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

Genomic epidemiology of Streptococcus dysgalactiae subsp. equisimilis strains causing invasive disease in Norway during 2018

Background

Streptococcus dysgalactiae subspecies equisimilis (SDSE) is an emerging global pathogen, yet the epidemiology and population genetics of SDSE species have not been extensively characterized.

Methods

We carried out whole genome sequencing to characterize 274 SDSE isolates causing bloodstream infections obtained through national surveillance program in 2018. We conducted multilocus sequence typing (MLST), emm-typing, core genome phylogeny, as well as investigated key features associated with virulence. Moreover, comparison to SDSE from other geographic regions were performed in order to gain more insight in the evolutionary dynamics in SDSE.

Results

The phylogenetic analysis indicated a substantial diversity of emm-types and sequence types (STs). Briefly, 17 emm-types and 58 STs were identified that formed 10 clonal complexes (CCs). The predominant ST-types were ST20 (20%), ST17 (17%), and ST29 (11%). While CC17 and CC29 clades showed a substantial heterogeneity with well-separated emm-associated subclades, the CC20 clade harboring the stG62647 emm-type was more homogenous and the most prevalent in the present study. Moreover, we observed notable differences in the distribution of clades within Norway, as well as several disseminated CCs and also distinct geographic variations when compared to data from other countries. We also revealed extensive intra-species recombination events involving surface exposed virulence factors, including the emm gene important for phylogenetic profiling.

Conclusion

Recombination events involving the emm as well as other virulence genes in SDSE, are important mechanisms in shaping the genetic variability in the SDSE population, potentially offering selective advantages to certain lineages. The enhanced phylogenetic resolution offered by whole genome sequencing is necessary to identify and delimitate outbreaks, monitor and properly characterize emerging strains, as well as elucidate bacterial population dynamics.

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

Group A Streptococcus is a globally significant human pathogen. The extensive variability of the GAS genome, virulence phenotypes and clinical outcomes, render it an excellent candidate for the application of genotype-phenotype association studies in the era of whole-genome sequencing. We have catalogued the distribution and diversity of the transcription regulators of GAS, and employed phylogenetics, concordance metrics and machine learning (ML) to test for associations. In this review, we communicate the lessons learnt in the context of the recent bacteria genotype-phenotype association studies of others that have utilised both genome-wide association studies (GWAS) and ML. We envisage a promising future for the application GWAS in bacteria genotype-phenotype association studies and foresee the increasing use of ML. However, progress in this field is hindered by several outstanding bottlenecks. These include the shortcomings that are observed when GWAS techniques that have been fine-tuned on human genomes, are applied to bacterial genomes. Furthermore, there is a deficit of easy-to-use end-to-end workflows, and a lag in the collection of detailed phenotype and clinical genomic metadata. We propose a novel quality control protocol for the collection of high-quality GAS virulence phenotype coupled to clinical outcome data. Finally, we incorporate this protocol into a workflow for testing genotype-phenotype associations using ML and ‘linked’ patient-microbe genome sets that better represent the infection event.

PRD-Containing Virulence Regulators (PCVRs) in Pathogenic Bacteria

Bacterial pathogens rely on a complex network of regulatory proteins to adapt to hostile and nutrient-limiting host environments. The phosphoenolpyruvate phosphotransferase system (PTS) is a conserved pathway in bacteria that couples transport of sugars with phosphorylation to monitor host carbohydrate availability. A family of structurally homologous PTS-regulatory-domain-containing virulence regulators (PCVRs) has been recognized in divergent bacterial pathogens, including Streptococcus pyogenes Mga and Bacillus anthracis AtxA. These paradigm PCVRs undergo phosphorylation, potentially via the PTS, which impacts their dimerization and their activity. Recent work with predicted PCVRs from Streptococcus pneumoniae (MgaSpn) and Enterococcus faecalis (MafR) suggest they interact with DNA like nucleoid-associating proteins. Yet, Mga binds to promoter sequences as a homo-dimeric transcription factor, suggesting a bi-modal interaction with DNA. High-resolution crystal structures of 3 PCVRs have validated the domain structure, but also raised additional questions such as how ubiquitous are PCVRs, is PTS-mediated histidine phosphorylation via potential PCVRs widespread, do specific sugars signal through PCVRs, and do PCVRs interact with DNA both as transcription factors and nucleoid-associating proteins? Here, we will review known and putative PCVRs based on key domain and functional characteristics and consider their roles as both transcription factors and possibly chromatin-structuring proteins.

MgaSpn is a negative regulator of capsule and phosphorylcholine biosynthesis and influences the virulence of Streptococcus pneumoniae D39

Global transcriptional regulators are prevalent in gram-positive pathogens. The transcriptional regulators of the Mga/AtxA family regulate target gene expression by directly binding to the promoter regions, that results in the coordinated expression of virulence factors. The spd_1587 gene of Streptococcus pneumoniae strain D39 encodes MgaSpn, which shares sequence similarity with global transcriptional regulators of the Mga/AtxA family. In this study, we demonstrated that MgaSpn regulates the biosynthesis of the capsule and phosphorylcholine, which play key roles in disease severity in S. pneumoniae infections. MgaSpn directly binds to the cps and lic1 promoters and affects the biosynthesis of the capsule and phosphorylcholine. MgaSpn binds to two specific sites on the promoter of cps, one of which contains the −35 box of the promoter, with high affinity. Consistently, low-molecular-weight capsule components were observed in the mgaSpn-null mutant strain. Moreover, we found that phosphorylcholine content was notably increased in the unencapsulated mgaSpn mutant strain. The mgaSpn null mutant caused more severe systemic disease than the parental strain D39. These findings indicate that the pneumococcal MgaSpn protein can inhibit capsule and phosphorylcholine production, thereby affecting the virulence of S. pneumoniae.

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

Group A Streptococcus (GAS) is a globally significant bacterial pathogen. The GAS genotyping gold standard characterises the nucleotide variation of emm, which encodes a surface-exposed protein that is recombinogenic and under immune-based selection pressure. Within a supervised learning methodology, we tested three random forest (RF) algorithms (Guided, Ordinary, and Regularized) and 53 GAS response regulator (RR) allele types to infer six genomic traits (emm-type, emm-subtype, tissue and country of sample, clinical outcomes, and isolate invasiveness). The Guided, Ordinary, and Regularized RF classifiers inferred the emm-type with accuracies of 96.7%, 95.7%, and 95.2%, using ten, three, and four RR alleles in the feature set, respectively. Notably, we inferred the emm-type with 93.7% accuracy using only mga2 and lrp. We demonstrated a utility for inferring emm-subtype (89.9%), country (88.6%), invasiveness (84.7%), but not clinical (56.9%), or tissue (56.4%), which is consistent with the complexity of GAS pathophysiology. We identified a novel cell wall-spanning domain (SF5), and proposed evolutionary pathways depicting the 'contrariwise' and 'likewise' chimeric deletion-fusion of emm and enn. We identified an intermediate strain, which provides evidence of the time-dependent excision of mga regulon genes. Overall, our workflow advances the understanding of the GAS mga regulon and its plasticity.