Analysis of key genes and pathways associated with the pathogenesis of Type 2 diabetes mellitus

Alur, Varun; Raju, Varshita; Vastrad, Basavaraj; Vastrad, Chanabasayya; Kotturshetti, Shivakumar

doi:10.1101/2021.08.12.456106

Cited by 2 publications

(2 citation statements)

References 241 publications

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“…Additionally, the rs781852-G variant in this gene has been identi ed as a risk allele for adipose hypertrophy and DM2 according to data from the GWAS Catalog (29). The gene's association with DM2 has also been described by Alur et al (30).…”

Section: Discussionmentioning

confidence: 98%

Precision Medicine in Obesity: Revealing the Role of SPON2 in Inflammation Through Gwas Analysis in Admixture Brazilian Population.

Costa,

Coelho,

Cruz

et al. 2024

Preprint

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Introduction: Obesity is recognized as a chronic condition with a multifactorial etiology, marked by persistent systemic low-level inflammation. Objectives: This study aims to explore and evaluate the role of genetic factors in predisposition to obesity within a diverse, mixed-race population. Methods: We conducted a Genome-Wide Association Study (GWAS) involving 1,036 individuals, comprising 333 eutrophic and 703 people with overweight, to pinpoint genetic variants linked to obesity. Genotyping was carried out using the MEGA chip by Illumina. Following this, imputation was performed using the CAAPA reference panel. We conducted in silico analyses using different platforms. Additionally, in a subset of 657 participants, we quantified levels of 11 cytokines (Eotaxin, IFNγ, IL-10, IL-6, IL-12, IL-13, IL-17A, IL-1β, IL-5, IL-8, and TNFα) in peripheral blood. We examined their relationship with the genotypes of the variants identified in the GWAS study. Results: We identified thirty-five variants that exhibited suggestive associations (5 x 10-8 < p-value < 1 x 10-5) with weight excess. Chromosome 4 harbored the main genes linked to this outcome (SPON2, RNF212, COL4A3, TMED11P and PCSK2) expressed in adipose tissue. Furthermore, we found a variant within the ZZEF1 gene on chromosome 17. Notably, variants such as rs10014526-T and rs77703123-T, in TMED11P displayed high linkage disequilibrium (LD) with variants in SPON2, rs75448245-G, rs11538062-T and rs75654334-T and in COL4A3, rs13419630-A, all negatively associated with the outcome. In contrast, the rs781851-G variant, in the ZZEF1 gene showed a positive association with the outcome. These polymorphic alleles were associated with variations in serum levels of the cytokines IL-6, IL-10, and IL-12. Conclusions: Our study implies that candidate genes linked to weight excess, notably SPON2, are connected to a perturbed immune pathway that underlies the characteristic inflammation seen in obesity. These novel uncovered associations in our study could potentially advance the field of precision medicine to treat obesity.

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Section: Discussionmentioning

confidence: 98%

Precision Medicine in Obesity: Revealing the Role of SPON2 in Inflammation Through Gwas Analysis in Admixture Brazilian Population.

Costa,

Coelho,

Cruz

et al. 2024

Preprint

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“…The relationship between the top genes from Figure 1A, such as MTRNR2L1 [21], INS [22], SERPINE1 [23], and CPE [24], and diabetes have been studied previously. Moreover, top statistically diabetes-associated genes (Figure 1D), such as RPL17, differentially expressed between T2DM and normal samples [25], UCHL1, exacerbating human islet amyloid polypeptide toxicity in β-cells [26], and CPE [24] have also been studied.…”

Section: Sr2 Links the Activities Of Genes To Biological Conditionsmentioning

confidence: 99%

SR2: Sparse Representation Learning for Scalable Single-cell RNA Sequencing Data Analysis

Zhao

Lin

2023

Preprint

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Single-cell RNA-sequencing (scRNA-seq) technology has been widely used to measure the transcriptome of cells in complex and heterogeneous systems. Integrative analysis of multiple scRNA-seq data can transform our understanding of various aspects of biology at the single-cell level. Many computational methods are proposed for data integration. However, few methods for scRNA-seq data integration explicitly model variation from heterogeneous biological conditions for interpretation. Modeling the variation helps understand the effect of biological conditions on complex biological systems.Our study proposes SR2 to capture gene expression patterns from heterogeneous biological conditions and discover cell identity simultaneously. Therefore, it can uncover the effect of biological conditions on the gene expression of cells and simultaneously achieve state-of-the-performance in cell identity discovery in our comprehensive comparison. Notably, SR2 is extended to model the effects of biological conditions on gene expression for cell populations, thus uncovering the effect of biological conditions on gene expression for cell populations and identifying putative condition-associated cell populations. To improve its scalability, we incorporate a batch-fitting strategy to ensure it is scalable to scRNA-seq data with arbitrary sample sizes. Moreover, the broad applicability of SR2 in biomedical studies has been demonstrated via applications. The complete package of SR2 is available athttps://github.com/kai0511/SR2.

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Analysis of key genes and pathways associated with the pathogenesis of Type 2 diabetes mellitus

Cited by 2 publications

References 241 publications

Precision Medicine in Obesity: Revealing the Role of SPON2 in Inflammation Through Gwas Analysis in Admixture Brazilian Population.

Precision Medicine in Obesity: Revealing the Role of SPON2 in Inflammation Through Gwas Analysis in Admixture Brazilian Population.

SR2: Sparse Representation Learning for Scalable Single-cell RNA Sequencing Data Analysis

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