Type of Document Dissertation Author Kidane, Yared Habteselassie Author's Email Address firstname.lastname@example.org URN etd-04132012-140041 Title The Landscape of Host Transcriptional Response Programs Commonly Perturbed by Infectious Pathogens: Towards Host-Oriented Broad-Spectrum Drug Degree PhD Department Genetics, Bioinformatics, and Computational Biology Advisory Committee
Advisor Name Title Lawrence, Christopher B. Committee Co-Chair Murali, T. M. Committee Co-Chair Bassaganya-Riera, Josep Committee Member Bevan, David R. Committee Member Melville, Stephen B. Committee Member Keywords
- drug resistance
- host-oriented broad-spectrum drug targets
- infectious diseases
- host transcriptional responses
Date of Defense 2012-03-27 Availability unrestricted AbstractThe threat from infectious diseases dates as far back as prehistoric times. Pathogens continue to pose serious challenges to human health. The emergence and spread of diseases such as HIV/AIDS, Severe Acute Respiratory Syndrome (SARS), avian influenza, and the threats of bioterrorism have made infectious diseases major public health concerns. Despite many successes in the discovery of anti-infective medications, the treatment of infectious diseases faces serious challenges, which include (i) the emergence and reemergence of infectious pathogens, (ii) the ability of pathogens to adapt and develop resistance to drugs, and (ii) a shortage in the development and discovery of new anti-infective drugs.
Host-Oriented Broad-Spectrum (HOBS) treatments have the promising potential to alleviate these problems. The HOBS treatment paradigm focuses on finding drug targets in human host that are simultaneously effective against a wide variety of infectious agents and toxins. In this dissertation, we present a computational approach to predict HOBS treatments by integrative analysis of three types of data, namely, (a) gene expression data representing host responses upon infection by a pathogen, (b) annotations of genes to pre-defined biological pathways and processes, and (iii) genes that are targets of known drugs. Our methods combine gene set-level enrichment with biclustering.
We applied our approach to a compendium of gene expression data sets derived from host cells exposed to bacterial or to fungal pathogens, to functional annotation data from multiple databases, and to drug targets from DrugBank. We present putative host drug targets and drugs with extensive support in the literature for their potential to treat multiple bacterial and fungal infections. These results showcase the potential of our computational approach to predict HOBS drug targets that may be effective against two or more pathogens.
Our study takes a clean-slate approach that promises to yield unsuspected or unknown associations between pathogens and biological processes, and thus discern candidate gene/proteins to be further probed as HOBS targets. Furthermore, by focusing on host responses to pathogens as captured by transcriptional data, our proposed approach stimulates host-oriented drug target identification, which has potential to alleviate the problem of drug resistance.
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