Evaluation of invA Diversity among Salmonella Species Suggests Why Some Commercially Available Rapid Detection Kits May Fail To Detect Multiple Salmonella Subspecies and Species

摘要

invA is a common molecular target for Salmonella-specific detection methods and is recommended by the U.S. Food and Drug Administration Bacteriological Analytical Manual as a target for PCR confirmation of putative Salmonella isolates. Novel assays designed for the rapid detection of foodborne pathogens are often validated according to guidelines provided by validation schemes, such as the AOAC International or the International Organization for Standardization. However, these validation guidelines allow for flexibility in the validation study experimental design, which may inflate the assay's ability to detect foodborne pathogens, especially for foodborne pathogens such as Salmonella, exhibiting tremendous species diversity with 2,600 confirmed serovars. This study was conducted to (i) describe the sequence diversity of invA, across a diverse set of Salmonella serovars and (ii) evaluate the ability of two commercially available, AOAC International-validated rapid detection assays to detect a diverse collection of Salmonella spp. strains. In silico analyses identified 362 of 2,058 nucleotide sites that were variable among invA sequences from a diverse collection, representing 86 unique serovars spanning all species and subspecies. Not surprisingly, the majority of variable sites (308 of 2,058) occurred in non-Salmonella enterica subsp. enterica strains, including Salmonella bongori and the other S. enterica subspecies. In vitro testing showed that both rapid detection assays, examined here, failed to detect all Salmonella strains at 1 log above the limit of detection, with assay A failing to detect S. enterica subsp. salamae, and assay B failing to detect S. bongori. Both strains were eventually detected at 100,000 times the limit of detection. Taken together, our study highlights the need to include non-subsp. S. enterica strains in the development and validation of rapid detection methods to limit false-negative test results.