Weakest-link dynamics predict apparent antibiotic interactions in a model cross-feeding community

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Abstract

With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, in which multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen. This study tests whether patterns of antibiotic interactions (synergy, antagonism, or independence/additivity) in monoculture can be used to predict antibiotic interactions in an obligate cross-feeding coculture. Using our previously described weakest-link hypothesis, we hypothesized antibiotic interactions in coculture based on the interactions we observed in monoculture. We then compared our predictions to observed antibiotic interactions in coculture. We tested the interactions between 10 previously identified antibiotic combinations using checkerboard assays. Although our antibiotic combinations interacted differently than predicted in our monocultures, our monoculture results were generally sufficient to predict coculture patterns based solely on the weakest-link hypothesis. These results suggest that combination therapy for cross-feeding multispecies infections may be successfully designed based on antibiotic interaction patterns for their component species.

Original languageEnglish (US)
Article numbere00465
JournalAntimicrobial agents and chemotherapy
Volume64
Issue number11
DOIs
StatePublished - Oct 20 2020

Bibliographical note

Funding Information:
We thank Jeremy Chacón, Lisa Fazzino, Sarah Hammarlund, Brian Smith, and Leno Bernard Smith, Jr., for their insights on this work. This work was supported by a Natural Sciences and Engineering Research Council of Canada postgraduate scholarship (PGSD2-487305-2016 to E.M.A) and by a National Institutes of Health award (1R01-GM121498 to W.R.H.). E.M.A and W.R.H. designed the research; E.M.A performed the research; E.M.A. analyzed the data; and E.M.A. and W.R.H. wrote the paper.

Publisher Copyright:
Copyright © 2020 Adamowicz and Harcombe. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Antibiotic resistance
  • Drug interactions
  • Microbial communities

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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