3D genome organization during lymphocyte development and activation

Schoonhoven, Anne van; Huylebroeck, Danny; Hendriks, Rudolf W.; Stadhouders, Ralph

doi:10.1093/bfgp/elz030

Cited by 17 publications

(10 citation statements)

References 93 publications

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“…Although we did not identify this interaction in our final SNP-interaction list upon re-examination of our workflow this interaction was present in our Hi-C dataset, but it was filtered out as the enhancer is not bound by FOXP3. Coordinated genome topology has also been shown in immune cell lineage commitment, both at a loci [134,135] and compartment level [136], consistent with the concept of immune transcriptional "factories" where genes congregate in regions of the nucleus to undergo coordinated transcriptional activation [137].…”

Section: Discussionsupporting

confidence: 72%

3DFAACTS-SNP: Using Regulatory T Cell-specific Epigenomics Data to Uncover Candidate Mechanisms of Type-1 Diabetes (T1D) risk

Liu

Sadlon

Wong

et al. 2021

Preprint

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BackgroundGenome-wide association studies (GWAS) have enabled the discovery of single nucleotide polymorphisms (SNPs) that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the identified variants lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. To address this problem, we developed a variant filtering workflow called 3DFAACTS-SNP to link genetic variants to target genes in a cell specific manner. Here we use 3DFAACTS-SNP to identify candidate SNPs and target genes associated with the loss of immune tolerance in regulatory T cells (Treg) in T1D. ResultsUsing 3DFAACTS-SNP we identified from a list of 1,228 previously fine-mapped variants, 36 SNPs with plausible Treg-specific mechanisms of action. The integration of cell-type specific chromosome conformation capture data in 3DFAACTS-SNP, identified 119 regulatory regions and 51 candidate target genes that interact with these variant-containing regions in Treg cells. We further demonstrated the utility of the workflow by applying it to three other SNP autoimmune datasets, identifying 17 Treg-centric candidate variants and 35 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of all known common (>10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 7,900 candidate variants and 3,245 candidate target genes, generating a list of potential sites for future T1D or autoimmune research. ConclusionsWe demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function and illustrate the power of using cell type specific multi-omics datasets to determine disease mechanisms. Our workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

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Section: Discussionsupporting

confidence: 72%

3DFAACTS-SNP: Using Regulatory T Cell-specific Epigenomics Data to Uncover Candidate Mechanisms of Type-1 Diabetes (T1D) risk

Liu

Sadlon

Wong

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Although we did not identify this interaction in our final SNP-interaction list upon re-examination of our workflow this interaction was present in our Hi-C dataset, but it was filtered out as the enhancer is not bound by FOXP3. Coordinated genome topology has also been shown in immune cell lineage commitment, both at a loci (151,152) and compartment level (153), consistent with the concept of immune transcriptional "factories"…”

Section: Chromhmm Statessupporting

confidence: 73%

3DFAACTS-SNP: Using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of Type-1 Diabetes (T1D) risk

Liu

Sadlon

Wong

et al. 2020

Preprint

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Background: Genome-wide association and fine-mapping studies have enabled the discovery of single nucleotide polymorphisms (SNPs) and other variants that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the SNPs lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. Methods: Autoimmunity results from a failure of immune tolerance, suggesting that regulatory T cells (Treg) are likely a significant point of impact for this genetic risk, as Treg are critical for immune tolerance. Focusing on T1D as a model of defective function of Treg in autoimmunity, we designed a SNPs filtering workflow called 3 Dimensional Functional Annotation of Accessible Cell Type Specific SNPs (3DFAACTS-SNP) that utilises overlapping profiles of Treg-specific epigenomic data (ATAC-seq, Hi-C and FOXP3-ChIP) to identify regulatory elements potentially driving the effect of variants associated with T1D, and the gene(s) that they control. Results: Using 3DFAACTS-SNP we identified 36 SNPs with plausible Treg-specific mechanisms of action contributing to T1D from 1,228 T1D fine-mapped variants, identifying 119 novel interacting regions resulting in the identification of 51 candidate target genes. We further demonstrated the utility of the workflow by applying it to three other fine-mapped/meta-analysed SNP autoimmune datasets, identifying 17 Treg-centric candidate variants and 35 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of any genetic variation using all common (>10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 7,900 candidate variants and 3,245 candidate target genes, generating a list of potential sites for future T1D or autoimmune research. Conclusions: We demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function and illustrate the power of using cell type specific multi-omics datasets to determine disease mechanisms. The 3DFAACTS-SNP workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

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“…Mammalian genomes are characterized by their higher order of chromatin structure in the 3D nuclear space, which is crucial for gene expression and the epigenetic regulation governing thymic development. The differentiation of several T cell lineages depends on multiple global and tissue specific transcription factors (histone modifiers and chromatin remodelers) controlling spatiotemporal gene expression (183)(184)(185). These proteins in association with cis-regulatory regions (enhancer and promoter elements) along the genome, define the dynamic conformation of chromatin.…”

Section: Discussionmentioning

confidence: 99%

3D Genome Organization as an Epigenetic Determinant of Transcription Regulation in T Cells

2022

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In the heart of innate and adaptive immunity lies the proper spatiotemporal development of several immune cell lineages. Multiple studies have highlighted the necessity of epigenetic and transcriptional regulation in cell lineage specification. This mode of regulation is mediated by transcription factors and chromatin remodelers, controlling developmentally essential gene sets. The core of transcription and epigenetic regulation is formulated by different epigenetic modifications determining gene expression. Apart from “classic” epigenetic modifications, 3D chromatin architecture is also purported to exert fundamental roles in gene regulation. Chromatin conformation both facilitates cell-specific factor binding at specified regions and is in turn modified as such, acting synergistically. The interplay between global and tissue-specific protein factors dictates the epigenetic landscape of T and innate lymphoid cell (ILC) lineages. The expression of global genome organizers such as CTCF, YY1, and the cohesin complexes, closely cooperate with tissue-specific factors to exert cell type-specific gene regulation. Special AT-rich binding protein 1 (SATB1) is an important tissue-specific genome organizer and regulator controlling both long- and short-range chromatin interactions. Recent indications point to SATB1’s cooperation with the aforementioned factors, linking global to tissue-specific gene regulation. Changes in 3D genome organization are of vital importance for proper cell development and function, while disruption of this mechanism can lead to severe immuno-developmental defects. Newly emerging data have inextricably linked chromatin architecture deregulation to tissue-specific pathophysiological phenotypes. The combination of these findings may shed light on the mechanisms behind pathological conditions.

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3D genome organization during lymphocyte development and activation

Cited by 17 publications

References 93 publications

3DFAACTS-SNP: Using Regulatory T Cell-specific Epigenomics Data to Uncover Candidate Mechanisms of Type-1 Diabetes (T1D) risk

3DFAACTS-SNP: Using Regulatory T Cell-specific Epigenomics Data to Uncover Candidate Mechanisms of Type-1 Diabetes (T1D) risk

3DFAACTS-SNP: Using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of Type-1 Diabetes (T1D) risk

3D Genome Organization as an Epigenetic Determinant of Transcription Regulation in T Cells

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