In contrast, pathological gene expression in terminal heart failure is accompanied by changes in active histone marks without major alterations in CpG methylation and repressive chromatin marks. Notably, cis-regulatory regions in cardiac myocytes are significantly enriched for cardiovascular disease-associated variants. This study uncovers distinct layers of epigenetic regulation not only during prenatal development and postnatal maturation but also in diseased human cardiac myocytes.Epigenetic mechanisms and transcription factor networks essential for differentiation of cardiac myocytes have been uncovered. However, reshaping of the epigenome of these terminally differentiated cells during fetal development, postnatal maturation, and in disease remains unknown. Here, we investigate the dynamics of the cardiac myocyte epigenome during development and in chronic heart failure. We find that prenatal development and postnatal maturation are characterized by a cooperation of active CpG methylation and histone marks at  How the cardiac myocyte epigenome is rearranged during development, postnatal maturation and disease is not well understood. Here, the authors investigate the human cardiac myocyte epigenome during development and chronic heart failure and identify distinct epigenetic programs regulating these processes. The general principles of cardiac development have been studied in great detail on a morphological and molecular basis. These studies have identified key signaling events and transcription factor networks that are involved in the specification and differentiation of cardiac myocytes (CMs)6. Many of these transitions involve changes of gene expression. This in turn is regulated by epigenetic processes including CpG methylation (mCpG) and histone modifications. However, the detailed epigenetic processes involved in maturation from fetal to adult CMs and in cardiac disease leading to terminal heart failure have not been fully uncovered, yet.The heart is the first organ to develop during embryogenesis8. Owing to technical restrictions, these studies were performed in heart tissue and therefore the affected cell type(s) could not be identified. Epigenetic mechanisms are highly cell-type-specific requiring cell separation techniques to determine epigenomic features in a specific cell type, especially when keeping in mind that the cellular composition of the human heart is highly dynamic. Previous studies in mice have used enzymatic dissociation by the Langendorff technique10 or additional purification of dissociated cardiac cells by flow cytometry12 to study cell-type-specific features of the CM transcriptome and epigenome, respectively. Purification of CM nuclei by fluorescence-assisted sorting has led to the identification of cell-type-specific mCpG and histone modification signatures in CMs during mouse heart development and maturation11. Based on this method14, we used a nuclear staining strategy to isolate CM nuclei from intact prenatal and postnatal human heart tissue and subjected these nuclei to comprehensive analysis of the epigenome during prenatal development, postnatal maturation, and in heart failure.Epigenetic studies in human hearts have identified altered mCpG in chronic heart failureHere we describe the human CM epigenome during prenatal development and postnatal maturation of the heart from infant to adult age and in terminal failure. We find that during normal lifespan of CMs gene regulation is mainly orchestrated by dynamic mCpG and canonical histone marks at distal regulatory and genic regions. In contrast to previous findings in heart tissue, expression of the pathological gene program in heart failure was not accompanied by changes in the CM DNA methylome but by active histone marks. In addition, our study provides a functional map of the non-coding genome of human CMs throughout life. Linking this functional annotation with known genetic polymorphisms revealed the presence of cardiovascular disease-associated polymorphisms in active CM enhancers.15. These markers are highly specific for postnatal and fetal CMs, respectively15. In addition, we identified and validated phospholamban (PLN) as a specific marker for prenatal and postnatal CM nuclei. In the heart, only CMs show high abundance and strong nuclear localization of PLN17. We confirmed the validity of nuclear PLN using dual labeling of CM nuclei with PCM1 by flow cytometry  tissue was analyzed at three stages  and at one disease condition of end-stage heart failure  from LV tissue Fig. . PurifieTNNT2) and troponin I1  gene Further segmentation of mCpG data resulted in UMRs and LMRs, which covered 3% of the genome each  Fig. . This afWe separately analyzed gUMR mCpG during prenatal development and postnatal maturation  in adult failing vs. non-failing CMs  and natriuretic peptides A and B , we identified concordant changes in active histone marks  and gene expression in failing CMs . For other histone modifications as well as for mCpG in gUMRs 28. To test whether genetic variations map to LMRs in CMs, we analyzed SNPs linked with different disease traits. SNPs associated with cardiac arrhythmia or coronary heart disease were most significantly enriched in CM LMRs 33.Notably, the negative correlation between mCpG and gene expression was not observed for a large group of early developmental genes that were marked with H3K27me3. These genes showed low mCpG despite very low transcriptional activity. Intriguingly, low mCpG was not restricted to genic regions for these genes but extended into surrounding genomic regions, which have previously been termed mCpG valleyscis-regulatory properties35. In myocyte nuclei, we identified >100,000 LMRs. The high degree of cell-type specificity of cis-regulatory domains with low mCpG is underlined by the strong enrichment of cardiac transcription factor motifs within these regions. Notably, motifs of different transcription factors were clustered within most cis-regulatory regions. The most prevalent combinations include T-box, GATA, or MEF2 motifs. Co-occupancy of these and other cardiac transcription factors has been described previously using a murine atrial cardiac muscle cell line36 and in vitro differentiated mouse CMs4. Alternatively, enhancers can be identified based on chromatin marks. Chromatin mark-based prediction using ChromHMM37 identified 216 Mb with strong enhancer signature in CMs. Cis-regulatory regions like LMRs represent either active enhancers or silent as well as repressive elements. We found a large overlap between cis-regulatory regions identified as LMRs and enhancers predicted by chromatin marks.Recent studies show that regions with low mCpG (LMR) have cis-regulatory regions38. Establishment of LMRs has been shown to depend on transcription factor occupancy39. LMRs show lowest mCpG values at accessible sites as revealed by parallel analysis of mCpG and accessibility in single cells40. Cis-regulatory sites marked by low mCpG can act as enhancers or silencers  in mouse and human prenatal and postnatal heartscis-regulatory regions identified as LMRs showed differential mCpG. Concordant gene expression and gUMR mCpG changes of adjacent genes suggest the functional relevance of these cis-regulatory sites. Affected genes were implicated in development and maturation of CMs highlighting the biological relevance of these observations. Regions gaining mCpG showed significant enrichment of motifs related to the GATA, T-box, and TEAD families. A similar combination of motifs has previously been found in murine HL-1 cells36. In contrast, loss of mCpG affected regions containing MEF2 and CTF/NF1 motifs. mCpG changes were associated with concordant changes in histone modifications. Notably, regions gaining mCpG during development already showed low levels of enhancer marks at the fetal stage. This may indicate that changes at the chromatin level precede altered mCpG. These differences in kinetics of histone modifications and mCpG have previously been observed during mouse organ development and were termed “epigenetic memory”42. In addition, regions losing mCpG were pre-marked with 5hmC, indicating active CpG demethylation43. These results clearly show that developmental mCpG of cis-regulatory sites is actively modified after lineage decision and orchestrates with dynamic modification of histones and gene expression.During prenatal development and postnatal maturation, >18% of CM 44 analyzing prenatal and postnatal mCpG dynamics in mouse organs, including heart, reports that continuous loss of mCpG at enhancer regions dominates prenatal development, while gain of mCpG is characteristic for postnatal stages. One reason for this discrepancy could be the different developmental stage of mice and men at birth. Another reason could be a postnatal change in cellular composition. In case of the heart, fetal and infantile hearts predominantly consist of CMs. After birth, CMs loose cell cycle activity. In contrast, endothelial and mesenchymal cell proliferation augments after birth14. This results in an approximately 50% lower proportion of CM nuclei in adult as compared to prenatal mouse11 and human hearts , multiple K+ channels , and voltage-gated Na+ channels 53. Further studies are needed to directly link enhancers containing genetic variants with CM genes.Genome-wide association studies for multiple diseases have identified a plethora of disease-associated variants in the non-coding genomeThe present study identifies mCpG and histone modifications as connected or separate layers of epigenetic regulation during human CM development and disease, respectively. This detailed insight into the CM epigenome in intact human hearts will be important for several areas of research. First, this CM epigenome may serve as a roadmap for further studies in embryonic stem cells or induced pluripotent stem cells to generate mature CMs in vitro for cell therapy of heart disease and for direct reprogramming of cells into CM in vivo. Second, our epigenetic data enable functional annotation of non-coding regions of the genome in CMs. This will be important to unravel the genetics of cardiac disease in non-coding regions of the genome. Further epigenetic studies of other cardiac cell types will help to better understand the contribution of individual cell types to cardiac disease. Third, this CM epigenome provides comprehensive insight into the molecular marks that are associated with physiological and pathological gene expression in CMs. Future studies mapping the three-dimensional architecture, protein complexes, and non-coding RNAs will help to develop new strategies for treatment of heart disease.LV biopsies from male hearts were used for CM nuclei isolation. These investigations were approved by the ethics committees of the Mount Sinai School of Medicine  and the Universities of Freiburg, Jena and Munich (Germany)  and resuspended in 1 ml PBS containing 1 mM ethylenediaminetetraacetic acid (EDTA). Nuclei were filtered  and incubated with Draq7  for 10 min. Nuclei were analyzed  and sorted by flow cytometry .All steps were performed at 4 °C to ensure integrity of chromatin and RNA. For nuclear RNA isolation RNAsin  was added to all buffers. Isolated nuclei were stained in 500 µl staining buffer  containing 1 % bovine serum albumin (BSA), 22.5 mg/ml glycine, 0.1% Tween 20) using anti-PCM1  and anti-PLN antibodies  for 30 min. For isotype control stainings, we used primary antibodies lacking target specificity . Subsequently, the corresponding Alexa488- and Alexa568-labeled secondary antibodies  were