Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci

Genetic Disruption of Adenosine Kinase in Mouse Pancreatic β-Cells Protects Against High-Fat Diet-Induced Glucose Intolerance

Islet β-cells adapt to insulin resistance through increased insulin secretion and expansion. Type 2 diabetes typically occurs when prolonged insulin resistance exceeds the adaptive capacity of β-cells. Our prior screening efforts led to the discovery that adenosine kinase (ADK) inhibitors stimulate β-cell replication. Here, we evaluated whether ADK disruption in mouse β-cells affects β-cell mass and/or protects against high-fat diet (HFD)-induced glucose dysregulation. Mice targeted at the Adk locus were bred to Rip-Cre and Ins1-Cre/ERT1Lphi mice to enable constitutive (βADKO) and conditional (iβADKO) disruption of ADK expression in β-cells, respectively. Weight gain, glucose tolerance, insulin sensitivity, and glucose-stimulated insulin secretion (GSIS) were longitudinally monitored in normal chow (NC)-fed and HFD-fed mice. In addition, β-cell mass and replication were measured by immunofluorescence-based islet morphometry. NC-fed adult βADKO and iβADKO mice displayed glucose tolerance, insulin tolerance and β-cell mass comparable to control animals. By contrast, HFD-fed βADKO and iβADKO animals had improved glucose tolerance and increased in vivo GSIS. Improved glucose handling was associated with increased β-cell replication and mass. We conclude that ADK expression negatively regulates the adaptive β-cell response to HFD challenge. Therefore, modulation of ADK activity is a potential strategy for enhancing the adaptive β-cell response.

GARLIC: a bioinformatic toolkit for aetiologically connecting diseases and cell type-specific regulatory maps

Genome-wide association studies (GWAS) have emerged as a powerful tool to uncover the genetic basis of human common diseases, which often show a complex, polygenic and multi-factorial aetiology. These studies have revealed that 70–90% of all single nucleotide polymorphisms (SNPs) associated with common complex diseases do not occur within genes (i.e. they are non-coding), making the discovery of disease-causative genetic variants and the elucidation of the underlying pathological mechanisms far from straightforward. Based on emerging evidences suggesting that disease-associated SNPs are frequently found within cell type-specific regulatory sequences, here we present GARLIC (GWAS-based Prediction Toolkit for Connecting Diseases and Cell Types), a user-friendly, multi-purpose software with an associated database and online viewer that, using global maps of cis-regulatory elements, can aetiologically connect human diseases with relevant cell types. Additionally, GARLIC can be used to retrieve potential disease-causative genetic variants overlapping regulatory sequences of interest. Overall, GARLIC can satisfy several important needs within the field of medical genetics, thus potentially assisting in the ultimate goal of uncovering the elusive and complex genetic basis of common human disorders.

Human genetics as a model for target validation: finding new therapies for diabetes

Type 2 diabetes is a global epidemic with major effects on healthcare expenditure and quality of life. Currently available treatments are inadequate for the prevention of comorbidities, yet progress towards new therapies remains slow. A major barrier is the insufficiency of traditional preclinical models for predicting drug efficacy and safety. Human genetics offers a complementary model to assess causal mechanisms for target validation. Genetic perturbations are 'experiments of nature' that provide a uniquely relevant window into the long-term effects of modulating specific targets. Here, we show that genetic discoveries over the past decades have accurately predicted (now known) therapeutic mechanisms for type 2 diabetes. These findings highlight the potential for use of human genetic variation for prospective target validation, and establish a framework for future applications. Studies into rare, monogenic forms of diabetes have also provided proof-of-principle for precision medicine, and the applicability of this paradigm to complex disease is discussed. Finally, we highlight some of the limitations that are relevant to the use of genome-wide association studies (GWAS) in the search for new therapies for diabetes. A key outstanding challenge is the translation of GWAS signals into disease biology and we outline possible solutions for tackling this experimental bottleneck.

Genetic association study of exfoliation syndrome identifies a protective rare variant at LOXL1 and five new susceptibility loci

Exfoliation syndrome (XFS) is the most common known risk factor for secondary glaucoma and a major cause of blindness worldwide. Variants in two genes, LOXL1 and CACNA1A, have previously been associated with XFS. To further elucidate the genetic basis of XFS, we collected a global sample of XFS cases to refine the association at LOXL1, which previously showed inconsistent results across populations, and to identify new variants associated with XFS. We identified a rare protective allele at LOXL1 (p.Phe407, odds ratio (OR) = 25, P = 2.9 × 10^-14) through deep resequencing of XFS cases and controls from nine countries. A genome-wide association study (GWAS) of XFS cases and controls from 24 countries followed by replication in 18 countries identified seven genome-wide significant loci (P < 5 × 10^-8). We identified association signals at 13q12 (POMP), 11q23.3 (TMEM136), 6p21 (AGPAT1), 3p24 (RBMS3) and 5q23 (near SEMA6A). These findings provide biological insights into the pathology of XFS and highlight a potential role for naturally occurring rare LOXL1 variants in disease biology.

High-Resolution Genetic Maps Identify Multiple Type 2 Diabetes Loci at Regulatory Hotspots in African Americans and Europeans

Interpretation of results from genome-wide association studies for T2D is challenging. Only very few loci have been replicated in African ancestry populations and the identification of the implicated functional genes remain largely undefined. We used genetic maps that capture detailed linkage disequilibrium information in European and African Americans and applied these to large T2D case-control samples in order to estimate locations for putative functional variants in both populations. Replicated T2D locations were tested for evidence of being regulatory hotspots using adipose expression. We validated a sample of our co-location intervals using next generation sequencing and functional annotation, including enhancers, transcription, and chromatin modifications. We identified 111 additional disease-susceptibility locations, 93 of which are cosmopolitan and 18 of which are European specific. We show that many previously known signals are also risk loci in African Americans. The majority of the disease locations appear to confer risk of T2D via the regulation of expression levels for a large number (266) of cis-regulated genes, the majority of which are not the nearest genes to the disease loci. Sequencing three cosmopolitan locations provided candidate functional variants that precisely co-locate with cell-specific chromatin domains and pancreatic islet enhancers. These variants have large effect sizes and are common across populations. Results show that disease-associated loci in different populations, gene expression, and cell-specific regulatory annotation can be effectively integrated by localizing these effects on high-resolution genetic maps. The cis-regulated genes provide insights into the complex molecular pathways involved and can be used as targets for sequencing and functional molecular studies.

