H3K4 tri-methylation breadth at transcription start sites impacts the transcriptome of systemic lupus erythematosus
© Zhang et al. 2016
Received: 16 November 2015
Accepted: 19 January 2016
Published: 2 February 2016
The autoimmune disease systemic lupus erythematosus (SLE) has a modified epigenome with modified tri-methylation of histone H3 lysine 4 (H3K4me3) at specific loci across the genome. H3K4me3 is a canonical chromatin mark of active transcription. Recent studies have suggested that H3K4me3 breadth has an important regulatory role in cell identity. This project examined H3K4me3 breadth at transcription start sites (TSS) in primary monocytes and its association with differential gene transcription in SLE.
Integrative analysis was applied to chromatin immunoprecipitation sequencing (ChIP-seq) and RNA sequencing (RNA-seq) data generated from primary monocytes as well as genomic data available in public repositories. Four distinctive H3K4me3 patterns of ChIP-seq peaks were identified at 8399 TSSs. Narrow peaks were highly enriched with genes related to housekeeping functions. The broader peaks with extended H3K4me3 immediately upstream and/or downstream of TSS were associated with immune response genes. Many TSSs had downstream H3K4me3 extended to ~650 bp, where the transition of H3K4me3 to H3K36me3, a transcriptional elongation mark, is often found. The H3K4me3 pattern was strongly associated with transcription in SLE. Genes with narrow peaks were less likely (OR = 0.14, p = 2 × 10−4) while genes with extended downstream H3K4me3 were more likely (OR = 2.37, p = 1 × 10−11) to be overexpressed in SLE. Of the genes significantly overexpressed in SLE, 78.8 % had increased downstream H3K4me3 while only 47.1 % had increased upstream H3K4me3. Gene transcription sensitively and consistently responded to H3K4me3 change downstream of TSSs. Every 1 % increase of H3K4me3 in this region leads to ~1.5 % average increase of transcription.
We identified the immediate TSS downstream nucleosome as a crucial regulator responsible for transcription changes in SLE. This study applied a unique method to study the effect of H3K4me3 breadth on diseases and revealed new insights about epigenetic modifications in SLE, which could lead to novel treatments.
KeywordsSystemic lupus erythematosus H3K4me3 Epigenome Integrative analysis Pattern recognition
Tri-methylation of histone H3 lysine 4 (H3K4me3) is a major chromatin mark regulating gene transcription . It is mostly found around transcription start sites (TSS) and strongly associated with active transcription [2, 3]. Active chromatin marks such as H3K4me3 are typically restricted to narrow regions over specific functional genomic motifs while repressive marks, such as H3K27me3, are often deposited over broader genomic regions . However, very broad peaks of H3K4me3 were recently identified in many cell types as marks that predicted cell identity . These broad peaks spanned up to 60 kb and highly differed between cell types [6, 7]. It was further shown that the breadth of H3K4me3 regions was positively correlated with transcriptional consistency . The relationship between RNA polymerase pausing and transcriptional consistency may explain the regulatory role of these broad regions of H3K4me3. However, a mechanism linking them definitively has not yet been identified. Control of transcriptional noise may be permissive for cell fate decisions, and therefore, tight regulation of transcriptional consistency may be required for full commitment to a phenotype . The goal of this study was to identify distinctive patterns of H3K4me3 peak breadth within a narrower region around TSSs and determine if H3K4me3 breadth specifically at TSSs represented an independent variable of transcription regulation, a topic not previously investigated in diseases.
Systemic lupus erythematosus (SLE) is a systemic autoimmune disease affecting nearly all types of hematopoietic cells. This polygenic disorder has a complex etiopathogenesis that involves the production of type I interferon, production of autoantibody, and T cell anomaly . Clinical manifestations of SLE include arthritis, nephritis, and dermatitis. We had previously noted a markedly altered epigenome in SLE [10–13]. In this study, we utilized our previous chromatin immunoprecipitation sequencing (ChIP-seq) and RNA sequencing (RNA-seq) data sets to re-analyze chromatin changes from the perspective of peak breadth rather than peak height. After finding that substantial differences in peak breadth were strongly associated with transcription regulation, we were able to identify that the nucleosome downstream of the transcription start site is more directly associated with differential transcription than the upstream nucleosome in SLE.