added. After 30 min of incubation, nuclei were pelleted by centrifugation  for 6 h at 37 °C with gentle shaking. After incubation, cells were centrifuged  and the supernatant was removed. BSA 1 mg/ml in PBS-Ca2+/Mg2 solution was added to the cellular pellet and was pipetted gently to dissociate the cells. After dissociation, cells were filtered and used for analysis. Cells were stained in ice for 1 h at a concentration of 2.5 × 106 cells/ml with anti-SIRPA-IgG-phycoerythrin-Cy7  and anti-CD90-allophycocyanin . For FACS, the cells were sorted at a concentration of 106 cells/ml in PBS-Ca2+/Mg2 with 10% fetal bovine serum using an Aria II cell sorter (BD Biosciences). To prevent cell death due to pressure and sheer stress, all sorts were performed with a 100-μm nozzle.Human fetal heart tissue was cut into small pieces and incubated in collagenase type II  in Hanks solution , the chromatin containing supernatant was used for ChIP experiments. The amount of chromatin (measured as DNA) was quantified using a Qubit 2.0 fluorometer (Life Technologies) and the corresponding Qubit dsDNA HS Assay Kit (Life Technologies). In all, 100–200 ng of isolated chromatin was used for ChIP experiments. Immunoprecipitation, reversal of cross-linking, and DNA purification were performed using the ChIP-IT High Sensitivity Kit (Active Motif) following the manufacturer’s manual and the antibodies listed in Supplementary Table Isolated CM nuclei were fixed with paraformaldehyde  for 2 min at room temperature. Fixation was quenched by adding glycine to a final concentration of 0.125 M. Nuclei were washed twice with washing buffer  supplemented with protease inhibitors . After addition of sodium dodecyl sulfate to a final concentration of 1%, nuclei were incubated for 10 min. Chromatin was sonicated using a Bioruptor (Diagenode) with 30 cycles . After centrifugation (13000 × 12. All libraries were sequenced on Illumina sequencers in paired-end mode.Nuclear RNA was extracted from sorted nuclei using either the RNeasy Micro Kit or the AllPrep DNA/RNA Micro Kit (Qiagen) including on-column DNAse digestion. RNA was reverse transcribed and amplified with the Nugen Ovation RNA-seq System V2 (Nugen). The resulting cDNA was fragmented . Sequencing libraries were constructed from 100 ng fragmented cDNA using the NEBNext Ultra DNA Library Prep Kit (NEB). The necessary PCR cycles were determined by qPCR after addition of Eva Green (Biotium). RNA-seq libraries for direct comparison of PLN- and PCM1-positive nuclei were prepared using the NEBNext Ultra RNA Library Prep Kit for Illumina (NEB) in combination with the RiboMinus Eukaryote System v2 (Life Technologies)54 was chosen. Libraries were sequenced on Illumina sequencers in paired- or single-end mode.WGBS-seq libraries were constructed from 250 ng DNA using the Ovation Ultralow Methyl-Seq Library System (Nugen) or from 0.5–1.5 µg DNA using the NEXTflex Bisulfite-Seq Kit (Bioo). For small DNA amounts between 5 and 30 ng from sorted fetal CMs, a tagmentation-based protocol (T-WGBS)DNA was extracted from sorted nuclei using the AllPrep DNA/RNA Kit (Qiagen). DNA was sheared in a volume of 100 µl at low intensity for 30 cycles (30 s on followed by 90 s off) using a Bioruptor (Diagenode). Sheared DNA was processed using the hydroxymethyl Collector–Seq Kit (Active Motif) according to the manufacturer’s instructions. The resulting DNA was used for library generation using the NEBNext Ultra DNA Library Prep Kit (NEB). Libraries were sequenced on Illumina sequencers in paired-end mode.55 was used. Bonferroni step-down correction was performed for multiple testing-controlled p-values. The GO term connectivity threshold was adjusted to 0.3  and only pathways with a p-value ≤ 0.01 were considered as significant. The resulting terms were functionally grouped and illustrated, whereby the most significant term in each group was selected as leading term.To identify enriched GO terms in the categories “biological process”, “cellular component” and “molecular function” ClueGO56. Quality and adapter trimming of sequencing reads was performed prior to mapping to remove low quality reads and adapter contaminations. RNA-seq data were mapped to the human genome (hg19) using Tophat257. ChIP-seq and 5hmC-seq data were mapped using Bowtie258 and WGBS-seq using Bismark59. PCR duplicates were removed using SAMtools60. Quality and adapter trimming of sequencing reads was performed prior to mapping to remove low quality reads and adapter contaminations. RNA-seq data were mapped to the human genome (hg19) using Tophat257. PCR duplicates were removed using SAMtools60.All bioinformatics tools used to analyze sequencing data were integrated in the Galaxy platform61, we used intronic and exonic regions of coding genes for gene expression analysis. Mapped RNA fragments were further processed using Cuffnorm62 to calculate FPKM values as an estimate of transcript expression. For differential gene expression, HTSeq count63 and Deseq264 were used. A q-value of <0.05 was considered as significant. Genes with expression values of <3 FPKM in all the compared groups were excluded from the differential gene expression analysis. We used Cufflinks62 to determine the main transcription start (TSS) and stop site (TES) of each coding gene using merged RNA-seq data from all assessed stages. To exclude biases, we analyzed alternative usage of TSS and TES sites using Cuffdiff62 and did not observe significant differences between fetal, infant, adult non-failing, and adult failing CMs.Owing to the high number of intronic reads arising from unspliced RNAs in nuclear RNA-seq data65. For quantification of H3K27ac, H3K9ac and H3K4me3 promoters (TSS ± 1 kb) and for H3K36me3, H3K9me3, H3K27me3, and H3K4me1 genic regions (TSS to TES) were used.For a comparison of differential gene expression with results obtained from ChIP-seq and WGBS, genes differentially regulated in failing as compared to non-failing hearts were ranked according to fold changes (log2(F/NF)). This ranking was applied to data obtained from ChIP-seq and WGBS. WGBS data were used to calculate differential mCpG of gUMRs and genic regions of the ranked genes (F–NF). The ratio of ChIP-seq data (F/NF) was calculated using bamCompare65. For comparison of different data sets, read counts were normalized to the geometric mean of the genome-wide read count. This normalization was performed for the different analyzed features . To combine results of different histone modifications, the normalized ratios of the individual marks were averaged on a logarithmic scale. For visualization, we merged the results of biological replicates.Sequencing data were processed using DeepTools37 was used to learn a 10-state model predicting chromatin states based on ChIP-seq data from non-failing adult CMs. For the annotation of chromatin states in CMs, we used ChIP-seq data generated for H3K27ac, H3K9ac, H3K36me3, H3K4me1, H3K4me3, H3K9me3, and H3K27me3. For comparison of chromatin states in adult CMs and heart tissues, we used the same ChIP-seq data from adult non-failing CMs except for H3K9ac, since these data are not available from Encode or Roadmap20. The derived states were classified as enhancer , promoter (H3K4me3), transcribed chromatin (H3K36me3), bivalent chromatin , heterochromatin (H3K9me3), polycomb repressed chromatin (H3K27me3), and silent undefined chromatin (no enriched chromatin mark).ChromHMM22. In vivo confirmed VISTA heart enhancers were also obtained from Dickel et al.22. For comparison of CM data with published heart data sets, we reanalyzed WGBS , and ChIP-seq data  generated by the Encode and Roadmap consortia20.Cardiac enhancers (predicted VISTA heart enhancers) derived from >35 human and mouse H3K27ac and p300 data sets were obtained from Dickel et al.https://github.com/dpryan79/MethylDackel). For segmentation21, we merged the replicates of the different stages to increase the coverage. In brief, genomic regions with highly disordered mCpG (PMRs) were identified using a 101 CpG sliding window with a step size of 1 CpG. A two-state Hidden-Markov model identified PMRs based on the deviation of methylation level distributions from the typical polarized distribution, which favors high and low methylation. Neighboring PMR segments were fused. After masking of PMRs, we smoothed the mCpG data using a running window of three CpGs to reduce noise. To identify suitable parameters  for the identification of hypomethylated regions, we calculated the false discovery rate (FDR) by comparing the number of identified regions in the original and a randomized methylome. For all methylomes generated in this study, at least 3 CpGs and a mCpG of ≤50% was chosen as parameter for the detection of hypomethylated sites. These parameters ensured in all assessed methylomes an FDR < 5%. We used a cut-off of 30 CpGs to discriminate LMR and UMR 21. This cut-off clearly separated CpG-low regions with methylation levels between 10 and 50% (LMRs) from un-methylated CpG-rich regions (UMRs)21.mCpG data were extracted from mapped reads and data of both strands were merged using MethylDackel  represent genomic regions not overlapping with CpG islands, LMRs, UMRs, and PMRs.Since the segmentation result is influenced by the local sequencing depth and surrounding mCpG variability, we merged the segments of the different stages using MergeBED68 for FMRs, CpG islands, LMRs, gUMRs, and PMRs. Since genic regions downstream of gUMRs were frequently partially methylated too, PMRs overlapping a single gene to at least 75% were removed from differential methylation analysis. For the analysis of differential mCpG, we used the results obtained from the individual biological replicates. A difference of >10% and a p-value ≤ 0.001 was considered as significant. A minimal coverage of one in all compared samples was required to include a CpG in the statistical analysis. Biological replicates with a median CpG coverage of ≤4 were excluded from the statistical analysis.Replicate-based differential methylation analysis (replicate DMR) was performed using Metilene68. For this analysis, we used Encode data from human left ventricle and compared it with the merged data obtained from CM nuclei from non-failing adult hearts. A difference of >10 % and a p-value ≤ 0.001 was considered as significant. DMRs spanning <10 CpGs were rejected.For the identification of DMRs between adult heart tissue and CMs, we used the binary segmentation algorithm implemented in MetileneFor visualization, we merged the results of biological replicates and smoothened the data using a running average of three CpGs.69 to perform a de novo motif analysis using the “size given” option. GREAT27 was used to identify genes in proximity (≤10 kb) to LMRs and to annotate genes within regulatory domains of SNPs. DeepTools65 and HiCExplorer70 were used to display traces and heatmaps and to perform cluster analysis. Annotations and genome files (hg19) were obtained from the UCSC71. Prism 5 (GraphPad) was used for statistical analysis and graphs.We used Homer72. Search terms were: cancer, digestive system disease, immune system disease, metabolic system disease, nervous system disease, and hypertension. In addition, we searched and combined loci retrieved for the search terms congenital heart and heart malformation , myocardial infarction, and coronary heart disease (combined term: coronary heart disease) as well as heart rate, PR interval, QT interval, and cardiac arrhythmia (combined term: cardiac arrhythmia). Duplicate SNPs were removed. SNAP73 was used to retrieve proxy SNPs in linkage disequilibrium 72 with the lead SNPs.Disease-associated SNPs were obtained from the NHGRI-EBI GWAS catalog for different traits on September 30, 2016All sequencing data sets reported in this manuscript are deposited in the Short Read Archive at the National Center for Biotechnology Information under the BioProject ID PRJNA353755. Additional data that support the findings of this study are available from the corresponding author upon request.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4


