Polyphyly in widespread Salmonella enterica serovars and using genomic proximity to choose the best reference genome for bioinformatics analyses

Salmonella is the most common cause of gastroenteritis in the world. Over the past 5 years, whole-genome analysis has led to the high-resolution characterization of clinical and foodborne Salmonella responsible for typhoid fever, foodborne illness or contamination of the agro-food chain. Whole-genome analyses are simplified by the availability of high-quality, complete genomes for mapping analysis and for calculating the pairwise distance between genomes, but unfortunately some difficulties may still remain. For some serovars, the complete genome is not available, or some serovars are polyphyletic and knowing the serovar alone is not sufficient for choosing the most appropriate reference genome. For these serovars, it is essential to identify the genetically closest complete genome to be able to carry out precise genome analyses. In this study, we explored the genomic proximity of 650 genomes of the 58 Salmonella enterica subsp. enterica serovars most frequently isolated in humans and from the food chain in the United States (US) and in Europe (EU), with a special focus on France. For each serovar, to take into account their genomic diversity, we included all the multilocus sequence type (MLST) profiles represented in EnteroBase with 10 or more genomes (on 19 July 2021). A phylogenetic analysis using both core- and pan-genome approaches was carried out to identify the genomic proximity of all the Salmonella studied and 20 polyphyletic serovars that have not yet been described in the literature. This study determined the genetic proximity between all 58 serovars studied and revealed polyphyletic serovars, their genomic lineages and MLST profiles. Finally, we enhanced the open-access databases with 73 new genomes and produced a list of high-quality complete reference genomes for 48 S. enterica subsp. enterica serovars among the most isolated in the US, EU, and France.

Introduction

For routine disease surveillance activities and outbreak investigations, the use of whole-genome sequencing (WGS) to identify and subtype foodborne bacterial pathogens has replaced traditional slide agglutination methods; likewise, to cluster and associate epidemiological strains, core genome multilocus sequence type (cgMLST) and single nucleotide polymorphism (SNP) analyses have replaced pulsed-field gel electrophoresis (PFGE) and multiple loci VNTR (MLVA) analyses. To meet the needs of real-time surveillance and ensure public health and economic benefits, the analysis of the complete genome is now routine for many reference laboratories around the world. cgMLST and SNP analyses are fast and several user-friendly tools exist for investigations of outbreak clusters (1–4). Nevertheless, although SNP phylogenetic core-genome analysis enables more detailed clustering between strains and better calculation of genomic distances between genomes, it requires complete genomes for processing the obtained data (3, 5). To ensure good-quality, complete reference genomes, which is essential for these epidemiological association analyses, we recently developed an open-source tool (SalmoDEST) that can download well-characterized good-quality, complete reference genomes from the open-access GenBank database (6). This tool can extract complete Salmonella genomes with a coverage higher than 50x and genome length over 4 Mb; it verifies the serovar to which genome belongs and identifies the corresponding MLST profile (6).

Nevertheless, although the number of compete genomes deposited in the open-access databases increases every year, a complete reference genome is still not available for several Salmonella serovars. The choice of a good reference genome is critical to ensure the sensitivity of the analyses performed when analyzing closely related genomes (5, 7). Selecting a reference genome close to the strains under study increases the fraction of the genome on which SNP variants can be screened for, thereby increasing method sensitivity. For instance, we have shown that the use of the reference genome Typhimurium LT2 led to an 11% loss of core genome information (89% of breadth coverage) in the SNP phylogenetic investigation of the Salmonella Wellikade outbreak occurred in 2016 in France (5). However, choosing the S. Gaminara strain SA20063285 reference genome provided 92% breadth coverage, corresponding to a loss of only 8% of core-genome information (5). When the complete genome is not available for the serovar studied, we proposed an operating protocol (5) that can be used in any laboratory involved in surveillance activities, outbreak management and emergency preparedness (5). The protocol identifies the closest complete genome to use for SNP phylogenetic analysis among the ones available in the EnteroBase Salmonella database (1, 8). We indicate how to query EnteroBase by searching for the closest hierarchical cluster (HC) 2,000 profile of the serovar under study and visualize results using the GrapeTree clustering analysis (5, 9).

