Significant regional variations were determined in fertility control behavior of rural women (p < 0.05). Almost all of its predictors explained by Theory of Planned Behavior (TPB) also showed significant regional variations (p < 0.05). Current fertility control policy should be strengthened more not only to improve fertility behavior of rural women but also to establish regional equity in fertility control by improving their reproductive decision-making in a rational way.Women’s fertility decision is quite difficult in male-dominant rural culture due to their poor reproductive autonomy. A cross-sectional survey was conducted in rural community of Bangladesh between November 2017 and February 2018 among 1285 respondents selected by multi-stage stratified sampling to explore regional variations of rural women’s fertility control behavior and its determinants using hierarchical and other inferential statistics. Data collection was done by face-to-face interview using questionnaire. Average parity was 2.5 per woman and 41% respondents had three or more children. About 60% of them used modern contraceptives (MCs) and oral contraceptive pill (OCP) was their first choice. Male participation in contraceptive use was less than 5%. Regional variation, women’s empowerment, fertility control knowledge, family planning (FP) attitude, social influence, perceived behavioral control (PBC) and fertility intention were significant predictors of fertility control behavior ( Fertility control behavior is the planned outcome of fertility decision that helps clients to choose an appropriate contraceptive method in a rational way according to their expressed needs and situation to carry out their reproductive intentions successfully ,2. Use oAccording to global context, total fertility rate (TFR) in 2015 was 2.5 live births per woman with a huge regional difference . The higIn 2016, Bangladesh had an estimated population of more than 160 million, which makes it the 8th most populous country in the world ,17. PopuAlthough decision-making in fertility control is a major concern in reproductive autonomy, most of the rural women are not familiar with rational ways of contraceptive decision-making even among the MC users . FertiliThis cross-sectional survey was conducted in rural areas of three divisions  of Bangladesh.The study was conducted among 1285 married women of reproductive age (15–49 years) having at least one child currently living together with their husbands in the rural area at least for 12 months before the survey.The respondents were selected by using multistage sampling technique . InitialData were collected by the researcher and 4 trained interviewers with previous exposure in health research between November 2017 and February 2018. Face-to-face interview was done through a structured questionnaire having 10 parts. Data collectors asked questions orally and filled out the responses. Socio-demographic and reproductive characteristics of respondents were described in the first part through 18 items such as age, income, number of children, etc. The second part was used to describe fertility control behavior of rural women using 6 items related to birth control and birth spacing. It was mixed-type, including both Likert as well as dichotomous types of answers. The third part described women’s autonomy using 10 items such as decision-making, economic access and mobility. Women’s fertility preference was assessed in the fourth part using 6 items such as preference for larger family and son preference. Part 5 included 8 items regarding information about contraceptive use and birth spacing to assess fertility control knowledge. Spousal relation was assessed in part 6 through 6 items such as spousal communication and male participation in FP. Part 7 was constructed to assess women’s FP attitude using 8 items. Social influence in fertility control was described by 8 items in part 8. Women’s PBC was assessed in part 9 through 6 items such as self-control on contraceptive-decision for birth control and birth spacing. Intention to fertility control was assessed in part 10 using 6 items such as readiness to use contraceptive and readiness for fertility control. Likert scale was followed from part 3 to part 10, except part 5, where dichotomous type was used. Equal numbers of positive and negative questions were included in the scales of fertility preference, fertility control knowledge, FP attitude and fertility control intention scales and only in PBC scale there were 4 positive and 2 negative questions. Cronbach’s Alpha (α) of fertility control behavior scale was 0.72. Cronbach’s α for women’s empowerment and fertility preference were 0.77 and 0.79, respectively. Cronbach’s α of FP attitude scale was 0.80 and for PBC scale it was 0.87. Cronbach’s α for social influence and fertility control intention were 0.72 and 0.78, respectively. Only spousal status and fertility control knowledge scales showed Cronbach’s α score between 0.6 and 0.7.Data processing and analyses were done using Statistical Package for Social Sciences (SPSS) version 20. Frequencies, percentage, mean and standard deviation (SD) were used for descriptive statistics. Hierarchical regression model was used to describe significance of predictors as well as regional effect on fertility control behavior. Kruskal–Wallis H and one-way analysis of variance (ANOVA) tests were used to describe the regional variations of fertility control behavior of rural women with its predictors. Factors related to fertility control behavior of rural women were arranged in groups for hierarchical analysis .Ethical clearances were taken from the Ethical Committee of Mahidol University (COA. No. MUPH 2017-193) and also from the Bangladesh Medical Research Council (BMRC) (Reg. No. 07427092017) to conduct the study.The typical rural picture was found to describe the socioeconomic status of the respondents. Nearly half of the respondents (44.5%) were in the group of 21–35 years. The average age of the respondents was 27.7 years with SD of 7.8 years . Around Among the respondents, about 80% had a history of early marriage and mean age of marriage was 16.6 years with SD of 1.1 years. Among the respondents, 78.6% had adolescent pregnancy and mean age of first pregnancy was 18.2 years with SD of 1.3 years. Around 43% had a history of pregnancy more than two times and 7% respondents had a history of abortion. Around 42% had a history of delivery more than two times and average parity was 2.5 per woman. Women having more than two living children were 41% and among the respondents, almost half of them (48.1%) conceived within two years of marriage. Average initial gap of first pregnancy from their marital age was 1.6 years with SD of 0.7 year .Almost half of them (48.6%) regularly used MCs and 11.3% irregularly used. Among the respondents, around 27% shared about switching in contraceptive use. Only around 8% used natural methods. Among the users of MCs, more than half (54.2%) used oral contraceptive pills (OCPs), 22.5% used injections, 3.6% used implant and 2.7% used Copper-T. Among the users of MCs, condom was used only in 7.5% cases and 9.5% gave a history of using permanent methods of contraceptives, either tubal ligation or vasectomy . p < 0.001) and accounted for 55.6% of the variation in fertility control behavior of rural women (p < 0.001). Finally, the area was added to find out the significant effect of regional variation on fertility control behavior. The inclusion of area to the regression model explained an additional 0.2% of the variation in fertility control behavior. Although this change in R² was small, it was significant  = 4.06, p < 0.05).The three-stage hierarchical multiple regression revealed that background factors at stage one contributed significantly to the regression model  = 199.49, al women . Next, cp < 0.001) (p < 0.001) (p < 0.05) from fertility control behavior of rural women of Chittagong division. Women’s age, family income, empowerment and fertility preference showed significance predicting fertility control behavior in the first model of background issues. Age and women’s empowerment persisted as significant predictors in the second model. Fertility control knowledge from background issues also showed significance in the second model. FP attitude, social influence, PBC and fertility intention were highly significant to predict fertility control behavior among the core items of TPB in the second model. When all independent variables were included in stage three of the regression model, socio-economic variables showed no significance predicting fertility control behavior of rural women. Only women’s empowerment and fertility control knowledge from background issues were significant to predict fertility control behavior in the final stage of the regression model (< 0.001) . FP atti< 0.001) . SpousalThe average score of women’s fertility control behavior for all divisions was 8.9 (±4.6). The average women’s empowerment score and average fertility preference score at the national level were 34.3 (±5.2) and 20.1 (±3.5), respectively. Mean score of fertility control knowledge was 5.6 (±1.7) and average FP attitude score among total respondents was 30.1 (±3.3). The average social influence score and average PBC score at national level were 29.0 (±3.6) and 21.6 (±4.3), respectively. The average fertility control intention score was 20.9 (±3.6) and mean score of spousal status among total respondents was 7.6 (±3.1) .2 (2) = 49.41, p < 0.001, with a mean rank fertility control behavior score of 544.28 for Chittagong, 669.55 for Dhaka and 715.91 for Rajshahi. Chittagong was very highly significantly different from Dhaka (p < 0.001) and Rajshahi (p < 0.001) but Dhaka and Rajshahi were not significantly different (p > 0.05). Significant regional variations of women’s empowerment (χ2 (2) = 64.03, p < 0.001), spousal status (χ2 (2) = 26.72, p < 0.001), PBC (χ2 (2) = 17.42, p < 0.001) and fertility control intention (χ2 (2) = 18.96, p < 0.001) were also confirmed by Kruskal–Wallis H tests. Post hoc tests confirmed the vulnerability of Chittagong from Rajshahi and Dhaka , FP attitude  = 12.58, p < 0.001) and social influence  = 25.10, p < 0.001) in fertility control were confirmed through one-way ANOVA tests. Tukey post hoc tests revealed the vulnerability of Chittagong from other divisions in fertility preference, FP attitude and social influence. In the case of fertility control knowledge, there was no statistically significant difference found among rural women of three divisions as determined by one-way ANOVA  = 2.12, p > 0.05) with a mean fertility knowledge score of 5.5 for Chittagong, 5.6 for Dhaka and 5.8 for Rajshahi. Although there was no significant difference, Rajshahi showed higher score  = 6.09, er score .This study showed that addressing women’s fertility decision remained challenging in a male-dominant rural community. Fertility control behavior as well as its predictors were explained through TPB in different backgrounds. Less than two-thirds (59.9%) of the respondents were MC users. The rate of MC use was still slightly higher in the study area in comparison to a recent Bangladesh Demographic and Health Survey (BDHS) . One of OCP was the most common method in contraceptive use, followed by injection in second choice, which was similar to the national findings of BDHS . Rural wAge, income, religion, family size, parity, etc. sometimes showed similarity with national findings of BDHS, 2014  and in aSES, women’s empowerment, fertility preference and fertility control knowledge were taken as background issues of TPB model of fertility control behavior in this study. Although women’s age and family income were significant in the primary stage of hierarchical regression model, in the final stage these were not significantly related to fertility control behavior of rural women in this study that differed from many other studies on fertility control ,22,23,26Women’s empowerment was in favor of fertility control behavior. Higher empowerment score showed better state in parity and contraceptive use. Importance of women’s empowerment in fertility control was also mentioned in other Bangladeshi papers ,9. WoldeAll the core factors of contraceptive use explained by TPB, except external control, showed significant effects on fertility control behavior of Bangladeshi rural women. Better FP attitude, positive social influence, higher PBC were in favor of better fertility control behavior of rural women that were described similarly in some studies using TPB ,21. HighRespondents were randomly selected only from 3 divisions not covering all divisions to describe regional variations of fertility control behavior of rural women. Multistage stratified sampling based on national TFR at first stage minimized the selection bias and made the sample representative including respondents from all age groups. Rural women might be more positive in a few cases towards the items related to individual issues in MC use. As it was a cross-sectional survey, the study had few inherent limitations to describe the significance of predictors with fertility control behavior of women. Except these, the study adopted TPB model nicely to explain regional variations of fertility control behavior of rural women and its predictors. A mixed-method or qualitative study can be effective to explain more the reasons of regional disparity in MC use in rural Bangladesh based on current study findings.Marked regional variations were observed in fertility control behavior of rural women along with its predictors. Regional disparity was an extra burden in male-dominant rural culture to achieve the national birth control target. Regional disparity should be minimized in contraceptive decision-making of rural women to ensure rational use of contraceptives. Fertility control awareness as well as income-generating programs for rural women could be helpful to improve their reproductive autonomy in fertility decision. The findings of this research will be helpful for planners to make current fertility control programs more region specific. Appropriate strategies should be included in the national fertility control program based on regional disparity in fertility indicators to make it more effective, not only to improve fertility control behavior of rural women but also to establish regional equity in fertility control.


