Necessary for long-term survival, quiescent chromatin is compact, hypoacetylated, and transcriptionally inactive. How transcription activates upon cell-cycle re-entry is undefined. Here we report robust, widespread transcription within the first minutes of quiescence exit. During quiescence, the chromatin-remodeling enzyme RSC was already bound to the genes induced upon quiescence exit. RSC depletion caused severe quiescence exit defects: a global decrease in RNA polymerase II (Pol II) loading, Pol II accumulation at transcription start sites, initiation from ectopic upstream loci, and aberrant antisense transcription. These phenomena were due to a combination of highly robust Pol II transcription and severe chromatin defects in the promoter regions and gene bodies. Together, these results uncovered multiple mechanisms by which RSC facilitates initiation and maintenance of large-scale, rapid gene expression despite a globally repressive chromatin state.Quiescence is a reversible G The quiescent state provides advantages to organisms: quiescence allows cells to remain dormant for long periods of time to survive harsh conditions or to prevent over-proliferation  cultures . Log phaferation . Notwithce entry . These ece entry . TogetheSaccharomyces cerevisiae  by the first 10 min of exit, despite the highly repressive chromatin architecture present in quiescence. We found that this hypertranscription  event isTo determine the earliest time at which transcription reactivates during quiescence exit, we fed purified quiescent cells YPD medium and took time points to determine the kinetics of Pol II C-terminal domain (CTD) phosphorylation by western blot analysis . UnexpecHighlighting the high level of transcription occurring in the first 10 min of quiescence exit, we observed a drop-off in Pol II occupancy levels around the first G2/M phase (240 min) . Indeed,Given the exceptionally high transcriptional response during the first 10 min of quiescence exit, we wondered whether chromatin changes reflected hypertranscription. To this end, we performed ChIP-seq analysis of H3 to measure nucleosome occupancy levels genome wide over time. Global H3 patterns during the early exit time points, especially at the 5 min time point, were more similar to that of the quiescent state than to the 240 min time point . HoweverWe next tested if a burst of histone acetylation occurred during these early exit time points to help overcome the repressive quiescent chromatin environment. To test this, we performed ChIP-seq analysis of H4ac using an antibody that recognizes penta-acetylated H4. Similar to nucleosome occupancy and positions, a modest increase in histone H4 acetylation occurred, but the levels did not reflect that of log cells . This sudst1∆), quiescent yeast cells exhibited defects in cell-cycle re-entry, where cells lacking TFIIS stall at the first G1 during exit, which is not the case during the mitotic cell cycle  exhibited increased nascent sense-strand RNA signals starting upstream of their normal TSSs, demonstrating widespread defects in TSS selection. Canonical NDRs at these sites were severely reduced in the absence of RSC . ExaminaGiven the robust transcriptional response during the early minutes of quiescence exit , we examIn this report, we have shown that there is a rapid and robust transcriptional response during the very early minutes of quiescence exit . This redst1∆. High numbers of stalled Pol II are present in cycling cells .Q exit experiments were performed as follows: Q cells were harvested and added to YPD to 1 OD660 into the appropriate YPD volume for a given experiment. Cells were grown for 16 hr and monitored for glucose exhaustion using glucose strips. Six hours after glucose exhaustion, 1 mg/mL of indole-3-acetic acid (IAA)  was added, in powder form, to the culture. IAA remained in the culture for 7 days before harvesting Q cells. Q cells were purified as described above, and depletion efficiency was determined by western blot analysis . Cells were incubated with 4tU for 5 min before pelleting  and flash frozen in liquid nitrogen. For the 10 min time points, 50 OD units of quiescent cells were released into YPD for 5 min before an additional 5 min incubation with 4tU at a final concentration of 5 mM. All time points were labeled with 4tU for a total of 5 min before pelleting and freezing. Total RNA was isolated using Ambion’s RiboPure Yeast Kit . S. cerevisiae cells were lysed in the presence of Kluvomyces lactis (K. lactis) cells in a 100:1 mixture. RNA was treated with DNAseI according to the TURBO DNase kit . Forty microgram RNA was then biotinylated with MTSEA biotin-XX (diluted in 20% DMF) at a final concentration of 16.4 µM in 20 mM HEPES pH 7.4 and 1 mM EDTA at room temperature for 30 min. Unreacted MTS-biotin was removed from samples by phenol:chloroform:isoamyl-alcohol extraction and resuspended in 100 µL nuclease-free water. Strepavidin beads (Invitrogen 65001) were washed with high-salt wash buffer  and blocked for 1 hr in high-salt wash buffer containing 40 ng/µL glycogen. Forty microliters of streptavidin beads were added to the RNA samples and incubated for 15 min at room temperature. Beads were washed three times in 1 mL high-salt wash buffer and eluted for 15 min at room temperature in 50 µL streptavidin elution buffer . The resulting RNA was then purified and concentrated using the Qiagen miRNeasy kit (#217084). Libraries were prepared from 5 ng of RNA using the Ovation SoLo kit . Libraries were subjected to 50 bp paired-end sequencing on an Illumina HiSeq 2500 at the Fred Hutchinson Cancer Research Center genomics facility.Generally, nascent RNA-seq experiments were performed as described in K. lactis (Ensembl ASM251v1) genomes and filtered reads using SAMtools as described above for ChIP-seq analysis. Reads were normalized to the spike-in control and RPKM. Differential expression analysis was performed using DESeq2  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Acceptance summary:Saccharomyces cerevisiae yeast cells emerge from quiescence, noting an unexpected, intense burst of expression from much of the genome. They focus primarily on the role of the nucleosome remodeler RSC in managing chromatin architecture over promoters during quiescence and as cells re-enter the cell cycle using a broad range of genome-wide measurements that strongly support the conclusions. This important process of cell cycle re-entry from the quiescent state is understudied but impacts a range of phenomena from development and carcinogenesis in multicellular organisms to adaptation of microorganisms to environmental cues, so the results will be of interest to a broad audience.Cucinotta et al. describe the transcription that occurs within minutes of nutrient repletion as Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Tim Formosa as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Jessica Tyler as the Senior Editor.Thank you for submitting your article "RSC primes the quiescent genome for hypertranscription upon cell cycle re-entry" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:Note that while the reviewers felt strongly that more integration of, and cross-correlation among the datasets would improve the impact of this work, the suggestions for ways to do that listed below are individually optional. The long list of recommendations may look daunting, but the authors are encouraged to view them as potential options, not as a list that has to be completed.1. Integration and correlation of different datasets should be enhanced. For example, a heatmap of the MNase-seq, TFIIB-seq, and RSC ChIP-seq data using the gene clusters in Figure 1C could be used to correlate induction of transcription with RSC occupancy, favorable NDR width, and binding of TFIIB. Another example would be to examine whether genes with transcription defects due to RSC depletion show greater changes in promoter nucleosome occupancy when compared to unaffected genes. Some attempt at this was made in Figures 6 and 7, but more analysis of this type would provide better support for the key conclusions.2. Improvements should be made to the bioinformatic and statistical handling of the data. The existing bioinformatic tools employed are appropriate, but they are not consistently applied for different experiments. How specific gene groups were chosen or clustered should be more carefully explained, and, if appropriate, cut-offs listed. Similar information should be included to support statements in the text . Some of the data is "normalized" but it isn't always clear what it is normalized to (spike-ins? Input?). Statistical analyses should be used to back up key results.3. Authors should acknowledge the impact of the delayed exit from quiescence following RSC depletion on their results. Cells that were able to exit are likely those with subtle differences in chromatin packing over a handful of genes required for exit. Cells that do not exit are left with quiescence signatures, which could be due to the lack of transcription as opposed to lack of RSC.4. The authors Pol II occupancy data via ChIP-seq with and without RSC depletion. Could these locations be compared with the transcription changes shown in Figure 1 to demonstrate which groups of genes are altered?5. In Figure 1, it is interesting that some of the clusters show sustained expression (Cluster 1), but some have hyper-transcription that is then reduced to log levels (Clusters 2 and 3). Is there a difference between these and RSC occupancy; or specific changes to one of these clusters when RSC is depleted? Furthermore, what genes make up these clusters?6. In Figure 3a, the authors show a decrease in fragile nucleosome occupancy through low MNase upon exit from quiescence. Is the effect more pronounced in a subset of genes such as just quartile 1, with the widest NDR? Similarly, does an analysis of MNase data at only those locations with the highest RSC occupancy produce a different outcome?7. Do the authors see decreased H3 occupancy (from ChIP-seq data) at the genes where the fragile nucleosome decreases (via MNase-seq data)?8. In Figure 4, the authors find that Pol II occupancy never reaches the 10-minute hyper-transcription enrichment when RSC is depleted. Do the cells ever reach this peak, or are they unable to obtain this hyper-transcription in the absence of RSC?9. Figure 5A shows that RSC occupancy changes to decrease over NDRs and increase over gene bodies. The authors then follow with inhibiting Pol II, and show an accumulation of Pol II at NDRs and decreasing over gene bodies. Do these data correlate? For example, does an ROC curve show correlation? Or if you compare the datasets do they show that the largest changes in Pol II distribution occur at the locations where RSC redistributes?10. Does the increased aberrant transcription shown in Figure 7 correlate with the level of RSC occupancy or the change in occupancy of Pol II?11. While Figure 8 accurately summarizes the key results of this study, more molecular insight either here or in the discussion would be helpful. One model is that RSC has a preference for "easy to remodel" nucleosomes, such as fragile nucleosomes. In quiescence, due to lack of ATP, RSC might be less able to evict these nucleosomes and becomes stuck at NDRs. Following the eviction of fragile nucleosomes at cells exit quiescence, RSC might move onto other "easy