Finally, when choosing the most suitable complete genome, polyphyletic serovars require special attention. A polyphyletic serovar derives from multiple independent ancestors (1). For example, a study of the phylogeny of the Salmonella Derby serovar showed that strains displaying the same antigenic pattern S. 1,4,[5],12: f,g: (10, 11) according to the White-Kauffmann-Le Minor scheme (12) — and, consequently, sharing the name Salmonella Derby — belonged to at least three distinct genomic lineages (13). A similar situation was reported for Salmonella Newport in 2013 (14). For Salmonella Derby, the three lineages were fully consistent with thoses identified by MLST analysis and were named according to their ST profile names (ST40, ST71, and ST682). The strains belonging to the ST40 lineage were distinct from those belonging to the ST71 lineage, differing by 26,957 SNPs with a standard deviation (SD) of 1,583. The genomes belonging to the ST682 lineage were the most genetically distant from ST40 and ST71, with an average of 33,961 SNPs and an SD of 4,102 SNPs (13). With such genomic distances between lineages, it seems evident that the choice of the appropriate reference genome for polyphyletic serovars is critical and cannot be based only on serovar name.

With the goal of providing a ready-to-use map of the genomic diversity of the Salmonella enterica subsp. enterica serovars prevalent in human health, animal health and the food sector, we carried out a phylogenetic study of the most frequently isolated serovars to give an overview of the main polyphyletic serovars and their genomic lineages.

Materials and methods

Selection of serovars and genomes

The serovars analyzed in this study are those identified as being the most frequently isolated in humans and the agri-food chain over a period of 10 years (from 2006 to 2016) in the United States (US), Europe (EU) and France (FR). The list of the most frequently isolated serovars was compiled based on data reported by the CDC, the USDA, the ECDC and the EFSA reports (11, 15–18). For FR, data from the official controls collected by the Salmonella Network, part of the Anses Laboratory for Food Safety (LSAl), and reports from the National Salmonella Reference Center were taken into account (10, 19). More than 1.5 million reported human cases and, animal and food isolates were compiled in six lists according to serovar prevalence. Three lists (i.e., one list for the US, one for EU and one for FR) were compiled for the serovars isolated from humans and three other lists for those collected from the agri-food sector. The three lists for human cases and the three lists for the agri-food isolates were used separately for the Venn diagram analysis that was carried out using the ggVennDiagram R package (v.1.2.1) (20). Finally, the serovars selected for this study were chosen according to the following criteria: being common to at least two lists and belonging to the leading 20 serovars of each list (Table 1).

TABLE 1

Table 1. List of the 58 Salmonella serovars identified as being the most frequently isolated in humans and the agri-food sector over a period of 10 years (from 2006 to 2016) in the United States, Europe and France.

For each of the serovars selected, the most common MLST profiles were identified using the data available in the EnteroBase Salmonella database on 19 July 2021. The MLST profiles with 10 or more genomes in the EnteroBase database were selected for this study. For each of these MLST profiles, three good-quality genomes were downloaded. The complete or contig genomes were searched and downloaded using the SalmoDEST tool (6) and manually via the GenBank and EnteroBase Salmonella databases. Good-quality genome criteria were a length > 4 Mb, coverage > 50x and an analysis of how well genome matched the predicted serovar using SeqSero2 (21). When available, genomes from the Anses LSAl collection were selected and sequenced for this study. One genome of S. Javiana was obtained from the strain S11LNR1976 (renamed 2019LSAL01686) from the French National Reference Laboratory Collection (LNR-Anses) in Ploufragan-Plouzané-Niort Laboratory. Three genomes of S. Paratyphi B were obtained from the strains CIP 106179, CIP 55.42 and CIP 106950 (renamed 2019LSAL01933, 2019LSAL01934 and 2019LSAL01936, respectively) of the French CIP collection (Collection de l'Institut Pasteut, Paris, https://www.pasteur.fr/en/public-health/biobanks-and-collections/collection-institut-pasteur-cip).

