Experimental evidence that network topology can accelerate the spread of beneficial mutations
Data files
Sep 22, 2023 version files 677.43 KB
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Instructions_for_reproducing_figures.txt
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Proportions.zip
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R_scripts.zip
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Raw_counts.zip
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README.md
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RKvalues.zip
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Simulations_data_and_code.zip
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Stats_results.zip
Abstract
Whether and how the spatial arrangement of a population influences adaptive evolution has puzzled evolutionary biologists. Theoretical models make conflicting predictions about the probability a beneficial mutation will become fixed in a population for certain topologies like stars, in which “leaf” populations are connected through a central “hub.” To date, these predictions have not been evaluated under realistic experimental conditions. Here, we test the prediction that topology can change the dynamics of fixation both in vitro and in silico by tracking the frequency of a beneficial mutant under positive selection as it spreads through networks of different topologies. Our results provide empirical support that metapopulation topology can increase the likelihood that a beneficial mutation spreads, broadens the conditions under which this phenomenon is thought to occur, and points the way towards using network topology to amplify the effects of weakly favored mutations under directed evolution in industrial applications.
README: Experimental evidence that network topology can accelerate the spread of beneficial mutations
Description of the data and file structure
This dataset was collected from a selection experiment where 4-deme metapopulations of Pseudomonas aeruginosa were grown in rich and complex laboratory media supplemented with a subinhibitory concentration of the antibiotic Ciprofloxacin. In one of the demes (Patch 3), a beneficial mutant of Pseudomonas aeruginosa that has an advantage for growing in the presence of Ciprofloxacin (PA14-gyrA) was inoculated at a low frequency (1:1000 ratio) along with the wild-type bacteria (PA14-LacZ). All the other patches were inoculated with the wild-type bacteria (PA14-LacZ). In the X-Gal supplemented solid growth media, PA14-LacZ colonies appear blue but PA14-gyrA colonies appear white. The increase in the frequency of the beneficial mutant (PA14-gyrA) in the whole metapopulation was measured by counting blue and white colonies and taking the proportion of white colonies in the total number of colonies counted.
All raw data (counts for blue and white colonies) can be found in the subfolders inside the "Raw counts" folder. Unless otherwise specified here, all datasheets contain variables describing the name of the network treatments (STAR/AMP or Well-mixed), the time/day of the evolution experiment, the identity of the biological replicates, blue colonies counted, white colonies counted, and the proportion of white colonies in that replicate metapopulation. The datasheets for the individual subpopulations (in the "Asym Subpopulations" and "Undirected subpopulations" subfolders) contain the variable proportion that is counted for individual subpopulations.
The data file in the subfolder "denovo evolution of LacZ at 20ng CIP" depicts the number of de novo Ciprofloxacin-resistant mutants recovered after evolving Pseudomonas aeruginosa for 10 days at high and low population sizes under the sub-inhibitory concentration of Ciprofloxacin used in the experiment. This is the datasheet that has been used to produce supplementary figure 8. The data file inside the "Competitions at sub-inhibitory Cip" subfolder shows the relative fitness of PA14-gyrA mutant at different subinhibitory concentrations of Ciprofloxacin and has been used to make supplementary figure 7.
The proportion data derived from these raw count datasheets has been extracted into the datasheets inside the "Proportions" folder for downstream analyses (making figures and statistical analyses) with R. This includes data from the simulation that have the same data structure as the experiments. The folders are structured according to the order of appearance of figures in the article. Please consult the "Instructions for reproducing figures" file provided with the dataset to reproduce the results of the article. The keys associating the file names with the experimental treatments are also inserted in each individual folder.
The folder "RKvalues" contains the R and K values derived from the nls fitting to the proportion datasets. These have been used for statistical analyses.
Code for the SANCTUM model can be found inside the "Simulation data and code" folder. The SANCTUM model is written in Python and can be uploaded to Google Colab and run in a web browser without installing dependencies. The instructions for running the simulation are included in the Python file itself. The data output from the SANCTUM model is divided into three folders. First, the "Undirected" folder. Datafiles with the phrase "histlong" in their name show the minimum generation required for the beneficial mutants to reach 50% of the frequency for each network treatment. Datafiles with "simmig" in the name show the proportion of beneficial mutants at each generation for each network treatment. Second, the "Undirected subpops" folder: datafiles starting with the phrase "sim" show the proportion of beneficial mutants in each subpopulation for each network treatment. Files inside the "Asymmetric" folder contain the proportion of the beneficial mutant for each asymmetric STAR/AMP and well-mixed network at different time points during the simulation.
The folder "R scripts" contains all the R codes used to make the figures and perform the statistical analyses More details on each of these files and how to use them are described in the "Instructions for reproducing figures" text file. These files should run in R versions released after 4.0.2.
Finally, the folder "Stats results" contains the data file with the results of all statistical analyses.
Methods
This data was collected from experimentally evolving laboratory populations of P. aeruginosa strain 14 (PA14) under a sub-inhibitory concentration of Ciprofloxacin and a novel agent-based SANCTUM model. The raw counts and proportions of the mutant bacteria PA14-gyrA wild-type non-mutant PA14-LacZ are both available. The proportions have been used to perform all the statistical analyses.