Interestingly, expression of this asXUT was also activated in wild-type cells upon oxidative stress, concomitant to ctt1 induction, indicating a potential attenuation feedback. This attenuation correlates with asXUT abundance, it is transcriptional, characterized by low RNAPII-ser5 phosphorylation, and it requires an histone deacetylase activity and the conserved Set2 histone methyltransferase. Finally, we identified Dicer as another RNA processing factor acting on ctt1 induction, but independently of Exo2. We propose that asXUTs could modulate the expression of their paired-sense genes when it exceeds a critical threshold, using a conserved mechanism independent of RNAi.Antisense (as)lncRNAs can regulate gene expression but the underlying mechanisms and the different cofactors involved remain unclear. Using Native Elongating Transcript sequencing, here we show that stabilization of antisense Exo2-sensitivite lncRNAs (XUTs) results in the attenuation, at the nascent transcription level, of a subset of highly expressed genes displaying prominent promoter-proximal nucleosome depletion and histone acetylation. Mechanistic investigations on the catalase gene  regulatory threshold”. This opens novel perspectives regarding the potential determinants of aslncRNA-dependent regulation, as previous models in budding yeast rather proposed that aslncRNA-mediated repression is restricted to lowly expressed genes.Examples of regulatory antisense (as)lncRNAs acting on gene expression have been reported in multiple model organisms. However, despite their regulatory importance, aslncRNAs have been poorly studied, and the molecular bases for aslncRNAs-mediated regulation remain incomplete. One reason for the lack of global information on aslncRNAs appears to be their low cellular abundance. Indeed, our previous studies in budding and fission yeasts revealed that aslncRNAs are actively degraded by the Xrn1/Exo2-dependent cytoplasmic 5’-3’ RNA decay pathway. Using a combination of single-gene and genome-wide analyses in fission yeast, here we report that the stabilization of a set of Exo2-sensitive aslncRNAs correlates with attenuation of paired-sense genes transcription. Our work provides fundamental insights into the mechanism by which aslncRNAs could regulate gene expression. It also highlights for the first time that the level of sense gene transcription and the presence of specific chromatin features could define the potential of aslncRNA-mediated attenuation, raising the idea that aslncRNAs only attenuate those genes with expression levels above a “ Eukaryotic genomes are pervasively transcribed , generatLong (l)ncRNAs are produced by RNA polymerase II (RNAPII), capped and polyadenylated, yet lack protein-coding potential ,5, althoSeveral lines of evidence suggest that they are functionally important. First, lncRNAs show tissue-specific expression  and respcis- and trans-regulation, mostly at the chromatin level, of genes important for development and cancer [Several classes of lncRNAs have been described . Among td cancer .cis or in trans have been described in the budding yeast Saccharomyces cerevisiae [Schizosaccharomyces pombe [Another class of lncRNAs consists of antisense transcripts, that are produced from the DNA strand antisense to genes . Severalrevisiae ,19,20,21es pombe ,23,24, ies pombe  and in mes pombe ,27.S. cerevisiae [S. pombe [S. cerevisiae, we defined among these antisense (as)XUTs a subgroup for which the sense-paired genes (referred to as class 1) undergo antisense-mediated transcriptional silencing [S. pombe.Our previous studies in budding and fission yeasts revealed that aslncRNAs are globally unstable and are mainly targeted by the cytoplasmic 5’-3’ RNA decay pathway dependent on the Xrn1 and Exo2 exoribonucleases in revisiae ,29 and SS. pombe , respectS. pombe ,29,30. Iilencing . Howeverctt1, confirmed that it is transcriptionally attenuated upon oxidative stress when its paired-asXUT is stabilized, and the level of the attenuation correlates with the abundance of the asXUT. The attenuation is characterized by low RNAPII Ser5-phosphorylation (Ser5-P) and requires histone deacetylase (HDAC) activity and the conserved Set2 histone methyltransferase (HMT). Finally, we identified Dicer as an additional regulator of ctt1 induction, acting independently of Exo2 and the asXUT. Together, our data support a model where asXUTs could modulate the expression of the paired-sense genes when it exceeds a critical threshold, using a conserved mechanism independent of RNAi.Here we used Native Elongating Transcript sequencing (NET-Seq) to identify genome-wide in fission yeast the genes attenuated at the nascent transcription level upon stabilization of their paired-asXUTs. This so-called class 1 corresponds to highly transcribed genes, displaying marks of active transcription at the chromatin level. Mechanistic investigation on a model class 1 representative, the inducible catalase-coding gene In budding yeast, stabilization of asXUTs results into the attenuation, at the transcriptional level, of a subset of paired-sense genes, which are referred to as class 1 . We receS. pombe, we performed NET-Seq in WT and exo2Δ cells. Although global mRNA synthesis was found to be unchanged upon exo2 inactivation  or not . Within each category, we distinguished genes with (classes 1 & 3) or without (classes 2 & 4) asXUTs  asXUTs  Tables.exo2Δ, 175 have asXUTs (class 1). Despite the proportion of class 1 genes among the attenuated genes is limited (24.2%), it is significantly higher than expected if presence of asXUT and sense gene attenuation were independent , suggesting that the attenuation could depend on the stabilized asXUTs, at least in some cases. On the other hand, the transcriptional down-regulation of class 2 (no asXUT) is likely to be an indirect effect reflecting the slow growth phenotype of the exo2Δ mutant [P = 1.36e-08) and “cellular component biogenesis (P = 1.04e-02), and it is known that the expression of genes involved in these biological processes directly depends on the growth rate [Among the 723 genes transcriptionally attenuated in Δ mutant . Consistwth rate . Altogetexo2Δ, transcription of class 1 falls to the low, basal level of class 3 , suggesting that it might be part of a natural attenuation mechanism (feedback loop) for ctt1 expression.Strikingly, we observed that  context . Furtherctt1 induction requires Exo2 activity for maintaining a low level of XUT0794, antisense to ctt1. Upon induction, the XUT is activated and could modulate expression of ctt1, in a similar way as shown for asXUT-associated genes in S. cerevisiae, such as GAL1-10 [In summary, our data suggest that  GAL1-10 ,20.ctt1 attenuation directly depends on antisense XUT0794.We designed several experiments in order to test whether cis, in WT cells, using a regulatable P41nmt1 promoter  at position 254/815 of XUT0794  and inteumulated , and cttWT cells . In the thout RZ . This im of ctt1 .XUT0794 is sufficient to attenuate ctt1 and that the level of the attenuation depends on the abundance of the asXUT, which is consistent with the hypothesis that the regulation is mediated by the asXUT but is not an indirect effect of Exo2 inactivation.We conclude that the 255–815 fragment of ctt1 induction occurs at the transcriptional level, we performed RNAPII ChIP experiments in WT and exo2Δ cells. Upon oxidative stress, RNAPII occupancy in the mutant showed a significant 2- to 4-fold decrease along the ctt1 locus  of Rpb1, the largest subunit of RNAPII, provided further insights into the mechanism of transcriptional attenuation. Ser5-P RNAPII is associated to the early stages of the transcription cycle and predominates in the promoter-proximal region of the gene, while Ser2-P RNAPII is associated to transcription elongation and increases along the gene core . Upon oxΔ mutant . In contΔ mutant .ctt1 gene body, especially in the XUT0794 overlapping region , an inhibitor of class I-II HDAC. When exposed to oxidative stress, TSA-treated exo2Δ cells accumulated ctt1 mRNA to the same level as the control (DMSO-treated) WT strain  but is specific to those with asXUT (class 1).On the basis of this observation, we predicted histone acetylation along  overlap ; probe Cs 1 gene , but notand cuf1 . This inclr6-1 point mutation [pst2Δ, we could successfully combine these mutations with exo2Δ. Attenuation of ctt1 was not suppressed in the exo2Δ clr3Δ, exo2Δ hos2Δ, exo2Δ png2Δ and exo2Δ clr6-1 mutants , Hos2 (a class I HDAC) and Clr6 (the ortholog of class I HDAC Rpd3). As Clr6 exists in at least two distinct complexes (Clr6-CI and -CII), we also tested a specific subunit for each them, namely the ING family protein Png2 (Clr6-CI) and the Sin3 family protein Pst2 (Clr6-CII), respectively . We usedmutation . Except  mutants , indicatnduction . In addith exo2Δ . This inctt1 requires an HDAC activity, suggesting that mechanisms of regulation of gene expression by lncRNAs have been conserved across the yeast clade.In conclusion, XUT-mediated attenuation of During transcription, elongating RNAPII recruits the histone methyltransferase (HMT) Set2, which methylates H3K36 across the gene body ,40. Set2ctt1 gene body in exo2Δ cells  and the 3’ extremity (probe E). Surprisingly, in the region overlapping XUT0794 (probe C), where Set2 occupancy and histone deacetylation are the most pronounced , involved in RNAi.The data presented above show that the Exo2-dependent RNA decay controls ctt1 attenuation is not suppressed in the exo2Δ dcr1Δ double mutant. Rather, ctt1 was found to be attenuated in the dcr1Δ single mutant, and we observed a synergic effect in the exo2Δ dcr1Δ double mutant. On the other hand, Dicer overexpression in exo2Δ cells had no impact on ctt1 attenuation  in exo2Δ) and class 3 (unchanged in exo2Δ). Class 1 corresponds to highly transcribed genes showing prominent nucleosome depletion and high histone acetylation levels at the promoter. In addition, class 1 displays high TSS-proximal antisense transcription, suggesting that the TSS region could be a possible determinant for aslncRNA-mediated regulation. In contrast, class 3 is weakly transcribed, and displays poor promoter-proximal nucleosome depletion and low histone acetylation. Upon stabilization of asXUTs, transcription of class 1 drops down to the basal