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Molecular Systems Biology Volume 15 ,Issue 5 ,2019-05-22
Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks
Articles
Jan‐Philipp Mallm 1 Murat Iskar 2 Naveed Ishaque 3 Lara C Klett 1 , 4 Sabrina J Kugler 5 , 6 Jose M Muino 7 Vladimir B Teif 8 Alexandra M Poos 1 , 4 , 9 , 10 Sebastian Großmann 1 Fabian Erdel 1 , 11 Daniele Tavernari 1 Sandra D Koser 12 Sabrina Schumacher 1 Benedikt Brors 12 Rainer König 9 , 10 Daniel Remondini 13 Martin Vingron 7 Stephan Stilgenbauer 6 Peter Lichter 2 , 14 Marc Zapatka 2 Daniel Mertens 5 , 6 Karsten Rippe 1
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DOI:10.15252/msb.20188339
Received 2018-03-23, accepted for publication 2019-04-17, Published 2019-04-17
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摘要

Abstract In chronic lymphocytic leukemia (CLL), a diverse set of genetic mutations is embedded in a deregulated epigenetic landscape that drives cancerogenesis. To elucidate the role of aberrant chromatin features, we mapped DNA methylation, seven histone modifications, nucleosome positions, chromatin accessibility, binding of EBF1 and CTCF, as well as the transcriptome of B cells from CLL patients and healthy donors. A globally increased histone deacetylase activity was detected and half of the genome comprised transcriptionally downregulated partially DNA methylated domains demarcated by CTCF. CLL samples displayed a H3K4me3 redistribution and nucleosome gain at promoters as well as changes of enhancer activity and enhancer linkage to target genes. A DNA binding motif analysis identified transcription factors that gained or lost binding in CLL at sites with aberrant chromatin features. These findings were integrated into a gene regulatory enhancer containing network enriched for B‐cell receptor signaling pathway components. Our study predicts novel molecular links to targets of CLL therapies and provides a valuable resource for further studies on the epigenetic contribution to the disease.

关键词

histone modifications;gene regulatory networks;enhancer;DNA;bivalent promoter

授权许可

© 2019 EMBO

图表

Chromatin feature annotation, open regions, and gene regulationChromatin features mapped here displayed differences between CLL patients and NBCs from healthy donors. As an example, the TCF4 locus is shown for CLL1 and NBC donor H7 samples. The TCF4 gene encodes for a transcription factor from the E protein family. Based on the increased H3K4me1, H3K27ac, and ATAC signal, two predicted enhancer loci were marked that became active in CLL. Note that the y‐axis for RNA‐seq is scaled differently for CLL (8,000) and NBCs (100) to visualize that the TCF4 gene was not completely silenced but lowly expressed also in NBCs as evident also from the H3K36me3 mark. Light gray depicts active chromatin region and dark gray the confined enhancer locus coinciding with an open chromatin region. The chromatin state annotation is described in panel (B).Chromatin segmentation of co‐occurring histone modifications by ChromHMM yielding a model with 12 chromatin states. The indicated emission parameters for the contributions of individual histone marks and the average amount of each state (Mb) for CLL and NBC samples are given.Chord diagram representation of genome‐wide chromatin state changes between CLL and NBC. The amount of chromatin change is proportional to the size of the segments with each tick representing 4 Mb of chromatin. Color coding of chromatin states as in panel (B).Distribution of ˜ 24,400 annotated differentially accessible regions (ATAC‐seq) in CLL compared to NBC samples (“CLL diff.”) according to the chromatin state annotation. In total, 7,605 regions gained an ATAC‐seq signal in CLL, while it was lost at 16,790 loci.Part of the computed B‐cell gene regulatory network showing TCF4 and its deregulated target genes as well as some of the adjacent nodes. The GRN was used to calculate the activity of regulators like TCF4 based on their target gene expression. Color code: TFs, red; target genes, gray; chromatin modifier, blue.