Giardia duodenalis genotypes in humans has been increasingly reported in recent years, data on possible differences in pathogen transmission between age groups and virulence between genotypes are scarce. The purpose of this study is to investigate the genetic diversity of G. duodenalis in humans in Spain and compare the distribution of G. duodenalis assemblages A and B between children and adults and clinical presentations between the two genotypes.Although the distribution of G. duodenalis.In the present study, 125 microscopy-positive fecal samples were collected from humans in Spain over a 7-year period. PCR and sequence analyses of the triosephosphate isomerase, β-giardin and glutamate dehydrogenase genes were used to identify the multilocus genotypes of G. duodenalis assemblages A (29) and B (66), with co-infections of the two in two patients. Among the sequences obtained in this study, four multilocus genotypes (MLGs) of the sub-assemblage AII were observed within assemblage A. In contrast, 19 MLGs were detected within assemblage B due to the high sequence diversity at each locus. One MLG, however, was found in 51.9% (27/52) of assemblage B samples. Children were more commonly infected by assemblage B (44/53 or 83%) than adults . Asymptomatic infection was more common in patients with assemblage A (4/29 or 13.8%) than in those with assemblage B , and the frequency of abdominal pain occurrence was higher in assemblage B patients (65/66 or 98.5%) than assemblage A patients .Sequence analysis of three genetic loci identified both G. duodenalis genotypes. They are useful in understanding the transmission of G. duodenalis in humans in Spain.These results illustrate the existence of differences in genotype distribution between children and adults and clinical presentations between  Giardia duodenalis is a common gastrointestinal pathogen in a wide range of vertebrates, including humans and domestic animals [G. duodenalis is mainly transmitted through contact with infected persons or ingestion of contaminated food or water [G. duodenalis every year [G. duodenalis infections [ animals . As one or water . It coloor water . Worldwiery year . Human iery year , 5. Infefections . Symptomfections , 7, whicfections . In devefections .Giardia duodenalis is a multispecies complex with at least eight recognized assemblages or genotypes (A-H) based on the genetic characterization of pathogens in clinical specimens [G. duodenalis genotype [pecimens , 11. Amogenotype . Thus, bgenotype .G. duodenalis-positive samples from humans indicates that assemblage B (accounting for ~ 58% giardiasis cases) has a higher prevalence than assemblage A (accounting for ~ 37% giardiasis cases) worldwide [G. duodenalis assemblages among some provinces. For instance, assemblages A and B were detected in 27.4 and 72.6%, respectively, of G. duodenalis-positive patients in Madrid [Molecular analysis of more than 2800 orldwide . The disorldwide , only asorldwide , while sorldwide , 17, 18.n Madrid , while an Madrid .G. duodenalis is a common human pathogen worldwide, few data are available on differences in pathogen transmission between children and adults and clinical presentations between genotypes. One study in Australia indicated that adults were more commonly infected with G. duodenalis assemblage A compared with children [G. duodenalis in humans [bg), glutamate dehydrogenase (gdh) and triosephosphate isomerase (tpi), were used in these studies. In addition, all samples in each survey were collected from the same city.Although children . Differechildren . In Spain humans , 20, 22.n humans , 24. Howbg, gdh and tpi) was used to characterize G. duodenalis in clinical samples collected from humans in multiple locations in Spain between 2012 and 2018. The distribution of G. duodenalis genotypes was compared between children and adults and clinical presentations compared between assemblages A and B to assess the possibility of differences in the transmission of G. duodenalis between the two populations and virulence between the two assemblages.To fill some of the knowledge gaps described above, in the present study, multilocus genotyping targeting the three classical genetic loci , 2015 (n = 27), 2016 (n = 33), 2017 (n = 25) and 2018 (n = 31) and submitted to a commercial laboratory, Synlab Diagnosticos Globales , for the detection of enteric pathogens, including common viruses, bacteria and parasites; samples positive for these bacteria and viruses were not further tested for enteric parasites. Each patient was supplied with one or more sterile plastic containers (1–3) with 5 ml of merthiolate-iodine-formaldehyde (MIF) solution to preserve the fecal samples; these were kept at room temperature until they were examined by microscopy. Among them, 118 were from patients suffering from diarrhea or other intestinal symptoms, while 7 were from asymptomatic persons who requested testing specifically for G. duodenalis because of unknown reasons. The patients were 1–75 years of age, with a median age of 10 years, and comprised 67 males and 58 females.A total of 125 fecal samples positive for G. duodenalis by direct microscopic examination of wet mount of fecal materials fixed with MIF solution as previously described [G. duodenalis were washed by centrifugation and stored in 70% ethanol at 4 °C before being shipped to the laboratory at the Centers for Disease Control and Prevention for molecular analysis.The fecal samples were analyzed for escribed . Brieflyg for 10 min to remove ethanol. Genomic DNA was extracted from the washed fecal material using a FastDNA SPIN Kit for Soil  as described [Approximately 500 μl of suspension of the ethanol-preserved fecal sample was washed twice with distilled water by centrifugation at 2000× escribed  and storG. duodenalis, a 532-bp fragment of the tpi gene, a 511-bp fragment of the bg gene and a 530-bp fragment of the gdh gene were amplified using nested PCR [To identify MLGs of http://technelysium.com.au/ChromasPro.html) and aligned with reference sequences  downloaded from the GenBank database using the software ClustalX  to identify the genotypes and subtypes of G. duodenalis. The evolution of assemblage B MLGs was assessed by using eBURST v.3.0 (http://eBURST.mlst.net/). Samples with apparent presence of double peaks at any of the loci during DNA sequencing were excluded in the MLG analysis of sequence data.All positive secondary PCR products from the three genetic loci generated in the study were sequenced in both directions using the secondary primers and the BigDye Terminator v.3.1 Cycle Sequencing Kit  on an ABI 3730 Genetic Analyzer (Applied Biosystems). The nucleotide sequences of each gene were assembled using ChromasPro v.1.32  together with its 95% confidence intervals (95% CI). Statistical analysis was performed using SPSS Statistics v.21.0 for Windows .The Chi-square test was used to compare differences in infection rates of assemblages among children and adult and clinical symptoms. Differences were considered significant at the level of G. duodenalis-positive fecal samples were included in this study. They were mostly diagnosed by microscopy detection of cysts in fecal samples , Endolimax nana (n = 2), Entamoeba coli (n = 2), Cryptosporidium sp. (n = 1) and Iodamoeba butschlii (n = 1). They were not included in the analysis of the associations between symptoms and assemblages of G. duodenalis.Altogether, 125 tpi, bg and gdh loci, respectively. The amplification rates were 8/9 (88.9%), 23/27 (85.2%), 25/33 (75.8%), 20/25 (80%) and 21/31 (67.7%) for samples collected during 2012, 2015, 2016, 2017 and 2018, respectively. Among the 97 genotyped samples, 78 were successfully sequenced at all three genetic loci; the remaining 19 samples were successfully genotyped at only one (n = 12) or two (n = 7) genetic loci. Two genotypes of G. duodenalis were detected among the 97 genotyped samples, namely assemblages A  and B , with two samples having both . Children under 12 years-old were more commonly infected by assemblage B (44/53 or 83.0%) than assemblage A (9/53 or 17.0%). In contrast, adults had similar distribution of assemblages A (20/42 or 47.6%) and B (22/42 or 52.4%). Among the 97 PCR-positive samples, 55 and 42 were from male and female patients, respectively. There was no significant difference in the distribution of G. duodenalis assemblages A and B between male  and female  patients .Among the 125 microscopy-positive samples, 46 (36.8%), 22 (17.6%) and 57 (45.6%) were from the age groups of 0–5, 6–12 and 23–75 years, respectively . The remaining 38 samples (30.4%) were collected from 9 other provinces, with 25 of them being PCR-positive. Among the latter, 5 and 20 samples were identified as assemblages A and B, respectively.Eighty-seven (69.6%) of the 125 microscopy-positive samples were collected from Barcelona. Among them, 72 samples were PCR-positive for Abdominal pain was the most common clinical symptom of giardiasis: 118 (94.4%) of the 125 patients had the symptom . Therefore, assemblage A-infected patients (4/29 or 13.8%) were more likely to have asymptomatic infection than assemblage B-infected patients (1/66 or 1.5%) . In addition, the frequency (% occurrence) of abdominal pain was significantly different in patients infected by assemblages A and B . Among the 29 assemblage A-infected patients, 25 (86.2%) had abdominal pain. In contrast, among the assemblage B-infected patients, 65 of 66 (98.5%) had this symptom. Other clinical symptoms such as bloating, diarrhea, nausea, dehydration, weight loss and epigastric pain were equally distributed between patients infected with the two assemblages  in 10 (35.7%) samples, AY072724 (A3) in 17 (60.7%) samples and AB469365 (A5) in one (3.6%) sample. Two subtypes were observed at the gdh locus among the 27 assemblage A-positive samples, with the sequences being identical to AY178737 (A2) in 9 (33.3%) samples and EF507651 (A4) in 18 (66.7%) samples  assemblage A-positive samples at the tpi locus among assemblage B samples in this study. Altogether, seven subtypes of assemblage B were observed at the tpi locus, 6 of which were identical to KX468986, L02116, EU272153, FJ560565, HM140723 and AB781124 in the GenBank. The remaining one had 99% sequence identity to KX469015, was named as TB1, and deposited in GenBank under the accession number MG754394. Among them, the KX468986 sequence type was the dominant subtype, being detected in 47 of 66 assemblage B-positive samples. The second most common sequence type was L02116, which was detected in 9 samples. The remaining 5 sequence types were only detected in one or two samples during the five years of study.More subtypes were detected among assemblage B samples at each of the three genetic loci  generated the expected products in PCR. The genotyping results