Population structure analysis of Salmonella serovar Muenchen to redefine geno-serotyping using genome indexing approaches

Accurate identification of <i>Salmonella</i> serovars for source attribution in foodborne illness outbreaks. Traditional serotyping, which relies on antigenic properties, continues to serve as gold standard; however, advances in whole-genome sequencing (WGS) have enabled to the development of in-silico serotyping tools such as SeqSero2 and <i>Salmonella In Silico</i> Typing Resource (SISTR). Genome-indexing methods, such as bettercallsal, integrate DNA sketching and genome proximity analysis, have emerged as a promising tool for improving serovar resolution. This study examines the performance of DNA sketching-based serotyping in conjunction with established in-silico methods, focusing especially on <i>Salmonella</i> Muenchen, a polyphyletic serovar that ranks among the top 20 serovars linked with human infections in the United States. In this study, SeqSero2 was employed for antigen-based serotyping, SISTR for core genome Multi-locus Sequence Typing (cgMLST)-based phylogenetic clustering, pangenome analysis using PIRATE for microevolutionary insights, and bettercallsal for genome-indexing-based serovar calls. The results demonstrate that bettercallsal, leveraging the National Centre for Biotechnology Information (NCBI) Pathogen Detection database, enhances serovar resolution by incorporating genome proximity calls. The integration of SeqSero2 with bettercallsal yields complementary insights, maintaining historical serotyping nomenclature while enhancing serovar classification. This dual-tool strategy improves the discrimination of genomically distinct but antigenically similar serovars, therefore addressing limitations of traditional and molecular serotyping. Overall, integrating genome indexing through DNA sketching with validated in-silico serotyping tools establishes a robust framework for pathogen characterization. In this study, the tool is specifically applied for <i>Salmonella</i> serovar characterization. This methodology enhances the source attribution accuracy in outbreak investigations and establishes a framework for updating serovar classification in the era of genomic epidemiology.