1887

Abstract

Microbial pathogens represent an increasing threat to human health. Although many infections can be successfully treated and cleared, drug resistance is a widespread problem. The existence of subpopulations of ‘tolerant’ cells (where a fraction of the population is able to grow above the population resistance level) may increase the rate of treatment failure; yet, existing methods to measure subpopulation effects are cumbersome. Here we describe , a computational pipeline that analyses photographs of disk diffusion assays to determine the degree of drug susceptibility [the radius of inhibition, (RAD)], and two aspects of subpopulation growth [the fraction of growth (FoG) within the zone of inhibition, (ZOI), and the rate of change in growth from no drug to inhibitory drug concentrations, (SLOPE)]. was used to examine the response of the human fungal pathogen to the antifungal drug fluconazole across different strain backgrounds and growth conditions. Disk diffusion assays performed under Clinical and Laboratory Standards Institute (CLSI) conditions led to more susceptibility and less tolerance than assays performed using rich medium conditions. We also used to quantify the effects of three drugs in combination with fluconazole, finding that all three combinations affected tolerance, with the effect of one drug (doxycycline) being very strain dependent. The three drugs had different effects on susceptibility, with doxycycline generally having no effect, chloroquine generally increasing susceptibility and pyrvinium pamoate generally reducing susceptibility. The ability to simultaneously quantitate different aspects of microbial drug responses will facilitate the study of mechanisms of subpopulation responses in the presence of antimicrobial drugs.

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2016-07-01
2020-04-02
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