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

Abstract

Bacterial "stand-alone" response regulators (RRs) are pivotal to the control of gene transcription in response to changing cytosolic and extracellular microenvironments during infection. The genome of group A Streptococcus (GAS) encodes more than 30 stand-alone RRs that orchestrate the expression of virulence factors involved in infecting multiple tissues, so causing an array of potentially lethal human diseases. Here, we analysed the molecular epidemiology and biological associations in the coding sequences (CDSs) and upstream intergenic regions (IGRs) of 35 stand-alone RRs from a collection of global GAS genomes. Of the 944 genomes analysed, 97% encoded 32 or more of the 35 tested RRs. The length of RR CDSs ranged from 297 to 1587 nucleotides with an average nucleotide diversity (π) of 0.012, while the IGRs ranged from 51 to 666 nucleotides with average π of 0.017. We present new evidence of recombination in multiple RRs including mga, leading to mga-2 switching, emm-switching and emm-like gene chimerization, and the first instance of an isolate that encodes both mga-1 and mga-2. Recombination was also evident in rofA/nra and msmR loci with 15 emm-types represented in multiple FCT (fibronectin-binding, collagen-binding, T-antigen)-types, including novel emm-type/FCT-type pairings. Strong associations were observed between concatenated RR allele types, and emm-type, MLST-type, core genome phylogroup, and country of sampling. No strong associations were observed between individual loci and disease outcome. We propose that 11 RRs may form part of future refinement of GAS typing systems that reflect core genome evolutionary associations. This subgenomic analysis revealed allelic traits that were informative to the biological function, GAS strain definition, and regional outbreak detection.

Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models

“… 13 . Legend: ANGAS473, an emm82 isolate, inferred to have been the result of an emm-switch event has been previously described 18 , 19 , and was observed in this study to be pgs-negative. …”

“…Furthermore, mga is encoded proximally to emm , and regulates the transcription of emm . Mathematically, we also have previously measured that mga2 had the highest number of unique allele types of 35 of the 53 RRs tested in this dataset 19 . Thus, biologically and mathematically, it is predicable that the importance score of mga2 would rank highly when inferring the emm -type.…”

“…5 ). This exception was emm 82 NGAS473 whose emm -switch event we have described previously 19 .

Figure 5 Phylogeny of group A Streptococcus E3 emm -cluster types.

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“…We have chosen to define the first of the chimeric emm - enn events as ‘contrariwise’ (33181V1T1_01) and the second as ‘likewise’ (K21246) in preference to synonymous and non-synonymous to avoid confusion with established molecular biology nomenclature. Furthermore, typing of these mutants should be emm 137 C (33181V1T1_01) and emm 77 C (K21246) for consistency with the prevailing convention for chimeric emm and M-like genes 11 , 19 , 21 . It should be noted that historically contrariwise events may have been labelled as emm -switches.…”

“…Furthermore, the horizontal transfer of emm between GAS strains has long been known 14 – 16 , and more recently termed ‘ emm -switching’ 17 , 18 . Other studies have also described deletion-fusion (chimeric) events spanning the emm locus 11 , 19 – 21 . This imposes limitations on the use of emm -typing in GAS strain definition.…”