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

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

10.21203/rs.3.rs-1175572/v1 ◽

2021 ◽

Author(s):

Ning Liu ◽

Timothy Sadlon ◽

Ying Ying Wong ◽

Stephen Martin Pederson ◽

James Breen ◽

...

Keyword(s):

Type 1 Diabetes ◽

Functional Annotation ◽

Target Genes ◽

Association Studies ◽

Regulatory Function ◽

Candidate Snps ◽

Cell Type ◽

Candidate Target ◽

Cell Type Specific

Abstract 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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A comprehensive integrated post-GWAS analysis of Type 1 diabetes reveals enhancer-based immune dysregulation

PLoS ONE ◽

10.1371/journal.pone.0257265 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257265

Author(s):

Seung-Soo Kim ◽

Adam D. Hudgins ◽

Jiping Yang ◽

Yizhou Zhu ◽

Zhidong Tu ◽

...

Keyword(s):

Type 1 Diabetes ◽

Target Genes ◽

Association Studies ◽

Regulatory Elements ◽

Immune Dysregulation ◽

Specific Gene ◽

Genome Wide Association Studies ◽

Gwas Analysis ◽

Regulatory Variants

Type 1 diabetes (T1D) is an organ-specific autoimmune disease, whereby immune cell-mediated killing leads to loss of the insulin-producing β cells in the pancreas. Genome-wide association studies (GWAS) have identified over 200 genetic variants associated with risk for T1D. The majority of the GWAS risk variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes substantially contribute to T1D. However, identification of causal regulatory variants associated with T1D risk and their affected genes is challenging due to incomplete knowledge of non-coding regulatory elements and the cellular states and processes in which they function. Here, we performed a comprehensive integrated post-GWAS analysis of T1D to identify functional regulatory variants in enhancers and their cognate target genes. Starting with 1,817 candidate T1D SNPs defined from the GWAS catalog and LDlink databases, we conducted functional annotation analysis using genomic data from various public databases. These include 1) Roadmap Epigenomics, ENCODE, and RegulomeDB for epigenome data; 2) GTEx for tissue-specific gene expression and expression quantitative trait loci data; and 3) lncRNASNP2 for long non-coding RNA data. Our results indicated a prevalent enhancer-based immune dysregulation in T1D pathogenesis. We identified 26 high-probability causal enhancer SNPs associated with T1D, and 64 predicted target genes. The majority of the target genes play major roles in antigen presentation and immune response and are regulated through complex transcriptional regulatory circuits, including those in HLA (6p21) and non-HLA (16p11.2) loci. These candidate causal enhancer SNPs are supported by strong evidence and warrant functional follow-up studies.

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New insights into promoter–enhancer communication mechanisms revealed by dynamic single-molecule imaging

Biochemical Society Transactions ◽

10.1042/bst20200963 ◽

2021 ◽

Author(s):

Jieru Li ◽

Alexandros Pertsinidis

Keyword(s):

Gene Expression ◽

Single Molecule ◽

Target Genes ◽

Regulatory Elements ◽

Specific Gene ◽

Single Molecule Imaging ◽

Cell Type ◽

Regulatory Information ◽

Cell Type Specific ◽

Target Promoters

Establishing cell-type-specific gene expression programs relies on the action of distal enhancers, cis-regulatory elements that can activate target genes over large genomic distances — up to Mega-bases away. How distal enhancers physically relay regulatory information to target promoters has remained a mystery. Here, we review the latest developments and insights into promoter–enhancer communication mechanisms revealed by live-cell, real-time single-molecule imaging approaches.

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Cell type–specific immune phenotypes predict loss of insulin secretion in new-onset type 1 diabetes

JCI Insight ◽

10.1172/jci.insight.125556 ◽

2019 ◽

Vol 4 (4) ◽

Cited By ~ 10

Author(s):

Matthew J. Dufort ◽

Carla J. Greenbaum ◽

Cate Speake ◽

Peter S. Linsley

Keyword(s):

Type 1 Diabetes ◽

Insulin Secretion ◽

Cell Type ◽

Onset Type ◽

Cell Type Specific ◽

Immune Phenotypes ◽

New Onset

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Single cell chromatin accessibility reveals pancreatic islet cell type- and state-specific regulatory programs of diabetes risk

10.1101/693671 ◽

2019 ◽

Cited By ~ 13

Author(s):

Joshua Chiou ◽

Chun Zeng ◽

Zhang Cheng ◽

Jee Yun Han ◽

Michael Schlichting ◽

...

Keyword(s):

