Tfcp2l1 coordinates IC and PC development. Tfcp2l1 induces the expression of IC specific genes, including specific H+-ATPase subunits and Jag1. Jag1 in turn, initiates Notch signaling in PCs but inhibits Notch signaling in ICs. Tfcp2l1 inactivation deletes ICs, whereas Jag1 inactivation results in the forfeiture of discrete IC and PC identities. Thus, Tfcp2l1 is a critical regulator of IC-PC patterning, acting cell-autonomously in ICs, and non-cell-autonomously in PCs. As a result, Tfcp2l1 regulates the diversification of cell types which is the central characteristic of 'salt and pepper' epithelia and distinguishes the collecting duct from all other nephron segments.Although most nephron segments contain one type of epithelial cell, the collecting ducts consists of at least two: intercalated (IC) and principal (PC) cells, which regulate acid-base and salt-water homeostasis, respectively. In adult kidneys, these cells are organized in rosettes suggesting functional interactions. Genetic studies in mouse revealed that transcription factor DOI:http://dx.doi.org/10.7554/eLife.24265.001 Nonetheless, despite their many differences, ICs and PCs function coordinately: for example, the absorption of Na+ by PCs creates a transepithelial electrical gradient which stimulates H+ secretion by ICs  and the ureteric bud (UB) . The Metn by ICs .Foxi1 exhibited a cell type that co-expressed mixtures of IC (Ca2) and PC (Aqp2) proteins, implying that Foxi1 restricted the otherwise facile interconversions of ICs to PCs  is a nuclear transcription factor and a member of the CP2 subfamily of the LSF/Grainyhead family , and then throughout its arborized Krt8+ stalks at E15-E18 , Tfcp2l1 was prominent in both PCs (Krt8+) and ICs  , but its most persistent location was the collecting duct system.d family . Tfcp2l1both LIF  and Wnt both LIF . In adding ducts . In fact E15-E18  when Tfc Atp6b1) , but dem Atp6b1) . Tfcp2l1Tfcp2l1, the development of collecting duct epithelia was characterized using signature proteins found in adult PCs and ICs. While the E13 and E15 collecting duct demonstrated homogeneous expression of proteins typical of PCs, there were rare cells co-expressing IC proteins (not shown). By E18 these cells became abundant and we designated them ‘double-positive’ progenitors, because they co-expressed IC and PC proteins  but also a subset of Aqp2+ PCs (24% of GFP-labeled cells were Aqp2+ cells expressing low levels of Atp6b1). In addition, rare (2%) GFP-labeled cells expressed equivalent levels of Aqp2 and Atp6b1, typical of ‘double positive’ cells (n = 80 ducts in four independent mice were inspected for each genetic label). Therefore, all adult ICs and PCs derive from HoxB7+ progenitors and all adult ICs and at least some PCs derive from HoxB7+ progenitors that subsequently express Atp6b1+.To further analyze the origin of ICs and PCs, we crossed cre-reporter mTmG;  with cre E15-E18 . All ICseys P60;  confirmiTfcp2l1 is important for the development of ‘double-positive’ progenitors or for later stages of IC and PC development, we created a floxed allele flanking the CP2 domain .To determine whether 2 domain , and we elopment , the Cdh develop , and Atpring ICs . In eachstaining . EIIA-CrTfcp2l1 deletion with EIIA-Cre or with Cdh16-Cre produced grossly normal kidneys at birth . Hence, Tfcp2l1 was critical to initiate the expression of IC signature genes which were first expressed in ‘double positive’ progenitors.at birth , except Tfcp2l1 by EIIA-Cre and Cdh16-Cre drivers which act before the appearance of IC cells, we found that deletion of Tfcp2l1 with Atp6b1Cre resulted in a normal number and distribution of ICs and PCs at P14. By P60, however, there were fewer than half the number of ICs  or PC (Aqp2>Atp6b1) phenotypes, but GFP+Tfcp2l1-deleted cells appeared strikingly similar to PCs .In contrast to the deletion of Tfcp2l1 was critical in the primary development of ‘double positive’ progenitors and the long-term maintenance of mature ICs which are otherwise capable of assuming a mixed or even a PC cell phenotype. In contrast, Tfcp2l1 was not likely to mediate IC development by regulating their survival, because we failed to find the apoptosis marker activated caspase 3 in any of our models (data not shown).Taken together, Tfcp2l1 that might explain IC regulation, we used an integrative approach that combined high-throughput data from two independent assays: (1) We found 843 differentially expressed genes in a comparison of f/fEIIA-Cre;Tfcp2l1 knockout and f/fTfcp2l1 control P1 mouse kidneys . According to GUDMAP, the comprehensive genitourinary development database (www.gudmap.org), our analysis showed that 62% of downregulated genes were expressed either by UB, cortical or medullary collecting ducts or by the TALH including multiple subunits of the H+ATPase complex  and IC specific Foxi1, Ca12, Ca2, Aqp6 and Oxgr1  We ChIP-Seq . Compari the TSS . In factromoters . In addil1 motif  .Taken together, Tfcp2l1 might regulate Notch signaling in ICs or in PCs.The analysis of our two datasets demonstrated significant over-representation of components of the Notch pathway suggesting that Jag1, Jag2, Dll1, Dll4), Receptors (Notch1-4) and Notch Signaling States . We found that Jag1 marked the development of ICs: Jag1 and Atp6b1 co-localize with Ktr8 in IC-PC ‘double positive cells’ at E18  and activated nuclear Hes1 were found in PCs  immediately surrounding the Jag1+ β-ICs (Notch1 (extracellular domain) was detected in other locations such as the basal membrane of some ICs, identified as Krt8-, Jag1-α-ICs , β-IC , and α-IC .We analyzed the Notch pathway using well characterized antibodies for Notch Ligands , Hes1 , Hey1  and Hey2 (-212)  . To broaxi1 ChIP . ConsideJag1 with either Cdh16-Cre or Atp6b1-Cre-drivers and the deletions were confirmed at P60 by immunostaining  we observed hydronephrosis (3/4 mice), similar to another collecting duct-specific Notch-inactivation model  and a decrease in Aqp2+ PC cells  and K+ absorption (IC)  . They maion (IC)  since PCcn2/NGAL . In sum,ure e.g. .Tfcp2l1 and Jag1 act on a primordial cell type which appears in the collecting duct between E15-E18 and expresses both IC and PC proteins  and by the loss of Jag1 expression in Tfcp2l1 knockouts , but by E18 there was clear excess expression of Jag1 in a subset of duct cells which displayed IC and PC proteins. Surrounding these IC progenitors were PCs with activated nuclear Hes1, a pattern which became distinct after birth , the upregulation of Foxi1 appeared to be a direct consequence of Notch-OFF because activated Notch signaling negatively regulated Foxi1 (Jag1 knockout and pharmacologic inhibition) generated diffuse expression of Foxi1 , or perhaps as a result of the expression of NUMB in some ICs . While this differential expression might contribute to cell speciation by driving higher levels of Jag1 expression in ICs, we note that Foxi1, which is downstream of Tfcp2l1, is not only critical for Jag1 expression, but it is reciprocally inhibited by Notch signaling . In conignaling  and 10. Hes1. In other developing organs, loss of Hes1 signaling resulted in a failure of tissue development  with LoxP sites. These exons included the CP2 conserved functional domain and their deletion created an open reading frame shift. The targeting construct was generated by using a modified NCI BAC recombineering protocol. Briefly, mouse (C57) Tfcp2l1 BAC clone  was recombineered using Loxp-Neomycin-Loxp (LNL) cassette with homologous arms upstream of exon 3 and Frt-Neomycin-Frt-Loxp (FNFL) cassette downstream of exon5. The Tfcp2l1 -targeting BAC DNA was electroporated into mouse KV1 ES cells (generated by Columbia University Transgenic Facility), followed by neomycin selection. Neomycin-resistant ES clones were screened using PCR and Southern Blots and selected clones were microinjected into C57B6 blastocysts to generate chimeras. The Tfcp2l1 chimeric mice were subsequently mated with EIIa-flpe C57B6 female mice for both germline transmission and flpe-based removal of the Frt-floxed neomycin cassette, which generated F1 Tfcp2l1-floxed mice. Cre mediated deletion was confirmed at the genomic level using PCR, at the RNA level using quantitative Real-Time PCR and at protein level using immunohistochemistry. Tfcp2l1 knockout and wild type kidneys were assayed using Affymetrix Mouse Genome 430.2 microarrays.EIIA-Cre (RRID:IMSR_JAX:003724), Cdh16-Cre (RRID:IMSR_JAX:012237) and f/fJag1 (RRID:IMSR_JAX:010618) were purchased from the Jackson Labs, Rosa26mTmG (RRID:IMSR_JAX:007576) was kindly provided by C Mendelsohn and Atp6b1-Cre was a gift from R Nelson, Utah. Rosa26mTmG genetic reporters express membrane-bound green fluorescein protein (GFP) upon Cre-mediated recombination  or goat IgG control (Jackson Immunoresearch). The ChIP antibody was extensively validated in ChIP-PCR assays. In brief, we used conservative Tfcp2l1 binding site at Krt7 promoter to validate the performance of the Tfcp2l1 antibody in a ChIP PCR assay. A ChIP Library was prepared according to the Illumina ChiPseq library preparation kit using size selection (150–250 bp). The library was sequenced on Illumina Genome Analyzer IIx . Sequences were aligned to mm9 genome assembly using Bowtie (http://www.bioconductor.org/packages/release/bioc/html/PWMEnrich.html)As described , P1 mousg Bowtie  and bindg Bowtie . We usedg Bowtie  for genehttps://bioconductor.org/packages/limma). Induced genes were validated by QPCR using Sybr Green  and relative levels of mRNA expression were normalized to β-actin mRNA. Primer pairs were designed using Primer3 software  and validated by product size , and synthesized cDNA using kits from Applied Biosystems. Preparation of templates and hybridization on Affymetrix Arrays