task had the capacity to provide reliable estimates of Pavlovian bias , in that parameter recovery in silico was greater than 80% . At the same time , in human participants stability was reduced down to ~ 10–15% . The return sensitivity parameter was more stable . However , the stability of task measures was considerably lower than those for IQ and even mood , and lower than that conventionally required for tracing developmental trajectories [41] . Model fit emerged as important , over and above individual parameters , being the most stable task measure . It classified participants into clusters with discernible longitudinal trajectories ( Fig 8 and S2 Appendix ) . The most striking differences were observed between a cluster consisting of people who showed poor at both time points ( 4 in Fig 8B and S2 Appendix ) and the rest . These participants were characterized by a higher Pavlovian bias and lower IQ than the rest , as well as by higher decision-noise parameters . Mood did not differ between any clusters . Poorly fitting participants may have followed a strategy less well captured by our models , or may have been irreducibly more stochastic . In either case , model-fitting causes decision-variability parameters to absorb this high variance , whether its cause is hidden cognitive variables that are as yet not represented in the models , or random noise . It would be interesting to use less constrained machine-learning models to estimate the upper limit on the amount of variance in the data that cognitive-mechanism models like ours hope to explain , and thus help interpret decision variability parameters [42] . Overall , model-fit emerged as a potentially important measure to classify developmental trajectories in future research . We found that IQ correlated with motivational exchange rates , and indeed model fit . That is , the behaviour of those with higher IQ was more affected by a unit improvement in objective outcome , over and above differences in learning and bias . This is what one would expect if IQ test scores were themselves dependent on how motivating the participants considered finding the correct answers in the IQ tests . This is in turn consistent with evidence that IQ test scores can be increased by material incentives and that motivation in the absence of additional incentives predicts real-life outcomes [43] . Likewise , confidence is linked to both IQ and motivation [44] . Our motivational exchange rates may thus reflect ability-dependent confidence , important for development during youth . Tasks involving simple inference about counter-intuitive contingencies , building on our Go-NoGo task , may be useful in exploring these relations . Furthermore , the neural process underpinning reductions in Pavlovian bias would be interesting to elucidate , as it has been suggested that top-down processes actively suppress this bias in more able participants [45] . Alternatively , reductions in Pavlovian bias occurring over time or across IQ groups in young people may relate to differences in fixed , for the duration of the experiment , Pavlovian parameters integral to associative-learning systems ( bpav in Eq 3 ) like those of our models . Though not affecting our central hypotheses , model-comparison analyses raised important questions as to how young people learn differentially from loss and gain events . The fact that behaviour could be explained almost equally well either by recourse to valenced-learning or to valenced-sensitivity , merits further study . Learning rates and sensitivities can be distinguished [46] , but the present task was not optimized to do so . A larger number of trials , for example , could help resolve this ambiguity . In terms of our models , subjects behaved either as if volatility were higher for loss compared to gain contexts ( faster aversive learning ) , or were loss averse ( higher aversive sensitivity ) . If appetitive and aversive sensitivities are valid concepts , they should be similar in comparable but learning-free settings , such as well-learnt versions of this task ( improved from [26] ) . Based on the present results , we hypothesize that in a task capable of simultaneously resolving the valence-dependence of preference ( valenced sensitivity ) and of learning ( valenced learning ) , the two sensitivities would be closer to each other than suggested by a pure valenced-sensitivity model , but would not be identical , as they would also be informed by loss aversion . We studied the orthogonalized Go-NoGo task because it is well established , widely used , specialized to assess Pavlovian bias , and could provide insights ( especially about decision noise and model fit ) likely to be relevant to other computational tasks . However , the findings reported here should be used with caution in other contexts . Further insights about Pavlovian bias may be provided by existing paradigms [47] , but testing for the putative experience-independent core of this bias with a brief task , suitable for developmental research , could be facilitated by an adaptive design . We suggest maximizing the number of plateau-performance trials by adaptively looking for the true indifference point between the effective values of the actions ( q , not Q , in Eq 3 ) . An experience-independent Pavlovian bias would mean that in the appetitive domain , the reward for the ‘No-Go’ action has to be ( adaptively ) boosted by an amount proportional to the mean reward of ‘Go’ and ‘No-Go’ in order to achieve indifference between these two choices . Similarly , in an aversive context the value of ‘No-Go’ has to be adaptively penalized by an amount proportional to the average of the loss returns for the two choices to achieve indifference . For symmetric average returns , the mean of the ( absolute ) adaptive boost and adaptive penalty components would be proportional to the Pavlovian bias , and the difference between these adaptive amounts would be the ‘Go bias’ . Methodologically , the present work extends the use of left-out-likelihood based model comparisons . These furnish outcomes that are directly intuitive and convenient for further statistical comparisons , as well as being free of approximations inherent in the BIC . On the other hand , they are much more computationally intensive and will require further refinement to render their use routine . In conclusion , we show that Pavlovian influences characterizing young people are well described at the epidemiological level by established reinforcement-learning models . Practice and higher IQ correlated with weaker Pavlovian influences , while higher IQ scores were also associated with higher motivation to attain a given reward , suggesting important neurodevelopmental relationships . However , neither the Pavlovian bias parameter nor other key task measures met conventional psychometric standards for temporal stability or for external validity with respect to age and psychiatric symptoms , attributes useful in characterizing individual variability and individual change . It is a matter for further research whether similar problems affect other computational tasks , but our study does give urgency to the work of establishing the psychometric properties of such tasks and the parameters associated with computational models them . Studies aiming to characterize individual trajectories of decision-making will benefit from psychometrically improved computational tasks , which better exclude experience-dependent components , as well as designs that include more follow-up points .
We used an orthogonalized Go-NoGo task that contrasts a propensity to act , rather than not to do so , in context involving opportunity ( ‘win’ ) versus threat ( ‘avoid loss’ ) . Participants were presented with four different abstract stimuli each of which had a constant , but unknown , association with a correct policy . The correct policy was either to emit ( ‘Go’ ) or to withhold ( ‘NoGo’ ) an action , here involving a button press . If the correct decision was made , the better of two outcomes was realised with probability 0 . 8 . This better outcome was null ( as opposed to a loss ) for two stimuli and positive ( as opposed to null ) for the other two . The task closely followed a previously published paradigm [25] , with some slight simplifications , unrelated to the core biases assessed by the task . These simplifications helped deliver it to a community sample , on a large scale and in the context of a multi-task battery . First , implementing the decision ‘Go’ was simpler , i . e . not dependent on any target features , unlike the original task in which the ‘Go’ action could be either ‘left’ or ‘right’ depending on the location of a target . This allowed trials to be shorter . However , time pressure from the remaining task battery ( to be reported separately ) , meant that subjects performed a more restricted sample of 144 trials . Second , task clarity was improved by informing participants before performing the task that the outcome probabilities were 0 . 8 and 0 . 2 . Third , motivation was made explicit by telling participants that they were playing for real money , that random performance would attract zero extra fee and excellent performance could be worth about five pounds sterling additional earnings . These changes were supported by piloting the whole battery in which the task was embedded , as we describe next . We took precautions to ensure that the fact that the task was delivered as part of a battery did not affect the power for testing the hypotheses in question . The battery of which this task was part of consisted of 7 tasks and took over 2 . 5 hours to complete , whereas the task analysed here took about 23 minutes to complete , longer than the average in the battery . We first examined data from previous , longer versions of the task and performed a pilot of 15 participants . In addition to quantitative data , these participants were de-briefed in detail by trained research assistants ( RAs ) who interviewed them as to whether they found the tasks tiring , interesting or difficult . Although quantitative data were in line with the literature , qualitative data suggested that some participants might , subjectively , be affected by tiredness but most importantly some found the task hard to work out and felt discouraged by this . Therefore , first , the randomization of the order of tasks in the battery was constrained , so that this task took place within the first hour of testing . Second , research assistants were assertive in enforcing short breaks between tasks and emphasizing the importance of attending to the task . Third , they reminded participants that they were playing for real money and that all decisions counted approximately equally , in monetary terms , encouraging attention to each decision rather than assume that shorter tasks paid as much as longer ones . Fourth , participants were reassured that they should not be discouraged if the best answers were not clear to them as the task progressed , but on the contrary they should proceed by trial and error and the best answers were then likely to gradually ‘sink in’ . This is consistent with the Rescorla-Wagner model that we used to analyse the data . After the first 50 participants were tested under careful RA supervision , an interim analysis of the whole battery and more limited feedback from RAs was reviewed . This gave no cause for concern with respect to the present task and the quantitative parameters extracted were reassuringly compatible with those from historical laboratory samples ( as well as the Results reported here ) . Participants were thoroughly informed about the task , including a veridical performance-related pay component . Community dwelling participants were recruited from within the volunteer database of the Neuroscience in Psychiatry Network Study [28] . Volunteers were invited to be approximately equally distributed by gender and age , between the ages of 14 and 24 years old , from Cambridgeshire ( 60% of sample ) and North London ( 40% ) . We excluded those with moderate or severe learning disability or serious neurological illness . Recruitment continued until a total sample of 820 young people agreed to participate at baseline . The Cambridge Central Research Ethics Committee approved the study ( 12/EE/0250 ) . Participants gave informed consent themselves if they were at least 16 years of age , otherwise the participant was fully informed and agreed to the study , but their parent or legal guardian provided formal informed consent . We explored variants of the core ( here called valenced-sensitivity ) model that [25] used to describe behaviour . First , the values of actions ( ‘Q values’ ) were calculated based on a learning rate λv . We use ‘v’ subscripts to indicate that , in different variants of the model , the parameter in question may be valenced , i . e . different for ‘win’ and ‘avoid-loss’ trials . In the valenced-sensitivity model all trial types shared the same learning rate but different motivational exchange rates ρv were used depending on trial valence: Qt+1=Qt ( at , st ) +λv ( ρvrt−Qt ( at , st ) ) ( 1 ) Only Q values pertaining to realized stimuli and actions were updated , with all others being carried forward from the previous trial . The model also kept track of the state values pertaining to each stimulus using the same parameters: Vt+1=Vt ( st ) +λv ( ρvrt−Vt ( st ) ) ( 2 ) Crucially , the conventional Q values were biased by two terms representing an overall tendency towards action ( ‘Go bias’ ) and a Pavlovian bias ( towards action or inaction ) that depended on valence ( state value ) : qt ( at , st ) =Qt ( at , st ) +bgo ( at ) +bpav ( at ) Vt ( st ) ( 3 ) where the two bias coefficients b are zero unless at=Go . In effect this means that the No-Go action was taken as a comparator , and the Go action was either penalized ( in aversive contexts ) or boosted ( in appetitive ones ) proportional to the value of the respective stimulus . Finally , the policy probability for choosing an action was given by the softmax function , modulated by a lapse rate parameter ξ: p ( at|st ) = ( 1−ξ ) exp ( q ( at , st ) ) ∑k ( exp ( q ( ak , st ) ) +ξ2 ( 4 ) Thus the motivational exchange rates ρv acted as an inverse temperature parameter for the softmax by scaling the outcome values in Eq 1 , which then fed into Eq 4 via Eq 3 . Several model variants were explored . First , separate learning rates were used depending on valence ( ‘valenced-learning model’ ) with a single outcome sensitivity . Second , an additional memory/forgetting parameter was introduced such that Q values pertaining to unexperienced state-action pairs decayed by a constant fraction per trial , rather than being carried forward intact ( ‘forgetting model’ ) , quantified by an additional ‘memory’ or ‘forgetting’ parameter [48] . In these ‘forgetting models’ models , we argued that during long periods when , by chance , a particular stimulus was not observed , the associated actions might drift back to zero if participants brought to the task a ( strictly unjustified , but common-sensical ) assumption that values might be subject to non-zero volatility . The other models assumed that the value of stimuli not seen in a particular trial would not change . Third , the appetitive and aversive sensitivities of the valenced-sensitivity model were transformed into an overall sensitivity and an appetitive/aversive ratio ( ‘sensitivity ratio model’ ) . This had two motivational exchange parameters , just like the valenced sensitivity model , but formulated them as an appetitive sensitivity and a sensitivity ratio . Therefore , at the level of the individual it was identical to the valenced sensitivity model . At the group level , the expectation-maximization fit used prior distributions for the parameters that were independent of each other . In the case of the valenced sensitivity model , this means that the distribution of appetitive sensitivity over the population was modelled as independent to the aversive one . For the sensitivity-ratio model , a positive mean for the population distribution of the eponymous parameter would encode a positive correlation between appetitive and aversive exchange rates . Model fits favoured the former model . We used hierarchical type-2-ML model-fitting , assuming that each wave of data could be described by a set of independent prior distributions for the mean and spread of each parameter . Waves of testing were fitted independently . Individuals’ parameters were optimized given point estimates of the mean and spread of the group they belonged to , which were themselves re-estimated . Specifically , we used the expectation-maximization algorithm in [25] , modifying MATLAB [49] code provided by Dr . Quentin Huys . We estimated the ‘integrated likelihood’ and ‘integrated BIC’ measures used in that work ( Eq 5 ) , using the same sampling technique . iLpt=ln∫p ( dpt|θ , Μ ) p ( θ|Θ ) dθiBIC=−2∑ptiLpt+Nparln ( ntrnpt ) ( 5 ) Where iLpt is the integrated likelihood for participant pt , dpt the data provided by this participant , θ the ‘micro’ parameters of that participant according to model M , Θ the ‘macro’ parameters describing the population distribution of θ , Npar the length of Θ , ntr the length of d ( same for each participant ) and npt the number of participants . For the purposes of model-fitting each parameter was transformed according to theoretical assumptions and the group distribution approximated by a Gaussian in the transformed space . For example , following [25] we assumed that bpav>0 so this parameter was fitted in log space , whereas parameters having both an upper and lower bound were logistic-transformed . We tested whether the model-fitting procedure was robust to assumptions about the distribution of parameter values in the population ( S1 Appendix ) . We tested two assumptions that were particularly relevant to Pavlovian bias . First , we allowed the population distribution of Pavlovian bias to be normal rather than log-normal . Using mean and variance population estimates derived from the real data , this translated to the presence of a small tail of negative values ( S1 Appendix ) . Second , we tested whether model-fitting was sensitive to the precise form of the population distributions used , e . g . gamma rather than log-normal . Recovery of Pavlovian bias parameters was reassuringly robust in the face of such twists ( S1 Appendix ) . In order to go beyond comparing models by using simple approximations like the BIC , we argued that better models should provide a higher likelihood for data on which they were not trained , compared to less good models . To do this , we fitted models as best as possible to everyone , leaving out certain test trials . Then we compared the sum-log-probability of the actual responses participants provided on these left-out trials , thus performing paired left-out-likelihood ( LOL ) comparisons . Confidence intervals around this difference of predictability-per-trial provide an intuitive measure of how much better one model is than another , especially when compared to the average predictability-per-participant-decision . For the LOL comparison to be optimal , models must be given the best possible chance to describe the individuals whose parameters are used to derive the LOLs . In order to do this , we first divided the sample into a 300-strong ‘group training’ set and a test set . The ‘group training set’ was used to provide the best possible descriptive statistics of the entire population in terms of the means and variances of each ( transformed ) parameter . These were the group-level parameters fitted by the type-2 maximum-likelihood procedure ( S3 Fig ) . We then used these group-level parameters to provide priors for fitting the remaining ‘test set’ , from which the ‘test trials’ were left out . Markov-chain monte-carlo ( MCMC ) fitting at the level of the individual participant was performed , using the sum-log-posterior over the included , non- left-out , trials only to derive posterior beliefs about parameters for each participant in the test sample . During fitting , MCMC efficiently provided sum-log-likelihood samples over the left-out-trials , thus forming the integrated LOL . Two trials were left-out , as described below . LOLs were not taken into account for parameter estimation , to avoid double-dipping . We repeated the procedure with different candidate models , thus obtaining ( paired ) model comparisons of their predictive power over the hidden trials only . For optimal performance , we first decided how many trials to leave out ( left-out trials , LOTs ) . When a limited amount of data per participant is available , greater numbers of LOTs result in noisier parameter estimates for each model , making it more difficult to detect differences between models . Furthermore , it is not a priori known how model fit may deteriorate as a function of the number of LOTs for different models . Hence , it makes sense to use the minimum number of LOTs and rely on our high number of participants to power model comparisons . In order to assess whether these considerations were important in practice , we generated synthetic data using the valenced-sensitivity model , the best in the literature . Using synthetic data we compared LOL using the procedure above with the true LOL according to the generative parameters . S4 Fig shows how increasing the number of LOTs significantly degraded the power of the true generative model to explain LOT data . Next , we assessed the effect of learning on LOL estimation . Because learning occurs in every trial , learners follow different trajectories in the included trials depending on what happens in the left-out trials . Thus , even if the LOL is not used during model fitting , information from the LOTs may influence the fitted parameters , thus potentially biasing fitted parameters towards values most consistent with the participant’s choices ( and hence high likelihood thereof ) in the LOTs . Thankfully , we do not have to guard against every possible such influence , but only to make sure that using information from the LOTs ( which stimuli where shown , which responses were performed and which returns were obtained ) does not unduly bias the estimation of parameters based on the included trials towards values that make the LOTs appear more likely . In order to reassure ourselves about this , we performed a series of numerical experiments where we compared using the information above , to marginalizing over the above responses and rewards . Consider a model M with a single parameter ε . Assume , furthermore , that we have a flat prior p0 ( ε ) ~1 over this parameter . If h is the left-out , or hidden , decision data and v is the included , or visible to the model , data , taking into account the flat prior gives: p ( h|v , M ) =∑εp ( h , ε|v , M ) =∑εp ( h|ε , M ) p ( v|ε , M ) ∑ε'p ( v|ε' , M ) ( 6 ) The question is how p ( v|ε , M ) ought to be calculated in order not to bias estimation of how good model M is , i . e . not to bias p ( h|v , M ) . As learning takes place from trial to trial , should the 'gaps' in v be filled in with the veritable choices of the participant , or be marginalized over ? To investigate this matter , we first performed a numerical experiment with a simple Rescorla-Wagner model with learning parameter ε , making binary choices between alternatives via a softmax function of known parameter τ = 0 . 1 ( i . e . a bare-bones version of our models ) . We generated 10000 x 28 trial epochs , for three levels of ε = 0 . 05 , 0 . 15 and 0 . 25 . Returns were deterministic returns ( action1 → 1 , action2 → 5 ) , starting values for each run: Q0 ( action1 ) = Q0 ( action2 ) = 0 . The first 8 trials were hidden . We looked for bias by examining how our estimate of p ( h|v , M ) depended on whether p ( v|ε , M ) is estimated using ‘informed’ visible trials , p ( i ) ( v|ε , M ) , or ‘agnostic’ ones ( i . e . , marginalizing over hidden trials ) , p ( a ) ( v|ε , M ) . A typical example of how p ( i ) ( v|ε , M ) may differ from p ( a ) ( v|ε , M ) is shown in S5 Fig . Although they gave different results for each individual subject , there was no difference ( and no bias ) with respect to the estimates over the hidden trials for any level of ε examined . This is shown in S6A Fig . It is interesting to note that some simple measures were biased in the expected way; for example , the maximum-likelihood estimate of the learning rate based on the ‘informed’ method was closer to the maximum-likelihood estimate over the hidden trials compared to the equivalent ‘agnostic’ estimate . In the case of ε = 0 . 15 this was by 0 . 03 log units , Wilcoxon p < 1e-08 . We then examined two further models , an η-greedy learner and an ‘observation-violating η-greedy’ learner . The latter was similar to the former , but , importantly , only updated action values for exploratory actions if they furnished a better-than-expected prediction error . We did not detect any bias in the simple η-greedy but we found a very small bias in the expected direction for the observation-violating model . The bias corresponded to 0 . 45% of the grand mean prediction probability . Given that this sequence of models was designed to showcase a difference between the more rigorous agnostic and the more practical informed approach , we concluded that any bias introduced by using the informed approach on our real data would be negligible . We first compared longitudinal change with paired nonparametric tests . We also examined change using latent-change-score ( LCS ) models [36] . To do this we transformed the distributions of each ( already transformed as above ) parameter at each timepoint to normal as described below . LCS models formulate the change between baseline and follow-up as a latent variable , and estimate its mean , variance but also its possible dependence on baseline values . Pure regression towards the mean endows this change parameter with a value of -1 . We used BIC and the likelihood ratio test to compare this model with nested , simpler models of change in the population distribution of parameters . Before we applied the latent-change-score formulation ( S3 Appendix ) , we forced the marginal distributions of the transformed parameters into a Gaussian form . This further transformation-to-Gaussian to was achieved by first , estimating the mean and SD of the parameter distribution in question . Second , estimating an empirical ( stepped ) cumulative distribution function for this parameter with the R function edcf [50] . Third , applying an inverse-gaussian-cdf with the same mean and SD as the original . We then applied the just-identified univariate latent change score model . This formulation does not extract from the data more than the statistics we might otherwise estimate–i . e . the means , variances and covariance of the baseline and long follow up measures , if we assume a bivariate normal distribution . It is however convenient in order to focus on change and phrase different hypotheses in terms of model comparison ( BIC , likelihood ratio etc . ) . We used the ‘Mood and Feelings Questionnaire’ ( MFQ ) as measure of mood and the Revised Children’s Manifest Anxiety Scale—2 ( RCMAS ) as a measure of anxiety [28 , 51 , 52] . General intelligence was measured by the full-scale IQ of the Wechsler Abbreviated Scale of Intelligence [53] . Measurements of IQ were performed on the same day as the task for the naturalistic longitudinal study . We used MFQ measurements taken near to the baseline testing session .
|
Choice behaviour is guided by Pavlovian influences , so that particular features of a situation , e . g . if one seeks to gain rewards to avoid losses , privilege certain decisions over others–here , to be active versus vs . inactive respectively . Such privileging may be useful but may also impair optimal instrumental behaviour . We examined the balance of Pavlovian and instrumental guidance of choice in healthy , 14-24-year-old participants and found that young people with higher IQ relied less on Pavlovian guidance . Experience with a task changed Pavlovian guidance in a rational manner , so that it was used less when unnecessary . On the other hand , the degree of Pavlovian influence was not a highly stable trait and did not depend on age or mood . The degree of unpredictability in choice emerged as a crucial individual characteristic , associated with stronger Pavlovian influences yet more stable than them as a disposition .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"learning",
"psychometrics",
"psychology",
"medicine",
"and",
"health",
"sciences",
"cognitive",
"psychology",
"emotions",
"mental",
"health",
"and",
"psychiatry",
"decision",
"making",
"human",
"learning",
"cognition",
"behavior",
"biology",
"and",
"life",
"sciences",
"social",
"sciences",
"cognitive",
"science",
"neuroscience",
"learning",
"and",
"memory"
] |
2018
|
Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood
|
In endemic areas with high transmission intensities , malaria infections are very often composed of multiple genetically distinct strains of malaria parasites . It has been hypothesised that this leads to intra-host competition , in which parasite strains compete for resources such as space and nutrients . This competition may have repercussions for the host , the parasite , and the vector in terms of disease severity , vector fitness , and parasite transmission potential and fitness . It has also been argued that within-host competition could lead to selection for more virulent parasites . Here we use the rodent malaria parasite Plasmodium yoelii to assess the consequences of mixed strain infections on disease severity and parasite fitness . Three isogenic strains with dramatically different growth rates ( and hence virulence ) were maintained in mice in single infections or in mixed strain infections with a genetically distinct strain . We compared the virulence ( defined as harm to the mammalian host ) of mixed strain infections with that of single infections , and assessed whether competition impacted on parasite fitness , assessed by transmission potential . We found that mixed infections were associated with a higher degree of disease severity and a prolonged infection time . In the mixed infections , the strain with the slower growth rate was often responsible for the competitive exclusion of the faster growing strain , presumably through host immune-mediated mechanisms . Importantly , and in contrast to previous work conducted with Plasmodium chabaudi , we found no correlation between parasite virulence and transmission potential to mosquitoes , suggesting that within-host competition would not drive the evolution of parasite virulence in P . yoelii .
Malaria is caused by a diverse group of parasites composed of at least six species of the genus Plasmodium . Genetic diversity within these species is high , with multiple strains often co-infecting the same host , and is driven and maintained by mutation and through recombination between strains . Concomitant infection of hosts , both man and mosquitoes , with multiple species and/or strains is a common occurrence in endemic areas [1–3] . Such infections may result from the bites of multiply infected mosquitoes or from the bites of multiple mosquitoes harbouring different species or strains . Co-infecting species or strains interact during their life cycles and such interactions may lead to intra-host competition with repercussions for the host , the parasite , and the vector in terms of disease severity , vector fitness , and parasite transmission potential and fitness . Within-host interactions between different parasite genotypes have been observed in both empirical human [4 , 5] and rodent [6] malaria studies , and these have often been observed to result in modulations of parameters such as infection dynamics ( suppression or enhancement of a particular strain or species in a mixed infection ) and virulence ( harm caused to the host ) . A series of experiments performed exclusively with strains of the rodent malaria parasite Plasmodium chabaudi , suggested that faster growing strains gained a competitive advantage over slower growing strains [7] , in that they often dominated mixed strain infections in terms of proportional numbers of parasites , and sometimes competitively excluded the slower growing strain at some point during the infection . This has been interpreted as suggesting that within-host competition could lead to the selection of virulence within a parasite population . The effects of competition between parasites on disease pathology is of particular relevance in malaria , as understanding the links between parasite genetic and disease severity will allow an understanding of how interventions , such as drugs and vaccines , that reduce parasite diversity will impact human health . There are conflicting theories as to how the presence of multiple strains ( and/or species ) of malaria parasites in an infection impact on disease . Reports from malaria endemic regions suggest that it is possible that disease pathology may be exacerbated by within-host competition [8] but also that it can result in decreased parasite burden and may protect against some clinical outcomes of disease [9–14] . Interactions between different Plasmodium strains or species concurrently infecting the same host ( vertebrate or vector ) may also influence the transmission dynamics of each species or strain , affecting their fitness , and driving the selection of those parasites that are good at competing . This has been observed in both human [3 , 15–18] and rodent [19 , 20] malaria infections . Laboratory experiments , field studies and mathematical modelling have been employed to describe the mechanisms driving the evolution of various phenotypes , including virulence . Virulence ( defined as “harm to the host” ) is often , but not always , linked to replication rate , with more virulent strains growing and replicating faster in the host than less virulent strains . Virulence differences can occur as a result of inherent genetic differences between strains , and/or through the influence of environmental factors , the most relevant of which to a parasite is the condition of the host [21] . It has been proposed that the evolution of virulence is driven by within-host competition between strains of malaria parasites in mixed infections [7 , 22] . This theory is based on the idea that faster multiplying parasites out-compete others in a mixed infection and therefore transmit more successfully to the vector . The consequences of within-host competition during the mosquito stage development of malaria parasites for both the vector and the parasite are very poorly understood . This is partially due to the fact that there is little understanding of how malaria parasite infection influences mosquito fitness . Some reports associate malaria parasite infection with decreased survival and reproduction of mosquitoes [23–29] , others find no effect [30] while there is also evidence to suggest that that malaria infection increases longevity in mosquitoes due to a trade-off with decreasing reproduction [31] . These contradictions notwithstanding , many lab-based and field studies have convincingly established that Plasmodium causes pathological changes not only in their vertebrate hosts , but also in insect vectors . This is particularly evident during earlier stages of Plasmodium infection in the mosquito whereby ookinetes penetrate mosquito midgut epithelium ( physical damage ) and provoke physiological stress [32] . As a result , the mosquito launches an immune response to curb the infection , which is an energetically demanding process [33] , hence reduced fitness . In addition , meta-analyses suggest that malaria parasites reduce mosquito fitness and survival [34] . Overall , Plasmodium infection is generally considered to be harmful to their mosquito hosts . What effect competition between parasite species and/or strains has on the pathology of the mosquito stages of malaria infections is largely unknown . Here we use the rodent malaria parasite Plasmodium yoelii to explore the consequences of within-host competition on disease severity and parasite fitness ( including transmission potential ) between isogenic parasite strains with varying degrees of virulence .
Infection parameters for all single and mixed infections are summarised in Table 1 . Of the three strains , only the virulent strain ( 17XL ) causes death of mice in single strain infections . In mice infected with this strain , death occurs early in the infection , by day 5 post-infection . Neither of the avirulent strains ( 17XNL and CU ) or the intermediately virulent strain ( 17X1 . 1pp ) cause host mortality at any time , and infections self-clear within 30 days . Peak parasitaemia; Mean ( ±SEM ) of highest parasitaemia , Cumulative parasitaemia; Mean of area under curve of the parasitaemia curve in single and mixed infections . Maximum Weight loss is the Mean ( ±SEM ) of maximum weight lost by the infected mice . Minimum RBC count is the Mean ( ±SEM ) of least Red Blood Cell number per mL of blood . Days post infections are given in parentheses . Data were generated from groups of 4 mice , and are representative of two independent repeat experiments . To analyse time course data we fitted general linear mixed models with Treatment and Time and their interaction as fixed factors , and Mouse as a random factor nested within treatment . To account for possible autocorrelation of errors through time , we compared the fit of models with and without an autocorrelation term [35] . Models were fitted using REML , and compared using Likelihood ratio tests . In cases where the autocorrelation term significantly improved the fit of the model it was retained for subsequent analysis of the fixed effects . The significance of fixed effects was then determined by fitting models with and without the term of interest using Maximum Likelihood , and comparing the fit of these models with a Likelihood ratio test . Parasitaemia values were log transformed prior to analysis to meet assumptions of homogeneity of variance whilst other response variables were analysed on the measured scale . All analyses were carried out using the LME function in R [36] . Time courses for infections involving the virulent strain were only analysed up to day 5 , after which point all mice had died . Both avirulent/avirulent ( CU+17XNL ) and avirulent/intermediately virulent ( CU+17X1 . 1pp ) strain mixed infections were characterised by protracted parasitaemia and prolonged chronicity of disease compared to their constituent strains growing in single strain infections . The mixed strain infections resulted in higher parasitaemia than either of the constituent strains in single infections late on in the infection ( treatment by time interaction term , L = 306 . 28 , df = 50 , P < 0 . 0001 , and L = 579 . 48 , df = 50 , P < 0 . 0001 , Fig . 1 , Panels A and B ) . For the avirulent/virulent ( CU+17XL ) mixed strain infections , the course of infection followed that of the most virulent constituent strain ( Fig . 1 Panel C , treatment by time interaction term , L = 111 . 69 , DF = 8 , P < 0 . 001 ) . Strikingly , infection with the avirulent/intermediately virulent mixture resulted in 75% host mortality infection late in the infection ( Log-rank test , χ2 = 8 . 165 , df = 2 , p = 0 . 0169 , Fig . 1 Panel E ) , and this was associated with the inability to clear parasites from the blood . In order to compare the virulence ( here defined as pathological harm to the host ) of mixed strain infections to that of single strain infections , we measured the red blood cell ( RBC ) density , and weight of mice daily throughout the course of the infections ( Fig . 1 , panels G-I and J-L , respectively ) , sampling ceasing when parasites were no longer visible in the blood by microscopy , or when mice succumbed to infection . Mixed infections of avirulent/avirulent ( CU + 17XNL ) resulted in lower RBC density and greater weight loss compared to either of the constituent strains in single infections during the latter stages of the infection ( Fig . 1 , Panels G , L = 97 . 90 , DF = 86 , P = 0 . 0004 and J , L = 131 . 38 , DF = 86 , P < 0 . 0001 ) , a phenomenon consistent with the prolonged chronicity of the mixed infection parasitaemia . In the avirulent/intermediately virulent ( CU + 17X1 . 1pp ) mixed infection , this effect was more pronounced , with dramatic and significantly lower RBC count ( Fig . 1 , panel H , L = 228 . 75 DF = 86 , P < 0 . 0001 ) and significantly greater weight loss ( Fig . 1 , panel K , L = 171 . 70 DF = 86 , P < 0 . 0001 ) compared to single strain infections , This weight loss and reduction in RBC density occurred in the latter part of the infection , reflecting the significantly higher parasitaemia of the mixed infection group during this period . Single infections of the most virulent strain ( 17XL ) result in acute anaemia and dramatic weight loss early in the infection , and this pattern is also observed in the mixed strain infection containing this strain plus an avirulent strain ( CU + 17XL ) ( Fig . 1 , Panels I and L ) . In order to determine whether mixed strain infections infect more mosquitoes and result in higher oocyst burdens than single strain infections , we allowed A . stephensi mosquitoes to feed on mice with single or mixed strain infections . As transmissibility varies dramatically throughout an infection , we allowed mosquitoes to feed at two separate time points; on day 3 post-infection , when the oocyst conversion rate ( OCR , the number of oocysts produced per gametocyte ) is at its highest in P . yoelii infections ( R . Culleton , unpublished observations ) , and on day 4 post-inoculation when OCR is lower . We observed no significant differences in the numbers of gametocytes produced in single strain infections compared to mixed strain infections , with the exception of the mixed infection composed of the avirulent CU and the intermediately virulent 17X1 . 1pp on day 3 pi , which contained significantly fewer gametocytes than the single 17X1 . 1pp infection ( S1 Fig . ) . All infections , regardless of virulence or whether mixed or single , resulted in a lower percentage of mosquitoes infected and lower oocyst burdens following feeding on day 4 pi compared to day 3 pi ( Fig . 2 ) . For the single infections , the greatest percentages of infected mosquitoes ( mean , n = 4 mice ) were found in those that had fed on CU ( 100% ) or 17X1 . 1pp ( 100% ) , followed by those fed on 17XNL ( 70 . 5% ) and 17XL ( 56 . 2% ) . The highest mean day 3 pi oocyst burdens were recorded for 17X1 . 1pp ( 105 oocysts per infected mosquito , p . i . m ) , followed by CU ( 96 oocysts p . i . m ) , 17XNL ( 17 oocysts p . i . m ) , and 17XL ( 10 oocysts p . i . m ) . This pattern was similar on day 4 pi , when the highest percentage of infected mosquitoes was achieved by 17X1 . 1pp ( 74 . 5% ) , followed by CU ( 65 . 6% ) , 17XNL ( 19 . 5% ) and 17XL ( 8 . 3% ) , with associated mean oocyst burdens of 13 , 8 , 3 , and 2 oocysts p . i . m , respectively . There was , therefore , no positive correlation between virulence and transmission potential . To test for differences in transmission potential ( defined as the percentage of mosquitoes carrying one or more oocyst following blood feeding on infected mice ) between mixed and single infections , we fitted a generalised linear mixed model , with treatment and day and their interaction as fixed factors , and mouse as a random factor to account for the repeated measures on each mouse . Since infection is a binary trait , we fitted a binomial error structure . Models were fitted using the glmer function in R with Likelihood ratio tests used to compare models with different fixed effects . In mixed infections containing two avirulent ( CU + 17XNL ) , There was no significant interaction between day and treatment , ( L = 2 . 231 , df = 2 , P > 0 . 05 ) , whilst treatment affected the proportion infected on both days ( L = 12 . 369 , df = 2 , P = 0 . 002 , with 17XNL infecting fewer mosquitos than either CU or the mixture ( Fig . 2 , Panel D ) . For the mixed infection containing one avirulent and one intermediately virulent strain ( CU + 17X1 . 1pp ) , again , there was no indication of an interaction ( L = 4 . 75 , df = 2 , P > 0 . 05 ) . Infection was lower on day 4 , than on day 3 ( L = 30 . 545 , df = 1 , P < 0 . 001 ) , but there was no effect of treatment ( L = 0 . 2656 , df = 2 , p > 0 . 05 , Fig . 2 , Panel E ) . When mosquitoes fed on mice infected with the avirulent/virulent ( CU + 17XL ) mixed infection , the effect of treatment on infection depended on day ( L = 6 . 681 , df = 2 , P < 0 . 05 ) , with CU having higher infectivity than both other treatments on day 4 , and than 17XL on day 3 , ( Fig . 2 , Panel F ) . In summary , mixed infections did not infect significantly different percentages of mosquitoes than one of the constituent strains ( CU , the most successful transmitter ) , with the exception of the CU+17XL infection when mosquitoes fed on day 4 of the infection . In this case , there were significantly fewer mosquitoes infected than in the CU single infection . To analyse oocyst number , we used a general linear mixed model with day and treatment as fixed factors , and mouse as a random factor . Data was logged prior to analysis to meet the assumptions of homogeneity of variance . In mixed infections containing two avirulent ( CU + 17XNL ) , there was a significant effect of treatment on oocyst numbers ( treatment main effect , F2 , 9 = 13 . 71 , p = 0 . 002 ) , with CU producing more oocysts that the mixture and 17XNL producing less . There were fewer oocysts on day 4 than day 3 ( F1 , 9 = 78 . 01 , p < 0 . 001 ) , but the effect of treatment was consistent on both days ( F2 , 9 = 0 . 781 , p = 0 . 4868 ) . Post-hoc tests revealed that the mixed infection produced significantly more oocysts than 17XNL ( P = 0 . 0031 ) , but oocyst numbers did not differ significantly from CU in a single infection ( Fig . 2 , Panel A ) . In the mixed infection composed of an avirulent and an intermediately virulent parasite ( CU+17X1 . 1pp ) , there were no significant differences in the numbers of oocysts produced by the mixed infection compared to the constituent strain single infections ( Fig . 2 , Panel B ) . When an avirulent and a virulent parasite strain co-infect a host ( CU + 17XL ) , thee numbers of oocysts produced in mosquitoes fed on single strain and mixed infections is significantly different ( treatment main effect , F2 , 9 = 18 . 28 , p < 0 . 0001 ) , with CU producing more oocysts than the mixture and 17XL producing less . There were fewer oocysts on day 4 than day 3 ( F1 , 9 = 52 . 14 , p < 0 . 001 ) , but the effect of treatment was consistent on both days ( F2 , 9 = 3 . 42 , p = 0 . 0787 ) . Post-hoc tests revealed that CU differs significantly from the mixture ( P = 0 . 003 ) , but for 17XL the difference is marginally non-significant ( Fig . 2 , Panel C ) . We measured the relative proportions of each of the strains within mixed infections by strain-specific qPCR every day throughout the course of the infection . In the mixed infection composed of the two avirulent strains CU and 17XNL , the proportions of the two strains fluctuate between 65% and 35% during the first 16 days of the infection , with 17XNL dominating for the majority of this period . From day 16 pi onwards , however , the proportion of 17XNL relative to CU drops daily until finally , at day 22 pi , there is complete competitive exclusion of 17XNL by CU ( Fig . 3 , panel A ) . In the mixed infection containing avirulent ( CU ) and intermediately virulent ( 17X1 . 1pp ) parasites , the intermediately virulent strain completely dominates the infection from day 4 pi until day 14 pi , during which period no avirulent parasites could be detected . However , this situation is dramatically reversed from day 16 pi , when the avirulent parasite re-emerges , and completely dominates the infection from day 20 until the infection is cleared , completely competitively excluding the intermediately virulent strain ( Fig . 3 , panel B ) . In the case of the mixed infection composed of a virulent ( 17XL ) and an avirulent ( CU ) parasite , the virulent parasite completely dominates the infection from day 4 pi , competitively excluding the avirulent parasite , as all mice die due to the infection on day 5 pi ( Fig . 3 , panel C ) . We next compared the numbers of parasites produced by each strain ( in terms of parasite density , defined as the number of parasites per mL of blood at a particular time-point ) throughout the course of single infections , with the numbers produced when in competition with another strain . The avirulent strain CU is competitively supressed by all strains during the first 20 days of the infection , with competitive suppression strongest when in competition with the virulent strain ( 17XL ) ( mice die at day 5 pi ) , followed by the intermediate strain ( 17X1 . 1pp ) and suppression mildest when in competition with the avirulent strain ( 17XNL ) . When in competition with the avirulent ( 17XNL ) and the intermediately virulent ( 17X1 . 1pp ) strains , competitive suppression ceases at day 20 pi , and competitive release occurs , with CU parasite densities reaching higher levels than in single infections ( Fig . 3 , Panel D ) . Thus , over the course of an infection in which CU is mixed with a strain of the same or higher virulence , both competitive suppression and facilitation occur . The avirulent parasite 17XNL is not supressed in competition with CU compared to growth in single infections during the first 16 days of the infection , after which time it suffers competitive suppression ( Fig . 3 , panel E ) . A similar trend is seen with the intermediately virulent 17X1 . 1pp strain , which is completely unaffected by the presence of CU in mixed infections up to day 16 , from which point on it suffers from competitive suppression ( Fig . 3 , panel F ) . The virulent 17XL strain is completely unaffected by the presence of an avirulent competitor ( CU ) in a mixed infection throughout the 5 days during which mice survive ( Fig . 3 , panel G ) . We measured the relative proportion of each of the strains in mixed infections in mosquitoes using qPCR on DNA extracted from mosquitoes with known numbers of oocysts , and compared the adjusted number of oocysts per strain ( total number of oocysts multiplied by the frequency of the strain measured by qPCR ) to the numbers produced in single infections . This analysis was performed on oocyst DNA extracted from mosquitoes fed on mice at days 3 and 4 pi . We fitted a general linear mixed model , with infection treatment and day fitted as fixed factors , and mouse fitted as a random factor nested within treatment . Adjusted oocyst number was log transformed to meet homogeneity of variance assumptions . For the mixed infection containing the virulent parasite 17XL , as there was very little transmission on day 4 , we were unable to use a general linear model , and t-tests were used in its place . The avirulent clone CU was significantly less successful at transmitting to mosquitoes in mixed infections with all competing strains on day 3 pi ( F3 , 12 = 7 . 78 , P = 0 . 0038 ) , when transmission capacity is at its peak in the P . yoelii / Mus musculus / Anopheles stephensi malaria system . The effect of infection treatment differed for the two days ( Treatment*Day interaction , F3 , 12 = 3 . 68 , P < 0 . 0433 ) with CU not producing significantly differing numbers of oocysts in single compared to mixed infections on day 4 ( Fig . 4 , panel A ) . Importantly , the virulence of the competing clone had no effect on the degree of competitive suppression of transmission potential . The transmission of the avirulent parasite 17XNL was severely and significantly reduced in mixed infections compared to single infections on both days 3 and 4 ( F1 , 9 = 30 . 48 , P = 0 . 0004 , Fig . 4 , panel B ) . The intermediately virulent strain 17X1 . 1pp suffered a less drastic and not statistically significant reduction in transmission success through competition with the avirulent strain CU on day 3 pi and there was no difference on day 4 ( Fig . 4 , panel C ) . Finally , the transmission of virulent strain 17XL to mosquitoes was severely and significantly reduced in the presence of an avirulent competitor on day 3 pi ( Student’s two-tailed t-test , t = 3 . 282 , df = 64 , P = 0 . 0017 ) , and completely abrogated on day 4 pi ( Fig . 4 , panel D ) . Using the oocyst data and relative proportions of each strain described above , we determined a fitness coefficient , reflecting the relative contribution of each strain to the products of fertilization in the mosquito midgut ( i . e . oocysts ) for each parasite strain either in single infections ( Fig . 5 , panel A ) , or in competition with the other strains ( Fig . 5 , panel B ) . The CU strain exhibits the highest fitness in single infections , followed by the intermediately virulent 17X1 . 1pp , the avirulent 17XNL , and finally the virulent 17XL . All strains are negatively affected by competition , with 17XL and 17XNL particularly severely compromised when in competition with CU . The CU strain is least affected by the presence of 17XL followed by 17X1 . 1pp and 17XNL . In order to test whether infections with avirulent parasite are infectious to mosquitoes during the latter stages of the infection , we allowed mosquitoes to feed on mice infected with 17XNL on day 18 . We found that these mosquitoes were infected with oocysts following feeding , and so transmission is possible during the latter stages of infection , at least with this strain . Finally , we assessed whether the proportion of strains measured in oocysts was representative of the proportion of strains inoculated into mice during mosquito feeding . We found a good correlation between the proportions of strains in oocysts , and the proportions in the blood of mice in infections resulting from inoculation of sporozoites from tested mosquitoes ( S2 Fig . ) .
It has been argued , on the basis of numerous experiments performed exclusively with P . chabaudi , that within-host competition leads to the selection of virulent malaria parasites [7 , 22 , 57–60] . This theory relies on the assumption that the “virulent” parasite ( typically , the one with the fastest growth rate ) outcompetes the less virulent parasites in the mammalian host , and then , crucially , is more successful at transmitting to mosquitoes , and subsequently into another mammalian host , than the less virulent parasites . Our results do not support this assumption . Firstly , the most virulent strain in a mixed infection does not always out-compete the least virulent . In the case of a mixed infection between an intermediately virulent and an avirulent parasite , it was ultimately the avirulent parasite that was responsible for the competitive exclusion of the intermediate virulence parasite . We hypothesise that the strain that dominates the infection during the acute phase ( the first 7 days of the infection ) , is subsequently targeted by a stronger strain-specific immune response than the competing strain , leading to the competitive release of the less virulent clone later on in the infection . Crucially , we found that P . y . yoelii infections are infectious to mosquitoes during the latter stages of such infections , following the competitive exclusion of the most virulent strain . In the case of a mixed infection with a highly virulent strain , the avirulent strain was competitively excluded by the fifth day of the infection , at which point the death of the host occurred , effectively restricting the transmission of both the virulent and avirulent parasites to the first five days of the infection . Secondly , we found no correlation between the virulence of a parasite and its transmission ability in single infections , with the avirulent strain ( CU ) resulting in the highest proportion of mosquitoes infected , and the highest number of oocysts per infected mosquitoes , than any of the other strains . Based on the transmission success of the clones in single infections , a fitness co-infection was derived which reflects the transmissibility of the strains on days three and four post-infection when transmissibility is at its highest in P . y . yoelii . This revealed that CU ( avirulent ) had the highest fitness in single infections , followed by 17X1 . 1pp ( intermediately virulent ) , 17XNL ( avirulent ) , and finally , the highly virulent 17XL . Furthermore , these relative finesses were calculated for only days three and four of the infections , and , as we found that transmission to mosquitoes was successful during the chronic phase of infection on the day on which it was tested , it is likely that the true relative fitness of the virulent 17XL is much lower than our estimates , as it kills the host on the fifth day of infection . Thirdly , there was no correlation between a strain’s virulence and the relative fitness cost of competition with another strain . For example , the relative fitness of the highly virulent strain 17XL in mixed infection with the avirulent strain CU was ~20% of its fitness in a single infection , whilst the relative fitness of the intermediately virulent 17X1 . 1pp in a mixed infection with CU was ~70% of its fitness in a single infection . Of all the strains , the avirulent 17XNL suffered the largest cost of competition with the avirulent CU strain , followed by the virulent 17XL , and the intermediately virulent 17X1 . 1pp . The avirulent strain CU was least affected by competition with the virulent XL , and most adversely affected by competition with the avirulent 17XNL . From these results we can infer that virulence is linked neither to competitive ability nor ‘fitness’ as measured through the ability of strains to transmit through mosquitoes in this species of malaria parasite , directly contradicting previous studies with P . chabaudi [7 , 22] , calling into question the validity of extrapolating general principles of the importance of within-host competition as a driver of the evolution of virulence from one parasite species to another . Malaria parasite species differ hugely in many important phenotypes , some of which , such as the timing of gametocytogenesis , will affect the evolutionary repercussions of within-host competition . Considering , for example , the cases of P . falciparum and P . vivax , the two most prevalent of the malaria parasites that infect humans , it may be reasonable to postulate that the evolution of P . vivax strains might be less influenced by within-host competition than P . falciparum , due to the former species’ propensity for producing gametocytes early on in infections [61] , before the influence of inter-strain within-host competition would manifest . In summary , previous experiments with P . chabaudi have appeared to show that within-host competition would drive the evolution of virulence; our results with P . yoelii contradict this , and this discrepancy is probably best explained by phenotypic differences between the species with respect to the timing of gametocyte production . We urge caution , therefore , when extrapolating the results of experiments dependent on variable phenotypic traits with one species of malaria parasite to any other . The fact that our experiments with P . yoelii yield contrasting results to those performed with P . chabaudi highlights the importance of parasite biology when considering informative models for the evolution of various traits , including virulence . Extrapolation from one species to another is problematic when parasite biology varies greatly between species . Furthermore , as host-parasite interactions are of crucial importance in these types of studies , it should be emphasized that the rodent malaria parasites are not , naturally , parasites of Mus musculus , but rather of Grammomys surdaster and Thamnomys rutilans ( Reviewed in [62] ) , and that the typical pathological outcomes of malaria parasite infections in these natural hosts is very different from that observed in laboratory mice . This point is illustrated further by studies showing that the outcome of within-host competition can be significantly different depending on the laboratory mouse strain used [59] . In summary , mixed strain infections of P . yoelii were found to cause more severe disease in mice than single infections of the constituent strains . There was no apparent increase in the infectivity of mixed infections to mosquitoes , and mixed infections did not result in greater oocyst burdens per infected mosquito . Within-host competition generally led to a reduction in parasite fitness , the degree of which varied between strains . Importantly , we found no evidence that virulent strains were more competitive than less virulent strains , and conclude that , in the case of P . y . yoelii , within-host competition would not lead to the selection of virulent strains .
Laboratory animal experimentation was performed in strict accordance with the Japanese Humane Treatment and Management of Animals Law ( Law No . 105 dated 19 October 1973 modified on 2 June 2006 ) , and the Regulation on Animal Experimentation at Nagasaki University , Japan . The protocol was approved by the Institutional Animal Research Committee of Nagasaki University ( permit: 1207261005–2 ) . We used four strains of rodent malaria parasite Plasmodium yoelii yoelii , three of which are phenotypically distinct lines that are isogenic except for polymorphisms at those loci that confer virulence [63] . These are Plasmodium yoelii yoelii 17XNL ( wild-type , non-virulent ) [64] , P . y . yoelii 17XL ( virulent ) [64] , and P . y . yoelii 17X1 . 1pp ( intermediate virulence ) [65] , and a genetically unrelated strain P . y . yoelii CU , which is of wild-type , non-virulent phenotype [65] . Eight-week old female CBA mice ( SLC Inc . , Shizuoka , Japan ) were housed at 26°C and fed on maintenance diet with 0 . 05% para-aminobenzoic acid ( PABA ) -supplemented water to assist with parasite growth . Anopheles stephensi mosquitoes , used in the transmission experiments , were housed in a temperature and humidity controlled insectary at 24°C and 70% humidity , adult flies being maintained on 10% glucose solution supplemented with 0 . 05% PABA . To address the question of whether within-host competition leads to increased virulence , we infected groups of mice with either of the strains on their own or together with a competitor strain . Densities of each strain in mixed infection were monitored using strain-specific real-time quantitative PCR [59 , 66] , replication rates were measured by asexual parasitaemia and virulence was quantified through monitoring anaemia , live-weight loss [67] and host mortality . Seven experimental groups of four mice each were set up to understand the effects of interactions between different parasite strains on the host and to compare the fitness of a strain in single versus mixed strain infections . Four of these groups were each singly-infected by i . v inoculation with CU , 17XNL , 17X1 . 1pp , or 17XL parasites ( 1 × 106 parasitised erythrocytes in 0 . 1mL ) . The remaining three groups each received a total of 2 × 106 parasites comprising a mixture of equal numbers of CU + 17XNL , CU + 17X1 . 1pp , and CU + P . yoelii 17XL . Inocula were prepared by taking blood from the tail vein of the donor mouse and diluting it in medium suitable for parasite maintenance ( 50% heat-inactivated foetal calf serum , 50% Ringer’s solution [27 mM KCl , 27 mM CaCI2 , 0 . 15 M NaCI] , with 20 units of heparin/ml mouse blood ) to the appropriate concentration for the inoculum size . The requisite volume of blood was calculated from the blood cell density and parasitaemia in donor mice counted immediately before experimental sub-inoculations . To accurately quantify the proportion of each strain used in the mixed strain infections , DNA was later extracted from a sample of each inoculum for real-time quantitative PCR ( qPCR ) analysis . Mouse red blood cell ( RBC ) densities and live-body weights were monitored as indicators of virulence . RBC densities were measured using a Coulter Counter ( Beckman Coulter , Florida ) from a 1:40 , 000 dilution of 2 μl sample of tail blood in Isoton ( Beckman Coulter , Florida ) solution . Parasite replication rate was assessed for 30 days by counting the proportion of RBCs infected by asexual parasites ( parasitaemia ) on Giemsa’s solution-stained thin blood smears from tail vein blood . Densities of gametocytes , the blood stage parasites that are transmissible to mosquitoes , were obtained by counting the number of RBCs containing mature gametocytes ( distinguishable from asexual parasites by their morphology and presence of pigment as detected by polarized light ) in the same thin blood smears used for counting asexual parasites . Asexual parasite density and gametocyte density were calculated from the product of RBC density and parasitaemia or gametocytaemia . Parasite densities in mixed strain infections were measured at specific time-points from day 1 to day 30 p . i using strain-specific qPCR from DNA prepared from 10-µl tail blood samples which were collected daily into physiological citrate saline solution , spun down and the pellet stored at −80°C prior to DNA extraction . The experiment was repeated once . To assess whether the outcome of the within-host competition was a determinant of transmission success , we fed mosquitoes on mice infected with either single or mixed infections . Transmission was measured by density of sexual forms , gametocytes , in the blood , the proportion of mosquitoes infected after taking a blood-meal from the mouse , and the numbers of oocysts present on infected mosquito midguts . The number of oocysts produced and the proportions of mosquitoes that were infected were also used as indicators of vector fitness . Twenty female Anopeheles stephensi mosquitoes ( seven- to eleven-days post-emergence ) were allowed to take blood meal from anaesthetised and immobilised mice in both single and mixed infections groups on day 3 p . i and the same was repeated with a fresh group of 20 mosquitoes day 4 p . i . Groups of mosquitoes that had fed on the same mouse were housed in individual pots . Seven to eight days post-feed , mosquitoes were immobilised and their midguts dissected to determine the number of oocysts and the percentage of oocyst-infected mosquitoes . To quantify the proportion of each strain in mixed-strain infections , mosquito midguts from the mixed-infection groups were suspended in PBS , spun down and the pellet stored at −80°C before DNA extraction and subsequent qPCR . The proportion of co-infecting strains in mixed infections was determined by using qPCR measurement of the copy number of parasite’s MSP-1 gene . The MSP-1 gene , located on chromosome 8 [68] contains regions of high sequence polymorphism between clones that facilitate the design of allele-specific primers that can act as clone-specific genetic markers . DNA was extracted from infected mouse blood and infected mosquito midguts using EZ1 DNA investigator kit ( Qiagen ) according to manufacturer’s instruction . The extracted DNA was used for qPCR using the Power SYBR Green PCR kit ( Applied Biosystems , UK ) on a 7500 Real Time PCR system ( Applied Biosystems , UK ) . Copy numbers of parasite msp-1 were quantified with reference to a standard curve generated from known numbers of plasmids containing the same gene sequence . Plasmodium yoelii CU msp1 and P . yoelii 17X msp1 were amplified as previously described [69] . Description of the use of quantitative microsatellite markers to measure the proportions of parasites carrying markers linked to the putative genetic driver of virulence in mice and mosquitoes is given in S1 Text . Fitness coefficients were determined for the four strains based on the numbers of oocysts produced per mosquito that fed on mice infected with each of the strains on days 3 and 4 post-inoculation . The mean number of oocysts observed on the mid-guts of mosquitoes fed on mice with infections of the various strains averaged between days 3 and 4 were taken as an infectivity index . These were then standardized against the strain with the highest infectivity ( CU ) , so that CU had a fitness coefficient of 1 . In mixed infections , the proportion of each strain was determined by qPCR , and the “adjusted number of oocysts” calculated for each strain ( number of oocysts multiplied by the strain frequency ) : Fitness coefficient = mean number of oocysts per mosquito fed on strain X ( day 3 + 4 ) / mean number of oocysts per mosquito fed on strain CU ( day 3 + 4 ) All graphs were generated using GraphPad Prism ( GraphPad Software Inc , USA ) . All statistical analyses were performed using R [36] . Detailed explanations of the statistical treatments used for each analysis are given in the relevant results section . All experiments were subject to full independent repeats with the exception of the experiment in which infectious mosquitoes were allowed to feed on naïve mice in order to measure whether parasite strain proportions present in mosquito oocysts were indicative of the proportions observed in mice following transmission , which were performed once .
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Malaria infections are very often composed of multiple strains of malaria parasites . It is thought that these strains may compete for resources such as space and nutrients within the host . Here we show that such “within-host competition” has repercussions for the virulence of the malaria infection , so that infections composed of multiple strains are more virulent in terms of disease severity , than single strain infections containing the constituent parasites . Following from this , it has been proposed that as such competition would favour those parasites with faster growth rates , then these parasites would be selected in nature when within-host competition is common . We show , however , that this is not necessarily the case , as parasites with faster growth rates in the mammalian host were no more successful at transmitting to mosquitoes than parasites with slower growth rates . These results show that a reassessment of our current understanding of the role of within-host competition in the selection of virulence in malaria parasites is required .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Within-host Competition Does Not Select for Virulence in Malaria Parasites; Studies with Plasmodium yoelii
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Visceral Leishmaniasis ( VL ) , caused by the intracellular protozoan Leishmania donovani , is characterized by relentlessly increasing visceral parasite replication , cachexia , massive splenomegaly , pancytopenia and ultimately death . Progressive disease is considered to be due to impaired effector T cell function and/or failure of macrophages to be activated to kill the intracellular parasite . In previous studies , we used the Syrian hamster ( Mesocricetus auratus ) as a model because it mimics the progressive nature of active human VL . We demonstrated previously that mixed expression of macrophage-activating ( IFN-γ ) and regulatory ( IL-4 , IL-10 , IL-21 ) cytokines , parasite-induced expression of macrophage arginase 1 ( Arg1 ) , and decreased production of nitric oxide are key immunopathologic factors . Here we examined global changes in gene expression to define the splenic environment and phenotype of splenic macrophages during progressive VL . We used RNA sequencing coupled with de novo transcriptome assembly , because the Syrian hamster does not have a fully sequenced and annotated reference genome . Differentially expressed transcripts identified a highly inflammatory spleen environment with abundant expression of type I and type II interferon response genes . However , high IFN-γ expression was ineffective in directing exclusive M1 macrophage polarization , suppressing M2-associated gene expression , and restraining parasite replication and disease . While many IFN-inducible transcripts were upregulated in the infected spleen , fewer were induced in splenic macrophages in VL . Paradoxically , IFN-γ enhanced parasite growth and induced the counter-regulatory molecules Arg1 , Ido1 and Irg1 in splenic macrophages . This was mediated , at least in part , through IFN-γ-induced activation of STAT3 and expression of IL-10 , which suggests that splenic macrophages in VL are conditioned to respond to macrophage activation signals with a counter-regulatory response that is ineffective and even disease-promoting . Accordingly , inhibition of STAT3 activation led to a reduced parasite load in infected macrophages . Thus , the STAT3 pathway offers a rational target for adjunctive host-directed therapy to interrupt the pathogenesis of VL .
Visceral leishmaniasis ( VL ) , caused by the intracellular protozoa Leishmania donovani and L . infantum ( syn L . chagasi ) , affects nearly a half-million people each year [1] . It occurs in tropical and subtropical regions of the world and is commonly associated with poverty . Infection is initiated when parasites are deposited in the skin by the sand fly vector . In most infected people , the infection is controlled by a type 1 cellular immune response and there are no signs of disease . However , some infected individuals develop a chronic progressive illness characterized by fever , splenomegaly , cachexia , pancytopenia and a relentlessly increasing parasite burden in the spleen , liver and bone marrow . Susceptibility is associated with decreased antigen-induced IFN-γ and IL-12 responses in peripheral blood mononuclear cells [2 , 3] , CD8 T cell exhaustion [4] , reduced T cell-mediated macrophage activation and parasite killing [5] , and increased IL-10 production [6–8] . In contrast to the in vitro finding of decreased antigen-induced IFN-γ , there is a high level of plasma and splenic IFN-γ production [6 , 9–11] and evidence of antigen-induced IFN-γ production in ex vivo whole blood assays [12] in patients with VL . The disconnect between what should be a protective IFN-γ response and the relentless parasite replication and disease progression in VL remains an enigma . In vitro models of L . donovani infection identified several pathways of impaired macrophage function [13] , but macrophage function in vivo has not been investigated . L . donovani infection of Syrian hamsters ( Mesocricetus auratus ) leads to disease that mimics the clinicopathological features of active human VL [14] . We have studied this model to better understand the immunopathogenic mechanisms that lead to progressive VL . We demonstrated that in the spleen of hamster with VL , like in human disease , there is strong expression of IFN-γ that inexplicably does not protect against the relentlessly increasing parasite burden [15 , 16] . This suggested that splenic macrophages did not effectively respond to classic macrophage activating signals , or responded in a way that was not protective . Indeed , expression of macrophage nitric oxide synthase ( NOS2 or iNOS ) , the primary anti-leishmanial effector mechanism in mice [17] , was impaired in macrophages in hamster VL [16 , 18] . We found evidence for several mechanisms that could account for this , including polarization of macrophages toward an M2-like phenotype with STAT6-dependent dominant arginase expression [19 , 20] , and simultaneous expression of the macrophage suppressive cytokines IL-4 and IL-10 [15 , 19 , 21] . To better understand the immunopathogenesis of this disease and macrophage function in the infected tissue environment , we determined global gene expression in infected spleens and splenic macrophages . We used RNA sequencing ( RNA-Seq ) with de novo transcriptome assembly because the hamster genome has not been fully sequenced and/or annotated . Other groups have used this approach to enable transcriptional profiling in non-model organisms [22–26] . RNA-Seq permits cost-effective , simultaneous sequencing at unprecedented scale and speed to quantitatively characterize gene transcription [27] . Analysis of the transcriptional profile in the L . donovani infected hamster spleen revealed a strikingly proinflammatory environment . There was a remarkable breadth and magnitude of upregulated transcripts related to interferon signaling in the spleen . However , splenic macrophages isolated from hamsters with VL showed fewer differentially expressed transcripts , expressed fewer IFN-response genes , and had a transcriptional profile indicative of a mixed M1- and M2-like activation phenotype . In fact , IFN-γ paradoxically enhanced parasite growth and induced the counter-regulatory molecules Arg1 , Ido1 and Irg1 in splenic macrophages . This was mediated , at least in part , through IFN-γ-induced STAT3 activation and expression of IL-10 , which suggests that splenic macrophages in VL are conditioned by the chronic inflammatory environment to respond to macrophage activation signals with an exuberant counter-regulatory response that contributes to the progressive infection . The STAT3 pathway offers a rational target for adjunctive host-directed therapy to interrupt the pathogenesis of VL .
We evaluated global gene expression in spleen tissue and splenic macrophages in the Syrian ( Golden ) hamster ( Mesocricetus auratus ) model of progressive VL . de novo assembly of a transcriptome was necessary because sequences derived from Chinese Hamster Ovary cells ( from its near relative Crisetulus griseus ) [28] , and a draft genome of Mesocricetus auratus via genome shotgun sequencing ( https://www . ncbi . nlm . nih . gov/bioproject/77669 ) , were incompletely sequenced and/or annotated . To avoid using low quality and artificial sequences , we first performed a quality control analysis of the raw RNA sequencing data . Phred score medians at all bases were ≥30 ( i . e . , error rate ≤ 0 . 001 ) and the majority of the reads had average phred score >37 ( S1A and S1B Fig ) . CG content per read was similar to the theoretical distribution ( S1C Fig ) and per base N content at each position was <5% ( S1D Fig ) . Control and infected samples generated high quality sequencing reads with a low frequency of sequence artifacts and low quality reads ( less than 2% ) , which were filtered out . We further removed reads that mapped to the Leishmania donovani genome ( NCBI BioProject PRJEA61817 ) [29] and assembled a high quality de novo transcriptome using Trinity software . Trinity has been proven effective in generating high quality de novo transcriptomes with low base-error rates and acceptable accuracy of RNA-Seq reads from non-model organisms [23] . A summary of the workflow is shown in Fig 1A . Trinity produced 187 , 847 transcripts ranging from 201 to 23 , 840 nucleotides in length . To validate the assembled results , we compared each transcript against the CHO RefSeq genome ( GenBank Assembly ID GCF_000223135 . 1 ) by Basic Local Alignment Search Tool ( BLAST ) , which can be exploited to assign a gene ID by identifying homologues in close species [30] . Using the hit with the lowest E-value and the largest alignment score , the largest reported E-value was 1e-5 and 78% of the hits had an E-value equal to 0 ( Fig 1B ) . The majority of the hits returned alignment scores >400 ( Fig 1C ) . These data indicated that the Syrian hamster de novo assembled transcriptome was highly homologous to sequences in the CHO-K1 genome , but that it contained more transcript sequences than what is represented or annotated in the CHO RefSeq genome . We used the BRANCH software to expand the Trinity transcriptome into a more complete transcriptome [22] . With BRANCH , 205 , 041 transcripts ranging from 201 to 23 , 840 nucleotides in length were obtained . 64% ( 131 , 021 ) of the BRANCH transcripts had a BLAST E-value <1e-3 when compared to the rat and mouse genomes . The transcripts that passed the E-value cutoff were typically longer and thus more informative than those that failed ( Fig 1D ) . After application of the <1e-3 cutoff to the assembled transcripts , BRANCH produced more transcripts compared to Trinity ( Fig 1E ) . These data indicated that BRANCH improved the Trinity assembly , so we pooled the BRANCH transcripts with an E-value <1e-3 to generate a draft reference transcriptome . Using the alignment software Bowtie2 ( v2 . 1 . 0 ) , the alignment of the RNA-Seq data to this draft transcriptome had a 92 . 76 ± 0 . 68% alignment rate , which was considerably higher than the 58 . 46 ± 1 . 38% and 37 . 77 ± 1 . 25% obtained when the sequences were aligned to the incomplete NCBI Mesocricetus auratus genome ( NCBI BioProject PRJNA210213 ) and CHO RefSeq transcripts . Additionally , we compared our assembled transcriptome with the NCBI Mesocricetus auratus genome and found >70% of NCBI transcripts could be found in our de novo assembled transcriptome , while <30% of our assembled transcripts were represented ( or annotated ) in the NCBI genome ( Fig 1F ) . Collectively , these data indicate that the de novo assembled and annotated transcriptome is to date the most complete compilation of hamster transcripts available . Consistent with our previous observations [31] , we determined that during the course of experimental VL , there is expansion of splenic macrophages ( Fig 2A ) . We demonstrated previously that this population is parasite-permissive and disease-promoting [16 , 19 , 20] . Therefore , their transcriptional profile , in the context of the diseased spleen , was of considerable interest . Antibodies for purification of hamster macrophages are not available , so we used adherence to isolate this population from whole spleen cells from control and infected hamsters . Heavily infected macrophages are less-adherent so may have been underrepresented in the purified population . The purity of this population was demonstrated by their typical macrophage morphology and intracellular expression of the macrophage marker CD68 ( Fig 2B ) . The enrichment of transcripts characteristic of the macrophage lineage , and the absolute or relative absence of transcripts specific to other cell lineages ( Fig 2C and S1 Table ) confirmed the purity of the isolated macrophages . Note that the transcription factor SPI1 ( PU . 1 ) , which is involved in the differentiation of macrophages , was also highly enriched in the splenic macrophage population . The enrichment of the fibroblast markers P4HB in the adherent spleen cells is most likely the consequence of expression by inflammatory macrophages [32 , 33] , but we cannot exclude the possible contamination with a small number of fibroblasts . Collectively , these data indicate that the adherent spleen cell population was highly enriched for splenic macrophages . The de novo assembled annotated transcriptome was used as a draft reference genome for differential gene expression analysis . Multidimensional Scaling ( Principal Component Analysis ) plots revealed that infected and uninfected samples were appropriately clustered in both spleen and splenic macrophage samples . However , the first dimensional coordinate separated spleen tissue but not splenic macrophage samples ( S2A and S2B Fig ) , which suggests a greater effect of Leishmania infection in the whole spleen samples than in the macrophage population . We performed differential expression analysis , using two R BioConductor packages EdgeR and DESeq2 [34–36] , between uninfected controls and Leishmania infected whole spleen and purified splenic macrophages ( both from 28-day infected hamsters ) . We chose this time point because at this time in the course of infection there is dramatic increase in spleen size , parasite burden and change of splenic macrophages to a more permissive phenotype [20 , 21 , 37] . We considered a transcript to have significant differential expression when it was detected by each of the 3 different approaches ( see methods ) . At a False Discovery Rate ( FDR ) cutoff <0 . 01 we identified 4 , 360 differentially expressed transcripts in the spleen samples , which included 2 , 340 ( 53 . 7% ) up-regulated and 2 , 020 ( 46 . 3% ) down-regulated genes ( Fig 3A ) . At the FDR <0 . 01 cutoff , splenic macrophages had substantially fewer differentially expressed transcripts ( n = 692 ) , which included 449 ( 64 . 9% ) that were up-regulated and 243 ( 35 . 1% ) that were down-regulated ( Fig 3B ) . Some differentially expressed transcripts were common to both the spleen and splenic macrophage samples ( 240 up-regulated and 64 down-regulated ) ( Fig 3A and 3B ) , but less <10% of the differentially expressed transcripts in the spleen samples were also differentially expressed in splenic macrophages . The number of differentially expressed transcripts in the spleen tissue and splenic macrophages was decreased to 2778 and 363 , respectively , by tightening the FDR to < 0 . 001 ( S2C Fig ) . We identified the molecular pathways significantly altered during infection using the Ingenuity Pathway Analysis ( IPA ) software package . The top 10 pathways identified as enriched in the 28-day infected spleen and splenic macrophages are shown in Fig 4A and 4B , respectively . A list of the top 50 canonical pathways for both samples can be found in S3A and S3B Fig . A number of the top pathways ( Hepatic fibrosis , pathogenesis of multiple sclerosis , atherosclerosis signaling , communication between innate , adaptive immune cells , and the glucocorticoid receptor signaling pathways ) identified in whole spleen tissue were also identified in splenic macrophages , supporting the central importance of macrophages in the immunopathogenesis of the splenic infection . A common characteristic shared by several of the enriched pathways in the infected spleen and splenic macrophages was the upregulation of inflammatory cytokines , chemokines and their receptors . The significance of these molecules was also confirmed by Gene Set Enrichment Analysis ( GSEA ) , which revealed that at least 4 out of the top 10 gene sets enriched in spleen and splenic macrophages , were associated with production , receptor activity and signaling of cytokines and chemokines . The list of these gene clusters and their interaction networks are shown in Figs 4C and S3C . Differentially expressed cytokines , chemokines , and their receptors identified the broad inflammatory nature of the spleen during VL and suggested involvement of multiple leukocyte populations ( Fig 5; S2 Table ) . In the spleen , transcription factors that drive inflammation , including those involved in interferon and cytokine responses ( STAT1 , STAT2 , STAT3 , IRF1 , IRF7 , XBP1 , LITAF ) and MHC expression ( XBP1 , NLRC5 ) , were both transcriptionally upregulated and predicted to be activated during infection ( S3 Table ) . Additionally , transcription factors involved in regulation of the inflammatory response , including the NF-kB complex ( RELA , RELB ) , TBX21 , NFATC2 ( T cell activation ) , STAT4 , IRF3/5 , IFI16 , HMGB1 , BCL10 ( NF-kB activator ) , CBP/P300 , and DDIT3 ( caspase activation , cytokine expression ) , were predicted to be activated in the infected spleen tissue despite some not being differentially expressed ( S3 Table ) . The predicted activation of lipopolysaccharide-induced TNF factor ( LTIF ) [38] and increased expression of TLR4 in the splenic macrophages suggested that circulating LPS may might be a contributor to the proinflammatory nature of the spleen in VL . Elevated circulating endotoxin levels resulting from increased intestinal permeability and bacterial translocation were observed during human VL [39] . In hamsters with VL we found serum endotoxin levels to be highly variable without a significant increase in mean levels in infected vs . uninfected hamsters ( 15 . 9 vs . 8 . 4 , p = 0 . 13; S4 Fig ) . However , the proportion of infected hamsters with a high circulating endotoxin level ( >30 EU/mL ) was significantly greater in the infected compared to uninfected animals ( 40% vs . 5%; p = 0 . 027 by Fisher Exact Test ) . Fewer cytokine and chemokine mRNAs were differentially expressed in splenic macrophages compared to whole spleen tissue , but all of the differentially expressed cytokine/chemokine transcripts in splenic macrophages were upregulated . Notably , proinflammatory macrophage-activating cytokines ( IFN-γ , IL-1β ) were upregulated , as were receptors that would be responsive to inflammatory signals ( e . g . toll-like receptor-4 [TLR4] , IL-15Rα , CSF2Rβ/IL-5Rβ [common subunit of the IL-3 , IL-5 , and GM-CSF receptors] and IL-21R ) ( Fig 5B; S2 Table ) . These findings distinctly contrast with data from in vitro infected mouse [40 , 41] and human [42] macrophages , which indicated that Leishmania infection had a broadly silent or suppressive rather than activating effect on macrophage inflammatory gene expression . Thus , our data suggest that the inflammatory signals generated in the infected spleen environment , which would be absent from in vitro infected macrophages , have considerable influence on the activation status of splenic macrophages . The expansion of myeloid cells in the spleen in VL may result from recruitment from the bone marrow , extramedullary hematopoiesis from in situ precursors [43] , and/or local proliferation of resident macrophages . Chemokines that act to recruit monocytes/macrophages ( CCL2 , CCL3 , CCL4 , CCL5 , CCL6 and CCL7 ) were highly expressed in the spleen during VL . The chemokine receptors CCR1 , CCR2 , and CCR5 were also increased on splenic macrophages ( Fig 5; S2 Table ) . Spleens from mice infected with Leishmania chagasi had sustained expression of CCL2 , which was associated with the influx of macrophages that enhanced the infection [44] . Transcription factors that regulate myelopoiesis ( Egr2 , SPI1 , IRF8 and AP1 ) [45 , 46] were enriched or predicted to be activated in the splenic macrophage population ( S1 and S3 Tables ) . Thus , local generation may also contribute to the accumulation of myeloid cells in the spleen . We have found that splenic macrophages are highly proliferative during active VL ( Osorio , Melby , manuscript in preparation ) . Expression of neutrophil ( CXCL2 , CXCL3 , CXCL5 , and CCL3 ) and eosinophil ( CCL11 and its receptor CCR3 ) chemoattractants was also increased significantly in the infected spleen , but we found no increase in these cells by morphological analysis of splenocytes ( Fig 2A ) . Better markers for hamster neutrophils are needed to exclude their accumulation in the spleen . It is also possible that the neutropenia commonly found in VL limits their accumulation in the spleen . Macrophages exhibit considerable plasticity in their activation state , which depends on cues received from the local environment [47 , 48] . The macrophage activation phenotype , and the signals that drive it , is critically important in VL because macrophages have the dual role of mediating intracellular parasite killing and controlling tissue damage and repair [49] . At the extremes of the polarization spectrum , M1 macrophages are important for the clearance of intracellular pathogens including Leishmania [50] , while M2 macrophages are protective against helminths and have anti-inflammatory and tissue repair functions [49 , 51] . However , accumulating evidence indicates that in tissue inflammation and infection [52 , 53] , the polarization of macrophages does not always fit neatly within the dichotomous M1–M2 classification system [48] . We evaluated the expression of genes known to be associated with macrophage activation/polarization ( see S4 Table for a full list of references ) . Splenic macrophages from hamsters with VL had a significantly increased expression of genes characteristic of both M1 ( CXCL9 , CXCL11 , IL1B , IL6 , FCGR1A , IDO , IRG1 , IFNγ , STAT1 , CCL3 , CCL5 ) ( Fig 6 , S4 Table ) and M2 ( Fig 7 , S4 Table ) polarization . The M1-associated genes showed a more consistent pattern of upregulation in the infected vs . uninfected groups compared to the M2-associated genes , perhaps suggesting a bias toward the M1 phenotype ( compare Figs 6 and 7 ) . Some M1-associated transcripts ( OASL , IRF1 , IRF7 ) were upregulated in the spleen tissue but not splenic macrophages , suggesting that other cell populations , possibly fibroblasts [37] , may have an immunoregulatory role . Other M1-associated markers ( NOS2 , CXCL13 , IFNGR , CD86 , CCR7 , CD80 , CD68 , IL7R , HRH1 , BCL2A1 , SPHK1 , PFKFB3 , PSMA2 , ATF3 ) were not upregulated in either spleen tissue or splenic macrophages ( data accessible in NCBI's Gene Expression Omnibus [54] through GEO Series accession number GSE91187; http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE91187 ) . The upregulation of IL-1β , IFN-γ and IL-6 in splenic macrophages ( Fig 6; S2 Table ) suggests an additional inflammatory effect on the macrophages through paracrine or autocrine activation . The M2 program of macrophage polarization was classically described as driven by IL-4- or IL-13-induced STAT6 activation [55] . However , there is a spectrum of macrophage phenotypes induced by anti-inflammatory signals such as IL-10 , TGF-β , glucocorticoids , and immune complexes [48 , 52] , which overlap with the IL-4/IL-13-polarized phenotype . In hamsters with VL , the splenic macrophages showed significantly increased expression of some M2-associated transcripts , including Arg1 , IL-10 , SOCS2 , CCL17 , Chi3L1 ( Fig 7 , S4 Table ) . We demonstrated previously that parasite-induced arginase-1 expression in macrophages in VL was dependent on STAT6 activation and amplified by IL-4 and growth factor receptor signaling [19 , 20] . Increased expression of IL-10 and IL-10R in splenic macrophages ( Fig 5; S2 Table ) also contributed to arginase expression [20] . IL-10 is also likely to dampen the effects of the proinflammatory cytokines and promote infection through suppression of T cell or macrophage effector function [56 , 57] . In contrast , other transcripts considered to be markers for M2 macrophages ( F13A1 , CCL11 , FN1 , CCL2 , CCL22 , PPARγ ) were not upregulated in splenic macrophages ( S4 Table ) . The M2-associated marker MSR1 ( Fig 7 , S4 Table ) was down-regulated . Furthermore , splenic macrophages did not display upregulation of angiogenic factors ( VEGFA , EPHB1/4 , DLL4 , LYVE1 , ANGPT1 , NRP1 ) , which are characteristic of M2 activation . CCL2 , which was highly upregulated in the spleen in VL , was demonstrated to drive the accumulation of tumor-associated macrophages and together with IL-6 ( also found to be upregulated in splenic macrophages ) promoted polarization and survival of M2-like macrophages [58] . To determine if the coexistent expression of M1- and M2-associated transcripts occurred within the same macrophages or within a mixed population of macrophages , we detected M1-associated ( Ido1 and Cxcl9 ) and M2-associated ( Arg1 ) mRNA transcripts in CD68+ splenic macrophages by in situ amplification and fluorescence hybridization [59] . We found that the spleen of hamsters with VL contained macrophages that were single-positive for either the M1 or M2 marker , and double-positive for both ( Fig 8 ) . Thus , splenic macrophages in VL have a diverse phenotype with evidence of M1 , M2 and mixed polarization . Co-expression of M1 and M2 associated genes has also been shown in peritoneal macrophages elicited with Toxoplama gondii [52] , adipose tissue macrophages [60] , GM-CSF knockout mice [61] and monocytes infected with human cytomegalovirus [53] . A mixed phenotype of macrophages could also be present during the transition from acute to chronic stages of infection [62] . To better define the regulation of the splenic macrophage transcriptional response we examined differential expression of mRNAs of M1/M2-relevant specific transcription factors . We also loaded the set of differentially expressed genes into IPA software to predict the transcription factors likely to be activated . mRNAs for transcription factors associated with M1 ( IRF1 , IRF7 , STAT1 , STAT5A/B ) and M2 ( IRF4 , CEBPβ , STAT3 ) were upregulated , and except for CEBPβ , were predicted to be activated ( S3 Table ) . The M2-associated transcription factor KLF4 was downregulated and predicted to be inhibited in the spleen ( S3 Table ) . In splenic macrophages , STAT1 mRNA was significantly upregulated and this transcription factor was predicted to be activated , while other M1- ( RELA , IRF3 , IRF7 , JUNB/AP1 , NFE2L2/NRF2 , IFI16 ) and M2-associated transcription factors ( CEBPα , β; NFKB1/P50 ) were predicted to be activated ( based on downstream gene targets ) without increase in their mRNA expression ( S3 Table ) . Overlapping network analysis suggests IRF7 as a primary regulator of M1-associated gene expression , and STAT1 , IRF1 and STAT3 as dual regulators of both M1 and M2 gene expression in VL ( Fig 9 ) . IRF7 was shown previously to mediate acquired resistance in the liver of L . donovani infected mice via expression of IFN-γ and iNOS [63] . STAT6 , which we showed was activated ( phosphorylated ) in the spleen in VL and was critically important for splenic arg1 expression [20 , 37] , was neither upregulated at the transcript level or predicted to be activated . This suggests that the effect of STAT6 activation is limited to a relatively small set of genes that do not reach the threshold for detection of pathway activation in the context of global gene expression . Consistent with our previous finding [20] , activation of STAT3 was predicted . Its activation may be driven by IL-21 , IL-6 and/or IL-10 , which were upregulated in the spleen and splenic macrophages . STAT3 may coordinately regulate with STAT6 the expression of M2-associated genes , including ARG1 [20] . STAT3 also has a key role in the expansion and suppressive activity of myeloid derived suppressor cells ( see further discussion below ) [64] . IRF4 , a transcription factor that contributes to M2 polarization [65] , showed upregulated mRNA but was not predicted to be activated . Consistent with previous studies in this experimental model [15 , 16 , 20 , 21] and human VL [6 , 9–11] , we found IFN-γ was highly upregulated in the spleens of infected hamsters ( >50-fold increase ) . We also found significantly increased splenic expression of >100 known IFN-responsive genes ( Figs 10A and S5 , S5 Table ) . These included members of the IFI gene family , interferon-stimulated genes ( ISG ) , guanylate binding proteins , interferon response factors , and antiviral effectors . The repertoire of interferon response genes ( compared to a manually curated reference set ) was significantly more extensive in the spleen tissue compared to splenic macrophages ( 99/148 [67%] vs . 52/148 [35%]; p = 0 . 0001 by Fisher Exact Test; S5 Table ) . Ingenuity Pathway Analysis confirmed that there were fewer IFN-response genes upregulated in splenic macrophages relative to the whole spleen ( Fig 10A and 10B ) . Collectively , these data suggest impaired or altered responsiveness of splenic macrophages to interferons in VL relative to other spleen cell populations . Although the production of IFN-γ is considered to be restricted to T cells and NK cells , we also found increased IFN-γ expression in splenic macrophages from hamsters with VL ( 11-fold increase ) ( Fig 6 , S4A Table ) . Other studies demonstrated that murine macrophages expressed IFN-γ in response to LPS , IFNγ , M . tuberculosis , and Streptococcus pyogenes [66–69] . In the environment of the chronically infected spleen , the high level of IFN-γ expression is ineffective in mediating exclusive M1 macrophage polarization , suppressing M2-associated gene expression , and restraining parasite replication and progressive disease . The generation of nitric oxide via macrophage inducible nitric oxide synthase ( NOS2 ) is a key anti-leishmanial effector mechanism in mice [17] . Consistent with our previous observations [16 , 18] , NOS2 was notably absent from the upregulated M1-associated repertoire of genes . NOS2 is a target of IFN-γ via the action of STAT1 and IRF1 . These transcription factors were predicted to be activated , so the absence of NOS2 expression suggests its suppression by other regulatory mechanisms . Specific sequences in the hamster NOS2 promoter that render it less responsive to IFN-γ-mediated transactivation ( also found in the human NOS2 promoter ) [18] may contribute to this . There is also a large body of evidence that supports NOS2 suppression by anti-inflammatory cytokines such as IL-10 ( see below ) . Evidence for a role of type 1 interferons in Leishmania infection is ambiguous . Early type 1 IFN production is protective in murine L . major infection , probably via promoting or shaping the adaptive Th1 response [70 , 71] . However , IFN-β impaired parasite killing in human macrophages [72] . Furthermore , sustained pathogen-induced type 1 interferon signaling can promote infection with intracellular bacteria [73–76] . Since many of the upregulated IFN-response genes in VL are induced by type 1 interferons ( either uniquely or in common with IFN-γ ) , we reasoned that the apparent unresponsiveness of infected macrophages to IFN-γ could be due to antagonistic crosstalk with type 1 IFN signaling ( reviewed in [77] ) . Pathway analysis revealed evidence of activation of the type 1 interferon signaling pathway with upregulation of numerous type 1 IFN-responsive genes in the spleen ( Figs 10A and S5; S5 Table ) . Surprisingly , we did not find increased expression of type 1 or type 3 interferon transcripts in the spleen of infected animals in the RNA-Seq dataset ( data accessible in NCBI's Gene Expression Omnibus [54] through GEO Series accession number GSE91187; http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE91187 ) . Since there is considerable heterogeneity in type 1 and type 3 interferons among different animal species , it is possible that some hamster IFN sequences were missed because they did not have sufficient homology to mouse , rat , or human sequences to meet the threshold of identification by BLAST . Alternatively , activation of the type 1 IFN pathway independent of type 1 IFNs , such as may occur via TLR-mediated activation of IRF3 or IRF7 [78 , 79] ( which were predicted to be activated ) , may contribute to the broad type 1 IFN-response signature . To evaluate the functional significance of IFN signaling we exposed hamster macrophages to IFN-α , IFN-γ or both . We found that both were effective in inducing the classic IFN-response genes , CXCL9 and CXCL10 ( Fig 10C ) . Pre-exposure to IFN-α did not blunt the IFN-γ-induced transcriptional response but amplified the expression of IFN-response genes ( Fig 10C ) . This indicates there was no IFN-α-induced inhibitory crosstalk . Pre-exposure of bone marrow derived macrophages to IFN-α , like IFN-γ [16] , did not lead to parasite killing ( Fig 10D ) . Strikingly , however , splenic macrophages isolated from hamsters with VL showed a dramatic increase in parasite burden when exposed to IFN-γ ( Fig 10E ) . This pathological effect increased over the course of the infection . Collectively , these data indicate that type 1 interferons have neither a direct pathologic or protective role , but IFN-γ has a paradoxical disease promoting effect on macrophages conditioned by the inflammatory environment of the spleen or the high intracellular parasite load in VL . We found evidence that IFN-γ induces immunomodulatory proteins that are likely to render the macrophage more permissive to parasite replication . Our previous work demonstrated a pathological role for macrophage Arg1 in VL [19 , 20] . Arg1 is a prototypic IL-4-induced M2 marker in most models ( as discussed above ) . The pathological expression of Arg1 in L . major infection in mice is driven by a dominant Th2 response [80–82] . In contrast , our data indicate that expression of Arg1 is not part of a conventional Th2-driven M2 macrophage phenotype , but occurs within the highly proinflammatory , IFN-dominated environment of the spleen . We show here for the first time that both IFN-α and IFN-γ are potent inducers of Arg1 in uninfected and infected macrophages ( Fig 10F ) . Furthermore , IL-4 and IFN-γ , both of which are expressed in the spleen in VL , were synergistic in their induction of Arg1 expression ( Fig 10G ) . Since Arg1 expression impairs macrophage and T cell effector function ( the latter through depletion of L-arginine ) , this may be part of the mechanism through which IFN-γ is paradoxically disease-promoting . The IFN-γ-inducible gene indoleamine 2 , 3-deoxygenase ( IDO-1 ) , which mediates the catalytic degradation of L-tryptophan [83] , was highly upregulated in the whole spleen ( 369-fold ) and splenic macrophages ( 40-fold ) of hamsters with VL ( Fig 6 , S4A Table ) . IDO-1 was induced in macrophages by IFN-γ and/or IFN-α , and this was amplified by infection with L . donovani ( Fig 10F ) . Previous studies demonstrated that Ido1 can suppress adaptive immunity through generation of T cell tolerance [83] , suppression of the T cell stimulatory capacity of dendritic cells [84] , generation of regulatory T cells [85] , and polarization of macrophages into an M2-like phenotype [86] . Ido1 has been proposed as a biomarker of active VL in humans [87] and a pathological determinant in experimental L . major infection [84 , 88] . Its role in the pathogenesis of VL remains to be determined . Another IFN-γ-induced transcript , immunoresponsive gene 1 ( Irg1 ) [89] was significantly up regulated in the infected spleen ( 365-fold ) and splenic macrophages ( 7 . 8-fold ) compared to uninfected controls ( Fig 6 , S4 Table ) . Irg1 expression was increased in macrophages following IFN-γ and/or IFN-α exposure , and this was amplified by infection ( Fig 10F ) . Recently , IRG1 was found to suppress macrophage activation via expression of the negative regulator A20 [90] . Thus , the high level of IFN-γ expression in VL may be paradoxically counterproductive by promoting the development of macrophage and/or T cell phenotypes that favor parasite growth and survival . This possibility needs further research . Concomitant with the IFN-γ expression , several anti-inflammatory cytokines that favor parasite growth and survival were expressed in the spleen and/or splenic macrophages . IL-10 and IL-10R were upregulated in splenic macrophages . IL-10 has emerged as a key cytokine that drives VL pathogenesis [6 , 12] through suppression of effector T cell responses and possibly macrophage function . The binding of immune complexes ( known to be abundant in active VL [91] ) to Fc-gamma receptors ( upregulated in splenic macrophages ) can also polarize macrophages to a regulatory IL-10-producing phenotype [92 , 93] . The pleiotropic cytokines IL-21 and IL-27 can induce the production of IL-10 through the activation of STAT3 [94 , 95] . Co-expression of splenic IL-21 , IL-27 and IL-10 , and IL-21-mediated induction of IL-10 , was shown in patients with VL [96] . In our model , Il27 mRNA ( IL-27p38 subunit ) was upregulated 2 . 7-fold ( FDR = 0 . 045 ) in the spleen in VL . However , the other subunit of the heterodimer , Eib3 , was downregulated ( 2 . 0-fold in the spleen and 2 . 7-fold in splenic macrophages; FDR<0 . 001 for both ) . IL-21 and its receptor were upregulated in the spleen , and IL-21R was upregulated in splenic macrophages . Besides its induction of IL-10 , IL-21 receptor signaling may also promote infection via M2 polarization of macrophages [97] , development of Th2 responses [98] , and suppression of dendritic cell activation and maturation [99] . It could also have a role in expansion of splenic hematopoietic progenitor cells [100] and B cells , both of which are associated with an increase in parasite burden [101] . Splenic B cells in turn may promote infection by production of IL-10 and suppression of protective Th1 cell responses [102–104] . Inflammatory cues in a number of infections and cancers lead to accumulation of a heterogeneous population of immature myeloid-derived suppressor cells ( MDSC ) that have profound anti-inflammatory and immunosuppressive effects [105] . These cells accumulate in the blood , bone marrow , and secondary lymphoid organs and may arise within either the granulocytic or monocytic MDSC lineages [106] . MDSCs are induced by pro-inflammatory cytokines ( TNF , IL-6 , IL-1 ) , such as we found in the inflammatory environment of the spleen in VL . MDSCs promote immune suppression primarily by dampening T cell responses [105 , 107] , but may also provide a permissive environment for replication of intracellular pathogens . Arg1 and Ido1 , which were highly upregulated in splenic macrophages in VL , are common mediators of the immunosuppression conferred by MDSC [105 , 108] . The high expression of CCL8 in the spleen in VL may also contribute to the expansion of immature myeloid cells having a regulatory phenotype [109] . While tools to detect surface markers for MDSCs in hamsters are not available , our data suggests that splenic MDSCs may have a role in disease progression in VL . We hypothesized that IFN-γ may increase parasite load in splenic macrophages through the STAT3 pathway , which was predicted by IPA analysis of the RNA-seq dataset to be activated during VL ( S3 Table; Fig 9 ) . STAT3 phosphorylation was significantly increased in splenic macrophages from hamsters with VL as early as 7 days post-infection and was sustained throughout the course of infection ( Fig 11A ) . This preceded the activation of STAT1 , which was increased at day 14 and 28 post-infection . We used a chemical inhibitor to block STAT3 phosphorylation in infected splenic macrophages exposed to IFN-γ ( Fig 11B ) . Inhibition of STAT3 activation abrogated the IFN-γ-induced increase in parasite load and ARG1 expression in in vitro infected bone marrow macrophages ( Fig 11C ) and ex vivo cultured splenic macrophages from hamsters with VL ( Fig 11D ) . We reasoned that IFN-γ could mediate the disease promoting effect through direct STAT3 activation , as has been demonstrated [110 , 111] , or through an indirect pathway involving STAT3-activating counter-regulatory cytokines , such as IL-10 . Using a STAT3 reporter assay , we found that exposure to L . donovani and IFN-γ induced STAT3 activation as early as 4 hrs after exposure ( Fig 11E ) . This early effect would not be dependent on de novo synthesis of a secondary protein mediator , and was not affected by IL-10 neutralization ( Fig 11E ) . However , at 48 hrs after exposure the increased IFN-γ-induced STAT3 activation was reduced by neutralization of IL-10 ( Fig 11E ) , suggesting an indirect effect that required synthesis of this cytokine . Lastly , we found that IFN-γ induced a STAT3-dependent IL-10 response in in vitro infected bone marrow-derived macrophages ( Fig 11F ) and ex vivo stimulated splenic macrophages from hamsters with VL ( Fig 11G ) . Collectively , these data indicate that IFN-γ promotes disease in experimental VL in part through direct and indirect ( via IL-10 ) activation of STAT3 , which then acts on downstream genes ( e . g . Arg1 ) that confer a permissive macrophage phenotype . The blockade of this process with STAT3 inhibitors , which are being extensively studies as non-cytotoxic chemotherapeutics for some cancers [112] , identifies STAT3 as a candidate target for adjunctive host-directed therapy in VL . IFN-γ is well-established as having a critical role in protection against Leishmania infection so our contrary finding of an infection-promoting effect deserves some contextual discussion . A number of previous studies in experimental models demonstrated that IFN-γ is required , but not sufficient , to protect against Leishmania infection [113] . The reason that the high endogenous IFN-γ production in the spleen is not protective in human VL has been an enigma . A clinical benefit of exogenous IFN-γ as adjunctive therapy was shown when it was combined with antileishmanial chemotherapy ( pentavalent antimony ) [114 , 115] . Since the anti-leishmanial activity of pentavalent antimony is mediated in part through modulating macrophage signaling [116] , this drug may make the cells more responsive to IFN-γ-induced activation . Use of IFN-γ as monotherapy in VL was beneficial in some but not all patients [117] , and the variable response was one of the reasons that IFN-γ was abandoned as a therapeutic agent for VL . Ex vivo cultures of splenic aspirates from patients with VL [118] showed that endogenous splenic IFN-γ was protective in most , but not all , subjects . In some subjects , the parasite burden decreased with IFN-γ neutralization , suggesting that It might play a pathological role . Collectively , these data suggest that a subset of patients fail to benefit from endogenous splenic IFN-γ expression and some are refractory to the effects of exogenous IFN-γ . Our data suggest a possible explanation for this enigma: that in some patients with VL , IFN-γ is part of a broad splenic proinflammatory response that drives an exuberant STAT3-dependent counter-regulatory response that promotes disease . Further investigation is needed to determine if this is the case .
Genome-wide expression analysis revealed evidence of a broad inflammatory signature that included an extensive array of upregulated interferon response genes in the spleen during progressive VL . This type of gene expression would be expected to drive macrophages toward a M1 phenotype and protect against Leishmania [119] . However , M1 polarization was not dominant and IFN-γ paradoxically enhanced parasite growth in splenic macrophages . Importantly , the parasite-promoting effect of IFN-γ was more pronounced in splenic macrophages isolated later in the course of infection . This suggests that as VL progresses , splenic macrophages in VL are conditioned by the chronic inflammatory environment to respond to macrophage activation signals in an aberrant , pathological way that contributes to the progressive infection . Several mechanisms could account for this . First , the finding of fewer upregulated IFN-response genes in splenic macrophages relative to the whole spleen , including transcripts known to be induced in macrophages , suggests relative macrophage unresponsiveness to IFN-γ . The absence of NOS2 upregulation is likely a central determinant of ineffective parasite killing . Impaired IFN-γ signaling , which has been well-described in in vitro infected macrophages [120–124] , is likely to contribute to impaired NOS2 expression during VL . Second , the co-expression of M1- and M2-associated transcripts , and the finding that IFN-γ induces Arg1 , are suggestive of a misdirected signaling in splenic macrophages during VL . There is a growing body of evidence that Arg1 has a significant pathological role in Leishmania infection , including human VL [19 , 20 , 80–82 , 125 , 126] . Our data indicate that expression of Arg1 is not part of a conventional Th2-driven M2 macrophage phenotype , but identify a previously unrecognized mechanism of IFN-induced Arg1 . This has significant bearing on the pathogenesis of VL so the mechanisms of IFN regulation of Arg1 in VL need further investigation . Third , parasite-derived signals and anti-inflammatory/regulatory cues ( e . g . IL-10 and IL-21 ) [19 , 20] may impair macrophage effector function and lead to disease-promoting gene expression . Fourth , the massive interferon response in the spleen appears to have a counter-protective effect through initiation of an exuberant counter-regulatory response mediated via STAT3 and IL-10 . The STAT3-dependent IFN-γ-induced Arg1 expression may paradoxically lead to impaired macrophage or T cell responses , as we and others have described previously in VL [19 , 20 , 80–82 , 125 , 126] . Lastly , the high expression of a broad array of chemokines in the spleen is likely to lead to accumulation of immature myeloid cell populations , which have some features consistent with myeloid-derived suppressor cells , that are less responsive to classical activation signals . Collectively , these data identify a number of molecules , pathways and transcription factors that contribute to the pathogenesis of VL . The STAT3 pathway in particular is an attractive target for adjunctive host-directed therapy for VL .
The animals used in this study were handled in strict accordance with the recommendations 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 of the University of Texas Medical Branch , Galveston , Texas ( protocol number 1101004 ) . 6–8 week old outbred Syrian hamsters females ( Mesocricetus auratus ) were obtained from Harlan Laboratories . Hamsters were either uninfected or infected ( n = 4–8 per group ) with 1x106 peanut agglutinin purified Leishmania donovani ( MHOM/SD/001S-2D ) metacyclic promastigotes by intracardial injection as we have described previously [127] . Hamsters were sacrificed at 28 days post-infection by CO2 inhalation and the spleens were collected in complete DMEM ( Gibco ) , supplemented with 2% fetal bovine serum ( FBS ) , 1 mM Sodium pyruvate ( Gibco ) , 1X MEM amino acids solution ( Sigma ) , 0 . 02% v/v/ EDTA , 10 mM HEPES buffer ( Cellgro ) and 100 IU/mL Penicillin/100mg/mL Streptomycin solution ( Cellgro ) . Adherent spleen cells were isolated after infiltrating the whole organ with the injection of 2 mL of Collagenase D ( Sigma ) at 2 mg/mL . The spleen tissue was cut into small pieces and incubated for 20 minutes at 37°C resuspended in the enzyme solution . The cell suspension and remaining tissue fragments were suspended in culture medium and gently passed using a syringe plug through a 100 μm cell strainer ( B-D ) to obtain a single cell suspension . Cells were cultured in a 75cm flasks and were allowed to adhere for 4 hr at 37°C and the non-adherent cells were removed after washing the monolayers with pre- warmed PBS ( 10 times ) , before the RNA was isolated . For isolation of RNA from the whole spleen , it was cut in small pieces , resuspended in 2 mL of lysis buffer and homogenized to isolate RNA using the RNAqueus kit ( Ambion-Life Technologies , CA ) following the manufacturers protocol . Adherent splenic macrophages were similarly lysed and the RNA isolated . Libraries for deep sequencing were constructed from poly-A RNA isolated from the spleens or splenic macrophages of uninfected and 28-day L . donovani-infected hamsters ( n = 4 per group ) using the Illumina TruSeq RNA Sample Preparation kit . The library quality was confirmed by Agilent Bioanalyzer . Paired-end 50-base sequencing was performed using TruSeq SBS kit v3 ( Illumina ) on an Illumina HiSeq 1000 . The quality of raw sequencing reads was checked using FastQC ( v0 . 10 . 1 ) [128] . To avoid the contamination of pathogen sequences , we filtered out reads ( using the FASTX Toolkit v0 . 0 . 13 ) that aligned to the Leishmania donovani BPK282A1 genome ( NCBI BioProject PRJEA61817 ) [29] using Bowtie 2 ( v2 . 0 . 0-beta5 ) [129] under default options . To reduce the effect of low quality reads , we further filtered out artifacts and reads having a phred score <28 in more than 10% of nucleotides using FASTX Toolkit ( v0 . 0 . 13 ) . Both forward and reverse reads were removed if any of them failed to pass the filters . To obtain a complete transcriptome , we used two steps . First , the cleaned sequencing reads from different spleen samples were pooled together and de novo assembled using Trinity software [23] . Second , the resulting transcriptome was combined with all cleaned reads from hamster spleen and splenic macrophages and the CHO-K1 RefSeq genome [28] to perform a second de novo assembly using BRANCH [22] . Both assembling steps were run in collaboration with Texas Advanced Computing Center ( TACC ) at the University of Texas at Austin . We first created a customized reference library using Rattus norvegicus ( Rnor_5 . 0 . 73 ) and Mus musculus ( GRCm38 . 73 ) genomes . We then used BLAST ( v 2 . 2 . 28+ ) [30] to align each transcript generated from BRANCH against the customized library to assign it a gene name based upon sequence similarity . An E-value cutoff of <1e-3 was used . Additionally , we compared the Trinity transcripts with the CHO-K1 Ref Seq genome . The spleen tissue and splenic macrophage RNA samples were collected and sequenced in two different experiments , so we analyzed them separately to avoid the batch effects . All the non-Leishmania-like raw sequencing reads were first mapped to our de novo reference genome using Bowtie2 ( v2 . 1 . 0 ) with default options , but allowing one read to map to as many as 500 different transcripts . We then measured the expression abundance , count of reads mapped to each transcript , using the software eXpress . The effective counts were recommended for RNA-Seq differential expression analysis because they correct biases caused by multiple hits and mismatches in alignment . To identify differentially expressed transcripts in each experiment , 3 different approaches were applied: exact tests of Robinson and Smyth [130] and generalized linear models with the likelihood ratio test using the BioConductor R package EdgeR [35] , and the Wald test using DESeq2 [36] . Only transcripts with at least 1 count per million in at least 3 out of 4 samples in control or experimental group were included in the analysis . A transcript was considered differentially expressed only when it was identified by all three different approaches . The transcriptome data have been deposited in NCBI's Gene Expression Omnibus [54] and are accessible through GEO Series accession number GSE91187 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE91187 ) . RNA samples were DNase treated with TURBO DNA-free kit ( Ambion ) and quantified using a NanoDrop Spectrophotometer ( Thermo Scientific ) and maintained at -80°C until used . 250–500 ng of RNA were used for cDNA synthesis using the high capacity cDNA reverse transcription kit ( Applied Biosystems ) . Gene expression was determined by SYBR green ( Applied Biosystems ) PCR using primers whose sequences were reported previously [20 , 21] or are shown in S6 Table , at a final concentration of 300–500 nM . With the exception of CCL17 , SOCS1 , and IRG1 primers were designed to span an intron , and were confirmed by analysis of dissociation curves to not generate primer dimers . Data was analyzed using the comparative Ct method , relative to uninfected control spleen or macrophages , and with the 18S rRNA gene as the normalizer . The expression of M1- and M2-associated transcripts in splenic macrophages was determined at the single cell level using the QuantiGene ViewRNA ISH cell Assay ( Affymetrix , Santa Clara , CA ) . RNA was visualized following the manufacturer protocol with the exception that MOWIOL ( Sigma , ST Louis , MO ) was used as mounting media . Spleen cells from infected hamsters and uninfected controls were allowed to attach to poly-L-lysine coated glass cover slips for 3 hr . After-fixation with 4% formaldehyde , adherent cells on coverslips were incubated with 1× detergent solution and digested with a protease solution ( 1:4000 ) . Cells were subsequently hybridized with specific probes sets ( 1:100 in diluent QF ) , conjugated to a specific fluorescent dye with different excitation wavelengths . Probes were designed to target the hamster IDO1 ( type1 fluorophore-550nm ) , CXCL9 ( type 1 fluorophore-550nm ) , Arg1 ( type 6 fluorophore-650nm ) and CD68 mRNA ( type 4 fluorophore-488nm ) using the accession numbers ( XM_005066560 , MH01025X1B04 , NM_001281645 and XM_005067542 ) , respectively ( Affymetrix ) . Cells were then hybridized with pre-amplifier ( 1:25 in amplifier diluent QF ) and amplifier ( 1:25 in amplifier diluent QF ) mix solutions . All hybridization steps were carried out at 40°C and were followed by three washes with washing buffer . All hybridized slides were examined by confocal microscopy and the number of fluorescent cells counted manually . Nuclei were stained with a 1X working DAPI solution in PBS . Cells incubated with no probe were used as negative controls . To determine effect of IFNs in macrophages , splenic macrophages were obtained as described above and BMDM were generated as we described previously [20] . Cells were infected with stationary phase L . donovani promastigotes ( 5 parasites per macrophage ) as described previously [20] . Uninfected or L . donovani infected macrophages were stimulated with recombinant hamster IFN-γ expressed in CHO cells ( 10% v/v supernatant ) or CHO cells transfected with empty vector ( mock supernatant control ) as described [16] , or with recombinant IFN-α ( Universal Type I Interferon , PBL Interferon Source ) at 100 U/mL . Infections and treatments were carried out for 24 hrs . For treatment with the STAT3 inhibitor , hamster BMDM or splenic macrophages were treated with 25–100 μM STAT3 inhibitor ( S31-201 , Cayman ) or DMSO vehicle control for 30 min before stimulation with recombinant hamster IFN-γ . The viability of cells after treatment was >95% ( CellTiter Glo , Promega ) . The parasite burden in infected macrophages was determined by quantitation of Leishmania 18S rRNA expression by qPCR . RNA was extracted and reverse-transcribed [20] and the Leishmania 18S rRNA amplified and quantified by SYBR-Green reaction ( Bio-Rad ) . The primers used were: For: 5’- CCAAAGTGTGGAGATCGAAG-5’ and Rev: 5’- GGCCGGTAAAGGCCGAATAG-3’ . The number of parasites was determined by extrapolation from a standard curve generated from purified splenic amastigotes , or by the relative expression determined by comparison to mock-infected cells ( background Ct value ) . Phosphorylation of STAT-1 and STAT-3 was determined by immunoblotting of splenic macrophages obtained at different times after infection of hamsters with L . donovani as described [20] . The following antibodies were used: p-STAT1 ( 9171 , Cell Signaling ) , p-STAT3 ( 9145 , Cell Signaling ) , GAPDH ( Clone 6C5 , Millipore ) and STAT3 ( Sc-80910 , SantaCruz ) . Bands were captured with a Chemi X T4 camera ( G BOX , SynGene ) and relative band intensity calculated by densitometry analysis with the softGeneTools Analysis Software ( Syngene ) . Hamster BHK-21 cell line was transiently transduced with lentiviral particles carrying a STAT-3 luciferase reporter construct ( Cignal Lenti Reporter , Qiagen ) . Cells ( 5 , 000 cells/100 μL ) were transduced at 20 MOI in 8 μg/mL polybrene in DMEM with 10% FBS . At 16 hrs post-transduction the medium was changed , and after 72h cells were exposed or not to L . donovani promastigotes ( 1:5 ) , treated with or without recombinant hamster IFN-γ , and pre-treated with or without 2 μg/mL of anti-IL-10 antibody ( mouse/rat anti-IL-10 , R&D , AF519 , R&D ) . Activity of the STAT-3 reporter was expressed as relative luciferase activity per number of cells determined with cell titer Glo ( Promega ) . Bacterial endotoxin level was determined by the Limulus Amebocyte Lysate ( LAL ) method ( Pierce LAL Chromogenic Endotoxin Quantitation Kit , Thermo Scientific ) in serum samples from uninfected or infected hamsters according the manufacturer instructions . We implemented “trimmed mean for M-values” ( TMM ) method for normalization and calculated the significance using tagwise dispersion in the RNA differential analysis . The Benjamin–Hochberg procedure was applied to gain the False Discovery Rate ( FDR ) from p values for multiple tests . Differences in mRNA expression determined by qRT-PCR between non-infected and 28 day infected animals were analyzed by two tail Mann-Whitney test or two tail unpaired t-test using GraphPad Prism version 5 . 01 for windows , GraphPad Software , San Diego California USA ( www . graphpad . com ) . In instances where groups were compared , ANOVA with a post hoc correction for multiple comparisons ( Bonferroni or Tukey ) was applied . All other statistical analyses are as described in the body of the paper or figure legends .
|
Visceral leishmaniasis ( VL ) is a neglected parasitic disease that is caused by the intracellular protozoan Leishmania donovani . Patients with this disease suffer from muscle wasting , enlargement of the spleen , reduced blood counts and ultimately will die without treatment . Progressive disease is considered to be due to impaired cellular immunity , with T cell or macrophage dysfunction , or both . We studied the Syrian hamster as an infection model because it mimics the progressive nature of human disease . We examined global changes in gene expression in the spleen and splenic macrophages during experimental VL and identified a highly inflammatory spleen environment with abundant expression of interferon and interferon-response genes that would be expected to control the infection . However , the high level of IFN-γ expression was ineffective in mediating a protective macrophage response , restraining parasite replication and halting progression of disease . We found that IFN-γ itself stimulated parasite growth in splenic macrophages and induced expression of counter-regulatory molecules , which may paradoxically make the host more susceptible . These data give insights into the nature of the immune response that promotes the infection , and identifies potential targets for therapeutic intervention .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Conclusions",
"Materials",
"and",
"Methods"
] |
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"blood",
"cells",
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"health",
"sciences",
"immune",
"cells",
"immune",
"physiology",
"spleen",
"immunology",
"tropical",
"diseases",
"vertebrates",
"parasitic",
"diseases",
"animals",
"mammals",
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"protozoans",
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"leishmania",
"neglected",
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"hamsters",
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"zoonoses",
"animal",
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"gene",
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"protozoan",
"infections",
"leishmania",
"donovani",
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] |
2017
|
Transcriptional Profiling in Experimental Visceral Leishmaniasis Reveals a Broad Splenic Inflammatory Environment that Conditions Macrophages toward a Disease-Promoting Phenotype
|
Past case reports have indicated that lymphatic filariasis ( LF ) occurs in Zambia , but knowledge about its geographical distribution and prevalence pattern , and the underlying potential environmental drivers , has been limited . As a background for planning and implementation of control , a country-wide mapping survey was undertaken between 2003 and 2011 . Here the mapping activities are outlined , the findings across the numerous survey sites are presented , and the ecological requirements of the LF distribution are explored . Approximately 10 , 000 adult volunteers from 108 geo-referenced survey sites across Zambia were examined for circulating filarial antigens ( CFA ) with rapid format ICT cards , and a map indicating the distribution of CFA prevalences in Zambia was prepared . 78% of survey sites had CFA positive cases , with prevalences ranging between 1% and 54% . Most positive survey sites had low prevalence , but six foci with more than 15% prevalence were identified . The observed geographical variation in prevalence pattern was examined in more detail using a species distribution modeling approach to explore environmental requirements for parasite presence , and to predict potential suitable habitats over unsurveyed areas . Of note , areas associated with human modification of the landscape appeared to play an important role for the general presence of LF , whereas temperature ( measured as averaged seasonal land surface temperature ) seemed to be an important determinant of medium-high prevalence levels . LF was found to be surprisingly widespread in Zambia , although in most places with low prevalence . The produced maps and the identified environmental correlates of LF infection will provide useful guidance for planning and start-up of geographically targeted and cost-effective LF control in Zambia .
Little has been reported about lymphatic filariasis ( LF ) in Zambia in the past . According to Buckley [1] , local medical reports from the 1930's and 1940's mentioned the recovery of microfilariae ( mf ) of Wuchereria bancrofti from patients in Zambia , but the history and movements of the infected individuals did not rule out the possibility that infections had been acquired elsewhere . These reports also mentioned that the condition of elephantiasis was seen in Zambia and was commonly referred to as “Serenje leg” or “Feira leg” after its frequent occurrence in the districts of Serenje and Feira ( now Luangwa ) . In 1946 , Buckley identified a few cases of W . bancrofti microfilaraemia in hospital patients in Lusaka , Ndola and Kasama , but none of the infected individuals had been permanent residents in the country [1] . During a small night blood survey carried out in Luangwa valley , Barclay [2] failed to identify W . bancrofti mf . In contrast , both Buckley and Barclay reported high prevalences of infection with another human filaria , Mansonella perstans , from their surveys . The first definite autochthonous case of LF due to W . bancrofti in Zambia was reported in 1975 by Hira [3] , [4] from a 25-year old fisherman from Luangwa who presented with a tender swelling in the right inguinal fossa and swollen ankles . Hira [4] , [5] afterwards observed more patients with W . bancrofti mf in Zambia , including cases acquired locally as well as cases that could have been acquired in neighboring countries . More recently , W . bancrofti mf were also reported from a 22-year old male from Southern Province [6] and from a 49-year old female from Northern Province who suffered from lower limb and vulval elephantiasis [7] . Although these observations suggested that LF was present and transmitted in Zambia , the geographical distribution , extent and prevalence pattern was largely unknown . In support of the World Health Assembly resolution of 1997 to eliminate LF globally as a public health problem , the government of Zambia therefore undertook a large-scale LF mapping survey from 2003 to 2011 . Volunteers from all districts of the country were examined for circulating filarial antigen ( a marker of W . bancrofti adult worm infection ) according to guidelines from the World Health Organization [8] . A first objective of this paper is to outline the LF mapping survey activities and to empirically present the CFA prevalences as observed at the numerous survey sites across Zambia . The presence of LF in an area is closely linked to the presence and abundance of the vector mosquitoes and to the physical requirements for parasite development within the vectors . Environmental conditions related to suitable mosquito habitats and to parasite growth and maturation in the vectors will often strongly influence the observed geographical prevalence patterns of LF [9] , [10] . The environmental drivers of LF distribution can be explored through spatial modeling frameworks , and can in turn be used to predict parasite presence at unsurveyed locations to further guide control programmes . A second objective of this paper is to take advantage of the large dataset available from the mapping survey to identify the most important ecological correlates associated with LF infection and to use these to produce maps delineating the presence of LF at different prevalence levels in Zambia .
The field surveys were carried out as a part of the Zambian Ministry of Health ( MoH ) Lymphatic Filariasis Control Programme ( 2003–2005 ) and Programme for Integrated Control of Neglected Tropical Diseases ( 2009–2011 ) , and followed protocols approved by the MoH for these programmes . The selected survey populations were called for meetings during which they were given detailed information about LF and the background , purpose and implications of the survey . Individuals volunteering to be examined provided oral informed consent under observation of both project staff and village authorities ( parents/guardians consented on behalf of children below 15 years ) . Oral consent is the traditional way for making agreements in the survey areas , where written consent is unfamiliar and would cause suspicion and refusal to participate . All 72 districts of Zambia existing at the start of the activity in 2003 ( some have later been split and/or reorganized ) were targeted for LF mapping . Based on previous reports and hospital records indicating possible cases of LF , 14 districts located in eight provinces were first selected . These were Choma and Sinazongwe ( Southern Province ) , Mpongwe ( Copperbelt Province ) , Kalabo , Sesheke and Senanga ( Western Province ) , Mbala and Chinsali ( Northern Province ) , Chama and Lundazi ( Eastern Province ) , Luangwa and Kafue ( Lusaka Province ) , Serenje ( Central Province ) and Zambezi ( North-Western Province ) . In each of these districts , three chiefdoms were selected to provide 100 volunteers each to be tested for circulating filarial antigen ( CFA ) during 2003–2005 . In the remaining 58 districts , which were considered less likely to have LF , one site was identified for the mapping exercise and 100 volunteers were targeted for CFA testing at each site during 2009–2011 . Selection of the sites was facilitated by local health personnel who led the survey team to areas where the population of people was high enough to allow the required number of people to be tested . Members of the community were usually called to one central place for the CFA test . A clinic or health centre was found to be convenient for the purpose . Local health personnel were requested to assist in the exercise , and their presence brought confidence and trust , or less suspicion , from the community members . Geographical coordinates ( longitude , latitude and elevation ) were taken at the survey sites using a hand held GPS receiver ( eTrex Summit , Garmin Corporation , Taiwan ) . Following WHO guidelines [8] , [11] , about 100 volunteers above the age of 15 years were tested for CFA from each survey site . At few sites , however , volunteers down to the age of 12 years were allowed to participate due to low numbers coming forward for the test . From each individual , 100 µl finger-prick blood was collected using a heparinized capillary tube . The blood was applied to the specimen pad of a rapid immunochromatographic test card ( ICT card , Binax Inc . , USA ) . The result was read as positive or negative ten minutes after the card was closed and was recorded on a survey form together with the name , sex and age of the volunteer . The data were entered in Excel , and later transferred to SPSS for exploratory analysis . Proxy environmental variables that may potentially influence the distribution of the filarial parasite-host-mosquito system and hence LF transmission [9] were extracted from freely accessible Remote Sensing ( RS ) sources at spatial and temporal resolutions shown in Table 1 . Daytime land surface temperature ( LST day ) , night time land surface temperature ( LST night ) and the Normalized Difference Vegetation Index ( NDVI ) were averaged over the period 2001–2010 representing the climatic period of the LF survey , according to Zambia's three distinct climatic seasons: i ) cold/dry season ( May–August ) , ii ) hot/dry season ( September–November ) and iii ) hot/rainy season ( December–April ) . Land cover data contained 23 different land cover classes for Zambia , which were re-classified into 7 categories; water bodies , wetlands , forests , urban areas , shrublands , grasslands and croplands and re-sampled to 1 km resolution . Rainfall estimates averaged over the climatic normal period 1950–2000 were obtained from the Worldclim project [12] . As a proxy for changes in the environment due to anthropometric activities , the human influence index ( HII ) [13] was used . A selection of maps of environmental predictors can be seen in Figure S1 in the supplementary material . The MODIS Reprojection Tool ( USGS ) was used to convert the RS data to geo-referenced maps . Further processing of the environmental data and distance calculation to the nearest water bodies was carried out in ArcMap v . 10 . 0 ( ESRI ) . Additional data processing was performed in Revolution R Enterprise version 4 . 0 ( Revolution Analytics; Palo Alto , USA ) and Stata/SE 10 ( StataCorp LP; College Station , USA ) . To elucidate potential co-linearity among the environmental variables , a correlation ( Pearson's test ) matrix was constructed based on 10 , 000 randomly extracted pixel values for each of the environmental predictors , with variables above a threshold of r>0 . 75 not allowed to enter the same model . To explore the ecological niche of the LF parasite-vector-host biocoenose in Zambia , a species distribution modeling approach was deployed . Species distribution models , also referred to as ecological niche models [14] , are commonly used to predict the geographic range of a species by extracting associations between point presence data and environmental data layers . The relationships are then used to characterize the environmental requirements of the species , and finally to predict suitable habitats across unsurveyed areas . Here , species distribution modeling was implemented using the MaxEnt approach [15] , commonly used to explore and predict environmental suitability for species , and has been shown to perform well compared to other predictive algorithms in comparative studies [16] , [17] . A brief explanation of MaxEnt modeling is given in Methods S1 in the supplementary material . Recently , the MaxEnt approach has also been applied in mapping the Africa continent-wide current and potential future distribution of LF [18] and schistososmiasis [19] . Specifically , MaxEnt , which builds on the principles of maximum entropy , was chosen as it allows a flexible modelling of the often complex non-linear associations of infection presence with environmental variables [16] , [18] . This flexibility can help facilitate an improved understanding of the ecological niche of a species , a prerequisite for a more reliable mapping of the potential distribution [20]–[22] . Furthermore , the MaxEnt method does not require absence data for the species being modeled; instead it takes advantage of the background environmental data for the entire study area through the background sampling procedure ( see supporting information for more details ) . An advantage of this is dealing with the risk of including ‘false’ absence records in the model that can arise from limitation of parasite detectability [23] and hence falsely indicate non-suitability of a location . Finally , MaxEnt copes relatively well with correlated variables ( which environmental variables often are ) through the inbuilt method for regularization ( L1-regularization ) known to be well-performing [24] , making it possible to explore a wider breath of potential environmental dependencies . Two separate models were explored , based on different prevalence value cut-offs: Model 1 was based on survey sites that had at least 5% prevalence , and model 2 used survey sites with at least 15% CFA prevalence as MaxEnt model input data . This was done to get an indication of the drivers of both the general distribution of endemic LF in Zambia ( represented by the distribution of at least 5% CFA prevalence ) , as well as the distribution of medium to high levels of infection prevalence ( at least15% prevalence ) . The spatial output of the MaxEnt model consists of a continuous range of relative probabilities indicating , in the case of this study , presence of the host–parasite system at the given prevalence threshold . The default logistic model that gives predicted estimates between 0 and 1 of the probability of infection presence for each pixel in the map was used . It was chosen to fit only linear , quadratic and product relationships , since more complex models can be difficult to specify a priori based on ecological theory [25] . Other parameterizations ( maximum number of iterations and convergence threshold ) followed recommendations by the model developers [15] , [26] . The importance of the environmental variables was evaluated by comparing estimates of the relative contribution of environmental factors to overall model training gain . The gain is a measure closely related to deviance , the goodness of fit measure used in generalized additive and generalized linear models [15] . Furthermore , the explanatory information in each variable when used in isolation and the information lost when omitted from the model was quantified using a jackknife cross-evaluation procedure . The continuous probability maps were furthermore converted into binary presence/absence maps of the LF host–parasite system , using the threshold indicating maximum training sensitivity plus specificity ( i . e . , that threshold which maximizes the sum of sensitivity and specificity for the training data ) . This is one of 11 thresholds calculated by MaxEnt , and is in considered one of the more robust of several standard thresholds for converting continuous probability surface to presence/absence surface [27] , [28] . This distribution was then used to define the spatial limits for each of the two categories ( ≥5% and ≥15% ) of infection prevalence . A validation procedure was implemented by randomly dividing the occurrence data in training and test data sets ( based on a 80–20% splitting of the data set ) . The evaluation focused on predictive performance at sites . Three statistics were applied; 1 ) the Area under the receiver operating characteristic Curve ( AUC ) , 2 ) correlation ( COR ) and 3 ) sensitivity and specificity , to assess the agreement between the prevalence recorded at sites and the predictions . AUC ranges from 0 to 1 , where an AUC≤0 . 5 indicates that model performance is equal to or worse than that of a random prediction while an AUC above 0 . 75 is normally considered useful [17] . COR was calculated as the Pearson correlation coefficient between the full range of prevalence values in the test dataset ( including negative sites ) and the model logistic prediction [16] , [26] . Sensitivity was calculated as the proportion of true positives/negatives ( ‘presence/absence’ points ) falling within the predicted presence/absence area , and specificity as the proportion of true negatives falling within the predicted absence area .
A total of 10193 volunteers from 108 survey sites located in all 72 districts and 9 provinces of Zambia were surveyed for CFA . Among these , 9964 ( 97 . 8% ) had a valid test card result and comprise the study population of examined individuals analyzed in this study . An overview of the survey sites , and the number , positivity for CFA , age and sex of the study population , is presented in Table 2 . A list of geographical coordinates for the study sites is given in Table S1 in the supplementary material . Most survey sites ( 83 or 76 . 9% ) had more than 90 examined individuals , whereas 14 sites ( 13 . 0% ) had less than 70 . The highest mean number of examined individuals per site ( 100 . 9 ) was in Copperbelt Province , whereas the lowest ( 55 . 9 ) was in Luapula Province . The age of examined individuals ranged from 12 to 96 years . The mean age for the survey sites ranged from 21 . 2 to 46 . 0 years , and the overall mean age was 34 . 0 years . Many more females than males were examined ( 6376 vs . 3585 ) , and the great majority of sites had more examined females than males ( 94 or 87 . 0% ) . CFA positive cases were identified at 84 ( 77 . 8% ) of the survey sites , where the prevalence ranged from 1 . 0 to 53 . 9% . The prevalence was ≥5% at 49 sites and ≥15% at 14 sites . The highest mean CFA prevalences were seen in Western ( 19 . 0% ) and Lusaka ( 18 . 8% ) provinces , whereas the lowest were in Copperbelt ( 3 . 4% ) and North-Western ( 2 . 5% ) provinces . The overall mean CFA prevalence for all examined sites was 7 . 4% . A graphical presentation of the measured CFA prevalence at the different survey sites is shown in Figure 1 , which thus gives an overview of the distribution pattern of LF in Zambia . All provinces had sites with a low CFA prevalence below 15% . However , six foci with CFA prevalences above 15% are clearly identified from the figure . Named by the district of location , these are the Kalabo and Senanga focus ( both in Western Province ) , the Luangwa and Kafue focus ( both in Lusaka Province ) , the Serenje focus ( Central Province ) and the Lundazi focus ( Eastern Province ) . The first four of these foci had sites with particularly high CFA prevalences of >25% , and among these the Kalabo focus had sites with >50% CFA prevalence . The importance of the environmental determinants of LF distribution in Zambia , as measured by their contribution to overall model training gain , varied substantially between the model based on ≥5% and the model based on ≥15% CFA prevalence data . The relative contribution of the 7 most important ( of a total of 16 ) of the environmental predictor variables is given in Table 3 . Between them , these 7 predictors were ranked in the top three of at least one of the two models . Overall the most important predictor was land cover , which in particular for model 1 ( CFA≥5% ) contributed to a significant part of model gain . In particular croplands and grasslands were associated with high probabilities of presence of infection , whereas forested areas were predicted as the least suitable of the land cover classes . The second most important predictor variable was day land surface temperature ( hot/dry season ) . It was especially important in the CFA≥15% model , where it contributed 22 . 4% of total training gain , which is in accordance with the jackknife procedure that indicated that it was also the variable with the highest model gain when used in isolation . The human influence index , HII , was also an important predictor in model 1 where it contributed 20 . 9% of total training gain , whereas it did not play a significant role in model 2 , contributing only 1 . 5% of total training gain . The least important environmental factors for both models , as judged from the total gain , were rainfall and night time LST . The environmental variable that decreased the gain most when omitted was the distance to surface water bodies , which therefore appeared to have the most information not present in the other variables . The functional relationship between the most important continuous predictor variables and the predicted probability of presence of either ≥5% or ≥15% CFA is depicted in the response curves in Figure 2 . Each curve is made by generating a MaxEnt model using only the corresponding predictor variable , disregarding all other variables . Maps of the MaxEnt predicted distributions of low ( ≥5% CFA ) and medium-high LF infection prevalence ( ≥15% CFA ) categories are presented in Figure 3a and 3b respectively . The heat map values represent the probabilities of ‘presence’ of each prevalence category , with relative probability values ranging from 0 ( green colors ) to 1 ( red colors ) . The scale is defined for each map so that red areas correspond to ‘presence areas’ as defined by the threshold indicating maximum training sensitivity plus specificity . Both maps indicate that LF infection potentially is present across Zambia with a somewhat patchy distribution , but with particularly high probability of presence in the floodplains of Western Province , the western part of North-western Province , the flood plain areas surrounding Zambezi River and its tributaries , the areas along Lake Kariba , the Kafue plains and the low plateau and river floodplains of Luangwa River . The most notable difference between the two maps is the much more confined presence areas predicted for the ≥15% prevalence category in the Northern and Luapula Provinces as compared to the relatively large areas predicted as potential ≥5% prevalence presence in these provinces . Superimposing the binary presence/absence maps to produce one risk map ( Figure 4 ) furthermore highlighted differences and similarities between the two model predictions . The orange color in this figure represents areas where only model 1 ( CFA≥5% ) predicts presence and the dark-brown color shows where model 2 ( CFA≥15% ) predicts presence ( nested within model 1 predicted presence areas ) . Finally , the light yellow color in the map delineates areas where none of the models predict presence , i . e . areas expected to have no or less than 5% infection prevalence . Measures of model accuracy are presented in Table 3 . AUC values ranged from 0 . 866 to 0 . 892 , indicating that the ‘suitability’ for LF infection was correctly ranked for 87–89% of the evaluated map pixels . The correlation ( COR ) between the MaxEnt model predicted suitability and the observed full range of CFA prevalences at all 108 localities ranged from 0 . 117–0 . 355 , and increased with CFA prevalence cut-off level ( Table 3 ) . This indicates that MaxEnt modeled the ‘true’ prevalence pattern of LF infection in Zambia better when using medium to high prevalence localities only ( model 2 ) , rather than the more general presence of infection ( ≥5% ) which showed a non-significant correlation to the observed CFA prevalences at survey sites . Based on the presence/absence map , model 2 also had the best predictive positive and negative performance as evaluated by its sensitivity ( 76 . 9% ) and specificity ( 64 . 5% ) meaning that 76 . 9% of the ≥15% prevalence data points were correctly identified within the predicted ‘≥15% prevalence zone’ , and that 64 . 5% of the true negatives were correctly identified within the ‘<15% prevalence zone’ .
The field survey reported in this paper was the first country-wide screening for LF in Zambia . More than 10 , 000 people from 108 sites located in all 72 districts and 9 provinces were examined for CFA during an 8-year period from 2003 to 2011 . The survey surprisingly indicated that LF is widely distributed in the country , with 78% of sites having CFA positive cases . In many of the sites prevalences were rather low , but a few identified foci had prevalences above 25% . The highest prevalences ( above 50% ) were recorded from Kalabo District in Western Province . The results from the survey , in particular the identification of the high endemicity foci , provide an important background for planning and initial implementation of LF control measures in Zambia . Females were much more eager to participate in the CFA screening than males . Overall , 64% of those examined were females , and at most survey sites ( 87% ) more female than male volunteers were examined . It is well known that the LF prevalence in most endemic areas is higher in adult males than adult females [28]–[30] . The recorded prevalences from the Zambian survey may therefore be an underestimation of the true values , especially at sites where the female to male ratio was high . Similarly the potential sampling biases introduced by involving local health personnel in the selection of study sites ( oversampling of suspected endemic areas ) and by examination of volunteers ( non-random sampling of study individuals ) should be kept in mind when interpreting findings . These are , however , practical arrangements that are often difficult to avoid during large-scale mapping surveys , and which are also recognized in the WHO guidelines for mapping surveys [8] , [11] . Some of the identified high prevalence foci were located near national borders , and it is possible these may be attached to foci in neighboring countries . Thus , the river Zambezi separates the Luangwa focus from areas of Zimbabwe where cases of LF have previously been documented [31] , [32] , and LF moreover appears to be common in the nearby Tete Province of Mozambique [33] . The Lundazi focus is close to Malawi , which also has widespread occurrence of LF although the prevalence in the western part of the country tends to be low [34] . Whether the Kalabo and Senanga foci extend into nearby Angola , or the Serenje focus extends into nearby Democratic Republic of Congo , is unclear as current information about the geographical distribution of LF in these neighboring countries is limited [33] , [35] . Infections with another species of filarial parasite , M . perstans , have also been reported from humans in Zambia [1] , [2] , but these do not seem to cross react in the CFA tests for W . bancrofti [36] . Knowledge about the vectors of LF in Zambia is limited , but recent surveys indicate that , as in most other parts of Sub-Saharan Africa , An . funestus and An . gambiae are the principal LF vectors [37; ST Shawa personal communication] . These species are also the main malaria vectors in Sub-Saharan Africa . As Zambia is one of the countries in this region that has received relatively high bed net coverage and coverage of indoor residual spraying for malaria control in recent years [38] , it cannot be excluded that these activities to some extent could have impacted the LF prevalences in some of the studied areas . Identifying the ecological correlates of LF presence and exploring its environmental distribution in Zambia is an important step required to produce accurate and reliable maps for geographically targeted and cost-effective intervention . Here a machine learning approach , that allows flexible modeling and exploration of potential complex associations between infection presence and environmental predictor variables in geographical space , was applied . This approach allowed visualization of the ‘ecological space’ for occurrence of LF at different levels of infection prevalence , and provided new insights as to how environmental variables may functionally influence the LF parasite-vector-host bioescone in Zambia . Of note it was found that the general distribution of LF ( ≥5% ) in Zambia appeared to be associated with human modified land areas , as indicated by the strong association with croplands and the Human Influence Index . These areas may sustain habitat-types that are particularly suitable breeding areas for the main vector mosquito species in Zambia ( Anopheles gambiae and A . funestus ) , and it is biological intuitive that the parasite is found in areas where the human host resides . It may , however , also partly be a reflection of a sampling bias towards ( more densely ) populated areas . Climatic factors on the other hand , were not important in model 1 , suggesting that climate per se may play a smaller role in determining the general distribution of LF in Zambia . The distribution of medium to high levels of LF ( model 2 ) on the other hand , was less associated with human influenced predictors ( only 1 . 9% HII ) and seemed to be more related to climatic factors , with daytime temperature variables being equally important to land cover as measured by contribution to model training gain ( Table 3 ) The functional relationship with day time temperature was positive ( Figure 2b–c ) , reaching a plateaux ( maximum ) at around 31°C in the rainy season and with a lower limit at around 22°C ( rainy and hot/dry season ) . This corresponds well with the findings from experimental studies showing that only few microfilariae will penetrate the gut of the mosquito at temperatures below 22°C and only little or no development occurs [39]–[42] . The rate of development then increases with rising temperatures , becoming optimal around 30°C [39] . Hereafter , the yield of infective larvae decreases due to increased filarial larval mortality [42] , [43] and lower survival rate of infective mosquitoes [41] . It also corresponds well to the findings from continental scale studies of the distribution of LF in Africa . For example Lindsay and Thomas [9] , who found that the temperatures at sites with presence of microfilaraemic individuals across Africa lie within the range between 22 to 30 degrees , and Slater and Michael [18] who found that the most suitable range for LF transmission across Africa lies between 25°C and 32 . 5°C ( mean maximum temperature ) . Besides suitable temperature ranges , water availability for mosquito breeding is a prerequisite for LF transmission . Rainfall however , did not contribute much to either models , and hence does not seem to be an important limiting factor for the distribution of LF in Zambia . However , distance from nearest permanent surface water body had the most information not present in the other variables in the models , and hence ( together with land cover and temperature ) appear to be an important determinant of LF distribution in Zambia . Similar environmental information as applied in the current study was recently used to predict the distribution and risk of malaria across Zambia [44] , although applying a different modeling approach ( Bayesian geostatistical modeling ) . Given that LF in Zambia is transmitted by the same vector mosquito species with the same ecological requirements as malaria , a certain similarity between the distributions of the two infections is to be expected . A visual comparison of the two maps indicate areas of co-inciding high risk in the low-lying floodplains and valleys surrounding Luangwa River , on the border between Northern and Eastern Provinces and in eastern parts of Lusaka Province . An area of medium-high risk malaria is also predicted in the floodplain areas in Western Province ( Zambezi River floodplains ) , although this is much more confined than that of the relatively large area predicted for LF in this part of the country . The biggest difference between the maps is the general high malaria risk predicted in large parts of Northern Province , where LF ( at medium-high prevalence levels ) is predicted to be less widespread . Similar patterns of contrasting spatial distributions of LF and malaria has also been observed in Uganda [45] and in some West African countries [10] . The present study has provided new and unexpected knowledge indicating widespread occurrence of LF in Zambia . It has moreover outlined its approximate geographical distribution , pointed to specific areas with high prevalence , and identified important environmental factors affecting its presence at various prevalence levels . This information will all be useful for planning and implementation of control of LF as a public health problem . In fact , the Ministry of Health in Zambia initiated mass drug administration in Kalabo District in late 2012 , based on the findings from the field surveys reported in this paper , and it is planned to scale up this activity across the country in the next few years . Although the applied modeling approach has proven useful to explore ecological correlates of LF and visualize environmentally suitable areas across unsurveyed areas in Zambia , it is important to stress that the resultant maps do not depict predicted prevalence: they show the relative probabilities of presence of the parasite-vector-host biocoenose . Given the relatively low correlation between these values and actual LF prevalence at sites , care should be taken not to interpret the maps as prevalence prediction maps . For this purpose , the full range of information in the survey data ( i . e age and gender ) also known to substantially influence LF prevalence/infection status , should be taken into consideration . Hence , a logical next step will be to build on the findings here and include individual level demographic data in a Bayesian geostatistical prediction model . Such an approach will allow an estimation of LF prevalence at unsurveyed locations , along with number of people at risk according to age and gender as done for instance for LF in Uganda [45] , which would be particularly useful for further improved geographically targeted and cost-effective intervention .
|
Lymphatic filariasis ( LF ) is a debilitating mosquito borne parasitic infection which worldwide affects more than 120 million people . It is also widespread in Sub-Saharan Africa . A World Health Organization coordinated Global Programme to Eliminate LF has targeted LF for elimination as a public health problem by the year 2020 , with annual mass drug administration ( MDA ) being the primary measure for this endeavor . An important first step before initiating MDA is the geographical mapping of infection in order to delimit the target areas . Past case reports have indicated that LF occurs in Zambia , but knowledge on its distribution and prevalence has been limited . Here we report on a country-wide survey carried out to map the geographical distribution and prevalence pattern across Zambia by screening adult volunteers for specific circulating filarial antigens ( CFA ) . The CFA prevalences observed at the numerous survey sites are presented and mapped to give an indication of LF distribution in the country . The observed geographical variation is furthermore examined using a species distribution modeling approach to explore environmental requirements for LF presence , and to predict potential suitable habitats over unsurveyed areas . The findings provide a firm background for planning and start-up of LF control in Zambia .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"ecology",
"global",
"health",
"epidemiology",
"biology",
"public",
"health"
] |
2014
|
Mapping the Geographical Distribution of Lymphatic Filariasis in Zambia
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The cyclooctadepsipeptide emodepside and its parent compound PF1022A are broad-spectrum nematicidal drugs which are able to eliminate nematodes resistant to other anthelmintics . The mode of action of cyclooctadepsipeptides is only partially understood , but involves the latrophilin Lat-1 receptor and the voltage- and calcium-activated potassium channel Slo-1 . Genetic evidence suggests that emodepside exerts its anthelmintic activity predominantly through Slo-1 . Indeed , slo-1 deficient Caenorhabditis elegans strains are completely emodepside resistant . However , direct effects of emodepside on Slo-1 have not been reported and these channels have only been characterized for C . elegans and related Strongylida . Molecular and bioinformatic analyses identified full-length Slo-1 cDNAs of Ascaris suum , Parascaris equorum , Toxocara canis , Dirofilaria immitis , Brugia malayi , Onchocerca gutturosa and Strongyloides ratti . Two paralogs were identified in the trichocephalids Trichuris muris , Trichuris suis and Trichinella spiralis . Several splice variants encoding truncated channels were identified in Trichuris spp . Slo-1 channels of trichocephalids form a monophyletic group , showing that duplication occurred after the divergence of Enoplea and Chromadorea . To explore the function of a representative protein , C . elegans Slo-1a was expressed in Xenopus laevis oocytes and studied in electrophysiological ( voltage-clamp ) experiments . Incubation of oocytes with 1-10 µM emodepside caused significantly increased currents over a wide range of step potentials in the absence of experimentally increased intracellular Ca2+ , suggesting that emodepside directly opens C . elegans Slo-1a . Emodepside wash-out did not reverse the effect and the Slo-1 inhibitor verruculogen was only effective when applied before , but not after , emodepside . The identification of several splice variants and paralogs in some parasitic nematodes suggests that there are substantial differences in channel properties among species . Most importantly , this study showed for the first time that emodepside directly opens a Slo-1 channel , significantly improving the understanding of the mode of action of this drug class .
In Ecdysozoa , large conductance potassium channels ( also BK or Maxi-K channels ) are encoded by slo-1 genes [1] . Due to their large conductance in the open state , typically exceeding 200 pS , Slo-1 channels are of major importance for repolarization of excitable cells . These channels are tetramers and , in almost every animal species , all subunits are encoded by a single gene – with teleost fish as the only known exception , encoding multiple slo-1 paralogs resulting from whole genome duplications [2] . The opening of Slo-1 channels is controlled by the membrane potential and intracellular free Ca2+-concentrations [Ca2+]i . Depolarization of the membrane as well as very high transient local Ca2+ concentrations are required to open Slo-1 channels [3] . Most other ion channels produce subtypes with altered physiological properties through a combination of different subunits as recently reviewed for acetylcholine and glutamate receptors [4] , [5] . In contrast , several different slo-1 splice variants have been described , e . g . for C . elegans , D . melanogaster , mice and humans , and differential splicing is known to affect channel properties [6]–[9] . In addition , the use of different tissue-specific promoters has been described for D . melanogaster [10] , [11] . Indeed , the number of different splice variants has dramatically increased during evolution , which is demonstrated by the fact that the human slo-1 ortholog kcnma1 encodes 13 alternative exons [12] . In fact , the number of splice variants in Mammalia and Diptera is so high that systematic analysis of the effects of the channel variants generated by alternative splicing on voltage- and Ca2+-sensitivity has only been performed for a few of them . The complexity of Slo-1 channel heterogeneity is further increased by the ability to form heterotetramers [13] containing different splice variants and the fact that channel responses to depolarization and Ca2+ are modulated by posttranslational modifications such as palmitoylation and phosphorylation at several distinct sites [14]–[16] . Splice variants in C . elegans are more clearly arranged , with four different splice sites giving rise to 12 well characterized splice variants [17] plus three additional variants annotated in WormBase ( Slo-1a-Slo-1m , Slo-1y , Slo-1z ) . Expression levels of these variants in whole C . elegans as well as voltage- and Ca2+-sensitivity profiles of different homomeric tetramers have been reported recently [6] , [17] . Nematode Slo-1 channels have received particular attention in recent years due to their central involvement in the mode of action of the broad-spectrum nematicidal cyclooctadepsipeptide emodepside [18] . C . elegans strains with slo-1 loss-of-function mutations are completely resistant to emodepside [19] . Emodepside sensitivity can be rescued by re-introducing Slo-1 from either C . elegans or the parasitic nematodes Ancylostoma caninum and Cooperia oncophora , but only partially by the human ortholog KCNMA1 [19]–[21] . These results can be explained by either a direct and specific interaction of emodepside with nematode Slo-1 channels or a necessary function of Slo-1 in a signal transduction pathway downstream of an emodepside target . Ectopic expression of Slo-1a in the pharynx muscle , which does normally not express Slo-1 channels , in a slo-1 deficient genetic background conferred emodepside sensitivity to pharyngeal pumping [21] , suggesting that this channel is either the direct target of the drug or that an unidentified target is present in pharyngeal muscle cells that is activated upstream of Slo-1 . Although Slo-1 channels are widely considered to be the most likely receptors for emodepside in nematodes [18] , [22]–[24] , direct interaction of the channel with emodepside or its activation by this drug have not been reported . This is in contrast to another putative emodepside target , the G protein-coupled receptor Lat-1 , an ortholog of mammalian latrophilin receptors . Binding of the drug to its target and activation of Lat-1 by PF1022A and emodepside have been demonstrated [25] . However , lat-1 loss-of-function mutations in C . elegans cause only partial emodepside resistance; the effects of emodepside on the pharynx but not on the body muscle were impaired [26]–[28] . Knowing the mode of action of new drugs has important advantages for the prediction of efficacy in new target species [29] and can exclude potential receptor-dependent deleterious side effects in host species [30] . Interpretation of structure-activity relationships are also facilitated if the receptor is known . Despite their relatively uniform body shape , nematodes are genetically and physiologically extremely diverse [31] , [32] . The cyclooctadepsipeptides have a very broad anthelmintic spectrum against parasitic nematodes [18] representing all major clades [33] , including strongylids ( clade V ) , Strongyloides ( clade IV ) , Ascaridoidea and Filarioidea ( clade III ) as well as the trichocephalids Trichuris and Trichinella ( clade I ) [18] . This broad spectrum nematicidal activity is in marked contrast to other anthelmintics that have been developed in the last decade including tribendimidine ( poor efficacy against Strongyloides stercoralis and Trichuris trichiura [34] ) , monepantel ( only limited efficacy against human hookworms and poor or no efficacy against parasites not belonging to clade V [35] ) as well as derquantel ( efficacy >95% against L4 and adults of Trichostrongylus and Nematodirus spp . as well as against adult H . contortus but suboptimal efficacies against L4 and adults of Teladorsagia circumcincta , L4 of H . contortus , and some large intestinal nematodes [36] ) . For S . ratti lack of monepantel efficacy is presumably due to the fact that no member the DEG-3/DES-2 subfamily of acetylcholine receptors , which are the known targets of this drug , are encoded in its genome [29] . Therefore , differences in susceptibility to anthelmintics among nematodes can in part be explained by presence of drug targets encoded in their genomes . slo-1 genes and gene products have thus far only been investigated in clade V of the phylum Nematoda [33] . Since parasitic nematodes of vertebrates are found in four of five clades [33] and Slo-1 is apparently a validated drug target , the present study aimed to analyze the diversity of Slo-1 channels within the phylum Nematoda . In addition , effects of emodepside on C . elegans Slo-1a channel opening were determined .
All animal experiments were approved by the local administrations in charge and were in accordance with local laws regarding animal welfare ( Animal Welfare Act in the United States and the “Tierschutzgesetze” in Germany and in Switzerland as well as in accordance with the European Union directive 2010/63/EU ) . Adult T . muris were obtained during controlled drug trials performed for other studies at Bayer HealthCare AG , Global Drug Discovery Animal Health in Monheim , Germany ( approved by Landesamt für Natur- , Umwelt- und Verbraucherschutz Nordrhein-Westfalen ( LANUV ) in Recklinghausen ( Germany ) under No . 200/V14 ) [37] and at the Swiss Tropical and Public Health Institute in Basel , Switzerland ( approved by the local veterinary office Basel-Stadt ( Switzerland ) based on Swiss cantonal and national regulations under permission no . 2070 ) [38] . Adult Parascaris equorum were collected in a controlled drug trial study conducted by von Samson-Himmelstjerna et al . that was approved by the Landesamt für Verbraucherschutz und Lebensmittelsicherheit ( LAVES ) in Hannover ( Germany ) under the reference number 33 . 9-42502-05-07A499 . Ascaris suum were obtained from a German slaughterhouse . Dirofilaria immitis macrofilariae were collected during routine necropsy of a naturally infected , moribund stray dog which was euthanized due to medical reasons ( Athens , GA , USA ) . No information regarding owners or the history of the dog is available . Macrofilariae of Onchocerca gutturosa were dissected from the nuchal ligament connective tissues , obtained from cattle post-slaughter in Banjul , Gambia , ( facilitated by the International Trypanotolerance Centre , Gambia ) . Consent of the owners of the carcasses was obtained . Brugia malayi microfilariae were provided by the NIAID/NIH Filariasis Research Reagent Resource center ( FR3 ) . Toxocara canis RNA was obtained from a previously published study [39] . Nematodes were homogenized on ice in Trizol ( Invitrogen ) or TriFast ( Peqlab ) reagent using a TissueRuptor and transparent disposable probes ( Qiagen ) . RNA was isolated according to the manufacturer's instructions , except that the volume of Trizol was increased to 5 ml per 100 mg wet tissue weight . Volumes of all subsequently used reagents were adjusted accordingly . RNAs were precipitated twice in the presence of glycogen ( Thermo Fisher Scientific ) , dissolved in water and stored at -80°C until use . For cDNA synthesis , 1 µg total RNA was mixed with 100 pmol of either random hexamer primers ( for PCR with degenerate primers ) or oligo dT primers ( for full-length PCRs; both primers from Thermo Fisher Scientific ) , incubated at 65°C for 5 min and then chilled on ice . Reverse transcription was performed in 20 µl using 200 U Maxima Reverse Transcriptase ( Thermo Fisher Scientific ) , 1 mM dNTPs and 20 U RiboLock RNase inhibitor ( Thermo Fisher Scientific ) by incubation at 42 °C for 30 min and 60 °C for 30 min . Finally , the enzyme was inactivated at 70 °C for 5 min . Alternatively , 200 U RevertAidM-MuLV reverse transcriptase ( Thermo Fisher Scientific ) were used with 20 U RiboLock RNase inhibitor , 0 . 5 mM dNTPs and 25 µM oligo-dT primers . Partial genomic data from T . muris and T . canis and a deduced full-length sequence of S . ratti ( for phylogenetic analysis only ) were provided by the Parasite Genomics group at the Wellcome Trust Sanger Institute and can be obtained from http://www . sanger . ac . uk/research/projects/parasitegenomics/ . Based on comparisons among available slo-1 cDNA sequences of H . contortus , C . oncophora and C . elegans , degenerate primers were designed and used to amplify and sequence small slo-1 fragments of P . equorum and A . suum . A 126 bp long cDNA T . canis slo-1 sequence published on nematode . net [40] was identified by BLAST . Specific primers for nested 5'- and 3'-RACE PCR were designed using these sequence data ( S1 Table ) . PCRs contained 0 . 3 mM dNTPs , 0 . 4 µM of the gene-specific and the universal primer , 1 µl cDNA and 0 . 5 µl Advantage 2 Polymerase Mix ( Clontech ) in 25 µl 1×Advantage buffer . For 5'-RACE multiple nested RACE PCRs were performed to proceed stepwise to the 5'-end of the cDNA . Full-length sequences were amplified using two gene-specific primers and the same PCR protocol . PCR fragments were gel purified , cloned into pCR4 TOPO ( Invitrogen ) and sent to GATC Biotech for sequencing . Primers for all full-length amplifications are provided in S1 Table . For other nematodes , primers derived from partial slo-1 sequences identified in the T . muris , D . immitis , Onchocerca volvulus and B . malayi genome projects were used . Using these sequences , primers for 5' and 3' RACE PCR were designed and RACE PCRs were carried out with the 5'/3' RACE Kit , second generation ( Roche Diagnostics ) as detailed previously [39] . PCRs were performed in 25 µl 1×Phusion II buffer containing 1 U Phusion II DNA polymerase ( Thermo Fisher ) , 0 . 3 mM dNTPs , 0 . 3 µM of each primer , 1×Q solution ( Qiagen ) and 1 µl cDNA synthesized with RevertAidM-MuLV reverse transcriptase . Thermocycling was conducted in a Biorad C1000 or S1000 cycler with initial denaturation at 98°C for 1 min followed by 35 cycles with denaturation at 98°C for 10 s , annealing at a primer pair specific temperature for 30 s and elongation at 72°C for 20 s to 2 min . For full-length PCRs , primers flanking the open reading frame were chosen . Full-length primers for B . malayi , D . immitis and O . gutturosa were obtained from genome data [41] . Further details about primer sequences are available in S1 Table . PCR products were gel-purified and cloned into the pCR4 TOPO blunt vector ( Invitrogen ) and sequenced by GATC Biotech . Molecular weight and putative isoelectric points were calculated with Clone Manager 9 ( Scientific and Educational Software ) . Localization of transmembrane regions was predicted using TMPred software [42] . Conserved domains and Prosite motifs were identified using CD-BLAST [43] , [44] and InterProScan [45] , [46] . Prediction of phosphorylation sites was performed using NetPhosK 1 . 0 [47] . Slo-1 sequences from the present study were aligned with homologs available in GenBank or Wormbase , and homologs from Meloidogyne incognita [48] and S . ratti ( obtained from the Sanger genome project ) . Deduced protein sequences were aligned using ClustalX2 [49] . Alignments were analyzed with Prottest 3 . 0 . 1 [50] to identify the most appropriate amino acid substitution model . PhyML 3 . 0 . 1 [51] , [52] was then used to determine tree topology and branch support as described [53] . In brief , the JTT model for amino acid substitution [54] with 16 Γ distributed substitution rate categories was used . PhyML was set to estimate Γ shape parameter and proportion of invariable sites while amino acid frequencies were set to be based on the substitution model . Nearest neighbor interchange ( NNI ) and subtree pruning and regraftment ( SPR ) moves were allowed to optimize the tree topology . Both Bayesian transformation and Shimodaira-Hasegawa-like modifications of the approximate likelihood test were used to calculate branch support . Tree optimization started with one neighbor-joining and 5 random trees and the tree with the highest likelihood was finally chosen and visualized in MEGA5 [55] . For analysis of the conserved alternative exon , ProtTest 3 . 0 identified the same model as for the full-length sequence and PhyML was executed using identical parameters as for the full-length sequence . The primers flanking the putative splice site ( S1 Table ) were used in a two-step RT-PCR as described above . PCR products were analyzed on 2 . 0% agarose gels and with the DNA 1000 kit on the Bioanalyzer 2100 ( Agilent ) . Gel-purified DNA fragments were cloned and sequenced . Quantification of fragments was performed using the Bioanalyzer Expert software . For RNA-seq experiments , total RNAs were extracted from larvae at day 10 [approximately 50 , 000 first and second stage larvae ( L1 and L2 ) from 5 experimentally infected pigs] , day 18 [15 , 000 third stage larvae ( L3 ) from 4 pigs] and day 28 p . i . [3 , 000 fourth stage larvae ( L4 ) from 2 pigs] , from whole adult male ( n = 10 ) , adult female ( n = 10 ) or from multiple stichosomes ( mixed sex; n = 10 ) , posterior portions of adult females ( n = 10 ) , including the eggs , and of adult males ( n = 10 ) using the TriPure reagent ( Roche ) . Yield and quality were verified using the 2100 Bioanalyzer ( Agilent ) . For library production , purification of polyadenylated ( polyA+ ) RNA from 10 µg total RNA from each sample was carried out using Sera-mag oligo ( dT ) beads ( Thermo Scientific ) . Then RNAs were fragmented to a size of 300–500 bp , reverse-transcribed using random hexamer primers , end-repaired and adapter-ligated according to the manufacturer's protocol ( Illumina ) . Ligation products of approximately 400 bp were eluted from agarose gels , PCR-amplified ( 15 cycles ) as recommended and purified on MinElute columns ( Qiagen ) . Finally , libraries were subjected to paired-end RNA-seq using HiSeq 2000 ( Illumina ) and assessed for quality and adapter sequences . After sequencing , raw reads were trimmed of Illumina adapters , filtered for length ( ≥ 40 nt ) and low-quality data ( reads containing 4 or more consecutive bases showing a PHRED quality below 20 ) . Transcripts for each stage and/or body portion as well as the Illumina RNA-seq data from a mixed-sex adult T . suis transcriptome generated previously [56] were reconstructed and quantified by Jex et al . [57] using the Tuxedo suite [58] and a draft assembly of the T . suis genome [57] . Differential transcription was assessed using NOISeq [59] , with 20% of the evaluated reads for each library used in 5 iterations to simulate technical replicates ( note: all RNA-seq libraries were constructed from multiple parasites , i . e . , 10 adults or 50 , 000 larvae retrieved from multiple host individuals to compensate for inter-individual biological variation ) . Full-length cDNAs encoding C . elegans Slo-1a and T . muris Slo-1 . 1a in pCR4TOPO ( Invitrogen ) were used . Plasmid DNA was linearized in 100 µl buffer containing 8 µg plasmid DNA and 100 U XbaI ( for C . elegans SLO-1a ) or 100 U BcuI ( for T . muris SLO-1 . 1a ) at 37°C for 2 h . Linearized plasmid DNA was purified via the GeneJet PCR Purification kit ( Thermo Fisher Scientific ) and eluted with 50 µl UltraPure DNase/RNase-free distilled water . Linearization of the plasmid DNA was monitored by agarose gel electrophoresis . DNA concentration was determined using the DNA 12000 kit and the 2100 Bioanalyzer ( Agilent Technologies ) according to the manufacturer's instructions . For cRNA synthesis from 1 µg linearized plasmid DNA , the mMessage mMachine T7 transcription kit ( Ambion ) was used according to manufacturer's instructions . To determine the integrity and concentration of the transcribed cRNA , the RNA 6000 Nano kit and the 2100 Bioanalyzer ( Agilent Technologies ) were used according to the manual . Defolliculated Xenopus laevis oocytes were obtained from EcoCyte Bioscience ( Castrop-Rauxel , Germany ) . After delivery , single oocytes were transferred into individual cavities of a 48 well plate containing Barth's solution ( 88 mM NaCl , 2 mM KCl , 1 mM MgCl2 , 15 mM Tris HCl , 0 . 5 mM CaCl2 , pH 7 . 4 ) and incubated for at least 2 h at 19°C before use . 75 nl of either 200 ng/µL C . elegans SLO-1a cRNA or T . muris SLO-1 . 1a were microinjected into each oocyte using the Roboinject ( Multi Channel Systems MCS GmbH ) . Oocytes injected with 75 nl water served as negative control . Position of impalement was set to 350 µm , whereas position of injection was set to 400 µm . Oocytes were incubated at 19°C for 3-4 days post microinjection . Barth's solution was replaced daily . Experiments were carried out using the Roboocyte ( Multi Channel Systems MCS GmbH ) . In the Roboocyte , oocytes were perfused with normal frog Ringer solution ( NFR: 90 mM NaCl , 2 mM KCl , 2 mM CaCl2 , 1 mM MgCl2 , 5 mM HEPES , pH 7 . 4 ) for 1 min before electrodes were inserted and membrane potential was clamped to −70 mV . Experiments were carried out as summarized in S1 Fig . . In general , initial current-voltage curves ( IVCs ) were recorded after another perfusion for 1 min . Currents were recorded at a frequency of 1000 Hz during clamping the membrane potential between −120 and +60 mV for 500 ms using voltage steps of 20 mV ( S1B Fig . ) and mean currents for each voltage step were calculated from these 500 individual values . Between individual steps , the membrane potential was clamped to −70 mV for 3 s . After initial recordings of IVCs , a perfusion with NFR was performed for 2 min before repeating the recording to ensure that responses were stable over time ( S1A ( I ) Fig . ) . Oocytes showing currents exceeding 250 nA at +60 mV were excluded from further experiments . To determine the effects of drugs , oocytes were first incubated with the vehicle ( 0 . 1% DMSO , 0 . 003% Pluronic F-68 ( Sigma Aldrich ) for 2 min . Vehicle , emodepside and blockers were always added manually into the well . IVCs were recorded and oocytes were again perfused for 1 min before incubation with emodepside ( 1 or 10 µM ) and another recording ( S1A ( II ) Fig . ) . Since the actual concentration of emodepside at its target site is completely unknown , 10 µM emodepside , which is the highest concentration soluble in water with 0 . 1% DMSO and 0 . 003% Pluronic F-68 , was compared with 1 µM emodepside to identify any potential concentration dependent effects . To test whether potential emodepside effects were reversible , oocytes were perfused for 5 to 10 min with NFR after an IVC recording in the presence of emodepside ( S1A ( III ) , ( IV ) Fig . ) . Effects of the blocker verruculogen ( 1 µM ) were determined both before and after addition of emodepside ( 1 µM ) to the oocytes ( S1A ( V ) , ( VI ) Fig . ) . Currents were recorded using Roboocyte ClampAmp software 2 . 2 . 0 . 15 . Mean currents during each voltage step were calculated for individual oocytes in Roboocyte ClampAmp . To obtain IVCs , mean currents ± SEM for all replicate oocytes were plotted against the voltage used to clamp the membrane potential in GraphPad Prism 6 . 00 . Mean currents between different groups of oocytes were compared separately for every step potential applied using the multiple t-test function in GraphPad Prism . P values were corrected for multiple comparisons using the Holm-Sidak method . Certain parts of the IVC were analyzed for different slopes and for linearity using the linear regression analysis in GraphPad Prism followed by the Wald-Wolfowitz runs test implemented in this analysis .
S2 Table summarizes the physico-chemical properties of predicted nematode Slo-1 subunits compared to homologous proteins from other nematodes and some orthologs from other species used as outgroup in further phylogenetic analysis . Only a single full-length cDNA sequence was identified from clade III nematodes ( B . malayi , O . gutturosa , P . equorum , A . suum and T . canis ) , although alternative exons were identified in partial PCR products . These sequences were not included in the initial analysis of differential splicing since no information regarding possible combinations of different exons is currently available for them . For B . malayi , six different isoforms ( BmaSlo-1c-h ) are annotated in Wormbase and isoform BmaSlo-1f was identical to the one identified in the present study ( S2 Fig . ) . For D . immitis two variants were identified differing only in a short region encoding amino acids 678 - 692 in DimSlo-1a , which is missing in DimSlo-1b ( S2 Fig . ) . In the clade I nematode T . muris , two partial genomic sequences encoding Slo-1 homologs were identified in contigs NODE_15952_length_5825_cov_10 . 724463 and NODE_133417_length_23144_cov_11 . 427541 . Full-length cDNAs of both paralogs were amplified and cloned . Comparison with Trichinella spiralis sequences in GenBank revealed that this clade I nematode also encodes two distinct Slo-1 homologs in its genome . The T . spiralis Slo-1 . 1 protein sequence can be found as two partial entries which cover the whole length under the GenBank accession numbers XP_003370273 . 1 and XP_003370274 . The T . spiralis Slo-1 . 2 database entries XP_003370270 . 1 , XP_003370271 . 1 and XP_003370272 . 1 correspond to the T . muris Slo-1 . 2 . In both trichocephalids , the two paralogs are juxtaposed in the genome in a tail-to-tail orientation . Two splice variants encoding full-length T . muris Slo-1 . 1 channels ( Slo-1 . 1a and Slo-1 . 1b ) were identified . In addition , two splice variants encoding severely truncated proteins were cloned; truncation is caused by retention of a partial or entire intron in the mature RNA leading to premature stop codons ( TmuSlo1 . 1c and TmuSlo-1 . 1d in S2 Fig . , respectively ) . Using primers flanking these introns , a semiquantitative estimate of the frequency of the splice variants was possible in two independent T . muris isolates . PCR products were separated on agarose gels ( Fig . 1A ) and the Bioanalyzer ( Fig . 1B ) . The latter was used to quantify the amount of DNA in the peaks ( Table 1 ) . In contrast to the initial impression from agarose gels , where the large PCR products encoding the truncated versions produced the brightest bands , the Bioanalyzer clearly shows that the smallest fragment encoding full-length channels represents the majority of amplicons in terms of molecules ( 77 and 84% ) . To reveal if these truncated channel versions are present in related species , data from a T . suis genome/transcriptome project were analyzed for the presence of different slo-1 splice products . A multi sequence alignment of the encoded protein sequences is given in fasta format in S3 Fig . . Truncated versions due to retained introns were observed in both TsuSlo-1 . 1 and TsuSlo-1 . 2 in the same region of the channel ( i . e . , the transmembrane helix S4 and the voltage sensor comprised of S5 and S6 ) , but the introns involved in these events were not at exactly the same position . Remarkably , alternative exons were found immediately before the retained introns , with one splice variant only present among cDNAs encoding full-length channels ( TsuSlo-1 . 1 . 4/7 ) and the alternative variant only in the truncated versions ( TsuSlo-1 . 1 . 2/5/10 and 14 ) . In addition , splice variants encoding even shorter channels than those found in T . muris were identified ( TsuSlo-1 . 1 . 1/3/8/12 ) . Quantitative analysis of transcriptome data revealed for Tsuslo . 1 . 1 that the most abundantly detected splice variant corresponds to the full-length splice variant Tsuslo-1 . 1 . 4 in terms of fragments per kilobase of exons per million fragments mapped ( FKPM ) , whereas the other full-length variant Tsuslo1 . 1-7 was found at much lower abundance ( Fig . 2 ) . The second most abundantly detected splice variant ( Tsuslo1 . 1 . 10 ) encodes a severely truncated protein of similar length as the truncated T . muris variants detected by RT-PCR ( S2 Fig . ) . Remarkably , there are very obvious differences in frequency of splice variants . For example , the frequency of Tsuslo-1 . 1 . 10 ranged between 20% in adults and 35% in L1 , with L3 and L4 showing intermediate frequencies ( Fig . 2A ) . Additional stage specific splicing is exemplified by the channels encoded by Tsuslo-1 . 1 . 2/5/14 and Tsuslo-1 . 1 . 3: While the former encodes a channel very similar to Tsuslo-1 . 1 . 10 , but differing in its NH2 terminus , the latter variant is extremely truncated ( S2 Fig . ) . Notably , the abundance of Tsuslo-1 . 1 . 10 is relatively high among slo-1 . 1 transcripts during larval development , peaking in the L1/L2 stage ( Fig . 2A ) . In contrast , transcription of this highly truncated slo-1 . 1 splice variant was relatively low ( statistically significant at a false discovery rate of 0 . 1 ) in most adult libraries , with the major exception being the stichosome , in which it displayed its highest abundance . Other minor slo-1 . 1 isoforms , including some slo-1 . 1 variants , showed evidence of differential transcription during development and in adulthood among genders/tissues . However , because single library replicates were used for each tissue and stage of T . suis , statistical assessment of the differential transcription of these minor isoforms is not advisable . We consider these differences both preliminary and qualitative at this stage . In contrast to Tsuslo-1 . 1 , little variation in transcription of Tsuslo-1 . 2 channel variants was observed among developmental stages or adult tissues/genders , with Tsuslo-1 . 2 . 6 ( the full length transcript ) being the major isoform ( Fig . 2C and D ) . Comparison of splice variants in parasitic nematodes with the 15 splice variants described for C . elegans reveals five main regions where differential splicing occurs ( S2 Fig . ) . The first site is an alternative exon ( i . e . , present or not ) , the absence of which results in the use of a downstream ATG start codon only in T . suis Slo-1 . 1 and 1 . 2 . Insufficient data are available to determine whether this splicing also occurs in T . muris . The first alternative exon identified in C . elegans ( region 2 in S2 Fig . ; also a ‘present or not’ type ) was not found in any of the parasitic nematode slo-1 cDNAs . Splicing region 3 refers to a larger region where differential splicing occurs in all four Trichuris genes in different positions . Many but not all of the encoded proteins are severely truncated . The fourth splicing site ( alternative exons ) has been conserved throughout the evolution of Slo-1 channels in nematodes and two versions are present in T . muris and T . suis Slo-1 . 1 . In all seven sequences cloned from ascarids and filariae , the corresponding region shows higher similarity to C . elegans Slo-1a . However , alternative sequences were identified in the genomes of A . suum , O . volvulus and B . malayi ( BmaSlo-1h in S2 Fig . and S2 Table ) . Alternative splicing occurs in exactly the same position in arthropods and vertebrates . This alternative exon immediately follows two highly conserved phosphorylation sites . Remarkably , two of the predicted B . malayi splice variants contain a partial duplication ( approximately two thirds of the whole exon BmaSlo-1c/d ) . Splicing site four in C . elegans ( position 5 in S2 Fig . ; ‘present or not’ type with two different 5' splice sites ) has no equivalent in any of the Trichuris sequences , but there is alternative splicing in the same area in B . malayi and D . immitis . The sixth splice region is found in C . elegans but is apparently not present in clade III and clade I parasitic nematodes . A maximum-likelihood approach was used to calculate a phylogenetic tree from the SLO-1 amino acid sequences ( Fig . 3A ) . As outgroup , three sequences from vertebrates , one from a mollusc and five from arthropods were included in the analysis . Slo-1 channels of nematodes and arthropods formed a monophyletic group in accordance with their phylogenetic position within the Ecdysozoa . In general , most of the tree topology is consistent with current views on nematode evolution . However , the position of the Slo-1 proteins from the clade IV nematodes M . incognita and S . ratti is basal to a group containing all clade III and clade V sequences , but the support values for this position are rather low in both versions of the approximate likelihood test ( Fig . 3A ) . The additional paralog Slo-1 . 2 present in clade I parasitic nematodes is placed as a sister operational taxonomic unit ( OTU ) to the Slo-1 . 1 paralog in this clade . This suggests that duplication of the slo-1 gene occurred after the trichocephalids diverged from the other groups and is not an ancestral feature of nematode genomes . To prove that differential splice variants were conserved throughout nematode evolution at the C . elegans alternative splice site four , only the amino acid sequences of this exon were aligned and subjected to phylogenetic analysis . For this purpose , alternative exons identified in the genomes of A . suum and O . volvulus were included . The partial duplication of this exon in BmaSlo-1c/d was also included as a separate OTU [BmaSlo-1c ( part . dupl . ) in S2 Fig] . Results are depicted in Fig . 3B . Due to the small size of the sequence , statistical support for individual nodes is lower than for full-length sequences . However , there is clearly a nematode-specific group with high similarity to the sequence in C . elegans Slo-1a , although the exon sequences in the trichocephalid species strongly diverge from those in clade III and V nematodes . The second group contains exon sequences from both nematodes and arthropods , including both alternative exons encoded in the D . melanogaster genome . Diversity within this group is much higher and deep divergence patterns could not be resolved . Therefore , the group consists of three major lineages , i . e . ( i ) Trichuris Slo-1 . 1b-like and arthropod sequences which might therefore be considered to be “ancestral” , ( ii ) the exon encoded in Slo-1 . 2 genes , ( iii ) the sequences derived from clade III and V nematodes . Voltage-dependent currents were measured in water-injected oocytes ( Fig . 4A ) . An inward-directed current was detected at very low negative step potentials ( -120 mV to -80 mV ) whereas an approximately linear increase of outward currents was detected between -60 mV and +60 mV ( Fig . 4A and 5A ) . In water as well as in CelSlo-1a or TmuSlo-1 . 1a injected oocytes no significant differences were observed between repeatedly recorded IVCs , indicating that the oocytes were in good physiological condition . Currents detected in water-injected oocytes were not significantly influenced by the vehicle ( 0 . 1% DMSO +0 . 003% Pluronic F-68 ) or by 10 µM emodepside ( Fig . 5A ) . In the absence of emodepside , basal IVCs between water and CelSlo-1a injected oocytes were very similar . However , there was a small but significant higher current at +20 mV in CelSlo-1a injected oocytes ( Fig . 5B ) , which corresponds to the voltage of approximately 0 to +20 mV needed to open CelSlo-1a at a low Ca2+ concentration of 10 µM [60] . This difference was also observed in the presence of the vehicle and there was no significant difference between basal IVCs and IVCs in the presence of the vehicle ( Fig . 5B ) . Interestingly , currents did not increase further when step potentials above +20 mV were applied ( Fig . 5B ) and no differences to water-injected oocytes were observed at +40 and +60 mV ( Fig . 5B ) . In addition , slightly larger negative and slightly higher positive currents were apparently observable at very low ( -120 to 100 mV ) and at slightly negative ( -40 to -20 mV ) voltages , respectively . Since the direct comparison of currents for individual voltages did not indicate significant differences , the range between -120 and -20 mV was subjected to linear regression . The runs test implemented in GraphPad Prism did not find any significant deviation from linearity for any of the data sets using the voltage range as shown in Fig . 5B . Remarkably , slopes for CelSlo-1a injected oocytes were significantly higher ( p<0 . 0001 ) than for water injected oocytes , no matter if DMSO was present in the medium of the CelSo-1a injected oocytes or not . Presence of the vehicle did not change the slope significantly ( p = 0 . 89 ) . The increased slopes demonstrate a voltage-independent increase in membrane conductance due to injection of Celslo-1a cRNA . No increase in currents at +20 mV was detected in oocytes injected with TmuSlo-1 . 1a cRNA ( S4 Fig . ) . In fact , currents measured in TmuSlo-1 . 1a-injected oocytes were significantly lower than in water-injected oocytes at 0 mV and +20 mV step potential . At high step potentials ( +40 - +60 mV ) , these IVCs deviated from linearity , suggesting a nonphysiological response of the oocytes . Linear regression of data between -120 and -20 mV again showed no deviation from linearity . In contrast to the data obtained after injection of CelSlo-1a , the slope was not increased but even decreased by TmuSlo-1a . Injection of more TmuSlo-1 . 1a cRNA ( up to concentrations of 750 ng/µL ) and longer incubation between injection and electrophysiological measurements ( up to six days ) did not improve the results . In contrast , higher cRNA amounts only increased deviation from linearity while longer incubation times had no detectable effects . In an attempt to increase the difference for CelSlo-1a or TmuSlo-1 . 1a , oocytes were preincubated in 2 µg/ml of the Ca2+ ionophore A23187 ( Sigma Aldrich ) in Ca2+ free Barth's medium for 15 min followed by three washes with normal Barth's medium . However , this pretreatment did not increase the response but caused severe leak currents . In the absence of a stimulus increasing [Ca2+]i , addition of emodepside to CelSlo-1a-injected oocytes resulted in dramatically increased currents ( Fig . 4C ) with significantly stronger effects at 10 µM than 1 µM emodepside ( Fig . 5C ) . Differences were significant at all step potentials except for +60 mV at both emodepside concentrations . IVCs obtained in the presence of emodepside are characterized by two very remarkable features . Firstly , currents increased even at step potentials far below the expected opening potential of Slo-1 channels , suggesting that channel opening can occur independently of membrane potential . Secondly , currents peaked at 0 mV and significantly decreased at higher step potentials , resulting in bell shaped IVCs ( Fig . 5C ) . At the highest step potentials used ( +60 mV ) , differences between vehicle controls and emodepside-treated oocytes were no longer significant . Wash out of 1 µm emodepside for 5 min and wash out of 10 µM emodepside for 10 min had no effect on IVCs . Even perfusion for 25 min did not reduce the emodepside effects ( n = 3 ) . Effects of verruculogen , a specific blocker of mammalian K+ channels with high conductance [61] , revealed that the order of addition strongly influenced the outcome . Preincubation of oocytes with verruculogen alone had no effects on IVCs; neither the slight increase in currents at +20 mV observed in CelSlo-1a-injected oocytes nor the increased slope in CelSlo-1 injected oocytes were blocked ( compare Fig . 5B and 5E ) . However , addition of verruculogen before emodepside completely prevented emodepside effects on IVCs ( Fig . 5E ) . This also shows that verruculogen effects on BK channels are not limited to mammalian channels . In contrast , when verruculogen was added to oocytes in the presence of emodepside , no decrease in emodepside-induced currents was detected ( Fig . 5F ) .
The mode of action of cyclooctadepsipeptides has long been a matter of debate , with ionotropic GABAA receptors [39] , [62] , the latrophilin receptor Lat-1 [25] , [63] and the Slo-1 channel [19]-[21] consecutively deemed to be the most likely receptors . Although Slo-1 is generally considered to be the most important target based on experiments using forward and reverse genetics , there has been no direct evidence for emodepside activation of the Slo-1 channel . Responses of excitable muscle/neuronal cells to emodepside develop much slower [21] , [64] than usually observed during direct gating of ionotropic receptors , which meant that indirect effects of emodepside on Slo-1 could never be completely excluded [64] . Using an oocyte expression system eliminating all nematode-related confounding factors , the present study for the first time demonstrates direct effects of emodepside on Slo-1 channels . In the presence of emodepside , highly increased currents were observed without depolarization up to a threshold of 0 mV and without any additional stimuli to artificially increase [Ca2+]i levels . These novel findings confirm that Slo-1 is a direct target of emodepside . The X . laevis expression system has been widely used to characterize the function of nematode ion channels and in particular receptors for neurotransmitters [65]-[67] . It has several advantages , including robustness , simultaneous expression of proteins ( e . g . , auxiliary proteins or multiple subunits ) [66] , and large cells which allow easy application of the two electrode voltage clamp technique ( TEVC ) [68] . However , there is also a drawback of using oocytes when working with Ca2+ responsive channels such as Slo-1 . In particular , the cell is so large that it is not possible to increase the [Ca2+]i to levels required for physiological activation of BK channels . Inside-out patch clamp recording would be required to perform this type of experiment [60] . Therefore , activation of the Slo-1 channel by simultaneously increasing the [Ca2+]i and depolarization of the cells was not possible in this configuration . Attempts to increase [Ca2+]i by preincubation of oocytes with the Ca2+ ionophore A23187 were not successful and resulted in oocytes looking very unhealthy and exhibiting large leak currents . Effects of emodepside on K+ currents in A . suum muscle flaps have been shown to be dependent extracellular Ca2+ [64] . Currently , data do not rule out completely that this is also the case for the experimental system used here since emodepside might itself increases [Ca2+]i directly in X . laevis oocytes although it had no effect in mammalian HEK293 cells [25] . A minor activity of CelSlo-1a channels was already observed in the absence of emodepside , i . e . the slope of the IVC was increased in oocytes injected with Celslo-1a cRNA . This increase in membrane conductance can probably be explained by a minor number of CelSlo-1a channels that are in an open state even in the absence of high [Ca2+]i and membrane depolarization . However , strong activity of the CelSlo-1a channel was only observed in the presence of emodepside , and currents mediated by TmuSlo-1 . 1a could not be observed at all . In the absence of any other known agonist of nematode Slo-1 channels and without the ability to prove functional expression of TmuSlo-1 . 1a by determining currents evoked by increased [Ca2+]i and depolarization , the failure to detect emodepside effects cannot be interpreted as emodepside unresponsiveness . In fact , currents in TmuSlo-1 . 1a-injected oocytes were lower than those observed in water-injected oocytes , suggesting that some non-physiological changes have occurred in the cells . This is supported by the fact that the IVCs in the absence of emodepside significantly deviated from linearity at high voltages , unlike currents observed in water-injected oocytes . Moreover , the slope of the IVC between -120 and -20 mV was lower than in the water-injected control oocytes . In marked contrast , CelSlo-1a increased the slope and therefore membrane conductance suggesting expression of functional CelSlo-1a but not TmuSlo-1 . 1a channels . Since T . muris is fully susceptible to emodepside [37] , [69] , [70] , it is unlikely that Slo-1 from this parasite should not respond to this drug . However , due to the presence of two slo-1 paralogs in the T . muris genome , it cannot be ruled out that only TmuSlo-1 . 2 is emodepside responsive . Other possibilities are that only certain T . muris Slo-1 . 1 splice variants respond to emodepside or that emodepside acts on T . muris only via the Lat-1 pathway . Optimized expression systems will be needed to distinguish between these possibilities in future experiments . For example , codon optimization might improve expression levels . Vectors that are particularly adapted for protein expression in X . laevis oocytes are available [71] but usage of this system resulted in extremely deteriorated oocyte morphology and oocyte rupture presumably due to toxicity of high levels of CelSlo-1a . Finally , other expression systems , such as insect or mammalian cells , should be considered for TmuSlo-1 . 1a . In the absence of emodepside , a highly reproducible increase in current was observed at +20 mV step potential , which corresponds to the opening potential reported for C . elegans Slo-1a at low Ca2+ concentrations [60] . This small current may represent a minor depolarization-dependent activation of a few channels even in the absence of Ca2+ signaling . However , it remains unclear why this was not observed at more positive step potentials . Moreover , increased currents at +20 mV and increased slope of the IVC were also observed in the presence of the Slo-1 channel blocker verruculogen regardless if addition of verruculogen was followed by exposure to emodepside . Preincubation with verruculogen completely abolished the effects of emodepside . In contrast , verruculogen had no significant effect when emodepside was applied first . This suggests that both drugs bind very tightly to CelSlo-1a and cannot displace each other once they have bound . High-affinity binding of emodepside to its target is also suggested by the fact that prolonged perfusion of the oocytes after removal of emodepside did not reverse channel opening . Very similar observations have been reported for effects of emodepside on A . suum muscle flaps [64] . Together with the slow onset of emodepside effects [64] , the fact that the drug effects were not reversible might suggest that emodepside does not bind to the extracellular domain of Slo-1 , but instead binds to an intracellular domain or to the transmembrane helices . The latter possibility is in agreement with its highly lipophilic nature . Emodepside opened the CelSlo-1a channel at virtually all step potentials , since currents were much higher than in the controls except at +60 mV . Unexpectedly however , the response did not increase linearly with step potential , but peaked at 0 mV , which is unusually low . The decreasing current at higher potentials and the lack of emodepside effects at +60 mV might be explained due to inhibition of Slo-1 channels by high intracellular concentrations of Ca2+ or Mg2+ as reported for mammalian BK channels ( see [72] and references therein ) . Although [Ca2+]i can be expected to be fairly low since there was no increased current in the absence of CelSlo-1 , the concentration of Mg2+ in X . laevis oocytes has been reported to be approximately 0 . 7 mM [73] . Whether this is also the case in the present study could not be resolved using the current voltage-clamp set up , but requires patch-clamp studies in which the concentration of these cations can be tightly controlled . Indeed , decreased currents at high step potentials in response to emodepside have not been observed for expression of C . elegans Slo-1 in HEK293 cells [74] . Despite their very similar morphology , genetic diversity of nematodes is known to be huge [75] . Although Slo-1 is a highly conserved channel in metazoans , it was not clear whether emodepside exerts its nematicidal effects through the same mechanisms in all nematode clades . The present study now offers the tools to compare the physiology of Slo-1 channels from different parasitic nematode lineages using optimized expression systems . The various slo-1 splice variants identified in parasitic nematodes show that evolution has occurred at highly conserved alternative splice sites ( splice region 4 with alternative exons present in nematodes , arthropods and vertebrates at the same position ) along with evolution of nematode or clade-specific splice variants . Effects of these variations and of possible heteromerization of different subunits on voltage and Ca2+ sensitivity can now be evaluated in patch-clamp experiments . That B . malayi and B . pahangi are rather unresponsive to emodepside in vivo and in vitro [76]–[78] cannot be easily explained by the primary structure of BmaSlo-1 , which is virtually identical to that of other filariae . Whether this difference in susceptibility is due to target site related differences ( e . g . different splice variants or combination of subunits ) or due to other ( not Slo-1 related ) mechanisms protecting Brugia from emodepside effects could be answered by comparing electrophysiological properties of different splice variants between filarial species . In general , differences in emodepside responsiveness between Slo-1 channels from different species could also be used to map the emodepside binding region . Expression of hybrid channels encoded partially from a susceptible nematode and partially from a resistant species such as an arthropod followed by determination of emodepside responsiveness in electrophysiological experiments would allow to scan the sequence for those regions in the primary structure required for opening by emodepside . The high number of splice variants encoding severely truncated Slo-1 proteins in both Trichuris species should be further analyzed . Truncated versions of CelSlo-1 with stop codons between the S4 membrane helix and the end of the first RCK domain ( regulator of conduction of potassium ) have been shown to be highly resistant to emodepside [19] , and a truncated version containing only the NH2 terminal part of a mouse Slo-1 ortholog from the start codon to the S6 transmembrane region forms a functional channel [79] . Therefore , a functional role of some of the truncated versions cannot be excluded , especially in light of the relatively high number of variants and the fact that expression levels also appear to be high in both Trichuris species . Truncated versions of the acetylcholine receptor subunits Unc-63 and Acr-8 have been implicated in resistance to levamisole [80] and truncated Slo-1 subunits might have dominant negative effects if they are able to heteromerize with full-length subunits but prevent the formation of a fully functional channel . That truncated channels are expressed in variants of both TsuSlo . 1 . 1 and TsuSlo-1 . 2 , and are apparently more abundant at least in some developmental stages and worm tissues than others suggests that these variants might have a physiological function , perhaps in modulating the activity of full-length gene products . The fact that temporal as well as spatial variation of splicing is de facto limited to TsuSlo-1 . 1 suggests that this splice variant is under less stringent evolutionary pressure than TsuSlo-1 . 2 . A more detailed spatio-temporal picture of the expression pattern of the splice variants would help define their physiological roles . In conclusion , these data show that the Slo-1 channel of C . elegans is a direct target of emodepside and that the channel is present in all important groups of parasitic nematodes of vertebrates . Sequence diversity of Slo-1 channels among these groups and within species involves several alternative splice variants and gene duplications . Interactions of subunit isoforms and effects on channel physiology and drug susceptibility are important aspects for more research on nematode neurobiology and parasitology .
|
Emodepside is an anthelmintic introduced into the market as an ingredient of different dewormers for cats and dogs , and is the only member of the cyclooctadepsipeptide class which has been commercialized . The voltage-gated and calcium-activated potassium channel Slo-1 has been implicated in the mode of action of emodepside , but evidence for direct emodepside-induced changes on the properties of Slo-1 channels has not been reported . Emodepside is active against a broad spectrum of parasitic nematodes , but the putative target Slo-1 has only been described for the model nematode Caenorhabditis elegans , and for relatively closely related strongylids . Here , Slo-1 channels of ascarids , filariae and trichocephalids are described . Unexpectedly , in the latter group two distinct Slo-1 channels are encoded in their genomes . The C . elegans Slo-1a channel was expressed in Xenopus laevis oocytes . After membrane depolarization without experimentally increasing intracellular Ca2+ , no Slo-1 specific currents were observed . In contrast , very large Slo-1-specific currents were observed after preincubation of oocytes with emodepside , suggesting that the drug opens the C . elegans Slo-1a channel irreversibly . This is the first report demonstrating direct interaction of a cyclooctadepsipeptide with a Slo-1 channel , which substantially enhances our understanding of the mode of action of this drug class .
|
[
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2014
|
Characterization of the Ca2+-Gated and Voltage-Dependent K+-Channel Slo-1 of Nematodes and Its Interaction with Emodepside
|
Triatomine vectors transmit Trypanosoma cruzi , the etiological agent of Chagas disease in humans . Transmission to humans typically occurs when contaminated triatomine feces come in contact with the bite site or mucosal membranes . In the Southern Cone of South America , where the highest burden of disease exists , Triatoma infestans is the principal vector for T . cruzi . Recent studies of other vector-borne illnesses have shown that arthropod microbiota influences the ability of infectious agents to colonize the insect vector and transmit to the human host . This has garnered attention as a potential control strategy against T . cruzi , as vector control is the main tool of Chagas disease prevention . Here we characterized the microbiota in T . infestans feces of both wild-caught and laboratory-reared insects and examined the relationship between microbial composition and T . cruzi infection using highly sensitive high-throughput sequencing technology to sequence the V3-V4 region of the 16S ribosomal RNA gene on the MiSeq Illumina platform . We collected 59 wild ( 9 with T . cruzi infection ) and 10 lab-reared T . infestans ( 4 with T . cruzi infection ) from the endemic area of Arequipa , Perú . Wild T . infestans had greater hindgut bacterial diversity than laboratory-reared bugs . Microbiota of lab insects comprised a subset of those identified in their wild counterparts , with 96 of the total 124 genera also observed in laboratory-reared insects . Among wild insects , variation in bacterial composition was observed , but time and location of collection and development stage did not explain this variation . T . cruzi infection in lab insects did not affect α- or β-diversity; however , we did find that the β-diversity of wild insects differed if they were infected with T . cruzi and identified 10 specific taxa that had significantly different relative abundances in infected vs . uninfected wild T . infestans ( Bosea , Mesorhizobium , Dietzia , and Cupriavidus were underrepresented in infected bugs; Sporosarcina , an unclassified genus of Porphyromonadaceae , Nestenrenkonia , Alkalibacterium , Peptoniphilus , Marinilactibacillus were overrepresented in infected bugs ) . Our findings suggest that T . cruzi infection is associated with the microbiota of T . infestans and that inferring the microbiota of wild T . infestans may not be possible through sampling of T . infestans reared in the insectary .
Chagas disease , caused by the protozoan parasite Trypanosoma cruzi , infects an estimated six million people residing in 21 endemic countries in the Americas , with 30 , 000 new infections yearly [1] . The most important method of T . cruzi transmission to humans is vector-borne: T . cruzi undergoes development exclusively in the gut of hematophagous triatomine species , and when the vector defecates during its blood meal , the parasite is inoculated via the bite site or mucosal surfaces [2] . Of the seven main vectors of T . cruzi across the Americas [3 , 4] , Triatoma infestans ( Klug , 1834 ) is the most important vector of Chagas disease in the Southern Cone of South America , where the insect lives in close association with human dwellings and domestic animals . Growing evidence suggests that the gut microbiota of insect vectors can affect the ability of human pathogens to colonize and persist in the vector or alter the vectors’ competence to transmit pathogens to the human host [5–8] by modulating insect immune responses or competing for resources or producing inhibitory molecules [6 , 9–12] . Therefore , to take advantage of these processes , manipulation of insect microbiota is becoming a novel avenue for controlling the spread of vector-borne infections [13–15] . Coprophagy ( i . e . the ingestion of feces within the insects’ colonies ) is a principal process by which symbiont intestinal microbes are acquired [16 , 17] . To date , all symbionts of triatomines belong to the Actinomycetes group [18] . In the wild , acquisition of non-symbiotic/environmental or host-associated microbes can also occur through coprophagy and during bloodmeals when in contact with the mammalian host [19] . The intestinal microbial composition has been shown to be affected by taking a blood meal [17] and blood meal source [20] . Because T . cruzi develops in the gut of triatomines ( and more specifically for T . infestans this development takes place in the rectum [21] ) and is expected to interact with gut microbes , several approaches to control T . cruzi infection via alterations of the triatomine gut microbiota have been explored [16 , 22] . For example , Beard et al leveraged the coprophagic habits of Rhodnius prolixus , another important triatomine vector of T . cruzi , to replace actinobacterial Rhodococcus rhodnii symbionts found ubiquitously in adult bugs with paratransgenic R . rhodnii carrying trypanocidal genes [16] . Another group utilized genetically modified R . rhodnii and Escherichia coli to induce RNA interference to affect redox state in the gut and reduce vector fitness [22] . Development of these and other novel vector control strategies is dependent upon a better understanding of the gut microbiome of triatomines and their relationship to T . cruzi infection and vector competence . Studies of triatomine microbiota to date have been limited by small sample size and insensitive technology , such as culture-dependent methods , denaturing gradient gel electrophoresis or conventional PCR [17 , 23 , 24] , though more recent high-throughput deep sequencing studies have been published [20 , 25–27] . In addition , many of these studies have predominantly focused on laboratory-reared insects only . The microbiome of triatomines have typically been found to be of low complexity , with only a few dominant genera which are not always the same across species , feeding status , blood meal source , or intestinal region sampled . Microbiota of wild insects has also been found to differ from laboratory insects [17 , 20 , 23 , 24 , 26–30] . Similarly , studies which investigated the interaction between T . cruzi and the triatomine gut microbiota have found variable effects , likely modulated by vector species , geographic location , development stage and sex , blood meal source , and other unknown confounders . In the lab , gut symbionts of Rhodnius prolixus have been shown to be disrupted by T . cruzi , as seen in the reduction of Serratia marcescens strains , which are associated with some triatomine species and have trypanolytic activity [31] . Triatomines , and more specifically Rhodnius prolixus , were the pioneering effort in demonstrating that manipulation of insect resident microbiota by the use of a paratransgenic vector expressing trypanocidal genes can thwart the development of a human pathogen [16] . Although microbiota-based efforts to control T . cruzi transmission are showing hints of promise , they are hampered by a solid knowledge of the triatomine microbiota . To our knowledge , there have been no examinations of the microbiome of wild Triatoma infestans by high-throughput sequencing , and there is only one study examining eight lab-reared T . infestans [25] . Using deep sequencing of the 16S ribosomal RNA gene at the variable V3-V4 locus we characterized the microbiome of 59 wild-caught and 10 laboratory-reared T . infestans from endemic communities in Arequipa , Perú . The objective of our study was to identify and compare the hindgut microbial composition of lab-reared and wild T . infestans and of T . cruzi-infected and uninfected insects and explore correlations between T . cruzi infection and specific microbiota profiles .
The Institutional Animal Care and Use Committee ( IACUC ) of the Universidad Peruana Cayetano Heredia reviewed and approved the entomological survey and house inspections protocol used for this study ( identification number 61287 ) and the triatomine colonies maintenance protocol ( identification number 62782 ) . The committee is registered with the United States National Institutes of Health ( PHS Approved Animal Welfare Assurance Number A5146-01 ) and adheres to the Animal Welfare Act of 1990 ( identification number 61287 ) . The committee is registered with the United States National Institutes of Health ( PHS Approved Animal Welfare Assurance Number A5146-01 ) and adheres to the Animal Welfare Act of 1990 . Insect rearing and mouse work were performed as described in Salazar et . al ( 2015 ) [32] . Laboratory triatomines ( n = 20 ) were reared in closed cages from eggs sourced from laboratory insect colonies maintained since 2008 at the Zoonotic Disease Research Laboratory , Unidad de Una Salud , Universidad Peruana Cayetano Heredia in Arequipa , Perú . The original colonies were initiated with eggs of local T . infestans from Arequipa . The insectary was located in an urban neighborhood of Arequipa ( altitude 2300 m above sea level ) . Chickens and mice used to feed the bugs were housed in an enclosed patio next to the insectary . Triatomines are not found in the neighborhood surrounding the laboratory . Approximately 21 days after hatching , the 20 insects were fed on chickens on which adult T . infestans had previously fed ( the adults were removed prior to the feeding of young insects ) , resulting in indirect coprophagy . Later , third instar stage insects were fed on either T . cruzi-infected or T . cruzi-negative BALB/c mice: 10 of the 20 triatomines were fed to repletion on BALB/c and the other 10 fed to repletion on uninfected BALB/c mice . Prior to insect feeding , the mice received an intraperitoneal inoculation with 103 T . cruzi parasites/100 μl of a local strain , confirmed as Discrete Typing Unit 1 using a published PCR-RFLP protocol [32] and were confirmed to be T . cruzi positive or negative by microscopy of whole blood . 45 days after this blood meal , with no interval blood-feeding , triatomines were examined for T . cruzi infection by microscopy and their fecal sample collected . Only the insects which met the following criteria were sampled: i ) had fed to repletion to ensure the probability of T . cruzi infection and guarantee molting to the fourth instar stage; ii ) had molted to fourth instar . Only insects that had T . cruzi status confirmed by fecal microscopy were included for further analyses . Three additional lab insects from the same colony were fed to repletion on uninfected mice in 2015 using the same rearing and feeding protocol to increase the sample size of our uninfected controls . In total for this study , we had 7 laboratory-reared insects from August 2013 ( n uninfected = 3 bugs , n infected = 4 ) and 3 uninfected laboratory-reared insects from August 2015 ) ( S1 Table ) . Wild triatomines ( n = 59 ) were captured by our research team and the Arequipa Ministry of Health vector control personnel as part of routine surveillance and treatment activities between November 2011 and May 2012 and in August 2015 . Of 59 insects , 53 ( 89 . 8% ) were collected in urban and periurban districts of the city of Arequipa ( Alto Selva Alegre , Alegre , Cayma , Hunter , Tiabaya and Yura ) and the remainder ( n = 6 , 10 . 2% ) from the rural district of Murco , located approximately 100 kilometers from the city of Arequipa . Triatomines were collected from household or peridomestic areas , including animal pens , after application of deltamethrin to these surfaces . Live triatomines were transported to the laboratory and analyzed for T . cruzi parasites by fecal microscopy on the day of capture . Wild triatomines were examined on the day of capture while still alive at the Arequipa insectary . Laboratory-reared bugs were examined and fecal material collected 45 days after last blood meal . A drop of fecal material was expressed onto a glass slide by applying gentle pressure to the bug’s abdomen . Fecal material was mixed with a drop of saline solution and examined by light microscopy at 400X for parasites by an experienced microscopist . Taking care to avoid contact between the insect and the paper , 2–5 drops of triatomine fecal material were expressed from the same bug by gentle abdominal pressure using tweezers onto Whatman filter paper ( grade 3MM , catalog number 3030–347 ) , allowed to air-dry completely at ambient temperature on the benchtop , and stored frozen in sealed plastic bags containing sterile silica desiccant packets at -20°C until cold-chain shipment and further laboratory procedures at the University of North Carolina at Chapel Hill , United States . DNA was extracted using a modified phenol-chloroform technique at the University of North Carolina at Chapel Hill . Initially , fecal spots were cut out of the filter paper with razor blades cleaned with 70% ethanol and placed in sterile snap-cap vials containing 180 μl of sterile Tail lysis buffer ( 50 mM Tris titrated to pH 7 . 5 , 100 mM EDTA , 100 mM NaCl , 1% SDS ) with 20 μl proteinase K . The specimens were incubated overnight at 50°C , then heated to 90°C for 5 minutes . After a brief centrifugation step to pellet the filter paper , the supernatant was diluted to 200 μl in sterile TE buffer and used for DNA extraction using a published phenol-chloroform extraction [33] in a fume hood with surfaces which were thoroughly cleaned with 70% ethanol and DNAZap ( Thermo Fisher Scientific ) decontaminant spray prior to specimen handling . DNA extraction negative controls were included ( empty snap-cap tubes to which Tail lysis buffer and all subsequent DNA extraction reagents were added ) and we used the High Sensitivity dsDNA Qubit assay ( Thermo Fisher Scientific ) to quantify the amount of contaminating DNA . The assay did not show a detectable level of double-stranded DNA and thus these negative controls were not included in the V3-V4 16S amplicon generation steps . T . cruzi infection status for each bug was confirmed using quantitative real time polymerase chain reaction ( qPCR ) on an Applied Biosystems Viia 7 real-time PCR machine with the published primers Cruzi 1 ( 5'-ASTCGGCTGATCGTTTTCGA-3’ ) and Cruzi2 ( 5'-AATTCCTCCAAGCAGCGGATA-3’ ) and the probe Cruzi3 ( FAM- CACACACTGGACACCAA-NFQ-MGB ) based on a published protocol [34] . In brief , 0 . 5 μl of Cruzi3 probe at 25 μM concentration and 0 . 5 μl each of Cruzi1 and Cruzi2 primers at 90 μM concentration were mixed with 5 μl template DNA , 18 . 5 μl water , and 25 μl ROX FastStart Universal Master Mix ( Roche ) and amplified under the following conditions: 50°C for 2 minutes , 95°C for 10 minutes , then 40 cycles of 95°C for 15 seconds followed by 58°C for 60 seconds . The V3-V4 region of the 16S rRNA gene was amplified using published primers [35] , producing amplicons of approximately 460 base pairs . Amplicon generation steps were performed in a laminar flow hood with surfaces sprayed with 70% ethanol and DNAZap decontaminant . To further reduce the likelihood of contamination from the laboratory environment , we used UV-irradiated plasticware and reagents ( with the exception of the Taq polymerase ) . PCR negative controls were included and contained all PCR components to which sterile water was added in place of specimen DNA . To facilitate multiplexing and pooling of amplicons during sequencing library preparation , a 10-nucleotide Multiplex Identifier ( MID ) barcode sequence was added to the 5′ end of the forward primer , a method previously validated by our group [36] . Fragments were amplified using reagents from the KAPA HiFi HotStart PCR Kit ( Kapa Bioystems ) under the following conditions: initial denaturation for 3 minutes at 95°C; 35 cycles of 98°C for 20 seconds , 55°C for 30 seconds , and 72°C for 30 seconds; and final extension at 72°C for 5 minutes . PCR products were visualized on a 1% agarose gel . PCR products were then purified using the Purelink Pro PCR cleanup kit ( Thermo Fisher Scientific ) and quantified using the QuantIT Picogreen dsDNA assay kit ( Thermo Fisher Scientific ) , after which they were pooled in equimolar amounts . The quantification assay did not detect contaminating DNA in our PCR negative controls , and as a result these were not included in the amplicon pool for sequencing . Illumina barcoded index adapters ( Bioo Scientific ) were ligated onto the pooled fragments after end filling and A tailing using Klenow and ligase ( Enzymatics ) . Resulting libraries were amplified for 8 cycles using the KAPA Library Amplification Kit ( Kapa Biosystems ) , cleaned using Kapa Pure beads ( Kapa Biosystems ) at a ratio of 0 . 6:1 beads to DNA following manufacturer instructions . Libraries were quantified using the Agilent 4200 TapeStation ( Agilent Technologies ) , pooled in equimolar amounts , then sequenced on the Illumina MiSeq platform by a paired-end 2x250 sequencing run at the High Throughput Sequencing Facility at the University of North Carolina at Chapel Hill . Raw Illumina FASTQ sequences were first inspected by the quality control software FastQC [37] . Next , the sequences were processed and analyzed in the QIIME software package ( v 1 . 9 . 1 ) [38] . The forward and reverse paired-end FASTQ reads were joined by invocation of the fastq-join with default parameters . A custom demultiplex step was applied to accommodate our two-index data structure in each read ( i . e . library-level index and sample-level 10 base pair barcode ) . Linker primer sequences and sample-level barcodes were trimmed . Low-quality sequences where 70% of the base pairs were below a Phred score of 24 were excluded . Operational Taxonomic Units ( OTUs ) were then defined with the uclust algorithm and the open reference approach [39] at a similarity threshold of 97% , followed by taxonomy assignment with respect to the GreenGenes database release 13 . 8 [40] . Sequences were aligned using PyNAST [41] and a phylogenetic tree was built with FastTree 2 . 1 . 3 [42] . The OTUs retained for downstream analyses passed the following quality filters: ( i ) a minimum OTU size of 10 sequences across all samples , ( ii ) a filtering depth of 0 . 01% ( i . e . any OTU making up less than 0 . 01% of the total observation count was excluded ) , ( iii ) singleton OTUs ( i . e . observed in less than 2 samples ) were excluded . If after the GreenGenes taxonomic assignment a taxon of interest was ambiguous at the genus level we then consulted the 16S sequences in the National Center for Biotechnology Information ( NCBI ) Genbank repository with the Basic Local Alignment Search Tool ( BLAST ) [43] . Diversity estimates ( α-diversity , within-group , and β-diversity , between group ) were calculated with respect to origin ( wild-caught vs . laboratory-reared T . infestans ) , T . cruzi infection , stage , and temporal and spatial factors . We estimated α-diversity analyses , including non-phylogenetic ( observed number of taxa and Chao1 ) and phylogenetic ( Faith’s Phylogenetic Diversity ) metrics were estimated using QIIME [38] at minimum sampling depths of 2000 sequences per subsample . We compared α-diversity between sample groups with non-parametric two-sample t-tests using 1000 Monte Carlo permutations to calculate the p-values . β-diversity was estimated in QIIME [38] using abundance-based Bray-Curtis , occurrence-based unweighted Unifrac [44] and quantitative abundance and phylogeny-based weighted Unifrac [45] at a minimum sub-sampling depth of 2000 sequences . Results were summarized and visualized through principal coordinate analysis ( PCoA ) implemented in QIIME [38] . To test whether sample groups were statistically different we used non-parametric ANOSIM ( ANalysis Of Similarities ) tests [46] and non-parametric two-sample t-tests with 1000 Monte Carlo permutations to derive p-values . To test correlations between sample groups and distribution of taxa , we computed Spearman’s Rho in QIIME [38] with bootstrapping and 1000 permutations by invocating the observation_metadata_correlation . py pipeline . To analyze which taxa were differentially abundant between samples groups , we employed two different methods: 1 ) non-parametric Kruskal-Wallis tests comparing the relative abundance of each taxon between sample groups with 1000 Monte Carlo permutations and Bonferroni-corrections to derive p-values and 2 ) with the DESeq2 method implemented in the differential_abundance . py pipeline [47] ( package version 1 . 18 . 1 ) . DESeq2 makes use of negative binomial generalized linear models and incorporates data-driven prior distributions to estimate dispersion and logarithmic fold changes between sample groupings [47] . Graphical visualizations were done with Graphpad PRISM ( La Jolla California , USA ) and with R package ggplot2 .
Fecal specimens from 69 T . infestans ( 59 wild-caught and 10 laboratory-reared ) were used in this study . Characteristics of these 69 insects are presented in S1 Table . Nine of the 59 wild bugs ( 15 . 3% ) and 4 of the 10 laboratory bugs ( 40 . 0% ) were T . cruzi-infected , confirmed by both microscopy of fresh fecal samples and qRT-PCR . Approximately half of the wild insects ( 32 , 54 . 2% ) were collected in the urban district of Alto Selva Alegre , but the majority of infected wild insects were captured in the rural district of Murco ( 5/9 , 55 . 6% ) . Of 59 wild insects , 29 were adults ( 49 . 2% ) , 17 ( 28 . 8% ) were fifth instars , 7 ( 11 . 9% ) were fourth instars and the remainder 6 insects ( 10 . 2% ) were third instars . Six of the 9 infected wild triatomines were fifth instars and the other 3 were adults . No wild infected third and fourth instars were sampled . Further sample details can be found in S1 Table . The V3-V4 region of the 16S rRNA gene was amplified and sequenced from the extracted DNA of all bugs . A total 2 , 051 , 172 reads were obtained from Illumina MiSeq platform with paired-end reads of 250 base pairs . The joined reads have been deposited in the Sequence Read Archive ( SRA ) under the accession PRJNA527262 . After demultiplexing and elimination of low-quality reads , 717 , 470 reads remained . Operational Taxonomic Units ( OTUs ) were defined using a similarity threshold of 97% followed by taxonomy assignment with respect to the GreenGenes database [40] . After quality filters were applied to exclude potentially spurious OTUs and Cyanobacteria ( comprising 0 . 01% and 0 . 05% of all reads , respectively ) , a total of 672 , 181 reads were retained for downstream analyses of α-diversity , β-diversity , and differential abundance of taxa ( mean number of reads per sample = 9 , 676; range = 673–23 , 174 ) . Only two samples belonging to wild insects had <1000 sequences and for many of the downstream analyses affected by sample depth , these two samples were excluded . The other samples of wild insects all had >2000 sequences . In the complete dataset of 69 triatomines we identified 32 taxa at the taxonomic rank of order ( S2 Table ) , 70 at the family level ( S3 Table ) , and 124 taxa at the genus level ( S3 Table ) . The genus-level bacterial composition of all lab insects ( n = 10 ) and all wild ( n = 59 ) is shown in Fig 1A and 1B , respectively . We first compared the α- and β-diversities of 2013 and 2015 laboratory-reared insects to confirm the validity of combining the data of these insects . The microbial composition of 2013 and 2015 insects did not differ significantly when comparing α-diversity with number of observed taxa as the metric ( p = 0 . 430 ) and β-diversity ( weighted Unifrac , ANOSIM R = -0 . 079 , p = 0 . 605 ) ( S1 Fig ) . For subsequent analyses , 2013 and 2015 bugs were analyzed together . Next , we compared the microbial composition between wild-caught and laboratory-reared T . infestans . All of the bacterial taxa identified in the complete dataset were seen in the wild-caught insects . The microbiomes of laboratory-reared T . infestans were a subset of the wild-caught T . infestans microbiota ( S2 Fig ) and included 56 of the total 70 bacterial families ( 80 . 0% , S3 Table ) and 96 of the total 124 genera identified ( 77 . 4% , S3 Table ) . The 28 genera absent from the lab-reared insects included both rare taxa in the wild insect population ( present in only 2–5 of the 59 wild insects ) and common taxa ( present in up to 40/59 wild insects ) ( S3 Table ) and represented mainly soil-associated bacteria [48] . By rarefaction analyses of number of observed taxa ( Fig 2A and 2B ) , the α-diversity of laboratory-reared T . infestans was significantly lower than that of wild-caught T . infestans ( p = 0 . 001 ) at increasing rarefaction depths ( 2000 , 5000 and 10000 sequences , S3 Fig ) and by the additional metrics Chao1 and Faith’s Phylogenetic Diversity ( S4 Fig ) . The greater α-diversity in wild compared to lab insects was evident when inspecting the taxa plots ( Fig 1 ) , as only three genera dominated the microbiomes of the lab-reared insects: 97 . 4% of the overall microbiome of lab-reared insects was composed of Enterococcus ( 58 . 7% ) , an unclassified Enterobacteriaceae genus ( 36 . 7% ) , and Bacillus ( 2 . 0% ) ( Fig 1A ) , with the remaining 2 . 6% of sequences assigned to the other 93 genera . The high prevalence of this unclassified Enterobacteriaceae genus is notable since it was present in only 0 . 25% of all 16S sequences of wild T . infestans ( S3 Table ) , the largest difference in relative abundances observed between wild and lab insects . In contrast to lab insects which were dominated by only three genera only overall , 22 genera were found to be responsible for 84 . 5% of the overall microbiome among all wild insects ( relative abundances among all sequences of wild insects ranging = 1 . 1–17 . 6% , Fig 1B ) ; the remaining 14 . 5% of wild insect sequences were assigned to the other 102 genera observed . Enterococcus and Bacillus , which had high relative abundances in lab insects , were among the 22 genera in wild insects , present in 5 . 7% and 3 . 7% of all 16S sequences of wild insects , respectively ( Fig 1B , S3 Table ) . Not all individual insects were dominated by the same genus and 19 distinct genera were observed to be dominant across individual insects ( i . e . having the highest within-sample frequency of all taxa identified in that sample ) with frequencies ranging from 11 . 9%-91 . 3% and generally reflecting the taxa seen at the population level ( Fig 1B ) . Serratia , Williamsia , and Staphylococcus genera , noted to be important components of triatomine microbiomes in other studies [16 , 22 , 31] , were highly prevalent in only a few samples ( Uninf05 , Uninf39 , and Inf9 , Fig 1B ) . 17 . 29% of all 16S sequences from wild insects were Actinomycetales compared to only 0 . 18% of all lab insect 16S sequences . Rhodococcus , Dietzia , and Corynebacterium , Actinomycetes postulated to be functionally relevant to the microbiome of triatomines [16] , were found in the majority of the insects ( S3 Table ) , but at low overall relative frequencies . We identified Wolbachia in 51/59 ( 86 . 4% ) of wild T . infestans ( 9/9 T . cruzi-infected and 42/50 uninfected ) and in 8/10 ( 80 . 0% ) of lab insects ( 4/4 T . cruzi-infected and 4/6 uninfected ) ( S3 Table ) . Wolbachia is a symbiont of many other insect species that act as vectors of human pathogens and has been found to be important for vector fitness and to influence the carriage of human pathogens by these vectors [13–15 , 49–54] . In our samples , however , Wolbachia was present at very low within-insect frequencies which averaged 0 . 06% in wild T . infestans and 0 . 02% in lab T . infestans , and they did not change with respect to T . cruzi-infection . Differences between wild and lab insects’ microbiota were also reflected in β-diversity analyses , with statistically significant ANOSIM tests and clear separation on PCoA between the wild and lab insect microbiomes by three different methods: weighted Unifrac ANOSIM R = 0 . 424 , p = 0 . 001 ( Fig 2B ) ; Bray-Curtis ANOSIM R = 0 . 796 , p = 0 . 001 ( S3C Fig ) ; and unweighted Unifrac ANOSIM R = 0 . 593 , p = 0 . 001 ( S3D Fig ) . In addition , analysis of differences in relative abundances by DESeq2 showed that almost half of the microbiome common to both wild and lab insects ( 44 . 8% , 43/96 genera ) was differentially abundant ( S5 Fig ) . To investigate factors influencing microbiome variation among wild uninfected T . infestans , we explored the effect of time of collection ( month of the year ) and of place of collection ( Arequipa districts ) on the microbial composition by independently analyzing uninfected T . infestans collected from the Alta Selva Alegre ( A . S . A ) district only ( n = 30 ) from November 2011 to May 2012 and uninfected T . infestans collected in March 2012 from three different districts ( Yura n = 2 , Hunter n = 7 , and A . S . A n = 7 ) . Month of insect capture was not associated with microbial composition by α-diversity ( observed number of taxa , p = 1 . 000 for all pairwise comparisons ) and β-diversity analyses ( R = 0 . 120; p = 0 . 080 , S6 Fig ) . Similarly , when time of collection was restricted to a single month , district of collection had minimal effect on microbial diversity of wild uninfected T . infestans from Yura , Hunter , and A . S . A districts , both in α-diversity ( number of observed taxa , range p for all pairwise comparisons = 0 . 960–1 . 000 ) and β-diversity analysis ( ANOSIM R = 0 . 019 , p = 0 . 348 , S7 Fig ) . The microbial composition across developmental stage ( S8 Fig , Panel A ) was not associated with α-diversity ( observed number of taxa , p = 1 . 000 among all comparisons , S8 Fig Panels B and C ) or β-diversity associated with developmental stage ( weighted Unifrac ANOSIM R = -0 . 042 , p = 0 . 767 , S8 Fig Panel D ) . However , small numbers of third and fourth instars may have had limited power to detect differences between older and younger developmental stages . We examined whether T . cruzi infection is associated with the microbiota composition of lab-reared and wild-caught T . infestans . Four of the 10 lab insects and 9 of the 59 wild-caught T . infestans were T . cruzi-infected . T . cruzi infection did not affect α-diversity of either lab or wild insects by any metric ( number of observed taxa Fig 3; Chao1 and Faith’s PD , S9A–S9D Fig ) . Next , we estimated β-diversity between T . cruzi-infected and uninfected lab insects by weighted Unifrac , tested the differences in variation by the ANOSIM test and the variation patterns were visualized by PCoA . β-diversity was not associated with T . cruzi infection in lab insects by weighted Unifrac ( ANOSIM R = 0 . 064 , p = 0 . 546 , Fig 4A ) ; this was also the case when unweighted and Bray-Curtis methods of β-diversity analysis were used ( S10 Fig ) . Variation in microbiome of lab-reared insects was better explained by the dominant bacterial genus within each sample ( Fig 4A ) . In wild insects , on the other hand , T . cruzi infection was associated with variation in β-diversity by weighted Unifrac analysis ( ANOSIM R = 0 . 269; p = 0 . 011 , Fig 4B ) as well as unweighted Unifrac ( R = 0 . 215 , p = 0 . 012 , S11 Fig ) and Bray-Curtis methods ( R = 0 . 250 , p = 0 . 012 , S11 Fig ) . The variation patterns were visualized by PCoA ( Fig 4B , S11 Fig ) . Ten bacterial genera were found to have relative abundances which were significantly different with respect to T . cruzi infection in wild insects by DESeq2 analysis ( Fig 5A and 5B , p<0 . 01 ) . Using the GreenGenes database , these 10 genera were identified as an unclassified genus and family of the order Rhizobiales , Dietzia , Alkalibacterium , Peptoniphilus , Nesterenkonia , Cupriavidus , Marinilactibacillus , and three unclassified genera of the Porphyromonadaceae , Phyllobacteriaceae , and Planococcaceae families . Porphyromonadaceae was also identified as being associated with T . cruzi-infected insects by all other association tests conducted: Kruskal-Wallis non-parametric statistic ( Bonferroni-corrected p<0 . 005 ) , Spearman’s Rho ( p<0 . 001 , with bootstrapping and 100 permutations ) and Fisher’s exact test ( OR 18 . 00; 95% CI = 2 . 62–136 . 55 ) . The unclassified Rhizobiales and Phyllobacteriaceae ( p<0 . 001 , Spearman’s Rho with bootstrapping and 100 permutations ) and Peptinophilus ( OR 9 . 17; 95% CI = 1 . 44–58 . 15 by Fisher’s exact test ) were also associated with increased odds of T . cruzi infection . For the Rhizobiales , Phyllobacteriaceae , Porphyromonadaceae , and Planococcaceae GreenGenes assignment was ambiguous at the genus level and thus we consulted the 16S sequences deposited in the NCBI repository GenBank by BLAST [43] . The likeliest taxonomic lineages for Rhizobiales , Phyllobacteriaceae , and Planococcaceae sequences are the genera Bosea ( sequence identity range = 98 . 65–100 . 00%; e = 0; query cover = 100 . 00% ) , Mesorhizobium ( sequence identity range = 97 . 91–100 . 00%; e = 0; query cover = 100 . 00% ) , and Sporosarcina ( sequence identity range = 97 . 89–100 . 00%; e = 0; query cover = 100 . 00% ) , respectively . Taxonomically , Phyllobacteriaceae are part of the Rhizobiales order [48] . GenBank sequences producing significant BLAST alignments with our Porphyromonadaceae sequences were all uncultured Porphyromonadaceae bacteria .