obtained suggest the occurrence of both assemblages A and B in Spain, with assemblage B (66/95 or 69.5%) being twice as common as assemblage A (29/95 or 30.5%). No assemblage E infection was detected in this study. This is consistent with observations in three previous studies in Spain, which had also shown a more frequent occurrence of assemblage B than assemblage A in humans in Zaragoza, Madrid and La Rioja provinces [G. duodenalis [In the present study, we assessed the genetic diversity of rovinces , 20, 22.odenalis , 28.G. duodenalis genotypes appears to be different between children and adults in Spain. In this study, 83% (44/53) of G. duodenalis-positive children were infected with assemblage B, compared with 52.4% (22/42) in adults . Similar results were obtained in one previous study in Egypt, where children had a higher frequency of assemblage B (24/34 or 70.6%) than assemblage A (10/34 or 29.4%), while adults had a similar frequency of both assemblages  [G. duodenalis could change in humans over age, perhaps as a reflection of the development of acquired immunity and/or differences in exposures.The distribution of 10/34 or .4%, whilctively) . These rG. duodenalis assemblage B were more likely to develop it than patients infected by assemblage A . This is different from data from the UK, in which the frequency of abdominal pain occurrence was similar between patients infected with the two assemblages [G. duodenalis genotypes.The clinical symptoms caused by assemblages A and B appear to be different in the Spanish population. In the present study, abdominal pain was the most common clinical symptom, but patients infected by emblages . In addiemblages –35. Howeemblages . Similaremblages , while tG. duodenalis in the Spanish population could be mostly anthroponotic in origin.Anthroponotic transmission appears to play an important role in giardiasis epidemiology in Spain. In the present study, almost all assemblage A-positive samples were identified as having sub-assemblage AII, which is known to preferentially infect humans . This isIn the present study, a higher genetic heterogeneity was observed in assemblage B than in assemblage A. Among the latter, most of the cases except one belonged to three MLGs within the sub-assemblage AII. In contrast, 19 MLGs were detected among assemblage B isolates, which is consistent with previous observations , 40, 41.G. duodenalis in the Spanish population, with a different distribution of the two genotypes between children and adults and some genotype-associated differences in clinical presentations or virulence. Despite the observation of high genetic diversity in G. duodenalis at the subtype level, several MLGs appear to have high occurrence in the human populations examined. The genetic identity of G. duodenalis at both the genotype and subtype levels suggests that anthroponotic transmission is important giardiasis epidemiology in the study area. Further studies using a better epidemiological design and more advanced molecular biologic tools are, however, needed to confirm these hypotheses.Results of the MLG analysis demonstrate a common occurrence of both assemblages A and B of Additional file 1: Table S1. Distribution of Giardia duodenalis subtypes in 21 human samples with partial or discrepant data at three genetic loci in Spain.


Background: The human retrovirus HTLV-1 inserts the viral complementary DNA of 9 kb into the host genome. Both plus- and minus-strands of the provirus are transcribed, respectively from the 5′ and 3′ long terminal repeats (LTR). Plus-strand expression is rapid and intense once activated, whereas the minus-strand is transcribed at a lower, more constant level. To identify how HTLV-1 transcription is regulated, we investigated the epigenetic modifications associated with the onset of spontaneous plus-strand expression and the potential impact of the host factor CTCF.Methods: Patient-derived peripheral blood mononuclear cells (PBMCs) and in vitro HTLV-1-infected T cell clones were examined. Cells were stained for the plus-strand-encoded viral protein Tax, and sorted into Tax+ and Tax– populations. Chromatin immunoprecipitation and methylated DNA immunoprecipitation were performed to identify epigenetic modifications in the provirus. Bisulfite-treated DNA fragments from the HTLV-1 LTRs were sequenced. Single-molecule RNA-FISH was performed, targeting HTLV-1 transcripts, for the estimation of transcription kinetics. The CRISPR/Cas9 technique was applied to alter the CTCF-binding site in the provirus, to test the impact of CTCF on the epigenetic modifications.Results: Changes in the histone modifications H3K4me3, H3K9Ac and H3K27Ac were strongly correlated with plus-strand expression. DNA in the body of the provirus was largely methylated except for the pX and 3′ LTR regions, regardless of Tax expression. The plus-strand promoter was hypomethylated when Tax was expressed. Removal of CTCF had no discernible impact on the viral transcription or epigenetic modifications.Conclusions: The histone modifications H3K4me3, H3K9Ac and H3K27Ac are highly dynamic in the HTLV-1 provirus: they show rapid change with the onset of Tax expression, and are reversible. The HTLV-1 provirus has an intrinsic pattern of epigenetic modifications that is independent of both the provirus insertion site and the chromatin architectural protein CTCF which binds to the HTLV-1 provirus. The main routes of infection are breast feeding, sexual contact and blood transfusion, each of which transmits cells carrying HTLV-1 and capable of infecting other cells in a new host. The majority of infected individuals remain asymptomatic throughout life. However some 5% develop adult T cell leukemia (ATL), and up to another 5% develop HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP)+ T cells. Each infected cell carries a single copy of the HTLV-1 provirus in a given location in the host genome4.HTLV-1 reverse-transcribes its 9 kb genomic RNA into complementary double-stranded DNA which is then inserted into the host cellular DNA upon infection. Thereafter the virus remains as a chromatinized provirus and is replicated as a part of the host genome. The virus mainly resides in CD47, accelerated cell cycle progression into S phase8, cell proliferation9 and DNA damage10. Tax also exerts a strong positive feedback on the promoter in the 5′ LTR by assembling other transcription activators  at the 5′ and 3′ ends, each of which serves as a promoter to drive the transcription of HTLV-1 from the plus- and minus-strand, respectively  , whereasHBZ is almost invariably detectable13, revealing a sharp contrast between the plus- and minus-strand transcription. However, the evidence of constitutive reactivity of cytotoxic T cells (CTLs) against Taxex vivo14 indicates that CTLs are frequently exposed to the viral antigen Taxin vivo, suggesting thattax is intermittently expressedin vivo. The current view of HTLV-1 expressionin vivo is thattax is usually silenced but undergoes intermittent expression, whereasHBZ is almost constantly expressed, yet at a low level1.In HTLV-1-infected individuals,15 (16. Plus-strand bursts are triggered by cell stress via p38 MAPK activation and require deubiquitylation at the HTLV-1 promoter of histone H2A (Lys119), the inhibitory transcriptional mark characteristic of polycomb repressive complex 1 (PRC1)16. In patient-derived HTLV-1-infected clonesin vitro, plus-strand bursts are more frequent in cells that lackHBZ mRNA and in cells in G0/G1 phase17, and more intense under conditions of physiological hypoxia18. In contrast, the abundance ofHBZ mRNA remains relatively stable during short-term culturein vitro  of cells, each cell containing hundreds of transcripts, leaving the other cells negative for the plus-strand expression . The cells were suspended (1×106 cells/ml) in RPMI-1640 supplemented with L-glutamine, penicillin/streptomycin and 10% fetal bovine serum, and incubated in 5% CO2 at 37°C overnight.Peripheral blood mononuclear cells (PBMCs) from patients with the HTLV-1-associated inflammatory disease HAM/TSP were separated from peripheral blood with Histopaque , washed in PBS, frozen in fetal bovine serum containing 10% DMSO and stored in liquid nitrogen until use. Upon thawing PBMCs, CD84 were maintained in RPMI-1640  supplemented with L-glutamine, penicillin/streptomycin and 20% fetal bovine serum  in 5% CO2 at 37°C. IL-2  was supplemented (100 unit/ml) into the culture twice a week. Raltegravir  was used at the concentration of 10 μM throughout the culture in order to prevent secondary infection.HTLV-1-infected T cell clones– population20. HTLV-1-infected T cell clones were stained with LIVE/DEAD and anti-Tax antibody . Then the viral protein Tax was stained intracellularly with Foxp3 staining kit  . CADM1 sntibody . The antChromatin was sonicated and sheared following cell and nuclear lysis. Sheared chromatin was incubated with each of the following antibodies: rabbit polyclonal anti-H3K4me3 , rabbit polyclonal anti-H3K9Ac , mouse anti-H3K27Ac , rabbit polyclonal anti-H3K36me3 , rabbit polyclonal anti-CTCF  and normal rabbit IgG . The resulting immune complexes were precipitated with Protein A+G magnetic beads . After washing the beads, the DNA fragments were recovered and purified.22. Namely, DNA fragments were incubated in hybridization buffer  at 65°C for 4 hours with human Cot-1 DNA  and biotinylated hybridization probes complementary to the HTLV-1 provirus sequence. After hybridization, the probes and associated DNA library fragments were recovered with Streptavidin beads , and the DNA fragments were PCR-amplified using NEBNext High-Fidelity 2X PCR Master Mix  with primers P5 (5′-AAT GAT ACG GCG ACC ACC GA-3′) and P7 (5′-CAA GCA GAA GAC GGC ATA CGA-3′) to a concentration of the order of 10 nM .Adaptor DNA was attached to the fragments obtained from chromatin immunoprecipitation using NEBNext Ultra II DNA Library Prep Kit . Then DNA library fragments were enriched for the HTLV-1 proviral sequence by probe capture hybridizationThe DNA library fragments were sequenced with Miseq Reagent Kit v3 (150 cycles) (Illumina).All reads obtained from an HTLV-1 clone TBX4B were aligned to an HTLV-1 reference genome J02029 ref.. The rea24. Paired reads were kept if they were aligned within 80-800 nucleotides. PCR duplications were removed with Picard 2.6.0. The data was converted into the bedgraph format with bedtools25, and visualised with the R Bioconductor package Sushi26.Paired 75 bp reads were aligned to the respective reference genome with BWADNA was extracted from fixed and flow-sorted HTLV-1-infected cells with the DNA FFPE Tissue Kit . DNA was sheared by sonication (Covaris) to obtain 200-600 bp fragments. Fragments containing methylated DNA were precipitated using MethylCollector Ultra Kit  with Low Salt Binding Buffer supplied by the manufacturer. The fragments were purified from either precipitated or unbound fraction with MinElute PCR purification Kit , and eluted into the same volume. For each locus in the HTLV-1 provirus indicated inDNA was extracted from fixed and flow-sorted HTLV-1-infected cells with the DNA FFPE Tissue Kit . Purified DNA was subject to bisulfite treatment with EpiTect Bisulfite Kit . A nested PCR was performed with FastStart Taq DNA Polymerase  to amplify the regions indicated in27. Namely, 2 μl of recombinant Cas9 protein (2.5 μg/μl)  and 0.5 μl ofin vitro-synthesised guide RNA (3 μg/μl)  as well as 1 μl of homologous DNA repair template (100 μM) (5′-AGG AAG CTG TGC TTG ACG GTT TGC TAT CCT TAG AAG AGG AAA GCC GCG GCC GGC TGC GAC GGG GCC CTC CAG GGG AGA AAG CCC CGC CAA GAG GTG AAA CGC ATC GTG ATC GGC AGC GAC GGG CTG AGG AGA AGA GGA AGC GAA AAA AAG AGC GGG AGA AAG AGG AGG AAA AGC AG-3′) (Integrated DNA Technologies) were combined with 8 μl of R resuspension buffer from Neon transfection system (Invitrogen). Cells (5 × 105) were suspended in buffer containing RNP complex and transfection was performed with Neon (Invitrogen) . When cells had recovered at around a week after transfection, DNA was extracted with DNA extraction solution . The absolute copy numbers ofgag and the mutant proviral sequence of the CTCF site in the DNA were quantified by qPCR  with respective standard curves, in order to estimate the frequency of mutant cells in the culture. Primers and probes forgag  and mutant CTCF-binding site  were used. Mutants were detected at a frequency of 1.6% to 3.1% in each of 4 HTLV-1-infected T cell clones applied. Cells were subcloned in order to isolate mutant cells by either limiting dilution or flow-sorting (BD FACSAria III) under Containment Level 3 conditions. Subclones were screened for the mutant proviral sequence of the CTCF site by PCR  with a mutation-specific primer indicated above. The DNA sequence of the putative mutants was confirmed by Sanger sequencing (GATC Biotech).Ribonucleoprotein (RNP) complex transfection was used17. The coverslips were imaged with an Olympus IX70 inverted widefield microscope with a 100× 1.35NA UPlanApo oil objective lens, a Spectra Light Engine illumination source (Lumencor) and an ORCA-Flash 4.0 V2 digital CMOS camera (Hamamatsu).HTLV-1-infected T cell clones were subjected to single-molecule RNA-FISH, targeting the plus- or minus-strand transcripts of HTLV-1, following the protocol described previouslyThe DNA samples obtained by ChIP using anti-CTCF antibody or normal rabbit IgG described above were quantified by droplet digital PCR. The reaction was set up using ddPCR Supermix  containing 750 nM each of forward and reverse primers and 250 nM probe to detect the HTLV-1 CTCF-binding site and TC-1 locus. Primers and probe for the HTLV-1 CTCF-binding site are listed inTo identify the epigenetic modifications associated with transcriptional activity in the provirus, we sorted the cells based on Tax protein expression and performed ChIP and DNA methylation analyses for each fraction  unless sIn the descriptions of the ChIP-seq data, we use the terms “5′ LTR junction” and “3′ LTR junction” to denote the regions of the HTLV-1 provirus adjoining the 5′ LTR and the 3′ LTR, respectively.in vitro HTLV-1-infected T cell clones, because the cells in each clone share the same provirus insertion site, so minimizing effects due to heterogeneity in the host genomic environment of the provirus. We performed ChIP on the Tax+ and Tax– populations from one of the HTLV-1-infected T cell clones to identify the histone modifications in the HTLV-1 provirus  were constantly detected, regardless of Tax expression. In particular, H3K4me3 was more highly enriched in those regions in the Tax– population (in vitro HTLV-1-infected T cell clone (11.65)  . The smaulation  is a cha+ population, and those marks appeared persistent in the 3′ LTR junction regardless of viral reactivation.Next, we examined patient-derived PBMCs  after overnight culture . The pat16; second, the HTLV-1-infected T cell clones demonstrate intermittent bursts of Tax expression29. Currently, there is no means of separating HBZ+ and HBZ– populations. However, we anticipate that the changes in histone marks in the 3′ LTR junction are likely to be small, and the minus-strand expression is much more constant than the plus-strand transcription17.Because we observed a similar pattern of histone marks in two independent HTLV-1-infected T cell clones with distinct provirus insertion sites, as well as in PBMCs with polyclonal insertion sites, the pattern of histone marks that we observed is not likely to be dependent on the host genomic environment of the provirus, but rather is a feature intrinsic to the HTLV-1 provirus. Two observations indicate that the changes in the observed pattern of histone marks are rapid and reversible: first, HTLV-1 reactivation takes place within a few hours of culture in patient-derived PBMCs19, we observed binding of the host protein CTCF in the HTLV-1 provirus  and examined 9 loci across the HTLV-1 provirus by qPCR . In the s locus . Note ths locus  despite s locus .gag toenv was heavily methylated. On the other hand, again, the pX and 3′ LTR regions were not methylated. Thepol2 site in Patient TED was not detected, perhaps because of sequence polymorphism in HTLV-1.We also examined patient-derived PBMCs (Patients TDZ and TED)  cultured19, this CTCF-binding site is situated at the observed border of DNA methylation  , among other transcription factor binding sites, upstream of the TATA box . The TREL36905) . Because+ population from Patient TED  and (f)  . In geneand (f) . Similarand (f) , with anin vitro, the 5′ LTR promoter was hardly methylated, regardless of whether Tax is expressed at a given time  in the provirus. Considering the known functions of CTCF, which include transcription regulation and the formation of an epigenetic barrier, we hypothesized that CTCF in the pX region of the HTLV-1 provirus controls the epigenetic modifications and viral transcription. To test this hypothesis, we applied CRISPR/Cas9 modification to alter the sequence of the CTCF-binding site, using ribonucleoprotein complex transfectionaltered . We prevsed Tax . We ther+ or Tax– populations . The 5′ LTR promoter was also hypomethylated, regardless of Tax expression in ΔCTCF-binding clones (We examined the epigenetic modifications in a ΔCTCF-binding clone (TBX4B-78). First, we confirmed that CTCF was no longer detected in either the Taxlations . The patlations  was larglations , in thatrovirus  was not rovirus :gag,po clones . These rSince the technique of ChIP-sequencing coupled with probe capture hybridization lacks precise quantification, we went on to quantify the kinetics of the plus- and minus-strand transcription of HTLV-1 more precisely, using our previously described protocol of single-molecule RNA-FISH, to detect any possible impact of CTCF-binding on HTLV-1 transcription.17. To test whether CTCF has an impact on the pattern of the plus- and minus-strand expression, we performed single-molecule RNA-FISH on the ΔCTCF-binding clones. Representative images are shown in17, a limited fraction of cells had a large number of plus-strand transcripts, and the remaining cells were negative. On the other hand, minus-strand transcripts were present in most cells. The number of transcripts per cell in the ΔCTCF-binding clones is presented inHBZ). We confirmed this result with another HTLV-1-infected clone (11.50)  . This rein vitro. It is likely that they were selected for strongin vitro growth, and so may have diverged phenotypically from the PBMCsin vivo; such differences could conceivably affect HTLV-1 transcription. Therefore, we wished to test the putative association between CTCF-binding and viral reactivation using PBMCs from HTLV-1-infected subjects.The putative impact of CTCF in the provirus was tested above on the HTLV-1-infected T cell clones. These clones were initially isolated and expanded from PBMCs of HTLV-1-infected individuals, and show robust growth31 that there are tens of thousands of different HTLV-1-infected T cell clones in a typical HTLV-1-infected individual. Each clone carries a single copy of the provirus inserted in a unique location in the genome. Whether the viral reactivation takes place has a strong dependence on the genomic insertion site of the provirus32. We asked if those clones that reactivate the plus-strand transcription have differential CTCF occupancy in the provirus from those that remain silent after the short-term culture. We performed a ChIP assay targeting CTCF on the Tax+ and Tax– populations after overnight culture, and performed droplet digital PCR to quantify the CTCF occupancy. The results showed no measurable difference in CTCF-binding between the two populations  there are two categories of HTLV-1-infected cellsin vivo - those with the 5′ LTR promoter methylated and those in which it is hypomethylated; (2) viral reactivation is allowed only in the hypomethylated cells; and (3) the DNA methylation in the body of the provirus does not influence viral reactivationex vivo. Whether proviral reactivation takes place depends strongly on the provirus insertion site32. It is therefore possible that DNA methylation in the 5′ LTR promoter is related to the provirus insertion site.DNA hypomethylation in the 5′ LTR promoter is associated with proviral transcription, as previously reported19, we hypothesized that CTCF imposes a boundary in the histone modifications and DNA methylation around the CTCF-binding site in the pX region. However, our results do not support the hypothesis that the pattern of epigenetic modifications in HTLV-1 depends directly on CTCF-binding. It is possible that changes in epigenetic modifications would take a longer time to become established after the provirus is mutated, or that CTCF imposes an epigenetic boundary at the pX region in the early stage of infection and becomes dispensable thereafter. However, the chromatin boundary in the pX region is not static: our observations indicate that, each time a cell goes through the cycle of plus-strand expression, the histone modifications change rapidly in the provirus, yet they always return to the marks present in the previous state of plus-strand expression, even without CTCF binding. We conclude that CTCF binding does not directly impose a barrier to the spread of these epigenetic modifications. Instead, it is possible that CTCF confers a benefit on HTLV-1 by making chromatin loops with the nearby host genome36. However, the consequences of HTLV-1 inserting an ectopic CTCF-binding site in the host genome vary widely according to the genomic integration site: it remains to be seen whether there is an additional impact of CTCF binding