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Modeling the Emergence of Antibiotic Resistance in the Environment: an Analytical Solution for the Minimum Selection Concentration.

ABSTRACT: Environmental antibiotic risk management requires an understanding of how subinhibitory antibiotic concentrations contribute to the spread of resistance. We develop a simple model of competition between sensitive and resistant bacterial strains to predict the minimum selection concentration (MSC), the lowest level of antibiotic at which resistant bacteria are selected. We present an analytical solution for the MSC based on the routinely measured MIC, the selection coefficient (sc) that expresses fitness differences between strains, the intrinsic net growth rate, and the shape of the bacterial growth dose-response curve with antibiotic or metal exposure (the Hill coefficient [?]). We calibrated the model by optimizing the Hill coefficient to fit previously reported experimental growth rate difference data. The model fit varied among nine compound-taxon combinations examined but predicted the experimentally observed MSC/MIC ratio well (R2 ? 0.95). The shape of the antibiotic response curve varied among compounds (0.7 ? ? ? 10.5), with the steepest curve being found for the aminoglycosides streptomycin and kanamycin. The model was sensitive to this antibiotic response curve shape and to the sc, indicating the importance of fitness differences between strains for determining the MSC. The MSC can be >1 order of magnitude lower than the MIC, typically by the factor sc? This study provides an initial quantitative depiction and a framework for a research agenda to examine the growing evidence of selection for resistant bacterial communities at low environmental antibiotic concentrations.

SUBMITTER: Greenfield BK

PROVIDER: S-EPMC5826132 | biostudies-other | 2018 Mar

REPOSITORIES: biostudies-other

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Modeling the Emergence of Antibiotic Resistance in the Environment: an Analytical Solution for the Minimum Selection Concentration.

Greenfield Ben K BK Shaked Shanna S Marrs Carl F CF Nelson Patrick P Raxter Ian I Xi Chuanwu C McKone Thomas E TE Jolliet Olivier O

Antimicrobial agents and chemotherapy 20180223 3

Environmental antibiotic risk management requires an understanding of how subinhibitory antibiotic concentrations contribute to the spread of resistance. We develop a simple model of competition between sensitive and resistant bacterial strains to predict the minimum selection concentration (MSC), the lowest level of antibiotic at which resistant bacteria are selected. We present an analytical solution for the MSC based on the routinely measured MIC, the selection coefficient (<i>sc</i>) that ex ...[more]

PMID: 29263062

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Project description:The origin of novel genes and beneficial functions is of fundamental interest in evolutionary biology. New genes can originate from different mechanisms, including horizontal gene transfer, duplication-divergence, and de novo from noncoding DNA sequences. Comparative genomics has generated strong evidence for de novo emergence of genes in various organisms, but experimental demonstration of this process has been limited to localized randomization in preexisting structural scaffolds. This bypasses the basic requirement of de novo gene emergence, i.e., lack of an ancestral gene. We constructed highly diverse plasmid libraries encoding randomly generated open reading frames and expressed them in Escherichia coli to identify short peptides that could confer a beneficial and selectable phenotype in vivo (in a living cell). Selections on antibiotic-containing agar plates resulted in the identification of three peptides that increased aminoglycoside resistance up to 48-fold. Combining genetic and functional analyses, we show that the peptides are highly hydrophobic, and by inserting into the membrane, they reduce membrane potential, decrease aminoglycoside uptake, and thereby confer high-level resistance. This study demonstrates that randomized DNA sequences can encode peptides that confer selective benefits and illustrates how expression of random sequences could spark the origination of new genes. In addition, our results also show that this question can be addressed experimentally by expression of highly diverse sequence libraries and subsequent selection for specific functions, such as resistance to toxic compounds, the ability to rescue auxotrophic/temperature-sensitive mutants, and growth on normally nonused carbon sources, allowing the exploration of many different phenotypes.IMPORTANCEDe novo gene origination from nonfunctional DNA sequences was long assumed to be implausible. However, recent studies have shown that large fractions of genomic noncoding DNA are transcribed and translated, potentially generating new genes. Experimental validation of this process so far has been limited to comparative genomics, in vitro selections, or partial randomizations. Here, we describe selection of novel peptides in vivo using fully random synthetic expression libraries. The peptides confer aminoglycoside resistance by inserting into the bacterial membrane and thereby partly reducing membrane potential and decreasing drug uptake. Our results show that beneficial peptides can be selected from random sequence pools in vivo and support the idea that expression of noncoding sequences could spark the origination of new genes.

Dataset Information

Modeling the Emergence of Antibiotic Resistance in the Environment: an Analytical Solution for the Minimum Selection Concentration.

Publications

Modeling the Emergence of Antibiotic Resistance in the Environment: an Analytical Solution for the Minimum Selection Concentration.

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