was performed at the Columbia Genome Center. Differential gene expression was identified using R Limma . Tissues were prepared by intracardial perfusion with PBS followed by 1% PFA/PBS, 30% Sucrose/PBS, OCT (Tissue-Tek) and cryosectioned at 50 µm. Blocking and staining was performed in PBS/3% BSA/0.2% Triton X-100 solution. Controls and experimental samples were always placed on the same slide, stained in parallel and imaged with the same parameters using confocal Zeiss LSM510 Confocal Microscope (Columbia Core Facilities). We used confocal microscopy to reconstruct whole cells from the image stacks and estimated the amount of protein per cell by measuring total fluorescent signal. Antibodies are listed in  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.TFCP2L1 Regulates the Patterning of Cells in the Collecting Duct of the Kidney" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Harry Dietz as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "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:This is a detailed analysis of the Tfcp2l1 knockout in the distal nephron. The authors reveal that this gene is necessary for promoting the intercalated cell phenotype within collecting ducts cells that are capable to be either principal or intercalated cells, at least partially through regulation of the Notch signaling pathway. These conclusions are drawn based on in situ antibody staining, RNA-Seq analysis and ChIP-seq. The authors devise a model whereby Tfcp21l interacts with Notch signaling to repress the intercalated cell fate in plastic collecting duct cells. This paper makes a number of important observations and establishes a new paradigm for collecting duct differentiation.Essential revisions:eLife, however there were major concerns from all three of the reviewers, all of which need to be addressed. They are:The work was found to be potentially interesting and worthy of publication in 1) The writing needs to be polished. The data presentation is so dense that one cannot determine whether the conclusion is reasonable or not. There are innumerable small images presented to support their conclusions, but many of them are over processed, and display a confusing array of overlapping colors , and from poorly defined tubule segments , that it is difficult to assess the overwhelming amount of data from the different animals and in vitro models used here. The overall major problem of information overload and seeming over interpretation of some of the images needs to be addressed. This will require serious editing and the removal of all but the essential data that support the hypothesis that Tfc2l1 is involved here.2) The authors might want to address in their  Discussion, physiological contexts in which the cellular composition of the collecting duct has been shown to change. For example, with lithium exposure.3) Citations to figures in the text are incorrect. Some panels are never referred to. Some data is presented but the location of that data is not mentioned . A lot of data is mentioned but there is no corresponding figure or supplemental figure. This needs to be corrected.4) A second issue is the experiments regarding the transfection of isolated MM with Tfcp2l1. This line of experimentation was very confusing to me until I read the Materials and methods section in which it was indicated that this was actually how this factor was discovered. However, that is not mentioned in the Results section. The reason this is confusing is because the manuscript focuses on the collecting ducts which are derived from a different lineage from the MM. Why they would use the MM instead of a collecting duct cell line is confusing. Further it is confusing as to how they are able to get cell types from both the MM lineage as well as the collecting duct lineage from this experiment. It appears as if they get an entire patterned nephron and collecting duct. Is Tfcp2l1 expressed in all these cells? Is this result different than if they treated with LIF? In the experiments where they compare the Tfcp2l1 transfected to GFP, do they treat with LIF (not clear from the Methods). If not, what are they comparing? An entire nephron to uninduced MM? If so, what is the relevance of these results? Without better explanation and rationale, I feel these results should be excluded from this paper.5) Some of the data if very difficult to see . In some cases, individual channels need to be shown. In others, higher magnification images would be of help.6) The authors need to clarify what Notch antibody they are using in their stainings. There are discrepancies between what they say in the text, how the label their figures and what the figure legends say.7) Figure 6A and B need to be divided up into distinct panels as each panel shows a different antibody combination from a different kidney (or at least region of the kidney). The single channel staining of pendrin is of low quality and hard to interpret. Co-stainings and/or higher mag should be shown.8) Figure 1, nuclear Tfcp staining is clear in the adults  but less so in the embryos . Do the authors have any better images? Also, is it clear that Tfcb is in all collecting duct cells in that levels are equal? Similarly, in 1M, the triple staining makes it hard to see whether Tfcb is in all cells. In 2E, it looks like the IC cells have more Tfcb for example.9) In Figure 3 the authors never show us the expression of Tfcp in the Adeno infected cultures, so it is really hard here to discern how much of the epithelia is due to Tfcb expression and how much is due to the supposed induction by LIF. Again, the multiple stains make it really hard to discern individual patterns. In 3D, it is hard for any reviewer to discern whether the Tfcb ChIP-seq peaks are meaningful and whether they localize to specific regions of genes or at the TSS. From the excel sheet, individual peaks may be significant but can the authors say something about the statistical significance of location of peaks? In other words, are peaks more likely to be near TSS rather than dispersed more randomly at target genes?10) In Figure 6, it would be nice to see Tfcb expression in the Jag1 KOs. In other words, does notch signaling alter levels of Tfcb and does this in turn reinforce more notch signaling to set boundaries between cell types?11) The model in Figure 7 assumes equal amounts of Tfcb, but this not clear. The transition from double positive to single positives, clearly require notch/jag1 and this depends on Tfcb, which is expressed in all cells (I think). But as notch signaling commences, do Tfcb levels remain the same or are they regulated by notch and does this then alter the amount of notch ligands to reinforce the pattern? The model assumes Hes1 and Foxi1 mediate the activation and repression, but again I am not sure Tfcb levels are equivalent in the two cell types. Essential revisions:The work was found to be potentially interesting and worthy of publication in eLife, however there were major concerns from all three of the reviewers, all of which need to be addressed. They are:1) The writing needs to be polished. The data presentation is so dense that one cannot determine whether the conclusion is reasonable or not. There are innumerable small images presented to support their conclusions, but many of them are over processed, and display a confusing array of overlapping colors , and from poorly defined tubule segments , that it is difficult to assess the overwhelming amount of data from the different animals and in vitro models used here. The overall major problem of information overload and seeming over interpretation of some of the images needs to be addressed. This will require serious editing and the removal of all but the essential data that support the hypothesis that Tfc2l1 is involved here.We appreciate the comments and have spent much effort reorganizing a number of images, displaying separate confocal channels, removing  all but the most essential data rather than document all of the IC and PC proteins at different stages in a single image, and we describe the location of the images . In all we have removed 20 panels but at the same time we are now showing individual channels. In particular, E15 data is no longer shown because of very rare presence of double positive cells before E18.We should mention that because we have reduced the complexity of the images, there are instances where we did have to indicate “data not shown”. We are happy to replace those images-perhaps in other types of files – according to your guidance.Next, we simplified the writing of the paper along with clarification of the data presentation, including removing the section on induction in vitro, the section on additional segments of the nephron, especially the connecting segment, and revisions to reduce redundant statements. As a result, we reduced the manuscript by 500 words in order to focus on the Tfcp2l1 pathway.2) The authors might want to address in their  Discussion, physiological contexts in which the cellular composition of the collecting duct has been shown to change. For example, with lithium exposure.We have expanded the section in the Discussion concerning medical settings in which IC and PC ratios might change.In addition, these studies are ongoing in our lab but the connection between different diseases of collecting ducts  and the underlying signaling pathways remains unexplored. We suspect a variety of signaling paradigms are recruited in different medical settings. In Consequently, we comment on the few known physiological examples of perturbation of the IC-PC ratio, but there is much unknown.3) Citations to figures in the text are incorrect. Some panels are never referred to. Some data is presented but the location