In silico characterisation of the two-component system regulators of Streptococcus pyogenes

Abstract

Bacteria respond to environmental changes through the co-ordinated regulation of gene expression, often mediated by two-component regulatory systems (TCS). Group A Streptococcus (GAS), a bacterium which infects multiple human body sites and causes multiple diseases, possesses up to 14 TCS. In this study we examined genetic variation in the coding sequences and non-coding DNA upstream of these TCS as a method for evaluating relationships between different GAS emm-types, and potential associations with GAS disease. Twelve of the 14 TCS were present in 90% of the genomes examined. The length of the intergenic regions (IGRs) upstream of TCS coding regions varied from 39 to 345 nucleotides, with an average nucleotide diversity of 0.0064. Overall, IGR allelic variation was generally conserved with an emm-type. Subsequent phylogenetic analysis of concatenated sequences based on all TCS IGR sequences grouped genomes of the same emm-type together. However grouping with emm-pattern and emm-cluster-types was much weaker, suggesting epidemiological and functional properties associated with the latter are not due to evolutionary relatedness of emm-types. All emm5, emm6 and most of the emm18 genomes, all historically considered rheumatogenic emm-types clustered together, suggesting a shared evolutionary history. However emm1, emm3 and several emm18 genomes did not cluster within this group. These latter emm18 isolates were epidemiologically distinct from other emm18 genomes in study, providing evidence for local variation. emm-types associated with invasive disease or nephritogenicity also did not cluster together. Considering the TCS coding sequences (cds), correlation with emm-type was weaker than for the IGRs, and no strong correlation with disease was observed. Deletion of the malate transporter, maeP, was identified that serves as a putative marker for the emm89.0 subtype, which has been implicated in invasive outbreaks. A recombination-related, subclade-forming DNA motif was identified in the putative receiver domain of the Spy1556 response regulator that correlated with throat-associated emm-pattern-type A-C strains.

In silico characterisation of stand-alone response regulators of Streptococcus pyogenes

“…Of the selected RRs, there were approximately twice as many repressors (or putative repressors) than activators. In a previous study we noted that all 14 of the GAS two-component system RRs possess helix-turn-helix (HTH) domains at their C-termini [ 82 ]. By contrast, 27 of the RRs tested had HTH domains at their N-termini, four were at the C-termini ( codY , lrp , msmR , and srv ), two were mid-protein ( adcR and ctsR ), and two lacked known HTH motifs ( vfr and vgl ).…”

Polymorphisms in Regulator of Cov Contribute to the Molecular Pathogenesis of Serotype M28 Group A Streptococcus

“…Accessory proteins interact with TCSs to fine-tune TCS regulatory activity, coordinate gene expression across multiple regulatory networks, and contribute to complex cellular phenotypes and virulence. 4,6e12 Across all M protein serotypes studied to date, GAS has 12 conserved TCSs, 20,90 including the well-studied CovRS TCS. 29e34, 37,91e94 RocA is an accessory protein to the CovRS TCS.…”

Lessons Learnt From Using the Machine Learning Random Forest Algorithm to Predict Virulence in Streptococcus pyogenes

“…We discovered examples of rare recombination of the mga regulon including mga2-switching, emm-switching, and chimeric emm-enn events and were also able to develop evolutionary models to explain them (Buckley et al, 2020;Buckley et al, 2021). Furthermore, we identified the deletion of a transporter gene (maeP) that stands as a biomarker for the invasive emm subtype 89.0 (Buckley et al, 2018). Collectively, these findings were significant because it has been suggested (Lees et al, 2019) that an ability to detect rare genotype anomalies enhances the discovery of rare clinically-relevant phenotypes.…”

“…We recently characterised the distribution and diversity of the nucleotide sequences of the two-component systems (TCSs) and stand-alone transcription regulators (TRs) in 944 GAS genomes, and then explored phenotype associations using phylogeny and concordance metrics (Buckley et al, 2018;Buckley et al, 2020). Subsequently, we applied the ML random forest (RF) algorithm to the allelic variation of the TRs to predict six metadata traits of the genomes (Buckley et al, 2021).…”