Loss of Function in Escherichia coli exposed to Environmentally Relevant Concentrations of Benzalkonium Chloride

Assessing the risk of resistance associated with biocide exposure commonly involves exposing microorganisms to biocides at concentrations close to the MIC. With the aim of representing exposure to environmental biocide residues, MG1655 was grown for 20 passages in the presence or absence of benzalkonium chloride (BAC) at 100 ng/L and 1000 ng/L (0.0002% and 0.002% of the MIC respectively). BAC susceptibility, planktonic growth rates, motility and biofilm-formation were assessed, and differentially expressed genes determined via RNA-sequencing. Planktonic growth rate and biofilm-formation were significantly reduced (p<0.001) following BAC adaptation, whilst BAC minimum bactericidal concentration increased two-fold. Transcriptomic analysis identified 289 upregulated and 391 downregulated genes after long-term BAC adaptation when compared to the respective control organism passaged in BAC-free-media. When the BAC-adapted bacterium was grown in biocide-free medium, 1052 genes were upregulated and 753 were down regulated. Repeated passage solely in biocide-free medium resulted in 460 upregulated and 476 downregulated genes compared to unexposed bacteria. Long-term exposure to environmentally relevant BAC concentrations increased the expression of genes associated with efflux and reduced gene expression associated with outer-membrane porins, motility and chemotaxis. This was manifested phenotypically through loss-of-function (motility). Repeated passage in a BAC-free-environment resulted in the up-regulation of multiple respiration-associated genes, which was reflected by increased growth rate. In summary, repeated exposure of to BAC residues resulted in significant alterations in global gene expression that were associated with minor decreases in biocide susceptibility, reductions in growth-rate and biofilm-formation, and loss of motility. Exposure to very low concentrations of biocide in the environment is a poorly understood risk factor for antimicrobial resistance. Repeated exposure to trace levels of the biocide BAC resulted in loss of function (motility) and a general reduction in bacterial fitness, but relatively minor decreases in susceptibility. These changes were accompanied by widespread changes in the transcriptome. This demonstrates the importance of including phenotypic characterisation in studies designed to assess the risks of biocide exposure. [Abstract copyright: Copyright © 2018 American Society for Microbiology.

Statistical characterisation of bacterial wild-type MIC value distributions and the determination of epidemiological cut-off values

MIC distribution data were obtained from a variety of international sources, and pooled after selection by a defined criterion. Sixty-seven of these datasets were subjected to a range of statistical goodness-of-fit tests. The log-normal distribution was selected for subsequent modelling. Cumulative counts of MIC distribution data were fitted to the cumulative log-normal distribution using non-linear least squares regression for a range of data subsets from each antibiotic-bacterium combination. Estimated parameters in the regression were the number of isolates in the subset, and (the log(2) values of) the mean and standard deviation. Optimum fits for the cumulative log-normal curve were then used to determine the wild-type MIC range, determined by calculating the MICs associated with the lower and upper 0.1% of the distribution, rounding to the nearest two-fold dilution, and calculating the probabilities of values higher and lower than these values. When plotted logarithmically, histograms of MIC frequencies appeared normal (Gaussian), but standard goodness-of-fit tests showed that the two-fold dilution grouping of MICs fits poorly to a log-normal distribution, whereas non-linear regression gave good fits to population (histogram) log-normal distributions of log(2) MIC frequencies, and even better fits to log-normal cumulative distributions. Optimum fits were found when the difference between the estimated and true number of isolates in the fitted subset was minimal. Sixteen antibiotic-bacterium datasets were fitted using this technique, and the log(2) values of the means and standard deviations were used to determine the 0.1% and 99.9% wild-type cut-off values. When rounded to the nearest two-fold dilution, > or = 98.5% of MIC values fall within the cut-off value range. Non-linear regression fitting to a cumulative log-normal distribution is a novel and effective method for modelling MIC distributions and quantifying wild-type MIC ranges