Sensitive and High Throughput ChIP Assays Enable Characterization of Chromatin State.
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Expression of eukaryotic genes during development requires complex spatial-temporal regulation. This complex regulation is often achieved through the coordinated interaction of transcription regulatory elements in the promoters of the target genes. The identification and mapping of regulatory elements in genome scale is crucial to understand how gene expression is regulated. Chromatin immunoprecipitation is a standard method for assessing the occupancy of DNA binding proteins in vivo in their native chromatin context using antibodies. However, standard chromatin immunoprecipitation procedure is time consuming, labor intensive and not suited for analyzing many samples simultaneously.
Recently, we have developed a simple ChIP protocol that requires fewer steps and less hands-on time. This protocol is compatible with both 96-well plate and single tube formats, and enables higher sensitivity and more reliable performance, as compared to conventional approaches.
We have successfully used this protocol to map various clinically relevant chromatin marks and controls across several cell types to quantitatively measure chromatin states. This analysis included a variety of marks corresponding to repressed, poised and active promoters, strong and weak enhancers, putative insulators, transcribed regions, as well as large-scale repressed and inactive domains. This study demonstrates the utility of this approach for the characterization of model cellular systems in perturbation studies with chemical probes.Keywords:
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The fundamental building block of chromatin, the nucleosome, occupies 150 bp of DNA in a spaced arrangement that is a primary determinant in regulation of the genome. The nucleosomal organization of some regions of the human genome has been described, but mapping of these regions has been limited to a few kilobases. We have explored two independent and complementary methods for the high-throughput analysis of mammalian chromatin structure. Through adaptations to a protocol used to map yeast chromatin structure, we determined sites of nucleosomal protection over large regions of the mammalian genome using a tiling microarray. By modifying classical primer extension methods, we localized specific internucleosomally cleaved mammalian genomic sequences using a capillary electrophoresis sequencer in a manner that allows high-throughput nucleotide-resolution characterization of nucleosome protection patterns. We developed algorithms for the automated and unbiased analysis of the resulting data, a necessary step toward large-scale analysis. We validated these assays using the known positions of nucleosomes on the mouse mammary tumor virus LTR, and additionally, we characterized the previously unreported chromatin structure of the LCMT2 gene. These results demonstrate the effectiveness of the combined methods for reliable analysis of mammalian chromatin structure in a high-throughput manner.
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Article12 January 2015Open Access A high-throughput ChIP-Seq for large-scale chromatin studies Christophe D Chabbert Christophe D Chabbert European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Sophie H Adjalley Sophie H Adjalley European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Bernd Klaus Bernd Klaus European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Emilie S Fritsch Emilie S Fritsch European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Ishaan Gupta Ishaan Gupta European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Vicent Pelechano Corresponding Author Vicent Pelechano European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Lars M Steinmetz Corresponding Author Lars M Steinmetz European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Stanford Genome Technology Center, Palo Alto, CA, USA Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Christophe D Chabbert Christophe D Chabbert European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Sophie H Adjalley Sophie H Adjalley European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Bernd Klaus Bernd Klaus European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Emilie S Fritsch Emilie S Fritsch European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Ishaan Gupta Ishaan Gupta