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

Background: 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. Methods: 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-ßH1) and contrasted these maps with Hi-C maps in EndoC-ßH1 cells and human islets and a promoter capture Hi-C map in human islets. Results: We found improvements in assay sensitivity of up to 33-fold and resolved ~3.6X 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. Conclusions: High resolution chromatin interaction profiles at selectively targeted loci can complement genome- and promoter-wide maps.

The nuclear receptor THRB facilitates differentiation of human PSCs into more mature hepatocytes.

To understand the mechanisms regulating the in vitro maturation of hPSC-derived hepatocytes, we developed a 3D differentiation system and compared gene regulatory elements in human primary hepatocytes with those in hPSC-hepatocytes that were differentiated in 2D or 3D conditions by RNA-seq, ATAC-seq, and H3K27Ac ChIP-seq. Regulome comparisons showed a reduced enrichment of thyroid receptor THRB motifs in accessible chromatin and active enhancers without a reduced transcription of THRB. The addition of thyroid hormone T3 increased the binding of THRB to the CYP3A4 proximal enhancer, restored the super-enhancer status and gene expression of NFIC, and reduced the 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 hPSC-hepatocytes resulted in the engraftment of human hepatocytes into the mouse liver without disrupting normal liver histology. This work implicates the environmental factor-nuclear receptor axis in regulating the maturation of hPSC-hepatocytes.

Deep learning reveals 3D atherosclerotic plaque distribution and composition.

Complications of atherosclerosis are the leading cause of morbidity and mortality worldwide. Various genetically modified mouse models are used to investigate disease trajectory with classical histology, currently the preferred methodology to elucidate plaque composition. Here, we show the strength of light-sheet fluorescence microscopy combined with deep learning image analysis for characterising and quantifying plaque burden and composition in whole aorta specimens. 3D imaging is a non-destructive method that requires minimal ex vivo handling and can be up-scaled to large sample sizes. Combined with deep learning, atherosclerotic plaque in mice can be identified without any ex vivo staining due to the autofluorescent nature of the tissue. The aorta and its branches can subsequently be segmented to determine how anatomical position affects plaque composition and progression. Here, we find the highest plaque accumulation in the aortic arch and brachiocephalic artery. Simultaneously, aortas can be stained for markers of interest (for example the pan immune cell marker CD45) and quantified. In ApoE-/- mice we observe that levels of CD45 reach a plateau after which increases in plaque volume no longer correlate to immune cell infiltration. All underlying code is made publicly available to ease adaption of the method.

Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D.

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.

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

Meta-analyses of genome-wide association studies (GWAS) have identified more than 240 loci that are associated with type 2 diabetes (T2D)1,2; however, most of these loci have been identified in analyses of individuals with European ancestry. Here, to examine T2D risk in East Asian individuals, we carried out a meta-analysis of GWAS data from 77,418 individuals with T2D and 356,122 healthy control individuals. In the main analysis, we identified 301 distinct association signals at 183 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 61 loci that are newly implicated in predisposition to T2D. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. Previously undescribed associations include signals in or near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect the differentiation of muscle and adipose cells3. At another locus, expression quantitative trait loci at two overlapping T2D signals affect two genes-NKX6-3 and ANK1-in different tissues4-6. Association studies in diverse populations identify additional loci and elucidate disease-associated genes, biology, and pathways.

Semantic text mining in early drug discovery for type 2 diabetes.

BACKGROUND: Surveying the scientific literature is an important part of early drug discovery; and with the ever-increasing amount of biomedical publications it is imperative to focus on the most interesting articles. Here we present a project that highlights new understanding (e.g. recently discovered modes of action) and identifies potential drug targets, via a novel, data-driven text mining approach to score type 2 diabetes (T2D) relevance. We focused on monitoring trends and jumps in T2D relevance to help us be timely informed of important breakthroughs. METHODS: We extracted over 7 million n-grams from PubMed abstracts and then clustered around 240,000 linked to T2D into almost 50,000 T2D relevant 'semantic concepts'. To score papers, we weighted the concepts based on co-mentioning with core T2D proteins. A protein's T2D relevance was determined by combining the scores of the papers mentioning it in the five preceding years. Each week all proteins were ranked according to their T2D relevance. Furthermore, the historical distribution of changes in rank from one week to the next was used to calculate the significance of a change in rank by T2D relevance for each protein. RESULTS: We show that T2D relevant papers, even those not mentioning T2D explicitly, were prioritised by relevant semantic concepts. Well known T2D proteins were therefore enriched among the top scoring proteins. Our 'high jumpers' identified important past developments in the apprehension of how certain key proteins relate to T2D, indicating that our method will make us aware of future breakthroughs. In summary, this project facilitated keeping up with current T2D research by repeatedly providing short lists of potential novel targets into our early drug discovery pipeline.

Reduced somatostatin signalling leads to hypersecretion of glucagon in mice fed a high-fat diet.

OBJECTIVES: Elevated plasma glucagon is an early symptom of diabetes, occurring in subjects with impaired glucose regulation. Here, we explored alpha-cell function in female mice fed a high-fat diet (HFD). METHODS: Female mice expressing the Ca2+ indicator GCaMP3 specifically in alpha-cells were fed a high-fat or control (CTL) diet. We then conducted in vivo phenotyping of these mice, as well as experiments on isolated (ex vivo) islets and in the in situ perfused pancreas. RESULTS: In HFD-fed mice, fed plasma glucagon levels were increased and glucagon secretion from isolated islets and in the perfused mouse pancreas was also elevated. In mice fed a CTL diet, increasing glucose reduced intracellular Ca2+ ([Ca2+]i) oscillation frequency and amplitude. This effect was also observed in HFD mice; however, both the frequency and amplitude of the [Ca2+]i oscillations were higher than those in CTL alpha-cells. Given that alpha-cells are under strong paracrine control from neighbouring somatostatin-secreting delta-cells, we hypothesised that this elevation of alpha-cell output was due to a lack of somatostatin (SST) secretion. Indeed, SST secretion in isolated islets from HFD-fed mice was reduced but exogenous SST also failed to suppress glucagon secretion and [Ca2+]i activity from HFD alpha-cells, in contrast to observations in CTL mice. CONCLUSIONS: These findings suggest that reduced delta-cell function, combined with intrinsic changes in alpha-cells including sensitivity to somatostatin, accounts for the hyperglucagonaemia in mice fed a HFD.

A Multi-tissue Transcriptome Analysis of Human Metabolites Guides 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 phenotype and gene expression. We developed an iterative modeling scheme for obtaining multi-SNP models of gene expression and applied this framework to generate expression models for 43 human tissues from the Genotype-Tissue Expression (GTEx) Project. We characterized the performance of single- and multi-SNP 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 did the top cis-eQTL (median 2-fold improvement); (B) predicted expression based on multi-SNP models was associated (false discovery rate < 0.01) with metabolite levels for 826 unique gene-metabolite pairs, but, after stepwise conditional analyses, 90% were dominated by a single eQTL SNP; (C) among 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; and (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 (19% when restricted to colocalized 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.

Loss of ZnT8 function protects against diabetes by enhanced insulin secretion.

A rare loss-of-function allele p.Arg138* in SLC30A8 encoding the zinc transporter 8 (ZnT8), which is enriched in Western Finland, protects against type 2 diabetes (T2D). We recruited relatives of the identified carriers and showed that protection was associated with better insulin secretion due to enhanced glucose responsiveness and proinsulin conversion, particularly when compared with individuals matched for the genotype of a common T2D-risk allele in SLC30A8, p.Arg325. In genome-edited human induced pluripotent stem cell (iPSC)-derived ß-like cells, we establish that the p.Arg138* allele results in reduced SLC30A8 expression due to haploinsufficiency. In human ß cells, loss of SLC30A8 leads to increased glucose responsiveness and reduced KATP channel function similar to isolated islets from carriers of the T2D-protective allele p.Trp325. These data position ZnT8 as an appealing target for treatment aimed at maintaining insulin secretion capacity in T2D.

AKR1D1 is a novel regulator of metabolic phenotype in human hepatocytes and is dysregulated in non-alcoholic fatty liver disease.

OBJECTIVE: Non-alcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome. Steroid hormones and bile acids are potent regulators of hepatic carbohydrate and lipid metabolism. Steroid 5ß-reductase (AKR1D1) is highly expressed in human liver where it inactivates steroid hormones and catalyzes a fundamental step in bile acid synthesis. METHODS: Human liver biopsies were obtained from 34 obese patients and AKR1D1 mRNA expression levels were measured using qPCR. Genetic manipulation of AKR1D1 was performed in human HepG2 and Huh7 liver cell lines. Metabolic assessments were made using transcriptome analysis, western blotting, mass spectrometry, clinical biochemistry, and enzyme immunoassays. RESULTS: In human liver biopsies, AKR1D1 expression decreased with advancing steatosis, fibrosis and inflammation. Expression was decreased in patients with type 2 diabetes. In human liver cell lines, AKR1D1 knockdown decreased primary bile acid biosynthesis and steroid hormone clearance. RNA-sequencing identified disruption of key metabolic pathways, including insulin action and fatty acid metabolism. AKR1D1 knockdown increased hepatocyte triglyceride accumulation, insulin sensitivity, and glycogen synthesis, through increased de novo lipogenesis and decreased ß-oxidation, fueling hepatocyte inflammation. Pharmacological manipulation of bile acid receptor activation prevented the induction of lipogenic and carbohydrate genes, suggesting that the observed metabolic phenotype is driven through bile acid rather than steroid hormone availability. CONCLUSIONS: Genetic manipulation of AKR1D1 regulates the metabolic phenotype of human hepatoma cell lines, driving steatosis and inflammation. Taken together, the observation that AKR1D1 mRNA is down-regulated with advancing NAFLD suggests that it may have a crucial role in the pathogenesis and progression of the disease.

Diabetes causes marked inhibition of mitochondrial metabolism in pancreatic ß-cells.

Diabetes is a global health problem caused primarily by the inability of pancreatic ß-cells to secrete adequate levels of insulin. The molecular mechanisms underlying the progressive failure of ß-cells to respond to glucose in type-2 diabetes remain unresolved. Using a combination of transcriptomics and proteomics, we find significant dysregulation of major metabolic pathways in islets of diabetic ßV59M mice, a non-obese, eulipidaemic diabetes model. Multiple genes/proteins involved in glycolysis/gluconeogenesis are upregulated, whereas those involved in oxidative phosphorylation are downregulated. In isolated islets, glucose-induced increases in NADH and ATP are impaired and both oxidative and glycolytic glucose metabolism are reduced. INS-1 ß-cells cultured chronically at high glucose show similar changes in protein expression and reduced glucose-stimulated oxygen consumption: targeted metabolomics reveals impaired metabolism. These data indicate hyperglycaemia induces metabolic changes in ß-cells that markedly reduce mitochondrial metabolism and ATP synthesis. We propose this underlies the progressive failure of ß-cells in diabetes.

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

BACKGROUND: 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 a non-coding sequence, complicating the task of defining the effector transcripts through which they operate. METHODS: 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 sub-networks. 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. RESULTS: 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 = 3.9 × 10- 5) when we combine the analysis of the islet-specific PPI network with a focus on the subset of T2D GWAS loci which act through defective insulin secretion. CONCLUSIONS: These analyses reveal a pattern of non-random functional connectivity between candidate causal genes at T2D GWAS loci and highlight the products of genes including YWHAG, SMAD4 or CDK2 as potential contributors to T2D-relevant islet dysfunction. The approach we describe can be applied to other complex genetic and genomic datasets, facilitating integration of diverse data types into disease-associated networks.

Electrophysiological properties of human beta-cell lines EndoC-ßH1 and -ßH2 conform with human beta-cells.

Limited access to human islets has prompted the development of human beta cell models. The human beta cell lines EndoC-ßH1 and EndoC-ßH2 are increasingly used by the research community. However, little is known of their electrophysiological and secretory properties. Here, we monitored parameters that constitute the glucose-triggering pathway of insulin release. Both cell lines respond to glucose (6 and 20 mM) with 2- to 3-fold stimulation of insulin secretion which correlated with an elevation of [Ca2+]i, membrane depolarisation and increased action potential firing. Similar to human primary beta cells, KATP channel activity is low at 1 mM glucose and is further reduced upon increasing glucose concentration; an effect that was mimicked by the KATP channel blocker tolbutamide. The upstroke of the action potentials reflects the activation of Ca2+ channels with some small contribution of TTX-sensitive Na+ channels. The repolarisation involves activation of voltage-gated Kv2.2 channels and large-conductance Ca2+-activated K+ channels. Exocytosis presented a similar kinetics to human primary beta cells. The ultrastructure of these cells shows insulin vesicles composed of an electron-dense core surrounded by a thin clear halo. We conclude that the EndoC-ßH1 and -ßH2 cells share many features of primary human ß-cells and thus represent a useful experimental model.

Understanding human fetal pancreas development using subpopulation sorting, RNA sequencing and single-cell profiling.

To decipher the populations of cells present in the human fetal pancreas and their lineage relationships, we developed strategies to isolate pancreatic progenitors, endocrine progenitors and endocrine cells. Transcriptome analysis of the individual populations revealed a large degree of conservation among vertebrates in the drivers of gene expression changes that occur at different steps of differentiation, although notably, sometimes, different members of the same gene family are expressed. The transcriptome analysis establishes a resource to identify novel genes and pathways involved in human pancreas development. Single-cell profiling further captured intermediate stages of differentiation and enabled us to decipher the sequence of transcriptional events occurring during human endocrine differentiation. Furthermore, we evaluate how well individual pancreatic cells derived in vitro from human pluripotent stem cells mirror the natural process occurring in human fetuses. This comparison uncovers a few differences at the progenitor steps, a convergence at the steps of endocrine induction, and the current inability to fully resolve endocrine cell subtypes in vitro.

Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation.

We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.

Patterns of differential gene expression in a cellular model of human islet development, and relationship to type 2 diabetes predisposition.

AIMS/HYPOTHESIS: Most type 2 diabetes-associated genetic variants identified via genome-wide association studies (GWASs) appear to act via the pancreatic islet. Observed defects in insulin secretion could result from an impact of these variants on islet development and/or the function of mature islets. Most functional studies have focused on the latter, given limitations regarding access to human fetal islet tissue. Capitalising upon advances in in vitro differentiation, we characterised the transcriptomes of human induced pluripotent stem cell (iPSC) lines differentiated along the pancreatic endocrine lineage, and explored the contribution of altered islet development to the pathogenesis of type 2 diabetes. METHODS: We performed whole-transcriptome RNA sequencing of human iPSC lines from three independent donors, at baseline and at seven subsequent stages during in vitro islet differentiation. Differentially expressed genes (q < 0.01, log2 fold change [FC] > 1) were assigned to the stages at which they were most markedly upregulated. We used these data to characterise upstream transcription factors directing different stages of development, and to explore the relationship between RNA expression profiles and genes mapping to type 2 diabetes GWAS signals. RESULTS: We identified 9409 differentially expressed genes across all stages, including many known markers of islet development. Integration of differential expression data with information on transcription factor motifs highlighted the potential contribution of REST to islet development. Over 70% of genes mapping within type 2 diabetes-associated credible intervals showed peak differential expression during islet development, and type 2 diabetes GWAS loci of largest effect (including TCF7L2; log2FC = 1.2; q = 8.5 × 10-10) were notably enriched in genes differentially expressed at the posterior foregut stage (q = 0.002), as calculated by gene set enrichment analyses. In a complementary analysis of enrichment, genes differentially expressed in the final, beta-like cell stage of in vitro differentiation were significantly enriched (hypergeometric test, permuted p value <0.05) for genes within the credible intervals of type 2 diabetes GWAS loci. CONCLUSIONS/INTERPRETATION: The present study characterises RNA expression profiles during human islet differentiation, identifies potential transcriptional regulators of the differentiation process, and suggests that the inherited predisposition to type 2 diabetes is partly mediated through modulation of islet development. DATA AVAILABILITY: Sequence data for this study has been deposited at the European Genome-phenome Archive (EGA), under accession number EGAS00001002721.

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.

Integrative network analysis highlights biological processes underlying GLP-1 stimulated insulin secretion: A DIRECT study.

Glucagon-like peptide 1 (GLP-1) stimulated insulin secretion has a considerable heritable component as estimated from twin studies, yet few genetic variants influencing this phenotype have been identified. We performed the first genome-wide association study (GWAS) of GLP-1 stimulated insulin secretion in non-diabetic individuals from the Netherlands Twin register (n = 126). This GWAS was enhanced using a tissue-specific protein-protein interaction network approach. We identified a beta-cell protein-protein interaction module that was significantly enriched for low gene scores based on the GWAS P-values and found support at the network level in an independent cohort from Tübingen, Germany (n = 100). Additionally, a polygenic risk score based on SNPs prioritized from the network was associated (P < 0.05) with glucose-stimulated insulin secretion phenotypes in up to 5,318 individuals in MAGIC cohorts. The network contains both known and novel genes in the context of insulin secretion and is enriched for members of the focal adhesion, extracellular-matrix receptor interaction, actin cytoskeleton regulation, Rap1 and PI3K-Akt signaling pathways. Adipose tissue is, like the beta-cell, one of the target tissues of GLP-1 and we thus hypothesized that similar networks might be functional in both tissues. In order to verify peripheral effects of GLP-1 stimulation, we compared the transcriptome profiling of ob/ob mice treated with liraglutide, a clinically used GLP-1 receptor agonist, versus baseline controls. Some of the upstream regulators of differentially expressed genes in the white adipose tissue of ob/ob mice were also detected in the human beta-cell network of genes associated with GLP-1 stimulated insulin secretion. The findings provide biological insight into the mechanisms through which the effects of GLP-1 may be modulated and highlight a potential role of the beta-cell expressed genes RYR2, GDI2, KIAA0232, COL4A1 and COL4A2 in GLP-1 stimulated insulin secretion.

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

This corrects the article DOI: 10.1038/sdata.2017.179.