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

HNF1A gene p.I27L is associated with early-onset, maturity-onset diabetes of the young-like diabetes in Turkey

BACKGROUND

The molecular basis of the Turkish population with suspected maturity-onset diabetes of the young (MODY) has not been identified. This is the first study to investigate the association between HNF1A-gene single-nucleotide polymorphisms (SNPs) and having early-onset, MODY-like diabetes mellitus in the Turkish population.

METHODS

All diabetic patients (N = 565) who presented to our clinic between 2012 and 2015 with a clinical suspicion of MODY were included in the study. Analysis of HNF1A, HNFB, HNF4A, GCK gene mutations was performed using real-time polymerase chain reaction sequencing. After genetic analysis, diabetics (n = 46) with HNF1A, HNF1B, HNF4A, GCK gene mutations (diagnosed as MODY) and diabetics (n = 30) with HNF1B, HNF4A, GCK gene SNPs were excluded. Patients with early-onset, MODY-like diabetes (n = 486) and non-diabetic controls (n = 263) were included. Genetic analyses for the HNF1A gene p.S487 N (rs2464196), p.A98V (rs1800574) and p.I27L (rs1169288) SNPs were performed using Sanger-based DNA sequencing among the control group.

RESULTS

p.S487 N and p.A98V was similar between the diabetics and controls in dominant and recessive models with no association (each, p > 0.05). p.I27L GT/TT carriers (GT/TT vs. GG, OR = 1.68, 95% CI: [1. 21-2.13]; p = 0.035) and p.I27L TT carriers had increased risk of having MODY-like diabetes (GT/GG vs. TT, OR = 1.56, 95% CI: [1. 14-2.57]; p = 0.048). Family inheritance of diabetes was significantly more common in patients with the p.I27L TT genotype. The p.I27L SNP was modestly associated with having diabetes after adjusting for body mass index and age (β = 1.45, 95% CI: [1. 2-4.2]; p = 0.036).

CONCLUSIONS

The HNF1A gene p.I27L SNP was modestly associated with having early-onset, MODY-like diabetes in the Turkish population. HNF1A gene p.I27L SNP might contribute to age at diabetes diagnosis and family inheritance.

Pancreatic islet chromatin accessibility and conformation reveals distal enhancer networks of type 2 diabetes risk

Genetic variants affecting pancreatic islet enhancers are central to T2D risk, but the gene targets of islet enhancer activity are largely unknown. We generate a high-resolution map of islet chromatin loops using Hi-C assays in three islet samples and use loops to annotate target genes of islet enhancers defined using ATAC-seq and published ChIP-seq data. We identify candidate target genes for thousands of islet enhancers, and find that enhancer looping is correlated with islet-specific gene expression. We fine-map T2D risk variants affecting islet enhancers, and find that candidate target genes of these variants defined using chromatin looping and eQTL mapping are enriched in protein transport and secretion pathways. At IGF2BP2, a fine-mapped T2D variant reduces islet enhancer activity and IGF2BP2 expression, and conditional inactivation of IGF2BP2 in mouse islets impairs glucose-stimulated insulin secretion. Our findings provide a resource for studying islet enhancer function and identifying genes involved in T2D risk.

Risk loci for type 2 diabetes (T2D) reside in pancreatic islet enhancers. Here, the authors generate high-resolution maps of islet chromatin conformation using Hi-C which they combine with ATAC-seq and ChIP-seq data to annotate candidate target genes of enhancers and validate IGF2BP2 activity in mouse islets.

DNA Methylation of Five Core Circadian Genes Jointly Contributes to Glucose Metabolism: A Gene-Set Analysis in Monozygotic Twins

The timing of daily fluctuations in blood glucose is tightly controlled by the circadian rhythm. DNA methylation accompanies the circadian clock, and aberrant DNA methylation has been associated with circadian disruption and hyperglycemia. However, the precise role of circadian genes methylation in glucose metabolism is unknown. Using a gene-set approach in monozygotic (MZ) twin pairs, we examined the joint effect of 77 CpGs in five core circadian genes (CLOCK, BMAL1, PER1, PER2, PER3) on glucose-related traits in 138 middle-aged, male-male MZ twins (69 pairs). DNA methylation was quantified by bisulfite pyrosequencing. We first conducted matched twin pair analysis to examine the association of single CpG methylation with glucose metabolism. We then performed gene-based and gene-set analyses by the truncated product method to examine the combined effect of DNA methylation at multiple CpGs in a gene or all five circadian genes as a pathway on glucose metabolism. Of the 77 assayed CpGs, only one site was individually associated with insulin resistance at FDR < 0.05. However, the joint effect of DNA methylation in all five circadian genes together showed a significant association with glucose metabolism. Our results may unravel a biological mechanism through which circadian rhythm regulates blood glucose, and highlight the importance of testing the joint effect of multiple CpGs in epigenetic analysis.

Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids

The concentrations of high- and low-density-lipoprotein cholesterol and triglycerides are influenced by smoking, but it is unknown whether genetic associations with lipids may be modified by smoking. We conducted a multi-ancestry genome-wide gene-smoking interaction study in 133,805 individuals with follow-up in an additional 253,467 individuals. Combined meta-analyses identified 13 new loci associated with lipids, some of which were detected only because association differed by smoking status. Additionally, we demonstrate the importance of including diverse populations, particularly in studies of interactions with lifestyle factors, where genomic and lifestyle differences by ancestry may contribute to novel findings.

Causal Associations in Type 2 Diabetes Development

CONTEXT

Obesity, glucose, insulin resistance [homeostatic model assessment, version 2, for insulin resistance (HOMA2-IR)], and insulin secretion (HOMA2-β) have been associated with type 2 diabetes (T2D) observationally. However, the causal, genetic contribution of each parameter to this risk is largely unknown and important to study because observational data are prone to confounding but genetic, causal data are free of confounding and reverse causation.

OBJECTIVE

We examined the causal, genetic contribution of body mass index (BMI), glucose level, C-peptide level, HOMA2-IR, and HOMA2-β to the risk of T2D in 95,540 individuals from the Copenhagen General Population Study and estimated the absolute 10-year risks.

METHODS

Cox regression analysis, instrumental variable analysis, and Poisson regression analysis were performed to estimate the observational hazard ratios, causal, genetic ORs, and absolute 10-year risks of T2D.

RESULTS

For 1-SD greater level, BMI was associated with an observational 66% (95% CI, 62% to 72%) and causal, genetic 121% (95% CI, 25% to 291%) greater risk of T2D; glucose with an observational 44% (95% CI, 41% to 46%) and causal, genetic 183% (95% CI, 56% to 416%) greater risk of T2D; and HOMA2-IR with an observational 30% (95% CI, 18% to 44%) and causal, genetic 12% (95% CI, 2% to 22%) greater risk of T2D. In contrast, for 1-SD greater level, HOMA2-β was associated with an observational 14% (95% CI, 11% to 16%) and causal, genetic 21% (95% CI, 8% to 32%) lower risk of T2D. The upper tertiles of HOMA2-IR were associated with absolute 10-year diabetes risks of 31% and 37% in obese women and men, age >60 years, and a glucose level of 6.1 to 11.0 mmol/L.

CONCLUSIONS

BMI, glucose level, HOMA2-IR, and HOMA2-β are causally associated with T2D.

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.

The Genetic Basis of Metabolic Disease

Recent developments in genetics and genomics are providing a detailed and systematic characterization of the genetic underpinnings of common metabolic diseases and traits, highlighting the inherent complexity within systems for homeostatic control and the many ways in which that control can fail. The genetic architecture underlying these common metabolic phenotypes is complex, with each trait influenced by hundreds of loci spanning a range of allele frequencies and effect sizes. Here, we review the growing appreciation of this complexity and how this has fostered the implementation of genome-scale approaches that deliver robust mechanistic inference and unveil new strategies for translational exploitation.

The Homeodomain Transcription Factor NKX2.1 Is Essential for the Early Specification of Melanocortin Neuron Identity and Activates Pomc Expression in the Developing Hypothalamus

Food intake is tightly regulated by a group of neurons present in the arcuate nucleus of the hypothalamus, which release Pomc-encoded melanocortins, the absence of which induces marked hyperphagia and early-onset obesity. Although the relevance of hypothalamic POMC neurons in the regulation of body weight and energy balance is well appreciated, little is known about the transcription factors that establish the melanocortin neuron identity during brain development and its phenotypic maintenance in postnatal life. Here, we report that the transcription factor NKX2.1 is present in mouse hypothalamic POMC neurons from early development to adulthood. Electromobility shift assays showed that NKX2.1 binds in vitro to NKX binding motifs present in the neuronal Pomc enhancers nPE1 and nPE2 and chromatin immunoprecipitation assays detected in vivo binding of NKX2.1 to nPE1 and nPE2 in mouse hypothalamic extracts. Transgenic and mutant studies performed in mouse embryos of either sex and adult males showed that the NKX motifs present in nPE1 and nPE2 are essential for their transcriptional enhancer activity. The conditional early inactivation of Nkx2.1 in the ventral hypothalamus prevented the onset of Pomc expression. Selective Nkx2.1 ablation from POMC neurons decreased Pomc expression in adult males and mildly increased their body weight and adiposity. Our results demonstrate that NKX2.1 is necessary to activate Pomc expression by binding to conserved canonical NKX motifs present in nPE1 and nPE2. Therefore, NKX2.1 plays a critical role in the early establishment of hypothalamic melanocortin neuron identity and participates in the maintenance of Pomc expression levels during adulthood.SIGNIFICANCE STATEMENT Food intake and body weight regulation depend on hypothalamic neurons that release satiety-inducing neuropeptides, known as melanocortins. Central melanocortins are encoded byPomc, and Pomc mutations may lead to hyperphagia and severe obesity. Although the importance of central melanocortins is well appreciated, the genetic program that establishes and maintains fully functional POMC neurons remains to be explored. Here, we combined molecular, genetic, developmental, and functional studies that led to the discovery of NKX2.1, a transcription factor that participates in the early morphogenesis of the developing hypothalamus, as a key player in establishing the early identity of melanocortin neurons by activating Pomc expression. Thus, Nkx2.1 adds to the growing list of genes that participate in body weight regulation and adiposity.

Integrated Functional Genomic Analysis Enables Annotation of Kidney Genome-Wide Association Study Loci

BACKGROUND

Linking genetic risk loci identified by genome-wide association studies (GWAS) to their causal genes remains a major challenge. Disease-associated genetic variants are concentrated in regions containing regulatory DNA elements, such as promoters and enhancers. Although researchers have previously published DNA maps of these regulatory regions for kidney tubule cells and glomerular endothelial cells, maps for podocytes and mesangial cells have not been available.

METHODS

We generated regulatory DNA maps (DNase-seq) and paired gene expression profiles (RNA-seq) from primary outgrowth cultures of human glomeruli that were composed mainly of podocytes and mesangial cells. We generated similar datasets from renal cortex cultures, to compare with those of the glomerular cultures. Because regulatory DNA elements can act on target genes across large genomic distances, we also generated a chromatin conformation map from freshly isolated human glomeruli.

RESULTS

We identified thousands of unique regulatory DNA elements, many located close to transcription factor genes, which the glomerular and cortex samples expressed at different levels. We found that genetic variants associated with kidney diseases (GWAS) and kidney expression quantitative trait loci were enriched in regulatory DNA regions. By combining GWAS, epigenomic, and chromatin conformation data, we functionally annotated 46 kidney disease genes.

CONCLUSIONS

We demonstrate a powerful approach to functionally connect kidney disease-/trait-associated loci to their target genes by leveraging unique regulatory DNA maps and integrated epigenomic and genetic analysis. This process can be applied to other kidney cell types and will enhance our understanding of genome regulation and its effects on gene expression in kidney disease.

The Continuing Evolution of Precision Health in Type 2 Diabetes: Achievements and Challenges

PURPOSE OF REVIEW

The purpose of this review was to summarize recent advances in the genomics of type 2 diabetes (T2D) and to highlight current initiatives to advance precision health.

RECENT FINDINGS

Generation of multi-omic data to measure each of the "biologic layers," developments in describing genomic function and annotation in T2D relevant tissue, along with the increasing recognition that T2D is a heterogeneous disease, and large-scale collaborations have all contributed to advancing our understanding of the molecular basis of T2D. Substantial advances have been made in understanding the molecular basis of T2D pathogenesis, such that precision health diabetes is increasingly becoming a reality. For precision diabetes to become a routine in clinical and public health, additional large-scale multi-omic initiatives are needed along with better assessment of our environment to delineate an individual's diabetes subtype for improved detection and management.

The associations of daylight and melatonin receptor 1B gene rs10830963 variant with glycemic traits: the prospective PPP-Botnia study

BACKGROUND

Seasonal variation in glucose metabolism might be driven by changes in daylight. Melatonin entrains circadian regulation and is directly associated with daylight. The relationship between melatonin receptor 1B gene variants with glycemic traits and type 2 diabetes is well established. We studied if daylight length was associated with glycemic traits and if it modified the relationship between melatonin receptor 1B gene rs10830963 variant and glycemic traits.

MATERIALS

A population-based sample of 3422 18-78-year-old individuals without diabetes underwent an oral glucose tolerance test twice, an average 6.8 years (SD = 0.9) apart and were genotyped for rs10830963. Daylight data was obtained from the Finnish Meteorological Institute.

RESULTS

Cross-sectionally, more daylight was associated with lower fasting glucose, but worse insulin sensitivity and secretion at follow-up. Longitudinally, individuals studied on lighter days at follow-up than at baseline showed higher glucose values during the oral glucose tolerance test and lower Corrected Insulin Response at follow-up. GG genotype carriers in the rs10830963 became more insulin resistant during follow-up if daylight length was shorter at follow-up than at baseline.

CONCLUSIONS

Our study shows that individual glycemic profiles may vary according to daylight, MTNR1B genotype and their interaction. Future studies may consider taking daylight length into account. Key messages In Western Finland, the amount daylight follows an extensive annual variation ranging from 4 h 44 min to 20 h 17 min, making it ideal to study the associations between daylight and glycemic traits. Moreover, this allows researchers to explore if the relationship between the melatonin receptor 1B gene rs10830963 variant and glycemic traits is modified by the amount of daylight both cross-sectionally and longitudinally. This study shows that individuals, who participated in the study on lighter days at the follow-up than at the baseline, displayed to a greater extent worse glycemic profiles across the follow-up. Novel findings from the current study show that in the longitudinal analyses, each addition of the minor G allele of the melatonin receptor 1B gene rs10830963 was associated with worsening of fasting glucose values and insulin secretion across the 6.8-year follow-up. Importantly, this study shows that in those with the rs10830963 GG genotype, insulin sensitivity deteriorated the most significantly across the 6.8-year follow-up if the daylight length on the oral glucose tolerance testing date at the follow-up was shorter than at the baseline. Taken together, the current findings suggest that the amount of daylight may affect glycemic traits, especially fasting glucose and insulin secretion even though the effect size is small. The association can very according to the rs10830963 risk variant. Further research is needed to elucidate the mechanisms behind these associations.

