T2DiACoD: A Gene Atlas of Type 2 Diabetes Mellitus Associated Complex Disorders

Genetic variants and type 2 diabetes in India: a systematic review and meta-analysis of associated polymorphisms in case-control studies

Background

India, with the largest population and second-highest type 2 diabetes mellitus (T2DM) prevalence, presents a unique genetic landscape. This study explores the genetic profiling of T2DM, aiming to bridge gaps in existing research and provide insights for further explorations.

Methods

We conducted a systematic review and meta-analysis of literature published up to September 2024 using databases like PubMed, Web of Science, Scopus, and Google Scholar to identify SNPs associated with T2DM in case-control studies within the Indian population. Data extraction followed a rigorously designed checklist independently verified by two reviewers. The quality of the studies assessed by utilizing Newcastle Ottawa scale, and heterogeneity through Cochran's Q, τ2, H2 and I 2 statistics. Fixed effect and random effect model was employed for meta-analysis based on heterogeneity, and publication bias was assessed by funnel plot analysis, Egger's and Begg's statistical test. In SNPs with adequate studies meta-regression was used to assess source of heterogeneity. Statistical analyses were performed using Stata 18.0 software.

Findings

Our search identified 1309 articles, with 67 included in the systematic review and 35 in the meta-analysis. These 67 case-control studies, involving 33,407 cases and 30,762 controls, analyzed 167 SNPs across 61 genes. Of these, 89 SNPs mapped to 46 genes showed significant associations with T2DM risk (P < 0.05), including 67 linked to increased risk and 16 with protective effects. Geographical analysis highlighted inter- and intra-regional variations. Meta-analysis of 25 SNPs revealed 12 SNPs with high T2DM risk compatibility. TCF7L2 gene exhibited a strong compatibility with an overall OR of 1.44 (95% CI 1.36-1.52) and S-value 112.41, while TCF7L2 variants rs7903146 and rs12255372, with OR 1.56 (95% CI 1.43-1.66) and S-value 89.036, OR of 1.36 (95% CI 1.17-1.35) with an S-value of 15.45 respectively.

Interpretation

Our study highlights the importance of considering the diverse ethnic groups of India for development of targeted and effective T2DM management strategies.

Funding

Department of Biotechnology (DBT) and Indian Council of Medical Research (ICMR), Government of India.

Non parametric differential network analysis: a tool for unveiling specific molecular signatures

BACKGROUND

The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential network analysis (DINA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DINA algorithms scrutinize alterations in interaction patterns derived from experimental data.

RESULTS

Introducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes.

CONCLUSIONS

Applying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions.

Understanding complex systems through differential causal networks

In the evolving landscape of data science and computational biology, Causal Networks (CNs) have emerged as a robust framework for modelling causal relationships among elements of complex systems derived from experimental data. CNs can efficiently model causal relationships emerging in a single system while comparing multiple systems, allowing to understand rewiring in different cells, tissues, and physiological states with a deeper perspective. Despite the existence of network models, namely differential networks, that have been used to compare coexpression and correlation structures, causality needs to be introduced in differential analysis to robustly provide direction to the edges of such networks, in order to better understand the flows of information, and also to better intervene in their functioning, for example for agricultural or pharmacological purposes. Resolved to reach this ambitious goal, we introduce Differential Causal Networks (DCNs), a novel framework that represents differences between two existing CNs. A DCN is obtained from experimental data by comparing two CNs, and it is a power tool for highlighting differences in causal relations. After a careful definition and design of DCNs, we test our algorithm to model possible differential causal relationships between genes responsible for the onset of type 2 diabetes mellitus-related pathologies considering patients' sex at the tissue level. DCNs allowed us to shed light on causal differences between sexes across nine tissues. We also compare differences among three possible definitions of DCNs to highlight similarities and differences of biological importance. Code, Data and Supplementary Information are available at https://github.com/hguzzi/DifferentialCausalNetworks .

The contribution of adiponectin to diabetic retinopathy progression: Association with the AGEs-RAGE pathway

Diabetic retinopathy (DR) is a chronic complication of diabetes. Given that adiponectin plays a key role in DR progression, this study aims to elucidate the molecular mechanisms of sDR progression related to adiponectin. First, we extracted the microarray dataset GSE60436 from the Gene Expression Omnibus (GEO) database to identify hub genes associated with DR. Pathway enrichment analysis revealed a focus on inflammation, oxidative stress, and metabolic disease pathways. Gene Set Enrichment Analysis (GSEA) identified nine significant pathways related to DR. Immune infiltration analysis indicated increased infiltration of fibroblasts and endothelial cells in DR patients. Second, at the gene level, single-cell RNA sequencing (scRNA-seq) results showed a decrease in ADIPOQ gene expression as the disease progressed in our mouse models. At the protein level, ELISA results from sera of 31 patients and 11 control subjects demonstrated significantly lower adiponectin expression in the proliferative diabetic retinopathy (PDR) group compared to controls. Our findings reveal that adiponectin is involved in the advanced glycation end products (AGEs) and receptor of advanced glycation end products (RAGE) axis, as evidenced by hub gene analysis, scRNA-seq, and ELISA. In conclusion, adiponectin acts as a central molecule in the AGEs-RAGE axis, regulated by ADIPOQ, to influence DR progression.

