Leveraging Transcription Factor Dependent RNA Profiling to Interrogate Gene Regulatory Networks in Human Disease Models

Gene regulation is a tightly controlled process that when disrupted can cause a multitude of cellular defects, increased disease risk, and in some cases disease itself. In order to understand the molecular basis of disease, a deep understanding of the gene regulatory networks that underlie healthy and diseased states is necessary. One approach to understand gene regulatory networks in cell-type and context-dependent states is through the application of transcriptional profiling in the presence or absence of key transcription factors (TFs). While TF dependent transcriptional profiling of coding genes has been commonly adopted to quantitatively relate genes in transcriptional hierarchies; identifying non-coding regulatory elements that drive context-specific gene regulatory programs is a relatively new focus. This paradigm change is driven in part by the finding that the majority of genetic variants associated to disease through genome wide association studies reside in the non-coding portion of the genome and are predicted to affect gene regulation. This dissertation outlines the application of TF dependent coding and non-coding transcriptional profiling to three independent contexts. In the first chapter, we identify cell-type specific gene regulatory programs in the developing retina and nominate regulatory elements that expand our knowledge of retinal biology. In the second chapter, TF-dependent transcriptional profiling reveals key players for inducing proliferation and insulin secretion in human beta islet cells, and defines a proliferative gene regulatory network. And in the third, TF dependent transcriptional profiling disentangles the functional relationships between atrial fibrillation, inflammation, and fibrosis at the level of gene expression in models of disease and rescue. While each context is unique, the three chapters of this dissertation revolve around the central theme of altered gene regulatory networks and leveraging transcriptional profiling to unveil deep insights on the mechanisms of disease and development.