Whole-genome sequencing analyses

Sequencing and assembly

Seventy-three genomes from Anses Salmonella Network collection were sequenced using the Illumina system producing paired-end reads as described in Cadel-Six et al. (22). The quality control, normalization and assembly were carried out with an in-house workflow called ARtWORK (23). The serovar and the multilocus sequence type (MLST) were attributed using the SeqSero2 (21) and MLSTseeman tools (24).

cgMLST analysis

The core-genome MLST (cgMLST) analysis was carried out with SeqSphere+ (Ridom® GmbH, Münster, Germany) under the EnteroBase cgMLST scheme based on 3002 loci (25).

Pan-genome phylogenetic analysis

The pan-genome kmer phylogenetic analysis was carried out with the QuickPhylo workflow as previously described (26), setting the Mash tool parameter to 1,000 selected kmers of 15 bases (27) and setting the DendroPy tool parameter to the neighbor-joining (NJ) method (28).

Tree annotation

Trees were visualized and annotated using R with the ggtree package (20, 29, 30).

Results

Salmonella serovars and genome selection

Fifty-eight S. enterica subsp. enterica serovars, the most frequently isolated in human cases and the agri-food sector in the US, EU and FR were selected for this study. The Venn analyses allowed selecting 47 prevalent common serovars in the US, EU and FR. The Venn diagrams in Figure 1 illustrate the intersections between the 25 and 50 most isolated serovars in humans and the agri-food sector in the US, EU and FR (Figure 1). The distribution of these 47 common serovars is illustrated in Supplementary Figure 1. Eleven other serovars were added because they belong to the leading 20 serovars of each list and were absent from the previous list comprising 47 common serovars. The 58 final serovars retained for this study are showed in Table 1.

FIGURE 1

Figure 1. Venn diagrams illustrating the intersection between the most frequently isolated serovars in the United States (US), Europe (EU) and France (FR) from human cases and from the agri-food sector. Venn analysis was carried out in two steps showed in (A,B). (A) Intersections between the top 25 serovars in human cases and the agri-food sector. The leading 25 human US, EU and FR serovars are included in the orange-, green- and red-outlined areas, respectively. The leading 25 agri-food sector US, EU and FR serovars are included in the yellow-, purple- and blue-outlined areas, respectively. The logical relation between the top 25 serovars in human cases and agri-food sector revealed 33 common serovars. The numbers within the intersections correspond to the common serovars. (B) Intersections between the leading 50 US, EU and FR agri-food sector serovars with the 33 serovars previously selected in (A). The top 50 US, EU and FR agri-food sector serovars are included in the green-, blue- and red-shaded areas, respectively. The previous 33 selected serovars in (A) are included in the purple-shaded area. The logical relation between these four groups revealed 14 other common serovars. The new 14 common serovars are surrounded by black circles. The numbers within the intersections correspond to common serovars. The resulting 47 common serovars obtained by Venn analyses are showed in the Supplementary Figure 1.

For these 58 major S. enterica subsp. enterica serovars, 639 genomes were collected from GeneBank, EnteroBase, CIP and the Anses Salmonella Network databases following the criteria described above. Eleven Salmonella samples from the other subspecies were also included. Genome ID, accession number, predicted serovar, MLST profile, genome length and coverage of the 650 genomes retained in this study is reported in Supplementary Table 1. Of the final total set of 650 genomes, 83 were complete genomes and 567 were contigs. Among the complete genomes, 77 belonged to the S. enterica subsp. enterica and 6 to other subspecies. Of the 567 contig genomes selected, 562 belonged to the subspecies enterica and 5 contig genomes belonged to other subspecies (Supplementary Table 1).

Phylogenetic analyses

The total set of 650 genomes was used for the first cgMLST analysis. From this analysis, a subset of 219 genomes were selected for the final phylogenetic cgMLST and pan-genome analyses. To conserve the genomic diversity of the first panel of 650 genomes, one genome per MLST profile was selected, favoring complete genomes when available. The subset of 219 genomes was composed of 74 complete genomes and 145 genomes in contigs. Genomes selected for the second 219 genome subset are shown in bold and gray fill color in the Supplementary Table 1.

cgMLST analysis of the panel of 650 genomes

Among the 650 genomes, the 11 genomes belonging to the other subspecies were used as outgroups. The cgMLST analysis shows that the 639 genomes belonging to S. enterica subsp. enterica were separated into four groups. Two of these groups, called groups A and B, included 90% of genomes (n = 580/639) and 50 of the 58 serovars studied. The 10% of the remaining S. enterica subsp. enterica genomes clustered into two separate groups, groups C and D (Figure 2).

FIGURE 2

Figure 2. Phylogenetic cgMLST distance tree of the 650 genome set of Salmonella. The tree is rooted on the Salmonella entrerica subsp. arizonae, diarizonae, houtenae, indica and salamae genomes. For better visualization of the groups, the tree is shown without branch lengths. Branches are colored to distinguish the four groups. Due to the high number of strains in the tree, strain labels are not shown and serovars are indicated along corresponding branches. Strongly supported subgroups are shaded in different colors (for the description of these subgroups see also Figure 3 and Table 3).

Group A included all genomes of serovars Anatum, Braenderup, Coeln, Dublin, Enteritidis, Gallinarum, Hadar, Heidelberg, Java, Kottbus, London, Manhattan, Muenchen, Newport, Ohio, Paratyphi B, Saintpaul, Stanley, Thompson, Typhimurium, 4,[5],12:i:-, Uganda and Virchow. Group A also included the genomes belonging to the MLST profiles Bareilly ST203, 362, 464, 909, 1,612, 2,129, 2,270, 2,553, Bovismorbificans ST142, 377, 1,499, Derby ST682, Infantis ST32, 603, 2,283, 2,146, Livingstone ST543, 1,941, 2,247, Reading ST1628 and Schwarzengrund ST2250.

Group B included all genomes of the serovars Agona, Albany, Brandenburg, Bredeney, Chester, Give, Goldcoast, Havana, Javiana, Johannesburg, Kedougou, Kentucky, Mbandaka, Minnesota, Montevideo, Muenster, Oranienburg, Panama, Poona, Rissen, Senftenberg, 1,3,19:z27:- and Tennessee. Along with these last genomes, the Group B included also the genomes belonging to the MLST profiles Banana ST683, 1,035, 4,745, 5,220, Bovismorbificans ST50, Cerro ST1291, Derby ST39, 40, 71, 72, Infantis ST79, Livingstone ST457, 638, Mississippi ST425, Reading ST93, 412 and Schwarzengrund ST96, 322.

Group C included all genomes of serovar Indiana, the genomes of serovar Cerro characterized by the MLST profiles ST367, 1,593, 2,407, Banana ST7024 and Bareilly ST5146.

Finally, Group D included all genomes of serovars Typhi, Veneziana, Napoli and the genomes of the serovar Mississippi characterized by MLST profiles ST448 and 5,834 (Figure 2 and Supplementary Figure 2).

cgMLST and pan-phylogenetic analyses of the subpanel of 219 genomes

Among the subset of 219 genomes, 214 belong to the subspecies enterica for the 58 serovars studied. The five genomes belonging to the other subspecies were used as outgroups for the cgMLST and pan-genome phylogenetic analyses. Both analyses revealed four groups in accordance with the results obtained with the first panel described above. Moreover, the comparison between the two phylogenetic approaches (cgMLST and pan-genome kmers) revealed the same composition of serovars and MLST profiles within each of the four groups of trees (Figure 3 and Supplementary Figure 3).