Network medicine based approach for identifying the type 2 diabetes, osteoarthritis and triple negative breast cancer interactome: Finding the hub of hub genes

The increasing prevalence of multi-morbidities, particularly the incidence of breast cancer in diabetic/osteoarthritic patients emphasize on the need for exploring the underlying molecular mechanisms resulting in carcinogenesis. To address this, present study employed a systems biology approach to identify switch genes pivotal to the crosstalk between diseased states resulting in multi-morbid conditions. Hub genes previously reported for type 2 diabetes mellitus (T2DM), osteoarthritis (OA), and triple negative breast cancer (TNBC), were extracted from published literature and fed into an integrated bioinformatics analyses pipeline. Thirty-one hub genes common to all three diseases were identified. Functional enrichment analyses showed these were mainly enriched for immune and metabolism associated terms including advanced glycation end products (AGE) pathways, cancer pathways, particularly breast neoplasm, immune system signalling and adipose tissue. The T2DM-OA-TNBC interactome was subjected to protein-protein interaction network analyses to identify meta hub/clustered genes. These were prioritized and wired into a three disease signalling map presenting the enriched molecular crosstalk on T2DM-OA-TNBC axes to gain insight into the molecular mechanisms underlying disease-disease interactions. Deciphering the molecular bases for the intertwined metabolic and immune states may potentiate the discovery of biomarkers critical for identifying and targeting the immuno-metabolic origin of disease.

Molecular mechanisms linking type 2 diabetes mellitus and late-onset Alzheimer's disease: A systematic review and qualitative meta-analysis

Research evidence indicating common metabolic mechanisms through which type 2 diabetes mellitus (T2DM) increases risk of late-onset Alzheimer's dementia (LOAD) has accumulated over recent decades. The aim of this systematic review is to provide a comprehensive review of common mechanisms, which have hitherto been discussed in separate perspectives, and to assemble and evaluate candidate loci and epigenetic modifications contributing to polygenic risk linkages between T2DM and LOAD. For the systematic review on pathophysiological mechanisms, both human and animal studies up to December 2023 are included. For the qualitative meta-analysis of genomic bases, human association studies were examined; for epigenetic mechanisms, data from human studies and animal models were accepted. Papers describing pathophysiological studies were identified in databases, and further literature gathered from cited work. For genomic and epigenomic studies, literature mining was conducted by formalised search codes using Boolean operators in search engines, and augmented by GeneRif citations in Entrez Gene, and other sources (WikiGenes, etc.). For the systematic review of pathophysiological mechanisms, 923 publications were evaluated, and 138 gene loci extracted for testing candidate risk linkages. 3 57 publications were evaluated for genomic association and descriptions of epigenomic modifications. Overall accumulated results highlight insulin signalling, inflammation and inflammasome pathways, proteolysis, gluconeogenesis and glycolysis, glycosylation, lipoprotein metabolism and oxidation, cell cycle regulation or survival, autophagic-lysosomal pathways, and energy. Documented findings suggest interplay between brain insulin resistance, neuroinflammation, insult compensatory mechanisms, and peripheral metabolic dysregulation in T2DM and LOAD linkage. The results allow for more streamlined longitudinal studies of T2DM-LOAD risk linkages.

DbKB a knowledge graph dataset for diabetes: A system biology approach

Diabetes has emerged as a prevalent disease, affecting millions of individuals annually according to statistics. Numerous studies have delved into identifying key genes implicated in the causal mechanisms of diabetes. This paper specifically concentrates on 20 functional genes identified in various studies contributing to the complexities associated with Type 2 diabetes (T2D), encompassing complications such as nephropathy, retinopathy, cardiovascular disorders, and foot ulcers. These functional genes serve as a foundation for identifying regulatory genes, their regulators, and protein-protein interactions. The current study introduces a multi-layer Knowledge Graph (DbKB based on MSNMD: Multi-Scale Network Model for Diabetes), encompassing biological networks such as gene regulatory networks and protein-protein interaction networks. This Knowledge Graph facilitates the visualization and querying of inherent relationships between biological networks associated with diabetes, enabling the retrieval of regulatory genes, functional genes, interacting proteins, and their relationships. Through the integration of biologically relevant genetic, molecular, and regulatory information, we can scrutinize interactions among T2D candidate genes [1] and ascertain diseased genes [2]. The first layer of regulators comprises direct regulators to the functional genes, sourced from the TRRUST database in the human transcription factors dataset, thereby forming a multi-layered directed graph. A comprehensive exploration of these direct regulators reveals a total of 875 regulatory transcription factors, constituting the initial layer of regulating transcription factors. Moving to the second layer, we identify 550 regulatory genes. These functional genes engage with other proteins to form complexes, exhibiting specific functions. Leveraging these layers, we construct a Knowledge Graph aimed at identifying interaction-driven sub-networks involving (i) regulating functional genes, (ii) functional genes, and (iii) protein-protein interactions.

