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Insights into an evolutionary strategy leading to antibiotic resistance.

ABSTRACT: Metallo-?-lactamases (MBLs) with activity towards a broad-spectrum of ?-lactam antibiotics have become a major threat to public health, not least due to their ability to rapidly adapt their substrate preference. In this study, the capability of the MBL AIM-1 to evade antibiotic pressure by introducing specific mutations was probed by two alternative methods, i.e. site-saturation mutagenesis (SSM) of active site residues and in vitro evolution. Both approaches demonstrated that a single mutation in AIM-1 can greatly enhance a pathogen's resistance towards broad spectrum antibiotics without significantly compromising the catalytic efficiency of the enzyme. Importantly, the evolution experiments demonstrated that relevant amino acids are not necessarily in close proximity to the catalytic centre of the enzyme. This observation is a powerful demonstration that MBLs have a diverse array of possibilities to adapt to new selection pressures, avenues that cannot easily be predicted from a crystal structure alone.

SUBMITTER: Hou CD

PROVIDER: S-EPMC5225480 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Insights into an evolutionary strategy leading to antibiotic resistance.

Hou Chun-Feng D CD Liu Jian-Wei JW Collyer Charles C Mitić Nataša N Pedroso Marcelo Monteiro MM Schenk Gerhard G Ollis David L DL

Scientific reports 20170111

Metallo-β-lactamases (MBLs) with activity towards a broad-spectrum of β-lactam antibiotics have become a major threat to public health, not least due to their ability to rapidly adapt their substrate preference. In this study, the capability of the MBL AIM-1 to evade antibiotic pressure by introducing specific mutations was probed by two alternative methods, i.e. site-saturation mutagenesis (SSM) of active site residues and in vitro evolution. Both approaches demonstrated that a single mutation ...[more]

PMID: 28074907

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Project description:The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the "morbidostat", a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations-an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution.

Dataset Information

Insights into an evolutionary strategy leading to antibiotic resistance.

Publications

Insights into an evolutionary strategy leading to antibiotic resistance.

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