Results and discussion
Classification of TSSs by their H3K4me3 breadth in primary monocytes
H3K4me3 breadth has been identified as a regulator of cell identity and transcriptional consistency in differentiating cells . We studied the H3K4me3 breadth and the pattern of H3K4me3 marks specifically around 27,588 unique TSSs in human primary monocytes to understand whether it was an independent variable of transcription regulation. The average H3K4me3, from 250 bp upstream to 250 bp downstream of TSSs, followed a bimodal distribution (Additional file 1: Figure S1A). The left peak corresponds to random background from sites without H3K4me3, whereas the right peak corresponds to the range of H3K4me3 levels at the other sites. We selected 14,217 TSSs with significantly higher H3K4me3 than the background for further analyses.
We used ENCODE ChIP-seq data generated from CD14+ monocyte to get a detailed histone landscape related to H3K4me3 breadth. We first repeated the classification of TSSs using the ENCODE H3K4me3 data. The two classifications agreed on 87 % of classified TSSs (kappa = 0.8, p < 2.2 × 10−16). None of the narrow peaks classified by one data set were re-classified as any of the broad peak patterns by the other data set, suggesting that narrow H3K4me3 at TSS is a stable feature. Furthermore, ENCODE data showed that other histone modifications complemented to the patterns of H3K4me breadth (Additional file 2: Figure S2). For example, H3K4me2 has almost the same pattern as H3K4me3, H3K4me1 tends to locate next to the outer edge of H3K4me3, and H3K36me3 often increases immediately after downstream extended H3K4me3 (Fig. 1c).
Association between H3K4me3 breadth at TSS and gene function
Selected overrepresented gene sets associated with each H3K4me3 pattern
tRNA metabolic process
Exon junction complex (EJC)
RNA polymerase II core binding
Systemic lupus erythematosus
Hematopoietic cell lineage
Staphylococcus aureus infection
Systemic lupus erythematosus
Response to interferon-alpha
RORA activates gene expression
DNA binding, bending
Systemic lupus erythematosus
Association of H3K4me3 breadth at TSS with gene transcription
The change of H3K4me3 breadth in SLE and its impact on gene transcription
Agreement between controls and SLE patients on classification of TSSs based on their H3K4me3 patterns
Since H3K4me3 of adjacent regions are closely correlated with each other, we re-analyzed the correlation between H3K4me3 and transcription changes at these three regions after removing their dependence on each other (Fig. 4b). The positive correlation between upstream H3K4me3 and transcription changes was diminished, suggesting that the previously observed association was an artifact and change of upstream H3K4me3 indirectly affected transcription through modifying the chromatin accessibility of other transcription regulators. On the other hand, the association between downstream H3K4me3 and transcription remained, suggesting that change in downstream H3K4me3 directly affected transcription probably through facilitating transcription elongation.
Protein-binding motifs associated with H3K4me3 breadth
Generalization of the H3K4me3 breadth-transcription association in inflammation
Several published data sets included matching transcriptome and H3K4me3 data from experiments that induced inflammatory responses in human cells. We downloaded two of these data sets from Gene Expression Omnibus and re-analyzed them using the same method as above. The GSE58310 data set included RNA-seq data from macrophages treated with or without LPS and matching ChIP-seq H3K4me3 data . To emphasize an epigenetic effect, the RNA-seq data was collected after LPS washout and cell culture for six more days. Genes maintaining differential transcription after the washout were identified from the RNA-seq and their average H3K4me3 changes were calculated at different regions (Fig. 7c). The other data set (GSE54000) included microarray transcriptome data from endothelium treated with TNF and matching ChIP-seq H3K4me3 data . The same analysis was applied to genes whose transcription was significantly modified by TNF (Fig. 7d). LPS and TNF are both inducers of inflammatory responses and our recent study reported elevated LPS level in circulating blood of SLE patients . In both cases, concordant changes of transcription and H3K4me3 were observed at all three regions around TSS; however, the largest H3K4me3 change always happened at TSS downstream region of genes with increased transcription. These analyses validate our observation in SLE and support a general theme of downstream H3K4me3 dictating capacity for stimulus-inducible transcription.