to the HTLV-1 provirus that is consistent in all clones. It is also possible that CTCF confers a higher rate of HTLV-1 transmission and increases viral persistencein vivo. However, long-term animal model experiments may be necessary to test this hypothesis. It remains an open question what regulates the distinct epigenetic modifications observed around the pX region in HTLV-1.Following the discovery that HTLV-1 binds CTCFThe following datasets are available from Open Science Framework:Dataset 1 (ChIP-seq on epigenetic marks):https://doi.org/10.17605/OSF.IO/4Q9RY37Dataset 2 (image data on smFISH):https://doi.org/10.17605/OSF.IO/4TBQY38Dataset 3 (qPCR and ddPCR data from ChIP and MeDIP):https://doi.org/10.17605/OSF.IO/8M76K39Dataset 4 (Sanger sequencing data from bisulfite assay):https://doi.org/10.17605/OSF.IO/D62H740These datasets are available under a CC0 1.0 Universal licence.Human subjects: All donors gave written informed consent in accordance with the Declaration of Helsinki to donate blood samples to the Communicable Diseases Research Tissue Bank, approved by the UK National Research Ethics Service (15/SC/0089). I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In the present study Miura et al., carried out a study aimed at investigating the epigenetic changes in the HTLV-1 genome that take place upon (re)activation of viral transcription. Based on their findings the authors conclude that the histone modifications H3K4me3, H3K9Ac and H3K27Ac correlated with plus-strand expression. Furthermore, plus-strand transcription was associated to promoter hypomethylation. This pattern of viral epigenetic modifications appeared to be independent of both the provirus insertion site and the binding of the CTCF chromatin architectural protein. The paper is clearly written and the pertinent literature is appropriately cited and discussed. The experiments are well laid-out and technically sound. The conclusions are well supported by the results and address an important aspect of the HTLV-1 life cycle. The methods are well described and appropriate. As the T-cell clones show a mixture of Tax positive and Tax-negative cells, it would be very interesting to synchronize viral expression in these cells (e.g. by cell cycle double block or by reversible inhibition of transcription) to better follow the temporal regulation of these expression bursts. This would also be an ideal model to study the mechanisms controlling such rapid changes in histone acethylation and DNA methylation. Minor points. Page 3 The authors describe the effects of Tax on cell turnover. It would be fair to mention its powerful  effect on plus strand transcription. The author state that, upon ex vivo culture of primary cells from infected patients, plus-strand expression is greatly increased while, the abundance of HBZ mRNA remains relatively stable. Although there is little doubt about the fact that plus strand expression is increased, previous studies showed that HBZ expression is increased as well . Page 6 The CHIP and MeDIP analyses were carried out on different patients. It would have been more informative to compare these results in the same patients. Page 10 The term CTCF mutant may be misleading as the mutation was introduced in the CTCF-binding site in the viral genome - not in the CTCF gene.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Minor points.Page 3The authors describe the effects of Tax on cell turnover. It would be fair to mention its powerful  effect on plus strand transcription.Response.  A sentence mentioning the positive feedback by Tax on the 5’ LTR promoter has been inserted in the 3rd paragraph in the Introduction section.The author state that, upon ex vivo culture of primary cells from infected patients, plus-strand expression is greatly increased while, the abundance of HBZ mRNA remains relatively stable. Although there is little doubt about the fact that plus strand expression is increased, previous studies showed that HBZ expression is increased as well .HBZ mRNA in patient-derived PBMCs by smFISH. Therefore, in the present paper we confine the discussion on the HBZ expression in ex vivo culture.Response.  We are currently investigating the quantity ofPage 6The CHIP and MeDIP analyses were carried out on different patients. It would have been more informative to compare these results in the same patients.Response.  Thank you for pointing out. We agree that it is informative to discuss results of different assays (ex. ChIP and MeDIP) in same patients, and also to compare the difference between patients for a same assay. To enable comparison of the results in this paper with data on the same subjects reported in other publications from our group, we have revealed in the revised paper the three-letter patient codes that are used in our other publications.Page 10The term CTCF mutant may be misleading as the mutation was introduced in the CTCF-binding site in the viral genome - not in the CTCF gene.Response.  We have systematically replaced the term “CTCF mutant” with “ΔCTCF-binding clone”. HTLV-1 has complex transcriptional regulation in early and established long-term infections in T-cells.   The Bangham group has previously shown that HTLV1 genomes establish an epigenetic patterns in latently infected T-cells, and how this corresponds to differences in viral transcription from the positive (Tax+) and negative (HBZ+) strands.  Several intriguing features were previously observed, including a boundary of DNA methylation and a CTCF binding site at the pX locus.  Additionally, a single cell study showed that transcription from the positive strand was dynamic, with some rare clonal bursts of TAX expression and viral reactivation, while the negative stand expression of HBZ was constitutive in all populations.  In this new study, the authors further characterize the epigenetic control of HTLV-1 infected PBMCs and T-cell clones by comparing populations actively expressing Tax from those not expressing Tax. The authors show by ChIP-Seq that histone modifications associated with transcription are activation at the 5’ LTR and genome body of the Tax expressing population of HTLV-1 cells.  In contrast, DNA methylation patterns as measured by MeDIP do not show significant variation in T-cell clones whether or not they express Tax.  Sequence specific DNA methylation as measured by bisulfite sequencing shows some differences in Tax positive relative to Tax negative PBMCs from HTLV-1 carriers.  Another major conclusion is that the CTCF binding to the proviral genome has no measurable function on viral gene expression or epigenetic patterning.  Although this CTCF mutation is essentially negative data, it is important to report in context to previously observed findings and expectations of CTCF function in regulation viral transcription and epigenetics. Specific Comments 1.   The dynamic expression of the HTLV-1 positive strand needs some further characterization. What percentage of cells express Tax in the PBMC carriers  and in the T-cell clones? Is this cell cycle, replicative age, or stress dependent?  2.  The evidence that the population not expressing TAX is still expressing the negative strand is the single cell FISH.  Can this also be detected by strand-specific RT-PCR to validate the FISH? 3.  Fig. 3.  ChIP-Seq data excludes the LTR.  Understandably, since the LTR is duplicated, it may be problematic to assign specific signals to the 5’ or 3’ region.  Nevertheless, the analysis of the combined LTR could be informative and should be included in the manuscript, either as a separate panel, or for a representative 5’ or 3’ LTR region for each histogram  4.  Fig 3B does not provide a ChIP-Seq for CTCF on PBMCs.  This is problematic since the paper focuses on the role of CTCF in various infection and latency scenarios.   If CTCF does not bind in PBMCs, this should be reported, and the temporal or stochastic nature of CTCF binding should be further investigated.  Is CTCF binding dependent on culture condition or clone specific? 5.  Fig 9 is difficult to interpret without additional controls.  A positive and negative control for CTCF binding, as well as an IgG control,  should be included in each sample (Tax+ vs Tax-).  Without these controls, or without the CTCF ChIP-Seq data in PBMCs from fig 3B, it is not possible to conclude that CTCF binds to the majority of genomes during primary infection in PBMCs.  This raises the issue of whether CTCF binding to the HTLV1 genome is highly variable for different viral integrations and isolated clones.  Along these lines, it may be worth examining multiple clonal isolates to determine how consistently CTCF binds to the proviral genome. 6.  The title may be misleading since it is not clear that the 5’ and 3’ LTRs are analyzed completely and separately from each other. Minor Issues:rd paragraph of Introduction:  “… transcription of… the plus and minus strand, respectively.  (REF)” 1.  A reference should be provided  for statement on p3. 3st column, Results section:  “…In the 3’ UTR and pX region, these three histone marks  were constantly detected, regardless of Tax expression.” 2.  p. 6, 1 I think this could be stated more clearly.  Perhaps starting with a  statement referring to the Tax negative population.  Also, the 3’UTR is not examined, but rather the boundary adjacent to the 3’ UTR is measured, according to the Figure Legend.    Finally, the levels of H3K4me3 at the 3’ end of the genome appears to be significantly enriched relative to the Tax positive population, perhaps suggesting these cells are expressing negative strand RNA and HBZ.  This could be tested experimentally.nd column, 1st sentence:  “…regardless of viral reactivation.”    I think “reactivation” may not be the appropriate designation, as TAX expression is the only readout, and this expression is dynamic through an unknown mechanism that may be distinct from viral reactivation that produces infectious particles.  The status of HBZ expression should be tested or discussed. 3.  p6, 2 4.  It is surprising that there is so little DNA methylation detected in the LTR of the stable T-cell clones as measured by bisulfite sequencing  compared to PBMCs from carriers .   Some consideration of this difference could be included in the Discussion section. 5.  Fig. 5D.   The bisulfite methylation may suggest that there is a trend toward selective methylation of primer region D in the Tax negative population. If the bisulfite conversion is not complete, this trend may suggest that DNA methylation is the mechanism of epigenetic silencing at the 5’ UTR.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Specific Comments 1.   