Respiratory syncytial virus (RSV) is a nonsegmented negative-strand RNA virus (NSV) and a leading cause of severe lower respiratory tract illness in infants and the elderly. Transcription of the ten RSV genes proceeds sequentially from the 3’ promoter and requires conserved gene start (GS) and gene end (GE) signals. Previous studies using the prototypical GA1 genotype Long and A2 strains have indicated a gradient of gene transcription extending across the genome, with the highest level of mRNA coming from the most promoter-proximal gene, the first nonstructural (NS1) gene, and mRNA levels from subsequent genes dropping until reaching a minimum at the most promoter-distal gene, the polymerase (L) gene. However, recent reports show non-gradient levels of mRNA, with higher than expected levels from the attachment (G) gene. It is unknown to what extent different transcript stabilities might shape measured mRNA levels. It is also unclear whether patterns of RSV gene expression vary, or show strain- or genotype-dependence. To address this, mRNA abundances from five RSV genes were measured by quantitative real-time PCR (qPCR) in three cell lines and in cotton rats infected with RSV isolates belonging to four genotypes . Relative mRNA levels reached steady-state between four and 24 hours post-infection. Steady-state patterns were non-gradient and genotype-specific, where mRNA levels from the G gene exceeded those from the more promoter-proximal nucleocapsid (N) gene across isolates. Transcript stabilities could not account for the non-gradient patterns observed, indicating that relative mRNA levels more strongly reflect transcription than decay. Our results indicate that gene expression from a small but diverse set of RSV genotypes is non-gradient and genotype-dependent. We propose novel models of RSV transcription that can account for non-gradient transcription. Respiratory syncytial virus (RSV) can infect individuals repeatedly and is the most common pathogen associated with severe lower respiratory tract disease in children worldwide –5. NumerRSV is a nonsegmented negative-strand RNA virus (NSV) classified into two major subgroups, A and B, largely distinguished by antigenic differences in the attachment (G) protein , 10. TheTranscription in RSV and other NSV is sequential, with genes transcribed in their order of occurrence from the 3’ promoter of the genome –22. EachBy homology with other NSV, it is widely assumed that transcription in RSV follows a gradient, where the extent to which a gene is transcribed falls with its distance from the 3' promoter , 32, 33.Here we explored the natural diversity of patterns of RSV gene expression by using qPCR to measure mRNA abundances of five different RSV genes  from isolates that we sequenced belonging to both subgroups and four genotypes . Genotype-dependent patterns were observed, all diverging from a gradient and all showing higher levels of G mRNA than expected. Transcript stabilities did not account for the non-gradient patterns, indicating that mRNA levels reflect transcription more than decay. We propose novel models incorporating the possible effects of stochastic transcription and/or the recycling of transcribing polymerases in order to begin rationalizing non-gradient RSV transcription.CT) values measured by real-time PCR for mRNA targets  and four genotypes  were used to infect HEp-2 cells (MOI = 0.01). Total mRNA abundances began to plateau at ~48 hours post-infection (pi) for all isolates , consistConsistent with sequential transcription, the mean relative level of NS1 mRNA decreased for all isolates after four hours pi . The sigAll four sets of steady-state mRNA levels were non-gradient, with levels of G mRNA exceeding levels of N mRNA . Steady-in vitro (We explored whether relative mRNA levels might change in the context of a fully immunocompetent host. A pair of cotton rats was infected with each virus isolate and both lung lavage (LL) and nasal wash (NW) samples were collected at four days pi. Relative mRNA levels were genotype-specific and similar in cotton rat LL and NW samples, and comparable to those measured in vitro .e(decay constant * 24 hr)). Estimated levels of gene expression remained non-gradient; thus, differential mRNA stabilities do not account for the non-gradient patterns observed  inhibitor GS-5734 then monitoring mRNA levels by qPCR over time. Decay was measured for all five mRNAs from each of the four isolates in HEp-2 cells . Exponenobserved . These din vitro using ribonucleoprotein (RNP) complex from an RSV Long strain and cell extract from uninfected HEp-2 cells Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: I Don't Know**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Piedra et al make observations that challenge the widely held belief that a mRNA gradient exists in RSV due to obligatorily sequential transcription with attenuation at gene junctions. mRNAs of five genes are quantitated over time, both in vitro and in vivo, and stability of each mRNA is also examined. This is an interesting and well-written study with original data, which show that steady state mRNA levels are genotype-dependent and do not follow a gradient, and that the non-gradient cannot be attributed to variations in mRNA stability. The study has implications for other NSV as well, and is in reasonable agreement with recent findings from other groups using distinct techniques. The use of 4 virus variants that cover the A and B subgroups, and confirmation of the findings in multiple cell lines as well as in infected cotton rats, is a notable strength of this paper. Statistical significance is not addressed for most of the data, and more discussion on some of their findings will help the reader interpret the work.Comments- Conclusions are in general well supported but any indication of significance of observed differences is lacking. Authors claim for example that NS1 levels decrease for all isolates after four hours, but the mentioned differences seem small and it’s not clear if they are significant. Also, in Fig. 2 it is not clear whether the means represent the mean of two independent experiments or the mean of all samples over two independent experiments.- Two major findings of mRNA quantitation (in addition to being non-gradient) are that G mRNA levels are relatively high and that relative NS2 mRNA levels vary considerably between A and B isolates; can the authors speculate on the meaning of their findings?- In the B isolates, the G:NS2 mRNA ratio appears to differ between cell lines and cotton rats. Can the authors say something about this result?- Lab-adaptation in previous studies is mentioned as one possible explanation for the difference in gradients or non-gradients measured between older and newer studies resp. Are the isolates used here not lab-adapted?- Read-through transcription is briefly mentioned in the discussion in an attempt to explain differences in NS2 mRNA levels. Could complex read-through transcription play an important role in non-gradient steady state levels?- NS2 mRNA was previously shown to have a short half-life. The current findings do not confirm the previous. Can the authors address this difference?- The authors propose two models, variable probability of transcription and polymerase recycling. However, rather than each representing a model, variable probability is more like an outcome, for which polymerase recycling could be one of the potential mechanisms. There is no data that aims to elucidate or support a model by which the virus regulates probability of transcription.Reviewer #2: In these studies the relative abundance of gene transcription is assessed across the RSV genome. Previous studies have suggested that the gene transcription is sequential whereas the authors of this manuscript provide preliminary data, verifying other data in the field, that there can be non-sequential gene transcription. The data are supportive of this concept and the discussion offers some suggested mechanisms of how this mechanism could occur. The order of gene transcription in different cell lines and in cotton rats are consistent and genotype specific. While interesting, there is no further exploration of the mechanisms that might differentially regulate these findings that is dependent upon genotype. Furthermore, and perhaps more interesting, no additional exploration of what might be the consequence to this mechanism in the different genotypes. Does the differential expression of different genes give an infective advantage? Perhaps it alters the host response to the virus in some manner? These data provide solid preliminary data for further, more indepth investigation as to the "biologic" function of such a mechanism and/or how this mechanism is facilitated/regulated by the different genotypes explored.A final issue is whether these mechanisms develop due to the time passaged in tissue culture, with the more recently derived strains  having a similar expression profile compared to the longer term strains, especially regarding the NS2 gene. The older, for example, have likely been differentially passaged many times in different long term cell lines, Hep2, Vero. This consideration is as likely to account for differences as any other pressure. The question may not be can it happen, rather does it normally happen and what impact does it have on the normal infectivity, success of the viral replication, and disease associated outcome in vivo.