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

描述（由申请人提供）：这是一个二级数据分析项目，通过使用NEI资助产生的两个微阵列数据集，鉴定负责调节小梁网细胞中基因表达的假定转录因子和相关顺式调节基序/模块，以响应两种细胞因子TNF和IL-1。这些细胞因子被认为介导激光小梁成形术的治疗效果，激光小梁成形术是青光眼的常见治疗方法。青光眼是一种常见的致盲性疾病，影响全球超过6700万人。青光眼视神经损伤的主要危险因素是眼内压（IOP）升高。激光小梁成形术是一种降低IOP的常见青光眼治疗方法，其治疗效果似乎是由细胞因子TNF和IL-1介导的。主要的假设是，这两种细胞因子的作用模式的转录调控模式将是共同的。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，但仍然是难以捉摸的，因为它们非常小，广泛分散在基因组的非编码区，难以使用常规方法定位。通过结合生物统计学和生物信息学工具，我们简化了对特定条件的推定转录因子调控网络的鉴定。我们将采用新一代的创新聚类方法，从微阵列数据中识别出紧密的潜在共调控基因簇，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推定的顺式调控基序/模块。TRANSFAC数据库是已知基序的最全面的数据库。 基因表达谱和随后的转录因子分析具有很大的潜力，以确定开发新的治疗方法的治疗靶点。它还将为利用下一代测序方法（如ChIP-Seq）设计未来的研究提供重要信息。本课题的成功完成将使我们进一步了解哪些特定的转录因子参与了青光眼治疗相关基因表达的改变 公共卫生相关性：青光眼是不可逆性失明的主要原因，主要风险因素是眼内压升高。我们建议确定公认的转录因子，调节基因表达的一种常见的治疗青光眼。该方案的成功完成将有助于阐明目前青光眼治疗中转录因子-基因调控网络的作用机制，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.