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A major agent of foodborne disease is non-typhoidal Salmonella enterica, of which there are greater than 1500 serotypes, conventionally defined by O and H antigen immunoreactivity. Just four or so of these serotypes are responsible for the majority of outbreaks, with predilections for certain foods; specifically, Enteritidis with eggs, Typhimurium with dairy products, Newport with leafy vegetables, and Heidelberg with poultry. In addition to gastroenteritis, S. enterica can cause life-threatening invasive infection, and correspondingly it is the leading cause of hospitalizations and death among foodborne pathogens in the U.S. Reducing these infections is a national goal of Healthy People 2020 (www.healthypeople.gov), but this will require, among other measures, “focused attention on determining the sources of Salmonella infections” (1).
Despite its limitations (particularly cost, lengthy turnaround time, and weak evolutionary correlation), Salmonella serotyping has for decades remained the starting point for epidemiological surveillance and outbreak investigation. It must be supplemented, however, by systems that provide higher resolution, most typically phage typing (for Typhimurium and Enteritidis only), antimicrobial susceptibility testing, and pulsed-field gel electrophoresis (PFGE). The latter has long been the gold standard of Salmonella subtyping, and benefits from an extensive national network and database (www.cdc.gov/pulsenet). With the exception of improved resolution, however, PFGE suffers from the same limitations as serotyping, along with issues of interpretation and portability (2).
Numerous DNA-based alternatives to Salmonella PFGE have been developed and tested, including MLST, MLVA, multiplex PCR, rep-PCR, RAPD, and ribotyping, but the two most promising appear to be the recently introduced whole genome SNP (single nucleotide polymorphism) and CRISPR analyses. The former essentially represents a major extension of MLST, analyzing >1000 rather than 7 loci and ca. 100,000 rather than 100 SNPs, and correspondingly yields maximal strain resolution (e.g., ref. 3). However, whole genome SNP analysis requires major investments in equipment, reagents, informatics, and technical expertise.
There are two Clustered Regularly Interspaced Short Palindromic Repeat regions in S. enterica, CRISPR1 and CRISPR2. Each have from 1 to 61 (median 17) repeats of a conserved 29 base pair sequence, separated by unique 32 base pair spacers. The combined polymorphism in repeat number and spacer sequence makes CRISPR elements an ideal target for sequence-based typing. CRISPR typing systems have been developed and validated for S. enterica by Fabre et al. (4) and Dudley and colleagues (5), and since 1996 have been widely used for “spoligotyping” of Mycobacterium tuberculosis. In some strains, however, the CRISPR regions are excessively long and hence difficult to ampify and sequence.
As demonstrated by phylogenetic analysis (SeCRISPR1 dendrogram, SeCRISPR2 dendrogram), S. enterica CRISPR sequences correlate well with serotype. They also correlate with MLST-based phylogenies, and correctly identified epidemiologically related isolates from documented outbreaks, leading Shariat & Dudley (6) to suggest that “CRISPR sequence analysis could provide a one-shot approach for both serotyping and subtyping with the benefit of much reduced time and expense to public health laboratories.” Towards this goal, MicrobiType offers SeCRISPR1 and SeCRISPR2 sequence typing to assist in your S. enterica surveillance and epidemiological investigations.
For strains with excessively long CRISPRs, MicrobiType offers SeMT1 (Fig. S1 sequence alignment from ref. 7), which we have recently shown in collaboration with USDA researchers to provide reliable sequence-based typing from poultry isolates (7 ). As illustrated in the SeMT1 dendrogram, this new locus provided excellent resolution of Typhimurium strains, while clustering those that are epidemiologically related.
Results are reported in dendrogram and sequence alignment formats, illustrating the relatedness of the submitted isolate to concurrently or previously submitted isolates from your lab, and to representative GenBank database strains.
(1) B.R. Jackson et al., 2013, Emerging Infect. Dis. 19:1239
(2) Wattiau et al., 2011, Appl. Environ. Microbiol. 77:7877
(3) Allard et al., 2012, BMC Genomics 13:32
(4) Fabre et al., 2012, PLoS ONE 7:e36995
(5) Liu et al., 2011, Appl. Environ. Microbiol. 77:1946
(6) Shariat & Dudley, 2014, Appl. Environ. Microbiol. 80:430
(7) Edlind et al., 2016, J Food Prot (in press)
(MicrobiType services are for research/investigational use only.)