Differential Network Interrogations of Epithelial to Mesenchymal Transition

描述（由申请人提供）：上皮-间充质转化（EMT）;肿瘤耐药性和转移的驱动因素，是一种复杂的机制，通过高度稳健的生物网络之间的复杂串扰而产生。在驱动EMT的网络中，关于最核心基因的信息很少，这主要是由于缺乏适当的计算工具。为了解决这个未解决的问题，两个PI（一个计算生物学家和一个分子生物学家）已经合作，以确定中央基因之间的差异表达上皮和间充质亚型在公认的温伯格的EMT细胞模型。虽然这些模型已经使用t检验和F检验的差异表达（DE）基因分析的主题，但由于存在不满足DE标准的额外基因，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，这样的分析不能提取间充质途径基因的EMT特异性表征;即，在整个共表达基因组中鉴定可能仅特异于EMT的间充质模式的区别集。在这里，我们提出了一个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，在正确的模型中，结合EMT相关基因的生物学验证的差分网络分析是至关重要的。为此，我们对温伯格的K-ras-HMLE（上皮）和K-ras-HMLE-SNAIL（间充质）4细胞系数据集进行了比较基因组微阵列表达研究。我们的分析揭示了一个显着的全球基因表达的亲本K-ras-HMLE和HMLE-SNAIL细胞之间的差异。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）攻击这些细胞。我们的新计算方法利用差分网络分析在细胞培养中的多个EMT模型中，并在动物肿瘤模型中（以验证肿瘤微环境对原位EMT的影响）。这将与更强大的生物验证相结合，在新的网络靶向药物的存在下。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网络中的中心基因的靶向策略的生物学验证和评估。将在mRNA和蛋白质表达水平上验证鉴定的中心基因，并在配对EMT模型中使用RNA干扰评价其因果关系。在网络驱动的药物设计中，EMT细胞将被一系列小分子药物（通过我们的化学文库筛选鉴定）作为单一药物或组合药物进行挑战，并验证药物治疗是否可以靶向中心基因。使用最强效SMI或其组合的疗效试验进行的额外验证 在动物肿瘤模型中的应用，将加强我们的网络衍生EMT靶向药物的临床应用。

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.