Analysis of gene signaling pathways with applications in obesity and diabetes

描述(申请人提供)：能够从差异表达(DE)基因或蛋白质列表中正确推断导致疾病的扰动途径相互作用可能是将目前丰富的高通量表达数据转化为生物学知识的关键。然而，目前旨在通过使用DE基因来识别显著受影响的途径来弥合这一差距的方法相当不成熟。即使不是所有的方法，也有许多这样的方法往往把这些途径当作简单的一组基因，而忽视或没有充分利用这些途径的本质：描述基因相互作用的复杂方式的图表。我们的初步结果表明，现有的通路分析方法经常提供不正确的结果。此外，它们提供的p值通过途径耦合现象不适当地受到共同途径基因的影响。这项提案的目标是通过开发实现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。考虑到一种疾病以高通量基因表达方法为特征，我们提出了一种影响分析技术，能够：i)识别显著受影响的途径，以及ii)提出可能被药物靶向的特定基因信号级联。该技术考虑了目前被现有途径分析工具忽略的生物学上的重要因素，包括：i)由途径图描述的基因相互作用，ii)基因类型和在给定途径中的位置，以及iii)扰动通过途径从一个基因传播到另一个基因的效率。此外，我们建议研究路径耦合，并为超几何、GSEA和路径影响分析方法开发适当的校正方法。这一分析将应用于糖尿病和肥胖症的研究。这里开发的新方法将被应用于低剂量CL 316,243(CL)处理的小鼠白色脂肪的微阵列数据，这种低剂量CL 316,243(CL)已被证明具有将白色脂肪转化为棕色脂肪(燃烧而不是储存能量)的潜力。我们还将把这种方法应用于3T3-L1前脂肪细胞诱导成脂后分化过程中收集的数据。这里的目标有三个：i)验证新的方法；ii)评估在脂肪形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，并构建一组与肥胖和糖尿病相关的定制路径；iii)确定在脂肪形成和脂肪组织重塑中重要的途径和信号级联。开发的方法将以BioConductor包的形式提供，以及免费的Java Web应用程序。我们的团队在开发用于高通量数据分析、多重假设检验以及肥胖和糖尿病的新算法方面拥有出色的资质和记录。

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.