to remodel" nucleosomes, such as those partially disrupted by Pol II. This, or some mechanism preferred by the authors, might be discussed to help the reader.Reviewer #1 (Recommendations for the authors):1) In the abstract, the authors state that ~60% of the genome is transcribed during the first 10 minutes after exit, but the data in figure 1 state that ~20% of genes are transcribed above the cutoff. It would be useful to reconcile this apparent discrepancy, and also to state whether the percentage of transcription is based on gene number or the total fraction of the genome.2) On line 156, the authors note a result in "Fig. 2-supplement 1B" but there is no figure that matches this description or seems to be related to the statement being supported.3) On line 169, the authors state that "the MNase-sensitivity of the +1 and +2 nucleosomes increased at the 10-minute time point" but this conclusion is not evident in Fig 3. The effect on the left side of the NDR in 3A-3B is clear, but the reader will need more guidance with seeing the change that supports the statement above.4) The flow cytometry result in Fig 4A +RSC seems like it should be similar to the results in Fig1-Sup-D and 2F, but instead it looks like a fully occupied G2 delay/arrest. Can the authors explain the difference?5) In Fig 5C, the authors describe the rise in Pol II ChIP signal at +1 Kb as a termination delay. It seems more appropriate to perform gene-averaging (TSS to TTS) or to align all termination sites instead of looking at 1 Kb past the TSS, which is early in some genes and late in others.6) The basis for placing genes in Class I and II in Fig 7 is not clear. The text says that Class I has high antisense and "generally reduced sense" transcripts. This seems mostly true, although about 20% of the Sense levels are up significantly. Class II, however, is described as having "modest changes in both sense and antisense transcript levels," but sense ranges from significantly up to significantly down, and antisense from down to strongly down. The authors should describe the rationale for defining these classes more clearly; are they based initially on levels of antisense transcripts? Without a clearer understanding of how these classes are defined, it is difficult to determine whether or not the conclusions drawn are supported.7) On line 327, it is not clear what an "incredibly strong" transcriptional response would be, so another adjective might be more appropriate.Reviewer #2 (Recommendations for the authors):I find the data very compelling and complete in the description of RSC regulating exist from quiescence. As mentioned in the public review, some aspects of data comparisons could be made. Some examples are listed below. While some may not be necessary, some of these may help to strengthen conclusions:1) The authors Pol II occupancy data via ChIP-seq with and without RSC depletion. Could these locations be compared with the transcription changes shown in Figure 1 to demonstrate which groups of genes are altered?2) In Figure 1, it is interesting that some of the clusters show sustained expression (Cluster 1), but some have hypertranscription that is then reduced to log levels (Clusters 2 and 3). Is there a difference between these and RSC occupancy; or specific changes to one of these clusters when RSC is depleted? Furthermore, what genes make up these clusters?3) In Figure 3a, the authors show a decrease in fragile nucleosome occupancy through low MNase upon exit from Q. This is a metaplot of all genes, and I am curious if the authors see more pronounced results if they show just quartile 1, with the widest NDR. Also, if the authors analyze the MNase data and show only locations that have RSC occupancy.4) Do the authors see decreased H3 occupancy (from ChIP-seq data) at the genes where the fragile nucleosome decreases (via MNase-seq data)?5) In Figure 4, the authors find that Pol II occupancy never reaches the 10-minute hypertranscription enrichment when RSC is depleted. Curiously, do the cells ever reach this peak, or are they unable to obtain this hypertranscription in the absence of RSC?6) Figure 5A shows that RSC occupancy changes to decrease over NDR and increase over gene bodies. The authors then follow with inhibiting Pol II, and show an accumulation of Pol II at NDRs and decreasing over gene bodies. Do these data correlate? For example, does an ROC curve show correlation? Or if you compare the datasets do they show that at the locations where RSC redistributes, there is the largest change in Pol II distribution?7) The locations that have increased aberrant transcription, as shown in Figure 7, are these genes that have RSC occupancy? If you compare these locations with Pol II altered occupancy, is there a correlation?Reviewer #3 (Recommendations for the authors):1. It is surprising that little attempt was made to integrate the results from the various analyses. For example, a heatmap of the MNase-seq, TFIIB-seq, and RSC ChIP-seq data using the gene clusters in Figure 1C would show that gene induction following Q exit is linked to RSC occupancy, favorable NDR width, and binding of TFIIB. Another example would be to examine whether genes with transcription defects due to RSC depletion show greater changes in promoter nucleosome occupancy when compared to unaffected genes. Some attempt at this was made in Figures 6 and 7, but I thought that more connections would better support the key results of this study.2. Improvements should be made to the bioinformatic and statistical handling of the data. The existing bioinformatic tools employed are fine, but they are not consistently applied for different experiments. How specific gene groups were chosen or clustered should be more carefully explained, and, if appropriate, cut-offs listed. Similar information should be included to support statements in the text . Some of the data is "normalized" but it isn't always clear what it is normalized to (spike-ins? Input?). Statistical analyses should be used to back up key results.3. Authors should acknowledge the impact of the delayed Q-exit following RSC depletion on their results. Cells that were able to exit are likely those with subtle differences in chromatin packing over a handful of genes required for Q exit (down the road identifying these might be possible using SGA?). Cells that do not exit are left with Q signatures, which could be due to the lack of transcription upon Q exit as opposed to lack of RSC.4. While Figure 8 accurately summarizes the key results of this study, more molecular insight either here or in the discussion would be helpful. One model is that RSC has a preference for "easy to remodel" nucleosomes, such as fragile nucleosomes. In Q, due to lack of ATP, RSC cannot evict these nucleosomes and becomes stuck at NDRs. Following the eviction of fragile nucleosomes at Q-exit, RSC moves onto other "easy to remodel" nucleosomes, such as those partially disrupted by PolII. Essential revisions:Note that while the reviewers felt strongly that more integration of, and cross-correlation among the datasets would improve the impact of this work, the suggestions for ways to do that listed below are individually optional. The long list of recommendations may look daunting, but the authors are encouraged to view them as potential options, not as a list that has to be completed.1. Integration and correlation of different datasets should be enhanced. For example, a heatmap of the MNase-seq, TFIIB-seq, and RSC ChIP-seq data using the gene clusters in Figure 1C could be used to correlate induction of transcription with RSC occupancy, favorable NDR width, and binding of TFIIB. Another example would be to examine whether genes with transcription defects due to RSC depletion show greater changes in promoter nucleosome occupancy when compared to unaffected genes. Some attempt at this was made in Figures 6 and 7, but more analysis of this type would provide better support for the key conclusions.We are very grateful for this suggestion and have added several panels addressing these questions to a new figure: Figure 4—supplement 1. In summary, we found the following: genes with widest NDRs (clusters 1 and 2) have high levels of TFIIB  and Pol II during early exit. While these two clusters were impacted by RSC depletion, some level of Pol II occupancy remained . We believe redundant factors may enable transcription to occur at these genes (ex: other remodelers or transcription factors). Furthermore, these genes (clusters 1 and 2) also had the widest NDRs and lowest H3 occupancy regardless of RSC, although we did see H3 occupancy rise in the absence of RSC . RSC occupancy levels did not correlate with transcription levels or NDR width , with cluster 1 transcription and NDR width deviating from RSC occupancy the most. We again believe this could be due to redundant factors at loci in cluster 1.2. Improvements should be made to the bioinformatic and statistical handling of the data. The existing bioinformatic tools employed are appropriate, but they are not consistently applied for different experiments. How specific gene groups were chosen or clustered should be more carefully explained, and, if appropriate, cut-offs listed. Similar information should be included to support statements in the text . Some of the data is "normalized" but it isn't always clear what it is normalized to (spike-ins? Input?). Statistical analyses should be used to back up key results.We thank the reviewers for this feedback and have updated the text to better describe how the analyses were completed, for example page 26, line 683. In addition, we added a section for analysis for each experiment type in the methods section.3. Authors should acknowledge the impact of the delayed exit from quiescence following RSC depletion on their results. Cells that were able to exit are likely those with subtle differences in chromatin packing over a handful of genes required for exit. Cells that do not exit are left with quiescence signatures, which could be due to the lack of transcription as opposed to lack of RSC.We agree this should be acknowledged and we have updated the text  to discuss this possibility.4. The authors Pol II occupancy data via ChIP-seq with and without RSC depletion. Could these locations be compared with the transcription changes shown in Figure 1 to demonstrate which groups of genes are altered?We have added this analysis using the same gene ordering as shown in Figure 1 and generated a new supplemental figure  to illustrate this comparison. These results are now discussed in page 12, lines 262-268.5. In Figure 1, it is interesting that some of the clusters show sustained expression (Cluster 1), but some have hyper-transcription that is then reduced to log levels (Clusters 2 and 3). Is there a difference between these and RSC occupancy; or specific changes to one of these clusters when RSC is depleted? Furthermore, what genes make up these clusters?We agree that the differences in gene expression varied over time across the clusters were interesting. We have added a heatmap of RSC occupancy in Q cells and during early exit at these loci , and discuss in page 12, lines 261-270. We note differences in the RSC profile: specifically striking to us was how much RSC remains in the NDR vs the gene body. Genes showing more RSC movement into the gene body (clusters 1 and 2) also had the highest