Predicting and testing gene function in the human cell division cycle

描述（由申请人提供）：基因组学面临着将高通量方法产生的大量信息整合到内聚数据集中以预测基因功能和途径的艰巨挑战。我们建议在非转化的人乳腺上皮细胞（HMEC）系中鉴定细胞周期调控基因，并直接将细胞周期调控基因与转化的HMEC系中的细胞周期调控基因进行比较。我们提出了一个计划，使用细胞周期调控的基因表达数据作为屏幕分配功能的未表征的细胞周期调控基因，然后通过细胞生物学方法实验测试这些计算产生的假设。为此，我们提出了实验，以确定细胞周期调控基因在三个不同的HMEC线和表征的细胞周期调控电路控制的FOXM1转录因子在扰动为基础的时间进程和ChIP芯片上的测定。该研究的一个关键方面是进一步开发贝叶斯集成方法以及新的计算工具，以分析探测细胞生长和增殖的基因组规模数据，目标是为未表征的基因提供基因功能的准确预测以及更完整的描述已知基因。使用功能预测作为指导，以前未表征的细胞周期调控基因将通过RNA干扰和高含量筛选进行分析，以测试功能预测。最后，通过DMA微阵列杂交分析每个未知的敲低，以提供表型的额外“功能读数”。所有这些数据将以迭代的方式整合回贝叶斯框架，以提高我们的预测能力。细胞周期控制是生长和发育的基本过程，其失调导致许多疾病，最显著的是癌症。因此，这项建议所针对的问题对公众健康十分重要。确定受调控的基因及其生物学功能可能有助于开发抗癌疗法和生物标志物。

DESCRIPTION (provided by applicant): Genomics faces the daunting challenge of integrating vast amounts of information generated by high- throughput methods into cohesive datasets to predict gene function and pathways. We propose to identify the cell cycle-regulated genes in a non-transformed human mammary epithelial cell (HMEC) line and directly compare the cell cycle-regulated genes to those in transformed HMEC lines. We present a plan to use the cell cycle-regulated gene expression data as a screen to assign functions to uncharacterized cell cycle- regulated genes, and then to experimentally test these computationally generated hypotheses by cell biological methods. To this end, we propose experiments to identify cell cycle-regulated genes in a three different HMEC lines and characterize the cell cycle regulatory circuitry controlled by the FOXM1 transcription factor in perturbation-based time courses and ChlP-on-chip assays. A key aspect of the study is to further develop Bayesian integration methods, as well as new computational tools, to analyze genome- scale data probing cell growth and proliferation with the goal of providing accurate prediction of gene function for uncharacterized genes as well as a more complete description of the known genes. Using the functional predictions as a guide, previously uncharacterized cell cycle-regulated genes will be analyzed by RNA interference and High Content Screening to test the functional predictions. Finally, knockdown of each unknown will be analyzed by DMA microarrays hybridization to provide an additional "functional readout" for phenotype. All of this data will be integrated back into the Bayesian framework in an iterative manner to increase our predictive power. Cell cycle control is a fundamental process of growth and development and it's deregulation leads to many diseases, most notably, cancer. Therefore, the problems addressed in this proposal are important to public health. Identifying the genes that are regulated and their biological function may aid in the development of anti-cancer therapies and biomarkers.