ELOVL6 can provide new insights into improving the composition of beneficial fats in cattle and expanding the knowledge of transcriptional regulation mechanism among domestic animals. In the current study, we found that bovine ELOVL6 expressed ubiquitously. Dual-luciferase reporter assay identified that the core promoter region (-130/-41 bp) was located in the second CpG island. In addition, the deletion mutation of binding sites demonstrated that sterol regulatory element binding transcription factor 1 (SREBF1) and specific protein 1 (SP1) both were able to stimulate bovine ELOVL6 promoter activity independently, while resulting the similar effect. To confirm these findings, further RNA interference assays were executed in bovine mammary epithelial cells (BMECs). In summary, these data suggest that bovine ELOVL6 expressed ubiquitously and is activated by SREBF1 and SP1, via two binding sites present in the ELOVL6 promoter region between -130 bp to -41bp.In mammals, very long chain fatty acids (VLCFAs) perform pleiotropic roles in a wide range of biological processes, such as cell membrane formation, cell signal transduction, and endocrine regulation. Beef and milk are abundant of palmitic acid which can be further elongated into stearic acid for synthesizing VLCFAs. Elongase of very long chain fatty acid 6 (ELOVL6) is a rate-limiting enzyme for converting palmitic acid to stearic acid. Consequently, investigating the tissue expression patterns and transcriptional regulation of bovine  In vertebrates, increasing evidence has shown that very long chain fatty acids (VLCFAs) which are the fatty acids of 20 carbon or more in length perform a vital role in maintaining global metabolic homeostasis and normal physiological function . Among VIn mammals, fatty acids with a chain length of up to 16 carbons are synthesized by fatty acid synthase(FASN), as well as are gained from diet. These short chain fatty acids (SCFAs) are further elongated and desaturated into VLCFAs . The eloELOVL6 was ubiquitously expressed, especially in tissues with high lipid content such as brown/white adipose tissue, liver and brain in mouse [ELOVL6 demonstrated that the deficiency of ELOVL6 in mouse protected against metabolic diseases such as insulin resistance [ELOVL6 induced cancer diseases, included breast cancer [ELOVL6 also regulates thermogenic capacity in brown adipose tissue [ELOVL6.C18 fatty acids has been proved to be the precursor for the synthesis of VLCFAs . ELOVL6 in mouse . The funsistance , nonalcosistance . Meanwhit cancer , cystic t cancer  and lungt cancer . ELOVL6 e tissue . ConsequELOVL6. In general, sequence-specific transcription factors have been grouped into two categories: proximal promoter factors, and enhancer binding factors. Direct evidence indicated that hepatic expression of ELOVL6 in mouse was regulated by SREBF1 via SREBF1 binding sites (SRE) present in the ELOVL6 promoter. Further ChIP assay showed that the proximal SRE-1 binding site on the promoter of ELOVL6 had higher affinity to SREBF1 than the distal one [ELOVL6 promoter activity in HepG2 cell lines [Recent attempts utilizing advanced molecular biological techniques have provided some novel insights of the transcriptional regulation of stal one . Meanwhill lines . It has ll lines .The SREBll lines . Additioll lines .ELOVL6 in bovine is conserved, we determined the tissue expression profile of bovine ELOVL6 in nine different tissues by quantitative real-time PCR (qPCR). In order to identify and narrow down the core promoter region of bovine ELOVL6, we constructed six dual-luciferase reporter plasmids harboring various 5’ flanking truncations and detected their promoter activities by dual-luciferase reporter assays. Seven transcription factor binding sites (TFBS) were predicated in the core promoter region of bovine ELOVL6. Subsequently, the dominant transcription factors were verified by site-directed deletion mutation and RNA interference assays. Our results suggest that bovine ELOVL6 expressed ubiquitously and is activated by SREBF1 and SP1, via two binding sites present in the ELOVL6 promoter region between -130 bp to -41bp.To address the question of whether the transcriptional pattern of All animal procedures were carried out in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals  and were approved by the Institutional Animal Care and Use Committee at the Northwest A&F University (Protocol NWAFAC1117). Cattles were raised under free food intake and humanely slaughtered in the Shannxi Kingbull Animal Husbandry Company, Ltd .ELOVL6 in spleen tissue. Total RNA was extracted using Trizol reagent  and quantified by the Nanodrop 2000 spectrophotometry . cDNA was subsequently synthesized by using the All-in-one RT MasterMix . The quantitative real-time PCR (qPCR) reactions were carried out in a CFX96 Real-Time PCR Detection System  employing the SYBR® Premix Ex Taq II . The Cq values were normalized to reference gene (GAPDH) run on the same plate. The primers which amplified the transcripts were listed in The tissues were collected from three 2 year-old male Qinchuan cattles, including heart, liver, spleen, lung, kidney, intestine, stomach, skeletal muscle and abdominal fat. The relative quantification was carried out against the quantification cycle (Cq) value of ELOVL6 promoter, multiple dual-luciferase reporter plasmids containing unidirectional truncations (from 5’ to 3’) were constructed. The primers were listed in MluI and XhoI, the fragments were cloned into pGL3-basic vector, respectively. The resulting constructs were designated as pGL3-F2, pGL3-F3, pGL3-F4, pGL3-F5, pGL3-F6 and pGL3-F7. Deletion mutation constructs were subsequently generated by overlapping extension methods using pGL3-F5 as template. All constructs were sequenced in both directions . The primers were listed in The 5’-flanking region of bovine ELOVL6 promoter was amplified and was identical to the GenBank database (Accession no. AC_000163). In order to determine the core region of bovine http://rnaidesigner.thermofisher.com/rnaiexpress/. SREBF1-siRNA sequences were as following, sence: 5'- UCUUCCAUCAAUGACAAGATT-3', anti-sence: 5'- UCUUGUCAUUGAUGGAAGATT-3'. SP1-siRNA sequences were as following, sence: 5'-GCCAAUAGCUACUCAACAATT-3', anti-sence: 5'-UUGUUGAGUAGCUAUUGGCTT-3'. Control-siRNA sequences were as following, sence: 5'- UUCUCCGAACGUGUCACGUTT-3', anti-sence: 5'- ACGUGACACGUUCGGAGAATT-3'. siRNAs were then transfected into BMECs following the manual of Lipofectamine™ 2000 . The expressions were measured by qPCR described above.All siRNAs were designed and synthesized according to the online prediction program at 5 cells per well in 48-well plates and incubated for 12 hours until they reached 80–90% confluent. Plasmids described above were transfected using Lipofectamine 2000  according to the manufacturer’s instructions. After 48 hours transfection, cells were washed with 1× PBS and lysed with 1× passive lysis buffer for 15 min. Dual-luciferase reporter assay was carried out by using Varioskan Flash instrument . The level of firefly luciferase activity was normalized to renilla luciferase activity. In RNA interference assays, BMECs were cultured in 1640 medium  which was supplemented with 10% fetal bovine serum (FBS)  in 5% CO2 and 100% humidity at 37℃ and passaged using standard cell culture techniques. Before transfection, cells were plated at a density of 7.5×105 cells per well in 48-well plates and incubated for 12 hours until they reached 70–80% confluent. siRNAs (20 umol/L) were transfected as 5 pmol mixed with 0.25 ul Lipofectamine 2000 per well. After 24 hours and 48 hours, samples of siRNA treatment were collected.3T3-L1  and 293A  cell lines were maintained in Dulbecco’s modified eagle’s medium (DMEM)  which was supplemented with 10% fetal bovine serum (FBS)  in 5% CO2 and 100% humidity at 37℃ and passaged using standard cell culture techniques. Before transfection, cells were plated at a density of 1.4×10ELOVL6 promoter was analyzed using a promoter prediction program at http://www.cbs.dtu.dk/services/Promoter/. The transcription factor binding sites were predicted by using an online prediction server at http://www.biobase-international.com/product/transcription-factor-binding-sites. The methylation CpG island of bovine ELOVL6 promoter was analyzed at http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi. For the phylogenetic analysis, other promoter sequences from different mammalian species were aligned with bovine ELOVL6 promoter by the Clustal X 2.1 program. A phylogenetic tree was constructed by using the MEGA program (version 6.0) with the neighbor-joining method. Bootstrap values were obtained from 1000 repetitions and listed as percentages at the nodes. The core regions of promoter sequences from different mammalian species were aligned and visualized by DNAman software (version 7.0).The bovine -ΔΔCT method. The results of each independent samples were normalized to the reference gene (GAPDH) run on the same plate. A one-way ANOVA test was conducted to determine the significant level. Mean values were compared by the LSD post-test. The results were expressed as mean ± SD, and the p value less than 0.05 was considered statistically significant.SPSS 19.0  software performed all statistical analyses. The relative expression quantification was evaluated by the algorithm of 2ELOVL6 in various tissues of Qin Chuan cattle, we investigated the expression profiles of bovine ELOVL6 in nine different tissues by qPCR. ELOVL6 was expressed ubiquitously on bovine . To understand potential evolutionary processes of bovine ELOVL6 promoter among mammal species, a neighbor-joining phylogenetic tree was constructed by MEGA program (version 6.0). Bos Taurus has the close relationship with Bos mutus and Bos bison among Bovidae family. Mus musculus and homo sapiens display the greatest distance from Bos taurus , we analyzed the cloned promoter fragment consisting 980 bp upstream of the transcription start site (TSS). Interestingly, neither a TATA box nor a CAAT box was identified at the upstream of TSS. However, this region was found to be GC-rich. The online program MethPrimer revealed two predicted CpG islands within the promoter region of the bovine ELOVL6 . ConservELOVL6 . SubsequELOVL6 promoter, we constructed six pGL3 reporter plasmids (designated as F2 to F7), which contained unidirectional truncations (from 5’ to 3’) of bovine ELOVL6 promoter, and then co-transfected with pRL-TK plasmid into 3T3-L1 and 293A cell lines for dual-luciferase reporter assay. In 3T3-L1 cell line, the dual-luciferase reporter assay showed that bovine ELOVL6 promoter activity gave a “down-up-down” pattern along with the piecewise truncation  which was compared with pGL3-F5 construct (-131/+225 bp) may be attributed to the loss of cis elements contained within the deleted DNA region. To confirm the hypothesis, we chose four predicted transcription factor binding sites involved in fatty acids synthesis pathway to investigate their effects on bovine promoter .SREBF1 and SP1 for 