H1d and H1c are depleted from GC- and gene-rich regions and active promoters, inversely correlated with H3K4me3, but positively correlated with H3K9me3 and associated with characteristic sequence features. Surprisingly, both H1d and H1c are significantly enriched at major satellites, which display increased nucleosome spacing compared with bulk chromatin. While also depleted at active promoters and enriched at major satellites, overexpressed H10 displays differential binding patterns in specific repetitive sequences compared with H1d and H1c. Depletion of H1c, H1d, and H1e causes pericentric chromocenter clustering and de-repression of major satellites. These results integrate the localization of an understudied type of chromatin proteins, namely the H1 variants, into the epigenome map of mouse ESCs, and we identify significant changes at pericentric heterochromatin upon depletion of this epigenetic mark.H1 linker histones facilitate higher-order chromatin folding and are essential for mammalian development. To achieve high-resolution mapping of H1 variants H1d and H1c in embryonic stem cells (ESCs), we have established a knock-in system and shown that the N-terminally tagged H1 proteins are functionally interchangeable to their endogenous counterparts  0, mapped using an overexpressing ESC line, shows similar features at active promoters but differential binding at repetitive sequences compared with H1d and H1c. Furthermore, using mutant ESCs that are deficient for multiple H1 variants, we demonstrate the role of H1 in chromocenter clustering and transcriptional repression of major satellites. Thus, these results connect this important repressive mark with the well understood ESC epigenome and identify novel functions of H1 in mammalian genome organization.Embryonic stem cells (ESCs) possess unique chromatin and epigenetic signatures, which are important in defining the identity and genome plasticity of pluripotent stem cells. Although ESC epigenomes have been extensively characterized, the genome localization of histone H1 variants, the chromatin structural proteins facilitating higher-order chromatin folding, remains elusive. Linker histone H1 is essential for mammalian development and regulates the expression of specific genes in ESCs. Here, by using a knock-in system coupled with ChIP–seq, we first achieve the high resolution mapping of two H1 variants on a genome-wide scale in mouse ESCs. Our study reveals the correlations of this underexplored histone family with other epigenetic marks and genome attributes. Surprisingly, we identify a dramatic enrichment of H1d and H1c at major satellite sequences. H1 In all eukaryotes, nuclear DNA is packaged into chromatin by its association with histones 0), germ cell specific H1s , as well as the recently characterized variant H1x in vivo is likely to provide significant insights, but has been challenging due to the lack of high quality antibodies that could accurately distinguish different H1 variants.The H1 histone family is the most divergent group among the highly conserved histone proteins. To date, 11 different H1 variants have been characterized in mammals, including somatic H1 variants (H1a to H1e), the replacement H1 (H1Pluripotent embryonic stem cells (ESCs) can differentiate into cells of all three germ layers, offering great potential in regenerative medicine. The epigenome is suggested to play a critical role in stem cell fate determination, and genome-wide mapping studies have revealed that ESCs have characteristic epigenetic landscapes that differ from differentiated cells 0 in ESCs by chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq). H1d and H1c are among the most abundant linker histones in mouse ESCs, accounting respectively for 32.6% and 16.4% of total H1, whereas the differentiation associated H1, H10, accounts for 2% of H1 in undifferentiated ESCs in vitrovs. quiescent cells 0 overexpressing ESCs, designated as respective H1dFLAG, H1cMyc, and fH10 cells. We demonstrate that tagged H1 variants maintain the biochemical properties of the endogenous H1s in vivo and that FLAG-H1d can substitute for H1d during mouse development. High resolution mapping reveals that H1d and H1c occupancies are highly correlated, both enriched at AT-rich regions, but also possess different binding specificity. Both H1d and H1c largely co-localize with H3K9me3, but show an inverse correlation with GC% or H3K4me3. Importantly, we discover that H1d and H1c are highly enriched at major satellite elements, which display a longer nucleosome repeat length than bulk chromatin in ESCs. Finally, we show that H1 depletion leads to chromocenter clustering and increased expression of major satellites independent of multiple epigenetic marks at these regions.In this study, we have achieved high resolution mapping of H1d, H1c and H1FLAG cell line was created by inserting the FLAG tag coding sequence at the endogenous H1d locus through homologous recombination  functions equivalently to endogenous H1d by assessing if the tagged H1d can rescue the embryonic lethality of H1 TKO mutants. Toward this end, we generated both H1c+/−H1d+/FLAGH1e+/− (“H1dFLAG”) and H1c+/−H1dFLAG/−H1e+/− (“H1d-trans”) ESC lines by transfection of the FLAG-H1d targeting vector . Analysis of histone extracts of chromatin prepared from cis-targeted H1dFLAG cells by HPLC and immunoblotting indicated that FLAG-H1d was associated with chromatin and eluted in the same fraction as the endogenous H1d, suggesting that FLAG-H1d has the same hydrophobicity as the endogenous H1d  cells, indicating a similar expression level of FLAG-H1d as the endogenous H1d  (KO) mice, demonstrating that FLAG-H1d can substitute for the endogenous H1d to fully rescue the lethal phenotype of H1 TKO mutants. HPLC, mass spectrometry and immunoblotting demonstrated that H1dFLAG/FLAG mice had full replacement of H1d by FLAG-H1d  for which highly specific antibodies exist. An H1dbination . H1c/H1eg vector  into theblotting . As expenous H1d . The ratnous H1d . As expeAG mice) i. These eKO mice iv. Taken+/MycH1d+/−H1e+/− ESCs (H1cMyc) by transfection of the H1cMyc targeting construct into the cis triply targeted H1c+/−H1d+/−H1e+/− ESCs and selected ESC clones that underwent homologous recombination at H1c locus (Myc cells (data not shown), and Myc-H1c was eluted in the same fraction as the endogenous H1c protein from HPLC analysis  using the Bowtie aligner vs. 22% of reads mapped to unique positions and multiple positions respectively, the H1c ChIP-seq libraries had 44% vs. 45% mapped to unique vs. multiple positions, suggesting that a higher proportion of H1c resides on repetitive sequences. Similarly, an overrepresentation of multi-match sequence reads (39% of mapped reads) occurred in H1d ChIP-seq libraries. A survey of sequencing signal intensities indicated that H1d and H1c were generally depleted from gene rich regions with the deepest dips around transcription start sites of active genes  as well as a 10 kb region centered around transcription termination sites (TTSs) using GenPlay software To better define the correlation of H1 occupancy with histone marks around TSSs and promoters, metagene analysis of H1 signals was performed for genes partitioned into 5 groups according to their levels of H3K9me3, H3K4me3, or H3K27me3, which displayed characteristic profiles around TSS . H1 signAlthough most H1d and H1c signals appeared universally distributed, we identified regions enriched for H1 binding using SICER vs. H3K4me3 on sex chromosomes were dramatically different from those of autosomes  , and botutosomes . This rea) H1d/H1c common peaks (regions highly enriched for both H1d and H1c) appeared similar to H3K9me3 peaks in genome attributes, except for satellite DNA which was relatively overrepresented in H1 peak regions; b) H1d/H1c common peaks were enriched at AT-rich sequences, satellite DNA, and chromosome G-bands but were absent from GC-rich regions, and genes or exons when compared with H3K4me3 or H3K27me3 peaks; c) comparison of H1d/H1c common peaks with H1d/H1c unique peaks (regions highly enriched for H1d or H1c but not both) showed similar features as the comparison of H1d/H1c common peaks with H3K4me3 or H3K27me3 peaks; d) comparison of H1d vs. H1c specific peaks indicated that H1d unique peaks were relatively enriched at GC-rich sequences and LINEs, whereas H1c unique peaks were more enriched at AT-rich sequences, Giemsa positive regions and satellite DNA; e) the overrepresentation analyses between H1d (or H1c) unique peak regions and histone mark peak regions exhibited similar features as comparisons using H1 common peaks. These results define common and unique features for H1d and H1c enriched regions.To gain a comprehensive view of the DNA features of H1d- and H1c- rich regions, we selected the regions highly enriched for H1 variants and histone marks, and performed cross-comparison of genome attributes using the statistical analysis software EpiGRAPH The EpiGRAPH overrepresentation analysis indicated that peak regions of H1d and H1c were enriched for satellite repeats. Indeed, examination of the top ranked H1 peak regions with especially high binding signals revealed that these regions overlap perfectly with major satellite sequences (examples shown in http://repeatmasker.org)  . AnalysiLAG ESCs .The level of H1 has been shown to be a determinant of nucleosome repeat length (NRL) with a higher level of H1 correlating with a longer NRL FLAG and H1cMyc cells displayed normal chromocenter clustering as WT ESCs . Furthermore, H10 enriched regions were significantly under-represented in gene regions but over-represented in distal intergenic regions with 80.1% of H10 peaks located in these regions (data not shown). Beside major satellites, H10 also appeared to be enriched at minor satellites and, to a lesser extent, at LINE L1 elements as determined by ChIP-seq and ChIP-PCR  shares the distinctive features of H1d and H1c in ESCs in genome-wide occupancy. It is worth noting, though, that endogenous H10 proteins are present at very low levels in undifferentiated WT ESCs and the genome-wide localization of H10 in ESCs may differ significantly from its binding patterns in differentiated cells. It would be interesting to systematically determine the genome-wide maps of histone variants in different cell types, particularly in light of a recent study reporting a distribution pattern change of H1.5 in cellular differentiation H1Drosophila cells The prevalent H1 variants binding with local troughs at active promoters we observed here in the mouse ESC genome is reminiscent of the previous results when ChIP-chip and a pan-H1 antibody were used to map H1 on a portion of the human genome in MCF-7 cells in vivo resonates with the previous observation that H1 is preferentially associated with scaffold associated regions (SAR) in vitro studies Correlation analyses indicate that H1d and H1c are inversely correlated with GC content, H3K4me3 mark, but positively correlated with H3K9me3 mark across the mouse ESC genome . Our finin vitro with HP1α DrosophilaThe co-localization of H1d and H1c with H3K9me3 suggests that these two variants are enriched at heterochromatin and may facilitate the maintenance of constitutive heterochromatin structure. Such association may be mediated through HP1, the heterochromatin protein binding to H3K9me3 and H3K9 methyltransferase Suv39h and facilitating spreading of heterochromatin marks 0 also appear to preferentially accumulate at satellite sequences in ESCs  mice. All animal work was performed according to procedures approved by the Institutional Animal Care and Use Committee (IACUC) at Georgia Institute of Technology.The H1d214), and the peak areas were recorded and analyzed with ÄKTA UNICORN 5.11 software. The A214 values of the H1 and H2B peaks were adjusted by the number of peptide bonds in each H1 variant and H2B. The H1/nucleosome ratio was determined by dividing the A214 of all H1 peaks by half of the A214 of the H2B peak. Fractions corresponding to different H1 variants from HPLC analysis were collected, lyophilized and analyzed with silver staining, Coomassie staining and Western blotting.Nuclei and chromatin of ESCs and mouse tissues were prepared and analyzed according to protocols described previously 0 (Santa Cruz 56695), anti-H1 (Milipore 05-457) and IgG (Millipore 12-370).The following antibodies were used in this study: anti-FLAG (Sigma-Aldrich F3165), anti-DYKDDDDK tag , anti-Myc-tag , anti-H3K4me3 (Millipore 07-473), anti-H3K9me3 (Abcam 8898), anti-H3K27me3 (Millipore 07-449), anti-H4K20me3 (Millipore 07-463), anti-H1ChIP assays were performed as described previously The libraries for massive parallel sequencing were prepared with the ChIP-seq Sample Preparation Kit (Illumina) according to the manufacturer's instructions. Briefly, 10 ng of immunoprecipiated