Transcriptional regulation of protein-coding genes is increasingly well-understood on a global scale, yet no comparable information exists for long non-coding RNA (lncRNA) genes, which were recently recognized to be as numerous as protein-coding genes in mammalian genomes. We performed a genome-wide comparative analysis of the promoters of human lncRNA and protein-coding genes, finding global differences in specific genetic and epigenetic features relevant to transcriptional regulation. These two groups of genes are hence subject to separate transcriptional regulatory programs, including distinct transcription factor (TF) proteins that significantly favor lncRNA, rather than coding-gene, promoters. We report a specific signature of promoter-proximal transcriptional regulation of lncRNA genes, including several distinct transcription factor binding sites (TFBS). Experimental DNase I hypersensitive site profiles are consistent with active configurations of these lncRNA TFBS sets in diverse human cell types. TFBS ChIP-seq datasets confirm the binding events that we predicted using computational approaches for a subset of factors. For several TFs known to be directly regulated by lncRNAs, we find that their putative TFBSs are enriched at lncRNA promoters, suggesting that the TFs and the lncRNAs may participate in a bidirectional feedback loop regulatory network. Accordingly, cells may be able to modulate lncRNA expression levels independently of mRNA levels via distinct regulatory pathways. Our results also raise the possibility that, given the historical reliance on protein-coding gene catalogs to define the chromatin states of active promoters, a revision of these chromatin signature profiles to incorporate expressed lncRNA genes is warranted in the future. Evidence for important, including essential, cellular and organismal roles of lncRNA in mammalian systems began to emerge prior to the advent of high-throughput genome and transcriptome sequencing. These early examples included the demonstration that the lncRNA XIST Many lncRNA transcripts, similarly to mRNAs, are 5′-capped, polyadenylated, frequently spliced with conventional GT-AG intron excision, and readily evident in cytoplasmic polyA + RNA preparations; thousands of lncRNAs have been discovered from cDNA libraries in vivo human brain These diverse functional mechanisms summarily indicate that jointly with TFs, lncRNAs are key regulators of protein-coding genes – including those that encode TFs. A prerequisite toward understanding the biology of lncRNAs is their assignment into tractable gene regulatory networks. We previously showed In this study, our goal was to computationally test the hypothesis that the global transcriptional regulatory programs of lncRNA genes and protein-coding genes are different. We set this problem within the framework of machine learning classification of promoters of these two broad gene classes. Previous studies We compared DNA sequence promoter patterns of protein-coding and lncRNA genes. We found that A/T-rich mono-, di- and tri-nucleotide patterns are enriched at the promoters of lncRNA genes, relative to the promoters of protein-coding genes  . CpG-derPalindromes, widespread regulatory elements in the promoters of protein coding genes in silico predicted the incidence of known transcription factor binding sequences (TFBSs) at the promoters of both gene types, using the HOCOMOCO We cis-regulated by two lncRNAs: an lncRNA containing primate-specific repetitive elements provides transcriptional regulation cis-antisense transcript acts post-transcriptionally Several TFs regulated by specific lncRNAs emerge as potential global regulators of lncRNA transcriptome in our analysis. A representative example is PGR (progesterone receptor), a nuclear hormone receptor (NHR), whose predicted TFBSs are differentially enriched at lncRNA promoters. The human PGR gene itself is cis-antisense lncRNA at its own locus cis and in trans by multiple lncRNAs in silico predicted binding site results for homeobox TFs at the promoters of lncRNA genes are consistent with a recent evolutionary study We additionally found that 14 FOX-family TFs, 6 SOX-family TFs, 3 members of the HOXD homeobox family, 3 members of the CEBP family, 3 NKX-family TFs, and 2 PPAR TFs  demonstrab initio motif discovery to genome-wide promoters, in order to complement the HOCOMOCO results. Ab initio identified motif families (MFs) generated by the Dragon Motif Finder The human proteome harbors approximately 1500 TFs de novo mutations may also alter a sequence of a particular binding site complicating known-TFBS discovery ab initio MFs might reflect condition-driven, or protein complex-dependent, deviations from known TFBS models. We therefore compared the ab initio identified MFs to those already associated with known TFs. We confirmed five models and added one new TFBS model (NKX3-2) to our roster of lncRNA-promoter-enriched TFBSs  in eight human cell lines Protein-coding gene promoters more often overlapped CSs associated with active, weak or inactive/poised promoters, and were also more strongly enriched for Polycomb-repressed regions. Relative to protein-coding gene promoters, those of lncRNA genes more often overlapped CSs associated with insulators, regions of transcriptional transition (regions located between the initiation and elongation histone marks), elongation, weak transcription and heterochromatin . After tTo understand the biological context of the heterochromatin CS enrichment at lncRNA gene promoters, we analyzed histone modification marks (HMs) in the ENCODE Tier 1 cell line GM12878. LncRNA gene promoters were significantly depleted of almost all histone modification marks, except for H3K27me3 and H3K9me3 . H3K27meSeveral lines of evidence indicate that the transcriptional regulation of lncRNAs may differ substantially from that of protein-coding genes. To computationally test for any evidence of this phenomenon, we leveraged recent advances in machine learning to fit an integrative model based on the information from all analyzed data types to distinguish the promoters of protein-coding genes from those of lncRNAs. Our fitted ensemble model correctly classified the promoters (lncRNA or protein-coding) with more than 80% accuracy. Hence, across the majority of the genome sequence space, genetic and epigenetic information is sufficient to confidently separate these two classes of promoters . InterroSince we had originally considered the regions of  bp around the TSS  as a putLncRNAs show lower expression in almost all cell types as compared to mRNAs We aimed to assess the extent of experimental support for transcriptionally permissive chromatin configurations across all TFBSs enriched at lncRNA promoters. We reasoned that binding sites should have an open chromatin configuration in the cell or tissue types where binding occurs. We leveraged the empirical genome-wide catalog of DNase I hypersensitive sites, indicative of open chromatin, derived for 125 human cell types by the ENCODE Consortium In parallel, we overlapped lncRNA-promoter-enriched TFBSs with ENCODE ChIP-seq experimental evidence for the corresponding TFs across all ENCODE ChIP-seq datasets We present the first genome-wide demonstration of a significant difference in sequence characteristics between the promoters of human lncRNA and protein-coding genes, suggesting distinct regulation of the two gene groups. In view of the frequent bidirectionality of human promoters that simultaneously give rise to protein-coding and lncRNA genes in silico predicted TFBSs that we have linked to global catalogs of lncRNA promoters and to lncRNA regulatory programs modulated by specific TFs. By virtue of their TFBS localization, such variants may emerge as direct functional candidates.Our observation that lncRNAs might be selectively regulated by a distinct set of TFs has substantial implications for systems biology: cells are potentially capable of harnessing a defined subset of regulatory switches to toggle the expression levels of lncRNAs without altering mRNA levels. Most of the disease-associated sequence variants in the human genome are non-coding Our lncRNA gene collection is a composite of three previously published lncRNA sources – Gencode Until recently, only protein-coding gene sets were used in characterizing general promoter features. Therefore, some widely accepted promoter features and chromatin state signatures may be biased as a consequence of having been inferred from protein-coding genes. In this context, it is hardly surprising that certain sequence and epigenetic features, more specific for protein-coding genes, are less pronounced at lncRNA promoters, while the chromatin states associated with lncRNA promoters are predominantly labeled as inactive promoters. However, these labels were based on manual annotation by biologists, predominantly of protein-coding and intergenic regions http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/refGene.txt.gz, download date: 14 January, 2013) for the human genome (version hg19). Out of the 44,140 transcripts, we considered only the 34,475 that were clearly protein-coding (i.e. having an NM RefSeqID) and that were located on chromosomes 1–22, X, and Y. To construct a non-redundant (a single reference transcript per gene) set, we considered at least 1 bp overlap in the entire genomic span  among all transcripts located on the same strand in the same locus, and we randomly selected one transcript per locus. Through these filtering steps, we ultimately arrived at 18,789 protein-coding non-redundant representative transcripts conforming to our one-transcript-per-gene data structure (We used RefSeq transcripts from the UCSC Genome Database (tructure .www.gencodegenes.org), a manually curated list of coding and non-coding genes that are supported by full-length cDNA, EST, RNAseq, and targeted-RTPCR evidence from public sources as well as from Gencode's ongoing validation efforts. We enhanced this collection with non-redundant lncRNAs from three additional sources: 4) NCBI RefSeq (NR identifier) non-coding transcripts that do not host any known small RNAs according to the UCSC Genome Database sno/miRNA repository, 5) human ESTs from the dbEST division of Genbank (NCBI) We also assembled 18,498  experimewww.galaxyproject.org/).For each gene's representative transcript, we considered the  bp around the TSS as the putative promoter region, except in the specific analyses listed under Results where an alternate  bp TSS set was used. We chose relatively large promoter regions with the purpose of incorporating alternative TSS, which in turn allowed us focus on gene-based rather than transcript-based analysis, since alternative promoter usage is a widespread phenomenon in human transcriptome To identify the regulatory patterns which may facilitate the computational discrimination between the promoters of protein-coding genes and lncRNA genes, we extracted features from several broad categories. These include various frequency-based properties of the promoters such as k-mers, word commonality, skew, palindromes; regulatory elements such as CpG islands, repetitive elements, TFBS found within the promoter regions; epigenetic features such as chromatin states and separate histone modification marks (see http://www.cbrc.kaust.edu.sa/hocomoco/Download.php) in the promoters of both protein-coding and lncRNA genes. Since the extent to which the original nucleotide composition of promoters is a cause or a consequence of the possible TFBS repertoires present in these promoters is unclear, we used the same strategy for both protein-coding and lncRNA promoters. For each PWM the threshold was set in the following way: for a random word generated by a background model (independent nucleotide distribution with nucleotide frequency of hg19) there was a fixed probability of 0.0005 to obtain the PWM score no less than the threshold. We generated 426 features using the binary value 0 or 1 (zero or non-zero hits above the threshold in a given promoter sequence in both strands). We selected significantly overrepresented TFBSs in promoters of protein-coding vs. promoters of lncRNA (and vice versa) gene sets  We predicted TFBSs using 426 position weight matrices (PWMs) for 401 human TFs from the HOCOMOCO http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeCshlLongRnaSeq/) and we quantified the expression levels as RPKM (read per kilobase of exon per million mapped reads) We used RNA-seq data from Gm12878, H1-hESC, K562 and HUVEC cell lines to check the model performance, when expression levels of lncRNAs and protein-coding genes are similar. We used the mappings, provided by ENCODE (www.uniprot.org) and the GeneCards resource (www.genecards.org) to compile a comprehensive list of human transcription factors that accounts for all name multiplicity, synonymity, and redundancy between the abbreviated transcription factor names used by HOCOMOCO (since Uniprot naming is in one-to-one relationship with the HOCOMOCO naming system) and ENCODE. We manually curated this list. We identified 106 . Green line corresponds to promoters of protein-coding genes; black line corresponds to lncRNA gene promoters. Sub-figure. a-d show distribution of the feature in a sliding window of 100 bp with a step of 50 bp, resulted in 39 windows on the plot. Sub-figure. e–f show the percentage of promoters where features were found. Transparent regions correspond to 5–95% bootstrap confidence interval of the statistics. WC: word commonality, PALIN: palindromes, CGI: CpG Islands, RE: repetitive elements. The enrichment score was calculated using right-sided exact Fisher's test Click here for additional data file.Figure S2Logos for over-represented ab initio identified motif families (MFs) from promoters of a) protein-coding genes in CPS, b) lncRNA genes in CPS, c) protein-coding genes in REFPS and d) lncRNA genes in REFPS. Logos for ab initio motif families (MFs), corresponding reverse complement (RC) MF and known TFBS match by TOMTOM system from promoters of e) lncRNA genes in CPS, f) lncRNA genes in REFPS.(PDF)Click here for additional data file.Figure S3Distribution of chromatin states in cell lines with normal karyotypes across promoters of protein-coding and lncRNA genes. Blue bar corresponds to promoters of coding genes from repeat-filtered promoter set (REFPS), green bar corresponds to promoters of coding genes from complete promoter set (CPS), red bar corresponds to promoters of lncRNAs from REFPS, and black bar corresponds to promoters of lncRNAs from CPS. This figure demonstrates fraction of all promoters overlapping with chromatin states. At the end of each bar 5–95% bootstrap confidence interval of the statistic is shown. AP: Active Promoter, WP: Weak Promoter, IP: Inactive Promoter, SE: Strong Enhancer, WE: Weak Enhancer, I: Insulator, TT: Transcriptional Transition, TE: Transcriptional Elongation, WT: Weakly Transcribed, PR: Polycomb Repressed, HC: Heterochromatin low signal, RP: Repetitive/Copy number variation.(PDF)Click here for additional data file.Figure S4Distribution of histone modification marks, modified histone H2AZ, CTCF, and the Polycomb-group protein (PRC2 complex component) EZH2 in cell lines across lncRNA and protein-coding gene promoters.(PDF)Click here for additional data file.Figure S5Distribution of chromatin states in cell lines with normal karyotypes across promoters of protein-coding and lncRNA genes with similar expression. Green bar corresponds to promoters of coding genes from complete promoter set (CPS), black bar corresponds to promoters of lncRNAs from CPS. This figure demonstrates percentage of all promoters overlapping with chromatin states. At the end of each bar 5–95% bootstrap confidence interval of the statistic is shown. AP: Active Promoter, WP: Weak Promoter, IP: Inactive Promoter, SE: Strong Enhancer, WE: Weak Enhancer, I: Insulator, TT: Transcriptional Transition, TE: Transcriptional Elongation, WT: Weakly Transcribed, PR: Polycomb Repressed, HC: Heterochromatin low signal, RP: Repetitive/Copy number variation.(PDF)Click here for additional data file.Figure S6Boxplot and Quartile-Quartile plot for expression value of protein-coding genes and lncRNA genes from complete promoter set (CPS) in different cell lines.(PDF)Click here for additional data file.Table S1Mono-, di-, and tri-nucleotides frequency and observed/expected ratio for both complete promoter set (CPS) and repeat-filtered promoter set (REFPS).(PDF)Click here for additional data file.Table S2Transcription factor binding sites overrepresented in promoters of protein-coding and lncRNA genes for complete promoter set (CPS) and repeat-filtered promoter set (REFPS) and support provided by DNAseI and ChIP-seq peaks.(PDF)Click here for additional data file.Table S3P-values of overrepresentation for chromatin states, CpG islands, repetitive elements and palindromes for complete promoter set (CPS) and repeat-filtered promoter set (REFPS).(PDF)Click here for additional data file.Table S4a. Summary of the results for separation of promoters of protein-coding and lncRNA genes using different combinations of features for the complete promoter set (CPS) and repeat-filtered promoter set (REFPS). For REFPS, we used all types of repeats except “simple repeats”, “low complexity regions” and “satellite repeats”. k-mer: mono-, di-,tri-nucleotide frequencies, CS: chromatin states, WC: word commonality, TFBS: transcription factor binding sites, CGI: CpG Islands, SKEW: A/T and C/G skews, PALIN: palindromes, RE: repetitive elements, COMBINE: combination of all types of features. b. Summary of the cross validation (CV) results for separation of promoters of protein-coding from lncRNA genes using all features (COMBINE) for completer promoter set (CPS) and repeat-filtered promoter set (REFPS).(PDF)Click here for additional data file.Table S5Results from execution of the computational model from promoters considering only upstream  of TSS, as well as from promoters considering only upstream  of TSS having no overlap with CGI.(PDF)Click here for additional data file.Table S6P-values of overrepresentation for chromatin states for similarly expressed genes promoter in complete promoter set (CPS).(PDF)Click here for additional data file.Text S1Supporting information for the methods applied and results obtained. The details of methods are described under Methods section. The details of results are described under Results section.(DOCX)Click here for additional data file.Dataset S1Set of a) RefSeq and b) lncRNA transcripts with hg19 human genome assembly coordinates in BED format for complete promoter set (CPS). Set of c) RefSeq and d) lncRNA transcripts with co-ordinates from hg19 in bed format for repeat-filtered promoter set (REFPS). Set of e) RefSeq and f) lncRNA promoters ([−250…+250]) with co-ordinates from hg19 in bed format for complete promoter set (CPS). Set of g) RefSeq and h) lncRNA promoters ([−250…+250]) with co-ordinates from hg19 in bed format for repeat-filtered promoter set (REFPS). i) “ENCODE_HOCOMOCO_mapping” - excel sheet contains mapping of ENCODE transcription factor name and HOCOMOCO V.8 motif name. Excel sheet “track_MEF2A_chip”, “track_GATA3_chip”, “track_ARI3A_chip” contains the track information for ENCODE ChIP-seq supported TFBS in CPS for MEF2A,GATA3 and ARI3A respectively.(ZIP)Click here for additional data file.Dataset S2Promoters, considering only upstream , of a) RefSeq and b) lncRNA transcripts with hg19 coordinates in BED format for complete promoter set (CPS). Promoters, considering only upstream , of c) RefSeq and d) lncRNA transcripts, having no overlap with CpG islands, with hg19 coordinates in BED format for CPS.(ZIP)Click here for additional data file.Dataset S3RNA-seq expression value in RPKM (read per kilobase of exon per million mapped reads) for lncRNA genes and protein-coding genes in complete promoter set (CPS).(ZIP)Click here for additional data file.


Meis1 overexpression induces tumorigenicity but its activity is inhibited by Prep1 tumor suppressor. Why does overexpression of Meis1 cause cancer and how does Prep1 inhibit? Tumor profiling and ChIP-sequencing data in a genetically-defined set of cell lines show that: 1) The number of Meis1 and Prep1 DNA binding sites increases linearly with their concentration resulting in a strong increase of “extra” target genes. 2) At high concentration, Meis1 DNA target specificity changes such that the most enriched consensus becomes that of the AP-1 regulatory element, whereas the specific OCTA consensus is not enriched because diluted within the many extra binding sites. 3) Prep1 inhibits Meis1 tumorigenesis preventing the binding to many of the “extra” genes containing AP-1 sites. 4) The overexpression of Prep1, but not of Meis1, changes the functional genomic distribution of the binding sites, increasing seven fold the number of its “enhancer” and decreasing its “promoter” targets. 5) A specific Meis1 “oncogenic” and Prep1 “tumor suppressing” signature has been identified selecting from the pool of genes bound by each protein those whose expression was modified uniquely by the “tumor-inducing” Meis1 or tumor-inhibiting Prep1 overexpression. In both signatures, the enriched gene categories are the same and are involved in signal transduction. However, Meis1 targets stimulatory genes while Prep1 targets genes that inhibit the tumorigenic signaling pathways. Changes of expression of oncogenes and tumor suppressors can cause cancer. However, it is not clear why the elevated levels of some transcription factors promote cancer. Although high throughput studies examining the oncogene or tumor suppressor activity of DNA binding proteins are available , 2, no sMyeloid ecotropic insertion site 1 (Meis1) is a DNA binding transcription factor of the TALE (three amino acid loop extension) homeodomain family , 4, a poIn this paper we have used a set of five isogenic MEF cell lines expressing different amounts of Meis1 and/or Prep1 to understand how the increased intracellular concentration of Meis1 or Prep1 leads to or inhibits cancer, respectively.We show that the number of Meis1 and Prep1 binding sites directly correlates with their expression level, second, that overexpression of Meis1 increases seven fold the number of Prep1 enhancer targets and, third, drastically changes the consensus Meis1 DNA binding motif. Upon overexpression in MEFs, the enriched DNA sequence is that of the AP-1 element, a target of the Jun/Fos family of transcription factor. Finally by integrating ChIP-seq and RNA-seq data from the various cell lines we identify a tumor-specific Meis1 signature and a suppression-specific Prep1 signature. Gene Ontology analysis of these signatures uncovers that Meis1 and Prep1 target the same gene categories regulating transcription and signal transduction. However, while Meis1 target genes stimulate, Prep1 targets inhibit these processes.i/iPrep1 MEFs (7) were infected either with an empty vector (ev cells) or with Meis1 (M cells) or Prep1 (P cells) expression vectors. Only the M cells become tumorigenic [Prep1 expression vector, generated the MP line that still produces tumors in vivo but is strongly inhibited. Ev, M and MP cells have been described previously [We performed ChIP-seq (chromatin immuno-precipitation coupled to DNA sequencing) with antibodies specific for Meis1 or Prep1 in five different isogenic cell lines bearing single gene differences. Hypomorphic origenic . The M ceviously . Littermeviously , Meis1 dp-value < 10−6 (based on the difference in reads between the immuno-precipitated and input samples). The total number of ChIP-seq peaks varied with the protein levels. In WT cells, Prep1 bound 2,949 genomic regions whereas in MP (expressing 4.2-fold more Prep1 than WT) and P cells (5-fold more Prep1) it bound 13,562 and 17,450 sites, respectively , accession numbers GSE54221 and GSE58802) were filtered using as threshold of significance a y Figure . Prep1 bLikewise, in WT cells Meis1 bound to 10,399 sites but this number decreased in the Prep1-hypomorphic ev cells , in which Meis1 level is 30% of WT and even more (3473) in P cells (Meis1 level being 10% of WT) in which Prep1 is overexpressed Figure . In M ceIn conclusion, the number of peaks or of genes bound by Prep1 and Meis1 is clearly in direct correlation with the intracellular level of the protein.DNA binding conditions in MEFs must be very different from those of the cells of the whole embryo trunk  and of eIn ev cells the number of Meis1 DNA-binding peaks Figure  is reducWe divide Meis1 and Prep1 peaks in transcription start site-associated (TSSA) if located within −500 to +100 bp from a TSS, intragenic (IG) if located within a transcription unit and intergenic if outside of a transcription unit. Close intergenic (CI) are < 20 Kb away, and far intergenic (FI) > 20 Kb from the TSS of an annotated gene. This subdivision may have functional relevance, because it tends to separate promoters from enhancers.The distribution of Meis1 peaks in the four genomic locations is essentially the same in WT, ev and M cells : Meis1 bIn WT cells, Prep1 peaks in the TSSA group are more abundant than Meis1 (3.35% vs. 0.78%), but are less frequent in the CI group (7.66% vs. 16.36%)  and S1B The overall distribution pattern of Meis1 peaks agrees with the previously published data on total E11.5 embryo trunk [Overall, the distance of the Meis1 and Prep1 peaks from the closest TSS, shows a similar distribution in the different cell types analyzed  and S1D.ChIP-seq shows 23% less Prep1 binding sites in MP cells, 13,562 v. 17,450, in agreement with the decrease of Prep1 (compare MP to P cells), with an extensive overlap Figure . Likewis+), whereas 26% of Meis1 peaks and 12% of Prep1 peaks overlap with the H3K4Me1+ enhancer mark [+; H3K27Ac+) increased 50%  than for Meis1 (0.98%) Figure . Moreovecer mark . In MEFscer mark  was pres% Figure . Despite% Figure .Therefore overexpression leads to more frequent binding of Prep1 to enhancer, and minor change of Meis1 binding to promoter sites.ChIP-seq on the whole embryo trunk and on embryonic stem cells was enriched in the binding consensus for Prep1 and Meis1: a decameric consensus sequence  for Prep1, or an octameric TGATXXAT (OCTA) and an hexameric TGACAX (Hexa) for Meis1 –13.In our MEF lines Meis1 and Prep1 consensus sequences changed see  when proAP-1 sites were enriched much less among Prep1 peaks Table . Overalla posteriori search for OCTA, DECA and AP-1 motifs confirmed this result on all sequence sets using FIMO [An ing FIMO  with defThe position of the OCTA and AP-1 consensus within Meis1 peaks was slightly different Figure . Among pWe next exploited the peak scores to compare the two consensus. A higher score does not directly indicate but must be related to higher affinity or binding to a higher fraction of the cells. In WT, P and MP cells, Prep1 peak score for DECA was on the average much higher than for AP-1 or DECA plus AP-1 consensus sites Figure . No diffIn vitro EMSA with a highly purified recombinant Meis1/Pbx1 heterodimer (see Methods) was used to show DNA binding to oligonucleotides containing OCTA, AP-1 or OCTA+AP-1 sites; likewise, the recombinant Prep1/Pbx1 heterodimer was tested with DECA, AP-1 or DECA+AP-1. The DNA sequences employed were chosen among the actual ChIP-seq peaks  of which 45% (n = 635/1395) were directly bound by Meis1 (n = 515) were upregulated upon Meis1 overexpression, suggesting an activatory role for Meis1. Since the M cells are tumorigenic, these genes should include those directly involved in Meis1 tumorigenesis.Our five-cell lines system allows to identify the genes responsible for the competing activities of Meis1 and Prep1. Total RNA was extracted from each cell line and the experiment was carried out in duplicate for WT, P and MP cells; in triplicate for ev cells and in quadruplicate for M cells. The RNA was deep-sequenced and analyzed by RNA-seq . Sequencby Meis1 . Of the We also compared RNA-seq of MP and ev cells to identify the Meis1 gene targets in the presence of overexpressed Prep1. Of the 1216 differentially expressed genes in MP cells , 38% 44. Howevern = 111) (n = 116)   and betwn = 116) . Likewisn = 116)  and S4G.n = 116)  and subtn = 116) . This tyThe analysis of Figure A different result was observed comparing the M-Meis1 Figure  to the MIn agreement with its oncogenic activity, the M-Meis1 signature Figure  was enrip = 2.13e−24) in differentiating normal cells, and 7 genes (p = 9.3e-6) upregulated in human lung cancer cells that overexpress a mutated form of Kras  which, like the OCTA consensus, is often very close to the peaks summit Figure . Bindingin vitro binding affinity to AP-1 is higher than Meis1-Pbx1 , because AP-1 becomes the main if not the only consensus sequence for Meis1 binding. Moreover, the inhibition of Meis1 tumorigenicity by co-overexpression of Prep1 (MP cells) correlates with the decrease of the number of the “extra” Meis1 targets, not the core genes, hence of those containing the AP-1 binding sites. The presence of AP-1 sites also among the Prep1-bound sequences is in line with the transcriptional competition between the two transcription factors . Indeed,1 Figure .The overlap of ChIP-seq and RNA-seq data has allowed to extract from the totality of their target genes a cancer-specific Meis1 and Prep1 signature Figure . In thisOverexpression of Meis1 and to a lesser extent of Prep1 preferentially leads to up-regulation of their target genes. We have therefore considered only those genes whose expression is enhanced by either Meis1 or Prep1. With these caveats, we consider the genes bound and overexpressed by Meis1 in M cells as potentially tumorigenic and those by Prep1 in MP cells as potentially tumor suppressive. Thus, Meis1 oncogenic signature, M-Meis1 signature Figure , includeThe importance of the AP-1 sites in Meis1 tumorigenicity was reinforced by the Gene Ontology analysis performed on the genes containing uniquely an AP-1 site in the peak sequence, and in which the AP-1 site coincided with the peak summit.ChIPs and ChIP re-ChIPs were carried out on various cell-types using standard methods . The detP-value cutoff of 10−6 and a false discovery rate of < 5% were selected for subsequent analysis. The sets of peak coordinates for Meis1 or Prep1 in one cell-type were intersected with the sets for Meis1 or Prep1 in the other cell-types. Two peaks were considered overlapped if they showed 50% or more overlapping sequences. Venn diagrams were generated using BioVenn software [All ChIPseq data set were mapped to the mouse (mm9) genome using Bowtie 1.0 software . The alisoftware .P-value cutoff of 0.05 was used. Intersects between ChIPseq and RNAseq data were performed on Galaxy platform.RNAseq data sets were aligned to mouse (mm9) genome using default parameters of TopHat . CufflinP-value cutoff of 10−4.Gorilla was used to assess gene ontology terms comparing the lists of genes bound and regulated by Meis1 or Prep1 to the list of genes in Ensembl v63 with a Details of cloning, expression and purification of recombinant proteins used in EMSAs are described in EMSA reactions were performed following the standard protocols. Details of the reactions are provided in


In addition, Cdk8 alone contributed to Ty1 gene expression regulation by altering the occupancy of the gene-specific transcription factor Ste12. Loss of STE12 and TEC1 suppressed growth phenotypes of the RNAPII-CTD truncation mutant. Collectively, our results implicate Ste12 and Tec1 as general and important contributors to the Cdk8, RNAPII-CTD regulatory circuitry as it relates to the maintenance of genome integrity.RNA polymerase II (RNAPII) contains a unique C-terminal domain that is composed of heptapeptide repeats and which plays important regulatory roles during gene expression. RNAPII is responsible for the transcription of most protein-coding genes, a subset of non-coding genes, and retrotransposons. Retrotransposon transcription is the first step in their multiplication cycle, given that the RNA intermediate is required for the synthesis of cDNA, the material that is ultimately incorporated into a new genomic location. Retrotransposition can have grave consequences to genome integrity, as integration events can change the gene expression landscape or lead to alteration or loss of genetic information. Given that RNAPII transcribes retrotransposons, we sought to investigate if the RNAPII-CTD played a role in the regulation of retrotransposon gene expression. Importantly, we found that the RNAPII-CTD functioned to maintaining genome integrity through inhibition of retrotransposon gene expression, as reducing CTD length significantly increased expression and transposition rates of Ty1 elements. Mechanistically, the increased Ty1 mRNA levels in the  RNA Polymerase II (RNAPII) is the enzyme responsible for the transcription of all protein-coding genes, many non-coding genes and retrotransposons. RNAPII has a unique C-terminal domain (CTD) that is composed of repeats of a seven amino acid sequence and is conserved across species. The CTD functions as a recruiting platform for regulatory and RNA processing factors, making the CTD a master orchestrator of transcription. Mutants containing reduced CTD length show defects in steady state transcription during growth in normal conditions, as well as defects in induced gene expression. However, how CTD length affects retrotransposon gene expression remains to be investigated. Here, we uncovered a direct role for CTD length in limiting retrotransposon gene expression and mobility, revealing a new role for the RNAPII-CTD in maintaining genome integrity. Rpb1, the largest subunit of RNAPII, contains a unique C-terminal domain (CTD) that is composed of heptapeptide repeats , which contain promoter and termination sequences required for their transcription [gag gene, which encodes a structural coat protein, and a pol gene, which encodes a polypeptide that is processed into the enzymes reverse transcriptase, protease and integrase. A crucial step in the replication cycle of retrotransposons is the production of a RNA intermediate by RNAPII [Retrotransposons constitute a major group of genetic elements transcribed by RNAPII, comprising over 3% of the genome and accounting for 5–10% of the total mRNA in haploid yeast ,17. In Scription . Retrotry RNAPII . RetrotrTEC1 significantly reduces both steady state Ty1 mRNA levels and transposition rates, while its overexpression results in increased rates of Ty1 transposition [Transposition events can have grave consequences for genome structure and function, making retrotransposons important sources of genome instability . Specifiposition –34.S. cerevisiae genome contains multiple retrotransposons, belonging to five families called Ty1 to Ty5 [The 1 to Ty5 ,35. Thes1 to Ty5 . In addi1 to Ty5 . These a5 phosphorylation, Tec1 and Ste12 at Ty1 promoters. Lastly, suggesting a broader role for these factors in RNAPII CTD function, we found that loss of STE12 or TEC1 suppressed RNAPII-CTD truncation mutant growth phenotypes, the latter likely in conjunction with CDK8.Building on previous work from our laboratory, which investigated the role of the RNAPII-CTD in the expression of most protein coding genes, we focused here on examining its contribution to retrotransposon biology . We founS. cerevisiae genome revealed significantly increased RNAPII occupancy levels in the rpb1-CTD11 mutant, which contained only 11 heptapeptide repeats, compared to wild type  and S7 (rpb1-S7A) phosphorylation sites on the RNAPII-CTD [7 phosphorylation had no effect on Ty1 or Ty2 mRNA levels  and indirectly (Tec1) regulated by Cdk8 [ste12Δ and tec1Δ single mutants both had reduced Ty1 mRNA levels compared to wild type  and phosphatase inhibitors . Chromatin was prepared as described previously  and incuReporter plasmids were generated by cloning ~1300 bp of the desired promoter region into the Sal1 and BamH1 sites of pLG669-Z . SpecifiOvernight cultures grown on YPD were diluted to 0.5 OD600, 10-fold serially diluted and spotted onto YPD plates with or without the indicated amounts of hydroxyurea (Sigma) or formamide (Sigma). Plates were incubated at the indicated temperatures for 2–4 days.TUB1 was used as a control given that its expression is not altered upon truncation of the RNAPII-CTD [RNA was extracted and purified using the Qiagen RNeasy Mini Kit. cDNA was generated using the Qiagen QuantiTect Reverse Transcription Kit. cDNA was analyzed using a Rotor-Gene 600 (Corbett Research) and PerfeCTa SYBR Green FastMix (Quanta Biosciences). Samples were analyzed in triplicate from three independent RNA preparations and the protein-coding gene APII-CTD . For meaHIS3 coding sequence inserted in the opposite orientation compared to the Ty1 element. The HIS3 gene is rendered nonfunctional by the insertion of an artificial intron in the same orientation as the Ty1 element, such that it is only spliced when transcribed from the Ty1 element. During the Ty1 transposition cycle, the Ty1 element is transcribed, the intron is spliced and the mature RNA is used for the synthesis of cDNA, which is then integrated into a new genomic location. Newly integrated Ty1 elements contain an undisrupted HIS3 open reading frame and can confer a HIS+ phenotype. Briefly, wild type and rpb1-CTD11 mutants were transformed with Pac1 digested pJR573 DNA. Transformants were selected in SC-URA media and all subsequent growth procedures were done in this media. Overnight cultures for 12 independent colonies for each strain were started. The next morning, cultures were diluted to OD600 0.3 and incubated at 20°C for 24 hours. Following, an aliquot was plated onto SC-URA and SC-HIS-URA to count the total number of cells, and cells with retrotransposition events, respectively. Plates were grown for 2–3 days until colonies were visible and counted. Results were analyzed using the Fluctuation AnaLysis CalculatOR (FALCOR) web tool using the MSS Maximum Likelihood Method to calculate mutation rates. [http://www.mitochondria.org/protocols/FALCOR.htmlThis assay tracks the mobility of a genome encoded Ty1 element ,39. The n rates. . Error bS1 FigS. cerevisiae genome. Increased RNAPII levels were observed at the 5’ and 3’ end of these elements in the rpb1-CTD11 mutant compared to wild type. Labeled boxes indicate the retrotransposon.Chromosome plots of relative RNAPII occupancy at both Ty3 retrotransposons in the (PDF)Click here for additional data file.S2 Figrpb1-CTD11 mutant strain at all, Ty1-, or Ty2-derived lone LTRs.Box plot showing differences in average MAT RNAPII occupancy scores between the wild type and the (PDF)Click here for additional data file.S3 Fig2 profiles normalized to input or RNAPII levels revealed strong enrichment toward the 3’ end of genes. Genes in the S. cerevisiae genome are aligned by their transcriptional start side (TSS), grouped into transcriptional frequency categories [5 profiles normalized to input (left) or RNAPII levels (right) revealed strong occupancy at the 5’ end of genes.