24 hours and 48 hours, respectively, both mRNA expression of ELOVL6 were significantly decreased. Relative to the experission in 0 hour, silencing of SREBF1 in 24 hours markedly decreased 65% expression of ELOVL6  [ELOVL6 [ELOVL6 promoter [ELOVL6 would be different within patterns on mouse, human or goat.VLCFAs are fatty acids with greater than 20 carbon atoms, which can be characteristically divided into saturated, monunsaturated and polyunsaturated fatty acids . VLCFAs te (SRE) . Further [ELOVL6 , 14. Thepromoter . ELOVL1,promoter . The trapromoter . We hypoELOVL6 mRNAs are ubiquitously expressed but their ratios vary across different tissues. In the current study, we demonstrated that the expression profile of bovine ELOVL6 slightly differ from that in human. In bovine, the transcript levels of ELOVL6 in intestine was higher than that in lung, whereas opposite observation was found in human as previously described by Ohno [ELOVL6. The small intestine is commonly thought of as a lipid storage organ, however, a recent research clarified a novel function of intestine that enterocytes stored the dietary fat in cytoplasmic lipid droplets (CLDs), when meals and diets containing large amounts of fat were consumed [ELOVL6 to elongate the palmitic acid into stearic acid for the storage in CLDs [ELOVL6 on cow.In general,  by Ohno . Howeverconsumed . In orde in CLDs . Since tELOVL6 promoter located at the second putative CpG island, harboring seven predicted TFBSs. The findings consisted with previous report that the DNA methylation of CpG island influenced the binding between transcription factor and its target DNA [ELOVL6 promoter proved our hypothesis that both SREBF1 and SP1 can activate the transcription of bovine ELOVL6 individually and exhibited equal effects on the promoter activities. For verification of results, we designed RNA interference of SREBF1 and SP1 in BMECs which was in line with previous findings.We have identified that the core region (-131/-41 bp) of bovine rget DNA . Among trget DNA . A recenrget DNA . NF-κB irget DNA . The sitELOVL6 promoter. We speculated that SREBF1 and SP1 synergistically regulated bovine ELOVL6 promoter activity, which should be verified by our further works.Study on murine demonstrated that SREBFs including three isoforms , are established as global lipid synthetic regulators . HoweverS1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.


NODAL/Activin signaling orchestrates key processes during embryonic development via SMAD2. How SMAD2 activates programs of gene expression that are modulated over time however, is not known. Here we delineate the sequence of events that occur from SMAD2 binding to transcriptional activation, and the mechanisms underlying them. NODAL/Activin signaling induces dramatic chromatin landscape changes, and a dynamic transcriptional network regulated by SMAD2, acting via multiple mechanisms. Crucially we have discovered two modes of SMAD2 binding. SMAD2 can bind pre-acetylated nucleosome-depleted sites. However, it also binds to unacetylated, closed chromatin, independently of pioneer factors, where it induces nucleosome displacement and histone acetylation. For a subset of genes, this requires SMARCA4. We find that long term modulation of the transcriptional responses requires continued NODAL/Activin signaling. Thus SMAD2 binding does not linearly equate with transcriptional kinetics, and our data suggest that SMAD2 recruits multiple co-factors during sustained signaling to shape the downstream transcriptional program.DOI:http://dx.doi.org/10.7554/eLife.22474.001 To allow a complex animal to develop from a small bundle of cells, the cells need to be able to communicate with each other to coordinate their activities. Furthermore, this communication needs to continue in adulthood to keep the body in balance and to prevent diseases such as cancer.The cells communicate by releasing signals that influence the behavior of their neighbors by activating proteins called transcription factors. These proteins then change the activity of particular genes in the nucleus by binding to specific places on a structure called chromatin (the structure in which the genes are packaged). One group of signaling molecules is known as the transforming growth factor beta superfamily, which is crucial for embryos to develop correctly. Failure to control these signals can also promote the growth of tumors. However, it is not clear how the detection of these signals at the surface of the cell leads to changes in the activity of genes inside the nucleus.Two transforming growth factor beta signals called Activin and NODAL cause a transcription factor known as SMAD2 to move into the nucleus where it can alter gene activity. Here Coda, Gaarenstroom et al. investigated how SMAD2 transmits the Activin/Nodal signal in mouse cancer cells. The experiments showed that SMAD2 can change the activities of genes in multiple ways. SMAD2 can bind to places in the chromatin that are either easy to access  as well as areas that are difficult to access . As a result, SMAD2 increases the activity of genes that were already active, but also switches on on genes that were previously inactive.Coda, Gaarenstroom et al. also found evidence that SMAD2 remained bound to chromatin after long periods of Activin/NODAL signaling. For some genes, this resulted in high gene activity, but in other cases this decreased the gene’s activity. Therefore, future experiments will investigate which other proteins help SMAD2 to change gene activity at later times.DOI:http://dx.doi.org/10.7554/eLife.22474.002 Patterning of tissues during embryonic development depends on the control of gene expression by extracellular signals. The signaling molecules involved frequently act as morphogens to impart positional information . It is bThis is best studied using TFs that are activated in response to extracellular signaling and the NODAL signaling pathway provides an excellent model system. NODAL is a member of the transforming growth factor β (TGF-β) superfamily which plays crucial roles in early embryonic development. It is required at low levels for maintenance of pluripotency in both early embryos and embryonic stem cells (ESCs), and at higher levels for mesendoderm differentiation and left–right patterning . MoreoveNODAL and the highly-related ligand Activin, which in vitro mimics the functional activities of NODAL, signal via heterotetrameric receptor complexes. These comprise type I and type II receptors, with NODAL additionally requiring the co-receptor TDGF1  . Ligand We previously established that SMAD2–SMAD4 complexes are unable to activate transcription on a naked DNA template, but instead require the context of chromatin , indicatHere we have taken a genome-wide approach to discover how a dynamic transcriptional program is executed in response to acute and prolonged NODAL/Activin-SMAD2 signaling. We have used the murine P19 embryonic teratoma cell line, which express pluripotency and mesendodermal genes in response to NODAL/Activin signaling. Most significantly, in contrast to ESCs, they do not differentiate when treated with the NODAL/Activin type I receptor inhibitor, SB-431542 , making Tdgf1 expression is controlled by NODAL/Activin signaling and TDGF1 is a very unstable protein, it is rapidly degraded when signaling is terminated  as SMAD2 is the predominant receptor-regulated SMAD downstream of NODAL/Activin in these cells . Robust rminated . As a reThe gene expression profiles  demonstrTo facilitate downstream analysis, we classified the target genes into four categories: ‘induced sustained’ (acutely upregulated genes whose transcription persists over time); ‘delayed’ ; ‘transient induced’ (acutely upregulated genes whose transcription subsequently declines); ‘repressed’  . These dIt was evident that, with the exception of the delayed genes, sustained signaling resulted in either repression or dampening of transcription. Even in the ‘induced sustained’ category where transcript levels were higher after 8 hr of Activin signaling compared with 1 hr, the rate of accumulation of transcripts decreased after 1 hr of Activin stimulation. Consistent with the modulation of gene expression profiles over time, we found that transcription of a substantial proportion of the delayed and repressed genes was perturbed by the presence of protein synthesis inhibitors . A subseSmad7 and Hes1 we found that when signaling was terminated after 1 hr their levels fell rapidly to baseline, presumably because of the very short half-life of these mRNAs, whereas in the context of continuous signaling, these genes were repressed in a more gradual fashion  to Activin-regulated genes, SMAD2 ChIP-seq data were integrated with the RNA-seq data, along with Pol II ChIP-seq data. For the latter, we performed ChIP-seq for the largest subunit of Pol II specifically phosphorylated on Ser5 or Ser2 of its C-terminal domain (CTD) to differentiate between initiating and elongating forms of Pol II respectively  Figure . By idenLefty1 and Lefty2  and elongating Pol II (Ser2P) on all genes and they showed the characteristic patterns, peaking at the TSS and TTS respectively  Figure . We noteexpected . Moreoveexpected .10.7554/Taking all these data together we conclude that Activin/NODAL signaling regulates transcription of target genes in response to signaling via de novo recruitment of Pol II, rather than via pause–release.Smad7 and Tbx3 .In contrast, histone H3 acetylation was observed around the majority of SMAD2 peaks, and in many cases the signal increased in response to Activin signaling . As expeLefty1 SBS, which was used as a representative example  were in an open conformation that did not change following 1 hr Activin treatment  and may reflect newly synthesized co-factors recruiting SMAD2 to secondary target genes , or return it to basal levels (transiently induced genes). This mechanism also seems to be true of some of the repressed genes, which are initially transiently activated in response to Activin, prior to their repression. We have not yet provided direct evidence for this secondary repression mechanism, but possible candidates for such repressors are TGIF, SKI, SKIL, ZEB1/2 or EVI-1 that can associate with activated SMAD2/3 to repress transcription . Thus, wDuring the course of this study we have compared the influence of ligand dose versus duration of signaling on the transcriptional response to NODAL/Activin signaling. Our data suggest that although some genes show a dose-dependent response, the major determinant of transcriptional output is signaling duration for the reasons outlined above. These results fit well with the observation that it is duration of NODAL signaling in zebrafish embryos, rather than amplitude, that determines cell fate specification .Tracking chromatin landscape changes over time at genes induced or repressed with different kinetics in response to Activin led us to the striking finding that at some loci SMAD2 binds to inactive chromatin. Its binding results in rapid nucleosome displacement and histone H3 acetylation on either side of the SMAD2-binding site. This subsequently leads to Pol II recruitment and acetylation at the TSS of the target gene with kinetics following the expression pattern of the transcript. The ability of activated SMAD2 to bind inactive chromatin and remodel it was unexpected, as it was previously suggested that SMAD2/3 only binds sites already occupied by master TFs, and that NODAL/Activin signaling does not affect nucleosome positioning at POU5F1-shared binding sites