H3K4me3 breadth has recently been identified as a key regulator of cell type identity . Very broad domains of H3K4me3 were identified as lineage specific markers in both human and mouse. These broad domains were typically extended over 5 kb and highly remodeled during cell differentiation. Enrichment of lineage specific transcription factors within these broad domains was observed, thereby supporting the concept that the domains were critically associated with lineage commitment. The H3K4me3 mark is deposited by members of the COMPASS/Trithorax family of methyltransferases and is removed by the JARID family of demethylases . The H3K4me3 marked nucleosome can be dynamically acetylated by p300 and CBP . This subsequent step may represent a mechanism by which H3K4me3 regulates transcription. We wished to understand the effects of H3K4me3 breadth at TSSs, a subject that had not been previously addressed. We had already identified significant changes in H3K4me3 peak height in the setting of SLE. This study was undertaken specifically to evaluate effects of H3K4me3 peak breadth in a human disease state.
We found that TSS H3K4me3 patterns were not markedly changed in SLE although monocyte behavior is markedly changed in SLE [12, 24–26], and transcription changes underlying the altered behavior are also substantial [11, 14–16]. It was surprising, therefore, to find that the H3K4me3 patterns themselves were largely stable in SLE monocytes.
We noted that the TSS and the downstream H3K4me3 changes were most closely aligned with differential transcription in SLE. Upstream changes in H3K4me3 were not directly associated with differential transcription once the data were corrected for dependence of adjacent regions on each other. The downstream extended category of H3K4me3 was the pattern most strongly associated with inflammation and immune responses. It is then expected that this would be the set of genes most altered in the setting of SLE. The nucleosome downstream of the TSS is important functionally. H3K4me1 tends to locate next to the outer edge of H3K4me3 (Fig. 1c), so its peak breadth is correlated with H3K4me3 peak breadth. H3K4me1 is required for the recruitment of factors that interact with H3K4me3 . This association may therefore relate to restriction of the activities nucleated on the downstream nucleosome. Many TSSs in this study had downstream H3K4me3 extended to ~650 bp, where H3K36me3, a transcriptional elongation mark, starts to increase (Fig. 1c). Release of RNA polymerase from pausing occurs at this location, and histone acetylation of this downstream nucleosome appears to be central to the process and is at least partly dependent on H3K4me3 [23, 28–30]. Therefore, modifications at this downstream nucleosome may control pivotal events in transcriptional elongation. These findings also have important implications for the analysis of ChIP-seq data that typically focuses on the nucleosome upstream of the TSS. These data highlight the importance of a comprehensive assessment of changes in analytic approaches.
This conclusion is seemingly contradictory to what was described in the seminal paper on H3K4me3 breadth . That paper reported the effect of H3K4me3 breadth on cell identity by comparing different cell types while the current study was focused on one cell type under different pathological states. Another source of the different conclusions between the two studies is the difference in the definition of broad H3K4me3 peaks. In that paper, the broad H3K4me3 domains spanned up to 60 kb and had minimal length of over 4 kb in hESC. The domains were largely intergenic. This study, on the other hand, only looked at a much narrower region of 1 kb around TSSs, which might have more direct association with transcription level and variability. The breadth of narrow and broad peaks usually differed only by hundreds of base pairs.
This study makes unique contributions by defining H3K4me3 patterns at TSSs and by identifying the nucleosome downstream of the TSS as directly associated with transcription. Given that many genes have the transcriptional initiation-elongation transition in this region [31, 32], it is plausible to hypothesize that increase of downstream H3K4me3 will facilitate the transition by making the nucleosome more accessible to elongation machinery. Nevertheless, the study has some important limitations. The sample size of the SLE patients was relatively small and included patients with mild or moderate disease activity. As higher levels of disease activity could recruit additional gene expression changes as well as H3K4me3 changes, our study may underestimate the effects. An additional limitation was the focus on the annotated TSS region. We chose to focus on the TSS in order to link our findings with our RNA-seq data.
In summary, our study highlights the importance of examining H3K4me3 breadth patterns as well as peak height in evaluating ChIP-seq data. This is also one of the first studies to examine the changes in H3K4me3 patterns related to a disease state. Furthermore, data mining analyses of extra data sets further suggested that the association between transcription and downstream H3K4me3 is common to inflammatory responses. Our results emphasize the stability of the patterns and the importance of the downstream nucleosome in regulating gene transcription.
Primary monocytes from six SLE patients with mild to moderate disease activity and six unrelated controls were isolated as described . The primary data on H3K4me3 and RNA-seq have been previously published [11, 34]. The SLEDAI scores on the day of sampling were between 0 and 7 (mean = 2.50). Detailed sample description was given in Supplemental Table 1 of . Chromatin immunoprecipitation of H3K4me3 was carried out as previously described [35–38]. Immunoprecipitation with anti-GST (Invitrogen, Camarillo, CA) and input were used to define background. The library preparation utilized the SOLiD ChIP-seq kit and was performed according to the manufacturer’s instructions. The same samples had parallel RNA-seq data, which was previously reported .
