In the course of demographic transitions (DTs), two large-scale trends becomeapparent: (i) the broadly positive association between wealth, status andfertility tends to reverse, and (ii) wealth inequalities increase and thentemporarily decrease. We argue that these two broad patterns are linked, througha diversification of reproductive strategies that subsequently converge aspopulations consume more, become less self-sufficient and increasingly depend oneducation as a route to socio-economic status. We examine these links using datafrom 22 mid-transition communities in rural Poland. We identify changingrelationships between fertility and multiple measures of wealth, status andinequality. Wealth and status generally have opposing effects on fertility, butthese associations vary by community. Where farming remains a viable livelihood,reproductive strategies typical of both pre- and post-DT populations coexist.Fertility is lower and less variable in communities with lower wealthinequality, and macro-level patterns in inequality are generally reproduced atthe community level. Our results provide a detailed insight into the changingdynamics of wealth, status and inequality that accompany DTs at the communitylevel where peoples' social and economic interactions typically takeplace. We find no evidence to suggest that women with the most educationalcapital gain wealth advantages from reducing fertility, nor that highereducational capital delays the onset of childbearing in this population. Rather,these patterns reflect changing reproductive preferences during a period ofprofound economic and social change, with implications for our understanding ofreproductive and socio-economic inequalities in transitioning populations. If reproductivestratification is driven by differences in educational capital, then (ii)educational capital should moderate how wealth influences fertility. This moderatingeffect should itself depend on how market-oriented the community is, and sinceconvergence on low fertility is already underway in more highly educated communities, we expen = 1255) of our respondents live in households partlyor mainly subsisting from farming, but only approximately 4% of householdsexclusively farm. Income-generating strategies combine farm and off-farm work,formal and informal wage-labour, allowing us to examine both‘traditional’ and ‘modern’ dimensions of wealth andstatus. These communities are undergoing late-stage DT, and fertility is declining.Nonetheless, completed fertility in our sample is dramatically higher than Polishnational estimates  of 1.38 in 2010, TFR of 2.16 in 1949,the most representative birth cohort), with a mean of 3.81 (s.d. 2.15) children perwoman, and significant between-community variation ). The distribution spans approximately 5.3 s.d., so women with themost educational capital were predicted to have approximately 35% (i.e.1-eβ5.3) fewer children than womenwith the least. But depending on the community, a 1 s.d. increase predictedanywhere from a 20% decrease ) to a 13%increase  ) in fertility. Thus, whenconsidered across the range in a particular community , women with the most educational capital are predicted tohave from approximately 60% fewer to approximately 54% morechildren than women with the least educational capital.Educational capital has the largest fixed effect on fertility. A 1 s.d. increaseis associated with a 7% reduction in fertility ), translatinginto approximately 24% lower fertility among the most market-integratedhouseholds compared with the least (the range spans approx. 5.5 s.d.). Thisvaries across communities from a 2% ) to an 8% ) decrease infertility or a difference of between 9 and 20% fewer children among womenin the most market-integrated households compared with those in the leastmarket-integrated ones.Similarly, a 1 s.d. increase in market integration is associated with a 5%decrease in fertility overall ), and therefore,approximately 20% higher predicted fertility among the wealthiest womencompared with the least wealthy (the range spans approx. 6 s.d.). This variesacross communities from an 11% increase ) to a 5% decrease ) in fertility, and thus adifference of between 55% more and 22% fewer children among womenwith the highest levels of non-farming wealth relative to the least wealthywomen.A 1 s.d. increase in non-farming wealth is associated with a 3% increasein fertility overall  ) and therefore approximately65% higher predicted fertility among the wealthiest women compared withthe least wealthy women (the range spans approx. 10 s.d.). This varies acrosscommunities from a 1% ) to an 8% ) increase in fertility. The mosttraditionally wealthy women are predicted to have between 5 and 34% morechildren than the least wealthy women in their community.A 1 s.d. increase in farming wealth is associated with a 5% increase infertility overall (ea) meansthat these effects should not be understood in isolation. These results show that the magnitude and sometimes the direction of theseeffects on fertility depend on the local context in which women reproduce, andimply non-trivial fertility differentials. Nonetheless, a significantinteraction between non-farming wealth and educational capital a meanstii) Educational capital modifies the effect ofnon-farming wealth on fertility.Figure 2a shows that overall, non-farmingwealth is positively associated with fertility when educational capital is low(top black line), but negatively associated with fertility when educationalcapital is high (bottom blue line). A 1 s.d. increase in a woman'seducational capital is associated with a 4% decrease in the magnitude ofthe positive relationship between non-farming wealth and fertility ). Women withhigher educational capital are therefore predicted to reducefertility with increasing wealth, whereas women with lower educational capitalare predicted to increase fertility with increasing wealth.However, the interaction itself varies by community and is not alwayssignificant (b). A 1 s.d. increase in educational capital isassociated with anywhere from a 3% increase ) to a 13% decrease ) in the magnitudeof the positive relationship between non-farming wealth and fertility.(nificant b. A 1 s.iii) Diverging reproductive strategies are more evidentwhere farming is viable.Figure 2b shows that the interaction abovetends to be stronger (i.e. more negative) and more often statisticallysignificant within communities with a high proportion of farmers  and exclusive farmers , and in communitieswith higher mean farming wealth  and lower mean educational capital. Thus,where farming livelihoods are viable, reproductive strategies are more variable.The interaction is not related to community sample size , nor to population density., or considering only post-reproductivewomen , andthe top quartiles are not significantly different across communities. Womenin the top quartile of educational capital do not differ in levels ofnon-farming wealth, either across the sample  or across communities , although they exhibitsignificantly less variance in wealth  and are substantially moremarket-integrated . Instead, post-reproductive women in the top quartile ofeducational capital have significantly shorter reproductive spans,indicating that later fertility regulation within marriage, rather thanpostponement of early reproduction, drives the patterns in our data. Neither themain effect of educational capital, nor its interaction with non-farming wealth,is driven by the inclusion of childless women in our sample , contrary to what has been found inother studies [We examined whether educational capital drives postponement of reproduction,either as a strategy to increase wealth among highly educated women , or beca studies ,27.(b)Overall, inequality across the sample is relatively low, with Gini coefficientsof 0.23 for educational capital, 0.21 for market integration, and 0.16 fornon-farming wealth. Farming wealth has a higher Gini coefficient of 0.42(measuring only farmers). However, inequality varies substantially acrosscommunities. Gini coefficients for educational capital vary from 0.13 to 0.26,indicating twice the amount of inequality in some communities compared withothers. Gini coefficients for market integration vary from 0.08 to 0.41, andthose for non-farming wealth vary from 0.06 to 0.27, in both cases indicating upto five times more inequality in some communities compared with others.Coefficients for farming wealth vary from 0.05 to 0.56, indicating ten times theamount of inequality in the most unequal community compared with the most equalone. Gini coefficients for different measures are not significantlyinter-correlated , so inequalityon one dimension does not imply inequality on others. Inequality is alsounrelated to population density or sample size .iv–v) Fertility is higher and more varied inunequal communities. We examined correlations betweencommunity-level Gini coefficients and the community-averaged fitted scores fromthe multi-level model (adjusted means and s.d. of predicted fertility). Figure 3ashows that there is significantly less variation in predicted fertility(measured in s.d.) in communities with lower inequality in non-farming wealth, butvariation in fertility is not related to other forms of inequality. Figure 3b,c showrespectively that mean predicted fertility is also significantly lower incommunities with lower inequality in both market integration  and non-farming wealth, butis not related to inequality in educational capital. Declining inequality inwealth and market integration is therefore related to convergence on lowfertility strategies, but not all kinds of inequality are equally important.(vi–vii) Community-level inequality reproducesmacro-level patterns. Finally, community-level inequalitiesreproduce macro-level patterns for some measures. Figure 3d,e showrespectively a clear decline in inequality in both market integration and non-farming wealth  in communities where the mean is higher.However, inequality in educational capital and farming wealth is not related tothe means of those measures. Figure 3f also confirms that in communitieswhere inequality in market integration is lower, mean educational capital issignificantly higher . This last resultis not driven by in-migration of highly educated people into moremarket-integrated communities. The proportion of migrants in the community isunrelated to inequality in market integration  and migrants are not more highlyeducated than non-migrants .(4.Transitions from hunting and gathering to subsistence agriculture appear typified bya diversification in the types of wealth and status that are inherited, and byincreasing reliance on ‘extrasomatic’ over ‘embodied’capital ,21. By cWe find that wealth and status have different effects on fertility, but theassociations vary by community, and they interact in certain contexts to producepatterns typical of both pre- and post-DT populations. In this mid-transitioncontext, wealthy women with higher levels of educational capital—a form ofstatus that itself emerges and becomes important during economicdevelopment—have fewer children, whereas wealthy women with lower educationalcapital have more children. These are not simply differences in fertility outcomesbetween high- and low-status individuals, but imply different reproductivestrategies, i.e. the use of resources to maximize different fitness currencies. These dWomen in the top quartile of educational capital were neither more likely to postponethe start of their reproductive careers, nor more materially wealthy, than theircounterparts in lower quartiles, either across communities or as a whole. Sofertility reduction does not appear to be an unambiguous strategy for acquiringmaterial advantages early in life, although women in the top quartile of educationalcapital do exhibit less variance in wealth. Rather, fertility reduction appearsdriven by reproductive choices after the onset of childbearing. This may have moreto do with changing preferences over the course of the reproductive career than withfundamental energetic trade-offs at the onset of reproduction: it may also berelated to time constraints owing to workforce participation at later life stages.These results contrast with recent evidence that low fertility, high educated womenhave wealth advantages , andthaIn line with macro-level and some historical evidence ,14,39, fUntil recently, this largely self-sufficient economy was reliant on cooperative farmwork and childcare ,62. With


