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Gene network analysis of type 2 diabetes mellitus.

机译:2型糖尿病的基因网络分析。

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摘要

Type 2 diabetes mellitus is a metabolic disorder that is believed to be caused by a combination of environmental factors and genetic disposition. Despite an impressive body of research and steady progress in understanding associated risk factors such as obesity and insulin resistance, the underlying disease mechanisms remain unknown. In this project, we described and developed a gene network analysis approach, called Gene Network Enrichment Analysis to study of type 2 diabetes mellitus. Gene networks encode relationships between genes; by accounting for such information in addition to gene expression, we show that Gene Network Enrichment Analysis is able to identify biological processes, signaling pathways, and individual genes associated with disease that are missed by standard analyses. We developed multiple variants of the algorithm and show that it is equally suited to analyzing individual experiments or as a meta-analysis across multiple, biologically related experiments. Application of Gene Network Enrichment Analysis on a large compendium of animal models of disease successfully identifies differential activity in insulin signaling, nuclear receptors, metabolism processes, and inflammatory response. Subsequent network analysis comparing diabetes resistant and diabetes prone mouse models demonstrated that differences in inflammation precedes any measurable metabolic differences, and may stem from differences in the actual number of inflammatory cells. Our results therefore suggest that dysregulation in inflammation, specifically T cell and macrophage activation, may be an early feature of disease.;We compare the performance of Gene Network Enrichment Analysis to a number of other tools used in the community including standard hypergeometric enrichment on differentially expressed genes and Gene Set Enrichment Analysis. While we focused on type 2 diabetes mellitus in this project, the Gene Network Enrichment Analysis algorithm that we have developed is generally applicable. To facilitate the adoption of the algorithm by the wider scientific community, we have implemented a publicly available and open-source web server for Gene Network Enrichment Analysis. The server is located at http://soteira.bu.edu .
机译:2型糖尿病是一种代谢性疾病,据信是由环境因素和遗传因素共同引起的。尽管进行了令人印象深刻的研究,并且在了解肥胖和胰岛素抵抗等相关危险因素方面取得了稳步进展,但潜在的疾病机制仍然未知。在这个项目中,我们描述并开发了一种基因网络分析方法,称为基因网络富集分析,用于研究2型糖尿病。基因网络编码基因之间的关系;通过考虑除基因表达之外的此类信息,我们表明基因网络富集分析能够识别生物学过程,信号传导途径以及与标准分析所遗漏的疾病相关的单个基因。我们开发了该算法的多种变体,并表明该算法同样适用于分析单个实验或作为跨多个生物学相关实验的荟萃分析。基因网络富集分析在大型动物疾病模型中的应用成功地确定了胰岛素信号传导,核受体,代谢过程和炎症反应中的差异活性。随后的比较糖尿病抗性和糖尿病易感小鼠模型的网络分析表明,炎症的差异先于任何可测量的代谢差异,并且可能源于炎性细胞实际数量的差异。因此,我们的结果表明,炎症异常,特别是T细胞和巨噬细胞活化失调,可能是疾病的早期特征。我们将“基因网络富集分析”的性能与社区中使用的许多其他工具进行了比较,包括差异性上的标准超几何富集表达的基因和基因集富集分析。尽管我们在此项目中专注于2型糖尿病,但我们开发的基因网络富集分析算法通常适用。为了促进更广泛的科学界对该算法的采用,我们为基因网络富集分析实现了一个公开可用的开源Web服务器。服务器位于http://soteira.bu.edu。

著录项

  • 作者

    Liu, Manway Michael.;

  • 作者单位

    Boston University.;

  • 授予单位 Boston University.;
  • 学科 Biology Genetics.;Biology Bioinformatics.;Biology Systematic.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 232 p.
  • 总页数 232
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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