Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms

The Effect of Short-Chain Fatty Acids on Growth of Cryptosporidium parvum In Vitro

In a previous study, we observed an increase in the severity of cryptosporidial infection corresponding to decreased levels of short-chain fatty acids (SCFAs). Therefore, we decided to examine the effect of SCFAs on Cryptosporidium growth in human ileocecal adenocarcinoma (HTC-8) cells. HTC-8 cells were infected with 1 × 105 C. parvum oocysts. After 48 h of incubation with selected SCFAs, cells were fixed and labeled with monoclonal antibody directed to all intracellular stages, and the number of parasites was quantitated using a fluorescent microscope. Acetate, butyrate, propionate and valproate significantly inhibited growth, with an EC50 between 4 and 10 mM. Additionally, when combined, butyrate, acetate and propionate showed increased efficacy. Butyrate also inhibited growth when incubated with sporozoites prior to infection of host cell monolayers. In addition, we looked at possible mechanisms of action of inhibition. A combination of C. parvum infection and butyrate treatment led to increases in apoptosis and certain inflammatory cytokines. We conclude that acetate, propionate and butyrate have direct inhibitory activities in host cells against C. parvum, and butyrate can also affect sporozoite infectivity directly. While not preventing infection, SCFAs may help in keeping the infection low or in check.

Trans-ancestral dissection of urate- and gout-associated major loci SLC2A9 and ABCG2 reveals primate-specific regulatory effects

Gout is a complex inflammatory arthritis affecting ~20% of people with an elevated serum urate level (hyperuricemia). Gout and hyperuricemia are essentially specific to humans and other higher primates, with varied prevalence across ancestral groups. SLC2A9 and ABCG2 are major loci associated with both urate and gout in multiple ancestral groups. However, fine mapping has been challenging due to extensive linkage disequilibrium underlying the associated regions. We used trans-ancestral fine mapping integrated with primate-specific genomic information to address this challenge. Trans-ancestral meta-analyses of GWAS cohorts of either European (EUR) or East Asian (EAS) ancestry resulted in single-variant resolution mappings for SLC2A9 (rs3775948 for urate and rs4697701 for gout) and ABCG2 (rs2622621 for gout). Tests of colocalization of variants in both urate and gout suggested existence of a shared candidate causal variant for SLC2A9 only in EUR and for ABCG2 only in EAS. The fine-mapped gout variant rs4697701 was within an ancient enhancer, whereas rs2622621 was within a primate-specific transposable element, both supported by functional evidence from the Roadmap Epigenomics project in human primary tissues relevant to urate and gout. Additional primate-specific elements were found near both loci and those adjacent to SLC2A9 overlapped with known statistical epistatic interactions associated with urate as well as multiple super-enhancers identified in urate-relevant tissues. We conclude that by leveraging ancestral differences trans-ancestral fine mapping has identified ancestral and functional variants for SLC2A9 or ABCG2 with primate-specific regulatory effects on urate and gout.

Pancreatic β-cells in type 1 and type 2 diabetes mellitus: different pathways to failure

Loss of functional β-cell mass is the key mechanism leading to the two main forms of diabetes mellitus - type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Understanding the mechanisms behind β-cell failure is critical to prevent or revert disease. Basic pathogenic differences exist in the two forms of diabetes mellitus; T1DM is immune mediated and T2DM is mediated by metabolic mechanisms. These mechanisms differentially affect early β-cell dysfunction and eventual fate. Over the past decade, major advances have been made in the field, mostly delivered by studies on β-cells in human disease. These advances include studies of islet morphology and human β-cell gene expression in T1DM and T2DM, the identification and characterization of the role of T1DM and T2DM candidate genes at the β-cell level and the endoplasmic reticulum stress signalling that contributes to β-cell failure in T1DM (mostly IRE1 driven) and T2DM (mostly PERK-eIF2α dependent). Here, we review these new findings, focusing on studies performed on human β-cells or on samples obtained from patients with diabetes mellitus.

Cohort profile: the Singapore diabetic cohort study

PURPOSE

The diabetic cohort (DC) was set up to study the determinants of complications in individuals with type 2 diabetes and examine the role of genetic, physiological and lifestyle factors in the development of complications in these individuals.

PARTICIPANTS

A total of 14 033 adult participants with type 2 diabetes were recruited from multiple public sector polyclinics and hospital outpatient clinics in Singapore between November 2004 and November 2010. The first round of follow-up was conducted for 4131 participants between 2012 and 2016; the second round of follow-up started in 2016 and is expected to end in 2021. A questionnaire survey, physical assessments, blood and urine sample collection were conducted at recruitment and each follow-up visit. The data set also includes genetic data and linkage to medical and administrative records for recruited participants.