The dynamic expression of the HTLV-1 positive strand needs some further characterization. What percentage of cells express Tax in the PBMC carriers  and in the T-cell clones? Is this cell cycle, replicative age, or stress dependent?HBZ mRNA molecules at the G0/G1 stage, and (2) a plus-strand burst (i.e. a rapid transcription) is more likely to be observed when a cell lacks detectableHBZ mRNAs. Indeed, the odds of a cell having a plus-strand burst at the G2/M stage is significantly lower than in cells in G0/G1 . These observations indicate an association between the plus-strand expression and the cell cycle in HTLV-1 T cell clones cultured in vitro. Response.  In HTLV-1-infected T cell clones, some 5-10% of the total population carry >4 plus-strand transcripts at any one time. We reported in our previous publication  that (1) HTLV-1-infected T cell clones have fewer In PBMCs isolated from HTLV-1 carriers, the HTLV-1 plus-strand reactivation is likely to be caused by environmental stress, as is observed in many other viruses. We are currently investigating the frequency and intensity of the HTLV-1 plus- and minus-strand expression during ex vivo culture of PBMCs by smFISH. Paragraph 5 in the Introduction has been modified to include these important points, and the appropriate papers cited. 2.  The evidence that the population not expressing TAX is still expressing the negative strand is the single cell FISH.  Can this also be detected by strand-specific RT-PCR to validate the FISH?tax mRNA expression and protein expression; third, the PCR does not give information at the single-cell level.  At present, we believe that smFISH is the most sensitive and specific technique available to detect mRNA, especially at the single-cell level. Response.  A possible alternative approach would be to sort the cells by flow cytometry for Tax protein and assay HBZ (minus strand) in the Tax-positive and Tax-negative fractions by RT-PCR. However, such approaches have certain limitations. Tax protein detection is less sensitive than smFISH, and crucially there is a time-lag between the onset of 3.  Fig. 3.  ChIP-Seq data excludes the LTR.  Understandably, since the LTR is duplicated, it may be problematic to assign specific signals to the 5’ or 3’ region.  Nevertheless, the analysis of the combined LTR could be informative and should be included in the manuscript, either as a separate panel, or for a representative 5’ or 3’ LTR region for each histogram Response.  We have revised Figure 3, Figure 7a and Supplementary Figure 1 to include reads aligned within the LTRs. We present those reads with grey colour in such a way that they can be differentiated from the rest of the reads that uniquely aligned. 4.  Fig 3B does not provide a ChIP-Seq for CTCF on PBMCs.  This is problematic since the paper focuses on the role of CTCF in various infection and latency scenarios.   If CTCF does not bind in PBMCs, this should be reported, and the temporal or stochastic nature of CTCF binding should be further investigated.  Is CTCF binding dependent on culture condition or clone specific? Response.  In Figure 3b, we omitted the ChIP-seq for CTCF in patient-derived PBMCs because of the limited amount of materials. In the revised Figure 9 we show that HTLV-1 binds CTCF in PBMCs regardless of the plus-strand expression. Also, in our previous publication reporting the binding of CTCF in the HTLV-1 provirus , we demonstrated that CTCF binds to various HTLV-1-infected cell lines as well as PBMCs isolated from HTLV-1-infected individuals. It is documented that CTCF-binding is affected by methylation at one of the cytosine residue in the CTCF binding-site. In HTLV-1, CTCF binds to the pX region in the provirus where DNA is rarely methylated  . Therefore, we anticipate that CTCF binding to the HTLV-1 provirus shows little variation among culture conditions or provirus insertion sites. However, it should be noted that a lack of variation in CTCF-binding to HTLV-1 does not necessarily mean that the CTCF binding is strong. We discuss the CTCF occupancy as a surrogate measure of binding strength in the next point. 5.  Fig 9 is difficult to interpret without additional controls.  A positive and negative control for CTCF binding, as well as an IgG control,  should be included in each sample (Tax+ vs Tax-).  Without these controls, or without the CTCF ChIP-Seq data in PBMCs from fig 3B, it is not possible to conclude that CTCF binds to the majority of genomes during primary infection in PBMCs.  This raises the issue of whether CTCF binding to the HTLV1 genome is highly variable for different viral integrations and isolated clones.  Along these lines, it may be worth examining multiple clonal isolates to determine how consistently CTCF binds to the proviral genome. Response.  In the revised paper, we show the results  of new experiments in which we carried out CTCF ChIP assay on patient-derived PBMCs with two biological replicates, including a negative control (rabbit IgG) and the TC-1 locus as a positive control.  The results show that CTCF binds to HTLV-1 after overnight culture, regardless of plus-strand expression. This observation is consistent with the conclusion that CTCF-binding has no discernible immediate impact on HTLV-1 expression. The results in the revised Figure 9 also show that the CTCF ChIP signal at the HTLV-1 CTCF-binding site was lower than at the TC-1 locus (positive control for CTCF binding).  CTCF occupancy (an approximation of binding strength) depends on the primary DNA sequence  and   can be estimated by ChIP readout.  We infer that CTCF occupancy at the HTLV-1 provirus is lower than that at TC-1. 6.  The title may be misleading since it is not clear that the 5’ and 3’ LTRs are analyzed completely and separately from each other. Response.  We acknowledge that we have not explicitly identified the epigenetic modifications within the two LTRs. Also, as pointed out by both of the reviewers, an important finding is that CTCF appeared to have no discernible impact on the epigenetic modifications around the pX region or on spontaneous transcription. Therefore, we have altered the title of the revised paper: Spontaneous HTLV-1 transcription and epigenetic changes around the pX region are CTCF-independent.Minor Issues: 1.  A reference should be provided  for statement on p3. 3rd paragraph of Introduction:  “… transcription of… the plus and minus strand, respectively.  (REF)” Response.  A reference  has been inserted (Ref #5 in the revised paper). 2.  p. 6, 1st column, Results section:  “…In the 3’ UTR and pX region, these three histone marks  were constantly detected, regardless of Tax expression.” I think this could be stated more clearly.  Perhaps starting with a  statement referring to the Tax negative population.  Also, the 3’UTR is not examined, but rather the boundary adjacent to the 3’ UTR is measured, according to the Figure Legend.    Finally, the levels of H3K4me3 at the 3’ end of the genome appears to be significantly enriched relative to the Tax positive population, perhaps suggesting these cells are expressing negative strand RNA and HBZ.  This could be tested experimentally. Response.  Thank you for the suggestion. In the revised paper, we have defined the terms “5’ LTR junction” and “3’ LTR junction” at the beginning of the Results section, to clarify how we refer to the regions adjoining the 5’ LTR and 3’ LTR.  Subsequently we systematically replaced “5’ LTR” and “3’ LTR” with “5’ LTR junction” and “3’ LTR junction” respectively. In relation to this comment, two sentences have been altered in the revised paper: (1) In the last sentence in the first paragraph (Page 11) (“These results are inconsistent with the idea that CTCF imposes an epigenetic border in the HTLV-1 provirus and regulates the distinct modifications between the 5′ and 3′ LTRs.”), we replaced the phrase “between the 5’ and 3’ LTR” with “around the pX region”. (2) In the last sentence in the Discussion section, we replaced “in the 5’ and 3’ LTRs in HTLV-1” with “around the pX region in HTLV-1”. Lastly, we state in the revised paper that H3K4me3 at the pX and adjoining the 3’ LTR is higher in the Tax– population, presenting additional evidence obtained by ChIP-qPCR to make a quantitative comparison. The new data are presented in the revised Supplementary Figure 1 . 3.  p6, 2nd column, 1st sentence:  “…regardless of viral reactivation.”    I think “reactivation” may not be the appropriate designation, as TAX expression is the only readout, and this expression is dynamic through an unknown mechanism that may be distinct from viral reactivation that produces infectious particles.  The status of HBZ expression should be tested or discussed.+ PBMCs that cells that restore the plus-strand transcription also produce unspliced transcripts (gag), which is associated with production of HTLV-1 infectious particles. HBZ expression is currently under investigation with smFISH in conjunction with plus-strand transcripts at the single-cell level. Response.  We have preliminary evidence from smFISH analysis of HTLV-1 4.  It is surprising that there is so little DNA methylation detected in the LTR of the stable T-cell clones as measured by bisulfite sequencing  compared to PBMCs from carriers .   Some consideration of this difference could be included in the Discussion section.+ T cell clones and PBMCs. First, single T-cell clones are isogenic in terms of the genomic environment of the provirus insertion site, whereas the polyclonal population of PBMCs contains proviruses inserted in tens of thousands of unique sites, each with a distinct genomic environment. Second, it is plausible that a single cell with a particular pattern of DNA methylation was selected at the beginning of T cell cloning, and that pattern has been stably inherited in the clone.  We infer that, at least in in vitro culture, DNA methylation at the 5’ LTR promoter is stable, and has little impact on the spontaneous expression of the plus strand. We have added this point in the paragraph entitled “DNA hypomethylation in the plus-strand promoter is a prerequisite for viral reactivation” in the Results section. Response.  There are two major points to discuss concerning the difference between HTLV-1 5.  Fig. 5D.   The bisulfite methylation may suggest that there is a trend toward selective methylation of primer region D in the Tax negative population. If the bisulfite conversion is not complete, this trend may suggest that DNA methylation is the mechanism of epigenetic silencing at the 5’ UTR. Response.  Our results show that the bisulfite conversion was highly efficient, as we could hardly see cytosine residues at non CpG sites after conversion.  To estimate to what degree DNA methylation accounts for the provirus silencing, independently of the position effect, would require simultaneous identification of insertion site, DNA methylation and virus transcription in single cells. However, this is beyond the scope of the present paper.


