Super-enhancers delineate disease-associated regulatory nodes in T cells

doi:10.1038/nature14154

. 2015 Apr 23;520(7548):558-62.

doi: 10.1038/nature14154. Epub 2015 Feb 16.

Super-enhancers delineate disease-associated regulatory nodes in T cells

Affiliations

¹ Lymphocyte Cell Biology Section, National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), National Institutes of Health (NIH), Bethesda, Maryland 20892, USA.
² Translational Immunology Section, NIAMS, NIH, Bethesda, Maryland 20892, USA.
³ Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland 20892, USA.
⁴ Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA.
⁵ The Jackson Laboratory for Genomic Medicine and Department of Genetic and Development Biology, University of Connecticut, Farmington, Connecticut 06030, USA.
⁶ Laboratory of Muscle Stem Cells and Gene Regulation, NIAMS, NIH, Bethesda, Maryland 20892, USA.

PMID: 25686607
PMCID: PMC4409450
DOI: 10.1038/nature14154

Super-enhancers delineate disease-associated regulatory nodes in T cells

Golnaz Vahedi et al. Nature. 2015.

. 2015 Apr 23;520(7548):558-62.

doi: 10.1038/nature14154. Epub 2015 Feb 16.

Authors

Affiliations

¹ Lymphocyte Cell Biology Section, National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), National Institutes of Health (NIH), Bethesda, Maryland 20892, USA.
² Translational Immunology Section, NIAMS, NIH, Bethesda, Maryland 20892, USA.
³ Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland 20892, USA.
⁴ Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA.
⁵ The Jackson Laboratory for Genomic Medicine and Department of Genetic and Development Biology, University of Connecticut, Farmington, Connecticut 06030, USA.
⁶ Laboratory of Muscle Stem Cells and Gene Regulation, NIAMS, NIH, Bethesda, Maryland 20892, USA.

PMID: 25686607
PMCID: PMC4409450
DOI: 10.1038/nature14154

Abstract

Enhancers regulate spatiotemporal gene expression and impart cell-specific transcriptional outputs that drive cell identity. Super-enhancers (SEs), also known as stretch-enhancers, are a subset of enhancers especially important for genes associated with cell identity and genetic risk of disease. CD4(+) T cells are critical for host defence and autoimmunity. Here we analysed maps of mouse T-cell SEs as a non-biased means of identifying key regulatory nodes involved in cell specification. We found that cytokines and cytokine receptors were the dominant class of genes exhibiting SE architecture in T cells. Nonetheless, the locus encoding Bach2, a key negative regulator of effector differentiation, emerged as the most prominent T-cell SE, revealing a network in which SE-associated genes critical for T-cell biology are repressed by BACH2. Disease-associated single-nucleotide polymorphisms for immune-mediated disorders, including rheumatoid arthritis, were highly enriched for T-cell SEs versus typical enhancers or SEs in other cell lineages. Intriguingly, treatment of T cells with the Janus kinase (JAK) inhibitor tofacitinib disproportionately altered the expression of rheumatoid arthritis risk genes with SE structures. Together, these results indicate that genes with SE architecture in T cells encompass a variety of cytokines and cytokine receptors but are controlled by a 'guardian' transcription factor, itself endowed with an SE. Thus, enumeration of SEs allows the unbiased determination of key regulatory nodes in T cells, which are preferentially modulated by pharmacological intervention.

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Figures

**Extended Data Figure 1. SE Structures Are Lineage-Specific**
(a) Histone acetyltransferase p300 is distributed asymmetrically across the genome in CD4⁺ T cells with a subset of enhancers (SEs) containing exceptionally high amounts of p300 binding. Graph demonstrates the ranked distribution of p300 binding measured by ChIP-seq in Th2 and Th17 cells. (b) Closely related CD4⁺ T cell populations have distinct SE landscapes. Common and cell-type specific SE domains in T cell subsets are illustrated for various fractions of overlapping genomic regions (f = 0.1, 0.3, 0.5, and 0.7). The overlapping pattern of SEs across CD4⁺ T cells was statistically significant when these annotations were shuffled across the genome (p-value<10⁻⁵). (c) Lineage-specific presence of SEs for master transcription factor genes in T cells. Genomic loci of genes encoding T-bet, GATA3, and RORγ exhibit SE structures in Th1, Th2, and Th17 cells, respectively. Black-bar represents the genomic location of SEs. (d) The genomic locus of gene encoding gp130, *Il6st*, accommodates an SE with high level of transcription. Black-bar represents the genomic location of SE.

