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

Mattia Zampieri, Balazs Szappanos, Maria Virginia Buchieri, Andrej Trauner, Ilaria Piazza, Paola Picotti, Sébastien Gagneux, Sonia Borrell, Brigitte Gicquel, Joel Lelievre, B. Papp, Uwe Sauer

Research output: Contribution to journalArticle

19 Citations (Scopus)

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.

Original languageEnglish
Article numbereaal3973
JournalScience Translational Medicine
Volume10
Issue number429
DOIs
Publication statusPublished - Feb 21 2018

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Metabolomics
Drug Discovery
Metabolome
Mycobacterium smegmatis
Pharmaceutical Preparations
Trehalose
Proteome
Microbial Drug Resistance
Lipid Metabolism
Mycobacterium tuberculosis
Genome
Anti-Bacterial Agents
Bacteria

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Zampieri, M., Szappanos, B., Buchieri, M. V., Trauner, A., Piazza, I., Picotti, P., ... Sauer, U. (2018). High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds. Science Translational Medicine, 10(429), [eaal3973]. https://doi.org/10.1126/scitranslmed.aal3973

High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds. / Zampieri, Mattia; Szappanos, Balazs; Buchieri, Maria Virginia; Trauner, Andrej; Piazza, Ilaria; Picotti, Paola; Gagneux, Sébastien; Borrell, Sonia; Gicquel, Brigitte; Lelievre, Joel; Papp, B.; Sauer, Uwe.

In: Science Translational Medicine, Vol. 10, No. 429, eaal3973, 21.02.2018.

Research output: Contribution to journalArticle

Zampieri, M, Szappanos, B, Buchieri, MV, Trauner, A, Piazza, I, Picotti, P, Gagneux, S, Borrell, S, Gicquel, B, Lelievre, J, Papp, B & Sauer, U 2018, 'High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds', Science Translational Medicine, vol. 10, no. 429, eaal3973. https://doi.org/10.1126/scitranslmed.aal3973
Zampieri, Mattia ; Szappanos, Balazs ; Buchieri, Maria Virginia ; Trauner, Andrej ; Piazza, Ilaria ; Picotti, Paola ; Gagneux, Sébastien ; Borrell, Sonia ; Gicquel, Brigitte ; Lelievre, Joel ; Papp, B. ; Sauer, Uwe. / High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds. In: Science Translational Medicine. 2018 ; Vol. 10, No. 429.
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