Comparative Genomics of Staphylococcus Reveals Determinants of Speciation and Diversification of Antimicrobial Defense

Abstract

The bacterial genus Staphylococcus comprises diverse species with most being described as colonizers of human and animal skin. A relational analysis of features that discriminate its species and contribute to niche adaptation and survival remains to be fully described. In this study, an interspecies, whole-genome comparative analysis of 21 Staphylococcus species was performed based on their orthologues. Three well-defined multi-species groups were identified: group A (including aureus/epidermidis); group B (including saprophyticus/xylosus) and group C (including pseudintermedius/delphini). The machine learning algorithm Random Forest was applied to prioritize orthologs that drive formation of the Staphylococcus species groups A-C. Orthologues driving staphylococcal intrageneric diversity comprised regulatory, metabolic and antimicrobial resistance proteins. Notably, the BraSR (NsaRS) two-component system (TCS) and its associated BraDE transporters that regulate antimicrobial resistance showed limited distribution in the genus and their presence was most closely associated with a subset of Staphylococcus species dominated by those that colonize human skin. Divergence of BraSR and GraSR antimicrobial peptide survival TCS and their associated transporters was observed across the staphylococci, likely reflecting niche specific evolution of these TCS/transporters and their specificities for AMPs. Experimental evolution, with selection for resistance to the lantibiotic nisin, revealed multiple routes to resistance and differences in the selection outcomes of the BraSR-positive species S. hominis and S. aureus. Selection supported a role for GraSR in nisin survival responses of the BraSR-negative species S. saprophyticus. Our study reveals diversification of antimicrobial-sensing TCS across the staphylococci and hints at differential relationships between GraSR and BraSR in those species positive for both TCS.

Limited effects of long-term daily cranberry consumption on the gut microbiome in a placebo-controlled study of women with recurrent urinary tract infections

“…Another 23 OTU41-characteristic genes were associated with metabolism, also representing a significant enrichment compared to the number of genes involved in metabolism across the genome as a whole (35% of OTU41-characteristic genes vs.17% of the entire genome, Fisher’s Exact test p < 10 − 5 ), in line with other bacterial comparative genomics studies showing metabolism as a driver of species diversification [ 60 – 62 ]. OTU41-characteristic metabolic genes included those affecting amino acids ( n = 8), nucleotides ( n = 3), carbohydrates ( n = 6), coenzymes ( n = 4), and lipids ( n = 1) (Additional file 3).…”

Investigating the effect of an identified mutation within a critical site of PAS domain of WalK protein in a vancomycin-intermediate resistant Staphylococcus aureus by computational approaches

“…In the present study, we detected mutation WalK-H364R, which locates in the Zn 2+ -binding residue of the PAS CYTO domain, in a laboratory-derived VISA strain compared to its wild-type strain. This mutation was also previously reported in a S. aureus strain after nisin selection [ 18 ]. In the present study, we attempted to generate a VISA mutant via the selection by vancomycin in a VSSA (vancomycin susceptible S. aureus ) strain.…”

Interactions between staphylococcal enterotoxins A and D and superantigen-like proteins 1 and 5 for predicting methicillin and multidrug resistance profiles among Staphylococcus aureus ocular isolates

“…Random Forest [28] model was applied for the prediction of outcome, a modern machine learning technique that permits exploration of nonlinear, complex interrelationships. It has been utilized to explore a large number of predictors and identify replicable sets of risk factors [35][36][37][38]. The Random Forest technology is related to recursive partitioning and classification tree analyses, wherein the variables that are most related to an outcome of interest are first optionally split to improve prediction, followed by more and more splits to create a tree.…”