Melatonin in type 2 diabetes mellitus and obesity

Despite considerable advances in the past few years, obesity and type 2 diabetes mellitus (T2DM) remain two major challenges for public health systems globally. In the past 9 years, genome-wide association studies (GWAS) have established a major role for genetic variation within the MTNR1B locus in regulating fasting plasma levels of glucose and in affecting the risk of T2DM. This discovery generated a major interest in the melatonergic system, in particular the melatonin MT₂ receptor (which is encoded by MTNR1B). In this Review, we discuss the effect of melatonin and its receptors on glucose homeostasis, obesity and T2DM. Preclinical and clinical post-GWAS evidence of frequent and rare variants of the MTNR1B locus confirmed its importance in regulating glucose homeostasis and T2DM risk with minor effects on obesity. However, these studies did not solve the question of whether melatonin is beneficial or detrimental, an issue that will be discussed in the context of the peculiarities of the melatonergic system. Melatonin receptors might have therapeutic potential as they belong to the highly druggable G protein-coupled receptor superfamily. Clarifying the precise role of melatonin and its receptors on glucose homeostasis is urgent, as melatonin is widely used for other indications, either as a prescribed medication or as a supplement without medical prescription, in many countries in Europe and in the USA.

Identification of 12 genetic loci associated with human healthspan

Aging populations face diminishing quality of life due to increased disease and morbidity. These challenges call for longevity research to focus on understanding the pathways controlling healthspan. We use the data from the UK Biobank (UKB) cohort and observe that the risks of major chronic diseases increased exponentially and double every eight years, i.e., at a rate compatible with the Gompertz mortality law. Assuming that aging drives the acceleration in morbidity rates, we build a risk model to predict the age at the end of healthspan depending on age, gender, and genetic background. Using the sub-population of 300,447 British individuals as a discovery cohort, we identify 12 loci associated with healthspan at the whole-genome significance level. We find strong genetic correlations between healthspan and all-cause mortality, life-history, and lifestyle traits. We thereby conclude that the healthspan offers a promising new way to interrogate the genetics of human longevity.

Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction

Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR) and genetic data. Our study cohort included 109, 490 individuals. In the first experiment, we extracted aggregated and longitudinal features from EHR. We applied logistic regression, random forests, gradient boosting trees, convolutional neural networks (CNN) and recurrent neural networks with long short-term memory (LSTM) units. In the second experiment, we applied a late-fusion approach to incorporate genetic features. We compared the performance with approaches currently utilized in routine clinical practice - American College of Cardiology and the American Heart Association (ACC/AHA) Pooled Cohort Risk Equation. Our results indicated that incorporating longitudinal feature lead to better event prediction. Combining genetic features through a late-fusion approach can further improve CVD prediction, underscoring the importance of integrating relevant genetic data whenever available.

Association of PPARG Gene Polymorphisms Pro12Ala with Type 2 Diabetes Mellitus: A Meta-analysis

BACKGROUND

Previous studies suggested that the single nucleotide polymorphisms of Pro12Ala located within the PPARG gene were significantly associated with the T2DM. Recently, the genetic studies on Pro12Ala were conducted in the different ethnic groups and the results of each study were shown to be inconsistent. Moreover, the systematic review has not been updated since 2000.

OBJECTIVE

To further validate the risk of Pro12Ala for T2DM disease based on the genetic data.

METHODS

The genetic studies on the Pro12Ala in the T2DM were searched in the PubMed and PMC database from January 2000 to October 2017. The meta-analysis was conducted with the CMA software.

RESULTS

The meta-analysis collected 14 studies including 20702 cases and 36227 controls. The combined analysis of all studies found that Pro12Ala was shown to be significantly associated with T2DM and the Ala allele played the increasing risks for the disease. Nevertheless, publication bias was detected in the combined analysis. The subgroup analysis indicated that Pro12Ala was found to be significant in the Caucasian and Chinese population. There was no heterogeneity and publication bias in these two groups.

CONCLUSION

The meta-analysis confirmed the evidence that the Pro12Ala was the susceptible variant for the decreasing risks for the T2DM.

Inferring the Molecular Mechanisms of Noncoding Alzheimer's Disease-Associated Genetic Variants

Most of the loci identified by genome-wide association studies (GWAS) for late-onset Alzheimer's disease (LOAD) are in strong linkage disequilibrium (LD) with nearby variants all of which could be the actual functional variants, often in non-protein-coding regions and implicating underlying gene regulatory mechanisms. We set out to characterize the causal variants, regulatory mechanisms, tissue contexts, and target genes underlying these associations. We applied our INFERNO algorithm to the top 19 non-APOE loci from the IGAP GWAS study. INFERNO annotated all LD-expanded variants at each locus with tissue-specific regulatory activity. Bayesian co-localization analysis of summary statistics and eQTL data was performed to identify tissue-specific target genes. INFERNO identified enhancer dysregulation in all 19 tag regions analyzed, significant enrichments of enhancer overlaps in the immune-related blood category, and co-localized eQTL signals overlapping enhancers from the matching tissue class in ten regions (ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, EPHA1, FERMT2, ZCWPW1). In several cases, we identified dysregulation of long noncoding RNA (lncRNA) transcripts and applied the lncRNA target identification algorithm from INFERNO to characterize their downstream biological effects. We also validated the allele-specific effects of several variants on enhancer function using luciferase expression assays. By integrating functional genomics with GWAS signals, our analysis yielded insights into the regulatory mechanisms, tissue contexts, genes, and biological processes affected by noncoding genetic variation associated with LOAD risk.

The influence of obesity-related factors in the etiology of renal cell carcinoma-A mendelian randomization study

BACKGROUND

Several obesity-related factors have been associated with renal cell carcinoma (RCC), but it is unclear which individual factors directly influence risk. We addressed this question using genetic markers as proxies for putative risk factors and evaluated their relation to RCC risk in a mendelian randomization (MR) framework. This methodology limits bias due to confounding and is not affected by reverse causation.

METHODS AND FINDINGS

Genetic markers associated with obesity measures, blood pressure, lipids, type 2 diabetes, insulin, and glucose were initially identified as instrumental variables, and their association with RCC risk was subsequently evaluated in a genome-wide association study (GWAS) of 10,784 RCC patients and 20,406 control participants in a 2-sample MR framework. The effect on RCC risk was estimated by calculating odds ratios (ORSD) for a standard deviation (SD) increment in each risk factor. The MR analysis indicated that higher body mass index increases the risk of RCC (ORSD: 1.56, 95% confidence interval [CI] 1.44-1.70), with comparable results for waist-to-hip ratio (ORSD: 1.63, 95% CI 1.40-1.90) and body fat percentage (ORSD: 1.66, 95% CI 1.44-1.90). This analysis further indicated that higher fasting insulin (ORSD: 1.82, 95% CI 1.30-2.55) and diastolic blood pressure (DBP; ORSD: 1.28, 95% CI 1.11-1.47), but not systolic blood pressure (ORSD: 0.98, 95% CI 0.84-1.14), increase the risk for RCC. No association with RCC risk was seen for lipids, overall type 2 diabetes, or fasting glucose.

CONCLUSIONS

This study provides novel evidence for an etiological role of insulin in RCC, as well as confirmatory evidence that obesity and DBP influence RCC risk.