FIGURE 3

Figure 3. Phylogenetic pan-genome kmer distance tree of the 219 genome subset of Salmonella. The tree is rooted on the Salmonella entrerica subsp. arizonae, diarizonae, houtenae, indica and salamae genomes. The tree is shown with branch lengths. Polyphyletic serovars are shaded in different colors. Complete genomes are indicated with circles and contigs with triangles. Labels contain the serovar and the MLST profile of each strain. The strongly supported subgroups listed in the Table 3 are highlighted in the margin of the tree. For example, A1 corresponds to the subgroup called “section Livingstone” in Table 3.

Polyphyletic serovars

Both cgMLST and pangenome kmers analyses revealed 25 polyphyletic serovars within the 58 serovars studied (Table 2). Of these 25 polyphyletic serovars, only the serovar Banana was scattered across four branches that distinguish four independent genomic lineages (three lineages in Group B and one in Group C). In our panel, we found seven serovars scattered across three lineages: Bareilly, Derby, Kottbus, Newport, Oranienburg, Reading and Saintpaul, with Reading shared between groups A and B, and Bareilly shared between groups A and C. Finally, the last 17 polyphyletic serovars were characterized by two lineages as shown in Table 2 and Figures 2, 3. Of these 17 serovars, 6 presented lineages in different phylogenetic groups: the serovars Bovismorbificans, Cerro, Infantis, Livingstone, Mississippi and Schwarzengrund. The serovars Bovismorbificans, Infantis, Livingstone and Schwarzengrund were shared between Groups A and B, Cerro was shared by groups B and C and Mississippi was shared by groups B and D.

TABLE 2

Table 2. Polyphyletic and monophyletic serovars studied.

The MLST profiles characterizing the different lineages of the polyphyletic serovars are compiled in the Table 2. The only difference between the cgMLST and pan-genome kmer analyses involved the genomes of serovar Saintpaul belonging to the MLST profile ST1934 that was observed in two different lineages (lineages II and III). All other data on the distribution of the MLST profiles among the different genomic lineages were concordant between the two types of phylogenetic analyses.

Description of genomic proximity between serovars

Nine subgroups of related serovars were observed in the cgMLST and pan-genome kmer analyses. In Group D, we found a subgroup — which we called “section Typhi” — comprising the genomes of serovar Typhi ST1 and 2, the genomes of the lineage Mississippi I (ST448, 5834), the genome of serovars Napoli and Veneziana (Table 3). Within Group A, we observed five subgroups, called sections “Livingstone,” “Enteritidis,” “Typhimurium,” “Newport” and “Muenchen.” The “section Livingstone” (section A1 in Figure 2) comprises the genomes of the lineage Livingstone I (ST543, 1941, 2247) and the genomes of the serovar Ohio. The “section Enteritidis” (section A5 in Figure 2), comprises the genomes of serovars Enteritidis (ST11, 183), Gallinarum (ST78, 331), Dublin (ST10, 73, 4406) and Berta (ST435). The “section Typhimurium” (section A4 in Figure 2), is composed of the serovars Typhimurium (ST19, 34, 36), Heidelberg (ST15), Coeln (ST1995, 2015), the genomes of the lineage Saintpaul I (ST49, 27, 50, 680, 3,602, 1,934) and the genomes of the lineage Reading I (ST1628). The “section Newport” (section A11 in Figure 2) is composed of serovar Kottbus ST212, 808, the genomes of the lineage Newport I (ST31, 45, 47, 132, 157, 614) and Newport II (ST156, 166). Finally, “section Muenchen”, (section A10 in Figure 2) composed of the genomes of the lineage Muenchen I (ST82, 112, 1,606, 2,769), Muenchen II (ST83) and the lineage Manhattan I (ST18, 44, 2,200). In Group B, there are two subgroups that we called sections “Montevideo” and “Bredeney.” The “section Montevideo” (section B9 in Figure 2) is composed of the genomes of the lineage Montevideo I (ST4, 195) and the genomes of the lineage Oranienburg I (ST179, 864, 1392, 1512). The “section Bredeney” (section B14 in Figure 2) is composed of the genomes of the lineage Bredeney I (ST241, 306, 505, 897), the genomes of the lineage Bredeney II (ST505), the genomes of the lineage Give II (ST654) and the genomes of the lineage Schwarzengrund II (ST96, 322) (Figures 2, 3). Within Group C, we observed the genomes of the lineages Banana I (ST7024), Bareilly I (ST5146) and Cerro I (ST367, 1,593, 2,407) with the genomes of the serovar Indiana.

