Chromosome contacts in activated T cells identify autoimmune disease candidate genes

Burren, Oliver S.; Garćıa, Arcadio Rubio; Javierre, Biola-Maria; Rainbow, Daniel B.; Cairns, Jonathan; Cooper, Nicholas; Lambourne, John; Schofield, Ellen; Dopico, Xaquín Castro; Ferreira, Ricardo C.; Coulson, Richard M. R.; Burden, Frances; Rowlston, Sophia; Downes, Kate; Wingett, Steven W.; Frontini, Mattia; Ouwehand, Willem H.; Fraser, Peter; Spivakov, Mikhail; Todd, John A.; Wicker, Linda S.; Cutler, Antony J.; Wallace, Chris

doi:10.1186/s13059-017-1285-0

Cited by 81 publications

(81 citation statements)

References 83 publications

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“…Row 1: promoter capture, odds that prey fragment overlaps enhancer in published T cell promoter-enhancer network (Cao et al, 2017). Row 2: promoter capture, odds that prey fragment overlaps regions of active chromatin called by CHROMHMM of the same cells (Burren et al, 2017b). Row 3: validation, odds that prey fragment was baited in the promoter capture.…”

Section: Mppc Provides Additional Information For Distinguishing Biolmentioning

confidence: 99%

“…Row 3: validation, odds that prey fragment was baited in the promoter capture. Row 4: validation, average gene expression (counts, log 2 scale) of gene associated with the baited promoter in RNA-seq analysis of the same cell type (Burren et al, 2017b). Predictors are CHiCAGO score (asinh transformed) and MPPC (sqrt transformed).…”

Section: Mppc Provides Additional Information For Distinguishing Biolmentioning

confidence: 99%

“…Promoter: link to active chromatin These cells had previously been assayed by ChIP-seq, and a 15 state CHROMHMM model fitted. 8 of these states showed characteristics of "active chromatin" and we combined these into a binary measure for active or inactive chromatin (Burren et al, 2017b). We used these results to quantify the overlap, for each prey fragment, with regions of active chromatin.…”

Section: Assessing Relationship Of Chicago Scores and Mppc To Outcomementioning

confidence: 99%

“…Validation: expression at linked promoter Given evidence that recruitment of prey fragments is associated with increased expression of the baited gene (Burren et al, 2017b), we expected that, amongst prey that did correspond to a baited promoter in the promoter capture experiment, the level of expression of the target gene should be higher when there was a direct contact. RNA-seq has previously been used to quantify transcription in these cells, and we used the expression of the target gene (log 2 (count + 1)) as an outcome measure in linear regression.…”

Section: Assessing Relationship Of Chicago Scores and Mppc To Outcomementioning

confidence: 99%

“…CHi-C has enabled identification of contacts made by promoters in primary human cells (Mifsud et al, 2015b;Cairns et al, 2016). The contact maps thus generated show a tendency for multiple contiguous fragments to be linked with the same promoter Burren et al, 2017b), but it is not clear whether enhancers overlapping all these fragments or only a subset of them are directly relevant to the promoter's regulation. Conversely, the same enhancer region can appear to interact with promoters of multiple genes (Dryden et al, 2014;Martin et al, 2015), while it remains unclear whether this reflects coregulation of these genes.…”

Section: Introductionmentioning

confidence: 99%

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Fine mapping chromatin contacts in capture Hi-C data

Eijsbouts

Burren

Newcombe

et al. 2018

Preprint

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Hi-C and capture Hi-C (CHi-C) are used to map physical contacts between chromatin regions in cell nuclei using high-throughput sequencing. Analysis typically proceeds considering the evidence for contacts between each possible pair of fragments independent from other pairs. This can produce long runs of fragments which appear to all make contact with the same baited fragment of interest. We hypothesised that these long runs could result from a smaller subset of direct contacts and propose a new method, based on a Bayesian sparse variable selection approach, which attempts to fine map these direct contacts.Our model is conceptually novel, exploiting the spatial pattern of counts in CHi-C data, and prioritises fragments with biological properties that would be expected of true contacts. For bait fragments corresponding to gene promoters, we identify contact fragments with active chromatin and contacts that correspond to edges found in previously defined enhancer-target networks; conversely, for intergenic bait fragments, we identify contact fragments corresponding to promoters for genes expressed in that cell type. We show that long runs of apparently co-contacting fragments can typically be explained using a subset of direct contacts consisting of < 10% of the number in the full run, suggesting that greater resolution can be extracted from existing datasets. Our results appear largely complementary to the those from a per-fragment analytical approach, suggesting that they provide an additional level of interpretation that may be used to increase resolution for mapping direct contacts in CHi-C experiments.

