Identification of novel functional CpG-SNPs associated with type 2 diabetes and coronary artery disease

The roles of the gut microbiota, metabolites, and epigenetics in the effects of maternal exercise on offspring metabolism

Metabolic diseases, including obesity, dyslipidemia, and type 2 diabetes, have become severe challenges worldwide. The Developmental Origins of Health and Disease (DOHaD) hypothesis suggests that an adverse intrauterine environment can increase the risk of metabolic disorders in offspring. Studies have demonstrated that maternal exercise is an effective intervention for improving the offspring metabolic health. However, the pathways through which exercise works are unclear. It has been reported that the gut microbiota mediates the effect of maternal exercise on offspring metabolism, and epigenetic modifications have also been proposed to be important molecular mechanisms. Microbial metabolites can influence epigenetics by providing substrates for DNA or histone modifications, binding to G-protein-coupled receptors to affect downstream pathways, or regulating the activity of epigenetic modifying enzymes. This review aims to summarize the intergenerational effect of maternal exercise and proposes that gut microbiota-metabolites-epigenetic regulation is an important mechanism by which maternal exercise improves offspring metabolism, which may yield novel targets for the early prevention and intervention of metabolic diseases.

Role of CaMKII in diabetes induced vascular injury and its interaction with anti-diabetes therapy

Diabetes mellitus is a metabolic disorder denoted by chronic hyperglycemia that drives maladaptive structural changes and functional damage to the vasculature. Attenuation of this pathological remodeling of blood vessels remains an unmet target owing to paucity of information on the metabolic signatures of this process. Ca2+/calmodulin-dependent kinase II (CaMKII) is expressed in the vasculature and is implicated in the control of blood vessels homeostasis. Recently, CaMKII has attracted a special attention in view of its chronic upregulated activity in diabetic tissues, yet its role in the diabetic vasculature remains under investigation.This review highlights the physiological and pathological actions of CaMKII in the diabetic vasculature, with focus on the control of the dialogue between endothelial (EC) and vascular smooth muscle cells (VSMC). Activation of CaMKII enhances EC and VSMC proliferation and migration, and increases the production of extracellular matrix which leads to maladaptive remodeling of vessels. This is manifested by activation of genes/proteins implicated in the control of the cell cycle, cytoskeleton organization, proliferation, migration, and inflammation. Endothelial dysfunction is paralleled by impaired nitric oxide signaling, which is also influenced by CaMKII signaling (activation/oxidation). The efficiency of CaMKII inhibitors is currently being tested in animal models, with a focus on the genetic pathways involved in the regulation of CaMKII expression (microRNAs and single nucleotide polymorphisms). Interestingly, studies highlight an interaction between the anti-diabetic drugs and CaMKII expression/activity which requires further investigation. Together, the studies reviewed herein may guide pharmacological approaches to improve health-related outcomes in patients with diabetes.

An update on the cell-free DNA-derived methylome as a non-invasive biomarker for coronary artery disease

Cardiovascular diseases are the foremost contributor to global mortality, presenting a complex etiology and an expanding array of risk factors. Coronary artery disease characterized by atherosclerotic plaque build-up in the coronary arteries, imposes significant mortality and financial burdens, especially in low- and middle-income nations. The pathogenesis of coronary artery disease involves a multifaceted interplay of genetic, environmental, and epigenetic factors. Epigenetic regulation contributes to the dynamic control of gene expression without altering the underlying DNA sequence. The mounting evidence that highlights the pivotal role of epigenetic regulation in coronary artery disease development and progression, offering potential avenues for the development of novel diagnostic biomarkers and therapeutic targets. Abnormal DNA methylation patterns are linked to the modulation of gene expression involved in crucial processes like lipid metabolism, inflammation, and vascular function in the context of coronary artery disease. Cell-free DNA has become invaluable in tumor biology as a liquid biopsy, while its applications in coronary artery disease are limited, but intriguing. Atherosclerotic plaque rupture causes myocardial infarction, by depriving heart muscles of oxygen, releasing cell-free DNA from dead cardiac cells, and providing a minimally invasive source to explore tissue-specific epigenetic alterations. We discussed the methodologies for studying the global methylome and hydroxy-methylome landscape, their advantages, and limitations. It explores methylome alterations in coronary artery disease, considering risk factors and their relevance in coronary artery disease genesis. The review also details the implications of MI-derived cell-free DNA for developing minimally invasive biomarkers and associated challenges.

