Nontyphoidal Salmonella (NTS) enterica serovars are the most important causes of bacterial gastroenteritis. Among the NTS serovars, Heidelberg is ranked as the second and third most frequent serovar recovered from clinical cases in Québec and Canada respectively [1]. In Québec between 2004 and 2014, 23% of S. Heidelberg clinical isolates were from blood specimens, compared to 7% for S. enterica serovar Enteritidis and 5% for S. enterica serovar Typhimurium, suggesting an increased capacity of this serovar to cause invasive systemic disease [2].

Pulsefield gel electrophoresis (PFGE) has been the gold standard method used by PulseNet Canada (PNC) since the 1990s for the molecular typing of Salmonella during outbreak investigations. However, a major drawback with the use of PFGE in outbreak investigation is the low resolution power of this technique that is further exacerbated when applied to S. Heidelberg typing owing to the extremely low genetic diversity of this serovar. For example, 70% of S. Heidelberg isolated in Québec belonged to pulsovar 2 [2].

Whole genome sequence (WGS) based methods owing to their growing availability and high genomic resolution are rapidly replacing traditional typing methods such as PFGE within major public health laboratories including PNC [3]. Two popular methods that are increasingly applied in the field of bacterial genomic epidemiology are: the gene-by-gene methods which is basically an extension of the 7 gene MLST typing technique to encompass the entire genome (whole genome MLST, wgMLST) or just the core genome (core genome MLST, cgMLST) [4, 5] and the single nucleotide variant (SNV) methods which identifies single nucleotide variants by comparing a population of target genomes against a reference [6, 7]. We recently found that the cgSNV method provided superior discriminatory power than traditional methods during outbreak investigations involving Salmonella Heidelberg [2].

The choice of the reference genome has been previously proposed as a potential consideration affecting core genome SNV (cgSNV)–based analysis and outcomes. For example, choosing a distantly related strain as a source of reference may tend to cluster isolates that are otherwise genetically distant. Another concern with the choice of reference genome is the sequencing status of the reference genome. It is generally perceived that high-quality complete genomes are preferred to ensure accurate and epidemiologically concordant phylogenetic analysis and outbreak investigation. These concerns were not addressed in our previous work on the cgSNV method [2]. Here using draft de novo assembled and completely sequenced or closed genomes of S. Heidelberg as well as a distantly related genome such as S. Dublin as references, we assessed the ability of the cgSNV methodology to differentiate amongst 145 S. Heidelberg strains involved in four distinct outbreaks and sporadic cases of salmonellosis in Québec.

In this study we assessed the impact of the choice of the reference genome on the resolution clustering of S. Heidelberg outbreak and sporadic isolates using the cgSNV methodology. Our results revealed that using a draft or completely sequenced S. Heidelberg genomes as references did not affect the ability of the cgSNV method to distinguish between four epidemiologically well characterized S. Heidelberg outbreak isolates and to separate these isolates from sporadic or background strains. In fact, all outbreak and sporadic isolates were clustered on distinct branches (Fig 1A and 1B). On the contrary, using S. Dublin as reference choice resulted in a tree with less resolution (Fig 2). Sporadic isolates clustered together with outbreak 1 isolates and in addition, the genetic distances observed within outbreak as well as between outbreak and sporadic isolates was overall reduced when S. Dublin was used as reference choice as opposed to S. Heidelberg reference genomes (Figs 1 and 2, Table 5). This finding is in agreement with a recent study on Salmonella which found that using a distantly related genome as a choice of reference failed to cluster S. Enteritidis outbreak strains concordantly [18]. These observations emphasize the need to choose an appropriate reference genome during laboratory investigations of foodborne outbreaks involving reference based methods.

The loss in resolution observed in this study can be due to the following reasons: Firstly, the SNVPhyl pipeline uses only core genome SNVs to build the phylogeny implying that a reference genome with high similarity with the isolates under investigation would have a larger core genome from which more SNVs can be produced to build a phylogeny as opposed to a dissimilar reference such as S. Dublin. However, the smallest core genome among all the reference genomes used in this study was equivalent to 84.76% of the total genome. Secondly, another issue to consider is that as the reference genome grows more distant from the sequences under analysis, more variation would be observed between the core regions of the reference genome and all other sequences leading to a long branch separating the reference genome from the rest of the other isolates. This was indeed what we observed using S. Dublin as reference (Fig 2) since the majority of the 18155 positions used to construct the phylogeny were indeed variations between the reference (17666 positions) and the other isolates.

The RF values for the trees constructed using S. Heidelberg draft genome and its corresponding closed genome equivalent was zero with the exception of the reference genome pair ID134609 (tree 7 vs. tree 8) whereby the RF topological distance was 24. This difference could be attributed to misidentification of SNVs linked to the presence of repetitive regions in the draft genome that were not properly detected. By nature, draft genomes are not entirely genomically accurate and for this reason, it is possible that the SPAdes assembly for this isolate may have collapsed repetitive regions larger than the read/read pair size into a single contig. Aligning reads to repetitive regions is problematic and has been reported to lead to potential misidentification of SNVs [19]. Although we used the software MUMmer to identify and filter the repetitive regions of the reference genome, for a closed genome, this method will be much more successful due to its intact, completely mapped sequence than for a draft genome, which may erroneously possess several copies of unmapped repeat regions. These unmapped repeats can lead to repeat reads mapping to a single contig resulting in the inclusion of additional SNVs and subsequent alignment issues and false SNV calls. Interestingly in this reference pair, 766 sites were used to generate the phylogeny using the draft genome as reference as opposed to 761 sites for the closed genome counterpart. Despite the slight differences in topology between this reference pair as well as in other tree pairs, both the draft and closed genomes were still able to differentiate the outbreak from sporadic isolates in concordance with epidemiological data. In agreement with our observations, a recent study on Listeria monocytogenes also demonstrated that phylogenetic clustering based on SNV analysis using a de novo assembled draft genome selected from within the group was similar to the phylogeny using a closely related closed genome [6].

Despite the increasing accessibility of WGS-based technologies and decreasing costs, the implementation of WGS for routine surveillance and outbreak investigation may still present many challenges as sequencing a genome to completion remains a costly and time-consuming endeavour and for many public-health laboratories, this is not a viable option. [20]. The results of our study indicates that draft genomes can be relied upon as a suitable reference choice during laboratory investigations of foodborne outbreaks using the cgSNVphyl pipeline. In fact, a recent study evaluated the quality score of 32000 genomes located in public repositories and concluded that most of these genomes were of sufficient quality to perform analysis on and only 10% of draft genomes were of poor quality and unsuitable for downstream analysis [21]. In conclusion, our results provide strong evidence that the choice of the reference genome does not impact the ability of the cgSNV methodology to distinguish between S. Heidelberg isolates involved in foodborne outbreaks. Our results also demonstrate that using a distantly related genome as reference could lead to a loss in resolution during cgSNV analysis. Although wgMLST was recently recommended as the primary subtyping tool moving forward by PNC and other foodborne surveillance networks [22], it is important to note that the cgSNV approach still remains an important method in the PNC molecular tool box. In fact, this method was recently used by PNC in collaboration with provincial public health laboratories to identify the source of a multi-provincial outbreak of S. Enteritidis [23]. Despite the advantages provided by cg/wgMLST approaches such as curability and standardization, the development and validation of schemas for each organism still remains a daunting task both financially and technically for public health laboratories operating with limited resources. The implementation of the cgSNV methodology described here could be a viable alternative for monitoring S. Heidelberg. Whether this method applies to other Salmonella serovars remains to be determined.