in ESCs . We uncoPitx2 SBS, even though it exhibits some of the same characteristics as the Lefty1 and Lefty2 SBSs (FOXH1-dependent and closed unacetylated chromatin at the SBS). This suggests that the chromatin remodelers involved in SMAD2 binding and nucleosome eviction are gene specific, and that additional factors are involved in their recruitment.Nucleosome remodelers have been implicated in SMAD2-mediated transcription, with the SWI/SNF ATPase SMARCA4 being required for a subset of TGF-β responses in both P19 and human epithelial cells . BPTF isLefty1 enhancer both in the SB-431542 state and after acute and chronic Activin induction. It remains unclear, however, how this modification is established prior to TF binding.Importantly, we found no evidence for a pioneer factor stably bound prior to SMAD2 recruitment at silent chromatin. In this respect these SBSs are analogous to so-called latent enhancers that are bound by stimulus-activated and lineage-determining TFs upon macrophage stimulation , and we SMAD2 binding correlates with an acute induction of H3K27Ac and H3K9Ac either side of the SBS, suggesting that SMAD2 induces acetylation through recruitment of HATs. H3 acetylation is limited to one to two nucleosomes flanking the SMAD2 peak. SMADs have been shown to bind the HATs EP300, PCAF and GCN5 . We haveLefty1, Lefty2, and Pitx2) FOXH1 recruits the active SMAD2-containing complex. FOXH1 is a member of the Forkhead family of TFs and one of the best characterized SMAD2 binding partners  at a final concentration of 20 nM. siRNAs were from Dharmacon  and are listed in P19 cells stably expressing MYC-FOXH1 were generated via transfection with a plasmid encoding MYC-tagged FOXH1  and pSUPTotal RNA was extracted using Trizol (Thermo Fisher Scientific) according to the manufacturer’s instructions. cDNA synthesis and qPCRs were performed as described . Primer TM resin coupled to the peptide according to the manufacturer’s instructions . We validated the antibody in several different ways. We proved that it recognised Myc-FOXH1 in a stably overexpressing P19 cell line by Western blot  according to the manufacturer’s instructions. qPCR was carried out as for gene expression analysis, with a standard curve consisting of 6 points and three-fold dilutions. Values for each IP sample were normalized relative to corresponding input chromatin for the same treatment. Primer sequences and antibodies are listed in Cells were crosslinked using 1% formaldehyde for 10 min at room temperature, followed by lysis in 5 mM HEPES (pH 8), 85 mM KCl, 0.5% NP-40. After centrifugation, nuclei were washed in 5 mM HEPES (pH 8), 85 mM KCl and then sonicated to 100–400 bp using 8–12 30” on-off cycles in 50 mM Tris-HCl (pH 8), 10 mM EDTA, 1% SDS using a Bioruptor Sonicator . The resulting chromatin was diluted 5x, pre-cleared with protein A/G Dynabeads (Invitrogen) and then incubated overnight with antibodies in immunoprecipitation (IP) buffer (50 mM HEPES (pH 7.5), 1% Triton X-100, 150 mM NaCl). Protein A or G Dynabeads (100 µl) blocked with BSA were then added for 6 hr, followed by six washes in IP buffer containing 0.1% sodium deoxycholate, 0.1% SDS and 1 mM EDTA, with the last three additionally containing 500 mM NaCl. A wash was then performed with 10 mM Tris-HCl pH 8, 250 mM LiCl, 1 mM EDTA, 1% NP-40 and 0.5% sodium deoxycholate followed by a final wash in 10 mM Tris-HCl pH 8, 1 mM EDTA. Chromatin was eluted in 1% SDS, 0.1 M NaHCOSamples for FAIRE-PCR were crosslinked, lysed and sonicated as for ChIP, followed by FAIRE as described .All RNA-seq experiments were performed as biological duplicates. Where the RNA-seq was performed in the presence of protein synthesis inhibitors, cycloheximide or emetine were added after the PBS washout, 5 min before the addition of Activin or SB-431542. Total RNA was prepared using Trizol followed by a clean up with an RNeasy kit (Qiagen). The quality of the RNA was assessed using a Bioanalyzer . Libraries were prepared using the TruSeq Stranded mRNA Sample Prep Kit . 101 bp single end reads were generated using an Illumina HiSeq 2500.Read quality was assessed with FastQC . QualityGenes with low read counts are susceptible to sampling error and as a consequence their counts can be unreliable. We therefore removed genes with a mean count across all samples below the 40th percentile of counts across all genes and all samples. This effectively removes the non-expressed and lowly expressed genes prior to statistical analysis. We accounted for differences in total read counts observed between the different samples using the normalization strategy found in the DESeq package from Bioconductor . This ap2FC filter of 0.7. We further manually filtered this list by discarding genes with <30 reads across all treatment conditions, as these likely represent background noise. We also removed genes only showing a significant change in the ‘Untreated’ condition, which might be due to long-term serum depletion of that sample compared to the others. This gave a total of 747 differentially-regulated genes.To determine differentially-expressed genes we used a negative binomial distribution implemented in DESeq to model the data. We ran pairwise comparisons of each time point against the SB-431542 sample to determine time point-specific differential expression, and identified differentially-expressed genes using an FDR threshold of 0.05 and a logTo cluster the genes according to their kinetic expression patterns, the following rules were used.2FC ≥0.7; 8 hr Activin log2FC >1 hr Activin log2FC; Untreated log2FC ≥0.Induced sustained: 1 hr Activin log2FC ≥0.7; 8 hr Activin log2FC <1 hr Activin log2FC and ≥0; Untreated log2FC ≤0.7 and ≥−0.7Transient induced: 1 hr Activin log2FC ≤0.7; 8 hr Activin log2FC ≥0.7; Untreated log2FC >0.Delayed: 1 hr Activin log2FC ≤0.7; Untreated log2FC ≤0.Repressed: 8 hr Activin log2FC was relative to the SB-431542 sample. For classification between ‘baseline on’ and ‘baseline off’, a gene was designated as ‘baseline off’ if less than 30 reads were detected in the SB-431542 sample. For classification as direct or indirect, a gene was defined as indirect if in both the cycloheximide- and emetine-treated 8 hr sample the log2FC did not reach >0.5 (for induced sustained and delayed genes) or < −0.5 (for repressed genes), or if 8 hr Activin log2FC >1 hr Activin log2FC (for transient induced genes).In all cases, the loghttp://sourceforge.net/projects/picard/). All ChIP-seq experiments were performed as biological duplicates.Samples for ChIP-seq with the corresponding inputs were prepared as described above for ChIP-PCR. Following end repair, poly-A-tailing and adapter ligation, Illumina TruSeq ChIP sample preparation kits were used to generate the libraries. The Illumina kit Phusion enzyme was replaced by Kapa HiFi HotStart ready mix . The PCR was run before gel isolation using the Invitrogen SizeSelect E-gel system . Post PCR we used AMPure XP beads  at a 1:1 ratio to maintain size integrity. Samples were multiplexed and 51 bp single end reads were generated on an Illumina HiSeq 2500. The raw reads were aligned to the mouse mm10 genome assembly using BWA 0.6.2-r126 , with a 6 and counted reads mapping to SMAD2 binding loci.To identify regions enriched with reads when compared to an input control we used MACS  q-value. We assoTo quantify gene-associated SMAD2 activity across the four time points we constructed a set of consensus binding sites by collapsing all binding sites identified from all comparisons. Binding loci were merged if they shared an overlap of at least a single nucleotide. Note that in our dataset the mean length of overlap between two MACS peaks was 200 bp, showing that the majority of the peaks have a ‘sizeable’ overlap. We quantified gene-associated SMAD2 activity by summing the normalized read counts across consensus loci that were associated with an individual regulated gene (SMAD2 footprint). Duplicate reads were removed prior to consensus peak quantification and reads were shifted to account for the fragment effect present in sequencing data.To select a list of SMAD2 peaks to take forward, we filtered out those peaks with low MACS fold enrichment scores (<3.0). Furthermore, we determined whether a peak contained sequences derived from repetitive elements by determining overlap with repeats reported in the repeatmasker track, UCSC Genome Browser. Peaks mapping to highly conserved repetitive sequence were removed.Accounting for variations in the sampling of the underlying sequence library caused by differences in the total number of reads sequenced is not straightforward for ChIP samples. Using total mapped reads has been shown to be unreliable, since variations in enrichment efficiency can have dramatic effects on the proportion of reads attributed to signal. A more appropriate normalization strategy is to use only reads derived from regions with high signal and low variance across all samples. Therefore, we used DiffReps 1.55.4  to calcuWe also used DiffReps to determine sites of differential enrichment of Pol II (either phosphorylated on Ser2 or Ser5 of the CTD) at each time point relative to SB-431542. For each ChIP-seq experiment, replicate samples for each time point were compared to the SB-431542 sample. The corresponding input samples were also processed with DiffReps for background noise estimation. The ‘--nsd’ and ‘--frag’ parameters were set to ‘broad’ and ‘200’, respectively, and all other parameters were kept as default. Sites of differential binding for Pol II Ser2P and Ser5P from all the comparisons were overlapped with respect to knownGene gene loci from the UCSC with the allowance for 2 kb of flanking sequence. More specifically, for each comparison, the sum of all differential bases found to overlap a given gene was calculated, and this was reported as a ratio of the length of the gene interval. Through inspection of the dataset it was found that genes exhibiting an overlap ratio ≥0.09 in any one of the comparisons were the most reliable. This gene list was then manually filtered to remove microRNAs and false positives, giving a dataset of 410 genes. This dataset represents the annotated genes which clearly show differential Pol II occupancy in response to signaling.Genes differentially expressed according to RNA-seq, as well as genes found to contain differential Pol II occupancy at any time point, were taken forward as SMAD2 target genes. Overlapping genes between these two lists were selected. Outliers on either side were additionally included based on the presence of SMAD2 peaks conserved between the two biological ChIP-seq replicates within 100 kb of an annotated TSS or TTS. Moreover, genes that had differential Pol II binding, but did not pass the stringent filter used for the RNA-seq data, were included if they were found to be differentially expressed according to less stringently filtered RNA-seq analysis and by manual inspection of the data. This led to a final list of 140 genes, associated with 478 SMAD2 consensus peaks.Histone modification changes or Pol II enrichment across the time points for specific peak and gene sets were visualized using metaprofiles. In all cases the reads for the two biological replicates were added together to generate more read depth. The read depth vectors for loci across SMAD2 binding sites  and SMAD2 target gene TSS loci were obtained. These vectors were then adjusted using the normalization factors generated by DiffReps described above, and metaprofiles were constructed by averaging per-nucleotide read depths across