(A) CHROMATRA plots  of RNAPItegories , and sor(PDF)Click here for additional data file.S4 Fig5 phosphorylation occupancy scores in the rpb1-CTD11, cdk8Δ and rpb1-CTD11 cdk8Δ mutant at Ty1 retrotransposons. (B) Average gene profiles of RNAPII-CTD phospho-S5 occupancy at Ty1 retrotransposons revealed elevated levels in the rpb1-CTD11 mutant which were normalized upon loss of CDK8.(A) Box plot showing differences in average RNAPII-CTD S(PDF)Click here for additional data file.S5 Figrpb1-CTD11 mutant compared to wild type. Immunoblots of whole cell extracts with flag antibodies to detect Ste12 (C) or Tec1 (D) protein levels. Tubulin was used as a loading control.RT-qPCR analysis of Ste12 (A) or Tec1 (B) mRNA levels in the (PDF)Click here for additional data file.S6 Fighttp://www.yeastract.com/). Average gene profile of gene identified by our data to be significantly bound by Ste12 (D) or Tec1 (E) in the wild type, rpb1-CTD11, cdk8Δ and rpb1-CTD11 cdk8Δ double mutant. Box plots showing significantly increased Ste12 (F) or Tec1 (G) binding at Ste12 or Tec1 regulated genes respectively in the cdk8Δ mutant compared to wild type.(A) Venn diagram highlighting a significant overlap between genes we identified to be significantly bound by Ste12 or Tec1, an effect consistent with previous reports . Venn di(PDF)Click here for additional data file.S7 FigRT-qPCR analysis of the indicated diploid strains.(PDF)Click here for additional data file.S8 Figrpb1-CTD11, tec1Δ and rpb1-CTD11 tec1Δ mutants. (E) Decreased levels of RPB1 in the rpb1-CTD11 mutant were not normalized upon loss of TEC1. The RPB1 RT-qPCR primers were designed such that they bound a region upstream of the sequence coding for the CTD.(A-D) RT-qPCR analysis of YKL145W, YIR034C, YML116W, and YCR061W mRNA levels in wild type, (PDF)Click here for additional data file.


Methyl CpG binding protein 2 (MeCP2) is a highly abundant chromosomal protein within the brain. It is hence not surprising that perturbations in its genome-wide distribution, and at particular loci within this tissue, can result in widespread neurological disorders that transcend the early implications of this protein in Rett syndrome (RTT). Yet, the details of its role and involvement in chromatin organization are still poorly understood. This paper focuses on what is known to date about all of this with special emphasis on the relation to different epigenetic modifications . We showcase all of the above in two particular important neurological functional alterations in the brain: depression (major depressive disorder [MDD]) and cocaine addiction, both of which affect the MeCP2 homeostasis and result in significant changes in the overall levels of these epigenetic marks. Three yted CpGs , 17. Dueted CpGs . Howeverted CpGs . It has ted CpGs . Therefoted CpGs . By virtThe initially identified form of MeCP2 turned out to be what is today known as the MeCP2-E2 isoform. It was not until 2004 that the MeCP2-E1 isoform  was discovered . MeCP2-EThe gene organization and schematic representation of the protein structure of these two MeCP2 isoforms are shown in Fig. At the protein level, the common methyl binding domain (MBD) Fig.  is the oIt took seven years since the discovery of MeCP2 for the number of publications on this protein Fig.  and the Although the presence of MeCP2 in the brain is exceedingly larger than in any other tissue , the lasi.e. H2A.Z, H2A.X, H2A.B, macro H2A, H3.3) [Chromatin is the name given to the nucleoprotein complex that results from the association of DNA and histones. Histones are basic proteins, rich in both Lysine and Arginine residues. They have been broadly classified into two structurally different major groups : core anA, H3.3) .However, what we have over the years referred to as histone variants  due to tRegardless of their ancestry or origin, replication-independent histone variants play an important role in brain development , 62. A fi.e. acetylation, methylation, phosphorylation and ribosylation, to name just a few [In addition to the histone variation, histones in general, regardless of their replication dependent or independent nature, can be post-translationally modified (PTM) at specific amino acid sites in their sequence. These chemical modifications (st a few ) set thest a few . From thst a few , 67, 68.The histone code involves a complex network of protein ‘writers’, ‘erasers’ and ‘readers’ responsible for the downstream functional implications of the histone PTMs. However, not all histone variants and PTMs have an exclusive epigenetic function. For instance, in addition to its epigenetic involvement, global histone acetylation of pan-acetylated H3/H4/H2A.Z can alter the structural organization of chromatin on their own , as is aIn addition to histone variants and their PTMs , other cAs we and others have experimentally shown, MeCP2 is a very abundant chromosomal protein within the brain, with approximately one MeCP2 mole for every nucleosome in the neurons of an adult brain , 9. The With regards to the potential role of MeCP2 in the organization of chromatin, and neuronal chromatin in particular, one of the most puzzling and yet unexplained observations is that of its early release during micrococcal nuclease digestion . Using aEqually intriguing is the difference in NRL observed between chromatin in the P fraction (approx. 200 bp) and that of the SE fraction (centered at approx. 174 bp). This difference is reminiscent of the transition in NRL observed during neuronal development (from 200 bp at the onset of development to 160-170 bp in mature neurons ) and forBased on these results, we would like to put forward a hypothetical model Fig.  that stiThe presence of two MeCP2 isoforms with putative, disparately different half-time lives may impinge in the dynamics of neuronal chromatin, giving it a very fluid organization. Although highly speculative, one could envision an organization in which MeCP2 that already binds highly dynamically to chromatin as determined by FRAP , 84 coulThe regulation of MeCP2 homeostasis and additional brain epigenetic markers, to be summarized in the following section, provide an extra layer of complexity to this chromatin organization and ultimately are responsible for the chromatin alterations that affect its function in the brain.Four main epigenetic contributors deserve special attention when it comes to the physiologically relevant aspects of MeCP2 and neuronal chromatin in normal and altered functional states of the brain: DNA methylation, MeCP2 phosphorylation, histone acetylation and microRNAs.MeCP2 is a methylated cytosine ‘reader’ and any changes in DNA methylation are going to have immediate downstream effects on the MeCP2-dependent organization of chromatin. Contrary to what was earlier believed, DNA methylation in neurons is highly dynamic and may change quite rapidly to alter the MeCP2 distribution and their connections or synapses. The process involves a group of enzymes called Tet (ten eleven translocation), which initially oxidize 5mC to 5hmC  and subsMeCP2 phosphorylation is by far the most studied PTM of this protein. MeCP2 has been reported to be phosphorylated at various serine residues, such as S13, S80, S149, S164, S229, S274, S401 and S421 , 90.Bdnf promoter III, acting as a repressor. Upon neuron depolarization, and following calcium influx, MeCP2 S421 becomes phosphorylated by a Camk2/4-dependent mechanism [Bdnf promoter III, allowing for its activity-dependent transcription. Phosphorylation at S421 has also been shown to be associated with accurate synapse development and behaviour [Among these MeCP2 serine residues, phosphorylation at residues S80 and S421 have been proposed to have opposite effects on neuronal activity , 34, 97.echanism  and is rehaviour . In an oehaviour . This opNpas4 transcription (a transcription factor that promotes the development of inhibitory synapses on excitatory neurons) and the ensuing activation of Bdnf transcription [More recently, neuronal depolarization has been shown to result in the phosphorylation of MeCP2 at threonine 308 in the transcriptional repressor domain (TRD) of the protein. Phosphorylation of this residue blocks the interaction of MeCP2 with the nuclear receptor co-repressor (NCoR) complex. Among other effects, this elicits cription .The direct or indirect molecular mechanisms behind the relation between MeCP2 and histone acetylation are obscure and require further analysis. Knockout mice in which MeCP2 expression has been completely ablated exhibit an almost 3-fold increase in the histone acetylation in neurons when compared to the wild type . ConversGlobal histone pan-acetylation results in a highly labile nucleosome  and affeMicroRNAs represent one of the most recently identified epigenetic constituents. MicroRNAs (miRNAs) are non-coding RNA transcripts that control gene expression by binding to complementary sequences  in the 3'-UTR of target mRNAs, thus controlling their degradation and translation . FurtherBdnf that induces miR-132 and represses MeCP2 translation. Taken together, these findings suggest a feedback loop involved in MeCP2 homeostasis [As was mentioned earlier, MeCP2 homeostasis plays an important role in the proper functional outcome of this protein. MicroRNA miR-132 has been found to play a crucial role in this regard. Blocking miR-132 in cultured rat neurons results in an increase of MeCP2 expression. This, in turn, increases the expression of eostasis .Another regulatory microRNA, miR-7b, is expressed in various regions of the adult mouse brain, and also targets MeCP2 at the 3'-UTR. Two CpG islands have been identified at the 5'-flanking region of the gene encoding for miR-7b. An increase in the methylation of these CpG islands during postnatal neuron maturation increases the recruitment of MeCP2 to these regions. This inhibits miR-7b gene expression and its repressive effect on MeCP2. Overall, miR-7b acts as a negative regulator of MeCP2 gene expression and is at the same time a down-stream target of MeCP2, forming a bi-directional feedback that may also be important for MeCP2 homeostasis during brain development .Igf2 (insulin-like growth factor – 2), has been found to regulate MeCP2 levels. This miRNA binds to the MREs in MeCP2 3'-UTR and inhibits MeCP2 gene expression. miR-483-5p is enriched in the fetal brain and is down-regulated after birth, thus controlling MeCP2 expression during human brain development [In humans, the intragenic miR-483-5p, derived from the gene elopment .The miR-155 and miR-802 also target MeCP2 and have recently shown to play an important role in Down syndrome pathology .The miR-181 family is expressed in astrocytes and has been shown to be involved in their neuro-inflammatory response. Despite the current lack of information, MeCP2 has been found to be a target for miR-181 .Bdnf, MeCP2 regulates the expression of many other similarly long genes that encode for proteins such as the calcium/calmodulin-dependent Camk2d kinase and those involved in axon guidance and synapsis formation [One of the more fascinating aspects of chromatin’s epigenetic involvement is, in our opinion, the connection between chromatin alterations in response to environmental cues, such as early life stress (ELS), and the resulting behavioral output. It has now been fully demonstrated in rodents that maternal/parental care can affect gene expression , 28 and ormation . Hence, ormation , 113–120ormation , 122. In2+ influx that triggers the action of neuronal kinases (such as Camk2d) that phosphorylate different substrates, including MeCP2, CREB (cAMP response element-binding protein) and histone H3S10, amongst others [cis acting regulatory elements, like MEF2C, which results in gene activation (such as that of Bdnf) [Whilst the genetic risk factors of both depression , 124 andt others . Phosphot others , which aof Bdnf) , 128.Avp). It was discovered that ELS was able to control the DNA methylation dynamics in post-meiotic neurons (see previous section), to result in stable persistent hypo-methylation of Avp expression that triggers the neuroendocrine and behavioral changes often observed in depression [Avp derepression is driven by neuronal activity that results in the Ca2 + - Camk2d dependent MeCP2 phosphorylation and chromatin dissociation described in the previous paragraph, followed by DNA hypo-metylation. A vicious cycle is thus established in which MeCP2 occupancy uncouples from the original stimulus, leading to the ELS hard-coding at the level of DNA methylation [Avp, MeCP2 also regulates the ELS-dependent programming of other genes, such as Crh (corticotropin releasing hormone) and Pomc (Proopiomelanocortin) [One of the first molecular connections between ELS and depression was established through the arginine vasopressin gene (pression . Early Ahylation . The sitocortin) , all of ocortin) .Avp, Crh, and Pomc, explain the connection between ELS and depression, Bdnf, as expected for any neuronal disturbance, also plays a very important role in depression [Bdnf III and IV promoters, a process that can be reverted with the use of histone methylation and HDAC5 inhibitors in mice [Bdnf in humans [Whilst pression , and itspression . With th in mice . It has n humans . The hisn humans  for a moMicroRNAs are also involved in depression. An increase was observed in the levels of miR-132 in the hippocampus of a rat model of stress-induced depression, and also in peripheral blood samples of patients with MDD. As expected from its role in MeCP2 homeostasis, a negative correlation between the expression levels of miR-132 and those of MeCP2 and BDNF was observed in these studies . Also, hbdnf expression [Cocaine abuse and MDD share some similarities, let alone the fact that ELS is often a common risk factor for addiction , 138, whpression , 146.The role of MeCP2 in cocaine addiction involves different aspects of the protein metabolism and its gene regulation and, in particular, the calcium-dependent MeCP2 S421 phosphorylation, one of its important PTMs that plays a crucial role , 148. Thc-Fos), and is observed within 30 minutes of a single cocaine injection (acute exposure). However, the effect fades away during chronic exposure. Conversely, the levels of H3 acetylation increased at promoters of genes such as Bdnf and Cdkl5 [Of particular interest to this review is the interplay at the chromatin level between the induced levels of MeCP2 expression observed during cocaine intake  and the nd Cdkl5 . A genomnd Cdkl5 , but ratnd Cdkl5 . They hand Cdkl5 . All of nd Cdkl5 , 154.bdnf through the feedback loop described in the previous section [Along with the shared molecular mechanisms with MDD, the cocaine-induced increase in MeCP2 represses the transcription of miR-132/miR-212 micro RNAs, which reduce the miRNA repression of  section , 155. Fu section .We would like to close this chapter with another intriguing aspect, which is that involving gender . As was It has already been twenty-four years since the first description of MeCP2 , 17. ForFinally, and although the broad attention by the scientific community to MeCP2 was triggered by the discovery of its massive involvement in RTT . To unde


This is followed by genome-wide GATA factor switching that mediates further induction of MEG-specific genes following lineage commitment. Interaction between GATA and ETS factors appears to be a key determinant of these processes. In contrast, ERY-specific lineage priming is biased toward GATA2-independent mechanisms. In addition to its role in MEG lineage priming, GATA2 plays an extensive role in late megakaryopoiesis as a transcriptional repressor at loci defined by a specific DNA signature. Our findings reveal important new insights into how ERY and MEG lineages arise from a common bipotential progenitor via overlapping and divergent functions of shared hematopoietic transcription factors.Combinatorial actions of relatively few transcription factors control hematopoietic differentiation. To investigate this process in erythro-megakaryopoiesis, we correlated the genome-wide chromatin occupancy signatures of four master hematopoietic transcription factors  and three diagnostic histone modification marks with the gene expression changes that occur during development of primary cultured megakaryocytes (MEG) and primary erythroblasts (ERY) from murine fetal liver hematopoietic stem/progenitor cells. We identified a robust, genome-wide mechanism of MEG-specific lineage priming by a previously described stem/progenitor cell-expressed transcription factor heptad  binding to MEG-associated  Hematopoiesis is driven by networks of transcription factors that establish tissue-specific programs of gene expression . Low-levErythrocytes and platelets originate from a common bipotential megakaryocyte-erythroid progenitor (MEP). A few lineage-restricted transcription factors, including erythroid (ERY)-enriched KLF1  and megakaryocyte (MEG)-enriched FLI1 and ETS1, regulate the divergence of MEG and ERY phenotypes . HoweverGATA1 and GATA2 are related transcription factors that recognize similar DNA motifs and act sequentially in blood development by orchestrating broad programs of gene activation and repression. Expression of GATA2 predominates in early hematopoiesis and declines during differentiation and maturation of most lineages, with a concomitant increase in GATA1 . GATA1 gcis-regulatory modules (CRMs)? (2) What is the scope and functional significance of GATA switching genome-wide, particularly in MEG? (3) How do the functions of GATA1 versus GATA2 overlap and differ in MEG? (4) To what extent does GATA switching play a role in hematopoietic lineage priming? We addressed these questions in primary cells (ERY) or primary cultured cells (MEG) expressing physiological levels of relevant transcription factors. As part of the Mouse ENCODE Project  What determines selective binding of GATA1 to lineage-specific  Project , we mapp+ CD71high; >97% purity); megakaryocytes cultured from primary fetal liver HSPCs , and HSPCs .  We mapped GATA1 and TAL1 occupancy sites (OSs) in fetal liver-derived MEG and ERY cells by chromatin immunoprecipitation followed by massively parallel DNA sequencing of transcription factor-bound DNA (ChIP-seq)  . We usedapproach . For casThe genome-wide occupancy patterns were overwhelmingly lineage-specific . SpecifiMEG-specific GATA1 and GATA2 OSs are enriched for ETS factor binding motifs . Having k . We used the kmer-SVM to discriminate between GATA1 OSs in ERY and MEG, and between TAL1 OSs in ERY and MEG. In each case the program was able to classify the test set sequences with high accuracy, both when OS sets were trained against each other  framework, kmer-SVM . The SVMch other  and agaich other . The lar1 in ERY , but k-m+, lin–), MEG and ERY transcriptomes on cDNA microarrays , and used the HSPC transcriptome as a reference point to define the developmental changes in gene expression during monolineage differentiation. Of 28,853 genes interrogated, 7513 were significantly expressed in at least one population . We designated genes as developmentally induced or repressed if their mRNA expression significantly (FDR < 5%) changed more than twofold relative to HSPC , Ppbp (platelet basic protein), Pf4 (platelet factor 4), and Gp1ba  appear to be regulated via this mechanism . These findings indicate the existence of a robust, genome-wide mechanism of MEG lineage priming. Given that our stringent criteria for peak calling and gene selection inevitably excluded many genes from analysis, we predict that the actual scope of MEG gene priming by the transcription factor heptad is substantially greater.To correlate transcription factor binding with expression levels in HSPCs, we considered two groups of MEG-induced genes: (1) genes classified as nonexpressed in HSPC (mRNA levels less than 2× array background), and (2) genes with higher starting expression levels (greater than 10-fold array background). These categories represent more extreme cases of transcriptionally nonprimed  and primed (expressed in HSPC prior to full induction in MEG) genes, respectively. There is selective enrichment of overlapping MEG/HPC-7 OSs in the “primed” gene category , right. A smaller fraction of ERY-induced genes appears to use a similar priming mechanism using the same heptad , but most of these genes are also induced in the MEG and are therefore not ERY-specific . Thus, iInf2  the repressive H3K27me3 methylation mark. Many MEG-specific genes that mediate platelet functions, including selectin, platelet (Selp), platelet factor 4 (Pf4), glycoprotein 9 (platelet) (Gp9), and CD42B alpha (Gp1ba) are regulated in a similar fashion (not shown).A typical developmentally primed gene, Inf2  coding fInf2 . In HPC-Itga2b (CD41) exemplifies a less typical MEG-primed gene , which raises several questions: (1) Do GATA1 and GATA2 regulate common or distinct sets of target genes? (2) Does binding of these factors result in similar or different functional outcomes? and (3) What determines whether a GATA2-bound element in HSPCs will undergo a GATA switch in late megakaryopoiesis or remain occupied by GATA2?cis elements . Only 455/2728 GATA2 OSs (< 17%) were shared by GATA1, suggesting unique binding preferences of these proteins for specific elements . Ingenuiby GATA2 . For exaopoiesis . In contopoiesis . Thus, wRoughly equal proportions of genes bound specifically by GATA2 during MEG development were activated, repressed, and unchanged compared to their expression in the HSPC population . AccordiWe used global transcriptome profiling and ChIP-seq to define and compare DNA occupancy by hematopoietic transcription factors, histone modifications, and mRNA expression during erythro-megakaryocytic differentiation. Integrating our findings with prior studies establishes a more coherent, genome-wide model for MEG and ERY-MEG lineage priming in HSPCs . AccordiUp to 40% of MEG GATA1 OSs are bound by GATA2 in HSPCs. This resembles the proportion of GATA1 OSs that were shown recently to undergo a GATA1 switch in the erythro-megakaryocytic cell line G1ME , althougOur data demonstrate that GATA1-TAL1 association distinguishes GATA1-mediated gene activation in MEG, similar to what is reported in ERY . Co-occucis-element binding motifs. Thus, although GATA1 and GATA2 can partially compensate for one another in gene targeting experiments , Thbd (CD141), and Plvap (MECA32), were below the 10th percentile of expressed genes , indicating no significant contamination of the purified HSPC population by endothelial cells. Primary erythroblasts (ERY) were obtained by immunostaining of fetal liver cells with PE-conjugated antibodies against Ter119 followed by magnetic bead purification. Primary fetal liver megakaryocytes were obtained by expansion and differentiation of fetal liver hematopoietic progenitor cells. KIT-positive progenitors were isolated directly from E14.5 mouse fetal livers by immunomagnetic selection and expanded in a medium with SCF and TPO for 7 d followed by terminal megakaryocyte differentiation for 5 d with TPO only. Differentiated megakaryocytes were further enriched to >97% purity by CD41 immunomagnetic bead selection. All cell populations were purified to at least 94% purity.Mouse fetal livers (E14.5) were used as the source of primary cells (P > 0.05) or less than 1.2-fold were considered to be unchanged . For MEG versus HSPC and ERY versus HSPC comparisons, genes that significantly (FDR < 5%) changed more than twofold relative to the expression level in HSPC were considered to be developmentally up- or down-regulated. Genes whose expression was altered insignificantly . The high stringency set of peaks was used for all analyses, and for cases in which the smaller number of peaks could affect the interpretation, the analysis was repeated for the larger set of reduced stringency peaks. A detailed description of experimental protocols and statistical analysis of the data is presented in Supplemental Methods.ChIP-seq was performed with the use of standardized ENCODE procedures, as previously described  under accession number GSE49664. Reads, peak calls, and signal tracks are also available from our customized genome browser (http://main.genome-browser.bx.psu.edu/), the UCSC Genome Browser (http://genome.ucsc.edu/), and the Mouse ENCODE Consortium website (http://www.mouseencode.org/publications/mcp08/).Mapped sequencing reads and cDNA microarray data have been submitted to the NCBI Gene Expression Omnibus (GEO;