Circulating Phylloquinone Concentrations and Risk of Type 2 Diabetes: A Mendelian Randomization Study

This study investigated the causal relation between circulating phylloquinone (vitamin K1) concentrations and type 2 diabetes by using a Mendelian randomization (MR) approach. We used data from three studies: the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study, Diabetes Genetics Replication and Meta-analysis (DIAGRAM), and the UK Biobank, resulting in 69,647 subjects with type 2 diabetes. We calculated a weighted genetic risk score including four genetic variants previously found to be associated with circulating phylloquinone concentrations. Inverse-variance weighted analysis was used to obtain a risk ratio (RR) for the causal relation between circulating phylloquinone concentrations and risk of type 2 diabetes. Presence of pleiotropy and the robustness of the results were assessed using MR-Egger and weighted-median analyses. Genetically predicted concentrations of circulating phylloquinone were associated with lower risk of type 2 diabetes with an RR of 0.93 (95% CI 0.89; 0.97) per every natural logarithm (Ln)-nmol/L-unit increase in circulating phylloquinone. The MR-Egger and weighted median analyses showed RRs of 0.94 (0.86; 1.02) and 0.93 (0.88; 0.98), respectively, indicating no pleiotropy. In conclusion, our study supports that higher circulating phylloquinone may be causally related with lower risk of type 2 diabetes, highlighting the importance of sufficient phylloquinone in the human diet.

Identification of dynamic molecular networks in peripheral blood mononuclear cells in type 1 diabetes mellitus

BACKGROUND

Type 1 diabetes mellitus (T1DM) is an autoimmune disease caused by the immune destruction of islet β cells. Gene expression in peripheral blood mononuclear cells (PBMCs) could offer new disease and treatment markers in T1DM. The objective of this study was to explore the coexpression and dynamic molecular networks in PBMCs of T1DM patients.

METHODS

Dataset GSE9006 contains PBMC samples of healthy volunteers, newly diagnosed T1DM patients, T1DM patients after insulin treatment, and newly diagnosed type 2 diabetes mellitus (T2DM) patients. Weighted correlation network analysis (WGCNA) was used to generate coexpression networks in T1DM and T2DM. Functional pathways in highly correlated modules of T1DM were enriched by gene set enrichment analysis (GSEA). We next filtered the differentially expressed genes (DEGs) and revealed their dynamic expression profiles in T1DM with or without insulin treatment. Furthermore, dynamic clusters and dynamic protein-protein interaction networks were identified. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was developed in dynamic clusters.

RESULTS

WGCNA disclosed 12 distinct gene modules, and distinguished between correlated networks in T1DM and T2DM. Two modules were closely associated with T1DM. GSEA showed that the immune response and response to cytokines were enriched in the T1DM highly correlated module. Next, we screened 44 DEGs in newly diagnosed T1DM compared with healthy donors, and 71 DEGs in 1-month and 97 DEGs in 4-month insulin treatment groups compared with newly diagnosed T1DM. Dynamic expression profiles of DEGs indicated the potential targets for T1DM treatment. Moreover, four molecular dynamic clusters were analyzed in newly diagnosed and insulin-treated T1DM. Functional annotation showed that these clusters were mainly enriched in the IL-17 signaling pathway, nuclear factor-ϰB signaling pathway, and tumor necrosis factor signaling pathway.

CONCLUSION

The results indicate potential drug targets or clinical efficacy markers, as well as demonstrating the underlying molecular mechanisms of T1DM treatment.

New considerations for hiPSC-based models of neuropsychiatric disorders

The development of human-induced pluripotent stem cells (hiPSCs) has made possible patient-specific modeling across the spectrum of human disease. Here, we discuss recent advances in psychiatric genomics and post-mortem studies that provide critical insights concerning cell-type composition and sample size that should be considered when designing hiPSC-based studies of complex genetic disease. We review recent hiPSC-based models of SZ, in light of our new understanding of critical power limitations in the design of hiPSC-based studies of complex genetic disorders. Three possible solutions are a movement towards genetically stratified cohorts of rare variant patients, application of CRISPR technologies to engineer isogenic neural cells to study the impact of common variants, and integration of advanced genetics and hiPSC-based datasets in future studies. Overall, we emphasize that to advance the reproducibility and relevance of hiPSC-based studies, stem cell biologists must contemplate statistical and biological considerations that are already well accepted in the field of genetics. We conclude with a discussion of the hypothesis of biological convergence of disease-through molecular, cellular, circuit, and patient level phenotypes-and how this might emerge through hiPSC-based studies.

Clinical significance of long noncoding RNA VIM-AS1 and CTBP1-AS2 expression in type 2 diabetes

BACKGROUND/AIMS

The risk of type 2 diabetes (T2D) is determined by a combination of genetic and environmental factors. Multiple studies have proposed that long noncoding RNAs (lncRNAs) are crucial molecules in regulating several biological processes and complex diseases. The study was aimed at investigating the association between the expression levels of lncRNA VIM-AS1, lncRNA CTBP1-AS2, and T2D susceptibility.

METHODS

lncRNA VIM-AS1 and lncRNA CTBP1-AS2 in the peripheral blood mononuclear cell (PBMC) of 100 healthy individuals and 100 T2D patients were collected for Quantitative Real-Time RT-PCR analysis. A logistic regression was performed to understand whether the likelihood of T2D can be predicted based on the expression levels of lncRNA VIM-AS1 and lncRNA CTBP1-AS2. Receiver operating characteristic (ROC) analysis was also performed to determine the statistical analysis of VIM-AS1 and CTBP1-AS2 levels in 200 samples.

RESULTS

Our results display that decreased levels of VIM-AS1 and CTBP1-AS2 in PBMC were associated with diabetes in Iranian population. The logistic regression revealed that Systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), Fasting blood glucose (FBG) and CTBP1-AS2 are substantial predictors of T2D. The ROC analysis of CTBP1-AS2 revealed the area under the ROC curve (AUC) of 0.68 with a sensitivity of 58.7% and specificity of 75.3% in distinguishing nondiabetic from diabetic subjects. The ROC analysis of VIM-AS1 determined AUC of 0.63 with a sensitivity of 56.1% and specificity of 68.37% in distinguishing the two diagnostic groups.

CONCLUSION

lncRNA VIM-AS1 and lncRNA CTBP1-AS2 expression levels are associated with T2D susceptibility.

Genome-wide profiling of histone H3K27 acetylation featured fatty acid signalling in pancreatic beta cells in diet-induced obesity in mice

AIMS/HYPOTHESIS

Epigenetic regulation of gene expression has been implicated in the pathogenesis of obesity and type 2 diabetes. However, detailed information, such as key transcription factors in pancreatic beta cells that mediate environmental effects, is not yet available.

METHODS

To analyse genome-wide cis-regulatory profiles and transcriptome of pancreatic islets derived from a diet-induced obesity (DIO) mouse model, we conducted chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-Seq) of histone H3 lysine 27 acetylation (histone H3K27ac) and high-throughput RNA sequencing. Transcription factor-binding motifs enriched in differential H3K27ac regions were examined by de novo motif analysis. For the predicted transcription factors, loss of function experiments were performed by transfecting specific siRNA in INS-1, a rat beta cell line, with and without palmitate treatment. Epigenomic and transcriptional changes of possible target genes were evaluated by ChIP and quantitative RT-PCR.