contributing loci. The metaprofiles were smoothened using local polynomials. In the case of TSS-centric profiles, genes in a reverse orientation were accounted for, and all plots run from 5' to 3'.To determine the similarity between the histone modifications at SMAD2 binding sites across the time points we clustered the DiffReps-normalized read count data. The normalized read count profiles were summed to give a single value per locus per sample (±2.5 kb relative to the TSS). We employed hierarchical clustering using euclidean distance to group the regions.2FC of Pol II (either Ser5P or Ser2P), the total numbers of reads mapping to the entire genomic extent of a gene (±2 kb) was determined, adjusting the counts from each sample by its DiffReps normalization factor. The mean values of these counts across the replicates was taken and compared to the mean normalized replicate value of the relevant SB-431542 sample. Hierarchical clustering was employed using euclidean distance. A similar analysis was performed with the H3K9Ac and H3K27Ac datasets focusing on either the TSS ± 2.5 kb or the SMAD2 binding sites.To quantify the log2FC of ≥0.7 cutoff for both acetylation states in the 8 hr Activin sample relative to the SB-431542 sample.From the analysis of histone modifications at SMAD2 binding sites described above, we defined regions of high or low acetylation for H3K9 and H3K27 by selecting the nodes obtained via hierarchical clustering that show either high or low H3K9Ac/H3K27Ac see . Taking The ChIP-seq and RNA-seq data have been submitted to the NCBI Gene Expression Omnibus (GEO) under the accession number GSE77488.For visualization of ChIP-seq data using the IGV browser, Pol II and histone acetylation tracks are shown normalized using the DiffReps-generated factors, read extension by 100 bp and smoothing over 10 bp windows, while the SMAD2 tracks have been extended and smoothened. The H3 tracks represent the raw coverage obtained from the aligned reads.Metacore was used to identify enriched gene ontology signaling pathways, molecular function and biological processes within the SMAD2 target gene list. Default settings were used and also background lists provided by the program itself.Taking all 478 consensus peaks associated with a SMAD2 target gene, the MACS-defined summits from each contributing MACS-called peak were selected . The 500 bp sequence surrounding each summit was extracted and submitted for known and de novo motif discovery using the MEME-ChIP suite. Default settings were used with the exception of MEME . To search for specific occurrence and location of user-defined motifs within this dataset, Fuzznuc was used . Motif sExperiments were performed at least twice independently  and the majority were performed at least three times. Within each qPCR experiment, technical duplicates were run. Statistical analyses were performed using an unpaired t-test unless otherwise specified. p<0.01 was considered statistically significant. 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 evaluated by Jessica Tyler as the Senior Editor and three reviewers, one of whom is a member of our Board of Reviewing Editors. The reviewers have opted to remain anonymous.Thank you for submitting your work entitled "Distinct modes of SMAD2 chromatin binding and remodeling shape the transcriptional response to NODAL/Activin signaling" for consideration by eLife.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in This study was designed to examine the SMAD2 genomic response in P19 embryonic teratoma cells treated with activin. The study is thorough and includes a variety of genome-wide methods, including RNA-seq and ChIP-seq. Together, the experiments reported provide a very useful data resource. This represents a major strength of the manuscript. However, the broad scope of the data presented did not lead to sufficient focus on conclusions that provide new insight into mechanisms of Smad2 signaling . The consensus opinion of the reviewers is that the manuscript needs to be re-organized to highlight data that supports new conclusions and that additional experimental analysis may be needed to fully establish such conclusions. This opinion led to the decision to return the manuscript to you at this time.Reviewer #1:This study was designed to examine the SMAD2 genomic response in P19 embryonic teratoma cells treated with activin. The study is thorough and includes a variety of genome-wide methods, including RNA-seq and ChIP-seq. Together, the experiments reported provide a very useful data resource. This represents a major strength of the manuscript.Three major conclusions are reported by the authors:1) Recruitment of SMAD2 to both open and closed regions of chromatin is detected. This is argued to be incompatible with a model of SMAD2 recruitment to pioneer transcription factors (PMID: 22036565 & PMID: 22036566), but for this function, pre-binding is not essential. Indeed, it has been previously reported that FOXH1 is recruited to chromatin following activin signaling (PMID: 21741376). Moreover, a mechanism for activin-stimulated recruitment of FoxH1 to closed chromatin has been reported to be mediated by TIF1g-Smad2/3 (PMID: 22196728). It is unclear that the present study extends these previous findings.2) SMARCA4 contributes to SMAD2 function at some sites. As noted by the authors, this has been noted elsewhere (PMID: 16990801 & PMID: 18003620).3) The authors report  that no significant enrichments of H3K27Me3 or H3K9Me3 were detected near mad binding sites. This finding contrasts with PMID: 25805847 (for at least some activin-dependent genes), but there is no follow up on this observation.On balance, this is a good study that provides an outstanding data resource that will be useful to researchers in the field of action/nodal signaling. However, enthusiasm for this study is dampened by the limited novelty of the major conclusions drawn by the authors.Reviewer #2:In this paper, the authors use several genome-wide approaches to investigate the mechanisms employed by Smad transcription factors to control transient gene expression profiles. A lot of data is generated and presented in this paper and from this the authors attempt to make several conclusions. However, overall, despite the large amount of work, the study lacks fundamentally new findings on how Smad transcription factors work. For example, the authors claim that Smad2 works through affecting polymerase recruitment and not pause release but the data are not entirely consistent with this view (see below). The authors also indicate that Smad binding kinetics do not necessarily correlate with gene expression profiles (and hence use different mechanisms at different genes). However, the authors do not take into account message stability, which is potentially reshaping the target gene expression profiles rather than a transcriptional effect per se. Another important issue is the inclusion of "media only" effects in timecourses of activin signalling and this is not really appropriate (see below) and impacts on the interpretation of the gene expression profiles. Two of the latter findings (i.e. involvement of FOXH1 and SMARCCA4) are interesting but are already well known and studied Smad effector proteins. Overall, the current paper is therefore not conclusive enough for publication, in part through the inclusion of too much information that is not always necessary. However, with streamlining and careful re-interpretation of the data, a more coherent story might emerge.1) The inclusion of the "untreated" samples in parts B and C is not entirely valid. This is not the same as treating with activin for 48 hrs and is actually a different question being asked i.e. what is the difference between acute activin signalling responses and endogenously generated sustained signalling. This of course impacts greatly on the profiles in part C, where the last time point should be deleted. The "sustained" signal is not actually central to the story and could easily be deleted from the paper. Indeed, many of the later figures do not use this condition anyway.2) Have the authors considered whether RNA stability is shaping the profiles in 3) In many places, the authors include "representative experiments" rather than biological replicates. As "representative" implies replicates have been done, then why are they not included? Can the authors explain their rationale for doing this? For timecourse experiments, this can be acceptable due to adjacent timepoints acting as replicates e.g.  but for 4) It is not clear how the replicate ChIP-seq experiments were processed to get the final list of Smad targets. How reproducible were the two ChIP-seq experiments? Also in generating "gene associated Smad activity" it is not clear why the authors took a "1bp" overlap in peaks. If looking at the same peak, then this is not stringent enough as the summits could be up to 500 bp apart and a parameter like summit-summit distance or% peak overlap should be used instead. If attempting to just associated total Smad binding with a gene, then the peak overlaps are not relevant. What happens if alternative ways of merging the Smad signal are used?5) 6) Another issue arises from 7) The authors claim that Smad has two modes of binding. One to open chromatin and one to closed chromatin. FAIRE data is provided to support this claim in a locus-specific manner (did the authors look genome-wide and what was the result?). Also, the authors plot H3 density across Smad binding regions. However, in 8) Related to point 7, the authors indicated that FOXH1 is not "pioneering" as binds at the same time as Smad2. However, if they are binding in the same complex, could FOXH1 still not be "pioneering in that context" i.e. the one that would access the closed chromatin?9) In the text, it is stated that H3K9Ac and K27ac show the same patterns. However, they are clearly different in 10) It was not clear whether the selected genes in 11) It is essential that biological replicates are included in Reviewer #3:In the present study, the authors examined the role of Smad2 in activin/nodal signaling-induced gene expression in P19 embryonic teratoma cells. I understand that the authors made great efforts to finish this paper. They provide many interesting observations including multiple modes of Smad2 binding to chromatin and the involvement of SMARCA4 in expression of a subset of Smad2 target genes. I think that the paper significantly contributes to further understanding of the Smad signaling pathway. However, I have a few concerns as follows.1) Activin/nodal signaling induces phosphorylation of Smad2 and Smad3 (as described in the third paragraph of the Introduction of this paper). However, the authors analyzed binding of Smad2 to chromatin but not that of Smad3. Is it appropriate to discuss activin/nodal signaling only referring Smad2? They should explain in the main text why they omitted arguments about Smad3. Other ChIP-seq papers on activin/nodal signaling analyzed both Smad2 and Smad3.Yoon et al. Genes Dev. 25, 1654-1661, 2011 (not cited)Kim et al. Dev. Biol. 357, 492-504, 2011 (cited)Chiu et al. Development 141, 4537-4547, 2014 (not cited)2) To conclude that Smad2 regulates target gene expression through de novo recruitment of pol II , the authors should directly show that knockdown of Smad2 results in failure of pol II to be enriched around the TSSs of target genes. I am afraid that Smad3 may also be involved. Same to the description in the fourth paragraph of the subsection “SMAD2 induces changes in the chromatin landscape