Interactions among insulin resistance, epigenetics, and donor sex in gene expression regulation of iPSC-derived myoblasts

About 25% of people in the general population are insulin resistant, increasing the risk for type 2 diabetes (T2D) and metabolic disease. Transcriptomic analysis of induced pluripotent stem cells differentiated into myoblasts (iMyos) from insulin-resistant (I-Res) versus insulin-sensitive (I-Sen) nondiabetic individuals revealed that 306 genes increased and 271 genes decreased in expression in iMyos from I-Res donors with differences of 2-fold or more. Over 30 of the genes changed in I-Res iMyos were associated with T2D by SNPs and were functionally linked to insulin action and control of metabolism. Interestingly, we also identified more than 1,500 differences in gene expression that were dependent on the sex of the cell donor, some of which modified the insulin resistance effects. Many of these sex differences were associated with increased DNA methylation in cells from female donors and were reversed by 5-azacytidine. By contrast, the insulin sensitivity differences were not reversed and thus appear to reflect genetic or methylation-independent epigenetic effects.

In silico identification and functional prediction of differentially expressed genes in South Asian populations associated with type 2 diabetes

Type 2 diabetes (T2D) is one of the major metabolic disorders in humans caused by hyperglycemia and insulin resistance syndrome. Although significant genetic effects on T2D pathogenesis are experimentally proved, the molecular mechanism of T2D in South Asian Populations (SAPs) is still limited. Hence, the current research analyzed two Gene Expression Omnibus (GEO) and 17 Genome-Wide Association Studies (GWAS) datasets associated with T2D in SAP to identify DEGs (differentially expressed genes). The identified DEGs were further analyzed to explore the molecular mechanism of T2D pathogenesis following a series of bioinformatics approaches. Following PPI (Protein-Protein Interaction), 867 potential DEGs and nine hub genes were identified that might play significant roles in T2D pathogenesis. Interestingly, CTNNB1 and RUNX2 hub genes were found to be unique for T2D pathogenesis in SAPs. Then, the GO (Gene Ontology) showed the potential biological, molecular, and cellular functions of the DEGs. The target genes also interacted with different pathways of T2D pathogenesis. In fact, 118 genes (including HNF1A and TCF7L2 hub genes) were directly associated with T2D pathogenesis. Indeed, eight key miRNAs among 2582 significantly interacted with the target genes. Even 64 genes were downregulated by 367 FDA-approved drugs. Interestingly, 11 genes showed a wide range (9-43) of drug specificity. Hence, the identified DEGs may guide to elucidate the molecular mechanism of T2D pathogenesis in SAPs. Therefore, integrating the research findings of the potential roles of DEGs and candidate drug-mediated downregulation of marker genes, future drugs or treatments could be developed to treat T2D in SAPs.

Analysis of age-dependent gene-expression in human tissues for studying diabetes comorbidities

The study of the relationship between type 2 diabetes mellitus (T2DM) disease and other pathologies (comorbidities), together with patient age variation, poses a challenge for medical research. There is evidence that patients affected by T2DM are more likely to develop comorbidities as they grow older. Variation of gene expression can be correlated to changes in T2DM comorbidities insurgence and progression. Understanding gene expression changes requires the analysis of large heterogeneous data at different scales as well as the integration of different data sources into network medicine models. Hence, we designed a framework to shed light on uncertainties related to age effects and comorbidity by integrating existing data sources with novel algorithms. The framework is based on integrating and analysing existing data sources under the hypothesis that changes in the basal expression of genes may be responsible for the higher prevalence of comorbidities in older patients. Using the proposed framework, we selected genes related to comorbidities from existing databases, and then analysed their expression with age at the tissues level. We found a set of genes that changes significantly in certain specific tissues over time. We also reconstructed the associated protein interaction networks and the related pathways for each tissue. Using this mechanistic framework, we detected interesting pathways related to T2DM whose genes change their expression with age. We also found many pathways related to insulin regulation and brain activities, which can be used to develop specific therapies. To the best of our knowledge, this is the first study that analyses such genes at the tissue level together with age variations.

Prioritization of genes associated with type 2 diabetes mellitus for functional studies

Existing therapies for type 2 diabetes mellitus (T2DM) show limited efficacy or have adverse effects. Numerous genetic variants associated with T2DM have been identified, but progress in translating these findings into potential drug targets has been limited. Here, we describe the tools and platforms available to identify effector genes from T2DM-associated coding and non-coding variants and prioritize them for functional studies. We discuss QSER1 and SLC12A8 as examples of genes that have been identified as possible T2DM candidate genes using these tools and platforms. We suggest further approaches, including the use of sequencing data with increased sample size and ethnic diversity, single-cell omics data for analyses, glycaemic trait associations to predict gene function and, potentially, human induced pluripotent stem cell 'village' cultures, to strengthen current gene functionalization workflows. Effective prioritization of T2DM-associated genes for experimental validation could expedite our understanding of the genetic mechanisms responsible for T2DM to facilitate the use of precision medicine in its treatment.

Hypothetical Strategies of Gene and Environmental Influence on Life Expectancy: A Brief Review

Almost all diseases have a genetic basis. However, several disorders stem from a combination of genes and environmental conditions. In the present study, databases including PubMed, Scopus and Google scholar were searched and reviewed and those relevant studies that investigated the association between environmental and genetic factors with the incidence of diseases were extracted and used. At the final step, it is concluded that in many cases, disorders have a multifactorial etiology. Having a gene related to a specific disorder is not the only reason for contracting the disease. Both genes and environmental factors play a role in human disease etiology. Everything outside of DNA, may affect health and even in many people with a positive family history of a specific disorder, environmental factors can facilitate or prevent the occurrence of the disease. Therefore, living a healthy lifestyle is important in reducing exposure to diseases, and long-life expectancy.

