DOI: 10.5061/dryad.gxd2547n4
title: Data for: Plasmids do not consistently stabilize cooperation across bacteria, but may promote broad pathogen host-range

description: Horizontal gene transfer via plasmids could favour cooperation in
 bacteria, because transfer of a cooperative gene turns non-cooperative
 cheats into cooperators. This hypothesis has received support from
 theoretical, genomic and experimental analyses. In contrast, we show here,
 with a comparative analysis across 51 diverse species, that genes for
 extracellular proteins, which are likely to act as cooperative ‘public
 goods’, were not more likely to be carried on either: (i) plasmids
 compared to chromosomes; or (ii) plasmids that transfer at higher rates.
 Our results were supported by theoretical modelling which showed that
 while horizontal gene transfer can help cooperative genes initially invade
 a population, it has less influence on the longer-term maintenance of
 cooperation. Instead, we found that genes for extracellular proteins were
 more likely to be on plasmids when they coded for pathogenic virulence
 traits, in pathogenic bacteria with a broad host-range.
description: The first dataset 'genome_data' contains information on
 the 1632 genomes analysed in our study. Each row in the data file
 corresponds to a replicon, which is lablled as either
 'chromosome' or 'plasmid' in the
 'replicon_type' column. Each replicon has its own
 'accession_number'. Each genome has one chromosome (except for
 <em>Vibrio parahaemolyticus</em> which has two) and at least
 one plasmid. All replicons from a genome have the same value in the
 columns 'species_name' and 'strain_name', which can be
 used by grouping variables in R. For each replicon, we used PSORTb v3.0 to
 predict the subcellular localisation of every protein. The number of
 proteins in each of the possible localisations (depending on the gram
 stain) is listed in the corresponding columns i.e. extracellular,
 cytoplasmic, periplasmic. Other data analysed in the paper are included in
 this dataset, including the pathogenicity & host-range category of
 the species, MOBsuite mobility predictions for each plasmid, and pangenome
 core & accessory %s from PanX. Also included is data on the number
 of environments each species's 16S rRNA was sequenced in, with a
 column for each of the five habitats and the total number. A '1'
 denotes it was sequenced in that environment, a '0' denotes it
 was not. These occur twice, once for the original data from Garcia-Garcera
 & Rocha (2020), labelled with 'gg' at the beginning, and
 second for our categorisation of species based on the
 literature. The second dataset
 'MP3_protein_predictions' contains MP3 pathogenicity predictions
 for every extracellular protein in all broad and narrow host-range genomes
 (one protein per row). The 'accession_number',
 'strain_name' and 'species_name' columns indicate
 which replicon, genome and species each protein is from. The
 hybrid prediction we used is in the 'Hybrid.Prediction'
 column. Using grouping variables in R means the number of
 'pathogenic' and 'non-pathogenic' proteins can be
 tallied for each replicon, and then combined with the data in
 'genome_data' for analysis.