European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Vicent Pelechano Corresponding Author Vicent Pelechano European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Search for more papers by this author Lars M Steinmetz Corresponding Author Lars M Steinmetz European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany Stanford Genome Technology Center, Palo Alto, CA, USA Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Author Information Christophe D Chabbert1,‡, Sophie H Adjalley1,‡, Bernd Klaus1, Emilie S Fritsch1, Ishaan Gupta1, Vicent Pelechano 1 and Lars M Steinmetz 1,2,3 1European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany 2Stanford Genome Technology Center, Palo Alto, CA, USA 3Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA ‡These authors contributed equally to this work *Corresponding author. Tel: +49 6221 3878542; Fax: +49 6221 387518; E-mail: [email protected] *Corresponding author. Tel: +49 6221 3878389; Fax: +49 6221 387518; E-mail: [email protected] Molecular Systems Biology (2015)11:777https://doi.org/10.15252/msb.20145776 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract We present a modified approach of chromatin immuno-precipitation followed by sequencing (ChIP-Seq), which relies on the direct ligation of molecular barcodes to chromatin fragments, thereby permitting experimental scale-up. With Bar-ChIP now enabling the concurrent profiling of multiple DNA–protein interactions, we report the simultaneous generation of 90 ChIP-Seq datasets without any robotic instrumentation. We demonstrate that application of Bar-ChIP to a panel of Saccharomyces cerevisiae chromatin-associated mutants provides a rapid and accurate genome-wide overview of their chromatin status. Additionally, we validate the utility of this technology to derive novel biological insights by identifying a role for the Rpd3S complex in maintaining H3K14 hypo-acetylation in gene bodies. We also report an association between the presence of intragenic H3K4 tri-methylation and the emergence of cryptic transcription in a Set2 mutant. Finally, we uncover a crosstalk between H3K14 acetylation and H3K4 methylation in this mutant. These results show that Bar-ChIP enables biological discovery through rapid chromatin profiling at single-nucleosome resolution for various conditions and protein modifications at once. Synopsis A new approach provides a rapid and accurate genome-wide overview of the chromatin status of multiple yeast chromatin-associated mutants at once. The simultaneous profiling of epigenetic marks in the mutants is achieved by multiplex immuno-precipitation of barcoded chromatin samples. Bar-ChIP is based on the immuno-precipitation of barcoded chromatin and permits sample multiplexing, thereby increasing the throughput of ChIP-Seq experiments. Application of the method to yeast chromatin-associated mutants enabled the concurrent generation of 90 ChIP-Seq datasets without the need for robotic instrumentation. The rapid chromatin profiling of the mutants at single-nucleosome resolution uncovered an association between intragenic H3K4 tri-methylation and cryptic transcription in set2∆. Introduction While ChIP-Seq and ChIP-on-chip remain the standard methods for global detection of binding sites associated with protein factors and histone chemical modifications, these approaches only allow profiling of a single protein modification per experiment. Given the vast number of post-translational modifications (PTM) implicated in biological processes (Kouzarides, 2007; Li et al, 2007a; Misteli & Soutoglou, 2009; Ransom et al, 2010; MacAlpine & Almouzni, 2013), their potential combinations on multiple amino acid residues and the possibility of interactions between these modifications, investigating chromatin biology requires generating ChIP-Seq or ChIP-on-chip data for numerous marks and across multiple physiological conditions. Therefore, studies of histone PTM dynamics necessitate a considerable number of individual experiments. For instance, a recent study, reporting the previously underestimated role of histone PTM in yeast stress response, required a total of thirty ChIP-on-chip assays to profile five histone marks across multiple points of a time course (Weiner et al, 2012). Similarly, determining histone modification patterns associated with cellular states in human cells often requires several hundred ChIP-Seq experiments (ENCODE Project Consortium, ). High-throughput ChIP-Seq approaches based on the use of robotic tools have