FINDINGS TO DATE

Data from the cohort have been used to identify determinants of diabetes and related complications. The longitudinal data of medical records have been used to analyse diabetes control over time and its related outcomes. The cohort has also contributed to the identification of genetic loci associated with type 2 diabetes and diabetic kidney disease in collaboration with other large cohort studies. About 25 scientific papers based on the DC data have been published up to May 2019.

FUTURE PLANS

The rich data in DC can be used for various types of research to study disease-related complications in patients with type 2 diabetes. We plan to further investigate disease progression and new biomarkers for common diabetic complications, including diabetic kidney disease and diabetic neuropathy.

Polygenic risk score for schizophrenia was not associated with glycemic level (HbA1c) in patients with non-affective psychosis: Genetic Risk and Outcome of Psychosis (GROUP) cohort study

INTRODUCTION

Type 2 diabetes (T2D) is a common comorbidity in patients with schizophrenia (SCZ). The underlying pathophysiologic mechanisms are yet to be fully elucidated, although it can be argued that shared genes, environmental factors or their interaction effect are involved. This study investigated the association between polygenic risk score of SCZ (PRS_SCZ) and glycated haemoglobin (HbA1c) while adjusting for polygenic risk score of T2D (PRS_T2D), and clinical and demographic covariables.

METHODS

Genotype, clinical and demographic data of 1129 patients with non-affective psychosis were extracted from Genetic Risk and Outcome of Psychosis (GROUP) cohort study. The glycated haemoglobin (HbA1c) was the outcome. PRS was calculated using standard methods. Univariable and multivariable linear regression analyses were applied to estimate associations. Additionally, sensitivity analysis based on multiple imputation was done. After correction for multiple testing, a two-sided p-value ≤.003 was considered to discover evidence for an association.

RESULTS

Of 1129 patients, 75.8% were male with median age of 29 years. The mean (standard deviation) HbA1c level was 35.1 (5.9) mmol/mol. There was no evidence for an association between high HbA1c level and increased PRS_SCZ (adjusted regression coefficient (aβ) = 0.69, standard error (SE) = 0.77, p-value = .37). On the other hand, there was evidence for an association between high HbA1c level and increased PRS_T2D (aβ = 0.93, SE = 0.32, p-value = .004), body mass index (aβ = 0.20, SE = 0.08, p-value = .01), diastolic blood pressure (aβ = 0.08, SE = 0.04, p-value = .03), late age of first psychosis onset (aβ = 0.19, SE = 0.05, p-value = .0004) and male gender (aβ = 1.58, SE = 0.81, p-value = .05). After multiple testing correction, there was evidence for an association between high HbA1c level and late age of first psychosis onset. Evidence for interaction effect between PRS_scz and antipsychotics was not observed. The multiple imputation-based sensitivity analysis provided consistent results with complete case analysis.

CONCLUSIONS

Glycemic dysregulation in patients with SCZ was not associated with PRS_SCZ. This suggests that the mechanisms of hyperglycemia or diabetes are at least partly independent from genetic predisposition to SCZ. Our findings show that the change in HbA1c level can be caused by at least in part due to PRS_T2D, late age of illness onset, male gender, and increased body mass index and diastolic blood pressure.

From Genetic Association to Molecular Mechanisms for Islet-cell Dysfunction in Type 2 Diabetes

Genome-wide association studies (GWAS) have identified over 400 signals robustly associated with risk for type 2 diabetes (T2D). At the vast majority of these loci, the lead single nucleotide polymorphisms (SNPs) reside in noncoding regions of the genome, which hampers biological inference and translation of genetic discoveries into disease mechanisms. The study of these T2D risk variants in normoglycemic individuals has revealed that a significant proportion are exerting their disease risk through islet-cell dysfunction. The central role of the islet is also demonstrated by numerous studies, which have shown an enrichment of these signals in islet-specific epigenomic annotations. In recent years the emergence of authentic human beta-cell lines, and advances in genome-editing technologies coupled with improved protocols differentiating human pluripotent stem cells into beta-like cells has opened up new opportunities for T2D disease modeling. Here we review the current understanding on the genetic basis of T2D focusing on approaches, which have facilitated the identification of causal variants and their effector transcripts in human islets. We will present examples of functional studies based on animal and conventional cellular systems and highlight the potential of novel stem cell-based T2D disease models.

