FisMat2017 - Submission - View

Abstract's title: The metabolic landscape of antibiotic modes of action
Submitting author: Mattia Zampieri
Affiliation: ETH Zurich
Affiliation Address: Auguste-Piccard-Hof 1
Country: Switzerland
Oral presentation/Poster (Author's request): Oral presentation
Other authors and affiliations: Authors: Mattia Zampieri * 1 ¥, Balazs Szappanos* 1,2, Maria Virginia Buchieri * 3, Andrej Trauner 4,5, Ilaria Piazza 6, Paola Picotti 6, Sébastien Gagneux 4,5 , Sonia Borrell 4,5 , Brigitte Gicquel 3, Joel Lelievre 7, Balazs Papp 2 and Uwe Sauer1. Affiliations: 1 Institute of Molecular Systems Biology, ETH Zürich, Switzerland. 2 Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary. 3 Mycobacterial Genetics Unit, Institute Pasteur, 25-28 Rue du Docteur Roux, 75015 Paris, France. 4 Swiss Tropical and Public Health Institute (Swiss TPH), Basel, Switzerland. 5 University of Basel, Basel, Switzerland. 6 Institute of Biochemistry, Department of Biology, ETH Zurich, Zurich, Switzerland. 7 Disease of the Developing World, GlaxoSmithKline, Severo Ochoa, Tres Cantos, Madrid 28760, Spain.

Despite rapid technological progress, the discovery of novel antibiotics has been stalled for the past 50 years. To combat the growing burden of antibiotic resistance, innovative drug discovery paradigms are required to improve and expedite the antibiotic discovery process. A crucial bottleneck in drug discovery is the identification of compounds’ Mode of Action (MoA). To address this problem we developed a rapid and systematic metabolome profiling strategy to classify the MoA of bioactive compounds. In contrast to existing methods based on phenotypic drug profiling, mostly on the basis of growth assays, we exploit here the intracellular response of about 1000 metabolites as a truly multiparametric readout of the cellular response. The specific advance over existing metabolic platforms is a faster throughput of 1-2 orders of magnitude, allowing our combined MS-based metabolomics and computational workflow to scale with the size of typical compound libraries. I will present how an entropy based measure of similarity can be used to predict antibiotic MoAs from large compendia of metabolome profiles, and the results obtained from analyzing an open access set of ~200 novel anti-tuberculosis compounds with unknown MoAs.