of that data is not mentioned . A lot of data is mentioned but there is no corresponding figure or supplemental figure. This needs to be corrected.We have reformatted all of the images and we have carefully paired the text and the figures.In order to cut down the amount of data (comment #1), we had to remove some of the figures particularly the evidence of very rare IC cells at E15, multiple combinations of IC and PC marker proteins, and demonstrations of loss of protein expression in knockouts. Instead we have indicated “not shown”. We are happy to replace any additional data as per the Editor’s recommendation.4) A second issue is the experiments regarding the transfection of isolated MM with Tfcp2l1. This line of experimentation was very confusing to me until I read the Materials and methods section in which it was indicated that this was actually how this factor was discovered. However, that is not mentioned in the Results section. The reason this is confusing is because the manuscript focuses on the collecting ducts which are derived from a different lineage from the MM. Why they would use the MM instead of a collecting duct cell line is confusing. Further it is confusing as to how they are able to get cell types from both the MM lineage as well as the collecting duct lineage from this experiment. It appears as if they get an entire patterned nephron and collecting duct. Is Tfcp2l1 expressed in all these cells? Is this result different than if they treated with LIF? In the experiments where they compare the Tfcp2l1 transfected to GFP, do they treat with LIF (not clear from the Methods). If not, what are they comparing? An entire nephron to uninduced MM? If so, what is the relevance of these results? Without better explanation and rationale, I feel these results should be excluded from this paper.Thank you for these comments. The reviewer is correct that we identified Tfcp2l1 from an induction screen – that is, we found that a single transcription factor can induce an entire patterned nephron and collecting duct. In fact, Tfcp2l1 is active without the addition of any other growth factors to metanephric mesenchymal cultures (LIF is not needed), and its induction rate approached 100% using two different adenoviral preparations. In the absence of growth factors, metanephric mesenchyme survives for one-two days, but after Tfcp2l1 induction, the mesenchyme expands and segmented nephrons appear. In our experiments, Tfcp2l1 was the only transcription transferred by Adenovirus to the mesenchyme that could reproduce LIF activity in rat metanephric mesenchyme.Tfcp2l1 is expressed weakly in proximal segments of the nephron, more strongly in the Talh and most strongly and persistently in the collecting ducts. This may be an explanation for its remarkable inductive activity of both proximal and distal nephron segments. We think that the readout not only reflected the earlier expression of Tfcp2l1 throughout the nephron, but may reflect that both the ureteric bud and the metanephric mesenchyme derive from the intermediate mesoderm.However, we appreciate that the induction of many types of epithelia including ureteric epithelia is not a classical finding in our field, and hence requires more explanation which is as the reviewer indicated is really distinct from the main findings of the paper. In addition, because our Tfcp2l1 knockout demonstrated striking loss of a specific cell type in the collecting duct rather than in other nephron segments, our paper focused on the collecting duct.Consequently, we have removed the figure of kidney induction as well as all gene analyses that utilized Tfcp2l1induced epithelia. Fortunately, the gene analysis is unchanged. The only mention of the inductive activity of Tfcp2l1 is a brief comment in Discussion.5) Some of the data if very difficult to see .We have replaced these figures and have divided the images into their component pieces.In some cases, individual channels need to be shown. In others, higher magnification images would be of help.We now show individual confocal channels to clarify the images. We have added higher power images in Figure 2—figure supplement 1, Figure 3—figure supplement 1, Figure 4, Figure 6, Figure 7, Figure 8, Figure 9.6) The authors need to clarify what Notch antibody they are using in their stainings. There are discrepancies between what they say in the text, how the label their figures and what the figure legends say.The antibodies are listed in the Supplementary file 5. The Notch antibodies are R&D AF1057 and Abcam Ab8925. We have reviewed the figures and legends many times and hopefully there are no longer discrepancies.7) Figure 6A and B need to be divided up into distinct panels as each panel shows a different antibody combination from a different kidney (or at least region of the kidney). The single channel staining of pendrin is of low quality and hard to interpret. Co-stainings and/or higher mag should be shown.These figures have been changed and separate channels are now shown throughout the manuscript . We have removed the low power image of pendrin staining substituting instead separate channels for ATPase in Figure 9.8) Figure 1, nuclear Tfcp staining is clear in the adults  but less so in the embryos . Do the authors have any better images? Also, is it clear that Tfcb is in all collecting duct cells in that levels are equal? Similarly, in 1M, the triple staining makes it hard to see whether Tfcb is in all cells. In 2E, it looks like the IC cells have more Tfcb for example.This is a very intriguing question concerning the regulation of Tfcp2l1 in different cell types. We changed Figure 1: E15 demonstrates an equivocal distribution of Tfcp2l1 but by E18 there is unequivocal nuclear localization co-incident with the first appearance of IC “double positive cells”. Nuclear localization of Tfcp2l1 is also clear in the adult.We now add data from spot imaging the adult collecting duct . It shows that IC cells, marked by Atp6b1, have a two-fold higher expression of Tfcp2l1, than do the PC cells, marked by Aqp2. Thank you for this question; we now mention in Discussion that a difference in Tfcp2l1 levels might contribute to the differentiation of IC-PC.9) In Figure 3 the authors never show us the expression of Tfcp in the Adeno infected cultures, so it is really hard here to discern how much of the epithelia is due to Tfcb expression and how much is due to the supposed induction by LIF. Again, the multiple stains make it really hard to discern individual patterns.Tfcp2l1 is an independent epithelial inducer, which is downstream of LIF. Consequently, no LIF is needed when Ad-Tfcp2l1 is added to metanephric mesenchyme. However, we removed the data from the paper concerning the inductive activity of the transcription factor according to suggestion #4 and in order to better focus the paper on Tfcp2l1’s role in the collecting duct.In 3D, it is hard for any reviewer to discern whether the Tfcb ChIP-seq peaks are meaningful and whether they localize to specific regions of genes or at the TSS. From the excel sheet, individual peaks may be significant but can the authors say something about the statistical significance of location of peaks? In other words, are peaks more likely to be near TSS rather than dispersed more randomly at target genes?Thank you for this question. We added a better plot in the figure  and indicated the accumulation of Tfcp2l1 peaks of both up and down regulated gene near the TSS . We show that ~30% of peaks are within 1kb of the TSS and 40% are accounted for within 10kb. In contrast, randomly selected genes do not demonstrate accumulation of peaks near TSS.10) In Figure 6, it would be nice to see Tfcb expression in the Jag1 KOs. In other words, does notch signaling alter levels of Tfcb and does this in turn reinforce more notch signaling to set boundaries between cell types?+and Krt8+) in the collecting duct, but no loss of expression of Tfcp2l1. Consequently we have proposed that Tfcp2l1 is upstream of Jag1.Thank you for this question; it is an important addition to the article. The knockout of Tfcp2l1 abolishes Jag1 staining but the deletion of Jag1 does not suppress Tfcp2l1, at least as detected by IF. We have included a new figure from Jag1 knockouts, Figure 9 and supplements, which show reversion to “double positive cells”  so there may be an additional feedback with promotes the differentiation of these cell types. We now mention this hypothesis in the Discussion.11) The model in Figure 7 assumes equal amounts of Tfcb, but this not clear. The transition from double positive to single positives, clearly require notch/jag1 and this depends on Tfcb, which is expressed in all cells (I think). But as notch signaling commences, do Tfcb levels remain the same or are they regulated by notch and does this then alter the amount of notch ligands to reinforce the pattern? The model assumes Hes1 and Foxi1 mediate the activation and repression, but again I am not sure Tfcb levels are equivalent in the two cell types.From the above #10, we think that Jag-Notch are downstream of Tfcp2l1 and hence we do not expect variation of Tfcp2l1 with Jag1 signaling. By using an in situ RNA labeling technique-pulldown assay that we developed (Unpublished), we do not find much difference in RNA content of these cells. However, to examine this question, we used spot imaging of the collecting duct and we measured a two- fold difference in immunodetectable Tfcp2l1.We emphasized in the Discussion that there is evidence for feedback loops at the level of Foxi1 and Jag1. While our data are strongest for this hypothesis, our new data suggests that modulation of Tfcp2l1 may enhance the distinction between IC and PC as well. Thank you for pointing out this possibility.


SPOP promoter was hypermethylated in CRC tissues and its methylation was correlated with poor survival. Transcription factor RXRA had a vital role in the regulation of SPOP gene. The data indicated that DNA methylation at −167 bp of the SPOP gene altered the binding affinity between transcription factor RXRA and SPOP promoter. Moreover, SPOP was found to associate with Gli2 and promoted its ubiquitination and degradation in CRC. Consequently, the expression level of Hh/Gli2 pathway-related apoptotic protein Bcl-2 was decreased and the function of resisting cell death was inhibited in CRC. It suggests that methylation status of SPOP promoter can be used as a novel epigenetic biomarker and a therapeutic target in CRC.Epigenetic silencing of tumor suppressors contributes to the development and progression of colorectal cancer (CRC). We recently found that speckle-type POZ protein (SPOP) was significantly downregulated and the inactivation of SPOP promoted metastasis in CRC. This study aimed to clarify its epigenetic alteration, molecular mechanisms and clinical significance in CRC. Our results revealed that the core region of  In this process, E3 enzyme determines the substrate specificity. Among the various E3 enzymes, Cul3-based ligase is known to regulate the cell apoptosis.18 SPOP has been identified to function as a substrate-specific adaptor that binds to Cul3  signaling pathway is crucial in tissue-patterning during embryonic development and tumorigenesis.22 Binding of Hh ligands to Patched (PTCH) relieves Smoothened (SMO), and subsequently initiates the cascade signaling to activate the transcriptional factors .23 However, the function and downstream substrate of SPOP remains unknown in colorectal cancer.Ubiquitin-dependent proteolysis has an important role in the regulation of a variety of cellular processes, including cell proliferation, differentiation and apoptosis.ul3 ref.  and exerSPOP promoter induces the transcriptional repression and decreased cell apoptosis in colorectal cancer. We suggested that demethylation of SPOP promoter region can be used as the novel epigenetic therapy for colorectal cancer.In this study, we found that hypermethylation of SPOP gene, we first analyzed which specific region within the SPOP gene promoter is critical to the regulation of gene transcription. Serial deletion constructs of the SPOP gene promoter region were generated. As shown in P<0.001).To evaluate the impact of DNA methylation pattern on transcriptional regulation of SPOP promoter for potential regions of DNA methylation, and a distinct CpG island was observed in the promoter region between −276 and −96 bp, which was within the core part of the promoter , suggesting that SPOP promoter methylation may serve as a poor prognostic predictor in CRC.The prognostic value of the SPOP promoter, we performed bisulfite sequencing (BSP) in three CRC cell lines Lovo, SW480, HCT116 and one human colon normal epithelium cell line FHC. The sequencing region was from −271 to −27 bp and included 13 CpG sites. As shown in versus nuclear SPOP protein distribution in these cells. As shown in To provide a detailed DNA methylation pattern within the core part of SPOP gene, two constructs of the SPOP gene promoter region were treated with SssI methylase in vitro, and then transfected into FHC cells . To confirm the interaction between RXRA and the SPOP gene, the luciferase assay was carried out. As shown in SPOP promoter activity was significantly reduced upon knockdown of RXRA and mutation of RXRA binding element. Besides, we also examined the response of wild-type and mutant reporters to 9-cis RA and exogenous RXRA overexpression. As shown in SPOP gene promoter is binding element for transcription factor RXRA. To strengthen this conclusion, an oligonucleotide probe and an in vitro-translated full-length RXRA protein were used in EMSA assays. Full-length RXRA protein exhibited high-affinity and sequence-specific binding to the proposed binding element  assay, we examined the RXRA protein levels in cell lines . ChIP-qPline FHC . In addiline FHC . Intriguline FHC . Transfeline FHC .SPOP gene affects CRC progression due to resisting cell death, knockdown or overexpression of the SPOP gene was induced in Lovo and HCT116 cells .To confirm the ubiquitination regulation of Gli2 mediated by SPOP, we investigated the transcription level and protein level of Gli2 . The mRN24 Although CRC pathogenesis is also associated with epigenetic alterations such as DNA hypermethylation and dysregulation of micro RNAs,1 epigenetic biomarkers for the diagnosis or prognosis of CRC have not yet been established. In our previous study, we suggested the potential role of SPOP in progression of CRC.13 However, the underlying mechanism remains unknown. In this study, we revealed that the tumor suppressor gene SPOP was inactivated by DNA methylation of its promoter region. We also examined the relationship between hypermethylation of the SPOP promoter and the clinical phenotypes of CRC patients. Intriguingly, the methylated SPOP promoter was associated with poor overall survival, suggesting that it may be a valuable prognostic biomarker in CRC. In particular, bisulfite sequencing is a simple and useful technique for single-base methylation pattern with a highly integrated resolution. The data indicate that DNA methylation at −167 bp of the SPOP gene is a key factor in the regulation of SPOP gene transcription by altering transcription factor RXRA binding affinity. To our knowledge, this is the first report to identify a critical regulatory region within the SPOP promoter.Hypermethylation is a common event in the silencing of tumor suppressor genes in most cancer types.21 Our results suggested a novel function of SPOP, that is, its apoptosis-promoting effect in CRC. The function of resisting cell death is important for the progression of CRC. Intriguingly, SPOP was found to associate with Gli2 and promoted its ubiquitination and degradation in CRC. Knockdown of SPOP increased the expression of Hh/Gli2 pathway-related anti-apoptotic proteins Bcl-2. Moreover, the immunohistochemistry assay result indicated a negative correlation between SPOP and Gli2 levels in CRC tissue microarray, which supports a hypothesis that the ubiquitination regulation of Gli2 is mediated by SPOP.As reported in several previous studies, SPOP is a novel substrate-specific adaptor binding to Cul3 and determines the specificity of ubiquitin-dependent proteolysis. However, as SPOP has a wide spectrum of substrates, it exerts tumor-promoting or tumor-inhibiting effects depending on the specific substrate in different tumors.25 We demonstrated that RXRA is a vital transcriptional regulator of the SPOP gene and the methylation of CpG site within the RXRA binding element interfered with the binding affinity. Treatment with the DNA demethylating agent 5-aza-dC rescued the binding affinity. Moreover, our results suggested that H3K9 me3, which is a transcriptional repression mark was enhanced, while the enrichment of active histone marks acetyl-H3 and H3K4 me3 was reduced. These interactions contributed to gene silencing caused by DNA methylation.DNA methylation may affect gene transcription by two major mechanisms. First, the methylated DNA and methyl-CpG-binding domain (MBD) proteins can recruit histone deacetylases and other chromatin remodeling proteins to the locus, which leads to the change of the chromatin structure. Second, methylated cytosine residue can interfere with the binding between transcription factors and their binding elements in the promoter region.et al.15 analyzed changes in the ubiquitin landscape induced by SPOP mutants (Tyr87Cys or Phel133Leu) in prostate cancer cells and revealed that DEK and TRIM24 were consistently upregulated by SPOP mutants. Here, we generated a common SPOP mutant (Tyr87Cys) by site mutation. Although Gli2 protein levels in SPOP mutant cells were markedly increased compared with SPOP-overexpression cells, mutation in the MATH domain of SPOP lightly attenuated its degradation activity on Gli2 protein. Further studies are needed to investigate the underlying mechanisms.In some other types of cancer with a high frequency of SPOP mutation, such as prostate and endometrial cancers with 8–14% rate of SPOP mutation, little is known about how SPOP mutations influence the stability of its substrates. The 374-residue SPOP protein contains three domains: an N-terminal MATH domain (residue 28–166) that recruits specific substrates, an internal BTB domain (residue 190–297) that binds Cul3 and a C-terminal nuclear localization domain (residue 365–374). To date, most identified mutation sites were located in the MATH domain, which determines the specific substrates, such as Ser14Leu in exon 2, Tyr87Cys in exon 4 and Phel133Leu in exon 5. Thus, these mutations in the MATH domain of SPOP might have influence on its substrate degradation. Indeed, Theurillat 26 However, the hypothetical best detection method for CRC is still missing. Promoter hypermethylation of tumor-related genes can be used as a sensitive marker for CRC early diagnosis, prognosis prediction and therapeutic target.28 Thus, recent developments for CRC screening have focused on the epigenetic basis of cancer development. Our results showed that hypermethylation of SPOP promoter was detected in 31/118 CRC patients and this methylation-associated inactivation of the SPOP gene contributed to the CRC cell anti-apoptosis, suggesting that hypermethylation of SPOP could be a potential marker for CRC diagnosis. Further studies are needed to clarify the possible screening methods combining hemoglobin test and SPOP methylation test for CRC.Easy-to-detect, robust and automatable tumor markers are heavily requested for early detection of CRC. To date, the established noninvasive stool tests, guaiac fecal occult blood tests (gFOBTs) and fecal immunochemical tests (FITs) are considered effective.SPOP gene regulates its transcriptional level by affecting the binding affinity between transcription factor RXRA and its binding element in CRC. In addition, SPOP regulates the ubiquitination and degradation of Gli2 in CRC, and consequently decreased the expression of Hh/Gli2 pathway-related apoptotic proteins. These data imply that the differential methylation pattern of the SPOP promoter in CRC and non-CRC tissues is a potential target for a novel epigenetic therapeutic reagent.In conclusion, our study shows that hypermethylation of a CpG island in the promoter region of the Colorectal cancer tissue was obtained from 118 patients with colorectal cancer who underwent radical resection in the Affiliated Hospital of Nantong Medical University from 2008 to 2010. All the patients were diagnosed pathologically according to the criteria of the American Joint Committee on Cancer. Clinicopathological details are provided in 29 The IRS was calculated by combining the quantity score with the intensity score. The quantity score documented the percentage of immunoreactive cells as 1 (0–25%), 2 (26–50%), 3 (51–75%) and 4 (76–100%). The intensity score documented the intensity of immunostaining as 0 (negative), 1 (weak), 2 (moderate) and 3 (strong). Multiplication of quantity score and intensity score resulted in an IRS ranging from 0 to 12. Under these conditions, samples with IRS 0–4 and IRS 6–12 were classified as low and high expression, respectively.Colorectal cancer tissue microarray (TMA) was constructed by the National Engineering Centre for Biochip, Shanghai, China. The number of CRC patients is 118. A standard protocol was used for immunostaining of the TMAs. Immunoreactivity was scored independently by two pathologists using a semi-quantitative immunoreactivity score (IRS).SssI methylase  was used as fully methylated and fully unmethylated controls in all PCRs, respectively.Genomic DNA was isolated from human colorectal cancer tissue and cells. The EZ DNA Methylation-Gold kit  was used for bisulfite treatment according to the manufacturer's instructions. Primer pairs that specifically amplified either methylated or unmethylated sequences spanning the CpG island were used. SPOP-MSP-M-F: 3′-TTTTTGAGTAGTTGGGATTATAGGC-5′, SPOP-MSP-M-R: 3′-ACACTTTAAAAAACCGAAACGAA-5′, SPOP-MSP-U-F: 3′-TTTTGAGTAGTTGGGATTATAGGTGT-5′, SPOP-MSP-U-R: 3′-TCAACACTTTAAAAAACCAAAACAA-5′. Normal blood genomic DNA untreated and treated with For BSP, bisulfite-treated DNA was amplified by PCR using the primers: SPOP-BSP-F: 5′-TGTTGTGAAATGTTTTTAATTGT-3′, SPOP-BSP-R: 5′-CCTAAATTTCCCTACTCCTCCTC-3′. PCR products were purified with a Wizard SV Gel and PCR Clean-up System . Then PCR products were cloned into a pGEM-T easy vector (Promega). Ten colonies were chosen randomly for plasmid DNA extraction using a Promega Spin Mini kit (Promega) and were then sequenced by an ABI 3130 Genetic Analyzer .