Quantitative Insight into Gene Cooperation

描述(申请人提供)：本研究的目的是为在基因表达水平上分析基因产物之间的复杂相互作用提供新的方法。大量研究表明，细胞暴露在两种不同信号下的结果不能通过对每种信号本身的反应来预测。这一一般原理适用于外源信号分子的协同效应以及基因表达和突变的影响。要详细分析产生这种相互作用的后果的机制，就需要用新的方法来剖析基因调控网络。由于已经就协同癌基因表达的后果对不同调控途径之间的相互作用进行了最详细的分析，这一领域对于新开发的分析工具的应用尤其有希望。该方法包括以下部分：(1)考虑基因表达数据的多维特征的一组差异表达基因的多变量搜索；(2)识别参与细胞对多个突变的协同反应的候选基因的子集；(3)重建作为多个突变对特定细胞功能的协同效应的基因信号网络，从而提供关于协同基因之间因果关系的必要信息。通过结合计算生物学、多元统计和实验研究的方法，可以极大地提高理解细胞对多个基因扰动的复杂反应机制的能力。虽然主要目标是开发一种将广泛适用于各种实验环境的通用方法学，但将进行生物学实验来研究两种致癌基因突变对生存和增殖等基本细胞功能的影响。这一实验计划可能还有其他好处，例如精确定位支持细胞增殖和/或生存的特定回路中的药物靶点。

DESCRIPTION (provided by applicant): The objectives of the proposed research are to provide novel methods for the analysis of the complex interactions between gene products at the level of gene expression. A great deal of research demonstrates that the outcome of exposure of a cell to two different signals is not predictable from the response to each signal on its own. This general principle holds true for the cooperative effects of exogenous signaling molecules as well as the effects of gene expression and mutation. Detailed analysis of the mechanisms by which the consequences of such interactions are created requires novel approaches to the dissection of gene regulatory networks. As the most detailed analysis of interactions between different regulatory pathways has been carried out in regard to the consequences of expression of cooperating oncogenes, this area is especially promising for the application of newly developed analytical tools. The proposed methodology includes the following components: (1) Multivariate search for a set of differentially expressed genes allowing for multidimensional characteristics of gene expression data, (2) Identification of a subset of candidate genes that participate in the cooperative response of a cell to multiple mutations, and (3) Reconstruction of a gene signaling network that underlies the cooperative effect of multiple mutations on a specific cell function, thereby providing the necessary information on causal relationships between cooperating genes. By combining methods of computational biology, multivariate statistics, and experimental studies it is possible to greatly enhance the ability to understand complex mechanisms of cell responses to multiple gene perturbations. While the main objective is to develop a general methodology that will be widely applicable in various experimental settings, biological experiments will be conducted to study the effects of two oncogenic mutations on such fundamental cell functions as survival and proliferation. This experimental program may have additional benefits such as pinpointing drug targets within a particular circuit supporting cell proliferation and/or survival.