DNA or input DNA were end repaired, 3′ adenylated and ligated with adapter oligos supplied by the manufacturer. DNA fragments within the range of 120∼500 bp were purified following gel electrophoresis and amplified with primers provided by the manufacturer. Library DNA was subsequently purified with a Qiagen DNA Isolation column, quantified and submitted for sequencing.http://bowtie-bio.sourceforge.net/index.shtml). The first 40 bp  or the first 35 bp  of the reads were used as seed sequences with up to two mismatches allowed for the alignment, and aligned number of reads were scored. Reads with multiple alignment positions were mapped randomly to one of the possible position. Reads for each ChIP-seq or input-seq library aligned to mm9 were normalized to 10 million reads, and IP-IN signals were calculated in each 100 bp sliding window by subtraction of normalized read counts per 10 million mappable reads of ChIP-seq library by that of its corresponding input-seq library using GenPlay software (http://genplay.einstein.yu.edu/wiki/index.php/Documentation) Sequencing was performed with Illumina Genome Analyzer II and Illumina HiSeq 2000 systems, and raw sequence reads containing more than 30% of ‘N’ were removed and adaptor sequences were trimmed. Clean sequences were aligned against mouse genome, mm9 (UCSC website), and 2,669 categories of mammalian repeats from RepBase version 14.07 The sum of signals (IP-IN) for each 1000 bp window  was used to calculate the correlation coefficients of H1 variants with GC% and different histone markers. Genome-wide and chromosome-wide correlation coefficients were calculated, and the scatter-plots were generated using Matlab.http://epigraph.mpi-inf.mpg.de/WebGRAPH/) Significantly enriched regions were identified using GenPlay or SICER v1.1 6 nuclei were resuspended in 200 µl of MNase digestion buffer  and digested at 37°C with 20 units of micrococcal nuclease (MNase) (Worthington) for time course analysis or 2 units of MNase (Worthington) for 5 min in analysis shown in ESC nuclei were extracted and MNase digestion was performed as described previously 2. The conversion factor of pixel/micron was 18.7 pixels per micrometer.FISH was performed as described previously GAPDH. The qRT-PCR primers for repetitive sequences are the same as in qChIP, and the primers for GAPDH are as described previously 1 µg of total RNA extracted from ESCs was treated with RNase free DNaseI (Sigma-Aldrich) and reverse transcribed using a SuperScript first-strand cDNA synthesis kit with random hexamers (Life Technologies). Triplicate PCR reactions using the iQ SYBR Green Supermix (Bio-Rad) were analyzed in a MyIQ Real-Time PCR Detection System (Bio-Rad). All samples were typically analyzed in two independent experiments. Relative expression units were calculated by subtracting the mock reverse-transcribed signals (RT−) from reverse transcribed signals (RT+) and normalizing the adjusted values with signals of the housekeeping gene 1 µg of DNA extracted from ESCs was treated with the CpGenome DNA modification Kit (Millipore) according to the manufacturer's manual. 20 ng of treated DNA was used in each PCR reaction as previously described Figure S1FLAG/FLAG mice and H1cMyc ESCs. (A) Generation and analysis of H1dFLAG/FLAG mice. i) Genotyping analysis of H1dFLAG/FLAG mice. The positions of WT and H1dFLAG PCR bands are indicated by arrows. ii) Reverse phase HPLC and mass spectrometry analysis of extracted histones from spleens of 1-year-old wildtype , H1c−/−H1d+/+H1e−/− , and H1c−/−H1dFLAG/FLAGH1e−/− mice . The insets are profiles generated by ESI-TOF mass spectrometry analysis of H1d/e fraction eluted from HPLC. iii) Silver staining (top) and immunoblotting (bottom) assays of individual H1 variants eluted from HPLC in (ii). 1: WT, 2: ceKO, 3: ceKO/H1dFLAG/FLAG. iv) H1/nucleosome ratio of histone extracts from mouse spleen. Values were calculated from HPLC analysis as shown in (ii). (B) Generation of H1cMyc knock-in ESCs. i) Schematic representation of the H1cMyc targeting vector and homologous recombination which results in insertion of the Myc tag at N-terminus of the coding sequence of the endogenous H1c gene. ii) Strategy of constructing H1cMyc knock-in ESCs and cis vs. trans configurations of the homologous recombination events. iii) Reverse phase HPLC analysis of total histone extracts from H1cMyc cells. iv) Coomassie stain (top) and immunoblotting (bottom) assay of the H1c and H1d/e peaks eluted from HPLC of histone extracts from cehet cells and H1cMyc cells.Generation of H1d(TIF)Click here for additional data file.Figure S2Hox clusters. (C) Metagene profiling analysis of H3K9me3 (left), H3K27me3 (middle) and H3K4me3 (right) around TSS in relation to levels of themselves.Distribution patterns of H1 variants and histone marks at genes. (A) Examples of binding signals of H1d, H1c, and histone marks at TSSs. (B) Occupancy of H1 variants and histone marks at 4 (TIF)Click here for additional data file.Figure S3−50 for all comparisons (with TSS) with paired t-test. The line in the box indicates the median, while the bottom and top of the boxes are the 25th and 75th percentiles, respectively. The red line represents the median signals at +/−10 Mb distal to TSS.Progressively elevated levels of H1 variants with increasing distance from TSS. Signal values of 100 bp windows at TSS and indicated flanking regions of genes with lowest H1 values  were plotted. Distal data points situated in the vicinity of other TSSs were removed from calculation. P<10(TIF)Click here for additional data file.Figure S4Annotation and distribution analysis of H1d and H1c enriched regions. (A) Examples of H1d and H1c enriched regions. (B) Pie diagram of distributions of H1d, H1c, H3K9me3, H3K27me3, and H3K4me3 enriched regions at genes, proximal regulatory regions, and distal intergenic regions.(TIF)Click here for additional data file.Figure S5vs. H1c, each H1 variant (H1d or H1c) vs. GC percentage and histone marks on individual chromosomes. Pearson's correlation was used to perform the analysis. P<10−100 for all correlation coefficients except for those labeled with “*”. *: P>0.001. (B) EpiGRAPH overrepresentation analyses of comparisons of H1d (or H1c) uniquely enriched regions vs. histone marks enriched regions as described in methods. H1d unique regions (left panels) or (H1c unique regions (right panels)) vs. H3K9me3 regions (i), vs. H3K27me3 regions (ii), vs. H3K4me3 regions (iii). Overrepresented repetitive elements are shown in the bottom half of each box. *: no significantly overrepresented features. All P values remained significant after multiple testing corrections with the FDR method and the more conservative Bonferroni method.Occupancy correlation and overrepresentation analysis of H1 variants and histone marks. (A) Correlation coefficients of H1d (TIF)Click here for additional data file.Figure S6qChIP analysis of H1d, H1c and histone marks at selected repetitive elements. Relative enrichment was calculated by normalizing the signals of ChIP over that of IgG. Data are presented as mean ± S.D.(TIF)Click here for additional data file.Figure S7−7 for all repeat classes shown except those marked with “*”. *: P>0.01. Error bars represent the differences between replicates. Data are presented as average ± S.E.M.Distribution of H1d, H1c, and histone marks on additional repetitive sequences. Fold enrichment of percent mapped repeats of H1d, H1c, H3K9me3, H3K27me3, and H3K4me3 ChIP-seq libraries over that of corresponding input-seq library. 14 most abundant repetitive sequences within the “other” repetitive group shown in (TIF)Click here for additional data file.Figure S8th and 75th percentiles, respectively. P<4×10−6 for all comparisons with H3K9me3 within each category by unpaired t-tests.Significant enrichment of H1 variants at major satellites. Box plots of the signals of three H1 variants and histone marks at major satellite repeats (left panel); TSS, 10 Mb distal to TSS, LINE L1, and IAP LTR repeats (right panel). Y axis: input subtracted, normalized signal values as tag counts per 100 bp window per 10 million mappable reads. The line in the box indicates the median, while the bottom and top of the boxes are the 25(TIF)Click here for additional data file.Figure S9trans ESC line. (A) Reverse phase HPLC of total histone extracts from H1d-trans cells. (B) Ratios of each H1 variant  to nucleosome of H1d-trans cells calculated from data shown in (A). (C) Western blotting indicating similar amount of FLAG-H1d in H1dFLAG and H1d-trans cells. (D) Fold enrichment of percent mapped repeats of H1d in H1dFLAG and H1d-trans ESCs. P values comparing ChIP-seq with input-seq libraries are less than 9.3×10−14 for all repeat classes shown. (E) qChIP analysis of H1d occupancy at indicated repetitive elements in H1d-trans cells. Relative enrichment was calculated by normalizing the signals of ChIP over that of IgG.Analysis of H1d-(TIF)Click here for additional data file.Figure S10HPRT gene in WT and H1 TKO ESCs. ChIP signals over IgG levels are presented as mean ± S.D. *: P<0.05; **: P<0.01; ***: P<0.001.H1 depletion leads to reduced NRLs and H1 occupancy at major and minor satellites. (A) Southern blotting analysis of partially digested nuclei using a major satellite probe. The tetra-nucleosome bands are indicated by asterisks. (B–C) Elevated NRLs at major satellites compared with bulk chromatin and minor satellites in H1 TKO ESCs. Data from EB-stained gel image  and corresponding Southern blots  are plotted in (C). The positions of di-nucleosome with a 10-minute MNase digestion are marked by asterisks in (B). The dashed line in (B) indicates the di-nuleosome position in major satellites, which is higher than that of bulk chromatin and minor satellites. NRLs in (C) were calculated by extrapolating the corresponding curves to time 0 as described (TIF)Click here for additional data file.Figure S11th and 75th percentiles, respectively. ****: P<0.000001.Chromocenter area in WT, H1 TKO and RES ESCs. Chromocenters of 80 nuclei from each cell line were analyzed. The line in the box indicates the median, while the bottom and top of the boxes are the 25(TIF)Click here for additional data file.Figure S120 over-expressing (fH10) ESCs. (A) Representative Western blots of H10 over-expressing cell clones. WT ESCs were transfected with vector expressing FLAG-H10, and stable ESC clones were picked and screened using an anti-FLAG antibody. Immunoblotting with anti-β-ACTIN antibody indicates equal loading of whole cell lysates. An H10 overexpressing clone with significant levels of FLAG-H10 is indicated with an asterisk. (B) RP-HPLC Profile of fH10 ESCs. (C) Ratio of individual H1 variant  to nucleosome of fH10 ESCs calculated from HPLC profile shown in (B). (D) Western blots indicating similar levels of H10 in H1 TKO cells and the FLAG-H10 in fH10 ESCs. H3 blots indicates equal loading of chromatin lysates.Generation of H1(TIF)Click here for additional data file.Figure S130 is depleted from active promoters. (A) Examples of H10 distribution at TSSs. (B–F) Metagene analysis of H10 levels over a 10 Kb window centered on TSSs partitioned according to the levels of expression (B), H3K9me3 (C), H3K4me3 (D), H3K27me3 (E), and the presence or absence of H3K4me3 and H3K27me3 (F).H1(TIF)Click here for additional data file.Figure S140 is enriched at satellite sequences. (A) A typical peak region of H10 at major satellites. (B) Fold enrichment of percent mappable repeats from the H10 ChIP-seq library over that of input-seq library on total repeats (left) and 20 most abundant repetitive sequences (right). P values calculated with Fisher's exact test comparing ChIP-seq with input-seq libraries are less than 1.8×10−21 for all repeats shown except those marked with “*”. *: P>0.01. (C) qChIP analysis of H10 occupancy at selected repetitive sequences. Relative enrichment was calculated by normalizing the signals of ChIP over that of IgG.H1(TIF)Click here for additional data file.Figure S15FLAG, H1cMyc, and fH10 ESCs. (A) Typical FISH images of indicated cells hybridized with a major satellite probe are shown in left panels. DNA was counterstained with DAPI (middle), and merged images are shown in right panels. Scale bar: 10 µm. (B) Box plots of the numbers of chromocenters in indicated ESCs. The line in the box indicates the median, while the bottom and top of the boxes are the 25th and 75th percentiles, respectively.FISH analyses of chromocenters in H1d(TIF)Click here for additional data file.Figure S160, H1dFLAG, and H1cMyc ESCs. Data are shown as mean ± S.D. a: P<0.05 in comparison with WT; b: P<0.05 in comparison with TKO.Expression analysis of major satellites in fH1(TIF)Click here for additional data file.Table S1List of read length, counts, and total mappable reads (to mm9) of the libraries.(DOC)Click here for additional data file.


Recent genome-wide studies suggested that in addition to genetic variations, epigenetic variations may also be associated with differential gene expression and growth vigor in plant hybrids. Maize is an ideal model system for the study of epigenetic variations in hybrids given the significant heterotic performance, the well-known complexity of the genome, and the rich history in epigenetic studies. However, integrated comparative transcriptomic and epigenomic analyses in different organs of maize hybrids remain largely unexplored.Here, we generated integrated maps of transcriptomes and epigenomes of shoots and roots of two maize inbred lines and their reciprocal hybrids, and globally surveyed the epigenetic variations and their relationships with transcriptional divergence between different organs and genotypes. We observed that whereas histone modifications vary both between organs and between genotypes, DNA methylation patterns are more distinguishable between genotypes than between organs. Histone modifications were associated with transcriptomic divergence between organs and between hybrids and parents. Further, we show that genes up-regulated in both shoots and roots of hybrids were significantly enriched in the nucleosome assembly pathway. Interestingly, 22- and 24-nt siRNAs were shown to be derived from distinct transposable elements, and for different transposable elements in both shoots and roots, the differences in siRNA activity between hybrids and patents were primarily driven by different siRNA species.These results suggest that despite variations in specific genes or genomic loci, similar mechanisms may account for the genome-wide epigenetic regulation of gene activity and transposon stability in different organs of maize hybrids. The chromatin states and genome activity in eukaryotes are regulated by a variety of different epigenetic mechanisms, mainly DNA methylation, histone modifications, and the RNA interference pathway -3. DNA mArabidopsis .McrBC-seq, ChIP-seq and sRNA-seq, see Additional file For the methods used in the analysis of data from mRNA-seq, 1: First filial generation; FDR: False-discovery rate; gDNA: Genomic DNA; GO: Gene Ontology; H3K36me3: Histone H3 tri-methylated at lysine 36; H3K4me3: Histone H3 tri-methylated at lysine 4; H3K9ac: Histone H3 acetylated at lysine 9; MACS: Model-based analysis of ChIP-seq; McrBC-seq: McrBC sequencing; miRNA: MicroRNA; MPV: Mid-parent value; mRNA: Messenger RNA; mRNA-seq: mRNA sequencing; PCR: Polymerase chain reaction; RdDM: RNA-directed DNA methylation; RPKM: Aligned reads per kilobase exon model (or genic region: or genomic region) per million mapped reads; siRNA: Small interfering RNA; SNP: Single-nucleotide polymorphism; sRNA: Small RNA; sRNA-seq: sRNA sequencing; TE: Transposable element; TSS: Transcription start site; TTS: Transcription termination site.cDNA: Complementary DNA; ChIP-seq: Chromatin immunoprecipitation sequencing; EST: Expressed sequence tag; FThe authors declare that they have no competing interests.XWD conceived of the project. XWD, XL and GH designed the experiments. GH, XL, MY, LL and YQ performed the experiments. GH, BC, XW, HH and LL conducted data analyses. GH, JL, LL, XW and XWD prepared the article. All authors read and approved the final manuscript.Table S1: Summary of total reads obtained from all sequencing libraries.Click here for fileFigures S1 to 11. Figure S1: Mean levels of exons and introns in shoots and roots of reciprocal hybrids. Figure S2: Distribution of H3K36me3 levels within and around differentially expressed genes. Figure S3: Experimental validation of methylated DNA regions by genomic bisulfite sequencing. Figure S4: A representative genomic region on maize chromosome 1 showing integrated maps of transcription and epigenetic modifications. Figure S5: Tree view of hierarchical clustering of H3K9ac and H3K36me3 levels. Figure S6: Relationships of variations in H3K36me3 and gene expression between organs and between genotypes. Figure S7: Functional categories of genes upregulated in shoots and roots of hybrids. Figure S8: Correlation of allelic expression bias between shoots and roots of Mo17 ´ B73. Figure S9: Coverage of 21 nt, 22 nt, and 24 nt siRNA clusters in and around protein-coding genes. Figure S10: Distribution of 21 nt and 22 nt siRNA clusters on maize chromosome 1. Figure S11: Correlation between 22 nt siRNAs and DNA-methylation levels at the same genomic loci.Click here for fileTables S2 and 3. Table S2: List of genes showing expression variation in shoots and roots of maize hybrids. Table S3: Number of small RNA reads associated with known maize microRNAs.Click here for fileSupplemental methods. Details of data processing and analyses.Click here for file


Transgenic plants overexpressing bHLH13 or bHLH17 displayed reduced JA responses. Furthermore, these bHLH factors functioned as transcription repressors to antagonize the transcription activators, such as MYC2 and the WD-repeat/bHLH/MYB complex, through binding to their target sequences. Coordinated regulation of JA responses by transcription activators and repressors would benefit plants by allowing fine regulation of defense and development, and survival in their frequently changing environment.Plants have evolved sophisticated systems for adaptation to their natural habitat. In response to developmental and environmental cues, plants produce and perceive jasmonate (JA) signals, which induce degradation of JASMONATE-ZIM-Domain (JAZ) proteins and derepress the JAZ-repressed transcription factors to regulate diverse aspects of defense responses and developmental processes. Here, we identified the bHLH subgroup IIId transcription factors  as novel targets of JAZs. These bHLH subgroup IIId transcription factors act as transcription repressors and function redundantly to negatively regulate JA responses. The quadruple mutant  Plants live in fixed places and have to evolve sophisticated systems for adaptation to their frequently changing environment. Plant hormones are essential for the regulation of these sophisticated systems which coordinately control plant growth, development, reproduction and defense. Jasmonates (JAs), a new class of cyclic fatty acid-derived plant hormone, regulate diverse aspects of plant defense and developmental processes. In response to external environmental signals and internal developmental cues, plants rapidly produce and efficiently perceive JA signals, which regulate a dynamic regulatory network to activate various downstream transcription factors essential for appropriate plant defense and development. Here, we identified the bHLH3, bHLH13, bHLH14 and bHLH17 transcription factors as novel transcription repressors of JA signaling. The coordinated regulation of JA-mediated plant defense and development by transcription activators and repressors would improve the survival of plants in their natural habitat and adaptation to the frequently fluctuating environment. Plant hormones are essential for the regulation of plant growth, differentiation, development, reproduction, and survival In response to external environmental signals and internal developmental cues, plants generate and perceive jasmonates (JA) signals to induce degradation of JASMONATE ZIM-Domain (JAZ) proteins Current studies have identified several key transcription factors as direct targets of JAZ proteins In this study, we identified the bHLH subgroup IIId transcription factors  as new targets of JAZ proteins. These bHLH subgroup IIId transcription factors function redundantly to negatively regulate JA-mediated plant defense and development. Furthermore, these bHLH factors act as transcriptional repressors to suppress JA responses, which antagonize the previously reported transcription activators (such as MYC2 and the WD-repeat/TT8/MYB75 complex) through binding to their downstream target sequences. Coordinated regulation of JA responses by transcription repressors and activators would benefit plants for adaptation to their frequently changing environment.Arabidopsis thaliana cDNA library using JAZ proteins (JAZ1 and JAZ8) as bait in the yeast two-hybrid (Y2H) system, and identified several transcription factors To understand molecular basis of jasmonate action, we exhaustedly screened the Arabidopsis bHLH family Phylogenetic analysis showed that bHLH13, together with bHLH3 (AT4G16430), bHLH14 (AT4G00870) and bHLH17 (AT2G46510/AtAIB), belongs to the subgroup IIId of the Nicotiana benthamiana. As shown in N. benthamiana leaves were reconstructed by coexpression of JAZ1-nYFP  with cYFP-bHLH3, cYFP-bHLH13, cYFP-bHLH14 and cYFP-bHLH17, respectively. Strong YFP signals were also detected when JAZ10 was coexpressed with these transcription factors in the BiFC assays  assays in leaves of C assays . These rTo investigate which domain of these bHLH factors is responsible for the interaction with JAZ proteins, we representatively divided the bHLH13 into the N-terminal fragment (bHLH13NT) containing the JAZ-Interaction-Domain (JID) To further examine which domain of JAZ proteins is critical for interactions with the bHLH subgroup IIId factors, we divided JAZ8 into the N-terminal fragment (JAZ8NT), the C-terminal part (JAZ8CT), and the Jas domain only (JAZ8Jas) . Our resbHLH3, bHLH13, bHLH14 and bHLH17 in plant tissues, we generated Arabidopsis plants transgenic for the GUS reporter driven by endogenous promoter of each bHLH factor. Histochemical staining of the GUS activity demonstrated that all these bHLH factors are expressed in various plant tissues (www.bar.utoronto.ca) COI1-dependent JA-induced gene expression was observed for bHLH13 and bHLH17, but not for bHLH3 and bHLH14  or the 5′UTR (for bHLH13)  or obviously reduced (for bHLH13) in their respective mutants  . Quantit mutants . ObservabHLH3, bHLH13, bHLH14 and bHLH17 display high similarity at the amino acid level and belong to the same subgroup IIId of the Arabidopsis bHLH family bhlh3, bhlh13, bhlh14 and bhlh17 mutants, to investigate whether these factors function redundantly in regulation of JA responses. Interestingly, we found that the anthocyanin accumulation was gradually increased in the double mutant bhlh3 bhlh17, the triple mutant bhlh3 bhlh13 bhlh17, bhlh3 bhlh13 bhlh14, bhlh3 bhlh14 bhlh17, bhlh13 bhlh14 bhlh17, and the quadruple mutant bhlh3 bhlh13 bhlh14 bhlh17 in response to MeJA treatment , while wild type and coi1-1 accommodated the majority of S. exigua larvae  , while the wild-type attracted less larvae than coi1-1   . In the  coi1-1) . These rIn summary, we demonstrated that the bHLH subgroup IIId factors  function as novel negative regulators of JA-mediated plant defense.bHLH17 overexpression lines, 17OE-2 and 17OE-4 with highly expressed bHLH17 transcripts  also exhibited reduced JA responses  negatively regulat JA responses, we examined whether these bHLH subgroup IIId factors function as transcriptional repressors using the GAL4-DNA- binding-domain (GAL4DB) and its binding sites (GAL4(4X)-D1-3(4X)-GUS)-based protoplast transient expression system Similarly, we observed that GAL4DB-fused bHLH3, bHLH13 or bHLH14 repressed the activity of GUS reporter, and that their repression function could be partially inhibited by JAZ1 . These rDFR-LUCP in which the LUC reporter was driven by the promoter of DFR  at the position from −182 to −154, was highly enriched in the anti-myc-immunoprecipitated chromatin of the myc-bHLH3 transgenic plant, but not in the controls   and transcription activator (such as MYC2 or TT8/MYB75) , S3, S4,c plant) , demonst of TAT1 .Taken together, our results suggest that bHLH3, bHLH13, bHLH14 and bHLH17 factors function as transcriptional repressors to repress the JA responses. These transcriptional repressors antagonize the previously reported transcription activators through binding to their downstream target sequences.The JAZ protéines bhlh3, bhlh13, bhlh14, bhlh17, bhlh3 bhlh17, bhlh3 bhlh13 bhlh17, bhlh3 bhlh13 bhlh14, bhlh3 bhlh14 bhlh17, bhlh13 bhlh14 bhlh17, bhlh3 bhlh13 bhlh14 bhlh17) showed that these bHLH subgroup IIId transcripiton factors function redundantly to repress JA responses  displayed no obvious alteration in JA-inhibitory root growth, but exhibited the enhanced sensitivity in JA-inducible anthocyanin accumulation and defense against insect Genetic and physiological analysis on the single, double, triple and quandruple mutants /MYB (MYB75 or GL1) complexes act as transcription activators which bind to and activate promoter sequences of their respective target genes, such as  and DFR  and 10,  and DFR . It is iMYC2 is a key gene that positively regulates a vast array of JA-responsive genes and diverse aspects of JA responses, including root growth Pst DC3000 Spodoptera littoralis and Helicoverpa armigera) MYC2 gene was shown to negatively regulate defense against necrotrophic fungi (B. cinerea and Plectosphaerella cucumerina) and expression of the related genes, such as PDF1.2Pst DC3000, necrotrophic pathogen B. cinerea, and insect S. exigua , and to additively or synergistically affect the myc2-associated increase of defense response against necrotrophic pathogen B. cinerea.The GAL4DB-based protoplast transient expression system is a well-established method for determination of transcription activators and repressors . exigua –7. GenerCOI1 for ubiquitination and subsequent degradation. As a result, the JAZ-targeted transcription activators and repressors  are released to antagonistically and coordinately regulate their target genes (such as TAT1 and DFR), which may further modulate expression of JA responsive genes essential for various JA responses , bhlh13 (CS466724/GK-696A04), bhlh14 (CS27164/GT1193.Ds3.05.24.99.b.288) and bhlh17 (CS874647/SAIL_536_F09) were obtained from the ABRC or NASC. The double, triple and quadruple mutants of the bHLH subgroup IIId factors were generated through genetic crossing among bhlh3, bhlh13, bhlh14 and bhlh17.The Arabidopsis thaliana seeds were sterilized with 20% bleach, plated on Murashige and Skoog medium , chilled at 4°C for 3 days, and transferred to a growth room under a 16-h (22–24°C)/8-h (16–19°C) light/dark photoperiod. Nicotiana benthamiana was grown in a growth room under a 16-h (28°C)/8-h (22°C) light/dark condition.The yeast two-hybrid screening method was described as previously JAZs, bHLH3, bHLH13, bHLH14, bHLH17, MYC2, MYC3, MYC4, TT8, GL3, EGL3, MYB75, GL1 and TTG1, and their domain derivatives were cloned into pLexA or pB42AD vectors. Primers used for the vector construction are presented in For Y2H assay, all the CDS of Arabidopsis JAZ1, JAZ10, bHLH3, bHLH13, bHLH14, bHLH17, MYC2, MYC3, MYC4, TT8, GL3, EGL3, MYB75 and GL1 were cloned into the binary nYFP or cYFP vector through enzyme digestion sites  or Gateway reaction with pDONR207 vector system (Invitrogen) Agrobacterium strains with indicated nYFP or cYFP vector were incubated, harvested, and resuspended in infiltration buffer . Equal concentrations and volumes of Agrobacterium strains were mixed and coinfiltrated into N. benthamiana leaves by a needleless syringe. After infiltration, plants were placed at 24°C for 50 h before observation. The experiments were repeated three biological times.For BiFC assays, full-length coding sequences of Arabidopsis seedlings were grown on MS medium for 11 days and then were treated with or without 100 µM MeJA for indicated time. In Arabidopsis seedlings were grown on MS medium supplied with or without indicated concentration of MeJA for eleven days. These materials were harvested for RNA extraction and subsequent reverse transcription.In ACTIN8 was used as the internal control. The primers used for real-time PCR analysis are presented in Real-time PCR analyses were performed with the RealMasterMix (SYBR Green I) (Takara) using the ABI7500 real-time PCR system as described previously Arabidopsis protoplast using the GUS reporter, the CDS of bHLH3, bHLH13, bHLH14, bHLH17 and MYC2 were fused with the GAL4DB under control of 35S promoter. The coding sequence of JAZ1 was cloned into the pGreenII 62-SK vector under control of 35S promoter firefly luciferase gene (LUC) under control of 35S promoter. Arabidopsis mesophyll protoplasts preparation and subsequent transfection were performed as described previously For transient expression assay in MYC2, TT8, MYB75, bHLH3 and bHLH17 were cloned into pGreenII 62-SK vectors under control of 35S promoter respectively. The ∼519 bp and ∼980 bp promoter sequences of DFR and TAT1 were amplified from genomic DNA and cloned into pGreenII 0800-LUC respectively For transient transcriptional activity assay using the LUC reporter, the CDS sequences of myc-bHLH3 transgenic plants and the wild-type (Col-0) plants treated with 100 µM MeJA for 40 minutes. Immunoprecipitation was performed using Mouse anti-MYC antibody and protein G agarose beads. Enrichment of promoter DNA was confirmed by qRT-PCR using ACTIN2 as normazlization control. Primers for 3′UTR region were used as negative control. Primers for the ChIP assays are listed in The ChIP experiment was performed as described previously Arabidopsis transgenic plants overexpressing bHLH13 and bHLH17, the full-length coding sequences of bHLH13 and bHLH17 were amplified and cloned into the modified pCAMBIA1300 vector under control of 35S promoter through the SalI and SpeI sites. To generate Arabidopsis myc-bHLH3 transgenic plants, coding sequence of bHLH3 was cloned into pROK2-myc vectors for fusion with six myc tags. These constructs were introduced into Arabidopsis plants using the Agrobacterium-mediated floral dip method. Two representative transgenic lines for bHLH13 and bHLH17 respectively were displayed in the article.To generate bHLH3, bHLH13, bHLH14 and bHLH17 were respectively amplified and cloned into pCAMBIA1391Z vector to drive the GUS genes for generation of bHLH3-GUSP, bHLH13-GUSP, bHLH14-GUSP and bHLH17-GUSP. These constructs were transformed into Agrobacterium strains GV3101, and transferred into Arabidopsis by floral dip methods. Seedlings, inflorescences and roots from the transgenic plants harboring bHLH3-GUSP, bHLH13-GUSP, bHLH14-GUSP or bHLH17-GUSP were used for histochemical staining of GUS. Histochemical staining for GUS activity assay was performed as described previously ∼2520 bp, ∼2500 bp, ∼1050 bp and ∼2570 bp promoter regions of bHLH3, bHLH13, bHLH14 and bHLH17 were respectively cloned into pEGAD vector for fusion with GFP under control of 35S promoter to generate the GFP-bHLH3, GFP-bHLH13, GFP-bHLH14 and GFP-bHLH17 constructs. The Agrobacterium containing the indicated constructs were resuspended in infiltration buffer , and infiltrated into N. benthamiana leaves by a needleless syringe. After infiltration, plants were placed at 24°C for 50 h before GFP observation.Coding sequences of Arabidopsis seedlings grown on MS medium with 0, 5, or 15 µM MeJA were measured as described previously For anthocyanin measurement, 11-d-old Seeds were grown on MS medium with 0, 5, 20 or 50 µM MeJA, chilled at 4°C for 3 days, and transferred to the growth room. Root lengths of fifteen 11-d-old seedlings for each genotype and treatment were measured and presented. The experiment was repeated three biological times.Flowering time of plants, grown in soil under long day condition with 16-h(22–25°C)/8-h(18–21°C) light/dark photoperiod, was recorded as the number of days from germination to the first appearance of buds in the rosette center. The experiment was repeated three biological times.Botrytis cinerea (106 spores/mL) solved in 0.025% tween or with 0.025% tween as control, placed in dark at the appropriate temperature (22°C) and high humidity (100%) for 36 hours, and transferred to a growth room under the growth conditions of a 16-h-light (21 to 23°C)/8-h-dark (16 to 19°C) photoperiod with ∼40% humidity. Infection symptoms were recorded at 7-day after infection. Infection ratings from 0 to 3 were assigned to the inoculated plants . For 7 spores/mL) solved in 0.025% tween or with 0.025% tween as control, placed in dark at the appropriate temperature (22°C) and high humidity (100%) for 36 hours, transferred to a growth incubator under the growth conditions of a 16-h-light (21 to 23°C)/8-h-dark (16 to 19°C) photoperiod with high humidity (>90%). Plant survival ratio were recorded at 9-day after infection. At least thirty plants from each genotype were used in each experiment. The experiment was repeated three biological times.For Pseudomonas syringae pv tomato (Pst) DC3000 suspension containing 108 (colony-forming units)/mL bacteria (OD600 = 0.2) with 0.02% Silwet L-77 or 0.02% Silwet L-77 as control. Infection symptoms were recorded at 3-day after infection. At least thirty plants from each genotype were used in each experiment. The bacterial population counts in the plant was determined as previously described Thirty-day-old plants were sprayed with S. exigua larvae (∼8 mg each) were weighted, and reared on leaves in one Petri dish, for each genotype using five independent replicates. Two days after feeding, the weight of the 10 larvae were measured. The increase of average larval weight was recorded.More than fifty mature rosette leaves with similar size from 4-week-old plants for each genotype were placed in one plastic Petri dishes (90 mm) containing wet filter paper. The 10 third-instar two-choice test, five rosette leaves from 4-week-old plants for each genotype were placed intervally in a circle in plastic Petri dishes (90 mm) containing wet filter paper. 40 newly hatched larvae were placed in the center of the Petri dishes for equal distance to the leaves. One day after incubation in the growth room, the numbers of larvae on leaves for each genotype were recorded. Four independent replicates were performed.In the three-choice test, three rosette leaves from 4-week-old plants for each genotype were placed intervally in a circle in plastic Petri dishes (90 mm) containing wet filter paper. 40 newly hatched larvae were placed in the center of the Petri dishes for equal distance to the leaves. One day after incubation in the growth room, the numbers of larvae on leaves for each genotype were recorded. Six independent replicates were performed.In the The Arabidopsis Genome Initiative numbers for genes mentioned in this article are as follows: JAZ1 (AT1G19180), JAZ2 (AT1G74950), JAZ3 (AT3G17860), JAZ4 (AT1G48500), JAZ5 (AT1G17380), JAZ6 (AT1G72450), JAZ7 (AT2G34600), JAZ8 (AT1G30135), JAZ9 (AT1G70700), JAZ10 (AT5G13220), JAZ11 (AT3G43440), JAZ12 (AT5G20900), bHLH3 (AT4G16430), bHLH13 (AT1G01260), bHLH14 (AT4G00870), bHLH17 (AT2G46510), MYC2 (AT1G32640), MYC3 (AT5G46760), MYC4 (AT4G17880), TT8 (AT4G09820), GL3 (AT5G41315), EGL3 (AT1G63650), MYB75 (AT1G56650), GL1 (AT3G27920), TTG1 (AT5G24520), DFR (AT5G42800), LDOX (AT4G22880), UF3GT (AT5G54060), VSP1 (AT5G24780), THI2.1 (AT1G72260), PDF1.2 (AT5G44420), LOX2 (AT3G45140), TAT1 (AT4G23600), ERF1 (AT3G23240) and ACTIN8 (AT1G49240).Figure S1bHLH3, bHLH13, bHLH14 and bHLH17 Function Redundantly to Repress JA-regulated Anthocyanin Accumulation. Anthocyanin contents of the 11-day-old seedlings in WT, the single bHLH mutants , the triple mutants , the quadruple mutant bhlh3,13,14,17 (Q4) and coi1-1 grown on MS medium (MS) or MS medium containing 5 or 15 µM MeJA. FW, fresh weight. Error bars represent SE (n = 3). Asterisks denote Student's t-test significance compared with WT plants: **, P<0.01.(TIF)Click here for additional data file.Figure S2bHLH3, bHLH13 and bHLH17 Cannot Interact with MYC2, MYC3, MYC4, GL3, EGL3, TT8, MYB75, GL1 and TTG1 in Y2H Assays.(TIF)Click here for additional data file.Figure S3N. benthamiana in BiFC Assays.bHLH3, bHLH13, bHLH14 and bHLH17 Cannot Interact with MYC2, MYC3 and MYC4 in (TIF)Click here for additional data file.Figure S4N. benthamiana in BiFC AssaybHLH3, bHLH13, bHLH14 and bHLH17 Cannot Interact with GL3, EGL3, TT8, MYB75 and GL1 in (TIF)Click here for additional data file.Table S1Primers Used for Vector Construction.(DOC)Click here for additional data file.Table S2Primers Used for Quantitative Real-time PCR Analysis and ChIP PCR.(DOC)Click here for additional data file.


S. cerevisiae. Together with Skp1 and Cul1, Dia2 forms the substrate-determining part of an E3 ubiquitin ligase complex, otherwise known as the SCF. Dia2 has previously been implicated in the control of replication and genome stability via its interaction with the replisome progression complex.Dia2 is an F-box protein found in the budding yeast, We identified components of the RSC chromatin remodelling complex as genetic interactors with Dia2, suggesting an additional role for Dia2 in the regulation of transcription. We show that Dia2 is involved in controlling assembly of the RSC complex. RSC belongs to a group of ATP-dependent nucleosome-remodelling complexes that controls the repositioning of nucleosomes. The RSC complex is expressed abundantly and its 17 subunits are recruited to chromatin in response to both transcription activation and repression. In the absence of Dia2, RSC-mediated transcription regulation was impaired, with concomitant abnormalities in nucleosome positioning.Our findings imply that Dia2 is required for the correct assembly and function of the RSC complex. Dia2, by controlling the RSC chromatin remodeller, fine-tunes transcription by controlling nucleosome positioning during transcriptional activation and repression. Saccharyomyces cerevisiae, the RSC complex has been shown to bind to transcribed parts of the genome and control nucleosome distribution in response to transcription in vivo is currently not clear.Nucleosomes pose formidable barriers to transcription. Sophisticated chromatin remodellers have evolved to specifically coordinate and fine-tune the accessibility of DNA to the basic transcription machinery. The RSC complex is the most abundant of such chromatin remodellers in eukaryotes. Comprised of 17 subunits, the RSC complex is an ATP-dependent chromatin remodelling complex that has been demonstrated to slide or disassemble nucleosomes Dia2) that has been proposed to constitute part of the replisome progression complex Dia2 is part of an E3 ubiquitin ligase  during gene induction GAL1 transcription . We surmised that if Dia2-mediated ubiquitylation of RSC subunits had a direct impact on RSC complex assembly, the RSC ubiquitylation pattern would be altered in the Δantibody . Sfh1 wadia2 strain lacked the two non-essential Htl1 and Rtt102 subunits . We therefore examined transcription of the GAL1 gene in Δhtl1 and Δrtt102 mutants  under both uninduced (raffinose) and induced  conditions. Furthermore, the reduction in nucleosome occupancy at these positions observed in wild type cells in response to GAL1 induction was no longer detectable in the absence of Dia2. It has recently been reported dia2 mutant. This could be due to the fact that a RSC core complex, comprising its essential subunits, was assembled independently of Dia2, so that the Δdia2 mutant may cause a distinct defect due to the missing RSC sub-module.If the abnormal composition of the RSC complex in the absence of Dia2 had functional significance, given the known function of the RSC complex as a chromatin remodeller, the observed transcriptional defects in the Δpromoter . When cel defect . InteresHTA1/HTB1 locus, including less sharply defined nucleosome positions and reduced changes in response to HU treatment . Further experiments are required to investigate these possibilities.In this study, we demonstrated that Dia2 genetically interacts with the RSC complex in the context of transcription and is required for complete RSC complex assembly. RSC is an essential chromatin remodelling complex involved in multiple aspects of chromatin metabolism during transcription, recombination, repair, and replication. We showed that Dia2 interacted genetically with subunits of the RSC complex and proposed a model whereby SCFne genes . Becausedia2 cells GAL1/GAL10 locus, which is known to come under control of the RSC complex, but whose regulation is cell cycle independent. We observed a delay and an overall inefficiency during gene induction in Δdia2 cells. The RSC complex is known to bind the GAL1/GAL10 promoter RSC mutant, rsc3-1ts, it has been previously suggested that RSC is required for efficient GAL1/GAL10 gene induction only when cells were shifted from a repressive condition (glucose) but not from non-induced conditions (raffinose) to galactose dia2 cells, however, we observed a delay in GAL1 induction when we added galacotse to cultures grown in raffinose. Rsc3 constitutes one of RSC's DNA binding subunits. Although we observed a reduction in the amount of Rsc3 present in the RSC complex  or Δdia2. Transformants were split onto plates containing either YPD (2% dextrose), in which the degron remains inactive, and YPG , in which the degron is activated by virtue of induced Ubr1 expression from pGAL1. G418 (for selection of knockout transformants) and 0.1 mM copper sulphate (to drive expression of the degron tagged RSC subunit) were also included in all media used. Plates were then incubated for 2 days at 30°C to allow colonies to develop. Colony counts were taken as a measure of viability following each transformation. Relative viability =  No. of colonies on test sample on YPG / No. colonies on YPD in the respective backgrounds. In galactose, where the degron was active, relative viability denoted the effect of growth inhibition in combination with the Δdia2 background. The control Δsuc2 did not result in reduced relative viability demonstrating that degron activation alone could not account for the results observed in the Δdia2 background.RSC degron strains were constructed and provided by the Logie lab 600 = 1.0 and treated with 1% formaldehyde for 30 minutes at room temperature for crosslinking. Cross-linking was halted by addition of 125 mM glycine for 5 minutes. Lysate preparation and immunoprecipitation of HA-tagged proteins was carried out in accordance with Ren et al. Yeast cells were harvested at O.D.GAL1, HTA1, HTA2, HHF1 or HHF2 expression, using primers specific to these transcripts. Primers specific to the ACT1 or TCM1 transcripts were used for normalization. Expression levels relative to the control were obtained using the following formula y = [2−C(t)/2−control C(t)]×100.Total RNA was extracted using the ‘RNeasy’ kit (Qiagen) and the corresponding RNase-free DNase (Qiagen). Equal amounts of total RNA were used as template for RT-qPCR to quantify et al. Tandem affinity purification of TAP-tagged proteins, Sfh1 and Htl1, was carried out as described in Puig Proteins were separated by SDS-PAGE and fixed overnight in 50% methanol for silver staining. For western blotting, SDS-PAGE gels were transferred to PVDF membranes according to a standard protocol. Antibodies used: Anti-HA (12CA5) 1∶1000, anti-calmodulin binding protein  = 1∶5000 and anti-ubiquitin   = 1∶1000.et al.600 = 1.0 and treated with 1% formaldehyde for 30 minutes at room temperature, followed by 125 mM glycine for 5 minutes. Cells were then treated with zymolyase (Seikagaku Biobusiness Corporation) in Buffer Z , 28 µl of β-ME  to digest cell walls and gently lysed using a hypotonic buffer . Chromatin was digested using micrococcal nuclease (USB Corporation) to achieve mononucleosomal-sized fragments. DNA was then extracted by two rounds of phenol/chloroform, followed by ethanol precipitation.Nucleosome positioning was assessed using an assay based on Bryant −MNaseC(t)/2−undigested C(t)]×100), then internally against a non-nucleosomal control region Micrococcal nuclease-digested DNA, and undigested genomic DNA were analysed by qPCR, using a panel of primers corresponding to the specified promoters and 5′ parts of the open reading frames. Data was normalised against undigested controls  that has been demonstrated by the Logie group to not interact genetically with the RSC complex. Relative viability is shown during degron-inducing  conditions corrected to viability in degron non-inducing (dextrose) conditinos. The tdrsc3 mutation conferred reduced viability relative to wild-type in both the Δdia2 and the control strain under inducing conditions. As such, it was impossible to deduce whether the potential interaction observed was genuine.(EPS)Click here for additional data file.Figure S2Transcription of histone genes in the Δdia2 mutant. Total RNA was extracted from yeast cells grown in YPD either in the absence (asyn) or after addition of 200 mM of hydroxyurea (HU) for 40 minutes to induce repression. RNA was reverse transcribed and analysed by quantitative PCR. Samples were normalised against expression of the ACT1 gene. Gene expression at the HTA1, HTA2 (representative of transcription of the histone H2A and H2B genes), HHF1 and HHF2 (representative of transcription of the histone H3 and H4 genes) loci were shown.(EPS)Click here for additional data file.Figure S3Dia2 is required for correct nucleosome positioning at the HTA1/HTB1 promoter. S3A) Diagramatic representation of predicted nucleosome and primer locations on the HTA1/HTB1 promoter. S3B) Nucleosome mapping by micrococcal nuclease protection assays in wild-type or Δdia2 cells grown in YPD (‘asyn’: triangles) or in the presence of 200 mM hydroxyurea (HU: circles). Error bars represent standard error of the mean from three biological replicates. Statistical significance is <0.0001 when comparing entire data sets- determined by 2-way ANOVA. During hydroxyurea-mediated repression, there is an expected increase in nucleosome deposition (top panel). This is particularly evident between primer positions 12–18 in the wild-type strain. In the Δdia2 strain however, nucleosome boundaries were generally less sharply defined . There is also a lack of protection to micrococcal nuclease digestion during repression suggestive of more open chromatin structure and less nucleosome deposition.(EPS)Click here for additional data file.Figure S4Htl1 is expressed in the Δdia2 mutant though it no longer associates with the RSC complex. Tandem affinity purification was performed using TAP-tagged Htl1 in the wild-type (WT) or the Δdia2 backgrounds. Left panel: Anti-calmodulin-binding protein (Anti-CBP) blot to indicate that immunoprecipitation was equally efficient in both samples and that Htl1 is adequately expressed in the absence of Dia2. Right panel: Western blotting using an anti-ubiquitin antibody (Anti-Ubq). Multiple ubiquitylated proteins were observed in the wild-type sample and some of these are absent in Δdia2 cells, indicated by *s. This reflected that the RSC complex was not efficiently pulled down in Δdia2 cells.(EPS)Click here for additional data file.Table S1List of yeast strains employed in this study.(DOC)Click here for additional data file.


Pcl3 in ESCs increases spontaneous differentiation, yet does not affect early differentiation decisions as assessed in teratomas and embryoid bodies, indicating that Pcl3 has a specific role in regulating ESC self-renewal. Consistent with Pcl3 promoting PRC2 function, decreasing Pcl3 levels reduces H3K27me3 levels while overexpressing Pcl3 increases H3K27me3 levels. Furthermore, chromatin immunoprecipitation and sequencing (ChIP-seq) reveal that Pcl3 co-localizes with PRC2 core component, Suz12, and depletion of Pcl3 decreases Suz12 binding at over 60% of PRC2 targets. Mutation of conserved residues within the Pcl3 Tudor domain, a domain implicated in recognizing methylated histones, compromises H3K27me3 formation, suggesting that the Tudor domain of Pcl3 is essential for function. We also show that Pcl3 and its paralog, Pcl2, exist in different PRC2 complexes but bind many of the same PRC2 targets, particularly CpG islands regulated by Pcl3. Thus, Pcl3 is a component of PRC2 critical for ESC self-renewal, histone methylation, and recruitment of PRC2 to a subset of its genomic sites.Polycomb repressive complex 2 (PRC2) trimethylates lysine 27 of histone H3 (H3K27me3) to regulate gene expression during diverse biological transitions in development, embryonic stem cell (ESC) differentiation, and cancer. Here, we show that Polycomb-like 3 (Pcl3) is a component of PRC2 that promotes ESC self-renewal. Using mass spectrometry, we identified Pcl3 as a Suz12 binding partner and confirmed Pcl3 interactions with core PRC2 components by co-immunoprecipitation. Knockdown of  Embryonic development requires coordinated changes in gene expression for the differentiation of specific cell types. Regulated changes in gene expression are also important for maintaining tissue homeostasis and preventing cancer. Histone modifications contribute to the control of gene expression by affecting chromatin structure and the recruitment of regulatory proteins. Polycomb repressive complex 2 (PRC2) catalyzes the methylation of a lysine residue on histone H3, an early step in gene repression. By investigating how PRC2 is recruited to genes, we have found that Polycomb-like 3 (Pcl3), a protein upregulated in diverse cancers, is a component of PRC2 that promotes its binding and function at target genes. Consistent with roles for PRC2 in regulating stem cell behaviors, Pcl3 is important for embryonic stem cell self-renewal. Thus, Pcl3 is a critical regulator of gene repression and stem cell self-renewal that acts by controlling PRC2 binding to target genes. The developmental plasticity of early embryos and embryonic stem cells (ESCs) requires the repression of cell-type specific genes. Two multiprotein complexes that participate in gene repression are Polycomb repressive complex 1 (PRC1) and Polycomb repressive complex 2 (PRC2) Whereas H3K27me3 is associated with repressed genes, H3K4me3 marks active genes. ESCs and a number of adult stem cells, however, contain a unique chromatin signature, termed bivalency, that is comprised of both H3K27me3 and H3K4me3 marks Disrupting core components of PRC2 causes a global reduction of H3K27me3 and misexpression of repressed genes, particularly bivalent genes Pcl mutant phenotype to that of the Polycomb mutant, have been found to modulate PRC2 activity Pcl1 is expressed minimally in ESCs, but promotes PRC2 function in adult tissues and male germ cells Beyond the core components of PRC2, accessory proteins such as Aebp2, Rbbp4/7, and Jarid2, influence PRC2 function Pcl3 shRNA-treated cells, we show that Pcl3 knockdown causes decreased H3K27me3 and Suz12 binding to the genome, indicating that Pcl3 regulates PRC2 binding at diverse target genes. Furthermore, Pcl3 localizes with Suz12 at a subset of PRC2 targets, including genes and microRNAs associated with differentiation and development. We also show that several Pcl3 Tudor domain residues are necessary for H3K27me3. Finally, we identify two GC-rich binding motifs that are enriched at Pcl3-dependent PRC2 targets, indicating that Pcl3 promotes PRC2 binding at CpG islands. Taken together, these results reveal that Pcl3 is an important regulator of PRC2 at a subset of target genes.Here, we show that mouse Pcl3 interacts with the core components of PRC2 and promotes complex function. By depleting Pcl3 in ESCs, we demonstrate that Pcl3 contributes to ESC self-renewal, but not differentiation, of the three germ layers. Using ChIP-seq of Suz12 (Suz12 gene trap (Gt/+Suz12) allele generated in a mouse ESC line to produce an allele that re-expresses Suz12 but that contains a loxP targeting site (Rev/+Suz12). Via a modified Floxin shuttle vector, we inserted an exon encoding amino acids 277–741 of Suz12 fused to a carboxy-terminal 6×His-3×Flag TAP tag. The resultant allele (Suz12TAP/+Suz12) expressed the full-length TAP-tagged Suz12 from the endogenous locus  tagged allele of Suz12  [54]. Inus locus . We measus locus . As expe amounts . Suz12Suild type .Suz12TAP/+Suz12 ESCs and identified co-purified proteins by mass spectrometry , mesoderm (T-brachyury), and endoderm (Hnf4) at least as well as control EBs, indicating that depletion of Pcl3 does not abrogate the ability of ESCs to differentiate to cell types of all three germ layers  and trimethylation of lysine 9 of histone H3 (H3K9me3) are involved in gene repression H3K27me3 .Pcl3 shRNA-associated phenotypes were due to decreased Pcl3 levels, we created a construct encoding a TAP-tagged Pcl3 not recognized by the Pcl3 shRNAs. Pcl3-TAP was detected by immunoblot and immunofluorescence and was able to bind core PRC2 components as assessed by co-immunoprecipitation, indicating that Pcl3-TAP functions similarly to wild type Pcl3 (Pcl3 expression in Pcl3-TAP-expressing ESCs was nearly 60% of endogenous levels as measured by qRT-PCR (Pcl3-TAP increased H3K27me3 levels in Pcl3 knockdown ESCs (To confirm that ype Pcl3 . Pcl3 ex qRT-PCR . Re-exprown ESCs . These dSuz12TAP/+Suz12 cells expressing either Pcl3 or control shRNA (GSE28325). Reads were aligned to the mouse reference genome  using bowtie, and only uniquely aligned reads were considered for further analysis  Knockdown of  density . Accordinockdown . ConsistPcl3 depletion  are unaffected by Pcl3  and repressive (H3K27me3) histone marks in ESCs epletion  [11], [6 mir-34a  [66], /L, where L is the genome size, and E[C] the expected correlation length of Skellam p-values across adjacent windows. Overlapping significant windows were concatenated, and we report the new p-values recomputed in the joined windows. More detailed descriptions are included in The data consisted of short reads from single-end 30-nucleotide sequencing. The short reads were aligned to the reference mouse genome (version MM9) by using bowtie with the parameters -n 2 -y –best -m 1 Pcl3 knockdown or scramble was computed in each bin. Gene density was computed by counting the number of RefSeq genes in each 1 Mbp bin. Pearson correlation was computed between gene density and log fold-change across bins. In The MM9 mouse genome was partitioned into disjoint 1 Mbp bins. The log fold-change of normalized counts in paired ChIP-seq data with To find miRNAs targeted by Suz12, we used a prior annotation of miRNA promoters th order Markov dependence We used a greedy algorithm called LeitMotif which models the background nucleotide distribution with the 9The ChIP-seq data are available from Gene Expression Omnibus under access number GSE28325.Figure S1Suz12TAP/+Suz12. The wild type endogenous allele is not pictured. Not to scale. (B) Staining of Suz12TAP/+Suz12 with anti-FlagM2 (red), which overlays with nuclei stained with DAPI (blue). Scale bar 10 µm. (C) Suz12 expression levels in wild type, Gt/+Suz12, Rev/+Suz12, and Suz12TAP/+Suz12 cell lines measured by qRT-PCR. Error bars indicate standard deviation. Asterisk denotes statistical significance of p<0.0001. Graph represents average expression of three different clones in three experiments assayed in quadruplet. (D) Wild type cells transfected with Pcl3-V5 and stained with anti-V5 (red). V5 staining overlays with nuclei stained with DAPI (blue). Scale bar 10 µm. Stainings were performed two times.Suz12-TAP and Pcl3-V5 localize to the nucleus. (A) Schematic of recombination events performed to create (TIF)Click here for additional data file.Figure S2Pcl3 shRNA clones assessed by immunoblot of lysates immunoprecipitated and probed with anti-Pcl3. For a positive control, Pcl3-TAP was immunoprecipitated and probed for FlagM2. Each lane represents a different clone. (B) Immunofluorescent staining of scramble and Pcl3-shRNA treated ESCs with Oct4, and Nanog. DAPI marks nuclei. Taken at 20×. (C) Scramble and Pcl3 shRNA ESCs stained for alkaline phosphatase activity. View of one well of a 6-well plate and magnified at 16×. (D) Pcl3 mRNA levels as measured by qRT-PCR in wild type and Pcl3 overexpressing cells. (E) Detection of Pcl3-TAP in wild type cells expressing Pcl3-TAP by immunoprecipitating and probing with anti-FlagM2. (F) Images of colonies stained with methylene blue formed from scramble and Pcl3 shRNA cells. Experiment was performed five times with two clones each of scramble and Pcl3 shRNA ESCs. (G) Pcl3 expression in scramble and Pcl3-shRNA derived teratomas. Representative of eight teratomas. (H) Teratomas expressing scramble and Pcl3 shRNA stained for the neural marker NeuN (green) and the neuron marker Tuj1 (red); muscle marker Actin (green); basal layer skin marker K14 (green). All images contain nuclear staining with DAPI (blue). Scale bar 20 µm. (I) Expression levels of Nestin, T-brachyury (T-bra), Hnf4, and Pcl3 which represent neuroectoderm, mesoderm, endoderm, and knockdown respectively, in scramble and Pcl3 shRNA expressing EBs. Experiment was performed with 3–6 clones. All immunoblots were performed 2–4 times, and α-tubulin was used as a loading control. All stainings were performed 2–3 times in 2–4 clones or teratoma samples. Expression analysis represents 3–4 experiments assayed in quadruplicate. Error bars indicate standard deviation and asterisks indicate statistical significance of p<0.005.Depletion of Pcl3 does not affect ESC differentiation to all three germ layers. (A) Protein levels of Pcl3 in scramble and (TIF)Click here for additional data file.Figure S3Pcl3 shRNA treated cells. Graph shows an approximate 80% decrease in H3K27me3 levels and represents ten experiments using 2–6 clones each. (B) qRT-PCR and (C) immunoblot indicating levels of Suz12 in cells treated with increasing amounts of Suz12 siRNA 48 hrs and 72 hrs post-transfection. β-actin was used as a loading control. (D) Transfection with Suz12 and Pcl3 siRNAs causes decreased expression of Suz12 and Pcl3 respectively as measured by qRT-PCR. (E) Pcl3-TAP localizes to the nucleus as assessed by immunofluorescent overlay of FlagM2 (green) and DAPI (blue). E-cadherin (red) marks the cell membrane. Scale bar 10 µm. Expression analysis was performed 2–4 times and assayed in quadruplet. Error bars indicate standard deviation, and asterisks indicate statistical significance of p<0.005. Staining was performed twice with three different clones.Depletion of Suz12 and Pcl3. (A) Quantification of H3K27me3 depletion in (TIF)Click here for additional data file.Figure S4Pcl3 knockdown causes the most significant depletion of Suz12 and H3K27me3 on chromosome 11. (A) Number of sequenced and aligned reads for ChIP-sequencing. (B) Partial sums of order statistics of binned read counts in the scramble and Pcl3 shRNA cells were computed, and their ratios are plotted for the Suz12 FlagM2 ChIP and H3K27me3 ChIP. The theoretical ratio of partial sums is almost linear when two samples are identically distributed, as shown by the red line. The blue vertical bar marks the quantile at which the ratio begins to deviate from linearity, and it effectively separates the background bins from ChIP-enriched bins. The ratio at this quantile was used to scale the Pcl3 ChIP-seq counts. (C) Boxplot comparing Suz12 binding sites to Marson et al. Pcl3 knockdown, while sites unaffected by Pcl3 knockdown are most often less significant Suz12 ChIP-seq binding sites. (E) Boxplot of log fold-changes in ChIP-seq read density within Suz12 binding sites. Suz12 binding and H3K27me3 in Pcl3 knockdown ESCs were decreased on all chromosomes. Chromosome 11 was the most significantly depleted . (F) Sites with decreased Suz12 binding upon Pcl3 knockdown tend to be devoid of E2f1 −53). (G) Suz12 ChIP-qRT-PCR indicating that at some sites Pcl3 depletion does not affect Suz12 binding. (H) ChIP-qRT-PCR for H3K4me3 in scramble and Pcl3 shRNA ESCs. Error bars indicate standard deviation, and asterisks indicate statistical significance of p<0.04. ChIP-qRT-PCRs was performed 2–3 times and assayed in quadruplicate.(TIF)Click here for additional data file.Figure S5−7 to 10−12. The boxplot shows the percentage of Pcl3 peaks at each p-value cutoff found to be overlapping with Suz12 binding sites that pass the p-value cutoffs 10−7, 10−8, 10−9, 10−10, 10−11, and 10−12.Pcl3 co-localizes with Suz12. (A) Aligning Suz12 and Pcl3 ChIP-seq reads at Pcl3 peak centers shows genome-wide co-localization of Pcl3 with Suz12. (B) To estimate the percentage overlap between Suz12 and Pcl3 binding sites, a sensitivity analysis was performed by varying the Skellam distribution p-value cutoff for calling peaks, ranging between 10(TIF)Click here for additional data file.Figure S6Pcl3 knockdown as measured by qRT-PCR and microarray. Cells were collected following selection for Pcl3 depletion, approximately 3–4 weeks. Microarray and qRT-PCR were performed with 2–6 clones each for control and Pcl3 knockdown ESCs. qRT-PCR analysis represents 2–3 different experiments performed with multiple clones and assayed in quadruplicate. For both graphs, error bars indicate standard deviation and data represents statistical significance of p<0.05. (A) Graph represents expression levels in ESCs that have not been pre-plated to remove differentiated cells. (B) Genes that show depleted Suz12 binding following Pcl3 knockdown and that are misregulated by microarray analysis. Cells in (C) were pre-plated to remove differentiated cells and then expression was assessed. (D) Binding profile of Suz12 and Pcl3 at the Suz12 locus. Turquoise puncta are background and not statistically significant.Pcl3 misregulates a subset of genes.  Relative expression of genes that either increased or decreased upon (TIF)Click here for additional data file.Table S1Pcl3 knockdown by microarray. Microarray data indicating fold changes in gene expression in ESCs expressing Pcl3 shRNA as compared to scramble shRNA ESCs. Comparison included six control samples and six Pcl3 shRNA clones.Gene expression changes upon (XLS)Click here for additional data file.Table S2Pcl3 shRNA ESCs by microarray. Microarray data indicating fold changes in gene expression in pre-plated ESCs expressing Pcl3 shRNA as compared to scramble shRNA ESCs. Comparison included two scramble clones and two Pcl3 shRNA clones.Gene expression changes in pre-plated (XLS)Click here for additional data file.Text S1Supplemental methods. Detailed description of the microarray and ChIP-sequencing methods as well as a list of the antibodies and primers used.(DOC)Click here for additional data file.


Nucleosomes can block access to transcription factors. Thus the precise localization of nucleosomes relative to transcription start sites and other factor binding sites is expected to be a critical component of transcriptional regulation. Recently developed microarray approaches have allowed the rapid mapping of nucleosome positions over hundreds of kilobases (kb) of human genomic DNA, although these approaches have not yet been widely used to measure chromatin changes associated with changes in transcription. Here, we use custom tiling microarrays to reveal changes in nucleosome positions and abundance that occur when hormone-bound glucocorticoid receptor (GR) binds to sites near target gene promoters in human osteosarcoma cells. The most striking change is an increase in measured nucleosome occupancy at sites spanning ∼1 kb upstream and downstream of transcription start sites, which occurs one hour after addition of hormone, but is lost at 4 hours. Unexpectedly, this increase was seen both on GR-regulated and GR-non-regulated genes. In addition, the human SWI/SNF chromatin remodeling factor (a GR co-activator) was found to be important for increased occupancy upon hormone treatment and also for low nucleosome occupancy without hormone. Most surprisingly, similar increases in nucleosome occupancy were also seen on both regulated and non-regulated promoters during differentiation of human myeloid leukemia cells and upon activation of human CD4+ T-cells. These results indicate that dramatic changes in chromatin structure over ∼2 kb of human promoters may occur genomewide and in response to a variety of stimuli, and suggest novel models for transcriptional regulation. C. Elegans to man in vivo) were much more frequently found in linker regions than in DNA covered by nucleosomes, and since start sites of active genes were frequently devoid of nucleosomes Nucleosomes can inhibit transcription by blocking the access of transcription factors to their sites on DNA. Since the linker DNA between nucleosomes (which averages ∼60 bp in length) is far more accessible than nucleosome bound DNA, the precise location of nucleosomes on DNA will functionally control transcription factor binding. Recent studies have established genomic microarray and multiparallel sequencing approaches for mapping endogenous nucleosome positions that can be orders of magnitude more efficient than standard approaches, such as indirect end labeling MMTV) promoter nucleosome, Nuc B, which occupies the promoter from ∼−250 to −100 and covers GRE elements as well as an essential NF1 binding site , as per MMTV-luciferase transgene MMTV-luc construct (SGK1 (GR-activated), GEM (GR-repressed) and CD44 (GR-unregulated)).To examine chromatin changes associated with transcriptional regulation by GR, we used UL3 cells, derivatives of U2-OS human osteosarcoma cells, which stably express GR and also contain a single integrated copy of a MMTV and MYC promoters are presented in MMTV in MDA-kb2 cells The nucleosome occupancy profiles for the We found that Dex addition eliminated the positioned nucleosome over MMTV Nuc B after 1 hr. . We usedMYC  into discrete nucleosome peaks at −900, −800 and −650 after dex addition for 1 hr , studies have shown that BRG1 is sufficient to coactivate through GR HSD11B2, . OccasioMMTV Nuc B, Nuc D, or Nuc F sequences  was usually intermediate between the -Dex and +Dex 1 hr values that were observed for cells transfected with control siRNA oligos . This suWe classified GR-regulated promoters into two groups: five which had well-mapped GR binding sites (GRBSes) in their proximal promoters  and four which had no promoter-proximal GRBSes but had well-mapped GRBSes located more than 1 kb upstream of TSSes  To measure of the effect of GR binding on immediately-surrounding chromatin, we plotted nucleosome density relative to all well-mapped GRBSes in two groups: promoter-proximal (within 500 bp of TSSes) and promoter-distal (between −20000 and −500 bp from TSSes), with some genes having GRBSes in more than one group + T-cells with or without activation by addition of anti-CD3 and anti-CD28 antibodies for 18 hours The observation of greatly increased promoter nucleosome occupancy in UL3 cells in response to dex, made us wonder whether similar chromatin changes might occur in response to other stimuli. Furthermore, the fact that similar effects were seen at genes which were regulated or not regulated by dex suggested that these changes in nucleosome occupancy might be occurring on most Pol II genes, genomewide . To address both of these issues, we examined the data from a recent study which used intensive multiparallel sequencing to compare genomewide nucleosome positions in CD4MYC , and CD4+ T-cells activated by addition of anti-CD3 and anti-CD28 antibodies  increases in response to these stimuli, perhaps as the result of new deposition of nucleosomes using S-phase independent chaperones. Intriguingly, one recent study revealed that, unlike the case for yeast promoters, human Pol II promoters have sequence characteristics which are expected to promote higher than average nucleosome occupancy It must be emphasized, however, that apparently increased occupancy could also be caused by other effects that might alter the abundance of nucleosomal MNase fragments from promoter regions in our samples. For instance, apparently low occupancy could result if association with nuclear matrix proteins prevented the release of mononucleosomes after MNase digestion, or if association with heterochromatin proteins, HMGs or variant linker histones blocked digestion between adjacent promoter nucleosomes  treatment, cells were split into phenol red free media 48 hours before harvest, dexamethasone (1000x stock in ethanol) was added to a final concentration of 10 nM. Control cells received ethanol alone. For BRG1 knock down experiments, cells were plated at a density of 2.95×104/cm2 and transfected 24 hrs later with 1.95 pmol/cm2 of BRG1 dsRNA (Santa Cruz) or control dsRNA(Santa Cruz) using oligofectamine (Invitrogen), essentially as per UL3 cells , with the inclusion of 1X Protease Inhibitor Cocktail (Pierce) in the lysis buffer. Luminescence units were normalized to protein concentration as measured by BCA protein assay (Bio-Rad). For Western blotting, PBS-washed cells were resuspended in RIPA lysis buffer with 1X Protease Inhibitor Cocktail (Pierce), lysed by freeze/thawing, and 10 µg of each sample resolved by 8% SDS-PAGE before transfer to PVDF membrane. Anti BRG1 rabbit polyclonal 2 plate surface area in ice cold lysis buffer , and chromatin collected by microcentrifugation at 1600 RPM for 15 min. at 4°C. Chromatin pellets were resuspended in 200 µL per 107 cells in storage buffer , flash frozen in liquid N2, and stored at −80°C until use. Concentration of DNA in chromatin samples was measured by dilution into 2 M NaCl, and spectrophotometry at 260 nm. 150 µg of DNA was incubated at 30°C with 212 U MNase in a volume of 100 µL reaction buffer  for 4 min. The reactions were stopped by adding 100 µL stopping buffer . 15 µL of proteinase K  was added and samples were incubated at 60°C for 2 hrs. Samples were extracted with an equal volume of phenol, treated for 1.5 hrs at 37°C with 18 µg RNAse (Qiagen), phenol extracted again, ethanol precipitated in the presence of 40 µg glycogen carrier, and separated by 4% PAGE. Bands corresponding with mononucleosome sized-fragments (∼146 bp) and altosome-sized fragments (∼200 bp) were excised from the gel and eluted in TE at 4°C with continuous shaking overnight. There were no evident differences in MNase digestion level or isolated band sizes for any sample or condition , with the accession number GSE25281.Mononucleosomal MNase footprint fragments from UL3 or HL60 cells  were isolated as described above and used to probe custom Nimblegen tiling arrays covering 25 gene loci, with a resolution of ∼10 bp and bearing two forward and two reverse oligos for each sequence see . The medQuantile normalized nucleosome/bare ratios for all oligonucleotides whose centers fell within 20 kb of a list of referenced genomic positions (TSSes or GRBSes) were placed into 20 bp bins, based on their position and orientation relative to the reference position, and averaged. For ease of representation, the data was smoothed using a 5-bin sliding window. To determine whether the difference between +dex 1 hr and no dex values for the 2 kb surrounding TSSes in binned data was significantly greater than for other 2 kb regions, average standard deviation for all 2 kb regions between −19 and +19 kb was estimated using the repeat dex 1 hr and no dex samples, and this was used in t-Tests comparing average (dex1 hr – nodex) values for the −1 to +1 kb region to all 18 other regions. Nucleosome end reads for activated and control CD4+ T-cells  all primer sequences used for MNase-footprint PCR, 2) additional details on methods used for normalization and smoothing of microarray data, estimation of nucleosome occupancy, and statistical analyses, 3) additional details on tests which argue against possible systematic biases in the data, and 4) references for papers cited in this file and/or in (DOC)Click here for additional data file.Figure S1MNase digestion differences cannot account for dex effects on chromatin. (A & B) Ethidium bromide stain of gels used to isolate mononucleosomal & altosomal MNase fragments. BP positions (based on NEB 50 bp and PhiX174/HaeIII ladders) are indicated on the left. (C) Gel A after isolation of mono, inter and dinucleosomal bands. (D) Quantitation of gel lanes with ImageQuant shows very little variability in MNase digestion. Note that there was little variability in MNase digestion between samples, which were all digested to >60% mononucleosome level. Furthermore, the modest differences in MNase digestion that do exist do not correlate with increased/decreased nucleosome occupancy . See also Additional Method in (TIF)Click here for additional data file.Figure S2Analytical methods for nucleosome mapping by microarray. (A) Example of F1, F2, R1 and R2 raw data, showing raw cy3 signal from MNase digested bare DNA at HSD11B2. Note how F1 and F2 track together, but often differ from R1 and R2. (B) Curves showing median-normalized nucleosome/bare signal versus percentile in dataset for each UL3 cell experiment. The average curve (black line) was used as the standard for quantile normalization. (C) Comparison of simple median normalization (top) versus quantile normalization (bottom) for the two independent –Dex samples at the GAPDH promoter. In both cases the data was smoothed as described in (D). (D) Example of deviation weighted exponential smoothing. Note how high peaks in the unsmoothed quantile-normalized data (brown line) correspond to regions where the standard error of the median between F1, F2, R1 and R2 for that position are high. Making use of this information in the smoothing function (red line) removes noise peaks that are not effectively removed using normal exponential smoothing (dotted orange line).(TIF)Click here for additional data file.Figure S3Increased nucleosome occupancy only occurs near TSSes and is not due to hybridization bias. We wished to know whether the increase of nucleosome occupancy surrounding transcription start sites (TSSes) with 1hr Dex treatment of UL3s and with DMSO treatment of HL60s was specific for promoter regions or was also true for other regions of low nucleosome density. In A & C, we tested for this type of systematic bias by calculating the “Dex effect” for each oligo on the array, where Dex effect =   -. Similarly, for HL60 cells, we calculated the DMSO effect: (nucl/bare +DMSO) – (nucl/bare control). We then split the data into two groups, “proximal” for all oligos w/in 2kb of a TSS and “distal” for all oligos more than 2kb away from a TSS. For each nucl/bare ratio under the control condition (-Dex or –DMSO) on the x axis, we then plotted the average Dex or DMSO effect on the y axis. If, for example, there was a hybridization artifact on our +DMSO array that gave an aberrantly high signal at all low-occupancy regions, this would result in a high DMSO effect at nucl/bare ratios less than 1 for both proximal and distal oligos. Instead, increased nucleosome occupancy +Dex1hr or +DMSO was only seen for proximal/promoter regions (blue lines). Furthermore, this was true for oligos showing both low and high occupancy under control conditions . In B & D, we plotted the fraction of all oligos showing any given nucl/bare ratio (y) versus nucl/bare ratio (x), comparing proximal and distal groups for each treatment condition. Note how the treatment (+DMSO or +Dex 1hr) and control (-DMSO or –Dex) curves for the distal oligos are precisely superimposable (compare red and green curves), indicating no systematic effect of treatment on the distribution of nucleosome occupancies in these regions. By contrast, for proximal oligos, the no treatment curves (blue) are shifted left relative to the treatment curves (yellow), reflecting increased nucleosome occupancy after treatment. Note, A & B show the average curves resulting from the all pairwise combinations of the repeated Nodex and Dex1hr samples. Similar results were also seen for each individual pair of Nodex and Dex1hr arrays.(TIF)Click here for additional data file.Figure S4GR activated genes: PCK1 & SGK1. Promoter nucleosome density of the GR activated genes PCK1/PEPCK (A) & (B), and SGK1 (C) & (D). (TIF)Click here for additional data file.Figure S5GR activated genes: SLC19A2 & SDPR. Promoter nucleosome density of the GR activated genes SLC19A2 (A) & (B), and SDPR (C) & (D), as described in (TIF)Click here for additional data file.Figure S6GR activated genes: ZBTB16 & TSC22D3. Promoter nucleosome density of the GR activated genes ZBTB16/PLZF (A) & (B), and GILZ/TSC22D3 (C) & (D), as described in (TIF)Click here for additional data file.Figure S7GR activated gene CYP3A4 and control gene GAPDH. Promoter nucleosome density for the GR activated gene CYP3A4 (A) & (B) and the GR- and hSWI/SNF-unregulated gene GAPDH (C) & (D), as described in (TIF)Click here for additional data file.Figure S8GR-activated MMTV promoter. (A) Same as Genome Res 17:928-39), and may also be consistent with indirect end-labeling results which sometimes showed multiple local peaks of nuclease sensitivity in the gaps between Nucs C through Nucs E (e.g. Trotter & Archer (2004) Mol Cell Biol 24:3347-58, Truss et al. (1995) EMBO J 14:1737-51, and Richard-Foy & Hager (1987) EMBO J 6:2321-28). Importantly, indirect end-labeling identifies positioned nucleosomes by the appearance of relatively strong MNase digestion sites flanking ∼146 bp regions of relative protection. These conditions can occur regardless of the fraction of gene copies that bear a positioned nucleosome. Thus, indirect end labeling is expected to provide information about nucleosome edges but will not provide accurate information about relative nucleosomal occupancy. For instance, while the c-myc promoter Nucleosomes 7 and 8 (identified by indirect end labeling) did appear as discrete nucleosome position peaks in our analysis, the relative weakness of these peaks suggests that these positioned nucleosomes are present on only a subset of gene copies (B) Effects of BRG1 knock down on MMTV promoter chromatin. Dotted lines show nucleosome density maps -Dex & +Dex 1hr from control cells (from A) and solid lines show –Dex and +Dex 1hr results from BRG1 knock down cells. Note that NucB decreases +Dex 1hr in the BRG1 knock down as well as control cells . However, the ability of hSWI/SNF to activate MMTV transcription does not perfectly correlate with its ability to increase NucB accessibility. For instance, mutations in the BAF60a hSWI/SNF subunit which reduce GR-hSWI/SNF interactions decreased MMTV activation by Dex by 10-fold, but only decreased NucB accessibility by two-fold  Mol Cell Biol 23:6210-20). This suggests that the low levels of BRG1 that remain after siRNA transfection in our studies, or the generally minor alternative ATPase hBRM, may be sufficient to disrupt NucB but not sufficient to make other chromatin changes at MMTV which are required for maximal transactivation.e copies . (B) Eff(TIF)Click here for additional data file.Figure S9GR repressed genes: GEM & PLK2. Promoter nucleosome density of the GR repressed genes GEM (A) & (B), and PLK2/SNK (C) & (D), as described in (TIF)Click here for additional data file.Figure S10GR repressed genes: POMC & MYC. Promoter nucleosome density of the GR repressed genes POMC (A) & (B), and MYC (C), as described in (TIF)Click here for additional data file.Figure S11Non-GR regulated cell cycle control genes: CCNA1 & CCND1. Promoter nucleosome density of the GR-unregulated genes CCNA1/cyclin A1 (A) & (B), and CCND1/cyclin D1 (C) & (D), as described in (TIF)Click here for additional data file.Figure S12Non-GR regulated cell cycle control genes: CCNE1 & CDK1. Promoter nucleosome density of the GR unregulated genes CCNE1/cyclin E1 (A) & (B), and CDK1/CDC2 (C) & (D), as described in (TIF)Click here for additional data file.Figure S13Non-GR regulated cell cycle control genes: CDKN1A & E2F1. Promoter nucleosome density of the GR unregulated genes CDKN1A/p21 (A) & (B), and E2F1 (C) & (D), as described in (TIF)Click here for additional data file.Figure S14Non-GR regulated, hSWI/SNF-regulated genes: CSF1 & CD44. Promoter nucleosome density of the GR-independent, hSWI/SNF activated genes CSF1 (A) & (B), and CD44 (C) & (D), as described in (TIF)Click here for additional data file.Figure S15Loss of Nuc B mononucleosomes is not due to altered dinucleosome formation by hSWI/SNF. (A) Mononucleosome levels at MMTV NucF, NucD and NucB at the indicated times, as measured by MNase footprint PCR (repeated from B) Altered dinucleosomes formed by hSWI/SNF  have an unusual ∼200 bp MNase footprint size, which allowed us to assay altosome levels on MMTV by subjecting ∼200 bp MNase-resistant chromatin fragments to MNase footprint PCR. The results showed that altosome levels at Nuc B did not rise significantly after 20 mins or 1 hour of dex treatment, and actually decreased at 4 hrs, arguing against the hypothesis that NucB is converted into altosomes. Interestingly, an increase in altosome formation was observed at Nuc F and D at 1 hr., which moved towards baseline levels after 4 hr. Because the footprint size of altosomes cannot arise from any arrangement of normal nucleosomes, and because no other remodeling complex is known to form altosomes, this observation suggests that hSWI/SNF affects nucleosomes up to ∼1 kb beyond its expected sites of recruitment (the GRE elements located within Nuc B). A return of altosomes to baseline levels at 4 hr is consistent with altosomes' innate tendency to revert to normal nucleosomes. (C) Prior studies indicate that, the partial agonist RU486 induces GR binding to MMTV and recruitment of hSWI/SNF, but does not support the recruitment of other coactivators, including pCIP, SRC1, and p300  J Biol Chem, 275:17771-7), allowing the examination of hSWI/SNF effects independent of other GR coactivators. Consistent with these studies, we find that RU486 decreases the amount of MMTV reporter induction compared to dexamethasone . When mononucleosome positions were analyzed by MNase footprint PCR for UL3 cells treated with RU486, we found that reduction of Nuc B occupancy was apparent after 20 min. and was increased at 1 and at 4 hrs, similar to dex treatment. (D) Altosome levels at upstream nucleosomes D and F also peaked at 1 hr. and declined by 4 hr. Intriguingly, altosomes at Nuc B were elevated throughout the RU486 time course, unlike dexamethasone treatment. The data may suggest that altosomes are formed at Nuc B by hSWI/SNF, but that the other coactivators recruited by Dex-bound GR, or the higher resulting transcription rate, cause them to be removed. In addition, the observation that altosome levels remain high after 4hrs of RU486 treatment only at NucB implies either continued localized hSWI/SNF action or localized stabilization of this product. Accumulation of altosomes at NucB may help to explain why a RU486 treatment was less effective than Dex at inducing restriction enzyme accessibility at NucB after 1 hr (Mymryk JS & Archer TK (1995) Mol Endocrinol 9:1825-1834).(TIF)Click here for additional data file.Figure S16Dex treatment does not significantly alter nucleosome density at transcription termination sites. As for (TIF)Click here for additional data file.Figure S17Remodeled chromatin extends further downstream of distal GR binding sites. (A) Average nucleosome occupancy mapped relative to GR binding sites that map to within 500 bp upstream of the TSS of their regulated genes . (B) Average nucleosome occupancy relative to GR binding sites greater than 1kb upstream of regulated TSSes . See (TIF)Click here for additional data file.Figure S18Changes in nucleosome occupancy relative to GR binding sites in human A549 lung cells. As for A) all six GR binding sites within 500 bp of TSSes. (B) all five GR binding sites between 1 and 2.5 kb of TSSes. (C) all 37 GR binding sites covered by our array and at least 6kb from TSSes. Dotted lines show the results from repeat samples. Solid lines show the average result.(TIF)Click here for additional data file.Figure S19Increased nucleosome occupancy with CD4+ T-cell activation is not due to general bias.A) We examined the nucleosome count data from Schones et al. using a similar metric to that used for our microarray data in B-D) The Schones et al data consists of more than 50 Illumina sequencing lanes per condition that appear to have been derived from several distinct samples. To determine whether variability in sample preparation could have caused the observed increased occupancy at TSSes with T-cell activation, we compared average nucleosome density around TSSes for the whole data set, resting (dotted lines) or activated (solid lines) to that for the first three and last three lanes for each dataset . The results for TSSes of genes upregulated (B), downregulated (C), or unaffected (D) by T-cell activation showed that, while some variability is evident between samples, this cannot account for the great increase in nucleosome density around all three classes of TSSes with T-cell activation.(TIF)Click here for additional data file.Table S1Genes covered by the nucleosome mapping Nimblegen arrays. Blue: genes activated by GR and Dex. Pink: genes repressed by GR and Dex. Grey: cell cycle control genes that are not regulated by GR and Dex in U20S cells. For citations given in the table, see Additional Methods, in (DOC)Click here for additional data file.Table S2Genomic locations and accession numbers for genes on the array. All coordinates are relative to the HG18 human genome build. TSS: transcription start site.(DOC)Click here for additional data file.