RESULTS

After long-term feeding with a high-fat diet, C57BL/6J mice were obese and mildly glucose intolerant. Among 39,350 islet cis-regulatory regions, 13,369 and 4610 elements showed increase and decrease in ChIP-Seq signals, respectively, significantly associated with global change in gene expression. Remarkably, increased H3K27ac showed a distinctive genomic localisation, mainly in the proximal-promoter regions, revealing enriched elements for nuclear respiratory factor 1 (NRF1), GA repeat binding protein α (GABPA) and myocyte enhancer factor 2A (MEF2A) by de novo motif analysis, whereas decreased H3K27ac was enriched for v-maf musculoaponeurotic fibrosarcoma oncogene family protein K (MAFK), a known negative regulator of beta cells. By siRNA-mediated knockdown of NRF1, GABPA or MEF2A we found that INS-1 cells exhibited downregulation of fatty acid β-oxidation genes in parallel with decrease in the associated H3K27ac. Furthermore, in line with the epigenome in DIO mice, palmitate treatment caused increase in H3K27ac and induction of β-oxidation genes; these responses were blunted when NRF1, GABPA or MEF2A were suppressed.

CONCLUSIONS/INTERPRETATION

These results suggest novel roles for DNA-binding proteins and fatty acid signalling in obesity-induced epigenomic regulation of beta cell function.

DATA AVAILABILITY

The next-generation sequencing data in the present study were deposited at ArrayExpress. RNA-Seq: Dataset name: ERR2538129 (Control), ERR2538130 (Diet-induced obesity) Repository name and number: E-MTAB-6718 - RNA-Seq of pancreatic islets derived from mice fed a long-term high-fat diet against chow-fed controls. ChIP-Seq: Dataset name: ERR2538131 (Control), ERR2538132 (Diet-induced obesity) Repository name and number: E-MTAB-6719 - H3K27ac ChIP-Seq of pancreatic islets derived from mice fed a long-term high-fat diet (HFD) against chow-fed controls.

Additional common variants associated with type 2 diabetes and coronary artery disease detected using a pleiotropic cFDR method

Genome-wide association studies (GWASs) have been performed extensively in diverse populations to identify single nucleotide polymorphisms (SNPs) associated with complex diseases or traits. However, to date, the SNPs identified fail to explain a large proportion of the variance of the traits/diseases. GWASs on type 2 diabetes (T2D) and coronary artery disease (CARD) are generally performed as single-trait studies, rather than analyzing the related traits simultaneously. Despite the extensive evidence suggesting that these two phenotypes share both genetic and environmental risk factors, the shared overlapping genetic biological mechanisms between these traits remain largely unexplored. Here, we adopted a recently developed genetic pleiotropic conditional false discovery rate (cFDR) approach to discover novel loci associated with T2D and CARD by incorporating the summary statistics from existing GWASs of these two traits. Applying the cFDR level of 0.05, 33 loci were identified for T2D and 34 loci for CARD, 9 of which for both. By incorporating pleiotropic effects into a conditional analysis framework, we observed that there is significant pleiotropic enrichment between T2D and CARD. These findings may provide novel insights into the etiology of T2D and CARD, as well as the processes that may influence disease development both individually and jointly.

The MTNR1B rs10830963 Variant in Interaction with Pre-Pregnancy BMI is a Pharmacogenetic Marker for the Initiation of Antenatal Insulin Therapy in Gestational Diabetes Mellitus

The rs10830963 variant of the Melatonin Receptor 1B (MTNR1B) gene is associated with the development of gestational diabetes mellitus (GDM). We hypothesized that carrying the rs10830963/G risk allele had effect on antenatal insulin therapy (AIT) initiation in GDM in a body mass index (BMI)-dependent manner. Design: In this post hoc analysis the MTNR1B rs10830963 genotype and the clinical data of 211 Caucasian GDM patients were assessed. As a first step, a pre-pregnancy BMI threshold was determined where the effect of MTNR1B rs10830963/G allele carrying on AIT initiation was the most significant using logistic regression. Maternal age adjusted real-life odds ratios (OR) values were calculated. The chi-square test was also used to calculate the p value and 10.000 bootstrap simulations were performed in each case to re-assess the statistical power and the OR. Carrying the MTNR1B rs10830963/G allele increased the odds of AIT initiation (OR = 5.2, p = 0.02 [χ² test], statistical power = 0.53) in GDM patients with pre-pregnancy BMI ≥ 29 kg/m². The statistical power reached 0.77, when the pre-pregnancy BMI cutoff of 27 kg/m² was used and the genetic effect on AIT initiation was still significant, but only using the logistic regression model. Carrying the MTNR1B rs10830963/G risk allele-in interaction with pre-pregnancy BMI-is likely be considered as a candidate pharmacogenetic marker of antenatal insulin therapy initiation and should be further assessed in precision medicine trials in GDM.

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).

Type 2 Diabetes-Associated Genetic Variants Regulate Chromatin Accessibility in Human Islets

Type 2 diabetes (T2D) is a complex disorder in which both genetic and environmental risk factors contribute to islet dysfunction and failure. Genome-wide association studies (GWAS) have linked single nucleotide polymorphisms (SNPs), most of which are noncoding, in >200 loci to islet dysfunction and T2D. Identification of the putative causal variants and their target genes and whether they lead to gain or loss of function remains challenging. Here, we profiled chromatin accessibility in pancreatic islet samples from 19 genotyped individuals and identified 2,949 SNPs associated with in vivo cis-regulatory element use (i.e., chromatin accessibility quantitative trait loci [caQTL]). Among the caQTLs tested (n = 13) using luciferase reporter assays in MIN6 β-cells, more than half exhibited effects on enhancer activity that were consistent with in vivo chromatin accessibility changes. Importantly, islet caQTL analysis nominated putative causal SNPs in 13 T2D-associated GWAS loci, linking 7 and 6 T2D risk alleles, respectively, to gain or loss of in vivo chromatin accessibility. By investigating the effect of genetic variants on chromatin accessibility in islets, this study is an important step forward in translating T2D-associated GWAS SNP into functional molecular consequences.

The Glymphatic System in Diabetes-Induced Dementia

The glymphatic system has emerged as an important player in central nervous system (CNS) diseases, by regulating the vasculature impairment, effectively controlling the clearance of toxic peptides, modulating activity of astrocytes, and being involved in the circulation of neurotransmitters in the brain. Recently, several studies have indicated decreased activity of the glymphatic pathway under diabetes conditions such as in insulin resistance and hyperglycemia. Furthermore, diabetes leads to the disruption of the blood-brain barrier and decrease of apolipoprotein E (APOE) expression and the secretion of norepinephrine in the brain, involving the impairment of the glymphatic pathway and ultimately resulting in cognitive decline. Considering the increased prevalence of diabetes-induced dementia worldwide, the relationship between the glymphatic pathway and diabetes-induced dementia should be investigated and the mechanisms underlying their relationship should be discussed to promote the development of an effective therapeutic approach in the near future. Here, we have reviewed recent evidence for the relationship between glymphatic pathway dysfunction and diabetes. We highlight that the enhancement of the glymphatic system function during sleep may be beneficial to the attenuation of neuropathology in diabetes-induced dementia. Moreover, we suggest that improving glymphatic system activity may be a potential therapeutic strategy for the prevention of diabetes-induced dementia.

A Review of Data of Findings on Night Shift Work and the Development of DM and CVD Events: a Synthesis of the Proposed Molecular Mechanisms

PURPOSE OF REVIEW

Night shift work has become highly prevalent in our 24/7 societies, with up to 18% of the US work force working alternate shift schedules. However, studies indicate that there may be adverse health effects of chronic night work across diverse populations. These effects are likely due to misalignment of the circadian system with work schedules, mediated by the system's primary marker melatonin as well as other downstream molecules.

RECENT FINDINGS

Melatonin has multiple biologic actions that are relevant to cardiometabolic disease, including modulation of oxidative stress, inflammation, and (via the melatonin receptor) vasoconstriction. Behavioral traits, such as chronotype and meal timing, have recently been shown to interact with the effects of night work on cardiometabolic health. Together with recent findings suggesting a role for circadian genes in cardiometabolic risk, the interactions of night shift work and behavioral traits are likely to facilitate novel treatment and prevention approaches for cardiovascular disease and type 2 diabetes, incorporating aspects of clock and timing.

Complex patterns of direct and indirect association between the transcription Factor-7 like 2 gene, body mass index and type 2 diabetes diagnosis in adulthood in the Hispanic Community Health Study/Study of Latinos

BACKGROUND

Genome-wide association studies have implicated the transcription factor 7-like 2 (TCF7L2) gene in type 2 diabetes risk, and more recently, in decreased body mass index. Given the contrary direction of genetic effects on these two traits, it has been suggested that the observed association with body mass index may reflect either selection bias or a complex underlying biology at TCF7L2.

METHODS

Using 9031 Hispanic/Latino adults (21-76 years) with complete weight history and genetic data from the community-based Hispanic Community Health Study/Study of Latinos (HCHS/SOL, Baseline 2008-2011), we estimated the multivariable association between the additive number of type 2 diabetes increasing-alleles at TCF7L2 (rs7903146-T) and body mass index. We then used structural equation models to simultaneously model the genetic association on changes in body mass index across the life course and estimate the odds of type 2 diabetes per TCF7L2 risk allele.

RESULTS

We observed both significant increases in type 2 diabetes prevalence at examination (independent of body mass index) and decreases in mean body mass index and waist circumference across genotypes at rs7903146. We observed a significant multivariable association between the additive number of type 2 diabetes-risk alleles and lower body mass index at examination. In our structured modeling, we observed non-significant inverse direct associations between rs7903146-T and body mass index at ages 21 and 45 years, and a significant positive association between rs7903146-T and type 2 diabetes onset in both middle and late adulthood.

CONCLUSIONS

Herein, we replicated the protective effect of rs7930146-T on body mass index at multiple time points in the life course, and observed that these effects were not explained by past type 2 diabetes status in our structured modeling. The robust replication of the negative effects of TCF7L2 on body mass index in multiple samples, including in our diverse Hispanic/Latino community-based sample, supports a growing body of literature on the complex biologic mechanism underlying the functional consequences of TCF7L2 on obesity and type 2 diabetes across the life course.

Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes

To define potentially causal variants for autoimmune disease, we fine-mapped^1,2 76 rheumatoid arthritis (11,475 cases, 15,870 controls)³ and type 1 diabetes loci (9,334 cases, 11,111 controls)⁴. After sequencing 799 1-kilobase regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89% of common variants. We defined credible sets of ≤5 causal variants at 5 rheumatoid arthritis and 10 type 1 diabetes loci. We identified potentially causal missense variants at DNASE1L3, PTPN22, SH2B3, and TYK2, and noncoding variants at MEG3, CD28-CTLA4, and IL2RA. We also identified potential candidate causal variants at SIRPG and TNFAIP3. Using functional assays, we confirmed allele-specific protein binding and differential enhancer activity for three variants: the CD28-CTLA4 rs117701653 SNP, MEG3 rs34552516 indel, and TNFAIP3 rs35926684 indel.

A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease

Genome-wide association studies (GWAS), relying on hundreds of thousands of individuals, have revealed >200 genomic loci linked to metabolic disease (MD). Loss of insulin sensitivity (IS) is a key component of MD and we hypothesized that discovery of a robust IS transcriptome would help reveal the underlying genomic structure of MD. Using 1,012 human skeletal muscle samples, detailed physiology and a tissue-optimized approach for the quantification of coding (>18,000) and non-coding (>15,000) RNA (ncRNA), we identified 332 fasting IS-related genes (CORE-IS). Over 200 had a proven role in the biochemistry of insulin and/or metabolism or were located at GWAS MD loci. Over 50% of the CORE-IS genes responded to clinical treatment; 16 quantitatively tracking changes in IS across four independent studies (P = 0.0000053: negatively: AGL, G0S2, KPNA2, PGM2, RND3 and TSPAN9 and positively: ALDH6A1, DHTKD1, ECHDC3, MCCC1, OARD1, PCYT2, PRRX1, SGCG, SLC43A1 and SMIM8). A network of ncRNA positively related to IS and interacted with RNA coding for viral response proteins (P < 1 × 10-48), while reduced amino acid catabolic gene expression occurred without a change in expression of oxidative-phosphorylation genes. We illustrate that combining in-depth physiological phenotyping with robust RNA profiling methods, identifies molecular networks which are highly consistent with the genetics and biochemistry of human metabolic disease.

The Common HNF1A Variant I27L Is a Modifier of Age at Diabetes Diagnosis in Individuals With HNF1A-MODY

There is wide variation in the age at diagnosis of diabetes in individuals with maturity-onset diabetes of the young (MODY) due to a mutation in the HNF1A gene. We hypothesized that common variants at the HNF1A locus (rs1169288 [I27L], rs1800574 [A98V]), which are associated with type 2 diabetes susceptibility, may modify age at diabetes diagnosis in individuals with HNF1A-MODY. Meta-analysis of two independent cohorts, comprising 781 individuals with HNF1A-MODY, found no significant associations between genotype and age at diagnosis. However after stratifying according to type of mutation (protein-truncating variant [PTV] or missense), we found each 27L allele to be associated with a 1.6-year decrease (95% CI -2.6, -0.7) in age at diagnosis, specifically in the subset (n = 444) of individuals with a PTV. The effect size was similar and significant across the two independent cohorts of individuals with HNF1A-MODY. We report a robust genetic modifier of HNF1A-MODY age at diagnosis that further illustrates the strong effect of genetic variation within HNF1A upon diabetes phenotype.

Anderson on behalf of METASTROKE and the ISGC, Boehnke M, Laakso M, Atzmon G, Glaser B, Mercader JM, Gaulton K, Flannick J, Getz G, Florez JC. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis

BACKGROUND

Type 2 diabetes (T2D) is a heterogeneous disease for which (1) disease-causing pathways are incompletely understood and (2) subclassification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper, we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity. First, we aimed to categorize genetic loci into groups representing likely disease mechanistic pathways. Second, we asked whether the novel clusters of genetic loci we identified have any broad clinical consequence, as assessed in four separate subsets of individuals with T2D.

METHODS AND FINDINGS

In an effort to identify mechanistic pathways driven by established T2D genetic loci, we applied Bayesian nonnegative matrix factorization (bNMF) clustering to genome-wide association study (GWAS) results for 94 independent T2D genetic variants and 47 diabetes-related traits. We identified five robust clusters of T2D loci and traits, each with distinct tissue-specific enhancer enrichment based on analysis of epigenomic data from 28 cell types. Two clusters contained variant-trait associations indicative of reduced beta cell function, differing from each other by high versus low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity mediated (high body mass index [BMI] and waist circumference [WC]), "lipodystrophy-like" fat distribution (low BMI, adiponectin, and high-density lipoprotein [HDL] cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster genetic risk scores were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease (CAD), and stroke. We evaluated the potential for clinical impact of these clusters in four studies containing individuals with T2D (Metabolic Syndrome in Men Study [METSIM], N = 487; Ashkenazi, N = 509; Partners Biobank, N = 2,065; UK Biobank [UKBB], N = 14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with approximately 30% of all individuals assigned to just one cluster top decile. Limitations of this study include that the genetic variants used in the cluster analysis were restricted to those associated with T2D in populations of European ancestry.

