Five linkage regions each harbor multiple type 2 diabetes genes in the African American subset of the GENNID Study

Differential Expression of lncRNA-miRNA-mRNA and Their Related Functional Networks in New-Onset Type 2 Diabetes Mellitus among Chinese Rural Adults

Increasing evidence suggested that the expression and inter-regulation of long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA) were related to the development of diabetes. Based on bioinformatics analysis, this study aimed to comprehensively analyze the dysregulated RNA molecules related to new-onset type 2 diabetes mellitus (T2DM). Twenty-four patients with new-onset T2DM were included as cases, and sex- and age-matched participants were included as controls. The differentially expressed lncRNAs, miRNAs, and mRNAs between the two groups were screened by RNA sequencing. LncRNA-miRNA-mRNA network and enrichment analysis were used to reveal the RNA molecules that were potentially associated with T2DM and their early changes. A total of 123 lncRNAs, 49 miRNAs, and 312 mRNAs were differentially expressed in the new-onset T2DM (fold change ≥ 1.5 and p value < 0.05). Functional analysis revealed that differentially expressed RNAs were likely to play essential roles in diabetes-related pathways. In addition, the protein–protein interaction (PPI) network screened multiple hub mRNAs, and lncRNA-miRNA-mRNA networks showed that a single miRNA could be related to multiple lncRNAs, and then they coregulated more mRNAs. SLC25A4, PLCB1, AGTR2, PRKN, and SCD5 were shown to be important mRNAs in T2DM, and miR-199b-5p, miR-202-5p, miR-548o-3p as well as miR-1255b-5p could be involved in their regulation. In conclusion, several new and previously identified dysregulated lncRNAs, miRNAs, and mRNAs were found to be vital biomarkers in T2DM. Their alterations and interactions could modulate the pathophysiology of T2DM. Those findings may provide new insights into the molecular mechanisms underlying the development of T2DM.

GRB10 regulates β cell mass by inhibiting β cell proliferation and stimulating β cell dedifferentiation

Decreased functional β-cell mass is the hallmark of diabetes, but the cause of this metabolic defect remains elusive. Here, we show that the expression levels of the growth factor receptor-bound protein 10 (GRB10), a negative regulator of insulin and mTORC1 signaling, are markedly induced in islets of diabetic mice and high glucose-treated insulinoma cell line INS-1cells. β-cell-specific knockout of Grb10 in mice increased β-cell mass and improved β-cell function. Grb10-deficient β-cells exhibit enhanced mTORC1 signaling and reduced β-cell dedifferentiation, which could be blocked by rapamycin. On the contrary, Grb10 overexpression induced β-cell dedifferentiation in MIN6 cells. Our study identifies GRB10 as a critical regulator of β-cell dedifferentiation and β-cell mass, which exerts its effect by inhibiting mTORC1 signaling.

The cross-talk between RAGE and DIAPH1 in neurological complications of diabetes: A review

Neuropathy, or dysfunction of peripheral nerve, is one of the most common neurological manifestation in patients with diabetes mellitus (DM). DM is typically associated with a hyperglycaemic milieu, which promotes non-enzymatic glycation of proteins. Proteins with advanced glycation are known to engage a cell-surface receptor called the receptor for advanced glycation end products (RAGE). Thus, it is reasonable to assume that RAGE and its associated molecule-mediated cellular signalling may contribute to DM-induced symmetrical axonal (length-dependent) neuropathy. Of particular interest is diaphanous related formin 1 (DIAPH1), a cytoskeletal organizing molecule, which interacts with the cytosolic domain of RAGE and whose dysfunction may precipitate axonopathy/neuropathy. Indeed, it has been demonstrated that both RAGE and DIAPH1 are expressed in the motor and sensory fibres of nerve harvested from DM animal models. Although the detailed molecular role of RAGE and DIAPH1 in diabetic neurological complications remains unclear, here we will discuss available evidence of their involvement in peripheral diabetic neuropathy. Specifically, we will discuss how a hyperglycaemic environment is not only likely to elevate advanced glycation end products (ligands of RAGE) and induce a pro-inflammatory environment but also alter signalling via RAGE and DIAPH1. Further, hyperglycaemia may regulate epigenetic mechanisms that interacts with RAGE signalling. We suggest the cumulative effect of hyperglycaemia on RAGE-DIAPH1-mediated signalling may be disruptive to axonal cytoskeletal organization and transport and is therefore likely to play a key role in pathogenesis of diabetic symmetrical axonal neuropathy.

Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study

BACKGROUND

To identify genetic associations of quantitative metabolic syndrome (MetS) traits and characterize heterogeneity across ethnic groups.

METHODS

Data was collected from GENetics of Noninsulin dependent Diabetes Mellitus (GENNID), a multiethnic resource of Type 2 diabetic families and included 1520 subjects in 259 African-American, European-American, Japanese-Americans, and Mexican-American families. We focused on eight MetS traits: weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein, triglycerides, fasting glucose, and insulin. Using genotyped and imputed data from Illumina's Multiethnic array, we conducted genome-wide association analyses with linear mixed models for all ethnicities, except for the smaller Japanese-American group, where we used additive genetic models with gene-dropping.

RESULTS

Findings included ethnic-specific genetic associations and heterogeneity across ethnicities. Most significant associations were outside our candidate linkage regions and were coincident within a gene or intergenic region, with two exceptions in European-American families: (a) within previously identified linkage region on chromosome 2, two significant GLI2-TFCP2L1 associations with weight, and (b) one chromosome 11 variant near CADM1-LINC00900 with pleiotropic blood pressure effects.

CONCLUSIONS

This multiethnic family study found genetic heterogeneity and coincident associations (with one case of pleiotropy), highlighting the importance of including diverse populations in genetic research and illustrating the complex genetic architecture underlying MetS.

Genetic and nongenetic profiling of milk β-hydroxybutyrate and acetone and their associations with ketosis in Holstein cows

Ketosis is a metabolic disorder of increasing importance in high-yielding dairy cows, but accurate population-wide binary health trait recording is difficult to implement. Against this background, proper Gaussian indicator traits, which can be routinely measured in milk, are needed. Consequently, we focused on the ketone bodies acetone and β-hydroxybutyrate (BHB), measured via Fourier-transform infrared spectroscopy (FTIR) in milk. In the present study, 62,568 Holstein cows from large-scale German co-operator herds were phenotyped for clinical ketosis (KET) according to a veterinarian diagnosis key. A sub-sample of 16,861 cows additionally had first test-day observations for FTIR acetone and BHB. Associations between FTIR acetone and BHB with KET and with test-day traits were studied phenotypically and quantitative genetically. Furthermore, we estimated SNP marker effects for acetone and BHB (application of genome-wide association studies) based on 40,828 SNP markers from 4,384 genotyped cows, and studied potential candidate genes influencing body fat mobilization. Generalized linear mixed models were applied to infer the influence of binary KET on Gaussian-distributed acetone and BHB (definition of an identity link function), and vice versa, such as the influence of acetone and BHB on KET (definition of a logit link function). Additionally, linear models were applied to study associations between BHB, acetone and test-day traits (milk yield, fat percentage, protein percentage, fat-to-protein ratio and somatic cell score) from the first test-day after calving. An increasing KET incidence was statistically significant associated with increasing FTIR acetone and BHB milk concentrations. Acetone and BHB concentrations were positively associated with fat percentage, fat-to-protein ratio and somatic cell score. Bivariate linear animal models were applied to estimate genetic (co)variance components for KET, acetone, BHB and test-day traits within parities 1 to 3, and considering all parities simultaneously in repeatability models. Pedigree-based heritabilities were quite small (i.e., in the range from 0.01 in parity 3 to 0.07 in parity 1 for acetone, and from 0.03-0.04 for BHB). Heritabilites from repeatability models were 0.05 for acetone, and 0.03 for BHB. Genetic correlations between acetone and BHB were moderate to large within parities and considering all parities simultaneously (0.69-0.98). Genetic correlations between acetone and BHB with KET from different parities ranged from 0.71 to 0.99. Genetic correlations between acetone across parities, and between BHB across parities, ranged from 0.55 to 0.66. Genetic correlations between KET, acetone, and BHB with fat-to-protein ratio and with fat percentage were large and positive, but negative with milk yield. In genome-wide association studies, we identified SNP on BTA 4, 10, 11, and 29 significantly influencing acetone, and on BTA 1 and 16 significantly influencing BHB. The identified potential candidate genes NRXN3, ACOXL, BCL2L11, HIBADH, KCNJ1, and PRG4 are involved in lipid and glucose metabolism pathways.