been developed (Garber et al, 2012; Aldridge et al, 2013) but remain restricted to a discrete number of laboratories that have access to the required instrumentation. Therefore, generation of comparative data from large-scale, genome-wide ChIP-Seq experiments remains cumbersome and costly to most laboratories. Here, we propose to implement a DNA barcoding step prior to chromatin immuno-precipitation to increase the speed and performance of ChIP-Seq experiments. The concept of DNA barcoding was recently applied to chromatin biology to investigate the biochemical mechanisms underlying the activity of histone PTM enzymes albeit in an in vitro context (Nguyen et al, 2014). Our barcoded high-throughput ChIP-Seq (Bar-ChIP) method relies on the direct barcoding of mono-nucleosomes derived from the isolation of yeast cell chromatin. As a proof of concept, we applied Bar-ChIP to the study of histone PTM in several Saccharomyces cerevisiae chromatin modifier mutants, which were profiled in parallel for five distinct histone modifications, thereby compressing 90 individual ChIP-Seq experiments into five. We show that barcoding and multiplexing of chromatin samples prior to immuno-precipitation greatly reduces the workload needed to perform ChIP-Seq experiments and permits a direct comparison between biological samples that are interrogated for specific histone PTM. We demonstrate that this method enables the faithful capture of histone PTM in S. cerevisiae in a genome-wide manner and confirm that the histone deacetylase complex, Rpd3S, upon activation by the methyl-transferase Set2, maintains a low level of histone acetylation in gene bodies. We show that Rpd3S activity is not restricted to histone H4 and lysine 9 and 56 of H3 (Venkatesh et al, 2012), but also targets H3K14. In addition, we report for the first time differential methylation on lysine 4 of H3 in a set2∆ mutant and demonstrate that intragenic H3K4me3 associates with the emergence of cryptic transcription. Validation of the newly discovered histone PTM trends underscores the power of Bar-ChIP to quickly and accurately screen for histone PTM patterns in multiple biological samples at once, which would not be feasible with traditional ChIP-Seq approaches. Results Bar-ChIP captures genome-wide distribution of H3K4me3 Bar-ChIP relies on the direct ligation of DNA molecular barcodes to fragmented chromatin using an adapted version of the classical Illumina DNA library preparation protocol (Fig 1). Barcoding of fragmented chromatin occurs prior to the immuno-precipitation step, hereby allowing for the pooling of numerous independently barcoded chromatin samples that will then be simultaneously subjected to the same assay. Therefore, for every protein or modification of interest, a single immuno-precipitation may be possible independently of the number of examined biological samples. In the current study, micrococcal nuclease (MNAse) was used to digest yeast crosslinked chromatin and isolate mono-nucleosome fractions to address the genome-wide distribution of histone post-translational modifications (PTM). Figure 1. Comparative representation of the ChIP-Seq and Bar-ChIP workflowsYeast cultures are crosslinked using formaldehyde. Chromatin is then extracted and fragmented using micrococcal nuclease (MNase) digestion. In a classical ChIP-Seq protocol, MNase-treated chromatin is directly immuno-precipitated with an antibody against the protein modification or factor of interest. Recovered DNA is then barcoded and used to generate an amplified DNA library ready for paired-end sequencing. In the Bar-ChIP protocol, fragmented chromatin is barcoded through ligation of molecular barcodes prior to immuno-precipitation. DNA recovered from the IP can directly be amplified by PCR using Illumina primers and deep-sequenced. The presence of the barcoding step early in the workflow allows for multiplexing of IP assays. Download figure Download PowerPoint To validate our approach, we first evaluated the impact of chromatin barcoding on the recovery of genome-wide patterns of H3K4me3. This mark was selected because of its prominent and well-characterized distribution on promoter regions (Shilatifard, 2008) and its association with active transcription (Pokholok et al, 2005; Hon et al, 2009). H3K4me3 was profiled for three independent biological replicates of S. cerevisiae cultures grown in rich media (YPD) using both Bar-ChIP and classical ChIP-Seq methods. Both barcoded and non-barcoded