Foodborne illness caused by Salmonella enterica species, particularly by S. Typhimurium and S. Heidelberg, accounts for 18.5% of salmonellosis annually in the United States . For accurate outbreak tracking and routine disease surveillance, it is critical that we employ rapid, efficient and robust subtyping methodologies. PFGE is the current gold standard for molecular subtyping of Salmonella and other methods include AFLP, MVLA and CRISPR-MVLST.
CRISPR sequence analysis is one of the cheaper and faster methods for Salmonella subtyping . For the majority of isolates analyzed, CRISPR-MVLST could be completed in less than 24 hours, including DNA isolation and analysis. Additionally, by virtue of their nature, sequencing data are more robust and tractable; this type of data is unequivocal and, with regards to inter-laboratory or database use, is highly consistent. They also provide increased downstream utilities that involve analysis of sequence information, such as phylogenetic studies. This approach is also in line with other high-throughput subtyping approaches, including real-time CRISPR analysis  and whole genome sequence analysis [43–47]. Conversely, although protocols exist that allow PFGE to be completed in 24 hours, it can often take 1–3 days, requires skilled personnel, inter-laboratory data analysis can be challenging and the data have no utility beyond subtyping. Given the advancement of whole-genome sequencing technologies, typing methods based on these are in development . While highly discriminatory, limitations to this approach that are not issues with either CRISPR-MVLST or PFGE include the time required for analysis and space required for data storage.
CRISPR spacer analysis alone has been used to analyze several different Salmonella serovars . Fabre and colleagues showed that among 50 isolates of S. Typhimurium and its I,4, ,12:i- variant, combined CRISPR1 and CRISPR2 sequence information is comparable to PFGE (D = 0.88 and 0.87, respectively). Both methods were more discriminatory than phage typing analysis of the same set of isolates. The same study also analyzed spacer content of S. Typhimurium and S. Enteritidis from 10 outbreaks and in all cases CRISPR sequences exhibited high epidemiologic concordance.
A preliminary investigation showed that addition of CRISPR spacer analysis to an MVLST scheme improves discrimination, beyond that provided by either approach independently, in eight out of nine of the most common illness-causing Salmonella serovars . We wanted to extend our evaluation of CRISPR-MVLST utility among predominant and clinically relevant Salmonella serovars. To date we have tested and compared CRISPR-MVLST to PFGE on large numbers of S. Enteritidis , S. Newport S, Heidelberg and S. Typhimurium isolates. Among the total 175 isolates analyzed here, we found significantly fewer alleles of fimH and sseL, compared to alleles of either CRISPR locus (Table 2; Figure 2). Given the reduced contribution of the virulence genes to defining STs, their addition may seem superfluous within this subtyping scheme. However, in this data set, fimH alleles define two STs, HST13 and TST20 and sseL alleles define five STs, TST16, TST19, TST23, TST29 and TST36. This further supports earlier findings showing that addition of MVLST to a CRISPR-based subtyping scheme increases discrimination in S. Enteritidis  as well as among a broad set of Salmonella enterica serovars .
Though the number of isolates for each serovar was similar, the number of STs within each serovar is surprisingly disparate: among 89 S. Heidelberg isolates we identified 21 HSTs and in 86 S. Typhimurium isolates, we identified 37 TSTs. This presumably reflects varied levels of clonality in different serovars. Independently of the number of STs defined for either serovar, the CRISPR loci are responsible for the vast majority of alleles: (S. Heidelberg – 83.3% and S. Typhimurium – 80%) (Figure 2). In S. Heidelberg, 50% of the different alleles identified were CRISPR1 alleles. Given that CRISPRs are of one of the more dynamic loci in bacteria [30, 31], this finding is not unexpected.
Although PFGE was more discriminatory than CRISPR-MVLST among 89 S. Heidelberg isolates (D = 0.81 versus 0.69, respectively), a combination of both techniques provided an improved value of 0.92. This represents a 92% probability that two unrelated strains can be separated. JF6X01.0022 is the most common PFGE pattern in PulseNet for S. Heidelberg  and is seen 30–40 times a month by the CDC. In our data set, 42% of the isolates have the JF6X01.0022 pattern and using CRISPR-MVLST, we were able to further separate these into seven distinct CRISPR-MVLST types (Figure 3b and d). Given the frequency at which this PFGE pattern occurs nationally, not all isolates that have this pattern may be associated with a specific outbreak, further enhancing the utility of CRISPR-MVLST as a complement to PFGE analysis. Collectively, these findings in S. Heidelberg show that the JF6X01.0022 pattern is analogous to the JEGX01.0004 pattern in S. Enteritidis, where the latter was observed in 51% of isolates analyzed and was separated into 12 distinct STs . A proposed improvement for discrimination in S. Heidelberg and S. Enteritidis by PFGE is to increase the number of enzymes used for PFGE analysis [50, 51], though the concurrent use of PFGE and CRISPR-MVLST would be much more efficient than this approach.
Regarding S. Heidelberg, our data are similar to that observed in a broad set of S. Enteritidis isolates : both serovars exhibit fewer number of STs identified and both require combining CRISPR-MVLST and PFGE to obtain a sufficient discriminatory power. This presumably reflects similar levels of clonality in S. Heidelberg and S. Enteritidis as compared to more heterogenous serovars such as S. Typhimurium where we observed many more STs present within a similar number of isolates examined.
Our data show that in S. Typhimurium, the discrimination provided by either PFGE or CRISPR-MVLST is similar (0.9486 versus 0.9415, respectively). When CRISPR-MVLST was applied to outbreak isolates, we were able to correctly identify the 20 isolates representing the two outbreaks, showing an extremely good epidemiologic concordance with this typing method. The epidemiologic concordance was better by CRISPR-MVLST than PFGE in identifying isolates from the 2004 bus trip outbreak and both methods had equal epidemiological concordance for the 2009 live poultry outbreak. Regarding the 2004 outbreak, the majority of isolates had the JPXX01.0146 pulsotype. In our initial study, this pulsotype was seen frequently, 16% of all isolates analyzed, and the 14 isolates with this pattern could also be represented by 7 distinct TSTs. Conversely, all isolates from this outbreak have TST59, which is unique and not seen in our initial data set showing that in this instance, CRISPR-MVLST may be a better subtyping approach. In analyzing the 2009 live poultry outbreak, it appears that PFGE is more discriminatory than CRISPR-MVLST, as CRISPR-MVLST also identified two non-outbreak related isolates as TST42. Given the available epidemiological data available, these two isolates do not appear to be associated with the outbreak. The fact that CRISPR-MVLST works better in some instances than others is not surprising and can also occur when other subtyping methods are used. ‘Problematic’ PFGE pulsotypes also exist and is one reason that second generation methods like MLVA and CRISPR-MVLST are being developed [33, 52]. As a recent example, isolates associated with the 2012 S. Typhimurium cantaloupe outbreak, had a common PFGE pattern so additional subtyping by MLVA was performed to correctly define the outbreak strain . That there is a strong association among closely related sequence types and closely related PFGE patterns for both S. Typhimurium (Figure 5) and S. Newport  provides further evidence that CRISPR-MVLST could serve as an appropriate alternative subtyping method.
Beyond the data shown here and in further evaluating the value of CRISPR-MVLST sequence typing, a recent study investigating S. Typhimurium isolates from a variety of animal sources showed an association of CRISPR-MVLST sequence types and resistance to antibiotics . As part of that study, the most frequent TSTs were TST10 and TST42, both of which were found in this current study. TST10 was also the most frequent clinical sequence type seen in this study (16/86 isolates) but only two isolates were TST42.