TABLE 3

Table 3. Identified subgroups.

Interestingly, our analyses reveal that the polyphyletic serovars Banana, Bovismorbificans, Derby, Newport, Muenchen and Reading arose independently on divergent branches of the tree strongly associated with genomes of other serovars (Table 3). For example, the lineage Banana I (ST7024) arose in Group C and is associated with the genomes of the lineage Bareilly I (ST5146), the lineage Cerro I (ST367, 1,593, 2,407) and the genomes of serovar Indiana (ST17, 2040). The lineages Banana II (ST4745), III (ST1035) and IV (ST683, 5,220) arose in Group B. Nevertheless, the lineage Banana II is associated with the lineages Derby II (ST39, 40) and Livingstone II (ST457, 638). The lineage Banana III is associated with the serovar Tennessee (ST319, 1565) and the lineage Rissen II (ST469) and the lineage Banana IV is associated with the lineage Derby III (ST71, 72). On the other hand, contrary to the lineages Derby II and III, the lineage Derby I (ST682) arose in Group A and is associated with serovar London (ST155).

Reference genome panel available in public databases

Given the selected set of high-quality complete genomes and the phylogenetic analyses carried out, we compiled a list of reference genomes with metadata and associated quality data (Supplementary Table 2). We selected 83 complete genomes from the initial set of 650 genomes, with 1 S. enterica subsp. salamae, 2 S. enterica subsp. arizonae, 1 S. enterica subsp. diarizonae, 2 S. enterica subsp. houtenae and 77 S. enterica subsp. enterica, representing 48 serovars and 71 MLST profiles.

Discussion

SNP phylogenetic analysis is the most suitable approach to use in investigations of outbreaks with the goal of clustering epidemiologically related strains and calculating pairwise distance between genomes of the same serovar. However, when analyzing genomic diversity between different serovars, the cgMLST and the pan-genome kmer analyses are more appropriate approaches. The cgMLST predictions based on the 3,002 gene scheme are extremely stable (1). In a comparison of more than 100 000 Salmonella genomes, the cgMLST scheme can predict serovars with fewer errors than the slide agglutination reference method (1, 12). Moreover, the cgMLST method is more widely employed than SNP analysis with large genome panels because it is computationally less demanding (1). Owing to these advantages, the hierarchical clustering of cgMLST sequence types was also chosen in EnteroBase as the method of choice to map new bacterial strains to predefined population structures at multiple levels of resolution (9). On the other hand, accessory genes contribute to ecological specialization and the pattern of horizontal gene transfer among phylogroups can provide important complementary information (31), so that analysis of the pan-genome can lead a better picture of microbial organism proximity (32). Moreover, the pan-genome analysis of thousands of prokaryote samples is possible on a standard desktop without compromising the accuracy of results (33). Last, but not least, neither cgMLST nor kmer pan-genome analyses need a reference genome. This is a crucial point when analyzing the diversity of Salmonella genomes represented by a large number of different subspecies and serovars as in this study.