fractions, containing mono-, di- and tri-nucleosomes, were then subjected to immuno-precipitation with an antibody specific for the histone mark (Fig 1). IP-DNA libraries derived from both protocols were PCR-amplified and deep-sequenced on an Illumina HiSeq 2000 instrument using paired-end technology. Input libraries were systematically included to control for potential biases in local chromatin solubility, enzyme accessibility and/or PCR amplification. No size-specific selection of mono-nucleosomal fragments was performed (Henikoff et al, 2011); however, only pairs of unambiguously mapped reads stemming from mono-nucleosomal particles were considered for downstream analysis (Supplementary Fig S1). Interestingly, mono-nucleosomes deriving from the fragments generated following the Bar-ChIP protocol and recovered after the IP were slightly longer (Supplementary Fig S1). A very good reproducibility was observed between biological replicates with a mean Spearman's correlation coefficient of 0.88 ± 0.04 for both ChIP- and Bar-ChIP-Seq (Supplementary Fig S2). Additionally, a high correlation between the two techniques was obtained for each IP DNA and input DNA, with a mean Spearman's correlation coefficient of 0.79 ± 0.07 (Fig 2A; Supplementary Fig S3). Figure 2. Direct comparison of the Bar-ChIP and ChIP-Seq methods Scatterplot illustrating the correlation between reads counts in the Bar-ChIP and ChIP-Seq datasets obtained for the same biological sample. 100 bp bins were used to compute these counts. Snapshot of coverage tracks of H3K4me3 around the GAL10 locus obtained using ChIP-Seq and Bar-ChIP protocols. Tracks corresponding to the associated chromatin inputs are also displayed. For a highly expressed gene such as CHS3 (black box (i)), H3K4me3 signal is much stronger at the 5′ end of the gene as compared to the input signal, which spreads along the entire gene body. On the other hand, while input signals display a rather homogeneous nucleosome spread on the GAL1 and GAL10 genes, H3K4me3 immuno-precipitation clearly shows an enrichment of the mark to the 3′ end of the gene (black box (ii)). This corresponds to the presence of actively transcribed antisense non-coding RNAs over the GAL10 and GAL1 genes that are responsible for the inhibition of GAL expression in dextrose-rich medium. Bedgraph format displaying the number of counts per base pair. TSS plot representing nucleosome occupancy around the TSS of annotated genes as observed in the chromatin input of both ChIP-Seq and Bar-ChIP methods. Occupancy levels are plotted as a function of the distance to the TSS. Midpoint of a nucleosome was approximated as being the center of the genomic locus intercepted by a read pair. Nucleosome counts were determined at each position around annotated TSS and estimated across all genes. Resulting counts were divided by the total number of observed nucleosomes to provide the genome-wide distribution of nucleosomes and occupancy at each position around the TSS. TSS plot representing H3K4me3 occupancy (normalized by chromatin input) around the TSS of annotated genes as observed in the ChIP-Seq and Bar-ChIP datasets. Evaluation of H3K4me3 counts with statistically significant difference between ChIP-Seq and Bar-ChIP. The mean normalized counts across all biological replicates are plotted as a function of the distance to the annotated TSS. Differential counts with statistical significance are indicated in red. Significant differences were called with the DESeq2 package after computing local FDR values based on the DESeq2 P-values. Only the positions with a log2 fold change greater than 0.05 and a local FDR smaller than 0.2 were considered significant. Download figure Download PowerPoint Signals for the presence of the H3K4me3 mark were equally well recovered by the two methods as confirmed by the high correlation obtained for regions with the PTM enrichment (Supplementary Fig S4) and by manual inspection of the coverage tracks for selected loci. For instance, the CHS3 promoter is enriched in H3K4me3 in comparison to total nucleosome occupancy (Fig 2B). Similarly, the GAL10 locus harbored high H3K4me3 levels on its 3′ end, consistent with active transcription of an antisense non-coding RNA that acts in GAL10 repression during growth in dextrose-containing medium (Houseley et al, 2008) (Fig 2B). These observations underline the comparable acquisition of biological information with either technique. To compare the two ChIP approaches at a genome-wide level, the