Ethnic disparities attributed to the manifestation in and response to type 2 diabetes: insights from metabolomics

Type 2 diabetes (T2D) associated health disparities among different ethnicities have long been known. Ethnic variations also exist in T2D related comorbidities including insulin resistance, vascular complications and drug response. Genetic heterogeneity, dietary patterns, nutrient metabolism and gut microbiome composition attribute to ethnic disparities in both manifestation and progression of T2D. These factors differentially regulate the rate of metabolism and metabolic health. Metabolomics studies have indicated significant differences in carbohydrate, lipid and amino acid metabolism among ethnicities. Interestingly, genetic variations regulating lipid and amino acid metabolism might also contribute to inter-ethnic differences in T2D. Comprehensive and comparative metabolomics analysis between ethnicities might help to design personalized dietary regimen and newer therapeutic strategies. In the present review, we explore population based metabolomics data to identify inter-ethnic differences in metabolites and discuss how (a) genetic variations, (b) dietary patterns and (c) microbiome composition may attribute for such differences in T2D.

Generic model for biological regulation

A substantial portion of molecules in an organism are involved in regulation of a wide spectrum of biological processes. Several models have been presented for various forms of biological regulation, including gene expression regulation and physiological regulation; however, a generic model is missing. Recently a new unifying theory in biology, poikilosis, was presented.  Poikilosis indicates that all systems display intrinsic heterogeneity, which is a normal state. The concept of poikilosis allowed development of a model for biological regulation applicable to all types of regulated systems. The perturbation-lagom-TATAR countermeasures-regulator (PLTR) model combines the effects of perturbation and lagom (allowed and sufficient extent of heterogeneity) in a system with tolerance, avoidance, repair, attenuation and resistance (TARAR) countermeasures, and possible regulators. There are three modes of regulation, two of which are lagom-related. In the first scenario, lagom is maintained, both intrinsic (passive) and active TARAR countermeasures can be involved. In the second mode, there is a shift from one lagom to another. In the third mode, reguland regulation, the regulated entity is the target of a regulatory shift, which is often irreversible or requires action of another regulator to return to original state. After the shift, the system enters to lagom maintenance mode, but at new lagom extent. The model is described and elaborated with examples and applications, including medicine and systems biology. Consequences of non-lagom extent of heterogeneity are introduced, along with a novel idea for therapy by reconstituting biological processes to lagom extent, even when the primary effect cannot be treated.

Generic model for biological regulation

A substantial portion of molecules in an organism are involved in regulation of a wide spectrum of biological processes. Several models have been presented for various forms of biological regulation, including gene expression regulation and physiological regulation; however, a generic model is missing. Recently a new unifying theory in biology, poikilosis, was presented.  Poikilosis indicates that all systems display intrinsic heterogeneity. The concept of poikilosis allowed development of a model for biological regulation applicable to all types of regulated systems. The perturbation-lagom-TATAR countermeasures-regulator (PLTR) model combines the effects of perturbation and lagom (allowed and sufficient extent of heterogeneity) in a system with tolerance, avoidance, repair, attenuation and resistance (TARAR) countermeasures, and possible regulators. There are three modes of regulation, two of which are lagom-related. In the first scenario, lagom is maintained, both intrinsic (passive) and active TARAR countermeasures can be involved. In the second mode, there is a shift from one lagom to another. In the third mode, reguland regulation, the regulated entity is the target of a regulatory shift, which is often irreversible or requires action of another regulator to return to original state. After the shift, the system enters to lagom maintenance mode, but at new lagom extent. The model is described and elaborated with examples and applications, including medicine and systems biology. Consequences of non-lagom extent of heterogeneity are introduced, along with a novel idea for therapy by reconstituting biological processes to lagom extent, even when the primary effect cannot be treated.

Type-2 diabetic rat heart: The effect of kolaviron on mTOR-1, P70S60K, PKC-α, NF-kB, SOD-2, NRF-2, eNOS, AKT-1, ACE, and P38 MAPK gene expression profile

It has been established that genetic factors partially contribute to type-2 diabetes and vascular disease development. This study determined the effect of kolaviron on the expression profile of genes associated with the insulin signaling pathway and involved in regulating glucose and lipid metabolism, oxidative stress, inflammation, vascular functions, pro-survival and the apoptosis pathway in the heart of type-2 diabetic rats. After induction and confirmation of type-2 diabetes seven days after, the rats were treated with kolaviron for twenty-eight days before being euthanized. Organs were harvested and stored at - 80 °C in a biofreezer. Total RNA was extracted from the ventricle, reverse transcribed to cDNA followed by a real-time quantitative polymerase chain reaction (RT-qPCR) analysis of the expression of mTOR-1, P70S60K, PKC-α, NF-kB, SOD-2, NRF-2, eNOS, AKT-1, ACE, p38 MAPK and the reference gene (GAPDH), after which they were normalized/standardized. The results show an increase in the relative mRNA expression of mTOR/P70S60K/PKCα /P38MAPK/NF-KB/ACE and a decrease in the relative mRNA expression of NRF2/SOD/AKT/eNOS in the heart of the diabetic rats. Nevertheless, kolaviron modulated the expression profile of these genes, which suggest a therapeutic effect and target for vascular dysfunction and complications in type-2 diabetes through the activation of the NRF-2/AKT-1/eNOS signaling pathway and suppression of the NF-kB/PKC signaling pathway.