For this study, the 58 most frequently isolated serovars in France, EU and the US with their major sequence-type profiles were selected with a view to human health, animal health and the agri-food safety sector at the national and international levels. From the first set of 650 genomes analyzed for these 58 prevalent serovars, the final taxon sampling genomes was composed of five outgroups and 214 ingroup S. enterica subsp. enterica strains. The cgMLST and pan-genome kmer phylogenetic analyses both uncovered a deep split that delineates four sister groups within S. enterica subsp. enterica, including the two previously partially described groups (14, 25, 31). Although many of the relationships reconstructed in this study are consistent with previous reports, our taxon dataset provides a more thorough interpretation of polyphyletic serovars than any other study. The large selection of serovars and sequence-type profiles included allowed deeply appreciating the relationship between serovars, their genomic lineages and MLST profiles.

For each serovar, we included a larger selection of sequence-type profiles than previously. This large diversity gave a good overview of the complexity of the genetic diversity in S. enterica subsp. enterica and identified 25 polyphyletic serovars, 17 of which have never been described before, such as Banana, Bareilly, Kottbus and Reading for which we identified three distinct lineages, with the exception of Banana characterized by four lineages. Finally, among the 25 polyphyletic serovars identified, one serovar was characterized by four distinct lineage, seven by three and 17 by two distinct lineages. All of these serovars, such as Newport and Derby (13, 34), likely derive from multiple independent ancestors during the evolutionary history of Salmonella. Interestingly, via the whole-genome comparisons, we demonstrated for Derby (4, 13), as previously shown for Newport (35), that heterogeneity between lineages mostly occurs in the prophage regions and that lineage-specific characteristics are also present in the Salmonella pathogenicity islands and fimbrial operons. Further analyses are needed to investigate the other polyphyletic serovars identified in this study.

Although 25 polyphyletic serovars have been identified in our taxon dataset, there are probably more. For example, the serovars Agona, Havana and Montevideo were not identified as polyphyletic in our study, but have been described as such previously (14, 25, 36). The MLST profiles selection parameters applied in this study (i.e., for each serovar, we included the MLST profiles with 10 or more genomes in the EnteroBase database on 19 July 2021) did not make it possible to highlight the polyphyly of these three serovars. Furthermore, the number of distinct lineages for a polyphyletic serovar also depends on the taxon dataset selected. For example, we previously described four distinct lineages for the serovar Derby (37). In the dataset selected for the study of the diversity of the serovar Derby in France, the genomes belonging to ST39, even if most closely related to ST40 genomes (i.e., with an average of 3,962 SNPs and an SD of 20 SNPs), were identified as an independent lineage.

The influence of the dataset on the results was also observed on the genomic groups identified. In our panel, we underlined strongly supported subgroups that confirm previous observations (14, 25, 31, 34, 38). We called these subgroups “sections” to echo previous descriptions. For example, in this study, “section Typhimurium” comprises the genomes of Salmonella Typhimurium, Heidelberg and Saintpaul (14) and encompasses the genomes of Salmonella Typhimurium ST19, 34, 36, Heidelberg ST15, Saintpaul ST27, 49, 50, 680, 1,934, 3,602 as well as Coeln ST1995, 2,015 and Reading ST1628. When describing genomic serovar associations, the ST profiles must also be mentioned. For example, in the “section Typhimurium,” only the genomes of serovar Reading ST1628 belong to this section, because this serovar is polyphyletic and the two other Reading lineages ST412 and 93 are genetically distant.

This study confirms that, since the advent of WGS and advances in knowledge on the genomic diversity in S. enterica subsp. enterica, it is no longer possible to cite a serovar without referring at least to its ST group (1, 8, 38). The serovar-based nomenclature is still useful to maintain a link with data collected in the past and to continue to ensure smooth communication at the international level with testing laboratories and countries that have limited access to molecular techniques. Furthermore, European regulations regarding zoonoses stipulate that the serovar name is the mandatory reference nomenclature for Salmonella. Nevertheless, MLST profile information should be added to the serovar whenever possible. When the genome is available, the information regarding the MLST profile is easily accessible on open-access bioinformatics platforms such as EnteroBase or via the Center for Genomic Epidemiology (CGE) website (http://www.genomicepidemiology.org/) (39). For some laboratories that do not have the resources to perform WGS, it is still possible to determine the MLST profile with a PCR thermocycler and small first-generation sequencers. These labs can also send the PCR amplicons to sequencing companies for a lower cost than for a WGS system.