Beta Cell ◽

Single Cell ◽

Islet Cell ◽

Target Genes ◽

Regulatory Function ◽

Cell Type ◽

Cell Clusters ◽

Risk Variants ◽

Cell Type Specific ◽

Accessible Chromatin

AbstractGenetic risk variants for complex, multifactorial diseases are enriched in cis-regulatory elements. Single cell epigenomic technologies create new opportunities to dissect cell type-specific mechanisms of risk variants, yet this approach has not been widely applied to disease-relevant tissues. Given the central role of pancreatic islets in type 2 diabetes (T2D) pathophysiology, we generated accessible chromatin profiles from 14.2k islet cells and identified 13 cell clusters including multiple alpha, beta and delta cell clusters which represented hormone-producing and signal-responsive cell states. We cataloged 244,236 islet cell type accessible chromatin sites and identified transcription factors (TFs) underlying both lineage- and state-specific regulation. We measured the enrichment of T2D and glycemic trait GWAS for the accessible chromatin profiles of single cells, which revealed heterogeneity in the effects of beta cell states and TFs on fasting glucose and T2D risk. We further used machine learning to predict the cell type-specific regulatory function of genetic variants, and single cell co-accessibility to link distal sites to putative cell type-specific target genes. We localized 239 fine-mapped T2D risk signals to islet accessible chromatin, and further prioritized variants at these signals with predicted regulatory function and co-accessibility with target genes. At the KCNQ1 locus, the causal T2D variant rs231361 had predicted effects on an enhancer with beta cell-specific, long-range co-accessibility to the insulin promoter, and deletion of this enhancer reduced insulin gene and protein expression in human embryonic stem cell-derived beta cells. Our findings provide a cell type- and state-resolved map of gene regulation in human islets, illuminate likely mechanisms of T2D risk at hundreds of loci, and demonstrate the power of single cell epigenomics for interpreting complex disease genetics.

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Chromatin architecture reveals cell type-specific target genes for kidney disease risk variants

BMC Biology ◽

10.1186/s12915-021-00977-7 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Aiping Duan ◽

Hong Wang ◽

Yan Zhu ◽

Qi Wang ◽

Jing Zhang ◽

...

Keyword(s):

Kidney Disease ◽

Target Genes ◽

Human Kidney ◽

Regulatory Elements ◽

Kidney Tubule ◽

Cell Type ◽

Altered Expression ◽

Risk Variants ◽

Cell Type Specific ◽

Tubule Cells

Abstract Background Cell type-specific transcriptional programming results from the combinatorial interplay between the repertoire of active regulatory elements. Disease-associated variants disrupt such programming, leading to altered expression of downstream regulated genes and the onset of pathological states. However, due to the non-linear regulatory properties of non-coding elements such as enhancers, which can activate transcription at long distances and in a non-directional way, the identification of causal variants and their target genes remains challenging. Here, we provide a multi-omics analysis to identify regulatory elements associated with functional kidney disease variants, and downstream regulated genes. Results In order to understand the genetic risk of kidney diseases, we generated a comprehensive dataset of the chromatin landscape of human kidney tubule cells, including transcription-centered 3D chromatin organization, histone modifications distribution and transcriptome with HiChIP, ChIP-seq and RNA-seq. We identified genome-wide functional elements and thousands of interactions between the distal elements and target genes. The results revealed that risk variants for renal tumor and chronic kidney disease were enriched in kidney tubule cells. We further pinpointed the target genes for the variants and validated two target genes by CRISPR/Cas9 genome editing techniques in zebrafish, demonstrating that SLC34A1 and MTX1 were indispensable genes to maintain kidney function. Conclusions Our results provide a valuable multi-omics resource on the chromatin landscape of human kidney tubule cells and establish a bioinformatic pipeline in dissecting functions of kidney disease-associated variants based on cell type-specific epigenome.

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Biomedical Data Commons (BMDC) prioritizes B-lymphocyte non-coding genetic variants in Type 1 Diabetes

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009382 ◽

2021 ◽

Vol 17 (9) ◽

pp. e1009382

Author(s):

Samantha N. Piekos ◽

Sadhana Gaddam ◽

Pranav Bhardwaj ◽

Prashanth Radhakrishnan ◽

Ramanathan V. Guha ◽

...

Keyword(s):

Type 1 Diabetes ◽

T Lymphocytes ◽

Genetic Variants ◽

B Lymphocyte ◽

Biomedical Data ◽

Cell Type ◽

Scoring Method ◽

Cell Type Specific ◽

Data Commons

The repurposing of biomedical data is inhibited by its fragmented and multi-formatted nature that requires redundant investment of time and resources by data scientists. This is particularly true for Type 1 Diabetes (T1D), one of the most intensely studied common childhood diseases. Intense investigation of the contribution of pancreatic β-islet and T-lymphocytes in T1D has been made. However, genetic contributions from B-lymphocytes, which are known to play a role in a subset of T1D patients, remain relatively understudied. We have addressed this issue through the creation of Biomedical Data Commons (BMDC), a knowledge graph that integrates data from multiple sources into a single queryable format. This increases the speed of analysis by multiple orders of magnitude. We develop a pipeline using B-lymphocyte multi-dimensional epigenome and connectome data and deploy BMDC to assess genetic variants in the context of Type 1 Diabetes (T1D). Pipeline-identified variants are primarily common, non-coding, poorly conserved, and are of unknown clinical significance. While variants and their chromatin connectivity are cell-type specific, they are associated with well-studied disease genes in T-lymphocytes. Candidates include established variants in the HLA-DQB1 and HLA-DRB1 and IL2RA loci that have previously been demonstrated to protect against T1D in humans and mice providing validation for this method. Others are included in the well-established T1D GRS2 genetic risk scoring method. More intriguingly, other prioritized variants are completely novel and form the basis for future mechanistic and clinical validation studies The BMDC community-based platform can be expanded and repurposed to increase the accessibility, reproducibility, and productivity of biomedical information for diverse applications including the prioritization of cell type-specific disease alleles from complex phenotypes.

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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Nature Communications ◽

10.1038/s41467-021-21583-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Rongxin Fang ◽

Sebastian Preissl ◽

Yang Li ◽

Xiaomeng Hou ◽

Jacinta Lucero ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Expression Patterns ◽

Regulatory Elements ◽

Cellular Heterogeneity ◽

Specific Gene ◽

Open Chromatin ◽

Cell Type ◽

Process Data ◽

Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

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