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

Abstract

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.

Weak Epistasis May Drive Adaptation in Recombining Bacteria

“…While considering genes in isolation is certainly inappropriate for largely clonal bacteria, we find that this is may also be the case for highly recombining bacteria thought to be "effectively sexual" (Smith et al 1993) from very low correlations between SNPs (r 2 , Figure S10). Short term adaptation proceeds rapidly even if the epistasis is weak (N|s i | ≈3-10) and loci are distantly spaced, conditions that would make interactions virtually invisible to selection in eukaryotes (although this depends on the timescale under consideration; Paixão & Barton 2016), allowing bacteria to harness these effects to quickly respond to novel pressures and maintain beneficial allelic combinations in the face of extensive recombination (Cui et al 2015;Skwark et al 2016). Selection may act on even weaker epistatic effects for fitness traits controlled by more than 10 loci (the maximum explored here), since increasing the number of loci with fitness effects increases the total amount of selection on the trait and thus relative amounts of recombination and selection (Neher and Shraiman 2009).…”

DCA for genome-wide epistasis analysis: the statistical genetics perspective

“…The first limitation is that DCA cannot be expected to yield meaningful results when recombination is weak. One example of such an effect was already given in [18] where also data from Streptococcus pyogenes was presented ( Fig. 6 of [18]).…”

“…Whole-genome sequences of carriage isolates from two birth cohorts of infants and their mothers in the Maela refugee camp (Thailand) [37,38] were reported in [39]. This data was filtered for positions (loci) that carry at most two alleles and a moderate amount of gaps, as described previously [18,21]. This procedure results in 3, 145 genotypes each containing 81, 506 loci, where the alleles at each locus can take three values (major, minor, gap).…”

ShinyOmics: collaborative exploration of omics-data

“…Common types of biologically meaningful networks include proteinprotein interaction [42], transcription regulatory [43] metabolic [44] and genetic interaction [45] networks. Depending on the organism, these networks can be manually curated, inferred bioinformatically [46][47][48], or might already be experimentally mapped out. The preloaded metabolic networks were generated by Jensen et al [18].…”