Chemogenomics and orthology‐based design of antibiotic combination therapies. Issue 5 (23rd May 2016)

• Record Type: Journal Article
• Authors: Chandrasekaran, Sriram; Cokol‐Cakmak, Melike; Sahin, Nil; Yilancioglu, Kaan; Kazan, Hilal; Collins, James J; Cokol, Murat
• Is Part Of: Molecular systems biology. Volume 12:Issue 5(2016:May)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-05-23
• Subjects: chemogenomics -- combination therapy -- drug resistance -- Mycobacterium tuberculosis -- Staphylococcus aureus; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.20156777
• Languages: English
• ISSNs: 1744-4292

Abstract

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. Synopsis: Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. INDIGO approach identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics. The analysis reveals a small set of genes in E. coli that are predictive of interaction outcomes and are surprisingly conserved between distant bacterial species. INDIGO can estimate the interaction outcomes in pathogens such as M. tuberculosis and S. aureus, based on the conservation of drug‐interaction‐related genes in E. coli . Novel predictions were experimentally validated in E. coli (66 combinations) and S. aureus (45 combinations). Abstract : Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms.