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, 353 (6304), 1147-51

Spatiotemporal Microbial Evolution on Antibiotic Landscapes

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Spatiotemporal Microbial Evolution on Antibiotic Landscapes

Michael Baym et al. Science.

Abstract

A key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front. While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front, we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behind more sensitive lineages. The MEGA-plate provides a versatile platform for studying microbial adaption and directly visualizing evolutionary dynamics.

Figures

Fig. 1
Fig. 1. 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 inwards. (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.
Fig. 2
Fig. 2. 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) The 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. As the intermediate step with no drug puts the highest and lowest concentrations adjacent, it serves as both the highest and lowest intermediate steps (dotted line).
Fig. 3
Fig. 3. Diverse genotypic strategies for adaptation to trimethoprim
(A) The number of observed mutations across individual isolates. Samples with a dnaQ mutation (filled symbols) consistently carried more mutations than those sampled with the wild-type dnaQ allele (‘x’ symbols). Data points are horizontally jittered for clarity. (B) The normalized ratio of non-synonymous 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) The number of distinct mutational events in genes that were mutated at least twice independently. Genes are colored by pathway per EcoCyc (33). Non-synonymous, synonymous and loss of function (including indel and nonsense) are indicated by the fill. Genes that only had one mutation across all samples were combined into the “unique” column. Individual mutations events were inferred through ancestry (movies S1 and S4). Inset: The multi-step MEGA-plate with samples containing the mutation soxR R20C (yellow) tracing mutational events from multiple samples and video.
Fig. 4
Fig. 4. 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 significantly 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 1–3), 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 dramatically higher than where they emerged, but not for the front. (E) Measured CPR MICs of the mutants from (D).

Comment in

  • Visualizing evolution as it happens.
    McNally L, Brown SP. McNally L, et al. Science. 2016 Sep 9;353(6304):1096-7. doi: 10.1126/science.aah5641. Science. 2016. PMID: 27609874 No abstract available.

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