Improved Identification of Epidemiologically Related Strains of Salmonella enterica by use of a Fusion Algorithm Based on Pulsed-Field Gel Electrophoresis and Multiple-Locus Variable-Number Tandem-Repeat Analysis

Abstract

Pulsed-field gel electrophoresis (PFGE) and multiple-locus variable-number tandem-repeat analysis (MLVA) are used to assess genetic similarity between bacterial strains. There are cases, however, when neither of these methods quantifies genetic variation at a level of resolution that is well suited for studying the molecular epidemiology of bacterial pathogens. To improve estimates based on these methods, we propose a fusion algorithm that combines the information obtained from both PFGE and MLVA assays to assess epidemiological relationships. This involves generating distance matrices for PFGE data (Dice coefficients) and MLVA data (single-step stepwise-mutation model) and modifying the relative distances using the two different data types. We applied the algorithm to a set of Salmonella enterica serovar Typhimurium isolates collected from a wide range of sampling dates, locations, and host species. All three classification methods (PFGE only, MLVA only, and fusion) produced a similar pattern of clustering relative to groupings of common phage types, with the fusion results being slightly better. We then examined a group of serovar Newport isolates collected over a limited geographic and temporal scale and showed that the fusion of PFGE and MLVA data produced the best discrimination of isolates relative to a collection site (farm). Our analysis shows that the fusion of PFGE and MLVA data provides an improved ability to discriminate epidemiologically related isolates but provides only minor improvement in the discrimination of less related isolates.