Molecular methods for serovar determination of Salmonella

Abstract

Salmonella is a diverse foodborne pathogen, which has more than 2600 recognized serovars. Classification of Salmonella isolates into serovars is essential for surveillance and epidemiological investigations; however, determination of Salmonella serovars, by traditional serotyping, has some important limitations (e.g. labor intensive, time consuming). To overcome these limitations, multiple methods have been investigated to develop molecular serotyping schemes. Currently, molecular methods to predict Salmonella serovars include (i) molecular subtyping methods (e.g. PFGE, MLST), (ii) classification using serovar-specific genomic markers and (iii) direct methods, which identify genes encoding antigens or biosynthesis of antigens used for serotyping. Here, we reviewed reported methodologies for Salmonella molecular serotyping and determined the “serovar-prediction accuracy”, as the percentage of isolates for which the serovar was correctly classified by a given method. Serovar-prediction accuracy ranged from 0 to 100%, 51 to 100% and 33 to 100% for molecular subtyping, serovar-specific genomic markers and direct methods, respectively. Major limitations of available schemes are errors in predicting closely related serovars (e.g. Typhimurium and 4,5,12:i:-), and polyphyletic serovars (e.g. Newport, Saintpaul). The high diversity of Salmonella serovars represents a considerable challenge for molecular serotyping approaches. With the recent improvement in sequencing technologies, full genome sequencing could be developed into a promising molecular approach to serotype Salmonella.

• Molecular serotyping
• salmonella serovar determination
• Salmonella subtyping

Acknowledgements

The authors would like to express their deepest gratitude to L.D. Rodriguez-Rivera for her critical reading of the manuscript.

Declaration of interest

This project was supported by USDA-National Integrated Food safety initiative grant 2008-51110-04333 as well as USDA-NIFA Special Research Grants 2009-34459-19750 and 2010-34459-20756. The National Natural Science Foundation of China (NSFC 31000779) supported C. Shi.

Supplementary material available online Supplementary Tables 1 and 2