**Extended Data Figure 2. Transcription Factor Enrichment at SEs**
(a) Lineage-specific transcription factors are enriched at cell type-specific SEs. Binding patterns of STAT4, STAT6, and STAT3 revealed preferential binding at Th1, Th2, and Th17-specific SE regions, respectively. Furthermore, master transcription factors T-bet, GATA3 and RORγt were enriched at lineage-specific SEs. Strong binding of BATF, BACH2, and IRF4 was present in SEs of the all three cell types. Maps of cell type-specific SEs were constructed as described in (Fig. 1b). Normalization of Y-axis takes into account the variable sizes of genomic regions and also the corresponding library size (i.e. the total read count) (Methods). (b) CTCF binding demarcates the boundaries of SEs. Normalized binding profile of CTCF protein revealed the enrichment of CTCF at boundaries of SE regions. (c) Comparing the enrichment of TFs at constituent enhancers of SEs and TEs reveals the preferential binding of STAT3 at SEs while other TFs demonstrated comparable binding at SEs and TEs.

**Extended Data Figure 3. Identity of SE Associated Genes**
(a) SEs delineate genes playing a central role in the biology of specific cell lineages. Gene ontology (GO) functional categories relevant to cytokine binding are enriched at SE-associated genes in T cells. In ES cells, SE structures primarily encompass DNA binding proteins and transcriptional repressor functions. In macrophages, chemokine and cytokine activity were the most prominent categories. Using a complementary approach to that described in Figure 2a, we characterized genes in proximity of SEs. The top GO molecular functions using GSEA were chosen. To calculate the statistical significance of these gene categories, we shuffled the SE regions around the genome 10 times, delineating the gene sets in proximity to the random genomic domains. We then assessed the relative proportion of a gene set captured in the actual data versus the shifted SEs. –log10 p-values for this permutation test are reported in the bar-graph. (b) Gene ontology (GO) functional category relevant to cytokines binding is enriched at SE-associated genes in T cells and, to a lesser extent, in macrophages but not mESCs or myotubes. To explore whether “cytokine binding” is specific to the SE structure in CD4⁺ T cells, we explored its association within the SE structures of other cell-types. The GO molecular function associated to cytokine binding (GO:0019955) was chosen. To calculate the statistical significance of this gene category, we shuffled the SE regions of mESC, macrophage, myotubes and CD4⁺ T cells around the genome 10⁵ times, delineating the gene sets in proximity to the random genomic domains associated to each cell type. We then assessed the relative proportion of the gene set captured in the actual data versus the shifted SEs. P-values for this permutation test are reported in the bar-graph. (c, d) BACH2 preferentially represses SE genes. Wildtype and Bach2-deficient CD4⁺ T cells were polarized to induced regulatory T cells (iTregs) and were subjected to total RNA extraction. RNA standards “spiked-in” were added in proportion to the number of cells present in the sample. The resulting transcriptome data measured by RNA-seq were processed by using standard normalization methods and then renormalized based on spiked-in reads (rpkm) (see Methods). Transcript abundance measured by RNA-seq was evaluated in wildtype and Bach2-deficient cells at SE and TE-associated genes compared to remaining genes (rpkm). Cumulative distribution (c) and violin plots (d) show the (log2) fold-change in gene expression for wildtype versus *Bach2*-deficient cells for these three groups of genes. SE genes are preferentially affected by loss of BACH2 compared to TE genes (p-value<2.2e-16, Kolmogorov-Smirnov test) or remaining genes (p-value<2.2e-16, Kolmogorov-Smirnov test). P-values for the violin plots (d) were calculated using Wilcoxon rank-sum test. (e) BACH2 selectively affects SE genes and such selectivity remains statistically significant when controlling for the higher levels of gene expression for the SE genes. Genes were ranked based on their transcriptional activity in Tregs. We focused on the top 500 highly expressed genes and explored the effect of Bach2 on three categories among them: genes with SEs (77), with TEs (125), and without either SEs or TEs (298). Expression levels among these three categories of genes were comparable (Wilcoxon rank-sum test p-value=0.644). However, Bach2 selectively affected highly expressed SE genes in contrast to those with TEs or no enhancers (Kolmogorov-Smirnov test p-value=9.813e-7 and 4.669e-8).