Drosophila, implicating a prevalent binding factor, GAF, in transcriptional pausing.Genome-wide studies in higher eukaryotes have revealed the presence of paused RNA polymerase II (RNA-Pol) at about 30–50 bp downstream of the transcription start site of genes involved in developmental control, cell proliferation and intercellular signaling. Promoter-proximal pausing is believed to represent a critical step in transcriptional regulation. GAGA sequence motifs have frequently been found in the upstream region of paused genes in Hsp70 during heat shock. By cytogenetic analyses on polytene chromosomes and chromatin immunoprecipitation (ChIP), we show that paused RNA-Pol of Hsp70 is substantially reduced in mutants. Conversely, a global increase in paused RNA-Pol is observed when GAF is over-expressed. Coupled analyses of transcriptome and GAF genomic distribution show that 269 genes enriched for upstream GAF binding are down-regulated in mutants. Interestingly, ~15 % of them encode transcriptional factors, which might control ~2000 additional genes down-regulated in mutants. Further examination of RNA-Pol distribution in GAF targets reveals that a positive correlation exists between promoter-proximal RNA-Pol density and GAF occupancy in WT, but not in mutants. Comparison of nucleosome profiles indicates that nucleosome occupancy is preferentially attenuated by GAF in the upstream region that strongly favors nucleosome assembly. Using a dominant eye phenotype caused by GAF over-expression, we detect significant genetic interactions between GAF and the nucleosome remodeler NURF, the pausing factor NELF, and BAB1 whose binding sites are enriched specifically in genes displaying GAF-dependent pausing.Using newly isolated mutants that retain only ~3 % normal GAF level, we analyzed its impacts on transcriptional regulation in whole animals. We first examined the abundance of three major isoforms of RNA-Pol on Our results provide direct evidence to support a critical role of GAF in global gene expression, transcriptional pausing and upstream nucleosome organization of a group of genes. By cooperating with factors acting at different levels, GAF orchestrates a series of events from local nucleosome displacement to paused transcription. The use of whole animals containing broad tissue types attests the physiological relevance of this regulatory network.The online version of this article (doi:10.1186/s13072-016-0082-4) contains supplementary material, which is available to authorized users. Eukaryotic transcription requires coordinated activities from distinct ensembles acting directly on transcriptional processes or chromatin architecture. Previous studies have shown that the transcriptional machinery undergoes stepwise catalytic transitions from initial assembly to termination upon the integration of regulatory information from general and gene-specific factors, while the overall organization and constituents of chromatin are dynamically modified to facilitate loading and progression of the transcriptional machinery . DespiteDrosophilaHeat shock 70 (Hsp70) and human c-Myc and c-Fos genes have revealed that RNA polymerase II (RNA-Pol) may stall for lengthy periods at about 30–50 bp downstream of the TSS, implicating a novel rate-limiting step prior to productive transcription [Analyses in cell-free systems have established that the supramolecular assembly of transcriptional complexes over the transcription start site (TSS) represents a critical rate-limiting step . Howevercription . Subsequcription . It has cription , 13. Onecription , 15. Botcription –21.Several features have also been noted for paused genes. For example, the TSS of these genes is clustered in narrow regions , which aHsp70, contain GAGA sequence motifs in the upstream region in Drosophila. Two sequence-specific DNA binding proteins, GAF and Psq factor, are known to bind this motif [A large fraction of paused genes, including is motif , 28. Genis motif , 29, 30.is motif –33. Seveis motif –37. Moreis motif , 39. Recis motif . WhetherGaf mutants to examine their effects on gene regulation in whole animals. We started with transcriptional analyses of Hsp70 and extended to genome-wide studies. We found that GAF, as an activator required for global gene expression, modulates the level of paused RNA-Pol and the nucleosome pattern specifically in the region immediately upstream of the TSS by cooperating with various sequence-independent and sequence-specific factors.Here we used newly isolated Gaf mutant larvae. The GAF-coding gene was previously designated as trithorax-like (Trl) residing in the left arm of the third chromosome, based on the observation that some mutant alleles could cause homeotic transformations like trithorax [EP3184  in CTD of RNA-Pol in wild type (WT) and rithorax . Howeverrithorax , 42. In Hsp70 genes [Hsp70. Drosophila contains six Hsp70 genes located in two cytogenetically discernible clusters at map positions 87A and 87C on polytene chromosomes. We used simultaneous staining of GAF and BEAF-32—a boundary element-binding protein [Hsp70 genes located between sections 2 and 3, and the 87C region contains four Hsp70 genes between sections 4 and 5 (marked by stars). Before induction, the signals of RNA-Pol isoforms including Hypo-p, Ser-5p and Ser-2p were either very weak or undetectable on these locations of both WT and mutant chromosomes  to measure the relative abundance of these isoforms in three intragenic regions centered at +58, +379 and +1702 as well as an upstream region centered at −182 for comparison  to quantitatively analyze the phosphorylation profile of RNA-Pol on Upon a brief heat shock , WT samples showed substantial increases in signal intensity for all three RNA-Pol isoforms at the +58 region and further downstream, as expected for fully active transcription. The increase in the Ser-5p signal was most pronounced at the promoter-proximal region, indicating the accumulation of considerable amounts of paused RNA-Pol. Clearly, the transition of RNA-Pol from paused to elongation states remains rate limiting during transcriptional activation. Similar to the uninduced condition, the Ser-5p signal showed a drastic reduction from WT to mutant samples. Again, the Ser-2p signal in mutant samples showed concomitant increases in the coding region, particularly at the 3′ end. Despite these changes, no significant differences were found between WT and mutant samples for total RNA-Pol, as measured on polytene chromosomes using an antibody recognizing the Rpb3 subunit of RNA-Pol ). The resultant chromosomes were intensely labeled with GAF signals and were thinner and more difficult to spread than WT ones. In proportion to the reduced chromosomal size, the overall BEAF-32 signals also appeared to be relatively weaker. Importantly, there was a substantial increase in Ser-5p signal  extracts. In contrast, both Hypo-p and Ser2-p did not show significant differences in dpp > GAF(3) extracts  extracts  signal was observed for 6 for GAF, ~34 × 106 for input), which were used for further analyses. Using the PICS program (Probabilistic inference for ChIP-Seq) [p = 1.6 × 10−788) [−4 by TOMTOM analysis)  to get more than thirty million reads (~32 × 10hIP-Seq) , we iden 10−788) . Althougbona fide physiological targets. Based on the number of putative GAF targets, our dataset is about 20–50 % larger than several earlier studies and similar to a recent study [These GAF binding peaks could be assigned to 1891 annotated genes, which are referred to as putative GAF-target genes. Among them, 1442 genes (~76 %) contain GAF peaks with values at least twofold higher than the corresponding regions in the input control Fig. b. Importnt study , 51–53. nt study , 55 . Among them, 15 genes were randomly chosen for verification by RT-qPCR. Nearly 70 % of selected genes showed similar changes   sin sinp < 0ρ = 0.42, p < 1 × 10−11) in WT, whereas this correlation is almost completely lost in Gaf mutants . For the gene body, a modest negative correlation is observed between GAF occupancy and RNA-Pol density . As expected from higher GAF occupancy, more extended GA repeats were found in ranks III and IV , 6.1 to 10.0 , 10.0 to 17.2 , 17.5 to 301 . The increase expanded to the proximal half in rank III (p = 8 × 10−3) and became most pronounced in rank IV (p = 3 × 10−3). Thus, nucleosome organization is affected in a dose-dependent manner. It is also important to note that the upstream nucleosome profile of rank IV became more consistent with the high SS-dinucleotide content in Gaf mutants, suggesting that GAF’s main function is to alleviate the constraint imposed by nucleosomes and underlying sequences, particularly in the proximal half of the upstream region.To determine the role of GAF in modulating nucleosome organization, we next examined how nucleosome profiles are altered in different ranks of GAF targets Fig. b–e. Supen = 210) of promoter-proximal RNA-Pol in Gaf mutant. Compared to the WT profile, no significant change was found in mutants . Interestingly, these regions showed substantial increases in nucleosome occupancy in mutants , we reasoned that GAF over-expression in eye disks might lead to its increased occupancy on these promoters and subsequent pausing, providing a convenient genetic assay. Using eyeless-Gal4 to drive a UAS-GAF-519 transgene located on second chromosome ) in eye imaginal tissues, we found that the size of the adult eyes was reduced by ~50 % in ey > GAF(2) flies  adults. We further examined the structure of larval eye disks. As expected, the size of eye disks of third-instar larvae was substantially reduced  disks. Thus, GAF over-expression appears to mainly affect the number of eye cells.We next asked whether any other factors might cooperate with GAF in regulating transcriptional pausing. Since GAF over-expression could result in striking increases in GAF occupancy and paused RNA-Pol globally Fig. b and sinies Fig. a, b. Deties Fig. b, d. Howced Fig. c. SpecifNurf mutant background, eye size was substantially restored 3-7—a suppressor of heterochromatin-mediated silencing [Previous studies have shown that NURF—an ATP-dependent nucleosome remodeling factor—may interact directly with GAF to re-organize nucleosomal spacing . In a Nudirectly . Specifiilencing —did not E values <5.2 × 10−37 or 6.3 × 10−103). Interestingly, motifs for BAB1  and MAD  were identified in rank IV only. Again, we tested their physiological relevance using the GAF-induced eye phenotype assay. The eye defects were substantially relieved in bab1 ), the role of MAD is less certain  are listed in FlyTF as putative transcriptional factors  is affected by  studies , 51. TheHsp70 gene, we demonstrate that GAF affects transcription primarily at post-initiation steps. Both cytogenetic and molecular analyses of the Hsp70 gene showed that the levels of Ser-5p and Ser-2p, but not Hypo-p, are substantially affected in Gaf mutants following heat induction. Since phosphorylation of RNA-Pol occurs after transcriptional initiation, these results clearly indicate that GAF exerts its effects at post-initiation steps. Our studies also revealed reciprocal changes for Ser-5p and Ser-2p in Gaf mutants; accompanying the large decrease in promoter-proximal Ser-5p signal, Ser-2p showed a substantial increase toward the distal end of Hsp70. Given the obligatory order for the appearance of Ser-5p and Ser-2p during transcription, these observations strongly suggest that the reduction of Ser-5p signal represents the primary effect of Gaf mutation. The increase in Ser2-p seems to be at odds with the observation that the majority of genes are down-regulated in Gaf mutants. Since GAF can physically interact with NELF and since active transcription of Hsp70 occurs when NELF is dissociated from RNA-Pol [Hsp70 promoter in Gaf mutant, resulting in less efficient withholding of paused RNA-Pol. In addition, we note that Hsp70 expression can still be induced by the potent activator HSF during heat shock from a transgene lacking GAF binding sites [Hsp70 may represent an unusual case in which multiple factors are critically involved in its activation. GAF knockdown in cultured cells has recently been reported to affect the accumulation of total RNA-Pol only in promoter-proximal region [For the inducible  RNA-Pol , 68, it ng sites . Thus, Hl region . Since tHsp70. We found that Gaf mutation results in reduced Ser-5p, but not Hypo-p or Ser-2p, on