Characterization of methicillin-resistant Staphylococcus aureus through genomics approach

Abstract

AbstractIn the present study, a total of 35 S. aureus isolates collected from two different geographical locations viz., Germany and Hungary were tested for their methicillin-resistant phenotype which revealed a high incidence of methicillin-resistant S. aureus. The quantitative test for biofilm production revealed that 73.3% of isolates were biofilm producers. The isolates were further characterized using a set of biochemical and genotypic methods such as amplification and analysis of S. aureus species-specific sequence and mecA gene. The 33 mecA positive isolates were then characterized by the amplification of SCCmec and pvl toxin genes. Further, based on the biofilm-forming phenotype, 15 isolates were selected and characterized through PCR–RFLP of coa gene, polymorphism of spa gene and amplification of biofilm-associated genes. The dendrogram prepared from the results of both biochemical and genotypic analyses of the 15 isolates showed that except for the isolates SA G5 and SA H29, the rest of the isolates grouped themselves according to their locations. Thus, the two isolates were selected for further characterization through whole-genome sequencing. Comparative genome analysis revealed that SA G5 and SA H29 have 97.20% ANI values with 2344 gene clusters (core-genome) of which 16 genes were related to antibiotic resistance genes and 57 genes encode virulence factors. The highest numbers of singleton genes were found in SA H29 that encodes proteins for virulence, resistance, mobile elements, and lanthionine biosynthesis. The high-resolution phylogenetic trees generated based on shared proteins and SNPs revealed a clear difference between the two strains and can be useful in distinguishing closely related genomes. The present study demonstrated that the whole-genome sequence analysis technique is required to get a better insight into the MRSA strains which would be helpful in improving diagnostic investigations in real-time to improve patient care.