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

Summary

This protocol establishes a stratified genomic workflow for Salmonella serotype prediction, utilizing MLST or SeqSero2S for routine surveillance, SISTR with core genome MLST for outbreak detection, and SeqSero2S for rapid screening. The protocol has been validated through a multicenter analysis to enhance surveillance in China.

Abstract

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.

Introduction

Salmonella is among the most significant foodborne pathogens globally, causing substantial morbidity and mortality^1,2. In China, it remains a leading cause of foodborne illnesses, with a limited number of serotypes accounting for the majority of infections³. Accurate serotype identification is crucial for surveillance and outbreak investigations, as serotype diversity is closely correlated with pathogenicity and public health risk. This is well-recognized in common serotypes such as Salmonella enteritidis and Salmonella typhimurium, but some less studied serot....

Protocol

The collection and handling of human fecal samples were conducted under approval from the Ethics Committee of West China Fourth Hospital, Sichuan University (Protocol Number: HXSY-EC-2022093), in accordance with the Helsinki Declaration and Good Clinical Practice. Informed consent was waived in accordance with national public health regulations.

NOTE: All procedures involving live Salmonella cultures and human fecal samples must be conducted in a Biosafety Level 2 (BSL-2) laboratory by personnel trained in standard microbiological practices and biosafety procedures. Wear appropriate personal protective equipment (PPE) at all times,....

Results

The protocol was validated using 315 Salmonella isolates from Southwest China, with 213 isolates (2/3) serving as the training set to compare five WGS-based serotype prediction methods (MLST, SISTR, SeqSero, SeqSero2, and SeqSero2S) against conventional serotyping results, while the remaining 102 isolates (1/3) were used as a validation set to assess MLST, SISTR, and SeqSero2S performance.

Training set analysis (n = 213)
Reproducibility assessment:

Discussion

This large-scale evaluation, encompassing 315 Salmonella isolates from Southwest China, demonstrates the superior reproducibility of WGS-based serotyping methods compared to conventional approaches (84.1% vs. 62.9% coverage, P<0.001). Our findings validate a comprehensive genomic workflow where critical initial steps of sample preparation and DNA quality control (sections 1-3) directly impact downstream reliability. The protocol emphasizes high-quality genome assemblies (preferably >100 kbp) for assembly.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
BioNumerics (version 7.6)	Applied Maths	https://bionumerics.software.informer.com/7.6/	MLST/serotype prediction/phylogenic analysis
CHROMagar Salmonella	Becton, Dickinson and Company	214983	Selective Salmonella chromogenic medium
Fastp (version 0.23.0)	Open-source	https://github.com/OpenGene/fastp	Read quality control
Illumina NovaSeq 6000 Sequencing System	Illumina	20012850	Whole-genome sequencing platform
M220 Focused-ultrasonicator	Covaris	500295	DNA shearing
Magbeads Fast DNA Kit for Soil	MP Biomedicals	MP116560200	Genomic DNA extraction kit
Microsoft Excel 2016	Microsoft	KB5002794	Data management
NEXTFLEX Rapid DNA-Seq Kit	Revvity	50-255-1124	Sequencing library preparation
Qubit 2.0 Fluorometer	Thermo Fisher Scientific	Q32866	DNA quantification
Qubit dsDNA High-Sensitivity Assay Kit	Thermo Fisher Scientific	Q32851	DNA quantification
R (version 4.1.2)	R Foundation	https://www.r-project.org/	Statistical computing
Salmonella Antiserum Kit	Statens Serum Institute	60898	O/H-antigen detection
SeqSero	Open-source	https://github.com/denglab/SeqSero	Serotype prediction
SeqSero2	Open-source	https://github.com/denglab/SeqSero2	Serotype prediction
SeqSero2S	Open-source	https://github.com/denglab/SeqSero2s	Serotype prediction
SISTR	Open-source	https://github.com/phac-nml/sistr_cmd	Serotype prediction
SPAdes (version 3.15.0)	Open-source	https://github.com/ablab/spades	Genome assembly
Swarm Agar	BD Biosciences	211519	Used for serotyping incubation
TBS-380 Fluorometer	Turner BioSystems	P/N 3800-003	DNA quantification
VITEK 2 Compact System	BioMérieux	95061-768	Automated microbial identification system