The microbial communities colonizing arthropod vectors of human pathogens influence the ability of pathogens to survive , reproduce and transmit to humans and other hosts [5–8] . Microbiota-based strategies to control and prevent vector borne illnesses have now garnered more attention , with efforts to curb dengue transmission using Wolbachia-based microbiota modifications of Aedes mosquitoes being the most advanced to date [13 , 15 , 51] . Such strategies are also being explored for triatomines , the insect vectors of Chagas disease . However , microbiome characterizations of triatomines remain relatively limited , particularly for T . infestans , the principal vector in the Southern Cone of South America . Our work describes , for the first time , the hindgut microbiota of T . infestans with sensitive high-throughput approaches in both laboratory-reared and wild-caught T . infestans and investigates the relationship between T . cruzi infection and microbiota profiles . The existing triatomine microbiota literature suggests that the largest differences in microbial composition are driven by triatomine species [20 , 23 , 24 , 26–28] . Indeed , we found differences between our T . infestans microbiota and that of other species’ microbiota , but also many similarities . We also observed notable differences between our T . infestans microbiota and that of T . infestans reported in other studies . We observed that Enterobacteriales and Bacillales , previously shown to dominate the microbiota of Rhodnius prolixus [28] and T . dimidiata [20] , respectively , but not T . infestans [25] , occurred at high relative abundances in both our lab ( Enterobacteriales , 37 . 0%; Bacillales 2 . 5% ) and wild insect populations ( Enterobacteriales , 7 . 6%; Bacillales 9 . 0% ) . Enterococcus and Enterobacteriaceae , which were found at high relative abundance in our lab-reared T . infestans , have also been found to be significant microbial components of lab-reared R . prolixus , T . protracta , T . recurva , and T . vitticeps [27] . When comparing our findings to the other reports of T . infestans microbiota [16 , 22 , 25 , 31] , we found that Clostridiales and Rhodocyclales were less prevalent in our study . Some genera which have been previously identified as important members of the microbial communities of triatomines , including of T . infestans , were absent from our insects ( Arsenophonus ) , observed in the majority of both lab and wild bugs but at low relative abundance ( Rhodococcus and Corynebacterium ) , or found at high relative abundance in only a few wild T . cruzi-uninfected samples ( Mycobacterium , Williamsia , Serratia ) . Arsenophonus and Corynebacterium are candidates for paratransgenic techniques to prevent . T . cruzi infection in T . infestans [25 , 55]; Rhodococcus has been already established in molecular strategies for T . cruzi control in another triatomine vector of Chagas , namely R . prolixus [16 , 22 , 55]; and the Serratia genus contains members shown in R . prolixus to have trypanolytic activity on specific T . cruzi strains [31] . Finally , the Wolbachia genus was detected in the majority of our wild and lab-reared triatomines but at low overall relative abundance ( 0 . 05% of all 16S sequences from wild insects and 0 . 02% of all 16S sequences from lab insects ) . This low overall relative abundance can also be due to our rectal sampling , because it has been shown that triatomine intestinal microbes are typically present in low numbers in the rectum [17] where T . cruzi undergoes development [21] . The Wolbachia genus contains members which are common symbionts in many insect species but best described in mosquito vectors of malaria , dengue , chikungunya [14 , 49–51 , 53 , 56] . Wolbachia has been demonstrated to affect vector survival [51 , 53 , 56] and the human pathogen as well [13 , 14 , 49–54 , 56] . Previously , Wolbachia has only been observed in various organs of Rhodnius pallescens [57] , but not in any other triatomine species [23 , 24 , 26 , 28 , 57] . Whilst we cannot establish based on our data that Wolbachia spp . are true symbionts of T . infestans , the fact that we observe this genus in 51/59 of wild insects and 8/10 of lab insects is indicative of symbiosis . Further efforts are needed to better understand whether Wolbachia and the other bacteria we found in the majority of both our lab and wild bugs , but at low relative abundance in the hindgut , are rare microbes or present at higher densities in other intestinal segments and what functional role , if any , they may play in the T . infestans lifecyle and T . cruzi infection . Recent studies of human and plant microbiomes have shown that even rare gut microbes can be implicated in key processes , such as preventing pathogen colonization and boosting host immunity [58–61] . Therefore , rare microbes may be equally as relevant in shaping the structure of the microbiome as the taxa with high relative abundance taxa and should not be discounted from characterizing a microbial ecosystem [62] . Microbiota varied substantially between our lab and wild insects , particularly with respect to environmental species and Actinomycetes . All known triatomine symbionts are members of the Actinomycetes [18] and Actinomycetes are the main producers of naturally-occurring antibiotics [63] . As in previous studies [23 , 24] we observed that the overall diversity of lab T . infestans was reduced compared to wild T . infestans . The missing microbes in lab vs . wild insects pertained primarily to soil-associated bacteria ( such as Rhizobiales and Burkholderiales ) . Even among the 96 genera which were common to both lab and wild bugs , we found significant differences: only three genera ( Enterococcus , an unidentified genus of Enterobacteriaceae , and Bacillus ) dominated the lab insects , whereas 22 distinct genera dominated the overall microbiota of the wild T . infestans . Whilst we detected Actinomycetes in all lab and all wild insects we sampled , their overall diversity and relative abundance was significantly reduced in lab vs . wild insects: 17 . 3% of all 16S sequences from wild insects were Actinomycetales ( representing 30 distinct genera ) compared to only 0 . 18% of all lab insect 16S sequences ( representing 22 distinct genera ) . Higher microbial complexity in field triatomines due to soil-associated bacteria has been previously reported for T . brasiliensis and T . pseudomaculata [24] . This suggests that environmentally-acquired microbes by the wild insects while off-host/ host-seeking is an important source of these differences . The low relative abundance of Actinomycetes symbionts in our lab insects can be due to rectal sampling or to our rearing protocol ( i . e . indirect coprophagy , whereby as third instars our lab insects fed on chickens on which adult triatomines previously fed on ) . The latter may not have seeded the full collection of gut symbionts . These differences reflect the overall decreased diversity of laboratory-reared triatomines and highlight that the microbiome of insectary-reared T . infestans may not represent that of wild populations , even in the same geographic area . The effect of environmentally-acquired bacteria and Actinomycetes symbionts on T . cruzi infection of wild T . infestans is not well characterized . Actinomycetes symbionts in triatomines have been shown to influence insect gut homeostasis by providing useful metabolites [18] . In addition , it has been postulated that acquisition of important Actinomycetes symbionts is in fact dependent on the environment [64] , because of coprophagy from soil contaminated with feces [16] . Numerous examples of the relevance of environmentally-acquired extracellular microbes to other insects’ fitness exist ( reviewed here [65] ) . For instance , it has been demonstrated that lab-hatched Ixodes scapularis ticks lacking the environmentally-acquired microbes carried a larger proportion of human pathogenic Rickettsia spp . than field-caught ticks , indicating that environmentally-acquired symbionts outcompete Rickettsia [66] . Our data suggest that environmentally-acquired species and/or Actinomycetes may be important to T . cruzi infections because of two observations . First , we found differences between T . cruzi-infected and uninfected wild insects and 8/10 genera differentially abundant between infected and uninfected bugs were environmental microbes , primarily found in soil , or Actinomycetes ( Bosea , Mesorhizobium , Dietzia , and Cupriavidus were underrepresented in infected bugs; Sporosarcina , Nestenrenkonia , Alkalibacterium , Marinilactibacillus were overrepresented in infected bugs ) . Second , the microbiome structure of our lab insects which were deficient in both environmental species and Actinomycetes was not associated with T . cruzi infection . Little is known about the relevance of the 10 different genera which differed significantly in their relative abundance between infected and uninfected wild bugs to T . infestans or T . cruzi . Dietzia is of particular interest because it has appeared in the triatomine microbiome literature [24–27 , 29 , 67] and it is an Actinomycetes [68] . In our sample , the Dietzia genus was underrepresented in infected vs . uninfected wild triatomines . Dietzia is a relatively newly described genus of the Actinomycetales order , suborder Corynebacterineae . In the past it has been misclassified as Rhodococcus spp . because of their striking similarity in Gram morphology and colony presentation [48] . Interestingly , genetically-modified Rhodococcus rhodnii expressing anti-trypanocidal agents in the triatomine gut was the first ever microbiota-based strategy of vector control [16] . Dietzia spp . have been described as symbionts of other insect vectors of human pathogens , including the tsetse fly Glossina pallidipes , the vector of African trypanosomiasis [69] , and of the myaisis-causing parasitic fly Wohlfahrtia magnifica [70] . Because Dietzia is postulated to contain species which may produce antimicrobials [68] , its metabolic richness in relation to T . infestans and T . cruzi cannot be overlooked , particularly in the context of widespread insect-Actinomycetes symbioses [71] , where insects gain essential metabolites ( such as B-complex vitamins in triatomines [18] ) or are known to actively recruit antibiotic-producing Actinobacteria for protection [72] . Like our study , previous studies of triatomine microbiomes and their relationship to T . cruzi infection have also found differences between infected and uninfected bugs , but these findings do not always agree: Castro et al . [73] reported that resident microbiota of R . prolixus may create a hostile environment for the parasite , as pre-treatment with antibiotics in the blood meal increased T . cruzi infectivity ten-fold; however , in vitro assays suggested that the parasites inhibited bacterial growth as well [73] . In contrast , other studies [25] found that lab-reared triatomines challenged or infected with T . cruzi had higher α-diversity . We did not find that α-diversity differed by infection status in either lab or wild bugs . Although we found associations between microbiota and T . cruzi infection in wild insects and found Dietzia to be a potentially important taxon to T . cruzi infection , we cannot infer the directionality of these relationships . Thus , we cannot conclude whether these differences in microbiome structure make the bugs susceptible to T . cruzi infection or whether T . cruzi itself changes the microbiome . To parse out the effect of microbial composition on triatomine susceptibility to T . cruzi infection , experiments that sample lab-reared insects before and after T . cruzi infection are needed . Work examining the effect of Rhodococcus on lab-reared R . prolixus microbiota suggested that the parasite does influence the bug’s microbiome [73]; however , other studies that have investigated the relationship between vector , T . cruzi and symbionts have suggested that the symbionts may affect T . cruzi presence and not the other way around , since T . cruzi has been shown to minimally affect the development and mortality of T . infestans , as long as the vector has an adequate food supply ( reviewed here [18 , 30] ) . This may be because whilst T . cruzi appears to compete for nutritional resources [30] , as long as the insects are not starved , these effects are negligible [30] . However , since regular meals cannot always be guaranteed in the wild , interactions between triatomines’ intestinal environment and T . cruzi may be quite different . We lacked data of potentially important variables in wild-caught insects , such as length of time since last feeding , blood meal source and duration of T . cruzi infection to assess these interactions in our wild bug population . We found significant microbiome variation among individual wild insects . We did not find evidence that seasonality , geography or developmental stage explain this variation . Whilst our findings are in line with a previous report of wild-caught T . protracta that geography does not explain microbiome variability [27] , our collections were spatially limited in relation to the T . infestans geographic range and our low sample sizes for some districts may have had limited power for spatial analyses . Thus , studies of the microbial structure of wild triatomines across a wider geographic area are needed . This is particularly relevant given our data and those of other studies indicating the importance of environmentally-acquired microbes to the microbiome of triatomines [16 , 25] . Our lack of microbiome changes along developmental stages is not in line with other reports of field T . sordida [29] and lab-reared R . prolixus [27] which found that the microbiome changes from development stage to the next . This calls for more investigations exploring how the microbiome develops with ontogeny , particularly when considering that all triatomine instars have the ability to transmit T . cruzi . Finally , a systematic temporal collection of wild triatomines was hindered by low infestation and infection rates at the time in Arequipa and seasonality should not be discounted as an important factor influencing the microbiota of triatomines . Our study had other limitations as well . It is unclear whether the lack of microbiota differences we observed between infected and uninfected lab insects is due to low sample size or due to our methodology of indirect coprophagy . Indirect coprophagy may not have seeded the lab nymphs with the full diversity of gut symbionts or with high enough numbers to establish gut colonization . We do not believe our laboratory insects to be aposymbiotic because we identified Actinomycetes in all lab insects . Finally , because eukaryotic sequencing was beyond the scope of this study we do not have data on the relationship between T . cruzi and other eukaryotes . Little is known about the eukaryotic microbiome of triatomines , but a single study testing pathogenic fungi as a biopesticide against R . prolixus found that T . cruzi infection rendered bugs more resistant to killing by the fungus [50] . The mycome and potentially other parasites undoubtedly have important influences on triatomine biology and immunology as well . To realize the potential of microbiome-based interventions to decrease the transmission of T . cruzi and burden of Chagas disease , further studies of vector-gut microbe-T . cruzi interactions are necessary . To enable a better understanding of the real significance of T . cruzi on its vectors , such studies of wild triatomines would need to include not only microbiome surveys ( including specimens from multiple species , developmental stages and geographic locations ) , but also metagenomic studies , similar to the study conducted by Carels et al . ( 2017 ) on lab-reared triatomines which identified major metabolic pathways of gut microbes [67] . Further microbiome and metagenomic characterizations will be particularly important when exploring what role , if any , demonstrated gut symbionts or putative symbionts , such as Dietzia or Wolbachia , play in T . cruzi-infection . Additionally , the field will need insectary-based studies of triatomine microbiome before and after T . cruzi infection to identify any pre-existing bacterial species that are protective , necessary , or encouraging to infection , with the caveat that there are large differences between laboratory-reared and wild bugs’ intestinal flora . These data are necessary to set up real-world trials of interventions that modify the bugs’ microbiomes to make them hostile to T . cruzi infection .
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Chagas disease in humans is caused by the parasite Trypanosoma cruzi and it is endemic to the Americas . Poor populations are most at risk . The parasite infects an estimated six million people of 21 endemic countries in the Americas , with 30 , 000 new infections yearly . The main mode of transmission is vector-borne by triatomine bugs , which tend to live in close association with humans . The main Chagas disease vector in the Southern Cone of South America , where the highest burden of disease exists , is Triatoma infestans . As blood-sucking insects , triatomines become infected when they bite a T . cruzi-positive human and once infected they transmit the parasites in their feces . Controlling the vector populations is the main strategy of Chagas disease transmission reduction efforts . Microbiota-mediated methods to control this vector-borne disease are now being explored to determine whether microbes typically found in the vectors’ gut have a detrimental effect on T . cruzi and how they may be used to modify the vector and curb the ability for T . cruzi to be transmitted to humans . To advance this new field , we first must gain better knowledge of the gut microbiota of triatomines . Our study is the first to use sensitive high-throughput methods to study the gut microbes of T . infestans , using both laboratory-reared and wild insects . We have found that the microbial composition of T . infestans in the laboratory does not reflect the complete collection of gut microbes of wild T . infestans and inferring the gut microbiota profile of wild insects through studying lab insects alone may not be possible . We also found evidence that in wild insects T . cruzi affects the composition of the gut microbiota and identified some bacterial taxa which may be important in modulating the T . infestans-T . cruzi relationship .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
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"invertebrates",
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"health",
"sciences",
"microbiome",
"microbiology",
"parasitic",
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"protozoans",
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"mathematics",
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"vectors",
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"insects",
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2019
|
Hindgut microbiota in laboratory-reared and wild Triatoma infestans
|
Two different Th2 subsets have been defined recently on the basis of IL-5 expression – an IL-5+Th2 subset and an IL-5−Th2 subset in the setting of allergy . However , the role of these newly described CD4+ T cells subpopulations has not been explored in other contexts . To study the role of the Th2 subpopulation in a chronic , tissue invasive parasitic infection ( lymphatic filariasis ) , we examined the frequency of IL-5+IL-4+IL-13+ CD4+ T cells and IL-5−IL-4 IL-13+ CD4+ T cells in asymptomatic , infected individuals ( INF ) and compared them to frequencies ( Fo ) in filarial-uninfected ( UN ) individuals and to those with filarial lymphedema ( CP ) . INF individuals exhibited a significant increase in the spontaneously expressed and antigen-induced Fo of both Th2 subpopulations compared to the UN and CP . Interestingly , there was a positive correlation between the Fo of IL-5+Th2 cells and the absolute eosinophil and neutrophil counts; in addition there was a positive correlation between the frequency of the CD4+IL-5−Th2 subpopulation and the levels of parasite antigen – specific IgE and IgG4 in INF individuals . Moreover , blockade of IL-10 and/or TGFβ demonstrated that each of these 2 regulatory cytokines exert opposite effects on the different Th2 subsets . Finally , in those INF individuals cured of infection by anti-filarial therapy , there was a significantly decreased Fo of both Th2 subsets . Our findings suggest that both IL-5+ and IL-5−Th2 cells play an important role in the regulation of immune responses in filarial infection and that these two Th2 subpopulations may be regulated by different cytokine-receptor mediated processes .
Th2 cells were initially characterized as expressing the cytokines – IL-4 , IL-5 and IL-13 [1] . Although Th2 cells can express a variety of other cytokines , these three cytokines remain the hallmark Th2 cytokines . Each Th2 cytokine has a well-defined and relatively specific function . While IL-4 is the major driving force behind Th2 differentiation , IgE class switching and alternative macrophage activation , IL-13 is an important mediator of goblet cell hyperplasia , mucus secretion and airway hyper reactivity [2] . IL-5 , in contrast , acts primarily on eosinophils and their precursors in the bone marrow to induce enhanced production , survival and activation of these cells [3] . While Th2 cells have generally been considered a homogenous population , recent reports provide evidence for subpopulations within the Th2 lineage [4] . Two of the main subsets identified recently are the IL-5 expressing Th2 subset ( co-expressing IL-4 , IL-5 and IL-13 ) and the non IL-5 expressing Th2 subset ( co-expressing only IL-4 and IL-13 ) [4] . Since the three established Th2 cytokines each play a non-redundant role in allergic disease pathology , it was postulated that these Th2 subsets might play an important role in allergic diseases . Indeed , IL-5+Th2 cells have been found in greater frequencies ( Fo ) in patients with eosinophilic gastrointestinal disease , while peanut allergy was found to be associated with higher Fo of IL-5−Th2 cells [5] . The canonical host immune response seen in human filarial infections is of the Th2 type and involves the production of cytokines – IL-4 , IL-5 , IL-9 , IL-10 and IL-13 , the antibody isotypes – IgG1 , IgG4 and IgE , and expanded populations of eosinophils and immunoregulatory monocyte [6] . Human filarial infection is known to be associated with down regulation of parasite-specific Th1 responses and T cell proliferation and but with augmented Th2 responses [7] . Thus , in human lymphatic filariasis ( LF ) patent filarial infection is associated with an antigen – specific expansion of Th2 cells ( mostly defined by IL-4 expression ) and enhanced production of IL-4 and IL-13 [7] . However , antigen – driven IL-5 production has been shown to be diminished in patently infected individuals [8] , [9] in some studies . Similarly , although protective immunity to filarial infections in mice is dependent primarily on IL-4 , IL-5 does not appear to play a role in resistance to infection [10] , [11] . Hence , filarial infections provide a natural setting in which to explore the differential role ( if any ) of Th2 subsets . We wanted to explore the hypothesis that Th2 subsets would be differentially regulated in asymptomatic infection compared to uninfected or individuals with chronic pathology . We , therefore , examined the Th2 cytokine expression patterns of CD4+ T cells in clinically asymptomatic patently infected ( INF ) individuals , filarial-uninfected endemic normal ( UN ) individuals , and in previously infected patients with filarial lymphedema ( CP ) both at homeostasis and following stimulation with parasite and control antigens . We show that active human LF is characterized by a significant enhancement in the Fo of both spontaneously-expressed and parasite antigen – driven IL-5− and IL-5+Th2 cells . We show that the Fo of IL-5+Th2 subpopulation is positively correlated with peripheral eosinophil and neutrophil numbers in filarial infections , while the IL-5−Th2 cells are strongly positively related to the levels of parasite specific IgE and IgG4 . We also show that these Th2 subpopulations appear to have differing programs of regulation by both IL-10 and TGFβ in filarial-infected individuals and that definitive treatment ( and subsequent cure ) of this infection causes reversion of the CD4+ Th2 subpopulation expansion to normal levels .
All individuals were examined as part of natural history studies approved by Institutional Review Boards of both the National Institutes of Allergy and Infectious Diseases and the National Institute for Research in Tuberculosis ( NCT00375583 and NCT00001230 ) , and informed written consent was obtained from all participants . The study took place in Tamil Nadu , South India where from 1000 subjects in the area , 70 subjects who were willing to provide blood were included . These subjects originate from hospital and surroundings and therefore may represent very different populations . The 70 individuals comprised of 32 clinically asymptomatic , infected ( hereafter INF ) individuals , 23 individuals with filarial lymphedema or elephantiasis ( hereafter CP ) and 15 uninfected , endemic normal ( hereafter UN ) individuals ( Table 1 ) . This was primarily a retrospective study using previously collected samples that had been fixed and cryopreserved . The samples obtained post-treatment were collected prospectively . The study individuals were recruited from individuals attending the Filariasis Clinic of the Government General Hospital , Chennai and from community screening of the areas where the individuals reside ( Puliayonthope and Ponneri areas ) . All CP individuals were circulating filarial antigen negative by both the ICT filarial antigen test ( Binax , Portland , ME ) and the TropBio Og4C3 enzyme-linked immunosorbent assay ( ELISA ) ( Trop Bio Pty . Ltd , Townsville , Queensland , Australia ) , indicating a lack of current active infection . The diagnosis of prior filarial infection was made by history and clinical examination as well as by positive Brugia malayi antigen ( BmA ) -specific IgG4 . BmA-specific IgE , IgG4 and IgG ELISA were performed exactly as described previously [12] . All INF individuals tested positive for active infection by both the ICT filarial antigen test and the TropBio Og4C3 ELISA and had not received any anti-filarial treatment prior to this study . All INF individuals were treated with a standard dose of diethylcarbamazine ( DEC ) and albendazole and follow – up blood draws were obtained one year later from 16 individuals . Among the 32 INF individuals , 25 were used for whole blood culture with parasite antigens and 7 were used for cytokine blocking studies . Among the 32 treated individuals , we were able to follow up only 16 individuals out of which 9 were circulating antigen negative ( Cured ) and 7 remained circulating antigen positive ( Not-cured ) . These individuals were used for post-treatment analysis . We also used 7 of the 32 INF individuals exclusively for performing cytokine blocking studies . All UN individuals were circulating filarial antigen negative and without any signs or symptoms of infection or disease . We have selected individuals from the same community and socio-economic backgrounds to account for exposure and socio-economic status . Saline extracts of B . malayi adult worms ( BmA ) and microfilariae ( Mf ) were used for parasite antigens and mycobacterial PPD ( Serum Statens Institute , Copenhagen , Denmark ) was used as the control antigen . Final concentrations were 10 µg/ml for BmA , Mf and PPD . Endotoxin levels in the BmA was <0 . 1 EU/ml using the QCL-1000 Chromogenic LAL test kit ( BioWhittaker ) . Phorbol myristoyl acetate ( PMA ) and ionomycin at concentrations of 12 . 5 ng/ml and 125 ng/ml ( respectively ) , were used as the positive control stimuli . Leukocyte counts and differentials were performed on all individuals using the Act-5 Diff hematology analyzer ( Beckman Coulter ) . The INF individuals had higher eosinophil counts but did not differ from the other two groups in any of the other hematological parameters ( data not shown ) . Whole blood cell cultures were performed to determine the Fo of intracellular cytokine-producing cells . . Briefly , whole blood was diluted 1∶1 with RPMI-1640 medium , supplemented with penicillin/streptomycin ( 100 U/100 mg/ml ) , L-glutamine ( 2 mM ) , and HEPES ( 10 mM ) ( all from Invitrogen , San Diego , CA ) and placed in 12-well tissue culture plates ( Costar , Corning Inc . , NY , USA ) . The cultures were then stimulated with BmA , Mf , PPD , PMA/ionomycin ( P/I ) or media alone in the presence of the co-stimulatory reagent , CD49d/CD28 ( BD Biosciences ) at 37°C for 6 hrs . FastImmune Brefeldin A Solution ( 10 µg/ml ) ( BD Biosciences ) was added after 2 hours . After 6 hours , whole blood was centrifuged , washed using cold PBS , and then 1× FACS lysing solution ( BD Biosciences , San Diego , CA , USA ) was added . The cells were fixed using cytofix/cytoperm buffer ( BD Biosciences , San Diego , CA , USA ) , cryopreserved , and stored at −80°C until use . The same procedure was used for both prospectively collected as well as for retrospectively stored samples . For cytokine neutralization experiments ( n = 7 ) , whole blood from INF individuals was cultured in the presence of anti-IL-10 ( 5 µg/ml ) or anti-TGFβ ( 5 µg/ml ) or isotype control antibody ( 5 µg/ml ) ( R& D Sytems ) for 1 h following which BmA and brefeldin A was added and cultured for a further 5 h . The cells were thawed and washed with PBS first and PBS/1% BSA later and then stained with surface antibodies for 30–60 minutes . Surface antibodies used were CD3 - Amcyan , CD4 - APC-H7 and CD8 - PE-Cy7 ( all from BD Biosciences ) . The cells were washed and permeabilized with BD Perm/Wash™ buffer ( BD Biosciences ) and stained with intracellular cytokines for an additional 30 min before washing and acquisition . Cytokine antibodies used were IL-4 FITC , IL-5 APC and IL-13 PE ( all from BD Pharmingen . Flow cytometry was performed on a FACS Canto II flow cytometer with FACSDiva software v . 6 ( Becton Dickinson ) . The lymphocyte gating was set by forward and side scatter and 100 , 000 gated lymphocyte events were acquired . Data were collected and analyzed using Flow Jo software . All data are depicted as Fo of CD4+ T cells expressing cytokine ( s ) . Frequencies following media stimulation are depicted as baseline Fo while Fo following stimulation with antigens or PMA/ionomycin are depicted as net Fo ( with baseline Fo subtracted ) . Data analyses were performed using GraphPad PRISM v6 . 0 ( GraphPad Software , Inc . , San Diego , CA , USA ) . Geometric means ( GM ) were used for measurements of central tendency . Comparisons were made using either the Kruskal-Wallis test with Dunn's multiple comparisons ( unpaired comparisons ) or the Wilcoxon signed rank test ( paired comparisons ) . Correlations were calculated using the Spearman rank test .
We first measured the spontaneously expressed and antigen - stimulated Fo of CD4+ T cells expressing IL-4 and IL-13 but not IL-5 ( CD4+IL4+IL13+IL5− ) as well as those expressing IL-4 , IL-13 , and IL-5 ( CD4+IL4+IL13+IL5+ ) in INF ( for representative flow plot , see Fig . S1 ) and contrasted these with the Fo of these subpopulations in UN and CP . As shown in Figure 1A , INF individuals exhibit significantly higher Fo of IL-5−Th2 cells in response to the parasite antigens BmA ( 2 . 2 fold ) and Mf ( 2 . 1 fold ) but not at baseline nor following PPD or PMA/ionomycin stimulation in comparison to UN individuals , INF individuals also had significantly higher Fo of IL-5+Th2 cells at baseline ( 1 . 2 fold ) and following BmA ( 2 . 6 fold ) and Mf ( 1 . 9 fold ) ( but not PPD or PMA/ionomcyin ) stimulation in comparison to UN ( Figure 1B ) . Similarly , INF individuals exhibit significantly higher Fo of IL-5−Th2 cells at baseline ( 1 . 8 fold ) and in response to BmA ( 3 fold ) and Mf ( 2 . 2 fold ) but not following PPD or PMA/ionomycin stimulation in comparison to CP individuals ( Figure 1A ) . Finally , INF individuals also had significantly higher Fo of IL-5+Th2 cells both at baseline ( 1 . 2 fold ) and following stimulation with BmA ( 2 . 1 fold ) and Mf ( 1 . 7 fold ) ( but not with PPD or PMA/ionomcyin ) in comparison to CP ( Figure 1B ) . Thus , filarial infection in this population was associated with expanded Fo of antigen – stimulated Th2 subpopulations , perhaps indicating a role for these cells in filarial infection and in the prevention of overt pathology . To determine whether the IL-5− and IL-5+Th2 subsets were associated with differential functions in filarial infections , we examined the relationship between the baseline Fo of IL-5− and IL-5+Th2 cells and peripheral eosinophil , neutrophil and basophil numbers in INF individuals ( n = 32 ) . As shown in Figure 2A , IL-5+ ( but not IL-5− ) Th2 cells had a significantly positive correlation between their Fo ex vivo and the absolute eosinophil count ( r = 0 . 4667 , p = 0 . 0071 ) as determined by Spearman rank correlation . Similarly , as shown in Figure 2B , IL-5+ ( but not IL-5− ) Th2 cells exhibited a significant positive association with the absolute neutrophil count ( r = 0 . 4115 , p = 0 . 0193 ) . In contrast , both IL-5− and IL-5+Th2 cells showed no correlation with the absolute basophil numbers in INF individuals ( Figure 2C ) . We next examined the relationship between the Fo of IL-5− and IL-5+Th2 cells ex vivo and IgE and IgG4 levels in INF individuals ( n = 32 ) . As shown in Figure 3A , IL-5− ( but not IL-5+ ) Th2 cells exhibited a significantly positive correlation between baseline Fo and the BmA – specific IgE levels ( r = 0 . 7717 , p<0 . 0001 ) as determined by Spearman rank correlation . Similarly , as shown in Figure 3B , IL-5− ( but not IL-5+ ) Th2 cells exhibited a significant positive association with the BmA – specific IgG4 levels ( r = 0 . 4115 , p = 0 . 0193 ) . To determine the role of IL-10 and TGFβ in the modulation of Th2 subpopulations in INF , we measured the frequency of IL-5−Th2 cells and IL-5+Th2 cells following short-term stimulation with the parasite antigen BmA in the presence or absence of anti-IL-10 or anti-TGFβ neutralizing antibody in INF individuals ( n = 7 ) . As shown in Figure 4A , both IL-10 and TGFβ neutralization resulted in significantly decreased Fo of IL-5−Th2 cells in INF individuals ( 2 . 3 and 1 . 9 fold respectively ) . In marked contrast , as shown in Figure 4B , both IL-10 and TGFβ blockade resulted in significantly increased Fo of IL-5+Th2 cells following BmA stimulation ( 1 . 5 and 1 . 3 fold respectively ) . Thus , both IL-10 and TGFβ play an important role in the modulation of Th2 subset Fo in filarial infections . To determine the role of antigen – persistence in the regulation of Th2 subsets in filarial infections , we measured the frequency of IL-5−Th2 and IL-5+Th2 cells in a subset of INF individuals ( n = 9 ) , who had been treated with anti-filarial chemotherapy and as a result had eliminated infection ( as demonstrated by the absence of circulating filarial antigen ) or those who had been treated but continued to harbor infection ( n = 7 ) . As shown in Figure 5A , treatment of filarial infection and consequent cure resulted in significantly decreased Fo of IL-5−Th2 cells upon filarial antigen stimulation ( 1 . 4 fold for BmA and 1 . 3 fold for Mf ) when compared to their pre-treatment levels . Similarly , treatment also resulted in significantly decreased Fo of IL-5+Th2 cells following filarial antigen stimulation ( 1 . 7 fold for BmA and 1 . 5 fold Mf ) ( Figure 5B ) . However , individuals who were treated but did not eliminate infection continued to exhibit significantly increased Fo of IL-5− and IL-5+Th2 cells in response to filarial antigens compared to pre-treatment Fo ( Figure 5 ) . Interestingly , this effect was specific to the filarial – antigen stimulated Fo of CD4+ Th2 subpopulations as neither responses to PPD nor to PMA/ionomycin were significantly among the groups . Thus , the antigen – driven expansion of Th2 subpopulations appears to be dependent on the continued presence of circulating filarial antigen ( an indicator of active infection ) .
Th2 responses are considered to be the hallmark of helminth infection and are indeed required for host resistance to a variety of helminths in animals [6] . The three prototypical Th2 cytokines – IL-4 , IL-5 and IL-13 have all been shown to play important but non – redundant roles in helminth immunity [13] . In addition , the recent explosion of interest in CD4+ T cell subpopulations and the availability of polychromatic cytokine staining has helped define heterogeneity within the Th2 compartment [4] . Thus , two major subsets of Th2 cells were recently described – an IL-5 expressing Th2 population , which is thought to play an important role in eosinophilic inflammation and an IL-5 non expressing Th2 population , which is thought to play an important role in other forms of allergic inflammation [4] . Moreover , it has been demonstrated that IL-5+ and IL-5−Th2 cells represent more and less highly differentiated Th2 subpopulation , respectively [14] . However , the role of these subsets in helminth infections is not known . The induction of prototypical Th2 response with high IL-4 , IL-5 and IL-13 secretion has long been considered to be the hallmark of active infection in human LF [7] . However , not all studies have consistently shown a predominant prototypical Th2 response in filarial infections . A recent study in Mali suggested that patent , long-standing filarial infection is associated with expanded adaptive regulatory T cell cells rather than an expansion of classical Th2 cells environment [15] . Previous studies have reported a down regulation of IL-5 upon parasite stimulation [8] , [9] , [16] . In addition , the role of Th2 responses in protection from or in the pathogenesis underlying the disease associated with LF has not been well characterized either . We therefore utilized two sets of comparisons to help elucidate the role of Th2 subsets in human filarial infections – ( 1 ) comparisons of Th2 subsets in INF and UN individuals , ( 2 ) comparisons of these subsets in INF and CP individuals . We were able to demonstrate that both Th2 subsets are expanded preferentially in active , subclinical infection but not in filarial disease ( without active infection ) . Our data on Ag – induced expression of CD4+ Th2 cell subpopulations also reveal interesting facets of T cell driven immune regulation in filarial infection and disease . First , the alterations in the CD4+ Th2 cell cytokine repertoire is filarial – antigen specific since the since these alterations in Th2 subpopulation Fo were primarily observed only in response to the filarial-derived BmA and Mf antigens but not to PPD nor in response to polyclonal stimulation . Second , the importance of antigen – persistence is clearly illustrated by our data on a small subset of individuals who cleared infection following treatment and were therefore proven to be filarial antigen negative . The follow up data on these individuals indicate a clear reversion to the normal/homeostatic levels of Th2 subset populations . On the other hand , the different Th2 subpopulations continue to expand in a control group of individuals , who also received treatment but failed to clear infection . Therefore , the expansion of antigen - specific Th2 subsets is closely associated with the presence of parasite antigen in vivo . Th2 cells are thought to play a counter-regulatory role in a variety of infectious and inflammatory conditions to offset pathology and promote tissue repair and wound healing mechanisms [17] . Th2 responses are considered to be fundamentally important in protection against the development of pathology both because of their ability to ameliorate Th1 induced inflammatory responses and because of their propensity to promote wound healing and tissue repair [18] . For example , IL-5 and IL-13 have pro-fibrotic activity and , in addition , IL-4 and IL-13 are critical mediators of alternative activation of macrophages . Our study of the Ag – stimulated expansion of Th2 subpopulations reveals a significant association of these cells with asymptomatic infection , confirming a previously reported association [19] . By contrasting these Th2 subpopulations in clinically asymptomatic patients to those with filarial lymphedema ( CP ) we may be able to infer a role for these expanded Th2 subsets in protection from the development of clinical pathology . Moreover , our data on the lower levels of Th2 subpopulations in CP supports the suggestion that unchecked parasite-specific Th1/Th17 cells may contribute to the pathological process in LF . Our data reveal clear distinctions in the relationship between IL-5− and IL-5+Th2 cells and expansion of innate leukocyte populations in filarial infections . Eosinophils are considered to be important innate effectors in immunity to helminth infections and have been shown to play a role in protection against S . mansoni and other helminths [20] , [21] . Similarly , basophils are known to act as effectors to promote parasite killing during challenge infections of immunized animals , perhaps through antibody dependent mechanisms [22] , [23] , while neutrophils have been demonstrated to attack helminth larvae in response to IL-4 and IL-5 [24] , [25] . Our study implicates the IL-5+Th2 subpopulation in this innate defense mechanism by promoting the recruitment and/or activation of eosinophils and neutrophils . Our study also demonstrates an important association of IL-5−Th2 cells in promoting Th2 associated ( IgE and IgG4 ) antibody responses in filarial infection . All helminth infections are characterized by the induction of antibody isotypes of the class – IgE and IgG4 ( IgG1 in mice ) , that are largely dependent on the IL-4 [26] . Not only did we assess the expansion of these Th2 subpopulations , we also examined the mechanisms regulating the expression of these cytokines in these two subpopulations . Since IL-10 and the TGFβ in chronic infections are known to play a role in modulating T cell expression of cytokines in filarial infections [19] , we examined the Fo of IL-5+ and IL-5−Th2 cells following either IL-10 or TGFβ blockade during in vitro stimulation with filarial antigen . Our data , through preliminary due to the small number of samples able to obtained , show clear differences in the modulation of the Th2 subsets . We demonstrate that the expansion of IL-5−Th2 cells is dependent on both IL-10 and TGFβ since blockade of these cytokines significantly reduces the frequency of IL-5−Th2 cells . Conversely , both IL-10 and TGFβ appear to impair the induction of the IL-5+Th2 subset . While it has been previously shown that IL-5 expression in Th2 cells is limited to the effector memory subset whereas IL-4 is expressed in both central and effector memory subsets [4] , this finding that IL-10 and TGFβ signaling may be critical to Th2 subpopulation expansion provide new insight into the interrelationship between the IL-5+ and IL-5−Th2 subpopulations and provides new avenues for the study of filarial-specific immune regulation and protection from immune-mediated pathology in LF . In summary , our study examines in depth the CD4+ Th2 cell subset repertoire in a chronic parasitic infection and sheds light on the role of these subsets in both the regulation of immune responses in active infection and in the pathogenesis of filarial lymphedema . While we have not performed longitudinal studies to define the development of pathology in filarial infection and this was a study using a combination of previously ( pre-treatment ) and prospectively ( post-treatment ) collected samples with inherent potential limitations with respect to bias , our strategy of contrasting immune responses in individuals with early or subclinical disease and those with late or clinical disease yields important information on the association of Th2 subsets in pathogenesis . However , the potential drawbacks in the study include potential bias in using both prospectively and retrospectively collected samples and lack of rigorous controls in eliminating potential confounders including socio-economic status of individuals in the study . The lack of proper information in the study area regarding the prevalence of the different clinical groups , adds to the problem of potential bias which means that our conclusions cannot be generalized . In addition , while we have demonstrated the presence of Th2 subsets in filarial infections , disease association is not formal proof of function and the elucidation of function needs to be explored in the future . Nevertheless , our study clearly defines and important association of filarial infection with heightened expansion of Th2 cells suggesting that these subsets play an important role in infection .
|
Th2 cells are CD4+ T cells that produce a unique set of cytokines - IL-4 , IL-5 and IL-13 . Th2 cells are commonly associated with allergies , asthma and helminth infections . A common helminth infection that infects over 120 million people worldwide is lymphatic filarial infection caused by filarial parasites . We show here data that filarial infection is associated with the expansion of two types of Th2 cells , one which produces IL-4 and IL-13 alone without IL-5 and the other which produces all three cytokines . Interestingly , while the former subset is associated with the levels of antibodies - IgG4 and IgE; the latter is associated with the presence of eosinophilia in filarial infected individuals . In addition , these subsets appear to be modulated differently by the immunoregulatory cytokines - IL-10 and TGFβ . Therefore , our study highlights a novel regulation of Th2 cells and suggests that the Th2 compartment is quite heterogeneous in phenotype with possible functional consequences .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"immune",
"cells",
"filariasis",
"t",
"cells",
"immunology",
"biology",
"parasitic",
"diseases",
"immune",
"response",
"helminth",
"infection"
] |
2014
|
Parasite-Antigen Driven Expansion of IL-5− and IL-5+ Th2 Human Subpopulations in Lymphatic Filariasis and Their Differential Dependence on IL-10 and TGFβ
|
Although treatment with interleukin-7 ( IL-7 ) was shown to transiently expand the naïve and memory T-cell pools in patients with chronic HIV-1 infection receiving antiretroviral therapy ( ART ) , it is uncertain whether a full immunologic reconstitution can be achieved . Moreover , the effects of IL-7 have never been evaluated during acute HIV-1 ( or SIV ) infection , a critical phase of the disease in which the most dramatic depletion of CD4+ T cells is believed to occur . In the present study , recombinant , fully glycosylated simian IL-7 ( 50 µg/kg , s . c . , once weekly for 7 weeks ) was administered to 6 rhesus macaques throughout the acute phase of infection with a pathogenic SIV strain ( mac251 ) ; 6 animals were infected at the same time and served as untreated controls . Treatment with IL-7 did not cause clinically detectable side effects and , despite the absence of concomitant ART , did not induce significant increases in the levels of SIV replication except at the earliest time point tested ( day 4 post-infection ) . Strikingly , animals treated with IL-7 were protected from the dramatic decline of circulating naïve and memory CD4+ T cells that occurred in untreated animals . Treatment with IL-7 induced only transient T-cell proliferation , but it was associated with sustained increase in the expression of the anti-apoptotic protein Bcl-2 on both CD4+ and CD8+ T cells , persistent expansion of all circulating CD8+ T-cell subsets , and development of earlier and stronger SIV Tat-specific T-cell responses . However , the beneficial effects of IL-7 were not sustained after treatment interruption . These data demonstrate that IL-7 administration is effective in protecting the CD4+ T-cell pool during the acute phase of SIV infection in macaques , providing a rationale for the clinical evaluation of this cytokine in patients with acute HIV-1 infection .
Although HIV-1 establishes a chronic active infection that evolves toward clinical immunodeficiency over a span of several years , accumulating evidence indicates that critical pathogenic events take place during the acute phase of infection , leading to a massive and seemingly irreversible depletion of CD4+ T cells , predominantly of the memory phenotype [1] , [2] . A large fraction of the T-cell pool in the body is harbored in the gut-associated lymphoid tissue ( GALT ) [3] , which has been identified as a primary anatomical site for CD4+ T-cell depletion in both HIV-1-infected patients [4]–[6] and SIV-infected nonhuman primates [1] , [2] , [7] , [8]; yet , the loss of CD4+ T cells within the early phase of infection appears to be a systemic phenomenon that involves all secondary lymphoid organs [1] , [9] , [10] . Taken together , these observations suggest that interventions aimed at preventing or reducing the immunologic damage caused by HIV-1 would be most effective if implemented during the earliest stages of infection , before the pool of memory CD4+ T cells becomes irreversibly compromised . In spite of extensive research over the past three decades , the mechanism of CD4+ T-cell depletion during the course of HIV-1 infection is still debated . Studies in SIV-infected macaques have highlighted the role of direct cytopathic effects of the virus during the course of acute primary infection [1] , [9] , [10] . However , indirect mechanisms , including bystander apoptosis , may also be important , as suggested by the increased levels of apoptosis detected in blood and lymphoid organs of macaques acutely infected with pathogenic SIV strains [2] , [11]–[15] , as well as in ex vivo-cultured T cells from individuals with acute HIV-1 infection [16]–[18] . Thus , the use of anti-apoptotic agents during primary HIV-1 infection may have beneficial effects for preserving the integrity of the CD4+ T-cell pool . We previously demonstrated that interleukin-7 ( IL-7 ) , a nonredundant cytokine that plays a critical role in the development and homeostasis of the T-lymphoid compartment of the immune system [19]–[21] , effectively reduces the levels of spontaneous apoptosis in both CD4+ and CD8+ T cells from HIV-1-infected individuals [22] . In lymphopenic hosts , the levels of endogenous IL-7 increase , causing transient proliferation of naïve and central memory ( CM ) CD4+ and CD8+ T cells , which eventually leads to the reconstitution of the physiological T-lymphocyte pool [20]–[22] . Owing to these unique biological properties , IL-7 is currently under clinical investigation as an immune-reconstitution agent in various forms of natural and iatrogenic immunodeficiencies , including those associated with AIDS and cancer [23] , [24] . Pre-clinical studies in macaques chronically infected with SIV [25]–[28] , as well as clinical studies in patients with HIV-1 infection or receiving treatment with immunosuppressive antineoplastic drugs [29]–[32] , have documented beneficial effects of short-term courses of IL-7 therapy , resulting in the proliferation and numerical expansion of naïve and CM CD4+ and CD8+ T cells in peripheral blood and secondary lymphoid organs . Whether adjuvant therapy with IL-7 may effectively lead to the long-term reconstitution of the immunologic function remains unclear . Moreover , IL-7 treatment has never been evaluated in acute HIV-1/SIV infection , a phase in which it may still be possible to avert the seemingly irreversible immunologic damage caused by massive viral replication prior to the appearance of virus-specific adaptive immune responses . In the present study , we administered fully glycosylated simian IL-7 to rhesus macaques during the acute phase of infection with a pathogenic SIV strain ( mac251 ) . The concomitant use of ART was deliberately avoided in order to exclude its confounding effects on pathogenesis since ART would have suppressed SIV replication , thereby preventing the pathologic depletion of CD4+ T-cells and making it impossible to evaluate the CD4-protective effects of IL-7 . Another important goal of our study was to examine the effects of IL-7 on SIV replication since IL-15 , a related common-γ-chain cytokine , was recently shown to dramatically increase SIV replication and accelerate disease progression when administered to acutely-infected macaques [33] . Our results demonstrate that treatment with IL-7 during the acute phase of SIV infection is safe and effective in preventing the decline of circulating naïve and memory CD4+ T cells without causing major increases in the levels of SIV replication .
None of the 6 rhesus macaques treated with IL-7 exhibited adverse clinical side effects throughout the treatment period . After the first IL-7 injection ( day −7 relative to SIV infection ) , plasma IL-7 levels peaked on day −5 to return to baseline on day 0 ( Figure 1A ) . The two subsequent injections ( day 0 and day 7 ) induced higher peak levels of plasma IL-7 and a greater area under the curve ( AUC ) , resulting in markedly elevated trough levels before each of the following injections . This pattern likely reflects the initial distribution of the cytokine to a high-affinity compartment that was saturated upon subsequent injections . Increased plasma levels of IL-7 , albeit considerably lower than in IL-7-treated animals , were also observed in untreated animals starting on day 28 post-infection in parallel with the most pronounced reductions in circulating lymphocyte counts ( Figure 1A ) . No significant correlations were found between plasma levels of IL-7 and various immunological parameters , including circulating CD4+ and CD8+ T-cell counts and expression of the specific chain of the IL-7 receptor ( IL-7Rα or CD127 ) ( data not shown ) . In agreement with previous studies [25]–[32] , IL-7 treatment initially caused a significant downmodulation of the IL-7 receptor ( CD127 ) in both naïve and memory T cells ( Figure 1B ) . Comparison of the two groups of animals showed that treatment with IL-7 did not induce significant increases in the levels of SIV plasma viremia at any time during the acute phase of infection and the follow-up period , including the peak of viral replication , the viral set point and the AUC , with the only exception of the earliest time point analyzed ( day 4 post-infection; p = 0 . 043 for the comparison between the two groups of animals by Wilcoxon rank sum test ) ( Figure 2A ) . However , there was a trend towards higher levels of viremia in IL-7-treated animals , particularly on days 35 and 41 post-infection , even though the statistical p values remained far below the threshold for significance ( Table S1 in Text S1 ) . Likewise , the two groups of animals showed no significant differences in the levels of SIV p27 antigenemia ( Figure 2B and Table S1 in Text S1 ) and proviral SIV DNA load detected in blood mononuclear cells on days 14 and 77 , in the GALT ( ileum ) on days 14–16 , and in axillary lymph nodes on days 25–27 post-infection ( Figure 2C and Table S1 in Text S1 ) . All the animals developed SIV-specific IgG antibodies , which became detectable between day 11 and day 21 of infection ( data not shown ) . During the follow-up period , 4 animals ( two untreated and two IL-7-treated ) developed early signs of progression to AIDS ( rapid progressors [RP] ) and were euthanized for terminal disease within 5 months of infection . The RP course has been suggested to represent a unique form of SIV disease , distinct from that of conventional progressors ( CP ) and associated with an unusual pathogenesis characterized by higher levels of SIV viremia , massive SIV replication in mononuclear phagocytic cells rather than in CD4+ T cells ( with consequent lack of depletion of CD4+ T cells ) , and severe SIV-related enteropathy in the absence of opportunistic infections [34] . Additional data on RP animals are presented in Supplementary Data and Figure S1 in Text S1 . Since the presence of RP animals could be a confounding factor in our study , all the virological and immunological data were analyzed both with the inclusion and after the exclusion of the 4 RP animals . When the analysis of SIV plasma viremia and antigenemia was restricted to CP animals , the statistical comparisons between IL-7-treated and untreated animals did not show any significant changes from those obtained with the inclusion of all the animals ( Figure S2A in Text S1 ) . While all the animals in the untreated group experienced a marked and sustained decline of circulating CD4+ T lymphocytes starting at the time of peak SIV replication ( day 14 post-infection ) , IL-7-treated animals showed no decline of CD4+ T-cell counts over the entire treatment period , with even a significant increase , relative to baseline , on day 41 ( Figure 3A ) . When the two groups of animals were compared , IL-7-treated macaques had significantly higher absolute numbers of peripheral CD4+ T cells at several time points , including day 14 post-infection ( statistical data not shown ) ; similar results were obtained by comparison of the changes in CD4+ T-cell counts from baseline in IL-7-treated versus untreated animals ( Figure S3 in Text S1 ) . To better characterize the effects of IL-7 on CD4+ T cells , the naïve , memory and effector subpopulations were analyzed separately . In the absence of IL-7 treatment , SIV infection caused significant decreases in the absolute numbers of naïve and memory CD4+ T cells , compared to pre-infection levels , throughout the acute phase of the infection ( Figure 3A ) . In contrast , IL-7 caused an initial increase in memory CD4+ T cells ( day 7 ) and subsequently prevented the decline of both naïve and memory CD4+ T cells throughout the treatment period; effector CD4+ T cells were increased at several time points ( Figure 3A ) . However , the protective effects of IL-7 were not sustained after treatment interruption with both naïve and memory CD4+ T cells becoming significantly decreased , compared to baseline values , on day 62 post-infection , 4 weeks after the last injection of IL-7 ( Figure 3A ) . When the absolute numbers of circulating naïve , memory and effector CD4+ T cells in the two groups of animals were compared , significant differences were detected at several time points ( statistical data not shown ) ; similar results were obtained by comparison of changes from baseline in IL-7-treated versus untreated animals ( Figure S3 in Text S1 ) . The statistical differences between treated and untreated animals remained significant when the analysis of total , naïve , memory and effector CD4+ T cells was restricted to CP animals , after exclusion of the 4 RP animals ( Figure S2B in Text S1 ) . A detailed subset analysis of the memory CD4+ T-cell population was performed at selected time points . While in untreated animals all memory CD4+ T-cell subsets ( CM , transitional memory [TM] and effector memory [EM] ) dramatically declined during the acute phase of infection , none of these subsets was quantitatively reduced in animals receiving IL-7 ( Figure 3B ) . The protective effects of IL-7 on memory CD4+ T cells were not sustained after treatment interruption as shown by the significant decline of all three subpopulations , compared to baseline , on day 62 post-infection ( Figure 3B ) . IL-7-treated animals experienced sustained increases in all subsets of circulating CD8+ T cells throughout the acute phase of infection , whereas a transient decline of naïve and memory CD8+ T cells was observed in untreated animals ( Figure 3A ) . When the two groups of animals were compared , IL-7-treated monkeys showed higher absolute numbers of peripheral CD8+ T cells at several time points ( statistical data not shown ) ; similar results were obtained by comparison of changes from baseline between IL-7-treated and untreated animals ( Figure S3 in Text S1 ) . Subset analysis of memory CD8+ T cells revealed no major changes in untreated animals , whereas IL-7 induced significant increases in all memory CD8+ T-cell subsets ( Figure 3B ) . However , on day 62 post-infection , CM CD8+ T cells returned to baseline values , while TM and EM CD8+ T cells were significantly decreased in both IL-7-treated and untreated animals ( Figure 3A and 3B ) . The statistical differences between treated and untreated animals remained significant when the analysis of total , naïve , memory and effector CD8+ T cells was restricted to CP animals , after exclusion of the 4 RP animals ( Figure S2B in Text S1 ) . Longitudinal analysis of the levels of proliferation in freshly isolated peripheral blood T cells revealed that IL-7 treatment induced only a transient increase in the proportion of Ki67-expressing CD4+ T cells during the first week of treatment ( day −5 and −3 prior to SIV infection ) , which returned to baseline levels at the time of SIV inoculation ( day 0 ) ; remarkably , there was no further increase in proliferation after any of the subsequent IL-7 injections ( Figure 4A ) . In contrast , CD8+ T cells showed elevated Ki67 expression both before SIV inoculation and at two time points ( day 4 and 11 ) after infection , even though the proportion of cycling cells returned to baseline thereafter ( Figure 4A ) . Analysis of apoptosis in circulating T cells by Annexin-V binding did not show significant elevations , compared to baseline , in either untreated or IL-7-treated animals throughout the acute phase of SIV infection ( Figure 4B ) , suggesting that the peripheral blood compartment is largely spared from in vivo apoptosis during primary infection; however , IL-7-treated animals exhibited significant and sustained increases in the intracellular levels of the anti-apoptotic protein Bcl-2 in both CD4+ and CD8+ T cells during the first three weeks of treatment ( Figure 4C ) . Representative histograms illustrating Ki67 expression , Annexin-V binding and Bcl-2 expression in CD4+ and CD8+ T cells from one untreated ( #749 ) and one IL-7-treated ( #746 ) animals are shown in Figure S4 in Text S1 . Altogether , these results suggested that numerical expansion due to proliferation was not a major factor contributing to the lack of decline of circulating CD4+ T cells seen in IL-7-treated animals while a decreased sensitivity to apoptosis was likely involved . In contrast , both proliferation and apoptosis reduction may have contributed to the sustained numerical increases documented in circulating CD8+ T cells . Next , we studied the effects of IL-7 in peripheral lymphoid tissues . Lymph node biopsies were collected from treated and untreated macaques on days 25–27 post-infection . The relative proportions of total , naïve , memory and effector CD4+ and CD8+ T cells in lymph nodes were not significantly different between IL-7-treated and untreated macaques , and the level of Ki67 expression was very low ( <1% ) in both groups of animals ( data not shown ) . Analysis of T-cell apoptosis by Annexin-V binding and intracellular Bcl-2 expression did not show significant differences between the two groups of animals despite a trend toward reduced Annexin-V binding in memory CD4+ and CD8+ T cells and effector CD8+ T cells from IL-7-treated macaques ( Figure 5A , upper panel ) ; however , these differences became statistically significant when the analysis was restricted to CP animals , after exclusion of the 4 RP animals ( Figure 5B , upper panel ) . In accordance with the Annexin-V data , lymph node CD4+ T cells from IL-7-treated macaques showed higher levels of intracellular Bcl-2 when RP animals were excluded from the analysis ( data not shown ) . Terminal ileum biopsies were obtained from all animals on days 14–16 post-infection . The yield of CD3+ T cells from these biopsies was highly variable ( range = 1 . 2–15 . 0% of the total cell populations ) , underscoring the inherent difficulties in obtaining mucosal specimens with comparable representation of the GALT via retrograde ileoscopy . Regardless of this limitation , no significant differences were detected in the relative proportions of total , naïve , memory and effector CD4+ T cells , as well as in the CD4/CD8 ratio in the intestinal tissues of IL-7-treated vs . untreated macaques ( data not shown ) ; likewise , the proportion of Annexin-V-positive CD4+ T cells was similar in the two groups ( Figure 5A , lower panel ) . In contrast , the proportion of Annexin-V-positive CD8+ T cells was lower in IL-7-treated animals ( p = 0 . 041 ) , primarily due to a reduction of apoptosis among memory CD8+ T cells ( p = 0 . 048 ) ( Figure 5A , lower panel ) , associated with a lower proportion of apoptosis-prone naïve CD8+ T cells ( p = 0 . 024 ) ( data not shown ) . The difference in the proportion of Annexin-V-positive CD8+ T cells remained significant when RP animals were excluded from the analysis ( Figure 5B , lower panel ) . SIV-specific T-cell responses were evaluated at multiple time points during and after the IL-7 treatment period by measuring the intracellular production of IFN-γ , IL-2 and MIP-1β in CD4+ and CD8+ T cells stimulated with peptide pools derived from the SIV Tat and Gag proteins . SIV-specific T-cell responses were detected at multiple time points in all animals , while we were unable to document the presence of SIV-neutralizing antibodies in serum at any time during acute primary infection in both untreated and IL-7-treated macaques ( data not shown ) . The total number of Tat-specific CD8+ T cells at the first time point analyzed ( day 21 post-infection ) was significantly higher in IL-7-treated than in untreated animals ( p = 0 . 017 ) , whereas the difference in Tat-specific CD4+ T-cell responses was close to but did not reach statistical significance ( p = 0 . 051 ) ( Figure 6 ) . Overall , IL-7-treated animals displayed higher numbers of Gag-responding CD4+ and CD8+ T cells at several time points , but the differences did not reach statistical significance ( Figure 6 ) . Qualitative analysis of SIV-specific T-cell responses revealed that initially most Tat-specific ( Figure 7A ) and Gag-specific ( Figure S5 in Text S1 ) CD4+ and CD8+ T cells were monofunctional in both groups of animals , producing a single cytokine ( single-producer , SP ) . When SP cells were analyzed separately from double- and triple-producer cells ( DP and TP ) , the difference between IL-7-treated and untreated animals on day 21 was significant for both CD4+ and CD8+ Tat-specific T cells ( p = 0 . 030 and 0 . 017 , respectively; Figure 7B ) . The quality of the T-cell responses evolved over time , with both Tat-specific ( Figure 7B ) and Gag-specific ( Figure S5 in Text S1 ) T cells acquiring some degree of polyfunctionality over time . This phenomenon was more prominent among IL-7-treated animals , as indicated by a significant difference in the proportion of the various functional subpopulations of Tat-specific CD8+ T cells on day 62 post-infection ( Figure 7C ) .
While the progressive refinement of multi-drug ART protocols has led to extraordinary advances in the treatment of chronic HIV-1 infection , there is still uncertainty as to whether a complete reconstitution of the immunologic function can be achieved even after years of sustained virologic suppression [35] . The often incomplete immunologic reconstitution in patients treated with ART , which is linked to an increased incidence of adverse clinical events [36] , has prompted consideration of adjuvant therapies , including treatment with cytokines of the common-γ-chain family , such as IL-2 , IL-7 and IL-15 , which are known to induce T-cell expansions . However , the ability of such treatments to regenerate fully competent naïve and memory T-cell pools remains questionable as multiple lines of evidence indicate that the initial damage caused by HIV-1 during acute primary infection is irreversible [1] , [2] , [10] , marking a critical event in the pathogenesis of HIV-1 disease and precluding the success of any restoration attempts enacted during the chronic phase . These considerations underscore the need to aim adjuvant treatment strategies toward immunologic preservation rather than reconstitution and , therefore , to implement such strategies at the earliest possible time after the diagnosis of HIV-1 infection . In this study , we used a macaque model to demonstrate that treatment with IL-7 , the principal T-cell homeostatic cytokine , can prevent the dramatic decline of circulating naïve and memory CD4+ T cells that occurs during acute primary SIV infection . Although in the clinical setting IL-7 would presumably be associated with ART , as it was done in phase-1 studies in patients with chronic HIV-1 infection [29] , [30] , we deliberately avoided the use of virus-suppressive drugs in order to allow for an unbiased evaluation of the effects of IL-7 on CD4+ T-cell depletion , which is the hallmark of HIV-1/SIV-induced pathogenesis during primary infection . In fact , ART by itself would have prevented the loss of CD4+ T cells , limiting the scope of our study . The fact that in acutely-infected macaques treatment with IL-7 was able to protect naïve and memory CD4+ T cells even in the absence of ART suggests that the combination of IL-7 with virus-suppressive drugs would be even more effective in maintaining the integrity of the CD4+ T-cell pool during one of the most critical phases in the pathogenesis of HIV-1 disease . To elucidate the mechanisms responsible for the CD4-protective effects of IL-7 , we examined the kinetics of CD4+ T-cell proliferation and apoptosis , as well as the induction of SIV-specific cellular immune responses during the course of IL-7 treatment . Some levels of proliferation , albeit low , were documented following the first injection of IL-7 , presumably leading to the observed initial expansion of the memory CD4+ T-cell subset . However , in agreement with data of IL-7 treatment in chronically SIV-infected macaques [28] , no further proliferation was seen after any subsequent IL-7 injection , suggesting that repeated administrations of fully glycosylated IL-7 at weekly intervals may in fact induce tachyphylaxis , at least concerning the proliferative effects of the cytokine . Conversely and in agreement with our previous ex vivo findings [22] , several observations pointed to a reduction of apoptosis as one of the mechanisms responsible for CD4+ T-cell preservation in our IL-7-treated macaques . First , while the levels of apoptosis in peripheral blood T cells did not change significantly during primary SIV infection in either treated or untreated animals , preventing the evaluation of the anti-apoptotic effects of IL-7 in this compartment , there was a sustained increase in the expression of the anti-apoptotic protein Bcl-2 , a marker of increased resistance to apoptosis , in circulating CD4+ and CD8+ T cells from IL-7-treated animals . Second , analysis of secondary lymphoid tissues ( lymph nodes and GALT ) demonstrated a reduction of apoptosis in selected CD4+ and CD8+ T-cell populations in IL-7-treated macaques , even though these tissues were only sampled at a single time point during the acute phase due to the inherent difficulties in obtaining multiple tissues from the same animal within a short period of time . This limitation reduced our chances to document the full spectrum of anti-apoptotic effects of IL-7 . Of note , analysis of the GALT at the time of peak SIV replication demonstrated that IL-7 reduced the proportion of Annexin-V-binding CD8+ T cells , particularly of the memory phenotype , but not CD4+ T cells . An important caveat that must be considered in this setting is the inconsistent quality of ileum specimens obtained by retrograde ileoscopy , which have a high probability of sampling error due to a markedly variable yield of lymphoid cells . Regardless , the fact that we did not observe even a trend toward reduced apoptosis among GALT CD4+ T cells may reflect the fact that IL-7 is inactive against the direct cytopathic effect of the virus , which has been identified as the principal mechanism responsible for CD4+ T-cell depletion in the gut during the acute phase of the infection , whereas the role of apoptosis in this context remains controversial [1] , [2] . Although a more extensive investigation is warranted to confirm this hypothesis , our data suggest that IL-7 treatment prevented the decline of circulating CD4+ T cells during the acute phase of SIV infection by inducing an initial expansion of these cells combined with their subsequent preservation via sustained pro-survival effects . Since IL-7 seemed to effectively preserve the pool of CD4+ T cells , which are the main cellular targets for SIV , one could have anticipated considerable increases in the levels of virus replication in acutely infected macaques treated with IL-7 . However , despite the absence of concomitant ART , we did not observe dramatic increases in the levels of SIV replication during both the treatment phase and the follow up period , with the only exception of the earliest time point analyzed ( day 4 post-infection ) in which the levels of SIV plasma viremia were significantly higher in IL-7-treated than in untreated macaques . A trend toward higher levels of SIV plasma viremia was seen at certain time points , particularly day 35 and day 41 post-infection , but it did not reach statistical significance even after exclusion from the analysis of the 4 animals with rapid disease progression . We cannot rule out that this lack of statistical significance was due to the low number of animals included in the study ( n = 6 per group ) , even though the p values remained far below the threshold for significance . Nevertheless , it is undisputable that , if an enhancing effect of IL-7 on SIV replication did occur , this effect was far less dramatic than that documented for a related common-γ-chain cytokine , IL-15 , in a similar experimental setting [33] . Indeed , IL-15 treatment of macaques during the acute phase of SIV infection was shown to induce a ∼3 log increase in viral load , most likely due to increased CD4+ T-cell proliferation and activation , and to accelerate the disease progression [33] . Various hypotheses can be formulated to explain these striking differences between the outcomes of IL-7 and IL-15 treatment . First , the lack of strong induction of CD4+ T-cell proliferation by IL-7 , unlike IL-15 , which was detectable only transiently and only after the first IL-7 injection; of note , this transient proliferative effect of IL-7 could have been responsible for the significant increase in SIV plasma viremia at the earliest time point tested ( day 4 post-infection ) . Second , the induction of early and vigorous SIV Tat-specific CD4+ and CD8+ T-cell responses in IL-7-treated animals , as compared to untreated animals , associated with the proliferation , expansion and activation of all CD8+ T-cell subpopulations . Since Tat is a regulatory protein expressed during the early phase of the viral life cycle , Tat-specific T-cell responses presumably were able to halt the infection before completion of a full replicative cycle , thereby preventing the spread of infectious virus to other target cells . Of note , a pronounced numerical increase was detected in EM CD8+ T cells , a functionally competent subset that was recently associated with effective vaccine-elicited protection in macaques [37] . Thus , it is plausible that the increased availability of target CD4+ T cells in IL-7-treated animals was counterbalanced by the effectiveness of virus-specific cell-mediated immune responses resulting in the observed minimal increases in SIV replication levels . An interesting correlate of our findings was provided by a recent study in mice acutely infected with LCMV , in which early treatment with IL-7 augmented the number and functionality of specific antiviral effector T cells , thereby reducing organ pathology and promoting viral clearance [38] . Conversely , it is also possible that the earlier and stronger Tat-specific CD4+ and CD8+ T-cell responses observed in IL-7-treated animals resulted from a more robust antigenic stimulation due to the higher amount of virus detected in these animals at day 4 post-infection; indeed , strong CD8+ T-cell responses were also detected in IL-15-treated animals , although they were incapable of controlling viral replication [33] . Thus , the different mechanisms of action of IL-7 and IL-15 on CD4+ T cells , with IL-15 inducing high levels of activation and proliferation mainly in memory CD4+ T cells , the preferential target of the virus , and IL-7 stimulating the proliferation and renewal of naïve T cells too , thereby contributing to the preservation and replenishment of the peripheral T-cell pool , may account for the divergent effects of treatment with these two cytokines . Although a more extensive investigation is warranted in the perspective of a potential clinical use of IL-7 in acute HIV-1 infection , it is important to emphasize that in a therapeutic regimen IL-7 would always be administered in combination with ART , thereby minimizing its possible enhancing effects on viral replication . Our results may have implications for devising new treatment strategies for acute HIV-1 infection , whose clinical management remains a challenge . A critical hurdle is the inherent difficulty in identifying and treating patients at the earliest possible stage in order to reduce the massive HIV-1 replication that occurs before the development of virus-specific adaptive immune responses and its deleterious effects on the immune system . The extent of viral replication during the acute phase of SIV infection in macaques has indeed been identified as a critical determinant of the natural history of the disease [39] . Although early ART treatment was reported to be beneficial on the induction and maintenance of HIV-specific cellular immune responses [39]–[41] , additional studies in patients [10] , [42] and macaques [43] have shown limited effects of ART alone on T-cell preservation in the intestinal lamina propria , underscoring the importance of devising effective adjuvant therapies . Indeed , even if ART is promptly initiated during primary infection , its effects may not be sufficient for fully preventing the immunologic damage caused by HIV-1 , particularly in the gastrointestinal tract , due to dishomogeneous drug biodistribution or inactivation by P-glycoproteins within the intestinal mucosa . Moreover , complete suppression of viral replication could take several weeks , and indirect mechanisms of cell destruction , such as bystander apoptosis , may remain active for some time after the virus has ceased to replicate . Our data provide a scientific basis for the clinical evaluation of IL-7 , in combination with ART , for the treatment of acute primary HIV-1 infection .
The animals were housed and fed according to regulations established in the Guide for the Care and Use of Laboratory Animals and the Animal Welfare Act . The animal experiments performed in this study were approved by the NIH Animal Care and Use Committee ( ACUC ) . Twelve colony-bred juvenile Rhesus macaques , housed at Bioqual Inc . ( Rockville , MD ) , were divided in two groups , according to HLA haplotype and baseline CD4+ T-cell counts ( Table S2 in Text S1 ) : untreated controls ( group 1 ) and IL-7-treated ( group 2 ) . Blood samples were obtained three times during the first week of IL-7 treatment , then twice weekly for the entire treatment period and then once per week; terminal ileum biopsies were obtained on day 14–16 post-infection; lymph node biopsies were obtained on day 25–27 post-SIV infection . Since one animal in the control group ( H744 ) was lost on day 14 post-infection during intestinal biopsy , all the data from day 14 onwards refer to a total of 11 animals . Recombinant glycosylated simian IL-7 ( Cytheris , France ) was employed in this study since it has a considerably longer half-life and greater stability ( M . Morre et al . , unpublished results ) than the non-glycosylated form that had been employed in several previous studies [27] , [28] . IL-7 was administered subcutaneously to the 6 monkeys included in group 2 at a concentration of 50 µg/kg of body weight once per week for a total of 7 consecutive administrations . To allow for the achievement of steady-state plasma levels of the cytokine prior to SIV infection , treatment was initiated 1 week before SIV inoculation ( day −7 ) . On day 0 , all 12 monkeys were inoculated intravenously with 100 macaque infectious doses of the pathogenic strain SIVmac251 , kindly provided by Dr . R . C . Desrosiers . Multiple clinical , immunological and virological parameters were monitored throughout the acute phase of infection , as well as for a follow-up period of 6 months post-infection . The concentration of IL-7 in serial plasma samples was measured using a high-sensitivity commercial ELISA ( Quantikine HS , R&D Systems , Minneapolis , MN ) . Peripheral blood was collected under sterile conditions in vacutainer tubes with EDTA as anticoagulant and complete blood cell count with differential was performed by a commercial laboratory ( Antech Diagnostics , Rockville , MD ) . Plasma was separated by spinning whole blood at 500×g for 20 min at 4°C without brake and stored at −80°C . PBMC were isolated by gradient centrifugation using Lymphocyte Separation Medium ( LSM; MP Biomedicals ) . Blood was diluted in Phosphate Buffered Saline ( PBS ) , stratified over LSM and centrifuged at 500×g for 25 min at 4°C without brake . The mononuclear cell ring was collected , and the PBMC were washed twice with PBS , counted and used for cytofluorimetric analyses . The following monoclonal antibodies ( mAbs ) were used for surface staining: CD28-FITC ( clone CD28 . 2 ) , CD4-PerCpCy5 . 5 ( clone L200 ) , CD95-APC or -PE ( clone DX2 ) , CD8-PECy7 ( clone SK1 ) , CD3-APCCy7 ( clone SP34-2 ) , all from BD Biosciences . The naïve ( CD28+CD95− ) , memory ( CD28+CD95+ ) and effector ( CD28−CD95+ ) T-cell subsets were identified as previously described [43] . At selected time points , a more detailed characterization of the memory T-cell compartment was performed by using a combination of the following mAbs: CD45RA-FITC ( clone 5H9 ) , CD28-PECy7 , CCR7-APC ( clone 3D12 ) , CD3-V450 , CD4-APCH7 ( all from BD Biosciences ) , and CD8-eFLOUR 605NC ( clone RPA-T8; eBioScience ) . As illustrated in Figure S6 in Text S1 , central memory T cells ( TCM ) were defined as CD45RA− CCR7+ CD28+ , transitional memory T cells ( TTM ) as CD45RA− CCR7− CD28+ , and effector memory T cells ( TEM ) as CD45RA− CCR7− CD28− . Additional mAbs were used to measure the expression of other cell-surface markers , including the IL-7 receptor-α/CD127-PE ( clone hIL-7R-M21 ) , CCR5-PE ( clone 3A9 ) , CXCR4-PE ( clone 12G5 ) and the activation markers HLA-DR-PE ( clone L243/G46-6 ) and CD25-PE ( clone M-A251 ) ( all from BD Biosciences ) . Stained cells were analyzed by flow cytometry using a BD FACSCanto collecting a minimum of 100 , 000 events per sample . Flow data were analyzed using the Flowjo software ( Tree Star ) or FCS Express ( DeNovo Software ) . Apoptosis was assessed by measuring the levels of Annexin-V binding , using Annexin V-APC conjugated ( BD Biosciences ) , after surface staining , as previously reported [22] . Bcl-2 expression levels and cellular proliferation were assessed as follows: after surface staining , the cells were fixed and permeabilized using BD Cytofix/Cytoperm solution ( BD Biosciences ) and incubated with mAbs anti-Bcl-2-PE ( clone Bcl-2/100 ) and anti-Ki67-PE ( clone B56 ) ( BD Biosciences ) . Annexin-V binding and Bcl-2 expression ( Mean Fluorescence Intensity , MFI ) were evaluated on various CD4+ and CD8+ T-cell subpopulation by multicolor flow cytometry using a BD FACSCanto , collecting a minimum of 100 , 000 events per sample , and flow data were analyzed with the Flowjo software ( Tree Star ) or FCS Express ( DeNovo Software ) applying a progressive gating strategy . Naïve memory and effector T-cell subsets were identified using a combination of anti-CD95 and anti-CD28 antibodies [44] on CD3+CD4+ and CD3+CD8+ gated cells , as described above for surface staining experiments . Plasma SIV RNA levels were measured using a quantitative real-time RT-PCR assay . Viral RNA was purified from 280 µl of cell-free plasma using the QIAamp Viral RNA kit ( Qiagen , USA ) , and stored at −80°C . The number of SIV RNA genome equivalents was determined using a single-tube real-time RT-RCR assay based on the AgPath-ID One-Step RT-PCR Kit ( Applied Biosystems , USA ) , under the following reaction conditions: 10 min at 45°C ( RT ) , 10 min at 95°C and 40 cycles of 15 sec at 95°C and 45 sec at 60°C ( PCR ) . Primers ( 300 nm ) and probe ( 200 nm ) were specifically designed within the SIV gag gene to amplify a fragment of 91 bp . Forward primer: 5′-GCAGAGGAGGAAATTACCCAGT-3′; reverse primer: 5′-ATTTTACCCAGGCATTTAATGTTC-3′ ( used for the RT phase ) ; TaqMan MGB probe , FAM-labelled: 5′-ACAAATAGGTGGTAACTATG-3′ . The copy number was determined by interpolation on a standard curve of a DNA plasmid carrying a fragment of the SIV gag gene containing the RT-PCR amplicon ( serial 10-fold dilutions from 100 copies/reaction to 106 copies/reaction ) . Forward cloning primer: 5′-GCAGAGACACCTAGTGATGGAAAC-3′; reverse cloning primer: 5′-TCTCCCACACAATTTAACATCTG-3′ . The SIV proviral DNA load was measured by real-time PCR using the same primers and probe as in the plasma viremia assay . Normalization for cell number was performed by quantification of a non-polymorphic single-copy gene , CCR5 . Additional details about the method have been reported [45] . SIV p27Gag antigenemia was measured by using the SIV Core Antigen Assay ( Coulter Corporation , Miami , USA ) according to the manufacturer's instructions . All samples were initially tested undiluted and retested at 1∶10 dilution if necessary . Plates were read using an end-point protocol with a microplate spectrophotometer ( Bio-Rad Instruments ) . Anti-SIV monkey IgGs were quantified using a home-made ELISA . Briefly , plates were coated overnight at 4°C with 1 µg/ml of total SIV protein extract , obtained by lysing with 1% Triton x-100 for 1 hour at room temperature the SIV isolate SIVmac251/SupT1-CCR5 CL30 ( from the AIDS & Cancer Virus Program , NCI , NIH , Frederick ) . The following day the plates were washed and blocked for 1 hour . Samples were then added to each well , initially undiluted and re-tested diluted 1∶5 or 1∶20 , whenever needed . Plates were then incubated with biotinylated goat anti-human IgG ( Sigma-Aldrich , #B1140 ) and HRP-conjugated streptavidin ( R&D Systems ) , and read at a wavelength of 450 nm with a reference set at 570 nm on a microplate spectrophotometer ( Bio-Rad Instruments ) . The presence of neutralizing anti-SIV antibodies in the sera of infected untreated and IL-7-treated animals was tested using an envelope-mediated fusion assay with PM1 cells chronically infected with SIVmac251 as effector cells and Hela ( TZM-bl ) cells expressing human CD4 and CCR5 as target cells . Ileum biopsies were performed by retrograde ileoscopy on day 14–16 post-infection . At least 6–8 punch biopsies were obtained from the terminal ileum of each animal , immediately placed in cold RPMI medium and then processed within 3 hours of excision . During the procedure , one animal ( H744 ) suffered an intestinal perforation and was lost . Ileum biopsies were digested in Iscove's media supplemented with 2 mg/ml Type II collagenase ( Sigma-Aldrich ) and 1 U/ml DNase I ( Sigma-Aldrich ) for 30 min at 37°C . After digestion , the samples were passed through a 70 µm strainer , and the suspended cells were washed twice with RPMI media supplemented with 10% heat-inactivated FBS . Lymph node excisional biopsies were performed on day 25–27 post-infection on axillary lymph nodes from all animals . Lymph nodes were cut longitudinally in two halves , one of which was stored at −80°C . The remaining half was finely minced using sterile scalpels and mechanically smashed to release lymphoid cells into the media . Cells were then washed , passed through a 70 µm strainer and stained for surface and intracellular markers as described above . SIV-specific CD4+ and CD8+ T-cell responses were analyzed by measuring intracellular cytokine production after antigen stimulation . Frozen samples of PBMC were thawed in RPMI 10% FBS and rested at 37°C for at least 5 hrs . Cells were then plated at 5×105 cells/well in a 96-well round-bottom plate in RPMI 10% FBS in the presence of purified anti-CD28 and anti-CD49d mAbs both at a final concentration of 1 ng/ml . Two pools of SIV-gag peptides and one pool of SIV-tat peptides ( from NIBSC , EVA Centre for AIDS reagents ) at a final concentration of 1 µg/ml ( each peptide ) and Staphylococcus aureus Enterotoxin B ( SEB , Sigma ) as positive control at a final concentration of 2 µg/ml were added to the samples in a total volume of 200 µl . A negative control with no stimulation was included . After 1 hr incubation at 37°C , 5% CO2 , 1 µl of Brefeldin A ( BD GolgiPlug , BD Biosciences ) was added to each well , and the plates were incubated for an additional 11 hrs . At the end of the incubation period the cells were transferred to 96-well V-bottom plates and washed twice with PBS before surface intracellular staining . Cells were stained first with LIVE/DEAD Fixable Dead Cell Stain reagent ( Invitrogen by Life Technologies ) at a concentration of 1 µl/106 cells for 20 min at 4°C and then with the mAbs to lineage antigens ( CD3-V450 and CD4-APCH7 , BD Biosciences ) , before fixation and permeabilization with BD Cytofix/Cytoperm Buffer ( BD Biosciences ) . After washing with BD PermWash Buffer ( BD Biosciences ) , the cells were incubated for 20 min at 4°C with anti-MIP-1β-PE ( clone D21-1351 ) , anti-IFN-γ-PECy7 ( clone P2G10 ) and anti-IL-2-APC ( clone MQ1-17H12; all from BD Biosciences ) , washed again and analyzed on a BD FACSCanto instrument . T-cell responses were analyzed using Flowjo ( Tree Star ) and Spice softwares . Statistical analysis was conducted using the softwares SAS ( version 9 . 1 for Windows ) , S-Plus ( version 6 . 2 for Windows ) , StatView ( version 5 . 0 . 1 for Macintosh ) and GraphPad Prism ( version 4 . 0b for Macintosh ) . Paired Student's t-tests were used for the comparison between different time points within the same animal group ( untreated or IL-7-treated ) . Non-parametric Wilcoxon rank sum tests were used to analyze differences between IL-7-treated and untreated animals . To compare untreated and IL-7-treated animals with respect to changes from baseline to multiple time points simultaneously the O'Brien test was used ( for a more detailed description see Supporting Information online ) .
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The development of highly effective cocktails of antiretroviral drugs has had a major impact on the survival and quality of life of individuals with HIV-1 infection . Yet , current protocols often fail to fully restore the immunologic function , a limitation that has prompted the clinical evaluation of immune-reconstitution agents , such as IL-7 , as adjuvant therapies . To date , however , IL-7 has been tested exclusively in patients with chronic HIV-1 infection , while it appears that the immune system is irreparably damaged during acute primary infection , within the first few weeks after encountering the virus . We used a macaque model to show that treatment with IL-7 has beneficial effects if implemented during the acute phase of infection with SIV , the simian AIDS virus . Early administration of IL-7 was safe and effectively protected CD4+ T cells , the primary target cells for the virus , from the marked decline that typically occurs during acute SIV infection . Furthermore , IL-7 boosted the development of antiviral immune responses . Thus , IL-7 might be an effective adjuvant therapy in acute HIV-1 infection , which can protect the pool of CD4+ T cells before it is irreversibly compromised by the action of the virus .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"biology"
] |
2012
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Treatment with IL-7 Prevents the Decline of Circulating CD4+ T Cells during the Acute Phase of SIV Infection in Rhesus Macaques
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Arthritogenic alphaviruses , including Ross River virus ( RRV ) and chikungunya virus ( CHIKV ) , are responsible for explosive epidemics involving millions of cases . These mosquito-transmitted viruses cause inflammation and injury in skeletal muscle and joint tissues that results in debilitating pain . We previously showed that arginase 1 ( Arg1 ) was highly expressed in myeloid cells in the infected and inflamed musculoskeletal tissues of RRV- and CHIKV-infected mice , and specific deletion of Arg1 from myeloid cells resulted in enhanced viral control . Here , we show that Arg1 , along with other genes associated with suppressive myeloid cells , is induced in PBMCs isolated from CHIKV-infected patients during the acute phase as well as the chronic phase , and that high Arg1 expression levels were associated with high viral loads and disease severity . Depletion of both CD4 and CD8 T cells from RRV-infected Arg1-deficient mice restored viral loads to levels detected in T cell-depleted wild-type mice . Moreover , Arg1-expressing myeloid cells inhibited virus-specific T cells in the inflamed and infected musculoskeletal tissues , but not lymphoid tissues , following RRV infection in mice , including suppression of interferon-γ and CD69 expression . Collectively , these data enhance our understanding of the immune response following arthritogenic alphavirus infection and suggest that immunosuppressive myeloid cells may contribute to the duration or severity of these debilitating infections .
Arthritogenic alphaviruses , including chikungunya virus ( CHIKV ) and Ross River virus ( RRV ) , are re-emerging , mosquito-transmitted alphaviruses that cause both endemic and explosive epidemics of debilitating musculoskeletal inflammatory disease [1] . CHIKV has caused outbreaks of unprecedented scale involving millions of persons in the Indian Ocean Islands [2] , India [3] , Southeast Asia [4 , 5 , 6] and Europe [7] . Most recently , CHIKV has emerged in the Western Hemisphere where ongoing epidemics on multiple islands in the Caribbean as well as in Central and South America have resulted thus far in more than one million suspected cases [8 , 9 , 10] . RRV , which causes ~4 , 000–7 , 000 cases in Australia and Papua New Guinea annually , has similarly caused explosive outbreaks [11] . For example , an RRV epidemic occurred in 1979–1980 with > 60 , 000 cases , where RRV spread from Australia to multiple islands in the Pacific Region including Fiji , the Cook Islands , and America Samoa [12 , 13 , 14] . Currently there are no specific therapies for the treatment of alphavirus-induced rheumatological disease and no licensed vaccines . CHIKV/RRV-induced disease is characterized by fever , intense pain and inflammation in joints , tendons , and muscles , and an impaired ability to ambulate [11] . This acute stage lasts for 1 to 2 weeks and is typically followed by convalescence . However , some disease signs and symptoms—such as joint swelling , joint stiffness , arthralgia , and tendonitis/tenosynovitis—can last for months to years , with up to 60% of patients reporting persistent rheumatological symptoms three years after initial diagnosis [15 , 16 , 17 , 18 , 19 , 20 , 21] . This chronic phase of the disease has been linked in both humans and animal models to persistent CHIKV/RRV infection in the affected musculoskeletal tissues [22 , 23 , 24 , 25] . Monocytes and macrophages can be activated by a variety of stimuli , resulting in a spectrum of activation phenotypes [26] . Macrophages that promote tissue repair/remodeling during wound healing and have immunoregulatory functions express arginase 1 ( Arg1 ) , an enzyme that hydrolyzes L-arginine [27] . High Arg1 expression has been associated with a variety of diseases such as chronic inflammation [28] , asthma [29] , and infectious diseases [30 , 31 , 32 , 33 , 34] . The expression of Arg1 by human and murine monocytes/macrophages , neutrophils , and myeloid-derived suppressor cells ( MDSCs ) has emerged as a major regulator of immune responses [35 , 36 , 37] . Indeed , Arg1 activity in myeloid cells impairs effective immunity against intracellular pathogens such as Mycobacterium tuberculosis and Toxoplasma gondii [38] , exacerbates tumor growth by suppressing T cell function [39 , 40] , and limits T cell-driven inflammatory tissue damage [41] . Arg1 activity has also been associated with higher viral loads and lower CD4+ T cell counts from HIV-seropositive patients [33 , 42] and with inhibition of CD8+ T cell responses in hepatitis B virus ( HBV ) and hepatitis C virus ( HCV ) -infected patients [34 , 43] . We previously showed that Arg1 was induced in the musculoskeletal inflammatory lesions and tissue-infiltrating macrophages of RRV- and CHIKV-infected mice [44] . We further showed that mice specifically deleted for Arg1 in myeloid cells had reduced viral loads , as well as improved tissue pathology , at late , but not early , times post-RRV infection , indicating that Arg1+ macrophages prevent efficient host control of RRV infection in musculoskeletal tissues [44] . We sought to expand our previous findings in the mouse model to human CHIKV infections . Here , we show that Arg1 is induced in peripheral blood mononuclear cells ( PBMCs ) from CHIKV-infected patients and that higher Arg1 expression levels were associated with higher viral loads and more severe disease . Using the RRV and CHIKV mouse models , we further investigated the mechanism ( s ) by which Arg1 regulates clearance of arthritogenic alphavirus infection . We found that Arg1-expressing myeloid cells inhibited the antiviral T cell response in RRV-infected mice , resulting in reduced expression of the antiviral cytokine interferon ( IFN ) -γ and modulation of T cell activation markers .
We have previously shown that Arg1 is highly induced in inflamed and infected musculoskeletal tissues and infiltrating macrophages of mice infected with RRV or CHIKV [44] . Moreover , mice specifically deleted for Arg1 in myeloid cells had reduced viral loads at late , but not early , times post-RRV infection , suggesting that Arg1 activity in macrophages inhibits efficient host control of RRV infection in musculoskeletal tissues [44] . To evaluate the role of Arg1 during CHIKV infection in patients , we analyzed the gene expression profile of PBMCs collected at four times post-illness onset ( PIO ) in a cohort of CHIKV-infected patients in Singapore: ( 1 ) acute phase ( median , 4 days PIO ) ; ( 2 ) early convalescent phase ( median , 10 days PIO ) ; ( 3 ) late convalescent phase ( 4–6 weeks PIO ) ; and ( 4 ) chronic phase ( 2–3 months PIO ) [45] . Compared to healthy controls , we found that Arg1 expression levels were significantly increased in circulating PBMCs during the acute phase and remained elevated into the chronic phase of the disease ( Fig 1A ) . By segregating the cohort into high and low viral load groups ( HVL and LVL , respectively ) , as previously defined [45] , higher Arg1 expression was observed to associate with higher viral loads in these patients ( Fig 1B ) . High viremia was also previously shown to correlate with increased disease severity during the acute phase of infection ( ref . [45] and S1A Fig ) . By segregating on disease severity , we were able to analyze Arg1 gene expression in PBMCs isolated from patients with mild or severe clinical disease throughout the disease time course . We found that although Arg1 expression levels were equally elevated in PBMCs isolated from patients with mild or severe disease during the acute , early convalescent , and chronic phases of disease ( S1B , S1C and S1E Fig ) , PBMCs isolated from patients with severe disease during the late convalescent phase ( 4–6 weeks PIO ) expressed Arg1 at significantly higher levels than PBMCs isolated from patients with mild disease ( P = 0 . 002 ) ( S1D Fig ) . Due to the low number of patients that complained of persistent arthralgia 2–3 months PIO in this study ( 6 of 23 patients ) , there was no association between Arg1 expression levels in PBMCs isolated from patients with persistent join pain during the chronic phase of disease ( 2–3 months PIO ) compared to those who had fully recovered . In addition to comparing Arg1 expression levels to viral loads and clinical disease , we compared Arg1 gene expression in PBMCs to plasma concentrations of C-reactive protein ( CRP ) and to the expression of other genes in PBMCs collected during the acute phase ( median , 4 days PIO ) or early convalescent phase ( median , 10 days PIO ) . Positive correlations were observed between Arg1 gene expression and the plasma concentration of CRP , IP-10 , and IL-4 ( Fig 1C ) as well as the gene expression of type I interferons ( IFNs ) , IL-6 , IP-10 , IL-10 , and NADPH oxidase 1 ( Nox1 ) in PBMCs isolated during the acute phase ( Fig 1D ) . This suggests that these factors are induced as part of the early response to CHIKV infection . Overall , these data show that , consistent with our studies in mice , Arg1 expression is induced in PBMCs isolated from humans infected with an arthritogenic alphavirus , and Arg1 expression levels are associated with viral load and disease severity . Additionally , other genes that have been shown to have suppressive functions , such as Nox1 , or to be associated with polarizing suppressive myeloid cells , such as IL-6 , are also elevated in PBMCs from CHIKV-infected patients . In sum , these data suggest that suppressive myeloid cells may be induced in human CHIKV infection . Since Arg1 is inducible in myeloid cells , we sought to test if exposure to CHIKV would induce Arg1 or other genes with immunoregulatory functions in human monocytes ex vivo . To do this we utilized the human fibrosarcoma cell line HS 633T , which has been shown to be susceptible to CHIKV infection [46] . To recapitulate the in vivo setting whereby monocytes infiltrate infected joint and muscle tissue , human monocytes isolated from PBMCs via negative selection were co-cultured with mock or CHIKV-infected HS 633T cells for 24 hpi , at which time all of the cells were harvested for gene expression analysis . The transcriptional profile of these groups was compared to mock or CHIKV-infected fibroblasts cultured in the absence of monocytes . Expression of the Arg1 , IL-6 , and Nox1 genes was significantly upregulated in the CHIKV-infected co-cultures ( Fig 2A ) . These data suggest that CHIKV-infection of human fibroblasts in vitro induces a transcriptional profile in co-cultured human monocytes that has features similar to myeloid suppressor cells . Next , we sought to determine if direct contact was required to induce Arg1 expression in monocytes . Monocytes were isolated from PBMCs and stimulated for 24 hours with supernatant from mock- or CHIKV-inoculated HS 633T cells collected at 24 hpi . Virus present in the cell culture supernatant was not inactivated or removed prior to incubation with monocytes . Arg1 , TGF-β , and Nox1 transcripts were induced in the monocytes cultured in the CHIKV-infected cell supernatant compared to those cultured in culture supernatants collected from the mock-inoculated cells ( Fig 2B ) . Following 24 h culture with fibroblast supernatant harvested at 48 hpi , IL-6 expression was significantly upregulated in the monocytes ( Fig 2B ) . These data indicate that direct contact with infected fibroblasts is not required for Arg1 induction in monocytes; instead , soluble factor ( s ) , which could include virus and/or other soluble factors such as cytokines , in the supernatant mediate activation of this transcriptional profile . To investigate if the presence of live virus in cell culture supernatants from alphavirus-infected cells is required for the induction of Arg1 in myeloid cells , we performed experiments utilizing J774 murine macrophages and C2C12 myoblasts that had been differentiated into myofibers to mimic muscle cells , which are a target cell of RRV and CHIKV infection in vivo ( S2 Fig ) . In these studies , supernatant transfer experiments were performed to determine whether RRV-infected muscle cells produce factors that induce the expression of Arg1 in macrophages . Stimulation of J774 macrophages with IL-4 ( a positive control ) or culture supernatants from RRV-infected differentiated C2C12 muscle cells induced Arg1 expression ( representative blot in S2A Fig , quantified in S2B Fig ) . In contrast , stimulation of macrophages with cell culture supernatants from mock-infected differentiated C2C12 muscle cells did not induce Arg1 expression above levels detected in control cells . To determine if the virus present in these cell culture supernatants was required for Arg1 induction in macrophages , we stimulated J774 macrophages with cell supernatant that was left untreated , treated with ultraviolet light ( UV ) or heat to inactivate the virus , or ultracentrifuged to eliminate virus . Additionally , macrophages were treated with virus that was purified via density gradient centrifugation . The ultra-centrifugation , UV , and heat inactivation treatments of the C2C12 cell culture supernatants effectively reduced the amount of live virus ( S2D Fig ) . Regardless of the presence of live or inactivated virus , the infected cell supernatant induced Arg1 expression in the macrophages , whereas purified virus did not ( representative blot in S2C Fig , quantified in S2E Fig ) . These data suggest that a factor ( s ) present in culture supernatants other than the virus itself mediates induction of Arg1 expression . Previously , we showed that Arg1 is expressed in infiltrating macrophages present in musculoskeletal tissues of CHIKV- or RRV-infected mice [44] . Since exposure of primary human monocytes to supernatants collected from CHIKV-infected cells induced Arg1 expression ( Fig 2 ) , we sought to further define the tissue compartments in which Arg1 was induced in CHIKV- and RRV-infected mice . The gastrocnemius muscle , circulating blood leukocytes , bone marrow , spleen , and draining popliteal lymph node were harvested from mock- , RRV- , or CHIKV-inoculated mice at 7 dpi for RT-qPCR analysis of Arg1 . As demonstrated previously [44] , Arg1 transcript was highly induced in inflamed muscle tissue of RRV-infected mice ( 245-fold increase , P = 0 . 002 ) as well as ankle joint tissue ( 8 . 2-fold increase , P = 0 . 06 ) ( Fig 3A ) . Additionally , Arg1 was induced in circulating blood leukocytes ( 8 . 6-fold increase , P = 0 . 02 ) . In contrast , Arg1 expression was not induced in lymphoid tissues such as bone marrow cells , cells from the draining popliteal LN , or spleen cells ( Fig 3A ) . Similar results were observed in tissues collected from CHIKV-infected mice , although the highest level of Arg1 induction was observed in ankle-joint associated tissue ( 54-fold increase , P = 0 . 002 ) as opposed to the gastrocnemius muscle in RRV-infected mice ( Fig 3B ) . These data indicate that , similar to our analysis of Arg1 in PBMCs from CHIKV-infected humans , Arg1 expression was elevated in circulating cells from CHIKV- and RRV-infected mice , albeit to a lesser extent . Arg1-expressing myeloid cells have immune-suppressive activity [41 , 47 , 48] . Additionally , we have previously shown that Arg1-expressing CD11b+F4/80+ macrophages present in the inflamed musculoskeletal tissues of RRV-infected mice suppressed T cell proliferation ex vivo in a dose-dependent manner by a mechanism that was partially Arg1-dependent [44] . Furthermore , mice specifically deleted for Arg1 in myeloid cells ( LysMcre;Arg1F/F mice ) have significantly lower viral loads in musculoskeletal tissues , suggesting that Arg1 activity in macrophages prevents efficient host control of RRV infection in musculoskeletal tissues [44] . Based on these data , we hypothesized that tissue-infiltrating myeloid cells inhibited RRV clearance by suppression of the antiviral T cell response . To investigate this hypothesis , WT and LysMcre;Arg1F/F mice were treated with anti-CD4 and anti-CD8α antibodies , or an isotype control antibody , on days 7 and 12 pi to deplete CD4+ and CD8+ T cells . Experiments were terminated at 14 dpi , spleens were analyzed for efficiency of T cell depletion , and muscle tissue was harvested for absolute quantification of RRV genome levels . Administration of anti-CD4 and anti-CD8α antibodies depleted ≥ 98% of both CD4+ and CD8+ T cell subsets as demonstrated by staining spleens for CD3 , CD4 , and CD8β ( Fig 4A and 4B ) . As with untreated mice [44] , control antibody-treated LysMcre;Arg1F/F mice had significantly lower viral RNA levels compared to control antibody-treated WT mice ( 3 . 2-fold decrease , P < 0 . 05 ) ( Fig 4C ) . The difference between control antibody-treated LysMcre;Arg1F/F mice and WT mice is not as substantial as we previously published in untreated LysMcre;Arg1F/F mice and WT mice [44]; this may be due to unanticipated effects of the isotype control antibody treatments . Interestingly , following depletion of both CD4+ and CD8+ T cells , LysMcre;Arg1F/F mice had similar viral loads to T cell-depleted WT mice ( P > 0 . 05 ) ( Fig 4C ) . Taken together , these data suggest that Arg1-expressing myeloid cells have suppressive activity and inhibit antiviral T cells . One mechanism by which myeloid cell Arg1 has been shown to suppress T cells is by inhibiting T cell proliferation [47 , 49] . To determine if myeloid cell Arg1 inhibits T cell stimulation and proliferation in lymphoid tissues , T cell populations in the spleen and draining popliteal lymph node ( LN ) were analyzed by flow cytometry at 7 , 10 , and 14 days post-RRV inoculation of WT and LysMcre;Arg1F/F mice . After gating on lymphocytes , CD8+ T cells were identified by staining positive for CD3 and CD8; CD4+ T cells were identified by gating on CD3+CD8- cells followed by gating on CD3+CD4+ cells ( S3A Fig ) . We found that WT and LysMcre;Arg1F/F mice had similar frequencies and total numbers of CD4+ and CD8+ T cells in both the spleen and draining popliteal LN at all of the time points analyzed ( S3B–S3E Fig ) . Since Arg1 is highly induced in inflamed musculoskeletal tissues but not lymphoid tissues of RRV-infected mice ( ref . [44] and Fig 3 ) , we hypothesized that the inhibitory effects of Arg1-expressing myeloid cells could be localized to sites of pathology in the musculoskeletal tissues . Thus , we next assessed the presence of CD4+ and CD8+ T cells in inflamed muscle tissue of WT versus LysMcre;Arg1F/F mice at 7 , 10 , and 14 dpi . CD4+ and CD8+ T cells were identified as described above ( Fig 5A ) . We again found similar frequency and number of total CD4+ and CD8+ T cells in the quadriceps muscle of both WT and LysMcre;Arg1F/F mice at all time points analyzed ( Fig 5B and 5C ) . In total , these data suggest that although myeloid cell Arg1 inhibits the antiviral T cell response , this is not through the suppression of total T cell numbers at immune inductive sites or the sites of infection and inflammation . Arg1-mediated suppression has been shown to inhibit antigen-specific T cell responses [50] . For example , in Leishmania-infected mice , parasite-specific T cell proliferation was suppressed at the site of pathology , where high arginase activity was detected , but not in the draining LN , where arginase activity was low or undetectable [48] . Thus , we next quantified virus-specific T cell responses in both lymphoid tissues and inflamed muscle tissue of RRV-infected mice . For these experiments we utilized a recombinant Ross River virus ( “RRV-LCMV” ) that was recently developed in our laboratory [51] and encodes a CD8 ( gp33 ) TCR immunodominant epitope of LCMV , enabling the identification and quantification of virus-specific CD8+ T cells in tissues via tetramer staining ( see Materials and Methods for description of virus generation ) . WT and LysMcre;Arg1F/F mice were inoculated with RRV-LCMV and gp33+CD8+ T cells were analyzed by flow cytometry at 10 dpi , a time point associated with extensive skeletal muscle inflammation and damage as well as high Arg1 expression [44] . To confirm the specificity of the gp33 tetramer staining , leukocytes from the spleens of mock- and WT RRV-infected mice were also stained for gp33-specific T cells . Similar to the analysis of bulk CD8+ T cells in WT RRV-infected mice ( S3 Fig ) , we found that the total number of CD8+ T cells in the spleen of RRV-LCMV-infected LysMcre;Arg1F/F mice was mildly elevated in comparison to WT mice ( Fig 6C ) . However , the total number of virus antigen-specific gp33+CD8+ T cells in the spleen of RRV-LCMV-infected LysMcre;Arg1F/F mice was similar to WT mice ( Fig 6E ) . Since Arg1 is highly induced in the inflamed muscle tissue of RRV-infected mice ( Fig 3 and ref . [44] ) , we next analyzed the bulk and virus-specific CD8 T cell response in muscle tissue of WT and LysMcre;Arg1F/F mice . The gp33+ gate was set such that ≤ 0 . 1% of CD8+ T cells from the muscle tissue of a WT RRV-inoculated mouse stained positive with the gp33 tetramer ( Fig 6F ) . Interestingly , RRV-LCMV-infected LysMcre;Arg1F/F mice had significantly increased frequency and number ( 2 . 5-fold ) of CD8+ T cells in the muscle tissue compared to RRV-LCMV-infected WT mice ( Fig 6G and 6H ) . This suggests that loss of myeloid cell Arg1 may have a more profound effect on CD8 T cells in the presence of a RRV expressing the CD8 T cell immunodominant epitope from LCMV , gp33 . Indeed , there was a slight but non-significant difference in the frequency of gp33-specific CD8+ T cells ( Fig 6I ) ; moreover , there was a significantly greater number ( 3 . 2-fold ) of gp33+CD8+ T cells detected in the inflamed quadriceps muscle tissue of RRV-LCMV-infected LysMcre;Arg1F/F mice than WT mice ( Fig 6J ) . These data are consistent with the data presented in Fig 3 , which shows elevated expression of Arg1 in skeletal muscle tissue but not lymphoid tissue . In addition , these data suggest that loss of myeloid cell Arg1 increased virus-specific T cell trafficking , proliferation , and/or avoidance of deletion at the site of inflammation . In addition to inhibiting T cell proliferation and trafficking , Arg1-expressing myeloid cells can also inhibit T cell activation [36 , 37] . We next sought to investigate the activation of virus-specific CD8+ T cell responses in WT and LysMcre;Arg1F/F mice . The three activation markers that we evaluated are known to be upregulated on antigen-experienced T cells: CD44 , CD11a , and CD69 [52] . Of the CD44+gp33+CD8+ T cells in WT and LysMcre;Arg1F/F mice , essentially all were also CD11a+ ( representative histogram shown in Fig 7A; quantified in Fig 7B ) . However , significantly more CD44+gp33+CD8+ T cells in LysMcre;Arg1F/F mice stained for CD69 than those cells in WT mice ( representative histogram shown in Fig 7A; quantified in Fig 7B ) , suggesting specific modulation of this activation marker on virus-specific T cells in the absence of arginase activity . These data suggest that the inflamed musculoskeletal tissue environment alters the T cell activation phenotype in an Arg1-dependent manner . To further explore the effects of Arg1 on T cell function , we investigated the cytokine expression levels in T cells sorted from muscle tissue of RRV-infected WT and LysMcre;Arg1F/F mice . At 10 dpi , quadriceps muscle tissue was dissected and enzymatically digested , and infiltrating leukocytes were stained for CD3 , CD19 , CD4 , and CD8 . CD4+ and CD8+ T cells were FACS-sorted by gating on CD3+CD19- cells and then gating individually on CD4+ T cells and CD8+ T cells ( Fig 8A ) . Gene expression analysis of the sorted T cell subsets was compared to respective T cells sorted from the spleen of a mock-infected mouse . Both CD4+ and CD8+ T cell subsets isolated from LysMcre;Arg1F/F mice expressed increased levels of IFN-γ compared to T cells sorted from WT mice ( Fig 8B ) . T cells sorted from the spleens of RRV-infected WT or LysMcre;Arg1F/F mice showed no difference in IFN-γ expression ( S4 Fig ) . CD4+ T cells sorted from LysMcre;Arg1F/F mice also expressed elevated levels of TNF-α ( Fig 8C ) and IL-10 ( Fig 8D ) transcripts . In contrast , CD8+ T cells sorted from WT or LysMcre;Arg1F/F mice expressed similar levels of TNF-α , and IL-10 expression was not detected in this T cell subset . Additionally , IL-2 expression was not detected in either T cell subset ( Fig 8E ) . To further confirm cytokine expression , T cells from RRV-infected WT mice isolated at 10 dpi were restimulated ex vivo with anti-CD3 and anti-CD28 antibodies followed by intracellular cytokine staining . A subset of CD4+ T cells produced both IFN-γ and IL-10 , whereas CD8+ T cells produced IFN-γ but very little IL-10 ( S5 Fig ) . These data suggest that Arg1 activity in macrophages inhibits cytokine expression , including IFN-γ , by T cells in musculoskeletal tissues , which may be one mechanism by which Arg1 influences viral loads . Suppressive myeloid cells have been shown to mediate T cell suppression through the induction or expansion of regulatory T ( Treg ) cells [53] , which can indirectly inhibit CD8+ T cells . Interestingly , we found that at 10 days post-RRV infection a subset of muscle-infiltrating CD4+ T cells produce IL-10 ( Fig 8D and S5 Fig ) , a cytokine that has anti-inflammatory functions and has been shown to regulate Arg1 expression levels in myeloid cells [53] . To determine if CD4+ T cells were contributing to Arg1 induction in musculoskeletal tissues following RRV infection , we treated mice with an anti-CD4 Ab or a control Ab on day 4 pi and harvested muscle tissues on day 7 pi for analysis of CD4 T cell numbers , macrophage numbers , RRV loads , and Arg1 expression . Administration of anti-CD4 Ab depleted ≥ 95% of CD4+ T cells in the spleen and muscle tissue as demonstrated by staining for CD3 and CD4 ( S6A–S6E Fig ) . Importantly , the depletion of CD4+ T cells did not reduce the total number of macrophages present in skeletal muscle tissue ( S6A , S6F and S6G Fig ) . Mice that were depleted of CD4+ T cells had reduced Arg1 expression in muscle tissue at 7 dpi compared to control Ab-treated mice ( 146-fold increase versus 271-fold increase ) , however these differences were not statistically significant ( S6H Fig ) . Consistent with viral loads in LysMcre;Arg1F/F mice at 7 dpi , mice depleted of CD4+ T cells had similar viral loads as control Ab-treated mice ( S6I Fig ) . These data suggest that Arg1 expression levels in tissues are predominantly regulated by a CD4+ T cell-independent mechanism ( s ) . Other groups have shown that IFN-γ production from T cells was critical for clearance of SINV RNA from neurons [54] , demonstrating a role for IFN-γ-mediated clearance of alphavirus RNA . To further investigate the role for T cell-derived IFN-γ in control of RRV infection , we adoptively transferred T cells from WT and Ifng-/- mice into Rag1-/- mice , which lack B and T cells , one day prior to RRV inoculation and analyzed RRV RNA levels at 14 dpi in muscle tissues compared to Rag1-/- mice that received media alone . T cell reconstitution was confirmed by flow cytometric analysis of spleen and muscle tissue at 14 dpi for the presence of cells staining positively for CD3 and CD4 or CD3 and CD8 and negatively for B220 and GR-1 ( Fig 9A ) . Although T cell engraftment varied between individual mice , the frequencies ( Fig 9B ) and total numbers ( Fig 9C ) of CD4 and CD8 T cells in the spleen and muscle tissue of mice receiving WT or Ifng-/- T cells were comparable . RRV RNA levels were lower in muscle tissue of mice that received WT T cells compared to mice that received media alone ( 9-fold decrease , P < 0 . 001 ) ( Fig 9D ) . Moreover , RRV RNA levels in Rag1-/- mice that received Ifng-/- T cells were similar to mice that received no T cells , which was significantly greater than RRV levels in Rag1-/- mice that received WT T cells ( 7 . 1-fold increase , P < 0 . 001 ) ( Fig 9D ) . These data indicate that IFN-γ production is critical for the antiviral effects of T cells following RRV infection . In sum , our data suggest that Arg1-mediated inhibition of T cell activation and IFN-γ production results in enhanced viral loads , perhaps contributing to viral persistence and chronic disease in humans .
We previously showed that RRV and CHIKV infection in mice resulted in elevated expression of Arg1 in inflamed musculoskeletal tissues and tissue-infiltrating macrophages [44] . Here , we demonstrate that Arg1 is also highly induced in PBMCs isolated from CHIKV-infected patients during the acute phase and remained elevated in PBMCs isolated from patients 2–3 months post-illness onset . These data suggest that CHIKV infection may result in the expansion of immunoregulatory myeloid cells that express high levels of Arg1 . Moreover , higher Arg1 transcript levels were associated with higher viral loads and with more severe disease , suggesting a relationship between viral levels , disease severity , and cells that express Arg1 . Immune-suppressive myeloid cells are heterogeneous populations of myeloid cells that are functionally defined by their potent ability to suppress T cell functions via a variety of mechanisms , including Arg1 activity [53] . We previously demonstrated that specific ablation of Arg1 in myeloid cells enhanced the clearance of RRV from musculoskeletal tissues and diminished muscle tissue pathology at late times post-RRV infection [44] . Here , we show that the enhanced control of RRV infection in LysMcre;Arg1F/F mice is likely due to more effective antiviral T cell responses . Several lines of evidence support this conclusion . First , in our previous study we found that CD11b+F4/80+ macrophages sorted from muscle tissue of RRV-infected mice suppressed T cell proliferation ex vivo in a mixed leukocyte reaction via a mechanism that was partially Arg1-dependent [44] . Here , depletion of CD4+ and CD8+ T cells from WT and LysMcre;Arg1F/F mice increased viral loads compared to the respective control antibody-treated mice at 14 dpi in inflamed muscle tissue , consistent with our previous studies that demonstrated a role for T cells in controlling RRV infection in muscle tissue [51] . However , viral loads in the muscle tissue of T cell-depleted WT and LysMcre;Arg1F/F mice at 14 dpi were not significantly different , suggesting that Arg1 activity in macrophages inhibited the antiviral activity of T cells at the sites of infection . Arginase-expressing myeloid cells can inhibit T cell functions via a variety of mechanisms , including reducing the bioavailablity of L-arginine , increasing the production of reactive nitrogen species such as peroxynitrite , and expanding regulatory T cell populations [53] . For instance , depletion of extracellular L-arginine levels from the local microenvironment via the activity of myeloid cell Arg1 has been shown to suppress T cells , including inhibiting T cell proliferation and suppressing other T cell functions such as IFN-γ production [47 , 49] . We found that WT and LysMcre;Arg1F/F mice had similar frequencies and numbers of CD4+ and CD8+ T cells in the spleen and draining popliteal LN at 7 , 10 , and 14 dpi , suggesting that myeloid cell Arg1 did not affect T cell numbers in lymphoid tissues . These data are consistent with our findings showing that Arg1 expression is highly induced in inflamed musculoskeletal tissues but not lymphoid tissues of RRV- and CHIKV-infected mice . This expression pattern is similar to mice infected with the Leishmania major parasite where high arginase activity was detected at the site of pathology but not in the draining lymph node [48] . Moreover , Arg1-expressing myeloid cell-mediated suppression has been shown to inhibit antigen-specific T cell responses [50] , as was shown for L . major-specific T cells at the site of pathology [48] . Thus , we hypothesized that virus-specific T cells , rather than bulk T cells , would be inhibited specifically at the site of pathology in musculoskeletal tissue . To this end we found that CD4+ and CD8+ T cell frequencies and numbers were similar in muscle tissue of WT and LysMcre;Arg1F/F mice at 7 , 10 , and 14 dpi . However , utilizing a recombinant RRV encoding the CD8 immunodominant epitope of LCMV , we found that LysMcre;Arg1F/F mice , as compared to WT mice , had significantly more virus-specific CD8+ T cells in the muscle tissue at 10 dpi , a time point preceding the significant difference in viral loads detected at 14 dpi . Further studies will be required to determine if this is a result of deletion of virus-specific T cells due to depletion of amino acids , the inability to detect gp33-specific T cells at the sites of pathology in WT mice , and/or a result of enhanced virus-specific T cell proliferation in muscle tissue of LysMcre;Arg1F/F mice . Preliminary studies show that a similar proportion of bulk and gp33-specific CD8+ T cells in spleen and muscle tissue stain for the proliferation marker Ki67 at 7 dpi ( S7 Fig ) , suggesting that arginase activity may not inhibit T cell proliferation in RRV-infected WT mice . Alternatively or additionally , an effect of myeloid cell Arg1 on CD4+ T cells , such as through the induction or expansion of Treg cells , another mechanism by which MDSCs mediate T cell suppression [53] , could indirectly inhibit virus-specific CD8+ T cells . If this mechanism of suppression was occurring in the context of RRV or CHIKV infection , loss of CD4+ T cells would result in less severe acute disease . Consistent with that hypothesis , Cd4-/- mice are protected from CHIKV-induced arthritis/swelling [55] , suggesting that CD4+ T cells may have a pathogenic role in acute CHIKV disease . In this study , we found that during RRV infection a subset of muscle-infiltrating CD4+ T cells produce IL-10 , a cytokine that has anti-inflammatory functions and has been shown to induce Arg1 expression in myeloid cells via signaling through STAT3 [53] . However , depletion of CD4+ T cells resulted in minimal effects on Arg1 expression levels in musculoskeletal tissues at 7 days post-RRV infection , suggesting that Arg1 expression levels are primarily regulated by CD4+ T cell-independent mechanisms , and studies are ongoing in our laboratory to define the role of specific cytokines , such as IL-10 and IL-6 , and other factors in the regulation of Arg1 expression in musculoskeletal tissues of RRV- and CHIKV-infected mice . In addition , further studies are required to determine if other cell ( s ) besides T cells are also inhibited by Arg1-expressing myeloid cells in the context of arthritogenic alphavirus infection . In addition to the increased number of gp33-specific CD8+ T cells in muscle tissue of RRV-LCMV-infected LysMcre;Arg1F/F mice , we also found that a greater frequency of these muscle-infiltrating virus-specific T cells stained for the activation marker CD69 compared to virus-specific T cells from WT mice . Importantly , no difference in the expression of another activation marker—CD11a—was seen on virus-specific T cells WT and LysMcre;Arg1F/F mice . These data suggest a modulation of specific activation markers on virus-specific CD8+ T cells in the presence or absence of arginase activity . CD69 is an early activation marker found on T cells that recently received stimulation through the TCR and has been shown to be persistently expressed at inflammatory foci [56] . We found that a larger proportion of virus-specific CD8+ T cells in muscle tissue of LysMcre;Arg1F/F mice were CD69+ at 10 dpi , suggesting that a greater number of T cells are restimulated in the muscle tissue of Arg1-deficient but not Arg1-sufficient mice , augmenting their activation and antiviral functions ( e . g . , IFN-γ production ) . Additionally , Cd69−/− T cells are not efficiently retained in lymphoid tissues and also fail to establish or sustain tissue residency [57 , 58] . Thus , arginase activity may inhibit virus-specific T cell retention in musculoskeletal tissues , resulting in reduced viral control . Additional studies are required to delineate the cause ( s ) of this differential T cell activation in WT versus LysMcre;Arg1F/F mice . Studies with Sindbis virus ( SINV ) , an alphavirus that causes encephalomyelitis in mice , have shown that mice unable to make antibodies can clear infectious virus from the brain stem and spinal cord but not the brain [54] . This was shown to be due at least in part through the action of IFN-γ produced by both CD4+ and CD8+ T cells , resulting in site-specific non-cytolytic clearance of virus from the CNS [54] . Consistent with a role for IFN-γ in controlling alphavirus infection , CHIKV-infected IFN-γ-/- mice had increased serum viral RNA levels compared to WT mice [55] . Here , we demonstrated that muscle-infiltrating CD4+ and CD8+ T cells express IFN-γ transcripts , and IFN-γ expression is higher in T cells isolated from muscle tissue of RRV-infected LysMcre;Arg1F/F mice compared to T cells isolated from muscle tissue of WT mice , suggesting that myeloid cell Arg1 activity inhibits cytokine expression by T cells in inflamed musculoskeletal tissues . Increased IFN-γ mRNA expression combined with an increased frequency of CD69+ T cells in musculoskeletal tissues of Arg1-deficient mice suggests that a lack of arginase activity may lead to more efficient T cell restimulation within the inflamed and infected musculoskeletal tissues . This results in more effective antiviral T cells and thus better viral clearance . The mechanism by which IFN-γ acts may include direct antiviral effects as well as regulatory functions important for other immune effector mechanisms , such as increasing expression of MHC class I and class II . Further studies demonstrated that adoptive transfer of naïve WT T cells but not T cells lacking IFN-γ could control RRV infection in muscle tissue of infected Rag1-/- mice , supporting a direct role for IFN-γ . Since uncontrolled cytokine production can be highly toxic , IFN-γ expression by T cells is tightly regulated at the transcriptional level [59] . Indeed , studies with effector CD8+ T cells during virus infection have shown that cytokine production terminates immediately following loss of antigen contact but is quickly initiated again after antigen contact is restored [59] . The increased IFN-γ expression by muscle-infiltrating T cells from RRV-infected LysMcre;Arg1F/F mice is another indication that the Arg1-driven immunosuppressive environment inhibits T cell responses . These studies provide important evidence for the role of Arg1-expressing myeloid cells in the control of arthritogenic alphavirus infection in humans and mice . Sustained expression of Arg1 throughout the course of disease suggests that activation of immunosuppressive myeloid cells may contribute to the duration of disease and/or the development of chronic disease . Thus , therapeutics that target the induction or activity of Arg1 could limit the severity or duration of these debilitating virus-induced diseases .
All mouse studies were performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . All mouse studies were performed at the University of Colorado Anschutz Medical Campus ( Animal Welfare Assurance #A 3269–01 ) using protocols approved by the University of Colorado Institutional Animal Care and Use Committee . All studies were performed in a manner designed to minimize pain and suffering in infected animals . CHIKV human PBMC samples were collected from 23 patients that were admitted to the Communicable Disease Centre at Tan Tock Seng Hospital during the 2008 Singapore CHIKF outbreak . All patients were diagnosed with CHIKF and blood was collected with written informed consent obtained from all participants . The study was approved by the National Healthcare Group’s domain-specific ethics review board ( DSRB Reference No . B/08/026 ) . A total of 23 PCR-confirmed CHIKV-positive individuals from the 2008 Singapore CHIKV outbreak were included in this study [45] . Peripheral blood mononuclear cell ( PBMC ) samples were isolated from patients admitted with acute CHIKV disease to the Communicable Disease Centre at Tan Tock Send Hospital during the outbreak from August 1 to September 23 , 2008 in Singapore . For a control group , PBMC samples were isolated during the same time period from 8 healthy volunteers ( controls ) residing in Singapore [60] . PBMCs from patients and controls were isolated using standard Ficoll-Paque density gradient centrifugation method and stored in -80°C until use . Samples were taken at 4 different time points: ( a ) acute phase ( median 4 days post illness onset ) , ( b ) early convalescent phase ( median 10 days post illness onset ) , ( c ) late convalescent phase ( 4–6 weeks post illness onset ) , and ( d ) chronic phase ( 2–3 months post illness onset ) . On the basis of their viral loads , quantified upon admission to hospital , the patients were classified into either high viral load ( HVL ) group ( n = 11 ) or low viral load ( LVL ) group ( n = 12 ) [45 , 60] . Based on the clinical parameters defined in earlier studies [45 , 61] , illness was defined as “severe” if a patient had either a maximum temperature greater than 38 . 5°C , or a maximum pulse rate greater than 100 beats/min , or a nadir platelet count less than 100 x 109/liter . Patients who did not fulfill these criteria were classified as “mild” [45 , 61] . Ten ( 91% ) of 11 patients with HVL ( median viral load , 9 . 97 x 105 pfu/mL; range , 1 . 42 x 105–5 . 62 x 108 pfu/mL ) presented with “severe” clinical illness , compared with 1 ( 19% ) of 12 patients with LVL ( median viral load , 2 . 02 x 104 pfu/mL; range , 1 x 102–5 . 36 x 104 pfu/mL ) ( see S1A Fig ) . The T48 stain of RRV was isolated from Aedes vigilax mosquitoes in Queensland , Australia [62] . Prior to cDNA cloning , the virus was passaged 10 times in suckling mice , followed by two passages on Vero cells [63 , 64] . The SL15649 strain of CHIKV was isolated from a serum sample collected from a febrile patient in Sri Lanka in 2006 . This virus was passaged two times in Vero cells prior to cDNA cloning [23] . Gradient-purified RRV was generated as previously described [65] . Briefly , virus particles were banded on a 60% to 20% discontinuous sucrose gradient by centrifugation at 24 , 000 rpm for 2 . 5 h at 4°C in a Beckman SW-28 rotor . Banded virus was collected and centrifuged through 20% sucrose at 24 , 000 rpm for 6 h 4°C in a Beckman SW-28 rotor . Virus pellets were then resuspended , aliquoted , and stored at −80°C . The recombinant “RRV-LCMV” was generated by inserting a tandem sequence , similar in design to a sequence inserted in the influenza virus genome that encodes the LCMV CD8 T cell receptor epitope gp33–41 ( KAVYNFATC ) and CD4 T cell receptor epitope gp61–80 ( GLKGPDIYKGVYQFKSVEFD ) [66] in-frame with the RRV structural polyprotein as previously described [51] . Stocks of infectious RRV , RRV-LCMV , or CHIKV ( SL15649 ) were generated from cDNAs and titered by direct plaque assay on BHK-21 cells as previously described [44] . The CHIKV isolate used in the experiments involving HS 633T cells and monocytes was originally isolated from a French patient returning from Reunion Island during the 2006 CHIKV outbreak ( IMT strain ) [67] . Virus stocks were prepared via numerous passages in Vero-E6 cultures , titered , washed , and precleared by centrifugation before storing at –80°C . These virus stocks were titered by plaque assay on Vero-E6 cells . The HS 633T human fibrosarcoma cell line is a kind gift of Philippe Gasque and his team at the University of La Réunion . HS 633T cells were grown in DMEM supplemented with 10% FBS . HS 633T cells were inoculated with CHIKV at a MOI of 1 in serum-free medium for 1 . 5 h . Inoculum was removed and fresh DMEM containing serum was added . Supernatant was harvested 24 h later . Fresh human PBMCs were isolated from whole blood by gradient centrifugation using Ficoll-Paque . Untouched monocytes were isolated using an indirect magnetic labeling system ( Monocyte Isolation Kit II , Miltenyi Biotec ) . Following selection , monocytes were either plated in serum-free IMDM for 1 h to adhere prior to stimulation with HS 633T cell supernatant for 24 hours post-inoculation ( hpi ) or co-cultured with mock or CHIKV-inoculated HS 633T cells for 24 hpi . Following the 24 h incubation , supernatant was removed and all of the cells were resuspended in TRIzol ( Life Technologies ) and stored at –80°C prior to RNA isolation . C2C12 cells ( ATCC CRL-1772 ) were grown in DMEM ( Sigma ) containing 10% FBS . To differentiate into myotubes , C2C12 myoblasts were plated in 12 well plates in DMEM containing 2% horse serum which was replaced every-other day . Myotubes were inoculated 6 days after plating and cultured for 48 hours in DMEM containing 2% FBS . C2C12 supernatant was collected into 1 . 5 mL Eppendorf tubes and centrifiuged at 13 , 000 rpm for 10 min at 4°C to remove any cellular debris . J774A . 1 murine macrophages ( ATCC TIB-67 ) were grown in DMEM containing 10% FBS . For co-culture and supernatant transfer experiments , J774A . 1 cells were plated in 48 well plates and cultured in DMEM containing 2% FBS . J774A . 1 cells treated with recombinant mouse IL-4 ( 5 ng/ml; R&D Systems ) were used as a positive control for Arg1 induction . J774A . 1 cells were inoculated with gradient-purified RRV at a multiplicity of infection ( MOI ) of 10 , which is approximately how much virus was present in the RRV-infected C2C12 supernatant . Prior to addition to J774A . 1 cells , identical samples of cell supernatant or purified virus were UV-treated for 15 min ( short wave ) or heat-treated at 56°C for 1 h to inactivate virus , or were centrifuged at 30 , 000 rpm in a swinging bucket ( SW50 . 1 Beckman rotor ) for 4 h at 4°C to remove live virus . J774A . 1 cells were cultured in these conditions for 24 hours . The cells were then washed with PBS and resuspended in RIPA buffer [H2O containing 50 mM Tris ( pH 8 . 0 ) , 150 mM NaCl , 1% NP-40 , 0 . 5% sodium deoxycholate , 0 . 1% sodium dodecyl sulfate , and 1x protease inhibitor complex ( Sigma ) ] for protein analysis . Protein lysates were separated by Tris-HCl–buffered 10% SDS-PAGE , followed by transfer to polyvinylidene difluoride membranes . Membranes were blocked in 5% milk in PBS containing 0 . 1% Tween and incubated in the appropriate Abs against the indicated proteins . GAPDH expression was used as a loading control . Anti-mouse Arg1 Ab ( V-20 ) was obtained from Santa Cruz; anti-mouse GAPDH Ab ( clone 71 . 1 ) was obtained from Sigma-Aldrich . Membranes were imaged on a ChemiDoc XRS Plus imager ( Bio-Rad ) , and bands for Arg1 and GAPDH were quantified using Bio-Rad Image Lab software . C57BL/6 wild-type ( stock # 000664 ) , Rag1-/- ( stock # 002216 ) , LysMcre ( stock # 004781 ) , Arg1F/F ( stock # 008879 ) , and Ifng-/- ( stock # 002287 ) mice were obtained from The Jackson Laboratory and bred in house . LysMcre;Arg1F/F mice were generated as previously described [44] . Animal husbandry and experiments were performed in accordance with all University of Colorado School of Medicine Institutional Animal Care and Use Committee guidelines . All mouse studies were performed in an animal biosafety level 3 laboratory . Three-to-four week old mice were used . Mice were inoculated in the left rear footpad with 103 PFU of RRV in diluent ( PBS/1% bovine calf serum ) in a 10-μl volume . Mock-infected animals received diluent alone . Mice were monitored for disease signs and weighed at 24-h intervals . Disease scores were determined by assessing grip strength , hind limb weakness , and altered gait , as previously described [68] . On the termination day of each experiment , mice were sacrificed by exsanguination , blood was collected , and mice were perfused by intracardial injection of 1x PBS . PBS-perfused tissues were removed by dissection and homogenized in TRIzol Reagent ( Life Technologies ) with a MagNA Lyser ( Roche ) . Alternatively , quadriceps muscles were dissected , minced , and incubated for 1 . 5 h with vigorous shaking at 37°C in digestion buffer ( RPMI 1640 , 10% FBS , 15 mM HEPES , 2 . 5 mg/ml Collagenase Type 1 [Worthington Biochemical] , 1 . 7 mg/ml DNase I [Roche] , 1x gentamicin [Life Technologies] , 1% penicillin/streptomycin ) . Following digestion , cells were passed through a 100-μm cell strainer ( BD Falcon ) and banded on Lympholyte-M ( Cedarlane Laboratories ) to isolate infiltrating leukocytes . Additionally , spleens and draining popliteal lymph nodes were dissected from mice and passed through a 100-μm cell strainer . Following red blood cell lysis ( spleens only ) , cells were washed in wash buffer ( 1x PBS , 15 mM HEPES , 1x gentamicin , 1% penicillin/streptomycin ) , and total viable cells were determined by trypan blue exclusion . Sera samples were titered by direct plaque assay on BHK-21 cells . Leukocytes isolated from enzymatically digested tissues were incubated with anti-mouse FcγRII/III ( 2 . 4G2; BD Pharmingen ) for 20 min on ice to block nonspecific Ab binding and then stained in FACS staining buffer ( 1x PBS , 2% FBS ) with the following Abs: anti-CD3-fluorescein isothiocyanate ( FITC , clone 145-2C11 ) , anti-CD3-allophycocyanin ( APC , 145-2C11 ) , anti-CD4-PerCP-Cy5 . 5 or Pacific Blue ( RM4-5 ) , anti-CD8α-phycoerythrin ( PE , 53–6 . 7 ) , anti-CD44-Pacific Blue ( IM7 ) , anti-CD69-FITC ( H1 . 2F3 ) , anti-B220-PE-Cy7 ( RA3-6B2 ) , and anti-CD19-FITC ( 6D5 ) ( all from BioLegend ) ; anti-CD8β-PE ( H35-17 . 2 ) , and anti-Gr-1-PE-Cy7 ( RB6-8C5 ) ( all from eBioscience ) . gp33-APC H-2Db KAVYNFATM tetramer was kindly provided by the National Institutes of Health Tetramer Core Facility . Cells were fixed overnight in 1% paraformaldehyde and analyzed on an LSR II using FACSDiva software ( Becton Dickinson ) . Further analysis was done using FlowJo Software ( Tree Star ) . Doublets were excluded using side- and forward-scatter height and width parameters . For intracellular cytokine analysis , wells in a 96-well plate was coated overnight with anti-CD3 Ab ( 10 μg/mL ) . Muscle-infiltrating leukocytes from RRV-infected mice were isolated at 10 dpi . 100 μl RPMI containing 7% FBS , Brefeldin A , and anti-CD28 Ab ( 2 μg/mL ) was added to the 96-well plate followed by addition of muscle-infiltrating leukocytes in 100 μl RPMI containing 7% FBS . Cells incubated in the absence of anti-CD3 and anti-CD28 Abs were used as a control . After 5 h incubation , cells were harvested , and incubated with anti-mouse FcγRII/III ( 2 . 4G2 ) for 20 min on ice followed by surface marker staining in FACS buffer for an additional 20 min on ice . After one wash step , cells were fixed and permeabilized in a 1% paraformaldehyde and saponin solution for 15 min at room temperature . Cells were washed with PBS containing saponin and then stained for intracellular cytokines for 45 min on ice in PBS with saponin . Finally , cells were washed 1X with PBS containing saponin , 1X with FACS buffer , and then stored overnight in 1% paraformaldehyde . The following antibodies from Bio X Cell were used for depletion studies: rat IgG2b control antibody ( anti-KLH , clone LTF-2 ) , CD4-depleting antibody ( rat IgG2b; clone YTS 191 ) , and CD8-depleting antibody ( rat IgG2b; clone YTS 169 . 4 ) . Mice were treated on days 7 and 12 post-inoculation via the intraperitoneal route ( i . p . ) with 200 μg of each CD4- and CD8-depleting antibody , or 400 μg of the control antibody , diluted in PBS to a final volume of 150 μl . Depletion efficiency was determined by flow cytometric analysis of spleen tissue at 14 dpi as described in the text for the presence of cells staining positively for CD3 and CD4 or CD8β . In separate experiments , mice were treated i . p . with 200 μg of CD4-depleting antibody or a control antibody diluted in PBS to a final volume of 150 μl on day 4 pi and harvested on day 7 pi . Depletion efficiency was determined by flow cytometric analysis of spleen and quadriceps muscle tissue as described in the text . For cell sorting , mice were sacrificed at 10 dpi and the quadriceps muscles were processed as described above . Cells were stained in FACS staining buffer with anti-CD19-FITC , anti-CD3-APC , anti-CD4-Pacific Blue , and anti-CD8α-PE antibodies ( BioLegend ) . Cells were sorted under BSL2 conditions on a FACSAria cytometer using FACSDiva software ( Becton Dickinson ) . CD19-CD3+ cells were gated on first , then CD4+CD8- or CD4-CD8+ cells were sorted separately . Cells were resuspended in TRIzol ( Life Technologies ) and stored at –80°C prior to RNA isolation . For T cell adoptive transfer experiments , T cells were isolated from the spleens of naïve wild-type C57BL/6 mice or Ifng-/- mice via negative selection using a pan-T cell isolation kit ( Miltenyi Biotec ) . Following isolation , cells were counted and 2 . 5 x 106 T cells were resuspended in RPMI containing 2% FBS in a total volume of 200 μl for i . p . injection into Rag1-/- mice one day prior to RRV infection . Control Rag1-/- mice received 200 μl of media alone . Of the transferred T cells , ≥ 95% of the cells were CD3+ and ~60% of the CD3+ T cells were CD4+ and ~35% were CD8+ . Additionally , ≤ 1% of CD19+ B cells remained in the injected cell preparations . T cell transfer was confirmed by flow cytometric analysis of spleen and muscle tissue at 14 dpi as described above for the presence of cells staining negatively for B220 and Gr-1 and positively for CD3 and CD4 or CD8α . For analysis of gene expression in mouse tissue samples or cells , RNA was isolated using a PureLink RNA Mini Kit ( Life Technologies ) , and 1 μg of total RNA was reverse-transcribed using Superscript III ( Life Technologies ) , random oligo ( dT ) primers , and RNaseOUT . Real-time qPCR experiments were performed using Taqman gene expression assays and a LightCycler 480 ( Roche ) . 18S rRNA was used as an endogenous control to normalize for input amounts of cDNA . The relative fold induction of amplified mRNA were determined by using the Ct method [69] . For analysis of gene expression in human cells , total RNA was extracted using RNeasy Mini Kit ( QIAGEN ) according to the manufacturer’s instructions . Quantification of total RNA was performed using a NanoDrop 1000 Spectrophotometer ( Thermo Scientific ) ; following quantification , RNA samples were diluted to 10 ng/μl . qRT-PCR was performed using QuantiFast SYBR Green RT-PCR Kit ( QIAGEN ) according to the manufacturer’s recommendations in a 12 . 5 μl reaction volume . All reactions were performed using 7900HT Fast Real-Time PCR System machine ( Applied Biosciences ) with thermal cycling conditions as described [70] . As above , the relative fold change for each gene between CHIKV-infected and mock-infected was calculated using the Ct method after normalization to GAPDH [70] . See S1 Table for forward and reverse primers used . RNA was isolated using a PureLink RNA Mini Kit ( Life Technologies ) as described above . Absolute quantification of RRV RNA was performed as previously described [44] . Briefly , a sequence-tagged ( small caps ) RRV-specific RT primer ( 4415 5’-ggcagtatcgtgaattcgatgcAACACTCCCGTCGACAACAGA-3’ ) was used for reverse transcription . A tag sequence-specific reverse primer ( 5’-GGCAGTATCGTGAATTCGATGC-3’ ) was used with a RRV sequence-specific forward primer ( 4346 5’-CCGTGGCGGGTATTATCAAT-3’ ) and an internal TaqMan probe ( 4375 5’-ATTAAGAGTGTAGCCATCC-3’ ) during qPCR to enhance specificity . To create standard curves , 10-fold dilutions , from 108 to 100 copies of RRV genomic RNAs , synthesized in vitro , were spiked into RNA from BHK-21 cells , and reverse transcription and qPCR were performed in an identical manner . The limit of detection was 100 genome copies . All data were analyzed using GraphPad Prism 5 software . Data were evaluated for statistically significant differences using a two-tailed , unpaired t test with or without Welch’s correction , a one-way analysis of variance ( ANOVA ) test followed by Tukey’s multiple comparison test , or a two-way ANOVA followed by a Bonferroni multiple comparison test . Comparison between the high viral load and low viral load group in the patient cohort was performed by two-tailed Mann Whitney U test . Similarly , for the in vitro infection studies , pair-wise comparison was performed using a two-tailed Mann Whitney U test . A P-value < 0 . 05 was considered statistically significant . All differences not specifically indicated to be significant were not significant ( P > 0 . 05 ) .
|
Mosquito-transmitted chikungunya virus ( CHIKV ) , Ross River virus ( RRV ) , and related alphaviruses cause epidemics involving millions of persons , such as on-going CHIKV outbreaks in the Caribbean and Central and South America . Infection with these viruses results in severe pain due to inflammation of musculoskeletal tissues that can persist for months and even years . There are no specific therapeutics or licensed vaccines for these viruses . Suppressive myeloid cells have been shown to inhibit anti-pathogen immune responses , including T cell responses , which can promote chronic disease . We showed previously that a gene associated with suppressive myeloid cells , arginase 1 ( Arg1 ) , was induced in musculoskeletal tissues and macrophages of mice infected with RRV or CHIKV , and mice that lacked Arg1 expression in myeloid cells had reduced viral loads at late times post-infection . Here , we demonstrate that Arg1 is induced in PBMCs isolated from CHIKV-infected patients , and Arg1 expression is associated with viral loads . Moreover , we found that Arg1-expressing myeloid cells inhibit the activation and function of antiviral T cells in RRV-infected mice . These studies underscore the role of suppressive myeloid cells in modulating the T cell response to arthritogenic alphaviruses and provide a therapeutic target to enhance viral clearance and potentially limit chronic disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Myeloid Cell Arg1 Inhibits Control of Arthritogenic Alphavirus Infection by Suppressing Antiviral T Cells
|
The epidermal growth factor receptor ( EGFR ) plays a key role in regulating cell proliferation , migration , and differentiation , and aberrant EGFR signaling is implicated in a variety of cancers . EGFR signaling is triggered by extracellular ligand binding , which promotes EGFR dimerization and activation . Ligand-binding measurements are consistent with a negatively cooperative model in which the ligand-binding affinity at either binding site in an EGFR dimer is weaker when the other site is occupied by a ligand . This cooperativity is widely believed to be central to the effects of ligand concentration on EGFR-mediated intracellular signaling . Although the extracellular portion of the human EGFR dimer has been resolved crystallographically , the crystal structures do not reveal the structural origin of this negative cooperativity , which has remained unclear . Here we report the results of molecular dynamics simulations suggesting that asymmetrical interactions of the two binding sites with the membrane may be responsible ( perhaps along with other factors ) for this negative cooperativity . In particular , in our simulations the extracellular domains of an EGFR dimer spontaneously lay down on the membrane in an orientation in which favorable membrane contacts were made with one of the bound ligands , but could not be made with the other . Similar interactions were observed when EGFR was glycosylated , as it is in vivo .
The epidermal growth factor receptor ( EGFR ) , a member of the Her ( ErbB ) family of cell-surface receptors , is critical to a variety of cellular processes and is implicated in the development of several forms of cancer and other diseases [1]–[3] . In normal cells , EGFR activation is initiated by the binding of extracellular ligands from the epidermal growth factor ( EGF ) family [4]–[7] , giving rise to the formation of active EGFR dimers , which transmit intracellular signals . It was first shown 30 years ago [8]–[12] that the Scatchard plots of EGF binding to EGFR are nonlinear ( concave up ) , which is indicative of heterogeneous binding affinity . It has been further suggested that the heterogeneity in EGFR ligand binding may play an important role in determining the signaling response to different ligand concentrations [12]–[14] . More recently , in a study conducted by Pike and colleagues [15] , a characterization of EGFR ligand binding based on a simultaneous fitting of binding isotherms from cells with different levels of EGFR expression showed that EGFR ligand binding may be described by a simple model ( shown in Fig . 1A ) . In this model , which is consistent with earlier results [16] , negative cooperativity underlies the heterogeneity of EGFR ligand binding [15] , [16]: The binding affinity of a ligand at one EGFR binding site is smaller when the other site is occupied ) . The structural origin of this negative cooperativity has been unclear . The existence of two structurally distinct binding sites in the doubly liganded dimer of Drosophila EGFR ( dEGFR ) [17] is consistent with its binding cooperativity . In crystal structures of the doubly liganded human EGFR dimer , however , the two binding sites are structurally virtually identical [18] , [19] . A recent investigation , based on structural and biochemical analyses [20] , suggested that the ligand-binding cooperativity may be explained by a conformational change in the ectodomain dimer . Although it is very plausible that such a scenario explains some of the negative cooperativity in EGFR , it is not clear that it represents the only , or even the main , contribution . Here we use molecular dynamics ( MD ) simulations to investigate the structural basis of the negative cooperativity in the ligand binding of human EGFR . We simulated the human EGFR ectodomain dimer anchored to a lipid membrane by EGFR transmembrane ( TM ) helices . In our simulations of both singly and doubly liganded ectodomains , the dimer began in an upright orientation , with the dimer's long axis perpendicular to the membrane , then spontaneously rotated and lay down on the membrane in such a way that one of the binding sites faced the membrane , while the other faced the bulk solvent . The ligand in the membrane-facing site developed extensive favorable interactions with the membrane , and our approximate free energy calculations suggest that these interactions contribute a significant fraction of the ligand binding free energy . These findings are consistent with Förster resonance energy transfer ( FRET ) experiments [21] , [22] , which showed that some EGFR-bound ligands are positioned within 40 Å of the membrane , while others are positioned beyond 70 Å . In further simulations of glycosylated EGFR ectodomains , we found that the ectodomain orientation and the membrane interaction of the bound ligand appear compatible with the in vivo glycosylation of EGFR . Based on our simulation findings , we suggest that the negative cooperativity in human EGFR ligand binding may arise in part from broken symmetry between the two bound ligands in an orientation in which the ectodomain rests on the membrane . Specifically , the simulations showed that the membrane favorably interacts with ligands bound to EGFR ectodomains resting on the membrane , and the results suggest that the high-affinity binding to an unliganded EGFR dimer may be attributed to this previously largely overlooked ligand-membrane interaction ( Fig . 1A ) . Such a structural explanation is supported by the experimental finding that when high-affinity ligand binding is abolished , the distance between bound ligands and the membrane increases [22] . The mechanism we propose here also offers a straightforward explanation of the observation that negative ligand-binding cooperativity in human EGFR is observed only when the receptor is embedded in the membrane .
We first simulated a dimer of the ectodomains ( domains I , II , III , and IV ) and the single-helix TM segment of EGFR . Based on available crystal structures [18] , [19] , [23] , the ectodomains were prepared in the form of an EGF-bound , back-to-back symmetric dimer , and the TM helices were prepared in the form of a TM dimer , with the N-terminal GxxxG-like motifs ( where G represents glycine , or another amino acid with a small side chain ) as the dimer interface [24]–[26] . The ectodomains were initially positioned upright , approximately perpendicular to the membrane ( Fig . 1B ) . In the simulation , the ectodomains lay down toward the membrane surface , and approximately 1 . 7 µs into the simulation , one of the bound ligands developed extensive interactions with the membrane ( Fig . 1C ) in a partial-resting orientation of the ectodomain dimer . Later in the course of the simulation , the ectodomains lay flat on the membrane , producing a full-resting orientation ( Fig . 1D ) . The simulation observations are supported by FRET measurements [21] , [22] , which have indicated that the EGFR ectodomain dimer may rest on the membrane . The simulation indicates that such orientations of the ectodomains are made possible because the linker segments between the ectodomains and the TM helices are not fully rigid , as has been previously suggested [23] . We previously simulated EGFR ectodomain monomers tethered to membrane-embedded TM helices . Although the ectodomain is ligand-free in the simulations , it came to rest on the membrane from an upright orientation in such a way that , if it were ligand-bound , the ligand would be in contact with the membrane ( Fig . 6A in ref . [26] ) . Notably , the orientation of the ectodomain dimer with respect to the membrane broke the symmetry between its two bound ligands: One of the ligands ( the membrane-facing ligand ) but not the other ( the solvent-facing ligand ) was in contact with the membrane ( Fig . 1C ) . Once the ectodomain dimer rested on the membrane , the membrane-facing ligand developed extensive favorable interactions with the lipids . In particular , the hydrophobic residues of the EGF ligand , such as Pro7 and Leu8 , were found to enter the hydrophobic interior of the membrane's extracellular leaflet ( Fig . 1C , inset ) . As discussed in detail in later sections , similar membrane interactions were also observed for one of the bound ligands in two other EGFR dimer simulations we performed . Although it is challenging to accurately calculate biomolecular binding free energies in simulation , generalized Born models can often provide a rough estimate . We estimated the binding free energy of each bound ligand using the molecular mechanics/generalized Born volume integration ( MM/GBVI ) model ( see Methods ) [27] . We initially calculated what we refer to as the ligand-protein interaction energy , which is an estimate for the free energy if there is little change in the protein structure on binding . As shown in Fig . 1E , the simplest application of the MM/GBVI method yields an interaction energy of an EGF ligand with EGFR of ∼80 kcal mol−1 . The ligand-membrane interaction energy contributes an additional ∼25 kcal mol−1 to the membrane-facing ligand but almost nothing to the solvent-facing one . The EGF-EGFR interaction energy of over 80 kcal mol−1 is much higher than the experimental value of the EGF binding free energy ( 10–15 kcal mol−1 [15] ) . As noted above , however , the computational quantity does not include the conformational free energy cost incurred when EGFR adopts its ligand-bound conformation during EGF binding . Using the structures of the active [18] , [19] and inactive [26] dimers , the MM/GBVI model estimates the cost of EGFR's transition to the ligand-bound conformation to be 89 kcal mol−1 per monomer in the EGFR dimer . This is qualitatively consistent with our previously reported simulations [26] , in which the ligand-bound conformation was not stable without the EGF ligands , and it suggests that the favorable EGF-EGFR interaction is approximately canceled out by EGFR adopting the unfavorable ligand-bound conformation . Despite this apparently satisfactory cancellation , we suspect that the individual canceling terms may still be overestimates , and we regard the use of the generalized Born model in the present context as qualitative . To assess whether interactions with the membrane could contribute significantly to the experimental ligand-binding free energy , we do not only look at the large ( ∼25 kcal mol−1 ) estimated binding energy , but we also look at how this energy compares to the estimated ligand-protein interaction energy . Since the protein-membrane energy is a substantial fraction even of the large ligand-protein interaction energy , the generalized Born model supports the conclusion that the membrane-facing ligand binds to the EGFR dimer with higher affinity than does the solvent-facing one . Intriguingly , we found that the hydrophobic patch formed by Pro7 and Leu8 is largely conserved in vertebrate EGF molecules ( Fig . 1F ) . Pro7 is especially well conserved in vertebrate EGF . Leu8 is less conserved , but this position is hydrophobic in the majority of vertebrate EGF members despite being solvent accessible . In vivo , human EGFR is glycosylated on the ectodomains [28] , and 10 of the receptor's 12 potential glycosylation sites are found to be fully or partially occupied by a variety of large , branching glycans [29] , [30] . It is conceivable that the relatively bulky glycans may preclude an EGFR ectodomain dimer from resting on the membrane . To test whether this is the case , we modeled and simulated an EGFR ectodomain–TM dimer system with full glycosylation ( Fig . 2A ) . We decorated the EGFR ectodomains at the 10 identified glycosylation sites [29] , [30] with three types of glycans ( BiS1F1 , Man6 , and Man8 ) that are common in EGFR glycosylation ( see Methods , Fig . S1 , and Table S1 ) . The simulation shows that the glycosylation does not disrupt the ligand-membrane contacts ( Fig . 2B ) we observed in nonglycosylated EGFR . In the course of the simulation starting from the partial-resting orientation ( Fig . 1C ) , the orientation of the ectodomains with respect to the membrane remained unchanged , with the membrane-facing ligand embedded in the membrane and the other ligand facing the solvent . The simulation showed that , in addition to interglycan interactions , the polar glycans interact extensively with the protein and the lipid head groups . The flexibility of glycans allowed the ectodomains to rest on the membrane . The glycans were found to be distributed adjacent to the protein surfaces , rather than protruding into the solvent ( Fig . S2 ) . The membrane-facing ligand of the glycosylated EGFR exhibited the same degree of membrane interactions in the simulation ( Fig . 2C ) as that of the nonglycosylated EGFR ( as indicated by the values of t = 0 in Fig . 2C ) . In fact , the membrane embedding of the glycosylated-EGFR membrane-facing ligand was slightly deeper than that of the membrane-facing ligand in nonglycosylated EGFR . We calculated that the membrane interaction contributes an additional estimated ∼30% to the membrane-facing ligand's MM/GBVI binding energy but virtually nothing to that of the solvent-facing ligand ( Fig . 2C ) . We thus conclude that robust interaction between EGFR-bound ligands and the membrane , which may contribute to the heterogeneous ligand binding in an EGFR dimer , is accessible to glycosylated as well as nonglycosylated EGFR . Having demonstrated that the ectodomains in a two-ligand EGFR dimer may rest on the membrane and that the two ligands may differ in their interactions with the membrane due to the ectodomain's orientation , we further simulated the one-ligand EGFR dimer . These simulations suggest that the ectodomains may rest on the membrane and that the ligand in a one-ligand EGFR dimer may also develop favorable interactions with the membrane , thus providing a structural model for high-affinity binding in the one-ligand EGFR dimer ( Fig . 1A ) . Because a crystal structure of a singly liganded ectodomain of an EGFR dimer is not yet available , we made a model based on the crystal structure of the two-ligand ectodomain dimer by removing one bound ligand from the crystal structure [26] . We here simulated the one-ligand ectodomain dimer in this conformation , connected with the TM segments , three times . In all three simulations , the ectodomain dimer , which was initially in an upright orientation , spontaneously lay down on the membrane ( Fig . 3B ) , allowing the ligand to come into contact and develop extensive interactions with the membrane . We again calculated the MM/GBVI energy of the ligand's interaction with the membrane ( Fig . 3C ) . The results suggest that the membrane interaction is energetically favorable , and that the free energy increase associated with the ligand's membrane interaction is a significant fraction of the free energy arising from its interaction with EGFR . The favorable nature of the ligand-membrane interaction strongly suggests that the membrane-facing binding sites are associated with high-affinity binding . This notion is notably supported by the observation from FRET experiments that abolishing the high-affinity binding leads to a significant increase in the average distance between the ligands and the membrane [22] . Assuming a thermodynamically equilibrated system , in one-ligand EGFR dimers the ligands predominantly occupy the high-affinity sites facing the membrane ( Fig . 1A ) . We also performed a similar simulation of a fully glycosylated , one-ligand ectodomain dimer attached to the TM segments . Starting form an upright conformation , the ectodomain dimer again spontaneously lay down , with its bound ligand coming into contact with the membrane ( Fig . 3B and 3C ) . This simulation suggests that a glycosylated one-ligand dimer may also prefer to rest on the membrane in such a way that its bound ligand faces the membrane , and that the ligand-membrane interactions are energetically favorable A simulation study of EGFR [22] has previously suggested that ectodomain interactions with the membrane may be at the root of the observed negative cooperativity of ligand binding . It was further suggested that the negative cooperativity may arise from the ectodomain's transition to a dEGFR-like asymmetric conformation , induced by interactions with the membrane . Although our simulations also suggest the important role of EGFR ectodomain interactions with the membrane , our simulations did not show a robust transition from a symmetric to an asymmetric conformation in the ectodomain dimer . Fig . 4A shows that , other than minor deviations due to the inherent flexibility of the loop regions , the dimer's two ectodomain subunits were nearly conformationally identical in our simulations . In particular , the conformations of domain II in the two subunits are highly similar , whereas in the dEGFR dimer the domain II is straight in one subunit and bent in the other , which ultimately leads to the different conformations of the two binding sites . This is illustrated in Fig . 4B , where the angle characterizing the bending of domain II is plotted . The angles of the two EGFR subunits were approximately the same in our simulations of the two-ligand dimer , much as they are in crystal structures [18] , [19] . Our MM/GBVI calculation supports the notion that the two receptors of the two-ligand EGFR dimer maintain similar binding-site conformations while resting on the membrane: The two ligands have comparable MM/GBVI interaction energies with the receptors ( Fig . 5B ) , including cases in which the receptors are glycosylated ( Fig . 2C ) . On the other hand , for the one-ligand dimer , which assumes asymmetric conformations , the angles differ significantly between the two subunits . Similarly , in our simulations the average root-mean-square deviation ( RMSD ) of the Cα atoms of domains I , II , and III between the two subunits was significantly lower for the two-ligand ectodomain dimer ( 2 . 7±0 . 3 Å when glycosylated and 2 . 6±0 . 5 Å when nonglycosylated ) than for the one-ligand dimer ( 4 . 4±0 . 2 Å when glycosylated and 4 . 7±0 . 3 Å when nonglycosylated ) . We performed three independent simulations of the two-ligand , nonglycosylated EGFR dimer . As discussed above , in one of these simulations , the ectodomain dimer first assumed a partial-resting orientation and eventually lay flat on the membrane ( Fig . 1; also shown as Simulation 1 in Fig . 5 ) . In another simulation ( Fig . 5A , Simulation 2 ) , the system arrived at a similar partial-resting orientation and remained there to the end of the simulation . In the third simulation ( Fig . 5A , Simulation 3 ) , the ectodomain dimer was found to rest sideways on the membrane surface . What is common to all three simulations , however , is that only one bound ligand made extensive contact with the membrane ( Fig . 5B , upper panels ) despite the variation in the orientation of the ectodomain dimer . The buried surface area of the membrane-facing ligand was consistently greater than that of the solvent-facing ligand ( Fig . 5B ) : 2 , 300±100 Å2 versus 1 , 600±100 Å2 . A substantial portion ( up to 200 Å2 ) of the approximately 700-Å2 difference is due to the embedding of Pro7 and Leu8 in the membrane . Our MM/GBVI calculations also consistently suggest that ligand-membrane interactions contribute a significant fraction to the free energy of ligand-receptor binding ( Fig . 5B ) . Earlier FRET measurements indicated that EGF bound to EGFR dimers falls into two groups: one in which the bound ligand is close to the membrane , and another in which it is farther away . Specifically , the FRET results showed that the N termini of the EGF molecules in the “close” group are no more than 35–40 Å from the membrane , and the N termini of the EGF molecules in the “far” group are no closer than 69–71 Å from the membrane [21] , [22] . Our simulation results ( Figs . 2C and 5B ) agree with these data ( see the description of the distance measurements in the Methods section ) and the simulation conformations are similar to those in the structural model proposed by Kästner et al . based on their FRET results [31] . In all of our simulations , the membrane-facing ligands were close to the membrane surface ( ∼10 Å ) , and thus belong to the former population . This population may also include the solvent-facing ligands in cases in which the ectodomain dimer rests flat on the membrane , such as at the end of Simulation 1 , where the membrane distance is ∼40 Å for the solvent-facing ligand . The latter population , on the other hand , may consist of the solvent-facing ligands in dimers such as those observed in Simulations 2 and 3 , as well as those in the upright dimers ( with distances of ∼80–120 Å ) . Combining the observations from the simulations with those from the FRET measurements , we suggest that it is unlikely that the negative cooperativity of ligand binding can be attributed to a single specific orientation of the ectodomain dimer . We instead suggest that the cooperativity is associated with an ensemble of ectodomain-dimer orientations , with the shared feature that the high-affinity ligand binding occurs at the membrane-facing binding site . This provides a straightforward explanation for the experimental observation that abolishing high-affinity ligand binding increases the average ligand-membrane distance [22] . Additionally , our simulations showed that free EGF molecules may interact favorably with and be attached to the membrane ( Fig . S3 ) . This simulation finding , combined with the observation that Spitz ligands ( which bind to dEGFR ) need to be palmitoylated ( and thus attached to membrane ) to activate dEGFR in vivo [32] , raises the possibility that the ligand-binding process of EGFR may occur at the membrane surface .
Our simulations suggest that an EGFR ectodomain dimer may rest on the membrane , and that the interaction between a bound ligand and the membrane may lead to a breaking of the symmetry between the two ligands , thus contributing to the negative cooperativity of EGFR ligand binding ( Fig . 1A ) . Our investigation is in part inspired by the FRET measurements of ligand distance from the membrane; based on these results , the orientation of EGFR ectodomains relative to the membrane was suggested to affect ectodomain conformations and give rise to the negative cooperativity [21] , [22] , [31] . The mechanism we propose here is particularly supported by the FRET finding that abolishing high-affinity ligand binding leads to a significant increase in the average distance between EGFR-bound ligands and the membrane [22] . Our simulations of glycosylated EGFR ( to our knowledge the first simulations of a fully glycosylated receptor ) showed that the mechanism we propose is compatible with EGFR glycosylation: A glycosylated ectodomain dimer may also rest on the membrane , and the attached glycans do not preclude interactions between the EGFR-bound ligand and the membrane . In this investigation , we have largely focused on EGFR dimers because they are central to the negative cooperativity of EGFR ligand binding [14] . EGFR monomers may also bind ligands at a high affinity comparable to that of EGFR dimers [15] , but the ectodomain structure of the ligand-bound EGFR monomer has not yet been resolved . In previous MD simulations , we showed that an EGFR monomer is similar to an EGFR dimer in that its ectodomains also rest on the membrane in a way that would allow membrane contact with the bound ligand [26] . From this observation , which is independent of any specific conformation of the ectodomains , it may be inferred that the high affinity of ligand binding in EGFR monomers could also be explained by favorable interactions between the membrane and the bound ligands . Our simulations suggest that the ectodomains of an EGFR dimer may rest on the membrane and that a bound EGF ligand may be in direct and energetically favorable contact with the membrane . Our earlier simulations also suggest that EGFR monomer ectodomains may also rest on the membrane [26] . This does not imply , however , that the ectodomains are fixed on the membrane in well-defined orientations . It is likely that , on a timescale much longer than our simulations , the ectodomains convert from one orientation to another in a dynamic equilibrium . While the orientations in which the ectodomains rest on the membrane may predominate , the ectodomains likely access the other orientations that could be crucial to the process of ligand binding or EGFR dimerization . A recent study [20] proposed that a conformational change from the so-called “flush” to the “staggered” arrangement between the two extracellular subunits in an EGFR dimer ( Fig . 6A ) may be at the root of the binding cooperativity of EGFR . While such a binding-cooperativity mechanism differs from the mechanism we propose here , these two mechanisms are not mutually exclusive . In agreement with the finding of Liu et al . [20] based on crystal structures , our simulations show that the two-ligand EGFR dimer prefers the staggered conformation and that the one-ligand and ligand-free EGFR dimers prefer the flush conformation [26] . Intriguingly , the ectodomain interaction with the membrane and the glycosylation of EGFR appear to strengthen this trend ( Fig . 6B ) . From this observation , we suggest that the membrane may be of critical importance to the negative cooperativity of EGFR ligand binding , not only for its asymmetric interactions with the bound ligands , but also for its effect on the accessible conformational space of the ectodomain dimers . Further investigation is certainly needed to quantify the contribution of the conformational dynamics of the ectodomains and the contributions of ligand-membrane interactions to the ligand-binding cooperativity of EGFR . Further investigation would also be needed to clarify whether the membrane interactions of the ectodomains have any role in autoinhibition . We have not addressed this question , but we have previously shown that the membrane interactions of the EGFR kinase domain do play an autoinhibitory role [25] , [26] . Experiments have shown that the ligand-binding cooperativity of EGFR is apparently missing for isolated EGFR dimer ectodomains in solution [12] . It was shown that the negative cooperativity may be partially recovered when the membrane is included in experiments of EGFR ectodomains attached to the TM helices [33] . Our suggested mechanism for the negative binding cooperativity , in which the membrane plays a central role , offers a straightforward explanation for these findings . If the asymmetry between the bound ligands in an EGFR dimer , and thus the binding cooperativity , is indeed associated with the difference in the interactions of bound ligands with the cell membrane , the absence of the membrane would naturally eliminate the binding cooperativity . Likewise , the lack of cooperativity for detergent-solubilized EGFR [34] may be explained by the absence of an extended membrane capable of interacting with EGFR-bound ligands . It has been shown that mutations at the intracellular domains of EGFR yield nearly linear Scatchard plots [33] . Although these Scatchard plots could reflect a weakened negative cooperativity due to these mutations , and thus suggest that the root of the negative cooperativity may lie beyond the ectodomains and the membrane , there is an alternative explanation: that the dimerization prior to ligand binding , which is a prerequisite of the binding cooperativity [15] , was weakened , leading to both a near-linear Scatchard plot and a difficulty in using the plot to reliably quantify binding cooperativity [20] . Our investigation of the relationship between the EGFR ectodomains and the cell membrane using atomistic , long-timescale MD simulations suggests a structural mechanism for the negative cooperativity of ligand binding of EGFR dimers; in this mechanism , the ectodomains may rest on the membrane , and the presence of the membrane may break the symmetry between the two binding sites . These results add further support to the emerging view that interactions between EGFR and the membrane play a central role in many aspects of the regulation of EGFR signaling [25] , [26] , [34]–[36] .
The simulations were performed on a special-purpose supercomputer , Anton [37] , using the Amber ff99SB-ILDN [38]–[40] force field , combined with the ff99SB* backbone correction [41] for proteins , the CHARMM C36 force field [42] for lipids , and TIP3P [43] as the water model . The simulated systems were solvated in water with 0 . 15 M NaCl , with residue protonation states corresponding to pH 7 . Additional Na+ ions were included to neutralize the net charges of the proteins ( −3 for the extracellular domains of each EGFR , −4 for each EGF ligand ) and the POPS lipids . As an equilibration stage , the protein backbone atoms were first restrained to their initial positions using a harmonic potential with a force constant of 1 kcal mol−1 Å−2 . The force constant was linearly scaled down to zero over at least 50 ns . Simulations were performed in the NPT ensemble with T = 310 K and P = 1 bar using the MTK algorithm [44] with 20-ps relaxation time . Water molecules and all bond lengths to hydrogen atoms were constrained using M-SHAKE [45] . The simulation time step was 1 fs for the equilibration stage and 2 fs for production simulations; the r-RESPA integration method was used , with long-range electrostatics evaluated every 6 fs [46] . The glycosylation of EGFR was modeled based on the mass-spectrometry analysis of the CL1-0 cell line [30] , which is broadly consistent with similar analysis on CL1-5 and A431 cell lines [29] , [30] . Since EGFR glycan attachments in the cell are very diverse—for every glycosylation site there is a large number of different glycan types that can be attached to it—we chose glycans among the most commonly found at the specific sites . These three common types are BiS1F1 , Man6 , and Man8 ( Fig . S1 and Table S1 ) . The glycan structures for the initial models were obtained using the Glycam web service [47] and then adjusted in VMD [48] to avoid clashes with protein and membrane . The simulations were performed with the GLYCAM06 force field [49] applied to the glycans . The simulated systems included the ectodomain–TM dimers with two EGF molecules bound ( three simulations of 2 . 6 , 1 . 2 , and 2 . 1 µs; ∼315 , 000 atoms ) and with one EGF molecule bound ( three simulations of 2 . 5 , 2 . 3 , and 0 . 9 µs; ∼300 , 000 atoms ) , a two-ligand glycosylated ectodomain–TM dimer ( 3 . 0 µs; ∼310 , 000 atoms ) , a one-ligand glycosylated ectodomain–TM dimer ( 8 . 3 µs; ∼300 , 000 atoms ) , and a single EGF molecule ( see SI; two simulations of 8 . 9 and 8 . 3 µs; ∼62 , 000 atoms ) ; a membrane was included in every case . Each system is set up such that each dimer is at least 25 Å from its periodic image . The model membrane consisted of POPC lipids , with 30% ( molar ) POPC randomly replaced by POPS in the intracellular leaflet of the bilayer ( only for the ectodomain–TM simulations ) to approximately mimic the charge distribution in the cellular membrane [26] , [50] . Modeling , analysis , and visualization were performed using VMD [48] . The distance between the EGF N terminus and the membrane , namely the distance from the N terminus to the plane through the phosphates of the extracellular lipid layer , was computed in a manner consistent with the FRET measurements [22] . The EGF-EGFR interaction energy estimation was based on the molecular mechanics/generalized Born volume integration ( MM/GBVI ) model [27] and performed using MOE software ( Chemical Computing Group ) [51] . The EGF-receptor binding energy was calculated for each snapshot from the difference of the energy of the EGF-receptor complex and the sum of isolated EGF and receptor energies . The EGF-membrane energy was calculated analogously . The conformational free energy of EGFR extracellular dimers was estimated based on the published coordinates of the full-length ligand-bound and ligand-free EGFR dimers [26] after energy minimization . Our calculations included domains I , II , III , and IV . The MM/GBVI energy is −34287 . 4 kcal mol−1 for the ligand-free dimer and −34110 . 2 kcal mol−1 for the ligand-bound dimer ( the EGF ligands were not included in the calculation ) , and thus the conformational free energy cost for each monomer is 88 . 6 kcal mol−1 .
|
Epidermal growth factor receptor ( EGFR ) molecules are of central importance in cellular communication . Embedded in the cell membrane , these receptors bind epidermal growth factor ( EGF ) molecules outside the cell and translate this binding into specific biochemical signals inside the cell , which in turn trigger cell proliferation , migration , or differentiation . EGFR dysfunction has been implicated in a variety of cancers , and EGFR-targeting drugs are commonly used in cancer treatments . It has been widely assumed that the extracellular portion of an EGFR molecule protrudes perpendicularly from the cell membrane . In detailed , atomic-level computer simulations , however , we find that it lies down on the membrane , placing its EGF-binding site adjacent to the membrane surface . We further show that EGF may interact with EGFR in two distinct ways ( with or without the involvement of the membrane ) . This may explain the experimental finding that an EGF molecule binds to EGFR more weakly at higher EGF concentration . This phenomenon , which is a manifestation of an underlying negative cooperativity , is an important but poorly understood characteristic of EGFR activity . In this study , we also model and analyze the glycan chains attached to EGFR , which are integral to its behavior in living cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biophysics",
"biology",
"and",
"life",
"sciences",
"computational",
"biology"
] |
2014
|
Membrane Interaction of Bound Ligands Contributes to the Negative Binding Cooperativity of the EGF Receptor
|
Mycobacterium leprae is the causative agent of leprosy and also known to possess unique features such as inability to proliferate in vitro . Among the cellular components of M . leprae , various glycolipids present on the cell envelope are well characterized and some of them are identified to be pathogenic factors responsible for intracellular survival in host cells , while other intracellular metabolites , assumed to be associated with basic physiological feature , remain largely unknown . In the present study , to elucidate the comprehensive profile of intracellular metabolites , we performed the capillary electrophoresis-mass spectrometry ( CE-MS ) analysis on M . leprae and compared to that of M . bovis BCG . Interestingly , comparison of these two profiles showed that , in M . leprae , amino acids and their derivatives are significantly accumulated , but most of intermediates related to central carbon metabolism markedly decreased , implying that M . leprae possess unique metabolic features . The present study is the first report demonstrating the unique profiles of M . leprae metabolites and these insights might contribute to understanding undefined metabolism of M . leprae as well as pathogenic characteristics related to the manifestation of the disease .
Mycobacterium leprae , the causative agent of leprosy , is an obligate intracellular pathogen having unique features . The doubling time of M . leprae is about 14 days as compared to approximately 24 hours of M . tuberculosis or the vaccine strain M . bovis BCG . The inability to cultivate M . leprae in vitro may be due to its intrinsic characteristics of inert nature , probably resulting from the reduced coding capacity of M . leprae genome . Comparative genomics have shown that there are numerous pseudogenes ( 1 , 116 pseudogenes versus 1 , 604 functional genes ) in M . leprae [1] . So far key studies including comparative genomics and proteomics have been performed , and its unique characteristics have been elucidated [1–4] . However , the interaction of the changes in genomic structures or protein expressions leading to the uncultivable nature of M . leprae is still unclear . On the other hand , the cell envelope components known to be highly involved in the differentiation of mycobacterial species are becoming clearer . Generally , various glycolipids and lipids , which are abundantly present in the outer layer of mycobacterial cell envelope , play an important role in achieving the pathogenicity including resistance to immune response and entrance into host cells [5] . M . leprae also has most of such glycolipids in common [6] , while the phenolic glycolipid-I ( PGL-I ) is shown to appear specifically in M . leprae as a one of major cellular components involved in pathogenicity [7–8] . Focusing on the cytosol of mycobacteria , various metabolites such as free amino acids or organic acid are present , which could be the valuable metabolic fuels , for subsequent intermediary metabolism . Therefore , understanding of the intricate balance of these metabolites , which play a role in driving and maintaining the cellular metabolism , is important . In cultivable organisms like M . tuberculosis , their dynamics has been investigated under different growth conditions and its alterations have been shown to be associated with the physiological feature as well as unique life cycle [9–12] . However , in the case of M . leprae , there have been no studies focusing on the fate of intracellular metabolites , which are assumed to be involved in the cellular metabolism . In some studies , the detection of intermediates was performed by labeling with isotopes or in other studies , enzymatic activity was measured by biochemical means [13–16] . Wheeler et al . showed that in M . leprae carbon from glycerol could be incorporated into the glycerol moiety of acylglycerols but not into the fatty acid moieties [16] . Unfortunately , most of other pathways and metabolism remain unknown probably due to the difficulties in culturing the bacilli . In addition , although the genes involved in basic metabolism are conserved without large deletions , sporadic distribution of pseudogenes was observed in M . leprae genome , [1] , which make the speculation of the metabolism from the genomic analyses of M . leprae difficult . These facts suggested that genomic analysis alone is not sufficient for elucidating whole metabolisms associated with its unique physiology . Recently , metabolomics approach using sera of the leprosy patients was undertaken . Significant increase of certain polyunsaturated fatty acids and phospholipids in high bacterial index patients were observed [17] . Also urinary metabolites could discriminate endemic controls from untreated patients , as well as leprosy patients with reversal reactions [18] . In this study , we focused on characterizing the quantitative and qualitative profile of intracellular metabolites in M . leprae by capillary electrophoresis-mass spectrometry ( CE-MS ) analysis , and compared them with those in Mycobacterium bovis BCG , which would consequently lead to the elucidation of pathogenic mechanisms of leprosy .
M . leprae Thai-53 strain was infected into the footpads of each nude mouse ( BALB/c nu/nu ) [19] . To propagate M . leprae , infected nude mice were maintained for 12 months in an isolated chamber . In order to exclude the possibility of getting wrong results , from the mouse-derived metabolites in M . leprae extract , we also prepared the uninfected nude mice which were maintained for the same period as M . leprae-infected nude mouse . Briefly , footpads dissected from M . leprae-infected and uninfected nude mouse were mechanically homogenized in Hanks' balanced salt solution ( HBSS ) as previously described [20 , 21] . An aliquot containing 2 . 5×1010 bacilli was taken from footpad homogenate of M . leprae-infected nude mouse and suspended in 5 ml of HBSS . In case of footpad homogenate from uninfected nude mouse , the volume equivalent to an average of above 2 . 5×1010 bacilli-contained aliquot was also taken and suspended in 5 ml of HBSS . For comparative study of the metabolites of M . leprae , we used M . bovis BCG Tokyo strain as control mycobacteria . 2 . 5×1010 bacilli were harvested from 1 week-culture in Middlebrook 7H9 broth supplemented with 10% ADC enrichment and suspended in 5 ml of HBSS . Above 5ml-HBSS suspensions containing 2 . 5×1010 mycobacterial cells and uninfected footpad homogenate were then incubated with 0 . 05% trypsin at 37°C for 1 hour . According to the metabolite extraction procedures [22–24] , trypsin-treated samples were collected by suction filtration using the Isopore Membrane Filter ( HTTP04700 ) ( Millipore , Massachusetts , USA ) . The collected samples were washed with Milli-Q water , and then exposed to methanol with Internal Standard Solution 1 ( Human Metabolome Technologies , Yamagata , Japan ) to obtain crude intracellular extracts . These were further treated with chloroform to remove the lipid components , and then filtrated with 5-kDa cut-off filter ( UFC3LCCNB ) ( Human Metabolome Technologies , Yamagata , Japan ) to yield the intracellular metabolite extract suitable for CE-MS analysis . In three groups , all procedures were independently performed in triplicate . For metabolite identification and quantification , Agilent CE-TOFMS System was basically performed on above prepared extracts , according to the conditions previously reported [22–24] . Capillary electrophoresis was carried out with a fused silica capillary whose diameter and length are 50 μm and 80 cm , respectively . Cationic and anionic metabolites were ionized in the positive ion mode ( 4 kV ) and negative ion mode ( 3 . 5 kV ) , respectively . The data were processed using MasterHands ver . 2 . 16 . 0 . 15 ( Keio University ) for retrieving the m/z value , migration time and peak area . Each metabolite was identified from its m/z value and migration time by searching against metabolite database ( Human Metabolome Technologies , Yamagata , Japan ) . Relative quantity of each metabolite was estimated by comparison of peak area with that of a standard compound in Internal Standard Solution 1 and resultant values were determined as relative peak area . In data processing of relative peak area , only the metabolites detected in triplicate were used for calculation of means , standard deviations and other analyses . By Welch’s t test , we assessed whether relative quantity of each metabolite was statistically different between two groups . Principal component analysis ( PCA ) was performed by using the MetaboAnalyst 3 . 0 ( University of Alberta and McGill University ) [25] . Animal experiments were carried out in strict accordance to "Act on Welfare and Management of Animals" enacted in 1973 . The protocol was approved by the Experimental Animal Committee of the National Institute of Infectious Diseases , Tokyo ( Permit Number: 214001 ) , whose guidelines are established by the Ministry of Health , Labour and Welfare , Japan ( MHLW ) .
Since M . leprae cannot be cultivated in vitro , the propagation in experimental animals such as nude mice and armadillo is currently the only way to obtain sufficient M . leprae for biochemical experiments . In this study , we propagated M . leprae in footpads of nude mice for 12 months . Footpads from M . leprae-infected and uninfected nude mice were processed in similar manner as described in the Methods , and the resultant extracts were analyzed . As shown in Fig 1 , CE-MS analysis showed that 206 and 49 metabolites were present in detectable amount in the extracts of footpads dissected from M . leprae-infected ( S1 Table ) and uninfected nude mice ( S2 Table ) , respectively ( n = 3 ) . Among 206 metabolites , 158 metabolites were specifically observed in M . leprae-infected nude mice , while only one compound was detected in uninfected nude mice ( Fig 1 ) . These observations mean that more than 75% of the detected metabolites in M . leprae-infected nude mice are derived from M . leprae itself . On the other hand , 48 metabolites from M . leprae-infected nude mice were also found in the extracts of uninfected nude mice . Quantitative comparison of 48 compounds between M . leprae-infected and uninfected nude mice revealed that most of relative peak areas from uninfected nude mice are much lower than those from M . leprae-infected nude mice ( S1 Fig ) . To acquire the authentic value of the metabolites derived from M . leprae , we used the background subtraction method . Mean value of each metabolite obtained from uninfected nude mice was subtracted from all triplicate value of M . leprae-infected nude mice and thereby retrieved 35 metabolites which were abundantly present in M . leprae-infected nude mice ( Fig 1 ) . Eventually , 193 metabolites were totally determined to be specifically present in the M . leprae extract ( S3 Table ) ( Fig 1 ) . Although it became apparent that M . leprae possessed 193 specific intracellular metabolites , it is unknown how their qualitative and quantitative profiles differ from other mycobacteria . M . leprae is known to have unique properties such as long-term obligate growth in vivo , which is quite different from other mycobacteria , indicating that it is difficult to select the control mycobacteria for comparison . In this study , for comparative profiling of the M . leprae metabolites , we chose in vitro-cultured M . bovis BCG . Unlike M . leprae , M . bovis BCG does not remain in the footpad where inoculated but get disseminated all over the body and sufficient bacilli cannot be retrieved for analyses . [26] . By performing the extraction procedures of in vitro-cultured M . bovis BCG under the same conditions as M . leprae , we obtained the metabolite extracts of M . bovis BCG for comparative study . CE-MS analysis of M . bovis BCG indicated that 137 compounds could be detected ( S4 Table ) . To obtain a comprehensive profile of metabolites , we performed the principal component analysis ( PCA ) on relative peak area of metabolites detected from three independent groups of M . leprae and M . bovis BCG ( Fig 2A ) . The results showed that the groups cluster within each species but their clusters are clearly separated , suggesting that the sort and quantity of detected metabolites from M . leprae are close within three analyzed groups , but they are quite distinguishable from those of M . bovis BCG groups whose profiles were also similar to each other ( Fig 2A ) . Therefore , this implied that metabolic profile of M . leprae and M . bovis BCG are functionally distinct . Comparison of detected metabolites between two species indicated that 84 metabolites are in common , while 109 and 53 metabolites are specifically observed in M . leprae and M . bovis BCG , respectively ( Fig 2B ) . Additionally , we assessed and classified the metabolites in terms of their function in each category of metabolism ( Fig 2C ) . As a consequence , we found that 56% of the total metabolites of M . leprae were categorized under the amino acid metabolism group , while in M . bovis BCG only 26% of the total metabolites belonged to the amino acid metabolism ( Fig 2C ) . On the other hand , as for the metabolites associated with central carbon and nucleic acid metabolism , it was observed that their proportion in M . bovis BCG was almost twice as much as the metabolites of M . leprae ( central carbon: 23% vs . 11%; nucleic acid: 20% vs . 12% in M . bovis BCG vs M . leprae respectively ) ( Fig 2C ) . These differences were more pronounced when the population of specifically detected metabolites from M . leprae and M . bovis BCG were considered . The amino acid-related compounds constitute 68% of 109 metabolites , which were specifically observed in M . leprae , but its proportion in 53 M . bovis BCG-specific metabolites was only 4% ( Fig 2C ) . On the contrary , the percentage of the compounds related to the central carbon and nucleic acid metabolism was shown to be quite high in M . bovis BCG-specific metabolites , compared to those in M . leprae-specific metabolites ( central carbon: 25% vs . 4%; nucleic acid: 26% vs . 9% in M . bovis BCG vs M . leprae respectively ) ( Fig 2C ) . These results indicated that , in M . leprae , amino acid-related compounds constituted a larger proportion of detected metabolites , while detection of metabolites associated with central carbon and nucleic acids were relatively small , compared to those of M . bovis BCG . To better explore the mechanisms governing the fate of M . leprae , we focused on the quantitative evaluation of 84 metabolites , which were common to both M . leprae and M . bovis BCG ( S5 Table ) . When the mean ratio of each relative peak area ( M . leprae/M . bovis BCG ) was arranged in decreasing order , it was found that 26% ( 22/84 ) of the metabolites significantly showed large differences ( >10-fold ) , while 14% ( 12/84 ) showed <0 . 1-fold differences ( Fig 3 ) . Additionally , functional classification of the metabolites having 10-fold difference in the mean ratio revealed that most of the compounds listed are involved in amino acid metabolism ( Table 1 ) . This is supported by the fact that M . leprae-specific metabolites were dominated by those related to amino acid metabolism ( Fig 2C ) , suggesting that amino acid and its derivatives abundantly accumulated as intracellular metabolites in M . leprae , when compared to those metabolites of M . bovis BCG . These results raise two possibilities regarding the metabolic aspects of M . leprae: ( 1 ) M . leprae itself has the capacity to produce high amount of amino acids , ( 2 ) M . leprae activates the uptake of amino acids from host , in order to maintain and control the metabolism suited for long-term , obligate growth in vivo . At present , it is not clear which mechanism could better explain the cause of amino acid accumulation , because no direct evidence was obtained from the M . leprae genomic analyses of the regions that could be involved . Probably there is yet unknown mechanism which causes the amino acid accumulation . On the contrary , Table 2 show that the metabolites in the cluster showing less than 0 . 1-fold differences in their mean ratio of relative peak area ( M . leprae/M . bovis BCG ) dominantly belonged to the intermediates related to the central carbon metabolism , for example metabolites such as glucose-6-phosphate , fructose-6-phosphate , sedoheptulose 7-phosphate , 6-phosphogluconic acid and ribose 5-phosphate . These metabolites play a critical role in cellular pathways of energy metabolism as well as other basic cellular processes . The same tendency is also observed when metabolites were classified according to their function ( Fig 2C ) , suggesting that the central carbon metabolism in M . leprae is strikingly declined or repressed compared to those in M . bovis BCG . These results are exemplified by the M . leprae genomics demonstrating that around half of genes related to energy metabolism tuned out to be the pseudogenes , which might lead to the functional defect [1] . It is generally hypothesized that lack of energy generation machinery is the main reason for the inability of the bacilli to proliferate in vitro , while there are only genomics analysis available to prove the hypothesis . Thus , phenotypically , our study of comparative metabolomics for the first time supported this hypothesis . M . tuberculosis is known to possess the ability to degrade and use cholesterol as an energy source and for the biosynthesis of mycobacterial lipids . In M . leprae , the presence of host-derived cholesterol plays important role in the process of intracellular survival [27] . However , M . leprae lost essentially all the genes associated with cholesterol catabolism but retained only the ability to oxidize cholesterol to cholestenone , indicating that cholesterol metabolism was not coupled to central carbon metabolism in M . leprae [28] . Lipids in the foamy macrophages and Schwann cells were shown to be derived from the host lipids , favoring bacterial survival [29 , 30] . In these contexts , M . leprae has its own unique metabolic pathways to sustain its growth and multiplication , which has to be further elucidated . Focusing on minute detail of the M . leprae genome , numerous pseudogenes are distributed in the genomic region of each metabolic pathway , suggesting that such unique features of the genome might be one of the factors influencing the characteristic profiles of intracellular metabolites . However , at present , it is difficult to identify the pseudogenes causing M . leprae-specific profiles because each pathway is interrelated and is not thoroughly investigated , which leads to complications in deciphering the metabolism in M . leprae . Therefore , generation of mutants having mutations that mimic the pseudogenes of M . leprae in cultivable mycobacteria might partly elucidate the relationship between uniqueness in genomic organization and the metabolic profile obtained by CE-MS analysis . Present study demonstrated that the dynamics of intracellular metabolites in M . leprae is quite different from those in M . bovis BCG . Although it is necessary to perform the metabolite profiling on other mycobacteria for more precise evaluation of M . leprae metabolism , the result retrieved from comparison with M . bovis BCG would partly contribute to the uncovering the M . leprae physiology associated with onset of leprosy . In M . tuberculosis , metabolomics analysis has been performed on in vitro-grown cells under conditions such as hypoxia and nutrient starvation which stimulate in vivo growth , while no study performing the metabolomics on actually in vivo-grown cells was reported [9–12] . Thus , our findings regarding in vivo-grown M . leprae might provide insights into the understanding of not only its physiology but also the metabolic behavior of mycobacteria in host , which remains unresolved .
|
Mycobacterium leprae , the causative agent of leprosy , has unique physiological features including being uncultivable in artificial media . This fact raises the possibility that M . leprae possesses specific metabolism that are different from other cultivable mycobacteria . Among the components of M . leprae , the glycolipids are known to be involved in pathogenicity , while the dynamics of intracellular metabolites such as organic acids , amino acids and nucleic acids remain unclear . Aiming to understand the metabolism of M . leprae , we characterized the profile of intracellular metabolites . Unexpectedly , we found that amino acid species are significantly accumulated , while most of intermediates related to central carbon metabolism markedly decreased in the metabolite fraction of M . leprae , as compared with that of other mycobacteria . These specific metabolic features of M . leprae was presented for the first time and these insights may contribute to understanding the mechanism of physiology including obligate growth in vivo , which is one of the key characteristics of leprosy .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"Discussion"
] |
[
"mycobacterium",
"leprae",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"intracellular",
"pathogens",
"pathogens",
"tropical",
"diseases",
"animal",
"models",
"bacterial",
"diseases",
"model",
"organisms",
"metabolites",
"neglected",
"tropical",
"diseases",
"bacteria",
"nucleic",
"acid",
"metabolism",
"research",
"and",
"analysis",
"methods",
"amino",
"acid",
"metabolism",
"infectious",
"diseases",
"mouse",
"models",
"actinobacteria",
"biochemistry",
"leprosy",
"biology",
"and",
"life",
"sciences",
"metabolism",
"organisms",
"mycobacterium",
"bovis"
] |
2016
|
Profiling of Intracellular Metabolites: An Approach to Understanding the Characteristic Physiology of Mycobacterium leprae
|
Mature Drosophila sperm are highly polarized cells—on one side is a nearly 2 mm long flagellar tail that comprises most of the cell , while on the other is the sperm head , which carries the gamete's genetic information . The polarization of the sperm cells commences after meiosis is complete and the 64-cell spermatid cyst begins the process of differentiation . The spermatid nuclei cluster to one side of the cyst , while the flagellar axonemes grows from the other . The elongating spermatid bundles are also polarized with respect to the main axis of the testis; the sperm heads are always oriented basally , while the growing tails extend apically . This orientation within the testes is important for transferring the mature sperm into the seminal vesicles . We show here that orienting cyst polarization with respect to the main axis of the testis depends upon atypical Protein Kinase C ( aPKC ) , a factor implicated in polarity decisions in many different biological contexts . When apkc activity is compromised in the male germline , the direction of cyst polarization within this organ is randomized . Significantly , the mechanisms used to spatially restrict apkc activity to the apical side of the spermatid cyst are different from the canonical cross-regulatory interactions between this kinase and other cell polarity proteins that normally orchestrate polarization . We show that the asymmetric accumulation of aPKC protein in the cyst depends on an mRNA localization pathway that is regulated by the Drosophila CPEB protein Orb2 . orb2 is required to properly localize and activate the translation of apkc mRNAs in polarizing spermatid cysts . We also show that orb2 functions not only in orienting cyst polarization with respect to the apical-basal axis of the testis , but also in the process of polarization itself . One of the orb2 targets in this process is its own mRNA . Moreover , the proper execution of this orb2 autoregulatory pathway depends upon apkc .
Polarity plays a central role in a diverse array of biological contexts in organisms ranging from single cell bacteria to complex multicellular eukaryotes . In multicellular eukaryotes , the steps involved in establishing , maintaining , and transmitting polarity are typically controlled by an interacting set of evolutionarily conserved atypical protein kinase C-partitioning defective proteins ( aPKC-PAR proteins ) . The classic model for polarity determination by the aPKC-PAR machinery is the establishment of the anterior-posterior axis in the C . elegans embryo [1] . Prior to fertilization , anterior determinants , the worm aPKC ortholog PKC-3 , PAR-3 and PAR-6 , are distributed in a complex around the entire cortex of the egg [2]–[4] , while the posterior factors , PAR-1 and PAR-2 , are cytoplasmic . PAR-2 is kept off the cortex by PKC-3 dependent phosphorylation , and a similar mechanism may apply to PAR-1 [5] , [6] . Sperm entry induces a cytoplasmic flux that relocalizes the PKC-3/PAR-3/PAR-6 complex in the posterior to the anterior cortex . Following the removal of PKC-3 activity from the posterior , PAR-1 and PAR-2 are able to associate with the cortex . Cortical PAR-2 in turn prevents re-association of anterior determinants with the posterior cortex ( for review: [7] ) . This generates a polarized cell in which the PKC-3/PAR-3/PAR-6 complex is distributed along the anterior cortex , while PAR-1/PAR-2 are localized on the posterior cortex . This process also serves to orient the mitotic spindle: the first cell division in the embryo is parallel to the anterior-posterior axes and as a consequence the two daughter cells receive different sets of embryonic determinants [1] . The aPKC-PAR machinery defines polarity in many other contexts besides the establishment of the anterior-posterior axis of the C . elegans embryo . Moreover , as in C . elegans , antagonistic interactions between aPKC-PAR family proteins are critical for establishing and propagate cell asymmetry in many systems ( for reviews: [8]–[11] . In most of the well-studied model systems , the aPKC-PAR machinery is deployed to generate undifferentiated cells that are then able to assume different identities . However , polarity can also be a distinguishing feature of terminally differentiated cells . One example of a highly polarized fully differentiated cell is the mature D . melanogaster sperm . At one end of the mature sperm cell is the sperm head , which contains the highly condensed haploid genome encased in a multilayer membrane . The rest of the cell is the nearly 2 mm long flagellar axoneme tail , which is connected to the head by a centrosome-derived structure called the basal body . The formation of this polarized cell commences after meiosis is completed and the 64 interconnected spermatids begin the process of differentiation ( Fig . 1A ) . Each haploid nucleus has a single basal body with a short axoneme surrounded by a membrane cap . In the first steps the basal body inserts into the nuclear envelope , where it functions to nucleate the assembly of the flagellar axoneme . The 64-cell cyst re-organizes so that the spermatid nuclei cluster together on the proximal or basal side of the cyst while the basal bodies and nascent flagellar axonemes localize to the opposite side . The flagellar axonemes then begin elongating towards the mitotic spermatogonia and stem cells at the apical end of the testis ( Fig . 1A ) . At the tip of elongating tails are ring canals ( that function to connect the 64 cells in the cyst ) and a ciliary sheath that encases the bundled elongating flagellar axonemes . The remainder of the axoneme is ensheathed by mitochondria and a plasma membrane . Once the spermatids are fully elongated , the process of individualization begins . A special actin based structure , called the individualization complex ( IC ) , forms from the mature spermatid nuclei , and then translocates down the axonemes , remodeling the membranes to separate the individual sperm tails and removing excess cytoplasm [12]–[16] . One of the critical steps in sperm differentiation is the polarization of the 64-cell spermatid cyst . There are multiple levels of polarization: At the cellular level , the round , seemingly symmetric spermatids must become polarized so that their nuclei are localized on one side , while the nascent flagellar initiate their growth on the opposite side . At the next level , polarization of individual spermatids in one cyst must be coordinated so that the all 64 of the spermatids in the cyst have the same orientation , nuclei clustered on one side of the cyst and nascent flagellar axonemes on the other . The two somatic cells that surround the cyst are also polarized: the head cyst cell encapsulates the clustered spermatid nuclei while the tail cyst cell covers the growing flagellar axonemes . During the following spermatid elongation and differentiation stages , the two somatic cells expand their volumes without divisions and retain their relative positions . Finally , the elongating spermatids must be oriented correctly with respect to the apical basal axis of the testes so that once differentiation is complete the mature sperm can be readily transferred into the seminal vesicle . Here we show that aPKC is required to orient the direction of spermatid cyst polarization with respect the apical-basal axis of the testis . When apkc activity is compromised , the direction of polarization appears to be randomized . As in many other biological contexts , aPKC protein is asymmetrically localized to the apical side of the spermatid cyst during polarization . However , the mechanisms used to spatially restrict aPKC accumulation are different from the canonical cross-regulatory interactions between this kinase and other cell polarity proteins that normally orchestrate polarization . Instead , an mRNA localization pathway that is regulated by one of the two Drosophila CPEB family translational regulators , orb2 , is responsible for spatially restricting the accumulation of aPKC protein during cyst polarization . We show that Orb2 binds to the 3′ UTR of a special apkc mRNA species , apkc-RA , which is only expressed in post-meiotic cysts , and regulates both its localization and translation . We also find that orb2 functions not only in orienting cyst polarization with respect to the apical-basal axis of the testis , but also in the process of polarization itself . One of the orb2 regulatory targets during polarization is its own mRNA , and this autoregulatory activity depends , in turn , upon apkc .
Although substantial amounts of aPKC can be detected in Western blots of testis extracts ( not shown ) , much of this protein is likely to be somatically derived . In whole mounts of wild type testes probed with aPKC antibody , only a low level of unlocalized protein is detected in spermatocytes before and during the first and second meiotic divisions ( Fig . 1B , arrowhead and arrow ) . However , after meiosis is complete and the 64-cell cysts show the first signs of polarization and axoneme nucleation , prominent aPKC signals can be detected . As shown in a newly polarized cyst in Fig . 1C , aPKC concentrates in “puncta” ( arrow ) that are clustered in a region of the cyst diametrically opposite to the spermatid nuclei . This region contains the nascent flagellar axonemes and it will form the tip of the elongating spermatid tails . As elongation proceeds and the structure of the elongating flagellar axonemes becomes more tightly organized , there is a band of aPKC protein located near the tip of the elongating sperm tails ( Fig . 1D , arrow , insert ) . During elongation , the protein appears to be arranged into a series of thin stripes that run parallel to the bundled sperm tails and are located near if not at the termini of the 64 growing flagellar axonemes ( Fig . 1E , arrow ) . These stripes remain associated with the tips of the elongated flagellar axonemes until after individualization commences ( not shown ) . In other biological contexts , proteins that function together with aPKC in cell polarization often exhibit similar or reciprocal localization patterns . To identify other potential players , we examined the expression of other classic polarity regulators Bazooka ( Baz: Drosophila Par3 ) , Discs large ( Dlg ) and Par-1 during spermatogenesis . As shown in Figure S1 , high levels of Baz , Dlg and Par-1 were seen at the apical end of the testis , in the region that contains the germline stem cells and the mitotic spermatogonia/spermatocytes and the somatic support cells . These proteins seemed to be mostly expressed in the somatic support cells that surround the developing mitotic and meiotic germline cysts ( Fig . S1A , C , E ) . However , unlike aPKC none showed localized accumulation later on during the differentiation of the spermatid cysts ( Fig . S1B , D , F ) . In many systems a critical step in the polarization pathway is the asymmetric localization of aPKC . Thus , a reasonable expectation is that the targeting of aPKC to the apical side of differentiating spermatid cysts will orchestrate some aspect ( s ) of cyst polarization . To explore this possibility we analyzed the effects of apkc mutations and RNAi knockdowns on sperm morphogenesis ( Fig . 2 ) . apkck06403 has a P-element insertion between the P2 and P3 promoters that could potentially affect mRNAs produced by the upstream P1 and P2 promoters ( see map in Fig . 3A ) . Since this allele is homozygous lethal , we looked for phenotypic effects in heterozygous mutant males . We also examined two viable hypomorphic alleles apkcex55 and apkcex48 that were generated by the excision of the apkck06403 P element [17] [18] . In wild type testes , the orientation of spermtid elongation is invariant: The sperm tails always extend towards the apical end of the testis while the sperm nuclei are pushed towards the base of the testis . As a result , spermatid nuclei clusters are not seen in the spermatogonia/spermatocyte region of the testis ( Fig . 2E ) . However , when apkc activity is compromised , spermatid nuclei clusters appear at the apical side of the testes ( Fig . 2B ) . Fig . 2A shows the frequency of testes that have differentiating sperm oriented in the opposite direction – the spermatid nuclei cluster located apically and the tails elongating basally . Whereas misoriented cysts were rarely if ever seen in wild type , about ¼ of the testes in males homozygous for either of the hypomorphic alleles had misoriented spermatid cysts . Within these testes , 5–20% of the cysts elongated in the incorrect direction . Arguing against simple background effects , we found that apkc was weakly haploinsufficient and incorrectly oriented spermatid cysts were also detected in males heterozygous for the strong loss of function allele , apkck06403 . It is worth noting that unlike other genes that have been implicated in cyst polarization ( e . g . , the exocyst complex: [19] ) , the defect in these hypomorphic apkc mutants appears to be in choosing the direction of polarization and not in coordinating or executing the polarization process itself . This is suggested by the fact that all 64 of the spermatids in the mutant cysts undergo seemingly normal elongation but in the incorrect direction and we never observed spermatids within the same cyst elongating in opposite directions . To provide further evidence that apkc is required to properly orient cyst polarization within the testis and also determine whether it functions in the germline , we knocked down apkc by combining UAS-apkc RNAi lines with a germline specific Gal4 driver nanos-Gal4 , or a triple Gal4 line ( pCOG-Gal4; NGT-40; nanos-Gal4 ) , MTD , that drives higher levels of UAS dependent expression in germline cells in most stages of spermatogenesis [20] , [21] . To control for off target effects from RNA interference , we tested two UAS-apkc RNAi lines , UAS-apkc::34332 and apkc:35140 , that express double strand RNA , dsRNA-HMS01320 and dsRNA-GL00007 , targeting two different exon regions that are common for all apkc mRNA species ( Fig . 3A ) ( see materials and methods ) [22] . Orientation defects like those observed in the hypomorphic mutants were also seen when apkc activity was reduced by RNAi knockdown ( Fig . 2D ) . For example , at 22o C 18% of the nanos-Gal4/UAS-apkc:35140 testes had at least one incorrectly oriented spermatid cyst ( Fig . 2A ) . Moreover , amongst the testes that had polarity defects , nearly 20% of the spermatid cysts were elongating in the wrong direction . The frequency of nanos-Gal4/UAS-apkc:35140 testes with incorrectly oriented elongating spermatid cysts increased from 18% to 45% when the temperature was raised from 22o C to 25o C . Even stronger effects were observed when UAS-apkc:35140 was combined with the triple Gal4 line MTD . In this case , every testis had multiple misoriented spermatid cysts ( Fig . 2A ) . In addition to these orientation defects , the spermatids in testes from the MTD/UAS-apkc:35140 knockdown were shorter than wild type ( not shown ) . This defect suggests that apkc activity might be needed to sustain the elongation of the flagellar axoneme . Since several of the proteins that usually collaborate with aPKC do not seem to be expressed in differentiating spermatids , other mechanisms are likely important for localizing aPKC asymmetrically and facilitating its function in correctly orienting cyst polarization . One such mechanism would be mRNA localization . Support for this possibility comes from the studies of Barreau et al . [23] . They identified a special group of genes that are transcribed only post-meiotically and encode mRNAs that localize in a striking “comet” pattern at the apical end of the elongating spermatid tails . The “head” of the comet , where these mRNAs are most highly concentrated , is in the same region of the elongating tails as the aPKC protein stripes . To determine if apkc mRNA is similarly localized in differentiating spermatids , we generated a set of oligo probes specific to the large exon that is common to all apkc mRNAs ( Fig . 3A ) . apkc mRNAs can first be detected in the stem cell region of the testis and they persist until late in spermatogenesis ( Fig . 4A ) . Significantly , in differentiating spermatid cysts apkc mRNAs are distributed in a “comet” pattern just like the post-meiotically expressed mRNAs identified by Barreau et al . ( Fig . 4A , arrow ) . The highest concentration of apkc mRNA is in the comet “head” which is close to the leading edge of the elongating sperm tails , while there is a comet “tail” with lower levels that extends backwards towards the spermatid nuclei . The apkc gene has four different promoters ( P1–P4 ) and , together with an extensive set of alternatively spliced exons and UTRs , it is predicted to generate more than 10 different mRNAs ( Fig . 3A ) . To identify apkc mRNA species that are expressed in the testis we used primers specific for different promoters and the alternatively spliced exons and UTRs ( e . g . , 1-2/3-4/PF-PR ) for RT-PCR ( Fig . 3A , 3B ) . These experiments indicate that testes have a complex mixture of many apkc mRNA species . Interestingly , one of these mRNAs , RA ( P1: 5–10; PF-PR ) , has an unusually long 3′ UTR that is not found in any of the other predicted mRNA species . As mRNA localization often depends upon special cis-acting elements in the 3′ UTR , we generated in situ probes specific for apkc-RA mRNA species . The apkc-RA expression pattern differs substantially from that of the “bulk” apkc mRNAs that hybridizes to the large common exon probe . Unlike “bulk” apkc mRNA , apkc-RA can not be detected in the pre-meiotic stages , while only low levels are present in the meiotic cysts ( Fig . 4B , arrow head & Fig . S2A ) . Its expression is first detected in the round spermatids after meiosis is complete ( Fig . 4B , white arrow ) , and increases to a maximum during spermatid differentiation ( Fig . 4B , yellow arrows & Fig . S2A ) . Initially apkc-RA is distributed uniformly through the cyst; however , this changes when polarization commences and the spermatid nuclei start to cluster on the basal side of the cyst . At this point apkc-RA mRNAs can be detected near the apical tip of the nascent flagellar axonemes much like aPKC protein ( Fig . S2B ) . As elongation proceeds , apkc-RA mRNAs accumulate in the characteristic comet pattern , and are most highly concentrated in the comet head near the apical tip of the elongating tails ( Fig 4B , yellow arrows ) . The apkc-RA mRNAs in the comet head are expected to be in close proximity to the aPKC protein stripes that mark the ends of the flagellar axonemes . When we simultaneously probed testes for apkc-RA mRNA and aPKC protein , the aPKC stripes partially overlap with the proximal side of the apkc-RA mRNA comet head ( Fig . 4C ) . This finding would fit with the idea that the apkc-RA mRNAs in the comet head are a source of the aPKC stripes at the leading edge of the growing flagellar axonemes . Several of the post-meiotic mRNAs identified by Barreau et al . have consensus CPEs ( Cytoplasmic Polyadenylation Elements ) in their 3′ UTRs . CPE motifs are recognized by a conserved family of RNA binding proteins , CPEBs , which in flies are known to function both in mRNA localization and translational regulation [24]–[26] . Proteins in this family have two RRM-type RNA binding domains in their C-terminal halves , while their N-terminal halves contain polypeptide sequences that have regulatory functions [27] , [28] . Inspection of the unique apkc-RA 3′ UTR indicates that it has three canonical CPE motifs ( Fig . 3A and Text S2 ) . Several of the other apkc mRNA species ( RD , RJ , RK , RL and RM ) , which share a different UTR sequence , have a single CPE sequence . Thus , an intriguing idea is that a fly CPEB protein binds to the RA mRNA species and perhaps to one or more of these other apkc mRNAs , facilitates their localization to the tip of the elongating sperm tails and controls their translation . Flies have two CPEB genes , orb and orb2 , and both are expressed in testes . Of these , orb is not likely to have either of these functions as its message is not translated until after spermatid elongation is complete [29] . orb2 , on the other hand , would be a plausible candidate for the regulatory factor . It is required at multiple steps during spermatogenesis including spermatid differentiation . We've found that one of its differentiation functions is to control the translation of post-meiotically expressed mRNAs like orb that have CPE motifs in their 3′ UTRs . Moreover , like these post-meiotic mRNAs , Orb2 protein is distributed in a comet pattern in elongating spermatids [29] . Two other findings lend support to the idea that orb2 might regulate the localization and/or translation of apkc-RA and perhaps other CPE containing apkc mRNAs species in the testis . First , we found that orb2 is required for the apical accumulation of aPKC in embryonic neuroblasts during asymmetric cell division [30] . Second , exogenous biotin tagged apkc-RA RNA can pull down Orb2 protein from adult fly brain extracts [31] . This hypothesis makes several predictions that we have tested for the apkc-RA mRNA species as it has a post-meiotic expression pattern . The experiments in the previous section demonstrate that Orb2 co-localizes with apkc mRNAs in elongating spermatid cysts and binds directly to the 3′ UTR to an apkc mRNA species that is specifically expressed at this stage of spermatogenesis . If this association is important for apkc activity during spermatid differentiation , orb2 should also be required for correctly polarizing the 64-cell cysts . To test this prediction we examined spermatid cyst polarization in two different orb2 mutants , orb236 and orb2ΔQ . orb236 is a null allele that lacks the entire Orb2 protein coding sequence . Meiosis is blocked in orb236 and 16-cell cysts , which have duplicated their DNA but not undergone the 1st meiotic division , accumulate in the mutant testes . These cysts ultimately exit meiosis and then attempt but fail to differentiate into mature sperm [29] . The other allele , orb2ΔQ , is a hypomorph . It has an in frame deletion that removes a short poly-glutamine domain in the N-terminal half of the protein . While the sequence of the Orb2 protein is altered , its level of expression remains the same [32] . Unlike the null , meiosis is unaffected in orb2ΔQ , and orb2ΔQ males produce some functional sperm and are fertile . However , in a subset of the spermatid cysts there are abnormalities in spermatid differentiation , including polarization defects ( Fig . 2A ) . The polarization phenotype in this hypomorphic orb2 allele closely resembles that seen for apkc , and we will discuss it first . One of the differentiation defects in orb2ΔQ is in properly orienting the direction of spermatid cyst polarization . This phenotype is observed in slightly over 20% of the orb2ΔQ testes ( Fig . 2 ) . Fig 5A and B show spermatid cyst nuclei and individualization complexes ( IC ) in orb2ΔQ testes visualized by Hoechst staining of DNA ( blue , arrowheads ) and phalloidin staining of actin ( green , arrows ) . The IC consists of 64 aligned actin cones . After elongation is complete , the IC assembles using the condensed spermatid nuclei as a scaffold and then travels along the flagellar axonemes separating syncytial spermatids from each other . The orb2ΔQ testes in Fig . 5A has two assembled ICs ( actin in green ) that are still associated with the condensed spermatid nuclei ( DNA in blue ) ; however , instead of being located towards the basal end of the testis as in wild type , the two spermatid nuclei clusters and associated ICs are in the spermatocyte region of the testis ( “*” marks stem cell position ) . ICs have a distinct morphology; the flat side faces the direction of IC motion , while the thinner , rounded side faces the cluster of spermatid nuclei . In orb2ΔQ testes , ICs moving in opposite directions were sometimes observed ( Fig 5B , yellow arrows indicate moving directions ) . While orb2ΔQ has defects in choosing the direction of cyst polarization with respect to the apical-basal axis of the testis , the spermatids within the cyst undergo a coordinated ( all in the same direction ) and otherwise seemingly normal polarization . In contrast , as illustrated by the disorganized distribution of the Boule translation factor and spermatid nuclei , the process of cyst polarization itself seems to be disrupted in orb236 . In wild type , Boule accumulates in a comet pattern near the tip of the elongating flagellar tails just like Orb2 ( Fig . 5C , arrows ) [29] , [33] . In the improperly polarized orb236 cyst in Fig . 5D ) , there are Boule “pseudo-comets” extending from both ends of the cyst towards the spermatid nuclei , which are scattered near the center of the cyst ( arrows ) . In the partially elongated cyst shown in 5E , Boule is distributed along the apical-basal axis with little evidence of polarization , while the spermatid nuclei are scattered throughout much of the cyst . Although these findings indicate that orb2 is essential for cyst polarization , the possibility remains open that the polarization defects arise at least in part from the block in meiosis . If the failure to properly orient the direction of spermatid polarization in orb2ΔQ arises because apkc requires orb2 activity in this process , we might expect to observe genetic interactions between these two genes . To explore this possibility we examined spermatid cyst polarization in different orb2/apkc trans-heterozygous mutant combinations . As described above , apkc is weakly haploinsufficient in cyst polarization , and in flies heterozygous for the strong loss of function allele apkck06403 about 15% of the testes had improperly oriented spermatid cysts ( Fig . 2A2 ) . By contrast , essentially no polarization defects were evident in testes from flies heterozygous for either orb2ΔQ ( not shown ) or orb236 ( Fig . 2A1 ) . On the other hand , about 40% of the testes from orb236/apkck06403 trans-heterozygous males have misoriented spermatid cysts ( Fig . 2A2 ) . While orb2ΔQ has at most only a minimal effect on the number of testes that have incorrectly oriented spermatid cysts when combined with apkck06403 , this allele significantly increases the frequency of cyst polarization defects when combined with the weak loss of function apkc mutants apkcex48 and apkcex55 ( Fig . 2A3 ) . In both trans-heterozygous combinations , the frequency of testes with misoriented spermatid cysts increases more than 10-fold . An even more dramatic genetic interaction is observed when the orb2 null allele , orb236 , is combined with either apkcex48 or apkcex55 ( Fig . 2A3 ) . In fact , the frequency of testes with misoriented cysts in these two trans-heterozygous combinations is equivalent to that observed in the testes of the corresponding homozygous apkc mutant . These synergistic genetic interactions support the idea that the functioning of apkc in orienting spermatid cyst polarization within the testis depends upon orb2 . An expectation of this hypothesis is that there should be defects in the targeting of apkc mRNA and/or in the localized expression of the aPKC protein when orb2 activity is compromised . We first examined the localization of apkc-RA and “bulk” apkc mRNAs in orb2ΔQ . In orb2ΔQ cysts that were polarized in the incorrect direction , the comet pattern was invariably lost and apkc-RA mRNA was instead distributed almost uniformly along the length of the flagellar axonemes ( Fig . 6A , B ) . Similar results were seen when we used the exon probe to visualize the “bulk” apkc mRNA ( Fig 6C ) indicating that the localization of other apkc mRNA species in the comet pattern is disrupted in incorrectly polarized cysts . On the other hand , in most , but not all of the orb2ΔQ cysts that polarized correctly and have their axonemes elongating towards the apical end of the testis , apkc-RA and the “bulk” apkc mRNAs were localized in the characteristic comet pattern as in wild type ( not shown ) . The pattern of aPKC protein accumulation in orb2ΔQ parallels that of apkc-RA mRNA: aPKC stripes are absent in flagellar axonemes that are polarized in the wrong orientation ( Fig . 6E ) . In contrast , in most of the cysts that are polarized in the correct orientation , aPKC protein stripes are localized at the tips of elongating flagellar axonemes just like wild type ( Fig . 6D ) . Interestingly , apkc-RA mRNA is readily detected in Orb2 immunoprecipitates of extracts from orb2ΔQ testes ( Fig . 3D ) . Thus , it seems likely that this deletion partially compromises a step subsequent to binding to the apkc-RA mRNA that is important for localizing the apkc-RA;orb2 mRNP complex and/or activating translation . We also examined apkc mRNA localization and translation in the null allele orb236 . In spite of the fact that meiosis never takes place , the apkc-RA transcript is still expressed in orb236 spermatid cysts . This was also true for orb , which , like apkc-RA , is transcribed post-meiotically in the germline of wild type males [29] . Fig . 6F and G show that the apkc-RA and also “bulk” apkc mRNAs are completely unlocalized in differentiating orb236 spermatid cysts . Likewise , the sharp stripes of aPKC protein seen near the ends of the growing sperm tails in wild type cysts are absent in the null mutant cysts ( not shown ) . Fully extended flagellar axonemes are up to 1 . 8 mm in length [12] , [13] . In order to mediate the localization and translational regulation of apkc or any other mRNA at the tips of the axonemes as the sperm tails elongate , there would have to be a continuous source of Orb2 in the comet head . One way to maintain high levels of Orb2 at the tip of the elongating axonemes would be a positive autoregulatory loop in which Orb2 helps direct the localization and translation of its own mRNA . An orb2 autoregulatory activity could also help promote the initial polarization of the spermatid cyst . Moreover , there is precedence for CPEB proteins having autoregulatory activity . In the female germline , a two step positive autoregulatory mechanism , in which Orb mediates the localization and translation of orb mRNA , is known to be important in targeting Orb to the oocyte when it is first specified and then ensuring that Orb continues to accumulate within oocyte as it develops [34] . Also consistent with this idea , orb2 mRNAs have consensus CPE motifs in their 3′ UTRs ( Text S2 ) . To explore this possibility further we first asked if orb2 mRNA is associated with Orb2 protein in testes extracts . We found that orb2 mRNA can be immunoprecipitated with Orb2 , but not control LacZ antibody ( Fig . 3D ) . We next asked whether orb2 mRNA and protein co-localize in elongating wild type and orb2 mutant spermatids . As shown in Fig . 7A , there is generally a close correspondence between orb2 mRNA and protein in elongating wild type spermatids . Importantly , both are concentrated and overlap extensively in the comet head . Further evidence that orb2 autoregulates the localization and translation of its own mRNA comes from analysis of orb2ΔQ testes . Just as was seen for apkc mRNAs and aPKC protein , the effects of orb2ΔQ depend upon the direction of polarization . In spermatids that are polarized in the incorrect orientation , orb2 mRNA and Orb2 protein do not accumulate to high levels in the comet head at the end of the elongating flagellar axonemes . Instead , the mRNA and protein are distributed uniformly throughout the elongating sperm tails ( Fig . 7B ) . In contrast , in most of the spermatids that are polarized in the correct direction , orb2 mRNA and protein are localized in the characteristic comet pattern seen in wild type testes ( not shown ) . The apparent failure of orb2 autoregulation in orb2ΔQ cysts that are incorrectly polarized could also explain why apkc mRNAs are not localized in the comet pattern and why there are no aPKC protein stripes . One explanation for the strong orb2-apkc genetic interactions is that the functioning of the orb2 autoregulatory loop at this stage of spermatogenesis could be intimately linked to apkc activity . To test this idea , we examined Orb2 protein expression in elongating spermatid cysts in testes from the apkcex55 allele and from the MTD/UAS-apkc:35140 RNAi knockdown . There were no obvious effects on Orb2 expression in the hypomorphic allele apkcex55 in either incorrectly or correctly oriented elongating spermatid cysts . On the other hand , we found that the accumulation of Orb2 protein in the characteristic comet pattern at the tips of the elongating flagellar axonemes is disrupted when apkc is knockdown using the MTD/UAS-apkc:35140 transgene combination ( Fig . 2G ) . In this case , the Orb2 comet pattern was absent in cysts that were oriented correctly as well as incorrectly . This finding would be consistent with the idea that spermatid cyst polarity is completely randomized in the strong MTD/UAS-apkc:35140 RNAi knockdown .
In order to produce functional sperm , the spermatid cyst must polarize so that all of the nuclei cluster together on one side of the cyst and the basal bodies and nascent flagellar axonemes on the other side . How the cyst orients with respect to the organ ( testes ) in which it resides is invariant: the nuclei cluster on the basal side of the cyst , while basal bodies localize on the opposite side of the cyst so that the axonemes can elongate apically towards the stem cell region of the testis . The factors involved in polarizing the cyst and in correctly orienting the polarized cyst within the testis are largely unknown . Here we have identified two genes that function to orient cyst polarization with respect to the apical-basal axis of the testis , apkc and orb2 . While the involvement of apkc in determining polarity has been extensively documented in many biological contexts , the spatial restriction of apkc activity by a mechanism that involves localized mRNA translational regulation is by contrast rather unusual . We have found that compromising apkc and orb2 activities can give rise to similar polarization phenotypes . In both instances , the system that orients cyst polarization relative to the apical-basal axis of the testes is disrupted and the sperm tails in a subset of the spermatid cysts elongate in the incorrect direction . During the first steps of polarization all of the spermatid nuclei in the cyst congregate on the basal side , while aPKC protein accumulates in a series of puncta on the other , apical side , near the ends of the nascent flagellar axonemes . As elongation progresses , the aPKC puncta transform into a series of short stripes at the ends of the elongating axonemes . That this spatially restricted pattern of aPKC accumulation is not only mediated by orb2 but is also likely to be relevant to properly orienting polarization with respect to the main axis of the testis is most clearly illustrated by the phenotypes in the hypomorphic allele , orb2ΔQ . All orb2ΔQ cysts that polarize in the incorrect orientation share a common set of defects . First , instead of being localized in the characteristic comet pattern , apkc-RA and “bulk” apkc mRNAs are spread uniformly though the cyst . Second , the aPKC protein stripes at the ends of the growing flagellar axonemes are absent . Third , while Orb2 protein and orb2 mRNA can be detected in these cysts , neither is localized . This latter finding would provide an explanation for the defects in both apkc mRNA localization and the expression of aPKC protein . By contrast , most but not all of the orb2ΔQ cysts that elongate in the correct direction resemble wild type: apkc and orb2 mRNAs and Orb2 protein are localized in the characteristic comet pattern , with aPKC stripes at the ends of the flagellar axonemes . One likely orb2 regulatory target is the apkc-RA mRNA . Although apkc mRNAs can be detected with a common exon probe in the adult male germline in all but the last stages of spermatogenesis , the apkc-RA mRNA is unusual in that it is expressed post-meiotically , and like other post-meiotic transcripts , it accumulates in a comet pattern in elongating spermatid cysts . This mRNA is transcribed from one of the four apkc promoters , and differs from the 12 other predicted apkc mRNA species in that it has a unique 3′ UTR . The RA 3′ UTR contain 3 CPE motifs and we've found that Orb2 binds to it both in vitro and in vivo . As is true for other mRNA targets of CPEB proteins , it is likely that orb2 promotes the localized production of aPKC by directly activating the translation of the apkc-RA mRNAs in the comet head . Consistent with this idea , the stripes of aPKC at the ends of the elongating flagellar axonemes are localized on the distal ( towards apical tip of the testes ) side of the Orb2 protein/apkc-RA mRNA comet head . Several other apkc mRNA species share a different UTR sequence that also contains CPE motifs . While we haven't examined the expression of the mRNAs containing this specific UTR sequence , it is possible that one or more is expressed at this stage of spermatogenesis . Since the apkc mRNAs detected with the common exon probe localize in a comet pattern that is similar to apkc-RA and this localization is disrupted in orb2 mutants , it would appear that if other apkc mRNA species are expressed at this stage of spermatogenesis they could also be orb2 regulatory targets . Significantly , the regulatory relationship between orb2 and apkc seems to be reciprocal . The first suggestion of cross-regulation came from the synergistic genetic interactions seen in trans-heterozygous combinations between orb2 and apkc mutants . Whereas polarization defects are rarely observed in the testes of males heterozygous for the hypomorphic apkcex48 or apkcex55 alleles , when they are combined in trans with the hypomorphic orb2ΔQ allele the frequency of testes with misoriented spermatid cysts increases by more than 10-fold . Trans-heterozygous genetic interactions between weak hypomorphic alleles are somewhat unusual , and raise the possibility that the two interacting genes are functionally interdependent . Consistent with a cross-regulatory connection , Orb2 is not localized in its characteristic comet pattern in elongating flagellar axonemes when apkc activity is knocked down in the MTD/UAS-apkc:35140 combination . Instead it is uniformly distributed in the elongating axonomes . This finding indicates that the localized accumulation of Orb2 protein in differentiating spermatid cysts depends upon apkc activity . Since Orb2 binds to its own mRNA and appears to be required for both the localization and translation of this mRNA during spermatid cyst differentiation , a plausible hypothesis is that apkc is a component of the orb2 positive autoregulatory loop . While apkc need not function directly , it is interesting to note that Orb2 has three ( high stringency ) predicted aPKC phosphorylation sites ( Ser146 , Ser273 , and Ser446 ) [35] , and all three are phosphorylated in Orb2 protein isolated from testes ( unpublished data ) . Thus it is possible aPKC phosphorylation facilitates the localized translation of orb2 mRNA , and in turn the localization and translation of apkc mRNAs , by phosphorylating Orb2 protein . In principle , this reciprocal regulatory relationship could help trigger the choice of orientation at the start of polarization and then serve to reinforce this choice . While our finding would be consistent with a model in which aPKC activity is spatially restricted by a mechanism that depends upon orb2 localizing and regulating the translation of apkc mRNAs , many important questions remain unanswered . One is whether apkc is required only for choosing the direction of cyst polarization within the testis , or if it also has a role in the process of polarization itself . Neither the apkc mutants nor the knockdowns are useful in answering this question as they certainly retain apkc activity . On the other hand , it seems likely that apkc is needed during the formation of the sperm tails since the elongating tails in the strongest RNAi knockdowns are considerably shorter than in wild type . As Orb2 protein accumulation is perturbed in this RNAi knockdown , one of the apkc functions during this phase of spermatogenesis is to maintain high levels of localized Orb2 protein . A similar question can be asked about orb2 . With the caveat that the orb236 cysts might have structural abnormalities arising from the failure to undergo meiosis , the phenotypes of this mutant argue that orb2 is required for polarization per se . If the only apkc function in this process is orienting the direction of polarization , then orb2 would have to have regulatory targets that control the actual process of polarization . In fact , there are several plausible candidates . Studies by Fabian et . al . [19] have shown that phosphatidylinositol 4 , 5-bisphosphate ( PIP2 ) and components of the exocyst complex are required for cyst polarization . The exocyst complex also mediates plasma membrane addition during spermatid elongation and localizes around the tip of the growing sperm tails . mRNAs encoding 4 of the 8 exocyst complex subunits ( sec3 , sec8 , sec10 , and sec15 ) have CPE sequences in their 3′ UTRs , and their localization and/or translation could be regulated by orb2 . Another possible orb2 regulatory target in cyst polarization is cdc42 . This small membrane anchored GTPase directs exocyst complex localization during ciliogenesis in kidney epithelial cells , interacting directly with Sec10 [36] , [37] . Cdc42 also functions in regulating apical-basal cell polarity by interacting with Par6 , and this interaction in turn recruits and localizes aPKC to the membrane [38] . Since mRNAs encoding both Cdc42 and Par6 have CPEs in their 3′ UTRs , their localization and translation could be controlled by orb2 . While we have not tested the effects of reducing cdc42 activity , we found that a subset of the spermatid cysts are polarized in the incorrect direction in par6 heterozygous males ( data not shown ) . The idea that orb2 has other mRNA targets in cyst polarization would also be consistent with the effects of orb2 mutations on asymmetric cell division in the embryo . We previously found that the accumulation of aPKC along the apical cortex of dividing neuroblasts depends upon orb2 [30] . However , one of the other orb2 asymmetric cell division phenotypes is a failure to properly orient the mitotic spindle . Since spindle orientation in dividing neuroblasts is thought to be independent of apkc [17] , it would appear that the localized expression of other polarity proteins must also depend upon orb2 . In addition to cdc42 , another potential orb2 target that has functions in spindle orientation would be the mRNA encoding Inscuteable ( insc ) . In fact , Hughes et . al . [39] have shown that apical localization of the insc mRNA is important for Insc function in neuroblast cell division , and like apkc-RA mRNA it has CPE sequences in its 3′ UTR . Thus , a plausible inference from their studies is that Orb2 may help localize insc mRNA to the apical side of the neuroblast , and then activate its on site translation . ( It is interesting to note that there are hints that mRNA localization may also be important when polarization is being initiated in epithelial cells [40] ) . While orb2 might regulate many of the same target mRNAs during both spermatid cyst polarization and asymmetric cell division , one apparent difference is in the penetrance of the phenotypes . Cyst polarization seems to be completely disrupted in the absence of orb2 function . In contrast , only a subset of the cells in the embryonic neuronal and muscle cell lineages show obvious defects in asymmetric cell division . One likely reason for this difference is that polarization during cell division can be mediated independently by cross-regulatory interactions between factors specifying the apical and basal domains . Since several of the polarity proteins do not seem to be expressed in the spermatid cysts , it is possible that these cross-regulatory interactions either do not exist , or are not sufficient for polarization . Conversely , the fact that there are even modest cell division phenotypes in neuronal and muscle precursor cell lineages in orb2 mutants also implies that these cross-regulatory interactions are not in themselves sufficiently robust to ensure that these cells always polarize correctly in the absence of orb2 function . In this context , it is worth noting that the role of orb2 in both asymmetric cell division and spermatid cyst polarization seems to differ from many of the previously documented functions of mRNA localization in processes that depend upon polarization such as cell fate or polarity axis determination . In most of the instances in which mRNA localization is known to have an important role in cell fate or polarity axis determination ( e . g . , prospero mRNA localization to the basal daughter cell during neuroblast cell division or oskar mRNA localization in the specification of the posterior pole of the oocyte/embryo ) , the underlying polarity of the cell , cyst , egg , embryo or organ is pre-defined by the activity of an upstream and seemingly distinct polarizing pathway , most typically involving the aPKC/PAR machinery . The mRNAs encoding the cell fate or axis determinants function primarily in the elaboration or execution of this polarity decision , and not in the initial definition of polarity [41]–[43] . In contrast , orb2 seems to be intimately involved in the upstream polarity pathway , helping the aPKC/PAR machinery define and then maintain the underlying polarity . In fact , in the process of correctly orienting spermatid cyst polarization the functional interdependence of orb2 and apkc would seem to be mechanistically equivalent to the cross-regulatory interactions that underpin “classical” polarization by the aPKC/PAR machinery . Other unresolved questions include the identity of the signals that initiate and orient cyst polarization relative to the testis itself . The latter , cyst orientation , most probably depends upon an external signal and the most likely source would be the two somatic support cells , the head and tail cyst cells , which encase the spermatid cyst ( Fig . 1A ) . These two cells arise from a population of somatic stem cells at the apical tip of the testis and associate with the newly formed germline daughter cell that ultimately gives rise to the 64-cell spermatid cyst . The two somatic cells grow without division , surrounding the germ cells as they undergo mitosis and meiosis . Interestingly , one of the cells ends up on the apical ( relative to the testes ) side of the newly formed cyst and it expresses the Dlg guanylate kinase . The other is on the basal side and doesn't express Dlg [44] . We confirmed this observation and found that another polarity regulator , Bazooka , seems to exhibit the same expression pattern . Either one of these cells could potentially produced a signal ( s ) that orients cyst polarization . It is also possible that polarization depends upon an autonomous signal generated in the germline when the cysts commence differentiation . Further studies will be required to answer these and other questions .
apkc mutant alleles apkck06403 , apkcex55 , apkcex48 and par1-GFP are kind gifts from Dr . Elizabeth Gavis , Dr . Yu-Chiun Wang and Dr . Eric Wieschaus at Princeton University . apkc RNAi lines are obtained from Bloomington Drosophila Stock Center ( stock # 34332 and 35140 ) , targeting two different exons common to all apkc mRNAs ( sequence: CTGGAGAAGACGATTCGTATA and CAAGCTGTTGGTGCACAAGAA ) [45] . MTD multiple driver line is obtained from Dr . Andrew Hudson and Dr . Lynn Cooley from Yale University . The orb2 mutant allele orb236 was generated in the lab [29] . orb2ΔQ is a gift from Dr . Barry Dickson from IMBA , Austria . Testes were dissected and fixed/stained following standard whole mount staining procedures [29] . DNA was dyed with Hoechst . Testes were further examined under epi-fluorescence microscopes . Percentage of testes with spermatid nuclei near the stem cell/spermatogonial region was recorded . Those were considered defective in orienting the polarization of the spermatid cysts . Whole mount staining was performed as in [29] . Antibodies used were as follows: mouse anti-Orb2 2D11 and 4G8 IgG ( undiluted , developed in the lab ) [29] , rabbit anti-Bol ( 1∶1000 , a gift from Steven Wasserman ) [46] , monoclonal anti-β-Tubulin E7 1∶50 ( Developmental Studies Hybridoma Bank ) , rabbit anti-aPKCζ ( 1∶1000 , clone c-20 , sc-216 , Santa Cruz Biotechnology ) , rabbit anti-GFP ( 1∶1000 , Cristea Lab , Princeton University ) . Rabbit polyclonal anti-Dlg-PDZ1 ( 1∶1000 ) and guinea pig anti-Bazooka ( 1∶500 ) were provided by Yu-Chiun Wang [47] . Actin was stained with Alexa488-phalloidin ( Invitrogen , Carlsbad , CA ) . DNA was stained with Hoechst ( 1∶1000 ) . Secondary antibodies used were goat anti-mouse IgG Alexa 488 , 546 or 647 , goat anti-rabbit Alexa 488 , 546 or 647 ( Molecular Probes , Inc . ) . Samples were mounted in Aqua-polymount on slides for an inverted Zeiss LSM510 or Leica SP5 confocal microscope . Fluorescence in situ hybridization was performed as described in [29] . Fluorescent antisense probes for apkc and orb2 were synthesized by Biosearch Technologies ( www . biosearchtech . com ) or synthesized in Tyagi lab . Forty non-overlapping 17 bp probes targeted at orb2 mRNA sequence from cctggacgatcagatgt to atatgttatttaatctcac were synthesized and labeled with Quasar 670 and used at 1∶100 dilution . For combined in situ hybridization-antibody staining experiment , the in situ hybridization was performed first , followed by a sample re-fix and then standard whole mount antibody staining .