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  22 Nov 2019November 21, 2019Dear Dr. Vargas and Reviewers:Thank you for reviewing our manuscript, ‘Non-gradient and genotype-dependent patterns of RSV gene expression.’ Thank you also for the opportunity to respond to the insightful comments made, and the important questions raised. We have done our best to answer fully. Please see the reviewers’ comments  and our responses (in bold) below.______________________________________________________________________Reviewer #1: Piedra et al make observations that challenge the widely held belief that a mRNA gradient exists in RSV due to obligatorily sequential transcription with attenuation at gene junctions. mRNAs of five genes are quantitated over time, both in vitro and in vivo, and stability of each mRNA is also examined. This is an interesting and well-written study with original data, which show that steady state mRNA levels are genotype-dependent and do not follow a gradient, and that the non-gradient cannot be attributed to variations in mRNA stability. The study has implications for other NSV as well, and is in reasonable agreement with recent findings from other groups using distinct techniques. The use of 4 virus variants that cover the A and B subgroups, and confirmation of the findings in multiple cell lines as well as in infected cotton rats, is a notable strength of this paper. Statistical significance is not addressed for most of the data, and more discussion on some of their findings will help the reader interpret the work.Comments- Conclusions are in general well supported but any indication of significance of observed differences is lacking. Authors claim for example that NS1 levels decrease for all isolates after four hours, but the mentioned differences seem small and it’s not clear if they are significant. Also, in Fig. 2 it is not clear whether the means represent the mean of two independent experiments or the mean of all samples over two independent experiments.Two kinds of observed difference are central to the paper: 1) the difference between measured RSV mRNA abundances and those expected from a gradient; and 2) differences among measured steady-state gene expression patterns belonging to strains from four different genotypes of RSV. It is worth stating that both kinds of difference are clear from the plotted data , and it does seem that the reviewer agrees with this point. The decrease in relative levels of NS1 mRNA after 4 hours post-infection (pi) is slight but consistent across genotypes (avg. = 11±8%). We find this interesting because a decrease in transcription from more 3’ proximal genes is consistent with the change that would be expected between pre-steady-state and steady-state relative mRNA levels resulting from sequential transcription. In addition, and as mentioned in the text, decreases for GA1 and BA were larger (avg. = 17±7%), and these strains showed lower total amounts of mRNA at t=4 hr pi. A steeper drop and lower total mRNA at the start of the experiment suggest that our 4 hr measurements occurred closer to the start of transcription for GA1 and BA than ON and GB1. Finally, each bar in each histogram of Fig2 represents the mean of two independent experiments, and not the mean of all wells over two independent experiments. - Two major findings of mRNA quantitation (in addition to being non-gradient) are that G mRNA levels are relatively high and that relative NS2 mRNA levels vary considerably between A and B isolates; can the authors speculate on the meaning of their findings?G protein is needed for both viral attachment to host cells and immunosuppression and evasion. Perhaps relatively high (and non-gradient) levels of G mRNA are required to make sufficient G protein (transmembrane and secreted) to support robust infection and transmission of virus. Also, both G protein and NS2 are involved in suppressing interferon signaling. It is possible that the G protein of subgroup B is more active than that of A in its NS2-like function, relaxing the need for the higher level of NS2 transcription seen here in the two strains belonging to the A subgroup. If this is true, and assuming a lack of translational differences, then similar patterns of transcription should be observed for other A and B strains. We have amended our discussion (lines 279-286) in order to incorporate our answer to this question. - In the B isolates, the G:NS2 mRNA ratio appears to differ between cell lines and cotton rats. Can the authors say something about this result?This could be a result of different transcript stabilities; relative mRNA abundances for NS1 and NS2 are, regardless of genotype, greater in cotton rat (CR) samples than samples from in vitro cell culture. Furthermore, the F:NS2 mRNA ratio is the most different between cell lines and CRs, followed by the G:NS2. These differences might also reflect the impact of the innate and adaptive immune response of the cotton rat on the life cycle of the virus. - Lab-adaptation in previous studies is mentioned as one possible explanation for the difference in gradients or non-gradients measured between older and newer studies resp. Are the isolates used here not lab-adapted?We mention in the discussion (lines 239-242) that most published data concerning RSV gene expression come from one of two lab-adapted strains (Long & A2) of subgroup A. This is stated more to indicate the dearth of viral diversity explored than to suggest a relationship between lab-adaptation and gradient gene expression. In fact, Aljabr et al. report non-gradient mRNA levels from an A2 strain. Our data do not support a relationship between gradient/non-gradient gene expression and more/less passage, and suggest that RSV gene expression depends more on subgroup and genotype. The two prototypic strains that we used (RSV/A/GA1/Tracy and RSV/B/GB1/18537) have been passaged more than ten times, while the two contemporaneous strains (RSV/A/ON/121301043A and RSV/B/BA/80171) have been passaged 6 and 7 times, respectively. Lines 256-257 (in discussion) and 362-365  have been added to make this clear. - Read-through transcription is briefly mentioned in the discussion in an attempt to explain differences in NS2 mRNA levels. Could complex read-through transcription play an important role in non-gradient steady state levels?In the simplest case, read-through transcription should have the effect of making relative mRNA levels  less different not more, and should flatten 3’ to 5’ negative gradients and not produce ‘bumps’ in transcription; but the transcriptional ‘bumps’ leading to non-gradient steady-state mRNA levels could result from the coupling of read-through transcription and polymerase recycling. - NS2 mRNA was previously shown to have a short half-life. The current findings do not confirm the previous. Can the authors address this difference?We are aware of published data :155-61) showing a short half-life for NS2 protein in BS-C-1 cells, but cannot come across data (outside of our own) for mRNA stability. Furthermore, Evans et al. showed the 40 kDa form of the NS2 protein to be stable while the 14.5 and 30 kDa forms rapidly disappeared during the pulse-chase experiments. - The authors propose two models, variable probability of transcription and polymerase recycling. However, rather than each representing a model, variable probability is more like an outcome, for which polymerase recycling could be one of the potential mechanisms. There is no data that aims to elucidate or support a model by which the virus regulates probability of transcription.With all due respect, we prefer to separate these distinct, albeit not incompatible, possibilities. Here, variable probability of transcription refers to the local probability of transcription initiation – i.e., the probability of transcription initiation at a GS signal when a viral polymerase is near or ‘on’ it (see lines 322-327). At the very least this probability depends on the GS signal and neighboring (including intergenic) sequence ). Polymerase recycling over a certain gene more than others can increase transcription from that gene even if the local probability of transcription initiation is constant across GS signals. Reviewer #2: In these studies the relative abundance of gene transcription is assessed across the RSV genome. Previous studies have suggested that the gene transcription is sequential whereas the authors of this manuscript provide preliminary data, verifying other data in the field, that there can be non-sequential gene transcription. The data are supportive of this concept and the discussion offers some suggested mechanisms of how this mechanism could occur. The order of gene transcription in different cell lines and in cotton rats are consistent and genotype specific. While interesting, there is no further exploration of the mechanisms that might differentially regulate these findings that is dependent upon genotype. Furthermore, and perhaps more interesting, no additional exploration of what might be the consequence to this mechanism in the different genotypes. Does the differential expression of different genes give an infective advantage? Perhaps it alters the host response to the virus in some manner? These data provide solid preliminary data for further, more indepth investigation as to the "biologic" function of such a mechanism and/or how this mechanism is facilitated/regulated by the different genotypes explored.We agree with the reviewer that our findings are solid and need to be studied in relation to their biological meaning within the human host. Such studies are currently ongoing but are not the topic of this report.A final issue is whether these mechanisms develop due to the time passaged in tissue culture, with the more recently derived strains  having a similar expression profile compared to the longer term strains, especially regarding the NS2 gene. The older, for example, have likely been differentially passaged many times in different long term cell lines, Hep2, Vero. This consideration is as likely to account for differences as any other pressure. The question may not be can it happen, rather does it normally happen and what impact does it have on the normal infectivity, success of the viral replication, and disease associated outcome in vivo.Our data do not show a relationship between RSV gene expression and passage number, and suggest that RSV gene expression patterns depend more on subgroup and genotype. The two prototypic strains that we used (RSV/A/GA1/Tracy and RSV/B/GB1/18537) have been passaged more than ten times, while the two contemporaneous strains (RSV/A/ON/121301043A and RSV/B/BA/80171) have been passaged 6 and 7 times, respectively. Lines 256-257 (in discussion) and 362-365  have been added to make this clear. ____________________________________________________________________We appreciate the chance to respond to the above comments and questions. We enthusiastically submit our revised manuscript and humbly await your decision. Sincerely,Dr. Felipe-Andrés PiedraDr. Pedro A. PiedraAttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 9 Dec 2019PONE-D-19-24823R1Non-gradient and genotype-dependent patterns of RSV gene expressionPLOS ONEDear Dr. Piedra,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. 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Varga, Ph.D.Academic EditorPLOS ONE[Note: HTML markup is below. Please do not edit.]Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #1: (No Response)Reviewer #2: All comments have been addressed**********2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: NoReviewer #2: N/A**********4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Piedra et al have adequately addressed most of the stated concerns. Questions about two of the concerns remain, see below.1) Statistical significance.As to the example in item 1), the mentioned decrease in NS1 levels after four hours is not clear in Fig. 2. No statistical significance has been added. The norm to determine and communicate the likelyhood that stated differences (whether appearing subtle or not) are real is to calculate p values.2) mRNA ratio variation between cell culture and cotton rats.* In answering this question, the authors argue that perhaps transcript stabilities cause this difference. However, a general point in the paper is that the non-gradient is not caused by variations in transcript stabilities. These seem to contradict?* This difference between in vitro and in vivo stands out and is interesting and could be important in assessing the value of vitro differences. Therefore the authors should include this finding in the discussion.Reviewer #2: The Authors have responded to all comments and have satisfactorily added discussion points and made changes to the manuscript.**********what does this mean?). If published, this will include your full peer review and any attached files.7. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  18 Dec 2019December 18, 2019Dear Dr. Vargas:Please see the remaining two comments  and our responses (in bold) below.______________________________________________________________________Reviewer #1: Piedra et al have adequately addressed most of the stated concerns. Questions about two of the concerns remain, see below.1) Statistical significance.As to the example in item 1), the mentioned decrease in NS1 levels after four hours is not clear in Fig. 2. No statistical significance has been added. The norm to determine and communicate the likelyhood that stated differences (whether appearing subtle or not) are real is to calculate p values.We have added a supplementary figure  depicting measured levels of NS1 at 4 hours post-infection (pi) and > 4 hours pi. The noted decrease is statistically significant when analyzed by regression modelling. The manuscript has been modified to incorporate this information (see lines 154-158 and 476-484). 2) mRNA ratio variation between cell culture and cotton rats.* In answering this question, the authors argue that perhaps transcript stabilities cause this difference. However, a general point in the paper is that the non-gradient is not caused by variations in transcript stabilities. These seem to contradict?The data we present indicate that transcript stabilities cannot account for the non-gradientness of the mRNA levels measured, not that transcript stabilities cannot at all affect relative mRNA levels. Moreover, in vitro transcript stabilities were measured, not in vivo stabilities. We therefore do not know what effect (if any) in vivo transcript stabilities might have on the difference between RSV mRNA levels in samples from cell culture and cotton rats. * This difference between in vitro and in vivo stands out and is interesting and could be important in assessing the value of vitro differences. Therefore the authors should include this finding in the discussion.We agree that the difference between in vitro and in vivo patterns of RSV transcription is interesting. We have added lines 292-95 to our discussion.____________________________________________________________________Thank you for the chance to respond to the above comments. We submit our revised manuscript and await your decision. Sincerely,Dr. Felipe-Andrés PiedraDr. Pedro A. PiedraAttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 23 Dec 2019Non-gradient and genotype-dependent patterns of RSV gene expressionPONE-D-19-24823R2Dear Dr. Piedra,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. 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Varga, Ph.D.Academic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 27 Dec 2019PONE-D-19-24823R2 Non-gradient and genotype-dependent patterns of RSV gene expression Dear Dr. Piedra:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. 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Binding of transcription factors to transcription factor binding sites (TFBSs) is key to the mediation of transcriptional regulation. Information on experimentally validated functional TFBSs is limited and consequently there is a need for accurate prediction of TFBSs for gene annotation and in applications such as evaluating the effects of single nucleotide variations in causing disease. TFBSs are generally recognized by scanning a position weight matrix (PWM) against DNA using one of a number of available computer programs. Thus we set out to evaluate the best tools that can be used locally  for creating PWMs from high-throughput ChIP-Seq data and for scanning them against DNA.de novo motif discovery tools that could be downloaded and installed locally using ENCODE-ChIP-Seq data and showed that rGADEM was the best-performing tool. TFBS prediction tools used to scan PWMs against DNA fall into two classes — those that predict individual TFBSs and those that identify clusters. Our evaluation showed that FIMO and MCAST performed best respectively.We evaluated a set of Selection of the best-performing tools for generating PWMs from ChIP-Seq data and for scanning PWMs against DNA has the potential to improve prediction of precise transcription factor binding sites within regions identified by ChIP-Seq experiments for gene finding, understanding regulation and in evaluating the effects of single nucleotide variations in causing disease.The online version of this article (doi:10.1186/s12859-016-1298-9) contains supplementary material, which is available to authorized users. The sequence-specific binding of transcription factors to transcription factor binding sites (TFBSs) is key to the mediation of transcriptional regulation . High thPrediction of transcription factor binding sites is generally performed by scanning a DNA sequence of interest with a position weight matrix (PWM) for a transcription factor of interest , 7 and vThere are many in vitro and in vivo experimental approaches that have been used to identify transcription factor binding sites and these are reviewed briefly here.Electro-Mobility Shift Assay (EMSA) [DNase I footprinting/protection assay combines the cleavage reaction of DNase I with EMSA [Systematic Evolution of Ligands by EXponential enrichment (SELEX) [In vitro methods include: (i) The y (EMSA)  which exith EMSA . A key pith EMSA . (iii) S (SELEX)  works by (SELEX) . A refin (SELEX) .Chromatin ImmunoPrecipitation (ChIP) assay, a variation of the ‘pull down’ class of assay [There has been a recent shift towards in vivo approaches . In the of assay , the DNAof assay . High thof assay , or the of assay .There are a number of advantages of using ChIP-Seq instead of ChIP-chip. Key improvements are in base pair resolution, avoiding non-linearity and saturation of ChIP-chip signal intensity, ability to analyze sequence repeat regions, and avoiding limitations from the limited selection of probes on a ChIP-chip array. Overall ChIP-Seq has a higher specificity and sensitivity compared with ChIP-chip , 15 and n matrix of scores for each of the 4 bases across each position in the binding motif) accounts for the preference for each of the four nucleotides at each position in the motif [Position Weight Matrices (PWMs) are the most widely used approach to modelling TFBSs. In contrast to a consensus model (which simply gives the most common base(s) at each position of a binding motif), a matrix-based PWM model  and CIS-BP [PWMs can be obtained from a number of resources including the commercial database TRANSFAC  and the d CIS-BP . HoweverWhile large scale ChIP experiments allow the genome-wide identification of binding regions for a specific transcription factor, these regions are much longer than the actual binding site for a specific transcription factor meaning that the actual transcription factor binding sites still need to be identified , 27.Various motif discovery methods have been developed and there have been several reviews of the approaches used , but using human ChIP-Seq data obtained from ENCODE . PerformTo evaluate performance, we identified experimentally-validated TFBSs from resources that, rather than just providing PWMs or approximate regions to which TFBSs bind, provide precise validated binding sites for a limited set of genes. Three sources of such data are available: PAZAR , ORegAnnPAZAR contains some redundancy (multiple instances of the same TFBS annotated for a given gene), so any duplicate TFBSs were removed.PAZAR contains 159 genes annotated with TFBSs that are contained in either JASPAR or ENCODE-ChIP-Seq data. This set contains data for 14 TFBSs with corresponding PWMs in JASPAR coming from a total of 156 human genes. This set is referred to below as ‘PAZAR-J’. The set also contains data for 12 transcription factors with binding data in the ENCODE-ChIP-Seq data which come from a total of 149 genes (‘PAZAR-E’). The PAZAR-J and PAZAR-E datasets overlap for 11 of the transcription factors  was performed for each of the 14 transcription factors in PAZAR-J by selecting the appropriate subset of the 156 genes in PAZAR-J having validated binding sites for the transcription factor in question.Evaluation of the motif discovery tools was performed for each of the 12 transcription factors in PAZAR-E by selecting the appropriate subset of the 149 genes in PAZAR-E having validated binding sites for the transcription factor in question and using the motif discovery tool selected in the initial evaluation (FIMO).Finally, re-evaluation of the motif scanning tools (using PWMs generated by rGADEM) was also performed for each of the 12 transcription factors in PAZAR-E by selecting the appropriate subset of the 149 genes in PAZAR-E having validated binding sites for the transcription factor in question.TFBSs can occur in the promoter region, in introns and exons, and far upstream of genes , 61. ConTP) were defined as predicted binding sites having a minimum overlap of 70 % of base pairs with known binding sites from PAZAR. Similarly, false positives (FP) were defined as predicted binding sites not having an overlap of at least 70 % with a known binding site and false negatives (FN) were defined as known binding sites that were not identified. Obtaining a true estimate of the total number of negative sites  is difficult and therefore we adopted the normal practice of avoiding performance measures that require true negative counts [all predicted component TFBSs within a region must overlap with known sites by at least 70 % of base pairs for a prediction to be regarded as a true positive.True positives (e counts . For cluAN=FP+TN) was defined as AN=L/lt where L is the length of the sequence and lt is the length of the PWM in question).As a control, all the DNA sequences were scrambled using the ‘shuffleseq’ program from the EMBOSS suite (version 6.4.0) . In thisSn=TP/(TP+FN)), positive predictive value (PPV=TP/(TP+FP)) and geometric accuracy .Performance was assessed by calculating sensitivity (=Sn.PPV) , averageThe methods used for deriving PWMs from the ENCODE-ChIP-Seq data are summarized in Fig. http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeSydhTfbs/) in FASTQ format. Only the ChIP-Seq data that had a corresponding control sample available were selected to help to control biases and artefacts that occur in the experimental protocol [http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and removed using the FASTX TOOLKIT (http://hannonlab.cshl.edu/fastx_toolkit/).ChIP-Seq data for the human transcription factors were obtained from the ENCODE project  have validated TFBSs in PAZAR, PWMs in JASPAR and are also represented in the ENCODE-ChIP-Seq data. BRCA1 was also found in all datasets, but has recently been removed from JASPAR since its sequence specificity has been questioned [As shown in Fig. estioned . While tLogically it makes sense to evaluate motif discovery methods first and then to evaluate the tools available for matching the derived PWMs to DNA sequences. However the evaluation of the performance of motif discovery methods requires a tool to test the performance of the resulting PWMs. Therefore we needed to select a motif scanning tool for this purpose. In earlier work we had tested the performance of a number of PWM scanning tools using older JASPAR matrices (JASPAR.2010). These results are summarized below and the best performing tool was then used for evaluating the motif discovery methods. Finally, the performance of the scanning tools was reassessed using motifs from the best performing motif discovery method.As stated above, in order to evaluate motif discovery methods, we need to scan the motifs against DNA and compare the predictions with a gold-standard set of known precise TFBSs. In work done in 2011, we evaluated the performance of different PWM scanning tools using the older JASPAR.2010 matrices [TFBS cluster prediction tools chosen were MCast , Baycis All tools having variable cutoffs for making predictions were evaluated to ensure the optimum cutoff was chosen by using 10 % steps for all parameters. In all cases, the default settings were found to give the best performance and were used for all future evaluations.Table We chose to evaluate four methods for motif discovery that have been developed especially for working with large genome-wide datasets and that are open source and well documented: rGADEM , HOMER , ChIPMunThe tools have parameters that can be adjusted for motif discovery and these were explored for all tools using a 10 % step size. It was found that the defaults produced PWMs that resembled well-established motifs for all tools with the exception of rGADEM where the e-value parameter had to be set to a value of 0.5 rather than the default value of 0.0. The motif discovery tools are also able to generate multiple possible motifs. During the exploration of parameters, it was found that the first PWM generated always best-resembled well-established motifs for the TFBSs used in this work, and consequently only the first PWM was used.Performance was evaluated by using the FIMO motif scanning tool comparing predictions of TFBS locations with the PAZAR-E data as a gold standard. Table http://www.bioconductor.org/packages/release/bioc/html/TFBSTools.html) in Bioconductor. Reverse complement matrices were also checked and the minimum distances recorded. Results for each matrix set comparison were averaged across the PWMs used. The normalised Euclidean distance ranges from 0 to 1 where 0 denotes complete identity and 1 denotes complete dissimilarity. Results are shown in Table The PWMs obtained using the different methods were compared with each other and with those in JASPAR: both the older set derived from SELEX and individual promoter assays (JASPAR.2010) and the newer matrices obtained from ChIP-Seq data (JASPAR.2014). Normalized Euclidean distances between equivalent PWMs were calculated using the TFBSTools package  shows the largest difference in PWMs from the worst performing method (MEME-ChIP). Clearly there are small but significant differences in the PWMs generated by different motif discovery tools. However all the motif discovery methods applied to the ENCODE-ChIP-Seq data show even greater differences from the old JASPAR.2010 PWMs generated using SELEX or individual promoter assays.every predicted site within the cluster must have a 70 % overlap with a true site), it might be expected that the sensitivity for cluster predictors would be lowered, while the specificity would be improved. Indeed the sensitivity of cluster predictors is somewhat lower than the individual site predictors. Since we do not have the true negative count, we cannot calculate specificity, but surprisingly the false positive rate on scrambled sequences (FPRs) for the cluster predictors is larger than that for single site predictors suggesting that the cluster predictors have lower specificity.Having shown that rGADEM generates better PWMs than other motif-discovery methods, we returned to the evaluation of tools for scanning PWMs against DNA. We repeated this evaluation using PWMs generated from the ENCODE-ChIP-Seq data using rGADEM, and results are shown in Table Using the JASPAR.2010 data, we had identified FIMO as the best tool for identifying individual TFBSs and MCast as the best cluster-based tool. Table MCast >Comet >ClusterBuster >Cister >Baycisfor cluster predictors andFIMO >Patser >PossumSearch >Clover >Matrix-Scanfor individual predictors.Cister, Comet and ClusterBuster all come from the same laboratory . These results suggest that Comet from 2002 outperforms ClusterBuster from 2003, but both have made progress over their initial 2001 software. However MCast significantly outperforms all three methods.As a comprehensive set of experimentally-characterized precise transcription factor binding sites is not available, having good reliable prediction methods is very important. While some experimental methods of identifying TFBSs are relatively accurate, identifying regions of around 10–20 b.p., methods such as ChIP-Chip, and more importantly the ‘gold standard’ ChIP-Seq method, identify DNA regions of 100–1000 b.p. which is much larger than the TFBS itself . Consequently, when these experimental methods are employed for identifying TFBSs, it is necessary to use a prediction tool to identify the TFBS within the much wider region. While the need for identifying TFBSs as an adjunct to gene prediction in the human genome has diminished, it is now much more important in order to have a full understanding of the regulation of gene expression and to be able to consider the potential phenotypic effects of mutations occurring in a TFBS.None of the ENCODE-ChIP-Seq data used to derive the PWMs for evaluating motif discovery tools overlapped the sequences obtained from genes present in PAZAR and consequently we know there is no overlap between the training and test sets. Table κB, the only transcription factor for which SELEX-seq, ENCODE-ChIP-Seq data and PAZAR data are available.The analysis here has focused on the use of data from ChIP-Seq experiments which, as described in the introduction, have largely superseded the earlier ChIP-chip approach; both of these are in vivo approaches. Another relatively new approach is the in vitro SELEX-seq  approachSn=0.913, PPV=0.810, ACCg=0.860, FPRs=0.004) is less than its counterpart derived from the ENCODE-ChIP-Seq data . However no firm conclusions can be drawn on the performance of SELEX-seq data in general on the basis of a single transcription factor.The performance of the SELEX-seq derived PWM (Another recently developed technology is ChIP-exo . Unfortuσ=3.7). A naïve scanning of PWMs against a DNA sequence can therefore result in a high false positive rate. It is therefore essential to optimize the methods used to scan a PWM against a DNA sequence in order to minimize the false positive rate.An inherent problem with TFBS prediction is their short and degenerate nature. The non-redundant vertebrate TFBS PWMs in JASPAR.2014 range from 5 b.p. (Pax4) to 30 b.p. (Prrx2), but with the majority being 9–15 b.p.  and the PWMs generated in this work using rGADEM. Similarly, using FIMO (part of the MEME suite) as a search tool, PWMs generated using MEME-ChIP do not perform as well as PWMs generated using rGADEM (Table Position Weight Matrices (PWMs) are the most widely used TFBS models, but are limited by the assumption of the model that positions within a binding site are independent, something which is not true in all cases . There hHowever it has been observed that classical PWM models tend to perform at least as well as more complex models  and thatWhile PWMs are not outperformed by more complex models for the majority of transcription factors, for a small number of individual transcription factors it has been found that more complex models do result in better performance . For exaWhile TFBS predictors which identify individual sites outperform those that identify clusters, the choice of the type of prediction tool depends on the context in which it is to be used. The evaluation used in this study was performed in the context of known TFBSs associated with genes. Consequently, if prior knowledge is available about the DNA sequence being scanned (i.e. the DNA sequence is that of a known protein coding gene) then using a predictor of individual TFBSs is probably a sensible strategy. When analyzing a stretch of DNA with no prior knowledge about the presence of a gene, it would be better to use a prediction tool that identifies clusters of TFBSs since the chance of a random match is much reduced , 96.In conclusion, we have analyzed motif discovery tools for generating PWMs from ChIP-Seq data using experimentally-validated precise TFBSs from PAZAR as a gold standard. We found that rGADEM out-performed other tools. We then evaluated a number of tools for scanning PWMs against DNA, both for identifying individual TFBSs and clusters of TFBSs. We found that FIMO and MCAST performed best respectively. We also found that there appears to be no dependence between the tool used for motif discovery and the tool used for motif scanning — in other words, using (for example) a motif scanning tool from the MEME suite does not perform better when using PWMs generated using a motif discovery tool from the MEME suite than when using an unrelated motif discovery tool.Additional file 1The PAZAR Reference Dataset. Entries from PAZAR, with the numbers of each TFBS that they contain. The spreadsheet also indicates whether the TFBSs are found in JASPAR and/or the ENCODE-ChIP-Seq data. (XLS 54 kb)Additional file 2Evaluation of search tools using JASPAR.2010 PWMs. Sensitivity, Positive predictive value, Geometric accuracy and False positive rate for the 14 TFBSs that are found in the PAZAR-J dataset. Separate sheets are provided for each of the search tools. (XLS 31.5 kb)Additional file 3Motif Discovery Tool Performance. Complete results for individual PWMs generated using different motif discovery tools and scanned against PAZAR-E data using the FIMO motif scanning tool. (XLS 14 kb)Additional file 4Sequence Logos. Sequence logos for the first PWM generated for the 12 TFBSs using each of the four motif discovery tools. (PDF 29.5 kb)Additional file 5Evaluation of search tools using PWMs generated from the ENCODE-ChIP-Seq data using rGADEM. Sensitivity, Positive predictive value, Geometric accuracy and False positive rate for the 12 TFBSs that are found in the PAZAR-E dataset. Separate sheets are provided for each of the search tools. (XLS 29.5 kb)


Whether non-integrated viral DNAs distribute randomly or target specific positions within the higher-order architecture of mammalian genomes remains largely unknown. Here we use Hi-C and viral DNA capture (CHi-C) in primary human hepatocytes infected by either hepatitis B virus (HBV) or adenovirus type 5 (Ad5) virus to show that they adopt different strategies in their respective positioning at active chromatin. HBV contacts preferentially CpG islands (CGIs) enriched in Cfp1 a factor required for its transcription. These CGIs are often associated with highly expressed genes (HEG) and genes deregulated during infection. Ad5 DNA interacts preferentially with transcription start sites (TSSs) and enhancers of HEG, as well as genes upregulated during infection. These results show that DNA viruses use different strategies to infiltrate genomic 3D networks and target specific regions. This targeting may facilitate the recruitment of transcription factors necessary for their own replication and contribute to the deregulation of cellular gene expression. Whether DNA viruses contact specific regions of host genomes or make random contacts is unclear. Here, the authors use Hi-C and show that HBV cccDNA and Ad5 DNA contact preferentially active chromatin at CpG islands for the former and at transcription start sites and enhancers for the latter. Chromatin loops resulting from the contacts between discrete, distant loci can notably result in functional interactions between co-regulated genes or enhancers and promoters. TADs are themselves segregated spatially in compartment A and B depending on their gene content .The three-dimensional organization of the genome has emerged in recent years as an important player in gene expression regulation. The cellular genome is compacted through folding of the DNA fiber that is organized into sub-Mb chromosomal “topologically associated domains” (TADs), i.e., DNA regions that display increased contact over themselves when analyzed through chromosome conformation capture experiments (Hi-C)4. However, the appreciation of the complexity of the higher-order organization of the host genome and its potential influence in the regulation of gene expression raises questions regarding the spatial arrangement of non-integrated viral DNA in this context. Notably, whether the overall distribution of the viral DNA will be random, or not.Viruses that transcribe their DNA within the nucleus have to adapt to the molecular mechanisms that govern transcriptional regulation. They can modify and/or re-direct the host’s transcriptional machinery in order to induce viral gene expression, as documented and supported by numerous studies5. cccDNA is a template for all HBV transcripts including pgRNA and is organized into a chromatin-like structure associated with histone and non-histone proteins6. Its transcription depends on cellular transcription factors (TFs) and chromatin-modifying enzymes, as well as on the viral regulatory protein HBx7. Human adenovirus is part of a large family of viruses that infect a wide range of vertebrate hosts. Adenoviruses infect and replicate at various sites of the respiratory tract causing respiratory diseases, but it can also infect the eyes and gastrointestinal tract as well as, albeit less frequently, urinary bladder and liver. Adenovirus is a non-enveloped virus containing a linear double-stranded DNA genome of ~40 kb. Upon infection, viral DNA is delivered to the nucleus, where it associates with histones and viral protein VII8. Adenovirus transcription is by convention separated into early and late phases separated by viral DNA replication8.To investigate this question, we focused on two DNA viruses, hepatitis B virus (HBV) and Human adenovirus. Chronic HBV infection is the prevalent cause of liver cancer. HBV is a small enveloped DNA virus of 3.2 kb which replicates its genome in the cytoplasm via reverse transcription of the encapsidated pre-genomic RNA (pgRNA) into a relaxed circular partially double-stranded DNA (RC-DNA). Upon internalization and transfer to the nucleus, the RC-DNA is converted into covalently closed circular DNA (cccDNA)9 and viral DNA capture (CHi-C) on primary human hepatocytes (PHH) infected by HBV or Hi-C on PHH infected by adenovirus type 5 (Ad5) to investigate the spatial nuclear organization of the viral DNAs. We showed that while both viral DNAs contact preferentially active chromatin, they adopt different strategies. HBV DNA contacts preferentially CpG islands (CGIs) that are enriched for Cfp1 factors, consistent with our finding that Cfp1 is recruited on the viral cccDNA and is required for H3K4me3 and full HBV transcription. Moreover we observed that CGIs contacted by HBV are enriched for CGIs associated with highly expressed genes and also associated with deregulated genes in HBV-infected PHH. Finally, we demonstrated that Ad5 interacts preferentially with transcription start sites (TSSs) and enhancers of highly expressed genes and genes upregulated during infection.Here, we applied Hi-C10  were also similar in PHH and fibroblasts12, as well as in non-infected and infected PHH  from the viewpoint of their genome organization. Hi-C libraries of PHH cells harvested 7 days after infection when cccDNA level is maximal2 from th 13 Fig. . Overall 13 Fig. , which d14. A total of 39,077 and 30,696 specific contacts between the host and the viral genome were characterized for donors 399 and 342, respectively  15 Fig. . Preferes18 Fig. 19. To inTSS Fig. , i.e., cq value < 0.05) . A total of 1139 mRNA (609 down- and 530 upregulated) and 31 LncRNA (15 up- and 14 downregulated) were differentially expressed between the two conditions (05) Fig. . Contact05) Fig. , suggest05) Fig. .Fig. 4HB22. The cellular factor CXXC finger protein 1 (CXXC1 or Cfp1) binds to non-methylated CGIs, establishing an active chromatin state in part through the recruitment of the methyltransferase Set1 responsible for H3K4me3 deposition23. Interestingly, DNA methylation as well as Set1-mediated H3K4 methylation of the viral cccDNA influence HBV transcription26. We silenced Cfp1 in HepG2-NTCP cells previously infected with HBV, as confirmed by RT-qPCR and western blotting  were generated  for non-infected PHH, and PHH infected with HBV wild type or HBV X-, were also similar  was derived from a 7-month-old donor infected by the human adenovirus serotype 5 (Ad5). To better understand the interplay between the Ad5 and PHH genome, we investigated using Hi-C, the positioning of Ad5 genome in the host nucleus, during infection (Methods) Fig. .Fig. 7Di13, and eigen vector decomposition partitioned the genome into compartments A (active) and B (inactive)  when Ad5 replication is low and high, respectively Fig.  and 14a.ve) Fig. , Methodsve) Fig. . At day ve) Fig. , correspve) Fig. . The conve) Fig. . Interes37. They are enriched at enhancer and TSS and cooperate with master TFs to recruit Mediator and SMC1A at actively transcribed genes38. E1A induces high rate of early viral promoters transcription through its interaction with Mediator and the formation of the preinitiation complexes39. Whether factor such as Mediator or cohesin present at FOXA binding motifs influence contacts between Ad5 and cellular chromatin or participate in Ad5 transcriptional activation via E1A interaction or both will need further investigation. It is also interesting to note that the second most enriched motif, CEBP motif, also known as CAAT box, that presents in a large proportion of cellular enhancers and promoters also regulates the expression of Adenovirus major late promoter (MLP)40. Again whether CAAT binding factors are involved in Ad5/cellular DNA contacts or only modulate Ad5 transcriptional regulation will need further analysis.We identified 3862 and 1,352,760 unique viral–host contacts  was confirmed in infected PHH  integrase that forms a complex with the cellular factor lens epithelium-derived growth factor (LEDGF)/p75 to promote the integration of HIV-1 into active gene-dense chromatin regions23. As a consequence, we propose that the preferential repositioning of cccDNA to CpG-rich regions favors the recruitment of Cfp1 to the viral molecule to establish transcription. Cfp1 contributes to transcription activation in part through the recruitment of Set1, which deposits H3K4me323. We showed here that Cfp1 depletion in the host cell correlates with both the decrease of HBV transcription and H3K4me3 deposition on the cccDNA, suggesting that Cfp1 is necessary for the recruitment of Set1 to the viral DNA so it can be properly transcribed as has been already demonstrated for cellular CGIs23. This is further supported by data showing that cccDNA transcription is controlled by Set1 recruitment24. As for the Ad5 genome, it preferentially repositions at TSS and enhancers, suggesting that this virus may also exploits some local molecular TFs to promote its own transcription. We observed that Ad5 contacts regions enriched for FOXA and CEBP binding motifs. While we do not know whether FOXA controls Ad5 transcription, it has been shown that it recruits factors required for transcriptional activation by E1A39. It will be interesting to further analyze whether FOXA and interacting partners modulate Ad5 transcription in PHH.HBV and Ad5 position at different active chromatin regions, suggesting that they provide a suitable and virus-specific environment for their transcription/replication. CGIs are bound by Cfp1 factors that maintain the region into a transcriptionally active state49. Moreover it has been shown that some genes in the cluster can influence the transcription of the other, highlighting the importance of this 3D organization in transcriptional regulation50. The mechanism of cooperation is not yet well understood but it is believed that the clustering may generate transcriptional areas with high local concentration of factors regulating transcription and creating thus a specialized microenvironment allowing efficient transcription. These mechanisms have been observed in the context of transcriptional activation of interferon type 1 beta (IFN-β). Virus infection triggers a NF-κB-dependent association between the INF-β gene and loci containing NF-kB binding sites and subsequent transcriptional activation43. DNA viruses may enhance the formation of such “transcriptional areas” by favoring the recruitment of cellular factors. In the case of HBV, we observed that HBV contacts are enriched at both CGIs associated with genes that are upregulated, but also downregulated upon HBV infection. It will be interesting to analyze whether HBV DNA could disrupt the spatial clustering of CGIs41 leading to the downregulation of some cellular genes.Viral–host genomes interactions in HBV-infected PHH may also influence the expression of cellular genes, as suggested by the enrichment of CGIs associated with deregulated genes in the set of HBV contacts. Similarly, the set of genes whose TSS are enriched in Ad5 contacts are upregulated in Ad5 infected arrested human fibroblasts, suggesting a causal relationship between viral contacts and gene expression changes. This set of observation raise the possibility that viral genomes interfere with cellular genes expression via DNA/DNA contacts. It will be interesting next to study how viral DNA contacts modulate cellular gene expression. Several studies identified clustering of co-regulated genes that cooperate with each other to enhance gene expressionOverall, this work provides a global overview of the physical contacts between two viral genomes and their host, as well as insights regarding the functional significance of these interactions. We propose a model where DNA viruses use different strategies to infiltrate the genomic 3D networks and contact active chromatin. We showed that HBV cccDNA contacts CpG islands known to be enriched for Cfp1, a factor involved in HBV transcription. This observation suggests that these viruses target specific active chromatin regions because these regions may provide an environment propitious to their own transcription/replication Fig. . This hy51. HepAD38 cells were maintained in Dulbecco's modified Eagle medium/F-12 with 10% fetal calf serum (FCS), 3.5 × 10–7 M hydrocortisone hemisuccinate, and insulin at 5 g per ml. Primary human hepatocytes (PHHs) were purchased from Corning  and maintained in the PHH medium  according to the manufacturer recommendations. HepG2-NTCP cells (A3 clone) derive from HepG2 cells and express the human sodium taurocholate cotransporting polypeptide (NTCP). HepG2-NTCP cells are grown in DMEM with 10% fetal calf serum (FCS)52.The HepAD38 cell line derives from HepG2 cells and contains the HBV genome (sub-type ayw) under tetracycline control−5 M hydrocortisone hemisuccinate, 5 mg/ml insulin, and 2% dimethylsulfoxide. HBV particles were concentrated from the clarified supernatant through overnight precipitation in 5% PEG 8000, followed by centrifugation at 4 °C (60 min at 5292 × g). Titers of enveloped