polytene chromosomes, suggesting that GAF can modulate the level of Ser-5p globally. This is substantiated by our analyses of the genome-wide distribution of RNA-Pol. In WT samples, GAF occupancy in the upstream region is positively correlated only with the RNA-Pol density in the promoter-proximal region or with PI. These results clearly reveal a dose-dependent relationship between paused RNA-Pol and GAF. Importantly, this positive correlation was lost in Gaf mutants, indicating a direct role of GAF in RNA-Pol pausing. Contrary to the reduction of paused RNA-Pol in Gaf mutants, GAF over-expression results in an overall increase in Ser-5p, but not other isoforms. These complementary results further strengthen the conclusion that GAF is critical for transcription pausing.Our results further show that the effect of GAF on Ser-5p is not unique to Consistent with earlier studies , 68, ourGaf mutants, resulting in closer matches to predicted nucleosome profiles. Thus, the upstream sequences of these genes may intrinsically favor higher levels of nucleosomes, potentially producing stronger physical constraints for the loading and assembly of transcriptional machinery. Apparently, the presence of high levels of GAF relieves such constraints by reducing the amount of nucleosomes and consequently makes promoters more accessible. Like RNA-Pol pausing, the modulation of nucleosome profiles by GAF is highly dependent upon the degree of occupancy. This dosage-dependent effect strongly supports a direct role of GAF. Thus, caution should be taken when making inferences about GAF’s role in regulation based on the GAF motif alone.In contrast to the downstream region, GAF-target genes contain more nucleosomes around the TSS and immediately upstream region than non-target genes. Overall, these patterns are consistent with the SS-dinucleotide content of the upstream region. However, considering the strong enrichment of SS-dinucleotide content in GAF targets, the increased nucleosome levels seem to be somewhat modest. It is also puzzling that nucleosomal profiles of different ranks of target genes appear to be largely indistinguishable, despite there being more extended SS-dinucleotide sequences in higher ranks. Nevertheless, the levels of nucleosomes were selectively increased in upstream regions from the TSS up to −500 for rank III and more so for rank IV in Gaf mutants.Since GAF can recruit NURF to remodel nucleosome organization in vitro  and sincThe role of GAF in RNA-Pol pausing and nucleosome remodeling has recently been reported, based on similar studies of a cultured cell line . While obab1 and Gaf mutants support cooperation between GAF and BAB1 in controlling transcriptional pausing. These results clearly show that the upstream region of GAF targets is intrinsically prone to nucleosome assembly, resulting in promoter occlusion. By its ability to trigger nucleosome re-organization, GAF may facilitate the promoter accessibility, assembly of the basal transcriptional machinery and its transition into initiation  by immobilization of the P-element. Nelf-AKG09483, Nelf-EEY07065, Df(3L)babAr07, Mad12 and ru cu ca were obtained from Bloomington Stock Centre. w; P{GawB}Cdk9NP0727/CyO was from Drosophila Genetic Resource Center. E(bx)Nurf301−9 and Su(var)3-714 were obtained from P. Badenhorst and P. Spierer, respectively.All flies were raised in standard media at 25 °C. n Trl13C  and a stGaf mutant larvae were lysed in SDS–urea buffer [GST-GAF-N was constructed by inserting a GAF N-terminal fragment (1–1089 nucleotides of ORF) into pGST5X-1 (Novagen). Bacterial fusion protein induced in strain BL21 (DE3) was gel-purified and used for immunization in rabbits by standard protocols . After pa buffer . ExtractThree pairs of salivary glands were isolated from third-instar larvae raised at 25 °C or treated for 30 min at 37 °C before dissection. Polytene chromosomes prepared from squashed glands were stained according to previously described procedures , 75. PriDrosophila M3 medium (Sigma); anterior-most parts containing imaginal disks, nerve cords and salivary glands  were collected. For heat-shock samples, tissue samples were processed according to published procedures [Thirty WT or mutant third-instar larvae were dissected in ocedures  and thenhttp://www.imb.sinica.edu.tw/mdarray/index.html). Briefly, cDNA was synthesized from 15 μg RNA and labeled with Alexa Fluor 555 (WT) or Alexa Fluor 647 (mutant) using a SuperScript Indirect cDNA Labeling System (Invitrogen). Labeled probes prepared from three samples were independently hybridized to a Drosophila Gene Expression Microarray (Agilent 4 × 44 K) containing 37,536 oligonucleotide probes, according to the manufacturer’s protocol. Microarrays were scanned on an Agilent G2565CA scanner and quantified using Feature Extraction software (FE version 10.5.1.1.1) for background subtraction and LOWESS normalization. GeneSpring GX11.5 analysis software (Agilent) was used for further analysis. Average values from triplicate sets were filtered on a Volcano plot, and an entity list was collected from probes with significant expression in WT samples .Total RNA was extracted from imaginal tissues of WT or mutant third-instar larvae by TRIzol (Invitrogen), treated with RNase-free DNase I (Worthington) and purified by RNeasy kit (Qiagen) according to the vendors’ protocols. Microarray analysis was conducted by our in-house Microarray Core Facility (Chromatin samples from WT and mutant larvae were prepared as described above. Immunoprecipitation was performed with affinity-purified antibodies against GAF or Rpb3. ChIP and input DNA samples were processed using an Illumina ChIP-Seq DNA Sample Prep kit (Illumina). After ligation to adaptors, DNA samples were amplified by PCR without size selections. Sequencing was performed on an Illumina Genome Analyzer IIx for 40 bp read length by the Biofuel High Throughput Sequencing Core Facility .2, 10 mM Tris pH 8, 0.1 % Triton X-100 and 0.5 mM DTT), for 60 larvae and homogenized in a Dounce homogenizer . Nuclei were collected by centrifugation at 4 °C, 720 g for 5 min, washed by buffer A and washed in buffer D . Nuclei were resuspended in buffer MN . Two hundred microliter of chromatin was digested with 20U of MNase (Worthington) for 3 h at 25 °C. Gel-purified DNA fragments ranging from 100 to 200 bp were then subjected to pair-end sequencing by an Illumina Genome Analyzer IIx.MNase-digested chromatin was prepared as described previously with some modifications , 77, 78.Details of bioinformatic analyses are presented in Additional file


Hsps). In this review, we describe the conserved structure of plant HSFs, the identification of HSF gene families from various plant species, their expression profiling under abiotic stress conditions, regulation at different levels and function in abiotic stresses. Despite plant HSFs share highly conserved structure, their remarkable diversification across plants reflects their numerous functions as well as their integration into the complex stress signaling and response networks, which can be employed in crop improvement strategies via biotechnological intervention.Abiotic stresses such as high temperature, salinity, and drought adversely affect the survival, growth, and reproduction of plants. Plants respond to such unfavorable changes through developmental, physiological, and biochemical ways, and these responses require expression of stress-responsive genes, which are regulated by a network of transcription factors (TFs), including heat stress transcription factors (HSFs). HSFs play a crucial role in plants response to several abiotic stresses by regulating the expression of stress-responsive genes, such as heat shock proteins ( Plants as sessile organisms are routinely confronted by a variety of abiotic or biotic stresses, such as water deficiency, high salt, extreme temperatures, chemical pollutants, oxidative stress, nematodes, herbivores, and pathogens  family  , Drosophila (1), Caenorhabditis elegans (1), and yeast  and cotton (40 members), the families of Arabidopsis and tomato are considered small. Currently, maximum of HSF genes were identified in wheat and soybean among monocots and eudicots, respectively. The multiplicity of HSFs in plants may be related to the gene duplications and whole-genome duplications at different points of evolution, followed by extensive gene loss  are found in all 5 monocots, not in eudicots, attributing to gene duplications on the monocot lineage. Higher number of class HSFC genes are identified in monocots, such as in wheat, maximum of 5 and 7 genes are assigned into subclass HSFC1 and C2, respectively, which is the most marked difference between monocots and eudicots  in response to HS, including 8 genes up-regulated by two-folds only during early heat shock (HS for 10 min) and 8 genes up-regulated at both short (HS for 10 min) and prolong (HS for 30 min) HS treatment, however, OsHSFC1a was noted to be down-regulated by the early HS treatment -inducible HSFA2 expression, activating AtHSFA2 transcription -binding protein 2A (DREB2A gene) directly regulates AtHSFA3 transcription via binding the two DRE core elements in the AtHSFA3 promoter  could activate the AtHSFA9 promoter based on an RY/Sph motif  as putative seed-related regulatory element in the AtHSFA9 promoter provided an essential binding site for ABI3. Interestingly, unlike Arabidopsis AtHSFA1d and A1e, AtHSFB1 and B2b are transcriptional repressors and negatively regulate the expression of HS-inducible HSFs including not only AtHSFA2 and A7a but also themselves , which can bind to the TATA box-proximal clusters of HSE in the AtHSFA2 promoter to activate its own transcription, attributing to exon skipping in the intron of the DBD encoding region  in their 5′ untranslated regions  residues, and Phe starvation is shown to alleviate translational repression by the uORFs. Once plants are suffered from pathogen challenge, the uncharged tRNAPhe will temporary increase and the eukaryotic initiation factor 2α (eIF2α) phosphorylation will be triggered, which may facilitate ribosome reattachment to the TBF1 translation start codon downstream of uORFs and release the inhibitory effects of uORFs to initiate TBF1 translation , ROF1 (FKBP62), and ROF2 (FKBP65) -mediated degradation, oligomerization, and interaction with other non-HSF proteins Figure . In ArabHSFs may be used for gene manipulation, contriving tolerance to HS in crops, while characterization of the functional plant HSFs under HS condition is the precondition.The major objective for agronomic research remains the enhancement of crop productivity under various abiotic stresses  with single or multiple mutants, and the role of master regulator for thermotolerance is shared among the four paralogs due to functional redundancy  and its constitutive expression pattern, GmHSFA1 may be the best candidate of master regulator in soybeans, which needs to be confirmed by an antisense silencing study. HSFA2 has been identified to be the dominant HSF in tomato and Arabidopsis based on its high activator potential for transcription of Hsp genes and the strong accumulation under conditions of long-term HS or repeated cycles of HS and recovery  mutant improved the thermotolerance, suggesting that HSFA2 can be active and functional in the absence of HSFA1s in Arabidopsis, and it is tempting to speculate that interactions between HSFA2 and other HSFs may exist in the quadruple knock-out mutants , TaRof1, galactinol synthase, and glutathione-S-transferase  ortholog histone acetyl transferase HAC1  in tomato HSFB1 is replaced by GSRMTETK in Arabidopsis HSFB1  from Chinese Wild Vitis pseudoreticulata in tobacco demonstrated that VpHSF1 acted as a negative regulator in basal thermotolerance and a positive regulator in acquired thermotolerance  at seedling stage under drought stress conditions, thus improving their drought-tolerance  role of HSFs in different stress conditions and establishment of an HSF network in relation to the crosstalk between abiotic stress responses and plant growth, development and metabolism, which may provide practical and biotechnological approaches to improve the crop plants tolerance to extreme environment conditions.In the future, a combination of advanced high throughput technologies, such as microarray, genomics, and proteomic approaches in various developmental stages and stress conditions will provide us with critical information to elucidate the whole complexity of MG, ML, and ZG conceived and designed the paper; MG, JL, XM, and DL collected and analyzed the literature; MG wrote the paper.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.