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After completion of meiosis , the 64 cells in the spermatid cyst begin differentiating into sperm . Sperm are highly polarized cells and a critical step in their differentiation is spermatid cyst polarization . Spermatids are also polarized within the testis , with the heads of the elongating spermatids located basally , while the tails extend apically . We show that aPKC accumulates preferentially on the apical side of the cyst during polarization and is required to correctly orient cyst polarization with respect to the apical-basal axis of the testis . Unexpectedly , aPKC activity is spatially restricted by a mechanism that depends upon the CPEB family translational regulator orb2 . orb2 is required to asymmetrically localize and activate the translation of apkc mRNAs during spermatid differentiation . In addition to correctly orienting the direction of cyst polarization , orb2 is required for the process of polarization itself . One of the orb2 regulatory targets in this process is its own mRNA , and this autoregulatory activity depends , in turn , upon apkc .
|
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2014
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Spermatid Cyst Polarization in Drosophila Depends upon apkc and the CPEB Family Translational Regulator orb2
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Small molecule drugs target many core metabolic enzymes in humans and pathogens , often mimicking endogenous ligands . The effects may be therapeutic or toxic , but are frequently unexpected . A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery . To map the intersection between drugs and metabolism , we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space . The results reveal the chemical space that has been explored for metabolic targets , where successful drugs have been found , and what novel territory remains . To aid other researchers in their drug discovery efforts , we have created an online resource of interactive maps linking drugs to metabolism . These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans . The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection . Chemical similarity links between drugs and metabolites predict potential toxicity , suggest routes of metabolism , and reveal drug polypharmacology . The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets . Thus , this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism .
Drug developers have long mined small molecule metabolism for new drug targets and chemical strategies for inhibition . The approach leverages the “chemical similarity principle” [1] which states that similar molecules likely have similar properties . Applied to small molecule metabolism , this principle has motivated the search for enzyme inhibitors chemically similar to their endogenous substrates . The approach has yielded many successes , including antimetabolites such as the folate derivatives used in cancer therapy and the nucleoside analog pro-drugs used for antiviral therapy . However , drug discovery efforts also frequently falter due to unacceptable metabolic side-effect profiles or incomplete genomic information for poorly characterized pathogens [2]–[4] . With the recent availability of large datasets of drugs and drug-like molecules , computational profiling of small molecules has been performed to create global maps of pharmacological activity . This in turn provides a larger context for evaluation of metabolic targets . For example , Paolini et al . [5] identified 727 human drug targets associated with ligands exhibiting potency at concentrations below 10 µM , thereby creating a polypharmacology interaction network organized by the similarity between ligand binding profiles . Keiser et al . [6] organized known drug targets into biologically sensible clusters based solely upon the bond topology of 65 , 000 biologically active ligands . The results revealed new and unexpected pharmacological relationships , three of which involved GPCRs and their predicted ligands that were subsequently confirmed in vitro . Cleves et al . [7] also rationalized several known drug side effects and drug-drug interactions based upon three-dimensional modeling of 979 approved drugs . However , despite the clear rationale and past successes in applying ligand-based approaches to drug discovery , global mapping between drugs and small molecule metabolism , the goal of this study , has been hindered by both methodological challenges and incomplete genomic information . The relatively recent availability of metabolomes for numerous organisms allows a fresh look on a large scale [8]–[13] . In this work , we link the chemistry of drugs to the chemistry of small molecule metabolites to investigate the intersection between small molecule metabolism and drugs . The Similarity Ensemble Approach ( SEA ) [6] was used to link metabolic reactions and drug classes by their chemical similarity , measured by comparing bond topology patterns between sets of molecules . Two types of molecule sets are used in this work . The first comprises drug-like molecules known to act at a specific protein target , and the second comprises the known substrates and products of an enzymatic reaction . While this approach is complementary to target and disease focused methods [5] , [14]–[23] , neither protein structure nor sequence information is used in the comparisons . Thus , these links provide an orthogonal view of metabolism based only upon the chemical similarity between existing drug classes and endogenous metabolites . To provide the results in the context of metabolism , drug “effect-space” maps were also created . For each of the 246 drug classes investigated in this work , effect-space maps enable visualization of the chemical similarities between drugs and metabolites painted onto human metabolic pathways , allowing a unique assessment of potential drug action in humans . In addition , to aid target discovery in pathogens , 385 species-specific effect-space maps were created to show the predicted effect-space of currently marketed drugs , painted onto metabolic pathways representing target reactions in model organisms and pathogens . Examples of these maps are provided below and their applications in predicting drug action , toxicity , and routes of metabolism are discussed . To enable facile exploration of the drug-metabolite links established by this analysis , interactive versions of both sets of maps are available at http://sea . docking . org/metabolism . Finally , using methicillin-resistant Staphylococcus aureus ( MRSA ) , a major pathogen causing both hospital- and community-acquired infections that is resistant to at least one of the antibiotics most commonly used for treatment [24]–[28] as an example , we show by retrospective analysis the use of species-specific maps for discovery and evaluation of drug targets . This also illustrates how additional types of biological information can be incorporated to enhance the value of these analyses .
To evaluate the chemical similarity between drug classes and metabolic reactions , links between sets of metabolic ligands and sets of drugs were generated according to SEA ( Figure 1 ) [6] . The similarity metric consists of a descriptor , represented by standard two-dimensional topological fingerprints , and a similarity criterion , the Tanimoto coefficient ( Tc ) . Expectation ( E ) values were calculated for each set pair by comparing the raw scores to a background distribution generated using sets of randomly selected molecules ( see Methods for further details ) . To represent metabolic ligand sets , the MetaCyc database , which includes enzymes from more than 900 different organisms catalyzing over 6 , 000 reactions , was used [12] . The substrates and products of each enzymatic reaction were combined to form a reaction set , each of which was required to contain at least two unique compounds ( Datasets S1 and S2 ) . Ubiquitous molecules called common carriers , which frequently play critical roles in reaction chemistry but do not distinguish the function of a specific enzyme , were removed , leaving a total of 5 , 056 reactions involving 4 , 998 unique compounds . To represent drugs , a subset of 246 targets of the MDL Drug Data Report ( MDDR ) collection , which annotates ligands according to the targets they modulate , was used ( Datasets S3 and S4 ) [30] . These sets contain 65 , 241 unique ligands with a median and mean of 124 and 289 ligands per target , respectively . Overall , 246 drug versus 5 , 056 reaction set comparisons involving 1 . 39×109 pairwise comparisons were made . Although drugs and metabolites typically differ in their physiochemical properties , significant and specific similarity links nonetheless emerged . Using SEA at an expectation value cutoff of E = 1 . 0×10−10 , a previously reported cutoff for significance [6] , 54% ( 132 of 246 ) of drug sets link to an average of 43 . 7 ( median = 10 ) or 0 . 9% of metabolic reactions . None of the remaining 46% ( 114 of 246 ) of drug sets link to any metabolic reaction sets . For instance , while the α-glucosidase drug set links to the α-glucosidase reaction ( E = 1 . 00×10−51 ) , the thrombin inhibitor drug set does not link significantly with any metabolic reaction . The thrombin inhibitor drug set targets the serine protease thrombin , which does not participate in small molecule metabolism , but rather plays a role in the coagulation signaling cascade . Likewise , 40% ( 2 , 044 of 5 , 056 ) of metabolic reactions hit an average of 2 . 8 ( median = 2 ) or 1 . 1% of drug sets at expectation value E = 1 . 0×10−10 or better . For instance , the metabolite set for retinal dehydrogenase reaction set links , as expected , to the retinoid drugs at E = 3 . 05×E−98 , but the valine decarboxylase reaction , which is not an MDDR drug target , does not link significantly to any drug sets . These strikingly similar results suggest both broad coverage ( 54% of drug sets and 40% of metabolite sets ) and specificity ( single sets link to just 0 . 9% of metabolite sets and 1 . 1% of drug sets , respectively ) . For full results , see Dataset S5 . To determine the utility of the method for recovery of known drug-target interactions , it was hypothesized that chemical similarity between MetaCyc reaction sets and corresponding MDDR drug sets could specifically recover the known drug-target interactions . The 246 MDDR drug set targets include 62 enzymes that could be mapped to MetaCyc via the Enzyme Commission ( EC ) number [31] describing the overall reaction catalyzed [32] . The results show that all 62 reaction sets for these targets link to at least one MDDR drug set . The majority of best hits ( 42 out of 62 ) were found at expectation values of E = 1 . 0×10−10 or better ( Table 1 ) . At expectation values better than E = 1 . 0×10−25 , 61% ( 19 of 31 ) of best hits recover either the specific known target or another enzyme in the same pathway . Examples of specific compounds linked by this analysis are given in Figure 2 for a selected group of these best-scoring hits . Other links recovered off-pathway hits , which often reflect known polypharmacology that is well-documented . For example , the glycinamide ribonucleotide formyltransferase ( GART ) inhibitor drug set hits both the GART reaction set ( E = 1 . 55×10−82 ) and the off-pathway but pharmacologically related antifolate target dihydrofolate reductase ( DHFR ) ( E = 1 . 02×10−134 ) . Other off-pathway hits reflect biological connections , or physical connections , between targets . For example , the adenosine deaminase reaction set links to the A1 adenosine receptor agonist drug set ( E = 7 . 69×10−159 ) ( Table 1 ) capturing the known interaction between A1 adenosine receptors and adenosine deaminase on the cell surface of smooth muscle cells [33] . Considering only the stringent case of exact matches based on EC numbers , a Mann-Whitney rank-sum test ( also referred to as the U-test ) shows that the expectation values for links between reaction sets and drug sets of known drug target enzymes were significantly better than the expectation values for links to reaction sets of non-target enzymes , i . e . , 62 known enzyme targets were recovered in a background of 4 , 920 non-target “other” enzymes at a statistical significance of P = 2 . 01×10−6 . In addition to recapitulating many known drug-target interactions , the links identified by these comparisons also suggest new hypotheses about drug-target interactions . One such new prediction involves the phospholipase A2 ( PLA2 ) inhibitor drug class . The substrates and products of PLA2 recapitulate its known link to the PLA2 inhibitor drug set ( E = 9 . 82×10−26 ) , however , the sterol esterase reaction returns an even better score against the PLA2 inhibitor set ( E = 3 . 18×10−44 ) ( Table 1 ) . Although this predicted pharmacological relationship has , to our knowledge , not been previously documented , the result is consistent with the known biological relationship between PLA2 and sterol esterase . Both enzymes are secreted by the pancreas and require phosphatidylcholine hydrolysis to facilitate intestinal cholesterol uptake [34] . Thus , this link suggests that therapeutic agents directed against PLA2 may also inhibit sterol esterase , perhaps even more strongly than their intended target . To present links between small molecule metabolites and drugs in the context of their known ( and potential ) metabolic targets , metabolic “effect-space” maps for currently marketed drugs were generated for each of the 246 drug classes investigated in this work . These maps enable visualization of the chemical similarities between drugs and metabolites painted onto human metabolic pathways , illustrating potential interactions between an individual drug class and specific metabolic enzymes in humans . Examples include the nucleoside reverse transcriptase , dihydrofolate reductase , and thymidylate synthase inhibitors which target pyrimidine nucleotide metabolism and biosynthesis of the essential coenzyme folate ( Figure 3 and Table 2 ) . Using the canonical human metabolic pathways from HumanCyc [35] , a subset of the BioCyc [12] database collection , reactions in each metabolic network have been colored according to their similarity to known drug classes ( Figure 3 ) . While Table 1 presents only the top link for each of 62 enzyme targets in MetaCyc against the 246 MDDR drug classes , the networks in Figure 3 detail all significant hits for selected drug classes against the pyrimidine and folate pathways . Interactive versions of these maps , one for each of the 246 drug classes included in our analysis , are available online ( see below ) . It has previously been shown that chemical similarity between known drugs often suggests novel drug-target interactions [5]–[7] , [14] . Consistent with these observations , effect-space maps such as those shown in Figure 3 can also be used to exploit chemical similarities between drugs and metabolites to indicate potential routes of drug metabolism and toxicity [3] , [11] , [36] , [37] . For example , the nucleotide reverse transcriptase inhibitors ( NRTIs ) used in HIV therapy are administered as pro-drugs . The effect-space map reflects this route of NRTI metabolism leading to viral inhibition . The top three hits yielded by the NRTI drug set queried against human metabolism – thymidine kinase ( E = 3 . 48×10−26 ) , thymidylate kinase ( E = 7 . 48×10−28 ) , and deoxythymidine diphosphate kinase ( E = 1 . 54×10−24 ) ( Figure 3 reaction numbers 2 , 3 , and 4; additional results in Table 2 ) – successively phosphorylate the NRTI pro-drugs into the pharmacologically active NRTI triphosphates [38] , [39] . The viral reverse transcriptase enzyme then incorporates the fully phosphorylated NRTIs into the growing DNA strand , thereby terminating transcription of the viral DNA . In this example , considerable toxicity mitigates the therapeutic value of inhibiting viral DNA transcription since the phosphorylated NRTIs directly inhibit human nucleotide kinases and mitochondrial DNA pol-γ . They also may be incorporated by pol-γ into the growing human mitochondrial DNA strand , and once incorporated are inefficiently excised by DNA pol-γ exonuclease [40] . Thus , the effect-space map illustrates both the route of metabolism and a mechanism of toxicity for NRTIs in humans . Drug effect-space maps also offer a broad glimpse of potential human metabolic interactions predicting new “polypharmacology” . From the ligand perspective , “drug polypharmacology” refers to a single drug or drug class that hits multiple targets . For example , dihydrofolate reductase ( DHFR , reaction number 7 in Figure 3 ) uses NADPH to reduce 7 , 8-dihydrofolate to tetrahydrofolate . Antifolate drugs inhibit DHFR , and , as expected , the DHFR drug set recovers the DHFR reaction substrates and products as the top similarity hit in human metabolism ( E = 1 . 46×10−82 ) ( Figure 3 , Table 2 , Figure 4 ) . However , at least 20 other reactions also use folate coenzymes in human metabolism [41]–[43] . Accordingly , SEA finds additional links between the DHFR drug set and established antifolate targets outside the pyrimidine and folate biosynthesis pathways such as serine hydroxymethyltransferase ( SHMT , E = 2 . 68×10−44 ) , phosphoribosyl-aminoimidazole-carboxamide formyltransferase ( AICAR transformylase , E = 2 . 21×10−39 ) , and phosphoribosyl-glycinamide formyltransferase ( GART , E = 2 . 21×10−39 ) ( Table 2 ) . The effect-space maps in Figure 3 illustrate the results from Table 2 and Figure 4 in a single view , illustrating drug polypharmacology with respect to critical metabolic pathways . Alternatively , from the target perspective , “target polypharmacology” may refer to a single target being modulated by multiple classes of drugs . For instance , thymidylate synthase ( TS ) is another classic antifolate target that uses a folate coenzyme to methylate deoxyuridine phosphate , generating deoxythymidine phosphate [44]–[47] . As expected , the TS reaction links to known antifolate drug classes such as GART inhibitors ( E = 4 . 76×10−73 ) and DHFR inhibitors ( E = 1 . 91×10−48 ) ( Table 3 and Figure 4 ) . However , TS is also effectively inhibited by uracil analogs such as fluoropropynyl deoxyuridine , which is not a folate , but rather a pyrimidine analog . Accordingly , the TS reaction also links to reverse transcriptase inhibitors , which include fluoropropynyl deoxyuridine and additional pyrimidine analogs such as azidothymidine ( AZT ) ( E = 5 . 68×10−11 ) ( Figure 4 ) . The target polypharmacology of the thymidylate synthase enzyme is mirrored by the drug polypharmacology of the thymidylate synthase inhibitors . The TS inhibitors link not only to the reactions of deoxyribonucleotide biosynthesis including thymidylate synthase ( E = 2 . 54×10−75 ) , but also the GART ( E = 1 . 50×10−60 ) and DHFR ( E = 1 . 96×10−123 ) reactions ( Figure 3 and Table 2 ) . Thus , SEA recapitulates the known polypharmacology of TS . Effect-space maps illustrate and clarify these pharmacological relationships . The great diversity of metabolic strategies , pathways , and enzymes present in humans , model organisms , and pathogenic species presents both opportunities and significant barriers to drug discovery . To address these issues , species-specific effect-space maps were created for each of 385 organisms from the BioCyc Database Collection . Target reactions existing in common and differentially between each of these species and humans are shown in these metabolic maps . As with the human effect-space maps , this set of maps is available in interactive form online . To show how these maps may be used to provide a context for drug discovery , MRSA is used as an example ( Figure 5 ) . The global view of drugs and metabolism provided by this species-specific map illustrates some of the daunting challenges to the selection of tractable metabolic drug targets in this organism . As described for Figure 3 , each node in the MRSA network in Figure 5 represents one reaction set , the substrates and products of a single metabolic reaction . Edges connect the reactions according to canonical BioCyc MRSA pathways . Each reaction in the network has been colored according the expectation value of the best link between the reaction set and any of the 246 MDDR drug sets . Lighter colored nodes have higher expectation values indicating less drug-like reaction sets , while darker colored nodes indicate more drug-like reaction sets . To provide therapeutic context , reactions that are also present in human metabolism have been faded , indicating that drug sets targeting these enzymes in MRSA may have the undesirable potential to inhibit the human enzymes as well . As with the other organisms represented in our online maps , most reactions in the MRSA subset have little chemical similarity to any MDDR drug set . Although 74% of the 469 MRSA metabolic reactions have measurable similarity to at least one MDDR drug set , only 36% of these links had expectation values of E = 1 . 0×10−10 or better . Several complete pathways of diverse chemical classes , including shikimic acid , phospholipid , peptidoglycan , teichoic acid , and molybdenum cofactor biosynthesis , lack links to any drug set at all . Only 18 of the 469 MRSA metabolic reactions are already known to be drug targets in MDDR . Fourteen of these are represented in Figure 5 ( as diamonds ) , but all 18 of these also appear in humans . Enzymes that catalyze these reactions in humans would likely be vulnerable to inhibitors developed against these MRSA targets , putting those drugs at risk for toxicity . Figure 6 illustrates how additional information can be used to further filter potential metabolic targets by painting additional biological or genomic data onto a species-specific map . Since successful modulation of a target may not alone be sufficient to kill a pathogen due to the presence of redundant pathways for the formation of critical metabolites , integration of such additional information into a metabolic map may provide added value in addressing the multi-dimensional challenges of drug discovery . Flux balance analysis of metabolic networks was used by several of the authors of this work to identify essential enzymes and metabolites required for the formation of all necessary biomass components for 13 strains of Staphylococcus aureus¸ including the methicillin-resistant N315 strain ( MRSA ) [48] . Using these results , 39 essential reactions and 19 synthetic lethal reaction pairs could be mapped to our dataset ( Figure 6 ) , highlighting those reactions for which inhibition is most likely to result in the death of the organism . Several of these reactions have been successfully targeted by currently marketed drugs , such as the previously discussed antifolate targets DHFR ( E = 1 . 02×10−134 ) , thymidylate synthase ( E = 2 . 54×10−75 ) , and dihydrofolate synthase ( E = 1 . 35×10−70 ) . This retrospective result illustrates the potential of such additional information in enriching for targets and drug chemistry that have been proven accessible . Other targets and pathways have not yet yielded successful drugs but are under investigation in MRSA or other pathogens , such as the shikimate pathway [49] in aromatic amino acid biosynthesis and the histidine biosynthesis pathway [50] . The combination of the essentiality data with the drug space mapping emphasizes the challenges to drug discovery against MRSA . Thus , while species-specific antifolates do exist , many antifolates such as methotrexate used in cancer therapy cause severe toxicity [43] . To avoid such toxicity , 14 of the 39 essential MRSA reactions that are also present in humans can be excluded from further consideration as drug targets in MRSA . A compilation of all of the metabolic network maps generated in this study is available at http://sea . docking . org/metabolism . These include interactive versions of the human effect-space maps shown in Figure 3 , one for each of the 246 MDDR drug classes analyzed in this work , and 385 species-specific maps such as that shown in Figure 5 . The species-specific maps were generated from the BioCyc database public collection , a compendium of 385 model organisms and pathogens whose genomes have been sequenced and their metabolomes deciphered . Of these , 65 have been designated as Priority Pathogens by the National Institute of Allergy and Infectious Diseases ( NIAID ) and include Bacillus anthracis , Brucella melitensis , Cryptosporidium parvum , Salmonella , SARS , Toxoplasma gondii , Vibrio cholerae , and Yersinia pestis [51] . Browse and similarity search tools are also provided , allowing exploration of the metabolic reaction sets and current drug classes used in this work , as well as comparison to user-defined custom ligand sets . These interactive tools enable facile exploration between the vast biological data on potential metabolic drug targets in these organisms and the drug chemistry currently available to prosecute those targets .
Using the SEA method , we have shown that comparison between ligand sets representing MDDR drug classes and ligand sets representing the substrates and products of metabolic reactions yields statistically significant links between known drugs and enzyme targets . Because the method is based on chemical similarity and requires only information from these molecule sets rather than the sequence , structure or physiochemistry of the targets , this ligand-based approach is independent from , and complementary to , protein structure and sequence based methods . Our results also suggest the potential of this method for predicting previously unknown interactions between drug classes and metabolic targets , recovering routes of metabolism and toxicity in humans , and identifying potential drug targets ( as well as challenges for target discovery ) in emerging pathogens . Thus , by mapping the chemical diversity of drugs to small molecule metabolism using ligand topology , this work establishes a computational framework for ligand-based prediction of drug class action , metabolism , and toxicity .
|
All humans , plants , and animals use enzymes to metabolize food for energy , build and maintain the body , and get rid of toxins . Drugs used to clear infections or cure cancer often target enzymes in bacteria or cancer cells , but the drugs can interfere with the proper function of human enzymes as well . Recent studies have mapped drugs to enzymes and many other targets in humans and other organisms , but have not focused on metabolism . In this study , we present a new method to predict what enzymes drugs might affect based on the chemical similarity between classes of drugs and the natural chemicals used by enzymes . We have applied the method to 246 known drug classes and a collection of 385 organisms ( including 65 National Institutes of Health Priority Pathogens ) to create maps of potential drug action in metabolism . We also show how the predicted connections can be used to find new ways to kill pathogens and to avoid unintentionally interfering with human enzymes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion"
] |
[
"chemical",
"biology/small",
"molecule",
"chemistry",
"infectious",
"diseases",
"genetics",
"and",
"genomics/comparative",
"genomics",
"pharmacology/drug",
"development",
"computational",
"biology/metabolic",
"networks",
"pharmacology",
"computational",
"biology/metagenomics",
"genetics",
"and",
"genomics",
"computational",
"biology/systems",
"biology"
] |
2009
|
A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism
|
In 2013–2014 , French Polynesia experienced for the first time a Zika outbreak . Two Aedes mosquitoes may have contributed to Zika virus ( ZIKV ) transmission in French Polynesia: the worldwide distributed Ae . aegypti and the Polynesian islands-endemic Ae . polynesiensis mosquito . To evaluate their vector competence for ZIKV , mosquitoes were infected per os at viral titers of 7 logs tissue culture infectious dose 50% . At several days post-infection ( dpi ) , saliva was collected from each mosquito and inoculated onto C6/36 mosquito cells to check for the presence of ZIKV infectious particles . Legs and body of each mosquito were also collected and submitted separately to RNA extraction and ZIKV RT-PCR . In Ae . aegypti the infection rate was high as early as 6 dpi and the dissemination efficiency get substantial from 9 dpi while the both rates remained quite low in Ae . polynesiensis . The transmission efficiency was poor in Ae . aegypti until 14 dpi and no infectious saliva was found in Ae . polynesiensis at the time points studied . In our experimental conditions , the late ability of the French Polynesian Ae . aegypti to transmit ZIKV added by the poor competence of Ae . polynesiensis for this virus suggest the possible contribution of another vector for the propagation of ZIKV during the outbreak , in particular in remote islands where Ae . polynesiensis is predominating .
Zika virus ( ZIKV; Flaviviridae: Flavivirus ) infection usually produces fever , skin rashes , conjunctivitis , muscle and joint pain , malaise and headache with potential neurological and auto-immune complications [1] . Isolated first in 1947 from a febrile monkey in the Zika forest in Uganda [2] , only sporadic humans infections were reported in Africa and Asia since the first large outbreak appeared in 2007 in Yap Island , Federated States of Micronesia [3] . ZIKV is a single-stranded positive sense RNA virus . Three genetic lineages reflecting geographic origin have been described: the two original West and East African lineages; and the Asian lineage [4] . French Polynesia is a French overseas Territory of about 270 000 inhabitants located in the South Pacific Ocean . Until ZIKV emerged in October 2013 , dengue virus ( Flaviviridae: Flavivirus ) used to be the only arbovirus recognized as circulating in French Polynesia [5] . ZIKV infections were reported in all five French Polynesian archipelagos ( Society , Marquesas , Tuamotu , Gambier and Austral Islands ) leading to the largest ZIKV outbreak ever reported at that date . Phylogenetic analysis determined that the virus belonged to the Asian lineage [5] . ZIKV has been described as being transmitted by peri-domestic Aedes mosquitoes , mostly Ae . aegypti but also Ae . albopictus that is able to survive at temperate climates [6–12] . Several other Aedes mosquito species have also been described as potential vectors for sylvatic transmission of ZIKV in Africa [8] . In the Pacific region , Ae . aegypti is present in almost all the region [13 , 14] . However in some islands where Ae . aegypti is absent or poorly present , endemic Aedes species like Ae . hensilli in Yap State predominate [15] . The potential role of Ae . hensilli in transmitting ZIKV during the outbreak in Yap island was suggested by its experimental ability to be infected and to disseminate the virus [15] . In French Polynesia the contribution of the endemic species Ae . polynesiensis in ZIKV spread was also suspected in addition of Ae . aegypti , supported by its ability to transmit arboviruses as DENV , chikungunya virus and Ross river virus [5 , 13 , 16 , 17] . In the present study , we assessed the vector competence of French Polynesian populations of Ae . aegypti and Ae . polynesiensis for ZIKV .
ZIKV strain PF13/251013-18 was isolated at Institut Louis Malardé from the serum of patient infected in October 2013 in the Marquesas Islands , French Polynesia . ZIKV was amplified on Ae . albopictus C6/36 cells [18] ( ATCC CRL-1660 , USA ) as described in Richard et al . [19] . After three successive passages , the infected-cell supernatant was harvested and concentrated by using Centricon Plus-70 centrifugal filter devices ( Millipore , Germany ) [20] . ZIKV concentrate was supplemented with heat-inactivated foetal bovine serum ( FBS , Life technologies , USA ) at 1:5 and stored at -80°C . For titrating the virus , C6/36 cells were inoculated with serial 10-fold dilutions of virus concentrate on a 96-wells plate . After six days , cells were fixed on the plate using 70% ice-cold acetone for 10 minutes . Cells were then incubated 45 minutes at 37°C with a specific hyperimmune mouse ascitic fluid provided by the Institut Pasteur of Dakar , Senegal , diluted 1:200 in PBS followed by 45 minutes of incubation at 37°C with fluorescein isothiocyanate-conjugated goat anti-mouse IgG ( Bio-Rad Laboratories , France ) diluted 1:100 . Viral titers were evaluated with the method of Reed and Muench in 50% tissue culture infectious dose ( TCID50/mL ) [21] . Ae . aegypti and Ae . polynesiensis colonies were established in 2014 from mosquito captured on Tahiti Island in Toahotu and Atimaono districts , respectively . F16 to F18 generation eggs of each mosquito colony were hatched . Larvae , pupae and adult mosquitoes were reared at 27°C , 80% relative humidity and 12:12h light-dark cycle inside a climate chamber ( Sanyo MLR-351H , Japan ) as previously described [19] . Five-days-old mosquitoes were starved for 24 hours and transferred into containers of about 60 mosquitoes each . The infectious meal was prepared using fresh washed bovine red cells ( SAEM Abattage de Tahiti , French Polynesia ) , viral concentrate ( 1:29 ) and adenosine triphosphate ( A6419 , Sigma-Aldrich , USA ) at 5 mM as phagostimulant . The titer of ZIKV in the blood meal was 7 log10 TCID50/mL . The blood meal was maintained at 37°C and presented during one hour through a Parafilm-M membrane to Ae . aegypti and through a porcine membrane to Ae . polynesiensis . To avoid horizontal transmission during sugar-feeding , fully-engorged females were transferred into individual plastic containers [22 , 23] . Mosquitoes were given access to 10% sucrose solution and maintained at 27°C , 80% relative humidity and 12:12h light-dark cycle for up to 21 days . Subsets of 18 hours starved mosquitoes were cold-anesthetized at 2 , 6 , 9 , 14 and 21 days post-infection ( dpi ) . To collect the saliva from each mosquito , legs and wings were removed and the proboscis was inserted into a filter tips ART ( Molecular BioProducts , USA ) containing 20 μL of FBS . Mosquitoes were invited to expectorate saliva during 30 min . The collected saliva was then expelled into a microtube containing 80 μL of 1% FBS cell-culture medium and preserved at -80°C . Each saliva sample was inoculated to C6/36 cells on a 96-well plate . Six days later , infectious cells were identified by indirect immunofluorescent assay as described above . Body ( thorax and abdomen ) and legs from each mosquito were also preserved in separate microtubes at -80°C . Individual mosquito bodies and legs were homogenized with metal beads for 4 min at 20 Hz ( Mixer Mill Retsch MM301 , Germany ) using cell-culture medium supplemented at 20% FBS for bodies and NucliSENS lysis buffer ( bioMérieux , France ) for legs [19] . Homogenate supernatants were recovered after centrifugation 5 minutes at 20 , 000 x g . Nucleic acids were extracted with the NucliSENS miniMAG system ( bioMérieux , France ) in accordance with manufacturer’s instructions . Real time RT-PCR were performed on a CFX96 Touch Real-Time PCR Detection System instrument using iTaq Universal Probes One-Step Kit ( Bio-Rad Laboratories , France ) and the primers and probes previously described [4] . Vector competence is defined as the ability of a given mosquito to allow the virus replicating , disseminating and finally being transmitted to a new susceptible host [24] . The ability of a mosquito species to become infected by the virus was given by the mosquito infection rate that corresponds to the proportion of females for which ZIKV was detected by real time RT-PCR in the body . The ability of the mosquito species to allow the virus spread outside the midgut was based on the detection of ZIKV in legs by real time RT-PCR . The evidence for the mosquito species to be able to transmit infectious virus was provided by the detection of replicative ZIKV particles from mosquito saliva inoculated on C6/36 cells . Viral dissemination and transmission efficiencies were defined as the number of mosquitoes with positive legs or infectious saliva divided by the number of mosquitoes tested . As the mortality rate of Ae . polynesiensis in laboratory conditions was high ( S1 Table ) , several trials were performed to obtain a sufficient number of mosquitoes for the different collecting days and especially for the late time point 14 dpi . The results obtained for each trial are detailed in S2 Table . Data of the trials performed with Ae . polynesiensis were pooled together before being analyzed . Chi-square test with or without Yates’ correction or Fisher’s exact test were performed to evaluate the differences between the two Aedes species at each time point and between two successive time points for each species ( Graph Pad Prism software , USA ) .
The mosquito infection rate was assessed at 6 , 9 , 14 and 21 dpi . To prevent any false positives due to remaining infectious blood meal in the midgut , infection rate was not evaluated at 2 dpi . The infection rate was 90% as early as 6 dpi for Ae . aegypti and increased slowly for Ae . polynesiensis from 11% at 6 dpi to 36% at 14 dpi ( Table 1 ) . The infection rate was significantly higher for Ae . aegypti compared to Ae . polynesiensis from 6 dpi to 14 dpi ( p<0 . 0001 ) . At 2 dpi , no ZIKV was detected in legs from Ae . aegypti and Ae . polynesiensis mosquitoes ( Table 1 ) . In Ae . aegypti , the dissemination efficiency increased over days , especially between 6 and 9 dpi ( p<0 . 0001; Fig 1 ) getting 75% at 9 dpi . We noticed that at 14 and 21 dpi , the dissemination efficiency was equal to the infection rate indicating that all infected mosquitoes had disseminated the virus ( Table 1 ) . In Ae . polynesiensis mosquitoes , ZIKV appeared in legs at only 9 dpi with 3% of dissemination efficiency ( Table 1 ) . Despite a significant increase ( p<0 . 01; Fig 1 ) the dissemination efficiency did not exceed 18% at 14 dpi . The dissemination rate in legs was significantly higher for Ae . aegypti compared to Ae . polynesiensis ( p<0 . 001 at 6 dpi , p<0 . 0001 at 9 and 14 dpi; Table 1 ) . Infectious saliva was detected from 6 dpi in Ae . aegypti females with 3% of transmission efficiency ( Table 1 ) . The transmission efficiency remained low at 9 dpi ( 8% ) then significantly increased to reach 36% at 14 dpi and 73% at 21 dpi ( p<0 . 01; Fig 1 ) . No ZIKV particle was found in the saliva from Ae . polynesiensis at any collecting days ( Table 1 ) .
In the present study we observed that Ae . aegypti early displayed high ZIKV infection rate but late ability to transmit the virus in our experimental conditions . Our results contrast with those previously reported for Ae . aegypti from Singapore in which as early as 6 dpi 100% of mosquitoes were potentially infectious with ZIKV detected in the salivary glands [10] . Contrariwise our results are in accordance with the recent studies carried in Senegalese and American populations of Aedes species [8 , 12] . Such a long extrinsic incubation period may limit the time window for an infectious vector to transmit ZIKV to susceptible people . Our laboratory results support that Ae . aegypti may have been a vector of ZIKV during the outbreak in French Polynesia , but maybe not the only one . In the endemic species Ae . polynesiensis , we found a moderate infection rate and the dissemination efficiency was low . No ZIKV particle was found in Ae . polynesiensis mosquito saliva even at the latest time point available for this species , i . e . 14 dpi . Due to the slower progression of ZIKV in this endemic species , the viral amount was maybe too low to allow detection at this time point . It is likely that ZIKV particles would have been detected in Ae . polynesiensis saliva at later time points . In a previous study , we had also observed that the dissemination and transmission of chikungunya virus in Ae . polynesiensis progressed slower than in Ae . aegypti [19] . The late ability of the two mainly distributed Aedes mosquito species in French Polynesia to transmit ZIKV raises questionings on the sole involvement of these vectors for having sustained the Zika outbreak , in particular in remote areas where Ae . aegypti is not or poorly present . In their study , Diagne et al . highlighted that the low transmission rates found in several African Aedes species are difficult to reconcile with continuous ZIKV transmission observed in Africa and also suggested the involvement of others vectors [8] . In Yap island , the predominant species Ae . hensilli was shown as a potential vector of ZIKV [15] . Nevertheless , despite a high level of infection ( 90% ) at 8 dpi , only 20% of infected mosquitoes disseminated the virus , while for chikungunya virus the dissemination rate reached 80% for an infection rate of 60% [15] . Interestingly , in the same study Ae . aegypti was only found at 0 , 1% on the island while Culex quinquefasciatus was found at ~30% making it the second main species on the island . Culex species and notably Culex quinquefasciatus are also present in all five archipelagos of French Polynesia [25] . The potential role of Culex species in ZIKV spreading was recently suggested in a few communications [12 , 26 , 27] . On 1st February 2015 , subsequent to the report of an increase of microcephaly cases and neurological complications , including Guillain-Barré Syndrome , in ZIKV affected countries , the World Health Organization declared ZIKV a Public Health Emergency of International Concern . Zika is asymptomatic in most cases and no vaccine is already available , thus vector control remains essential to limit outbreaks and consequently to limit the occurrence of clinical cases with pathologic complications . Identifying the main vector ( s ) for the current pandemic strain of ZIKV is essential to properly adapt vector control strategies .
|
From 2007 , Zika virus has caused several outbreaks in the Pacific including French Polynesia . Aedes aegypti mosquito which is present in almost all Pacific Island Countries is reasonably expected to have been involved in the Zika outbreaks . In addition endemic Aedes mosquito species may have sustained Zika virus transmission in the less urbanized and most remote islands . In the present study we provide for the first time data about the vector competence of the endemic Ae . polynesiensis species for Zika virus . We found under experimental conditions a weak competence of Ae . polynesiensis for the virus . Furthermore we demonstrated a late ability of the French Polynesian population of Ae . aegypti to transmit Zika virus . These findings raise questions about the potential involvement of other vector ( s ) in Zika virus transmission in place or together with the Aedes mosquitoes . In a context where innovative vector control strategies are mostly focused on targeting the mosquito species considered as the main arbovirus vectors , the potential for others vector species to take the lead in transmitting such arboviruses should not be neglected .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"legs",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"geographical",
"locations",
"microbiology",
"limbs",
"(anatomy)",
"saliva",
"animals",
"ethnicities",
"viruses",
"rna",
"viruses",
"insect",
"vectors",
"infectious",
"diseases",
"musculoskeletal",
"system",
"aedes",
"aegypti",
"medical",
"microbiology",
"epidemiology",
"microbial",
"pathogens",
"arboviral",
"infections",
"disease",
"vectors",
"insects",
"polynesians",
"arthropoda",
"people",
"and",
"places",
"mosquitoes",
"french",
"polynesia",
"anatomy",
"flaviviruses",
"oceania",
"viral",
"pathogens",
"physiology",
"biology",
"and",
"life",
"sciences",
"population",
"groupings",
"viral",
"diseases",
"organisms",
"zika",
"virus"
] |
2016
|
Vector Competence of French Polynesian Aedes aegypti and Aedes polynesiensis for Zika Virus
|
Invasive fungal infections caused by the pathogen Candida albicans have transitioned from a rare curiosity to a major cause of human mortality . This is in part due to the emergence of resistance to the limited number of antifungals available to treat fungal infections . Azoles function by targeting the biosynthesis of ergosterol , a key component of the fungal cell membrane . Loss-of-function mutations in the ergosterol biosynthetic gene ERG3 mitigate azole toxicity and enable resistance that depends upon fungal stress responses . Here , we performed a genome-wide synthetic genetic array screen in Saccharomyces cerevisiae to map ERG3 genetic interactors and uncover novel circuitry important for azole resistance . We identified nine genes that enabled erg3-mediated azole resistance in the model yeast and found that only two of these genes had a conserved impact on resistance in C . albicans . Further , we screened a C . albicans homozygous deletion mutant library and identified 13 genes for which deletion enhances azole susceptibility . Two of the genes , RGD1 and PEP8 , were also important for azole resistance acquired by diverse mechanisms . We discovered that loss of function of retrograde transport protein Pep8 overwhelms the functional capacity of the stress response regulator calcineurin , thereby abrogating azole resistance . To identify the mechanism through which the GTPase activator protein Rgd1 enables azole resistance , we selected for mutations that restore resistance in strains lacking Rgd1 . Whole genome sequencing uncovered parallel adaptive mechanisms involving amplification of both chromosome 7 and a large segment of chromosome 3 . Overexpression of a transporter gene on the right portion of chromosome 3 , NPR2 , was sufficient to enable azole resistance in the absence of Rgd1 . Thus , we establish a novel mechanism of adaptation to drug-induced stress , define genetic circuitry underpinning azole resistance , and illustrate divergence in resistance circuitry over evolutionary time .
The evolution of drug resistance in fungal pathogens poses grave concern given the limited number of antifungal drugs currently available to treat systemic infections . These infections have increased in prevalence in recent years and are now a major cause of human mortality worldwide [1] , with the most vulnerable populations being those with suppressed immune function due to infection with HIV or immunosuppressive treatments for cancer or organ transplantation . Candida albicans is the most prevalent opportunistic human fungal pathogen and the fourth leading cause of iatrogenic bloodstream infections [2 , 3] . It can colonize almost every niche in the human body and is a commensal of the mucosal microbiota , where maintenance of a stable host-fungus relationship is crucial for avoiding disease [4 , 5] . Impaired host immunity enhances risks of deadly disseminated infection , with systemic candidiasis associated with mortality rates of 40% despite state-of-the-art antifungal therapies [1–3] . With the increasing prevalence of immunocompromised individuals , and the spread of drug-resistant fungal infections , there is a dire need for novel antifungal treatment options . Currently , the most widely prescribed antifungal class is the azoles . Fluconazole has dominated this class in the clinic due to its desirable pharmacokinetics , and efficacy within the host [6 , 7] . The azoles target the ergosterol biosynthetic enzyme lanosterol-14α-demethylase , Erg11 , and inhibit its activity by binding to the iron heme group of its active site [6 , 7] . This leads to a block in the production of ergosterol and the production of a toxic sterol intermediate by the ergosterol biosynthetic enzyme Δ-5 , 6-desaturase , Erg3 [6–8] . Accumulation of this toxic sterol coupled with the depletion of ergosterol destabilizes the cellular membrane , halting fungal growth and imposing a severe membrane stress upon the cell [6 , 8] . The major liability of azoles is their fungistatic activity , merely inhibiting growth of the fungal population as opposed to killing the fungal cell [6 , 7] . Long-term and prophylactic azole therapy coupled with a fungistatic mechanism of action generates a strong selective pressure for the evolution of azole-resistance , and azole-resistant Candida species have been recognized as a severe threat to public health [9] . Canonical molecular mechanisms of resistance to the azoles have been well-characterized in both laboratory-derived resistant strains , as well as clinical isolates . These include overexpression and/or alteration of the drug target Erg11 , as well as overexpression of several multidrug efflux transporters , including Cdr1 , Cdr2 , and Mdr1 [6 , 8] . Fungi exposed to fluconazole also exhibit high levels of aneuploidy , with a segmental aneuploidy involving an isochromosome of the left arm of chromosome 5 flanking a centromere ( i ( 5L ) ) frequently observed in fluconazole-resistant isolates [10–12] . C . albicans also possesses numerous stress response pathways that enable survival during exposure to diverse stresses in the host , including the stress induced by antifungal drugs . Stress response pathways are crucial for basal tolerance of antifungals , as well as for resistance acquired by diverse mechanisms , including loss-of-function mutations in the ergosterol biosynthesis gene , ERG3 [8 , 13 , 14] . Key stress response regulators that are crucial for erg3-mediated resistance include the protein phosphatase calcineurin [14 , 15] , protein kinase C Pkc1 [16] , and molecular chaperone Hsp90 [14 , 15] . The genetic circuitry through which these regulators enable resistance remains largely elusive , and it is likely that other key regulators remain to be identified . Loss of function of erg3 provides a powerful context for defining circuitry enabling antifungal drug resistance given that erg3 loss of function has been identified in clinical isolates [13 , 17] , and that key regulators of erg3-mediated azole resistance are also required for resistance acquired by diverse mechanisms in the host [15 , 16 , 18] . Systematic analysis of circuitry enabling cellular responses to drugs can now be performed with an unprecedented level of resolution by leveraging high throughput genomic analyses to define mutants with enhanced susceptibility to a drug . Such approaches have been pioneered with the model yeast Saccharomyces cerevisiae for which facile genetics enables a multitude of approaches to map gene function and define functional relationships in the cell [19 , 20] . One approach that has yet to be applied to drug resistance , is based on synthetic genetic array ( SGA ) methodology , which enables the systematic mapping of synthetic genetic interactions by engineering high-density arrays of double gene deletion mutants [21–24] . Genetic interactions manifest as unexpected double mutant phenotypes and are typically assessed by quantitative measurement of fitness or morphology [20 , 25] , with the classic example being synthetic lethal interactions . The principle of synthetic lethality provides a rational approach to dramatically expand the target space for antimicrobial discovery based on identifying combinations of agents that target members of synthetic lethal pairs [26] . For example , aggravating genetic interaction partners of ERG3 may represent novel drug targets for combination therapies with azoles to treat fungal infections , as in principle they should mitigate the emergence of resistance through this mechanism . Although combination therapies are the standard for treating infections such as HIV [27] , malaria [28] , and tuberculosis [29] , this approach has remained largely unexplored for treating fungal infections . Identifying targets for combination therapy in fungal pathogens such as C . albicans is hindered by the lack of a defined meiotic cycle . An alternative strategy is to use S . cerevisiae as a model system , which is justified based on the conservation of key cellular regulators between the species [30] , including the key drug resistance determinants Hsp90 , calcineurin , and Pkc1 [14–16] . In this manuscript , we mapped genetic interactors of ERG3 in S . cerevisiae in order to uncover novel genetic circuitry important for azole resistance . By screening 4 , 308 double mutants for growth in the presence of miconazole , we identified nine genes that enable erg3-mediated resistance in the model yeast . Only two of these genes had a conserved impact on erg3-mediated azole resistance in the fungal pathogen C . albicans , highlighting substantial divergence in the circuitry orchestrating response to drug-induced stress between the species . As an alternative approach to identify cellular responses to azoles in C . albicans , we screened a C . albicans homozygous deletion library with 1 , 152 mutants covering 674 genes spanning ~11% of the genome . We identified 13 C . albicans genes for which deletion enhances susceptibility to azoles . Two of the genes , RGD1 and PEP8 , were also important for azole resistance acquired by diverse mechanisms . We discovered that loss of function of Pep8 overwhelms the functional capacity of calcineurin in response to azole stress , thereby compromising cellular responses to membrane damage and abrogating azole resistance . In contrast , loss of function of Rgd1 did not impair canonical resistance mechanisms or stress response pathways . To identify the mechanism by which Rgd1 enables azole resistance , we selected for mutations that restore resistance in strains lacking Rgd1 . Whole genome sequencing of four independent lineages uncovered parallel adaptive mechanisms involving amplification of chromosome 7 and a large segment of chromosome 3 . Additional mechanistic studies demonstrated that upregulation of a transporter gene on the right arm of chromosome 3 , NPR2 , was sufficient to confer azole resistance in strains lacking Rgd1 . Thus , we define complex genetic circuitry underpinning azole resistance and establish a novel mechanism through which genomic plasticity enables adaptation to drug-induced stress , with broad implications for the development of strategies to combat drug-resistant fungal infections .
We leveraged the power of genome-scale synthetic genetic analysis in S . cerevisiae to define genetic interactors of a key regulator of membrane homeostasis and drug resistance , ERG3 . By monitoring fitness in the absence and presence of azole , we aimed to define condition-specific genetic interactions , and new determinants of drug resistance . Using SGA technology , we mapped genetic interactions by generating double deletion mutants using an erg3Δ query strain and the non-essential gene deletion library [21] . In the absence of a genetic interaction , double-mutant fitness is a multiplicative combination of each single-mutant fitness , with genetic interactions defined as deviations from the expected double-mutant fitness [25] . These interactions are assigned an SGA score , with the magnitude of the score indicative of the strength of the genetic interaction . We identified 100 genetic interaction partners of ScERG3 under standard laboratory conditions with an SGA score ≤ -0 . 25 [20] , including genes involved in diverse biological processes such as lipid metabolism , Golgi vesicle transport , transcription , cellular amino acid metabolic process and cell wall organization ( Fig 1A and S1 Table ) . Next , we compared our genetic interaction profile with that observed for other query genes , given that similarity of genetic interaction profiles reveals functional relationships between genes . As expected , the ERG3 genetic interaction profile was significantly correlated with that observed for genes involved in ergosterol biosynthesis including ERG6 , ERG24 , ERG2 , and ERG25 ( S1 Table ) . This analysis supports the importance of Erg3 in ergosterol biosynthesis and implicates interactions with other diverse biological processes . In order to specifically identify genes that are required for azole resistance acquired by ERG3 loss of function , we extended the SGA approach to screen the full collection of double mutants in the presence of a fixed concentration of the azole miconazole and monitored growth relative to the erg3Δ strain ( S2 Table ) . For the double mutants identified as sensitive to miconazole in the primary screen , we performed a complementary drug susceptibility test by performing a fluconazole minimum inhibitory concentration ( MIC ) assay . Through this analysis we validated nine out of 14 genes for which deletion suppressed the erg3-mediated azole resistance phenotype and whose complementation with the wild-type allele restored erg3-mediated azole resistance ( PHO2 , SPT3 , UPC2 , UME6 , SGF73 , CNB1 , CSF1 , SST2 , and SIN4 ) ( Fig 1B ) . The remaining four genes ( BAP2 , TRP4 , MDM39 and BRE1 ) did not validate in our follow up analysis . Notably , we identified known determinants of the erg3-mediated resistance phenotype , including the gene encoding the catalytic subunit of calcineurin , CNB1 , validating our approach . Thus , by leveraging SGA , we identified genome-scale functional relationships with ERG3 , and genes important for azole resistance . We next assessed whether the nine genes for which deletion suppressed erg3-mediated azole resistance in S . cerevisiae had a conserved impact on azole resistance in the fungal pathogen C . albicans . To do so , we generated homozygous deletions of each gene in a C . albicans erg3Δ/erg3Δ mutant background . Due to difficulty in constructing homozygous deletion mutants for two genes , SPT3 and orf19 . 3473 , we engineered conditional expression strains in which we deleted one allele and replaced the native promoter of the remaining allele with a tetracycline-repressible promoter to enable transcriptional repression by the tetracycline analog doxycycline . As expected , homozygous deletion of the gene encoding the catalytic subunit of calcineurin , CNA1 , or a transcription factor required for the upregulation of drug efflux pumps , UPC2 , abrogated erg3-mediated azole resistance of C . albicans ( Fig 1C ) [15 , 31–33] . In contrast , deletion or transcriptional repression of the remaining six genes tested had no impact on azole resistance ( Fig 1C ) , revealing substantial divergence in the genetic circuitry underpinning azole resistance between the two species . Strains of C . albicans are often capable of proliferating in the presence of azoles independent of specific adaptive mutations , a phenomenon referred to as tolerance . Based on the findings that regulators of erg3-mediated azole resistance are often important for basal tolerance of wild-type cells to azoles , as is the case with CNA1 and UPC2 [15 , 33] , we took a complementary approach to define regulators of azole resistance . We screened a C . albicans library of homozygous deletion mutants spanning approximately 11% of the genome [34] , for hypersensitivity to a sub-inhibitory concentration of fluconazole ( Fig 2A ) . Using this approach , we identified 13 sensitive strains that had an OD600 < 80% that of the wild type in the presence of azole , with minimal growth defect in the absence of drug ( Fig 2B ) : ERG5 , KIC1 , PEP8 , APM1 , GZF3 , STT4 , PBS2 , RGD1 , RCY1 , SSK2 , MRR2 , SET6 , and orf19 . 2378 . Although five of these genes , ERG5 , KIC1 , GZF3 , PBS2 , and MRR2 , had previously been linked to azole tolerance [35] , the remaining eight genes had previously unreported roles . To determine if the genes that influence basal tolerance also enable erg3-mediated azole resistance , we deleted both ERG3 alleles in eight of the 13 homozygous deletion mutant backgrounds . The remaining five genes were not evaluated based on difficulty with mutant construction . This identified two of the eight genes as important for erg3-mediated azole resistance , RGD1 and PEP8 ( Fig 2C ) . RGD1 encodes a RHO-GTPase activating protein ( RHO-GAP ) , which facilitates the GTP hydrolysis of its cognate RHO-GTPases Rho3 , Rho4 , and Cdc42 [36] , and PEP8 encodes a vacuolar protein component of the retromer that is essential for endosome-to-Golgi retrograde protein transport [37] . To our knowledge , neither PEP8 nor RGD1 have been previously implicated in azole resistance in C . albicans . We found that the pep8 and rgd1 homozygous deletion mutants had a sensitivity to specific cell membrane stresses , as the mutants were hypersensitive to the membrane perturbing agents SDS and terbinafine , but were not sensitive to another membrane perturbing agent amphotericin B or to cell wall stress induced by the echinocandin caspofungin ( S1A Fig ) . Thus , our screen for genes enabling fluconazole tolerance identified two novel determinants of erg3-mediated azole resistance in C . albicans . Although ERG3 loss-of-function mutations are relatively common among clinical isolates [13 , 38 , 39] , we wanted to assess whether PEP8 and RGD1 have an impact on azole resistance that evolved in a human host . We focused on two strains from a series of C . albicans oral isolates from an HIV-infected individual sampled over a 2-year period of treatment with fluconazole; CaCi-2 represents one of the first isolates collected over this time period and CaCi-17 is the final isolate collected [40] . Throughout the sampling period , these isolates acquired diverse mutations including point mutations in UPC2 , ERG11 and TAC1 , which led to the overexpression of efflux pumps and the azole target ERG11 [40–42] . We engineered RGD1 and PEP8 homozygous deletion mutants in each clinical isolate background and assessed azole susceptibility phenotypes . Homozygous deletion of either RGD1 or PEP8 enhanced the fluconazole susceptibility of both clinical isolates to differing extents ( Fig 2D and S1B Fig ) . Deletion of PEP8 had the strongest effect on resistance , reducing the fluconazole minimum inhibitory concentration by approximately four-fold in the early isolate , and 128-fold in the late clinical isolate . The enhanced susceptibility due to RGD1 deletion was less pronounced , with an approximately two-fold effect in both the early and late clinical isolates . Thus , identifying genes important for azole tolerance and erg3-mediated azole resistance provides a powerful strategy to discover novel regulators of clinically-relevant azole resistance . Determining the mechanisms through which novel regulators of azole resistance enable cellular responses to drug-induced stress is crucial for understanding circuitry enabling drug resistance and identifying new targets for intervention . In the context of PEP8 , members of the retrograde transport pathway have been implicated in facilitating tolerance to a wide variety of cellular stresses including alkalinity , SDS , and high concentrations of Ca2+ in C . albicans [43] . Disruption of retrograde trafficking results in perturbed vacuolar morphology , and increased levels of calcineurin-dependent transcripts , which are necessary to survive the stress imposed by a retrograde transport defect [43] . We hypothesized that the erg3Δ/erg3Δ pep8Δ/pep8Δ double deletion mutant may experience a state of cellular stress that overwhelms the functional capacity of calcineurin upon fluconazole exposure . To test this , we first verified that the pep8Δ/pep8Δ mutant displayed perturbed vacuolar morphology , as visualized by DIC microscopy and FM4-64 staining ( Fig 3A and S2 Fig ) . Indeed , vacuoles in pep8Δ/pep8Δ mutant were less well-defined compared to the wild type or erg3Δ/erg3Δ mutant . This effect was exacerbated in an erg3Δ/erg3Δ pep8Δ/pep8Δ double mutant , with vacuoles almost completely fragmented ( Fig 3A and S2A Fig ) . We also observed fragmented vacuoles when PEP8 was deleted from clinical isolates CaCi-2 and CaCi-17 , highlighting this was not specific to the erg3 mutant background ( S2B Fig ) . Next , we tested whether deletion of PEP8 resulted in an increase in calcineurin-dependent transcripts . Using quantitative RT-PCR , we observed that the pep8Δ/pep8Δ and erg3Δ/erg3Δ pep8Δ/pep8Δ mutants had significantly elevated levels of the calcineurin-dependent transcripts CRH11 and UTR2 relative to the wild type and erg3Δ/erg3Δ parental strains in the presence of fluconazole , consistent with elevated calcineurin activity ( Fig 3B ) . CRH11 and UTR2 expression levels were also increased in the erg3Δ/erg3Δ pep8Δ/pep8Δ double mutant in the absence of fluconazole relative to the individual deletion mutants , suggesting that the protein phosphatase was hyperactivated under basal conditions in this double mutant ( Fig 3B ) . Notably , in an erg3 mutant , constitutive calcineurin activation does not abrogate azole resistance as homozygous deletion of ERG3 in a strain lacking the autoinhibitory domain of calcineurin [44] , did not lead to an enhanced susceptibility to azoles ( S3A Fig ) . This highlights that constitutive activation of calcineurin alone is not sufficient to abrogate erg3-mediated resistance implicating additional cellular consequences upon loss of function of Pep8 . Collectively , these results confirm that deletion of PEP8 results in perturbed vacuolar morphology and increased levels of calcineurin effector genes , and these effects are exacerbated in combination with deletion of ERG3 under basal conditions . Our findings are consistent with the model that deletion of both ERG3 and PEP8 results in an increase in cellular stress that overwhelms the functional capacity of calcineurin . An additional prediction of the model is that erg3Δ/erg3Δ pep8Δ/pep8Δ double mutants would be hypersensitive to calcineurin inhibition . We tested the sensitivity of wild type , erg3Δ/erg3Δ , pep8Δ/pep8Δ , and erg3Δ/erg3Δ pep8Δ/pep8Δ strains to the calcineurin inhibitor FK-506 . Although the pep8Δ/pep8Δ mutant displayed some sensitivity to FK-506 relative to the wild-type strain , the erg3Δ/erg3Δ pep8Δ/pep8Δ double mutant showed striking hypersensitivity to FK506 relative to either single mutant or the parental strain ( Fig 3C and S3B Fig ) . Yet another prediction of our model is that the erg3Δ/Δ pep8Δ/Δ double mutant would be hypersensitive to Hsp90 inhibition , given that inhibition of Hsp90 destabilizes the catalytic subunit of calcineurin and impairs calcineurin activation in response to stress [18] . As predicted , the pep8Δ/pep8Δ and erg3Δ/erg3Δ mutants both displayed enhanced susceptibility to the Hsp90 inhibitor geldanamycin relative to the wild-type strain , and this sensitivity was further enhanced in the erg3Δ/erg3Δ pep8Δ/pep8Δ double mutant ( Fig 3C and S3B Fig ) . Together , these findings support a model in which loss of Pep8 induces cellular stress which is further exacerbated by loss of Erg3 , thereby overwhelming the demand for calcineurin and impairing crucial calcineurin-dependent cellular stress responses and abrogating azole resistance . We next sought to evaluate the mechanism by which Rgd1 influences azole resistance , by monitoring the impact of this RHO-GAP on known resistance circuitry . First , we quantified the expression of known resistance determinants , including key multidrug transporters and the azole target ERG11 . In strains lacking RGD1 , both the basal levels of ERG11 and the induction in response to fluconazole were comparable to the wild-type strain for the rgd1Δ/rgd1Δ mutant and comparable to the erg3Δ/erg3Δ control for the erg3Δ/erg3Δ rgd1Δ/rgd1Δ double mutant ( S4A Fig ) . Similarly , homozygous deletion of RGD1 in either a wild type or erg3Δ/erg3Δ background did not lead to a decrease in expression of efflux transporters CDR1 , CDR2 , or MDR1 , and in some cases actually led to an increase in expression in the presence of fluconazole ( S4A Fig ) . Second , we tested the impact of Rgd1 on protein kinase C ( PKC ) signaling , given that PKC signaling enables azole resistance in C . albicans [16] , and that Rgd1 has been implicated as a positive regulator of PKC signaling in S . cerevisiae and C . albicans in response to low pH stress [45–47] . We found that the terminal MAP kinase in the Pkc1 cascade , Mkc1 , was phosphorylated in response to fluconazole in both wild-type and rgd1Δ/rgd1Δ strains ( S4B Fig ) , suggesting that Rgd1 is not required for activation of this cascade in response to drug-induced stress . Intriguingly , Mkc1 was phosphorylated under basal conditions in both erg3Δ/erg3Δ and erg3Δ/erg3Δ rgd1Δ/rgd1Δ mutants , suggesting that the Pkc1 pathway is constitutively activated in strains lacking Erg3 ( S4B Fig ) . Finally , to test if deletion of RGD1 resulted in impaired signaling through other stress response pathways important for azole resistance , we monitored calcineurin activation by measuring the induction of the calcineurin-dependent transcript , UTR2 . Both the basal expression and upregulation of UTR2 in response to fluconazole were comparable between strains lacking Rgd1 and the controls ( S4C Fig ) , suggesting that the azole sensitivity of the erg3Δ/erg3Δ rgd1Δ/rgd1Δ mutant cannot be attributed to a defect in calcineurin signaling . Thus , RGD1 enables azole resistance independent of effects on expression of the drug target gene or activation of key stress response pathways implicated in azole resistance . As a complementary approach to determine the mechanism through which Rgd1 enables azole resistance , we tested whether known physical interaction partners of Rgd1 were important for this phenotype . First , we focused on Rho3 and Rho4 , which are RHO-GTPases that are activated by Rgd1 in S . cerevisiae and involved in actin cytoskeleton re-organization and polarized cell growth [48] . We found that homozygous deletion of RHO3 or RHO4 had no impact on fluconazole susceptibility in an otherwise wild-type background or in an erg3 mutant background ( S4D Fig ) . Next we focused on Pik1 , a phosphoinositide kinase that regulates localization of Rgd1 to the plasma membrane in S . cerevisiae [49] . As with RHO3 and RHO4 , we found that homozygous deletion of the C . albicans homolog of PIK1 , PIKα , did not enhance azole susceptibility ( S4D Fig ) . Thus , Rgd1 enables cellular responses to azoles independent of known interacting partners . Given that the mechanism by which Rgd1 enables azole resistance remained elusive , we turned to an unbiased approach to identify mutations that could restore azole resistance to a strain lacking Rgd1 . To do so , we plated ~2x108 cells on agar medium containing a high concentration of miconazole for the sensitized erg3Δ/erg3Δ rgd1Δ/rgd1Δ strain , as well as the CaCi-17 rgd1Δ/rgd1Δ strain . We were unable to recover resistant mutants in the erg3Δ/erg3Δ rgd1Δ/rgd1Δ background . In contrast , many resistant isolates from independent lineages were obtained from the CaCi-17 rgd1Δ/rgd1Δ background , each of which had enhanced azole resistance relative to the progenitor in addition to a growth defect in the absence of drug ( Fig 4A and 4B ) . Thus , there are fitness constraints that may limit the evolution of azole resistance in the absence of Rgd1 . To identify mutations that could restore resistance in the absence of Rgd1 , we performed whole genome sequencing of four azole-resistant isolates from independent selection experiments , along with the respective progenitor strain . Genome sequence analysis did not identify any single nucleotide variants or small insertions or deletions in coding regions that were common to the evolved lineages and absent from the progenitor . In contrast , we discovered several large structural changes that were common among the azole-resistant isolates , involving copy number expansions of chromosome 3 and 7 ( Fig 4C ) . In particular , all four evolved lineages had a common breakpoint on the left arm of chromosome 3 that simultaneously resulted in a loss ( monosomy ) of 35% of the left portion of chromosome 3 and amplification of 65% of the right portion of chromosome 3 . All four evolved clones had different levels of chromosome 3 amplification ( Fig 4C ) , and allele ratios within the amplification indicate that these were likely independent events ( Fig 4D ) . For example , the monosomic region of chromosome 3 was composed of haplotype A in two of the lineages ( R1 and R2 ) and haplotype B in the other two evolved lineages ( R3 and R4 ) ( Fig 4D ) . Thus , dramatic structural rearrangements of chromosome 3 and 7 restore azole resistance in a clinical isolate lacking RGD1 . We scanned the genes within the expanded region of chromosomes 3 and 7 to identify any with annotated roles in response to azoles or membrane transport and identified several candidate resistance modifiers . For example , the amplified region of chromosome 3 contains: orf19 . 344 , which is upregulated in azole-resistant strains and is thought to be regulated by the efflux regulator Tac1 [50 , 51]; orf19 . 304 , which is a putative transporter similar to MDR proteins [52]; and Npr2 , which is a putative urea transporter [53] . Chromosome 7 harbors the cellular stress response regulator Hsp90 [15] , the major facilitator transporter Flu1 with reported roles in fluconazole susceptibility [54] , and other sugar transporters including Hgt12 and Hgt13 . We quantified expression of these candidate genes by qRT-PCR to determine if the increased copy number was associated with increased expression and confirmed elevated transcript levels of all genes in the resistant isolates relative to the parental control ( S5A Fig ) . A concomitant increase in resistance to the Hsp90 inhibitor geldanamycin was also observed in all four resistant isolates ( S5B Fig ) . Finally , we monitored the accumulation of rhodamine 6G ( R6G ) in our parental and evolved isolates . R6G is a fluorescent substrate that is effluxed from the cell by many of the transporters that are involved in fluconazole efflux [55] . Deletion of RGD1 from the azole-resistant isolate CaCi-17 resulted in an increased accumulation of the fluorescent substrate within the cell relative to CaCi-17 ( S6 Fig ) , suggesting that this strain was impaired in drug efflux . However , in all four evolved lineages R6G accumulation was reduced relative to the CaCi-17 rgd1Δ/rgd1Δ background ( S6 Fig ) , highlighting these strains had improved efflux relative to their fluconazole-susceptible parent . Thus , amplification of chromosome 3 and 7 enables the evolution of antifungal drug resistance , likely through the upregulation of multiple genes involved in efflux and stress response signaling . To further define the gene ( s ) that enable azole resistance in the absence of RGD1 , we plated one of our evolved resistant isolates , CaCi-17 rgd1Δ/Δ R1 , for single colonies onto YPD agar and allowed the culture to grow in the absence of drug stress for three days . Whole genome sequencing of selected colonies identified one lineage that had lost the chromosome 7 aneuploidy but retained the unusual chromosomal 3 aneuploidy ( Fig 5A and Fig 5B ) . This isolate retained the fluconazole resistance phenotype as evidenced by minimum inhibitory concentration assays , highlighting that the gene ( s ) responsible for azole resistance are likely located on chromosome 3 ( Fig 5C ) . Based on this prediction , we focused our analysis on those genes with annotated roles in response to azoles or membrane transport that were located in the amplified region of chromosome 3 . In the CaCi-17 rgd1Δ/Δ strain , we overexpressed orf19 . 344 , orf19 . 304 or NPR2 by replacing the native promoter of one allele with the strong tetracycline-repressible promoter . In the absence of tetracycline or its analog doxycycline ( DOX ) , each of these transcripts was significantly overexpressed , and expression was reduced upon growth of cells with DOX ( Fig 5D ) . Drug susceptibility profiling demonstrated that overexpression of NPR2 increased azole resistance relative to the CaCi-17 rgd1Δ/Δ parent ( Fig 5E ) , confirming that overexpression of NPR2 is sufficient to enable azole resistance in the absence of Rgd1 . Notably , overexpression of orf19 . 344 or orf19 . 304 had no effect on azole susceptibility ( Fig 5E ) . Thus , we implicate the putative urea transporter , Npr2 , as a mediator of azole resistance in the absence of RGD1 and identify a fascinating example of parallel evolution in which the amplification of a transporter gene on chromosome 3 is the preferred adaptive mechanism to enable azole resistance in the absence of this RHO-GTPase activating protein .
In this study , we uncovered novel genetic circuitry that enables resistance to the most widely deployed class of antifungal drugs , the azoles . Leveraging genome-scale synthetic genetic analysis in S . cerevisiae , we identified ERG3 genetic interactors under basal conditions and in the presence of miconazole , nine of which proved to be determinants of azole resistance . Our analysis revealed considerable divergence in genes important for azole resistance in C . albicans , motivating an alternate approach to screen for genes that enable basal tolerance to azoles directly in this leading human fungal pathogen . A functional genomic screen of C . albicans mutants covering ~11% of the genome , identified 13 genes important for azole tolerance . Two of these genes , RGD1 and PEP8 , were required for azole resistance acquired by diverse mechanisms . We found that loss of Pep8 overwhelms the functional capacity of calcineurin in response to azole stress , impairing crucial responses to drug-induced cell membrane stress . In contrast , loss of Rgd1 did not impair canonical resistance mechanisms or signaling through key stress response pathways implicating novel genetic circuitry governing azole resistance in the absence of Rgd1 . Whole genome sequencing of four independent lineages revealed that amplification of chromosome 7 and the right portion of chromosome 3 can restore resistance in strains lacking Rgd1 , suggesting that Rgd1 may enable azole resistance by influencing the expression or activity of genes in these amplified regions . Consistent with this model , we found that overexpression of the transporter gene NPR2 , located on the amplified right portion of chromosome 3 , was sufficient to restore azole resistance in a strain lacking Rgd1 . Thus , we establish novel circuitry important for antifungal drug resistance , and implicate a new facet of genomic plasticity in pathogen adaptation to drug-induced stress . Our findings highlight evolutionary divergence of circuitry underpinning azole resistance . Although there are many examples of circuitry rewiring between S . cereivisae and C . albicans [56–59] , there is conservation of most canonical drug resistance mechanisms , including alteration of the drug target Erg11 , activation of efflux pumps , and signaling through key hubs of cellular stress responses [6 , 8] . As poignant examples , the molecular chaperone Hsp90 , and its client proteins calcineurin and Slt2/Mkc1 have all been implicated in azole resistance of diverse fungi [15 , 16 , 60] . In contrast , we observed a conserved impact on azole resistance in C . albicans for only two of the nine genes for which deletion abrogates erg3-mediated resistance in S . cerevisiae; one encoding the transcriptional regulator Upc2 and the other encoding the catalytic subunit of calcineurin Cna1 . This suggests that although key hubs in the genetic network governing drug resistance are conserved , there is extensive divergence in circuitry controlling crucial cellular responses to drug-induced stress . This resonates with previous findings that the circuitry downstream of both calcineurin and Slt2/Mkc1 enabling membrane stress responses have been rewired between S . cerevisiae and C . albicans [14–16] . The genetic evidence for divergence in resistance circuitry is also complemented by chemical biology screens , which have revealed distinct sets of molecules that potentiate azole activity against different fungal species [61 , 62] . Together , this emphasizes the necessity of studying drug resistance directly in fungal pathogens . Our functional genomic screen in C . albicans defined a new role for the retromer component , Pep8 , in azole resistance mediated via effects on cellular demand for the protein phophastase calcineurin . The stability of calcineurin depends on Hsp90 , which interacts with the catalytic subunit under basal conditions , stabilizing calcineurin and keeping it poised for activation [63] . PEP8 has recently been shown to interact genetically with HSP90 in C . albicans [64] , which may reflect that perturbation of Pep8 induces cellular stress . Consistent with this model , mutants defective in retrograde signaling , including vps15Δ/vps15Δ , vps51Δ/vps51Δ and pep8Δ/pep8Δ are hypersensitive to cell membrane stress [43] . Defects in endosomal trafficking have also been shown to modulate stress response signaling , with homozygous deletion of VPS21 , a gene important for membrane trafficking through the late endosome/prevacuolar compartment , leading to increased intracellular calcium levels and enhanced expression of calcineurin effectors [65] . Strikingly , loss-of-function of VPS21 leads to enhanced azole tolerance [65 , 66] , the opposite of what we observed upon loss of Pep8 , highlighting complex functional relationships between regulators of endosomal transport and cell membrane stress responses . The complexity of circuitry governing cellular responses to membrane stress is further emphasized by our discovery of Rgd1 as a novel effector of azole resistance that operates independent of established resistance determinants . Our results suggest that Rgd1 enables erg3-mediated azole resistance independent of its cognate GTPases Rho3 and Rho4 , and independent of Pikα . By leveraging selection for genetic alterations that restore azole resistance in strains lacking Rgd1 coupled to whole genome sequencing , we discovered a new link between genomic plasticity and azole resistance . Exposure to fluconazole is known to promote aneuploidy formation in C . albicans due to alterations in cell cycle progression and the establishment of multinucleate trimera intermediates that are often resolved as aneuploid progeny [67] . The most well-characterized aneuploidy contributing to fluconazole resistance involves an isochromosome formation of the left arm of chromosome 5 , which leads to azole resistance via amplification of genes encoding the drug target Erg11 and the efflux pump transcriptional regulator Tac1 [10–12] . Although we were unable to obtain isolates with restored azole resistance in an erg3Δ/erg3Δ rgd1Δ/rgd1Δ background , such isolates were readily obtained in the late clinical isolate CaCi-17 with both RGD1 alleles deleted and all of the independent isolates shared a common aneuploidy signature with amplification of chromosome 7 and the right portion of chromosome 3 . Such aneuploidies of the smaller chromosome have been associated with azole resistance [12 , 68] , including increased copy number of chromosomes 3 and 7 , although the genes on these chromosomes important for enabling resistance remain largely enigmatic . Our analysis demonstrated that overexpression of the putative urea transporter gene NPR2 , which resides on chromosome 3 , can restore azole resistance in the absence of Rgd1 . Although C . albicans aneuploidies often do not come at a fitness cost [12] , we observed that the azole-resistant aneuploid isolates recovered in the clinical isolate background lacking Rgd1 had a fitness cost in the absence of drug and we were unable to recover resistant isolates in the background lacking both Erg3 and Rgd1 . Thus , fitness constraints may limit the evolution of drug resistance in the absence of Rgd1 , suggesting that it may be an attractive target for thwarting the emergence of drug resistance . In this era of antimicrobial resistance , there is a pressing need to identify novel strategies to block the evolution of drug resistance and abrogate resistance once it has evolved . Defining the genes and genetic circuitry required for drug resistance provides a powerful approach for the rational design of combination therapies to achieve this goal [69] . Classic examples of this approach , include the molecular chaperone Hsp90 , protein phosphatase calcineurin , and protein kinase Pkc1 [15 , 16 , 18] . The central challenge for targeting these cellular regulators for treatment of fungal infectious disease arises from their conservation and importance in the human host , necessitating the development of inhibitors that can selectively engage with the fungal counterpart [15 , 70 , 71] . Our discovery that Rgd1 and Pep8 regulate drug resistance expands the target space for antifungal drug development , and highlights that we are far from saturation given that screens with limited genomic coverage continue to yield novel resistance regulators . Progress with the development of more comprehensive genomic resources for fungal pathogens will enable elegant studies to elucidate the genetic interaction networks governing cellular stress response and drug resistance [26 , 72 , 73] . These genomic advances coupled with novel approaches to chemical synthesis and chemical biology screens will have a transformative impact on the discovery of molecules and mechanisms to evade drug resistance and treat life-threatening infectious disease [74–76] .
Strain stocks were maintained at -80°C in 25% glycerol . All strains were cultured in YPD ( 2% bactopeptone , 1% yeast extract , and 2% glucose ) for liquid culture growth , or YPD with 2% agar for solid media growth at 30°C unless otherwise specified . Strains used in this study are listed in S3 Table . Cloning procedures were performed following standard protocols . Plasmids used in this study are listed in S4 Table . The absence of nonsynonymous mutations in plasmids was verified by sequencing . Primers used in this study are listed in S5 Table . SGA analysis was conducted in biological duplicate as described in [21–24] . The fitness of double mutants was evaluated by automated measurement of colony size . The epsilon score measures the extent to which colony size of a double mutant deviates from the colony size expected from combining two mutations together . The data includes both negative ( putative synthetic sick/lethal ) and positive interactions ( potential epistatic or suppression interactions ) involving the gene ( s ) of interest . The magnitude of the score is indicative of the strength of interaction . Based on statistical analysis , we determined a default cutoff for our quantitative genetic interactions consisting of an SGA score ≤ -0 . 25 in YPD medium . This strict confidence threshold was applied based on previous genome-wide SGA studies [23–25] , and based on analysis of biological duplicates . Further , strict normalization strategies were employed to obtain accurate fitness measurements , as described previously [25] . When comparing the genetic interaction profiles with other query strains , genetic profile similarity is based on Pearson correlation . To assess the double deletion mutants for azole sensitivity , erg3Δ query strains were crossed with 4 , 309 non-essential deletion mutants to generate double deletion mutants via several automated selection steps . This erg3 SGA library was grown overnight in YPD in 96-well plates at 30°C . The next morning 0 . 5 μL of culture was added to YPD without and with 400 ng/mL of miconazole . Strains were incubated 30°C for 48 hours at which point the absorbance was determined at 600 nm using a spectrophotometer ( Molecular Devices ) and corrected for background from the corresponding medium . Strains identified as hypersensitive to miconazole ( growth defect >60% ) were verified for their azole sensitivity by performing minimum inhibitory concentration assays with the azole fluconazole . To identify regulators of C . albicans azole tolerance , a homozygous deletion mutant collection consisting of 1 , 152 mutant strains covering 674 genes or roughly ~11% of the C . albicans genome [34] was screened in the absence and presence of fluconazole . Each strain from the library was grown overnight in 200 μL of YPD in 96-well plates . The next morning , the cultures were diluted 1:100 by pinning 0 . 5 μL into 50 μL of YPD . The 1:100 dilution was then pinned ( 0 . 5 μL ) into 200 μL of YPD or 200 μL of YPD with 0 . 6 μg/mL of fluconazole . The plates were incubated at 30°C for 48 hours , at which point OD600 was measured using a spectrophotometer ( Molecular Devices ) and corrected for background from the corresponding medium . Growth for each strain in the presence of fluconazole was normalized to growth in YPD alone . In total , 103 strains showed growth inhibition in fluconazole ≥ 75% compared to the no drug control . For genes for which multiple mutants were generated , the drug susceptibility phenotype of both mutants was confirmed . These were all tested for fluconazole susceptibility using a standard dose response minimum inhibitory concentration assay . The 13 strains that validated and showed the most robust phenotype were selected for follow-up analysis . Antifungal susceptibility was measured in flat bottom , 96-well microtitre plates ( Sarstedt # 83 . 3924 ) using a broth microdilution protocol described in [79] . In brief , minimum MIC assays were set up in 2-fold serial dilutions of fluconazole ( Sequoia Research Products Ltd . ) , miconazole ( Sigma-Aldrich ) , FK506 ( A . G . Scientific , Inc . ) , geldanamycin ( A . G . Scientific , Inc . ) , terbinafine ( Sigma-Aldrich ) , SDS ( Bioshop ) , amphotericin B ( Sigma-Aldrich ) , or caspofungin ( generously provided by Terry Roemer from Merck Research Laboratories ) in a final volume of 200 μL per well . Where applicable , doxycycline ( BD Biosciences #631311 ) was added to a final concentration of 1 μg/mL . All drug stocks were prepared in DMSO except for fluconazole , caspofungin , and SDS which were prepared in sterile ddH2O . Cell densities of overnight cultures were determined and dilutions were prepared such that ~103 cells were inoculated into each well . Plates were incubated in the dark at 30°C for 24–48 hours as indicated , at which point the absorbance was determined at 600 nm using a spectrophotometer ( Molecular Devices ) and corrected for background from the corresponding medium . Growth was normalized to the no drug treatment well for the relevant strain , unless stated otherwise . Plotted are the average optical density values of technical duplicate measurements . Each strain was tested in duplicate in three biological replicates . MIC data was quantitatively displayed with color using the program Java TreeView 1 . 1 . 3 ( http://jtreeview . sourceforge . net ) . For growth kinetics assays , cultures were grown overnight in YPD , diluted to an OD600 of 0 . 1 in 100 μL of YPD without or with fluconazole in 96-well plates , and grown at 30°C with continuous shaking ( TECAN GENios ) . OD595 was measured every 15 minutes for 48 hours with XFluor4 software . All cultures were grown in technical triplicates . Growth curves were generated on at least two separate occasions . Data was plotted using GraphPad Prism . To assess the phosphorylation status of Mkc1 in response to fluconazole , overnight cultures of each strain were grown to saturation in YPD at 30°C . Cultures were then sub-cultured to an OD600 of 0 . 2 in 25 mL YPD , and allowed to grow for 3 hours at 30°C . Cultures were then split into two separate 10 mL cultures for treatment without or with fluconazole ( 8 μg/mL ) . Cultures were grown in the presence or absence of drug for 40 minutes at which point 1mL of cells was pelleted and washed in cold Tris buffered saline ( TBS ) . Pellets were then re-suspended in 300 μL 0 . 1N sodium hydroxide , pelleted , and the supernatant removed . The pellet was re-suspended in 60 μL 1X sample buffer containing 0 . 35M Tris-HCl , 10% ( w/w ) SDS , 36% glycerol , 5% β-mercaptoethanol , and 0 . 012% bromophenol blue for SDS-PAGE . Samples were boiled at 95°C for 5 minutes and then separated on 8% SDS-PAGE gel . Proteins were transferred from the polyacrylamide to a PVDF membrane through electrotransfer ( Bio-Rad laboratories Inc . ) . PVDF membranes were blocked with 5% BSA in TBS with 0 . 1% tween20 ( TBS-T ) . To detect the phosphorylated form of Mkc1 , an α-phospho-P44/42 MAPK ( Thr202/Tyr204 ) antibody ( Cell Signaling , 9102 ) , was diluted 1:1500 and hybridized to the blot in the presence of 5% BSA in TBS-T . Actin was detected using an α-Act1 antibody ( Santa Cruz Biotechnology , sc47778 ) at a 1:3333 dilution in 5% BSA in TBS-T . Overnight cultures of C . albicans grown in YPD at 30°C with shaking were diluted to an OD600 of 0 . 1 in a total volume of 10 mL YPD . Cells were grown for 2 hours , at which point fluconazole was added at a final concentration of 16 μg/mL . Cells were grown for an additional 2 hours . To monitor gene expression of evolved mutants , strains were subcultured to an OD600 of 0 . 1 and allowed to grow to mid-log phase . To prepare samples for RNA extraction , 10 mL of subculture was harvested by centrifugation at 1300g for 5 min . The pellet was washed with 2 mL cold ddH2O before being flash-frozen and stored at -80°C overnight . RNA was isolated using the QIAGEN RNeasy kit and cDNA was generated using the AffinityScript cDNA synthesis kit ( Stratagene ) . qRT-PCR was carried out using the Fast SYBR Green Master Mix ( Thermo Fisher Scientific ) in 384-well plates with the following cycle conditions: 95°C for 10 min , repeat 95°C for 10 sec , 60°C for 30 sec for 40 cycles . The melt curve was completed with the following cycle conditions: 95°C for 10 sec and 65°C for 5 sec with an increase of 0 . 5°C per cycle up to 95°C . All reactions were done in triplicate and are representative of biological duplicates . Data were analyzed in the Bio-Rad CFX manager 3 . 1 . Data was plotted and significance was assessed using GraphPad Prism . For vacuole membrane staining with FM 4–64 ( Thermo Fisher Scientific , # T3166 ) , cells were grown overnight in YPD medium 30°C with agitation . The next morning , cells were diluted to an OD600 of 0 . 2 and allowed to grow for 3 hours . Cells were incubated with 8 μM FM 4–64 for 30 minutes at 30°C . Cells were washed and continuously pulsed with FM 4–64 for an additional 60 minutes with 8 μM FM 4–64 . Cells were resuspended in SC media and visualized on a Zeiss Imager M1 upright microscope and AxioCam Mrm with AxioVision 4 . 7 software . For fluorescence microscopy , an X-cite series 120 light source with ET HQ tetramethylrhodamine isothiocyanate ( TRITC ) /DsRED filter set from Chroma Technology ( Bellows Falls , VT ) was used . For selection of azole resistance by a rapid , one-step regime , CaLC3601 ( CaCi-17 rgd1Δ/rgd1Δ ) was grown overnight in YPD at 30°C . To establish overnight cultures , four independent colonies were selected to establish four independent lineages . Cell counts were performed with a hemacytometer , and ∼2 x 109 cells were plated on SD agar or YPD agar supplemented with 18–20 μg/mL miconazole ( Sigma-Aldrich ) . Plates were left to incubate for 5 days at 30°C in the dark before individual colonies were selected for drug susceptibility and whole genome sequencing analyses . To identify mutants that had lost the chromosome 7 aneuploidy , a single colony of CaLC4349 ( CaCi-17 rgd1Δ/rgd1Δ R1 ) was grown overnight in YPD and cultures were plated for single colonies on YPD agar in the absence of drug . Plates were left to incubate for 3 days at 30°C in the dark before individual colonies were selected for whole genome sequencing analysis . Genomic DNA was isolated with phenol chloroform , as described previously [80] . Libraries were prepared using the NexteraXT DNA Sample Preparation Kit following the manufacturer’s instructions ( Illumina ) . Libraries were purified with AMPure XP beads ( Agencourt ) and library concentration was quantified using a Bioanalyzer High Sensitivity DNA Chip ( Agilent Technologies ) and a Qubit High Sensitivity dsDNA fluorometric quantification kit ( Life Technologies ) . DNA Libraries were sequenced using paired end 2x250 flow cells on an Illumina MiSeq ( Creighton University ) . Copy number variation was visualized using YMAP [81] . All genome sequencing information has been deposited into the NCBI BioProject Accession number: PRJNA323475 .
|
Fungal infections caused by the pathogen Candida albicans pose a serious threat to human health . Treating these infections relies heavily on the azole antifungals , however , resistance to these drugs develops readily demanding novel therapeutic strategies . We performed large-scale systematic screens in both C . albicans and the model yeast Saccharomyces cerevisiae to identify genes that enable azole resistance . Our genome-wide screen in S . cerevisiae identified nine determinants of azole resistance , only two of which were important for resistance in C . albicans . Our screen of C . albicans mutants identified 13 genes for which deletion enhances susceptibility to azoles , including RGD1 and PEP8 . We found that loss of Pep8 overwhelms the functional capacity of a key stress response regulator , calcineurin . In contrast , amplification of chromosome 7 and the right portion of chromosome 3 can restore resistance in strains lacking Rgd1 , suggesting that Rgd1 may enable azole resistance by inducing genes in these amplified regions . Specifically , overexpression of a gene involved in transport on chromosome 3 , NPR2 , was sufficient to restore azole resistance in the absence of Rgd1 . Thus , we establish novel circuitry important for antifungal drug resistance , and uncover adaptive mechanisms involving genomic plasticity that occur in response to drug induced stress .
|
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2018
|
Global analysis of genetic circuitry and adaptive mechanisms enabling resistance to the azole antifungal drugs
|
A small mobile protein , encoded by the FLOWERING LOCUS T ( FT ) locus , plays a central role in the control of flowering . FT is regulated positively by CONSTANS ( CO ) , the output of the photoperiod pathway , and negatively by FLC , which integrates the effects of prolonged cold exposure . Here , we reveal the mechanisms of regulation by the microRNA miR172 target SCHLAFMÜTZE ( SMZ ) , a potent repressor of flowering . Whole-genome mapping of SMZ binding sites demonstrates not only direct regulation of FT , but also of many other flowering time regulators acting both upstream and downstream of FT , indicating an important role of miR172 and its targets in fine tuning the flowering response . A role for the miR172/SMZ module as a rheostat in flowering time is further supported by SMZ binding to several other genes encoding miR172 targets . Finally , we show that the action of SMZ is completely dependent on another floral repressor , FLM , providing the first direct connection between two important classes of flowering time regulators , AP2- and MADS-domain proteins .
Throughout their lives , plants progress through distinct developmental phases , from germination and vegetative growth to flowering and , finally , senescence . The transition from vegetative growth to flowering is of particular importance because the correct timing of this switch is mandatory to ensure reproductive success . Plants have therefore evolved an elaborate genetic network that integrates endogenous and environmental signals to guarantee that flowering commences when conditions are most favorable . Genetic and molecular analyses in Arabidopsis thaliana and other plants have identified several distinct genetic pathways that are involved in regulating the floral transition [1] , [2] . On the basis of genetic interactions , one can distinguish between the gibberellic acid pathway , the autonomous pathway , and the vernalization pathway . Finally , light , and especially day length , is an important stimulus that is integrated into the flowering time regulatory network by the photoperiod pathway [3] , [4] . A . thaliana is a facultative long-day plant , which means that it will flower more rapidly when day length exceeds a critical minimum . Interestingly , plants measure photoperiod in the leaves and not at the shoot apex where the new flowers will be formed . It has therefore been long postulated that the light-exposed leaves produce a flower-forming substance to regulate the formation of flowers at the shoot apex [5] , [6] . This ultimately led to the formation of the “florigen” hypothesis , which postulated that a substance , “florigen , ” is produced in leaves under inductive photoperiod and is transported to the shoot apex to induce flowering [7] . It was later demonstrated that such a flower-inductive substance could be transmitted from one plant ( donor ) via grafting to another plant ( receptor ) that had been cultivated under noninductive conditions . An important factor that allows Arabidopsis to discriminate between short day ( SD ) and inductive long day ( LD ) is the B-box–type zinc finger protein CONSTANS ( CO ) [8] . The regulation of CO at both the mRNA and protein levels ensures that the protein will accumulate and activate flowering only under LD conditions [4] , [9] , [10] . Interestingly , CO appears to carry out its function in leaves , where it acts in the phloem companion cells to regulate a systemic signal that induces photoperiodic flowering [11] , [12] . Several lines of evidence suggest that the protein FLOWERING LOCUS T ( FT ) acts as a florigen to convey flowering time signals from the leaves to the apex [13] , [14] . First , it was established that FT is the major target of CO in leaves [15] , [16] . It was further demonstrated that the FT protein interacts at the shoot apex with another flowering time regulator , the bZIP transcription factor FD , to induce downstream flower-specific targets such as the MADS-domain proteins APETALA1 ( AP1 ) and FRUITFULL ( FUL ) [16] , [17] . The finding that FT is transcribed in leaves but acts at the apex implied that FT can move , either as mRNA or as protein . Later experiments were unable to detect FT mRNA movement but provided evidence that FT protein is able to reach the apex when expressed in the vasculature [18]–[23] . Interestingly , the induction of flowering under LD by CO/FT is counteracted by several factors that either prevent FT expression in the leaf or act downstream of FT to modulate its function at the shoot apex . In particular , MADS-domain transcription factors have been shown to act as repressors of flowering . The most prominent of these is FLOWERING LOCUS C ( FLC ) , which represses flowering in winter annual accessions of Arabidopsis before the plants have been exposed to a prolonged period of cold [24] . It has recently been shown that FLC , when expressed either from the phloem-specific SUC2 promoter or the meristem-specific KNAT1 promoter , efficiently represses flowering and that these effects are additive . Further , it was demonstrated that FLC directly binds to the regulatory regions of three positive regulators of flowering , FT , FD , and SUPRESSOR OF OVEREXPRESSION OF CONSTANS 1 ( SOC1 ) , presumably to repress these genes [25] . Two other MADS-domain transcription factors , FLM and SVP , have also been shown to repress flowering . In contrast to FLC , which is involved in the vernalization pathway , these two genes seem to be involved predominately in the photoperiod pathway , and FLM and SVP act as partners [26]–[28] . There is , however , also evidence that implicates SVP and FLM in temperature-dependent regulation of flowering in Arabidopsis , and SVP has recently been shown to interact with FLC in a repressor complex [29] . In addition , SVP has also been shown to directly bind to regulatory regions of FT and SOC1 [29] , [30] . More recently , two more transcription factors , TEMPRANILLO 1 ( TEM1 ) and TEM2 , have been shown to redundantly repress flowering [31] . In contrast to FLC , FLM and SVP , which are MADS-domain transcription factors , each TEM gene encodes an AP2 domain as well as a B3-type DNA binding domain . TEM1 is most strongly expressed in leaves , where its expression is regulated in a circadian fashion [31] . TEM1 was further shown to directly bind to the 5′ UTR of FT [31] . This is in contrast to FLC , which bound most strongly to the first intron of FT , indicating that FT is regulated by different repressors in different regions . Yet another family of six AP2-like transcription factors also act as repressors of flowering . This clade of proteins comprises APETALA 2 ( AP2 ) itself , the three TARGET OF EAT ( TOE ) proteins ( TOE1 , TOE2 , and TOE3 ) , and SCHLAFMÜTZE ( SMZ ) and its paralog SCHNARCHZAPFEN ( SNZ ) [32]–[34] . All six genes have in common that they are predicted targets of microRNA172 ( miR172 ) , expression of which is regulated by GIGANTEA ( GI ) to control flowering in a CO-independent manner [35] . It has previously been shown that TOE1 and TOE2 act as repressors of flowering: toe1 mutants are significantly early flowering , and this effect is enhanced in a toe1 toe2 double mutant [33] , [35] . However , plants that expressed miR172 constitutively were found to flower much earlier than even the toe1 toe2 double mutant , indicating that the other AP2 family members most likely act redundantly with TOE1 and TOE2 to repress flowering [33] , [36] . A good candidate for such a repressor is SMZ , which was originally identified in an activation-tagging screen because of its dominant late-flowering phenotype [34] . Additionally , SNZ , a paralog of SMZ , has been shown to repress flowering when expressed at high levels [34] . However , it was unclear whether SMZ and SNZ normally act as repressors of flowering . Here , we show that the miR172 targets SMZ and SNZ are bona fide floral repressors and act redundantly with TOE1 and TOE2 to delay flowering specifically under LD conditions . Plants expressing SMZ at high levels are late flowering , which is due to an almost complete block in FT induction . The effects of SMZ on FT expression appear to be direct , as chromatin immunoprecipitation coupled to hybridization to tiling arrays ( ChIP-chip ) identified FT as a target of SMZ . In addition , several other known regulators of flowering time were identified as SMZ targets . Among them are SMZ itself , SNZ , AP2 , and TOE3 , suggesting a complex feedback regulation among miR172 targets . Finally , we found that repression of flowering by SMZ is independent of the potent floral repressor FLC , but requires FLM for its function , providing a direct connection between two important classes of flowering time regulators , AP2- and MADS-domain proteins .
smz-D was originally isolated as a dominant late-flowering mutant in an activation-tagging screen under LD conditions . SMZ is expressed in young seedlings and is developmentally regulated , as deduced from microarray data ( Figure 1A ) and confirmed by a genomic SMZ∶GUS reporter ( Figure 1B–1D ) . Expression of SMZ declines with increasing age , but SMZ is induced again in seeds during maturation . In addition , analysis of publicly available microarray data ( “The diurnal project”; http://diurnal . cgrb . oregonstate . edu/ ) revealed that SMZ exhibits a diurnal expression with a maximum at Zeitgeber 15 under LD conditions [37] . To better understand where SMZ functions in respect to the known flowering time pathways , we first investigated the flowering time behavior in this mutant under different day lengths ( Figure 2A and Table 1 ) . We found that smz-D delays the onset of flowering specifically under inductive LD conditions , where it produced 45 . 1±1 . 7 leaves before flowering compared to wild-type ( 15 . 5±0 . 6 leaves ) . In contrast , under noninductive SD conditions , smz-D ( 79 . 1±2 . 2 leaves ) plants flowered similarly to wild-type ( 73 . 0±1 . 6 leaves ) , indicating that smz-D represses flowering specifically under inductive LD , but has little effect under SD conditions . To investigate whether SMZ is indeed functioning as a floral repressor , we isolated homozygous SMZ loss-of-function alleles from T-DNA insertion collections . Neither of the individual smz mutant lines displayed any obvious phenotypes; in particular , the total number of leaves did not significantly differ from that of wild-type plants ( Figure 2B and Table 1 ) . Most notably , the plants were not early flowering , as one would have expected to result from the loss of a putative floral repressor . Also , a double mutant lacking SMZ and its closest paralog SNZ was found to be indistinguishable from the wild type ( Figure 2B ) . Together with AP2 , TOE1 , TOE2 , and TOE3 , SMZ and SNZ form a clade of six AP2-domain transcription genes . Because functional redundancy has been observed within this clade in respect to the timing of floral induction of the toe1 toe2 double mutant , we first focused on the function of TOE1 , TOE2 , SMZ and SNZ rather than that of AP2 and TOE3 , which are predominately expressed at the meristem , and created a mutant line that lacks toe1 toe2 smz snz functions . This quadruple mutant was found to flower significantly earlier than Col-0 , toe1 , and even toe1 toe2 double-mutant plants ( Figure 2B and Table 1; p<0 . 001 in all comparisons ) . This result confirms that SMZ and its paralog SNZ are indeed acting as floral repressors redundantly with TOE1 and TOE2 . This effect is only apparent in the sensitized toe1 toe2 mutant background , as TOE1 normally masks the effects of smz and snz loss of function . The early flowering we observed in certain combinations of smz , snz , toe1 , and toe2 loss-of-function alleles was associated with a reduced number of rosette leaves , whereas the number of cauline leaves remained constant ( Figure S1 ) . It is interesting to note that even the toe1 toe2 smz snz quadruple mutant still flowers significantly later than plants that constitutively express miR172 , which have been reported to produce on average two to three rosette leaves before bolting [33] , [36] . This strongly suggests that the two remaining miR172 targets , AP2 and TOE3 , also act to repress flowering , which is especially interesting given that these two genes are predominately expressed at the meristem . We conclude from these results that SMZ and its homolog SNZ are bona fide floral repressors that , partly redundant with other members of the miR172 target family , act to delay flowering in A . thaliana under LD conditions . Two genes play key roles in the photoperiod pathway: CO , which constitutes the main readout of the circadian clock , and FT , which has been shown to be an important part of the mobile signal that conveys the information to induce flowering from the leaves to the apex [18]–[23] . To test the genetic position of SMZ in relation to these two factors , we introduced smz-D into established plant lines that expressed CO or FT under the control of the constitutive 35S promoter ( Figure 2C ) . Lines expressing either of these two genes at a high level are extremely early flowering . We observed a substantial delay in flowering in smz-D 35S::CO plants ( 11 . 3±0 . 4 leaves ) compared to the CO overexpressing line ( 5 . 4±0 . 3 leaves ) ( Figure 2C and Table 1 ) . In contrast , smz-D had a much smaller effect on the flowering of plants expressing FT at high levels ( 6 . 3±0 . 3 leaves; compared to 4 . 9±0 . 2 leaves observed in 35S::FT ) . These findings are compatible with the idea that SMZ acts as a repressor of flowering and counteracts the flower-promoting activity of CO . Next , we tested the dependence of SMZ on the presence of functional FLC , as FLC is a well-described repressor of flowering , integrating environmental signals such as vernalization and ambient temperature [24] . FLC has been shown to directly bind to regulatory sequences of the FT gene as well as to the promoters of SUPRESSOR OF OVEREXPRESSION OF CONSTANS 1 ( SOC1 = AGL20 ) and FD [29] . We therefore tested whether smz-D acts through FLC to repress flowering . When we introduced smz-D into the strong flc-3 deletion mutant background , which lacks part of the 5′UTR and the first exon , and is a genetic null allele of FLC , we observed no difference in flowering time in smzD flc-3 ( 43 . 8±1 . 9 leaves ) when compared to smz-D ( 45 . 6±1 . 9 leaves ) , showing that smz-D represses flowering independently of FLC ( Figure 2D and Table 1 ) . As mentioned above , SMZ and SNZ share a miR172 target site . To test whether the mRNAs of these two genes are indeed targeted for degradation and are cleaved at the predicted positions , we carried out RACE-PCR to map the 5′ end of miR172 cleavage products . We found that the SMZ mRNA was cleaved at the predicted site in all clones analyzed ( n = 12; Figure S2 ) . Similarly , correct cleavage of SNZ mRNA was observed in 12/13 ( Figure S2 ) cases , confirming that both SMZ and SNZ are indeed miR172 targets . To further investigate whether the regulation of SMZ by miRNA172 plays a role in controlling the floral transition , we introduced into plants a version of SMZ mRNA ( rSMZ ) that carried silent mutations in the miR172 complementary site , rendering the mRNA resistant to miR172-directed cleavage ( Figure S2 ) . Strong expression of rSMZ from the constitutive 35S promoter caused plants to remain vegetative throughout their life ( Figure 3C and 3G , and Table 2 ) . In addition , the leaves of these plants displayed a crinkled phenotype and remained smaller than those of either wild-type controls or plants transformed with the SMZ ORF . The failure of 35S::rSMZ plants to initiate flowering suggests that miR172-directed cleavage of SMZ mRNA in smz-D and 35S::SMZ plants limits the effects of overexpressing the native version of SMZ . Besides FLC , two other MADS-domain proteins , SHORT VEGETATIVE PHASE ( SVP ) and FLOWERING LOCUS M ( FLM = MAF1 ) , have been shown to function as floral repressors [26] , [27] , [30] . On the basis of genetic analysis of mutant alleles , it has been suggested that FLM and SVP act as coregulated partners in the same pathway [28] . To test whether either of these two genes is required for SMZ function , 35S::SMZ and 35S::rSMZ constructs were transformed into established svp and flm T-DNA mutant lines . Loss of SVP did not affect the SMZ overexpression phenotype , i . e . , svp plants carrying either the 35S::SMZ or the 35S::rSMZ transgene flowered just as late as control plants ( Figure 3G and Table 2 ) . In contrast , the late flowering , which usually would result from SMZ overexpression , was completely abolished in the flm mutant background ( Figure 3E and 3G , and Table 2 ) . Even constitutive expression of the miR172-resistant form of SMZ ( rSMZ ) was no longer able to delay flowering ( Figure 3F and 3G , and Table 2 ) in flm mutants . This is in contrast to the extreme effect of ( r ) SMZ on flowering in wild-type control transformations ( Figure 3B , 3C , and 3G , and Table 2 ) . Interestingly , we also observed that expression of rSMZ in flm resulted in reduced growth and crinkly leaves similar to what we had observed in wild-type control plants transformed with 35S::rSMZ ( Figure 3C ) , despite the fact that the plants now flowered with a normal number of leaves . In addition , apical dominance was reduced in these lines , giving the plants a bushy appearance . The fact that expression of rSMZ causes phenotypes even though no flowering time defects were observed rules out that the transgene was silenced in the flm background . In addition , levels of transgene expression were found to be comparable in Col-0 and flm background when analyzed by quantitative reverse transcription PCR ( qRT-PCR ) ( unpublished data ) . Because high levels of FLM have been shown to delay flowering , we therefore examined the expression of FLM in smz-D by qRT-PCR , but did not find any evidence that FLM levels were increased ( unpublished data ) . Therefore , it does not appear that SMZ is simply up-regulating FLM transcription . Our previous results demonstrated that SMZ acts as a floral repressor in the photoperiod pathway . A characteristic of this pathway is the spatial separation of the perception of inductive photoperiod in the leaves and the formation of flowers at the shoot apical meristem . Recently , the FT protein has been shown to be an important component of the signal that transmits flowering time information from the leaves to the apex [18]–[23] . In the course of these studies , it was shown that for FT to exert its function in the photoperiod pathway , it is both necessary and sufficient for this gene to be expressed in leaf phloem companion cells [19] , [20] , [22] . Therefore , if the function of SMZ in flowering time is indeed to negatively regulate FT expression , misexpression of SMZ in phloem companion cells and the resulting repression of FT in this tissue should be sufficient to recapitulate the late-flowering phenotype observed in smz-D . As expected , expression of SMZ from the phloem companion cell-specific SUC2 promoter efficiently delayed flowering and resulted in late-flowering plants that were phenotypically indistinguishable from 35S::SMZ plants ( Figure 4A and 4B , and Table 2 ) . Similarly , rSMZ driven from the phloem-specific promoter SUC2 did not cause plants to flower later than the miR172-susceptible SMZ ORF driven from the same promoter ( Figure 4A and 4B , and Table 2 ) . Furthermore , SUC2::rSMZ plants did not display any of the additional defects in leaf or shoot morphology that were evident in 35S::rSMZ ( Figure 4A ) , indicating that these phenotypes were caused by misexpression of rSMZ in tissues other than the vasculature . An alternative explanation could be that miR172 is normally not expressed in phloem companion cells , in which case , SMZ and rSMZ overexpression would have similar effects . This seems unlikely , however , as miR172 has been cloned from phloem exudates in Brassica [38] . In contrast , expression of SMZ from the shoot meristem-specific FD promoter had only the most modest effect on bolting time ( Figure 4A and 4B , and Table 2 ) . Even in FD::rSMZ plants , the number of rosette leaves ( 14 . 2±1 . 5 ) was similar to that in controls , indicating that high levels of SMZ at the shoot apex are not sufficient to delay the onset of flowering . The number of cauline leaves , however , was vastly increased in FD::rSMZ plants ( 13 . 8±6 . 3 ) compared to wild type ( 2 . 9±0 . 2 ) ( Figure 4B and Table 2 ) . Additionally , these plants displayed a shoot phenotype reminiscent of a double mutant lacking both the meristem identity gene LEAFY ( LFY ) and AP1 , in that flowers were replaced by leaf-like organs , which were frequently subtended by bracts ( Figure 4A ) . Taken together , these results suggest that SMZ can affect different sets of target genes in leaves and at the shoot meristem . An alternative explanation would be that the FD promoter becomes active too late in development to delay flowering but in time to repress flower development , causing this shoot phenotype . As we showed earlier , genetic analyses clearly place SMZ in the photoperiod pathway and tissue-specific misexpression of SMZ suggests that regulation of flowering time by SMZ occurs predominantly in the leaves . To analyze the molecular cause for the late flowering of SMZ overexpressing plants , we carried out quantitative RT-PCR on the putative target gene FT , which is normally induced in leaves under LD . As expected , FT mRNA was not detectable in noninductive SD conditions in flc-3 mutants , which served as a background for this experiment , irrespective of the presence or absence of smz-D ( Figure 5A ) . FT transcription was rapidly and strongly induced in flc-3 1 d after plants were shifted to inductive LD conditions ( Figure 5A ) . Levels of FT mRNA increased even further after exposure to four consecutive LD . In contrast , smz-D flc-3 plants completely failed to induce FT , even after 4 LD . These results indicate that the late flowering observed in smz-D is largely caused by the inability of smz-D plants to induce FT even under inductive LD . Furthermore , the ability of SMZ to repress FT did not depend on the presence of a functional FLC allele , as already suggested by genetic analyses ( Figure 2D ) . Genetic analyses had demonstrated that , in contrast to FLC , FLM is strictly required for SMZ to repress flowering . We therefore tested FT expression in a flm mutant and compared it to that in a flm 35S::SMZ line ( Figure 5B ) . As expected , FT was readily induced in flm after 3 d of inductive LD . FT levels were actually higher in flm than in Col-0 wild-type control plants , suggesting that FLM is normally involved in FT repression . In 35S::SMZ plants , however , FT induction was strongly attenuated , and FT levels reached only 8% of those observed in Col-0 . In contrast , FT was strongly expressed in a 35S::SMZ flm line , indicating that SMZ requires functional FLM in order to suppress FT induction . This is in perfect agreement with our genetic analyses , which had shown that a mutation in FLM completely suppresses the late flowering of SMZ overexpression . To test whether SMZ normally represses FT , we analyzed its expression in the toe1 toe2 smz snz quadruple mutant . We found that FT is expressed at high levels in this mutant background when compared to wild-type control plants throughout the first 2 wk of development ( Figure 5C ) . This supports the idea that SMZ , together with the other AP2 family members , represses flowering by regulating FT expression . To determine the effects of SMZ overexpression on the transcriptome in greater detail , we performed a microarray analysis in leaves and at the shoot meristem of flc-3 and smz-D flc-3 plants . SMZ was significantly ( RankProducts , percentage false positives [pfp]<0 . 01 ) up-regulated in smz-D plants at all time points in both tissues investigated ( Figure 6A ) . In contrast , expression of GIGANTEA ( GI ) and CO , which both act upstream of FT in the photoperiod pathway , remained unchanged in smz-D plants ( Figure 6A ) . Furthermore , the diurnal expression normally observed of CO and GI was unaltered ( Figure S3 ) , indicating that SMZ is not regulating flowering by modulating expression of these genes . Analysis of FT expression by qRT-PCR had revealed that FT expression is strongly attenuated in smz-D ( Figure 5A ) . In agreement with this , we found that FT was significantly ( pfp<0 . 01 ) induced in flc-3 leaves 1 and 3 d after plants were transferred to inductive LD but that FT induction in leaves was completely blocked by smz-D ( Figure 6B ) . FT mRNA was not detectable at the shoot meristem at any time point in all samples . Interestingly , TWIN SISTER OF FT ( TSF ) followed the expression of FT in that it was substantially up-regulated in leaves of flc-3 plants , but was not induced at any other time point in any tissues investigated ( Figure 6B ) . This supports the idea that FT and TSF act partially redundantly in promoting the floral transition . Statistical analysis revealed that there was only one other transcript besides FT that was significantly ( pfp<0 . 01 ) induced in leaves of flc-3 plants in response to inductive photoperiod 1 and 3 d after shift to LD that was not also up-regulated in smz-D . This gene encodes a β-amylase ( BMY1; At4g15210 ) and has not previously been implicated in the regulation of flowering . Taken together , these results indicate that FT , and to a lesser extent TSF , are major targets of SMZ in leaves . Such a repression of FT could either be due to a direct effect of SMZ on the FT locus or through the activation of other floral repressors . To investigate the possibility that SMZ is acting indirectly on FT by activating transcription of another floral repressor , we examined our smz-D microarray data . Many of the known repressors of flowering encode MADS-domain transcription factors , but neither FLC , FLM , nor SVP RNA was induced in smz-D flc-3 , when compared to flc-3 ( Figure 6C ) . This is of particular importance , as both FLC and SVP have been shown to bind to the FT locus . These results indicate that the delay in flowering observed in smz-D plants is not simply caused by FLC and/or SVP activation . It is , however , possible that SMZ activates or stabilizes FLC and/or SVP protein by an unknown mechanism . Recently , two AP2-domain transcription factors , TEM1 and TEM2 , have also been shown to regulate FT . In fact , direct binding of TEM1 to a regulatory region of FT has been demonstrated [31] . Although TEM1 expression is unaltered in both the leaves and at the shoot meristem of smz-D plants , TEM2 was found to be significantly ( pfp<0 . 01 ) up-regulated in leaves and shoot meristem samples of smz-D at all time points ( Figure 6C ) . This suggests that at least part of the effect of SMZ on flowering may be mediated by TEM2 . Interestingly , expression of SNZ , the closest paralog of SMZ , is significantly ( pfp<0 . 05 ) down-regulated in leaves of smz-D flc-3 1 and 4 d after plants were shifted to LD ( Figure 6C ) . Indeed , even before the shift ( day 0 ) , a substantial repression of SNZ can be observed . Similarly , levels of AP2 , TOE1 , and TOE3 mRNA are reduced in SMZ overexpressing lines , although to a lesser extent , suggesting widespread feedback regulation among the miR172 target genes ( Figure S4 ) . Positive regulators of flowering such as FUL , LFY , CAL , and SOC1 were all induced significantly ( pfp<0 . 05 ) at the apex of flc-3 plants after the shift to LD ( Figure 6D ) . Similarly , FD , which physically and genetically interacts with FT , was substantially ( but not significantly ) induced at the meristem ( Figure 6D ) . Neither of these genes was , however , induced in smz-D flc-3 , indicating that high levels of SMZ are sufficient to completely block the transition to flowering at the shoot apex . In agreement with this finding , homeotic genes such as AP1 , PI , and AG are also not induced in smz-D flc-3 , but are readily detectable at the meristem of flc-3 ( Figure 6E ) . To determine whether SMZ acts as a regulator of transcription , we established lines that express SMZ in fusion with an N-terminal GFP tag and drove them in the leaves by the 35S promoter . To sustain SMZ function , it was necessary to place a flexible linker consisting of ten Gly-Ser pairs between the GFP and SMZ . Among the T1 lines , several late-flowering individuals were recovered . Late flowering in these lines was confirmed in the T2 generation ( Figure 7 ) , indicating that functional GFP∶SMZ protein persisted in these lines at high levels . As expected for a putative transcription factor , the GFP signal was predominately nuclear localized in GFP∶SMZ plants even though signal intensity was rather low when compared to control plants expressing a nuclear-localized 3xVENUS YFP ( Figure S5 ) . The nuclear localization of the GFP fusion protein enabled us to perform chromatin immunoprecipitation on whole-genome tiling arrays ( ChIP-chip ) in order to identify regions in the Arabidopsis genome bound by SMZ . In total , 434 regions in the genome exhibited statistically significant enrichment to GFP∶SMZ at a false discovery rate ( FDR ) of 5% or less when compared to a line expressing a nuclear-localized YFP ( Dataset S5 ) . Of these 434 peak regions , only 33 were not directly associated with genes ( ±2 . 5 kb of the coding sequence [CDS] ) , whereas the great majority ( 401 peaks ) fell within 2 . 5 kb of annotated genes . The latter were associated with 395 unique Arabidopsis gene models with six annotated loci having two peaks of significant binding . We observed several interesting enrichments for particular gene ontology ( GO ) categories among the 307 of 395 genes for which assignments exist . Indeed , genes associated with flower development ( GO:0009908 ) were significantly overrepresented at a FDR p<0 . 0005 among the list of potential SMZ target genes , demonstrating a functional specificity to binding and a nonrandom distribution of the peaks identified by ChIP-chip . The second biological process found to be overrepresented at a FDR p<0 . 0005 comprises genes involved in “response to stimuli” ( GO:0050896 ) , in particular to water ( GO:0009415 , GO:0009414 ) and jasmonic acid ( GO:0009753 ) . Among the genes bound by SMZ were many known regulators of flowering , suggesting that the effects of SMZ on flowering time are rather direct . Most notably , the second most strongly enriched locus in the entire genome analysis was located less than 2 kb upstream of the transcription start site of the miR172 target gene TOE3 ( Figure 8C ) . Binding at the TOE3 locus was highly statistically significant ( FDR<0 . 000001 ) . Closer inspection of the list of high-confidence SMZ targets ( FDR<5% ) revealed that three other miR172 targets were also significantly bound . Among them were SMZ itself , its paralog SNZ , and AP2 ( Figure 8A , 8B , and 8D ) . In fact , SMZ was one of only six loci genome-wide that was the closest locus to two peaks of high-confidence binding . No high-confidence binding by SMZ was detected to the last two members of the clade of miR172 targets , TOE1 and TOE2 . The finding that four out of six miR172 targets were significantly bound by SMZ is more than expected by chance ( Fisher exact test; p<0 . 0001 ) . It should be noted that the expression levels of SNZ , AP2 , and TOE3 were reduced in leaves of smz-D plants as measured by microarrays ( Figure 6C and Figure S4 ) . Taken together , these results strongly suggest a complex negative regulatory feedback mechanism among the miR172 targets . In addition , two other known repressors of flowering , TEM1 and FRI , were also bound by SMZ ( Figure 8E and 8F ) . Most interestingly , FT was also among the genes that showed significant binding by SMZ approximately 1 . 5 kb downstream of the FT CDS ( Figure 8G ) . Assuming that SMZ acts as a transcriptional repressor , the binding of SMZ to the FT locus readily explains the failure of smz-D and 35S::SMZ plants to induce FT ( Figure 5A and 5B ) and the high levels of FT expression in the toe1 toe2 smz snz quadruple mutant ( Figure 5C ) . The regulatory landscape around the FT locus appears to be rather complex , with TEM1 binding to the 5′UTR , FLC binding to the first intron , and finally , SMZ binding downstream of the coding region [25] , [29] , [31] . Further , the effect of high SMZ levels on flower development is most likely not only due to a repression of FT , but also to SMZ repressing other flowering time regulators as well . This idea is supported by the finding that , besides FT , the floral integrator and flower development genes SOC1 and AP1 were also identified by ChIP-chip as high-confidence ( FDR<5% ) targets of SMZ ( Figure 8H and 8I ) . In each of these cases , binding occurred directly upstream of the transcription start site , suggesting strongly that expression of these genes is under direct negative regulation by SMZ . Similar to what we had observed for FT , smz-D plants did not induce SOC1 or AP1 either in leaves or at the shoot meristem when shifted from SD to inductive LD ( Figure 6D and 6E ) . Enrichment of loci identified by ChIP-chip was confirmed by quantitative PCR for all genes discussed ( Figure 8J ) . To test whether SMZ and its paralogs affect expression of the genes bound by SMZ , we analyzed their expression by quantitative PCR in the toe1 toe2 smz snz quadruple mutant . As already described for FT ( Figure 5C ) , AP1 and SOC1 were strongly induced in the quadruple mutant ( Figure S6 ) . So were LFY and FUL ( Figure S6 ) , which are not or only weakly bound by SMZ . The latter most likely is due to indirect activation of these genes . The expression of AP2 and TOE3 was also increased . This is in agreement with the proposed negative feedback regulation among the miR172 target genes . In contrast , FRI was only marginally up-regulated ( Figure S6 ) . However , the FRI allele of the Col-0 accession , is recessive and essentially nonfunctional , making it hard to interpret this result [39] . Our results strongly support the notion that SMZ , and by extension the other miR172 targets as well , act as direct repressors of the transition to flowering . Consistent evidence from both gene expression and ChIP-chip experiments suggests that SMZ directly represses the transcription of a range of known flowering time genes and that the delay in flowering time caused by high levels of SMZ is most likely a result of repression of a number of flowering time regulators in both the leaves and the shoot meristem .
To ensure reproductive success , plants have evolved a complex regulatory network that integrates various endogenous and environmental factors to ensure that flowering occurs when conditions are most favorable . Many of the key regulators that control flowering time have been identified and the majority of them are putative transcription factors . Extensive epigenetic regulation of several key regulators of flowering complicates the situation even further [40] . Based on genetic analyses , pathways that control the transition to flowering have been defined , but the details of how this transcription factor network functions at a molecular level is poorly understood . Here , we have combined genetic analysis with high-throughput microarray technologies to understand in detail how the AP2-like transcription factor SMZ represses flowering . A model summarizing our findings regarding the genetic interactions of SMZ and its position in the network regulating flowering in response to photoperiod is represented in Figure 9 . SMZ is predominately expressed in young leaves , suggesting that this is the tissue where it normally functions [35] , [41] . Leaves play a crucial role in the perception of day length , and it has recently been demonstrated that the information to induce flowering can be conveyed from the leaves to the shoot apex via transport of the FT protein [18]–[23] . The regulation of FT expression is therefore of the utmost importance for a plant to ensure the correct timing of flowering . Plants achieve this control by a combined effect of activators of FT expression , such as CO , and repressors , such as FLC , FLM , SVP , and the TEM proteins , some of which have been shown to directly bind to regulatory regions of the FT locus [25] , [29] , [31] . Expression of SMZ from a leaf-specific promoter recapitulated the late-flowering phenotype of constitutive SMZ overexpression , indicating that presence of SMZ in the vasculature was sufficient to repress flowering . Molecular analyses indicate that SMZ directly contributes to the regulation of FT in leaves . The evidence for this is 2-fold: first , plants expressing SMZ at high levels fail to induce FT in response to LD , and second , SMZ binds directly to the FT locus . Taken together , these results strongly indicate that SMZ acts as a floral repressor and that FT is a major transcriptional target of SMZ in leaves . Whether FT constitutes the sole mobile signal that conveys the instruction to flower from leaves to the apex is still an open question . Several other classes of molecules have been implicated as long-distance flowering signals in various plant species [42] , [43] . Carbohydrates in general , and sucrose in particular , have been suggested to play a role in the induction of flowering in Arabidopsis [44] , [45] . The mechanism by which sugars affect flowering is not entirely clear , but the finding that SMZ binds to and represses BMY1 , which encodes a cytosolic β-amylase , may provide new insights into this issue . Misexpression of miR172-resistant SMZ from a meristem-specific promoter had a marked effect on flower development , and FD::rSMZ plants phenotypically resembled ft lfy , fd lfy , or lfy ap1 double mutants [16] , [17] , [46] . Although we did not detect high-confidence binding of SMZ to LFY , strong , significant binding was observed to SOC1 and AP1 , so it is possible that the reduced abundance of these factors at the meristem may be at least partly responsible for the observed phenotype . Along these lines , it has recently been reported that a soc1 ful double mutant reverts to a vegetative state after flowering had been induced , resulting in a perennial growth habit of the double mutant and demonstrating the importance of these genes in robustly inducing flowering [47] . Furthermore , SMZ does not act alone in repressing flowering but instead redundantly with related proteins , all of which are miR172 target genes . It has previously been shown that miR172 overexpression did not dramatically alter the mRNA levels of its targets . This has been interpreted as evidence for translational repression being more important than mRNA cleavage [36] . Later , however , it was shown that at least one of these genes , AP2 , can repress its own transcription , demonstrating that a negative feedback very much confounds this conclusion [48] . It was , however , not clear whether this repression was direct or indirect . Also , it was unclear just how widespread this negative feedback regulation among the miR172 targets really was . Our genome-wide ChIP binding and gene expression studies indicate that SMZ is not only binding to its own genomic region , but regulates at least three other family members as well , demonstrating that the negative feedback is direct and common among the miR172 targets . The function of SMZ appears to strictly depend on the presence of the MADS-domain transcription factor FLM . The mechanistic details of this interaction remain unknown , but one can imagine several possible scenarios . SMZ might directly interact with FLM protein to form a repressor complex . However , at least when tested in yeast , we did not find any indication for direct interaction between SMZ and FLM ( unpublished data ) . An alternative would be that FLM needs to be present at target loci in order to facilitate either SMZ binding or activity , but without physical interaction between the two . The dependence of SMZ on FLM seems to be rather specific , as inactivation of the MADS repressors FLC nor SVP , both of which have been shown to directly repress FT , does not prevent SMZ function . It has been suggested that FLM and SVP genetically act as partners in repressing flowering time [28] . However , at least in the case of repressing SMZ activity , FLM and SVP functions are clearly separate and not interchangeable . In addition to the genes discussed so far , TEM1 , a known repressor of flowering , was also bound by SMZ ( Figure 8E ) . How binding of TEM1 by SMZ could possibly regulate flowering is currently unclear . It should be noted that the expression levels of TEM1 were not changed in leaves and at the meristem by smz-D ( Figure 6C ) . In contrast , the expression levels of TEM2 , the closest paralog of TEM1 , were significantly up-regulated in leaves and meristem samples ( Figure 6C ) . One may hypothesize that , similar to what we have observed among the miR172 targets , control of TEM1 and TEM2 expression also involves a regulatory feedback mechanism . Regardless of the precise mechanisms that control TEM expression , the increased TEM2 levels in smz-D very likely contribute to the repression of FT . Finally , FRIGIDA ( FRI ) was also identified among the genes most strongly bound by SMZ ( Figure 8F ) . FRI is a potent activator of FLC , and together these two genes are to a large extent responsible for the winter-annual behavior of certain Arabidopsis accessions [24] , [39] , [49] . Col-0 carries a recessive FRI allele , and it is therefore unlikely that the binding of SMZ to the FRI promoter is responsible for the delay in flowering we observe in smz-D . However , SMZ and the other miR172 targets could very well contribute to the control of FRI expression in late-flowering accessions that carry a functional FRI allele . This would provide Arabidopsis with a way to regulate FLC levels by modulating FRI expression . To test such a scenario , one would need to analyze the effect of gain- and loss of function of miR172 targets on flowering time and especially FLC expression levels in a FRI dominant background . Our results indicate that the miR172/SMZ module functions as a rheostat in flowering time by SMZ binding to several genes encoding miR172 targets and other flowering time regulators . The importance of this regulatory module is highlighted by the finding that overexpression of miR172 strongly accelerates flowering [33] , [36] , whereas constitutive expression of SMZ ( or AP2 , TOE1 , or TOE2 ) has the opposite effect [33] , [34] , [36] . In nature , to tightly control flowering time , Arabidopsis must achieve a careful balance between miR172 and its targets . Negative feedback of SMZ onto the other miR172 targets likely contributes to this regulation . miR172 and its targets are not specific to Arabidopsis , but are conserved in other dicotyledonous as well as monocotyledonous plant species , suggesting that these genes play an important role in plant development in general [50] . In maize , for example , miR172 promotes vegetative phase change and onset of reproductive development [51] , indicating that the function of the miR172/AP2 module is largely conserved . In addition , miR172 and its target indeterminate spikelet1 ( ids1 ) have been shown to participate in sex determination and meristem cell fate in maize [52] . Thus , our findings about the regulatory module consisting of AP2-like transcription factors and their microRNA will likely be relevant to many other plants . In summary , we provide evidence for a complex regulatory feedback mechanism among the miR172 target genes that directly controls the expression of FT . In addition , we show that several other known flowering time regulators such as SOC1 and AP1 are also directly targeted and repressed by SMZ . The intricate regulatory interactions we uncovered by just looking at just one single factor , SMZ , demonstrate how complex regulation of flowering time at the molecular level is . To fully understand this complex trait , a concerted effort of the flowering time community will be required to systematically study the genes and proteins involved in floral transition on a genome-wide scale .
Wild-type plants were of the Columbia ( Col-0 ) accession . All T-DNA insertion mutants used in this work are in Col-0 accession [53] , [54] . flc-3 , flm-3 , svp-31 , toe1-2 , and toe2-1 have been described before [24] , [30] , [33] , [55] . Two T-DNA insertion lines for SMZ ( smz-1 and smz-2 ) and one SNZ loss-of-function allele ( snz-1 ) were isolated as part of this work ( Table S1 ) . smz-2 was used for genetic analysis throughout this work . Mutant plants were confirmed by PCR-based genotyping . All plants were grown in growth chambers in a controlled environment ( 23°C , 65% relative humidity ) . Plants were raised on soil under a mixture of Cool White and Gro-Lux Wide Spectrum fluorescent lights , with a fluence rate of 125 to 175 µmol m−2 s−1 . All light bulbs were of the same age . Long day ( LD ) is defined as 16 h light , 8 h dark , and short days ( SD ) as 8 h light , 16 h dark . For flowering time measurements , plants were randomized with the respective controls , and the flowering time phenotype was determined without prior knowledge of the genotype . All flowering time assays were performed at least twice . ORFs were amplified using the Pfu DNA polymerase ( New England Biolabs ) , cloned into Gateway entry vectors using T4 DNA ligase and subsequently recombined into Gateway-compatible binary vectors suitable for plant transformation . Constructs for constitutive and tissue-specific expression of SMZ were obtained by amplification of the SMZ ORF using oligonucleotide primers G-3323 and G-5638 , cloning the PCR product into pJLSmart , resulting in pJM9 , and recombination into Gateway-compatible plant binary vectors , providing promoters for expression in plants , generating pJM34 ( 35S::SMZ ) , pJM66 ( SUC2::SMZ ) , and pJM50 ( FD::SMZ ) . To generate the miRNA172-resistant form of SMZ ( rSMZ ) , synonymous mutations were introduced into the miR172 binding site by site-directed mutagenesis using oligonucleotide primers G-2050 and G-2051 , resulting in pFK37 . Cloning of rSMZ into Gateway-compatible entry and destination vectors was as described above , resulting in pJM36 , pJM68 , and pJM52 ( for 35S::rSMZ , SUC2::rSMZ , and FD::rSMZ , respectively ) . To separate SMZ from the GFP tag , a Gly-Ser linker was added to the N-terminus of the SMZ ORF in a two-step PCR . In a first PCR , the SMZ ORF was amplified using primers G-16615 , which replaced the start codon of SMZ with 30 bases encoding for five Gly-Ser pairs , and G-16616 . In a second step , the Gly-Ser linker was extended to its final length of 60 bases , encoding for ten Gly-Ser pairs , using G-18665 and G-16616 . The resulting PCR product was cloned into the SmaI site of the pJLSmart Gateway-compatible entry vector by blunt end ligation ( pFK478 ) . The GS10∶SMZ ORF was subsequently recombined into a pGREEN-IIS based Gateway-compatible destination vector ( pFK247 ) , which provided 35S promoter for expression in plants and an in-frame fusion with an N-terminal eGFP , resulting in pFK480 . For the genomic SMZ∶GUS reporter , a 10 . 6-kb EcoRV-SacI , including the whole SMZ 5′ and 3′ regions , was cut from BAC T15C9 and cloned into the pGREEN-IIS plant binary vector . The GUS ORF , including stop codon , was amplified by PCR using primers G-8237 and G-8238 , introducing AgeI sites in the process . The GUS ORF was cloned in frame with the SMZ start codon in an AgeI site present in the first exon of SMZ . All sequences amplified by PCR were confirmed by sequencing . All enzymes used were purchased from Fermentas unless otherwise indicated . Complete sequences of constructs used are available on request . For sequences of the primers used to amplify ORFs , see Table S1 . For plant transformation , constructs were transformed into Agrobacterium tumefaciens strain ASE by electroporation . Arabidopsis plants of the Col-0 accession were transformed by the floral-dip procedure [56] . Transgenic plants were selected with 0 . 1% glufosinate ( BASTA ) on soil or 50 µg/ml kanamycin on plates . At least 20 T1 plants were analyzed for each construct . Total RNA was extracted from plant tissue using either the Plant RNeasy kit ( Qiagen ) or Trizol reagent ( Invitrogen ) according to the manufacturer's instructions . 2 µg of total RNA was DNase I-treated and single-stranded cDNA was synthesized using oligo ( dT ) and the RevertAid First Strand cDNA Synthesis Kit ( Fermentas ) . Quantitative real-time PCR was performed on an Opticon Continuous Fluorescence Detection System ( MJR ) using the Platinum SYBR Green qPCR Supermix-UDG ( Invitrogen ) . Gene expression was calculated relative to β-Tubulin using the ΔΔCT method . Results are reported for triplicate measurements of one of several biological replicates . For each genotype and replicate , a minimum of 10 seedlings was pooled for RNA extraction . Oligonucleotide primers used for qRT PCR are listed in Table S1 . For the analysis of the leaf transcriptome in Arabidopsis flc-3 and smz-D flc-3 , plants were grown under SD conditions for 14 days and shifted to LD to induce flowering synchronously . Rosette leaves one to three from 10 plants were collected zero , 1 and 4 d after the plants were shifted to LD in duplicate , and total RNA was extracted using Qiagen Plant RNeasy columns ( Qiagen ) . Biotinylated antisense RNA was prepared from 1 µg of total RNA using the MessageAmp II-Biotin Enhanced Kit ( Ambion ) according to the manufacturer's instructions . A total of 13 . 5 µg of fragmented amplified RNA ( aRNA ) was hybridized to an Arabidopsis ATH1-121501 gene expression array ( Affymetrix ) . Arrays were washed and stained on a GeneChip Fluidics Station 450 ( Affymetrix ) and scanned on an Affymetrix GeneChip scanner GS300 7G . Analysis of the shoot meristem transcriptome was carried out as described above except that plants were grown for 25 days under SD before transfer to LD . RNA from shoot apices was isolated as described [34] . For visualization , normalized expression estimates were obtained by directly importing . CEL files into GeneSpring 10 using gcRMA ( Agilent Technologies ) and baseline transformation as a normalization routine . All microarray data are freely available from the ArrayExpress database ( http://www . ebi . ac . uk/arrayexpress; accession numbers: E-MEXP-2040 ( leaf samples ) and E-MEXP-2041 ( apices ) ) . Lists of statistically significantly expressed genes ( Datasets S1 , S2 , S3 , and S4 ) were calculated for pairwise comparisons between time points within a given genotype or between genotypes at a given time point using RankProducts ( version 2 . 6 . 0 ) implemented in R ( version 2 . 4 . 0; GUI 1 . 17 ) on gcRMA ( version 2 . 6 . 0 ) normalized expression estimates [57] , [58] . The entire ChIP-chip experiment from sonication through array analysis was performed on technical duplicate samples from both 35S::NLS-3xVenusGFP and 35S::GFP-SMZ seedlings and then repeated on biological replicate samples . Briefly , seedlings grown for 9 LD were fixed at the end of the day as described previously [59] . Frozen tissue was ground , filtered three times through Miracloth ( Calibrochem ) , and washed as described previously thorough buffers M1 , M2 , and M3 [59] . Nuclear pellets were resuspended in sonic buffer as described ( 1 mM PEFA BLOC SC [Roche Diagnostics] was substituted for PMSF ) , split into duplicate samples , and sonicated with a Branson sonifier at continuous pulse ( output level 3 ) for eight rounds of 2×6 s and allowed to cool on ice between rounds . Immunoprecipitation ( IP ) reactions were performed by incubating chromatin with 2 . 5 µl of anti-rabbit GFP antibody ( ab290 , Abcam ) overnight at 4°C , as described [59] . The immunoprotein–chromatin complexes were captured by incubating with protein A-agarose beads ( Santa Cruz Biotechnology ) , followed by consecutive washes in IP buffer and then elution as described [59] . Immunoprotein-DNA was then incubated consecutively in RNase A/T1 mix ( Fermentas ) and Proteinase K ( Roche Diagnostics ) as described , after which DNA was purified using Minelute columns ( Qiagen ) [59] . Recovered DNA was amplified using the Sigma WGA GenomePlex kit ( Sigma-Aldrich ) , after we performed a comparison to other systems , which showed this protocol gives improved amplification consistency and minimal amplification bias , in accordance with a previous study [60] . One microgram of DNA was fragmented , labeled , and hybridized to Affymetrix Arabidopsis tiling 1 . 0F arrays ( Affymetrix ) . Chromatin size distribution and fragmentation performance was confirmed on an Agilent Bioanalyzer prior to array hybridization ( Agilent Technologies ) . Regions found to be enriched by ChIP-chip were confirmed by manual ChIP . We performed triplicate qPCR on chromatin samples from 35S::SMZ-GFP and 35S::GFP-NLS plants . As a negative control , we used a region 3 . 5 kb away from the FT peak that was not enriched in ChIP-chip analysis . Tiling array data were processed using the CisGenome suite [61] . Briefly , raw . CEL files were quantile normalized and peaks were called using TileMapv2 . Analysis was performed in MA mode with window size 5 , and only peaks detected with a FDR of better than 0 . 05 were analyzed . EasyGO was used to do GO-based enrichment analysis [62] . Genome-wide visualization was performed with Affymetrix Integrated Genome Browser after normalization with Affymetrix Tiling Array Software ( Affymetrix ) . All tiling array data are freely available from the ArrayExpress database ( http://www . ebi . ac . uk/arrayexpress; accession numbers: E-MEXP-2068 ) .
|
Flowering is a pivotal event in the life cycle of many plants and is therefore under tight control . The ability to detect the daily photoperiod is of particular importance in many plant species , as it enables them to enter the reproductive phase in response to seasonal changes in day length . When the photoperiod is permissive to flowering , a signal is produced in leaves that is transported to the shoot meristem , where it initiates the formation of flowers . It is now widely accepted that an important component of this long-distance signal is the flowering protein FT . Here , we show that the AP2-like transcription factor SMZ , which represses flowering and is a target of the regulatory miRNA172 microRNA , functions together with related proteins to directly regulate FT expression . Using chromatin immunoprecipitation coupled to genome tiling arrays , we find that SMZ binds directly to the FT genomic locus and to several other key flowering-related loci . Unexpectedly , the ability of SMZ to repress flowering strictly depends on the presence of the MADS-domain transcription factor FLM . In addition , SMZ binds to its own regulatory sequences and those of three closely related genes , providing evidence of strong negative feedback between SMZ and the other AP2-like miRNA172 targets .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/gene",
"expression",
"genetics",
"and",
"genomics/functional",
"genomics",
"developmental",
"biology/plant",
"growth",
"and",
"development",
"plant",
"biology/plant",
"growth",
"and",
"development",
"plant",
"biology/plant",
"genetics",
"and",
"gene",
"expression",
"genetics",
"and",
"genomics/plant",
"genetics",
"and",
"gene",
"expression"
] |
2009
|
Repression of Flowering by the miR172 Target SMZ
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Chikungunya virus ( CHIKV ) is a re-emerging , pathogenic alphavirus that is transmitted to humans by Aedes spp . mosquitoes—causing fever and debilitating joint pain , with frequent long-term health implications and high morbidity . The CHIKV lifecycle is poorly understood and specific antiviral therapeutics or vaccines are lacking . In this study , we investigated the role of host-cell chloride ( Cl- ) channels on CHIKV replication . We demonstrate that specific pharmacological Cl- channel inhibitors significantly inhibit CHIKV replication in a dose-dependent manner , suggesting that Cl-channels are pro-viral factors in human cells . Further analysis of the effect of the inhibitors on CHIKV attachment , entry , viral protein expression and replicon replication demonstrated that Cl- channels are specifically required for efficient CHIKV genome replication . This was conserved in mosquito cells , where CHIKV replication and genome copy number was significantly reduced following Cl- channel inhibition . siRNA silencing identified chloride intracellular channels 1 and 4 ( CLIC1 and CLIC4 , respectively ) as required for efficient CHIKV replication and protein affinity chromatography showed low levels of CLIC1 in complex with CHIKV nsP3 , an essential component of the viral replication machinery . In summary , for the first time we demonstrate that efficient replication of the CHIKV genome depends on cellular Cl- channels , in both human and mosquito cells and identifies CLIC1 and CLIC4 as agonists of CHIKV replication in human cells . We observe a modest interaction , either direct or indirect , between CLIC1 and nsP3 and hypothesize that CLIC1 may play a role in the formation/maintenance of CHIKV replication complexes . These findings advance our molecular understanding of CHIKV replication and identify potential druggable targets for the treatment and prevention of CHIKV mediated disease .
Chikungunya virus ( CHIKV ) is a mosquito-borne virus of the Alphavirus genus in the Togaviridae family . It was first isolated during an outbreak in Tanzania in 1952 [1] , since which its geographic range has expanded globally to include almost 40 countries [2] . Following transmission , CHIKV replicates in the fibroblasts of the dermis and disseminates through the blood-stream to several tissues including muscle , joints and the liver [3] . CHIKV causes chikungunya fever which is characterized by high fever , maculopapular rash , myalgia and debilitating arthralgia [4] . In some cases more severe symptoms occur—including encephalitis , encephalopathy , myocarditis , hepatitis and Guillain Barré syndrome [5 , 6] , however chikungunya fever-associated death is rare [7] . Due to the chronic debilitating symptoms and sequela that persist for up to 3 years [8] , CHIKV has a major impact on morbidity and loss of economic productivity within at-risk populations [9] . Treatment of CHIKV-associated disease is limited to the relief of symptoms with no licensed vaccines or direct acting antivirals currently available . CHIKV is a small , enveloped virus , with a single-stranded positive-sense RNA genome of ~12 kilobases . The genome possesses a type-0 5’ 7-methyl-GpppA cap , a 3’ poly ( A ) tail and two open reading frames ( ORFs ) . The first ORF ( ORF-1 ) encodes the non-structural polyprotein P1234 that is processed to yield four mature non-structural proteins ( nsP1-4 ) . The second ORF ( ORF-2 ) encodes the structural polyprotein that is processed into the capsid protein , E3 , E2 , 6K , and E1 . Virus attachment onto mammalian cells is mediated by the CHIKV E2 protein and the cell adhesion molecule Mxra8 [10] . Internalization is achieved via clathrin-mediated endocytosis , although clathrin-independent pathways have also been identified [10 , 11] . Following CHIKV trafficking to early endosomes , the E1 glycoprotein facilitates endosomal fusion and capsid release into the cytoplasm [11 , 12] . Replication of the viral genome is not well studied but through analogy to other alphaviruses proteolytic cleavage of P1234 in cis by the protease function of nsP2 releases the RNA-dependent RNA polymerase nsP4 , initiating the synthesis of minus-strand RNA . Subsequent proteolytic cleavage of the remaining P123 polyprotein initiates synthesis of genomic and sub-genomic RNAs from the minus-strand template [13] . Viral RNA replication occurs in membrane-bound replication complexes , termed spherules , located at the plasma membrane [13 , 14] , facilitating an optimal environment for replication and protection of dsRNA intermediates from host cell detection . The structural polyprotein is translated from sub-genomic RNA and is co- and post- translationally cleaved by viral and host proteases . Virus assembly and budding takes place at the plasma membrane . The regulation of ionic homeostasis , mediated through cellular ion channels , has emerged as a requirement for a number of virus infections [15] . It has previously been shown that two pore domain ( K2P ) potassium channels play a role during the trafficking of Bunyamwera virus ( family Peribunyaviridae ) in endosomes [16 , 17] and that Hazara virus ( family Nairoviridae ) similarly requires endosomal potassium to permit release from endosomes [18] . Ebola virus ( family Filoviridae ) is known to require endosomal calcium channels for entry into its host cells [19] , while entry of Influenza virus ( family Orthomyxoviridae ) depends on binding of the hemagglutinin to the voltage-dependent calcium channel Cav1 . 2 [20] . Hepatitis C virus ( family Flaviviridae ) replication is inhibited by Cl- channel blockers [21] whilst the Cl- channel CLC6 was identified as a pro-viral factor in a genome-wide loss of function screen during CHIKV infection [22] . In this study , we used a panel of ion channel modulating compounds to demonstrate that CHIKV requires Cl- channel activity during its lifecycle , in both mammalian and mosquito cells , for efficient replication of the viral genome . Through RNAi silencing , two Cl- intracellular channels , CLIC1 and CLIC4 , were identified as pro-viral factors for CHIKV replication , with CLIC1 potentially interacting with CHIKV nsP3—further implicating this channel as a significant host cell factor during CHIKV infection . These findings expand our understanding of CHIKV pathogenesis and reveal Cl- channels as a potential host cell target for the development of much needed CHIKV antivirals .
Huh7 cells ( hepatocytes derived from human hepatocellular carcinoma ) and BHK-21 cells ( fibroblasts derived from Syrian golden hamster kidney ) were a gift from M . Harris ( University of Leeds , UK ) . C6/36 cells ( Aedes albopictus larva ) were a gift from S . Jacobs ( The Pirbright Institute , UK ) . All cell lines tested negative for mycoplasma . Mammalian cells were maintained in Dulbecco’s modified Eagle’s medium ( DMEM , Sigma ) supplemented with 10% fetal bovine serum , 100 units/ml penicillin , 100 μg/ml streptomycin and non-essential amino acids ( Lonza ) in a humidified incubator at 37°C and 5% CO2 . Invertebrate cells were maintained at 28°C in Leibovitz’s media ( Gibco ) supplemented with Tryptose Phosphate Broth ( Thermo Fisher Scientific ) and 100 units/ml penicillin , 100 μg/ml streptomycin . The CHIKV ICRES and CHIKV-Fluc replicon ( SGR ) , in which the ORF-2 region encoding the structural proteins has been replaced with sequence encoding firely luciferase , cDNA clones were previously described [23] . Both are based on the isolate LR2006 OPY1 , representing the East Central South African genotype . CHIKV TST-nsP3 was generated by synthesizing a 129 bp DNA fragment containing the twin-strep-tag ( TST ) encoding sequence flanked by two SpeI restriction sites ( ThermoFisher Scientific ) . This fragment was cloned into the CHIKV-Fluc SGR using a unique SpeI site in region corresponding to the hypervariable domain of nsP3 ( position 5222 ) . The TST-nsP3 fragment was then excised and cloned into the CHIKV ICRES plasmids using the unique KfII and the AgeI restriction sites . All CHIKV plasmids were linearized using NotI and in vitro transcribed using the mMESSAGE mMACHINE SP6 Kit ( Ambion ) . 1 μg of RNA was electroporated into 1 . 2 × 106 BHK-21 cells using a single square wave pulse ( 260 V , 25 ms ) . Cells were seeded into a 75cm2 flask and incubated for 48 hrs at 37°C . Supernatant was harvested , clarified by centrifugation for 5 min at room temperature ( RT ) , aliquoted and stored at -80°C . The CHIKV titer was determined by standard plaque assay on BHK-21 monolayers and expressed as plaque forming units/ml ( PFU/ml ) . Huh7 cells were seeded into 96 well plates at 10 000 cells/well ( 20 000 cells/well for C6/36 cells ) one day prior to treatment with increasing doses of Ribavirin ( Fluorochem ) ; 4 , 4'-Diisothiocyanato-2 . 2'-stilbenedisulfonic acid ( DIDS , Sigma ) ; 9-Anthracenecarboxylic acid ( 9-ACA , Sigma ) ; indanyloxyacetic acid-94 ( IAA-94 , Cayman Chemical ) , 5-Nitro-2- ( 3-phenylpropylamino ) benzoic acid ( NPPB , Santa Cruz Biotechnology ) and dimethyl sulfoxide ( DMSO , Fisher Chemical ) . Compound stocks were dissolved in DMSO and stored at -20°C in aliquots until dilution into complete DMEM/Leibovitz’s for addition onto cells . After 6 hrs and 24 hrs incubation respectively , media/compound was removed and cells incubated in Opti-MEM ( Gibco ) plus 1 mg/ml 3- ( 4 , 5-Dimethylthiazol-2-yl ) -2 , 5-Diphenyltetrazolium Bromide ( MTT ) for 30 min at 37°C . After removal of Opti-MEM/MTT , cells were lysed in DMSO and absorbance determined at 570 nm on an Infinite F50 microplate reader ( Tecan ) . Absorbance of samples was normalized to untreated control cells . Three independent experiments were conducted , each repeat consisting of three wells/dose . A two-way ANOVA was employed and a Sidak’s or Dunnett’s multiple comparisons test was performed , comparing each compound dose to the average of untreated samples . The MNTD was defined as the highest compound concentration that does not show a significant reduction in normalized absorbance to the untreated samples . Huh7 cells were treated with MNTDs of Ribavirin , DIDS , 9-ACA , IAA-94 , NNPB and DMSO ( control ) as follows . Huh7 cells were seeded into 12 well plates at 100 000 cells/well . The next day , CHIKV at MOI 2 ( 2xPFU as determined on BHK-21 monolayers/Huh7 cell number ) and the respective inhibitor were diluted in 150 μl complete DMEM and adsorbed to the cells for 1 hr at 37°C . CHIKV/compound was removed , cells washed with PBS and incubated for 12 hrs in DMEM/compound . Supernatant was then harvested the titer of released CHIKV determined by plaque assay . To determine the virucidal activity of the compounds , the inhibitors and CHIKV were diluted into 150 μl with complete DMEM and incubated at 37°C for 1 hr prior to adsorption to the cells . Treatment with DEPC was conducted by diluting CHIKV and DEPC ( 2 mM final concentration ) in PBS and incubating at RT in the dark . CHIKV and compounds were adsorbed to cells for 1 hr at 37°C and were then removed and cells incubated for 12 hpi in complete DMEM . The supernatants were harvested and CHIKV titer determined by standard plaque assay . The impact of the compounds on CHIKV attachment to the cell surface was investigated by performing CHIKV adsorption in the presence of the inhibitors at 4°C for 1 hr to prevent uptake . Cells were then rigorously washed with cold PBS and returned to 37°C in complete DMEM . At 24 hpi supernatants were harvested and CHIKV titer determined by standard plaque assay at 24 hpi . The first two hours of the CHIKV lifecycle were investigated by treating the cells with the inhibitors in 1 ml DMEM 1 hr prior to absorbing the virus in the presence of the inhibitors . After removal of the virus , cells were incubated one further hour in 1 ml complete DMEM plus inhibitors before these were removed by washing in PBS . Cells were then kept in complete DMEM until the titer of the released virus was determined at 12 hpi . C6/36 cells were seeded into 12 well plates at 200 000 cells/well . The next day , cells were infected with CHIKV at MOI 2 ( 2xPFU as determined on BHK-21 monolayers/C6/36 cell number ) and treated with the MNTD of compounds as described for Huh7 cells . The supernatants were harvested and CHIKV titer determined at 24 hpi . All experiments were performed in three independent repeats , each consisting of 2 wells/condition . A one-way ANOVA was employed and a Dunnett’s multiple comparisons test was performed , comparing treated samples to untreated sample . Huh7 cells were seeded into 6 well plates at 300 000 cells/well . The next day , cells were infected with CHIKV ( MOI 0 . 1 , 2 , 10 ) in the presence of the MNTD of the inhibitors as described above . After removal of the virus , cells were incubated in complete DMEM/inhibitor until cells were washed in PBS , detached by trypsinization , and lysed in lysis buffer ( 25 mM Tris•HCl pH 7 . 4 , 150 mM NaCl , 1% NP-40 , 1 mM EDTA , 5% glycerol ) at 24 hpi . Cell lysate was cleared by centrifugation at >16 0000 x g for 10 mins at 4°C and total protein concentration determined by Pierce BCA Protein Assay ( Thermo Scientific ) . Equal amounts of protein were resolved by SDS-PAGE and proteins transferred to PVFD membranes using semi-dry transfer . Western blots were probed with antibodies against nsP1 ( 1:1000 , rabbit polyclonal , in-house ) [24] , nsP3 ( 1:1000 , rabbit polyclonal , in-house ) [24 , 25] , capsid ( 1:1000 , rabbit polyclonal , in-house ) , CLIC1 ( 1:1000 , mouse monoclonal , Abcam , ab77214 ) , CLIC4 ( 1:200 , mouse monoclonal , Santa Cruz Biotechnology , sc-135739 ) and the housekeeping protein actin ( clone AC-15 , mouse monoclonal , Sigma ) overnight at 4°C diluted in Odyssey Blocking Buffer ( Li-Cor ) . After washing in PBS , the western blot was incubated for 1 hr at RT with the respective secondary antibodies ( IRDye 800CW Donkey anti-Mouse; IRDye 680LT Donkey anti-Rabbit; Li-Cor ) . The membrane was dried an imaged using the Odyssey Fc Imaging System ( Li-Cor ) . Huh7 cells were seeded into 12 well plates at 100 000 cells/well . The next day , cells were washed and incubated in live cell imaging solution ( 140 mM NaCl , 2 . 5 mM KCl , 1 . 8 mM CaCl2 , 1 mM MgCl2 , 20 mM HEPES pH 7 . 4 , 20 mM Glucose , 1% BSA ) supplemented with the MNTD of inhibitory compounds for 50 mins at 37°C . Cells were incubated on ice for 10 mins and Alexa Fluor 488 EGF complex ( Invitrogen E13345 ) was added to a final concentration of 0 . 8 μg/ml . After 45 mins , cells were fixed in 4% Formaldehyde/PBS and imaged using the IncuCyte ZOOM system ( Essen Bioscience ) . The default software parameters for a 12 well plate ( Corning ) with a 10× objective was used for imaging 4 fields of view/well . A processing definition was established to automate identification of Alexa Fluor 488 positive objects . The green object count/well was extrapolated by the IncuCyte ZOOM software . Two wells/conditions were analyzed in three biological repeats . A one-way ANOVA was employed and Dunnett’s multiple comparisons test was performed comparing each sample to the untreated sample . A modest , but significant decrease in Alexa Fluor 488 EGF uptake upon treatment with the carrier control ( DMSO ) was controlled for by additionally comparing the samples to DMSO-treated samples in the Dunnett’s multiple comparison test . The CHIKV-Fluc SGR contains a Firefly Luciferase ( Fluc ) reporter gene which is expressed from the sub-genomic promotor [23]; therefore , detection of Fluc activity is indicative of CHIKV genome replication . The plasmid was linearized using NotI and in vitro transcribed using the mMESSAGE mMACHINE SP6 Kit ( Ambion ) . 400 ng CHIKV-Fluc SGR RNA was co-transfected with 100 ng Renilla Luciferase ( Rluc ) encoding RNA using 1 μl Lipofectamine 2000 ( Thermo Fisher ) into Huh7 cells seeded the previous day into 24 well plates at 50 000 cells/well . During the time of transfection , cells were incubated with Opti-MEM/inhibitor . Cells were lysed in passive lysis buffer ( Promega ) at 6 hrs post-transfection . The dual luciferase assay was performed according to the manufacturer’s protocol ( Promega ) . The Fluc signal was adjusted to the Rluc signal as follows: ( average Rluc signal ( untreated control ) / Rluc signal ( sample X ) ) x Fluc signal ( sample X ) . Three independent repeats were performed , with each repeat consisting of 2 wells/conditions , which were analyzed as 2 technical repeats each . A one-way ANOVA was employed and Dunnett’s multiple comparisons test was performed comparing each sample to the untreated sample . Huh7 cells were infected with CHIKV and treated with inhibitory compounds as described above . At 6 hpi , total RNA was extracted from cells using TRI Reagent Solution ( Applied Biosystems ) according to the manufacturer’s instructions . Strand-specific qPCR ( ssqPCR ) was performed according to the protocol described by Plaskon and colleagues [26] . Briefly , 500 ng of RNA were reverse-transcribed with gene specific primers ( S1 Table ) using the SCRIPT cDNA Synthesis Kit ( Jena Bioscience ) according to the manufacturer’s protocol . 100ng of strand-specific cDNA was used as template for the quantitative PCR performed with the qPCRBIO SyGreen Blue Mix Lo-ROX ( PCR Biosystems ) with gene specific primers ( S1 Table ) amplifying a 94 bp region of the CHIKV nsP1 encoding sequence using the following PCR program: 95°C for 2 mins , 40 x ( 95°C for 5 sec , 60°C for 30 sec ) , dissociation curve 60°C-95°C as pre-defined by the Mx3005P thermal cycler ( Agilent technologies ) . In vitro transcribed CHIKV ICRES RNA was reverse transcribed and a cDNA dilution series employed as a standard to quantify copy numbers in the respective samples . All experiments were performed in four independent repeats , each consisting of 2 wells/condition . A one-way ANOVA was employed and Dunnett’s multiple comparisons test was performed comparing each sample to the untreated control sample . C6/36 cells were infected with CHIKV ( MOI 2 ) and treated with inhibitory compounds as described above . At 24 hpi , total RNA was extracted from cells using TRI Reagent Solution ( Applied Biosystems ) according to the manufacturer’s instructions . RNA integrity was confirmed by denaturing ageraose gel electrophoresis before reverse transcription of 1 μg of RNA using the High-Capacity RNA-to-cDNA Kit ( Applied Biosystems ) according to the manufacturer’s protocol . Quantitative PCR was performed using the qPCRBIO SyGreen Blue Mix Lo-ROX ( PCR Biosystems ) with primers amplifying a 78 bp region of the CHIKV nsP1 encoding sequence ( fwd primer: 5’CCGACTCAACCATCCTGGAT’3 , rev primer: 5’GGCAGACGCAGTGGTACTTCCT’3 ) , 100ng of cDNA template and a PCR program as described above . In vitro transcribed CHIKV ICRES RNA was reverse transcribed and a cDNA dilution series employed as a standard to quantify copy numbers in the respective samples . All experiments were performed in three independent repeats , each consisting of 2 wells/condition . A one-way ANOVA was employed and Dunnett’s multiple comparisons test was performed , comparing each sample to the untreated control sample . Huh7 cells were seeded into 12 well plates at 100 000 cells/well . The next day , cells were transfected with 75 pmol/well CLIC1 , CLIC4 and control-B ( = scrambled ) siRNA ( Santa Cruz Biotechnology ) , respectively , with each siRNA representing a pool of three 19–25 nt siRNAs . Transfection was conducted according to the manufacturer’s protocol ( Santa Cruz Biotechnology sc-29528 ) with a siRNA to transfection reagent ratio of 1:1 . At 24 hrs post transfection , the transfection was repeated to achieve a clearly discernable reduction in protein expression level . At 48 hrs post initial transfection , cells were infected with CHIKV ( MOI 10 ) as described above and a sample harvested for analysis on western blot . Supernatant was harvested at 24 hpi and standard plaque assays performed to determine viral titer . Cells were lysed and analyzed on western blots as described . For double knock down of CLIC1 and CLIC4 , a total of 150 pmol siRNA/well was transfected into the cells . Three independent repeats were performed and CHIKV titers of CLIC1/CLIC4 knock down samples were expressed as percentage of scrambled siRNA control sample per repeat . A one-way ANOVA was employed and Dunnett’s multiple comparisons test was performed , comparing each sample to the scrambled siRNA control sample . Huh7 cells were infected with CHIKV ICRES TST-nsP3 ( MOI 10 ) as described above . At 24 hpi , cells were lysed in lysis buffer ( 2x , pH 7 . 2 , 20mM PIPES , 240mM KCl , 60mM NaCl , 10mM MgCl2 , 2% Triton x-10 , 20% Glycerol ) and total protein concentration of clarified cell lysate determined by BCA assay ( Pierce ) . Strep-Tactin Sepharose 50% suspension ( iba , 2-1201-002 ) was washed with wash buffer ( 100 mM Tris-Cl , 150 mM NaCl , 1 mM EDTA; pH 8 ) before equilibrating with lysis buffer . 1mg/ml total protein was incubated with the sepharose o/n at 4°C . Sepharose was washed once with lysis buffer followed by wash buffer ( 100 mM Tris/HCl , pH 8 . 0; X mM NaCl; 1 mM EDTA ) with increasing and then decreasing stringency ( 150 mM , 275 mM , 500 mM , 150 mM NaCl ) . Sepharose was boiled in standard SDS sample buffer and bound proteins analyzed on western blots as described above .
We first assessed well-characterized Cl- channel blockers—including diisothiocyanostilbene-2 , 20-disulfonic acid ( DIDS ) , 9-anthracene carboxylic acid ( 9-ACA ) , indyanyloxyacetic acid 94 ( IAA-94 ) and 5-nitro-2-3-phenylpropylamino benzoic acid ( NPPB ) for their effects on CHIKV infection . For these assays , maximal non-toxic doses ( MNTD ) for human hepatoma cells ( Huh7 ) were determined for each compound by MTT assay ( S1 Fig ) . Next , Huh7 cells were infected with CHIKV ( MOI 2 ) in the presence of the MNTD of each compound; at 12 hpi titers of released virus were determined . Treatment with NPPB resulted in an 18-fold reduction in CHIKV progeny compared to untreated cells , while treatment with DIDS and 9-ACA led to an 8-fold decrease in CHIKV titer ( p ≤ 0 . 05 ) ( Fig 1A ) . IAA-94 had no detectable effects . Ribavirin , shown previously to be active against CHIKV [27 , 28] , was included as a positive control and significantly inhibited production of CHIKV virions ( 56-fold decrease ) . To confirm these findings , the expression of CHIKV nsP1 and capsid protein in the presence each Cl- channel inhibitor were determined by western blot analysis . Fig 1B shows reduced expression of nsP1 and capsid in DIDS , 9-ACA and NPPB-treated cells compared to untreated/DMSO-treated cells and only in the presence of IAA-94 were nsP1/capsid levels unaffected . DIDS , 9-ACA , NPPB and Ribavirin were found to inhibit CHIKV replication in a dose dependent manner ( Fig 1Ci–1Civ ) . This data further implies that functional Cl- channels are required during the CHIKV lifecycle . The inhibitory effect on viral protein expression was consistent in cells infected with greater and smaller MOIs ( S2 Fig ) . To exclude any direct virucidal activity of the compounds , CHIKV virions were incubated with the MNTD of each compound in vitro at 37°C for one hour prior to their dilution in media and infection onto untreated cells ( Fig 1D ) . DEPC was included in this assay as a positive control , as it has previously been shown to modify histidine’s on CHIKV glycoproteins , preventing viral fusion [29] . Treatment with DIDS , 9-ACA , NPPB and Ribavirin had no significant effects on the CHIKV titer following direct virion treatment , implying that the compounds do not inactivate the viral particle . Taken together , these data provide the first reported requirement for DIDS- , 9-ACA- and NPPB-sensitive cellular Cl- channels during the CHIKV lifecycle . We next sought to identify the stage of the CHIKV lifecycle that is sensitive to Cl- channel inhibition . The CHIKV lifecycle begins with virus attachment to host cells mediated by the CHIKV E2 protein . To analyze cell attachment , CHIKV ( MOI 2 ) was adsorbed onto cells at 4°C ( to prevent virus internalization ) for 1 hr in the presence of either DIDS , 9-ACA , NPPB or Ribavirin . Both virus and compound were then removed by rigorous washing and cells were warmed to 37°C to permit the internalization of attached virus particles . Virus infection efficiency was lower in this assay compared to infections carried out at physiological temperature . Thus , cell supernatants were assayed for infectious CHIKV progeny at 24 hpi ( Fig 2A ) , rather than 12 hpi as in other assays . Treatment with 9-ACA , NPPB and Ribavirin had no influence on CHIKV titers , indicating that these compounds do not impact CHIKV E2 mediated cell surface attachment . DIDS had a modest but non-significant effect on virus binding , implying it may partially impact this lifecycle stage . The first CHIKV particles fuse with endosomal membranes as early as 2 mins post-infection , making CHIKV entry a rapid process ( 12 ) . Indeed it has been reported that almost all CHIKV particles escape the endosomal system within the initial 22 mins of infection . Following endosomal fusion , the viral capsid is released , genome is translated and replication complexes begin to form . We investigated the requirement of Cl- channels during this early lifecycle stage by adsorbing CHIKV onto pre-treated cells for 1 hr at 37°C ( MOI 2 ) . Virus and compound were then removed and non-cell associated viruses were washed from cells . Cells were treated with each compound for a further 1 hr and after their removal , CHIKV titers were assessed at 12 hpi . As shown in Fig 2B , 9-ACA , NPPB and Ribavirin had no effect on CHIKV infection when treatment was limited to the early lifecycle stages . DIDS-treated cells , however , displayed reduced CHIKV titers ( p ≤ 0 . 05 ) , consistent with its modest effects on virus attachment . Some of the inhibitory effect associated with DIDS-treatment appeared to be non-specific and associated with general inhibition of clathrin-mediated endocytosis , since cells accumulated lower levels of Alexa Fluo 488 EGF following DIDS-treatment ( Fig 2C ) . NPPB-treatment in this assay led to a small increase in EGF uptake by an as yet undescribed mechanism . Taken together , these data indicate that neither 9-ACA or NPPB inhibit CHIKV at the stages of virus attachment or internalization , whilst DIDS inhibits CHIKV early attachment and internalization . We then investigated the effects of the Cl- channel inhibitors on efficient CHIKV RNA replication using a CHIKV-Fluc SGR ( Fig 3A ) and Fluc luminescence as a proxy of CHIKV RNA synthesis . For these assays , cells were briefly pre-treated with the MNTD of DIDS , 9-ACA , NPPB or Ribavirin before co-transfection with the CHIKV-Fluc SGR and a 5’capped Rluc mRNA in trans , to control for differences in transfection efficiency . Cells were assessed for CHIKV replication by the measurement of luciferase activity 6 hrs post-transfection . NPPB-treatment significantly inhibited CHIKV-Fluc SGR replication 16-fold ( p ≤ 0 . 0001 ) while DIDS-treatment led to a 9-fold decrease in CHIKV-Fluc SGR replication . As expected , ribavirin also significantly lowered CHIKV-Fluc SGR replication 37-fold ( p ≤ 0 . 0001 ) . 9-ACA treatment produced only a comparatively modest ~3-fold reduction ( p ≤ 0 . 01 ) in CHIKV-Fluc SGR replication ( Fig 3B ) . To confirm these findings , we quantified CHIKV genomic ( Fig 3C ) and intermediate complimentary minus-strand ( Fig 3D ) RNA copy number ( Fig 3C ) by ssqPCR . For these assays , cells were infected ( MOI 2 ) in the presence of each Cl- channel inhibitory compound and total RNA extracted at 6 hpi . Significantly lower levels of the CHIKV genomic RNA were observed in cells treated with DIDS ( p ≤ 0 . 05 ) , 9-ACA ( p ≤ 0 . 05 ) , NPPB ( p ≤ 0 . 05 ) or Ribavirin ( p ≤ 0 . 01 ) ( Fig 3C ) , consistent with the SGR data ( Fig 3B ) . Similarly , copy numbers of the intermediate complimentary minus-strand RNA were significantly reduced with DIDS- ( p ≤ 0 . 001 ) , 9-ACA- ( p ≤ 0 . 01 ) , NPPB- ( p ≤ 0 . 001 ) and Ribavirin- ( p ≤ 0 . 0001 ) treatment . From this data , we inferred that the efficiency of CHIKV genome replication is dependent on the function of Cl− channels that are sensitive to DIDS , 9-ACA and NPPB–via either a direct role in replication of the CHIKV genome or indirectly through upregulation of ORF-1 translation . All of the Cl- channel compounds used in this study inhibit a broad range of cellular Cl- channels . To date , ≥40 Cl- channels have been identified that fall into six classes; 1 ) Cl- intracellular channels ( CLICs ) , 2 ) voltage-gated Cl- channels ( CLCs ) , 3 ) cystic fibrosis transmembrane conductance regulator ( CFTR ) , 4 ) calcium-activated Cl- channels ( CaCCs ) , 5 ) ligand-gated Cl- channels and 6 ) volume-regulated Cl- channels ( VRAC ) [30 , 31] . We focused on CLIC1 and CLIC4 as candidate channels , due to their known sensitivity to DIDS , 9-ACA and NPPB and involvement in other virus infections such as hepatitis C virus and Merkel cell polyomavirus [21 , 32] . Initially , we confirmed by western blot that CHIKV infection did not induce expression of CLIC1 or CLIC4 ( S3 Fig ) . CLIC1 and CLIC4 were then silenced using siRNA ( Fig 4Ai ) , cells were then infected with CHIKV ( MOI 10 ) and released virus titers determined at 24 hpi . Compared to control siRNA transfected cells , in those with reduced CLIC1 or CLIC4 expression levels , replication of CHIKV was significantly inhibited ( p ≤ 0 . 05 and p ≤ 0 . 001 , respectively ) ( Fig 4B ) , confirming a role for both CLIC1 and CLIC4 in efficient CHIKV replication . Co-silencing of both CLIC1 and CLIC4 ( Fig 4Aii ) did not result in synergistic inhibition of CHIKV replication ( Fig 4B ) , implying that both channels are required for the same or a related stage in the virus lifecycle . Given the inhibitory effects of chloride channel inhibition on CHIKV genome replication ( Fig 3 ) , we reasoned that the channels may form part of the virus replicase complex . To assess this , cells were infected with CHIKV twin-strep-tag nsP3 ( CHIKV TST-nsP3 ) ( MOI 10 ) , which encodes a 28 amino acid TST-tag within the hypervariable domain of nsP3 . At 24 hpi , cells were lysed and TST/strep-tactin affinity pull-downs performed—whereby the TST-tag of nsP3 selectively binds to Strep-Tactin Sepharose . Following elution , proteins interacting with CHIKV nsP3 were identified by western blot analysis . Negative controls for this analyses included cells infected with wild-type CHIKV ( i . e . expressing untagged nsP3 ) and uninfected cells . We observed that untagged nsP3 , capsid , and to lesser extent CLIC1 , bound non-specifically to the Sepharose resin , albeit to low levels ( Fig 4C ) . However , compared to both controls , enriched levels of CLIC1 were evident in complex with TST-nsP3—implying low levels of direct/indirect interaction between CLIC1 and nsP3 . CLIC4 was not identified as an nsP3 interacting partner , suggesting an independent function outwith the CHIKV replicase complex . Previously published immunoprecipitation studies demonstrated an interaction between alphavirus capsid protein and nsP3 [33] , which was included as a positive control to validate the pull-downs ( Fig 4C ) . Taken together , these data suggest that CLIC1 interacts either directly or indirectly with nsP3 , an essential component of the CHIKV replication machinery . As an arbovirus , CHIKV infects both humans and mosquitos . We thus investigated if the dependency of CHIKV on cellular Cl- channels is limited to human cells or extends to those of its mosquito host . To assess this , C6/36 cells derived from the larva of Ae . albopictus were infected with CHIKV in the presence of the MNTD ( S1B Fig ) of NPPB , the Cl- channel inhibitor that exhibited the most significant inhibitory effects on CHIKV replication in mammalian cells ( Figs 1 and 3 ) . A 357-fold reduction in CHIKV titer , following infection ( MOI 2 ) of NPPB-treated C6/36 cells , was observed at 24 hpi ( p ≤ 0 . 001 ) , implying that Cl- channels are required during the CHIKV lifecycle in mosquito host cells . Ribavirin-treatment , used as a positive control , led to a 39-fold decrease in CHIKV titer ( p ≤ 0 . 001 ) ( Fig 5A ) . The effects of the other Cl- channel inhibitors ( DIDS , 9-ACA and IAA-94 ) tested in mammalian cells , were also investigated in infections of C6/36 cells ( MOI 2 ) ( S4 Fig ) . Only DIDS-treatment led to a significant reduction in CHIKV replication at 24 hpi . As this could have been due to a similar non-specific effect as observed in Fig 2 . —DIDS , 9-ACA and IAA-94 were not pursued further in C6/36 cell infection studies . By analogy to the data from mammalian cells , the effects of NPPB on viral genome replication were investigated using the CHIKV-Fluc SGR . Though the cytotoxic effect of CHIKV-Fluc SGR transfection in combination with NPPB-treatment prevented replicon analysis , CHIKV RNA copy numbers could still be determined by reverse transcription qPCR 24 hpi . Viral RNA copy numbers were reduced 137-fold in NPPB-treated cells ( Fig 5B ) and 15-fold in Ribavirin-treated cells ( p ≤ 0 . 0001 ) . Taken together , these data demonstrate that NPPB-sensitive Cl- channels are involved in the CHIKV lifecycle in both human and mosquito host cells .
In this study we demonstrate that cellular Cl- channels have a significant pro-viral role during CHIKV infection . Our data shows that Cl- channel modulators inhibit efficient CHIKV RNA synthesis and that the intracellular Cl- channels CLIC1 and CLIC4 are specifically required for CHIKV replication . CLIC1 was found in complex with CHIKV nsP3 , albeit to low levels , suggesting a direct involvement in CHIKV replication complex formation or nsP3-mediated viral functions . For the first time , these data highlight the requirement of cellular ion channels during the CHIKV lifecycle . Cl- channels at the cell membrane regulate an array of cellular processes including cell-volume control , fluid transport and cell excitability . Intracellularly , Cl- transport across organelle membranes regulates endosome , lysosome and Golgi acidification . We observed inhibitory effects for three out of four assessed Cl- channel inhibitors on CHIKV replication in mammalian cells ( Fig 1A ) . The tested compounds do not display virucidal activity ( Fig 1D ) nor influence virus binding or the early stages of the viral lifecycle ( Fig 2 ) . Intriguingly , 9-ACA- and NPPB-sensitive Cl- channels were required specifically for efficient RNA replication—as evidenced by the effect of the inhibitors on the CHIKV-Fluc SGR and copy number of the CHIKV genomic and intermediate minus-strand RNA ( Fig 3 ) . NPPB-sensitive Cl- channels appear to have a conserved function in mosquito cells , as application of the inhibitor to infected C6/36 cells led to a reduction in CHIKV replication ( Fig 5 ) . To identify specific Cl- channels required for CHIKV replication , we focused on two CLIC family members , namely CLIC1 and CLIC4 . CLIC proteins are small proteins ( 236–253 aa , with the exception of CLIC5B and CLIC6 ) , harboring an N-terminal GST-like domain and a C-terminal alpha-helical domain ( reviewed in [34] ) . CLICs are metamorphic proteins that reversibly alternate between soluble cytoplasmic and membrane-associated forms , by rearrangement of the GST-like fold under oxidative conditions . Consequently , CLICs are multifunctional , exerting GST-like enzymatic functions as well as functions in membrane trafficking , endosomal sorting and functioning as Cl- channels . In Merkel cell polyomavirus infected cells , both CLIC1 and CLIC4 , mediate small T antigen induced cell motility via their Cl- channel activity [32] . In this study , CLIC1 and CLIC4 silencing inhibited CHIKV replication and , given that these effects mirrored those of the Cl- modulators , we reasoned that this effect was likely due to Cl- channel activity . Interestingly , we identified CLIC1 as a potential interacting partner of nsP3—an essential component of the CHIKV replication complex . We thus speculate that CLIC1 is in its membrane-inserted conformation and interacts with nsP3 and/or other ns-protein ( s ) bound to nsP3 as part of the replicase , to ensure optimal replication conditions within this membrane imbedded complex . These functions may align with the known roles of intracellular Cl- function , namely the regulation of organelle pH to maintain complex stability and genome integrity . In addition , isolation of CHIKV replication complexes has shown that all proteins needed for viral genome replication are present in the membrane fraction , and no soluble proteins are required [35] , further supporting a role for membrane associated CLIC1 . The double knock down of CLIC1 and CLIC4 did not have a synergistic effect on inhibiting CHIKV replication ( Fig 4B ) and CLIC4 was not observed to directly interact with nsP3 . These results imply that , although required for efficient CHIKV replication , CLIC4 functions through an alternative mechanism or different stage in a pathway to CLIC1 . Using a combination of broad acting inhibitors and siRNA screens , the involvement of several Cl- channel/transporter proteins in the hepatitis C virus lifecycle has previously been reported [21] , with CLC2 , CLC3 , CLC5 , CLC7 being specifically required for genome replication . A Cl- channel from the same family , CLC6 , has been identified as a pro-viral factor in a genome-wide loss of function screen performed with a CHIKV reporter virus [22] . This raises possibility that further Cl- channels are involved in the replication of the CHIKV genome or other stages of the virus lifecycle . For example , it is conceivable that Cl- channels not targeted by the compounds used in this study , or other ion channels , may play a role in post-entry events , leading up to genome replication—i . e . trafficking or uncoating as observed for e . g . Bunyamwera virus [16 , 17] . Anion channels have been shown to be important in other viral systems . Cl- channels were identified as part of the Semliki Forest virus , that is closely related to CHIKV , replication complex by quantitative proteomics [36] , supporting our CLIC1/nsP3 findings . In addition , the replication of Tomato bushy stunt virus ( family Tombusviridae ) was shown to depend on Cl—proton exchanger function [37] . Gef1p Cl-silencing , a homologue of the mammalian CLC proteins in yeast , inhibited replication through its downstream effects on Cu2+ homoeostasis , inhibiting the functionality of the viral replicase . Direct interaction of the voltage-dependent anion channel 1 with VP1 and VP3 of infectious bursal disease virus ( family Birnavirdae ) was shown to stabilize the ribonucleoprotein complex , allowing full activity of the viral polymerase [38] . It may be possible that CLIC1 , as an nsP3 interacting protein , is required for CHIKV genome replication for a similar mechanism . Interestingly , CLIC-dependent Cl- efflux has been shown to act during NLRP3 inflammasome activation and signaling [39] and it has recently been shown that the NLRP3 inflammasome is activated in CHIKV infected humans [40] . A small molecule inhibitor of the inflammasome abrogated inflammatory pathology in mice without influencing the CHIKV titer . This could imply that CLIC Cl- channel function is also involved in the inflammatory response to CHIKV infection , in addition to its role in efficient CHIKV genome replication . Inhibitors specific to CLIC1 and CLIC4 may hold potential as both CHIKV antivirals and inhibitors of the CHIKV inflammatory response . Specific inhibitors would exclude possible off-target effects caused by the currently available broad acting Cl- channel inhibitors . In conclusion , for the first time this study identifies Cl- channels as essential host cell factors for efficient CHIKV replication and in conjunction with other recent studies highlights the significance of ion channel modulation as a druggable target to inhibit virus infection . Notably , we show that the requirement for cellular Cl- channels is conserved between human and mosquito host cells and it is likely that other Cl- channels are required at various stages of the CHIKV lifecycle . In general , Cl- channels may represent a potential target for the development of antivirals acting against a broad variety of ( arbo- ) viruses and encouragingly Cl- channel inhibitors are in clinical or pre-clinical use [30] , potentially facilitatatig the development of Cl- channel specific compounds for future anti-CHIKV strategies .
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Chikungunya virus ( CHIKV ) is a mosquito-borne virus that infects humans and causes chikungunya fever—characterized by fever , rash and chronic arthralgia . Treatment of chikungunya fever is limited to the alleviation of the symptoms and no vaccine is available to prevent infection . Consequently , new anti-CHIKV therapies or targets are urgently required . Since its initial isolation in Tanzania in 1952 , CHIKV has spread widely across tropical/sub-tropical and more temperate regions . It has been responsible for epidemics in >70 different regions of the world , resulting in high morbidity and financial burden . Despite this , the CHIKV lifecycle is poorly understood . Here , we demonstrate for the first time that CHIKV requires host-cell ion channels that mediate chloride ion ( Cl- ) flux through the membranes of infected cells , to complete its lifecycle . Specifically , using pharmacological compounds , we show that Cl- channels are required for efficient replication of the virus genome , identify two specific channels ( CLIC1 and CLIC4 ) required for replication and demonstrate that CLIC1 may interact with viral non-structural protein 3 ( nsP3 ) —an essential component of the CHIKV replicase complex . These findings advance our understanding of CHIKV replication and identify potential druggable targets for the treatment and prevention of CHIKV mediated disease .
|
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2019
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Chikungunya virus requires cellular chloride channels for efficient genome replication
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The insulin/IGF-activated AKT signaling pathway plays a crucial role in regulating tissue growth and metabolism in multicellular animals . Although core components of the pathway are well defined , less is known about mechanisms that adjust the sensitivity of the pathway to extracellular stimuli . In humans , disturbance in insulin sensitivity leads to impaired clearance of glucose from the blood stream , which is a hallmark of diabetes . Here we present the results of a genetic screen in Drosophila designed to identify regulators of insulin sensitivity in vivo . Components of the MAPK/ERK pathway were identified as modifiers of cellular insulin responsiveness . Insulin resistance was due to downregulation of insulin-like receptor gene expression following persistent MAPK/ERK inhibition . The MAPK/ERK pathway acts via the ETS-1 transcription factor Pointed . This mechanism permits physiological adjustment of insulin sensitivity and subsequent maintenance of circulating glucose at appropriate levels .
The insulin signaling pathway is a highly conserved regulatory network coordinating animal metabolism and growth with nutritional status . In mammals , energy metabolism is regulated by insulin , and tissue growth by insulin-like growth factors ( IGFs ) , through their respective receptors ( for review see [1] ) . Drosophila has a single insulin-like receptor protein ( InR ) , which is activated by a family of insulin-like peptides ( ILPs ) and mediates physiological responses related to both metabolism and growth ( for review see [2] ) . InR stimulation leads to activation of the phosphatidylinositol 3-kinase ( PI3K ) /AKT pathway . AKT is recruited to the plasma membrane through phosphatidylinositol-3 , 4 , 5-triphosphate ( PIP3 ) , which is generated by phosphorylation of PI-4 , 5-P2 by PI3K ( for review see [3] ) . Membrane-recruited AKT is activated through phosphorylation by PDK1 and by TOR complex 2 [4]–[6] . AKT has several downstream effectors , including FOXO , a Forkhead transcription factor . AKT-mediated phosphorylation promotes retention of FOXO in the cytoplasm , thereby limiting FOXO activity [7] . AKT also promotes the activity of TOR complex 1 by phosphorylating two of its upstream regulators , TSC2 and PRAS40 [8]–[11] . Insulin signaling is involved in homeostatic regulation , gradually adjusting physiological processes in response to variable nutritional conditions . This tuning mode of regulation differs from many developmental signaling pathways , which produce a limited range of outputs ( e . g . cell fate ) . Therefore , it is perhaps not surprising that cellular insulin sensitivity is modulated by input from other signaling pathways . For example , TORC1-regulated S6 kinase ( S6K ) inhibits expression of the IRS scaffold proteins , which are recruited to activated insulin/IGF receptors , thereby making cells more insulin resistant [12] , [13] . Inflammatory signals , on the other hand , are known to cause insulin resistance by c-Jun N-terminal kinase-mediated phosphorylation of the IRS proteins ( for review see [14] ) . This is likely to contribute to the pathogenesis of type 2 diabetes . While regulation of insulin sensitivity is clearly of physiological importance , identifying novel regulatory mechanisms through genetic screens has been challenging due to the need for a sensitive readout that is also amenable to large-scale screening . The Drosophila eye provides such a system . Because insulin signaling limits FOXO activity , overexpression of FOXO can challenge the regulatory capacity of the insulin pathway , creating a sensitized genetic background [6] , [15] . Using this strategy to identify in vivo modulators of insulin pathway activity , we have uncovered a novel regulatory mechanism influencing insulin sensitivity . We show that the extracellular signal-regulated kinase ( ERK ) /MAP kinase signaling pathway ( for review see [16] ) influences cellular insulin responsiveness controlling the expression of the insulin-like receptor ( inr ) gene . This transcriptional regulation is mediated through the Ets-1 orthologue , Pointed , a transcription factor regulated by the MAPK/ERK pathway [17] . This mechanism provides a means for integration of signaling input via the Epidermal growth factor receptor ( EGFR ) -regulated MAPK/ERK with insulin-like signaling to control systemic glucose homeostasis .
To identify novel modulators of insulin-like signaling we screened for modifiers of FOXO overexpression , which produces small rough eyes . This phenotype has earlier been shown to respond to changes in insulin-like signaling in a highly sensitive manner [6] , [15] . As insulin-like signaling is a known regulator of growth , we focused on screening RNAi lines that had earlier shown tissue undergrowth in a wing-based screen ( [18] , Figure S1 ) . Our screen identified the PI3K gene , which serves as a positive control . The screen also identified kinase suppressor of ras ( ksr ) as an enhancer of the FOXO gain-of-function phenotype . Downregulation of ksr by RNAi enhanced the FOXO phenotype , but on its own , did not reduce eye size ( Figure 1A , quantified in Figure 1B ) . The lack of an obvious eye phenotype resulting from ksr depletion alone presumably reflects the magnitude of KSR downregulation generated with the GMR-GAL4 driver during the phase of eye imaginal disc growth . Enhancement of the FOXO overexpression phenotype was also observed when one copy of the ksr gene was removed ( Figure S2 ) . Removing one copy of the ksr gene on its own did not reduce eye size , indicating the utility of the sensitized background to identify subtle modulators of pathway activity . FOXO is regulated at multiple levels , including nuclear localization [7] . FOXO is nuclear in cells devoid of growth factors , but upon insulin stimulation FOXO accumulates in the cytoplasm through AKT-mediated phosphorylation ( [7]; Figure 1C ) As expected , RNAi-mediated depletion of PI3K limited the insulin-induced shift toward cytoplasmic FOXO . Depletion of KSR by RNAi produced a similar effect ( Figure 1C; representative images in Figure S3 ) . Does KSR act via AKT or does a parallel pathway override AKT-mediated FOXO regulation ? To address this , we monitored insulin-induced activation of AKT . Depletion of KSR suppressed insulin-induced phosphorylation of the activating ‘hydrophobic motif’ site S505 on AKT ( Figure 2A ) . The known functions of KSR are related to MAPK/ERK activation [19] , [20] . To ask if changes in the canonical MAPK/ERK pathway would explain the KSR effect on insulin signaling , we depleted D-MEK ( MAP kinase kinase ) . This produced an effect comparable to that of KSR ( Figure 2B ) . Insulin-induced AKT activation involves increase in the level of plasma membrane phosphoinositide PIP3 through the activity of PI3K [3] . To assess PIP3 levels , we used a GFP-linked pleckstrin homology ( PH ) domain from GRP1 [21] . Insulin treatment induced prominent membrane accumulation of the GRP1-PH domain , which was prevented by depletion of PI3K ( Figure 2C ) . Similarly , RNAi-mediated depletion of KSR reduced membrane localization of GRP1-PH in response to insulin . To test whether other downstream targets of AKT besides FOXO were affected by MAPK/ERK inhibition , we analyzed phosphorylation of S6K , a target of TORC1 [22] . Insulin-induced phosphorylation shifts some of the S6K protein into a ladder of slower migrating forms [e . g . [23]] , which was reduced by depletion of KSR or D-MEK ( Figure 2D ) . In both experiments the effects were comparable to depletion of PI3K . To test , whether MAPK/ERK signaling affected PI3K activity independent of insulin , we overexpressed activated PI3K in the absence of insulin stimulation and monitored AKT phosphorylation . In this setting , knockdown of KSR had no influence on pathway activity ( Figure S4 ) . In sum , these data suggest that reduced MAPK/ERK activity lowers sensitivity to insulin stimulation , but does not hamper PI3K from activating AKT . The data above suggested that MAPK signaling regulates the insulin-like pathway above the level of PI3K . We therefore sought to monitor InR expression level as well as activation making use of a shift in its electrophoretic mobility caused by phosphorylation ( Figure 3A , lanes 1 and 4 ) . Surprisingly , we found that depletion of KSR led to a reduction in the total level of InR protein . This was observed in both insulin-treated and untreated cells . Depletion of PI3K did not produce a comparable effect . To confirm that InR expression is regulated by the canonical MAPK/ERK pathway , we silenced the expression of Raf and D-MEK , which also showed reduced InR expression ( Figure 3B ) . Increasing MAPK/ERK pathway activity acts in the opposite direction: depletion of Gap1 , the GTPase activator of RAS , led to activation of MAPK/ERK signaling visualized by phospho-specific antibody against the active form of ERK ( Figure 3C ) as well as elevated InR expression ( Figure 3B ) . Thus , regulation of InR appears to be a specific MAPK/ERK pathway effect and InR levels are sensitive to both positive and negative changes in the MAPK/ERK activity . How does MAPK/ERK signaling affect InR expression ? Using quantitative RT-PCR we observed a significant reduction in the levels of the mature inr mRNA and the unspliced inr primary transcript upon KSR depletion in S2 cells ( Figure 3D ) . The reduction in inr primary transcript levels upon KSR depletion suggests that MAPK/ERK activity is likely to regulate inr transcription . If so , we would expect transgene-directed expression of inr to be refractory to the effects of MAPK pathway modulation . This proved to be the case . A transfected version of inr under control of a heterologous promoter was insensitive to KSR depletion ( Figure 4A ) . As an in vivo test we used GMR-Gal4 as a heterologous promoter to direct expression of a UAS-InR transgene in the eye . Under these conditions , KSR depletion did not enhance the FOXO overexpression phenotype ( Figure 4B , 4C ) . The insensitivity of transgene-directed InR to the effects of KSR depletion is consistent with the hypothesis of a mechanism involving control of endogenous inr transcription . pointed ( pnt ) encodes an ETS-1 transcription factor that is activated by MAPK/ERK through phosphorylation of a conserved threonine residue , T151 [24] , [25] . We tested its involvement in the regulation of inr expression by depletion of Pointed from S2 cells by RNAi . This caused a prominent reduction of inr mRNA ( Figure 5A ) . To narrow down the regulatory region of the inr gene we systematically analyzed >20 kb upstream of the inr coding region by preparing a series of luciferase reporter constructs ( Figure S5 ) , which lead to identification of a minimal cis-regulatory region of 0 . 8 kB ( Figure 5B ) . Overexpression of Pointed-P2 in S2 cells increased reporter activity directed by this 0 . 8 kB region ( Figure 5C ) . This fragment contained one site perfectly matching the consensus Pointed binding site ( 5′- ( C/G ) ( A/C/G ) GGA ( A/T ) ( A/G ) -3′; [26] ) . Mutating the consensus site reduced the ability of Pointed to induce reporter expression ( Figure 5C ) , suggesting binding to this site contributes to Pointed-mediated regulation of inr gene expression . Reciprocally , Pointed depletion in S2 cells led to a decrease in the level of InR protein ( Figure 5D ) , and to a reduction of insulin-induced AKT S505 phosphorylation ( Figure 5E ) . To further test this relationship in vivo , we asked whether reducing pointed levels would influence the severity of the FOXO overexpression phenotype . Removing one copy of the pointed gene using three independent alleles modestly but significantly enhanced the FOXO overexpression phenotype in the eye ( Figure 5F , Figure S6 ) . These findings suggest that the MAPK/ERK pathway acts via the Ets-1 transcription factor Pointed to control cellular insulin sensitivity . To begin to explore the physiological role of regulation of InR levels by the MAPK/ERK pathway in vivo , we performed a survey of larval tissues and found that KSR depletion led to significant reduction of inr transcript levels in imaginal discs and larval fat body , Drosophila equivalent of liver and adipose tissue ( Figure 6A ) . KSR depletion also led to a reduction of InR protein levels ( Figure 6B ) as well as nuclear FOXO accumulation in the larval fat body ( Figure 6C ) . MAPK/ERK signaling can be regulated by a variety of receptor tyrosine kinases ( RTKs ) . We next made use of pumpless-GAL4 to manipulate RTK activity . Pumpless is active mainly in the fat body , but also other tissues , such as parts of larval gut . Inhibition of Epidermal growth factor ( EGF ) signaling by expression of a dominant negative form of EGFR ( dnEGFR ) led to downregulation of inr mRNA ( Figure 6D ) and protein levels ( Figure 6E ) in the isolated fat body . This suggests that EGFR is a physiologically relevant upstream regulator of MAPK/ERK-mediated control in Inr expression in vivo . Systemic regulation of InR activity has been shown to influence metabolic homeostasis [reviewed in [27]] . In this context , the effects of KSR depletion on inr expression and FOXO localization in fat body were suggestive of a link to energy metabolism . To ask whether reduction of inr to half of normal levels was sufficient to cause a metabolic disturbance , we made use of larvae carrying one copy of the deletion Df ( 3R ) BSC678 , which fully removes the inr gene ( Figure 5B ) . Quantitative RT-PCR was used to confirm that inr mRNA levels were reduced to ∼50% in these animals ( Figure 7A ) . Notably , this result indicates that there is little or no feedback from InR signaling on inr expression , and suggests that there is limited output from InR via the MAPK pathway in Drosophila . In addition , we observed that flies with modestly reduced inr levels showed impaired capacity to limit FOXO activity in the eye ( Figure S7 ) . Larvae lacking one copy of the inr gene showed no significant change in levels of stored glycogen and triglycerides or trehalose ( Figure S8 ) , a circulating disaccharide synthesized by the fat body through glycogenolysis ( [28] ) . However , levels of circulating glucose in the hemolymph were substantially increased ( Figure 7B ) , suggesting compromised clearance of dietary glucose from the circulation . To ask whether this phenotype could be achieved by independent genetic means , we used pumpless-GAL4 to drive expression of a UAS-inrRNAi transgene to deplete InR ( Figure 7C ) . These animals showed elevated glucose in their hemolymph , verifying that regulation of InR levels is physiologically important in vivo in maintaining levels of circulating glucose . This conclusion was further supported by the finding that InR overexpression modestly , but significantly , decreased levels of circulating glucose ( Figure S9 ) . These findings are consistent with what has been reported in flies in which the ability to produce insulin-like peptides was compromised by genetic ablation of the insulin-producing neurosecretory cells [29] . These flies showed a prominent metabolic change at the level of circulating glucose levels [29] . Similarly , insulin signaling in mammals regulates glucose uptake and reduced insulin sensitivity is linked to hyperglycemia , metabolic syndrome and type-2 diabetes [30] , [31] . To test whether MAPK/ERK-regulation of InR expression is involved in maintaining systemic glucose homeostasis , we assessed the effects of KSR RNAi . Depletion of KSR led to elevated levels of circulating glucose ( Figure 7D ) . If the effect of KSR depletion is due to reduced InR levels , we would expect restoring inr expression under Gal4 control to lower glucose levels toward normal . This proved to be the case ( Figure 7D ) . As expected , expression of a dnEGFR using pumpless-GAL4 driver also resulted in elevated circulating glucose ( Figure 7E ) . The glucose levels were restored by simultaneous overexpression of Pointed , which is in agreement with the view that Pointed acts as a downstream effector of the pathway ( Figure 7F ) . These observations suggest a physiological role for EGFR-MAPK/ERK-Pointed activity in control of glucose homeostasis via regulation of InR levels . The insulin signal transduction pathway is regulated by cross-talk from several other signaling pathways . This includes input from the amino-acid sensing TOR pathway into regulation of insulin pathway activity by way of S6 kinase regulating IRS [12]–[14] . Signaling downstream of growth factor receptors has also been linked to regulation of insulin signaling [32] , [33] . The active form of the small GTPase Ras can bind to the catalytic subunit of PI3K and promote its activity . Expression of a form of PI3K that cannot bind Ras allows insulin signaling , but at reduced levels [33] . The work reported here provides evidence for a second mechanism through which growth factor receptor signaling through the MAPK/ERK pathway modulates insulin pathway activity . Transcriptional control of inr gene expression by EGFR signaling may provide a means to link developmental signaling to regulation of metabolism . In this context , we noted a statistically significant correlation between EGFR target gene sprouty and inr gene expression at different stages during Drosophila development ( Figure S10 ) . Several steps of the insulin pathway can be regulated by phosphorylation . Given that the MAPK/ERK pathway is a kinase cascade , a priori , the possibility of phosphorylation-based interaction between these pathways would seem likely . However , this appears not to be the case . Acute pharmacological inhibition of the MAPK/ERK pathway proved to have no impact on insulin pathway activity ( Figure S11 ) . Thus short-term changes in MAPK/ERK pathway activity do not seem to be used for transient modulation of insulin pathway activity . Instead , the MAPK/ERK pathway acts through the ETS-1 type transcription factor Pointed to control expression of the inr gene . Transcriptional control of inr suggests a slower , less labile influence of the MAPK pathway . Taken together with the earlier studies [32] , [33] , our findings suggest that growth factor signaling can regulate insulin sensitivity by both transient and long-lasting mechanisms . Why use both short-term and long-term mechanisms to modulate insulin responsiveness to growth factor signaling ? The use of direct and indirect mechanisms that elicit a similar outcome is reminiscent of feed-forward network motifs [34] . Although these motifs are often thought of in the context of transcriptional networks , the properties that they confer are also relevant in the context of more complex systems involving signal transduction pathways . In multicellular organisms , feed-forward motifs are often used to make cell fate decisions robust to environmental noise [35] . Our findings suggest a scenario in which a feed-forward motif is used in the context of metabolic control , linking growth factor signaling to insulin responsiveness . In this scenario , growth factor signaling acts directly via RAS to control PI3K activity and indirectly via transcription of the inr gene to elicit a common outcome – sensitization of the cell to insulin . This arrangement allows for a rapid onset of enhanced insulin sensitization , followed by a more stable long-lasting change in responsiveness . Thus a transient signal can both allow for an immediate as well as a sustained response . The transcriptional response also makes the system stable to transient decreases in steady-state growth factor activity . We speculate that this combination of sensitivity and stability allows responsiveness while mitigating the effects of noise resulting from the intrinsically labile nature of RTK signaling . As illustrated by our data , failure of this regulation in the fat body leads to elevated circulating glucose levels , likely reflecting impaired clearance of dietary glucose from the circulation by the fat body . Maintaining circulating free glucose levels low is likely to be important due to the toxic effects of glucose [28] . In contrast , circulating trehalose , glycogen or triglyceride levels showed no significant change in animals with reduced InR expression , suggesting that these aspects of energy metabolism can be maintained through compensatory mechanisms in conditions of moderately impaired insulin signaling . Earlier studies by Puig and coworkers have shown that the transcription of the inr gene is under dynamic control [36] , [37] . Activation of FOXO in the context of low insulin signaling leads to upregulation of inr transcription , thus constituting a feedback regulatory loop . Thus , InR expression appears to be under control of two receptor-activated cues , which have opposing activities: inr expression is positively regulated by the EGFR-MAPK/ERK module , but negatively regulated by its own activity on FOXO . In the setting of this study , the cross-regulatory input from the MAPK/ERK pathway was found to dominate over the autoregulatory FOXO-dependent mechanism . If conditions exist in which the FOXO-dependent mechanism was dominant , we would expect to observe a limited potential for crossregulation by the MAPK/ERK pathway . Whether Pointed and FOXO display regulatory cooperativity at the inr promoter is an intriguing question for future study .
UAS-InR , pntΔ88 , pnt07825 , UAS-Pnt-P2 , ksrS-627 , Df ( 3R ) Exel6186 , Df ( 3R ) ED6076 and Df ( 3R ) BSC678 flies were obtained from the Bloomington Stock Center . UAS-RNAi-PI3K , UAS-RNAi-KSR and UAS-RNAi-InR lines were from the Vienna Drosophila RNAi center . pUAST-FOXO-GFP flies were provided by Aurelio Teleman . pUAST-dnEGFR flies were provided by Pernille Rørth . pntT5 flies were provided by Christian Klämbt . S2 cells were grown at 25°C in SFM ( Gibco ) supplemented with L-glutamine . dsRNA was prepared using MegascriptT7 ( Ambion ) with the following templates: PI3K , nucleotides 358–857 of Pi3K92E coding sequence ( FBpp0083348 ) ; ksr , nt 2224–2710 ( FBpp0078413 ) ; D-MEK , nt 961–1191 of the ORF plus the first 83 nt of the 3′UTR ( FBtr0071313 ) ; Raf , nt 522–912 ( FBpp0110324 ) ;GAP1 , nt 153–646 ( FBpp0076096 ) ; pnt , nt 1541–1957 ( 718AA isoform , FBpp0088658 ) ; GFP , nt 17–633 of EGFP2 , was used as control . S2 cells were treated with 37 nM dsRNA . Cells were transfected using effectene reagent ( QIAGEN ) with pMT-GAL4 , pUAST-FOXO-GFP or pUAST-Myc-Dp110CAAX , or pMT-GFP-PH or pMT-InR-Flag or pMT-Pnt-P2 . 0 . 7 mM CuSO4 was used to induce FOXO , Dp110 , GFP-PH , InR or Pnt expression after transfection . The following primers were used to clone InR-Flag into pMT vector with EcoRI , NotI and XhoI sites by fusion: 5′-GGTACCTACTAGTCCAGTGTGGTGGAATTCATGTTCAATATGCCACGGGGAGTGAC-3′; 5′- TTCGAAGGGCCCTCTAGACTCGAGCGGCCGCTTACTTGTCATCGTCGTCCTTGTAGTCCGCCTCCCTTCCGATGAATCCA-3′; 5′- ACGTTGCGCTCGAGCCAGAGCTCGA-3′ and 5′- TCGAGCTCTGGCTCGAGCGCAACGT-3′ . The primers used to clone pntp2 into pMT-Myc by SLIC at EcoRI site were: forward , 5′-AGTGCAACTAAAGGGGAATTCATGGAATTGGCGATTTGTAAAACAG-3′; reverse , 5′- GATAAGCTTCTGCTCGAATTCATCCACATCTTTTTTCTCAATCTTAAG-3′ . The primers used to clone the inr gene regulatory region into pGL3-Basic at HindIII and XhoI sites were: forward , 5′- GCGTGCTAGCCCGGGCTCGAGTGAGAGTTTCATGTGTCAGA -3′; reverse , 5′- AAGCTTACTTAGATCGCAGATGTTAATTGCACAGCAAGCTC-3′ . The primers used to mutate the predicted Pnt consensus site with QuickChange II XL kit were: forward , 5′-GAGAATGCCGGAGATGAAGACGCGAACGAAGATGAAGTCGATG-3′; reverse , 5′- CATCGACTTCATCTTCGTTCGCGTCTTCATCTCCGGCATTCTC-3′ . For FOXO-GFP localization , live S2 cells were imaged using a Leica SP5 confocal microscope . Images were taken of random fields within 15 min after 10 µg/ml insulin boost for 30 min and scored for GFP localization ( scoring was done ‘blind’ ) . For GFP-PH images were taken within 10 min after 10 µg/ml insulin boost for 5 min . The ratio of membrane to cytoplasmic GFP levels was measured as pixel intensity along the white line as indicated in Figure 2C ( left panel ) . For fat body FOXO immunofluorescent staining , newly hatched 1st instar larvae were seeded at 50/vial and reared at 25°C . Wandering 3rd instar larvae were dissected . Tissues were fixed in PBS with 4% paraformaldehyde at room temperature for 20 min . Anti-FOXO antibody [36] was used at 1∶1000 dilution . Fat body connected with salivary gland was imaged using a Zeiss LSM700 confocal microscope . Cells were homogenized in SDS sample buffer , boiled and resolved by SDS-PAGE before transfer to nitrocellulose membranes for antibody labeling . Antibodies to phospho-S505-AKT , AKT , P-InR and Myc were from Cell Signaling Technology . Anti-Kinesin was from Cytoskeleton . Phospho-ERK antibody was from Sigma . Anti-S6K is described in [38] . Anti-dInR is described in [37] . Total RNA was extracted from S2 cells using QIAGEN RNeasy Mini Kit and treated with On-Column DNase ( QIAGEN RNase-Free DNase Set ) at room temperature for 15 min to eliminate genomic DNA contamination . Reverse transcription to synthesize the first strand used oligo-dT primers and Superscript RT-III ( Invitrogen ) . PCR was performed using POWER SYBR GREEN Master Mix ( Applied Biosystems ) and analyzed on Applied Biosystems 7500 fast real-time PCR system . Results were normalized to Kinesin mRNA , and rp49 was used as a control . The following primers were used: Kinesin-f , 5′-GCTGGACTTCGGTCGTAGAG-3′; Kinesin-r , 5′- CTTTTCATAGCGTCGCTTCC-3′; rp49-f , 5′- GCTAAGCTGTCGCACAAA-3′; rp49-r , 5′- TCCGGTGGGCAGCATGTG-3′; InR-f , 5′- CTGGTGGTGCTGACAGAGAA-3′; InR-r , 5′- GCAGCTGACAACTGGCATTA-3′; pri-InR-f , 5′- CAAGAGACAGCAACAAAAGG-3′; pri-InR-r , 5′- GCTTGCATGTGTTGGTGAGC-3′; KSR-f , 5′- AGCCGAGCGAAGATTGTAAA-3′; KSR-r , 5′- TCCCGATACATGCCTACACA-3′; pnt-f , 5′- CGATGCGAATGCCTACTACACG-3′; pnt-r , 5′- TGCTGGTGTTGTAGCCTGAAC-3′ . Newly hatched 1st instar larvae were seeded at 50/vial and reared at 25°C . Hemolymph was extracted from wandering stage 3rd instar larvae . 2 µl of pooled hemolymph was diluted with 8 µl Tris buffered saline ( pH 6 . 6 ) and incubated at 70°C for 5 min before clarification by centrifugation at 20 000×g for 1 min . Glucose was measured in 6 µl supernatant using the GAGO-20 kit ( Sigma ) and normalized to the same amount of TBS as blank control . For trehalose measurement , supernatant was incubated with 7 . 5 µg trehalase ( Sigma ) overnight at 37°C and measured using GAGO-20 kit as well . For glycogen and triglyceride measurements , 3rd instar larvae were homogenized using Sartorius Potter-S tissue homogenizer in water with 0 . 05% Tween . Supernatant was collected after 5 min of heat inactivation at 70°C and centrifugation at 13000 rpm for 3 min . Glycogen and protein levels were measured using Glycogen assay kit ( Bio Vision ) and Bio-Rad protein assay reagent , respectively . Triglyceride was measured using Sigma Triglyceride kit . Data were normalized to total protein .
|
Insulin signaling is an important and conserved physiological regulator of growth , metabolism , and longevity in multicellular animals . Disturbance in insulin signaling is common in human metabolic disorders . For example insulin resistance is a hallmark of diabetes and metabolic syndrome . While the core components of the insulin signaling pathway have been well established , the mechanisms that adjust the insulin responsiveness are only known to a limited extent . Here we present results from a genetic screen in Drosophila that was designed to identify regulators of cellular insulin sensitivity in an in vivo context . Surprisingly , we discovered cross-talk between the epidermal growth factor receptor ( EGFR ) –activated MAPK/ERK and insulin signaling pathways . This regulatory mechanism , which involves transcriptional control of insulin-like receptor gene , is utilized in vivo to maintain circulating glucose at appropriate levels . We provide evidence for a regulatory feed-forward mechanism that allows for dynamic transient responsiveness as well as more stable , long-lasting modulation of insulin responsiveness by growth factor receptor signaling . The combination of these mechanisms may contribute to robustness , allowing metabolism to be appropriately responsive to physiological inputs while mitigating the effects of biological noise .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology"
] |
2011
|
MAPK/ERK Signaling Regulates Insulin Sensitivity to Control Glucose Metabolism in Drosophila
|
The accurate diagnosis and clinical management of the growth restriction disorder Silver Russell Syndrome ( SRS ) has confounded researchers and clinicians for many years due to the myriad of genetic and epigenetic alterations reported in these patients and the lack of suitable animal models to test the contribution of specific gene alterations . Some genetic alterations suggest a role for increased dosage of the imprinted CYCLIN DEPENDENT KINASE INHIBITOR 1C ( CDKN1C ) gene , often mutated in IMAGe Syndrome and Beckwith-Wiedemann Syndrome ( BWS ) . Cdkn1c encodes a potent negative regulator of fetal growth that also regulates placental development , consistent with a proposed role for CDKN1C in these complex childhood growth disorders . Here , we report that a mouse modelling the rare microduplications present in some SRS patients exhibited phenotypes including low birth weight with relative head sparing , neonatal hypoglycemia , absence of catch-up growth and significantly reduced adiposity as adults , all defining features of SRS . Further investigation revealed the presence of substantially more brown adipose tissue in very young mice , of both the classical or canonical type exemplified by interscapular-type brown fat depot in mice ( iBAT ) and a second type of non-classic BAT that develops postnatally within white adipose tissue ( WAT ) , genetically attributable to a double dose of Cdkn1c in vivo and ex-vivo . Conversely , loss-of-function of Cdkn1c resulted in the complete developmental failure of the brown adipocyte lineage with a loss of markers of both brown adipose fate and function . We further show that Cdkn1c is required for post-transcriptional accumulation of the brown fat determinant PR domain containing 16 ( PRDM16 ) and that CDKN1C and PRDM16 co-localise to the nucleus of rare label-retaining cell within iBAT . This study reveals a key requirement for Cdkn1c in the early development of the brown adipose lineages . Importantly , active BAT consumes high amounts of energy to generate body heat , providing a valid explanation for the persistence of thinness in our model and supporting a major role for elevated CDKN1C in SRS .
Silver-Russell syndrome ( SRS; MIM 180860 ) , Beckwith Weidemann Syndrome ( BWS; MIM 130650 ) and IMAGe syndrome ( MIM 614732 ) are all rare imprinted developmental disorders that occur as a result of genetic or epigenetic alterations primarily at human chromosome 11p15 [1 , 2] . Recent studies highlight the potential involvement of one maternally expressed imprinted gene , CYCLIN DEPENDENT KINASE INHIBITOR 1C ( CDKN1C ) , in all three disorders [3] . Loss-of-function or loss-of-expression of CDKN1C is a common feature of BWS , either through direct DNA mutation , epigenetic misregulation or loss of the maternal chromosome [4] . The rare IMAGe syndrome , which has the major features of fetal growth restriction , metaphyseal displasia , adrenal hypoplasia congentia and genital abnormalities , is associated with genetic mutations in the CDKN1C gene [5 , 6] . The changes associated with growth restriction are gain-of-function mutations of the PCNA domain , limited to a handful of rare familial cases highlighted in a recent review [3] , that may increase the stability of the protein [6 , 7] . SRS is characterised by severe pre and post natal growth restriction combined with some of the following: neonatal hypoglycaemia , excessive sweating , triangular shaped face , head circumference of normal size but disproportionate to a small body size , clinodactyly , feeding problems , low body mass index manifesting as extreme thinness , no catch up growth and increased risk of delayed development and learning disabilities [8] . Numerous genetic and epigenetic alterations have been reported in SRS patients but identifying the causal gene mutation ( s ) has been challenging . Some studies suggest loss of function of the paternally expressed growth factor INSULIN-LIKE GROWTH FACTOR 2 ( IGF2 ) [9] . However , there are SRS patients that carry an extra copy of maternally derived 11p15 without loss-of-function of IGF2 [10] . Maternal duplications spanning the complex imprinted domain at 11p15 have been independently reported in a number of studies [11–16] and the majority are associated with unbalanced translocations suggesting that increased dosage of a maternally expressed imprinted gene may be important in SRS . The minimal region of maternal microduplication in SRS encompasses CDKN1C and three other maternally expressed protein coding genes POTASSIUM CHANNEL , VOLTAGE GATED KQT-LIKE SUBFAMILY Q , MEMBER 1 ( KCNQ1 ) , PLECKSTRIN HOMOLOGY-LIKE DOMAIN , FAMILY A , MEMBER 2 ( PHLDA2 ) and SOLUTE CARRIER FAMILY 22 , MEMBER 18 ( SLC22A18 ) [17 , 18] . Since CDKN1C is a maternally expressed gene [19 , 20] , these SRS patients are predicted to have twice the normal level of CDKN1C expression . We , and others , have shown that loss of Cdkn1c in mice results in a late fetal overgrowth and disrupted placental development [21–25] consistent with a key role for this gene in BWS . CDKN1C , which is maternally expressed in both humans and mice [19 , 20] , belongs to the Kip cyclin dependent kinase inhibitor family that induce cell cycle arrest and limit proliferation [26] . In mice , Cdkn1c is widely expressed during embryonic development in cells exiting differentiation [27–29] . Cdkn1c also functions to orchestrate cell fate determination targeting key transcription factors [30–36] and in stem cell self-renewal and quiescence in a number of embryonic [32–34 , 37–40] and adult [41–43] stem/progenitor cells . These multiple roles may account for complex phenotypic consequences in response to alterations in the dosage of this gene . We previously reported growth restriction in mice carrying a bacterial artificial chromosome ( BAC ) transgene spanning Cdkn1c , Phlda2 and Slc22a18 [24] . This alteration essentially models the minimal microduplicated region observed in some SRS patients [17 , 18] . The mice exhibited significant fetal growth restriction from embryonic day ( E ) 13 . 5 with the absence of catch-up growth . We were able to attribute the fetal growth restricting properties of this microduplication to two-fold expression of Cdkn1c consistent with the phenotype observed in SRS . Fetal growth restriction per se is a relatively generic phenotype and more specific features of SRS would lend greater support to the hypothesis that altered expression of CDKN1C contributes significantly to SRS in human patients . To provide further evidence for or against a key role for CDKN1C in SRS , we examined the microduplication mice for additional SRS-associated phenotypes . This work revealed low birth weight with a relative sparing of the head , neonatal hypoglycaemia , small sized adults with substantially less white adipose tissue , all of which were genetically attributable to just two-fold expression of Cdkn1c . These findings support a major role for elevated CDKN1C in SRS . Importantly , we identified a novel function for Cdkn1c in directly promoting the development of brown adipose tissue early in life , a finding that could account for the prevalence of thinness in SRS .
Cdkn1c is expressed from the BAC in a number of tissues including the pituitary , the hypothalamus and the pancreas [51] that may stimulate the browning of WAT . However , elevated Cdkn1c expression within transgenic rpWAT ( Fig 2G ) suggested the potential for a direct role for Cdkn1c in brown adipogenesis . Expression of Cdkn1c has been reported in the epididymal white adipose tissue of adult mice [52] . At postnatal day 7 ( P7 ) , Cdkn1c expression was detectable within several adipose depots with levels positively correlating with the brown adipose-like nature of these depots [53] . Cdkn1c was found to be most highly expressed in the interscapular-type brown fat depot ( iBAT ) , which is composed of a classical or canonical type of BAT sharing a developmental origin with myoblasts [48] , with moderate expression in rpWAT and subcutaneous ( sc ) WAT and lowest expression in mesenteric ( mes ) WAT ( Fig 3A ) . At E16 . 5 , when iBAT is discernable as a discrete depot , a few Cdkn1c-positive cells were identifiable by both in situ hybridisation and immunohistochemistry ( Fig 3B ) . At P7 , Cdkn1c was more widely expressed within the iBAT depot ( Fig 3C , left panel ) . Cdkn1c was also expressed within a few discrete niches in P7 rpWAT ( Fig 3D , left panel ) . Importantly , BACx1 and BACx2 rpWAT and iBAT displayed a similar expression pattern to WT depots by in situ hybridisation ( Fig 3C and 3D , middle panels ) indicating that Cdkn1c was not ectopically expressed from the transgene in these depots . β-galactosidase staining of dissected intact depots from BAC-lacZ pups revealed blue staining niches consistent with expression originating from the transgene in both depots ( Fig 3C and 3D , far right panels ) . To determine whether expression of Cdkn1c was imprinted in adipose tissue , we made use of the Cdkn1c restriction fragment length polymorphism ( RFLP ) assay [54] . Mus musculus domesticus BL6 mice possess an AvaI restriction enzyme site within an exon of Cdkn1c that is absent in Mus spretus mice ( Fig 3E ) . P7 pups were generated from crosses between pure BL6 females and BL6 males carrying a copy of the Mus spretus Cdkn1c region . AvaI digestion of a PCR product amplified across the polymorphic region from genomic DNA demonstrated that both alleles were physically present . Digestion of the PCR product amplified from cDNA revealed the predominant presence of only the maternally inherited BL6 allele ( lower band ) in both P7 iBAT and P7 rpWAT ( Fig 3E ) . Similarly , depots from adult mice displayed predominantly maternal-allele expression ( Fig 3E ) . Differential DNA methylation spanning the predicted Cdkn1c promoter region [55] was also discernable in both adipose depots at P7 and in the adult ( Fig 3F ) . These data demonstrated that Cdkn1c was both expressed and imprinted in post-natal adipose tissue , and that both expression and imprinting was maintained into adulthood . The in situ hybridisation analysis ( Fig 3D ) and further histological examination of rpWAT at P7 suggested that the phenotypic differences present in adult mice were apparent at this much earlier timepoint ( Fig 4A ) . Electron microscopic imaging of BACx1 P7 rpWAT depots revealed clusters of cells that possessed BEIGE characteristics including a larger volume of cytoplasm , numerous mitochondria and smaller , multilocular , lipid droplets not readily observed in matched WT depots ( Fig 4B ) . QPCR demonstrated that Cdkn1c expression was significantly elevated in BACx1 and BACx2 P7 rpWAT , by 1 . 5- and 2 . 2-fold respectively ( Fig 4C ) . Several markers of BAT were also elevated including peroxisome proliferator-activated receptor gamma , coactivator 1 alpha ( Ppargc1a ) , cell death-inducing DFFA-like effector a ( Cidea ) , Ucp1 , Elovl3 and PR domain containing 16 ( Prdm16 ) in BACx1 and BACx2 P7 rpWAT ( Fig 4D ) . Consistent with 10-fold higher expression of Ucp1 mRNA , UCP1 protein was more readily detectable in BACx1 rpWAT than in WT rpWAT in a within litter comparison ( Fig 4E ) . PRDM16 , a brown fat determinant [48] , was also more readily detectable in BACx1 rpWAT depots than wild type depots ( Fig 4E ) . Importantly , P7 rpWAT from the reporter line BAC-lacZ had a normal appearance and neither Cdkn1c nor key BAT markers were elevated ( S3A Fig ) . The presence of BEIGE-like cells in the BACx1 and BACx2 rpWAT and their absence in the BAC-lacZ model , in which Cdkn1c was expressed at a normal level , identified Cdkn1c as a gene that promotes the “browning” of WAT . Importantly , this phenotype was apparent when WAT first emerged as a distinct depot in very young mice . Elevated expression of Cdkn1c also had an effect on iBAT . P7 BACx1 and BACx2 iBAT depots were heavier as a proportion of total body weight , by 30% and 60% respectively , than WT iBAT depots ( Fig 5A ) . This was not due to increased lipid deposition as BACx1 and BAC2 iBAT depots displayed increased cellularity , confirmed by cell counting ( Fig 5B ) . As in rpWAT , Cdkn1c expression was significantly elevated in BACx1 and BACx2 iBAT , by 1 . 5- and 2 . 2-fold respectively ( Fig 5C ) . QPCR analysis revealed near wild type expression of the adipogenesis regulators retinoblastoma 1 ( Rb1 ) , peroxisome proliferator-activated receptor-γ ( PPARγ ) and CCAAT-enhancer-binding protein-α ( C/EBPα ) but elevated expression of CCAAT-enhancer-binding protein-β ( C/EBPβ ) in both BACx1 and BACx2 depots ( Fig 5C ) . BAT markers Ppargc1a , Ucp1 and Elovl3 were significantly elevated in BACx1 . All five BAT markers examined were significantly elevated in the higher dosage line , BACx2 ( Fig 5C ) . Critically , BAC-lacZ iBAT appeared morphologically normal and neither Cdkn1c nor key BAT markers were elevated ( S3B Fig ) genetically assigning these alterations to the increased dosage of Cdkn1c in BACx1 and BACx2 . The ratio of mitochondrial DNA to nuclear DNA can be used as an estimate of mitochondrial load . Both BACx1 and BAC2 P7 iBAT depots contained significantly increased mitochondrial DNA content compared to WT ( Fig 5D ) . Consistent with a greater mitochondrial load , expression of the nuclear mitochondrial marker cytochrome c , somatic ( Cycs ) was significantly elevated in BACx2 and both Cycs and the mitochondrion-encoded cytochrome c oxidase subunit II ( Cox2 ) were elevated in BAC1 and BACx2 iBAT ( Fig 5C ) . Fully functional iBAT at birth is important for maintaining newborn body temperature . 33°C approaches thermoneutrality and corresponds to the temperature within litters of newborn mice in contact with their mother [56] whereas 26°C elicits near-maximal thermogenesis by brown adipose tissue [57] . P2 WT and BACx2 pups kept at 33°C and then exposed to room temperature ( 22°C ) for a 20 minute period both lost body heat at the same rate despite significant differences in their body weights ( Fig 5E ) , consistent with functional iBAT at this timepoint . Taken together , these data identified a novel function for Cdkn1c in boosting the development of both BEIGE and iBAT early in post-natal life , with increasing expression of Cdkn1c associated with the increased development of brown adipose . In contrast to the increase in classic iBAT in response to elevated Cdkn1c , loss-of-expression of Cdkn1c resulted in the loss of iBAT . Mice inheriting a targeted deletion of Cdkn1c maternally ( loss-of-function ) die in the neonatal period [21] . Cdkn1c-/+ ( KOMAT ) embryos examined a day prior to neonatal demise , at E18 . 5 , possessed poorly discernable iBAT depots lacking the characteristic butterfly shape normally observed at this stage of development ( Fig 6A ) . H&E staining of the isolated KOMAT depots revealed a disorganised morphology with large areas of lipid ( Fig 6B ) . QPCR analysis confirmed considerably reduced expression of Cdkn1c . KOMAT expressed relatively normal levels of Rb1 , PPARγ and C/EBPα ( Fig 6C ) . C/EBPβ was expressed at 50% the wild type level reciprocal to the increased expression observed in response to elevated Cdkn1c . Pan-adipocyte markers fatty acid binding protein 4 ( Fabp4 ) and perilipin 1 ( Plin1 ) were also markedly reduced . Prdm16 was expressed at wild type levels while expression of peroxisome proliferator-activated receptor gamma , coactivator 1 alpha ( Ppargc1a ) , which encodes a transcriptional coactivator that is involved in the activation of brown fat cells , was markedly elevated indicating that the initiation of brown adipocyte commitment was not prevented by loss of Cdkn1c . Nonetheless , there was a marked reduction in expression of downstream genes required for brown adipose development and function including the cAMP-inducible gene , Ucp1 , and the cAMP insensitive genes Cidea and Elovl3 ( Fig 6C ) . Expression of the nuclear mitochondrial marker Cycs and the mitochondrion-encoded Cox2 were also diminished , by 25–30% ( Fig 6C ) . Mitochondrial DNA content was 40% less than the wild type level ( Fig 6D ) . UCP1 and PRDM16 proteins were barely detectable in Cdkn1c KOMAT iBAT ( Fig 6E and 6F ) , all indicative of severely compromised BAT development . Loss of function of PRDM16 in iBAT early in life results in a switch from an iBAT identity towards a skeletal muscle identity [48] . Consistent with the loss of PRDM16 , Cdkn1c KOMAT iBAT expressed two-fold higher levels of the skeletal muscle-selective genes myogenic factor 5 ( Myf5 ) and myogenic differentiation 1 ( Myod1 ) ( Fig 6C ) further supported by western analysis for MYOD1 ( Fig 6G ) . These data identified a requirement for Cdkn1c in the development of classic BAT . To determine whether Cdkn1c could function intrinsically to boost brown adipogenesis , we performed an ex-vivo adipogenesis assay . Mouse embryonic fibroblasts ( MEFs ) are multipotent and have the potential to differentiate into brown adipocytes . MEFs were isolated from E12 . 5 BACx1 and WT fetuses and induced to differentiate using a standard adipogenic protocol [58] . The expression profile of Cdkn1c in both WT and BACx1 MEFs followed similar pattern of up regulation by day 2 and down regulation by day 8 of differentiation , with BACx1 MEFs expressing consistently higher levels of Cdkn1c at each time point ( Fig 7A ) . Having confirmed elevated expression of Cdkn1c in the differentiating MEFs , a single copy of the Cdkn1c transgene was genetically combined with a maternally inherited targeted deletion of Cdkn1c ( KOMAT ) to generate MEFs of four genotypes: WT , BACx1 , KOMAT and KO+BACx1 . After 8 days of adipocyte-directed differentiation Cdkn1c was expressed 1 . 4-fold the WT level in BACx1 MEFs and at barely detectable levels in KOMAT MEFs ( Fig 7B ) . KO+BACx1 MEFs , which carried both the transgene and the targeted allele , expressed Cdkn1c at WT levels ( Fig 7B ) . All four genotypes differentiated into lipid-containing cells , as evidenced by Oil-Red O staining and mRNA levels for general adipogenic markers ( Fig 7C and S4 Fig ) . As in vivo , key markers of BAT fate and function Cidea , Ucp1 and Elovl3 were elevated in BACx1 D8 MEFs ( Fig 7D ) . Importantly , KO+BACx1 MEFs , in which Cdkn1c was expressed at WT levels , did not display altered expression of these markers ( Fig 7D ) . After 8 days of adipocyte-directed differentiation BACx2 D8 MEFs displayed 2 . 4-fold elevated expression of Cdkn1c and further elevated expression of several BAT markers ( Fig 7E ) consistent with the dosage-related function of Cdkn1c in inducing a BAT-like gene program . Confocal imaging suggested more mitochondria in the BACx1 differentiated samples ( Fig 7F ) , consistent with in vivo data ( Fig 4B ) . UCP1 protein was detectable in BACx1 D8 MEFs but not WT MEFs , a difference further highlighted by exposure to the positive regulator of Ucp1 gene transcription , retinoic acid [59 , 60] ( Fig 7G ) . Taken together , these data demonstrated that Cdkn1c can drive a BAT-like cell fate in adipocyte-differentiated fibroblast cells ex-vivo , and in a dosage-sensitive manner . There was a considerable loss of PRDM16 protein in Cdkn1c KOMAT iBAT ( Fig 6F ) but Prdm16 mRNA levels were relatively unaltered ( Fig 6C ) suggesting a function for Cdkn1c in the post transcriptional regulation of PRDM16 . Consistent with this role , CDKN1C protein co-localised with the brown fat determinant , PRDM16 , to the nucleus of rare cells present within P7 iBAT ( Fig 8A ) . Acute loss of CDKN1C , driven by siRNA transfection of the brown preadipocyte cell line HIB-1B [61] , resulted in a reduction of PRDM16 protein ( Fig 8B ) . Prdm16 and Cdkn1c are both known to be functionally important for adult haematopoietic stem cells [41 , 62] and adult neural stem cells [63 , 64] . 5-bromo-2-deoxyuridine ( BrdU ) label retention has been defined as a characteristic attributed to slow-cycling adult stem cells [65] . In two independent experiments , pregnant females were injected with BrdU ( single dose at E16 . 5 or four doses of BrdU from E16 . 5 ) . Within the adult iBAT from offspring of these pregnancies CDKN1C/PRDM16 double positive cells retained BrdU for six to eight weeks after embryonic labeling ( Fig 8C ) . Taken together , all our data suggest that CDKN1C functions to support the post transcriptional accumulation of PRDM16 in a progenitor cell , and thus promotes the development of brown fat .
Here we provide in vivo evidence for key features of SRS in a novel mouse model of the minimal microduplicated region reported in some patients with this syndrome [66 , 67] including low birth weight , head sparing , neonatal hypoglycaemia , smallness as adults and an extreme lack of body fat . Critically , we show that these phenotypes were due solely to the two-fold increased dosage of Cdkn1c consistent with the predicted expression levels in SRS patients . In our model , Cdkn1c was not ectopically expressed nor was Cdkn1c expressed at excessively high levels thus our findings are physiologically relevant . In addition to providing compelling evidence for a major role of elevated CDKN1C is SRS , we demonstrated in vivo and ex-vivo that Cdkn1c promotes the formation of brown adipose tissue , both the classic form exemplified by the iBAT depot and also the BIEGE form that emerges within WAT depots which persists into adulthood . Moreover , our data suggest that Cdkn1c functions to boost BAT , in part , by supporting protein accumulation of the brown fat determinant , Prdm16 . This work has implications both for the diagnosis of SRS and the clinical management of SRS patients . Microduplication mice were born low birth weight with a relative sparing of the head and neonatal hypoglycaemia . As adults the mice failed to catch-up in weight with their littermates and possessed substantially less white adipose tissue . We were able to exclude a role for two other genes present on the BAC ( Phlda2 and Slc22a18 ) in driving these phenotypes by using a reporter line in which expression of the BAC copy of Cdkn1c was replaced by lacZ [51] . While fetal growth restriction and low birth weight are relatively common complications of pregnancy that can have numerous origins , the more specific features of SRS support a major role for elevated CDKN1C expression in SRS . Currently the diagnosis of SRS is hampered by the complexity of alterations reported in different patients and the variable presentation of phenotypes . Moreover , some alterations may have an epigenetic origin not detectable by traditional DNA based approaches or not present in accessible tissues . The greater certainty that CDKN1C is a major contributor to SRS should lead to the development of better diagnostic tools and potentially the improved sub-classification of patients . It would now seem pertinent to examine BAT in SRS patients and , conversely , to assess individuals with a diagnosis of fetal growth restriction followed by extreme thinness for alterations in the expression of CDKN1C . In addition to observing several defining features of SRS in our microduplication model , we identified Cdkn1c as a gene that functions in vivo , and in a dosage sensitive manner , to boost the amount of brown adipose tissue that develops early in life . Elevated Cdkn1c was associated with an increased amount of BEIGE adipose ( non-classic BAT ) located within the rpWAT depot in very young and in adult mice . Transgenic rpWAT depots had a marked appearance of BAT-like niches and expressed much higher levels of several BAT markers including Elovl3 and Cidea , markers that are insensitive to cAMP . Both UCP1 and PRDM16 protein were readily detectable in BACx1 rpWAT depots in comparison to wild type depots in within litter comparisons . Elevated Cdkn1c also resulted in a larger iBAT depot relative to body weight in young mice and augmented the existing brown adipose gene program . The function of Cdkn1c in boosting the formation of BAT early in life would explain neonatal hypoglyceamia and the failure of our mice to lay down sufficient stores of white adipose tissue into adulthood manifesting as thinness . While Cdkn1c was expressed from the BAC in a number of tissues including the pituitary , the hypothalamus and the pancreas [51] that may stimulate the browning of WAT , Cdkn1c was expression and imprinted within both rpWAT and iBAT depots . Importantly , elevated Cdkn1c enhanced the expression of brown adipose marker genes in adipogenically-differentiated MEFs . Normalising Cdkn1c by combining a single copy of the transgene with maternal inheritance of the targeted Cdkn1c allele in this same experiment resulted in wild type levels of both Cdkn1c and the BAT markers . This experiment demonstrated the intrinsic ability of Cdkn1c to drive a BAT-like gene program ex-vivo . Our findings that Cdkn1c plays a key role in promoting BAT development is novel and has important implications both for our understanding of BAT development . Elevated Cdkn1c boosted the development of BAT while loss-of function of Cdkn1c resulted in abnormal morphology of the iBAT depot alongside a striking reduction in the expression of several brown adipose markers , and loss of UCP1 and PRDM16 protein . Classic iBAT derives from a common progenitor to skeletal muscle and a switch between these two lineages is thought to be controlled by Prdm16 [48] . Consistent with loss-of-function of Prdm16 , Cdkn1c KO iBAT expressed elevated levels of two muscle-specific genes . While PRDM16 protein was barely detectable , Cdkn1c KO iBAT expressed normal levels of Prdm16 mRNA . Acute knock-down of CDKN1C in a brown fat cell line resulted in the loss of PRDM16 protein suggesting that Cdkn1c acts to regulate the post transcriptional accumulation of PRDM16 . A precedent exists for Cdkn1c in regulating the post-transcriptional accumulation of several other transcription factors [30–36 , 68 , 69] . Moreover , co-expression of CDKN1C and PRDM16 in the nucleus of a rare , BrdU label-retaining cell in iBAT suggests that regulation takes place with an adult brown adipose progenitor cell . Prdm16 and Cdkn1c are both already known to be functionally important for adult HSC [41 , 62] and adult NSC [63 , 64] . However , it remains controversial whether label retention is a definitive feature of stem cells and further work is required demonstrate that the PRDM16/CDKN1C double positive cells are indeed brown fat progenitors . What is clear is that both Prdm16 and Cdkn1c are required for the proper determination of BAT cell fate , as evidenced by elevated expression of the myogenic markers Myf5 and MyoD in response to loss-of-function of Prdm16 [48] and Cdkn1c ( Fig 6 ) . Rather than participating in cell fate decisions , we propose that Cdkn1c modulates the accumulation of PRDM16 to promote “brownness” , acting downstream of cell fate choice . Our mouse model recapitulated several defining features of SRS but there are potential limitations with this study . Firstly , the human and mouse CDKN1C predicted proteins share amino acid sequence conservation in the cyclin-dependent kinase inhibitory domain and in the QT domain , but the internal proline-rich and an acidic repeat domains found in the mouse sequence are replaced by a single PAPA repeat in the human sequence [27] . A key question that therefore arises is whether CDKN1C functions in humans to regulate brown adipogenesis ? Although a low body mass index is consistent with more brown adipose tissue , we can find no report examining brown adipose tissue in SRS patients . However , recent data suggest that increased methylation at CDKN1C is associated with a higher BMI in a normal population [70] which holds promise . Secondly , while the transgenic model partially recapitulates the minimal microduplication observed in SRS , some key Cdkn1c enhancers located at a distance from the gene are absent from the mouse transgene [51] . We have examined the consequences of increased dosage in only a subset of tissues in which Cdkn1c is normally expressed , which excludes skeletal muscle and cartilage . However , this is likely to also be true for the SRS syndrome patients with smaller microduplications as the human CDKN1C enhancers are also located at a distance from the gene body [55] . Loss-of-function of CDKN1C in humans has been reported in cases of BWS . Excessive weight gain , which might be anticipated from a lack of BAT , is not a feature of BWS . BWS children can display neonatal hypoglycemia and one recent study reported early onset diabetes in a family with a mutation in CDKN1C [71] , all of which could suggest a metabolic function for CDKN1C in humans . There are differences in the epigenetic regulation between humans and mice with some expression from the paternal allele in humans [55] which may attenuate the phenotype in BWS . Our findings may therefore have implications for several rare human imprinting disorders . There is now sufficient evidence from animal models and human studies to indicate a key role for the imprinted CDKN1C gene in SRS , BWS and IMAGe syndrome ( Fig 9 ) . This knowledge will undoubtedly improve our understanding of these complex childhood growth disorders and their longer term implications . From an evolutionary perspective , our finding that Cdkn1c acts early in life to promote the formation of brown adipose tissue in mice is also intriguing . Thermogenesis is critical for the survival of young mammals before the development of subcutaneous fat and hair but comes at an energetic cost to the individual . Cdkn1c is both a BAT-promoting gene and one that negatively regulates embryonic growth [24 , 25] . Our data predict that silencing of Cdkn1c by the paternal genome , which occurred after mammals diverged from marsupials [72] , would result in larger offspring with the simultaneous reallocation of resources away from maintaining body temperature towards supporting the enhanced growth , providing a clear competitive advantage and lending support to the hypothesis that thermogenesis is an arena for genomic conflict in mammals [73] . In conclusion , this work provides genetic evidence from a physiological relevant animal mode that Cdkn1c functions to boosts the development of BAT in mice . This work fundamentally establishes that Cdkn1c gene dosage , rather than gene function per se , plays a key role in this process . We critically show that relatively small ( < two-fold ) changes in gene expression can have a dramatic consequence for development in mice with long lasting consequences . If these functions hold true in humans , this information will provide a step change in our understanding of the pathologies that occur in SRS and potentially other disorders including BWS and IMAGe syndrome , leading to improved diagnosis and the clinical management of patients .
All animal studies and breeding was approved by the University of Cardiff ethical committee and performed under a UK Home Office project license ( RMJ ) . Mice were housed on a 12 hour light–dark cycle with lights coming on at 06 . 00 hours with a temperature range of 21°C +/- 2 with free access to tap water and standard chow . BAC transgenic lines Cdkn1cBACx1 , Cdkn1cBACx2 and Cdkn1c BAC-lacZ , were bred onto a C57BL/6J ( BL6 ) background for >12 generations and genotyped as described [51] . The Cdkn1ctm1Sje allele [21] for historical reasons was maintained on the 129S2/SvHsd ( 129 ) background . Cdkn1c-RFLP mice were generated by crossing a M . m . spretus male with a BL6 female and selecting for the Cdkn1c AvaI RFLP for >8 generations . Basal body temperatures of group-housed , experimentally naive female transgenic mice were monitored with a rectal probe ( IN005A , Vet Tech solution ) . Surface temperature of P2 pups was recorded using a thermal imaging camera ( Optris P1200 ) . Glucose concentrations in whole blood were determined in neonatal pups in the fed state with the HemoCue system . β-galactosidase ( lacZ ) staining , H&E staining and in situ hybridisation were performed as previously described [51] . CDKN1C immunohistochemistry: 10 μm sections were prepared from E16 . 5 fetuses fixed overnight in 4% PFA at 4°C and paraffin embedded . Slides were dewaxed in xylene and rehydrated through graded ethanols , submerged in 1X Citrate Buffer ( DAKO ) and heated in a pressure cooker for 20 minutes . Slides were cooled and blocked for 20 minutes in Peroxidase Block ( Envsion ) , then 30 minutes in 10% normal rabbit serum and 1% BSA in PBS , and then incubated in primary antibody ( Santa Cruz P57 M-20; SC-1039 ) overnight at room temperature diluted 1:50 in 10% rabbit serum and 1% BSA in PBS , washed in PBS , incubated with 1:200 dilution of HRP-conjugated rabbit anti-goat IgG secondary ( DAKO ) for 1 hour at room temperature , washed in PBS 3 x 5 minutes at room temperature and visualized with DAB ( DAKO ) . Slides were counterstained in Mayers Haematoxylin , dehydrated , cleared and mounted in DPX mounting medium . For electron microscopy , rpWATs from P7 mice were fixed overnight in 2% PFA/2% gluteraldehyde in 0 . 1M Sorensons PB , post fixed in 1% osmium tetraoxide for 2 hours and stained in uranyl acetate overnight at 4°C . After sequential dehydration , samples were embedded in pure araldite and ultra-thin sections were visualised under Philips TEM 208 transmission electron microscope ( Phillips ) . Cryosections were incubated with primary antibodies ( 1:100 dilution ) for 3 hours at room temperature , washed in PBS before incubation with fluorescent secondary antibodies ( 1:1000 dilution ) for one hour at 4°C followed by 4’ , 6-diamidino-2-phenylindole ( DAPI ) staining . Slides were mounted using Fluoromount aqueous media ( Sigma ) and imaged using Leica TCS SP2 AOBS laser confocal microscope , and Leica Confocal software . Samples were scanned with appropriate excitation and emission settings ( S2 Table ) . To identify label-retaining cells in iBAT , we performed two BrdU pulse chase experiments . WT pregnant mice were intraperitoneally administered injections of BrdU at 80 mg/kg/time ( Sigma , USA ) either once at E16 . 5 or twice daily from E16 . 5 for two days . Offspring born from these pregnancies were euthanised 6–8 weeks after the last BrdU injection . iBAT was harvested and cryosections were incubated with the primary antibodies to CDKN1C , PRDM16 , BRDU and fluorescent secondary antibodies as described above . Samples were scanned with appropriate excitation and emission settings ( S3 Table ) . Genomic DNA was bisulphite treated using an EZ DNA Methylation Kit ( Zymo Research ) . Sodium modification treatments were carried out in duplicate for each DNA sample and at least three independent amplification experiments were performed for each individual examined . The region spanning the Cdkn1c was amplified by PCR using primers 5’-tgggtgtagagggtggatttagtta-3’s and 5’- cccacaaaaaccctaccccc-3’ and hemi-nested primer 5’- gtattgttaggattaggatttagttggtagtagtag . The PCR products were cloned into pGEM-T ( Promega , Madison , WI , USA ) and an average of 20 clones per sample were sequenced using M13 reverse primer and an automated ABI Prism 3130xl Genetic Analyzer ( Applied Biosystems , Foster city , CA , USA ) as previously described [74] . Quantitative RT-PCR was performed in duplicate on four independent samples obtained from two litters as described [75] . Mitochondrial DNA was quantitated by comparing the nuclear mitochondrial marker cytochrome c , somatic ( Cycs ) with the mitochondrion-encoded cytochrome c oxidase subunit II ( Cox2 ) by quantitative PCR . Primers are given in S1 Table . RFLP analysis was performed on cDNA prepared from iBAT and rpWAT obtained from crossing a BL6 female with a Cdkn1c-RFLP male . Western blot analysis: total proteins ( 30 μg ) were resolved by SDS-PAGE , transferred to PVDF ( Millipore Corp . , Bedford , MA ) , blocked in TBS-T ( 10 mM Tris , 150 mM NaCl , 0 . 05% Tween 20 , 5% skimmed milk ) , incubated with primary antibodies ( Sigma SAB4500071 CDKN1C; Sigma SAB1300006 PRDM16; Abcam ab10983 UCP1; R&D sytems MAB5966 MYOD; Sigma A5316 β-ACTIN ) and visualised using secondary horseradish peroxidase-linked antibodies ( Invitrogen ) and ECL . For differentiation experiments , MEFs isolated from E12 . 5 embryos and cultured in DMEM/F12 ( Invitrogen ) , 10% fetal bovine serum ( Invitrogen ) , 2 mM glutamine ( Sigma ) and 50 μg/ml penicillin/streptomycin ( Sigma ) for two passages were used . Differentiation of two-day-post confluent MEFs ( D0 ) was performed by incubation with 170 nM insulin ( Sigma ) , 250 nM dexamethasone ( Sigma ) , 2 . 5 nM rosiglitazone ( Axxora ALX-350-125-M025 ) and 0 . 5 mM isobutylmethylxanthine ( IBMX ) ( Sigma ) for 2 days and medium containing only 170 nM insulin and 2 . 5 nM rosiglitazone for 6 additional days , changing the medium every 48 hours . For ORO , cells were fixed for 20 minutes in paraformaldehyde vapour and stained for 15 minutes with Oil Red O solution ( 0 . 6% ( w/v ) in isopropanol:water 60:40 ) , washed and photographed . For UCP1 western blots , cells differentiated for 8 days were harvested , or treated with vehicle ( dimethyl sulfoxide ) or 9-cis-retinoic acid ( 1 μM in dimethyl sulfoxide ) over 48 hours with protein extraction at intervals . For confocal microscopy , MEFs underwent differentiation in 5 cm glass bottom plates ( Mat Tek ) . After 8 days of differentiation , cells were stained with 5 μg/ml Hoechst 33342 ( Invitrogen ) and Rhodimine-123 ( Sigma Aldrich ) for 30 minutes at 37°C . Dyes were removed , and cells were washed for 5 minutes in media . Further staining with 7 . 5 μg/ ml HCS CellMask Red ( Invitrogen ) for 10 minutes was performed followed by three washes in ddH20 . Samples were imaged using Leica TCS SP2 AOBS laser confocal microscope and Leica Confocal software . HIB-1B cells were maintained in DMEM/F12 ( Invitrogen ) supplemented with 10% fetal bovine serum ( Invitrogen ) , 2 mM glutamine ( Sigma ) and 50μg/ml penicillin/streptomycin ( Sigma ) . The siRNA sequence for Cdkn1c-depletion was p57 siRNA ( m ) ( Santa Cruz Biotechnology sc-37621 ) . Control siRNA-A ( Santa Cruz Biotechnology sc-37007 ) was used as the scrambled sequence . Lipid complexes were prepared and reverse transfected according to manufacturer instructions ( INTERFERin , Polyplus ) in 12-well plates with 10 pmole of the siRNAs complexed with 2 μl of INTERFERin in OPTIMEM with a repeat transfection performed at 24 hours . Cells were harvested 48 hours after transfection and analysed by western blotting . Experiments were performed in three separate occasions in duplicate ( ECL ) or triplicate ( fluorescent ) wells . Statistical significance ( Probability values ) was determined using the Student’s t-Test ( two tailed distribution and two sample unequal variance ) . For qPCR analysis , Mann-Whitney test was performed on ∆Ct values between groups .
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Silver Russell syndrome is a severe developmental disorder characterised by low birth weight , sparing of the head and neonatal hypoglycemia . SRS adults are small and can be extremely thin , lacking body fat . Numerous genetic and epigenetic mutations have been linked to SRS primarily involving imprinted genes , but progress has been hampered by the lack of a suitable animal model . Here we describe a mouse model of the rare micro duplications reported in some SRS patients , which recapitulated many of the defining features of SRS , including extreme thinness . We showed that these mice possessed substantially more of the energy consuming brown adipose tissue ( BAT ) , driven by a double dose of the imprinted Cdkn1c gene . We further show that Cdkn1c is required for the postranscriptional accumulation of the BAT determinant PRDM16 and that these proteins co-localise to the nucleus of in a rare label-retaining cell within BAT . These data suggest that Cdkn1c contributes to the development of BAT by modulating PRDM16 and supports a major role for this gene in SRS .
|
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2016
|
Cdkn1c Boosts the Development of Brown Adipose Tissue in a Murine Model of Silver Russell Syndrome
|
PCNA ubiquitylation on lysine 164 is required for DNA damage tolerance . In many organisms PCNA is also ubiquitylated in unchallenged S phase but the significance of this has not been established . Using Schizosaccharomyces pombe , we demonstrate that lysine 164 ubiquitylation of PCNA contributes to efficient DNA replication in the absence of DNA damage . Loss of PCNA ubiquitylation manifests most strongly at late replicating regions and increases the frequency of replication gaps . We show that PCNA ubiquitylation increases the proportion of chromatin associated PCNA and the co-immunoprecipitation of Polymerase δ with PCNA during unperturbed replication and propose that ubiquitylation acts to prolong the chromatin association of these replication proteins to allow the efficient completion of Okazaki fragment synthesis by mediating gap filling .
It is well established that the replication machinery encounters a variety of obstacles and is thus designed with a degree of flexibility . This plasticity of DNA replication depends on both alternative components and regulation by post-translational modification . For example , while genetic and physical studies indicate that the leading and lagging strands are primarily replicated by DNA polymerase ε ( Polε ) and DNA polymerase δ ( Polδ ) , respectively [1–4] , this assignment is flexible: Polδ synthesises the leading strands on rare occasions [5–7] , synthesises both strands during viral replication [8] and can sustain cell viability in the absence of Polε [9] . Key to orchestrating enzymes for DNA replication is PCNA , which serves as a scaffold for recruiting many of the numerous enzymes involved , including the replicative DNA polymerases . In addition , PCNA ubiquitylation on lysine164 regulates DNA damage tolerance ( DTT ) . When replication is blocked by damaged DNA bases the Rad6-Rad18 E2-E3 ligase complex binds to single stranded DNA coated with RPA and mono-ubiquitylates PCNA to promote translesion DNA synthesises by non-canonical polymerases [10 , 11] . Subsequent to mono-ubiquitylation , PCNA can be poly-ubiquitylated by the Ubc13-Mms2-Rad5 complex [12 , 13] to initiate damage bypass by HR-dependent template switching [14] . The level and duration of PCNA ubiquitylation is additionally regulated by constitutive deubiquitylation [15–17] . The prevailing view is that PCNA ubiquitylation is a DNA damage-induced phenomena . This is consistent with the budding yeast situation , where PCNA ubiquitylation is barely detectable in unperturbed S phase but robustly induced in response to replication-blocking DNA lesions [10 , 12] . However , PCNA is robustly ubiquitylated during unperturbed replication in fission yeast [18] and significant levels of PCNA ubiquitylation are evident during unperturbed replication in frog extracts and metazoan cells [19 , 20] . Several observations suggest that PCNA ubiquitylation is linked to DNA replication: PCNA ubiquitylation is upregulated in response to an increase in canonical replication intermediates [21–23] and a recent synthetic genetic array analysis in budding yeast showed that the PCNA ubiquitylation pathway is genetically correlated with the mechanism of lagging strand DNA synthesis [24] . Moreover , in vitro reconstitution of PCNA ubiquitylation demonstrates that efficient mono-ubiquitylation is coupled to DNA synthesis by Polδ [25] . Despite the accumulating evidence that PCNA ubiquitylation is linked to the processes of DNA replication , there have been no reports that examine if the process of unperturbed DNA replication is influenced by the ubiquitylation of PCNA and the role of this modification during unperturbed S phase remain unclear . To address this question experimentally , we investigated how replication dynamics are influenced by PCNA ubiquitylation in fission yeast . We find that , in the absence of PCNA ubiquitylation DNA replication is slower and that there is an increase in single stranded DNA gaps in S phase cells . We also observe that PCNA ubiquitylation increases the amount of chromatin associated PCNA and influences the recruitment of Polymerase δ . We propose that PCNA ubiquitylation facilitates the completion of Okazaki fragment synthesis .
If incomplete lagging strand synthesis activates PCNA ubiquitylation , it is possible that PCNA-Ub participates in the completion of Okazaki fragment synthesis . To examine this possibility , we first determined the contribution of PCNA ubiquitylation to the progression of unperturbed S phase by assessing replication dynamics in synchronised populations ( Fig 1B–1F ) . Since S . pombe Pcn1 can be modified on lysine 164 by either ubiquitin or SUMO , we first examined cells defective for the Rhp18 E3-ligase ( Rhp18 is the S . pombe homolog of S . cerevisiae Rad18 . For clarity , we refer to this E3 ligase as Rad18 through the text ) . While S phase entry was slightly delayed in rad18Δ cells ( Fig 1C ) , bulk replication progression proceeded with similar kinetics when assessed by total bromodeoxyuridine ( BrdU ) accumulation ( Fig 1C ) . In contrast , while cells carrying the mutation of the ubiquitylated PCNA residue , pcn1-K164R , also slightly delayed S phase entry , their progress through S phase was also defective ( S2A and S2B Fig ) . Importantly , rad18Δ was epistatic with pcn1-K164R for the slight delay to S phase entry ( S2A Fig ) , confirming that the delay seen in rad18Δ cells is PCNA ubiquitylation dependent . It is unclear why the pcn1-K164R mutation also conferred a ubiquitylation-independent defect in S phase progression ( S2A Fig ) . We observed that replication timing was also perturbed and that Polε DNA association during S phase was reduced ( see below ) in a manner that was independent of the Pli1 SUMO ligase . As pcn1-K164R is thus clearly acting as a hypomorphic allele , we concentrated our analysis on the rad18 deletion mutant cells . To establish if PCNA ubiquitylation affected the DNA replication kinetics of specific loci we examined enrichment of BrdU across the genome during mid to late S phase by BrdU-IP in rad18+ and rad18Δ cells ( Fig 1D ) . This showed changes to the replication dynamics , with advanced replication close to origins and delayed replication for the inter-origin regions . Because relative BrdU enrichment between two samples does not directly reflect relative replication kinetics ( the two samples will not be at exactly the same point in S phase ) , we performed independent replication time courses for rad18+ and rad18Δ cells and normalised for replication progression in order to directly compare DNA replication timing across the genome ( Fig 1E , see Materials and methods for details ) . Replication progression was calculated at each local region of the genome when the global genome replication level was either 25 , 50 or 75% . I . e . we used the global extent of replication to standardise comparisons between rad18+ and rad18Δ strains such that the extent of local replication was compared between strains with equivalent global levels . rad18Δ cells showed delayed replication at regions distal to replication origins which are , relative to origins , late replicating ( light blue , Fig 1E ) . This was compensated for by higher local replication at many origin-associated regions that are relatively early replicating ( light red , Fig 1E ) . Some additional peaks were also observed , for example regions 1770-kb region in Chr . II and 3320-kb in Chr . III , suggesting reduced fork progression rates are partially compensated for by firing cryptic origins [26] . The distribution of BrdU at genomic regions surrounding origins would be expected to become wider as S phase progressed ( ultimately it would be flat at the end of S phase ) . Consistent with our hypothesis that replication fork progression is subtly delayed in rad18Δ cells ( Fig 1E ) , we observed that deletion of rad18 resulted in a narrower distribution of BrdU later in S phase when compared to rad18+ control cells ( S3A–S3E Fig ) . Control experiments where we allowed cells to progress into S phase in the presence of hydroxyurea confirmed that the two strains initiated S phase at the same origins and confirm that our sequencing methodology is reproducible ( S3B Fig ) . To examine further whether rad18Δ caused delayed replication in regions that replicate late , a meta-analysis was performed by computationally identifying replication origins and analysing the relatively late replicating inter-origin regions . As shown in Fig 1F the local replication extent of the early replicating origins was not perturbed in rad18Δ . In contrast , later replicating regions show a significant decrease in their extent of replication , even when adjusted for the global replication amounts . This effect was particularly striking in the regions that were amongst the last to be replicated ( Fig 1G ) . Analysis of the specific loci that were most under-replicated in rad18Δ cells ( S4A and S4B Fig ) showed they correspond to those loci that we previously demonstrated to be the last to be replicated in wild type cells [5] . These data demonstrate that the lack of PCNA ubiquitylation delays replication fork progression , with the cumulative effect manifesting most obviously at late replicating regions . PCNA is loaded during DNA replication , functions as the replicative clamp and remains chromatin associated until the polymerase has finished replication and ligation is complete . We speculated that PCNA ubiquitylation may contribute to PCNA retention on the chromatin . However , in native cell extracts PCNA is progressively deubiquitylated , compromising the ability to measure PCNA ubiquitylation during chromatin association assays . To overcome this limitation , we increased the level of PCNA ubiquitylation by engineering a strain , Purg1-rad18 , where rad18+ is under the control of an inducible promoter ( Fig 2A ) . Fractionation of cell extracts following rad18 induction revealed that ubiquitylated PCNA was preferentially associated with chromatin ( Fig 2B ) in a manner dependent on K164 ubiquitylation ( Fig 2C and 2D ) . This suggests the modification contributes to the stability of PCNA chromatin association . Consistent with this , shut-off of rad18+ transcription from Purg1 when combined with induced Rad18 degradation resulted in rapid PCNA disassociation from chromatin , concomitant with deubiquitylation ( S5 Fig ) . Because it is not practical to assay native fission yeast extracts for endogenous levels of ubiquitylated PCNA on chromatin due to its deubiquitylation by isopeptidase in native extracts we compared the total chromatin-associated PCNA in rad18+ and rad18Δ cells during S phase . In rad18+ cells , PCNA accumulated in S phase and gradually diminished towards the completion of replication . Comparatively , in rad18Δ cells , the amount of chromatin associated PCNA decreased during the late stages of replication ( Fig 2E ) . This is reminiscent of the predominant effect of loss of PCNA ubiquitylation manifesting at late replicating regions ( Fig 1F and 1G ) . We verified the observed effect of Rad18 loss on PCNA chromatin association using a photo-activated localization microscopy ( PALM ) -based technique that directly visualises DNA-associated PCNA [27] . Briefly , this method exploits motion blurring to selectively eliminate signals arising from rapidly diffusing molecules , allowing visualisation of low mobility signals derived from DNA-associated molecules ( Fig 2F ) . Previously we reported that low mobility PCNA ( mEos3 . 1-Pcn1 ) is notably enriched during S phase [27] . Deletion of rad18 significantly reduced the fraction of these molecules ( Fig 2F , right ) , thus confirming that PCNA-K164 ubiquitylation results in increased amounts of loaded PCNA during unperturbed S phase . One possible explanation for the increased amount of chromatin-associated PCNA accompanying K164 ubiquitylation is that this contributes to the function of DNA polymerases during DNA replication . In unchallenged cells we could detect the association of Polδ , but not Polε , with PCNA by immunoprecipitation ( S6A and S6B Fig ) . This would be consistent with the higher PCNA-dependency of Polδ function [28–30] , but may equally reflect the lower levels of DNA-associated Polε during S phase when compared to Polδ . Increased PCNA ubiquitylation ( by Rad18 overexpression via Purg1-rad18 ) increased Polδ co-immunoprecipitation with anti-PCNA without influencing cell cycle profiles ( Fig 3A and 3B ) . PCNA ubiquitylation and co-immunoprecipitation were also both enhanced by hydroxyurea treatment of rad18+ and Purg1-rad18 cells . Thus , Polδ: PCNA co-immunoprecipitation intensity scaled with PCNA ubiquitylation ( Fig 3A and 3B ) . We also noted that the PCNA which co-immunoprecipitated with Polδ was biased toward ubiquitylated forms ( Fig 3C ) and that the loss of poly-ubiquitylation ( ubc13 deletion ) showed an intermediate decrease in co-immunoprecipitation of Polδ when compared to loss of all ubiquitylation ( pcn1-K164R ) ( Fig 3D ) . Using the PALM motion blurring assay ( see Fig 2F ) we did not detect a decrease in the Polδ immobile fraction in untreated S phase rad18Δ cells ( Fig 3E ) , possibly because our assay is insufficiently sensitive . However , when rad18Δ cells were arrested within S phase by hydroxyurea treatment , the fraction of low mobility Polδ molecules decreased when compared to rad18+ controls , providing support for the contention that PCNA ubiquitylation contributes to Polδ function . PCNA recruits DNA polymerases after it is loaded [31] and the affinity of PCNA: Polδ binding is not influenced by K164 ubiquitylation [32] . Thus , the increased Polδ-PCNA association could be accounted for purely by the increased amount of PCNA on DNA due to ubiquitylation inhibiting clamp unloading . This predicts that increasing PCNA chromatin association independently of its ubiquitylation status would lead to increased Polδ: PCNA co-immunoprecipitation . To address this , we examined Polδ-PCNA association in cells deleted for elg1 , where PCNA chromatin association is enhanced due to inactivation of the Elg1 unloader ( Fig 3F ) [33] . Loss of Elg1 resulted in an increase in Polδ co-immunoprecipitation with PCNA in both rad18+ and rad18Δ backgrounds ( Fig 3G ) . This result demonstrated that the amount of loaded PCNA relates to the level of PCNA-polymerase association , although we cannot rule out the possibility that additional factors that directly respond to PCNA ubiquitylation can also influence the association . PCNA ubiquitylation is proposed to help ‘replace’ replicative polymerases with non-canonical polymerases . We therefore examined co-immunoprecipitation of several DNA damage tolerant polymerases , Polη , Polκ and Polζ , with PCNA ( S6C Fig ) . Marginal Polη: PCNA co-immunoprecipitation was observed in Purg1-rad18 cells , where PCNA ubiquitylation levels were high , consistent with the ubiquitin-binding zinc-finger domain of Polη directing PCNA association . Co-immunoprecipitation of Polκ or Polζ with PCNA was not detectable , presumably due to sparse protein levels ( S6C Fig ) . Taken together , these data indicated that the non-canonical polymerases do not appreciably outcompete Polδ for association with ubiquitylated PCNA . Consistent with this , neither of Polη , Polκ nor Polζ were responsible for the altered BrdU incorporation observed in rad18Δ cells during unperturbed S phase ( S6D Fig ) . To establish if PCNA modification influences Polδ and Polε function we examined synthetic genetic interactions between rad18Δ and temperature sensitive ( ts ) polymerase mutations ( Fig 4A ) . For cdc6-23 , ( Polδ-ts ) , concomitant rad18Δ reduced the restrictive temperature , consistent with PCNA ubiquitylation enhancing Polδ activity . Importantly , this synthetic genetic interaction was also observed for pcn1-K164R and combining both rad18Δ and pcn1-K164R showed no additive effect ( Fig 4B ) . For cdc20-m10 ( Polε-ts ) rad18Δ did not affect the restrictive temperature , suggesting Polε activity is not influenced by PCNA ubiquitylation . Consistent with this , when we examined the fraction of low mobility Polε in S phase cells using PALM motion blurring , we did not detect a significant change in when rad18 was deleted ( S6E Fig ) . Interestingly , when we examined Polε mobility in a pcn1-K164R background , a significantly lower fraction of Polε displayed low mobility in S phase cells ( S6E Fig ) . This phenomenon was not observed in a pli1 deletion mutant ( S6F Fig ) . Thus , the K164R mutation has effects beyond that of PCNA ubiquitylation ( c . f . S2 Fig ) which are unlikely to be related to modification by small Ub-like molecules . During DDT ubiquitylation of PCNA promotes ssDNA gap filling opposite DNA lesions [22 , 34 , 35] . We have confirmed ( S1E Fig ) that PCNA ubiquitylation is induced following dysfunction of Okazaki fragment synthesis and demonstrated that this increases the fraction of Polδ co-immunoprecipitating with PCNA ( Fig 3A–3C ) and can contribute to the chromatin association of this lagging strand polymerase ( Fig 3E ) . Since Polδ repeatedly disassociates from and re-associates with the template during synthesis [32] , relatively long lived ssDNA gaps may occur stochastically between Okazaki fragments . We reasoned that PCNA ubiquitylation could act to supress or repair such events via a DTT-like gap filling mechanism during unperturbed S-phase ( Fig 4C ) . This predicts that the absence of PCNA ubiquitylation would result in ssDNA gap accumulation during DNA replication . To estimate the extent of ssDNA gaps in vivo , we first utilised an S1 nuclease-based assay [36] previously developed for detecting ssDNA in replicated molecules ( Fig 5A ) . By calculating the distribution of DNA fragment sizes from gel intensities ( S7 Fig ) we infer that rad18Δ cells displayed increased DNA fragmentation when compared to rad18+ cells , with small ( < 1 kb ) fragments accumulating in rad18Δ throughout S phase ( Fig 5B–5D ) . As an alternative assay , we BrdUTP labelled ssDNA gaps in genomic DNA prepared in agarose plugs . When DNA from rad18Δ cells was compared to rad18+ , increased signal was evident in mid to late S phase ( Fig 5E–5G ) . These two experiments support a model where PCNA ubiquitylation occurs between Okazaki fragments ( Fig 4C ) and prevents the accumulation of ssDNA gap during unperturbed S phase ( see Discussion ) .
Here we have used the fission yeast model to demonstrate that , in addition to its known role in DNA damage tolerance , PCNA K164-ubiquitylation contributes to the timely completion of unperturbed DNA replication . Our results show that PCNA association with chromatin is stabilised by PCNA-K164 ubiquitylation during S phase . We also observed an increased co-immunoprecipitation of Polδ with PCNA when PCNA is ubiquitylated and we provide evidence that the chromatin association of Polδ is promoted by PCNA ubiquitylation . In budding yeast , PCNA ubiquitylation is barely detectable in unperturbed S phase [22] and robustly induced in response to DNA lesions that block the canonical replicative DNA polymerases [10 , 12] . Consequently , PCNA ubiquitylation has been studied almost exclusively in the context of its key role in DNA damage tolerance [37] . In contrast , in fission yeast PCNA is robustly ubiquitylated during unperturbed S phase [18] and this is not significantly further induced if DNA is damaged during S phase . Budding and fission yeast thus represent opposite ends of what appears to be a spectrum . We note that both yeasts have approximately similar genome sizes and there is no evidence to suggest that fission yeast suffers from elevated levels of spontaneous DNA damage . Interestingly , mammalian cells exhibit both S phase-dependent PCNA ubiquitylation and DNA damage induced PCNA ubiquitylation ( see S8A and S8B Fig ) . It is currently not known what underlies the differences between organisms in terms of PCNA ubiquitylation in unperturbed S phase . However , as Rad18 is activated by regions of single stranded DNA it is possible that PCNA ubiquitylation is reflecting the extent of ssDNA present when DNA replication is active . In support of this , in budding yeast a defect in short-flap Okazaki fragment processing caused by compromising the function of the Fen1 flap endonuclease , which normally processes the 5’end of Okazaki fragments , induced detectable levels of PCNA ubiquitylation [24] . This is explained by the accumulation of long 5’ ssDNA flaps that bind RPA and activate Rad18 ubiquitylation . However , when Okazaki fragment-processing is proficient , the vast majority of flap structures are cleaved by Fen1 when they are 1 or 2 nucleotide in length [38] . Thus , Okazaki fragment processing is unlikely to be a significant source of ssDNA during unperturbed S phase . We have shown here that the lack of PCNA ubiquitylation leads the accumulation of ssDNA gaps during S phase in fission yeast . We propose that the dynamics of Polδ disassociation from PCNA result in stochastic formation of transient gaps during lagging strand synthesis . These gaps trigger Rad18-dependent ubiquitylation of PCNA , which stabilises PCNA on the DNA , allowing association of Polδ and rapid gap resolution . In the absence of PCNA ubiquitylation , a proportion of these gaps persist and thus gaps are detected in our assays . The generation of transient gaps during lagging stand synthesis likely explains the fact that PCNA is ubiquitylated during S phase in this organism . In support of fission yeast generating increased regions of ssDNA during unperturbed DNA replication ( when compared to budding yeast ) we note that the abrogation of recombination pathways in fission yeast ( e . g . rad51Δ or rad52Δ mutants ) causes a much more severe growth defect than the equivalent loss of recombination pathways in budding yeast and that the combination of rad51 deletion with rad18 or pcnl-K164R results in synthetic lethality ( S9 Fig ) . This suggests that homologous recombination and DTT pathways cooperatively repair ssDNA gaps , which may be abundant compared to S . cerevisiae . In considering the origin of ssDNA during S phase that we observe in S . pombe and the differential PCNA ubiquitylation between S . pombe and S . cerevisiae in unperturbed S phase , it is interesting to consider that the kinetics of Polδ holo-enzyme dissociation from PCNA . It has recently been reported [32] that the S . cerevisiae enzyme is more processive than its human counterpart: human Polδ dissociates more rapidly from PCNA than its budding yeast counterpart and it was estimated that ~14–31% of human Okazaki fragments are completed by two independent Polδ: PCNA association events . Conversely , >99% of budding yeast Okazaki fragments are predicted to be completed by a single Polδ: PCNA interaction . While the kinetics of S . pombe Polδ dissociation from PCNA has not been studied , the fact that PCNA ubiquitylation is strongly influenced by the intermediates of lagging strand DNA synthesis [21–23] ( see S1E and S1F Fig ) is consistent with the fission yeast PCNA ubiquitylation pathway , in addition to regulating translesion synthesis during DTT , functioning to maintain accurate Okazaki fragment synthesis in the face of frequent Polδ: PCNA dissociation . Okazaki fragment synthesis is necessarily coupled , either directly or indirectly , to the movement of replication forks . Approximately 105 and 107 Okazaki fragments are synthesised per cell cycle in fission yeast cells and human cells , respectively . The potential for failure during this process as a consequence of premature Polδ dissociation would therefore need to be minimised by ensuring the re-association of Polδ and completion of Okazaki fragment synthesis . We propose that this is facilitated by PCNA ubiquitylation , which ensures that PCNA is not prematurely unloaded . The fact that we show that the loss of PCNA ubiquitylation results in the accumulation of ssDNA gaps during unperturbed S phase in S . pombe ( Fig 5 ) supports our model . Intriguingly , preliminary analysis ( S10 Fig ) showed the positive effect of PCNA ubiquitylation on PCNA chromatin association is evident only when the Elg1 unloader complex is active , suggesting that PCNA ubiquitylation may inhibit its unloading by Elg1 , a PCNA unloading factor currently characterised only in in S . cerevisiae [33] . In S . cerevisiae yeast , SUMOylated PCNA is the predominant modification during unperturbed S phase [12 , 39] . Previous work showed that Elg1 preferentially interacts with SUMO-modified PCNA [40] . However , Elg1 unloads both unmodified and SUMOylated forms of PCNA , an event which in budding yeast requires the ligation of Okazaki fragments [41] . However , in fission yeast , and in human cells , SUMOylated PCNA is much harder to detect and the SUMO-interacting motifs identified in S . cerevisiae Elg1 are not conserved . Thus , the influence of PCNA SUMOylation is unlikely to be prominent and we propose that the effect of PCNA ubiquitylation on stabilising PCNA is more predominant in fission yeast cells and potentially higher eukaryotes . It has also been suggested that the unloading of PCNA in response to HU or MMS in S . cerevisiae is dependent on its ubiquitylation and concomitant activation of the DNA damage checkpoint [42] . One interpretation of this apparent contradiction could be that , under extensive replication stress , checkpoint activation changes the response to PCNA ubiquitylation . Alternatively , this may again reflect a difference between the two organisms in the regulation of PCNA unloading . In fission yeast a significant proportion of PCNA is ubiquitylated during unperturbed S phase . To avoid a global engagement of error prone DNA polymerases , we propose that the replicative polymerases remain the preferred binding partners for ubiquitylated PCNA . However , when a replicative polymerase is stalled at a blocking lesion , the ubiquitin binding domain-containing polymerases are provided an increased opportunity to sample the damaged base . In budding yeast the situation is distinct: PCNA is not significantly ubiquitylated in unperturbed S phase , but is robustly ubiquitylated in response to a replicative polymerase arrested at a lesion . Thus , we would predict that the binding kinetics for the replicative and error-prone DNA polymerases will be different between the two organisms in order to maintain the same biological outcomes: an appropriate balance between unsuitable use of error prone DNA polymerases during unperturbed S phase ( to minimise constitutive mutagenesis ) and their appropriate use during DNA damage tolerance to maximise cell survival in response to DNA damage [43] . In summary , our analysis shows that PCNA ubiquitylation , in addition to controlling DNA damage tolerance pathway usage , also participates in the timely completion of unperturbed DNA synthesis . We propose that this function is related to the increased association of ubiquitylated PCNA with chromatin . We suggest that , when Polδ stochastically dissociates during Okazaki fragment synthesis , the consequent ssDNA results in PCNA ubiquitylation which ensures it remains DNA-associated to facilitate the recapture of Polδ and completion of Okazaki fragment synthesis .
Standard S . pombe genetic and molecular techniques were employed as described previously [44] . The BrdU-incorporating strains have been already reported [45] . Polδ-GFP cells were constructed by introducing the sequence encoding GFP into the N-terminal of the cdc6 gene on S . pombe genome based on the Cre-loxP method [46] . Polε-GFP cells were constructed by introducing GFP at the C-terminal of cdc20 gene using PCR-based integration [47] . Purg1-rad18 strains were based on rad18Δ cells in which ORF of the rad18 gene fused with the AID degron construct [48] was used to replace the endogenous urg1 ORF [49] . U2OS cells were cultured in Dulbecco’s modified Eagle’s medium supplemented with 10% foetal bovine serum ( DMEM-FBS10% ) in a 5% CO2 atmosphere . The medium was exchanged with one containing 400 ng/ml of nocodazol . Following 18 hr incubation , mitotic cells were detached by gentle shaking of the culture vessel and passaged in DMEM-FBS10% . Cells were then either UV-irradiated ( 254 nm peak; 20J/m2 ) , or not , 2 hr prior to sampling . At the indicated time points cells were sampled and then subjected to immunoblotting with anti-PCNA antibody ( mouse monoclonal , PC10 clone , Abcam ) . To determine the S-phase fraction of the synchronised cells , 5μM if EdU was added into an aliquot and EdU positive cells scored 2 hr after EdU addition [50] . 1BR3hTERT cells were cultured in DMEM-FBS10% and the medium was exchanged with DMEM without FBS . Following 15 days , cells were passaged into DMEM-FBS10% . Cells were UV-irradiated and scored for S-phase fraction as described . Cell lines from GDSC collection . Authenticated 2015 by STR profiling . cdc25-22 cells harbouring the constructs for BrdU-incorporation were grown to exponential phase ( 0 . 2 x106 /ml ) at 25°C and synchronised at G2 phase by incubation at 36°C for 3 . 5 hr . After adding bromodeoxyuridine ( 0 . 5 μM ) , cells were further incubated at 25°C . At relevant time points , 1x108 cells were pelleted and subjected to genomic DNA extraction . To detect total BrdU incorporation , dot blotting was performed as previously described [34] . The intensity of BrdU-incorporation was established by quantifying the signal using an ImageQuant LAS 4000 imager ( GE Healthcare Life Sciences ) . Global replication rates for each time point after release from G2 phase were estimated by dividing signal intensities at each time-points by that for 150 min , at which genome replication was completed . Local replication rates were established from BrdU-IP-Sequencing . Paired-end reads from high throughput sequencing were aligned to the S . pombe genome sequence ( ASM294v2 . 23: chromosomes I , II and III , downloaded from 'PomBase’ website ) using bowtie2–2 . 2 . 2 . From the alignment data the position of the centre of each read was calculated and the number of reads in 300bp-bins across genome counted . The Perl program converting alignment data to count data: ‘sam-to-count . pl’ is available on the GitHub website ( https://github . com/yasukasu/sam-to-bincount ) . The counts at the chromosome coordinate x , CB ( t , x ) –the BrdU-IP sample derived from cells at the t-min time point , CI ( 0 , x ) –the input sample derived from cells before release from G2 ( t = 0 ) , were normalised with the total number of reads: NB ( t , x ) = CB ( t , x ) /ΣCB ( t , x ) , NI ( t , x ) = CI ( t , x ) /ΣCI ( t , x ) . Enrichments for BrdU-incorporated fragments were calculated: E ( t , x ) = NB ( t , x ) /NI ( t , x ) . As BrdU is an analogue of thymine and its enrichment is thus likely to be biased towards A/T rich regions , the dataset of enrichment was normalised using the A/T-ratio of each 300-bp bin AT ( x ) : E’ ( t , x ) = E ( t , x ) /AT ( x ) . Moving average of E’ ( t , x ) with 8 bins at both side were calculated and plotted ( Fig 1D ) . To estimate the extent of local replication , enrichments across the genome were multiplied by the global replication amount G ( t ) determined from the dot-blot assay ( Fig 1C ) : L ( t , x ) = E’ ( t , x ) ×G ( t ) . These were then normalised with that of the last time point , at which all the cells had completed genome replication: L’ ( t , x ) = L ( t , x ) /L ( 160 , x ) . To obtain a function of local replication extent , data of multiple time points at each 300 bp ( L’ ( 70 , x ) , L’ ( 75 , x ) , L’ ( 80 , x ) , L’ ( 85 , x ) , L’ ( 90 , x ) ) were fitted with a cumulative normal distribution function in which the global replication amount is variable , F ( G , x ) . Using this function , Local replication extent when the global replication was 25% , 50% or 75% completed was determined: F ( 0 . 25 , x ) , F ( 0 . 5 , x ) and F ( 0 . 75 , x ) . Fig 1E–1G and S4 Fig is derived from these datasets . The custom R scripts used for this computational analysis are available on request . Whole cell extracts were prepared by spheroplast lysis using Zymolyase 100T ( Seikagaku ) and lysing enzyme ( Sigma-Aldrich ) . Extracts were fractionated into soluble and chromatin-bound fractions by centrifugation through a sucrose cushion [51] . 5 x 108 exponentially growing cells in 50 ml YE medium were treated with 1% formaldehyde for 15 min at RT under agitation . The crosslinking reaction was quenched by adding 2 . 5 ml of 2 . 5 M glycine . Cells were washed with ice-cooled PBS , pelleted and re-suspended in 700 μl pf RIPA buffer ( 50mM HEPES pH7 . 5 , 1mM EDTA , 140 mM NaCl , 1% Triton X-100 , 0 . 1% ( w/v ) sodium deoxycholate ) supplemented with complete protease inhibitor ( Roche ) , 1 mM AEBSF & 1μg/ml pepstatin ( Sigma-Aldrich ) . After adding zirconia/silica beads ( biospec ) , cells were ribolysed ( 6 bursts of 30 sec at speed 6 . 5 in a FastPrep ribolyser ( MP-Biomedicals ) . 300μl of cell lysate was sonicated ( 7 cycles; 30 sec on , 30 sec off ) using a bioruptor pico ( diagenode ) , l μl of benzonase ( novagen ) added and incubated for 20 min on ice . The lysate was then centrifuged ( 14000 rpm for 30 min in a microfuge ) and the supernatant transferred to new tube . 30 μl was kept as the ‘input’ sample . 2 μg of anti-GFP antibody ( rabbit IgG , A11122 , Life technologies ) or anti-PCNA antibody [18] was added to the isolated cell extract . After a 3 hr incubation at 4°C with gentle agitation , 20 μl of magnetic G protein dynabeads ( Life technologies ) was added and incubated for a further 1 hr . Beads were washed twice with RIPA buffer and once with TE . Following addition of 60 μl of elution buffer , beads were incubated at 65°C for 15 min . Supernatant was isolated as the ‘IP’ sample . Laemmli buffer was added into both ‘IP’ and ‘input’ samples and western blots were interrogated with anti-PCNA or anti-GFP ( mouse IgG clones 7 . 1 and 13 . 1 , Roche ) . 1 x 108 cells were incubated in YE media containing 50 μg/ml of BrdU and subjected to genomic DNA extraction [44] . 2 μg of extracted DNA was digested with 1 μl of S1-nuclease ( Life Technologies ) using the manufactures buffer in a 20 μl reaction mixture . The reaction was stopped by the addition 2 μl of 0 . 5 M EDTA and heating to 70°C for 10 min . The complete reaction mixture was subjected to agarose ( 1 . 5% ) electrophoresis . DNA was transferred onto GeneScreen Plus membrane ( PerkinElemer ) by neutral capillary transfer and the BrdU signal detected by the immunoblotting [34] . Normalisation of the BrdU incorporation intensities to the fraction of S1-digested fragments was performed as previously described for alkaline digested DNA [52] . 4 x 107 cells were harvested and subjected to Zymolyase 100T ( 0 . 5 mg/ml , Seikagaku ) and lysing enzyme ( 1mg/ml , Sigma-Aldrich ) treatment in 1ml of spheroplasting buffer ( 20 mM citrate-phosphate buffer , 50 mM EDTA and 1 . 2M sorbitol ) . After spheroplasting , cells were re-suspended in 80 μl of spheroplasting buffer without enzymes , mixed with 80 μl of 2% agarose ( SeaPlaque GTG agarose ) and then 20 μl volume of agarose plugs were prepared . Plugs were washed with detergents and treated with Protease K as described previously [53] . Three plugs were subjected to treatment with T4 polymerase ( 6 units , New England Biolabs ) and dNTP with BrdUTP ( Sigma-Aldrich ) instead of dTTP ( 200 μM each ) in 100 μl at 37°C overnight . DNA was recovered from plugs by phenol/chloroform extraction and applied to dot blots ( Scie-Plas Ltd . ) . BrdU signal was detected as described above . Analysis of DNA binding in vivo by PALM was performed as previously described using a custom-built microscope system [27] . Photoconversion and excitation of mEos3 molecules was controlled by continuous wave illumination with 405nm and 561nm laser light . The intensity of the 405nm laser was modulated during the imaging such that the number of photoconverted molecules for any one frame was kept low to reduce the chances of overlapping static molecules , or the possibility of blurring molecules masking static localisations . Laser intensities at the sample were calculated as 0 . 1-1W/cm2 ( 405nm ) and 1kW/cm2 ( 561nm ) . Camera EM gain was set at 250 and exposure time for each frame was 350ms . Typical data acquisition consisted of 3000–4000 frames and 6000–10000 frames for polymerases and PCNA respectively . Data sets were built from of a minimum of 3 biological repeats . Raw image data were processed using a custom ImageJ 2D-Gaussian fitting routine as previously described23 . Code available on GitHub: https://github . com/aherbert/GDSC-SMLM and as a Fiji update site ( GDSC SMLM ) . Scale bar 1 . 5 micometers . Data files for BrdU-IP sequence have been deposited in the Gene Expression Omnibus database under accession number GSE70033 .
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PCNA is a homotrimeric complex that clamps around the DNA to provide a sliding platform for DNA polymerases and other replication and repair enzymes . The covalent modification of PCNA by ubiquitin on lysine reside 164 has been extensively studied in the context of DNA repair: it is required to mediate the bypass of damaged template bases during DNA replication . Previous work has shown that PCNA is modified by ubiquitin during normal S phase in the absence of DNA damage , but the significance of this modification has not been explored . Here we show that , in addition to regulating bypass of damaged bases , lysine 164 ubiquitylation plays a role in ensuring the completion of unperturbed DNA replication .
|
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2017
|
PCNA ubiquitylation ensures timely completion of unperturbed DNA replication in fission yeast
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Progesterone and estrogen are critical regulators of uterine receptivity . To facilitate uterine remodeling for embryo attachment , estrogen activity in the uterine epithelia is attenuated by progesterone; however , the molecular mechanism by which this occurs is poorly defined . COUP-TFII ( chicken ovalbumin upstream promoter transcription factor II; also known as NR2F2 ) , a member of the nuclear receptor superfamily , is highly expressed in the uterine stroma and its expression is regulated by the progesterone–Indian hedgehog–Patched signaling axis that emanates from the epithelium . To further assess COUP-TFII uterine function , a conditional COUP-TFII knockout mouse was generated . This mutant mouse is infertile due to implantation failure , in which both embryo attachment and uterine decidualization are impaired . Using this animal model , we have identified a novel genetic pathway in which BMP2 lies downstream of COUP-TFII . Epithelial progesterone-induced Indian hedgehog regulates stromal COUP-TFII , which in turn controls BMP2 to allow decidualization to manifest in vivo . Interestingly , enhanced epithelial estrogen activity , which impedes maturation of the receptive uterus , was clearly observed in the absence of stromal-derived COUP-TFII . This finding is consistent with the notion that progesterone exerts its control of implantation through uterine epithelial-stromal cross-talk and reveals that stromal-derived COUP-TFII is an essential mediator of this complex cross-communication pathway . This finding also provides a new signaling paradigm for steroid hormone regulation in female reproductive biology , with attendant implications for furthering our understanding of the molecular mechanisms that underlie dysregulation of hormonal signaling in such human reproductive disorders as endometriosis and endometrial cancer .
Establishment of uterine receptivity is mandatory for successful embryo apposition , attachment , and implantation; failure to manifest this uterine state is an underlying cause of most pregnancy failures in women . A multitude of signaling molecules have been shown to play key roles in the elaboration of this uterine response through mesenchymal–epithelial interaction . Among numerous factors involved in these primary events of pregnancy , two steroid hormone receptors , progesterone receptor ( PR ) and estrogen receptor ( ER ) , and their cognate ligands , undoubtedly play central roles in this biological process [1–3] . Although estrogen activity is essential for an integrated uterine response , it has been shown that excessive estrogen activity can prematurely close the implantation window [4] , suggesting that estrogen activity is tightly controlled during the peri-implantation period to allow normal development of the receptive uterus . Importantly , progesterone is known to attenuate estrogen-induced gene expression in uterine epithelial cells [5] . Intriguingly , this suppression is mediated by stromal progesterone receptors [6 , 7] , suggesting that the coordinated action of estrogen and progesterone depends on crosstalk between the epithelial and stromal compartments of the uterus . Although the inhibitory effect of progesterone on epithelial estrogen activity has been described [6 , 7] , the mechanism by which progesterone suppresses estrogen action remains poorly defined . Lydon et al have shown that female PR-null mice are infertile [8] . The expression of Indian hedgehog ( Ihh ) , a gene highly expressed in the uterine epithelium , is greatly reduced in these null mutants , indicating that Ihh is a downstream target of the progesterone receptor [9] . To understand the role of Ihh in reproduction , conditional null mutant mice of Ihh were generated [10] . These mutants exhibit defects in both implantation and decidualization , indicating that epithelial Ihh regulates the decidual response through Patched/Smoothened ( Ptch/Smo ) signaling in the stroma . Recently , it has been shown that COUP-TFII ( chicken ovalbumin upstream promoter transcription factor II; also known as NR2F2 ) is a downstream target of Ihh in the uterine tissue [9 , 10] . COUP-TFII is highly expressed in the uterine stromal compartment , and its expression is significantly reduced in the Ihh mutant mice , suggesting that COUP-TFII might mediate the effects of Ihh signaling in the uterine stroma [10] . This notion is consistent with our previously findings that COUP-TFII is a downstream target of sonic hedgehog ( Shh ) signaling [9–13] and conditional ablation of COUP-TFII in the foregut mesenchyme resembles Shh-null mutant phenotypes [14] . COUP-TFII belongs to the orphan nuclear receptor superfamily , and has been well characterized [15 , 16] . Genetic ablation of COUP-TFII results in early embryonic lethality due to cardiovascular defects [17] . COUP-TFII heterozygous female mice have shown significant reduced fecundity , which is attributed to ovarian and uterine defects [18] , but the uterine-specific function of COUP-TFII remains largely undefined . We have recently established a COUP-TFIIflox/flox mouse model in order to generate tissue- or cell-lineage–specific knockouts of COUP-TFII . Phenotypes exhibited by conditional mutants lacking COUP-TFII in endothelial cells [19] , limbs [20] , stomach [14] , or diaphragm [21] are all consistent with the notion that COUP-TFII might play an important role in reciprocal epithelial–mesenchymal cellular cross-talk . To define COUP-TFII uterine function , we have ablated COUP-TFII specifically in cell lineages of the uterus that express PR by crossing COUP-TFIIflox/flox with PR-Cre knockin mice [22] . Ablation of COUP-TFII in the uterine stroma results in decidualization failure , resembling the conditional ablation of Ihh . In addition , we showed that the expression of bone morphogenetic protein 2 ( BMP2 ) is greatly reduced in the COUP-TFII mutants , and that reintroduction of BMP2 into uterine horn rescues the decidualization defects . Thus , we established a genetic pathway in which progesterone receptor regulates Ihh , which in turn regulates COUP-TFII through Ptch/Smo signaling , and finally , COUP-TFII regulates BMP2 to confer decidualization in the uterus . Surprisingly , we also found that ER activity and target gene expression in the uterine epithelial cells are markedly elevated in conditional COUP-TFII knockout mice , which alters the window of receptivity and affects embryo attachment and implantation . Since stromal PR has been implicated to suppress epithelial ER activity , we further asked whether stromal PR expression is downregulated in the COUP-TFII conditional mutants . Indeed , PR expression is reduced significantly in the uterine stroma , while no obvious change is seen in the epithelium . This finding indicates an indispensable role for stromal COUP-TFII in the maintenance of progesterone suppression of uterine epithelial ER activity , a prerequisite for the establishment of normal uterine receptivity . This study also substantiates the importance of epithelial–stromal cross-communication and sheds new light on a complex signaling circuit that spans the uterine epithelial–stromal divide that is indispensable for the development of the receptive uterus and subsequent decidualization . In addition , not only does this finding further our understanding of steroid hormonal control of uterine receptivity , but it provides a novel signaling paradigm for steroid hormonal dysregulation shown to underlie such female reproductive pathologies as endometriosis and endometrial , ovarian , and breast cancers .
Conditional knockout mice of COUP-TFII , PRCre/+ COUP-TFIIflox/flox were generated by crossing PR-Cre knockin mice with COUP-TFIIflox/flox mice . PR is highly expressed in the uterine stroma and epithelium , while COUP-TFII is highly expressed in the stroma , but rarely expressed , if ever , in the uterine epithelium [18 , 22] . Immunohistochemistry of the reproductive tract indicated that COUP-TFII is efficiently ablated in the stroma of the mutant uterus by the PR-Cre ( Figure 1A and 1B ) . It is also evident from this figure that COUP-TFII is highly expressed in the stroma compartment but is hardly detectable in the luminal and the glandular epithelia of the uterus . In contrast , PR is expressed in the granulosa cells , while COUP-TFII is expressed in the theca cells of the ovary [18 , 22] . Since PR and COUP-TFII are not expressed in the same cell , COUP-TFII is not ablated in the theca cells . As expected , the expression of COUP-TFII in the theca cells of the ovary is not altered in the conditional COUP-TFII mutants comparison with controls as shown by immunostaining ( Figure 1C–1E ) . To ensure there is no disruption of ovarian function in COUP-TFII mutants , we transferred ovaries from control and mutant mice to wild-type recipients and observed reproduction for a 6-mo period . Healthy newborns were yielded from PRCre/+ COUP-TFIIflox/flox ovaries in a similar manner as the controlled PRCre/+ and PRCre/+ COUP-TFIIflox/+ ovaries ( Table 1 ) . In addition , the litter size from the mutant ovaries was not significantly reduced compared with that of controls , indicating that the PRCre/+ COUP-TFIIflox/flox females have no ovarian defects ( Table 1 ) . Thus , the implantation failure observed in our conditional mutants is likely due to impaired uterine , but not ovarian , function . PRCre/+ COUP-TFIIflox/flox mutant mice and COUP-TFIIflox/flox control mice were mated with wild-type males ( B6SJLF1; Taconic ) and observed for 6 mo to compare breeding capacity . PR-Cre mice were also used as a control to distinguish the contribution of the PR-Cre allele . Pups were not born from mutant females , while both types of controls gave birth regularly ( Figure 1F ) , indicating that ablation of COUP-TFII in the uterus leads to infertility . The hormone profile during pregnancy showed no significant difference in estradiol ( control , 45 . 3 ± 3 . 8 pg/ml; mutant , 47 . 9 ± 3 . 2 pg/ml; n = 14 , 3 . 5 d postcoitus [3 . 5 dpc] ) and progesterone levels ( control , 15 . 8 ± 4 . 4 ng/ml; mutant , 18 . 6 ± 2 . 9 pg/ml; n = 12 , 3 . 5 dpc ) between mutants and controls , further supporting the fact that PRCre/+ COUP-TFIIflox/flox mice have no obvious ovarian defect as stated above ( Figure 1C–1E; Table 1 ) . To dissect the cause of infertility , we examined whether embryos properly attach to the uterine lumen , an early event of pregnancy that is initiated at midnight of pregnancy day 4 ( 4 dpc ) . We dissected mice on the morning of pregnancy day 5 ( 4 . 5 dpc ) and counted the number of implantation sites by injecting Chicago Blue dye . Implantation sites were not detected in the mutant uterine horns , while normal implantation sites were scored in the controls ( Figure 2A–2C ) . Histological examination also showed embryos failed to attach to the uterine lumen of mutant mice , while normal attachment and induction of the decidual response was observed in all controls ( Figure 2D–2E ) . Embryo-attachment failure is most likely caused by an altered uterine receptivity response in the mutant model , since the blastocyst still contains an unaltered COUP-TFII allele , and even mutant embryos are able to implant in wild-type mothers as previously described [17] . Decidualization is the subsequent step in the implantation process [23] . Although it is not possible to compare the decidual response in natural pregnancies of these mice , decidualization was assayed after hormonal induction [24] . Induction of decidualization was normal in COUP-TFIIflox/flox control mice using two types of stimuli ( oil injection into the uterine lumen or needle scratching on the antimesometrial side of luminal epithelia ) . Uterine horns of PRCre/+ COUP-TFIIflox/flox mutant mice failed to decidualize under treatment of either stimuli ( Figure 3A , 3B , and 3E ) , and alkaline phosphatase activity , an indicator of stromal cell differentiation in response to decidualization , was absent ( Figure 3C and 3E ) . In addition to the failure of decidualization , stromal cell proliferation is also affected since the size of mutant uterine horns appear small at 4 . 5 dpc ( Figure 2A and 2B ) . Immunostaining of phosphorylated histone H3 ( phospho-H3 ) demonstrated that stromal cell proliferation was significantly decreased as indicated by the phospho-H3–positive cells ( Figure 3F and 3G ) . The numbers of phospho-H3–positive cells in stroma are quantified and shown in Figure 3H . In contrast to the stroma , the numbers of phospho-H3–positive cells in the epithelia are increased in the mutant ( Figure 3F and 3G ) . The increase in the numbers of proliferating cells in the mutant epithelium are quantified and shown in Figure 3I . In addition to the decreased proliferation in the stroma , vessel density visualized by lectin staining was also lower in the mutant uterus ( Figure 3J and 3K ) . Reduced angiogenesis could partly contribute to the decrease in size of the uterine horn . BMP2 is a known specific marker for decidualization in the uterus , and its expression is greatly induced upon decidualization [25 , 26] . To explore the molecular mechanism of decidualization failure in the COUP-TFII mutant mice , we asked whether expression of BMP2 is altered . Basal Bmp2 expression levels were unaffected in the mutants in comparison with the controls . However , the induced expression of Bmp2 upon decidualization was greatly diminished in the mutant uterus ( Figure 4A ) . Immunohistochemistry confirmed no stromal expression of BMP2 in the mutant uterus ( Figure 4B and 4C ) . The above results suggest that BMP2 is a downstream target of COUP-TFII that regulates the decidual response . To address this , we asked whether BMP2 could rescue the decidualization defect exhibited by the COUP-TFII conditional mutant . Along with artificially stimulating the uterus , recombinant human BMP2 was administered into the uterine lumen . Mice were dissected 48 h later , and the decidual response was measured . BMP2 treatment restored the decidual response in the mutant uterine horns ( Figure 4D and 4G ) as measured by the enhancement of alkaline phosphatase activity in the stimulated horns , while no activity was detected in the vehicle ( BSA ) –treated mutant horns ( Figure 4E , 4F , 4H , and 4I ) . These results strongly support that BMP2 is a major COUP-TFII effector that lies downstream of COUP-TFII to mediate uterine decidualization . BMP2 has also been shown as a downstream target of hedgehog signaling in other tissues [27 , 28] , and conditional ablation of Bmp2 results in decidualization defects , but embryo attachment is unaffected ( Lee et al . , unpublished data ) . Therein , our finding provides new evidence in support of the existence of a uterine Ihh–COUP-TFII–BMP2 axis that is required for decidualization . The lack of embryo attachment indicates that ablation of COUP-TFII not only affects the physiology of uterine stromal cells but also affects the endometrial epithelial compartment . One of the major roles of progesterone is to down-regulate ER activity in the uterine luminal epithelium , which consequently opens the uterine receptivity window . Since COUP-TFII mutants have a receptivity defect ( Figure 2A–2E ) , we wondered whether COUP-TFII is a mediator of progesterone's suppression of ER activity in the epithelia . If so , ER activity in this compartment should increase in COUP-TFII mutants . To address this , the expression level of estrogen-responsive genes was examined by quantitative real-time RT-PCR analysis ( qRT-PCR ) . The expression of lactoferrin ( Ltf ) , a known estrogen-responsive target in the uterine epithelia [29] , is significantly elevated in the mutant uterus at 3 . 5d pc ( Figure 5A ) . To exclude the possible involvement of other factors , we also examined the expression of Ltf in mice exogenously treated with hormones , mimicking 3 . 5 dpc of pregnancy ( 30 h after progesterone and estrogen [Pe] treatment; see Materials and Methods ) . Although the fold changes vary , Ltf expression level is consistently significantly higher in mutant mice ( Figure 5B ) . Immunohistological staining detected high lactoferrin expression in mutant epithelia ( Figure 5C–5F ) , demonstrating that estrogen activity is indeed enhanced in the uterine epithelial compartment . Other well-documented estrogen-responsive genes in the uterine epithelia , including complement component 3 ( C3 ) and chloride channel calcium activated 3 ( Clca3 ) [30 , 31] , were also elevated in the mutant mice ( Figure 5G and 5H ) , indicating that estrogen activity is upregulated in the uterine luminal epithelium of mutant mice . Mucin 1 ( MUC1 ) is known to be one of the important markers determining uterine receptivity [32] . MUC1 is an estrogen-responsive target , and its expression is attenuated at the time of implantation to facilitate epithelial remodeling [33 , 34] . Persistent expression of MUC1 during the peri-implantation period prevents uterine receptivity and embryo attachment [32] . qRT-PCR showed high expression levels of Muc1 in the mutant uterus ( Figure 6A ) . In addition , immunohistochemistry detected high expression levels of MUC1 in the apical surface of mutant luminal epithelia ( Figure 6B and 6C ) . These results suggest that high estrogen activity might be the underlying cause of the uterine receptivity defect displayed by the mutant model . Consistent with this notion , Clca3 , a gene important for the overproduction of mucus protein [35] , is also shown to be highly upregulated in the mutant uterus ( Figure 5H ) . Therefore , upregulation of many ER target genes suggests that stromal COUP-TFII is essential for the PR-mediated downregulation of ER activity in the epithelium to open up the receptivity window . The membrane transformation of uterine epithelia is well documented as a marker of uterine receptivity [36] . Long microvilli of the epithelial surface are characteristically present under estrogen influence , while progesterone shortens these structures . Microvilli flattening occurs before implantation and is an important process to facilitate embryo attachment [36] . Electron microscope ( EM ) studies revealed that mutant epithelia fail to undergo appropriate remodeling to flatten the microvilli ( Figure 6D and 6E ) . In addition , mutant microvilli exhibit increased glycocalyx expression ( Figure 6F and 6G ) , which is consistent with high expression of MUC1 [36] . Both MUC1 expression and glycocalyx formation prevent embryo attachment [34 , 37 , 38] . It has been reported that a series of glycosylation enzymes are involved in the glycosylation of mucins , and among them , UDP-N-acetyl-alpha-D-galactosamine: polypeptide N-acetylgalactosaminyltransferase 1 ( GALNT1 ) is a key enzyme [39] . We examined Galnt1 expression in qRT-PCR and observed that its expression level is increased by 20% ( Figure 6H ) , and , most remarkably , another glycosylation enzyme Galnt7 expression is upregulated by almost 70% in the mutant uterus ( Figure 6H ) . Although it has not been well established in the mouse uterus , activation of GALNT7 catalytic activity requires prior glycosylation by other enzymes [40] , and GALNT7 cooperatively functions with GALNT1 [41] . The high expression of these enzymes might account for hyperglycosylation of the apical surface of the mutant uterine luminal epithelium . Another important parameter for uterine epithelial maturation is the presence of desmosomes [36 , 42 , 43] , adherent junctions of the lateral plasma membrane . Desmosomes are normally lost before implantation to facilitate embryo invasion into the uterine stroma . However , desmosomes are persistently present in the mutant epithelia ( Figure 6I and 6J ) . As expected , the expression level of desmocollin-2 ( Dsc2 ) , one of the ubiquitous desmosomal components [44] , is high in the mutant uterus ( Figure 6K ) . This inappropriate regulation of Dsc2 might contribute to desmosome dysregulation . Taken together , the high estrogen activity observed in the mutant epithelium alters the uterine receptivity in the mutant mice , which is reflected by striking structural abnormalities in the apical–lateral regions of mutant luminal epithelial cell . PR in the stroma has been implicated to play a critical role in modulating ER activity in the epithelium [6 , 7] . Since the activity of ER is enhanced in COUP-TFII mutants , an important question is whether ablation of COUP-TFII in the uterine stroma alters the expression level of stromal PR . To address this possibility , we used PR-specific immunostaining to assess the expression of PR in the uterus of controls and mutants . The result clearly shows that the expression level of PR is significantly reduced in the stroma of COUP-TFII mutants ( Figure 7A and 7B ) . In contrast , there is no significant change in the PR expression levels in the luminal epithelium or the glandular epithelium . This result indicates that downregulation of PR in the stroma in the absence of COUP-TFII could disrupt stromal–epithelial interactions and contribute to the enhanced ER activity . In an attempt to further dissect the molecular mechanism of enhanced estrogen activity in the mutant uterine epithelium , we first asked whether uterine ERα levels are altered in mutants . qRT-PCR showed a 40% increase in levels of ERα ( Esr1 ) mRNA in the whole-uterine tissues of COUP-TFII mutants ( Figure 7C ) . Immunohistological staining using ERα-specific antibody further confirmed an increased expression of ERα in the epithelial compartment of COUP-TFII mutants ( Figure 7D and 7E ) . To quantify the difference in expression levels , we isolated uterine epithelia from whole uterus and examined ERα expression by western blot analysis . The result showed that ERα expression is increased 2- to 3-fold in the mutant uterine epithelia ( Figure 7F ) . To further ask whether these receptors are activated or not , we examined the phosphorylation status of ERα using antiphosphorylated ERα antibody and observed increased phosphorylation of ER in the uterine epithelium of COUP-TFII mutants ( Figure 7F–7H ) . Increased phosphorylation levels of ER seem proportional to increased expression levels of ER , but this modification has been shown to couple with growth factor signaling , which might be controlled under paracrine mechanism [45] and is less likely to be an autophosphorylation; therefore , this finding really supports the notion that stromal–epithelial communication is dysregulated in the COUP-TFII mutant uterus . In addition to ER , members of the steroid receptor coactivator ( SRC ) /p160 family , SRC-1 and SRC-2 , have been shown to play a major role in regulating ER activity and uterine function [46–48] . Thus , we examined the expression of coactivators by both immunohistochemistry and western blot analysis . We showed that SRC-1 is upregulated in mutant uterine epithelia ( Figure 7F , 7I , and 7J ) , while SRC-2 and SRC-3 are unchanged ( unpublished data ) . Taken together , the increase in ER , phosphorylated ER , and SRC-1 levels in the mutant uterine epithelium can together contribute to enhanced uterine ER activity in the COUP-TFII mutant .
Uterine receptivity has been intensely studied in recent years because of its clinical importance [49 , 50] . Mouse models generated by gene-knockout technology revealed that multiple factors are involved in this process [1–3] . Although individual factors have proven to be essential for uterine receptivity , most of them are directly or indirectly controlled by estrogen and/or progesterone . Therefore , we assume that the balance in activities between these two hormones is a major determinant of successful uterine receptivity . Indeed the levels of estrogen used in in vitro fertilization procedures have recently been suggested as a likely contributor to lower pregnancy successes when using artificial reproductive techniques [51] . This reappraisal prompts the question of how to control estrogen activity during the peri-implantation period so that higher success rates with in vitro fertilization can be achieved . An important step toward addressing this question is to define the mechanism by which progesterone modulates estrogen activity in the uterine epithelium . Understanding this pivotal control mechanism would enable the formulation of better clinical protocols to induce and preserve the receptive uterus . Based on the findings described herein , we propose a new model to explain estrogen and progesterone control of uterine implantation . In this model , progesterone activates the Ihh–COUP-TFII–BMP signaling axis to elicit stromal cell differentiation that is required for decidualization . Importantly , COUP-TFII also mediates progesterone-induced suppression of epithelial estrogen action through decreasing epithelial ER and SRC-1 levels and inhibition of ER activation ( phosphorylation ) during the peri-implantation period ( Figure 8 ) . All these effects are likely due to its regulation of stromal PR level , which was shown to be responsible for the downregulation of ER activity [6 , 7] . Because COUP-TFII is expressed in the stroma , a paracrine mechanism of action is proposed by which stromal-derived COUP-TFII controls epithelial ER activity through as-yet-unknown mediator ( s ) that transmits the inhibitory signal from the stromal to the epithelial compartment . Although beyond the scope of this study , identification of this paracrine signal represents the next most important step to fully understand the complete circuitry of progesterone/estrogen action in reproduction . The model of progesterone signaling described here is most likely oversimplified . Many other players in both epithelial and stroma compartments may also participate in the overall regulation . For example , ER in the stromal compartment and the very low expression of COUP-TFII , if any , in the epithelial compartment , may also participate in some way in this scheme . Only by compartmental-specific deletion of these genes can we validate and dissect the contributions of these proteins to embryo implantation in the future . As reported previously , COUP-TFII is regulated by progesterone through Ihh signaling , which emanates from the epithelial compartment of the uterus , but we should not underestimate the roles of stromal PR because tissue-recombinant experimental models have demonstrated that epithelial estrogen activities are suppressed by stromal PR [6 , 7] . Indeed decreased expression of stromal PR is expectedly observed in COUP-TFII mutant mice , which was examined under proper comparison ( PRCre/+ versus PRCre/+ COUP-TFIIflox/flox; Figure 7A and 7B ) , although detailed regulatory mechanism of this interdependence has yet to be defined . In addition , leukemia-inhibiting factor ( LIF ) , which has been well documented , may also participate in this scheme . Lif-null mice exhibit defects in embryo attachment , in which the specific ultrastructural and immunohistological features associated with a receptive uterus are lost [52] . Since observed phenotypes in Lif-null mice are similar to COUP-TFII mutant mice , LIF also could be placed in our scheme . LIF is known to be estrogen responsive; when examined , we did not find significant changes in LIF by qRT-PCR and by immunocytochemistry in mutants in comparison to the controls . It is possible that more complex mechanism underlies our model , but it is still unequivocal that COUP-TFII has access to the principal part of steroid receptor regulation in the uterine biology . The finding that COUP-TFII antagonizes ER action is intriguing . ER has been shown to regulate the expression of many glycoproteins during the peri-implantation period [34 , 53] . Downregulation of the expression of such glycoproteins ( including MUC1 ) is known to pave the way for remodeling of the epithelial surface to facilitate embryo attachment . Although COUP-TFII has been shown to compete with ER binding in vitro in the regulation of Ltf [54 , 55] , COUP-TFII is not expressed in the same compartment as lactoferrin and MUC1 , and thus it is unlikely that it regulates their expression directly in vivo . Using tissue-recombinant studies , Buchanan et al . showed that epithelial lactoferrin expression is not only regulated by epithelial ER but also regulated by stromal ER [56] . This raises the possibility that COUP-TFII might compete with stromal ER and alter the epithelial ER function . Another possible mechanism is that COUP-TFII regulates local estrogen levels , since COUP-TFII has been shown to compete with SF-1 to regulate aromatase expression [57] . However , aromatase expression was not altered in the COUP-TFII conditional mutant mice ( unpublished data ) . We also showed that the expression of ER , phosphorylated ER , and SRC-1 are all increased in the COUP-TFII mutants . Enhanced expression of these molecules will no doubt contribute to the observed increased ER activity and the subsequent activation of the downstream ER targets . Since COUP-TFII is highly expressed in the stroma but is barely detectable in the epithelia , the up-regulation of ER activity in the epithelium is unlikely a consequence of direct regulation of the above molecules by COUP-TFII . It is more likely that the stromal COUP-TFII regulates PR to control a paracrine signal , which acts through its epithelial receptor to suppress epithelial ER activity as well as ER and its coregulator expression . Unlikely as it might be , we can not exclude the possibility that the low levels of epithelial COUP-TFII expression is sufficient to synergize with other epithelial factors to suppress epithelial ER activity directly . In conclusion , COUP-TFII controls early molecular and cellular changes in the uterus that are required for embryo implantation and subsequent decidualization . Based on our previous observation that COUP-TFII is a mediator of the Shh pathway in motor neurons and the stomach [11 , 14] , it is not surprising that COUP-TFII mediates progesterone–Ihh signaling to regulate decidualization . We also show that BMP2 can rescue the decidual defect elicited by the loss of COUP-TFII , which places BMP2 downstream of the COUP-TFII pathway . Unexpectedly , stromal COUP-TFII also promotes PR expression to mediate progesterone-induced suppression of estrogen activity in the uterine epithelium; local suppression of estrogen activity is required to establish a receptive uterus . Therefore , progesterone control of epithelial estrogen activity is projected from the stromal compartment via COUP-TFII through a complex epithelial–stromal cross-communication pathway . The abnormal increase in estrogen activity following the removal of COUP-TFII may help our understanding of the molecular events that control uterine receptivity as well as female reproductive health .
Generation of COUP-TFIIflox/flox mice and PR-Cre knockin mice has been previously described [14 , 22] . To obtain uterine tissues of pregnant mice , we started mating with wild-type males ( B6SJLF1; Taconic , http://www . taconic . com ) at 7 wk of age and designated the day of vaginal plug as pregnant day 1 . Ovariectomy was performed at 6 wk of age and followed by the hormone regimen as described below . For priming with 1 μg of 17β-estradiol ( E2; Sigma-Aldrich , http://www . sigmaaldrich . com ) was dissolved in 1 ml sesame oil ( Sigma-Aldrich ) , and 0 . 1 ml was subcutaneously administered in a single dose for each mouse . For daily treatment of Pe , 10 mg progesterone ( Sigma-Aldrich ) and 67 ng 17β-estradiol ( nidatory estrogen ( e ) ) were dissolved in 1 ml sesame oil , and 0 . 1 ml was subcutaneously administered in a single dose for each mouse . In the implantation study , 1% Chicago Sky Blue 6B ( Sigma-Aldrich ) was prepared in 0 . 9% saline , and 0 . 1 ml was intravenously injected for each mouse before dissection . For the rescue of decidualization , 25 μg recombinant human BMP2 ( Fitzgerald Industries International , http://fitzgerald-fii . com ) was reconstituted by 10% BSA , and 10 μl was administered for each uterine horn . All procedures for animal study were approved by the institutional animal care guidelines at Baylor College of Medicine . All assays were repeated at least three times . We followed the ovary transfer procedure described previously [58] . Ovaries from 6-wk-old controls , PRCre/+ or PRCre/+ COUP-TFIIflox/+ mice , or mutant PRCre/+ COUP-TFIIflox/flox mice were isolated and then transferred to a B6129-F1 female mouse . At 2 wk after transfer , the mice were mated with B6SJL-F1 male mice for a period of 2 to 6 mo . Each litter was genotyped in order to characterize the origin of the pups . When two litters came from the transferred ovary , the mating was stopped and the experiment was considered a success . The details of this method have been previously described [24] . Briefly , after 2 wk of ovariectomy , we first primed mice with 100 ng of estradiol ( E2 ) for 3 d and then started the daily treatment of 1 mg progesterone and 6 . 7 ng E2 ( Pe ) 2 d later . Mechanical stimulation was added 54 h after the first Pe treatment ( 54 hPe ) , and mice were dissected 48 h later for decidual response measurement . The same hormone regimen was used for exogenous hormone treatment mimicking 3 . 5 dpc . Tissues were isolated at 30 hPe . Isolated uterine tissues were fixed in 4% paraformaldehyde ( PFA ) /PBS , dehydrated through graded ethanol , and processed for paraffin embedding . Primary antibodies used in this study are as follows: mouse monoclonal anti-COUP-TFII ( 1:1 , 000; Perseus Proteomics , http://ppmx . com ) , rabbit polyclonal anti–phospho-H3 ( 1:200; Upstate Biotechnologies , http://www . upstate . com ) , goat polyclonal anti-BMP2 ( 1:100; Santa Cruz Biotechnology , http://www . scbt . com ) , rabbit polyclonal anti-lactoferrin ( 1:5 , 000; Abcam , http://www . abcam . com ) , rabbit polyclonal anti-MUC1 ( 1:400; Abcam ) , rabbit polyclonal anti-PR ( 1:200; Dako , http://www . dako . com ) , rabbit polyclonal anti-ERα ( 1:500; Santa Cruz Biotechnology ) , rabbit polyclonal anti–phosphorylated ERα ( S118 , 1:100; Abcam ) , and rabbit polyclonal anti–SRC-1 ( 1:500; Santa Cruz Biotechnology ) . Biotinylated antibodies ( 1:400; Jackson ImmunoResearch , http://www . jacksonimmuno . com ) were used as secondary antibodies , followed by horseradish peroxidase–conjugated streptavidin ( 1:200; Molecular Probes , http://probes . invitrogen . com ) , and signals were developed with 3 , 3′-diaminobenzidine ( DAB ) substrate kit ( Vector Laboratories , http://www . vectorlabs . com ) or Alexa fluor 488–conjugated tyramide signal amplification ( TSA ) kit ( Molecular Probes ) . Hematoxylin or methyl green ( Vector Laboratories ) was used for counterstaining in immunohistochemistry . Isolated tissues were fixed in 2% PFA/PBS , cryoprotected by 30% sucrose/PBS , and embedded in OCT compounds ( Sakura , http://www . sakura . com ) . The sections were stained with 5-bromo-4-chloro-3-indolyl phosphate ( BCIP ) and Nitro blue tetrazolium chloride ( NBT ) solution ( pH 9 . 5; Roche , http://www . roche . com ) . Nuclear Fast Red ( Vector Laboratories ) was used for counterstaining . A total of 25 μg biotinylated-lycopersicon esculentum lectin ( Vector Laboratories ) was intravenously injected , and then uterine tissues were isolated , fixed in 4% PFA/PBS , cryoprotected by 30% sucrose/PBS , and embedded in OCT compounds . The sections were incubated in horseradish peroxidase–conjugated streptavidin , and signals were developed with the TSA kit . Isolated tissues were quickly stabilized in RNAlater RNA stabilization reagent ( QIAGEN , http://www . qiagen . com ) . Total RNA was extracted using an RNeasy Mini kit ( QIAGEN ) and reverse transcribed using TaqMan Reverse Transcription Reagents ( Applied Biosystems , http://www . appliedbiosystems . com ) . Gene expression assay was performed by running the ABI PRISM 7700 Sequence Derector System ( Applied Biosystems ) . TaqMan Universal Master Mix reagents and inventoried primer/probe mixture ( Applied Biosytems ) were used for the reaction . The primers/probes used in this study are the following: Bmp2 ( Mm01962382_s1 ) , Ltf ( Mm00434787_m1 ) , C3 ( Mm00437858_m1 ) , Clca3 ( Mm00489959_m1 ) , Muc1 ( Mm00449604_m1 ) , Galnt1 ( Mm00489148_m1 ) , Galnt7 ( Mm00519998_m1 ) , Dsc2 ( Mm00516355_m1 ) , Esr1 ( Mm00433149_m1 ) , COUP-TFII ( Mm00772789_m1 ) . Standard curves were generated by serial dilution of a preparation of total RNA , and mRNA quantities were normalized against 18S RNA determined by using eukaryotic 18S rRNA endogenous control reagents ( Applied Biosystems ) . Mice were perfused with 2 . 5% glutaraldehyde in 0 . 1 M cacodylate buffer before isolation of uterine tissues . Tissues were cut into 1-mm3 pieces , immersed in 2 . 5% glutaraldehyde and 2 . 0% formaldehyde in cacodylate buffer with 2 mM CaCl2 , washed , and then postfixed by 1% OsO4 in 0 . 1 M cacodylate buffer . Next , the tissues were dehydrated through graded ethanol then dehydrated further in propylene oxide , and embedded in Spurr resin . Ultrathin sections ( 80 nm ) were cut with an MT6000 XL Ultramicrotome ( RMC Inc . , http://www . rmcproducts . com ) , stained with aqueous uranyl acetate and lead citrate , and examined under a Hitachi-H7500 TEM ( http://www . hitachi-hta . com ) at 80 kV . Uterine luminal epithelial cells were isolated as previously described [59] . Briefly , isolated uteri were placed into Hanks balanced salt solution ( HBSS; Ca2+-free , Mg2+-free ) , and cut into 1-mm segments . The cut uteri were placed into 1% trypsin/HBSS solution for 1 . 5 h at 4 °C , and then washed with cold HBSS . The uteri were placed into 20% FBS/HBSS solution for 5 min , and washed with cold HBSS . The uteri were incubated with DNase solution for a minute to break down DNA . The uterine luminal epithelium was gently removed from the uterine stroma under a dissecting microscope . The isolated uterine luminal epithelium was lysed with 1× RIPA buffer ( 150 mM NaCl , 10 mM Tris-Cl [pH 7 . 5] , 0 . 1% SDS , 1% Triton X-100 , 1% deoxycholate , and 5 mM EDTA ) containing proteinase inhibitors and phosphatase inhibitors . The whole-uterine luminal epithelial cell lysates were separated on 8% and 15% SDS–polyacrylamide gels and transferred to a nitrocellulose membrane ( Amersham Biosciences , http://www . amersham . com ) . The membranes were blocked in TBST buffer ( 20 mM Tris [pH 7 . 6] , 137 mM NaCl , and 0 . 05% Tween 20 ) containing 1% casein for 1 h and then incubated overnight at 4 °C in 0 . 5% casein containing primary antibody . The membrane was washed several times with TBST buffer and incubated with horseradish peroxidase–conjugated secondary antibody . After 1 h , the blot was washed several times with TBST buffer and developed with ECL reagents ( Amersham Biosciences ) . The serum progesterone and estradiol levels were measured with radioimmunoassay by the core laboratory of University of Virginia Center for Research in Reproduction Ligand Assay and Analysis Core .
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Pregnancy is established and maintained through a series of precisely choreographed cellular and molecular events that are controlled by two sex hormones , estrogen and progesterone . Both hormones exert their actions through their distinct nuclear receptors . During the peri-implantation period , estrogen activity is attenuated by progesterone to facilitate epithelial remodeling and embryo attachment , but the detailed molecular mechanism of how this process is achieved remains largely undefined . COUP-TFII ( chicken ovalbumin upstream promoter transcription factor II; also known as NR2F2 ) , a member of the nuclear receptor superfamily , is highly expressed in the uterine stroma , and its expression is controlled by progesterone–Indian hedgehog–Patched signaling from the epithelium to the stroma . To assess the uterine function of COUP-TFII , uterine-specific COUP-TFII knockout mice were generated . These mutant mice are infertile due to failure of implantation . We identified a novel genetic pathway in which the epithelial Ihh regulates the stroma COUP-TFII to control BMP2 and regulates decidualization . Interestingly , enhanced epithelial estrogen activity , which impedes the maturation of receptive uterus , was clearly noted in the absence of COUP-TFII . This finding reveals that COUP-TFII plays a critical role in maintaining the balance between estrogen and progesterone activities to establish proper implantation . This finding also provides new insights into women's health care associated with uncontrolled estrogen activity , such as breast cancer and endometriosis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"mammals",
"physiology",
"eukaryotes",
"vertebrates",
"mus",
"(mouse)",
"animals",
"genetics",
"and",
"genomics"
] |
2007
|
COUP-TFII Mediates Progesterone Regulation of Uterine Implantation by Controlling ER Activity
|
Vertebrate genomes contain numerous copies of retroviral sequences , acquired over the course of evolution . Until recently they were thought to be the only type of RNA viruses to be so represented , because integration of a DNA copy of their genome is required for their replication . In this study , an extensive sequence comparison was conducted in which 5 , 666 viral genes from all known non-retroviral families with single-stranded RNA genomes were matched against the germline genomes of 48 vertebrate species , to determine if such viruses could also contribute to the vertebrate genetic heritage . In 19 of the tested vertebrate species , we discovered as many as 80 high-confidence examples of genomic DNA sequences that appear to be derived , as long ago as 40 million years , from ancestral members of 4 currently circulating virus families with single strand RNA genomes . Surprisingly , almost all of the sequences are related to only two families in the Order Mononegavirales: the Bornaviruses and the Filoviruses , which cause lethal neurological disease and hemorrhagic fevers , respectively . Based on signature landmarks some , and perhaps all , of the endogenous virus-like DNA sequences appear to be LINE element-facilitated integrations derived from viral mRNAs . The integrations represent genes that encode viral nucleocapsid , RNA-dependent-RNA-polymerase , matrix and , possibly , glycoproteins . Integrations are generally limited to one or very few copies of a related viral gene per species , suggesting that once the initial germline integration was obtained ( or selected ) , later integrations failed or provided little advantage to the host . The conservation of relatively long open reading frames for several of the endogenous sequences , the virus-like protein regions represented , and a potential correlation between their presence and a species' resistance to the diseases caused by these pathogens , are consistent with the notion that their products provide some important biological advantage to the species . In addition , the viruses could also benefit , as some resistant species ( e . g . bats ) may serve as natural reservoirs for their persistence and transmission . Given the stringent limitations imposed in this informatics search , the examples described here should be considered a low estimate of the number of such integration events that have persisted over evolutionary time scales . Clearly , the sources of genetic information in vertebrate genomes are much more diverse than previously suspected .
The integration of a DNA copy of the retroviral RNA genome into the DNA of infected cells is an essential step in the replication of these viruses . Portions of DNA tumor virus genomes can also become integrated into cellular DNA , but this is a relatively rare event , detected by selection of a clone of cells that express the viral oncogene ( s ) . While such integration events occur routinely in somatic cells , retroviral DNA sequences are also integrated in the germlines of many hosts , giving rise to inherited , endogenous proviruses . It has been reported that sequences from viruses that contain RNA genomes and do not replicate through a DNA intermediate , may also be copied into DNA and become integrated into the germline cells of plants and insects [1] , [2] , [3] . That such events can have biological impact was demonstrated in the case of sequences derived from the positive strand RNA genome of a Dicistrovirus ( Israeli acute paralysis virus ) , which were integrated into the germline of bees from different hives [2] . Bees with genomes that contain sequences encoding a portion of the structural protein of this virus are resistant to infection by this same virus . Similar observations have been made in mice with endogenous retroviral sequences related to a capsid gene ( Fv-1 locus ) which confers resistance to infection by some retroviruses [4] . These observations suggest that chronic infections of a host with both retroviruses and non-retro RNA viruses can result in germline integration events that produce a host expressing some viral functions that confer an advantage to the species; resistance to subsequent infection by that virus . With these ideas in mind , we undertook a search in the germline genomes of vertebrates for DNA sequences that may be related to any of the known non-retroviral families of viruses that contain single-stranded RNA genomes . As our analyses were being completed , an independent group of investigators reported that sequences derived from the nucleocapsid gene ( N ) of ancient relatives of such a virus , the Borna disease virus ( BDV ) , are integrated in the genomes of several mammalian species [5] . Here we report the results of our comprehensive search in which 5 , 666 sequences from non-retroviruses with RNA genomes were compared with the DNA sequences in the genomes of 48 vertebrate species . Our studies have not only confirmed the integration of BDV N-related sequences , but they have also revealed that sequences related to the matrix and polymerase genes of this virus have been integrated into the germlines of various vertebrate species . In addition , we have discovered genome integrations of viral gene sequences from other members of the order Mononegavirales , with the most prominent related to Ebolaviruses and Lake Victoria Marburgvirus . It is noteworthy that these viruses exhibit extremely high mortality rates in some susceptible species , for example reaching 80% in horses that develop Borna disease , and up to 90% in humans infected with Ebolavirus [6] . In addition to possessing linear non-segmented , negative sense single-stranded RNA genomes , the Mononegavirales have several other features in common , including a similar gene order and transcription strategy in which genes are flanked by specific transcription start and stop sites and are expressed in a gradient of decreasing abundance ( Figure 1 , for review see: [7] ) . The 8 . 9 Kb BDV genome encodes information for at least six proteins . These viruses form a unique family , the Bornaviridae , and they are the only viruses in the Order to replicate and transcribe their genomes within the nucleus of the infected cell [8] . Sheep , horses , and cows are among the natural hosts for this enzootic virus; while there are a number of other experimental hosts , virus replication under such conditions is poor , chronic , and slow [8] . Many tissues can be infected in susceptible hosts , but disease symptoms are commonly neurological . Natural infections of humans are at best controversial , and infectious virus has been isolated from this source only infrequently [9] . Given that the BDV is an RNA virus , its genome sequence conservation among isolates of many mammalian species , separated in both time and geographic locations , is surprisingly high . This suggests strong selection pressure to retain a core sequence for virus viability in a reservoir species with which an evolutionary equilibrium has been established . The Ebola ( EBOV ) - and Marburg ( MARV ) - viruses comprise the two genera of the family Filoviridae . Their approximately 19 Kb genomes are replicated and transcribed in the cytoplasm of infected cells . EBOV and MARV cause highly lethal hemorrhagic fever in humans and have high potential for individual-to-individual transmission . Several strains of EBOV are known , including the Zaire and Sudan strains in Africa , and the Reston strain in the Philippines . The latter has only been associated with monkeys , but a recent report also found infection by this strain in domestic swine , and the presence of antibodies in six exposed farm workers [10] . Recent evidence suggests that bats are the natural reservoir of these zoonotic agents [[11] , and references therein , [12]] .
To conduct this survey , a BLAST program ( see Methods ) and the NCBI viral Refseq database of virus sequences were employed ( October 2009 release ) which , at the time , contained a total of 79 , 001 viral protein sequences , among them 5 , 666 sequences from viruses with single-stranded RNA genomes that replicate without a DNA intermediate . The latter sequences included all 4 known Orders of animal viruses ? with single-stranded RNA genomes , and represented all 38 recognized families , as well as 9 additional unclassified viral genera with such genomes . These viral sequences were compared with 48 complete vertebrate genomes , to determine if any could be identified in the vertebrate genomes . The results were striking , revealing numerous genomic sequences related primarily to two currently circulating virus families with single , negative strand RNA genomes , the Bornaviruses and Filoviruses ( Table 1 ) . Selected examples are listed in Table 2 , with a complete list provided in Supporting Tables S1 , S2 , S3 , S4 , S5 , S6 and S7 and Figures S3 and S4 . The most numerous of these virus-like sequences were related to the nucleocapsid N ( p40 ) gene of BDV , but sequences related to the BDV RNA-directed RNA polymerase ( L ) , and to the genes encoding the major nucleocapsid protein , NP , and the minor nucleocapsid , polymerase complex cofactor protein ( VP35 ) of EBOV/MARV , were also detected in several vertebrate genomes . Sequences related to the matrix protein ( M ) gene of BDV were detected in the lemur and medaka genomes , and to the L gene of EBOV/MARV in the opossum genome . Altogether , we discovered BDV-like sequences in at least 13 species , and EBOV/MARV-like sequences in at least 6 species . A single , high confidence example of sequences related to the L gene of Midway/Nyamanini virus was detected in the zebrafish genome . A sequence related to the Tamara Bat virus in the medaka genome was the lone example related to a positive strand RNA viral genome . In many of these examples no synteny was observed among chromosome locations of the sites in different related vertebrate genomes and we conclude that most represent independent integration events , possibly taking place over extended time periods . In other cases , both synteny of chromosomal locations and copy number stability in a genome is observed for virus-related sequences , through lines of inheritance . The genes of viruses in the Order Mononegavirales are transcribed as mono- or dicistronic mRNAs ( Figure 1 ) . The distribution of endogenous virus-like sequences that were detected here , appear to be limited to one or very few per specie . This , and the fact that single genes are represented in diverse locations , is suggestive of a mechanism that involved the reverse transcription and integration of DNA copies of viral mRNAs by LINE elements , much as cellular pseudogenes are produced . Indeed , we found several cases in which landmarks , or remnants of landmarks , characteristic of Line element-mediated insertion are associated with specific Bornavirus- and Filovirus-related integrations . These include direct repeats flanking transcription start sites and 3′ polyA sequences ( Table 3 ) . In many additional cases , only 3′ polyA sequences were observed ( data not shown ) . The fact that direct repeats are not found for some endogenous sequences is not surprising , as these repeats may be just 2 nucleotides long and likely have experienced numerous mutations from the time of initial integration . However , from the informative examples in Table 3 we conclude that some , if not all , RNA virus-related sequences have been integrated into their host genomes by LINE elements via target-primed reverse transcription from ancient viral mRNAs . In some cases , the integrations of virus-related genes were observed in closely related species descended from each other , allowing an estimate of the oldest common ancestor of these integrations . For example , a rodent lineage ( including mice and rats ) contains BDV gene N- and L-related endogenous sequences , and a separately derived primate lineage ( comprising marmosets , macaques , chimps , and humans ) contains endogenous BDV gene N-related sequences integrated into seven different places in the genomes . The rodent and primate lines differ from each other in their integration sites , but within both lineages identical sites of integration and stable copy numbers of genes are observed , indicating decent through lineages of viral genes integrated in the past . In the primate line these sites first appear in the present day marmosets and have been retained over forty million years from a common ancestor of marmosets and humans ( Figure 2 ) . Based on the degree of sequence homology of BDV-related genes in different host genomes , most of these integrations seem likely to have originated in the same time frame , with the exception of the integration in squirrels , which has much higher sequence homology to the present day virus ( Table S1 ) . We stress that integration events illustrated in Figure 2 appear to have been independent events , and do not come from a single ancient integration: no synteny in integrated sequences and adjacent chromosome is observed across species . The timing of integrations of the EBOV/MARV-related sequences is less clear . The examples of these viral gene sequences fail to distinguish between the present day strains of EBOV and Lake Victoria MARV suggesting an ancient ancestor of both ( Figure 3 and Figure S1 ) . Because the integration events appear to predate the split between these genera , we consider them together , and have estimated their ages indirectly . We start with the assumption that at the time of integration , functional protein-coding sequences were free of stop codons . Some of these integrated viral sequences appear to be under positive selection to the present day , because they have retained their open reading frames . Other integrated viral gene sequences have not retained open reading frames and have mutation rates that are measurable . We can employ the latter to estimate the age of an integration event . The typical rate of vertebrate genetic drift ranges from 0 . 12% of nucleotides per million years in primates to 2–4 times that value in rodents [13] , [14] , [15] , [16] . There are three stop codons and nineteen codons that can become stop codons with a single base change . Assuming an equal frequency of all possible single nucleotide changes , there is a 12% probability that a random codon change will produce a stop codon in one mutational step . Genomic sequences that once encoded proteins , but are now non-functional pseudogenes , are therefore expected to develop stop codons at a rate of one per 1/ ( 0 . 12×3×0 . 0012 ) ≈2310 positions for each million years of evolution of primates , and 2–4 times more frequently in rodents . We next analyzed virus-derived integrations for the presence of stop codons in the stretches of aligned peptide sequences , as shown in Table 4 ( additional integrations are listed in Table S8 ) . According to the calculations described above , the two least conserved , near full-length integrations of BDV-related genes in humans , hsEBLN-3 and hsEBLN-4 , appear to be 48 and 40 million years old respectively , consistent with our earlier estimates based on primate phylogeny . Integrations in rodents appear to be more recent , or have lost their protein coding ability at a later time , about 21 million years ago for rodEBLL and 19 million years for rodEBLN-2 and rodEBLN-4 . Interestingly , the mouse integrations appear to be under stronger selection that those in rats . The EBOV/MARV-related integrations in the opossum genome appear to be 32–53 million years old ( assuming 0 . 13% neutral rate for nucleotide drift per million years [17] ) . The ages cited here are rough estimates , as rates of genetic drift vary in time and across different stretches of DNA . Other integrations have similar sequence identity with the present day viruses and appear to originate from the same time in history . However , we do not explicitly cite their ages due to the preliminary nature of the scaffold assemblies for carrier species ( Table 4 and Table S8 ) . The absence of the stop codons in some integrations points to strong selective pressures towards maintenance of full-length open reading frames . This is in contrast to the actual peptide sequences that appear to be undergoing neutral drift . Over the 20 million years of evolution in rodents and 40 million years in other mammals , we expect a 5–10% nucleotide change or approximately 15–30% codon change , if there is no selective pressure against fixation of such events in the population . Accordingly , one would expect to observe a stop codon in 1 . 8–3 . 6% of the codons . This is , indeed , the case for the majority of the integrations ( Table 4 and Table S8 ) . In contrast , several integrations show signs of strong positive selection , namely those related to the BDV N gene in humans , microbats , rodents , and other animals , and both the EBOV/MARV NP and VP35 gene-related integrations in bats and tarsier . Some integration events , including the BDV N-like sequences in humans ( e . g . hsEBLN-1 ) and the EBOV VP35-like sequences in microbats ( mlEEL35 ) have maintained nearly full-length open reading frames ( Table 2 ) . The probability of having no stop codon in the longest of these , the BDV gene N-like integration in humans , is one in eight hundred , suggesting that at some time , past or present , there was strong selective pressure to keep and express this ancestral viral gene . Expressed sequence tags ( EST ) were identified for four integrated copies of the BDV N-related genes in humans ( hsEBLN-1 through hsEBLN-4 ) . The chromosome 3 integration ( hsEBLN-2 ) is actually tiled on Affymatrix chips to detect mRNAs from human tissues . Analysis of a very large diversity of tissue types show low levels of this transcript in most tissues tested , intermediate levels in thymus , olfactory bulb , fetal thyroid , liver , prefrontal cortex , CD34 cells , endothelial cells and dendritic cells , and high levels in CD4 and CD8 T-cells ( Figure S2 ) . In susceptible species , BDV replicates mainly in cells of the nervous system , but viral nucleic acids and proteins have been isolated from peripheral blood mononuclear cells . It is clear that several BDV N-like endogenous sequences are expressed as mRNAs in human tissues . Expression of mRNA from these endogenous sequences was also detected in several cell lines in cell culture [5] . BDV is an enzootic virus , with natural infections occurring in sheep , horses , and cattle [18] , in which serious , often fatal , neurological symptoms are observed . These animals have no detectable copies of the BDV-related endogenous sequences . Furthermore , species in the primate and mouse/rat lineages , which contain endogenous N-like sequences , are generally resistant to the virus , or the virus is observed to replicate poorly with little or no symptoms in these animals [19] ( Table 5 ) . In cows , which do have endogenous sequences related to the BDV N gene , there is apparently no present day selection for its coding capacity ( Table 4 ) , and cows are known to be susceptible to Borna disease . Thus , there appears to be a general correlation between natural resistance to the pathogenic effects of the virus and the potential for expression of BDV N-like endogenous sequences in a host . However , as has been observed with Fv-1 in mice [20] , natural resistance can be overcome under experimental conditions in which animals or cell cultures may be subjected to large doses of the virus ( Table 5 ) . The X-ray crystal structure of the N protein of BDV has been solved , and a number of critical features determined [21] . The protein is organized in two domains , separated by a short linker , and assembles into a homotetramer . We find that open reading frames in two endogenous human inserts , hsEBLN-1 and hsEBLN-2 , are long enough to encode folded N-terminal domains ( Figure 4 ) , while an open reading frame in hsEBLN-1 also encodes a complete C-terminal domain . When expressed , either of these proteins could conceivably affect the proper assembly of the BDV ribonucleoprotein complex . Production of N-related antibodies might also inhibit virus replication . An open reading frame is also observed in the integration in squirrels ( stEBLN ) , encompassing a complete C-terminal domain . The BDV gene N-related integration in the genome of the microbat Myotis lucifugus ( mlEBLN-1 ) , might also be found to carry a full-protein open reading frame when the preliminary assembly of this genome undergoes final revision . EBOV and MARV are zootropic viruses that cause infections with some of the highest mortality rates in humans , primates , and pigs . Recent studies have suggested that megabats , specifically Hypsignathus monstrosus , Epomops franqueti , and Myonycteris torquata , could be potential natural reservoirs for EBOV [22] . Later studies also identified microbat Mops condylurus , as well as several other megabats , as potential reservoirs [11] . Some of the bats actually carry live virus , yet exhibit no visible symptoms of disease . There are more than 1 , 100 recognized species of bats , comprising about a fifth of all mammalian species [23] , but the genomes of only two bat species have been sequenced . Our results show that at least one of them , the microbat Myiotis lucifugus , has detectable integrations of EBOV/MARV-like sequences , with several of these showing strong selective pressure for maintaining open reading frames ( Table 4 ) . The most widespread EBOV/MARV integrations observed in this study are derived from the major viral nucleocapsid gene NP and the minor nucleocapsid and polymerase complex cofactor gene VP35 . The endogenous sequences related to the NP protein align with the amino-terminal region ( Figure 5 ) , which is conserved among these viruses and the Paramyxovirus family , and is critical for NP-NP protein interactions [24] , [25] . The microbat sequence mlEELN-1 , for example , covers most of this region , including a highly conserved stretch of amino acids and part of a structurally disordered acidic region , which is thought to play a role in the incorporation of the protein into virus particles [24] . Determination of the X-ray crystal structure of the interferon inhibitory domain ( IID ) of the EBOV VP35 protein has identified two interacting sub-domains , the C-terminal of these includes a cluster of basic residues , centering on R312 , which are critical for RNA binding [26] . The microbat endogenous sequence mlEEL35 encompasses the entire IID domain as well as a good portion of the N-terminal domain , which is required for VP35 oligomerization as well as viral replication and transcription ( Figure 6 ) . A comparison of the sequences shows that residues important for interactions between the IID sub-domains are largely conserved in mlEEL35 [27] , [28] . However , while an arginine residue corresponding to R312 is retained in microbats and the tarsier , two or more of the surrounding acidic residues are substituted in each of these endogenous sequences . Substitution of these residues in EBOV VP35 diminishes RNA binding and abrogates the interferon antagonist function of this protein [26] , [27] . Furthermore , viruses that carry these relevant mutations are non-pathogenic in normally susceptible guinea pigs , and animals infected with this mutated virus develop antibodies that render them resistant to subsequent challenge [29] . Our sequence search also uncovered what appear to be remnants of ancient integrations of virus-like glycoprotein genes ( G ) , which are most similar to the glycoproteins from the Order Mononegavirales ( Table 6 ) . A BDV gene G-like integration in primates was acquired sometime before the split between humans and old world monkeys , and there are several integrations that most resemble the Filovirus glycoprotein genes ( GP ) . In the Filoviruses , the GP precursor protein is cleaved to form two bound peptides , GP1 and GP2 . We found no traces of receptor-binding GP1 [30] in the vertebrate genomes analyzed . However , we identified several sequences related to the second peptide , GP2 , which is involved in glycoprotein trimerization [31] , and is highly conserved among known Filoviruses ( Table 6 ) . Because GP2 shares sequence elements with the avian sarcoma/leukosis virus , the flanking regions of the top BLAST glycoprotein hits were checked for retroviral sequences , LTR elements and gag-pol genes ( as described in Methods ) , and integrations that show no known adjacent retroviral elements were identified . Nevertheless , some ambiguity remains due to the preliminary nature of several of the vertebrate genome assemblies . Assuming that the endogenous glycoprotein encoding sequences are , indeed , related to viruses in the Order Mononegavirales , their integration may also play role in virus resistance . For example , expression of a GP2 peptide from endogenous sequences may affect the trimerization of GP from a related infecting virus . Recent studies have indicated that over-expression of Filovirus GP in host cells may prevent subsequent infection with the virus [32] . Whether expression of integrated GP-like sequences can stimulate such cellular immunity or other types of resistance to infection remains to be explored .
This survey has uncovered a fossil record for currently circulating RNA virus families that stretch back some 40 million years in the evolution of host species . The error rate per replication of the DNA genomes of the hosts is much lower than the error rates of RNA-dependent RNA synthesis , the mechanism by which these viruses replicate their genomes . Consequently , the host genome contains a more accurate record of the archival genes of viruses with RNA genomes than the related present-day viruses . Considering the relatively high rate of mutation in RNA viruses , and the stringent criteria we utilized to detect homologies , what is reported here should be taken as an underestimate of such viral gene integration events . The most common events we detected derive from certain viruses that contain negative single strand RNA genomes . This might be a reflection of some unusual properties of such viruses and their hosts . For example , the viruses could have high sequence conservation or the hosts could have been selected to retain specific viral sequences that confer resistance to subsequent infection . However , the results of this search are as interesting for what was not found as what was found . The endogenous viral sequences that were identified with highest confidence are all related to currently circulating viruses in the Order Mononegavirales , which contain single negative strand RNA genomes . Furthermore only two of the four recognized families in this Order are represented , the Bornaviruses ( BDV ) and Filoviruses ( EBOV and MARV ) . In one species , zebrafish , we also found endogenous sequences related to members of a possible new Taxon in this viral Order , comprising Midway and Nyamanini viruses [33] . These results seem especially noteworthy , as the genomic insertions reported in plants and insects are all derived from viruses with plus strand RNA genomes , such as the Flaviviruses and the Picornaviruses [1] , [2] , [3] . Furthermore , the data presented here ( Tables 3 and S1 ) indicate that the endogenous sequences in vertebrate genomes were likely integrated via target-primed reverse transcription of ancestral viral mRNAs by LINE elements . As all viruses produce mRNAs during active infection , the selection or retention of endogenous sequences from mainly one viral Order , is all the more striking . The cellular location of viral replication does not appear to be a critical factor in the insertion of endogenous sequences , because the Bornaviruses replicate in the nucleus and the Filoviruses , in the cytoplasm . We note , in addition , that no endogenous sequences were found that are related to viruses in the Orthomyxovirus family , such as the influenza viruses , which contain segmented negative strand RNA genomes and also replicate in the nuclei of infected cells . However , it is possible that some feature of the mRNAs produced by these viruses is recognized preferentially by LINE machinery , or can promote access to such machinery in the nucleus , and such notions can now be tested . LINE elements are known to be active in the germline [34] , and it is possible that the germline cells of some infected vertebrates may have been especially susceptible to infection by the ancestors of these viruses . Finally , DNA copies of mRNAs from other RNA viruses may , indeed , have been integrated into the germlines of infected vertebrates , but are no longer recognizable . Once DNA copies are inserted into the host genome one would expect the mutation rate of these sequences to be reduced by about four orders of magnitude compared to the genes in replicating RNA viruses , rapidly separating the virus sequences of today from the those of the past . Indeed , a DNA copy of an RNA viral genome trapped in a host chromosome is a window on the RNA virus sequences of the past . In this context , the high conservation of the BDV genome [35] , [36] may partially explain our ability to detect the related endogenous sequences . By far the most readily observable endogenous virus-like elements uncovered in our study were related to BDV . For example , these germline integrations persisted for millions of years as recognizable copies of the N gene in primate and rodent lineages , and of the N and the L genes in bats . Furthermore , an initial event appears to slow or stop further integration events , suggesting that the viral gene product ( s ) can inhibit further virus infection , or eliminates the need to further select for the new integration event . Several integrations also appear to have been selected for their protein coding capacity , with no stop codons emerging over the past forty million years . This is particularly striking because the amino acids in these genes appear to be undergoing the expected frequency of neutral drift , at least among shared integrations in the primate lineage . There are several possible mechanisms by which an endogenous viral gene product may inhibit the subsequent infection of a cell or animal by the same virus . For example , synthesis from the endogenous sequence of an RNA molecule that is partially complementary to the infecting viral RNA could trigger an early interferon or RNA interference response . In addition , translation of an mRNA from the endogenous viral sequence would lead to production of a protein or peptide that is similar , but not identical to that of the infecting viral protein . In the case of nucleocapsid-like proteins ( N , NP ) , such an endogenous gene product could block virus replication or result in the assembly of faulty , non-infectious particles . This would require genetic drift to produce missense mutations but no stop codons , which is the case for some endogenous sequences that we have discovered . Because the function of these proteins requires appropriate multimerization , even a small number of abnormal or defective , endogenously produced monomers could exert a substantial biological effect . Sequence differences in proteins expressed by the endogenous L- and VP-35-like genes could also result in assembly of defective virus particles . Such particles might then become good immunogens , providing immune protection in the host . It is also possible that production of glycoprotein peptides encoded in endogenous viral sequences might block infections by viruses with similar glycoproteins . Examples of the various resistance mechanisms cited above have been shown to exist with several virus groups . This includes experiments in rats , where ectopic expression of individual proteins of the Bornavirus N , X , and P genes , but not their mRNA , inhibits virus replication [37] . There is likely strong selection pressure to establish a resistance mechanism against Bornavirus and Ebolavirus/Marburgvirus , given their high mortality rates in susceptible species . We have noted that the natural hosts of BDV , such as cows and horses , have no detectible sequences related to the BDV N gene ( Table 1 ) , or that the integration is under no present-day selection ( Table 4 ) . It has also been reported that resistance to the neurological symptoms of BDV is genetically inherited in rats and is encoded in an unknown host gene [38] . It would now be quite interesting to test whether or not that gene is the BDV-related rodEBLN sequence . It would also be interesting to examine the endogenous sequences in the human population in greater detail , to determine if there are polymorphisms or deletions that might correlate with neurological diseases , which could lead to a re-examination of the role of BDV in such conditions . Natural resistance to currently circulating EBOV and MARV may allow species to serve as asymptomatic reservoirs for these viruses . In microbats , we identified endogenous sequences related to the NP and VP35 genes of these Filoviruses , in addition to the N and L genes of BDV . Bats of different species have been identified as possible natural reservoirs of EBOV and MARV in areas of human outbreaks in Africa [39] , [40] , [41] . Recent studies confirm that these viruses co-circulate in Gabon , where bats infected by each virus are found . It should now be possible to ask if there is any correlation between the presence and properties of the endogenous sequences in the various bat species and their ability to serve as natural reservoirs for these negative strand RNA viruses . In summary , our studies have made it clear that ancient relatives of some RNA viruses have left DNA copies of their sequences in the germline cells of their vertebrate hosts . The sources of vertebrate genetic inheritance are , therefore , considerably more diverse than previously appreciated . A number of recent reports from tissue culture experiments or clinical studies have presented evidence for the incorporation of DNA sequences corresponding to all or part of the genomes of a variety of infecting RNA viruses into host cell DNA [e . g . 5] [42] , [43] , indicating that such events might occur in somatic tissues with some frequency . However , the mechanisms of integration seem to be varied , and the biological impacts have yet to be elucidated . Whether the germline integrations that we have identified are simply accidents or , as we suspect , may sometimes provide the host with an important selectable advantage , can now be tested .
Analysis of genome integrations was conducted based on viral protein sequences available at NCBI FTP website ( ftp://ftp . ncbi . nih . gov/refseq/release/viral/ ) . Most recent sequences were downloaded on October 28 , 2009 . A total of 79 , 001 sequences were included in that distribution , with each representing an individual viral protein . This number slightly overestimates the actual number of unique sequences , as some proteins may be part of a polyprotein . However , the discrepancy is small , as a total of only 561 sequences are labeled as polyproteins . Finally , every individual virus encodes more than one protein . The complete list of viral proteins was further narrowed down to include only single stranded RNA viruses with no known DNA phase in their replication . For this purpose , we used the list in the NCBI taxonomy database , downloaded on the same date as the viral protein sequences ( http://www . ncbi . nlm . nih . gov/Taxonomy/ ) . This screening procedure yielded 5 , 666 independent viral protein sequences . Again , small overlap is possible due to dual representation in polyproteins . Viral sequences were then screened against publicly available genomic assemblies of 48 sequenced vertebrates and a few close siblings . Vertebrate sequences were downloaded from the UCSC genome website , and when not available , directly from the sequencing center websites or from the Ensembl database ( release 56 ) . The list of species considered is given in Table S9 . The initial search was performed using BLAST 2 . 2 . 17 with parameters -p tblastn -M BLOSUM62 -e 1e-4 . A direct search produced 14 , 281 results , with BLAST E-value cutoff at 10−04 . The vast majority of hits arose from homology between viral proteins and a few host proteins . By far the most widespread homology was between the gene for a 60–70 kDa protein in plant viruses and vertebrate heat shock proteins ( HSP70 in humans ) . Similarly , several viral genes had homologies with GIMAP8 , BIRC8 , PARP14 , and the DNAJC14 families of genes . We removed from further consideration any viral protein that had homology with known mRNAs in humans , cows and mice at the same time . Any integrations in this group would likely represent host pseudogenes , rather than integrations of viral origin . As a final crosscheck , all integrated sequences were reverse-searched against all known nucleotide and protein sequences in the NCBI database using BLAST algorithm , to ensure that a putative integration is indeed from a virus with a single strand RNA genome , and is not a homologous protein from another virus or organism . Additionally , all reported sequences have 30–50% identity with the present-day virus proteins . These values are common for many homologous proteins in Ensembl database , and support an evolutionary relationship between the integrated sequences we have identified and present day virus proteins . Altogether we identified strong hits from seven viral proteins from three different viruses/families ( Table 1 ) , all within the Order Mononegavirales ( non-segmented single stranded negative sense RNA viruses ) . The sole exception that resembles a Flavivirus-derived gene is discussed below . All of the Mononegavirales-derived hits come from nucleocapsid ( N , NP ) , and matrix ( M ) proteins , as well as the viral RNA-dependent DNA polymerase ( L ) , and the polymerase complex cofactor ( VP35 ) . Additionally , weaker hits were associated with glycoproteins ( G , GP ) of the same viruses . Extra care has to be taken here , as glycoproteins are encoded in many viral genomes including retroviruses , which are commonly integrated in the germ lines . We did the following checks to eliminate potential retroviral glycoproteins from further consideration: regions of 10 kb extending both downstream and upstream of each potential glycoprotein-like integration were downloaded and checked for retroviral gag- and pol-genes , as well as for LTR-signatures . Retroviral pol genes were chosen for their highest conservation among all retroviral genes . Altogether , gag- and pol- genes were downloaded from approximately 50 different retrovirus families , and searched using blastx algorithm of the BLAST program , with E-value threshold of 10−3 . Search for LTR-sequences was conducted using LTR-FIND tool ( http://tlife . fudan . edu . cn/ltr_finder/ ) [44] . While all aforementioned integrations were related to members of the Mononegavirales , one putative integration on scaffold 1104 of medaka is most similar to a virus with a positive strand RNA genome , the Flavivirus , Tamana Bat virus . Integration with putative coordinates 26500-2900 on scaffold 1104 has low sequence similarity to Tamana Bat virus and several other Flaviviruses . However , sequence similarity of this integration is fairly low ( BLAST value 10∧-7 for a 190 amino acid fragment of a 600 amino acid protein , with sequence identity of just 28% ) . Additionally , the entire scaffold is not yet mapped to a chromosome , has no known genes , and is not readily aligned with other species . It therefore remains to be seen if this is an actual integration of a positive-sense virus , some accidental sequence , or the result of laboratory contamination . The possibility of somatic cell integration , as opposed to germ-line integration , also remains open , as medaka sequencing relies on genomic DNA from adult bodies [45] .
|
Vertebrate genomes contain numerous copies of retroviral sequences , acquired over the course of evolution . Until recently they were thought to be the only type of RNA viruses to be so represented . In this comprehensive study , we compared sequences representing all known non-retroviruses containing single stranded RNA genomes , with the genomes of 48 vertebrate species . We discovered that as long ago as 40 million years , almost half of these species acquired sequences related to the genes of certain of these RNA viruses . Surprisingly , almost all of the nearly 80 integrations identified are related to only two viral families , the Ebola/ Marburgviruses , and Bornaviruses , which are deadly pathogens that cause lethal hemorrhagic fevers and neurological disease , respectively . The conservation and expression of some of these endogenous sequences , and a potential correlation between their presence and a species' resistance to the diseases caused by the related viruses , suggest that they may afford an important selective advantage in these vertebrate populations . The related viruses could also benefit , as some resistant species may provide natural reservoirs for their persistence and transmission . This first comprehensive study of its kind demonstrates that the sources of genetic inheritance in vertebrate genomes are considerably more diverse than previously appreciated .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"and",
"genomics",
"virology"
] |
2010
|
Unexpected Inheritance: Multiple Integrations of Ancient Bornavirus and Ebolavirus/Marburgvirus Sequences in Vertebrate Genomes
|
Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure . This spontaneous activity has also been shown to play a key role in the response to external stimuli . To better understand this role , we proposed a viewpoint , “memories-as-bifurcations , ” that differs from the traditional “memories-as-attractors” viewpoint . Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input , known as a bifurcation in dynamical systems theory , wherein the input modifies the flow structure of the neural dynamics . Learning , then , is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input . Based on this novel viewpoint , we introduce in this paper an associative memory model with a sequential learning process . Using a simple Hebbian-type learning , the model is able to memorize a large number of input/output mappings . The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input , and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns . These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input , which thus increases the capacity for learning . This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals . In addition , the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in our previous study .
The way in which neural processing of sensory inputs leads to cognitive functions is one of the most important issues in neuroscience . Neural activity in the presence of sensory stimuli [1]–[4] and during the execution of cognitive tasks in response to sensory inputs have been measured experimentally [5] , [6] , and neural network models that exhibit the requested responses to the inputs have been investigated theoretically [7]–[12] . Learning algorithms have also been proposed to memorize several input/output ( I/O ) mappings [12]–[16] . The response activity has been the main focus both in modeling studies and experiments , while pre-stimulus , i . e . , spontaneous , activity has been dismissed simply as background noise . However , spontaneous activity has recently been garnering more attention since experimental measurements have revealed that the spontaneous activity is not random noise and that it shows characteristic spatiotemporal patterns [17]–[19] . Furthermore , many observations have revealed that the response activities to external stimuli [20] , [21] or cognitive tasks depend on the spontaneous activity [22] , [23] . Evoked responses are generated not only by external inputs but also through the interplay of the spontaneous activity and external stimuli . Thus , to establish a neural basis for the cognition and computation in a neural system , it is important to understand the nature of this interplay . Spontaneous activity has been analyzed theoretically over the last few decades by using neural network models of rate-coding or spiking neurons with random , designed , or biologically realistic connections [24]–[28] . However , apart from a few publications [29] , [30] , the relationship between the spontaneous activity and response to external input has rarely been investigated . Furthermore , how the learning shapes the spontaneous activity and its response to an input is still an open question , but recent experimental studies suggest that learning and developmental processes modify and shape the spontaneous activity [31] , [32] . In the present paper , we analyze how the spontaneous activity is formed when I/O mappings are memorized . We do this by introducing a simple learning rule to the neural dynamics in order to study the interplay between the spontaneous activity and input-evoked response . To analyze the formation of the spontaneous activity and its response to the memorized input through the learning of I/O mappings , we previously proposed a novel view on memory in [33] , [34] , which we called “memories as bifurcations” in contrast to the traditional theoretical viewpoint of “memories as attractors . ” According to the memories-as-attractors viewpoint , each memory is embedded in one of the attractors in a unique neural dynamical system [11] . An input specifies an initial condition of the dynamical system , and from that initial state , the neural activity reaches an attractor that matches the target corresponding to the given input . Thus , the initial states are determined by the given inputs , but the neural activity in the absence of inputs is not examined . In contrast , according to the memories-as-bifurcations viewpoint , an input modifies the neural dynamics as a parameter , and the flow structure of the neural activity is also changed from that without an input . In the absence of input , the neural activity evolves and corresponds to spontaneous activity . In the presence of a learned input , the flow structure in the neural dynamics changes and an attractor that matches the requested target corresponding to the applied input emerges . With an increase in the input strength , the flow structure changes via a sequence of bifurcations in terms of dynamical systems theory . Here , the flow structure can be changed substantially by applying different memorized inputs . Thus , for this viewpoint , memories are embedded in the flow structure of the neural dynamics such that they enable appropriate bifurcations to appear upon input application . Previously , we designed a neural-network connection matrix through correlations among memorized inputs and targets so that an output that matches a target is generated , as a result of bifurcations from the spontaneous activity , by applying the corresponding input [34] . In the model , similarity between the spontaneous and evoked activities was demonstrated and is consistent with recent observations in experimental studies [32] , [35]–[37] . Although the simplicity of the model is an advantage for analyzing the relationship between spontaneous and evoked neural activities , it remains unclear whether the simplistic structure in the designed network in [34] is the only way to store associative memories or if there exists a variety of networks that show similar behavior and generate a sufficient memory capacity . Also , how such network structures for memorizing I/O mappings are formed by learning through a widely-accepted synaptic plasticity rule , such as the Hebbian rule , is still open for debate . In the present study , we introduce a sequential learning model with a simple Hebbian-type learning rule that changes the synaptic strength according to the activities of the pre- and postsynaptic neurons . From extensive numerical simulations , we have confirmed that through this learning the networks memorize mappings ( where is the number of elements ) satisfying the memories-as-bifurcations viewpoint . Here , spontaneous activity shows chaotic behavior with approaches to memorized output patterns . By applying each memorized input , this activity is transformed ( after a sequence of bifurcations ) into different attractors that generate the target pattern corresponding to the applied input . In spite of the sequential learning scheme , the neural network does not lose the memory it learned earlier; it has a capacity of up to . This capacity is not so small , and interestingly it is not possible in conventional sequential learning models in which the learning of a new I/O mapping easily pushes out previous memories . As long as the memorized targets are attractors in the same dynamical system , the formation of a new attraction to a novel attractor will easily destroy the attraction to earlier target patterns . Our model differs in that the different targets are attractors in the presence of the corresponding input , i . e . , they are embedded in different neural dynamical systems , so that attractors for earlier targets are not destroyed . Here , the spontaneous activity is flexible; it is possible to apply an input so that a new target is embedded in the network structure without destroying the information of the previous targets . Remarkably , the network generated through the learning process to obtain a high memory capacity is found to have a similar structure to the network designed in [18] . Although the learning process can generate a huge variety of networks , which are not similar to the designed network , a common structure is generated by the learning . A simple learning rule for synaptic change is sufficient for generating such a network .
We first select two random binary patterns , and , as the input and target patterns , respectively . The neural activity evolves in the presence of whose strength is constant during the learning process for . The synaptic connection also evolves according to ( 2 ) where is a learning parameter that is the inverse of the time scale ratio of the synaptic to neural dynamics . The above synaptic dynamics are determined by correlations between the activities of the pre- and postsynaptic neurons . This learning rule takes a similar form as the perceptron learning rule where the synaptic connection is changed by correlations between activities of elements in the input and output layers [16] . Here , although the validity of this learning rule is not mathematically proven in contrast to the perceptron , it is expected by the following argument . According to Eq . 1 , the change in the neural activity during with the connection modified by the learning , , is given by ( 3 ) ( 4 ) Following the synaptic dynamics in Eq . 2 , the change in the neural activity due to is given by ( 5 ) where is a positive value determined by and differential coefficient . Thus , when is larger ( smaller ) than , increases ( decreases ) , respectively . Hence , the change in the synapses will drive the successive activity toward the target . Note , however , that the distance between the neural activity and the target is not necessarily guaranteed to decrease monotonically through the learning , because the total change in the neural activity depends also on . The learning process stops automatically when the neural activity matches the target since in this case , otherwise , the learning process continues . Here we impose several I/O mappings to be successively learned , and after learning the preceding mapping , another input pattern with the same strength as the previous learning is applied while giving a new target pattern . The learning process for each single I/O mapping is called a learning step in what follows . In this learning algorithm , which belongs to a class of palimpsest learning models [38]–[40] , each mapping is learned sequentially and previously learned mappings are overwritten by the latest mapping . Thus , it is possible that older mappings are forgotten through the learning process . During the learning process , double ( neural and synaptic ) dynamics run concurrently , and the neural and synaptic states have to be set as initial states: the neural and synaptic states are randomly selected from with a uniform probability and from a binary ensemble of with equal probability , respectively . In this model , fully-connected networks without self-connections are used . Through different learning processes , different sets of mappings are learned so that the generated networks are also different . For a statistical analysis , we take an average over many networks shaped through different learning processes . As our purpose in this study is to analyze the relationship between the spontaneous and evoked dynamics , we analyze the neural dynamical system in the absence and presence of input after learning . After the learning is completed , the synaptic connections are fixed and only the neural activities evolve . Note that there is no need for the input strengths for learning and memory recall to be identical: we can set the input strength used during the recall process after the learning and independently of the input strength used during the learning process . For example , after learning with , we can analyze the evoked dynamics by applying the input with . To distinguish the two clearly , the input strength used in the learning process is denoted by and that used in the analysis of the neural activities after learning is denoted by . The spontaneous and evoked dynamics are given by and , respectively . As recall and memory for the memories-as-bifurcations viewpoint are defined differently from those for the memories-as-attractors viewpoint , we outline the definitions of recall and then memory here . A network succeeds in recalling a target for an input of , if , on application of input for = , the overlap of the evoked activity with the target is higher than the overlap with any other pattern . Here , is a transposed vector of and the inner product is given by . By considering a case in which the evoked attractor is not a fixed-point attractor , the temporal average overlap is taken as this criterion . By denoting the temporal average overlap with the target as , the criterion for the successful recall of corresponding to the applied input is given by ( 6 ) where we measure the avaraged overlaps in the presence of the input and is the pattern that has the largest overlap with the activity among other targets and inputs , as well as other random patterns . Memory is defined as the ability of a network to recall a target for most initial states . The condition for whether a network memorizes an I/O ( / ) mapping is ( 7 ) where represents the average over the initial states of this network . By extending this criterion , we adopt a condition for determining whether networks memorize the I/O mapping for a certain parameter as ( 8 ) where denotes the average over different networks .
Fig . 1 exhibits a learning process shown as a raster plot and the time series of the overlap with the target for . After wandering over many neural activity patterns , the neural activity reaches the target pattern and the learning process is completed . The learning process does not stop by becoming trapped in a local minimum , nor does it continue to wander over the neural patterns . We confirmed that in all trials with parameters of , the learning was completed . During a learning process , the flow structures of the spontaneous and evoked activities change . Hence , the recall process also changes through the learning process . Fig . 2 shows a recall process before and after learning for and . Before learning , an attractor matching the applied input pattern is generated when that input is applied ( in Fig . 2A ) , but the overlap with the required target is not high and the network thus fails to recall the target . After learning , two types of neural dynamics are generated depending on the parameter values ( ) ( see also Table 1 ) : We now analyze the spontaneous and evoked neural dynamics in these two regimes . First , to reveal the dependence of the evoked dynamics on the parameters , as a function of and , is shown in Fig . 3A . In the R regime , for larger and smaller values , only the target attractor exists and the average overlap is equal to one , while in the NR regime , both the target and reverse-target attractors exist and the average overlap is lower than that in the R regime . As decreases or increases , the volume of the reverse-target attractor basin increases and that of the target attractor decreases so that the average overlap with the target also decreases . The dotted line in Fig . 3A represents the boundary between the R and NR regimes computed using the spontaneous activity , as discussed below . To analyze the spontaneous dynamics , we note that due to the symmetry , the mean overlap for each target over time is generally zero because the orbit can approach both the target and reverse-target with equal probability . Thus , we measure the standard deviation ( SD ) of the overlap to quantify the approach to each target . The SD ( ) of an overlap with the -th target over time is computed as . If this SD is much larger than that for the overlap with a random pattern , then the spontaneous activity selectively approaches the target ( and its reverse ) . A numerical computation of the SD as a function of and is plotted in Fig . 3B . In the R regime , chaotic behavior appears and the SD takes a finite positive value , while in the NR regime , fixed-point attractors exist and so the SD is zero . Interestingly , a band that has a higher SD , which stretches from ( 2 . 6 , 0 . 001 ) to ( 16 , 1 ) , and whose ridge divides the R and NR regimes appears in the figure . In Fig . 3B , the ridge is shown as the dotted line , which is also plotted as a reference in Fig . 3A . Around the ridge , the SD of the spontaneous activity is much higher than that in other areas , and the chaotic spontaneous activity shows switching behavior between the target and reverse target . While the target and reverse-target attractors are unstable , their ruins still exist and the neural dynamics intermittently visit them . In Figs . 3A and B , the boundary defined by the SD might be slightly ambiguous because of the finite-size effect . However , by extrapolating the result for larger system sizes ( to be discussed later ) , it is expected that , in the absence of inputs , all the networks in the NR regime show fixed-point behavior and those in the R regime show chaotic behavior , in the thermodynamic limit . By increasing or decreasing , the minimum distance between the activity and the target ( or the reverse-target ) increases in the R regime . Thus , in this limit , the SD in the NR regime is zero . It suddenly increases to nearly one at the transition point , and then gradually decreases in the R regime . The ridge of the SD thus indicates the transition between the NR and R regimes well . The area with the average overlap taking nearly one above the dotted line in Fig . 3A is expected to remain even in the thermodynamic limit . However , this area is included in the NR regime , since according to the analysis of the neural dynamics after multiple learning steps , to be discussed later , no more than a single pattern is recalled , as in the rest of the NR regime . We also show how spontaneous activity changes into evoked activity with an increase in in each regime , as shown in Fig . 3C . In the R regime , by increasing from zero , the neural activity shows successive bifurcations such that the overlap with the target increases to approach unity at . The fixed-point attractor matching the target appears at . In the NR regime , the target and reverse target attractors do not change on application of the input , but the basin volumes of the attractors increase . We analyze the connection matrix that is shaped through the learning process , in the R and NR regimes , by measuring the element of the matrix C which is projected onto and , as defined by ( 9 ) where = . Note that for a given binary pattern , if the system has a large matrix element , then pattern is more stable in the absence of inputs for the neural dynamics in Eq . ( 1 ) . Similarly , when is larger , is less stable . Fig . 4 shows a time series of the elements , and for the NR , and R , regimes . In the NR regime , only the element is much larger than the others after learning , while in the R regime , both and take salient positive values and and take salient negative values . The result that dominates in the NR regime means that the generated connection matrix takes a similar form to that of the Mattis model in a spin system [41] , which corresponds to the Hopfield network with only one memorized pattern . In the network where is larger and the other elements are much smaller , the target and reverse-target patterns remain highly stable . This is consistent with the above analysis in the NR regime . In the R regime , in contrast , the connection matrix shows a form distinct from those of the matrices in Mattis and Hopfield-type networks . Remarkably , the matrix takes a similar form to that of the model in [34] , where was adopted . Indeed , the behaviors of the spontaneous and evoked activities in this regime agree with that observed in that model [34] . In general , the behaviors are strongly dependent on the matrix elements . In Fig . 5 , the elements as a function of are plotted . For , all of the elements deviate saliently from zero , and as decreases , the elements , and decrease rapidly , while does not change . The regime changes from the R to NR regime as this occurs . We now analyze why such connection matrices are formed through the learning process . The evolution of the matrix element is also determined by Eq . ( 2 ) as follows: ( 10 ) ( 11 ) Although also evolves temporally , we set as a constant value , because relative scale of the elements is relevant for understanding the behavior . In the same way , the evolutions of the other elements are determined by ( 12 ) ( 13 ) ( 14 ) In both the regimes , the activity approaches a target and thus is greater than zero ( and smaller than ) for most of the learning process . Thus , is positive for most of the learning process and then , takes a large positive value . In contrast , the change in the other elements is distinct between both regimes , which is explained by the initial behavior of the learning process . In the R regime , the overlap with the input increases in the early stage of the learning process as is directed toward by the input , as shown in Fig . 4A ( ii ) . It is estimated that is and positive , which is much larger than . Thus , and are negative in the R regime , while is positive . These estimates of the sign of the elements are consistent with the matrix elements in Fig . 4B . For the NR regime , in which is smaller and is larger , the increase in the overlap with the input in the early stage is much smaller than that in the R regime; if is small , the neural activity does not respond strongly to the input , whereas if is large , the learning is completed before the overlap with the input increases . Thus , the temporal changes in , and are much smaller . Hence , only takes a large value , and thus the Mattis-type network is generated . Neural activities that are shaped through multiple learning steps are analyzed for I/O mappings that are learned sequentially , as shown in Fig . 6 . In the presence of each input ( as indicated by the colored bars above the plot ) , the neural activity converges to the target to be memorized in the same way as in the learning process of a single mapping ( shown in Fig . 1 ) . Note that although the learning process changes the synaptic connections and flow structure of the neural activity , some of the structure generated in earlier learning steps is preserved because the change in the flow structure in each learning step occurs in the presence of a different input pattern . We mainly present the results after the learning of 40 mappings and analyze the behaviors of spontaneous and evoked activities for later 30 mappings in the following analysis . ( We choose 30 mapping because memory capacity is almost 20 as shown later . The number 30 and 40 can be arbitrary , as long as they are chosen to be larger than the many capacity . ) Corresponding to each phase in the one-step learning , we also found two distinct behaviors in the multiple learning: ( i ) Neural activity responds to multiple inputs so that an attractor that matches each learned pattern is generated respectively upon each input . Thus , multiple mappings are successfully memorized . ( ii ) The neural activity does not respond to any input . The two attractors that match the latest learned target and its reverse pattern exist in the absence and presence of the input . Recall in response to an input is not observed either . We call these the R and NR regimes , respectively , in the same manner as the analysis for one-learning step . In Fig . 7 , we plot the neural dynamics in the presence and absence of inputs after 40 learning steps for in the R regime . The recall processes of 1st , 5th , and 30th targets are shown by the overlaps with for and 30 in the absence and presence of the 1st , 5th , and 30th input , respectively . From here on , the index ( 5 , and in this case ) denotes the order of the I/O mapping beginning with the most recent , i . e . , the 1st mapping is the latest learned one , while the 5th is that learned 5 steps earlier , and so forth . In the R regime , by applying an input , the overlap with the required target increases and takes on the highest value of all overlaps . In particular , in the presence of the latest input , the overlap with the latest target takes a much higher value , of nearly one , and an attractor that matches the latest target is generated . Thus , the latest target is successfully recalled by applying the corresponding input . In the presence of earlier inputs , the overlaps with the requested targets take smaller values than that with the latest target , but they are still larger than the overlaps with other patterns ( see Fig . S1 ) , as long as the retrieved mapping is not one that was learned much earlier ( as shown below ) . ( The overlaps with the applied inputs also take higher values than the overlaps with other patterns , as well as the overlaps with the required targets . Thus , we compare the overlaps with the targets with those with the inputs in the following part . ) For example , the overlap with the 5th target is highest among the overlaps with others , in particular higher than that with the 5th input ( Fig . 7B ) . Thus , the 5th target is also recalled according to Eq . 4 . From almost all initial values , the neural activity evolves to an attractor that gives the corresponding target pattern upon application of the appropriate input . Thus , the 1st and 5th targets are always recalled . According to the definition of memory in Eq . 6 , the 1st and 5th mappings are memorized in this network . In contrast , the overlap with the 30th target , which is learned much earlier , takes a much smaller value and is lower than the overlap with the 30th input . Thus the network cannot recall the 30th target , i . e . , the target has not been memorized . Hence the memory capacity of the present network lies between 5 and 30 . To examine the memory capacity , we compute the average overlaps with the targets in the presence of each earlier input , as well as the average overlap with the input itself , as shown in Fig . 7B . The overlap with an earlier target upon application of the corresponding input gradually decreases with an increase in , while the overlap with the applied input increases . The difference between the average overlaps with the -th target and input under the -th input = decreases with an increase in . Here , eventually crosses 0 at around 20 . According to definition of memory in Eq . 8 , the system in this regime succeeds in recalling the target by applying the corresponding input to 20 I/O mappings . To reduce the artifact from the fluctuations of the overlap on memory capacity due to the finite size effect , we modify the definition of the memory capacity slightly as ( 15 ) Here , we set , however , as long as the value is small , there is no essential change in the memory capacity . According to this modified definition , is computed to be 19 . We also analyze the spontaneous neural dynamics that underlie the responses to the learned inputs analyzed above in the R regime . The spontaneous neural activity shows noisy behavior , and no fixed pattern is stable , as shown in Fig . 7A . Irrespectively of the noisy behavior , the overlaps with the memorized targets often show high values from time to time . We compute the distributions over time of these overlaps and present them in Fig . 7C . The overlap distribution with the latest target is much broader than that with a random pattern , and thus , the neural activity gets selectively closer to the latest target from time to time , even in the absence of input . The distributions of the overlaps with earlier targets are also broader than that with a random pattern , even though the magnitude is smaller than that of the overlap with the latest target . Following the analysis introduced in the single-step learning , we measure the SDs of the distributions of the overlaps with all the targets , as represented by dots in Fig . 7D . We also compute the SD by averaging over the networks , as shown in Fig . 7D as the light blue line . As shown , the SDs of the later targets decrease as increases . The major source of decrease in the SD comes from a decrease in the amplitude of the overlap . Therefore , the spontaneous activity approaches the learned targets from time to time and the closeness to the target during the spontaneous dynamics decreases with . The SD decreases approximately as a power law as , with . This decay rate roughly agrees with that of the evoked activity , which is approximated by with . Both of the exponents are computed from a fit of the overlap and averaged SD to and , respectively , by using the least-squares method . We will analyze the dependence of the decay rates on the parameters and below . In the NR regime , in contrast , the latest target and its reverse pattern exist as attractors in the absence and presence of inputs for ( see Fig . S2 ) . This is identical to the NR-regime behavior after one learning step , for which was nearly zero . Due to the stability of the latest target attractor , the neural activity does not respond to the earlier input ( ) either , so that is also nearly zero . According to the definition of memory , Eq . 15 , . By decreasing or increasing , the reverse target attractor is less stable in the presence of the latest input , and loses stability at some parameter values , while this attractor is still stable in the absence of the input . In this region , is equal to one , while there is still no response to an earlier input , and thus in this region , = 1 . So far , we have analyzed the spontaneous neural activity with 0 and the evoked activity with . We now examine how the spontaneous activity is transformed into the evoked activity with , as is increased . This change with changing is regarded as a bifurcation or a sequence of bifurcations in terms of the dynamical system theory . The bifurcations of the neural activity , revealed by increasing for the 1st , 5th , and 30th input strengths for the network given in Fig . 7 , are shown in Fig . 8 . In the R regime , the overlap with the 1st ( i . e . , latest ) target increases monotonically and continuously by increasing the strength of the 1st input . Finally , the fixed point that matches the 1st target is generated for not only the network used in the figure , but also most of the networks in the R regime . The change to a fixed point is understood as a low-dimensional bifurcation , while the whole sequence of neural activity changes involves higher-dimensional dynamics . For the 5th and 30th inputs , the overlap with the corresponding input is increased continuously with an increase in the input strength , in a similar manner as the bifurcation diagram for the 1st input . In contrast to the latest input , however , the attractor is not a fixed-point attractor even for , where the evoked activity still shows chaotic behavior . Apart from the change to a fixed-point attractor , the bifurcation sequences involve a large degree of freedom in a high-dimensional ( ) space . Hence , plotting a few macroscopic variables , i . e . , the overlaps of the neural activity with a few targets , is not sufficient to capture the entire bifurcation sequence . Therefore , to consider the chaotic dynamics , we measured the Lyapunov spectrum for the neural activity dynamics . With an increase in the input strength , the number of positive Lyapunov exponents decreases , implying the existence of successive bifurcations from a high-dimensional attractor to a lower-dimensional attractor ( see Fig . 8 ) . Accordingly , the dimension of the neural-activity attractor also decreases . No positive Lyapunov exponents exist once the fixed-point attractor is reached for the input that was just learned , while even for the application of an earlier input , a decrease in the number of positive exponents is observed but the number does not reach zero . In the NR regime , the latest target and reverse-target fixed-point attractors exist with . Even by increasing the input strength , these attractors remain stable and no bifurcation occurs . The dependence of the spontaneous and evoked activities on the two parameters , and , are analyzed through the capacity and SD . The dependence of the evoked activity is explored by measuring the capacity according to Eq . 15 , with the results shown in Fig . 9A . In the R regime with a larger and smaller , a high capacity is observed , while in the NR regime with a smaller and larger , the capacity is zero or one . Over the entire parameter space , the overlap with the requested target in the presence of an earlier input decreases , i . e . , decreases as increases , while that with the corresponding input increases . However , the decay rate of the overlap with the target as a function of and the growth rate of the overlap with the input are dependent on and . For a large and small , e . g . , as shown in Fig . 7B , the decay rate of the overlap with the target as a function of is small , as well as the growth rate of the overlap with the input . In general , when the capacity is higher , response to an earlier input is higher and the decay rates are lower . As the parameters approach the NR regime and the memory capacity decreases with a decrease in and increase in , these rates become larger ( see Figs . 7B and 9D ( i ) ) . Finally , in the NR regime , the rates reach maximal value , and the network responds only to the most recently learned input and not to any other input , i . e . ( see Fig . 9D ( ii ) ) . To explore the dependence of the spontaneous activity , we measure the average SD of the spontaneous activity over the learned mappings , ( 16 ) as shown in Fig . 9B , where is set to 30 . When is larger , the decay rate of SD ( ) is smaller . For a large and small , , where takes a higher value , the spontaneous activity approaches not only the latest target , but also an earlier target from time to time , as shown in Fig . 7D . The closeness to the target , as seen by the decrease in the SD of the overlap with an earlier target , decreases for targets memorized earlier . As decreases and increases , and the system approaches the NR regime , the average SD decreases and this decay rate increases; the spontaneous activity approaches the latest target selectively as shown by the small distance between the spontaneous activity and the latest target ( see Fig . 9C ( i ) ) . Finally , in the NR regime , the activity in the absence of input falls on the latest target and reverse-target pattern ( or the localized fluctuations around these patterns ) ( see Fig . 9C ( ii ) ) . The decay rates of the overlap with the evoked activity and the SD of the spontaneous activity in the R regime were seen to obey power laws of and , respectively , and the two exponents and have a similar value for and dependence on , as shown in Fig . 9E . This suggests that the approach of the spontaneous activity to the target is correlated with the activity evoked in response to the corresponding input . Both of the two exponents decrease for a larger and smaller . For much larger and much smaller values , these decreases become saturated , and the curves of and SD ( ) as functions of no longer change with an increase in . Thus , the capacities for different values become also saturated and take a common value of ( Fig . 9F ) . In other words , for a sufficiently large and small , the capacity in this model with takes a constant value of 20 . Further , from results for , and , we have confirmed that this capacity is proportional to ; the capacity has a universal limit of ( see Fig . S3 ) . Note that the R and NR regimes are clearly distinguishable mathematically . Although the boundary between them might slightly ambiguous , as seen in Figs . 9A and B for because of the finite-size effect , it is clearer with the increase in , and , in the thermodynamic limit , it is expected that the memory capacity is equal to one ( or zero ) as is the fixed-point spontaneous activity , i . e . , , for all networks in the NR regime . In the R regime , in contrast , spontaneous activity shows chaotic behavior , i . e . , , for all networks , and the memory capacity increases linearly with size , as . The proportion coefficient 0 . 2 may be slightly varied according to the criterion for the memory capacity , but the proportionality to is invariant . Hence , the boundary between R and NR is clearly defined . Finally , we analyze the connection matrix by measuring the elements of the matrix , , and as defined in Eq . 9 . In Fig . 10 , we show the elements in both the R and NR regimes and also in the border between them . The elements in the R regime take comparable values for each , and decrease with an increase in , but the decay rates are rather small compared with those in the NR regime . Thus , for each mapping , the analysis of the network structure in the R regime after a single learning step is also valid after multiple learning steps . The network structure in which underlies the chaotic spontaneous activity with high closeness to the learned target patterns and successful recall of the target upon application of the corresponding input . At the border between the R and the NR regimes , is much larger , while for decreases rapidly with an increase in . This network structure makes the approach of the spontaneous activity to the latest target ( and reverse-target pattern ) much closer as shown in Fig . 9C ( i ) . In the NR regime ( i . e . , with a much smaller and much larger ) , the decay rate of is much larger than that in the R regime and , only takes a significant value . For the latest mapping , the network structure is similar to that in the NR regime after one learning step as analyzed above . This is consistent with the existence of only the latest target and reverse-target attractors in the spontaneous activity and the absence of response to any input .
We introduced a sequential learning rule , to match the memories as bifurcations viewpoint , by adopting a simple rule based on the correlations between the activities of the pre- and postsynaptic neurons; the rule is similar to the perceptron learning rule [13] , [16] . Sequential learning or palimpsest learning have been studied over a few decades [38]–[40] , [42] , [43] , and it has been shown that learning a new I/O mapping can easily destroy traces of previously memorized target patterns to such an extent that the memory capacity is lower than that for non-sequential learning . Methods have been proposed to alleviate the decrease in the capacity by decreasing the degree of synaptic plasticity , for example , by decreasing the number of the synapses that change simultaneously [42] . However , the destruction of earlier attractors due to the formation of new attractors is still a general trend as long as the memorized targets are attractors in the same dynamical system . From our viewpoint , in contrast , the attraction to a new learned target is shaped under a “different” dynamical system because each system exists in the presence of a different input pattern , and as we demonstrated , the neural network does not completely lose the memory learned earlier; the capacity is for an input strength that is sufficiently large and a rate of synaptic change that is sufficiently smaller than that of the change in the neural activity . For larger values , the system under the new input deviates farther from that without input and from that with the previously learned inputs so that the traces of the previously learned memories are not destroyed . For small , in contrast , the system under the input is close to that without input , so that the traces are easily destroyed . For a larger , on the other hand , the change in the synaptic connection is larger so that traces of previously learned memories are destroyed , while the synaptic connection is enhanced and selectively stabilizes the new target pattern . Indeed , for a larger and smaller area , only a highly stable attractor that matches the latest target is generated by removing earlier memories , and thus multiple mappings are not memorized . The simplicity of our learning scheme may have potential applications for the learning algorithm of I/O mappings . A limitation in our model is that the target information is supplied to all neurons because we used all-to-all recurrent connections . This limitation can be overcome by appropriately introducing a layered network structure and reinforcement learning algorithm [33] into the present learning algorithm . In addition , the present scheme is based on Hebbian-type synaptic changes that use only the pre- and postsynaptic neural activities and the target information under the presence of input; this means it may be plausible to expect the existence of such synaptic dynamics in biological neural system . There have been extensive experimental studies on the responses of neural activities to external stimuli in the sensory cortex [1]–[4] and higher cortex area [5] , [6] . Pre-stimuli , spontaneous activity had been dismissed as a background noise in these studies , but in recent experimental studies , it has been demonstrated that spontaneous neural activity without sensory input is not simple noise but is in fact highly structured in time and space [17] , [44] . In particular , spontaneous activity is often found to exhibit transitory behavior among several activity patterns that are similar to those evoked by external stimuli [32] , [35] , [37] , [45] , [46] . In other words , spontaneous activity includes some patterns evoked by external stimuli [18] . Thus , spontaneous activity that is widespread and wanders over many patterns converges to one patterns by applying an input . If one observes a discontinuous change in the neural activity by increasing the input strength , we expect that the change will be interpreted as a bifurcation . In the present study , we analyzed the transformation of the spontaneous to evoked activity from the memories-as-bifurcations viewpoint; we found that spontaneous activity that is chaotic but that often approaches the memorized targets is shaped by learning . This is reminiscent of the similarity between the spontaneous and evoked activities noted in the above experimental studies . Interestingly , if the spontaneous activity makes a closer approach to some target patterns , the inputs corresponding to those targets generate a higher neural activity response . This correlation between the responsiveness to a given input and the spontaneous activity may suggest a possible role of the spontaneous activity in preparing the response to the input . There have been several studies of neural-network models of the spontaneous activity in neural dynamics in random networks or models of working memory [24]–[27] . Spontaneous activities that visit several patterns have been investigated as chaotic itinerancy over patterns [47] , [48] or heteroclinic channels [49] . Our focus here lies in understanding whether such structure can be shaped by a simple learning rule and elucidating the characteristic behavior of the shaped spontaneous activity . Thus , our findings can also shed some light on how such transitory neural dynamics are generated . We should note that , as an alternative approach contesting the memories-as-attractors viewpoint , the so-called liquid state machine was proposed [12] , [50] , [51] , where learning I/O mapping was also achieved without multiple attractors . In this machine , there is a “reservoir” that stores the trace of the input and a “read-out unit” that detects this trace and transfers it to the desired output , while learning modifies only the read-out unit to generate the desired output . In our study , in contrast , there is no read-out unit , but the internal neural-activity dynamics ( which corresponds to the reservoir ) is modified during the learning process . With this approach , we can study spontaneous neural activity dynamics and evoked activity dynamics , which are not considered in the liquid-state machine . A recent study by Berkes et al . , [32] has demonstrated that the similarity between the spontaneous and evoked neural activities is not an innate property but is shaped through a developmental process; the dynamics of the activities are expected to be modified by the experience-dependent synaptic plasticity , and as a result , the similarity is believed to be shaped . We have shown that such a similarity is shaped through sequential Hebbian learning . In addition , we have found that in the network connection matrix , the characteristic pattern of the matrix elements ( Eq . 7 ) is also shaped , although the learning rule can form another characteristic pattern of network connections . In a parameter regime without any memory capacity , only the element is significant . In striking contrast , in a regime with memory capacity of IO mappings , the values of the elements of , and are of a comparable order , with the former two being positive and the latter two being negative . This network structure ( the sign of each element ) is found to be in common with the network in [34] , which was designed to achieve appropriate bifurcations upon certain inputs by superposing connections generated by the correlation between each target and input pattern with equal weight . In the present study , such connections , even though the weights are biased to recently memorized patterns , are generated as a result of a simple learning rule . This demonstrates the generality of the memories-as-bifurcation viewpoint and the existence of a variety of connections for its implementation . Finally , we briefly discuss the biological plausibility of our learning rule . Indeed , it does not follow the Hebbian unsupervised learning adopted in standard models for the cerebrum cortex with recurrent neural connections . Still , our learning rule also satisfies a minimum requirement for a biological neural system [42]: a learning rule needs only local information for pre- and postsynaptic cells and does not require any global information , which is difficult for each neuron to obtain . In fact , our learning model given by Eq . 2 needs information on only the neural activity of the pre- and postsynaptic cells and the target activity in the postsynaptic cell . The learning rule ( Eq . 2 ) consists of two parts: an anti-Hebbian part [52] , [53] , , and the supervised part , . First , a possible interpretation of the anti-Hebbain rule can be provided by introducing an interneuron . It is known that the excitatory neurons ( pyramidal neurons ) are connected through inhibitory neurons ( interneurons ) in the sensory cortex . When activations of pre- and post excitatory neurons are correlated and synapses between the presynaptic excitatory neuron and the inhibitory interneuron and those between the interneuron and the postsynaptic neuron are strengthened by the Hebbian rule , the efficacy between the pre- and postsynaptic neurons is effectively weakened . Instead of taking into account these intermediate neurons explicitly , one could eliminate variables for the interneurons and consider effective direct coupling between and , as in our model . In this case , the coupling between and follows anti-Hebbian plasticity of the synapse . To discuss the plausibility of the supervised part , let us consider another network whose activity represents target pattern and which projects onto the network in our model . Here , the target pattern does not represent a signal to error of the output behavior , as often used in supervised learning models in the cerebellum cortex [54] , [55] , but represents only the neural activity pattern to be learned . The term represents a simple Hebbian change between the presynaptic neurons in the network and the other network representing the target . This Hebbian change enables learning the correlation between the activities in the target network and in the presynaptic neurons . This example is only one possible way to implement our model in a biological neural network , and future studies are needed to establish a link between our learning rule and more biological neural-network dynamics .
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The neural activity without explicit stimuli shows highly structured patterns in space and time , known as spontaneous activity . This spontaneous activity plays a key role in the behavior of the response to external stimuli generated by the interplay between the spontaneous activity and external input . Studying this interplay and how it is shaped by learning is an essential step toward understanding the principles of neural processing . To address this , we proposed a novel viewpoint , memories-as-bifurcations , in which the appropriate changes in the activity upon the input are embedded through learning . Based on this viewpoint , we introduce here an associative memory model with sequential learning by a simple Hebbian-type rule . In spite of its simplicity , the model memorizes the input/output mappings successively , as long as the input is sufficiently large and the synaptic change is slow . The spontaneous neural activity shaped after learning is shown to itinerate over the memorized targets in remarkable agreement with the experimental reports . These dynamics may prepare and facilitate to generate the learned response to the input . Our results suggest that this is the possible functional role of the spontaneous neural activity , while the uncovered network structure inspires a design principle for the memories-as-bifurcations .
|
[
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] |
[
"physics",
"mathematics",
"biology",
"computational",
"biology",
"nonlinear",
"dynamics",
"biophysics",
"neuroscience"
] |
2013
|
Embedding Responses in Spontaneous Neural Activity Shaped through Sequential Learning
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One mechanism of information storage in neurons is believed to be determined by the strength of synaptic contacts . The strength of an excitatory synapse is partially due to the concentration of a particular type of ionotropic glutamate receptor ( AMPAR ) in the post-synaptic density ( PSD ) . AMPAR concentration in the PSD has to be plastic , to allow the storage of new memories; but it also has to be stable to preserve important information . Although much is known about the molecular identity of synapses , the biophysical mechanisms by which AMPAR can enter , leave and remain in the synapse are unclear . We used Monte Carlo simulations to determine the influence of PSD structure and activity in maintaining homeostatic concentrations of AMPARs in the synapse . We found that , the high concentration and excluded volume caused by PSD molecules result in molecular crowding . Diffusion of AMPAR in the PSD under such conditions is anomalous . Anomalous diffusion of AMPAR results in retention of these receptors inside the PSD for periods ranging from minutes to several hours in the absence of strong binding of receptors to PSD molecules . Trapping of receptors in the PSD by crowding effects was very sensitive to the concentration of PSD molecules , showing a switch-like behavior for retention of receptors . Non-covalent binding of AMPAR to anchored PSD molecules allowed the synapse to become well-mixed , resulting in normal diffusion of AMPAR . Binding also allowed the exchange of receptors in and out of the PSD . We propose that molecular crowding is an important biophysical mechanism to maintain homeostatic synaptic concentrations of AMPARs in the PSD without the need of energetically expensive biochemical reactions . In this context , binding of AMPAR with PSD molecules could collaborate with crowding to maintain synaptic homeostasis but could also allow synaptic plasticity by increasing the exchange of these receptors with the surrounding extra-synaptic membrane .
Ligand-gated neurotransmitter receptors in the post-synaptic membrane respond to neurotransmitter release and thereby mediate rapid signaling at neuronal synapses . Efficient synaptic signaling demands that these receptors be concentrated at high densities in order to optimally respond to rapidly diffusing neurotransmitter molecules . For instance , at excitatory glutamatergic synapses of the central nervous system , alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors ( AMPARs ) are highly concentrated ( 1000 receptors/µm2 ) relative to the extra-synaptic membrane ( 2 . 7 receptors/µm2 ) [1] , [2] . AMPARs are concentrated in a large membrane-associated protein complex called the post-synaptic density ( PSD ) [3] . The current model of AMPAR aggregation into the PSD comprises of several steps . First , AMPARs are trafficked to the synapse [4] where they are inserted in the extra-synaptic membrane via exocytosis [5] . While in the extra-synaptic membrane AMPARs undergo lateral diffusion and are randomly captured by the PSD through direct and indirect biochemical interactions with multiple partners [6] , [7] , [8] , [9] , [10] , [11] . Within the PSD , AMPARs continue a more restricted diffusion process [10] , [12] , [13] . Upon unbinding , receptors can diffuse out of the PSD and are then recycled via endocytosis or targeted for degradation . Although AMPARs bind to scaffolding proteins in the PSD it seems that biochemical interactions alone cannot explain the retention of AMPARs required to achieve long lasting changes in synaptic strength . For example , genetic manipulation of PSD scaffolding protein levels does not abolish basal synaptic transmission and leaves the amplitude of spontaneous excitatory potentials unchanged [14] , [15] , [16] . About 42% of the PSD mass is composed of proteins that do not necessarily bind to AMPAR [3] . The presence of large numbers of high-molecular weight proteins significantly restricts protein diffusion within cells [17] , [18] , an effect known as macro-molecular crowding [19] . Molecular crowding can cause a breakdown of the laws of mass transport by causing anomalous diffusion of macro-molecules , i . e . the mean-squared displacement ( MSD ) of the molecule is no longer linear over time [20] . Anomalous diffusion due to molecular crowding is a physical process fundamentally different from considerations of excluded volume at steady-state , such as tortuosity [21] , [22] . Molecular crowding affects not only the diffusion of molecules but also their reaction kinetics [17] , [23] . Structural and imaging studies suggest that the PSD is likely a crowded environment , such that even lipids undergo confined diffusion [24] , [25] . If molecular crowding affects the diffusion of AMPAR , this would require a reassessment of the biophysical mechanisms that control the homeostatic and dynamic concentration of these receptors in the PSD . Here we used Monte Carlo simulations to study the effects of PSD molecular crowding on AMPAR concentration . We found that the expected levels of molecular crowding in the PSD result in anomalous diffusion of receptors and are capable of reproducing the characteristic non-linear dependence of the MSD of AMPARs as a function of time . Our simulations indicate that the extent of AMPAR trapping in the PSD is a switch-like function of the concentration of PSD molecules . We also studied receptor accumulation after synaptic stimulation by allowing PSD molecules to bind to AMPARs [11] , [26] , [27] . Binding allowed AMPARs trapped in a crowded PSD to increase their net mobility [24] . In summary , our results suggest that molecular crowding can maintain homeostatic synaptic concentrations of AMPARs without requiring energetically expensive biochemical modifications , while binding to PSD proteins allows synaptic plasticity by increasing the mobility of these receptors .
The PSD of glutamatergic synapses is characterized by a high density network of scaffolding proteins located below the membrane , as well as trans-membrane proteins including receptors , ion channels and adhesion molecules . All of these molecules are linked together into a coherent structure by extensive protein-protein interactions [3] . Moreover , several PSD molecules , such as PSD-95 , 4 . 1N , and AKAP , can undergo lipid modification that allows them to intercalate into the plasma membrane , bringing them close to the cytoplasmic domains of diffusing receptors [36] , [37] . Thus , AMPAR diffusion can be sterically hindered by PSD molecules even in the absence of direct binding ( Fig . 1A ) . We simulated the diffusion of receptors within synaptic and extra-synaptic regions over a discrete lattice of 2×2 µm , with periodic boundary conditions to obviate finite size effects ( see Materials and Methods ) . The large concentrations of macro-molecules such as the one found in the PSD , the cytoplasm or membrane of cells is referred to as molecular crowding [17] . In our models , we represented molecular crowding as populating a fraction ( C ) of grid points occupied by PSD molecules , such that at C = 0 , no obstacles are found on the membrane and an AMPAR can freely diffuse ( with diffusion coefficient Dfree ) . We first studied the effects of molecular crowding of PSD molecules on the movement of AMPARs in a membrane in the case where the AMPARs do not bind to PSD molecules . Classical theories of diffusion suggest that the effect of elastic collisions with static obstacles is to reduce the Dfree of AMPAR to a lower constant value known as the apparent diffusion coefficient ( Dapp ) [17] , [22] . This effect is referred to as tortuosity [38] . Instead , we found that this relationship grew increasingly non-linear as a function of C ( Fig . 1B ) . Diffusion under such conditions is described by a different diffusion equation known as anomalous diffusion [18] , [39] , [40]: ( 6 ) where is the anomalous exponent . Clearly , the power law dependence is linear when plotting the logarithm of the MSD/time against the logarithm of time ( Fig . 1C ) ( 7 ) The value of depends on the value of C ( α = α ( C ) ) , such that at C = 0 diffusion is unobstructed and the dependency between MSD and time follows a linear relationship characteristic of normal diffusion ( eq . 6 with α = 1 ) . Thus , experimental measurements that quantify α can determine whether diffusion is normal ( α = 1 ) and therefore can be analyzed with traditional mass action formalism , or whether diffusion is anomalous ( α<1 ) showing strong deviations from traditional steady-state analysis [40] . Further analysis of the dependence of α as a function of C showed a switch-like behavior . The sigmoidal shape of the plot shown in Fig . 1D can be divided into three regions . The first region , C<0 . 3 , shows a small effect of molecular crowding on AMPAR movement , resulting in normal diffusion ( α = 1 ) . Conversely , for C>0 . 5 the value of α is practically zero , indicating that AMPAR movement is severely reduced , but still possible . In this case , AMPARs are continuously colliding with PSD molecules being trapped in very small pockets within the PSD . Upon escaping one pocket , the diffusing receptors fall into a different area of high density of PSD molecules where they again continue to diffuse in a restricted manner [18] . For intermediate values , 0 . 3<C<0 . 5 , there is a steep decrease in the value of α , with the corresponding deviation from normal diffusion . It is important to note that the fractional power-law dependency of the MSD vs . t is not expected from classical theories of diffusion or by assuming tortuosity . Tortuosity is characterized by a constant diffusion coefficient ( α = 1 ) that is lower than what would be expected from unobstructed diffusion [22] . In anomalous diffusion there is no characteristic time constant for the system and the apparent diffusion coefficient decays with time [18] , [40] , [41] , making α the appropriate physical variable to describe a diffusion process under such conditions . There are multiple physical consequences that arise from anomalous diffusion , from increases in temporal correlation of the position of the diffusing particles to changes in biochemical reactions rates [40] . The effect of molecular crowding results in a process where no equilibrium of concentration is achieved over the duration of our simulations [19] . Diffusing over a random field of PSD molecules results in randomly distributed areas where AMPARs move almost freely , while in other areas the steric interactions practically confine movement without the need for binding to other proteins [18] . The trajectories that diffusing particles follow under anomalous diffusion differ from the ones measured under normal conditions ( Fig . 2A and B ) [10] . While trajectories under normal diffusion visit the entire membrane ( Fig . 2A ) , particles undergoing anomalous diffusion spend more time in some areas than in others ( Fig . 2B ) . In order to relate our simulations to experimentally measured quantities , we calculated the instantaneous Dapp . In this case , Dapp = Δx2/4Δt , where Δx is the displacement from the initial position after a set time Δt determined by the observer . Under conditions of free diffusion ( C = 0 ) the histogram of values of Dapp results in a median Dapp equal to that reported experimentally ( Fig . 2C solid line ) [10] . In contrast , under diffusion under anomalous diffusion due to PSD molecular crowding the histogram of Dapp is reduced sharply , resulting in a higher probability of finding AMPARs with extremely low Dapp even in the case of moderate values of molecular crowding ( C = 0 . 36 , Fig . 2C dashed line ) [10] , [32] . The median value of Dapp decays rapidly as a function of molecular crowding ( Fig . 2D ) . Qualitatively , the distribution of Dapp observed in our model resembles that reported experimentally using single particle tracking of AMPARs in neurons [10] , [13] . Overall , the analyses shown in Fig . 1 and 2 suggest that diffusion inside synapses can be severely hampered by molecular crowding . The effects of molecular crowding can obscure the contribution of binding to the retention of AMPARs in the PSD and could also contribute to the retention of glutamate receptors inside the synapse . Since the effects of increasing molecular crowding could be classified as binding to static PSD molecules , we examined the potential contribution of the PSD molecules to the retention of AMPARs inside the PSD . We simulated the diffusion of AMPARs on a square PSD 0 . 5 µm in width [24] placed in the center of a 1 by 1 µm membrane with toroidal boundary conditions to avoid finite-size effects . In each simulation an AMPAR was released at a random position inside the boundaries of the PSD . After 1 sec , we determined whether an AMPAR remained inside or escaped outside the area occupied by the PSD ( Fig . 3A ) . Fig . 3B shows how the probability of finding a receptor within the area of the PSD varies according to the fraction of that area occupied by PSD molecules ( C ) . At C = 0 the probability of a receptor residing within the PSD is , as expected , equal to the fraction of the membrane occupied by the PSD ( 0 . 25 ) . For C>0 . 6 , AMPARs initially positioned inside the PSD remain confined there . Within a concentration range of 0 . 3–0 . 5 , the relative trapping of receptors showed a very steep dependence on small changes in PSD molecule concentration . In this regime , AMPAR can still move in and out of the PSD , but their net movement is significantly slowed by molecular crowding . As a consequence , the time spent inside the PSD is extended , a result similar to that produced by a model of receptor dynamics based on molecular interactions [42] . The difference between the two models is that our model does not require biochemical interactions between protein receptors and PSD scaffold molecules . We note that a random distribution of PSD molecules is essential in generating anomalous diffusion; a regular distribution of obstacles increases tortuosity but yields normal diffusion ( see Figs . 1 and 2 in Text S1 ) . Therefore , these results indicate that the steady-state concentration of AMPAR in the PSD can be maintained by physical confinement of AMPARs due to molecular crowding within the PSD . The time that an AMPAR is trapped inside the PSD depends on the amount of molecular crowding and position with respect to the PSD boundary . Equation 6 can be used to predict the time an AMPAR released in the center of a PSD would take to reach the boundary and escape . Using Dfree as the diffusion coefficient and a range of 0 . 3<α<0 . 4 , which corresponds to a minute difference in molecular crowding from C = 0 . 44 to 0 . 42 , an AMPAR would reach the boundary of an average PSD of 125 nm in 4–76 seconds ( denoted by the dashed line in Fig . 4A ) ; which is in very good agreement with experimental observations [10] , [13] , [43] . However , retention time is very sensitive to PSD radius; for example , in a large PSD ( 250 nm radius ) AMPARs will remained trapped for 100–10 , 000 seconds ( 2 . 4 minutes to 2 . 15 hours; solid line in Fig . 4A ) . Further analysis shows the sensitivity of residence time to PSD diameter and the value of α ( Fig . 4B ) . Thus , if molecular crowding has an effect in the movement of AMPAR , small changes due to re-arrangements of the PSD , insertion of transmembrane proteins , or conformational changes that allow steric interactions , could dramatically influence the time an AMPAR remains trapped inside a PSD without the need for strong biochemical interactions with scaffold proteins . The model originally developed by Kusumi et al . [44] is widely used to analyze the diffusion of AMPARs [10] , [13] , [24] , [43] , [45] . As opposed to our model , Kusumi's model assumes that a diffusing transmembrane protein is tethered to a loose cytoskeleton or is physically confined into an area bounded by reflecting walls . This model determines the confinement length ( L ) of the area explored by a molecule as it diffuses ( MSD = ( L2/3 ) ( 1−exp ( −10Dfreet/L2 ) ) ) ) . If the assumption is that the molecules are confined to diffuse within compartments , then the calculated diffusion coefficient is equal to Dfree . We fitted Kusumi's model to the MSD plots obtained in Fig . 1 . This analysis indicates that for a molecular crowding of C = 0 . 40 the confinement length is L = 250 nm , which corresponds to the classified ‘slow’ moving AMPARs in PSDs [24] . This same group report the presence of ‘fast’ moving AMPAR with L = 600 nm , which corresponds to a value of C = 0 . 38 ( see Fig . 3 in Text S1 ) . Interestingly , the mobility of AMPAR increases with the application of glutamate , resulting in an increase of the diameter of area explored ( from 225±3 nm during control to 239±15 nm during glutamate stimulation ) . Consistent with our hypothesis that steric interactions in the PSD hinder diffusion this same study shows that lipids also undergo confined diffusion inside the PSD [24] . It is important to note that ‘slow’ ( or immobile ) and ‘fast’ moving particles naturally emerge as part of the same process of anomalous diffusion [46] . We fitted eq . 6 to several reported curves of MSD against time for extra-synaptic and synaptic AMPAR diffusion [10] , [24] , [43] , [45] . Remarkably , for diffusion inside synapses most of the fits resulted in crowding values close to C = 0 . 44 , while for extra-synaptic diffusion C∼0 . 40 ( Fig . 4 in Text S1 ) . Overall , our analyses in Figs . 1–4 , and Figs . 3 and 4 in Text S1 suggest that molecular crowding in the PSD could be of the order of C = 0 . 40–0 . 46 and that a small change in crowding could significantly regulate the mobility of AMPARs . The value of C for synaptic diffusion obtained from fitting eq . 6 to experimental data is in good agreement with calculations of excluded volume based on the spatial dimensions and density of PSD proteins ( see Discussion ) . Synaptic activity regulates the number of AMPAR and their mobility in and out of the PSD [10] . This regulation of AMPAR density inside the PSD could be direct , with synaptic activity leading to unbinding or binding of AMPAR complexes to PSD molecules; or could be indirectly caused by spatial rearrangement of the PSD structure , with the resulting change in molecular crowding allowing AMPARs to move in and out of this structure . To understand the relative contributions of binding and crowding to AMPAR regulation in the PSD , we initially measured the effects of increasing the fraction of PSD molecules ( P ) that could bind to AMPAR . Independent of the precise binding and unbinding probabilities of AMPAR to other proteins or the potential multi-step nature of receptor-scaffold interactions , the range of values for hydrogen bonds for protein-protein interaction motifs such as the PDZ domains is 2–13 kBT [47] , [48] . Figure 5 shows the results of simulations in which the membrane was covered by PSD molecules with a density of C = 0 . 45 and the binding energies for the interaction of PSD molecules with AMPAR were uniformly distributed over 4–8 kBT . We considered the possibility that synaptic activity causes PSD molecules or receptors to be modified so that they can bind to each other at random times . As previously shown in Fig . 1 , when all molecules exclusively act as obstacles , AMPAR diffusion is initially anomalous and receptors are trapped in the PSD ( P = 0 in Fig . 5A ) . As the fraction of PSD molecules that can bind to AMPAR increases , there is a resulting increase in the net mobility of AMPAR; this is evident as an increase in the MSD of AMPAR ( Fig . 5A ) . A logarithmic transform of the data shows that for even a small fraction of activation of PSD molecules ( P>0 . 2 ) results in diffusion of AMPARs that is almost normal , albeit slow ( Fig . 5B , with a small period of anomalous diffusion in the first 10 ms ) . This high sensitivity to the degree of binding is clearer in the plot of the anomalous exponent as a function of the fraction of activated PSD molecules ( Fig . 5C ) . Thus , binding to PSD molecules increases the mobility of AMPARs otherwise trapped by molecular crowding and the diffusion process resulting from this binding to PSD molecules is practically normal . We compared the results shown in Figs . 5A–C with simulations in which all the PSD molecules could bind to AMPARs ( P = 1 ) with variable binding energies . Figure 5D shows a plot of MSD versus time for simulations with binding energies ( E ) ranging from 1 to 10 kBT . The logarithmic analysis shows that although anomalous diffusion is present over a short period of time , AMPAR diffusion returns to a normal process characterized by tortuosity ( Dfree>Dapp = constant ) ( Fig . 5E ) . As binding strength increases there is a delay in the transition from anomalous to normal diffusion ( not shown ) [49] . This is due to the effect of binding that traps an AMPAR in a single position in space for a period of time . However , this effect is strong only with large binding energies ( E>10 kBT ) . Note that since this behavior resulted when all molecules covering 45% of the PSD bind strongly to AMPAR we consider this result to be an extreme case . As has been previously shown , the strength of the binding increases the cross-over time from anomalous to tortuous diffusion [49] . A linear fit to the late part of the diffusion process , indicated by the thick lines in Fig . 4E , results in a value of α close to 1 for all binding energies ( Fig . 5F ) . It is important to note that although diffusion is normal it is very slow compared to AMPAR diffusion over a membrane without obstacles . Thus , a PSD in which all the molecules bind to AMPARs cannot reproduce the non-linear relationship between MSD and t observed in experimental results and , for a physiological range of binding energies , results in normal diffusion in less than 1 sec . We next studied the general principle of how transient alterations in binding of PSD molecules can affect the concentration of AMPAR in the context of molecular crowding . Such reactions can be very complex , such as in long term potentiation ( LTP ) or depression ( LTD ) [50] , [51] , [52] , [53] . We assumed that transient post-synaptic activation results in a post-translational modification - of either the C-terminal domains of the receptors [54] or the scaffold proteins in the PSD [11] , [50] , and that this transformation enhances the binding of PSD molecules to AMPAR . In order to determine a baseline influence of crowding on AMPAR retention after synaptic stimulation we assumed that a random subset of PSD molecules was allowed to bind to AMPAR with the same value of binding energies . After the period of synaptic activity ended , the active PSD molecules lost their ability to bind and returned to being obstacles . We simulated a large PSD that occupied a square area of 0 . 5×0 . 5 µm over a 2×2 µm patch of membrane . We explored the influence of a wide range of molecular crowding , from 0 . 00–0 . 60 , and of binding energies , from 0–14 kBT . For each combination of molecular crowding and binding energies we ran 1000 different simulations in which an AMPAR was randomly placed in the membrane . After each set of 1000 simulations we calculated the percentage change of AMPARs found inside the PSD before and after the stimulation . Each simulation consisted in 500 ms before the stimulus , 100 ms of stimulation , and 700 ms after the stimulus , we calculated the average concentration in the first and last 400 ms of the simulation . The stimulus randomly activated only 10% of all the PSD molecules . Accumulation of AMPARs in the PSD depended on the amount of molecular crowding and strength of the stimulus . Figure 6A shows several traces of the relative concentration of AMPARs in the PSD as a function of time when C = 0 . 44 for different binding strengths . At a binding energy of 2 kBT more AMPARs left the PSD than were absorbed . As the binding strength increased AMPAR started to accumulate in the PSD . Figure 6B shows examples of the dependence of the fraction of AMPAR accumulation in the PSD as a function of binding for three different crowding values . The summary plot presented in Fig . 6C shows that for low values of binding strength and crowding , there is no accumulation of AMPARs . At high values of molecular crowding and binding there is always an increase of AMPAR in the PSD . However , there a transition region in which combinations of crowding and binding results in a slight depletion of AMPARs in the PSD . In this transition region the trapping of AMPARs is weakened by the increased net mobility of AMPARs due to binding . However , the low binding energies are not enough to recruit more AMPARs into the PSD . Increasing the fraction of PSD molecules binding to AMPAR would transform the PSD into a high-capacity buffer with slow kinetics , ultimately trapping more AMPARs . Thus , binding energies and molecular crowding result in different patterns of AMPAR accumulation in the synapse; after activity is terminated , molecular crowding retains the receptors within the PSD structure for long periods of time . Taken together , our results suggest that , under conditions of molecular crowding and anomalous diffusion , steric interactions can have a significant effect in AMPAR retention inside the PSD . Steric interactions in combination with molecular binding can provide synapses the ability to retain AMPARs for prolonged periods of time and the flexibility to allow stimulus evoked AMPAR trafficking with the surrounding membrane .
There are three fundamental physical assumptions common to all membrane protein diffusion studies that apply to the diffusion of AMPAR in and out of the PSD . First , when diffusing in an obstacle-free membrane AMPARs undergo normal diffusion ( eq . 1 ) . Second , AMPARs bind non-covalently and reversibly with PSD molecules [55] . Third , an elastic collision occurs if an AMPAR encounters , but does not bind to , a PSD molecule . There is extensive experimental data showing that AMPAR movement in the extra-synaptic membrane can be described as an elastic random walk with elastic collisions [9] . This is plausible in neuronal membranes since water molecules are the main carriers of energy and there are more collisions between water molecules and proteins than between proteins . In any case , energy loss between collisions would only further the observed effects by decreasing the diffusion of AMPAR molecules . The presence of membrane anchored proteins acting as obstacles for membrane protein diffusion has been documented in other cell types [56] . Since the PSD consists of a dense assembly of transmembrane and submembranous scaffold proteins , these proteins could sterically interact with AMPARs or other molecules through their C- or transmembrane-domains . For example , a variant of NCAM has been shown not to accumulate in the PSD [13] . However , the diffusion of NCAM is determined by the different splice variants which have different cytosolic domains . NCAM has been shown to undergo normal or anomalous diffusion depending on which one of the three splice variants is being studied [57] . Although , the NCAM data supports the hypothesis that steric interactions with the C-terminus are more important than collisions with transmembrane proteins in the PSD there are other forms of compartmentalization than arise from interactions with the dense extra-cellular matrix [45] . Since steric interactions are due to the physical presence of molecules and is not dependent on their identity , the diffusion of AMPAR should be affected by the total concentration of all macro-molecules . Even though a single molecular species might be regularly distributed over the PSD , the collection of all molecules could generate a dense mesh that effectively constitutes a random distribution of macro-molecules [17] , [18] . Our simulations show that if steric interactions exclude AMPAR from diffusing in even 30% of the synaptic area , then net receptor diffusion will be severely hampered . The total mass of a 360 nm diameter and 30 nm thickness PSD has been calculated to be 1 . 1±0 . 36 GigaDaltons ( or 1 . 83×10−15 gr ) , with a volume of 3 . 06×106 nm3 [58] . The average protein density for macro-proteins is assumed to be constant at around 1 . 4×10−21 gr/nm3 [59] , thus a solid PSD would contain 7 . 18×10−15 gr . Therefore , the fraction of PSD occupied by macro-molecules is ( 1 . 83×10−15/4 . 27×10−15 ) 43% by mass . The volume occupied in a PSD with the same dimension can be calculated by using the assumption that in a PSD there are 10 , 000 molecules of 100 kD [3] , which results in an occupied volume of 50% ( radius of a molecule is [ ( 0 . 75/π ) M . W . / ( 1 . 4e−21 gr/nm3 * A ) ]1/3 , with A being Avogadro's number , and M . W . the molecular weight ) . Although , not all cases of molecular crowding necessarily result in anomalous diffusion [60] , the experimental measurements show that the PSD has the levels of molecular crowding necessary to observe the anomalous diffusion effects proposed by our model . An alternative structural mechanism to confine receptor diffusion is the picket-and-fence model [61] . This model suggests that anchored molecules have specific arrangements that can trap molecules [10] , [24] , however , there is no anatomical evidence showing a picket-and-fence structure in the PSD . Furthermore , all experimental evidence shows that AMPARs execute a random walk on the extra-synaptic membrane or inside the PSD [10] , [62] , [63] , [64] . Our work , as well as other computational studies [65] , have shown that a picket-and-fence model does not produce the considerable anomalous diffusion observed for AMPAR . Dissociation constants for binding of AMPAR , or transmembrane AMPA receptor regulatory proteins ( TARPs ) , such as stargazin , to the C-terminal domain binding proteins ( CTDBP ) , such as GRIP , PICK and MAGUK proteins such as PSD-95 , are in the range of 1–10 µM [48] , [66] , [67] . These values are in the upper part of our range of 2–13 kBT ( , where R is the gas constant , Keq the equilibrium constant and 2 . 5 converts from kJ/mol to kBT units ) . Although no data is available to determine the percentage of PSD molecules that might bind to AMPAR or their strength , our simulations predict that changes in only a few of them are necessary to flip the ‘molecular switch’ from AMPAR retention to AMPA diffusion . These interaction energies may be directly measured using optical trapping techniques , as for the case of diffusing cadherin and transferrin receptors [68] . Also , in our simulations we assumed a uniform distribution of binding energies which might not be the case in a crowded PSD [17] . Deviations in the distribution of binding energies for PSD proteins would result in either higher or lower numbers of binding PSD needed to allow AMPAR mobility . Although AMPAR-TARP interactions can be disrupted by glutamate [69] , our results show that the observed increased mobility after bath application of glutamate of AMPAR inside the PSD ( see Fig . 3 in [24] ) could be a consequence of binding to static PSD molecules . The biophysical property of this assumption is the isotropic release of AMPAR after unbinding from a PSD molecule . As our other hypotheses , this assumption is not exclusive to our model . The well-stirred assumption , used to build and analyze all mass-action models of synaptic plasticity , implicitly assumes no directionality of binding and unbinding . Although this hypothesis is rarely addressed elsewhere , there are two physical properties of molecules that support such an assumption . The first one is the well-documented property of rotational diffusion ( Drot ) [70] , [71] , [72] , [73] . The characteristic times of Drot range from 10–100 µs . Rotational diffusion in combination with conformational changes can significantly modify the diffusional environment of diffusing molecules in the PSD . Molecular aggregation can modify this rotation to the point of stopping it [74] , [75] . A different assumption that results in an average effective isotropic release after binding is the random orientation of the binding site of a population of anchored molecules . However , under conditions of molecular crowding the probability of interacting with the binding site can be severely reduced and the mobility of AMPAR would not vary in the presence or absence of binding . We assumed that isotropy of release was due to rotational diffusion . The major consequence of this assumption is that after unbinding , AMPARs can continue traveling beyond the point that was previously forbidden due to the volume exclusion imposed by the PSD protein . The combined effects of molecular crowding and binding can regulate the density of AMPAR in the PSD in such a way that molecular crowding retains the AMPAR diffusion in the absence of synaptic activity while binding allows their movement . Unfortunately , measurement of rotational diffusion has not been performed on AMPARs or any PSD molecules . Such measurements would determine whether the PSD behaves as a solid structure or if it is a crowded , yet mobile , system . Experimental evidence suggests that the PSD is a tight mesh of proteins that can be considered stationary [76] , [77] , [78] . In general , our model predicts that by changing the concentration of non-interacting PSD molecules AMPAR residence time in the PSD could be modified . Our results can be tested by studying the dynamics of receptor mobility in synapses where both AMPARs and PSD scaffold molecules ( such as PSD-95 ) are fluorescently labeled , with super-ecliptic phluorin ( SEP-GFP ) or photo-activable GFP and mCherry respectively . Monitoring the fluorescence recovery after photobleaching or the loss of fluorescence after photo-activation will yield a measure of the diffusivity of AMPARs , correlated with the amount of scaffold proteins ( as measured by fluorescence intensity of mCherry-PSD-95 ) . The amount of PSD scaffolds can be modified by manipulation of various protein-protein interaction domains of PSD-95 that mediate its retention in the synapse [79] . Finally , the role of specific interactions between receptors and scaffolds can be studied by observing the dynamics of transferrin receptors labeled by SEP-GFP ( which marks membrane receptors ) . Since transferrin receptors are not known to interact with PSD-95 , their mobility will allow the assessment of the contribution of crowding to receptor retention without being confounded by specific binding interactions . The role of binding interactions can be directly assessed by high resolution analysis of single particle tracks of receptors where direct interactions between receptors ( or associated proteins ) and scaffolds have been abrogated [11] . While this latter work did report increases in diffusivity of receptors when receptor-scaffold interactions were abolished , it was not clear whether this was due to lack of entry of receptors into synapses ( purely extra-synaptic diffusion ) or due to the fact that receptors entered the synapse but were not retained . Measuring rotational diffusion of AMPARs or non-interacting molecules , such as NCAM , in the synaptic and extra-synaptic space would provide valuable information . We hypothesize that when AMPAR binds to scaffolding molecules its Drot will decrease , thus measurements of Drot in time would show changes from high to low values . However , if the receptors are in a crowded environment without binding then we expect to have a similar distribution of the values of Drot inside and outside the synapse . Overall , our modeling efforts suggest that molecular collisions can keep AMPAR inside the PSD without requiring extra energetic processes [42] other than keeping the PSD crowded . This zero-energy model means that the PSD can maintain a difference in the concentration of AMPARs with respect to the surrounding membrane over long periods of time . Since NMDA receptors also undergo lateral diffusion then we expect the same processes to apply to their concentration in the PSD [106] .
The average spatial displacement of a particle diffusing in a two dimensional membrane is described by ( 1 ) Where t is time , Dfree is the diffusion coefficient , and MSD is the average mean square displacement of the particle . The instantaneous MSD can be calculated over an ensemble of multiple independently diffusing particles , ( 2 ) Where ri is the relative position of particle i to its initial position at time t , and N is the number of particles . The membrane model consisted of a square mesh with toroidal boundary conditions . Particles crossing over the edge of the diffusing space would appear on the opposite edge in the next time step . The size of the rectangular mesh is specified in each simulation , but it was at least equivalent to 1 µm in side . The simulations were fully characterized by the time step Δt and the diffusion coefficient of AMPAR in the extra-synaptic membrane which we assume is the closest to an unobstructed system ( Dfree = 0 . 200×10−3 µm2/ms which results in a median Dfree = 0 . 138×10−3 µm2/ms; [10] ) . At every time step the particle could move in any of the four directions defined in the rectangular mesh . In order to achieve this , a random number was drawn from a homogenous distribution to determine the axis of movement , and a second homogeneous random number was drawn to determine the direction of movement along the chosen axis . The physical size of the mesh was determined using the expected displacement of a molecule given a Δt = 1×10−3 ms ( 3 ) which resulted in a value of Δx = 8 . 9×10−4 µm . This mesh size did not affect the calculation of the diffusion coefficient or the overall results of this study since performing the same simulations with 1000×Dfree resulted in the same values of anomalous diffusion with only a rescaling of the simulation time [18] . The PSD is a disk-like structure composed of several thousand molecules that is 200–800 nm in diameter and 30–50 nm thick [3] , [107] . Most of the PSD molecules are a few nanometers below the post-synaptic membrane , with some transmembrane protein complexes . The spatial arrangement of the PSD facing the membrane is smooth compared to the cytoplasmic side [108] . Since the life time of PSD molecules is longer than the synaptic plasticity effects studied here [109] and have low mobility [65] , we consider them as essentially static [76] , [77] , [84] , [85] . Based on the aforementioned properties , we modeled the PSD molecules as particles that did not diffuse and therefore occupied a single fixed position in the square mesh . In our algorithm a PSD was represented as an occupied point in the lattice . Diffusing molecules could collide with a static PSD molecule . A collision resulted in the AMPAR returning to its original position [65] . Binding between large biological molecules occurs mainly through non-covalent bonds . Most protein-protein interactions are mediated by hydrogen bonds and van der Waals interactions [47] . The range of binding energies of hydrogen bonds is from 1–13 kBT and less than 1 kBT for van der Waals [47] , [83] . We modeled the binding of diffusing AMPARs to scaffold proteins in the PSD as a stochastic second-order reaction . An AMPAR that moved into the position occupied by a PSD molecule had an initial probability of bouncing off pbounce . ( 4 ) If the AMPAR succeeded in binding to the PSD molecule , then the probability of remaining bound was given by an exponential potential ( 5 ) Where kB is the Boltzmann's constant and T is the temperature in Kelvin . Binding to scaffolding proteins could be a multi-order process; however , in this work we approximated binding with a single exponential energy barrier . As a reference , the 3 hydrogen bonds that make up a PDZ domain-ligand interaction have a total binding energy of about 10 kBT [81] . After binding , the AMPAR molecule remains fixed in that position until another homogenously distributed random number . After unbinding we assumed that the molecule has an equal probability of moving in any direction . There are three possibilities that can determine the movement of AMPAR after unbinding: 1 ) AMPAR undocks and diffuses in the half-plane defined by the PSD molecule to which the AMPAR was bound; 2 ) conformational changes of the underlying PSD molecules remove the steric interaction , thus allowing AMPAR to move freely in any direction; 3 ) rotational diffusion of the AMPAR-PSD complex allows AMPAR to move in any direction . The rotational diffusion of cell membrane complexes is well known and has characteristic time constant ranging from 10–100 µs [70] , [71] , [72] , [73] . In general , we assumed an isotropic direction of movement upon unbinding . Under the isotropic release paradigm , the newly freed AMPAR could move to any of the neighboring mesh points as long as they were un-occupied by another PSD molecule . Each simulation was independent; therefore , we did not model AMPAR-AMPAR interactions . A typical simulation consisted of running 200–1000 AMPARs over the same membrane model with different initial conditions of the random number generator . We recorded the position of all the simulated particles every 1 ms . We tracked the spread of AMPARs from their point of origin at t = 0 over for up to 2000 ms [13] . The models were implemented using Matlab ( Natick , MA ) in combination with Star-P ( Interactive Supercomputing , Waltam , MA ) . Star-P allowed us to utilize and run the original Matlab model in parallel at the Computational Biology Initiative high performance cluster at UTSA ( http://www . cbi . utsa . edu ) .
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One of the most accepted theories of information storage in neurons is that it is partially localized in the strength of synaptic contacts . Evidence suggests that at the cellular level , in combination with other cellular mechanisms , this is implemented by increasing or decreasing the concentration of a particular type of membrane molecules . Two opposing mechanisms have to coexist in synapses to allow them to store information . On one hand , synapses have to be flexible , to allow the storage of new memories . On the other hand , synapses have to be stable to preserve previously learned information . Although much is known about the molecular identity of synapses , the biophysical mechanisms by which molecules can enter , leave and remain in the synapse are unclear . Our modeling work uses fundamental biophysical principles to quantify the effects of molecular collisions and biochemical reactions . Our results show that molecular collisions alone , between the diffusing proteins with anchored molecules in the synapse , can replicate known experimental results . Molecular collision in combination with biochemical binding can be fundamental biophysical principles used by synapses for the formation and preservation of memories .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biophysics/cell",
"signaling",
"and",
"trafficking",
"structures",
"neuroscience/theoretical",
"neuroscience"
] |
2010
|
Quantifying the Effects of Elastic Collisions and Non-Covalent Binding on Glutamate Receptor Trafficking in the Post-Synaptic Density
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Short and dysfunctional telomeres are sufficient to induce a persistent DNA damage response at chromosome ends , which leads to the induction of senescence and/or apoptosis and to various age-related conditions , including a group of diseases known as “telomere syndromes” , which are provoked by extremely short telomeres owing to germline mutations in telomere genes . This opens the possibility of using telomerase activation as a potential therapeutic strategy to rescue short telomeres both in telomere syndromes and in age-related diseases , in this manner maintaining tissue homeostasis and ameliorating these diseases . In the past , we generated adeno-associated viral vectors carrying the telomerase gene ( AAV9-Tert ) and shown their therapeutic efficacy in mouse models of cardiac infarct , aplastic anemia , and pulmonary fibrosis . Although we did not observe increased cancer incidence as a consequence of Tert overexpression in any of those models , here we set to test the safety of AAV9-mediated Tert overexpression in the context of a cancer prone mouse model , owing to expression of oncogenic K-ras . As control , we also treated mice with AAV9 vectors carrying a catalytically inactive form of Tert , known to inhibit endogenous telomerase activity . We found that overexpression of Tert does not accelerate the onset or progression of lung carcinomas , even when in the setting of a p53-null background . These findings indicate that telomerase activation by using AAV9-mediated Tert gene therapy has no detectable cancer-prone effects in the context of oncogene-induced mouse tumors .
Telomeres are nucleoprotein structures localized at the ends of eukaryotic chromosomes , which are essential to protect them from degradation and end-to-end chromosome fusions . In mammals , telomeric DNA consists of TTAGGG tandem repeats bound by a 6-protein complex known as shelterin [1 , 2] . Telomerase is a reverse-transcriptase able to elongate telomeres by the de novo addition of telomeric repeats onto chromosome ends [3] . Telomerase is composed by the telomerase reverse transcriptase catalytic subunit ( TERT ) and by a RNA component ( Terc ) , that is used as a template for telomere elongation . Telomerase is active in pluripotent cells where it elongates telomeres within each generation , but is silenced after birth in the majority of tissues . In mice , it has been shown that both Terc and Tert are downregulated in the majority of tissues post-natally with some exceptions like the testis and the hematopoietic tissues [4–6] . In particular , adult mouse lungs , kidney , heart and brain lack detectable telomerase activity [4–6] . Thus , in the adult organism telomeres shorten associated to cellular division owing to the end-replication problem [7 , 8] . When telomeres reach a critically short length this is insufficient to warrant telomere protection thus leading to the activation of a persistent DNA damage response at chromosome ends , ultimately inducing senescence or apoptosis . Several studies have shown that the presence of short/dysfunctional telomeres in the cell rather than the mean telomere length is what negatively impacts on cell division [9 , 10] . Telomere shortening is considered one of the hallmarks of aging as short telomeres have been shown to be sufficient to induce organismal aging [6 , 11 , 12] . Even though mice are born with longer telomeres than humans they show a 100-fold faster rate of telomere shortening than humans in blood cells [13 , 14] . Indeed , by using a single-cell telomere length analysis using a quantitative FISH technique , we have shown that mouse telomeres shorten with aging in all mouse tissues . In support of telomeres being rate limiting for mouse aging , first generation telomerase-deficient mice have shorter telomeres than normal and show a decreased mouse longevity , a phenotype that is anticipated with increasing mouse generations in the absence of telomerase [13 , 15–21] . Thus , there is mounting evidence that although mice have on average , longer telomeres than humans , they also suffer telomere shortening with aging , and indeed this shortening is relevant for aging [13 , 21] . Similar to the telomerase-deficient mouse models , human germline mutations in telomerase and other telomere-related genes are causative of the so-called telomere syndromes ( ie . , aplastic anemia and pulmonary fibrosis ) owing to presence of much shorter telomeres than normal which lead to premature loss of the regenerative capacity of tissues [22] . Interestingly , owing to its ability to confer unlimited proliferative potential , TERT is also found over-expressed and mutated in the vast majority of human cancers including lung cancer where it is thought to allow cancer cell growth by ensuring a minimal telomere length to warrant telomere protection [23–31] . Telomerase is also upregulated in mouse tumors [32–34] . Tert transgenic mouse models with a constitutive telomerase expression in adult tissues indicated that although Tert over-expression does not have per se an oncogenic activity , its persistent expression throughout organismal lifespan could favor cancer appearance at older ages [35–38] . Of note , transgenic over-expression of Tert in the context of cancer-resistant mice results in longer telomeres in the adult organism and in an increased mouse longevity by 40% [39] , thus demonstrating that telomerase has an anti-aging activity by virtue of its ability to maintain telomeres . More recently , our group has also shown that reactivation of telomerase activity in adult mice by using non-integrative gene therapy vectors , which in proliferating cells , only allow temporary expression of telomerase , is sufficient to extend mouse longevity and delay many different age-related conditions , without increasing cancer incidence [6] . In particular , we used adeno-associated viral vectors ( AAV ) to deliver telomerase to adult tissues . These vectors present many desirable properties as they are non-integrative , show a poor immunogenicity and an excellent safety profile [40] . They are able to transduce both dividing and quiescent cells in a wide range of tissues and maintain the expression for a long time [41] . Moreover , some AAV vectors ( as AAV9 ) have the capability of crossing the blood-brain-barrier and target brain cells upon intravenous injection in adult mice [42 , 43] . In particular , expression of Tert using AAV9 vectors can delay physiological aging and extend longevity in wild-type mice without increasing cancer [6] . Moreover , a single treatment with AAV9-Tert vectors showed therapeutics effects in preventing death by heart failure after induction of myocardial infarction in mice , as well as in preventing or reversing disease in mouse models of aplastic anemia and idiopathic pulmonary fibrosis associated to short telomeres [44–46] . Importantly , AAV9-Tert gene therapy has not been shown to increase cancer incidence either in the context of mouse longevity studies [6] or in the context of the above-mentioned mouse models of disease owing to short telomeres [44–46] . However , mice are short lived species compared to humans , and although mice also spontaneously develop cancer with aging , a potential long-term pro-tumorigenic effect of telomerase may be missed . To circumvent this , here we set to study the safety of AAV9-Tert treatment in the context of cancer prone mouse models . In particular , here we tested the long-term effects of AAV9-Tert gene therapy in an oncogene-induced lung cancer mouse model . To this end , we tested our AAV9-Tert gene therapy vectors in the well-established lox-stop-lox-K-RasG12V knock-in mouse model in which endogenous expression of the K-RasG12V oncogene is induced upon Cre expression [47] . Our results show that AAV9-Tert gene therapy treatment does not affect tumor onset and/or development suggesting the safety of this therapy in the context of a cancer-prone background in mice . Interestingly , we observe that telomerase inhibition previous to the induction of the oncogene is tumor protective .
Here we set out to address the impact of telomerase activation by using Tert gene therapy in a well-established mouse model of lung cancer initiation and progression . To this end , we used the well-established oncogenic K-Ras lung carcinogenesis model [47] . This mouse model harbors one copy of the K-RasG12V oncogene ( K-Ras+/ LSLG12Vgeo ) containing a STOP codon flanked by loxP sites . Expression of the Cre recombinase leads to the excision of the stop cassette and consequent expression of K-RasG12V and its β-galactosidase ( β-geo ) reporter ( Fig 1A ) . The Cre recombinase is delivered by intratracheal instillation with replication-defective adenoviruses encoding the Cre recombinase ( Adeno-Cre ) [48] . The telomerase gene is packaged in adeno-associated virus type 9 ( AAV9 ) and is delivered systemically by intravenous tail injection [6] . First , we checked whether lung cells could be co-infected with adeno-Cre and with AAV9 viruses . To this end , we simultaneously transduced mice with adeno-Cre and with AAV9 carrying GFP ( AAV9-eGFP ) . One week after the viral transductions , mice were sacrificed and lung samples were taken for immunohistochemistry staining of β-galactosidase ( β-geo ) , a surrogate marker co-expressed with the K-RasG12V , and of GFP ( S1 Fig ) . One week after induction of oncogenic K-Ras , β-Gal positive cells appear in small clusters of 4–8 cells that show a cytoplasmic foci staining [49] ( S1 Fig ) . Double staining with anti-β-Gal ( brown ) and with anti-GFP ( purple ) of these samples revealed that these cell clusters were also positive for GFP , demonstrating that lung cells can be co-infected with adeno viruses and adeno-associated viruses ( S1 Fig ) . Next , to address the impact of AAV9-Tert treatment in the context of oncogenic K-Ras expression , we tested two possible scenarios . First , we used a “pre-treatment” strategy in which young 8-week-old mice were first infected by tail vein injection with either AAV9-Null , AAV9-Tert or a catalytically inactive AAV9-Tert-DN vectors . TERT-DN acts as a dominant negative and has been previously described by us to inhibit endogenous telomerase activity and to impair the growth of cancer cell lines [50 , 51] . Four weeks after treatment with the viral vectors , we induced the expression of the oncogenic K-Ras by intratracheal instillation with replication-defective adenoviruses encoding the Cre recombinase ( Adeno-Cre ) ( Fig 1B ) . In a second experimental setting , we used a “simultaneous treatment” strategy in which 12-week-old mice were treated with either one of the three AAV9 vectors ( AAV9-Null , AAV9-Tert and AAV9-Tert-DN ) by tail injection at the same time that they were intratracheally treated with Adeno-Cre to activate K-Ras ( Fig 1B ) . In both strategies , we included a group of 12-weeks old mice that were not infected with any of the AAV9 vectors but were treated with adeno-Cre , as positive control for oncogenic K-Ras tumorigenesis in the absence of AAV9 viral vectors . Two months after oncogene activation by adeno-Cre inoculation , tumor growth was longitudinally followed by using computed tomography ( CT ) every two months ( Fig 1B ) . Mice were sacrificed 40 weeks after oncogene activation and samples were taken for histological analysis ( Fig 1B ) . In vivo tumor follow-up by CT showed that AAV9-Tert treated mice showed the same number of mice affected with tumors as well as the same number of tumors per mouse and the same tumor area as the AAV9-Null treated mice and the untreated control group both in the “pre-treatment” and “simultaneous treatment” experimental settings ( Fig 1C–1I ) . Interestingly , mice pre-treated with AAV9-Tert-DN vectors before oncogene activation ( “pre-treatment group” ) , showed a significant decrease in the percentage of mice developing tumors at 32 weeks post-oncogene activation ( Fig 1C ) . In addition , pre-treated AAV9-Tert-DN mice showed less number of tumors per mouse and a reduced tumor area compared to either AAV9-Tert treated , AAV9-Null treated mice or to the untreated control group at 32 weeks post-oncogene activation ( Fig 1D , 1E and 1I ) . In contrast , in the “simultaneous treatment” group , we observed no significant differences between the AAV9-Tert-DN and the other groups both in the percentage of mice with tumors , in the number of tumors per mice or in the tumor area ( Fig 1F and 1I ) . We also studied the impact of AAV9-Tert gene therapy in K-Ras-induced lung tumorigenesis in a p53-deficient background , a more aggressive scenario in which lung tumors develop even more rapidly [48] . To this end , we first treated the mice with the AAV9 viruses and then activated oncogenic K-Ras ( “pre-treatment” strategy; Fig 1A ) . Mice were sacrificed 5 months post-adeno-cre infection for macroscopic quantification of tumor burden ( S2 Fig ) . In this p53-deficient genetic background , 100% of the experimental mouse groups developed lung tumors and no significant differences in the number of tumors and in tumor size were detected between AAV9-Tert treated group as compared to AAV9-null and untreated control groups ( S2 Fig ) . All together , these results clearly indicate that telomerase gene therapy has no effect in tumor onset or in tumor development in a context of oncogenic K-Ras lung tumorigenesis even in a p53-deficient background , in mice . In contrast , telomerase inhibition by using a dominant negative Tert gene , administered previously but not simultaneously to oncogene activation significantly impairs tumor growth . To analyze the degree of malignancy of the K-RasG12V lung tumors that appeared in the different experimental cohorts , we performed hematoxylin and eosin staining in serial sections of paraffin embedded lungs at 40 weeks post-oncogene activation in the “pre-treatment” group , which was the one that showed significant differences in tumor growth in the AAV9-Tert-DN cohort . Lesions were classified either as hyperplasias , adenomas , or carcinomas . Hyperplasic lesions showed alveolar-like structures with uniform nuclei and similar to healthy lung tissue . Adenomas contained cells with slightly enlarged nuclei with prominent nucleoli and disturbed the adjacent tissue . Carcinomas presented cells with very large , pleomorphic nuclei , high mitotic index with aberrant mitosis , and hyperchromatism ( Fig 2A–2C ) . We observed no significant differences in the total number of hyperplasic lesions between the different mouse cohorts ( Fig 2A ) . Similarly , mice pre-treated with AAV9-Tert did not show any significant differences in the incidence of adenomas and carcinomas compared to mice treated with the AAV9-Null or to mice not treated with viruses ( mock ) ( Fig 2B–2C ) . Interestingly , we observed a significant reduction in the number of adenomas in the AAV9-Tert-DN pre-treated mice compared to mock and AAV9-Null pre-treated mice ( Fig 2B ) . Furthermore , the total number of carcinomas was also lower in mice pre-treated with AAV9-Tert-DN compared to mock , AAV9-Null and AAV9-Tert treated mice ( Fig 2C ) . These results demonstrate that AAV9-Tert gene therapy does not increase the malignancy of K-ras induced lung tumors . Again , telomerase inhibition by using AAV9-Tert-DN gene therapy vector previous to oncogene-induction had a significant impact in decreasing both tumor onset and tumor malignancy . To confirm that the tumors originated from cells simultaneously infected with adeno-cre and the AAV9 vectors , we first determined K-RasG12V expression in tumors by detecting the expression of its surrogate β-galactosidase marker by immunohistochemistry ( Materials and Methods ) . We found that all tumors originated were from adeno-cre infected cells ( Fig 2D ) . We next determined the presence of the AAV9-Tert/AAV9-Tert-DN viral genomes ( vg ) in the tumors at the end-point from the different experimental groups . We performed a PCR from total tumor DNA as template and using primers annealing at the 5’-end within the CMV promoter that drives the expression of the Tert transgene and at the 3’-end within the Tert/Tert-DN ORF ( Fig 2E ) . We analyze a total of 26 tumors belonging to AAV9-Null , AAV9-Tert and AAV9-Tert-DN treated mice from the “pre-treatment” from the “simultaneous” group . We found that the AAV9-Tert/Tert-DN vg was detected in 88% of all the tumors analyzed from the “pre-treatment” and in 75% of the tumors analyzed from the “simultaneous” group ( Fig 2E ) . These results clearly show that K-RasG12V induced tumors aroused from cells also infected with the AAV9 vectors ( Fig 2D and 2E ) . Next , to study whether the effects of the different viral vectors carrying the wild-type and mutant Tert genes could be related to their expression levels , we studied the transcriptional expression levels of both Tert and Tert-DN mRNAs in treated lungs both at 8 weeks and 40 weeks post-oncogene activation by using quantitative PCR ( qPCR ) analysis . We found a similar upregulation of both Tert and Tert-DN mRNA levels at 8 weeks after oncogene activation and this up-regulation was maintained , although to lower levels , at 40 weeks after oncogene activation in both the “pre-treatment” and “simultaneously treatment” cohorts ( Fig 3A–3C ) . We showed previously that AAV9 vectors target preferentially alveolar type II cells ( ATII ) in mouse lungs ( 80% of AAV9-infected lung cells are ATII cells ) [46] . To address the effects of treatment with AAV9-Tert and AAV9-Tert-DN viral vectors in telomere length in whole lung tissue and specifically in ATII cells , we performed an immuno-telomereFISH using an anti-Sftpc antibody as a marker for ATII cells and a telomeric quantitative FISH probe to measure telomere fluorescence on lung sections [46] . Telomere fluorescence was measured in healthy whole-lung tissue and in ATII cells at 8 weeks post-oncogene activation in the “pre-treatment” group , which was the one that showed differences in tumor growth between the different viral treatments ( Fig 4 ) . The results show that AAV9-Tert treated lungs show significantly higher telomere fluorescence and a lower percentage of short telomeres ( telomeres below 25th percentile of telomere fluorescence ) in both healthy whole-lung tissue and in ATII cells compared to AAV9-Null and AAV9-Tert-DN treated lungs ( Fig 4A–4C ) . Telomere fluorescence was also measured in healthy whole-lung tissue , in ATII cells , and in tumors at the experimental end-point ( 40 weeks post-oncogene activation ) in both pre-treated ( Fig 5 ) and simultaneously treated groups ( Fig 6 ) . At the end-point , AAV9-Tert treated mice in both groups , “pre-treatment” and “simultaneous” , showed longer telomeres and lower percentage of short telomeres both in the whole lung and in ATII cells compared to untreated , AAV9-Null and to AAV9-Tert-DN control groups ( Fig 5A–5C and Fig 6A–6C ) . When telomere length was determined in tumors , we also observed a significant increase in average telomere length and a decrease in the percentage of short telomeres in AAV9-Tert treated samples compared to untreated , AAV9-Null and to AAV9-Tert-DN control groups ( Fig 5D and 5E; Fig 6D and 6E ) . To understand at the molecular level the impact of different AAV9 treatments on lung tumorigenesis , we next determined DNA damage ( γH2AX-positive cells ) , apoptosis ( C3A-positive cells ) , and proliferation ( Ki67-positive cells ) in tumors at 40 weeks post-oncogene activation both in the “pre-treatment” and “simultaneous treatment” groups by using immunohistochemistry . Interestingly , in both experimental settings , tumors appearing in the AAV9-Tert-DN treated mice showed significantly less Ki67-positive cells compared to AAV9-Tert treated and to control groups ( no viral treatment and mice treated with AAV9-Null ) ( Fig 7A ) , which is in agreement with significantly less tumors in this group ( Fig 1D and 1E ) . Importantly , no significant differences in Ki67-positive cells were observed between AAV9-Tert treated and control mice ( no viral treatment and mice treated with AAV9-Null ) neither in the “pre-treatment” nor in the “simultaneous” groups ( Fig 7A–7C ) , which is in agreement with similar tumor burdens in these groups ( Fig 1G and 1H ) . Next , we determined the impact of different AAV9 treatments on DNA damage induction by quantifying the percentage of γH2AX positive cells in the tumors at 40 weeks after oncogene-induction both in the “pre-treatment” and “simultaneous treatment” groups . We found that lung tumors appearing in the mice with AAV9-Tert-DN presented increased number of cells with DNA damage compared to AAV9-Tert treated and control mice ( no viral treatment and treated with AAV9-Null ) . Again , the tumors appearing in mice treated with AAV9-Tert showed a similarly low abundance of cells positive for γH2AX to the untreated and AAV9-Null treated cohorts ( Fig 7D–7F ) . In accordance with higher DNA damage in tumors from mice pre-treated with AAV9-Tert-DN vectors , we observed increased numbers of apoptotic cells in the AAV9-Tert-DN treated mice compared to AAV9-Tert treated mice and control mice ( untreated and AAV9-Null treated cohorts ) ( Fig 7G and 7I ) . Again , in the “pre-treatment” group we found no significant differences in the number of apoptotic cells between the AAV9-Tert and control mice ( Fig 7G and 7I ) . However , no differences in the number of apoptotic cells were observed in the “simultaneous treatment” among the different mouse cohorts ( Fig 7H and 7I ) , in agreement with similar tumor burden in all cohorts within the simultaneous treatment group ( Fig 1G–1H ) Altogether , these results indicate that tumor burden in the different cohorts correlates with proliferation , DNA damage and apoptosis , namely tumors appearing in the AAV9-Tert-DN “pre-treatment” group have less proliferation and more DNA damage and apoptosis . Importantly , telomerase overexpression in the AAV9-Tert treated mice did not influence any of these parameters either in the “pre-treatment” or in the “simultaneous treatment” groups , in agreement with the fact that AAV9-Tert treatment did not increase lung tumorigenesis . In order to study the effects of the different gene therapy vectors on DNA damage specifically located at telomeres , we performed a double immunofluorescence using antibodies against 53BP1 to mark DNA damage foci and TRF1 to mark telomeres in lung tumor sections . To this end , we quantified the number of cells presenting ≥4 53BP1 foci within the tumor in the “pre-treatment” and “simultaneous treatment” groups ( Fig 8A–8C ) . In the “pre-treatment” group , tumors appearing in the AAV9-Tert-DN treated mice showed a 5-fold increase in 53BP1-positive cells compared to tumors appearing in untreated mice or mice treated with AAV9-Null and AAV9-Tert vectors ( Fig 8A ) . In the “simultaneous” group , however , no differences in the percentage of damaged cells among the four mouse cohorts were detected ( Fig 8B ) . In addition , the percentage of damaged cells presenting ≥ 2 telomere induced foci ( TIF ) was 5-fold higher in tumors from the AAV9-Tert-DN treated group compared to untreated , AAV9-Null and AAV9-Tert treated cohorts in the “pre-treatment” group ( Fig 8D ) . In the “simultaneous treated group” , however , no differences in the percentage of cells presenting ≥ 2 TIFs among the four mouse cohorts were detected ( Fig 8E ) . These results suggest that telomerase inhibition by using AAV9-Tert-DN gene therapy vectors previous to oncogene activation results in increased telomere damage associated to oncogenic K-Ras tumorigenesis . This is in agreement with the reduced tumor burden observed in mice in which telomerase activity was inhibited by AAV9-Tert-DN before oncogene activation ( Fig 1C–1E and Fig 2C ) . The observation that AAV9-Tert treatment leads to similar numbers of tumor cells with telomere damage ( TIFs ) than the AAV9-Null controls , underlies the non-oncogenic effects of telomerase gene therapy . Previous studies showed that K-ras mediated lung carcinogenesis induces senescence in the pre-neoplastic lesions ( adenomas ) and that this senescence is overcome in the more aggressive lesions [52–55] . Interestingly , short/dysfunctional telomeres have been proposed to be at the origin of this oncogene-induced senescence [56 , 57] . We set to assess whether treatment with the AAV9-Tert and AAV9-Tert-DN vectors had any effects on the levels of the senescence marker p16 compared to untreated mice and to mice treated with AAV9-Null . To this end , we determined p16 mRNA levels at 8 and at 40 weeks post-oncogene activation by using q-PCR ( Fig 8G–8I ) [58] . Interestingly , in the “pre-treatment” group , AAV9-Tert treated lungs showed significantly lower p16 expression compared to AAV9-Tert-DN treated mice and to both untreated and AAV9-Null treated mice at 8 weeks post-oncogene activation ( Fig 8G ) , suggesting that telomerase expression previous to oncogene activation inhibits senescence induction at early times during K-Ras induced lung carcinogenesis . However , at 40 weeks post-oncogene activation these differences were lost and we observed similar p16 mRNA expression levels in the different mouse cohorts both in the “pre-treatment” and in the “simultaneous” group ( Fig 8H and 8I ) , most likely owing to the more advanced tumor stage . These results indicate that pre-treatment with AAV9-Tert leads to reduced senescence associated to oncogene-activation , however , this does not seem to be important for overall tumor progression and aggressiveness .
