Data from: Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis

Recent advances in the scale and diversity of population genomic datasets
for bacteria now provide the potential for genome-wide patterns of
co-evolution to be studied at the resolution of individual bases. Here we
describe a new statistical method, genomeDCA, which uses recent advances
in computational structural biology to identify the polymorphic loci under
the strongest co-evolutionary pressures. We apply genomeDCA to two large
population data sets representing the major human pathogens Streptococcus
pneumoniae (pneumococcus) and Streptococcus pyogenes (group A
Streptococcus). For pneumococcus we identified 5,199 putative epistatic
interactions between 1,936 sites. Over three-quarters of the links were
between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of
which are critical in determining non-susceptibility to beta-lactam
antibiotics. A network-based analysis found these genes were also coupled
to that encoding dihydrofolate reductase, changes to which underlie
trimethoprim resistance. Distinct from these antibiotic resistance genes,
a large network component of 384 protein coding sequences encompassed many
genes critical in basic cellular functions, while another distinct
component included genes associated with virulence. The group A
Streptococcus (GAS) data set population represents a clonal population
with relatively little genetic variation and a high level of linkage
disequilibrium across the genome. Despite this, we were able to pinpoint
two RNA pseudouridine synthases, which were each strongly linked to a
separate set of loci across the chromosome, representing biologically
plausible targets of co-selection. The population genomic analysis method
applied here identifies statistically significantly co-evolving locus
pairs, potentially arising from fitness selection interdependence
reflecting underlying protein-protein interactions, or genes whose product
activities contribute to the same phenotype. This discovery approach
greatly enhances the future potential of epistasis analysis for systems
biology, and can complement genome-wide association studies as a means of
formulating hypotheses for targeted experimental work.