levels of class 3. This suggests the existence of a regulatory threshold, ie asXUTs would modulate expression of their associated sense genes, only if expression is above this threshold. This hypothesis contrasts with a previous model based on the analysis of sense-antisense RNA levels in budding yeast, which proposed that antisense-mediated repression would be restricted to low sense expression [In a previous study, we reported that the asXUT-associated genes are globally less transcribed than the ‘solo’ ones (without asXUT), displaying an hypoacetylated promoter and hyperacetylation across the gene body . Here wepression .S. cerevisiae [S. pombe [S. cerevisiae, disruption of several aslncRNAs results into increased protein synthesis from their paired-sense mRNAs, indicating a role of these aslncRNAs in the control of protein abundance [Our data suggests that a subset of asXUTs could regulate gene expression at the transcriptional level, reducing sense transcription, as previously shown in revisiae ,28. XUTsS. pombe . In S. cbundance . Future ctt1, for further characterization. Induction of ctt1 in response to oxidative stress was attenuated in exo2Δ cells, correlating with antisense XUT0794 accumulation. Our data indicate that ctt1 attenuation in Exo2-deficient cells occurs at the transcriptional level and is mediated by HDAC activity. Our attempts to identify the HDAC involved in the attenuation mechanism were unsuccessful, most likely due to redundancy of HDAC activities, considering the results obtained upon TSA treatment. On the other hand, we show that the Clr6CI-II complexes  are required for ctt1 induction . Sanger sequencing confirmed the correct orientation of the insert and the absence of mutation. Hammerhead ribozyme [XUT0794 by two-step PCR, giving a 3.2 Kb final product corresponding to ctt1 mRNA coordinates +/- 500 bp that was cloned in pREP41. After verification of absence of additional mutations by Sanger sequencing, the ribozyme-containing construct was excised and transformed in the YAM2534 strain (ctt1::ura4). Transformants were selected on 5-FOA plates and analyzed by PCR on genomic DNA. Deletion of exo2 was performed subsequently.All the strains used in this study are listed in ribozyme  was inse595 0,5) in YES or EMM-L medium. For ctt1 induction, 1 mM H2O2 was added for 15 minutes [P41nmt1 was repressed by growing cells in EMM-L + 15 μM thiamine for 24 hours.Strains were grown at 32°C to mid-log phase  cells and sequenced as previously described [rpb3-flag) were described in the same previous report [NET-Seq libraries were constructed from biological duplicates of YAM2507  using version 0.12.8 of Bowtie , with a exo2Δ using DESeq [P-value <0.05, adjusted for multiple testing with the Benjamini-Hochberg procedure) in the mutant were defined as class 1 & 2.Differential analysis was performed between the IP samples from WT and ng DESeq . Genes shttp://vm-gb.curie.fr/mw3.Raw sequences have been deposited to the NCBI Gene Expression Omnibus (accession number GEO: GSE106649). A genome browser for visualization of NET-Seq processed data is accessible at 2O (Ambion) and quantified using a NanoDrop 2000c spectrophotometer.Total RNA was extracted from exponentially growing cells using standard hot phenol procedure, resuspended in nuclease-free Hctt1 mRNA and U3B were detected using AMO2063 and AMO2081 oligonucleotides, respectively (see 32P-labelled probes were hybridized overnight at 42°C in ULTRAhyb-Oligo hybridization buffer (Ambion). Quantitation used a Typhoon Trio PhosphorImager and the ImageQuant TL v5.2 sofware .10 μg of total RNA were loaded on denaturing 1.2% agarose gel and transferred to Hybond-XL nylon membrane . vely see . 32P-labStrand-specific reverse transcription (RT) reactions were performed from at least three biological replicates, using 1 μg of total RNA and the SuperScript II Reverse Transcriptase kit (Invitrogen), in the presence of 6,25 μg/ml actinomycin D. For each sample, a control without RT was included. Subsequent quantitative real-time PCR were performed on technical duplicates, using a LightCycler 480 instrument (Roche). Oligonucleotides used are listed in 595 0,5) cells were fixed for 10 minutes at room temperature using formaldehyde , then glycine was added  for 5 minutes. Chromatin was sonicated using a Bioruptor sonication device (Diagenode). Antibodies used were 8WG16 (Covance) for RNAPII, H14 (Covance) for RNAPII S5-Pho, 3E10 (Millipore) for RNAPII S2-Pho, ab1791 (Abcam) for histone H3, 05–1355 (Millipore) for acetyl-H4 (Lys5/8/12/16), 07–353 (Millipore) for acetyl-H3 (Lys14), ab9050 (Abcam) for H3K36me3 and 9E10  for Myc. Quantitative real-time PCR were performed in technical duplicates on a StepOnePlus machine (Applied Biosystems) or on a LightCycler 480 instrument (Roche). Oligonucleotides used are listed in ChIP analysis was performed from three biological replicates, for each strain. Exponentially growing , sn(o)RNAs (black) and XUTs (red). B. Metagene view of NET-Seq signals along class 1–4 genes in WT cells. For each class, normalized signal  along mRNA transcription start site (TSS) +/- 1000 nt (sense strand) and the antisense (as) strand were piled up, in a strand-specific manner. Average signal for each strand was plotted for class 1 (red), 2 (blue), 3 (green) and 4 (black). The shading surrounding each line denotes the 95% confidence interval. C. Box-plot of NET-Seq signal  for XUTs antisense to class 1 and class 3 genes in WT and exo2Δ (Δ) cells. D. Metagene view of nascent antisense transcription (NET-Seq) signal around the sense gene TSS of class 1–4 genes, in WT cells. The shading surrounding each line denotes the 95% confidence interval. E. Metagene of H3 levels for class 1–4 genes in WT cells. The analysis was performed using previously published ChIP-Seq data [F. Metagene view of H3K14 acetylation for class 1–4 genes in WT cells. ChIP-Seq libraries construction and sequencing were previously described [2) for H3K14ac and H3.Seq data . Metagenescribed . Metagen(TIF)Click here for additional data file.S2 FigA. Snapshot of total RNA-Seq signals along the ctt1 locus in WT and exo2Δ cells. RNA-Seq data were previously published [exo2Δ strains are represented as black and grey lines, respectively. B. Exo2-deficient cells display a slow growth phenotype. YAM2400 (WT) and YAM2402 (exo2Δ) cells were grown in rich (YES) medium, at 32°C. OD595 was measured every hour. OD595 at time 0 was set to 1, for each strain. Data are expressed in a log scale. C. Loss of Exo2 confers sensitivity to hydrogen peroxide. Serial 1:10 dilutions of YAM2400 (WT) and YAM2402 (exo2Δ) cells were dropped on solid rich medium (YES) containing or not 2 mM H2O2. Plates were incubated at the indicated temperature for 3–4 days.ublished . Densiti(TIF)Click here for additional data file.S3 FigA. Attenuation of ctt1 mRNA upon overexpression of XUT0794 in cis. Strains YAM2400 (WT) and YAM2474 (P41nmt1-XUT0794) were grown for 24 hours to mid-log phase in EMM medium +/- 15 μM thiamine, before addition of H2O2 for 15 min. Levels of XUT0794 (red) and ctt1 mRNA (blue) were quantified from total RNA using strand-specific RT-qPCR and normalized on the level of the U3B snoRNA. Data are presented as mean +/- SEM from three biological replicates. B. Induction of ctt1 mRNA upon XUT0794 overexpression in trans. YAM2475 (empty vector) and YAM2476  cells were grown for 24 hours to mid-log phase in EMM-L medium, before addition of H2O2 for 15 min. Determination of XUT0794 and ctt1 mRNA levels and data presentation are as above.(TIF)Click here for additional data file.S4 Figact1 gene in WT (upper panels) and exo2Δ (lower panels) cells. In each panel, the signal corresponding to the sense (+) and antisense (-) strand is shown in blue and pink, respectively. Blue arrows and boxes represent the mRNAs and coding sequences, respectively. NET-Seq data for the WT strain were previously described [Snapshot of total (input) and nascent (IP) NET-Seq signals along the escribed . The snaescribed .(TIF)Click here for additional data file.S5 FigA. ChIP analysis of H3K14 acetylation along ctt1. Culture, cross-linking and chromatin extraction were as described in act1, then on the level of histone H3, immunoprecipitated from the same chromatin. Data are presented as mean +/- SEM, calculated from three biological replicates. *p<0.05; **p<0.01; ns, not significant upon t-test. B-C. ChIP analysis of H4 K5/8/12/16 acetylation along the class 2 genes cuf1 (B) and ptb1 (C) in WT and exo2Δ cells. Cultures were as described in (TIF)Click here for additional data file.S6 FigA. Effect of the Clr3 class II HDAC. YAM2400 (WT), YAM2402 (exo2Δ), YAM2407 (clr3Δ) and YAM2444 (exo2Δ clr3Δ) cells were grown in rich medium before (white) or after addition of 1 mM H2O2 for 15 minutes (black). ctt1 mRNA levels were quantified by strand-specific RT-qPCR from total RNA as described in p<0.05; **p<0.01; ns, not significant upon t-test. B. Effect of Hos2 class I HDAC. Same as above using YAM2400 (WT), YAM2402 (exo2Δ), YAM2471 (hos2Δ) and YAM2472 (exo2Δ hos2Δ). *p<0.05; ***p<0.001; ns, not significant upon t-test. C. Effect of the Png2 subunit of the Clr6CI complex. Same as above using YAM2400 (WT), YAM2402 (exo2Δ), YAM2561 (png2Δ) and YAM2562 (exo2Δ png2Δ). *p<0.05; **p<0.01; ***p<0.001; ns, not significant upon t-test. D. Effect of Clr6 class I HDAC. Same as above using YAM2400 (WT), YAM2402 (exo2Δ), YAM2798 (clr6-1) and YAM2814 (clr6-1 exo2Δ). *p<0.05; ***p<0.001 upon t-test. E. Effect of the Pst2 subunit of the Clr6CII complex. Same as above using YAM2400 (WT), YAM2402 (exo2Δ) and YAM2815 (pst2Δ). *p<0.05; **p<0.01; ***p<0.001 upon t-test.(TIF)Click here for additional data file.S7 FigA-C. WT cells with pREP-nmt1/LEU2 empty vector , and exo2Δ cells with pREP-nmt1/LEU2 or pREPNFLAG-Dcr1  plasmids [2O2 for 15 min. ctt1 mRNA (A), XUT0794 (B) and dcr1 mRNA (C) were quantified from total RNA using strand-specific RT-qPCR and normalized on the level of the U3B snoRNA. Average values and SEM were calculated from three biological replicates. **p<0.01; ***p<0.001; ns, not significant upon t-test. D. ChIP analysis of H3K36 trimethylation (H3K36me3) along ctt1 in dcr1Δ cells. Strains YAM2400 (WT) and YAM2406 (dcr1Δ) were grown as in plasmids  were gro(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.


