Overview of the INDIGO approach. INDIGO identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics. INDIGO identifies genes that are predictive of interaction outcomes. INDIGO can estimate interaction outcomes in less-studied pathogens such as M. tuberculosis and S. aureus, based on conservation of drug-interaction related genes in E. coli
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. The Collins lab has developed a computational approach called INDIGO that 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 E.coli. Our analysis revealed a core set of genes and pathways (e.g., central metabolism) that are predictive of antibiotic interactions. By identifying 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.