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.

Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution

Trans-ethnic meta-analysis of genome-wide association studies (GWAS) across diverse populations can increase power to detect complex trait loci when the underlying causal variants are shared between ancestry groups. However, heterogeneity in allelic effects between GWAS at these loci can occur that is correlated with ancestry. Here, a novel approach is presented to detect SNP association and quantify the extent of heterogeneity in allelic effects that is correlated with ancestry. We employ trans-ethnic meta-regression to model allelic effects as a function of axes of genetic variation, derived from a matrix of mean pairwise allele frequency differences between GWAS, and implemented in the MR-MEGA software. Through detailed simulations, we demonstrate increased power to detect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios of heterogeneity in allelic effects between ethnic groups. We also demonstrate improved fine-mapping resolution, in loci containing a single causal variant, compared to these meta-analysis approaches and PAINTOR, and equivalent performance to MANTRA at reduced computational cost. Application of MR-MEGA to trans-ethnic GWAS of kidney function in 71,461 individuals indicates stronger signals of association than fixed-effects meta-analysis when heterogeneity in allelic effects is correlated with ancestry. Application of MR-MEGA to fine-mapping four type 2 diabetes susceptibility loci in 22,086 cases and 42,539 controls highlights: (i) strong evidence for heterogeneity in allelic effects that is correlated with ancestry only at the index SNP for the association signal at the CDKAL1 locus; and (ii) 99% credible sets with six or fewer variants for five distinct association signals.

Physical Interactions and Expression Quantitative Traits Loci Identify Regulatory Connections for Obesity and Type 2 Diabetes Associated SNPs

The mechanisms that underlie the association between obesity and type 2 diabetes are not fully understood. Here, we investigated the role of the 3D genome organization in the pathogeneses of obesity and type-2 diabetes. We interpreted the combined and differential impacts of 196 diabetes and 390 obesity associated single nucleotide polymorphisms (SNPs) by integrating data on the genes with which they physically interact (as captured by Hi-C) and the functional [i.e., expression quantitative trait loci (eQTL)] outcomes associated with these interactions. We identified 861 spatially regulated genes (e.g., AP3S2, ELP5, SVIP, IRS1, FADS2, WFS1, RBM6, HORMAD1, PYROXD2), which are enriched in tissues (e.g., adipose, skeletal muscle, pancreas) and biological processes and canonical pathways (e.g., lipid metabolism, leptin, and glucose-insulin signaling pathways) that are important for the pathogenesis of type 2 diabetes and obesity. Our discovery-based approach also identifies enrichment for eQTL SNP-gene interactions in tissues that are not classically associated with diabetes or obesity. We propose that the combinatorial action of active obesity and diabetes spatial eQTL SNPs on their gene pairs within different tissues reduces the ability of these tissues to contribute to the maintenance of a healthy energy metabolism.

Mechanisms of Type 2 Diabetes Risk Loci

PURPOSE OF REVIEW

Deciphering the mechanisms of type 2 diabetes (T2DM) risk loci can greatly inform on disease pathology. This review discusses current knowledge of mechanisms through which genetic variants influence T2DM risk and considerations for future studies.

RECENT FINDINGS

Over 100 T2DM risk loci to date have been identified. Candidate causal variants at risk loci map predominantly to non-coding sequence. Physiological, epigenomic and gene expression data suggest that variants at many known T2DM risk loci affect pancreatic islet regulation, although variants at other loci also affect protein function and regulatory processes in adipose, pre-adipose, liver, skeletal muscle and brain. The effects of T2DM variants on regulatory activity in these tissues appear largely, but not exclusively, due to altered transcription factor binding. Putative target genes of T2DM variants have been defined at an increasing number of loci and some, such as FTO, may entail several genes and multiple tissues. Gene networks in islets and adipocytes have been implicated in T2DM risk, although the molecular pathways of risk genes remain largely undefined. Efforts to fully define the mechanisms of T2DM risk loci are just beginning. Continued identification of risk mechanisms will benefit from combining genetic fine-mapping with detailed phenotypic association data, high-throughput epigenomics data from diabetes-relevant tissue, functional screening of candidate genes and genome editing of cellular and animal models.

CDK5 Regulatory Subunit-Associated Protein 1-like 1 Negatively Regulates Adipocyte Differentiation through Activation of Wnt Signaling Pathway

CDK5 Regulatory Subunit-Associated Protein 1-like 1 (CDKAL1) was identified as a susceptibility gene for type 2 diabetes and body mass index in genome-wide association studies. Although it was reported that CDKAL1 is a methylthiotransferase essential for tRNA^Lys(UUU) and faithful translation of proinsulin generated in pancreatic β cells, the role of CDKAL1 in adipocytes has not been understood well. In this study, we found that CDKAL1 is expressed in adipose tissue and its expression is increased during differentiation. Stable overexpression of CDKAL1, however, inhibited adipocyte differentiation of 3T3-L1 cells, whereas knockdown of CDKAL1 promoted differentiation. CDKAL1 increased protein levels of β-catenin and its active unphosphorylated form in the nucleus, thereby promoting Wnt target gene expression, suggesting that CDKAL1 activated the Wnt/β-catenin pathway—a well-characterized inhibitory regulator of adipocyte differentiation. Mutant experiments show that conserved cysteine residues of Fe-S clusters of CDKAL1 are essential for its anti-adipogenic action. Our results identify CDKAL1 as novel negative regulator of adipocyte differentiation and provide insights into the link between CDKAL1 and metabolic diseases such as type 2 diabetes and obesity.

KCNQ1 gene polymorphism is associated with glycaemic response to treatment with DPP-4 inhibitors

AIMS

Only afew gene variants were associated with the response to dipeptidylpeptidase-4 inhibitors (DPP4I). KCNQ1 gene variants were previously related both to type 2 diabetes (T2D) and incretin effect. We hypothesized that T2D related KCNQ1 variants would be associated with smaller glucose-lowering effect of DDP4I.

METHODS

We performed a retrospective study in 137 Caucasian subjects with T2D who were followed for 6months after initiation of DPP4I treatment. Genotyping for KCNQ1 rs163184 and rs151290 was performed using PCR-HRMA and PCR-RFLP methods, respectively. The main clinical outcome was reduction in HbA1c (ΔHbA1c) after 6-month DPP4I treatment.

RESULTS

KCNQ1 rs163184 T>G variant was associated with the response to DPP4I treatment in genetic additive model (β=-0.30, p=0.022). For each G allele in the rs163184 genotype, we observed a 0.3% (3.3mmol/mol) less reduction in HbA1c during treatment with a DPP4I. Both the GG homozygotes and G-allele carriers had significantly smaller HbA1c reduction in comparison with the TT homozygotes.

CONCLUSIONS

KCNQ1 rs163184 T>G variant was associated with a reduced glycaemic response to DPP4I. The difference of 0.6% (6.5mmol/mol) in HbA1c reduction between the TT and GG homozygotes might be of clinical significance if replicated in further studies.

Associations between a fetal imprinted gene allele score and late pregnancy maternal glucose concentrations

Aim

We hypothesised that some of the genetic risk for gestational diabetes (GDM) is due to the fetal genome affecting maternal glucose concentrations. Previously, we found associations between fetal IGF2 gene variants and maternal glucose concentrations in late pregnancy.

Methods

In the present study, we tested associations between SNP alleles from 15 fetal imprinted genes and maternal glucose concentrations in late pregnancy in the Cambridge Baby Growth and Wellbeing cohorts (1160 DNA trios).

Results

Four fetal SNP alleles with the strongest univariate associations: paternally-transmitted IGF2 rs10770125 (P-value = 2 × 10^–4) and INS rs2585 (P-value = 7 × 10^–4), and maternally-transmitted KCNQ1(OT1) rs231841 (P-value = 1 × 10^–3) and KCNQ1(OT1) rs7929804 (P-value = 4 × 10^–3), were used to construct a composite fetal imprinted gene allele score which was associated with maternal glucose concentrations (P-value = 4.3 × 10^–6, n = 981, r² = 2.0%) and GDM prevalence (odds ratio per allele 1.44 (1.15, 1.80), P-value = 1 × 10^–3, n = 89 cases and 899 controls). Meta-analysis of the associations including data from 1367 Hyperglycaemia and Adverse Pregnancy Outcome Study participants confirmed the paternally-transmitted fetal IGF2/INS SNP associations (rs10770125, P-value = 3.2 × 10^–8, rs2585, P-value = 3.6 × 10^–5) and the composite fetal imprinted gene allele score association (P-value = 1.3 × 10^–8), but not the maternally-transmitted fetal KCNQ1(OT1) associations (rs231841, P-value = 0.4; rs7929804, P-value = 0.2).

Conclusion

This study suggests that polymorphic variation in fetal imprinted genes, particularly in the IGF2/INS region, contribute a small but significant part to the risk of raised late pregnancy maternal glucose concentrations.