Bidirectional relationship between type 2 diabetes mellitus and coronary artery disease: Prospective cohort study and genetic analyses

BACKGROUND

While type 2 diabetes mellitus (T2DM) is considered a putative causal risk factor for coronary artery disease (CAD), the intrinsic link underlying T2DM and CAD is not fully understood. We aimed to highlight the importance of integrated care targeting both diseases by investigating the phenotypic and genetic relationships between T2DM and CAD.

METHODS

We evaluated phenotypic associations using data from the United Kingdom Biobank ( N = 472,050). We investigated genetic relationships by leveraging genomic data conducted in European ancestry for T2DM, with and without adjustment for body mass index (BMI) (T2DM: Ncase / Ncontrol = 74,124/824,006; T2DM adjusted for BMI [T2DM adj BMI]: Ncase / Ncontrol = 50,409/523,897) and for CAD ( Ncase / Ncontrol = 181,522/984,168). We performed additional analyses using genomic data conducted in multiancestry individuals for T2DM ( Ncase / Ncontrol = 180,834/1,159,055).

RESULTS

Observational analysis suggested a bidirectional relationship between T2DM and CAD (T2DM→CAD: hazard ratio [HR] = 2.12, 95% confidence interval [CI]: 2.01-2.24; CAD→T2DM: HR = 1.72, 95% CI: 1.63-1.81). A positive overall genetic correlation between T2DM and CAD was observed ( rg = 0.39, P = 1.43 × 10 -75 ), which was largely independent of BMI (T2DM adj BMI-CAD: rg = 0.31, P = 1.20 × 10 -36 ). This was corroborated by six local signals, among which 9p21.3 showed the strongest genetic correlation. Cross-trait meta-analysis replicated 101 previously reported loci and discovered six novel pleiotropic loci. Mendelian randomization analysis supported a bidirectional causal relationship (T2DM→CAD: odds ratio [OR] = 1.13, 95% CI: 1.11-1.16; CAD→T2DM: OR = 1.12, 95% CI: 1.07-1.18), which was confirmed in multiancestry individuals (T2DM→CAD: OR = 1.13, 95% CI: 1.10-1.16; CAD→T2DM: OR = 1.08, 95% CI: 1.04-1.13). This bidirectional relationship was significantly mediated by systolic blood pressure and intake of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, with mediation proportions of 54.1% (95% CI: 24.9-83.4%) and 90.4% (95% CI: 29.3-151.5%), respectively.

CONCLUSION

Our observational and genetic analyses demonstrated an intrinsic bidirectional relationship between T2DM and CAD and clarified the biological mechanisms underlying this relationship.

Coronary artery disease patients with rs7904519 (TCF7L2) are at a persistent risk of type 2 diabetes

AIMS

Type 2 diabetes (T2D) and coronary artery disease (CAD) often coexist and share genetic factors.This study aimed to investigate the common genetic factors underlying T2D and CAD in patients with CAD.

METHODS

A three-step association approach was conducted: a) a discovery step involving 943 CAD patients with T2D and 1,149 CAD patients without T2D; b) an eliminating step to exclude CAD or T2D specific variants; and c) a replication step using the UK Biobank data.