Painting a new picture of personalised medicine for diabetes

The current focus on delivery of personalised (or precision) medicine reflects the expectation that developments in genomics, imaging and other domains will extend our diagnostic and prognostic capabilities, and enable more effective targeting of current and future preventative and therapeutic options. The clinical benefits of this approach are already being realised in rare diseases and cancer but the impact on management of complex diseases, such as type 2 diabetes, remains limited. This may reflect reliance on inappropriate models of disease architecture, based around rare, high-impact genetic and environmental exposures that are poorly suited to our emerging understanding of type 2 diabetes. This review proposes an alternative 'palette' model, centred on a molecular taxonomy that focuses on positioning an individual with respect to the major pathophysiological processes that contribute to diabetes risk and progression. This model anticipates that many individuals with diabetes will have multiple parallel defects that affect several of these processes. One corollary of this model is that research efforts should, at least initially, be targeted towards identifying and characterising individuals whose adverse metabolic trajectory is dominated by perturbation in a restricted set of processes.

Melatonin signalling and type 2 diabetes risk: too little, too much or just right?

Of the associations of genetic variants with type 2 diabetes, the one of an SNP in an intron of the gene encoding the melatonin receptor 1B (MTNR1B) has been remarkably robust. Work from our group and others has provided support for a model where carriers of this risk G allele exhibit increased MTNR1B expression in islets of Langerhans. Most published studies to date favour that melatonin's action on the beta cell is inhibition of insulin secretion. Hence, our model proposes that this inhibitory effect of melatonin is exaggerated in carriers of the MTNR1B risk G allele. This would explain why this genetic association causes reduced insulin secretion and greater risk of future type 2 diabetes, as has been observed in numerous studies. Concurrently, another body of work has shown that rare MTNR1B alleles, which could perturb receptor function, also associate with type 2 diabetes. In this commentary, it is suggested that such apparently conflicting observations can be reconciled by the fact that non-coding (intronic; frequent) and coding (exonic; rare) alleles of MTNR1B give rise to different phenotypes. Thus, altered gene transcription may explain why SNPs, which do not alter coding sequences, exhibit cell-specific effects. In contrast, SNPs that change protein sequences are more likely to exert generalised effects since an altered protein will appear in all cells expressing the gene.

Late dinner impairs glucose tolerance in MTNR1B risk allele carriers: A randomized, cross-over study

BACKGROUND & AIMS

Late-night dinner eating is associated with increased risk for type-2 diabetes. The underlying mechanism is unclear. One explanatory hypothesis is that the concurrence of elevated circulating melatonin and high glucose concentrations (characterizing late eating) leads to impaired glucose tolerance. However, to date no study has tested the influence of physiological melatonin concentrations on glucose-tolerance. The discovery of melatonin receptor MTNR1B as a diabetes risk gene provides evidence for a role of physiological levels of melatonin in glucose control. The aim of our study was to test the hypothesis that elevated endogenous melatonin concentrations worsen glucose control when eating late. Registered under ClinicalTrials.gov Identifier no. NCT03003936.

METHODS

We performed a randomized, cross-over trial to compare glucose tolerance in the presence (late dinner) or absence (early dinner) of elevated physiological melatonin concentrations and we compared the results between homozygous carriers and non-carriers of the MTNR1B risk allele.

RESULTS

The concurrence of meal timing with elevated endogenous melatonin concentrations resulted in impaired glucose tolerance. This effect was stronger in MTNR1B risk-carriers than in non-carriers. Furthermore, eating late significantly impaired glucose tolerance only in risk-carriers and not in the non-risk carriers.

CONCLUSIONS

The interaction of dinner timing with MTNR1B supports a causal role of endogenous melatonin in the impairment of glucose tolerance. These results suggest that moving the dinner to an earlier time may result in better glucose tolerance specially in MTNR1B carriers.

CLINICAL TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT03003936.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

Computational analyses of type 2 diabetes-associated loci identified by genome-wide association studies

BACKGROUND

Genome-wide association studies (GWAS) of type 2 diabetes (T2D) have discovered a number of loci that contribute to susceptibility to the disease. Future challenges include elucidation of functional mechanisms through which these GWAS-identified loci modulate T2D disease risk. The aim of the present study was to comprehensively characterize T2D associated single nucleotide polymorphisms (SNPs) and genes through computational approaches.

METHODS

Computational biology approaches used in the present study included comparative genomic analyses and functional annotation using GWAS3D and RegulomeDB, investigation of the effects of T2D-associated SNPs on miRNA binding and protein phosphorylation, and gene ontology, pathway enrichment, protein-protein interaction (PPI) networks and functional module analysis of T2D-associated genes from previously published GWAS.

RESULTS

Computational analysis identified a number of T2D GWAS-associated SNPs that were located at protein binding sites, including CCCTC-binding factor (CTCF), E1A binding protein p300 (EP300), hepatocyte nuclear factor 4alpha (HNF4A), transcription factor 7 like 2 (TCF7L2), forkhead box A1 (FOXA1) and A2 (FOXA2), and potentially affected the binding of miRNAs and protein phosphorylation. Pathway enrichment analysis confirmed two well-known maturity onset diabetes of the young and T2D pathways, whereas PPI network analysis identified highly interconnected "hub" genes, such as TCF7L2, melatonin receptor 1B (MTNR1B), and solute carrier family 30 (zinc transporter), member 8 (SLC30A8), that created two tight subnetworks.

CONCLUSIONS

The results provide objectives and clues for future experimental studies and further insights into the molecular pathogenesis of T2D.

Foxa2 and Pdx1 cooperatively regulate postnatal maturation of pancreatic β-cells

OBJECTIVE

The transcription factors (TF) Foxa2 and Pdx1 are key regulators of beta-cell (β-cell) development and function. Mutations of these TFs or their respective cis-regulatory consensus binding sites have been linked to maturity diabetes of the young (MODY), pancreas agenesis, or diabetes susceptibility in human. Although Foxa2 has been shown to directly regulate Pdx1 expression during mouse embryonic development, the impact of this gene regulatory interaction on postnatal β-cell maturation remains obscure.

METHODS

In order to easily monitor the expression domains of Foxa2 and Pdx1 and analyze their functional interconnection, we generated a novel double knock-in homozygous (FVFPBF^DHom) fluorescent reporter mouse model by crossing the previously described Foxa2-Venus fusion (FVF) with the newly generated Pdx1-BFP (blue fluorescent protein) fusion (PBF) mice.

RESULTS

Although adult PBF homozygous animals exhibited a reduction in expression levels of Pdx1, they are normoglycemic. On the contrary, despite normal pancreas and endocrine development, the FVFPBF^DHom reporter male animals developed hyperglycemia at weaning age and displayed a reduction in Pdx1 levels in islets, which coincided with alterations in β-cell number and islet architecture. The failure to establish mature β-cells resulted in loss of β-cell identity and trans-differentiation towards other endocrine cell fates. Further analysis suggested that Foxa2 and Pdx1 genetically and functionally cooperate to regulate maturation of adult β-cells.

