Differential Network Interrogations of Epithelial to Mesenchymal Transition

上皮细胞向间质细胞转化的微分网络询问

• 批准号: 8492867
• 负责人: Ramzi M. Mohammad
• 金额: $ 19.84万
• 依托单位: WAYNE STATE UNIVERSITY
• 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/8492867
• 关键词: Address; Algorithms; Animals; Antineoplastic Agents; Behavior; Biological; Cell Culture Techniques; Cell Line; Cell model; Cells; Clinical; Complex; Computing Methodologies; Coupled; Data; Data Set; Development; Disseminated Malignant Neoplasm; Drug Design; Drug Targeting; Epithelial; Epithelial Cells; Evaluation; Future; Gene Cluster; Gene Expression; Genes; In Situ; Individual; Investigation; Malignant Neoplasms; Measures; Mesenchymal; Modeling; Molecular; Neoplasm Metastasis; Network-based; Parents; Pathway Analysis; Pathway interactions; Pattern; Pharmaceutical Preparations; Pharmacology; Phenotype; Population; Process; RNA; RNA Interference; Resistance; Testing; Tissue-Specific Gene Expression; Validation; clinical application; comparative genomics; computerized tools; design; dosage; effective therapy; efficacy trial; epithelial to mesenchymal transition; in vivo; inhibitor/antagonist; insight; mRNA Expression; meetings; mouse model; novel; preclinical efficacy; protein expression; public health relevance; screening; small molecule; small molecule libraries; subcutaneous; tumor; tumor microenvironment

DESCRIPTION (provided by applicant): Epithelial-to-Mesenchymal Transition (EMT); a driver of tumor resistance and metastasis, is a complex mechanism that arises through an intricate cross talk between highly robust biological networks. There is minimal information on the most central genes in the networks that drive EMT primarily due to the lack of proper computational tools. To address this unmet problem, two PIs (a computational biologist and a molecular biologist) have teamed together to identify the central genes that are differentially expressed between epithelial and mesenchymal subtypes in well recognized Weinberg's EMT cell models. While these models have been the subject of differentially expressed (DE) gene analyses using the t-test and the F-test, it is not sufficient to interrogate the entire EMT phenomena due to the presence of additional genes that do not meet the DE criteria. Existing models for network analysis, co-expression analysis, and gene clustering can only provide information about a group of genes with similar behavior. However, such analysis cannot extract EMT-specific characterization of mesenchymal pathway genes; i.e. identifying the distinguishing set of mesenchymal patterns in the entire co-expressed gene groups that may be specific to EMT only. Here, we propose a network-based differential analysis model for analyzing the topological differences between two gene networks constructed from the expression data. We hypothesize that for deeper understanding of EMT a differential network analysis coupled with biological validation of the EMT associated genes in the correct models is critical. To this end, we performed comparative genomic microarrays expression investigations on Weinberg's K-ras-HMLE (Epithelial) and K-ras-HMLE-SNAIL (Mesenchymal) 4 cell lines datasets. Our analyses revealed a significant global gene expression difference between parent K-ras-HMLE and HMLE-SNAIL cells. As they are SNAIL driven EMT models, we challenged these cells with a small molecule inhibitor (SMI) against SNAIL (GN-25). Our new computational approach utilizes differential network analysis in multiple EMT models in cell culture, and in animal tumor model (to verify the influence of tumor microenvironment on EMT in situ). This will be coupled with more robust biological validation in the presence of newer network targeted drugs. Therefore, our Specific Aims are 1) Identifying EMT central genes using differential network-based algorithms and 2) Biological validation and evaluation of targeted strategies against centralized genes in the EMT networks. The identified central gene will be validated at the mRNA and protein expression level and their cause-effect relationship will be evaluated using RNA interference in the paired EMT models. In a network-driven drug design, the EMT cells will be challenged with a repertoire of small molecule drugs (identified through our chemical library screening) as single agent or in combination and verify whether drug treatments could target the central genes. Additional validation using efficacy trial of the most potent SMI or its combination in animal tumor models will fortify the clinical application of our network derived EMT targeted drugs.

描述（由申请人提供）：上皮-间质转化（EMT）；肿瘤抵抗和转移的驱动因素是一种复杂的机制，是通过高度强大的生物网络之间错综复杂的串扰而产生的。主要由于缺乏适当的计算工具，关于驱动 EMT 的网络中最核心基因的信息很少。为了解决这个未解决的问题，两名 PI（一名计算生物学家和一名分子生物学家）联手鉴定了在公认的 Weinberg 的 EMT 细胞模型中上皮亚型和间质亚型之间差异表达的核心基因。虽然这些模型已成为使用 t 检验和 F 检验进行差异表达 (DE) 基因分析的主题，但由于存在不符合 DE 标准的其他基因，因此不足以询问整个 EMT 现象。现有的网络分析、共表达分析和基因聚类模型只能提供一组具有相似行为的基因的信息。然而，此类分析无法提取间充质途径基因的 EMT 特异性特征；即识别整个共表达基因组中可能仅针对 EMT 的独特间充质模式集。在这里，我们提出了一种基于网络的差异分析模型，用于分析从表达数据构建的两个基因网络之间的拓扑差异。我们假设，为了更深入地了解 EMT，微分网络分析加上正确模型中 EMT 相关基因的生物学验证至关重要。为此，我们对 Weinberg 的 K-ras-HMLE（上皮细胞）和 K-ras-HMLE-SNAIL（间充质细胞）4 个细胞系数据集进行了比较基因组微阵列表达研究。我们的分析揭示了亲本 K-ras-HMLE 和 HMLE-SNAIL 细胞之间存在显着的整体基因表达差异。由于它们是 SNAIL 驱动的 EMT 模型，我们用针对 SNAIL (GN-25) 的小分子抑制剂 (SMI) 来挑战这些细胞。我们的新计算方法在细胞培养的多个 EMT 模型和动物肿瘤模型中利用微分网络分析（以验证肿瘤微环境对原位 EMT 的影响）。这将与更新的网络靶向药物的存在下更强大的生物验证相结合。因此，我们的具体目标是 1) 使用基于差分网络的算法识别 EMT 中心基因，2) 针对 EMT 网络中中心基因的靶向策略进行生物学验证和评估。确定的中心基因将在 mRNA 和蛋白质表达水平上进行验证，并在配对的 EMT 模型中使用 RNA 干扰来评估它们的因果关系。在网络驱动的药物设计中，EMT 细胞将面临一系列小分子药物（通过我们的化学库筛选确定）作为单一药物或组合的挑战，并验证药物治疗是否可以针对中心基因。使用最有效的 SMI 或其组合的功效试验进行额外验证 在动物肿瘤模型中的应用将加强我们网络衍生的 EMT 靶向药物的临床应用。