Spatiotemporal microbial evolution on antibiotic landscapes

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Spatiotemporal microbial evolution on antibiotic landscapes by Michael Baym, Tami D. Lieberman, Eric D. Kelsic, Remy Chait, Rotem Gross, Idan Yelin, and Roy Kishony Science Volume 353(6304):1147-1151 September 9, 2016 Published by AAAS

Fig. 1 An experimental device for studying microbial evolution in a spatially structured environment. An experimental device for studying microbial evolution in a spatially structured environment. (A) Setup of the four-step gradient of trimethoprim (TMP). Antibiotic is added in sections to make an exponential gradient rising inward. (B) The four-step TMP MEGA-plate after 12 days. E. coli appear as white on the black background. The 182 sampled points of clones are indicated by circles, colored by their measured MIC. Lines indicate video-imputed ancestry. (C) Time-lapse images of a section of the MEGA-plate. Repeated mutation and selection can be seen at each step. Images have been aligned and linearly contrast-enhanced but are otherwise unedited. Michael Baym et al. Science 2016;353:1147-1151 Published by AAAS

Fig. 2 Initial adaptation to low drug concentrations facilitates later adaptation to high concentrations. Initial adaptation to low drug concentrations facilitates later adaptation to high concentrations. (A) Frames from a section of the TMP intermediate-step MEGA-plate over time (TMP, movie S4; CPR, movie S5). The first frame showing a mutant in the highest band is indicated by a blue box. (B) Rates of adaptation in the intermediate-step experiments across TMP and CPR, showing the necessity of intermediate adaptation for the evolution of high levels of resistance. Error bars show the appearance times of multiple lineages in the highest concentration. Because the intermediate step with no drug puts the highest and lowest concentrations adjacent, it serves as both the highest and lowest intermediate steps (dashed line). Michael Baym et al. Science 2016;353:1147-1151 Published by AAAS

Fig. 3 Diverse genotypic strategies for adaptation to trimethoprim. Diverse genotypic strategies for adaptation to trimethoprim. (A) Numbers of observed mutations across individual isolates. Samples with a dnaQ mutation (solid symbols) consistently carried more mutations than those sampled with the wild-type dnaQ allele (crosses). Data points are horizontally jittered for clarity. (B) The normalized ratio of nonsynonymous to synonymous substitutions of isolates compared with the ancestor for samples with normal and highly mutated phenotypes. Error bars are the standard deviation of the Bayesian posterior estimate for the binomial parameter. (C) Numbers of distinct mutational events in genes that were mutated at least twice independently. Genes are colored by pathway per EcoCyc (37). Nonsynonymous, synonymous, and loss-of-function mutations (including indel and nonsense) are indicated. Genes that only had one mutation across all samples were combined into the “unique” column. Individual mutation events were inferred through ancestry (movies S1 and S4). Inset: The multistep MEGA-plate with samples containing the mutation soxR R20C (yellow) tracing mutational events from multiple samples and video. Michael Baym et al. Science 2016;353:1147-1151 Published by AAAS

Fig. 4 Compensatory mutations can be spatially trapped. Compensatory mutations can be spatially trapped. (A) Ciprofloxacin experiment still frame with locations of 24 isolates showing a full-fitness mutation (cyan) or yield-deficient mutation followed by a compensatory mutation (purple). (B) Optical density at the marked points in (A) over the course of the experiment. The two example traces (indicated by a square and triangle) correspond to the points marked by the same glyph in (A). (C) Mutants isolated behind the front can have markedly higher resistance than the front at the time it passed the same location. Resistance of the front was measured by the concentration at which front progression stopped; isolate MICs were measured in vitro. (D) The front (marked F) and three compensatory mutants (marked 1 to 3) were sampled at 162 hours, and immediately inoculated ahead of the front as indicated by the arrows. Growth of the moved mutants is evident for the three compensatory mutants, despite being inoculated at a CPR concentration much higher than where they emerged, but not for the front. (E) Measured CPR MICs of the mutants from (D). Michael Baym et al. Science 2016;353:1147-1151 Published by AAAS