The majority of adult cell types in both humans and mice do not express telomerase and this results in progressive shortening of telomeres and increased telomere-associated DNA damage with aging [4–6 , 13 , 21 , 58–64] , a phenomenon that is proposed to be among the primary causes of organismal aging [11] . Mice are born with longer telomeres than humans but the rate of telomere shortening in blood cells from mice is 100-fold higher than in humans and telomeres do shorten very significantly during the mouse lifespan [14 , 21] . Thus , although mice have on average longer telomeres than humans , they also suffer telomere shortening with aging , and indeed this shortening is relevant for aging [13] . In contrast to telomere shortening in healthy cells , the majority of cancer cells aberrantly reactivate telomerase to achieve unlimited proliferative potential , one of the hallmarks of cancer [65] . Indeed , many different human cancer types show mutations in either the promotor region or coding regions of the telomerase catalytic subunit TERT gene [23–31 , 66] . Also , a wide variety of mouse cancers activate telomerase [32–34] . This frequent re-activation of telomerase in cancer cells , has led to the idea that the postnatal suppression of telomerase activity in the adult organism may have a role as a tumor suppression mechanism . However , there is mounting evidence that short telomeres can also lead to increased cancer , especially owing to accumulation of chromosome aberrations if DNA damage checkpoints are lost , such as p53 loss [61 , 67 , 68] . Indeed , cancer incidence increases with aging and it is also more elevated in the so-called telomere syndromes [69] . Interestingly , constitutive telomerase expression in telomerase transgenic mouse models shows that telomerase does not act as an oncogene and only leads to slightly higher incidence of some spontaneous cancer at old ages [36 , 37] . More recently , we have demonstrated that telomerase activation using non-integrative gene therapy vectors ( adeno associated vectors ) does not lead to increased cancer both in the context of normal physiological aging [6] and in the context of mouse models of disease including heart infarct [44] , aplastic anemia associated to short telomeres [45] , and pulmonary fibrosis owing to short telomeres and low doses of a damaging agent to the lungs [46] . Furthermore , we recently demonstrated that chimeric mice with a high percentage of cells having much longer telomeres than those that are normal for the species are in fact cancer protected in agreement with the fact that these mice accumulate less DNA damage with aging , thus suggesting that maintaining long telomeres during aging protect from cancer [70] . However , whether telomerase activation can lead to more cancer in the context of tumor prone contexts remained to be formally addressed . Here , we demonstrate that telomerase gene therapy does not affect ( either increasing or decreasing ) tumorigenesis in a well-established mouse model of lung carcinogenesis induced by oncogenic K-ras even in a p53-defficient background [47] . We demonstrate this by using two independent strategies , one in which we over-express Tert before the induction of the oncogene and another in which we activate Tert at the same time that we induce the oncogenic K-Ras allele . This result suggests that endogenous telomerase activation associated to oncogene-induced tumorigenesis is sufficient to allow carcinogenesis and that extra telomerase activation provided by the gene therapy vectors does not affect tumor initiation . Interestingly , when we treated mice with gene therapy vectors carrying a catalytically dead mutant allele of Tert that is unable to catalyze the addition of new telomeric repeats , we found a decreased cancer incidence but only when the vector carrying the catalytically dead Tert mutant was administered prior to oncogene activation . This is in agreement with previous finding from our group showing that catalytically dead Tert acts as a dominant negative blocking endogenous telomerase activity and inhibiting cancer cell growth [51] . Furthermore , we show here that expression Tert dominant negative prior to oncogene activation results in lower proliferation and increased DNA damage and apoptosis , thus contributing to block initiation of K-Ras carcinogenesis . Although we cannot rule out that AAV9-Tert-DN might also have some telomere-independent mechanism to suppress tumor growth , however , the fact that the tumors appearing in mice transduced with AAV9-Tert-DN showed a five-fold increase in telomere induced foci ( TIFs ) clearly indicates that Tert-DN expression suppress tumor growth by inducing telomere damage . Of note , this did not occur in the simultaneous treatment group , suggesting that telomerase inhibition once the tumor is already induced has less effect blocking tumorigenesis than when telomerase is inhibited prior to oncogene induction most likely owing to the fact that endogenous telomerase is activated with tumorigenesis . The tumor suppressive effects observed with AAV9-Tert-DN treatment prior to oncogene induction are remarkable since telomerase activity is dispensable for transformation of cells with long telomeres [71] . Indeed , telomerase abrogation in the context of cancer-prone mouse models , including the K-Ras+/G12D lung tumorigenesis mouse model , only showed anti-tumorigenic activity after several mouse generations in the absence of telomerase when telomeres reached a critically short length [67 , 72–74] . However , the fact that AAV9-Tert-DN treatment prior to oncogene activation significantly delays tumor onset and progression by increasing telomere-induced DNA damage and apoptosis suggests that telomere length is rate limiting in the early steps of oncogene-induced lung tumorigenesis in mice . These results open new therapeutic opportunities using AAV9-Tert-DN gene therapy to prevent tumor induction in cancer prone settings . Finally , we make here the very intriguing finding that AAV9-Tert treatment prior to oncogene induction significantly reduced the levels of the p16 senescence marker . It was been previously proposed that DNA damage associated to oncogene-induced senescence is largely produced by short/dysfunctional telomeres [57] . Our results support this notion as pre-treatment with telomerase is able to significantly decrease senescence in the early steps of lung carcinogenesis . Nevertheless , our results also indicate that senescence does not seem to correlate with tumor burden , as AAV9-Tert treatment does not impact on the final number of adenomas and carcinomas . A limitation for any gene therapy as a treatment for human diseases is the potential immunogenic response elicited either by the viral particles or by the product encoded by the transgene . We use , however , AAV vectors that are weak immunogens [75] . Our AAV9 vectors do not carry any viral gene and no immunogenic response against AAV9 have been reported in mice [76] . In line with this , we did not observe a significant difference in the tumor burden between the untreated and the AAV9-null treated mice , although there was a trend to lower tumor burden in the mice that received the AAV9 vectors in some of the parameters measured . Regarding the potential immune response against the AAV9-encoded transgene product , namely telomerase , we think it is unlikely since the transgene encodes the endogenous mouse telomerase . In agreement with this , we found no significant difference in the tumor burden between the AAV9-Null and the AAV9-Tert treated mice . Indeed , we have tested the AAV9-Tert gene therapy in several previous works and never observed an immunogenic effect of mouse Tert in mice [6 , 44–46] . However , the potential immunogenic response of human telomerase in humans deserves further clinical research . The experimental design of this work poses the limitation that the delivery of the Cre recombinase to induce oncogenic K-Ras expression and of telomerase were performed using different viral vectors , adeno virus and adeno associated virus , respectively . Hence , there was no mechanism to select for cells that were infected with both virus . Furthermore , owing to the packaging limit of AAV vectors to 5 kilobases [77] , it was not possible to carry both the Cre and the Tert genes in the same AAV9-vector in the “simultaneous strategy” . Nevertheless , we observed that the K-Ras expressing tumors also expressed the AAV9-transduced Tert/Tert-DN genes , indicating that the majority of the tumors originated from cells transduced with the AAV9 vectors . Furthermore , we show that one week after co-infection with adeno-Cre and with AAV9-GFP the β-gal positive cells also expressed GFP , demonstrating that lung cells are susceptible to be co-infected with adeno and adeno-associated virus . However , we cannot rule out the possibility that some tumors arise from cells only infected by the adeno-Cre and not by the AAV9 vectors . One could argue than in a long-lived specie like humans , the exogenous expression of Tert in a wild-type scenario may take several years/decades to facilitate cancer development , the time needed to acquire additional oncogenic mutations that eventually lead to malignant transformation and tumor development . To model the impact of AAV9-Tert in cancer in short lived mice , we forced oncogenic Ras expression to induce tumorigenesis both in wild-type and p53-null genetic backgrounds , thereby avoiding this lagging time . In addition , to avoid that long-term expression of telomerase could facilitate cancer development after several decades , we use non-integrative AAV9 vectors , which allow only for a transitory expression of telomerase due to the fact that as cells divide the virus load is progressively diluted until eventually cells lose the expression of the transgene ( Tert ) . In summary , the results shown here demonstrate that telomerase activation by adeno associated vectors does not increase lung carcinogenesis even in the context of an activated K-Ras oncogene , highlighting the safety of therapeutic strategies based on telomerase activation using AAV9 vectors .
K-Ras+/G12V mice were generated as previously described [47] . K-Ras+/G12V mice were crossed with p53-/- mice ( Jackson Labs , http://jaxmice . jax . org/strain/002101 . html ) to generate the compound K-Ras+/G12V p53-/- mouse . Separated groups of mice were tail-vein injected with 2x1012 vg ( viral genomes ) /mouse of either AAV9-Null , AAV9-Tert or AAV9-Tert-DN , a catalytically inactive form of mouse TERT . All mice were maintained at the Spanish National Cancer Centre under specific pathogen-free conditions in accordance with the recommendations of the Federation of European Laboratory Animal Science Associations ( FELASA ) . All animal experiments were approved by the Ethical Committee ( CEIyBA ) and performed in accordance with the guidelines stated in the International Guiding Principles for Biomedical Research Involving Animals , developed by the Council for International Organizations of Medical Sciences ( CIOMS ) . Adeno-associated viral vectors ( AAV9 ) were generated and purified as previously described [78] . The vectors used were ( i ) AAV9-Null ( ii ) AAV9-Tert that express murine catalytic subunit of telomerase ( iii ) AAV9-Tert-DN that express murine catalytically inactive telomerase ( iv ) AAV9-GFP [6] . AAV9 particles were purified using 2 cesium chloride gradients , dialyzed against phosphate-buffered saline ( PBS ) and filtered . Viral genome particle titers were determined by a quantitative real-time polymerase chain reaction ( PCR ) method . Twelve-week-old mice were treated once with intratracheal adeno-Cre vectors ( Gene Vector Core , University of Iowa , 1x1010 pfu/ml ) instillation with 1x108 pfu/mouse of virus after anesthesia by intraperitoneal injection of ketamine-medetomidine ( Domitor , 1mg/ml; Orion Corporation ) . To wake up the mice after the instillation , they were injected with 0 . 05 mg of atipamezole ( Antisedan , 5mg/ml; Orion Corporation ) . Eight weeks after adeno-Cre inoculation , an in vivo follow-up of tumor growth was achieved by four computed tomographies ( CT ) every 8 weeks . CT analyses were performed as previously described [79] . Briefly , Micro‐CT imaging was performed on a high resolution scanner ( Locus , General Electric HealthCare , London , Ontario , Canada ) . The scanning protocol operates at 80 kVp and 50 mA , 400 projections and collected in one full rotation of the gantry in approximately 10 , minutes . The reconstruction was done with a modified Feldkamp cone‐beam algorithm . Micro‐CTimages were analyzed using MicroView Analysis + ( v2 . 2 , General Electric Healthcare , London , Ontario , Canada ) . Lungs were fixed in 10% buffered formalin , embedded in paraffin wax and sectioned at 5 mm . For pathological examination sections were stained with hematoxylin and eosin , according to standard procedures . Antibodies used for immunohistochemistry in lung tumor sections included those raised against: γH2AX Ser 139 ( Millipore ) , Ki67 ( Master diagnostica ) , C3A ( Cell Signaling Technology ) , β-GAL ( 3A9A; CNIO Monoclonal Antibodies Core Unit , AM ( 3A9A ) ) and GFP ( Cell Signaling ) . For β-GAL and GFP double staining , the immunohistochemical reaction was developed using 3 , 30-diaminobenzidine tetrahydrochloride ( DAB ) ( Chromomap DAB , Ventana , Roche ) and purple chromogen ( Discovery Purple Kit , Ventana , Roche ) , respectively . Nuclei were counterstained with Harrys’s hematoxylin . Pictures were taken using Olympus AX70 microscope . Total RNA from cells was extracted with the RNeasy kit ( QIAGEN ) and reverse transcribed was using the iSCRIPT cDNA synthesis kit ( BIO-RAD ) according to manufacturer’s protocols . Quantitative real-time PCR was performed with the QuantStudio 6 Flex ( Applied Biosystems , Life Technologies ) using Go-Taq qPCR master mix ( Promega ) according to the manufacturer’s protocol . Samples were run in triplicates . Primers used are as follows: TBP1-F 5’-ACCCTTCACCAATGACTCCTATG-3’; TBP1-R 5’-TGACTGCAGCAAATCGCTTGG-3’; TERT-F 5’-GGATTGCCACTGGCTCCG-3’; TERT-R 5’-TGCCTGACCTCCTCTTGTGAC-3’; P16-F 5’-TACCCCGATTCAGGTGAT; P16-R 5’-TTGAGCAGAAGAGCTGCTACGT-3’; CMV-F 5’-CAATTACGGGGTCATTAGTTCATAGC-3’ . Quantitative telomere fluorescence in situ hybridization ( Q-FISH ) was performed directly on parafinn-embedded lung sections as previously described [21] and analysed by Definiens software . The incidence of short telomeres was calculated as the percentage of telomeres below the 25th percentile of telomere fluorescence in AAV9-null samples . For immunofluorescence analyses , tissue sections were fixed in 10% buffered formalin ( Sigma ) and embedded in paraffin . After desparaffination and citrate antigen retrieval , sections were permeabilized with 0 . 5% Triton in PBS and blocked with 1%BSA and 10% Australian FBS ( GENYCELL ) in PBS . The antibodies were applied overnight in antibody diluents with background reducing agents ( Invitrogen ) . Primary antibodies: polyclonal rabbit anti-SFTPC ( Sigma ) , rat polyclonal anti-TRF1 ( homemade ) , anti-53BP1 ( Novus Biologicals ) . Immunofluorescence images were obtained using a confocal ultraspectral microscope ( Leica TCS-SP5 ) . Quantifications were performed with Definiens software . A double immunofluorescence using antibodies against 53BP1 to mark DNA damage foci and TRF1 to mark telomeres was performed in lung tumor sections to assay for telomeric DNA damage specifically located at telomeres .
|
The ends of our chromosomes , or telomeres , shorten with age . When telomeres become critically short cells stop dividing and die . Shortened telomeres are associated with onset of age-associated diseases . Telomerase is a retrotranscriptase enzyme that is able to elongate telomeres by coping an associated RNA template . Telomerase is silenced after birth in the majority of cells with the exception of adult stem cells . Cancer cells aberrantly reactivate telomerase facilitating indefinite cell division . Mutations in genes encoding for proteins involved in telomere maintenance lead the so-called “telomere syndromes” that include aplastic anemia and pulmonary fibrosis , among others . We have developed a telomerase gene therapy that has proven to be effective in delaying age-associated diseases and showed therapeutic effects in mouse models for the telomere syndromes . Given the potential cancer risk associated to telomerase expression in the organism , we set to analyze the effects of telomerase gene therapy in a lung cancer mouse model . Our work demonstrates that telomerase gene therapy does not aggravate the incidence , onset and progression of lung cancer in mice . These findings expand on the safety of AAV-mediated telomerase activation as a novel therapeutic strategy for the treatment of diseases associated to short telomeres .
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2018
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AAV9-mediated telomerase activation does not accelerate tumorigenesis in the context of oncogenic K-Ras-induced lung cancer
|
CD8+ T cell responses to Epstein-Barr virus ( EBV ) lytic cycle expressed antigens display a hierarchy of immunodominance , in which responses to epitopes of immediate-early ( IE ) and some early ( E ) antigens are more frequently observed than responses to epitopes of late ( L ) expressed antigens . It has been proposed that this hierarchy , which correlates with the phase-specific efficiency of antigen presentation , may be due to the influence of viral immune-evasion genes . At least three EBV-encoded genes , BNLF2a , BGLF5 and BILF1 , have the potential to inhibit processing and presentation of CD8+ T cell epitopes . Here we examined the relative contribution of these genes to modulation of CD8+ T cell recognition of EBV lytic antigens expressed at different phases of the replication cycle in EBV-transformed B-cells ( LCLs ) which spontaneously reactivate lytic cycle . Selective shRNA-mediated knockdown of BNLF2a expression led to more efficient recognition of immediate-early ( IE ) - and early ( E ) -derived epitopes by CD8+ T cells , while knock down of BILF1 increased recognition of epitopes from E and late ( L ) -expressed antigens . Contrary to what might have been predicted from previous ectopic expression studies in EBV-negative model cell lines , the shRNA-mediated inhibition of BGLF5 expression in LCLs showed only modest , if any , increase in recognition of epitopes expressed in any phase of lytic cycle . These data indicate that whilst BNLF2a interferes with antigen presentation with diminishing efficiency as lytic cycle progresses ( IE>E>>L ) , interference by BILF1 increases with progression through lytic cycle ( IE<E<<L ) . Moreover , double-knockdown experiments showed that BILF1 and BNLF2a co-operate to further inhibit antigen presentation of L epitopes . Together , these data firstly indicate which potential immune-evasion functions are actually relevant in the context of lytic virus replication , and secondly identify lytic-cycle phase-specific effects that provide mechanistic insight into the immunodominance pattern seen for CD8+ T cell responses to EBV lytic antigens .
Members of the human herpes family of viruses have co-evolved with their hosts to persist as largely asymptomatic , latent infections . However , under conditions of immune T cell impairment as seen for example in immunosuppressed transplant recipients , herpesviruses may reactivate , often with clinical symptoms [1]–[4] . This reflects the vital role of T cell-mediated immune responses in controlling , albeit not eliminating , persistent herpesvirus infections [5]–[8] . The ability of these viruses to persist and be transmitted by the immune host is achieved through two strategies: firstly , the establishment of a latent infection with minimal if any viral antigen expression in long lived cell types , and secondly , the synthesis of viral proteins that interfere with antigen processing pathways in the infected cell during the virus-productive phase of replication . Multiple immune evasion proteins have been identified within herpesviruses of the α and β families ( e . g . , herpes simplex virus , HSV , and cytomegalovirus , CMV , respectively ) and these proteins have been shown to cooperate with each other during lytic cycle replication of the individual viruses . Whether the γ-herpesvirus immune evasion mechanisms similarly cooperate with each other is unknown . The prototypic human γ-herpesvirus , Epstein-Barr virus ( EBV ) , establishes latency in the memory B lymphocyte pool [9] . Studies of infectious mononucleosis patients suggest that during primary infection , EBV seeds this compartment as a reservoir of infected cells by inducing a growth-transforming infection of B lymphocytes through the coordinated expression of 8 transformation-associated proteins [9] . Upon establishment of virus persistence , such growth-transformed cells are well controlled by latent antigen-specific CD8+ T cells , and the virus is maintained in a latent and immunologically silent state in resting B cells . Periodically the virus reactivates into its lytic or virus productive phase of replication to allow infection of new cells and transmission to other hosts . Lytic replication is characterized by the sequential expression of two immediate-early ( IE ) genes ( BZLF1 and BRLF1 ) , around 30 early ( E ) genes followed by around 30 late ( L ) genes . This provides a potentially diverse repertoire of antigens for immune targeting and strong responses are made to epitopes drawn from the immediate early and some early expressed antigens . A testament to the efficacy of the lytic and latent epitope-specific CD8+ T cell responses is that although 90% of adults worldwide are infected with EBV , infection remains largely asymptomatic [10] . However , high levels of viral particles have been proposed to be synthesised and shed in such immune hosts [11] . Additionally in vitro models show that in the absence of immune effectors , B cells reactivating from latency in to lytic cycle can remain viable and go on producing virus for several days [12] . In vivo therefore , T cell recognition within this extended window of replication has the potential to limit virus production , and evading recognition would clearly be of an advantage to the virus in increasing its chances of transmission from the virus-carrying host . Following the observation that HLA-class I expression at the cell surface of EBV-infected cells was decreased upon entry into lytic cycle [13] , it was also demonstrated that there was increasing evasion of CD8+ T cell recognition by cells replicating EBV as they progressed through lytic cycle [14] . Thus EBV-specific CD8+ T cells which targeted antigens expressed in the IE wave of expression recognised their target epitopes relatively well , while CD8+ T cells specific for E expressed proteins recognised their target epitopes at an intermediate level , and L epitope-specific effectors were relatively poor at recognising their targets . Subsequently , three EBV lytic cycle genes were shown by ectopic expression in EBV-negative cell models to encode proteins that interfere with the HLA class I antigen processing pathway [15]–[18] . These proteins are: BNLF2a , which associates with the Transporter associated with Antigen Processing ( TAP ) to block translocation of peptide fragments from the cytosol to the endoplasmic reticulum , thus preventing their access to HLA class I molecules [15] , [19]–[21]; BGLF5 , which encodes an exonuclease that degrades mRNA and thus reduces global levels of host cell transcripts , including those for HLA and TAP [17] , [22] , [23]; and BILF1 , which binds to HLA class I/peptide complexes and both interferes with their transport to the cell surface and increases the turnover of pre-existing cell-surface HLA class I/peptide complexes , targeting them for lysosomal degradation [16] , [24] , [25] . Although the individual EBV evasion genes have been well-studied in model systems , little is known about their contributions to evasion in the context of natural EBV lytic cycle . The limited information available suggests that BNLF2a may only be effective during the IE- and E-phases of lytic cycle [19] , and yet cells in the L-phase show greatest resistance to EBV-specific CD8+ T cells . To better understand why L-phase viral antigens are less immunogenic , we have knocked-down BNLF2a , BGLF5 and BILF1 expression in spontaneously lytic LCLs and examined the efficiency of recognition of these cells by IE , E and L antigen-specific CD8+ T cell clones . The data show that of these three gene products , BNLF2a and BILF1 are the major effectors of evasion and they cooperate to provide immune protection across all three phases of the lytic cycle .
A panel of EBV transformed B-cell lines ( LCLs ) from suitable HLA-typed donors was first selected in which more than 1% of cells expressed the lytic switch protein BZLF1 as detected by intracellular staining and flow cytometry . These lines therefore contained significant numbers of cells spontaneously entering into lytic cycle , allowing them to be used as targets in T cell recognition assays . To examine the relative contribution of BNLF2a , BGLF5 and BILF1 to the inhibition of CD8+ T cell recognition of EBV infected B cells during lytic cycle we devised a strategy to knockdown the expression of these genes in LCLs using a lentivirus-delivered shRNA . Sequences for these shRNAs were identified by screening candidate siRNA sequences for their ability to silence ectopic expression of BNLF2a , BGLF5 and BILF1 in model systems ( data not shown ) and incorporating the selected sequences into shRNA lentiviral expression vectors . Each lentivirus expressed a puromycin resistance gene to enable antibiotic enrichment of transduced cells , and a fluorescent tag to monitor transduction efficiency ( Table 1 ) . The efficiency of BNLF2a , BGLF5 and BILF1 knockdown in LCLs using respective shRNA-lentiviruses was first examined by measuring transcript levels by qRT-PCR and protein levels by western blot where relevant antibodies were available . Figure 1a shows a representative example of the relative level of BNLF2a transcript knockdown in shBNLF2a-transduced LCLs compared to non-target shRNA ( shControl ) -transduced LCLs . As lytic cycle entry is spontaneous and the frequency of entry is unique to individual LCL cultures , differences in the frequency of cells spontaneously undergoing lytic cycle replication in the two LCL lines were accounted for by relating BNLF2a transcript levels to those of the IE lytic BZLF1 transcripts . In all shBNLF2a-transduced LCLs used in this study , the median knockdown of BNLF2a transcripts was 80% ( range 70–85% ) . The knockdown of BNLF2a transcripts ( Figure 1A ) corresponded to a reduction in BNLF2a protein expression in these same transduced LCLs ( Figure 1B ) . Similar efficiencies of knockdown of BGLF5 transcripts ( Figure 1C; median knockdown = 75% , range = 70–85% ) and BILF1 transcripts ( Figure 1E; median knockdown = 80% , range = 75–90% ) were observed in replicate experiments . The lack of available antibodies to detect BILF1 precluded confirmation of knockdown for this EBV protein , but antibodies to BGLF5 confirmed efficient knockdown of BGLF5 protein in LCLs transduced with shBGLF5 ( Figure 1D ) . To investigate the effect of BNLF2a on epitope presentation during the IE , E and L-phases of lytic cycle , pairs of LCLs transduced with either the shControl- or shBNLF2a-lentiviruses were established from a range of different donors with HLA allele matches for HLA-A2 and/or HLA-B7 . These LCLs were used as targets for panels of effector CD8+ T cell clones restricted through HLA-A2 or HLA-B7 and specific to epitopes generated during the IE , E and L-phases of lytic cycle , as shown in Table 2 . T cell recognition was assayed by co-incubation of LCL targets with effector T cells for 18 hours , then measuring by ELISA the amount of IFN-γ released into the supernatant by the T cells . To account for differing levels of spontaneous lytic cycle in the LCLs pairs , and potential indirect effects of knockdown of one lytic gene on other lytic cycle genes , the measured level of T cell recognition was adjusted according to the amount of each target antigen . Levels of target antigen were assayed by measuring their transcript levels , and not protein levels , as target peptides are predominantly derived from defective ribosomal products ( DRiPs ) rather than mature proteins . The raw T cell function and antigen expression data corresponding to the normalised results in Figure 2 are provided in Supplementary Information ( Figures S2–S5 ) . To facilitate comparison between different target/effector T cell combinations , T cell recognition of shBNLF2a LCLs was expressed relative to the amount of recognition of shControl ( non-target , scrambled shRNA ) after adjusting for differences in target antigen expression . Thus , in Figure S2A , recognition of the shBNLF2a LCL by BRLF1-specific T cells is about 18-fold better than recognition of the control LCL , but as expression of BRLF1 is about 50% higher in the shBNLF2a LCL ( Figure S2C ) , the normalised increase in T cell recognition is reduced to 12 . 5-fold ( Figure S2C ) . Figure 2 shows data from six representative experiments using shBNLF2a-LCLs from 5 donors to examine the effect BNLF2a has on HLA-A2 restricted ( Figures 2A , 2B ) and HLA-B7 restricted ( Figure 2C ) epitope presentation during IE ( hollow bars ) , E ( gray bars ) and L stages ( black bars ) of lytic cycle . As shown in figure 2A ( upper graph ) , BNLF2a-knockdown in donor 3 LCLs ( shBNLF2a-LCLs ) resulted in 13-fold better recognition of the YVL epitope originating from the BRLF1 IE antigen compared to shControl-LCLs . There was a lower but still substantial 9-fold increase in recognition of the GLC epitope of the BMLF1 E antigen in shBNLF2a-LCLs , and a marginal 2-fold increase in the recognition of the FLD epitope of the BALF4 L antigen . This panel of effector T cells was assayed on donor 4 target LCLs ( Figure 2A , lower graph ) with the same pattern of increased recognition of IE>E>>L epitopes being reproduced , albeit with different magnitudes of increased recognition . Another panel of HLA-A2 restricted T cells , specific for IE ( YVL epitope of BRLF1 ) , E ( TLD epitope of BMRF1 ) and L ( WQW epitope of BNRF1 ) antigens gave a similar pattern of increased recognition of IE , E and L derived epitopes in shBNLF2a-LCLs relative to shControl-LCLs derived from different donors ( Figure 2B ) . Recognition experiments were also performed using a panel of HLA-B7 restricted T cells recognising the DPY epitope derived from the BZLF1 IE antigen , the RPG epitope of the BNLF2b E antigen and the YPR epitope of the BNRF1 L antigen . As shown in Figure 2C , the pattern of recognition paralleled what was observed with HLA-A2 restricted epitopes; i . e . the reduction of BNLF2a expression led to a pattern of increased recognition of IE>E>>L antigens . These experiments were repeated and extended into other donors , a summary of which is provided in Table 3 . It should be noted the data in this table include some experiments in which it was not possible to determine recognition of the LCLs by a complete panel of IE , E and L specific T cells in parallel assays and so only one or two such T cell specificities were used . Together , these data are consistent with the interpretation that inhibition of TAP-mediated peptide transport into the ER by BNLF2a is more dominant during the IE and E phases of lytic cycle , and that BNLF2a appears to have a much weaker effect at the L stage of lytic cycle . The role of BGLF5 in interfering with epitope presentation during lytic cycle was then similarly investigated using BGLF5 knockdown LCLs as targets for T cells specific to IE , E and L lytic epitopes . As shown in Figures 3A–C , the knockdown of BGLF5 resulted in a modest , if any , increase in recognition of the IE-YVL and -DPY epitopes that was never more than three times above shControl . Similarly , the increase in recognition of E epitopes in the absence of BGLF5 was not more than 2-fold . Although there is a hint that the knockdown of BGLF5 increased recognition of the L-WQW epitope , this was not reproducible for all L-epitopes and donors in replicate experiments . A summary of all experiments performed is provided in Table 4 . Overall , these data suggest that relative to BNLF2a , BGLF5 plays a rather minimal role in interfering with antigen presentation during lytic cycle , contributing only a small effect across all stages of lytic cycle . We next examined the effect that BILF1 expression had on CD8+ T cell recognition of IE , E and L lytic epitopes using similar experimental approaches as described above . To this end , shBILF1-LCLs were generated and used as targets for T cells specific for epitopes drawn from the IE , E and L-phases of lytic cycle . As shown in Figure 4A ( upper graph ) , in marked contrast to the results observed for BNLF2a-depleted LCLs , donor 2 LCLs with reduced expression of BILF1 resulted in a 25-fold increase in recognition of the L antigen ( FLD epitope of BALF4 ) compared to recognition of shControl-LCLs . There was a substantial , though smaller 8-fold increase in recognition of the E antigen ( GLC epitope of BMLF1 ) , and no increase in recognition of the IE antigen ( YVL epitope of BRLF1 ) . This same panel of T cells when used as effectors against Donor 3 LCLs ( Figure 4A , lower graph ) gave the same pattern of results , albeit a marginal 2-fold increase was observed for recognition of the IE antigen ( YVL epitope of BRLF1 ) . A second panel of HLA-A2 restricted T cells , which included specificities towards the TLD epitope of the BMRF1 E antigen and the WQW epitope from the BNRF1 L antigen , again revealed a similar pattern of enhanced recognition of L and E epitopes ( Figure 4B ) . Furthermore , the pattern was consistent when using our panel of HLA-B7 restricted T cells ( Figure 4C ) . The experiments shown in Figure 4 were repeated and extended to include other donor LCLs , and summarised in Table 5 . Taken together , these data show that BILF1 plays a more dominant role in interfering with antigen presentation during L stage lytic cycle ( IE<<E<L ) at a time when the effects of BNLF2a are diminished . The data presented in Figures 2–4 and Tables 3–5 were derived from experiments where the effects of knocking down BNLF2a , BGLF5 and BILF1 on epitope recognition were assayed separately . However , in a small number of experiments , it was possible to examine in parallel the effect of knocking down expression of each of these three genes on CD8+ T cell recognition of IE , E and L derived epitopes . Figure 5 shows one such representative example of two replicate experiments . The results are consistent with the general conclusions drawn from Figures 2–4 and Tables 3–5 , which are: ( i ) that during the IE stage of lytic cycle BNLF2a plays a dominant role in interfering with antigen presentation while BILF1 contributes a small effect , ( ii ) at E stage lytic cycle both BILF1 and BNLF2a impair presentation , ( iii ) at L stage lytic cycle , BILF1 seemingly plays a dominant role , with BNLF2a contributing a small effect , and ( iv ) BGLF5 appears to only minimally impact on presentation throughout lytic cycle . We considered the possibility that the lack of effect of BGLF5 might possibly be due to insufficient knockdown of this gene . We therefore employed a complementary experimental approach in which we used as targets a panel of LCLs generated with rEBV in which BNLF2a , BGLF5 or BILF1 genes were knocked out . Whilst this approach was hampered by the fact that only a few LCLs demonstrated sufficient spontaneous lytic gene expression , we were able to generate sufficient data for direct comparison with the knockdown data in Figure 5 . As shown in Figure 6 ( and Figures S6–S8 ) these recombinant EBV LCLs , which completely lacked expression of BNLF2a , BGLF5 or BILF1 , revealed the same pattern of results as was obtained with shRNA-mediated knockdown LCLs . One factor that might contribute to the differential effects of BNLF2a , BGLF5 and BILF1 on CD8+T cell recognition of IE , E , and L antigens is the initial kinetics of their expression during lytic cycle . To address this possibility , we analysed the expression kinetics of these genes the using the EBV infected-Akata Burkitt lymphoma cell line , in which synchronous initiation of the lytic cycle of the resident virus can be induced by ligation of the B-cell receptor . Following induction of lytic cycle , aliquots of cells were taken at sequential time points and qRT-PCR analysis of lytic gene expression was performed . As shown in Figure 7A , BNLF2a expression is first detectable 2 h post-induction , almost coincident with the BZLF1 IE gene , and before expression of the representative E gene , BMRF1 . BNLF2a expression then steadily increases and peaks 8–24 h before steadily decreasing thereafter . Thus , although BNLF2a is considered an E expressed lytic gene by virtue of its transcript being sensitive to new protein synthesis and independent of viral DNA replication , it is temporally more akin to an IE gene . Notably , the expression of BNLF2a transcript remains high , at around 73% of maximum at 24 h post-induction when maximal levels of the representative L antigen , BALF4 , transcripts are expressed . However , it is known that BNLF2a protein expression is markedly diminished from 12 h post induction [19] despite the maintenance of this relatively high level of transcripts . The kinetics of expression therefore offers an explanation for why BNLF2a is most effective at interfering with antigen presentation during IE and E phase lytic cycle . BGLF5 transcripts can be detected at the same time as BILF1 , ( 4 h post induction ) , after which its expression level increases more slowly , peaking at 24 h during L-phase lytic cycle ( Figure 7B ) . The initial expression of BILF1 is detected at around 4 h post-induction ( Figure 7C ) , coincident with expression of BMRF1 transcripts . Having reached peak levels at 8 h , BILF1 transcripts decline slightly but are maintained at near maximal levels well into the L-phase of lytic cycle , at 24 h and beyond . This may explain why BILF1 has a subtle effect on the presentation of IE lytic epitopes and a stronger effect on L-lytic epitope presentation . Taken together these kinetics data suggest that the roles that BNLF2a and BILF1 play in interfering with antigen presentation are at least in part a consequence of timing of their expression . One question arising from the preceding observations is whether there is any redundancy or co-operation between BILF1 and BNLF2a during the IE phase when individually BNLF2a is more dominant and at the L stage , when BILF1 has the strongest effect . To address this question , LCLs were transduced with both shRNA-BILF1 and shRNA-BNLF2a vectors to generate double-knockdown target LCLs . The recognition of IE ( YVL from BRLF1 ) and L ( FLD from BALF4 ) epitopes presented by these cells was then assessed , alongside the recognition of their single knockdown and shControl-LCL counterparts . A representative example of two repeat experiments is shown in Figure 8A , the knockdown of both BNLF2a and BILF1 expression in target cell lines increased the recognition of the IE YVL epitope 15-fold versus 9-fold for BNLF2a knockdown and 10-fold versus 5-fold for BNLF2a knockdown in two different donor LCLs . In both cases there was a minimal effect from the knockdown of BILF1 expression only . The increase in recognition using dual knockdown however suggests a level of synergy or cooperation between these two immune evasion proteins at the IE stage of lytic cycle . Representative results obtained using L-FLD antigen specific effector CD8 T cells on the same two donor LCLs ( Figure 8B ) , showed a clear increase in L-FLD recognition of dual knockdown LCLs compared to BILF1 only knockdown LCLs ( 12 . 5-fold versus 7 . 5-fold , and 16-fold versus 10 . 5-fold ) . This reproducible increase in recognition suggests that BILF1 and BNLF2a cooperate with each other at L-phase as well as at IE-phase of lytic cycle .
These experiments reveal that the relative contribution of BNLF2a , BGLF5 and BILF1 towards interference with antigen presentation differs during the three different phases of lytic cycle . BNLF2a has a more dominant role during the IE- and E-phases of lytic cycle , with its effect decreasing as lytic cycle progresses ( IE>E>>L ) . Conversely , BILF1 becomes more dominant as lytic cycle progresses ( IE<E<<L ) , coincident with declining effects of BNLF2a . Unexpectedly , our experiments revealed that the effect of BGLF5 on antigen presentation is weak throughout lytic cycle , despite its expression and host shut-off function during the E and especially L stages . Experiments using recombinant EBV deleted for the BGLF5 gene also demonstrated comparatively little effect on CD8+ T cell recognition ( Figure 6 ) , ruling out the theoretical possibility that the results in Fig . 3 and Table 4 were due to insufficient knockdown of BGLF5 by the shRNA approach . The minimal effect of BGLF5 on epitope presentation is surprising , given that the ectopic expression of BGLF5 can result in a decrease in MHC class I surface expression and to significant impairment of EBV epitope recognition [17] , [23] . A possible explanation for this observation is that removal of BGLF5 might cause a counteracting upregulation of other immune evasion genes . This seems not to be the case in respect of BNLF2a or BILF1 ( Figure S9 ) although we cannot rule out the possibility that another as yet unidentified immune evasion gene is so affected . On the available evidence , we are drawn to conclude that the global down regulation of host mRNAs by BGLF5 confers little protection from CD8+ T cell recognition in the context of EBV infection of normal B lymphocytes . Since as few as 10 MHC/peptide molecules on the cell surface may be sufficient for recognition by CD8+ T cells [11] , LCLs would appear to express a huge excess of MHC class I molecules . A BGLF5-mediated partial reduction in the availability of newly synthesised HLA class I molecules might therefore be inconsequential in comparison to the effects of BNLF2a and BILF1 on the available MHC class I/peptide complexes at the cell surface . The main function of BGLF5 , therefore , most likely involves the generation and processing of linear viral genomes [26] rather than to protect virus-producing cells from CD8+ immune T cells . The minimal immune evasion effect of BGLF5 contrasts notably with HSV , where silencing of the virion host shut-off ( vhs ) gene results in an increase in recognition by virus specific CD8+ T cells [27] . Why EBV ( a γ-herpesvirus ) and HSV ( an α-herpesvirus ) differ in this respect is unclear , but could be influenced by the different host cell tropism , differences in duration of lytic cycle , and differences in the molecular mechanisms of host-shut off . With regards to this final point , it will be of interest to know whether the host shut-off protein of the only other human γ-herpesvirus , Kaposi sarcoma-associated herpesvirus ( KSHV ) , impacts on antigen presentation in the context of KSHV lytic cycle . The molecular mechanism of the KSHV SOX protein is more similar to EBV BGLF5 than to HSV vhs [18] , [22] , [28] . It should be noted that β-herpesviruses ( such as HCMV ) do not contain a host shut-off gene , so this function is clearly not a conserved and essential mechanism for herpesvirus modulation of the MHC class I antigen processing pathway . Whilst the different kinetics of initiation of BNLF2a and BILF1 expression ( Figure 7 ) and the subsequent posttranslational downregulation of BNLF2a protein [19] may account for their phase-specific immune-evasion functions , they might also be predicted to limit the possibilities for co-operation at the IE and L-phases of lytic cycle . Nevertheless , we did observe such cooperation . This may be because there is a window of about 6–24 h after lytic cycle entry when BNLF2a and BILF1 are co-expressed along with IE , E and L-phase antigens . Another factor to consider is that whilst both BNLF2a and BILF1 respectively can impair the generation of MHC/peptide complexes and their transport to the cell surface , BILF1 can also target pre-existing surface MHC-I/peptide complexes for degradation [16] , [24] . Consequently , those MHC/peptide complexes ( be they IE , E , or L antigen-derived ) that survive initial evasion mechanisms to reach the cell surface , will continue to be targeted by BILF1 even after the reduction of BNLF2a protein . That multiple viral evasion genes should demonstrate cooperation is not unexpected; indeed such cooperation is well-documented for the β-herpesvirus , CMV [7] , [15] , [29] . Cooperation between multiple evasion genes provides an evolutionary advantage to the virus . In addition to a generally greater efficiency of evasion , it also allows the virus to cope with peptides presented by different MHC class I allotypes . For example , EBV BILF1 only marginally affects presentation through HLA-C alleles [25] , whereas BNLF2a will target all TAP-dependent peptides . This parallels the resistance of HLA-C to US2 and US11 of HCMV [30] and the targeting of TAP by HCMV US6 [31]–[33] . However , our present study highlights an additional feature of cooperation , which is to maximally impair presentation through different phases of lytic cycle . This may be particularly important for γ-herpesviruses , such as EBV , which have a relatively prolonged lytic cycle , and less important for α-herpesviruses , such as HSV , where lytic virus replication is more rapid . Our data beg the question as to why EBV would downregulate the expression of BNLF2a at the L-phase of lytic cycle , when it is clearly such a potent immune evasion mechanism ? One possibility is that excessive immune-evasion mechanisms contributing to the down regulation MHC class I levels could leave cells too vulnerable to NK cell destruction [34] , [35] . In this scenario , it is envisaged that controlled expression of BNLF2a and BILF1 is perhaps an eloquent trait of EBV , in order to maximise protection from CD8+ T cell recognition , while minimising NK cell induced destruction . In this context it may be relevant that BILF1 preferentially targets HLA-A and HLA-B MHC class I molecules , while it does not down regulate the surface expression of HLA-C molecules which would act as NK inhibitory ligands [25] . It should also be noted that many immune-modulating viral genes have other functions relevant to the efficient replication of virus . In the case of EBV , BILF1 is a G-protein-coupled receptor whose signalling functions are dispensable for evasion from CD8+ T cell recognition [18] , [24] , [36] , [37] . To date , no function for BNLF2a other than its inhibition of TAP has been defined , but the possibility remains that it has a second function in lytic replication for which prolonged high expression during late lytic cycle might be detrimental to the virus . Previous studies have shown that the immune response to EBV is unique amongst the herpesviruses in that EBV-specific CD8+ T cell responses directed towards lytic antigens show a different pattern of immunodominance [14] . These EBV-specific T cell responses are more frequently skewed towards IE-phase and some E-phase lytic antigens than L-phase antigens [14] . This is likely to be due in part to the role that EBV infected B lymphocytes play in the stimulation of EBV-specific T cells . Although EBV lytic cycle can occur in both epithelial cells and in B lymphocytes [9] , it appears from observations on X-linked lymphoproliferative disease ( XLP ) patients or heterozygous carriers of this disease that infected B cells drive stimulation of CD8+ T cell responses to EBV lytic cycle antigens [38]–[40] . Importantly , IE and E specific T cell responses are less able to recognise and lyse EBV infected cells that are at the L phase of lytic cycle , i . e . expressing VCA , despite continued expression of the IE and E target antigens ( Pudney et al , [14]; and Figure S10 ) . It is therefore likely that CD8+ T cells in vivo will be very inefficient at preventing the spread of EBV virus from infected cells that have already entered late lytic cycle . Extrapolating from the kinetics of lytic cycle induction in the Akata cell model ( Figure 7 ) there would be a rather small window of perhaps 4–6 hours during E lytic cycle in which lytic EBV infected cells can be recognised and lysed in order to prevent the subsequent release of virus particles . Thereafter , the cells may produce virus for several days [12] unthreatened by immune T cell responses . Understanding that endogenous antigen presentation in lytically infected B cells is the predominant source of stimulation for CD8+ T cell responses to lytic cycle antigens , as opposed to cross-presentation via dendritic cells as is common for other herpesviruses such CMV [41]–[43] , places greater importance on the role of the phase-specific interference of antigen presentation identified in the present work . In this context our new data implicate a significant contribution of BILF1 to the pattern of immunodominance that is seen for EBV . However , whilst our data demonstrate that BILF1 and BNLF2a cooperate to afford evasion across all three phases of lytic cycle , they do not obviously suggest that BILF1 is substantially more potent at the L-phase than is BNLF2a at the IE-phase . Although such differences in potency could be masked by the experimental design of our experiments , we consider it likely that there is yet to be identified one ( or more ) additional immune evasion gene that preferentially modulates recognition by CD8+ T cells specific for L-stage antigens . From the data presented in this study , a model is proposed ( Figure 9 ) . In conclusion , the present study identifies lytic cycle phase-specific effects of viral immune evasion genes targeting the MHC class I antigen processing pathway which provides mechanistic insight into the pattern of immunodominance of EBV lytic antigen specific CD8+ T cell responses that sets EBV apart from other herpesvirus infections .
Written , informed consent was given by all donors for the collection and use of blood samples , and all experiments were approved by the West Midlands ( Black Country ) Research Ethics Committee ( 07/Q2702/24 ) . For the generation of replication-defective lentivirus , the packaging cell line FT293 ( Invitrogen ) was co-transfected , using lipofectamine 2000 ( Invitrogen ) , with lentiviral vector plasmids ( shBILF1-YFP , shBNLF2a-CFP or shBGLF5-FP635 ) , ( Sigma-Aldrich; Table 1 ) , the envelope plasmid-pMD2G and the packaging plasmid-psPAx2 ( Invitrogen ) . Supernatants containing virus were harvested 72 hours after transfection , filtered through a 0 . 22 µm pore and subsequently concentrated by centrifugation prior to infection of target EBV-transformed lymphoblastoid cell lines ( LCLs ) . LCLs were generated by transforming B-lymphocytes from donors of known HLA type with the B95 . 8 strain of EBV as previously described [19] . B95 . 8 transformed LCL cultures were selected on the basis of containing at least 1% of cells expressing BZLF1 protein detected by intracellular staining and flow cytometry . All LCLs were maintained in standard media ( RPMI-1640 with 10% FCS ) . Replicate cultures of LCLs were transduced in parallel with the appropriate knockdown and control shRNA-lentiviruses ( Table 1 ) . Transduced cultures were maintained and expanded in standard media plus 1 µg/ml puromycin where necessary . For target cell lines that were more than 70% transduced after expansion , cells were used immediately in T cell recognition assays . For transduced lines in which less than 70% were transduced , enrichment was achieved by sorting on the expression of CFP , YFP or FP635 using Cytomation MoFlo fluorescence activated cell sorting . Cells were then re-cultured and maintained in standard media , until numbers were sufficient for use in T cell recognition assays . Wild-type recombinant EBV based on the B95 . 8 genome , 2089 , and null recombinants for BNLF2a , BGLF5 , or BILF1 , or BZLF1 have been described elsewhere [19] , [26] , [44] , [45] . The 2089 , ΔBGLF5 , ΔBILF1 and ΔBZLF1 recombinant viruses were kindly provided by Henri Jacques Delecluse and Regina Feederle , Heidelberg . LCLs carrying these recombinant EBVs were generated by transforming B lymphocytes from donors of known HLA type with the B95 . 8 strain of EBV as previously described [19] . CD8+ T cell clones were generated as previously described [14] , [46] using limiting dilution or IFN-γ capture T cell cloning . All novel HLA-B7 restricted T cell clones were generated using limiting dilution cloning while HLA-A2 restricted effector clones were from IFN-γ capture and limiting dilution T cell cloning . The clones used in this study are shown in Table 2 . CD8+ T cell recognition of lytic epitopes presented by shRNA-transduced LCLs was measured using a standard IFN-γ ELISA assay as previously described [47] . Briefly , triplicate aliquots of 105 target LCLs were incubated with 104 effector T cells for 18 h in standard media . To measure T cell recognition of the target cells , 50 µl of the supernatant from each well was assayed for IFN-γ . Total RNA was extracted from 0 . 5×106 to 106 cells using RNeasy kit ( Nugen ) followed by Turbo DNA-free ( Applied biosystems ) treatment to remove any contaminating DNA . A 500 ng sample of RNA was reverse transcribed into cDNA using qScript cDNA supermix , as per manufacturer's protocol ( Quanta biosciences ) . Quantitative-PCR was then performed using specific EBV lytic gene primers ( Alta Bioscience ) and probes ( Eurogentec ) ( Table S1 ) . Expression normalised to GAPDH expression and the data displayed as relative to expression in shNon-target LCLs , or relative to the maximal level of transcript for each gene . T cell recognition assays relied upon target LCLs spontaneously entering lytic cycle replication , the efficiency of which varies between lines and within lines over time . Since this directly impacts the level of antigen available for presentation , and therefore CD8+ T cell recognition , it was important to measure the level of target antigen expression in each cell line in every experiment . As peptides for presentation to T cells are generally considered to originate predominantly from the products of defective translation ( DRiPs ) rather than through degradation of mature protein [48]–[50] , we measured the level of mRNA transcript of each antigen to which our T cells were specific . This allowed us to normalise the amount of IFN-γ release ( T cell recognition ) against target antigen expression . For example , for a CD8+ T cell which recognises the YVL epitope , derived from the lytic antigen BRLF1 , if the mRNA level of BRLF1 in the reference target line ( shControl LCLs ) was y , and in T cell recognition ( IFN-γ release ) was x , then the amount of IFN-γ released by YVL specific T cells incubated with the reference line was adjusted ( normalised ) by dividing x by y . This was performed on all lines which enabled us to express the recognition data as fold increase in epitope recognition of knockdown LCLs as a ratio of recognition of shControl LCLs . The validity of this experimental approach was demonstrated by the direct correlation between the level of target antigen-mRNA and CD8+ T cell recognition , as shown in Figure S1 . Thus , by measuring the mRNA-expression level of specific target antigens we can accurately account for differences in the amount of lytic cycle in individual LCL target cell lines on the day of assay . Examples of raw T cell recognition and mRNA expression data alongside the subsequent normalised data are shown in Figures S2–S5 . Western blotting was performed as described previously [51] . Briefly , total cell lysates were prepared in reducing sample buffer ( 2% sodium dodecyl sulphate ( SDS ) , 72 . 5 mM Tris-HCl pH 6 . 8 , 10% glycerol , 02 . M sodium 2-mercaptoethanesulfonate , 0 . 002% bromophenol blue ) , sonicated and heated to 100°C for 5 min . Solubilised proteins equivalent to 2×105 cells/20 µl sample were separated by SDS-polyacrylamide gel electrophoresis on 4–12% Bis-Tris NuPage mini-gels with morpholinepropanesulfonic acid running buffer ( Invitrogen ) , then transferred to polyvinylidene difluoride membranes . Specific proteins were detected by incubating membranes with primary antibodies at 4°C overnight . Rabbit anti-BGLF5 serum [52] was diluted 1/6 , 000 , clone 5B9 rat anti-BNLF2a [19] culture supernatant was used at a dilution of 1/100 , clone BZ1 purified mouse anti-BZLF1 [53] and goat anti-calregulin ( sc6467; Santa Cruz Biotechnology ) were used at 1 µg/ml . Primary antibody binding was detected by incubation with appropriate alkaline phosphatase conjugated secondary antibody and subsequently developed using CDP-star detection kit ( Applied Biosystems ) . The reactivation of Akata-BL cells into lytic cycle was performed by cross-linking surface IgG molecules as previously described [54] . Cells were then harvested at the indicated time points for qRT-PCR analysis .
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Epstein Barr Virus ( EBV ) , an oncogenic herpesvirus , infects and persists asymptomatically in the majority of humans . In immunocompetent individuals , EBV co-exists with its host as a lifelong infection in the face of strong anti-viral CD8+ T-cell responses . Evasion of this immune-response is presumed to be due in part to immune-modulating mechanisms of certain EBV-encoded proteins expressed during lytic cycle replication . Three such proteins ( BNLF2a , BGLF5 and BILF1 ) have been identified biochemically as able to interfere with HLA-class I antigen presentation . In this study we investigated these proteins in the context of EBV-infected cells in lytic cycle , and their functional recognition by EBV virus-specific CD8+ T-cells . A novel feature of EBV biology was revealed; rather than demonstrating simple redundancy , evasion proteins effect optimum temporal protection at different phases of lytic cycle . BNLF2a strongly inhibited CD8+ T-cell recognition immediately after the EBV-infected cells entered lytic cycle , with its influence waning upon progression to later phases of lytic cycle . Conversely , BILF1 strongly inhibited recognition predominantly at the late phase of lytic cycle . Unexpectedly , despite its well-characterised molecular functions , BGLF5 had relatively little effect on recognition at any stage of lytic cycle . Our results help to explain the previously-identified unusual pattern of immunodominance of anti-EBV CD8+ T-cell responses .
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2014
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Cooperation between Epstein-Barr Virus Immune Evasion Proteins Spreads Protection from CD8+ T Cell Recognition across All Three Phases of the Lytic Cycle
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The diversity of viruses probably exceeds biodiversity of eukaryotes , but little is known about the origin and emergence of novel virus species . Experimentation and disease outbreak investigations have allowed the characterization of rapid molecular virus adaptation . However , the processes leading to the establishment of functionally distinct virus taxa in nature remain obscure . Here , we demonstrate that incipient speciation in a natural host species has generated distinct ecological niches leading to adaptive isolation in an RNA virus . We found a very strong association between the distributions of two major phylogenetic clades in Tula orthohantavirus ( TULV ) and the rodent host lineages in a natural hybrid zone of the European common vole ( Microtus arvalis ) . The spatial transition between the virus clades in replicated geographic clines is at least eight times narrower than between the hybridizing host lineages . This suggests a strong barrier for effective virus transmission despite frequent dispersal and gene flow among local host populations , and translates to a complete turnover of the adaptive background of TULV within a few hundred meters in the open , unobstructed landscape . Genetic differences between TULV clades are homogenously distributed in the genomes and mostly synonymous ( 93 . 1% ) , except for a cluster of nonsynonymous changes in the 5′ region of the viral envelope glycoprotein gene , potentially involved in host-driven isolation . Evolutionary relationships between TULV clades indicate an emergence of these viruses through rapid differential adaptation to the previously diverged host lineages that resulted in levels of ecological isolation exceeding the progress of speciation in their vertebrate hosts .
Evolutionary diversification has resulted in a myriad of virus species [1 , 2] , but—despite their great importance as agents of diseases of humans , crops , and livestock—our understanding of their functional partitioning and distribution across hosts is strongly limited . The evolution of novel viral features and their molecular basis is best understood in laboratory and medical settings in which new virus types can arise quickly through genetic adaptation to specific host environments [3 , 4] . Ultimately , such adaptive processes may lead to ecological isolation of specialized virus types due to their reduced ability to use alternative hosts [5] . Heterogeneity in the environment of a virus population , e . g . , through the simultaneous presence of different hosts , can also drive differential adaptation to divergent ecological niches resulting in “sympatric speciation” [6] . Although these principles of virus diversification have been demonstrated under controlled experimental conditions , our knowledge of the mechanisms that generate and maintain functionally distinct viruses in nature is very scarce [7] . The emergence of natural virus species is often investigated with a top-down approach reconstructing macroevolutionary patterns of diversification in the past [8] . Phylogenetic relationships of virus and host species can provide valuable information about long-term virus–host coevolution or ancient host-shift events [9] . However , divergent phylogenetic clades can be found within many natural virus species [10–15] . While genetic structuring below the species level could derive from selectively neutral processes like geographic isolation and random genetic drift , different virus clades may also represent functionally distinct entities resulting from differential adaptation in a heterogeneous host environment [16 , 17] . Still , the ecological conditions that promote virus divergence and potentially lead to the emergence of new virus species in nature are generally unclear . Here , we implemented a bottom-up approach to investigate the origin of high genetic diversity in Tula orthohantavirus ( TULV ) and the role of host divergence as a potential driver of virus speciation . TULV ( family Hantaviridae , formerly Bunyaviridae ) is a horizontally transmitted , single-stranded RNA virus with a three-segmented genome of about 12 kilobases [18] . Genetic diversity in TULV is partitioned into several phylogenetic clades with allopatric or parapatric distribution across Europe [12 , 19] . On a large scale , the combined geographic ranges of specific TULV clades appear similar to those of several morphologically cryptic evolutionary lineages in its reservoir host , the common vole ( M . arvalis ) , for which speciation processes started during or after the last glaciation [20–22] . We used a natural hybrid zone between evolutionary lineages in the rodent host to test on a local spatial scale whether TULV diversity was neutral or adaptive and determined the likely targets of host-induced isolation in the TULV genome . Implementing a methodical framework typically used to investigate speciation in sexual organisms [23] , our comprehensive analyses demonstrate speciation processes in a hantavirus most likely initiated by a host shift between prediverged lineages of a single natural host species .
We found a very narrow geographic contact between two major phylogenetic clades of TULV in the hybrid zone between the Central and Eastern evolutionary lineages in the common vole ( Fig 1 ) . Molecular screening of 1 , 309 common voles from 109 locations detected 306 TULV-infected individuals at 65 locations in two replicate sampling transects . Viral sequences of the small genome segment ( S-segment; see Methods ) showed up to 37 . 6% sequence divergence at this local geographic scale . Phylogenetic analyses with reference sequences spanning the known TULV distribution assigned them to the two most recently diverged virus clades ( Fig 2 ) . We termed these TULV clades Central South ( TULV-CEN . S ) and Eastern South ( TULV-EST . S ) given their spatial association with different common vole host lineages and their respective geographic distribution relative to other , more diverged TULV clades in the northern range of the same host lineages ( Fig 2 ) . Sequences from the western parts of both transects ( n = 111 ) were all most closely related with TULV strains found within the geographic distribution of the Central host lineage , whereas sequences from the eastern parts ( n = 195 ) were all most similar to strains associated with the Eastern host lineage ( [12]; Figs 1 and 2 ) . Despite dense geographic sampling , we detected only at two adjacent locations in the Porcelain transect and one in the Bavaria transect TULV sequences from both viral clades ( Fig 1 and S1 Fig ) , and two host individuals from these locations carried genome segments from different TULV clades . The spatial distribution of TULV clades in the common vole hybrid zone was strongly associated with the transition between the Central and Eastern evolutionary host lineages along both transects . Sequencing of the mitochondrial cytochrome b gene ( mitochondrial DNA [mtDNA]; see Methods ) and phylogenetic analyses identified 207 voles with Central lineage mtDNA in the western parts of the transects and 357 individuals with Eastern lineage mtDNA in the eastern parts ( S2 Fig , Fig 3A and 3D ) . Bayesian clustering analyses of autosomal nuclear DNA ( nucDNA; 16 microsatellite loci ) of the host ( n = 1 , 521 ) showed a clinal transition between two major genetic clusters in each transect with extensive admixture in individuals from locations in the core contact region ( S3 Fig ) . Geographic clines demonstrated that the transition between TULV clades was spatially congruent but significantly steeper compared to the transition between evolutionary lineages in their hosts ( Fig 3 , Table 1 ) . The widths of the clines in TULV were estimated as 0 . 3 km ( two log-likelihood support limits: 0 . 0–2 . 0 km ) in the Bavaria transect and 2 . 5 km ( 1 . 1–5 . 1 km ) in the Porcelain transect ( Table 1 ) . Depending on transect and marker type , the widths of the zones of hybridization and admixture between host lineages were estimated between 13 . 2 km ( 8 . 6–19 . 3 km ) and 61 . 1 km ( 44 . 4–89 . 0 km ) , which is eight to 40 times wider than the virus contact ( Fig 3 , Table 1 ) . Sequences from parts of the medium ( M; n = 54 ) and large ( L; n = 60 ) genome segments of TULV sampled in and around the locations where the virus clades were in contact ( Fig 1 ) demonstrated that the divergence between TULV clades in all segments was highly spatially consistent ( S4 and S5 Figs ) . High-throughput sequencing of six TULV-CEN . S and six TULV-EST . S genomes ( three from either side in both transects , see Fig 1; S1 Table ) confirmed strong genome-wide divergence between the clades , with an overall nucleotide diversity of 0 . 144 in coding regions ( Fig 4 ) . The only complete TULV genome publicly available to date ( see Methods ) clustered in phylogenetic analyses consistently with the TULV-EST . S genomes from both transects despite its origin about 250 km to the east ( Figs 1 and 2 ) . Our analyses provided no evidence for recombination between different TULV clades . However , recombination breakpoints located between the M and L TULV genome segments were detected within the TULV-EST . S clade ( Porcelain transect; p = 0 . 0074; Bavaria transect; p = 4 × 10−5; S6 Fig ) . TULV-CEN . S and TULV-EST . S genomes from the contact zone showed fixed differences at 579 nucleotide positions in coding regions ( 11 , 169 nucleotides total ) of which 539 ( 93 . 1% ) were synonymous and only 40 ( 6 . 9% ) altered the amino acid sequence of the encoded protein ( Table 2 and S2 Table ) . This very low ratio of nonsynonymous to synonymous substitutions is consistent with pervasive purifying selection acting overall on these viral genomes . However , statistical tests for selection revealed signals of diversifying selection between TULV clades in specific regions of the M- and L-segments ( S3 Table ) . Branch-site models and mixed-effect models of evolution in the softwares PAML and HYPHY ( see Methods ) identified individual codons under potential positive selection , but only estimates for codon 17 in the M-segment were significant with both methods ( S4 and S5 Tables ) . A sliding-window analysis showed an accumulation of nonsynonymous but not of synonymous differences in the same region of the M-segment encoding the viral envelope glycoprotein , which resulted in a conspicuously high local dN/dS ratio ( Fig 4 ) . Taken together , the signal of diversifying selection on a few TULV amino acid positions suggests that these sites could be responsible for effective ecological isolation of differentially adapted virus clades .
Our local-scale analyses demonstrate a stage in the speciation process of a natural hantavirus in which ecological separation in the virus precedes complete reproductive isolation in the host ( Fig 3 ) . The patterns of evolutionary divergence between TULV clades revealed by the complementary top-down perspective ( Fig 2 ) indicate that functional divergence among TULV-CEN . S and TULV-EST . S was established after a host shift in the secondary contact between previously diverged host lineages . Allopatric divergence of common vole lineages [20–22] has likely provided the evolutionary substrate for adaptive separation and the emergence of ecologically isolated TULV clades . At first sight , this may appear like the result of codivergence between virus clades and host lineages—a pattern once assumed to be typical for the evolution of hantaviruses in general [24–26] . However , despite the respective association of TULV-CEN . S and TULV-EST . S with the Central and Eastern common vole lineages , additional more divergent TULV clades are present in the northern parts of the ranges of the same host lineages ( Fig 2 ) . This is in conflict with a scenario of allopatric virus–host codivergence ( e . g . , in glacial refugia ) under which virus clades in the same host lineage should be closest related [27] . It is therefore more likely that only the postglacial formation of the vole hybrid zone [21 , 22] provided the opportunity for a transmission of the TULV-CEN . S and TULV-EST . S ancestor between host lineages with subsequent specialization . Error prone replication and very large effective population sizes in RNA viruses generally facilitate rapid diversification and adaptation to novel hosts [28] . In heterogeneous host environments , differentially specialized viruses may emerge [6 , 29–31] if the improved adaptation to one ecological resource outweighs the reduced ability to use an alternative resource , representing an evolutionary tradeoff [32] . The secondary contact between two prediverged common vole lineages after postglacial recolonization [20–22] strongly increased local heterogeneity in the TULV host environment . TULV-CEN . S and TULV-EST . S have therefore likely diverged as a direct consequence of diversifying natural selection in the secondary contact zone . The congruence of spatial separation ( Fig 1 ) and sequence divergence across all genome segments ( Figs 2 and 4 , S4 and S5 Figs ) indicates pervasive barriers between TULV-CEN . S and TULV-EST . S despite frequent current gene flow through hybridization among host lineages ( S3 Fig ) . Recombination or genome reassortment has been suggested for TULV and other hantaviruses [33–36] . We found limited evidence for reassortment of TULV genome segments between TULV-EST . S sequences but not in TULV-CEN . S or between clades ( S6 Fig ) . While recombination or genome reassortment may play a role in TULV evolution in general , our data from two replicate transects demonstrate that their impact on the integrity of TULV-CEN . S and TULV-EST . S clades in this virus contact zone is insignificant . Both clades co-occurred in three host populations ( Figs 1 and 3 and S1 Fig ) , and two voles from these populations showed evidence for co-infection and thus opportunity for potential virus reassortment . However , clade affiliation was consistent for all TULV segments from all other individuals ( among these 23 from mixed TULV populations ) including 12 complete genomes ( Fig 1 ) . It remains to be shown whether reassortment is generally impeded between differentially adapted viruses in co-infected hosts , e . g . , by genetic incompatibilities [37] . Overall , the strong and pervasive evolutionary separation between TULV-CEN . S and TULV-EST . S is , in several aspects , consistent with the criteria for complete biological speciation [38 , 39] , despite incomplete reproductive isolation among the host lineages [21] . The comparative analysis of admixture patterns at an ecologically relevant spatial scale also provides clues on the adaptive landscape [e . g . , 40 , 41] of the TULV clades . Historical and ongoing gene flow in the common vole hybrid zone has resulted in a gradual and relatively smooth transition between two divergent TULV host environments ( Fig 3A , 3B , 3D and 3E ) . We see no possibility for extrinsic factors such as landscape connectivity or climatic differences at a geographic scale of a few kilometers to block TULV transmission without blocking dispersal in the host as well . The local frequencies of TULV-CEN . S and TULV-EST . S are therefore informative about their relative fitness in host populations . Very steep transitions between virus clades ( Fig 3C and 3F , Table 1 ) indicate strongly isolated fitness optima such that small overall changes in host allele or genotype frequencies translate to a major shift in the TULV adaptive landscape . Ecological separation between TULV-CEN . S and TULV-EST . S is therefore probably mediated by a matching-allele type of virus–host interaction [42] . Vole populations of intermediate admixture were predominantly infected by the TULV-EST . S clade , shifting the virus contact towards the Central side of the host hybrid zone ( Fig 3 ) . This is consistent with unequal lineage-specific selective effects , e . g . , through genetic dominance of Eastern host lineage alleles , mediating higher general fitness of TULV-EST . S in the heterozygous background of admixed hosts . Adaptive divergence between TULV-CEN . S and TULV-EST . S is apparently driven by a low number of amino acid differences ( Table 2 and S2 Table ) , probably affecting key functions of the virus–host interactions [5 , 6] . TULV is frequently detected as spillover infection in several arvicoline rodent species [12 , 43 , 44] , which demonstrates its ability to infect hosts that are genetically much more diverged than common vole lineages . Adaptive divergence between TULV-CEN . S and TULV-EST . S is therefore mediated by a mechanism affecting virus persistence rather than initial host infection . Higher relative fitness of TULV-CEN . S in the Central host lineage and vice versa has probably resulted in host-induced ecological isolation . The patterns of genomic variation suggest a particular importance of the 5′-terminal coding region of the M-segment for differential host adaptation of TULV-CEN . S and TULV-EST . S ( Fig 4 , Table 2 and S2–S5 Tables ) . The M-segment encodes the viral glycoprotein precursor ( GPC ) and its cotranslational cleavage products , which are crucial for virus–host interactions [45] . The N-terminal part of the GPC represents a signal peptide in other hantaviruses [18] , but the exact GPC processing in TULV has not been investigated . Adaptive variation in regulatory elements can mediate immune escape through interference with the intracellular processing of virus-derived proteins [46] , e . g . , via the major histocompatibility complex ( MHC ) class I antigen presentation pathway [47] . Alternatively , if the signal sequence is not fully cleaved [48] , this region may directly interact with host receptors or components of the immune system as part of the N-terminal ectodomain in the mature viral glycoprotein spike complex [49] .
Here , we demonstrated that the emergence of two highly diverged clades in TULV was likely driven by adaptive differentiation rather than neutral processes mediated by relatively few genetic changes in the viral genome . Similar processes may act during the establishment of genetic structuring and potentially speciation in other viruses , including human pathogenic rabies virus [10] , Lassa arenavirus [11 , 14] , or Puumala orthohantavirus ( PUUV ) [13] , but comparable data from ecologically relevant spatial scales are scarce . Taking potential population structure and movements of hosts in natural systems explicitly into account requires considerable effort [50] but offers the potential to bridge the gap to experimental insights on virus evolution and lead to a more complete understanding of the emergence of novel virus species .
Rodent trapping occurred after ethical evaluation and approval by the Bernese cantonal commission on animal experimentation under permits BE-90/10 and BE-33/14 issued by the cantonal veterinary offices . Common voles were sampled in the border region between Germany and the Czech Republic , where earlier analyses detected a relatively narrow hybrid zone between the evolutionary lineages Central and Eastern in the Bavaria transect [21] . The current study increased the density of sampling in the Bavaria transect considerably and added the completely new and more extensive transect Porcelain approximately 100 km to the north ( Fig 1 ) . Sampling occurred particularly at the center of the hybrid zone at ecologically relevant spatial scales such that distances between localities could be covered by individual voles within a few hours [51–53] , and gene flow patterns suggest frequent dispersal ( Fig 3 ) . The Porcelain transect was 124 km long , covering 65 localities and 1 , 234 common vole individuals , and the Bavaria transect ( 88 km ) contained 27 sampling localities from which 778 voles were obtained . Voles were trapped with snap traps and stored at −20 °C immediately after collection . DNA was extracted from a tissue sample following a standard phenol-chloroform protocol . We used mtDNA and nucDNA markers to determine the spatial distribution of the evolutionary lineages in the vole hosts and assess the admixture status . For mtDNA , we sequenced a part of the cytochrome b gene following [54] for at least three individuals per population if possible , resulting in 253 novel sequences total . Additional mtDNA data for the Bavaria transect was taken from Beysard and Heckel [21] . Phylogenetic analysis was done using MrBayes version 3 . 2 . 6 [55] on the CIPRES platform [56] . Implementing reversible-jump sampling over the entire general time-reversible substitution model space [57] , Metropolis-coupled Markov chain Monte Carlo ( MCMC ) sampling was performed for 107 generations in four independent runs comprising four chains . After discarding a burn-in fraction of 25% , samples were recorded every 103 generations . Host individuals were assigned to evolutionary lineages according to phylogenetic clustering of their mtDNA sequences with reference data as in [58] ( S2 Fig ) . Analogous to Beysard and Heckel [21] and Beysard and colleagues [59] , the structure of the hybrid zone and admixture patterns were inferred with nucDNA data from 16 microsatellite loci [20 , 60 , 61] using Bayesian genetic clustering with Structure version 2 . 3 . 4 [62] . We analyzed a total of 1 , 521 individuals—1 , 015 from the Porcelain transect and 506 from the Bavaria transect separately—implementing the admixture model with correlated allele frequencies [63] in four independent runs of an MCMC search ( 106 generations after 105 burn-in iterations ) for each number of clusters ( K = 1–10 ) . The optimal number of clusters was subsequently determined using the method from Evanno and colleagues [64] implemented in Structure Harvester [65] . In agreement with earlier analyses in the Bavaria transect [21] , this clearly supported two genetic clusters in both transects . Common voles of at least 20 g body weight were screened for TULV infection because younger individuals are typically protected by maternal antibodies [66] . TULV infection was assessed by amplification of 540 nucleotides of the gene encoding the viral nucleocapsid protein on the S-segment using the RT-PCR assay described in [67] . RNA extractions from lung tissue and sequencing of amplified fragments followed Schmidt and colleagues [12] . Phylogenetic analyses of TULV sequences were performed as described above , while evolutionary parameters of combined first + second and third codon positions were estimated independently . Sequences from two PUUV strains ( S7 Table ) served as outgroups , and complete coding sequences ( CDSs ) of all three genome segments were concatenated for the analysis of TULV genomes ( Fig 1 ) . TULV samples were assigned to larger phylogenetic clades with allopatric or parapatric distribution in Europe using sequences from Schmidt and colleagues [12] as references . For samples originating from locations in which TULV-CEN . S and TULV-EST . S clades occurred together and from adjacent populations ( 50 samples from 9 populations total ) , sequences were additionally generated from the M and L genome segments using primers C1 and C2 [43] and HanLF1 and HanLR1 [68] according to their respective protocols . Phylogenetic reconstruction was done as described above . We estimated the maximum genetic divergence between TULV strains sampled in the same population as the largest distance along branches ( sum of branch lengths ) connecting a sequence pair from the same location in a phylogenetic tree ( [19]; S1 Fig ) . Geographic clines were inferred along one-dimensional transect axes crossing the contact zones for mtDNA and nucDNA host markers and TULV S-segment data using the HZAR package [69] . Transect axes were defined as the projection line minimizing the sum of rectangular distances from individual sampling locations to that line . The distance between locations along the cline axis is the distance between projection points . Three cline models with increasing levels of parametrization were implemented: model 1 ( free cline center and width; cline ends fixed to minimum and maximum observed frequency ) , model 2 ( additional free minimum and maximum frequency ) , and model 3 ( additional free parameters for independent exponential tails ) . Likelihood scores of the cline models were compared for each data set , and cline parameters were estimated for the model with highest likelihood performing 105 generations of an MCMC search in three independent chains after 104 burn-in iterations . Cline concordance and coincidence were inferred with a likelihood ratio test [70 , 71] . For mtDNA , nucDNA , and TULV data , the likelihood profile for the parameters’ cline center and width were explored . Model likelihoods were determined for 20 fixed values for cline center and width , respectively . Cline centers were fixed to equal steps between kilometers 20 to 80 and 20 to 60 along the transect axes and cline width to values between 1 to 60 and 1 to 40 km for the Porcelain and Bavaria transect , respectively . The test statistic was calculated as two times the difference in log-likelihood between a null model assuming concordance and the alternative model supporting different cline widths for different markers [71] . Significance was determined based on a χ2 distribution with one degree of freedom equal to the difference in parameters estimated between the alternative model and the null model . Twelve TULV positive samples were chosen for shotgun sequencing of the virus genome based on their origin relative to the TULV contact in each transect . Libraries were prepared from total RNA extracted from lung tissue with no pre-amplification of the viral genome using the Illumina TruSeq kit . Libraries were sequenced on 1 . 5 lanes of an Illumina MiSeq at 2 × 300 bp paired-end and one lane of a HiSeq3000 at 100 bp single-end ( see S1 Table ) . In every sequencing run , libraries from both TULV clades were analyzed together , and sequence reads of the same sample from multiple lanes were combined . Reads were trimmed using default settings in Trimmomatic version 0 . 32 [72] and initially mapped to the only available full TULV genome ( GenBank accession numbers NC005227 S-segment , NC005228 M-segment , NC005226 L-segment ) using the mem algorithm of BWA version 0 . 7 . 15 [73] with parameters–B 3 –k 14 . The MarkDuplicates function using default settings and AddOrReplaceGroups of Picard version 1 . 99 ( http://broadinstitute . github . io/picard ) were implemented to remove duplicates , and BAM files were subject to local realignment using GATK version 3 . 7 . 0 [74] . Genotypes were called with GATK’s UnifiedGenotyper and the following parameters:—output_mode EMIT_ALL_SITES—min_base_quality_score 10—standard_min_confidence_threshold_for_calling 10 . FastaAlternateReferenceMaker from GATK was used to generate individual consensus sequences for every genome assembly . Once a first consensus sequence was obtained , raw sequencing reads from the individual samples were mapped back to their own consensus sequence using BWA’s mem with more stringent parameters–B 4 –k 19 . For samples that yielded multiple contigs per genome segment in the first assembly , the consensus sequence of another sample from the same transect and of the same TULV clade was used to fill a few remaining gaps between contigs . A second consensus sequence was then generated as described above . Sanger sequences from the partial S-segment were used to confirm consensus sequences indicating a mismatch every 4 . 63 × 10−4 nucleotides on average . Mapping statistics were collected implementing samtools version 1 . 3 . 1 [75] stats command , and coverage was inferred with DepthOfCoverage from GATK . Details on assembly statistics and sequence coverage are given in S1 Table . We checked for a potential TULV clade-specific assembly bias by performing additional reference-guided assemblies for every sample using consensus sequences from both transect ends as references . Genome sequences from both assemblies were identical , providing no evidence for a reference-induced bias . Gene CDSs from all three genome segments were concatenated for further analyses . Multiple sequence alignments were performed in BioEdit [76] using CLUSTAL W [77] . Phylogenetic reconstruction was done in MrBayes as described above using PUUV as outgroup . DnaSP version 5 [78] was used to estimate genome-wide nucleotide sequence diversity and the number of polymorphic sites and to perform sliding-window analyses implementing the Jukes and Cantor correction [79] . A full exploratory recombination scan was performed on concatenated gene CDSs of all three genome segments in RDP4 [80] implementing the RDP , GENECOV , and MaxChi methods . Recombination events detected in the initial scan were re-analyzed using all available methods , and p-values were taken from MaxChi . We used the codeml program from the PAML package version 4 . 8 [81] to test for signals of selection implementing the branch-site model [82] and clade model C [83] . Model fitting was based on tree topologies inferred using RAxML version 8 . 2 . 10 [84] on the CIPRES platform [56] . Model likelihoods were compared to a null hypothesis using a likelihood ratio test with a χ2 distribution . Comparisons between random sites models were used to test for positively selected codons ( M2a versus M1a , and M8 versus M7 and M8a ) ( S6 Table ) . Sites under positive selection were identified by Bayes’ Empirical Bayes analysis implemented in PAML . To incorporate rate variation at synonymous sites , we also investigated positive selection using the FUBAR [85] and MEME [86] methods in HYPHY [87] on the Datamonkey webserver [88] . Sites with posterior probabilities > 0 . 8 or p < 0 . 1 were deemed to be positively selected .
|
Natural biodiversity is driven by stochastic processes and evolutionary adaptation to ecological niches . In viruses , adaptation to specific hosts may cause diversification and eventually lead to the emergence of novel viruses . Here , we studied diversity in Tula orthohantavirus ( TULV ) in relation to evolutionary divergence in its natural rodent host , the European common vole ( Microtus arvalis ) . In a geographical region in which two distinct evolutionary lineages in the common vole interact and interbreed ( a hybrid zone ) , we found two substantially different TULV clades . Phylogenetic analyses revealed that the divergence among virus clades was likely triggered by a shift of an ancestral virus between the previously diverged host lineages in the hybrid zone . The strong association between virus clades and host lineages at a fine geographical scale results in effective separation of TULVs , despite incomplete reproductive isolation and frequent gene flow among local host populations . Virus genome sequences pointed to the amino-terminal part of the envelope protein as an important region for functional differentiation among these virus clades .
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2019
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Secondary contact between diverged host lineages entails ecological speciation in a European hantavirus
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One of the major goals of comparative genomics is to understand the evolutionary history of each nucleotide in the human genome sequence , and the degree to which it is under selective pressure . Ascertainment of selective constraint at nucleotide resolution is particularly important for predicting the functional significance of human genetic variation and for analyzing the sequence substructure of cis-regulatory sequences and other functional elements . Current methods for analysis of sequence conservation are focused on delineation of conserved regions comprising tens or even hundreds of consecutive nucleotides . We therefore developed a novel computational approach designed specifically for scoring evolutionary conservation at individual base-pair resolution . Our approach estimates the rate at which each nucleotide position is evolving , computes the probability of neutrality given this rate estimate , and summarizes the result in a Sequence CONservation Evaluation ( SCONE ) score . We computed SCONE scores in a continuous fashion across 1% of the human genome for which high-quality sequence information from up to 23 genomes are available . We show that SCONE scores are clearly correlated with the allele frequency of human polymorphisms in both coding and noncoding regions . We find that the majority of noncoding conserved nucleotides lie outside of longer conserved elements predicted by other conservation analyses , and are experiencing ongoing selection in modern humans as evident from the allele frequency spectrum of human polymorphism . We also applied SCONE to analyze the distribution of conserved nucleotides within functional regions . These regions are markedly enriched in individually conserved positions and short ( <15 bp ) conserved “chunks . ” Our results collectively suggest that the majority of functionally important noncoding conserved positions are highly fragmented and reside outside of canonically defined long conserved noncoding sequences . A small subset of these fragmented positions may be identified with high confidence .
Comparative sequence analysis has had a major impact on molecular biology and genetics . Comparison of the sequences of protein-coding genes between multiple species has enabled prediction of gene function [1] , identification of protein domains [2] , prediction of functional amino acid residues [3 , 4] , and detection of signals of natural selection at the level of whole genes [5] and individual codons [6 , 7] . Inferring non-neutral sequence elements in the human genome is of considerable interest even without a specific a priori hypothesis concerning their possible functional role ( s ) . On a general level , for example , sequence conservation may considerably inform human genetic studies seeking to identify allelic variants associated with disease phenotypes , particularly in noncoding regions [8] . The effect of human SNPs at the level of molecular function and phenotype depends on the importance of the individual nucleotide position , whereas the information of the sequence region as a whole is not necessarily relevant . For example , about half of human SNPs within protein coding genes are represented by synonymous variants , which are likely to be of limited importance , even though they are embedded within highly conserved exonic sequences . In addition , a subset of individual nucleotides conserved in four mammalian genomes were shown to be under selective pressure [9] . A position-specific measure of selective constraint is therefore highly suitable for analysis of positions that are polymorphic within the human population . Several algorithms have been developed for detection and scoring of sequence conservation in the context of a multispecies sequence alignment . However , to date these approaches have been applied almost exclusively to detect discrete regions with elevated average sequence conservation that typically extend for up to hundreds of contiguous bases [10–14] . Such regions encompass canonical coding exons , as well as so-called “conserved noncoding sequences” that presumably result from purifying selection , and are thereby indicative of functional importance [15 , 16] . Recently , comparative genomic sequence of unprecedented depth has been generated by sequencing of multiple mammalian and other vertebrate genomes orthologous to 1% of the human genome defined by the ENCODE regions [17 , 18] . Several alignment techniques have been applied to construct multiple sequence alignments within ENCODE regions [18] . These alignments have in turn been subjected to analysis with existing sequence conservation detection algorithms , including phastCons[10] , GERP [11] , and BinCons [13] . The conserved regions identified by these analyses show statistically significant overlap with experimentally identified coding and noncoding functional elements . However , the majority of experimentally characterized noncoding functional elements fall outside of currently delineated conserved regions , and , conversely , most conserved regions were located outside of experimentally detected elements [18] . The fact that many functional elements reside in noncoding regions that do not exhibit uniformly high conservation is perhaps not surprising given that binding sites for transcriptional factors that mediate many biological processes are quite plastic evolutionarily [19] . Conversely , many individual nucleotides located outside of well-defined conserved regions exhibit sequence conservation across multiple species . Such conservation may be due to mere chance or , for a certain fraction of these nucleotides , may reflect their importance for fitness and hence function . The aforementioned observations emphasize the need for higher resolution methods for analysis of evolutionary conservation within functional elements and generally across the genome . Here we develop an approach for analyzing sequence conservation at the individual base-pair level , with an aim toward correlating conservation with human genetic variation and with functional genomic annotations . We present a new probabilistic conservation score , SCONE ( Sequence Conservation Evaluation ) . SCONE provides conservation scores for individual nucleotide positions , and can be applied to predict continuous sequence regions with an elevated level of conservation . We apply SCONE to the study of annotated functional elements and human sequence polymorphism . We focus on the statistical distribution of position-specific conservation scores rather than on the bulk overlap between conserved regions and functional features . It is clear from the outset that the power to detect conservation at the single base-pair resolution is limited , even when comparing multiple species [20] . We surmount this obstacle by deriving considerable statistical power from combined analysis of numerous individual nucleotide positions from many genomic regions . While this analysis does not allow us to detect individual functional positions accurately , we can show that , collectively , a subset of noncontiguous individual positions are important . A key advantage of the analysis of the distribution of position-specific scores is that it is unbiased with respect to the pattern of conservation along a given sequence region . SCONE thus has the potential to analyze putative functional elements in which the conservation signal is not homogeneous or manifested by exon-like contiguous conserved stretches . We report herein on the relationship between sequence conservation , functional sequence elements , and human allelic variation , as revealed by single-nucleotide conservation analysis .
Analysis of population sequence polymorphism is an effective and widely used tool for detecting the influence of ongoing or recent selection . Sites experiencing purifying selection will tend to show a decrease in the density of polymorphism and average heterozygosity , as well as a shift in allele frequencies toward more rare derived ( nonancestral ) alleles . We hypothesized that sites adjudged to be constrained by SCONE would evince ongoing purifying selection , which should in turn affect the distribution of population polymorphism . Differences in allele frequency distributions between positions under strong constraint and unconstrained positions , both inside and outside of contiguous conserved sequence regions , would thus indicate the functional significance of those positions . We employed the most comprehensive SNP dataset available for these regions , produced by the International Haplotype Map project [22] . The HapMap project resequenced ten 500 kb ENCODE regions ( total 5 Mb ) in 48 unrelated individuals from four separate population sets ( Yoruba , Han Chinese , Japanese , and CEPH ) . Subsequent genotyping was performed in each population on the basis of this SNP discovery and SNPs in the dbSNP database; this is likely to introduce biases toward frequent SNPs ( i . e . , those most likely to be shared between populations ) and artificially reduce the apparent fraction of rare SNPs . We chose to rely on the parental subset of the Yoruba population from Ibadan , Nigeria ( YRI ) , for our SNP analysis , which , after filtering out SNPs in CpG positions , included a total of 13 , 490 SNPs in ten ENCODE regions . In all of our analysis , we ignored SNPs within coding regions , since selective effects on coding SNPs are comparatively well-studied . We detect a significant difference ( p < 0 . 0004 , Fisher exact test ) in the fraction of rare derived alleles ( Figure 2 ) between conserved ( SCONE p-value < 0 . 005 , Fisher exact test ) and nonconserved noncoding positions . The higher fraction of rare derived alleles in conserved ( slowly evolving ) positions indicates that these positions are experiencing purifying selection . Because allele frequency distributions are unaffected by mutation rate heterogeneity , our results suggest that this effect is due to sites that are evolving slowly due to selection rather than merely due to chance . For comparison , we examined the allele frequency distribution in noncoding conserved sequence regions , using the ENCODE multispecies conserved sequence ( MCS ) element set to define contiguous conserved elements . These were defined on the basis of agreement between at least two out of three regional conservation scores ( phastCons , BinCons , and GERP ) that identify regions of sequence with elevated average conservation . The shift in allele frequency distributions is stronger for SCONE-conserved positions than it is for MCS elements ( p < 0 . 05 , Fisher exact test ) , suggesting that these positions are either enriched for functional positions compared to MCS elements , or are on average under stronger selection . We employed a simple model of evolution that assumes constant population size and no demographic events to estimate the average heterozygous selection coefficient ( s ) for functional noncoding positions that best explains the observed shift in allele frequencies between SCONE conserved and nonconserved positions . We assumed , based on the false discovery rate in these positions ( see Methods ) , that 61 . 6% of SCONE conserved positions are functional . We estimate s in the range of 10−4–10−3 . An excess of low-frequency alleles in conserved regions was reported in several earlier studies [23–25] . The main question pertinent to the analysis of position-specific conservation is whether the majority of deleterious alleles within a population reside in conserved regions , or whether individually conserved positions not incorporated into longer conserved elements are also under purifying selection . To address this question , we examined the distribution of allele frequencies in positions outside of MCS elements . After partitioning these positions according to their SCONE rate estimates ( as above ) , we were able to detect a significant difference ( p < 0 . 009 ) in rare derived allele frequency between high- and low-scoring positions . This strong shift may be an indication that a significant subset of functional positions lie outside of MCS elements [9] , and that a greater portion of functional positions may be identifiable via the contribution of position-specific analysis than can be found through the identification of conserved elements alone . This suggests that a search for phenotypically important human genetic variation should not be limited to conserved regions , and information on the conservation level of individual base pairs is of importance for prioritizing SNPs in studies of genetics of specific human phenotypes . Population genetic analysis indicates that a significant fraction of functional positions lies outside MCS elements . It is natural to seek confirmation of this fact by inquiring whether these positions coincide with identifiable regulatory and other functional elements , and whether we may observe a similar distribution of conserved positions and MCS elements with regard to annotated functional regions . In addition to a highly accurate annotation of protein coding genes , the ENCODE project has produced large-scale identification of transcribed regions , a composite of putative sequence-specific binding sites , and regions with significantly increased histone modification ( EIGRs ) likely to be involved in transcription regulation , and DNase I Hypersensitive sites ( DHSs ) , which are heavily validated markers of human cis-regulatory sequences [18] . A subset of transcribed regions identified by hybridization of RNA to tiling genomic DNA microarrays consist of nonprotein coding transcribed regions of unclear functional significance . Analysis of conservation may therefore potentially support functionality of such regions , or may enlighten the current debate concerning background transcription in the human and other large genomes [26] . Promoters represent a specific type of regulatory element , whereas EIGR and DHS tracks are more generic in nature and may therefore encompass a spectrum of functional elements with differing regulatory roles . We therefore analyzed the distribution of SCONE p-values in all of the above types of functional elements , as well as CpG islands , a well-known sequence-based marker of functional elements . We found all categories of functional elements to exhibit statistically significant deviation from the uniform neutral distribution . The deviation of all classes of functional regions from the neutral distribution was explained almost entirely by a subset of positions with low SCONE p-values . The excess of conserved individual positions in functional regions compared to putatively neutral regions ( ancestral repeats ) is shown in Figure 3 . In many classes of functional elements , much of the signal of conservation falls outside of MCS elements . With the exception of coding sequences and 3′ UTRs , a majority of individually conserved positions do not fall within MCSs . The pattern of conservation differs markedly across different functional features; even a high overall level of conservation ( as in CpG islands ) does not imply that these putatively functional positions can be easily grouped into functional elements . The true proportion of functional positions may thus be underrepresented by requiring them to be grouped into windows . Transcribed regions and EIGRs show similar fractions of conserved positions . DHSs , although also significantly enriched in conserved positions , are least conserved compared to all other functional elements . 5′ UTRs and CpG Islands display the highest level of conservation among putative regulatory regions , although generally sequence regions proximal to genes are more conserved . What fraction of ENCODE regions are functional ? If conservation is interpreted as a signal of selective constraint , SCONE may be employed to identify the number of functional positions in a genomic region ( that is , a set of sites , not necessarily contiguous ) . The false discovery rate at a particular p-value threshold estimates the fraction of sites with p-values below that threshold that must be functional in order to explain the deviation from the expected neutral distribution . By relaxing the p-value threshold sufficiently , nearly all conserved functional sites may be included . Based on the distribution of SCONE p-values and the false discovery rate , we estimate that between 5 . 5% and 11% of positions in ENCODE regions are conserved because of function . However , Figure 3 should not be taken as representative for the human genome as a whole , since ENCODE regions have a twice higher fraction of coding positions compared to the genome-wide average . In addition , stochastic variation in the mutation process and sampling variance inevitably introduce noise into the distribution , reducing the accuracy of the estimate . A substantial fraction of conserved positions lies outside of MCS elements ( 65% at a SCONE p-value threshold for conservation <0 . 025 , where the fraction of all ENCODE positions called conserved according to SCONE p-value or membership in MCS elements is approximately equal ) . Are these positions randomly distributed , or do they exhibit clustering , as we might expect if this observed sequence conservation is due to binding sites or other short functional elements ? To evaluate the extent of clustering of conserved nucleotides , we identified all conserved islands ( see Methods ) , using a p-value threshold of T = 0 . 05 . These represent optimally bounded islands of conservation whose overall conservation cannot be increased by expanding them in either direction . We restricted our analysis to short conserved islands , with lengths ranging from five to 12 bases . These islands are heavily overrepresented in functional sequences , with the majority found in and around coding sequences or promoter regions ( Figure 4A ) . We compared the density of short conserved islands ( number of clusters/total number of positions ) in functionally annotated regions to the density in putatively neutral ancestral repeat regions . As seen from Figure 4B , all functionally annotated regions show considerable over-representation of conserved islands with lengths ranging from five to 12 bases when compared to ancestral repeat regions . To verify that this is not simply due to a simple excess of conserved positions and is indeed due to significant clustering of conserved bases , we randomly shuffled SCONE p-value scores across all DHS sites and compared the proportion of short conserved islands predicted in these “shuffled” DHS positions; we find a 5-fold excess in density in unshuffled DHS sites , suggesting that the overrepresentation in functional regions is due to clustering . Stretches of two or three conserved bases do not show any over-representation in comparison to the shuffled sequence ( unpublished data ) . In agreement with the results of the analysis of human genetic variation described above , over-representation of short stretches of conserved positions in functionally annotated regions suggests the functional importance of these short islands . Can SCONE scores be used to identify isolated individual functional noncoding positions ? In general , the rate of false discovery is too high across the study regions to allow ascertainment of such positions . However , in a subset of sites with either a higher expected mutation rate or greater sequence coverage , this may be possible . We chose a more stringent p-value threshold ( p < 0 . 001 ) ; at this threshold , the computed false discovery rate in noncoding , non-MCS regions was 39% , meaning 61% of these positions are putatively functional . Based on the observation of enrichment of short conserved sequences , we looked for clusters of three non-MCS noncoding positions , each with a SCONE p-value < 0 . 001 , that fell within a 10 bp window . We identified 5 , 562 such clusters in the study regions . A majority of them ( 80% ) lie in CpG islands , as expected since conserved positions there will tend to have greater significance as determined by SCONE , due to their much higher mutation rate . These clusters are highly enriched amongst annotated regions such as DHS sites , showing a 520-fold increase in density compared to ancestral repeat regions and an 88-fold increase compared to regions lacking an annotation ( that is , excluding exons , CpG islands , transcribed sequences , and DHS sites ) . Shuffling positions within DHS sites also greatly reduces enrichment ( 44 clusters identified within shuffled DHS positions compared to 1 , 079 within unshuffled positions ) . This enrichment is weaker , but still persists , even if we apply more stringent standards , restricting ourselves to positions with p < 0 . 001 that are at least 50 bp from the nearest MCS element or CpG island; clusters identified using these thresholds still show a 59-fold increase in density within DHS sites compared to AR regions , and a 10-fold increase compared to unannotated regions . Although further validation of these positions is difficult , the strong degree of enrichment in annotated regions suggests that these positions are highly likely to be conserved due to function .
Detailed knowledge of the structure of coding sequences makes them much more tractable to conservation analysis . The genetic code , by itself , imposes significant constraints on such sequences and provides us with a framework by which we may better understand them . A number of methods have been developed that exploit this knowledge to better predict functional and selective constraints on coding positions [5–7] . In coding regions , the functional significance of a given position is highly contingent upon the surrounding bases , since a protein , to some extent , behaves as a single coherent functional , and thus evolutionary , unit . The constraints imposed by this contingency means the influence of purifying selection on a site will be much easier to trace through its evolutionary history , since it is anchored by other sites that are similarly constrained . Finally , the existence of the genetic code dictates that the evolution of coding sequences is based almost wholly on their informational content . In noncoding sequences , however , this situation does not persist . Few noncoding elements are as well-characterized in terms of structure and function as coding sequences are , but undoubtedly many elements will show markedly different patterns of evolution from what we have come to expect in coding sequences . These differences suggest that paradigmatic assumptions about conservation cannot be carried over from coding sequences , and conservation analysis in noncoding regions must proceed on fundamentally different grounds . In noncoding sequences , we might expect to find elements that , though highly functional , lack the same tight relationship between relative position and function . If , for example , the sequence composition bias of a region has a strong functional significance , the region might evolve to conserve the overall necessary property while showing very little constraint at individual sites . Structural properties of the sequence may show as much or more influence than informational content . Such properties may not depend on strict sequence conservation , and the evolutionary retention of such properties would therefore be invisible to traditional sequence conservation analysis . Functional noncoding elements that show strong constraint might only be short , partially degenerate words only a handful of base pairs in length . Control of gene expression , for example , may depend on brief , influential segments ( e . g . , transcription factor binding sites ) interspersed by relatively unimportant noise . Without the benefit of long contiguous stretches of functional positions to indicate important elements , identification of such words amidst the general rubble is considerably harder . Our method attempts to take into account some of the above difficulties . SCONE is not based on any model of pattern of conservation along the sequence and is focused instead on individual nucleotide positions . Conservation of a specific nucleotide position does not directly depend on conservation of its neighbors . Along the same lines , when we score sequence regions , we use a purely additive scoring scheme . Additionally , SCONE optionally incorporates insertions and deletions . The importance of insertions and deletions for the analysis of conservation is probably greater in noncoding sequences than in genes , where most insertions and deletions will lead to frame-shifting mutations and thus are extremely rare . Our analysis has revealed that many individually conserved nucleotides not embedded in conserved elements are of functional significance , as evident from analysis of the allele frequency distributions of human SNPs within these positions and analysis of individually conserved positions within functional features . These results suggest that future efforts at identifying functional positions in noncoding regions via analysis of conservation would benefit from methods that are hypothesis-free with respect to the distribution of constrained positions .
A number of well-known parametric models for nucleotide substitutions exist [29] . Most of these do not consider the effect of sequence context on mutation rate , which can have profound effects on a site-specific measure of conservation . However , several recent studies derived context-dependent multiparametric models from deep mammalian phylogenies using diverse computational strategies [30–32] . An alternative strategy is to limit the analysis to very close genomes , where multiple substitutions per site are extremely rare and fully general mutation models can be derived using simple counting methods . Limiting the analysis to very close genomes avoids the complex problem of estimation of a very large number of parameters which arises in maximum likelihood or Bayesian methods which use deep phylogenies . We employed human–chimpanzee comparison to infer mutation rates . The substitution rate between human and chimpanzee is extremely low [33] , so that the chance of observing double substitutions in the lineage at a particular site is negligible . At the same time , there is almost no incidence of shared polymorphism [33 , 34] . In the absence of multiple substitutions , transition matrix for nucleotide substitutions can be estimated by a simple counting approach . The ancestral state and the directionality of substitutions can be inferred by using baboon as an outgroup . As the divergence time between baboon and human/chimp is much greater , the probability of double substitutions is higher in this lineage . It is therefore necessary to correct for the occurrence of nonparsimonious situations . To do so , we used a first-order model of dependence on neighboring positions in order to capture all context-dependent effects on mutation rate; a higher-order model would have been both unnecessary ( unpublished data ) and more computationally intensive . We made alignments of human , chimpanzee , and baboon sequence taken from ENCODE regions using the multiple sequence aligner TBA . The frequency of all trinucleotide triples within background positions in the aligned sequences was counted ( ignoring any triples containing gaps ) . In order to correct for nonparsimonious situations , trinucleotide substitution rates in the human lineage , p ( A → B ) , were computed in a manner similar to Jordan et al . [35] . This allows us to build a Markov transition rate matrix Q , where Q ( i , j ) = p ( i → j ) , i ≠ j , and . If we operate under the assumption that the human lineage after divergence from chimp is short enough that multiple mutation events are unlikely at a single site , this transition rate matrix , if scaled to a unit time , will be identical to the instantaneous rate matrix for the human lineage . Under a standard continuous-time Markov model , we may compute a transition rate matrix P for an arbitrary time t according to the matrix exponential . We compute the matrix exponential using the Padé approximation [36] . Our estimate compares well against other previous estimates of context-dependent mutation rates [30–32] . Direct comparison shows our matrix is extremely similar ( R2 = 0 . 96 ) to the matrix produced by Siepel and Haussler , while a matrix produced without context dependency , estimated via PAML [37] on the same data using the HKY85 model [38] , fares much worse ( R2 = 0 . 25 ) . SCONE optionally allows inclusion of a model of insertion/deletion mutations in assessing conservation . Indel rates were estimated using a procedure similar to that for mutation rates . Four classes of insertions and deletions ( of size 1 , 2 , 3 , or greater than 3 ) were considered . Human/chimp/baboon alignments were used to infer the total number of insertion and deletion events of each class in the human lineage for the entire sample ( with baboon as the outgroup to determine the ancestral state ) , as well as the number of ancestral bases , Na . The frequency of an insertion of size k in a window of size n is ins ( k ) *n / Na , where ins ( k ) is the number of insertions of size k observed in the sample; the probability of seeing a deletion of size k at a single site is ( k + n − 1 ) *del ( k ) / Na , where del ( k ) is the number of deletions of size k observed in the sample . Indel events are comparatively rare , more than an order of magnitude less frequent than substitutions ( Cooper et al . , 2004 ) . Thus , we assumed the absence of double-hit events on individual branches of the mammalian tree . The probability of an indel event therefore scales linearly according to time . Although this is not ideal , it avoids the considerable computational complications imposed by considering convolutions of indels . SCONE requires as input a rooted phylogenetic tree for all species in an alignment intended to be scored , with branch lengths denoted in substitutions per site . The algorithm proceeds in two phases: first , it computes an estimate of the rate of evolution of the site based on the observed alignment columns; then it computes a p-value for the rate score . The rate estimate is computed as follows . Consider an alignment of N sequences and a column i of the alignment . Let s ( i , n ) be the “state” of the n-th sequence in the i-th column , where possible states are S = {A , T , G , C , 1 , 2 , 3 , 4} , either nucleotide bases ( A , T , G , C ) , or the size of an overlapping gap ( 1 , 2 , 3 , 4+ ) . Then c ( i , n ) = ( s ( i − 1 , n ) , s ( i , n ) , s ( i + 1 , n ) ) describes the sequence context of the i-th column in the n-th sequence . To compute the rate , we begin by labeling the leaves of a phylogenetic tree Ψ with the sequence state c ( i , n ) for all n ∈ N sequences ( species ) in the alignment . We define the transition probability between two states a = ( Ai−1 , Ai , Ai+1 ) , b = ( Bi−1 , Bi , Bi+1 ) as p ( a , b , t ) = subst ( a , b , t ) *indel ( a , b , t ) , where subst ( a , b , t ) = Pt ( a , b ) and indel ( a , b , t ) is the probability of the specific insertion or deletion event between a and b as derived from our gap model above ( 1 if we are ignoring gaps when computing rate ) for time t . We further define a conditional transition probability between two states a , b as: or , alternatively , if Ψ is a branch of length t in Ψ between nodes with states a and b , p′ ( Ψ ) = p′ ( a , b , t ) . Using this conditional probability , we compute the likelihood , recursively: where π ( a ) is the probability of seeing state a and for a node n with children n1 , n2: The state ( Ni−1 , N , Ni+1 ) of node n is taken from the sequence , if n is a leaf; if n is an internal node , the values of Ni−1 and Ni+1 are taken from the most parsimonious labeling of the tree at positions i−1 and i+1 . Iteration thus proceeds over all possible states only for N , while Ni−1 and Ni+1 are fixed . L is the likelihood of the observed state under the assumption of neutrality . We introduce the factor ω to represent the rate of evolution of the site; this represents a scaling of the entire tree , not the individual branches of the tree . That is , in a site evolving at rate ω , all branch lengths in the given ( neutral ) tree are uniformly scaled by the factor ω . If ω < 1 , the site is evolving slower than expected by neutrality , etc . The transition probability p′ between states a and b for a site evolving at rate ω is p′ ( a , b , ωt ) , and similarly for L , e . g . , : Note that ω is independent of the mutation rate at the site . We may take two routes from this point . The simplest is to estimate ω by finding the value that maximizes L ( by , for example , golden section search ) . The rate of evolution is the best possible discriminator we can use to distinguish between sites . However , our ability to accurately estimate ω depends on the mutation rate and depth of the tree at the site . For sites with very low mutation rate or very little sequence coverage , maximum-likelihood estimates of ω will be inaccurate due to sampling errors . A Bayesian estimate of ω may be more appropriate . A number of Bayesian estimators and choice of prior distribution are available . SCONE allows the choice of multiple prior distributions , including a Gamma distribution and a log-normal distribution ( or an arbitrary user-specified distribution ) . In the analysis presented here , rather than exploring these options exhaustively , we opted for simplicity and selected a relatively conservative estimator , ω , at the median value of the posterior probability distribution , and a simple prior , a uniform distribution [0 , 1] . SCONE allows either the Bayes estimate of ω or the maximum-likelihood estimate of ω to be reported . This rate estimate is called the “SCONE score” for the site . Exact computation of a p-value for the above rate estimate is theoretically possible; however , enumerating across the space of all possible leaf states increases in computational cost exponentially as the number of species in the tree grows , and is usually prohibitively expensive . This computation is also apparently intractable to dynamic programming methods . To avoid such costs , we approximate p-values via Monte Carlo simulation , which allows an arbitrary degree of precision . In practice , we usually perform 104 iterations . For present purposes , this level of error is sufficient . In each iteration , we perform a forward simulation of neutral evolution at a site . We first label the root of the phylogenetic tree with a random state a = ( i , j , k ) , i , j , k ∈ {A , T , G , C} . Next , each daughter node b is labeled by random sampling according to the transition probability p ( a , b , t ) , where t is the length of the intervening branch . This process is repeated until the entire tree is labeled . Special dispensations must be made for insertions , which propagate toward the root of the tree , symbolically . Therefore , insertion events are computed independently after the initial labeling , then propagated along the tree . Subsequently , the rate estimate is computed for the given labeling of the tree . Sufficient iteration allows us to determine the approximate shape of the distribution of rates for the given phylogenetic tree . We may then approximate a p-value for a given rate estimate as the fraction of rates in the Monte Carlo sample that exceed the given rate . Iteration is performed once for each tree topology encountered—the resulting distribution is then memorized and used to compute p-values for subsequent positions with the same topology . We estimate the false discovery rate for a particular p-value threshold p as follows . Under a simple model of evolution , we assume that some fraction N of all sites under consideration are evolving neutrally , while the remainder 1−N are functional . Furthermore , some fraction C of all sites will be scored by SCONE with a p-value below p . Because SCONE scores are uniformly distributed in neutral sites , the fraction of neutral positions with a p-value below p will be simply p . The fraction of functional positions with a p-value below p is the unknown true-positive rate t . Therefore C = pN + t ( 1−N ) . And N = ( C−t ) / ( p−t ) . We may observe some further constraints . If C < p , then N > 1 , and so C ≥ p . Also , if t < C , then N < 0; therefore t ≥ C . Under these constraints , the false discovery rate pN/C has a maximum when t = 1 . To estimate the minimal proportion of functional positions , we may conservatively assume that t = 1 . The false discovery rate is thus conservatively given by: where p and C are known quantities . From this we may estimate both the false discovery rate and the proportion of functional sites in a genomic region . In any multiple sequence alignment of a given syntenic region , some species may show incomplete coverage , whether due to incomplete sequencing , alignment failure due to divergence , or differences in the evolutionary history of the site . We deal with missing information in several ways . If sequence information from certain species is missing from the alignment for whatever reason at a given site , then those species are pruned from the phylogenetic tree and SCONE scores are computed using the pruned tree for the site . If the tree has an empty root ( that is , the site represents a recent insertion in the lineage of the reference sequence ) , scores are computed using the subset of the tree containing the insertion ( as it makes little sense to track the evolution of a nonexistent site in the full tree ) . A major potential source of inaccuracies in the SCONE model is variation in mutation rate . Heterogeneity in mutation rate is well-documented [39] and is believed to vary at a scale somewhere between 1 Mb and 15 Mb [28] . As neutral divergence depends only on mutation rate [29] , such heterogeneities are a potential source of model failure . Regions with lower mutation rate will tend to have an excess of apparently conserved positions , while regions with higher mutation rate will be depleted in conserved positions . We quantified the deviation in SCONE p-value distributions for each sequence region as the mean p-value for that region ( the expected mean for a [0 , 1] uniform distribution is exactly 0 . 5 ) . We estimated mutation rate in non-CpG neutral positions for each region by counting the number of substitutions or indels per site between human and chimpanzee . According to these simple measures , deviation and mutation rate are significantly correlated ( Figure S1C , R2 = 0 . 26 ) . Some fraction of the deviation of SCONE p-value distributions from uniform expectation may thus be explained as the result of mutation rate variation between genomic regions . It is possible to partially correct for mutation rate effects simply by scaling the length of the phylogenetic tree according to local rate variation . Estimates of local mutation rates should be made using nucleotide divergence , which may lead to artificial correlations between mutation rate and sequence conservation , due to the influence of purifying selection on divergence rates . Though this correlation may be mitigated by making mutation rate estimates based on putatively neutrally evolving regions over a megabase scale , such estimates would require the input of external genomic annotations into SCONE . These corrections may be externally applied as seen fit; we have elected to avoid such heuristic corrections within our model . Future developments in the understanding of the causes of mutation rate heterogeneity will hopefully allow the construction of more precise mutation models . A number of methods already exist that make use of multiple sequence alignments to score conservation , including several single-position scores [11 , 13 , 14] . SCONE is similar to previous single-position scores , in that it relies on a given phylogenetic tree and compares expected versus observed rates of evolution . Like GERP [11] and the parsimony-based p-value score available as part of the FootPrinter software package[13] ( and unlike the phylogenetic shadowing method of Boffelli et al . ) , SCONE has no model for functional sequences , and instead is based on a null model of neutrality . Although SCONE may be run using the simple parsimonious substitution-counting method employed by Margulies et al . , its intended mode relies on estimation of the rate of evolution of a site . SCONE differs from GERP and phylogenetic shadowing in that it computes a p-value for each position , in addition to making an estimate of the rate of evolution of the site . SCONE is the only method of scoring-sequence conservation that makes use of a context-dependent model of mutation . We compared SCONE against several scores that were employed in generating the ENCODE MCS elements set ( viz . , phastCons [10] , BinCons , and GERP ) . BinCons is a truly regional measure of sequence conservation , i . e . , it scores conservation of sequence regions rather than individual nucleotides . PhastCons assigns scores in the form of Bayesian posterior probabilities to individual positions . However , these scores depend on the overall level of regional conservation . GERP scores conservation of individual nucleotide positions independently . We examined the utility of various conservation metrics in distinguishing between functional and neutral positions . We employed nondegenerate coding positions as the best available model of functional positions ( although we are more broadly interested in applying SCONE to analysis of noncoding sites ) . Figure S2 represents ROC curves for all scores in terms of their ability to discriminate nondegenerate positions from ancestral repeat positions ( Figure S2A ) or 4-fold degenerate positions ( Figure S2B ) . From the ROC curves for each conservation score ( Figure S2A ) , it is evident that regional measures of conservation , such as phastCons or BinCons , are able to return a more efficient partition of functional and nonfunctional sites than position-specific measures ( GERP and SCONE ) , as regional scores rely on the interdependence of conservation amongst neighboring positions in functional elements . However , a similar comparison between positions showing heterogeneous conservation , using 4-fold degenerate coding positions as the “neutral” dataset ( Figure S2B ) , paints a very different picture . Region-based conservation scores are less suited to distinguishing between contiguous conserved and nonconserved positions . In comparing position-specific scores , SCONE is much more efficient at distinguishing nondegenerate coding and AR positions . This suggests that the context-dependent model employed by SCONE improves the identification of conserved positions . However , GERP more successfully discriminates nondegenerate coding positions from 4-fold degenerate coding positions . The difference between GERP and SCONE in this test is solely due to fully conserved positions and can be attributed to SCONE's context-dependent matrix; running SCONE with the Kimura two-parameter model recapitulates GERP's behavior . This discrepancy is unlikely to be explained by a difference in mutation rate patterns between synonymous and noncoding positions . Raw mutation rates and context-dependent effects ( most notably CpG effect ) are similar between coding and noncoding regions [40] . Thus , the most likely explanation is the well-documented effect of natural selection in favor of C and G in degenerate positions [41 , 42] . The top 1% of highly constrained sites according to SCONE are fully conserved positions within CpG dinucleotides , or positions within CpG di-nucleotides containing only a single substitution . The mutation rate for CpG dinucleotides is greatly elevated , and coding sequences are no exception . Such a high fraction of conserved CpG positions is extremely unexpected by chance and suggests they are maintained by selection . Another observation suggesting that conservation of these positions is due to selection is the clustering of these highly conserved 4-fold degenerate C and G nucleotides along the sequence . In 55% of cases , a 4-fold degenerate position neighboring a highly constrained 4-fold degenerate position is also conserved ( SCONE p-value < 0 . 05 ) . For the purposes of the analysis performed in this paper , SCONE scores were generated using Bayesian estimates of rate based on the most parsimonious labeling of the tree and including indels in the model . The phylogenetic tree was provided by the ENCODE MSA group . Human sequence was excluded from score generation; since SNP positions may appear in human consensus sequence and thus be counted as substitutions , significant artificial correlations between allele frequency and conservation may result from the inclusion of human sequence . Conserved elements are defined according to an additive score . For a given confidence threshold T and a series of bases numbered 1 . . N: where Xi is the SCONE score at position i . Here T represents the threshold of conservation for individual positions; relaxing this threshold results in larger contiguous elements being defined as conserved . A conserved element represents an optimally bounded region of sequence , for which the sum S cannot be increased by extension in either direction , which may be identified by an efficient linear search . We begin with starting position j = 1 and compute S ( j , j+k ) for k = j . . N . Then k0 is the first value of k > j where S ( j , j+k ) < 0 , and . For this interval , ( j , kmax ) are the optimal bounds of a conserved element , with score S ( j , j+kmax ) . We define a new starting position j = kmax+1 and continue iterating until we walk off the end of the sequence . Although there is currently no well-accepted annotation of strictly neutral regions in the human genome , and indeed each passing day seems to further whittle down those portions of the genome believed to be neutral , we made a best guess by excluding any features annotated as functional . A number of studies have employed mammalian ancestral repeats [13 , 18] as a neutral standard . These regions comprise repetitive elements that have been retained since the mammalian ancestor . We used these as our neutral standard and additionally excluded all sites falling within 50 bp of exon boundaries , regulatory regions identified by quantitative chromatin profiling [18] , and CpG islands . This was the “neutral” set employed in all of our analysis . We restricted ourselves to mammalian species in all our conservation analysis ( excluding humans ) , which left 22 species in the ENCODE alignments , viz . : armadillo , baboon , chimp , colobus monkey , cow , dog , dusky titi , elephant , galago , hedgehog , human , macaque , marmoset , oppossum , mouse , mouse lemur , owl monkey , platypus , rabbit , rat , bat , shrew , and tenrec .
|
The structure of the human genome remains largely unknown , including which parts of the genome are functionally relevant and which parts are “junk . ” The availability of genomic sequence from a large number of mammals allows a more detailed exploration of this structure , using comparison of related sequences from different species to identify portions of the genome that have remained unchanged , conserved by the action of natural selection , and thus likely to be functionally significant . To date , most efforts focused on localizing the functional fraction of the human genome have been based on identifying contiguous stretches of positions conserved in multiple species . Here , we present an analysis that is based instead on a single-position measure of conservation called SCONE . Our analysis suggests that the majority of conserved and putatively functional positions are highly fragmented and lie outside contiguous regions of conserved sequence . A subset of these fragmented positions may be identified based on local clustering .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"homo",
"(human)",
"mammals",
"computational",
"biology"
] |
2007
|
Analysis of Sequence Conservation at Nucleotide Resolution
|
Mycobacterium tuberculosis causes the majority of tuberculosis ( TB ) cases in humans; however , in developing countries , human TB caused by M . bovis may be frequent but undetected . Human TB caused by M . bovis is considered a zoonosis; transmission is mainly through consumption of unpasteurized dairy products , and it is less frequently attributed to animal-to-human or human-to-human contact . We describe the trends of M . bovis isolation from human samples and first-line drug susceptibility during a 15-year period in a referral laboratory located in a tertiary care hospital in Mexico City . Data on mycobacterial isolates from human clinical samples were retrieved from the laboratory’s database for the 2000–2014 period . Susceptibility to first-line drugs: rifampin , isoniazid , streptomycin ( STR ) and ethambutol was determined . We identified 1 , 165 isolates , 73 . 7% were M . tuberculosis and 26 . 2% , M . bovis . Among pulmonary samples , 16 . 6% were M . bovis . The proportion of M . bovis isolates significantly increased from 7 . 8% in 2000 to 28 . 4% in 2014 ( X2trend , p<0 . 001 ) . Primary STR resistance was higher among M . bovis compared with M . tuberculosis isolates ( 10 . 9% vs . 3 . 4% , p<0 . 001 ) . Secondary multidrug resistance ( MDR ) rates were 38 . 5% and 34 . 4% for M . bovis and M . tuberculosis , respectively ( p = 0 . 637 ) . A rising trend of primary STR monoresistance was observed for both species ( 3 . 4% in 2000–2004 vs . 7 . 6% in 2010–2014; p = 0 . 02 ) . There is a high prevalence and a rising trend of M . bovis isolates in our region . The proportion of pulmonary M . bovis isolates is higher than in previous reports . Additionally , we report high rates of primary anti-tuberculosis resistance and secondary MDR in both M . tuberculosis and M . bovis . This is one of the largest reports on drug susceptibility of M . bovis from human samples and shows a significant proportion of first-line anti-tuberculosis drug resistance .
Tuberculosis ( TB ) remains an important health problem in several regions of the world , especially in countries with a high prevalence of HIV infection . Mycobacterium tuberculosis complex ( MTBC ) includes closely related species among which M . tuberculosis , M . bovis , and M . africanum are the most frequently associated with human disease . [1] Unlike M . tuberculosis , M . bovis can infect a broad range of mammals , including cattle and , therefore , it is considered a zoonosis . The main mechanism of contagion in humans is the consumption of unpasteurized dairy products and , less frequently , animal-to-human and human-to-human contact . [2–4] Historically , the burden of human TB caused by M . bovis ( HTBMb ) has been closely related to that of bovine TB ( BTB ) in the same region . [5] Unfortunately , data from most developing countries , where there is still inappropriate BTB control , is scarce . [6 , 7] There are several factors that explain the underreporting of HTBMb in these regions . First , control programs rely on acid-fast bacilli ( AFB ) smears as the primary diagnostic method in suspected TB cases; and mycobacterial culture is performed only when drug resistance is suspected , or treatment is failing . Second , most laboratories use culture medium containing glycerol , and this reduces the probability of M . bovis isolation . Lastly , only a few laboratories can identify MTBC at the species level . [8 , 9] In Mexico , one national reference laboratory ( Instituto Nacional de Diagnóstico y Referencia Epidemiológicos ) and 32 public health laboratories are capable of performing mycobacterial cultures . However , species-level identification is not routinely performed . Consequently , data regarding first-line anti-tuberculosis drug susceptibility for MBTC are scarce . Therefore , describing the burden of HTBMb and first-line drug susceptibility pattern has important implications for treatment , referral , and public health policies in developing countries . We describe the trends of M . bovis isolation and first-line drug susceptibility for a referral laboratory in a tertiary care hospital during a 15-year period .
Institutional approval for this study was obtained from the Comité Institucional de Investigación Biomédica en Humanos . All data analyzed were anonymized . A search was performed using the Laboratory of Clinical Microbiology database to investigate all MTBC isolates from January 2000 to December 2014 . This database includes both local and referred samples . Only the first isolate was considered for the analysis when multiple samples within a 6-month period were available for a single patient . Whenever there were additional isolates from the same patient ≥ 6 months apart , they were considered as separate episodes and included for analysis . Data on mycobacterial species , drug susceptibility , sample source , treatment , and referral status were obtained . HIV status was obtained for local samples only . Primary resistance ( new cases ) was defined as those samples from patients without previous anti-tuberculosis treatment . Secondary resistance ( treated cases ) was defined as those samples from patients who had previously received any anti-tuberculosis treatment . Resistance to both isoniazid ( INH ) and rifampin ( RIF ) was defined as multidrug resistance ( MDR ) . Polydrug resistance was defined as resistance to two or more drugs but excluding those classified as MDR . Samples from sputum , bronchoalveolar lavage , endotracheal aspirate , gastric aspirate , lung and pleural biopsy , and pleural fluid were classified as pulmonary . Liver , spleen , and gastrointestinal biopsies , as well as ascites fluid , and fecal samples were classified as abdominal . Additionally , if samples from the same patient were obtained from a pulmonary and an extrapulmonary source , the case was classified as pulmonary . As a laboratory standard procedure , all samples obtained from bronchoalveolar lavage , cerebrospinal fluid , and biopsies from any tissue or abscess were cultured in mycobacteria-specific culture medium , regardless of TB suspicion . Sputum and urine samples were cultured for mycobacteria at the request of the treating physician . Samples were digested and decontaminated by the NALC-NaOH method as previously described . [10 , 11] After digestion , samples were inoculated in both Löwenstein-Jensen medium and MGIT tubes ( Becton-Dickinson , Sparks , MA , USA ) according to the manufacturer’s specifications . Biopsy samples were additionally inoculated in Stonebrink culture medium . Additionally , smears from all samples were prepared for Ziehl-Neelsen and Auramine-rhodamine stain . Isolates obtained from MGIT tubes were sub-cultured in Stonebrink and Löwenstein-Jensen medium . All positive cultures were further identified by DNA probe ( Accuprobe , GEN-PROBE , San Diego , CA ) . Biochemical tests ( niacin production , nitrate reduction , thiophen-2-carboxylic acid anhydride susceptibility , and pyrazinamidase deamidation ) for the identification of M . bovis were performed in those positive cultures with dysgonic growth . [12] Spoligotyping was performed for local isolates as previously described , and data was entered into an international database ( www . mbovis . org ) . [13] Susceptibility testing for anti-tuberculosis drugs was performed for INH , RIF , streptomycin ( STR ) and ethambutol ( EMB ) . For this purpose , and according to the manufacturer’s specifications , the radiometric BACTEC 460 TB culture system ( Becton-Dickinson , Sparks , MA , USA ) was used from the years 2000 to 2010 with the following drug concentrations: INH ( 0 . 1 μg/mL ) , RIF ( 2 . 0 μg/mL ) , EMB ( 7 . 5 μg/mL ) , and STR ( 6 . 0 μg/mL ) ; then , from 2010 on , the BACTEC MGIT 960 culture system ( Becton-Dickinson , Sparks , MA , USA ) was used with the following concentrations: INH ( 0 . 1 μg/mL ) , RIF ( 1 . 0 μg/mL ) , EMB ( 5 . 0 μg/mL ) , STR ( 2 . 0 μg/mL ) . This laboratory is subjected to regular quality control evaluations by the Centers for Disease Control and Prevention , and the College of American Pathologists for identification and susceptibility testing of mycobacteria species . Statistical analysis was performed using STATA 11 . 0 software ( StataCorp , College Station , TX , USA ) . Categorical data was summarized using frequency tables , and the X2 test was used for comparison between groups . The M . bovis case proportion by year was analyzed obtaining a X2 for trend by the Armitage test ( regression ) . A p-value <0 . 05 was determined as statistically significant for all tests .
During the study period , 81 , 521 samples were processed for mycobacterial culture ( Fig 1 ) . Among these , 1 , 165 MTBC isolates were identified , 583 ( 50 . 0% ) as local samples and 582 ( 49 . 9% ) as referrals after eliminating duplicate cultures . Of these , 73 . 7% ( 859/1 , 165 ) were identified as M . tuberculosis , and 26 . 2% ( 306/1 , 165 ) as M . bovis . Sixty-two percent of the M . tuberculosis isolates and 35 . 2% of the M . bovis isolates were obtained from pulmonary samples ( p<0 . 001; Table 1 ) . Data on AFB stain were available for 954 isolates; the proportion of positive AFB stain was 75 . 9% ( 378/680 ) for M . tuberculosis , and 24 . 1% ( 120/274 ) for M . bovis ( p = 0 . 001 ) . Among the AFB-positive M . bovis isolates , 45 . 8% ( 55/120 ) were pulmonary , and 54 . 1% ( 65/120 ) were extrapulmonary ( p = 0 . 018 ) . Conversely , among the AFB-positive M . tuberculosis isolates , 79 . 3% ( 300/378 ) were pulmonary and 20 . 6% ( 78/378 ) were extrapulmonary ( p<0 . 001 ) . One hundred and twelve ( 19 . 2% ) local isolates were from HIV-infected patients; 63 . 3% ( 71/112 ) were M . tuberculosis , and 36 . 6% ( 41/112 ) were M . bovis ( p = 0 . 054 ) . Among the samples from HIV-infected patients , 52 . 6% ( 59/112 ) were pulmonary samples . Of these , 76 . 2% ( 45/59 ) were identified as M . tuberculosis , and 23 . 7% ( 14/59 ) were M . bovis . The overall proportion of M . bovis isolation significantly increased from 7 . 8% in 2000 to 28 . 4% in 2014 ( X2trend , p<0 . 001; Fig 2 ) . Spoligotype pattern was available for 63 . 5% ( 108/170 ) of the local samples ( S1 Table ) . Data on first-line anti-tuberculosis drugs susceptibility were available for 1 , 139 ( 97 . 7% ) isolates ( Table 2 ) . When considering monoresistance among all isolates , 10 . 9% of M . bovis and 3 . 2% of M . tuberculosis were resistant to STR ( p<0 . 001 ) . This association remained after stratifying by new and treated cases ( p<0 . 001 and p = 0 . 032 , respectively ) . Total MDR among all cases was 11 . 9% for M . tuberculosis and 7 . 6% for M . bovis ( p = 0 . 038 ) . This same association was observed among new cases ( 6 . 8% vs . 3% , p = 0 . 026 ) . However , among treated cases no difference was observed . An increasing trend of STR monoresistance among new cases was found when considering both species ( 3 . 4% in 2000–2004 vs . 7 . 6% in 2010–2014 , p = 0 . 02 ) ( Table 3 ) .
This report demonstrates a high prevalence and a rising trend in the proportion of M . bovis isolation in our laboratory . This report is one of the largest on first-line anti-tuberculosis drug profile of M . bovis and shows a noteworthy proportion of first-line anti-tuberculosis drug resistance and secondary MDR isolates . The proportion of M . bovis isolates in this study ( 26 . 2% ) is much higher than that reported by other hospital-based studies in Latin America ( 0 . 4% ) and by other hospitals in Mexico ( <1% ) . [14 , 15] This may be explained by the larger study period of the present report and the high proportion of samples from immunosuppressed patients who are at a greater risk for M . bovis infection , as documented in previous studies . [16] In fact , isolates obtained from HIV-infected patients accounted for 19 . 2% of the local samples . [17] We also identified a rising trend in the proportion of cases caused by M . bovis across time . HMBTb is considered a reflection of the BTB burden in the region . In fact , we recently identified a high burden of bovine and human TB in a dairy production facility in rural Mexico . [2] This also correlates with previous reports of M . bovis among artisanal dairy products , which have been linked to HMBTb cases in Mexico and along the south border cities of the United States . [18 , 19] Mexico is considered a country of “sporadic occurrence” of BTB by the World Organization for Animal Health . However , like in many countries in the region , the test-and-slaughter strategy for bovine tuberculosis control is not universally implemented . [6] The main obstacles for BTB control in Mexico are financial and cultural . Official government data reports an overall prevalence of BTB of 2 . 05% in 2015 , but it reaches 16 . 5% among dairy farms . [20] The reason for this difference may be explained by the fact that meat producing regions require to be certified as BTB free for cattle export . On the contrary , in dairy production farms , BTB only mildly affects production and pasteurization eliminates M . bovis . [21] Unfortunately , about 30% of the milk production in Mexico is sold without pasteurization , mostly to small retailers and artisan cheese producers . [22] The respiratory route of contagion is considered less efficient for M . bovis than for M . tuberculosis . However , recent data detailing outbreaks in the community and hospitals demonstrated that human-to-human contagion is not as unlikely as previously believed . [3 , 23 , 24] Interestingly , we observed an important proportion ( 16 . 6% ) of M . bovis isolates recovered from pulmonary samples from a mainly urban population . Therefore , it may be hypothesized that airborne human-to-human transmission of M . bovis may occur in the community , but remains undetected in our region given that mycobacterial culture is not routinely performed . Unfortunately , as an important limitation of this study , the lack of clinical and epidemiologic data precludes us from reaching a definite conclusion . We also report a high rate of first-line anti-tuberculosis resistance for MBTC . The proportion of INH ( 14% ) , RIF ( 7 . 6% ) , and STR ( 5 . 5% ) primary resistance found among new cases is considerably higher for INH and RIF than in previous reports from our group in 1995 ( INH 6% , RIF 2% , STR 6% ) . [25] These proportions are also higher than those from other reports in Mexico ( 1995 to 2006 ) among new cases ( INH 9% , RIF 3% , and MDR 4 . 5% ) , and are also higher than recent data from the National Survey on TB Drug Resistance in Mexico ( INH 3 . 5% , RIF 0 . 1% , STR 4% and MDR 2 . 3% ) . [26 , 27] This discrepancy may be explained by the different periods from which data was collected and the dissimilar patient population ( ours being hospital-based and including more HIV-infected , and immunosuppressed patients ) . When comparing the resistance profiles of M . bovis and M . tuberculosis , we observed a considerably higher primary MDR M . tuberculosis proportion , and a significantly higher STR monoresistance among M . bovis isolates . Data regarding drug susceptibility for M . bovis TB in humans and animals is limited . A study from San Diego reported 7% resistance for INH and 1% for RIF among 167 M . bovis TB cases . [28] Drug susceptibility has also been reported from outbreaks caused by MDR strains; however , most other case series of HTBMb report full susceptibility to all first-line anti-tuberculosis drugs . [14 , 15 , 29–32] A report from the National TB Genotyping Service of the United States informed of 17% of STR resistance among 165 M . bovis isolates; however , no explanation for this was proposed . [33] Drug susceptibility from M . bovis isolates collected from farm animals or wildlife is almost uniformly reported as fully susceptible to anti-tuberculosis drugs . [34–36] . We believe that a high proportion of primary resistance for STR among M . bovis isolates , may be explained by the use of aminoglycosides for treating other diseases in cattle . [37] Some have suggested that primary resistance to INH and RIF in M . bovis may indicate human-to-human transmission . [32] However , we surveyed BTB in a dairy farm , where samples from cows were obtained during necropsy . We recovered 150 M . bovis isolates; among these , we observed an even higher rate of STR resistance ( 15 . 6% ) and a similar rate of INH ( 9 . 2% ) and RIF ( 3 . 4% ) resistance . ( M . Bobadilla , Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán , Mexico City , personal communication ) . It has been suggested that HTBMb cases may be at a higher risk for developing MDR strains if natural resistance to pyrazinamide is not considered and monoresistance to INH or RIF is present . [38 , 39] We identified a similar proportion of secondary MDR M . bovis and M . tuberculosis isolates . Unfortunately , only a few cases were analyzed and no data on previous treatments or outcomes were available , therefore we are unable to conclude if this assumption is true . Nevertheless , it should be recognized that TB cases caused by MDR M . bovis may result in disease that is harder to treat on a second- line drug regimen . This highlights the need for performing species-level identification and drug susceptibility testing whenever M . bovis is suspected . In conclusion , we believe that data contained in this study is relevant in terms of public health and highlights the need for more stringent control of BTB in our country . It also underscores the importance of proper identification of M . bovis given that the considerable rate of primary resistance to INH and RIF along with the natural pyrazinamide resistance may result in treatment failures and select for MDR strains .
|
Human tuberculosis caused by Mycobacterium bovis ( HTBMb ) is a lesser-known form of the disease . The main route of transmission of HTBMb is the consumption of unpasteurized dairy products , causing mostly extrapulmonary disease . M . bovis is naturally resistant to pyrazinamide , a drug that allows for a shorter treatment course . Therefore , if M . bovis is not properly identified or if there is resistance to other drugs , proper treatment may be hindered . Most laboratories in developing countries do not routinely perform mycobacterial cultures , and only a few laboratories can identify M . bovis . Therefore , HTBMb cases are believed to be underestimated . We report a large proportion of M . bovis isolates and an increasing isolation trend across time . We report a large proportion of M . bovis isolates from pulmonary samples , suggesting the possibility of human-to-human airborne transmission . Also , we showed that M . bovis isolates were more frequently resistant to streptomycin , perhaps as a result of antibiotic usage in cattle . This work underscores the need for identification to the species level , proper susceptibility testing , as well as a stricter control of bovine tuberculosis .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Trends of Mycobacterium bovis Isolation and First-Line Anti-tuberculosis Drug Susceptibility Profile: A Fifteen-Year Laboratory-Based Surveillance
|
The mammalian circadian clock relies on the transcription factor CLOCK:BMAL1 to coordinate the rhythmic expression of 15% of the transcriptome and control the daily regulation of biological functions . The recent characterization of CLOCK:BMAL1 cistrome revealed that although CLOCK:BMAL1 binds synchronously to all of its target genes , its transcriptional output is highly heterogeneous . By performing a meta-analysis of several independent genome-wide datasets , we found that the binding of other transcription factors at CLOCK:BMAL1 enhancers likely contribute to the heterogeneity of CLOCK:BMAL1 transcriptional output . While CLOCK:BMAL1 rhythmic DNA binding promotes rhythmic nucleosome removal , it is not sufficient to generate transcriptionally active enhancers as assessed by H3K27ac signal , RNA Polymerase II recruitment , and eRNA expression . Instead , the transcriptional activity of CLOCK:BMAL1 enhancers appears to rely on the activity of ubiquitously expressed transcription factors , and not tissue-specific transcription factors , recruited at nearby binding sites . The contribution of other transcription factors is exemplified by how fasting , which effects several transcription factors but not CLOCK:BMAL1 , either decreases or increases the amplitude of many rhythmically expressed CLOCK:BMAL1 target genes . Together , our analysis suggests that CLOCK:BMAL1 promotes a transcriptionally permissive chromatin landscape that primes its target genes for transcription activation rather than directly activating transcription , and provides a new framework to explain how environmental or pathological conditions can reprogram the rhythmic expression of clock-controlled genes .
Virtually every mammalian cell harbors a circadian clock that regulates rhythmic gene expression to enable biological functions to occur at the most appropriate time of day . Circadian clocks rely on transcriptional feedback loops which are initiated in mammals by the heterodimeric transcription factor CLOCK:BMAL1 [for review , 1] . CLOCK:BMAL1 rhythmically binds to DNA to activate the rhythmic transcription of the core clock genes Period ( Per1 , Per2 , Per3 ) , Cryptochrome ( Cry1 and Cry2 ) , Rev-erb ( Rev-erbα and Rev-erbβ ) and Ror ( Rorα , Rorβ and Rorγ ) . Upon expression and maturation , PERs and CRYs form a repressive complex that rhythmically inhibits CLOCK:BMAL1-mediated transcription first on-DNA and then off-DNA [2–5] . Furthermore , REV-ERBs and RORs rhythmically regulate Bmal1 expression by repressing or activating its transcription , which promotes robustness of circadian oscillations [6 , 7] . In addition to activating the rhythmic transcription of core clock components , CLOCK:BMAL1 also regulates rhythmic expression of thousands of clock-controlled genes to generate oscillations in biochemistry , physiology and behavior , and thus control the rhythmic organization of most biological functions [8–10] . Characterizing the mechanisms through which CLOCK:BMAL1 regulates expression of its target genes has largely been carried out by determining how CLOCK:BMAL1 regulates the transcription of core clock genes ( Per , Cry and Rev-erb ) , and target genes ( e . g . , Dbp , named for D site of albumin promoter binding protein ) . Results from many laboratories show that the rhythmic binding of CLOCK:BMAL1 to e-boxes located in core clock gene promoters is necessary and sufficient for rhythmic transcription [4 , 5 , 11–13] . Upon DNA binding during the light phase , CLOCK:BMAL1 promotes chromatin modifications by recruiting histone-modifying enzymes to core clock gene promoters and enhancers . These enzymes include the histone acetyltransferases p300 and CBP , which mediate the acetylation of H3K9 and H3K27 , and the histone methyltransferases MLL1 and MLL3 ( Myeloid/Lymphoid Or Mixed-Lineage Leukemia 1 and 3 ) , which promote the tri-methylation of H3K4 [3 , 14–19] . CLOCK:BMAL1 rhythmic DNA binding was also recently shown to promote rhythmic nucleosome removal , thereby generating a chromatin landscape that is favorable for the binding of other transcription factors at its enhancers [20] . Finally , CLOCK:BMAL1 recruits transcriptional co-activators , including components of the mediator complex and RNA Polymerase II ( Pol II ) to initiate core clock gene transcription [3 , 21 , 22] . During the repression phase in the early night , binding of the PER/CRY complex to DNA-bound CLOCK:BMAL1 is accompanied by the co-recruitment of histone deacetylases and demethylases and the removal of the H3K9ac , H3K27ac and H3K4me3 marks [19 , 23–27] . While these mechanisms are required for CLOCK:BMAL1-mediated transcription activation of core clock genes , it still remains unclear if the same mechanisms regulate the rhythmic expression of clock-controlled genes . The recent characterization of CLOCK and BMAL1 mouse liver cistromes revealed that although CLOCK:BMAL1 binds synchronously during the middle of the day to thousands of enhancers and promoters , the transcription of its target genes is highly heterogeneous [3 , 28–30] . Indeed , not all CLOCK:BMAL1 target genes are rhythmically expressed , and a large fraction of the rhythmically expressed target genes are transcribed at night , in antiphase to maximal CLOCK:BMAL1 DNA binding [28] . These data therefore suggest that the mechanisms by which CLOCK:BMAL1 regulates transcription of core clock genes differs from the regulation of other clock-controlled genes , and that additional mechanisms account for the activation of rhythmic gene expression by the circadian clock . To uncover these mechanisms and to delineate the transcriptional logic underlying CLOCK:BMAL1 heterogeneous transcriptional output , we performed a meta-analysis of genome-wide datasets investigating the molecular events occurring at CLOCK:BMAL1 DNA binding sites , including CLOCK:BMAL1 rhythmic DNA binding , epigenetic modifications and transcription activation . Our analysis reveals that while CLOCK:BMAL1 DNA binding is sufficient to decondense the chromatin and prime its enhancers for transcriptional activation , it is not sufficient to generate transcriptionally active enhancers . Our results also indicate that many transcription factors bind to CLOCK:BMAL1 enhancers , and their recruitment likely contributes to CLOCK:BMAL1 clock-controlled transcriptional output . Altogether , our data support that CLOCK:BMAL1 regulation of clock-controlled gene expression relies on the cooperation between CLOCK:BMAL1 and other transcription factors . Furthermore , our data also suggest that a major role of CLOCK:BMAL1 is to generate a permissive chromatin landscape to rhythmically prime its enhancers for the recruitment of other transcription factors , rather than directly promoting transcription activation .
To characterize the mechanisms by which CLOCK:BMAL1 regulates the transcriptional activity of its target genes at the genome-wide level in the mouse liver , we first generated a list of high-confidence CLOCK:BMAL1 DNA binding sites by determining the overlap between CLOCK and BMAL1 ChIP-Seq peaks in the mouse liver [3] . This analysis resulted in a list of 3217 CLOCK:BMAL1 binding sites , of which 2458 peaks can be assigned to a direct target gene ( i . e . , a CLOCK:BMAL1 peak located between -10kb of a target gene transcription start site and +1kb of a target gene transcription termination site; see S1 Fig , S1 Table , and methods section for details ) . To determine the extent to which rhythmic CLOCK:BMAL1 DNA binding contributes to rhythmic transcription activation at the genome-wide level , we used a public mouse liver Nascent-Seq dataset that characterized the levels of nascent RNA expression over the course of a 24-hr day [28] . A Nascent-Seq dataset was preferred over RNA-Seq because nascent RNA expression directly reflects transcription activation , and is unaffected by the post-transcriptional regulations that contribute to rhythmic mRNA expression in the mouse liver [3 , 28 , 31] . We found that only a small fraction of CLOCK:BMAL1 target genes are rhythmically transcribed ( ~26%; S1 Fig ) . Noticeably , not all rhythmic target genes are transcribed during the day , i . e . , coincidently with rhythmic CLOCK:BMAL1 DNA binding ( ZT02-ZT12 ) . Indeed , 38% of the rhythmic CLOCK:BMAL1 target genes exhibit a peak of transcription between ZT12 and ZT02 , out-of-phase with the rhythmic DNA binding of CLOCK:BMAL1 ( n = 124 CLOCK:BMAL1 peaks ) ( Fig 1A–1C; S1 Fig ) . Importantly , our analysis also reveals that the majority of CLOCK:BMAL1 direct target genes are either arrhythmically transcribed ( AR; n = 654 CLOCK:BMAL1 peaks ) or not expressed ( NE; n = 291 CLOCK:BMAL1 peaks ) ( Fig 1A–1D; S1 Fig ) . To determine if this result may be due to comparing samples collected in constant darkness ( ChIP-Seq ) and in a light:dark ( LD ) cycle ( Nascent-Seq ) , we also analyzed a mouse liver BMAL1 ChIP-Seq rhythm performed under LD condition [29] . BMAL1 binding phase and ChIP-Seq signal under LD condition both exhibit a remarkably high level of similarity to those under DD conditions , and this even for the AR or NE target genes ( S2 Fig ) . This therefore suggests that the large number of arrhythmically transcribed or not expressed CLOCK:BMAL1 target genes is not a consequence of using datasets generated under different lighting conditions . Taken together , these results indicate that the mechanisms underlying CLOCK:BMAL1-mediated rhythmic transcription of core clock genes ( i . e . , Per1 , Per2 , Per3 , Cry1 , Cry2 , Rev-erbα , Rev-erbβ and Dbp ) are not prevalent at the genome-wide level . They also suggest that the rhythmic recruitment of CLOCK:BMAL1 at its target gene promoters and enhancers is not sufficient to activate transcription for the majority of its target genes . To investigate the mechanisms underlying CLOCK:BMAL1 heterogeneous transcriptional output , we first examined if differences in the phase , intensity or location of CLOCK:BMAL1 DNA binding might explain the differences in transcription activation . The phase of CLOCK:BMAL1 DNA binding was found to be indistinguishable between all four transcriptional output categories , as both CLOCK and BMAL1 rhythmically bind to DNA with a peak between ZT3 and ZT9 for almost all target genes ( Fig 1A , 1B and 1E ) . We then used CLOCK and BMAL1 ChIP-Seq signal as a readout to determine DNA binding intensity , and found that both CLOCK and BMAL1 ChIP-Seq signals are significantly higher at DNA binding sites targeting the in-phase transcriptional cyclers ( Rinφ ) when compared to peaks targeting the 3 other groups ( out-of phase cyclers , arrhythmically expressed and non-expressed target genes ) ( Fig 1A , 1B and 1F; Kruskal-Wallis test , p < 0 . 05 ) . Remarkably , the binding intensity of CLOCK and BMAL1 at non-expressed target genes ( NE ) is similar to the binding intensity observed at the out-of-phase transcriptional cyclers ( Ro/φ ) and arrhythmically transcribed ( AR ) target genes , suggesting that CLOCK:BMAL1 DNA binding alone does not directly activate transcription at most of its target genes ( e . g . , comparisons between Fig 1D and 1F ) . To verify that these results are not due to the cut-offs we used to partition CLOCK:BMAL1 transcriptional output , we performed similar analyses using direct correlations between BMAL1 or CLOCK ChIP-Seq signal and the phase of rhythmic transcription , as well as by partitioning rhythmic target genes in five groups of equal sizes . These analyses confirmed our results ( S3 and S4 Figs ) . While rhythmically transcribed target genes peaking from ZT5 to ZT13 exhibit higher BMAL1 and CLOCK ChIP-Seq signal , no differences in DNA binding signal were observed between the rhythmically expressed targets peaking from ZT13 to ZT5 and the AR and NE groups ( S3 Fig ) . In addition , we did not find any significant correlation between either CLOCK or BMAL1 ChIP-Seq signals and the phase of DNA binding or the phase of rhythmic transcription ( S4 Fig ) . Because CLOCK:BMAL1 peaks targeting core clock genes are enriched in the Rinφ and Ro/φ groups and exhibit higher ChIP-Seq signal than clock-controlled ( output ) genes , we also compared CLOCK and BMAL1 ChIP-Seq signals between groups after removing peaks targeting the core clock genes ( i . e . , comparing clock-controlled genes only ) . Whereas BMAL1 ChIP-Seq signal intensity was still significantly higher at the Rinφ target genes compared to the three other groups , CLOCK DNA binding intensity was similar between all 4 groups ( Fig 1F ) . Our data therefore indicate that while higher BMAL1 DNA binding signal may contribute to Rinφ transcription , the different transcriptional output of CLOCK:BMAL1 target genes cannot be explained solely by differences in CLOCK:BMAL1 DNA binding intensity . We also examined if differences in the location of CLOCK:BMAL1 DNA binding sites are associated with differences in transcriptional output by mapping CLOCK:BMAL1 peaks to either the transcription start site ( TSS ) , gene body or extended promoter ( -10 kb to -1 kb from the TSS ) of their target genes . While the AR and NE groups were found to be statistically different ( chi square test; p < 0 . 05 ) , we did not observe any differences between the rhythmic target groups ( Rinφ and Ro/φ ) and the arrhythmically or not expressed groups ( Fig 1G ) . The vast majority of CLOCK:BMAL1 peaks were located within enhancers ( i . e . , gene body or extended promoter ) , and only ~10–19% of CLOCK:BMAL1 peaks were mapped to TSS . Finally , we examined if differences in the number of genes targeted by multiple CLOCK:BMAL1 peaks were associated with differences in transcriptional output . We found that in-phase transcriptional cyclers were more frequently targeted by multiple CLOCK:BMAL1 peaks , and that conversely , non-expressed target genes were less frequently targeted by multiple peaks ( S5 Fig ) . However , the lack of differences between the Ro/φ and AR groups indicates that the presence of multiple ChIP-Seq peaks does not directly influence the rhythmicity of CLOCK:BMAL1 target genes . Taken together , our analysis indicates that CLOCK:BMAL1 heterogeneous transcriptional output can not be simply attributed to differences in the phase , intensity or location of CLOCK and BMAL1 binding to the DNA . While stronger DNA binding intensity may contribute to rhythmic transcription during the light phase , additional mechanisms are likely to contribute to CLOCK:BMAL1 transcriptional output heterogeneity . Circadian repression in mammals is initiated at the beginning of the night by the recruitment of the PER/CRY repressive complex and its associated histone deacetylases and methyltransferases to CLOCK:BMAL1 on DNA [23–25 , 27 , 32] . Because a differential recruitment of PERs and CRYs at CLOCK:BMAL1 DNA binding sites could lead to differences in CLOCK:BMAL1-mediated transcriptional output ( e . g . , decreased recruitment at arrhythmically transcribed target genes , delayed recruitment of out-of-phase transcriptional cyclers , etc . ) , we investigated the DNA binding profile of PER1 , PER2 , CRY1 and CRY2 at CLOCK:BMAL1 DNA binding sites for each of the four transcriptional output groups using publically available ChIP-Seq datasets [3] . Our analysis shows that PER1 , PER2 and CRY2 are rhythmically recruited at CLOCK:BMAL1 DNA binding sites with little difference between the four transcriptional output groups ( S6 Fig ) . Maximal DNA binding for PER1 , PER2 and CRY2 occur at CT12-16 for all groups , and differences were mostly observed for CRY2 , where higher ChIP-Seq signal was found for rhythmically expressed target genes ( S6 Fig ) . On the other hand , analysis of CRY1 recruitment to CLOCK:BMAL1-bound enhancers revealed more pronounced differences between all four groups . CRY1 is a potent circadian repressor that is preferentially recruited at the beginning of the light phase just prior CLOCK:BMAL1 transcription activation ( i . e . , CT0-4 ) , a mechanism proposed to poise CLOCK:BMAL1 for transcription activation [3] . We found that CRY1 recruitment at CT4 is significantly higher for rhythmically transcribed target genes ( both Rinφ and Ro/φ ) than for arrhythmically transcribed and non-expressed genes ( S6 Fig ) . In addition , CRY1 recruitment was significantly decreased in non-expressed CLOCK:BMAL1 target genes than arrhythmic genes at CT4 . These data thus suggest that CRY1 recruitment to CLOCK:BMAL1 DNA binding sites is , in addition to its well-characterized repressive effect , linked to rhythmic transcription activation . Consistent with this hypothesis are the higher levels for Ro/φ at CT12 compared to Rinφ ( S6 Fig ) . Based on the mechanisms mediating the delayed transcription of the CLOCK:BMAL1 target gene Cry1 [33] , a model incorporating the nuclear receptors Rev-erb ( repressor ) and Ror ( activator ) , and the D-box transcriptional factors E4bp4 ( also called Nfil3; repressor ) , Dbp , Hlf and Tef ( activators ) has been proposed to explain the different phases of rhythmic gene expression in the mouse liver [33 , 34] . In this model , co-binding of D-box transcription factors with CLOCK:BMAL1 is proposed to delay the phase of CLOCK:BMAL1 target genes from the morning to the afternoon ( i . e . , from ~ZT6 to ~ZT12 ) , and additional binding of REV-ERBs and RORs would further delay the phase of transcription to the night ( e . g . , ~ZT18 ) . To test if the binding of REV-ERBs and D-box transcription factors contribute to the delay of the out-of-phase CLOCK:BMAL1 target genes , we used publicly available ChIP-Seq datasets to determine REV-ERBα , REV-ERBβ [35] , and E4BP4 [36] DNA binding intensity at CLOCK:BMAL1 enhancers . We find that REV-ERBα and REV-ERBβ DNA binding , which peaks at ZT10 for all target genes [35] , is significantly higher at CLOCK:BMAL1 peaks targeting genes transcribed during the night [consistent with the model proposed based on Cry1 expression; 33 , 34] , and no differences were observed between Rinφ , AR and NE target genes ( Fig 2A and 2B; Kruskal-Wallis test , p < 0 . 05 ) . The binding of E4BP4 , which is maximal at ZT22 [36] , was also enriched at CLOCK:BMAL1 enhancers targeting the Ro/φ genes , but to a lesser extent than what was observed for the REV-ERBs ( Fig 2C ) . In particular , no significant difference in enrichment was observed between the Rinφ and the Ro/φ groups , perhaps because co-binding of both CLOCK:BMAL1 and D-box transcription factors drives rhythmic transcription in the afternoon around ZT12 , a time used for our cut-off to differentiate the in-phase from out-of-phase transcription cyclers . In summary , our analysis indicates that the binding of REV-ERBα and REV-ERBβ ( and eventually E4BP4 ) at CLOCK:BMAL1 enhancers may , as suggested by others [33 , 34] , contribute to the delayed transcription of rhythmically expressed CLOCK:BMAL1 target genes . Our inability to detect substantial differences in CLOCK:BMAL1 DNA binding that would explain the heterogeneity of CLOCK:BMAL1 transcriptional output suggests that mechanisms other than the recruitment of core clock proteins to target gene promoters control CLOCK:BMAL1-mediated transcription . The recent finding that CLOCK:BMAL1 promotes the removal of nucleosomes when bound to DNA may represent one of these mechanisms [20] . Indeed , by mediating the removal of nucleosomes , CLOCK:BMAL1 would enable other transcription factors to access CLOCK:BMAL1 enhancers ( most transcription factors bind better to naked DNA than DNA wrapped around nucleosomes ) . To test if CLOCK:BMAL1-mediated nucleosome removal can contribute to the heterogeneity of CLOCK:BMAL1 transcriptional output , we examined mouse liver nucleosome signal over the 24-hr day at CLOCK:BMAL1 DNA binding sites for each of the transcriptional output groups , using a public MNase-Seq dataset ( micrococcal nuclease digestion of mouse liver chromatin at 6-time points and high-throughput sequencing of mononucleosomes [20] ) . Our analysis reveals that nucleosome signal is rhythmic at CLOCK:BMAL1 DNA binding sites for each of the transcriptional output categories , i . e . , even at CLOCK:BMAL1 DNA binding sites targeting arrhythmically transcribed and non-expressed genes ( Fig 3A–3D; S7 Fig ) . Importantly , the phase of the rhythms is similar for all groups and minimal nucleosome signal coincides with maximal CLOCK:BMAL1 DNA binding during the light phase . Closer inspection of the levels of nucleosome signal and rhythm amplitude reveals important differences between each of the four transcriptional output categories ( Fig 3E ) . First , the amplitude of the rhythms is significantly decreased for arrhythmically transcribed target genes . While minimal levels of nucleosome signal during the day are similar between the AR and Rinφ groups , nucleosome signal remains low during the night ( i . e . , when CLOCK:BMAL1 is not bound to DNA ) at CLOCK:BMAL1 peaks targeting AR genes ( Fig 3E ) . This suggests that some transcription factors may be still bound to DNA during the night in the AR group ( when CLOCK:BMAL1 is not bound to DNA ) , thereby preventing the reformation of nucleosomes . This may promote transcription at night and thus lead to arrhythmic transcription . Second , the overall nucleosome signal is significantly lower at CLOCK:BMAL1 peaks targeting Ro/φ genes than for Rinφ genes , without any significant effect on the amplitude of the rhythm ( Fig 3E ) . In addition , the time of minimal nucleosome signal is delayed by 4 hours between Rinφ and Ro/φ: while it coincides with the time of maximal CLOCK:BMAL1 DNA binding for Rinφ genes ( ZT06 ) , minimal nucleosome signal is observed at ZT10 for Ro/φ genes . This delayed nucleosome signal for the out-of-phase transcriptional cyclers may be explained by the significant recruitment of REV-ERBα and REV-ERBβ ( Fig 2A and 2B ) . Indeed , CLOCK:BMAL1 has been recently proposed to facilitate circadian repression by promoting the recruitment of REV-ERBα through chromatin decondensation [37] . Thus , the increased binding of REV-ERBs at CLOCK:BMAL1 enhancers at ZT10 may promote a further decrease in nucleosome signal . Furthermore , anti-phase binding of the RORs on ROREs during the night would prevent a full nucleosome re-compaction , thereby promoting lower levels of nucleosome signal at CLOCK:BMAL1 peaks targeting Ro/φ target genes . Finally , there are no significant differences of nucleosome signal between CLOCK:BMAL1 DNA binding sites targeting in-phase transcriptional cyclers than those targeting non-expressed target genes ( Fig 3E ) . This intriguing result suggests that although CLOCK:BMAL1 is unable to promote transcription activation at NE target genes , its rhythmic DNA binding still mediates a rhythm in nucleosome signal . One possible explanation for this result is that CLOCK:BMAL1 decondenses the chromatin to facilitate the binding of other transcription factors , but those would not be recruited at NE target genes except under specific conditions ( e . g . environmental stressors ) , thereby preventing activation of transcription under standard conditions . Our data indicate that CLOCK:BMAL1 rhythmic DNA binding promotes the rhythmic removal of nucleosomes at all four transcriptional output categories . We then asked if CLOCK:BMAL1 can also promote the formation of transcriptionally active enhancers . To address this question , we used public datasets [3 , 36 , 38] to examine the rhythmic pattern of two independent markers of enhancer activity at CLOCK:BMAL1 DNA binding sites: the post-translational modification H3K27 acetylation ( H3K27ac ) , which positively correlates with enhancer activity at almost all enhancers and TSS [39] , and the expression levels of enhancer RNA ( eRNA ) , which are relatively short non-coding RNA molecules ( 50–2000 nucleotides ) transcribed at active enhancer regions [40] . While mouse liver H3K27ac ChIP-Seq signal is rhythmic and high during the light phase at CLOCK:BMAL1 DNA binding sites targeting in-phase transcriptional cyclers ( consistent with CLOCK:BMAL1 directly facilitating the acetylation of H3K27; Fig 3F and S8 Fig ) , significant differences were observed at CLOCK:BMAL1 DNA binding sites targeting the other 3 transcriptional output categories . Rhythmic H3K27ac rhythm is delayed for the out-of-phase transcriptional cyclers , and the amplitude of H3K27ac rhythm is significantly dampened at CLOCK:BMAL1 DNA binding sites targeting arrhythmically transcribed genes ( Fig 3F and S8A Fig ) . Remarkably , levels of H3K27ac are close to background levels at CLOCK:BMAL1 peaks targeting non-expressed genes . Given that CLOCK:BMAL1 rhythmically binds to relatively similar levels for all four transcriptional output categories , our analysis suggests that CLOCK:BMAL1 DNA binding does not directly contribute to the acetylation of H3K27 . To extend on this observation , we then examined another marker of enhancer transcriptional activity by assessing eRNA expression at CLOCK:BMAL1 DNA binding sites using a publicly available GRO-Seq dataset [36] . The analysis confirmed the results obtained with H3K27ac ( Fig 3G ) . Rhythmic eRNA expression is only observed at CLOCK:BMAL1 enhancers targeting rhythmically transcribed genes , and eRNA expression at enhancers targeting non-expressed genes is dramatically decreased to levels close to background ( Fig 3G ) . Importantly , these differences in eRNA expression between the four CLOCK:BMAL1 transcriptional output categories are further corroborated by similar variations in RNA Polymerase II ( Pol II ) ChIP-Seq signal at CLOCK:BMAL1 enhancers ( S8C Fig ) . Altogether , our analysis therefore demonstrates that , contrary to what has been typically found for core clock genes , CLOCK:BMAL1 DNA binding is not sufficient to promote the activation of its enhancers . Instead , our results suggest that CLOCK:BMAL1 rhythmically opens the chromatin to facilitate the binding of other transcription factors at its enhancers , and that the nature of these transcription factors ( e . g . , activators , repressors ) significantly contributes to CLOCK:BMAL1 transcriptional output . To test our hypothesis that transcription factors bind at CLOCK:BMAL1 enhancers to contribute to their transcriptional activity and thereby impact on CLOCK:BMAL1-mediated transcription , we assessed if transcription factors were differentially recruited at CLOCK:BMAL1 DNA binding sites within each transcriptional output group . To this end , we performed a DNA binding motif analysis using HOMER Software Suite that we further validated using mouse liver transcription factor ChIP-Seq datasets . As expected , the motif analysis revealed that CLOCK:BMAL1 DNA binding motif ( e-box of the sequence CACGTG ) is highly enriched at CLOCK:BMAL1 enhancers for all transcriptional output categories ( Fig 4A ) . Surprisingly however , we found that motifs for liver-specific transcription factors ( e . g . , Cebp , Hnf1 , Hnf4 and Hnf6 ) were also enriched for all four transcriptional output categories , and thus even at CLOCK:BMAL1 enhancers targeting non-expressed genes ( Fig 4B , S9 Fig , and S2 Table ) . On the contrary , motifs for ubiquitous transcription factors ( u-TFs; broadly expressed transcription factors with a transcriptional activity regulated by external factors ) were almost always enriched for specific CLOCK:BMAL1 transcriptional output groups ( Fig 4C , S9 Fig , and S2 Table ) . For example , CRE motif was enriched at all CLOCK:BMAL1 enhancers except those targeting out-of-phase transcriptional cyclers , and FXR motif was enriched at all CLOCK:BMAL1 enhancers except those targeting out-of-phase transcriptional cyclers . Noticeably , the motifs for NF-κB [which binds to DNA and becomes transcriptionally active upon infection and inflammation; 41 , 42] , and CTCF [which establishes discrete functional chromatin domains by promoting DNA looping; 43 , 44 , 45] were enriched at enhancers targeting non-expressed genes . To assess the relevance of this difference of motif enrichments between tissue-specific ( ts-TFs ) and u-TFs , we determined the DNA binding pattern of several transcription factors at CLOCK:BMAL1 enhancers in the mouse liver using publicly available transcription factors ChIP-Seq datasets [46–51] . This in vivo analysis largely confirmed the computational motif analysis: most liver-specific TFs were found to bind at CLOCK:BMAL1 DNA binding sites independently of the transcriptional output , whereas u-TFs were more specifically enriched in specific CLOCK:BMAL1 transcriptional output groups ( Fig 4D , E , S10 Fig , and S3 Table ) . For example , Hnf4a and Hnf1 are the only liver-specific TF to exhibit a differential binding between CLOCK:BMAL1 transcriptional output groups of the six TFs tested ( Foxa1 , Foxa2 , Hnf1 , Hnf4A , Hnf6 and Cepba ) . Conversely , all twelve u-TFs investigated exhibit DNA binding differences at CLOCK:BMAL1 enhancers between categories of transcriptional output ( Fig 4D and 4E , S10 Fig ) . Although each u-TF bound to different subsets of CLOCK:BMAL1 enhancers , u-TF recruitment was generally higher in rhythmically expressed target genes and lower in non-expressed target genes compared to the arrhythmic CLOCK:BMAL1 target group . ( Fig 4D and 4E , S10 Fig , and S3 Table ) . To further characterize the differences in TF DNA binding between CLOCK:BMAL1 , u-TFs and ts-TFs , we computed a TF DNA binding variability index by calculating the standard deviation of the ChIP-Seq signal between the 4 CLOCK:BMAL1 transcriptional output groups ( see methods for details ) . We found that the DNA binding variability at CLOCK:BMAL1 peaks is comparable between CLOCK:BMAL1 and ts-TFs , whereas there is significantly more variability for u-TFs than for CLOCK and BMAL1 when peaks targeting core clock genes are removed from the analysis ( Fig 4G ) . While there are variability index differences among ts-TFs and u-TFs , this analysis further supports our finding that u-TF recruitment at CLOCK:BMAL1 peaks is globally more variable than for ts-TF ( Fig 4G ) . Altogether , our data indicate that the mechanisms by which CLOCK:BMAL1 regulates transcription of clock-controlled genes differ from the well-characterized CLOCK:BMAL1-mediated regulation of core clock gene expression . Specifically , our data show that although CLOCK:BMAL1 mediates rhythmic nucleosome removal at its enhancers , it is not sufficient to generate an active enhancer or drive rhythmic transcription . We thus propose a model whereby CLOCK:BMAL1 regulates transcription of clock-controlled genes by rhythmically opening chromatin to facilitate the binding of other transcription factors at its enhancers ( Fig 5A ) . This possibility is supported by results showing that nucleosome signal is rhythmic at the DNA binding sites of several TFs when those sites are located close to a CLOCK:BMAL1 peak , and not rhythmic when CLOCK:BMAL1 binding is absent ( S11 Fig ) . Consequently , the transcriptional activities of these transcription factors would dictate the transcriptional outcome of clock-controlled genes rather than CLOCK:BMAL1 ( Fig 5A ) . For example , binding of positive transcription factors along with CLOCK:BMAL1 would activate enhancers and lead to transcription activation during the day , whereas binding of transcriptional repressors ( e . g . , REV-ERBα/β ) would inhibit CLOCK:BMAL1 enhancer activity and thereby contribute to rhythmic transcription peaking during the night , in anti-phase with CLOCK:BMAL1 DNA binding ( Fig 5A ) . If no transcription factors are recruited ( e . g . , inducible transcription factors ) , CLOCK:BMAL1 enhancers remain silent and target genes are not expressed or are arrhythmically expressed ( Fig 5A ) . Arrhythmically expressed genes at CLOCK:BMAL1 enhancers may also have positive transcription factors bound at all times overriding the absence of CLOCK:BMAL1 DNA binding at night ( see result section about rhythmic nucleosome signal and Fig 3E ) . Our results also suggest that u-TFs regulate CLOCK:BMAL1 transcriptional output more prevalently than ts-TFs . It may be that ts-TFs facilitate the binding of CLOCK:BMAL1 at tissue-specific enhancers rather than contributing to CLOCK:BMAL1 transcriptional output ( see discussion ) . To validate this model experimentally , we investigated how i ) Bmal1 knockout , and ii ) changes in environmental conditions ( that alter u-TFs transcriptional activities ) affect CLOCK:BMAL1 transcriptional output . If the activity of u-TFs contributes to CLOCK:BMAL1 regulation of clock-controlled gene transcription , then , a knockout of Bmal1 ( which eliminates CLOCK:BMAL1-mediated transcription [52] ) should differentially affect the expression of CLOCK:BMAL1 target genes . Specifically , target gene expression levels in Bmal1-/- mouse should be arrhythmic and low for the in-phase transcriptional cyclers ( no recruitment of positive transcription factors at CLOCK:BMAL1 enhancers ) , while they should be arrhythmic and high for the out-phase transcription cyclers ( no recruitment of repressors at CLOCK:BMAL1 enhancers ) . These effects should also be more obvious during the light phase when CLOCK:BMAL1 binds to DNA . In addition , Bmal1 knockout should have a reduced effect on arrhythmically and non-expressed target genes . These predictions were confirmed by analyzing a public dataset that characterized the genome-wide effect of Bmal1 knockout in the mouse liver using RNA-Seq of rRNA-depleted total RNA ( Fig 5B and 5C ) [53] . For both intronic and exonic RNA-Seq signal , the expression of Rinφ genes in Bmal1-/- mouse liver is at the trough level of wild-type mice , and at peak levels in Ro/φ genes . Moreover , Bmal1 knockout does not significantly affect the expression levels of arrhythmic and non-expressed CLOCK:BMAL1 target genes ( Fig 5B and 5C ) . Our model also predicts that the transcriptional output of CLOCK:BMAL1 target genes can be altered by environmental changes that affect u-TF DNA binding capacity . External signals that activate or repress the binding of u-TFs are predicted to impact CLOCK:BMAL1 cooperation with other transcription factors , and thereby change the transcriptional output of CLOCK:BMAL1 target genes . For example , signals that enable the recruitment of positive transcription factors at CLOCK:BMAL1 enhancers could increase the amplitude of rhythmic transcription and/or initiate the rhythmic expression of target genes that are arrhythmic under control conditions . Conversely , signals that inhibit the binding of transcription factors that normally cooperate with CLOCK:BMAL1 could blunt the rhythmic expression of some CLOCK:BMAL1 target genes . To test this hypothesis , we analyzed how fasting , which is known to affect the transcriptional activity of many u-TFs [54 , 55] , alters CLOCK:BMAL1 target gene expression in the mouse liver using a public dataset [56] . Strikingly , while the expression of Clock , Bmal1 and several direct rhythmic target genes ( e . g . , Phf17 , Slc16a2 ) are unaffected by fasting , some other targets exhibit a significantly altered gene expression profile ( Fig 5D–5G , S12 Fig for additional examples ) . For example , some rhythmic target genes become arrhythmically expressed under fasting ( e . g . , Sgk2 , Flcn ) while other targets exhibit an increased amplitude of expression ( e . g . , Gnat1 , Gm129 ) ( Fig 5E ) . Remarkably , some direct CLOCK:BMAL1 target genes that are arrhythmically or not expressed under ad libitum condition become rhythmically expressed under fasting condition ( Fig 5F and 5G ) . Because not all CLOCK:BMAL1 target genes are equally affected by fasting , these results cannot simply be explained by a global change in CLOCK:BMAL1 transcriptional activity under fasting condition . One possibility is that fasting enhances or represses the transcriptional capabilities of several u-TFs that cooperate with CLOCK:BMAL1 , thereby altering the transcriptional output of many direct CLOCK:BMAL1 target genes . Similar results were found by analyzing a public dataset investigating the effect of high-fat diet on rhythmic gene expression in the mouse liver ( S13 Fig ) [57] .
Based on the mechanisms by which CLOCK:BMAL1 regulates the expression of several core clock genes , it is commonly assumed that the rhythmic binding of CLOCK:BMAL1 to DNA is necessary and sufficient to drive the rhythmic transcription of its target genes . However , the recent characterization of CLOCK and BMAL1 cistromes in the mouse liver revealed that CLOCK:BMAL1 target gene transcription is highly heterogeneous , thereby suggesting that CLOCK:BMAL1 regulation of clock-controlled gene expression relies on more complex mechanisms than those underlying core clock gene rhythmic transcription [3 , 28–30] . We report here that CLOCK:BMAL1 heterogeneous transcriptional output does not stem from differences in the DNA binding profiles of CLOCK and BMAL1 , or the PER/CRY circadian repressive complex . Instead , we found that while CLOCK:BMAL1 rhythmically promotes chromatin decondensation at its enhancers , it is not sufficient to promote transcription activation . Based on these data and the characterization of transcription factor DNA binding profiles at CLOCK:BMAL1 enhancers , we propose that CLOCK:BMAL1 regulates the expression of clock-controlled genes by generating a permissive chromatin landscape that facilitates the binding of other transcription factors at its enhancers rather than directly promoting rhythmic transcription . Interestingly , analysis of a random set of genes not directly targeted by CLOCK:BMAL1 but exhibiting similar profiles of expression of the four CLOCK:BMAL1 transcriptional output categories suggests that this mechanism is largely specific to CLOCK:BMAL1 ( S14 Fig ) . The current models describing the regulation of rhythmic gene expression by circadian clocks in other eukaryotes are also based on how core clock components regulate their own transcription via transcriptional feedback loops . For example , the mechanisms underlying transcriptional regulation by CLOCK:BMAL1 orthologs in Neurospora ( WCC for White Collar Complex ) and Drosophila ( CLK:CYC heterodimer ) are based largely on how they regulate the expression of the core clock genes frequency ( in Neurospora ) , and period and timeless ( in Drosophila ) [2 , 58–60] . Given that the circadian clock mechanisms are highly conserved in eukaryotes , it is likely that both WCC and CLK:CYC also regulate their target gene expression by remodeling the chromatin and facilitating the binding of other transcription factors . Consistent with this hypothesis , WCC and CLK:CYC transcriptional outputs are also heterogeneous [61 , 62] , and both recruit chromatin remodelers to promote nucleosome eviction at their binding sites [63–67] . The recent characterization of many transcription factor cistromes revealed that the number of transcription factor DNA binding sites often exceeds the number of anticipated target genes , suggesting that many of these DNA binding sites are non-functional [68 , 69] . Although many CLOCK:BMAL1 DNA binding sites could be considered as non-functional because they target arrhythmically or not expressed genes , the observation that CLOCK:BMAL1 rhythmically promotes nucleosome eviction at enhancers targeting both arrhythmically expressed ( albeit with a decreased amplitude ) and non-expressed genes instead indicates that CLOCK:BMAL1 rhythmic DNA binding is not “silent” . More specifically , our data suggest that the majority of CLOCK:BMAL1 DNA binding events are functional , in that they rhythmically shape the chromatin landscape , and that transcription activation requires additional downstream events to be initiated ( e . g . , recruitment of other transcription factors ) . This hypothesis is further supported by our finding that CLOCK:BMAL1 does not directly generate a transcriptionally active enhancer . Indeed , both H3K27ac ChIP-Seq signal and eRNA transcription are minimal at CLOCK:BMAL1 enhancers targeting non-expressed genes , and are delayed at CLOCK:BMAL1 enhancers targeting out-of-phase transcriptional cyclers ( Fig 3F and 3G ) . The observation that H3K27ac ChIP-Seq signal at CLOCK:BMAL1 enhancers correlates with CLOCK:BMAL1 transcription output rather than CLOCK:BMAL1 DNA binding phase/intensity seems inconsistent with the well-described interactions between core clock proteins and histone modifiers [3 , 14–19 , 26] , and thus raises the question on whether or not CLOCK:BMAL1 DNA binding occurs with enzymatically activate histone modifiers . Interestingly , instances of enhancers bound by p300/CBP but lacking H3K27ac ( and transcriptional activity ) have been described at enhancers targeting developmental genes in human ES cells [70 , 71] . Those enhancers , which are termed poised enhancers , share most of the properties of active enhancers , including similar levels of nucleosome depletion , p300 , and chromatin remodelers binding . However , these poised enhancers are unable to drive gene expression in ES cells until they acquire H3K27ac signal during differentiation [71] . Here we found that the binding of CBP and p300 at non-expressed target genes is above background levels , and that the differences in CBP and p300 DNA binding between non-expressed and expressed target genes are smaller than those observed for H3K27ac and Pol II ChIP-Seq signal ( Figs 3Fand 4F , S10 Fig ) . It is thus tempting to speculate that the concept of poised enhancers extends to the circadian field , with CLOCK:BMAL1 rhythmically priming the chromatin landscape of “circadian poised enhancers” . While those circadian poised enhancers would share properties of active enhancers ( similar CLOCK:BMAL1 DNA binding , nucleosome eviction rhythm , etc . ) , they would be transcriptionally inactive and require the binding of other transcription-associated factors needed to trigger H3K27ac and rhythmic transcription . Investigation of the transcription factors that are recruited at CLOCK:BMAL1 enhancers revealed a surprising difference between u-TFs and ts-TFs . In particular , ts-TFs are recruited at similar levels between expressed and non-expressed CLOCK:BMAL1 target genes , suggesting that they do not significantly contribute to the heterogeneity of CLOCK:BMAL1 transcriptional output . Because ts-TFs are known to establish tissue-specific enhancers and enable the binding of u-TFs in a tissue-specific manner [72–75] , it is likely that ts-TFs contribute primarily to the binding of CLOCK:BMAL1 at tissue-specific enhancers and thus enable the generation of a tissue-specific circadian transcriptional program [8–10 , 76] . Contrary to ts-TFs , u-TFs appear to bind at CLOCK:BMAL1 enhancers targeting specific transcriptional output categories , suggesting that their nature ( i . e . , activator or repressor , constitutively active or inducible ) , as well as mode of cooperation with CLOCK:BMAL1 , likely contributes to the heterogeneity of CLOCK:BMAL1 target gene transcription . For example , the transcriptional repressors REV-ERBα and REV-ERBβ are enriched at CLOCK:BMAL1 enhancers targeting out-of-phase transcriptional cyclers , agreeing with the recently proposed model of facilitated repression whereby CLOCK:BMAL1 remodels its enhancer chromatin to facilitate the recruitment of REV-ERBs and delay the transcriptional output of some of its target genes [37] . Since rhythmically expressed genes tend to exhibit higher u-TF ChIP-Seq signal than arrhythmic and non-expressed genes ( S10 Fig ) , and given the low expression of in-phase transcriptional cyclers in Bmal1-/- mice , we propose that a major function of CLOCK:BMAL1 is to facilitate the recruitment of both positive and negative transcription factors to drive the rhythmic transcription of clock-controlled genes ( i . e . , not just to facilitate the binding of the circadian repressors REV-ERBα/β ) . Although the mechanisms underlying of this cooperation between CLOCK:BMAL1 and other transcription factors are still unknown , nucleosome-mediated cooperation between transcription factors is not unprecedented [77–82] , and several papers have shown that two non-interacting TFs can synergistically bind to DNA through a mechanism whereby the first TF leads to partial unwrapping of nucleosomal DNA , thus making the site of the second TF more accessible and thereby increasing DNA binding . This cooperation between CLOCK:BMAL1 and other TFs may explain why a large fraction of CLOCK:BMAL1 target genes are not expressed: u-TF recruitment is not sufficient to activate CLOCK:BMAL1 enhancers and promote transcription . In support of this idea , CLOCK:BMAL1 enhancers targeting non-expressed target genes are enriched for the NF-κB transcription factor motif , which is known to mediate transcriptional response to immune and inflammatory responses [41] . Because the genome-wide characterization of circadian clock mechanisms has mostly been carried out in healthy mice raised in standard laboratory conditions , NF-κB is likely inactive , sequestered in the cytosol and its target genes are not expressed . CLOCK:BMAL1 may thus prime NF-κB DNA binding upon inflammation or immune response , thereby triggering a rhythmic response to acute infection . Interestingly , such a mechanism may explain , at least in part , why the immune host response oscillates based on the time-of-day bacterial infection [83–86] . We also found that CLOCK:BMAL1 enhancers at non-expressed target genes are enriched for the transcription factor CTCF ( CCCTC-binding factor; Fig 4A ) . CTCF is known to promote long-range interactions between two or more genomic sequences , and thus bring sequences that are far apart in the linear genome into close proximity [44] . This may suggest that some CLOCK:BMAL1 DNA binding sites situated in non-expressed gene loci actually target other clock-controlled genes located hundreds of kilobases apart through long-range interactions , as recently described for one CLOCK:BMAL1 DNA binding site in the mouse liver [87] . Although it is impossible to assess the prevalence of CLOCK:BMAL1 binding sites mediating long-range chromatin interactions without the appropriate experiments , we found a few examples suggesting that this is a likely possibility ( S15 Fig ) . Transcription regulation in higher eukaryotes relies on the activity of multiple enhancers [88 , 89] . It is thus likely that CLOCK:BMAL1 target gene expression results from a complex integration between CLOCK:BMAL1 enhancers and other enhancers . Our results indicate that enhancers targeting the same gene typically share the same transcriptional activity profiles ( H3K27ac signal , eRNA levels , and Pol II ChIP-Seq signal; S8 Fig ) . Based on these observations , we cannot exclude that other enhancers targeting arrhythmically expressed CLOCK:BMAL1 target genes outcompete CLOCK:BMAL1 enhancers , to produce constitutive expression . Further experiments aimed at revealing hierarchical influences of enhancers on the regulation of gene expression at the genome-wide level will be required to directly test this hypothesis . It was recently proposed that altering the environmental conditions can reprogram circadian transcriptional programs ( e . g . , high-fat diet and antibiotics treatment in the liver , LPS treatment in the lung [57 , 90–92] ) . Our model that CLOCK:BMAL1 regulates the expression of clock controlled genes by facilitating the binding of other TFs represents a mechanistic framework for explaining how environmental signals can mediate this transcriptional reprogramming . Indeed , activation of new signaling pathways by environmental changes is likely to modulate multiple transcriptional programs , thereby altering how CLOCK:BMAL1 cooperate with those programs to drive rhythmic gene expression . Importantly , this mechanism may also explain , at least in part , why the number and nature of rhythmically expressed genes vary between datasets and laboratories [93–95] . Indeed , differences in diet , light environment and housing may all lead to changes in u-TF transcriptional activity , which may in turn affect clock-controlled gene expression . In conclusion , our data indicate that the mechanisms by which CLOCK:BMAL1 regulates the transcription of core clock genes do not apply to clock-controlled genes , and suggest that the primary function of CLOCK:BMAL1 is to regulate the chromatin landscape at its enhancers to facilitate the binding of other transcription factors . Our results therefore highlight the emerging role of other transcription factors in regulating the ~15% of genes that are rhythmically expressed in a given mammalian tissue , and suggests that clock-controlled gene expression relies more on the interplay between the circadian clock and other signaling pathways . Given that the majority of CLOCK:BMAL1 target genes are either arrhythmically or not expressed under standard conditions , our data also suggest that these non-oscillating genes may become rhythmically expressed under other environmental and/or pathological conditions , and thus expand the total number of genes under circadian control to more than 50% in mammals [10] . Finally , because the clockwork mechanisms are highly conserved between eukaryotes ( e . g . , heterogeneous transcriptional output , poor reproducibility between datasets characterizing circadian gene expression , regulation of chromatin landscape by core clock components ) , it is likely that the mechanisms we uncovered largely apply to all eukaryotic circadian clocks .
Unless notified below , publically available datasets used in this paper were downloaded from the NCBI or EBI websites in either sra or fastq formats ( see Table 1 for accession numbers ) . Files in sra format were converted to fastq files using the sratoolkit ( version 2 . 3 . 5–2 ) . Fastq files were mapped to the mouse genome ( version mm10 ) using bowtie2 [96] or tophat2 [97] . For all datasets , we only considered reads that mapped uniquely to the mouse genome ( i . e . , one unique genomic location ) . Datasets were further filtered to remove duplicated reads using samtools ( rmdup function ) or a custom-made script . Additional information is provided for each dataset as Supplementary Materials and Methods . Genomic locations of CLOCK and BMAL1 DNA binding sites in the mouse liver provided in the original paper ( supplementary S2 Table ) [3] were used to generate our list of high confidence CLOCK:BMAL1 DNA binding sites . Genomic locations were converted to the mm10 version of the mouse genome using UCSC genome browser liftOver tools , and processed as indicated in S1 Fig to generate our list of high confidence CLOCK:BMAL1 DNA binding sites . Overlap between CLOCK and BMAL1 ChIP-Seq peaks was determined using bedtools ( intersectBed ) and coordinates from BMAL1 ChIP-Seq datatsets were further kept to generate a list of 3217 CLOCK:BMAL1 peaks . We also used the original data provided by the authors in their S2 Table to assign CLOCK:BMAL1 peaks to their putative target genes ( original analysis performed using HOMER tools ) . In particular , we defined a CLOCK:BMAL1 target gene as a gene with at least one CLOCK:BMAL1 peak located between -10kb of the transcription start site and +1kb from the transcription termination site . Using this criteria , 2458 CLOCK:BMAL1 peaks were assigned to a target gene , and the remaining 759 peaks were assigned as an intergenic CLOCK:BMAL1 DNA binding site . The 2458 CLOCK:BMAL1 peaks assigned to a target gene were then parsed based on the transcription profile of their target genes using the Nascent-Seq datasets from Menet et al . , 2012 [28] . We directly used the original Nascent-Seq expression values and the assessment of their rhythmic expression from the original paper without performing new analysis . Details on how genes were determined to be rhythmically transcribed are provided in Supplementary Materials and Methods . Using these data , CLOCK:BMAL1 peaks were parsed into 4 different categories of transcriptional output ( see also S1 Fig ) : The list of the 3217 CLOCK:BMAL1 peaks parsed into the different transcriptional output categories is provided in S1 Table . The phase of rhythmic CLOCK:BMAL1 DNA binding , ChIP-Seq signal , and genomic location of CLOCK:BMAL1 DNA binding sites were retrieved from the Koike et al . , 2012 original paper supplementary S2 Table [3] and processed to generate the analysis presented in Fig 1 . ChIP-Seq , MNase-Seq and GRO-Seq signal was retrieved from bam files containing uniquely mapped reads ( and duplicated reads removed ) at CLOCK:BMAL1 enhancers using custom-made scripts [20] . Specifically , signal was retrieved at: and normalized to the sequencing depth . Differences in the window size were calculated based on the width of the ChIP-Seq signal at CLOCK:BMAL1 DNA binding sites ( e . g . , H3K27ac ChIP-Seq signal is significantly wider than any transcription factor ChIP-Seq signal ) . Because we aimed at assessing the role of CLOCK:BMAL1 in removing a nucleosome at its DNA binding site , we chose a narrower window size of 150bp ( see Fig 3A–3D ) . All analyses were performed at individual CLOCK:BMAL1 ChIP-Seq peaks , and this even for peaks targeting the same gene . Data presented in S4 Fig examined the role of multiple peaks targeting the same gene on BMAL1 and CLOCK ChIP-Seq signals . For all datasets , ChIP-Seq signal is displayed as the number of reads/bp per 100 , 000 , 000 reads . Enhancers lying into CLOCK:BMAL1 target gene loci ( -10kb from the transcription start site to +1kb from the transcription termination site ) were identified using a public mouse liver DNAse-Seq dataset ( see above ) [98] and bedtools ( intersectBed function ) . Enhancers were then parsed based on the presence or not of a CLOCK:BMAL1 ChIP-Seq peak ( 3155 out of the 3217 CLOCK:BMAL1 ChIP-Seq peaks are located into a DNaseI hypersensitive site ) . Because a majority of the 104 , 556 DHS peaks only displayed low levels of ChIP-Seq ( transcription factors , Pol II , H3K27ac ) and GRO-Seq signals [as shown in the ENCODE project , 99 , 100] , we filtered the number of DHS lying into a CLOCK:BMAL1 target gene by only considering those being into the top 40 , 000 DHS list ( based on DNase-Seq signal ) , obtaining the following number of DHS peaks: H3K27ac and Pol II ChIP-Seq signals , as well as GRO-Seq ( eRNA ) signal , were retrieved at those DHS sites ( as well as those overlapping with a CLOCK:BMAL1 peak ) using the DHS peak coordinate and normalized to 100 , 000 , 000 reads . Signal was then normalized to the coordinate length ( in bp ) to obtain the signal displayed as reads/bp per 100 , 000 , 000 reads . The coordinates used were , for the same reason as above for CLOCK:BMAL1 DNA binding sites: Because our analysis revealed the existence of small but significant overall variations of H3K27ac and Pol II ChIP-Seq signal between time points ( see S8 Fig ) , we further normalized the datasets by performing either a mean normalization ( H3K27ac ) or a ranking analysis ( Pol II ) . For H3K27ac ChIP-Seq datasets [3 , 38] , averaged H3K27ac signal was calculated at the top 40 , 000 DHS peaks ( the top 40 , 000 DHS peaks concentrate the majority of TFs DNA binding sites; peak center ± 1 kb; total of 104 , 556 total DHS peaks; dataset from Ling et al . , 2010 [98] ) for each time point . This averaged signal was then used to normalize the raw H3K27ac ChIP-Seq signal , by calculating for each time point the ratio between H3K27ac signal for each peak and this averaged signal ( see S8 Fig ) . Pol II ChIP-Seq dataset [22] were normalized by performing a ranking normalization ( method similar to a quantile normalization ) . To this end , Pol II ChIP-Seq signal was calculated at all 104 , 556 DHS peaks ( peaks mapped in Ling et al . , 2010 paper [98] ) , and sorted based on the ChIP-Seq values . The raw values for each DHS peak were then normalized using the sorted averaged ChIP-Seq signal at each of the 104 , 556 ranks for all time points . Motif analysis was performed at CLOCK:BMAL1 enhancers ( original peak coordinates ) for each of the transcriptional categories using the findsMotifGenome . pl script from the HOMER suite . Parameters were as the following: -size given–len8 . The resulting table was sorted by the q-value and a q-value less than 0 . 05 was considered significant . Percent enrichment ( percent of target sequences with motifs / percent of background sequences with motif ) was then calculated for motifs found to be significant in at least one of the CLOCK:BMAL1 transcriptional output category . Results of the motif analysis are provided as S2 Table . To determine the variance of each TF DNA binding ( CLOCK , BMAL1 , ts-TFs and u-TFs ) between the four CLOCK:BMAL1 transcriptional output categories , we computed a TF DNA binding “variability index” based on the analysis performed in S10 Fig . The variability index was calculated by summing up the standard deviation of the ChIP-Seq signal between the 4 transcriptional output groups , which was calculated for each decile ( 0 . 1 to 0 . 9 ) and normalized to the averaged signal for each decile ( the standard deviation is higher for upper deciles because ChIP-Seq signals are higher ) . This index reflects differential DNA binding strength between groups , as similar binding between the 4 groups results in small standard deviation values for each decile , and thus a small variability index . Conversely , differences in DNA binding signal between groups result in larger standard deviation values and thus a larger variability index . To determine if the results described in this paper are specific to CLOCK:BMAL1 , we also performed an analysis on genes not targeted by CLOCK:BMAL1 , but exhibiting similar profiles of expression to the 4 CLOCK:BMAL1 transcriptional output categories ( Rinφ , Ro/φ , AR and NE ) . To this end , 125 genes were randomly selected for each of the 4 groups , using criteria similar to those used to define CLOCK:BMAL1 transcriptional output ( see above ) . Levels of expression for each group were not significantly different to those of CLOCK:BMAL1 target genes ( Kruskal-Wallis test ) . Nucleosome signal , H3K27ac ChIP-Seq signal , Pol II ChIP-Seq signal , eRNA expression , tissue specific and ubiquitous transcription factor ChIP-Seq signal were all calculated as described above for CLOCK:BMAL1 target genes . Statistical analysis was also performed similarly to CLOCK:BMAL1 transcriptional output . Statistical analysis was done using JMP , Version Pro 12 . 0 . 1 . SAS Institute Inc . , Cary , NC , 1989–2007 . Differences in sequencing signal , represented in the boxplot graphs , were analyzed for statistical enrichment using the nonparametric Kruskal-Wallis test . Rhythmic analysis of nucleosome signal and ChIP-Seq signal was performed using a Fourier analysis ( Fig 3A–3D ) ( see Supplementary Materials and Methods for details ) . Differences in the amplitude of nucleosome signal rhythm ( Fig 3E ) were analyzed using a 2-way ANOVA . Differences in CLOCK:BMAL1 ChIP-Seq peaks genomic location were analyzed using a chi-square test ( Fig 1G ) , and differences in the number of CLOCK:BMAL1 peaks per target genes ( S5A Fig ) were analyzed by a Fisher’s exact test . Differences were considered significant when p < 0 . 05 .
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Circadian clocks in mammals rely on the heterodimeric transcription factor CLOCK:BMAL1 to drive rhythmic gene expression and allow biological functions to perform best at the most appropriate time of the day . Investigation of the mechanisms by which CLOCK:BMAL1 regulates its target genes transcription has led to the paradoxical observation that while CLOCK:BMAL1 DNA binding is rhythmic and occurs during the day for all target genes , its transcriptional output is highly heterogeneous . To address this issue , we analyzed independent genome-wide datasets and found that CLOCK:BMAL1 DNA binding during the day is associated with a reorganization of the chromatin that is favorable to transcription activation for all target genes . However , this diurnal CLOCK:BMAL1 DNA binding and chromatin remodeling is not sufficient to promote a transcriptionally active enhancer , therefore suggesting that CLOCK:BMAL1 cooperates with other factors to control the transcription of most of its target genes . This hypothesis is supported by our finding that ubiquitous transcription factors , but not tissue-specific transcription factors , are differentially recruited at CLOCK:BMAL1 enhancers . Altogether , our data highlight the critical role of transcription factors recruited at CLOCK:BMAL1 enhancers in regulating transcription , and present a new mechanistic framework to understand how changes in the environment can reprogram circadian transcriptional programs .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"gene",
"regulation",
"regulatory",
"proteins",
"dna-binding",
"proteins",
"dna",
"transcription",
"transcription",
"factors",
"dna",
"epigenetics",
"chromatin",
"genomic",
"signal",
"processing",
"transcriptional",
"control",
"chromosome",
"biology",
"proteins",
"gene",
"expression",
"nucleosomes",
"biochemistry",
"signal",
"transduction",
"cell",
"biology",
"nucleic",
"acids",
"genetics",
"biology",
"and",
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] |
2018
|
Regulation of circadian clock transcriptional output by CLOCK:BMAL1
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The Cytotoxic Necrotizing Factor 1 ( CNF1 ) is a protein toxin which is a major virulence factor of pathogenic Escherichia coli strains . Here , we identified the Lutheran ( Lu ) adhesion glycoprotein/basal cell adhesion molecule ( BCAM ) as cellular receptor for CNF1 by co-precipitation of cell surface molecules with tagged toxin . The CNF1-Lu/BCAM interaction was verified by direct protein-protein interaction analysis and competition studies . These studies revealed amino acids 720 to 1014 of CNF1 as the binding site for Lu/BCAM . We suggest two cell interaction sites in CNF1: first the N-terminus , which binds to p37LRP as postulated before . Binding of CNF1 to p37LRP seems to be crucial for the toxin's action . However , it is not sufficient for the binding of CNF1 to the cell surface . A region directly adjacent to the catalytic domain is a high affinity interaction site for Lu/BCAM . We found Lu/BCAM to be essential for the binding of CNF1 to cells . Cells deficient in Lu/BCAM but expressing p37LRP could not bind labeled CNF1 . Therefore , we conclude that LRP and Lu/BCAM are both required for toxin action but with different functions .
Urinary tract infections ( UTIs ) are among the most common bacterial infections of humans . More than 80% of UTIs are caused by Uropathogenic Escherichia coli ( UPEC ) strains [1] . Many pathogenic Escherichia coli strains including UPEC and strains inducing meningitis or soft tissue infections produce Cytotoxic Necrotizing Factor 1 ( CNF1 ) , a protein toxin which contributes to virulence [2] . Of major importance for its role as a virulence factor is the effect of CNF1 on epithelial barrier- and immune cell functions [3] . Both features are controlled by Rho GTPases , which are directly targeted by the toxin . CNF1 deamidates a specific glutamine ( Gln63/61 ) of Rho proteins , which is crucial for GTP hydrolysis and therefore , the Rho proteins are arrested in a constitutively activated state [4] , [5] . Rho family GTPases are regulated in a GTPase cycle by the following cellular proteins: GEFs ( guanine nucleotide exchange factors ) activate Rho proteins by GDP/GTP-exchange , GAPs ( GTPase-activating proteins ) stimulate the intrinsic GTP hydrolysis , thereby accelerating inactivation . GDIs ( guanine nucleotide dissociation inhibitors ) predominantly bind the inactive form of Rho GTPases blocking nucleotide exchange [6] . Active Rho proteins interact with several effectors , which govern a variety of cellular processes including organization of the actin cytoskeleton . Consequently , cells treated with CNF1 show a strong network of actin stress fibers , filopodia and membrane ruffles [7] . CNF1 belongs to a family of single chain AB-toxins with an N-terminal domain important for membrane translocation and the C-terminal catalytic domain encompassing deamidase activity . The toxin is taken up into mammalian cells by receptor-mediated endocytosis . It has been shown that the cytosolic precursor protein ( 37LRP ) of the non-integrin laminin receptor ( 67LR ) interacts with the N-terminus ( aa 1–342 ) of CNF1 in a yeast-two-hybrid-screen [8] . Processing of 37LRP to the mature , membrane localized 67LR is still unclear but may originate from homo- or hetero-dimerization of 37LRP with another 37LRP molecule or with galectin3 [9] . Moreover , it has been suggested by knockdown of 37LRP that 67LR mediates the uptake of CNF1 into cells [8] . Here , we identified the Lutheran ( Lu ) adhesion glycoprotein/basal cell adhesion molecule ( BCAM ) as a cellular receptor for CNF1 and show that it is essential for the intoxication of cells . The transmembrane adhesion molecule Lu/BCAM is a member of the immunoglobulin ( Ig ) superfamily with five Ig-like domains on the extracellular site , a single transmembrane domain and a short , C-terminal cytoplasmic tail . This protein , like 67LR is a receptor for the extracellular matrix protein laminin . Two transcript variants , encoding different isoforms , have been found for this gene called Lu and BCAM . The difference between Lu and BCAM is the length of the cytoplasmic tail ( 59 amino acids in Lu and 19 amino acids in BCAM ) , which is thought to mediate intracellular signaling because it contains an SH3 binding motif and 5 phosphorylation sites [10] . Lu/BCAM binds to laminin α5 , which is the major laminin α-chain in the basement membrane [11] . In humans , Lu plays a role in vaso-occlusion of red blood cells ( RBCs ) in sickle cell patients . In sickle cell RBCs , epinephrine increases a Lu/BCAM-mediated adhesion of the cells to laminin α5 in a cAMP and protein kinase A-dependent manner [12] . However , Lu/BCAM is also expressed in many other cells and tissues with a strong appearance in epithelial and endothelial cells ( for review see [13] , [14] ) . We show that CNF1 binds to Lu/BCAM on mammalian cells and that this interaction is crucial for the uptake of the toxin . The interaction of CNF1 with Lu/BCAM occurs within a region located in close proximity to the C-terminal catalytic domain , whereas the N-terminus of CNF1 interacts with the 37LRP precursor of the non-integrin laminin receptor [8] . Therefore , CNF1 seems to contact two different laminin binding proteins , using separate domains .
Identification of the cellular receptor is a crucial task in understanding bacterial toxins . For CNF1 it has been reported that the N-terminal 342 amino acids of the toxin interact with the 37 kDa laminin receptor precursor ( 37LRP ) [8] . This intracellular protein matures to the 67 kDa cell surface-localized laminin receptor ( 67LR ) . We confirmed a week interaction of CNF1 with 67LR by overlay assays ( not shown ) . Therefore , we used purified 67LR from cells , which secrete this receptor into the culture medium [15] . However , some cell lines like RBL ( rat basophil leukemia ) or HeLaS3 ( human cervix carcinoma , subclone 3 ) cells express 37LRP but are not intoxicated by CNF1 , suggesting that another structure on the cell surface may be necessary for efficient binding and endocytosis of the toxin . In line with this , it has been shown , using monoclonal antibodies , that besides the N-terminal receptor binding domain , a second part of CNF1 is involved in binding to mammalian cells [16] . We screened for cell surface protein receptors CNF1 binds to by incubating HeLa cells with a double-tagged GST-CNF1-GST fusion protein at 4°C . As controls we used GST alone or the analogue double-tagged form of the CNF family member CNFY ( GST-CNFY-GST , Yersinia pseudotuberculosis toxin CNFY ) . This toxin is known to interact with a different , yet unknown receptor on mammalian cells [17] . Following binding , we lysed the cells and precipitated the toxin together with associated molecules , using anti-GST magnetic beads . Eluates were separated on SDS-PAGE and the eluted proteins were subsequently identified by nanoLC-MS/MS . The only hit unique to the CNF1-precipitate was the Lutheran ( Lu ) adhesion glycoprotein/basal cell adhesion molecule ( BCAM ) ( Fig . S1 ) . This surface protein has a large extracellular Ig-like structure and is widely expressed . Interestingly , Lu/BCAM like the proposed CNF1 receptor 67LR interacts with laminin , suggesting that the receptor-binding domain of CNF1 could interact with both laminin binding structures on the cell surface . To verify the CNF1-Lu/BCAM interaction , we repeated the precipitation assay with HEK293 ( Fig . 1A ) and HeLa cells ( Fig . 1B ) and analyzed the presence of Lu/BCAM in the precipitate by Western-blotting with a specific antibody against Lu/BCAM . As shown in Fig . 1 , Lu/BCAM was exclusively co-precipitated with GST-CNF1-GST but not with GST-CNFY-GST or GST alone . Notably , we could not detect 37LRP/67LR in any lane by Western-blotting although the protein was expressed in HeLa and in HEK293 ( human embryonic kidney ) cells ( Fig . S2 ) . We asked whether Lu/BCAM is an alternative receptor in the absence of 67LR or whether binding to Lu/BCAM is generally crucial for toxin uptake . In the latter case , blocking the interaction of CNF1 with Lu/BCAM should inhibit the uptake of the toxin into cells . Uptake of CNF1 can be monitored by the shift of deamidated RhoA to higher molecular weight in SDS-PAGE [5] and by the morphological changes of cells induced by the toxin . To this end , we performed competition experiments with recombinant BCAM ( rBCAM ) and CNF1 on HeLa cells . We incubated the toxin with rBCAM ( in a molar ratio CNF1∶rBCAM of 1∶1 , 1∶10 and 1∶100 , respectively ) for 20 min and then treated cells with the mixture . As shown in Fig . 2A , preincubation of CNF with rBCAM inhibited uptake of the toxin in a concentration-dependent manner , indicating direct interaction of the proteins . In a second approach , we blocked the binding of CNF1 to Lu/BCAM directly on the cell surface . For these experiments , HeLa cells were treated with an anti Lu/BCAM antibody ( AB B12 ) that binds to the extracellular domain of Lu/BCAM . As control we used an anti-Lu/BCAM antibody ( AB C16 ) directed against the intracellular part of the glycoprotein . Toxin uptake was determined by the amount of modified RhoA ( shift in SDS-PAGE ) after 2 h of incubation . Fig . 2B shows that pre-incubation of cells with the antibody against Lu/BCAM extracellular domain inhibited the uptake of CNF1 , whereas the control antibody had no effect . As expected , the antibodies added to the cells without the toxin had no effect . The data indicate that binding of CNF1 to Lu/BCAM is required for the uptake of the toxin into cells . To visualize CNF1 on the cell surface , we fluorescently labeled GST-CNF1 ( green ) and studied colocalization with Lu/BCAM using an antibody against the receptor and a second rhodamine labeled antibody ( red ) . As negative control we used labeled GST-CNFY . Binding of the toxins was performed at 4°C and the cells incubated for further 30 min at 37°C to allow endocytosis . The cells were fixed and stained for Lu/BCAM . As shown in Fig . 2C CNF1 colocalized with Lu/BCAM , whereas there was no colocalization of CNFY with this receptor . Colocalization occurred in vesicular structures . It is known that CNF1 is released from endosomes following acidification [17] . Therefore , we confirmed the colocalization of CNF1 with the endosomal marker EEA1 , which exclusively localizes to early endosomes . For immunofluorescence we used an antibody against EEA1 and a second rhodamine labeled antibody ( red ) . As shown in Fig . 2C ( top ) the toxin was localized to early endosomes . To corroborate the requirement of Lu/BCAM for toxin binding and/or uptake we made use of human immortalized myelogenous leukemia cells ( K562 ) , which do not express Lu/BCAM . As control the same cell line stably expressing recombinant Lu/BCAM ( K562-Lu/BCAM ) was employed [18] . We verified expression of Lu/BCAM and analyzed the presence of 37LRP in the two cell-lines by Western-blotting . Whereas 37LRP is expressed in both cell lines , Lu/BCAM could only be detected in K562-Lu/BCAM but not in K562 cells ( Fig . S2 ) . To study the uptake of CNF1 , leukemia cells and Lu/BCAM-expressing cells were treated with the toxin for different time periods and , subsequently , the uptake of CNF was analyzed by the shift of modified RhoA . The CNF1-induced shift of RhoA was incomplete in K562 cells not expressing Lu/BCAM even after overnight intoxication ( Fig . 3A , bottom ) . By contrast , in Lu/BCAM expressing cells deamidation of RhoA occurred within 1 h ( Fig . 3A , top ) . Apparently a low amount of toxin was taken up into the leukemia cells which do not express Lu/BCAM . This may occur by unspecific pinocytosis or by binding to the 67LR present on both cell lines . The data indicate that Lu/BCAM is essential for efficient uptake of CNF1 . To further support this finding we performed competition experiments with rBCAM and CNF1 on K562-Lu/BCAM cells . We pre-incubated the toxin with rBCAM ( in a molar ratio CNF1∶rBCAM of 1∶1 , 1∶10 and 1∶100 , respectively ) for 20 min and treated the cells with the mixture . As expected , preincubation of CNF with rBCAM inhibited uptake of the toxin in a concentration-dependent manner ( Fig . 3B ) . We intended to distinguish between binding and uptake of the toxin . Therefore , we performed fluorescence activated cell sorting ( FACS ) analysis with DyLight488-labeled GST-CNF1 . First , we added increasing amounts of labeled toxin to HeLa cells , incubated the cells for 15 min on ice and washed them with PBS . Afterwards we analyzed the fluorescence of the cells by FACS measurements . As expected , increasing concentrations of the labeled toxin shifted the cells to higher fluorescence , indicating specific binding of GST-CNF to the cells ( Fig . S3 , left ) . Data from 3 independent experiments were quantified ( Fig . S3 , right ) . We further studied the toxin Lu/BCAM interaction and performed competition experiments firstly with recombinant BCAM and in a second approach with antibodies . We either used increasing amounts of an anti-Lu/BCAM antibody directed against the extracellular domain of the receptor or an antibody directed against 37LRP . All competitors were used in increasing amounts ( molar ratio CNF1∶competitor from 1∶0 to 1∶50 , respectively as indicated ) . The fluorescence decreased to lower levels in the presence of the competitors rBCAM ( Fig . 4A ) and anti-Lu/BCAM ( Fig . 4B ) , respectively but not in the presence of a 50-fold molar excess of anti-37LRP ( Fig . 4C ) . Whereas GST-CNF1 binding was decreased with recombinant BCAM to about 25% of control , the effect was less pronounced but significant with the antibody directed against Lu/BCAM . No change in fluorescence was detected in the presence of the antibody against 37LRP . The data indicate that the toxin specifically binds to Lu/BCAM . To verify binding of the toxin to Lu/BCAM , we performed FACS analysis with K562 cells ( Fig . 4D ) and with receptor expressing K562-Lu/BCAM cells ( Fig . 4E ) . At first cells were incubated with DyLight488-labeled GST-CNF1 ( 2 µg/mL ) , washed and analyzed . In line with our precipitation experiments , K562 cells only marginally shifted to higher fluorescence , whereas the K562-Lu/BCAM cells showed a broad spectrum of fluorescent cells ranging from low to high fluorescence . The latter may correspond to the degree of Lu/BCAM overexpression in these cells , which seems to vary from cell to cell . Pre-incubation of labeled GST-CNF1 with rBCAM concordantly reduced the amount of toxin bound to these cells but did not completely block CNF-binding . This effect may be based on the high amount of Lu/BCAM expressed . Lu/BCAM is known to be modified by glycosylation . Four N-glycosylation sites have been identified , which account for approximately 15% weight composition of the protein [13] . Some bacterial toxins are recruited to cellular membranes by binding to sugar chains before a high affinity interaction with a surface protein occurs . This has been shown for example for tetanus neurotoxin [19] . In our co-precipitation assay ( Fig . 1 ) only the high molecular weight form of Lu/BCAM was enriched by binding to CNF1 , whereas the lower molecular weight form was not precipitated with CNF , although it appeared as the most prominent band in the lysate . This indicates that N-glycosylation may be important for interaction with the toxin . Therefore , we tested , whether N-glycosylation of Lu/BCAM plays a role for CNF1 binding . The recombinant BCAM used in this study was purified from Lu/BCAM-overexpressing mammalian cells . The protein is fully glycosylated . We treated recombinant BCAM with PNGaseF and analyzed deglycosylation by SDS-PAGE . Deglycosylated rBCAM should run lower according to its lower molecular weight . As shown in Fig . S4A , PNGaseF-treated rBCAM runs at a lower molecular weight ( 67 kDa ) as compared with the glycosylated rBCAM ( 84 kDa ) . This is indicative of effective deglycosylation [14] . We spotted CNF1 on nitrocellulose and repeated the overlay assay as described above with glycosylated recombinant BCAM and PNGaseF-treated deglycosylated rBCAM . In this assay CNF1 still interacts with deglycosylated rBCAM ( Fig . S4B ) . Moreover , treatment of cells with PNGaseF and subsequent FACS-analysis revealed that the toxin binds with even higher affinity to the cells ( Fig . S4C ) . This may be due to a higher accessibility of the protein part of the receptor . The data indicate that N-glycosylation is not necessary for the interaction of CNF1 with Lu/BCAM . The exclusive precipitation of the high molecular weight form of the protein from cells may indicate that this is the surface exposed form of Lu/BCAM . Two sites of CNF1 have been described to be necessary for its interaction with mammalian cells: The N-terminal receptor binding domain ( aa 1–342 , [16] ) and a small part of CNF1 adjacent to the catalytic domain ( aa 683–730 , [16] ) . To narrow down the region of CNF1 , which interacts with Lu/BCAM , at first we studied direct protein-protein interaction . We performed dot-blot and surface plasmon resonance ( Biacore ) studies with recombinant BCAM and several CNF1 fragments . Additionally , we used the CNF-family member CNFY , which is identical in length and shares amino acid identity to CNF1 of 61% ( CNFY ) [20] . All proteins were tested for correct folding/activity in an in vitro Rho shift assay , which indicates deamidation of recombinant RhoA . As expected , only the N-terminal fragment of CNF1 ( aa 1–342 ) , which does not contain the catalytic domain , did not catalyze the deamidation of RhoA ( Fig . S5 ) . For dot-blots , we spotted 3 µM solutions of GST-CNFs , GST-CNF fragments and GST alone , respectively , onto nitrocellulose . After membrane blockage , recombinant BCAM was added and bound rBCAM was detected using anti-BCAM antibody . Equal protein load was analyzed by visualizing the GST part of the spotted proteins with an anti GST-antibody . As shown in Fig . 5A-top , significantly more rBCAM bound to CNF1 as compared with CNFY . In contrast , rBCAM did not interact with GST . The data indicate that CNF1 binds with higher affinity to rBCAM as compared to CNFY . This is in line with previous competition experiments , which indicate that CNF1 and CNFY apparently bind to a different receptor [17] , [20] . Moreover , the binding site seems to be located in close proximity to the catalytic domain . The CNF1 fragments 426–1014 , 709–1014 and 720 to 1014 exhibited strong binding to rBCAM , indicating that amino acids 720 to 1014 of CNF1 are sufficient for the interaction with this receptor ( 5A-bottom ) . In contrast , the N-terminal part ( aa 1–342 ) , which was previously suggested to be responsible for CNF-receptor interaction , did not interact with rBCAM . For quantitative analysis of the protein-protein interaction we used plasmon resonance spectroscopy ( Biacore ) . For these studies we used recombinant Lu/BCAM , which contains a C-terminal human IgG domain for purification ( Sino biology ) . We covalently coupled an antibody against human IgG to two lanes of a CM5-biacore chip . For analysis of the rBCAM-CNF1 interaction , rBCAM as ligand ( only to lane 2 ) and in a second step , CNF proteins as analyte ( both lanes ) were guided over the lanes . This allows for correction of unspecific protein binding to the antibody . In Fig . 5B bound protein is given as relative units ( RU ) corrected for the unspecific binding to lane 1 . The data show that the strongest binding to rBCAM occurs with the full-length toxin and with the C-terminal part of CNF1 , whereas no interaction with rBCAM could be detected with CNFY or with the N-terminal part of CNF1 ( aa 1–342 ) . The fragment 426–1014 was weaker in its interaction to Lu/BCAM as compared to the C-terminal fragments 709–1014 or 720–1014 . The difference between CNF1 ( aa 426–1014 ) and the shorter fragment may be based on different protein stability or better accessibility of the receptor binding domain in the shorter fragments which needs further investigation . The data suggest that the interaction site of CNF1 with Lu/BCAM is located within amino acids 720 to 1014 and does not occur with the N-terminus ( aa 1–342 ) . To confirm these results , we repeated the FACS experiments as described above with labeled GST-CNF1 and increasing amounts of unlabeled CNF1 fragments ( molar ratio CNF1∶competitor from 1∶0 to 1∶30 , respectively as indicated ) . Moreover , we used untagged CNF1 for competition . As expected , the C-terminal fragments of the toxin ( aa 709–1014 and aa 720–1014 ) were able to inhibit binding of CNF1 to the cells whereas pre-incubation with the N-terminus ( aa 1–342 ) had no effect ( Fig . 6 ) . In line with the dot-blot analysis , the fragment 426–1014 was weaker in its interaction to Lu/BCAM as compared to the C-terminal fragments . The catalytic part of CNF1 has been localized to amino acids 720 to 1014 [21] . Our data show that the binding site of the toxin to Lu/BCAM is located adjacent to this part and even overlap . Therefore , we tested , whether the toxin deleted for the N-terminus but containing a translocation domain , receptor binding and catalytic part may be sufficient for intoxication of cells . We incubated HeLa cells with the CNF1 fragment ( aa 343 to 1014 ) , which contains the catalytic domain , the Lu/BCAM binding site and additionally the two hydrophobic regions H1 and H2 suggested to mediate insertion into the endosomal membrane [22] . We lysed the cells and analyzed the modification of RhoA using the Rho shift assay . Even a high amount of the toxin part ( 1 , 5 µM ) added to mammalian cells was not sufficient to intoxicate the cells ( not shown ) . This indicates that besides the CNF1-Lu/BCAM interaction , binding to p37LRP is required for the intoxication of the cells . One explanation could be that the protein is not released into the cytosol . However , further experiments are required , to analyze this feature . Our data are summarized in Fig . 7 .
Our study sheds new light on the uptake mechanism of the bacterial toxin CNF1 . It is widely accepted that the toxin enters the cytosol of mammalian cells from endosomes , following receptor-mediated endocytosis [23] . Inhibition of endosome acidification with bafilomycin A completely blocks intoxication of cultured cells [17] , [24] . Here , we identified Lu/BCAM as a crucial receptor for the toxin . For precipitation of CNF1-interacting proteins , we used a method recently employed for the identification of the alpha-toxin receptor [25] . Following binding to the surface of living HeLa cells and subsequent lysis , we isolated the toxin-protein complexes , using magnetic beads . This method ensures correct folding and orientation of membrane proteins during toxin binding . Moreover , intracellular proteins and cytosolic domains of membrane proteins are excluded as interaction partners . MALDI-TOF analysis of the precipitate identified Lu/BCAM as interaction partner for CNF1 . We verified CNF1 Lu/BCAM interaction by several methods: Western-blotting revealed that Lu/BCAM was exclusively co-precipitated with CNF1 but not with CNFY . Second , dot-blot analysis with several CNF1 fragments and recombinant BCAM delineated the site of interaction to the C-terminus ( aa 720 to 1014 ) . Finally , we supported our findings with surface plasmon resonance measurements ( Biacore ) . In this system we were not able to displace CNF1 from BCAM using several conditions like high salt buffer ( 500 mM NaCl , 20 mM Tris/HCl , pH 7 . 4 ) basic or acidic buffer ( 50 mM NaOH , 10 mM glycine , pH 2 . 5 ) . Therefore , it was not possible to calculate the dissociation constant ( Kd ) of the rBCAM-CNF interaction . Binding to Lu/BCAM occurred with amino acids 720–1014 with comparable affinity as determined for the full-length protein . Using FACS analysis , we show that this part of the toxin is sufficient for interaction with the cell surface . Moreover , it is sensitive for competition with antibodies against Lu/BCAM or with an excess of recombinant BCAM . In previous studies the non-integrin laminin receptor precursor 37LRP has been identified as interaction partner for the N-terminal part of CNF1 in a yeast two-hybrid screen [8] . This part has been suggested as the toxins receptor binding domain , because an excess of a corresponding CNF1 peptide incubated together with full-length CNF1 blocks intoxication of cells [21] . In line with this , the N-terminal binding partner 37LRP/67LR seems to be required for the action of the toxin . It has been shown that 37LRP/67LR is important for intoxication of mammalian cells and for the opening of the brain barrier in a mouse model [26] , [27] . No direct binding to the cell surface was proven and p37LRP/p67 may have other functions like transport into the cell . Surprisingly , we could not find any interaction with Lu/BCAM and the previously suggested N-terminal receptor binding site . This suggests that 37LRP/67LR and Lu/BCAM are not alternative receptors , although they are both laminin-binding proteins . We found Lu/BCAM to be crucial for binding , because cells deficient in Lu/BCAM but expressing LRP could not bind labeled CNF1 . This indicates that 37LRP/67LR is not sufficient for toxin binding . Therefore , we conclude that 37LRP/67LR and Lu/BCAM are both required for toxin action but with different functions . We suggest two interaction sites in CNF1: The N-terminus binding to 37LRP/67LR as postulated by Kim [8] , [26] and the C-terminus ( probably a region around amino acids 683–730 , [16] ) binding with high affinity to Lu/BCAM . In line with this , two different cell interaction sites of CNF1 have been identified by McNichol and coworkers , which both are necessary for efficient intoxication of cells [16] . Our data show that Lu/BCAM is required for toxin binding . However , it is not sufficient for intoxication of mammalian cells , since CNF1 deleted for the N-terminal 342 amino acids can bind to cells but is not able to intoxicate them . What is the role of the N-terminus and its interaction with 37LRP/67LR ? We observed that the N-terminal part of CNF1 forms SDS stable tetramers , although only a small fraction of the purified protein was found in the oligomer-band ( data not shown ) . Further studies are required to analyze a possible oligomerization of CNF1 . We speculate that 37LRP/67LR may bind to the pre-formed oligomer and may play a crucial role for the uptake of CNF1 into the cytosol . Initial binding of the toxin to the cell surface requires Lu/BCAM . It remains to be analyzed , whether interaction of an oligomer is required for pore-formation in the endosomal membrane and/or for transport of the catalytic domain into the cytosol . Taken together , we present a novel model of the action of CNF1 , which is outlined in Fig . 7 . We suggest that CNF1 binds to the surface of mammalian cells with its receptor binding domain located adjacent to the catalytic domain . The crystal structure of the catalytic domain ( aa 720–1014 ) reveals an additional helix ( aa 720 to 734 ) sticking out of the otherwise globular structure ( 735 to 1014 ) . This part does not directly contribute to the catalytic core domain of the toxin [28] but may connect the receptor binding domain of CNF1 with the catalytic domain or even is part of the Lu/BCAM interaction site . Further studies are required to analyze this important feature . We have shown recently that cleavage of CNF1 enhances its biologic activity and that this cleavage does not occur in the cytosol but either on the cell surface or within endosomes [29] . Two surface binding sites ( one at the N-terminus and the other near the C-terminus ) would allow toxin cleavage on the membrane/vesicle surface without losing the interaction of separated toxin parts . This allows subsequent pore formation and translocation . Thus , while some toxins ( e . g . , diphtheria toxin ) are functionally connected by SS-bridges , CNF might use two cell surface binding sites for efficient up-take .
