Analysis of gene signaling pathways with applications in obesity and diabetes

基因信号通路分析及其在肥胖和糖尿病中的应用

基本信息

批准号：
8515396
负责人：
SORIN DRAGHICI
金额：
$ 29.29万
依托单位：
WAYNE STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-07-15 至 2015-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8515396
关键词：
3T3-L1 Cells Address Adipocytes Adverse effects Aging Algorithms Area Bioconductor Biological Biological Sciences Brown Fat Burn injury Cell Line Complex Computer Analysis Computer Simulation Coupling Custom DNA Microarray Chip Data Data Analyses Data Set Databases Development Diabetes Mellitus Disease Dose Drug Targeting Fatty acid glycerol esters Feedback Gene Expression Gene Proteins Generic Drugs Genes Goals Graph Internet Intervention Java Knowledge Lead Life Malignant Neoplasms Measures Metabolic Pathway Methods Molecular Molecular Biology Molecular Genetics Mus Obesity Organism Pathway Analysis Pathway interactions Pharmaceutical Preparations Phenotype Positioning Attribute Protein Microchips Proteins Public Health Research Signal Pathway Signal Transduction Signaling Pathway Gene Specificity System Systems Biology Techniques Testing Therapeutic Intervention Time Tissues adipocyte differentiation gene interaction high throughput analysis improved in vivo lipid biosynthesis metabolomics mouse model neglect novel novel strategies protein metabolite public health relevance simulation tool

项目摘要

DESCRIPTION (provided by applicant): Being able to correctly infer the perturbed pathways interactions that cause the disease from a list of differentially expressed (DE) genes or proteins may be the key to transforming the now abundant high- throughput expression data into biological knowledge. However, the current methods that aim to bridge this gap by using the DE genes to identify significantly impacted pathways are rather unsophisticated. Many if not all such methods often treat the pathways as simple sets of genes, and either ignore or under-utilize the very essence of such pathways: the graphs that describe the complex ways in which genes interact with each other. Our preliminary results show that the existing pathway analysis methods often provide incorrect results. In addition, the p-values they provide are inappropriately influenced by common pathway genes through a pathway coupling phenomenon. The goal of this proposal is to address the problems above by developing methods that implement a systems biology approach for the analysis of gene signaling pathways. Given a disease characterized using a high throughput gene expression approach, we propose an impact analysis technique able to: i) identify the significantly impacted pathways, and ii) propose specific gene signaling cascades that could potentially be targeted by drugs. This technique takes into consideration biologically important factors currently neglected by the existing pathway analysis tools including: i) the gene interactions as described by the pathway graph, ii) the gene type and position in the given pathways, and iii) the efficiency with which perturbations propagate from one gene to another across the pathway. Furthermore, we propose to study the pathway coupling and develop appropriate correction methods for the hypergeometric, GSEA and pathway impact analysis methods. This analysis will be applied to diabetes and obesity research. The novel approach developed here will be applied to microarray data from white fat of mice treated with low dose CL 316,243 (CL), which has been shown to have the potential to transform white fat into brown fat (which burns energy rather than store it). We will also apply this approach on data collected during the differentiation of 3T3-L1 pre-adipocytes after induction of adipogenesis. The goal here is three-fold: i) to validate the novel approach; ii) to assess the efficiency with which gene perturbations propagate on each KEGG pathway during adipogenesis and fat tissue remodeling, and construct a custom set of pathways relevant to obesity and diabetes; and iii) to identify pathways and signaling cascades that are important in adipogenesis and fat tissue remodeling. The methods developed will be made available as a Bioconductor package, as well as a free Java web application. Our team has excellent qualifications and track record in developing novel algorithms for the analysis of high-throughput data, multiple hypothesis testing, as well as obesity and diabetes.

描述（由申请人提供）：能够正确地推断出从差异表达（DE）基因或蛋白质列表中引起疾病的扰动途径相互作用可能是将现在丰富的高吞吐量数据转化为生物学知识的关键。但是，目前旨在通过使用DE基因识别显着影响的途径来弥合这一差距的方法是相当不同意的。许多这样的方法通常将途径视为简单的基因集，而忽略或忽略了这种途径的本质：描述基因相互作用的复杂方式的图。我们的初步结果表明，现有的途径分析方法通常会提供不正确的结果。另外，通过途径偶联现象，它们提供的P值受到共同途径基因的影响不当。该建议的目的是通过开发实施系统生物学方法来分析基因信号通路的方法来解决上述问题。鉴于使用高吞吐量基因表达方法来表征的疾病，我们提出了一种能够：i）确定受影响的途径的影响分析技术，ii）提出了可能由药物靶向的特定基因信号级联。该技术考虑了当前被现有途径分析工具所忽略的生物学上重要因素，包括：i）途径图所述的基因相互作用，ii）ii）在给定途径中的基因类型和位置，iii）在整个途径上从一个基因到另一个基因的敏感效率传播的效率。此外，我们建议研究途径耦合并开发适当的校正方法，用于高几何，GSEA和途径影响分析方法。该分析将应用于糖尿病和肥胖研究。此处开发的新方法将应用于用低剂量CL 316,243（CL）处理的白脂的微阵列数据，该数据已证明具有将白脂肪转化为棕色脂肪（燃烧能量而不是储存）的潜力。我们还将将这种方法应用于诱导脂肪形成后3T3-L1前脂肪细胞的分化期间收集的数据。这里的目标是三个方面：i）验证新方法； ii）评估在脂肪生成和脂肪组织重塑过程中，基因扰动在每个KEGG途径上传播的效率，并构建与肥胖和糖尿病有关的自定义途径； iii）确定在脂肪形成和脂肪组织重塑中很重要的途径和信号级联。开发的方法将作为生物处理程序包以及免费的Java Web应用程序提供。我们的团队在开发新型算法中具有出色的资格和记录，用于分析高通量数据，多个假设测试以及肥胖和糖尿病。