Data set overviewAssignment of CLL samples to B‐cell developmental stages based on DNA methylation patterns.Unsupervised hierarchical clustering of the samples from Pearson's correlation coefficient (average linkage) for DNA methylation from WGBS. The analysis was carried out considering the most variable 1 million CpG sites.Same as panel (B) but computed from the gene expression profiles of 2,000 genes from RNA‐seq.Same as panel (B) but computed for histone modifications at promoters from ChIP‐seq. Samples cluster according to the modifications, underlining specificity of the experimental data, and separate inactive (H3K9me3 and H3K27me3) from the other active histone marks.Top: Unsupervised hierarchical clustering of the samples from Spearman's correlation coefficient (average linkage) for chromatin accessibility from ATAC‐seq calculated from ˜ 120,000 accessible regions. Bottom: Distribution of fold changes in ATAC‐seq signal from DiffBind between CLL and NBC samples. The data were fitted to a sum of three Gaussian functions. Threshold values were determined from the indicated cross‐over points as described in Materials and Methods.Exemplary comparison of ATAC‐seq data of all analyzed CLL IGHV mutated (n = 11), CLL IGHV unmutated (n = 8), and NBC (n = 7) samples in replicates (except for H10, H12, and H13) at the EBF1 locus. It contains regions with lost ATAC‐seq signal in CLL as compared to NBC controls determined by the DiffBind analysis (red bars in bottom track “Difference”). Representative ChromHMM state annotations of CLL1 and H6 are depicted as color bars above the corresponding group.

Large partially methylated domains identified in CLLLeft, example of a large PMD on chromosome 2 derived from a consensus of CLL samples (n = 11). Right, genome‐wide quantification of PMDs across CLL samples (n = 11) and NBCs (n = 6). The PMDs mapped with this set of 11 CLL samples were used for further analysis in figure panels (A–E) in combination with the RNA‐seq and ChIP‐seq analysis of the samples listed in Appendix Figure S1A. Red, methylated DNA; blue, unmethylated DNA.Expression of genes located inside (blue) and outside (gray) the PMD regions. In the boxplot, maximum, third quartile, median, first quartile and minimum are indicated.Fraction of differentially expressed genes inside or outside PMD regions. Up‐ and downregulated genes are shown in red and green, respectively.Upper panel: Average signal of histone modification marks normalized to H3 and standard deviation in 5‐kb windows around the ± 50 kb flanking regions of PMD boundaries. Normalized fold changes were calculated by dividing to the average signal flanking outside the PMD boundaries. Blue box, within PMDs; thin line, outside PMDs, norm.—normalized. Lower panel: Distribution of bound CTCF sites in CLL cells as determined by ChIP‐seq (blue line) around the ± 50 kb flanking regions of PMD boundaries in 5‐kb windows. The height of the curves gives the sum of the next nearest CTCF peak at the given distance to the PMD boundary.Percentage of somatic mutations located inside (blue) or outside (gray) the PMD regions. Red line represents the expected ratio based on the genomic length of PMD and non‐PMD regions. Mutation data were from Puente et al (2015).

Characterization of partially methylated domains and DMRsExample of a large PMD on chromosome 1 derived from a consensus of CLL samples (n = 11) in comparison with the NBC reference (n = 6).Similarity of PMDs found in CLL, medulloblastoma (Group3/WNT; Hovestadt et al, 2014), colon cancer (Berman et al, 2011), pancreas (Schultz et al, 2015), and placenta tissues (Schroeder et al, 2013). Medullobl., medulloblastoma; Gr3, group3; Rep, replicate. Lower triangular part refers to the fraction of the PMD on the y‐axis overlapping with the PMD sample on the x‐axis and vice versa for the upper triangular.Normalized coverage plot of lamina‐associated domains (Guelen et al, 2008) and Hi‐C B compartments (Fortin & Hansen, 2015) around the ± 50 kb flanking regions of PMD boundaries in 2.5‐kb windows.Distribution of ATAC around the ± 50 kb flanking regions of PMD boundaries in 5‐kb windows. Normalized fold changes were calculated by dividing to the average signal flanking outside the PMD boundaries. Blue box, within PMDs; thin line, outside PMDs, nor.—normalized.Methylation changes in DMRs from CLL vs. NBCs plotted against the methylation changes of the same regions during normal B‐cell maturation from naive B cells to class‐switched memory B cells (hiMBC). Data for DMRs were averaged across multiple CpG sites and across replicate samples for each experimental class. CLL‐specific DMRs (red, CLL‐specific) displayed a higher change of more than 0.2 b‐value in comparison with B‐cell programming. The parameter n indicates the total number of CLL‐specific DMRs, hypo‐ and hyper‐methylated.Enrichment of TF binding motifs at ATAC‐seq peaks overlapping with DMRs. As background, all the consensus ATAC‐seq peaks outside the DMRs were used. Size indicates the percentage of DMR‐overlapping ATAC‐seq peaks with the motif.

H3K4me3 and nucleosome positioning changes at promotersCorrelation function of H3K4me3 ChIP‐seq reads. A broadening of H3K4me3 domains in CLL by 1–2 nucleosomes was detected. The number of replicates analyzed was 11 (CLL) and 4 (NBC), respectively. Error bars represent the SEM.H3K4me3 peak width distribution at common promoters in CLL and NBC controls. In the boxplot, maximum, third quartile, median, first quartile and minimum are indicated. The number of replicates analyzed was 11 (CLL) and 4 (NBC), respectively.Distribution of nucleosome occupancy calculated from histone H3 ChIP‐seq averaged over a 1,000‐bp window within promoters. H3K4me3 regions displayed higher nucleosome density for CLL as compared to NBC samples. The boxplot representation and number of samples was the same as in panel (B).Exemplary region at the TAF13 promoter showing higher H3K4me3 levels upstream of the TSS with lost ATAC signal (gray bar) as compared to the NBC control.TF motif analysis of ATAC signal lost at CLL promoters with broadened H3K4me3 regions.Heatmap of genome‐wide histone modification patterns at promoters within −2 to 5 kb around the TSS (x‐axis) for an NBC (H3) and a patient (CLL1) sample. Each line on the y‐axis corresponds to one promoter. The clustering revealed one cluster with loss of H3K4me3 at bivalent promoters in CLL.TF motif analysis at bivalent promoters that lost H3K4me3 in CLL.

Analysis of H3K4me3 peak broadening and loss of bivalent states at CLL promotersH3K4me3 distribution around TSS for NBCs and CLL. Both samples types displayed a similar H3K4me3 distribution up‐ and downstream of the TSS. However, for CLL extended peaks were found that centered around the TSS, indicating a gain of nucleosomes in these regions.Cluster plot of nucleosomes occupancy at all CLL‐specific promoters that gain nucleosomes in CLL. Left: K‐means clustering of nucleosome occupancy for NBC samples. Right: CLL samples with the same ordering as for NBC controls. A fraction of promoters in the bottom cluster displayed a particularly pronounced gain of nucleosomes at the TSS, which reflects the profile of the promoters with the extended H3K4me3 signal depicted in panel (A).Nucleosome profile at the promoter region of NFKB1 with higher nucleosome density for CLL at the TSS (red line) compared to NBCs (black line). The light blue circle indicates SP1 binding sites in the lymphoblastoid cell line GM12878.GO enrichment analysis of genes with gained nucleosomes at their promoters.Ratio of read counts for H3K4me3 and H3K27me3 at bivalent promoters for CLL samples grouped according to their IGVH mutation status and compared to NBC samples (***P < 0.001, t‐test).Cluster heatmap of histone demethylase and methyltransferase expression values. Samples were clustered according to expression similarities (hierarchical clustering), while enzymes were sorted according to their target modification.

Comparison of enhancers identified with previous data sets and exemplary lociOverlap of CLL and NBC annotated active chromatin states (1, 8, 9, and 11) identified here with previous data sets. Venn diagram showing the total chromatin (Mb) of recurrent predicted enhancer chromatin state, occurring in at least three samples, compared to published corresponding states E7–11 of peripheral blood CD19+ B cells and CD3+ T cells from the Roadmap Epigenome project. In our union set of enhancers, 89% of known B‐cell and 71% of known T‐cell chromatin annotated as predicted enhancers were present.Enrichment of ENCODE TF binding sites (TFBS) in chromatin states. The average profile is shown for each cluster of TFBSs (TFBS C1–C2) with the TFBS description given in Appendix Table S5.Enrichment of bidirectionally expressed RNA from all samples and chromatin states changed in CLL in comparison with published data sets. These included the enhancer catalogs from Vista, modifications from ENCODE, bidirectional RNA from FANTOM, and p300 sites from ENCODE. The majority of known enhancers from diverse tissues corresponded to our active 1–3 states. Inactivation of these states in CLL occurred mostly via the bivalent state.Chromatin feature maps at the CREB3L2 for a CLL patient (CLL1) and an NBC donor (H7). An intragenic predicted enhancer region downstream of the TSS that became active in CLL is highlighted. Light gray depicts active chromatin region and dark gray the confined enhancer locus coinciding with an open chromatin region. For color coding of ChromHMM states, see panel (B).Same for the FMOD locus with two predicted enhancer regions with enhanced activity in CLL marked in gray.

Differential enhancer activity in CLL and NBCsOverlap of active regions identified in CLL and NBCs by ChromHMM, ATAC peaks, or bidirectionally expressed RNA loci labeled as “Bidi”. Venn diagram showing the total number of overlapping regions (not area‐proportional).Chromatin transitions within differential active states between NBC and CLL. Heatmap representation of the amount of chromatin (log2 Mb + 1) transitioning from a particular state in NBC (rows) to CLL (columns). Transitions were considered for all recurrent active chromatin state regions (states 1, 8, 9, and 11) present in a minimum of three samples even if the consensus state was not an active state. Accordingly, the matrix includes transitions between non‐active states at low frequencies.Chromatin states at bidirectionally transcribed predicted enhancers loci. All Bidi loci identified in NBC samples (n = 961) and CLL samples (n = 8,530) are shown. The Bidi loci show an enrichment of the states “Active 2 (predicted active enhancer)” and “Active 1 (predicted transcription start sites)”.Clustering of samples via expression of bidirectionally regions that are differential between NBCs and CLL and quantified using DESeq2.Volcano plot of differential super‐enhancers targeting known leukemia and cancer genes. Examples include SE loss at CDKN1A, PI3KC2B, and KMT2B (MLL2) and SE gain at FMOD, CREB3L2, CTLA4, TCF4, LEF1, and BCL2. Points represent non‐differential SEs (gray) and differential SEs (FDR < 0.01) with fold change > 1 (orange).RNA expression changes of selected genes associated with differential SEs. Top: genes significantly (FDR < 0.05) upregulated by SEs in CLL. Bottom: genes significantly downregulated by SEs in CLL. In the boxplot, maximum, third quartile, median, first quartile and minimum are indicated. The number of replicates analyzed was 19 (CLL) and 7 (NBC), respectively.Comparison of normalized gene expression of histone deacetylases between CLL and NBCs. Histone deacetylases significantly upregulated in CLL are shown in bold. The boxplot representation and number of samples was the same as in panel (F).HDAC activity and its inhibition by panobinostat in B cells from CLL patients (red) in comparison with healthy donors (gray). Error bars indicate standard deviation measured in four biological replicates.Genome browser view of H3K27ac tracks (in gray) at exemplary genes for NBCs and CLL cells 24 h after mock and after panobinostat treatment. At genes such as CDKN1A (cell cycle control) and KLF13, reduced H3K27ac signal in CLL was increased upon HDAC inhibition to the level found in NBCs. WNT11 is shown as an example of a de novo gain of an active enhancer due to treatment with panobinostat.Heatmap displaying changes in H3K27ac read occupancy in CLL upon panobinostat treatment for 24 h. A general gain of H3K27ac in enhancers upon panobinostat treatment was observed.

ATAC‐seq and TF binding motif analysis of enhancersPlot of the most enriched TF binding motifs in regions that showed gained ATAC‐seq signal at ChromHMM states 1, 8, 9, and 11 (predictive for enhancers). Color coding represents different TF classes. The size of the spots is proportional to the percentage of target sequences with a given motif.H3K27ac (left) and DNA methylation (right) at NFAT binding sites. CLL cells showed both an H3K27ac enrichment and DNA hypomethylation at NFAT target sites, suggesting a higher activity of TFs from the NFAT family in CLL.Same as panel (A) but for lost ATAC‐seq signal.ATAC footprints for E2A, EBF, and CTCF motifs from the Homer analysis. The E2A motif footprint (binding site of E protein family TFs like TCF4) displayed an increased binding signal in CLL, while sites with the EBF and CTCF motif lost the ATAC signal.Expression of the genes nearest to target enhancers with constitutively bound (“stable”) CTCF vs. enhancers that lost CTCF in CLL. Loss of CTCF binding correlated with reduced gene expression.Correlation matrix of simultaneously open regions computed from the scATAC‐seq data. For all loci, the pair‐wise correlation coefficients were calculated and plotted to visualize how different loci were wired to each other. As an example, the TCF4 locus on chromosome 1 is depicted.Enhancer–promoter rewiring at the NFKB2 locus. Top: Clustering of NBC and CLL samples according to gene expression of NFKB2, PSD and FBXL15. NBC samples were in the left cluster with high NFKB2 expression, which was reduced in the right cluster containing the CLL samples. Bottom: A switch of interactions between the NFKB2 promoter and two different enhancers in CLL (red line) vs. NBCs (gray line) was observed. Based on the CTCF ChIP‐seq analysis, both intronic enhancers at the NFKB2 and the FBXL15 gene show constitutively bound CTCF nearby, even though the targets of the two enhancers were switched.

Analysis of CTCF binding by ChIP‐seqUnsupervised clustering of CTCF ChIP‐seq reads. The ChIP‐seq signal separates NBC and CLL samples.MA plot of CTCF ChIP‐seq regions with differentially occupied CTCF ChIP‐seq regions marked in red. The plot showed a large‐scale loss of CTCF binding activity in CLL.Chromatin state annotation of differential CTCF ChIP‐seq binding sites.Histogram displaying the distance of PMD to the TAD boundaries reported previously for a lymphoblastoid cell line (Rao et al, 2014). Size distribution (average and std. dev) was 1,156 kb ± 125 kb (PMDs, this study) and 258 kb ± 20 kb [TADs (Rao et al, 2014)].Histogram displaying distance of CTCF sites to TAD boundaries for stable and lost CTCF sites.Histogram displaying the distance of lost and stable CTCF sites to enhancers that change their gene target in CLL.

Integration of chromatin state changes, TF binding, and gene expressionRelation of chromatin states and gene expression changes. For 81% of differentially expressed genes, a change in a regulatory chromatin feature was observed.Scheme of chromatin feature changes and associated TFs identified in this study (Appendix Table S2).Pathway analysis of identified core TFs and their target genes from our B‐cell GRN. Significantly enriched gene sets (P < 0.05) relevant to CLL pathophysiology were retrieved (BCR signaling, NF‐κB signaling, and MAPK signaling).CLL GREN. TFs identified here, associated chromatin modifiers, and differentially expressed target genes were integrated into a network. A part that includes TCF4 and EBF1 is shown. The different TSSs of the TCF4 gene (Fig 1A) were combined into a single gene target for the corresponding SE. Several enhancers of EBF1 target genes were active in NBCs (H402, H441, H464, H1000) but became silenced in CLL. As an example, H464 and SNX22 are highlighted by a red rectangle. These enhancers represent candidate enhancers for involving EBF1 binding. Color code: active enhancers, dark, light, and medium green for CLL only (“C”), only in NBCs (“H”), and both cell types (“CH”), respectively; TFs, red; target genes, gray; chromatin modifier, blue.Analysis of regulation of SNX22 by the intronic H464 enhancer shown in panel (B). This enhancer lost its ATAC signal at the predicted EBF1 binding site. EBF1 ChIP‐seq analysis validated that EBF1 is indeed lost at this site. Although H3K27ac at the locus was only slightly reduced, transcription of SNX22 was largely reduced. These findings are consistent with a mechanism where EBF1 binding drives gene expression of SNX22 by binding to H464.

通讯作者

1. Daniel Mertens.Mechanisms of Leukemogenesis, German Cancer Research Center (DKFZ), Heidelberg, Germany;Department of Internal Medicine III, University Hospital Ulm, Ulm, Germany.daniel.mertens@uniklinik-ulm.de
2. Karsten Rippe.Division of Chromatin Networks, German Cancer Research Center (DKFZ) and Bioquant, Heidelberg, Germany.daniel.mertens@uniklinik-ulm.de

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Jan‐Philipp Mallm,Murat Iskar,Naveed Ishaque,Lara C Klett,Sabrina J Kugler,Jose M Muino,Vladimir B Teif,Alexandra M Poos,Sebastian Großmann,Fabian Erdel,Daniele Tavernari,Sandra D Koser,Sabrina Schumacher,Benedikt Brors,Rainer König,Daniel Remondini,Martin Vingron,Stephan Stilgenbauer,Peter Lichter,Marc Zapatka,Daniel Mertens,Karsten Rippe. Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks. Molecular Systems Biology ,Vol.15, Issue 5(2019)

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