Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
PLOS GENETICS, Volume 13, issue 2
AbstractRecent 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 forsystems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work.
Skwark , M J , Croucher , N J , Puranen , S , Chewapreecha , C , Pesonen , M , Xu , Y , Turner , P , Harris , S R , Beres , S B , Musser , J M , Parkhill , J , Bentley , S D , Aurell , E & Corander , J 2017 , ' Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis ' , PLoS Genetics , vol. 13 , no. 2 , e1006508 . https://doi.org/10.1371/journal.pgen.1006508