Our ability to overcome the challenges behind metabolic disorders will require a detailed understanding of the regulation of responses to nutrition. The Creb3 transcription factor family appears to have a unique regulatory role that links cellular secretory capacity with development, nutritional state, infection, and other stresses. This role in regulating individual secretory capacity genes could place this family of transcription factors at an important regulatory intersection mediating an animal’s responses to nutrients and other environmental challenges. Interestingly, in both humans and mice, individuals with mutations in Creb3L3/CrebH, one of the Creb3 family members, exhibit hypertriglyceridemia (HTG) thus linking this transcription factor to lipid metabolism. We are beginning to understand how Creb3L3 and related family members are regulated and to dissect the potential redundancy and cross talk between distinct family members, thereby mediating both healthy and pathological responses to the environment. Here, we review the current knowledge on the regulation of Creb3 family transcription factor activity, their target genes, and their role in metabolic disease. The recent rise in obesity and the chronic diseases associated with it are a result of many factors, including increased consumption of sugars and fats. Raising awareness of the potential adverse effects of these products in order to reduce their consumption has been a challenging task for many reasons. At the level of biomedical research, efforts are under way to identify possible therapeutic targets for nutritional disorders associated with our modern lifestyles. New strategies in the intervention of metabolic diseases are crucial to human health and will likely become ever important in the future.Drosophila CrebA protein, which was identified as binding to cyclic-AMP responsive DNA elements (CRE)  . Later, ts (CRE) . Creb3, ts (CRE) . More rets (CRE) . BecauseDrosophila embryos , Creb3L2/BBF2H7, Creb3L3/CrebH, and Creb3L4/AlbZIP/Atce1/Tisp40/Creb4. For the purpose of a standard nomenclature, we refer to these proteins by their Creb3 names. Creb3 transcription factors are highly conserved from sponges to humans . One of  embryos , which s embryos . Togethevia peptide cleavage through regulated intramembrane proteolysis, which releases the N-terminus of the transcription factor to shuttle into the nucleus. The cleavage process of the transmembrane domain is similar to the transcription factors ATF6 and sterol regulatory element-binding protein (SREBP) , which allows it to anchor in the ER. Transcriptional activation then requires that the anchoring be removed  (SREBP) , another (SREBP) . The lumAll Creb3 proteins also share a unique adjacent to bZip (ATB) domain, which is located N-terminally to the bZIP . The ATBThe transcriptional activity of Creb3 family members responds dynamically to various environmental cues and during various times in the life of an organism. Genetic studies on the mammalian Creb3 genes have focused on their developmental roles. Many of these studies suggest that this transcription factor family regulates protein secretion through the transcriptional of regulation of genes involved in COPII vesicle formation. Recently, studies indicate that several family members also have possible roles in responding to nutrients.Both Creb3L1 and Creb3L2 have roles in development. During skeletal development, they regulate secretion of matrix proteins by the transcriptional regulation of the secreted protein, type 1 collagen (Col1a1), and secretory machinery genes involved in COPII vesicle formation . In humaCreb3L4, by contrast, is linked to chromatin organization during spermiogenesis  and prosOriginally, Creb3L3 was thought to have roles in liver development. However, Creb3L3 knockout mice develop normally . Recent Recently, genome-wide association studies strongly associate mutations in mice deficient for the recruitment factor of Creb3 (CREBRF) with increased body mass index and obesity . OriginaDrosophila Creb3 member CrebA in the fly embryo  , a demetAnother possible co-activator of Creb3 proteins is the CREB-regulated transcriptional coactivator 2 (CRTC2 or TORC2). Just like HCF, it is also implicated in metabolic responses . Based oThe available data indicate that Creb3 family members lie at a unique regulatory node receiving developmental and environmental signals, both metabolic and inflammatory, to regulate gene expression, including secretory capacity. Creb3 proteins can regulate genes involved in COPII vesicle formation by directly binding to their promoters. Although Creb3L3 regulation is the best studied in response to nutrition and metabolic disease, studies of other family members, such as Creb3 and Creb3L4, suggest that they also have roles in responding to nutrient status.While Creb3 family members’ potential regulation of secretory capacity is an interesting mechanism to couple nutrient status to a system-wide physiological response, we currently do not have a complete understanding of the secretory genes Creb3 family transcriptionally regulate. Most of the genome-wide data comes from RNA studies, while ChIP studies have generally focused on a few target genes. The mRNA-seq studies have been very useful in identifying a list of putative Creb3 family target genes. Several ChIP or transcriptional reporter studies have looked at individual or a combination of putative targets. There are antibodies available for most of the family members which have been used for immunoprecipitations and ChIP. These antibodies should be used for ChIP-seq experiments. Genome-wide ChIP studies will aid our global understanding of which secretory machinery genes as well as other genes Creb3 transcription factors directly regulate. Knowing these direct targets will benefit our understanding of the physiological role that these transcription factors have in responding to metabolic challenges.Furthermore, what is the physiological impact of regulating these secretory genes? Clearly, the export of hormones responding to the nutritional statues of an organ would impact inter-organ communication. Which hormones are impacted is an important question. However, changes in the secretory machinery could also alter lipid export and thus lipid metabolism, as well. In addition, changes in nutrition may also cause changes in the morphology of the ER and Golgi, as well as changes in membraneless cellular structures, such as processing bodies and stress granules , which aBoth authors contributed equally toward this work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Better understanding of epigenetic regulation of hepatocellular carcinoma (HCC) will help us to cure this most common malignant liver cancer worldwide. The underlying mechanisms of HCC tumorigenesis are genomic aberrations regulated by genetic and epigenetic modifications. Histone H3 lysine modifications regulate histone structure and modulate transcriptional factor binding with target gene promoters. Targetting genes include VASH2, fatty acids synthase, RIZ1, FBP1, MPP1/3, YAP, which affect tumorigenesis, metabolisms, angiogenesis, and metastasis. Signal pathway studies demonstrate that the HGF-MET-MLL axis, phosphatase and tensin homolog (PTEN)-PI3K-Akt axis; WNT-β-catenin signal pathway is involved in histone H3 modification. A variety of factors such as virus infection, reactive oxygen species, food-borne toxins, irradiation, or non-coding RNA cause hepatocellular DNA damage or modification. Dysfunctional DNA repair mechanisms, including those at the epigenetic level are also major causes of HCC tumorigenesis. The development of therapies based on epigenetic regulatory mechanisms has great potential to advance the care of HCC patients in the future. The well-established definition of an epigenetic modification is a ‘stably heritable phenotype resulting from changes in a chromosome without alterations in DNA sequence’ . HoweverHepatocellular carcinoma (HCC) is the most common malignant liver cancer and it causes 662,000 deaths every year worldwide . Nearly Histone H3 modifications have been intensively investigated in various cancer models, including HCC. There are several major histone H3 modifications during HCC development: histone methylation and acetylation, histone phosphorylation and ubiquitination. Amongst all the histone modifications, histone methylation and acetylation have been intensively investigated as playing a role in HCC development.There are abnormally high H3K4 trimethylation and H3 acetylation, as well as a lower H3K27 trimethylation at putative vasohibin 2 (VASH2) promoter in HCC, which results in an abnormally high expression of VASH2 in HCC. In contrast, silence of VASH2 is known to inhibit the HCC proliferation while enhancing apoptosis. Moreover, the secreted VASH2 from HCC also enhances human umbilical vein endothelial cells growth and proliferation, indicating that VASH2 may functions as growth factor . Sterol Human HCC cell in culture display a nucleosome density that is relatively lower than control, which accompanies histone H3 acetylation at lysine 9 (K9), regardless of their transcriptional activation status. The present study indicates that K9 acetylation may play an important role in nucleosome relaxing and in tumorigenesis initiation . The retThe connection between HCC development and histone H3 lysine methylation has also been widely investigated. Early studies show that a higher level of H3K4 trimethylation is associated with a poor prognosis of HCC , and HCC2O2 treatment of the hepatocellular cell lines studies show that histone is modified from an active chromatin (trimethyl-H3K4 and acetylated-H4K16 dominant) to a repressive chromatin (trimethyl-H3K27 dominant) status. These results indicate that oxidative stress alters the chromatin status, which leads to abnormal methylation of TSGs and contributes to hepatocarcinogenesis in CHC patients [in vitro and in vivo [Enrichment of H3K27 trimethylation, independent of H3K9 dimethylation, trimethylation, and DNA methylation, was an early event in the silence of p16 (INK4a) during the tumor development . Chronicpatients . Concurrpatients . By H3K2The relationship of acetylation and methylation on histone lysines is sophisticatedly and precisely regulated by different enzymes. Histone H3 acetylation and methylation of several lysine residues could happen simultaneously. Moreover, other targetting amino acids of histone H3, such as H3K18 and H3K56 or arginine, were also modified as HCC develops. However, there are just a few reports describing their role in the process of tumorigenesis of HCC ,29.Long non-coding RNA (lncRNA) is a member of non-coding RNA family, which is characterized as longer than 200 base pairs. unlike miRNA and circRNA, lncRNA can form secondary structure and can be post-translationally processed through splicing or 5′capping. Accumulating evidence shows that lncRNA are involved in inflammatory process, especially in chronic inflammatory process. The major targetting signal pathways of lncRNAs include NF-κB pathway, JAK-STAT pathway, and MAPK pathway. Aberrant regulation of these signal pathways causes cell inflammatory response, cell apoptosis, or neurodegerative diseases . MoreoveMLL is a transcriptional coactivator playing a central role of HCC early development. MLL binds with transcription factors and drives target gene expression. MLL also plays critical role in epigenetic regulation of cell cycle and self-renewal of stem cells . In cellIn vivo experiments have shown that exogenous implanting HepG2 with overexpressing JARID1B results in larger tumor size compared with control. Signal pathway analysis showed that higher level of JARID1B in HCC inhibited PTEN expression by demethylating H3K4me3 associated PI3K-AKT signal pathway activation, which eventually promotes metastatic behaviors of HCC [JARID1B is a member of the family JmjC domain-containing proteins that removes the methyl group from methylated lysine 4 on histone H3 (H3K4). Comparing with normal liver tissue, HCC has significantly higher mRNA and protein level of JARID1B which is associated with metastasis and poor survival rate. Silencing of JARID1B in HCC cell lines, such as HepG2, SNU423, or SK-Hep1, inhibited their proliferation, migration, or invasion. s of HCC . A recens of HCC   is involved in many hepatocellular metabolisms, such as lipogenesis, protein synthesis, glucogenesis, and bile acid (BA) homeostasis. SIRT1 has been found to be aberrantly high in HCC compared with health control. Signal pathway assays showed that higher level of SIRT1in HCC up-regulates AMPK. AMPK negatively regulates mTORC1 and p70S6K signal axis accompanying histone H3 K4 and K9 trimethylation. Both of them result in farnesoid X receptor (FXR) expression inhibition. Moreover, comparing with control, hepatectomy of the overexpression SIRT1 mice increased mice mortality, decreased proliferation of hepatocytes which was accompanied by decreased expression and activity of farnesoid X receptor (FXR). The aberrant expression and deacetylation of farnesoid X receptor (FXR) induced by higher level of SIRT1 resulted in hepatocytocellular abnormality, such as BA metabolic dysfunction, hepatocellular tumorigenesis . By epigin vitro and in vivo. EZH2 negatively regulates TP53, a tumor suppressor in HCC. But EZH2 positively regulates E2F1, NOTCH2 expression in HCC. CHIP assay showed that EZH2 and H3K27me3 coincidently occupied the promoter of cyclin-dependent kinase inhibitor (CDKN2A), but not the TP 53 promoter. The results indicate these genes are regulated by EZH2 in a H3K27me3-dependent or H3K27me3 independent way. Moreover, survival rate of the patients expressing EZH2 and menin was significantly lower than the patients not expressing EZH2 or menin. Further, inhibition of H3K27me3 alone or inhibition of H3K4me3 together effectively blocked the aggressive phenotype of HCC cells [Polycomb group (PcG) proteins are highly conserved epigenetic gene silencers that play important roles in the maintenance of cell fates. On HCC model studies showed that reduced one component of polycomb repressive complex 2(PRC2), enhancer of zeste homolog 2 (EZH2), effectively decreases invasion, and proliferation of liver cancer cell CC cells   induced HCC tumorigenesis by decrease of H3K9me3, genomic DNA demethylation, and down-regulation of RIZ1, a histone lysine methyltransferase tumor suppressor gene [Exposure to chemicals is also an important cause for HCC tumorigenesis and metastasis. Diethylnitrosamine/tetrachloride (CClsor gene . In a disor gene .Oxidative stress is another important reason for the HCC tumorigenesis and development by direct DNA damage . Hepatit6-methylguanine-DNA methyltransferase (MGMT) by hypermethylation in its promoter has close relationship with aflatoxin B1-mediated P53 mutation in HCC [In addition to DNA damages, impaired DNA repairing mechanism, including unbalance-epigenetic repairing mechanism, also caused hepatocarcinogenesis. Early study showed that many genomic and epigenetic abnormalities have been found in p53, β-catenin, p16CDK1, and DNA mismatch repair genes. However, no specific abnormal genetic or epigenetic changes for HCC have been found . Recentln in HCC . Analysin in HCC ,53. HoweAberrant regulation of miR200 family by epigenetic mechanism is also linked to hepatocellular carcinogenesis. DNA methyltransferase I (DNMT1) and another histone methyltransferase, PcG protein enhancer of zeste homolog 2 (EZH2) bind with miR200a/b/429 promoter region and inhibit miR200a/b/429 expression in HCC and glioma cells. Silencing of EZH2 decreased DNMT1 binding on the miR200a/b/429 promoter and increased of miR200a/b/429 expression. These results indicated that EZH2 and DNMT1 silencing contributed to the progress of HCC or glioblastoma by regulation of miR200a/b/429 at epigenetic level .Oxidative stress induced by various factors such as chemicals, virus, or irradiation also plays important role in epigenetic alteration and role in HCC development. ROS-mediated epigenetic instability results in two types of DNA alteration: (1) ROS induces the transcriptional factor, snail expression. Snail induces and recruits DNMT and HDACs to the TSG promoter, leads to TSG expression inhibition ,56. A ROHistone H3 modifications directly or indirectly regulate HCC oncogene expression. These regulatory properties for HCC oncogenes have been successfully adopted for clinical purposes. Application of SAHA with 5-aza-dC together has a significant synergic effect on SAHA-mediate antiproliferation of HCC . Pan-DACAlthough more attention has been paid on epigenetic regulation of the HCC, the mechanism and clinical significance are still unclear. For example, in addition to histone H3, the importance of other histones like H4 or H1 and H2 are largely unknown. Even in histone H3, many critical amino acids, such as serines, lysine 14, 57 are relatively poor understand with regards to their role in HCC development. The effects of other modifications, such as phosphorylation, ubiquitination are also largely unknown.Taken together, histone3 lysine modification is involved in carcinoma genes initialization, metabolism, angiogenesis, and metastasis. Altering these Histone3 lysine modifications significantly change HCC tumorigenesis properties and outcomes. These novel studies have been adopted in clinical trials and could become promising therapies for the HCC patients in the near future.


Coordinated experiments focused on transcriptional responses and chromatin states are well-equipped to capture different epigenomic and transcriptomic levels governing the circuitry of a regulatory network. We propose a workflow for the genome-wide identification of epigenomic and transcriptional cooperation to elucidate transcriptional networks in cancer. Gene promoter annotation in combination with network analysis and sequence-resolution of enriched transcriptional motifs in epigenomic data reveals transcription factor families that act synergistically with epigenomic master regulators. By investigating complementary omics levels, a close teamwork of the transcriptional and epigenomic machinery was discovered. The discovered network is tightly connected and surrounds the histone lysine demethylase KDM3A, basic helix-loop-helix factors MYC, HIF1A, and SREBF1, as well as differentiation factors AP1, MYOD1, SP1, MEIS1, ZEB1, and ELK1. In such a cooperative network, one component opens the chromatin, another one recognizes gene-specific DNA motifs, others scaffold between histones, cofactors, and the transcriptional complex. In cancer, due to the ability to team up with transcription factors, epigenetic factors concert mitogenic and metabolic gene networks, claiming the role of a cancer master regulators or epioncogenes. Significantly, specific histone modification patterns are commonly associated with open or closed chromatin states, and are linked to distinct biological outcomes by transcriptional activation or repression. Disruption of patterns of histone modifications is associated with the loss of proliferative control and cancer. There is tremendous therapeutic potential in understanding and targeting histone modification pathways. Thus, investigating cooperation of chromatin remodelers and the transcriptional machinery is not only important for elucidating fundamental mechanisms of chromatin regulation, but also necessary for the design of targeted therapeutics. The cancer systems biology team of Professor Fabian V. Filipp at the University of California Merced introduces a universal workflow for elucidation of regulatory cooperation networks. The authors show that by integrating different genomic features of complementary epigenomic and transcriptomic data a highly specific and hyperconnected network can be identified. On the one hand epigenomic target regions define accessible chromatin, on the other hand motifs of differentially expressed transcripts determine the target genes controlled by the cooperative forces. In cancer, epigenetic master regulators govern the epigenome and accomplish target specificity of their phenotypic program by cooperation with members of the transcriptional machinery. The research establishes how genome-wide data in combination with systems biology analyses ascertains master regulators and new drug targets of therapy resistance in cancer. Chromatin remodelers and transcription factors are in close communication via recognition of post-translational histone modifications.8 The regulome describes the interplay between genes and their products and defines the control network of cellular factors determining the functional outcome of a genomic element. The reconstruction of regulatory gene networks is stated as one of the main objectives of systems biology.10 However, an accurate description of the regulome is a difficult task due to the dynamic nature of epigenetic, transcriptional, and signaling networks. Systems biology has the ability to integrate genome-wide omics data recorded by ChIP-Seq, assay for transposase-accessible chromatin using sequencing (ATAC-Seq), whole genome bisulfite sequencing (WGBS-Seq), and RNA sequencing (RNA-Seq) technology to identify gene targets of a regulatory event.11 The integrated analysis of such data—on the one hand based on gene networks, on the other hand based on sequence features of high-resolution sequencing data—captures cooperation among regulators. Effective experimental design and data analysis of complementary epigenomic and transcriptomic platforms are required to decipher such epigenomic and transcriptional cooperation, which has a profound impact in development and disease.Regulatory networks in biology are intrinsically hierarchical and governed by interactions and chemical modifications.12 In particular histone 3 lysine 9 methylation (H3K9) is regarded as an epigenetic mark associated with suppressed gene activity.13 The H3K9 lysine demethylase 3 A  demethylates mono-methylated or di-methylated histone marks, thereby activating gene regulation within spermatogenesis, metabolism, stem cell activity and tumor progression.16 Genome-wide ChIP-Seq data of KDM3A identified specific gene targets and transcriptional networks in androgen response, hypoxia, glycolysis, and lipid metabolism, emphasizing the importance of cooperation with transcription factors.5 However, among epigenetic profiling experiments, a common observation is that enrichment studies provide significance for multiple transcription factors and not just one single, prioritized hit. This underscores the concept of transcriptional cooperation among epigenetic players but also emphasizes the need to design a reliable workflow that includes cross-validation with complementary, multi-omics platforms and analysis techniques.We took advantage of published, information-rich transcriptomic and epigenomic data to study regulatory networks surrounding histone lysine demethylation. The presence or absence of methylation on histone lysine residues correlates with altered gene expression and is an integral part of the epigenetics code.4 However, different genome-scale data platforms and analysis techniques result in the detection of significant, yet only occasionally overlapping, insight into regulatory networks. To address this, the intersection of multi-omics data levels is useful to augment and validate epigenetic regulation of transcriptional programs. At the same time, there are unique, platform-specific insights, which need to be analyzed accordingly.The regulatory landscape of an epigenomic player includes histone modifications, non-enzymatic chromatin interactions, cooperation with transcription factors, transcriptional modulation of gene target networks, and eventually stimulation of specific effector pathways. With the help of hierarchical experimental design, the complementary power of epigenomic and transcriptomic data can be leveraged, and thus used to address distinct levels of the regulome.17 Importantly, computations of significance of enrichment may be performed at the level of either the gene or the sequence. Furthermore, different members of transcription factor families have the ability to recognize the same sequence motif. Therefore, dedicated searches may account for such ambiguity and overcome potential gene-specific database biases of individual transcription factors , and 1866 genes as negatively regulated by KDM3A activity. Using this data we defined the set of 56.9% differentially expressed genes as positively regulated by KDM3A activity (down in the CRL-2505 prostate cancer line with shRNA knockdown of KDM3A), and 43.1% of differentially expressed genes as negatively regulated by KDM3A activity. KDM3A binding locations were defined by a loss of ChIP-Seq signal following knockdown of KDM3A. Concurrently, H3K9me1/2 histone marks following KDM3A knockdown are recognized as target regions of KDM3A histone lysine demethylation mediated by KDM3A. Changes in these ChIP-Seq marks upon KDM3A knockdown were contrasted against reference genomic DNA input or control non-coding shRNA samples. Quantification of the activity-based ChIP-Seq array matched with knockdown of KDM3A resulted in 37525 peaks associated with KDM3A binding, 45246 and 32665 H3K9 mono- and di-demethylation (H3K9me1/2-KDM) events, respectively. Overall, the peak counts of both histone marks showed a gain of signal upon knockdown of KDM3A reflecting the demethylase activity. By integrating continuous ChIP-Seq signals, an average profile of a meta-gene can be generated and functional coordinates analyzed for regulatory control. In such a meta-gene profile, promotor regions are located within 1000 bp upstream of the gene-coding body, with the transcription start site (TSS) as the start of the gene-coding body at the zero position, and intergenic regions as the remaining regions outside of the gene body. KDM3A localized to the response element-rich promoter regions and demethylated H3K9me1/2 histone marks in the proximity of the TSS. Taken together, the meta-gene analysis classified areas important for transcriptional regulation and defined genomic sequence coordinates relevant for cooperation with transcription factors.Coordinated ChIP-Seq and transcriptomic data classified genome-wide interactions of the chromatin demethylase KDM3A using antibodies specific for KDM3A, and its histone marks H3K9mep value cut-off of 0.05  of the PAS domain factors (TFClass 1.2.5) is detected with an adjusted p value below 1.0E-100 by AME in the ChIP-Seq data. Other members of the same family like the aryl hydrocarbon receptor nuclear translocator  show similar significance, since the detection is based on the same sequence logo, highlighting the lack of ability to differentiate between structurally homologous transcription factors. Therefore, we intersected all four sets of AME ChIP-Seq, TFT ChIP-Seq, URA transcriptomics, and TFT transcriptomics, and narrowed down 21 transcription factor families supported by all datasets  are detected but show different signs of regulation depending on the factor of interest, the response of its target genes, and the change of expression of the factor itself. After intersection of all four datasets, 11 transcription factors belonging to 10 transcription factor families were determined Fig. . This seChIP-Seq based approaches provide sequence resolution but detection of enriched transcription factor motifs is ambiguous and is most appropriately accomplished at the transcription factor family level to account for and include homologous factors. In contrast, transcriptomics studies provide directionality of regulation—transcriptional activation or repression upon epigenomic activity—an important aspect lacking in coordinate-based ATAC-Seq or ChIP-Seq experiments. Integration of different sequence, gene, or network-based approaches prioritizes high-fidelity cooperation partners in epigenomic regulation. Therefore, any combination of complementary data from sequence, gene, or network-based approaches is identified as desirable input for reliable regulatory systems biology analyses.14 Transcriptionally silenced genes contain methylation marks on the H3K9 subunit18 and arrays of ChIP-Seq experiments