Polymorphism in Adiponectin and Adiponectin Receptor Genes in Diabetes Mellitus Pathogenesis

The role played by hereditary factors in the development of diabetes mellitus type 2 (DM2) has not yet been fully established. Therefore, the purpose of our study was to investigate the prevalence of adiponectin and polymorphism in its gene receptors in connection with the primary symptoms of DM2 pathogenesis. Genomic DNA was isolated from the whole blood of 94 patients with an established diagnosis of DM2 using the phenol–chloroform method. Gene polymorphisms were determined using real-time polymerase chain reaction (PCR). The most common polymorphic variants in patients with DM2 were the genotypes AA (rs11061971) and GG (rs16928751) on the ADIPOR2 gene. A strong correlation was found between the rs16928751 polymorphism on the ADIPOR2 gene and increased body mass index (BMI). TG (rs2275737) ADIPOR1 gene genotype carriers were found to have the highest levels of glycosylated hemoglobin (HbA1), whereas TT (rs2275738) caused stable hyperglycemia. In addition, the rs16928751 ADIPOR2 gene polymorphism showed an association with the development of key mechanisms of DM2 in the Russian population, although a number of genomic searches failed to show any association of this gene with DM2. Unique gene variants associated with the risk of developing DM2 in the Crimean population were established.

Dual-agonist incretin peptides from fish with potential for obesity-related Type 2 diabetes therapy - A review

The long-acting glucagon-like peptide-1 receptor (GLP1R) agonist, semaglutide and the unimolecular glucose-dependent insulinotropic polypeptide receptor (GIPR)/GLP1R dual-agonist, tirzepatide have been successfully introduced as therapeutic options for patients with Type-2 diabetes (T2DM) and obesity. Proglucagon-derived peptides from phylogenetically ancient fish act as naturally occurring dual agonists at the GLP1R and the glucagon receptor (GCGR) with lamprey GLP-1 and paddlefish glucagon being the most potent and effective in stimulating insulin release from BRIN-BD11 clonal β-cells. These peptides were also the most effective in lowering blood glucose and elevating plasma insulin concentrations when administered intraperitoneally to overnight-fasted mice together with a glucose load. Zebrafish GIP acts as a dual agonist at the GIPR and GLP1R receptors. Studies with the high fat-fed mouse, an animal model with obesity, impaired glucose-tolerance and insulin-resistance, have shown that twice-daily administration of the long-acting analogs [D-Ala²]palmitoyl-lamprey GLP-1 and [D-Ser²]palmitoyl-paddlefish glucagon over 21 days improves glucose tolerance and insulin sensitivity. This was associated with β-cell proliferation, protection of β-cells against apoptosis, decreased pancreatic glucagon content, improved lipid profile, reduced food intake and selective alteration in the expression of genes involved in β-cell stimulus-secretion coupling. In insulin-deficient Glu^CreERT2;ROSA26-eYFP transgenic mice, the peptides promoted an increase in β-cell mass with positive effects on transdifferentiation of glucagon-producing to insulin-producing cells. Naturally occurring fish dual agonist peptides, particularly lamprey GLP-1 and paddlefish glucagon, provide templates for development into therapeutic agents for obesity-related T2DM.

Association of ACE I/D and PAI-1 4G/5G polymorphisms with susceptibility to type 2 diabetes mellitus

BACKGROUND

A number of studies were carried out to assess the association of angiotensin I converting enzyme (ACE) I/D and plasminogen activator inhibitor-1 (PAI-1-1) 4G/5G polymorphisms with susceptibility to type 2 diabetes mellitus (T2DM). However, there are a few studies in Iranian patients with T2DM. Here, we tested for an association of ACE I/D and PAI-1 4G/5G polymorphisms with T2DM risk.

METHODS

One hundred-eighteen patients with T2DM and 125 healthy subjects were participates in this study. The ACE I/D (rs4340) and PAI-1 4G/5G (rs1799889) polymorphisms was genotyped by conventional and PCR-RFLP assays, receptively. The associations was evaluated by calculating the odds ratio (OR) and 95% confidence interval (95% CI).

RESULTS

The genotype distribution of ACE I/D and PAI-1 4G/5G polymorphisms were not deviated from the Hardy-Weinberg equilibrium in healthy controls. The ACE II, ID, and DD genotype frequencies were 18.6%, 48.3%, and 33.1% in the T2DM patients versus 24.0%, 45.6% and 30.4% in healthy subjects, respectively. The PAI-1 4G/4G, 4G/5G, and 5G/5G genotype frequencies were 16.9%, 51.7%, and 31.4% in cases versus 24.8%, 57.6% and 17.6% in controls, respectively. There is a significant distribution in genotype/allele of PAI-1 4G/4G between cases with T2DM and healthy control, but not for ACE I/D. Moreover, the 5G/5G genotype is significantly (OR = 2.139, CI 95% 1.171-3.907, p = 0.013) increased the risk of T2DM by two folds in the cases than healthy controls.

CONCLUSIONS

Our findings suggest that PAI-1 4G/5G may be likelihood risk factor for the development of T2DM in the Iranian patients. The higher frequency of PAI-1 5G/5G genotype in patients with T2DM revealed that individuals with the 5G allele may be at higher risk of T2DM development than those with 4G. However, there was no significant association between ACE I/D polymorphism and T2DM in our population. Future rigorous, well-designed studies with larger sample should replicate this study to confirm our findings in Iranian T2DM patients.

