[Source: PLoS Pathogens, full page: (LINK). Abstract, edited.]
OPEN ACCESS / PEER-REVIEWED / RESEARCH ARTICLE
On the evolutionary ecology of multidrug resistance in bacteria
Sonja Lehtinen , François Blanquart, Marc Lipsitch, Christophe Fraser, with the Maela Pneumococcal Collaboration
Published: May 13, 2019 / DOI: https://doi.org/10.1371/journal.ppat.1007763 / This is an uncorrected proof.
Resistance against different antibiotics appears on the same bacterial strains more often than expected by chance, leading to high frequencies of multidrug resistance. There are multiple explanations for this observation, but these tend to be specific to subsets of antibiotics and/or bacterial species, whereas the trend is pervasive. Here, we consider the question in terms of strain ecology: explaining why resistance to different antibiotics is often seen on the same strain requires an understanding of the competition between strains with different resistance profiles. This work builds on models originally proposed to explain another aspect of strain competition: the stable coexistence of antibiotic sensitivity and resistance observed in a number of bacterial species. We first identify a partial structural similarity in these models: either strain or host population structure stratifies the pathogen population into evolutionarily independent sub-populations and introduces variation in the fitness effect of resistance between these sub-populations, thus creating niches for sensitivity and resistance. We then generalise this unified underlying model to multidrug resistance and show that models with this structure predict high levels of association between resistance to different drugs and high multidrug resistance frequencies. We test predictions from this model in six bacterial datasets and find them to be qualitatively consistent with observed trends. The higher than expected frequencies of multidrug resistance are often interpreted as evidence that these strains are out-competing strains with lower resistance multiplicity. Our work provides an alternative explanation that is compatible with long-term stability in resistance frequencies.
Antibiotic resistance is a serious public health concern, yet the ecology and evolution of drug resistance are not fully understood. This impacts our ability to design effective interventions to combat resistance. From a public health point of view, multidrug resistance is particularly problematic because resistance to different antibiotics is often seen on the same bacterial strains, which leads to high frequencies of multidrug resistance and limits treatment options. This work seeks to explain this trend in terms of strain ecology and the competition between strains with different resistance profiles. Building on recent work exploring why resistant bacteria are not out-competing sensitive bacteria, we show that models originally proposed to explain this observation also predict high multidrug resistance frequencies. These models are therefore a unifying explanation for two pervasive trends in resistance dynamics. In terms of public health, the implication of our results is that new resistances are likeliest to be found on already multidrug resistant strains and that changing patterns of prescription may not be enough to combat multidrug resistance.
Citation: Lehtinen S, Blanquart F, Lipsitch M, Fraser C, with the Maela Pneumococcal Collaboration (2019) On the evolutionary ecology of multidrug resistance in bacteria. PLoS Pathog 15(5): e1007763. https://doi.org/10.1371/journal.ppat.1007763
Editor: Erwin Schurr, McGill University, CANADA
Received: October 30, 2018; Accepted: April 15, 2019; Published: May 13, 2019
Copyright: © 2019 Lehtinen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: SL and CF were funded by MIDAS (U01GM110721-01). FB was funded by Marie Sklodowska-Curie Actions (657768). ML was funded by National Institute Of General Medical Sciences (U54GM088558). FB and CF also received funding from the Li Ka Shing foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: ML has received consulting fees/honoraria from Merck, Pfizer, Affinivax, and Antigen Discovery, Inc and grant support not related to this paper from Pfizer and PATH Vaccine Solutions.
Keywords: Antibiotics; Drugs Resistance.