Data from: Host-parasite coevolution promotes innovation through deformations in fitness landscapes

Data files

Dec 04, 2025 version files 63.24 KB

Competition_assays.xlsx

16.98 KB
Phage_densities_for_coevolutionary_replay_experiment.csv

4.54 KB
README.md

3.21 KB
Simulation_results.xlsx

38.51 KB

Abstract

During the struggle for survival, populations occasionally evolve new functions that give them access to untapped ecological opportunities. Theory suggests that coevolution between species can promote the evolution of such innovations by deforming fitness landscapes in ways that open new adaptive pathways. We directly tested this idea by using high throughput gene editing-phenotyping technology (MAGE-Seq) to measure the fitness landscape of a virus, bacteriophage λ, as it coevolved with its host, the bacterium Escherichia coli. Through computer simulations of λ’s evolution on the empirical fitness landscape, we showed that λ was more likely to evolve to use a new receptor if it experienced a shift in its fitness landscape caused by coevolution. Detailed examination of a single coevolving laboratory population showed that the first mutation λ evolved required the presence of the ancestral host, whereas later steps in λ’s evolution required the shift to a resistant host. When replays of the coevolution experiment were run with an intervention that disrupted coevolution, λ did not evolve to use OmpF. This study provides direct evidence for the role of coevolution in driving evolutionary novelty and provides a quantitative framework for predicting evolution in coevolving ecological communities.

Host-parasite coevolution promotes innovation through deformations in fitness landscapes

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Description of the data and file structure

'Simulation_results.xlsx' contains data for simulation results of frequency of OmpF-use evolution observed in different conditions. This data was used to construct Figure 2 (first sheet in the file) and Figure 2—figure supplement 2 (rest of the sheets). Sheet names indicate the simulation parameters—for example, “N, T by 2”, “N by 10”, etc., where N is the population size and T is the number of generations over which lambda was evolved. Additional labels in the sheet names refer to specific simulation setups:
- Regular: simulation started with ancestral host and shifted to malT- host
- malT to malT: landscape shifts between two malT- host landscapes
- anc to anc: landscape shifts between two ancestral host landscapes
- Common genotypes: simulations started using only the genotypes for which fitness was measured on both landscapes
  
  In all sheets, each run number represents a batch of 30 simulation runs. The last three columns—'Average', 'Std Dev', and '95% Confidence'—correspond, respectively, to the mean of 10 runs for a given host-switch day, the standard deviation of those runs, and the 95% confidence interval. The confidence interval is computed using the Student’s t-distribution based on the standard deviation and sample size.
'Competition_assays.xlsx' contains data for competition assays run between different lambda genotypes from the lineage that evolved OmpF function (WT-A-ABC-ABCDE) against different host types--ancestor and malT-. This data was used to construct Figure 3 and Figure 3—figure supplement 1. Each competition assay was conducted using three independent experimental replicates. The selection rate was calculated as the difference in growth rates between the competing genotypes, where the growth rate is defined as the logarithm of the ratio of final to initial phage density.
'Phage_densities_for_coevolutionary_replay_experiment.csv' contains phage densities data (in pfu/ml) for the coevolutionary replay experiment with two different types of hosts for Figure 4—figure supplement 2. The column with 'R' prefix corresponds to phage densities in coevolution with ancestor host, and the column with 'M' prefix corresponds to coevolution with malT- host.

Sharing/Access information

Links to other publicly accessible locations of the data:
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Data was derived from the following sources:

Amplicon sequencing data used to generate fitness landscapes in this study have been deposited in the NCBI Sequence Read Archive under BioProject PRJNA646809

Code/Software

Scripts to reproduce figures and analysis for this paper including simulations can be found at https://github.com/anigupta12/fitness-landscape-paper. The scripts are organized in folders in the repository with descriptive folder names.