monitoring H3K9 methylation marks revealed global histone demethylation effects of KDM3A. Combined assessment of histone demethylation events and gene expression changes indicated major transcriptional activation, suggesting that distinct oncogenic regulators, in particular transcription factors, may synergize with the epigenetic patterns controlled by KDM3A. Furthermore, the epigenetic factor was shown to cooperate with the androgen receptor to control prostate tissue-specific gene target networks introducing the concept of the epioncogene and transcriptional cooperation.19For the oncogenic nature and target specificity of an epigenomic master regulator, epigenomic and transcriptional cooperation with transcription factors is key. KDM3A is able to support initiation of transcription by its ability to specifically remove mono-methylation and di-methylation marks from the H3K9 residue leading to chromatin de-condensation.21 Such cooperative coactivation of the androgen receptor with KDM3A features a role for KDM3A as an active force in commencing oncogenesis in prostate epithelial cells. KDM3A is known to control the transcription and function of oncogenic transcription factors.23 However, an expanded study outlining the effects of perturbed KDM3A H3K9 demethylation upon human transcription factor response element recognition in cancer has so far been missing.While KDM3A is able to control chromatin accessibility, the mechanism by which it targets specific genes is of current interest and may influence understanding of epigenetic dysregulation in human disease. While several cancers exhibit deregulated KDM3A activity, in prostate adenocarcinoma it functions as a transcriptional coregulator with the androgen receptor.25 Epigenomic regulation of SREBF1 activity has been reported to stimulate lipogenesis, and SREBP1 regulates lipid accumulation and cell cycle progression in androgen independent prostate cancer cell lines.28 KDM3A regulates the transcriptional program of the AR, serves epigenomic master regulator by epigenomic and transcriptional cooperation of prostate adenocarcinoma.5 Despite some factors including the forkhead box (FOX) factors (TFClass: 3.3.1) family were frequently detected at the transcription factor family levels, lack of consistent overlap of epigenomic and transcriptomic data eventually excluded prominent cancer drivers like forkhead box A1 , forkhead box M1 , forkhead box O1 , or forkhead box O3 . Despite FOX factors are known to cooperate with nuclear hormone receptors (TFClass: 2.1.1), it is possible that the putative association with KDM3A steams from the fact that the closely cooperating AR frequently has FOX motifs nearby. Systematic, genome-wide surveys have elucidated that FOX motifs are adjacent androgen response elements (AREs),30 thereby facilitating cooperation at the level of transcription factors and promotion of prostate cancer progression.The cooperation network includes previously validated interactions of MYC, HIF1A, and AR in cancer but also highlights close association of KDM3A with transcriptional networks of factors rather studied in development and tissue differentiation like JUN, CEBPB, MYOD, SREBF1, SP1, MEIS1, ZEB1, or ELK1 , on the other hand as differentially expressed transcripts (target genes)—serves as input for four different analysis platforms . The utilized cellular model is a variant derived from a xenograft and simulates castration-induced regression and relapse typical of human prostate carcinoma epithelial cells independent of dihydroxytestosterone stimulation. Furthermore, KDM3A is activated by somatic copy number amplification in lung, prostate, uterine, bladder, testicular germ cell, ovarian, cervical, breast, sarcoma, melanoma, and other cancers making its cooperation network an important target of broad interest in oncology.KDM3A coordinates transcriptional activation by H3K9 demethylation thereby enabling chromatin accessibility and replacement of components of nucleosome-stalled polymerase complexes by tissue-specific transcription factors. The workflow is exemplified by reprocessing published data on KDM3A activity recorded with matching knockdown conditions in a human prostate carcinoma epithelial cellular model deposited in NCBI GEO entries GSE109748 and GSE7049838 Peak-calling utilized a model-based analysis of ChIP-Seq (MACS) algorithm.40 Significant ChIP-Seq genomic locations relative to nearby gene bodies were annotated by ChIPSeek.41 ChIP-Seq peak regions were sorted and filtered by BEDtools.42 Average ChIP enrichment profiles over specific genomic features were calculated using the cis-regulatory element annotation system tool.44 ChIPSeq binding profiles were visualized in the integrative genomics viewer (IGV).45 Utilized conditions include ChIP-Seq profiles of antibodies specific for chromatin marks H3K9me1, H3K9me2, and the epigenomic modifier KDM3A in combination with small hairpin RNA (shRNA) knockdown of KDM3A matched with coordinated transcriptomic data of control and KDM3A knockdown cells using human transcriptome platform GPL10558 .5 The epigenomic and transcriptomics datasets contained 77911 features and 4356 differentially expressed transcripts upon KDM3A knockdown, respectively, with p values and q values below 0.05 adjusted for multiple hypothesis testing.Illumina HiSeq 2000  fastq files were aligned to the reference human genome 19 using the Bowtie software package.46 Transcription factor binding and promoter sites were annotated utilizing transcription factor databases.47 For transcription factor binding site searches we built manual or utilized deposited position site-specific matrices or sequence logos of curated, non-redundant transcription factor databases. Statistically significant enrichment of these transcription factor motifs was determined using find individual motif occurrences (FIMO) and motif enrichment tools of the motif-based sequence analysis toolkit (MEME) suite.48 Upstream regulators were determined by ingenuity pathway analysis  based on differentially expressed genes with an adjusted p value below 0.05. Significant enrichment of target gene networks with consistent transcription factor motifs was calculated for all target genes with annotated transcription factor motifs in the 3′ promoter region of their transcription start sites.49Human transcription factors were annotated according to their Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC) identification number using the using the multi-symbol checker tool. Discovered transcription factors were classified by shared DNA binding domains according to the hierarchical classification of human transcription factors (TFClass) database.GSE109748 and GSE70498. Identified transcription factors and statistics are assembled in the Supplementary Information. All data supporting the findings of this study is openly available within the paper and the Supplementary Information deposited at the npj Systems Biology and Applications website. A preprint version of this manuscript is made available to the scientific community on the preprint server bioRxiv309484.50Data is deposited in NCBI GEO entries Supplementary Information. Supplementary Table Supplementary Table Supplementary Information


Sequence-specific transcription factors (TFs) represent one of the largest groups of proteins that is targeted for SUMO post-translational modification, in both yeast and humans. SUMO modification can have diverse effects, but recent studies showed that sumoylation reduces the interaction of multiple TFs with DNA in living cells. Whether this relates to a general role for sumoylation in TF binding site selection, however, has not been fully explored because few genome-wide studies aimed at studying such a role have been reported. To address this, we used genome-wide analysis to examine how sumoylation regulates Sko1, a yeast bZIP TF with hundreds of known binding sites. We find that Sko1 is sumoylated at Lys 567 and, although many of its targets are osmoresponse genes, the level of Sko1 sumoylation is not stress-regulated and the modification does not depend or impinge on its phosphorylation by the osmostress kinase Hog1. We show that Sko1 mutants that cannot bind DNA are not sumoylated, but attaching a heterologous DNA binding domain restores the modification, implicating DNA binding as a major determinant for Sko1 sumoylation. Genome-wide chromatin immunoprecipitation (ChIP-seq) analysis shows that a sumoylation-deficient Sko1 mutant displays increased occupancy levels at its numerous binding sites, which inhibits the recruitment of the Hog1 kinase to some induced osmostress genes. This strongly supports a general role for sumoylation in reducing the association of TFs with chromatin. Extending this result, remarkably, sumoylation-deficient Sko1 binds numerous additional promoters that are not normally regulated by Sko1 but contain sequences that resemble the Sko1 binding motif. Our study points to an important role for sumoylation in modulating the interaction of a DNA-bound TF with chromatin to increase the specificity of TF-DNA interactions. Transcription factors bind the genome at specific sites to control the expression of target genes. Although the DNA sequence of these sites is critical for determining where they bind, additional factors must act to ensure that only appropriate sites remain bound and regulated by transcription factors. Here we demonstrate that SUMO post-translational modification functions in transcription factor binding site selection. We show that a yeast transcription factor, Sko1, becomes sumoylated after it binds DNA, and that the modification reduces the interaction of Sko1 with DNA. We propose that this is important to ensure that Sko1 remains associated only with actual Sko1 genomic binding sites. Indeed, we find that a mutant form of Sko1 that cannot be sumoylated binds hundreds of sites on the genome that are not normally bound by Sko1. Most of these additional binding sites contain sequences that somewhat resemble the normal Sko1 binding site sequence, suggesting that sumoylation increases the stringency of Sko1 binding site selection. As numerous transcription factors are known to be targets of SUMO modification, our work suggests a general role for sumoylation in promoting specificity of binding to genomic sites in eukaryotic cells. Small Ubiquitin-like Modifier) peptide to specific lysine residues on substrate proteins through a three-enzyme cascade that is analogous to ubiquitination .,59.SKO1-Materials and Methods). Nonetheless, a large number of overlapping peaks were identified in each analysis between the two replicates  from the overlapping peak sets. Nearly all the 207 Sko1-WT binding sites were also bound by Sko1-MT, but Sko1-MT