Predicting transcript factors in response to TNF or IL-1 treatment on TM cells

描述(申请人提供)：这是一个次级数据分析项目，通过使用NEI拨款产生的两个微阵列数据集，识别假定的转录因子和相关的顺式调控基序/模块，负责调节小梁细胞对两种细胞因子--肿瘤坏死因子和白介素1的基因表达。这些细胞因子被认为介导了激光小梁成形术的治疗效果，这是青光眼的一种常见治疗方法。青光眼是一种常见的致盲疾病，全世界有6700万人患有青光眼。青光眼视神经损伤的主要危险因素是眼压升高。激光小梁成形术是一种常见的降低眼压的青光眼治疗方法，其疗效似乎是由细胞因子、肿瘤坏死因子和IL-1介导的。主要的假设是，这两种细胞因子的作用模式有许多共同的转录调控模式。转录因子和相应的顺式调控元件被认为是基因调控的关键组成部分，但仍然难以捉摸，因为它们非常小，广泛分布在基因组的非编码区，很难使用传统方法进行定位。通过结合生物统计学和生物信息学工具，我们简化了特定条件下假定的转录因子调控网络的识别。我们将使用新一代创新的聚类方法从微阵列数据中识别潜在共调控基因的紧密簇，然后使用TRANSFAC数据库识别DNA序列数据中常见的已知基序，并使用最新的统计算法预测假定的顺式调控基序/模块。TRANSFAC数据库是已知基序最全面的数据库。基因表达谱和随后的转录因子分析具有确定治疗靶点的巨大潜力，用于开发新的治疗方法。它还将为利用CHIP-SEQ等下一代测序方法设计未来的研究提供关键信息。该项目的成功完成将大大提高我们对哪些特定转录因子参与青光眼治疗相关基因表达变化的理解 公共卫生相关性：青光眼是导致不可逆性失明的主要原因，而眼压升高是主要的危险因素。我们建议识别可能的转录因子，以调节基因表达，以响应青光眼的常见治疗。该方案的成功完成可以阐明目前青光眼治疗的转录因子-基因调控网络机制，进而可能为进一步研究更有效的治疗靶点提供线索。

DESCRIPTION (provided by applicant): This is a secondary data analysis project to identifying putative transcription factors and associated cis-regulatory motifs/modules that are responsible for regulating gene expressions in trabecular meshwork cells in response to two cytokines, TNF and IL-1, by employing two microarray data sets generated by an NEI grant. These cytokines are thought to mediate the therapeutic efficacy of laser trabeculoplasty, a common treatment for glaucoma. Glaucoma is a common blinding disease affecting over 67 million persons worldwide. The primary risk factor for glaucomatous optic nerve damage is elevated intraocular pressure (IOP). The therapeutic effect of laser trabeculoplasty, a common glaucoma treatment that reduces IOP, appears to be mediated by the cytokines, TNF and IL-1. The main hypothesis is that a number of transcriptional regulation patterns will be common to these two cytokine's modes of action. Transcription factors and corresponding cis-regulatory elements are considered key components in gene regulation, but continue to remain elusive because they are very small, scattered widely over the genome's noncoding regions, and difficult to locate using conventional approaches. By combining biostatistics and bioinformatics tools, we streamlined the identification of putative transcription factor regulatory networks specific for conditions. We will employ a new generation of innovative clustering methods to identify tight clusters of potentially coregulated genes from microarray data, and then identify common known motifs in the DNA sequence data using TRANSFAC database as well as predict putative cis-regulatory motifs/modules by using a latest statistical algorithms. TRANSFAC database is the most comprehensive database of known motifs. Gene expression profiles and subsequent transcription factor analysis have a great potential to identify therapeutic targets for developing new treatments. It will also provide crucial information to design future studies utilizing next generation sequencing method such as ChIP-Seq. The successful completion of this project will significantly enhance our understanding on what specific transcription factors are involved in the changes of gene expressions associated with the glaucoma therapy PUBLIC HEALTH RELEVANCE: Glaucoma is a leading cause of irreversible blindness, and a primary risk factor is elevated intraocular pressure. We propose to identify putative transcription factors that regulate gene expressions in response to a common treatment to glaucoma. The successful completion of this proposal can elucidate transcript factor-gene regulation network mechanism of current treatment of glaucoma, which in turn may provide clues for a more effective treatment target for further research.