CONCLUSIONS

Our data show that the maturation of pancreatic β-cells requires the cooperative function of Foxa2 and Pdx1. Understanding the postnatal gene regulatory network of β-cell maturation will help to decipher pathomechanisms of diabetes and identify triggers to regenerate dedifferentiated β-cell mass.

Hsa_circ_0054633 in peripheral blood can be used as a diagnostic biomarker of pre-diabetes and type 2 diabetes mellitus

AIMS

The purpose of the current study was to investigate the characteristic expression of circular RNAs (circRNAs) in the peripheral blood of type 2 diabetes mellitus (T2DM) patients and their potential as diagnostic biomarkers for pre-diabetes and T2DM.

METHODS

CircRNAs in the peripheral blood from six healthy individuals and six T2DM patients were collected for microarray analysis, and an independent cohort study consisting of 20 normal cases, 20 pre-diabetes patients and 20 T2DM patients was conducted to verify the five chosen circRNAs. We then tested hsa_circ_0054633 in a third cohort (control group, n = 60; pre-diabetes group, n = 63; and T2DM group, n = 64) by quantitative real-time polymerase chain reaction (Q-PCR).

RESULTS

In total, 489 circRNAs were discovered to be differentially expressed between the two groups, and of these, 78 were upregulated and 411 were downregulated in the T2DM group. Five circRNAs were then selected as candidate biomarkers and further verified in a second cohort. Hsa_circ_0054633 was found to have the largest area under the curve (AUC). The diagnostic capacity of hsa_circ_0054633 was tested in a third cohort. After introducing the risk factors of T2DM, the hsa_circ_0054633 AUCs for the diagnosis of pre-diabetes and T2DM slightly increased from 0.751 (95% confidence interval [0.666-0.835], P < 0.001) to 0.841 ([0.773-0.910], P < 0.001) and from 0.793 ([0.716-0.871], P < 0.001) to 0.834 ([0.762-0.905], P < 0.001), respectively.

CONCLUSIONS

Hsa_circ_0054633 presented a certain diagnostic capability for pre-diabetes and T2DM.

Sexual dimorphisms in genetic loci linked to body fat distribution

Obesity is a chronic condition associated with increased morbidity and mortality and is a risk factor for a number of other diseases including type 2 diabetes and cardiovascular disease. Obesity confers an enormous, costly burden on both individuals and public health more broadly. Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes. Body fat distribution is distinct from overall obesity in measurement, but studies of body fat distribution can yield insights into the risk factors for and causes of overall obesity. Sexual dimorphism in body fat distribution is present throughout life. Though sexual dimorphism is subtle in early stages of life, it is attenuated in puberty and during menopause. This phenomenon could be, at least in part, due to the influence of sex hormones on the trait. Findings from recent large genome-wide association studies (GWAS) for various measures of body fat distribution (including waist-to-hip ratio, hip or waist circumference, trunk fat percentage and the ratio of android and gynoid fat percentage) emphasize the strong sexual dimorphism in the genetic regulation of fat distribution traits. Importantly, sexual dimorphism is not observed for overall obesity (as assessed by body mass index or total fat percentage). Notably, the genetic loci associated with body fat distribution, which show sexual dimorphism, are located near genes that are expressed in adipose tissues and/or adipose cells. Considering the epidemiological and genetic evidence, sexual dimorphism is a prominent feature of body fat distribution. Research that specifically focuses on sexual dimorphism in fat distribution can provide novel insights into human physiology and into the development of obesity and its comorbidities, as well as yield biological clues that will aid in the improvement of disease prevention and treatment.

Genomics of Islet (Dys)function and Type 2 Diabetes

Pancreatic islet dysfunction and beta cell failure are hallmarks of type 2 diabetes mellitus (T2DM) pathogenesis. In this review, we discuss how genome-wide association studies (GWASs) and recent developments in islet (epi)genome and transcriptome profiling (particularly single cell analyses) are providing novel insights into the genetic, environmental, and cellular contributions to islet (dys)function and T2DM pathogenesis. Moving forward, study designs that interrogate and model genetic variation [e.g., allelic profiling and (epi)genome editing] will be critical to dissect the molecular genetics of T2DM pathogenesis, to build next-generation cellular and animal models, and to develop precision medicine approaches to detect, treat, and prevent islet (dys)function and T2DM.

Genetic regulatory signatures underlying islet gene expression and type 2 diabetes

Genome-wide association studies (GWAS) have identified >100 independent SNPs that modulate the risk of type 2 diabetes (T2D) and related traits. However, the pathogenic mechanisms of most of these SNPs remain elusive. Here, we examined genomic, epigenomic, and transcriptomic profiles in human pancreatic islets to understand the links between genetic variation, chromatin landscape, and gene expression in the context of T2D. We first integrated genome and transcriptome variation across 112 islet samples to produce dense cis-expression quantitative trait loci (cis-eQTL) maps. Additional integration with chromatin-state maps for islets and other diverse tissue types revealed that cis-eQTLs for islet-specific genes are specifically and significantly enriched in islet stretch enhancers. High-resolution chromatin accessibility profiling using assay for transposase-accessible chromatin sequencing (ATAC-seq) in two islet samples enabled us to identify specific transcription factor (TF) footprints embedded in active regulatory elements, which are highly enriched for islet cis-eQTL. Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions. Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints. Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions. Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition.

Decreased STARD10 Expression Is Associated with Defective Insulin Secretion in Humans and Mice

Genetic variants near ARAP1 (CENTD2) and STARD10 influence type 2 diabetes (T2D) risk. The risk alleles impair glucose-induced insulin secretion and, paradoxically but characteristically, are associated with decreased proinsulin:insulin ratios, indicating improved proinsulin conversion. Neither the identity of the causal variants nor the gene(s) through which risk is conferred have been firmly established. Whereas ARAP1 encodes a GTPase activating protein, STARD10 is a member of the steroidogenic acute regulatory protein (StAR)-related lipid transfer protein family. By integrating genetic fine-mapping and epigenomic annotation data and performing promoter-reporter and chromatin conformational capture (3C) studies in β cell lines, we localize the causal variant(s) at this locus to a 5 kb region that overlaps a stretch-enhancer active in islets. This region contains several highly correlated T2D-risk variants, including the rs140130268 indel. Expression QTL analysis of islet transcriptomes from three independent subject groups demonstrated that T2D-risk allele carriers displayed reduced levels of STARD10 mRNA, with no concomitant change in ARAP1 mRNA levels. Correspondingly, β-cell-selective deletion of StarD10 in mice led to impaired glucose-stimulated Ca²⁺ dynamics and insulin secretion and recapitulated the pattern of improved proinsulin processing observed at the human GWAS signal. Conversely, overexpression of StarD10 in the adult β cell improved glucose tolerance in high fat-fed animals. In contrast, manipulation of Arap1 in β cells had no impact on insulin secretion or proinsulin conversion in mice. This convergence of human and murine data provides compelling evidence that the T2D risk associated with variation at this locus is mediated through reduction in STARD10 expression in the β cell.

