An Integrative Approach to Construct a Regulatory Network Effected by TDZs

综合构建以经济技术开发区为主导的监管网络

• 批准号: 8640941
• 负责人: Kyoung Jae Won
• 金额: $ 20万
• 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/8640941
• 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; receptor binding; 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）：肥胖是代谢疾病的主要危险因素。肥胖通常会导致功能失调的白脂肪组织（WAT）的积累，这进一步导致代谢失调，脂肪酸循环升高并增加促炎性脂肪因子的分泌。人类中发现脂肪燃烧的棕色脂肪组织（BAT）的发现提高了蝙蝠的令人兴奋的可能性，可能是治疗肥胖症和Metablic疾病的新方法。噻唑烷二酮（TZD）具有将WAT转化为“棕色”状态的功能。除此之外，TZD已被用作糖尿病的疗法。但是TZD的临床使用受到限制，因为 安全问题，例如潜在的心血管风险。了解该机制将确定有效但较低的代谢性疾病风险药物靶标。 TZDS Activatin PPAR作用？ （过氧化物酶体增殖物激活受体？）。但是，我们对目标的理解？和其他辅助因子有限。了解TZD的作用并进一步研究褐变 效果，我们建议开发一种新型算法，以预测远程启动子 - 增强剂的相互作用并构建转录网络。为了预测远程相互作用，我们将采用一种机器学习算法，该算法使用增强子RNA（ERNA）水平和基因转录水平，从全局跑步测序（Groseq）数据获得。 Groseq是预测长期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包括PPAR的已知结合位点，包括PPAR？，GR，C/EBP，SMRT和RXR，我们将构建一个全面的TF-GENE网络。预测的相互作用为研究TZD的基因调节提供了有用的环境。我们将研究距离，相对位置，多个TF结合位点的组合如何影响基因表达。我们还将通过在网络中包括蝙蝠特异性的TF结合数据来研究TZD的褐变效应。将通过HE TF-GENE网络分析BAT特异性结合信息的转录规则。总体而言，这些研究使用一种创新和创造性的方法来整合各种类型的数据来研究TZD的基因调节。通过重新处理复杂的基因组数据集，该算法在分析褐变效应的调节规则方面提供了独特的观点，这将极大地增强我们对TZDS的GEN调节的理解并确定糖尿病的潜在治疗靶标。