β-actin was used as an internal control. All the procedures were performed in triplicate.Total RNA was extracted from the cells using Trizol plus kit . First-strand cDNA synthesis was performed using Promega kit. Synthesized cDNA was used for qRT-PCR analysis using SYBR Premix Ex Taq Kit (Takara) based on the manufacturer's instructions. β-actin , Bcl-2 , cleaved Caspase-3 , cleaved PARP  were used. All immunoblots were performed with triplicate.Total protein was isolated from cells using cell extraction buffer (50 mM Tris–HCl (pH 7.4), 150 mM NaCl, 1% Triton X-100, 0.1% SDS, 1 mM EDTA and protease inhibitor cocktail). Protein concentrations were measured using a BCA Protein Assay kit . Antibodies against SPOP , Gli2 , RXRA , SPOP  was amplified from cDNA of FHC cells. The primers were as follows: F: 5′-AGAGAATTCATGTCAAGGGAAATCTTTGC-3′, R: 5′-AGAGGATCCTTAGGATTGCTTCAGGCGTT-3′. To construct the lentivirus production containing SPOP, the ORF of SPOP was subcloned into the pLenti-CMV-GFP vector .The full-length ORF of The synthesized DNA fragments encoding the short-hairpin RNA (shRNA) used for the knockdown of endogenous SPOP were inserted into the pGPU6/GFP/Neo vector . The sequences of the shRNAs were as follows: shSPOP, 5′-AACGCCTGAAGCAATCCTACTCGAGTAGGATTGCTTCAGGCGTT-3′, NC, 5′-TTCTCCGAACGTGTCACGTCTCGAGACGTGACACGTTCGGAGAA-3′. All plasmids were verified by sequencing.SPOP promoter region  were amplified by PCR and cloned into a pGL3-basic luciferase vector (Promega). A NheI site was added to the 5′-primer, and a XhoI site was added to the 3′-primer. The constructs were verified by sequencing of both DNA strands. The sequences of mutated sites were synthesized by Genescript .Serial deletion fragments of FLAG-Gli2, HA-Ub and pcDNA3.1(-)A-RXRA were purchased from GenePharma.30 using antibodies for RXRA , RNA polymerase II (Abcam), H3Ac , H3K4 me3 (Millipore), H3K9 me3 (Millipore). The primers were as follows: F: 5′-GCCTCTATTTGGTCCTCGCA-3′, R: 5′-CAAGTCAGGGCCCGGGGATT-3′.ChIP was performed as previously described,Reporter vectors were transfected into cells together with phRL-TK vectors. After transfection for 48 h, luciferase activity was measured by the Dual Luciferase Assay system (Promega). The phRL-TK vectors were used for standardization of the data. All the procedures were performed in triplicate.Ubiquitination assays of Gli2 were performed by co-transfection of FLAG-Gli2 and HA-Ub. After immunoprecipitation by FLAG antibodies, ubiquitinated Gli2 was detected by HA antibodies in western blot.For immunoprecipitation assays, supernatant of the cell lysates was incubated with indicated antibodies and protein-G agarose beads at 4 °C overnight. The beads were washed with the buffer , and the coprecipitated protein was detected with subsequent immunoblotting.Lovo-NC and Lovo-shSPOP cells were treated with 0.5 mM CHX  for different times as indicated, followed by SDS-PAGE and western blotting analysis.μg of pcDNA3.1(-)A-RXRA or empty vector as DNA template was used according to the protocol of the TNT T7/SP6 Coupled Reticulocyte Lysate System (Promega). EMSA was performed by using LightShift Chemiluminescent EMSA Kit  following the manufacturer's protocol. The probe sequences are as follows: 5′-CCCGCGCCCTGCGCGCGACCCCCTCCAAGTT-3′. The biotin-labeled oligonucleotide probes were purchased from Sangon .RXRA protein was obtained when 1 The trypsinized cells (2000 cells) were suspended in 2 ml complete medium plus 0.3% agar (Sigma). The agar–cell mixture was plated as a top layer onto a bottom layer comprising 1% complete medium agar mixture. After 2 weeks of incubation, colonies with size >0.1 mm in diameter were counted.Flow cytometry and Terminal-deoxynucleoitidyl Transferase Mediated Nick End Labeling (TUNEL) were used to analyze cell apoptosis. For flow cytometry, the cells were stained with Alexa Fluor 647 Annexin V and 7-AAD  according to the manufacturer's instructions. The cells were then tested on a FACScan flow cytometer (Becton Dickinson). Upper (late apoptotic cells) and lower (early apoptotic cells) right quadrants are counted. TUNEL assays were conducted using the TUNEL apoptotic cell detection kit , according to the manufacturer's instructions.6 cells/100 μl per flank). For bioluminescence imaging, mice were injected with 15 mg/ml luciferin dissolved in PBS at a dose of 10 μl luciferin per gram of body weight. Luciferase activity was visualized using the Xenogen IVIS 2000 small-animal In Vivo Imaging System . Mouse protocols were approved by the Animal Care and Use Committee of Nantong University.Male BALB/c nude mice (5 weeks old) were purchased from Vitalriver . A total of 24 mice were randomly divided into four groups. Lovo-NC, Lovo-shSPOP, HCT116-Vec and HCT116-SPOP cells were injected subcutaneously into the flanks of the mice .


Escherichia coli contains seven rRNA operons, each consisting of the genes for three rRNAs  and one or two tRNA genes in the spacer between 16S and 23S rRNA genes and one or two tRNA genes in the 3’ proximal region. All of these rRNA and tRNA genes are transcribed from two promoters, P1 and P2, into single large precursors that are afterward processed to individual rRNAs and tRNAs by a set of RNases. In the course of Genomic SELEX screening of promoters recognized by RNA polymerase (RNAP) holoenzyme containing RpoD sigma, a strong binding site was identified within 16S rRNA gene in each of all seven rRNA operons. The binding in vitro of RNAP RpoD holoenzyme to an internal promoter, referred to the promoter of riRNA , within each 16S rRNA gene was confirmed by gel shift assay and AFM observation. Using this riRNA promoter within the rrnD operon as a representative, transcription in vitro was detected with use of the purified RpoD holoenzyme, confirming the presence of a constitutive promoter in this region. LacZ reporter assay indicated that this riRNA promoter is functional in vivo. The location of riRNA promoter in vivo as identified using a set of reporter plasmids agrees well with that identified in vitro. Based on transcription profile in vitro and Northern blot analysis in vivo, the majority of transcript initiated from this riRNA promoter was estimated to terminate near the beginning of 23S rRNA gene, indicating that riRNA leads to produce the spacer-coded tRNA. Under starved conditions, transcription of the rRNA operon is markedly repressed to reduce the intracellular level of ribosomes, but the levels of both riRNA and its processed tRNAGlu stayed unaffected, implying that riRNA plays a role in the continued steady-state synthesis of tRNAs from the spacers of rRNA operons. We then propose that the tRNA genes organized within the spacers of rRNA-tRNA composite operons are expressed independent of rRNA synthesis under specific conditions where further synthesis of ribosomes is not needed. Escherichia coli is composed of three species of rRNA  and a total of 55 species of ribosomal protein. rRNA plays fundamental roles as the structural and functional components of the ribosome. The genome of E. coli contains seven rRNA operons, each consisting of 16S, 23S, and 5S rRNA in this order. All seven rRNA operons contain, besides three rRNA genes, specific tRNA genes within the spacer between 16S and 23S rRNA genes and after 23S rRNA gene. The complete genome sequence of E. coli was first determined for two K-12 strains, MG1655 .rrn operons is initiated from tandem promoters (upstream strong P1 and downstream weak P2) in a control region upstream of the 16S rRNA gene , RNAP RpoZ operon [rpoZ (RNAP RpoZ subunit)-spoT (ppGpp pyrophosphatase)-trmH (tRNA methyltransferase)-recG (DNA helicase)] and RNAP RpoD operon [rpsU (S21)-dnaG (primosome)-rpoD (RNAP RpoD subunit)]. These composite operons include the genes encoding components of the core machineries for replication, transcription and translation, thereby making coordination in their intracellular level. The intracellular levels of tRNAs in E. coli are also maintained in coordination with the levels of ribosomes, and the ratio of tRNA to ribosomes decreases with the increasing growth rate , DNA-RNAP complexes were isolated by using anti-RpoC antibody. This SELEX cycle was repeated four rounds and the sequence analysis of RNAP-bound DNA fragments was performed by SELEX-chip (tilling array) method.The genomic SELEX system was used as described previously . For therrnE operon, were synthesized by PCR using a set of primers . PCR products were purified using the QIAquick PCR purification kit (Qiagen). For gel shift assays, each of the probes (10 nM) was incubated at 37°C for 30 min with various amounts of RNAP in the gel-shift buffer . The mixture was directly subjected to 4% PAGE. DNA probes were visualized by staining with ethidium bromide or SYBR Green.Gel mobility shift assay was performed as described previously ,81. A se primers , E. colirrnE operon segment, probe A1 and probe A2  at 37°C. RNA synthesis is initiated by adding 0.25 mM each of ATP, GTP, UTP and 0.05 mM 32P-CTP (3000Ci/mmol). The reactions were stopped and RNA products were isolated by phenol extraction followed by ethanol precipitation. Samples were mixed with 0.01 ml of formaldehyde loading buffer and loaded onto a 6% sequencing gel and analyzed using FLA 9000 Phosphoimager .Linear DNA templates  were ligated into pRS551 for construction of a series of the reporter assay plasmids. The construction of the plasmids was confirmed by DNA sequencing. Each plasmid was transformed into E. coli wild-type K-12 BW25113. Cultures grown overnight in LB medium were diluted 1:1,000 into fresh medium, and cells were grown for 4 h to an optical density at 600 nm (OD600) of 0.4 to 0.5. The activity of LacZ was measured according to the standard Miller method. The measurement was performed three times to obtain average values. Reporter assay was performed in triplicate. The values were indicated using the average of three independent colonies with standard deviation.To measure the activity and regulation of promoters, a LacZ reporter assay was employed by using plasmid vector pRS551 ,81. ProbrrnE operon region-specific reverse primers . RNA purity was checked by electrophoresis on 2% agarose gel in the presence of formaldehyde followed by staining with Ethidium bromide. Northern blot analysis was performed essentially as described previously ,84. FluoEscherichia coli K-12 contains seven copies of the rRNA-tRNA composite operon, each consisting of 16S rRNA-tRNA(s)-23S rRNA-5S rRNA-tRNAs in this order. Using the Genomic SELEX screening, a constitutive promoter was identified within the 16S rRNA gene that was recognized by RNAP RpoD holoenzyme alone Click here for additional data file.S2 Figrrn operons of E. coli K12 W3100 are summarized. The numbers of sequence difference from the consensus sequences among seven rrn operons are shown in each operon of 16S and 23S rRNA genes. Total numbers of sequence differences are 33 (2.14%) for 16S rRNA and 77 (2.65%) for 23S rRNA.The sequence differences of rRNA genes between seven (PDF)Click here for additional data file.S3 FigrrnB, rrnC, rrnD and rrnG) contains tRNAGlu gene inside the spacer between 16S and 23S rRNA genes. The 72-bp long sequence of tRNAGlu gene is identical between gltT (rrnB), gltU (rrnC), gltV (rrnD) and gltW (rrnG). The sequence difference between 16S rRNA and tRNAGlu genes  and between tRNAGlu and 23S rRNA genes are shown. Sequence of the rrnD operon used for preparation of probes in this study are shown in blue while the sequences different from the most conserved sequences are shown in red.Type-A rrn operons ((PDF)Click here for additional data file.S1 Tablein vitro transcription assay (probe-T series), reporter assay (probe-R series) and Northern blot analysis (probe-N series) (for the list see Probes used for gel shift assay (probe-G series), (PDF)Click here for additional data file.S2 TablerrnB, rrnC, rrnD and rrnG operons contain tRNAGlu gene inside the spacer between 16R and 23S rRNA genes. The position on the genome and the length of each gene or spacer are described.The (PDF)Click here for additional data file.