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Section: Mppc Provides Additional Information For Distinguishing Biolmentioning

confidence: 99%

Section: Mppc Provides Additional Information For Distinguishing Biolmentioning

confidence: 99%

Section: Assessing Relationship Of Chicago Scores and Mppc To Outcomementioning

confidence: 99%

Section: Assessing Relationship Of Chicago Scores and Mppc To Outcomementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fine mapping chromatin contacts in capture Hi-C data

Eijsbouts

Burren

Newcombe

et al. 2018

Preprint

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Mapping long‐range contacts between risk loci and target genes in human diseases with Capture Hi‐C

Cao

Lin

et al. 2020

Clinical & Translational Med

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Dear Editor, Ill-shaped genome conformations contribute to gene dysregulation through long-range chromatin contacts and are responsible for biological phenotypes of disease and carcinogenesis, particularly in breast cancer, 1,2 colorectal cancer, 3,4 and autoimmune diseases. 5-7 Here, we review the identification of long-range contacts between risk loci and putative target genes in the above diseases using Capture Hi-C technology. We hope that this review will provide insights into the related molecular mechanisms via the three-dimensional (3D) genome structure of these diseases. Current research shows that over 95% of singlenucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) are located in noncoding regions. 7 However, the roles of these GWAS SNPs in human disease development are unclear. A large proportion of such GWAS SNPs have been identified as DNase I hypersensitive sites, 7 which overlap with the binding sites of different transcription factors, such as CTCF, FOXA1, GATA3, P300, and ER-α, 1 or are enriched with active histone marks in different cell types with cell specificity 4 (Figure 1A and B). Therefore, it is important to link risk loci with GWAS SNPs, as distal regulatory elements, to their target genes through long-range chromatin interactions. Capture Hi-C technology was developed to detect chromatin interactions between regions of interest (e.g., noncoding SNP regions as potential regulatory elements) and their target genes with reduced sequencing costs and improved read depth. 8 In breast cancer, 110 genes linked to 33 risk loci and seven genes linked to three risk loci have been identified using Capture Hi-C technology, respectively 1,2 (Table S1). Many risk loci in these two studies had multiple target genes, and especially, 75% of these 36 risk loci had target genes other than their nearest genes. If the "nearest genes" annotation method was used, most target genes of these risk loci could not be found. In this sense, Capture Hi-C is more effective This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Transcriptional enhancers and their communication with gene promoters

Ray-Jones

Spivakov

2021

Cell. Mol. Life Sci.

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Transcriptional enhancers play a key role in the initiation and maintenance of gene expression programmes, particularly in metazoa. How these elements control their target genes in the right place and time is one of the most pertinent questions in functional genomics, with wide implications for most areas of biology. Here, we synthesise classic and recent evidence on the regulatory logic of enhancers, including the principles of enhancer organisation, factors that facilitate and delimit enhancer–promoter communication, and the joint effects of multiple enhancers. We show how modern approaches building on classic insights have begun to unravel the complexity of enhancer–promoter relationships, paving the way towards a quantitative understanding of gene control.

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Chromosome contacts in activated T cells identify autoimmune disease candidate genes

Abstract: Background: Autoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4 + T cells. Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates identification of candidate disease genes.

Cited by 81 publications

References 83 publications

Fine mapping chromatin contacts in capture Hi-C data

Fine mapping chromatin contacts in capture Hi-C data

Mapping long‐range contacts between risk loci and target genes in human diseases with Capture Hi‐C

Transcriptional enhancers and their communication with gene promoters

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