Literature is limited on the effects of high prevalence HIV on fertility in the absence of treatment, and the effects of the introduction of sustained access to antiretroviral therapy (ART) on fertility. We summarize fertility patterns in rural northeast South Africa over 21 years during dynamic social and epidemiological change.We use data for females aged 15–49 from the Agincourt health and socio-demographic surveillance system (1993–2013). We use discrete time event history analysis to summarize patterns in the probability of any birth.Overall fertility declined in 2001–2003, increased in 2004–2011, and then declined in 2012–2013. South Africans showed a similar pattern. Mozambicans showed a different pattern, with strong declines prior to 2003 before stalling during 2004–2007, and then continued fertility decline afterwards. There was an inverse gradient between fertility levels and household socioeconomic status. The gradient did not vary by time or nationality.The fertility transition in rural South Africa shows a pattern of decline until the height of the HIV/AIDS pandemic, with a resulting stall until further decline in the context of ART rollout. Fertility patterns are not homogenous among groups. By the 1990s, South Africa was well advanced in its transition to low fertility, having halved fertility between the 1960s and 1990s. Up until the 1980s, fertility decline was gradual , while iSocioeconomic development and household socioeconomic status has long been hypothesized to contribute to fertility decline though evidence in sub-Saharan Africa (SSA) has been inconsistent, and it has been difficult to identify levels of socioeconomic development or affluence that are associated with initiation of decline or that trigger stalls in fertility decline –8. At thHIV has been theorized to lower fertility through several biological and social mechanisms; for example: reduced coital frequency; sexually transmitted infection (STI) co-infection; delayed onset of sexual debut and/or first union; reduced pre-marital sex and re-marriage; increased spousal separation and marriage dissolution; increased condom use; increased post-partum amenorrhea; reduced pregnancy rates and increased fetal loss; and reduced sperm production. HIV has also been theorized to increase fertility through other mechanisms, such as reduced breastfeeding, reduced post-partum abstinence, and child replacement due to increased infant mortality . EvidencEarlier work on the effects of HIV on fertility in South Africa have stressed the important role of antiretroviral treatment (ART) roll-out in shaping both the HIV epidemic and fertility . QualitaWe aim to summarize overall patterns in fertility in rural South Africa over 21 years during dynamic social and epidemiological change, including the emergence of the HIV pandemic and the rollout of ART. We use a longitudinal, robust population surveillance dataset and analyze trends using a unified, statistical framework that permits detailed hypothesis testing of differences and incorporation of uncertainty in our estimates. Finally, we examine variation in fertility patterns according to social groups, including nationality and household socioeconomic status.This study will further the literature on fertility in SSA in several ways. First, the long period of follow-up allows for the analysis of trends over a period of two decades, shedding light on how immigrant fertility assimilates to host population fertility, the robustness of SES effects on fertility over time, the effects of high prevalence HIV on fertility in the absence of treatment, and the effects of the introduction of sustained access to ART on fertility in a high HIV prevalence setting. Literature investigating these relationships has largely been limited to short periods of follow-up or cross-sectional analyses and thus unable to observe how these effects behave over time within a community. Second, previous methods used from Agincourt on fertility were direct calculation of rates (or decomposition of those rates) and did not supply confidence intervals , 5. UsinWe use household data collected from 1993–2013 by the Agincourt Health and Demographic Surveillance System (AHDSS) that has been tracking the population living in the Agincourt subdistrict of Bushbuckridge, Mpumalanga province, South Africa . AnnuallOver 30 % of the population comprises Mozambican immigrants, formerly refugees who entered the area during/following the Mozambican civil war . We codeHousehold socioeconomic status (SES) has been measured biannually since 2001 using a validated, 34-item asset index . We calcWe categorize time periods to both detect change along the dimensions of our statistical models and contextualize the dynamics of the HIV epidemic in the study population over time. Most time periods include 4-year intervals to simultaneously capture temporal change as well as sufficient births in each cell. Similarly, we categorize female ages of 15–49 years into standard 5-year age-groups to both detect discrete change as well as to facilitate comparisons with other demographic studies.As SES was measured biannually since 2001, we impute missing values from 2001–2013 using partial mean matching (based on nearest two neighbors) with five imputations. Because of the multilevel nature of the data, we follow the recommendations of Gelman, Hill  and deriWe model fertility using discrete time event history analysis for repeating events . We organ = 285) and are not presented. All analyses were completed using Stata 13 [We also test a model of multiple births using multilevel, multinomial logistic regression including a random intercept for the individual and covariates of age and time period. Given the rarity of triplets, we categorize the outcome into no birth, single, and multiple births. The predictions from the multiple birth outcomes were unstable  was reviewed and approved by the Committee for Research on Human Subjects  of the University of the Witwatersrand (protocol M960720 and M081145). A record of participant consent is kept of the household respondent who consented to the interview.Table Figure p < 0.001) and resulted in the final model. Estimation results are presented in model M1 . The probability of birth then increased in 2004–2007 relative to 2001–2003 (ages 25–29 p = 0.0170) but remained lower than the earliest time period (ages 25–29 p = 0.0480). The probability of birth decreased in the latest time period relative to 2008–2011 (ages 25–29 p = 0.002). The peak age also shifted in the last time period from ages 25–29 to ages 20–24 (p = 0.047).The odds ratios in model M1 describe the changes in a female’s probability of birth as a function of sex and age. Figure p < 0.001), and nationality and time (p < 0.001) significantly improved model fit and resulted in the final model. Estimation results are presented in model M2  and ages 40–49 .For South Africans, in Fig. For Mozambicans, in Fig. p = 0.604), and SES and time (p = 0.979) were not significant. Estimation results are presented in model M3 (Table p < 0.001) and between the high compared to the middle SES tertiles (p < 0 .001).To examine variations in fertility across household socioeconomic status, we estimated the model M1 (from Table p = 0.836). Estimation results are presented in model M4 (Table p = 0.003) and between the high compared to the middle SES tertiles (p = 0.003).Finally, we jointly modeled nationality and SES using the imputed SES values and restricted to years 2001–2013. We estimated the model M2 (from Table Several factors may explain these fertility trends, such as the HIV epidemic at its peak in 2004–2007. This may suggest that women were practicing replacement with elevated child mortality. Additionally, ART became available starting in 2007 and more widely in 2009 , which mThe stall in fertility decline in this population from 2004–2007 has been documented by Ibisomi et al.  who founThe probability of a live birth declined with higher levels of SES, which is consistent with findings from other studies in sub-Saharan Africa using similar wealth indices. Adebowale et al.  suggestsWe acknowledge several strengths and limitations. We used a robust dataset with careful tracking of vital events over a long period of time. The site uses extensive training and quality control procedures to ensure high-quality data . BecauseOur findings of overall declining fertility in 2013 raise the importance of family planning issues. Since transmission of HIV remains a major concern, notably among adolescents and young adults, it is increasingly important to ensure effective discussion and provision of family planning in the course of antenatal attendances and after delivery. Access to sound and accessible family planning advice, linked with personal protection against HIV infection, are vital and support the importance of integrated family planning and reproductive health services, especially given the growing numbers on long-term ART . ParticuWe summarized trends in fertility over time and age, and examined differences in fertility patterns by social groups. Overall fertility declined in 2001–2003, increased in 2004–2011, and then declined in 2012–2013. South Africans showed a similar pattern to the overall trends. Mozambicans showed a different pattern, with strong declines prior to 2003 before stalling during 2004–2007, and then continued fertility decline afterwards. There was an inverse gradient between fertility levels and household SES, with those in lower SES tertiles having higher levels of fertility. The gradient did not vary by time or nationality. Overall at the population level, the fertility transition in rural South Africa shows a pattern of decline until the height of the HIV/AIDS pandemic, with a resulting stall until further decline in the context of ART rollout. This study provides new insights about how current fertility behaviors are affected by the evolving HIV epidemic and continued availability of ART over a long period of follow-up, and how fertility behaviors differ between South African and Mozambican populations living in the study area.


