An integrative approach to construct a regulatory network effected by TDZs

构建受 TDZ 影响的监管网络的综合方法

• 批准号: 8491522
• 负责人: Kyoung Jae Won
• 金额: $ 24万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8491522
• 关键词: 2,4-thiazolidinedione; Adipose tissue; Affect; Algorithms; Antidiabetic Drugs; Binding; Binding Sites; Biological; Blood Circulation; Brown Fat; Burn injury; CCAAT-Enhancer-Binding Proteins; Calories; Chemicals; Classification; Clinical; Complex; Data; Data Analyses; Data Set; Development; Diabetes Mellitus; Disease; Distal; Drug Targeting; Enhancers; Environment; Exposure to; Fatty Acids; Fatty acid glycerol esters; Gene Expression; Gene Expression Regulation; Gene Targeting; Genes; Genetic Transcription; Genomics; Heating; Housing; Human; Machine Learning; Measures; Metabolic; Metabolic Diseases; Methodology; Methods; Modeling; Mus; Nature; Non-Insulin-Dependent Diabetes Mellitus; Nucleic Acid Regulatory Sequences; Obesity; Peroxisome Proliferator-Activated Receptors; Positioning Attribute; Prevention approach; Property; RNA; RXR; Regulation; Relative (related person); Risk; Risk Factors; Role; Running; Safety; Therapeutic; Thiazolidinediones; Time; Transcription factor genes; Transcriptional Regulation; Variant; adipokines; base; cardiovascular risk factor; cofactor; design; epigenome; gene interaction; genome-wide; in vivo; innovation; insulin sensitivity; meetings; novel; novel strategies; promoter; public health relevance; response; therapeutic target; time use; transcription factor

DESCRIPTION (provided by applicant): Obesity is a major risk factor for metabolic disorders. Obesit typically leads to accumulation of dysfunctional white adipose tissue (WAT), which further causes metabolic dysregulation with elevated circulation of fatty acids and increased secretion of proinflammatory adipokines. The discovery of fat burning brown adipose tissue (BAT) in humans has raised the exciting possibility of BAT may be targeted as a novel method to treat obesity and metablic diseases. Thiazolidinediones (TZDs) have a function to convert WAT into a "brownlike" state. Beside, TZDs have been used as a remedy for diabetes. But the clinical use of TZDs has been limited because of the safety concerns such as potential cardiovascular risks. Understanding the mechanism will identify efficacious but lower risk drug targets for the metabolic disorders. TZDs act by activatin PPAR? (peroxisome proliferator-activated receptor ?). However, our understanding about the targets f PPAR? and other cofactors is limited. To understand the role of TZDs and further study the browning effect, we propose to develop a novel algorithm to predict long-range promoter-enhancer interaction and construct a transcriptional network. To predict the long-range interactions, we will employ a machine learning algorithm that uses the enhancer RNA (eRNA) levels and gene transcription levels obtained from global run-on sequencing (GROseq) data. GROseq is a useful dataset to predict long-range interactions, as the eRNA levels highly correlate with the gene transcription level. Applyingthe obtained interactions to the known binding sites of TFs including PPAR?, GR, C/EBP, SMRT, and RXR, we will construct a comprehensive TF-gene network. The predicted interaction provides a useful environment to study gene regulation of TZDs. We will study how the distance, relative position, an the combination of multiple TF binding sites affect gene expression. We will also investigate the browning effects by TZDs by including BAT-specific TF binding data in the network. The transcriptioal rule of the BAT-specific binding information, in combination with other TFs, will be analyzed from he TF-gene network. As a whole, these studies use an innovative and creative approach to integrate various types of data to study gene regulation of TZDs. By reprocessing complex genomic datasets ino a comprehensive regulatory network, the proposed algorithm provides a unique view in analyzing the regulatory rules of the browning effect, which will greatly enhance our understanding about the gen regulation of TZDs and identify potential therapeutic targets for diabetes.

描述（由申请人提供）：肥胖是代谢紊乱的主要危险因素。肥胖通常会导致功能失调的白色脂肪组织（WAT）积聚，从而进一步导致代谢失调，导致脂肪酸循环增加和促炎脂肪因子分泌增加。人体燃烧脂肪的棕色脂肪组织 (BAT) 的发现提出了令人兴奋的可能性，BAT 可能成为治疗肥胖和代谢性疾病的新方法。噻唑烷二酮类 (TZD) 具有将 WAT 转化为“棕色”状态的功能。此外，TZD 已被用作治疗糖尿病的药物。但 TZD 的临床使用受到限制，因为 潜在的心血管风险等安全问题。了解该机制将确定针对代谢紊乱的有效但风险较低的药物靶点。 TZD 通过激活 PPAR 发挥作用？ （过氧化物酶体增殖物激活受体？）。然而，我们对PPAR目标的理解是什么？和其他辅助因子是有限的。了解 TZD 的作用并进一步研究褐变 效应，我们建议开发一种新的算法来预测长程启动子-增强子相互作用并构建转录网络。为了预测长程相互作用，我们将采用机器学习算法，该算法使用从全局连续测序 (GROseq) 数据获得的增强子 RNA (eRNA) 水平和基因转录水平。 GROseq 是预测长程相互作用的有用数据集，因为 eRNA 水平与基因转录水平高度相关。将获得的相互作用应用于已知的 TF 结合位点，包括 PPAR?、GR、C/EBP、SMRT 和 RXR，我们将构建一个全面的 TF 基因网络。预测的相互作用为研究 TZD 的基因调控提供了有用的环境。我们将研究多个 TF 结合位点的距离、相对位置、组合如何影响基因表达。我们还将通过在网络中包含 BAT 特异性 TF 结合数据来研究 TZD 的褐变效应。 BAT特异性结合信息与其他TF结合的转录规则将从TF基因网络中进行分析。总的来说，这些研究采用创新和创造性的方法来整合各种类型的数据来研究 TZD 的基因调控。通过在综合调控网络中重新处理复杂的基因组数据集，所提出的算法为分析褐变效应的调控规则提供了独特的视角，这将极大地增强我们对 TZD 基因调控的理解并确定糖尿病的潜在治疗靶点。