binding of Pol II in exit. All genes had reduced Pol II occupancy however clusters 1 and 2 still had detectable levels of Pol II occupancy. We included a new figure showing the top five GO terms for each cluster , and discuss on page 7, lines 129-131. Cluster 1 had the highest enrichment of GO terms and contained genes involved in rRNA processing and translation.6. In Figure 3a, the authors show a decrease in fragile nucleosome occupancy through low MNase upon exit from quiescence. Is the effect more pronounced in a subset of genes such as just quartile 1, with the widest NDR?We see a moderate effect of Q-exit on MNase-digestion ability after the data are subset into quartiles .Similarly, does an analysis of MNase data at only those locations with the highest RSC occupancy produce a different outcome?We thank the reviewers for this question and have added this analysis to the paper. We see a difference in MNase-digestion levels comparing high RSC levels to the lowest RSC levels .7. Do the authors see decreased H3 occupancy (from ChIP-seq data) at the genes where the fragile nucleosome decreases (via MNase-seq data)?Yes. We see decreased H3 occupancy at these genes when cells exit. We show these metaplots in a new figure .8. In Figure 4, the authors find that Pol II occupancy never reaches the 10-minute hyper-transcription enrichment when RSC is depleted. Do the cells ever reach this peak, or are they unable to obtain this hyper-transcription in the absence of RSC?We have performed ChIP-seq analysis to the 30-minute time point and did not observe transcription levels reaching this peak. We have updated the text to emphasize that the level could potentially reach this later on .9. Figure 5A shows that RSC occupancy changes to decrease over NDRs and increase over gene bodies. The authors then follow with inhibiting Pol II, and show an accumulation of Pol II at NDRs and decreasing over gene bodies. Do these data correlate? For example, does an ROC curve show correlation? Or if you compare the datasets do they show that the largest changes in Pol II distribution occur at the locations where RSC redistributes?We have added a figure to reflect the clusters shown in Figure 1C . We believe that this illustrates the difference of RSC localization with and without phenanthroline, as there are further differences at different clusters. The most striking changes are in clusters 1 and 2, where Pol II is normally highly bound. At these sites, RSC movement is dramatically sequestered to the NDR when phenanthroline is added, supporting our conclusion that RSC co-transcriptionally moves into gene bodies. This result is now discussed in page 13, lines 294-297.10. Does the increased aberrant transcription shown in Figure 7 correlate with the level of RSC occupancy or the change in occupancy of Pol II?We thank the reviewers for this question. While it would have been very interesting if so, we did not observe any correlation with the levels of RSC occupancy or Pol II occupancy. We believe that increased aberrant transcription occurs when an unaffected or “wide-enough” NDR is present and increased transcription is thus an indirect effect of RSC depletion rather than direct action of RSC at these loci .11. While Figure 8 accurately summarizes the key results of this study, more molecular insight either here or in the discussion would be helpful. One model is that RSC has a preference for "easy to remodel" nucleosomes, such as fragile nucleosomes. In quiescence, due to lack of ATP, RSC might be less able to evict these nucleosomes and becomes stuck at NDRs. Following the eviction of fragile nucleosomes at cells exit quiescence, RSC might move onto other "easy to remodel" nucleosomes, such as those partially disrupted by Pol II. This, or some mechanism preferred by the authors, might be discussed to help the reader.We are grateful to the reviewers for these suggestions and are in favor of this as a potential model. We have added these ideas to the discussion on page 19 line 479-491.


Mb) is the sarcoplasmic heme protein primarily responsible for meat’s color. The transcription pattern of the porcine Mb gene has not been studied because its genome structure information has not been officially annotated. In our study, we attempted to reveal the possible mechanism of pig-meat color formation by integrating the public data of genome and transcriptome.Myoglobin , consisting of 16 adaptive transcription factors and 23 secondary transcription factors. The verification of gene expression in longissimus dorsi muscle showed that the Mb mRNA and encoded protein were significantly (p < 0.05) more abundant in Bama pigs than Yorkshire pigs. The qRT-PCR  validation on genes of the Mb-TFs network showed that FOS, STAT3, STAT1, NEFL21, NFE2L2 and MAFB were significant positive regulatory core transcription factors of Mb-TFs network in Bama pigs, whereas ATF3 was the secondary transcription factor most responsible for the activation of the above transcription factors. Our study provides a new strategy to unravel the mechanism of pork color formation, based on public transcriptome and genome data analysis.Myoglobin is a key chemical component that determines meat’s color and affects consumers’ purchase intentions. In this work, we firstly identified the promoter sequence of the  With the development of society and the improvement of human living standards, people’s focus on meat consumption has gradually shifted from “quantity” to “quality” . The WesMb content and chemical form [Mb in meat exists in three forms: oxygenated oxymyoglobin (OxyMb), oxidized metmyoglobin (MetMb) and reduced deoxymyoglobin (DeoMb) [Mb constitute different meat colors valued by the consumer when buying meat. Consumers generally prefer the bright red flesh color formed by muscle-oxygenated protein [The different degrees of meat color depend on cal form . Mb in m (DeoMb) . Differe protein . The glo protein . Pork is protein . In ligh protein . The lip protein . Several protein ,17. Stud protein .Mb is an ubiquitous protein found in striated muscle of nearly all vertebrate taxa. Studies have shown that the heritability estimate for porcine longissimus myoglobin concentration was 0.27, with moderate heritability [Mb during myogenesis and in various adult striated muscle-fiber types has prompted investigators to study the putative myoglobin promoters and the various transcription factors that are responsible for the regulation of Mb transcription. Using cultured adult myoblasts obtained from 11 to 12-day-old chicken embryonic pectoral muscle, researchers have demonstrated that a 167 bp myoglobin promoter region (−371 to −205), containing a 57 bp cis-acting element, enhanced reporter activity in differentiated myotubes and concluded that the 2 kb putative Mb promoter region contains a muscle-specific enhancer region that is essential for the regulation of Mb transcription during skeletal muscle cell differentiation [Mb gene has not been identified in the reference genome, and its transcriptional regulation pattern has not been studied, due to the lack of sequence information of the promoter region [tability . The obs−371 to −5, contai−371 to −5, contair region .Mb genome location and its transcription pattern in pigs, we performed a data mining of pig genomic and transcriptomic data available in a public database, in addition to revealing the influence of the Mb transcription regulation pattern in pork color formation through in vitro validation.In order to investigate the Mb and its related transcription factors.Three male Bama pigs (~45 kg for 6 months old) and three male Yorkshire pigs (~100 kg for 6 months old) were selected separately for slaughter; tissue samples from the sixth to seventh back ribs of the longissimus dorsi muscle, and the total RNA, were extracted and quantified. The Reverse Transcription Kit  was used to remove genomic DNA at 42 ℃ for 2 min. The samples were directly retrotranscribed for 15 min to convert total RNA to cDNA, for the post-study of Mb gene, including all exons and introns, can be retrieved from the primary assembly of Wuzhishan minipigs in the Ensembl database (http://asia.ensembl.org/index.html (accessed on 15 May 2021)). A region ~2 kb upstream of the transcription start site of the Mb gene is defined as a potential promoter for the prediction of transcription factor-binding sites. The putative transcription factor binding sites (TFBSs) in this region were analyzed using the online analysis server LASAGNA (https://biogrid-lasagna.engr.uconn.edu/lasagna_search/ (accessed on 15 May 2021)). We used JASPAR CORE matrices as matrix-derived models for transcription factors, and the filter for the predicted results was the cut-off value: p < 0.01.The genomic information of the p < 0.05 and a fold change ≥2.0.The gene expression profiling datasets GSE119368 and GSE99092 were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between local and commercial pig breeds were analyzed with the DESeq2 package in a Bioconductor (version 3.8) , with a p-value < 0.05 was defined as the cut-off criteria.To elucidate potential biological processes, Yu et al. conducted the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, which was carried out by clusterProfiler to expound the promising signaling pathways correlated with the core transcription factors and secondary transcription factors . The adjThe core TF (Transcription Factor)-secondary TF interaction network was constructed using the STRING online database. TTI  pairs with a combined score ≥ 0.4 were used to construct the TTI network. Transcription factors were annotated by TRRUST v2 . Then, tThe value of meat color was measured by the Germany Matthaus meat color tester Opto-Star, and the Opto value  was used to evaluate the color score of pork. The “OPTO-STAR” gun is an intelligent probe for measuring the brightness of meat, whose multiple functions greatly assist the user’s task. As the measurement method, the measurement head was placed on the cross section of the longissimus dorsi muscle and the measurement was completed in 3 s. In the “MEASURE” routine, we used the calibration-bloc for simulating a meat-measurement. We put the sensor onto color “0”, and left it for three seconds. The display then counts down to zero: if it is near to zero (+1/−1) then the bloc is recognized from the system; press “ENT”. We repeated the procedure with the second color “90”. After this, the system checked plausibility and jumped automatically back to “MEASURE”, if calibration was done properly. The measurement was carried out 24 h after slaughter. Opto value reference standard: Excellent: Opto-wert ≥ 63; Good: 53 ≤ Opto-wert < 63; Inferior: Opto-wert ≤ 53.Western blotting was performed on the same set of samples used in the DIGE  analysis. Briefly, 50 μg pooled protein extracts of each group were fractionated by 10% SDS-PAGE  and transferred to a PVDF (Polyvinylidene Fluoride) membrane as previously described . The sigβ-actin [https://primer3.ut.ee/ (accessed on 12 March 2021) and shown in The quantity and purity of the samples were assessed with a Nanodrop ND-2000 spectrophotometer . The qRT-PCR reaction was performed on an ABI PRISM 7900HT thermal cycler  using FastStart Universal SYBR Green Master . Each sample was repeated three times, and the data represent the average of at least three measurements. The Ct value of the target gene