Bayesian variable selection for high dimensional predictors and self-reported outcomes

Background

The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error.

Methods

We adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women’s Health Initiative SNP Health Association Resource, which includes extensive genotypic (>900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women.

Results

Simulation studies show improved sensitivity of our proposed method when compared to a naive approach that ignores error in the self-reported outcomes. Application of the proposed method resulted in discovery of several single nucleotide polymorphisms (SNPs) that are associated with risk of type 2 diabetes in a dataset of 9,873 African American and Hispanic participants in the Women’s Health Initiative. There was little overlap among the top ranking SNPs associated with type 2 diabetes risk between the racial groups, adding support to previous observations in the literature of disease associated genetic loci that are often not generalizable across race/ethnicity populations. The adapted Bayesian variable selection algorithm is implemented in R. The source code for the simulations are available in the Supplement.

Conclusions

Variable selection accuracy is reduced when the outcome is ascertained by error-prone self-reports. For this setting, our proposed algorithm has improved variable selection performance when compared to approaches that neglect to account for the error-prone nature of self-reports.

Comparative analysis of gene expression profiles in children with type 1 diabetes mellitus

Type 1 diabetes (T1D) is an autoimmune disease that is typically diagnosed in children. The aim of the present study was to identify potential genes involved in the pathogenesis of childhood T1D. Two datasets of mRNA expression in children with T1D were obtained from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) in children with T1D were identified. Functional analysis was performed and a protein‑protein interaction (PPI) network was constructed, as was a transcription factor (TF)‑target network. The expression of selected DEGs was further assessed using reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) analysis. Electronic validation and diagnostic value analysis of the identified DEGs [cytokine inducible SH2 containing protein (CISH), SR‑related CTD associated factor 11 (SCAF11), estrogen receptor 1 (ESR1), Rho GTPase activating protein 25 (ARHGAP25), major histocompatibility complex, class II, DR β4 (HLA‑DRB4) and interleukin 23 subunit α (IL23A)] was performed in the GEO dataset. Compared with the normal control group, a total of 1,467 DEGs with P<0.05 were identified in children with T1D. CISH and SCAF11 were determined to be the most up‑ and downregulated genes, respectively. Heterogeneous nuclear ribonucleoprotein D (HNRNPD; degree=33), protein kinase AMP‑activated catalytic subunit α1 (PRKAA1; degree=11), integrin subunit α4 (ITGA4; degree=8) and ESR1 (degree=8) were identified in the PPI network as high‑degree genes. ARHGAP25 (degree=12), HNRNPD (degree=10), HLA‑DRB4 (degree=10) and IL23A (degree=9) were high‑degree genes identified in the TF‑target network. RT‑qPCR revealed that the expression of HNRNPD, PRKAA1, ITGA4 and transporter 2, ATP binding cassette subfamily B member was consistent with the results of the integrated analysis. Furthermore, the results of the electronic validation were consistent with the bioinformatics analysis. SCAF11, CISH and ARHGAP25 were identified to possess value as potential diagnostic markers for children with T1D. In conclusion, identifying DEGs in children with T1D may contribute to our understanding of its pathogenesis, and such DEGs may be used as diagnostic biomarkers for children with T1D.

