title: Chemogenomics and orthology-based design of antibiotic combination therapies.
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.
authors.affiliation: Harvard Society of Fellows, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA Broad Institute of MIT and Harvard, Cambridge, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, USA chandrasekaran@fas.harvard.edu murat_cokol@hms.harvard.edu.
authors.forename: Sriram
authors.identifier: 0000-0002-8405-5708
authors.initials: S
authors.lastname: Chandrasekaran
authors.affiliation: Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey.
authors.forename: Melike
authors.initials: M
authors.lastname: Cokol-Cakmak
authors.affiliation: Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey.
authors.forename: Nil
authors.initials: N
authors.lastname: Sahin
authors.affiliation: Department of Molecular Biology and Genetics, Uskudar University, Istanbul, Turkey.
authors.forename: Kaan
authors.initials: K
authors.lastname: Yilancioglu
authors.affiliation: Department of Computer Engineering, Antalya International University, Antalya, Turkey.
authors.forename: Hilal
authors.initials: H
authors.lastname: Kazan
authors.affiliation: Broad Institute of MIT and Harvard, Cambridge, MA, USA Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, USA Department of Biological Engineering, Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA.
authors.forename: James J
authors.initials: JJ
authors.lastname: Collins
authors.affiliation: Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, USA Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA chandrasekaran@fas.harvard.edu murat_cokol@hms.harvard.edu.
authors.forename: Murat
authors.initials: M
authors.lastname: Cokol
mesh_terms: D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D019295:Computational Biology / Q000379:methods*; D062126:Databases, Chemical; D030541:Databases, Genetic; D004347:Drug Interactions; D004359:Drug Therapy, Combination; D004926:Escherichia coli / Q000187:drug effects / Q000235:genetics*; D053263:Gene Regulatory Networks / Q000187:drug effects; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000187:drug effects; D009169:Mycobacterium tuberculosis / Q000187:drug effects / Q000235:genetics*; D013211:Staphylococcus aureus / Q000187:drug effects / Q000235:genetics*
chemical_list: D000900:Anti-Bacterial Agents
keywords: Mycobacterium tuberculosis; Staphylococcus aureus; chemogenomics; combination therapy; drug resistance