S-glutathionylation, the covalent attachment of a glutathione (GSH) to the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-glutathionylation remains unknown. Based on a total of 1783 experimentally identified S-glutathionylation sites from mouse macrophages, this work presents an informatics investigation on S-glutathionylation sites including structural factors such as the flanking amino acids composition and the accessible surface area (ASA). TwoSampleLogo presents that positively charged amino acids flanking the S-glutathionylated cysteine may influence the formation of S-glutathionylation in closed three-dimensional environment. A statistical method is further applied to iteratively detect the conserved substrate motifs with statistical significance. Support vector machine (SVM) is then applied to generate predictive model considering the substrate motifs. According to five-fold cross-validation, the SVMs trained with substrate motifs could achieve an enhanced sensitivity, specificity, and accuracy, and provides a promising performance in an independent test set. The effectiveness of the proposed method is demonstrated by the correct identification of previously reported S-glutathionylation sites of mouse thioredoxin (TXN) and human protein tyrosine phosphatase 1b (PTP1B). Finally, the constructed models are adopted to implement an effective web-based tool, named GSHSite (http://csb.cse.yzu.edu.tw/GSHSite/), for identifying uncharacterized GSH substrate sites on the protein sequences. S-glutathionylation is a redox-dependent PTM that involves the covalent attachment of glutathione (GSH) to the thiol group of cysteine residues and regulates by physiological GSH level and GSH/GSSG ratio through spontaneous or enzymatic reaction [S-glutathionylation also serves to modulate cancer migration, cell death and survival, energy metabolism and glycolysis, as well as protein folding and degradation from bacteria to human [S-glutathionylation also participate in pathogenesis of many diseases, such as neurodegenerative diseases, metabolic disorders and cancers [in vivo S-glutathionylation, the detail characteristics and mechanisms of S-glutathionylation still await to be clarified. To our knowledge, the protein database of human, mouse, or rat possess only consist of approximate 2% cysteine residues. However, only cysteine residue containing lower pKa in a basic environment perhaps in close three-dimensional proximity to Arg, Lys, or His residues is more accessible by GSH modification [reaction –5. In adto human . The var cancers ,7. Due tfication .S-glutathionylomics using various biological systems to investigate and identify more than thousands of S-glutathionylated targets and sites [S-glutathionylated peptides, a curated database is of urgent need to facilitate further biological investigation of S-glutathionylated proteins and the substrate specificities of S-glutathionylation sites. Bioinformatic approaches are powerful tools for prediction of the susceptibility of individual cysteine residues to S-glutathionylation. Although several algorithms and public servers have been developed to analyze and predict the reactive state of cysteine [S-glutathionylation targets and sites is reported. Sun et al. have computationally identified S-glutathionylation motifs by functional annotation and S-glutathionylation sites prediction by collecting 43 experimentally S-glutathionylated proteins and 227 corresponding sites [S-glutathionylation motifs and substrate site specificities remains unclear.To date, numerous methods have been directed toward mass spectrometry-based nd sites –11. As fcysteine ,13 and ocysteine ,15, few ng sites . Other pS-glutathionylation motifs in primary amino acid sequence, the in silico characterization, i.e. amino acid composition (AAC) and accessible surface area (ASA), of protein S-glutathionylation sites is applied to distinguish the S-glutathionylation sites versus non-S-glutathionylation sites. In this study, we anticipate to characterize the S-glutathionylation sites with the consideration of substrate specificity of GSH. This study presents a statistical method for identifying S-glutathionylation sites and potential consensus motifs by maximal dependence decomposition (MDD) [S-glutathionylation data from published database (independent set) are used to test the effectiveness of the models in cross-validation. To facilitate the study of protein S-glutathionylation, the identified substrate motifs were exploited to implement a web-based resource for identifying S-glutathionylation sites with potential motifs.To further investigate potential on (MDD) . With thS-glutathionylomic data, the experimentally verified S-glutathionylated cysteines from mouse macrophages [S-glutathionylated cysteines on these S-glutathionylated proteins were used as the negative data. As shown in S-glutathionylated proteins were obtained. In order to avoid a biased prediction performance for a binary classification between positive and negative data, the negative training data was balanced with the positive training data. A K-means clustering method based on sequence identity [K, which denoted the number of samples obtained from the negative set. This resulted in an equal number of positive and negative sequence fragments from the training data , while other 62 non-S-glutathionylated cysteines were used as the negative data set. This study focused on the sequence-based analysis of substrate specificity of cysteine S-glutathionylation. The ability to distinguish the S-glutathionylated cysteine from the non-S-glutathionylated cysteine of the identified motifs would be evaluated based on cross-validation.For independent testing set, the experimentally verified  RedoxDB  and SGDB RedoxDB . A totalS-glutathionylated protein sequences with more than 30% identity were defined as homologous sequences. Two homologous sequences were specified to re-align the fragment sequences using a window length of 2n+1, centered on the S-glutathionylation sites using BL2SEQ [S-glutathionylation site on homologue fragment sequence in training set was kept while the other in testing set was discarded. Redundancy was removed by retaining only one record in the event of finding multiple records of the same site position and accession number. The non-redundant negative data were generated using the same approach as positive one. After the removal of redundant data, 254 positive sequence fragments and 1054 negative sequence fragments with cysteine residues were obtained for independent testing.After the cross-validation of training set, the model with highest accuracy was further evaluated by using an independent test set. However, the positive data of independent test set may include the sequences that were homologous to training data. As for classification, the prediction performance of the trained models may be overestimated owing to the over-fitting of a training set. To this, the homologous sequences between training set and independent test set were removed. With reference to the reduction of the homology of the training set in MASA , two S-gg BL2SEQ . For twoaccessible surface areas (ASA) around the S-glutathionylation sites were also investigated. Amino acid sequences with a cysteine in the center were individually extracted from positive and negative training sets using a window of length 2n+1 centered on substrate sites, where n was set to ten in this study. An orthogonal binary coding scheme was adopted to transform amino acids into numeric vectors, in the so-called 20-dimensional binary coding. For example, glycine was encoded as “10000000000000000000;” alanine was encoded as “01000000000000000000,” and so on. The number of feature vectors represented the flanking amino acids surrounding the S-glutathionylation site was (2n+1) × 20. A total of p vectors {xi, i = 1,. . ., p} were used to represent all p sequence fragments in the training data. Each vector in positive or negative cysteines was labeled with the class of its corresponding protein (e.g. positive or negative). For the composition of 20 amino acids surrounding the S-glutathionylation sites, the vector xi had 20 elements for the amino acid composition (AAC) and 441 elements for the amino acid pair composition (AAPC). The 20 elements were defined as the occurrence frequencies of 20 amino acids in a sequence fragment, and the 400 elements were defined as the occurrence frequencies of 400 amino acid pairs in a sequence fragment. When the fragment sequences at N- or C-terminus are less than 21-mer, non-existing residues were filled with “X” in the corresponding position. A total of 21 types of amino acids and 441 types of amino acid pairs were presented in our setting. Using the BLOcks SUbstitution Matrix (BLOSUM62) matrix [Aside from the composition of flanking amino acids (AA), the ) matrix , the givS-glutathionylated cysteines was determined using non-homologous training data. The PWM specified the relative frequency of amino acids that surrounded the S-glutathionylation sites, and was utilized in encoding the fragment sequences. A matrix of m × w elements was used to represent each residue of a training dataset, where w stands for the window size and m consists of 21 elements including 20 types of amino acids and one for terminal signal. In addition, WebLogo [S-glutathionylation sites.Refer to the method of SulfoSite , the pos WebLogo ,25 was aS-glutathionylation sites. The matrix of (2n+1)×20 elements had rows centered on substrate site, extracted from the PSSM profile, where 2n+1 represented the window size and 20 represented the position specific scores for each type of amino acid. After that, the (2n+1)×20 matrix was transformed into a 20×20 matrix by summing up the rows that were associated with the same type of amino acid. Finally, every element in 20×20 matrix was divided by the window length 2n+1 and then normalized using the formula: In the viewpoint of structural environment, several amino acid residues of a protein can be mutated without changing its structure, and two proteins may have similar structures with different amino acid compositions. Position Specific Scoring Matrix (PSSM) profiles, which have been extensively utilized in protein secondary structure prediction, subcellular localization and other bioinformatics problems ,26–28, aaccessible surface area (ASA) was used to evaluate the characteristics of S-glutathionylation sites. Since most of the experimental S-glutathionylated proteins did not have corresponding protein tertiary structures in PDB [S-glutathionylation sites were inputted to RVP-Net to compute the ASA value of all of the residues. The ASA values of amino acids around the S-glutathionylation sites were extracted and normalized to be between zero and one.A side-chain of amino acid that undergoes post-translational modification prefers to be accessible on the surface of a protein . Thus, ts in PDB , an effes in PDB ,33, was S-glutathionylation sites based on the amino acid sequences. Due to the difficulty of detecting the conserved motifs for the sequence data with a larger size, this work applied maximal dependence decomposition (MDD) [S-glutathionylation site into subgroups, which had statistically obvious motifs. MDDLogo has been reported that the grouping of protein sequences into smaller groups is prior to computationally identifying PTM sites [n+1. Since n is set to 10, the position Ai is defined from the range of -10 to +10. Then, a contingency table of the amino acids occurrence between two positions Ai and Aj is generated. In order to extract the motifs that had conserved biochemical property of amino acids, the 20 types of amino acids were categorized into five groups, including polar, acidic, basic, hydrophobic, and aromatic groups  to evaluate the dependence of amino acid occurrence between two positions Ai and Aj surrounding the GSH sites. The chi-square test was defined as:mn represented the number of sequences that had the amino acids of group m in position Ai and had the amino acids of group n in position Aj, for each pair  with i≠j. Emn was calculated as mR = Xm1+ …+Xm5, XCn = X1n+ …+X5n, and X denoted the total number of sequences. If a strong dependence were detected  between two positions, it will be proceeded as described by Burge and Karlin [Ai, Score(Ai), is calculated as follows:Ai with a maximal dependence value, Score(Ai), is applied to cluster sequences. When applying MDD to cluster sequences, a parameter, i.e., the maximum-cluster-size, should be set. If the size of a subgroup is less than the cutoff value of maximum-cluster-size, the subgroup will not be divided any further. The MDD process terminates after all of the subgroup sizes are less than the value of the specified maximum-cluster-size.The aim of this study was to investigate the motifs of on (MDD)  to clustTM sites –40. As ic groups . Next, Md Karlin . A depenS-glutathionylation.MDD clustering