Literature DB >> 29467300

High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds.

Mattia Zampieri1, Balazs Szappanos2,3, Maria Virginia Buchieri4, Andrej Trauner5,6, Ilaria Piazza7, Paola Picotti7, Sébastien Gagneux5,6, Sonia Borrell5,6, Brigitte Gicquel4, Joel Lelievre8, Balazs Papp3, Uwe Sauer2.   

Abstract

Rapidly spreading antibiotic resistance and the low discovery rate of new antimicrobial compounds demand more effective strategies for early drug discovery. One bottleneck in the drug discovery pipeline is the identification of the modes of action (MoAs) of new compounds. We have developed a rapid systematic metabolome profiling strategy to classify the MoAs of bioactive compounds. The method predicted MoA-specific metabolic responses in the nonpathogenic bacterium Mycobacterium smegmatis after treatment with 62 reference compounds with known MoAs and different metabolic and nonmetabolic targets. We then analyzed a library of 212 new antimycobacterial compounds with unknown MoAs from a drug discovery effort by the pharmaceutical company GlaxoSmithKline (GSK). More than 70% of these new compounds induced metabolic responses in M. smegmatis indicative of known MoAs, seven of which were experimentally validated. Only 8% (16) of the compounds appeared to target unconventional cellular processes, illustrating the difficulty in discovering new antibiotics with different MoAs among compounds used as monotherapies. For six of the GSK compounds with potentially new MoAs, the metabolome profiles suggested their ability to interfere with trehalose and lipid metabolism. This was supported by whole-genome sequencing of spontaneous drug-resistant mutants of the pathogen Mycobacterium tuberculosis and in vitro compound-proteome interaction analysis for one of these compounds. Our compendium of drug-metabolome profiles can be used to rapidly query the MoAs of uncharacterized antimicrobial compounds and should be a useful resource for the drug discovery community.
Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

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Year:  2018        PMID: 29467300      PMCID: PMC6544516          DOI: 10.1126/scitranslmed.aal3973

Source DB:  PubMed          Journal:  Sci Transl Med        ISSN: 1946-6234            Impact factor:   17.956


  83 in total

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Review 7.  Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research.

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Review 10.  Technologies for High-Throughput Identification of Antibiotic Mechanism of Action.

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