RESULTS

Ten genetic loci were associated with T2D in CAD patients. Three variants were specific to either CAD or T2D. Five variants lost significance after adjusting for covariates, while two SNPs remained associated with T2D in CAD patients (rs7904519*G: TCF7L2 and rs17608766*C: GOSR2). The T2D susceptibility rs7904519*G was associated with increased T2D risk, while the CAD susceptibility rs17608766*C was negatively associated with T2D in CAD patients. These associations were replicated in a UK Biobank data, confirming the results.

CONCLUSIONS

No significant common T2D and CAD susceptibility genetic association was demonstrated indicating distinct disease pathways. However, CAD patients carrying the T2D susceptibility gene TCF7L2 remain at higher risk for developing T2D emphasizing the need for frequent monitoring in this subgroup.

Integrated epigenome, whole genome sequence and metabolome analyses identify novel multi-omics pathways in type 2 diabetes: a Middle Eastern study

BACKGROUND

T2D is of high prevalence in the middle east and thus studying its mechanisms is of a significant importance. Using 1026 Qatar BioBank samples, epigenetics, whole genome sequencing and metabolomics were combined to further elucidate the biological mechanisms of T2D in a population with a high prevalence of T2D.

METHODS

An epigenome-wide association study (EWAS) with T2D was performed using the Infinium 850K EPIC array, followed by whole genome-wide sequencing SNP-CpG association analysis (> 5.5 million SNPs) and a methylome-metabolome (CpG-metabolite) analysis of the identified T2D sites.

RESULTS

A total of 66 T2D-CpG associations were identified, including 63 novel sites in pathways of fructose and mannose metabolism, insulin signaling, galactose, starch and sucrose metabolism, and carbohydrate absorption and digestion. Whole genome SNP associations with the 66 CpGs resulted in 688 significant CpG-SNP associations comprising 22 unique CpGs (33% of the 66 CPGs) and included 181 novel pairs or pairs in novel loci. Fourteen of the loci overlapped published GWAS loci for diabetes related traits and were used to identify causal associations of HK1 and PFKFB2 with HbA1c. Methylome-metabolome analysis identified 66 significant CpG-metabolite pairs among which 61 pairs were novel. Using the identified methylome-metabolome associations, methylation QTLs, and metabolic networks, a multi-omics network was constructed which suggested a number of metabolic mechanisms underlying T2D methylated genes. 1-palmitoyl-2-oleoyl-GPE (16:0/18:1) - a triglyceride-associated metabolite, shared a common network with 13 methylated CpGs, including TXNIP, PFKFB2, OCIAD1, and BLCAP. Mannonate - a food component/plant shared a common network with 6 methylated genes, including TXNIP, BLCAP, THBS4 and PEF1, pointing to a common possible cause of methylation in those genes. A subnetwork with alanine, glutamine, urea cycle (citrulline, arginine), and 1-carboxyethylvaline linked to PFKFB2 and TXNIP revealed associations with kidney function, hypertension and triglyceride metabolism. The pathway containing STYXL1-POR was associated with a sphingosine-ceramides subnetwork associated with HDL-C and LDL-C and point to steroid perturbations in T2D.

CONCLUSIONS

This study revealed several novel methylated genes in T2D, with their genomic variants and associated metabolic pathways with several implications for future clinical use of multi-omics associations in disease and for studying therapeutic targets.

A bibliometric analysis of DNA methylation in cardiovascular diseases from 2001 to 2021

BACKGROUND

DNA methylation is a dynamically reversible form of epigenetics. Dynamic regulation plays an important role in cardiovascular diseases (CVDs). However, there have been few bibliometric studies in this field. We aimed to visualize the research results and hotspots of DNA methylation in CVDs using a bibliometric analysis to provide a scientific direction for future research.

METHODS

Publications related to DNA methylation in CVDs from January 1, 2001, to September 15, 2021, were searched and confirmed from the Web of Science Core Collection. CiteSpace 5.7 and VOSviewer 1.6.15 were used for bibliometric and knowledge-map analyses.