Association of Lower Extremity Vascular Disease, Coronary Artery, and Carotid Artery Atherosclerosis in Patients with Type 2 Diabetes Mellitus

The motive of this article is to present the case study of patients to investigate the association between the ultrasonographic findings of lower extremity vascular disease (LEAD) and plaque formation. Secondly, to examine the association between the formation of coronary artery and carotid artery atherosclerosis in patients with type 2 diabetes mellitus. 124 patients with type 2 diabetes (64 males and 60 females with the age group 25-78 years) are considered for the research studies who have registered themselves in the Department of Endocrinology and Metabolism from April 2017 to February 2019. All participants have reported their clinical information regarding diabetes, alcohol consumption, smoking status, and medication. The blood samples from subjects are collected for measurement of HbA_1c, total cholesterol, triglycerides, HDL-c, and LDL-c levels. Two-dimensional ultrasound has been used to measure the inner diameter, peak flow velocity, blood flow, and spectral width of the femoral artery, pop artery, anterior iliac artery, posterior tibial artery, and dorsal artery and to calculate the artery stenosis degree. Independent factors of atherosclerosis are determined by multivariate logistic regression analysis. The results are evaluated within the control group and it is found that there is no significant impact of gender, age, and body mass index (P > 0.05) on the lower extremity vascular diseases. Those with smoking, alcohol consumption, hypertension, and dyslipidemia have higher positive rate (P < 0.05). The type 2 diabetes mellitus group has higher diastolic blood pressure and lower triglyceride (P < 0.05). Diastolic blood pressure, HbA_1C, total cholesterol, HDL-c, and LDL-C are not remarkably dissimilar between the type 2 diabetes mellitus group and the control group (P > 0.05). Compared with the control group, the type 2 diabetes mellitus group has higher frequency of lower extremity vascular diseases in the dorsal artery than in the pop artery (P < 0.05). The blood flow of type 2 diabetes mellitus group is found to be lower than that of the control group, especially in the dorsal artery (P < 0.05). The blood flow velocity of the dorsal artery is accelerated (P < 0.01). Among 117 patients of type 2 diabetes mellitus (94.35%) with a certain degree of injury, there are 72 cases of type I carotid stenosis (58.06%), 30 cases of type II carotid stenosis (24.19%), and 15 cases of type III carotid stenosis (12.10%). Out of 108 subjects in the control group, there are 84 cases of type 0 carotid stenosis (77.78%), 19 cases of type I carotid stenosis (17.59%), 5 cases of type II carotid stenosis (4.63%), and 0 case of type III carotid stenosis (0.00%). Compared with the control group, carotid stenosis is more common in patients with type 2 diabetes mellitus (P < 0.05). Age, smoking, duration of diseases, systolic blood pressure, and degree of carotid stenosis are found to be associated with atherosclerosis. The findings suggest that the color Doppler ultrasonography can give early warning when applied in patients with carotid and lower extremity vascular diseases to delay the incidence of diabetic macroangiopathy and to control the development of cerebral infarction, thus providing an important basis for clinical diagnosis and treatment.

Correlation between Alzheimer's disease and type 2 diabetes using non-negative matrix factorization

Alzheimer's disease (AD) is a complex and heterogeneous disease that can be affected by various genetic factors. Although the cause of AD is not yet known and there is no treatment to cure this disease, its progression can be delayed. AD has recently been recognized as a brain-specific type of diabetes called type 3 diabetes. Several studies have shown that people with type 2 diabetes (T2D) have a higher risk of developing AD. Therefore, it is important to identify subgroups of patients with AD that may be more likely to be associated with T2D. We here describe a new approach to identify the correlation between AD and T2D at the genetic level. Subgroups of AD and T2D were each generated using a non-negative matrix factorization (NMF) approach, which generated clusters containing subsets of genes and samples. In the gene cluster that was generated by conventional gene clustering method from NMF, we selected genes with significant differences in the corresponding sample cluster by Kruskal-Wallis and Dunn-test. Subsequently, we extracted differentially expressed gene (DEG) subgroups, and candidate genes with the same regulation direction can be extracted at the intersection of two disease DEG subgroups. Finally, we identified 241 candidate genes that represent common features related to both AD and T2D, and based on pathway analysis we propose that these genes play a role in the common pathological features of AD and T2D. Moreover, in the prediction of AD using logistic regression analysis with an independent AD dataset, the candidate genes obtained better prediction performance than DEGs. In conclusion, our study revealed a subgroup of patients with AD that are associated with T2D and candidate genes associated between AD and T2D, which can help in providing personalized and suitable treatments.

The Emerging Role of TXNIP in Ischemic and Cardiovascular Diseases; A Novel Marker and Therapeutic Target

Thioredoxin interacting protein (TXNIP) is a metabolism- oxidative- and inflammation-related marker induced in cardiovascular diseases and is believed to represent a possible link between metabolism and cellular redox status. TXNIP is a potential biomarker in cardiovascular and ischemic diseases but also a novel identified target for preventive and curative medicine. The goal of this review is to focus on the novelties concerning TXNIP. After an overview in TXNIP involvement in oxidative stress, inflammation and metabolism, the remainder of this review presents the clues used to define TXNIP as a new marker at the genetic, blood, or ischemic site level in the context of cardiovascular and ischemic diseases.