Comparison of efficacy of SHENQI compound and rosiglitazone in the treatment of diabetic vasculopathy analyzing multi-factor mediated disease-causing modules

Atherosclerosis-predominant vasculopathy is a common complication of diabetes with high morbidity and high mortality, which is ruining the patient's daily life. As is known to all, traditional Chinese medicine (TCM) SHENQI compound and western medicine rosiglitazone play an important role in the treatment of diabetes. In particular, SHENQI compound has a significant inhibitory effect on vascular lesions. Here, to explore and compare the therapeutic mechanism of SHENQI compound and rosiglitazone on diabetic vasculopathy, we first built 7 groups of mouse models. The behavioral, physiological and pathological morphological characteristics of these mice showed that SHENQI compound has a more comprehensive curative effect than rosiglitazone and has a stronger inhibitory effect on vascular lesions. While rosiglitazone has a more effective but no significant effect on hypoglycemic. Further, based on the gene expression of mice in each group, we performed differential expression analysis. The functional enrichment analysis of these differentially expressed genes (DEGs) revealed the potential pathogenesis and treatment mechanisms of diabetic angiopathy. In addition, we found that SHENQI compound mainly exerts comprehensive effects by regulating MCM8, IRF7, CDK7, NEDD4L by pivot regulator analysis, while rosiglitazone can rapidly lower blood glucose levels by targeting PSMD3, UBA52. Except that, we also identified some pivot TFs and ncRNAs for these potential disease-causing DEG modules, which may the mediators bridging drugs and modules. Finally, similar to pivot regulator analysis, we also identified the regulation of some drugs (e.g. bumetanide, disopyramide and glyburide etc.) which have been shown to have a certain effect on diabetes or diabetic angiopathy, proofing the scientific and objectivity of this study. Overall, this study not only provides an in-depth comparison of the efficacy of SHENQI compound and rosiglitazone in the treatment of diabetic vasculopathy, but also provides clinicians and drug designers with valuable theoretical guidance.

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

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

Genome-wide association study of coronary artery calcified atherosclerotic plaque in African Americans with type 2 diabetes

Background

Coronary artery calcified atherosclerotic plaque (CAC) predicts cardiovascular disease (CVD). Despite exposure to more severe conventional CVD risk factors, African Americans (AAs) are less likely to develop CAC, and when they do, have markedly lower levels than European Americans. Genetic factors likely contribute to the observed ethnic differences. To identify genes associated with CAC in AAs with type 2 diabetes (T2D), a genome-wide association study (GWAS) was performed using the Illumina 5 M chip in 691 African American-Diabetes Heart Study participants (AA-DHS), with replication in 205 Jackson Heart Study (JHS) participants with T2D. Genetic association tests were performed on the genotyped and 1000 Genomes-imputed markers separately for each study, and combined in a meta-analysis.

Results

Single nucleotide polymorphisms (SNPs), rs11353135 (2q22.1), rs16879003 (6p22.3), rs5014012, rs58071836 and rs10244825 (all on chromosome 7), rs10918777 (9q31.2), rs13331874 (16p13.3) and rs4459623 (18q12.1) were associated with presence and/or quantity of CAC in the AA-DHS and JHS, with meta-analysis p-values ≤8.0 × 10⁻⁷. The strongest result in AA-DHS alone was rs6491315 in the 13q32.1 region (parameter estimate (SE) = −1.14 (0.20); p-value = 9.1 × 10⁻⁹). This GWAS peak replicated a previously reported AA-DHS CAC admixture signal (rs7492028, LOD score 2.8).

Conclusions

Genetic association between SNPs on chromosomes 2, 6, 7, 9, 16 and 18 and CAC were detected in AAs with T2D from AA-DHS and replicated in the JHS. These data support a role for genetic variation on these chromosomes as contributors to CAC in AAs with T2D, as well as to variation in CAC between populations of African and European ancestry.

Electronic supplementary material

The online version of this article (10.1186/s12863-017-0572-9) contains supplementary material, which is available to authorized users.

Genetics of Type 2 Diabetes in African Americans

Type 2 diabetes (T2D) is a global health problem showing substantial ethnic disparity in disease prevalence. African Americans have one of the highest prevalence of T2D in the USA but little is known about their genetic risks. This review summarizes the findings of genetic regions and loci associated with T2D and related glycemic traits using linkage, admixture, and association approaches in populations of African ancestry. In particular, findings from genome-wide association and exome chip studies suggest the presence of both ancestry-specific and shared loci for T2D and glycemic traits. Among the European-identified loci that are transferable to individuals of African ancestry, allelic heterogeneity as well as differential linkage disequilibrium and risk allele frequencies pose challenges and opportunities for fine mapping and identification of causal variant(s) by trans-ancestry meta-analysis. More genetic research is needed in African ancestry populations including the next-generation sequencing to improve the understanding of genetic architecture of T2D.

An Efficient Stepwise Statistical Test to Identify Multiple Linked Human Genetic Variants Associated with Specific Phenotypic Traits

Recent advances in genotyping methodologies have allowed genome-wide association studies (GWAS) to accurately identify genetic variants that associate with common or pathological complex traits. Although most GWAS have focused on associations with single genetic variants, joint identification of multiple genetic variants, and how they interact, is essential for understanding the genetic architecture of complex phenotypic traits. Here, we propose an efficient stepwise method based on the Cochran-Mantel-Haenszel test (for stratified categorical data) to identify causal joint multiple genetic variants in GWAS. This method combines the CMH statistic with a stepwise procedure to detect multiple genetic variants associated with specific categorical traits, using a series of associated I × J contingency tables and a null hypothesis of no phenotype association. Through a new stratification scheme based on the sum of minor allele count criteria, we make the method more feasible for GWAS data having sample sizes of several thousands. We also examine the properties of the proposed stepwise method via simulation studies, and show that the stepwise CMH test performs better than other existing methods (e.g., logistic regression and detection of associations by Markov blanket) for identifying multiple genetic variants. Finally, we apply the proposed approach to two genomic sequencing datasets to detect linked genetic variants associated with bipolar disorder and obesity, respectively.

Natural variation in the histone demethylase, KDM4C, influences expression levels of specific genes including those that affect cell growth

DNA sequence variants influence gene expression and cellular phenotypes. In this study, we focused on natural variation in the gene encoding the histone demethylase, KDM4C, which promotes transcriptional activation by removing the repressive histone mark, H3K9me3, from its target genes. We uncovered cis-acting variants that contribute to extensive individual differences in KDM4C expression. We also identified the target genes of KDM4C and demonstrated that variation in KDM4C expression leads to differences in the growth of normal and some cancer cells. Together, our results from genetic mapping and molecular analysis provide an example of how genetic variation affects epigenetic regulation of gene expression and cellular phenotype.

The short isoform of the ubiquitin ligase NEDD4L is a CREB target gene in hepatocytes

During cycles of fasting and feeding, liver function is regulated by both transcriptional and post-translational events. Regulated protein degradation has recently emerged as a key mechanism to control abundance of specific hepatic proteins under different nutritional conditions. As glucagon signaling through cAMP and PKA is central to glucose output during fasting, we hypothesized that this signaling pathway may also regulate ubiquitin ligases in the fasted state. Here we show that fasting stimuli promote expression of the short isoform of the E3 ubiquitin ligase Nedd4l in primary mouse hepatocytes. Nedd4l-short mRNA and NEDD4L (short isoform) protein accumulate in glucagon-treated primary mouse hepatocytes and in liver tissues during fasting. We identified a functional cAMP response element in the alternate Nedd4l-short promoter; mutation of this element blunts cAMP-induced expression of a Nedd4l reporter construct. CREB occupies the endogenous Nedd4l locus near this element. CREB and its co-activator CRTC2, both activated by fasting stimuli, contribute to glucagon-stimulated Nedd4l-short expression in primary hepatocytes. siRNA-mediated Nedd4l depletion in primary hepatocytes did not affect gluconeogenic gene expression, glucose output or glycogen synthesis. Our findings reveal a new mechanism of Nedd4l transcriptional regulation in liver cells.