**Extended Data Figure 4. BACH2 Acts as a Guardian Transcription Factor**
(a,b) Loss of BACH2, STAT4, and STAT6 have the most selective impact on the expression of SE-genes. The fold-change in expression (in rpkm) between wildtype and knockout samples was calculated for SE-genes and an equal number of randomly selected TE genes (a). For each transcription factor, the difference between SEs and TEs was quantitated using Kullback-Leibler distance (b). The larger difference between SEs and TEs for Bach2, STAT4, and STAT6 suggests the more selective impact of these transcription factors on SEs. STAT4 and T-bet transcriptome data were under Th1, STAT6 under Th2, STAT3, BATF and IRF4 under Th17 and BACH2 under iTreg conditions. (c) SE-associated genes in CD4⁺ T cells are repressed by Bach2. To ensure accurate inference of the effect of Bach2 on transcriptome, spiked-in RNA standards were added. The gene-set-enrichment-analysis (GSEA) of SE-associated genes revealed that SE genes were enriched in genes repressed by Bach2 when transcript levels were renormalized using spiked-in RNA standards. (d) BACH2 acts as a repressor of SE-associated genes. Comparison of the transcriptome data measured by RNA-seq in wildtype and Bach2-deficient cells (DE-seq analysis for three wildtype and knockout samples, FDR<0.05 and fold-change > 1.5) revealed that 348 SE-genes were repressed while 176 were induced by this protein. Integration of Bach2 binding data measured by ChIP-seq characterized the direct targets of BACH2. (e) The gene-set-enrichment-analysis (GSEA) of TE-associated genes revealed that TE genes are not enriched in genes repressed by Bach2. (f,g) BACH2-associated transcriptional repression at some SE domains correlates with the downregulation of nearby genes such as *Rbpj* (f) and Socs1 (g). (h) Genes and noncoding transcripts endowed with SE architecture in CD4⁺ T cells are tightly and negatively controlled by the “guardian” transcription factor Bach2 which itself has a rich cassette of regulatory elements. Examples were selected based on direct binding of Bach2 at the gene-body or SE regions measured by ChIP-seq.

**Extended Data Figure 5. Rheumatoid Arthritis Risk Genes with SE Structure Are Selectively Targeted by a Janus Kinase Inhibitor, tofacitinib**
(a) Genetic variants in high linkage disequilibrium (LD) with SNPs associated with autoimmune disorders such as RA, IBD, MS, and T1D exhibit preferential enrichment in SEs versus TEs in human CD4 T cells. Variants in LD with SNPs in each disease were determined from 1000 Genomes Project using r2=0.9 and distance limit=500 by SNAP toolbox. The heatmap depicts the percentages of SNPs and total number of SNPs per 10MB within SEs and TEs. (b) Tofacitinib treatment has a selective impact on SE versus TE genes in human T cells. Violin plots depict the fold-change (log2) in transcript levels due to tofacitinib treatment at SE-versus TE-genes in CD4⁺ T cells. The p-values were calculated based on Wilcoxon signed-rank test. (c) Highly expressed genes in T cells with SEs are selectively affected by tofacitinib. For each donor, the top 100 highly expressed genes in non-treated cells were selected and categorized as having SEs or not. The p-values were calculated based on Wilcoxon signed-rank test. (d) RA risk genes with SEs are selectively targeted by a Jak-inhibitor, tofacitinib. Violin plots depict the fold-change in expression (log2) after tofacitinib treatment of human CD4⁺ T cells at RA-risk genes with or without SEs (a donor with no spiked-in standard in RNA-seq). P-values were calculated using F-test.

**Extended Data Figure 6. Tofacitinib Selectively Affects Autoimmune Disease Risk Genes with SE Structure in T Cells**
(a) RA, IBD, and MS risk genes are linked to SEs in CD4⁺ T cells. The candidate genes associated to RA, IBD, MS, and T2D were chosen based on recent meta-analyses of GWAS data. More than half of RA risk genes (53/98) contained SEs in CD4⁺ T cells. In line with the enrichment of SNPs associated to IBD and MS in T cell SEs (Figure 4a), around half of IBD (91/216) and MS risk genes (36/87) were associated with SEs in T cells. In contrast, T2D risk genes showed little association with SEs (4/65) (Fisher’s exact test, p-value=0.4). (b-d) RA and IBD risk genes with SEs are selectively targeted by a Jak-inhibitor, tofacitinib. Cumulative plots depict the fold-change in expression (log2) at RA (b), IBD (c) and MS (d) risk genes with or without SEs after 0.3uM tofacitinib treatment of human CD4⁺ T cells (p-values Kolmogorov-Smirnov test)

**Figure 1. SE Structure Predicts Lineage and Stage-Specific Transcription**
(a) Histone acetyltransferase p300 is distributed asymmetrically across the genome in CD4⁺ T cells with a subset of enhancers (SEs) containing exceptionally high amounts of p300 binding (Table S1). (b) Closely related CD4⁺ T cell populations have distinctive SE landscapes. Venn diagram depicts shared and unique SE domains in T cell subsets. (c) SE-associated genes are highly transcribed compared to typical-enhancer (TE)-associated genes. Proximity measures were used to assign SEs and TEs to their target genes (P-values Wilcoxon rank-sum test). (d) Presence of lineage-specific SEs predicts cell-selective expression. Three groups of genes associated to unique SE structure in each lineage were defined as Th1, Th2, and Th17-specific SE genes. The expression of lineage-specific SE-associated genes was more significant in the corresponding lineage (P-values Wilcoxon rank-sum test). (e) SE domains are themselves transcribed in CD4⁺ T cells. The list of ncRNAs was derived from the map of intergenic transcripts in T cell subsets. One-third of ncRNAs in T cells, (501/1,524), were transcribed from an SE. (f-h) The SE structure differentiates highly lineage-specific and dynamic noncoding transcripts from constitutively expressed transcripts across T cell lineages. Pearson correlation coefficients for transcription levels between each pair of differentiation stages were calculated for 501 ncRNAs with SEs (f) and 1,023 ncRNAs without SEs (g). (h) ncRNA transcripts with SEs have larger standard deviation (s.d.) across differentiation stages compared to those without SEs.

**Figure 2. Transcription Factors with Major Roles in T Helper Cell Differentiation Occupy SEs**
(a-c) Lineage-predicting transcription factors are enriched at SE domains. The catalogue of SEs in CD4⁺ T cells was constructed by merging Th1, Th2, and Th17 SEs. Binding patterns of STAT1, STAT3, STAT4 and STAT6 (a), BATF, T-BET, BACH2 and IRF4 (b), and HIF1a, RORγt, and GATA3 (c) are demonstrated at SEs. (d) Binding of lineage-specific transcription factors correlates with the presence of lineage-specific SEs in T cells (log2 tags-per-million) (Table S2). (e) Gene ontology (GO) functional categories relevant to cytokines and cytokine receptors are enriched at SE-associated genes in T cells. GO analysis for SE regions was performed using GREAT .

**Figure 3. *Bach2* is Endowed with the Highest p300-Enriched SE in T cells**
(a) Ranked order of p300-loaded enhancers in T cell subsets demonstrates *Bach2* as the strongest SE-associated gene in CD4⁺ T cells. (b) *Bach2* locus, the top ranked SE, exhibits an exceptional amount of p300 binding. (c, d) BACH2 preferentially represses SE genes. Wildtype and *Bach2*-deficient CD4 T cells were polarized to induced regulatory T cells (iTregs) and were processed for total RNA extraction (n=3). Normalized transcript abundance measured by RNA-seq (rpkm) was evaluated in wildtype and *Bach2*-deficient cells at SE and TE-associated genes and compared to the remaining genes. Cumulative distribution (c) and violin plots (d) show the (log2) fold-change in gene expression for wildtype versus *Bach2*-deficient cells (Table S3). (e) The gene-set-enrichment-analysis (GSEA) of SE-associated genes reveals that SE genes are enriched in genes repressed by BACH2. (f) BACH2 affects a subset of noncoding transcripts at SE domains. Overall, 56 ncRNAs with SE structure are repressed while 32 transcripts are induced by BACH2 (Table S3). (g) BACH2-associated repression of a noncoding transcript with an SE architecture correlates with the transcriptional repression of a nearby gene (*Ifngr1*). Direct BACH2 binding along with the transcript levels in wildtype and *Bach2*-deficient cells measured by RNA-seq were depicted in a 140kbp window accommodating *Ifngr1* gene.