ACE2 Interaction Networks in COVID-19: A Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019; COVID-19) is associated with adverse outcomes in patients with cardiovascular disease (CVD). The aim of the study was to characterize the interaction between SARS-CoV-2 and Angiotensin-Converting Enzyme 2 (ACE2) functional networks with a focus on CVD.

METHODS

Using the network medicine approach and publicly available datasets, we investigated ACE2 tissue expression and described ACE2 interaction networks that could be affected by SARS-CoV-2 infection in the heart, lungs and nervous system. We compared them with changes in ACE-2 networks following SARS-CoV-2 infection by analyzing public data of human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). This analysis was performed using the Network by Relative Importance (NERI) algorithm, which integrates protein-protein interaction with co-expression networks. We also performed miRNA-target predictions to identify which miRNAs regulate ACE2-related networks and could play a role in the COVID19 outcome. Finally, we performed enrichment analysis for identifying the main COVID-19 risk groups.

RESULTS

We found similar ACE2 expression confidence levels in respiratory and cardiovascular systems, supporting that heart tissue is a potential target of SARS-CoV-2. Analysis of ACE2 interaction networks in infected hiPSC-CMs identified multiple hub genes with corrupted signaling which can be responsible for cardiovascular symptoms. The most affected genes were EGFR (Epidermal Growth Factor Receptor), FN1 (Fibronectin 1), TP53, HSP90AA1, and APP (Amyloid Beta Precursor Protein), while the most affected interactions were associated with MAST2 and CALM1 (Calmodulin 1). Enrichment analysis revealed multiple diseases associated with the interaction networks of ACE2, especially cancerous diseases, obesity, hypertensive disease, Alzheimer's disease, non-insulin-dependent diabetes mellitus, and congestive heart failure. Among affected ACE2-network components connected with the SARS-Cov-2 interactome, we identified AGT (Angiotensinogen), CAT (Catalase), DPP4 (Dipeptidyl Peptidase 4), CCL2 (C-C Motif Chemokine Ligand 2), TFRC (Transferrin Receptor) and CAV1 (Caveolin-1), associated with cardiovascular risk factors. We described for the first time miRNAs which were common regulators of ACE2 networks and virus-related proteins in all analyzed datasets. The top miRNAs regulating ACE2 networks were miR-27a-3p, miR-26b-5p, miR-10b-5p, miR-302c-5p, hsa-miR-587, hsa-miR-1305, hsa-miR-200b-3p, hsa-miR-124-3p, and hsa-miR-16-5p.

CONCLUSION

Our study provides a complete mechanistic framework for investigating the ACE2 network which was validated by expression data. This framework predicted risk groups, including the established ones, thus providing reliable novel information regarding the complexity of signaling pathways affected by SARS-CoV-2. It also identified miRNAs that could be used in personalized diagnosis in COVID-19.

Functional Interactomes of Genes Showing Association with Type-2 Diabetes and Its Intermediate Phenotypic Traits Point towards Adipo-Centric Mechanisms in Its Pathophysiology

The pathogenic mechanisms causing type 2 diabetes (T2D) are still poorly understood; a greater awareness of its causation can lead to the development of newer and better antidiabetic drugs. In this study, we used a network-based approach to assess the cellular processes associated with protein–protein interaction subnetworks of glycemic traits—HOMA-β and HOMA-IR. Their subnetworks were further analyzed in terms of their overlap with the differentially expressed genes (DEGs) in pancreatic, muscle, and adipose tissue in diabetics. We found several DEGs in these tissues showing an overlap with the HOMA-β subnetwork, suggesting a role of these tissues in β-cell failure. Many genes in the HOMA-IR subnetwork too showed an overlap with the HOMA-β subnetwork. For understanding the functional theme of these subnetworks, a pathway-to-pathway complementary network analysis was done, which identified various adipose biology-related pathways, containing genes involved in both insulin secretion and action. In conclusion, network analysis of genes showing an association between T2D and its intermediate phenotypic traits suggests their potential role in beta cell failure. These genes enriched the adipo-centric pathways and were expressed in both pancreatic and adipose tissue and, therefore, might be one of the potential targets for future antidiabetic treatment.

Evaluation of Relationship Between Arylesterase-Based Activity and Genetic Variants of Paraoxonase1 in T2DM Patients within Golestan Province

Arylesterase activity of Paraoxonase-1 (PON1) enzyme may be play important role in initiation and progression of several diseases. Activity or serum level of Arylesterase can be affected by many genetic alterations such as SNPs. The reduction in the activity and serum level of Arylesterase could be involved in Type2 diabetes mellitus (T2DM). The aim of this investigation is to examine the association between Arylesterase activity and promoter polymorphism (- 108C > T) of PON1gene in patients with T2DM in Golestan Province, northern area of Iran. Achievement of this purpose was due to DNA obtaining from blood then SNP determination using PCR-RFLP and Arylesterase activity measurement in the serum of 90 normal individuals and 90patients suffering diabetes. Data was processed by SPSS software version 16. The significant association was observed between the Arylesterase activity and three genotypes of PON1 gene such as CC, CT, and TT in subjects with T2DM. In the present study, it has been shown level of Arylestrase activity of PON1 in patients (117.33 ± 63.96) is lower than it in control group (289.33 ± 68.38); P < 0.05. Our results declared that activity of Arylesterase in diabetic patients was significantly lower than the healthy individuals.

Ramipril and resveratrol co-treatment attenuates RhoA/ROCK pathway-regulated early-stage diabetic nephropathy-associated glomerulosclerosis in streptozotocin-induced diabetic rats

Clinical studies have shown that hyperglycemia can induce early-stage diabetic nephropathy (DN). Furthermore, oxidative stress, tubular epithelial-mesenchymal transition and extracellular matrix accumulation promote the progression of DN to chronic kidney disease and tubulointerstitial fibrosis. It is necessary to initiate treatment at the early stages of DN or even during the early stages of diabetes. In this work, rats with streptozotocin (STZ)-induced diabetes mellitus (DM) presented early DN symptoms within 45 days, and collagen accumulation in the glomerulus of the rats was primarily mediated through the RhoA/ROCK pathway instead of the TGF-β signaling pathway. Resveratrol (15 mg/kg/day) and ramipril (10 mg/kg/day) co-treatment of STZ-induced DN rats showed that glomerulosclerosis in early-stage DN was reversible (P < .05 compared with that in STZ-induced DM rats). The results of this study support early intervention in diabetes or DN as a more efficient therapeutic strategy.

HDAC4 mutations cause diabetes and induce β-cell FoxO1 nuclear exclusion

Background

Studying patients with rare Mendelian diabetes has uncovered molecular mechanisms regulating β‐cell pathophysiology. Previous studies have shown that Class IIa histone deacetylases (HDAC4, 5, 7, and 9) modulate mammalian pancreatic endocrine cell function and glucose homeostasis.

Methods

We performed exome sequencing in one adolescent nonautoimmune diabetic patient and detected one de novo predicted disease‐causing HDAC4 variant (p.His227Arg). We screened our pediatric diabetes cohort with unknown etiology using Sanger sequencing. In mouse pancreatic β‐cell lines (Min6 and SJ cells), we performed insulin secretion assay and quantitative RT‐PCR to measure the β‐cell function transfected with the detected HDAC4 variants and wild type. We carried out immunostaining and Western blot to investigate if the detected HDAC4 variants affect the cellular translocation and acetylation status of Forkhead box protein O1 (FoxO1) in the pancreatic β‐cells.

Results

We discovered three HDAC4 mutations (p.His227Arg, p.Asp234Asn, and p.Glu374Lys) in unrelated individuals who had nonautoimmune diabetes with various degrees of β‐cell loss. In mouse pancreatic β‐cell lines, we found that these three HDAC4 mutations decrease insulin secretion, down‐regulate β‐cell‐specific transcriptional factors, and cause nuclear exclusion of acetylated FoxO1.

Conclusion

Mutations in HDAC4 disrupt the deacetylation of FoxO1, subsequently decrease the β‐cell function including insulin secretion, resulting in diabetes.

Identification and characterization of differentially expressed genes in Type 2 Diabetes using in silico approach

Diabetes mellitus is clinically characterized by hyperglycemia. Though many studies have been done to understand the mechanism of Type 2 Diabetes (T2D), however, the complete network of diabetes and its associated disorders through polygenic involvement is still under debate. The present study designed to re-analyze publicly available T2D related microarray raw datasets present in GEO database and T2D genes information present in GWAS catalog for screening out differentially expressed genes (DEGs) and identify key hub genes associated with T2D. T2D related microarray data downloaded from Gene Expression Omnibus (GEO) database and re-analysis performed with in house R packages scripts for background correction, normalization and identification of DEGs in T2D. Also retrieved T2D related DEGs information from GWAS catalog. Both DEGs lists were grouped after removal of overlapping genes. These screened DEGs were utilized further for identification and characterization of key hub genes in T2D and its associated diseases using STRING, WebGestalt and Panther databases. Computational analysis reveal that out of 99 identified key hub gene candidates from 348 DEGs, only four genes (CCL2, ELMO1, VEGFA and TCF7L2) along with FOS playing key role in causing T2D and its associated disorders, like nephropathy, neuropathy, rheumatoid arthritis and cancer via p53 or Wnt signaling pathways. MIR-29, and MAZ_Q6 are identified potential target microRNA and TF along with probable drugs alprostadil, collagenase and dinoprostone for the key hub gene candidates. The results suggest that identified key DEGs may play promising roles in prevention of diabetes.

Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program

The Million Veteran Program (MVP) was established in 2011 as a national research initiative to determine how genetic variation influences the health of US military veterans. Here we genotyped 312,571 MVP participants using a custom biobank array and linked the genetic data to laboratory and clinical phenotypes extracted from electronic health records covering a median of 10.0 years of follow-up. Among 297,626 veterans with at least one blood lipid measurement, including 57,332 black and 24,743 Hispanic participants, we tested up to around 32 million variants for association with lipid levels and identified 118 novel genome-wide significant loci after meta-analysis with data from the Global Lipids Genetics Consortium (total n > 600,000). Through a focus on mutations predicted to result in a loss of gene function and a phenome-wide association study, we propose novel indications for pharmaceutical inhibitors targeting PCSK9 (abdominal aortic aneurysm), ANGPTL4 (type 2 diabetes) and PDE3B (triglycerides and coronary disease).

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks.