Decoding visceral adipose tissue molecular signatures in obesity and insulin resistance: a multi-omics approach

IF 4.2 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM Obesity Pub Date : 2024-10-13 DOI:10.1002/oby.24146
Dipayan Roy, Raghumoy Ghosh, Ritwik Ghosh, Manoj Khokhar, Ma Yin Yin Naing, Julián Benito-León
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Abstract

Objective

Obesity-associated insulin resistance (IR) is responsible for considerable morbidity and mortality globally. Despite vast genomic data, many areas, from pathogenesis to management, still have significant knowledge gaps. We aimed to characterize visceral adipose tissue (VAT) in obesity and IR through a multi-omics approach.

Methods

We procured data on VAT samples from the Gene Expression Omnibus (GEO) for the following two groups: 1) populations with obesity (n = 34) versus those without (n = 26); and 2) populations with obesity and IR (n = 15) versus those with obesity but without IR (n = 15). Gene set enrichment, protein-protein interaction network construction, hub gene identification, and drug-gene interactions were performed, followed by regulatory network prediction involving transcription factors (TFs) and microRNAs (miRNAs).

Results

Interleukin signaling pathways, cellular differentiation, and regulation of immune response revealed a significant cross talk between VAT and the immune system. Other findings include cancer pathways, neurotrophin signaling, and aging. A total of 10 hub genes, i.e., STAT1, KLF4, DUSP1, EGR1, FOS, JUN, IL2, IL6, MMP9, and FGF9, 24 TFs, and approved hub gene-targeting drugs were obtained. A total of 10 targeting miRNAs (e.g., hsa-miR-155-5p, hsa-miR-34a-5p) were associated with obesity and IR-related pathways.

Conclusions

Our multi-omics integration method revealed hub genes, TFs, and miRNAs that can be potential targets for investigation in VAT-related inflammatory processes and IR, therapeutic management, and risk stratifications.

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解码肥胖和胰岛素抵抗中的内脏脂肪组织分子特征:一种多组学方法。
目的:肥胖相关的胰岛素抵抗(IR)是全球发病率和死亡率的重要原因。尽管有大量的基因组数据,但从发病机制到管理等许多领域仍存在重大的知识空白。我们旨在通过多组学方法描述肥胖和胰岛素抵抗中内脏脂肪组织(VAT)的特征:我们从基因表达总库(Gene Expression Omnibus,GEO)中获取了以下两类人群的内脏脂肪组织样本数据:1)肥胖人群(34 人)与非肥胖人群(26 人);2)肥胖和红外人群(15 人)与肥胖但无红外人群(15 人)。研究人员进行了基因组富集、蛋白质-蛋白质相互作用网络构建、枢纽基因鉴定和药物-基因相互作用,随后进行了涉及转录因子(TF)和微RNA(miRNA)的调控网络预测:结果:白细胞介素信号通路、细胞分化和免疫反应调控揭示了增值税与免疫系统之间的重要交叉对话。其他发现还包括癌症通路、神经营养素信号传导和衰老。共获得了 10 个枢纽基因,即 STAT1、KLF4、DUSP1、EGR1、FOS、JUN、IL2、IL6、MMP9 和 FGF9,24 个 TFs,以及已批准的枢纽基因靶向药物。共有10个靶向miRNA(如hsa-miR-155-5p、hsa-miR-34a-5p)与肥胖和红外相关通路有关:我们的多组学整合方法揭示了枢纽基因、TFs和miRNAs,这些基因、TFs和miRNAs可能成为研究与增值税相关的炎症过程和IR、治疗管理和风险分层的潜在靶点。
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来源期刊
Obesity
Obesity 医学-内分泌学与代谢
CiteScore
11.70
自引率
1.40%
发文量
261
审稿时长
2-4 weeks
期刊介绍: Obesity is the official journal of The Obesity Society and is the premier source of information for increasing knowledge, fostering translational research from basic to population science, and promoting better treatment for people with obesity. Obesity publishes important peer-reviewed research and cutting-edge reviews, commentaries, and public health and medical developments.
期刊最新文献
Issue Information Cardiometabolic characteristics of weight cycling: results from a mid-South regional comprehensive health care system Early changes in the gut microbiota are associated with weight outcomes over 2 years following metabolic and bariatric surgery In silico and functional analysis identifies key gene networks and novel gene candidates in obesity-linked human visceral fat Machine learning-based clustering identifies obesity subgroups with differential multi-omics profiles and metabolic patterns
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