Usefulness of High-Quality Core Genome Single-Nucleotide Variant Analysis for Subtyping the Highly Clonal and the Most Prevalent Salmonella enterica Serovar Heidelberg Clone in the Context of Outbreak Investigations

Nontyphoidal Salmonella enterica strains are important bacterial agents of salmonellosis in humans and animals (1) and represent up to 125,000 cases annually of foodborne gastroenteric disease arising from sporadic and outbreak events in Canada (2). More than 2,500 Salmonella enterica serovars have been described, but only a few have been associated with cases of human illness (3, 4). Salmonella Heidelberg ranks third and fourth among serovars causing human illness in Canada (5) and the United States (6), respectively, and is commonly detected in retail meat samples and food animals. While the majority of Salmonella infections are mild and self-limiting, S. Heidelberg can cause more severe diseases, including septicemia, myocarditis, extraintestinal infections, and death (7, 8).

Pulsed-field gel electrophoresis (PFGE) is the gold standard method used by Canadian public health laboratories for the molecular typing of S. Heidelberg, following standardized procedures set out by the PulseNet Canada guidelines. A well-recognized limitation of this classic typing method is that strains bearing highly common PFGE patterns occasionally render PFGE ineffective at detecting foodborne outbreaks from background sporadic cases, thus limiting the strength of laboratory evidence to support case linkages. Specifically, the S. Heidelberg PFGE pattern SHEXAI.0001/SHEBNI.0001 is the most common in all of North America.

In the province of Quebec (QC) and according to the Laboratoire de santé publique du Québec (LSPQ) surveillance program, S. Heidelberg was the second most prevalent serovar recovered from clinical cases between 2005 and 2014. PFGE analysis alone revealed little genetic diversity among S. Heidelberg isolates. Between 2004 and 2014, 70% of QC isolates exhibited pulsotype 2 (SHXAI.0001/SHBNI.0001). Approximately 23% of the isolates were isolated from blood, supporting the increased invasive rate associated with this serovar, compared to 7% for S. enterica serovar Enteritidis and 5% for S. enterica serovar Typhimurium.

With decreasing costs and increasing feasibility, whole-genome sequencing (WGS) technology has emerged as an attractive method for the real-time investigation of bacterial foodborne outbreaks and for routine surveillance purposes (9–11). The increasing readiness of WGS technology combined with the widely hypothesized and previously reported high concordance of WGS-based typing approaches with epidemiological data have resulted in the widespread adoption of WGS surveillance and outbreak response by global contemporaries responsible for public health and food safety, and this method is positioned to replace PFGE-based molecular epidemiology of foodborne pathogens (10, 12–14). This revolution in global disease surveillance has created the urgency for Canadian public health laboratories to apply WGS technology to routine public health activities.

Prior to implementation of a new technology that will inform public health and food safety decision-making, it is critical that validation studies be performed to (i) determine if the method indeed demonstrates increased discriminatory power relative to traditional subtyping tools in resolving S. Heidelberg events and (ii) understand how to responsibly interpret the data for public health decisions. Imperative to robust validation, WGS analyses must be applied to outbreak events wherein strong epidemiological evidence allowed the resolution of outbreaks from endemic strains.

In this study, we assessed the usefulness of a high-quality core genome single-nucleotide variant (hqSNV) analysis to distinguish between three epidemiologically defined outbreaks typed by PFGE and sharing the most commonly occurring PFGE pattern (SHEXAI.0001/SHEBNI.0001) of S. Heidelberg cases in North America.

In this study, we undertook the WGS and analysis of three retrospective and epidemiologically characterized S. Heidelberg outbreak events that were indistinguishable by PFGE typing alone to assess whether WGS combined with hqSNV analysis has the discriminatory capacity required to distinguish among these events. S. Heidelberg is a highly clonal serovar in Canada and North America, dominated by the two-enzyme PFGE pattern combination SHXAI.0001/SHBNI.0001 (Canadian PulseNet designation) corresponding to JF6X01.0022/JF6A26.0001 (PulseNet USA designation). This study confirms the high genetic clonality of this serovar and demonstrates that predominance of the PFGE pattern SHXAI.0001/SHBNI.0001 is not attributable to the ubiquitous nature of this pattern but rather to the limited discriminatory power of PFGE typing. During outbreak investigations, factors defining the inclusion and exclusion of isolates from the event are based on a combination of laboratory and epidemiological evidence. Because the three outbreaks occurred as distinguished and delineated sources and events (i.e., restaurants and wedding), the epidemiological linkage of isolates to a specific event was clearly established by epidemiological trace-back investigation. However, because each of the selected outbreak isolates was associated with the most prevalent PFGE pattern encountered in North American clinical cases, the laboratory evidence is considered weak and does not strongly support isolate inclusion or exclusion in an event (20). As a result of these weakened criteria, the combined evidence does not enable source attribution during a foodborne outbreak. The pervasiveness of food distribution in Canada and beyond consequently requires a public health and food safety system that critically relies on laboratory evidence to detect and investigate foodborne disease outbreaks. In other words, outbreaks attributed to food products that are not consumed locally (at a single restaurant or social event, for example) will go undetected, thus preventing strong laboratory evidence to link human cases to each other and to potential sources. Recent studies have demonstrated the strength of WGS subtyping of outbreaks using hqSNV analysis for several foodborne outbreaks or outbreaks attributed to other serovars of Salmonella (4, 10, 12, 14, 21), including the previously problematic clonal S. Enteritidis (13). This study yielded very strong laboratory evidence of genetic relatedness between epidemiologically related isolates where previous conventional laboratory evidence obtained by PFGE and phage type was weak at best. Both WGS and hqSNV analysis clearly delineated each outbreak as a well-separated, distinct event on the maximum-likelihood phylogenetic tree, demonstrating 100% concordance with the epidemiological data. Isolates within an outbreak event were separated by a small number of hqSNVs, varying from 0 to 4 hqSNVs. This result indicates that these isolates are very close genetic matches, and these findings are highly promising for the application of WGS-based methods to future S. Heidelberg surveillance activities and outbreak investigations. In contrast to our findings, Leekitcharoenphon et al. (10) reported slightly increased single-nucleotide polymorphism (SNP) distances separating S. Heidelberg outbreak-related isolates (4 to19 SNPs). The slightly lower SNP distances revealed among S. Heidelberg isolates may be indicative of inherent levels of genetic diversity across the Salmonella serovars, with different serovars consisting of populations demonstrating variable genetic diversity, with no two serovars exactly alike. An additional possibility is that the previously reported findings are more reflective of the SNP calling methods applied to the analysis of the isolates and the high stringency with which hqSNVs are extracted and qualified using the SNVPhyl pipeline, which uses two variant callers to predict SNVs, SAMtools/BCFtools and FreeBayes. Another factor affecting SNP calls is the choice of a reference genome, with the differences in selection of a reference genome impacting the reference mapping and subsequent variant calling stages of the analysis. In this study, SL476, a complete S. Heidelberg genome, was used as the reference, whereas in the study by Leekitcharoenphon et al., isolates were mapped to a less closely related isolate, S. Enteretidis P125109 (10).

In this study, phylogenomic analysis revealed great hqSNV distances between outbreaks occurring at different periods. However, our analysis also revealed that certain background sporadic isolates had closer-than-expected hqSNV distances to outbreak-associated isolates. In these instances, the sporadic isolates were excluded from the event solely on the basis of their isolation outside the defined time frame of the outbreak event. This finding lends support to previous reports that genomic data should be considered in conjunction with epidemiological evidence to determine the inclusion and exclusion of isolates from outreach events (10, 22, 23). This study did not reveal a correlation between the number of different bands observed by PFGE subtyping and the number of hqSNV differences between isolates with different PFGE patterns. Indeed, isolates with indistinguishable PFGE patterns can have high genetic distances, and isolates that would have been routinely considered related in fact have no genetic matches. A consistent correlation between the phage type and the number of hqSNVs was also observed in this study, despite the use of only the core genome, thus confirming the discriminatory power of core hqSNV analysis.

This study demonstrates the increased discriminatory power of a WGS-based method and hqSNV analysis relative to a conventional typing method to distinguish between outbreak-associated isolates. hqSNV analysis enabled source attribution of a foodborne event. Moreover, although PFGE subtyping pointed to a highly clonal genetic population, WGS permitted the unveiling of additional genetic diversity, thus facilitating the resolution of isolates with a matching PFGE pattern into phylogenetically defined and epidemiologically concordant groups. Since our analysis revealed that isolates recovered across geography and time can also be genetic matches, we strongly recommend that WGS-based typing results be used to inform S. Heidelberg outbreak event case definitions along with the consideration of epidemiological evidence. This WGS-based analysis will next be extended to cover increased PFGE diversity and to map the genetic population of S. Heidelberg isolates responsible for human disease across North America.