CONCLUSION

Our approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports the use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.

Type 2 diabetes-associated variants of the MT2 melatonin receptor affect distinct modes of signaling

Melatonin is produced during the night and regulates sleep and circadian rhythms. Loss-of-function variants in MTNR1B, which encodes the melatonin receptor MT₂, a G protein-coupled receptor (GPCR), are associated with an increased risk of type 2 diabetes (T2D). To identify specific T2D-associated signaling pathway(s), we profiled the signaling output of 40 MT₂ variants by monitoring spontaneous (ligand-independent) and melatonin-induced activation of multiple signaling effectors. Genetic association analysis showed that defects in the melatonin-induced activation of Gα_i1 and Gα_z proteins and in spontaneous β-arrestin2 recruitment to MT₂ were the most statistically significantly associated with an increased T2D risk. Computational variant impact prediction by in silico evolutionary lineage analysis strongly correlated with the measured phenotypic effect of each variant, providing a predictive tool for future studies on GPCR variants. Together, this large-scale functional study provides an operational framework for the postgenomic analysis of the multiple GPCR variants present in the human population. The association of T2D risk with signaling pathway-specific defects opens avenues for pathway-specific personalized therapeutic intervention and reveals the potential relevance of MT₂ function during the day, when melatonin is undetectable, but spontaneous activity of the receptor occurs.

Genetic susceptibility in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is present in 1.5-2.5% of the population and can result in substantial lifelong disability. It is characterized by intrusive thoughts, sensations, and urges and by repetitive behaviors that are difficult to control despite, in most cases, preserved insight as to their excessive or irrational nature. The causes and underlying pathophysiology of OCD are not well understood, which has limited the development of new treatments and interventions. Despite evidence for a substantial genetic contribution to disease risk, identification and replication of genetic variants associated with OCD have been challenging. Decades of candidate gene association studies have provided little insight. They are now being supplanted by modern genomewide approaches to discover both common and rare sequence and structural variants. Studies to date suggest potential novel therapeutic avenues such as modulators of glutamatergic and immune pathways; however, individual genetic findings are not yet statistically robust or replicated. Further efforts are clearly needed to identify specific risk variants and to confirm vulnerable pathways by studying much larger cohorts of patients with comprehensive variant discovery approaches. Mouse knockout models have already made notable inroads into our understanding of OCD pathology; their utility will only increase as specific risk alleles are identified.

Tpcn2 knockout mice have improved insulin sensitivity and are protected against high-fat diet-induced weight gain

Type 2 diabetes is a complex disorder affected by multiple genes and the environment. Our laboratory has shown that in response to a glucose challenge, two-pore channel 2 ( Tpcn2) knockout mice exhibit a decreased insulin response but normal glucose clearance, suggesting they have improved insulin sensitivity compared with wild-type mice. We tested the hypothesis that improved insulin sensitivity in Tpcn2 knockout mice would protect against the negative effects of a high fat diet. Male and female Tpcn2 knockout (KO), heterozygous (Het), and wild-type (WT) mice were fed a low-fat (LF) or high-fat (HF) diet for 24 wk. HF diet significantly increases body weight in WT mice relative to those on the LF diet; this HF diet-induced increase in body weight is blunted in the Het and KO mice. Despite the protection against diet-induced weight gain, however, Tpcn2 KO mice are not protected against HF-diet-induced changes in glucose or insulin area under the curve during glucose tolerance tests in female mice, while HF diet has no significant effect on glucose tolerance in the male mice, regardless of genotype. Glucose disappearance during an insulin tolerance test is augmented in male KO mice, consistent with our previous findings suggesting enhanced insulin sensitivity in these mice. Male KO mice exhibit increased fasting plasma total cholesterol and triglyceride concentrations relative to WT mice on the LF diet, but this difference disappears in HF diet-fed mice where there is increased cholesterol and triglycerides across all genotypes. These data demonstrate that knockout of Tpcn2 may increase insulin action in male, but not female, mice. In addition, both male and female KO mice are protected against diet-induced weight gain, but this protection is likely independent from glucose tolerance, insulin sensitivity, and plasma lipid levels.

Glucose and Insulin-Related Traits, Type 2 Diabetes and Risk of Schizophrenia: A Mendelian Randomization Study

BACKGROUND

The link between schizophrenia and diabetes mellitus is well established by observational studies; however, the cause-effect relationship remains unclear.

METHODS

Here, we conducted Mendelian randomization analyses to assess a causal relationship of the genetic variants related to elevated fasting glucose levels, hemoglobin A1c (HbA1c), fasting insulin levels, and type 2 diabetes with the risk of schizophrenia. The analyses were performed using summary statistics obtained for the variants identified from the genome-wide association meta-analyses of fasting glucose levels (up to 133,010 individuals), HbA1c (up to 153,377 individuals), fasting insulin levels (up to 108,557 individuals), type 2 diabetes (up to 659,316 individuals), and schizophrenia (up to 108,341 individuals). The association between each variant and schizophrenia was weighted by its association with each studied condition, and estimates were combined using an inverse-variance weighted meta-analysis.

FINDINGS

Using information from thirteen variants related to fasting insulin levels, the causal effect of fasting insulin levels increases (per 1-SD) on the risk of schizophrenia was estimated at an odds ratio (OR) of 2·33 (p = 0·001), which is consistent with findings from the observational studies. The fasting glucose associated single nucleotide polymorphisms (SNPs) had no effect on the risk of schizophrenia in Europeans and East Asians (p > 0·05). Nonsignificant effects on the risk of schizophrenia was observed with raised HbA1c and type 2 diabetes, and consistent estimates were obtained across different populations.

INTERPRETATION

Our results suggest a causal role of elevated fasting insulin levels in schizophrenia pathogenesis.

Circadian dysrhythmia-linked diabetes mellitus: Examining melatonin’s roles in prophylaxis and management

Diabetes mellitus is a chronic, life-threatening metabolic disorder that occurs worldwide. Despite an increase in the knowledge of the risk factors that are associated with diabetes mellitus, its worldwide prevalence has continued to rise; thus, necessitating more research into its aetiology. Recent researches are beginning to link a dysregulation of the circadian rhythm to impairment of intermediary metabolism; with evidences that circadian rhythm dysfunction might play an important role in the aetiology, course or prognosis of some cases of diabetes mellitus. These evidences thereby suggest possible relationships between the circadian rhythm regulator melatonin, and diabetes mellitus. In this review, we discuss the roles of the circadian rhythm in the regulation of the metabolism of carbohydrates and other macronutrients; with emphasis on the importance of melatonin and the impacts of its deficiency on carbohydrate homeostasis. Also, the possibility of using melatonin and its analogs for the “prophylaxis” or management of diabetes mellitus is also considered.

NetControl4BioMed: a pipeline for biomedical data acquisition and analysis of network controllability

BACKGROUND

Network controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combined multi-drug therapy in order to induce a desired response from a cell; this can lead to developments of novel therapeutic approaches for systemic diseases like cancer.