Tremendous advances have been made recently in the identification of genes and signaling pathways associated with the risks for psychiatric disorders such as schizophrenia and bipolar disorder. However, there has been a marked reduction in the pipeline for the development of new psychiatric drugs worldwide, mainly due to the complex causes that underlie these disorders. G-protein coupled receptors (GPCRs) are the most common targets of antipsychotics such as quetiapine and aripiprazole, and play pivotal roles in controlling brain function by regulating multiple downstream signaling pathways. Progress in our understanding of GPCR signaling has opened new possibilities for selective drug development. A key finding has been provided by the concept of biased ligands, which modulate some, but not all, of a given receptor’s downstream signaling pathways. Application of this concept raises the possibility that the biased ligands can provide therapeutically desirable outcomes with fewer side effects. Instead, this application will require a detailed understanding of the mode of action of antipsychotics that drive distinct pharmacologies. We review our current understanding of the mechanistic bases for multiple signaling modes by antipsychotics and the potential of the biased modulators to treat mental disorders. Despite tremendous success identifying genetic risk factors for major psychiatric illnesses such as schizophrenia, bipolar disorder, and depression, the field of psychiatric disorders still lags far behind other therapeutic areas of medicine in that not even a single gene has been conclusively demonstrated to provoke any of their symptoms. This is in large part due to the complex genetic as well as environmental and epigenetic risk factors that underlie these diseases. Furthermore, the rational design of multitarget drugs such as multi-acting receptor targeted antipsychotics (MARTA) has encountered considerable challenges in optimizing multiple structure-activity relationships while maintaining drug-like properties. These factors have led to a dramatic worldwide decline in the discovery and development of new psychiatric drugs ,2,3. ThuG-protein coupled receptors (GPCRs) , also knNeurons communicate with each other via neurotransmitters through two mechanisms referred to as fast and slow synaptic transmission, which involve two distinct classes of receptors. Ionotropic receptors comprise ligand-gated ion channels that induce fast synaptic transmission. In contrast, metabotropic receptors constitute GPCRs that cause slow synaptic transmission through intracellular signal transduction as well as induction of gene expression to exert antipsychotic actions ,8,9. IntGPCR activation elicits downstream G protein-dependent signaling followed by phosphorylation of the receptor by G protein-coupled receptor kinases (GRKs) . The phoAccumulating evidence indicates that selective biased signaling may be extended to a wide variety of GPCRs currently targeted by pharmacological agents. The conventional agonists and antagonists are simultaneous stimulators and blockers of both G-protein-mediated signaling and β-arrestin-dependent signaling, respectively. The specificity of the ‘biased ligands’, which selectively target either G-protein-mediated signaling or β-arrestins-dependent signaling, is able to separate the benefits and the adverse effects of conventional drugs.2+, and Inositol 1,4,5-triphosphate (IP3). These second messengers in turn trigger distinct signaling cascades. Notably, multiple distinct GPCRs can couple with the same Gα protein and the same receptor can also couple with more than one Gα protein. Gβγ subunits have both regulatory and signaling functions, serving either as scaffolds for kinases such as phosphatidylinositol 3-kinase (PI3K) and protein kinase D (PKD) or as modulators of ion channels  pathways A. In G pchannels A 5,6]. ,6. 2+, aDopamine is a catecholamine neurotransmitter that is deeply involved in CNS disorders such as schizophrenia, bipolar disorder, Parkinson’s disease, attention deficit hyperactivity disorder, and obsessive–compulsive disorder ,12,13,14In the striatopallidal neurons, D2R activation evokes Gi/o-mediated signaling to inhibit intracellular cAMP accumulation and the protein kinase A (PKA)/dopamine and cAMP-regulated phosphoprotein of 32 kDa (DARPP-32) pathway, which can mediate numerous behavioral effects of dopamine ,35,36 FB. Gs-couAntagonism of D2Rs in basal ganglia is the primary mode of action of antipsychotics, the first-line treatment for schizophrenia and bipolar disorder that share high levels of polygenic and pleiotropic molecular architecture ,15,47. ISeveral findings argued against hyperdopaminergia as the singular cause of manifestation of schizophrenia , and addAripiprazole is a third-generation antipsychotic drug initially approved for the treatment of schizophrenia but found to be effective in bipolar disorder and major depressive disorder ,61. AripQuetiapine, an atypical antipsychotic drug antagonizing multiple GPCRs including D2R and 5-HT2A ,76, has The function of D2Rs has been examined through the use of pharmacological agents and by genetic deletion approaches. Recent studies using cell type-specific knockouts illustrate the necessity of D2Rs for many behavioral and physiological functions ,92,93. SSeveral studies have illustrated the importance of cAMP–PKA–DARPP32 signaling in striatopallidal MSNs for antipsychotic-induced phosphorylation of ribosomal protein S6 and histone H3, as well as the expression of c-fos and egr-1 ,98. On tAlthough atypical antipsychotics show beneficial effects on positive symptoms in schizophrenia with less side effects such as extrapyramidal symptoms, none of these drugs are expected to effectively improve negative symptoms and cognitive dysfunction. However, recent efforts support the idea that biased D2R ligands effectively restore these impairments. Allen et al. have discovered several functionally selective biased D2R-βarr2 ligands, UNC9975, UNC0006, and UNC9994, by exploring multiple regions of the aripiprazole template . As descA series of observations obtained by biased D2R ligands indicate that a functionally selective compound, which activates D2R-βarr2 pathway and reduces Gi activity in hypodopaminergic PFC while simultaneously blocking D2R-βarr2 pathways in hyperdopaminergic striatum, would exhibit beneficial effects on schizophrenic symptoms . It is iA tremendous number of studies with advanced technologies including next-generation sequencing in recent years have revealed that major psychiatric disorders, such as schizophrenia and bipolar disorder, share molecular and genetic architecture. However, not even a single gene that provokes any psychiatric symptoms has not been identified so far. GPCRs, especially D2R, are well known therapeutic targets for the disorders. A biased GPCR ligand has the potential to improve mental illness with fewer adverse effects.


The angiogenic capacity of local tissue critically regulates the response to ischemic injury. Elevated reactive oxygen species production, commonly associated with ischemic injury, has been shown to promote phosphorylation of the vascular endothelial growth factor receptor 2 (VEGFR2), a critical regulator of angiogenesis. Previous data from our lab demonstrated that diminished levels of the antioxidant glutathione positively augment ischemic angiogenesis. Here, we sought to determine the relationship between glutathione levels and oxidative stress in VEGFR2 signaling. We reveal that decreasing the ratio of GSH to GSSG with diamide leads to enhanced protein S-glutathionylation, increased reactive oxygen species (ROS) production, and enhanced VEGFR2 activation. However, increasing ROS alone was insufficient in activating VEGFR2, while ROS enhanced VEGF-stimulated VEGFR2 activation at supraphysiological levels. We also found that inhibiting glutathione reductase activity is sufficient to increase VEGFR2 activation and sensitizes cells to ROS-dependent VEGFR2 activation. Taken together, these data suggest that regulation of the cellular GSH:GSSG ratio critically regulates VEGFR2 activation. This work represents an important first step in separating thiol mediated signaling events from ROS dependent signaling. 2O2 is insufficient to activate VEGFR2; however decreasing GSH:GSSG results in ligand independent activation of VEGFR2.Changes in GSH:GSSG but not ROS alone activates VEGFR2. This is a brief summary of the manuscript demonstrating that the addition of exogenous H Scavenging metabolically derived reactive oxygen species (ROS) is a primary function of GSH. GSH is used in many enzymatic reactions to buffer ROS. One of the by-products of these enzymatically driven reactions involving GSH is oxidized glutathione (GSSG). GSSG is either recycled to GSH by the enzyme glutathione reductase (GR) or exported out of the cell to maintain the intracellular redox balance between GSH and GSSG, which, depending on cell type normally ranges from 200:1 – 30:1 The dynamic changes in the glutathione redox couple control several cellular processes including cell proliferation, differentiation, and apoptosis. Cellular processes are driven by thiol-disulfide switches or “nano-switches” (a phrase used by Schafer and Buettner), which are regulated by the GSH redox couple Glutathione has been shown to regulate protein signaling through a process called S-glutathionylation, a redox-dependent post-translational modification. A potential mechanism to stimulate S-glutathionylation is by increasing intracellular GSSG levels, allowing modification of cysteine residues through a thiol-disulfide exchange Our lab previously demonstrated that altering basal glutathione levels affects angiogenesis in a murine model of ischemic vascular remodeling through an increase in VEGF production 22.1Human aortic endothelial cells (HAEC) were purchased from Lonza at p3 and sub-cultured in MCDB 131 media supplemented with bovine brain extract, fetal bovine serum, and heparin. HAECs were propagated at 37 °C in 5% CO2. All experiments were performed between passages 7–10.2.22O2  for 30 min. The experimental groups combining diamide or H2O2 with VEGF-A treatment received VEGF-A after 15 min of diamide or H2O2 treatment. Cell groups treated with peg-catalase were preloaded for 16 h with 500 μM before addition of diamide. Cells were then collected in 2X laemmli buffer. The cell lysate was boiled at 95 °C and stored at −80 °C until use. Cell lysates were separated using a 7.5% SDS gel and transferred to PVDF membranes. Antibodies used were phospho Y1175 VEGFR2, total VEGFR2, β-tubulin , total glutathionylation antibody (EMD Millipore) and secondary antibodies from Cell Signaling and Jackson ImmunoResearch.Briefly, a confluent endothelial monolayer was serum depleted (0.5%) for 20 h, then treated with increasing concentrations  of diamide or H2.3High-performance liquid chromatography (HPLC) was employed to measure GSH and GSSG using a previously described technique 2.42DCF-DA (Molecular Probes) was used to measure total intracellular ROS production with and without diamide or H2O2 treatment using manufacturer's protocol. Briefly, cells were starved overnight, washed, loaded with dye for 30 min at 37 °C and then treated with either diamide, positive control (H2O2), or vehicle for another 30 min at 37 °C. Fluorescence intensity (492ex/527em) of each of the samples was normalized to control to calculate fold-change. CM-H2DCF-DA should not be used as a measure of specific species of ROS, but rather as a pan-ROS indicator. Kalyanaraman et al. CM-H2.5165) released into the media after diamide stimulation was quantified using a human VEGF quantikine ELISA kit (R&D systems) following the manufacturer's protocol.The concentration of VEGF-A , 1,3-bis-(2-chloroethyl)-1-nitrosourea (BCNU), and diamide. BSO selectively inhibits gamma-glutamyl cysteinyl ligase and inhibits de novo formation of GSH but has been shown to not directly affect GSH:GSSG Diamide exposure resulted in a dose-dependent depletion of intracellular GSH while the concentrations of GSSG did not show significant changes among the treatment groups A. GSSG l3.2in vivo, we used diamide as a tool to oxidize GSH in our in vitro system using human aortic endothelial cells. Endothelial cell proliferation and migration, important for blood vessel remodeling, is driven by complex signaling pathways initiated by VEGF binding to VEGFR2. To determine if changes in the GSH redox state in endothelial cells affects VEGF-dependent VEGFR2 activation, we assessed the phosphorylation state of VEGFR2 at tyrosine (Y) 1175 following treatment with increasing concentrations of diamide. Phosphorylation of VEGFR2 at Y1175 is important to trigger downstream signaling through recruitment of phospholipase Cγ (PLCγ) and phosphatidyl inositol 3-phosphate kinase (PI3K) leading to both proliferation and migration of endothelial cells. Our results show that at all concentrations of diamide VEGF-dependent VEGFR2 activation is enhanced  levels, we measured the release of ROS in HAECs following diamide treatment. We used CM-Hreatment C.3.32O2 alone is sufficient to activate VEGFR2. We treated HAECs with increasing concentrations of H2O2  for 30 min. Similar to our experiments using diamide treatment, we treated HAECs with and without VEGF-A and tested for VEGFR2 activation using Y1175 as our readout. The results show that H2O2 alone does not activate the receptor and does not enhance VEGF-dependent VEGFR2 activation until concentrations reach 1 mM  to inhibit PTPs and found no detectable activation of VEGFR2  in Finally we verified that Hcatalase D. We alr4VEGFR2 signaling is essential to vascular development, under both pathologic and physiologic conditions. The production of ROS also plays a major role in vascular development, with the addition of antioxidants such as N-Acetyl Cysteine unsurprisingly interrupting the majority of signaling pathways, due to their ability to scavenge ROS. This approach to treat disease is a simplistic view of a major signaling dynamic. We believe a major determinant in angiogenic signaling pathways is the local cellular redox environment. Endogenous antioxidants, such as GSH, are less well studied in the context of signaling during vessel growth, however research in this area is rapidly expanding.Two studies have demonstrated ligand independent VEGFR2 activation illustrating potential alternate signaling pathways. Jin et al. showed that shear stress in bovine arterial cells activated VEGFR2 in a ligand independent manner This is the first study to demonstrate the regulation of VEGFR2 by the glutathione redox state . Glutathione exists in the cell in both reduced (GSH) and oxidized (GSSG) forms that constitute the major redox couple of the cell These data support the hypothesis that arterial signaling is regulated in a thiol dependent manner. This study opens an avenue to more thoroughly understand VEGFR2 signaling that was previously thought to rely heavily on alterations in ROS production and the subsequent cysteine oxidation steps leading to various protein activity changes. Quite possibly the more important signaling contributors are the available thiols, including but not limited to glutathione, hydrogen sulfide, and persulfide pools in the cell. These thiol pools fluctuate during many disease states and may or may not need ROS changes to contribute to protein modifications.A clear limitation of the current study is a lack of specific mechanisms driving the activation of VEGFR2. Our lab is currently exploring several avenues of activation. VEGFR2 appears to be glutathionylated however the modification is transient and finding the specific timepoint of glutathionylation has been challenging. Another potential mechanism of activation is in the Src pathway. Studies of both mechanisms are currently underway.