was normalized with the average RNA expression of the housekeeping gene β-actin ,27. The t-tests. A value of p < 0.05 indicated a significant difference.Statistical analyses were performed using the software GraphPad Prism for Windows . Values were expressed as the mean ± SD. Comparisons between two groups were performed by Student’s Mb gene has no annotation information on the official porcine reference genome. We performed the homology alignment of the Mb mRNA sequence on the primary assemblies of 12 pig breeds, and found that the Mb gene had complete genomic information on the primary assembly  of the Wuzhishan mini-pig  from the GEO database, and analyzed the gene differential expression. The results showed that 2620 up-regulated DGEs in muscle tissue of Dapulian pigs included 74 transcription factors, while 493 up-regulated DGEs in muscle tissue of Wei pigs included 13 transcription factors. Among the 87 activated transcription factors, 16 core transcription factors were found to bind to the promoter region of Mb gene, and 23 secondary transcription factors interacted with the Mb gene and its core transcription factors  response element on the MyoD promoter, thus inhibiting myoblast in mice [Although ntiation . FOS inh in mice . Ten tra in mice .STAT1 and STAT3 are not only significantly up-regulated, but also have more binding sites  at the promoter region of Mb in our results. STAT1 and STAT3 are signal transducers and transcriptional activators that play an important role in regulating cell proliferation, differentiation and migration [STAT1 [STAT3 was beneficial to improving the meat quality of cattle. Guillet-Deniau et al. suggested that activation of the JAK-STAT pathway may be associated with myogenic differentiation. Skeletal muscle serotonin increases myostatin expression and triggers activation of the JAK-STAT pathway in primary myogenic cells [ATF3, which belongs to the secondary transcription factor of Mb and does not bind to the promoter of Mb. Glal et al. found that IL-22 can up-regulate ATF3 in intestinal epithelial cells, and ATF3 is necessary for transmitting IL-22 signal to lead to STAT3 phosphorylation and subsequent AMP (Adenosine monophosphate) induction. ATF3 itself does not directly act on STAT3. On the contrary, ATF3 regulates p-STAT3, including SHP2 and PTP-Meg2, by negatively targeting protein tyrosine phosphatase (PTPs) [Studies in the past years have shown that many cytokines and hormones regulate lipid metabolism by activating JAK-STAT signaling pathways. Activated STATs can directly regulate lipid metabolism by affecting the expression of enzymes. igration ,40. Grohn [STAT1 . Meng etic cells . Interese (PTPs) . NFE2L2 and NFE2L1 do not have many up-regulated multiple and binding sites, as CNC-bZIP family transcription factors with basic lysine zipper structures, they play an important role in resisting oxidative stress and maintaining redox homeostasis. Hou et al. found that subcutaneous adipose tissue (SAT) of a series of adipocyte-specific NFE2L1 knockout (NFE2L1 (f)-KO) mice significantly reduced mass insulin resistance adipocyte hypertrophy and severe adipose inflammation. The results of NFE2L1 deficiency may interfere with the expression of lipolysis genes in adipocytes, lead to adipocyte hypertrophy, and then lead to inflammation, cell pyrogenesis and insulin resistance [NFE2L2 deficiency in hepatocytes inhibits the expression of peroxisome proliferator-activated receptor gamma (PPAR γ) and its downstream adipogenic genes in the liver and/or primary hepatocytes induced by HFD (High Fat Diet) and palmitate exposure. However, gene activation through NFE2L2 can be blocked by small MAF proteins, such as MAFG and MAFK, to balance NFE2L2 action and regulate the intracellular oxidation levels [MAFB was up-regulated simultaneously with Mb and NFE2L2 in the longissimus dorsi of local pigs. MAFB is a transcription factor that plays a regulatory role in muscle development and lipid metabolism. It is a member of the MAF transcription factor family. Its members contain a basic leucine zipper (bZIP) domain that binds to DNA. These bZIP domains are located in the MAF recognition element (MARE) and regulate the transcription of the target gene by binding to the acidic domain in the gene [MAFB expression is induced by the liver X receptor (LXR), which is activated by oxidized low-density lipoprotein (LDL) and regulates the macrophage apoptosis inhibitor (AIM), and adipose tissue macrophages (ATM). The expression of AIM in adipocytes plays a role in lipolysis of adipocytes [MAFB -/- fetal liver cells, and feeding these mice with a high-fat diet can inhibit the increase in fat mass [CXCL2, GADD45A) and decreased MAFB correlated with the change in leg lean mass [Mb gene contains only one MAFB binding site, so its regulatory effect deserves further attention.In addition, although sistance . NFE2L2 n levels . In our the gene . Recentlipocytes . Tran etfat mass . In addiean mass . The proAs high-throughput public data published on the NCBI  increases, a large number of pig transcriptome data are expected to contribute to the construction of the Mb-TFs network. At present, local pig breeds in China are distributed in six major regions  . UnfortuMb gene is not found in the pig genome reference annotation file (GFF), and genome aligners such as HISAT2 and BWA are unable to perform SNP calling and calculate reads for the Mb gene. The above problems can only be solved by relying on the database to accumulate more and more high-throughput sequencing data of local pig muscle tissues in the future.In our preliminary experiments, we also examined the transcriptome data of some local pig breeds in public databases, and principal component analysis revealed that only a few samples in the dataset could meet the threshold for differential gene expression analysis. In addition, another reason for the scarcity of studies on pig meat color is that the Mb gene, constructed the transcription model of the Mb gene and identified the potential specific activation transcription factors in the Bama pig breed through in vivo experiments. To summarize, through data mining, we found the complete genomic information of the Our research strategy provides an experimental basis for further research, revealing the formation of pig meat color and its relationship with molecular mechanisms on lipid metabolism.


MT) are associated with a high-risk disease phenotype with increased proliferation, epigenetic therapeutic resistance, and poor survival outcomes. We performed a multi-omics interrogation to define gene expression and chromatin remodeling associated with ASXL1MT in chronic myelomonocytic leukemia (CMML). ASXL1MT are associated with a loss of repressive histone methylation and increase in permissive histone methylation and acetylation in promoter regions. ASXL1MT are further associated with de novo accessibility of distal enhancers binding ETS transcription factors, targeting important leukemogenic driver genes. Chromatin remodeling of promoters and enhancers is strongly associated with gene expression and heterogenous among overexpressed genes. These results provide a comprehensive map of the transcriptome and chromatin landscape of ASXL1MT CMML, forming an important framework for the development of novel therapeutic strategies targeting oncogenic cis interactions.Myeloid neoplasms are clonal hematopoietic stem cell disorders driven by the sequential acquisition of recurrent genetic lesions. Truncating mutations in the chromatin remodeler ASXL1 (ASXL1 MT) are associated with poor clinical outcome, however, their impact on chromatin dynamics remains unexplored. Here the authors use a multi-omics approach for chronic myelomonocytic leukemia (CMML) and investigate the transcriptome and chromatin landscape of ASXL1MT CMML.‘Mutations in the chromatin remodeler ASXL1 (ASXL1 Within myeloid neoplasms, chronic myelomonocytic leukemia (CMML) represents an attractive disease model since it is characterized by both myelodysplastic and myeloproliferative features, while retaining a relatively simple clonal composition3. CMML shares the typical repertoire of genetic driver lesions with other myeloid neoplasms and is particularly enriched in truncating mutations involving ASXL1 3. The presence of truncating ASXL1 mutations in CMML is associated with proliferative disease features, resistance to epigenetic therapies, and adverse outcomes6. Due to their independent prognostic significance, ASXL1 mutations have been incorporated in all three contemporary molecularly integrated CMML-specific prognostic models8. Given the paucity of effective therapies for CMML, delineating the molecular mechanisms of ASXL1-mutant CMML (ASXL1MT) is of particular interest from a therapeutic standpoint.Chronic myeloid neoplasms are malignant clonal hematopoietic stem cell disorders driven by recurrent genetic events, with an inherent risk of transformation to acute myeloid leukemia (AML)ASXL1 mutations promote leukemogenesis by transcriptional up-regulation of leukemogenic drivers including posterior HOXA genes, and that these mutations recruit several effectors to alter the epigenome through histone modifications, increases in chromatin accessibility, and remodeling of enhancers14. However, to our knowledge, no studies have been able to extensively query the epigenome in primary patient samples. Given the complexity of human transcriptional regulation in vivo and the multitude of potential epigenetic mechanisms cooperating to regulate transcriptional activity, questions remain about the interplay of regulatory mechanisms in patients with CMML. To elucidate this interplay, we interrogated the genome, transcriptome, and epigenome of patients with ASXL1-wildtype (ASXL1WT) and ASXL1MT CMML. In this work, we integrated somatic mutations, transcription, (hydroxy)methylation, histone modifications, and chromatin accessibility to reveal the complexity of the epigenetic landscape, the simultaneous presence of multiple regulatory mechanisms affecting drivers of leukemogenesis, and remodeling of the enhancer landscape as an important driver of intratumoral heterogeneity. These insights into the epigenetic landscape of ASXL1MT CMML generated from primary patient samples are of considerable interest for the development of novel targeted therapeutic strategies for patients with ASXL1MT CMML.The sum of evidence from mechanistic studies suggests that ASXL1 has a complex interactome, that truncating MT (n = 8) and ASXL1WT (n = 8) CMML . This included several modulators of DNA methylation including TET2, DNMT3A, and IDH2 . As previously reported, G646W (c.1934dup) was the most prevalent ASXL1 mutation and the observed variant allele frequencies of all ASXL1 mutations were consistent with heterozygosity16. The presence of truncating ASXL1 mutations was associated with increased all-cause mortality in the larger patient population seen at our institution (n = 375) from which the 16 patients is this study were sampled  showed gene expression profiles intermediate between G646W and ASXL1WT patients . Both samples in question clustered with the other ASXL1MT samples with regards to their epigenetic features despite the apparent differences in their gene expression profiles  for all subsequent analyses. Functional annotation of the differentially expressed genes revealed several affected cellular