**Figure 4. Rheumatoid Arthritis Risk Genes with SE Structure Are Selectively Targeted by Janus Kinase Inhibitor, tofacitinib**
(a) Single-nucleotide polymorphisms (SNPs) associated with autoimmune diseases including rheumatoid arthritis (RA), inflammatory bowel disease (IBD), multiple sclerosis (MS), and type 1 diabetes (T1D) are preferentially enriched at the SE structure of human CD4⁺ T cells. In contrast, SNPs associated with disorders in which CD4⁺ T cells play limited roles, such as T2D and cancer, are not enriched in these genomic domains. A catalogue of 1,426 SEs in human T cells was constructed by aggregating SE predictions in human Th1, Th2, and Th17 cells using H3K27ac data (Table S4). We divided the number of SNPs enriched in SEs/TEs by the total size of SEs (66.5338 MB) and TEs (63.12915 MB) and reported the number of SNPs within every 10 MB of the genome (P-values permutations test). (b) RA risk genes are linked to SEs in CD4⁺ T cells. The 98 candidate genes associated to RA were from. (c) RA risk genes with SEs are selectively targeted by a Jak-inhibitor, tofacitinib. Violin plots depict the fold-change in expression (log2) after tofacitinib treatment of human CD4⁺ T cells at RA-risk genes with or without SEs (three donors). To ensure accurate inference of the effect of tofacitinib on transcriptome, spiked-in RNA standards were added and gene expression levels (rpkm) were renormalized based on the spiked-in standards (P-values Wilcoxon rank-sum test).

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References

1. Andersson R, et al. An atlas of active enhancers across human cell types and tissues. Nature. 2014;507:455–461. doi:nature12787 [pii] 10.1038/nature12787. - PMC - PubMed
1. Hnisz D, et al. Super-enhancers in the control of cell identity and disease. Cell. 2013;155:934–947. doi:S0092-8674(13)01227-0 [pii] 10.1016/j.cell.2013.09.053. - PMC - PubMed
1. Loven J, et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell. 2013;153:320–334. doi:S0092-8674(13)00393-0 [pii] 10.1016/j.cell.2013.03.036. - PMC - PubMed
1. Whyte WA, et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell. 2013;153:307–319. doi:S0092-8674(13)00392-9 [pii] 10.1016/j.cell.2013.03.035. - PMC - PubMed
1. Parker SC, et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc Natl Acad Sci U S A. 2013;110:17921–17926. doi:1317023110 [pii] 10.1073/pnas.1317023110. - PMC - PubMed

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[1] Andersson R, et al. An atlas of active enhancers across human cell types and tissues. Nature. 2014;507:455–461. doi:nature12787 [pii] 10.1038/nature12787. - PMC - PubMed

[2] Andersson R, et al. An atlas of active enhancers across human cell types and tissues. Nature. 2014;507:455–461. doi:nature12787 [pii] 10.1038/nature12787. - PMC - PubMed

[3] Hnisz D, et al. Super-enhancers in the control of cell identity and disease. Cell. 2013;155:934–947. doi:S0092-8674(13)01227-0 [pii] 10.1016/j.cell.2013.09.053. - PMC - PubMed

[4] Hnisz D, et al. Super-enhancers in the control of cell identity and disease. Cell. 2013;155:934–947. doi:S0092-8674(13)01227-0 [pii] 10.1016/j.cell.2013.09.053. - PMC - PubMed

[5] Loven J, et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell. 2013;153:320–334. doi:S0092-8674(13)00393-0 [pii] 10.1016/j.cell.2013.03.036. - PMC - PubMed

[6] Loven J, et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell. 2013;153:320–334. doi:S0092-8674(13)00393-0 [pii] 10.1016/j.cell.2013.03.036. - PMC - PubMed

[7] Whyte WA, et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell. 2013;153:307–319. doi:S0092-8674(13)00392-9 [pii] 10.1016/j.cell.2013.03.035. - PMC - PubMed

[8] Whyte WA, et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell. 2013;153:307–319. doi:S0092-8674(13)00392-9 [pii] 10.1016/j.cell.2013.03.035. - PMC - PubMed

[9] Parker SC, et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc Natl Acad Sci U S A. 2013;110:17921–17926. doi:1317023110 [pii] 10.1073/pnas.1317023110. - PMC - PubMed

[10] Parker SC, et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc Natl Acad Sci U S A. 2013;110:17921–17926. doi:1317023110 [pii] 10.1073/pnas.1317023110. - PMC - PubMed

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Super-enhancers delineate disease-associated regulatory nodes in T cells

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Super-enhancers delineate disease-associated regulatory nodes in T cells

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