was found at an additional 277 sites . This isP < 1.0 x 10−4), which matches the results of a previous examination of the Sko1 regulon [We then examined the nature of the Sko1-WT and Sko1-MT peaks to determine whether binding sites unique to Sko1-MT have common or distinguishing features. Genes situated nearest the Sko1-WT peaks are involved in diverse pathways, but Gene Ontology (GO) term analysis indicates that glucose, hexose, and ethanol metabolic processes are significantly enriched among these , release of Sko1 , or no observable change in Sko1 occupancy in response to NaCl treatment . To validate these results, we performed independent ChIP experiments followed by qPCR analysis of the promoter regions of the eight representative Sko1 target genes. Indeed, most genes showed statistically significantly higher levels of Sko1-MT compared with Sko1-WT in untreated and NaCl-treated samples  resulted in reduced RNA levels of one gene, FSH1, both prior to and after NaCl treatment, but had no significant effect on expression of the inducible genes STL1, PRR2, or MPC3  of 0.2. Then, cells of each strain were spotted side-by-side in five-fold serial dilutions on solid-media plates with or without indicated osmotic stress conditions. Plates were incubated at 30°C and images were taken after the indicated durations.595 nm of 0.5 to 0.7). Osmotic stress was induced by adding NaCl to a final concentration of 0.4 M for the indicated time points. Cells were then harvested by centrifugation at 3000 g for 5 min, followed by a wash with IP buffer , 1X yeast protease inhibitor cocktail (BioShop), 1 mM phenylmethylsulfonyl fluoride (PMSF), and 2.5 mg/mL N-ethylmaleimide (NEM)). Cells were lysed with glass beads in IP buffer containing 0.1 mM dithiothreitol for 30 min at 4˚C. The lysed material was isolated from the beads and centrifuged twice to remove insoluble materials. Samples were either analyzed by immunoblot or used for IP. For IP experiments, an aliquot of yeast lysate was retained as input sample, and the remainder was incubated overnight with anti-HA agarose beads or Protein G agarose with HA antibody at 4˚C. IPs were washed three times with ice-cold IP buffer plus 0.1% NP40, and twice with IP buffer alone. Beads were then resuspended and boiled with SDS loading buffer for 3 min, prior to analysis by the indicated immunoblots.Yeast cultures (40–50 mL) were grown in appropriate liquid medium to mid-log phase (OD595 nm of ~0.65. Cells were harvested by centrifugation at 3000 g for 5 min and washed with 20% trichloroacetic acid (TCA). Washed cells were then lysed with glass beads in 20% TCA. Lysates were precipitated and washed with 5% TCA and resuspended with modified SDS buffer  prior to boiling for 5 min. Boiled samples were centrifuged at room temperature for 10 min. An aliquot (40 μL) of the supernatant was retained as input sample and the remainder was diluted with denaturing IP buffer  containing 0.5 mg/mL of bovine serum albumin. Diluted samples were then incubated overnight with anti-HA agarose beads at 4˚C. IPs were washed three times with ice-cold denaturing IP buffer. Beads were then resuspended and boiled with SDS loading buffer for 3 min prior to analysis by the indicated immunoblots.Yeast cultures (50 mL) were grown in YPD medium to an ODg for 5 min. To the isolated supernatants, additional NaCl was added to a final concentration of 225 mM. For IP, salt-adjusted supernatants were incubated with 15 μL magnetic Protein G beads  pre-bound with 2 μg rabbit anti-HA (Novus) or mouse anti-MYC (NEB) antibodies. IPs were washed with four different buffers for 4 min in the following order: (1) ChIP buffer with 275 mM NaCl; (2) ChIP buffer with 400 mM NaCl; (3) A buffer containing 10 mM Tris-HCl, pH 8, 0.25 M LiCl, 1 mM EDTA, 0.5% NP-40 and 0.5% sodium deoxycholate; (4) Tris-EDTA buffer . Washed beads were then incubated in ChIP elution buffer  for 10 min at 65°C. Bound material was isolated on a magnet, treated with RNase for 30 min at 37°C, then with proteinase K at 42°C for 1 h. To reverse crosslinking, samples were incubated overnight at 65°C. DNA was purified and recovered using GeneJet Gel Extraction Kit (Thermo Fisher) followed by a quantitative PCR using primers listed in PMA1, for other ChIP analyses. ChIP experiments were repeated at least three times and the averages were plotted with the standard deviations shown as error bars. Pair-wise statistical analyses were performed by Student’s t-test  were grown in YPD medium and induced by osmotic stress as indicated for IP procedure. Cells were then cross-linked with 1% formaldehyde for 20 min, followed by 5 min of quenching with 282 mM of glycine. Cells were pelleted by centrifugation and washed twice with ice-cold TBS , then in ChIP buffer . Washed samples were resuspended in ChIP buffer and lysed with glass beads using a mini bead beater. The lysed materials were isolated from glass beads and sonicated to yield an average DNA fragment size of 300 to 500 bp in length. Samples were then centrifuged at 14,000 est Figs  and S6, RNA was isolated as previously reported from 10 mL yeast culture and subjected to DNase treatment . ReverseFor ChIP-seq analysis, ChIP was performed as described above, but scaled up as appropriate. Namely, culture volumes were 200 mL in rich (YPD) medium, and 6 μg of HA antibody was pre-bound to 40 μL of Dynabeads. Two replicates of the ChIP-seq experiment described above were performed, with sequencing libraries generated for both IP and input samples for Replicate 1, but only IP samples were sequenced for Replicate 2. Libraries were prepared using the NEBNext Ultra II DNA library prep kit (New England Biolabs), and paired-end sequencing (two times 126 bases) was performed using an Illumina HiSeq 2500 instrument at The Centre for Applied Genomics (TACG) at the Hospital for Sick Children (Toronto). Raw and processed sequencing data files have been uploaded to the NCBI Gene Expression Omnibus (GEO) with accession number GSE118655.q-value, which is the false-discovery rate (FDR)-adjusted p-value, calculated using the Benjamini-Hochberg correction. Fold enrichment (FE) is calculated as the fold enrichment for each peak summit against a random Poisson distribution with local lambda. A set of consensus peaks was assembled using DiffBind (version 2.2.12), such that each peak was identified in at least two independent samples from both replicates, or as otherwise noted. Binding affinities were determined and compared using DiffBind based on the number of ChIP read counts   running Cutadapt (version 1.10) with the following parameters: quality score cut-off of 25, six nucleotides were removed from 5′ ends, Illumina universal adapter sequences were removed, stringency setting of 5, sequences shorter than 40 nt after trimming were discarded, and only pairs of reads were retained. Trimmed forward and reverse reads were then aligned to the sacCer3 reference genome using Bowtie2 (version 2.3.2) . Peak catracted) . Becausetracted)  using thFor peak number analysis Figs  and S2, S1 Fig(A) C-terminal 6xHA tag on Sko1 does not affect cell growth in normal or osmotic conditions. Spot assays (as in (B) Detection of Sko1 sumoylation in lysates prepared under denaturing conditions. IP-immunoblot analysis was performed with protein samples prepared under denaturing conditions (TCA precipitation). Asterisks (*) indicate position of the major (mono-) sumoylated form of Sko1 in each immunoblot. Open circles (○) indicate position of putative Sko1 degradation products, detectable in both HA and SUMO immunoblots. (C) A yeast strain expressing sumoylation-deficient Sko1 shows no growth defect. Spot assays in which growth of indicated yeast strains were compared on rich (YPD) or synthetic complete (SC) medium, as in D) Sko1 sumoylation levels are unaffected by stress. Relative Sko1 sumoylation levels were quantified after IP-immunoblot analyses as in t-test, there is no significant statistical difference among the samples. (E) Blocking Sko1 sumoylation does not affect its abundance. HA and GAPDH immunoblot analysis of lysates from SKO1.-WT.HA or sko1-MT.HA strains grown in SC medium treated with 0.4 M NaCl for indicated times. Sumoylated forms of Sko1.WT cannot be seen in this short exposure. (F) Blocking Sko1 sumoylation does not prevent its Hog1-mediated phosphorylation. HA immunoblot analysis, as in HOG1 and expressing Sko1.HA was included as a control. Analysis using standard SDS-PAGE analysis is shown at bottom.ys as in  in which(PDF)Click here for additional data file.S2 Fig(A) Number of binding sites (peaks) identified from Replicate 2 ChIP-seq analysis of SKO1-WT.HA and sko1-MT.HA strains, either untreated or treated with 0.4 M NaCl for 5 min, with a q-value less than 0.05 (blue bars). Subset of peaks having a q-value less than 0.05 and a fold enrichment (FE) value greater than 2 are also indicated (red bars). (B) Venn diagram, as in q < 0.05) shared among the four samples in Replicate 2. (C) Venn diagrams indicating numbers of peaks identified in both Replicate 1 and 2, for each of the four samples. Peaks found in both replicates (i.e. intersects) for each sample constitute the “Overlapping Peak Sets.” At right, similar analysis comparing peaks from Replicate 1 and the subset of peaks from Replicate 2 that have an FE greater than 2. All analyzed peaks have q-values less than 0.05. (D) Number of binding sites for each of four samples in the overlapping peak sets, which includes only peaks identified in both replicates (as indicated in (C) left). (E) Venn diagram showing number of common and unique peaks in the overlapping peak sets from the four samples.(PDF)Click here for additional data file.S3 Fig(A) Distribution of aggregate ChIP-seq peak numbers (for overlapping peak sets) around the nearest transcriptional start site (TSS) for indicated peak sets in untreated and NaCl-treated samples. (B) De novo motif discovery was performed, using the MEME analysis tool, for “WT & MT” and “MT only” peak sets, in untreated and NaCl-treated samples. Only one significant motif was identified for each of the untreated and +NaCl MT-only peak sets , and the most significant motifs for the WT & MT peak sets are shown . Number of peaks contributing to the motif (“Sites”) is indicated in each case.