processes including up-regulation of cell division , DNA replication and repair , gene expression , signal transduction , and antigen presentation  pathways  and chromatin accessibility (ATAC-seq) by fitting a 7-state hidden Markov model . This transition was mainly driven by H3K27ac and H3K4me1 gains in promoter regions. To verify that changes in promoter occupancy by the discovered chromatin states could serve as a plausible explanation for the observed transcriptional activity, we tested the associations between the presence of chromatin states and gene expression in ASXL1MT CMML. As expected, the presence of active chromatin states was associated with increased gene expression  and chromatin with isolated accessibility (04) towards both active (05) and repressed states (02). Among the up-regulated genes there was a marked increase of the active promoter state (05) around the transcription start site and losses of active chromatin states  in the flanking regions. We did observe a gain in active chromatin states and / or loss of poised and repressed chromatin states in the promoter regions of the majority of up-regulated genes including mitotic kinases and several HOXA cluster genes methylation of promoter regions of the up-regulated genes methylation status, we performed a stratified analysis that demonstrated no significant difference in global DNA (hydroxy)methylation . These data support the notion that gene body (hydroxy)methylation is associated with increased gene expression. However, the lack of differential (hydroxy)methylation between ASXL1WT and ASXL1MT for the up-regulated genes make it an unlikely explanation for the observed increase in transcriptional activity.Given this heterogeneity of promoter chromatin states across the up-regulated genes, we sought to interrogate potential alternative epigenetic regulatory mechanisms that may explain the observed transcriptional activity. Aberrant genome-wide DNA methylation is associated with adverse cytogenetics features, increased leukemic transformation rates, and inferior survival outcomes in CMMLup- Fig.  or down-up- Fig. . Similarup- Fig. . Again, up- Fig.  or down-up- Fig. . In addipes Fig. . To ensu22. Enhancers have been associated with aberrant transcriptional activity and response to treatment in CMML23. We identified candidate cis-regulatory elements by integrating chromatin accessibility and histone acetylation data. To explain increased transcriptional activity among ASXL1MT patients, we hypothesized that the responsible cis-regulatory elements would only be present in ASXL1MT but not ASXL1WT CMML (genotype-specific cis-regulatory elements). Regions with chromatin accessibility (ATAC-seq) which were concurrently marked by H3K27ac and unique to ASXL1MT were considered candidate regions  and this enrichment was slightly more pronounced in the super-enhancer regions . To associate the enhancers with putative target genes, we used a neighboring gene approach incorporating proximity on the linear genome, experimentally determined regulatory domains, and localization within conserved topologically associating domains (TADs)31. We excluded candidate regions overlapping with promoter regions and focused on distal enhancers (91% of the identified regions). These 3214 ASXL1MT-specific distal enhancers were implicated in 4981 cis-interactions with 3308 genes . Among these 97 up-regulated target genes were MEIS1 and 17 mitotic kinases including CDK1, CCNE1, and CDC20 . By virtue of the applied selection algorithm (GREAT) and additional constraints (TADs), most ASXL1MT-specific distal enhancers were localized between 5 and 200 kb of their associated target gene  was comparable to a 20% increase in active promoter chromatin states or in gene body methylation. A measure of the relative importance of the different epigenetic regulatory mechanisms is shown in Supplementary Fig. MT-specific distal enhancers are independently associated with increased oncogenic gene expression and their presence can serve as a plausible explanation for the observed increased transcriptional activity including the overexpression of mitotic kinases.Cis-regulatory elements are powerful regulators of transcription and frequently altered in myeloid neoplasmsons Fig. . There wons Fig. . The vasons Fig.  and 2% bors Fig. 28. Interons Fig. 29. BRD4 nes Fig. . Among tene Fig. . The preene Fig. . The effene Fig. . The gening Fig. . To undeMT-specific distal enhancers in proximity to several of the up-regulated genes, we performed single-cell ATAC-seq on three patients with ASXL1WT (6832 cells) and three patients with ASXL1MT CMML (5360 cells) to validate our bulk ATAC-seq findings , we again observed an enrichment of ETS family transcription factor motifs . These data support the notion that ASXL1MT CMML is characterized by an increase in intratumoral heterogeneity with increased chromatin accessibility for transcription factors binding ASXL1MT-specific distal enhancers.Having identified ETS family transcription factors predicted to bind the ASXL1ifs Fig. . Among t ASXL1WT 832 cellsifs Fig. . SimilarMT CMML is characterized by overexpression of proliferative genes, mirroring the clinical phenotype of leukocytosis, splenomegaly, resistance to epigenetic therapies (DNA methyltransferase inhibitors), and increased leukemic transformation rates35. Gaining a better understanding of the molecular mechanisms underlying this aberrant transcriptional activity is important for the development of novel therapies for patients with ASXL1MT CMML. In this study we interrogated the epigenome of patients with ASXL1MT CMML and observed a transition from poised and inactive to active chromatin states in promoter regions and increased chromatin accessibility exposing de novo distal enhancers correlating with the transcription of affected genes. We furthermore observed an increase in intratumoral heterogeneity due to an extended repertoire of transcription factor motifs, again involving ASXL1MT-specific distal enhancers. These results suggest an important role of oncogenic cis interactions for the sustained expression of key drivers of leukemogenesis in ASXLMT CMML.The gene expression profile of ASXL1Drosophila, a protein that regulates the balance of trithorax (activating) and polycomb (repressive) functions. In myeloid neoplasms, the ASXL1 gene is frequently affected by nonsense and frameshift mutations leading to truncation of the protein at the C-terminus and loss of the plant homeodomain (PHD)15. Hitherto, the associated proteins for the PHD of the Asx family remain unknown36. Investigations employing different disease models have shed light on the transcriptomic and epigenetic changes associated with ASXL1 loss and truncation. ASXL1 is thought to recruit chromatin modulators and transcription factors to alter transcriptional activity of genes involved in leukemogenesis. The exact molecular mechanisms associated with truncating ASXL1 mutations however remain to be defined. Based on the interaction between ASXL1 and BAP1, a predominant loss of polycomb repressive complex 1 (PRC1)-mediated histone de-ubiquitination was the initial expected mechanism regulating transcriptional activity in ASXL1MT patients37. However, observations in ASXL1MT AML cell lines suggested a predominant loss of polycomb repressive complex 2 (PRC2)-mediated histone methylation (loss of H3K27me3) to be the regulatory mechanism at work and implicated the deregulation of posterior HOXA cluster genes as key factors in the ensuing leukemogenesis9. Observations from an ASXL1MT murine model suggested a gain of function of PRC1-mediated effects rather than altered PRC2 activity (unaffected H3K27me3)13. The notion that increased de-ubiquitination is facilitating the transcriptional up-regulation through enhanced activity of the ASXL1-BAP1 complex was further confirmed in different ASXL1MT cell lines, however, a decrease in H3K27me3 was observed at the same time in this disease model11. Loss of both H3K27me3 and H2AK119Ub promoting the expression of leukemogenic posterior HOXA genes has since been confirmed in cell lines and a murine model10. Another ASXL1MT murine model revealed an increase in chromatin accessibility along with the recruitment of the bromodomain and extra-terminal domain family member BRD4, resulting in enhanced expression of genes involved in stem-cell maintenance and myeloid differentiation14. Overall, the sum of evidence from these mechanistic studies suggests that ASXL1 has a complex interactome, that truncating ASXL1 mutations promote leukemogenesis by transcriptional up-regulation of leukemogenic drivers including posterior HOXA genes, and that these mutations recruit several effectors to alter the epigenome through histone modifications, increases in chromatin accessibility, and remodeling of enhancers14.The three ASXL proteins  are mammalian homologs of Addition of sex combs (Asx) in MT CMML using primary patient samples. Employing conservative statistical methods and relying on a robust number of biological replicates, we observed significant changes in promoter chromatin states and distal enhancers in ASXL1MT CMML. In general, these changes were less extreme than the ones observed in data generated from different hematopoietic precursor and leukemic cell lines11. We interpret these differences as a consequence of greater biological variability in primary patient samples and the statistical framework employed in this study. While previous studies have focused on global changes in histone modifications and considered them in isolation, we integrated several omics layers using an unbiased machine learning approach to capture the complexity of the epigenetic landscape to a greater extent. In doing so, we observed significant differences between global trends in chromatin remodeling and changes affecting the differentially expressed genes of interest. This may indicate that global assessments of chromatin remodeling are only part of the information required to develop effective targeted epigenetic therapies for specific leukemogenic mechanisms. For many of the up-regulated genes, we observed chromatin state transitions towards active states driven by H3K27ac deposition in promoter regions. Similarly, we observed a loss of H2K27me3 affecting the promoter regions of several of the up-regulated genes (either in isolation or in concert with the aforementioned gains in active chromatin states). For several up-regulated, yet relatively lowly expressed genes the model did not reveal marked state transitions in the promoter regions. The global increase in the active promoter state also included gains in H3K4me1, which is of particular interest since KMT2A fusions, partial tandem duplications, abnormalities of KMT2C/KMT2D/SET1D/2D (COMPASS family) are exceedingly rare in CMML38. Another open question concerns the locus-specific effects of truncating ASXL1 mutations. While we observed widespread remodeling of the epigenome, we do not fully understand how truncated ASXL1 exerts its effects selectively across the epigenome. This represents a limitation of the current study and future work in suitable disease models may help further refine the list of targets of truncated ASXL1. While ChIP-seq studies with antibodies directed against truncated ASXL1 may reveal genomic loci of direct interaction, other ASXL1-mutant-specific effects may be mediated