A Stratified Genomic Framework for Salmonella Serotype Prediction: Evaluation of MLST, SeqSero, SeqSero2, SeqSero2S, and SISTR in Southwest China

Accurate serotyping of Salmonella is essential for effective surveillance and outbreak investigation, as serotype diversity directly impacts pathogenicity and public health risk assessment. However, conventional slide agglutination methods are limited by poor reproducibility, labor intensity, and high costs, which hinder their application in high-throughput monitoring programs. To address these limitations, we developed and validated a genomic workflow integrating Multilocus Sequence Typing (MLST), the Salmonella In Silico Typing Resource (SISTR), SeqSero, SeqSero2, and SeqSero2S for serotype prediction using whole-genome sequencing data. This protocol was evaluated through a multicenter analysis of 315 Salmonella isolates collected from food and human sources in Southwest China. The findings of this study demonstrated significantly higher concordance among genomic approaches (up to 100%/99.1%/90.1% in the training set and 100%/97.1%/93.1% in the validation set for SISTR/MLST/SeqSero2S, respectively) compared to traditional serotyping. The workflow includes recommendations for selecting appropriate prediction methods based on surveillance context, emphasizing MLST and SeqSero2S for routine monitoring, SeqSero2S for rapid screening, and SISTR with core genome MLST for outbreak investigations. This approach facilitates the integration of genomic serotyping into public health practice, reducing reliance on traditional serology and improving reproducibility and scalability in Salmonella monitoring programs.