Functional Investigations of HNF1A Identify Rare Variants as Risk Factors for Type 2 Diabetes in the General Population

Variants in HNF1A encoding hepatocyte nuclear factor 1α (HNF-1A) are associated with maturity-onset diabetes of the young form 3 (MODY 3) and type 2 diabetes. We investigated whether functional classification of HNF1A rare coding variants can inform models of diabetes risk prediction in the general population by analyzing the effect of 27 HNF1A variants identified in well-phenotyped populations (n = 4,115). Bioinformatics tools classified 11 variants as likely pathogenic and showed no association with diabetes risk (combined minor allele frequency [MAF] 0.22%; odds ratio [OR] 2.02; 95% CI 0.73-5.60; P = 0.18). However, a different set of 11 variants that reduced HNF-1A transcriptional activity to <60% of normal (wild-type) activity was strongly associated with diabetes in the general population (combined MAF 0.22%; OR 5.04; 95% CI 1.99-12.80; P = 0.0007). Our functional investigations indicate that 0.44% of the population carry HNF1A variants that result in a substantially increased risk for developing diabetes. These results suggest that functional characterization of variants within MODY genes may overcome the limitations of bioinformatics tools for the purposes of presymptomatic diabetes risk prediction in the general population.

Differentiation of Diabetes by Pathophysiology, Natural History, and Prognosis

The American Diabetes Association, JDRF, the European Association for the Study of Diabetes, and the American Association of Clinical Endocrinologists convened a research symposium, "The Differentiation of Diabetes by Pathophysiology, Natural History and Prognosis" on 10-12 October 2015. International experts in genetics, immunology, metabolism, endocrinology, and systems biology discussed genetic and environmental determinants of type 1 and type 2 diabetes risk and progression, as well as complications. The participants debated how to determine appropriate therapeutic approaches based on disease pathophysiology and stage and defined remaining research gaps hindering a personalized medical approach for diabetes to drive the field to address these gaps. The authors recommend a structure for data stratification to define the phenotypes and genotypes of subtypes of diabetes that will facilitate individualized treatment.

The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases

The Metabolic Syndrome in Men (METSIM) study is a population-based study including 10,197 Finnish men examined in 2005-2010. The aim of the study is to investigate nongenetic and genetic factors associated with the risk of T2D and CVD, and with cardiovascular risk factors. The protocol includes a detailed phenotyping of the participants, an oral glucose tolerance test, fasting laboratory measurements including proton NMR measurements, mass spectometry metabolomics, adipose tissue biopsies from 1,400 participants, and a stool sample. In our ongoing follow-up study, we have, to date, reexamined 6,496 participants. Extensive genotyping and exome sequencing have been performed for essentially all METSIM participants, and >2,000 METSIM participants have been whole-genome sequenced. We have identified several nongenetic markers associated with the development of diabetes and cardiovascular events, and participated in several genetic association studies to identify gene variants associated with diabetes, hyperglycemia, and cardiovascular risk factors. The generation of a phenotype and genotype resource in the METSIM study allows us to proceed toward a "systems genetics" approach, which includes statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein, or metabolite levels, to provide a global view of the molecular architecture of complex traits.

Exome-Wide Association Study Identifies Low-Frequency Coding Variants in 2p23.2 and 7p11.2 Associated with Survival of Non-Small Cell Lung Cancer Patients

INTRODUCTION

A growing body of evidence has suggested that low-frequency or rare coding variants might have strong effects on the development and prognosis of cancer. Here, we aim to assess the role of low-frequency and rare coding variants in the survival of NSCLC in Chinese populations.

METHODS

We performed an exome-wide scan of 247,870 variants in 1008 patients with NSCLC and replicated the promising variants by using imputed genotype data of The Cancer Genome Atlas (TCGA) with a Cox regression model. Gene-based and pathway-based analysis were also performed for nonsynonymous or splice site variants. Additionally, analysis of gene expression data in the TCGA was used to increase the reliability of candidate loci and genes.

RESULTS

A low-frequency missense variant in chaperonin containing TCP1 subunit 6A gene (CCT6A) (rs33922584: adjusted hazard ratio [HR_adjusted] = 1.75, p = 6.06 × 10^-4) was significantly related to the survival of patients with NSCLC, which was further replicated by the TCGA samples (HR_adjusted = 4.19, p = 0.015). Interestingly, the G allele of rs33922584 was significantly associated with high expression of CCT6A (p = 0.019) that might induce the worse survival in the TCGA samples (HR_adjusted = 1.15, p = 0.047). Besides, rs117512489 in gene phospholipase B1 gene (PLB1) (HR = 2.02, p = 7.28 × 10^-4) was also associated with survival of the patients with NSCLC in our samples, but it was supported only by gene expression analysis in the TCGA (HR_adjusted = 1.15, p = 0.023). Gene-based and pathway-based analysis revealed a total of 32 genes, including CCT6A and 34 potential pathways might account for the survival of NSCLC, respectively.

CONCLUSION

These results provided more evidence for the important role of low-frequency or rare variants in the survival of patients with NSCLC.

Association Study with 77 SNPs Confirms the Robust Role for the rs10830963/G of MTNR1B Variant and Identifies Two Novel Associations in Gestational Diabetes Mellitus Development

CONTEXT

Genetic variation in human maternal DNA contributes to the susceptibility for development of gestational diabetes mellitus (GDM).

OBJECTIVE

We assessed 77 maternal single nucleotide gene polymorphisms (SNPs) for associations with GDM or plasma glucose levels at OGTT in pregnancy.

METHODS

960 pregnant women (after dropouts 820: case/control: m99'WHO: 303/517, IADPSG: 287/533) were enrolled in two countries into this case-control study. After genomic DNA isolation the 820 samples were collected in a GDM biobank and assessed using KASP (LGC Genomics) genotyping assay. Logistic regression risk models were used to calculate ORs according to IADPSG/m'99WHO criteria based on standard OGTT values.