distributions of nucleosomes derived from both classical and Bar-ChIP were analyzed within −1,000 bp to + 1,500 bp around annotated transcription start sites (TSS) (Fig 2C). The map of nucleosome occupancy for nucleosomal DNA produced using classical ChIP-Seq revealed an array of 150 bp-spaced nucleosomes on either side of the TSS, as previously reported (Yuan, 2005; Jiang & Pugh, 2009; Weiner et al, 2010). An identical trend of nucleosome occupancy was observed for nucleosomal DNA generated using Bar-ChIP, confirming our ability to capture nucleosome distributions even when DNA adapters are directly ligated to fragmented chromatin (Fig 2C). Mapping of H3K4me3 occupancy around annotated TSS for both protocols showed a clear enrichment for nucleosomes carrying the PTM at the 5′ end of genes (up to + 500 bp) (Fig 2D). Local statistical differences between the distributions of the histone mark obtained by either traditional ChIP or Bar-ChIP were computed using the DESeq2 package (Love et al, 2014) to provide an estimate of potential disparities between the two methods (see 4). We found that enrichment signals obtained with Bar-ChIP were of slightly greater amplitude than those generated with classical ChIP-Seq (Fig 2D and E; Supplementary Figs S5 and S6). This suggests that regions enriched in H3K4me3-marked nucleosomes were more prominently detected with Bar-ChIP, which tended to also exaggerate the depletion signals originating from H3K4me3-poor regions. Despite these differences, the same genomic regions were identified by both techniques for H3K4me3 enrichment or depletion, indicating that Bar-ChIP faithfully captured the distribution of H3K4me3-marked nucleosomes. Bar-ChIP enables rapid and simultaneous generation of ChIP-Seq data sets Current protocols for ChIP-Seq suffer from several practical constraints, including cost, laboriousness, as well as the limitation of having only one modification or protein profiled per assay. To demonstrate the multiplexing potential of Bar-ChIP and its suitability for rapid and systematic profiling of histone modifications across multiple yeast samples, the approach was applied to five histone PTM in a panel of four S. cerevisiae chromatin modifier mutants using biological triplicates (Fig 3A). As these mutants and histone marks have been mostly characterized using ChIP-on-chip methods, our objective was to comparatively assess the resolution provided by Bar-ChIP and address the interplay between histone modifications. Figure 3. Highly multiplexed ChIP experiment based on the Bar-ChIP protocol Schematic representation of the experimental design. Cultures corresponding to distinct yeast strains were harvested, crosslinked and their MNase-treated chromatin was barcoded to enable sample tracking. Aliquots from each of the barcoded chromatin samples were pooled together prior to immuno-precipitation against the histone modifications of interest. DNA recovered from each IP was amplified and sequenced using paired-end technology. Finally, barcode sequences were used to demultiplex sequencing datasets and attribute each read to the proper biological sample. Normalized proportion of reads attributed to each strain. One sequencing lane corresponded to multiplexed samples submitted to one IP assay. For each sequencing lane, read counts attributed to each biological sample were first divided by the total number of reads recovered from the lane (Supplementary Fig S6). For each biological sample, the resulting ratio was normalized using the proportion of reads in the chromatin input lane that was attributed to that biological sample to correct for biases in the initial pooling of fragmented chromatin samples. Note that each set1∆ library represents less than 0.17% of the total number of reads recovered for the IPs against H3K4 methylation while the set2∆ libraries represent not more than 0.8% of the total reads recovered for the IP against H3K36me3. Absolute numbers for recovered sequencing reads are indicated in Supplementary Fig S7. Download figure Download PowerPoint To ensure consistency in the data, all these chromatin-associated mutants were derived from the BY4741 strain background, from which the initial Saccharomyces cerevisiae deletion collection was generated (Winzeler, 1999). Our set of mutants included set1Δ, deleted for Set1p, a component of the COMPASS complex, which contains a SET domain and is the only protein capable of catalyzing the deposition of mono-, di- and trimethyl groups on lysine 