Genetic regulatory signatures underlying islet gene expression and type 2 diabetes

The majority of genetic variants associated with type 2 diabetes (T2D) are located outside of genes in noncoding regions that may regulate gene expression in disease-relevant tissues, like pancreatic islets. Here, we present the largest integrated analysis to date of high-resolution, high-throughput human islet molecular profiling data to characterize the genome (DNA), epigenome (DNA packaging), and transcriptome (gene expression). We find that T2D genetic variants are enriched in regions of the genome where transcription Regulatory Factor X (RFX) is predicted to bind in an islet-specific manner. Genetic variants that increase T2D risk are predicted to disrupt RFX binding, providing a molecular mechanism to explain how the genome can influence the epigenome, modulating gene expression and ultimately T2D risk

Cellularity and Adipogenic Profile of the Abdominal Subcutaneous Adipose Tissue From Obese Adolescents: Association With Insulin Resistance and Hepatic Steatosis

We explored whether the distribution of adipose cell size, the estimated total number of adipose cells, and the expression of adipogenic genes in subcutaneous adipose tissue are linked to the phenotype of high visceral and low subcutaneous fat depots in obese adolescents. A total of 38 adolescents with similar degrees of obesity agreed to have a subcutaneous periumbilical adipose tissue biopsy, in addition to metabolic (oral glucose tolerance test and hyperinsulinemic euglycemic clamp) and imaging studies (MRI, DEXA, (1)H-NMR). Subcutaneous periumbilical adipose cell-size distribution and the estimated total number of subcutaneous adipose cells were obtained from tissue biopsy samples fixed in osmium tetroxide and analyzed by Beckman Coulter Multisizer. The adipogenic capacity was measured by Affymetrix GeneChip and quantitative RT-PCR. Subjects were divided into two groups: high versus low ratio of visceral to visceral + subcutaneous fat (VAT/[VAT+SAT]). The cell-size distribution curves were significantly different between the high and low VAT/(VAT+SAT) groups, even after adjusting for age, sex, and ethnicity (MANOVA P = 0.035). Surprisingly, the fraction of large adipocytes was significantly lower (P <0.01) in the group with high VAT/(VAT+SAT), along with the estimated total number of large adipose cells (P <0.05), while the mean diameter was increased (P <0.01). From the microarray analyses emerged a lower expression of lipogenesis/adipogenesis markers (sterol regulatory element binding protein-1, acetyl-CoA carboxylase, fatty acid synthase) in the group with high VAT/(VAT+SAT), which was confirmed by RT-PCR. A reduced lipo-/adipogenic capacity, fraction, and estimated number of large subcutaneous adipocytes may contribute to the abnormal distribution of abdominal fat and hepatic steatosis, as well as to insulin resistance in obese adolescent

Chaste : Cancer, Heart and Soft Tissue Environment

Funding: UK Engineering and Physical Sciences Research Council [grant number EP/N509711/1 (J.K.)].Chaste (Cancer, Heart And Soft Tissue Environment) is an open source simulation package for the numerical solution of mathematical models arising in physiology and biology. To date, Chaste development has been driven primarily by applications that include continuum modelling of cardiac electrophysiology (‘Cardiac Chaste’), discrete cell-based modelling of soft tissues (‘Cell-based Chaste’), and modelling of ventilation in lungs (‘Lung Chaste’). Cardiac Chaste addresses the need for a high-performance, generic, and verified simulation framewor kfor cardiac electrophysiology that is freely available to the scientific community. Cardiac chaste provides a software package capable of realistic heart simulations that is efficient, rigorously tested, and runs on HPC platforms. Cell-based Chaste addresses the need for efficient and verified implementations of cell-based modelling frameworks, providing a set of extensible tools for simulating biological tissues. Computational modelling, along with live imaging techniques, plays an important role in understanding the processes of tissue growth and repair. A wide range of cell-based modelling frameworks have been developed that have each been successfully applied in a range of biological applications. Cell-based Chaste includes implementations of the cellular automaton model, the cellular Potts model, cell-centre models with cell representations as overlapping spheres or Voronoi tessellations, and the vertex model. Lung Chaste addresses the need for a novel, generic and efficient lung modelling software package that is both tested and verified. It aims to couple biophysically-detailed models of airway mechanics with organ-scale ventilation models in a package that is freely available to the scientific community.Publisher PDFPeer reviewe

Multiomic Profiling Identifies cis-Regulatory Networks Underlying Human Pancreatic β Cell Identity and Function.

EndoC-βH1 is emerging as a critical human β cell model to study the genetic and environmental etiologies of β cell (dys)function and diabetes. Comprehensive knowledge of its molecular landscape is lacking, yet required, for effective use of this model. Here, we report chromosomal (spectral karyotyping), genetic (genotyping), epigenomic (ChIP-seq and ATAC-seq), chromatin interaction (Hi-C and Pol2 ChIA-PET), and transcriptomic (RNA-seq and miRNA-seq) maps of EndoC-βH1. Analyses of these maps define known (e.g., PDX1 and ISL1) and putative (e.g., PCSK1 and mir-375) β cell-specific transcriptional cis-regulatory networks and identify allelic effects on cis-regulatory element use. Importantly, comparison with maps generated in primary human islets and/or β cells indicates preservation of chromatin looping but also highlights chromosomal aberrations and fetal genomic signatures in EndoC-βH1. Together, these maps, and a web application we created for their exploration, provide important tools for the design of experiments to probe and manipulate the genetic programs governing β cell identity and (dys)function in diabetes

A Common Type 2 Diabetes Risk Variant Potentiates Activity of an Evolutionarily Conserved Islet Stretch Enhancer and Increases C2CD4A and C2CD4B Expression

Genome-wide association studies (GWASs) and functional genomics approaches implicate enhancer disruption in islet dysfunction and type 2 diabetes (T2D) risk. We applied genetic fine-mapping and functional (epi)genomic approaches to a T2D- and proinsulin-associated 15q22.2 locus to identify a most likely causal variant, determine its direction of effect, and elucidate plausible target genes. Fine-mapping and conditional analyses of proinsulin levels of 8,635 non-diabetic individuals from the METSIM study support a single association signal represented by a cluster of 16 strongly associated (p &lt; 10−17) variants in high linkage disequilibrium (r2 &gt; 0.8) with the GWAS index SNP rs7172432. These variants reside in an evolutionarily and functionally conserved islet and β cell stretch or super enhancer; the most strongly associated variant (rs7163757, p = 3 × 10−19) overlaps a conserved islet open chromatin site. DNA sequence containing the rs7163757 risk allele displayed 2-fold higher enhancer activity than the non-risk allele in reporter assays (p &lt; 0.01) and was differentially bound by β cell nuclear extract proteins. Transcription factor NFAT specifically potentiated risk-allele enhancer activity and altered patterns of nuclear protein binding to the risk allele in vitro, suggesting that it could be a factor mediating risk-allele effects. Finally, the rs7163757 proinsulin-raising and T2D risk allele (C) was associated with increased expression of C2CD4B, and possibly C2CD4A, both of which were induced by inflammatory cytokines, in human islets. Together, these data suggest that rs7163757 contributes to genetic risk of islet dysfunction and T2D by increasing NFAT-mediated islet enhancer activity and modulating C2CD4B, and possibly C2CD4A, expression in (patho)physiologic states

A type 2 diabetes-associated functional regulatory variant in a pancreatic islet enhancer at the ADCY5 locus

Molecular mechanisms remain unknown for most type 2 diabetes genome-wide association study identified loci. Variants associated with type 2 diabetes and fasting glucose levels reside in introns of ADCY5, a gene that encodes adenylate cyclase 5. Adenylate cyclase 5 catalyzes the production of cyclic AMP, which is a second messenger molecule involved in cell signaling and pancreatic β-Cell insulin secretion. We demonstrated that type 2 diabetes risk alleles are associated with decreased ADCY5 expression in human islets and examined candidate variants for regulatory function. rs11708067 overlaps a predicted enhancer region in pancreatic islets. The type 2 diabetes risk rs11708067-A allele showed fewer H3K27ac ChIP-seq reads in human islets, lower transcriptional activity in reporter assays in rodent b-cells (rat 832/13 and mouse MIN6), and increased nuclear protein binding compared with the rs11708067-G allele. Homozygous deletion of the orthologous enhancer region in 832/13 cells resulted in a 64% reduction in expression level of Adcy5, but not adjacent gene Sec22a, and a 39% reduction in insulin secretion. Together, these data suggest that rs11708067-A risk allele contributes to type 2 diabetes by disrupting an islet enhancer, which results in reduced ADCY5 expression and impaired insulin secretion

Comparing individual-based approaches to modelling the self-organization of multicellular tissues.

The coordinated behaviour of populations of cells plays a central role in tissue growth and renewal. Cells react to their microenvironment by modulating processes such as movement, growth and proliferation, and signalling. Alongside experimental studies, computational models offer a useful means by which to investigate these processes. To this end a variety of cell-based modelling approaches have been developed, ranging from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended shapes. However, it remains unclear how these approaches compare when applied to the same biological problem, and what differences in behaviour are due to different model assumptions and abstractions. Here, we exploit the availability of an implementation of five popular cell-based modelling approaches within a consistent computational framework, Chaste (http://www.cs.ox.ac.uk/chaste). This framework allows one to easily change constitutive assumptions within these models. In each case we provide full details of all technical aspects of our model implementations. We compare model implementations using four case studies, chosen to reflect the key cellular processes of proliferation, adhesion, and short- and long-range signalling. These case studies demonstrate the applicability of each model and provide a guide for model usage