through protein-protein interactions40. Furthermore, our gene expression data did not support the gene expression profiles observed in different murine myeloid disease models (e.g. lack of HHEX up- and lack of SOX6 down-regulation in ASXL1MT CMML) and it remains debatable how closely these models recapitulate human disease biology, given the inherent heterogeneity of human myeloid neoplasms41. In search of additional regulatory mechanisms for the up-regulated genes, we revisited promoter and gene body methylation without discovering compelling evidence for either mechanism being at work in ASXL1MT CMML, despite the high prevalence of concurrent TET2 mutations35. This is consistent with previous observations in CMML pointing towards the importance of hematopoiesis-specific enhancers and may further be explained by the relatively balanced distribution of co-mutations in this study21. In CMML, aberrant methylation is known to be associated with distinct clinical features such as high-risk karyotypes and therapeutic resistance23. At the same time, the most commonly employed treatment strategy for CMML is the inhibition of DNMT with DNA methyltransferase inhibitors , which are thought to epigenetically restore normal hematopoiesis in a subset of patients without altering the mutational burden or inherent risk of leukemic transformation44. Furthermore, subsequent translational investigations in CD14-selected peripheral blood mononuclear cells demonstrated that differential promoter methylation does not correlate with transcription in CMML45. Our DNA immunoprecipitation and microarray methylation studies using unselected bone marrow mononuclear cells do confirm these findings, so do our RNA-seq results in CD34-selected, CD14-selected, and unselected bone marrow mononuclear cells. Extending the search for plausible additional regulatory mechanisms, we discovered a group of distal enhancers with interesting characteristics. First, these enhancers were specific for ASXL1MT CMML. Second, these enhancers associated with several of the genes up-regulated in ASXL1MT CMML. Third, these enhancers tightly correlated with transcription independent of the prevalent promoter chromatin states and gene body (hydroxy)methylation. A model including promoter chromatin states, ASXL1MT-specific distal enhancers, and gene body (hydroxy)methylation explained almost half of the variation in gene expression and adds to our understanding of the complexity of transcriptional regulation in ASXL1MT CMML. These ASXL1MT-specific distal enhancers were highly enriched in ETS family transcription factor motifs as well as BRD4 and explained part of the observed increase in intratumoral heterogeneity at the single-cell level. The mapping of these enhancers and their association with key up-regulated leukemogenic drivers such as MEIS1 and several mitotic kinases is of considerable interest given that the presence of such enhancers represents an explanation for the lineage- and context-specific transcriptional effects of novel therapeutic agents such as BET bromodomain inhibitors47. Observing BRD4 enrichment in the ASXL1MT-specific distal (super-)enhancers, further support the exploration of these novel therapeutics for individualized treatment approaches in ASXL1MT CMML. Here we drew a detailed map of oncogenic distal enhancers unique to a high-risk phenotype of CMML, laying the foundation for future mechanistic studies defining the viability of these aberrant cis interactions to serve as therapeutic targets. Genotype-specific oncogenic chromatin interactions may be exploited for therapeutic benefit and serve as the rationale for early phase clinical trials with emerging epigenetic small molecule therapeutics in a disease that has not seen significant therapeutic advances in the last 25 years51.Given the complexity of human transcriptional regulation in vivo and the multitude of potential epigenetic mechanisms cooperating to regulate transcriptional activity, we interrogated the genome, transcriptome, and epigenome of ASXL1ASXL1 mutations. We performed targeted next generation sequencing of DNA, whole transcriptome RNA sequencing (RNA-seq), immunoprecipitation of DNA hydroxymethyl and methyl residues (DIP-seq), immunoprecipitation of the histone modifications H2AK119ub, H3K4me1, H3K4me3, H3K27ac, and H3K27me3 (ChIP-seq), and DNA transposase accessibility assays (ATAC-seq). The RNA-seq data were used to define the transcriptional activity in ASXL1MT CMML and then correlated with known epigenetic regulatory mechanisms by integrating the ChIP-, DIP-, and ATAC-seq data. CMML is a malignant proliferation of monocytes and, unlike in acute myeloid leukemia, the fraction of CD34+ blasts is usually below 5%35. CD14 on the other hand is a marker of terminally differentiated monocytes as they appear in the peripheral blood and it is debatable whether the CD14+ cell population in the bone marrow represents the malignant cell population of interest in CMML. We refrained from further CD34- or CD14-sorting the bone marrow mononuclear cells for several reasons: First, the cell attrition rates due to CD34- and CD14-sorting would have made the multi-omics interrogation of primary patient samples for this study impossible (particularly the cell requirements for the ChIP-seq experiments). Second, we observed a tight correlation between the gene expression profiles of sorted and unsorted cells, raising the question whether cell sorting would have influenced the results significantly . Genomic DNA was isolated using the ACCEL-NGS 1 S Plus DNA Library Kit . Total RNA was analyzed by preparing cDNA libraries generated in accord with the RNA-seq preparation protocol . Viable bone marrow mononuclear cells were stored in freezing medium  at −80 °C.To survey the (epi-)genetic landscape of human CMML, we obtained bone marrow mononuclear cells from 16 patients with WHO-defined CMML . Libraries derived from each DNA sample were prepared using NEB Ultra II  and individually barcoded by dual indexing. Sequencing was performed on an HiSeq 4000 (Illumina) with 150 bp paired-end reads. Forty-eight pooled libraries per lane were sequenced to a median read depth of ~400x. The custom panel of target regions covered all coding regions and consensus splice sites from the following 36 genes: 53. Reads were aligned to the GRCh38 reference genome and transcript counts calculated using featureCounts (v2.0.0)54. Differential expression was performed using DESeq2 (v1.30.1), clustering using heatmap.2 , and plotting using EnhancedVolcano 55. Pathway analyses were performed using gProfiler (v2021-05-01) and the Reactome Pathway Knowledgebase (v2021-5-7)57. Potential therapeutic targets among the up-regulated genes were identified by querying DGIdb (v3.0)17. For the validation of RNA-seq results we analyzed a subset of target genes by quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR). Total RNA from the RNA-seq experiments was used to synthetize cDNA with the High-Capacity cDNA Reverse Transcription Kit . A 1/3 dilution of the total cDNA was amplified by real-time PCR. Samples were prepared with PerfeCTa SYBR Green FastMix , the primer sets are given in Supplementary Data RNA quality was determined using an Agilent Bioanalyzer RNA Nanochip or Caliper RNA assay. Library preparation was performed using TruSeq Stranded Total RNA (Illumina) and sequenced on an HiSeq 2500 (Illumina) with paired-end reads. RNA-seq sequencing reads were processed through the MAPRSeq bioinformatics workflow as previously described DESeq2 v.30.1, cl3, 1.0% SDS), incubated at 65 °C for 90 min. Protein digestion was performed on the eluted DNA samples at 50 °C for 30 min using protease mix . ChIP DNA was purified using Sera-Mag beads (Thermo Fisher Scientific) with 30% PEG before library construction. H2AK119ub and H3K27ac ChIP-seq was performed as previously described58. The antibodies were used at a dilution of 1:250 (H3K27me3), 1:500 (H3K4me1 and H3K4me3), and 1:1000 (H2AK119ub and H3K27ac). Libraries were prepared by following a modified Illumina paired-end protocol and sequenced on an HiSeq 2500 (Illumina) to a median depth of ~25 million (H3K4me1 and H3K4me3) or ~50 million reads (H3K27me3 and Input). Reads were aligned to the GRCh38 reference genome using bowtie2 (v2.3.3.1)59.Approximately 100,000 cells from each sample were used for input for native chromatin immunoprecipitation (N-ChIP). Cells were lysed on ice for 20 min in lysis buffer containing 0.1% Triton X-100, 0.1% deoxycholate, and protease inhibitor. Extracted chromatin was digested with 90U of MNase enzyme (New England Biolabs) for 6 min at 25 °C. The reaction was quenched with 250 µM of EDTA post-digestion. A mix of 1.0% Triton X-100 and 1.0% deoxycholate was added to the digested samples and incubated on ice for 20 min. Digested chromatin was pooled and pre-cleared in IP buffer  plus protease inhibitors with pre-washed Protein A/G Dynabeads  at 4 °C for 1.5 h. Supernatants were removed from the beads and transferred to a 96-well plate containing the antibody-bead complex. Following an overnight 4 °C incubation, samples were washed twice with low salt buffer  and twice with high salt buffer . DNA-antibody complexes were eluted in elution buffer  array (Illumina) according to the manufacturer’s specifications. Signal intensities were processed and normalized using Minfi (v1.36.0) using subset within-array quantile normalization62. CpGs below the detection threshold (p < 0.010) as well as unreliable and cross-reactive probes were removed, leaving 787403 CpGs for downstream analyses. Differentially methylated regions were identified using bumphunter (v1.32.0)63.Genomic DNA was isolated and submitted to the Mayo Clinic Epigenomics Development Laboratory (EDL) for DNA immunoprecipitation and library preparation. The 5mC-33D3 (C15200081) monoclonal antibody , an in-house developed 5hmC antibody (EDL), and the 53017 bridging antibody  were used. The antibodies were used at a dilution of 1:110 (5hmC) and 1:340 (5mC). Libraries were prepared by following a modified Illumina paired-end protocol and sequenced on a HiSeq 4000 (Illumina). Reads were aligned to the GRCh38 reference genome using the Burrows-Wheeler AlignerCorces et al. with modifications using the Nextera kit (Illumina)64. The cells were lysed for 3 min on ice and transposed for 30 min at 37 °C following clean-up. The DNA libraries were prepared with 5–10 cycles of PCR amplification with the NEB High Fidelity Master Mix . Clean-up was done using the Zymo DNA Clean and Concentrator kit  and followed with AMPure XP  bead clean-up to remove primer dimers and under-digested chromatin. Sequencing was performed on an HiSeq 4000 (Illumina) to a depth of ~30 million reads per sample. Reads were aligned to GRCh38 using bowtie259. For the single-cell chromatin transposase accessibility assays (scATAC-seq) cryopreserved bone marrow mononuclear cells were thawed and resuspended following an established workflow  and approximately 100000 viable mononuclear cells per sample were subjected to transposase assays (exposing buffered nuclei to Tn5 transposase) before proceeding to single-cell partitioning into gel beads in emulsion, barcoding, library construction, and sequencing following established 10X Genomics protocols. The target cell recovery was approximately 2000 cells per sample. For details on the 10X Genomics Chromium platform including demonstrated protocols on sample preparation, library construction, instrument settings, and sequencing parameters please see the manufacturer’s resources (https://support.10xgenomics.com/single-cell-atac). Genomic libraries were sequenced on an HiSeq 4000 (Illumina) before demultiplexing, alignment to the reference genome, and post-alignment quality control. The 10X Genomics Cell Ranger ATAC software (v2.0) was used for demultiplexing, alignment of the reads to the GRCh38 reference genome, filtering and quality control, counting of barcodes and unique molecular identifiers, identification of transposase cut sites, detection of accessible chromatin peaks, count matrix generation for peaks and transcription factors.DNA for ATAC-seq was prepared from 50,000 cells following the OMNI-ATAC procedures as described by 66. Peak calling with default parameters was performed before subjecting the peaks to the MSPC (v5.4.0) consensus peak calling algorithm67. By convention H3K4me1, H2AK119ub, and H3K27me3 peaks were called as “broad peaks” and the remaining marks were called as “narrow peaks”. To leverage the power of several biological replicates per analysis we employed a standard peak calling threshold in MACS2 (FDR < 0.050) before applying a more stringent threshold in the subsequent MSPC step . The UCSC Genome Browser and deepTools (v3.5.0) were used for signal visualization69. For visualization purposes averaged, input-corrected average signal tracks were created (except ATAC-seq where no input was used) using wiggletools mean (v1.2) and deepTools bamCoverage / bigwigCompare70.Aligned reads from the different immunoprecipitation and accessibility sequencing experiments were sorted and indexed using samtools (v1.9) and peaks were called using MACS2 (v3.0.0a6) with input controls (except ATAC-seq)71. Overall survival was defined as the time from diagnosis to death and patients who were alive at the end of follow-up were censored. Leukemia-free survival was defined as the time from diagnosis to leukemic transformation or death and patients who were alive and free of acute leukemia at the end of follow-up were censored. The log-rank test was used to compare time to event data in subgroups. Multivariable-adjusted (Cox) proportional hazards regression models were used to assess the association between clinical and genetic parameters of interest and overall survival72. Violations of the proportional hazards assumption were evaluated using Schoenfeld residuals. Hidden Markov modeling (ChromHMM v1.22) was used to discover and characterize the presence of chromatin states in promoter regions (TSS ± 2000bp)18. We fit 5- to 15-state models and judged their goodness of fit by each model’s ability to discriminate important chromatin states  without creating an excessive number of combinatorial states within each epigenetic mark. Based on the ability to sufficiently discriminate the activity of promoters and enhancers, the 7-state model was felt to be the most parsimonious model with acceptable fit for the data. Candidate cis-regulatory elements were validated using the ENCODE, GeneHancer, and Hacer databases27. To annotate and associate these regions with potential target genes, the Genomic Regions Enrichment of Annotations Tool (GREAT v4.0.4) was used30. To increase the specificity of the predicted (enhancer-promoter) cis-interactions, we included only those that did not violate the boundaries of topologically associating domains established in K562 cells31. Super-enhancers were identified by intersecting the candidate cis-regulatory elements with known super-enhancers in K562 cells26. De novo motif analysis was performed using Hypergeometric Optimization of Motif EnRichment (HOMER v3.0)28. Candidate transcription factors predicted to bind these regions were then validated by querying the ReMap 2020 database29. To estimate the independent effect of each epigenetic regulatory mechanism on gene expression, we employed multivariable-adjusted linear regression. Given the heavily left-skewed distribution and overdispersion of transcript count data we performed a log-transformed of the transcript count data (adding a pseudocount). We then used ordinary least squares regression to model the transformed transcript count data and exponentiated the obtained regression coefficients for ease of interpretation of the effect sizes (eβ − 1). The model included the active and inactive chromatin states, gene body (hydroxy)methylation, and the presence of an associated distal enhancer as parameters. We used the lmg metric  as an estimate of relative importance for each model paramaters74. Routine regression diagnostics were employed to evaluate model assumptions and model fit . Observations with excessive leverage (h > [(2 * k)/n]) and influence (Cook’s D > [4/(n − k − 1)]) were removed (n denoting the sample size and k the number of model parameters). Quality control, integration, normalization (TF-IDF), scaling, feature selection, clustering, and dimensionality reduction (SVD) of the scATAC-seq data was performed using Seurat (v4.0.5)75. Batch correction was performed using Harmony (v0.1.0)76. Single-cell entropies were calculated using BioQC (v1.18.0)78.Data are presented as median (range) unless denoted otherwise. Medians were the preferred measure of central tendency and non-parametric hypothesis tests were used for comparisons unless stated otherwise. Continuous variables were compared using the Mann-Whitney-U test, categorical variable using Fischer’s exact test. Trends across ordered groups were assessed using Cuzick’s test for trend. The equality of single-cell entropy distributions was evaluated using the two-sample Kolmogorov-Smirnov test. Overall and leukemia-free survival estimates were calculated using the Kaplan and Meier methodFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting Summary


Understanding the factors that underlie the epigenetic regulation of genes is crucial to understand the gene regulatory machinery as a whole. Several experimental and computational studies examined the relationship between different factors involved. Here we investigate the relationship between transcription factors (TFs) and histone modifications (HMs), based on ChIP-seq data in cell lines. As it was shown that gene regulation by TFs differs depending on the CpG class of a promoter, we study the impact of the CpG content in promoters on the associations between TFs and HMs. We suggest an approach based on sparse linear regression models to infer associations between TFs and HMs with respect to CpG content. A study of the partial correlation of HMs for the two classes of high and low CpG content reveals possible CpG dependence and potential candidates for confounding factors in our models. We show that the models are accurate, inferred associations reflect known biological relationships, and we give new insight into associations with respect to CpG content. Moreover, analysis of a ChIP-seq dataset in HepG2 cells of the HM H3K122ac, an HM about little is known, reveals novel TF associations and supports a previously established link to active transcription. Epigenetic modifications play a crucial role in the gene regulatory system. They allow a single DNA molecule to be interpreted in different cell types and throughout different developmental stages, by directly interacting with other regulatory factors or by reorganizing the DNA accessibility in the cell and thereby affecting gene transcription . The genHere, we consider the interplay between two important layers of gene regulation, DNA-binding transcription factors (TFs), and post-translational modification of histone residues (HMs) of the nucleosome complex. Co-localization and interactions between both these layers have been observed and have proven to be useful for determining the expression level of nearby genes –19. ApprIn the past, different computational models have been proposed to elucidate the role of Epigenetic factors in gene regulatory networks and, in particular, to discover patterns and associations from epigenetic data about TF binding and HM occurrence. From the data perspective, the models can be categorized into three different groups, 1) unsupervised approaches using TF and HM data, 2) the prediction of TF binding based on HM data, and 3) the prediction of HM binding based on TF data.Successful unsupervised approaches leveraged graphical models for discovering interactions between TF and HMs. Lasserre et al.  combinedFor the purpose of predicting HM information based on TF data, different methods have been employed. Whitaker et al. use DNA binding motifs to make a binary prediction of the presence or absence of an HM peak employing a LASSO logistic regression model to filter predictive motifs and a Random Forest on the filtered motifs to classify whether an HM is present at a specific region . BenveniIn our work we build up on the previous approaches by extending the existing ideas to capture more complex relationships, investigating the impact of CpG content on the regulatory machinery, and studying new data. In contrast to Benveniste et al., we use a LASSO regression based approach to be able to investigate negative associations between TFs and HMs and to use the coefficients as a mean to measure the strength of their association. Furthermore, a LASSO approach implicitly selects a subset of TFs that impact HM occupancy, by driving other coefficients to zero. This implicit feature selection allows us to distill the factors relevant for HMs from a large set of TFs without prior knowledge. We extend the work of Perner et al. by using a larger set of TFs, not restricting to chromatin modifiers, to be able to derive novel, unknown interactions, or associations between TFs and HMs. Furthermore, we binned the region around the TSS into windows of 100bp length to improve resolution and added a fusion penalty to enforce similar coefficients for adjacent bins in order to better resemble the input signal. Apart from these technical extensions, our main contribution lies in the analysis of the impact of promoter CpG content on the derived models by splitting the gene set into promoters with high and low CpG content, following the ideas of Karlic et al., and Saxonov et al. , 35. It m TFs as input (features) and the abundance of an HM as output (response). Thus, we can formulate the prediction of the abundance abd(HMY) of a Histone Modification HMY asβ0 is the model bias term and βi, i = 1‥n are model coefficients subject to optimization. In our case, the abundance of both TFs and HMs is obtained by using ChIP-seq measurements in non-overlapping windows from 500bp upstream to 500bp downstream of the transcription start site (TSS) of all annotated protein coding genes. To get a more refined resolution of the ChIP-seq data for the features, we further bin those windows into segments of 100bp length, in which read counts of the TF ChIP-seq data are accumulated (norm) instead of using the raw read counts. Thus, we modify k bins for each of the m TFs. To gain more interpretability, we impose regularizations on the coefficients. First, we add an L1 norm of the coefficients as a penalization term to achieve sparsity, thus reducing the number of TFs selected for an HM to the ones with the strongest relationship to that HM. One approach to solve linear regression with L1 norm is LASSO and has been