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.
The RNA binding protein Larp1 was originally shown to be involved in spermatogenesis, embryogenesis and cell-cycle progression in Drosophila. Our data show that mammalian Larp1 is found in a complex with poly A binding protein and eukaryote initiation factor 4E and is associated with 60S and 80S ribosomal subunits. A reduction in Larp1 expression by siRNA inhibits global protein synthesis rates and results in mitotic arrest and delayed cell migration. Consistent with these data we show that Larp1 protein is present at the leading edge of migrating cells and interacts directly with cytoskeletal components. Taken together, these data suggest a role for Larp1 in facilitating the synthesis of proteins required for cellular remodelling and migration.
Most genetic association signals for type 2 diabetes risk are located in noncoding regions of the genome, hindering translation into molecular mechanisms. Physiological studies have shown a majority of disease-associated variants to exert their effects through pancreatic islet dysfunction. Systematically characterizing the role of regional transcripts in b-cell function could identify the underlying disease-causing genes, but large-scale studies in human cellular models have previously been impractical. We developed a robust and scalable strategy based on arrayed gene silencing in the human b-cell line EndoCbH1. In a screen of 300 positional candidates selected from 75 type 2 diabetes regions, each gene was assayed for effects on multiple disease-relevant phenotypes, including insulin secretion and cellular proliferation. We identified a total of 45 genes involved in b-cell function, pointing to possible causal mechanisms at 37 diseaseassociated loci. The results showed a strong enrichment for genes implicated in monogenic diabetes. Selected effects were validated in a follow-up study, including several genes (ARL15, ZMIZ1, and THADA) with previously unknown or poorly described roles in b-cell biology. We have demonstrated the feasibility of systematic functional screening in a human b-cell model and successfully prioritized plausible disease-causing genes at more than half of the regions investigated.Type 2 diabetes risk is determined by a complex interplay between environmental and genetic factors, with heritability estimates ranging from 20% to 80% (1). Over the past decade, genome-wide association studies (GWAS) of everincreasing size have discovered more than 100 regions of the genome (loci) associated with type 2 diabetes risk (2). Studies in individuals with diabetes have demonstrated that a large number of these association signals exert their effects on disease susceptibility through pancreatic islet dysfunction (3).Despite these advances, progress in translating genetic findings into disease biology has been relatively slow. The majority of risk variants are located in noncoding regions of the genome and pinpointing the underlying causal genes or "effector transcripts" has proved challenging (4). Recent efforts have focused on identifying structural or functional links between association signals and regional genes (5,6). A complementary strategy uses candidate gene biology to prioritize genes located near association signals. High-throughput screening could facilitate the identification of genes implicated in b-cell function and thereby highlight potential effector transcripts at type 2 diabetes GWAS loci. To date, such approaches have been limited by the inadequacies of available human cellular models and the high cost of insulin immunoassays (;$2 per data point), the gold standard for measuring insulin. To circumvent these issues, previous studies have relied on rodent b-cell models and either used reporter assays as a proxy for insulin measurements or focused on cellular proliferation (7-11).Recent...
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