AI Article Synopsis

  • A new RNase H2-dependent PCR (rhPCR) genotyping assay has been developed to identify specific lineages and sub-lineages of Salmonella Heidelberg using single-nucleotide polymorphisms (SNPs).
  • The assay involves a series of 28 reactions targeting 14 specific DNA bases, effectively distinguishing 15 potential genetic groups of SH.
  • It demonstrates accuracy in identifying Salmonella strains, correlates with whole genome sequencing data, and shows promise for practical use in outbreak investigations and tracking sources of infection.

Article Abstract

We report a novel RNase H2-dependent PCR (rhPCR) genotyping assay for a small number of discriminatory single-nucleotide polymorphisms (SNPs) that identify lineages and sub-lineages of the highly clonal pathogen Salmonella Heidelberg (SH). Standard PCR primers targeting numerous SNP locations were initially designed in silico, modified to be RNase H2-compatible, and then optimized by laboratory testing. Optimization often required repeated cycling through variations in primer design, assay conditions, reagent concentrations and selection of alternative SNP targets. The final rhPCR assay uses 28 independent rhPCR reactions to target 14 DNA bases that can distinguish 15 possible lineages and sub-lineages of SH. On evaluation, the assay correctly identified the 12 lineages and sub-lineages represented in a panel of 75 diverse SH strains. Non-specific amplicons were observed in 160 (15.2%) of the 1050 reactions, but due to their low intensity did not compromise assay performance. Furthermore, in silico analysis of 500 closed genomes from 103 Salmonella serovars and laboratory rhPCR testing of five prevalent Salmonella serovars including SH indicated the assay can identify Salmonella isolates as SH, since only SH isolates generated amplicons from all 14 target SNPs. The genotyping results can be fully correlated with whole genome sequencing (WGS) data in silico. This fast and economical assay, which can identify SH isolates and classify them into related or unrelated lineages and sub-lineages, has potential applications in outbreak identification, source attribution and microbial source tracking.

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http://dx.doi.org/10.1016/j.mimet.2018.12.021DOI Listing

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