EAPSI: Locating New Therapeutic Targets to Combat Drug Resistance

Erickson Keesha E, Boulder CO

Investigators

Abstract

Drug-resistant microorganisms and cancers result in more than 8 million deaths per year globally. While there has been extensive research into the mechanisms for bacterial or cancer drug resistance, there is no comprehensive method that allows the scientific community to easily search and draw comparisons across existing datasets. This project aims to build such a tool, deemed the Resistome. This project is conducted in collaboration with Prof. Juan Hsueh-Fen at the National Taiwan University, an expert in systems biology with invaluable experience in elucidating complex mechanisms underlying human disease. The Resistome will enable large-scale computational studies that were previously impossible, which will lead to new realizations about the nature of drug-resistance and will promote the eventual development of novel therapeutics that hinder resistance. The initial vision for the database was to enable tracking of resistance-conferring mutations across diverse cell types, which will allow information to be analyzed using automated machine learning techniques. However, it is increasingly evident that transient responses at the gene expression level also promote resistance, without necessarily being reflected at the genome level. Thus, this major aim of this project is to expand the current Resistome database to allow for the incorporation of transcriptome-level data. Continuous collaboration with the Juan lab will have maximum benefit during this time, such that value fields in the database can be properly selected to broadly apply to bacterial and cancer drug resistances. A central database that includes both resistance-conferring mutations and resistance-conferring gene expression changes will support novel and complex computational efforts to identify the fundamental interactions underlying drug resistance. This award under the East Asia and Pacific Summer Institutes program supports summer research by a U.S. graduate student and is jointly funded by NSF and the Ministry of Science and Technology of Taiwan.