Biology subjects

van de Bunt, M.

Publications and source records attributed to van de Bunt, M..

7 recordsLinked to original sources

bioRxiv2022

Inferring causal genes at type 2 diabetes GWAS loci through chromosome interactions in islet cells

Resolving causal genes for type 2 diabetes at loci implicated by genome-wide association studies (GWAS) requires integrating functional genomic data from relevant cell types. Chromatin features in endocrine cells of the pancreatic islet are particularly informative and recent studies leveraging chromosome conformation capture (3C) with Hi-C based methods have elucidated regulatory mechanisms in human islets. However, these genome-wide approaches are less sensitive and afford lower resolution than methods that target specific loci. To gauge the extent to which targeted 3C further resolves chromatin-mediated regulatory mechanisms at GWAS loci, we generated interaction profiles at 23 loci using next-generation (NG) Capture-C in a human beta cell model (EndoC-{beta}H1) and contrasted these maps with Hi-C maps in EndoC-{beta}H1 cells and human islets and a promoter capture Hi-C map in human islets. We found improvements in assay sensitivity of up to 33-fold and resolved 4.8X more chromatin interactions. At a subset of 18 loci with 25 co-localised GWAS and eQTL signals, NG Capture-C interactions implicated effector transcripts at five additional genetic signals relative to promoter capture Hi-C through physical contact with gene promoters. Therefore, high resolution chromatin interaction profiles at selectively targeted loci can complement genome- and promoter-wide maps.

genetics↗

bioRxiv2022

A nuclear receptor facilitates differentiation of human PSCs into more mature hepatocytes

The capacity to generate functional hepatocytes from renewable human pluripotent stem cells (hPSCs) could address limited supplies of primary human hepatocytes. However, hepatocytes differentiated from hPSCs in vitro are functionally immature. To understand mechanisms regulating maturation of in vitro derived hepatocytes, we developed a 3D spheroid differentiation system and compared gene regulatory elements in uncultured human primary hepatocytes with those in hepatocytes that were differentiated in 2D or 3D conditions from human PSCs by RNA-seq, ATAC-seq, and H3K27Ac ChIP-seq. Three-dimensional differentiation improved enhancer activity and expression of transcription factor ONECUT1, but was insufficient to upregulate human-specific mature hepatocytes marker gene CYP3A4 or super-enhancer regulated transcription factor gene NFIC. Regulome comparisons showed reduced enrichment of thyroid receptor THRB motifs in accessible chromatin and in active enhancers without reduced transcription of THRB, suggesting the regulation at the level of THRB ligands in PSC-differentiated hepatocytes. Addition of thyroid hormone T3 to the PSC-differentiated hepatocytes increased CYP3A4 expression. T3 increased binding of THRB to the CYP3A4 proximal enhancer and restored the super-enhancer status and gene expression of NFIC and reduced expression of AFP. The resultant hPSC-hepatocytes showed gene expression, epigenetic status and super-enhancer landscape closer to primary hepatocytes and activated regulatory regions including non-coding SNPs associated with liver-related diseases. Transplanting the 3D PSC-hepatocytes into immunocompromised mice resulted in engraftment of human hepatocytes in the mouse liver parenchyma without disrupting normal liver histology at 6 months after transplantation. This work provides insights into the functions of nuclear receptor THRB and highlights the importance of the environmental factors-nuclear receptors axis in regulating maturation of human PSC-differentiated cell types.

developmental biology↗

bioRxiv2019

A multi-tissue transcriptome analysis of human metabolites guides the interpretability of associations based on multi-SNP models for gene expression

There is particular interest in transcriptome-wide association studies (TWAS) - gene-level tests based on multi-SNP predictive models of gene expression - for identifying causal genes at loci associated with complex traits. However, interpretation of TWAS associations may be complicated by divergent effects of model SNPs on trait phenotype and gene expression. We developed an iterative modelling scheme for obtaining multi-SNP models of gene expression and applied this framework to generate expression models for 43 human tissues from the Genotype-Tissues Expression (GTEx) Project. We characterized the performance of single- and multi-SNP TWAS models for identifying causal genes in GWAS data for 46 circulating metabolites. We show that: (a) multi-SNP models captured more variation in expression than the top cis-eQTL (median 2 fold improvement); (b) predicted expression based on multi-SNP models was associated (FDR<0.01) with metabolite levels for 826 unique gene-metabolite pairs, but, after step-wise conditional analyses, 90% were dominated by a single eQTL SNP; (c) amongst the 35% of associations where a SNP in the expression model was a significant cis-eQTL and metabolomic-QTL (met-QTL), 92% demonstrated colocalization between these signals, but interpretation was often complicated by incomplete overlap of QTLs in multi-SNP models; (d) using a \"truth\" set of causal genes at 61 met-QTLs, the sensitivity was high (67%), but the positive predictive value was low, as only 8% of TWAS associations at met-QTLs involved true causal genes. These results guide the interpretation of TWAS and highlight the need for corroborative data to provide confident assignment of causality.

genomics↗

bioRxiv2019

Identification of type 2 diabetes loci in 433,540 East Asian individuals

Meta-analyses of genome-wide association studies (GWAS) have identified >240 loci associated with type 2 diabetes (T2D), however most loci have been identified in analyses of European-ancestry individuals. To examine T2D risk in East Asian individuals, we meta-analyzed GWAS data in 77,418 cases and 356,122 controls. In the main analysis, we identified 298 distinct association signals at 178 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 56 loci newly implicated in T2D predisposition. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. New associations include signals in/near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect muscle and adipose differentiation. At another locus, eQTLs at two overlapping T2D signals act through two genes, NKX6-3 and ANK1, in different tissues. Association studies in diverse populations identify additional loci and elucidate disease genes, biology, and pathways.\n\nType 2 diabetes (T2D) is a common metabolic disease primarily caused by insufficient insulin production and/or secretion by the pancreatic {beta} cells and insulin resistance in peripheral tissues1. Most genetic loci associated with T2D have been identified in populations of European (EUR) ancestry, including a recent meta-analysis of genome-wide association studies (GWAS) of nearly 900,000 individuals of European ancestry that identified >240 loci influencing the risk of T2D2. Differences in allele frequency between ancestries affect the power to detect associations within a population, particularly among variants rare or monomorphic in one population but more frequent in another3,4. Although smaller than studies in European populations, a recent T2D meta-analysis in almost 200,000 Japanese individuals identified 28 additional loci4. The relative contributions of different pathways to the pathophysiology of T2D may also differ between ancestry groups. For example, in East Asian (EAS) populations, T2D prevalence is greater than in European populations among people of similar body mass index (BMI) or waist circumference5. We performed the largest meta-analysis of East Asian individuals to identify new genetic associations and provide insight into T2D pathogenesis.

genetics↗

bioRxiv2018

Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data

Genome wide association studies (GWAS) have identified several hundred susceptibility loci for Type 2 Diabetes (T2D). One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes. However, the causal variants identified by GWAS mostly fall into non-coding sequence, complicating the task of defining the effector transcripts through which they operate. Here, we describe implementation of an analytical pipeline to address this question. First, we integrate multiple sources of genetic, genomic, and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals. Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner Tree approach) which uses external, experimentally-confirmed protein-protein interaction (PPI) data to generate high confidence subnetworks. Third, we use GWAS data to test the T2D-association enrichment of the \"non-seed\" proteins introduced into the network, as a measure of the overall functional connectivity of the network. We find: (a) non-seed proteins in the T2D protein-interaction network so generated (comprising 705 nodes) are enriched for association to T2D (p=0.0014) but not control traits; (b) stronger T2D-enrichment for islets than other tissues when we use RNA expression data to generate tissue-specific PPI networks; and (c) enhanced enrichment (p=3.9xl0-5) when we combine analysis of the islet-specific PPI network with a focus on the subset of T2D GWAS loci which act through defective insulin secretion. These analyses reveal a pattern of non-random functional connectivity between causal candidate genes atT2D GWAS loci, and highlight the products of genes including YWHAG, SMAD4 or CDK2 as contributors to T2D-relevant islet dysfunction. The approach we describe can be applied to other complex genetic and genomic data sets, facilitating integration of diverse data types into disease-associated networks.\n\nAuthor summaryWe were interested in the following question: as we discover more and more genetic variants associated with a complex disease, such as type 2 diabetes, will the biological pathways implicated by those variants proliferate, or will the biology converge onto a more limited set of aetiological processes? To address this, we first took the 1895 genes that map to ~100 type 2 diabetes association signals, and pruned these to a set of 451 for which combined genetic, genomic and biological evidence assigned the strongest candidacy with respect to type 2 diabetes pathogenesis. We then sought to maximally connect these genes within a curated protein-protein interaction network. We found that proteins brought into the resulting diabetes interaction network were themselves enriched for diabetes association signals as compared to appropriate control proteins. Furthermore, when we used tissue-specific RNA abundance data to filter the generic protein-protein network, we found that the enrichment for type 2 diabetes association signals was enhanced within a network filtered for pancreatic islet expression, particularly when we selected the subset of diabetes association signals acting through reduced insulin secretion. Our data demonstrate convergence of the biological processes involved in type 2 diabetes pathogenesis and highlight novel contributors.

bioinformatics↗

bioRxiv2017

Electrophysiological properties of human β-cell lines EndoC-βH1 and -βH2 conform with human β-cells

The electrophysiological and secretory properties of the human {beta}-cell lines EndoC-{beta}H1 and EndoC-{beta}H2 were investigated. Both cell lines respond to glucose (6-20mM) with 2-to 3-fold stimulation of insulin secretion, an effect that was mimicked by tolbutamide (0.2mM) and reversed by diazoxide (0.5mM). Glucose-induced insulin release correlated with an elevation of [Ca2+]i, membrane depolarization and increased action potential firing. KATP channel activity at 1mM glucose is low and increasing glucose to 6 or 20mM reduced KATP channel activity to the same extent as application of the KATP channel blocker tolbutamide (0.2mM). The upstroke of the action potentials in EndoC-{beta}H1 and -{beta}H2 cells observed at high glucose principally reflects activation of L- and P/Q-type Ca2+ channels with some small contribution of TTX-sensitive Na+ channels. Action potential repolarization involves activation of voltage-gated Kv2.2 channels and large-conductance Ca2+-activated K+ channels. Exocytosis (measured by measurements of membrane capacitance) was triggered by membrane depolarizations >10ms to membrane potentials above -30mV. Both cell lines were well-granulated (6,000-15,000 granules/cell) and granules consisted of a central insulin core surrounded by a clear halo. We conclude that the EndoC-{beta}H1 and -{beta}H2 cells share many features of primary human {beta}-cells and that they represent a useful experimental model.

cell biology↗

bioRxiv2017

Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci

Human genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D). However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS). We characterised patterns of chromatin accessibility (ATAC-seq, n=17) and DNA methylation (whole-genome bisulphite sequencing, n=10) in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks. We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant. At several loci (including CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants. The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.

genomics↗