Genus-targeted markers for the taxonomic identification and monitoring of coagulase-positive and coagulase-negative Staphylococcus species

Abstract

The Staphylococcus genus comprises multiple pathogenic and opportunistic species that represent a risk to public health. Epidemiological studies require accurate taxonomic classification of isolates with enough resolution to distinguish clonal complexes. Unfortunately, 16 S rRNA molecular analysis and phenotypic characterization cannot distinguish all species and do not offer enough resolution to assess intraspecific diversity. Other approaches, such as Multilocus Sequence Tagging, provide higher resolution; however, they have been developed for Staphylococcus aureus and a few other species. Here, we developed a set of genus-targeted primers using five orthologous genes (pta, tuf, tpi, groEs, and sarA) to identify all Staphylococcus species within the genus. The primers were initially evaluated using 20 strains from the Collection of Microorganisms of Interest in Animal Health from AGROSAVIA (CMISA), and their amplified sequences were compared to a set of 33 Staphylococcus species. This allowed the taxonomic identification of the strains even on close species and the establishment of intraspecies diversity. To enhance the scope and cost-effectiveness of the proposed strategy, we customized the primer sets for an Illumina paired-end amplicon protocol, enabling gene multiplexing. We assessed five genes across 177 strains, generating 880 paired-end libraries from the CMISA. This approach significantly reduced sequencing costs, as all libraries can be efficiently sequenced in a single MiSeq run at a fraction (one-fourth or less) of the cost associated with Sanger sequencing. In summary, this method can be used for precise identification and diversity analysis of Staphylococcus species, offering an advancement over traditional techniques in both resolution and cost-effectiveness.

Keywords: Epidemiology; Molecular typing; Phylogenetics; Protein-coding genes; Staphylococcus.

Fig. 1

Average number of mismatches between the primer sets and the orthologs from the 33 evaluated genomes (Panel A). Assess the relationship between the evolutionary rate and the number of mismatches (Panel B)

Fig. 2

Amplification of the tpi, tuf, pta, groEs, and sarA genes in Staphylococcus strains selected to evaluate the genetic targets

Fig. 3

Molecular Phylogenetic analysis of 20 Staphylococcus strains using concatenated sequences of the five genes. The evolutionary history was inferred using the concatenated genes tuf, pta, groEs, sarA, and tpi sequences. The Maximum Likelihood method is based on the Kimura 2-parameter model. The percentage of trees, only > 50%, in which the associated taxa clustered together is shown next to the branches. All positions containing gaps and missing data were eliminated. There were a total of 1002 positions in the final dataset. Streptococcus pyogenes is the outgroup. In blue is the Staphylococcus coagulase-negative, in yellow is the coagulase-positive, and in red is the coagulase variable response

Fig. 4

Estimates of evolutionary divergence between strains using concatenated gene sequences. The square root of the number of base substitutions per site (P-distance) between strains and reference species is shown. The analysis was conducted using the Kimura 2-parameter model. Evolutionary studies were conducted in MEGA7 (Kumar et al. 2016). In blue is the Staphylococcus coagulase-negative, in yellow is the coagulase-positive, and in red is the coagulase variable response

Fig. 5

Phylogenetic separation of 177 strains with pta, tuf, sarA, and groEs gene in agreement with the proposed by Lammer. A. Clade (CA), B. clade (CB) and Staphylococcus species (spp.), C. clade (CC), D. clade (CD)

Fig. 6

Molecular phylogenetic analysis of 177 isolates using concatenated sequences of the five genes (tuf, pta, groEs, sarA and tpi). The evolutionary history was inferred by using the Maximum Likelihood method and Kimura 2-parameter model. The tree with the highest log likelihood (-39108.15) is shown. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value