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

Padmini Ramachandran^1*Kranti Konganti¹Amanda M. Windsor¹Christopher John Grim¹Abani Kumar Pradhan²

• ¹US Food and Drug Administration Human Foods Program, College Park, United States
• ²University of Maryland, College Park, United States

Precise identification of Salmonella serovars is essential for source attribution in foodborne illness outbreaks. Traditional serotyping, based on antigenic properties, remains the gold standard; however, advances in whole-genome sequencing (WGS) have led to the development of in-silico serotyping tools such as SeqSero2 and Salmonella In Silico Typing Resource (SISTR). More recently, genome-indexing methods like bettercallsal, which integrates DNA sketching and genome proximity analysis, have shown promise for enhanced serovar resolution. This study assesses the effectiveness of DNA sketching-based serotyping combined with established in-silico methods, focusing on Salmonella Muenchen, a polyphyletic serovar among the top 20 serovars associated with human infections in the United States. We applied SeqSero2 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. Our findings demonstrate that bettercallsal, leveraging the National Centre for Biotechnology Information (NCBI) Pathogen Detection database, enhances serovar resolution by incorporating genome proximity calls. The combined use of SeqSero2 and bettercallsal provides complementary insights, preserving historical serotyping nomenclature while refining serovar classification. This dual-tool approach improves the discrimination of genomically distinct but antigenically similar serovars, addressing limitations of traditional and molecular serotyping. The integration of genome indexing through DNA sketching with validated in-silico serotyping tools provides a robust framework for pathogen characterization in general. In this study, we demonstrate its utility specifically for Salmonella serovar characterization. This methodology will enhance the accuracy of source attribution in outbreak investigations and provides a framework for updating serovar classification in the era of genomic epidemiology.