Differential Network Interrogations of Epithelial to Mesenchymal Transition

描述(申请人提供)：上皮到间充质转化(EMT)；肿瘤耐药和转移的驱动因素，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。关于驱动EMT的网络中最核心的基因的信息很少，这主要是因为缺乏适当的计算工具。为了解决这个未解决的问题，两位PI(一位计算生物学家和一位分子生物学家)联合起来，在公认的Weinberg的EMT细胞模型中识别在上皮和间充质亚型之间差异表达的中心基因。虽然这些模型一直是使用t检验和F检验进行差异表达(DE)基因分析的对象，但由于存在不符合差异表达(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，这种分析不能提取间充质途径基因的EMT特异性特征；即在整个共表达的基因组中识别可能仅针对EMT的一组区分的间质模式。在这里，我们提出了一个基于网络的差异分析模型，用于分析由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，差异网络分析结合正确模型中EMT相关基因的生物学验证是至关重要的。为此，我们在Weinberg的K-ras-HMLE(上皮)和K-ras-HMLE-Snail(间充质)4个细胞系数据集上进行了比较基因组芯片的表达研究。我们的分析表明，亲本K-ras-HMLE和HMLE-Snail细胞的整体基因表达存在显著差异。由于它们是蜗牛驱动的EMT模型，我们用小分子抑制剂(SMI)攻击这些细胞，以对抗蜗牛(GN-25)。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(以验证肿瘤微环境对原位EMT的影响)中使用了差分网络分析。这将伴随着在存在较新的网络靶向药物的情况下进行更强大的生物验证。因此，我们的具体目标是1)使用基于差异网络的算法识别EMT中心基因，2)针对EMT网络中集中基因的靶向策略进行生物学验证和评估。已识别的中心基因将在mRNA和蛋白质表达水平上进行验证，并将在成对的EMT模型中使用RNA干扰来评估它们之间的因果关系。在网络驱动的药物设计中，EMT细胞将接受一系列小分子药物(通过我们的化学文库筛选确定)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，将加强我们网络衍生的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.