RESULTS

The most important risk alleles associated with GDM were rs10830963/G of MTNR1B (OR = 1.84/1.64 [IADPSG/m'99WHO], p = 0.0007/0.006), rs7754840/C (OR = 1.51/NS, p = 0.016) of CDKAL1 and rs1799884/T (OR = 1.4/1.56, p = 0.04/0.006) of GCK. The rs13266634/T (SLC30A8, OR = 0.74/0.71, p = 0.05/0.02) and rs7578326/G (LOC646736/IRS1, OR = 0.62/0.60, p = 0.001/0.006) variants were associated with lower risk to develop GDM. Carrying a minor allele of rs10830963 (MTNR1B); rs7903146 (TCF7L2); rs1799884 (GCK) SNPs were associated with increased plasma glucose levels at routine OGTT.

CONCLUSIONS

We confirmed the robust association of MTNR1B rs10830963/G variant with GDM binary and glycemic traits in this Caucasian case-control study. As novel associations we report the minor, G allele of the rs7578326 SNP in the LOC646736/IRS1 region as a significant and the rs13266634/T SNP (SLC30A8) as a suggestive protective variant against GDM development. Genetic susceptibility appears to be more preponderant in individuals who meet both the modified 99'WHO and the IADPSG GDM diagnostic criteria.

The InterAct Consortium. Investigation of gene-diet interactions in the incretin system and risk of type 2 diabetes: the EPIC-InterAct study

AIMS/HYPOTHESIS

The gut incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) have a major role in the pathophysiology of type 2 diabetes. Specific genetic and dietary factors have been found to influence the release and action of incretins. We examined the effect of interactions between seven incretin-related genetic variants in GIPR, KCNQ1, TCF7L2 and WFS1 and dietary components (whey-containing dairy, cereal fibre, coffee and olive oil) on the risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct study.

METHODS

The current case-cohort study included 8086 incident type 2 diabetes cases and a representative subcohort of 11,035 participants (median follow-up: 12.5 years). Prentice-weighted Cox proportional hazard regression models were used to investigate the associations and interactions between the dietary factors and genes in relation to the risk of type 2 diabetes.

RESULTS

An interaction (p = 0.048) between TCF7L2 variants and coffee intake was apparent, with an inverse association between coffee and type 2 diabetes present among carriers of the diabetes risk allele (T) in rs12255372 (GG: HR 0.99 [95% CI 0.97, 1.02] per cup of coffee; GT: HR 0.96 [95% CI 0.93, 0.98]); and TT: HR 0.93 [95% CI 0.88, 0.98]). In addition, an interaction (p = 0.005) between an incretin-specific genetic risk score and coffee was observed, again with a stronger inverse association with coffee in carriers with more risk alleles (0-3 risk alleles: HR 0.99 [95% CI 0.94, 1.04]; 7-10 risk alleles: HR 0.95 [95% CI 0.90, 0.99]). None of these associations were statistically significant after correction for multiple testing.

CONCLUSIONS/INTERPRETATION

Our large-scale case-cohort study provides some evidence for a possible interaction of TCF7L2 variants and an incretin-specific genetic risk score with coffee consumption in relation to the risk of type 2 diabetes. Further large-scale studies and/or meta-analyses are needed to confirm these interactions in other populations.

The Genetic Architecture of Diabetes in Pregnancy: Implications for Clinical Practice

The genetic architecture of diabetes mellitus in general and in pregnancy is complex, owing to the multiple types of diabetes that comprise both complex/polygenic forms and monogenic (largely caused by a mutation in a single gene) forms such as maturity-onset diabetes of the young (MODY). Type 1 diabetes (T1D) and type 2 diabetes (T2D) have complex genetic etiologies, with over 40 and 90 genes/loci, respectively, implicated that interact with environmental/lifestyle factors. The genetic etiology of gestational diabetes mellitus has largely been found to overlap that of T2D. Genetic testing for complex forms of diabetes is not currently useful clinically, but genetic testing for monogenic forms, particularly MODY, has important utility for determining treatment, managing risk in family members, and pregnancy management. In particular, diagnosing MODY2, caused by GCK mutations, indicates that insulin should not be used, including during pregnancy, with the possible exception of an unaffected pregnancy during the third trimester to prevent macrosomia. A relatively simple method for identifying women with MODY2 has been piloted. MODY1, caused by HNF4A mutations, can paradoxically cause neonatal hyperinsulinemic hypoglycemia and macrosomia, indicating that detecting these cases is also clinically important. Diagnosing all MODY types provides opportunities for diagnosing other family members.

Transcriptional regulation of the uncoupling protein-1 gene

Regulated transcription of the uncoupling protein-1 (UCP1) gene, and subsequent UCP1 protein synthesis, is a hallmark of the acquisition of the differentiated, thermogenically competent status of brown and beige/brite adipocytes, as well as of the responsiveness of brown and beige/brite adipocytes to adaptive regulation of thermogenic activity. The 5' non-coding region of the UCP1 gene contains regulatory elements that confer tissue specificity, differentiation dependence, and neuro-hormonal regulation to UCP1 gene transcription. Two main regions-a distal enhancer and a proximal promoter region-mediate transcriptional regulation through interactions with a plethora of transcription factors, including nuclear hormone receptors and cAMP-responsive transcription factors. Co-regulators, such as PGC-1α, play a pivotal role in the concerted regulation of UCP1 gene transcription. Multiple interactions of transcription factors and co-regulators at the promoter region of the UCP1 gene result in local chromatin remodeling, leading to activation and increased accessibility of RNA polymerase II and subsequent gene transcription. Moreover, a commonly occurring A-to-G polymorphism in close proximity to the UCP1 gene enhancer influences the extent of UCP1 gene transcription. Notably, it has been reported that specific aspects of obesity and associated metabolic diseases are associated with human population variability at this site. On another front, the unique properties of the UCP1 promoter region have been exploited to develop brown adipose tissue-specific gene delivery tools for experimental purposes.

Hypothalamic transcriptomes of 99 mouse strains reveal trans eQTL hotspots, splicing QTLs and novel non-coding genes

Previous studies had shown that the integration of genome wide expression profiles, in metabolic tissues, with genetic and phenotypic variance, provided valuable insight into the underlying molecular mechanisms. We used RNA-Seq to characterize hypothalamic transcriptome in 99 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP), a reference resource population for cardiovascular and metabolic traits. We report numerous novel transcripts supported by proteomic analyses, as well as novel non coding RNAs. High resolution genetic mapping of transcript levels in HMDP, reveals both local and trans expression Quantitative Trait Loci (eQTLs) demonstrating 2 trans eQTL 'hotspots' associated with expression of hundreds of genes. We also report thousands of alternative splicing events regulated by genetic variants. Finally, comparison with about 150 metabolic and cardiovascular traits revealed many highly significant associations. Our data provide a rich resource for understanding the many physiologic functions mediated by the hypothalamus and their genetic regulation.

DOI:http://dx.doi.org/10.7554/eLife.15614.001

Metabolism is a term that describes all the chemical reactions that are involved in keeping a living organism alive. Diseases related to metabolism – such as obesity, heart disease and diabetes – are a major health problem in the Western world. The causes of these diseases are complex and include both environmental factors, such as diet and exercise, and genetics. Indeed, many genetic variants that contribute to obesity have been uncovered in both humans and mice. However, it is only dimly understood how these genetic variants affect the underlying networks of interacting genes that cause metabolic disorders.

Measuring gene activity or expression, and tracing how genetic instructions are carried from DNA into RNA and proteins, can reliably identify groups of genes that correlate with metabolic traits in specific organs. This strategy was successfully used in previous studies to reveal new information about abnormalities linked to obesity in specific tissues such as the liver and fat tissues. It was also shown that this approach might suggest new molecules that could be targeted to treat metabolic disorders.

A brain region called the hypothalamus is key to the control of metabolism, including feeding behavior and obesity. Hasin-Brumshtein et al. set out to explore gene expression in the hypothalamus of 99 different strains of mice, in the hope that the data will help identify new connections between gene expression and metabolism.

This approach showed that thousands of new and known genes are expressed in the mouse hypothalamus, some of which coded for proteins, and some of which did not. Hasin-Brumshtein et al. uncovered two genetic variants that controlled the expression of hundreds of other genes. Further analysis then revealed thousands of genetic variants that regulated the expression of, and type of RNA (so-called "spliceforms") produced from neighboring genes. Also, the expression of many individual genes showed significant similarities with about 150 metabolic measurements that had been evaluated previously in the mice.

This new dataset is a unique resource that can be coupled with different approaches to test existing ideas and develop new ones about the role of particular genes or genetic mechanisms in obesity. Future studies will likely focus on new genes that show strong associations with attributes that are relevant to metabolic disorders, such as insulin levels, weight and fat mass.

DOI:http://dx.doi.org/10.7554/eLife.15614.002

Sleep Control, GPCRs, and Glucose Metabolism

Modern lifestyles prolong daily activities into the nighttime, disrupting circadian rhythms, which may cause sleep disturbances. Sleep disturbances have been implicated in the dysregulation of blood glucose levels and reported to increase the risk of type 2 diabetes (T2D) and diabetic complications. Sleep disorders are treated using anti-insomnia drugs that target ionotropic and G protein-coupled receptors (GPCRs), including γ-aminobutyric acid (GABA) agonists, melatonin agonists, and orexin receptor antagonists. A deeper understanding of the effects of these medications on glucose metabolism and their underlying mechanisms of action is crucial for the treatment of diabetic patients with sleep disorders. In this review we focus on the beneficial impact of sleep on glucose metabolism and suggest a possible strategy for therapeutic intervention against sleep-related metabolic disorders.

Adiposity-Dependent Regulatory Effects on Multi-tissue Transcriptomes

Obesity is a global epidemic that is causally associated with a range of diseases, including type 2 diabetes and cardiovascular disease, at the population-level. However, there is marked heterogeneity in obesity-related outcomes among individuals. This might reflect genotype-dependent responses to adiposity. Given that adiposity, measured by BMI, is associated with widespread changes in gene expression and regulatory variants mediate the majority of known complex trait loci, we sought to identify gene-by-BMI (G × BMI) interactions on the regulation of gene expression in a multi-tissue RNA-sequencing (RNA-seq) dataset from the TwinsUK cohort (n = 856). At a false discovery rate of 5%, we identified 16 cis G × BMI interactions (top cis interaction: CHURC1, rs7143432, p = 2.0 × 10(-12)) and one variant regulating 53 genes in trans (top trans interaction: ZNF423, rs3851570, p = 8.2 × 10(-13)), all in adipose tissue. The interactions were adipose-specific and enriched for variants overlapping adipocyte enhancers, and regulated genes were enriched for metabolic and inflammatory processes. We replicated a subset of the interactions in an independent adipose RNA-seq dataset (deCODE genetics, n = 754). We also confirmed the interactions with an alternate measure of obesity, dual-energy X-ray absorptiometry (DXA)-derived visceral-fat-volume measurements, in a subset of TwinsUK individuals (n = 682). The identified G × BMI regulatory effects demonstrate the dynamic nature of gene regulation and reveal a functional mechanism underlying the heterogeneous response to obesity. Additionally, we have provided a web browser allowing interactive exploration of the dataset, including of association between expression, BMI, and G × BMI regulatory effects in four tissues.

Mass production of functional human pancreatic β-cells: why and how?

Diabetes (either type 1 or type 2) is due to insufficient functional β-cell mass. Research has, therefore, aimed to discover new ways to maintain or increase either β-cell mass or function. For this purpose, rodents have mainly been used as model systems and a large number of discoveries have been made. Meanwhile, although we have learned that rodent models represent powerful systems to model β-cell development, function and destruction, we realize that there are limitations when attempting to transfer the data to what is occurring in humans. Indeed, while human β-cells share many similarities with rodent β-cells, they also differ on a number of important parameters. In this context, developing ways to study human β-cell development, function and death represents an important challenge. This review will describe recent data on the development and use of convenient sources of human β-cells that should be useful tools to discover new ways to modulate functional β-cell mass in humans.

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

Changes in the expression of the type 2 diabetes-associated gene VPS13C in the β-cell are associated with glucose intolerance in humans and mice

Single nucleotide polymorphisms (SNPs) close to the VPS13C, C2CD4A and C2CD4B genes on chromosome 15q are associated with impaired fasting glucose and increased risk of type 2 diabetes. eQTL analysis revealed an association between possession of risk (C) alleles at a previously implicated causal SNP, rs7163757, and lowered VPS13C and C2CD4A levels in islets from female (n = 40, P < 0.041) but not from male subjects. Explored using promoter-reporter assays in β-cells and other cell lines, the risk variant at rs7163757 lowered enhancer activity. Mice deleted for Vps13c selectively in the β-cell were generated by crossing animals bearing a floxed allele at exon 1 to mice expressing Cre recombinase under Ins1 promoter control (Ins1Cre). Whereas Vps13c(fl/fl):Ins1Cre (βVps13cKO) mice displayed normal weight gain compared with control littermates, deletion of Vps13c had little effect on glucose tolerance. Pancreatic histology revealed no significant change in β-cell mass in KO mice vs. controls, and glucose-stimulated insulin secretion from isolated islets was not altered in vitro between control and βVps13cKO mice. However, a tendency was observed in female null mice for lower insulin levels and β-cell function (HOMA-B) in vivo. Furthermore, glucose-stimulated increases in intracellular free Ca(2+) were significantly increased in islets from female KO mice, suggesting impaired Ca(2+) sensitivity of the secretory machinery. The present data thus provide evidence for a limited role for changes in VPS13C expression in conferring altered disease risk at this locus, particularly in females, and suggest that C2CD4A may also be involved.

Genome-wide association meta-analysis in Chinese and European individuals identifies ten new loci associated with systemic lupus erythematosus

Systemic lupus erythematosus (SLE; OMIM 152700) is a genetically complex autoimmune disease. Genome-wide association studies (GWASs) have identified more than 50 loci as robustly associated with the disease in single ancestries, but genome-wide transancestral studies have not been conducted. We combined three GWAS data sets from Chinese (1,659 cases and 3,398 controls) and European (4,036 cases and 6,959 controls) populations. A meta-analysis of these studies showed that over half of the published SLE genetic associations are present in both populations. A replication study in Chinese (3,043 cases and 5,074 controls) and European (2,643 cases and 9,032 controls) subjects found ten previously unreported SLE loci. Our study provides further evidence that the majority of genetic risk polymorphisms for SLE are contained within the same regions across both populations. Furthermore, a comparison of risk allele frequencies and genetic risk scores suggested that the increased prevalence of SLE in non-Europeans (including Asians) has a genetic basis.

Key Considerations and Methods in the Study of Gene-Environment Interactions

With increased involvement of genetic data in most epidemiological investigations, gene-environment (G × E) interactions now stand as a topic, which must be meticulously assessed and thoroughly understood. The level, mode, and outcomes of interactions between environmental factors and genetic traits have the capacity to modulate disease risk. These must, therefore, be carefully evaluated as they have the potential to offer novel insights on the "missing heritability problem", reaching beyond our current limitations. First, we review a definition of G × E interactions. We then explore how concepts such as the early manifestation of the genetic components of a disease, the heterogeneity of complex traits, the clear definition of epidemiological strata, and the effect of varying physiological conditions can affect our capacity to detect (or miss) G × E interactions. Lastly, we discuss the shortfalls of regression models to study G × E interactions and how other methods such as the ReliefF algorithm, pattern recognition methods, or the LASSO (Least Absolute Shrinkage and Selection Operator) method can enable us to more adequately model G × E interactions. Overall, we present the elements to consider and a path to follow when studying genetic determinants of disease in order to uncover potential G × E interactions.

Is Transforming Stem Cells to Pancreatic Beta Cells Still the Holy Grail for Type 2 Diabetes?

Diabetes is a progressive disease affecting millions of people worldwide. There are several medications and treatment options to improve the life quality of people with diabetes. One of the strategies for the treatment of diabetes could be the use of human pluripotent stem cells or induced pluripotent stem cells. The recent advances in differentiation of stem cells into insulin-secreting beta-like cells in vitro make the transplantation of the stem cell-derived beta-like cells an attractive approach for treatment of type 1 and type 2 diabetes. While stem cell-derived beta-like cells provide an unlimited cell source for beta cell replacement therapies, these cells can also be used as a platform for drug screening or modeling diseases.

Genome-edited human stem cell-derived beta cells: a powerful tool for drilling down on type 2 diabetes GWAS biology

Type 2 diabetes (T2D) is a disease of pandemic proportions, one defined by a complex aetiological mix of genetic, epigenetic, environmental, and lifestyle risk factors. Whilst the last decade of T2D genetic research has identified more than 100 loci showing strong statistical association with disease susceptibility, our inability to capitalise upon these signals reflects, in part, a lack of appropriate human cell models for study. This review discusses the impact of two complementary, state-of-the-art technologies on T2D genetic research: the generation of stem cell-derived, endocrine pancreas-lineage cells and the editing of their genomes. Such models facilitate investigation of diabetes-associated genomic perturbations in a physiologically representative cell context and allow the role of both developmental and adult islet dysfunction in T2D pathogenesis to be investigated. Accordingly, we interrogate the role that patient-derived induced pluripotent stem cell models are playing in understanding cellular dysfunction in monogenic diabetes, and how site-specific nucleases such as the clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system are helping to confirm genes crucial to human endocrine pancreas development. We also highlight the novel biology gleaned in the absence of patient lines, including an ability to model the whole phenotypic spectrum of diabetes phenotypes occurring both in utero and in adult cells, interrogating the non-coding 'islet regulome' for disease-causing perturbations, and understanding the role of other islet cell types in aberrant glycaemia. This article aims to reinforce the importance of investigating T2D signals in cell models reflecting appropriate species, genomic context, developmental time point, and tissue type.

Type 2 diabetes: genetic data sharing to advance complex disease research

As with other complex diseases, unbiased association studies followed by physiological and experimental characterization have for years formed a paradigm for identifying genes or processes of relevance to type 2 diabetes mellitus (T2D). Recent large-scale common and rare variant genome-wide association studies (GWAS) suggest that substantially larger association studies are needed to identify most T2D loci in the population. To hasten clinical translation of genetic discoveries, new paradigms are also required to aid specialized investigation of nascent hypotheses. We argue for an integrated T2D knowledgebase, designed for a worldwide community to access aggregated large-scale genetic data sets, as one paradigm to catalyse convergence of these efforts.

The impact of personalized medicine of Type 2 diabetes mellitus in the global health context

Advances in the fields of genomic sciences have given rise to personalized medicine. This new paradigm draws upon a patient's genetic and metabolic makeup in order to tailor diagnostics and treatment. Personalized medicine holds remarkable promises to improve prevention and management of chronic diseases of global relevance, such as Type 2 diabetes mellitus (T2DM). This review article aims at summarizing the evidence from genome-based sciences on T2DM risk and management in different populations and in the Global Health context. Opinions from leading experts in the field were also included. Based on these findings, strengths and weaknesses of personalized approach to T2DM in a global context are delineated. Implications for future research and implementation on that subject are discussed.

Transcriptome-Wide Analysis Reveals Modulation of Human Macrophage Inflammatory Phenotype Through Alternative Splicing

OBJECTIVE

Human macrophages can shift phenotype across the inflammatory M1 and reparative M2 spectrum in response to environmental challenges, but the mechanisms promoting inflammatory and cardiometabolic disease-associated M1 phenotypes remain incompletely understood. Alternative splicing (AS) is emerging as an important regulator of cellular function, yet its role in macrophage activation is largely unknown. We investigated the extent to which AS occurs in M1 activation within the cardiometabolic disease context and validated a functional genomic cell model for studying human macrophage-related AS events.

APPROACH AND RESULTS

From deep RNA-sequencing of resting, M1, and M2 primary human monocyte-derived macrophages, we found 3860 differentially expressed genes in M1 activation and detected 233 M1-induced AS events; the majority of AS events were cell- and M1-specific with enrichment for pathways relevant to macrophage inflammation. Using genetic variant data for 10 cardiometabolic traits, we identified 28 trait-associated variants within the genomic loci of 21 alternatively spliced genes and 15 variants within 7 differentially expressed regulatory splicing factors in M1 activation. Knockdown of 1 such splicing factor, CELF1, in primary human macrophages led to increased inflammatory response to M1 stimulation, demonstrating CELF1's potential modulation of the M1 phenotype. Finally, we demonstrated that an induced pluripotent stem cell-derived macrophage system recapitulates M1-associated AS events and provides a high-fidelity macrophage AS model.

CONCLUSIONS

AS plays a role in defining macrophage phenotype in a cell- and stimulus-specific fashion. Alternatively spliced genes and splicing factors with trait-associated variants may reveal novel pathways and targets in cardiometabolic diseases.

Genome-wide association study identifies multiple susceptibility loci for multiple myeloma

Multiple myeloma (MM) is a plasma cell malignancy with a significant heritable basis. Genome-wide association studies have transformed our understanding of MM predisposition, but individual studies have had limited power to discover risk loci. Here we perform a meta-analysis of these GWAS, add a new GWAS and perform replication analyses resulting in 9,866 cases and 239,188 controls. We confirm all nine known risk loci and discover eight new loci at 6p22.3 (rs34229995, P=1.31 × 10(-8)), 6q21 (rs9372120, P=9.09 × 10(-15)), 7q36.1 (rs7781265, P=9.71 × 10(-9)), 8q24.21 (rs1948915, P=4.20 × 10(-11)), 9p21.3 (rs2811710, P=1.72 × 10(-13)), 10p12.1 (rs2790457, P=1.77 × 10(-8)), 16q23.1 (rs7193541, P=5.00 × 10(-12)) and 20q13.13 (rs6066835, P=1.36 × 10(-13)), which localize in or near to JARID2, ATG5, SMARCD3, CCAT1, CDKN2A, WAC, RFWD3 and PREX1. These findings provide additional support for a polygenic model of MM and insight into the biological basis of tumour development.

Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes

Insights into the genetic basis of type 2 diabetes mellitus (T2DM) have been difficult to discern, despite substantial research. More is known about rare forms of diabetes mellitus, several of which share clinical and genetic features with the common form of T2DM. In this Review, we discuss the extent to which the study of rare and low-frequency mutations in large populations has begun to bridge the gap between rare and common forms of diabetes mellitus. We hypothesize that the perceived division between these diseases might be due, in part, to the historical ascertainment bias of genetic studies, rather than a clear distinction between disease pathophysiologies. We also discuss possible implications of a new model for the genetic basis of diabetes mellitus subtypes, where the boundary between subtypes becomes blurred.

Diabetes in the post-GWAS era

Large sample sizes, high-resolution arrays and comprehensive imputation are pushing genetic fine-mapping of complex trait loci to its limits without, in most cases, pinpointing a unique variant-gene combination. Superimposing these results on sophisticated maps of functional chromatin elements promises to break this logjam, as a new study of type 2 diabetes compellingly demonstrates.

Increased Melatonin Signaling Is a Risk Factor for Type 2 Diabetes

Type 2 diabetes (T2D) is a global pandemic. Genome-wide association studies (GWASs) have identified >100 genetic variants associated with the disease, including a common variant in the melatonin receptor 1 b gene (MTNR1B). Here, we demonstrate increased MTNR1B expression in human islets from risk G-allele carriers, which likely leads to a reduction in insulin release, increasing T2D risk. Accordingly, in insulin-secreting cells, melatonin reduced cAMP levels, and MTNR1B overexpression exaggerated the inhibition of insulin release exerted by melatonin. Conversely, mice with a disruption of the receptor secreted more insulin. Melatonin treatment in a human recall-by-genotype study reduced insulin secretion and raised glucose levels more extensively in risk G-allele carriers. Thus, our data support a model where enhanced melatonin signaling in islets reduces insulin secretion, leading to hyperglycemia and greater future risk of T2D. The findings also imply that melatonin physiologically serves to inhibit nocturnal insulin release.

Impact of Common Diabetes Risk Variant in MTNR1B on Sleep, Circadian, and Melatonin Physiology

The risk of type 2 diabetes (T2D) is increased by abnormalities in sleep quantity and quality, circadian alignment, and melatonin regulation. A common genetic variant in a receptor for the circadian-regulated hormone melatonin (MTNR1B) is associated with increased fasting blood glucose and risk of T2D, but whether sleep or circadian disruption mediates this risk is unknown. We aimed to test if MTNR1B diabetes risk variant rs10830963 associates with measures of sleep or circadian physiology in intensive in-laboratory protocols (n = 58-96) or cross-sectional studies with sleep quantity and quality and timing measures from self-report (n = 4,307-10,332), actigraphy (n = 1,513), or polysomnography (n = 3,021). In the in-laboratory studies, we found a significant association with a substantially longer duration of elevated melatonin levels (41 min) and delayed circadian phase of dim-light melatonin offset (1.37 h), partially mediated through delayed offset of melatonin synthesis. Furthermore, increased T2D risk in MTNR1B risk allele carriers was more pronounced in early risers versus late risers as determined by 7 days of actigraphy. Our results provide the surprising insight that the MTNR1B risk allele influences dynamics of melatonin secretion, generating a novel hypothesis that the MTNR1B risk allele may extend the duration of endogenous melatonin production later into the morning and that early waking may magnify the diabetes risk conferred by the risk allele.

Current Understanding on Role of the Wnt Signaling Pathway Effector TCF7L2 in Glucose Homeostasis

The role of the Wnt signaling pathway in metabolic homeostasis has drawn our intensive attention, especially after the genome-wide association study discovery that certain polymorphisms of its key effector TCF7L2 are strongly associated with the susceptibility to type 2 diabetes. For a decade, great efforts have been made in determining the function of TCF7L2 in various metabolic organs, which have generated both considerable achievements and disputes. In this review, I will briefly introduce the canonical Wnt signaling pathway, focusing on its effector β-catenin/TCF, including emphasizing the bidirectional feature of TCFs and β-catenin post-translational modifications. I will then summarize the observations on the association between TCF7L2 polymorphisms and type 2 diabetes risk. The main content, however, is on the intensive functional exploration of the metabolic role of TCF7L2, including the disputes generated on determining its role in the pancreas and liver with various transgenic mouse lines. Finally, I will discuss those achievements and disputes and present my future perspectives.