4 of H3 in S. cerevisiae (Roguev et al, 2001; Krogan et al, 2002; Santos-Rosa et al, 2002). Set1p is strongly active at the 5′ end of actively transcribed genes, where it results in peaks of H3K4me3. Loss of Set1p results in the absence of methylation on H3K4 and in the local emergence of new transcripts from previously silent loci (Venkatasubrahmanyam et al, 2007; Lenstra et al, 2011). Another SET mutant analyzed in our study was set2Δ, as Set2p is the only histone methyltransferase responsible for deposition of methyl groups on lysine 36 of H3 (H3K36me1, 2, 3) in S. cerevisiae (Strahl et al, 2002). Set2p associates with the elongating form of RNA polymerase II, when it is phosphorylated on serine 2 of its carboxyl-terminal domain (CTD) (Krogan et al, 2003; Li, 2003). Set2p then deposits the elongation mark H3K36me3 on nucleosomes toward the 3′ end of genes (Pokholok et al, 2005). Deposition of this mark enables activation of the deacetylase complex, Rpd3S, which maintains low histone acetylation levels within the coding region of transcribed genes, thereby preventing cryptic transcript initiation (Carrozza et al, 2005; Keogh et al, 2005; Li et al, 2007c; Drouin et al, 2010). H3K36me3-dependent regulation of Rpd3S involves two subunits: Rco1p, which possesses a PHD zinc finger domain, permitting binding to histones regardless of their PTM, and a chromodomain-containing protein, Eaf3p, that recognizes the H3K36me3 mark (Li et al, 2007b). To better understand the interactions between these proteins, the interplay between their enzymatic activities and the presence of specific histone marks, both rco1Δ and eaf3Δ mutants, were profiled for histone PTM. Given the functions of the proteins described above, five distinct histone modifications were selected: H3K14ac and H3K4me3, two marks located at the 5′ end of genes and associated with active transcription; H3K36me3, associated with transcription elongation; and H3K4me2 and H3K4me1 marks, whose roles in transcriptional processes are not as well delineated. Due to the complete absence of methylation of H3K4 in set1Δ, this mutant was used as an internal control for the IP specificity when profiling H3K4me1, H3K4me2 and H3K4me3. Similarly, the set2Δ mutant constituted a control for the IP specificity of H3K36me3. For each yeast strain, three biological replicates were grown in YPD, crosslinked, and their chromatin was subjected to MNase-mediated fragmentation. Chromatin samples were then barcoded and pooled prior to parallel immuno-precipitation. Recovered DNA libraries were PCR-amplified and deep-sequenced using paired-end technology, with one library per sequencing lane. Each amplified library corresponded to DNA products obtained from one IP against a specific histone mark, albeit for 15 biological samples at once. As before, input DNAs were also sequenced to control for potential biases in MNase accessibility and sequencing. Consequently, the Bar-ChIP method applied to a unique experiment yielded the equivalent of 90 ChIP-Seq datasets with only five chromatin IP assays (Fig 3A). One hundred and twenty million reads were recovered on average from each sequencing lane and demultiplexed. The distribution of reads between the 15 biological samples reliably reflected the original chromatin composition expected for each pool from the distinct ChIP assays (Supplementary Fig S7). Of 4 to 9 million unique molecules were retrieved per pool for each histone modification, indicative of a rather low resolution of the data (Supplementary Fig S8). However, normalization of the data using the input read counts for every strain showed a clear depletion in H3K4me1, 2, 3 and H3K36me3 levels for the set1Δ and set2Δ strains, respectively, as was expected (Fig 3B). These results confirmed our capacity to perform chromatin IP of barcoded and pooled chromatin fragments, suggesting that Bar-ChIP can be used to study the genome-wide patterns of histone marks. Multiplexed experiments provide overview of genome-wide distribution of histone marks To evaluate the value of Bar-ChIP for investigating chromatin-associated processes, the 90 datasets were explored for potential interactions between histone PTM. For each profiled strain, data from the biological replicates were pooled together, thereby increasing resolution and permitting an accurate comparison between datasets. Analysis of the genome-wide nucleosome distributions for wild-type and mutant strains showed that eaf3Δ and set1Δ chromatin was generally