Deciphering the transcriptional regulatory network (TRN) governing a biological process in mammalian systems is essential to our understanding of basic mechanisms underlying normal physiology as well as disease etiology. It is a daunting task because too many links in the TRN are unknown. The emergence of ChIP-chip and ChIP-seq technologies has enabled the mapping of the genome-wide binding sites of many transcription factors (TFs) known to be key regulators in a biological process. However, these two technologies are limited to the known regulators with ChIP-quality antibodies. We found that TF binding is often associated with a dynamic histone mark signature and can be computationally predicted from the genome-wide histone mark dynamics. Therefore, we hypothesize that with time-course nucleosome-resolution ChIP-seq of a few informative histone marks and RNA-seq data of gene expression, and effective computational modeling, we could infer the TRNs in mammalian biological processes. Specifically, we propose to develop effective computational algorithms to achieve Aim1: first, predict TF binding from nucleosome-resolution histone mark dynamics;second, identify target genes from TF binding, histone marks and gene expression profiles;and third, infers the TRN over a time course. We also propose to apply the above algorithms in two biological systems in Aim 2. One is the mouse myoblast cell line C2C12 differentiation into bone, fat, or muscle, and the other is the human apocrine breast cancer cell line MDA-MB-453 reversible reprogramming to epithelial cells with vitamin D treatment. Through time-course nucleosome-resolution histone mark ChIP-seq and RNA-seq profiling, we will computationally infer and experimentally validate the TRNs in these two systems.
We propose a systematic and unbiased approach to infer the transcriptional regulatory networks (TRNs) governing two biological processes in mammalian systems, which can be cost- effectively extended to other processes. In particular, we expect our approaches to benefit the better understanding of stem cell fate control and cell identity reprogramming, and facilitate treatments for many devastating diseases and injuries. The TRNs obtained from the two biological systems in Aim 2 will unravel new molecular mechanisms of transcriptional and epigenetic regulation. They may help identify new targets for therapeutic intervention for obesity and cancer. The resulting computational algorithms in Aim 1 and time-course high throughput data in Aim 2 will be made publicly available as valuable resources for the biomedical research community.
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