Zdbf2 gene—involved in growth control—provides a valuable model to study this window: upon exit from naïve pluripotency and prior to tissue differentiation, it undergoes a switch from a distal to a proximal promoter usage, accompanied by a switch from polycomb to DNA methylation occupancy. Using a mouse embryonic stem cell (ESC) system to mimic this period, we show here that four enhancers contribute to the Zdbf2 promoter switch, concomitantly with dynamic changes in chromatin architecture. In ESCs, the locus is partitioned to facilitate enhancer contacts with the distal Zdbf2 promoter. Relieving the partition enhances proximal Zdbf2 promoter activity, as observed during differentiation or with genetic mutants. Importantly, we show that 3D regulation occurs upstream of the polycomb and DNA methylation pathways. Our study reveals the importance of multi-layered regulatory frameworks to ensure proper spatio-temporal activation of developmentally important genes.During early mammalian development, the chromatin landscape undergoes profound transitions. The  During the early stages of mammalian development, as the embryo implants into uterine wall, the pluripotent cells that will go on to form somatic tissues transition from ‘naïve’ to ‘primed’ for lineage specification . One halOct4  relies on an naïve and primed cell-specific enhancers  is active. As cells exit naïve pluripotency, a promoter switch occurs, resulting in activation of the proximal Zdbf2 promoter (pZdbf2), located 73 kilobases (kb) downstream. From the primed state and then throughout life, pZdbf2 is the functional promoter while pLiz is constitutively silenced. It should be noted that despite the genomic distance, there is no change in the message between the Liz and Zdbf2 isoforms, thus the promoter switch does not result in protein diversity. Rather, there is a stratified relationship between the two promoters: pLiz activity is absolutely required for imprinted deposition of DNA methylation at a somatic differentially methylated region (sDMR). The sDMR DNA methylation, in turn, antagonizes a block of polycomb-mediated repression, freeing pZdbf2 -mediated contacts at the locus change dynamically during differentiation, and contribute to the sequential activity of the Zdbf2 promoters. Saliently, 3D organization of the locus appears to exert its effect epistatically with respect to the DNA methylation and polycomb pathways. This implies that there are two layers of chromatin-based control of Zdbf2: one at the level of chromatin marks and the other at the level of chromatin architecture. The highly regulated nature of Zdbf2 underscores the importance of structural chromosome topology occurring in concert with chromatin marks to control proper spatio-temporal expression of developmentally consequential genes.We show here using a cell-based approach that several enhancers cooperate to regulate the dynamics activity of the different Zdbf2 alternative promoter usage during de novo DNA methylation, we performed an assay for transposase-accessible chromatin followed by sequencing (ATAC-Seq)   on DNA hTAC-Seq) . A protrTAC-Seq)  (GreenbeTAC-Seq) , which dTAC-Seq)  (Bao et TAC-Seq) . For clahylation . As expe in ESCs .pLiz (E1-3), and one adjacent to a CpG island (CGI) that is an apparent border to the polycomb H3K27me3 block in ESCs (E4) (pLiz. Therefore we assayed for enrichment of H3K27 acetylation (H3K27ac), a mark of active enhancer elements  . Given telements . E1-3 apelements . Thus, welements   during differentiation , we did not observe distal looping that occurred at high frequency in either ESCs or D7 EpiLCs , which is active in D7 EpiLCs while E1-4 are all marked by H3K27ac. Our 4C-seq revealed that pZdbf2 exhibited increased contacts with all four of the putative enhancers in D7 EpiLCs  (We previously showed that DNA methylation accumulates at  ablates . Interesccupancy . Liz tra in vivo . In the  in vivo . This waz (∆Liz) .pLiz-to-pZdbf2 promoter switch, E4 becomes enriched for H3K27ac. We hypothesized that a deletion for E4 would have minimal impact on pLiz, but may affect pZdbf2 activity  gene  gene —a core c  instructive chromosome conformation allowing for proper pLiz and pZdfb2 activity, and 2) a Liz-dependent epigenetic switch to evict polycomb at pZdbf2. This tiered model, with chromosome conformation acting upstream, emphasizes the exquisite choreography that can occur to program developmentally important genes during the exit from the naïve pluripotent state. Interestingly, global depletion of CTCF leads to upregulation of Zdbf2 in ESCs  while restricting the other’s (pZdbf2). Only upon removal of CTCF binding, the opportunity for pZdbf2 is created to contact its enhancers. On the face of it, such a mechanism resembles the imprinted Igf2/H19 locus, where CTCF binding near the H19 gene on the maternal allele restrict interactions between a shared set of enhancers and the more distal Igf2 gene . Therefore, when generating an in-house Eed-/- line, we used the same genetic background for consistency.All cell lines are listed in the Key Resource Table. For all experiments, the parental WT line was used as a control for mutant lines generated in that background (E14 or J1). 4C-Seq was performed using clone 36 Tigre locus), the auxin analog indole-3-acetic acid (IAA) was added to the media at a final concentration of 500 µM from a 1000x stock, and incubated with the cells for two days.Feeder-free ESCs were grown on gelatin-coated flasks. Serum culture conditions were as follows: Glasgow medium (Sigma) supplemented with 2 mM L-Glutamine (Gibco), 0.1 mM MEM non-essential amino acids (Gibco), 1 mM sodium pyruvate (Gibco), 15% FBS, 0.1 mM β-mercaptoethanol and 1000 U/ml leukemia inhibitory factor . Cells were passaged with trypsin replacement enzyme (Gibco) every two days. 2i culture conditions were as follows: N2B27 medium , 50% DMEM/F12 (Gibco), 2 mM L-glutamine (Gibco), 0.1 mM β- mercaptoethanol, NDiff Neuro2 supplement (Millipore), B27 serum-free supplement (Gibco)) supplemented with 1000 U/ml LIF and 2i . Cells were passaged every 2–4 days with Accutase (Gibco). Vitamin C (Sigma) was added at a final concentration of 100 µg/ml. For EZH2 inhibition experiments, the EZH2 incubator UNC 1999  was added to media at a 1 µM final concentration for four days. To induce degradation of CTCF in the E14TG2a_CTCF-AID-eGFP, Tir1 cell line (with Tir1 targeted to the 5 cells/cm2 on Fibronectin (10 µg/ml)-coated plates in N2B27 medium supplemented with 12 ng/ml Fgf2 (R and D) and 20 ng/ml Activin A (R and D). EpiLCs were passaged with Accutase at D4 of differentiation.To induce EpiLC differentiation, cells were gently washed with PBS, dissociated, and replated at a density of 2 × 10Cells were regularly tested for presence of mycoplasma by sending used media to GATC/Eurofins for analysis.Cas9 gene. All sgRNA sequences are listed in Eed mutation, loss-of-function was further confirmed by immunoblotting.All deletions in this study were generated with two CRISPR single guide RNAs (sgRNAs) specific to the target sequences followed by Cas9 nuclease activity and screening for non-homologous end joining. sgRNAs were designed using the online CRISPOR online program (crispor.tefor.net) and cloned into the pX459 plasmid harboring the t-test using GraphPad Prism6 software.Genomic DNA from cells was isolated using the GenElute Mammalian Genomic DNA Miniprep Kit (Sigma), with RNase treatment. Bisulfite conversion was performed on 500–1000 ng of DNA using the EpiTect Bisulfite Kit (Qiagen). Bisulfite-treated DNA was PCR amplified and analyzed by pyrosequencing. Pyrosequencing was performed on the PyroMark Q24 (Qiagen) according to the manufacturer’s instructions, and results were analyzed with the associated software. All bisulfite primers are listed in WGBS data from ESCs were previously generated  and EpiLATAC-Seq was performed as described in ChIP was performed exactly as described in DpnII or its isoschiszomer MboII (New England Biolabs) were chosen as the primary restriction enzyme, and NlaIII (New England Biolabs) as the secondary restriction enzyme. ESC and EpiLC material were harvested from 150 cm2 culture flasks (TPP Techno Plastic Products AG), which provided ample material for up to four technical replicates presuming cells were healthy and near confluency. To avoid technical artifacts, crosslinking and library preparation were performed in parallel for each experiment. For each VP, approximately 1 µg of library material was amplified using 16 individual PCR reactions with inverse primers containing indexed Illumina TruSeq adapters , then DNase-treated and column purified (Qiagen RNeasy Kit). To generate cDNA, RNA was reverse transcribed with SuperscriptIII (Life Technologies) primed with random hexamers. RT-qPCR was performed using the SYBR Green Master Mix on the Viia7 thermal cycling system (Applied Biosystems). Relative expression levels were normalized to the geometric mean of the Ct for housekeeping genes RNA-seq libraries were prepared from 500 ng of DNase-treated RNA with the TruSeq Stranded mRNA kit (Illumina). Sequencing was performed in 100pb paired-end reads using the Illumina HiSeq2500 platform.Western blots were visualized using the ChemiDoc MP (Biorad). The antibodies are listed in the Key Resource Table.http://broadinstitute.github.io/picard/). Tracks were created using HOMER software v4.7  using Bwa mem v0.7.5a  with defare v4.7 .Whole-genome bisulfite sequencing data were analyzed as described in http://hannonlab.cshl.edu/fastx_toolkit/index.html). Adapter sequences were removed with Cutadapt v1.3 ((apt v1.3  and readapt v1.3  with Bowapt v1.3  and defa2 × 100 bp paired-end reads were mapped onto the mouse reference genome (mm10) using STAR v2.5.0a  reportinhttps://www.bioinformatics.babraham.ac.uk/projects/trim_galore/. Adapters were first trimmed using Trim Galore: v0.4.0, Samples were demultiplexed using the script provided with the FourCSeq R package (v1.12.0) . InverseRaw and processed sequencing data reported in this paper have been submitted to GEO, accession number GSE121405. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, including Michael Buszczak as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Kevin Struhl as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity:; Alvaro Rada-Iglesias (Reviewer #2).Thank you for submitting your article "Dynamic enhancer partitioning instructs activation of a growth-related gene during exit from naïve pluripotency" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:Zdbf2 locus, which undergoes a switch from distal (pLiz) to proximal (pZdbf2) promoter usage, accompanied by a switch in polycomb to DNA methylation occupancy. The authors define a number of different enhancer elements (E1-4). E1-3 are active in ESCs, while E4 becomes active in EpiLCs. The authors use high resolution 4C to show enhancer-promoter dynamic interactions during the process of differentiation. They then delete specific enhancers to demonstrate their functional significance in regards to the regulation of chromatin marks throughout the region and the expression of Zdbf2 itself. The authors provide evidence that Liz transcription and polycomb function play a minor role in the 3D architecture of the region during differentiation. They end by showing that a CTCF binding site acts to partition the Zbdf2 locus in ESCs. Interestingly, they show that in the absence of this partition, polycomb mediated silencing keeps Zdbf2 expression at relatively low levels. Removal of both the partition site and polycomb activity results in a dramatic increase of Zdbf2 expression in ESCs.Recent studies have begun to reveal how specific transcription factors (TFs) serve to change the global chromatin landscape as naïve pluripotent embryonic stem cells transition to a primed pluripotent epiblast-like state. This study builds upon these findings and explores the cause and effect relationship between changes in 3D organization and changes in enhancer activity during this developmental transition. As a model, the authors focus on the regulation of the The manuscript is well written and robust data generally support the authors' conclusions. The paper will be of interest to the field and will significantly contribute to our understanding of how chromatin organization and architecture change during developmental transitions.Essential revisions:Zdbf2 expression are not actually observed (E4 deletion or CTCF_PS deletion). Furthermore, it is also unclear whether a 7 day differentiation is always used when referring to EpiLC (e.g. H3K27ac ChIPs). If the authors want to use this 7 day EpiLC differentiation, they should better characterize or describe which cells they are truly working with.1) The state of the cells being examined needs to be better defined. EpiLC differentiation, as originally described by the Saitou lab, typically takes 2-3 days and this recapitulates the postimplantation epiblast (E5.5-6). Some authors refer to this state as formative, rather than primed pluripotency, which in vitro might be best represented by EpiSCs. However, in the manuscript the authors use a 7 days EpiLC differentiation protocol. What kind of cells are the authors really obtaining after 7 days? Are they in fact in the process of adapting their ESC to EpiSC (not EpiLC) conditions? If the typical 2-3 day EpiLC differentiation protocol is considered, then some of the changes in pLiz and pZdbf2 needs to be examined in greater detail. There are concerns that clonal differences within the ES cell population could account for some of the described results. This point needs to be addressed in the text and by using another complementary experimental approach such as FISH.2) The hierarchical activity of Zdbf2 proximal promoter (and perhaps E4), while E4 might become dominant in controlling later Zdbf2 expression. The authors should conditionally delete or inactivate E1-3 (e.g. CRISPRi) during EpiLC differentiation to more conclusively support their model.In addition, there is the possibility that E1-3 and E4 enhancers are sequentially used and stage-specific, with E1-3 being important to initially activate the Eed knockout could have secondary effects. The authors could use an EZH2 inhibitor to more acutely inhibit PRC2 function in their experiments. This would complement their current data and potentially avoid major confounding effects.3) There are concerns that the Zdbf2 flanking genes upon deletion of the CTCF PS site in both ESCs and EpiLCs.4) There are concerns about the authors' claims regarding the CTCF partition. Considering the possibility that loss of CTCF partitioning can lead to a loss of insulation, the authors should evaluate the expression of the In addition, using an alternative experimental approach such as FISH (see point 2 above) to evaluate their various deletion cells would help support their model.Without CTCF knockout, the authors should temper their language regarding whether CTCF itself is needed for the partition. Alternatively, they could attempt to conditionally knockdown CTCF. This was recently done in ESCs using an AID degree system  and the authors could perhaps request those cells to save time.eLife. Your revised article has been favorably evaluated by Kevin Struhl (Senior Editor), a Reviewing Editor, and two reviewers.Thank you for resubmitting your work entitled "Dynamic enhancer partitioning instructs activation of a growth-related gene during exit from naïve pluripotency" for further consideration at The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:The reviewers appreciate that you addressed most of concerns raised during the first round of the review. Reviewer #2 raised a few points that other reviewers agreed to be important points. These points can be simply addressed by textual changes, and we would like you to edit the text to reflect on those points. After that, the reviewing editor should be able to editorially accept the manuscript.eLife to publish your work. We look forward to receive the revised version soon.Thank you very much for choosing Reviewer #2:I am overall satisfied with how the authors addressed our previous suggestions.It is true that in some cases they have not been able to solve our concerns due to technical reasons, but it is also true that some of the experiments we proposed were not trivial. In those cases, for example the possibility of E1-3 and E4 enhancers acting in a hierarchical manner, I would suggest that they mention this possibility in their discussion.Zdbf2 up regulation they observe in the CTCF-AID ESC even if it does not fit their model. I think they can comment on the potential reasons explaining the differences between deleting a single CTCF site and a global loss of CTCF in the Discussion section.They should also incorporate the The Author response images 1 A and B should be included as supplementary material, since it helps [to define] what their cells truly are.Reviewer #3:I agree that the authors more or less addressed the previous suggestions. I also agree that bringing  Essential revisions:1) The state of the cells being examined needs to be better defined. EpiLC differentiation, as originally described by the Saitou lab, typically takes 2-3 days and this recapitulates the postimplantation epiblast (E5.5-6). Some authors refer to this state as formative, rather than primed pluripotency, which in vitro might be best represented by EpiSCs. However, in the manuscript the authors use a 7 days EpiLC differentiation protocol. What kind of cells are the authors really obtaining after 7 days? Are they in fact in the process of adapting their ESC to EpiSC (not EpiLC) conditions? If the typical 2-3 day EpiLC differentiation protocol is considered, then some of the changes in Zdbf2 expression are not actually observed (E4 deletion or CTCF_PS deletion). Furthermore, it is also unclear whether a 7 day differentiation is always used when referring to EpiLC (e.g. H3K27ac ChIPs). If the authors want to use this 7 day EpiLC differentiation, they should better characterize or describe which cells they are truly working with.Zdbf2 gene. However, in our EpiLC differentiation protocol, the promoter switch does not occur until roughly day 4, and Liz is not fully shut down (at the population level) until day 7, which we published in Greenberg et al., 2017. Hence, it was important we used these timepoints in our study. We now justify our strategy in the text, subsection “Two Classes of Putative Enhancers Lie in the Zdbf2 Locus”, and have added a new panel to that depicts expression dynamics . Furthermore, we clarify throughout the manuscript the day of differentiation for each experiment. We also provide below some RNA-seq analyses that we are not including in the manuscript, but may help answer the reviewer’s question of what cells these are. We performed hierarchical clustering of our ESCs and EpiLCs at various timepoints  with recently published ESC, D2 EpiLC and (in vitro) EpiSC RNA-seq data  . But without more thorough characterization, we feel it is more prudent continue referring to the cells as D7 EpiLCs.We thank the reviewers for raising this issue, as it is an important point that we did not adequately address in the original manuscript. For the purpose of this study, we wanted to emphasize the promoter switch that occurs at the ., 2018) . Indeed,., 2018) . This is2) The hierarchical activity of pLiz and pZdbf2 needs to be examined in greater detail. There are concerns that clonal differences within the ES cell population could account for some of the described results. This point needs to be addressed in the text and by using another complementary experimental approach such as FISH.Eed in Greenberg et al., 2017 and here), as well as in different CRISPR clones for the Eed mutation (data not presented). Finally, we used a PRC2 inhibitor as suggested (described in detail below), which was wholly in line with our genetic mutant cell line. The subtlest result was the deletion of the CTCF-binding site. For this revision, we also used the CTCF-degron line, which also points to CTCF-based regulation of Liz/Zdbf2 (See below).We agree with the reviewers that clonal variation can lead to misleading interpretations. That said, we are confident in the results presented here, bolstered by some of the experiments performed for this revised version. The enhancer deletion effects are strong on the expression of the genes—in addition to being coherent with the 4C-seq results—that it is highly likely these are regulatory elements. Moreover, experiments in polycomb mutants have been performed in multiple PRC2 genes , we performed single-cell RT-qPCR, which showed that Liz and Zdbf2 promoter activity is mutually exclusive.With regards to performing DNA FISH, this is an experiment we would be eager to perform in principle. However, the locus is quite compact—the distal enhancers (E1-3) and proximal promoter are only separated by about 60kb of genomic space. A DNA FISH experiment would mean implementing a high-resolution microscopy technique that we are not equipped to perform at this moment. We hope the reviewers understand and accept our rationale. Relatedly, we would also like to note that RNA FISH does not allow us to distinguish the In addition, there is the possibility that E1-3 and E4 enhancers are sequentially used and stage-specific, with E1-3 being important to initially activate the Zdbf2 proximal promoter (and perhaps E4), while E4 might become dominant in controlling later Zdbf2 expression. The authors should conditionally delete or inactivate E1-3 (e.g. CRISPRi) during EpiLC differentiation to more conclusively support their model.Zdbf2 activation. For example, as the reviewers suggested, perhaps we could resolve whether E1-3 are required for maintaining Zdbf2 expression after the initial activation event. We were able to induce dCas9-KRAB at both the protein and RNA levels  inducible Tet-on promoter driving expression of a dCas9-KRAB chimeric construct. We targeted the construct to the ROSA26 locus to ensure proper induction. In parallel we designed a single plasmid containing sgRNAs targeted to E1, E2, and E3 . Once the dCas9-KRAB line established, we stably transfected the sgRNA plasmid under blasticidin selection. The idea was that we could induce KRAB-mediated silencing of E1-3 at different points during differentiation , and theA levels . Unfortunduction . This co3) There are concerns that the Eed knockout could have secondary effects. The authors could use an EZH2 inhibitor to more acutely inhibit PRC2 function in their experiments. This would complement their current data and potentially avoid major confounding effects.Eed mutant cell line, and we have added a panel to Figure 4—figure supplement 3E. This strongly confirms our previous result, and bolsters our interpretation that both polycomb and 3D organization instruct Zdbf2 activation.Indeed, we agree with the reviewers that there is always a concern with secondary effects in constitutive mutants, especially after prolonged time in culture. Therefore, as suggested, we incubated both our WT and ∆CTCF_PS ESCs in presence of a potent EZH2 inhibitor  and a negative