We refined our previously described “CHOP-seq” pipeline to process the ChIP-seq data . After the sequencing reads were mapped to human reference genome (hg19), all reads were extended to 200 bp long at the 3′ end to cover the isolated region of nucleosome occupancy. The sequencing depth around 27,588 uniquely located transcription start sites (TSSs) annotated by RefSeq was summarized to obtain a measurement for each TSS and sample. A normalized measurement of −1.0 and 1.0, respectively, corresponds to average depth two times lower and higher than the background. The measurements were first normalized between samples and then adjusted by subtracting background signal measured by input controls. The detail of these steps was described in our previous paper .
We started with the H3K4me3 at 27,588 annotated TSSs of six control samples and then related the H3H4me3 patterns identified from these samples to relative H3K4me3 changes in six SLE samples. The normalized depth at single bases in the −1 to 1 kb region around TSS was averaged into 50 bp bins to obtain two 27,588 × 41 matrixes for the control and SLE group, respectively. The EM algorithm for mixtures of univariate normals method (https://cran.r-project.org/web/packages/mixtools) was applied to three different regions (TSS, 400–500 bp upstream, and 600–700 bp downstream) to identify patterns of H3K4me3 breadth, using only TSSs with substantially higher H3K4me3 level than background. To analyze gene set overrepresentation of each pattern, we collected predefined gene sets from various sources such as BioSystems, KEGG, and OMIM. The hypergeometric test was applied to each gene set to identify those significantly enriched within genes having a specific H3K4me3 breadth pattern. All genes having detectable H3K4me3 at their TSS were used as test background.
RNA-seq results from the same samples were obtained from our previous study . Genes with significant differential transcription between control and SLE samples (p < 0.01) were used to establish associations between differential H3K4me3 and differential transcription. Genes with multiple TSSs classified into different H3K4me3 patterns were not included. RNA-seq reads from whole blood of three patients with very early-onset inflammatory bowel disease and their six unaffected parents were aligned to the reference genome GRCh37 using Tophat2. Transcriptome assembly was performed using Cufflinks, after which the assemblies were merged across samples using Cuffmerge. Differentially expressed genes between the probands and their parents were identified using Cuffdiff (FDR < 0.05). The publication of this data set is in preparation.
Extra ChIP-seq data of CTCF and 11 histone modifications in CD14+ monocyte, such as H3K27ac and H3K36me3, were obtained from ENCODE (https://genome.ucsc.edu/ENCODE). These data were processed in the same way to get a 27,588 × 41 matrix for each histone modification. A combined set of 2414 protein-binding DNA motifs was collected from public resources, such as TRANSFAC and ENCODE. Matches to these motifs were searched for within −1 to +1 kb of all TSSs and considered as potential transcription factor binding sites (TFBSs). Enrichment and distribution of matches were compared across TSS subgroups with different H3K4me3 patterns. We re-analyzed two published data sets including matching H3K4me3 and transcriptome samples treated with TNF or LPS. Processed data of both data sets (GSE 54000 and GSE58310) were downloaded from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo).
Source code of all data analysis is publically available at https://goo.gl/22csxb. A cloud-based interactive online tool (http://awsomics.org/project/sle_h3k4me3_breadth) was created to share the data and results of this study. This tool integrates the data of this project with a variety of public genomic data we are continuously collecting from public repositories through Amazon Web Service. It allows users to explore, query, and visualize the data in an integrative way.
Availability of supporting data
We designed an innovative way for research to share their genomic data and presented it for the first time through this study. An online tool was implemented and made publicly available (http://awsomics.org/project/sle_h3k4me3_breadth) for users to access data of this study and perform exploratory analysis and customized visualization themselves (Additional file 3: Figure S3 and Additional file 4: Figure S4). This tool was built upon a cloud-based framework and integrated into a variety of collection of genomic information, such as predefined gene sets and protein-binding motifs used in this study.
tri-methylation of histone H3 lysine
systemic lupus erythematosus
transcription factor binding site
transcription start site
The authors gratefully acknowledge the patients who participated in this study. This study was supported in part by the Wallace Chair of Pediatrics, RO1 AR058547, and The Children’s Hospital of Philadelphia. The Hopkins Lupus Cohort was supported by AR 43727.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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