(PDF)Click here for additional data file.S4 Fig(A) Differential binding analysis of different groups of peaks. Boxplots comparing mean read concentrations  in each of the four indicated ChIP-seq analyses for the 52 peaks that are found in both replicates of all four ChIP-seq sets (left) or the 212 peaks that are found in both replicates of the Sko1-WT sets in both untreated and +NaCl conditions (right). (B) Validation of ChIP-seq analysis. Five independent standard ChIP experiments were performed with SKO1-WT and sko1-MT strains. Sko1.HA occupancy levels at promoter regions of eight representative genes were determined by qPCR, at 0 or 5 min after the addition of 0.4 M NaCl. For each gene, occupancy is shown relative to Sko1-WT in untreated samples. Error bars represent standard deviations. P-values from two-factor ANOVA analysis of WT vs MT sets for each gene are shown. Asterisks (*) indicate that the two data sets (WT and MT) are statistically different Click here for additional data file.S5 Fig(A) Quantitative RT-PCR analysis of mRNA levels of indicated representative Sko1-target genes at 0, 10, 20 and 30 min after treatment of SKO1-WT or sko1-MT cultures with 0.4 M NaCl. Error bars represent standard deviations of three independent replicates. P-values from two-factor ANOVA analysis of WT vs MT sets for each gene are shown. Asterisks (*) indicate that the two data sets (WT and MT) are statistically different  Quantitative RT-PCR analysis of mRNA levels of a selection of genes that are bound by Sko1-MT, but not Sko1-WT, at 0 and 10 min after treatment of SKO1-WT or sko1-MT strains with 0.4 M NaCl. Statistical analysis indicates no significant difference between WT and MT sets. Error bars represent standard deviations of four independent replicates.(PDF)Click here for additional data file.S6 FigSKO1-WT and sko1-MT strains at 0, 5, or 15 min after treatment with NaCl. Data are represented as fold occupancy (relative to occupancy at the PMA1 locus which is not targeted by Hog1 or Sko1). Error bars represent standard deviations of three independent replicates. Asterisks (*) indicate that the compared data pairs are statistically different Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 TableData relates to (XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


The specific target for PL2-6 appears to include the nucleosome acidic patch. Thus, within the epichromatin and chromomeric regions, this epitope is ‘exposed’. Considering that histones possess unstructured ‘tails’ , our perception of these chromatin regions becomes more ‘fuzzy’ (less defined). We suggest that epichromatin cationic tails facilitate interactions with anionic components of NE membranes. We also suggest that the unstructured histone tails , with their presumed promiscuous binding, establish multivalent binding that stabilizes each chromomere as a unit of chromatin higher order structure. We propose an ‘unstructured stability’ hypothesis, which postulates that the stability of epichromatin and chromomeres (as well as other nuclear chromatin structures) is a consequence of the collective contributions of numerous weak histone IDPR binding interactions arising from the multivalent nucleosome, analogous to antibody avidity.‘Epichromatin’, the surface of chromatin beneath the interphase nuclear envelope (NE) or at the surface of mitotic chromosomes, was discovered by immunostaining with a specific  Indetobacco) ,16: intetobacco) –19; an ePL2-6 is a bivalent mouse IgG2b antibody. In order to examine the significance of antibody bivalency to the remarkable peripheral chromatin nuclear staining, we generated a monovalent form of PL2-6, employing papain digestion . To our in vitro into granulocytes and macrophage forms [paucity of various histone post-translational modifications associated with transcription or repression of transcription [not an anti-DNA antibody and that the DNA sequences enriched from HL-60/S4 cells are possibly unique to these cells. Certainly, Drosophila, C. elegans and tobacco cells do not have retrotransposon Alu, among other differences. A recent study [Several major questions arise from the unusual chromatin surface binding of interphase epichromatin by bivalent PL2-6: What are the properties of this chromatin? Do these properties change during cell differentiation? What type of DNA is present in interphase epichromatin? These questions were explored employing ChIP-Seq on the human myeloid leukaemia HL-60/S4 cell line, which can be differentiated ge forms . In summcription . Indeed,nt study , employiMuch less can be said about chromomeres, visualized with the monovalent Fab fragments of PL2-6. Chromomeres appear to have ‘exposed’ nucleosome acidic patches which are not highlighted by bivalent PL2-6. It is possible that some chromomeres present transcriptional ‘target regions’ on their surface, maintaining a more ‘open’ display of surface nucleosomes , but we How do we explain the different immunostaining patterns of bivalent PL2-6 and of monovalent Fab fragments? Conceivably, at least four factors might affect the bivalent versus the monovalent antibody staining pattern: (i) epitope local concentration; (ii) geometric arrangements of the multivalent epitopes; (iii) blocking by competing binders; and (iv) unique properties of epichromatin as a ‘surface’ . It is interesting that a zig–zag arrangement of nucleosomes along a single chromatin fibre  yields 2.The 1950s and 1960s were the beginning of exciting times for structural biology: deduction of the protein α-helix and β-sheet from X-ray diffraction data of polypeptides, determination of the collagen triple helix, the DNA double helix, haemoglobin subunit allosteric interactions, predictability of RNAse peptide folding, tRNA structure, etc. In many respects, the cell became regarded as a ‘bag’ of highly regular macromolecular structures, functioning by ‘lock-and-key’ mechanisms with predictable secondary, tertiary and quaternary higher order folding, while being immersed in defined intracellular solutions. An amazing documentation of the successes with structure determination can be seen in the beautiful poster available online from the PDB (Molecular Machinery: A Tour of the Protein Data Base). This confidence in structural predictability, combined with fibre X-ray diffraction data (i.e. the method employed to decipher the DNA double helix) employed on isolated chromatin fibres led to two proposals for helical models of chromatin, both of which were wrong  –36. InstIn vitro, the presence of histone H1 is required to condense polynucleosomal chains at physiological ionic strength. (iii) The H1 C-terminal peptide is more important in the formation of chromatin higher order structure than is the N-terminal peptide (which still plays a role). (iv) There appears to be an insignificant amount of ‘free’ (unbound) H1 in the nucleus. (v) The residence time for H1 on a nucleosome is shorter than the residence time for the inner histones, but longer than for transcription factors or HMG proteins. (vi) H1 exhibits numerous types of post-translational modifications  that are dynamic during the cell cycle and during cell differentiation. (vii) Genetic loss of certain histone isotypes can apparently be compensated by H1 redundancy, until the stoichiometry of H1/nucleosome becomes too low. It is clear that H1 proteins are dynamic within the nucleus, with complex and varying roles in influencing chromatin higher order structure and transcription regulation.Describing the internal nucleosomal organization of chromomeres presents a distinct challenge. Histone H1 is a particularly attractive candidate for stabilizing super-nucleosomal clusters (chromomeres) within live cells. A considerable amount of research has been performed on the different H1 isotypes  –62. A fea). As an analogue to the multivalent mononucleosome, we suggest considering the multivalent IgM (macro-immunoglobulin) molecule. The IgM molecule is composed of five IgG-like subunits, connected by their Fc regions, generating 10 antibody binding sites on a single macromolecule. The individual binding constants are usually relatively weak; but collectively, based upon the avidity principle, an IgM molecule has among the strongest association constant of any immunoglobulin. As a prototype for inter-nucleosome binding, we also cite the histone H4 tail, which can interact with the acidic patch of an adjacent nucleosome [figure 10c), analogous to the interaction between PS and the protein MARCKS at the plasma membrane [figure 10b). In this manner, nuclear architecture and chromatin higher order structure can be regarded as being the product of the multivalent mononucleosome, with its binding force derived from the avidity principle.An additional challenge to the concept of highly defined chromatin structures comes from the emerging realization that the constituent histones of the nucleosome are rich in ‘unstructured’ intrinsically disordered peptide region (IDPR) –66. IDPRfigure 10cleosome . We suggmembrane ,76. The 3.(1) We suggest that the conformational plasticity of nucleosomal higher order structure is in large measure due to the multiplicity of IDPR histone tail interactions.2+ [(2) Our conception of interphase and mitotic epichromatin is of a chromatin surface covered with unaffiliated histone tails, forming a cloud of (+) amino acid residues. Interphase epichromatin might be electrostatically attracted to inner nuclear membrane anionic phospholipids. During mitotic chromosome condensation, a rise in cellular Mg2+  might wein vivo, at least some of the histone tails are associated with ‘distant’ nucleosomal DNA.(3) We speculate that inner histone and H1 tails act like ‘sticky tape’ holding multivalent nucleosomes in a coordinated bundle (chromomeres). The effectiveness of this ‘sticky tape’ to bind nucleosomes together can be influenced by post-translational modifications. In vitro experimental and computational evidence supports that the histone tails can bind to DNA within chromatin –80. Thus(4) Chromomeres likely represent only one level in the super-nucleosome organization. If chromatin possesses ‘liquid droplet’-like properties , phase s4.collective contributions of the numerous IDPR–binding partner interactions in establishing these chromatin structures. Furthermore, we propose that the promiscuity of IDPRs will generate many functionally equivalent binding partners , resulting in considerable redundancy, such that mutations in the binding sites can be readily compensated by alternative binding interactions. However, this hypothesis must be consistent with the conception that histone post-translational modifications of IDPRs can modulate (i.e. destabilize or hyper-stabilize) these binding interactions. This hypothesis also does not exclude the importance of specific ‘lock-and-key’ interactions by other chromatin-associated proteins or specific ‘induced fit’ mechanisms by modifying enzymes [We propose that various chromatin structures (e.g. epichromatin and chromomeres) are stably maintained by the interactions of the unstructured intrinsically disordered peptide regions (IDPRs) of the inner histones and of histone H1 on multivalent mononucleosomes. This hypothesis stresses the  enzymes , which m