Chemogenomics and orthology‐based design of antibiotic combination therapies
Name
Chandrasekaran-2016-Chemogenomics and or.pdf
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1.57 MB
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Author(s)
Chandrasekaran, Sriram
Cokol‐Cakmak, Melike
Sahin, Nil
Yilancioglu, Kaan
Kazan, Hilal
Collins, James J.
Cokol, Murat
Date Issued
May 2016
Journal
Molecular Systems Biology
Publisher
EMBO Press
Citation
Chandrasekaran, Sriram, Melike Cokol‐Cakmak, Nil Sahin, Kaan Yilancioglu, Hilal Kazan, James J. Collins, and Murat Cokol. "Chemogenomics and orthology‐based design of antibiotic combination therapies." Molecular Systems Biology 12:5 (2016), 872.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Synthetic Biology Center
Harvard University--MIT Division of Health Sciences and Technology
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Creative Commons Attribution 4.0 International License
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DOI of Published Version
http://dx.doi.org/10.15252/msb.20156777
