Structure and Function of Immune Gene Regulatory Networks
Gene regulatory networks (GRNs) are central to almost all biological processes. Our search focusses on understanding the structure and logic of human GRNs with the ultimate goal of devising strategies for therapeutic interventions. Current gaps in our understanding of GRNs include: determining how combinations of transcription factors (TFs) regulate specific target gene expression patterns, identifying mechanisms by which different genes are co-regulated to effect a given biological response, determining how GRNs are rewired in response to environmental cues, and designing strategies to manipulate GRNs to modulate biological outcomes. Cytokines present an ideal model system to study GRNs because cytokines genes are highly regulated at the transcriptional level and because this regulation involves a complex interplay between cell type-specific TFs and TFs activated by different signaling pathways. In the lab, we tackle the challenge of studying the structure and regulatory logic of the cytokine GRN by integrating complementary methods to map protein-DNA interactions, functional perturbations, and phenotypic characterizations. Our long-term goal is to answer central questions in immune regulation such as: through which mechanisms are immune genes regulated during inflammatory processes? How do pathogens perturb immune GRNs? How can we modulate the GRN to more effectively respond to infectious diseases and pathological conditions? Overall, our work will identify general principles and generate a framework to study and manipulate GRNs which will ultimately lead to novel strategies impacting human health.
Functional Characterization of Cancer Noncoding Variants
Cancer driver mutations in noncoding regions are difficult to identify in the context of thousands of passenger (non-functional) mutations. Additionally, in most cases, the mechanisms by which noncoding disease variants affect gene regulation have not been determined. Our goal is to tackle these open problems in Biomedical research by generating an end-to-end pipeline to identify and functionally characterize hundreds of cancer driver noncoding variants. This includes developing computational tools to predict driver variants, validating these variants using massively parallel reporter assays, and determining transcription factor binding differences between alleles by using an enhanced yeast one-hybrid approach we recently developed that overcomes many of the limitations associated with chromatin immunoprecipitation and motif binding predictions. These studies will provide mechanistic insights into how noncoding variants alter gene expression in cancer, and may provide personalized therapeutic options.