HeLa and Hek293 cells were grown in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% fetal calf serum , 1% nonessential amino acids , penicillin ( 4 mM ) , and streptomycin ( 4 mM ) . K562 wt and K562 Lu/BCAM ( provided by Anne Filipe , INSERM Paris ) were grown in RPMI ( RPMI-1640 ) containing 10% fetal calf serum , penicillin ( 4 mM ) and streptomycin ( 4 mM ) . K562 cells expressing Lu/BCAM were grown in medium containing 1 mM Geniticin . T47D cells were cultured in DMEM supplemented with 10% fetal calf serum , 1% nonessential amino acids , penicillin ( 4 mM ) , and streptomycin ( 4 mM ) and 80 iU/l insulin . All cell lines were cultivated in a humidified atmosphere containing 5% CO2 at 37°C . For competition experiments cells were grown to subconfluency and treated with appropriate amounts of toxin for 1 to 16 h , as indicated . For competition assays cells were pre-incubated with antibodies against Lu/BCAM , or the toxins were pre-treated with recombinant BCAM ( Sinobiologics ) prior to intoxication as indicated . Cell lysates were generated as follows: Cells were washed twice with phosphate buffered saline ( PBS ) and lysed with GST-Fish buffer ( 10% glycerol , 50 mM Tris/HCl , pH 7 . 4 , 100 mM NaCl , 1% NP-40 , 2 mM MgCl2 and 1 mM PMSF ) . Lysates were cleared by centrifugation ( 20 min , 21 , 000 g , 4°C ) . To separate cytosolic and membrane fractions , lysates were centrifuged ( 1 h , 100 , 000 g , 4°C ) . Membrane pellets were dissolved in hot sample buffer ( 100 mM Tris ( pH 6 . 8 ) , 10% sodium dodecyl sulfate ( SDS ) , 10% glycerol , 100 mM dithiotreitol ( DTT ) ) . In-gel digests were performed as described in standard protocols . Briefly , the excised gel bands were destained with 30% ACN , shrunk with 100% ACN , and dried in a Vacuum Concentrator ( Concentrator 5301 , Eppendorf , Hamburg , Germany ) . Digests with trypsin were performed overnight at 37°C in 0 . 1 M NH4HCO3 ( pH 8 ) . About 0 . 1 µg of protease was used for one gel band . Peptides were extracted from the gel slices with 5% formic acid . All LC-MS/MS analyses were performed on an ion trap mass spectrometer ( Agilent 6340 , Agilent Technologies ) coupled to an 1200 Agilent nanoflow system via a HPLC-Chip cube ESI interface . Peptides were separated on a HPLC-Chip with an analytical column of 75 µm i . d . and 150 mm length and a 40-nL trap column both packed with Zorbax 300SB C-18 ( 5 µm particle size ) . Peptides were eluted with a linear acetonitrile gradient with 1%/min at a flow rate of 300 nL/min ( starting with 3% acetonitrile ) . MS/MS analyses were performed using data-dependent acquisition mode . After a MS scan ( standard enhanced mode ) , a maximum of three peptides were selected for MS/MS ( CID , standard enhanced mode ) . The automated gain control was set to 350000 . The maximum accumulation time was set to 300 ms . Mascot Distiller 2 . 1 ( Matrix Science , UK ) was used for raw data processing and for generating peak lists , essentially with standard settings for the Agilent ion trap . Mascot Server 2 . 2 ( Matrix Science , UK ) was used for database searching with the following parameters: peptide mass tolerance: 1 . 1 Da , MS/MS mass tolerance: 0 . 3 Da , 13C: 1 , enzyme: trypsin with max . 2 missed cleavage , variable modifications: Gln→pyroGlu ( N-term . Q ) , oxidation ( M ) and carbamidomethyl ( C ) . The SwissProt database was used for database searching . Scaffold 4 . 0 . 4 ( Proteome Software , USA ) was used for statistical analysis and filtering of the search results ( protein threshold: 99% , peptide threshold: 80% , minimum number of peptides: 2 ) . The 67LR was purified from T47D human breast carcinoma cells ( obtained from Marc Hirschfeld , Albert-Ludwigs-Universität; Frauenklinik ) . After rotational incubation these cells shed the receptor into the medium . Cells were grown up to 70–75% confluency , washed with calcium-free PBS and harvested by trypsination for 5 minutes with TE ( Trypsin-EDTA ) . The cells were centrifuged at 800 g for 10 minutes . Approximately 2×107 cells were suspended in 1 ml ice-cold PBS and incubated for 4 h at 37°C under rotation . After removal of the cells by centrifugation at 16000 g for 15 min the shed receptor was precipitated from the supernatant with acetone or frozen at −80°C . Cell lysates were separated by 12 . 5% SDS-PAGE ( for RhoA-shift assay 15% urea-SDS-PAGE ) and transferred onto a polyvinylidene difluoride membrane . Membranes were either blocked with skimmed milk ( 5% ) or bovine serum albumin ( BSA , 3% ) for 1 h at room temperature . Proteins were detected using anti-CNF1 monoclonal antibody ( 1∶3000; Santa Cruz ) , anti-RhoA monoclonal antibody ( 1∶500; Santa Cruz ) , anti-Lu/BCAM ( 1∶2500; Abcam; 1∶1000; Santa Cruz ) or anti-LRP ( 1∶1000 , Santa Cruz ) and a respective horseradish peroxidase-coupled second antibody . Detection occurred by enhanced chemiluminescence . Hek293 and HeLa cells ( 2–3 subconfluent 15 cm dishes ) were incubated with GST-CNF1-GST , GST-CNFY-GST or GST ( 500 ng/ml , respectively ) for 20 min at 4°C . Cells were harvested and lysed and the IP was conducted using anti-GST magnetic beads according to the manufacturers manual ( Miltenyi Biotech ) . CNF proteins in given concentrations were spotted onto a nitrocellulose membrane at 4°C . After drying , membranes were incubated with 5 nM recombinant BCAM in TBST , 30 min at 4°C and blocked in 5% milk for 1 h at room temperature . The membranes were washed 3 times with TBST ( Tris buffered saline; 0 . 05% Tween-20 ) . Binding of rBCAM was detected using anti-Lu/BCAM antibodies ( 1∶2500; Abcam ) . GST served as control and was detected with anti-GST antibodies ( 1∶500; Santa Cruz ) . Detection occurred by enhanced chemiluminescence with horseradish peroxidase-coupled second antibodies . An antibody against human IgG ( Santa Cruz , 20 µg/mL ) was covalently coupled to two lanes of a CM5-biacore chip with 400 mM N-ethyl-N-dimethylaminopropyl-carbodiimide ( EDC ) and 100 mM N-hydroxy-succinimide ( NHS ) . The lanes were saturated with 1M ethanolamine . As ligand recombinant BCAM containing a C-terminal human IgG domain ( 0 . 2 µM ) was exclusively guided over lane 2 . In a second step , CNF proteins as analyte ( 1 µM ) were guided over both lanes . Bound protein was determined as relative units ( RU ) corrected for the unspecific binding to lane 1 . Regeneration occurred with 10 mM glycine/HCl , pH 2 . 5 . Cells were detached from culture dishes by incubation with 10 mM EDTA/PBS . The cell suspension was washed twice with PBS and kept on ice . 250 , 000 cells were incubated 20 min at 4°C with different concentrations of labeled toxins , and fluorescence was measured with a fluorescence-activated cell sorter ( FACS ) using the BD FACSCalibur platform . Cell surface-bound fluorescence was detected with an argon-ion laser ( 488 nm ) and the 530-nm-band-pass filter ( FITC ) . Toxins were labeled according to the manufacturer's manual ( DyLight 488 carboxylic acid succinimidyl ester ( Invitrogen ) . For competition assays cells were either pre-incubated with anti-Lu/BCAM ( Santa Cruz ) or the toxins were pre-incubated with recombinant BCAM ( Sinobiologics ) . Cells were washed with phosphate buffered saline ( PBS ) and prefixed with ice cold methanol supplemented with 1 mM EGTA . After 10 min cells were transferred to 4% formaldehyde in PBS , washed , permeabilized with 0 . 15% Triton X-100 in PBS for 10 min and blocked by 1% BSA in PBS for 30 min . Incubation with the primary antibody ( anti-EEA1 1∶200 , Santa Cruz ) ( anti-Lu/BCAM 1∶150 , Santa Cruz ) was overnight at 4°C in PBS . Cells were washed with PBS and incubated with the suitable secondary antibody in PBS for 1 h at room temperature . Cells were washed , dried and embedded with Mowiol supplemented with DABCO ( Sigma , St . Louis , MO , USA ) . Cells were analyzed with an inverted Axiovert 200 M microscope ( Carl Zeiss GmbH , Jena , Germany ) , driven by Metamorph imaging software ( Universal Imaging , Downingtown , PA , USA ) , with a Yokogawa CSU-X1 spinning disc confocal head ( Tokyo , Japan ) with emission filter wheel , with a Coolsnap HQ II digital camera and with 488 nm and 561 nm laser lines . Images were processed with Metamorph software .
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We study a crucial virulence factor produced by pathogenic Escherichia coli strains , the Cytotoxic Necrotizing Factor 1 ( CNF1 ) . More than 80% of urinary tract infections ( UTIs ) , which are counted among the most common bacterial infections of humans , are caused by Uropathogenic Escherichia coli ( UPEC ) strains . We and others elucidated the molecular mechanism of the E . coli toxin CNF1 . It constitutively activates Rho GTPases by a direct covalent modification . The toxin enters mammalian cells by receptor-mediated endocytosis . Here , we identified the protein receptor for CNF1 by co-precipitation of cell surface molecules with the tagged toxin and subsequent Maldi-TOF analysis . We identified the Lutheran ( Lu ) adhesion glycoprotein/basal cell adhesion molecule ( BCAM ) as receptor for CNF1 and located its interaction site to the C-terminal part of the toxin . We performed direct protein-protein interaction analysis and competition studies . Moreover , cells deficient in Lu/BCAM could not bind labeled CNF1 . The identification of a toxin's cellular receptor and receptor binding region is an important task for understanding the pathogenic function of the toxin and , moreover , to make the toxin accessible for its use as a cellbiological and pharmacological tool , for example for the generation of immunotoxins .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"biology"
] |
2014
|
Lu/BCAM Adhesion Glycoprotein Is a Receptor for Escherichia coli Cytotoxic Necrotizing Factor 1 (CNF1)
|
Extensive electrophysiology studies have shown that many V1 simple cells have nonlinear response properties to stimuli within their classical receptive field ( CRF ) and receive contextual influence from stimuli outside the CRF modulating the cell's response . Models seeking to explain these non-classical receptive field ( nCRF ) effects in terms of circuit mechanisms , input-output descriptions , or individual visual tasks provide limited insight into the functional significance of these response properties , because they do not connect the full range of nCRF effects to optimal sensory coding strategies . The ( population ) sparse coding hypothesis conjectures an optimal sensory coding approach where a neural population uses as few active units as possible to represent a stimulus . We demonstrate that a wide variety of nCRF effects are emergent properties of a single sparse coding model implemented in a neurally plausible network structure ( requiring no parameter tuning to produce different effects ) . Specifically , we replicate a wide variety of nCRF electrophysiology experiments ( e . g . , end-stopping , surround suppression , contrast invariance of orientation tuning , cross-orientation suppression , etc . ) on a dynamical system implementing sparse coding , showing that this model produces individual units that reproduce the canonical nCRF effects . Furthermore , when the population diversity of an nCRF effect has also been reported in the literature , we show that this model produces many of the same population characteristics . These results show that the sparse coding hypothesis , when coupled with a biophysically plausible implementation , can provide a unified high-level functional interpretation to many response properties that have generally been viewed through distinct mechanistic or phenomenological models .
As we seek to understand how sensory nervous systems process information about their environment , one of the most common quantitative descriptors of neural coding has been the notion of a classical receptive field ( CRF ) [1] . In general , the CRF is a measurement of the portion of the stimulus space that causes a change in a neuron's response when a stimulus is presented ( or removed ) . For example , beginning with the pioneering work of Hubel and Wiesel [2] , simple cells in the primary visual cortex ( V1 ) have been characterized as feature detectors with CRFs that are selective for location , orientation and spatial frequency . Unfortunately , a simple linear-nonlinear model based on the measured CRF ( e . g . , linear filtering with the CRF followed by nonlinear thresholding or saturation ) is insufficient to explain many response properties of V1 cells . For example , extensive electrophysiology studies have shown that many V1 simple cells also receive contextual influence where stimuli not part of the CRF can modulate the cell's response to CRF stimuli ( reviewed in [3] ) . Furthermore , when driven by rich stimuli within the CRF , simple cells exhibit complex nonlinear response properties that cannot be captured by thresholding or saturation alone [4] . We use the term non-classical receptive field ( nCRF ) effects to collectively refer to these contextual modulations and nonlinear response properties . Understanding nCRF effects is likely critical for understanding the coding of natural stimuli because they arise under stimulus conditions that are more complex and ecologically relevant than the stimuli often used in CRF mapping experiments ( e . g . , sinusoidal gratings , white noise , sparse dots ) . Indeed , recent electrophysiology experiments with natural video stimuli have shown contextual influence in V1 responses [5]–[8] . Furthermore , observed V1 nCRF effects have been related to perceptual contextual effects such as contour integration [9] . Given the wide range of different nCRF effects reported in the literature , it is still unclear how these effects are related or what collective role they play in sensory coding . Many individual nCRF effects have been successfully described in terms of potential underlying circuit mechanisms ( i . e . , mechanistic models , reviewed in [10] ) or compact stimulus/response descriptions ( i . e . , phenomenological models , reviewed in [3] ) . While valuable , these approaches do not fully address the functional significance of nCRF effects or illuminate their role in sensory information processing . In another direction , individual nCRF effects have also been connected to potential benefits in specific tasks ( e . g . , curvature detection [11] , contour integration as reviewed in [12] , figure-ground segregation as reviewed in [13] ) . While these approaches are also valuable , these types of models have limited explanatory power because they only address narrow subsets of biological vision ( i . e . , individual tasks ) and they do not show that the processing strategies represented by nCRF effects are optimal for the given tasks . In short , models constructed for individual effects do not connect this broad range of response properties to the optimal sensory coding strategies that can provide a parsimonious description in terms of fundamental system goals . One central goal of theoretical and computational biology is to provide functional insight into biological phenomenon by using high-level models ( often abstracting away specific experimental detail ) to generalize and explain disparate observations . Regarding CRF properties in biological vision , one model that has had success in this regard is the sparse coding hypothesis . Sparse coding conjectures an optimal coding goal where a population of cells encodes a stimulus at a given time using as few active units as possible . Specifically , the model of interest optimizes population sparsity , which is distinct from lifetime sparsity ( a single cell being active a small fraction of the time ) . In seminal results , the high-level sparse coding model ( combined with unsupervised learning using the statistics of natural images ) has been shown to be sufficient to explain the emergence of V1 CRF shapes both qualitatively [14] and quantitatively [15] . In addition to this success providing functional insight into CRF properties , distributed sparse neural codes have many potential benefits ( e . g . , explicit information representation and easy decodability at higher processing stages [16] , metabolic efficiency [17] , increased capacity of associative and sequence memory models [18] , [19] ) and are consistent with many recent electrophysiology experiments [20] . Despite the success accounting for the emergence of CRF properties , there has been little work showing that sparse coding can account for response properties observed in V1 cells . There have been several recent experimental results showing that stimuli in the CRF surround can cause individual cell responses with higher lifetime sparsity than expected ( e . g . [5] , [6] , [8] , reviewed in [21] ) . While this experimental observation provides encouraging support for the sparse coding hypothesis , it does not imply that a sensory coding model optimizing sparsity is sufficient to account for V1 response properties ( including nCRF effects ) . Sparse coding is one interpretation of the efficient coding hypothesis [22] ( conjecturing that neural coding should successively remove stimulus redundancy ) , and other models related to efficient coding have shown individual model cells that produce some nCRF effects ( reviewed in detail in the Discussion section ) . However , few of these models have shown the broad spectrum of observed nCRF effects in single cells , and none have yet demonstrated the diversity of population response properties reported in the literature for a single effect . Taken together , the evidence of sparsity in experimental observations and the prior success of other related models gives motivation for investigating the potential role of sparse coding in producing nonclassical response properties . In this paper we demonstrate that a wide variety of nCRF effects are emergent properties of a sparse coding model implemented in a neurally plausible network structure . Specifically , we use the experimental paradigms described in the literature for a wide variety of nCRF effects ( e . g . , end-stopping , surround suppression , contrast invariance of orientation tuning , cross-orientation suppression , etc . ) to replicate these electrophysiology experiments on a dynamical system implementing optimal sparse coding . In the first contribution of this paper , we show that this model produces individual units that reproduce a wide variety of canonical nCRF effects . While another recent model [23] has also shown nearly all of these effects in a unified model along with some increased sparsity of the responses , the present work is the first to show that these effects can arise in a model that has only sparsity as the coding objective . In the second contribution of this paper , when the population diversity of an nCRF effect has been reported in the literature ( either through population statistics or multiple individual cells with varying response properties ) , we also show that this simulated population demonstrates much of the same population heterogeneity reported in the literature . Notably , the results we report are produced with a single set of model parameters ( i . e . , parameters are not tuned to produce each different effect ) , despite the system only being designed to optimize sparsity and not constructed to produce nCRF effects . These results show that the sparse coding hypothesis , when coupled with a biophysically plausible implementation , can provide a unified high-level functional interpretation to many population response properties that have generally been viewed through distinct models .
The sparse coding model proposes that V1 encodes an image patch with pixels as approximately a linear superposition of ( ) dictionary elements , ( 1 ) where the coefficients represent the population activity ( e . g . , average firing rates ) [14] . In this model , a neural population encoding the image would calculate activity levels that minimize an energy function that is a weighted combination of a data fidelity term ( e . g . , mean-squared error ) and a sparsity penalty ( e . g . , the coefficient magnitudes ) , ( 2 ) Here is a system parameter that controls the trade-off between the fidelity of the representation and the sparsity of the coefficients . The sparse coding model is a functional model that can be implemented through many different mechanisms , including using generic convex optimization algorithms designed for digital computers . In this study we use a dynamical system proposed in [24] that employs neurally plausible computational primitives . Specifically , we implemented the sparse coding model by simulating the dynamical system given by: ( 3 ) where is an internal state variable for each node ( e . g . , membrane potential ) , is the system time constant , and is an inner product over the spatial dimensions . In the system dynamics , captures the feedforward filtering while captures the recurrent interactions that implement competition between cells to represent the stimulus . Note that the recurrent interaction between those cells is inhibitory if and excitatory if ( since in our model ) . is the soft thresholding function:The input stimulus can be changed dynamically ( e . g . , a drifting sinusoidal grating ) , in which case the time-varying coefficients will track approximate solutions , with the solution accuracy determined by the time scale of the input changes relative to the system dynamics . We note that recent theoretical work has demonstrated several network architectures that can efficiently implement other versions of sparse coding with various degrees of biological plausibility [15] , [25] , [26] . The network architecture being used in this study provably solves the optimization in Eq . ( 2 ) with strong convergence guarantees [27] , can implement many variations of the sparse coding hypothesis ( i . e . , different sparsity-inducing cost functions ) [28] , and is implementable in neuromorphic analog circuits [29] . In our implementation , a dictionary optimized for sparse coding with natural scenes was determined via unsupervised learning under sparsity constraints using whitened natural scenes as the training set ( whitening is a first-order approximation of retinal processing ) . The learned dictionary was overcomplete with effective dictionary elements for the pixel image patches used as stimuli . The training set , whitening and learning rule were all exactly as in [14] , while the sparse codes during training ( i . e . , solutions to ( 2 ) ) were calculated using a standard software package [30] ( for computational efficiency ) with . We interpret these dictionary elements as the classical spatial receptive fields ( CRFs ) of the simulated neurons . This interpretation is supported by our own simulated receptive field mapping experiment ( results not shown ) using sparse dot stimuli , similar to previous studies ( e . g . , see Fig . 4b in [14] ) . The results demonstrated in this study are based on the responses of 72 units in this dictionary that had CRFs well-localized within the available image patch ( shown in Fig . 1 ) . The system parameters described above ( i . e . , membrane time constant , sparsity level ) are kept the same for every simulation in this paper ( details given in Materials and Methods ) . In other words , no attempt was made to tailor the system to reproduce each effect individually ( some interesting exceptions where parameter changes correspond to apparently conflicting results in the literature are shown in the Supporting Information ) . We interpret the sparse coefficients in Eq . ( 2 ) as the trial-averaged instantaneous spike rate of neurons in the model population . To do this , we also impose a positivity constraint and extend the dictionary matrix by including both the original dictionary elements and the negative of the dictionary elements ( i . e . , doubling the size of the matrix to use the same effective dictionary as if there were both positive and negative coefficients ) . This mirrored receptive field structure is reminiscent of the push-pull feedforward input structure in the visual simple cells [31] . In the following sections , we highlight several common nCRF effects from the literature and illustrate that this sparse coding model can largely reproduce both reported individual response properties and much of the reported response diversity across V1 neurons . For each nCRF effect the simulation was constructed to match as closely as possible the experimental protocol described in the experimental procedures section of the corresponding electrophysiology paper , including stimulus construction parameters and data analysis ( details given in Materials and Methods ) . We classify the studied nCRF effects into three groups: suppressive effects that are evoked by the presence of stimuli outside the classical receptive field ( CRF surround effects ) , effects where the response modulation depends on the orientation of the stimulus in the surround ( CRF surround orientation effects ) and effects that reflect the nonlinearity of the CRF center ( nonlinear CRF effects ) . Stimuli in the region surrounding the CRF can have a modulatory effect on a neuron's response despite not inducing significant response in isolation ( by definition of the CRF ) . In perhaps the simplest form of this suppressive modulation , it has long been known that some V1 neurons exhibit end-stopping where the spike rate decreases for a cell responding to an optimally-oriented bar stimulus when the bar length is increased beyond the CRF boundaries . An example figure depicting the end-stopping effect as observed in cat electrophysiology recordings [32] is reproduced in Fig . 2A . When simulating this experiment [32] on the sparse coding model , some of the model cells ( such as the target cell shown in Fig . 2B ) exhibit the same characteristic suppression with increasing bar length . The end-stopping effect was previously shown in [33] to emerge in the sparse coding model . The end-stopping effect can be simply understood in terms of the goals of sparse coding . When the bar is short , the CRF of the target cell is the most efficient description of the stimulus and that cell has the strongest response . However , when the bar is long enough that it is better explained by the CRFs of other cells , the target cell becomes suppressed by these competitors so as to maintain a sparse representation . The Discussion section contains a detailed look at how the network interactions supporting the sparse coding model can produce this effect . Similar to end-stopping , some V1 neurons also exhibit surround suppression where their response to a sinusoidal grating patch decreases as the patch size increases beyond the CRF . Additionally , the tuning curve for patch size often exhibits receptive field expansion at low contrast , meaning that the patch size achieving the maximum response increases at low contrast ( Fig . 3A ) . As illustrated in the response of an example model cell shown in Fig . 3B , the sparse coding model can exhibit the same basic suppression and receptive field expansion properties observed in electrophysiology experiments . In addition , we note that the slight increase of response level ( i . e . , response rebound ) at large stimulus size visible in Fig . 3B is also visible in Fig . 3A and discussed elsewhere [34] . The network interactions giving rise to surround suppression are presumably similar to that of end-stopping , but are more difficult to specify given the added dynamics of the drifting grating stimulus . In particular , due to the suboptimal match of the target CRF to the larger stimulus , competition from other cells ( that better match the larger stimulus ) can suppress the target cell's response . This competition can also be modulated by the stimulus contrast and may contribute to the receptive field expansion . Specifically , at low contrast the competing cells have lower response levels ( resulting in a weaker suppressive effect on the target cell ) , enabling the response of the target cell to grow with the stimulus size . Despite the evidence detailed above that some biological and model V1 neurons exhibit surround suppression , a single example cell is insufficient to quantify the prevalence of this effect in a population encoding sensory information . While many nCRF effects are reported as single cell response properties , some studies have attempted to quantify how strongly an effect is expressed across the population . In the case of surround suppression , two metrics have been used to quantify the degree of suppression and receptive field expansion demonstrated by a cell . One is the suppression index ( SI ) , calculated as the ratio between the ( suppressed ) response value at large stimulus sizes and the peak response value ( indicated by arrows in Fig . 3B ) . The second metric is the RF expansion ratio , calculated as the ratio of the size tuning peak location at high contrast against that at low contrast . In many physiological studies ( both in monkeys [35] and in cats [36] ) , a large proportion of cells actually show little suppression , with relatively few cells exhibiting strong suppression . An example SI distribution from cat V1 is shown in Fig . 3C , demonstrating a dominant peak at zero suppression and a relatively uniform distribution among more suppressive cells . A similar population distribution emerges from the sparse coding model cells , as illustrated in Fig . 3D . Another characteristic of the surround suppression index is that it is largely invariant to the stimulus contrast . In other words , the difference in SI at high and low contrast is close to zero ( Fig . 3E ) with a mean value of 0 . 06 [37] . We also observed this characteristic in the sparse coding model cells ( Fig . 3F ) , with a mean SI difference of 0 . 02 . We note here that some studies ( e . g . [38] ) recorded unusually high percentage of cells showing significant surround suppression , perhaps due to a different experimental preparation . Interestingly , the sparse coding model can qualitatively reproduce these apparently conflicting results by using a different set of parameters to encourage more sparsity ( see Fig . S2 which is described in Supporting Information Text S1 ) . A scatterplot of RF expansion ratios for V1 cells in macaque [37] shows clearly that on average , the CRF size is larger at low contrast than at high contrast ( Fig . 4A ) . A scatterplot of expansion ratios for the sparse coding model population shows the same qualitative trend of expanding CRF size at low contrast . We note that the mean expansion ratio in the sparse coding model cells ( 1 . 16 ) is lower than typically reported values in the electrophysiology literature ( e . g . , 2 . 3 in [37] ) . This quantitative difference may be due to variations in the RF expansion ratio definitions ( e . g . , the study in [37] uses a difference of Gaussians fit rather than tuning curve peaks ) , the lack of contrast saturation in the present model ( see Discussions ) , or biased sampling of neurons in the electrophysiology literature [39] . The possibility that the true expansion ratio might be lower than previously reported is corroborated by a recent study reporting that as many as 40% of cat V1 neurons show length tuning peaks that are invariant to contrast changes [40] . The modulatory effects seen from surround stimulation can depend on a number of stimulus properties , including contrast , spatial extent ( relative to the CRF ) , and stimulus orientation in the surround . In particular , modulation is often most suppressive when the surrounding stimuli are at orientations parallel to the preferred CRF orientation ( iso-oriented ) , and less suppressive ( or even facilitatory ) when the stimuli are perpendicular to the preferred CRF orientation ( ortho-oriented ) . For example , when stimulating a cell with an optimally oriented sinusoidal grating just covering the CRF ( i . e . , the orientation eliciting the strongest response ) , a grating in the annulus surrounding the CRF often suppresses the cell when it is iso-oriented and has little effect when it is ortho-oriented . An example of this surround orientation tuning in macaque V1 cells [41] is shown in Fig . 5A . The sparse coding model cells can also demonstrate the same type of surround orientation tuning , as illustrated by the model cell response shown in Fig . 5B . This tuning behavior in the model is likely due to the difference in the strength of competition with different stimulus surround orientations . In particular , the competing cells stimulated by iso-oriented surrounds are likely to have stronger CRF overlaps with the target cell and therefore induce more competition than the cells stimulated by ortho-oriented surround stimuli . Orientation tuned surround effects can have substantial variations , even with minor changes in the stimulus . For example , the modulatory effect can be facilitatory at some surround orientations , causing a net increase in the response of the cell to CRF stimulation alone . This facilitatory effect is often seen when using a center stimulus slightly larger than the optimal size [41] , as shown in Fig . 5C for the same cell as in Fig . 5A . Interestingly , increasing the size of the center stimulus for a model cell can likewise induce facilitation when the surround stimulus is close to ortho-oriented ( shown in Fig . 5D for the same cell as in Fig . 5B ) . As with surround suppression , a single example of facilitation in the surround orientation tuning does not characterize the prevalence of this effect in a population of V1 cells encoding a stimulus . The degree of facilitation expressed by a neuron can be characterized by measuring the ratio between the maximum of the surround orientation tuning ( the maximum of the solid line in Fig . 5B ) and the response to the center at the optimal orientation with no surrounding stimulus ( the maximum of the dashed line in Fig . 5B ) . In macaque V1 [42] , the median of the facilitation ratio across the measured population was found to be 1 . 44 at high contrast and 1 . 71 at low contrast . The sparse coding model cells show a similar dependency on contrast levels , with the median facilitation ratio ranging from 1 . 15 at high contrast and 1 . 31 at low contrast . The surround orientation tuning properties described above can be substantially influenced by the contrast difference between the center and the surround . For example , if the center contrast is fixed and the surround contrast is varied , the most significant suppression in individual macaque neurons was observed with the iso-orientated stimuli at high surround contrast ( see Fig . 6A ) [35] . Similarly , when plotting the responses as a function of center contrast for various surround settings ( e . g . , no surround , iso-oriented , and ortho-oriented ) , the suppressive effects in macaque were most pronounced with the iso-oriented stimuli at high center contrast ( see Fig . 6C ) [43] . Both of these dependencies on contrast can also be observed in the sparse coding model cells , as shown in Fig . 6D and Fig . 6B . Again we note that in some physiological studies an apparently conflicting result is reported where cat V1 neurons show facilitation with iso-oriented surround stimuli at low CRF contrast [44] ( Fig . S3A which is described in Supporting Information Text S1 ) . Interestingly , the sparse coding model can also reproduce this behavior when using a different set of parameters ( see Fig . S3B which is described in Supporting Information Text S1 ) . Even when the stimulation is confined to the CRF with no involvement of the surround , cells in V1 exhibit several nonlinear effects that cannot be explained by a canonical linear-nonlinear model [4] . One example of such an effect is the contrast invariance of orientation tuning for V1 cells . In a linear-nonlinear model based on CRFs , higher contrast stimuli evoke stronger responses that more readily exceed the spiking threshold , thus broadening the orientation tuning curve for higher contrast stimuli ( the “iceberg effect” [45] ) . However , as reported in the cat physiology literature , the orientation tuning width is largely contrast invariant [46] as demonstrated in Fig . 7A . Cells from the sparse coding model can also display this contrast invariance in the width of their orientation tuning curves , as shown in Fig . 7B . This invariance can potentially be attributed to recurrent inhibition from competing cells at orientations where the target cell is not the most efficient description ( e . g . , ortho-oriented stimuli ) . Even though these competing cells may not have large overlap with the CRF of the target cell , as the contrast increases they will become more active and induce stronger inhibition , thereby narrowing the tuning width of the target cell compared to the low-contrast response . Indeed , compared to the predictions of a linear-nonlinear model ( not shown ) , the tuning width from our model is much narrower . The degree to which the width of the orientation tuning curve changes for a cell can be quantitatively measured by calculating the half-width at half-height of the Gaussian fit to the tuning curve for various contrast levels [47] . The population statistics can be plotted as a histogram tabulating the slope of the best linear fit to the width expansion with contrast for each cell . An example of this measure from ferret V1 demonstrating that the tuning curve width is almost constant with contrast is shown in Fig . 7C [47] . In this same measure , the sparse coding model also exhibits strong contrast invariance properties across the population , as shown in Fig . 7D . Both the ferret V1 population and the sparse coding model have a slope tightly concentrated around zero in these histograms , with mean values of 0 . 002 and 0 . 032 respectively . The mean values of the half-width at high contrast measured in physiology ( ) [47] and the model ( ) are also similar . An example of a nonlinear CRF effect using a more complex stimulus is cross orientation suppression , where a plaid ( i . e . , an ortho-oriented mask grating superimposed on an iso-oriented test grating ) suppresses the response of the cell to the test alone . Fig . 8A and Fig . 8B show examples of this suppressive tuning property from cat V1 [48] , as well as from a single cell in the sparse coding model . This kind of facilitatory effect may be due to a number of factors , including excitatory connections between cells ( i . e . , other cells in the population encouraging the target cell to represent the stimulus when they are unable to do so ) or dis-inhibition , where a distant cell inhibits an intermediate cell that subsequently releases an inhibitory effect on the target cell [49] . The degree of cross orientation suppression depends on other factors beyond the orientation of the mask stimulus , including the contrast levels of the test stimulus . This contrast dependency was observed in cat V1 ( shown in Fig . 8C ) [50] , and is also visible in the sparse coding model neurons as shown in Fig . 8D . Note that while the qualitative trends in the contrast dependency are the same in the model and in physiology , the lack of contrast saturation in the present model is evident in this figure ( see Discussions ) . The degree of cross orientation suppression expressed in a population of cells can be characterized by comparing the response to the plaid with the response to the test alone . A scatter plot of the normalized spike rate of cat V1 cells shown in Fig . 8E for the test versus plaid stimuli demonstrates that most cells have a suppressive response to the plaid ( as depicted in the single cell response in Fig . 8A ) [51] . Furthermore , the scatter plot indicates that the suppression is more pronounced for lower test contrasts . As shown in Fig . 8F , the sparse coding model population exhibits the same qualitative properties , with most cells exhibiting plaid suppression that increases with lower test contrast . Quantitatively , the mean cross orientation suppression ratio between the test and plaid responses for cat V1 was measured at 0 . 11 for low test contrast and 0 . 71 for high test contrast [51] . The sparse coding model cells have mean cross orientation suppression ratios of 0 . 59 and 0 . 95 for low and high test contrasts ( respectively ) . While the model shows the same qualitative trend and overlaps in range , the specific values for these ratios are slightly higher than the reported experimental values . This small quantitative discrepancy might be due to the presence of contrast saturation in the physiology ( visible in Fig . 8C ) and its absence in the sparse coding model ( Fig . 8D; ( see Discussions ) .
Electrophysiology research in V1 has revealed a wide variety of nCRF effects that may appear to be due to many different aspects of neural coding or cortical processing . The functional interpretation of these effects is especially complex given the heterogeneity of the responses exhibited across populations of cells reported in the literature . We have demonstrated for a wide variety of nCRF effects that both the canonical individual cell response properties and a substantial diversity of population response properties are emergent characteristics of a simple dynamical system implementing a sparse coding model . This model appears to produce a very good qualitative match to many measures of population response statistics , and in many cases produces quantitative measures of these statistics that are in a similar range to reports in the physiology literature . By demonstrating a coding model that can account for these response properties , these results provide a potential functional insight into the role of nCRF effects in optimal sensory coding . While not mutually exclusive of other functional models that may also play a role in neural coding , the sparse coding model is one of the few models ( along with [52] ) able to substantially reproduce some nCRF effects as well as account for the emergence of localized , oriented , and frequency-selective CRFs [14] . In particular , despite not being constructed to produce nCRF effects , the present model appears able to capture population properties of nCRF effects that have been difficult for other functional models to produce ( e . g . the contrast invariance of surround suppression index in Fig . 3F , as discussed in [53] ) . There are several existing results that share a similar goal of providing high-level functional interpretation of nCRF effects . Perhaps most closely related to the present study is the PC/BC model [23] , [52] , [54]–[56] , which has also been able to reproduce most of the nCRF effects demonstrated in this paper [23] . It is interesting to note that although it has other functional goals , the PC/BC model does exhibit high sparsity [52] and has accounted for classic CRF tuning properties [52] . While there is significant overlap in the demonstrated nCRF effects , the present work is unique in exhibiting the sufficiency of a model derived from sparse coding to produce the observed effects and to reproduce the population diversity seen in physiology ( which the PC/BC model has yet to demonstrate ) . Given the similar behavior of the PC/BC model and the present model , it is possible that there is a deeper underlying relationship between the PC/BC model and sparse coding than is presently understood . Other example related works include the basic predictive coding model [57] , where a subpopulation of model neurons communicating prediction errors exhibits some of the single cell nCRF effects documented in the present study . Another example is the divisive normalization model [58] , where contextual effects emerge from a population interaction that modulates the gain in an attempt to maximize the independence of neighboring units . While both of these models account for some individual effects , they are not currently known to reproduce the population diversity seen in physiology or to alone be sufficient to also account for the emergence of known CRF properties ( without an added sparsity constraint ) . More recent models capture the center-surround homogeneity ( e . g . orientation co-alignment ) in the natural scenes through a generalized form of divisive normalization [59] or capture the covariance structure between pixels in natural scenes [60] . While each of these models demonstrates some individual nCRF effects , these models are also not currently known to reproduce the population diversity seen in physiology ( in particular , [59] simulates responses using a single generic unit and not a diverse population ) and neither model currently has a fully specified implementation in a biologically plausible circuit ( although an approximate form of the model in [60] may enable such an implementation ) . Another related model was described in [61] , which demonstrated that a spiking input targeted divisive inhibition mechanism gives rise to competition among sensory feature detectors and non-classical-like effects . While this model have some interesting features that the present model does not have ( e . g . , biologically realistic spiking behavior ) , the stimuli and CRF representations were 1D idealized functions and it's not clear how the results extend to 2D images . An important feature of the present work is that the same model ( with the same parameters ) is used to produce all of the presented results ( i . e . , parameters were tuned once and fixed for all experiments in the main text ) . The qualitative and quantitative matches observed in this paper rely on these parameter settings combined with the dynamical system implementation of the sparse coding rule . For example , changes in the system that would actually encourage responses with higher sparsity ( e . g . , increasing , solving Eq . ( 2 ) using a conventional digital algorithm , running the dynamical system implementation with more integration time steps/faster non-biological time constants ) would often generate similar single cell nCRF effects [62] as presented here ( results not shown ) , but those effects would be too strong to be a quantitative match to the population properties ( e . g . , a far higher percentage of model cells would show strong surround suppression than is reported in physiology; see Fig . S1 which is described in Supporting Information Text S1 ) . The Supporting Information demonstrates some instances where simple parameter changes in the model can actually account for apparently conflicting reports regarding nCRF effects in the experimental literature . We speculate that different settings of in the model may reflect differences in experimental preparations , such as different species and various levels of anesthesia . Indeed , anesthesia is known to influence the sparsity level in sensory systems [63] , [64] , and some perceptual contextual effects only occur in awake animals [65] . These observations about changes in the results with varying sparsity levels indicates that the sparse coding objective appears to be sufficient to produce the nCRF modulations , but the dynamical system implementation ( with biophysical time constants ) is required to produce the heterogeneity necessary to be a good quantitative fit . We also note that the role the dynamical system plays in the present work is similar to recent work [15] showing that learned dictionaries can be a much better quantitative match with measured macaque CRFs when the sparse coding model is implemented in a neurally-plausible network model . It is presently unclear if a different dynamical system minimizing the sparse coding objective would also result in the heterogeneity necessary to still be a good quantitative fit to physiology . Similar variations in the quantitative fits ( especially to population data ) are expected when using other sparsity penalties beyond the norm used here [28] , or when using sparse coding implementations that encourage more “hard” sparsity ( i . e . , more elements that are exactly zero ) [15] . In a similar vein , the present study uses a four-times overcomplete dictionary optimized for sparsity under natural scenes , and this model component is also likely important to the presented results . Though investigating the role of the dictionary would be an interesting avenue of further exploration , we expect that larger dictionaries may enable more sparse responses which also may demonstrate more suppression than what is seen in the current model . The recurrent interactions between cells in the sparse coding model implement a rich nonlinear response where cells compete to represent stimulus features . While it has been noted that stimuli in the CRF surround can produce sparse responses [5] , [6] , [8] , the surprising finding of this work is that the particular form of inhibition and excitation necessary to implement a sparse coding model is sufficient to explain so many individual and population nCRF properties . At a high level , these effects likely arise from the present model because the observed responses produce a more efficient representation of the stimulus than alternative population responses . While a detailed investigation of how the network interactions give rise to the response properties is an interesting open question for future investigation , in general this is difficult to determine due to the interactions between the network dynamics and the stimulus dynamics ( i . e . , the response properties arise from the average response over a drifting grating , in addition to being influenced by network dynamics ) . In the case of end-stopping , the stimuli is not drifting and we can see more explicitly how this effect arises from the principles of sparse coding . In response to a given fixed stimuli , the steady-state network response is composed of a combination of feedforward excitation , recurrent excitation and recurrent inhibition . When plotting these three components of the steady-state response as a function of the bar length ( Fig . 9A ) , it is evident that the overall response is mostly driven by the feedforward component and the recurrent inhibition . The feedforward excitation saturates as a result of the stimulus growing out of the CRF , but the recurrent inhibition keeps growing with increased bar length . To see the spatial extent of the recurrent influence , Fig . 9B shows the CRF locations and orientations of the cells influencing the target cell . As expected , inhibition mostly comes from cells with overlapping and co-linear CRFs that represent a more efficient description of the stimulus as the bar length increases . There has been a long history of debate over the mechanisms underlying various nCRF effects [3] , with each effect generally having a substantial literature attempting to answer questions about the detailed aspects underlying the modulatory response properties ( e . g . , the relative role of intra-cortical connections versus feedforward projections from thalamus in contrast invariant orientation tuning [66] , as well as the role of feedback connections [67] ) . The implementation used in this work ( see Materials and Methods ) would appear to suggest that these contextual effects can emerge from recurrent network structure in the absence of nonlinearities in the thalamic input or feedback from higher cortical areas . However , mechanistic interpretation of functional models must be cautious as there are often many possible mappings of the model to circuitry and biophysical mechanisms . For example , past work has shown that it is possible to have mappings of functional models onto circuitry that are very different from their original intuitive mappings ( e . g . , divisive normalization [68] and predictive coding [54] ) . The sparse coding dynamical system used in this study is open to the same variety of mechanistic interpretations . For example , the recurrent inhibitory influences could be implemented [69] via local inhibitory interneurons receiving convergent inputs from local excitatory neurons [70] and having dense ( many-to-one ) output connections with these excitatory neurons [71] . Alternately , it is possible that these inhibitory influences could be implemented via a mechanism based on long term depression of synaptic connections between excitatory cells in cortical layer 4 [72] and global inhibition [73] . For another example , as demonstrated in [68] , it might be possible to achieve similar computational goals through nonlinearities in the feed-forward thalamocortical circuit , rather than a recurrent network . For yet another example , the recurrent competition could be implemented through subtraction as in our model , or through division as in [23] . It remains an open question to determine the most biophysically appropriate mapping of the present model onto a circuit implementation . While the mechanisms underlying individual nCRF effects is an interesting area of investigation , another related question of interest is to determine which aspects of the model are responsible for the observed population variability . In the present model , the dictionary serves to define both the activity driving each cell through the CRF , as well as determining the synaptic weights that define the recurrent influences in the network dynamics . Because the present dictionary was learned from the sparse coding objective on natural images , it is optimal for this coding strategy and demonstrates significant variability as observed in biological CRFs . While a detailed investigation of how the model gives rise to the response diversity is also a challenging and interesting open question for future investigation , one interesting preliminary question is what role the variability in the dictionary plays in the observed nCRF response variability . As a specific example , we have found the surround suppression index to be significantly anti-correlated with the CRF size ( Fig . 10; correlation coefficient ; ) . While we are unaware of studies investigating this relation in the physiology literature , there are several studies that do suggest this type of anti-correlation . One piece of evidence [74] shows that cortical layers with larger CRFs also tend to have lower SIs and vice versa . Another corroborating study [75] shows that suppressive V1 cells have smaller CRFs compared to plateaued and facilitative cells . This anti-correlation may be present simply because there are fewer cells with larger CRF size in the model ( visible in Fig . 10 ) and in V1 [76] , making these cells more likely to be used in an efficient coding model whenever the stimulus grows past a certain size . It is also possible that the limited stimulus sizes used in the current model and many physiology studies ( e . g . [77] ) could be producing a boundary effect that contributes to some of these observations . It is presently unclear if the inherent variability in the dictionary is alone sufficient to produce the response variability observed in biology ( i . e . , if another coding model could produce this same variability when using CRFs from this same type of learned dictionary ) or if significant response heterogeneity requires the interaction of a learned dictionary with a dynamical system implementing sparse coding . Some contextual effects , especially ones that involve perception such as perceptual pop-out , figure ground segregation [13] , and contour integration [12] operate over a larger range ( e . g . over 8 times the CRF size in [78] ) and are likely to be mediated by long-range lateral connections [79] . The present study did not test the emergence of these types of effects in the sparse coding model due to the limited size of the dictionary elements . The sparse coding model simulated here used a substantially overcomplete dictionary ( see Materials and Methods ) , thus the size of the visual field we were able to simulate is limited by the current computational complexity of learning large scale dictionaries from the statistics of natural images . While it may seem unlikely that long-range effects could emerge from the present model when the only direct influences are between cells with overlapping receptive fields ( see Materials and Methods ) , it is conceivable that second order effects ( e . g . , dis-inhibition , where a distant cell inhibits an intermediate cell that subsequently releases an inhibitory effect on the target cell ) may play a central role that would only be discovered in a study using larger visual fields . An alternative is to incorporate long-range lateral connections explicitly into a sparse coding model [80] . Despite the wide variety of nonlinear properties observed in the sparse coding model , this model alone is unable to reproduce some nCRF effects because it lacks the stereotypical saturating contrast response function [81] . While this contrast saturation would be a simple addition to the model , the present study focuses on the basic sparse coding model to isolate the response properties due to the nonlinear interactions required to achieve sparsity . It is interesting to note that the model can still reproduce several contrast dependent contextual effects even without an explicit contrast saturation mechanism . Indeed , it has been previously suggested that some of these contrast dependent effects may be independent of the response saturation [42] . Nevertheless , we expect that including some type of contrast saturation in the model may improve the quantitative fit of the current model to some nCRF effects . For example , introducing contrast saturation in the surround suppression simulation ( Fig . 3 ) may further restrict the size tuning curve peak at high contrast and lead to a closer match to the expansion ratios reported in the physiology literature . Contrast saturation could be included in this model through several mechanisms , including modifying the cost function to encourage saturating spike rates ( although by itself this mechanism may not accurately capture saturating membrane potentials [82] ) , including LGN saturation [54] , modifying the network implementation to include contrast-dependent shunting inhibition [4] , or coupling the sparse coding model with a model such as the previously reported divisive normalization [58] .
To implement sparse coding in a neurally plausible network architecture , we solve the dynamical system in equation ( 3 ) using a first order Euler method with an integration time step of ms , 25 integration time steps per stimulus ( i . e . , corresponding to a stimulus presentation of approximately 1/30 second per frame of a video ) , a sparsity level of and a membrane time constant of ms ( within the range of physiological values between 10 ms and 100 ms [83] ) . In simulations using static stimuli we measured the response after 1000 integration time steps to assure full convergence . Stimuli such as bars and sinusoid gratings were generated as pixel image patches , whitened ( to mimic retinal processing ) , and overlaid on a gray background with the same mean as the gratings . Finally , for all stimuli we used a contrast ( defined as the range of the intensity values of the sinusoid grating or bar ) of 0 . 3 unless otherwise noted . As in physiological experiments studying nCRF effects ( e . g . [36] ) , we first picked an arbitrary “target” neuron from the population that we would “record” from , pinpointed the center of its CRF ON-region by hand ( interpreting the dictionary element as approximating the CRF ) , and searched for an optimal circular sinusoidal static grating patch stimulus ( i . e . , having the size , orientation , spatial frequency , and phase that gave rise to the maximal response of the target neuron in the model ) . We performed this search by a two-step exhaustive search over the parameter space using the following ranges: size of the grating was between 1 pixel and 16 pixels in diameter using 0 . 5 pixel increments; orientation was between 0 and 175 degrees using 5 degree increments; spatial frequency was between 0 . 5 to 2 radians/pixel using 0 . 25 radians/pixel increments; phase was between 0 to 2 using radian increments . We used this approach to map the optimal stimuli for a total of 72 simulated cells ( each with CRFs well-localized within the limited visual field used in the simulation ) . In most experiments we used drifting sinusoid gratings as stimuli ( as described in the experimental literature for each effect ) . We simulated a drifting grating in discrete time by a series of static gratings at progressive phases . We fixed the temporal frequency of the grating to be about 3 Hz , which is typical of the preferred frequency of cortical neurons [83] . To simulate the dynamic effect of the neural response , we simulated the dynamical system in equation ( 3 ) through the entire experiment with the driving input switched at the appropriate time to match the drift speed of the grating . We measured the response to a full cycle of the grating presentation by the mean or F1 ( first harmonic ) component , depending on the measure used in physiology literature for the particular effect under consideration . In the end-stopping experiment we found an optimal static bar stimulus for the target neuron by fixing the bar width to 2 pixels , the orientation to be the same as the optimal sinusoid grating orientation , and the bar length to be the same as the optimal grating size . We then found the optimal bar location by translating the bar around a 5-pixel neighborhood of the grating center and searching for the maximal model response for that cell . After the optimal bar stimulus location was found , we increased its length from 1 to 16 pixels and recorded the steady-state response from the model . In the surround suppression simulation , we varied the contrast of the sinusoid grating stimuli from 0 . 05 to 0 . 5 with increments of 0 . 1 , and we varied the size from 1 to 16 pixels in diameter with an increment of 1 pixel ( other parameters were fixed ) . We measured the spike rate in response to the drifting grating by the F1 component . We defined the surround suppression index as , where represents the peak response across all stimulus sizes at a certain contrast , and represents the minimum response at a radius larger than the peak . Response to high contrast was measured at 0 . 5 and low contrast at 0 . 05 . In all orientation tuning studies , we stepped the orientation of the stimulus from 0 to 180 degrees in increments of 5 degrees . We measured the mean spiking response to the drifting grating . When studying the contrast invariance property , we stepped the contrast from 0 . 1 to 0 . 5 in increments of 0 . 1 . In the population study of the tuning width , we measured tuning curve half-width at half-height by 1 . 17 times the standard deviation of the Gaussian fit to the orientation tuning curves . When measuring the slope of half-width vs . contrast , we normalized the contrast to 100 [47] . Five neurons in the simulated population had small unipolar CRFs and therefore showed very little orientation tunings . We could not fit Gaussians successfully to the tuning curves for these neurons , and therefore did not include their orientation tuning properties in the population study . In the center surround orientation tuning experiment , the surround annulus grating had a thickness of 2 pixels and the center and the surround were phase-locked . When measuring the surround orientation tuning , we fixed the center orientation at the optimal orientation and measured the response to the center alone as well as the center plus the surround . We measured the response measurement for two different center radii: the optimal and the optimal plus one pixel . In the experiment that studied the contrast's effect on the center surround orientation tuning , the center contrast took on values on a logarithmic scale ( 0 , 0 . 03 , 0 . 06 , 0 . 12 , 0 . 25 , 0 . 5 ) and we kept the surround contrast constant at 0 . 5 . Similar to the observation in physiology ( Fig . 8E ) , there are many cells with weak response at low contrast in the simulation . Due to the present simulation having more cells than the study in [48] , this clustering around zero made the low contrast responses difficult to read when plotted . To better visualize the suppression effect of the plaid for weakly responsive neurons , we plotted the low-contrast population responses with the maximum response normalized to 1 ( effectively spreading the points out over the full range to better see their position above or below the diagonal line ) . High-contrast responses were similarly normalized to plot on the same scale .
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Simple cells in the primary visual cortex ( V1 ) demonstrate many response properties that are either nonlinear or involve response modulations ( i . e . , stimuli that do not cause a response in isolation alter the cell's response to other stimuli ) . These non-classical receptive field ( nCRF ) effects are generally modeled individually and their collective role in biological vision is not well understood . Previous work has shown that classical receptive field ( CRF ) properties of V1 cells ( i . e . , the spatial structure of the visual field responsive to stimuli ) could be explained by the sparse coding hypothesis , which is an optimal coding model that conjectures a neural population should use the fewest number of cells simultaneously to represent each stimulus . In this paper , we have performed extensive simulated physiology experiments to show that many nCRF response properties are simply emergent effects of a dynamical system implementing this same sparse coding model . These results suggest that rather than representing disparate information processing operations themselves , these nCRF effects could be consequences of an optimal sensory coding strategy that attempts to represent each stimulus most efficiently . This interpretation provides a potentially unifying high-level functional interpretation to many response properties that have generally been viewed through distinct models .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
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Visual Nonclassical Receptive Field Effects Emerge from Sparse Coding in a Dynamical System
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Functional-effective connectivity and network topology are nowadays key issues for studying brain physiological functions and pathologies . Inferring neuronal connectivity from electrophysiological recordings presents open challenges and unsolved problems . In this work , we present a cross-correlation based method for reliably estimating not only excitatory but also inhibitory links , by analyzing multi-unit spike activity from large-scale neuronal networks . The method is validated by means of realistic simulations of large-scale neuronal populations . New results related to functional connectivity estimation and network topology identification obtained by experimental electrophysiological recordings from high-density and large-scale ( i . e . , 4096 electrodes ) microtransducer arrays coupled to in vitro neural populations are presented . Specifically , we show that: ( i ) functional inhibitory connections are accurately identified in in vitro cortical networks , providing that a reasonable firing rate and recording length are achieved; ( ii ) small-world topology , with scale-free and rich-club features are reliably obtained , on condition that a minimum number of active recording sites are available . The method and procedure can be directly extended and applied to in vivo multi-units brain activity recordings .
Understanding the relationships between structure and function , dynamics and connectivity of neuronal circuits are a challenge of the modern neurosciences , especially as the characterization of neuronal interaction in terms of functional and effective connectivity [1–3] is concerned . Functional connectivity is an observable phenomenon defined as statistical dependency between remote neurophysiological events; it is usually inferred on the basis of correlations among neuronal activity measurements , by means of different approaches ranging from basic cross-correlation[4] to model-based methods[1 , 5] . Effective connectivity refers explicitly to the influence that a neuron or neural system exerts on another one , either at synaptic or population level; it can be inferred by perturbing the activity of a neuron , and then by measuring the other neurons activity changes . Structural or anatomical connectivity is related to the physical connections ( i . e . , synapses ) among neurons [2] . In this paper , we refer to the more general framework of functional connectivity , even if , by using the proposed correlation-based method , directed graphs ( i . e . causal relationships ) can be derived ( cf . Materials and Methods sect . ) . The complexity of the nervous system and the difficulties of multi-site parallel recordings in in vivo experimental models , hampered the systematic study of emergent properties of complex networks . At the same time , the availability of validated methods able of reliably inferring functional connections down to synaptic level is still limited . To this end , we adopted a reductionist approach making use of in vitro experimental models coupled to Micro-Electrode Arrays ( MEAs ) . In this context , large-scale neural networks developing ex vivo and chronically coupled to MEAs [6] , represent a well-established experimental system for studying the neuronal dynamics at population level [7] . Despite their simplicity , they show recurrent synchronized periods of activity , as also observed in vivo during sleep or anesthesia , and even quiet wakefulness [8 , 9] . These model systems represent a good trade-off between controllability-observability and similarity to the in vivo counterpart , allowing accessibility and manipulation from both chemical and electrical point of view . Recent advances in multichannel recording techniques have made possible to observe the activities of thousands of neurons simultaneously with the acquisition of massive amount of empirical data [10] . These methods are very attractive since they allow the detailed monitoring of the on-going electrophysiological spatio-temporal patterns of complex networks [11–14] . Reconstructing the detailed functional connectivity of a neuronal network from spikes data is not trivial , and it is still an open issue , due to the complexities introduced by neuron dynamics and high anatomical interconnectivity [15 , 16] . Statistical analysis of spike trains was pioneered by Perkel [17] and followed by more than four decades of methodology development in this area [18] . Cross-correlation based methods remain the main statistics to evaluate interactions among the elements in a neuronal network , and produce a weighted assessment of the connections strength . Weak and non-significant connections may tend to obscure the relevant network topology made up of strong and significant links , and therefore they are often discarded by applying an absolute or a proportionally weighted threshold [19] . Correlation-based techniques include independent components analysis , synchrony measures [20] , cross-correlation [21 , 22] , correlation coefficients [7 , 23] , partial-correlation [24] . Other widespread techniques to infer functional connectivity are based on Information Theory ( IT ) methods [10 , 25 , 26] , Granger causality [27 , 28] and dynamical causal modeling [1] . With few exceptions [29 , 30] , all the recently introduced and revisited methods concentrate on excitation , ignoring inhibition or admitting the failure in reliably identifying inhibitory links [26] . In this work , we focus attention on cross-correlation histogram ( CCH ) based methods . We present a new algorithm able to efficiently and accurately infer functional excitatory and inhibitory links; we validate the method on simulated neuronal networks; finally , we study connection properties in large-scale ex vivo neuronal networks showing how to directly and reliably derive the topological properties of such networks . There are three different connectivity conditions that , theoretically , influence the temporal correlation between neurons: pairs of excitatory neurons , pairs of inhibitory neurons , and inhibitory-excitatory pairs [31] . The first term is the one usually estimated and from which we obtain the inferred functional excitatory network usually represented by a ( directed ) graph . The second term is negligible as inhibitory-inhibitory links are physiologically very sparse [32] . The last term , when it is exerted by a GABAergic interneuron to cortical excitatory neurons , acts by reducing the activity and decreasing the spontaneous fluctuations ( i . e . , feedforward inhibition ) . On the contrary , when it is exerted by cortical excitatory neurons to GABAergic interneurons , it acts by increasing the activity of such neurons that , in turn , form inhibitory synaptic contacts with the glutamatergic cortical cells ( i . e . , feed-back inhibition ) [33] . In other studies [34–36] , it was noticed the primary effect of inhibition is a trough in the cross-correlogram: to detect this interaction a background of postsynaptic spiking against which the inhibitory effect may be exercised ( i . e . , high and tonic firing rates ) is needed [22] . From experimental works related to in vivo multi-unit recordings , it was shown the sensitivity to excitation is much higher than the sensitivity to inhibition [37] ( due to the low firing rates of neurons ) . Finally , it should be underlined the analysis of interactions in neuronal networks is a quite demanding computational process , and all the currently proposed methods for analyzing multiple spike trains rely on quantities that need to be computed through intensive calculations [38] . By using the ad-hoc developed CCH , we could derive functional connectivity maps ( both for excitation and inhibition ) and to reliably extract topological characteristics from multiple spike trains in large-scale networks ( i . e . , thousands of neurons ) monitored by large-scale MEAs ( i . e . , thousands of micro-transducers ) .
Starting from the standard definition of the cross-correlation [22] ( cf . , Materials and Methods sect . ) , we adopted the normalization approach described in [21 , 39] to obtain the “raw” Normalized Cross-Correlation Histogram ( NCCH ) . We formalized our hypothesis that , the extraction of negative peaks ( rather than troughs ) obtained by a filtering operation on the NCCH and followed by distinct thresholding operations for excitatory and inhibitory connections allows to identify a significant percentage of inhibitory connections with a high-level accuracy at low computational cost . Theoretically , cross-correlation is able to detect both an increase and a decrease of the synchrony between spike trains related to putative interconnected neurons . However , in real experimental data , the cross-correlogram is very jagged making difficult the detection of small peaks and troughs , and , apart from specific conditions ( i . e . , high and tonic firing rate ) [4] , hindering the detection of inhibition . Our approach consists in a simple post processing of the cross-correlation histogram , thus obtaining what we called Filtered and Normalized Cross-Correlation Histogram ( FNCCH , curly brackets in Eq ( 1 ) ) . Stated a reference neuron x and a target neuron y , Eq ( 1 ) provides the mathematical definition of the absolute peak of the FNCCH . FNCCHxypeak=Cxy ( τ ) |=argmaxt{|Cxy ( t ) −1W∑v=−W2v=W2Cxy ( v ) |} ( 1 ) where W is the time window where FNCCH is evaluated . The filtering procedure ( cf . Materials and Methods sect . ) consists in subtracting the mean value of the cross-correlogram ( in the time window W ) from the values of the normalized cross-correlogram Cxy ( ν ) , ν ∈ [-W/2 , W/2] . The subsequent peaks extraction operation is performed by considering the absolute values , and it allows to compute the highest peak . In this way , it is possible to distinguish between peaks and troughs by taking into account the original signs: a positive value refers to an excitatory link , and a negative value refers to an inhibitory one . Details about further refinements needed to avoid detection of false inhibitory connections can be found in the Supplementary Information ( cf . , Sect . S1 ) . In the next sections , we show the validation of the method with the aid of large-scale in silico networks; then , we present the results , in terms of functional connectivity maps and network topology , obtained from the analysis of multi-electrode parallel recordings of in vitro neuronal populations . Such populations are coupled to both 60 channels MEAs ( MEA-60 ) and high-density MEAs with 4096 micro-transducers ( MEA-4k ) ( cf . Materials and Methods sect . ) . We applied the FNCCH ( time window W = 25 ms and time bin 1 . 0 ms ) to 10 realizations of in silico neural networks made up of 1000 randomly connected neurons , characterized by an average ratio between inhibitory and excitatory connections of 1/4 ( cf . , Materials and Methods sect . ) . The model was tuned to reproduce the dynamics exhibited by in vitro neuronal networks . Simulations show the typical signature characterized by a mix of spiking and bursting activities as displayed by the raster plot and the Instantaneous Firing Rate ( IFR ) traces of the excitatory ( red ) and inhibitory ( blue ) neuronal populations of Fig 1A . From a topological point of view , both the excitatory and inhibitory structural sub-networks follow a random connectivity , as the incoming degree distributions of Fig 1B ( inset ) display . Each neuron receives 100 connections from the other neurons: excitatory neurons receive 80% of excitatory and 20% of inhibitory links , respectively , ( reflecting the ratio of the excitatory and inhibitory populations ) ; inhibitory neurons receive only excitatory connections ( cf . S2C Fig ) . Further details about the dynamics and connectivity of the simulated neuronal networks can be found in the Supplementary Information ( cf . , Sect . S2 ) . Fig 1C and 1D quantify the performances of the FNCCH by means of the Receiver-Operating-Characteristic ( ROC ) [40] curve and the Matthews Correlation Coefficient ( MCC ) [41] . Fig 1C shows the ROC curves obtained by comparing the Synaptic Weight Matrix ( SWM ) of the model ( i . e . , the ground truth ) with the computed Functional Connectivity Matrix ( FCM ) , and Fig 1D shows the MCC curve ( cf . , Materials and Methods sect . ) . The ROC curve relative to the detection of inhibitory connections ( blue curve in Fig 1C ) is very close to the perfect classifier , with an Area Under Curve ( AUC ) of 0 . 98 ± 0 . 01 ( blue bar in the inset of Fig 1C ) . The MCC curve relative to the inhibitory links ( blue curve in Fig 1D ) has a maximum value of 0 . 87 ± 0 . 04 , showing a good precision in the identification of inhibition . Then , we compared the sensitivity of the FNCCH for the detection of excitatory links ( red curves in Fig 1C and 1D ) with the standard NCCH’s one ( for excitation , black curves in Fig 1C and 1D ) to underline the improved detection capabilities obtained by the filtering procedure . We observed not only a significant ( p < 0 . 001 ) AUC increase ( 0 . 92 ± 0 . 01 vs . 0 . 72 ± 0 . 02 , Fig 1C inset ) , but also significant improvements in both ROC and MCC curve shapes for low values of false positive rates ( FPR ) . In particular , we can notice ( Fig 1D ) , that the FNCCH excitatory curve has a maximum value of about 0 . 75 with respect to the correspondent NCCH value ( for the same false positive rate ) that is negative ( suggesting a disagreement between prediction and observation ) . Further details about false and true positive detection can be found in the Supplementary Information ( Sect . S5 ) . The above results justify the use of a hard threshold procedure ( cf . , Materials and Methods sect . ) to select the strongest and significant functional connections . The Thresholded Connectivity Matrix ( TCM ) is thus directly computed from the FCM by using a threshold equal to ( μ + 1 σ ) , ( mean plus one standard deviation of the connections strength ) for the inhibitory links , and ( μ + 2 σ ) for the excitatory ones , obtaining estimated links with a very high-level of accuracy ( cf . Materials and Methods sect . ) : R2 = 0 . 99 for the inhibitory links and R2 = 0 . 94 for the excitatory ones . To investigate whether the reconstructed functional connectivity network resembles the one of the model , we calculated the excitatory and the inhibitory ( Fig 1B ) links degree distribution after the thresholding procedure from TCM . The computed degree-distributions fit a Gaussian distribution ( Fig 1B , R2 = 0 . 99 for the inhibitory links and R2 = 0 . 98 for the excitatory ones ) , in accordance with the original distributions used to generate the structural ( random ) connectivity of the model ( Fig 1B inset ) . It can be noticed that the mean and standard deviation values of the functional Gaussian distribution for the excitatory links are in good agreement with the structural ones ( μfunct = 87 , σfunct = 13 . 2 and μstruct = 80 , σstruct = 19 . 6 ) . On the other hand , for the inhibitory links , such values are higher than the structural ones due to the presence of many polysynaptic interactions ( μfunct = 48 , σfunct = 9 . 3 and μstruct = 25 , σstruct = 14 . 5 ) . Finally , we computed the delay distribution for both the excitatory and the inhibitory links from the TCM ( Fig 1E ) . The extracted delay distribution for the excitatory links qualitatively reflects the one used to generate the model ( uniform distribution in the interval [0 , 20] ms ) . The estimated inhibitory distribution , instead , exhibits a more confined range which reflects the one used to produce the model ( constant delay set at 1 ms ) , but with a spread and a median value at about 5 ms ( cf . , Materials and Methods sect . ) . The disagreement can be explained by the presence of multiple and polysynaptic interactions ( due to the combination of excitatory and inhibitory inputs on a single neuron; cf . , Discussion sect . ) . Further validation of the proposed method was pursued by implementing a scale-free ( with small-world features ) network . The results ( cf . Supplementary Information , S3 Fig ) are less striking than those obtained for random connectivity; nevertheless , FNCCH outperforms standard cross-correlation and the identification of inhibitory links is still maintained with a similar general trend . The FNCCH was applied to neuronal networks coupled to two different devices: MEA-60 and MEA-4k . Fig 2 shows the two utilized microtransducers ( Fig 2A and 2D ) and illustrative images of networks coupled to the two ( Fig 2B and 2E ) . Such networks are the morphological substrate originating the complex electrophysiological activity characterized by an extensive bursting dynamics ( i . e . , highly synchronized network bursts ) and a random spiking activity . Fig 2C and 2F show two examples of spontaneous activities recorded by a MEA-60 ( Fig 2C ) and a MEA-4k ( Fig 2F ) . We can observe silent periods , desynchronized spiking activity , and peaks of activity ( of different duration and called network bursts ) , which cause a rapid increase of the Instantaneous Firing Rate ( IFR ) ( Fig 2C and 2F , bottom panels ) . More details about the spiking and bursting dynamics originated by networks coupled to MEA-4k are reported in the Supplementary Information ( S1 Table ) . We analyzed three cortical and three striatal networks coupled to the MEA-60 ( FNCCH parameters: time window W = 25 ms and time bin 0 . 1 ms ) and three cortical networks coupled to the MEA-4k ( FNCCH parameters: time windows W = 24 ms and time bin of 0 . 12 ms ) after they reached a stable stage ( i . e . , after 21 Days In Vitro , 21 DIV ) . Fig 3A and 3G show connectivity graphs of cortical and striatal networks coupled to a MEA-60 device ( Fig 3B and 3H and 3C and 3I show the contribution of excitation and inhibition , respectively ) . All the graphs were obtained by applying the hard threshold approach and the spatio-temporal filtering to prune co-activations ( cf . , Materials and Methods sect . ) . Then , we looked , for the cortical networks , the presence of privileged sub-networks constituted by the most connected nodes ( i . e . , rich club ) , by computing the Rich Club Coefficient ( RCC ) curve [42] ( cf . , Materials and Methods sect . , Eq ( 10 ) ) . The nodes of these sub networks are highlighted in yellow and cyan ( Fig 3B and 3C ) . For the striatal culture , the qualitative prevalence of inhibitory connections is clearly visible . To characterize the detected links for the cortical cultures , we computed the box plots of the functional connection peak delays ( Fig 3D ) and lengths ( Fig 3E ) of the excitatory ( red ) and inhibitory ( blue ) connections . Similar graphs derived from a cortical network coupled to a MEA-4k were obtained ( Fig 4A ) . Links strength is represented by two color codes ( arbitrary unit ) for excitation ( hot-red color code ) and inhibition ( cold-blue color code ) . The two detected subnetworks are also shown in Fig 4B and 4C . Moreover , the box plots pointing out the connection peak delays and lengths are depicted in Fig 4F and 4G . Noteworthy it is that the inhibitory links are slower , and with possible slightly longer connections than the excitatory ones , as reported in literature for structural and functional connectivity in brain slices [43] . Considering the high number of connections found by using the MEA-4k , we point out the two hundred strongest connections for excitation and the fifty strongest connections for inhibition ( Fig 4D and 4E ) , illustrating how these main links include both short and long interactions with a prevalence of short interactions for excitatory connections . We also computed the inhibitory links percentage with respect to the total number of detected links for the three different experimental conditions and three experiments for each condition . As expected , we found that striatal cultures have a higher percentage of inhibition and inhibitory links ( about 60% ) [44 , 45] than cortical ones ( about 25% ) . It is worth noticing that for the cortical cultures the excitatory/inhibitory ratio is detected quite independently of the number of recording sites ( Figs 3F and 4H ) , although it tends to stabilize with a shorter recording time for the MEA-4k . Interestingly enough , the found ratio ( about 1/4 ) in cortical networks between inhibitory and excitatory links is roughly the same as the ratio of inhibitory and excitatory neurons as estimated by immunostaining in similar experimental preparations [8] . In order to derive the topological features [46] of the analyzed cortical networks , we computed the Clustering Coefficient , CC ( Fig 5A ) and the average shortest Path Length , PL ( Fig 5B ) . Then , we extracted the Small-World Index ( SWI ) by comparing the CC and the PL of the analyzed networks with the mean values of CC and PL of 100 realizations of a random network with the same degree-distribution , as recently proposed [26] . We found that when cortical networks are coupled to MEA-4ks devices , we can see the emergence of a clear small-world ( SW ) topology ( Fig 5C ) ; on the other hand , for cortical networks coupled to MEA-60s devices , we cannot infer any SW topology . From the measurements performed by MEA-4ks , we can state that both inhibitory and excitatory subnetworks with their small world index , SWI >>1 ( 9 . 2 ± 3 . 5 for the inhibitory links and 5 . 2 ± 2 for the excitatory ones ) contribute to ‘segregation’ . Moreover , both inhibitory and excitatory links with their fraction of long connections contribute also to network ‘integration’ ( i . e . , communication among the SWs ) . To further characterize the topology of these neuronal assemblies , we also investigated the possible emergence of scale-free topologies [47] by evaluating the presence of hubs[48] and power laws for the excitatory ( Fig 5D ) , inhibitory ( Fig 5E ) and global ( Fig 5E , inset ) link degree distributions . In agreement with previously published model systems [49] and other studies [43] , we obtained that such distributions fit a power law with R2 higher than 0 . 92 , in all the three cases . Finally , we searched for the presence of privileged sub-networks made up of the most connected nodes ( i . e . , rich club ) of the investigated networks by computing the RCC curve . For the analyzed cortical cultures , we found privileged sub-networks as indicated by the computed RCC values with a maximum value of 2 . 7 ± 0 . 5 . Fig 4B and 4C show the rich club networks identified for one neural network coupled to the MEA-4k , represented by means of blue circles ( for excitatory subnetwork ) and pink circles ( for inhibitory subnetworks ) . Fig 3B and 3C are the analogous for a cortical neural network coupled the MEA-60 ( yellow for the excitatory nodes and light blue for the inhibitory ones ) . Similar cortical networks coupled to the MEA-60 devices show no clear SW topology ( Fig 5C ) ; these networks seem to be characterized by a ( sub ) -random topology with SWI of 0 . 4 ± 0 . 1 for the excitatory and 0 . 2 ± 0 . 2 for the inhibitory links . These cortical networks are of the same type as the ones coupled to the MEA-4k ( i . e . , similar density of neurons , same age , same culture medium ) , and the apparent estimated random topology should be attributed to the low number of recording sites ( i . e . , 60 channels ) that are not enough to reliably infer topological features . To determine how the number and density of electrodes are crucial , we computed the SWI by considering a reduced number of electrodes for the functional connectivity analysis from the MEA-4k recording , as described in Fig 5F . In particular , we started from the full resolution of the MEA-4k ( i . e . , 4096 electrodes ) , and we progressively decreased the electrode density to 60 electrodes ( inter-electrode distance 189 μm , electrode density 19 electrode/mm2 ) to obtain a configuration comparable with the MEA-60 devices , as previously reported[50] . The obtained results are shown in Fig 5G: the SWI decreases down to a random topology becoming variable and unstable when the number of the considered electrodes is less than 100 . This last result is referred to the excitatory links and the same analysis was not applied to the inhibitory connections . Such inhibitory links are much less than the excitatory ones , thus leading to an inhibitory topology reconstruction that is strongly influenced by the decimation scheme applied to reduce the number of electrodes .
Generally , by inspecting a CCH , we can notice an increase or a decrease of the fluctuations [22] . In some studies , it was noticed that the primary effect of inhibition on the cross-correlogram is a trough near the origin , and for this interaction to be visible there must be present a background of postsynaptic spiking against which the inhibitory effect may be exercised ( high-tonic firing rate regime ) [4 , 35] . From experimental works related to the analysis of connectivity from cortical multi-unit recordings [55] , a good sensitivity for excitation is obtained , while the situation is considerably worse for inhibition . This is due to a low sensitivity of CCH for inhibition , especially under the condition of low firing rates [4 , 56] . The difference in sensitivity may amount to an order of magnitude , and it was demonstrated that for inhibition , the magnitude of the departure relative to the flat background is equal to the strength of the connection , whereas for excitation it involves an additional gain factor [4] . As a whole , the lack of efficiency in the detection of inhibition , simply reflects the disproportionate sensitivity of the analysis tool [57] . In our work , we introduced a cross-correlogram filtering approach ( FNCCH ) developed to overcome the inhibition detectability issue . As Fig 1 shows , the FNCCH is able to detect , with high accuracy , the inhibitory links when applied to in silico neural networks with similar dynamics with respect to the actual ones . The filtering procedure improves also the detectability of the excitatory links , resulting in a reshaping of the ROC curve ( Fig 1A ) with an increase of both precision ( MCC curve , Fig 1B ) and AUC with respect to the standard cross-correlation ( NCCH ) . However , the presented FNCCH , being a CC-based method , has some limitations in the inhibitory links detection that we tried to investigate with our in silico models . The main factor affecting the detectability of inhibition , is the variability of CC . In order to reduce this variability , it is possible to increase the number of coincidences per bin by widening the bin-width ( that is , down-sampling with loss of information in the acquired electrophysiological data ) , or by increasing the number of involved events ( which can be obtained with high firing rate and/or by raising the recording time ) [58] . Another influencing factor depends on the balance of excitatory and inhibitory neuronal inputs ( i . e . , balanced model ) and it is referred to the relative strength between inhibitory and excitatory inputs . In fact , when the neuron is not balanced , excitation is , on average , stronger than inhibition . Conversely , when the neuron is balanced , both excitation and inhibition are strong and detection of inhibitory links improves [22 , 31 , 57] . Starting from the in silico model , we were able to investigate the impact of rates variability on excitation/inhibition detectability , and to try to define a reasonable threshold ( criterion for detectability [22 , 56] ) . In particular , we varied the firing rate of the inhibitory neurons from 20 spikes/s to 2 spikes/s , while maintaining a firing rate of 2–3 spikes/s for the excitatory neurons . We found that the detectability of the functional inhibitory links is preserved with our method , down to a firing of about 6 spikes/s , and then decreases significantly . We investigated also the inhibition identification with respect to the recording time . Starting from 1 hour of simulation , we reduced ( 10 min steps ) the recording time , and we found that there is a decrease in the inhibition detectability below 30 minutes of recording ( cf . S4 Fig ) . Finally , we investigated the performances of the FNCCH in a scale-free and small-world network . The detection of inhibition was still possible with relatively good results , even if the global performances of the algorithm decreases . This shall not be attributed to the scale-free topology , but to the reduced firing rate for both inhibitory and excitatory neurons and to possible unbalances between inhibition and excitation ( cf . S3 Fig ) . Nevertheless , the method could reliably capture the topology of the network and qualitatively estimate the synaptic in-degree distribution . Thus , the obtained results enabled us to apply the FNCCH to in vitro large-scale neural networks , and allowed us to infer topology and functional organization . The described procedure could be also directly applied to Multi Unit Activity ( MUA ) from in vivo multi-site measurement recordings . Other methods ( e . g . , partial correlation , transfer entropy ) were not taken explicitly into consideration for comparison , either for their computational costs , or for the inability to identify inhibitory links [59] . The cortical networks probed with MEA-4k showed a clear small-world topology . The inhibitory functional links had a SWI equal to 9 . 2 ± 3 . 5 , higher than the value extracted from the excitatory links ( 5 . 1 ± 1 . 9 ) . Conversely , the cortical networks coupled to the MEA-60 showed a random organization topology ( 0 . 21 ± 0 . 212 for the inhibitory links and 0 . 38 ± 0 . 1 for the excitatory ones ) . These apparent random organizations are due to the low number of recording sites of the acquisition system; in fact , it is worth to remember that the SWI is computed by comparing cluster coefficient ( CC ) and average shortest path length ( PL ) of the analyzed networks to the corresponding values for surrogate random equivalent networks ( same number of nodes and links ) . From the obtained results , unlike recently presented findings [42] , we demonstrated that the emergence of small-worldness , cannot be reliably derived or observed in a neuronal population probed by a reduced number ( < 100 ) of recording sites . To characterize connectivity properties , besides the importance of well-defined statistical tools used for the analysis , it is fundamental to probe network activity by using large-scale microtransducer arrays ( i . e . , with at least 200 electrodes ) . As a whole , the issue related to the low number of recording sites should be carefully taken into account when extracting dynamical features as well as organizational principles of complex networks . Finally , it should be underlined that we focused our attention on CC based methods . We mentioned , in the Introduction , the widespread use of Information Theory ( IT ) based techniques . Beside the relative novelties of such methods , and the good performances ( for a review see [38] and references therein ) , they showed high computational costs and , to our knowledge , the inability to reliably estimate inhibitory connections [26] . Although theoretically , IT based methods such as Transfer Entropy ( TE ) and Mutual Information ( MI ) are able to detect inhibitory links , we are not aware of studies consistently reporting a successful identification of inhibitory connections . The problem is in the incapability of distinguishing between excitatory and inhibitory links , rather than in the detection of inhibition as pointed out in the Supplementary Information ( S6 Fig ) .
Primary neurons were obtained from rat embryos ( 18 days , E18 ) from Sprague Dawley pregnant rats ( Charles River Laboratories ) . The experimental protocol was approved by the European Animal Care Legislation ( 2010/63/EU ) , by the Italian Ministry of Health in accordance with the D . L . 116/1992 and by the guidelines of the University of Genova ( Prot . N . 24982 , October 2013 ) . Cross-correlation ( CC ) [22] measures the frequency at which a neuron or electrode fires ( “target” ) as a function of time , relative to the firing of an event in another one ( “reference” ) . Mathematically , the correlation function is a statistic representing the average value of the product of two random processes ( the spike trains ) . Given a reference electrode x and a target electrode y , the correlation function reduces to a simple probability Cxy ( τ ) of observing a spike in one train y at time ( t + τ ) , given that there was a spike in a second train x at time t; τ is called the time shift or the time lag . In this work , we use the standard definition for the cross-correlation computation , following a known normalization approach on the CC values [39] . We define the cross-correlation as follows: Cxy ( τ ) =1NxNy∑s=1Nxx ( ts ) y ( ts−τ ) ( 2 ) where ts indicates the timing of a spike in the x train , Nx is the total number of spikes in the x train and Ny is the total number of spikes in the y train . Cross-correlation is limited to the interval [0 , 1] and it is symmetric Cxy ( τ ) = Cyx ( -τ ) . The cross-correlogram is then defined as the correlation function computed over a chosen correlation window ( W , τ = [-W/2 , W/2] ) . Different shapes of cross-correlograms can be obtained from pairs of analyzed spike trains . The occurrence of significant departures from a flat background in the cross-correlogram ( i . e . , a peak or a trough ) is an indication of a functional connection[4] . In particular , a peak corresponds to an excitatory connection and a trough to an inhibitory link . The different amplitude of the peaks can be related to the existence of different levels of synchronization between neural spike trains . Generally , a correlogram can reflect a so-called direct excitatory connection between two neurons when a one-sided peak is evident ( displaced from the origin of time by latency corresponding to the synaptic delay ) . The use of spike train data offers the possibility to optimize the cross-correlation algorithm efficiency . To overcome the lack of efficiency of many of the proposed CC computation strategies , we present an alternative approach based on the “direct” spike time stamps inspection that avoids un-necessary calculations on the binarized spike trains . In fact , the only important information is stored in the bins containing a spike ( i . e . , spike time stamp ) , that are significantly less than null bins . If we consider that the average mean firing rate in neural networks oscillates between 0 . 2 spikes/s and 20 spikes/s [60] , at a sampling frequency of 10 kHz , it yields only 2% of bin with spikes and 98% of meaningless bins: thus , we developed a new version of the CCH as indicated in Fig 6E . Let us consider a reference neuron x and a target neuron y , and let us suppose that we computed the NCCH between x and y . After the NCCH computation , the maximum value ( i . e . , the peak ) is used as a value reflecting the strength of the estimated functional link . If x and y share an excitatory link , this procedure works well ( Fig 6A and 6B ) . On the other hand , when x inhibits y , the inhibitory trough will be discarded in favor of the NCCH peak ( Fig 6C ) , with a misleading excitatory link detection . The CCH shapes are similar also in the correlograms derived from experimental data , although with an even more jagged behavior . Fig 6F and 6G show two examples of detected putative excitatory and inhibitory connections . Eq ( 1 ) gives the mathematical definition of the FNCCH computation that overcomes this problem . We refer to the filtered peak value as entity of the peak . In this way , it is possible to distinguish between peaks and troughs by taking into account the sign . A positive peak is referred to an excitatory links ( Fig 6A and 6B ) , conversely , a negative peak is referred to an inhibitory link ( Fig 6D ) . We implemented and applied also a post-computation filtering procedure to improve the detectability of inhibitory links on noisy spike trains ( cf . , Supplementary Information , S1 Fig ) . The block diagram and pseudocode depicted in Fig 6E show the sequence of operations executed by the FNCCH . The starting point is the first bin containing a spike in the target train . The binning procedure is directly performed on the time stamps . For each couple of neurons , starting from the first spike of the target train , we slide the time stamps of the reference electrode to find the first spike whose correlation window contains the target spike . Then , we continue to move over the target train to build the entire cross-correlogram ( for that reference spike ) . When the correlation window for the reference spike is completed ( i . e . , when we have counted the number of spikes for all the bin of the target spike train ) , we move to the next spike of the reference train , and re-iterate the procedure starting from the first target spike into the correlation window , centered at the current reference spike . Then , we normalize the CC and repeat all the aforementioned operations for the other electrodes . Exploiting the symmetry of the CC function we consider only half of the electrodes for the computation . Moreover , for each pair , we select , as target train , the one with the smallest number of spikes to reduce the number of operations . Once the NCCH is obtained , we apply the filtering operation described by Eq ( 1 ) to compute the FNCCH values . Finally , we take the maximum absolute value as estimation of the correlation strength between the two electrodes . If it is negative , the found connection is considered a putative inhibitory link , otherwise is considered an excitatory one . We applied a Spatio-Temporal Filter directly to the functional connectivity matrix ( FCM ) originated by the FNCCH . The procedure we implemented follows the one devised by Maccione et al . , [61] by defining a distance-dependent latency threshold . More in detail , we evaluated the links length ( using the Euclidean distance ) and the functional delays for each electrodes pair . We assumed as maximum propagation velocity a value of 400 mm/s[62] . If a functional connection has a temporal delay not compatible with such maximum velocity , it is discarded . Finally , we introduced also a minimum delay of about 1 ms , compatible with fast excitatory AMPA synaptic transmission . Thus , we refined the FCM by removing all the links related to putative non-physiological connections . Cross-correlation , as well as any other connectivity method , provides a full n x n Connectivity Matrix ( CM ) , whose generic element ( i , j ) is the estimation of the strength of connection between electrodes i and j . A thresholding procedure is thus needed to eliminate those values that are only relative to noise and not to real functional connections . In the literature , there are several thresholding procedures , with different levels of complexity: the simplest one is to use a hard threshold , defined as ( μ + n · σ ) , where μ and σ are the mean and the standard deviation computed among all the CM’s elements , respectively , and n is an integer[24] . There are other more sophisticated approaches based on shuffling methods that consist in destroying all the temporal correlations within the spike trains and compute a null hypothesis to test the significance of the connections[63] . However , shuffling procedures require many resources in terms of memory and computational times . In this work we proved , by means of the in silico network model , that a simple hard threshold method is sufficient . We found that significant levels of accuracy can be obtained with a threshold equal to ( μ + σ ) for both excitatory and inhibitory links ( cf . , Results sect . ) . The ROC curve[40] is a common metrics used to evaluate the performances of a binary classifier by comparing prediction and observation . In our study , the prediction is represented by the computed Thresholded functional Connectivity Matrix ( TCM ) , and the observation corresponds to the Synaptic Weight Matrix ( SWM ) of the neural network model ( i . e . , the ground truth ) . We can define the True Positive Rate ( TPR ) and the False Positive Rate ( FPR ) as follows: TPR=TPTP+FN ( 3 ) FPR=FPFP+TN ( 4 ) where TP are the True Positive links , and TN , FP and FN are the True Negative , False Positive and False Negative connections , respectively . The ROC curve is then obtained by plotting TPR versus FPR . The Area Under Curve ( AUC ) is a main parameter extracted to have a single number describing the performances of a binary classifier: a random guess will correspond to 0 . 5 , while a perfect classifier will have a value of 1 . Another important metrics that can be extracted from a ROC analysis is the accuracy , defined as: ACC=TP+TNTP+TN+FP+FN ( 5 ) The MCC curve[40] is a common metrics , alternative to the ROC analysis , used to evaluate the performances of a binary classifier by comparing prediction and observation . Using the quantities defined in the previous paragraph , changing the threshold used to compute the TCM , we can define MCC as: MCC=TP*TN−FP*FN ( TP+FP ) ( TP+FN ) ( TN+FP ) ( TN+FN ) ( 6 ) The MCC assumes values in the interval [–1 , 1] and the MCC curve is obtained by plotting the MCC value versus the false positive rate . Let x be a generic node and vx the total number of nodes adjacent to x ( including x ) . Let u be the total number of edges that actually exist between x and its neighbors . The maximum number of edges that can exist among all units within the neighborhood of x is given by vx ( vx -1 ) /2 . The Cluster Coefficient ( Cx ) for the node x , is defined as: Cx=2*uvx ( vx−1 ) ( 7 ) The Average Cluster Coefficient , obtained by averaging the cluster coefficient of all the networks nodes , is a global metric often used to quantify the segregation at network level . Let x and y be two generic nodes of a network V of n nodes . Let d ( x , y ) be the shortest distance between the nodes x and y . We define the Average Path Length ( L ) as: PL=2n ( n−1 ) ∑x≠yd ( x , y ) ( 8 ) This topological parameter is commonly used to evaluate the networks level of integration . To detect the emergence of small-world network [64] , it is possible to combine the metrics previously introduced , defining the Small-World Index ( SWI ) : SWI=CnetCrndLnetLrnd ( 9 ) where Cnet and Lnet are the cluster coefficient and the path length of the investigated network , respectively , and Crnd and Lrnd correspond to the cluster coefficient and the path length of random networks equivalent to the original network ( i . e . , with the same number of nodes and links ) . A SWI higher than 1 suggests the emergence of a small-world topology . A graph representing a complex network is said to have a rich-club organization if the hub nodes of such a graph are more strongly connected with each other than expected by their high degree alone[42] . It is possible to infer such an organization by computing the Rich Club Coefficient ( RCC ) . The RCC value at a specific k level is computed by evaluating the cluster coefficient among the nodes with a degree higher than k: RCC ( k ) =2E>kN>k ( N>k−1 ) ( 10 ) where N>k is the number of nodes with a degree higher than k , and E>k represents the edges between them . Evaluating the RCC with k varying from 1 to the maximum degree allows to build the RCC curve . The RCC curve is normalized by the corresponding average value for a set of surrogated random neural networks equivalent to the investigated one ( i . e . , networks with the same number of nodes and edges ) . If the maximum RCC normalized coefficient value is higher than one , a privileged sub-network ( i . e . , a rich club ) is found . The network model was made up of 1000 neurons randomly connected . The dynamics of each neuron is described by the Izhikevich equations[65] . In the actual model , two families of neurons were taken into account: regular spiking and fast spiking neurons for excitatory and inhibitory populations , respectively ( S2A Fig ) . The ratio of excitation and inhibition was set to 4:1 as experimentally founded in cortical cultures [8] . In the model , each excitatory neuron receives 100 connections from the other neurons ( both excitatory and inhibitory ) of the network . Such incoming connections reflect the same ratio of the neuronal population , i . e . , 80% of excitatory and 20% of inhibitory links . ( S2C Fig ) . Each inhibitory neuron receives 100 input only from excitatory neurons . Autapses are not allowed . All the inhibitory connections introduce a delay equal to 1 ms , otherwise excitatory ones range from 1 to 20 ms [66] . Synaptic weights were extracted from a Gaussian distribution with mean equal to 6 and -5 for excitatory and inhibitory weights ( S2B Fig ) . Standard deviations were set to 1 . Excitatory weights evolve following the spike timing dependent plasticity ( STDP ) rule with a time constant equal to 20 ms[67] . The spontaneous activity of the neuronal network was generated by stimulating a randomly chosen neuron at each time stamp injecting a current pulse extracted from a normal distribution ( Istm , exc = 11 ± 2; Istm , inh = 7 ± 2 ) . The network model was implemented in Matlab ( The MathWorks , Natick , MA , USA ) , and each run simulates 1 hour of spontaneous activity . Cortical and striatal neurons were dissociated from rat embryos ( E18 ) Sprague Dawley ( Charles River Laboratories ) . The day before plating , Micro-Electrode Arrays ( both MEA-4k and MEA-60 ) were coated with the adhesion proteins laminin and Poli-Lysine ( Sigma-Aldrich ) . The final density of plating was about 1200 cells/mm2 for the MEA-60 and 700 cells/mm2 for the MEA-4k . MEAs were maintained for four weeks in a humidified incubator ( 5% CO2 , 37°C ) in Neurobasal medium supplemented with B27 . More details about cell cultures can be found in previous works[50 , 68] . Recordings were performed using two kinds of MEAs: ( i ) MEA-60 ( Multi Channel Systems , Reutlingen , Germany ) constituted by 60 planar Ti/TiN microelectrodes 200 μm spaced with a diameter of 30 μm and arranged in a 8 by 8 square grid ( electrodes in the four corners are not present ) . ( ii ) MEA-4k ( 3Brain , Wadenswill , Switzerland ) constituted by 4096 square microelectrodes 42 μm spaced , 21 μm side length , arranged in a 64 by 64 square grid . Recordings of spontaneous activity were performed during the fourth week in vitro . We recorded 1 hour of spontaneous activity at the sampling frequency of 10 kHz ( MEA-60 ) and of 9046 Hz ( MEA-4k ) . Data analysis was performed off-line using Matlab and C# ( Microsoft , US ) . Spike detection . The algorithm used to detect extracellularly recorded spikes was the Precise Timing Spike Detection ( PTSD ) [69] . Practically , the detection was performed by setting three parameters: a differential threshold , evaluated as 8 times the standard deviation of the noise of each electrode; a peak life time period ( set at 2 ms ) and the refractory period ( set at 2 ms ) . Spike sorting was not performed as it is often difficult to distinguish different shapes during bursts due to overlapping spikes [60] . Burst detection . Burst at single electrode level and network bursts were detected by using previously developed and validated algorithms . Single electrode bursting activity was detected by considering at least 5 spikes with a maximum Inter Spike Interval ( ISI ) of 100 ms [70] . Functional connectivity and topological analysis . The FNCCH used to infer functional connectivity , as well as the metrics used to characterize the topological features of the cortical networks ( Small-World Index , Clustering Coefficient , average shortest Path Length ) were collected in an update version of the SpiCoDyn software [71] . Data are expressed as mean ± standard deviation of the mean . Statistical analysis was performed using Matlab . Since data do not follow a normal distribution ( evaluated by the Kolmogorov-Smirnov normality test ) , we performed a non-parametric Kruskal-Wallis test . Significance levels were set at p < 0 . 001 . In the box plot representation , the median value and 25th-75th percentiles are indicated by the box , mean value is indicated by the small hollow square , and whiskers indicate 5th-95th percentiles . The developed FNCCH is available to the scientific community on the Neuroimaging Informatics Tools and Resources Clearinghouse , ( NITRC ) repository ( http://www . nitrc . org/ ) . In particular , the FNCCH has been embedded in a new release ( v3 . 0 ) of the software tool SpiCoDyn ( https://www . nitrc . org/projects/spicodyn/ ) .
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The balance between excitation and inhibition is fundamental for proper brain functions and for this reason is precisely regulated in adult cortices . Impaired excitation/inhibition balance is often associated with several neurological disorders , such as epilepsy , autism and schizophrenia . However , estimating functional inhibitory connections is not an easy task and few methods are available to identify such connections from electrophysiological data . Here we present a cross-correlation based method to identify both excitatory and inhibitory functional connections in large-scale neuronal networks . The method is applicable to both in vitro and in vivo spike data recordings . Once a connectivity map ( i . e . a graph ) is obtained , we characterized the associated topology by means of classical graph theory metrics to unveil functional architecture . In this work , we analyze in vitro cortical networks probed by means of large-scale microelectrode arrays ( i . e . , 4096 sensors ) and we derive network topologies from spike data . The functional organization found is called “small-world and scale-free” and is the same organization found in cortical in vivo brain regions by means of different experimental methods . We also show that to obtain reliable information about network architecture at least a network with a hundred of nodes-neurons is needed .
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2018
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Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings
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Neutrophils release extracellular traps ( NETs ) in response to planktonic C . albicans . These complexes composed of DNA , histones , and proteins inhibit Candida growth and dissemination . Considering the resilience of Candida biofilms to host defenses , we examined the neutrophil response to C . albicans during biofilm growth . In contrast to planktonic C . albicans , biofilms triggered negligible release of NETs . Time lapse imaging confirmed the impairment in NET release and revealed neutrophils adhering to hyphae and migrating on the biofilm . NET inhibition depended on an intact extracellular biofilm matrix as physical or genetic disruption of this component resulted in NET release . Biofilm inhibition of NETosis could not be overcome by protein kinase C activation via phorbol myristate acetate ( PMA ) and was associated with suppression of neutrophil reactive oxygen species ( ROS ) production . The degree of impaired NET release correlated with resistance to neutrophil attack . The clinical relevance of the role for extracellular matrix in diminishing NET production was corroborated in vivo using a rat catheter model . The C . albicans pmr1Δ/Δ , defective in production of matrix mannan , appeared to elicit a greater abundance of NETs by scanning electron microscopy imaging , which correlated with a decreased fungal burden . Together , these findings show that C . albicans biofilms impair neutrophil response through an inhibitory pathway induced by the extracellular matrix .
Candida albicans is a widespread nosocomial fungal pathogen and frequent cause of bloodstream infection [1] . One of the most common risk factors of invasive candidiasis is medical device placement , with nearly 80% of patients having vascular catheters [2] . On these and other medical devices , C . albicans adopts a biofilm lifestyle . As an adherent microbial community , Candida is capable of withstanding conventional antifungals and host defenses [3–9] . This biofilm mode of growth presents a significant obstacle for effective treatment of candidiasis . Despite advancements in antifungal therapies and diagnostics , the mortality associated with invasive candidiasis remains exceedingly high , near 30% , and is even higher for patients with biofilm-infected devices that are retained [1 , 2] . Little is known about the host immune system response to device-associated Candida infection and why these biofilms are so resilient . One defining biofilm characteristic is the production of a protective extracellular matrix [10] . Recent analysis of C . albicans matrix identified α-mannan and β-1 , 6-glucan as the most abundant polysaccharides [11] . However , when compared to cell wall polysaccharides , striking differences were noted in their structures . For example , the α-1 , 2 branched α -1 , 6-mannan found in the matrix contained over 50-fold more residues than cell wall mannan [12] . Also , the mannan co-isolated with linear β-1 , 6-glucan , a structure distinct from the branched structure described for cell wall glucan [13] . A complex of these unique matrix polysaccharides was shown to encase the cells , promoting drug resistance and providing biofilm structure [14] . Leukocyte recognition of biofilms likely involves these and other matrix components , which are shielding the cell wall . Neutrophils are an essential host component required for control of numerous invasive fungal infections , including invasive candidiasis [15–18] . Patients with neutropenia are particularly prone to severe , life-threatening disease and those who remain neutropenic are at risk for relapse [19] . A recent study examining the host response to device-associated Candida biofilms identified neutrophils as the primary biofilm-associated leukocyte [20] . This finding was observed for multiple animal models mimicking clinically relevant biofilm infections , including a vascular catheter , a urinary catheter , and a denture-associated C . albicans infection [20] . However , it remains a mystery how neutrophils respond to these common biofilm infections and why this response is ineffective . While neutrophils are capable of phagocytosing Candida yeast , the larger hyphal forms trigger the release of neutrophil extracellular traps ( NETs ) [21 , 22] . These protruding fibrillary structures are composed of granular proteins and histones on a web of DNA [23] . NETs kill both yeast and hyphal forms of C . albicans in vitro , with antifungal activity linked to NET-bound calprotectin , which chelates cations required for proliferation of microorganisms [21 , 24] . NETs are critical for control of in vivo infection due to their activity against hyphae , which are too large to be ingested [22] . It would reason that NETs would be an ideal response against aggregated biofilm communities . In the current investigation , we show that C . albicans biofilms impair NET release as a mechanism to resist killing by neutrophils . We describe how the biofilm architecture and matrix drive the inhibition of this important immune function .
We examined neutrophil-biofilm interactions by co-culturing isolated human neutrophils with C . albicans biofilms . We found biofilms to be over 5-fold more resistant to killing by neutrophils when compared to planktonic cells ( Fig 1A ) . This is consistent with prior studies showing that biofilms resist neutrophil killing [7 , 25 , 26] . To examine if this impairment in neutrophil function may be due to a difference in release of NETs , we performed several complementary experiments [21 , 23 , 27 , 28] . First , we utilized the cell-impermeable Sytox Green dye to measure free DNA as an estimate of NETs [27] . Over the course of 4 hours , planktonic C . albicans triggered elevated free DNA , increasing more than 20-fold , consistent with release of NETs ( Fig 1B ) . This rise mirrored the response to phorbol myristate acetate ( PMA ) , a potent inducer of NET release . In comparison , no elevation of free DNA was observed in response to biofilms . To determine if the free DNA we were detecting was due to NET release , we utilized immunofluorescence imaging with labeling of citrullinated histones , a modification present in NETs ( Fig 1C ) [29] . Upon co-culture of planktonic Candida and neutrophils , thin strands of immunofluorescence were observed to encompass groups of Candida , consistent with the presence of NETs . However , these were rarely visualized when neutrophils were exposed to C . albicans biofilms . We further examined the neutrophil response by scanning electron microscopy ( Fig 1D ) [27] . After a 4 h exposure to planktonic cells , numerous web-like fibrillary structures coated the fungi , consistent with the release of NETs . The structures were spread over yeast cells as well as the hyphae , which had developed in response to the temperature and media of the assay . In contrast , upon neutrophil exposure to biofilm , neutrophils associating with the biofilm appeared rounded . The rounded neutrophil appearance accompanied by the lack of NETs in response to biofilm prompted us to examine if biofilms were recognized by neutrophils . To investigate this possibility , we observed the neutrophil response to C . albicans biofilms growing in microfluidic devices with time lapse microscopy . C . albicans biofilms were propagated on the channel sidewall and neutrophil migration was imaged over a course of 90 min ( Fig 2A and S1 File ) . Neutrophils were observed to migrate and adhere to the biofilm with a predilection for hyphae . Upon contact , the neutrophils elongated and migrated along hyphae . However , they appeared to stall before reaching the deepest basal biofilm layer . In contrast to this , neutrophils engulfed planktonic Candida and did not appear to elongate ( Fig 2A and S2 File ) . We further explored this interaction with scanning electron microscopy ( Fig 2B ) . Neutrophils were observed to release NETs in response to planktonic cells over the course of 4 h . Upon biofilm presentation , neutrophils were rounded and beginning to adhere to hyphae . By 1 h , neutrophils had clearly fastened to hyphae and exhibited extended filopodia , often stretching over multiple hyphae . However , by 4 h , the neutrophils again appeared rounded and inactive . Time lapse imaging confirmed that the neutrophils remained viable at this time point , excluding the propidium iodide stain ( S3 File ) . Taken together , these time course studies show an active interaction between C . albicans biofilms and neutrophils , where neutrophils adhere to the biofilm , elongate , and migrate . However , this process ends without NET release . Biofilms are heterogeneous communities of adherent cells encased in an extracellular matrix . We questioned if this covering may be a critical factor for the altered recognition and impairment of NET release . To test this , we physically dispersed C . albicans biofilms and examined the neutrophil response . In contrast to intact biofilm , which did not elicit NET release , the dispersed biofilms triggered a 5-fold increase in free DNA release by Sytox Green ( Fig 3A ) . We also used scanning electron microscopy to examine NET release in response to disrupted biofilm . Consistent with the Sytox Green assay results , many NETs were observed to associate with the edge of the biofilm that had been disrupted ( Fig 3B ) . This was in stark contrast to the intact biofilm , where neutrophils appeared rounded and very few neutrophils had released NETs ( Fig 1D ) . The observation that dispersed or disrupted biofilm induced NET release suggested that inhibition of NET release may be dependent on neutrophil contact with the biofilm . To further test this hypothesis and exclude the possibility of a soluble inhibitor of NETs , we investigated the impact of biofilm supernatants on neutrophil function . We utilized PMA , an inducer of NET release , and examined NET production in the presence of biofilm supernatants [30] . While exposure to PMA alone generated NETs , C . albicans biofilms inhibited PMA-induced NETs ( Fig 3C ) . This inhibition was not reproduced when biofilm supernatants were combined with PMA , suggesting that the phenotype is not due to a soluble factor ( Fig 3D ) . Instead , the finding is consistent with a contact-dependent mechanism of NET suppression . Our studies show impairment of NET release by C . albicans biofilms . We next sought to determine if NETs were capable of killing biofilm-associated cells . To examine if NETs would demonstrate anti-biofilm activity , we utilized PMA for NET induction . When neutrophils were simultaneously presented with both PMA and biofilm , NETs were inhibited ( Fig 3C ) . However , neutrophils could be induced to produce NETs by pre-stimulation with PMA prior to addition to the biofilm for 90 min ( Fig 3E ) . Even with this pre-stimulation step , biofilms still demonstrated significant NET inhibition , with NETs only reaching approximately one third of the levels of PMA alone . When applied to biofilms , these pre-stimulated neutrophils exhibited anti-biofilm activity . Collectively , these studies show C . albicans biofilms inhibit NET release , but if this can be overcome , NETs would likely be an effective killing mechanism against biofilms . To uncover factors governing C . albicans biofilm inhibition of NET release , we utilized a library of strains with genetic disruption of β-1 , 6 glucan and mannan synthesis and processing pathways ( S1 Table ) [14] . This library had been previously constructed to identify genes involved in the synthesis of the C . albicans extracellular matrix , with candidate genes selected based on the chemical structure of the abundant mannan-glucan complex [11] . We screened a total of 36 C . albicans mutants , including 30 with genetic disruption in mannan pathways and 6 with disruption in β-1 , 6 glucan pathways . C . albicans strains were grown as biofilms and Sytox Green was used to estimate NET release . The screen identified 14 strains which elicited fluorescence at least 1 . 5 fold higher than the reference strain , demonstrating that these pathways likely play a role in the suppression of NETs by C . albicans biofilms ( S2 Table ) . Considering 10 of these strains had disruption in mannan pathways , we chose this mutant subset for further investigation . For the C . albicans mannan mutants selected from our screen , we confirmed the phenotype of the triggering of NETs for at least 3 neutrophil donors . Five of the mutant strains consistently elicited higher NET release as estimated by Sytox Green ( Fig 4A ) . The four strains with the most pronounced phenotype ( alg11Δ/Δ , mnn9Δ/Δ , pmr1Δ/Δ and vrg4Δ/Δ ) had previously been shown to be involved in production of the extracellular matrix mannan-glucan complex , suggesting a role for this polysaccharide in impairment of NET release [14] . However , a biofilm defect was noted for two of these strains ( alg11Δ/Δ and vrg4Δ/Δ ) in our model ( S1 Fig ) . Therefore , we chose to focus on pmr1Δ/Δ , the mutant strain which elicited the highest NET release , while still forming a mature biofilm . PMR1 encodes a P-type Ca2+/Mn2+-ATPase which transports ions into the Golgi [31] . As mannosyltransferases require Mn2+ , disruption of PMR1 impacts both O- and N-mannan , resulting in truncated cell wall mannans . During biofilm growth , PMR1 is also required for production of extracellular matrix polysaccharides , including a large mannan structure which ultimately associates with glucan [14] . Consistent with the role of PMR1 in biofilm matrix production , we found the PMR1 transcript level to be more abundant during biofilm growth when compared to planktonic conditions ( 2 . 7 +/- 0 . 3-fold by RT-PCR ) . By scanning electron microscopy , the pmr1Δ/Δ biofilm had an appearance very similar to the reference strain consisting primarily of hyphae with few yeast forms ( Fig 4B and S1 Fig ) . Consistent with results of the Sytox Green assays , an increase in NETs was observed in response to the pmr1Δ/Δ biofilm , when compared to the reference strain biofilm ( Fig 4B ) . The mutant phenotype was mostly reversed with complementation of a single allele ( Fig 4C ) . The pmr1Δ/Δ+PMR1 biofilm triggered fewer NETs than the pmr1Δ/Δ biofilm , approaching the number for the reference strain . This is consistent with prior studies examining PMR1 that have demonstrated a similar pattern , with incomplete phenotype restoration upon integration of a single allele [14 , 32] . To assess if the pmr1Δ/Δ phenotype was unique to the biofilm mode of growth , we examined the neutrophil interactions with planktonic cells . Similar to the reference strain , the pmr1Δ/Δ mutant elicited NET release on co-culture ( Fig 4D ) . This suggests that the increased NET induction by the pmr1Δ/Δ biofilm is not due to a cell wall defect and it is specific to the biofilm mode of growth . The generation of ROS and its subsequent processing by myeloperoxidase are key steps in production of NETs [33 , 34] . ROS triggers the release of neutrophil elastase from granules to the cytosol where it degrades F-actin to halt migration before translocating to the nuclease for partial cleavage of histones , allowing for chromatin decondensation . To test the possibility that biofilm formation may be disrupting this process , we measured neutrophil production of ROS in response to C . albicans biofilms . Neutrophils were pre-treated with a free radical sensor ( CM-H2DCFDA ) and ROS was measured by fluorescence . Upon exposure to biofilms alone , neutrophils generated approximately 4-fold less ROS compared to a similar number of planktonic cells ( Fig 5A and 5B ) . Disruption of the biofilm architecture through biofilm dispersion increased ROS production to more than 60% of that measured in response to planktonic C . albicans . We additionally examined ROS in response to matrix disruption using the pmr1Δ/Δ mutant . In contrast to the reference strain , the pmr1Δ/Δ biofilm allowed for ROS activation , inducing over 3-fold higher ROS levels ( Fig 5C ) . We further examined the mechanism of neutrophil impairment using PMA to induce NETs . PMA is an activator of protein kinase C , which mediates activation of NADPH , an enzyme complex responsible for ROS generation during NETosis [35] . In agreement with prior investigations , PMA alone induced neutrophil generation of ROS ( Fig 5A ) [34 , 36] . However , C . albicans biofilm abrogated PMA-inducted ROS . This finding is consistent with the induction of an inhibitory pathway of NADPH oxidase which is downstream of protein kinase C activation ( Fig 5D ) . Collectively , these studies show C . albicans biofilms likely impair NET release by inhibiting NADPH-oxidase and ROS production in a manner dependent on intact biofilm structure and matrix production . To examine if the increased NET release associated with the pmr1Δ/Δ mutant resulted in increased anti-Candida activity , we examined the Candida burden following co-culture with neutrophils . The viable fungal burden was estimated by XTT assay after lysis of neutrophils . While neutrophils demonstrated minimal activity against the reference strain biofilm , the pmr1Δ/Δ biofilm burden was reduced by approximately 25% ( Fig 5E ) . This degree of killing is similar to the results from the studies adding pre-stimulated neutrophils to biofilm ( Fig 3F ) . Neutrophils were similarly effective against both the reference strain and pmr1Δ/Δ planktonic cells ( Fig 5F ) . Taken together , these studies show PMR1 is required for impairment of NET release and the resistance to killing by neutrophils during biofilm growth . To assess the role of mannosylation and matrix production on neutrophil function in vivo , we utilized a rat vascular catheter model of biofilm infection where biofilms are propagated on jugular venous catheters following luminal inoculation [37 , 38] . We analyzed biofilm architecture and host response with scanning electron microscopy . The reference strain produced a confluent layer of yeast and hyphae , as has been previously described ( Fig 6A ) [37 , 38] . The pmr1Δ/Δ mutant also produced a robust biofilm in vivo . However , many host cells were found associating with the biofilm and it was covered with numerous thread-like structures . On higher magnification , these fibrils were seen protruding from a host cell , ultimately forming a web , consistent with the release of NETs . This fibrillary material was less apparent for the reference strain biofilm . We next asked if the increase NET release in response to the pmr1Δ/Δ mutant correlated with increase fungal killing . Infected rat venous catheters were collected for viable burden determination . Compared to the reference strain , the pmr1Δ/Δ mutant exhibited a 70% reduction in microbial burden ( Fig 6B ) . This corroborates in vitro studies demonstrating increased killing of the pmr1Δ/Δ mutant biofilm . Strikingly , the phenotype was even more pronounced in vivo . The studies show that mannosylation of Candida biofilms inhibits NETs , leading to decreased killing in vivo .
The propensity of Candida to form biofilms provides a means for it to withstand host defenses and antifungal therapies , a major obstacle for effective treatment [4 , 39 , 40] . Neutrophils are essential for immunity to many invasive fungal infections including candidiasis [15–18] . In response to many pathogens , including those too large to by phagocytosed , these cells release NETs which entrap and kill various microorganisms , including C . albicans [21–23] . Prior reports have revealed that C . albicans biofilms resists killing by neutrophils , an essential leukocyte for immunity to invasive fungal infections [6 , 7 , 25] . Here we show that unlike planktonic C . albicans , biofilms fail to trigger the release of NETs . This manner of avoiding neutrophil killing is unique to the biofilm lifestyle and sheds light on why Candida biofilms are so resilient . This study provides multiple , complementary lines of evidence that the impairment of NET release in response to C . albicans biofilms is linked to the presence of an extracellular matrix . First , dispersion of microtiter plate biofilms interrupted the inhibitory process and permitted NET release . Likewise , disruption of biofilms growing on a coverslips triggered NETs . Additionally , multiple C . albicans mutants lacking a mature extracellular matrix elicited NETs . The increase in NETs correlated with susceptibility to neutrophil killing both in vitro and in vivo in a rat venous catheter model of biofilm infection . Furthermore , we showed that neutrophils pre-stimulated to induce NETs are active against biofilms , highlighting the importance of this inhibitory pathway . Together , these studies show the C . albicans biofilm matrix inhibits NET release , which contributes to immune evasion and provides a survival advantage . Recent investigations have provided a refined characterization of the C . albicans extracellular matrix , identifying polysaccharide structures distinct from those found in the cell wall [11 , 14] . The most abundant matrix polysaccharide , α-mannan , was in structures over 50-fold larger than those described for mannan of the cell wall [12] . A screen for genetic regulators of matrix mannan production identified 7 genes involved in mannan pathways [14] . These genes served roles in production or delivery of matrix mannan , biofilm architecture , and biofilm-associated drug tolerance via antifungal sequestration . Our current investigation identified 4 of these same mannan genes in a screen designed to find genes regulating the biofilm inhibition of NETs ( ALG11 , MNN9 , PMR1 , and VRG4 ) . These findings suggest that the core group of genes responsible for matrix production and drug tolerance also contribute to resistance to neutrophil attack . One limitation of this study is that genetic disruption of Candida matrix production can also impact the cell wall integrity , as the machinery for these two processes is overlapping [14] . For example , during biofilm growth , PMR1 is required for production of extracellular matrix polysaccharides , including a large mannan structure which ultimately associates with glucan [14] . However , disruption of PMR1 also impacts both O- and N-mannan , resulting in truncated cell wall mannans [31] . Our current studies identify a phenotype for the pmr1Δ/Δ mutant during biofilm formation , consistent with disruption of matrix , a biofilm-specific process . Differences in the triggering of NETs or the susceptibility to neutrophils were not observed between the pmr1Δ/Δ mutant and reference strain during planktonic growth . In addition , disruption of matrix by dispersion of the intact biofilm also mimicked the phenotype . In our studies , the C . albicans biofilm inhibition of NETs correlated mechanistically with dampened neutrophil ROS . Biofilm alone did not induce neutrophil ROS above the baseline and completely inhibited PMA induction of ROS . Conversely , the pmr1Δ/Δ biofilm , which triggered NETs , also elicited neutrophil ROS . A similar pattern of ROS release was observed in response to dispersed biofilm . Both of these observations are consistent with a role for biofilm matrix for this process . Most NET pathways involve NADPH-oxidase produced ROS with processing by myeloperoxidase , including NET induction by C . albicans [33 , 41 , 42] . In the current study , C . albicans biofilm suppression of both ROS and NET release was not overcome by PMA activation of protein kinase C , suggesting the presence of an inhibitory pathway of NADPH-oxidase which is downstream of protein kinase C . Interestingly , a process of ROS-independent rapid NET release has been observed for Candida when primed neutrophils are presented with fibronectin [43 , 44] . We considered the possibility of this pathway in response to C . albicans biofilm , but NET release was not identified by Sytox green staining or scanning electron microscopy imaging at early time points ( 15 min-1 h ) . Multiple moieties on the cell wall of planktonic Candida have been shown to be involved in phagocyte recognition , including N- and O-linked mannans , β-glucans , and chitin [18] . Investigations have begun to explore a role for receptors recognizing these pathogen-associated molecular patterns ( PAMPs ) in NETosis [22 , 43 , 45] . The induction of NETs in response to immobilized β-glucan and fibronectin was found to be dependent on β-glucan recognition by complement receptor 3 , but not by Dectin-1 [43] . In contrast , studies examining the NET response to the fungal pathogen Paracoccidioides brasiliensis discovered a key role for dectin-1 [45] . In C . albicans , this receptor has been shown to modulate NETosis [22] . Upon phagocytosis of a mutant C . albicans , which was “yeast-locked” and unable to filament , dectin-1 was implicated in negative regulation of NETosis [22] . In the current investigation , we show impaired NET release upon exposure to C . albicans biofilm . One intriguing possibility is that the biofilm matrix is masking cell wall epitopes important for neutrophil recognition and NET production . Alternatively , a unique epitope in the matrix material may prompt activation of a pathway distinct from NETosis . It is also interesting to postulate the NET response to alternative Candida biofilms . For example , biofilms with a less dense , more permeable matrix , such sexual biofilms with mating type locus a/a or α/α , may be expected allow for higher NETosis [46 , 47] . Prior investigations have identified diverse mechanisms of resisting NETs for a variety of pathogens . One of the intriguing mechanisms of NET evasion for bacterial pathogens is the production of extracellular deoxyribonucleases with the capacity to degrade NETs [48–53] . Enzymes produced by bacteria , such a catalase , are also protective through degradation of NET-associated reactive oxygen species [54] . Several mechanisms account for NET evasion for Aspergillus [55] . A . fumigatus conidia express a hydrophobin ( RodA ) which suppresses NET formation [55 , 56] . The hyphal forms appear to be protected from NETs by galactosaminogalactan , a positively charged exopolysaccharide which is proposed to inhibit binding of NET components [56] . For Cryptococcus spp . , the surrounding capsule modulates the neutrophil response [57] . For example , C . gattii capsular components can induce NET release , but are protective from NET killing [57 , 58] . In contrast , the capsular polysaccharide glucuronoxylomannan of C . neoformans inhibits the production of NETs [58] . Mechanisms of NET inhibition for Candida spp . have not previously been described . To our knowledge , the present study represents the first study showing the inhibition of NET release in a biofilm-specific manner . We and others have shown that Candida biofilms are many fold more resistant to neutrophil killing when compared to planktonic cells [7 , 25 , 26] . Here we describe a pronounced impairment of NET release in response to biofilm which accounts for resistance to neutrophils . However , as disruption of the biofilm matrix did not completely reverse this phenotype or elicit NET formation to the level of planktonic cells , other mechanisms are likely in play as well . In addition , the inhibition of NETs may have more broad implications beyond providing protection from neutrophil killing . In vivo , these NETs may play a critical role in preventing dissemination to distant sites , unmasking epitopes for fungal recognition , or recruiting additional inflammatory cells [59 , 60] . A role for NETs in leukocyte recruitment would provide an answer to why the difference in killing between the pmr1Δ/Δ biofilm the reference strain was greater in vivo . Our studies identify a mechanism of neutrophil impairment by C . albicans that is unique to biofilm formation . Our data show how biofilm extracellular matrix alters neutrophil recognition of Candida , inhibiting release of NETs and neutrophil associated fungal killing . Further understanding of this pathway may assist in the development of strategies to augment the host immune response to biofilm infections .
C . albicans strains used in this study are listed in S1 Table . The library of mutants with disruption of mannan and glucan genes has previously been described [14] . C . albicans SC5314 was used as the wild type strain and SN250 was used as the reference strain for mutants [14 , 61 , 62] . Strains were stored in 15% ( vol/vol ) glycerol stock at -80°C and maintained on yeast extract-peptone-dextrose ( YPD ) medium + uridine ( 1% yeast extract , 2% peptone , 2% dextrose , and 80 μg/ml uridine ) prior to experiments . Cultures were propagated overnight in YPD supplemented with uridine at 30°C on an orbital shaker at 200 RPM . For biofilm experiments , C . albicans was resuspended in RPMI-MOPS at a concentration of 1 . 5 x 106 cell/ml and 200 μl was added to wells of 96-well plates followed by a 24 h incubation at 37°C , unless otherwise specified [63] . For experiments using planktonic Candida , 1 ml of an overnight culture was inoculated into 20 ml of fresh YPD broth and incubated at 30°C on an orbital shaker at 200 RPM for 2 hours , washed twice with phosphate buffered saline ( -calcium , -magnesium ) DPBS ( Hyclone Laboratories Inc . , Logan , UT ) , and enumerated by hemocytometer . To assess biofilm burden and determine an equivalent burden of planktonic organisms , an XTT ( 2 , 3-Bis- ( 2-Methoxy-4-Nitro-5-Sulfophenyl ) -2H-Tetrazolium-5-Carboxanilide ) assay was performed as an estimate of viable burden [63] . Background absorbance at 492 nm was recorded prior to the addition of XTT and subtracted from final readings . Results showed a burden of 1 . 5 x 106 planktonic cells/well to be similar to the biofilm burden ( S2 Fig ) . Therefore , this number of planktonic cells was used in neutrophil co-culture experiments comparing the response to biofilm and planktonic cells [63] . Blood was obtained from volunteer donors with written informed consent through a protocol approved by the University of Wisconsin Internal Review Board ( IRB ) . Primary human neutrophils were purified by negative antibody selection using the MACSxpress Neutrophil Isolation and MACSxpress Erythrocyte Depletion kits ( Miltenyi Biotec Inc . , Auburn , CA ) . Experiments with neutrophils were performed in RPMI 1640 ( without phenol red ) supplemented with 2% heat-inactivated fetal bovine serum ( FBS ) and supplemented with glutamine ( 0 . 3 mg/ml ) . Incubations were at 37°C with 5% CO2 . As a measure of NET release , a Sytox Green assay was adapted for use in biofilm assays [27] . C . albicans biofilms were grown in wells of 96-well opaque plates and neutrophils were added to a final concentration of 2x105 cell/well . After a 4 h incubation , Sytox Green ( Life Technologies , Eugene , OR ) was added at a final concentration of 1 μM and fluorescence ( excitation 500 nm/emission 528 nm ) was measured in an automated plate reader . To reduce the background signal , plates were read immediately following the addition of Sytox Green . Background signals on average were 9% of positive controls . For a subset of experiments , PMA ( 100 nM ) was included . For experiments involving biofilms supernatants , C . albicans biofilms were grown in 6-well plates for 24 hours and washed twice with DPBS prior to addition of fresh media for 2 h . Supernatants were collected and centrifuged at 1200xg at 4°C for 20 min . For experiments with dispersed biofilms , biofilms were physically dispersed by gentle pipetting . A similar process for planktonic cells did not impact the NET response by Sytox Green ( S3 Fig ) . Experiments using planktonic cells were similarly performed by adding and allowing 1 . 5 x 106 cells/well to settle prior to the addition of neutrophils . Background fluorescence for each condition was subtracted from total fluorescence values . For measurement of neutrophil ROS production , an oxidative stress assay was adapted for use in biofilm [64 , 65] . Briefly , neutrophils were stained with the fluorescent dye CMH ( 2 ) CFDA ( Life Technologies , Eugene , OR ) in DPBS for 10 min at room temperature in the dark and added to biofilms growing in 96-well opaque plates to final concentration of 2x105 neutrophils/well . Fluorescence ( excitation 495 nm; emission 527 nm ) was recorded every 30 min for 3 h and data are shown for 2 . 5 hours , as this represented the max reading , prior to over reads or decline . Experiments using planktonic cells were similarly performed with the addition of 1 . 5 x 106 cells/well . Background fluorescence was determined for each C . albicans condition and subtracted from total fluorescence values prior to data analysis . Briefly , an XTT ( 2 , 3-bis- ( 2-methoxy-4-nitro-5-sulfophenyl ) -2H-tetrazolium-5-carboxanilide ) metabolic assay was used to estimate C . albicans viability following co-culture with neutrophils [6 , 63] . Following a 12 or 24 h incubation period , biofilms were washed with DPBS . To compare the killing of biofilm and planktonic cells , 24 h biofilms were compared to planktonic cells at a concentration of 1 . 5 x 106 cells/well . Neutrophils were added to a final concentration of 1 . 5 x 106 cells/well ( effector:target of 1:1 ) . For a subset of experiments , PMA 100 nM was included in select wells to induce NETs [21] . Following a 5 h incubation , neutrophils were lysed for 20 minutes at 37°C in TritonX-100 at a final concentration of 0 . 03% with 50 RPM agitation . Following lysis , 90 μL of 9:1 XTT working solution ( 0 . 75 mg/ml XTT in PBS with 2% glucose: phenazine methosulfate 0 . 32 mg/ml in ddH2O ) was added to each well . After a 25 min incubation , samples were transferred to a Falcon 96 well U bottom plate and centrifuged at 1 , 200×g to pellet cells . Supernatants ( 110 μl ) were then transferred to a 96 well flat bottom plate for absorption reading at 492 nm . To determine percent killing , values were compared to wells without neutrophils after subtraction of baseline absorbance . A subset of experiments was similarly performed using 12 h biofilms and 1 . 5 x 106 neutrophils/well to examine the impact of pre-stimulation of NETs and disruption of PMR1 . For microfluidic experiments , biofilms were grown in straight channels of a microfluidic device ( Iuvo Microchannel 5250 , Thermo Fisher ) . Channels were pretreated with fibrinogen 10 μg/ml in DPBS for 1 h prior and rinsed three times with RPMI-MOPS prior to loading 2 μl C . albicans at a concentration of 106 cells/ml . The plate was incubated for 1 h at room temperature on its vertical access to allow for settling and adherence to the sidewall . The plate was incubated for an additional 18 h vertically at 37°C . Biofilms were then gently washed 3 times with RPMI supplemented with 2% FBS and 3 μl of fluorescently labeled human neutrophils at 2×106 cells/ml were added . These cells had previously been stained with Calcein AM ( ThermoFischer Scientific , Waltham , MA ) at 0 . 5 μg/ml in DPBS for 10 min at room temperature in the dark . Candida-neutrophil interactions were analyzed by time lapse microscopy with microfluidic devices incubating at 37°C . For imaging of the initial ( 0–90 min ) interaction with biofilms , images were obtained every 60 sec using bright field and fluorescence ( excitation 480 nm , emission 525nm ) at 10x for 90 minutes on an inverted microscope ( Nikon Eclipse TE300 ) equipped with a motorized stage ( Ludl Electronic Products ) , charge-coupled device camera ( CoolSNAP ES2 ) , and MetaVue imaging software v6 . 2 . Images and videos were compiled using ImageJ . Video is shown at 5 frames per second . For imaging of neutrophil viability ( 3–5 h ) , Calcein AM-labeled neutrophils were incubated with Candida biofilms for 3 h prior to imaging . After the addition of 4 μl propidium idodide ( 3 μM ) , devices were incubated for an additional 15 min . Images were then obtained every 2 min using bright field and fluorescence ( excitation 480 nm , emission 525nm and excitation 565 , emission 620 ) for 2 h and compiled at 7 frames per second . Experiments were similarly performed with planktonic C . albicans in glass coverslip bottom petri dishes ( MatTek , Ashland , Ma ) . As a qualitative measure of NETs , immunofluorescent imaging for histone citrullination was performed . Biofilms were grown in glass coverslip bottom petri dishes ( MatTek , Ashland , Ma ) or two channel microfluidic devices ( Beebe lab , UW-Madison ) pre-treated with 10μg/ml fibrinogen ( Sigma-Aldrich , St . Louis , MO ) in 0 . 1% gelatin ( Millipore , Temecula , CA ) . Biofilms were washed 3 times after 22 h of growth and neutrophils were added at a concentration of 2×106 cells/ml . After 4 h , biofilms were fixed with 4% formaldehyde in DPBS for an additional 4 h . Fixed co-cultures were rinsed 3×5 min with DPBS and incubated with antibody blocking buffer ( 2% w/v bovine serum albumin ( BSA ) and 0 . 02% v/v Tween 20 in PBS ) overnight at 4°C . All steps were performed very gently to preserve NETs . Following rinsing with antibody binding buffer ( 0 . 1% BSA w/v and 0 . 005% v/v Tween 20 in PBS ) , primary antibody ( anti-histone H4 , citrulline3 ) in antibody binding buffer at 1:1000 was added for 2 h at room temperature [29] . Samples were rinsed gently 6× 5 min and secondary antibody ( goat-anti rabbit IgG Fc DyLight 594 conjugated ) at 1:200 in antibody binding buffer was added for a 2 h incubation in the dark . Samples were rinsed 6×5 min with antibody binding buffer and brightfield and fluorescent ( excitation 565 , emission 620 ) images were obtained using the 20x objective on an inverted microscope ( Nikon Eclipse TE300 ) equipped with a charge-coupled device camera ( CoolSNAP ES2 ) and MetaVue imaging software v6 . 2 . Images were processed using ImageJ . A coverslip model of biofilm formation was adapted for use with neutrophils [14] . Briefly , C . albicans resuspended in RPMI-MOPS at 106 cells/ml was added to poly-L-lysine coated coverslips ( 13 mm , Thermonax plastic for cell culture ) for 30 min at 30°C . After the adherence period , non-adherent cells were removed and fresh RPMI-MOPS was added . Biofilms were grown for 24 h at 37°C on an orbital shaker at 50 RPM and washed with DPBS . Neutrophils ( 5 x 105 ) were added and coverslips were collected for scanning electron microscopy , as described below , after 15 min , 1 h , or 4 h . Specific-pathogen-free Sprague-Dawley rats weighing 350 g ( Harlan Sprague-Dawley , Indianapolis , Ind . ) were used for all studies . A jugular vein rat central venous catheter biofilm infection model was used as previously described [38 , 66] . Briefly , 24 h following surgical jugular venous catheter insertion , C . albicans at 106 cells/ml was instilled in the catheter lumens . After 24 h , the inocula were removed and catheters were harvested and collected for imaging by scanning electron microscopy , as described below , or viable burden measurement . Viable burdens were determined by plating of serial dilutions on Sabouraud Dextrose Agar in triplicate . Rat vascular catheter biofilms were processed and imaged by scanning electron microscopy as previously described [38] . Coverslips of biofilm and planktonic co-culture with neutrophils were similarly processed . Briefly , after washing with DPBS , samples were fixed overnight ( 4% formaldehyde , 1% glutaraldehyde , in PBS ) . They were then washed with PBS , treated with 1% osmium tetroxide , and washed again . Samples were dehydrated through series of ethanol washes followed by critical point drying and mounted on aluminum stubs . Following sputter coating with either gold or platinum , samples were imaged in a scanning electron microscope ( LEO 1530 ) at 3kV . RNA was purified from 24 h biofilms and planktonic cells using the RNeasy Minikit ( Qiagen ) and quantified using a NanoDrop spectrophotometer [67] . TaqMan primer and probe sets were designed as previously described [67] . Primers for ACT1 included AGCTTTGTTCAGACCAGCTGATT ( ACT1 RT For ) , GGAGTTGAAAGTGGTTTGGTCAA ( ACT1 RT Rev ) , and /56-FAM/CCAGCAGCTTCCAAACCT/36-TAMSp/ ( ACT 1 Probe ) . Primers for PMR1 included TGGATGGGCAATAAGGATGAC ( PMR1 RT For ) , TGTTAACTTAGCCTCTGCAGG ( PMR1 RT Rev ) , and /56-FAM/AGTGATGGCTAATGTGATCGATATGGCG/36-TAMSp/ ( PMR1 Probe ) . The QuantiTect probe RT-PCR kit ( Qiagen ) was used in a CFX96 real-time PCR detection system ( Bio-Rad ) with the following program: 50°C for 20 min , initial denaturation at 95°C for 15 min , and then 40 cycles of 94°C for 15 s and 55°C for 1 min . Reactions were performed in triplicate . The quantitative data analysis was completed using the delta-delta CT method [68] . The comparative expression method generated data as transcript fold-change normalized to a constitutive reference gene transcript ( ACT1 ) and as transcript fold-change relative to planktonic cells . Experiments were performed at least 3 times using neutrophils from different donors on different days . Statistical analyses were performed by Student’s t-test using Sigma Stat or Excel software . Differences of P<0 . 05 were considered significant . For studies with neutrophils from human subjects , written informed consent was obtained from healthy donors at the time of blood draw with the approval of the University of Wisconsin-Madison Center for Health Sciences Human Subjects Committee . All animal procedures were approved by the Institutional Animal Care and Use Committee at the University of Wisconsin according to the guidelines of the Animal Welfare Act , The Institute of Laboratory Animal Resources Guide for the Care and Use of Laboratory Animals , and Public Health Service Policy under protocol MV1947 .
|
Candida spp . avidly adhere to medical device surfaces , forming resilient , drug-tolerant biofilms which are encased in a protective extracellular matrix . These infections are notoriously difficult to eradicate and little is known about how they evade host defenses . Here we show a mechanism by which C . albicans biofilms inhibit the activity of neutrophils , leukocytes critical for protection from Candida . When exposed to biofilm , neutrophils fail to release neutrophil extracellular traps ( NETs ) , web-like structures of DNA , histones , and proteins capable of fungal killing . The biofilm extracellular matrix appears to have a major role in this inhibition , as disruption of matrix polysaccharides by physical or genetic means reverses the impairment . C . albicans biofilms block neutrophil production of reactive oxygen species ( ROS ) , a signaling pathway involved in NET production . Impaired NET release results in decreased fungal killing in vitro and in an animal model of biofilm infection . This represents a novel mechanism of immune evasion specific to the biofilm mode of growth .
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2016
|
The Extracellular Matrix of Candida albicans Biofilms Impairs Formation of Neutrophil Extracellular Traps
|
Toxoplasma gondii is a zoonotic protozoan parasite which infects nearly one third of the human population and is found in an extraordinary range of vertebrate hosts . Its epidemiology depends heavily on horizontal transmission , especially between rodents and its definitive host , the cat . Neospora caninum is a recently discovered close relative of Toxoplasma , whose definitive host is the dog . Both species are tissue-dwelling Coccidia and members of the phylum Apicomplexa; they share many common features , but Neospora neither infects humans nor shares the same wide host range as Toxoplasma , rather it shows a striking preference for highly efficient vertical transmission in cattle . These species therefore provide a remarkable opportunity to investigate mechanisms of host restriction , transmission strategies , virulence and zoonotic potential . We sequenced the genome of N . caninum and transcriptomes of the invasive stage of both species , undertaking an extensive comparative genomics and transcriptomics analysis . We estimate that these organisms diverged from their common ancestor around 28 million years ago and find that both genomes and gene expression are remarkably conserved . However , in N . caninum we identified an unexpected expansion of surface antigen gene families and the divergence of secreted virulence factors , including rhoptry kinases . Specifically we show that the rhoptry kinase ROP18 is pseudogenised in N . caninum and that , as a possible consequence , Neospora is unable to phosphorylate host immunity-related GTPases , as Toxoplasma does . This defense strategy is thought to be key to virulence in Toxoplasma . We conclude that the ecological niches occupied by these species are influenced by a relatively small number of gene products which operate at the host-parasite interface and that the dominance of vertical transmission in N . caninum may be associated with the evolution of reduced virulence in this species .
Toxoplasma gondii and Neospora caninum are closely related tissue-dwelling Coccidia – intracellular protozoan parasites of the phylum Apicomplexa . T . gondii can infect essentially any warm-blooded vertebrate and is found in nearly one third of humans , arguably being the world's most successful zoonotic parasite [1]; it causes neonatal mortality , spontaneous abortion and blindness [2] . T . gondii is most often transmitted horizontally following ingestion of environmentally resistant oocysts excreted by its definitive host ( cats ) , or via ingestion of persistent asexual stages ( bradyzoites ) residing in the tissues of intermediate hosts . The biology of T . gondii has been intensively studied , but despite advances in understanding host cell invasion , the role of secreted kinases in parasite virulence [3] , [4] and its population and evolutionary biology [5] , [6] , the molecular mechanisms responsible for its highly promiscuous nature remain unknown . Neospora caninum is a close relative of T . gondii [7] . They are both tissue-dwelling Coccidia and share many common morphological and biological features [8] . Each is able to develop in intermediate hosts , reproducing asexually , or to move between intermediate and definitive hosts , reproducing sexually . Neospora was initially misidentified as Toxoplasma , but was subsequently differentiated based on host preferences , etiology , morphological and genetic differences [8] . Despite these similarities the two species differ in their definitive host: while Toxoplasma completes its sexual cycle in felids , Neospora does so exclusively in canids [9] . Unlike Toxoplasma , Neospora appears not to be zoonotic , having a more restricted host range [10] , [11] in which it occupies a unique ecological niche showing a striking capacity for highly efficient vertical transmission in bovines [12] . N . caninum is one of the leading causes of infectious bovine abortion , resulting in significant economic losses to the dairy and beef industries [13] . The molecular determinants of host specificity and in particular zoonotic capability in the Apicomplexa are not known . It is possible that a large part is played by the host cell invasion machinery common to all Apicomplexa which involves surface antigens and specialized apical secretory organelles named rhoptries , micronemes and dense granules [14] , but this is yet to be substantiated by experimental evidence . The process of host cell invasion has been well studied in Toxoplasma and components of the invasion machinery are also involved in host cell modification and interaction with the host immune system [3] , [4] . Attachment to host cells is mediated by a family of highly abundant surface antigens [15] , after which the micronemes release adhesins which engage an actin-myosin motor to provide the driving force for host cell invasion [16] , [17] , [18] . Rhoptry neck proteins are then released to form a tight region of contact with the host cell , known as the moving junction , which acts as a scaffold for the parasite to enter the cell and form the parasitophorous vacuole ( PV ) in which it resides [19] . The rhoptries also release a range of proteins that modulate host cell function [20] , [21] , [22] , in particular , virulence-related rhoptry kinases interact with host defenses; for example , ROP18 inactivates host immunity-related GTPases ( IRGs ) that would otherwise rupture the PV membrane and kill the parasite [23] , [24] . Whilst it is anticipated that the overall process of host-parasite interaction in Neospora is likely to be similar , we hypothesize that the molecular characteristics of this interface are likely to be the key determinant in the unique biological features of the two parasites . In fact , small but defining differences in the biology of these two closely related organisms provide a unique opportunity to identify the mechanisms which underlie the basis of host specificity , pathogenesis and zoonotic potential not only in these important parasites , but also in the wider members of the phylum . This includes several groups of organisms of key interest to human and animal welfare ( e . g . Plasmodium , Cryptosporidium and Eimeria ) . In order to exploit this opportunity we have sequenced the genome of N . caninum and the transciptomes of both N . caninum and T . gondii , undertaking the first comparative transcriptome analysis of any apicomplexans at single base-pair resolution . We show that Neospora caninum and Toxoplasma gondii have very similar genomes with largely conserved gene content and synteny . As predicted , differences are most common amongst groups of genes which interact with the host . We find that surface antigen gene families are expanded in N . caninum suggesting that larger repertoires of such genes may be important in becoming a more host-restricted coccidian parasite , although data from a more extensive range of related parasites would be required to test this hypothesis . We also find that some rhoptry genes are highly variant between species and demonstrate that the pseudogenisation of ROP18 in N . caninum leads to a functional change in the interaction of the parasite with host immune mechanisms . We propose that such mutations may be associated with changes in transmission strategy . In addition to these biological insights , our data provides a vital community resource for comparative genomics in this important phylum of medical and veterinary parasites .
We sequenced the genome of N . caninum Liverpool strain using Sanger sequencing to ∼8-fold depth . It was assembled into 585 supercontigs with an N50 of 359 kb totaling 61 Mb ( Table 1 ) . We constructed a set of N . caninum pseudochromosomes by aligning 242 supercontigs to the fourteen publicly available T . gondii Me49 chromosomes [25] based on predicted protein sequence similarity ( Figure 1A ) . It has been shown previously using our partially assembled sequencing data that N . caninum and T . gondii genomes are largely syntenic [26] . Here we show that for almost all regions where conservation of gene order ( synteny ) is interrupted , corresponding orthologous regions are found elsewhere in the genome . This suggests that while there may have been a small number of chromosomal rearrangements , there has been very little net gain or loss of genetic content ( Figure 1B ) . The N . caninum Liverpool genome sequence has been added to the European Nucleotide Archive as project CADU00000000 . To determine gene expression differences between species and to improve genome annotation we sequenced the transcriptome of the invasive stage ( tachyzoite ) of N . caninum Liverpool and T . gondii VEG using mRNA sequencing ( mRNAseq ) on an Illumina GAIIx machine ( Tables S5 & S6 ) . The parasites were grown asynchronously for a period of six days in cell culture . We took samples of RNA at days three , four and six . We found that days three and four showed fairly similar expression profiles within species and so we have pooled this data for most analyses . We found however that day six N . caninum parasites were showing expression of bradyzoite ( quiescent stage ) marker genes ( Text S1 ) . These parasites had not fully converted into bradyzoites , but may be preparing to do so . We did not observe expression of these markers at day six in T . gondii , so we did not seek to compare transcriptomes of the two species at this timepoint . Transcriptome sequencing data has been submitted to ArrayExpress with accessions E-MTAB-549 for N . caninum sequences and E-MTAB-550 for T . gondii sequences . Combining de novo gene predictors and mRNAseq evidence we identified 7121 protein-coding genes in N . caninum and produced a revised T . gondii ME49 gene count of 7286 , a reduction of 9% from previous predictions ( Table 1 ) . This was achieved predominantly by merging adjacent genes based on mRNAseq evidence . In N . caninum we detected the expression of 74% of genes during the tachyzoite stage . In T . gondii 80% were expressed , significantly more than the 49% recently reported using microarrays suggesting greatly improved sensitivity [27] . Using a combination of automated orthologue identification and manual curation we identified a small number of unique ( i . e . organism-specific ) genes in both genomes that might underlie their phenotypic differences ( Figure 2A ) . Excluding surface antigen families ( discussed later ) , we found 231 genes unique to T . gondii and 113 to N . caninum , i . e . with no orthologue or paralogue based on our orthologue analysis . Of these , 72 from T . gondii and 43 from N . caninum had Pfam domains or proteomics-based evidence ( Table S1 ) . These genes represent good candidates for understanding organism-specific differences and are enriched for those involved in host-parasite interactions . The remainder had no detectable homologues or proteomics-based evidence , although most had good transcriptome evidence . Only one organism-specific multigene family , with no homologues in the other species was identified: a family that we have named Toxoplasma-specific family ( TSF; Figure S1 ) . This family is located largely in chromosomal regions with no similarity to N . caninum ( regions 3 , 5 and 17 in Figure 1B ) and varies in size between T . gondii strains . We found that all ten members of TSF from T . gondii Me49 were expressed during the tachyzoite stage . No significant domains , motifs or signal peptides were identified; however a putative transmembrane helix was predicted between amino acids 195 and 217 on TgTSF1 . Another previously unidentified family was present in N . caninum , but was expanded in T . gondii ( Figure S2 ) . This family comprises three genes from N . caninum and seven from T . gondii Me49 . Sequences were scanned using InterProScan [28] but no significant domains or motifs were identified . Lysine-arginine rich motifs are however present in the sequences suggesting possible nuclear localization signals . We have therefore named this family Lysine-Arginine rich Unidentified Function ( KRUF ) . KRUF genes appear to be highly expanded in the GT1 strain of T . gondii , with up to twenty members [25] . Two of the three N . caninum members are expressed in the tachyzoite and early bradyzoite stages ( NCLIV_002020 and NCLIV_002030 ) . Most of the T . gondii members are expressed , some at very high levels ( esp . TGME49_051170 ) . While 32% of the genes shared by T . gondii and N . caninum have orthologues in a range of eukaryotes , we found that ∼39% of the shared genes do not have orthologues in other apicomplexans sequenced to date ( Figure 2B ) . Furthermore , while ∼29% of the shared genes not found outside apicomplexans have orthologues in at least one apicomplexan , only 0 . 3% are shared between all apicomplexans with completed genome sequences . Due to the assumptions behind this analysis we have likely underestimated the similarity between Apicomplexa and more detailed manual analysis will no doubt reveal more divergent orthologues . However our results suggest that the genome content of apicomplexans is very diverse and that many novel and divergent genes are found within the Coccidia . To determine whether their divergent lifestyles are associated with differences in metabolism we compared the predicted repertoires of metabolic enzymes and pathways of N . caninum to those of T . gondii [25] . The pathways identified in N . caninum appeared identical to those in T . gondii and we found no single metabolic gene specific to either species suggesting that changes in metabolism do not play a large role in host restriction and zoonotic compatibility in these species . Although a small number of metabolic genes were differentially expressed between species , we found little evidence that these were clustered in any particular pathway , although there is some evidence that nitrogen metabolism may be upregulated in N . caninum and porphyrin metabolism may be upregulated in T . gondii ( Figure 2C ) . Previous estimates from rRNA analysis have suggested that N . caninum and T . gondii diverged between 12 and 80 million years ago ( mya ) [6] , [29] . To gain a more accurate estimate we examined a large number of orthologue alignments , determining synonymous substitution rates between N . caninum and T . gondii and between malarial parasites of human and non-human homonidae: Plasmodium falciparum and P . reichenowi , respectively . We assumed constant evolutionary rates between the Plasmodium spp . and Coccidia , excluding genes which were found to have evolved in a non-clock-like manner . We used a previously determined estimate of 2 . 49 mya for the split between P . falciparum and P . reichenowi [30] . This allowed us to date the speciation of N . caninum and T . gondii to 28 . 0 mya or between 21 . 7 and 42 . 7 mya using the confidence intervals of the P . falciparum and P . reichenowi divergence time . This suggests that speciation of N . caninum and T . gondii occurred after the speciation of their definitive hosts ( estimated at 54–67 mya ) [31] . The ability to reject non-clock-like genes is dependent on gene length and so we also calculated the divergence time using only the longest 25% of the orthologous groups . This led to a divergence time of 26 . 9 mya , very close to that calculated using all groups , suggesting that a tendency to exclude longer genes using the clock test has not biased our results . Examination of gene gain and loss and differential expression implicated two host-interaction gene families: SAG1-Related Sequence ( SRS ) and ROPK , as among the most divergent features of the two species ( Figure 2D , Figure 3 ) . SAG1 was the first SRS protein identified and is the major surface antigen of Toxoplasma . SRS proteins localize to the cell surface of both T . gondii and N . caninum . They are thought to play a key role in attachment to host cells , modulation of host immunity and regulation of parasite virulence [32] . Wasmuth et al . ( submitted ) found 109 functional genes and 35 pseudogenes in T . gondii Me49 with similar numbers across several different strains . They are present sometimes in single copies , often in tandem arrays . They are dispersed across all chromosomes rather than showing a preference for subtelomeric regions as is found for some large gene families in Plasmodium , Babesia and Theileria ( Figure 1A ) . It has been suggested that the large number of SRS genes is present in T . gondii to accommodate the wide spectrum of potential host-cell molecular interactions presented by its exceptionally large host range [33] , [34] . However , our data refute this; we found the SRS gene family to be substantially expanded in N . caninum compared to T . gondii with a total of 227 N . caninum SRS genes ( NcSRSs ) and 52 NcSRS pseudogenes ( Figure 1A ) . Expression data suggest however that T . gondii expresses a greater number of its SRS repertoire ( 55 vs . 25 in N . caninum ) during the tachyzoite stage ( Figure 1A ) . In N . caninum we found in most cases that only a single SRS gene was expressed at a multigene locus , whereas in T . gondii we often found several . Extending our gene expression studies beyond the rapidly growing and invasive tachyzoite stage , we noticed that N . caninum cultures maintained until day six showed expression of known bradyzoite-specific genes ( e . g . BAG1 , SRS13 , SAG4 ) , suggesting they were beginning to convert into the slow-growing stage ( Text S1 ) . We observed a greater number of NcSRS genes ( 36 vs . 25 ) expressed at day six than at earlier points in the culture . Despite this it remains unclear whether most members of this expanded family in N . caninum are expressed and further expression data are required from all life-stages before the role of these genes can be better understood . SRS genes consist of one or more copies of the SAG domain family , which has been classified into eight subfamilies ( Figure S3; Wasmuth et al . , submitted ) . The doubling of SRS gene numbers in N . caninum compared to T . gondii is largely accounted for by expansion of a particular subfamily with a 7–8 domain architecture . No novel SAG domain subfamily has evolved in either lineage , however several domain combinations are found in low copy numbers in only one or the other species ( Table S2 ) . Since a particular SRS locus tends to contain genes with the same domain architecture in both species , expansion has likely occurred by tandem duplication . We found evidence that gene conversion may have occurred at , at least , one locus ( SRS19; Figure S4A ) , whereas one of the most highly expressed loci in both organisms ( SRS29 , containing the SAG1 gene ) showed no evidence of gene conversion ( Figure S4B ) , perhaps due to functional constraints . SUSA genes ( SAG-Unrelated Surface Antigen genes ) are a superfamily of surface antigens unrelated to SRSs but which are also postulated to interact with the host immune system [35] . In common with the SRS superfamily we found that N . caninum had an expanded number of SUSA genes but that a greater number were expressed in T . gondii ( Figure 1A ) . In fact none of the NcSUSA genes were expressed in the tachyzoite stage . Two NcSUSA genes ( NCLIV_067570 and NCLIV_067920 ) were however expressed at day six of culture . The apical complex is the defining characteristic of the Apicomplexa . It includes the rhoptry , microneme and dense granule secretory organelles , which are essential for cell invasion . Figure 3 shows how the repertoires and expression of gene products known or predicted to be localized to these organelles differs between T . gondii and N . caninum . Several T . gondii rhoptry genes ( ROP18 , ROP16 and ROP5 ) have been implicated in virulence based on a genetic cross between the type II and III [3] and type I and III [4] lineages of T . gondii . N . caninum differs from T . gondii at each of these loci , but shares some similarities with low virulence strains . Expression of TgROP18 is associated with virulence in mice [4] and in some hosts high ROP18 expression may reduce parasite fitness by causing rapid host death [36] . It is involved in preventing the host interferon-gamma ( IFN-γ ) response , during which the host loads immunity-related GTPases ( IRGs ) onto the parasitophorous vacuole ( PV ) leading to its disruption and parasite cell death in avirulent strains [37] . Virulent T . gondii strains express high levels of ROP18 , which phosphorylates and inactivates IRGs to safeguard the PV [24] , [37] . We found that in Neospora Nc-Liv ROP18 is a pseudogene due to several interrupting stop codons in the sequence syntenic with the Toxoplasma gene . We confirmed the presence of these stop codons in a further four strains of the parasite isolated from different geographic locations and hosts ( Table S3 ) . To determine whether N . caninum is able to phosphorylate IRGs without a functional copy of ROP18 we examined the loading of Irga6 ( a member of the host IRG GTPase family ) onto the PV by immunofluorescence studies . We observed that , in both N . caninum and T . gondii infections , host cells responded by loading Irga6 onto the PV but only T . gondii was able to phosphorylate Irga6 and thereby presumably inactivate the IRG protein ( Figure 4A ) . This suggests that N . caninum is unable to prevent its host from using IRGs to attack the PV . In T . gondii ROP16 directly interferes with host signaling pathways ( e . g . Stat3 , Stat6 ) to modulate the proinflammatory host cytokine IL-12 [21] , [38] . A single polymorphic residue on TgROP16 determines the strain-specific activation and phosphorylation of Stat3 [39] . We found that ROP16 was highly expressed in T . gondii VEG ( type III ) tachyzoites and it has been shown elsewhere to be highly expressed in all T . gondii strain types [27] . While its orthologue in N . caninum , NcROP16 , possesses the key active-site leucine residue for Stat3 activation , the gene was not expressed in our experiments . Although it is possible that NcROP16 could be expressed in other cell types , our experiments predict that N . caninum infection does not activate Stat3 due to its lack of expression . Several additional T . gondii rhoptry genes are missing from N . caninum ( Figure 3 ) , most notably the entirety of the locus which encodes ROP2A , ROP2B and ROP8 . The TgROP5 multigene locus accounts for 50% of inherited variation in Toxoplasma virulence [40] . The relationship between ROP5 genotype and virulence in T . gondii is however not clear . The most virulent , type I T . gondii strain ( e . g . RH ) has six copies , while the less virulent type II T . gondii strain ( e . g . Me49 ) has around ten copies and the least virulent type III strain ( e . g . VEG ) has four . N . caninum Liverpool encodes only two copies of the ROP5 gene both of which are highly expressed in the tachyzoite stage ( Figure 4B ) . The secreted proteins of the microneme organelles play a crucial role in host cell attachment and invasion by mediating gliding motility [16] . We identified thirteen previously undescribed genes putatively encoding micronemal proteins by virtue of conserved domain architectures . Of these newly identified genes , MIC26 ( a MIC2 paralogue ) and MIC19 ( a PAN domain-containing gene ) are unique to N . caninum . Some differences also exist between the species in dense granule genes which are involved in the modification and function of the parasitophorous vacuole ( PV ) [41] . Dense granule genes GRA11 and GRA12 were absent from the N . caninum genome sequence . Serine proteases are important to the maturation of both rhoptry and microneme proteins and their inhibition blocks parasite replication and rhoptry formation [42] . TgSUB2 , a subtilisin-like serine protease has been identified as a likely processor of several rhoptry proteins [43] and whilst T . gondii is vulnerable to a variety of protease inhibitors , including serine protease inhibitors , N . caninum invasion is inhibited only by aspartyl protease inhibitors [44] . We found that while all 12 identifiable T . gondii subtilases had orthologues in N . caninum , there was a significant decrease in expression of these proteases ( hypergeometric test; p = 0 . 003 ) compared with T . gondii . This suggests that subtilisin-like serine protease activity may not be used to the same extent in N . caninum as in T . gondii and may explain why N . caninum is less susceptible to its inhibition . The ApiAP2 family represents the major group of apicomplexan transcription factors . They have been implicated , for example , in control of the intraerythrocytic development cycle ( IDC ) [45] and sporozoite development [46] of malaria parasites . Twenty-nine such genes have been identified in Plasmodium and 68 in Toxoplasma [47] . We found N . caninum orthologues for all 68 TgAP2 genes but detected significant differences in the expression of eleven of them ( Figure 3 ) , which in turn may be responsible for expression differences we have observed in other genes . It has been suggested for instance that rhoptry genes are regulated by AP2 transcription factors in Plasmodium [48] . We found that 54 of 68 NcAP2s and 61 of 68 TgAP2s were expressed during the tachyzoite stage , more than a previous study [49] . This is surprising considering that one would expect the principal family of transcription factors in organisms with a complex life cycle to be highly specific to different life stages . As expected , the repertoire of ncRNA genes of known function ( e . g . t-RNAs , snoRNAs , snRNAs etc . ) is almost identical between Toxoplasma and Neospora . However , we were able to identify an expansion of a previously unidentified candidate structured non-coding RNA family in N . caninum . This suggests that ncRNA repertoires are divergent in these species , although the functions of these RNAs remain to be identified ( Text S2 , Figures S5 & S6 ) .
We have used genome and transcriptome sequencing to probe the apicomplexan parasites Toxoplasma gondii and Neospora caninum for differences which might underlie their divergent host ranges , transmission strategies and zoonotic potential . We have demonstrated that the two genomes show a high degree of synteny , with a one-to-one correspondence between most protein-coding genes . We calculated that speciation occurred around 28 mya , after the divergence of their respective definitive hosts , the cat and dog . This is consistent with two possibilities: 1 ) one or both parasite species may have switched to a new definitive host since their divergence , 2 ) a common ancestor used both cats and dogs as definitive hosts but during divergence N . caninum and T . gondii eventually became restricted to their present day definitive hosts . Our data clearly show that genes interacting most closely with the host have diverged to the greatest extent and we have therefore been able to narrow investigations to a relatively small number of candidate gene families and individual genes . Although many genes of unknown function remain to be characterized in these organisms , the majority of these are conserved . We have identified two novel protein-coding gene families ( TSF and KRUF ) and a putative ncRNA family which differs between species and that warrant further experimental characterization . The principle surface antigen gene family , the SRSs , was the most divergent family . This result was expected because in all Apicomplexa examined so far , including several malaria parasite genomes , the surface antigens are the largest , most rapidly evolving of all gene families [50] . However , the observation that N . caninum has more than twice as many SRS genes as T . gondii is striking and rather unexpected . It had been assumed that T . gondii requires a large number of these genes to accommodate its extraordinarily large host range and cover all potential host-cell molecular interactions with corresponding parasite proteins [33] , [34] . The much smaller host range of N . caninum would suggest that this hypothesis is not supported and that perhaps conversely , a larger number of SRS genes might be advantageous in evolving a narrower host range . Transcriptome evidence however suggests that N . caninum uses fewer SRSs than T . gondii during the tachyzoite stage , suggesting that they may be of more importance in other parts of the life cycle . In fact , in N . caninum there is rarely more than one SRS gene expressed at each locus , while in T . gondii there are frequently multiple genes expressed . This implies there have been significant changes in regulation of these host-interacting genes between species , although the mechanisms of regulation of these genes remain unknown . Interestingly it is a small number of subfamilies which have been expanded in N . caninum , in particular the fam7-8 architecture , the most common in both species . It may be that the more limited host range of N . caninum is related to specialization of this subset of the SRS genes . In common with SRS genes , important species-specific differences were identified in rhoptry organelle genes where the divergence of key genes of known function may help to explain phenotypic differences between Toxoplasma and Neospora . In particular ROP18 is a key virulence determinant in T . gondii which protects the parasitiphorous vacuole from attack by the mouse immune system [23] . We showed that this gene is pseudogenised in N . caninum and that N . caninum is unable to perform ROP18-mediated inactivation of immunity-related GTPases ( IRGs ) in murine cells . Our data suggest a reduced role for T . gondii virulence factor orthologues in N . caninum , for example , in relation to the virulence-associated rhoptry proteins ROP18 , ROP16 and ROP5 . The loss of ROP18 function in N . caninum might be adaptive , preventing killing of its host and promoting parasite survival in the species to which it is restricted . Intriguingly , it has been proposed and shown experimentally in viruses that reduced virulence is associated with the evolution of vertical transmission [51] , [52] , one of the most striking characteristics of N . caninum transmission in cattle . N . caninum may have increased successful vertical transmission from cow to calf by reducing virulence mechanisms , thus reducing the likelihood of host mortality . Alternatively , if ROP18 is only relevant to a subset of T . gondii intermediate hosts , its loss in N . caninum may reflect the fact that these intermediate host species are less important to N . caninum . Indeed , the importance of the cat-mouse cycle in the epidemiology of T . gondii may explain the evolution of ROP18-mediated inactivation specifically of murine IRGs , a mechanism which is certainly less relevant to N . caninum in which canids rather than felids are the definitive host . In fact IRG homologues are known to be present in the bovine genome [53] and novel N . caninum rhoptry genes could mediate IRG-defense in these hosts . Both of these scenarios suppose that N . caninum has become more specialized in its host range , suggesting that the common ancestor of N . caninum and T . gondii had a wide host range . In order to test this hypothesis it will be necessary to examine the genomes of coccidian outgroups such as Sarcocystis and Eimeria and to better characterize the function of those rhoptry proteins specific to N . caninum . The genomic resource we present will be useful in generating further understanding of apicomplexan genome evolution in general and coccidian biology in particular . Furthermore our description of these parasites will help to kick-start large-scale population-based studies to understand how genetic variation affects their biology .
Neospora caninum Liverpool strain was originally isolated from the cerebrum of a congenitally infected dog [54] . N . caninum Liverpool and Toxoplasma gondii VEG tachyzoites were maintained as described previously [55] . Paired end reads of N . caninum DNA were generated from random subclone libraries and additional reads were directed to close gaps and improve the data coverage of low quality regions . All sequencing was performed using BigDye terminator chemistry and used AB 3730xl analyzers ( Life Technologies ) . In total , 920k reads were obtained , quality-clipped and screened for contamination . 92% of reads were used in the final assembly . Based on an estimated genome size of 62 Mb for N . caninum the sequencing coverage was ∼8x . Sequence reads were assembled using PHRAP ( P . Green , unpublished ) into 960 supercontigs with an N50 of 354 kb . To reanalyse the ROP5 locus these reads were reassembled using Arachne [56] . N . caninum pseudochromosomes were generated by aligning supercontigs to T . gondii ME49 chromosomes using PROmer [57] . 242 contigs ( 90 . 4% of the sequence ) aligned successfully to the 14 chromosomes of T . gondii . Of the remaining 718 contigs , 375 were removed due to contamination , poor quality or if they were <1 kb in length . The remaining 343 contigs were grouped as UnAssigned Contigs ( UACs ) and used in further analysis alongside the pseudochromosomes . Telomeres were identified by examining chromosome ends for the typical TTTAGGG septameric repeat . A genome resequencing library for N . caninum were prepared as in [58] . Sequencing was performed on an Illumina GAIIX as for transcriptome libraries . Illumina paired-end reads were mapped using SSAHA2 [59] . The ROP18 region of five N . caninum isolates ( Table S3 ) was amplified in two overlapping sections using the following primer pairs: F1+R3 and F3+R1 ( exp . product 1268 bp and 890 bp respectively ) ( supplied by Eurofins ) . ROP18_F1 – GAGTGCCACGGTCCTCTAAG , ROP18_R3 – ATTTGTCCGACGCAAAATTC , ROP18_F3 – GGCTTCTGCTCCAGTATTCG , ROP18_R1 – GCCTTATAAACCACCCGTCA . PCR Reagents were supplied by Qiagen . Toxoplasma genome sequences and gene models were downloaded from ToxoDb v5 . 2 ( http://www . toxodb . org ) ; they were generated at the J . Craig Venter Institute and have been kindly provided by the Toxoplasma research community . N . caninum gene models were created using several algorithms [60]–[66] trained on T . gondii Me49 ( ToxoDB v4 . 2 ) and using ESTs from N . caninum Liverpool and NC-1 strains collected by the Gene Index Project [67] . The models were examined using the Artemis Comparison Tool [68] and where possible corrected based on evidence from synteny and sequence conservation with T . gondii and transcriptome sequencing evidence . We found a large number of erroneously unfused gene models . Using our T . gondii transcriptome data a total of 354 pairs of adjacent , same-strand genes were linked by reliably mapped bridging read-pairs . A further 449 genes in T . gondii were found to be parts of an adjacent gene but did not have spanning read pairs , usually being likely UTR segments . This resulted in a large drop in the predicted T . gondii gene count and we incorporated these corrections into our subsequent analysis . We used orthoMCL [69] to identify an preliminary set of orthologous groups between T . gondii and N . caninum . These results was modified using 679 manually identified orthologue pairs . We identified 6348 one-to-one orthologous gene pairs , which we then used to determine whether genes in these organisms tend to be shared with other apicomplexan species . We performed an orthoMCL with representative genes for the one-to-one ( core ) T . gondii/N . caninum set as well as predicted protein sequences for the following species: the plant Arabidopsis thaliana , the piroplasmic apicomplexan Babesia bovis , the apicomplexan Cryptosporidium parvum , the slime-mould Dictyostelium discoidium , human , the haemosporian apicomplexan Plasmodium falciparum , the yeast Saccharomyces cerevisiae , the piroplasmic apicomplexan Theileria annulata , the kinetoplastid Trypanosoma brucei and the diatom Thalassiosira pseudonana . Where a core gene was found to have an orthologue in three or more non-apicomplexan eukaryotes , we defined it as eukaryotic . Where it was not eukaryotic , but conserved amongst all apicomplexa , we defined it as conserved apicomplexan , If a gene was not conserved apicomplexan , but found in one or more apicomplexan species , other than T . gondii and N . caninum it was defined as apicomplexan . Remaining genes were considered specific to T . gondii/N . caninum . SRS genes and SAG domains were identified as in Wasmuth et al . ( submitted ) . We identified pseudogenes as clusters of significant BLAST hits which did not overlap valid gene models . Putative pseudogenes were manually checked to determine whether rational gene models could be made and whether Ilumina resequencing data supported any stop codons . N . caninum SAG domains were clustered as in Wasmuth et al . , to identify any novel domain subfamilies . N . caninum ROP , MIC , GRA and AP2 genes were initially determined by manually identifying orthologues of known T . gondii genes with reference to various studies [70]–[77] . Where homologous families of proteins fell into these groups , e . g . ROPK family for ROP , TRAP and MAR for MIC , novel members were sought using BLAST and HMMer . Poly A+ mRNA was purified from total RNA using oligo-dT dyna bead selection followed by metal ion hydrolysis fragmentation with the Ambion RNA fragmentation kit . 1st strand cDNA was synthesized using randomly primed oligos followed by 2nd strand synthesis to produce dscDNA . Fragments were selected for 200–250 bp inserts amplified by PCR to enrich for properly ligated template strands . Libraries were sequenced using the Illumina Genome Analyzer IIX in paired end mode for 2×76 cycles using proprietary reagents according to the manufacturer's recommended protocol . RNA-seq reads were aligned against the reference genomes using SSAHA2 [59] . Reads were included only where one end of the pair aligned uniquely to the genome and the distance between the pairs was within the expected insert size range , plus the expected intron length ( 80–4000 bp ) . We used Reads Per Kilobase of exon model per Million mapped reads ( RPKM ) normalised by the unique length of the gene as a measurement of expression level . We excluded positions which were non-unique from the length calculation using a kmer window of 75 bp , 37 bp either side of that position . Non-uniquely mapped reads were excluded by removing reads with a score <10 . In order to determine whether or not a gene was expressed we calculated an RPKM threshold ( Figure S7 ) . We used DESeq to determine differentially expressed genes [78] . In each pairwise comparison of two conditions A and B ( e . g . N . caninum day 4 tachyzoites with T . gondii day 4 tachyzoites ) biological replicates were used for both A and B to gain more accurate estimates of experimental variation . Genes with an adjusted p-value of <1e-5 were considered differentially expressed . When considering differential expression between species rather than between different time points in the same species we considered only genes identified as pairwise orthologues . Orthologues of N . caninum and T . gondii are often different lengths and therefore we normalised the read counts for T . gondii genes based on the gene length of the N . caninum orthologue . We determined orthologous relationships between N . caninum , T . gondii , P . falciparum and P . reichenowi using orthoMCL [69] . Orthologous groups containing a single gene from each species were aligned using muscle [79] and those with less than 50% conserved positions across all four species ( including gaps ) were excluded , leaving 184 alignments . We further excluded those orthologous groups which we determined have not evolved in a clock-like manner . To do this we used a likelihood ratio test for a constant rate of evolution [80] . Likelihood computations on a fixed species tree under a model where branch lengths are free to vary , and under a model in which branch lengths were constrained to be clock-like were performed in PAUP 4b10 [81] , under a general time-reversible model [82] incorporating both a proportion of invariant sites and a gamma distribution of rates across sites [83] , [84] . The GTR+I+G model of evolution was applied for each locus independently . We performed this test for single-copy orthologous sequences , determined as above , from T . gondii , N . caninum , P . falciparum , P . reichenowi and P . berghei . Twelve of the 184 alignments were excluded because they were determined to have evolved in a nonclock-like manner . As clock-like evolution is the null hypothesis in this test , failure to reject a molecular clock can be either due to the true process of evolution being clock-like , or very close to clock-like for a particular locus , or because of a lack of power in the data to reject the clock . To test that this was not introducing a significant bias in our results , we looked at the estimate of divergence times from two different sets of loci: all loci that fail to reject the clock model , and only those genes in the top quartile of alignment length ( which should have the most statistical power to reject the clock ) . Codeml [85] was used to calculate the maximum likelihood value of dS in pairwise runmode with the JTT model allowing 2 or more dN/dS ratios for branches . Using all 172 alignments the median 4-fold coding site synonymous substitution rate ( dS ) across pairs of N . caninum/T . gondii orthologues was 0 . 856 substitutions per site . Between P . falciparum and P . reichenowi this was 0 . 076 , similar to that calculated by Neafsey et al . [86] ( 0 . 068; 95% CI [0 . 060–0 . 077] ) . We assume that these Plasmodia diverged 2 . 49 mya ( 95% CI [1 . 93–3 . 79] ) [30] . We thus dated the split for N . caninum and T . gondii to 28 mya , or taking into account the confidence intervals for the Plasmodium divergence estimate , between 21 . 7 and 42 . 7 mya , after the divergence of the definitive hosts around 52 . 9 mya [87] . If we calculate the median dS values using only those longest 25% of the 172 orthologous groups , we get a dS of 1 . 230 for N . caninum/T . gondii and 0 . 114 for P . falciparum/P . reichenowi . This translates to a divergence time of 26 . 9 mya , This value is very close to that calculated using all 172 alignments . This suggests that a any tendency to exclude longer genes using the clock test has not biased our results . Enzyme Commission ( EC ) number mappings were extracted from the KEGG database [88] from 23 different species covering prokaryotes , archaea and eukaryotes and were mapped on to the corresponding genes in OrthoMCL database [69] . All N . caninum proteins that shared orthology with these enzymes were transitively assigned one or more EC number . KEGG pathway mapping/coloring tools were used to map EC numbers to pathways . The final set of N . caninum metabolic pathways was compared to that of T . gondii ( EC numbers assigned and used in similar fashion to Neospora ) . Pathways containing significantly high numbers of gene expression differences were determined as discussed in Statistical analysis . Cell culture was performed as described in [37] . The following immunoreagents were used ( dilutions in parentheses ) . From J . C . Howard ( University of Cologne ) : mouse anti-Irga6 monoclonal antibody ( mAb ) 10E7 ( 1∶500 ) [89] , anti Irga6 phosphopeptide Ab T102-555 ( 1∶5000 ) [23] Alexa 350/488/546/555/647-labelled donkey anti-mouse , rabbit and goat sera ( Molecular Probes ) , donkey anti-rabbit- ( GE Healthcare ) , donkey antigoat- ( Santa Cruz Biotechnology ) and goat anti-mouse- HRP ( horseradish peroxidase ) ( Pierce ) antisera ( all 1∶1000 ) . From P . Bradley ( UCLA ) : mouse anti-N . caninum ROP2 family member monoclonal antibody 20B5D5 ( 1∶2000 ) . 4′ , 6-Diamidine-2′-phenylindole dihydrochloride ( DAPI , Invitrogen ) was used for nuclear counterstaining at a final concentration of 0 . 5 mg ml−1 . Saponin permeabilization and immunostaining were performed as described in [90] , [91] , except for slides stained with T102-555 which were permeablized in ice cold methanol for 20 min and blocked in 1% BSA in PBS for 30 min . N . caninum proteins were purified from a tachyzoite pellet and resolved into 127 contiguous bands using acrylamide gel electrophoresis . Bands were excised and digested with trypsin . LC MS/MS was carried out using an LTQ ion trap mass spectrometer ( Thermo Fisher Scientific Inc , Waltham , MA , USA ) with an electrospray ionization source , Tryptic peptides were eluted using a linear gradient of 0–50% ( v/v ) acetonitrile/0 . 1% ( v/v ) formic acid over 140 minutes followed by 100% ( v/v ) ACN/0 . 1% formic acid for 20 minutes and a further 20 minutes of 0% ( v/v ) acetonitrile/0 . 1% ( v/v ) formic acid . Protein identifications were made as in [92] , those above 1% false discovery rate were discarded . 1053 proteins were found to have at least one significantly matching peptide . To determine whether certain gene functions were overrepresented in differentially expressed genes we assigned GO terms using InterPro2GO [93] . The hypergeometric test was used to determine overrepresented GO terms in pooled day three and four expression data with a DESeq q-value cutoff of 1e−5 . The Benjamini-Hochberg method was used to correct for multiple hypothesis testing . Values of P<0 . 05 were considered significant . The hypergeometric test was also used in the same way to determine whether KEGG metabolic pathways were enriched in differentially expressed genes .
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Coccidian parasites have a major impact on human and animal health world-wide and are among the most successful and widespread parasitic protozoa . They include Neospora caninum which is a leading cause of abortion in cattle and one of its nearest relatives , Toxoplasma gondii . Despite its close phylogenetic relationship to Toxoplasma , Neospora has a far more restricted host range , does not infect humans and its epidemiology depends predominantly on efficient vertical transmission . The divergent biology of these two closely related species provides a unique opportunity to study the mechanisms of host specificity , pathogenesis and zoonotic potential not only in these , but other Coccidia . We have sequenced the genome of Neospora and the transcriptomes of both species to show that despite diverging some 28 million years ago , both genome and gene expression remain remarkably conserved . Evolution has focused almost exclusively on molecules which control the interaction of the parasite with the host cell . We show that some secreted invasion-related proteins and surface genes which are known to control virulence and host cell interactions in Toxoplasma are dramatically altered in their expression and functionality in Neospora and propose that evolution of these genes may underpin the ecological niches inhabited by coccidian parasites .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"veterinary",
"diseases",
"genomics",
"molecular",
"cell",
"biology",
"zoology",
"evolutionary",
"biology",
"biology",
"computational",
"biology",
"proteomics",
"genetics",
"and",
"genomics",
"veterinary",
"science"
] |
2012
|
Comparative Genomics of the Apicomplexan Parasites Toxoplasma gondii and Neospora caninum: Coccidia Differing in Host Range and Transmission Strategy
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The poliovirus vaccine field is moving towards novel vaccination strategies . Withdrawal of the Oral Poliovirus Vaccine and implementation of the conventional Inactivated Poliovirus Vaccine ( cIPV ) is imminent . Moreover , replacement of the virulent poliovirus strains currently used for cIPV with attenuated strains is preferred . We generated Cold-Adapted Viral Attenuation ( CAVA ) poliovirus strains by serial passage at low temperature and subsequent genetic engineering , which contain the capsid sequences of cIPV strains combined with a set of mutations identified during cold-adaptation . These viruses displayed a highly temperature sensitive phenotype with no signs of productive infection at 37°C as visualized by electron microscopy . Furthermore , decreases in infectious titers , viral RNA , and protein levels were measured during infection at 37°C , suggesting a block in the viral replication cycle at RNA replication , protein translation , or earlier . However , at 30°C , they could be propagated to high titers ( 9 . 4–9 . 9 Log10TCID50/ml ) on the PER . C6 cell culture platform . We identified 14 mutations in the IRES and non-structural regions , which in combination induced the temperature sensitive phenotype , also when transferred to the genomes of other wild-type and attenuated polioviruses . The temperature sensitivity translated to complete absence of neurovirulence in CD155 transgenic mice . Attenuation was also confirmed after extended in vitro passage at small scale using conditions ( MOI , cell density , temperature ) anticipated for vaccine production . The inability of CAVA strains to replicate at 37°C makes reversion to a neurovirulent phenotype in vivo highly unlikely , therefore , these strains can be considered safe for the manufacture of IPV . The CAVA strains were immunogenic in the Wistar rat potency model for cIPV , inducing high neutralizing antibody titers in a dose-dependent manner in response to D-antigen doses used for cIPV . In combination with the highly productive PER . C6 cell culture platform , the stably attenuated CAVA strains may serve as an attractive low-cost and ( bio ) safe option for the production of a novel next generation IPV .
There are two vaccines that can effectively protect against poliomyelitis which have been available for more than 60 years and are still used today . The Inactivated Poliovirus Vaccine ( IPV ) , today referred to as conventional ( c ) IPV , was developed in 1955 by Jonas Salk and contains three formalin inactivated , wild-type and neurovirulent poliovirus strains ( Mahoney , MEF-1 and Saukett ) [1] . In the 1960s Albert Sabin introduced the second vaccine against poliomyelitis: the Oral Poliovirus Vaccine ( OPV ) , a trivalent formulation of three live , attenuated strains ( Sabin 1 , 2 and 3 ) [2 , 3] . OPV and IPV have dramatically reduced the incidence of poliomyelitis since their introduction; with only 74 wild-type poliomyelitis cases worldwide in 2015 , restricted to Afghanistan and Pakistan , eradication of the disease is extremely close [4] . Despite the efficacy of OPV , the Sabin strains have the propensity to revert to neurovirulent form [5] . In OPV vaccinees these reverted neurovirulent strains can cause Vaccine-Associated Paralytic Poliomyelitis ( VAPP ) , and via shedding , circulating Vaccine Derived Polioviruses ( cVDPVs ) [6] can cause poliomyelitis outbreaks in areas of low vaccination coverage [7] . Therefore the cessation of OPV use in routine immunization and full implementation of vaccination with the safer , but more expensive , IPV is required to enable the final stages of eradication and sustain a polio-free status in the years thereafter [8 , 9] . However , even if eradication is achieved , immunization against poliomyelitis will remain necessary to maintain a polio-free world [10] , as the risk of re-emergence of polioviruses from several potential sources ( spills of laboratory stocks [11] or vaccine production facilities [12] , undetected viruses in remote locations , long term shedders after OPV vaccination [13] , bioterrorism , etc . ) will persist [14 , 15] . To minimize these risks , replacing cIPV , which is made from wild-type ( virulent ) strains , with an IPV made from attenuated ( non-virulent ) strains , is an approach actively promoted by the World Health Organization ( WHO ) and its collaborators [16] . Currently the Sabin strains used in OPV are the preferred candidates to replace the wild-type strains . Sabin-based IPV’s ( sIPV ) have been recently licensed in Japan [17] and China [18] , and several others are currently in development [19] . The local licensure and worldwide efforts for clinical development of sIPV illustrate the potential of this vaccine to replace the widely used cIPV . However , uncertainties that exist with regard to affordability of large scale production [20 , 21] , complexities in sIPV dosing strategy due to differences in antigenic content , and absence of standardization compared to cIPV [22–24] , have prompted investment in alternative strategies to develop next generation IPVs . Implementation of modern vaccinology techniques to enable the generation of novel vaccine strains that display desired characteristics such us reduced pathogenicity and an immunogenic profile identical to the well-established cIPV strains has been proposed [25 , 26] . Our aim was to develop novel attenuated strains for IPV manufacture that can address the biosafety issues of cIPV without altering immunogenicity . Our approach for viral attenuation was to develop strains with impaired growth at physiological temperature ( ≥37°C ) but that are still capable of replication to high infectious titer yields at lower ( manufacturing ) temperatures . We hypothesized that inability to replicate at 37°C would impede reversion to neurovirulent form , resulting in a non-pathogenic phenotype in the natural host . Cold-adaptation ( adaptation to growth at low ( <37°C ) temperature by serial passage ) is often associated with reduced replication ( or sensitivity ) at higher temperatures ( 37–40°C ) ) . Historically , cold-adaptation has been frequently used to generate attenuated RNA and DNA viruses ( reviewed in [27] ) , including influenza [28 , 29] , measles [30] , and rubella vaccine strains [31] . Cold-adapted polioviruses have also been generated in the past by passage at 23–30°C [32–34] . In general , these attenuated polioviruses were also temperature sensitive , as defined by increased replication at lower temperatures ( ≤37°C ) as compared to growth at higher ( ~40°C ) temperatures , but did not necessarily show a complete loss of replication capacity at 37°C ( as is also the case for the Sabin strains [35–37] ) . By combining empirical and rational methods of attenuation , we generated temperature sensitive poliovirus strains incapable of replication at physiological temperature , that grow to high titers at 30°C , and that have the antigenic profile of ( wild-type ) cIPV strains . The strains were obtained via serial passage at low temperature and genetic engineering , ultimately resulting in three Cold-Adapted Viral Attenuation ( CAVA ) vaccine strains , namely: CAVA-1 Mahoney , CAVA-2 MEF-1 and CAVA-3 Saukett . We characterized the CAVA strains with respect to in vitro temperature sensitivity , in vivo attenuation and in vivo immunogenicity , and we investigated the mechanism of , and mutations responsible for , their phenotype .
The highly temperature sensitive poliovirus strains were derived from , Brunenders , a Type I partially-attenuated poliovirus [38] , by serial passage in vitro . The passaging was performed 34 times in PER . C6 cells at low temperature ( 26–30°C ) , at low Multiplicity of Infection ( MOI = 0 . 01 ) and harvested 3–4 days post infection . Adaptation for increased growth at 30°C on PER . C6 cells was observed after 11 and 28 passages , but impairment of growth at 37°C was not detected ( S1 Fig ) . Upon clonal selection , where approximately 1000 clones were screened for infectivity ( Cytopathic Effect , CPE ) at 30 and 37°C , three clones showed delayed replication at 37°C ( 1–3 days later ) as compared to 30°C . This impaired growth at 37°C was confirmed by comparing growth kinetics in suspension PER . C6 cell cultures at both temperatures ( Fig 1A ) . The three clones showed slower replication rates and a 100- to 1000-fold reduction in maximum titer as compared to the parental Brunenders strain at 37°C , and faster growth at 30°C ( Fig 1A ) which indicates an adaptation to lower temperature . Each clone had 18 nucleotide mutations ( either shared or unique ) with respect to the parental Brunenders strain . Overall , 31 distinct mutations were detected across the three different clones ( see Fig 2 for a schematic representation of all viruses and S1 Table for details regarding the specific mutations ) . All 31 mutations were cloned into the Brunenders cDNA plasmid and transfection of the resulting in vitro transcribed RNA in PER . C6 cells resulted in the rescue of the CAVA backbone virus . Remarkably , the combination of 31 mutations was not lethal and viable virus was rescued at 30°C . However , at 37°C , this CAVA backbone virus was incapable of replication ( defined as no increase of infectious units ( TCID50 ) over a 2 day period ) . Virus replication at 30°C was unaffected when compared to the Brunenders parental strain ( Fig 1B ) . Temperature sensitivity of the CAVA backbone virus was confirmed when infections were left up to 14 days at 37°C , in multiple cell lines ( Vero , L20B , Hela , SK-N-MC , Hek293 ) , and its neuroattenuation in vivo was demonstrated ( S2 Table ) in CD155 transgenic mice , a susceptible model for poliovirus neurovirulence [39] . To generate CAVA-IPV vaccine strains for poliovirus serotypes 1 , 2 and 3 , the capsid sequence of the CAVA backbone was replaced by the capsid sequences of each of the three cIPV strains , to mimic their antigenic profiles . This resulted in three new synthetically-derived viruses named CAVA-1 Mahoney , CAVA-2 MEF-1 and CAVA-3 Saukett . The remainder of the genome maintained 24 of the CAVA mutations spread over the 5’ Untranslated Region ( 5’UTR ) and Non-Structural proteins ( see Fig 2 ) . As was observed with the CAVA-backbone , the three CAVA vaccine strains showed no replication at 37°C , whilst growth kinetics and maximum yields at 30°C were similar to the parental Brunenders strain ( Fig 1C ) . To visualize signs of infection , PER . C6 cells were infected with CAVA-1 Mahoney at 37°C and 30°C , at an MOI of 1 and crude harvests were taken 24–48 hours post infection for examination by electron microscopy ( EM ) . PER . C6 cells were infected using the same conditions with either wild-type Mahoney or PBS ( mock ) at 37°C as positive and negative controls , respectively . Fig 3 depicts representative cells from the inoculated cell cultures; CAVA-1 Mahoney at 30°C resembled the wild-type Mahoney PV infection at 37°C with cells being either dead or dark and apoptotic . Within infected cells , large virus-induced rearrangements of Endoplasmic Reticulum ( ER ) membranes were visible as well as highly structured virus lattices ( Fig 3 Panels B and D ) . However , at 37°C the CAVA-1 Mahoney infected cells resembled the PBS mock infected samples ( Fig 3 Panels A and C ) . All cells were healthy and not one cell of the >360 cells in the EM preparations showed signs of infection . The input virus was verified by infectious titer determination . The inability to visualize signs of virus replication by EM is in line with the replication kinetics data shown in Fig 1C . We used intracerebral ( i . c . ) inoculation in of CD155 transgenic mice [39] to determine whether the temperature sensitive phenotype of the three CAVA vaccine strains translated to in vivo attenuation as compared to cIPV strains , and how this level of attenuation compared to that of the Sabin strains ( Table 1 ) . Experiment 1 aimed to determine if the CAVA strains were attenuated . For this purpose , three mice per strain were inoculated i . c . at the highest dose ( constrained by the maximum inoculation volume and concentration of the virus sample ) . To confirm results from experiment 1 , we inoculated five mice per strain in experiment 2; in parallel the TCID50 ( infectious units ) required to induce paralysis ( or death ) in 50% of the mice ( PLD50 ) was calculated where possible . Upon i . c . inoculation of CD155 transgenic mice all three CAVA vaccine strains showed a highly attenuated phenotype . The maximum dose possible ( 8 . 2–8 . 4 Log10 TCID50/mouse ) did not induce paresis or paralysis in any of the mice 21 days post inoculation ( Table 1 ) . Similar observations were made for mice inoculated with Sabin strains 2 and 3 when inoculated with the maximum dose possible ( 7 . 7 and 8 . 4 Log10 TCID50/mouse , respectively ) . For Sabin 1 , a total of three of the eight inoculated mice with the maximum dose of 8 . 0 Log10TCID50/mouse showed signs of paresis and/or paralysis . Nonetheless , all three CAVA and Sabin strains were highly attenuated in this model and the PLD50 was above the maximum dose tested . By contrast , the neurovirulent cIPV strains induced paralysis in all of the mice at the tested doses ( Table 1 ) . The levels of neurovirulence measured here for the cIPV and Sabin strains in this mouse model are in agreement with those reported elsewhere [40 , 41] .
As eradication of poliomyelitis draws closer , the poliovirus field is moving towards novel vaccines and vaccination strategies . To serve as novel IPV strains , we generated three attenuated poliovirus strains using a combination of empirical and rational attenuation methods with specific focus on ( genetic stability of ) attenuation , immunogenicity , and affordability . Our approach for viral attenuation was to develop strains with impaired growth at physiological temperature ( ≥37°C ) with high replicative capacity at ( manufacturing ) temperature . The CAVA strains were empirically derived by serial passage at low temperature , much like the Sabin strains; however , the subsequent synthetic combination of multiple mutations into one genome was essential to obtain a complete block in viral replication at 37°C . The CAVA strains showed no sign of successful in vitro infection at physiological temperature . Indeed , decreases in infectious units and viral RNA level were measured and no viral proteins or visual signs of infection , such as the presence of replication vesicles or virus lattices , could be observed . This indicates that , at 37°C , the CAVA virus replication cycle is blocked at protein translation , RNA replication , or earlier . Moreover , serial passage at 37°C did not show outgrowth of revertants . Instead all quantifiable virus was lost after the first passage . This suggests that the viruses are locked into the temperature sensitive phenotype by the combination of CAVA mutations , where the inability to replicate also negates reversion and recombination at physiological temperature . To our knowledge , no poliovirus has been described with such a complete block in replication at 37°C . An historic poliovirus strain Mabie ( PP3 ) was reported to lack replicative capacity at 36°C [42] , although it induced CPE at physiological temperature denoting maintenance of at least some low level replicative capacity . Impaired growth at physiological temperature has been also extensively shown for more recently described ( rationally ) attenuated polioviruses [43–48] , including the Sabin strains . However , their ability to replicate at 37°C was not abolished , implying a residual risk of reversion and recombination in vivo . The intermediate viruses with CAVA mutations in the IRES or Non-Structural proteins showed impaired , but not completely halted , growth at 37°C . Therefore the combination of CAVA mutations in these regions is required . More specifically , a combination of 14 mutations within those two regions was sufficient to cause the CAVA temperature sensitive phenotype . This was confirmed by introduction of the 14 mutations into other wild-type and attenuated polioviruses of differing serotypes , indicating that the mechanism of action is unique and independent of the parental Brunenders backbone . The CAVA temperature sensitivity is likely exerted by multiple molecular mechanisms ( as exemplified by the synergistic , combinatorial effect of the CAVA mutations ) which work together to hamper replication at 37°C . However , at 30°C these mutations do not appear to obstruct virus replication , protein translation , or RNA replication . One explanation for this may be that the introduction of multiple mutations decreases the thermal stability of the viral proteins and/or RNA , resulting in folding defects , conformational changes and subsequent losses of biological functionality of the viral ( precursor ) proteins and/or functional RNA elements . When environmental thermal energy is lowered ( for example at 30°C ) the decreased thermal stability may not be sufficient to cause significant changes in protein/RNA structure and function to such an extent that virus replication is restricted . For example , the CAVA mutations in the IRES may destabilize the secondary RNA structure of this essential RNA element . Predicted secondary RNA structures of the CAVA and Brunenders IRES-domains II and VI show that the free energy ( ΔG ) is raised in the CAVA domains ( as well as an altered domain II structure ) indicating decreased thermostability ( Fig 8 ) . The changes in free energy and structure would likely hamper folding and thermostability of the IRES and therefore disrupt ( initiation of ) translation . Alterations in Domain II of poliovirus IRESs have previously been reported to show defects in translation [49 , 50] , which gives further credence that these IRES mutations inhibit viral infection at 37°C by hampering translation . However , at lower temperature this intrinsic free energy of the IRES domains may still be permissive for successful protein translation and infection . The CAVA mutations in the 2C and 3D proteins , which reverted after gradual increase of infection temperature ( and which are part of the 14 selected mutations ) , may play particularly prominent roles in inducing the CAVA temperature sensitivity . However , the compensatory impact of the other CAVA mutations , or the additional new mutations identified after regaining replication capacity at 37°C , cannot be ruled out . The highly conserved 2C protein has multiple functions in host-cell membrane alteration , encapsidation , viral RNA binding and RNA replication [51–54] , however , the CAVA mutations in the 2C fall outside any of the known functional domains of this protein . CAVA mutation 4428 at residue 101 is in close proximity to the cis acting replication element ( cre ) , an RNA functional domain essential for RNA replication [55] . If the change in nucleotide 4428 alters the secondary structure of the cre element in such a way that ( at 37°C ) replication may be impaired , this may explain part of the temperature sensitive phenotype . The CAVA mutation 3D[74] is close to residue 73 in the palm of 3D , which in Sabin 1 has been implicated to play a role in temperature sensitivity via a temperature dependent decrease of VPg-uridylation compared to wild-type [56] . It is plausible that the CAVA mutation at residue 74 induces similar temperature sensitive defects in the CAVA viruses . The CAVA mutation at residue 286 of the 3D is located in the middle finger domain of the polymerase close to a putative translocation domain [57] , which is required for nascent RNA chain synthesis . It is conceivable that the amino acid substitution at this position may exert effects during RNA synthesis by temperature dependent , conformational interference with elongation . Further research is required to pinpoint the exact molecular mechanisms of CAVA temperature sensitivity , and the responsible mutations . However , the vast number of mutations and combinations thereof makes full understanding of the CAVA phenotype a challenging endeavor . To illustrate , even the extensively studied Sabin strains do not have a fully understood molecular mechanism to explain their attenuation [58] . As expected , the inability to propagate at 37°C in vitro translated to high attenuation in vivo . The CAVA viruses showed a level of attenuation that was at least as high as the Sabin strains , significantly higher than the cIPV strains , and comparable to previously described attenuated poliovirus strains tested in the same animal model [43 , 45 , 46] . Follow up studies to this work will focus on comparing the level of attenuation of the CAVA and Sabin strains . This will require the administration of a more sensitive but complicated route of inoculation ( i . e . intra spinal ) . Nonetheless , the intra cerebral model used here is a widely used and accepted model to measure poliovirus neurovirulence which has been used in the field for other attenuated poliovirus strains [41 , 43 , 48] . With it the CAVA strains maintained a highly attenuated phenotype after five serial passages , which exceeds the number of passages required to produce final manufacturing seed lots on the PER . C6 cell platform ( which likely requires 3 passages ) . Remarkably , none of the mice inoculated with any of the passaged CAVA strains showed any signs of paralysis . Factors other than passage number , such as infection scale , temperature , cell type , and MOI , can influence the dynamics of a virus population . We attempted to control these variables as far as technically possible , to mimic envisioned vaccine manufacturing conditions . The extended passage performed here showed few alterations in viral sequence and only 0–1 reversions of the 24 incorporated CAVA mutations , with no effects on in vitro or in vivo attenuation . Formaldehyde-inactivated versions of the CAVA vaccine strains were immunogenic and induced high neutralizing antibody titers in vivo in a standard rat potency assay . CAVA type 2 and 3 components showed reduced vaccine potency relative to the reference cIPV at the same dosing . However , increasing the dose to 150% ( of the standard human dose for cIPV ) resulted in a significant increase in CAVA immunogenicity , albeit only slightly for type 2 , rendering the response of all CAVA strains comparable to the cIPV reference . The reason for the observed lower immunogenicity shown by the CAVA inactivated vaccine is currently unknown . The differences in immunogenicity are most likely induced by the many differences in production processes used to generate the small scale PER . C6-based CAVA IPV and the cIPV international reference standard , which was produced on the Vero cell platform using a validated and optimized process . Virus culture conditions , virus purification , inactivation and/or D-antigen measurements for production of a CAVA-based IPV therefore require further investigation and optimization . Despite the need for further investigation , the CAVA strains showed inactivation rates and D-antigen recoveries during inactivation that were in the same range as those observed for the cIPV strains produced on the PER . C6 cell platform ( S4 Fig ) . Thus the differences observed in immunogenicity are not necessarily induced by differences in formalin inactivation of the CAVA antigens as compared to those of the cIPV strains . Although unpredicted , another explanation for the slightly lowered immunogenicity could be an incompatibility of the CAVA-backbone with the cIPV capsids and/or the lower culture temperature which may slightly alter the conformation of the virion during virus assembly causing changes in antigenicity and immunogenic potency . Indeed , biophysical characterization of the CAVA vaccine strains as compared to their cIPV counterparts did result in some unexpected differences . For example , antigenicity as measured by D-antigen unit per infectious unit ( DU/TCID50 ) was similar for CAVA-1 Mahoney , higher for CAVA-2 MEF-1 and lower for CAVA-3 Saukett as compared to their cIPV counterpart ( S4 Table ) . Furthermore , thermal stability as measured by capsid melting temperature ( Tm ) showed that all CAVA strains had a melting temperature above 37°C ( S5 Table ) , but that CAVA-2 MEF-1 and CAVA-3 Saukett displayed significantly lower Tm as compared to their cIPV counterpart . The biological relevance of these in vitro differences and their impact on the ultimate vaccine immunogenicity in rats is currently unknown and therefore requires further investigation . Currently , work is in progress to determine whether the observed differences in immunogenicity are intrinsic to the CAVA viruses or caused by the differing production processes of the CAVA vaccine and the cIPV reference standard . It is important to strive for comparability to cIPV immunogenicity as this vaccine has been used successfully for more than half a century inducing high titers of antibodies with an extensive duration of protection against poliomyelitis . A similar immunogenic profile may therefore facilitate clinical development of a novel vaccine candidate . Cost of IPV is a critical parameter to enable immunization of the developing world , an essential endeavor to achieve and maintain eradication . Therefore , strategies to increase IPV affordability are encouraged by the WHO[59] . Virus production during vaccine manufacture with high yields can significantly decrease costs of goods . We have previously demonstrated that the use of the PER . C6 platform increases volumetric productivity of infection harvests as compared to the Vero-based platform for cIPV strains [60] and for the Sabin strains [61] . Use of the highly productive PER . C6 platform for propagation of the CAVA vaccine strains resulted high infectious titer yields ( 9 . 4–9 . 9 Log10 TCID50/ml ) , comparable to those reached with the Sabin strains ( 9 . 2–9 . 9 Log10 TCID50/ml ) , which demonstrates potential for significantly reducing cost of goods and consequent vaccine pricing required for global roll-out of an affordable IPV . Next generation IPV vaccine strains should ideally portray a non-infectious phenotype to reduce the risk of transmission and disease , should dissemination into the environment occur . The novel CAVA strains are characterized by an inability to replicate at 37°C and capacity to propagate to high titers at 30°C . Their unprecedented temperature sensitivity translated to a high level of in vivo neuroattenuation and suggests that the CAVA strains are non-infectious at physiological temperature . Their use can therefore decrease biosafety risks associated with cIPV manufacturing . These novel attenuated strains are designed to be antigenically equal to the cIPV strains , although further work is required to demonstrate equivalent immunogenicity . In combination with the highly productive PER . C6 cell culture platform , the stably attenuated CAVA strains may serve as an attractive low-cost and ( bio ) safe option for the production of a next generation IPV which can aid in achieving and maintaining a polio free world .
The Brunenders , MEF-1 and Saukett viruses were derived from virus seeds kindly donated by SBL ( former Swedish Bacteriological Laboratories ) . Sabin 1 , 2 and 3 were purchased at The National Institute for Biological Standards and Control ( NIBSC , catalogue number: 01/528 , 01/530 , and 01/532 , respectively ) . The Mahoney virus was purchased at the European Virus Archive ( EVA ) . All remaining viruses used were rescued via RNA transfection for which the RNA was transcribed in vitro with a T7 polymerase using a synthetic cDNA plasmid as a template , as described previously [38 , 62] . The cDNA plasmids were synthetically generated at Genscript and contained the entire viral genome sequence downstream of a T7 promoter . A schematic overview of all synthetic viruses and the incorporated mutations is shown in Fig 2 . PER . C6 cells [63] ( Janssen proprietary cell line , derived from primary human retina cells ) were maintained as described previously [60] . All infections were performed in suspension PER . C6 cells using a cell seeding density of 107 viable cells per ml and infection volumes ranged between 5–15 ml in shaker flasks to 250 ml in roller bottles . Infections were performed at differing temperatures ( 26–30 , 33 , 35 or 37°C ) as well as MOI ( 0 . 01–2 TCID50/cell ) , indicated per experiment . Time of Harvest ranged from t = 0–4 days post infection . Brunenders was passaged 34 times at low MOI ( 0 . 01 TCID50/cell ) on PER . C6 cells with 107 cells/ml at 26–30°C . Infectious titer determination was performed in multi-well 96 plates seeded with 6 . 5x104 adherent PER . C6 cells per well in DMEM supplemented with 10% FBS and 10mM Magnesium Chloride . Eleven serial virus dilutions with a five-fold dilution factor were prepared and added to the cells with subsequent incubation for 13 days at 30°C for all titration assays , unless indicated differently . On day 13 each well was scored for CPE and titers were calculated by method of Spearman and Kärber [64] . EM was performed at Leiden University Medical Center . Infection harvests were fixed at 1 hour at room temperature in 1 . 5% glutaraldehyde in 0 . 1 M cacodylate buffer ( pH 7 . 4 ) and stained with 1% osmium tetroxide for 1 hour . Samples were pelleted in 3% agar where resulting pellets were cut and gradually dehydrated with an ethanol series . The samples were then infiltrated for 1 hour with a 1:1 mixture of propylene oxide and epoxy LX-112 resin ( Ladd Research ) . After an additional hour in 100% epoxy LX-112 , the samples were polymerized for 48 h at 60°C . Cell sections of 50 nm were cut , placed onto carbon-coated formvar grids , and counterstained with 7% uranyl acetate and lead citrate for 20 and 10 minutes , respectively . Imaging was performed with a Tecnai 12 BioTwin transmission electron microscope ( FEI company ) operated at 120 kV . For detection of viral proteins 100 μg of total protein from clarified crude harvests ( after 3 freeze thaw cycles ) was loaded into a NuPAGE Novex Bis-Tris 4–12% protein gel ( Life Technologies ) and blotted onto Nitrocellulose membranes ( Life Technologies ) . Membranes were blocked in 5% non-fat dried milk ( Bio-Rad ) and incubated overnight with a 1:1000 dilution of goat polyclonal antibodies against poliovirus types 1 , 2 , 3 ( ProSci ) , followed by 2 hours with a 1:15000 dilution of donkey anti-goat IRDye 800CW secondary antibody ( Westburg ) . Proteins were visualized using Odyssey infrared imaging system ( Li-Cor , BioSciences ) . Quantification of poliovirus RNA was performed by RT-qPCR using viral RNA isolated from clarified , freeze-thawed infection harvests using a QIAamp viral RNA isolation kit ( Qiagen ) . Viral RNA was reverse transcribed to cDNA and subsequently amplified with the Power SYBR Green RNA-to-Ct 1-Step Kit ( Life Technologies ) , using 400nM forward primer ( 5’ TCTCCTAGCCCAATCAGGAA 3’ ) and 400nM reverse primer ( 5’ TCTCCCATGTGACTGTTTCAA 3’ ) flanking an amplicon ( 86nt in length ) in the 3D polymerase gene . Real-time PCR was performed in a 7500 Fast thermocycler ( Life Technologies ) starting with 30 min at 48°C for reverse transcription and 10 min at 95°C for activation of DNA polymerase , followed by 40 amplification cycles of 15 sec at 95°C for denaturation and 1 min at 63°C for annealing and extension . Purified , in vitro transcribed RNA of the CAVA backbone virus , with known concentration number of poliovirus genome copies , was used as a standard to allow RNA quantification of tested samples . Sequencing of the full viral genomes was performed by RT-PCR and Sanger sequencing as described previously [38] . RNA secondary structures predictions of the IRES domains II and IV of CAVA and Brunenders were executed by the MFOLD program ( http://mfold . rna . albany . edu/ ? q=mfold/RNA-Folding-Form ) developed by M . Zuker . Neurovirulence testing was performed at Stony Brook University using transgenic mice expressing the poliovirus receptor ( CD155 ) [39] . For each poliovirus serotype studied , they were 3 experimental groups ( CAVA and Sabin strains ) and one control group ( wild-type strains ) . Two to three mice were housed per cage . For the neurovirulence testing , groups of three and five ( n = 3 and n = 5 ) CD155 transgenic mice ( 8±2 weeks of age , male and female ) were anesthetized and then inoculated i . c . ( 30 μL per mouse , dose in TCID50 per mouse , Table 1 ) . For the wild-type cIPV strains ( control group ) doses ranged from 102–106 TCID50 per mouse . All inoculations took placed in a biosafety cabinet type II . Intraperitoneal injection of Ketamine ( 100 mg/kg ) / Xylazine ( 10 mg/kg ) combination was used as anesthetic producing short-term surgical anesthesia with good analgesia facilitating i . c inoculation in the anesthetized mice . After inoculation , mice were examined daily for 21 days for signs of paresis or paralysis , where scoring was done according to the WHO’s Standard Operating Procedure for poliovirus neurovirulence testing in transgenic mice [65] . The virus titer that induced paralysis ( or death ) in 50% of the mice ( ( P ) LD50 ) was calculated by the method of Reed and Muench [66] . CAVA harvests were treated with domiphen bromide to remove host cell DNA and consequently clarified of a series of filters . Prior to Cation Exchange Chromatography ( CEX ) the clarified harvests were acidified using 25mM sodium citrate . CEX was performed using Sartobind S cationic membranes . The CEX eluate was subjected to a Size Exclusion Chromatography step for further purification ( polish ) and buffer exchange . The SEC eluate was conditioned using M199 and glycine prior to inactivation . Inactivation was performed according to the EP guidelines and in line with Salk’s description of poliovirus inactivation procedure in the 1950s [67] . Briefly , the purified batches were filtered ( 0 . 22μm ) prior to formalin addition ( 0 . 009% formalin or 3 . 3mM formaldehyde ) and incubated for 13 days at 37°C and shaking at 75 rpm . Filtration was performed at days 6 and 13 of inactivation . The inactivated purified virus bulks were tested for purity ( OD and SDS PAGE ) and sterility ( mycoplasma , endotoxin , bioburden ) . Rat Potency testing was performed at the National Institute of Biological Standardization ( NIBSC ) . The purified and inactivated monovalent CAVA samples were tested for D-antigen content by ELISA . This D-antigen ELISA utilizes polyclonal capture and monoclonal detection antibodies raised against active Sabin viruses . The inactivated CAVA viruses were consequently tested for monovalent in vivo immunogenicity in the rat potency model . Four groups of Wistar female rats ( n = 10 ) were immunized with a full dose , or a 1:2 , 1:4 and 1:16 dilution of the full dose of each of the inactivated CAVA vaccine strains , or the reference vaccine BRP2 . The 100% full human dose represents 40 , 8 or 32 D-antigen units of Type 1 , 2 and 3 respectively , which is the minimal required dosing of cIPV . The 150% full human dose represents 60 , 12 or 48 D-antigen units of Type 1 , 2 and 3 respectively . After three weeks , sera were collected . Neutralizing antibodies against all three poliovirus types were measured in separate assays using 100 TCID50 of Sabin 1 , Sabin 2 or Sabin 3 poliovirus strains as assay challenge viruses and Hep2C as indicator cells . Sera-virus incubation was overnight at 4°C , followed by 3 hours at 35°C [68] . Assay was stopped after 6 days of incubation at 35°C by staining the plates with Naphthalene black . Virus neutralization titers were expressed as a score based on the last serum dilution with no signs of cytopathic effect ( CPE ) . Relative Potency was calculated based on the number of seroconverting animals for each vaccine in relation to the reference BRP2 using Combistats analysis software . This was performed for each poliovirus serotype separately . Current NIBSC seroconversion limits are ≥4 , ≥362 , and ≥6 for types 1 , 2 and 3 , respectively , and are set based on a minimum of three repeated tests with the reference vaccine . A cut-off value is determined as the mid-point on a log2 scale of the minimum and maximum geo-mean titers , according to the European Pharmacopoeia [68] . All mice used for in vivo neurovirulence testing at Stony Brook University have been maintained under specific-pathogen-free conditions and animals experiments were performed in strict compliance with the national guidelines provided by “The Guide for Care and Use of Laboratory Animals” and The Stony Brook University Institutional Animal Care and Use Committee ( IACUC ) . The IACUC of the Stony Brook University approved all animal experiments presented here ( permit #267166 ) . CD155 transgenic mice were bred in the Division of Laboratory Animal Resources ( DLAR ) at Stony Brook University . All mice were housed in a pathogen-free mouse facility at the DLAR facility . NIBSC’s Animal Welfare and Ethical Review Body approved the application for Procedure Project Licence Number 80/2523 which was approved by the UK Government Home Office and under which animal care and protocols for Rat potency testing were conducted . All animal care and protocols used at NIBSC adhere to UK regulations ( Animals , scientific procedures , Act 1986 that regulates the use of animals for research in the UK ) and to European Regulations ( Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes ) . The experiments in rats shown here were carried out following protocol 1 within Home Office Procedure Project Licence Number 80/2523 referred above .
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The vaccines that are used to protect against poliovirus infection have been available since the 1950s and have brought the eradication of poliomyelitis to our doorstep . For the post-eradication era , an Inactivated Poliovirus Vaccine ( IPV ) based on attenuated Sabin strains is recommended , as these strains are currently the only option to move to safer manufacturing of IPV . Here we describe three novel poliovirus strains that cannot replicate at 37°C . Their lack of pathogenicity was confirmed by intracerebral inoculation of susceptible transgenic mice that subsequently did not develop any symptoms of poliomyelitis . The inability to replicate at 37°C is caused by multiple mutations which do not revert to virulence after passage in cells . Furthermore , when used as vaccines , these viruses were capable of inducing a potent immune response in rats . At low temperature ( 30°C ) these viruses showed high productivity on the PER . C6 cell line , which has the potential to significantly reduce costs of goods , as previously shown for conventional poliovirus strains . Taken together , these new strains could contribute to a safe , genetically stable , efficacious and affordable IPV .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"medicine",
"and",
"health",
"sciences",
"microbial",
"mutation",
"immune",
"physiology",
"pathology",
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"laboratory",
"medicine",
"pathogens",
"immunology",
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"pathogens",
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"ribosome",
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"viral",
"replication",
"enteroviruses",
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] |
2016
|
Cold-Adapted Viral Attenuation (CAVA): Highly Temperature Sensitive Polioviruses as Novel Vaccine Strains for a Next Generation Inactivated Poliovirus Vaccine
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How oncogenes modulate the self-renewal properties of cancer-initiating cells is incompletely understood . Activating KRAS and NRAS mutations are among the most common oncogenic lesions detected in human cancer , and occur in myeloproliferative disorders ( MPDs ) and leukemias . We investigated the effects of expressing oncogenic KrasG12D from its endogenous locus on the proliferation and tumor-initiating properties of murine hematopoietic stem and progenitor cells . MPD could be initiated by KrasG12D expression in a highly restricted population enriched for hematopoietic stem cells ( HSCs ) , but not in common myeloid progenitors . KrasG12D HSCs demonstrated a marked in vivo competitive advantage over wild-type cells . KrasG12D expression also increased the fraction of proliferating HSCs and reduced the overall size of this compartment . Transplanted KrasG12D HSCs efficiently initiated acute T-lineage leukemia/lymphoma , which was associated with secondary Notch1 mutations in thymocytes . We conclude that MPD-initiating activity is restricted to the HSC compartment in KrasG12D mice , and that distinct self-renewing populations with cooperating mutations emerge during cancer progression .
Self-renewal is integral to the malignant phenotype [1] . In principle , the ability of cancer cells to self-renew may be intrinsic to the compartment in which the tumor-initiating mutation occurs , or may be acquired as a consequence of mutations in more differentiated cells . The hematopoietic system has proven highly informative for addressing how cancer-associated mutations and cell of origin interact to establish malignant self-renewing populations . Accumulating evidence supports the idea that many hematopoietic malignancies exist in a hierarchy of differentiation with only a minor population capable of propagating and maintaining the disease in vivo [2] . These cells are termed leukemia-initiating cells or leukemia stem cells ( LSCs ) , and manifest some biologic properties of normal hematopoietic stem cells ( HSCs ) . However , the precise relationship between these populations is uncertain and appears to depend , in part , on both the leukemia subtype and on the effects of specific mutations . For example , overexpressing MLL fusion proteins found in human acute myeloid leukemia transforms both murine HSCs and more differentiated progenitors [3 , 4] . By contrast , inactivation of the JunB transcription factor must occur in the HSC compartment for initiation of myeloid malignancies [5] . These proof-of-concept experiments underscore the importance of understanding how oncogenes and tumor suppressors that are commonly mutated in human cancers perturb self-renewal and growth control . Importantly , the functional characteristics of LSCs that distinguish them from HSCs and how these properties are modulated by oncogenes are poorly understood . RAS gene mutations are highly prevalent in pancreatic ( >80% ) , colorectal ( 40%–50% ) , endometrial ( 40% ) , lung ( 30% ) , and cervical cancers ( 20%–30% ) , as well as in myeloid malignancies ( 20%–40% ) [6] . Of the genes in the canonical RAS family , KRAS accounts for ∼90% of cancer-associated mutations , whereas HRAS mutations are rare . In hematologic cancers , NRAS is mutated 2–3 times more often than KRAS [6] . Cancer-associated RAS mutations , which introduce amino acid substitutions at codons 12 , 13 , or 61 , result in oncogenic Ras proteins that accumulate in the active , GTP-bound conformation because of defective guanine nucleotide hydrolysis [7] . Elevated levels of GTP-bound Ras , in turn , deregulate signaling in cancer cells by altering the activation of effector cascades that include the Raf/MEK/ERK , phosphatidylinositol 3-kinase ( PI3K ) /Akt , and Ral-GDS pathways [8] . Chronic and juvenile myelomonocytic leukemias ( CMML and JMML ) are aggressive myeloid malignancies that are classified as myeloproliferative disorders ( MPDs ) [9] . Both diseases are characterized by leukocytosis with excess monocytes in blood and bone marrow , and by significant infiltration of malignant myeloid cells into the liver , spleen , and other organs . Hyperactive Ras is strongly implicated in the pathogenesis of JMML and CMML . Somatic NRAS and KRAS mutations are found in ∼40% of CMML specimens [10 , 11] , and ∼85% of JMML patients have mutations in KRAS , NRAS , NF1 , or PTPN11 ( reviewed in [12] ) . The latter two genes encode proteins that regulate Ras-GTP levels . Importantly , children with germline mutations in NF1 or PTPN11 are at markedly increased risk of developing JMML , which argues strongly that deregulated Ras signaling can initiate this MPD [12] . This hypothesis is further supported by studies of Nf1 , Kras , and Ptpn11 mutant mice , all of which develop MPDs that resemble JMML and CMML [13–16] . Mutations that alter other signaling molecules also cause MPD . For example , the BCR-ABL fusion gene is the hallmark of chronic myeloid leukemia , and JAK2 mutations are found in nearly all cases of polycythemia vera ( reviewed in [17] ) . While mutations affecting signaling molecules are found in nearly all types of MPD , additional cooperating mutations are rare . MPDs are therefore genetically straightforward and tractable malignancies for understanding how aberrant signal transduction contributes to cancer by perturbation of stem and progenitor cell fates . Mice expressing oncogenic KrasG12D in hematopoietic cells develop a fatal MPD with 100% penetrance that is characterized by leukocytosis , splenomegaly , and anemia , with death at approximately 3 mo of age [13 , 14] . In this system , injecting Mx1-Cre , KrasLSL-G12D mice with polyinosinic-polycytidylic acid ( pIpC ) induces expression of Cre recombinase , which removes an inhibitory loxP-STOP-loxP ( LSL ) element and activates the KrasG12D allele ( we hereafter refer to Mx1-Cre , KrasLSL-G12D mice that have been treated with pIpC as KrasG12D mice ) . Mx1-Cre , KrasLSL-G12D mice provide a robust experimental system for investigating how expressing oncogenic Kras from the endogenous promoter affects HSCs and their progeny in vivo . Here , we identify tumor initiating cells in the KrasG12D model of MPD and find that , unlike some myeloid oncogenes , KrasG12D does not confer aberrant self-renewal properties to committed progenitor cells . However , small numbers of primitive KrasG12D cells can initiate MPD . This population shows excessive proliferation and rapidly dominates multilineage hematopoiesis in vivo . These data indicate that hyperactive Ras signaling is sufficient for the competitive advantage demonstrated by mutant HSCs and further implicate the HSC compartment as critical for therapy of JMML and CMML . Transplanted KrasG12D HSCs efficiently initiate T lineage acute lymphoblastic leukemia/lymphoma ( T-ALL ) , which is associated with Notch1 mutations and with acquisition of LSC activity in differentiating thymocytes . These results further demonstrate that distinct self-renewing populations can arise through cooperating oncogenic mutations during cancer progression .
Although basal Mx1-Cre activity is low [18 , 19] , many Mx1-Cre , KrasLSL-G12D mice that are not injected with pIpC ultimately succumb with MPD ( [14] and unpublished data ) . This observation suggests that HSCs and/or progenitor cells that activate KrasG12D expression have a substantial proliferative advantage in vivo . To assess the kinetics of this process , we analyzed recombination in the myeloid progenitors of young Mx1-Cre , KrasLSL-G12D mice that were not treated with pIpC . Bone marrow cells from 3–5-wk-old animals were plated in methylcellulose medium to enumerate granulocyte-macrophage colony forming unit progenitors ( CFU-GM ) , and individual myeloid colonies were genotyped by PCR . Surprisingly , nearly all CFU-GM in untreated animals were recombined as early as 3 wk of age ( Figure 1A ) . We then examined more primitive populations in Mx1-Cre , KrasLSL-G12D mice crossed to a ROSA26 yellow fluorescent protein ( YFP ) reporter strain ( Figure 1B ) [20] . In these mice , cells expressing Cre are identified by YFP expression . The frequency of YFP+ cells in wild-type ( WT ) mice was within the expected range of background Cre expression [18 , 19] . However , we found a much higher incidence of YFP expression in bone marrow cells of Mx1-Cre , KrasLSL-G12D , ROSA26-YFP mice . This result was consistent among all populations analyzed , including the primitive Flk2− Lin−/lo Sca1+ c-kit+ ( Flk2− LSK ) compartment , which is highly enriched for HSCs [21] . To confirm that YFP expression correlated with KrasG12D expression , we also directly genotyped colonies formed by single Flk2− LSK cells . This method again revealed a predominance of KrasG12D-expressing cells in naïve Mx1-Cre , KrasLSL-G12D mice ( Figure 1C ) . Together , these data are consistent with an advantage for Cre-expressing cells in untreated Mx1-Cre , KrasLSL-G12D mice . The apparent outgrowth of KrasG12D Flk2− LSK cells in mice that were not injected with pIpC suggested that KrasG12D expression might increase proliferation in this compartment . To test this hypothesis , we stained bone marrow cells collected from KrasG12D mice and WT littermates 2 wk after pIpC treatment with antibodies to cell surface proteins and with dyes that stain DNA and RNA ( 7-aminoactinomycin D [7-AAD] and pyronin Y , respectively ) . These studies revealed a significant reduction in the number of quiescent Flk2− LSK cells in KrasG12D animals , which are identified by having a 2n DNA content and low pyronin Y staining ( Figure 2A and 2B ) . Whereas the Flk2− subset of WT LSK contains roughly 80% cells in the G0 phase of the cell cycle , KrasG12D Flk2− LSK are only 50% quiescent . As an initial exploration of mechanisms regulating cell cycle progression , we analyzed D- and E-type cyclin expression by quantitative PCR in sorted Flk2− LSK cells from KrasG12D and WT mice . KrasG12D Flk2− LSK cells had significantly higher expression of cyclin D1 ( Figure 2C ) . Bone marrow mononuclear cells from KrasG12D mice display a hypersensitive pattern of CFU-GM progenitor growth , which is a cellular hallmark of JMML [12–14] . We found that this abnormal CFU-GM activity resides primarily in the common myeloid progenitor ( CMP ) compartment , and that these cells demonstrate enhanced proliferation in vivo ( [22] and unpublished data ) . To investigate if this population could initiate and maintain MPD , we collected Lin−/lo Sca1− c-kit+ CD34+ FcγRII/III− CMPs from 5-wk-old KrasG12D mice and WT littermates by FACS , and transferred 10 , 000 of these cells into lethally irradiated recipients with 106 WT marrow cells for radioprotective support . Transplanted tester cells , recipient cells , and support cells were marked by expression of different isoforms of CD45 , allowing them to be distinguished by flow cytometry . Transplanted KrasG12D or WT CMPs demonstrated robust day 8 spleen colony-forming unit ( CFU-S8 ) activity , with colony size somewhat larger for KrasG12D input CMPs ( Figure 3A ) . However , we detected less than 0 . 1% of circulating myeloid cells derived from transplanted KrasG12D or WT CMPs 1 mo after transplantation ( Figure 3B and 3C ) . As expected from previous studies [23 , 24] , KrasG12D and WT CMPs made minor contributions to the circulating B cell compartment with a statistically insignificant trend towards greater B cell production from KrasG12D cells . Taken together , these data indicate that KrasG12D CMPs do not initiate a hematologic disease . By contrast , transferring 500 Flk2− LSK cells from KrasG12D animals into lethally irradiated recipients rapidly resulted in durable multilineage reconstitution . KrasG12D cells dominated the T cell and B cell compartments sooner and to a higher degree than the progeny of WT Flk2− LSK cells in control animals . The myeloid series demonstrated a more variable time course , but KrasG12D derived cells also eventually out-competed WT cells ( Figure 4 ) . Recipients of KrasG12D Flk2− LSK cells that were euthanized 3 mo after transplantation had mild to moderate MPD , manifested as leukocytosis and splenomegaly with myeloid and erythroid infiltration ( Figure 5 ) . As discussed below , we also found that all recipients of KrasG12D Flk2− LSK cells developed T-ALL 2–4 mo after adoptive transfer . Although early mortality from T-ALL precluded analyzing recipient mice beyond 3 mo , KrasG12D Flk2− LSK cells recapitulate the essential features of MPD seen in the original Mx1-Cre , KrasLSL-G12D model [13 , 14] . We next asked how KrasG12D expression affects HSC function . Immunophenotypic analysis revealed a 2-fold reduction in the number of marrow Flk2− LSK cells in KrasG12D mice 2 wk after pIpC injection ( Figure 6A ) . This was mostly offset by an increased number of splenic Flk2− LSK cells . The reduction in marrow Flk2− LSK cells persisted in older animals ( Figure S2 ) . We also performed limit dilution studies to assess functional HSC activity . In these experiments , lethally irradiated recipients received decreasing numbers of whole bone marrow cells from either KrasG12D mice or WT littermates that had been injected with pIpC 2 wk earlier . Recipients were bled monthly , and flow cytometry was performed to assess whether CD45 . 1+ donor cells were able to provide durable ( >2 mo ) multilineage ( myeloid , B cell , and T cell ) engraftment . These studies demonstrated a striking 10-fold decrease in the number of long-term repopulating stem cells in KrasG12D animals compared to the WT littermate controls ( Figure 6B; Table S1 ) . Progression of MPD despite a reduction in the size of the HSC compartment suggests an increased production of mature cells by each KrasG12D HSC . To address this possibility , we examined the patterns of reconstitution from either KrasG12D or WT HSCs in mice that received the limit dilution dose ( 1 × 105 KrasG12D and 1 × 104 WT bone marrow cells ) . As ∼50% of these recipients were engrafted with donor cells , the Poisson distribution predicts that approximately 70% of the engrafting mice received a single HSC and ∼24% received two HSC . By 2 mo after transplantation , donor KrasG12D marrow cells made a markedly greater contribution to recipient hematopoiesis than WT cells ( Figure 6C ) . We were able to compare repopulation of the stem cell compartment by WT and KrasG12D HSCs in a few lethally irradiated recipient mice before the onset of T-ALL . In these experiments , lethally irradiated recipients ( CD45 . 2 ) were reconstituted with equal numbers of KrasG12D ( CD45 . 1 ) and WT ( CD45 . 1/CD45 . 2 ) Flk2− LSK cells , as well as 106 CD45 . 2 whole bone marrow cells ( CD45 . 2 ) for radioprotection . Whereas only one of six recipients euthanized 2 mo after transplantation showed a clear bias towards KrasG12D-derived Flk2− LSK cells , mice that survived for 3 mo without evidence of diffuse T-ALL demonstrated an overwhelming bias towards KrasG12D-derived Flk2− LSK cells , myeloid progenitors , and mature myeloid cells ( Figure S3 ) . Recipients that were injected with KrasG12D Flk2− LSK cells , either alone or in a 1:1 ratio with WT Flk2− LSK cells , uniformly became moribund 8–14 wk after transplantation . Examination of euthanized mice revealed massive thymic enlargement with an arrest in T cell development at the CD4/CD8 double positive stage , and variable infiltration of blast cells within the liver , spleen , and bone marrow ( Figure 7A ) . Importantly , two of three animals from the bone marrow limit dilution transplantation assay that were engrafted with a single KrasG12D repopulating unit developed T-ALL that was identical to that seen in animals repopulated with 500 KrasG12D Flk2− LSK cells ( unpublished data ) . Next , we asked if T-ALL arose within the bone marrow or the thymus of mice transplanted with KrasG12D HSCs . To do this , we took advantage of our prior observation that recipients conditioned with sublethal irradiation fail to engraft with KrasG12D bone marrow [13] . Therefore , sublethally irradiated recipients can exclusively select for hematologic malignancies with more aggressive biologic behavior . We isolated bone marrow cells and thymocytes from primary recipients of KrasG12D Flk2− LSK cells that developed T-ALL , and injected each population into sublethally irradiated secondary recipients ( Figure 7B ) . As expected , no animals demonstrated multilineage engraftment or MPD . Animals transplanted with thymocytes quickly succumbed with an identical T-ALL as primary recipients; however , none of the mice transplanted with bone marrow developed leukemia . Thus , whereas bone marrow-derived KrasG12D HSCs efficiently give rise to T-ALL , the T-ALL LSC population is initially restricted to the thymus in primary recipient mice . These results suggested that one or more secondary mutations might have developed in a novel T-lymphoid clone . Somatic NOTCH1 mutations are common in human and murine T-ALL [25–28] . To determine if a similar mechanism might contribute to the evolution of KrasG12D HSCs to T-ALL LSCs , we performed Western blot analysis to detect cleaved ( activated ) Notch1 protein . Cleaved Notch1 was observed in thymocytes from diseased primary recipients , but not in bone marrow cells ( Figure 7C ) , a finding that is consistent with the secondary transplant data . Direct sequencing around the PEST domain uncovered frameshift mutations in exon 34 of Notch1 in thymocytes from five of six animals transplanted with KrasG12D Flk2− LSK cells that developed T-ALL , but no mutations in thymocytes from control mice that received WT HSCs alone and remained well . The presence of a somatically acquired Notch1 mutation in a large fraction of the tumor provides a molecular indication of clonality . The propensity of KrasG12D HSCs to generate T-ALL led us to investigate the effects of KrasG12D expression in early T-lineage cells . Flow cytometry of the bone marrow revealed no expansion of Lin− Flk2+ IL-7Rα+ c-kitint Sca1int common lymphoid progenitors ( Figure 8A ) [29] . However , KrasG12D mice demonstrated consistent thymic enlargement compared to age-matched littermate controls , even prior to the onset of T-ALL ( Figure 8B ) . Immunophenotyping of primary thymocytes demonstrated an essentially normal distribution of CD4+ and CD8+ expression , with a slight trend toward an increased number of CD4/CD8 double negative ( DN ) cells ( after excluding Mac1+ and Gr1+ infiltrating myeloid cells ) ( Figure 8B ) . By contrast , further examination of the DN compartment using the cell surface markers CD44 and CD25 uncovered skewed development in Kras mutant mice ( Figure 8C ) . Together , these data demonstrate that oncogenic KrasG12D perturbs thymic homeostasis , particularly in early stages of thymocyte maturation .
We find that oncogenic KrasG12D expression in HSCs confers a strong in vivo growth advantage , increases proliferation , and results in MPD and T-ALL . In MPD , as in normal marrow , stem cell activity is restricted to the Flk2− LSK population , which represents less than 0 . 1% of nucleated marrow cells . While pathologic behaviors of more mature cells may contribute to tissue infiltration , anemia , and organomegaly , self-renewal is confined to this very primitive population . Therefore , hyperactive Ras signaling promotes excess proliferation in multiple hematopoietic compartments without immortalizing non–self-renewing cells . Similar data have been described in murine models of MPD based on BCR-ABL overexpression or loss of JunB , both of which also deregulate cytoplasmic signaling networks [5 , 30] . By contrast , recent experiments have provided direct evidence that some oncogenic transcription factors allow committed myeloid progenitors to acquire self-renewal ability [3 , 4] . Taken together , studies of myeloid oncogenes performed to date support the general idea that mutations that predominately alter cytoplasmic signaling networks and those that affect transcription factors controlling cell fate decisions comprise discrete complementation groups for the fully transformed phenotype [31 , 32] . Our limit dilution transplantation data demonstrate that oncogenic Kras confers a dramatic growth advantage in the HSC compartment . Under stringent conditions in which the contribution of a single WT HSC can barely be detected , the progeny of one ( or at most three ) KrasG12D HSC comprise a substantial fraction of the hematopoietic compartment . These studies provide direct experimental evidence that the outgrowth of malignant cells in MPD can be attributed to hyperactive Ras signaling in HSC . Our data suggest a pathogenic model in which JMML or CMML is initiated by a somatic mutation that deregulates Ras signaling in a single HSC . This idea is consistent with limited data from human patients and xenograft studies that implicate the HSC as the cell of origin for JMML [33–38] . To begin to address the mechanism by which mutant HSCs outgrow their WT counterparts , we analyzed the cell cycle in KrasG12D Flk2− LSK cells and found they are preferentially in cycle . The overexpression of cyclin D1 in KrasG12D cells we observed is consistent with many prior studies in cultured cell lines engineered to overexpress oncogenic Ras [39 , 40] . Intriguingly , HSCs in mice lacking D-type cyclins demonstrate severe proliferative defects and accumulate in the G0 and/or G1 phases of the cell cycle [41] . If increased cyclin D1 levels conversely result in excessive proliferation of HSC , then MPD in KrasG12D mice may be mediated in part by cyclin D1 , similar to the requirement for cyclin D1 in a model of Ras-mediated breast cancer [42] . We demonstrate a substantial early growth advantage of HSCs that express KrasG12D; however , it is also possible that oncogenic Ras expression has a negative long-term impact on HSC function . Increased proliferation or oncogenic stress may ultimately detract from self-renewal capacity . Reduced HSC fitness was observed in Pten−/− mice , in which phosphatidylinositol 3-kinase signaling is hyperactive [43 , 44] . Similar effects of KrasG12D are suggested by the reduced numbers of HSCs in KrasG12D mice , although this finding could also reflect changes in the composition of the Flk2− LSK population or cell-extrinsic effects related to alteration of the marrow microenvironment . The rapid demise of primary KrasG12D mice from MPD , and of transplant recipients from T-ALL , precluded serial transplantation experiments to test the long term fitness of KrasG12D HSC . In addition , the 5-fold discrepancy between HSC numbers that were measured by flow cytometry versus limit dilution transplantation suggests a defect in engraftment of KrasG12D HSC . This idea is consistent with a prior report in which retroviral transduction of mutant NRAS appeared to reduce engraftment potential [45] , and with extensive data showing that proliferating HSCs fail to engraft efficiently [46–50] . Despite the reduced number of HSC in the bone marrow of KrasG12D mice , our data are not entirely consistent with the idea that KrasG12D is a cell-intrinsic negative regulator of HSC self-renewal . If it were , we would expect specific loss of KrasG12D cells and outgrowth of WT or KrasLSL-G12D cells , because conditional models using Mx1-Cre typically retain a small pool of cells with the unrearranged locus [51 , 52] . However , we observed preferential retention of KrasG12D cells , even within the diminishing Flk2− LSK compartment . This observation suggests that residual WT HSCs are unable to compensate for the reduced HSC number . Therefore , we favor the hypothesis that the reduction in HSC number in KrasG12D mice reflects a disordered bone marrow microenvironment with reduced supportive capacity rather than a purely cell-intrinsic effect of KrasG12D in HSC . The natural history of hematologic disease was different in primary KrasG12D mice than in recipients of transplanted KrasG12D HSC . A subtle but important finding is that MPD is established earlier in primary KrasG12D mice than in transplanted recipients . ( Figure 5 and [13]; also see [16 , 53] ) . There are several possible explanations for this observation . The hematopoietic microenvironment in young mice may be more permissive for MPD than the irradiated bone marrow of an adult . Additionally , pIpC administration in Mx1-Cre mice may quickly create a field of KrasG12D myeloid progenitors that contributes to the rapid evolution of MPD in primary mice through cytokine-mediated autocrine and/or paracrine mechanisms [54] . It is also possible that nonhematopoietic stromal cells in Mx1-Cre , KrasLSL-G12D mice express K-RasG12D and contribute to the rapid onset of MPD in primary KrasG12D mice . The kinetics of MPD development relate directly to the high frequency of T-ALL we observed in transplanted recipients as compared to primary KrasG12D mice . The apparent incidence of T-ALL is highly subject to selection bias , because animals that die from MPD cannot be evaluated for subsequent emergence of T-ALL . For example , we have observed ∼10%–15% of KrasG12D mice develop T-ALL on an inbred C57BL/6 strain background ( unpublished data ) , but median time to death from MPD is shorter than the typical latency of T-ALL , and lymphoid tumors exclusively appear in mice with a relatively late onset of MPD . Similarly , we have not observed spontaneous T-ALL in F1 ( C57BL/6 × 129Sv/Jae ) mice , which die from MPD at a younger age than the C57BL/6 strain described here ( [13] and unpublished data ) . In transplant recipients , aggressive T-ALL arose in mice that also had evidence of underlying MPD that was not yet severe enough to kill the animal . Together , these observations suggest that the attenuation of MPD in transplant recipients was central to the apparent increase in the incidence of T-ALL in the transplant setting . Cooperation of hyperactive Ras and deregulated Notch signaling in T-ALL has recently been shown [55–57] . Our data extend these studies by demonstrating the remarkable efficiency with which KrasG12D HSCs can initiate T-ALL , and delineating how multiple cell types may participate in the stepwise acquisition of oncogenic mutations in hematologic cancers . In the Mx1-Cre , KrasLSL-G12D model , T-ALL is initiated by oncogenic Kras expression in HSC , but full transformation occurs when cooperating Notch1 mutations arise in a T-lineage cell . In this sense , both KrasG12D HSCs and the fully transformed thymocytes can be considered different types of malignant stem cells with distinct leukemogenic potentials . One potential implication of these results is that the initiating Kras mutation creates conditions favorable for acquisition of cooperating mutations by increasing the size of susceptible lymphoid progenitor pools and/or conferring resistance to apoptotic signals during thymic selection . KrasG12D appears to most greatly affect the DN population that is characteristically undergoing TCR rearrangement , selection , and proliferation [58 , 59] . Interestingly , K-RasG12D protein expression may substitute for the pre-T cell receptor rearrangement at this critical checkpoint , thereby allowing propagation of thymocytes that would normally be edited [60] . Consistent with this idea , a patient with impaired lymphoid homeostasis and multiple lymphoid malignancies was recently reported to have a germline NRASG13D mutation , and oncogenic NRAS suppressed apoptosis of lymphocytes after cytokine withdrawal [61] . By contrast , Kindler et al . recently reported reduced thymic cellularity in Mx1-Cre , KrasG12D mice [56] . We speculate that the proliferative effects of K-RasG12D in the T cell compartment were obscured in those studies by the short interval between pIpC injection , which induces systemic interferon production , and histologic analysis . The idea that patients may harbor a variety of genetically distinct LSC is consistent with studies of patients with chronic myeloid leukemia in blast transformation [62] , and has important therapeutic implications . The need to eliminate partially transformed but self-renewing cells , like KrasG12D HSCs , will depend on their propensity to initiate a life-threatening disease . Targeted therapies that are directed against onco-proteins such as K-RasG12D will effectively eliminate premalignant clones only if the targeted lesions are initiating rather than secondary mutations . For example , inhibition of Notch signaling is an attractive therapeutic strategy for T-ALL that is being investigated in the clinic . However , if these cancers arise from aberrant HSCs that do not contain a NOTCH1 mutation and are not eradicated by treatment , relapse could occur through the acquisition of distinct cooperating mutations in a self-renewing preleukemic population . Consistent with this idea , studies of human T-ALL suggest that NOTCH1 mutation occurs as a secondary mutation in at least some cases , with some patients developing recurrent disease having distinct NOTCH1 alleles [63] . Finally , our data have implications for understanding the nature of cancer stem cell populations in nonhematopoietic malignancies . KRAS is the most frequent target of dominant oncogenic mutations in human cancer , and it is particularly important in carcinomas of the lung , pancreas , and colon . Analogous cancers arise in strains of mice expressing conditional oncogenic Kras alleles in these tissues [64–68] . Importantly , whereas oncogenic Kras expression efficiently initiates tumorigenesis in murine lung and pancreas , colon cancer is observed only when the tumor suppressor Apc is inactivated as well [68] . These data are consistent with studies of human patients , which imply that KRAS mutation occurs early in pancreatic cancer but typically after APC mutation in colon carcinoma [69–71] . Lung cancer in KrasG12D mice appears to be initiated in a distinct bronchio-alveolar stem cell population [72] . On the basis of these observations and our data , we speculate that , like HSCs , cells initiating pancreatic and lung cancer will possess inherent self-renewal potential , and that KRAS mutations only contribute to colon tumorigenesis in cells that have already acquired a mutation that enhances self-renewal . Uncovering specific proteins and pathways that are essential for the self-renewal and survival of Kras mutant cancer stem cell populations may reveal novel targets for therapeutic intervention in a variety of human cancers .
All animals were handled in strict accordance with good animal practice as defined by the relevant national and local animal welfare bodies , and all animal work was approved by the institutional animal research committee ( University of California , San Francisco IACUC ) . Animals were housed in a barrier facility at the University of California , San Francisco . All mice were of the C57BL/6 strain . As described [13] , 21-d-old Mx1-Cre , KrasLSL-G12D were injected IP with 250 μg pIpC ( Sigma ) . ROSA26 LSL-YFP reporter mice were a gift from C . Lowell . Ptprc ( CD45 ) congenic mice were from Jackson Labs . To assay CFU-GM , nucleated bone marrow cells ( 105 ) or splenocytes ( 2 × 105 ) were suspended in 1 ml methylcellulose medium ( M3231 , StemCell Technologies ) with 10 ng/ml murine GM-CSF ( PeproTech ) . Colonies were counted after 8 d . To assay Flk2− LSK cells , 100 sorted cells were plated in 1 ml methylcellulose medium ( M3434 , StemCell Technologies ) , which is provided containing IL-3 , IL-6 , stem cell factor , and erythropoietin , and incubated at 37 °C for 14 d . For Kras genotyping , individual colonies were picked with a 10 μl pipette and frozen overnight in 10 μl ddH2O prior to PCR analysis [13] . Flow cytometry was performed as described [21 , 24] . Staining was carried out in FACS Staining Buffer ( FSB; HBSS with 2% heat inactivated FCS ) at 0 °C unless otherwise stated . HSCs were defined as Lin−/lo Sca1+ c-kit+ Flk2− , and CMP as Lin−/lo Sca1− c-kit+ CD34+ FcγR− . Antibodies were from eBioscience except as specified . To identify HSCs , cells were first stained for 1 h with unconjugated lineage antibodies: CD3 ( 17A2 , BioLegend ) ; CD4 ( RM4–5 ) ; CD5 ( 53–7 . 3 ) ; CD8 ( 53–6 . 7 ) ; B220 ( RA3-6B2 ) ; Ter119 ( TER-119 ) ; Mac1 ( M1/70 ) ; Gr1 ( RB6-8C5 ) . Cells were washed then incubated for 30 min with TriColor goat-anti-rat F ( ab′ ) 2 ( Invitrogen ) and murine IgG ( Sigma ) . Cells were washed again , stained for 30 min with Pacific Blue anti-Sca1 ( D7 , BioLegend ) ; APC anti-c-kit ( 2B8 , BioLegend ) ; PE anti-Flk2 ( A2F10 , BioLegend ) ; and 7-AAD ( Sigma ) 5 μg/ml for dead cell exclusion . IL-7Rα was detected using a PE conjugate ( eBioscience ) . In this case , or when analyzing YFP+ cells , Flk2 was detected with biotinylated anti-Flk2 and APC-Alexa Fluor 750 streptavidin ( Invitrogen ) . We found no difference in Flk2− LSK staining when Mac1+ cells were identified an on independent channel but not excluded . For myeloid progenitor analysis , PE anti-Flk2 was replaced with PE anti-FcγRII/III ( 93 ) and FITC anti-CD34 ( RAM34 ) , and cells were stained for an additional 30 min prior to analysis . Mature cells were assigned lineage using Pacific Blue anti-Mac1 and anti-Gr1 ( myeloid ) , FITC anti-CD3 and anti-CD5 ( T ) , and PE anti-B220 ( B ) . T cell subsets in mice with T-ALL were distinguished with Pacific Orange anti-CD4 ( Invitrogen ) , APC anti-CD8 , FITC anti-CD3 , and PE anti-CD5 . To discern origin of cells in chimeric mice , antibodies to CD45 . 1 ( A20; PE-Cy7 ) and CD45 . 2 ( 104; Alexa Fluor 700 ) were added . Primary thymocytes were analyzed with PE-Cy7 anti-CD4 , Alexa 647 anti-CD8 , PE anti-CD25 , Pacific Blue anti-CD44 , and FITC anti-Mac1 and anti-Gr1 . Prior to sorting , c-kit+ cells were enriched with anti-CD117 microbeads and an AutoMACS ( Miltenyi ) . Cell sorting was performed on a FACSAria and flow cytometry on an LSRII , both using FACSDiva software ( BD ) . Data were analyzed using FlowJo software ( TreeStar ) . Recipients received a single fraction of 950 rads for lethal irradiation , or 450 rads for sublethal irradiation , from a cesium source . Donor cells were prepared in 100 to 200 μl of FSB , and injected retro-orbitally into anesthetized mice . Recipients received water with neomycin and polymyxin for 2 wk . Blood counts were monitored monthly using a Hemavet 950FS ( Drew Scientific ) . Limit dilution was performed as described [73] . Lethally irradiated recipients were transplanted with 1 × 104 , 3 × 104 , or 1 × 105 nucleated bone marrow cells along with 106 unfractionated WT bone marrow cells for radioprotection . Peripheral blood was analyzed monthly by flow cytometry; mice with detectable engraftment in myeloid , B and T lineages 2 mo after transplantation were scored as positive and L-Calc software ( StemCell Technologies ) was used for statistical analysis . For the CFU-S8 assay , lethally irradiated mice received 105 CMPs without support cells . Spleen colonies were observed 8 d later by gross examination at harvest and after 48 h fixation in 10% formalin . Sections were stained with hematoxylin and eosin . Cells were lysed in 1% NP-40 with 30 mM NaF , 30 mM β-glycerophosphate , 20 mM Na4P2O7 , 1 mM Na3VO4 , and Complete ( Roche ) and analyzed after SDS-PAGE using cleaved Notch1 and β-actin antibodies ( Cell Signaling ) . Genomic DNA was PCR-amplified sequenced bidirectionally using 5′-ATAGCATGATGGGGCCACTA-3′ and 5′-GCCTCTGGAATGTGGGTGAT-3′ . Staining with 7-AAD and pyronin Y was performed as described [74] in nucleic acid staining solution ( NASS; 0 . 1M phosphate-citrate buffer [pH 6 . 0] [Sigma] , 5mM EDTA , 0 . 15M NaCl , 0 . 5% BSA ) , with 0 . 02% saponin ( Sigma ) . Nucleated bone marrow cells were stained for 30 min with FITC lineage antibodies ( CD3 , CD4 , CD5 , CD8 , B220 , Ter119 , and Gr1 ) , Pacific Blue anti-Sca1 , unconjugated anti-CD16/32 ( 2 . 4G2 , UCSF hybridoma core ) , and biotinylated anti-Flk2 . Cells were washed and then stained with APC-Alexa Fluor 750 streptavidin . Cells were washed again and resuspended in 500 μl of NASS with 1 μg/ml 7-AAD ( Sigma ) , and incubated at room temperature for 30 min , then on ice for 5 min . Pyronin Y ( Sigma ) was then added to 1 μg/ml , and cells were incubated for an additional 10 min before being washed and resuspended in 200 μL of FSB . Cells were finally stained with APC anti-c-kit and FITC anti-Mac1 . Differences in the quiescent fraction of HSCs were analyzed using an unpaired t-test . The assay was performed as described [50] . Flk2− KLS cells from animals pooled by genotype were double-sorted directly into RNA binding/lysis buffer from the RNEasy kit ( Qiagen ) , and total RNA was extracted per instructions . First strand cDNA synthesis was performed using a SuperScript III kit ( Invitrogen ) per manufacturer's instructions . Reactions were performed in an ABI-7900 sequence detection system using SYBR green according to manufacturer's instructions ( Applied Biosystems ) . Each amplification was performed in 10 μl with a template cDNA equivalent of 100 sorted HSCs . Each sample was tested in triplicate with each primer pair , and normalized to β-actin expression . Due to limiting numbers of doubly sorted cells , final cell purity was not analyzed; however the staining characteristics of c-kit-enriched and singly sorted cells are presented in Figure S1 . To analyze repopulation by a single HSC , we identified a cohort of mice receiving a cell dose yielding engraftment in only 50% of recipients . The Poisson distribution indicates that the probability of a mouse receiving k HSCs is given by where n is the average HSC number per mouse . The average that yields k = 0 HSC at a rate of 0 . 5 is given by 0 . 5 = e−n , which is solved to give n = 0 . 693 . Using this value for the average number of HSC per mouse , the Poisson distribution can be used to estimate the likelihood of any mouse receiving a given number of HSC: f ( 0 ) = e−ln 2 = 0 . 5; f ( 1 ) = ( ln 2 ) e−ln 2 = 0 . 346; f ( 2 ) = 1/2 ( ln 2 ) 2e−ln 2 = 0 . 120; f ( 3 ) = 1/6 ( ln 2 ) 3e−ln 2 = 0 . 028 . The proportion of mice expected to receive four or more HSCs is the remainder , 1 − ( 0 . 5 + 0 . 346 + 0 . 12 + 0 . 028 ) = 0 . 006 , or 0 . 6% .
|
Ras proteins act as molecular switches that relay growth signals from outside the cell . This mechanism is often subverted in cancer , and Ras proteins are activated directly by RAS gene mutations in approximately one-third of human malignancies . We have modeled this in mice engineered to have a Ras mutation . These mice develop a disease similar to chronic leukemias in humans called myeloproliferative disorders . It is marked by a fatal accumulation of mature and immature cells in the blood and bone marrow . We investigated whether some or all of these neoplastic cells were immortal . In agreement with the “cancer stem cell” hypothesis , we found that immortal cells were extremely rare in the bone marrow of diseased mice . They were found only in the same cell populations that contain normal bone marrow stem cells . However , these cells had high rates of replication and produced large numbers of daughter cells . Furthermore , many mice went on to develop acute lymphoid leukemia after acquiring additional mutations in maturing lymphoid cells . These studies exemplify the evolution of malignant stem cells during cancer progression . They also highlight the importance of rare , long-lived cells in the genesis and , potentially , therapy of high-risk chronic leukemias caused by abnormal Ras proteins .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"oncology",
"developmental",
"biology",
"oncology/myeloproliferative",
"disorders,",
"including",
"chronic",
"myeloid",
"leukemia",
"cell",
"biology",
"hematology"
] |
2009
|
Oncogenic Kras Initiates Leukemia in Hematopoietic Stem Cells
|
The diversification of neoavian birds is one of the most rapid adaptive radiations of extant organisms . Recent whole-genome sequence analyses have much improved the resolution of the neoavian radiation and suggest concurrence with the Cretaceous-Paleogene ( K-Pg ) boundary , yet the causes of the remaining genome-level irresolvabilities appear unclear . Here we show that genome-level analyses of 2 , 118 retrotransposon presence/absence markers converge at a largely consistent Neoaves phylogeny and detect a highly differential temporal prevalence of incomplete lineage sorting ( ILS ) , i . e . , the persistence of ancestral genetic variation as polymorphisms during speciation events . We found that ILS-derived incongruences are spread over the genome and involve 35% and 34% of the analyzed loci on the autosomes and the Z chromosome , respectively . Surprisingly , Neoaves diversification comprises three adaptive radiations , an initial near-K-Pg super-radiation with highly discordant phylogenetic signals from near-simultaneous speciation events , followed by two post-K-Pg radiations of core landbirds and core waterbirds with much less pronounced ILS . We provide evidence that , given the extreme level of up to 100% ILS per branch in super-radiations , particularly rapid speciation events may neither resemble a fully bifurcating tree nor are they resolvable as such . As a consequence , their complex demographic history is more accurately represented as local networks within a species tree .
The rich biodiversity of many organismal groups is the result of bursts of rapid species diversifications , with extreme examples in angiosperms [1] and vertebrates [2] . Among the latter , birds are one of the most speciose groups with a total of >10 , 500 recognized species that are proposed to be the result of mostly recent accelerations of diversification rates [3] . Nevertheless , the deep roots of 95% of these species lie within the ancient adaptive radiation of Neoaves , comprising all contemporary avian lineages except Palaeognathae ( ratites and tinamous ) and the Galloanserae ( chicken and ducks ) . This massive radiation exhibits the highest known diversification rate among deep vertebrate radiations [2] , coincides with the Cretaceous-Paleogene ( K-Pg ) boundary , and gave rise to 36 extant bird lineages within <15 million years ( MY ) [4] . Simulations suggest that the distribution of neoavian internode lengths causes a very high probability of gene tree–species tree incongruences [5] , i . e . , hemiplasy derived from incomplete lineage sorting ( ILS ) [6] . ILS denotes the persistence of ancestral polymorphisms across multiple successive speciation events and is followed by stochastic allele fixation in each descendant lineage , potentially making phylogenetic inference at the level of individual loci problematic . Past studies on the extent of ILS during speciation have been restricted to recent divergences because homoplasy needs to be low . For example , divergences among great apes show that ~30% of the gorilla genome exhibits nucleotide substitution patterns incongruent with the human/chimpanzee/gorilla species tree [7] . In contrast , the characteristics of ILS remain to be explored in adaptive radiations . As virtually homoplasy-free phylogenetic rare genomic changes [8 , 9] , retrotransposed elements ( REs ) exhibit conflicting phylogenetic signals only when their insertions occurred on short internodes; they can thus be used to localize and quantify ILS even on very deep timescales [8 , 10–13] .
Homoplasy via independent RE insertion requires the retrotransposition of the same RE subtype into precisely the same genomic location , in the same orientation , and featuring an identical target site duplication . In addition to these factors that make independent insertions very rare , the LTR retrotransposons studied here have a low copy number ( e . g . , 3 , 138 copies in the zebra finch genome ) , were active only for a short time period around the neoavian radiation [10] , and show no target site preference among thousands of reconstructed ancestral target sequences of inserted elements ( S2 Fig ) . We therefore propose that the probability of homoplasy caused by independent insertions among our RE markers is extremely low . Homoplasy via precise excision is the deletion of the RE insertion and one copy of the duplicated target site , but not a single bp more or less than that . These requirements make the occurrence of precise excisions very rare and we therefore visually inspected all of our markers for precise boundaries of presence/absence states and coded imprecise or poorly aligned boundaries as missing data . Altogether , we suggest that our 2 , 118 RE markers contain negligible homoplasy , and conflicts are instead due to ILS-derived hemiplasy . To verify that incongruences constitute ILS-derived hemiplasy , Hormozdiari et al . [14] proposed to test for topological consistence between each RE marker and a sequence tree derived from its flanking nucleotides . However , we note that failure of this test for some of their RE markers does not equal homoplasy of RE markers . Alternative and more plausible causes for inconsistencies are homoplasy or tree reconstruction uncertainties in the flanking sequence trees and the fact that recombination may cause different topologies between adjacent loci [15] . Unfortunately , single-locus sequence trees of Neoaves have an average topological distance of 63% for introns and 66% for ultraconserved elements ( UCEs ) from the main Jarvis et al . tree [4] . This means that the average nonexonic locus fails to congruently resolve most of the neoavian internodes . We note that it is therefore not possible to independently verify hemiplasy in neoavian RE markers by comparison to flanking sequence trees . Nevertheless , if homoplasy was prevalent in our RE markers , we would expect to see an equal distribution of RE incongruences across all of the sampled clades of Neoaves . While we find dozens of presence/absence markers with incongruences affecting the short branches within the neoavian radiation ( S1 Table; e . g . , the core landbirds and core waterbirds clades ) , there is not a single RE incongruence in our presence/absence matrix ( S1 Table ) affecting well-accepted internal relationships within postradiation taxa , such as passerines , parrots , eagles , penguins , the woodpecker/bee-eater clade , the hummingbird/swift clade , and the flamingo/grebe clade . Such an imbalance of RE incongruences strongly implies that homoplasy is indeed negligible among our 2 , 118 RE markers . We analyzed the RE presence/absence matrix using Felsenstein’s polymorphism parsimony [16] and obtained a single most parsimonious RE ( MPRE ) tree , whose branches are supported by a total of 1 , 373 conflict-free insertion events across the neoavian radiation ( Fig 1B ) . The topology is very similar to previous phylogenomic estimates using mostly noncoding nucleotide data [4 , 10 , 17–21] , including relationships previously strongly supported in whole-genome sequence analyses [4] ( Fig 1A ) , such as the sunbittern/tropicbird , bustard/turaco , and mesite/sandgrouse clades . From these three groups , only the sunbittern/tropic clade was previously recovered in some multilocus analyses [19–21] . The remaining 745 retrotransposon markers show different degrees of gene tree–species tree incongruence . This is best explained by the persistence of ancestral polymorphisms across successive speciation events , followed by reciprocal allele fixation in each of the descendant lineages , i . e . , ILS . We define the extent of ILS as corresponding to weak conflict ( persistence across two speciation events; Fig 1C ) , moderate conflict ( three events; Fig 1D ) , or strong conflict ( more than three events; Fig 1E ) . Per-branch counts of ILS-affected RE insertion events show that incongruences are pronounced on some internodes and are nearly absent on others ( Fig 1A and 1B ) , with greater conflict in deeper internodes . The internodes among core waterbirds exhibit weaker discordances and large amounts of conflict-free RE markers , which is in line with the observation that the RE relationships are fully congruent with the genome-level sequence analyses in Jarvis et al . [4] . Within core landbirds , the MPRE tree is fully congruent with a genome-level tree based on sequences from UCEs [4] , yet discordant with the main tree from Jarvis et al . [4] with regards to the position of mousebirds . The deepest divergences of core landbirds contain many ILS-affected markers with strong discordances when mapped on the main Jarvis et al . tree [4] ( Fig 1A ) , but slightly less so when mapped on the MPRE topology ( Fig 1B ) . Furthermore , the placement of owls in the MPRE tree ( Fig 1B ) is in agreement with our preliminary analysis of owl REs [4] . However , we emphasize that an alternative grouping of owls with eagles and New World vultures received nearly as strong RE support [4] , which suggests that the position of the owls may in fact approximate a trifurcation . Among the remaining neoavian divergences , nine internodes are discordant between our MPRE tree and the genome-scale sequence tree ( Fig 1A and 1B ) , all of which are characterized by scarcity of ILS-free RE markers and dominance of RE presence/absence patterns that show complex incongruences resulting from ILS across at least four consecutive speciation events ( sensu Fig 1E ) . It is striking that the conflicting placements of mousebirds are well-supported in the main Jarvis et al . tree [4] on the one side and our MPRE tree and the genome-scale UCE tree [4] on the other side , respectively . One explanation for this could be hybridization of two distinct and diverged ancestral species , e . g . , the ancestor of mousebirds and the ancestor of the woodpecker/bee-eater/hornbill/trogon/cuckoo-roller clade , which would lead to well-supported alternative topologies with conflicts across multiple well-supported branches . This form of hybridization would then be distinguishable from ILS by an over-representation of RE markers supporting one alternative , species tree-incongruent topology and an under-representation of markers supporting the remaining alternative topologies . Such a situation was recently suggested for the very base of the rodent phylogeny [22] . We therefore analyzed all six possible positions of mousebirds within Afroaves ( core landbirds without the passerine/parrot/falcon/seriema clade ) for their respective support by RE markers . We also analyzed RE support for a grouping of mousebirds as sister to the remaining core landbirds , which was previously suggested in limited RE studies that used the zebra finch genome as only query species [10 , 23] . The strongest support ( 29 RE markers ) was found for mousebirds as the sister taxon of the remaining Afroaves ( Fig 2A ) , and the six alternatives were recovered by two to eleven markers each ( Fig 2B–2G ) , with four markers supporting the Jarvis et al . topology of mousebirds being sister to Coraciimorphae s . str . [4] ( Fig 2D ) . The fact that we found no excess of markers supporting the main Jarvis et al . topology [4] over the other alternatives suggests that the mousebird conflict was not caused by hybridization . Instead , the nearly symmetric distribution of support among the six non-MPRE topologies indicates that the presence/absence patterns of these RE markers result from stochastic sorting of alleles after persistence of ILS across the early diversification of core landbirds . We thus suggest that the whole-genome and intron-specific sequence trees [4] recover a locally anomalous topology [15] driven by the known problematic behavior of mousebirds in sequence analyses [24] . We emphasize that the genome-scale UCE tree [4] supports exactly the same mousebird affinities as the majority of REs herein ( Figs 1B and 2A ) , raising the question as to how the UCE phylogenetic signal was overruled by intronic signal within the main genome-scale sequence analysis of Jarvis et al . [4] . Mapping RE markers on a dated time tree of the main Jarvis et al . analysis [4] enabled us to estimate the temporal dynamics of ILS across the very short internodes of the neoavian radiation ( Fig 3A ) . Jarvis et al . infer the onset of Neoaves diversification at around the K-Pg boundary [4] , which is in stark contrast to most mitochondrial and multilocus nuclear studies ( but see refs . [17 , 26] ) that estimate the deepest neoavian divergences at >82 million years ago ( MYA ) [3 , 27] or even >100 MYA ( reviewed by ref . [28] ) . We anticipate that this debate will persist for the upcoming years . However , given that the Jarvis et al . [4] estimates are the first based on genome-scale data , we consider these to be the most reliable molecular dates currently available . We found a negative correlation between branch length and the percentage of ILS-affected RE markers per branch ( Spearman's ρ = −0 . 6888 , p = 7 . 1×10−5; Fig 3C ) , which corroborates our assumption that ILS is indeed the driving force for most ( if not all ) of the observed incongruences . This is due to the fact that ILS has a higher probability of occurring if the time between consecutive speciation events is short [29 , 30] , and we would expect no such correlation if the conflicts we refer to as ILS-derived were instead caused by homoplasy . Strikingly , the per-branch estimates of ILS ( Fig 3A ) suggest that all those branches ( or their 95% credible interval of divergence times in ref . [4] ) overlapping with the K-Pg boundary exhibit 40%–100% ILS and are mostly incongruent with our MPRE tree ( Figs 1B and 3C ) . Furthermore , the three deepest branches within the post-K-Pg diversification of core landbirds are affected by 59%–81% ILS , including the two branches involved in the aforementioned mousebird conflict . This means that with the exception of the core waterbird/sunbittern/tropicbird branch and the core landbird branch , all branches affected by ≥40% ILS were incongruent with our MPRE tree ( Figs 1B and 3C ) . We then tested if temporal variation in RE insertion rates ( Fig 3B ) may account for some of the irresolution . While there is considerable rate variation between branches ( Fig 3B ) , there is no correlation between branch length and RE insertion rates ( Fig 3D ) or between RE insertion rates and degree of ILS ( Fig 3E ) . Altogether , this suggests that the low amount of ILS-free markers on the problematic branches is not the result of very low RE insertion rates ( <10 REs per MY ) . This is further supported by the notion that most of the post-K-Pg branches within core landbirds and core waterbirds have similarly short branch lengths and mostly low RE insertion rates , yet also low ILS ( e . g . , core waterbird branch with 12 RE insertions per MY and 20% ILS ) . We thus propose that the high prevalence of ILS ranging from 40%–100% across the deepest relationships among Neoaves is not an issue of taxon or marker sample size and rather reflects the biology of very rapid speciation . Complex gene tree–species tree incongruences of retrotransposon markers might be more accurately represented in phylogenetic networks where data conflicts are evident as reticulate relationships among taxa and alternative topologies are visible even within well-supported lineages [12 , 29] . The resultant neighbor-net [31] ( Fig 4A ) illustrates the differential distribution of ILS-derived incongruences across the neoavian radiation , with well-supported , low-conflict branches leading to the core landbird and core waterbird clades , respectively . Together with the relatively large amount of ILS of polymorphisms originating deep within each of these two clades , and at the very base of Neoaves ( Figs 1A–1B and 3A ) , this conclusively reveals that neoavian evolution went through three adaptive radiations [4] . Notably , the differences in the extent of ILS among these radiations imply that the tempo or demography of speciation may have varied considerably under the circumstances of accelerated diversification . More precisely , most of the 18% ILS-affected RE insertion events within the core waterbird radiation did not sort completely across two speciation events ( Fig 4B ) , whereas 27% of the insertions in the core landbird radiation did not sort across mostly two to three speciation events ( Fig 4C ) . These percentages of total ILS are comparable to the 34% genome-wide ILS found among human/chimpanzee/gorilla gene trees [7] . Finally , the deepest radiation of Neoaves exhibits discordances in 73% of the RE markers , mostly explained by persistence of ILS across five to seven speciation events ( Fig 4D ) . This is consistent with a highly reticulate network structure ( Fig 4A ) , which is restricted to those internodes that overlap with the K-Pg transition ( Fig 2A ) . Notably , only these neoavian relationships remained unresolvable in whole-genome sequence analyses [4] , and ILS-free RE insertions are scarce on these internodes ( Figs 1A–1B and 3A ) . Retrotransposon loci affected by ILS are distributed across the avian genome , irrespective of the duration of ILS per intronic or intergenic marker ( Fig 5A ) . This situation likely applies to the ancestral Neoaves genome , as the avian karyotype is unusually stable , including conserved synteny of the Z sex chromosome and ubiquitous presence of numerous microchromosomes [32 , 33] . The Z chromosome ( no recombination in female meiosis ) represents a low recombination environment [34–37] and is affected by ILS to a similar extent ( 34% ) as the genome-wide average of 35% ( S2 Table ) , and a tree based on RE markers from the Z chromosome yields similar topological discordances with the MPRE tree ( Fig 5B and 5C , S3 Fig ) . This is contrary to the observation of previous studies ( on nonrapid diversifications ) , where low-recombination autosomal regions and sex chromosomes generally exhibit less ILS due to the lower effective population size ( Ne ) of regions with low recombination [7 , 30] . Finally , it is striking that the incongruences among our chromosomal trees of rare genomic changes almost perfectly overlap with conflicts among whole-genome sequence trees derived from concatenated or coalescence-based analyses of various data partitions in Jarvis et al . [4] ( Fig 5D , S4 Fig ) and again yield a highly reticulate structure at the base of Neoaves ( cf . Fig 4A ) . Taken together , this reveals that many of these deepest neoavian divergences receive considerable support in some and strong refutation in other analyses , suggesting that the consecutive arrangement of their very short internodes may potentially represent Rosenberg’s “anomaly zone” [38] , i . e . , observing stronger support for a gene tree than for the actual species tree [15] . The probability for the occurrence of ILS depends on Ne in relation to the time between consecutive speciation events [29 , 30] , with Ne correlating positively and time negatively with the expected extent of ILS , respectively . The observed complex genealogical fates of ancestral RE insertion polymorphisms during the initial super-radiation ( Fig 4D ) therefore suggest that the onset of neoavian diversification was characterized by a large number of near-simultaneous speciation events of an ancestral species with large Ne . Considering the sheer amount of differing allelic combinations that are possible to result from stochastic sorting of ancestral biallelic genetic variation after up to 17 speciation events ( Fig 4D ) , we hypothesize that such complex signals might overrule the underlying species tree-concordant signal , because the latter can be expected to occur rarely under the complex sorting scenario envisioned ( cf . Figs 1A–1B and 3A ) . Considering all theoretically possible RE presence/absence patterns in a five-taxon tree ( Fig 1C–1E ) , ILS across four speciation events requires allelic sorting in each of the descendant lineages , permitting 22 different character distributions that are discordant with the species tree ( Fig 6A , S5 Table ) . Under the model of stochastic sorting of polymorphisms of RE presence/absence ( Fig 6 ) or other types of biallelic variation ( e . g . , single nucleotides ) , the probability for the occurrence of hemiplasy surpasses 90% after an ILS duration of seven speciation events ( Fig 6D , S5 Table ) . This may explain why the deepest neoavian bifurcations receive various alternative topologies in the different genome-scale sequence trees of Jarvis et al . [4] ( Fig 5D ) . However , the high bootstrap support ( >90% ) for some alternative bifurcations could also mean that there are several comparably likely relationships , thus resembling a local network . Alternatively , Salichos & Rokas recently proposed that bootstrapping in phylogenomic analyses can lead to strong support for bifurcations even in the light of strong conflict [39] . Even if one of these genome-scale bifurcating trees reflects the actual neoavian species tree , the verification of such a phylogenetic hypothesis remains challenged by the underlying complex discordances . Finally , the nearly star-shaped topology of this super-radiation ( Figs 4A and 5D ) may reflect population complexity of the ancestral species , especially if the succession of population isolation during explosive speciation happened in disagreement with prior population structure [40 , 41] . We conclude that Neoaves diversification is more complex than can be shown in fully bifurcating trees and exhibits a dynamic picture of ILS . The timing of the highly ILS-affected initial super-radiation coincides with the K-Pg extinction of nonavian dinosaurs and archaic birds [42] , suggesting that the abrupt availability of ecological niches [4] was followed by near-simultaneous population isolations [41] via specializations and led to several network-like relationships . The subsequent , decelerated adaptive radiations of waterbirds and raptorial [4] landbirds exhibit less ILS and likely took place after the K-Pg boundary [4 , 17] . Interestingly , this time span is similar to placental mammal diversification [43] , which was accompanied by localized and less pronounced ILS than shown here for Neoaves [12 , 13] . Finally , and contrary to the expectation that complete genomes will permit full resolution of phylogenies [44] , our genome-level analyses of rare genomic changes yield a broadly bifurcating species tree of Neoaves [4] with local network-like reticulations that probably lie in the anomaly zone . Our study thus provides empirical evidence for a locally confined “hard” polytomy [41] , and we predict that future genome-wide studies of ILS in other adaptive radiations will reveal further examples where a fully bifurcating , universal species tree is an oversimplification of the underlying complexity of speciation .
Our taxon sampling comprises the genome assemblies of 48 recently sequenced birds [45] and thus contains the same species that were used in the genome-scale sequence analyses of Jarvis et al . [4] . We focused on identifying RE insertion events during early neoavian evolution and therefore excluded non-neoavian genomes ( chicken , duck , ostrich , tinamou , turkey ) from the set of query species used for extracting RE candidate loci . All neoavian genomes were utilized as queries , with the exception of close relatives of ingroup species ( as they do not add much more information ) such as zebra finch ( i . e . , four remaining passerines ) , white-tailed eagle ( i . e . , bald eagle ) , budgerigar ( i . e . , kea ) , and adelie penguin ( i . e . , emperor penguin ) . This taxon sampling contains representatives of all major neoavian lineages [4] , and we thus consider it sufficient for estimating ILS during Neoaves diversification . We expect that the addition of more taxa via sequencing of additional genomes would not result in an improved resolution of our RE data but rather lead to an increase of missing data and the detection of additional ILS across internodes that lie outside the three neoavian radiations reported herein . We analyzed a total of ~130 , 000 copies of hitchcock-related LTR retrotransposons [46] that were previously shown to be REs characteristic of early bird evolution [10 , 47] and constitute the majority of RE activity during the neoavian radiation [10] . After repeat annotation of the sampled genomes using RepeatMasker [48] version 3 . 2 . 9 , we extracted all TguLTR5d elements for each query species , including 1-kb flanks per RE locus . These sequences were then compared to the remaining query species via BLASTn [49] ( cutoff E < 10−30 ) , followed by extraction of the BLASTn hits and generation of locus-specific alignments in MAFFT [50] ( version 6 , E-INS-i ) . These alignments were postfiltered to exclude loci exhibiting less than ten species , missing flanks , plesiomorphic RE insertions ( i . e . , orthologous presence among all query species ) , or autopomorphic RE insertions ( i . e . , presence only in one query species ) . Furthermore , we omitted loci that were redundant or potentially paralogous . Among the ~8 , 000 remaining candidate loci , we manually identified phylogenetically informative RE insertions ( including additional RE insertions in the sequences flanking the TguLTR5d query ) in ~3 , 000 loci . For these marker candidates , we compiled final multispecies alignments after BLASTn searches ( cutoff E < 10−10 ) of 2-kb flanks against the full taxon sampling of 48 birds . RE markers serve as virtually homoplasy-free estimators of ILS-derived hemiplasy after minimizing potential errors that might arise from misalignment or incorrect scoring . Therefore , we carefully inspected the 48-species presence/absence alignment of each of the ~3 , 000 marker candidates by eye and manually coded binary character states using strict standard criteria [10 , 51 , 52] . Character state “1” requires the presence of an orthologous RE insertion ( i . e . , identical insertion point , orientation , RE subtype , and target site duplication ) in an orthologous genomic locus ( i . e . , single-copy flank regions ) . Character state “0” constitutes the absence state of a particular RE insertion , as indicated by the presence of a nonduplicated target site and an alignment gap precisely corresponding to the RE presence/absence boundaries . If neither of the conditions necessary for character states “1” or “0” were met , character states were treated as missing data and coded as “ ? ” . The same was done in the case of a gap in the genome assembly or a large unspecific deletion of the insertion locus . Marker candidates that did not meet the aforementioned strict criteria were omitted . This overall procedure led to a reduction of the ~3 , 000 marker candidates to a final set of 2 , 118 RE markers . Note that from these markers , 61 were previously published as a preliminary analysis of owl retrotransposons with the focus on determining the owl sister group [4 , 53] . We emphasize that these 2 , 118 markers encompass all RE insertion events during the neoavian radiation that were identified with our screening approach , with the exception of shallower internodes because they were not the main focus of our analyses of neoavian ILS . In the latter cases , we recorded a subset of the numerous marker candidates for these internodes , namely the woodpecker/bee-eater/hornbill , hummingbird/swift , pelican/egret/ibis/cormorant , and flamingo/grebe clades , as well as internodes within these . We manually recorded target site duplications ( i . e . , direct repeats of 5 bp flanking the studied LTR retrotransposons ) for each of our RE markers ( S1 Table ) . This was done by visually inspecting the left and right flanks in our 48-species marker alignments to parsimoniously infer the putative ancestral states of lineage-specific nucleotide changes in each orthologous target site . We therefore suggest that these reconstructed motifs approximate the respective target sequences at the time points of RE insertion . We analyzed the motifs in 5′–3′ orientation ( relative to the LTR orientation ) using WebLogo [54] . The resultant sequence logo [55] contains near-equal frequencies of the four possible nucleotides per motif position ( S2 Fig ) , which suggests that there is no target site preference among the REs studied herein . We analyzed the 1/0-coded presence/absence matrix of 2 , 118 RE markers using the Dollop program in PHYLIP [56] version 3 . 695 under polymorphism parsimony and standard parameters with randomized input order of species ( 7 times to jumble , random seed “11111” ) . The Dollop output contained the resultant MPRE tree and the parsimony-inferred per-branch character states for each RE marker , which we used to calculate the amount of ILS-free markers per internode , and to infer the duration of ILS across speciation events in incongruent insertions . We also ran Dollop using the main Jarvis et al . tree [4 , 53] as input tree under the same aforementioned parameters , which was followed by estimation of the amount and duration of ILS across internodes . Subsequently , Z-chromosomal and microchromosomal RE trees were generated using Dollop under the same parameters as the MPRE tree . Finally , Splitstree [31] version 4 . 13 . 1 was used for neighbor-net analysis of conflict within our RE presence/absence matrix and supernetwork analyses of conflict between different tree topologies based on REs ( S2 Data ) or nucleotide sequences [4 , 53] . Our phylogenetic analyses yielded a reconstruction of transitions of character states for each RE marker , thus allowing the analysis of ILS-derived hemiplasy under the assumed negligibility of homoplasy . We defined an ILS-free marker ( i . e . , duration of ILS across maximally one speciation event ) as one that required a single step when mapped on the analyzed tree . In the Dollop output , this is coded as a single transition to the presence state ( “1” ) on the branch where the RE insertion occurred . If a presence/absence pattern required more than one step when mapped on the given tree , it was defined as an ILS-affected marker ( i . e . , duration of ILS across minimally two speciation events ) . Under polymorphism parsimony , such a pattern results from a polymorphic RE insertion ( “P” ) that occurred on a branch prior to the conflicting branches and then persisted as a polymorphism across two or more speciation events , followed by stochastic allele sorting in the descendant lineages . We thus counted the total number of transitions to the presence ( “1” ) and the absence ( “0” ) allele necessary to explain the given topological conflict as a measure for the minimal amount of independent allele fixation events . Considering that n speciation events give rise to n + 1 lineages ( Fig 6 ) , our estimates of the duration of ILS correspond to the minimum of speciation events across which ILS persisted when counting the minimal amount of lineages that must have independently sorted under the given RE presence/absence pattern . Finally , we manually counted all possible allelic fates for ILS across two to four speciation events ( Fig 6A ) and derived a formula to calculate the amount of species tree-incongruent presence/absence patterns theoretically resulting from any duration of ILS ( Fig 6 , S5 Table ) . Dividing this number of hemiplasious character distributions by the amount of all theoretically possible presence/absence patterns yielded the probability of occurrence of hemiplasy in a biallelic polymorphism ( Fig 6C , S5 Table ) .
|
The rise of modern birds began after the mass extinction of nonavian dinosaurs and archaic birds at the Cretaceous-Paleogene ( K-Pg ) boundary , about 66 million years ago . This coincides with the super-rapid adaptive radiation of Neoaves ( a group that contains most modern birds ) , which has been difficult to resolve even with whole genome sequences . We reconstructed the genealogical fates of thousands of rare genomic changes ( insertions of selfish mobile elements called retrotransposons ) , a third of which were found to be affected by a phenomenon known as incomplete lineage sorting ( ILS ) , namely a persistence of polymorphisms across multiple successive speciation events . Astoundingly , we found that near the K-Pg boundary , speciation events were accompanied by extreme levels of ILS , suggesting a near-simultaneous , star-like diversification process that appears plausible in the context of instantaneous niche availability that must have followed the K-Pg mass extinction . Our genome-scale results provide a population genomic explanation as to why some species radiations may be more complex than a fully bifurcating tree of life . We suggest that , under such circumstances , ILS bears witness to the biological limitation of phylogenetic resolution .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
The Dynamics of Incomplete Lineage Sorting across the Ancient Adaptive Radiation of Neoavian Birds
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The vacuolating cytotoxin ( VacA ) of the gastric pathogen Helicobacter pylori binds and enters epithelial cells , ultimately resulting in cellular vacuolation . Several host factors have been reported to be important for VacA function , but none of these have been demonstrated to be essential for toxin binding to the plasma membrane . Thus , the identity of cell surface receptors critical for both toxin binding and function has remained elusive . Here , we identify VacA as the first bacterial virulence factor that exploits the important plasma membrane sphingolipid , sphingomyelin ( SM ) , as a cellular receptor . Depletion of plasma membrane SM with sphingomyelinase inhibited VacA-mediated vacuolation and significantly reduced the sensitivity of HeLa cells , as well as several other cell lines , to VacA . Further analysis revealed that SM is critical for VacA interactions with the plasma membrane . Restoring plasma membrane SM in cells previously depleted of SM was sufficient to rescue both toxin vacuolation activity and plasma membrane binding . VacA association with detergent-resistant membranes was inhibited in cells pretreated with SMase C , indicating the importance of SM for VacA association with lipid raft microdomains . Finally , VacA bound to SM in an in vitro ELISA assay in a manner competitively inhibited by lysenin , a known SM-binding protein . Our results suggest a model where VacA may exploit the capacity of SM to preferentially partition into lipid rafts in order to access the raft-associated cellular machinery previously shown to be required for toxin entry into host cells .
The vacuolating cytotoxin ( VacA ) is an intracellular-acting toxin generated by the pathogen , Helicobacter pylori , which infects the gastric epithelium of humans and is a significant risk factor for the development of peptic ulcer disease , distal gastric adenocarcinoma , and gastric lymphoma in humans [1] . VacA has been demonstrated to be important for H . pylori colonization and disease pathogenesis [2]–[5] . Intoxication with VacA results in multiple consequences , including vacuolation and apoptosis of epithelial cells [6] . Analogous to many other bacterial toxins [7] , VacA interacts with the plasma membrane of sensitive cells as the first step during intoxication [8] . Subsequent to binding , VacA is internalized by a novel pinocytic-like mechanism , and functions from an intracellular compartment [8]-[13] . There has been considerable interest in identifying target cell receptors for VacA because , for most toxins , the presence or absence of specific receptors on the surface of target cells largely dictates cellular tropism [14] . Several studies have characterized VacA binding to the plasma membrane of sensitive cells as non-specific [10] , [15] , suggesting that VacA may bind to multiple receptors and/or a highly abundant membrane component . Receptor protein tyrosine phosphatases ( RPTP ) , α and β , have been demonstrated to confer cellular sensitivity to VacA and directly interact with VacA [16]-[18] , but the importance of RPTP-α or RPTP-β for binding VacA to the plasma membrane surface has not been demonstrated , and the role , if any , of these proteins as primary binding determinants for VacA is uncertain . VacA interacts in vitro with heparan sulfate [19] and several glycosphingolipids [20] , but neither the interaction of VacA with these molecules on the surface of mammalian cells , nor the importance of these potential interactions for toxin function has been demonstrated . Likewise , although VacA interacts with lipid preparations and binds to artificial membranes [21]–[26] , the specificity and importance of VacA-lipid interactions for toxin function remains to be established . Finally , VacA association with lipid rafts has been demonstrated to be important for the toxin's vacuolation activity [21] , [27]–[29] , but it is not clear whether VacA binding to lipid or protein components within these rafts is important for toxin function . Thus , the molecular basis of VacA interactions with the plasma membrane and , in particular , the identity of receptors important for VacA binding , remains poorly understood . To provide further insight into the molecular basis underlying the capacity of VacA to bind to the surface of epithelial cells and induce vacuolation activity , we evaluated the importance of several common membrane lipids for toxin function . Here , we provide evidence that the membrane lipid , sphingomyelin ( SM ) , comprising sphingosine , a fatty acid , a phosphate group , and choline , modulates the sensitivity of epithelial cells to VacA . SM is a key structural component of the cell membrane and , in particular , specialized membrane domains called lipid rafts , while at the same time serving a significant functional role , as it is the parent compound of several lipid mediators [30] . We demonstrate that SM is important for VacA binding to cells and association with lipid rafts on the plasma membrane . Finally , VacA binds in vitro to SM in a manner that is competitively inhibited by the SM-specific binding protein , lysenin . These results suggest that SM functions as a receptor for VacA by promoting association of the toxin with plasma membrane lipid rafts .
Monolayers of HeLa cells were preincubated at 4 °C with 50 μM exogenous SM , PC , PI , or PE in order to increase the concentration of these selected lipids , which are commonly found in the plasma membrane of mammalian cells . After 1 h , VacA was added to the monolayers at a low concentration ( 10 nM ) , and the cells were further incubated with both toxin and exogenous lipids at 37 °C . After 24 h , only modest levels of vacuolation were visible in the absence of exogenous lipids ( Figure 1A ) . However , cellular vacuolation was visibly increased only in monolayers that had been preincubated with exogenous SM , but not in monolayers that had been preincubated with the other lipids ( Figure 1A ) . Essentially identical results were obtained , regardless of whether monolayers were preincubated with SM for 1 h prior to toxin addition , or whether mixtures of VacA and SM were added simultaneously to cells ( Figure S1 ) . None of the lipids alone induced vacuolation ( data not shown ) . Notably , the SM content of cells ( Table S1 ) as well as vacuolation ( Figure 1B ) increased as a function of exogenous SM concentration . SM is an important and abundant sphingolipid on the plasma membrane of many mammalian cells [31] . To evaluate the effects of reducing cellular SM on VacA-mediated vacuolation , HeLa cells were preincubated for 1 h with SMase C , a phosphodiesterase that hydrolyzes plasma membrane SM to ceramide and free phosphorylcholine at the aqueous-lipid interface , and then further incubated for 24 h with a higher concentration of VacA ( 100 nM ) . SMase was previously used to demonstrate binding to SM on the surface of cells by lysenin , the first identified SM-binding protein originally discovered within the coelomic fluid of the earthworm Eisenia foetida [32] , [33] . Pretreatment of cells with SMase C visibly reduced VacA-mediated cellular vacuolation ( Figure 1C ) . Moreover , SMase C pretreatment visibly reduced plasma membrane SM , as indicated by the reduced fluorescence associated with cells stained with Venus-lysenin ( Figure S2 ) . Cellular vacuolation ( Figure S3 ) and SM content ( Table S2 ) were both quantitatively reduced as a function of SMase C concentration . VacA-mediated cellular vacuolation and cellular SM levels were also reduced when cells were pretreated with inhibitors of de novo sphingolipid biosynthesis , Fumonisin B1 ( 50 μg/mL ) or PDMP ( 50 μM ) ( data not shown ) . However , in subsequent experiments we used SMase C to deplete cellular SM , because of the high level of specificity of SMase C for SM [34] , [35] , as well as the capacity to specifically hydrolyze SM on the plasma membrane surface . In addition to HeLa cells , we found that SM was critical for VacA-mediated vacuolation of AGS and AZ-521 cells , two human gastric epithelial cell lines ( Figure S3 ) , as well as for CHO-K1 and Vero cells ( data not shown ) , demonstrating that the importance of plasma membrane SM for toxin function is not idiosyncratic to HeLa cells . In subsequent experiments , we chose to use HeLa cells because they have been the most widely used model to investigate VacA association with and entry into host cells [8] , [10]–[13] , [15] , [29] , [36] . However , during the course of these studies , we repeated each of the experiments at least once using AZ-521 cells , which yielded essentially identical results to those obtained for experiments employing HeLa cells . Significantly higher VacA concentrations were required to induce detectable vacuolation in cells preincubated with SMase C relative to untreated cells ( Figure 1D ) ; at the highest toxin concentration tested ( 400 nM ) , the level of vacuolation was approximately only 30% of that detected for cells in which SM had not been depleted . Furthermore , adding exogenous SM ( 50 μM ) back to cells that had been preincubated with SMase restored the sensitivity of both HeLa ( Figure 1E ) and AZ-521 cells ( data not shown ) to VacA . In contrast , adding exogenous PC , PE , PI , or cholesterol back to HeLa cells that had been preincubated with SMase did not restore cellular sensitivity to VacA , even at very high concentrations of lipids ( 200 μM ) ( Figure S4 ) . These results demonstrate the importance of plasma membrane SM for conferring sensitivity to VacA . We next evaluated whether the inhibitory effects of SMase C were primarily attributable to the loss of SM or , alternatively , to the generation of ceramide , which is a potent mediator of cellular signaling [37]–[39] . Preincubation of HeLa cells with exogenous ceramide had no effect on VacA-mediated vacuolation ( Figure S5A ) . However , VacA-mediated vacuolation was inhibited in a dose-dependent fashion when HeLa cells were preincubated with SMase D ( Figure S5B ) , a phosphodiesterase that cleaves SM at the choline-phosphate linkage to yield ceramide-1-phosphate [34] , [40] , [41] , which has a different and distinct role from ceramide in cell signaling [42] , [43] . Finally , adding exogenous SM back to cells that had been preincubated with SMase C restored sensitivity of these monolayers to VacA in a manner that was dependent on the concentration of exogenous SM ( Figure 2 ) . These results are consistent with the idea that the inhibitory action of SMase C is primarily due to the loss of plasma membrane SM rather than the generation of ceramide , and further illustrate that the presence of SM at the plasma membrane is important for VacA cellular intoxication . We also evaluated whether contaminating phospholipase C ( PLC ) activity within our SMase preparations might contribute to the inhibition of vacuolation . PC-PLC cleaves PC into phosphorylcholine and diacylglycerol ( DAG ) , which is a potent activator of protein kinase C ( PKC ) signaling ) . For these studies , we purchased SMase C that had undergone quality control testing to ensure the presence of minimal contaminating PLC activity . Nonetheless , we directly tested and found that neither SMase C nor SMase D induced detectable decreases in cellular PC content ( Table S3 ) . Moreover , we determined that SMase C pretreatment inhibited VacA-mediated cellular vacuolation to the same extent in the presence or absence of the PKC inhibitor , BIM I ( 0 – 5 μM; data not shown ) , indicating that PKC activation due to PC cleavage was not likely to be responsible for the inhibition of cellular vacuolation . Taken together , these results further support the model that SM depletion , and not low levels of PC cleavage , is responsible for the inhibitory action of SMase C . In the studies described above , VacA-induced vacuolation was modulated in response to preincubating monolayers with exogenous SM or SMase C . To probe the functional role of SM for VacA cellular intoxication , HeLa cells were first incubated with VacA at 4 °C , which promotes toxin binding to the plasma membrane while preventing internalization [10] . The cells were then incubated with exogenous SM or SMase C to increase or reduce plasma membrane SM , respectively . Finally , the cells were incubated at 37 °C for 24 h , and evaluated for cellular vacuolation by measuring neutral red uptake . In contrast to experiments in which cells were preincubated with exogenous SM or SMase ( Figure 1 and 3A ) , vacuolation was not altered when cells with pre-bound VacA were subsequently treated with SM or SMase C ( Figure 3A ) . One interpretation of these data is that pre-bound VacA blocks SMase access to its substrate on the surface of cells . Alternatively , these results may indicate that only the first step along the VacA intoxication pathway – toxin binding to the plasma membrane surface - is sensitive to depletion or supplementation of plasma membrane SM . In either case , these results suggest that plasma membrane SM may be important for toxin association with the plasma membrane surface . We next evaluated the importance of SM for the binding of VacA to the surface of sensitive cells . DIC/fluorescence microscopy revealed that relative to untreated cells , visibly more Alexa Fluor 488-labeled VacA associated with the plasma membrane of HeLa cells preincubated with exogenous SM , while considerably less toxin was visible on the surface of cells pretreated with SMase C ( Figure 3B ) . Flow cytometry measurements revealed that depletion of plasma membrane SM reduced VacA binding by more than 80% ( Figure 3C and 3D ) , while not affecting the binding of transferrin ( Tf ) or the cholera toxin B fragment ( CTB ) ( Figure 3D ) . As a control , we demonstrated that pretreatment of cells with SMase C also significantly reduced binding of Venus-lysenin ( Figure 3D ) . Finally , we demonstrated that the level of VacA binding was strongly dependent ( R2 = 0 . 95 ) on the levels of SM in cells pretreated with different concentrations of SMase C ( Figure S6 ) . To further evaluate the importance of plasma membrane SM for VacA interactions with the cell surface , we next determined whether restoring plasma membrane SM back to cells that had been previously depleted of SM was sufficient to rescue VacA binding . As expected , VacA binding was significantly decreased by pretreatment of cells with SMase C ( Figure 4 ) . However , when these SM-depleted cells were incubated with exogenous SM prior to exposure to toxin ( all at 4 °C ) , VacA binding increased as a function of SM concentration , and was fully restored at 200 μM SM to levels measured in controls using untreated cells ( Figure 4 ) . Similar results were obtained when these experiments were conducted with Venus-lysenin , which was used a control because this protein demonstrates absolute binding specificity for SM on the surface of cells ( Figure 4 ) . The increase in cellular binding of Venus-lysenin as a function of SM concentration also supports the premise that exogenously added SM properly inserted into the plasma membrane . In contrast to those results obtained with SM , VacA cellular binding was not restored in the presence of exogenous PC , PE , PI , or cholesterol , even at high concentrations of exogenous lipids ( 200 μM ) ( Figure S7 ) . Thus , restoring plasma membrane SM on cells previously depleted of plasma membrane SM was sufficient to rescue binding of VacA to the cell surface . VacA association with lipid raft microdomains on the plasma membrane of mammalian cells is important for the toxin's vacuolating activity [27]–[29] . To explore whether SM is an important determinant for VacA association with lipid rafts , we used gradient density centrifugation to fractionate DRMs , which are the biochemical correlate of lipid rafts , that had been extracted from cells incubated with VacA , cholera toxin ( CT ) , or mock treated with PBS pH 7 . 2 at 4 °C . As previously reported [15] , [27] , [29] , most of the cell-associated VacA was found in low density fractions , although VacA was also detected within the less buoyant fractions near the bottom of the gradient ( Figure 5A ) . Analysis of DRMs from VacA-treated cells that had been pretreated with SMase C revealed that VacA was visibly reduced in the low-density fractions compared with cells that had not been pretreated with SMase C , but only modestly decreased within the less buoyant fractions near the bottom of the gradient ( Figure 5A ) . In contrast , depletion of cellular SM with SMase C had no effect on the total cellular binding and distribution within the density gradient fractions of either CT ( Figure 5B ) , in agreement with a recent report [44] , or the endogenous transferrin receptor ( Tf-R ) ( Figure 5C ) . These results distinguish VacA- and CT-populated DRMs by their sensitivity to SM depletion , and indicate the importance of SM for VacA association within lipid rafts . We next explored the possibility that VacA binds directly to SM , using an ELISA-based assay which has been extensively used for characterizing the binding of lysenin to SM [32] , [45] , [46] . Significantly more VacA was bound to wells coated with SM than to wells with PE , PI , or in wells not coated with lipids ( Figure 6A ) , similar to what was previously reported for lysenin [33] . VacA binding to SM was dose-dependent , saturable , and detectable at concentrations as low as 2 . 5 nM VacA ( Figure 6B ) . Consistent with previous reports [10] , [15] , VacA binding to HeLa or AZ-521 cells was not saturable and only slightly inhibited ( <20% ) in the presence of a molar excess of unlabeled VacA ( data not shown ) , which we speculate may be explained , at least in part , by the high abundance of SM in the outer leaflet plasma membrane . Notably , VacA did not bind to ceramide ( Figure 6A ) , which is a product of SMase C-mediated SM hydrolysis lacking the phosphorylcholine headgroup . However , VacA bound SM and PC to approximately the same extent , suggesting that the phosphorylcholine head group common to both SM and PC may be an important determinant for VacA binding . We discuss below possible reasons why VacA binds to PC in vitro , while PC was not found to confer cellular sensitivity to VacA ( Figure 1A and 1B ) . Finally , we investigated whether lysenin competitively inhibits VacA binding to SM-coated plates . Microtiter plates whose wells were coated with SM were incubated with VacA ( 10 nM ) alone or mixed with different molar ratios of recombinant Venus-lysenin [47] . These experiments revealed that a molar excess of Venus-lysenin inhibited VacA binding to SM in vitro in a dose-dependent fashion ( Figure 6C ) . In contrast , VacA cellular binding and vacuolation were only slightly inhibited ( <20% ) in the presence of a high molar excess ( >1000-fold ) of Venus-lysenin ( data not shown ) , and we speculated that the inability to completely antagonize VacA binding may be explained in part by the high levels of SM within the plasma membrane . Notably , lysenin was not found to competitively inhibit VacA binding to PC in vitro ( data not shown ) , which is consistent with previous reports that lysenin binds specifically to SM [32] , [48] .
By modulating important properties of eukaryotic cells , many toxins remodel the host environment to create a suitable niche for pathogenic organisms to colonize and persist during infection [14] . Many of the most potent toxins act upon intracellular targets . As the first step in cellular entry , intracellular-acting toxins bind to one or more plasma membrane surface receptors [7] . Notably , cells lacking toxin receptors are generally resistant to intoxication , underscoring the importance of the toxin-receptor complex [14] . Our results indicate that the important plasma membrane sphingolipid , SM , functions as a receptor for VacA . SM is the first plasma membrane component identified whose presence or absence is crucial for the sensitivity of epithelial cells to VacA , while at the same time dictating the extent to which the toxin binds to the cell surface . In contrast , although the plasma membrane proteins RPTP-α and RPTP-β confer cellular sensitivity to VacA and directly associate with the toxin in vitro [16]–[18] , the significance of these proteins for VacA interactions with the cell surface has not been established . Likewise , while VacA has been reported to interact with several potential membrane components in vitro , including various lipid preparations , heparan sulfate , and glycosphingolipids , [19]–[23] , [25] , [49] , [50] , the significance of these interactions on the cell surface for VacA function remains to be experimentally addressed . Our data indicate that VacA binds directly to SM in vitro ( Figure 6 ) . VacA also bound to PC in vitro , but not ceramide , suggesting that the phosphorylcholine head-group common to SM and PC may serve as a binding determinant for VacA . Surprisingly , incubation with exogenous PC did not increase the sensitivity of cells to VacA ( Figure 1A , B ) . Moreover , exogenous dipalmitoyl-phosphatidylcholine ( DPPC ) or distearoyl-phosphatidylcholine ( DSPC ) did not increase VacA binding to HeLa cells ( Figure S8 ) . Depleting plasma membrane PC with phospholipase D type VII , which cleaves PC to produce phosphatidic acid and choline , did not decrease VacA binding to HeLa cells ( data not shown ) . Neither SMase C nor SMase D pretreatment of monolayers detectably altered cellular PC levels ( Table S3 ) , indicating that decreased VacA cellular binding is unlikely to be due to contaminating PC-phospholipase C or phospholipase D activities within the SMase preparations used in these studies . Taken together , these data suggest that other differences between SM and PC not related to their common phosphorylcholine head group may be important for toxin vacuolation activity . Although SM and PC are both abundant in mammalian membranes [31] , fundamental differences between the acyl chains of these two lipids result in SM partitioning predominantly to tightly packed , liquid-ordered phase domains ( lipid-rafts ) , especially in the presence of cholesterol [30] , [51] , while PC is found predominantly in a liquid-disordered phase ( non-rafts ) [52] . Moreover , the molecular length of SM is significantly greater for SM than PC , and atomic force microscopy has revealed that SM protrudes out of the membrane , while PC is more embedded within the bulk membrane [52]–[54] , which we speculate could result in PC being less accessible for VacA binding . Future work to evaluate the affinity of VacA for SM , as well as to define the structure-activity relationships of the lipid and the structure-function relationships of VacA important for this interaction , will be important for establishing the molecular basis and degree of VacA selectivity for this sphingolipid . The propensity of SM to partition into lipid rafts may underlie , at least in part , the functional significance of this sphingolipid for VacA cellular intoxication . Several groups have demonstrated the importance of VacA association with lipid rafts for toxin-mediated cellular vacuolation [21] , [27]–[29] , but the molecular basis of raft association had been poorly understood previously . Here , we revealed that plasma membrane SM is critical for VacA association with lipid rafts ( Figure 5 ) . While it is not currently clear whether VacA binds directly to “pre-formed” raft domains , or whether VacA binding to SM induces the nucleation of new rafts , it is noteworthy that approximately 60% of total membrane SM has been estimated to partition normally into lipid rafts [55] , and SM-rich membrane domains have been reported as a discrete subclass of lipid rafts [47] , [48] . In considering a functional role for SM-rich rafts in VacA cellular intoxication , it is noteworthy that VacA association with lipid rafts precedes and is functionally important for the uptake of the toxin into cells [27] , [29] , [56] . In accordance , we found that HeLa cells depleted of plasma membrane SM internalized significantly less VacA compared to mock-treated cells ( Figure S9 ) . In contrast , CT and Tf were taken up to the same extent by SM-depleted and mock-treated cells ( Figure S9C ) , suggesting that the cellular uptake pathways of these two proteins remained fully functional after depletion of plasma membrane SM . How might the SM-dependent association of VacA with rafts promote toxin uptake ? VacA is internalized into HeLa and AGS cells by a novel Cdc42-dependent , pinocytic-like mechanism , involving association of the toxin with rafts rich in glycosylphosphatidylinositol ( GPI ) -anchored proteins , although GPI-anchored proteins were not found to directly bind VacA [9] , [11]–[13] . In considering potential roles for SM in VacA internalization , it is notable that Cdc42-dependent mechanisms of cell entry are regulated by levels of membrane sphingolipids such as SM , which are constantly recycled between the plasma membrane and several intracellular destinations [57] . One possibility , that remains to be determined , is that upon binding SM , VacA is taken up into cells by the same mechanism used for SM recycling from the cell surface [55] . A second possibility is that SM may function in concert with additional cell surface components , such as RPTP-β , to promote toxin entry into cells . It was recently reported that following the binding of VacA to AZ-521 cells , RPTP-β translocates from the [9] bulk membrane into lipid rafts [56] , suggesting the interesting possibility that rafts might function as platforms where SM and RPTP-β function together to promote toxin uptake . However , additional raft components may also be important for toxin function as well . GPI-anchored proteins are enriched within lipid raft microdomains , but while one study indicated that enzymatic removal of these proteins decreased VacA function , there were no detectable effects on VacA interactions with DRMs [15] . Cholesterol is also enriched within lipid rafts , and facilitates SM packing within DRMs [58]–[60] , and it is possible that cholesterol could function directly as a co-receptor for VacA cellular binding . However , we earlier reported that attempts to detect VacA interactions with cholesterol in vitro were unsuccessful [29] . In addition , several reports have indicated that extracting plasma membrane cholesterol with cyclodextrin compounds such as methyl-beta-cyclodextrin ( MβCD ) only partially reduces VacA association with DRMs and overall cell binding [27] , [29] , [56] . Although the membrane content of SM and cholesterol have been reported to be closely interdependent [61] , we found treatment of cells with SMase D ( 100 μM ) or SMase C ( 100 mU/mL ) decreased plasma membrane content by only approximately 10% and 20% respectively ( Figure S10 ) . These same treatments decreased total cellular cholesterol levels by only approximately 10% and 9% , respectively , as compared to treatment with MβCD ( 4 mg/mL ) , which decreased total cellular cholesterol by approximately 60% ( data not shown ) . Finally , exogenous cholesterol did not rescue VacA cellular vacuolation ( Figure S4 ) or binding ( Figure S7 ) in cells pretreated with SMase C . Based on these data , we suggest that rather than functioning as a VacA co-receptor , cholesterol may have an alternative function , such as contributing to the structural integrity of VacA-bound DRMs . A third possibility is that SM is critical for the initial interaction of VacA with rafts on the cell surface , but does not participate directly in the internalization of the toxin . However , such a mechanism may not be likely , as VacA has been reported to remain associated with rafts subsequent to initial entry into cells [11] . Several studies have indicated that H . pylori produces a SMase in vitro [62] , [63] . Currently it is unknown whether the H . pylori SMase is produced in vivo during infection , and if so , whether it is present at concentrations sufficient to deplete plasma membrane SM of gastric cells in vivo . Nonetheless , the possibility that H . pylori might generate both a SMase and SM-binding toxin in vivo suggests a potential post-translational mechanism by which H . pylori could regulate VacA activity during infection . SM is an important structural component of lipid rafts within the cell membrane , while at the same time serving an important functional role in cell signaling , as it is the parent compound of several lipid mediators [30] . Here we provide the first example of a bacterial virulence factor that exploits this important membrane lipid as a cellular receptor . These results also provide a conceptual framework for future efforts to understand the molecular basis of toxin interactions with SM on the cell surface , and to develop inhibitors to block the action of VacA on sensitive host cells .
H . pylori 60190 ( 49503; ATCC ) , were cultured , and VacA was purified as described in ref . [64] . HeLa ( CCL-2; ATCC ) and AZ-521 cells ( 3940; Japan Health Science Foundation ) were maintained in minimum essential medium ( MEM; Sigma ) . AGS cells ( CRL-1739; ATCC ) were grown in F-12K medium ( ATCC ) . Tissue culture media were supplemented with 2 mM glutamine , 100 U penicillin/mL , 1 mg streptomycin sulfate/mL ( Sigma ) , and 10% fetal bovine calf serum ( FBS; JRH Biosciences ) , and maintained at 37 °C in a humidified atmosphere under 5% CO2 . Recombinant SMase D [34] and Venus-lysenin [47] were expressed and purified as previously described in refs [65] , and [47] , respectively . VacA was activated as described [66] , and added to cells in the presence of 5 mM NH4Cl [67] . Cells were preincubated with the indicated concentrations of SM ( 860062; Avanti ) , phosphatidylcholine ( PC , 840053; Avanti ) , dipalmitoyl-phosphatidylcholine ( DPPC , 850355C; Avanti ) , or distearoyl-phosphatidylcholine ( DSPC , 850365C; Avanti ) , phosphatidylethanolamine ( PE , 840022; Avanti ) , phosphatidylinositol ( PI , 840042; Avanti ) , or ceramide ( ceram , 22244; Sigma ) for 1 h prior at to intoxication at 37 °C . Cells were never exposed to greater than 1% solvent ( methanol ) . In other experiments , monolayers were preincubated with the indicated concentrations of bacterial sphingomyelinase C ( SMase C; Sigma ) , SMase D , or MβCD ( Sigma ) , which alone did not cause a decrease in cell viability ( as indicated by propidium iodide uptake ) . Cells were mock treated with the same volume of solvent or PBS as added with lipid or SMase C , SMase D , or MβCD respectively . To inhibit de novo SM synthesis , cells were treated with or without Fumonisin B1 ( Sigma ) , which inhibits sphingosine N-acyltransferase [68] , or ( ± ) -threo-1-Phenyl-2-decanoylamino-3-morpholino-1-propanol ( PDMP; Sigma ) , which inhibits SM synthase at concentrations≥25 mM [69] . Prior to administration of VacA , cells were incubated for 24 h with or without Fumonisin B1 ( 50 μg/mL ) or PDMP ( 50 μM ) in MEM ( without FBS ) +0 . 1% BSA . Monolayers were visually examined using a Fisher Scientific MicroMaster inverted microscope , outfitted with a Nikon , Coolpix 43000 camera . For quantitative analysis , neutral red uptake was measured , as previously described [70] . Relative vacuolation was calculated by dividing neutral red uptake of lipid- or SMase-treated cells by neutral red uptake of cells incubated with VacA alone . For all experiments , neutral red uptake of mock-treated cells ( e . g . minus VacA ) was subtracted from the neutral red uptake of cells incubated with VacA . SM levels were measured using the Amplex Red Sphingomyelinase Assay Kit ( Invitrogen/Molecular Probes; Eugene , OR ) , according to manufacturer's instructions [71] . Plasma membrane SM was visualized by incubating cells at 4 °C with Venus-lysenin , and then visualized by DIC/epifluorescence microscopy . PC levels were measured using the Amplex Red Phosphatidylcholine-Specific Phospholipase C Assay Kit ( Invitrogen/Molecular Probes ) , according to manufacturer's instructions . Total cellular cholesterol levels were measured using the Amplex Red Cholesterol Estimation Kit ( Invitrogen/Molecular Probes ) , according to manufacturer's instructions . Plasma membrane cholesterol was evaluated by staining cells with Filipin III ( 50 μg/ml; Sigma ) , and analyzing by flow cytometry using UV laser excitation , as previously described [72] . As a standard quality control measure , cellular SM , PC , or cholesterol levels were quantified for every experiment involving depletion or supplementation of cellular SM , PC , or cholesterol . Purified VacA was conjugated with Alexa Fluor 488 , using the Alexa Fluor labeling kit ( Invitrogen ) according to the manufacturer's instructions . Labeling was experimentally determined to not alter VacA-mediated cellular vacuolation activity . Alexa Fluor 488 labeled-Tf and -CTB were from Invitrogen . Chamber slides were analyzed using a Delta Vision RT microscope ( Applied Precision ) , EX 490/20 and EM 528/38 , using an Olympus Plan Apo 60x oil objective with NA 1 . 42 and working distance of 0 . 17 mm . DIC images were collected for all fields . Images were processed using SoftWoRX Explorer Suite . Analytical flow cytometry was carried out using a Coulter EPICS XL-MCL™ flow cytometer equipped with a 70-μm nozzle , 488 nm line of an air-cooled argon-ion laser , and 400 mV output . The band pass filter used for detection of cell fluorescence was 525/10 nm . Cell analysis was standardized for scatter and fluorescence by using a suspension of fluorescent beads . Events were recorded on a log fluorescence scale and the geometric mean fluorescence values were determined using FCS Express 3 . 00 . 0311 V Lite Standalone . The data were gated so that only viable cells , which were readily evident by their forward and side scatter properties , were considered , while non-viable cells ( which demonstrated lower forward scatter ) were excluded from analysis . Experiments analyzed by DIC/epifluorescence microscopy were performed with monolayers of cells in 8-well chamber slides ( Nunc ) , while experiments analyzed by flow cytometry were carried out with suspended cells ( 106 cells/mL ) . As specified , cells were preincubated for 1 h in the presence of SM or SMase C . Cells pre-chilled to 4 °C were incubated for 1 h at 4 °C with activated Alexa Fluor 488-labeled VacA ( 10 or 100 nM ) , Alexa Fluor 488-labeled CTB ( 100 nM ) , Alexa Fluor 488-labeled Tf ( 60 nM ) , or Venus-lysenin ( 1 μM ) . For binding , cells were washed twice with PBS and fixed with paraformaldehyde ( 4% ) on ice . For internalization , cells were washed once with ice-cold PBS 7 . 2 , and then incubated at 37 °C in MEM plus 10% FBS ( prewarmed to 37 °C ) . After 1 h , cells were incubated for 5 min on ice with trypan blue ( 0 . 5% in PBS 7 . 2 ) , which is a membrane-impermeable , Alexa Fluor 488 fluorescence quenching agent [73] , [74] , and then analyzed immediately by flow cytometry . In preliminary experiments , we confirmed by microscopy that trypan blue quenched the fluorescence of membrane-bound , but not intracellular , Alexa Fluor 488-labeled VacA . Relative binding or internalization was calculated by dividing the geometric mean fluorescence of lipid- or SMase-treated cells by the geometric mean fluorescence of cells incubated with VacA alone . For all experiments , the geometric mean fluorescence of mock-treated cells ( e . g . minus VacA ) was subtracted from the geometric mean fluorescence of cells incubated with VacA . DRM preparation and analysis were performed as previously described [29] . DRMs were fractionated using OptiPrep ( Sigma ) density centrifugation , and proteins were detected by Western blot analysis using VacA rabbit antiserum ( Rockland Immunochemicals ) , anti-cholera rabbit antiserum ( Sigma ) , anti-Tf-R mouse antibodies ( Zymed ) , anti-rabbit or anti-mouse immunoglobulin G-alkaline phosphatase conjugates ( Sigma ) , and Lumi-phos substrate ( Pierce ) . VacA binding to lipids was assessed by ELISA , as described previously [32] , [46] , in lipid-coated Immulon 1B microtiter plates ( Fisher ) . VacA binding was detected using VacA antiserum , followed by HRP conjugated anti rabbit antibodies ( Rockland ) and signal was detected using Ultra TMB-ELISA substrate ( Pierce ) . Relative VacA binding was calculated by dividing the absorbance at 450 nm in the presence of lipid by that detected in the absence of lipid . Unless otherwise indicated , each experiment was performed at least three independent times . For those studies requiring statistical analysis , data are from a representative experiment conducted in triplicate . All statistical analyses were performed using Microsoft Excel ( Version 11 . 0 ) . Error bars represent standard deviations . All P values were calculated with the Student's t test using paired , two-tailed distribution . Asterisks indicate statistical significance ( P<0 . 05 ) .
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Sensitivity to toxins produced by pathogenic bacteria is largely dictated by the presence or absence of toxin receptors on the plasma membrane of host cells . VacA is an important toxin produced by the pathogenic bacterium Helicobacter pylori , which infects the human stomach and causes gastric ulcer disease and stomach cancer . VacA binds and enters human cells , and induces several changes resulting ultimately in the death of the intoxicated cells . However , the identity of the VacA receptor responsible for toxin binding and function has remained a topic of debate . In this paper , we demonstrate that sphingomyelin , a lipid on the surface of cells with important membrane structural and signaling properties , functions as a VacA receptor . We demonstrate that VacA binds to sphingomyelin , and that presence or absence of sphingomyelin on the plasma membrane dictates how much VacA binds to the cell surface , and therefore , how sensitive cells are to the toxin . The identification of sphingomyelin also provides a conceptual framework for how VacA may enter cells through specialized functional domains on the surface of cells . This is the first example of a bacterial toxin that exploits sphingomyelin as a receptor , and future work will focus on developing strategies to block VacA interactions with sphingomyelin , thereby protecting cells from the downstream consequences of toxin action .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis"
] |
2008
|
Sphingomyelin Functions as a Novel Receptor for Helicobacter pylori VacA
|
All pancreatic endocrine cell types arise from a common endocrine precursor cell population , yet the molecular mechanisms that establish and maintain the unique gene expression programs of each endocrine cell lineage have remained largely elusive . Such knowledge would improve our ability to correctly program or reprogram cells to adopt specific endocrine fates . Here , we show that the transcription factor Nkx6 . 1 is both necessary and sufficient to specify insulin-producing beta cells . Heritable expression of Nkx6 . 1 in endocrine precursors of mice is sufficient to respecify non-beta endocrine precursors towards the beta cell lineage , while endocrine precursor- or beta cell-specific inactivation of Nkx6 . 1 converts beta cells to alternative endocrine lineages . Remaining insulin+ cells in conditional Nkx6 . 1 mutants fail to express the beta cell transcription factors Pdx1 and MafA and ectopically express genes found in non-beta endocrine cells . By showing that Nkx6 . 1 binds to and represses the alpha cell determinant Arx , we identify Arx as a direct target of Nkx6 . 1 . Moreover , we demonstrate that Nkx6 . 1 and the Arx activator Isl1 regulate Arx transcription antagonistically , thus establishing competition between Isl1 and Nkx6 . 1 as a critical mechanism for determining alpha versus beta cell identity . Our findings establish Nkx6 . 1 as a beta cell programming factor and demonstrate that repression of alternative lineage programs is a fundamental principle by which beta cells are specified and maintained . Given the lack of Nkx6 . 1 expression and aberrant activation of non-beta endocrine hormones in human embryonic stem cell ( hESC ) –derived insulin+ cells , our study has significant implications for developing cell replacement therapies .
Innovative strategies for diabetes therapy aim to replace lost insulin-producing beta cells by reprogramming other cell types or by deriving beta cells from pluripotent cells . Ectopic expression of the transcription factors Pdx1 , Neurogenin 3 ( encoded by the Neurog3 gene; Ngn3 ) , and MafA has been shown to reprogram pancreatic exocrine acinar cells into beta-like cells [1] . Similarly , some success in reprogramming of liver cells into beta cells has been reported after misexpression of Pdx1 , Ngn3 , MafA , NeuroD , or Nkx6 . 1 [2]–[6] . Moreover , recent studies have demonstrated that pancreatic endocrine alpha cells can spontaneously convert into beta cells after near complete ablation of beta cells in adult mice [7] . Conversely , loss of beta cell identity and partial conversion of beta cells into other endocrine cell types has recently been identified as an early event marking beta cell failure in diabetes [8] . Thus , substantial plasticity exists between pancreatic cell types , and this plasticity could potentially be exploited to halt diabetes progression or to replenish beta cells in diabetic individuals . However , little is still known about the factors that control this plasticity . During embryonic development , all endocrine cell types are derived from a common endocrine precursor population marked by the transcription factor Ngn3 [9] , [10] . Ngn3 activity is required for the specification of all endocrine cells [11] and the expression of Arx and Pax4 , two transcription factors that control endocrine subtype choices downstream of Ngn3 . Arx-deficient mice display a loss of alpha cells and concomitant increase in beta and delta cells , while Pax4-deficiency results in the opposite phenotype of reduced beta and delta cells but increased alpha cells [12] , [13] . Strikingly , forced expression of Pax4 in endocrine precursors and their differentiated progeny imparts a beta-like cell identity to differentiating precursors , resulting in hyperplastic islets with an excess of beta-like cells at the expense of the other endocrine cell types [14] . However , despite their increased beta cell mass , mice misexpressing Pax4 eventually become diabetic and succumb prematurely , suggesting that sustained expression of Pax4 is not compatible with normal beta cell function . Since Pax4 is normally absent from beta cells and only transiently expressed in endocrine precursors during embryogenesis [15] , it is possible that proper beta cell development and maturation requires Pax4 downregulation . Similar to Pax4 , misexpression of Pdx1 in endocrine precursors has also been shown to favor a beta cell fate choice over other endocrine cell types [16] . Unlike ectopic Pax4 expression , forced expression of Pdx1 did not reduce the numbers of delta and PP cells , but selectively affected the ratio between beta and alpha cells . Therefore , Pdx1 activity appears to primarily control the alpha versus beta cell fate decision , which is consistent with its expression in both beta and delta cells [13] . Nkx2 . 2 has recently been identified as a beta cell maintenance factor and stabilizes beta cell fate by repressing the alpha cell fate determinant Arx [17] . While these studies have provided insight into the factors involved in endocrine cell type specification and maintenance , still little is known about how these factors interact to establish and maintain gene expression programs characteristic of each endocrine cell type . In particular , it is unclear which molecular mechanisms operate in beta cell precursors to ensure that alternative endocrine lineage programs are repressed , while beta cell-specific programs are activated . Given the simultaneous initiation of multiple endocrine subtype programs in one cell with current human embryonic stem cell ( hESC ) differentiation protocols [18] , [19] , such knowledge is critical for refining these protocols to support the differentiation of mature and functional beta cells in vitro . In addition to MafA and Mnx1 ( also called Hb9 ) [20]–[22] , in the adult pancreas Nkx6 . 1 is among the few transcription factors exclusively detected in beta cells . During development , Nkx6 . 1 is first expressed in multipotent pancreatic progenitors , where it specifies an endocrine identity by repressing the pre-acinar transcription factor Ptf1a [23] . At later developmental stages , Nkx6 . 1 expression persists in common progenitor cells for the ductal and endocrine cell lineages before becoming eventually restricted to the beta cell lineage [24] . Whether or not Nkx6 . 1 plays a role in beta cell specification and maintenance remains unknown , largely due to the lack of appropriate genetic models to study this question . Excessive early acinar cell specification and reduced numbers of Ngn3+ cells in Nkx6 . 1 null mutant mice preclude their utility for such studies . To determine the function of Nkx6 . 1 in endocrine cell type specification and beta cell maintenance , we generated novel genetic mouse models to conditionally inactivate or misexpress Nkx6 . 1 after endocrine precursors have been specified . Our studies reveal that Nkx6 . 1 is both necessary and sufficient to specify the beta cell lineage . Nkx6 . 1 prevents alpha cell specification in cooperation with Pdx1 by directly repressing Arx through competition with the Arx gene activator Isl1 . Furthermore , inactivation of Nkx6 . 1 in beta cells causes loss of beta cell identity and conversion into delta cells . Our findings identify Nkx6 . 1 as a beta cell programming factor and uncover a transcriptional network that initiates and maintains beta cell-specific gene expression programs , while repressing programs of alternative endocrine lineages .
During pancreatic development , Nkx6 . 1 is expressed in a subset of Ngn3+ cells and is then exclusively maintained in beta cells [25] , suggesting that the specification of non-beta endocrine cell types might require Nkx6 . 1 downregulation . To explore whether expression of Nkx6 . 1 in all , or at least the majority of , Ngn3+ cells is sufficient to allocate precursors to the beta cell lineage , we heritably expressed Nkx6 . 1 in Ngn3+ cells , utilizing a mouse line that allows for conditional overexpression of Nkx6 . 1 after expression of Cre recombinase ( Nkx6 . 1OE mice ) . In Nkx6 . 1OE mice , concomitant expression of Nkx6 . 1 and enhanced green fluorescent protein ( eGFP ) is induced by Cre recombinase-mediated excision of a lacZ expression cassette flanked by loxP sites . The Nkx6 . 1OE transgene design is analogous to the Z/EG transgene , in which Cre recombinase induces expression of GFP by recombining loxP sites flanking a lacZ cassette . Therefore , Z/EG mice were used as controls for the Nkx6 . 1OE strain ( Figure 1A , 1B ) . We induced transgene recombination with Ngn3-Cre and compared the relative contribution of recombined GFP+ cells to each of the five endocrine cell types in Ngn3-Cre;Nkx6 . 1OE and Ngn3-Cre;Z/EG control mice . Notably , due to mosaic expression of the transgenes , not all hormone+ cells expressed GFP . At postnatal day ( P ) 2 , 55 . 1±1 . 7% of the GFP+ cells expressed insulin in Ngn3-Cre;Z/EG control mice , while 86 . 1±2 . 5% of the GFP+ cells were insulin+ in Ngn3-Cre;Nkx6 . 1OE mice ( Figure 1C , 1H , 1M; P<0 . 001 ) , suggesting that Nkx6 . 1 favors a beta cell fate choice . Consistent with this notion , Nkx6 . 1-expressing endocrine precursor cells displayed a significantly decreased propensity to differentiate into glucagon+ , somatostatin+ , pancreatic polypeptide ( PP ) + , or ghrelin+ cells compared to endocrine precursor cells from control mice ( Figure 1D–1G , 1I–1M ) . Since Nkx6 . 1 expression did not affect cell replication , as shown by analysis of the proliferation marker Ki67 ( Figure 1N ) , or survival [23] , these data indicate that Nkx6 . 1 promotes beta cell differentiation from endocrine precursors at the expense of alternative endocrine fates . Similar to Nkx6 . 1 , conditional expression of Pax4 in mouse endocrine precursors results in beta cell specification at the expense of all other endocrine cell types [14] . The expression of Pax4 leads to oversized islets and is eventually accompanied by beta cell dysfunction and diabetes . To determine whether transgenic Nkx6 . 1 expression similarly causes islet and beta cell hyperplasia , we compared islet size in Ngn3-Cre;Nkx6 . 1OE and control mice at P2 and at 5 months of age . Consistent with our observation that Nkx6 . 1 overexpression in adult beta cells does not stimulate beta cell expansion [26] , Ngn3-Cre;Nkx6 . 1OE mice displayed normal islet cell mass ( Figure S1A , S1B ) . Furthermore , 5-month-old Ngn3-Cre;Nkx6 . 1OE mice exhibited normal glucose tolerance ( Figure S1C ) , showing that sustained expression of the Nkx6 . 1 transgene does not perturb glucose homeostasis . To next explore the extent of endocrine precursor reprograming and to assess the maturity of beta cells in Ngn3-Cre;Nkx6 . 1OE mice , we analyzed insulin+ progeny of targeted endocrine precursors for the expression of critical beta cell markers and possible ectopic expression of non-beta endocrine cell markers . As expected , insulin+GFP+ cells in Ngn3-Cre;Nkx6 . 1OE mice expressed the beta cell marker Pdx1 , MafA , and Pax6 at P2 , showing that transgenic Nkx6 . 1 expression in endocrine precursors and their progeny does not impair beta cell maturation ( Figure S1D–S1I ) . Next , to determine whether Nkx6 . 1 is sufficient to fully repress alternative endocrine lineage programs during endocrine cell differentiation , we analyzed insulin+ cells in Ngn3-Cre;Nkx6 . 1OE mice for expression of Arx , Brn4 , glucagon , and somatostatin . At P2 , targeted GFP+ cells in Ngn3-Cre;Nkx6 . 1OE mice were largely indistinguishable from their counterparts in control mice and rarely displayed coexpression of insulin with any of these non-beta endocrine markers ( Figure S1J–S1Q; arrowheads ) , suggesting that Nkx6 . 1 is effective in fully establishing a beta cell expression program . We next sought to determine whether Nkx6 . 1 acts by inducing cell fate conversion during differentiation of Ngn3+ endocrine precursors or by converting already differentiated non-beta endocrine cells . Since we observed residual glucagon+ , somatostatin+ , pancreatic polypeptide ( PP ) + , and ghrelin+ cells misexpressing Nkx6 . 1 at P2 ( Figure 1M ) , we first examined whether these cells convert into beta cells during postnatal life , as observed after Pdx1 expression in Ngn3+ cells [16] . Different from Ngn3-Cre;Pdx1OE mice , we found that targeted non-beta endocrine cells persisted in 5-month-old Ngn3-Cre;Nkx6 . 1OE mice ( Figure S2A–S2H ) . Notably , the endocrine cell type ratios observed in Ngn3-Cre;Nkx6 . 1OE mice at P2 are largely maintained at 5 months of age ( Figure S2I ) , suggesting that additional non-beta-to-beta cell fate conversion does not occur postnatally . To directly test whether forced expression of Nkx6 . 1 in differentiated alpha cells triggers their conversion into beta cells , we induced recombination of the Nkx6 . 1OE transgene with Glucagon-Cre ( Glc-Cre ) . Consistent with the persistence of targeted non-beta endocrine cells in adult Ngn3-Cre;Nkx6 . 1OE mice , we failed to observe insulin+ cells expressing GFP ( Figure S3 ) . These findings pinpoint Nkx6 . 1 beta cell programming activity to a period between the Ngn3+ state and activation of hormone gene expression . Since global loss of Nkx6 . 1 impairs the generation of Ngn3+ endocrine precursors [23] , it has remained unclear whether beta cell development requires Nkx6 . 1 activity downstream of Ngn3 . To investigate a potential requirement for Nkx6 . 1 in this process , we constructed a conditional mutant allele for Nkx6 . 1 by flanking exon 2 with loxP sites ( Figure S4A ) . Cre recombinase-mediated deletion of exon 2 eliminates a large portion of the DNA-binding homeodomain and additionally introduces a frameshift , resulting in three premature stop codons in exon 3 , which cause termination of translation [27] . Importantly , mice heterozygous or homozygous for the Nkx6 . 1flox ( Nkx6 . 1f ) allele show no abnormalities , suggesting that the floxed allele of Nkx6 . 1 is fully functional . To verify that Cre-mediated recombination of the Nkx6 . 1f allele generates a null allele , we intercrossed Nkx6 . 1f/+;Prm1-Cre and Nkx6 . 1+/− mice to induce recombination of the Nkx6 . 1f allele in germ cells ( Nkx6 . 1Δf/− allele ) . As expected , these Nkx6 . 1Δf/− mice phenocopied Nkx6 . 1 germline null mutant mice [28] , and died immediately after birth , manifesting paralysis of their upper extremities and asphyxia ( Figure S4B ) . Western blot analysis of Nkx6 . 1 protein expression in pancreata from Nkx6 . 1Δf/− embryos at embryonic day ( e ) 14 . 5 showed a complete absence of Nkx6 . 1 ( Figure S4C ) . The pancreas of Nkx6 . 1Δf/− embryos was of normal size , but displayed a drastic reduction in insulin+ cells at e18 . 5 ( Figure S4D–S4F ) , phenocopying Nkx6 . 1−/− mice [29] . To determine whether Nkx6 . 1 is required for beta cell formation from Ngn3+ precursors , we utilized Ngn3-Cre to simultaneously induce recombination of the Nkx6 . 1f allele and the Z/EG reporter transgene for stable lineage tracing of all progeny derived from Ngn3+ cells . In Nkx6 . 1f/−;Ngn3-Cre;Z/EG embryos , Cre recombines the loxP sites in both the Nkx6 . 1f allele and the Z/EG transgene to produce cells that are deficient for Nkx6 . 1 and express eGFP ( Figure 2A , 2B ) . At e15 . 5 , when Ngn3 expression peaks [11] , Nkx6 . 1 was detected in a large subset of Ngn3+ and GFP+ cells derived from the Ngn3-expressing domain in Ngn3-Cre;Z/EG control embryos ( Figure 2C ) . In Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice , GFP+ cells were devoid of Nkx6 . 1 ( Figure 2D , Figure S5B ) , showing the Ngn3-Cre transgene efficiently deletes Nkx6 . 1 in Ngn3+ cells and their progeny . Ngn3 was similarly expressed in Nkx6 . 1f/−;Ngn3-Cre;Z/EG and control embryos ( Figure 2C , 2D ) , demonstrating that loss of Nkx6 . 1 in endocrine precursors does not affect Ngn3 expression . Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice were born at the expected Mendelian frequency , but died within the first few days after birth from dehydration and hyperglycemia; a phenotype indicative of a beta cell defect . To determine whether loss of Nkx6 . 1 affects the cell fate choice of endocrine precursors , we analyzed the fate of Ngn3+ cells in Ngn3-Cre;Z/EG and Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice . Based on our previous finding that Nkx6 . 1 prevents acinar cell fate specification [23] , we first examined whether loss of Nkx6 . 1 in Ngn3+ cells allocates endocrine precursors to the acinar lineage . The ability of Ngn3+ cells to undergo endocrine-to-acinar cell fate conversion has been previously demonstrated in conditions of reduced Ngn3 gene dosage or impaired Notch signaling activity [30] , [31] . At P2 , we found GFP+ cells to be exclusively restricted to endocrine islets in both Ngn3-Cre;Z/EG and Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ( Figure 2E , 2F ) , revealing that Nkx6 . 1 deletion in Ngn3+ cells does not cause endocrine-to-acinar fate conversion . Thus , unlike multipotent pancreatic progenitors , which adopt an acinar cell identity in the absence of Nkx6 . 1 activity [23] , Ngn3+ endocrine precursors are no longer competent to activate acinar gene expression programs after deletion of Nkx6 . 1 . Because Nkx6 . 1-deficient endocrine precursors differentiate into endocrine cells ( Figure 2F ) , we next sought to determine whether loss of Nkx6 . 1 affects the relative proportion of the different endocrine cell types arising from Ngn3+ cells . To examine the endocrine cell fate choice of Ngn3+ cells , we quantified how many of the recombined Ngn3-expressing cells were allocated to each endocrine cell lineage by co-staining for GFP as a lineage marker of Ngn3-cell progeny together with each of the five hormones individually . At P2 , 55 . 1±1 . 7% of recombined cells were insulin+ in Ngn3-Cre;Z/EG control mice , while only 16 . 6±5 . 4% of recombined cells were insulin+ in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ( Figure 2G , 2L , 2Q; P<0 . 01 ) , suggesting that Nkx6 . 1-deficient precursors have a lower propensity to differentiate into insulin+ cells . To test whether Nkx6 . 1-deficient Ngn3+ cells instead adopt non-beta endocrine cell identities , we compared the percentage of Ngn3+ cells that contributed to each non-beta endocrine cell lineage in Ngn3-Cre;Z/EG and Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice . Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice at P2 exhibited significantly more glucagon+GFP+ ( 35 . 9±0 . 8% vs . 24 . 6±2 . 0%; P<0 . 05 ) , somatostatin+GFP+ ( 16 . 6±2 . 1% vs . 7 . 2±0 . 2%; P<0 . 05 ) , PP+GFP+ ( 23 . 3±4 . 1% vs . 7 . 5±0 . 1%; P<0 . 025 ) , and ghrelin+GFP+ ( 11 . 1±1 . 5% vs . 5 . 5±0 . 7%; P<0 . 05 ) cells than Ngn3-Cre;Z/EG control mice ( Figure 2H–2K , 2M–2Q ) . Together , these findings suggest that endocrine precursor cells require Nkx6 . 1 activity to differentiate into beta cells and that Nkx6 . 1 prevents precursors from adopting non-beta endocrine fates . To ascertain that the differences in islet cell type composition between Ngn3-Cre;Z/EG and Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice are indeed the result of preferential precursor cell fate choices and not due to different proliferation or survival rates , we analyzed GFP+ cells in each genotype for their rates of proliferation and apoptosis at P2 . Both insulin+GFP+ and insulin−GFP+ endocrine cells displayed similar proliferation and apoptotic rates in Ngn3-Cre;Z/EG and Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ( Figure 2R , 2S ) , demonstrating that loss of Nkx6 . 1 does not affect proliferation or survival . Together , we show that Nkx6 . 1 controls the fate choice between beta and non-beta endocrine cell lineages in endocrine precursor cells , without favoring any one non-beta endocrine cell type in particular . To investigate whether Nkx6 . 1 mediates beta cell specification downstream of Ngn3 by regulating transcription factors necessary for beta cell development , we analyzed expression of the beta cell progenitor markers Pax4 , MafB , and Pdx1 in Ngn3-Cre;Z/EG and Nkx6 . 1f/−;Ngn3-Cre;Z/EG embryos during the peak period of beta cell differentiation at e15 . 5 . Confirming our previous findings in Nkx6 . 1 null mutant embryos [24] , Pax4 expression was not affected in Nkx6 . 1f/−;Ngn3-Cre;Z/EG embryos ( Figure 3A , 3B; 9 . 6% of GFP+ cells expressed Pax4 in control mice vs . 10 . 1% in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ) . In contrast , the marker of newly-born alpha and beta cells , MafB , was absent from the majority of targeted cells in Nkx6 . 1f/−;Ngn3-Cre;Z/EG embryos ( Figure 3C , 3D; 76 . 2% of GFP+ cells expressed MafB in control mice vs . 20 . 7% in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ) , suggesting that MafB , similar to its homolog MafA [20] , is controlled by Nkx6 . 1 . Likewise , while a large percentage of Ngn3+ cell progeny were Pdx1+ in control embryos , only a small percentage expressed Pdx1 in Nkx6 . 1-deficient embryos ( Figure 3E , 3F; 81 . 0% of GFP+ cells expressed Pdx1 in control mice vs . 18 . 6% of GFP+ cells in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ) . Thus , Nkx6 . 1 controls the expression of MafB and Pdx1 , but not Pax4 in embryonic beta cell precursors . Although reduced in numbers , we still observed targeted GFP+ cells expressing insulin in neonatal Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice , raising the question of whether these Nkx6 . 1-deficient insulin+ cells properly differentiate into beta cells . To investigate how lack of Nkx6 . 1 affects beta cell gene expression programs , we analyzed insulin+ cells in neonatal Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice for the expression of Pax6 , Pdx1 , and MafA . Expression of the islet cell marker Pax6 was not affected in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ( Figure S5C , S5D; 68 . 4% of insulin+GFP+ cells expressed Pax6 in control mice vs . 72 . 7% in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ) . By contrast , confirming our findings at embryonic stages ( Figure 3E , 3F ) , Pdx1 expression was markedly reduced in Nkx6 . 1-deficient insulin+ cells at P2 ( Figure 4A , 4B; 76 . 5% of insulin+GFP+ cells expressed Pdx1 in control mice vs . 30 . 2% in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ) . Similarly , the mature beta cell marker MafA was absent from insulin+ cells in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ( Figure 4C , 4D; 79 . 3% of insulin+GFP+ cells expressed MafA in control mice vs . 0% in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ) . This demonstrates that beta cells require Nkx6 . 1 activity during their differentiation to initiate MafA expression and to maintain high levels of Pdx1 . These findings are consistent with the phenotype of Nkx6 . 1 null mutant mice , in which limited numbers of insulin+ cells lacking MafA are observed [20] , [24] . We conclude that insulin expression can still be initiated in the absence of Nkx6 . 1 , but that these insulin+ cells lack key features of normal beta cells . The lack of beta cell-specific markers in Nkx6 . 1-deficient insulin+ cells raised the question of whether these insulin+ cells also carry features of alternative endocrine lineages . To determine whether loss of Nkx6 . 1 in endocrine precursors results in the activation of mixed endocrine gene expression programs , we analyzed insulin+ cells in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice for coexpression of the alpha cell lineage determinants Arx and Brn4 . As expected , in Ngn3-Cre;Z/EG control mice virtually no colocalization of Arx and Brn4 with insulin was observed at P2 ( Figure 4E , 4G; 0 . 2% of insulin+GFP+ cells expressed Arx and 0% Brn4 in control mice ) . In contrast , a subset of recombined insulin+GFP+ cells in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice also expressed Arx and Brn4 ( Figure 4F , 4H; arrowheads; 26 . 1% of insulin+GFP+ cells expressed Arx and 26 . 5% Brn4 in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ) , showing aberrant activation of alpha cell differentiation genes . Notably , Nkx6 . 1 deletion or misexpression did not affect Arx expression in the immediate progeny of endocrine precursors at e15 . 5 ( Figure S6A–S6D ) , pinpointing Nkx6 . 1-mediated regulation of Arx to a time window between e15 . 5 and birth . At P2 , loss of Nkx6 . 1 activity was also associated with aberrant expression of glucagon in insulin+ cells ( Figure 4I , 4J; arrowheads in J; 0 . 5% of insulin+GFP+ cells expressed glucagon in control mice vs . 29 . 8% in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ) . Moreover , we found that many of the targeted insulin+ cells ectopically expressed somatostatin in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ( Figure 4K , 4L; arrowheads in L; 0% of insulin+GFP+ cells expressed somatostatin in control mice vs . 19 . 6% in Nkx6 . 1f/−;Ngn3-Cre;Z/EG mice ) . These findings demonstrate that Nkx6 . 1 is critical for repressing alternative endocrine lineage programs and that beta cell-specific programs can only be induced to a limited extent when Nkx6 . 1 is lost . Previous studies have shown that the number of glucagon+ cells is increased in Pdx1 heterozygous mutant mice [32] , [33] . Furthermore , beta cells lose Nkx6 . 1 expression upon Pdx1 deletion in beta cells [34] . Combined with our observation that Nkx6 . 1 maintains Pdx1 expression during beta cell differentiation , these findings raise the possibility that Pdx1 and Nkx6 . 1 cooperate through a positive feedback loop to establish and maintain beta cell identity and to repress non-beta endocrine lineage programs . To test this idea , we analyzed wild type , Nkx6 . 1f/+;Ngn3-Cre , Pdx1+/− , and compound heterozygous Nkx6 . 1f/+;Ngn3-Cre;Pdx1+/− mice for the ectopic expression of non-beta endocrine hormones in insulin+ cells at P2 . While we saw no coexpression of somatostatin or PP with insulin in any of the four genotypes ( Figure 5A–5D; data not shown ) , glucagon and insulin co-positive cells were occasionally detected in all genotypes , including wild type mice ( Figure 5A–5D , 5I ) . Quantification of the percentage of insulin+ cells also expressing glucagon revealed significantly more dual hormone-positive cells in compound heterozygous Ngn3-Cre;Nkx6 . 1f/+;Pdx1+/− mice than in either single heterozygous mutant or in wild type mice ( Figure 5A–5D , 5I ) . As previously reported [32] , [33] , Pdx1+/− mice displayed an increase in the glucagon to insulin cell ratio that was also seen in compound heterozygous Ngn3-Cre;Nkx6 . 1f/+;Pdx1+/− but not in Nkx6 . 1f/+;Ngn3-Cre mice ( Figure 5J ) . Thus , haploinsufficiency for Pdx1 but not Nkx6 . 1 increases alpha cell numbers , which may reflect a non-cell autonomous effect on alpha cell proliferation , as recently shown in a mouse model of conditional deletion of Pdx1 in beta cells [35] . To determine whether the mixed lineage identity of insulin+glucagon+ cells is associated with the expression of Arx , we co-stained pancreatic sections of mice at P2 from all genotypes for insulin , glucagon , and Arx . The majority of insulin+glucagon+ cells expressed Arx in all genotypes , although occasional insulin+glucagon+Arx− cells were also detected ( Figure 5E–5H; arrowheads ) . The observed increase in cells exhibiting mixed alpha/beta cell identity and Arx expression in Nkx6 . 1/Pdx1 compound heterozygous mice supports the notion that Pdx1 and Nkx6 . 1 cooperate in beta cell fate specification by preventing activation of alpha cell-specific gene expression programs during endocrine cell differentiation . To further explore how Nkx6 . 1 prevents endocrine precursors from adopting alpha cell identity , we examined the relationship of the Nkx6 . 1 and Arx expression domains in progeny of Ngn3-expressing cells during development . Consistent with the dependence of Arx expression on Ngn3 [12] , Arx was confined to a domain that marks descendants of Ngn3-expressing cells ( Figure 6A′″ , 6B′″; note , occasional Arx+GFP− cells in A and B can be explained by mosaic expression of the Z/EG and/or Ngn3-Cre transgenes ) . At e14 . 5 , preceding the onset of the major wave of beta cell differentiation , the majority of Arx+ cells also expressed Nkx6 . 1 and Arx+Nkx6 . 1− cells were rare ( Figure 6A; arrowheads ) . By e16 . 5 , however , when large numbers of beta cells arise [36] , GFP+ cells seldom coexpressed Nkx6 . 1 and Arx ( Figure 6B; arrowheads ) . Thus , endocrine precursors initially activate Nkx6 . 1 and Arx concomitantly , but their expression domains become mutually exclusive during beta cell differentiation . Together with our finding that Nkx6 . 1 regulates Arx during this time window , the observed expression pattern of Arx and Nkx6 . 1 raised the possibility that Nkx6 . 1 functions as a transcriptional repressor of Arx . To explore this hypothesis , we tested whether Nkx6 . 1 occupies Arx regulatory sequences and is capable of repressing Arx transcription . We identified two conserved Nkx6 . 1 binding motifs within 5 kb of the 5′ end flanking region from the Arx transcriptional start site ( Figure 6C; site A and B ) as well as 12 potential Nkx6 . 1 binding sites located in two previously characterized enhancers within the third intron of the Arx genomic sequence ( Figure 6C; site C , Re1 ) and the 3′ flanking region ( Figure 6C; site D , Re2 ) [37] . Recently , the Arx Re1 and Re2 enhancers have been shown to be required for Isl1-mediated activation of Arx in alpha cells , suggesting that these two enhancers are critical for Arx transcription [37] . Chromatin immunoprecipitation ( ChIP ) analyses for Nkx6 . 1 revealed that Nkx6 . 1 directly and specifically associates with the Arx Re1 enhancer ( site C ) in the Min6 beta cell line ( Figure 6D ) and in embryonic endocrine precursors isolated by fluorescence-activated cell sorting ( FACS ) from Ngn3eGFP/+ embryonic pancreata at e15 . 5 ( Figure 6E ) . Discordant with a previous report , which reported binding of Nkx6 . 1 to site B in a beta cell line [17] , no association was observed with the other sites ( sites A , B , or D ) containing Nkx6 . 1 motifs ( Figure 6D ) . Transfection of the αTC1–6 alpha cell line with an expression plasmid for Nkx6 . 1 and a luciferase reporter construct containing the Arx Re1 enhancer sequence revealed that Nkx6 . 1 significantly reduced reporter gene activity ( Figure 6F ) . Confirming previous findings [37] , transfection of an expression plasmid for Isl1 activated the Re1 enhancer ( Figure 6G ) . Co-transfection of CMV-Nkx6 . 1 abolished the ability of Isl1 to activate the Re1 enhancer in a dosage-dependent manner , showing that Nkx6 . 1 and Isl1 regulate Arx antagonistically through competition at the Re1 enhancer . However , in the presence of Nkx6 . 1 , addition of CMV-Isl1 was not sufficient to revert Nkx6 . 1-mediated repression ( Figure 6G ) , indicating dominance of Nkx6 . 1 repressive over Isl1 activator activity . While our experiments show that Nkx6 . 1 is able to repress the Arx Re1 enhancer in alpha cell lines , Nkx6 . 1 cannot evoke an alpha-to-beta cell fate change when misexpressed in differentiated alpha cells ( Figure S3 ) . Thus , Nkx6 . 1-dependent Arx repression through the Re1 enhancer appears to be functionally most relevant during endocrine cell type specification , when Nkx6 . 1 prevents initiation of Arx expression ( Figure 4E , 4F ) . It has recently been shown that Nkx2 . 2 is an obligatory repressor of Arx in differentiated beta cells and that the absence of Nkx2 . 2 repressor activity causes beta-to-alpha cell conversion in mice [17] . Since Nkx2 . 2 is expressed in both beta and alpha cells , it has been speculated that beta cell-specific repression of Arx might depend on Nkx6 . 1 [17] . To directly test this hypothesis , we deleted Nkx6 . 1 selectively in beta cells , using the rat insulin promoter II ( RIP ) -Cre transgene to recombine the Nkx6 . 1f allele and the Rosa26 ( R26 ) -YFP reporter allele . As expected , the YFP lineage label was largely confined to beta cells in control Nkx6 . 1f/+;RIP-Cre;R26-YFP mice at 6 weeks of age ( Figure 7A–7E , 7O ) . In striking contrast , only a few Nkx6 . 1-deficient , YFP-labeled cells expressed insulin in Nkx6 . 1f/−;RIP-Cre;R26-YFP mice ( Figure 7F , 7G , 7O ) , suggesting that cells that once activated the insulin promoter no longer expressed insulin . Analysis of YFP expression in conjunction with glucagon , somatostatin , and PP revealed that Nkx6 . 1-deficient beta cells adopted delta cell identity , but did not convert into alpha or PP cells ( Figure 7H–7J , 7O ) . Thus , loss of Nkx6 . 1 in differentiated beta cells no longer causes activation of glucagon and PP , as observed after Nkx6 . 1 inactivation in Ngn3+ endocrine precursors . These findings suggest that Nkx6 . 1 is necessary to repress delta cell-specific genes in beta cells , but that expression of alpha and PP cell-specific genes are inhibited through an Nkx6 . 1-independent mechanism once beta cells have formed . To further test whether Nkx6 . 1 deficiency in beta cells could lead to partial activation of an alpha cell gene expression program , we examined YFP+ cells for Arx expression . YFP+ cells rarely expressed Arx in both control and Nkx6 . 1f/−;RIP-Cre;R26-YFP mice ( Figure 7K , 7M ) , demonstrating that Nkx6 . 1 is no longer necessary for Arx repression after beta cells have differentiated . Likewise , Pdx1 expression in adult islet cells was Nkx6 . 1-independent , as somatostatin+ cells that arose from Nkx6 . 1-deficient insulin-expressing cells were Pdx1+ ( Figure 7L , 7N ) . Our data reveal that gene regulation by Nkx6 . 1 is highly context-dependent . While Nkx6 . 1 is necessary for Arx repression and Pdx1 activation in beta cell precursors , both genes are regulated by Nkx6 . 1-independent mechanisms in mature beta cells .
We found that forced expression of Nkx6 . 1 allocates endocrine precursors to the beta cell lineage . While a similar activity has been described for Pdx1 [16] , Nkx6 . 1 and Pdx1 display different kinetics of beta cell programming . As evidenced by cells coexpressing insulin , glucagon , and Arx at birth [16] , forced expression of Pdx1 in endocrine precursor cells initially produces cells with mixed alpha and beta cell identity . In contrast to Ngn3-Cre;Pdx1OE mice , we rarely observed cells exhibiting both alpha and beta cell features in neonatal Ngn3-Cre;Nkx6 . 1OE mice , suggesting that Nkx6 . 1 plays a critical role early during cell fate specification of endocrine precursors . This notion is consistent with our finding that Nkx6 . 1 acts as a direct transcriptional repressor of the Arx gene . While Pax4 has also been shown to act as a direct repressor of Arx [38] , our observation that Pax4 expression is not affected by Nkx6 . 1 ablation suggests that Pax4 alone is not sufficient to repress Arx in beta cell precursors . Given the dependence of Nkx6 . 1 expression on Pax4 activity [15] , it is possible that the observed derepression of Arx and glucagon in Pax4 mutant mice [12] , [38] is a consequence of Nkx6 . 1-deficiency . In addition to repressing alpha cell-specific genes through the regulation of Arx , Nkx6 . 1 also reallocated delta , PP , and epsilon cell precursors to the beta cell lineage . Likewise , inactivation of Nkx6 . 1 in endocrine precursors resulted in increased production of cells of all non-beta endocrine cell types and ectopic expression of non-beta cell hormones in insulin+ cells . The phenotype observed upon conditional activation or deletion of Nkx6 . 1 in endocrine precursors identifies Nkx6 . 1 as a potent general repressor of non-beta endocrine gene expression programs . While our study establishes Nkx6 . 1 as a direct repressor of the alpha cell fate determinant Arx , Nkx6 . 1 likely represses additional cell fate determinants critical for the specification of delta , PP , and epsilon cells . As little is known about the transcription factors mediating non-beta endocrine cell fate choices , identification of additional direct target genes for Nkx6 . 1 in endocrine cell fate specification will have to await future studies . Similar to Nkx6 . 1 , forced expression of Pax4 also conferred beta cell identity to precursors of all endocrine cell lineages [14] . However , in contrast to Pax4 misexpressing mice [14] , we did not observe oversized islets or diabetes in adult mice misexpressing Nkx6 . 1 . Our observations in Ngn3-Cre;Nkx6 . 1OE mice are consistent with our previous study , showing that transgenic overexpression of Nkx6 . 1 in beta cells does not stimulate beta cell proliferation or perturb beta cell function [26] . Thus , despite their shared property as a direct repressor of Arx [38] , Nkx6 . 1 and Pax4 must also have distinct targets in endocrine cells . The lack of adverse effects on beta cell function makes Nkx6 . 1 an excellent candidate for beta cell programming strategies . Unlike Pax4 expression , which we found to be independent of Nkx6 . 1 , maintenance of Pdx1 expression downstream of Ngn3 required Nkx6 . 1 activity . Previous genetic studies in mice have shown that the expression of Nkx6 . 1 in beta cells also depends on Pdx1 activity [34] , suggesting that these two “pro-beta” transcription factors reinforce each other's expression . Since both Pdx1 and Nkx6 . 1 control the expression of critical genes for beta cell function , such as MafA and Glut2 ( [20] , [25] , [34]; this study ) , the positive regulatory loop between Pdx1 and Nkx6 . 1 might be critical for initiating and stabilizing beta cell-specific gene expression programs during endocrine cell differentiation . The mutual reinforcement in gene expression between these two transcription factors also explains why the combined deletion of one copy of Pdx1 and Nkx6 . 1 was sufficient to destabilize the beta cell fate choice and to cause ectopic expression of glucagon in insulin+ cells . Nkx6 . 1 could regulate Pdx1 either directly or indirectly by repressing an inhibitor of Pdx1 expression . Since Nkx6 . 1 directly represses Arx and Arx has previously been shown to repress both Pdx1 and Nkx6 . 1 [39] , loss of Pdx1 in Nkx6 . 1-deficient precursors is most likely the consequence of Arx derepression ( Figure 8B ) . We found that expression of Nkx6 . 1 did not force all Ngn3+ cells to differentiate into beta cells and that small numbers of alpha , delta , PP , and epsilon cells expressing Nkx6 . 1 persisted during adulthood . This implies that the competence of precursors to adopt a beta cell fate upon Nkx6 . 1 activation is limited to a short period during development when cells are still plastic and lineage-specific gene expression programs have not been fully established . Our observation that misexpression of Nkx6 . 1 in alpha cells failed to induce conversion of alpha into beta cells supports the notion that cells quickly lose the competence to respond to Nkx6 . 1 repressive cues as they undergo endocrine differentiation . Our study shows that Nkx6 . 1 occupies the Arx enhancer in beta cells ( Figure 6D ) , but unlike its inactivation in endocrine precursors , Nkx6 . 1 inactivation in beta cells does not activate Arx . This finding suggests that Nkx6 . 1 is only capable of repressing Arx during endocrine cell differentiation . One potential mechanism that could account for Nkx6 . 1-independent Arx repression in beta cells is DNA methylation , which has recently been shown to occur at the Arx locus [40] . Interestingly , deletion of the DNA methyltransferase Dnmt3a or Dnmt1 in beta cells results in Arx derepression and spontaneous conversion of beta into alpha cells [17] , [40] , suggesting that DNA methylation alone is sufficient to keep Arx repressed in beta cells . Dnmt3a is recruited to Arx by Nkx2 . 2 and loss of Nkx2 . 2 repressor activity causes spontaneous conversion of beta into alpha cells [17] , demonstrating that Arx expression in beta cells can be readily induced when Nkx2 . 2 repressor function is removed . Whereas Nkx2 . 2 forms a complex with Dnmt3a in the 5′ regulatory region of Arx [17] , we show that Nkx6 . 1 occupies an intronic Arx enhancer , where it competes with Isl1 and prevents Isl1 from activating Arx . Thus , it appears that DNA methylation-dependent repression of Arx is particularly important for keeping Arx repressed in beta cells , whereas Nkx6 . 1 prevents Arx activation in differentiating beta cell precursors . Overall , the role of DNA methylation in restraining cell plasticity during development is still poorly understood . Knowledge of how islet cell type-specific genes are epigenetically modified as cells differentiate and how this process can be reversed will prove important for devising effective cell programming and reprogramming strategies . Our study highlights the importance of Nkx6 . 1 as a beta cell programming factor during endocrine cell differentiation and shows that insulin expression can be initiated independent of Nkx6 . 1 and Pdx1 . Strikingly , the insulin+Nkx6 . 1−Pdx1− cells observed after conditional inactivation of Nkx6 . 1 in Ngn3+ cells display a similar molecular profile as insulin+ cells generated in vitro with current hESC differentiation protocols . Like Nkx6 . 1-deficient insulin+ cells in Nkx6 . 1f/−;Ngn3-Cre mice , hESC-derived insulin+ cells are polyhormonal and fail to express Nkx6 . 1 , Pdx1 , MafA , and critical glucose transporters ( [18] , [19]; Sander laboratory , unpublished data ) , which suggests that Nkx6 . 1 is required for complete beta cell programming in mice and humans . Our studies now pave the way for exploring the effectiveness of Nkx6 . 1 in ( re ) -programming strategies to generate functional beta cells for diabetes therapy .
CAG-Bgeo , -Nkx6 . 1 , -eGFP ( Nkx6 . 1OE ) [23] , Nkx6 . 1+/− [29] , Pdx1+/− [41] , Neurog3eGFP [42] , Prm1-Cre [43] , Ngn3-Cre [44] , RIP-Cre [45] , Rosa26-YFP [46] , CAG-Bgeo , -eGFP ( Z/EG ) [47] , and Glc-Cre mice [48] have been previously described . To create the Nkx6 . 1flox ( Nkx6 . 1f ) allele , a targeting vector consisting of two loxP sites inserted into the first and second introns of Nkx6 . 1 was generated ( Figure S4A ) . The herpes simplex virus-thymidine kinase gene was placed outside of the Nkx6 . 1 gene homology region for negative selection . After electroporation of 129S6-derived mouse embryonic stem cells , 375 clones survived neomycinR selection . Southern blotting identified 14 clones as correctly targeted . Two clones carrying the Nkx6 . 1flox-PGK-Neo allele were independently injected in mouse blastocysts , and chimeric mice bred with C57BL/6J mice for germline transmission screening . The FRT-flanked neomycinR gene in intron 2 was subsequently removed by crossing Nkx6 . 1flox-PGK-Neo mice with ActB-FlpE mice ( JAX; more information at http://www . mmrrc . org/strains/29994/029994 . html ) . Midday on the day of vaginal plug appearance was considered e0 . 5 . Glucose tolerance tests were performed as previously described [26] . Tissue preparation , immunofluorescence and TUNEL staining were performed as previously described [49] . For detection of nuclear antigens , antigen retrieval was performed in pH 6 . 0 citrate buffer and sections were permeabilized in 0 . 15% Triton X-100 in PBS . The following primary antibodies were used at the given dilutions: mouse anti-insulin ( Sigma ) , 1∶5000; guinea pig anti-insulin ( Dako ) , 1∶2000; guinea pig anti-glucagon ( Sigma ) , 1∶2000; rabbit anti-PP ( Dako ) , 1∶2000; goat anti-ghrelin ( Santa Cruz ) , 1∶1000; rabbit anti-somatostatin ( Dako ) , 1∶3000; goat anti-chromogranin A ( Santa Cruz ) , 1∶500; rat anti-GFP ( C . Kioussi ) , 1∶1000; mouse anti-Nkx6 . 1 ( BCBC clone #2023; against C-terminal part of Nkx6 . 1 ) , 1∶500; guinea pig anti-Ngn3 [24] , 1∶2000; rabbit anti-Pax6 ( Chemicon ) , 1∶1000; rabbit anti-Brn4 ( M . Rosenfeld ) , 1∶500; rabbit anti-Ki67 ( Lab Vision ) , 1∶500; rabbit anti-MafB ( Bethyl Labs ) , 1∶1000; rabbit anti-MafA ( Bethyl Labs ) , 1∶1000; rabbit anti-Pax4 ( B . Sosa-Pineda ) , 1∶100; rabbit anti-Arx ( P . Collombat ) , 1∶500; rabbit anti-Arx ( K . Morohashi ) , 1∶250; guinea-pig anti-Pdx1 ( C . Wright ) , 1∶10 , 000 . Staining with antibodies raised in mice was conducted using the M . O . M . Kit ( Vector Labs ) in conjunction with streptavidin-conjugated secondary antibodies ( Jackson ImmunoResearch ) . When necessary , nuclei were counterstained with Hoechst 33342 ( Invitrogen ) at 10 µg/ml . Primary antibodies were detected with donkey-raised secondary antibodies conjugated to Cy3 , Cy5 , DyLight488 ( Jackson ImmunoResearch ) or Alexa488 ( Molecular Probes ) at 1∶1500 dilution ( 1∶500 for Cy5 ) . ApoTome images were captured on a Zeiss Axio Observer Z1 microscope with Zeiss AxioVision 4 . 8 and figures prepared with Adobe Photoshop/Illustrator CS4 . Where necessary , the Cy5 channel was pseudo-colored white . Images were processed in accordance with the Journal of Cell Biology figure manipulation guidelines . For all morphometric analyses and cell quantifications , a total of 10 sections per mouse from at least three mice per genotype were analyzed . For TUNEL , proliferation , and cell lineage analyses , the number of GFP+Hoechst+marker+ cells was manually counted , divided by the total number of GFP+Hoechst+ ( Ngn3-Cre-mediated lineage tracing ) or marker+Hoechst+ ( RIP-Cre-mediated lineage tracing ) cells , and multiplied by 100 . For the analysis of Pdx1 and Nkx6 . 1 single and compound heterozygous mice , sections were stained for insulin , glucagon , and somatatostatin and all marker+ cells ( on average 2100 cells per pancreas ) were manually counted . Insulin+ cells were analyzed for the expression of glucagon or somatostatin . For quantification of GFP+marker+ cells , on average 500 GFP+ cells from at least five different sections per mouse were counted . For islet cell mass measurements , images covering an entire pancreas section were tiled using a Zeiss Axio Oberver Z1 microscope with the Zeiss ApoTome module . The hormone+ area and total pancreas area were measured using ImagePro Plus 5 . 0 . 1 software ( Media Cybernetics ) and islet cell mass was calculated as follows: ( hormone+ area/total pancreatic area ) multiplied by pancreatic weight . Western blot analysis using anti-Nkx6 . 1 ( P . Serup ) and anti-HDAC1 ( Santa Cruz ) antibodies was performed as previously described [26] . Based on previously identified motifs [50] , [51] , a custom positional weight matrix was used to identify putative Nkx6 . 1 binding sites . ChIP assays were performed as described [52] , using Nkx6 . 1 ( P . Serup , 1∶250 ) or rabbit IgG antisera . Immunoprecipitations were performed on Min6 cells ( ATCC ) or on 1 . 5×106 GFP+ cells isolated from 300 pancreata of Neurog3eGFP/+ embryos at e15 . 5 by fluorescence activated cell sorting . Each ChIP assay was quantified in triplicate by qPCR . The following primer sequences were used: ( site A ) 5′-CAT CCG GTG ATA CTG GAA GCC C -3′ and 5′-GTC TTT ATC TGA GGG GGG GCT G -3′; ( site B ) 5′-GCA GAG GGG GGA GGA GGG -3′ and 5′- CGG CAG GGA AAT CCA CAA AAC -3′; ( site C; Re1 ) 5′- CCA TTT GAA GGC AAA ATG CT -3′ and 5′- GTA TGG GCT GCA AAC ACC TT -3′; ( site C; Re2 ) 5′- TGA AGT GGC TGA ATG AGA GC -3′ and 5′- AGT TGG AGC GCG TTT TGT AG -3′; glucagon 5′- AAG CAG ATG AGC AAA GTG AGT G -3′ and 5′- AGG CTG TTT AGC CTT GCA GAT A -3′; and intergenic control 5′- CAC TCA GAT CCT GAG CCA CA -3′ and 5′- GCT CTC TGC CTT CCA CTT TG -3′ . The Isl1 Re1 enhancer and CMV-Isl1 constructs as well as procedures for transient transfections and luciferase assays have been described previously [53] . Unless indicated otherwise , 0 . 1 µg of CMV-Nkx6 . 1 was transfected . Luciferase and Renilla expression were measured 48 hours post transfection . For each data point relative luciferase activity was quantified as the total luciferase units divided by the total Renilla units . All reporter gene analyses were performed in triplicate . All values are shown as mean ± standard error of the mean ( SEM ) ; p-values were calculated using student's 2-tailed t-test; P<0 . 05 was considered significant .
|
Diabetes is a disease caused by the loss or dysfunction of insulin-producing beta cells in the pancreas . Recent studies suggest that modification of the beta cells' differentiation state is among the earliest events marking the progressive failure of beta cells in diabetes . Currently , very little is known about the factors that instruct cells to adopt beta cell characteristics and maintain the differentiated state of beta cells . We have discovered that a single transcription factor can instruct precursor cells of other endocrine cell types to change their identity and differentiate into beta cells . Conversely , inactivation of the transcription factor in endocrine precursors prevents their differentiation into beta cells and results in excess production of other endocrine cell types . When the factor is specifically inactivated in beta cells , beta cells lose their identity and adopt characteristics of other endocrine cell types , similar to what is seen in animal models of diabetes . Thus , we have identified a single factor that is both sufficient to program beta cells and necessary for maintaining their differentiated state . This factor could be an important target for diabetes therapy and could help reprogram other cell types into beta cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"cell",
"fate",
"determination",
"gene",
"regulation",
"genetics",
"molecular",
"genetics",
"biology",
"genetics",
"and",
"genomics",
"cell",
"differentiation",
"gene",
"function"
] |
2013
|
Nkx6.1 Controls a Gene Regulatory Network Required for Establishing and Maintaining Pancreatic Beta Cell Identity
|
Yellow fever virus ( YFV ) is a member of the Flaviviridae family . In Brazil , yellow fever ( YF ) cases have increased dramatically in sylvatic areas neighboring urban zones in the last few years . Because of the high lethality rates associated with infection and absence of any antiviral treatments , it is essential to identify therapeutic options to respond to YFV outbreaks . Repurposing of clinically approved drugs represents the fastest alternative to discover antivirals for public health emergencies . Other Flaviviruses , such as Zika ( ZIKV ) and dengue ( DENV ) viruses , are susceptible to sofosbuvir , a clinically approved drug against hepatitis C virus ( HCV ) . Our data showed that sofosbuvir docks onto YFV RNA polymerase using conserved amino acid residues for nucleotide binding . This drug inhibited the replication of both vaccine and wild-type strains of YFV on human hepatoma cells , with EC50 values around 5 μM . Sofosbuvir protected YFV-infected neonatal Swiss mice and adult type I interferon receptor knockout mice ( A129-/- ) from mortality and weight loss . Because of its safety profile in humans and significant antiviral effects in vitro and in mice , Sofosbuvir may represent a novel therapeutic option for the treatment of YF . Key-words: Yellow fever virus; Yellow fever , antiviral; sofosbuvir
Yellow fever virus ( YFV ) is a single-strand positive-sense RNA virus which belongs to the Flaviviridae family . Yellow fever ( YF ) outbreaks were very common throughout the tropical world until the beginning of the 20th century , when vaccination and vector control limited the urban virus circulation [1] . Classically , sylvatic and urban cycles of YFV transmission occur . Non-human primates are sylvatic reservoirs of jungle YFV and non-immunized humans entering the rain forest and those living in the ecotone ( between preserved rain forest and urban area ) are highly susceptible to YFV , which is transmitted by mosquitoes from Haemagogus and Sabethes genera [2] . The virus is usually brought to urban settings by viremic humans infected in the jungle [2] . The urban cycle involves transmission of the virus among humans by vectors like Aedes spp . mosquitoes [2] . Brazil , an endemic country for YF , failed to vaccinate a large proportion of the susceptible population . This scenario of low human vaccinal coverage along with increased sylvatic YFV activity in primates has been occurring in Brazil since 2016 , leading to bursts of human cases of YF . For instance , between the second semester of 2017 and March 2018 , 4 , 847 epizootics were reported and 920 human cases were confirmed . There were 300 deaths associated with this outbreak [3 , 4] . In fact , cases of YF increased 1 . 8-times compared to the previous 35 years [3] . Altogether , these data also show that YFV spread from Brazilian rain forests to the outskirt of major cities in the Southeastern region of the country . Despite the detection of YFV in some urban areas in humans and primates during this recent reemergence , the Brazilian Ministry of Health ( MoH ) has argued this was a sylvatic cycle with no urban autochthonous transmission . Indeed , most of the recent activity of YFV was observed in areas adjacent to the Atlantic forest , where the genotype I was introduced two times in 2005 ( 95% interval: 2002–2007 ) and 2016 ( 95% interval: 2012–2017 ) , spilling over from the Amazon forest [5] . YFV causes massive lethality in new world monkeys and around 30% in humans [6] . Once displaying signs of severe infection , such as bleeding , shock , liver function decay and jaundice , infected individuals are likely to progress to poor clinical outcomes . Most often , acute hepatic failure occurs rapidly . No specific treatment options to YFV exist and patients solely receive intensive palliative care . Therefore , antivirals with anti-flavivirus activity may represent an important alternative for drug repurposing in an attempt to improve patient outcome . Developed in the 1930s , YF 17D live-attenuated vaccine confers long-lasting immunity to its recipients . Vaccination is recommended for individuals aged ≥ 9 months who are living in or travelling to areas at risk for YF . Contraindications include hypersensitivity to vaccine components , severe immunodeficiency , and age under ≤6 months [7] . Even though 17D is highly effective and one of the safest vaccines in history , rare severe adverse events have been reported . YF vaccine-associated neurological ( YEL-AND ) and viscerotropic ( YEL-AVD ) diseases are similar to classic YF caused by wild-type ( WT ) virus . Reporting rates of YEL-AND and YEL-AVD are 0 . 8 and 0 . 4 cases per 100 , 000 doses distributed [8] . Specific treatment would be of utmost importance for individuals with YF vaccine-associated diseases and for YFV-exposed people for whom vaccination is contraindicated . Our group and others have recently shown that sofosbuvir , a clinically approved anti-hepatitis C virus ( HCV ) drug , is also endowed with anti-Zika virus ( ZIKV ) antiviral activity in vitro and in vivo [9–11] . It has also been shown that sofosbuvir also blocks dengue virus ( DENV ) replication [12] . Animal model studies of sofosbuvir on Flaviviruses reveal that this drug is more effective when used prophylactically or as early as possible . Sofosbuvir was approved by the Food and Drug Administration ( FDA ) in 2013 . It has been used in therapy regimens thereafter in large worldwide scale to treat HCV-infected individuals , with infrequent registers of toxicity and adverse effects even for complex patients , such as those co-infected with both HIV/HCV or with substantial liver damage provoked by HCV [13–15] . Sofosbuvir is very effective against HCV genotype 1/2/3 , and safe doses may range from 400 to 1200 mg daily for up to 24 weeks [13–15] . When compared to pan-antiviral drugs such as ribavirin , sofosbuvir is considered safer for pregnant woman [16 , 17] . In the intention to have a safe and an active antiviral compound to inhibit YFV replication , we tested whether the virus was susceptible to sofosbuvir . We found that sofosbuvir binds to conserved amino acid residues on the YFV RNA polymerase ( NS5 ) , inhibiting virus replication in human hepatoma cells , diminishing YFV-associated mortality and improving the hepatic condition in animal models .
The antiviral sofosbuvir was kindly donated by Dr . Jaime Rabi ( Microbiológica Química e Farmacêutica , Brazil; part of the BMK Consortium ) . Ribavirin and AZT were provided by Instituto de Tecnologia de Farmacos ( Farmanguinhos , Fiocruz ) . Drugs were dissolved in 100% dimethylsulfoxide ( DMSO ) and subsequently diluted at least 104-fold in culture medium before each assay . The final DMSO concentration showed no cytotoxicity . The materials for cell culture were purchased from Thermo Scientific Life Sciences ( Grand Island , NY ) unless otherwise mentioned . Human hepatoma cell lines ( Huh-7 and HepG2 ) and African green monkey ( Vero ) cells were cultured in DMEM supplemented with 10% fetal bovine serum ( FBS; HyClone , Logan , Utah ) , 100 U/mL penicillin , and 100 μg/mL streptomycin [18 , 19] . Cells were incubated at 37°C in 5% CO2 . Aedes albopictus C6/36 cells were cultured at 28°C in Leibovitz L15 medium supplemented with 2 mM L-glutamine , 0 . 75 g/L sodium bicarbonate , 0 . 3% tryptose phosphate broth , non-essential amino acids and 5% FBS . YFV vaccinal and WT strains were passaged at an multiplicity of infection ( MOI ) of 0 . 01 in either Vero ( for 24–72 h at 37°C ) or C6/36 ( for 6 days at 28°C ) cells . Virus titers were also determined in Vero cell cultures by TCID50/mL [20] or plaque forming units ( PFU ) /mL ( described below ) . The vaccine strain 17D was donated by the Reference Laboratory for Flavivirus , Fiocruz , Brazilian Ministry of Health , whereas the WT strain was isolated in Vero cells from the serum of a symptomatic patient with confirmed RT-PCR result for YFV ( GenBank accession #MH018072 ) [21] . Monolayers of 1 . 5 x 104 hepatoma cells in 96-well plates were treated for 5 days with various concentrations of sofosbuvir or ribavirin as a control . Then , 5 mg/ml 2 , 3-bis- ( 2-methoxy-4-nitro-5-sulfophenyl ) -2H-tetrazolium-5-carboxanilide ( XTT ) in DMEM was added to the cells in the presence of 0 . 01% of N-methyl dibenzopyrazine methyl sulfate ( PMS ) . After incubating for 4 h at 37°C , the plates were read in a spectrophotometer at 492 nm and 620 nm [22] . The 50% cytotoxic concentration ( CC50 ) was calculated by a non-linear regression analysis of the dose-response curves . Monolayers of 5 . 5 x 106 Huh-7 cells in 6-well plates were infected with YFV at an MOI of 0 . 1 for 1 h at 37°C . The cells were washed with PBS to remove residual viruses , and various concentrations of sofosbuvir , or ribavirin , in DMEM with 1% FBS were added . After 24 h , the cells were lysed , the cellular debris was cleared by centrifugation , and the virus titers in the supernatant were determined . A non-linear regression analysis of the dose-response curves was performed to calculate the concentration at which each drug inhibited the plaque-forming activity of YFV by 50% ( EC50 ) . Huh-7 and HepG2 cells were seeded on black 96-well microplates with clear bottom ( Greiner Bio-One , Kremsmünster , Austria ) and infected with YFV at an MOI of 1 . After 1 hour , the viral inoculum was removed and cells were incubated with growth medium containing sofosbuvir , Ribavirin or AZT for 2 days . Cells were then fixed with 4% paraformaldehyde in PBS for 20 min at room temperature . The fixative was removed and cells monolayers were washed with PBS . Blocking of unspecific binding of the antibody and permeabilization were performed with 3% bovine serum albumin ( BSA , Sigma Aldrich ) and 0 . 1% Triton X-100 in PBS for 20 min at room temperature . SCICONS J2 antibody ( Scicons , Hungary ) , which recognizes double-stranded RNA , was diluted 1:1000 in PBS and incubated for 1 h at room temperature . The primary antibody was removed and cell monolayer was washed twice with PBS . Secondary antibody Donkey anti-mouse IgG coupled to AlexaFluor488 fluorochrome ( Thermo Fisher Scientific ) was diluted 1:1000 in PBS and incubated for 40 min at room temperature . After washing cells with PBS , nucleus staining with DAPI diluted 1:10 , 000 in PBS was performed at room temperature for 10 min and then washed with PBS . Cells were imaged using a Nikon TE300 ( Tokyo , Japan ) inverted microscope coupled to a Leica DFC310FX camera ( Leica Biosystems , Wetzlar , Germany ) . Images referent to AlexaFluor488 and DAPI signals were merged using the microscope software . Huh-7 and HepG2 cells were seeded in 6-well plates at density of 6 x104 cells/well and 2 . 5 x 105 cells/well , respectively . For infection , the growth medium was replaced by serum-free medium containing the virus at an MOI of 1 . Mock-infected cells were incubated with conditioned medium from uninfected cells prepared exactly as performed for viral propagation . After 1 hour , inoculum was removed and replaced by growth medium containing either the vehicle or the antivirals at different concentrations and incubated for 48 h ( for HepG2 ) or 72 h ( for Huh-7 ) cells . After that , cells were harvested by treatment with a 0 . 25% trypsin solution . Cells were fixed with 4% paraformaldehyde ( Sigma-Aldrich ) in phosphate buffered saline ( PBS ) for 15 min at room temperature and washed with PBS . Cells were permeabilized with 0 . 1% Triton X-100 ( Sigma Aldrich ) in PBS , washed with PBS , and blocked with PBS with 5% FBS . Cells were incubated with 4G2 , a pan-flavivirus antibody raised against the envelope E protein produced in 4G2-4-15 hybridoma cells ( ATCC ) , diluted 1:10 in PBS with 5% FBS . Cells were labeled with donkey anti-mouse Alexa Fluor 488 antibody ( Thermo Scientific , Waltham , MA , USA ) diluted 1:1000 in PBS with 5% FBS , and were analyzed by flow cytometry in a BD Accuri C6 ( Becton , Dickinson and Company , Franklin Lakes , NJ , USA ) for YFV infection . The gate strategy to assure accuracy in the analysis is displayed as S1 Fig . Virus titers were determined as PFU on Vero cells . Supernatants containing virus were serially diluted and incubated over confluent monolayers . After 1 h , cells were overlaid with semisolid medium , alpha-MEM ( GIBCO ) containing 1 . 4% carboxymethyl cellulose ( Sigma-Aldrich ) and 1% FBS ( GIBCO ) . Cells were further incubated for 4 to 5 days . Cells were fixed in 4% formaldehyde and stained with 1% crystal violet in 20% ethanol for plaque visualization . The sequences encoding the C-terminal portion of the RNA polymerase from Flaviviruses were acquired from the complete sequences deposited in GenBank . An alignment was performed using the ClustalW algorithm in the Mega 6 . 0 software . The sequences were analyzed by disparity index per site . Compared regions are displayed in the S1 Table . The amino acid sequence encoding YFV RNA polymerase ( UniProtKB code: P03314 ) was obtained from the EXPASY proteomic portal [23] ( http://ca . expasy . org/ ) . The template search was performed using the Blast server ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) with the Protein Data Bank [24] ( PDB; http://www . pdb . org/pdb/home/home . do ) as the database and the default options . The T-COFFEE algorithm was used to generate the alignment between the amino acid sequences of the template proteins and YFV RNA polymerase . Subsequently , the construction of the YFV RNA polymerase complex was performed using MODELLER 9 . 19 software [25] , which employs spatial restriction techniques based on the 3D-template structure . The preliminary model was refined in the same software , using three cycles of the default optimization protocol . The structural evaluation of the model was then performed using two independent algorithms in the SAVES server ( http://nihserver . mbi . ucla . edu/SAVES_3/ ) : PROCHECK software [26] ( stereochemical quality analysis ) and VERIFY 3D [27] ( compatibility analysis between the 3D model and its own amino acid sequence by assigning a structural class based on its location and environment and by comparing the results with those of crystal structures ) . The procedures described in this study were in accordance with the ethical and animal experiments regulations of the Brazilian Government ( Law 11794/2008 ) , guidelines published at the Brazilian participant Institutions and National Institutes of Health guide for the care and use of laboratory animals . The study is reported in accordance with the ARRIVE guidelines for reporting experiments involving animals [28] . Swiss albino mice ( Mus musculus ) ( pathogen-free ) from the Oswaldo Cruz Foundation breeding unit ( Instituto de Ciência e Tecnologia em Biomodelos; ICTB/Fiocruz ) were used for these studies . The animals were kept at a constant temperature ( 25°C ) with free access to chow and water in a 12-h light/dark cycle . The experimental laboratory received pregnant mice ( at approximately the 14th gestational day ) from the breeding unit . Pregnant mice were observed daily until delivery to accurately determine the postnatal day . We established a litter size of 10 animals for all experimental replicates . The Animal Welfare Committee of the Oswaldo Cruz Foundation ( CEUA/FIOCRUZ ) approved and covered ( license number L-016/2016 ) the experiments in this study . In parallel , some experiments were conducted using type I interferon receptor deficient mice ( A129−/− ) , SV129 background , obtained from Bioterio de Matrizes da Universidade de Sao Paulo ( USP ) . Adult male and female A129−/− mice were bred and kept at Immunopharmacology Laboratory of the Universidade Federal de Minas Gerais ( UFMG ) under specific pathogen-free conditions . Mice were kept at a constant temperature ( 25°C ) with free access to chow and water in a 12-h light/dark cycle . Experimental protocol was approved by the Committee on Animal Ethics of the UFMG ( CEUA/UFMG , Permit Protocol Number 84/2018 ) .
We initially compared the disparities among regions encoding the RNA-dependent RNA polymerase ( RDRP ) region of the contemporary Flaviviruses DENV , ZIKV and YFV ( Table 1 ) . The YFV RNA polymerase shares a conserved domain for catalytic activity with the orthologous enzymes ( S1 Table ) . Next , the crystal structures of DENV NS5 complexed with viral RNA ( PDB code: 5DTO ) [30] , HCV NS5B in complex with Sofosbuvir diphosphate ( PDB code: 4WTG ) [31] , and complexed to UTP ( PDB code: 1GX6 ) [32] were selected and used in a comparative modeling procedure , covering 100% of the YFV RNA polymerase sequence considered here ( residues Thr252-Ile878 ) . These three template proteins represent orthologous viral RNA polymerases from the Flaviviridae family . Consequently , the resulting 3D model of YFV RNA polymerase in complex with Sofosbuvir triphosphate showed good structural quality . The analysis of the YFV RNA polymerase model suggests that sofosbuvir triphosphate binds between the palm and the fingers regions , making hydrogen bonds with Gly538 , Trp539 , Ser603 , and Lys 693 residues and salt bridge interactions with Lys359 and two Mg2+ ions . Interestingly , these interactions are all described as relevant for incorporation of natural ribonucleotides [31] ( Fig 1 ) . Therefore , these results motivate further testing in biologically relevant models to YFV replication .
Brazil has been challenged in recent years by the ( re ) emergence of arboviruses . Massive cases of microcephaly associated with ZIKV circulation were registered in 2015/16 [36] . Two different genotypes of chikungunya virus ( CHIKV ) started to co-circulate from 2014 onward [37 , 38] . The four DENV serotypes are hyperendemic throughout the country [39] . More recently , YFV activity increased , affecting both non-human primates and humans without constituted immunity [3 , 6] . These facts clearly demonstrate that strategies of vector control failed and highlights the necessity of alternative strategies to control or even mitigate the diseases provoked by the arboviruses . In the context of this article , re-emergence of YFV also points out that poor vaccination coverage left human population living in or entering the ecotone susceptible to this virus . Unvaccinated individuals may quickly progress to severe YF , with hepatic and even neurological impairment [1] . In addition , due to massive YF vaccine campaigns , a fair amount of vaccine-related severe adverse events may be expected . Thus , finding antiviral drugs to treat YFV-infected individuals is critical for medical intervention over cases provoked by WT or even vaccine strains . Several groups demonstrated that the clinically approved anti-HCV drug sofosbuvir is endowed with antiviral activity towards other flaviviruses , such as ZIKV [10 , 40] and DENV [12] . Considering that these agents belong to the same family , it was plausible to test sofosbuvir against YFV . Indeed , we observed that sofosbuvir targets the YFV RNA polymerase in silico and inhibits YFV RNA replication and infectious virus production . The in vitro antiviral results display that sofosbuvir’s EC50 towards YFV is in micromolar range and comparable to what has been described for ZIKV and DENV [10 , 12 , 40] . Our data showed that sofosbuvir inhibits YFV in hepatoma cell lines . This is particularly relevant since YFV targets hepatocytes and the liver is the most affected organ in YF [41] . The degree of liver damage measured by elevated aminotransferase levels and jaundice is associated with higher mortality [42] . Our Swiss mouse neonate model of virus infection reproduced the deleterious association among increased ALT , viral loads , and mortality . By inhibiting virus replication , sofosbuvir attacked this deleterious loop towards the liver protection and enhancement of the survival . In humans , massive apoptosis and necrosis of hepatocytes are reported in fatal cases [43] . Additionally , impaired synthesis of clotting factors caused by YFV-induced liver injury is key to the pathogenesis of haemorrhagic manifestations in severe YF [44] . Experimental cases of liver transplant after YFV infection have been conducted during recent outbreak in Brazil [4 , 45 , 46] with 50% survival rate . We envision that sofosbuvir may improve liver function in YF patients , and hopefully enhance the survival rates of transplanted individuals . In the highly virulent infection mouse model , A129-/- mice infected with WT YFV , sofosbuvir also diminished virus replication in the brain and improved liver condition . Both viscerotropic and neurological replication injuries are hallmarks of YFV pathogenesis [8 , 47 , 48] . Our data point to a possible benefit of early treatment with sofosbuvir for patients that may progress to complications at late stages in the natural history of infection . Interestingly , although sofosbuvir was more efficient when used prophylactically , it was able to improve the hepatic condition of the infected animals receiving a post-infection treatment . Sofosbuvir reduced the YFV-induced mortality and lack of weight gain in mice models . Considering our data and the safety history of sofosbuvir in hepatitis , it is not a hyperbolic conclusion to consider sofosbuvir in clinical use for YF infection in humans . Primarily , sofosbuvir could be worthwhile for acutely infected individuals and those displaying neurotropic and viscerotropic diseases provoked by virus replication . Since vaccine is not recommended for special groups of individuals at higher risk of severe adverse events , such as elderly and those with immunodeficiencies , sofosbuvir could be used prophylactically . Most commonly , we used ribavirin , a pan-antiviral drug with anti-YFV activity demonstrated both in vitro and in vivo [33–35] , as a positive control to inhibit YFV replication and to compare with sofosbuvir . Our in vitro data showed that sofosbuvir was more efficient than ribavirin in reducing viral genome replication , number of infected cells , and production of infectious viral particles in Huh-7 cells . Moreover , we thus understand that sofosbuvir possesses advantages over ribavirin in terms of safety and efficacy . Ribavirin is more toxic than sofosbuvir , especially for critically ill hepatic and renal patients [49] , such as those with YF . According to FDA categorization of risk during pregnancy; sofosbuvir is class B ( drugs with the second to lowest risk of causing malformations ) , whereas ribavirin is class X ( forbidden , even for men having intimate contact with women at gestational age ) . Thus , ribavirin would have a more limited scope of use . Indeed , when ribavirin was used in a clinical trial against a flavivirus , the Japanese encephalitis virus , it failed to be effective [50] . Although our results are translational , it is important to cite that further studies are necessary to precisely determine sofosbuvir’s mechanism ( s ) of action towards YFV life cycle and the dose adjustment to treat patients with YF . YFV RNA polymerase is the likely target , based on docking onto this enzyme and reduction of viral dsRNA levels . As with other RNA polymerases from positive-sense RNA viruses , well-conserved motifs are found the YFV and HCV RNA polymerase , such as D-x ( 4 , 5 ) -D and GDD , which are spatially juxtaposed , wherein Asp binds Mg2+ and Asn selects ribonucleotide triphosphates over dNTPs , determining RNA synthesis [9] . It would not be surprising to see sofosbuvir triphosphate bound to these critical residues for catalysis , because even other positive-sense RNA virus beyond members of the Flaviviridae family are susceptible to sofosbuvir [51] . Sofosbuvir inhibits HCV RNA polymerase as chain terminator [31] . Nevertheless , this drug is considered a non-obligate chain terminator , due to the presence of 3’-OH moiety . Indeed , sofosbuvir inhibited ZIKV replication by targeting its RNA polymerase directly and provoking A-to-G mutation in the virus genome [40] . Whether sofosbuvir acts directly on YFV RNA polymerase , induces an error-prone virus replication by its incorporation in the virus genome or inhibition inosine monophosphate dehydrogenase and/or independent mechanisms–it remains to be elucidated . We also observed that sofosbuvir inhibits 100- to1000-times more potently HCV than YFV production/replication [13 , 31 , 52] . These differences in potencies need to be interpreted in light of the sofosbuvir’s concentrations found in anatomical compartments and its long-half life [16] , which may already be enough to compensate the limitation in the in vitro pharmacological potency . Sofosbuvir’s chemical structure allows its vectorization to the liver , where it is found at 77 μM when patients are treated with the reference dose of 400 mg/day [53] . YFV production was reduced up to 3-log10 when hepatoma cells where treated with sofosbuvir at 50 μM . Since sofosbuvir has been used clinically at doses up to 1200 mg/day [16] , it is possible to have enough sofosbuvir in the liver to inhibit or reduce YFV replication in humans remains . Only clinical trials will reveal the best regimen and posology for sofosbuvir towards YF . In the our mouse models , sofosbuvir efficacy was observed at the reference pre-clinical dose previously studied for HCV , 20 mg/kg/day [15] . The results described here demonstrate for the first time the antiviral activity of sofosbuvir to YFV , which caused a recent outbreak in Brazil , providing primary scientific evidence for a new potential use of a clinically approved antiviral drug and reinforcing that its chemical structure may be used to generate selective anti-YFV specific drugs .
|
Yellow fever virus is transmitted by mosquitoes and its infection may be asymptomatic or lead to a wide clinical spectrum ranging from a mild febrile illness to a potentially lethal viral hemorrhagic fever characterized by liver damage . Although a yellow fever vaccine is available , low coverage allows 80 , 000–200 , 000 cases and 30 , 000–60 , 000 deaths annually worldwide . There are no specific therapy and treatment relies on supportive care , reinforcing an urgent need for antiviral repourposing . Here , we showed that sofosbuvir , clinically approved against hepatitis C , inhibits yellow fever virus replication in liver cell lines and animal models . In vitro , sofosbuvir inhibits viral RNA replication , decreases the number of infected cells and the production of infectious virus particles . These data is particularly relevante since the liver is the main target of yellow fever infection . Sofosbuvir also protected infected animals from mortality , weight loss and liver injury , especially prophylatically . Our pre-clinical results supports a second use of sofosbuvir against yellow fever .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"flow",
"cytometry",
"medicine",
"and",
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"and",
"laboratory",
"medicine",
"pathogens",
"biological",
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"geographical",
"locations",
"microbiology",
"dna-binding",
"proteins",
"hepatoma",
"cells",
"vaccines",
"animal",
"models",
"organisms",
"viruses",
"model",
"organisms",
"polymerases",
"rna",
"viruses",
"experimental",
"organism",
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"infectious",
"disease",
"control",
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"infectious",
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"south",
"america",
"animal",
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"proteins",
"medical",
"microbiology",
"microbial",
"pathogens",
"rna",
"polymerase",
"mouse",
"models",
"viral",
"replication",
"brazil",
"spectrophotometry",
"people",
"and",
"places",
"biochemistry",
"cytophotometry",
"flaviviruses",
"virology",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"cultured",
"tumor",
"cells",
"spectrum",
"analysis",
"techniques"
] |
2019
|
Yellow fever virus is susceptible to sofosbuvir both in vitro and in vivo
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Cap Analysis of Gene Expression ( CAGE ) in combination with single-molecule sequencing technology allows precision mapping of transcription start sites ( TSSs ) and genome-wide capture of promoter activities in differentiated and steady state cell populations . Much less is known about whether TSS profiling can characterize diverse and non-steady state cell populations , such as the approximately 400 transitory and heterogeneous cell types that arise during ontogeny of vertebrate animals . To gain such insight , we used the chick model and performed CAGE-based TSS analysis on embryonic samples covering the full 3-week developmental period . In total , 31 , 863 robust TSS peaks ( >1 tag per million [TPM] ) were mapped to the latest chicken genome assembly , of which 34% to 46% were active in any given developmental stage . ZENBU , a web-based , open-source platform , was used for interactive data exploration . TSSs of genes critical for lineage differentiation could be precisely mapped and their activities tracked throughout development , suggesting that non-steady state and heterogeneous cell populations are amenable to CAGE-based transcriptional analysis . Our study also uncovered a large set of extremely stable housekeeping TSSs and many novel stage-specific ones . We furthermore demonstrated that TSS mapping could expedite motif-based promoter analysis for regulatory modules associated with stage-specific and housekeeping genes . Finally , using Brachyury as an example , we provide evidence that precise TSS mapping in combination with Clustered Regularly Interspaced Short Palindromic Repeat ( CRISPR ) -on technology enables us , for the first time , to efficiently target endogenous avian genes for transcriptional activation . Taken together , our results represent the first report of genome-wide TSS mapping in birds and the first systematic developmental TSS analysis in any amniote species ( birds and mammals ) . By facilitating promoter-based molecular analysis and genetic manipulation , our work also underscores the value of avian models in unravelling the complex regulatory mechanism of cell lineage specification during amniote development .
Single-molecule sequencing technology in combination with Cap Analysis of Gene Expression ( CAGE ) allows mapping and quantification of transcription start sites ( TSSs ) at single base pair resolution [1 , 2] . Application of this technology to a large collection of human and mouse samples , covering almost all known cellular steady states , generated a comprehensive overview of mammalian promoter usage and revealed a complex architecture of cell type–specific and ubiquitously expressed gene promoters and their transcription factor occupancies [3–5] . Integration of such high-throughput TSS profiling data from vertebrate , invertebrate , and fungal model organisms is giving us a fresh look into eukaryotic promoter structure and evolution [6] . Understanding developmental ontogeny of steady states in the adult requires time-course analysis of undifferentiated or differentiating embryonic and fetal tissues . Doing so is ethically prohibited in the human and technically demanding in the mouse for early-stage embryos . Among the 975 human samples profiled in Functional Annotation of Mammalian Genome ( FANTOM ) 5 for CAGE-based TSSs , human embryo was not included; of the mouse developmental samples similarly profiled ( approximately a quarter of the total 399 ) , all were from embryos older than E11 ( halfway through their development ) [3] . Such limitations can be circumvented by using nonmammalian vertebrate models , in which early embryonic samples are relatively easy to acquire . In a recent study using the zebrafish model , for example , an early developmental process called maternal-to-zygotic transition was analyzed through CAGE-based TSS profiling , and 2 fundamentally different transcription initiation codes were uncovered to regulate maternal and zygotic gene transcription [7 , 8] . In this work , we chose the chick as an amniote model for the exploration of ontogenetic evolution of TSS dynamics and generated genome-wide TSS profiles using embryonic samples covering the entire period of chick development , from egg-laying to hatching [9] . Both birds and mammals are amniotic vertebrates . Their development is evolutionarily conserved and highly stereotypic . Among all nonmammalian model organisms currently in use , the chick is the best experimental system for human developmental studies [10 , 11] . About 60% of the 22 , 000 human protein-coding genes have 1-to-1 orthologs in the chick [12–14] . Developmental programs leading to the formation of the 3 principal germ layers and their subsequent lineage specification are highly similar between these 2 species [10] . Importantly , the most critical period of vertebrate lineage diversification , gastrulation and early postgastrulation development , taking place after implantation and a difficult time for tissue isolation in most mammalian species , is readily accessible in the chick model . To achieve our aim , we collected chicken embryos from 16 developmental stages and carried out a comprehensive developmental CAGE-based TSS analysis of the chicken genome .
Chicken samples from 16 developmental stages ( Fig 1A and 1B; S1 Table ) , covering the entire prehatching developmental period from Hamburger and Hamilton ( HH ) stage 1 ( HH1 ) to HH45 [9] , were collected . At HH1 , most of the cells in the embryo are pluripotent [15–17]; at HH45 , most have reached a steady state after terminal lineage differentiation [18–20] . Ten additional samples of both embryonic and primary cell origins ( 2 limb bud , 2 extraembryonic , 3 aortic smooth muscle , 2 hepatocyte , and 1 bone marrow-derived mesenchymal stem cell [MSC] ) were included for comparison ( S1 Table ) . RNAs isolated from these samples were used for CAGE library construction and sequencing following established protocols [3] ( Materials and methods ) . CAGE reads were mapped to galGal5 , the latest chicken genome assembly ( Genbank ID: GCA_000002315 . 3; NCBI link https://www . ncbi . nlm . nih . gov/assembly/GCA_000002315 . 3/ ) . The median read depth for all chicken samples was 2 . 7 million , comparable to that of 4 million for human samples [3] . Peaks of TSS activity were identified across the genome using a decomposition-based peak identification ( DPI ) method [3] . In total , 142 , 924 peaks were identified with a permissive threshold of at least 1 TSS having 3 tag counts in 1 of the samples . Among them , 31 , 863 peaks remained after filtering with the robust threshold that requires 1 TSS with more than 10 tag counts in 1 sample ( equivalent of 1 normalized tag per million [TPM] ) . Similar to the ratios seen in CAGE-based TSS analyses with human and mouse samples [3] , 60% ( 19 , 172 ) of all robust chicken TSSs could be mapped to annotated TSSs of coding or noncoding gene models within 500 base pairs ( based on chicken RefSeq , Ensembl , and EST databases for coding genes and chicken NONCODE [21] , ALDB-lincRNA [22] , and other lincRNA databases [23] for noncoding genes ) ( S1 Fig ) . The remaining 40% ( 12 , 644 ) of unannotated TSSs represented likely alternative promoters and noncoding RNA genes , which are yet to be characterized . About 31%–46% of all robust TSSs in any given sample ( median 10 , 557 TSSs; 33% of total TSSs ) had peak values greater than 3 TPM ( S1 Table ) . Following the general practice in CAGE analysis , their corresponding genes were considered to have an abundance of roughly 1 transcript per cell and were categorized as expressed [3 , 24] . Because development is a continuous process , promoter activity profiles from different stages are causally linked . To assess sample-stage relationships based on their genome-wide TSS landscape , principal coordinates analysis ( PCoA , a multiple-dimensional scaling approach ) was performed . PCoA of all developmental samples revealed close correlation between TSS-based sample distance and embryonic stage–based developmental progression ( Fig 1C ) . Samples from the first day of chicken embryogenesis , covering from pregastrulation to early somite stages ( roughly corresponding to the second and third weeks of human development ) were tightly clustered , suggesting that these stages possess shared TSS features . Among the remaining samples , groups representing mid development ( D1 . 5–D4; from cardiac to hepatic differentiation ) , mid-late development ( D5–D10; from definitive hematopoiesis to chondrogenesis ) , and late development ( D15 and D20; bone and feather formation and terminal differentiation of most other cell lineages ) could be readily distinguished ( Fig 1C ) . When all samples were compared together , primary cells formed separate clusters ( Fig 1D ) , reflecting transcriptional homogeneity in purified cell types , regardless of whether they represented the differentiated or multipotent lineages . The extraembryonic and limb bud samples , representing subpopulations of developmental tissues , were seen to group more closely to the whole embryos than to primary cells ( Fig 1D ) . ZENBU genome browser was developed for the visualization of human CAGE data [25 , 26] . We adopted this platform and created a chicken-based ZENBU configuration ( hereafter called Chicken-ZENBU; example shown in S2 Fig ) for interactive and web-based exploration of chicken CAGE data ( accessible at http://fantom . gsc . riken . jp/zenbu/gLyphs/#config=b1zZI1gUFZ6mHX6-4Gvxr ) . Briefly , Chicken-ZENBU allowed visual representation of TSS positions and peak values on galGal5 , the latest version of chicken genome assembly . It was also fully integrated with the latest chicken genome annotation resources , including Ensembl , RefSeq , EST , NONCODE , ALDB-lincRNA , and highly conserved elements ( HCEs ) from 48 representative avian species [27 , 28] . Genes and TSSs could be searched and visualized at desirable genomic resolutions , from single nucleotide to chromosome levels . This allowed intuitive assessment of TSS consistency across all available sample data points . For example , inspection of TSS mapping results for the housekeeping gene GAPDH ( Fig 2A; full view in S2 Fig ) revealed in all samples a robust and single peak at the 5′ end of RefSeq transcript NM_204305 and Ensembl transcript ENSGALT00000023323 . 5 , covering a 5 base pair–wide region and with the peak center mapped with single base pair precision to Chr1:76 , 952 , 888 ( Fig 2A , right ) . This lends very strong support to the accuracy of both RefSeq and Ensembl gene models for GAPDH . In many cases , however , RefSeq and Ensembl gene models diverge on TSS prediction , and our CAGE-based TSS mapping could resolve such discrepancies ( e . g . , for beta actin gene ACTB , Fig 2B ) . When neither of 2 gene models matched our CAGE-based TSS mapping ( e . g . , for ribosomal protein gene RPL32 , Fig 2C ) , future revision in annotation is recommended . In addition to genome-wide precision mapping of chicken TSSs , our data allowed us to track changes in TSS positions and peak values throughout chicken development . With regard to genomic position , half of mapped annotated genes had a single associated TSS peak in all samples ( S2 Table ) , indicating that these genes are transcribed from a stably positioned TSS regardless of developmental stage or cellular origin . The other half of the genes exhibited 2 or more TSS peaks ( S2 Table ) , likely reflecting alternative promoter utilization . This ratio is comparable to alternative promoter utilization reported for the human [3] and Drosophila [29] genomes . TSS peak values , on the other hand , were stable only for a small percentage of all reported TSSs ( see the housekeeping gene section below ) . For most TSSs , their peak values exhibited prominent variation among developmental samples . Such variation could be detected in our analysis with high sensitivity , capturing CAGE TSS peak values spanning 5 orders of magnitude . This allowed us to make quantitative assessment of promoter activities in heterogeneous cell populations in the embryo . For example , loss of epiblast pluripotency during the first day of embryonic development was clearly revealed by tracking expression changes of the TSS peak of the NANOG gene ( Chicken-ZENBU views in Fig 2D and S3A Fig; quantification in Fig 3 ) . Similar changes were observed for many other pluripotency-associated genes ( e . g . , POU5F3 , MYC , and EOMES ) ( Fig 3 ) . Likewise , following the developmental progression , lineage-specific differentiation in each of the 3 principal germ layers ( the ectoderm , mesoderm , and endoderm ) could be readily assessed . For instance , a single TSS peak was detected in the GFAP locus ( Chicken-ZENBU views in Fig 2E and S3B Fig; quantification in Fig 3 ) , and its rapid rise in activity in the second half of embryogenesis marked the differentiation of astrocytes , a derivative of the ectoderm germ layer . Furthermore , TSS profiles for many other ectoderm-derived , lineage-specific markers , such as ASL1 ( lens ) , GRIA4 ( neurons ) , and PLP1 ( oligodendrocytes ) , and for mesoderm- and endoderm-derived lineage-specific markers ( e . g . , TNMD [tendon] , TNNT3 [fast-twitch muscle] , MYH7B [slow-twitch muscle] , and LDB3 [striated muscle] for the mesoderm and ALB , F2 , and SPP2 [liver] and GKN1 [stomach] for the endoderm ) could also be precisely tracked throughout development ( Fig 3 ) . Interestingly , in a subset of genes with multiple associated TSS peaks , each peak exhibited unique developmental profiles , likely reflecting alternative promoter activities under stage-specific transcriptional regulation . For example , the CDYL gene , encoding an uncharacterized chromodomain protein , had 2 robust TSS peaks ( S4A Fig ) . One of them ( TSS1 ) was expressed ubiquitously ( S4B and S4C Fig ) , whereas the other ( TSS2 ) showed high levels of expression only during early development ( S4B and S4C Fig ) . To evaluate stage- and cell type–specific TSSs systematically , we applied a promoter activity cutoff of 10 TPM and asked whether any TSSs had over 10-fold enrichment over the cross-sample mean in any particular sample group . In total , 2 , 296 TSSs were found to be highly enriched in primary cells ( 1 , 175 in hepatocytes , 449 in aortic smooth muscle cells [SMCs] , and 672 in MSCs ) ( S3 Table ) and 608 TSSs in developmental samples ( 172 in early stage samples , 118 in mid stage samples , 184 in mid-late stage samples , 493 in late stage samples , 292 in extraembryonic samples , and 81 in limb samples ) ( S3 Table ) . It is worth noting that many of these stage- or cell type–specific promoter activities had been previously unreported or poorly characterized ( examples shown in S5 Fig ) . Many genes are involved in the maintenance of basic cellular machinery and are expressed ubiquitously in all cell types . Some of them are also highly expressed ( e . g . , genes encoding GAPDH , tubulin , actin , and ribosomal proteins ) . These genes are commonly used as loading/calibration controls in expression analysis . However , recent studies have shown that most of such housekeeping genes are expressed neither uniformly nor stably across different tissue/cell types [30] . In our study , out of the 31 , 863 TSS peaks identified in the chicken genome , 3 , 631 ( 11 . 4% ) were active in all 26 samples ( >3 TPM ) and could be considered as ubiquitously expressed . Among them , 189 ( 0 . 59% ) were highly expressed ( >100 TPM ) and 79 ( 0 . 25% ) were extremely highly expressed in all samples ( >300 TPM cutoff and >700 TPM cross-sample mean ) . Most of the commonly used housekeeping genes ( e . g . , GAPDH and ACTB ) were in this last group ( S4 Table ) . Despite their ubiquitous and high levels of expression , however , very significant cross-sample variations were noted for these genes , an observation in support of Eisenberg and Levanon’s general assessment on housekeeping genes [30] . They applied alternative criteria ( ubiquitous expression , low tissue variance , and no exceptional expression ) and found 3 , 804 human genes as bona fide housekeeping genes . Using a similar approach ( >10 TPM , σ ( log2TPM ) <1 ) , we identified 1 , 254 chicken housekeeping genes ( 1 , 399 TSSs ) ( S5 Table ) , among which 787 ( 63% ) were shared between the human and the chicken ( S6 Table ) . Gene ontology ( GO ) analysis of all 1 , 254 chicken housekeeping genes indicated an exclusive functional association with basic cellular machinery , including translation , transcription , subcellular organization , metabolism , and protein trafficking ( S7 Table ) . The vast majority of those housekeeping genes were expressed much more stably than GAPDH ( ranked 1 , 077 ) and ACTB ( not considered as a housekeeping gene according to this criterion ) ( S5 Table ) . Among the top 30 most stable genes ( Fig 4 ) , the top 10 in expression stability were IK , EIF3K , EIF3I , FBXW2 , MED21 , RPL7 , SAP18 , RALGAPB , DERL2 , and SNX4 ( Fig 4 ) ; and the top 10 in expression levels were RPL7 , H3F3C , VAPA , EIF3K , EIF3I , NAA20 , PSMC6 , SAP18 , ZMAT2 , and POLDIP3 ( S6 Fig ) . These genes are recommended as the bona fide housekeeping genes for expression normalization in avian studies . Genome-wide TSS mapping enabled us to analyze general features of chicken promoters , such as CpG island overlap , GC content , length and shape , and their usage across stages and cell types . Based on bioinformatics analysis of the chicken genome , approximately 70 , 000 CpG islands in total were predicted , and 48% of them were associated with a gene [12 , 31] . We found that 13 , 701 out of all robust TSSs ( 43% ) overlap a CpG island , a ratio higher than what was reported for human TSSs ( 33% ) [3] . This is in agreement with an overall higher GC content in the chicken genome ( chicken 52%; human 41% ) [32] . We also analyzed the correlation of peak shape ( as an index of precision in the choice of TSS ) with other promoter features . Peak shape , whether broad or sharp , was assessed by “shape index” ( SI ) [33] . A sharp peak was defined as having SI > −1 and a broad peak as having SI < −1 , with the highest possible SI being 2 , representing 100% single-base position mapping of all tags . Broad peaks exhibited higher GC content ( within a 100 bp region; ± 50 bp around the TSS representative position ) ( S7A Fig ) and , consequently , showed higher presence of CG-rich motifs ( S7B Fig bottom; position weight matrix visualization for aligned “sharp” and “broad” 100 bp promoter regions ) . Furthermore , they overlap CpG islands with higher frequency ( S7C Fig ) , although the overall percentage of CpG-associated TSSs ( among all expressed TSSs with >3 TPM ) did not exhibit prominent variation among the samples ( S7F Fig ) . Broad peaks were also associated with a greater bias towards non-tissue–specific genes ( S7D and S7E Fig ) . Sharp peaks , on the other hand , were clearly associated with TATA box motif by position weight matrix visualization ( S7B Fig top ) . These results agree with previous studies on the mammalian [3] and Drosophila [33] promoter architecture . We further investigated promoter usage by merging TSS peaks ( >1 TPM ) within 100 bp distance into super clusters ( Materials and methods ) [3] . In total , 21 , 632 super clusters were obtained; 14 , 422 ( 71% ) of them had 1 TSS , and the remainder had 2 or more TSSs . A general shift to a broader super cluster size was seen with developmental samples ( S7G Fig ) , suggesting that there is an overall trend to have more TSSs active as development proceeds . Systems-level studies of avian development are yielding unprecedented details on how transcriptional regulatory networks control lineage specification [34–38] . Such studies could be further aided by a genome-wide understanding of promoter and enhancer architecture , which has not yet been reported in the chick . Using data from 48 representative avian species , recent work on avian comparative genomics [27 , 28] showed that over 99% of approximately a quarter million Avian-Specific Highly Conserved Elements ( ASHCEs; a subset of all HCEs ) were located in noncoding regions of the genome , pointing to their potential roles in transcriptional regulation . Among the TSS peaks identified in this work , 110 ( 0 . 34% ) directly overlapped the ASHCEs , and 2 , 660 ( 8 . 35% ) directly overlapped the HCEs . Not surprisingly , GO analysis of the top 500 ASHCE-associated genes suggested a strong bias towards developmental functions [28] . To facilitate future analysis of ASHCEs in developmental regulation , we mapped all ASHCEs to galGal5 and integrated this information into Chicken-ZENBU ( as HCE and ASHCE tracks; label 11 in S2 Fig ) . Examples of HCEs and ASHCEs in the MSX1 and MSX2 loci are shown in S8 Fig . Other vertebrate comparative genomics resources , such as the 0 . 6 million Conserved Non-Exonic Elements ( CNEEs ) identified through whole-genome alignment of a broad spectrum of vertebrate species [39] , could , in the future , be similarly mapped and visualized on Chicken-ZENBU . In addition to allowing integration of conserved regulatory elements and TSS activities , our data also enabled systematic profiling for transcription factor binding sites ( TFBSs ) in any promoter ( s ) of interest . As a proof of principle , we asked whether lineage-specific binding motifs could be revealed through TFBS profiling of promoter sequences ( arbitrarily defined as −300 to +100 base pair region ) around the identified TSSs . TSSs enriched in each sample group ( >10 TPM ) ( S3 Table ) were profiled for TFBSs by using Analysis of Motif Enrichment ( AME ) motif-based sequence analysis tool ( Meme-Suite . org , see Materials and methods ) [40] . The top 50 motifs for enriched TSSs in each of the 10 sample categories and for housekeeping genes are shown in Fig 5A . All enriched motifs are shown in S8 Table . A small percentage of the top 50 motifs were shared among sample groups ( 0 for embryo groups , 3/50 for primary cells , and 0 for other tissues ) or with the housekeeping gene group ( 0 for all ) ( Fig 5B , S9 Table ) . Most of the identified top 50 TFBS motifs were highly specific to individual sample category ( 31/50 for early embryo group , 32/50 for middle embryo group , 20/50 for mid-late embryo group , 8/50 for late embryo group , 24/50 for aortic SMCs , 35/50 for MSCs , 26/50 for hepatocytes , 47/50 for extraembryonic tissue , 6/6 for wing buds , and 13/17 for leg buds ) ( Fig 5B , S9 Table ) , suggesting that they were primarily engaged in lineage-specific transcriptional regulation . Similar results were obtained when the comparison was performed with top 100 TFBS motifs ( S9 Fig ) . Moreover , when TFBS analysis was extended to all TSSs with either sharp or broad peaks ( as described in the previous section ) , distinct groups of enriched motifs were seen for the sharp ( TA-rich motifs , including TBP binding site TATAAA ) and broad ( CG-rich motifs ) TSS peaks ( S10 Fig ) . These data demonstrate that TSS mapping combined with TFBS profiling enables us to identify enriched regulatory motifs with potentially specific roles in developmental- and lineage-related transcriptional regulation . Clustered Regularly Interspaced Short Palindromic Repeat ( CRISPR ) /Cas system is used by bacteria and archaea to recognize and cleave viral DNA sequences . As shown in mammalian models , this genome editing technology is also applicable to avian cells and embryos [41 , 42] . A modified Cas9 protein , with its nuclease activity dead and fused with 10 copies of VP16 transactivation domain , was able to recruit transcription initiation machinery to gene promoters guided by single guide RNA ( sgRNA ) [43] . This technique is termed “CRISPR-on” for CRISPR/dCas9-mediated gene activation . Previous studies showed that the highest efficiency of CRISPR-on was achieved by using multiple sgRNAs targeted to the first 200–300 base pairs upstream of TSS , whereas sgRNAs targeted to sequences further upstream had low efficiency and those targeted to sequences downstream of TSS had negative effect [43] . The CRISPR-on technique can potentially be used to activate any endogenous genes , many of which are ill-suited for conventional plasmid-based cloning and overexpression . Its success , however , relies heavily on the precise knowledge of TSSs of interest , and up to now , this technique has not been used in any avian or mammalian developmental study . To test whether our genome-wide TSS mapping could expedite CRISPR-on application in chick developmental analysis , we targeted the Brachyury ( T ) gene as a proof of principle for CRISPR-on–mediated activation . A single TSS peak was identified at the 5′ end of chicken Brachyury ( Fig 6A ) , a gene marking mesoderm precursors and axial mesoderm cells at early developmental stages . The promoter region was analyzed for suitable sgRNA design ( crispr . mit . edu ) [44] , and 4 sgRNA sequences located within 300 bp of the TSS were selected ( Fig 6B ) . The expression construct “pAC154-dual-dCas9VP160-sgExpression” ( addgene #48240 ) was used for sgRNA expression . Early gastrulation-stage embryos ( HH2–HH3 ) were electroporated with combined 4 sgRNA-expressing constructs and were grown to HH10 , followed by Brachyury in situ hybridization analysis . CRISPR-on–activated Brachyury+ signals were widely and ectopically seen in somitic and neural tube cells ( Fig 6C and 6D ) in addition to Brachyury’s normal expression domains in pre-ingressing mesoderm progenitors and notochord cells [45] . Several other genes tested were also robustly activated through CRISPR-on ( S11 Fig and S12 Fig ) , suggesting that CRISPR-on technique ( with the help of TSS mapping ) can be efficiently applied to avian developmental research .
TSSs were investigated in the past through nuclease protection , primer extension , and 5′ rapid amplification of cDNA end ( RACE ) assays . In the era of high-throughput sequencing , transcriptome profiling through RNA-seq massively expanded our ability to reconstruct transcript structure at the genome level . However , conventional RNA-seq techniques target random RNA fragments and are not best suited for accurate identification of TSSs [46] . CAGE technology , targeting and enriching 5′ capped RNA fragments , has emerged as the most widely adopted method for genome-wide TSS mapping , in comparison with other approaches such as dRNA-seq [47] , TagRNA-seq [48] , RNA annotation and mapping of promoters for analysis of gene expression ( RAMPAGE ) [29] , and single-molecule real-time ( SMRT ) [49] . Applying the CAGE technology , we generated genome-wide TSS profiles for chicken embryos covering the entire developmental period from pregastrulation to hatching . Developmental TSSs were mapped to the latest chicken genome assembly and are open for interactive exploration on Chicken-ZENBU . Promoter activities reflecting complex lineage specification events , as well as housekeeping gene functions , were readily captured , yielding a rich resource for the analysis of ontogenetic evolution of steady state transcriptional activities in the adult . As the first genome-wide TSS mapping in birds and the first comprehensive developmental TSS analysis in amniotes , our data pave the way for the refinement of chicken genome annotation in combination with the latest improvement in genome assembly [14] and for systems-level analysis of promoters ( for transcriptional regulation ) and 5′ untranslated regions ( for translational regulation ) of developmentally important genes . As a proof of principle , we demonstrated that TSS profiling enabled us to find stage-dependent enriched TFBS motifs and to achieve high efficiency in CRISPR-on–mediated transcriptional activation of a mesoderm regulator . The latter is the first report of CRISPR-on technique in developmental studies , applicable in principle to any gene . CRISPR-on and the similar CRISPR-mediated transcriptional inhibition [50] and CRISPR-mediated epigenetic modification [51] techniques , all requiring precise TSS knowledge , have the potential to revolutionize developmental studies in model systems not amenable to traditional genetics-based analysis . A recurring issue in high-throughput analysis is cellular heterogeneity regardless of sample source ( e . g . , a tumor or an embryo ) . This is being tackled in the field with the development of computational deconvolution algorithms on one hand [52–54] and of nanogram- and pictogram-scale RNA-seq platforms on the other , including CAGE-adapted nanoCAGE and C1 CAGE [55–57] . Unlike tumor tissues , which are inhomogeneous by nature and variable in their relative cellular composition , embryos are composed of highly stereotypic and reproducible cell populations , with their fates and contributions well mapped out . In our current analysis , early-stage embryos contain relatively few distinct cell populations , whereas late-stage embryos are composed of hundreds of steady state cell types . Early embryonic tissues are therefore ideally suited for testing and optimizing deconvolution algorithms . Sensitivity in CAGE peak identification also implies that minor cell populations can be readily detected and quantified in either simple or complex mixed populations . Chicken germ cells , for example , represent an extreme case in which only less than 0 . 1% of all embryonic cells are involved at any developmental stage . Yet TSSs representing germ cell–specific markers such as DDX4 ( CVH ) could still be detected from stage HH1 to stage HH6 , suggesting that most lineage specification events , even during late embryogenesis ( when tissue complexity increases dramatically ) , are captured in our analysis . Taken together , we conclude that our CAGE-based developmental TSS profiling and proof-of-principle analyses will significantly advance avian and mammalian developmental studies . With future improvement in deconvolution , CAGE library-making and RNA-sequencing platforms , avian embryos offer a rare physiological scenario for systems-level study of ontogenetic evolution of cell lineage specification .
Fertilized hens’ eggs were purchased from a local farm ( Shiroyama Farm , Kanagawa , Japan ) . Eggs were incubated at 38 . 5°C to desired stages . For HH1 to HH26 samples ( 1 . 5-hour to 5-day ) , RNAs were isolated from both embryonic and extraembryonic territories . For HH32 , HH37 , HH41 , and HH45 samples ( 7-day , 10-day , 15-day , and 20-day ) , RNAs were isolated from the embryonic territory only . Multiple embryos ( 2–32 ) were used for the collection of samples younger than 3 days old , and single embryos were used for 3-day to 20-day samples . The extraembryonic samples were prepared from extraembryonic tissues ( the amnion , chorion , allantois , and yolk sac ) collected from HH32 and HH41 embryos . The HH41 extraembryonic sample had low sequencing reads and was not included in the PCoA . The fore- and hind-limb bud samples were collected from eggs purchased from Granja Gibert ( Cambrils , Spain ) . The eggs were incubated to stage HH20 . Fore- and hind-limb buds were dissected separately in cold PBS and RNA extracted with Trizol . Primary cell samples were purchased from Cell Applications ( aortic SMCs: #CAC35405 , strain unknown; hepatocytes: custom preparation , Cornish cross strain; MSCs: custom preparation , Rhode Island red strain ) . For electroporation of sgRNA and dCAS9-VP160 expressing constructs , fertilized eggs were grown to stage HH2–HH3 in ovo . Embryos were then isolated , electroporated with expression constructs ( 1 ug/ul final concentration for each construct ) , and cultured ex ovo in a New culture setting as previously described [16 , 58] . Electroporated embryos were monitored periodically for GFP activity ( co-electroporated ) , and after reaching HH10 , the embryos were fixed and processed for RNA in situ hybridization [16] . For expression validation of limb bud–related genes , RNA in situ hybridization was performed in whole-mount following standard procedures using digoxigenin-labeled antisense riboprobes . The probes used were PRRX1 , VEGFD , LHX9 , HOXA11 , CRABP1 , and HOXD10 kindly provided by A . Nieto , M . Davey , and J . Fallon or generated by PCR at MR lab . CAGE libraries were prepared following a standard protocol using 5 ug of total RNA per sample [59 , 60] . Libraries were then subjected to sequencing on the HeliScope single-molecule sequencer following the manufacturer directions [1] . The sequencing data were aligned to the chicken galGal5 genome assembly using delve ( downloadable from fantom . gsc . riken . jp/software/ ) , which is a hidden Markov model–based alignment method developed in house . Sequences that aligned uniquely to the reference genome with 85% identity and 99% accuracy were kept for all downstream analyses . CAGE TSSs were defined for each sample by extracting the first position of all mapped reads and counting their abundance , collapsing all the overlapping positions relative to the strand orientation . Sample details and total mapped tags are listed in S1 Table . Peaks ( promoters ) were defined by applying DPI method as described previously [3] . Promoters were then associated to the closest gene within 500 bp ( both upstream and downstream of the gene TSS ) . Both Ensembl and RefSeq gene models were used in order to achieve the highest coverage . The avian CAGE dataset is available for download at DDBJ ( DNA Data Bank of Japan ) with the accession number DRA004812 . Expression normalization was calculated sample by sample as TPM . An additional normalization step was performed by R bioconductor edgeR package [61] , normalization option “RLE” ( relative log expression ) . Expression enrichment calculation for identification of sample/stage-specific promoters was calculated as the logarithm base 10 of the normalized expression over the median expression across all samples . Several functions in the edgeR package were also used for differential expression ( DE ) analysis between pairs of sample replicates , GO enrichment analysis for differentially expressed genes , and PCoA plots showing the development progression and the samples’ separations . Raw data for PCoA can be accessed at http://fantom . gsc . riken . jp/5/suppl/Lizio_et_al_2017/ . CpG island boundaries were downloaded from UCSC genome browser and the overlaps with TSSs were calculated using bedtools “intersectBed” function . The SI and the GC percent calculation were based on the same procedure applied previously [33] . Promoters were extended up to 100 bp ( ± 50 bp ) from the representative TSS position ( the position with highest expression ) before GC percentage and SI calculation and were subdivided into stage- , tissue- and non-specific in order to assess differences in promoter shape and GC content preference . Grouping into super clusters was achieved by applying the function “mergeBed” from the bedtools suite , by requiring a merging distance of 100 bp between TSSs . This distance ensures that single peak TSSs remain as such , while neighboring TSSs become part of a broader , multimodal promoter . In order to assess promoter size shifts , super clusters were defined for all robust TSSs expressed in 26 samples ( the approximately 31 , 000 peaks ) and also for each stage- and cell-type group separately ( aortic , hepatic , mesenchymal , early devel , mid devel , mid-late devel , late devel , buds , extra-embryo ) . To generate super clusters for each separate group , only TSSs that expressed 1 TPM and above in at least 1 sample of the group were considered , to make them comparable to the entire set of robust TSSs . For TFBS motif analysis , ( −300 , +100 ) bp regions around the TSSs were extracted by bedtools 2 . 25 . The extracted sequences were then scanned for local enrichment estimation using AME tool ( MEME Suite 4 . 11 . 2 ) separately for each of the following subgroups of promoters: aortic , mesenchymal and hepatic primary cells , and early , middle , mid-late , and late embryo developmental tissues . Housekeeping TSSs were also tested as a separate group . The motifs that the TSSs were compared against were taken from JASPAR CORE database for vertebrates ( 2016 ) . The top 50 enriched motifs ( adjusted p-value ≤ 0 . 05 ) for embryo ( all embryo development subgroups ) , primary cell ( aortic , msc , and hepatocyte ) , and other tissue ( buds , extraembryonic ) groups were selected and compared to the housekeeping group in order to identify common motifs .
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Early development cannot be studied in humans . Analysis of embryogenesis using avian models , which are phylogenetically closely related to mammals , can help us understand the complex regulatory mechanism of cell lineage specification during human development . We monitored the 3 weeks of chicken embryonic development from newly-laid egg to the point of hatching via CAGE expression profiling and revealed the first avian genome-wide set of genuine transcription start sites ( TSSs ) critical for differentiation and maturation from pregastrulation to hatching . By analyzing stage-specific expression profiles , we have identified enriched transcription factors responsible for lineage commitment and their corresponding regulatory modules . In addition , we reported a set of stable housekeeping genes more suitable for cross-sample normalization and calibration . Finally , we demonstrated the utility of a CAGE-based TSS dataset in developmental studies . Using the Brachyury gene as an example , we showed that CRISPR/Cas9-based genome-editing tools can be efficiently employed to target and transcriptionally activate the promoters of virtually any endogenous genes , enabled by the knowledge of their precise TSS locations . Our data , made available for easy exploration through the open ZENBU platform , represent an invaluable resource to study early development in amniotes .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"discussion",
"Conclusions",
"Materials",
"and",
"methods"
] |
[
"methods",
"and",
"resources",
"vertebrates",
"chickens",
"animals",
"dna",
"transcription",
"developmental",
"biology",
"sequence",
"motif",
"analysis",
"molecular",
"biology",
"techniques",
"embryos",
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"and",
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"embryology",
"birds",
"bioinformatics",
"gene",
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"gamefowl",
"fowl",
"molecular",
"biology",
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"poultry",
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"genetics",
"biology",
"and",
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"sciences",
"genomics",
"amniotes",
"organisms"
] |
2017
|
Systematic analysis of transcription start sites in avian development
|
Dengue is a febrile illness transmitted by mosquitoes , causing disease across the tropical and sub-tropical world . Antibody prevalence data and serotype distributions describe population-level risk and inform public health decision-making . In this cross-sectional study we used data from a pediatric dengue seroprevalence study to describe historical dengue serotype circulation , according to age and geographic location . A sub-sample of 780 dengue IgG-positive sera , collected from 30 sites across urban Indonesia in 2014 , were tested by the plaque reduction neutralization test ( PRNT ) to measure the prevalence and concentration of serotype-specific neutralizing antibodies according to subject age and geography . PRNT results were obtained from 776 subjects with mean age of 9 . 6 years . 765 ( 98 . 6% ) neutralized one or more dengue serotype at a threshold of >10 ( 1/dil ) . Multitypic profiles were observed in 50 . 9% of the samples; a proportion which increased to 63 . 1% in subjects aged 15–18 years . Amongst monotypic samples , the highest proportion was reactive against DENV-2 , followed by DENV-1 , and DENV-3 , with some variation across the country . DENV-4 was the least common serotype . The highest anti-dengue antibody titers were recorded against DENV-2 , and increased with age to a geometric mean of 516 . 5 [1/dil] in the oldest age group . We found that all four dengue serotypes have been widely circulating in most of urban Indonesia , and more than half of children had already been exposed to >1 dengue serotype , demonstrating intense transmission often associated with more severe clinical episodes . These data will help inform policymakers and highlight the importance of dengue surveillance , prevention and control .
Dengue is a febrile illness caused by dengue virus ( DENV ) infection . The clinical manifestations of dengue occur on a spectrum , ranging from asymptomatic or a mild flu-like syndrome known as classic dengue fever ( DF ) , to a more severe form known as dengue hemorrhagic fever ( DHF ) and the potentially fatal dengue shock syndrome ( DSS ) [1] . DENV , which belongs to the family Flaviviridae , is transmitted by mosquitoes of the genus Aedes; predominantly Aedes aegypti . There are four evolutionarily distinct , antigenically related DENV serotypes; DENV-1 , -2 , -3 , and -4 causing disease across the tropical and sub-tropical world [2 , 3] . Neutralizing antibodies ( NAbs ) against the four serotypes are considered a critical component of the protective immune response which is achieved when adequate , specific antibody titers circulate [4] . Accordingly , plaque reduction neutralization tests ( PRNT ) , which quantify serum concentrations required to neutralize live viruses , are the most specific assays for detecting flavivirus exposure history [5] . The dengue PRNT is able to target individual viral serotypes , and therefore can infer serotype-exposure history , however , interpretation of heterotypic responses is complicated for reasons including original antigenic sin [6 , 7] . Indonesia is the largest archipelago country in the world with over 17 , 000 islands , inhabited by around 240 million people . Dengue was first reported in 1968 , and has been expanding ever since , in both incidence and geography , with an annual burden of >750 , 000 cases [8] . The disease is likely hyperendemic across most islands [9 , 10] . Reporting of DHF in Indonesia is mandatory within 72 hours of diagnosis , health centers and public/private hospitals use the World Health Organization’s ( WHO ) 1997 case definitions [11] and only DHF/DSS cases are reported . Laboratory confirmation of dengue is rare , especially in health services with limited facilities although dengue IgG/IgM and NS1 rapid tests are increasingly used in hospitals and health clinics . Indonesia does not conduct nationally-representative dengue serotype surveillance . Genotypic and serological surveillance has been undertaken by some Indonesian institutions , on a project basis which confirmed the dengue serotypes in symptomatic individuals [12–14] . Those studies include in Makassar , South Sulawesi from 2007–2010 , where dengue infection was confirmed in >100 patients , many of whom were aged 11–20 years old . Serotyping revealed that DENV-1 was the most common form ( 41% ) followed by DENV-2 ( 31% ) , DENV-3 ( 20% ) , and DENV-4 ( 7% ) [15] . In Surabaya , East Java , in 2012 , dengue RNA was isolated from 79 of 148 suspected dengue patients ( 53% ) , with DENV-1 as the predominant serotype ( 73% ) , followed by DENV-2 ( 8% ) , DENV-4 ( 8% ) , and DENV-3 ( 6% ) , while 5% were found to have mixed serotypes [16] . In Semarang , Central Java in 2012 , 66 of 120 suspected cases ( 55% ) were serologically confirmed and viral RNA was detected in 31 samples [12] . DENV-1 was the predominant serotype , followed by DENV-2 , DENV-3 , and DENV-4 . DENV-1 predominance has also been reported from other studies and cities in Indonesia , including Surabaya [17] and Makassar [15] . Finally , from urban and rural areas of Bali ( Denpasar and Gianyar ) , in 2015 , 205 adult patients with suspected dengue were recruited in a prospective cross-sectional study . Of these , 161 patients had virologically-confirmed dengue; DENV-3 was predominant ( 48% ) , followed by DENV-1 ( 28% ) , DENV-2 ( 17% ) , and DENV-4 ( 4% ) . Five samples ( 3% ) were detected which contained two different serotypes , and it was noted that the proportions varied in urban and rural areas [18] . Understanding antibody prevalence is an important consideration in the interpretation of epidemiological data , especially when reviewing interactions with other flaviviruses or considering vaccine introduction . The co-circulation of multiple dengue serotypes is a population-level risk factor for severe dengue disease because of the increased likelihood of a second or subsequent infection , and also due to the fact that sequential infections are associated with increased severity [19] . Serotype distribution may be predictive of future epidemiology and is important information for dynamic transmission models . The objective of this study was to use data from a dengue seroprevalence survey to describe the historical serotype ( DENV-1 , 2 , 3 , 4 ) circulation based on the prevalence of serotype-specific anti-DENV antibodies , according to age and geographic location , in a pediatric population in Indonesia .
In this cross-sectional study , serum samples and data from a national-level pediatric dengue seroprevalence study were used to describe historical dengue serotype circulation , according to age and geographic location . Dengue IgG-positive sera , collected from 30 sites across urban Indonesia , were tested by the PRNT to measure the prevalence and concentration of serotype-specific dengue neutralizing antibodies according to subject age and geography . Surveillance and sample collection methods were previously described [20] . Ethical approval was obtained from the Health Research Ethics Committee of Faculty of Medicine of University of Indonesia ( No . 462/H2 . F1/ETIK/2014 ) . Briefly , between 30 October 2014–27 November 2014 , blood samples were collected from 3 , 210 children aged 1–18 years in 30 urban Indonesian subdistricts , randomly selected from west to east based on the probability proportional to population size . The blood samples were to be tested for dengue IgG by enzyme-linked immunosorbent assay ( ELISA ) . A sub-sample of 780 dengue IgG positive sera was used to estimate the prevalence of serotype-specific neutralizing antibodies by PRNT . The sample size was estimated to provide 95% confidence and a margin error of 5%; this is accounting for the 30 clusters with a design effect of two and assuming the “worst case” of 50% exposure to any one serotype . The sample was not strictly representative of the dengue IgG positive population as the samples were selected equally from each of the four age groups , i . e . 195 samples per age group , and , to provide geographical representativeness , from clusters in proportion to dengue IgG seroprevalence rates . This method over-sampled from younger subjects to; 1 ) increase the number of samples tested from children recently infected with dengue , to provide a record of recent dengue circulation; 2 ) reduce the number of PRNTs performed on samples from older children , likely to have been infected with many serotypes , which may therefore be impossible to meaningfully interpret . The PRNT method was performed based on optimized and validated PRNT50 assay for the detection of neutralizing antibodies to four serotypes of DENV [21] . Each serum sample was heat inactivated at 56°C and assayed in four separate PRNT runs , which corresponded to four different DENV serotype challenge viruses . Vero cells ( CCL-81 ) were obtained from American Type Culture Collection ( ATCC ) . Cells were grown and maintained in Minimum Essential Medium ( MEM ) ( Gibco-Thermo Fisher Scientific , CA , USA ) , supplemented with 5% heat-inactivated Fetal Bovine Serum ( FBS ) , 2 mM of L-glutamine , and 1% of antibiotic/antimycotic ( Gibco-Thermo Fisher Scientific , CA , USA ) at 37°C in an atmosphere of 5% CO2 . Working banks of Vero cells were prepared in-house , qualified , and confirmed to be free of any microbial , mycoplasma , and viral contaminants . Purified mouse monoclonal antibodies ( MAbs ) specific to the DENV serotype envelope protein were used as the primary antibodies for virus detection according to the corresponding serotype: anti-DENV-1 ( D2-1F1-3 ) , anti-DENV-2 ( 3H5-1-12 ) , anti-DENV-3 ( 8A1-2F12 ) , and anti-DENV-4 ( 1H10-6-7 ) ( Biotem , Le Rivier d’Apprieu , France ) . Alkaline phosphatase-conjugated goat anti-mouse IgG ( Jackson Immunoresearch Laboratories , West Grove , PA , USA ) was used as the secondary antibody . The parental DENVs of the recombinant CYD vaccine viruses , i . e . , DENV-1 strain PUO-359 , DENV-2 strain PUO-218 , DENV-3 strain PaH881/88 , and DENV-4 strain 1228 , were used as challenge viruses in the PRNT . The initial source , and the suitability of these four DENV serotypes to be used in dengue neutralization assay have been described elsewhere [21–23] . Dengue-antibody positive and negative human serum sample controls were obtained from healthy adult donors from Indonesia . The serum controls were used in each assay run , and served to monitor its performance and validity . The neutralization titer ( PRNT50 ) of the test serum sample was defined as the reciprocal of the highest test serum dilution for which the virus infectivity was reduced by 50% when compared with the average plaque count of the challenge virus control , calculated using a four-point linear regression method . Since the lowest starting dilution of serum in the assay was 1:5 , the theoretical lower limit of quantitation of the assay was a titer of 10 ( reciprocal dilution ) . This is a descriptive analysis , no hypotheses were tested . The study population mean age was calculated and geographic distribution described . Dengue serotype specific PRNT profiles were defined according to the following algorithm; categorizing samples as naïve ( no previous dengue infection ) , monotypic ( infection with one serotype ) , or multitypic ( >1 serotype ) [24]: PRNT profile distribution by age and geography were described . PRNT profile prevalence and their 95% confidence intervals ( 95% CI ) were calculated , the clusters results were aggregated at province level and a map was generated using QGIS 2 . 16 . 2 “Nødebo” . The mean PRNT titer , GMT ( Geometric Mean Titer ) , and the 95% CI for each age group and dengue serotype was calculated for all samples based on their DENV PRNT results . To calculate the GMT , samples with an antibody titer T <10 ( 1/dil ) were given the value 5 and the mean titer was calculated using the equation: GMT^jh=10^∑1nlog10Tin Where GMT^jh is the mean titer for the dengue serotype h of the age group j , Ti is the PRNT titer of the subjects i and n the number of subjects with a PRNT titer in the age group j for the serotype h . All statistical analyses were performed using Excel 2013 .
Blood samples were collected from 3 , 210 children aged 1–18 years in 30 urban Indonesian subdistricts , randomly selected from west to east . From a sub-sample of 780 dengue IgG positive sera , PRNT50 results were obtained from 776 participants , equally sampled from each age group ( 1–4 , 5–9 , 10–14 and 15–18 years old ) . In the youngest , 1–4 years old group , four serum samples were of insufficient quantity to be tested . The mean age was 9 . 6 years old ( 95% CI [9 . 3–10 . 0] . The 30 clusters were represented with 14–39 samples per cluster . Of these , 765 ( 98 . 6% ) neutralized one or more dengue serotypes at a threshold of >10 ( 1/dil ) , a proportion which varied by age: 95 . 3% in the 1–4 years old , 99 . 5% in the 5–9 years old , 99 . 5% in the 10–14 years old and 100% in the 15–18 years old . Samples were categorized according to PRNT50 profile . Multitypic profiles were observed in 50 . 9% of the subjects , with 28 . 3% in those aged 1–4 years old , 48 . 2% in the 5–9 years old , 63 . 6% in the 10–14 years old and 63 . 1% in those aged 15–18 ( Fig 1 ) . The proportion of monotypic profiles decreased with increasing age , representing 67 . 0% of those aged 1–4 years , 51 . 3% of the 5–9 year old group , 35 . 9% of the 10–14 years old group , 36 . 9% of the 15–18 years old and 47 . 7% of the overall sample . There were no naïve subjects in the 15–18 years old group whereas 4 . 7% of the 1–4 years old group; 0 . 5% of the 1–9 and 10–14 years old groups , and 1 . 4% of the overall sample had no detectable neutralizing dengue antibodies at the 10 ( 1/dil ) threshold . Amongst monotypic samples , the highest proportion of samples were reactive against DENV-2 , followed by DENV-1 , and DENV-3 , a trend which was also observed in the two youngest age groups , while the three serotypes were more evenly distributed amongst the 10–14 and 15–18 years old age groups ( Fig 1 ) . The clusters were aggregated within 14 provinces , resulting in samples per province ranging from 15 to 183 serum samples . In seven provinces multitypic profiles were the most common ( from 52 . 2% to 69 . 4% of samples ) . In seven provinces the monotypic profile was more prevalent ( from 49 . 7% to 68 . 8% ) . DENV-4 was dominant in one province , in the 13 other provinces DENV-1 , DENV-2 , DENV-3 or a combination of these serotypes were dominant , with DENV-2 dominance being more common ( Fig 2 ) . The four monotypic serotypes were identified in every province , with the exception of DENV-2 in Nanggroe Aceh Darussalam and DENV-4 in Sulawesi Tenggara and Sulawesi Selatan . GMTs increased with age . DENV-2 had the highest GMT overall ( 406 . 5 [1/dil] ) and for three of the four age groups with titers of 208 . 8 , 502 . 2 , 497 . 4 and 516 . 5 [1/dil] , respectively ( Fig 3 ) . DENV-4 had the lowest GMT for each age group ( 51 . 2 , 98 . 9 , 138 . 1 and 128 . 2 [1/dil] ) and overall ( 97 . 6 [1/dil] ) . In the oldest subjects , titers against DENV-1 were highest ( 593 . 08 [1/dil] ) followed by DENV-3 ( 550 . 2 [1/dil] ) and DENV-2 .
We conducted a dengue seroprevalence study which identified serological evidence for the circulation of all four dengue serotypes across urban areas of Indonesia , in children who were exposed to infection from 1996 to 2013 . The proportion of children with exposure to >1 serotype increased with age , and children were more likely to have been infected with DENV-2 , DENV-1 and DENV-3 than DENV-4 . Nonetheless , these results show that all four serotypes have been widely circulating in most of Indonesia , as is common in hyper-endemic countries . This study generated data on serotype-specific prevalence in areas where little or no data were previously available , with the exception of historical data from Yogyakarta , Java island [32] . Available dengue serotype data collated from 1994 to 2012 ( n = 596 ) [25] and recent publications from all over Indonesia confirm the concomitant presence of the four DENV serotypes [10 , 12 , 15–18 , 26–28] . Samples were collected from suspected cases and therefore suffer a potential selection bias towards serotypes associated with more symptomatic/severe cases . The serological data we report here indicate a consistent pattern of distribution of serotypes , a finding which may indicate that the cases captured within these surveillance studies is broadly reflective of the DENV serotype circulation in the country . PRNT enables the interrogation of samples according to their exposure history . In this study , it was remarkable to observe that in this pediatric population more than half ( 50 . 9% ) had already been exposed to >1 dengue serotype , a proportion which increased with age . This rate is important because it demonstrates early and intense transmission in Indonesia; and we know that second infections have been described as more likely to be symptomatic , severe and hemorrhagic [29] . Individuals of an age likely to have received one natural exposure , but before their second , may represent an attractive target for dengue vaccination programs [30] . The observed GMT increase with age is most likely explained by continuous re-exposure to DENVs over time , further boosting antibody levels . These profiles imply that existing vector control activities in urban areas are largely insufficient at preventing infection; and that investments in novel methods may be warranted . The prevalence of multitypic profiles further reinforces the requirement for development of a safe and effective , quadrivalent dengue vaccine which could be used in children at highest risk of developing symptomatic and severe disease episodes . Additionally , these data can be useful for the calibration of dengue transmission models which may help to understand disease dynamics and the likely effects of dengue control interventions . There are several limitations to our study . Sera collected during the convalescent phase represent infection history in the population , but are limited by the sensitivity and specificity of the serological methods used to quantify antibodies . We had the benefit of analyzing samples in this study by PRNT; however interpretation of data can be confused by heterotypic cross-neutralization between serotypes . For this reason , we did not interpret the serotype distributions of multitypic infections . Only samples positive for dengue IgG in ELISA screening assay were selected to undergo PRNT , therefore these may not be fully representative of dengue positive sera . We also observed discrepancies between IgG ELISA and PRNT data in which some samples that were positive by IgG ELISA were negative in PRNT ( 1 . 4% ) . This may be a consequence of the well-documented serological cross-reactivity across the flavivirus group [31] Our sample collection was also limited to urban areas and subjects consenting to the study which may have introduced additional bias . In conclusion , this study confirmed the distribution of multiple dengue serotypes across urban Indonesia . Many children were infected with multiple serotypes , and the accompanying risk of severe disease , from an early age . DENV-1 , DENV-2 and DENV-3 may play a more significant epidemiological role than DENV-4 . It is hoped that these data influence policymakers to afford increased attention to dengue surveillance , prevention and control .
|
Dengue is a febrile illness transmitted by mosquitoes , causing disease across the tropical and sub-tropical world . Antibody prevalence data and serotype distribution describe population-level risk and inform public health decision-making . We present data from a dengue seroprevalence study in children in Indonesia; circulation of the four dengue serotypes ( DENV-1 , -2 , -3 , -4 ) was assessed , by age and location . Samples collected from 30 urban Indonesian sites were tested using the plaque reduction neutralization test ( PRNT ) , which enabled us to measure prevalence and concentration of antibodies specific to dengue virus serotypes . Results were obtained from 776 subjects ( mean age: 9 . 6 years ) . 765 ( 98 . 6% ) neutralized ≥1 dengue serotype; the highest proportion was reactive against DENV-2 , followed by DENV-1 , and DENV-3 , with some variation across the country . Reaction to multiple serotypes was observed in 50 . 9% of samples . The highest anti-dengue antibody titers were recorded against DENV-2 , and increased with age . The fact that all four dengue serotypes have been widely circulating in urban Indonesia , and more than half of children had been exposed to >1 dengue serotype , shows intense transmission , often associated with more severe clinical episodes . These data will help inform policymakers and highlight the importance of dengue surveillance , prevention and control .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"dengue",
"virus",
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"proteins",
"infectious",
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"geography",
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"medical",
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"dengue",
"fever",
"microbial",
"pathogens",
"immunoassays",
"historical",
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"and",
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"biochemistry",
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] |
2018
|
Dengue virus serotype distribution based on serological evidence in pediatric urban population in Indonesia
|
Burkholderia pseudomallei is an environmental Gram-negative bacillus and the cause of melioidosis . B . thailandensis , some strains of which express a B . pseudomallei-like capsular polysaccharide ( BTCV ) , is also commonly found in the environment in Southeast Asia but is considered non-pathogenic . The aim of the study was to determine the distribution of B . thailandensis and its capsular variant in Thailand and investigate whether its presence is associated with a serological response to B . pseudomallei . We evaluated the presence of B . pseudomallei and B . thailandensis in 61 rice fields in Northeast ( n = 21 ) , East ( n = 19 ) and Central ( n = 21 ) Thailand . We found BTCV in rice fields in East and Central but not Northeast Thailand . Fourteen fields were culture positive for B . pseudomallei alone , 8 for B . thailandensis alone , 11 for both B . pseudomallei and B . thailandensis , 6 for both B . thailandensis and BTCV , and 5 for B . pseudomallei , B . thailandensis and BTCV . Serological testing using the indirect hemagglutination assay ( IHA ) of 96 farmers who worked in the study fields demonstrated that farmers who worked in B . pseudomallei-positive fields had higher IHA titers than those who worked in B . pseudomallei-negative fields ( median 1:40 [range: <1:10–1:640] vs . <1:10 [range: <1:10–1:320] , p = 0 . 002 ) . In a multivariable ordered logistic regression model , IHA titers were significantly associated with the presence of B . pseudomallei ( aOR = 3 . 7; 95% CI 1 . 8–7 . 8 , p = 0 . 001 ) but were not associated with presence of B . thailandensis ( p = 0 . 32 ) or BTCV ( p = 0 . 32 ) . One sequence type ( 696 ) was identified for the 27 BTCV isolates tested . This is the first report of BTCV in Thailand . The presence of B . pseudomallei and B . thailandensis in the same field was not uncommon . Our findings suggest that IHA positivity of healthy rice farmers in Thailand is associated with the presence of B . pseudomallei in rice fields rather than B . thailandensis or BTCV .
Burkholderia pseudomallei is a soil-dwelling Gram-negative bacterium and the cause of melioidosis , a frequently fatal infectious disease of humans and animals . Humans acquire the disease following skin inoculation , inhalation or ingestion of the bacterium from the environment . The disease is highly endemic in Southeast Asia and Northern Australia [1] , and is increasingly being reported in South Asia , Africa and Central and South America [2 , 3] . A recent modeling study estimated that there are about 165 , 000 human melioidosis cases per year worldwide , of whom 89 , 000 ( 54% ) die [4] . The current diagnostic standard for melioidosis is microbiological culture [5] . However , melioidosis is difficult to diagnose due to its diverse clinical manifestations , the inadequacy of conventional bacterial identification methods , and a lack of microbiology laboratories in tropical developing countries [5] . An indirect haemagglutination assay ( IHA ) is the most frequently used serological test for melioidosis , but may be misleading when used for the diagnosis of melioidosis in disease-endemic regions [5] . This is because the background seropositivity ( IHA titers ≥1:160 ) ranges from 4% to 32% in healthy individuals living in areas where melioidosis is endemic [6–8] . Therefore , IHA is recommended as a serological standard to assess exposure to B . pseudomallei [5] . Burkholderia thailandensis was first recognized by Wuthiekanun et al . in 1996 [9] . The organism is genetically closely related to B . pseudomallei , can be isolated from environmental soil and water , and is non-pathogenic [9–11] . The colony morphology of B . thailandensis and B . pseudomallei are very similar , but B . thailandensis can assimilate L-arabinose [12 , 13] . In addition , B . thailandensis has polysaccharide-related genes that are distinct from B . pseudomallei ( 74 . 8% and 72 . 8% nucleotide and protein similarity , respectively ) and usually lacks the virulence-associated capsular polysaccharide ( also referred to as CPS or CPS-I ) of B . pseudomallei [14–17] . The geographical distribution of B . thailandensis is uncertain but the organism has rarely been isolated from fields that are culture positive for B . pseudomallei [18 , 19] . It was recently shown that B . pseudomallei can inhibit the growth and motility of B . thailandensis in the laboratory [20] . However , previous environmental studies did not systematically evaluate the presence of both organisms , so the presence of B . thailandensis and co-localization of both organisms may have been underestimated [18 , 19] . In an experimental mouse model , lipopolysaccharide extracted from B . thailandensis induced measurable IgG and IgM , and provided partial protection against melioidosis [21] . The association between exposure to environmental B . thailandensis and IHA seropositivity in humans is still largely unknown . A variant of B . thailandensis originally isolated from soil in Cambodia ( E555 ) that contained genes encoding a B . pseudomallei-like capsular polysaccharide cluster ( BTCV ) was described in 2010 [17] . This organism exhibited several B . pseudomallei-like phenotypes including colony wrinkling , resistance to human complement binding , and intracellular macrophage survival . However , in mice E555 was avirulent [17] , induced higher levels of IgG and gave better protection against melioidosis than non-capsulated B . thailandensis [22] . The capsular polysaccharide ( CPS ) biosynthesis gene cluster of E555 and that of B . pseudomallei are highly similar ( 94 . 4% and 96% nucleotide and protein similarity , respectively ) [17] , and nuclear magnetic resonance spectroscopy has shown that the structures of CPS produced by E555 and that of B . pseudomallei are identical [23] . Previously , BTCV has been isolated from human blood in the USA in 2003 ( strain CDC3015869; ST101 , USA [24] ) and from environmental samples in Cambodia in 2010 ( strain E555; ST696 ) , Gabon in 2013 ( strain D50; ST1126 [25] ) and Laos in 2015 ( strain ST_10; ST696 [26] ) . BTCV has not been reported in Thailand and its distribution is unknown . We recently reported the presence of B . pseudomallei in 61 rice fields in the Northeast , East and Central Thailand , and its association with soil physicochemical properties [27] . Here , we report the presence of B . thailandensis and co-localization between B . pseudomallei and B . thailandensis in the same rice fields , and provide the first report of the BTCV in Thailand . In addition , we explored whether exposure to B . thailandensis and BTCV is associated with background seropositivity to B . pseudomallei by evaluating IHA levels in healthy adults who worked in the sampled rice fields .