Recognition of a mobile conceptus by the endometrium is critical by days 14–16 post-ovulation (PO), when endometrium produces PGF, initiating luteolysis. The objective of this study was to evaluate endometrial gene expression changes based upon pregnancy status via RNA sequencing. This experiment utilized a cross-over design with each mare serving as both a pregnant and non-mated control on days nine, 11, and 13 PO (n = 3/status/day). Mares were randomly assigned to collection day and pregnancy confirmed by terminal uterine lavage at the time of endometrial biopsy. Total RNA was isolated and libraries prepared using Illumina TruSeq RNA sample preparation kit. Reads were mapped and annotated using HISAT2 and Stringtie. Expression values were evaluated with DESEQ2 (P ≤ 0.05 indicated significance). On day nine, 11, and 13 there were 1435, 1435 and 916 significant transcripts, respectively. Multiple genes with splice variants had different expression patterns within the same day. These are the first data to evaluate the endometrial transcriptome during MRP on days nine, 11, and 13.Equine maternal recognition of pregnancy (MRP) is a process whose signal remains unknown. During MRP the conceptus and endometrium communicate to attenuate prostaglandin F Bioinformatic analysis was performed on the Galaxy web platform  and usedh sample . Transcrh sample . To be chttps://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis). Threshold values from DESeq2 were set at p ≤ 0.05 and fold change ≥ 1.5 in order to be utilized for IPA.Differentially expresses genes were evaluated within Ingenuity Pathway Analysis (IPA) to biological processes being targeted , Quality filtering and removal of adapter sequence and low-quality sequence resulted in an average read length of 89 base pairs for all samples. Samples generated on average 33,810,516 reads . Mapping efficiency to build three of the equine genome (EquCab3) was 94%.p ≤ 0.05) in abundance between endometrial samples from pregnant or non-pregnant mares, but 682 were unannotated. Of all significant transcripts, 693 were more abundant in samples from pregnant mares and 743 were more abundant in samples from non-pregnant mares  identified in endometrial samples. Of these transcripts, 845 were more highly abundant in samples from pregnant mares and 590 were more abundant in samples from non-pregnant mares (p ≤ 0.05). Within these transcripts, 446 were unannotated. Of all of the significant transcripts identified, 400 were more highly abundant in samples from pregnant mares and 516 were more highly abundant in samples from non-pregnant mares. On day 11, there were 1435 transcripts  identified on day nine, 678 (47.2%) identified on day 11, and 446 (48.7%) identified on day 13 were not previously annotated. The most highly abundant non-ribosomal, gene in day nine endometrial samples from pregnant mares was Early Growth Response 1 (EGR1) and from endometrial samples from non-pregnant mares it was an unannotated transcript (MSTRG.8258.4) followed by annotated transcript , Homocysteine Inducible ER Protein with Ubiquitin Like Domain 1 (HERPUD1). Interestingly, the annotated transcript with the largest fold change  between samples from pregnant compared to non-pregnant mares was Hook Microtubule Tethering Protein 1 (HOOK1). p ≤ 0.05) were analyzed with IPA to determine the canonical pathways they have been associated with experimentally. Only annotated transcripts were used for this analysis. Some of the top biological pathways that were stimulated due to the presence of an embryo (P+) in our dataset on day nine were AMPK (AMP-activated protein kinase) signaling (z-score = 2.1), androgen signaling (z-score = 2.0) and GnRH signaling (z-score = 1.6). Some of the top biological pathways that were inhibited in our dataset on day nine included Aryl hydrocarbon receptor signaling (z-score = −1.3), neuroinflammation signaling (z-score = −1.1), and neuregulin signaling (z-score = −1.0). Other biological pathways of interest that were inhibited included ILK (integrin-linked kinase) signaling and actin cytoskeleton signaling. The biological processes with z-score associations are in Transcripts  and the most abundant transcript in samples from non-pregnant mares was Aldehyde Dehydrogenase 1 Family Member A1 (ALDH1A1). The largest fold change (Log2 fold change = 27.5) difference between groups was unannotated transcript MSTRG.25465.6, more abundant in samples from pregnant mares. The largest fold change of an annotated transcript (Log2 fold change = 13.6) was Membrane Bound O-Acyltransferase Domain Containing 2 (MBOAT2), also more abundant in samples from pregnant mares. Interestingly, of the top ten transcripts with the largest fold change, only one was an annotated transcript, and only one was higher in abundance in samples from non-pregnant mares (unannotated transcript MSTRG.6103.11). The top ten fold changes of annotated transcripts are presented in p ≤ 0.05) were evaluated. In total, transcripts on day 11 were associated with 395 biological processes, but only 82 were associated with z-scores indicating association with stimulating or inhibiting that biological process based upon pregnancy status  signaling (z-score = −2.2). Other biological processes that were inhibited included endocannabinoid neuronal synapse pathway, Wnt/β-catenin signaling, GnRH signaling, and TGF-β signaling. Some of the top biological pathways of interest are in The most abundant transcript in samples from pregnant mares was an unannotated transcript . This resulted in a Log2 fold change of 30.0 between samples from pregnant mares compared to non-pregnant mares. The most abundant transcript in samples from non-pregnant mares was Fos Proto-Oncogene, AP-1 Transcription Factor Subunit (FOS). The largest fold change was unannotated transcript MSTRG.25465.3. The largest fold change in annotated transcripts was Acyl-CoA Oxidase 1 , more abundant in samples from non-pregnant mares. Interestingly, the second largest old change in annotated transcripts on day 13 was the same as on day 11, MBOAT2 (Log2 fold change = 13.9), more abundant in samples from pregnant mares. Interestingly, on day 13 only seven biological pathways were stimulated based upon pregnancy status in our dataset. These biological pathways included thrombin signaling, signaling by Rho family GTPases, GnRH signaling, and actin cytoskeleton signaling. In contrast, the most heavily inhibited biological process from our dataset on day 13 was p53 signaling (z-score = −2.0). Other inhibited biological processes of interest included RhoGDI signaling, ILK signaling, integrin signaling, and (in contrast to day 11) PPAR signaling. p ≤ 0.05). One transcript was unannotated, MSTRG.27075.4. It was more abundant across all three days in samples from pregnant mares . The other transcript was gene Gametogenetin Binding Protein 2 (GGNBP2). This gene was actually more abundant in samples from pregnant mares on days nine and 11  and more abundant in samples from non-pregnant mares on day 13 (Log2 fold change = −2.3).Interestingly there were only two transcripts that were significant on all three days . This study utilized days nine, 11, and 13 in order to evaluate the transcriptome before, during and after MRP. What is also unique about this study is that it not only evaluated the genes, but also the transcripts (splice variants) within those genes. In addition to identifying known genes, we also identified a population of unannotated genes/transcripts.An interesting phenomenon noted during this analysis was the change in expression status between variants of a gene. Multiple genes had many transcripts that were significant within a day, but sometimes the transcripts would vary in whether they were more highly expressed in samples from pregnant mares or non-pregnant mares. An example was USP36 on day nine, ubiquitin specific peptidase 36. Transcript 1 was more abundant in samples from pregnant mares whereas transcript 2 was more abundant in samples from non-pregnant mares. This occurrence of transcripts of the same gene having opposing expression patterns happened with many genes throughout the results suggesting that alternative splicing could be occurring in the endometrium during this time frame. Alternative splicing is the combination of different splice sites joining together to form the gene . Others Another observation from this study was the large number of genes/transcripts that were present in only samples from pregnant or non-pregnant mares. During this time frame, pregnant and non-pregnant mares have the same hormonal profile . TherefoA biological pathway that was targeted by many transcripts, on multiple days, was integrin signaling. Subsequently, it was also a pathway of great interest in regard to maternal recognition of pregnancy. Integrin linked kinase (ILK) signaling was also heavily impacted by transcripts within our study. Previous reports suggested that focal adhesions are present in equine endometrium during the time of maternal recognition of pregnancy and serum samples from pregnant and non-pregnant mares contain exosomes that are transporting microRNA (miRNA) targeting focal adhesions ,31. Focap ≤ 0.05) in samples from pregnant mares on day 11. On day 13 the main gene responsible for this pathway enrichment was RAPGEF, rap guanine nucleotide exchange factor 1. This gene is more closely related to actin dynamics within the cell and adheres to focal adhesions versus signaling from focal adhesions [In this study, integrin signaling was inhibited on days nine and 13 and enhanced on day 11. The reason for this variability is due to the genes contributing to the biological process on the specified days. On day nine one of the key contributors to inhibition of integrin signaling is RHOH. This gene is required to maintain integrins, specifically integrin LFA-1 . In our dhesions . This coAlthough not significant in this study between groups, another interesting observation was the identification of CATSPERD, CATSPERG, and CATSPERB, all subunits of CATSPER that are required to form a functional ion channel, within the endometrium . PreviouThis is the first study evaluating equine endometrium before, during, and after maternal recognition of pregnancy utilizing RNA sequencing. A large number of genes/transcripts were identified that were unique to pregnancy status and day, including many novel transcripts with unknown functions. Interestingly, alternative splicing was identified, yet the importance of these needs to be determined. Further research is needed to determine the role of the genes identified in this study in order to elucidate the signaling that is occurring during maternal recognition of pregnancy.