DOI: 10.5061/dryad.ht76hdrhq
title: Data from: Host-parasite coevolution promotes innovation through deformations in fitness landscapes

description: 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.
description: # Title of Dataset Host-parasite coevolution promotes innovation through
 deformations in fitness landscapes [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.ht76hdrhq) ## 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: N/A 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](https://www.ncbi.nlm.nih.gov/bioproject/?term=\(PRJNA646809\)%20AND%20bioproject_sra[filter]%20NOT%20bioproject_gap[filter]) ## Code/Software Scripts to reproduce figures and analysis for this paper including simulations can be found at [https://github.com/anigupta12/fitness-landscape-paper](https://github.com/anigupta12/fitness-landscape-paper). The scripts are organized in folders in the repository with descriptive folder names.