The discovery of a new catalytic domain, the Trigger Loop, revealed that RNA polymerase can actively choose the correct substrates. Also, the intrinsic proofreading activity was found to proceed via a ribozyme-like mechanism, whereby the erroneous nucleoside triphosphate (NTP) helps its own excision. Factor-assisted proofreading was shown to proceed through an exchange of active centres, a unique phenomenon among proteinaceous enzymes. Furthermore, most recent  Current Opinion in Microbiology 2018, 42:13–18Cell regulationThis review comes from a themed issue on Rita Tamayo and Jan-Willem VeeningEdited by Issue and the EditorialFor a complete overview see the Available online 29th September 2017http://dx.doi.org/10.1016/j.mib.2017.08.004http://creativecommons.org/licenses/by/4.0/)..1369-5274/© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (Gene expression relies on the accurate copy of genetic information. The fidelity of RNA synthesis results from the accuracy of correct NTP selection (versus non-complementary NTPs and complementary 2'-deoxy NTPs), the proofreading of misincorporation events, and the efficiency of extension of the misincorporated nucleotide. In this review, we summarize the structural and biochemical determinants of transcription fidelity that have been uncovered in the last decade, and we describe very recent insights on the consequences that stalled misincorporated complexes may have on cellular functions and gene expression.2+) mechanism within a relatively rigid active centre. However, at saturating NTPs concentrations (close to cellular levels), such a ‘motionless’ active site would provide as low as ~10-fold kinetic discrimination against some non-complementary NTPs (though 103 for certain misincorporations), and would not discriminate at all against complementary 2'-deoxy NTPs [i + 1 site (grey in i + 1 site cannot induce productive folding of the TL because of the wrong geometry of base pairing with the template (in case of non-complementary NTPs) or the lack of critical contacts of the NTP's sugar moiety with the TL (in case of complementary deoxy NTPs) [For a long time, the catalysis of phosphodiester bond formation by RNA polymerase (RNAP) was thought to be performed solely via a two metal ion Mg+ mechanioxy NTPs , 3. TL ioxy NTPs , 4. The  grey in  induces  grey in , which, i + 1 site [The affinity discrimination against non-complementary NTPs takes place due to their weaker base pairing with the template, and may increase discrimination by more than an order of magnitude. Furthermore, the TL competes with non-cognate NTPs in the + 1 site . Such ‘a3 to 105 fold, depending on the particular misincorporation [5–107 fold, although this may differentially drop according to the concentrations of NTPs in the cell. It should also be noted that some accessory factor may influence RNAP accuracy, such as, in E. coli, the global transcription regulator DksA, that binds close to the RNAP active centre and slows down the incorporation of erroneous nucleotides [It must be noted that, while the above-mentioned mechanisms are general and conserved, their efficiencies may vary greatly depending on the identity of incoming NTP, the acceptor base in the template DNA as well as surrounding sequences , 6, 7. Fleotides •.Misincorporation does happen occasionally. Because of the absence of Watson-Crick base pairing with the template, the RNA 3' end becomes misaligned in various ways, relatively to the rest of RNAP active centre. This impairs the catalysis of the subsequent NMP addition, though to various extents depending on the mismatched pair at the 3' end of RNA , the incin vitro it was shown to proofread most misincorporation events before the wrong transcript is extended, even in high concentrations of substrates [RNAP active centre is able to hydrolyse the phosphodiester bonds of the transcript •. This rbstrates ••.In vitro, Thermus aquaticus GreA stays bound to the elongation complex, but is inactive during correct synthesis, and substitutes for the TL only upon misincorporation or occasional backtracking [E. coli GreB, however, was shown to dissociate quickly from the elongation complex, reflecting possible different modes of regulation of the activities of different Gre factors [In vitro, GreA proofreads almost all misincorporation events before their extension [Most organisms possess factors that strongly stimulate hydrolysis of the phosphodiester bonds in the transcript and thus proofreading of transcription. In bacteria these are the Gre factors, while archaea and eukaryotes employ homologues of RNA polymerase II factor TFIIS. These accessory factors stabilise the second catalytic metal ion and activate the attacking water molecule , 15. To tracking ••. E. co factors . In vitrxtension ••, but txtension , 20•.in vivo relied for a long time on lacZ reporter genes carrying a nonsense codon in the open reading frame [-5–10-4 [greA mutant of Streptococcus pneumoniae [dksA mutant of E. coli [Saccharomyces cerevisiae gave contradictory results on the role of TFIIS [In bacteria, the study of transcriptional fidelity ng frame , 22, 23.[-5–10-4 , 22, andeumoniae •, and in E. coli , 24. Comof TFIIS , 26, 27.E. coli, a greA mutant strain showed over 100-fold increase in error rate, similarly to a double greA greB mutant, while deletion of greB alone did not have any effect, revealing a major role for GreA in transcription proofreading [greA [A new reporter assay, based on the suppression of a missense mutation in the active site of Cre recombinase, has recently been developed for the detection of G?A (misincorporation of A instead of G) errors , 29. In freading . Overexpng [greA . In yeasSaccharomyces cerevisiae and E. coli, respectively [In recent years, next-generation sequencing technologies have allowed the study of transcription fidelity in greater detail , 31. Nasectively ••, a mucectively ••. A somectively ••, thougectively ••.in vitro observations [E. coli these hotspots are ~8 times more abundant in untranslated regions compared to protein coding sequences, while no difference was observed in S. cerevisiae [The misincorporation pattern showed a strong bias towards G?A misincorporation , 30••, irvations , 6, 7, arvations ••. Interrevisiae ••.in vivo remains challenging, but several reports have linked transcription errors to detrimental cellular phenotypes in eukaryotes [The study of transcription fidelity karyotes , 37, 38.E. coli, deletion of both greA and greB, but not single deletions alone, considerably increased the switching frequency of the lac operon [ack-1 mutation of RNAP also promoted the switching [In bacteria, transcriptional infidelity was shown to be a significant source of molecular noise, which could lead to heritable phenotypic changes via activation of a bistable switch , 40. Bisc operon , 40, andwitching ••.in vitro because of backtracking [in vivo, leading to detrimental consequences such as double strand brakes and genome instability [in vivo, and therefore may be a major source of conflicts with fellow RNAPs and replication complexes [It seems now questionable whether transcription infidelity influences cellular phenotypes via the actual production of erroneous proteins. Misincorporation events cause long-lived pauses tracking , 42. Bactability , 44•. Ittability •. The suomplexes ••. In thin vivo may be the resolution of stalled misincorporated complexes [E. coli strains lacking greA and dksA is reduced when DNA repair is compromised [greA greB dksA grows extremely slowly and with a high degree of filamentation [greA mutant of S. pneumoniae, which does not encode other cleavage factors nor DksA homologues [Consequently, the most relevant role of cleavage factors Gre and TFIIS (and its homologues) omplexes , 20•. Gromplexes , 45. Forpromised •. Also, entation , 47 and Recent biochemical, genetic and next-generation sequencing advances have revised and improved our view of the mechanisms and the roles of transcription fidelity in both bacteria and eukaryotes. However, a number of questions remain unanswered. For example, the exact structural basis for the differences in discrimination against various misincorporation events remains only hypothetical. Also, the involvement of transcription factors such as DksA, or RNA polymerase II subunits such as Rbp9, in transcription accuracy is still unclear. Most interestingly, the mechanisms by which cells resolve the apparently detrimental misincorporated complexes in the absence of proofreading factors remain elusive.• of special interest•• of outstanding interestPapers of particular interest, published within the period of review, have been highlighted as:


The general structure and action of all eukaryotic and archaeal RNA polymerases machinery have an astonishing similarity despite the diversity of core promoter sequences in different species. The goal of our work is to find common characteristics of DNA region that define it as a promoter for the RNA polymerase II (Pol II).The profiles of a large number of physical and structural characteristics, averaged over representative sets of the Pol II minimal core promoters of the evolutionary divergent species from animals, plants and unicellular fungi were analysed. In addition to the characteristics defined at the base-pair steps, we, for the first time, use profiles of the ultrasonic cleavage and DNase I cleavage indexes, informative for internal properties of each complementary strand.Schizosaccharomyces pombe has similar structural organization. Its mechanical and 3D structural characteristics have singular properties at the positions of TATA-box. The minor groove is broadened and conformational motion is decreased in that region. Special characteristics of conformational behavior are revealed in metazoans at the region, which connects the end of TATA-box and the transcription start site (TSS). The intensities of conformational motions in the complementary strands are periodically changed in opposite phases. They are noticeable, best of all, in mammals. Such conformational features are lacking in the core promoters of S. pombe. The profiles of Saccharomyces cerevisiae core promoters significantly differ: their singular region is shifted down thus pointing to the uniqueness of their structural organization. Obtained results may be useful in genetic engineering for artificial modulation of the promoter strength.DNA of the core promoters of metazoans and The online version of this article (doi:10.1186/s12864-016-3292-z) contains supplementary material, which is available to authorized users. In eukaryotes transcription initiation starts when Pol II in complex with transcription factors (TFs) interacts with the core promoter –3. TATA A. thaliana, S. cerevisiae and H. sapiens with oligonucleotides that contain octanucleotides TATAAAAG or TATAAAAA in their center show that eight base-pair TATA-element binds to the concave surface of TBP by bending towards the major groove , taken from DiPro DB or Additional file


The identification of genetic variants affecting gene expression, namely expression quantitative trait loci (eQTLs), has contributed to the understanding of mechanisms underlying human traits and diseases. The majority of these variants map in non-coding regulatory regions of the genome and their identification remains challenging. Here, we use natural genetic variation and CAGE transcriptomes from 154 EBV-transformed lymphoblastoid cell lines, derived from unrelated individuals, to map 5376 and 110 regulatory variants associated with promoter usage (puQTLs) and enhancer activity (eaQTLs), respectively. We characterize five categories of genes associated with puQTLs, distinguishing single from multi-promoter genes. Among multi-promoter genes, we find puQTL effects either specific to a single promoter or to multiple promoters with variable effect orientations. Regulatory variants associated with opposite effects on different mRNA isoforms suggest compensatory mechanisms occurring between alternative promoters. Our analyses identify differential promoter usage and modulation of enhancer activity as molecular mechanisms underlying eQTLs related to regulatory elements. Expression quantitative trait loci (eQTL) are widely studied, yet the mechanisms by which they exert their effects are largely unknown. Here, performing CAGE-seq on 154 lymphoblastoid cell lines, the authors map regulatory variants associated with promoter usage (puQTLs) and enhancer activity (eaQTLs). However, understanding the consequences of these regulatory variants on the human transcriptome remains a major challenge for accurate interpretation of GWAS signals and for the precise identification of causal variants. This issue has been addressed in population studies combining individual genotypes and transcriptome profiles; a design capable of finding associations between SNVs and mRNA levels, namely expression quantitative trait loci (eQTLs)4.For more than a decade, numerous genome-wide association studies (GWAS) have identified thousands of single nucleotide variants (SNVs) associated with human traits and diseases. The contribution of SNVs located within promoter and enhancer elements to disease etiology is well established6. In addition, regulatory variants have been found associated with alternative transcript usage8, including variation in mRNA 5′-end position. Also, given that human genes have, on average, more than four TSSs9 , differential TSS, or promoter usage deserves to be further investigated to better understand eQTL effects.Several observations support the functional implication of eQTLs on gene promoters. First, eQTLs have been recurrently found enriched within promoter regions of their associated genes11. Briefly, enhancers are cis-regulatory regions located remotely from promoters and contribute to the regulation of gene expression by increasing transcription levels and providing information not encoded in proximal promoters, such as the developmental timing or tissue specificity of expression. Enhancers contain binding sites for transcription factors and chromatin looping mediator proteins necessary for them to act on target genes in a distance-independent manner12. Yet, the systematic identification of enhancers’ target genes requires precise description of enhancer–promoter interactions, based on chromatin conformation assays or functional experiments using genome editing. As a consequence, the target genes of most enhancers remain poorly annotated, increasing the difficulty of interpreting regulatory variants located within enhancers, and in understanding their contribution to human disease.Moreover, eQTLs are also enriched in enhancer elements13 transcriptome profiling and test the resulting molecular phenotypes for association with nearby genetic variants to discover cis-QTLs TLs Fig. . We repo6 (±4.3 × 106) reads uniquely mapping to the reference genome  and can be classified as either antisense promoters (n = 271) or putative promoters localized within a gene or in an intergenic region (n = 244)  Fig. . Interes23, Supplementary Fig. p value = 8.99 × 10−5) involved in immune and inflammatory responses, IKZF1  implicated in the regulation of lymphocyte differentiation and BCL11A  downregulated during hematopoietic cell differentiation and thus possibly associated with B-cell malignancies. We also found enrichment for the transcriptional co-activator, EP300 , similarly to the GTEx study6 observation on splicing-QTLs.The general features of puQTLs are similar to eQTLs. They localize close to TSSs Fig. , within 24. We found 1024 puQTLs (out of 4876 unique variants) overlapping linkage disequilibrium (LD) intervals containing GWAS hits . Such significant enrichment has been previously reported for QTLs associated with other molecular phenotypes . To refine the analysis and decipher whether the effect of the puQTLs and GWAS variants are concordant , we applied the regulatory trait concordance (RTC)26 method. Briefly, RTC accounts for local LD structure and regresses out the genetic effect of the GWAS variant from the CAGE peak expression data, to measure if the puQTLs association is still significant. The RTC scores range between 0 and 1, reflecting not concordant or concordant effects of the pair of puQTL and the GWAS hit, respectively. We found 51 puQTLs passing the high-confidence concordance threshold  from group-3. TTC23 is represented in our data set with three CAGE peaks, localized in two promoter regions 1.6 kb apart  and FAM76B (ENSG00000077458.8) genes . The seven DENND2D CAGE peaks are distributed in three distinct promoters within a 4.3 kb region  for total mRNA levels  and TLR1 (ENSG00000174125.3)  associated with enhancer transcriptional activity at the significance threshold of 5% FDR     paired with ARL4C   and an enhancer region localized 50 kb upstream of it  Fig. . An illu.5) Fig. . The rs1p2) Fig. , as an eL4C Fig.  and as a−7) Fig. . These ocit R package)31 for 92 triplets (39 eaQTL-associated enhancers paired with 78 promoters), we tested independently two models that considers enhancer transcriptional activity as the molecular mediator of gene expression and vice versa  the eaQTL variants, (2) the enhancer transcriptional activity, and (3) the paired promoter expression values. Using causal inference testing  underlying enhancer related eQTLs.We described here a collection of 5376 puQTLs and 110 eaQTLs; regulatory variants associated with promoter usage and enhancer activity, respectively. By levering the CAGE technology to quantify these molecular phenotypes, this study highlights how CAGE transcriptome profiling coupled with QTL mapping can help dissect the genetic mechanisms underlying eQTLs and potentially disease-associated variants.32. While the analysis of exon usage with RNA-seq requires increased sequencing depth33, it enables an exon-wise quantification that complements the specific TSS information provided by CAGE. The combination of the two approaches  therefore constitutes an effective strategy to give a broader view of the molecular mechanisms underlying regulatory variants. We leveraged these advantages to discover puQTLs involved in differential promoter usage and by extension to differential transcript isoform production. Our approach identified, among genes of groups-2, -3, and -5  by performing single nucleotide resolution TSS phenotyping in fruit-flies using CAGE34. They describe tssQTLs not affecting transcript levels, in line with our observations.We find extensive overlap between puQTLs mapped from CAGE data and eQTLs mapped from RNA-seq, as a result of the expected high correlation between the mRNA quantification provided by both technologies35 limit the power to identify lncRNA-eQTLs36. Nevertheless, eQTL studies on lncRNAs, even restricted to a few hundred non-coding genes, established a substantial contribution of lncRNA-associated regulatory variants to human phenotypes39. As anticipated by a recent report40, and revealed in our study with the detection of puQTLs associated with 489 lncRNAs and 271 antisense transcripts, the precise genome-wide TSS mapping and the accurate quantification of low-expressed non-coding transcripts are complementary features that CAGE can provide for conducting QTL mapping, compared to RNA-seq. Moreover, our strategy could further contribute to the characterization of potential roles for lncRNAs in human traits, by combining it with the recently produced atlas of putative functional human lncRNA41. This atlas has over 9000 lncRNAs, including about 3000 enhancer-associated lncRNAs.The low expression level and poor annotation of lncRNAs42. Although high-throughput reporter assays have been used to test regulatory consequences of non-coding variants on reporter genes44, the lack of native chromatin context represents the main limitation of these methods that do not investigate epistatic interactions between multiple variants. The approach developed in our study to map eaQTLs constitutes a potential strategy to unravel regulatory mechanisms involving multiple variants within enhancer elements.Evidence supporting the so called “multiple enhancer variant” hypothesis for GWAS traits has been reported for loci carrying multiple regulatory variants within enhancers and cooperatively altering the expression of target genesOverall, this study reveals that differential promoter usage is an important consequence of functional variation in the human genome. Our eaQTL mapping analysis provides the opportunity to dissect mechanisms underlying regulatory variants located within enhancer elements. Finally, our study highlights how CAGE transcriptome profiling coupled with QTL mapping furthers our understanding of eQTL effects and contributes to the effective interpretation of disease-associated variants.n = 86, with the authorization of the ethical committee of the University of Geneva Medical School) or from the GenCord collection 45 were cultured in conventional medium for LCLs  with 15% fetal bovine serum (Gibco), 50 U ml−1 penicillin and 50 µg ml−1 streptomycin (Gibco). While harvesting cells in growing phase (<106 cells ml−1), culture media were systematically tested for mycoplasma infection  prior to proceeding with RNA extraction.EBV-transformed LCLs  and centrifuged for 5 min at 10,000×g (4 °C). Nuclei pellets were washed twice with lysis buffer before resuspension in TRIzol Reagent (Life Technologies). The RNeasy kit (Qiagen) was used according to the manufacturer’s protocol to extract nucleus-enriched RNA fractions. During the RNA purification process, samples were treated with DNase I  following the manufacturer’s recommendations.For each cell line, a nucleus-enriched RNA fraction was isolated from 20 million cells, as detailed in Fort and colleagues47. Briefly, the initial reverse transcription was performed using random primers and in the presence of sorbitol and trehalose. Then, the enrichment of capped RNAs was obtained with initial oxidation of the 5′-cap RNA diol group, resulting in a dialdehyde that was then coupled with long-arm biotin hydrazide before capture of biotinylated RNA/cDNA hybrids on streptavidin-coated magnetic beads. Samples were treated with RNase-I, cleaving single-stranded RNA and discarding cDNA that did not reach the 5′-cap. Finally, RNA/cDNA hybrids were denatured with alkali to recover cap-selected single-stranded cDNAs. Sample multiplexing was achieved by introducing barcode sequences within 5′-linkers that were ligated to the 3′ extremity of first-strand cDNA. The 5′-linkers were also used for priming the second-strand cDNA synthesis and for CAGE tag generation using the EcoP15I restriction enzyme. Following 3′-linker ligation and prior to loading on the sequencing platform, a final CAGE library amplification using nine PCR cycles was performed.CAGE libraries were prepared from 3 µg of RNA, using the reagents and following the protocols published by Takahashi and colleagues48. Reads were of 26–42 bases in length. CAGE tags were mapped to the reference genome hg19/GRCh37 using Delve (V1.0)49 and Burrows-Wheeler Aligner (BWA V0.5.6)50. Two mismatches were allowed for the mapping procedure and only reads with MapQ values over 20, and therefore mapping to single loci of the reference genome, were used in our analyses. Finally, reads mapping to ribosomal RNA were eliminated.CAGE libraries were sequenced on the Illumina HiSeq 2500 platform with a read length of 50 bases. Sequences with ambiguous base calling were discarded, samples reads split by barcodes and artefactual linker/adapter sequences removed using TagDust (v2.2)6 mapped CAGE tags were discarded, in line with sequencing depth recommendations for the CAGE technology47.Of the 164 samples originally sequenced, 8 with less than 5 × 10http://fantom.gsc.riken.jp/5/data/. CAGE tag counts per CAGE peak were normalized for sequencing depth, converting tag counts to tags per million mapped reads (TPM) and, similarly to the FANTOM promoter atlas9, TPM values were further normalized between samples using the relative logarithmic expression (RLE) normalization procedure from edgeR51. We applied a minimum expression threshold on the mean expression over all individuals included in the study of 0.5 RLE-TPM 52 and IMPUTE2 (V2.3.2)53, yielding 9.1 × 106 SNVs. The genotyping data for the CEU individuals included in our study were retrieved from the whole-genome sequencing analyses performed by the 1000 Genomes Project Consortium14. We combined these two data sets and filtered for an information score above 0.5 and a minimum alternative allele counts of 10. We were left with genotype data at 7,508,202 autosomal variants for the 154 individuals.GenCord individuals were genotyped with the Illumina 2.5M Omni chip in a previous studyQTLtools package V1.1)16. Of the 156 samples passing the sequencing depth threshold, no amplification bias was detected and samples from two individuals, with suspicion of cross-sample contamination, were removed from the study  methods (QTLtools (V1.1)17, with the following sets of covariates: for the puQTLs, the first 3 PCs were derived from genotype data and the first 20 PCs were derived from promoter-normalized expression values. We controlled for stratification due to sample collections 54. We finally extracted puQTL or eaQTL with q value <0.05, which corresponds to a 5% FDR.We delimited the set of variants to be tested per molecular phenotype by using TADs, as defined by Hi-C data on LCLsQTLtools fenrich module (V1.1)17 tests if a set of QTLs fall within functional annotations more often than is expected by chance. We used this module with annotations retrieved from the UCSC Table Browser on September 2016. These data are available for the GM12878 LCL, including 75 ChIP-seq experiments for four histone marks  and 71 transcription factors (ENCODE uniform peaks)23 as well as DNase I hypersensitivity (ENCODE/University of Washington). 1000 permutations were performed on the functional landscape, here all promoters detected , to obtain expected overlap values.The 24 or near to a GWAS variant (±500 kb) with a R-squared greater than 0.5. To estimate the expected overlap by chance of puQTLs and GWAS hits, we preformed 1000 permutations using random variants with allelic frequencies and distances to TSSs that are similar to those of puQTLs.To assess how many puQTLs are overlapping GWAS variants, we selected puQTLs either matching the variants reported in the GWAS catalogQTLtools rtc module (V1.1)17 using the GWAS catalog24 (accessed in May 2017).The RTC analysis was performed using the p value for each CAGE peak of corresponding puQTL-associated genes were calculated, and puQTL-promoter associations with a p value <0.05 were considered. We grouped genes associated with puQTLs having significant concordant regulatory effects  into group-4 and group-5 based on the effect size ratio (ER = |max(ß)|/|min(ß)|), grouping genes with different effects on CAGE peaks when ER > 2.We initially grouped puQTL-associated genes with either a single CAGE peak or with several CAGE peaks when less than 200 nt apart. The other multi-promoter genes were categorized based on the effect of puQTLs on their different CAGE peaks. To this end, the effect size and associated 5 17 module with nominal pass, gene expression matrixes and, as set of covariates, we used the first 3 PCs derived from genotype data and the first 20 PCs derived from gene-normalized expression values.We retrieved mapped RNA-seq data (BAM files) for 154 Central European individuals20 transcripts reference set and the ChromHMM21 segmentation based on ENCODE ChIP-seq histone marks from the LCL GM12878. Our hierarchical annotation procedure has four steps. First, FANTOM CAGE peaks within 500 nt upstream of annotated TSSs or residing within a 5′-UTR first exon or a 5′-UTR first intron were annotated as “Annotated genes”. The other CAGE peaks were annotated as “Not annotated transcript” and further categorized in “enhancer,” “promoter,” or “other” based on epigenetic features. Finally, the “promoter” was subdivided into “Anti-sense promoter” and “Putative promoter” based on genomic localization.The CAGE peaks retrieved from the FANTOM promoter atlas were annotated using the GENCODE-V19 where they detected enhancers based on balanced bidirectional transcriptional hallmarks22. First, 63,991 autosomal enhancer regions were retrieved in May 2016 from the FANTOM atlas (http://fantom.gsc.riken.jp/5/data/). Enhancer elements characterized with bidirectional transcription patterns in our samples were selected. To this end, we first produced CAGE tag clusters using the Paraclu method (V9)55, with CAGE tag 5′ genomic coordinates as input and (i) a minimum of five tags per cluster, (ii) a (maximum density)/(baseline density) ≥ 2 and (iii) a maximal cluster length of 200 nt. To select enhancer regions with a bidirectional transcriptional pattern, we required the overlap of two CAGE tag clusters on opposite DNA strands within a 400 nt window from the enhancer region center.Enhancer regions transcriptionally active in our cohort of LCLs were selected with a procedure similar to the approach detailed in the FANTOM enhancer atlas9, TPM values were further normalized between samples using the RLE normalization procedure from edgeR51. We then filtered enhancer regions to have non-promoter-like directionality pattern, requiring |D| < 0.8. Finally, we summed the twice normalized expression of the 200 nt flanking windows to assign a single expression value to each enhancer, and discarded enhancers with null expression in more than 50 individuals. We built an expression matrix for 3558 enhancers for 154 individuals.We then calculated normalized expression for both flanking 200 nt windows (F and R) to determine, for each enhancer region, a directionality score, D = (F−R)/(F + R). Counts of CAGE tags per F and R windows were normalized for sequencing depth, converting tag counts to tags per million mapped reads (TPM) and, similarly to FANTOMwww.ebi.ac.uk/arrayexpress) under accession number E-MTAB-5835. Derived data supporting the findings of this study are available from the corresponding author on request.The sequencing CAGE data generated in this study are available in the ArrayExpress database at EMBL-EBI (Peer Review FileSupplementary Information