more sensitive to MNase digestion (Fig 4A), as suggested by the disappearance of the di-nucleosome and widening of the mono-nucleosome signal reproducibly observed by bioanalyzer (Supplementary Fig S9). Maps of nucleosome occupancy revealed that nucleosome-depleted regions located approximately 100 bp upstream of annotated TSS were more pronounced and 50 to 100 bp wider in eaf3Δ and set1Δ. In contrast, each other mutant exhibited typical nucleosome organization around the TSS (Fig 4A; Supplementary Fig S10), except for an unusually wide profile of the −1 nucleosome. We attribute this difference to a greater heterogeneity of the fragments obtained by MNase digestion of the corresponding regions. Additionally, the average size of mono-nucleosome fragments was smaller in the two mutants despite simultaneous treatment of all samples with the same amount of MNase (Supplementary Fig S11). This difference was taken into consideration when performing the comparative downstream analyses. Figure 4. TSS plots depicting the distribution of the histone post-translational modifications profiled in the multiplexing experiment A. Nucleosome occupancy around annotated TSS for the wild-type and mutant yeast strains profiled in the study. B. Occupancy of the various marks profiled in the experiment in the wild-type strain BY4741. C–E. H3K14ac (C), H3K4me3 (D) and H3K4me2 (E) occupancies around the annotated TSS in the mutants profiled in the study. Data information: Histone mark occupancies were normalized using the counts corresponding to chromatin inputs. Download figure Download PowerPoint These maps of nucleosome occupancy were then used to examine enrichment profiles around the TSS obtained for the various histone PTM and yeast strains. Enrichments obtained in the wild-type strain for each profiled histone mark confirmed the specificity and consistency of the IPs (Fig 4B). As expected, H3K14ac and H3K4me3 peaked at the 5′ end of genes. High levels of H3K4me2 were located about 500 bp after annotated transcription initiation sites, as previously reported in single gene and genome-wide studies (Santos-Rosa et al, 2002; Ng et al, 2003; Liu et al, 2005; Pokholok et al, 2005). H3K4me1-enriched nucleosomes were present ~600 bp downstream of the TSS. Finally, H3K36me3, the mark associated with elongating RNA polymerase II, was enriched near the 3′ end of genes (Pokholok et al, 2005; Li et al, 2007a). Despite the aforementioned wider profile of the −1 nucleosome, comparison of the H3K4me3 enrichment patterns obtained in both comparative and multiplex experiments did not show any significant difference (Supplementary Fig S12). Additionally, to assess the possibility of cross-contamination during sample pooling, the enrichment patterns derived from the remnant reads for H3K4 methylation and H3K36me3 in set1∆ and set2∆, respectively, were examined. These generally did not resemble those of the wild-type strain or of the other mutants (Supplementary Fig S13), although traces of H3K4 methylation were still detected in set1∆ (Supplementary Fig S13E and F), albeit corresponding to a very low number of sequencing reads (Supplementary Fig S7). Altogether, these observations confirmed that IP experiments performed on barcoded chromatin were successful in capturing an enriched fraction of nucleosomes carrying the targeted histone PTM. The profiles of H3K36me3 were similar between wild-type and set1Δ, eaf3Δ and rco1Δ strains, except for an exaggerated depletion around the TSS for set1Δ (Supplementary Fig S14). The pattern of H3K14 acetylation in this mutant closely resembled that of the wild-type strain. In contrast, set2Δ, eaf3Δ and rco1Δ mutants displayed an equal and comparable distribution of the acetylation mark along the entire gene body, reflecting the globally high levels of histone acetylation present genome-wide upon deletion of these chromatin-associated genes (Fig 4C). In the set2Δ mutant, distributions of H3K4me3, H3K4me2 and H3K4me1 to a lesser extent differed from those observed in the wild-type strain (Fig 4D and E; Supplementary Fig S14), suggesting that the deletion of SET2 impacts the methylation profile of H3K4. While the main peak of H3K4me3 occupancy was present near the 5′ end of genes, similar to the wild-type profile, a modest enrichment of H3K4 trimethylation was detected at the 3′ end of genes, beyond the first 500 bp. This was also confirmed by manual examination of gene coverage tracks (Supplementary Fig S15). The peak for H3K4me2 was slightly shifted toward the 3′ end of genes in comparison to the wild-type strain, such that high levels of the mark were then maintained in the 3′ region of gene bodies beyond 1,000 bp, while regions between 500 bp and 1,000 bp appeared to be depleted in H3K4 di-met