East , Central and Northeast Thailand consist of 7 , 21 and 20 provinces , cover 34 , 381 , 93 , 005 and 168 , 854 km2 , and had estimated populations in 2013 of 3 . 9 , 18 . 7 and 23 . 3 million , respectively [28] . Northeast Thailand is a plateau surrounded by mountain ranges , and most of the arable land consists of tropical sandy soil . East Thailand is characterized by short mountain ranges alternating with alluvial plains . Central Thailand is a large plain consisting of clay soil . Rice farming is the predominant form of agriculture in all three regions . In Thailand , for administrative purposes each province is sub-divided into districts , sub-districts , communes and villages . The majority of the population in all three regions live in rural settings and most adults are engaged in agriculture , particularly rice farming . In 2013 , areas used for agriculture were 57% , 48% and 60% in East , Central and Northeast Thailand , respectively [29] . We conducted a cross-sectional environmental survey as described previously [27] . All B . pseudomallei , B . thailandensis and BTCV reported in this work were from the same environmental survey [27] . In brief , we collected soil from randomly-selected rice fields in the East , Central and Northeast regions during the dry season ( from April–June ) in 2013 , 2014 and 2015 , respectively . We sampled rice fields that had been used for rice farming in the 12 months prior to the sampling date . We collected the blood from farmers who were exposed to the sampled rice fields in the 12 months prior to the blood collection date . Written , informed consent was obtained from land owners and farmers prior to soil sampling and blood collection , respectively . The study protocol was approved by the Ethics Committee of the Faculty of Tropical Medicine , Mahidol University ( MUTM 2013-021-01 ) and the Oxford Tropical Research Ethics Committee , University of Oxford ( OXTREC 1013–13 ) . The method used for soil sampling was described previously [27 , 30] . In brief , each rice field was divided into a 10x10 grid system to generate 100 sampling points per field . At each sampling point , around 30 grams of soil was removed from the base of a 30-cm hole . All soil samples were processed within 48 hours of collection for the identification of B . pseudomallei and B . thailandensis , and for soil physicochemical properties . One kilogram of soil from each sampling field was made by aggregating 100 soil samples ( 10 g per each sampling point ) and evaluated for soil physicochemical properties as described previously [27] . Ten grams of soil from each sampling point was mixed with 10 ml of enrichment broth consisting of threonine-basal salt solution plus colistin 50mg/L ( TBSS-C50 broth ) and incubated at 40°C in air for 48 hours . Ten microliters of surface liquid was then streaked onto Ashdown agar containing gentamicin 8mg/L and crystal violet 5mg/L using a calibrated loop , incubated at 40°C in air , and examined every day for 4 days for bacterial colonies suggestive of B . pseudomallei or B . thailandensis [31 , 32] . B . pseudomallei can have seven colony morphotypes on Ashdown agar [32] , and cannot readily be distinguished from the colonies of B . thailandensis [9 , 12 , 13] . For each soil specimen , a total of up to five presumptive colonies of B . pseudomallei or B . thailandensis were picked and evaluated by a latex agglutination test that is highly specific for B . pseudomallei CPS [16 , 33 , 34] , and the L-arabinose assimilation test . B . pseudomallei was defined based on a positive latex agglutination test and negative L-arabinose assimilation . B . thailandensis was defined based on a negative latex agglutination test and positive L-arabinose assimilation [9 , 16 , 17] . Colonies which had positive results for both the latex agglutination and L-arabinose assimilation tests were defined as BTCV , as previously described [17] . The monoclonal antibody used in the latex agglutination test has been shown to be positive for B . pseudomallei and BTCV ( strains E555 and CDC3015869 ) , and negative for B . thailandensis [35 , 36] . Because BTCV was found in rice fields in East and Central Thailand , we randomly selected three isolates of BTCV from each culture positive field and genotyped these using multilocus sequence typing ( MLST ) [37] . The alleles at each of the seven loci were assigned by comparing the sequences to those on the B . pseudomallei MLST website ( https://pubmlst . org/bpseudomallei/ ) . Information and the sequence type of BTCV reported in this work has been deposited in the global MLST database ( https://pubmlst . org/bpseudomallei/ ) . Blood samples were collected from rice farmers who had worked in the sampled rice fields in the 12 months prior to the sampling date . The detection of antibodies against B . pseudomallei was performed using the IHA , as described previously [38 , 39] . The antigen used in the IHA was derived from a pool of two clinical B . pseudomallei isolates , 199a and 207a , obtained from melioidosis patients in Ubon Ratchathani , Thailand . The negative control was pooled sera from three patients with no detectable IHA titers . The positive control was pooled sera from three patients with known positive IHA . In this study , for binary comparisons , an IHA titer of <1:80 was defined as negative based on the previous report that healthy US donor could have IHA titers up to 1:40 [40] . To determine the optimal sample size , we performed a pilot study of soil sampling in four rice fields in Chachoengsao province , East Thailand . Three and four rice fields were culture positive for B . pseudomallei ( 75%; 3 of 4 ) and B . thailandensis ( 100%; 4 of 4 ) , respectively . We calculated that 60 rice fields ( 3 rice fields per province ) and 60 rice farmers were needed to determine environmental factors associated with presence of B . pseudomallei and B . thailandensis , and evaluate the association between IHA titers and presence of both organisms , respectively , with a power of 80% at an alpha error of 5% . We evaluated ( 1 ) positivity of B . thailandensis and BTCV in the rice fields , ( 2 ) co-localization and correlation between B . pseudomallei , B . thailandensis and BTCV in the rice fields , ( 3 ) soil properties associated with presence of B . thailandensis and BTCV , and ( 4 ) association between IHA levels and presence of those organisms in the rice fields . Fisher’s exact test and Mann-Whitney test were performed to compare binary and ordinal variables , respectively . McNemar’s test was used to compare the presence of two organisms . We assessed co-localization between organisms in the rice fields by using Kappa value . The Kappa value was used to describe the agreement of presence and absence of the organism in rice fields , 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 [41] . A Spearman rank correlation coefficient ( Spearman’s rho ) was used to assess correlations among the total number of sampling points culture-positive for B . pseudomallei , B . thailandensis and BTCV in the rice fields . Spearman’s rho close to 0 indicates no correlation , while Spearman’s rho close to 1 ( or -1 ) indicates a strong positive ( or negative ) correlation between the organisms [42] . We used ordered logistic regression to determine the associations between IHA level and presence of the three organisms . As we sampled more than one rice farmer per field , the analysis was stratified by rice field . Multivariable ordered logistic regression models were developed using a purposeful selection method [43] . In brief , a univariable ordered logistic regression model was used to preliminarily evaluate the crude association between presence of B . pseudomallei , B . thailandensis and BTCV and IHA titers . We decided a priori to evaluate their independent associations in a multivariable ordered logistic regression model , and presence of B . pseudomallei , B . thailandensis and BTCV were all included in the final multivariable model . Sensitivity analysis was conducted by grouping B . thailandensis and BTCV as B . thailandensis . All statistical tests were performed using STATA version 14 . 0 ( StataCorp LP , College Station , Texas ) . The final database with the data dictionary are publicly available online ( https://doi . org/10 . 6084/m9 . figshare . 4928993 ) .
Of 6 , 100 soil samples collected from 61 rice fields , 826 ( 14% ) were culture positive for B . thailandensis . The percentages of rice fields that were culture-positive for B . thailandensis were 29% ( 6 of 21 rice fields ) , 63% ( 12 of 19 rice fields ) and 57% ( 12 of 21 rice fields ) in Northeast , East and Central Thailand , respectively ( Fig 1 ) . There was borderline evidence that culture-positivity for B . thailandensis was higher in the East than the Northeast ( 63% vs . 29% , p = 0 . 055 ) , while there was no significant difference between East and Central ( 63% vs . 57% , p = 0 . 76 ) or Northeast and Central regions ( 29% vs . 57% , p = 0 . 12 ) . For the rice fields that were culture-positive for B . thailandensis , the median numbers of positive sampling points were 6 . 5 ( range 1 to 27 ) , 26 . 5 ( range 1 to 100 ) and 10 . 5 ( range 1 to 85 ) in Northeast , East and Central Thailand , respectively ( S1 Table ) . There was a trend towards the median number of positive sampling points for B . thailandensis being lower in the Northeast than the East ( p = 0 . 08 ) , while there was no significant difference between Central versus Northeast or East Thailand ( p = 0 . 40 and 0 . 52 , respectively ) . BTCV was isolated from 11 of 61 ( 18% ) rice fields in the East ( 7 fields ) and Central ( 4 fields ) regions but it was not isolated from rice fields in Northeast Thailand ( Fig 1 ) . Overall , the proportion of fields positive for the BTCV was lower than that for B . thailandensis ( 18% vs . 49% , p<0 . 001 ) . The percentage of rice fields that were culture positive for BTCV in the East and Central regions was not significantly different ( 37% [7/19] vs . 19% [4/21] , p = 0 . 29 ) . The median numbers of positive sampling points in the East and Central regions were also not significantly different ( 3 [range 1 to 24] vs . 4 [range 1 to 8] , p = 0 . 45 ) . We previously reported the isolation of B . pseudomallei from 30 of 61 rice fields included in this study [27] . Fig 2 shows the number of rice fields from which B . pseudomallei , B . thailandensis and BTCV were isolated . Of 61 rice fields , 14 ( 23% ) were positive for B . pseudomallei alone , 8 ( 13% ) positive for B . thailandensis alone , 11 ( 18% ) positive for both B . pseudomallei and B . thailandensis , 6 ( 10% ) positive for B . thailandensis and BTCV , and 5 ( 8% ) positive for B . pseudomallei , B . thailandensis and BTCV . Co-localization of B . pseudomallei and B . thailandensis in the same rice field was not more frequent than expected by chance ( Kappa value 0 . 08 , p = 0 . 26 ) . The numbers of sampling points per rice field that were culture positive for B . pseudomallei and for B . thailandensis were not correlated ( Spearman’s rho -0 . 02 , 95%CI -0 . 27 to 0 . 23 , p = 0 . 89 ) . A sensitivity analysis was conducted by considering BTCV as B . thailandensis , which gave a comparable result ( Spearman’s rho -0 . 02 , 95%CI -0 . 27 to 0 . 23 , p = 0 . 87 ) . All eleven fields culture positive for BTCV were also culture positive for B . thailandensis ( Fig 2 ) . There was a fair agreement between presence of B . thailandensis and BTCV ( Kappa value 0 . 37 , p<0 . 001 ) , and a strong correlation between the total number of sampling points culture positive for the two organisms ( Spearman’s rho 0 . 68 , 95% CI 0 . 51 to 0 . 79 , p<0 . 001 ) . Of 6 , 100 soil samples collected , 975 ( 16% ) were positive for B . pseudomallei alone , 706 ( 12% ) positive for B . thailandensis alone , 24 ( 0 . 4% ) positive for BTCV alone , 69 ( 1% ) positive for both B . pseudomallei and B . thailandensis , 1 ( 0 . 02% ) positive for B . pseudomallei and BTCV , 50 ( 0 . 8% ) positive for B . thailandensis and BTCV , and 1 ( 0 . 02% ) positive for B . pseudomallei , B . thailandensis and BTCV . There was a slight agreement between presence of B . thailandensis and BTCV in the same soil sample ( Kappa value 0 . 09 , p<0 . 001 ) . Co-localization of B . pseudomallei and B . thailandensis in the same soil sample was also not greater than that expected by chance ( p>0 . 99 ) . Associations between soil physicochemical properties and the presence of B . thailandensis were not observed ( S2 Table ) . Presence of BTCV was negatively associated with cation exchange capacity , which represents the total nutrient fixing capacity of soil ( p = 0 . 05 ) , and associated with the level of total nitrogen ( p = 0 . 04; S3 Table ) . The associations were also observed in the multivariable model ( S4 Table ) . A total of 27 isolates of BTCV from 76 culture positive sampling points for BTCV in 11 rice fields in East and Central Thailand were randomly selected for MLST ( S5 Table ) . All 27 isolates belonged to sequence type ( ST ) 696 , which was identical to the ST of BTCV strain E555 reported from soil in Cambodia [17] . We had previously reported a single B . pseudomallei isolate ( strain A-330-05-1-04 ) from drinking water in Ubon Ratchathani , northeast Thailand , as ST696 [44] . The isolate was re-evaluated . The isolate was found to be positive for both latex agglutination and L-arabinose assimilation and was thus re-classified as BTCV . Of 96 rice farmers included in the analysis , 29 , 35 and 32 were from Northeast , East and Central Thailand , respectively . The median number of farmers per rice field was 1 ( range 1 to 5 ) . Sixty-two farmers ( 65% ) were male and median age was 51 years ( range 23–75 years ) . Six farmers ( 6% ) had a known diagnosis of diabetes . Overall , 27 ( 28% ) farmers had a positive IHA ( IHA titers ≥1:80 ) . Forty eight farmers who worked in rice fields culture-positive for B . pseudomallei had higher IHA titers than the 48 farmers who worked in rice fields culture-negative for the organism ( median 1:40 [range: <1:10–1:640] vs . <1:10 [range: <1:10–1:320] , p = 0 . 002 ) ( Fig 3 ) . Proportion of farmers who had positive IHA ( IHA ≥1:80 ) was also significantly higher in rice field culture-positive for B . pseudomallei than the farmers who work in rice field culture-negative for B . pseudomallei ( 44% vs . 13% , p = 0 . 001 ) . In the univariable ordered logistic regression model , IHA titers were associated with the presence of B . pseudomallei ( OR = 3 . 39; 95% CI 1 . 66–6 . 90 , p = 0 . 001 ) but not with presence of B . thailandensis ( OR = 0 . 92; 95% CI 0 . 44–1 . 91 , p = 0 . 82 ) or BTCV ( OR = 1 . 04; 95% CI 0 . 60–1 . 80 , p = 0 . 89 ) . A multivariable ordered logistic regression model was used to evaluate independent association between IHA titers of rice farmers and presence of each organism . IHA titers were independently associated with the presence of B . pseudomallei ( aOR = 3 . 72; 95% CI 1 . 76–7 . 84 , p = 0 . 001 ) but not associated with the presence of B . thailandensis ( p = 0 . 32 ) or BTCV ( p = 0 . 32 , Table 1 ) .
Here , we present data on the spatial distribution of B . pseudomallei , B . thailandensis and BTCV in Northeast , East and Central Thailand . This is the first study to report the isolation of BTCV from soil in Thailand , although we did find that a Thai water isolate previously identified as B . pseudomallei was actually an example of BTCV [44] . B . thailandensis was commonly isolated in all three regions , while BTCV was less common but associated with B . thailandensis . Co-localization of B . thailandensis and B . pseudomallei was not uncommon . Our findings also suggest that IHA positivity of healthy rice farmers was associated with exposure to B . pseudomallei rather than to B . thailandensis or BTCV . This supports the recommendation that IHA could be used to measure exposure to environmental B . pseudomallei [5] , even in areas containing other closely related Burkholderia species . Our finding of co-localization of B . pseudomallei and B . thailandensis is consistent with a previous environmental study in Khon Kaen , northeast Thailand [11] . B . thailandensis has been reported from many melioidosis-endemic countries; including Thailand , Laos , Vietnam [37] , Cambodia ( https://pubmlst . org/bpseudomallei/ ) , Australia [45] , Papua New Guinea [46] , Kenya [37] and Gabon [25] . This is probably highly influenced by the locations of melioidosis research groups . B . thailandensis has also been reported in two non melioidosis-endemic countries; France and the United States [24 , 37] . B . thailandensis strain 82172 ( ST73 ) was isolated from the intestine of a foal in France in 1982 , while B . thailandensis strain CDC2721121 ( ST73 ) was isolated from the pleural wound of a 76 year old male from Louisiana , USA , in 1997 [24 , 37] . As strain CDC2721121 was isolated from a wound sample its pathogenicity in humans cannot be assumed and this strain was avirulent in a mouse model [22] . Although BTCV was common in both Central and East Thailand , its prevalence is lower than that of wild-type B . thailandensis . Our analysis suggested that BTCV was associated with soil with low cation exchange capacity and high levels of total nitrogen . Although Sim et al . raised the possibility that acquisition of the B . pseudomallei-like CPS in E555 might improve its environmental fitness [17] , this is not supported by our findings . It is also possible that the environment we studied is not representative of the environmental niche which induced B . thailandensis to acquire the B . pseudomallei-like gene cluster . The finding that all BTCV isolates obtained from different geographical areas in Thailand , Laos and Cambodia were ST696 suggests that these may have arisen from a single ancestor . BTCV isolates in USA ( ST101 ) [24] and Gabon ( ST1126 ) [25] are single- and triple-locus variants of ST696 , respectively ( S5 Table ) . Previous phylogenetic analysis suggested that ST101 and ST696 are closely related and possibly share the same ancestor [17] . Studies using whole genome sequencing of BTCV and B . thailandensis from different regions are required to further understand the genetic diversity and evolution of this organism . Our results suggest that exposure to environmental B . thailandensis and BTCV makes a limited contribution to IHA seropositivity in farmers . In animal models , antibodies can be detected after intraperitoneal inoculation of B . thailandensis and BTCV [22] . Intraperitoneal inoculation can lead to rapid dissemination of B . thailandensis or BTCV by bypassing natural host defenses [22] , and induces a serological response . Nonetheless , human exposure to B . thailandensis and BTCV in the natural environment rarely if ever leads to infection , unlike exposure to B . pseudomallei . This is also supported by the finding that intra-nasal inoculation of high dose B . thailandensis or BTCV ( 106 colony forming unit [CFU] ) did not cause death in BALB/c mice with rapid bacterial clearance and no visible abscess formation in sacrificed mice , whilst the LD50 of B . pseudomallei was less than 300 CFU in the same experiment [17] . Our study has several limitations . Soil sampling was performed during the dry season over a period of three years . We chose to sample during the dry season to control the variation in presence of the three organisms , human exposure and soil physicochemical properties associated with seasonal changes . It is possible that the presence of B . thailandensis and BTCV could vary according to the season . Farmers may work in multiple rice fields , and be exposed to B . pseudomallei in untested fields . Our study may have also detected more positive samples for B . thailandensis and BTCV if more than five colonies had been tested from each sampling point or using other identification methods such as PCR . In summary , our large cross-sectional environmental survey has defined the distribution of B . thailandensis and BTCV in Thailand . This is the first report of BTCV in Thailand , which appears to be less common than wild-type B . thailandensis . Our findings also suggest that exposure to B . thailandensis or BTCV in the environment makes a limited contribution to IHA positivity amongst healthy farmers .
|
Burkholderia thailandensis is a non-pathogenic soil-dwelling bacterium and is genetically closely related to Burkholderia pseudomallei , the cause of melioidosis . In mouse models , inoculation of a variant of B . thailandensis which express a B . pseudomallei-like capsular polysaccharide ( BTCV ) induces antibodies and partial protection against melioidosis . Here , we evaluated the presence of B . pseudomallei , B . thailandensis , and BTCV in 61 rice fields in Northeast , East and Central Thailand , determined whether they co-existed , and if their presence was associated with a serological response in farmers . We report the presence of BTCV in Thailand for the first time and describe the distribution of the three organisms . Co-localization between these organisms in the same rice fields was not uncommon . Our findings suggested that serological positivity based on the indirect hemagglutination assay ( a test commonly used to detect antibodies to B . pseudomallei ) in healthy rice farmers in Thailand was associated with exposure to B . pseudomallei , but not exposure to B . thailandensis or BTCV .
|
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2018
|
Presence of B. thailandensis and B. thailandensis expressing B. pseudomallei-like capsular polysaccharide in Thailand, and their associations with serological response to B. pseudomallei
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Chagas disease is one of the most important public health problems and a leading cause of cardiac failure in Latin America . The currently available drugs to treat T . cruzi infection ( benznidazole and nifurtimox ) are effective in humans when administered during months . AmBisome ( liposomal amphotericin B ) , already shown efficient after administration for some days in human and experimental infection with Leishmania , has been scarcely studied in T . cruzi infection . This work investigates the effect of AmBisome treatment , administered in 6 intraperitoneal injections at various times during acute and/or chronic phases of mouse T . cruzi infection , comparing survival rates and parasitic loads in several tissues . Quantitative PCR was used to determine parasitic DNA amounts in tissues . Immunosuppressive treatment with cyclophosphamide was used to investigate residual infection in tissues . Administration of AmBisome during the acute phase of infection prevented mice from fatal issue . Parasitaemias ( microscopic examination ) were reduced in acute phase and undetectable in chronic infection . Quantitative PCR analyses showed significant parasite load reductions in heart , liver , spleen , skeletal muscle and adipose tissues in acute as well as in chronic infection . An earlier administration of AmBisome ( one day after parasite inoculation ) had a better effect in reducing parasite loads in spleen and liver , whereas repetition of treatment in chronic phase enhanced the parasite load reduction in heart and liver . However , whatever the treatment schedule , cyclophosphamide injections boosted infection to parasite amounts comparable to those observed in acutely infected and untreated mice . Though AmBisome treatment fails to completely cure mice from T . cruzi infection , it impedes mortality and reduces significantly the parasitic loads in most tissues . Such a beneficial effect , obtained by administrating it over a short time , should stimulate studies on using AmBisome in association with other drugs in order to shorten recovery from T . cruzi infection .
Chagas disease is a leading cause of cardiac failure and the most important parasitic disease in terms of morbidity and mortality in Latin America . Its causal agent , the protozoan parasite Trypanosoma cruzi , belonging to the family of Trypanosomatidae , currently infects 8 to 10 million persons . It is usually transmitted by the faeces of bloodsucking insect vectors ( Hemiptera Reduviidae ) , human-to-human by infected blood products ( and solid organ transplants ) , and from mother-to-child [1]–[3] . Large-scale migration of Latin Americans over the last few decades has contributed to Chagas disease becoming a global health issue [4] . There is currently an increased risk of transmission via infected blood products and/or congenital transmission in non-endemic countries , particularly in United States and Europe [5]–[8] . After an acute phase , generally asymptomatic though sometimes fatal in children , the infection evolves to an asymptomatic and silent chronic phase . Decades after primary infection , 30 to 40% of infected individuals develop a symptomatic chronic either cardiac ( the most frequently encountered ) and/or digestive clinical form of Chagas disease ( megacolon and/or megaoesophagus ) , responsible for an important morbi-mortality [1] , [3] . The currently used trypanocidal drugs , benznidazole and nifurtimox , were developed empirically in the 1960s and 1970s , respectively . They are more effective in early acute infection than in the late well established chronic phase . Though the lack of good markers complicates the validation of parasitic cure , these drugs appear preventing progression of cardiac chronic lesions [1] , [9]–[11] . However , such drugs have to be taken for 1 to 3 months and can induce severe side effects conducing to stop the treatment [12] . Thus , the chemotherapy of Chagas disease remains an unsolved problem , and alternative or novel drugs are needed . Numbers of different compounds have been assayed in a variety of ways , even though none emerged as a new efficient treatment [13] , [14] . The macrolide polyene amphotericin B is known to bind to sterols of eukaryotic cell membranes , inducing alterations of cell permeability and cell death . While amphotericin can bind to the cholesterol component of mammalian cells , inducing cytotoxic effects , it has a higher affinity for ergosterol , a component of the fungal cell membrane , leading to their specific killing . To minimize the toxic side-effects of amphotericin , a liposomal formulation of this molecule named AmBisome has been developed [15] , and is a current and potent treatment of invasive fungal infections with Candida and Aspergillus [16]–[18] . As trypanosomatids also present ergosterol as component of their membranes [19] , AmBisome might also be effective against infections with such parasites . Clinical trials have demonstrated the high efficacy of AmBisome treatment in human visceral leishmaniasis , leading to consider it as the first-line treatment for this disease [20]–[24] . Although ergosterol is also the predominant membrane sterol of T . cruzi [25] , [26] , few data are currently available on the effect of amphotericin on this parasite . Though several studies showed its in vitro trypanocidal activity [27]–[33] , only one report described the in vivo effect of four amphotericin B formulations in mice acutely infected with T . cruzi . This latter study showed that a single dose of 25 mg/kg of AmBisome suppresses acute infection ( on the basis of blood microscopic observations ) , whereas other amphotericin B lipid formulations increased the survival rate but did not eradicate infection in all animals [34] . On the basis of these results and aiming to obtain more information on the efficacy of AmBisome as a potential drug for Chagas disease , we have investigated thoroughly its effect in both acute and chronic phases of mouse T . cruzi infection . We have tested various schemes of treatment and studied by quantitative PCR the parasitic loads in several organs known to host parasite multiplication ( heart , skeletal muscle , adipose tissue , spleen and liver ) .
BALB/cJ mice were obtained from Janvier ( Le Genest-St-Isle , France ) and were maintained in our animal facilities in compliance with the guidelines of the ULB ( Université Libre de Bruxelles ) Ethic Committee for the use of laboratory animals ( protocol 51 approved by CEBEA , Brussels , Belgium ) . Mice were infected at 6 weeks-old by intra-peritoneal ( i . p . ) injection of 1 , 000 blood trypomastigotes of the Tulahuen strain of T . cruzi ( genotype TcVI; [35] ) . Blood parasitaemias were regularly determined by microscopic examination of tail vein blood , with a detection limit of 10 , 000 parasites/mL [36] . Mice were treated with 6 i . p . injections of AmBisome ( Gilead , Paris , France; 25 mg/kg ) given on alternate days starting either on the first day post-inoculation ( dpi 1 ) , during the acute parasitemic phase ( dpi 10 ) , the chronic phase ( dpi 45 ) or both phases of infection . ( dpi 10 and dpi 45 ) . The tested dose ( 25 mg/kg ) derived from the previous report of Yardley et al . [34] . Schedules and doses of AmBisome treatments , as well as mouse groups , are described in Table 1 . Some chronically-infected mice were submitted to cyclophosphamide ( Endoxan , Baxter , Belgium ) immunosuppressive treatment ( 4 i . p . injections of 200 mg/kg on alternate days ) as previously described [37] . On days 21 ( acute infection ) or 74 post-inoculation ( chronic infection ) , mice were bled out under gazeous anesthesia via retro-orbital puncture and blood collected in citrated microtubes . Heart , liver , spleen , thigh muscle , and white adipose tissue ( dorsal subcutaneous ) were harvested after thoroughly flushing the entire mouse body with PBS [38] , in order to avoid contamination of collected tissues with blood parasites . Blood and tissue samples were aliquoted and stored at −80°C until DNA extraction . Organ pieces ( 50 mg ) were disrupted using Magna Lyser instrument ( Roche Diagnostics , Brussels , Belgium ) at 6 , 500 rpm for 50 s in Green Beads tubes ( Roche Applied Science , Brussels , Belgium ) containing 400 µl of DNA Tissue buffer ( Mole Genetics AS , Lysaker , Norway ) . Then 800 µg Proteinase K ( Roche Applied Science , Brussels , Belgium ) were added to the disrupted samples , and incubated for 4 h at 56°C . DNA extraction was performed on 200 µL of blood or organ digested samples using GeneMole apparatus and DNA Blood/Tissue kits ( Mole Genetics AS , Lysaker , Norway ) , and eluted in 200 µL of GeneMole Elution buffer , according to the manufacturer recommendations . The standards for the quantitative PCR ( qPCR ) reactions were generated from tissue homogenates of non-infected mice ( 50 mg of heart , liver , spleen , skeletal muscle , adipose tissue , prepared as mentioned above ) , to which 106 T . cruzi culture trypomastigotes were added , as previously described [39] . DNA ( from tissues spiked with parasites ) , extracted as mentioned above , was serially diluted with 25 µg/mL of DNA obtained from tissues without added parasites . The 10-fold diluted prepared standards contained DNA from 105 to 10−2 parasites equivalents per 50 ng of total DNA . A standard curve was generated from these standards to determine the DNA parasitic load in organs of infected mice . Infected blood standards were prepared by 10-fold serial dilutions of 500 µL of fresh mouse blood artificially spiked with 108 T . cruzi trypomastigotes , as already described [40] . DNA was extracted from each dilution as described above and a standard curve ranging from 2×108 to 2×10−1 parasites/mL ( corresponding to 2×105 to 2×10−4 parasite equivalents per assay ) was generated to determine the parasitic DNA load of infected mouse blood . Real-time PCR was performed using a LightCycler® 480 system ( Roche Diagnostics Brussels , Belgium ) according to the manufacturer's instructions . Reactions were performed in a 25 µL final volume with either 160 nM T . cruzi 195-bp repeat DNA specific primers ( Invitrogen , Carlsbad , California ) TcZ1 5′-CGAGCTCTTGCCCACACGGGTGCT-3′ and TcZ2 5′-CCTCCAAGCAGCGGATAGTTCAGG-3′ [41] or 160 nM GAPDH Forward 5′-GACTTCAACAGCAACTCCCAC-3′ and GAPDH Reverse 5′-TCCACCACCCTGTTGCTGTA-3′ ( from RTPrimer Database ) and Perfecta SYBRGreen SuperMix ( Quanta Biosciences , Gaithersburg USA ) . Each PCR reaction contained 50 ng genomic tissue DNA or 1 µL of eluted blood DNA . Amplification protocol consisted in a denaturation phase at 95°C for 5′ ( RampRate 4 . 40°C/s ) , then 50 cycles of amplification ( 95°C 3′ , ( RampRate 4 . 40°C/s ) , 65°C 1′ ( RampRate 2 . 20°C/s ) ) . Fluorescence emission was measured at the end of the elongation step . A melting curve phase program was applied with a continuous fluorescence measurement between 50°C and 95°C ( RampRate 2 . 20°C/s ) . The identity of the amplified products was checked by analysis of the melting curve carried out at the end of amplification . Each LightCycler run contained 2 negative controls ( no DNA added to the reaction ) , and each DNA sample was quantified in duplicate . Duplicate values for each DNA sample were averaged and parasite equivalent load was calculated automatically by plotting the CP values against each standard of known concentration and calculation of the linear regression line of this curve . To normalize the amount of tissue analyzed in each PCR reaction , we choose a housekeeping gene ( GAPDH ) to correct the intra-sample variations of the initial sample amount , DNA recovery and/or sample loading . Normalization with an external standard was possible because the amplification of T . cruzi genomic and murine GAPDH sequences occurred with the same efficiency ( TcZ: 1 . 912; GAPDH: 1 . 930 ) [39] . For normalization , the TcZ DNA value in each tissue sample was divided by the value of the murine GAPDH DNA in the same sample . Results were presented as means ± SEM . Comparisons of means between groups were performed using the Mann-Whitney U-test . To assess differences between survival curves , a long rank test of Kaplan-Meier was performed . All tests were performed using Graph Pad software ( Prism 5 version 5 . 02 ) .
Infection of BALB/c mice with the Tulahuen strain of T . cruzi resulted in an acute parasitaemic phase easily detectable by standard microscopic examination from 12 to 30 days post-inoculation ( dpi ) , peaking at 3 . 8±1 . 5×106 parasites/mL on dpi 21 ( NT group , Fig . 1A ) . This acute phase led to the death of 30% of infected animals ( Fig . 1B ) . Afterwards , the infection evolved to a chronic phase during which blood parasites became undetectable by standard microscopic examination . However , when such chronically infected mice received the immunosuppressive cyclophosphamide drug , they displayed a drastic increase of their blood parasite levels easily detectable by microscopic examination , reaching 5 . 4±3 . 6×106 parasites/mL ( on day 14 after the first cyclophosphamide injection ) , i . e . levels comparable to those previously observed in acute phase ( Fig . 1A ) . Quantitative PCR determination of parasitic DNA and parasitaemia determined by microscopic observation were statistically correlated ( R = 0 . 953 , P = 0 . 0003 ) and levels estimated in the acute phase of infection ( on dpi 21 ) were close to those determined by microscopic observation . qPCR analysis of blood samples collected on dpi 74 ( chronic phase ) allowed the detection of parasite DNA in all infected mice , corresponding to a mean level of 3 , 380±1 , 440 parasite equivalents/mL ( NT group , Fig . 2A ) , i . e . values under the detection limit of microscopic observation . In chronically infected mice receiving cyclophosphamide , circulating parasite DNA levels drastically increased to values corresponding to 19 . 4±10 . 7×106 parasite equivalents/mL ( NT group , Fig . 2A ) . Levels of parasite DNA in tissues of T . cruzi-infected mice are shown in Fig . 2 . On dpi 21 ( acute phase ) , heart , spleen , skeletal muscle and adipose tissue displayed mean levels of parasitic DNA ranging between 6 , 220 and 108 , 000 equivalent parasites per 50 ng of total DNA , while parasite DNA amount was particularly low in liver ( 85 . 9±18 . 6 parasite equivalents per 50 ng of DNA ) . On dpi 74 ( chronic phase ) , parasite DNA amounts were roughly reduced by 10-fold in liver , 100 fold in muscle and heart , 1 , 000 fold in spleen and 5 , 000-fold in adipose tissue as compared to acute phase . In both phases , skeletal muscle depicted the highest parasite DNA load . Again , cyclophosphamide injections induced an increase in parasite DNA levels by around 1 , 000 times in adipose tissue , 100-fold in hepatic tissue and only 10-fold in cardiac , spleen and skeletal muscular tissues . As shown in Fig . 1B , administration of AmBisome to acutely infected mice ( TA; dpi 10 ) prevented mice from fatal issue ( P<0 . 05 as compared with untreated animals ) and all treated mice survived until the end of the experiment ( 74 dpi ) . This observation , along with the fact that cell blood counts remained similar in treated and untreated animals ( data not shown ) , suggested the absence of major toxic effects of treatment at the used dose . As shown in Fig . 1A , microscopic observation of blood samples showed acutely infected mice treated with AmBisome significantly reducing their acute phase mean parasitaemias by 5 times , though blood parasites remained detectable at dpi 21 in all mice ( TA: 6 . 7±0 . 8×105 parasites/mL; NT: 3 . 8±1 . 5×106 parasites/mL; P = 0 . 015 ) . qPCR analyses of blood samples of treated mice showed a similar tendency to reduce by 4 times ( not statistically significant ) parasite DNA amounts compared to those observed in untreated mice ( Fig . 2A , Table 2 ) . By contrast , qPCR analyses of spleen and adipose tissue showed AmBisome treatment reducing their parasite loads by 449 to 1 , 361 as compared with NT mice , whereas such reduction was only by 6 to 51 in the other tissues ( Fig . 2 B–F , Table 2 ) . We also evaluated the effect of AmBisome given during the acute phase on tissue parasite loads in chronic phase . If parasitaemia on dpi 74 remained microscopically undetectable in AmBisome-treated mice , qPCR , detected similar blood parasite DNA levels in TA mouse group and in untreated mice ( NT , Fig . 2A , Table 2 ) . By contrast , all other tissues collected on dpi 74 from TA mice exhibited a significant reduction in parasite DNA amounts compared with NT animals ( by 437 in liver and 8 to 176 in the other tissues ) ( P<0 . 05 , Fig . 2B–F; Table 2 ) . Since AmBisome treatment given during the acute phase of infection did not eliminate totally the parasites , experiments were also performed adding a second round of treatment during chronic phase to mice previously treated during the acute phase ( TAC group ) . In parallel , other mice received injections of AmBisome only during the chronic phase of infection ( TC ) . Treatment in chronic phase consisted on 6 i . p . injections of 25 mg/kg on alternate days starting on dpi 60 . TC mice showed blood parasite DNA amounts roughly similar to those of NT animals , ( dpi 74 , Fig . 2A ) , whereas they displayed a significant reduction of parasite DNA loads in all tested tissues when compared to untreated mice ( dpi 74 , Fig . 2B–F , P<0 . 05 ) . Such reduction was stronger in liver and heart ( dpi 74 , Fig . 2B–C , Table 2 , P<0 . 05 ) . When compared to TA group , TC mice did not display major changes in tissue parasite DNA loads ( dpi 74 , Fig . 2B–F , Table 2 ) . TAC mice also presented similar blood parasite DNA amounts than NT and TA chronically infected mice ( dpi 74 , Fig . 2A ) . However , this second round of AmBisome allowed a significant reduction of parasite DNA loads in all other tested tissues when compared to NT mice ( dpi 74 , Fig . 2B–F , P<0 . 05 ) . These parasite loads remained lower than one parasite equivalent per 50 ng tissue DNA , except in muscular tissue . This double treatment scheme significantly improved the effect previously observed in liver from the TA group ( dpi 74 , Fig . 2C , P<0 . 05 ) , as mentioned by the calculated fold decreases ( Table 2 ) . We also investigated whether an earlier administration ( starting on dpi 1 ) was able to improve the treatment efficiency of AmBisome . All treated mice ( TeA ) survived and displayed reduced parasitaemias ( microscopic determination ) compared to NT group ( dpi 21 , TeA: 4 . 88±0 . 82×105; P<0 . 05 ) . Comparison of qPCR analyses performed in TeA and NT mice ( on dpi 21 ) showed such early treatment lowering parasite loads in all tissues ( Table 2; P<0 . 01 except for blood ) , the more potent effect being observed in spleen . Interestingly , early treatment starting on dpi 1 had more pronounced effect than that starting on dpi 10 in reducing parasite loads in spleen and liver ( by 6 to 8 fold; P<0 . 01 ) , similar effect on muscle . However , in adipose tissue the reducing effect of the early AmBisome treatment ( TeA ) was less pronounced than in TA mice ( P<0 . 01 ) . The potential long term effect of such early treatment was also investigated by determining the tissue parasite loads on dpi 74 . Excepted for adipose tissue , the latter were decreased by 7 to 113 fold as compared to NT group , as indicated by the calculated fold decreases ( Table 2 ) . As reported in Fig . 1A–2A , a drastic increase of blood parasite levels was observed 14 days after the first cyclophosphamide injection ( 7 days after the 4th injection ) in all TC and TAC AmBisome-treated mice , both by microscopical and qPCR analyses , reaching parasite amounts comparable to that observed in acutely infected NT mice . We also observed that cyclophosphamide injections similarly boosted parasite DNA levels in tissues of all of these mice , as in NT mice ( Fig . 2B–F ) . Such drastic reactivation of parasite multiplication clearly showed that animals were not completely cured from T . cruzi infection . Cyclophosphamide immunosuppression test was not applied to TA and TeA mouse groups , since at the end of AmBisome treatment in acute phase , parasites were still observable in blood by standard microscopic examination , indicating they were not completely cured ( see above; Fig . 1A ) .
Taken together , these results indicate that AmBisome , at the used doses ( i . p . administration ) , prevents mice from fatal issue in the acute phase of infection , contributes to drastically reduce parasite loads in heart , liver , spleen , skeletal muscle and adipose tissues in acute , as well as in chronic infection , but fails to completely cure animals from T . cruzi infection . An earlier administration of AmBisome ( on dpi 1 ) has a better effect in reducing parasite loads in spleen and liver in acute phase , whereas repetition of treatment in chronic phase improves the reduction of parasite loads in heart and liver . Survival rate and parasitaemias ( microscopic examination ) observed in untreated mice are in agreement with our previous report using the same mouse and parasite strains [42] , [43] . Our qPCR data obtained in tissues/organs from infected mice can be considered as reliable since possible contaminations by DNA from blood trypomastigotes have been drastically reduced by flushing the entire circulatory system of mice . Moreover , such data agree with those of previous works exploring T . cruzi infection in adipose or muscular tissues of mice by normalised qPCR [39] , [44] . The high amount of parasite DNA observed in skeletal muscle and heart both in acute and chronic infection can be explained by the known muscular tropism of the used parasite strain ( TcVI genotype; [45] ) . The lowest amount detected in liver likely relates to the involvement of this organ as a major site of immunological elimination of parasites [46] . The high amount of parasite DNA in mouse adipose tissue also confirms previous reports [44] . The more important effect of dpi 10 ( TA ) - vs . dpi 1-treatment ( TeA ) in reducing parasite DNA in adipose tissue might indicate a later parasite invasion of this tissue compared to others . The comparisons of tissue parasitic loads through the course of infection indicate that the AmBisome treatment initiated in acute phase ( TeA and TA ) induces a global decrease of parasitic loads in all studied organs , and that this beneficial effect is long-lasting since still observed in chronic phase . The treatment given in chronic phase only ( TC ) has also a significant beneficial effect in reducing such organ parasitic loads . However , treatment repeated in acute and chronic phases ( TAC ) does not present a significant advantage over TC treatment . However , considering blood , if a significant reduction of parasitaemia ( microscopic determination ) can be observed after the acute phase treatments ( TeA and TA ) , the estimations of parasite DNA in chronic phase ( qPCR determination ) remain similar in NT and treated mice , whatever the scheduled treatments . This latter observation might relate to a release of T . cruzi DNA into blood circulation , subsequent to an intra-tissue lysis of parasites by AmBisome . Such parasitic DNA release probably also occurs in acute phase . However , during this phase , high levels of inflammatory molecules , such as the serum amyloid P protein ( SAP ) , are abundantly produced in response to T . cruzi infection [47] . SAP is known to capture DNA and be rapidly eliminated in liver [48] , [49] , which might contribute to decrease the detectable circulating DNA levels in acute , but not in chronic phase . This indicates that qPCR determination of parasitic DNA in blood does not reflect the actual parasitic load in other tissues and is not sufficient enough to appreciate the effect of a treatment . Our results confirm that AmBisome treatment increases the survival rate of acutely infected animals , although it does not cure them , even if multiple injections of drug are used instead of only one as previously indicated [34] , and if treatment is started close to inoculation date . Indeed , treated mice still displayed low levels of parasites or parasite DNA in blood and other tissues both in early ( on dpi 21 ) as well as in late infection ( on dpi 74 ) . Our experiments with the cyclophosphamide immunosuppressive drug confirm the presence of residual tissue infection in various organs . The lack of complete curative activity of the drug might be related to a sub-estimation of its efficacy since it has been administered by i . p . instead of intravenous route , known to be more effective for diffusing liposome-encapsulated drugs [18] , [34] . Amphotericin B is known to induce an immediate lysis of Trypanosomatidae parasitic protozoa whatever their strain [50] , due to the interaction of its large macrolactone ring with ergosterol and other 24-alkyl sterols contained in membranes , triggering the formation of aqueous pores . Consequently , another possible explanation for the absence of complete curative effect might relate to the preferential tropism of the used parasite strain ( TcVI ) for muscle tissues ( see above ) , whereas the AmBisome targeting , by its liposomal formulation , is more directed toward liver , spleen and lungs [51] , [52] . So , the present results do not exclude a more pronounced curative effect using intravenous administration , or in T . cruzi infection with other parasite strains having different tissue distribution . Moreover , a beneficial effect of AmBisome treatment might be also expected in T . cruzi congenital infection , in which parasites are preferentially targeted to the liver by the fetal circulation [2] , [53] , since our results show the early treatment being able to reduce drastically parasite loads in liver and spleen , in addition to allowing survival of all infected animals . Another information derived from the present study is the significant reduction of DNA parasite load observed in tissues ( notably in cardiac tissue ) of mice treated during the chronic phase of infection . This phase is frequently encountered in human T . cruzi infection and cumulative data indicate that treatment of such infected subjects with the standard benznidazole drug significantly reduce the progression of cardiac Chagas disease and increase the frequency of negative seroconversion [11] . This could stimulate studies on using association of drugs including AmBisome ( requiring only some injections ) in T . cruzi- infected patients , in order to improve and/or accelerate such beneficial evolution and definitive cure .
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Chagas disease is a leading cause of cardiac failure and the most important parasitic disease in terms of morbidity and mortality in Latin America . After an acute parasitaemic phase , infection naturally evolves to a long chronic phase . If the currently available trypanocidal drugs , benznidazole and nifurtimox , are effective in recent infection , they have to be administered during months and induce side effects . AmBisome , an already safe patented lipid formulation of amphotericin B , has been previously shown efficient using short time administration in treating human and experimental Leishmania ( another Trypanosomatidae parasite ) and fungal infections . This report evaluates the effect of AmBisome in mice infected with T . cruzi . Besides parasitologic evaluation , quantitative PCR was used to evaluate the parasite loads in tissues . Injections of AmBisome in acute infection allowed survival of all animals and drastically reduced the parasite loads in most tissues , whereas its administration in chronic phase strongly decreased the parasite loads in heart and liver , without completely curing the animals . Such results should encourage investigations on using AmBisome in association with standard drugs in order to improve the treatment of T . cruzi infection .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"animal",
"models",
"medicine",
"infectious",
"diseases",
"model",
"organisms",
"parasitic",
"diseases",
"chagas",
"disease",
"biology",
"microbiology",
"host-pathogen",
"interaction",
"mouse",
"infectious",
"disease",
"control",
"quantitative",
"parasitology",
"parasitology"
] |
2011
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Parasitic Loads in Tissues of Mice Infected with Trypanosoma cruzi and Treated with AmBisome
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Immunoglobulin G ( IgG ) is an essential plasma-derived medicine that is lacking in developing countries . IgG shortages leave immunodeficient patients without treatment , exposing them to devastating recurrent infections from local pathogens . A simple and practical method for producing IgG from normal or convalescent plasma collected in developing countries is needed to provide better , faster access to IgG for patients in need . IgG was purified from 10 consecutive minipools of 20 plasma donations collected in Egypt using single-use equipment . Plasma donations in their collection bags were subjected to 5%-pH5 . 5 caprylic acid treatment for 90 min at 31°C , and centrifuged to remove the precipitate . Supernatants were pooled , then dialyzed and concentrated using a commercial disposable hemodialyzer . The final preparation was filtered online by gravity , aseptically dispensed into storage transfusion bags , and frozen at <-20°C . The resulting preparation had a mean protein content of 60 . 5 g/L , 90 . 2% immunoglobulins , including 83 . 2% IgG , 12 . 4% IgA , and 4 . 4% IgM , and residual albumin . There was fourfold to sixfold enrichment of anti-hepatitis B and anti-rubella antibodies . Analyses of aggregates ( <3% ) , prekallicrein ( 5-7 IU/mL ) , plasmin ( 26 . 3 mU/mL ) , thrombin ( 2 . 5 mU/mL ) , thrombin-like activity ( 0 . 011 U/g ) , thrombin generation capacity ( < 223 nM ) , and Factor XI ( <0 . 01 U/mL ) activity , Factor XI/XIa antigen ( 2 . 4 ng/g ) endotoxin ( <0 . 5 EU/mL ) , and general safety test in rats showed the in vitro safety profile . Viral validation revealed >5 logs reduction of HIV , BVDV , and PRV infectivity in less than 15 min of caprylic acid treatment . 90% pure , virally-inactivated immunoglobulins can be prepared from plasma minipools using simple disposable equipment and bag systems . This easy-to-implement process could be used to produce immunoglobulins from local plasma in developing countries to treat immunodeficient patients . It is also relevant for preparing hyperimmune IgG from convalescent plasma during infectious outbreaks such as the current Ebola virus episode .
Plasma products to treat congenital bleeding and immunological diseases are made in industrialized countries using complex technologies unavailable in the developing world [1] . Low- to medium-income countries may have little or no access to these life-saving products; these nations urgently need practical processing methods to produce them affordably . We have introduced the concept of small-scale ( “minipool” ) plasma processing methods implementable with minimum infrastructural requirements . We developed viral inactivation and protein purification technologies in single-use equipment to prepare virally safe solvent/detergent-filtered ( S/D-F ) plasma for transfusion as well as minipool S/D-F cryoprecipitate to treat bleeding disorders [2–4] . Similarly simple technologies are desperately needed to make safe immunoglobulin G ( IgG ) , a product on the Essential Medicine List of the World Health Organization , to treat immune-deficient patients . Thus we describe here a small-scale caprylic acid IgG fractionation process that requires minimal capital investment and uses disposable equipment . This production approach could increase the supply of IgG in developing countries and improve treatment of immunodeficient patients . It is also a realistic approach to consider in the preparation of convalescent immunoglobulins during infectious outbreaks such as the current Ebola virus epidemic [5 , 6] .
Whole blood was collected with CPD-A anticoagulant/preservative solution ( ratio: 14ml/100ml of blood ) from regular volunteer non-remunerated donors at Shabrawishi Hospital Blood Bank ( Giza , Cairo , Egypt ) . Donors received information prior to donation in compliance with national regulations . The procedure was approved by the Institutional Review Board from Cairo University ( Number N-5–2014 ) . The blood bank is licensed ( license number N°7 ) by the General Directorate for Blood Transfusion Affairs , Ministry of health and is ISO certified ( ASR number 1230 ) . Non-leuco-reduced blood was centrifuged at 3600x g for 12 minutes within 4 hours of collection . Plasma was transferred into storage bags , frozen in a -40°C freezing room , and stored at ≤-25°C for a maximum of 12 months . The preparation of the IgG fraction is summarized in Fig . 1 . Plasma from 20 blood donations tested for anti-A and anti-B titer < 32 ( Micro Typing Cards with NaCl; DiaMed AG , Cressier sur Morat , Suisse ) , or from the same blood group , were subjected to in-bag cryoprecipitation [2 , 7] . The cryoprecipitate-poor supernatant ( approximately 200mL ) was transferred into a transfusion bag , frozen and stored at <-30°C . Supernatants were thawed at 30–35°C . After thawing , caprylic acid ( Merck , Darmstadt , Germany ) was added within one minute to each bag under constant manual stirring to 5% ( v/v ) final concentration , pH 5 . 5 +/- 0 . 1 , and the mixture incubated at 31+/- 0 . 5°C for 90 minutes at 150 rpm in a temperature-controlled shaker-incubator ( Lab Therm LT-W , Kühner , Switzerland ) [2] . Precipitated proteins were removed by centrifugation ( KR4i , Jouan , St Herblain , France ) at 3500x g for 45 minutes . The clear supernatants ( approximately 2 . 8 L ) were pooled under laminar flow into a SD Virus Inactivation Bag Cascade ( VIPS SA , Colombier , Switzerland ) and concentrated ( typically 60 g/L ) using a sterile single-use hemodialyzer ( F6HB , Fresenius , Bad Homburg , Germany ) , a hemodialysis pump and monitoring equipment ( Terumo BCT , Lakewood , CO , United States ) . The solution was progressively diluted with 5 volumes of sterile pyrogen-free saline solution and subjected to diafiltration to remove caprylic acid and concentration . The Ig fraction was centrifuged ( Jouan ) at 3500x g for 45 minutes at 2–4°C to remove any particulates , then filtered by gravity through a pyrogen-free pharmaceutical-grade BC0025L60SP03A cartridge ( 3M Cuno , Cergy-Pontoise , France ) and a 0 . 2 μm Mini-Kleenpak sterilizing filter ( Pall Corporation , Dreieich , Germany ) and directly dispensed under laminar flow into sterile plastic storage bags and stored frozen at <-25°C . Total protein was determined by Biuret ( Protein Kit 110307 , Merck Millipore , MA , USA ) . Zone electrophoresis was performed on agarose gels ( Hydragel 7 protein kit , Sebia , Evry , France ) , staining with amidoblack and densitometric analysis by a semi-automated Hydrasys instrument ( Sebia ) . Sodium dodecylsulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) , under non-reducing and reducing conditions , used 4%~12% Bis-Tris Gel ( NuPAGE , Novex Life Technologies , CA , USA ) as before [8] . Albumin was measured photometrically using bromocresol green ( DiaSys Diagnostic Systems , Holzheim , Germany ) . IgG , IgA , and IgM were determined by immunoturbidimetry [9] . Anti-hepatitis B and anti-rubella immunoglobulins G titres were determined using Architect Anti-HBs Reagent and Architect Rubella IgG reagent , respectively ( Abbott Laboratories , North Chicago , IL , USA ) . Molecular size distribution was analyzed by size exclusion chromatography on a TSKGel G3000SWXL column ( 7 . 8mm ID X 30 cm L ) protected with a TSKGel guard column ( 6 . 0mm ID X 4 . 0 cm L ) , equipped with isocratic pump model SDS 9414 , UV-VIS detector model S3210 , Rhiodyne manual injector and PeakSimple Chromatography Data System SRI Model333 as data integrator ( Schemback , Germany ) . The mobile phase was 0 . 1 M sodium sulfate , 50 mM sodium acetate , 0 . 05% sodium azide , pH5 , flow-rate was 0 . 5 ml/min , and the detection wavelength was 280nm . Thrombin generation assay ( TGA ) used Technothrombin fluorogenic substrate and RC High reagent ( Technoclone , Vienna , Austria ) , and prekallikrein activator ( PKA ) , plasmin , thrombin , thrombin-like amidolytic activities used S-2302 , S-2251 , S-2238 , and S-2288 chromogenic protease substrates ( Chromogenix , Milan , Italy ) , respectively [10] . Factor XI coagulant activity was measured by one-stage thromboplastin time coagulation assay with human factor XI—deficient and reference plasma ( DiaMed , Cressier , Switzerland ) , and FXI/FXIa antigen with Human Factor XI quantitative sandwich ELISA ( Abcam , Cambridge , UK ) as before [10] . Endotoxins were determined by the LAL assay . A licensed human IgG preparation produced by a combined ethanol fractionation-chromatography process was used as a control . Data are expressed as the mean ± standard deviation . The 0 . 5-mL samples were mixed with 1mL ice-cold methanol and incubated overnight in a deep freezer at -80°C . Samples were centrifuged at 4000x g for 20 minutes and 1 mL of the supernatant was taken and filtered through a 0 . 45μm syringe filter to a 1 . 5 ml clean tube . Samples were processed and analyzed by HPLC ( Schemback SFD GmbH , Bad Honnef , Germany ) equipped with analytical pump ( SFD 9414 ) , UV/VIS detector ( S 3210 ) , Rheodyne manual injector model 7725i , Peak Simple Data System model 333 ( SRI , Torrance , California , USA ) , and Luna 5u , C8 ( 2 ) 100Å ( 150 mm x 4 . 6 mm ) column chromatography ( Phenomenex , Torrance , USA ) ; 0 . 1% trifluoroacetic acid ( TFA ) in a 80:20 mixture of methanol ( Fisher Scientific , UK ) and water was used as mobile phase at 0 . 8 mL/min . Caprylic acid detection was done at 214nm wavelength Di ( 2-ethylhexyl ) phthalate ( DEHP ) was assessed on the starting plasma and final IgG . Samples were processed and analyzed as before [2] by HPLC ( Schemback SFD GmbH , Bad Honnef , Germany ) equipped with analytical pump ( SFD 9414 ) , UV/VIS detector ( S 3210 ) at a wavelength of 202 nm , using a Lichrospher 100 RP 18–5μ ( 250 mm x 4 . 6 mm ) column ( CS-Chromatographie Service GmbH , Langerwehe , Germany ) . A mixture of 85:15 of acetonitrile and methanol ( Merck ) was used as mobile phase at 1 . 5 mL/min for 8 minutes analysis time . The capacity of the caprylic acid treatment to inactivate/remove viruses was assessed at Texcell ( Evry , France ) , a specialized laboratory working under GLP compliance awarded by the ANSM , France’s National Agency for Medicines and Health Products Safety . The process was scaled-down by a factor of 10 ( 40 mL ) . Validations were performed in duplicate under worst-case conditions using cryo-poor plasma as starting material , 4 . 8% caprylic acid , and a temperature of 28 . 5–30 . 5°C . The study followed Good Laboratory Practices and CPMP recommendations [11] . Plasma tested negative for HBsAg; HIV-1/HIV-2 Ab+P24 Combo assay; Anti-HCV by Abbott Architect Chemiluminescence ( Abbott Laboratories ) ; HBV , HIV and HCV individual-donor-Nucleic Acid Test ( NAT ) ( Tigris; Grifols Diagnostic Solutions Inc . , Emeryville , CA , USA ) using the Procleix Ultrio assay . Cryo-poor plasma samples were prepared at the Shabrawishi Hospital Blood bank , frozen at -30°C and shipped with dry ice to Texcell . HIV-1 ( Lai strain ) , bovine viral diarrhoea virus ( BVDV; NADL strain; ATCC VR-534 ) , and pseudorabies virus ( PRV; Aujeszky disease virus; Kojnock strain; ATCC VR-135 ) were used for spiking , and P4-CCR5 , MDBK cell lines ( ATCC CCL-22 ) and Vero ( Molecular Virology Laboratory , Institut Pasteur , Paris , France ) , respectively , for titration assays . Cryo-poor plasma was transferred into the reaction container . When temperature reached 28 . 5–30 . 5°C , the material was spiked with virus-inoculum ( 2 . 0% [v/v] ) . Spiked starting material was homogeneized and positive controls were collected; 4 . 8% ( v/v; final concentration ) caprylic acid was added in less than one minute . The spiked solution was kept at 28 . 5–30 . 5°C under continuous transversal agitation . Samples were taken right after caprylic acid addition ( T0 ) and at 15 , 45 , 60 , and 120 minutes after , and were immediately centrifuged at 3500x g for 45 minutes at 4°C . Supernatants were recovered and diluted 30 folds ( BVDV ) or 50 folds ( PRV and HIV ) with culture medium to stop the reaction , and were frozen and stored at -70°C . Control samples prepared under these conditions were verified not to induce cellular toxicity . Spiked samples were titrated by validated end-point dilution assay , and the viral clearance of the steps was assessed in terms of infectivity . Viral titers were calculated and expressed as 50% tissue culture infective dose per milliliter ( TCID50/mL ) using the Sperman Kaber formula . Infectious titers were calculated at the non-interfering dilution after large volume titration . The safety of the purified IgG was evaluated in Sprague—Dawley rats as described before [2] , after approval from the National Cancer Institute ( Cairo , Egypt ) , where the study was performed . It was conducted according to institution guidelines on animal studies and following recommendations specified in the Code of Federal Regulations Title 21 , except that the injection was done intravenously , not intraperitonally , and the observation period was 14 days instead of 7 . Twenty-one healthy rats weighing 80–100 g and not used previously for any test purpose were divided into three groups of seven rats . Animals received a dose of 6 . 5 mL/kg of saline , commercial IgG ( control ) or minipool IgG . The rats were observed once daily for abnormal behavior or clinical signs . Body weight , and consumption of water and food was recorded at 10 time-points over the observation period . The study was only observational and did not require anesthesia , sacrifice nor dissection .
The main characteristics of the final preparations are summarized in Table 1 . The product was clear , with a slight bluish color , and not turbid . Sodium content and osmolality were close to physiological value . Mean protein concentration was 60 . 2 g/L , with a content of gamma-globulins close to 90% , and traces of albumin , alpha-1 , alpha-2 , and bêta-proteins revealed by zone electrophoresis ( Fig . 2A and B ) . The relative abundance of IgG was 82–85% , IgA 11–13% , and IgM 4–5% , close to the physiological proportion . Albumin was less than 3 g/L . Content of high molecular weight proteins/aggregates was less than 3% and monomers and dimers more than 90% by HPLC . Anti-A and anti-B isoagglutinin titer in all batches was less than 1/32 . Titer of anti-hepatitis B and anti-rubella immunoglobulin G showed enrichment factors compared to plasma of 5 . 8 and 4 . 1 , respectively . Proteolytic and thrombogenic activity were also assessed . Mean PKA was 6 . 1 ± 1 . 1 IU/ml ( control: 3 IU/ml ) , well below the maximum limit of 35 IU/ml in the European Pharmacopoeia . TGA data showed a peak thrombin of 0–223 nM ( control: 56 . 8 nM ) below the threshold value of 350nM associated with thromboembolic activity in some IVIG preparations . Mean plasmin was 26 . 3 mU/mL ( control: 20 . 3 mU/mL ) , thrombin 2 . 5 mU/mL ( control: 24 mU/mL ) , thrombin-like proteolytic activity 0 . 011 U/g protein ( control: 0 . 06 U/g protein ) , Factor XI activity < 0 . 01 IU/mL , and Factor XI/XIa 2 . 4 ng/g protein . SDS-PAGE ( Fig . 3 ) under non-reducing conditions ( A ) evidenced that most proteins migrated at a MW close to 150–160 kDa ( immunoglobulins G and A ) . Minor protein bands were detected at MW close to 25 , 60 , and 80–90 kDa . Under reducing conditions ( B ) , two major protein bands with MW of 50 and 25 kDa ( immunoglobulin G heavy and light chains , respectively ) were detectable . The minipool IgG pattern was similar to control apart for additional protein bands with MW of approximately 150–160 , 90 , and 70kDa under reducing conditions . Caprylic acid in the final preparation was <750ppm , and DEHP <5ppm . Endotoxin content was less than 0 . 5 EU/ml . Viral validation data ( Table 2 ) showed that ( a ) viral infectivity was not affected after spiking to the starting material , and ( b ) HIV-1 , BVDV , and PRV inactivation was fast and complete within 15 minutes after caprylic acid addition . Reduction factors ( duplicate experiments ) were > 5 . 69 and > 5 . 74 log for HIV-1 , > 5 . 23 and > 5 . 35 log for BVDV , and > 5 . 10 and >5 . 10 log for PRV . General safety tests did not induce rat mortality nor behavioral changes in the three groups and there was no significant difference in body weight increase rate over 7 days ( Table 3 ) . The water and food consumption was not noticeably different among the three groups , ranging between 4–13% and 9–14% of total available , respectively , over the 14 days of observation post infusion .
Producing a 90% pure immunoglobulin fraction in disposable , single-use devices is feasible . This method could be used to produce immunoglobulins from local plasma in developing countries to protect immunodeficient patients against infectious agents , and could be of interest for preparing hyperimmune IgG from convalescent plasma collected during infectious outbreaks such as the current Ebola virus episode . Clinical evaluations of this preparation in immunodeficient children are on-going and indicate good tolerance and normal IgG half-life .
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Plasma-derived immunoglobulin G ( IgG ) is on WHO’s Essential Medicines List , yet developing countries face severe shortages of this critical treatment . Infusion of IgG prepared from locally-collected plasma provides an advantageous mix of antibodies to viral and bacterial pathogens found in the living environment , and this can reduce recurrent infections in immune-deficient patients . We developed a simple manufacturing process using disposable equipment ( blood bags , hemodialyzer , and filters ) to isolate immunoglobulins from minipools of 20 plasma donations . This process yields a ca . 90% pure virally-inactivated immunoglobulin fraction at 50–60% recovery . Anti-hepatitis B and anti-rubella immunoglobulins were enriched fourfold to sixfold . The product was free of in-vitro thrombogenic and proteolytic activity , confirming its expected clinical safety profile . Virus validations showed caprylic acid treatment robustly inactivated or removed infectivity of lipid-enveloped viruses , including human immunodeficiency virus ( HIV ) and hepatitis C virus model . This simple and cost-effective process is implemented in Egypt to prepare experimental batches for clinical evaluation . It can enhance immunoglobulin supplies to treat immunodeficient patients through passive transmission of antibodies directed against local pathogens . The method requires minimal training and reasonable infrastructure , and is a practical means to prepare convalescent hyperimmune IgG during infectious outbreaks such as the current Ebola episode .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Minipool Caprylic Acid Fractionation of Plasma Using Disposable Equipment: A Practical Method to Enhance Immunoglobulin Supply in Developing Countries
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Visual neuroscientists have discovered fundamental properties of neural representation through careful analysis of responses to controlled stimuli . Typically , different properties are studied and modeled separately . To integrate our knowledge , it is necessary to build general models that begin with an input image and predict responses to a wide range of stimuli . In this study , we develop a model that accepts an arbitrary band-pass grayscale image as input and predicts blood oxygenation level dependent ( BOLD ) responses in early visual cortex as output . The model has a cascade architecture , consisting of two stages of linear and nonlinear operations . The first stage involves well-established computations—local oriented filters and divisive normalization—whereas the second stage involves novel computations—compressive spatial summation ( a form of normalization ) and a variance-like nonlinearity that generates selectivity for second-order contrast . The parameters of the model , which are estimated from BOLD data , vary systematically across visual field maps: compared to primary visual cortex , extrastriate maps generally have larger receptive field size , stronger levels of normalization , and increased selectivity for second-order contrast . Our results provide insight into how stimuli are encoded and transformed in successive stages of visual processing .
Studies of visual cortex typically measure responses to a narrow set of stimuli designed to investigate a particular phenomenon . For example , a study might use sinusoidal gratings varying in contrast to study contrast response functions [1] , [2] , another study might use silhouettes to study shape tuning [3] , [4] , and yet another study might use arrays of line segments to study texture representation [5] , [6] . This approach provides valuable insights , but different effects are studied in isolation and different models ( e . g . , linear filtering , static nonlinearities , divisive normalization , MAX ) are proposed for different effects . To advance our understanding , we seek to develop an integrated model that explains responses to a wide range of stimuli ( Figure 1 ) . In this study , we measure functional magnetic resonance imaging ( fMRI ) responses in early visual cortex to a wide range of band-pass grayscale images , and we develop a model that starts with images and predicts these responses . The model has a cascade architecture and comprises four main components . The first component is a set of V1-like Gabor filters that are applied to the image . These filters are adapted from our previous work on modeling fMRI responses [7] . The second component is a divisive normalization operation that is applied to filter outputs . Divisive normalization is a well-established computation that accounts for several nonlinear response properties of V1 neurons [8]–[10] . The third component is a compressive static nonlinearity that is applied after summation of contrast-energy across the visual field . We recently found that this nonlinearity is important for accurately predicting responses to stimuli varying in position and size [11] . The fourth component is a variance-like nonlinearity that is used in the summation of contrast-energy . This nonlinearity generates selectivity for second-order contrast and shares some similarities with filter-rectify-filter models that have been proposed for texture perception [12] , [13] . We provide software code that implements the complete model along with example datasets at http://kendrickkay . net/socmodel/ . This is useful for the goal of reproducible research [14] and provides the opportunity for others to improve upon our work . We welcome efforts to consider potential alternative models—including models developed in psychophysics , computer vision , and the theoretical literature , as well as models that posit specific circuit-level mechanisms—and to determine whether these models better account for the experimental measurements we have made . We hope the open exchange of data and code will spur further modeling efforts . This paper is structured as follows: We start by motivating each component of our model through targeted examples of stimuli and responses . We then use cross-validation to show that the full model does not overfit the data but in fact improves prediction accuracy . Finally , we examine the parameters of the model and inspect the effect of the parameters on the behavior of the model . This examination reveals that compared to primary visual cortex , extrastriate maps generally have larger receptive field size , stronger levels of normalization , and increased selectivity for second-order contrast .
The model we developed for predicting the BOLD response consists of a sequence of operations ( Figure 2A ) . The BOLD response is predicted by applying V1-like Gabor filters to the luminance image ( V1 energy ) , normalizing the filter outputs by local population activity ( Divisive normalization ) , summing contrast-energy across a specific region of the visual field ( Spatial summation ) using a variance-like nonlinearity ( Second-order contrast ) , and applying a compressive static nonlinearity ( Compressive nonlinearity ) . The key novel component of the model is the computation of second-order contrast ( Figure 2B ) , hence the name of the model . The model has eight free parameters ( Figure 2A , bracketed variables ) and is fit to the response amplitudes of each voxel . To motivate and explain the second-order contrast ( SOC ) model , we start with simpler versions of the model and incrementally build up to the full model ( Figure 2C ) . At each step of the process , we assess how well a simple model explains responses to a range of stimuli and improve performance by adding a new component to the model . A caveat to this approach is that increasingly complex models may provide better fits , but these improvements may simply reflect overfitting to the noise in the data . In a later section , we use cross-validation to obtain unbiased estimates of model accuracy and verify that the more complex models are indeed more accurate than the simpler models . The simplest model is the complex-cell energy ( CC ) model , which involves computing V1 energy and summing across the visual field . Previous studies indicate that the CC model is a reasonable starting point: the CC model accounts for substantial variance in BOLD responses in early visual areas to grayscale natural images [7] and a closely related model accurately characterizes BOLD responses to a checkerboard pattern positioned at different visual field locations [15] . For the purposes of this project , the summation weights in the CC model were constrained to be Gaussian across space and equal for different orientations; this is a reasonable approximation for voxel responses [7] . We assessed how well the CC model accounts for responses to a set of stimuli that included oriented gratings and mixtures of oriented gratings presented at different contrast levels ( henceforth referred to as grating stimuli ) . Results for an example voxel in V1 are shown ( Figure 3 ) . Responses increase with contrast and with number of orientations , consistent with recent fMRI measurements [16] , [17] . This pattern of results is qualitatively reproduced by the CC model ( Figure 3 , red curve ) . However , the CC model fails quantitatively: it does not account for the fact that responses tend to saturate at low contrasts . To improve performance , we augmented the CC model with divisive normalization , a computational mechanism that explains a variety of nonlinear behaviors of V1 neurons including contrast saturation [8]–[10] . The divisive normalization ( DN ) model fits the data accurately ( Figure 3 , orange curve ) . To test the DN model on a wider range of stimuli , we measured responses to noise patterns covering different portions of the visual field ( henceforth referred to as spatial stimuli ) . Results for an example voxel in V2 are shown ( Figure 4 ) . The DN model does a reasonable job capturing the pattern of responses to the stimuli ( Figure 4 , orange curve ) . However , the model underestimates responses to stimuli covering a small portion of the receptive field and overestimates responses to stimuli covering a large portion of the receptive field . This can be seen most clearly by inspecting responses to the stimuli labeled ‘Bottom to top’ . We observed this pattern of underestimation and overestimation of spatial responses in a previous study [11] and resolved the issue by applying a compressive static nonlinearity after spatial summation . Intuitively , the compressive nonlinearity boosts responses to stimuli that only partially overlap the receptive field , and can be interpreted as providing tolerance for changes in stimulus position and size [11] . We attempted to improve the performance of the DN model by incorporating , in an analogous fashion , a compressive nonlinearity after spatial summation . We find that the compressive spatial summation ( CSS ) model better fits the data ( Figure 4 , blue curve ) . The CSS model accurately fits responses to the spatial stimuli; and since the CSS model is a more general case of the DN model , the CSS model accurately fits responses to the grating stimuli . However , the CSS model fails to fit responses to the two sets of stimuli simultaneously . For example , if the CSS model is fit to the spatial stimuli , the predicted responses to the grating stimuli substantially overestimate the actual responses ( Figure 5A , blue curve ) . This failure suggests that the CSS model is incomplete and must be modified to account for the full range of responses . Under the CSS model , the predicted response co-varies with the total amount of contrast-energy within a certain region of the visual field ( subject to a compressive nonlinearity ) . This explains why the model predicts large responses to the grating stimuli , as these stimuli contain contrast-energy throughout the spatial extent of the stimulus . Suppose , however , that BOLD responses are not driven by contrast-energy per se , but by variations in contrast-energy . This might explain why the grating stimuli elicit relatively weak BOLD responses . To improve the performance of the CSS model , we incorporated a variance-like nonlinearity into the spatial summation stage of the model . This nonlinearity suppresses responses to stimuli with spatially homogeneous distributions of contrast-energy and enhances responses to stimuli with spatially heterogeneous contrast-energy distributions . We find that the new model , which is the full second-order contrast ( SOC ) model , simultaneously fits both the spatial stimuli and the grating stimuli ( Figure 5A , green curve ) . The noise patterns used for the spatial stimuli consist of contours that are spatially separated from one another; this spatial separation gives rise to variation in contrast-energy and generates large responses from the SOC model . We hypothesized that reducing the spatial separation of the contours would reduce variation in contrast-energy and lead to reduced BOLD responses . To test this hypothesis we measured responses to noise patterns with different levels of contour separation ( Figure 5B ) . As expected , we find that the response is lowest at the smallest separation and increases at larger separations . This pattern of results is accurately predicted by the SOC model ( Figure 5B , green curve ) but not the CSS model ( Figure 5B , blue curve ) . To systematically evaluate the merit of the SOC model , we fit that model and each of the simpler models ( CC , DN , CSS ) independently to the data using five-fold cross-validation . Cross-validation produces a prediction of each data point based on a model that is not fit to that data point . Models are evaluated by how well model predictions match the data . Because the SOC model subsumes the simpler models , it is guaranteed to produce the best fits for a given set of data . However , there is no guarantee that the SOC model will cross-validate well , i . e . generalize to unseen data . The SOC model will cross-validate well only if the effects described by the model are sufficiently large and there are sufficient data to estimate model parameters accurately . Cross-validation controls for model complexity since overly complex models will tend to fit noise in the data and , as a result , generalize poorly . Alternative methods for model selection include AIC and BIC , and these methods produce similar results ( see Supporting Figure S1 ) . In all visual field maps , we find that the SOC model has the highest cross-validation accuracy ( Figure 6 ) . The accuracy of the SOC model is slightly lower than the noise ceiling , i . e . , the maximum performance that can be expected given the noise in the data . Using the metric of explainable variance which takes into account the noise ceiling ( see Methods ) , we find that on average , the SOC model accounts for 88% , 92% , 89% , and 84% of the explainable variance in V1 , V2 , V3 , and hV4 , respectively ( median across voxels in each map ) . These values indicate the high predictive power of the SOC model . Metrics like variance explained are convenient for summarizing model accuracy , but it is important to examine the specific aspects of the data that drive these metrics . To visualize results from a large number of voxels on a single plot , we adopt the strategy of averaging data across voxels and averaging the predictions of each model across voxels . Note that this averaging is only for sake of visualization; cross-validation accuracy is computed on a voxel-by-voxel basis and does not involve averaging data . Examining the data and model predictions for a representative visual field map , we see that the SOC model clearly outperforms the other models ( Figure 7 ) . In interpreting this plot , keep in mind that the predictions of a model may depend on the specific stimuli to which the model is fit . For example , when fit to a wide range of stimuli , the DN model fails to predict responses to the grating stimuli ( Figure 7 , orange curve ) , despite the fact that the DN model succeeds when the model is fit only to the grating stimuli ( see Figure 3 ) . As another example , the CC model performs quite poorly for the stimuli tested in this study ( Figure 7 , red curve ) , which may seem surprising given previous reports that the CC model ( or variants thereof ) can characterize responses to grayscale natural images [7] and retinotopic mapping stimuli [15] . However , the results are not inconsistent . The key realization is that the CC model may perform well if fit and tested on stimuli that probe a limited range of stimulus dimensions ( e . g . a limited range of contrasts ) . With a wide range of stimuli , failures of the CC model become evident , and more complex models are necessary to explain the data . We developed the SOC model using carefully controlled stimuli and have demonstrated that the model accurately characterizes responses to these stimuli . A major advantage of controlled stimuli is ease of interpretation: with controlled stimuli , it is relatively easy to identify the stimulus properties that drive effects in the data [18] . However , a stimulus set composed of controlled stimuli is inherently biased towards certain stimulus types at the exclusion of others , leaving open the question of how well the model characterizes responses to stimuli in general . To estimate general accuracy , in a separate experiment we measured responses to 35 objects and quantified how well the SOC model—with parameters derived from the controlled stimuli—predicts the responses . On average , the SOC model accounts for 65% , 72% , 69% , and 59% of the explainable variance in V1 , V2 , V3 , and hV4 , respectively ( median across voxels in each map ) . These values are lower than the corresponding values obtained for the controlled stimuli , underscoring the fact that summary metrics of model performance are highly dependent on the type of stimuli used . Nevertheless , the values are encouragingly high and confirm that the SOC model has predictive power for ecologically relevant stimuli [19] . One interpretation of the reduced performance on object stimuli is that such stimuli contain higher-order features that are not accurately represented by the SOC model; investigating these features can be the focus of future studies . In the divisive normalization stage of the SOC model , the population activity used to normalize filter outputs consists of the sum of the outputs of filters at the same position but different orientations ( see Methods ) . The reason we assumed the population has the same spatial extent as the filter outputs is simplicity: by making that assumption , the space of model parameters is vastly reduced and the interpretation of the divisive normalization stage is simplified . However , divisive normalization models of V1 neurons often consist of a central excitatory region that is normalized by a larger surround region [20] , [21] , and such models are used to account for surround suppression , a phenomenon that is closely related to second-order contrast ( see Discussion ) . Thus , one might speculate that if the spatial extent of the population were enlarged , the resulting model might be sufficient to account for our data . To address this issue , we tested a version of the DN model in which the spatial scale over which normalization occurs is flexible and fit to the data . The hypothesis is that this model might account for the data as well as ( or better than ) the more complex SOC model . We find that the DN model with flexible normalization ( Figure 8B , yellow bar ) outperforms the original DN model ( Figure 8B , orange bar ) but does not achieve the same accuracy as the SOC model ( Figure 8B , green bar ) . This indicates that simply enlarging the normalization pool is not sufficient and that the additional computations in the SOC model are necessary to account for the data . We also tested several other control models , including a model that demonstrates that the squaring operation in the computation of second-order contrast is critical ( Figure 8B , cyan bar ) . One of the control models ( RM2 ) omits the Divisive normalization component of the SOC model , and performs about as well as the full SOC model . This can be attributed to the fact that the effect of Divisive normalization on the overall response of the model can be approximated , through suitable choice of parameters , by the other components of the model , most notably the Compressive nonlinearity component . For a simple example of this phenomenon , suppose we have a cascade of two power-law nonlinearities , each with exponent 0 . 5 . If the first nonlinearity is omitted , the overall input-output relationship can still be preserved if the exponent of the second nonlinearity is set to 0 . 25 . While a compressive nonlinearity is not an exact substitute for divisive normalization , it approximates many of the same effects within our measurements . We have chosen to include the Divisive normalization component in the SOC model for two reasons . One is to maintain historical continuity , as previous studies have incorporated divisive normalization immediately following a linear filtering stage [e . g . 9] . The second reason is that even though normalization ( immediately after the linear filtering stage ) is not essential for the current set of data , it is likely that normalization will prove essential at finer scales of measurement ( sub-millimeter voxels ) . For example , a major effect explained by normalization is cross-orientation suppression at the level of single neurons in V1 [10]; this effect is largely obscured at the current scale of measurement ( 2 . 5-mm voxels ) . This observation highlights the fact that the model inferences we make are limited by the resolution of our BOLD measurements and that there is value in developing models at finer scales of measurement . We now turn to examining the parameters of the SOC model . There are three parameters of interest , σ , n , and c . The σ parameter controls the size of the 2D Gaussian over which contrast-energy is summed , the n parameter controls the strength of the compressive nonlinearity , and the c parameter controls the strength of the variance-like nonlinearity that generates selectivity for second-order contrast ( see Methods for details ) . To summarize the n and c parameters , we calculate the median parameter value across voxels in each map . To summarize the σ parameter , we fit a line relating receptive field eccentricity and σ and extract the σ value at 2° eccentricity . For each parameter of interest , we plot the summary value observed in each visual field map ( Figure 9 , top ) . Because raw parameter values are difficult to interpret , we also perform simulations that clarify the effect of the parameter values on the overall stimulus-response relationship ( Figure 9 , bottom ) . In these simulations , we calculate the response of the SOC model using the typical parameter values found in each visual field map ( thus , four instances of the SOC model were simulated ) . These simulations directly reflect the behavior of the SOC model as fitted to each map and do not incorporate any assumptions beyond what is determined from the data and the model . Inspecting the variation in parameter values , we find that the σ parameter increases from V1 to V2 to V3 to hV4 , reflecting an increase in receptive field size ( Figure 9A ) . We find that the n parameter decreases from V1 to V2 to V3 to hV4 , reflecting an increase in normalization ( Figure 9B ) . Finally , we find that the c parameter is higher in V2 and V3 than it is in V1 and hV4 , reflecting increased selectivity for second-order contrast ( Figure 9C ) . All pairwise differences between visual field maps are statistically significant ( p<0 . 05 , two-tailed randomization test ) with the exception of n in V3 vs . hV4 and c in V2 vs . V3 .
We have developed a model that predicts BOLD responses to a wide range of stimuli . Stimulus-driven BOLD responses arise principally from metabolic demands of peri-synaptic neural activity [22] , [23] . Hence , BOLD is one of the many ways that neural activity can be measured , and our model of BOLD responses is a model of neural population responses . However , the spatial resolution of our BOLD measurements ( 2 . 5-mm voxels ) is lower than the resolution required to analyze and dissect neural circuits , and this may lead some to conclude that our model of BOLD responses does not actually provide much insight into neural computation . We believe this view to be in error . To explain our position , it is useful to highlight the distinction between functional models and circuit models . Functional models are stimulus-referred ( i . e . start with the stimulus ) and specify what aspects of the stimulus drive responses in a given area . Building functional models has a long history in electrophysiology [for review] , , where researchers explain the spiking activity of neurons in terms of relatively simple computations applied to the stimulus . Circuit models go further than functional models by identifying the specific neural circuitry that gives rise to the observed responses . Hence , functional models may be simpler than circuit models and multiple competing circuit models may be consistent with a given functional model . There is value in functional characterizations of neural responses , especially if one seeks to link neural circuits to perceptual judgments and behavior [26] . To illustrate the distinction between functional models and circuit models , consider a model that explains the spiking activity of a V1 simple cell by the application of an oriented linear filter to the stimulus , followed by a rectification nonlinearity . This model , known as an LN or linear-nonlinear model [24] , is a functional but not a circuit model—it describes how stimuli relate to responses , but does not characterize the many stages of processing performed by the visual system before V1 ( e . g . retina , LGN ) nor the specific neural circuit by which orientation tuning arises [e . g . feedforward computation on LGN afferents or intracortical processing within V1—see 27] . Nevertheless , the model is useful for understanding how stimuli are represented in the visual system . The SOC model developed in this paper is a functional model—it characterizes the relationship between visual stimuli and measured BOLD responses . Like functional models of neuronal responses , the SOC model does not propose specific neural circuits . Rather , the SOC model provides insight at the functional level , that is , in identifying the aspects of the stimulus that drive responses in different visual field maps . For example , the model indicates that second-order contrast is an important factor that drives population responses in V2 and V3 , and we can reasonably infer that this same stimulus property drives responses of individual neurons in these maps . To test and expand upon this hypothesis , one could adapt the stimuli and model used in this study to single-unit electrophysiology and assess how well neuronal responses are accounted for . In doing so , we may find it necessary to extend the model to account for response properties that are evident at the level of individual neurons but which are not readily observed at the population level . Neural activity is coupled to the BOLD response through a complex set of neurovascular mechanisms [23] , [28] . Thus , physiological responses measured using BOLD fMRI reflect both neural activity and these coupling mechanisms . Since the coupling mechanisms are not explicitly modeled in the present work , an implicit assumption in the interpretation of our results is that the BOLD response provides a linear ( or approximately linear ) measure of some aggregated neural activity . Under this assumption , we attribute the various nonlinear operations in the SOC model to nonlinearities arising in neural processing . However , there may be nonlinearities in neurovascular coupling , and this possibility limits the inferences we can make from our BOLD measurements . For example , if there is a nonlinearity in the relationship between the total amount of neural activity in a voxel and the strength of the BOLD response measured from that voxel , then the level of compression estimated by the Compressive nonlinearity component of the SOC model may differ from the level of compression associated with the underlying neural activity . Going forward , we believe that developing a better understanding of the different types of neural activity ( e . g . synaptic activity , spiking activity ) and the mechanisms that couple these various types of neural activity to the BOLD response is of high importance . The SOC model has a cascade architecture , consisting of a series of computations that are applied to the stimulus . The success of the SOC model is consistent with the long-standing hypothesis that the visual system can be characterized as a cascade of operations [29]–[34] . However , cascade models come in a variety of different forms and vary in essential characteristics such as the number of stages in the model and the computations that are applied at each stage . Our work contributes to the field by proposing a specific model and showing that this model quantitatively accounts for a sizable range of experimental measurements in the living human brain . The SOC model is most similar to the cascade model that is being developed by Heeger , Landy , and colleagues [34] , [35] . These authors propose that the stimulus is transformed through two or more stages of canonical operations , each stage consisting of filtering , which is a linear operation ( L ) ; rectification , which is a nonlinear operation ( N ) ; and normalization , which is a nonlinear operation ( N ) . Mapping these operations onto the SOC model , we see that the SOC model is a two-stage cascade model with an overall form of LNNLNN ( Figure 2A ) . There are differences between the SOC model and the Heeger-Landy model . First , the SOC model is fully computable , starting with images and predicting physiological responses . Second , the filtering operation in the second stage of the SOC model is generic: variance in contrast-energy drives responses irrespective of how contrast-energy is arranged in the stimulus . In contrast , the Heeger-Landy model uses oriented second-order filters . Third , the normalization operation in the second stage of the SOC model is implemented as a compressive nonlinearity . This is reasonable because under certain conditions , the effects of divisive normalization can be approximated with a compressive nonlinearity [11] . It is common in cascade models to designate different stages as corresponding to different visual areas . Thus , it is tempting to view the first stage of operations in the SOC model ( the first LNN ) as corresponding to primary visual cortex ( V1 ) and the second stage of operations ( the second LNN ) as corresponding to extrastriate areas . However , this interpretation is complicated by the fact that the full two-stage SOC model predicts V1 responses more accurately than the one-stage DN model ( see Figure 6 ) . To reconcile this finding , we hypothesize that the computation of first-order contrast ( the first LNN ) occurs in V1 ( or is inherited from earlier processing ) , the computation of second-order contrast ( the second LNN ) occurs downstream from V1 , and feedback introduces second-order effects into V1 responses . Some support for this circuit-level hypothesis comes from studies reporting that surround suppression—which , as we later explain , is intimately related to second-order contrast—is mediated by feedback from extrastriate areas to V1 [36] , [37] . A key component of the SOC model is a nonlinearity that computes variance in contrast-energy within a specific region of the visual field . This nonlinearity enhances responses to stimuli that have heterogeneous distribution of contrast-energy and suppresses responses to stimuli that have homogeneous distribution of contrast-energy . We find that the nonlinearity is substantially stronger in extrastriate areas V2 and V3 compared to V1 , suggesting that selectivity for second-order contrast is mainly a feature of extrastriate cortex . We do find , however , that the strength of the nonlinearity in hV4 is comparable to that in V1 , indicating that in hV4 first-order contrast is relatively effective at driving responses . The concept of second-order contrast—or , more generally , second-order stimuli—has a long history in visual psychophysics [for review] , [ see 12 , 13] and other sensory modalities [38] . Second-order stimuli involve modulation of a stimulus property ( e . g . contrast ) across space or time in such a way that the modulation cannot be detected by a first-order filter . For example , consider a sinusoidal grating whose amplitude is modulated by a sinusoidal grating of lower spatial frequency . Such a stimulus varies in contrast across space , but this variation cannot be detected by a first-order luminance filter since average luminance remains constant throughout the extent of the stimulus . To explain the perception of second-order stimuli , researchers have proposed filter-rectify-filter ( FRF ) models in which first-order filters are applied to the stimulus , the outputs of these filters are rectified , and second-order filters are applied to the rectified outputs . Extending results from animal models [e . g . 39] , [40] , [41] , several fMRI studies have found evidence of second-order processing in human visual cortex [35] , [42] . These studies used adaptation techniques to infer selectivity for second-order modulation of contrast [42] and orientation [35] , [42] , and proposed a variant of the FRF model to account for their results [35] . Our results are consistent with these adaptation studies in finding that second-order effects exist in many visual field maps including V1 . We extend these studies by executing a different experimental and modeling approach: We demonstrate second-order effects directly in visually evoked responses . Moreover , we develop a model that operates on images and quantitatively predicts responses at the level of single voxels . Our finding that selectivity for second-order contrast is particularly strong in extrastriate areas is consistent with the fact that sparsely distributed contours strongly activate such areas [43] . This is because sparsely distributed contours give rise to large amounts of contrast variation . Our results are also consistent with the results of a study that developed and compared models of neural responses in V1 and V2 [44] . In that study , neural responses were characterized using a model in which V1-like filters are applied to the stimulus , the outputs of the filters are rectified , and then a flexible set of weights on the rectified filter outputs is used to predict responses . Importantly , fitted weights tended to be more negative in V2 than in V1 . This suppression may serve to reduce responses to stimuli that are spatially homogeneous in contrast-energy , similar to the variance-like nonlinearity we propose in the SOC model . A quantitative comparison of these models is an important future direction . Second-order contrast is a key feature of the SOC model , and it is useful to clarify the connection between second-order contrast and phenomena that have been extensively studied in the visual system . One such phenomenon is surround suppression , which has been studied both psychophysically and physiologically and is thought to underlie perceptual processes such as scene segmentation [45] , perceptual constancies [46] , and enhancement of salience differences [47] . A basic form of surround suppression is size tuning , whereby the response of a neuron is highest for a grating of a certain size and is suppressed if the grating is enlarged [20] , [48] . The SOC model was not specifically designed to account for size tuning , but a simulation demonstrates that the SOC model does in fact exhibit size tuning ( Figure 10 ) . Intuitively , response suppression for large gratings stems from the absence of variation in contrast-energy; conversely , response enhancement for small gratings stems from the presence of variation in contrast-energy . This simulation demonstrates the close relationship between second-order contrast and surround suppression . The SOC model's explanation of surround suppression differs from that provided by traditional models of surround suppression . In such models , a central excitatory region is divisively normalized by a larger surround region , and response suppression for large gratings stems from increased stimulation of the surround [20] , [21] . The fact that surround suppression might have different computational explanations—either divisive normalization over a large spatial extent or second-order mechanisms—has been previously recognized [35] . We find that divisive normalization by itself does not fully account for our data , even if the spatial extent of normalization is enlarged ( see Figure 8B , yellow bar ) . Thus , our results suggest that second-order mechanisms play an essential role in producing surround suppression effects . The ability to tease apart computational explanations such as these is made possible by our approach of measuring responses to a wide range of stimuli and testing general models that operate on arbitrary stimuli . Second-order contrast also has an interesting connection to the statistics of natural images . The distribution of local contrast in a natural image tends to be sparse , with local contrast often near zero [50]–[53] . We reasoned that because of this sparseness , the amount of second-order contrast in natural images should be relatively high . To verify this hypothesis , we constructed a collection of natural image patches and quantified the amount of second-order contrast in each image by computing the response of the SOC model to the image . For comparison we also computed responses of the SOC model after scrambling the phase spectrum of each patch . The responses of the SOC model are , on average , higher for the natural image patches ( Figure 11A ) . Reduced responses to the phase-scrambled patches can be attributed to the fact that phase-scrambling takes localized structures ( which induce high variation in contrast-energy ) and disperses them throughout the image ( Figure 11B ) . The fact that natural stimuli have relatively high amounts of second-order contrast is consistent with previous analyses of natural image statistics [54] . We suggest that selectivity for second-order contrast can be interpreted as an efficient coding strategy in which the visual system is tuned to the statistical features of natural scenes [49] . Stated simply , the idea is that the visual system is tuned in such a way that commonly experienced stimuli ( e . g . stimuli with second-order contrast ) evoke stronger responses than less commonly experienced stimuli ( e . g . stimuli without second-order contrast ) . Our simulations show that scrambling the phase spectra of natural image patches reduces variation in contrast-energy and leads to reduced responses from the SOC model . In general , reduction in contrast-energy variation may explain why phase scrambling tends to reduce activation levels in the visual system . For example , phase-scrambling line and edge stimuli reduces variation in contrast-energy and , as expected , reduces BOLD responses in early visual areas [55] . Of course , the phase spectrum consists of other stimulus characteristics besides variation in contrast-energy , and the visual system might also be sensitive to these characteristics . One example is alignment of phases across spatial frequencies , which occurs at edges in natural images [56] . The SOC model has high accuracy but is not perfect , especially when tested on naturalistic object stimuli ( see Results ) . To improve performance , future work could continue the approach taken in the present study of designing controlled stimuli , assessing model predictions , and introducing new model components as necessary . It may be productive to consider how well the SOC model predicts responses to simple icons and shapes as such stimuli have been previously used to study the tuning properties of extrastriate areas [57]–[59] . Future work could also be directed towards expanding the range of stimuli for which the SOC model operates . For tractability we restricted the stimuli in this study to a band-pass range of spatial frequencies . A natural step would be to extend the SOC model to operate on stimuli with arbitrary spatial frequency content . This could be done , for example , by replicating the model architecture at multiple spatial scales and allowing the predicted response to be a weighted sum across scales . Ultimately , additional stimulus properties such as color , motion , and depth will need to be considered .
Three experienced fMRI subjects ( three males; age range 29–39; mean age 33 ) participated in this study . All subjects had normal or corrected-to-normal visual acuity . Informed written consent was obtained from all subjects , and the experimental protocol was approved by the Stanford University Institutional Review Board . One subject ( JW ) was an author . Subjects participated in 1–2 scan sessions for the main experiment , and one subject participated in an additional scan session for the object experiment . Subjects also participated in 1–4 separate scan sessions to identify visual field maps [details in 60] . We used a randomized event-related design to minimize anticipatory and attentional effects . Stimuli were presented in 8-s trials , one stimulus per trial . During the first 3 s of a trial , the nine images comprising a given stimulus were presented in random order at a rate of 3 images per second ( duty cycle: 167-ms ON/167-ms OFF ) . Then for the next 5 s , no stimulus was presented . For the main experiment , the 156 stimuli were randomly divided into four groups . In each run , the stimuli from one of the groups were presented once and in random order . To establish the baseline signal level , each run also included null trials in which no stimuli were presented ( “blank” stimuli ) . Two null trials were inserted at the beginning and end of each run , and one null trial was inserted after every five stimulus trials . Each run lasted 6 . 7 minutes . Each scan session consisted of three sets of four runs ( thus , each stimulus was presented three times over the course of the session ) . For the object experiment , the 35 stimuli were presented once and in random order in each run . Null trials were included to establish the baseline signal level as in the main experiment . Each run lasted 6 . 0 minutes , and each scan session consisted of ten runs . To improve signal-to-noise ratio for the main experiment in subjects 1 and 2 , two independent scan sessions were conducted . The stimulus ordering in the second session was matched to that in the first session , and the data from the two sessions were directly averaged together ( after data pre-processing ) . Functional MRI data were collected at the Stanford Center for Cognitive and Neurobiological Imaging using a 3T GE Signa MR750 scanner and a Nova 32-channel RF head coil . In each scan session , 22 slices roughly parallel to the parieto-occipital sulcus were defined: slice thickness 2 . 5 mm , slice gap 0 mm , field-of-view 160 mm×160 mm , phase-encode direction anterior-posterior . A T2*-weighted , single-shot , gradient-echo EPI pulse sequence was used: matrix size 64×64 , TR 1 . 337702 s , TE 28 ms , flip angle 68° , nominal spatial resolution 2 . 5×2 . 5×2 . 5 mm3 . The TR was matched to the refresh rate of the display such that there were exactly 6 TRs for each 8-s trial . For post-hoc correction of EPI spatial distortion , measurements of the B0 magnetic field were performed . Field maps were collected in the same slices as the functional data using a 16-shot , gradient-echo spiral-trajectory pulse sequence . Two volumes were successively acquired , one with TE set to 9 . 091 ms and one with TE increased by 2 . 272 ms , and the phase difference between the volumes was used as an estimate of the magnetic field . To track slow drifts in the magnetic field ( e . g . due to gradient heating ) , field maps were collected before and after the functional runs as well as periodically between functional runs . Voxels in each visual field map were pooled across subjects . Unless otherwise indicated , error bars represent ±1 standard error ( 68% confidence intervals ) across voxels and were obtained using bootstrapping . We fit the SOC model to each voxel using response amplitudes to the SPACE , ORIENTATION , GRATING , PLAID , CIRCULAR , and CONTRAST stimuli . Model fitting was performed using nonlinear optimization ( MATLAB Optimization Toolbox ) with the objective of minimizing squared error . The predicted response to a given stimulus was obtained by computing the response of the model to each of the nine images comprising the stimulus and then taking the average across these responses . Fitting all of the parameters in the SOC model ( r , s , x , y , σ , c , n , g ) simultaneously is computationally prohibitive . To reduce computational requirements , we determined a single set of canonical values for the r and s parameters before fitting the remaining parameters ( detailed below ) . This strategy has the additional benefit of simplifying the interpretation of the model; for example , voxel-to-voxel differences in the overall strength of normalization can be solely attributed to differences in the n parameter and not differences in the r and s parameters ( see Figure 9B ) . Our fitting approach was as follows . To determine a single set of canonical values for the r and s parameters , we selected from each subject the ten voxels in V1 with the highest GLM cross-validation accuracy and exhaustively evaluated each combination of r and s , where r is chosen from { . 01 . 05 . 1 . 2 . 3 . 4 . 5 . 6 . 7 1 1 . 5 2} and s is chosen from { . 002 . 005 . 01 . 02 . 05 . 1 . 2 . 5 1 2 4 8} . For each combination of r and s , we optimized x , y , σ , and g with c fixed to 0 . 9 and n fixed to 0 . 5 , and then optimized all of these parameters simultaneously . On average across voxels , the values that produced the best fits were r = 1 and s = 0 . 5 . We then fixed the r and s parameters to these values and fit the remaining parameters of the model for every voxel . To guard against local minima , we used a variety of initial seeds for the c and n parameters . For every combination of c and n , where c is chosen from { . 1 . 4 . 7 . 8 . 85 . 9 . 95 . 975 . 99 . 995} and n is chosen from { . 05 . 1 . 2 . 3 . 4 . 5 . 6 . 7 1} , we optimized x , y , σ , and g with c and n fixed , and then optimized all of these parameters simultaneously . The SOC model was fit using two different resampling schemes . In the full fit scheme , we fit the model to the entire set of responses . This was used to derive best estimates of the parameters of the SOC model . In the cross-validation scheme , we fit the model using five-fold cross-validation ( random selection of folds ) . This was used to obtain unbiased estimates of the accuracy of the SOC model . Accuracy was quantified as the percentage of variance explained ( R2 ) in the measured response amplitudes by the cross-validated predictions of the response amplitudes:where di indicates the ith measured response amplitude and mi indicates the ith predicted response amplitude . The R2 value indicates the percentage of variance relative to 0 that is predicted by the model . Note that defining R2 with respect to deviations from 0 as opposed to deviations from the mean ( which is the typical statistical formulation ) avoids the arbitrariness of the mean , which varies depending on the specific data points under consideration . Model accuracy was compared to the noise ceiling , defined as the maximum accuracy that a model can be expected to achieve given the level of noise in the data [70] , [71] . Noise ceiling estimates were obtained using Monte Carlo simulations in which a known signal and noisy measurements of the signal are generated and the expected R2 between the signal and the measurements is calculated . In these simulations , the signal and noise are assumed to be Gaussian-distributed with parameters matched to the response amplitudes and associated error bars obtained from each voxel [see 11 for additional details] . Model accuracy was also compared to a flat response model that simply predicts the mean response for every stimulus . To obtain a metric of model accuracy that is adjusted for the noise ceiling and the flat response model , we define percent explainable variance aswhere R2 indicates the raw performance of the model , FR indicates the performance achieved by the flat response model , and NC indicates the noise ceiling . For example , 50% explainable variance means that the amount of variance predicted by a model is halfway between the amount of variance predicted by the flat response model and the maximum amount of variance that can be predicted given the noise in the data . As an additional assessment of model accuracy , we took the fits of the SOC model from the main experiment ( full fit scheme ) and predicted the response amplitudes in the object experiment . To compensate for instability in the gain of response amplitudes across scan sessions ( e . g . due to imperfections in co-registration ) , we allowed a non-negative scale factor to be applied to the predicted response amplitudes before computing R2 values . For fair comparison , the simulations used to estimate the noise ceiling for the object predictions also included the scale adjustment . Example datasets and code implementing the SOC model are provided at http://kendrickkay . net/socmodel/ .
|
Much has been learned about how stimuli are represented in the visual system from measuring responses to carefully designed stimuli . Typically , different studies focus on different types of stimuli . Making sense of the large array of findings requires integrated models that explain responses to a wide range of stimuli . In this study , we measure functional magnetic resonance imaging ( fMRI ) responses in early visual cortex to a wide range of band-pass filtered images , and construct a computational model that takes the stimuli as input and predicts the fMRI responses as output . The model has a cascade architecture , consisting of two stages of linear and nonlinear operations . A novel component of the model is a nonlinear operation that generates selectivity for second-order contrast , that is , variations in contrast-energy across the visual field . We find that this nonlinearity is stronger in extrastriate areas V2 and V3 than in primary visual cortex V1 . Our results provide insight into how stimuli are encoded and transformed in the visual system .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"visual",
"system",
"fmri",
"computational",
"neuroscience",
"sensory",
"systems",
"biology",
"computational",
"biology",
"neuroscience",
"neuroimaging",
"coding",
"mechanisms"
] |
2013
|
A Two-Stage Cascade Model of BOLD Responses in Human Visual Cortex
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Niemann-Pick Protein C2 ( npc2 ) is a small soluble protein critical for cholesterol transport within and from the lysosome and the late endosome . Intriguingly , npc2-mediated cholesterol transport has been shown to be modulated by lipids , yet the molecular mechanism of npc2-membrane interactions has remained elusive . Here , based on an extensive set of atomistic simulations and free energy calculations , we clarify the mechanism and energetics of npc2-membrane binding and characterize the roles of physiologically relevant key lipids associated with the binding process . Our results capture in atomistic detail two competitively favorable membrane binding orientations of npc2 with a low interconversion barrier . The first binding mode ( Prone ) places the cholesterol binding pocket in direct contact with the membrane and is characterized by membrane insertion of a loop ( V59-M60-G61-I62-P63-V64-P65 ) . This mode is associated with cholesterol uptake and release . On the other hand , the second mode ( Supine ) places the cholesterol binding pocket away from the membrane surface , but has overall higher membrane binding affinity . We determined that bis ( monoacylglycero ) phosphate ( bmp ) is specifically required for strong membrane binding in Prone mode , and that it cannot be substituted by other anionic lipids . Meanwhile , sphingomyelin counteracts bmp by hindering Prone mode without affecting Supine mode . Our results provide concrete evidence that lipids modulate npc2-mediated cholesterol transport either by favoring or disfavoring Prone mode and that they impose this by modulating the accessibility of bmp for interacting with npc2 . Overall , we provide a mechanism by which npc2-mediated cholesterol transport is controlled by the membrane composition and how npc2-lipid interactions can regulate the transport rate .
Cholesterol , ubiquitously present in all vertebrate cells , regulates the structure and permeability of cellular membranes [1 , 2] . It comprises typically 20–25 mol % of all lipids in the plasma membrane [1] and acts as a precursor for many bioactive molecules such as steroid hormones , bile acids , oxysterols , and vitamin D [2] . While cholesterol is vital for health , its excessive accumulation results in pathologies , such as cardiovascular diseases , the complications of which account for about 30% of deaths globally [2] . Because most cells cannot catabolize cholesterol , its efflux is crucial to prevent cholesterol overloading [2] . There is a network of cellular signaling and transport systems that tightly controls cholesterol trafficking [1] . One of the key proteins involved in cholesterol efflux from late endosomes/lysosomes is Niemann-Pick Protein C2 ( npc2 ) , an intralysosomal and secretory protein found in epididymal fluid , milk , plasma , and the bile [3] . Mutations in npc2 or its transmembrane partner npc1 result in accumulation of lipids such as unesterified cholesterol and sphingolipids [4 , 5] . This fatal genetic lysosomal storage condition called Niemann-Pick C disease results in progressive neuronal degeneration in the brain and early death [6 , 7] . In the late endosome/lysosome , a “tag team duo” composed of npc1 and npc2 is responsible for egress of endocytosed cholesterol [4 , 8] . npc2 captures cholesterol from the internal membranes of late endosomes/lysosomes and transfers it to npc1 [9 , 10] for cholesterol egress from these compartments [5] . There has been recent resurgence of npc1 structures characterized by crystallography and cryo-electron microscopy [11–15] , reviving interest in molecular investigation of late endosomal/lysosomal cholesterol trafficking . These structural studies have been complemented by computational investigations focused on the cholesterol transfer process between the N-terminal domain of npc1 and npc2 [16 , 17] , as well as sterol-binding to npc2 [18] . Meanwhile , npc2 also works independently of npc1 [19] . In particular , it has been shown that normalizing abca1 expression can bypass the effects of npc1 mutations , but not those of npc2 mutations [20] , highlighting the importance of the npc1-independent cholesterol transport by npc2 . In this work , we investigate the npc1-independent mechanism , namely the direct specific binding of npc2 onto the internal late endosomal/lysosomal membranes to cargo cholesterol between them [19 , 21] . npc2 mainly functions in the acidic environment of late endosomes and lysosomes . These organelles manifest a multivesicular appearance due to the presence of a unique anionic phospholipid called bis ( monoacylglycero ) phosphate ( bmp ) , also known as lysobisphosphatidic acid ( lbpa ) [22] . bmp is abundant within the internal membranes of late endosomes/lysosomes , yet it is absent from the cytoplasmic leaflet of the limiting membrane [22] . The intraluminal vesicles are the sites of sphingolipid degradation , with sphingomyelin ( sm ) representing the most abundant sphingolipid [23] . When bound to a membrane , npc2 interacts specifically with bmp , which modulates its efficiency in cholesterol transport [21 , 24–26] . On the other hand , sm strongly inhibits cholesterol transfer by npc2 [27] . Nevertheless , the molecular interactions between npc2 and different lipid components , and in particular how bmp and sm contributes to this dual regulation of npc2-dependent cholesterol transport remain unclear . We used atomistic molecular dynamics ( MD ) simulations to perform an extensive investigation of npc2-membrane interactions in a variety of different membrane lipid mixtures of neutral ( 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( popc ) , N-palmitoyl-D-erythro-sphingosylphosphorylcholine ( sm ) ) and charged ( s-s 2 , 2’-dioleoyl lysobisphosphatidic acid ( bmp ) , 1 , 2-dioleoyl-sn-glycero-3-phosphatidylglycerol ( dopg ) ) lipids together with cholesterol ( chol ) . We determined the lipid-dependent membrane-binding free energies using well-tempered metadynamics ( wt-mtd ) on three collective variables ( cvs ) that define the orientation and the position of npc2 with respect to the membrane . The results highlight that i ) npc2 binds charged membranes favorably; ii ) npc2 binds membranes in two different competitively favorable orientations; iii ) bmp is decisive for strong npc2 binding in the orientation that places the cholesterol-binding pocket of npc2 in direct contact with the membrane; and iv ) sm hinders the formation of the aforementioned orientation . We characterized specific lipid-protein interactions in different orientations to understand the bases of the observed binding and inhibition mechanisms induced by bmp and sm . Altogether , the results provide an atom-scale picture for npc2-membrane binding and insights into how the membrane composition modulates the efficiency of npc2-dependent cholesterol transport .
We considered a total of 8 different membrane systems ( see Table 1 ) composed of bmp , chol , sm , dopg , and popc . For 5 of these membrane systems ( systems 1 , 3–5 , 7 in Table 1 ) , we performed unbiased simulations with npc2 in its cholesterol-bound ( npc2chol-bound ) and apo ( npc2apo ) forms . Seven 400 ns long repeats were performed , each starting from a randomly chosen independent initial configuration , adding up to a total of 70 unbiased simulations . We also performed ( biased ) free energy calculations using well-tempered metadynamics ( wt-mtd ) [28] for all membrane systems except System 4 with npc2chol-bound and npc2apo . The total simulation time for unbiased and biased simulations add up to 28 μs and ∼148 μs , respectively . The preparation of all systems including the membrane mixtures is described in detail in S1 Text . All simulations were performed using gromacs 5 . 0 [29] employing the Amber ff99sb-ildn force field [30] for the protein , the Slipids force field [31] for the lipids , and the tip3p model for water [32] . The equations of motion were integrated using a leap-frog algorithm with a 2 fs time step . All bonds were constrained using the lincs algorithm [33] . Long-range electrostatic interactions were treated by the smooth particle mesh Ewald scheme ( spme ) [34 , 35] with a real-space cutoff of 1 . 0 nm , a Fourier spacing of 0 . 16 nm , and a fourth-order interpolation . The van der Waals interactions were treated with a Lennard-Jones potential with a cutoff of 1 . 0 nm . Long-range dispersion corrections were applied for energy and pressure [36] . Before production runs , successive steepest descent minimizations were carried out , followed by short equilibration simulations in the NVT and NpT ensembles at a temperature of 310°K using the v-rescale thermostat [37] with a time constant of 0 . 1 ps . The pressure was kept at 1 atmosphere using the Berendsen barostat [38] with a time constant of 0 . 5 ps . All production simulations were performed in the NpT ensemble . Protein , solvent ( water and ions ) , and lipids were coupled to separate temperature baths at 310°K using the Nosé-Hoover thermostat [39 , 40] with a time constant of 0 . 5 ps . Pressure was kept at 1 atmosphere with a time constant of 10 ps and a compressibility of 4 . 5 × 10−5 bar−1 using the Parrinello-Rahman barostat [41 , 42] semi-isotropically for membrane-containing systems and isotropically for others . For the free energy calculations , all biased simulations employed gromacs 5 . 0 . 4 [29] patched with plumed 2 . 1 [43] . Details of these free energy computations are discussed in S1 Text , which also includes a more detailed description of the simulation protocol .
To investigate the role of bmp , sm , and chol in npc2–membrane binding , we prepared a number of membrane models with different lipid compositions listed in Table 1 . For most of the membrane systems , we first carried out unbiased md simulations , where we initially placed apo ( npc2apo ) [44] or chol-bound npc2 ( npc2chol-bound ) [3] above the membrane surface in seven different , randomly chosen orientations such that the minimum z-distance between the protein and the membrane surface is 30–35Å ( see Fig 1A for a typical initial configuration ) . In these unbiased simulations , the membranes containing bmp ( bmp:chol:sm:dopg:popc = 100:0:0:0:0 ( System 4 ) , 20:10:0:0:70 ( System 5 ) , and 20:10:20:0:50 ( System 7 ) ) resulted in spontaneous association of npc2 with the membrane in less than 60 ns ( Fig 1B and 1C ) . For comparison , if the membrane considered did not include bmp ( bmp:chol:sm:dopg:popc = 0:10:0:0:90 ( 1 ) and 0:10:20:0:70 ( System 3 ) ) , then no npc2 binding was observed . The only exception was chol:sm:popc = 10:20:70 , where one out of 14 simulations resulted in npc2 binding to the membrane . Monitoring the distance between the membrane surface and the residue at the opening of the cholesterol-binding pocket ( G61 ) , we observed that npc2 binds the membranes in two distinct modes based on the membrane-adsorbed state: membrane binding orientation 1 ( Prone mode , Fig 1B ) , in which the cholesterol-binding pocket is in direct contact with the membrane surface and G61 is inserted into the membrane; and orientation 2 ( Supine mode , Fig 1C ) , in which G61 is facing away from the membrane surface . Supine mode , formed by rotating the protein ∼180° along its long axis with respect to Prone mode , places the cholesterol-binding pocket away from the membrane to face the water phase . In these simulations , Supine mode is observed more frequently than Prone mode for both npc2apo and npc2chol-bound ( Table 1 ) and the orientations do not interconvert within 400 ns . Table 1 shows the number of times each orientation is observed for each membrane system based on the location of G61 with respect to the membrane surface . Based on the results , one can conclude that i ) the simulations reveal two functional binding modes with favorable energetics , and ii ) the npc2-membrane interaction is so strong that once the binding has taken place , the protein is trapped in one of the two local minima and the orientation does not change during the simulation time scale of 400 ns . Characterizing the aforementioned membrane binding modes both energetically ( to compute the free energy of binding ) and structurally ( to determine the mechanism of binding ) requires extensive sampling of the relevant degrees of freedom . To this end , we used a combination of well-tempered metadynamics ( wt-mtd ) [28] , umbrella sampling , and bias exchange [45] . A similar combination of wt-mtd and umbrella sampling was previously used in coarse-grained simulations [46] . A schematic representation of our sampling approach is shown in Fig 2 and the details can be found in S1 Text . Briefly , we defined three collective variables ( cvs ) that describe the position and the orientation of the protein with respect to the membrane: We divided the |z| range that extends from the membrane surface to bulk solvent into 4Å windows that overlap 1Å with each other using half harmonic restraints . Within each window , we performed three separate wt-mtd simulations [28] , in each biasing one of |z| , θ , or ϕ . This adds up to 51–54 200 ns-long simulations ( 17–18 windows ) for each system . To further improve the sampling efficiency , we coupled the neighboring simulations through the bias exchange scheme [45] as shown in Fig 2 . wt-mtd helps overcome barriers along |z| , ϕ , and θ by flattening the underlying free energy landscape , while half-harmonic restraints keep sampling within manageable blocks . Besides , bias exchange improves the statistical efficiency and the convergence of the free energy estimations . This approach allows reconstruction of potential of mean force ( pmf ) profiles for complex systems using a reweighting scheme that combines a time-independent locally-converging free energy estimator for metadynamics [47] and a non-parametric variant of the weighted histogram analysis method [48] , discussed in S1 Text . Based on these free energy simulations , we reconstructed one-dimensional ( 1d ) and two-dimensional ( 2d ) pmf profiles for each simulated system as functions of the biased collective variables ( see S1 Text for details ) . All reconstructed free energy surfaces and their errors are given separately for the anionic and neutral membranes . 1d pmfs as a function of |z| are given in S1A Fig . 2d pmfs and their local errors for |z| vs ϕ are given in S1B and S2 Figs for anionic membranes and in S3B and S4 Figs for neutral membranes . The pmfs for |z| vs θ are given in S5B and S6 Figs for anionic membranes and in S7B and S8 Figs for neutral membranes . To capture the depth of insertion into the membrane , we set the origin ( z = 0 ) to the z-coordinate of the center of mass ( comz ) of the upper leaflet P atoms and reconstructed the pmfs also as functions of the minimum of the protein Cα z-coordinates , which is here denoted as min zr , r being the residue number . See Fig 3A for 1d pmfs , Fig 3B and S9 Fig for 2d pmfs for charged membranes , and S10 and S11 Figs for 2d pmfs for neutral membranes . Here , we discuss the general features of npc2-membrane binding . In agreement with the above-discussed unbiased simulations , the results show enhanced npc2 binding only to anionic membranes rich in bmp . In neutral membranes without bmp ( bmp:chol:sm:dopg:popc = 0:10:0:0:90 ( 1 ) , 0:35:0:0:65 ( System 2 ) , and 0:10:20:0:70 ( System 3 ) ) , the affinity for the membranes is low ( Fig 3A and S1A Fig ) . The binding to neutral membranes is unspecific in terms of ϕ ( S10 and S3 Figs ) and θ ( S7 Fig ) , as no particular binding orientation can be identified . In anionic membranes ( bmp:chol:sm:dopg:popc = 100:0:0:0:0 ( System 4 ) , 20:10:0:0:70 ( System 5 ) , 20:35:0:0:45 ( System 6 ) , 20:10:20:0:50 ( System 7 ) , and 0:10:0:20:70 ( System 8 ) ) , on the other hand , we observe the two distinct high-affinity binding orientations ( ϕ >∼140° and ϕ <∼40° in Prone and Supine modes , respectively ) with overall enhanced affinity in Supine mode ( Fig 3B and S1B Fig ) . The pmf profiles ( S5 Fig ) reveal a single specific high affinity binding orientation for the anionic membranes in terms of θ in a range of about 80° < θ < 100° . Supine mode has generally lower free energies than Prone mode and is more plastic as indicated by the wider distribution of ϕ in Supine mode ( Fig 3 and S1 Fig ) . This also suggests that Supine mode is more flexible in terms of membrane-interacting residues and may be better described as a membrane-adsorbed state rather than as a specific membrane-bound state . On the other hand , Prone mode is characterized by a tighter distribution of ϕ and deeper insertion into the membrane ( Fig 3B ) . This deeply inserted state , also apparent in 1d pmf profiles ( Fig 3A ) , is uniquely associated with Prone mode and forms by the membrane insertion of a loop ( V59-M60-G61-I62-P63-V64-P65 ) at the opening of the cholesterol-binding pocket . From here on , we refer to this hydrophobic loop as the membrane insertion loop ( mil ) . The position of the cholesterol-binding pocket and three residues ( F66 , V96 , and T100 ) that act as gates in sterol transfer [18] with respect to the membrane surface , as well as the membrane insertion of the loop strongly suggest that Prone mode is the conductive state for cholesterol uptake/release . Because the interconversion between Prone and Supine modes can determine the efficiency of cholesterol uptake/release , it is potentially an important process for npc2 function . The interconversion can take place by protein detaching from the membrane and reattaching in either of the orientations . This process requires surpassing the barrier equal to the free energy of membrane binding , which in BMP-containing membranes is typically 7 − 15 kJ/mol and hence quite large ( Table 1 ) . Alternatively , the protein could roll around its long axis without detaching from the membrane . The latter process has a barrier of about 6 − 7 kJ/mol ( about 2 . 5 to 3 kBT ) along ϕ ( Fig 3B ) , which is quite low compared to thermal energy . We conclude that npc2 changes the binding orientation by rocking around its long axis while staying adsorbed on the membrane surface . bmp is known to improve npc2-mediated cholesterol transfer between membranes [27] . To investigate the effect of bmp on the free energy of npc2-membrane binding , we performed membrane binding free energy simulations for both npc2apo and npc2chol-bound in the presence of bmp . We focus our discussion here on the investigations with the anionic bmp:chol:sm:dopg:popc = 20:10:0:0:70 ( System 5 ) mixture together with the neutral membranes that do not contain bmp ( Table 1 ) . Both npc2apo and npc2chol-bound bind strongly in two specific binding orientations to bmp membranes , the binding free energies being around or larger than ΔG = −11 kJ/mol . In neutral membranes , the binding affinity is much lower ( ΔG = −3 to −4 kJ/mol ) and manifests unspecific adsorption in terms of ϕ and θ as mentioned above ( S10 , S3 and S7 Figs ) . The 2d pmf in Fig 3B ( 1st row ) shows that Prone mode is almost as favorable as Supine mode , especially when npc2 is cholesterol-bound . Moreover , bmp also promotes membrane anchoring by the membrane insertion loop ( mil ) as evidenced by deeper inserted states captured in the pmf profiles . In essence , bmp does not only improve npc2 adsorption onto the membrane surface , but it also promotes the binding mode ( Prone ) in which cholesterol uptake and release can take place , along with membrane anchoring by mil . The electrostatic potential profiles ( S12 Fig ) lack a substantial difference between membrane compositions that could explain how and why npc2 , which has +4e at pH = 5 ( see S1 Text ) , favors negatively charged bmp . To further test whether the effects of bmp on npc2-membrane binding are specific or can simply be attributed to its negative charge , we performed free energy simulations , where bmp is substituted with dopg ( bmp:chol:sm:dopg:popc = 0:10:0:20:70 ( System 8 ) ) . Phosphatidylglycerol ( pg ) lipids are not only negatively charged , but they are also precursors in bmp synthesis [49] . Although the negative charge of dopg clearly enhances membrane adsorption ( ΔG = −9 to −10 kJ/mol ) when compared to the neutral membranes , the dopg membrane has ∼ 3 kJ/mol lower affinity for both npc2apo and npc2chol-bound when compared to bmp-containing membranes ( Fig 3A and S1A Fig ) . Moreover , dopg does not favor npc2-binding in Prone mode as strongly as bmp , and does not foster the penetration of the membrane insertion loop into the membrane ( Fig 3B; last row ) . Because pg cannot reproduce the effects of bmp , we conclude that specific bmp-npc2 interactions are important in both membrane binding and cholesterol uptake/release . The higher cholesterol concentration is expected to shift the equilibrium between the npc2apo and npc2chol-bound towards the cholesterol-bound state . We further hypothesized that cholesterol concentration in the membrane can also affect npc2-membrane binding properties . Increasing the cholesterol concentration from 10 to 35 mol% in the presence of 20 mol% bmp ( bmp:chol:sm:dopg:popc = 20:10:0:0:70 ( System 5 ) and 20:35:0:0:45 ( System 6 ) ; see Table 1 ) enhances membrane adsorption for npc2chol-bound by ∼3 kJ/mol ( at min zr = ∼ 3Å ) without affecting that of npc2apo . However , higher cholesterol concentration has the opposite effect for the deeper inserted binding mode of npc2 ( Prone mode ) . The deeper inserted state ( around min zr = −6 Å ) is destabilized by ∼5 kJ/mol for npc2chol-bound , while it is stabilized by ∼3 kJ/mol for npc2apo ( Fig 3A ) . The same effect can be more clearly seen in 2d pmf profiles ( Fig 3B , 1st and 2nd rows ) . Comparing 10 mol% and 35 mol% chol membranes , the minimum for Prone mode shifts up towards the membrane surface and its free energy is increased in npc2chol-bound , while it is shifted downwards in npc2apo . In other words , while both npc2apo and npc2chol-bound adsorb on the bmp-rich membranes with high affinity , the higher chol concentration shifts the free energy minimum towards the deeper inserted Prone mode in npc2apo and away from it in npc2chol-bound . This result implies that the cholesterol levels in the internal membranes of late endosomes/lysosomes can affect the rate of npc2-mediated cholesterol transport by affecting the equilibrium between Prone and Supine modes , taking into account that deeper inserted Prone mode is conductive for cholesterol uptake and release . sm is one of the components of the suborganelle fractions of late endosomes [22] . Furthermore , sm has been shown to strongly inhibit cholesterol-transport by npc2 [27] . To investigate if and how sm affects npc2-membrane binding , we performed unbiased and free energy simulations to membranes containing 20 mol% sm in the presence and absence of bmp ( bmp:chol:sm:dopg:popc = 20:10:20:0:50 ( System 7 ) and 0:10:20:0:70 ( System 3 ) ; see Table 1 ) . Like all other neutral membranes , npc2 has small affinity for non-specific adsorption onto the sm-membrane surface ( ΔG = −3 to −4 kJ/mol; Fig 4A ) . The bmp-sm membrane , on the other hand , has approximately the same affinity for npc2 when compared to bmp-membrane without sm but with 10 mol% chol ( ΔG = −12 to −13 kJ/mol; Fig 4A ) . However , the important observation is that the deeper inserted binding mode of npc2chol-bound ( Fig 4A ) is clearly inhibited by sm . Meanwhile , the deeper inserted state for npc2apo has ∼4 kJ/mol higher free energy in all 20 mol% bmp membranes regardless of the presence of sm . Therefore , sm does not have any clear effect on membrane binding of npc2apo ( Fig 4A ) . These observations are also confirmed by comparing the 2d pmf data ( Fig 4B ) for sm-containing and sm-free membranes , as well as the lack of adsorption in Supine mode to sm-containing membranes in unbiased simulations ( Table 1 ) . The results offer a mechanism as to how sm inhibits npc2-mediated cholesterol transport: sm can hinder cholesterol transfer between membranes by interfering with formation of the deeper inserted Prone mode , and thus , the cholesterol release from npc2chol-bound . Since bmp-sm membranes have similar affinity to those with only bmp , the sm-containing membrane fractions of the internal lysosomal/late endosomal membranes may arrest substantial amount of npc2 in non-conductive Supine mode . This can , in turn , reduce the effective concentration of npc2 available for cholesterol exchange , hindering it even further . Having identified two functional binding modes for npc2-membrane binding and the importance of bmp in the process , we moved on to characterize specific interactions between bmp and npc2 using all membrane-binding free energy simulations with bmp-membranes . To this end , for each orientation in each bmp-membrane free energy simulation , we calculated the normalized contact frequency ( ncf ) ( S1 Text ) . For a given residue r , ncf describes the contact frequency with bmp compared to the total number of contacts with any lipids in a membrane . ncf captures if and how much bmp-npc2 interactions are enhanced with respect to those with other lipids in the membrane . In other words , for a particular residue , the values of ncf close to 1 indicate that its contact with bmp is likely specific , while values close to 0 indicate that either the contact was not observed or it is unspecific . Fig 4A shows the average of ncf over all bmp-membrane free energy simulations , and its standard deviation . In Prone mode , the loop at the opening of the cholesterol-binding pocket ( V59-M60-G61-I62-P63-V64-P65 ) along with several residues nearby in sequence has the highest probability of interacting with bmp when compared to any other lipid in the membrane ( Fig 4A ) . Moreover , a stretch of residues from 96–108 that also line the bottom surface in Prone mode , as well as several residues around R118 , are involved in bmp contacts . Likely due to the flexibility of the N-terminus , residues around K4 also form substantial contacts with bmp . Moreover , F66 , V96 , and T100 , which have been previously implicated as “reversible gate keepers” in sterol uptake and release [18] , are among the residues with enhanced specificity to bmp in Prone mode . This further supports the role of Prone mode and bmp in cholesterol transport . Comparison of Fig 4A and 4B panels shows that Supine mode has considerably fewer specific contacts with bmp than Prone mode . The comparison also reveals that the residues in contact with bmp in the two orientations are anti-correlated except near the termini . This is partly the effect of the restricted definition of Supine mode used here . Supine mode appears to be more plastic than Prone mode and the low free energy region near Supine mode extends up to ∼100° . While many polar and non-polar residues contact bmp in Prone mode , only positively charged residues interact with bmp in Supine mode . This is due to the deeper insertion taking place in Prone mode , which is stabilized by several nearby charged residue-bmp interactions ( Fig 4 ) . Moreover , H56 , which is highly conserved among homologs of npc2 , is likely involved in deep insertion of the loop into the membrane by interacting strongly with bmp . This residue is protonated in all of our simulations based on the pKa estimations done on the crystal structures [3 , 44] . Indeed , H56 may play a key role in the pH dependence of npc2-mediated cholesterol transport [27] by modulating the formation of deeply inserted Prone mode . Having established the role of bmp and its interaction with the membrane insertion loop for the formation of Prone mode , we also explored how the lipids studied here , in particular chol and sm , can affect this interaction at the molecular level . We showed that chol concentration differentially affects Prone mode in npc2apo and npc2chol-bound , and sm hinders its formation . An important stabilizing factor for Prone mode is the anchoring by the membrane insertion loop , which is further stabilized by bmp . The insertion , on the other hand , is only favorable if the desolvation penalty of the loop and the cavity it inserts into are compensated by favorable interactions with lipids . To indirectly assess the penalty of membrane insertion due to chol and sm , we use the quantity sasa-ratio = sasamil/sasaW . Here , sasaW stands for the solvent accessible surface area ( sasa ) estimated using a probe with a radius of 0 . 14 nm to approximate water as solvent; and sasamil stands for sasa estimated using a probe with a radius of 0 . 58 nm to approximate the membrane insertion loop ( mil ) . The probe radius of 0 . 58 nm for mil is approximated by R m i n = 0 . 066 M 1 2 [50] , where mass , M , of mil is 694 Da . Note that Rmin is the minimum radius of a sphere in which the protein of mass M can fit [50] . We calculated the sasa-ratio ( reported as mean ± standard deviation ) for all the lipid components collectively ( sasa-ratiomembrane ) and for only bmp in the context of all lipid components ( sasa-ratiobmp ) . For calculations , we used only free energy simulation trajectories ( the last 50–100 ns ) , where the protein ( npc2apo or npc2chol-bound ) is kept at a non-interacting distance to the membrane ( i = 45–53 , Fig 2 ) to ensure that membranes are unaffected by the protein . sasa-ratio is a dimensionless quantity ( within the interval of [0 , 1] ) that shows the relative accessibility of the surfaces by mil when compared to water , with values of ∼1 implying almost equal accessibility by mil and water , and values of ∼0 implying almost no accessibility by mil . The three studied systems ( bmp:chol:sm:dopg:popc = 20:10:0:0:70 ( System 5 ) , 20:35:0:0:45 ( System 6 ) , and 20:10:20:0:50 ( System 7 ) ) have a similar sasa-ratiomembrane ( the values being 0 . 53 ± 0 . 02 , 0 . 51 ± 0 . 02 , and 0 . 51 ± 0 . 02 , respectively ) . This shows that the sasa-ratio does not depend substantially on the membrane size and composition , when all the membrane components are considered collectively . Interestingly , we found that increasing chol concentration from 10 to 35 mol % increased the sasa-ratiobmp from 0 . 23 ± 0 . 06 to 0 . 32 ± 0 . 05 . Meanwhile , inclusion of sm decreased the sasa-ratiobmp to 0 . 20 ± 0 . 05 . That is , chol enhances bmp presentation to mil , while sm decreases it . Overall , this analysis suggests that bmp presentation through crevices on a membrane surface contributes strongly to formation of the deep-inserted Prone mode , and that bmp presentation is modulated by the relative concentrations of chol and sm . While chol elevates bmp presentation , sm has an opposite effect . The thermodynamic cycle shown in Fig 5A allows us to infer about the cholesterol-binding free energy difference between the solvated ( aqueous ( aq ) ) and membrane-bound forms of npc2 , which can be expressed as ΔΔGCHOL-binding=ΔGmembrane-boundapo→CHOL-bound−ΔGaqapo→CHOL-bound ( 1 ) =ΔGaq→membrane-boundCHOL-bound−ΔGaq→membrane-boundapo , ( 2 ) where the apo → chol-bound and aq → membrane-bound transformations are shown with dashed and solid arrows , respectively , in Fig 5A . Expressing the aq → membrane-bound transformation in terms of min zr , we can calculate the relative cholesterol binding free energy , ΔΔGchol-bound ( min zr ) ( Fig 5B ) , by subtracting the pmf data for npc2apo from those of npc2chol-bound shown in Fig 3A . The cholesterol binding affinity of npc2 is modulated by the composition of the membrane and the binding mode of npc2 . For most of the studied membrane systems , the cholesterol binding free energy decreases upon formation of the deeply-inserted state of npc2 ( Fig 5 ) . However , for the low cholesterol bmp-membrane ( bmp:chol:sm:dopg:popc = 20:10:0:0:70 ( System 5 ) ) , the cholesterol binding affinity clearly increases up to ∼5 kJ/mol in the deeply-inserted membrane-bound state . The cholesterol-binding affinities are not affected in the shallow binding Prone mode , since the cholesterol-binding pocket is still exposed to bulk solvent . However , the deeply inserted state both changes the environment of the hydroxyl group of cholesterol by exposing it to the lipid head groups and likely affects the cholesterol-binding pocket . In summary , membrane composition does not only determine the membrane binding properties of npc2 , but also its cholesterol-binding affinity .
We performed an extensive set of atomistic simulations of the cholesterol-carrier protein npc2 . The work done provides insights into the membrane-binding mechanism of npc2 , its dependence on specific lipids , and the cholesterol transfer process associated with npc2 . The results from the interactions between bmp-containing membranes and npc2 are summarized schematically in Fig 6 . A simplified putative cycle for cholesterol transport between cholesterol-poor and cholesterol-rich membranes that contain bmp is represented by solid arrows . In this cycle , npc2apo ( i ) binds a cholesterol-rich membrane in Prone mode , ( ii ) loads cholesterol , ( iii ) detaches from the membrane , ( iv ) binds a cholesterol-poor membrane in Prone mode , ( v ) unloads cholesterol , and ( vi ) detaches from the membrane . Based on this schematic , the interconversion between the deeply-inserted and the surface-adsorbed states , which have a free energy barrier of ∼6 − 7 kJ/mol depending on the membrane composition , competes with processes that are conductive to cholesterol exchange between membranes . npc2-sm membrane binding also competes with these processes by favoring the surface adsorbed states over the deeply inserted states . We propose a concerted regulatory mechanism of npc2 by bmp and sm . In this mechanism , npc2-membrane binding is facilitated and stabilized by direct interaction of npc2 with bmp . While the negative charge of bmp is involved in pulling npc2 to the membrane surface , specific interactions , such as those with H56 , are necessary especially for the formation of Prone mode and the insertion of the membrane insertion loop . Meanwhile , sm regulates membrane binding indirectly by modulating the presentation of bmp to npc2 . Interestingly , this indirect regulation does not necessarily change the affinity of npc2 to the membrane , but instead alters its propensity to bind in Prone mode . Our results have various implications about the mechanism and regulation of npc2-mediated cholesterol transport . Abdul-Hammed et al . have performed assays that showed bmp to enhance both processes , but sm to inhibit them [27] . Since the productive configuration for cholesterol exchange between the membranes and npc2 is likely to be Prone mode , our results explain the underlying molecular mechanism for the roles of bmp and sm . Essentially , bmp supports npc2-mediated cholesterol transport by enhancing binding in Prone mode , and sm in turn inhibits it by the reverse mechanism . Besides , acid sphingomyelinase regulates npc2-mediated cholesterol transport by converting sm to ceramide [51] . The functional role of these two different binding orientations in cholesterol transport can be explained by this regulation mechanism . That is , cholesterol transport rate can be controlled by the composition of the membrane interacting with npc2 such that npc2-binding in Prone mode is favored , or disfavored . Another functional role for two distinct high affinity surfaces of the protein is potentially related to npc2’s fusogenic function and its regulation [27] . npc2 contains many acidic and basic residues , mainly on its surface . The bovine and human orthologs of the protein have a net charge of +2e and +1e , respectively , based on the number of charged residues . In the low pH of lysosomes , at least some acidic residues and histidines are expected to be protonated attaining neutral or positive charges and increasing the overall positive charge of the protein . Indeed , in our simulations , we used bovine npc2 protonated at E110 ( Q in human ) , H31 ( S in human ) and H56 ( strongly conserved ) based on pKa estimations on the crystal structures to approximate an environmental pH of ∼5 . However , our simulations do not account for dynamic shifts in pKa due to changes in the local environment . Several acidic residues are located on the membrane interacting surfaces in Prone mode and Supine mode . When npc2 adsorbs onto the membrane surface , the local electrostatic potential due to the anionic lipid head groups will increase the intrinsic pKa of these residues . That is , some acidic residues can get protonated depending on the membrane composition and depending on where they are located with respect to the membrane in a particular membrane binding mode . This can , in turn , alter membrane binding modes and their affinities , especially in the anionic membranes . For example , D113 , which is on the membrane-interacting surface in Prone mode , has been shown to be vital for cholesterol transport , but not for cholesterol binding [24] . The dynamic and differential protonation of residues like D113 may modulate npc2-binding to membranes with different compositions . Besides , they likely prevent very tight npc2-membrane binding . This is essential for efficient shuttling of npc2 between disconnected membranes to transport cholesterol . Glycosylation of npc2 has been implicated in proper protein sorting , protection against degradation by the lysosomal enzymes , and in modulating cholesterol transfer rates [26] . Monoglycosylated ( glycosylated at N39 ) and diglycosylated ( glycosylated at both N39 and N116 ) forms of npc2 have been identified in lysosomes [26 , 52] . Cholesterol transfer by the diglycosylated form has been shown to be slower than that by the monoglycosylated form [26] , but the molecular mechanism accounting for the observed difference remains unknown . Based on our work , N39 and N116 are located on the membrane interacting surfaces in Supine mode and Prone mode , respectively . Glycosylation of N39 may destabilize Supine mode and shift the equilibrium towards Prone mode . This shift , in turn , may enhance cholesterol uptake and release , since this process presumably takes place in Prone mode , but not in Supine mode . On the other hand , glycosylation at N116 is likely to interfere with the membrane interactions in Prone mode , especially close to the C-terminal side of the protein ( Fig 4A ) . Cholesterol uptake and release could still take place , albeit slower , because the stretch of residues in the proximity of the hydrophobic loop may still anchor the protein to the membrane . In summary , our results provide an atom-scale mechanism that explains the differential regulatory roles of bmp and sm in npc2-membrane binding . npc2 binds a membrane in two different binding orientations depending on the lipid composition . bmp is required for npc2-membrane binding and cannot be substituted by other anionic lipids . On the other hand , sm counteracts bmp and hinders the formation of the deep-inserted mode that places the cholesterol-binding pocket in direct contact with the membrane surface and is conductive for cholesterol uptake/release .
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Cholesterol plays essential structural and functional roles in all vertebrate cells . Abnormalities in cholesterol metabolism are associated with severe and wide-spread diseases . Cholesterol efflux is one of the essential steps in its metabolism and is mediated by Niemann-Pick Protein C2 ( npc2 ) in endosomes and lysosomes . Mutations in npc2 result in the fatal genetic disease , called Niemann-Pick C disease , characterized by neuronal degeneration resulting in early death . We performed an extensive set of atomistic molecular dynamics simulations employing enhanced sampling approaches to investigate how npc2 mediates cholesterol transport and the roles of membrane lipids in the process . We show that the binding of npc2 to endosomal/lysosomal membranes and the consequent trafficking of cholesterol are concertedly regulated by bis ( monoacylglycero ) phosphate and sphingomyelin . Moreover , our results explain the molecular mechanism of experimentally observed affect of lipids on cholesterol transport rate . In essence , our findings provide key insights into the regulation of the lysosomal and endosomal cholesterol trafficking by npc2 and reveal key lipid-protein interactions .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
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"cellular",
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"thermodynamics",
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"lipids",
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2017
|
Concerted regulation of npc2 binding to endosomal/lysosomal membranes by bis(monoacylglycero)phosphate and sphingomyelin
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Phenotypic diversity is considered beneficial to the evolution of contingent cooperation , in which cooperators channel their help preferentially towards others of similar phenotypes . However , it remains largely unclear how phenotypic variation arises in the first place and thus leads to the construction of phenotypic complexity . Here we propose a mathematical model to study the coevolutionary dynamics of phenotypic diversity and contingent cooperation . Unlike previous models , our model does not assume any prescribed level of phenotypic diversity , but rather lets it be an evolvable trait . Each individual expresses one phenotype at a time and only the phenotypes expressed are visible to others . Moreover , individuals can differ in their potential of phenotypic variation , which is characterized by the number of distinct phenotypes they can randomly switch to . Each individual incurs a cost proportional to the number of potentially expressible phenotypes so as to retain phenotypic variation and expression . Our results show that phenotypic diversity coevolves with contingent cooperation under a wide range of conditions and that there exists an optimal level of phenotypic diversity best promoting contingent cooperation . It pays for contingent cooperators to elevate their potential of phenotypic variation , thereby increasing their opportunities of establishing cooperation via novel phenotypes , as these new phenotypes serve as secret tags that are difficult for defector to discover and chase after . We also find that evolved high levels of phenotypic diversity can occasionally collapse due to the invasion of defector mutants , suggesting that cooperation and phenotypic diversity can mutually reinforce each other . Thus , our results provide new insights into better understanding the coevolution of cooperation and phenotypic diversity .
How to understand the emergence and persistence of cooperation is a key problem in evolutionary biology [1–12] , since individuals sticking to cooperation produce benefits to others at a cost to themselves . The Prisoner’s Dilemma game , as an effective paradigm , has been widely employed to characterize and elucidate the issues surrounding the evolution of cooperation [5 , 13–16] . In a typical Prisoner’s Dilemma game , two individuals simultaneously decide either to cooperate or to defect . When both cooperate , they each get the reward R . When both defect , they each get the punishment P . When a cooperator encounters a defector , the former receives the sucker’s payoff S while the later the temptation payoff T . The payoff parameters satisfy the inequality T > R > P > S . It can be easily obtained that the best response for an individual is to always defect no matter what strategy the opponent adopts in one-shot interaction . For iterated interactions , one additional payoff condition often required is 2R > T + P . Under these conditions , the aggregate payoff of any two interacting individuals arrives at the highest if both have cooperated among the four possible combinations in terms of the two individuals’ strategies . The strategy maximizing an individual’s payoff ( defection ) and that maximizing the group’s payoff ( cooperation ) do not coincide , leading to the social dilemma [1 , 2 , 4] . To surmount this conflict between group interest and self-interest , some mechanisms must be at work to sustain costly cooperative behaviors . These mechanisms include direct reciprocity [17] , indirect reciprocity [18] , group selection [19] , network reciprocity [5] , and kin selection [20] . Besides , it is found that cooperation can arise without reciprocity when individuals preferentially donate to partners sufficiently similar to them [21] . This kind of similarity-mediated interaction is the decisive mechanism that promotes conditional helping behavior ( also termed as contingent cooperation or more generally in-group favoritism [22] ) . In the original model in Ref . [21] , each individual has a tag τ and a tolerance Q . Individuals acting as donors each have a pre-assigned number of potential recipients to interact with . Their donations are just distributed towards those recipients who share sufficiently similar tags within their tolerance threshold Q . The maintenance of cooperation is realized by the successive replacement of one cooperative cluster over another , along with the rise and fall of their tolerance levels . Nevertheless , two open questions remain to be addressed: every agent is a potential donator and thus no absolute defectors ( namely , who always defect ) are present , and the reflective boundary at Q = 0 is biased for the emergence of cooperation . In response to these two questions raised in Ref . [23] , the authors of Ref . [21] further corroborated in Ref . [24] that similarity can still breed cooperation even if the option of ‘never donate’ ( i . e . , negative Q values ) is allowed , provided that mutations are not biased to ‘never donate’ so strongly as assumed in Ref . [23] . Inspired by this work [21] , Traulsen et al . constructed a minimal model for tag-based cooperation [25] . By discretizing the tags and tolerances , they explored the evolutionary dynamics in the well-mixed population of infinite size . They later extended this model to structured populations [26] and finite populations [27] . Concerning spatial populations , the authors of Ref . [28] have considered the dynamics of tag diversity in the context of Prisoner’s Dilemma game . Simulation results show that tight coupling between tag and behavioral strategy leads to very drastic oscillations of the dynamics , responsible for the rapid loss of tag diversity and thus of the cooperation . Loose coupling does weaken the oscillation of the dynamics , inducing high levels of overall cooperation in the long run . The model was also analyzed mathematically by omitting the rare event in which recombinations of behavioral strategies and of tags occur simultaneously . A second set of models analyze the evolution of cooperation under selection-mutation dynamics [29–31] . In the pioneering work [29] , just two strategies , contingent cooperation and defection , are considered . Each individual has a phenotype . Cooperators only cooperate with these individuals of the same phenotype . In a birth-death event , both strategy and phenotype can mutate separately . Combining the coalescent theory and perturbation theory , the authors gave the very beautiful criteria , ( R - P ) ( 1 + 3 ) > T - S , for cooperation to evolve . Later , Tarnita et al . considered the multiplexity of tags; that is , each individual belongs to n groups out of m groups and derived the conditions under which cooperation can evolve [30] . Very soon they extended the above framework to study the competition of multiple strategies , and derived the succinct condition for a specific strategy to be selected [31] . Mathematical tractability of these models is possible in the limit of weak selection , which leads to the separation of game payoff and structural coefficients , just depending on the strategy mutation rate and the set mutation rate . In these models [21 , 25 , 27–30] , phenotype , group , and set can be generally regarded as tags . They are visible and evolving features . Moreover , current interactions neither involve memory of past experience , nor depend on whether partners are altruistic towards third-parties . In a broad sense , reputation [32] , social influence , social institutions , accents , and even scientific paradigms [33] can also serve as criteria based on which individuals select their partners [21] . Recent experimental studies have demonstrated that even an isogenic population shows high level of diversity in phenotypic traits [34–41] . Individuals can switch between phenotypes adaptively apart from the induction of mutation , when confronted with changing environments and thus unpredictable threat of survival . To face and surmount these challenges , individuals sometimes randomly switch between different phenotypes . To be more accurate , when cooperators cooperate probabilistically , the cooperative act itself as phenotype exhibits diversity [36 , 39] . This work [39] has pointed out that under deme-structured environment the phenotype-mediated interaction environments ( assortment ) are sufficient to evolve cooperation . Not limited to the evolution of cooperation , the phenotype diversity can also determine the competence of cells in Bacillus subtilis [37] . In response to fluctuating environments , cells may tune the switching rates between phenotypes to maximize their fitness [34] . Even the cell’s phenotype is subject to the density of the inducers in the ambient environment [43] . A key point in these studies seems that phenotype diversity by itself can confer a survival advantage . These prior studies mainly focus on the importance of stochastic phenotypic expression for the viability of organisms [34–41] . The environmental change is weakly affected by , or totally controlled by some factors independent of , the organisms living in the environment . To realize this stochastic expression , organisms are endowed with multiple phenotypes and they can switch between these phenotypes , a phenomenon known as phenotypic variation ( or noise ) [37 , 40] . These studies have mainly dealt with species-environment systems [34–38 , 40 , 41] , while interactions between subpopulations with different phenotypes are largely unconsidered or simply characterized by mutation at a constant rate [34 , 36] . Furthermore , although previous studies concerning tag-based cooperation [21–31] have taken into consideration interactions between subpopulations with different phenotypes , these models focus almost exclusively on how phenotypic diversity affects cooperation . Therefore , how such phenotypic diversity emerges in the first place has yet to be fully answered . Our study shall combine individuals’ strategic behavioral interactions , characterized by the two-player Prisoner’s Dilemma game , with evolvable phenotypic diversity and set out to probe the resulting coevolutionary dynamics . In this paper , we are interested in the key question of how natural selection leads to phenotype diversity when cooperation is contingent on the phenotypic similarity . Here phenotypes are observable and cooperators channel their help only to these of similar phenotypes . Some questions naturally arise: Why does there exist phenotypic variations in the first place ? How does natural selection lead to the emergence of multiple distinctive phenotypes that are expressed in the population ? Whether does the contingent cooperation coevolve with diverse phenotypes ? We will address the question using a coevolutionary model of phenotypic diversity and cooperation . Our present model does not require any prescribed level of phenotypic diversity , but rather lets it be an evolvable trait . We will show that phenotypic diversity and contingent cooperation can coevolve under wide conditions , and moreover , natural selection favors an optimum level of phenotypic diversity .
Consider a finite asexual population of N individuals . Each individual i is characterized by a triplet ( Gi , Si , Ki ) , where Gi is the current phenotype expressed , Si is the behavioral strategy , and Ki is the total number of potential phenotypes individual i can express . The behavioral strategy Si is further determined by a pair of variables within the unit square , Si = [pi , qi] ∈ [0 , 1]2 . Here pi is the probability that i cooperates with similar others , and qi is the probability that i cooperates with others of different phenotypes . For simplicity , we will focus our analysis on two discrete strategies , C = [1 , 0] and D = [0 , 0] . It is straightforward to extend the behavioral strategy to the full space similarly as in Ref . [64] . The population is well-mixed , and individuals interact with everyone else . The interactions are characterized by the simplified Prisoner’s Dilemma game ( donation game ) . A ( conditional ) cooperator pays a cost c for a compatible recipient , of the same phenotype , to receive a benefit b ( see Fig 1 ) . Defectors pay no costs and distribute no benefits . Each individual i incurs a cost κi for retaining phenotypic variation and expression . κi is assumed to be proportional to Ki , κi ( Ki ) = θKi . Here we choose the simplest possible phenotype cost function . The net payoff πi determines the reproductive success ( fitness ) fi of an individual i . Here the fitness is an exponential function of payoff fi = eβπi , where β is the intensity of selection . The evolutionary updating occurs according to a frequency-dependent Moran process . At each time step , an individual is chosen with probability proportional to its fitness to reproduce an offspring . Following birth , a random individual in the population dies . The population size is thus constant throughout the evolution . Reproduction is however subject to mutation . With probability μ , the offspring randomly adopts one of the two behavioral strategies and also acquires a random number K i ′ of potentially expressible phenotypes at a cost θ K i ′ . This mutant expresses one phenotype at random out of the total K i ′ possible phenotypic variations . When it comes to phenotypic switching , we consider the random case . Here we have a combinational mutation that can change behavioral strategy and phenotypic diversity . We now briefly elucidate the general procedure for calculating fixation probabilities . Suppose the population consists of i type A individuals and N − i type B individuals . Each type A individual possesses KA potentially expressible phenotypes and its strategic behavior is sA . Each type B individual possesses KB potentially expressible phenotypes and its strategic behavior is sB . sA = 1 if A is a cooperator , and sA = 0 otherwise . So does sB . Denote by ϕi the fixation probability that the population eventually arrives at the state consisting of N type A individuals when it starts with the state consisting of i type A individuals . The updating event is frequency-dependent Moran process . The payoff for an A and a B can be respectively written as PA = ( i − 1 ) ( b − c ) sA + ( N − i ) ( bsB − csA ) δ − θKA , and PB = i ( bsA − csB ) δ + ( N − i − 1 ) ( b − c ) sB − θKB , with δ being one if A and B have expressed the same phenotype and zero otherwise . Self-interaction is obviously not included . The fitness for A and B reads fA = eβPA , and gB = eβPB , respectively . The intensity of selection β measures how much payoff contributes to fitness . In an updating event , the probability for the number of type A individuals in the population to increase by one , decrease by one and remain unchanged is given respectively as T i , i + 1 = i f A i f A + ( N - i ) g B · N - i N , T i , i - 1 = ( N - i ) g B i f A + ( N - i ) g B · i N , and Ti , i = 1 − Ti , i+1 − Ti , i−1 . Then we have ϕ i = T i , i + 1 ϕ i + 1 + T i , i - 1 ϕ i - 1 + T i , i ϕ i Combining with the boundary conditions ϕ0 = 0 and ϕN = 1 , we can obtain the fixation probability as ϕ 1 = ( 1 + ∑ l = 1 N - 1 ∏ k = 1 l T k , k - 1 T k , k + 1 ) - 1 Individuals possessing too many available phenotypes will be easily invaded by those who are endowed with a modest number of phenotypes for possessing cost increases linearly . In the long run , their fraction is almost negligible . We can thus assume that individuals can be endowed with at most M potentially expressible phenotypes . In the limit of small mutation , the population is quite frequently located at one of these 2M homogeneous states . This state is from time to time disturbed by the mutant . Very soon either the mutant is wiped out and the homogeneous state is recovered , or it successfully invades and wipes out the residents and thus transits the population to a new homogeneous state . Therefore , the population dynamics of 2M strains can be well approximated by an embedded Markov chain between these M full defective states and M full cooperative states . For convenience’s sake , we label cooperative strains with even numbers 2KC , and defective strains with odd numbers , 2KD − 1 , for 1 ≤ KC ≤ M and 1 ≤ KD ≤ M . For strain X having KX potentially expressible phenotypes and strain Y with KY potentially expressible phenotypes , the expected transition rate from state X to state Y is r ( X , Y;KX , KY ) as shown by Eq ( 1 ) in Results Section . Going a further step , we can get the transition matrix A with dimension 2M by 2M . The ijth entry of matrix A is r ( i , j;Ki , Kj ) for i ≠ j , and the iith entry is one minus the sum of all other entries in the ith row . It should be noted that we have analytically derived the transition rates between any two competing strains and thus the transition matrix . We then use built-in numerical methods in Matlab to solve the left eigenvector of the transition matrix corresponding to the eigenvalue of one . This left eigenvector , after normalization as needed , gives the stationary distribution of these 2M full states . Summing all the elements with even indices in the normalized eigenvector , we can get the overall cooperation level [44 , 45] .
Let us start with the simplest case of two competing strains , which constitutes the basis for analyzing the general population dynamics . One strain has the potential of expressing KX distinctive phenotypes; the other has the potential of expressing KY different phenotypes . Mutations among these two strains are bidirectional and occur at a sufficiently small rate . At this limit of small mutation rates , the competition dynamics can be simplified by investigating transition rates between homogeneous population states ( All C vs . All D ) . Let ρ X → Y s be the fixation probability that a single mutant X takes over the resident population Y when X and Y are of the same phenotype . Let ρ X → Y d be the fixation probability that a single mutant X takes over the resident population Y when X and Y have expressed different phenotypes . Then the expected transition rate ( omitting the mutation rate μ for notational brevity ) from state X to Y , r ( X , Y;KX , KY ) , is given by r ( X , Y ; K X , K Y ) = H ( K Y - K X ) [ 1 2 α Y ρ Y → X s + 1 2 ( 1 - α Y ) ρ Y → X d ] + [ 1 - H ( K Y - K X ) ] { 1 2 K X - K Y K X ρ Y → X d + 1 2 K Y K X [ α Y ρ Y → X s + ( 1 - α Y ) ρ Y → X d ] } . ( 1 ) H ( ⋅ ) is the Heaviside step function . H ( x ) = 1 for x ≥ 0 , and H ( x ) = 0 for x < 0 . For random phenotypic switching , αX = 1/KX , where X ∈ {C , D} . It is the same case with αY . Some explications concerning this transition rate are necessary . When the number of potentially expressible phenotypes that strain Y possesses exceeds or equates with that strain X possesses , strain Y has the chance αY to express the same phenotype with strain X . At this time , the population moves from state X to state Y with the probability ρ Y → X s . With probability 1 − αY , strain Y expresses different phenotype from strain X . Once this happens , the population moves from state X to state Y with the probability ρ Y → X d . The coefficient 1 2 means that the mutant can be either a cooperator or a defector with equal probability . The sum constitutes the first term in the right-hand side of r ( X , Y;KX , KY ) . Following the same logic , we can arrive at the second term . We can use Eq ( 1 ) to analytically derive the transition rates between different population states in the limit of rare mutations and for any intensity of selection β . In particular , Eq ( 1 ) can be greatly simplified in the limit of strong selection , β → ∞ ( see S1 Supplementary Information ) . Fig 2 shows the transition rate with respect to four different strategy combinations of mutants and residents . When a defector attempts to invade the otherwise defector population , the dynamical scenario is easily understandable . Defectors pay no cost and bring no benefit to others . Their fitness is totally dependent on the number of potentially expressible phenotypes . As long as the cost of possessing potentially expressible phenotypes is nonzero , the more potentially expressible phenotypes a defector possesses , the higher cost it bears . Fitness-driven competition puts such defectors in a disadvantageous place , as illustrated in Fig 2D . Actually , we can rigorously confirm this observation . Assume that the invading defectors have KY available phenotypes , while the resident defectors are endowed with KX phenotypes . No matter what the composition of the population is , and independent of their actual expressions , the fitness is e−βθKY and e−βθKX , for an invader and a resident , respectively . After a simple calculation , we can obtain the accurate expressions of the fixation probability as ρ Y → X s = ρ Y → X d = 1 - e β θ ( K Y - K X ) 1 - e N β θ ( K Y - K X ) , which are also exactly the transition rates for both KY > KX and KY < KX . It is very easy to verify that the transition rate is decreasing with respect to KY , and increasing with respect to KX , respectively . Therefore , a peak appears when the residents are endowed with 50 potentially expressible phenotype and the invaders only with 1 . When the mutants are cooperators and strive to dominate the defector residents , the evolutionary race proceeds in a different way . Not only the cost of phenotypic variation and expression is involved , the payoff resulting from the game interactions is also integral component of fitness . It is better for these cooperators to express different phenotype from the defectors . Thus they can escape from the exploitation of the defectors . In the light of mathematical language , the fixation probability ρ C → D d is larger than ρ C → D s . We can approximately speak that ( 1 - α Y ) ρ C → D d is far larger than α Y ρ C → D s especially for large KY . It is worth noting that for KC > KD , r ( D , C ; K D , K C ) = 1 2 α C ρ C → D s + 1 2 ( 1 - α C ) ρ C → D d; for KC < KD , r ( D , C ; K D , K C ) = 1 2 ( 1 - α D ) ρ C → D d + 1 2 α D ρ C → D s . The analysis bespeaks that the transition rate is mainly controlled by ρ C → D d , and responsible for the similarity of the transition rate relying on KC and KD for cases that cooperators invade defectors ( see Fig 2B ) , and that defectors invade defectors . In the former case , the transition rate sees an appreciable increase . This should be attributed to the mutual breed of the invading cooperators once more than one cooperators emerge . We next probe how the transition rate is dependent on KC and KD when defectors attempt to invade the cooperator residents ( see Fig 2C ) . In order to avoid being exploited by the invading defectors , cooperators have no other choice but to increase the number of their potentially expressible phenotypes . In doing so , cooperators need to bear higher cost of phenotypic variation . At the same time , they complicate their own ‘code’ , which takes defectors longer time to decipher , thereby allowing contingent cooperators to benefit from their mutual breed for a longer period . As a result , it to some extent reduces the rate that defectors invade cooperators , favoring the persistence of cooperators . These are two driving forces acting on the coevolutionary dynamics . In the given parameter scope ( KC ≤ 50 ) , the positive effect owing to the similarity-mediated interactions predominates . The extent of such predominance is sensitive to the increase in KC . We should bear in mind that once setting the parameters of b and c , the relative advantage of defectors to cooperators is constant for each interaction . The cost of phenotypic variation nonetheless rises to infinity as KC goes to infinity . As a consequence , there exists a threshold of KC above which the reciprocity coming from similarity-mediated interactions is completely offset by the prohibitively high cost of maintaining too many phenotypes . Similar sensitivity on KC can be observed when cooperators attempt to invade cooperators . Since the invading cooperators are also able to accomplish the reciprocity between themselves , the residents’ strength in resisting invasion is greatly discounted , especially for large KC . This perfectly explains why the transition rate depends on KC in a U-shaped way ( see Fig 2A ) . With the simplified dynamics being scrutinized , it naturally steers us to analyze the full population dynamics . Consider the competition of an arbitrary number of strains ( = 2M ) in the population of finite size ( = N ) . As we have pointed out , individuals endowed with very large numbers of available phenotypes are destined to be wiped out only if phenotypic variation is costly . It makes sense to assume M as a finite number . In the limit when mutation rarely happens , we are able to analytically compute the average frequency of each of these 2M strains in the long run . In this limit , either the initially rare mutants are assimilated by the residents or the mutants successfully invade and take over the whole population before the occurrence of next mutation . Thus there will be at most two different strains present in the population simultaneously . As the transition rate between any two strains has been derived in Eq ( 1 ) , the embedded Markov chain is well defined . We can thus get the average fraction of time the population spends in each of these 2M homogeneous states by computing the stationary distribution of the transition matrix . The stationary distribution is given by the normalized eigenvector of the transition matrix corresponding to its maximal eigenvalue one . Though the approximation is obtained in the limit , it proves valid for a wider range of mutation rate , as we have corroborated by numerical simulations . Fig 3 illustrates the stationary distribution of these 2M strains for different values of parameter θ . The abscissa value denotes the number of potentially expressible phenotypes that the corresponding strain can express . As to which phenotype the strain specifically expresses , it depends entirely on the outcome of ‘throwing a dice’ when the strain first emerges as a mutant . Some remarkable features are revealed in this plot . On the one hand , of all cooperative strains there exists an optimal solution K C * in term of phenotypic variation . This optimal number lies in between one and the maximally allowed number and is subject to the change of θ . For the cooperative strain possessing this optimal number of potentially expressible phenotypes , it attains the highest fraction among all M cooperative strains . For the defective strain , possessing just one potentially expressible phenotype is always the best choice . On the other hand , an increase in θ depresses the overall cooperation level , and reduces the diversity as well . For θ = 0 . 05 and 0 . 1 , not only the cooperation level is significantly higher than the defection level , the cooperative strain possessing K C * available phenotypes also accounts for the largest fraction in the long run . As θ increases to ∼0 . 3 , cooperation still enjoys appreciable dominance over defection , whereas the defective strain with KD = 1 is most prevalent . When θ is as high as 0 . 5 , the diversity , especially of cooperative strains , drastically shrinks . Concomitantly , the cooperation level drops below 50% . Furthermore , the diversity almost gets lost completely and cooperation level slumps towards zero for extremely high costs such as θ = 1 . We thus confirm that cooperation does coevolve with the phenotypic diversity . A little differently , for θ = 0 , the more potentially expressible phenotypes cooperative strain possesses , the more prevalent it is . Our findings have demonstrated that there exists an optimal number of phenotypic variations for cooperators and such cooperators account for the highest fraction in the stationary distribution . Even more , such cooperators can be dominated by defectors if the cost of phenotypic variation further increases . However , cooperators still enjoy a higher overall fraction . Reasons responsible for these observations are interesting and fundamental . Fig 4 offers a graphic illustration . For convenience of describing this evolutionary path , we denote by Low , Middle , and High level when the average phenotype diversity is located in the interval ( 0 , 3] , ( 3 , 38] , and ( 38 , 50] , respectively . Denote by CL the state of cooperators with Low average phenotype diversity , and the like . When the population is occupied by CL , the dynamics are extremely stable and stay put with a probability as high as 0 . 99978 . The coexistence states , CL + DL and CM + DL , are also visited quite frequently . When CL + DL dominates the population , the dynamics are quite unstable . The population either maintains the state with probability 50 . 55% , or enters into the state CM with probability 27 . 4% . When CM + DL dominates the population , the dynamics are less stable . The population is as likely as 52 . 56% to stay in the current state , and also has an odds of 44 . 22% to enter the state CM directly . Once the state CM dominates the population , the dynamics are also extremely stable and stay put with a probability as high as 0 . 99977 . Switching between these four states constitutes the core component of the population dynamics and thus explains the macroscopic observations . It it worth noting that though DM can stabilize the dynamics with probability 0 . 99976 , paths arriving at this state are so parsimoniously few that this state can produce no essential effects in the long run . Following common practice , we investigate how the overall cooperation level and corresponding optimal phenotypic diversity ( i . e . , K C * ) vary with the key parameter θ , and intensity selection β [46–48] , respectively . Fig 5A shows that the cooperation level monotonically decreases as θ rises from 0 to 1 . For θ being zero , cooperation is stabilized at a level as high as 0 . 91 . Accordingly , the optimal phenotype diversity is the largest allowable number . At this point , complicating phenotype diversity proves more effective in dodging defectors’ exploitation . As long as θ is non-zero , the effectiveness is compromised by the cost of having more potentially expressible phenotypes . Consequently for a wide spectrum of θ , K C * comes in between 1 and M . The higher the cost , the lower K C * is ( see Fig 5B ) . These results further corroborate our aforementioned findings . Of interest , Fig 5C shows that the cooperation level sees a non-monotonous dependency on the selection intensity β . That is , the cooperation level peaks at βc ≈ 0 . 0398 . Below this value , increasing β contributes to the positive effect of cooperative strain clustering relatively far more than the competitive advantage of defective strains over cooperative ones . Once β exceeds this threshold βc , β’s increment further intensifies the competition , which leaves less space for cooperative strains to escape from defective strains’ stalking and subsequent exploitation . This leads to the decline of cooperation level as β rises beyond βc . Similar dependency of the optimal diversity level on β can be observed ( see Fig 5D ) . Moreover , although for the extreme values of β ( 0 and ∞ ) the optimal diversity does not exist , the underlying rationales are different . For the former case , evolution of cooperation follows the neutral drift . All strains account for equal fraction in the long run . For the later case , the defective strain with just one potentially expressible phenotype overwhelmingly predominates the evolutionary race , leaving negligible odds for other strains to prevail .
How altruistic behavior emerges and persists is a key issue to be answered [49–57] . The results of our model provide a possible path for cooperation to get established , as the evolving phenotypic diversity plays a crucial role [58–61] . As far as defective strains are concerned , competition weakens the survivability of the ones endowed with large numbers of potentially expressible phenotypes , thereby suppressing the phenotypic diversity of defective strains . It is the other way around for cooperative strains . In addition to the strain with the optimum level of phenotypic diversity , K C * , those endowed with the number of potentially expressible phenotypes close to K C * almost share the same strong ability to withstand the invasion of other strains . It follows that all these cooperative strains account for commensurate fractions , and as a consequence , the phenotypic diversity of cooperative strains is preserved . Thanks to the positive effect of phenotypic diversity on cooperation , the aggregate competence of cooperative strains can readily outperform that of defective strains , leading to the overall cooperation level higher than 50% . It is noteworthy that as θ increases to 0 . 3 , the fraction of the most prevalent defective strain is higher than that of the cooperative strain with K C * . Even so , the overall cooperation level is still over 0 . 5 , suggesting the vital role of diversity in the establishment of cooperation once again . For very large θ , contribution to fitness generated by game interactions is incommensurate to that of the cost of phenotypic variation and expression . In other words , the effect of the former is negligible . Only cooperative strains with a very few available phenotypes are likely to be favored by selection . This undoubtedly inhibits the diversity of cooperative strains . Ensuing comes the parsimoniously low level of overall cooperation . On this front , cooperation coevolves with phenotypic diversity . Our model integrates the subpopulation interactions with different phenotypes , and well captures the underlying rationale for many observations in the biological sphere . For instance , to surmount the competition from the commensal microbiota , S . typhimurium needs to express T1 to induce gut inflammation . The expression is costly , and the inflammation benefits the mutants as well . In direct competition , the cooperating S . typhimurium would absolutely get eliminated by the mutants ( i . e . , defectors ) . Real situation goes this way . Only a fraction of the cells in the population of S . typhimurium express T1 , while the remaining do not . In a natural way , the expression can be regarded as conducting cooperative action . By virtue of this probabilistic expression of phenotype , the population of S . typhimurium outperforms the mutants [42] . Another relevant example may be the side-blotched male lizards found on the Pacific Coast of North America . These male lizards exhibit differing phenotypes in their throat color , either yellow or orange or blue [62 , 63] . It is most probably that the phenomenon is the result of development rooted in our model philosophy . This conjecture has implications for and awaits the confirmation of field studies . The impact of environmental variability on population survival in ecological systems has been intensely investigated , especially from the perspective of experimentation [34 , 40 , 41 , 43] . In our model , due to the fact that individuals possess diverse potentially expressible phenotypes , there are multiple potential states for the population to inhabit due to random expression and inherent phenotype diversity . Cooperators , in competition with defectors , may have different viability as the population state varies . How cooperators fare when the population switches between these states is still unclear . We originally address this issue by combining phenotype-similarity based interaction and inherent phenotype diversity . Allowing individuals to vary in the number of phenotypes they can switch to provides many potential states for the population to reside . In some of these states , cooperative strains dominate the population , and the dynamics are stable in the sense that the population is less likely to be invaded by other strains . In other states , the dynamics manifest drastic oscillations; averagely speaking , cooperative strains prevail in the evolutionary competition with defective strains . Experimental findings have established that the bet-hedging strategy ( stochastic switching between phenotypes ) can persist by its inherent adaptation to the fluctuating environmental conditions [34–38] . Our work generates qualitatively similar results that natural selection favors cooperation if contingent cooperators are able to switch to novel phenotypes at random , as well as that the population resides most frequently in the states exhibiting the optimal phenotypic diversity . Possessing diverse phenotypes while expressing one plays a similar role as the recombination of tag-trait does in Ref . [28] , both loosening the coupling between tag and trait . In this sense , our mechanism can be added into ‘other mechanisms that can accomplish the same stabilizing effect’ as the authors of Ref . [28] have suggested . The application of the framework we introduce here includes , but not limited to , the study of cooperation . It is a general theory for studying situations where in-group members play one game while out-group members play another game [64] . Using proper types of game to characterize in-group and out-group interactions , we can investigate and explain the evolution of parochial altruism and homophily [22] , and corresponding results will be presented in another work . Applying this model to a variety of other social behaviors , such as coordination , trust and bargaining , may reveal more about such evolutionary dynamics and be worthy of further investigation [65–67] , as is exploring situations where individuals are members of multiple groups with competing allegiances . Extending this framework to group interactions [68–70] , which cannot always be viewed as the sum of pairwise interactions [71 , 72] , will be useful .
|
Phenotypic variation is commonly observed from human cells to the intestinal pathogen Salmonella enterica serovar Typhimurium to the wrinkly-spreader morphs . Such phenotypic diversity proves effective in promoting cooperation , or confers survival advantage against unfavorable environmental changes . Prior studies show that interactions based on phenotypic similarity can promote cooperation . Yet in these models , the level of phenotypic diversity is prescribed such that individuals each possess the same number of available phenotypes , and thereby no evolution of phenotypic diversity per se . We here take into consideration important aspects of the diversity of phenotype and contingent cooperation and show that phenotypic diversity coevolves with cooperation under a variety of conditions . Our work provides a potential mechanism for the evolution of cooperation , and individuals , especially cooperators , endowed with diverse phenotypes constitute the backbone in inducing the coevolution .
|
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2017
|
Coevolutionary dynamics of phenotypic diversity and contingent cooperation
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Pneumonic plague is a highly virulent infectious disease with 100% mortality rate , and its causative organism Yersinia pestis poses a serious threat for deliberate use as a bioterror agent . Currently , there is no FDA approved vaccine against plague . The polymeric bacterial capsular protein F1 , a key component of the currently tested bivalent subunit vaccine consisting , in addition , of low calcium response V antigen , has high propensity to aggregate , thus affecting its purification and vaccine efficacy . We used two basic approaches , structure-based immunogen design and phage T4 nanoparticle delivery , to construct new plague vaccines that provided complete protection against pneumonic plague . The NH2-terminal β-strand of F1 was transplanted to the COOH-terminus and the sequence flanking the β-strand was duplicated to eliminate polymerization but to retain the T cell epitopes . The mutated F1 was fused to the V antigen , a key virulence factor that forms the tip of the type three secretion system ( T3SS ) . The F1mut-V protein showed a dramatic switch in solubility , producing a completely soluble monomer . The F1mut-V was then arrayed on phage T4 nanoparticle via the small outer capsid protein , Soc . The F1mut-V monomer was robustly immunogenic and the T4-decorated F1mut-V without any adjuvant induced balanced TH1 and TH2 responses in mice . Inclusion of an oligomerization-deficient YscF , another component of the T3SS , showed a slight enhancement in the potency of F1-V vaccine , while deletion of the putative immunomodulatory sequence of the V antigen did not improve the vaccine efficacy . Both the soluble ( purified F1mut-V mixed with alhydrogel ) and T4 decorated F1mut-V ( no adjuvant ) provided 100% protection to mice and rats against pneumonic plague evoked by high doses of Y . pestis CO92 . These novel platforms might lead to efficacious and easily manufacturable next generation plague vaccines .
Plague , also known as Black Death , is one of the deadliest infectious diseases known to mankind . Yersinia pestis , the etiologic agent of plague , is a Gram-negative bacterium transmitted from rodents to humans via fleas [1] . The bite of an infected flea results in bubonic plague which can then develop into secondary pneumonic plague , resulting in person-to-person transmission of the pathogen through infectious respiratory droplets [2] . Pneumonic plague can also be caused by direct inhalation of the aerosolized Y . pestis , leading to near 100% death of infected individuals within 3–6 days [2] , [3] . Due to its exceptional virulence and relative ease of cultivation , aerosolized Y . pestis poses one of the greatest threats for deliberate use as a biological weapon [4] . Since the disease spreads rapidly , the window of time available for post-exposure therapeutics is very limited , usually 20–24 h after the appearance of symptoms [3] . Although levofloxacin has recently been approved by the Food and Drug Administration ( FDA ) for all forms of plague ( http://www . fda . gov/NewsEvents/Newsroom/PressAnnouncements/ucm302220 . htm ) , prophylactic vaccination is one of the most effective means to reduce the risk of plague . Stockpiling of an efficacious plague vaccine has been a national priority since the 2001 anthrax attacks but no vaccine has yet been licensed . Previously , a killed whole cell ( KWC ) vaccine was in use in the United States , and a live attenuated plague vaccine ( EV76 ) is still in use in the states of former Soviet Union [5] . However , the need for multiple immunizations , high reactogenicity , and insufficient protection made the KWC vaccine undesirable for mass vaccination , and , consequently , it was discontinued in the United States [6] . In fact , the live-attenuated vaccine may not meet FDA approval because of the highly infectious nature of the plague bacterium and the virulence mechanisms of vaccine strains have not been fully understood [6] , [7] . A cautionary tale related to this is the recent fatality of a researcher as a result of exposure to the attenuated pigmentation-minus Y . pestis strain , KIM/D27 ( http://en . wikipedia . org/wiki/Malcolm_Casadaban] . The focus in the past two decades , thus , has shifted to the development of recombinant subunit vaccines [3] , [6] , [8] , [9] containing two Y . pestis virulence factors , the capsular protein ( Caf1 or F1; 15 . 6 kDa , Figure 1A and B ) and the low calcium response V antigen ( LcrV or V; 37 . 2 kDa , Figure 1A and C ) , which is a component of the type 3 secretion system ( T3SS ) . F1 assembles into flexible linear fibers via a chaperone/usher mechanism [10] , forming a capsular layer that allows Y . pestis to adhere to the host cell and escape phagocytosis [11] ( Figure 1A and 1B ) . The V antigen forms a “pore” at the tip of the “injectisome” structure of the T3SS needle , creating a channel that delivers a range of virulence factors , also known as the Yersinia outer membrane proteins ( Yops ) , into the host cytosol ( Figure 1A ) [12] . The V antigen is also critical for impairment of host's phagocytic responses [13] . Abrogation of these functions by F1 and V antibodies appears to be one of the mechanisms leading to protection of the host against lethal Y . pestis infection . Two types of F1/V recombinant vaccines have been under investigation , one containing a mixture of F1 and V antigens [14] , and another , a single F1-V fusion protein [15] , [16] . Although both induce protective immunity against Y . pestis challenge in rodent and cynomolgus macaque models , protection of African Green monkeys was insufficient and highly variable [6] , [17] . A phase I clinical trial in humans showed that a vaccine consisting of a mixture of F1 and V proteins was immunogenic , however , the antibody titers varied over a wide range leading to concerns about the consistency of vaccine efficacy [18] . One of the problems associated with the current plague vaccines is that the naturally fibrous F1 polymerizes into heterodisperse aggregates , compromising the quality and overall efficacy of the vaccines [15] , [19] , [20] , [21] , [22] . Second , the subunit vaccines do not induce adequate cell-mediated immune responses that also appear to be essential for optimal protection against plague [23] . Third , it is unclear if inclusion of other Y . pestis antigens such as the YscF , the structural unit of the injectisome needle ( Figure 1A and 1D ) , can boost the potency of the F1/V vaccines . This is particularly important as F1-minus strains of Y . pestis exist in nature which are as virulent as the wild-type strains [24] , [25] and significant diversity in the LcrV sequence of these strains might render the current F1/V vaccines ineffective [26] , [27] . Finally , the reported immunosuppressive property of V antigen [13] , [28] and whether it could compromise the innate immunity of humans , is a significant concern . These questions must be addressed to generate a next generation plague vaccine that could pass licensing requirements , as well as be manufactured relatively easily for stockpiling . Recently , we have developed a novel vaccine delivery system using the bacteriophage T4 nanoparticle [29] , [30] , [31] , [32] . The T4 capsid ( head ) is an elongated icosahedron , 120 nm long and 86 nm wide , composed of three essential capsid proteins: a major capsid protein , gp23*; vertex protein , gp24*; and a portal protein , gp20 ( Figure 1E ) . It is decorated with two non-essential proteins , Soc , the small outer capsid protein , and Hoc , the highly antigenic outer capsid protein . Binding sites for these proteins appear following head “expansion , ” a major conformational change that increases the outer dimensions of the capsid by ∼15% and inner volume by ∼50% [33] . Approximately 870 molecules of the tadpole- shaped Soc protein ( 9 kDa ) assemble into trimers at the quasi three-fold axes , clamping to adjacent capsomers and forming a reinforced cage around the shell ( Figure 1E ) [34] . This stabilizes an already stable head that can withstand harsh extracellular environment ( e . g . , pH 11 ) [34] . Hoc , on the other hand , is a linear “fiber” containing a string of four domains , three of which are immunoglobulin ( Ig ) -like [35] . One hundred and fifty five copies of Hoc fibers , with their NH2-termini projected at ∼160 Å distance from the capsid assemble at the center of each capsomer ( Figure 1E ) . Hoc binds to bacterial surfaces , apparently enriching the phage near its host for infection [36] . Although Soc and Hoc provide survival advantages , they are completely dispensable under laboratory conditions showing no significant effect on phage productivity or infectivity [37] . Purified Soc ( or Hoc ) protein binds to Hoc− Soc− capsid with high specificity and nanomolar affinity , properties that are not compromised by attachment of a pathogen antigen at the NH2- and COOH-termini [29] , [30] , [31] , [32] . Individual domains , or full-length proteins as large as 90 kDa , or multilayered oligomeric complexes of >500 kDa fused to Soc can be arrayed on T4 capsid , making it a robust antigen delivery platform [29] , [30] . Here , we describe two basic approaches to generate next generation plague vaccines , structure-based immunogen design and T4 nanoparticle delivery ( Figure 1 ) . We designed an F1 mutant that retained the T cell epitopes but folded into a soluble monomer rather than into an insoluble fiber ( Figure 1B ) . The mutated F1 was fused to V antigen to produce a bivalent F1mut-V immunogen that was also expressed as a soluble monomer . We then constructed an oligomerization deficient YscF mutant ( Figure 1D ) as well as a V mutant without the putative immunomodulatory sequence ( Figure 1C ) . The mutated antigens were fused to Soc and arrayed on phage T4 nanoparticle ( Figure 1F ) . The F1mut-V monomer induced robust immunogenicity , and the T4-decorated F1mut-V without any adjuvant , in addition , induced balanced TH1 and TH2 responses . Both the soluble and T4 decorated F1mut-V provided 100% protection to mice and rats against intranasal challenge with high doses of Y . pestis CO92 . Inclusion of YscF showed a slight enhancement in the potency of F1-V plague vaccine , whereas replacement of V with V10 mutant , which lacks the putative immunosuppressive sequence , did not significantly alter vaccine efficacy . These results provided new insights into plague vaccine design and produced next generation plague vaccine candidates by overcoming some of the concerns associated with the current subunit vaccines .
The X-ray structure and biochemical studies established that F1 polymerizes into a linear fiber by head to tail interlocking of F1 subunits through a donor strand complementation mechanism [10] ( Figure 1B ) . Each subunit has an Ig-like domain consisting of a four-stranded anti-parallel β-sheet . Of the four β-strands , three belong to one subunit forming a cleft into which the NH2-terminal β-strand of the “n+1” subunit locks in , resulting in a bridge connecting adjacent subunits ( inter-molecular complementation ) ( Figure 1B ) . Stringing of subunits in this fashion leads to assembly of linear F1 fibers of varying lengths . Caf1M chaperone is required for this process because prior to filling the cleft , a “spare” β-strand of Caf1M temporarily occupies the cleft until it is replaced by the β-strand of the incoming subunit with the assistance of an outer membrane usher protein , Caf1A . Over-expression of the F1 gene in a heterologous system such as E . coli ( Figure 2 ) exposes the unfilled hydrophobic cleft , resulting in uncontrolled aggregation of F1 subunits into insoluble inclusion bodies . This is demonstrated in Figure 2B in which all of the over-produced F1 protein partitioned into the pellet ( lane 8 ) and none was detected in the supernatant ( lane 7 ) . Denaturation of the insoluble protein recovered some of the F1 protein into the soluble fraction but it still aggregated rapidly leading to precipitation ( in the Histrap column ) upon removal of the denaturant . Similar aggregation behavior of F1 was observed in previously published studies [38] , [39] . We hypothesized that shifting of the NH2-terminal β-strand of F1 to the COOH-terminus should reorient the β-strand such that it fills its own cleft ( intra-molecular complementation ( Figure 1B ) , and furthermore , it should no longer require the assistance of chaperone or usher proteins . To test this hypothesis , we constructed an F1 mutant ( F1mut1 ) by deleting the NH2-terminal donor strand [amino acid ( aa ) residues 1–14] and fusing it to the COOH-terminus with a short ( Serine-Alanine ) linker in between ( Figure 2A ) . The recombinant F1mut1 , as predicted , folded into a soluble protein in the absence of Caf1M or Caf1A , and approximately 70% of the protein partitioned into the cell-free lysate ( Figure 2B , lanes 9 and 10 ) . In addition , for reasons unknown , the mutated F1 protein was expressed at significantly higher levels than that of the native F1 protein after IPTG induction ( Figure 2B , compare lane 5 with lane 3 ) . The gel filtration profile showed that the F1mut1 eluted as a symmetrical peak corresponding to a molecular mass of ∼19 kDa ( Figure 2C ) , a monomer , suggesting that the interlocking mechanism had shifted from inter- to intra-molecular interactions . A bioinformatics approach was used to determine if the strand shifting might have disrupted the NH2-terminal epitopes of F1 . The aa residues 7 to 20 are reported to contain a mouse H-2-IAd restricted CD4+ T cell epitope [40] . Of the fifty-three predicted 9-mer CD8+ T cell epitopes that encompassed 46 human MHC-I alleles ( Table S1 in Text S1 ) , four peptides ( aa residues: 9–17 , 10–18 , 11–19 and 13–21 ) fell in this region , and of the 9 peptides predicted to contain CD4+ T cell epitopes ( Table S2 in Text S1 ) , only one ( aa residues 1–18 ) belonged to this region . We determined that the integrity of these potential linear epitopes could be restored by extending the sequence of the switched strand by up to the aa residue 21 , which would duplicate the residues 15 to 21 at the COOH-terminus . Thus , the F1mut2 was constructed ( Figure 2A ) and tested . The F1mut2 behaved in a similar manner as the F1mut1 with respect to over-production and solubility ( Figure 2B , lanes 12–15 ) and was also purified as a monomer ( data not shown ) . Fusion of F1mut2 to V would generate a bivalent plague vaccine . Consequently , a mutated F1-V fusion protein ( F1mut-V ) was produced by fusing F1mut2 to the NH2-terminus of V with a two aa linker in between ( Figure 3A ) , and its solubility was compared to that of the native polymeric F1-V . The native F1-V protein , as reported previously [20] , [22] , was insoluble and partitioned into inclusion bodies ( Figure 3B; lanes 5 and 6 ) . Denaturation and refolding solubilized some of the protein but it also eluted , as was reported previously [20] , over a wide range of high molecular weight sizes in a gel filtration column ( Figure 3C , red profile ) . F1mut-V protein , on the other hand , was nearly 100% soluble ( Figure 3B; lanes 7 and 8 ) and eluted as a symmetrical peak corresponding to a molecular weight of ∼64 kDa , equivalent to the mass of monomeric F1mut-V fusion protein ( Figure 3C , blue profile ) . The yield of F1mut-V was quite high , ∼20 mg pure protein per liter of the E . coli culture . Furthermore , its stability to trypsin digestion was similar to that of the native F1-V ( Figure 3D ) . The Y . pestis V antigen has been reported to induce interleukin ( IL ) -10 and suppress the production of pro-inflammatory cytokines such as interferon ( IFN ) -γ and tumor necrosis factor ( TNF ) -α , which could lead to lowering of innate immunity in vaccinated individuals [41] . A truncated V in which the COOH-terminal 30 aa residues ( 271–300 ) were deleted ( referred to as “V10” mutation ) was reported to lack this immunomodulatory function [41] . A mutated F1mut-V10 recombinant was therefore constructed by deleting these residues ( Figure 3A ) . This mutant protein was also over-produced in E . coli , which was also highly soluble and could be purified as a monomer ( Figure 3C , green profile ) . Inclusion of YscF might expand the breadth of efficacy of F1-V plague vaccine formulation to Y . pestis strains containing variant V antigens [26] , or of those strains devoid of capsule but highly virulent in nature [24] , [25] . Since YscF is a structural component of the injectisome , over-production of this protein caused aggregation [42] . A mutant YscF was constructed by mutating the aa residues Asn35 and Ile67 , that are involved in oligomerization ( Asn35 changed to Ser , and Ile67 changed to Thr ) ( Figure 4A ) [43] . The resultant YscF35/67 mutant protein was soluble and the gel filtration profile showed two peaks , a high molecular weight aggregate near the void volume , and a second peak corresponding to a molecular mass of ∼22 kDa , which is equivalent to a dimer ( Figure 4B , blue profile; C ) . The native YscF , on the other hand , eluted over a wide range of high molecular weight sizes consistent with the formation of heterodisperse aggregates ( Figure 4B , red profile ) . The mutant dimer did , however , show slow aggregation during concentration and storage , as evident by the appearance of small amounts of precipitates . A large number of F1 , V , F1-V , and YscF recombinant proteins , both in native and mutated forms , were fused to the NH2- and/or the COOH-termini of either phage T4 Soc or the T4-related phage RB69 Soc and screened for their solubility as well as ability to bind to T4 phage ( Figure 5A , and data not shown ) . Our previous studies showed that the RB69 Soc binds to T4 capsid at nearly the same affinity as T4 Soc [34] . The RB69 Soc-fused plague antigens , with the exception of the native F1-Soc , produced soluble proteins whereas the T4 Soc-fused antigens were insoluble . Several of the RB69 immunogens were purified ( Figure 5B ) and tested for binding to T4 using our previously established in vitro assembly system . A typical result is shown in Figure 5C and D , which also exemplifies the versatility of the T4 nanoparticle display . Consistent with the crystal structure of Soc , which showed that both the NH2- and COOH-termini are exposed on the capsid surface , the plague immunogens F1mut and V could be efficiently displayed as an F1mut-V fusion protein that in turn was fused to the NH2-terminus of Soc ( Figure 5C ) . At the same time , its COOH-terminus could be fused to YscF35/67 , and the resultant F1mut-V-Soc-YscF35/67 fusion protein containing all three plague immunogens could be displayed on T4 capsid ( Figure 5G , lane 4 ) . The 66 kDa F1mut-V-Soc bound to T4 even at a relatively low 1∶1 ratio of F1mut-V-Soc molecules to Soc binding sites ( Figure 5C , red arrow ) . Binding increased with increasing ratio and reached saturation at 20–30∶1 . The copy number of bound F1mut-V-Soc per capsid ( Bmax ) was 663 , which meant that ∼76% of the Soc binding sites were occupied , and its apparent binding affinity ( Kd ) was 292 nM , which was ∼4-fold lower than that of Soc binding ( Kd = 75 nM ) [34] ( Figure 5D ) . This is consistent with the expectation that the 66 kDa F1mut-V-Soc , unlike the 10 kDa Soc , would encounter steric constraints to occupy all the binding sites on the capsid exterior . Given this limitation , the observed copy number was remarkably high , with the capsid surface presumably tightly packed with the F1mut-V molecules ( model shown in Figure 1F ) and exposing , consequently , the plague antigen epitopes for presentation to the immune system . Indeed , cryo-electron microscopy showed that these T4 capsids , unlike the wild-type capsids ( Figure 5E ) , were decorated with a layer of F1mut-V molecules , seen as fuzzy protrusions around the perimeter of the capsid wall ( Figure 5F ) . A series of nanoparticle decorated plague immunogens were prepared , including all three plague immunogens displayed on the same capsid using the F1mut-V-Soc-YscF35/67 fusion protein ( Figure 5G , lane 4 ) . The immunogenicity of mutated F1 immunogen was tested in a mouse model . The animals ( Balb/c ) were immunized according to the scheme shown in Figure 6 A and B and antibody titers in the sera were determined by ELISA . The data showed that all the three plague antigens adjuvanted with alhydrogel induced antigen-specific antibodies ( Figure 6C ) . The V antigen induced the highest titers with the end point titer reaching as high as 7×106 . The YscF antigen was the least immunogenic ( Figure 6C , panel III ) , with the endpoint titers about 1–2 orders of magnitude lower than that of F1 and V antigens ( Figure 6C , panels I and II ) . No significant differences in F1-specific antibody titers were observed among the various groups ( i . e . , F1-V versus F1mut-V versus F1-V+YscF; panel II ) . Importantly , the monomeric F1mut-V induced comparable antibody titers as the native polymeric F1-V , suggesting that the capsular structure of F1 per se did not afford a significant advantage to induction of antibodies . However , unexpectedly , the V-specific IgG titers were at least an order of magnitude higher when YscF was also included in the vaccine ( p<0 . 001 ) ( Figure 6C , panel I; compare F1-V to F1-V+YscF ) . Intranasal challenge of animals with 90 LD50 of Y . pestis CO92 [1 LD50 = 100 colony forming units ( CFU ) in Balb/c mice] , one of the most lethal strains , showed that all the control mice died by day 3 . However , the mice immunized with native V immunogen showed 83% survival ( two of twelve mice died ) , whereas the mice immunized with F1-V , F1mut-V , or F1-V plus YscF were 100% protected ( Figure 6D ) . The surviving mice were then re-challenged with a much higher dose , 9 , 800 LD50 , of Y . pestis CO92 on day-48 post-first challenge . The purpose of re-challenge was to determine if a strong adaptive immunity was generated after first infection with Y . pestis , which should in turn confer a much higher level of protection against subsequent challenges . Indeed , our data showed that all of the mice survived the re-challenge except two mice in the native F1-V group that succumbed to infection ( 83% protection ) ( Figure 6D ) . All of the naïve animals of same age which were used as a re-challenge control died as expected . These efficacy results showed that the monomeric F1mut-V was as efficacious as or even slightly better than the native F1-V polymer . The immunogenicity of nanoparticle decorated plague antigens was tested by vaccinating mice with phage T4 particles ( Figure 7A ) . The amount of the antigen was kept the same as that of the soluble preparations ( Figure 6 ) ; however , the T4 formulations contained no adjuvant . The data showed that the T4 displayed plague antigens induced comparable antibody titers as the adjuvanted soluble antigens ( Figure 7B ) . The challenge data showed that all the T4 decorated plague antigens , including the V alone group , provided 100% protection to mice against intranasal challenge with 90 LD50 of Y . pestis CO92; all the control animals died by day 4 . Upon re-challenge on day 48 post-first challenge with 9 , 800 LD50 ( Figure 7C ) , all of the mice were completely protected . As expected , the control re-challenge group of mice succumbed to infection . Overall , these data suggested that the T4 nanoparticle arrayed plague antigens might be more potent than the soluble antigens , as two deaths in each of the V and F1-V groups of mice occurred with the soluble vaccines ( Figure 6D ) but not with the T4 vaccines . Stimulation of both arms of the immune system , humoral ( TH2 ) and cellular ( TH1 ) , is probably essential for protection against Y . pestis infection [6] , [23] , [44] , [45] . In mice , the TH1 profile involves induction of antibodies belonging to IgG2a subclass whereas the TH2 profile primarily involves the induction of IgG1 subclass . To determine the specificity of antibodies induced by soluble vs T4 displayed antigens , the subclass IgG titers were determined by ELISA ( Figure 8 ) . These data showed that the soluble antigens and the T4 displayed antigens induced comparable IgG1 titers ( TH2 response ) ( Figure 8A ) whereas the T4 antigens evoked 1–2 orders of magnitude higher IgG2a titers than the soluble antigens ( TH1 response ) ( Figure 8B ) . These results suggested that the T4 decorated plague immunogens stimulated stronger cellular responses as well as humoral responses , whereas the soluble antigens showed a bias towards the humoral responses as was observed in the previous studies [46] . The immunogenicity and protective efficacy of F1mut-V vs F1mut-V10 was evaluated by three criteria: F1- and V-specific antibody titers , cytokine responses , and protection against Y . pestis CO92 challenge . Both the F1- and V-specific IgG antibodies ( Figure 9B ) and subclass IgG titers ( Figure 8A and B ) were not significantly different between the F1mut-V and F1mut-V10 immunized groups of mice when the immunogen used was soluble and alhydrogel-adjuvanted . However , when decorated on phage T4 nanoparticle with no adjuvant , F1mutV elicited higher total IgG ( Figure 9B ) and IgG1 titers ( Figure 8A ) than F1mutV10 ( p<0 . 05 ) . These trends were also reflected in the production of the TH2 cytokines , IL-4 and IL-5 , by splenocytes of immunized mice stimulated ex vivo with F1-V . Similar levels of IL-4 and IL-5 were produced by the soluble F1mut-V and F1mut-V10 antigens or the T4-displayed F1mut-V , whereas the T4 displayed F1mut-V10 showed slightly reduced levels ( Figure 10 ) . The induction of proinflammatory cytokines , such as IL-1α and IL-1β was also similar , irrespective of whether the antigens were soluble or T4 displayed ( Figure 10 ) . However the levels of TNF-α , an inflammatory mediator that synergistically acts with IFN-γ to help bridge the gap between innate and cell-mediated immune responses , were significantly higher in mice immunized with soluble F1mut-V10 than those immunized with F1mut-V ( Figure 10 ) . However , the trend was opposite when F1mut-V and F1mut-V10 immunogens were T4 displayed , although the data did not reach statistical significance . In fact , the T4 displayed F1mut-V10 induced overall weaker IFN-γ and cytokine responses when compared to its F1mut-V counterpart . With respect to animal survival , both the F1mut-V and F1mut-V10 immunogens , either soluble or T4 displayed , provided 100% protection to mice upon intranasal challenge with 5 , 350 LD50 of Y . pestis CO92 ( Figure 9C ) , with the control animals dying by day 3 . When the mice were re-challenged with an extremely high LD50 ( 20 , 000 ) on day 88 post-first challenge , all the groups showed 100% protection except the T4-displayed F1mut-V10 group in which one mouse died ( 92% protection ) ( Figure 9C ) . All of the naïve re-challenge control animals died by day 4 . To further test the efficacy of the mutated inmunogens , a rat study was conducted . Rats [47] , the natural host of Y . pestis , were vaccinated with alhydrogel adjuvanted F1mut-V , and F1mut-V10 as well as the T4 nanoparticle displayed F1mut-V and F1mut-V10 ( Figure 11A ) . The same immunization scheme as shown in Figure 6B was used and the animals were challenged with a 5 , 000 LD50 of Y . pestis CO92 . The data showed that all the control animals died by day 4 whereas all the F1mut-V and F1mut-V10 immunized animals were 100% protected ( Figure 11B ) .
Since the deadly anthrax attacks in 2001 , stockpiling of recombinant anthrax and plague vaccines to protect masses against a potential bioterror attack became a national priority . However , no plague vaccine has yet been licensed . The reasons include poor stability , insufficient immunogenicity , and/or manufacturing difficulties associated with the current formulations . New immunogen designs and vaccine platforms that could overcome some of these problems would be of great interest not only to stockpile efficacious biodefense vaccines but also to develop vaccines against a series of infectious diseases of public health importance . Here , by using structure-based immunogen design and T4 nanoparticle delivery approaches , we have engineered new and efficacious plague vaccines that could be manufactured relatively easily and provide complete protection against pneumonic plague in two rodent models . The surface-exposed Y . pestis antigens F1 and V have been the leading candidates for formulating a subunit plague vaccine for nearly two decades [14] , [15] , [16] , [17] . Although poorly immunogenic by themselves , their immunogenicity could be enhanced by adjuvantation with Alum [15] or by fusion with a molecular adjuvant such as flagellin [19] . While complete protection was observed in rodent models [17] , these vaccines impart partial and varied protection in African Green monkeys [6] , [17] . Another concern has been that the naturally polymeric F1 has high propensity to aggregate ( Figure 2 ) . When produced in a heterologous system such as E . coli , the recombinant F1-V protein partitions into insoluble inclusion bodies [15] , [19] , [20] , [21] , [22] ( Figure 3 ) . Although it can be partially recovered in soluble form by denaturation and re-folding , the preparation still consists of a mixture of heterogenous aggregates and varying amounts of the misfolded protein [20] . These might also trap contaminants , compromising the overall purity , stability , and efficacy of the vaccine . Attempts to produce a monomeric vaccine by mutating the lone cysteine residue in V have not been successful [20] . We proposed three hypotheses to design a soluble monomeric plague vaccine , yet retaining its structural and epitope integrity . First , we hypothesized that the β-strand that connects the adjacent F1 subunits requires repositioning . This was achieved by transplanting the NH2-terminal β-strand to the COOH-terminus in such a way that the reoriented β-strand fitted into its own β-sheet cleft rather than that of the adjacent F1 subunit . It also eliminated the need for chaperone and usher mediated oligomerization as there would no longer be an unfilled β-sheet pocket exposed in the F1 subunit . Second , by using epitope predictions , the NH2-terminal aa residues 15–21 of F1 flanking the β-strand were duplicated at the COOH-terminal end to restore any potential T-cell epitopes that might have been lost during the switch . This is important because in a previous study , a simple β-strand switch produced a less stable monomer with diminished immunogenicity [48] . Third , the mutated F1 was fused to the NH2-terminus of V with a flexible linker in between to minimize interference between the F1 and V domains . The bivalent F1mut-V immunogen thus produced showed a remarkable shift in solubility , from an insoluble F1-V polymer to a completely soluble monomer ( Figure 3 ) . The monomer could be purified from cell-free lysates at high yields , ∼20 mg of pure protein from a liter of E . coli culture , which we believe could be substantially increased under optimized conditions in a fermentor . Several lines of evidence demonstrated that the F1mut-V monomer was as efficacious as , if not better than , the native F1-V polymer . In four separate immunization studies and two animal models ( Figures 6 , 7 , 9 , and 11 ) , F1mut-V induced robust immunogenicity and protective efficacy . It showed similar levels of F1- and V-specific antibody titers as the native F1-V , and no significant differences were observed in TH1 vs TH2 specific IgG subclass titers . Furthermore , F1mut-V overall showed stronger cytokine responses and conferred 100% protection in vaccinated mice and rats , including when very high doses of Y . pestis CO92 , ∼5 , 350 LD50 for first challenge and ∼20 , 000 LD50 for re-challenge , were administered by the intranasal route ( Figure 9 ) . The native F1-V , on the other hand , showed slightly lower protection ( ∼83% ) upon re-challenge ( Figure 6 ) . The possibility of increasing the breadth and potency of F1-V vaccine by inclusion of YscF was tested by constructing an oligomerization deficient YscF35/67 mutant [43] . Such a vaccine might be effective even against those Y . pestis strains that contain variant V antigens or lack the capsule , but are highly virulent [26] . The mutated protein , purified as a soluble dimer , elicited YscF-specific antibodies on its own , and , when it was mixed with F1-V , it enhanced the induction of V-specific antibody titers as well as survival rate in mice ( Figure 6 ) . While these results indicated enhanced potency of F1-V vaccine in the presence of YscF , more studies are needed to determine if the cost of an additional protein can be justified for vaccine manufacture . On the other hand , the T4 displayed trivalent vaccine , F1mut-V-Soc-YscF ( Figures 5 and 7 ) , might offer an alternative to incorporate YscF into the plague vaccine formulation . Y . pestis infection stimulates IL-10 production which in turn suppresses the production of proinflammatory cytokines IFN-γ and TNF-α . Both IFN-γ and TNF-α are important for innate immunity , as well as to elicit TH1 immune responses that might be essential for protection against pneumonic plague [49] , [50] , [51] . These immunomodulatory functions , in part , were attributed to the V antigen , specifically to the NH2-terminal aa residues 31–49 [49] . Deletion of these residues , or of the COOH-terminal aa residues 271–300 ( V10 mutation ) , have been reported to abrogate the suppression of IFN-γ and TNF-α [41] , presumably by preventing the interaction of V with toll like receptor 2 ( TLR2 ) and CD14 , the receptors of the innate immune system [49] , [52] . Our studies showed that both the F1mut-V and F1mut-V10 immunogens produced similar levels of IFN-γ and other proinflammatory cytokines , such as IL-1α and IL-1β , upon stimulation ex-vivo of splenocytes from immunized mice with F1mut-V . However , TNF-α was induced to significantly higher levels in the F1mut-V10 group ( Figure 10 ) , consistent with the published report [41] . However , the T4 nanoparticle decorated F1mut-V10 showed opposite trend , producing much reduced levels of TNF-α as well as IFN-γ and other cytokine responses than its F1mut-V counterpart , a result also correlated with lower protection against re-challenge [92% protection with T4 displayed F1mut-V10 vs 100% protection with T4 displayed F1mut-V upon re-challenge with 20 , 000 LD50 ( Figure 9C ) ] . Thus , our data did not show consistent enhancement of proinflammatory cytokines by the V10 mutation , hence it is questionable that replacing native V with V10 mutant would lead to a significant beneficial effect in a new plague vaccine design . On the other hand , from a structural standpoint , deletion of the aa residues 271–300 disrupts the coiled coil bridge between the NH2- and COOH-domains of V [12] , which would likely make V10 mutant a conformationally more flexible molecule and could adversely affect vaccine stability and efficacy . Although humoral immune responses are critical for protection against plague , several studies have shown that cell-mediated immunity also plays important roles [23] , [53] , [54] . Wang et al . [53] established the role of CD8+ T cells in protection of mice against pneumonic plague evoked by Y . pestis KIM 1001 strain . This study corroborated the earlier report of Parent et al . [23] , which concluded that plague vaccines that generate both humoral- and cell-mediated immune responses will be most effective . Likewise , Philipovskiy and Smiley ( 3 ) reported that mice vaccinated with a live Y . pestis vaccine primed both CD4+ and CD8+ T cells , which when passively transferred to naïve mice , provided protection against pulmonary Y . pestis infection [54] . The adjuvant-free T4 nanoparticle decorated F1mut-V induced robust F1- and V-specific antibody responses , as well as provided 100% protection to mice and rats against very high doses of Y . pestis challenge ( Figures 7 , 9 and 11 ) . In addition , T4 delivery induced balanced TH1 and TH2 responses with a potent TH1 response , as evident from the induction of subclass IgG2a specific antibodies . Similar patterns were observed in our previous studies with the T4 displayed HIV-1 p24 immunogen [32] . Presumably , the large size of the T4 phage particle ( capsid , 120 nm×86 nm; tail , 100 nm ) allows for its efficient uptake by the antigen presenting cells and cross-presentation to both MHC-I and MHC-II molecules , stimulating both the humoral and cellular arms of the immune system . It is also possible that the T4 phage DNA containing CpG might potentially serve as a TH1-type of adjuvant . Indeed , studies have shown that F1-V vaccine adjuvanted with CpG or poly IC ( also a TH1 type adjuvant ) , given by the intranasal route , induced both TH1 and TH2 responses , providing better protection to mice against bubonic and pneumonic plague [55] , [56] . Thus , T4 might be a particularly useful platform for plague vaccine design since clearance of the Y . pestis bacterium may require a balanced response that is generally not seen with the current F1-V vaccines [46] . We also note that , although the mechanistic basis for T4 responses is currently unknown , no adverse effects to T4 vaccination have been observed in many preclinical studies performed in mouse , rat , rabbit , and rhesus macaque models [31] , [57] , [58] , or in a human trial where T4 phage was given orally [59] . There has been a considerable urgency to develop a recombinant plague vaccine , but several concerns precluded licensing of current formulations . Our studies have established that the F1mut-V recombinant vaccine is efficacious and easily manufacturable and should be seriously considered as a next generation plague vaccine . Future studies would include preclinical evaluation of protection against Y . pestis infection in cynomolgus macaques as well as African Green monkeys , potentially leading to human clinical trials . Although the soluble F1mut-V vaccine adjuvanted with alum would be relatively easy to manufacture , the phage T4 nanoparticle-decorated F1mut-V vaccine offers certain advantages . First , the T4 formulation provided enhanced vaccine potency in small animal models . Second , the T4 vaccine would not require an extraneous adjuvant , and third , additional antigens from other biodefense pathogens , such as the protective antigen ( PA ) from Bacillus anthracis could be incorporated into the same formulation generating a dual vaccine against both inhalation anthrax and pneumonic plague . Our recent study demonstrated that the T4 displayed PA provided complete protection to Rhesus macaques against aerosol challenge with Ames spores of B . anthracis [51] . Fourth , the large interior of T4 head which has the capacity to package ∼171 kb DNA can also be used to deliver DNA vaccines [60] . By combining protein display outside and DNA packaging inside the T4 nanoparticles can simultaneously deliver vaccine antigen ( s ) as well as vaccine DNAs , similar to that of the prime-boost strategy , potentially inducing robust and long-lasting immune responses . Finally , such prime-boost vaccines could be targeted to antigen-presenting dendritic cells ( DCs ) by displaying a DC-specific ligand on the capsid using Hoc , further stimulating the cell-mediated immunity . One or two doses of such potent nanoparticle vaccines might be sufficient to afford protection against multiple biothreat agents . With the recent data demonstrating the proof of concept [60] , we are currently developing these novel vaccine platforms , not only to defend against biowarfare pathogens but also to generate efficacious vaccines against complex infectious agents such as HIV-1 and malaria .
This study was conducted in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocols were reviewed and approved by the Institutional Animal Care and Use Committees of the University of Texas Medical Branch , Galveston , TX , ( Office of Laboratory Animal Welfare assurance number: A3314-01 ) and The Catholic University of America ( Office of Laboratory Animal Welfare assurance number: A4431-01 ) . The T7 promoter containing E . coli expression vector pET28b ( Novagen , MA ) was used for recombinant plasmid construction . The template DNAs containing Y . pestis F1 , V , or YscF were kindly provided by Dr . Richard Borschel from the Walter Reed Army Institute of Research ( Silver Spring , MD ) . E . coli XL-10 Gold cells ( Stratagene , CA ) were used for the initial transformation of clones . The plasmid DNAs were then re-transformed into E . coli BL21 ( DE3 ) RIPL ( Novagen , MA ) for expression of recombinant proteins . The Hoc− Soc− phage T4 was propagated on E . coli P301 and purified by CsCl gradient centrifugation . The DNA encoding F1 , V , or YscF were amplified by PCR using primers containing appropriate restriction site ( s ) ( NheI/XhoI for F1 and YscF , and NheI/HindIII for V ) . The PCR products were purified , digested with appropriate restriction enzymes , and ligated with pET-28b vector DNA digested with the same restriction enzymes . The resulting plasmids had F1 , V , or YscF coding sequences fused in-frame with the 23 aa vector sequence containing a hexa-histidine tag at the NH2-terminus . The YscF mutant , YscF35/67 , which contained point mutations at aa 35 ( Asn to Ser ) and 67 ( Ile to Thr ) was amplified by overlap PCR [61] followed by digestion with NheI and XhoI enzymes . YscF35/67 DNA was then ligated into the linearized pET28b vector . The F1mut1 , in which the first 14 aa residues were deleted and fused to the COOH-terminus with a two aa ( SA ) linker , was constructed by two rounds of PCR . The first round of PCR was performed to amplify F1 fragment in which the NH2-terminal 14 aa residues were deleted . This PCR product was used as a template for the second round of PCR using a forward primer containing NheI restriction site and a reverse primer containing the NH2-terminal 14 aa residues and XhoI restriction site . The PCR fragment was then inserted into NheI and XhoI linearized pET28b vector . To construct F1mut2 in which aa residues 15 to 21 were duplicated at the COOH-terminus , a reverse primer with a 5′-tag corresponding to the 15 to 21 aa sequence and XhoI restriction site was used for PCR amplification . The F1mut2 fragment was then inserted into NheI and XhoI linearized pET28b vector . To construct F1-V recombinants , V was first amplified and inserted into BamHI and HindIII linearized pET28b vector to generate the pET-V clone . F1 and F1mut2 were amplified with primers containing NheI and BamHI restriction sites , digested with NheI and BamHI , and ligated with the pET-V vector DNA digested with the same restriction enzymes . The resulting F1-V and F1mut-V plasmids contained F1 or F1mut in-frame fusion with V and a 23-aa vector sequence containing the hexa-histidine sequence at the NH2-terminus of F1 . The F1mut-V10 was amplified by overlap PCR using F1mut-V as the template and the mutated DNA was inserted into the NheI and HindIII linearized pET28b vector . T4 Soc gene or RB69 Soc gene was fused with V , F1 , or YscF with two aa ( GS ) linker by overlap PCR and the amplified DNA was inserted into the pET28b vector . The fused products V-T4 Soc , F1-T4 Soc , V-RB69Soc , and F1-RB69 Soc were further fused to YscF by overlap PCR to generate V-Soc ( T4 or RB69 ) -YscF and F1-Soc ( T4 or RB69 ) -YscF . Two aa residues , GS , were used as a linker between Soc and YscF . To construct F1-V-Soc clones , RB69 Soc gene was first amplified with end primers containing HindIII and XhoI restriction sites and inserted into the HindIII and XhoI linearized pET28b vector . This clone was then linearized by digestion with NheI and HindIII restriction enzymes . F1mut-V and F1mut-V10 DNAs were amplified by using the end primers containing NheI and HindIII restriction sites and inserted into the above plasmid . The resulting clones contained F1mut-V or F1mut-V10 fused in-frame to the NH2-terminus of RB69 Soc and also the flanking vector sequences containing two hexa-histidine tags at both NH2- and COOH-termini . The F1mut-V-Soc was then fused with YscF by overlap PCR with a two aa linker , GS , between Soc and YscF . All of the clones were sequenced ( Retrogen , CA ) and only the clones containing 100% sequence accuracy were used for protein purification . The primer sequences used and clones generated in these studies will be available upon request . The structural models of F1 , V , YscF , and T4 phage nanoparticle ( Figure 1 ) were constructed using Chimera version1 . 4 . 1 [62] . The T cell epitopes were predicted using MetaMHC , a new web server which integrates the outputs of leading predictors by several popular ensemble strategies [63] . This was shown to generate statistically significant results that were more reliable than the individual predictors [63] . For the CD4+ T cell epitope prediction , F1 protein sequence was screened against 14 human MHC-II alleles . Peptides identified as positive ones by at least one predictor method were considered as potential CD4+ T cell epitopes . For the CD8+ T cell epitope prediction , F1 was screened against 57 human MHC-I alleles . Peptides identified as positive by at least one ensemble predictor approaches were considered to be potential CD8+ T cell epitopes . Default values were used for both the T cell epitope predictions . The E . coli BL21 ( DE3 ) RIPL cells harboring various plague recombinant plasmids constructed as above were induced with 1 mM IPTG for 1 to 2 h at 30°C . The cells were harvested by centrifugation at 4 , 000 g for 15 min at 4°C and the pellets were resuspended in 50 mM Tris-HCl ( pH 8 . 0 ) . Solubility analysis was carried out using bacterial protein extraction reagent ( B-PER ) ( Thermo Fisher Scientific Inc . , Rockford , IL ) . The cells were lysed with B-PER and centrifuged at 12 , 000 g for 10 min . The soluble supernatant and insoluble pellet fractions were analyzed by SDS-polyacrylamide gel electrophoresis ( PAGE ) as follows . The samples were boiled in a buffer containing SDS and β-mercaptoethanol , and were electrophoresed on a 12% or 15% ( w/v ) polyacrylamide gel . Since the protein aggregates will be dissociated into monomers under these conditions . The molecular weight differences observed in Figure 2B reflect sizes of the polypeptide chains of F1 , F1mut1 , and F1mut2 . For example , F1mut1 and Fmut2 are approximately 1 . 6 kDa and 2 . 2 kDa larger than F1 because F1mut1 has a two amino acid linker ( SA ) and an eight amino acid His-tag ( LEHHHHHH ) ( orange ) at the C-terminus . F1mut2 , in addition , has the duplicated T cell epitope ( EPARITL ) ( blue ) ( Figure 2A ) . For protein purification , the cells were resuspended in binding buffer ( 50 mM Tris-HCl pH 8 . 0 , 300 mM NaCl and 20 mM imidazole ) containing proteinase inhibitor cocktail ( Roche , USA ) . The cells were lysed by French press ( Aminco , IL , USA ) at 12 , 000 psi and the soluble fractions containing the His-tagged fusion proteins were isolated by centrifugation at 34 , 000 g for 20 min . The supernatants were filtered through 0 . 22 µm filters ( Sartorius Stedim Biotech , Germany ) and loaded onto 1 ml HisTrap column ( AKTA-prime , GE Healthcare ) pre-equilibrated with 20 ml of binding buffer . After washing with the binding buffer containing 50 mM imidazole , the proteins were eluted with 20–500 mM linear imidazole gradient . The peak fractions containing the desired protein were concentrated by Amicon Ultra-4 centrifugal filtration ( 10 kDa cut-off; Millipore ) . The proteins were further purified by gel filtration on Hi-load 16/60 Superdex 200 column ( AKTA-FPLC , GE Healthcare ) in a buffer containing 20 mM Tris-HCl , pH 8 . 0 and 100 mM NaCl . The peak fractions containing the purified proteins were concentrated and stored at −80°C . The native F1 recombinant proteins were purified from the pellet containing the insoluble inclusion bodies . The pellet was dissolved in the binding buffer containing 8 M urea and loaded onto 1 ml HisTrap column ( AKTA-prime , GE Healthcare ) pre-equilibrated with the same buffer . The proteins were renatured by washing the column with a decreasing urea gradient ( 8 to 0 M ) in the binding buffer . The bound proteins were then eluted with 20–500 mM linear imidazole gradient . If necessary , the peak fractions from the HisTrap column were concentrated by Amicon Ultra-4 centrifugal filtration ( 10 kDa cut-off ) . The proteins were further purified by gel filtration on Hi-load 16/60 Superdex 200 column as described above . The levels of lipopolysaccharide ( LPS ) contamination in the purified recombinant Y . pestis antigens from E . coli; F1 , LcrV , YscF , and F1mut-V , were determined using Endosafe PTS system ( Charles River Laboratories International , Inc . , Wilmington , MA ) . This system consists of a handheld spectrophotometer and utilizes FDA approved disposable cartridges . At least three batches of each antigen were tested . The endotoxin levels ranged from 0 . 05 to 0 . 8 EU/ml , substantially lower than the maximum recommended in gene vectors and subunit vaccines , 10 and 20 EU/ml respectively , for preclinical research [64] . In vitro binding of plague-Soc fusion protein on Hoc− Soc− T4 phage was carried out as previously described [29] , [30] , [32] . About 3×1010 phage particles were sedimented for 45 min at 34 , 000 g in LoBind Eppendorf tubes and resuspended in phosphate-buffered saline ( PBS ) buffer ( pH 7 . 4 ) . Various Soc fusion proteins were incubated with the resuspended Hoc− Soc− phage at 4°C for 45 min . The phage particles were sedimented at 34 , 000 g for 45 min and the supernatant containing the unbound protein was discarded . The phage pellet containing the bound plague antigen ( s ) was washed twice with excess buffer containing 20 mM Tris-HCl pH 8 and 100 mM NaCl . The final pellets were resuspended in PBS buffer ( pH 7 . 4 ) and analyzed by SDS-PAGE . The gels were stained with Coomassie Blue R250 ( Bio-Rad , USA ) and the protein bands were quantified by laser densitometry ( PDSI , GE Healthcare ) . The density of Soc fusion protein , gp23* , and gp18 ( major tail sheath protein; 70 kDa ) bands were determined for each lane separately and the copy number of bound plague antigen molecules per capsid was calculated using the known copy numbers of gp23* ( 930 molecules per capsid ) or gp18 ( 138 molecules per capsid ) . A saturation binding curve relating the number of bound plague protein-Soc molecules per capsid ( Y ) and the concentration of unbound protein in the binding reaction ( X ) was generated by SigmaPlot software . The apparent Kd ( association constant ) and Bmax ( maximum copies of Soc fusion protein bound per capsid ) were determined using the equation Y = BmaxX/ ( Kd+X ) as programmed in the SigmaPlot software . Six to eight weeks female Balb/c mice ( 17–20 g ) were purchased from Jackson Laboratories ( Bar Harbor , Maine ) and randomly grouped and acclimated for 7 days . Equivalent amounts of plague immunogen molecules , either soluble or phage-bound , were used for each immunization . For immunization of soluble antigens , the purified proteins ( 10 µg/mouse/immunization ) were adsorbed on alhydrogel ( Brenntag Biosector , Denmark ) containing 0 . 19 mg of aluminum per dose . For the T4 displayed antigens , the phage particles were directly used without any adjuvant ( 10 µg of plague antigen/mouse/immunization ) . On days 0 , 21 and 42 , mice were vaccinated via the intramuscular route . Alternate legs were used for each immunization . Blood was drawn from each animal on days 0 ( pre-bleeds ) , 35 and 49 and the sera obtained were stored frozen at −70°C . On day 56 , mice were intranasally challenged with Y . pestis CO92 BEI strain [65] using the indicated LD50 . Animals were monitored and recorded twice daily for mortality or other symptoms for 48 to 88 days . The animals that survived were re-challenged intranasally at 48 or 88 days post-first challenge with the indicated LD50 and monitored twice daily for a further 48 days . Female Brown Norway rats ( 50–75 g ) were purchased from Charles River ( Houston , TX ) . Upon arrival , animals were weighed and randomized into the treatment groups and were acclimated for several days before manipulation . The plague immunogens were prepared as described above . On days 0 , 21 and 42 , rats were vaccinated via the intramuscular route with 15 µg antigen in 50 µl PBS buffer . Alternate legs were used for each immunization . On day 56 , animals were intranasally challenged with 5 , 000 LD50 of Y . pestis CO92 BEI strain and were monitored twice daily for 30 days and clinical symptoms of disease and survival recorded . The IgG titers were determined by ELISA . Briefly , 96-well microtiter plates ( Evergreen Scientific , Los Angeles , CA ) were coated with 10 ng/well of purified F1 , V , YscF , F1-V or F1mutV antigen at 4°C overnight . Following blocking and washing , sera from naïve and immunized mice were serially diluted and incubated with the affixed antigens for 1 h at room temperature . Following several washes , horseradish peroxidase ( HRP ) -conjugated goat anti-mouse IgG secondary antibody was added to the wells at a dilution of 1∶10 , 000 . After incubation for 1 h at room temperature , the unbound antibody was removed and the wells were washed several times and the TMB ( 3 , 3′ , 5 , 5′-tetramethylbenzidine ) substrate was added . Following a 20 min incubation to develop the color , the reaction was quenched by the addition of 2 N H2SO4 and the absorbance was read at 450 nm using an ELISA reader . For IgG subtypes , horseradish peroxidase-conjugated goat anti-mouse IgG1 or IgG2a secondary antibodies were used . Seven days after the second boost ( day 49 ) , mice were sacrificed and spleens were harvested to prepare splenocytes using the lymphocyte separation medium . The isolated lymphocytes were adjusted to ∼5×106 cells/ml and 1 ml of lymphocytes seeded into each well . Triplicate cultures from each group were stimulated with purified F1-V ( 10 µg/ml ) . Additional control stimulators included medium only and concanavalin A ( 5 µg/ml ) . After approximately 48 h incubation at 37°C in a humidified ( 5% CO2 in air ) incubator , culture supernatants were collected . Cytokines were measured using a multiplex assay ( Millipore , Billerica , MA ) . The results were analyzed in Prism and the statistical significance was determined by one way ANOVA with Bonferroni correction . F1 capsule antigen ( caf1 ) [GeneID: 1172839 , Sequence: NC_003134 . 1 ( 85950 . . 86462 ) ] , lcrV [GeneID: 1172676; Sequence: NC_003131 . 1 ( 21935 . . 22915 , complement ) ] , yscF [GeneID: 1172700 , Sequence: NC_003131 . 1 ( 41026 . . 41289 ) ] , and soc ( RB69Soc ) [GeneID: 1494143 , Sequence: NC_004928 . 1 ( 14980 . . 15216 , complement ) ] .
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Plague caused by Yersinia pestis is a deadly disease that wiped out one-third of Europe's population in the 14th century . The organism is listed by the CDC as Tier-1 biothreat agent , and currently , there is no FDA-approved vaccine against this pathogen . Stockpiling of an efficacious plague vaccine that could protect people against a potential bioterror attack has been a national priority . The current vaccines based on the capsular antigen ( F1 ) and the low calcium response V antigen , are promising against both bubonic and pneumonic plague . However , the polymeric nature of F1 with its propensity to aggregate affects vaccine efficacy and generates varied immune responses in humans . We have addressed a series of concerns and generated mutants of F1 and V , which are completely soluble and produced in high yields . We then engineered the vaccine into a novel delivery platform using the bacteriophage T4 nanoparticle . The nanoparticle vaccines induced robust immunogenicity and provided 100% protection to mice and rats against pneumonic plague . These highly efficacious new generation plague vaccines are easily manufactured , and the potent T4 platform which can simultaneously incorporate antigens from other biothreat or emerging infectious agents provides a convenient way for mass vaccination of humans against multiple pathogens .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"biology"
] |
2013
|
Mutated and Bacteriophage T4 Nanoparticle Arrayed F1-V Immunogens from Yersinia pestis as Next Generation Plague Vaccines
|
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