Ten-Eleven Translocation (TETs)proteins mediate the oxidation of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). Tet1 is expressed at high levels in mouse embryonic stem cells (ESCs), where it mediates the induction of 5hmC decoration on gene-regulatory elements. While the function of Tet1 is known, the mechanisms of its specificity remain unclear.We perform a genome-wide comparative analysis of 5hmC in pluripotent ESCs, as well as in differentiated embryonic and adult cells. We find that 5hmC co-localization with Polycomb repressive complex 2 (PRC2) is specific to ESCs and is absent in differentiated cells. Tet1 in ESCs is distributed on bivalent genes in two independent pools: one with Sin3a centered at non-hydroxymethylated transcription start sites and another centered downstream from these sites. This latter pool of Tet1 co-localizes with 5hmC and PRC2. Through co-immunoprecipitation experiments, we show that Tet1 forms a complex with PRC2 specifically in ESCs. Genome-wide analysis of 5hmC profiles in ESCs following knockdown of the PRC2 subunit Suz12 shows a reduction of 5hmC within promoter sequences, specifically at H3K27me3-positive regions of bivalent promoters.In ESCs, PRC2 recruits Tet1 to chromatin at H3K27me3 positive regions of the genome, with 5hmC enriched in a broad peak centered 455 bp after the transcription start site and dependent on the PRC2 component Suz12. These results suggest that PRC2-dependent recruitment of Tet1 contributes to epigenetic plasticity throughout cell differentiation. In eukaryotic cells, 5-methylcytosine (5mC) occurs almost exclusively within a CpG context, and is catalyzed by the family of DNA methyltransferase (DNMT) enzymes -3. More PRC2 is the enzymatic complex that mediates the trimethylation of lysine 27 of histone H3 (H3K27me3) on developmental genes that determine whether the chromatin remains either open or fully inaccessible. It is formed by a core complex that includes Enhancer of Zeste 1 or 2 homolog (Ezh1 or Ezh2), Suppressor of Zeste 12 homolog (Suz12), Embryonic ectoderm development (Eed), and other accessory subunits, many of which are specific to ESCs -41. PRC2Using genome-wide analysis of 5hmC distribution in ESCs with respect to embryonic and adult tissues, we found that the overlap between the repressive modification H3K27me3 and 5hmC is ESC-specific, and we studied the molecular mechanism of the interplay between Tet1 and PRC2 in ESC.We mapped the genome-wide distribution of 5hmC using the glucosylation, periodate oxidation, biotinylation (GLIB) method followed by sequencing using an Illumina platform (GLIB-Seq)  in ESCs,Heatmaps plotted using the gene expression levels obtained from RNA sequencing (RNA-Seq) analysis showed a similar distribution pattern in all samples, with 5hmC enrichment typically on gene bodies with respect to the transcription start site (TSS) Figure . ClassifFor each cell type, we then plotted the expression level (reads per kb of exon per million mapped reads (RPKM) log10) and the 5hmC intensity normalized for the gene length for each gene to further clarify the role of 5hmC in gene expression  of all genes, ordered by expression level, showed that H3K27me3 is more abundant on promoters of genes with low expression in all cell types. However, in ESCs only, H3K27me3 promoters showed a significant enrichment of 5hmC Figure .The above data show a correlation between 5hmC and H3K27me3 distribution on ESC promoters. Further analysis of Tet1 distribution, using published chromatin immunoprecipitation sequencing (ChIP-Seq) datasets 32], on , on 32],Moreover, Tet1-Sin3a co-bound peaks showed high CpG island content, whereas the number of CpG islands was significantly decreased in Tet1-H3K27me3 co-bound regions . After separating the results into two clusters (k = 2), we obtained two different Tet1 binding profiles: cluster 1, with a profile centered on the TSS and enriched at K4-only genes; and cluster 2, with a larger profile centered downstream from the TSS enriched on bivalent promoters Figure . Next, wThe data above suggest interplay between Tet1 and PRC2 in ESCs. Next, we examined whether Tet1 could form a complex with PRC2 by co-immunoprecipitation of the endogenous proteins from ESCs and MEFs. Tet1 was co-immunoprecipitated with the PRC2 subunits Suz12 and Ezh2 in ESCs Figure . The recGenome-wide analysis of 5hmC profiles in control and Suz12 knockdown ESCs showed a reduction of 5hmC at the promoter regions of H3K27me3-positive genes within the Tet1 co-bound regions but not at H3K27me3-negative regions  supplemented with 15% FBS , 0.1 mmol/l nonessential amino acids (Invitrogen), 1 mmol/l sodium pyruvate (Invitrogen), 0.1 mmol/l 2-mercaptoethanol, 1500 U/ml Leukemia Inhibitory Factor , 25 U/ml penicillin, and 25 µg/ml streptomycin. MEFs were derived from 13.5 day pregnant female mice and cultured in high-glucose DMEM supplemented with 10% FCS. Mouse tissues were extracted from 8-week-old mice. For silencing of Suz12, ESCs were transfected as previously described  with theGenomic DNA was extracted using a DNeasy Blood and Tissue kit .GLIB was precipitated using a Hydroxymethyl Collector . Libraries were generated with a ChIP-Seq Sample Prep Kit  and sequenced on an HiScanSQ Platform (Illumina) .Sequencing data were mapped to the mouse genome (mm9 assembly) using Bowtie (version 0.12.7), reporting only unique hits with up to two mismatches. Redundancies were collapsed, and peak calling was performed using MACS (version 1.4.1). For comparative analysis, we downloaded GEO Datasets data for the ESC histone modifications GSE12241 ,54 and Get al. [et al. [LCP-ICP-HCP promoters were defined as described by Weber et al. , H3K4me3 [et al. , and act [et al. ,61. To oData mapped on the mouse mm8 assembly was transposed to the mm9 assembly using the liftOver tool. Heatmaps and comparative analyses were performed using custom Perl scripts. Clustering of Tet1 binding profiles was performed using the MATLAB implementation of the k-means algorithm using the Pearson correlation coefficient as the 'distance' between profiles. Using 100 realizations of the clustering (using different random seeds), we foudn that the composition of clusters was remarkably stable. The fitting procedure of the average Tet1 binding profile was performed using the gnuplot least squares fitting algorithm. The test function was a superposition of two  Gaussian distributions .2, 5 µmol/l ZnCl2), then cells were resuspended in isotonic buffer supplemented with 1% NP-40 to isolate nuclei. The isolated nuclei were resuspended in digestion buffer , and treated with microccocal nuclease at 30°C for 10 min.Cells were harvested in 1× PBS and resuspended in isotonic buffer  for 2 h at 4°C. Samples were washed four times with digestion buffer supplemented with 0.1% NP-40 at room temperature. Proteins were eluted by incubating with 0.4 M NaCl TE buffer for 30 min, and analyzed by western blotting as previously described .+ membranes .Each ChIP experiment was performed at least three independent times, as previously described . OligonuThe antibodies used were anti-Tet1, anti-H3K27me3, anti-H3K4me3 , anti-ssDNA , anti-Suz12and anti-Ezh2 , anti-actin , anti-Sin3a , and anti-5mC and anti-5hmC (both Active Motif).The Gene Expression Omnibus accession number for 5hmC profiling of ESC, MEF, brain and liver reported in this paper is GSE44566.5mC: 5-methylcytosine; 5hmC: 5-hydroxymethylcytosine; ChIP: chromatin immunoprecipitation; Eed: Embryonic ectoderm development; ESC. Embryonic stem cell; Ezh1: Enhancer of Zeste homolog 1; GLIB: Glucosylation: periodate oxidation: biotinylation; HCP: High CpG; ICP: Intemediate CpG; LCP: Low CpG; MEF: Mouse embryonic fibroblast; PRC2. Polycomb repressive complex 2; RPKM: Reads per kb of exon per million mapped reads; Suz12: Suppressor of Zeste 12 homolog; TET: Ten-eleven translocation; TSS: Transcription start siteFN and SO designed the experiments and co-wrote the paper. FN and DI performed the ChIP-Seq experiments and analyzed the data. AP and RZ contributed to the bioinformatic analysis. AK performed the tissue preparations and the biochemical experiments. CP and SR contributed to cell culture and DNA cloning. All authors read and approved the final manuscript.PDF document containing four supplemental figures , and one supplemental table (Table S1).Click here for file