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arXiv · 1301.5656

Designing antibiotic cycling strategies by determining and understanding local adaptive landscapes

Christiane P. Goulart·Mentar Mahmudi·Kristina A. Crona·Stephen D. Jacobs·Marcelo Kallmann·Barry G. Hall·Devin C. Greene·Miriam Barlow
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Abstract

The evolution of antibiotic resistance among bacteria threatens our continued ability to treat infectious diseases. The need for sustainable strategies to cure bacterial infections has never been greater. So far, all attempts to restore susceptibility after resistance has arisen have been unsuccessful, including restrictions on prescribing [1] and antibiotic cycling [2,3]. Part of the problem may be that those efforts have implemented different classes of unrelated antibiotics, and relied on removal of resistance by random loss of resistance genes from bacterial populations (drift). Here, we show that alternating structurally similar antibiotics can restore susceptibility to antibiotics after resistance has evolved. We found that the resistance phenotypes conferred by variant alleles of the resistance gene encoding the TEM β-lactamase (blaTEM) varied greatly among 15 different β-lactam antibiotics. We captured those differences by characterizing complete adaptive landscapes for the resistance alleles blaTEM-50 and blaTEM-85, each of which differs from its ancestor blaTEM-1 by four mutations. We identified pathways through those landscapes where selection for increased resistance moved in a repeating cycle among a limited set of alleles as antibiotics were alternated. Our results showed that susceptibility to antibiotics can be sustainably renewed by cycling structurally similar antibiotics. We anticipate that these results may provide a conceptual framework for managing antibiotic resistance. This approach may also guide sustainable cycling of the drugs used to treat malaria and HIV.

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Christiane P. Goulart, Mentar Mahmudi, Kristina A. Crona, Stephen D. Jacobs, Marcelo Kallmann, Barry G. Hall, Devin C. Greene, Miriam Barlow. 2013-01-23. Designing antibiotic cycling strategies by determining and understanding local adaptive landscapes. https://doi.org/10.1371/journal.pone.0056040

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