RESULT

We develop a novel bioinformatics data analysis pipeline called NetControl4BioMed based on the concept of target structural control of linear networks. Our pipeline generates novel molecular interaction networks by combining pathway data from various public databases starting from the user's query. The pipeline then identifies a set of nodes that is enough to control a given, user-defined set of disease-specific essential proteins in the network, i.e., it is able to induce a change in their configuration from any initial state to any final state. We provide both the source code of the pipeline as well as an online web-service based on this pipeline http://combio.abo.fi/nc/net_control/remote_call.php .

CONCLUSION

The pipeline can be used by researchers for controlling and better understanding of molecular interaction networks through combinatorial multi-drug therapies, for more efficient therapeutic approaches and personalised medicine.

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.

The research data management platform (RDMP): A novel, process driven, open-source tool for the management of longitudinal cohorts of clinical data

Background

The Health Informatics Centre at the University of Dundee provides a service to securely host clinical datasets and extract relevant data for anonymized cohorts to researchers to enable them to answer key research questions. As is common in research using routine healthcare data, the service was historically delivered using ad-hoc processes resulting in the slow provision of data whose provenance was often hidden to the researchers using it. This paper describes the development and evaluation of the Research Data Management Platform (RDMP): an open source tool to load, manage, clean, and curate longitudinal healthcare data for research and provide reproducible and updateable datasets for defined cohorts to researchers.

Results

Between 2013 and 2017, RDMP tool implementation tripled the productivity of data analysts producing data releases for researchers from 7.1 to 25.3 per month and reduced the error rate from 12.7% to 3.1%. The effort on data management reduced from a mean of 24.6 to 3.0 hours per data release. The waiting time for researchers to receive data after agreeing a specification reduced from approximately 6 months to less than 1 week. The software is scalable and currently manages 163 datasets. A total 1,321 data extracts for research have been produced, with the largest extract linking data from 70 different datasets.

Conclusions

The tools and processes that encompass the RDMP not only fulfil the research data management requirements of researchers but also support the seamless collaboration of data cleaning, data transformation, data summarization and data quality assessment activities by different research groups.

Shared Genetic Contribution of Type 2 Diabetes and Cardiovascular Disease: Implications for Prognosis and Treatment

PURPOSE OF REVIEW

The increased cardiovascular disease (CVD) risk in subjects with type 2 diabetes (T2D) is well established. This review collates the available evidence and assesses the shared genetic background between T2D and CVD: the causal contribution of common risk factors to T2D and CVD and how genetics can be used to improve drug development and clinical outcomes.

RECENT FINDINGS

Large-scale genome-wide association studies (GWAS) of T2D and CVD support a shared genetic background but minimal individual locus overlap. Mendelian randomisation (MR) analyses show that T2D is causal for CVD, but GWAS of CVD, T2D and their common risk factors provided limited evidence for individual locus overlap. Distinct but functionally related pathways were enriched for CVD and T2D genetic associations reflecting the lack of locus overlap and providing some explanation for the variable associations of common risk factors with CVD and T2D from MR analyses.

Genomic insights into the causes of type 2 diabetes

Genome-wide association studies have implicated around 250 genomic regions in predisposition to type 2 diabetes, with evidence for causal variants and genes emerging for several of these regions. Understanding of the underlying mechanisms, including the interplay between β-cell failure, insulin sensitivity, appetite regulation, and adipose storage has been facilitated by the integration of multidimensional data for diabetes-related intermediate phenotypes, detailed genomic annotations, functional experiments, and now multiomic molecular features. Studies in diverse ethnic groups and examples from population isolates have shown the value and need for a broad genomic approach to this global disease. Transethnic discovery efforts and large-scale biobanks in diverse populations and ancestries could help to address some of the Eurocentric bias. Despite rapid progress in the discovery of the highly polygenic architecture of type 2 diabetes, dominated by common alleles with small, cumulative effects on disease risk, these insights have been of little clinical use in terms of disease prediction or prevention, and have made only small contributions to subtype classification or stratified approaches to treatment. Successful development of academia-industry partnerships for exome or genome sequencing in large biobanks could help to deliver economies of scale, with implications for the future of genomics-focused research.

Imaging endocrinology in animal models of endocrine disease

Endocrine organs secrete a variety of hormones involved in the regulation of a multitude of body functions. Although pancreatic islets were discovered at the turn of the 19th century, other endocrine glands remained commonly described as diffuse endocrine systems. Over the last two decades, development of new imaging techniques and genetically-modified animals with cell-specific fluorescent tags or specific hormone deficiencies have enabled in vivo imaging of endocrine organs and revealed intricate endocrine cell network structures and plasticity. Overall, these new tools have revolutionized our understanding of endocrine function. The overarching aim of this Review is to describe the current mechanistic understanding that has emerged from imaging studies of endocrine cell network structure/function relationships in animal models, with a particular emphasis on the pituitary gland and the endocrine pancreas.

Proneurotensin Predicts Cardiovascular Disease in an Elderly Population

CONTEXT

The gut hormone neurotensin promotes fat absorption, diet-induced weight gain, and liver steatosis. Its stable precursor-hormone fragment "proneurotensin" predicts cardiometabolic disease in middle-aged populations, especially in women.

OBJECTIVE

To test if proneurotensin predicts cardiovascular disease (CVD) and diabetes development in an elderly population and whether there are gender differences in this respect.

DESIGN, SETTING, AND PARTICIPANTS

Fasting proneurotensin was measured in plasma from 4804 participants (mean age 69 ± 6 years) of the Malmö Preventive Project and subjects were followed up for development of CVD and diabetes during 5.4 years.

MAIN OUTCOME MEASURES

Multivariate adjusted Cox proportional hazard models CVD were used to relate the proneurotensin to the risk of incident CVD and diabetes in all subjects and in gender-stratified analyses.

RESULTS

In total, there were 456 first CVD events and 222 incident cases of diabetes. The hazard ratio [HR (95% confidence interval)] for CVD per 1 standard deviation (SD) increment of proneurotensin was 1.10 (1.01 to 1.21); P = 0.037, and the above vs below median HR was 1.27 (1.06 to 1.54); P = 0.011, with similar effect sizes in both genders. There was no significant association between proneurotensin and incident diabetes in the entire population (P = 0.52) or among men (P = 0.52). However, in women proneurotensin predicted diabetes incidence with a per 1 SD increment HR of 1.28 (1.30 to 1.59); P = 0.025 and an above vs below median HR of 1.41 (1.10 to 1.80); P = 0.007.

CONCLUSIONS

In the elderly population, proneurotensin independently predicts development of CVD in both genders, whereas it only predicts diabetes in women.

The molecular functions of hepatocyte nuclear factors - In and beyond the liver

The hepatocyte nuclear factors (HNFs) namely HNF1α/β, FOXA1/2/3, HNF4α/γ and ONECUT1/2 are expressed in a variety of tissues and organs, including the liver, pancreas and kidney. The spatial and temporal manner of HNF expression regulates embryonic development and subsequently the development of multiple tissues during adulthood. Though the HNFs were initially identified individually based on their roles in the liver, numerous studies have now revealed that the HNFs cross-regulate one another and exhibit synergistic relationships in the regulation of tissue development and function. The complex HNF transcriptional regulatory networks have largely been elucidated in rodent models, but less so in human biological systems. Several heterozygous mutations in these HNFs were found to cause diseases in humans but not in rodents, suggesting clear species-specific differences in mutational mechanisms that remain to be uncovered. In this review, we compare and contrast the expression patterns of the HNFs, the HNF cross-regulatory networks and how these liver-enriched transcription factors serve multiple functions in the liver and beyond, extending our focus to the pancreas and kidney. We also summarise the insights gained from both human and rodent studies of mutations in several HNFs that are known to lead to different disease conditions.