Genetic characterization and in silico serotyping of 62 Salmonella enterica isolated from Korean poultry operations.

BACKGROUND

The conventional method of antigen-based serotyping for Salmonella poses challenges due to the necessity of utilizing over 150 antisera. More recently, in silico Salmonella serotyping has emerged as a predictive alternative. The purpose of this study was to predict the serovars of 62 Salmonella enterica strains isolated from Korean poultry operations and their genetic characteristics using whole genome sequencing. The analysis employed diverse methods, including ribosomal, and core genome multi-locus sequence typing (MLST), based on Salmonella In Silico Typing Resource (SISTR). Pangenome, clusters of orthologous groups (COG) analysis, and identification of virulence and antibiotic resistance genes were conducted.

RESULTS

Salmonella enterica subspecies enterica serovars were observed and clustered based on the pangenome and phylogenetic tree: 21 Salmonella Albany (Albany), 13 Salmonella Bareilly (Bareilly), and 28 Salmonella Mbandaka (Mbandaka). The most frequently observed sequence types for the three serovars were ST292 in Albany, ST203 in Bareilly, and ST413 in Mbandaka. 18 antibiotic resistance genes showed varying presences based on the serovars, including Albany (qacEdelta1, tet(D), CARB-3 (blaCARB-3), and dfrA1) and Bareilly (aac(6')-ly). Intriguingly, a mutated gyrA (Ser83 → Phe, serine to phenylalanine) was observed in all 21 Albany strains, whereas Bareilly and Mbandaka carried the wild-type gyrA. Among 130 virulence genes analyzed, 107 were present in all 62 Salmonella strains, with Mbandaka strains exhibiting a higher prevalence of virulence genes related to fimbrial adherence compared to those of Albany and Bareilly.

CONCLUSIONS

The study identified distinct genetic characteristics among the three Salmonella serovars using whole genome sequencing. Albany carried a unique mutation in gyrA, occurring in the quinolone resistance-determining region. Additionally, the virulence gene profile of Mbandaka differed from the other serovars, particularly in fimbrial adherence genes. These findings demonstrate the effectiveness of in silico approaches in predicting Salmonella serovars and highlight genetic differences that may inform strategies for antibiotic resistance and virulence control, such as developing rapid diagnostic tools to detect the AMR (e.g. tet (D), and gyrA) or targeting serovar-specific virulence factors like fimbrial adherence genes in Mbandaka to mitigate pathogenicity.