Chemogenomics and orthology‐based design of antibiotic combination therapies |
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| Authors: | Sriram Chandrasekaran Melike Cokol‐Cakmak Nil Sahin Kaan Yilancioglu Hilal Kazan James J Collins Murat Cokol |
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| Institution: | 1. Harvard Society of Fellows, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA;2. Broad Institute of MIT and Harvard, Cambridge, MA, USA;3. Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, USA;4. Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey;5. Department of Molecular Biology and Genetics, Uskudar University, Istanbul, Turkey;6. Department of Computer Engineering, Antalya International University, Antalya, Turkey;7. Department of Biological Engineering, Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA;8. Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA;9. Harvard‐MIT Program in Health Sciences and Technology, Cambridge, MA, USA;10. Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, USA;11. Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA |
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| 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. |
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| Keywords: | chemogenomics combination therapy drug resistance
Mycobacterium tuberculosis
Staphylococcus aureus
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