control compound . After 4 days, H3K27me3 was undetectable by western blot, as shown in Figure 4—figure supplement 3D, whereas there was a strong signal in the negative control. RT-qPCR from these cells confirmed our results from the 4) There are concerns about the authors' claims regarding the CTCF partition. Considering the possibility that loss of CTCF partitioning can lead to a loss of insulation, the authors should evaluate the expression of the Zdbf2 flanking genes upon deletion of the CTCF PS site in both ESCs and EpiLCs.Eef1b2 and Ndufs1, which are immediately upstream of Liz; Gpr1, which overlaps Liz; and Adam23, which is immediately downstream of Liz/Zdbf2 . There is no significant change in expression for any of these genes during EpiLC differentiation when the CTCF_PS is deleted. Our interpretation of this result is that Eef1b2 and Ndufs1 remain insulated from the Zdbf2 locus because of CTCF bound in between Eed1b2 and Gpr1 , which does not deplete during differentiation. Our 4C and available HiC data suggest that Adam23 also exists within its own interacting domain separated from Zdbf2 possibly because of CTCF-mediated insulation between Zdbf2 and Adam23 . This result indicates that the CTCF at the partition site plays no role in this insulation. Finally, we have long had interest in Gpr1’s possible role in this locus, but we never have observed any major impact on Gpr1 expression in any of our cis or trans mutations in the ESC-EpiLC system. This experiment is consistent with prior results.This is a good point, and we thank the reviewer’s for raising it. We now have included RT-qPCR graphs for In addition, using an alternative experimental approach such as FISH (see point 2 above) to evaluate their various deletion cells would help support their model.While we agree, this experiment was too technically challenging to undertake for the purpose of this revision (see above).Without CTCF knockout, the authors should temper their language regarding whether CTCF itself is needed for the partition. Alternatively, they could attempt to conditionally knockdown CTCF. This was recently done in ESCs using an AID degree system  and the authors could perhaps request those cells to save time.Liz expression is decreased compared to the wild-type control. Our interpretation is that CTCF levels are already lower in the untreated cell line due CTCF protein instability . Adding auxin on results in a marginal, statistically insignificant further decrease in expression. Perhaps as even in the untreated cells, lower CTCF levels create a landscape where Liz expression is decreased due to decreased enhancer-promoter contacts, and this has reached a minimal threshold. Incidentally, we also observed de-repression of the proximal, Zdbf2 promoter in both untreated and especially auxin-treated cells. As opposed to our ∆CTCF_PS deletion line where we did not observe a significant effect on Zdbf2, globally depleting CTCF apparently leads to upregulation. Whether this is somehow due to direct recruitment of polycomb via CTCF or indirect regulation is a worthwhile avenue of investigation for a future study. As the latter result was somewhat difficult to interpret without further experimental interrogation, we opted to withhold this information in this version of the manuscript.We have taken the reviewers’ advice, and moderated our language on the role of CTCF, per se, in the manuscript. Additionally, as suggested, we utilized the CTCF Degron line described in Nora et al. We converted the cells to 2i+vitC (12 days), and then incubated with or without auxin for two days as described in Nora et al. The results are indeed intriguing, and are now incorporated in Figure 4—figure supplement 2D,E. In short, even in the untreated Tir, CTCF-AID line, Finally, we also performed EpiLC differentiation in the absence of CTCF, but we observed so much cell death compared to control cells, we chose not to include those results as we found the data difficult to interpret due to potentially confounding effects.The reviewers appreciate that you addressed most of concerns raised during the first round of the review. Reviewer #2 raised a few points that other reviewers agreed to be important points. These points can be simply addressed by textual changes, and we would like you to edit the text to reflect on those points. After that, the reviewing editor should be able to editorially accept the manuscript.Reviewer #2:I am overall satisfied with how the authors addressed our previous suggestions.It is true that in some cases they have not been able to solve our concerns due to technical reasons, but it is also true that some of the experiments we proposed were not trivial. In those cases, for example the possibility of E1-3 and E4 enhancers acting in a hierarchical manner, I would suggest that they mention this possibility in their discussion.We now discuss a possible hierarchical model of enhancer regulation in the first paragraph of the Discussion.They should also incorporate the Zdbf2 up regulation they observe in the CTCF-AID ESC even if it does not fit their model. I think they can comment on the potential reasons explaining the differences between deleting a single CTCF site and a global loss of CTCF in the Discussion section.pLiz Programming of pZdbf2” and then in the Discussion paragraph two.This figure has been added , and mentioned in the Results subsection “CTCF Partitioning Fine-tunes The Zdbf2 Locus”, and incorporated figure panels . We have added the D2 and D4 RNA-seq data to the GEO accession.We now discuss the cell state in subsection “Two Classes of Putative Enhancers Lie in the Reviewer #3:I agree that the authors more or less addressed the previous suggestions. I also agree that bringing See above.


H exon. CSR depends on non-coding (CSRnc) transcription of donor Iμ and acceptor IH exons, located 5′ upstream of each CH coding gene. Although, our knowledge of the role of CSRnc transcription has advanced greatly, its extension and importance in healthy and diseased humans is scarce. We analyzed CSRnc transcription in 70,603 publicly available RNA-seq samples, including GTEx, TCGA, and the Sequence Read Archive using recount2, an online resource consisting of normalized RNA-seq gene and exon counts, as well as, coverage BigWig files that can be programmatically accessed through R. CSRnc transcription was validated with a qRT-PCR assay for Iμ, Iγ3, and Iγ1 in humans in response to vaccination. We mapped IH transcription for the human IGH locus, including the less understood IGHD gene. CSRnc transcription was restricted to B cells and is widely distributed in normal adult tissues, but predominant in blood, spleen, MALT-containing tissues, visceral adipose tissue and some so-called “immune privileged” tissues. However, significant Iγ4 expression was found even in non-lymphoid fetal tissues. CSRnc expression in cancer tissues mimicked the expression of their normal counterparts, with notable pattern changes in some common cancer subsets. CSRnc transcription in tumors appears to result from tumor infiltration by B cells, since CSRnc transcription was not detected in corresponding tumor-derived immortal cell lines. Additionally, significantly increased Iδ transcription in ileal mucosa in Crohn's disease with ulceration was found. In conclusion, CSRnc transcription occurs in multiple anatomical locations beyond classical secondary lymphoid organs, representing a potentially useful marker of effector B cell responses in normal and pathological immune responses. The pattern of IH exon expression may reveal clues of the local immune response  in health and disease. This is a great example of how the public recount2 data can be used to further our understanding of transcription, including regions outside the known transcriptome.Antibody class switch recombination (CSR) to IgG, IgA, or IgE is a hallmark of adaptive immunity, allowing antibody function diversification beyond IgM. CSR involves a deletion of the IgM/IgD constant region genes placing a new acceptor Constant gene, downstream of the VDJ Affinity maturation and class switch recombination (CSR) are tightly regulated, molecular processes that occur in a specialized microenvironment within secondary lymphoid organs known as the germinal center (GC). Upon T-dependent antigen stimulation, IgM+ naive B cells re-localize into the B cell follicle, undergo clonal expansion within the GC. The GC reaction is an iterative process of mutation-selection that leads to the generation of antigen-specific high affinity, class-switched memory B cells and long-lived antibody secreting plasma cells  (n = 18). Written informed consent was obtained from each volunteer in each blood sample draw. All procedures in human subjects were performed after Institutional Review Board approval from the National Institute of Public Health (CI: 971/82-6684). Plasma and PBMCs were isolated according to the manufacturer's instructions, aliquoted and stored at −80°C and liquid N2, respectively. Total RNA was extracted from PBMCs with TRIzol and stored at −80°C until used.Pre-immune (day 0), day 7, 15, 30, and 180 post-vaccination peripheral blood samples (18 mL) were obtained by venipuncture in 2 × 8 mL Vacutainer® CPT™ tubes from healthy volunteers vaccinated with Hepatitis B and/or Tetanus toxoid/Diphteria .Cryopreserved PBMCs were thawed at 37°C and resuspended in RPMI 10% FBS, washed with PBS 1X and fixed with 1% paraformaldehyde for 20 min at room temperature. After washed with FACS solution , cells were incubated for 30 min at 4°C with the following antibody cocktail: anti-CD3 PerCP/Cy5.5 , anti-CD19 FITC , anti-CD20 PE Cy7 , anti-CD27 APC , and anti-CD38 PE . Flow cytometry analysis was performed in a FACS Aria II . Doublets and CD3+ events were gated out. Plasmablasts were defined as CD36 B cells were seeded per well on 6-well plate incubated in RPMI medium supplemented with 10% FBS, streptomycin and penicillin at 37°C with 5% CO2. Two activation conditions were stablished at different time points , 1 μg/ml anti-human CD40 (G28.5), 5 μg/ml CpG ODN 2006 (Invivogen) and 25 ng/ml recombinant IL-21 (eBiosciences). For T-independent activation, 5 μg/ml CpG ODN 2006 (Invivogen), 0.05% S. aureus Cowan , 5 ng/ml Pokeweed Mitogen (Sigma).PBMCs were isolated by Ficoll-Paque™ density gradient from blood bank buffy coats. B cells were enriched through negative selection using B cell Isolation Kit II . 1 × 10IGHM, IGHG1, IGHG3, and AID gene was performed using specific primers and TaqMan probes(IDT). The primers and probes used to quantify the CSRnc transcripts are detailed in Table HPRT with PrimeTime® Predesigned qPCR Assays was performed as the reference gene. The fold difference was calculated using 2ΔΔCT, using resting enriched B cells as calibrator and non-B cells as negative control. HPRT was used as normalizer for every condition.Total RNA from PBMCs was extracted using TRIzol (Invitrogen). The integrity of the RNA was measured with Agilent RNA 6000 Nano. SuperScript™ III One-Step RT-PCR(Invitrogen) was used for reverse transcription and amplification. Quantitative PCR of CSRnc transcripts for H are upstream of the corresponding switch region (SH), thus we first mapped S regions based on the frequency distribution of the AGCT motif in 500 bp bins along the whole IGH locus   that can be programmatically accessed through the R programming language  are not mapped as such in the current version of the human genome GRCh38). I8. IH arelanguage . To map s Viewer . CSRnc rrecount2 was used to extract read counts from each of the nine CH constant region coding genes , as well as, from the corresponding CSRnc I exon coordinates as a GRanges object (2-transformed CH (coding) and CSRnc RPKM average per sample was used as an approximation of abundance of transcription. For CSRnc transcription, log2 RPKM per sample average adopted a quasi-normal distribution with a mean of 2.65 log2 RPKM (SD ± 3.79), which corresponds to 6.29 RPKM. As an initial exploration to which tissues and in which diseases CSRnc transcription takes place, we used the log2 RPKM average as a cut-off to define “high” expression (>2.65 log2 RPKM) or “low” expression (< 2.65 log2 RPKM). The mean CH transcription average log2 RPKM was 7.82 (SD ± 5.16). Given the difference between coding and CSRnc transcription, and to address the relative expression between coding and CSRnc transcription for each Ig loci, coding and CSRnc log2RPKM values for each Ig gene were standardized by transformation to Z-scores.s object . The logAlthough all RNA-seq samples in TCGA and GTEx follow a homogeneous ontology categorization, metadata associated to SRA projects is widely heterogeneous and commonly insufficient. To obtain a more homogenous categorization of nearly half of our dataset, we used disease  annotati0 is that there is no difference in the probability distribution between Z clusters and tissue. A 2 × 2 contingency table was built for each tissue between the number of samples belonging to a given Z cluster and the remaining samples not belonging to that cluster. A two-sided Fisher's test was performed with the R function fisher test . P-value adjustment with the Benjamini-Hochberg method was performed to correct for multiple testing using the R function p. adjust . A False Discovery Rate (FDR) < 0.01 was considered as a significant enrichment.To define CSRnc transcription profile variation in healthy tissue (GTEx dataset), we performed tissue sample enrichment analysis according to Z-cluster using a two sided Fisher's Exact Test. The Hk-means clustering using the Cluster software 3.0 (https://sourceforge.net/projects/jtreeview/).To define CSRnc transcription profiles, Z-scores for each I-exon were subjected to ware 3.0 . Ten clulimma R package v3.34  and was included for further analysis  that were selected for downstream analysis  at ENSEMBL  region within the s Figure . This remyelomas , 24. Fors Figure . Iγ2 ove ENSEMBL . The genprojects , 36, indprojects . Overallprojects ; howeverin vitro for 3 and 6 days. We obtained whole blood total RNA and quantified Iμ, Iγ3, and Iγ1 CSRnc and AID transcripts by qRT-PCR . Transcription of AID showed the same pattern as CSRnc transcripts  or at day 14 , however at day 14 was higher than at day 7 (p = 0.01)  nor at day 14 (mean 1.02 ± 0.9. p = 0.57)  and increased at day 14 . As expected, Iγ3 transcription at day 14 was higher than at day 7 (p = 0.0002)  Figure . Iγ1 tra) Figure . Contras) Figure . No chan) Figure .p = 0.011), and returned to pre-vaccine levels 14 days post-vaccination, as previously described . However, plasmablast and Iμ transcription correlated at day 7 . Interestingly, plasmablast fold-change at day 7 negatively correlated with Iγ3 transcription . No changes in Iμ, Iγ1, and Iγ3 transcription was detected in response to influenza vaccination  in any of the 10 IH exons in a substantial fraction of the whole dataset  (Table H log2-transformed RPKM average (2.65) for each non-zero RPKM sample  was used to define an expression cutoff. “High” CSRnc transcription was defined as a mean log2 RPKM of 2.65 or higher. Only 29.8% of RNA-seq samples  were above this expression cutoff. “Low” CSRnc transcription was defined as a mean log2 RPKM < 2.65 . The CSRnc transcription levels varied according to IH. Higher transcription (log2 RPKM), as well as, a more widespread transcription (proportion of samples) was found for Iμ in all datasets  obtained with 5 Figure . Table 3s Figure , supportH relative transcription profile. To do so, the entire non-zero dataset including GTEx, TCGA, and SRA Z-scores was clustered in 10 groups using k-means clustering . Clusters Z0, Z2, and Z8 were characterized by low (mean log2 RPKM < 2.65 or Z score < 0) CSRnc transcription in all IH classes. Clusters Z4, Z6, and Z7 were characterized by “high” expression of Iμ only (mean log2RPKM > 2.65). Cluster Z3 showed “high” expression in Iμ and Iα2. Cluster Z1 showed high expression in Iμ, Iγ1, and Iγ4. Finally, clusters Z5 and Z9 showed “high” expression in all IH's  such as terminal ileum, transverse colon, stomach, lung, and esophageal mucosa  and sigmoid colon (mean 2.1 ± 3.2). Interestingly, salivary gland expression was among the tissues with highest transcription, and non-mucosal tissues such as thyroid and pituitary gland showed high average IH transcription. Another notable difference in average log2 RPKM was between visceral adipose tissue  and subcutaneous adipose tissue (mean 1.9 ± 1.5). Conversely, samples of tissues such as brain, skeletal and cardiac muscle, skin, as well as, chronic myelogenous leukemia cell line K562 and transformed dermal fibroblast cell lines showed the lowest CSRnc transcription levels (mean log2 RPKM < 2.65)  Figure .H by tissue type. The transcription pattern of each IH in whole blood was significantly different (p < 2.2e−16)   Figure . Iμ tran Figures , in whicα1.1 and Iα1.2 transcription however, some differences were noted. Iα1.1 transcription was higher in terminal ileum and transverse colon than in blood (p < 0.001). In contrast, Iα1.2 transcription was higher in spleen and salivary gland than in blood (p < 2e−16). Iα1 transcription was similar to Iα1.2 transcription, but also was higher in stomach, esophageal mucosa than in blood (p < 1.1e−05)  Figure . Thus, Iγ4 and Iε. Transcription for both was higher in spleen, terminal ileum and EBV-transformed B-cells than in blood (p < 0.01). However, Iγ4 transcription was higher in thyroid, visceral adipose tissue (omentum), testis, than in blood (p < 2e−16). Similarly, Iε transcription was higher in in thyroid than in blood (p < 0.00003)  Figure .H relative expression (Z-score) revealed that terminal ileum, spleen, transverse colon, whole blood and salivary gland share a similar expression pattern and are highly enriched (FDR < 0.001) in clusters Z5 and Z9   (FDR < 0.001), whereas others such as breast, vagina, liver were enriched in cluster Z3 and Z6 (Iμ only). Testis, thyroid, pituitary, and omentum, characterized by the highest Iγ4 transcription, were enriched in cluster Z1  (FDR < 0.001). Finally, the remaining tissues such as subcutaneous adipose tissue, arteries, brain, skeletal, and cardiac muscle, skin, transformed fibroblasts and K562 cell lines, were enriched in Z0, Z2, and Z8  (FDR < 0.001)  (FDR < 0.001).We tested if a particular CSRnc expression profile was associated to a particular tissue. Consistently with the regression analysis, de-convolution of CSRnc transcription according to I) Figure . Some ti) Figure , and in H coding transcript log2RPKM as a proxy of the amount of B cells in the tissue. In general, CH transcription was 10–100-fold higher than CSRnc transcription. To correlate CSRnc transcription with corresponding CH transcription, Z-scores were used. As expected for each class, CSRnc and CH Z-scores where significantly correlated (p < 1.0e−16), suggesting that the higher numbers of B cells or plasma cells in a given tissue, higher CSRnc transcription. However, we have noticed that for most classes, a fraction of samples deviates from the expected orthogonal correlation, indicating higher CSRnc transcription relative to the CH transcript. This is particularly notable for Iμ, but also, Iγ1, Iγ4, and Iα2 , lymphoid-primed multipotent progenitors , common lymphoid progenitors , thymic CD34+CD7− CD1a− CD4−CD8− (Thy1) precursors and fully B cell-committed progenitors  expressed Iμ  in kidney, brain and muscle.We analyzed CSRnc expression in fetal tissue by using project SRP055513 data, which reported an extensive RNA-seq analysis in twenty fetal tissues during gestational weeks 9–22 . Remarkak Figure . Higher H average log2 RPKM distribution) across 33 cancer types  and acute myeloid leukemia (AML) were the highest. In general, CSRnc transcription in neoplastic tissue mimicked its non-neoplastic counterparts, being high in lung, stomach, and testicular germ cell carcinomas. Conversely, CSRnc transcription was low in glial cell and skin cancers  Tumors where average CSRnc transcription is lower than in healthy tissue, such as in DLBCL, prostate, thyroid, liver, and colon cancer  Tumors where average CSRnc transcription was higher than its healthy tissue counterpart, such as breast, rectum, testicular germ cell, pancreas carcinomas, ovarian cystadenoma, and skin melanoma  Tumors with no difference in CSRnc transcription between healthy and neoplastic counterpart, such as stomach and esophagus  and neoplastic tissue allowed the identification of three distinct patterns Figure : (1) Tum Figures . (2) Tuma Figure . (3) Tums Figure . Interess Figure . In adres Figure .H transcription   Table S. However66  Tablen Figure .recount2  located in the IGHM-IGHD intergenic region was identified, which overlaps with a previously described repeat Σμ region, downstream of an atypical switch region (σδ) involved in non-classical CSR to IgD in mice (+ EBV-transformed cell lines and human tonsillar B cells undergo μ-δ CSR by non-homologous recombination using σδ as acceptor switch region (δ (Σμ) can be actively transcribed , suggesting that CSRnc transcription may not be the result of plasmablast mobilization to peripheral blood. Moreover, we observed increased Iγ1 and Iα1 transcription in natural H7N9 infection, but not upon influenza vaccination , and its transcription is required for CSR regardless of the acceptor class , but not subcutaneous adipose tissue. Visceral adipose lymphoid B cells are immunologically active cells implicated in adipose tissue homeostasis that may play important pro-inflammatory roles associated with metabolic syndrome and obesity , testis (Z1 and Z2), terminal ileum (Z5 and Z9), and whole blood  indicate CSRnc transcriptional heterogeneity in tissue donors involved in the GTEx project. This could result from different tissue microenvironments , as well as, genetics, which may modify CSRnc transcription patterns and possibly CSR itself. A recent study identified four SNP's in the human IgH locus presumably involved in CSR that affect immunoglobulin levels  The anti-tumor response is largely mediated by the presentation of tumor neo-antigens to T cells and Treg balance. However, antibody-mediated anti-tumor activity can be achieved by antibody—dependent cytotoxicity or other mechanisms (2) The presence of ectopic (or tertiary) lymphoid structures (ELS) (3) The tumor microenvironment, including certain cytokines may modify CSR patterns in infiltrating and ELS B cells, regardless the antibody effector function. (4) AID activity is a known contributor to off-target mutagenesis and genomic instability in B cell malignancies  Thees (ELS) , 62 and es (ELS) , breast es (ELS) , and coles (ELS) . (3) Thegnancies . Aberrangnancies .H expression in certain tumors analyzed in the TCGA project resembles their non-neoplastic counterpart; however, some cancer types have significantly less CSRnc transcription, whereas others show the opposite. Most of the evidence we have gathered so far indicate that the origin of IH transcription is in the tumor infiltrating and ELS B cells, rather than the tumor cells per se .This study was conceived and designed by HK-M and JM-B. The experimental procedures were performed by H-KM, MO-M, HV-T, and JT-S. Flow cytometry acquisition and analysis was conducted by HK-M and LCB. RNA-seq data bioinformatics and statistical analysis were done by HK-M, LC-T, and JM-B, and supervised by AJ. HK-M and JM-B drafted the manuscript. LCB, LC-T, and AJ critically reviewed the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Precise promoter annotation is required for understanding the mechanistic basis of transcription initiation. In the context of complex genomes, such as herpesviruses where there is extensive genic overlap, identification of transcription start sites (TSSs) is particularly problematic and cannot be comprehensively accessed by standard RNA sequencing approaches. Kaposi's sarcoma-associated herpesvirus (KSHV) is an oncogenic gammaherpesvirus and the etiological agent of Kaposi’s sarcoma and the B cell lymphoma primary effusion lymphoma (PEL). Here, we leverage RNA annotation and mapping of promoters for analysis of gene expression (RAMPAGE) and define KSHV TSSs transcriptome-wide and at nucleotide resolution in two widely used models of KSHV infection, namely iSLK.219 cells and the PEL cell line TREx-BCBL1-RTA. By mapping TSSs over a 96 h time course of reactivation we confirm 48 of 50 previously identified TSSs. Moreover, we identify over 100 novel transcription start site clusters (TSCs) in each cell line. Our analyses identified cell-type specific differences in TSC positions as well as promoter strength, and defined motifs within viral core promoters. Collectively, by defining TSSs at high resolution we have greatly expanded the transcriptional landscape of the KSHV genome and identified transcriptional control mechanisms at play during KSHV lytic reactivation. Kaposi's sarcoma-associated herpesvirus (KSHV) is an oncogenic gammaherpesvirus and the etiological agent of Kaposi’s sarcoma and the B cell lymphoma primary effusion lymphoma (PEL). Despite identification of the virus over 20 years ago there is still an incomplete understanding of how many RNAs are transcribed from the viral genome and the location these RNAs are derived from. To fill this gap in knowledge we determined the landscape of transcription initiation on the KSHV genome. Our analyses more than tripled the number of known TSCs and thus viral-expressed RNAs. Furthermore, we identified key sequence features associated with the regulation of viral transcription start sites. This study provides the first transcriptome-wide characterization of KSHV transcription initiation sites as well as a framework for future studies to define functions of novel viral transcripts and viral gene regulatory elements. Regulated gene expression is an essential process for all eukaryotic cells as well as the pathogens that infect them. A fundamental step in the regulation of gene expression is the initiation of RNA synthesis. RNA Polymerase II (RNAP II) transcription begins with the binding of gene-specific regulatory factors within the core promoter located near the transcription start site (TSS). Sequence elements found within core promoters include the TATA element, BRE (Transcription factor II B recognition element), Initiator (Inr), and downstream promoter element (DPE) . These eModels of transcription initiation often depict RNA synthesis initiating from a single nucleotide. However, the application of high-throughput 5’-end sequencing technologies, such as Cap Analysis of Gene Expression (CAGE), have demonstrated that most RNAP II promoters have an array of closely spaced TSSs instead of the expected single TSS –7. Thus,Viruses employ a parasitic lifestyle thus they exploit many, and in some cases, all, of the host gene expression machineries. The co-option of cellular processes allows viruses to dramatically downsize their genomes, however, it necessitates the incorporation of host cell-specific gene regulatory mechanisms and features that enable the use of such mechanisms. Additionally, the compact nature of viral genomes requires the encoding of viral proteins and RNAs within a relatively small amount of genomic space.Kaposi sarcoma-associated herpesvirus (KSHV) is a human oncogenic virus and a member of the gammaherpesvirus subfamily , 17. WhiAs with other herpesviruses, KSHV displays two distinct phases of its viral lifecycle, latency and lytic infection. During latent infection KSHV exists in a dormant state in which viral gene expression is dramatically restricted such that few viral antigens and no viral particles are produced. In contrast, during the lytic cycle, which is driven by the viral-encoded transcription factor replication and transcription activator (RTA) , 28, theDespite identification of KSHV over two decades ago we still lack fundamental knowledge regarding how viral gene expression is controlled at the level of transcription initiation. Additionally, although several studies have employed RNA-seq to define the transcriptional output of the KSHV genome as well as the temporal kinetics of viral gene expression, it is often difficult to accurately identify TSSs from traditional RNA-seq data alone. Identification of TSSs is particularly important in complex transcriptomes, such as that of KSHV, where there is extensive overlapping transcription of viral genes and the appearance of polycistronic RNAs. To fill this gap in fundamental knowledge we have mapped all transcription initiation events on the viral genome using RNA annotation and mapping of promoters for analysis of gene expression (RAMPAGE) . LeveragPrecise promoter annotation is required for understanding the mechanistic basis of condition- and tissue-specific gene regulation. While the transcriptional landscape of KSHV has been characterized by RNA-seq there is still a gap in fundamental knowledge regarding the location of viral TSSs, and thus it has not been feasible to comprehensively define viral promoters and the sequence motifs embedded within them. Moreover, whether there are condition (i.e. latent vs. lytic) or cell type specific differences in TSS usage is not known. To fill this gap in knowledge we took advantage of two well established models of KSHV infection, namely, iSLK.219 and TREx-BCBL1-RTA cells. While iSLK.219 cells are of clear cell renal cell carcinoma origin and thus of lesser biological relevance , the iSLLeveraging iSLK.219 and TREx-BCBL1-RTA cells we mapped TSSs transcriptome-wide at nucleotide resolution across a 96 h Dox-induced reactivation time course using RAMPAGE. RAMPAGE, which combines the two orthogonal 5’-selection approaches of template-switching and cap-trapping, enables TSS identification with high specificity through the high-throughput sequencing of 5′‐complete complementary DNAs . AdditioThe resultant RAMPAGE sequencing reads were aligned to the human (GRCh38) and KSHV GQ994935.1) genomes and TSCs were identified by Paraclu [.1 genomeWithin 12 h of Dox addition to the culture media there was an increase in TSSs that continued through the time course Figs . In totaAn advantage of RAMPAGE over other 5’-end sequencing technologies is that sequencing is performed in a pair-ended format and thus more information regarding transcript content is present in the data. Leveraging the pair-end information of RAMPAGE we were able to assign many TSSs to ORFs that were previously lacking TSS information . For exaTranscriptional regulation is highly complex and chromatin structure and modifications directly contribute to transcription initiation. The open chromatin landscape of the KSHV genome in PEL has been investigated by formaldehyde-assisted isolation of regulatory elements sequencing (FAIRE-seq) . PreviouTREx-BCBL1 cells have been the subject of extensive investigation regarding the presence and location of various chromatin modifications, including the activating mark H3K4me3 and the repressive mark H3K27me3, as well as the KSHV latent protein LANA and cellular RNAP II . By inteWe next sought to determine whether KSHV promoter usage is regulated in a cell-type specific manner. Intersection analysis of TSCs identified in iSLK.219 and TREx-BCBL1-RTA cells revealed that viral gene expression is highly cell-type specific. We identified 129 TSCs were present in both cell lines, we identified 35 and 163 TSCs present exclusively in either iSLK.219 or TREx-BCBL1-RTA cells, respectively . ImportaInspection of our data uncovered that many immediate early genes, such as K6, tend to be associated with multiple TSCs, all of which are shared between both iSLK.219 and TREx-BCBL1-RTA cells. Previous studies have demonstrated that immediate early genes can be expressed at a low basal level during latency, and are subsequently robustly induced upon lytic reactivation. Given that both multiple shared TSCs are present in both cell-types we hypothesized that basal and induced gene expression may be controlled by different promoters. Indeed, inspection of TSCs that drive expression of K6 uncovered multiple promoters, with a prominent switch in TSC usage upon lytic reactivation . As showAs noted above we also identified cell-type specific TSCs. For example, in TREx-BCBL1-RTA cells we identified 11 ORFs expressed by TSCs only present within TREx-BCBL1-RTA cells. In contrast, we identified a distinct set of 11 ORFs that were expressed from TSCs only identified in iSLK.219 cells. For instance, we identified a TSC for LANA2 (K10.5) in TREx-BCBL1 cells while it was absent from iSLK.219 cells . This isIn highly complex genomes with extensive genic overlap, 5’-end RNA sequencing is more accurate in measuring transcript expression than traditional RNA-seq as it avoids ambiguous read assignment and transcript length normalization. We thus quantified viral gene expression in both cell lines based on the intensity of the 5’ RAMPAGE signal . MoreoveUnsupervised hierarchical clustering analysis further demonstrated that viral reactivation in iSLK.219 and TREx-BCBL1 cells follows different kinetic programs . While tGiven that unsupervised hierarchical clustering demonstrated distinct kinetic programs we next sought to investigate whether the individual programs were associated with unique core promoter compositions. It has previously been suggested that TSCs within a 50 bp window are under control of the same transcription initiation complex , thus weTo identify sequence elements associated with the distinct clusters we extracted nucleotide sequences 50 bp upstream and downstream of the maximally expressed nucleotide within each cluster (MaxTSN) and searched for position-specific ultra-short motifs by kplogo analysis. All clusters, with the exception of TREx-BCBL1-RTA cluster 2, display a preference for a Py-Pu dinucleotide at the −1/+1 position and a similar preference has been observed at both mouse and human TSCs . TREx-BCiSLK.219 clusters 1 and 3, which exhibit maximum expression 48 h and 96 h post-induction, respectively, and TREx-BCBL1-RTA cluster 1, which displays maximum expression at 96 h post-induction, all harbor an AT rich region 25–31 upstream of the MaxTSN . Moreove+1) is found enriched at TSCs within TREx-BCBL1-RTA cluster four. An Inr motif is similar to a TATA motif in that it can directly recruit the general transcription factor TFIID. However, the Inr recruits TFIID through interactions with TAF1 and TAF2 rather than TBP. The enrichment of Inr motifs in cellular genes that lack TATA motifs has been previously observed and it has been speculated that promoters with an Inr are less dependent on a TATA box and vice versa [While neither an AT rich region or TBP motif was found in the other clusters, an initiator (Inr) motif  can be found dispersed among the moderately and highly expressed cellular TSCs within iSLK.219 cells  was maintained in RPMI 1640 medium (Invitrogen) supplemented with 10% fetal bovine serum , 200 μM L-glutamine, 100 U/ml penicillin/streptomycin. iSLK.219  and Human embryonic kidney 293 T (HEK293T) cells (purchased from ATCC) were grown in Dulbecco’s modified Eagle medium  supplemented with 10% FBS. Lytic reactivation of TREx-BCBL1-RTA and iSLK.219 cells was induced by the addition of 1 μg/ml doxycycline (Fisher Scientific).Fluorescence in situ hybridization and flow cytometry (FISH-Flow) of PAN RNA in iLSK.219 and TREx-BCBL1-RTA cells was done as previously described , 71. Bri2O. 1 μl of reverse-transcription primer (400 μM) and 1 μl of barcoded template-switching oligo (4 mM) were added before denaturation . Ice-cold reverse-transcription mix , 7.5 μl Sorbitol/Trehalose mix (3.3 M/0.66 M), 1.9 μl DTT (100 mM), 5.6 μl betaine (5 M), 4 μl Invitrogen SuperScript III) was added and the reaction was incubated in a thermal cycler . The RNAs and their associated 5’-complete cDNAs were purified by RNAClean XP, quantified by quantitative PCR and pooled. Pool volumes were adjusted to 40 μl by RNAClean XP precipitation. The oxidization buffer  was added and the samples were incubated on ice in the dark for 45 min. After quenching the reaction with 2 μl 40% glycerol, 14 μl of 1 M Tris-HCl pH8.5 was added prior to RNAClean XP purification. The samples were eluted in 40 μl H2O. Biotinylation was performed by adding the biotinylation mix ) and incubated at room temperature for 14–15 hours in the dark. For RNase I digest, we added the digestion mix ) and incubated the samples for 45 min at 37°C, and then 5 min at 65°C. After RNAClean XP purification, the samples were resuspended in 40 μl H2O. The biotinylated capped RNAs and their associated 5’-complete cDNAs were captured by streptavidin-coated magnetic beads (Thermo Scientific) for 30 min at room temperature followed by extensive washing: once with wash buffer (WB) 1 , once with WB2 , twice with WB3  and twice with WB4 . The cDNAs were eluted by incubation with 65 μl 50 mM NaOH for 10 min at room temperature, and purified with AMPure XP (Agencourt). The cDNAs were then amplified with the KAPA HiFi HotStart PCR kit (KAPA Biosystems). The size selection (~300 to 1000 bp) was conducted with two rounds AMPure XP Cleanup and final libraries were eluted in 20 μl H2O. The libraries were run on a DNA HS Bioanalyzer chip for quality control, quantified by quantitative PCR, and sequenced on a NextSeq 500 for PE75 run. Raw RAMPAGE sequencing files can be found in GEO accession # GSE129902. Oligonucleotides used in RAMPAGE analysis are in RAMPAGE was performed as previously described with minor modifications . RNA wasData processing was performed according to the original RAMPAGE method with minor modifications. Briefly, raw reads in fastq files were demultiplexed and barcodes removed using cutadapt . TrimmedStart site clusters in mapped RAMPAGE data were extracted using Paraclu  with thehttp://bioconductor.org/packages/release/bioc/html/seqPattern.html), sequences were ordered by MaxTSN expression value (TPM). Transcripts initiating from each of the merged TSS were counted using Rsubread and normalized to the total uniquely mapped reads as TPM (tag per million). The mean expression value of duplicates at each time point were used in the K-means analysis with kmeans function from cluster  and visualized with factoextra, the optimal cluster number K was determined by Average Silhouette Method . The K-means clustered heatmaps were generated with pheatmap . Kplogo was used to search for enriched k-mers at each position [For motif analysis, TSCs within 50 bp were merged and the maximum expressed nucleotide (MaxTSN) was identified. Sequences 50 upstream and downstream of the MaxTSN were extracted and subjected to motif analysis and pattern density visualization. DNA motif pattern density and position were visualized by R package seqPattern . Processed fastq reads were mapped to GQ994935.1 and GRCh38 genome using default STAR parameters, and peaks identified using MACS2 [CTCF ChIP-seq (SRR503420), FAIRE-seq , and RTA ChIP-seq , LANA , PolII (SRR1030375), H3K27me3 (SRR1030374) and H3K4me3  were downloaded from Sequence Read Archive (SRA) use sra-tools (Novel RTA-associated TSSs were synthesized as gBlock gene fragments (IDT). gBlock fragments were amplified using KAPA HiFi DNA polymerase (Kapa Biosystems), digested with XhoI and HindIII and cloned into pGL4.28 (Promega). pFLAG-RTA was a kind gift from Dr. Melanie M. Brinkmann (Helmholtz Centre for Infection Research).HEK293T cells, at 80% confluency, were transfected with 50 ng pGL4.28 luciferase reporter and 450 ng pFlag-RTA  plasmid using PolyJet In Vitro DNA Transfection Reagent (SignaGen Laboratories). Cells were collected and used to measure firefly luciferase activity using luciferase assay system (Promega) on a GloMax 20/20 Luminometer (Promega).S1 Fig(TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 Fig(TIFF)Click here for additional data file.S5 Fig(TIFF)Click here for additional data file.S6 Fig(TIFF)Click here for additional data file.S7 Fig(TIFF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


Transcription factors are managers of the cellular factory, and key components to many diseases. Many non-coding single nucleotide polymorphisms affect transcription factors, either by directly altering the protein or its functional activity at individual binding sites. Here we first briefly summarize high-throughput approaches to studying transcription factor activity. We then demonstrate, using published chromatin accessibility data , that the genome-wide profile of TF recognition motifs relative to regions of open chromatin can determine the key transcription factor altered by a perturbation. Our method of determining which TFs are altered by a perturbation is simple, is quick to implement, and can be used when biological samples are limited. In the future, we envision that this method could be applied to determine which TFs show altered activity in response to a wide variety of drugs and diseases. Transcription factors (TFs) are the managers of the cellular factory, controlling everything from cellular identity to response to external stimuli . BecauseMoreover, most disease-causing mutations are found in regulatory regions ,15, e.g.The relationship between many diseases and transcription factors has led to tremendous interest in global investigations of transcription factor activity. To decipher transcription factor activity requires understanding of the two major functions of a transcription factor: binding to DNA and modification of transcription. Transcription factors bind to specific DNA sequences, a TF recognition motif. A number of techniques have been utilized to identify and characterize such recognition motifs . HoweverExtensive attention has focused on determining where in the genome transcription factors bind ,29,30. TAn alternative approach to detecting individual protein–DNA binding locations is to infer a large collection of binding events via DNA footprinting ,33,34. DDespite these limitations, DNA footprinting assays uncovered a distinct function for transcription factors: altering DNA accessibility. When chromatin accessibility data is considered in the context of known TF sequence motifs  ,38,39,40Altering transcription is the second major function of transcription factors . BecauseNascent transcription assays (GRO-seq and PRO-seq) directly profile RNA associated with engaged cellular polymerases ,50. ConsThe Assay for Transposase-Accessible Chromatin followed by sequencing (ATAC-seq), a method for identifying regions of open chromatin, is particularly attractive because it is quick, easy, inexpensive, and deployable in small cell count samples. Additionally, recent work has shown that changes in chromatin accessibility can inform on TF activity. Specifically, BagFoot  combinedfl/fl) and perturbations , we assessed changes in accessibility over all putative TF sequence recognition motifs , developed with simplicity and ease of implementation in mind, focused around inferring changes in TF activity from ATAC-seq data. Using nascent transcription data, we had previously developed the motif displacement score (MD-score), a metric that assesses TF-associated transcriptional activity. As such, the MD-score reflects the enrichment of a TF sequence motif within a small radius (150 bp) of enhancer RNA (eRNA) origins relative to a larger local window (1500 bp) . While A database).p-value p-value cutoff  at a shorter time point (1 h) . The MD-p-value p-value p-value p-value We next analyzed differential ATAC-data gathered by Denny et al. to examine whether Nfib promotes metastasis via increasing chromatin accessibility. For this question, they examined two human small cell lung carcinoma (SCLC) cell lines (H524 and NJH29), profiling by ATAC-seq before and four hours after doxycycline treatment. Using the MD-score approach, we detected changes in TF activity for multiple members of the NFI family a,b. An ifl/fl mouse embryonic stem cells (ESCs) with tamoxifen for 72 h to ablate BRG1 expression. When compared to the unperturbed mouse ESC control, we observed lowered MD-scores for SOX2, POU5F1 (Oct4), and NANOG in the BRG1-depleted cells  as a co-localization measure of ATAC-seq peak midpoints to TF sequence motif sites across the genome, we correctly detect changes in TF activity. Second, our approach independently confirms the results obtained by BagFoot , as the We believe there are two distinct advantages to the MD-score approach to assessing TF activity. First, the MD-score is calculated relative to a local background window. Consequently, it cleanly accounts for the localized sequence bias observed at promoters and enhancers , which lOur MD-score statistic was originally developed for analysis of nascent transcription data  and focufastq-dump v2.8.0 with argument –split-3. Paired-ended raw reads were trimmed using trimmomatic v0.36 at a fixed length with options PE -phred33 CROP:36 HEADCROP:6. After verifying the dataset quality with FastQC v0.11.5, the reads were aligned to the hg19 or mm10 reference genome, using Bowtie v2.2.9 with arguments -p32 -X2000. The resulting SAM files were converted to BAM format using samtools v1.3.1 using the view -q 20 -S -b arguments and sorted with the sort -m500G arguments. Bam files were then converted to BedGraph format for easier processing using bedtools v2.25.0 with arguments -bg -ibam INPUT_BAM_FILE -g GENOME_REFERENCE and read counts were normalized by the millions mapped. Finally, MACS v2.1.1.20160309 was used to call broad peaks from the ATAC-seq BAM files with arguments callpeak -n ASSAY_PREFIX –nomodel –format BAMPE –shift -100 –extsize 200 -B –broad.Each ATAC-seq dataset was subjected to a standard data processing pipeline. The SRR datasets were converted to FASTQ format using p-value cutoff of -max-stored-scores 10,000,000 –thresh 1 ×.The human motif sites calculated in Azofeifa et al.  for the We used samples from the following public GEO datasets for our analysis: GSE58740 (samples SRR1448793 and SRR1448795), GSE81255/GSE81258 , GSE87822 (samples SRR4413799 and SRR4413811), and GSE104720 (samples SRR6148318 and SRR6148319).https://biof-git.colorado.edu/dowelllab/DAStk for download. We used 641 PWMs of human motifs in the HOCOMOCO [p-value cutoff of The Differential ATAC-seq toolkit (DAStk) is a collection of scripts publicly available at HOCOMOCO  databaseportions .


Thermus thermophilus HB8 transcriptional repressor TTHA0973 using the selection method Restriction Endonuclease Protection, Selection and Amplification (REPSA), massively parallel sequencing, and bioinformatic analyses. We identified a consensus TTHA0973 recognition sequence of 5′–AACnAACGTTnGTT–3′ that exhibited nanomolar binding affinity. This sequence was mapped to several sites within the T. thermophilus HB8 genome, a subset of which corresponded to promoter regions regulating genes involved in phenylacetic acid degradation. These studies further demonstrate the utility of a biochemistry-centric approach for the facile identification of potential biological functions for orphan transcription factors in a variety of organisms.Advances in genomic sequencing have allowed the identification of a multitude of genes encoding putative transcriptional regulatory proteins. Lacking, often, is a fuller understanding of the biological roles played by these proteins, the genes they regulate or regulon. Conventionally this is achieved through a genetic approach involving putative transcription factor gene manipulation and observations of changes in an organism’s transcriptome. However, such an approach is not always feasible or can yield misleading findings. Here, we describe a biochemistry-centric approach, involving identification of preferred DNA-binding sequences for the  Transcriptional regulation is the primary means by which most organisms, both prokaryotic and eukaryotic, control gene expression. Transcriptional regulation occurs through the combinatorial recognition of specific DNA elements by sets of proteins, commonly referred to as transcription factors, that affect access of RNA polymerase to gene promoter regions and rates of productive transcription. Understanding the sets of genes regulated by a particular transcription factor, or regulon, provides insights into the biological function of this protein and its role in an organism’s physiology.With the advent of massively parallel sequencing techniques, the genomes of hundreds of thousands of organisms are presently known . Within Escherichia coli and can be seen in the wealth of information presently available on its transcription factors and the genes they regulate, as exemplified by the database RegulonDB ) ) . Publiclov/geo/) , SuperSe


Corynebacterium glutamicum ATCC 13032 harbors five sigma subunits of RNA polymerase belonging to Group IV, also called extracytoplasmic function (ECF) σ factors. These factors σC, σD, σE, σH, and σM are mostly involved in stress responses. The role of σD consists in the control of cell wall integrity. The σD regulon is involved in the synthesis of components of the mycomembrane which is part of the cell wall in C. glutamicum. RNA sequencing of the transcriptome from a strain overexpressing the sigD gene provided 29 potential σD-controlled genes and enabled us to precisely localize their transcriptional start sites. Analysis of the respective promoters by both in vitro transcription and the in vivo two-plasmid assay confirmed that transcription of 11 of the tested genes is directly σD-dependent. The key sequence elements of all these promoters were found to be identical or closely similar to the motifs -35 GTAACA/G and -10 GAT. Surprisingly, nearly all of these σD-dependent promoters were also active to a much lower extent with σHin vivo and one (Pcg0607) also in vitro, although the known highly conserved consensus sequence of the σH-dependent promoters is different (-35 GGAAT/C and -10 GTT). In addition to the activity of σH at the σD-controlled promoters, we discovered separated or overlapping σA- or σB-regulated or σH-regulated promoters within the upstream region of 8 genes of the σD-regulon. We found that phenol in the cultivation medium acts as a stress factor inducing expression of some σD-dependent genes. Computer modeling revealed that σH binds to the promoter DNA in a similar manner as σD to the analogous promoter elements. The homology models together with mutational analysis showed that the key amino acids, Ala 60 in σD and Lys 53 in σH, bind to the second nucleotide within the respective -10 promoter elements . The presented data obtained by integrating in vivo, in vitro and in silico approaches demonstrate that most of the σD-controlled genes also belong to the σH-regulon and are also transcribed from the overlapping or closely located housekeeping (σA-regulated) and/or general stress (σB-regulated) promoters. Corynebacterium glutamicum is a Gram-positive bacterium, which is applied in industrial biotechnology and also considered a model species for other corynebacteria as well as related actinobacterial genera such as Mycobacterium and Rhodococcus. The C. glutamicum genome encodes 7 σ factors: A primary σA, a primary-like σB and five σ factors of Group IV , which are involved in various stress responses.Most bacterial genes are transcribed by an RNA polymerase (RNAP) holoenzyme that includes a primary sigma subunit (σ factor) responsible for the transcription of housekeeping genes. In addition to the primary σ factor, a number of alternative sigma factors control various functions of the bacterial cells under specific nutritional, growth and environmental conditions. Group IV  also calPseudomonas aeruginosa  which drive transcription from the same promoter  in Listeria monocytogenes  involved in cell surface stress responses were also found in Bacillus subtilis   controlled by different σ factors were found to constitute a modular network in ruginosa . Individromoters . Accordiromoters . In contpromoter . Microarytogenes . Signifisubtilis . A singl and σL) . OverlapclpB) and chaperones (dnaK and dnaJ2) was found to be activated by both σH (involved in heat and oxidative stress response) and σE (involved in cell surface stress response) in C. glutamicum  at the same C. glutamicum promoters was suggested, but has not yet been proven  often result in damaged proteins, which are then degraded by proteases or re-folded by chaperones during the stress response. Expression of some genes encoding proteases  and probably some other σD-dependent genes were found to contribute to lysozyme resistance. No clear σD regulon induction was observed in this strain under any other stress conditions tested  (We have recently found by using RNA-seq that σntegrity . Similarchniques . In C. gicillin) .C. glutamicum  and single-promoter analysis (in vitro transcription and an in vivo two-plasmid system for the assignment of σ factors to genes/promoters). In silico homology modeling completed the different approaches to analyzing the sigma-promoter relationships.In this study, we discovered that nearly all of the σEscherichia coli DH5α  according to the recommendations of the manufacturer. The specific oligonucleotide primers for mutagenesis were designed by NEBaseChangerTM v1.2.8 (New England BioLabs® Inc.).DNA isolation, PCR, cutting by restriction enzymes, ligation and transformation of chniques . C. glutporation . DNA fraCorynebacterium glutamicum ATCC 13032 was cultivated in minimal medium CGXII with glucose (2%) or phenol (3.4 mM). The sigD-overexpressing C. glutamicum strain was used as described previously . After additional DNase treatment, RNA samples were purified with an RNA Clean&Concentrator-5 kit (Zymo Research) and quantified with a DropSense 16 (Trinean). The quality of total RNA was controlled with an RNA 6000 Nano kit in an Agilent 2100 Bioanalyzer (Agilent Technologies). To construct the whole transcriptome cDNA library, 2.5 μg total RNA (RIN > 9) was used for the depletion of rRNA with a Ribo-Zero rRNA Removal Kit (Bacteria) according to manufacturer’s instructions (Illumina). The rRNA removal was checked with an Agilent RNA Pico 6000 kit and the Agilent 2100 Bioanalyzer (Agilent Technologies). The mRNA obtained was converted to a cDNA library according to the TruSeq Stranded mRNA Sample Preparation guide (Illumina). The quality and quantity of the cDNA library was checked with an Agilent High Sensitivity DNA kit and the Agilent 2100 Bioanalyzer (Agilent Technologies). Sequencing was performed on an Illumina HiSeq 1500 using 70 bases read length (Illumina).eviously . Total RUACCCUAG (index in bold) to the 5′-monophosphorylated ends. The primary 5′-triphosphate ends were converted to 5′-mono-phosphate by RNA 5′-polyphosphatase (RPP) treatment (Epicenter) to ligate the 5′-adapter to the 5′-ends of primary transcripts. Finally, reverse transcription with a stem-loop DNA adapter and library amplification was performed. The quality and quantity of the cDNA library was checked in the same way as for the whole transcriptome cDNA library. Prior to sequencing, primary 5′-end cDNA libraries were purified and size-selected for fragments approximately 100–1000 bases long via gel electrophoresis, and quantified again. Paired end sequencing was performed in an Illumina MiSeq using MiSeq Reagent Kits v3 with a read length of 2 × 75 bases (Illumina).A primary 5′-end specific cDNA library was synthesized using 2 × 2.5 μg total RNA (RIN > 9) as described previously . BrieflyC. glutamicum ATCC 13032 reference genome sequence accession number BX927147  were detected essentially as described by C. glutamicum grown with and without phenol (3.4 mM), including normalization, was performed using the whole transcriptome data and Bioconductor package DESeq2  was calculated by the log2 mean of normalized read counts, and the signal intensity ratio  by log2 fold-change. The evaluation of the differential RNA-seq data was performed using an adjusted p-value cut-off of P ≤ 0.01 and a signal intensity ratio  cut-off of ≥1 or ≤-1.Differential gene expression analysis of e DESeq2  includede DESeq2 . The sigin vitro transcription assay was carried out essentially as described previously  was mixed with the respective σ factor  in a molar ratio of 1:30. The holo-RNAP was assembled for 10 min at 37°C. The transcription mixture was incubated for 15 min at 37°C. The transcripts labeled with [α-32P]UTP were separated in 5.5% polyacrylamide gel. In vitro transcription assays were done 2 or 3 times for each promoter, with essentially the same results.The eviously . The holin vivo using the two-plasmid system for C. glutamicum described recently . Two-plasmid strains were cultivated in 2xYT medium (containing Km and Tc) for 24 h and the cell samples were collected. The cells were disrupted with a FastPrep homogenizer . The fluorescence of the cell-free extracts was determined with a Saphire2 microplate spectrophotometer . Cells harboring the pEPR1 construct and the expression vector without a sig gene were used as a control. To determine the background fluorescence of C. glutamicum cells, the strain only carrying the promoter-less vector pEPR1 was used. Protein concentration in the extract was determined by Bradford assay, and promoter activity was expressed in arbitrary units/mg protein.Sigma factors were assigned to individual promoters recently  and simi E. coli . In prineviously  were useD and σH domains which recognize the -10 and -35 sequences of the respective promoters were produced by using the Swiss-Model server    and PDBient GTC)  were usege AMBER  and LinuC. glutamicum WT strain, a strain overexpressing sigD and a sigD deletion (ΔsigD) strain was performed previously , RNA-seq of 5′-enriched primary transcripts of C. glutamicum overexpressing sigD was used. The sequencing of primary 5′-end specific cDNA library allows the exact mapping of TSS. To detect these TSS, the data obtained from the primary 5′-end cDNA sequencing were mapped to C. glutamicum ATCC 13032 reference genome sequence, accession number BX927147 , peptidoglycan formation (lppS), inhibition of σD activity (rsdA) or have hypothetical functions  were only used. This assay may directly prove whether a promoter is specifically recognized by RNAP associated with the chosen σ factor, and thus distinguish between direct and indirect control by the tested σ. As shown in Figure D-specific. The only exception was the promoter of cg0607 (Pcg0607), which was active with both σD and σH. The in vitro transcript level with σH was even higher than with σD . The σH-specific clpP1 promoter  and time point 6 (T6) although it was at the same level of the control. It is an unusual feature of this two-plasmid system in some cases, which we described in our previous paper . Interestingly, the trend of changes in the σH-directed promoter activity was mostly the opposite of that with σD: the activity with σH was highest 3 h after the addition of IPTG, then mostly decreased at T6, and the difference in Gfp level between the cells with IPTG-induced and non-induced σH-overproduction nearly vanished at T24 in most cases  after the addition of the inducer (IPTG) in all cases and highest after 24 h (T24) in 10 of the 11 tested promoters. The promoters PH is directly responsible for the minor activity of σD-controlled promoters in vivo, we tried to overexpress sigH in the ΔsigD strain also harboring pEPR1 with the target promoters. The ΔsigD strain grew slightly slower than the wild type strain , moreover, only on a solid agar medium. The increase in promoter activity, (induction ratio of WT and ΔsigD strains with sigH overexpressed), was 2.1 and 1.8, respectively, for Pcmt2, 2.2 and 3.5, respectively, for PlppS and 1.1 and 6.1, respectively, for PrsdA , in contrast to C. glutamicum ECF sigma factors σH, σE, and σM, which exhibit a 25–38% mutual identity of AAs. However, when the regions which are thought to interact with the -35 and -10 promoter sequences  and σH (AT vs. GTT at -10 and GTAACA/G vs. GGAAT/C at -35). To identify the key AAs responsible for recognizing different nucleotides at the second position of these consensus sequences and to understand why σH is, to a certain extent, able to initiate transcription from the σD-dependent promoters, we used computer modeling tools. First, we created homology models of those σD and σH domains, which recognize the -10 and -35 sequences of the respective promoters. The structure of E. coli σE based on the crystal protein in complex with promoter DNA was used as a template  as well as the -35 element . This also corresponds to the similarity of the AA sequences of E. coli σE and C. glutamicum σH which are supposed to interact with these nucleotides (We have shown that the consensus sequences of the ) and σH  differ  is in close contact with the surface of the σ subunit in our homology models based on the E. coli σE crystal structure 4LUP  sigma factors described, and a model of the complete DNA transcription bubble is not yet available. The key nucleotide at the second position of the P Figure . AccordiD Figure . In contH Figure .D and σH in complex with the respective -35 elements can be considered highly plausible. To verify the stability and reliability of these homology models, we introduced a water envelope into the models and carried out 50-ns MD simulations. It was found, that the hydrophobic interactions of the methyl group of T at the second position of the σD-consensus of the -35 element with the side chains of Val 169 and Ala 166 in σD are crucial for its recognition , whose methyl group interacts with the side chains of the hydrophobic AAs Val 169 and Ala 166 in σD  in detail, we again performed an extensive 50-ns MD simulation. It was found that the side chain of Thr 168 in σH can rotate, and its methyl group then interacts with the methyl group of the thymine within GTAAC, which undoubtedly leads to a hydrophobic stabilization. Moreover, the hydroxyl group from the Ser 171 side chain forms a hydrogen bond with the oxygen atom of the thymine. To test whether the replacement of the AA sequence 168-ThrValMetSerArg-172 with AlaValArgValAla from σD can improve the recognition of σD-controlled promoters by σH, we designed the mutant σH168-AlaValArgValAla-172 for the in vivo analysis.With the -35 element within the σD Figure , interacH Figure . To asseD-controlled promoters were to a lesser extent also activated by σH, although the highly conserved sequences of the σH-specific promoters  differ from those of the identified σD-specific promoters . Computer modeling showed that σH can bind to the key regions of promoter DNA in a similar manner as σD to the analogous elements. Based on the results of the sequence similarity analysis and computer modeling, we designed mutations to make σH more σD-like and possibly more efficient at transcription from σD-dependent promoters. We introduced the mutations into the sigH gene cloned in the expression vector pEC-XT99A, which resulted in the production of the mutant proteins σHLys-53-Ala or σH168-AlaValArgValAla-172. These mutant genes were used in the two-plasmid assays with the promoters which exhibited the highest activity with σH: Pcg0607, Plpps, Pcmt2, and PrsdA. We assumed that the overproduced σHLys-53-Ala and σH168-AlaValArgValAla-172 would trigger higher promoter activity in two-plasmid assays with the chosen σD-dependent promoters than σH. This assumption was only confirmed with σHLys-53-Ala and Pcg0607  most likely interact with the -10 promoter elements.Most of the σH168-AlaValArgValAla-172 recognized Pcg0607 less efficiently. To assess how the mutant σH168-AlaValArgValAla-172 could interact with the σD-controlled promoters, we analyzed the homology model with this mutant σ factor in more detail. Using a 50-ns MD simulation, we found that the complex of σH168-AlaValArgValAla-172 with the -35 element of the σD-controlled promoter is probably not stable. This was something of a surprise given that the native σH was able to efficiently bind σD-controlled promoter DNA in previous MD simulations. A detailed analysis of the MD trajectories showed that this instability appeared to be due to the absence of Arg at the final position of the AA sequence AlaValArgValAla . The positively charged Arg 172 side chain of σH was found to create a significant stabilizing interaction  with the negatively charged backbone of promoter DNA. This hypothesis, which might explain the low efficiency of the mutant σH168-AlaValArgValAla-172, may be tested in the future by creating additional mutations suggested by the in silico analysis.Contrary to the expectation, σD-controlled promoters . Promoters which we consider to be σH-dependent according to the sequences of the putative -35 (GGAA) and -10 (GTT) elements were found upstream of lppS and fadD2 genes. The detection of all TSS in the upstream regions of the σD-dependent genes was important to avoid effects of transcription started from the additional promoters. The position of the cloned promoter fragments for in vivo and in vitro assays could thus be chosen in such a way, that only the activity of the supposed σD-controlled promoters was determined. The organization of the multiple promoters within the upstream regions of the 8 genes is shown in Figure fadD2, the studied σD-dependent P1fadD2 and the additional σH-dependent P2fadD2 partially overlap and the activity of the additional σH-specific promoter might thus contribute to the overall σH-dependent activity of the fragment. However, such activity was not detected by in vitro transcription. In vivo, the σH-dependent activity was similarly low as in other cases. The σA- and/or σB-dependent promoters P3cmt1, P2cg0420, P2cg0607, P2cg2047, P3lppS, and P2cmt2 . Out of the conditions tested, only phenol exhibited a stronger inhibitory effect on the growth of the ΔsigD strain carrying the vector pEC-XT99A than on the growth of the analogous WT strain. We found that growth rate of C. glutamicum WT culture in 2xYT medium after the addition of 7.5 mM phenol decreased from 0.88 to 0.46 h-1 (1.9-fold), whereas the growth of the C. glutamicum ΔsigD decreased from 0.68 h-1 to 0.22 (3.1-fold). A difference in the effect of phenol was not significant in plasmidless strains.We have tested a number of stress conditions  to detect a slower growth of the C. glutamicum to phenol stress, we performed RNA-seq using the RNA isolated from the C. glutamicum WT cells grown in minimal medium with glucose (2%) or phenol (3.4 mM). Differential gene expression analysis including normalization, was performed using the whole transcriptome data and Bioconductor package DESeq2  cut-off of ≥1 or ≤-1. According to the results  or phenol (3.4 mM). The promoter activity was measured in the extract of the cells before the addition of IPTG and at the end of exponential growth phase.The effect of phenol on the activity of the promoters Pcg0607, PlppS, PrsdA, and Plpd) in the strains with overexpressed sigD was clearly higher after cultivation with phenol . This is one of a few examples of the stress response to the action of phenolic compounds regulated by the ECF sigma factors in C. glutamicum , C. callunae (93%) and C. efficiens (90%)]. A lower level of identity (70–79%) was found with σD proteins from most of the phylogenetically more distant Corynebacterium species. The identity of C. glutamicum σD with σD proteins from other bacteria of the Mycolata group  exhibited a range of 54–57%. All bacteria with a σD identity level higher than 45%  belong to Actinobacteria. However, the functions of the genes of the σD regulons in these related bacteria are partially different. In addition to mycolate synthesis, the M. tuberculosisσD regulon is involved in virulence, lipid metabolism and gene expression under starvation conditions  or no consensus sequences suggested for σD-dependent promoters were identified upstream of the homologous genes in the vast majority of other Corynebacterium species. Conservation of the putative σD-dependent promoter sequences among various bacteria was only found in the genes cmt1 and cmt2, encoding trehalose corynomycolyl transferases.A comparison of the promoter sequences of the putative σrsdA promoter was found in 38 Corynebacterium species encoding the anti-sigma factor RsdA. In other Actinobacteria , a putative σD-dependent promoter is not present upstream of the rsdA gene but upstream of the sigD gene. Such an arrangement indicates that the sigD-rsdA operon is autoregulated by σD in these bacteria, in contrast to C. glutamicum, where σD seems to only control rsdA gene expression but not its own synthesis. Only two vegetative promoters were localized upstream of the C. glutamicum ATCC 13032 sigD gene  of the -10 and -35 sequences (GTAAC - 15 nt – CTCGAT) in the PigD gene .D played a dominant role in the transcription from all promoters tested in this study, however, low σH activity with most of these promoters was detected by the in vivo two-plasmid assay. This activity of the promoters was not observed when using in vitro transcription (with one exception). The promoter Pcg0607 was an exception and its efficiency documented by in vitro transcription was even higher with σH than with σD , is therefore considered to be a candidate for a global regulatory molecule . Thus, it was apparently σH, which drove the detected activity of the σD-dependent promoter P2lppS in the ΔsigD mutant. Thus, σH was able to partially substitute for missing σD.To prove that the promoters are recognized directly with RNAP+σe Figure . The traD-dependent genes by different methods in C. glutamicum ATCC 13032 than those used in C. glutamicum R , are not present on the C. glutamicum R chromosome. Another gene, lpd was not found to be σD-dependent in C. glutamicum R. The promoter P2lpd was localized 360 nt upstream of the translation initiation codon. There is a possibility that a potential small RNA, which was detected by RNA-seq, is transcribed from this promoter. P2lpd has a GTT sequence in the -10 region which is the consensus of σH-dependent promoters. The lpd gene (encoding dihydrolipoamide dehydrogenase) is known to play a role in the oxidative stress response in bacteria  were higher transcribed under the phenol treatment. The promoters Pcg0607, PlppS, Prsd and in addition Plpd, were also found to be induced by phenol in minimal medium by two-plasmid analysis  necessary for plasmid maintenance.Phenol in complete medium did not significantly slow the growth of the ΔD-dependent genes and the organization of their transcription suggest that these genes play an important role not only in stress response, but also in cell homeostasis and in building a cell envelope during rapid growth under optimum conditions. Under optimal growth conditions, the transcription of the genes involved in corynomycolate synthesis is probably driven from additional σA- and/or σB-dependent promoters or σH-dependent promoters, which we detected upstream of many σD-dependent genes . Moreover, the regulatory overlaps of σH with transcriptional regulators(D seem to control a number of physiological functions in C. glutamicum including heat, SOS, oxidative, chemical and cell surface stress responses. The accumulated evidence indicates that overlaps between the regulons of C. glutamicum ECF sigma factors are rather a common regulatory strategy how to cope with complex environmental stresses than an unusual exception.The possible global regulator σegulators and within vitro transcription assays. JN performed the sequence analyses. IB carried out in silico analyses. All authors analyzed the results. MP and JN drafted the initial manuscript. All authors contributed to writing the final manuscript, reading, and approving the submitted version.MP and JK conceived the project and led the studies performed in Prague and Bielefeld, respectively. HD, TB, LR, and VŠ carried out the most experiments. TB and HD carried out RNA-seq and processed the data. JH did the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.