Novel Molecular Networks and Regulatory MicroRNAs in Type 2 Diabetes Mellitus: Multiomics Integration and Interactomics Study
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ABSTRACT:
SUBMITTER: Khokhar M
PROVIDER: S-EPMC11135235 | biostudies-literature | 2022 Jan
REPOSITORIES: biostudies-literature
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Project description:Type 1 diabetes (T1D) is a chronic metabolic disorder characterized by the autoimmune destruction of insulin-producing pancreatic islet beta cells in genetically predisposed individuals. Genome-wide association studies (GWAS) have identified over 60 risk regions across the human genome, marked by single nucleotide polymorphisms (SNPs), which confer genetic predisposition to T1D. There is increasing evidence that disease-associated SNPs can alter gene expression through spatial interactions that involve distal loci, in a tissue- and development-specific manner. Here, we used three-dimensional (3D) genome organization data to identify genes that physically co-localized with DNA regions that contained T1D-associated SNPs in the nucleus. Analysis of these SNP-gene pairs using the Genotype-Tissue Expression database identified a subset of SNPs that significantly affected gene expression. We identified 246 spatially regulated genes including HLA-DRB1, LAT, MICA, BTN3A2, CTLA4, CD226, NOTCH1, TRIM26, PTEN, TYK2, CTSH, and FLRT3, which exhibit tissue-specific effects in multiple tissues. We observed that the T1D-associated variants interconnect through networks that form part of the immune regulatory pathways, including immune-cell activation, cytokine signaling, and programmed cell death protein-1 (PD-1). Our results implicate T1D-associated variants in tissue and cell-type specific regulatory networks that contribute to pancreatic beta cell inflammation and destruction, adaptive immune signaling, and immune-cell proliferation and activation. A number of other regulatory changes we identified are not typically considered to be central to the pathology of T1D. Collectively, our data represent a novel resource for the hypothesis-driven development of diagnostic, prognostic, and therapeutic interventions in T1D.
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Project description:Insulin-dependent diabetes mellitus (T1D) is an organ-specific auto-immune disease caused by the selective destruction of the pancreatic beta cells by inflammatory cells, especially auto-reactive CD8+ T lymphocytes. In this study we evaluated the differential large scale gene expression profiling using cDNA microarrays of T (CD4+ and CD8+) and monocyte (CD14+) cells. In addition, considering that HLA class II profile may influence the expression of these molecules on the surface of peripheral blood cells, and considering that the mechanisms by which HLA class II susceptibility alleles drive the auto-immune response have not been elucidated, we intend to further stratify T1D patients according to the HLA class II profile. 20 pre-pubertal recently diagnosed T1D patients were selected, HLA-DRB1/DQB1 allele typing and separated in two groups. The group 1(G1) had patients with susceptibility alleles and group 2 (G2) with at least one protection allele. To established relationships between genes, the GeneNetwork 1.2 algorithm was used, 6 networks were obtained, TCD4+ G1 patients X controls, TCD4+ G2 patients X controls, and same situation to TCD8+ and CD14+.
2011-06-15 | E-MEXP-3223 | biostudies-arrayexpress
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Project description:The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rapidly became a global health challenge, leading to unprecedented social and economic consequences. The mechanisms behind the pathogenesis of SARS-CoV-2 are both unique and complex. Omics-scale studies are emerging rapidly and offer a tremendous potential to unravel the puzzle of SARS-CoV-2 pathobiology, as well as moving forward with diagnostics, potential drug targets, risk stratification, therapeutic responses, vaccine development and therapeutic innovation. This review summarizes various aspects of understanding multiomics integration-based molecular characterizations of COVID-19, which to date include the integration of transcriptomics, proteomics, genomics, lipidomics, immunomics and metabolomics to explore virus targets and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers an abridgment of omics investigations related to disease pathogenesis and virulence, the role of host genetic variation and a broad array of immune and inflammatory phenotypes contributing to understanding COVID-19 traits. Insights into this review, which combines existing strategies and multiomics integration profiling, may help further advance our knowledge of COVID-19.
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Project description:BackgroundThe high prevalence of type 2 diabetes mellitus (T2DM) in individuals over 65 years old and cognitive deficits caused by T2DM have attracted broad attention. The pathophysiological mechanism of T2DM-induced cognitive impairments, however, remains poorly understood. Previous studies have suggested that the cognitive impairments can be attributed not only to local functional and structural abnormalities but also to specific brain networks. Thus, our aim is to investigate the changes of global networks selectively affected by T2DM.MethodsA resting state functional network analysis was conducted to investigate the intrinsic functional connectivity in 37 patients with diabetes and 40 healthy controls who were recruited from local communities in Beijing, China.ResultsWe found that patients with T2DM exhibited cognitive function declines and functional connectivity disruptions within the default mode network, left frontal parietal network, and sensorimotor network. More importantly, the fasting glucose level was correlated with abnormal functional connectivity.ConclusionThese findings could help to understand the neural mechanisms of cognitive impairments in T2DM and provide potential neuroimaging biomarkers that may be used for early diagnosis and intervention in cognitive decline.
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Project description:Diabetes Mellitus Type 1 miRnome
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Project description:Cardiovascular disease (CVD) is the major macrovascular complication of diabetes mellitus. Recently, although CVD morbidity and mortality have decreased as a result of comprehensive control of CVD risk factors, CVD remains the leading cause of death of patients with diabetes in many countries, indicating the potential underlying pathophysiological mechanisms. MicroRNAs are a class of noncoding, single-stranded RNA molecules that are involved in β-cell function, insulin secretion, insulin resistance, skeletal muscle, and adipose tissue and which play an important role in glucose homeostasis and the pathogenesis of diabetic complications. Here, we review recent progress in research on microRNAs in endothelial cell and vascular smooth muscle cell dysfunction, macrophage and platelet activation, lipid metabolism abnormality, and cardiomyocyte repolarization in diabetes mellitus. We also review the progress of microRNAs as potential biomarkers and therapeutic targets of CVD in patients with diabetes.
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Project description:ObjectivesTo assess the potential value of some miRNAs as diagnostic biomarkers for mild cognitive impairment (MCI) among patients with type2 diabetes mellitus (T2DM) and to identify other risk factors for MCI among them.MethodsThis study enrolled 163 adults with T2DM using face to face interview. Cognitive function with its domains was assessed using Adenbrooke's Cognitive Examination III (ACE III). Lipid profile, glycated hemoglobin, and miR-128, miR-132, miR- 874, miR-134, miR-323, and miR-382 expressions, using quantitative real-time PCR, were assessed.ResultsMCI was detected among 59/163 (36.2%) patients with T2DM. Plasma expression of miR-132 was significantly higher in T2DM patients with MCI compared to those without MCI and to normal cognitive healthy individuals (median = 2, 1.1 and 1.2 respectively, P < 0.05. Logistic regression analysis showed that higher miR-132 expression with adjusted odds ratio (AOR): 1.2 (95% CI 1.0-1.3), female gender (AOR:2.1; 95%CI 1.0-4.3), education below postgraduate (secondary and university education with AOR: 9.5 & 19.4 respectively) were the significant predicting factors for MCI among T2DM patients. Using ROC curve, miR-132 was the only assayed miRNA that significantly differentiates T2DM patients with MCI from those with normal cognition with 72.3% sensitivity, 56.2% specificity, and 63.8% accuracy (P < 0.05). Other studied miRNAs showed lower sensitivity and specificity for detecting MCI among studied T2DM participants.ConclusionMCI affects nearly one-third of adult patients with T2DM. A significantly over expression of miR-132 was detected among T2DM with MCI compared to those with normal cognition.
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