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Mammals contain over 200 different cell types, yet nearly all have the same genomic DNA sequence. It is a key question in biology how the genetic instructions in DNA are selectively interpreted by cells to specify various transcriptional programs and therefore cellular identity. The structural and functional organization of chromatin governs the transcriptional state of individual genes. To understand how genomic loci adopt different levels of gene expression, it is critical to characterize all local chromatin factors as well as long-range interactions in the 3D nuclear compartment. Much of our current knowledge regarding protein interactions in a chromatin context is based on affinity purification of chromatin components coupled to mass spectrometry (AP-MS). AP-MS has been invaluable to map strong protein-protein interactions in the nucleus. However, the interaction is detected after cell lysis and biochemical enrichment, allowing for loss or gain of false positive or negative interaction partners. Recently, proximity-dependent labeling methods have emerged as powerful tools for studying chromatin in its native context. These methods take advantage of engineered enzymes that are fused to a chromatin factor of interest and can directly label all factors in proximity. Subsequent pull-down assays followed by mass spectrometry or sequencing approaches provide a comprehensive snapshot of the proximal chromatin interactome. By combining this method with dCas9, this approach can also be extended to study chromatin at specific genomic loci. Here, we review and compare current proximity-labeling approaches available for studying chromatin, with a particular focus on new emerging technologies that can provide important insights into the transcriptional and chromatin interaction networks essential for cellular identity.
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Abstract Expression of eukaryotic genes during development requires complex spatial-temporal regulation. This complex regulation is often achieved through the coordinated interaction of transcription regulatory elements in the promoters of the target genes. The identification and mapping of regulatory elements in genome scale is crucial to understand how gene expression is regulated Chromatin immunoprecipitation is a standard method for assessing the occupancy of DNA binding proteins in vivo in their native chromatin context using antibodies. However, standard chromatin immunoprecipitation procedure is time consuming, labor intensive and not suited for analyzing many samples simultaneously. Recently, we have developed a simple chromatin immunoprecipitation protocol, which utilizes high capacity protein A/G coated magnetic beads and 96 well plate. This protocol requires fewer steps, little hands-on time and is compatible with multi-channel pipetting and liquid handling robot. This protocol allows genome wide mapping of transcription regulatory network in a high throughput manner. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3011. doi:10.1158/1538-7445.AM2011-3011
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PAP-LMPCR for improved, allele-specific footprinting and automated chromatin fine structure analysis
The analysis of chromatin fine structure and transcription factor occupancy of differentially expressed genes by in vivo footprinting and ligation-mediated-PCR (LMPCR) is a powerful tool to understand the impact of chromatin on gene expression. However, as with all PCR-based techniques, the accuracy of the experiments has often been reduced by sequence similarities and the presence of GC-rich or repeat sequences, and some sequences are completely refractory to analysis. Here we describe a novel method, pyrophosphorolysis activated polymerization LMPCR or PAP-LMPCR, which is capable of generating accurate and reproducible footprints specific for individual alleles and can read through sequences previously not accessible for analysis. In addition, we have adapted this technique for automation, thus enabling the simultaneous and rapid analysis of chromatin structure at many different genes.
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