to regulate transcription”.3) How did the authors validate specificity of the anti-FoxH1 antibody prepared by themselves? [Editors’ note: the author responses to the first round of peer review follow.][…] Reviewer #1:This study was designed to examine the SMAD2 genomic response in P19 embryonic teratoma cells treated with activin. The study is thorough and includes a variety of genome-wide methods, including RNA-seq and ChIP-seq. Together, the experiments reported provide a very useful data resource. This represents a major strength of the manuscript.We were pleased to see that the reviewer recognized a strength of the paper as an outstanding data resource. We have now performed a large number of new experiments and analyses to corroborate the data and strengthen our new mechanistic insights into SMAD2 signalling. We have also rewritten the manuscript to better communicate the novelty of the major conclusions.The point of the study, which I think didn’t come over sufficiently strongly in the original version, was to answer fundamental questions about how transcription factor binding regulates transcription. Over the last decade a huge effort has been put into genome-wide mapping of transcription factor binding sites, histone modifications and chromatin accessibility. However, it is still unclear whether transcription factor occupancy induces histone modifications, or if the chromatin landscape dictates recruitment. Furthermore, much of this mapping has been carried out in steady state conditions. As a result, we know little about the sequence of events that occur from transcription factor binding to RNA Polymerase II regulation, or about how transcription factor binding influences the temporal patterns of gene expression in the longer term. The best way to study these critical issues is to use a system where the transcription factor binds in response to extracellular signalling, and to do this we have chosen to focus on Activin/NODAL signalling.In this paper for the first time we have delineated the sequence of events that occur from SMAD2 binding to transcriptional activation, and the mechanisms underlying them. Crucially, our work establishes new paradigms for signal-dependent transcriptional regulation.1) We have provided unequivocal evidence that transcription factor binding causes histone modification and chromatin remodelling with the discovery that SMAD2 binds to closed chromatin and remodels it.Lefty1 and Pmepa1 SBSs results in nucleosome eviction  that SMADs act passively and only bind to ‘preconditioned’ chromatin.2) We demonstrate that SMAD2 regulates RNA Polymerase II via de novorecruitment to target promoters. We have considerably strengthened this conclusion in the revised manuscript by analysing metaprofiles of Pol II Ser2P (see revised 3) We demonstrate that SMAD2 chromatin binding does not directly correlate with transcription of target genes, and thus have overturned the assumption in the field that transcription factor binding linearly equates with transcriptional kinetics.We have now explored this latter issue in much more detail with an SB-431542 chase experiment see . The resThree major conclusions are reported by the authors:1) Recruitment of SMAD2 to both open and closed regions of chromatin is detected. This is argued to be incompatible with a model of SMAD2 recruitment to pioneer transcription factors (PMID: 22036565 & PMID: 22036566), but for this function, pre-binding is not essential. Indeed, it has been previously reported that FOXH1 is recruited to chromatin following activin signaling (PMID: 21741376). Moreover, a mechanism for activin-stimulated recruitment of FoxH1 to closed chromatin has been reported to be mediated by TIF1g-Smad2/3 (PMID: 22196728). It is unclear that the present study extends these previous findings.The reviewer argues that the two papers from the Young and Zon labs (PMID: 22036565 & PMID: 22036566), which conclude that master transcription factors (TFs) dictate cell type-specific responses to TGF-β/BMP signalling, could in fact be consistent with our finding that SMAD2 exhibits the two distinct modes of chromatin binding.It is true that in principle, the activated SMADs could bind DNA simultaneously with the master TFs, but neither of these papers actually addressed this issue. They did not investigate whether the co-occupancy of SMADs with master TFs was a result of pre- binding of the master TF, or whether the SMADs and master TFs bind simultaneously. To do this they would have had to investigate the chromatin state and presence/absence of master TFs in the non-signalling state, which they did not. Our paper is the first to address this issue.Moreover, the Young paper (PMID 22036565) favours the pre-binding model as it states that master transcription factors help direct SMAD3 binding by establishing open chromatin that contains SBEs, allowing SMAD3 to bind DNA and form a physical complex with the master transcription factors. As a result of these papers the dogma developed in the field that SMADs act passively and only bind to already accessible chromatin. Our paper overturns this dogma and is the first to show that the SMADs bind closed unacetylated chromatin and displace nucleosomes.EOMES and GSC genes in human ESCs. However, the predominant increase in FOXH1 binding that correlates with SMAD2/3 binding actually occurs at 24 hr post Activin stimulation. Therefore, it is not clear how this relates to the rapid simultaneous binding of FOXH1 and SMAD2 at closed chromatin that we observe. Moreover, they interpret the role of FOXH1 as a pioneer factor , which is not what we conclude.The reviewer mentions that it has previously been reported that FOXH1 is recruited to chromatin following Activin signalling (PMID: 21741376). These authors did report inducible binding of FOXH1 and SMAD2/3 at the The reviewer also mentions a paper (PMID: 22196728) which they say reports a mechanism for Activin-stimulated recruitment of FOXH1 to closed chromatin mediated by TIFγ-SMAD2/3. However, this paper does not address FOXH1 binding at all, and even for the SMAD2/3–SMAD4 complex binding to the proximal promoter element, it is not shown how binding of TIF1γ together with SMAD2/3 at the upstream site actually facilitates this.2) SMARCA4 contributes to SMAD2 function at some sites. As noted by the authors, this has been noted elsewhere (PMID: 16990801 & PMID: 18003620).The reviewer is correct that the interaction of SMAD2 with SMARCA4 and a functional effect on transcription has been noted previously. In fact, one of these papers came from my lab. It is not known, however, at what level SMARCA4 is required. Here we show definitely that it is required for the nucleosome displacement at SMAD2 binding sites at ‘baseline off’ target genes.3) The authors report  that no significant enrichments of H3K27Me3 or H3K9Me3 were detected near mad binding sites. This finding contrasts with PMID: 25805847 (for at least some activin-dependent genes), but there is no follow up on this observation.HOX clusters. The paper that the reviewer refers to uses hESCs and is focused on H3K4me3, which we did not investigate. They did not investigate H3K9Me3 at all. In addition, they did observe basal H3K27me3, but concluded that it is independent of Activin/Nodal signalling.We found no enrichment of H3K27Me3 or H3K9Me3 at Activin target genes in P19 cells. Because of this we did not investigate these marks further. Their absence was not due to a failure of these ChIPs though, as we did find other regions of the genome enriched in these marks, such as the Reviewer #2:In this paper, the authors use several genome-wide approaches to investigate the mechanisms employed by Smad transcription factors to control transient gene expression profiles. A lot of data is generated and presented in this paper and from this the authors attempt to make several conclusions. However, overall, despite the large amount of work, the study lacks fundamentally new findings on how Smad transcription factors work. For example, the authors claim that Smad2 works through affecting polymerase recruitment and not pause release but the data are not entirely consistent with this view (see below). The authors also indicate that Smad binding kinetics do not necessarily correlate with gene expression profiles (and hence use different mechanisms at different genes). However, the authors do not take into account message stability which is potentially reshaping the target gene expression profiles rather than a transcriptional effect per se. Another important issue is the inclusion of "media only" effects in timecourses of activin signalling and this is not really appropriate (see below) and impacts on the interpretation of the gene expression profiles. Two of the latter findings (i.e. involvement of FOXH1 and SMARCCA4) are interesting but are already well known and studied Smad effector proteins. Overall, the current paper is therefore not conclusive enough for publication, in part through the inclusion of too much information that is not always necessary. However, with streamlining and careful re-interpretation of the data, a more coherent story might emerge.We thank the reviewer for their extremely useful insights. We have addressed all of the criticisms and comments with a large number of new experiments, as outlined in the point-by-point rebuttal below. We have also rewritten the manuscript to better communicate the novelty of the major conclusions.The point of the study was to answer fundamental questions about how transcription factor binding regulates transcription. We wanted to discover whether transcription factor occupancy induces histone modifications, or if the chromatin landscape dictates recruitment. We wanted to define the sequence of events that occur from transcription factor binding to RNA Polymerase II regulation, and discover how transcription factor binding influences the temporal patterns of gene expression in the longer term. The best way to study these critical issues is to use a system where the transcription factor binds in response to extracellular signalling, and to do this we chose to focus on Activin/NODAL signalling.In this paper for the first time we have delineated the sequence of events that occur from SMAD2 binding to transcriptional activation, and the mechanisms underlying them. We have now established three new paradigms for signal-dependent transcriptional regulation.1) We have provided unequivocal evidence that transcription factor binding causes histone modification and chromatin remodelling with the discovery that SMAD2 binds to closed chromatin and remodels it.This discovery also overturns the dogma in the field that SMADs act passively and only bind to ‘pre-conditioned’ chromatin.2) We demonstrate that SMAD2 regulates RNA Polymerase II via de novorecruitment to target promoters. (See below for details about how we have corroborated these data.)3) We demonstrate that SMAD2 chromatin binding does not directly correlate with transcription of target genes, and as a result have overturned the assumption in the field that transcription factor binding linearly equates with transcriptional kinetics.We have now explored this latter issue in much more detail with an SB-431542 chase experiment see . The res1) The inclusion of the "untreated" samples in parts B and C is not entirely valid. This is not the same as treating with activin for 48 hrs and is actually a different question being asked i.e. what is the difference between acute activin signalling responses and endogenously generated