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Joint Faculty Recruit Seminar: Emily Miraldi, Ph.D., New York University
January 20, 2016 @ 3:30 pm - 4:30 pm
Joint Faculty Recruit Seminar
DCM&B and Division of Infectious Diseases
Emily Miraldi, Ph.D.
Simons Center for Data Analysis, Simons Foundation
Rich Bonneau Lab, Center for Genomics and Systems Biology, NYU
Dan Littman Lab, Skirball Institute for Biomolecular Medicine, NYU School of Medicine
Title: Molecular- to population-level Models of the Gut Immune-microbial Ecosystem Abstract: The gut microbiome not only contributes to gut-proximal autoimmune diseases (e.g., inflammatory bowel disease) but also systemic immunity, influencing susceptibility to rheumatoid arthritis and multiple sclerosis. In animal models, autoimmune pathogenesis often involves the coordination of multiple gut-resident host cell types and microbial genotypes, highlighting the need for engineering approaches to gain insight into this dynamic, multivariate ecosystem. My research leverages recent breakthroughs in genomics technologies to build both ecological and molecular-level models of the gut ecosystem. At the ecological level, inference of interactions (e.g., mutualistic, competitive) among the gut microbial populations from amplicon-sequencing datasets (e.g., 16S rRNA) is a crucial first step to building an ecological model. In collaboration with Zach Kurtz and Christian Muller, Ph.D., I developed a Gaussian graphical modeling framework SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), which addresses several important statistical challenges inherent to the data. At the molecular level, my goal is transcriptional regulatory network (TRN) inference in the immune cell populations that sense and respond to the gut microbiota. Given the scarcity of many of these populations and the desire to model in a physiological setting, I developed methods to derive candidate transcription factor (TF) – target gene interactions from the recently developed Assay for Transposase Accessibile Chromatin (ATAC)-seq, providing this ChIP-seq-like information from orders of magnitude less sample material. The ATAC-seq “prior” of putative TF- gene interactions, in combination with gene expression measurements, serves as input to our sparse TRN inference method, the Inferelator. I first validated this method in the well-studied context of in vitro Th17 cell differentiation, for which we generated ATAC-seq data and could compare to a published gold-standard network based on TF knockout and ChIP-seq data. I then applied the method to ex vivo RNA-seq and ATAC-seq data from the recently-discovered, gut-resident immune cell types, innate lymphoid cells (ILCs), resulting in TRNs that provide molecular underpinnings to the physiological roles of ILCs in the gut.