established in the machine learning community, known to recover features that are important to explain the response variable and setting feature coefficients which are weakly correlated with the response to zero [G = , such that for each pair of features (vertices) connected by an edge, a penalization for the absolute difference between their coefficients is added to the optimization function. In our case, E is the set of pairs of adjacent bins.HM presence has been associated with the presence of several TFs , 31, 34.umulated  top. To  to zero . Second, to zero , 40 inco1 and λ2 are Lagrangian multipliers adjusting the contribution of the two penalization terms to the overall loss.Thus, the final objective function that is optimized to find a set of coefficients γ = 0 applies only fusion and γ = 1 enforces similar sparsity and fusion penalties in the model. The parameter γ is explored within the set of values {0} ∪ {10i|i = −5, −4, ⋯, 5}. The λ2 values corresponding to the knots at the solution path are then derived for each γ value.To train the models we use the generalized fused LASSO implementation provided by Arnold et al. . For a gα = 0.005 to assess the statistical significance of the derived coefficients. We computed the F-statistic as0 denotes the residual sum of squares for the reduced model by excluding the TF we test for, RSSfull is the RSS of the full model [0) by setting the coefficients of the TF we test for to zero to estimate the F-statistic.For each of the models, we examined the impact of each TF on the response signal of an HM. For this, we summed the coefficients over all bins of a TF and performed an F-test with significance cutoff ll model . The vared LASSO . As re-tα as important and significant. This ensures that, on the one hand all TFs that we consider are statistically significant for predicting the response, but on the other hand maintains a comprehensible subset of TFs. The overall workflow of our approach is depicted in Once we computed p-values for each TF, we report the TFs with the ten lowest p-values that are smaller than It is known that CpG content has an impact on different components of the gene regulatory system , 42. ForTo investigate the influence of genomic CpG content on the relationship of TFs and HMs, we partition the promoter regions of protein coding genes into those with high, and those with low CpG content for the given reference genome. We thus obtain a criterion that allows us to split the samples of each experiment into two groups, that we can model separately to reveal CpG dependent trends. This split into two groups does arise naturally from the promoter regions, as Saxonov et al. showed by looking at frequencies of CpG content across genes . That reHere, we reproduced their results for hg19, the reference genome build for the processed data, by counting the number of CpGs from 500bp upstream to 500bp downstream of TSSs of protein coding genes, counting CpGs in bins of width 5. This yields a bimodal distribution analogous to Saxonov et al. , which cX and Y given a set of other variables ρ between the residuals of the two linear regression models predicting X with Y with X and Y after removing the information of other variables X and Y given n = 10000 permutations of the residuals σ indicating that these residuals are permuted and keep n, and a significance threshold of α = 0.01 is set for Bonferroni corrected p-values. The results are summarized in Partial correlation of two variables All models were generated on data of the immortalized human liver carcinoma cell line HepG2 and leukemia cell line K562. The HM data is a set of files derived from ChIP-Seq experiments produced within the DEEP project  represenhttps://www.ebi.ac.uk/ega/home). For the K562 cell line both HM and TF, as well as HepG2 TF data can be accessed through the encode portal (https://www.encodeproject.org/matrix/?type = Experiment). S1 and S2 Tables in The HepG2 cell line HM data has been obtained through the DEEP project. This data has been deposited at the European Genome-Phenome Archive (EGA) under the accession number EGAS00001001937  and the CpG content. In In addition to comparing models based on the HM’s function, we can contrast them based on the genes’ CpG content. In general, we observe that, in models trained for a given HM, often one of the CpG classes, either models with CpG poor or CpG rich genes, outperforms the respective other class significantly, for instance in all histone marks associated with active genes. To confirm their significance, we performaned a Wilcoxon test with Benjamini-Hochberg test correction  is a well known TF linked to transcriptional activation and is crucial for the expression of most genes as a recruiter for RNA Polymerase II . FurtherThe transcription factors ARID4B and SAP130 are both subunits of SIN3A, a co-repressor complex with histone deacetylase (HDAC) activity. More specifically, SIN3A contains HDAC1 and HDAC2 subunits , and is Further examples for the biological relevance of our findings include the Structure Specific Recognition Protein 1 (SSRP1), which is a subunit of the FACT complex, acting as a histone chaperone to reorganize chromosomes to enable transcriptional elongation . H3K27acWe can also derive associations with respect to the CpG content classes. For example, THAP11 is associated with binding to HCFC1, a protein complex known to bind to CpG islands and to recruit different chromatin modifying enzymes including KDM1A, a H3K9 demethylase . In our As a final example, we examine the relationships of the RNA polymerase II-associated factor 1 homolog (PAF1). This factor interacts with RNA Polymerase II to stimulate transcriptional activity . This beSo far, we showed that many of the derived interactions of our models are consistent with reports in the literature as well as results of a ChIP-MS study. This suggests that also novel inferred associations between TFs and HMs reflect biologically meaningful relationships. For instance, the Transcriptional Enhancer Factor TEF-5 (syn. TEAD3) is a protein important for cell growth and tumor suppression . So far,So far, we examined the relationships of TFs with well known HMs and showed that those yield interesting characteristics with regard to CpG content and are biological meaningful. Here, we apply our methodology to H3K122ac—a modification for which the precise function is still unknown—to elucidate its role in gene regulation. First studies suggest that this mark, located at the dyad axis of the nucleosome, destabilizes the nucleosome complex, thus loosening up densely packed chromatin and stimulating transcription . Our modHowever, the partial correlation study  indicateIn our analysis, we show that with the suggested models we were able to accurately predict histone modifications based on TF abundance and thereby infer biologically meaningful relationships. We observe a difference between models trained on modifications that are linked to active genes, such as H3K27ac and H3K4me3, performing better than models of marks that are mostly associated with repressed genes, such as H3K9m3 and H3K27me3. One reason could be that the latter are reported as marks for heterochromatic regions, densely packed chromatin, where it is in general more difficult for TFs to bind as most of the DNA is not directly accessible. Thus, there is less information available to the model, because our input is TF ChIP-seq measurements. We also observe a drop in performance when looking at H3K36me3 compared to other models. This mark is an elongation mark and as such, it is mostly found in the gene body , whereasWithin the scope of the literature search to assess biological relevance of the found associations, we discovered subtle relationships that could not be explained by individual literature alone. For example, ARID4B and SAP130 are both subunits of the SIN3A complex, which is involved in histone deacetylation . HoweverWe also find that certain TFs, such as GATAD2A show binding preferences depending on the CpG content. For GATAD2A, there seems to be a binding preference indicated by high coefficients for certain bins of the promoter window for H3K27ac and H3K122ac. There is no such trend apparent in the CpG rich models of marks associated with transcriptional activation, where this TF is also generally not picked as an important feature of the model compare . This suIn this work we extended existing approaches that investigate the relationship between TFs and HMs in the light of gene regulation based on ChIP-seq data. In particular, we studied TF–HM relationships in the context of sequence CpG content and expand the work to the H3K122ac mark, about little has been known so far. We use the regularized regression method Fused LASSO  to prediFor the modeling, we complement the method of Benveniste et al.  by usingTo assess the biological relevance of the discovered associations, we additionally searched the literature for the results of the HepG2 cell line. We were able to explain many of the derived relationships directly by, for instance, interaction of TFs with a chromatin modifying complex, or TFs building a subunit of such a complex. Several other discovered associations could be explained by reportedly strong connections of the TF with active transcription, where the positively associated marks were linked to active transcription and negatively associated marks to repressed transcription. We also found relationships that were more subtle, such as ARID4B and SAP130 associated with H3K27ac, leaving room for speculation. The results of our model can also be used to guide future work on discovering novel interaction partners between TFs and HMs, as some of the discovered associations have not been reported in the literature and thus could serve as a starting point for interaction studies.As a new result, we investigated the influence of CpG content on the prediction performance and discovered associations. We found that the model performance is dependent on the CpG content, with the CpG rich models being superior to the CpG poor models for H3K4me3 and H3K27ac, but inferior for the H3K4me1 models. Moreover, we discovered that certain TFs can only be found as important and significant in one of the two groups, such as THAP11. This suggests that CpG content is a key factor for TF-HM relationships and thus should be incorporated in studies of TF and HM data.To summarize, we showed that our regularized regression approach performs well and was able to recover known positive as well as negative associations between TFs and HMs and thus could be used to guide future search for novel interactions. Furthermore, we found that the CpG content has an impact on regression performance, which could be explained by the the binding preference of certain TFs to only CpG poor respectively rich genes. As only little research has focused on this dependence, we were only able to back up the findings for THAP11 by literature. Nonetheless, these results indicate that the CpG content is an important factor in the gene regulatory system and its information content should be leveraged in computational models, if possible. Finally, our investigation of H3K122ac suggests a role as a mark for active regions, which is not only evident in the regression models but is also supported by the partial correlation study.S1 File(PDF)Click here for additional data file.