sustained signalling. This of course impacts greatly on the profiles in part C, where the last time point should be deleted. The "sustained" signal is not actually central to the story and could easily be deleted from the paper. Indeed, many of the later figures do not use this condition anyway.The reviewer raises a valid point. We wanted to include the chronic signalling condition for exactly the reason that the reviewer states, i.e. to understand the difference between acute signalling and endogenously-generated chronic signalling. To develop a comprehensive picture of how Activin/NODAL signalling through SMAD2 regulates transcription in both the short and long term, we think that this sustained signalling condition is essential. The most important insight it has given us is that SMAD2 remains bound to chromatin as long as signalling is active and directly contributes to the transcriptional responses .The issue we faced was how to plot the responses to chronic signalling on the same graphs as the acute signalling responses. We considered that it was valid to plot it as a 48 hr Activin time point as we found that the levels of pSMAD2 were the same, as were the target gene responses we measured. However, on further analysis see  we found2) Have the authors considered whether RNA stability is shaping the profiles in This is a very important point that we have now explored in more detail in the revised manuscript. To sum up our results, RNA stability obviously has an impact on the RNA-seq profiles, but is not responsible for the differences between ‘induced sustained’ and ‘transient induced’.T and Tdgf1 are both very stable transcripts, yet T is a delayed gene and Tdgf1 is in the ‘induced sustained’ category. Similarly, Lefty1 and Hes1 have relatively short half-lives, and are in the ‘induced sustained’ and ‘transient induced’ categories respectively.We have now measured the mRNA stability of a number of representative target genes and find no correlation between mRNA stability and the gene category  In many places, the authors include "representative experiments" rather than biological replicates. As "representative" implies replicates have been done, then why are they not included? Can the authors explain their rationale for doing this? For timecourse experiments, this can be acceptable due to adjacent timepoints acting as replicates e.g.  but for All experiments have been performed at least twice and the vast majority three or more times. This is now explicitly stated in the Materials and methods .In the revised version of the manuscript we have now presented combined experiments for the majority of experiments rather than representative ones see new . For theNote that for the spatial experiments like those shown in The only experiments that we are still showing as representative experiments are: time courses like 4) It is not clear how the replicate ChIP-seq experiments were processed to get the final list of Smad targets. How reproducible were the two ChIP-seq experiments? Also in generating "gene associated Smad activity" it is not clear why the authors took a "1bp" overlap in peaks. If looking at the same peak, then this is not stringent enough as the summits could be up to 500 bp apart and a parameter like summit-summit distance or% peak overlap should be used instead. If attempting to just associated total Smad binding with a gene, then the peak overlaps are not relevant. What happens if alternative ways of merging the Smad signal are used?Assuming that the reviewer is referring to the SMAD2 ChIP-seq experiments, then the replicates were very good, but the enrichment in the second biological replicate was significantly lower across the whole genome. This is obvious looking at the scales in For the other ChIP-seq experiments, both biological replicates were used by the DiffReps program to identify the statistically significant loci of enrichment (see subsection “Pol II (Ser5P and Ser2P) and H3K27Ac/H3K9Ac ChIP-seq analysis”, first paragraph).Concerning the merging of SMAD2 peaks. We used a minimum of 1 bp as an overlap, but as now stated in the text  the majority of peaks have a sizable overlap and the mean is 200 bp see . The ide5) The reviewer says that there is clear evidence of a paused polymerase in the inhibited condition at the TSS of the ‘baseline on’ genes. I think there may be a difference in interpretation here. The reviewer appears to suggest that a peak of Pol II Ser5P at the TSS per seis a sign of a paused polymerase. However, this is not the case, and to clarify the situation we have now added metaprofiles for Pol II Ser2P . For the ‘baseline off’ genes, the critical result is that there is no peak of Pol II Ser5P at the TSS and no Pol II Ser2P in the SB- 431542-treated state. Therefore, activation of these genes must be via Pol II recruitment, as the reviewer states see new .The ‘baseline on’ genes, in contrast, in all conditions show the typical active gene profile see new , which iMost importantly, for these ‘baseline on’ genes, Activin/NODAL signalling does not change the shape of the Pol II Ser5P and Pol II Ser2P profiles. It simply changes the amplitude, which reflects changes in Pol II recruitment. This is also the case for genes in the other categories. See subsection “Activin/NODAL signaling regulates Pol II via recruitment”, second paragraph.Thus, we conclude that the step regulated by NODAL/Activin signalling is Pol II recruitment.We have not performed ChIP-seq with an antibody recognizing total RNA polymerase as we consider that using the antibodies specific to the different forms is more informative.6) Another issue arises from We thank the reviewer for these comments as they inspired us to perform an SB- 431542 chase experiment that has been incredibly informative, and has provided critical evidence to support our claim that SMAD2 recruits additional factors to shape subsequent responses.To discover whether Activin/NODAL signalling through SMAD2 induces an initial transcriptional up-regulation that is then modulated through other factors, or whether the long-term responses require continuous signalling, we treated cells for 1 hr with Activin and then with or without the type I receptor inhibitor SB-431542 for different times. The results are very striking  The authors claim that Smad has two modes of binding. One to open chromatin and one to closed chromatin. FAIRE data is provided to support this claim in a locus-specific manner (did the authors look genome-wide and what was the result?). Also, the authors plot H3 density across Smad binding regions. However, in We have now performed additional experiments and analyses to convincingly show that SMAD2 does exhibit two distinct modes of binding.The reviewer considers that the chromatin is already open in the ‘baseline off’ samples . This is not the case, as it is clear that Activin induction results in a dramatic decrease in H3 occupancy at these SBSs and also a substantial induction of acetylation in the flanking nucleosomes. To corroborate this result we subdivided this category of SBSs further to focus on those whose acetylation is highly induced upon signal induction see . In thisLefty1 and Pmepa1 are uniformly high at the SBS and 5’ and 3’ flanking nucleosomes in the absence of signalling and that Activin induction results in nucleosome displacement at the SBS  Related to point 7, the authors indicated that FOXH1 is not "pioneering" as binds at the same time as Smad2. However, if they are binding in the same complex, could FOXH1 still not be "pioneering in that context" i.e. the one that would access the closed chromatin?This is an interesting issue. The point we were making was that FOXH1 is not pre-bound and acting as a ‘landing pad’ for the activated SMAD complex. Given that both FoxH1 and the activated SMADs bind simultaneously and they are interdependent on each other for binding, it is impossible to say which component is responsible for accessing the closed chromatin. One of the other key results in the paper is that SMARCA4 is required for opening up of the chromatin at some of these loci and it is SMAD2 that interacts with SMARCA4. We now favour a model of dynamic assisted loading as hypothesised by Swinstead et al. 2016 (PMID 27633730), which we mention in the first paragraph of the subsection “The role of SMAD2-cooperating transcription factors”.9) In the text, it is stated that H3K9Ac and K27Ac show the same patterns. However, they are clearly different in Our data show clearly that the induction of H3K27Ac and H3K9Ac after 1 hr Activin are similar and correlate with initial SMAD2 binding  It was not clear whether the selected genes in Lefty1, Lefty2, and Pmepa1, are in the ‘baseline off’ category; the others are in the ‘baseline on’ category and are there for comparison. This is now made clear in the text (see subsection “SMARCA4 is required for nucleosome eviction at a subset of SMAD2 binding sites”). The categories for a given gene are in 11) It is essential that biological replicates are included in We have now combined the biological replicates for both Pitx2 and Smad7 in the ligand-induced state despite the different baselines. However, the reduction in accessibility in the absence of SMARCA4 seen for these two genes is clearly not sufficient to affect ligand-induced transcription, SMAD2 binding or local histone acetylation .We have now combined the FAIRE data to be clear about the statistical significance see . The revtion see . The revReviewer #3:[…] 1) Activin/nodal signaling induces phosphorylation of Smad2 and Smad3 (as described in the third paragraph of the Introduction of this paper). However, the authors analyzed binding of Smad2 to chromatin but not that of Smad3. Is it appropriate to discuss activin/nodal signaling only referring Smad2? They should explain in the main text why they omitted arguments about Smad3. Other ChIP-seq papers on activin/nodal signaling analyzed both Smad2 and Smad3.Yoon et al. Genes Dev. 25, 1654-1661, 2011 (not cited)Kim et al. Dev. Biol. 357, 492-504, 2011 (cited)Chiu et al. Development 141, 4537-4547, 2014 (not cited)The reason that we concentrated on SMAD2, was that there are undetectable levels of SMAD3 in these cells. This is obvious in all the Western blots in the paper where we used an antibody that recognizes SMAD2 and SMAD3 equally (BD 610843)  To conclude that Smad2 regulates target gene expression through de novo recruitment of pol II , the authors should directly show that knockdown of Smad2 results in failure of pol II to be enriched around the TSSs of target genes. I am afraid that Smad3 may also be involved. Same to the description in the fourth paragraph of the subsection “SMAD2 induces changes in the chromatin landscape to regulate transcription”.As explained above, we find SMAD3 to be undetectable in P19 cells. We show that for the ‘baseline off’ genes that there is no Pol II enriched at the TSS’s of target genes when the pathway is switched off with SB-431542, but that Pol II Ser5P becomes enriched at the TSS and in the gene bodies upon Activin stimulation. Because SMAD2 is the major R-SMAD, we ascribed the activity to SMAD2. However, to make the result more general we have rewritten the text to state that Activin/NODAL signalling regulates target gene expression through de novo recruitment of Pol II .3) How did the authors validate specificity of the anti-FoxH1 antibody prepared by themselves?We validated the antibody in several different ways. It is not quite sensitive enough to see endogenous FOXH1 in a Western blot. However, as we show in