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Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments

Author

Listed:
  • Remy Chait

    (Harvard Medical School
    Institute of Science and Technology)

  • Adam C. Palmer

    (Harvard Medical School)

  • Idan Yelin

    (Faculty of Biology, Technion, Israel Institute of Technology)

  • Roy Kishony

    (Harvard Medical School
    Faculty of Biology, Technion, Israel Institute of Technology)

Abstract

Antibiotic-sensitive and -resistant bacteria coexist in natural environments with low, if detectable, antibiotic concentrations. Except possibly around localized antibiotic sources, where resistance can provide a strong advantage, bacterial fitness is dominated by stresses unaffected by resistance to the antibiotic. How do such mixed and heterogeneous conditions influence the selective advantage or disadvantage of antibiotic resistance? Here we find that sub-inhibitory levels of tetracyclines potentiate selection for or against tetracycline resistance around localized sources of almost any toxin or stress. Furthermore, certain stresses generate alternating rings of selection for and against resistance around a localized source of the antibiotic. In these conditions, localized antibiotic sources, even at high strengths, can actually produce a net selection against resistance to the antibiotic. Our results show that interactions between the effects of an antibiotic and other stresses in inhomogeneous environments can generate pervasive, complex patterns of selection both for and against antibiotic resistance.

Suggested Citation

  • Remy Chait & Adam C. Palmer & Idan Yelin & Roy Kishony, 2016. "Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments," Nature Communications, Nature, vol. 7(1), pages 1-8, April.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms10333
    DOI: 10.1038/ncomms10333
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    Cited by:

    1. Nghe, Philippe & Mulder, Bela M. & Tans, Sander J., 2018. "A graph-based algorithm for the multi-objective optimization of gene regulatory networks," European Journal of Operational Research, Elsevier, vol. 270(2), pages 784-793.

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