is a recursive process that divides all sequences into tree-like subgroups. After the detection of maximal dependence of flanking positions, as the example illustrated in K = exp(−γ||Si − Sj||2) was used as the kernel function of the SVMs. The LIBSVM library could output a value of probability estimated ranging from 0 to 1 for each prediction. According to that, the values of probability estimated from each SVM classifier trained with the best feature corresponding to a specific motif were adopted to form an input vector for second-layered SVM.The support vector machine (SVM) was adopted to learn the predictive model from the positive and negative data of the training set. Based on binary classification, the concept behind SVM was to map the input samples into a higher dimensional space using a kernel function, followed by finding a hyper-plane that can discriminating the two classes with maximal margin and minimal error. A public SVM library, LIBSVM , was empPrior to the construction of a final model, the predictive performance of models using different features was evaluated by performing five-fold cross validation. As shown in S-glutathionylation, in this study, we focused on the sequence-based analysis of substrate specificity for S-glutathionylation. Here, a web-based tool TwoSampleLogo [S-glutathionylated cysteine, the non-homologous S-glutathionylated cysteines were centered on position 0, and the flanking amino acids (-10 ~ +10) were graphically visualized as sequence logos. Contrast between 1783 S-glutathionylation sites and 8423 non-S-glutathionylation sites, the TwoSampleLogo revealed that the most pronounced feature of S-glutathionylation sites was the abundance of charged amino acids, especially the positively charged Lysine (K) and Arginine (R), at positions -10 ~ -5, -3, +2, +3, and +7 ~ +10 , Phenylalanine (F), and Tryptophan (W), locating around non-S-glutathionylated cysteines at position -7, -6, -2, -1, +1, +3, +4, and +6, was depleted in the negative dataset . As shown in S-glutathionylated and non-S-glutathionylated cysteines, suggesting low preference of solvent accessibility in S-glutathionylation sites. Moreover, the adjacent amino acids neighboring the centered S-glutathionylation sites had relatively higher preference of solvent-accessible surface area than that of non-S-glutathionylation sites. The result suggested that the flanking amino acids having hydrophilic characteristics may regulate the S-glutathionylation on cysteine residues due to the relative surface solvent accessibility.In addition to the composition of amino acids in linear sequences, we further used RVP-Net algorithm to analyze the correlation of S-glutathionylation sites compared to the non-S-glutathionylation sites, the predictive models were trained with various features, such as 20D binary code, BLOSUM62, AAC, AAPC, ASA, PWM, and PSSM. Using cross validation, four predictive powers, including sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews correlation coefficient (MCC), were also evaluated. As shown in S-glutathionylation sites, the predictive sensitivity, specificity, accuracy, and MCC of the best model were 0.65, 0.66, 0.66, and 0.24, respectively. Additionally, the predictive power of the model trained with the hybrid combination of BLOSUM62 and AAPC provided the best performance of the predictive sensitivity, specificity, accuracy, and MCC at 0.66, 0.67, 0.67, and 0.26, respectively. Thus, BLOSUM62 combined with AAPC was selected as the training feature for the construction of two-layered SVM model.To determine what features provide the best performance to identify the S-glutathionylation data set, we further applied the maximal dependence decomposition (MDD) to cluster all 1783 identified S-glutathionylated peptide sequences. Here, 12 subgroups of S-glutathionylation motifs can be obtained from the most significant dependencies of amino acid composition between specific positions , which contains one S-glutathionylation site at cys-215 [S-glutathionylation site at position 215 was also correctly predicted by GSHSite.A mouse thioredoxin , which contains one t Cys-73 , is used cys-215  and is nS-nitrosylation and S-glutathionylation can target on the same cysteine residues of proteins, here, we further investigated the relationship between these two modifications. Based on the experimentally verified data set from dbSNO [S-glutathionylation sites and total 2159 S-nitrosylation sites  in case 1 study was categorized into C(X)7K/R/H motif . S-glutathionylation and S-nitrosylation contributed for translation and generation of precursor metabolites and energy. Moreover, more proteins involving in structural constituent of ribosome, structural molecule activity, and nucleotide binding were presented. In addition to the mitochondrial proteins, most proteins were located in cytosol and ribonucleoprotein complex. Similar biological processes and molecular functions were presented in only S-glutathionylated proteins  involved in oxidation reduction, 246 proteins (25%) played roles in nucleotide binding, and 333 (34%) proteins located in mitochondrion  for cross talk of 328 proteins . For thehondrion .S-glutathionylation substrate specificity from experimentally verified S-glutathionylomic data. The analysis of position-specific amino acids composition reveals that the most pronounced feature of S-glutathionylation sites is the abundance of positively charged amino acids at surrounding positions, especially on the positions from -5 to +3. This investigation also implicates that the distant amino acids in sequence (around position -7 and -6), which may be close to S-glutathionylation cysteines in three-dimensional structure, have notable difference between S-glutathionylation sites and non-S-glutathionylation sites. Moreover, the flanking amino acids around S-glutathionylation sites have higher preference of solvent-accessible surface area than that around non-S-glutathionylation sites. According to the five-fold cross-validation, the model trained with the combined features of BLOSUM62 and amino acid pair composition gets the highest sensitivity, specificity, accuracy, and MCC.In this study, we reported a systematic informatics investigation on the S-glutathionylation sites based on the amino acid sequences. However, it is difficult to explore the conserved motifs from large-scale S-glutathionylome data set. Thus, this work applies MDDLogo algorithm to cluster all sequences of S-glutathionylation site into 12 subgroups. According to the chi-square test of the dependence in flanking positions, surprisingly, all of the MDD-clustered subgroups have the conserved motifs of positively charged amino acids  at a specific position. Particularly, subgroups GSH2 have the conserved motifs of positively charged amino acids at two specific positions (-6 and +8). Although the newly identified motifs could not be experimentally verified, it still worthy to be noticed that MDD clustering can help the biologist investigating the potential substrate motifs of S-glutathionylation sites. More noteworthy is that the MDD-clustered motifs can be applied to improve the predictive power of computationally identifying S-glutathionylation sites with various substrate specificities. According to the evaluation of five-fold cross-validation, the models trained with combined MDD-clustered motifs are increased for the predictive accuracy of 0.71, comparing to the model trained without MDD clustering. This analysis indicates that the S-glutathionylated sequences with a larger size can be alternatively clustered by MDD method in order to enhance the signal of amino acids motif and improve the performance of the predictive model.Due to the abundance of experimental data, this study focuses on investigating the motifs of S-glutathionylation data is needed to re-calibrate more accurate MDD-clustered motifs. The proposed method can be improved by considering the motifs that are intrinsically included in the test data. Consequently, the models with MDD clustering method are applied to implement a novel web-based tool, named GSHSite, for identifying cysteine S-glutathionylation. Correct prediction on two experimentally verified S-glutathionylated proteins demonstrated the effectiveness of GSHSite. In this web-based tool, the detail information, annotation, and 3D structure provided from PDB of proteins are also included. This approach not only provides the prediction yet experimental S-glutathionylation site information, but also can be used to explore the potential substrate specificity of S-glutathionylation.Finally, the independent testing indicates that the predictive model by MDDLogo-clustered SVMs can generate the best performance compared with the single SVM model. The acquisition of additional experimentally verified http://csb.cse.yzu.edu.tw/GSHSite/. All of the data set used in this work is also available for download in the website.The proposed method is implemented as a web-based resource, which is now freely available to all interested users at S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigS-glutathionylation and S-nitrosylation in upper panel were compared with un-modified cysteines in lower panel (p < 0.01). (B) The significant amino acids around S-glutathionylated cysteine residue were enriched from the positive dataset and presented in upper panel (p < 0.01). Relatively, the high frequency of amino acids around S-nitrosylated cysteines were depleted from the negative dataset and presented in lower panel.(A) The identically common cysteines for (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.


The aim of our study was to investigate whether the Somnolter® is useful in family practice to identify patients with OSAHS.Obstructive sleep apnea and hypopnea syndrome (OSAHS) is a disorder that causes clinical symptoms  that may increase the risk of traffic accidents, cardiovascular disease, type 2 diabetes and reduce the quality of life. A recently developed device  practices.® measuring SaO2, mandibular movements, body position, heart rate, nasal air flow and thoracic and abdominal breathing movements. The Somnolter® automatically analyses the data and different parameters to detect OSAHS. Afterwards, the data were manually revised by the researchers.Based on the questionnaire and body mass index, patients with an increased risk of OSAHS were contacted to collaborate in the study. In this convenience sample, 18 patients were successfully tested with the SomnolterOut of 365 subjects, 31 met the inclusion criteria and 18 were successfully tested at home. Sixteen out of 18 patients had an Apnea Hypopnea Index (AHI) ≥ 5, ten of them had mild OSAHS, 3 were categorized as moderate OSAHS and finally 3 matched the criteria of severe OSAHS.®. In the future the Somnolter® might be a feasible alternative to diagnose OSAHS.The proposed case-finding strategy still needs optimization, but is considered helpful in selecting patients at high risk of OSAHS. OSAHS was detected in 14 out of 18 patients tested with the SomnolterThe online version of this article (doi:10.1186/1756-0500-7-616) contains supplementary material, which is available to authorized users. Sleep apnea is a pathological phenomenon which can result in clinical symptoms, e.g. daytime fatigue, excessive sleepiness, impaired concentration, depression. The combination of symptoms and proven breathing abnormalities during polysomnography (PSG) is historically framed in more or less distinguished syndromes. The obstructive sleep apnea syndrome (OSAS) is originally described by Gulleminault in 1976].].Append


Notch1 and Jagged1. Thus, we examined the molecular and biological links between estrogen and the Notch signaling in postmenopausal osteoporosis in vitro. hBMSCs were obtained from healthy women and patients with postmenopausal osteoporosis. Notch signaling molecules were quantified using real-time polymerase chain reaction  and Western Blot. Luciferase reporter constructs with putative EREs were transfected into hBMSCs and analyzed. hBMSCs were transduced with lentiviral vectors containing human Notch1 intracellular domain (NICD1). We also used N-[N--l-alanyl]-(S)-phenylglycine t-butyl ester, a γ-secretase inhibitor, to suppress the Notch signaling. We found that estrogen enhanced the Notch signaling in hBMSCs by promoting the expression of Jagged1. hBMSCs cultured with estrogen resulted in the up-regulation of Notch signaling and increased proliferation and differentiation. Enhanced Notch signaling could enhance the proliferation and differentiation of hBMSCs from patients with postmenopausal osteoporosis (OP-hBMSCs). Our results demonstrated that estrogen preserved bone mass partly by activating the Notch signaling. Because long-term ERT has been associated with several side effects, the Notch signaling could be a potential target for treating postmenopausal osteoporosis.Estrogen deficiency is the main reason of bone loss, leading to postmenopausal osteoporosis, and estrogen replacement therapy (ERT) has been demonstrated to protect bone loss efficiently. Notch signaling controls proliferation and differentiation of bone marrow-derived mesenchymal stem cells (BMSCs). Moreover, imperfect estrogen-responsive elements (EREs) were found in the 5′-untranslated region of The online version of this article (doi:10.1007/s11010-014-2021-7) contains supplementary material, which is available to authorized users. Osteoporosis is a systematic skeletal disorder characterized by decreasing bone mineral density (BMD) and deteriorating bone structure due to imbalanced bone remodeling . FracturNotch1 was blocked by mNotch-1 shRNA, the expressive level of several neuron-specific markers was increased, suggestive of an essential role of Notch signaling in the differentiation of BMSCs into neurons in vitro [The Notch signaling pathway is highly conserved and associated with cell-fate determination, self-renewal potential, and apoptosis . Notch sin vitro . Other sin vitro  also shoin vitro , and it in vitro . WhetherThe aim of this study was to determine the molecular and biological links between estrogen and Notch signaling in the proliferation and differentiation of hBMSCs. In addition, we also investigated whether activation of the Notch signaling pathway could improve the proliferation and differentiation of OP-hBMSCs.Control 30.75 ± 2.22) and four patients with postmenopausal osteoporosis (OP 71.5 ± 2.38) after informed consent to the research protocol. Detailed information regarding the hBMSC donors is provided in Supplementary Table 1. Ethical approval was obtained from the ethics committee of the Fourth Military Medical University for this procedure (20110405-5). hBMSCs were harvested in a sterile environment and cultured as described previously [Primary hBMSCs were obtained from bone marrow aspirates of 4 healthy women (osis OP 7.5 ± 2.385 cells) harvested from healthy subjects grew to 80 % confluency in 24-well plates, they were transfected with a luciferase reporter plasmid containing different ERE fragments including Notch1-a , Notch1-b (−339 to 327 bp), Jagged1-a (−1339 to 1327 bp), Jagged1-b (−1627 to 1615 bp), Jagged1-c (−50 to 38 bp) EREs, or an empty vector with lipofectamine 2000. After 24 h, 10−7 M 17β-estradiol was added to the growth medium. After a 24 h incubation with 17β-estradiol, luciferase values were detected using the Dual-Glo® Luciferase Assay System . All procedures were performed according the instructions of the manufacturer.When hBMSCs . Transgenes were amplified from a human complementary DNA library  and directionally inserted into the GV205 vector, which was purchased from GeneChem, Shanghai. An Ubi-MCS-3FLAG internal site fragment was added to check the transfection efficiency. An empty vector was used as a negative control. Virus vector particles were obtained by transiently transfecting 293 T cells with transfer vector and packaging plasmids as previously described [Lentiviral transfer vectors were created with the human escribed –16. PrimAll experiments were performed repeatedly in three times. The data are expressed as the mean ± SD, and samples were evaluated by the ANOVA test using SPSS 16.0.In our previous studies, the expression level of key Notch signaling molecules was compared in hBMSCs between patients with postmenopausal osteoporosis and healthy women by real-time PCR. Our results showed that Notch signaling was impaired in OP-hBMSCs .Runx2, ALP, and Osterix by real-time PCR . On days 3 after osteogenic induction, the 17β-estradiol-treated OP-hBMSCs significantly expressed an elevated level of Runx2 and Osterix. Furthermore, the addition of ICI-182780  almost completely blocked the enhancement in osteoblastic differentiation that was induced by 17β-estradiol . Therefore, our findings demonstrated that 17β-estradiol enhanced osteoblastic differentiation of OP-hBMSCs.To test the relationship between estrogen and osteoblastic differentiation of OP-hBMSCs, we exposured OP-hBMSCs to an osteogenic induction medium containing 17β-estradiol and detected the expression of the osteoblastic markers Notch1 and Jagged1 were found to be significantly up-regulated. Hes1, which is known as a target of canonical Notch signaling, also showed more than threefold increase of its expression level. Furthermore, the addition of ICI-182780 almost completely blocked the increase in Notch signaling that was induced by 17β-estradiol , which is much greater than that of the other ERE fragments. This result indicated that 17β-estradiol activated Notch signaling mainly through the activation of the Jagged1-a ERE. Together, these findings demonstrated that 17β-estradiol promotes ligand-induced Notch signaling in hBMSCs.To determine the potential molecular link between Notch signaling and estrogen, we examined the binding affinity of estradiol to several EREs in the 5′-flanking region of the To elucidate whether 17β-estradiol could promote the proliferation and osteoblastic differentiation of hBMSCs partially by activating the Notch signaling pathway, we used DAPT, a Notch signaling antagonist, to block Notch signaling in hBMSCs. First, we found that 17β-estradiol significantly increased the number of hBMSCs, while DAPT addition reversed the elevated number of hBMSCs Fig. a. The coRunx2 and Osterix by real-time PCR  in OP-hBMSCs. The lentiviral transduction of OP-hBMSCs was confirmed by detecting NICD1 expression by real-time PCR and FLAG expression by Western Blotting . An empty lentiviral vector was used as a negative control.To further address whether Notch signaling mediates the enhanced proliferation and osteoblastic differentiation of hBMSCs by estradiol, we used a lentiviral vector system to efficiently overexpress ing Fig. a, b. In PCR Fig. a. By enfNICD1-transduced OP-hBMSCs appeared as typical clusters of spindle-shaped cells, compared with the fibroblast-like OP-hBMSCs in the control group. To elucidate whether Notch signaling could rescue the defective proliferation of OP-hBMSCs, the cell proliferative rate was evaluated by a growth curve and colony forming assay. . This finding stated that Jagged1 expression was enhanced by 17β-estradiol, and estrogen enhanced Notch downstream signaling in a ligand-dependent manner. The results are consistent with a study by Soares [The molecular link between estrogen and Notch signaling has been detected in breast cancer and endothelial cells , 13 but Runx2 and Osterix have crucial roles in osteogenesis [Hes1 cooperated with Runx2 to stimulate the Osteopontin or Osteocalcin promoters and then enhance osteogenesis. Osterix is downstream of Runx2 and has been shown to have a major role in the later stage of osteogenesis [Runx2 and Osterix of NICD-transduced OP-hBMSCs, evaluated by real-time PCR, indicating the activation of Notch signaling improved the proliferation and enhanced osteoblastic differentiation of OP-hBMSCs.It is well known that ogenesis , 20. Qi ogenesis  also demNotch signaling regulates osteoblastic differentiation in a cell type- and cell stage-dependent manner , 23. PreBased on our data, we demonstrated that estrogen regulated the differentiation and proliferation of hBMSCs partially by activating the Notch signaling pathway. An optimal utilization of Notch signaling could effectively improve bone mass of postmenopausal osteoporosis. The Notch signaling pathway could be a potential strategy for treating postmenopausal osteoporosis in vivo. However, Notch signaling also contributes to cancer and tumor angiogenesis. Therefore, balancing the efficacy to treat postmenopausal osteoporosis and Notch signaling side effects must be considered for future clinical applications. In the future, we hope to develop a Notch activator only targeting and promoting osteogenic differentiation, and to maintain bone mass. Meanwhile, it could be applied to some fragile bones which are easier to fracture, so as to prevent fractures and improve the quality of life of postmenopausal women.Supplementary material 1 (DOCX 1041 kb)Below is the link to the electronic supplementary material.


Human T-lymphotropic virus types 1 and 2 (HTLV-1 and –2) are prevalent at low-level among United States blood donors, but recent data on their prevalence is lacking.Data on all first-time blood donors in a large network of United States blood centers was examined during the period 2000-2009. Anti-HTLV-1 and -2 was measured by enzyme immunoassay (EIA) screening with type-specific confirmation by immunofluorescence or RIBA. Prevalence and odds ratios (OR) and 95% confidence intervals (CI) for associations with demographic characteristics were assessed using multivariable logistic regression.Among 2,047,740 first-time donors, 104 donors were seropositive for HTLV-I  per 100,000) and 300 donors were seropositive for HTLV-2 infection  per 100,000). Prevalence was lower than reported in the 1990’s but stable from 2000 to 2009. HTLV-1 seropositivity was associated with female sex ; older age; and Black  and Asian  race/ethnicity. HTLV-2 seropositivity was associated with female sex ; older age; and non-white race/ethnicity; residence in the Western  and Southwestern  U.S.; and lower educational level.HTLV-1 and -2 prevalences among U.S. blood donors declined since the early 1990’s but were stable since 2000. Higher prevalence of HTLV-2 in the West and Southwest may be attributed to endemic foci among Amerindians.


Chemotaxis, a directional cell migration guided by extracellular chemoattractant gradients, plays an essential role in the recruitment of neutrophils to sites of inflammation. Chemotaxis is mediated by the G protein-coupled receptor (GPCR) signaling pathway. Extracellular stimuli trigger activation of the PLC/PKC/PKD signaling axis, which controls several signaling pathways. Here, we concentrate on the novel functions of PLC/PKC/PKD signaling in GPCR-mediated chemotaxis of neutrophils. Not onlα subunit from the Gβγ dimer . Th. Thβ/PI3activity . Taken t2 or PIP2) into phosphatidylinositol-triphosphate P3 or PIP3) and the phosphatase and tensin homolog (PTEN) converts PIP3 back to PIP2. Leading-edge localization of PI3K and trailing-edge localization of PTEN are key features of gradient sensing and polarization and are essential requirements for chemotaxis in neutrophils and Dictyostelium discoideum [α subunit of PI3K to enhance its interaction with PTEN, leading to polarized PTEN activity and thereby regulating neutrophil migration [ D. discoideum and neutrophils. The C2 domain of PTEN is required but not sufficient to recruit D. discoideum PTEN to the plasma membrane [ D. discoideum cells [The PLC/PKC/PKD1 signaling pathway might also regulate the localization and functions of other key components involved in neutrophil chemotaxis. PI3Ks phosphorylate phosphatidylinositol-biphosphate Pscoideum , 63–65. igration . Moreovemembrane . Li et amembrane . Recentlum cells . One cluum cells , 68. It um cells . It is oβ, PLCγ, and PLCε in GPCR-mediated neutrophil chemotaxis, specifically PLC isoform-specific activation and the function of downstream effectors such as PKCs.In this review, we strove to summarize recent findings regarding novel functions of the classic PLC/PKC/PKD signaling axis in neutrophil chemotaxis. Future research should focus on revealing isoform-specific functions of PLC


Dizziness is a common complaint in HAM/TSP and can occur due to vestibulospinal tract dysfunction. This tract can be assessed through Vestibular Evoked Myogenic Potential (VEMP). The aim was to correlate the result of VEMP generated by acoustic stimuli and dizziness in individuals with HTLV-1-asymptomatic infection and HAM/TSP. VEMP was recorded from sternocleidomastoid muscle of 60 HTLV-1-negative adults (56 ± 5 years) and 60 individuals infected with HTLV-1, being 30 asymptomatic (56 ± 8 years) and 30 with HAM/TSP (59 ± 8 years). In all groups, 90% of the participants were women. The acoustic stimuli were short tone bursts , stimulation rate of 5 Hz and the analysis time for each response was 60 ms; 200 responses were averaged for each run. The electromyographic signals were amplified and band-pass filtered between 10 and 1.5 KHz. Of 60 HTLV-1-negative individuals, 14(23%) reported dizziness; VEMP was normal in all. In the HTLV-1-asymptomatic group, 11(37%) complained of dizziness (P=0.22); VEMP was altered in 4(40%) subjects with dizziness and in 1(5%) without dizziness (P=0.05). In the group with HAM/TSP, dizziness was reported by 17(57%) subjects (P=0.002); VEMP was altered in 11(64%) with dizziness and in 5(38%) without dizziness (P=0.15). Damage of vestibulospinal tract seems to occur in the early stages of HAM/TSP. VEMP was previously shown to be a useful test for the follow-up of asymptomatic carriers. Dizziness without an apparent cause in HTLV-1-asymptomatic carriers deserve investigation about a possible spinal cord involvement.


Plasmodium falciparum DBL family of erythrocyte binding proteins, which are considered as prospective candidates for malaria vaccine development. The EBA-140 ligand is a paralogue of the well-characterized P. falciparum EBA-175 protein. They share homology of domain structure, including Region II, which consists of two homologous F1 and F2 domains and is responsible for ligand-erythrocyte receptor interaction during invasion. In this report we describe, for the first time, the glycophorin C specificity of the recombinant, baculovirus-expressed binding region (Region II) of P. falciparum EBA-140 ligand. It was found that the recombinant EBA-140 Region II binds to the endogenous and recombinant glycophorin C, but does not bind to Gerbich-type glycophorin C, neither normal nor recombinant, which lacks amino acid residues 36–63 of its polypeptide chain. Our results emphasize the crucial role of this glycophorin C region in EBA-140 ligand binding. Moreover, the EBA-140 Region II did not bind either to glycophorin D, the truncated form of glycophorin C lacking the N-glycan or to desialylated GPC. These results draw attention to the role of glycophorin C glycans in EBA-140 binding. The full identification of the EBA-140 binding site on glycophorin C molecule, consisting most likely of its glycans and peptide backbone, may help to design therapeutics or vaccines that target the erythrocyte binding merozoite ligands.The erythrocyte binding ligand 140 (EBA-140) is a member of the  Plasmodium merozoites, are involved .Flow cytometry analysis. The recombinant Region II (4 ug) was incubated with 3x105 native or treated with trypsin (Sigma-Aldrich) or neuraminidase  human normal or Gerbich-negative erythrocytes in phosphate buffered saline (PBS), pH 7.4 for 2 h at 4°C. Alternatively, recombinant Region II (4 ug) was incubated with 1x106 CHO cells [HO cells  expressiHO cells . Then, aImmunoblotting (overlay assay). The proteins of erythrocyte membranes or purified erythrocyte glycophorins [ophorins  were fraophorins , 38.Surface plasmon resonance (SPR) analysis. The SPR assay using BIACORE T200 instrument (Biacore) was developed to analyze Region II interaction with human erythrocytes. The Ni-NTA chip (Biacore) was coated with Ni+2 by injection of 10 ul (0.5 nM) NiCl2 at a flow rate of 10ul/min. The His-tagged recombinant Region II was captured on the Ni-coated chip by injecting Region II (10ug/ml) for one minute at a flow rate of 10ul/ml. Human erythrocytes, suspended in PBS pH 7.4 containing 0.5 mg/ml BSA, of different hematocrits  were injected at flow rate of 3ul/min for 4 minutes in flow cells coated with RII. Dissociation was monitored for 300 seconds. The surface was regenerated by injecting 350 mm EDTA and 0.005% P20 surfactant at a flow rate of 30ul/min. A flow cell without treatment with Region II or NiCl2 was used as the control.P. falciparum clone and was expressed in the Sf9 line of insect cells using recombinant baculovirus obtained from Genescript (titer 2x108). Pilot experiments determined that 50 hours of SF9 cell culture at a virus multiplicity of infection (MOI) of 5 gave optimal expression of Region II to cell culture medium. The recombinant protein of the expected molecular mass (~75 kDa) was present in a total cell lysate and in a soluble and an insoluble cell fraction and medium fluid as well and was not degraded  based assay using Biacore T200 was also developed . The binP. falciparum EBA-140 ligand was preliminarily characterized in immunoblotting using human erythrocyte membranes and purified glycophorins [The binding specificity of the recombinant ophorins  Fig. 6)P. falcipophorins , and delThe binding of recombinant Region II to freshly donated normal and Gerbich-negative erythrocytes was examined using flow cytometry . The inaRecombinant Region II binding was also determined using CHO cells stable transfected with vectors encoding GPC and its deletion forms type Yus and Gerbich  Fig. 8)Fig. 8). Recombinant Region II binding was also determined using CHO cells stable transfected with vectors encoding GPC and its deletion forms type Yus and Gerbich  Fig. 8)Fig. 8). Plasmodium falciparum erythrocyte invasion ligand that engages GPC on host erythrocytes [P. falciparum alters the receptor binding specificity. The GPC specificity of EBA-140 ligand was defined by only one sequence (VSTK) of four Region II polymorphic amino acid residues [The erythrocyte-binding antigen 140 (EBA-140) is a hrocytes , 18, 22.hrocytes , 22. Althrocytes  or CHO-Khrocytes . Studiesresidues , 21. HowOur results were generally accordant with earlier findings obtained with parasite culture supernatants or with the recombinant EBA-140 Region II expressed on cells, but were for the first time obtained with the purified baculovirus-expressed recombinant Region II of the EBA-140 ligand. The ligand was shown to bind to erythrocyte and recombinant form of GPC and no distinct binding to other erythrocyte membrane components was observed. The role of GPC oligosaccharide chains in this reaction was confirmed by the lack of the EBA-140 Region II binding to desialylated GPC and much lower binding to desialylated erythrocytes. It was shown that the EBA-140 Region II was not bound either to Gerbich deletion mutant of GPC (lacking a.a. residues 36–63) or to GPD (truncated GPC lacking a.a. residues 1–21), but showed the binding to the recombinant Yus variant of GPC (lacking a.a. residues 17–35).P. falciparum is common [In view of these results the binding site on GPC molecule for the EBA-140 ligand is still a puzzle. GPC has many sialylated oligosaccharide chains, one N-glycan bound to Asn8 and several O-linked glycans  Fig. 9)Fig. 9) as common . This frThere is another possibility that the lack of the EBA-140 binding to Gerbich variant of GPC is the result of different structure of its N-glycosidic chain. Unlike normal GPC the variant GPC Gerbich-type migrate as a diffuse band on SDS-PAGE . StudiesP. falciparum invasion inhibitory therapeutics and vaccines.We recently performed the first mass spectrometry analysis of the GPC N-glycan structure which revealed the presence of a small portion of complex-type structures containing repeating polylactosamine units with the terminal Fucα residues, while the short antenna were terminated with Siaα residues. Based on these results we propose that the amount of elongated, Fuc-terminated polylactosamine chains may be elevated in GPC-Gerbich, in agreement with previous results . This di


Human T-lymphotropic virus type I (HTLV-1) integration is known to be weakly biased towards genomically active regions but can occur throughout the genome. We have shown that there are of the order of 10^4 HTLV-1-infected T-cell clones in each individual, each clone defined by a unique genomic integration site of the single-copy HTLV-1 provirus. HTLV-1 dysregulates many host genes by expressing a transcriptional transactivator, Tax, but it remains unknown whether HTLV-1 also alters host gene expression by insertional mutagenesis. In recent work  we have shown that HTLV-1 encodes a functional binding site for CCCTC binding factor (CTCF), a known mediator of chromatin looping. We hypothesize that the HTLV-1 provirus forms CTCF-mediated chromatin loops with the flanking host genome and dysregulates the expression of host genes. To test this hypothesis, we performed a circular chromatin conformation capture assay followed by high-throughput sequencing (4C-seq) which enables high resolution screening of physical long range interactions between chromosomes. Using the proviral CTCF site as bait, we have detected long-range interactions between the proviral and the host genome which suggest the formation of novel chromatin looping in clones of HTLV-1-infected T cells from both cases of ATL and from non-malignant cases of HTLV-1 infection. Chromosome conformation capture using specific primers (3C) confirmed clone-specific interactions. We are now testing whether these long-range interactions are associated with clone-specific alterations in the expression of host genes.


Behavior is shaped to a dramatic degree by the occurrence of rewards, through both pavlovian and instrumental conditioning processes; these mechanisms give rise to both normal and abnormal behavior. It is crucial to understand the neural mechanisms that give rise to normal actions and how they lead to pathological behaviors, such as overeating and drug addictions. Two theories regarding the role for dopamine neurons in learning include the concepts that their activity serves as a (1) mechanism that confers incentive salience onto rewards and associated cues and/or (2) contingency teaching signal reflecting reward prediction error. While both theories are provocative, the causal role for dopamine cell activity in either mechanism remains controversial. In this study mice that either fully or partially lacked NMDARs in dopamine neurons exclusively, as well as appropriate controls, were evaluated for reward-related learning; this experimental design allowed for a test of the premise that NMDA/glutamate receptor (NMDAR)-mediated mechanisms in dopamine neurons, including NMDA-dependent regulation of phasic discharge activity of these cells, modulate either the instrumental learning processes or the likelihood of pavlovian cues to become highly motivating incentive stimuli that directly attract behavior. Loss of NMDARs in dopamine neurons did not significantly affect baseline dopamine utilization in the striatum, novelty evoked locomotor behavior, or consumption of a freely available, palatable food solution. On the other hand, animals lacking NMDARs in dopamine cells exhibited a selective reduction in reinforced lever responses that emerged over the course of instrumental learning. Loss of receptor expression did not, however, influence the likelihood of an animal acquiring a pavlovian conditional response associated with attribution of incentive salience to reward-paired cues (sign tracking). These data support the view that reductions in NMDAR signaling in dopamine neurons affect instrumental reward-related learning but do not lend support to hypotheses that suggest that the behavioral significance of this signaling includes incentive salience attribution. Behavior is shaped to a dramatic degree by the occurrence of rewards, through both pavlovian and instrumental conditioning processes; these mechanisms give rise to both normal and abnormal behavior. It is crucial to understand the neural mechanisms that give rise to normal actions and how they lead to pathological behaviors, such as overeating and drug addictions. Though dopamine neurotransmission has often been implicated in reward-related learning, the specifics of this role remain poorly understood. The set of studies described in this manuscript reveals that NMDA/glutamate-mediated dopamine transmission contributes to the acquisition of instrumental reward-seeking actions, possibly highlighting these mechanisms as targets of interventions designed to alter the occurrence of reward-related actions, like drug seeking and drug taking.The electrical activity of dopamine neurons, and associated activity-dependent synaptic release of dopamine, is thought to be critical to reward-related learning and behavior . BecauseA considerable body of evidence, derived mostly from electrophysiological recordings of midbrain neurons in nonhuman primates, implicates brief event-related, high-frequency discharge activity of dopaminergic neurons, and the associated phasic, nonlinear increases in the quantity of transmitter released , as a neAn alternate perspective regards dopaminergic transmission as the mechanism by which rewarding events and reward-predictive stimuli are imbued with incentive motivational properties, transforming them from merely pleasurable, or “liked,” to “wanted” attractors of motivated behavior and attention . A varieThe prediction error and the incentive salience perspectives are often both supported by the results of experimental manipulations of dopamine transmission. For example, optogenetic simulation of dopamine neuron burst firing acts as an unconditioned stimulus that reinforces instrumental and pavlovian behaviors . While tBecause NMDA/glutamate receptors BkmnSlc6a3/J  mice, each heterozygous for a mutated dopamine transporter (DAT) gene expressing Cre recombinase, and B6.129S4-Grin1tm2Stl/J  mice were purchased from The Jackson Laboratory. In DATcre+ mice, Cre recombinase cDNA was inserted into the 3' untranslated region of the DAT gene for bicistronic mRNA translation; Cre-mediated recombination is detectable in this line as early as E15 and is primarily restricted to the substantia nigra, ventral tegmental area, and retrorubral field within the midbrain . Male DATcre+ mice were also separately crossed to female B6.129S4-tm1SorGT(ROSA)26Sor/J  reporter mice  in a temperature- and humidity-controlled room on a 14/10 h light/dark cycle. Behavioral testing was conducted during the light cycle. Food was available m MgCl2, 36 ml PBS, 60 mg X-gal, and 800 µl dimethylformamide. The solution was allowed to react with brain slices at 37°C for 48 h; the slices were then rinsed, counterstained, and mounted on slides.DATcre+ mice also expressing the ROSA26-LacZ gene were killed by isoflurane overdose, then transcardially perfused with freshly mixed, cold 4% paraformaldehyde. Brains were stored in paraformaldehyde for 1 d before being switched to a 30% sucrose/PBS solution. Slices of 40 µm width were cut on a cryostat and rinsed in PBS. The staining solution contained 85.33 mg potassium ferrocyanide, 64 mg potassium ferricyanide, 4 ml of 20 mflox/wt, n = 9; DATcre–;NR1flox/flox, n = 8; DATcre+;NR1flox/wt, n = 10; DATcre+;NR1flox/flox, n = 8) were killed by rapid decapitation and tissue samples were collected from the ventral striatum. Samples were frozen for subsequent analyses of monoamines and their metabolites using HPLC. Tissue was homogenized in 0.1 m perchloric acid, centrifuged for 25 min, and the content of 200 μl of supernatant was quantified by reverse-phase column HPLC (BAS) at 0.7 V applied, using a 7% acetonitrile-based mobile phase. Protein content was quantified using the Lowry method  was characterized by placing subjects in clean, standard acrylic animal cages that were novel to the mouse (24 × 40 cm), with a thin layer of bedding. Each cage was equipped with Opto M3 locomotor activity monitors (Columbus Instruments) fitted with 1” spaced x-axis infrared beam emitters. Locomotor behavior was monitored for 30 min (data collected in 5 min time bins). Locomotor data for 36 mice was lost because of equipment failure, leaving n = 36, n = 32, n = 31, and n = 31 for the four genotype groups, respectively.The locomotor behavior of 165 DATcre;NR1 mice . Bottle positions  were switched on the second day of habituation. Testing began the following day, bottle positions were again switched, and data were collected for 2 d; a final switch, followed by 2 d of data collection, concluded the procedure. Data presented are averages of consumption levels on the second day of placement on each side.flox/wt, n = 26; DATcre–;NR1flox/flox, n = 27; DATcre+;NR1flox/wt, n = 22; DATcre+;NR1flox/flox, n = 26; reflects data exclusion from 11 mice due to technical failures with the operant chambers, e.g., pellet dispenser or lever failures) were introduced to limited access to chow in their home cages in order to achieve body weights ∼85% of free-feeding levels. Mice were exposed to 0.5 g of the reinforcer pellets  in their home cages during the first day of food restriction. Body weight was maintained at this level throughout the experiment, and standard chow was provided in the home cage at least 1 h after daily testing. Mice were trained on sequential days in extra wide aluminum and polycarbonate Med Associates modular mouse-testing chambers, each stationed inside a sound-attenuating chamber and equipped with a white noise generator, house light , and a tone generator. A horizontal array of five illuminable nose-poke apertures formed one side of the box, and on the other resided an illuminable pellet-delivery magazine with an entry-detection photocell. Chambers also contained two retractable ultrasensitive mouse levers ; these were positioned one each on both sides of the food magazine.An experimentally naive set of 112 mice  resulted in a 50 ms tone pulse, which was accompanied by pellet delivery and a 2 s illumination of the magazine light upon completion of the ratio schedule. The first 10 pellets per session were delivered on a fixed-ratio 1 schedule; subsequently, pellets were delivered on a variable-ratio 2 schedule. Responses to the inactive lever were recorded but had no programmed consequence. A 0.5 s timeout followed each pellet delivery, during which responses could not elicit delivery of another reward, but did count toward completion of the next reinforcement schedule.flox/wt mice were phenotypically similar to DATcre−;NR1flox/wt and DATcre—;NR1flox/flox control groups . We also tested DATcre—;NR1flox/wt animals  to provide further empirical support for the validity of comparisons between DATcre+;NR1flox/wt and DATcre+;NR1flox/flox animals. The same schedule of caloric restriction described above was initiated prior to behavioral training. Animals first underwent 2 d of magazine training in which 30 food pellets were delivered to the magazine on a variable-time 60 s schedule. Fifteen daily sessions of sign-tracking/goal-tracking conditioning began the next day. These sessions consisted of 15 presentations on a variable-time 180 s schedule of a CS (“lever-CS”). Each lever-CS involved a 20 s extension of the lever to the right of the food magazine; two food pellets were delivered to the magazine coincident with lever-CS termination. Actuations of the lever-CS were recorded but had no programmed consequences.Methods for sign-tracking/goal-tracking pavlovian learning were modeled after l groups , indicatOn the day following the last conditioning session, all mice underwent a single test of conditioned reinforcement, wherein the two most lateral nose-poke apertures were illuminated. Responses to the active aperture  resulted in a 5 s extension of the lever-CS, while responses to the inactive aperture were recorded but were without programmed effect. No food was delivered during this session. The session ended 60 min after the first active aperture response or after 90 min had elapsed, whichever occurred first.flox/wt vs flox/flox) zygosity were entered as between-subjects factors. In sign-tracking/goal-tracking experiments, for comparisons between DATcre+;NR1flox/wt and DATcre+;NR1flox/flox mice, NR1 genotype was the singular between-subjects factor; for comparisons between DATcre—;NR1flox/wt and DATcre+;NR1flox/wt mice, DATcre genotype was the singular between-subjects factor.Statistical tests, outlined in t tests where appropriate. For locomotor and learning experiments, we found significant departures from assumptions of traditional repeated-measures ANOVA, including violations of sphericity and/or heterogeneous, correlated residuals. These were not entirely unexpected, especially in our learning experiments, because correlations between testing days change as behavior progressively changes. Because population-level analysis often does not accurately characterize individual learning curves  were analyzed across 5 min time bins; the bin was treated as a linear covariate. Free-food consumption (ml/kg consumed) was analyzed with day of measurement as a repeated measure. Because water consumption levels were negligible, these data were not analyzed. Dopamine utilization was analyzed as the ratio of metabolite DOPAC content to dopamine content.Locomotor behavior measures ; if no quadratic effect of day was detected, it was removed, leaving the linear effect. For instrumental learning, reinforcers earned across days were analyzed, as were active and inactive lever presses. For sign-tracking/goal-tracking data, we analyzed genotype effects on behavioral data acquired across successive sessions, mirroring the analysis in We also calculated proposed conditional response “bias” measures described by Data from six subjects on day 8 were lost due to technical failure. These data points were treated as missing at random in mixed model analysis.Measures of responding for conditioned reinforcement included number of lever-CSs earned and number of active and inactive aperture nose pokes. Because sign tracking has been associated with greater conditioned reinforcement in rats , we alsoFigures are presented as mean ± SE line plots or as Tukey box-plots, the latter demonstrating spread about a group median with plus symbols (+) demarcating group means.F = 0.01, p = 0.932; NR1: F = 1.51, p = 0.229; DATcre x NR1: F = 0.14, p = 0.713, ain Table 1).In flox/wt, DATcre–;NR1flox/flox, DATcre+;NR1flox/wt, and DATcre+;NR1flox/flox mice were initially characterized for total ambulatory activity in a novel environment. As depicted in p = 0.731; NR1: Wald p = 0.171; DATcre × NR1: Wald p = 0.474; DATcre × NR1 × time bin: Wald p = 0.863, b), indicating that that locomotor behavior was unaffected by genetic manipulation of NMDAR in dopamine cells. All mice increased consumption of the sweetened condensed milk solution across successive days of access  = 12.38, p = 0.0005), but as depicted in F = 0.06, p = 0.804; NR1: F= 0.02, p = 0.902; DATcre × NR1: F= 0.64, p = 0.423; DATcre × NR1 × day: F = 0.001, p = 0.962, c).DATcre–;NR1p = 0.039), NR1 × day , and DATcre x NR1 × day interactions . The NR1 × day interaction was significant within DATcre+ animals , and successive Bonferroni-corrected contrasts revealed that while behavior during the initial training sessions did not differ, DATcre+;NR1flox/flox mice earned fewer reinforcers than DATcre+;NR1flox/wt mice on days 3–6 . Similar findings were obtained when the omnibus interaction was explored via simple effects within NR1 genotypes . DATcre+;NR1flox/flox earned fewer reinforcers than DATcre—;NR1flox/flox mice on day 5 , and a similar trend was found on day 6 . Importantly, no differences in instrumental behavior between DATcre–;NR1flox/flox, DATcre–;NR1flox/wt, and DATcre+;NR1flox/wt mice were detected .Reinforcers earned during the instrumental conditioning sessions are depicted in p = 0.004, j) was decomposed  to reveal that fewer active lever presses were made by DATcre+;NR1flox/flox mice relative to DATcre+;NR1flox/wt mice, again on days 3–6 . Fewer active lever presses were also made by DATcre+;NR1flox/flox mice relative to DATcre+;NR1flox/wt on day 5 , with near-significant differences on day 6 . On the other hand, no interactions with genotypes were found for inactive lever pressing , indicating that the impairment in instrumental behavior observed in animals lacking NMDA receptors in dopamine neurons was selective to the active lever.To provide evidence that this difference in instrumental responding reflected differences in associative behavior, similar analyses were performed on number of active (reinforced) lever B and inThe acquisition of both sign-tracking and goal-tracking conditional responses is depicted in flox/flox mice and DATcre+;NR1flox/wt mice revealed no effects of genotype on the probability of making a magazine head entry during the lever-CS ; moreover, there were no effects of genotype on number of magazine entries during the lever-CS . We also analyzed the discrimination ratio between CS and pre-CS period responding, again finding no genotype effects . Though a significant day × genotype effect on latency to enter the magazine upon lever-CS onset was found , post hoc tests did not reveal any significant differences between groups on any of the 15 d of training . Thus, with the exception of a marginal omnibus test suggesting deviations in response latency, these analyses do not support altered discriminated goal approach after loss of NMDA receptors in dopamine neurons.Analyses comparing goal-tracking behavior , making similar numbers of lever contacts  and doing so with latencies that did not differ .Surprisingly, analyses of the development of corresponding lever-CS approach/sign-tracking behaviors . DATcre—;NR1flox/wt and DATcre+;NR1flox/wt differed in their probability of actuating the lever during a lever-CS . Nevertheless, like DATcre+;NR1flox/wt mice, when DATcre—;NR1flox/wt animals were compared with DATcre+;NR1flox/flox knock-out mice lacking both NR1 alleles in dopamine neurons, no differences were found . DATcre—;NR1flox/wt and DATcre+;NR1flox/wt expressed other sign-tracking measures at similar rates . Thus, the behavior DATcre—;NR1flox/wt and DATcre+;NR1flox/wt was generally equivalent, supporting the use of DATcre+;NR1flox/wt as controls with DATcre+;NR1flox/flox knock-outs for the main comparisons described above.We also compared NR1p = 0.003; probability vs latency, Spearman’s ρ = 0.934, p < 0.001; latency vs response, Spearman’s ρ = 0.328, p = 0.009; Days 13–15: probability vs response, Spearman’s ρ = 0.720, p < 0.001; probability vs latency, Spearman’s ρ = 0.969, p < 0.001; latency vs response, Spearman’s ρ = 0.698, p < 0.001, ee). The three scores were then averaged to form a summary bias score, as described previously (p = 0.994), and no genotype differences were found by the conclusion of testing .Because sign-tracking responses tend to come at the expense of goal-tracking responses, and vice versa, we calculated relative response bias scores on the basis of probabilities, responses, and latencies to respond to the lever-CS versus the food magazine . Individual scores, averaged across 3 d blocks, demonstrated significant pairwise correlations that increased in magnitude across training . Relative rates of phenotype designations did not differ between DATcre+;NR1flox/flox mice and DATcre+;NR1flox/wt mice . Reasoning we might be able to more sensitively observe differences in the rate of learning by examining only their respective conditional response, we assessed the acquisition of goal-tracking behaviors in goal trackers exclusively and the acquisition of sign-tracking behaviors in sign trackers exclusively, as shown in flox/flox mice express a greater degree of sign-tracking behavior–opposite of the hypothesized effect. However, because comparisons of summary bias score distributions did not reach traditional levels of statistical significance, no additional exploratory statistical evaluations of these data were conducted.We designated mice as sign trackers or goal trackers according to whether their final 3 d average summary bias scores were positive or negative. A bias toward the sign-tracking conditional response, under this scheme of categorization, occurred in fewer mice than did goal tracking  = 0.526, p = 0.601), nor did the number of active aperture (t(61) = 0.559, p = 0.551) or inactive aperture (t(61) = 0.553, p = 0.581, hh) responses. However, as occurs in rats, mice designated sign trackers exhibited higher levels of conditioned reinforcement than mice designated goal trackers, earning more lever-CS presentations .Finally, the ability of the lever-CS to support new learning via conditioned reinforcement, a phenomenon elevated in sign-tracking animals  and consHere, the effects of genetic excision of the NMDAR from dopamine neurons on associative reward-related learning were evaluated. Mice lacking NMDAR in dopamine neurons exhibited impaired instrumental learning but normal sign-tracking and goal-tracking responses in a pavlovian conditioning procedure. These results are presented against the backdrop of normal exploratory locomotion and palatable food consumption, eliminating these ancillary phenotypes as likely explanations for the observed learning effects.Loss of NMDAR in dopamine neurons resulted in slower acquisition of instrumental responding; this finding is in general agreement with results gathered earlier using a similar mouse model . QualitaThis result suggests a causal role for NMDA-mediated neurotransmission in dopamine neurons in instrumental learning. One possibility is that the loss of NMDA receptors disables one mechanism that contributes to phasic, stimulus-related dopamine neuron firing . AdditioOur data are, however, consistent with a number of optogenetic studies wherein response-contingent optical activation of dopamine neurons either facilitated an appetitive instrumental response or was sufficient to support responding alone . MoreoveSign-tracking rats—those that approach and interact with a predictive cue  during an autoshaping task, often at the expense of approaching the location of reward delivery —are thouBecause NMDAR loss in dopamine neurons results in attenuation of the magnitude of phasic dopamine release to ∼30% that of controls , this geIn addition to failing to support the incentive salience perspective of dopamine activity in reward learning, we also did not yield evidence of a contribution of NMDA-mediated dopamine activity and/or phasic release to pavlovian goal approach, nor have several others using a similar mouse genetics approach . Given tflox/flox mice that have a dramatic, albeit not full, diminution of phasic dopamine release. There was no indication of this in our data. Alternatively, it is possible that a loss of NMDAR-mediated synaptic plasticity or other NMDAR-dependent physiological mechanisms in mice lacking NMDAR in dopamine neurons obscured the observation of a behavioral difference in sign tracking. Additionally, it is possible that these behaviors may be supported by dopaminergic projections to the basolateral amygdala or prefrontal cortex, as these cells express very little DAT , but 20–25% then developed overt sign-tracking conditional responses, some without any appreciable accompanying goal tracking ; future studies are needed to address this limitation.Here, we utilized a mouse model of compromised NMDA-dependent dopamine activity to characterize multiple components of reward-driven associative learning. Complementing the temporal precision of the optogenetics approaches, this approach allowed us to study the behavioral impact of putatively dampened endogenously generated phasic dopamine signals and loss of NMDAR-related synaptic plasticity. Our data revealed a clear role of NMDA activity in dopamine neurons in the acquisition of instrumental learning. We then tested causally, for the first time, predictions about the role of phasic dopamine in reward learning made by the incentive salience that contrast with those made by prediction error accounts. Though dopamine voltammetry data indicated a relationship between elevated CS-evoked dopamine activity and sign-tracking behavior , our resTherefore, our results are not fully consistent with the incentive salience perspective of phasic dopamine. They also are not necessarily uniformly consistent with a prediction error account of dopamine because putatively diminished phasic dopamine release did not affect pavlovian approach learning in any measured outcome. Thus, our results may be more congruent with a multifaceted conceptualization of dopaminergic transmission, wherein the behavioral significance of phasic dopamine could shift adaptively between prediction error, incentive salience attribution, and other forms of behavioral invigoration and flexibility, or combinations thereof, depending on the particular configuration of biological demands, internal goal states, and motivators present in the environment. This view is consistent with traditional views of dopamine as a neuromodulator, interacting with and adjusting ongoing circuit activity in a manner that can give rise to a multiplicity of context-dependent behavioral phenomena.