DNA-containing viral particles were determined by immunoprecipitation with an anti-preS1 antibody , followed by qPCR quantification of viral RC-DNA using RC primers  for 3 days before processing the cells53. HepG2-NTCP cells were infected with normalized amounts of virus at an MOI of 100 vp/cell in presence of 4% PEG 8000. Twenty-four hours after inoculation, cells were washed with PBS 1X and transfected with the indicated siRNA as described in the siRNA section. Eight days after infection, HBV transcription was analyzed by RT-qPCR. The level of HBV RNA in HepG2-NTCP cells transfected with Si Ctr set to 1.For virus production, HepAD38 cells were grown in Williams E medium supplemented with 5% FCS, 7 × 10PHH (345) were seeded and maintained in the culture for 4 or 7 days. Ad5 infection was followed by measuring adenovirus gene expression of E1A and E1B (early phase) and protein II  (Methods), as well as by tracking the amount of DNA. E1A expression was also confirmed by immunostaining and immunoblotting.54. For relative quantifications, Rhot2 was used as a reference gene because of its low variation coefficient in human liver tumors and cell lines55. Values were calculated according to the ΔCt quantification method with ΔCt = Ct HBV – Ct Rhot2. Results are expressed as the average of at least three independent experiments. Standard error of the mean (SEM) are indicated. p-values were determined by Mann–Whitney test. The primers HBV RNAall-F and HBV RNAall-R amplify all HBV transcripts (pgRNA as well as the 2.4 and 2.1 kb mRNA), except the 0.8 Kb transcript encoding HBx  and TURBO DNase (Ambion). RNA (500 ng) was retrotranscribed using random primers and RevertAid H Minus M-MuLV reverse transcriptase (Fermentas). RT-qPCR experiments were carried out as described2, 10 mM KCl), lysed by adding 1% NP-40, and shaken using a vortex for 30 s. Nuclei were pelleted by centrifugation (10 min at 1600 × g) and washed in buffer I. Following centrifugation, DNA was extracted using QIAamp DNA blood mini kit (Qiagen). For HBV cccDNA quantification, DNA was pre-treated with 10 U plasmid-safe DNase (Epicenter) for 1 h at 37 °C, and cccDNA was amplified using cccDNA primers  with minor modifications. Cells were fixed with 1% formaldehyde for 15 min at room temperature (RT) and quenched with 0.125 M glycine for 5 min. For nuclear extracts preparation, cells were lysed in PBS supplemented with protease inhibitors and homogenized with CK14 Precellys beads (KT03961-1-203.05) at 6700 rpm, pulse for 20″ on and 30″ off, for 3 cycles. After centrifugation, nuclei were suspended in ChIP buffer . After sonication and centrifugation, chromatin was immunoprecipitated overnight at 4 °C using 4 μg of H3K4ME3 (miilipore 07-043) and anti-Cfp1 (ab 56035). Immunoprecipitations with irrelevant non-specific immunoglobulins (Millipore PP64B) were included in each experiment as a negative control. Immune complexes were then incubated with 30 μl of protein G agarose beads for 3 h at 4 °C. Immunoprecipitated complexes were washed and eluted in Elution buffer AM4. After purification of DNA, qPCR was performed using specific cccDNA primers  supplemented with diamidino phenyl indol (DAPI) for nuclear staining. Fluorescent images were acquired on an Axio Observer.Z1 microscope with an Apotome Camera with Plan-Apochromate 63x/N.A. 1.40. Images were acquired with AxioVision software .12 with minor modifications. Five million PHH cells were collected and incubated for 10 min at RT in culture medium containing 1% formaldehyde. The reaction was stopped by addition of 2.5 M glycine  and incubated for 5 min at RT followed by 15 min on ice. The suspension of cross-linked cell was centrifuged at 300 × g for 10 min, and the pellet suspended in 500 μl ice-cold Hi-C lysis buffer  with 100 μl of protease inhibitors . The suspension was incubated on ice for 15 min, centrifuged (2500 × g for 5 min), and the cell pellet suspended in 1 ml of digestion buffer 1X (NEB). The cells were transferred in a 2 mL tube Soft tissue homogenizing CK14 (BERTIN Instruments), then lysed using tissue homogenizer . Lysate was transferred into loBind tubes (Eppendorf). Precellys beads were washed with 800 μl NEB buffer subsequently pooled with lysate. Total lysate was split into six 300 μl aliquots. Each aliquot was mixed with 200 μl NEB buffer 1X, followed by the addition of 15 μl SDS 10% and incubation at 65 °C for 10 min. SDS was quenched by adding 100 μl of 10% Triton X-100 in each tubes which were kept at 37 °C for 30 min. Chromatin was digested overnight at 37 °C by adding 1000 Units of BglII (NEB). DNA ends were filed and labeled with biotin in klenow end-filling reaction with 1.5 μl of 10 mM dATP, 1.5 μl of 10 mM dGTP, 1.5 μl of 10 mM dTTP, 37.5 μl of 0.4 mM biotin-14-dCTP , and 40 U of Klenow  incubated at 37 °C for 45 min. Digested DNA was then ligated by adding 900 μl of ligation buffer containing 120 μl of 10X NEB T4 DNA ligase buffer, 100 μl of 10% Triton X-100, 12 μl of 10 mg per ml Bovine Serum Albumin, 663 μl of water, and 5 μl of 400 U per μl T4 DNA Ligase , and incubation at RT for 4 h under slow rotation. After cross-link reversal and protein degradation with proteinase K , DNA was purified using phenol-chloroform extraction and suspended in TE 1X supplemented with RNase A . DNA was then sheared using a Covaris LE220 apparatus . After shearing DNA was run on 1% agarose gel and bands ranging from 300 to 800 pb were extracted from the gel using minielute gel extraction kit (Qiagen). DNA fragments were immobilized using MyOne Streptavidin C1 dynabeads (Life Technologies), end repaired, adenine tailed and ligated to custom made paired-end adapters (compatible with Illumina primers). The immobilized Hi-C DNA was amplified using Illumina primers PE PCR 1.0 and PE PCR 2.0 with 8–12 PCR amplification cycles. PCR products were further used for direct Illumina pair-end sequencing using nextSeq 500 High output kit v2 (Illumina), or processed through an additional step of capture C (CHi-C).Hi-C was carried out as described previouslyCapture Hi-C of HBV sequences were carried out using custom-designed xGen Lockdown probes (Integrated DNA Technology) of 120 bp, each covering the entire HBV genome genotype D with twofold coverage. A total of 500–1000 ng of HI-C DNA were enriched for HBV DNA sequences according to the manufacturers’ xGen rapid capture protocol v2, using custom paired-end blockers. Fifty microliters of Dynabeads MyOne streptavidin C1 (Life Technologies) were used for capture reaction. DNA-enriched libraries were then amplified with four PCR cycles using PE PCR 1.0 and PE PCR 2.0 primers (KAPA HIFI HotStart ReadyMix) and sequenced using Illumina pair-end sequencing.56. Read-pairs were aligned independently against the reference human genome (NCBI hg19) and the virus genomes using Bowtie 2, using an iterative procedure57 . The HBV and adenovirus sequences used were HBVayw genotype D (https://www.ncbi.nlm.nih.gov/nuccore/V01460.1) and adenovirus C JX173077.1 Human adenovirus C strain human/ARG/A8649/2005/2[P2H2F2], respectively.PCR duplicates for each Hi-C library were filtered using the random tags present on the custom oligonucleotides58. The genome was binned into 100 kb or 400 kb segments, and the corresponding contact maps were generated and normalized using the sequential component normalization procedure (SCN11). SCN alleviates the intrinsic experimental and sequencing variations of the Hi-C protocol  The normalized contact value between two bins corresponds to a number between 0 and 1 that corresponds to the relative contact frequency between this pair of bins compared with all other pairs involving one of those bins. The logarithmic color scale representation helps the visualization.To generate contact maps, each mapped position was assigned to a BglII restriction fragment (RF). Non-informative events such as self-circularized restriction fragments or uncut adjacents RFs were discarded by taking into account the read-pairs relative directions and the distribution of the different configurations57. Positive and negative eigen values were arbitrarily chosen to correspond to the A and B compartments, respectively. This was done using a positive Pearson correlation coefficient with a signal of histone marks associated with active chromatin (H3K4Me3).Eigen vector decomposition was done as follows: the effect of genomic distance was calculated with the normalized 400-kb binned matrix for each chromosome. Each element of the latter matrix was detrended by dividing it with the expected value according to the genomic distance effect. The correlation matrix was then generated and the eigen values and first eigen vector were computed. The latter vector allows the compartment A or B attribution as previously shown inFor the computing and display of the contact signal profile of virus genome along human chromosomes, we assigned each pairs of read-pairs involving virus and human chromosome to the corresponding 400 kb bins.For HBV profile, Hi-C and CHi-C data were merged.Histone modification ChIP-seq datasets were recovered from publicly available databases, and the corresponding reads are aligned along the NCBI hg19 genome using Bowtie. For each contact made by the virus, the number of reads of the histone mark signal was aggregated over a 7-kb window centered on the position of contact (windows ± 3.5 kb around start of read). The same procedure was applied for the corresponding input signal. The ratio of both sums provides the strength of contacts between the virus and a given histone mark. This value was compared with values obtained using a null (random) model. As for the overlap strategy (below), the genomic positions of random positions were chosen by taking into account the differences in detectability of each Hi-C experiment . All the contacts detected in a same Hi-C library were loaded into computer memory. A group of positions was then randomly picked (with replacement) among them to have the same number of virus-human chromosomes contacts. Doing this, a group of fragments that has been detected a lot of times in a Hi-C library will also have more chances to be picked in the random positions generations respecting the difference of detectability between genomic regions inherent to a Hi-C experiment. The mean of 100 realizations was computed to estimate the expected strength of contact for genomic positions of random group. The log2 ratio between the virus contact strength and the random positions strength is computed and plotted for five histone marks.The dataset used in this work correspond to the following references on the short-read archive (SRA) database: SRR227563 for H3K4me3, SRR227466 for H3K4me2, SRR227575 for H3K27ac, SRR227598 for H3K27me3, SRR568329 for H3K9me3, and SRR227552 for input.https://www.encodeproject.org/files/ENCFF558TCP, file: hepatocytes-DS32057A.peaks.fdr0.01.hg19.bed. For a group of N contacts between virus and human chromosomes, we generate a group of N random contacts by sampling with replacement the group of positions already detected in the whole Hi-C library. Doing that, a genomic region that is well detectable (and that will have more detected positions in the whole Hi-C library) will have more chance to be picked up and in contact with the virus. We reiterate this procedure 1000 or 10,000 times. From the set of these realizations, we computed the expected distribution of proportion of contacts overlapping a biological group and displayed the result as a violin plot. A p-value was computed by comparing the detected proportion of contact involving the virus (HBV or Ad5).We developed a bootstrap strategy to determine whether the proportion of contacts made by the virus and overlapping a biological set of genomic positions was significant. The pair-end reads for which one read of the pair contacts the virus and the other read the human genome were processed as follow. First, 7 kb windows centered on the start of each read mapped on the human genome were selected and for each window, the number of positions of various categories of functional elements was computed . The positions of lamins (track: laminB1Lads), TSS , CpG (track: cpgIslandExt), origins of replication  were retrieved from USCS database on the hg19 assembly. Enhancers positions were extracted from the ENCODE project  with the following parameters: “P” for Poisson distribution, “Dplus” for the extended block-diagonal model and the maximal number of change-points was set to the size of the matrice divided by five.TADs boundaries were determined with the HiCseg software16. The duplicates in the biological group positions were removed using R “unique” function.The positions of HBV integration within the genome of non-tumoral cells were retrieved from the work ofCGIs associated with highly and poorly expressed genes were established as follows: the list of transcript expressions was sorted according to their values in FPKM. Then, the 5000 first elements and the 5000 last elements were retained. Duplicated genes were removed. CGIs were associated to their corresponding genes with windows ± 5 kb surrounding the TSS which gives a total of 3698 CGI and 2213 CGI for highly expressed genes and poorly expressed genes, respectively. The group of deregulated genes includes genes whose log2 ratio of expression was above +2.0 (upregulated) or below −2.0 (downregulated). This procedure allows us to have large groups, which was necessary to have significance.p-value estimated from the number of realizations with a contact strength above or equal to the one detected with the virus contacts group.The number of overlaps with the set of positions of a group of genomic features was computed with the function “findOverlaps” from the R package GenomicRanges. The null models were built as followed: genomic positions of all the contacts from a Hi-C library were loaded into memory. From this set of genomic positions, a group of positions with the same number of virus contacts were randomly picked with replacement. This procedure allows to take into account variations in local detectability due to the Hi-C protocol  10,000 or 1000 random realizations were generated and the To visualize virus contact profile around positions of interest, we summed all the contacts made by the virus with 50 kb bins within 5 Mb windows centered on the positions of genomic features of interest . To normalize for variability in detectability, we divide the previous signal by the Hi-C coverage of the same bins.60. Gene enrichment was evaluated by using Agilent CpG island gene list provided by the database as background.TSS/CGI genes in contact with the viral genome were analyzed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool (v6.8)61 with default parameters (hg19 ER_MotifOutput/ -size 200 -mask).To identify recognition binding sequences of known transcription factors in regions enriched in contacts with the virus, we binned the contact signal of Ad5 at 2 kb resolution. We computed the Hi-C coverage at the same resolution. Peaks of enriched contacts for Ad5 were defined as the ones with a ratio of contact signal over the Hi-C coverage above five, resulting in 2976 sequences. The genomic positions of the corresponding bins along the hg19 genome were used as input for the HOMER softwareWe applied a receiver operating characteristic (ROC) analysis to test for significant associations between TSSs enriched in contacts with Ad5 and genomic features of interest . TSSs were sorted according to a “virus occupancy score” computed by summing the number of contacts done by the virus around a TSS (centered window of 7 kb) divided by the Hi-C coverage of the region . From this ranked list, each TSS is labeled “true positive rate” if TSS belongs to a type of genomic features, and “false positive rate” if not. The proportion of positive and negative TSS is plotted along the ranked list of virus occupancy scores.Transcriptomics data from GSE32340 study (GSE32340_RPKM_mock_e1a_samples.txt) were used to determine highly and poorly expressed as well as up- and downregulated genes with E1A protein infection.RNAseq analysis of PHH was used to group genes according to their expression levels, with 3249 highly expressed genes corresponding to genes whose expression is above 20 RPKM (Reads Per Kilobase per Million mapped reads). 20,141 poorly expressed genes were pooled together, corresponding to genes whose expression levels are below 0.5 RPKM.32. Genes up- and downregulated with E1A protein infection were defined as those displaying a log2 fold change superior to +1 or inferior to −1, respectively. Housekeeping genes were retrieved from62.Genes up- and downregulated during Ad5 infection were defined as those displaying a log2 fold change superior to +1 or inferior to −1, respectively, in the transcriptomics datasiRNAs control (ON-TARGETplus Non-targeting Pool #D-001810-10-0X) and siRNA directed against cfp1 were purchased from Santa Cruz Biotechnology. Twenty-four hours after infection, HepG2 NTCP cells were transfected with 40 nM of si Ctr or si Cfp1 (sc-35055) using Lipofectamine RNAiMAX  reagent according to the manufacturer’s protocol. RNA was extracted 8 days after infection.63. The expression of lncRNAs and mRNA was assessed by Cuffdiff (http://cole-trapnell-lab.github.io/cufflinks/cuffdiff/index.html). Square of Pearson Correlation Coefficient between the biological replicates was >0.94. Differentially expressed genes had a q value < 0.05. Enrichment pathways were analyzed using KEGG enrichment analysis.Three biological replicates were used for each conditions (infected and non-infected). PHH were infected as described above. Total RNA was extracted and purified using TRIzol reagent (Invitrogen) and TURBO DNase (Ambion). cDNA library, sequencing using illumina PE 150 and RNAseq analysis were performed by Novogen. On average, ∼95 million reads were generated per sample. Clean reads were aligned to the reference genome with Tophat2Anti-tubulin  was purchased from SIGMA , Anti-beta actin  was purchased from SIGMA , anti-E1A (dilution 1/1000 for WB and 1/200 for immunofluorescence IF) from Santa Cruz Biotechnology , anti-CGBP  from Santa Cruz Biotechnology , HBc  was from DAKO  and prediluted anti-HBs (no dilution) from sigma .Samples were resolved by SDS-PAGE and electro-transferred to nitrocellulose membranes. Blots were incubated with the indicated primary antibodies and then probed with Dye-conjugated secondary antibodies. Fluorescent immunoblot images were acquired and quantified by using an Odyssey scanner and Odyssey 3.1 software (Li-CorBiosciences). Uncropped scans of blots are reported in the Supplementary Information as Supplementary Figure Histone modification marks dataset were from the Encode project generated using hepatic HepG2 cells (GSE29611).https://github.com/axelcournac/virus_Hi-C_Analysis.Codes produced in this study can be accessed at Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5