BME PhD Dissertation Defense - Paul Iazzetti

Starts:
1:00 pm on Friday, August 22, 2014
Location:
44 Cummington Mall, Room 203
Title: "High-Throughput Binding Characterization of Bacterial Transcription Factors"

Committee:
James Galagan, Ph.D. (Advisor)
Associate Professor of Biomedical Engineering and Microbiology
Igor Kramnik, M.D., Ph.D.
Associate Professor of Medicine
Ahmad Khalil, Ph.D. (Chair)
Assistant Professor of Biomedical Engineering
Wilson Wong, Ph.D.
Assistant Professor of Biomedical Engineering

Abstract:
Tuberculosis (TB) is a global pandemic responsible for the deaths of 1.5 million people annually. A third of the world’s population is thought to harbor the latent form of the disease, and a disproportionate majority of TB cases are reported in Asia and Africa where poor infrastructure impedes proper treatment. Non-adherence to drug regimens has helped foster the rise of multi drug-resistant tuberculosis, with implications for those in the developed world. Treatment of the disease is hindered by an inadequate understanding of its causative agent, the pathogen Mycobacterium tuberculosis (MTB). Decreases in the cost of gene sequencing have heralded a new era of genomic technologies such as chromatin immunoprecipitation sequencing (ChIP-Seq), which can generate comprehensive in-vivo genomic binding data for transcription factors and help elucidate the workings of bacterial regulatory networks.

In this work we explore the in vitro binding behavior of MTB transcription factors important to disease pathogenesis using electrophoretic mobility shift assays (EMSAs) and High-Throughput Sequencing – Fluorescent Ligand Interaction Profiling (HiTS-FLIP). We compare this data to high-throughput in vivo binding data generated by ChIP-Seq to assessing binding patterns across in vitro and in vivo conditions, and demonstrate the use of HiTS-FLIP as a powerful complement to ChIP-Seq for accurately characterizing transcription factor binding affinity. The results of this work lay the foundation for an integrated experimental workflow combining high-throughput in vitro and in vivo transcription factor binding data to better understand transcription factor behavior in vivo.