RESULTS

A total of 2617 publications were included in 912 academic journals by 15,584 authors from 963 institutions from 85 countries/regions. Among them, the United States of America, China, and England were the top 3 countries contributing to the field of DNA methylation. Harvard University, Columbia University, and University of Cambridge were the top 3 contributing institutions in terms of publications and were closely linked. PLoS One was the most published and co-cited journal. Baccarelli Andrea A published the most content, while Barker DJP had the highest frequency of co-citations. The keyword cluster focused on the mechanism, methyl-containing substance, exposure/risk factor, and biomarker. In terms of research hotspots, references with strong bursts, which are still ongoing, recently included "epigenetic clock" (2017-2021), "obesity, smoking, aging, and DNA methylation" (2017-2021), and "biomarker and epigenome-wide association study" (2019-2021).

CONCLUSIONS

We used bibliometric and visual methods to identify research hotspots and trends in DNA methylation in CVDs. Epigenetic clocks, biomarkers, environmental exposure, and lifestyle may become the focus and frontier of future research.

In silico identification of single nucleotide variations at CpG sites regulating CpG island existence and size

Genetic and epigenetic modifications of genes involved in the key regulatory pathways play a significant role in the pathophysiology and progression of multifactorial diseases. The present study is an attempt to identify single nucleotide variations (SNVs) at CpG sites of promoters of ACAT1, APOB, APOE, CYBA, FAS, FLT1, KSR2, LDLR, MMP9, PCSK9, PHOX2A, REST, SH2B3, SORT1 and TIMP1 genes influencing CpG island (CGI) existence and size associated with the pathophysiology of Diabetes mellitus, Coronary artery disease and Cancers. Promoter sequences located between -2000 to + 2000 bp were retrieved from the EPDnew database and predicted the CpG island using MethPrimer. Further, SNVs at CpG sites were accessed from NCBI, Ensembl while transcription factor (TF) binding sites were accessed using AliBaba2.1. CGI existence and size were determined for each SNV at CpG site with respect to wild type and variant allele by MethPrimer. A total of 200 SNVs at CpG sites were analyzed from the promoters of ACAT1, APOB, APOE, CYBA, FAS, FLT1, KSR2, LDLR, MMP9, PCSK9, PHOX2A, REST, SH2B3, SORT1 and TIMP1 genes. Of these, only 17 (8.5%) SNVs were found to influence the loss of CGI while 70 (35%) SNVs were found to reduce the size of CGI. It has also been found that 59% (10) of CGI abolishing SNVs are showing differences in binding of TFs. The findings of the study suggest that the candidate SNVs at CpG sites regulating CGI existence and size might influence the DNA methylation status and expression of genes involved in molecular pathways associated with several diseases. The insights of the present study may pave the way for new experimental studies to undertake challenges in DNA methylation, gene expression and protein assays.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Exploring the Pleiotropic Genes and Therapeutic Targets Associated with Heart Failure and Chronic Kidney Disease by Integrating metaCCA and SGLT2 Inhibitors' Target Prediction

Background

Previous studies have shown that heart failure (HF) and chronic kidney disease (CKD) have common genetic mechanisms, overlapping pathophysiological pathways, and therapeutic drug—sodium-glucose cotransporter 2 (SGLT2) inhibitors.

Methods

The genetic pleiotropy metaCCA method was applied on summary statistics data from two independent meta-analyses of GWAS comprising more than 1 million people to identify shared variants and pleiotropic effects between HF and CKD. Targets of SGLT2 inhibitors were predicted by SwissTargetPrediction and DrugBank databases. To refine all genes, we performed using versatile gene-based association study 2 (VEGAS2) and transcriptome-wide association studies (TWAS) for HF and CKD, respectively. Gene enrichment and KEGG pathway analyses were used to explore the potential functional significance of the identified genes and targets.

Results

After metaCCA analysis, 4,624 SNPs and 1,745 genes were identified to be potentially pleiotropic in the univariate and multivariate SNP-multivariate phenotype analyses, respectively. 21 common genes were detected in both metaCCA and SGLT2 inhibitors' target prediction. In addition, 169 putative pleiotropic genes were identified, which met the significance threshold both in metaCCA analysis and in the VEGAS2 or TWAS analysis for at least one disease.

Conclusion

We identified novel variants associated with HF and CKD using effectively incorporating information from different GWAS datasets. Our analysis may provide new insights into HF and CKD therapeutic approaches based on the pleiotropic genes, common targets, and mechanisms by integrating the metaCCA method, TWAS and VEGAS2 analyses, and target prediction of SGLT2 inhibitors.

Identification of novel functional CpG-SNPs associated with Type 2 diabetes and birth weight

Genome-wide association studies (GWASs) have identified hundreds of genetic loci for type 2 diabetes (T2D) and birth weight (BW); however, a large proportion of the total trait heritability remains unexplained. The previous studies were generally focused on individual traits and largely failed to identify the majority of the variants that play key functional roles in the etiology of the disease. Here, we aim to identify novel functional loci for T2D, BW and the pleiotropic variants shared between them by performing a targeted conditional false discovery rate (cFDR) analysis that integrates two independent GWASs with summary statistics for T2D (n = 26,676 cases and 132,532 controls) and BW (n = 153,781) which entails greater statistical power than individual trait analyses. In this analysis, we considered CpG-SNPs, which are SNPs that may influence DNA methylation status, and are therefore considered to be functionally important. We identified 103 novel CpG-SNPs for T2D, 182 novel CpG-SNPs for BW (cFDR < 0.05), and 52 novel pleiotropic loci for both (conjunction cFDR [ccFDR] < 0.05). Among the identified novel CpG-SNPs, 33 were annotated as methylation quantitative trait loci (meQTLs) in whole blood, and 145 displayed at least some effects on meQTL, metabolic QTL (metaQTL), and/or expression QTL (eQTL). These findings may provide further insights into the shared biological mechanisms and functional genetic determinants that overlap between T2D and BW, thereby providing novel potential targets for treatment/intervention development.

Identification of novel SNPs associated with coronary artery disease and birth weight using a pleiotropic cFDR method

Objectives: Clinical and epidemiological findings indicate an association between coronary artery disease (CAD) and low birth weight (BW). However, the mechanisms underlying this relationship are largely unknown. Here, we aimed to identify novel single-nucleotide polymorphisms (SNPs) associated with CAD, BW, and their shared pleiotropic loci, and to detect the potential causal relationship between CAD and BW.

Methods: We first applied a genetic pleiotropic conditional false discovery rate (cFDR) method to two independent genome-wide association studies (GWAS) summary statistics of CAD and BW to estimate the pleiotropic enrichment between them. Then, bi-directional Mendelian randomization (MR) analyses were performed to clarify the causal association between these two traits.

Results: By incorporating related traits into a conditional analysis framework, we observed the significant pleiotropic enrichment between CAD and BW. By applying the cFDR level of 0.05, 109 variants were detected for CAD, 203 for BW, and 26 pleiotropic variants for both traits. We identified 11 CAD- and/or BW-associated SNPs that showed more than three of the metabolic quantitative trait loci (metaQTL), protein QTL (pQTL), methylation QTL (meQTL), or expression QTL (eQTL) effects. The pleiotropic SNP rs10774625, located at ATXN2, showed metaQTL, pQTL, meQTL, and eQTL effects simultaneously. Using the bi-directional MR approach, we found a negative association from BW to CAD (odds ratio [OR] = 0.68, 95% confidence interval [CI]: 0.59 to 0.80, p = 1.57× 10^-6).

Conclusion: We identified several pleiotropic loci between CAD and BW by leveraging GWAS results of related phenotypes and identified a potential causal relationship from BW to CAD. Our findings provide novel insights into the shared biological mechanisms and overlapping genetic heritability between CAD and BW.