Open AccessChemogenomics and orthology‐based design of antibiotic combination therapies
Author(s) -
Chandrasekaran Sriram,
CokolCakmak Melike,
Sahin Nil,
Yilancioglu Kaan,
Kazan Hilal,
Collins James J,
Cokol Murat
Publication year - 2016
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.15252/msb.20156777
Subject(s) - biology , indigo , computational biology , antibiotics , drug discovery , escherichia coli , mycobacterium tuberculosis , antagonism , gene , microbiology and biotechnology , bioinformatics , tuberculosis , genetics , medicine , art , receptor , pathology , visual arts
Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO , which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli . Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms.