Laboratory Investigation of Salmonella enterica serovar Poona Outbreak in California: Comparison of Pulsed-Field Gel Electrophoresis (PFGE) and Whole Genome Sequencing (WGS) Results.

INTRODUCTION: Recently, Salmonella enterica serovar Poona caused a multistate outbreak, with 245 out of 907 cases occurring in California. We report a comparison of pulsed-field gel electrophoresis (PFGE) results with whole genome sequencing (WGS) for genotyping of Salmonella Poona isolates.

METHODS: CA Salmonella Poona isolates, collected from July to August 2015, were genotyped by PFGE using XbaI restriction enzyme. WGS was done using Nextera XT library kit with 2x300 bp or 2x250 bp sequencing chemistry on the Illumina MiSeq Sequencer.  Reads were mapped to the de novo assembled serovar Poona draft genome (48 contigs, N50= 223,917) from the outbreak using CLCbio GW 8.0.2. The phylogenetic tree was generated based on hqSNPs calling. Genomes were annotated with CGE and PHAST online tools. In silico MLST was performed using the CGE online tool.

RESULTS: Human (14) and cucumber (2) Salmonella Poona isolates exhibited 3 possibly related PFGE patterns (JL6X01.0018 [predominant], JL6X01.0375, JL6X01.0778).  All isolates that were related by PFGE also clustered together according to the WGS. One isolate with a divergent PFGE pattern (JL6X01.0776) served as an outlier in the phylogenetic analysis and substantially differed from the outbreak clade by WGS. All outbreak isolates were assigned to MLST sequence type 447. The majority of the outbreak-related isolates possessed the same set of Salmonella Pathogenicity Islands with few variations. One outbreak isolate was sequenced and analyzed independently by CDC and CDPH laboratories; there was 0 SNP difference in results. Additional two isolates were sequenced by CDC and the raw data was processed through CDPH and CDC analysis pipelines. Both data analysis pipelines also generated concordant results.  Discussion: PFGE and WGS results for the recent CA Salmonella enterica serovar Poona outbreak provided concordant assignment of the isolates to the outbreak cluster. WGS allowed more robust determination of genetic relatedness, provided information regarding MLST-type, pathogenicity genes, and bacteriophage content. WGS data obtained independently at two laboratories showed complete agreement.