Furthermore, to avoid creating a two-tier system, it is the role of reference laboratories to provide genomic phylogenetic analyses, such as the analyses carried out in this study, to allow other laboratories to locate their strains on the trees.

Associating the historical name of the serovar with its MLST profile is an essential step toward the future of the nomenclature of Salmonella, which should have the advantage of clearly identifying the genomic lineage to which it belongs and indicating possible close links with other serovars (8, 9, 40). This association will also make it possible to obtain a more precise vision of the prevalence of certain serovars (and their genomic lineages) in various sectors, e.g., are all three Reading serovars prevalent in the poultry sector or is it only one of its three genomic lineages adapted to this sector?

In this study, we also provide a list of complete genomes that can be used as references and point out the absence of complete genomes for the following serovars: Banana, Kedougou, Mississippi and Veneziana. We also note the absence of complete genomes for the following lineages: Bareilly L2, Bovismorbificans L2, Chester L1, Infantis L2, ParatyphiB L1, Reading L2 and L3, Rissen L1, Saintpaul L2 and Schwarzengrund L1. We are currently sequencing short and long reads of these serovars to provide high-quality reference genomes for them.

Finally, in response to future outbreak situations or One Health surveillance of prevalent Salmonella and other emerging serovars, our study opens the way to a better understanding of the genomic diversity of S. enterica subsp. enterica and sheds light on the prevalent polyphyletic serovars at the national and international levels.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.

Author contributions

Conceptualization and supervision: SC-S. Methodology, writing – original draft preparation, writing – review & editing, and visualization: EC and SC-S. Formal analysis: GI and EC. Data curation: EC, VN, and SC-S. All authors read and approved the final manuscript.

Funding

This work was supported by the project Cross-sectoral framework for quality Assurance Resources for countries in the European Union (CARE), which has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 773830.

Acknowledgments

We thank the ANSES Salmonella Network team of the LSAl-Anses, Laetitia Bonifait of the LNR-Anses and Dominique Clermont of the CIP (Institut Pasteur) for providing isolates for sequencing. We would like to thank Corinne Danan for the suggestions given during the proofreading of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2022.963188/full#supplementary-material

Supplementary Table 1. Accession number, taxonomic affiliation, MLST profiles and genomics data for the panel of 650 Salmonella strains analyzed. Genomes selected for the second 219 genome subset are shown in bold and gray fill color.

Supplementary Table 2. List of reference genomes with metadata and associated quality data for the Salmonella serovars identified as being the most frequently isolated in humans and the food sector over a period of 10 years (from 2006 to 2016) in the United States, Europe and France. Complete genomes selected for the second 219 genome subset are shown in bold and gray fill color.

Supplementary Figure 1. Venn diagrams illustrating the 47 Salmonella serovars identified to be common to the United States, Europe and France by logical relation analyses showed in Figure 1. The serovars are illustrated separately for agri-food isolates and human cases. To these 47 serovars, eleven others were added because they belong to the top 20 serovars from each country but were not common to all countries. The final list of 58 serovars retained for this study is showed in Table 1.

Supplementary Figure 2. Phylogenetic cgMLST distance tree of the 650 genome set of Salmonella. The tree is rooted on the Salmonella entrerica subsp. arizonae, diarizonae, houtenae, indica and salamae genomes. The tree is shown with branch lengths. Branches are shaded with different colors to distinguish the five groups identified.

Supplementary Figure 3. Phylogenetic cgMLST distance tree of the 219 genome subset of Salmonella. The tree is rooted on the Salmonella entrerica subsp. arizonae, diarizonae, houtenae, indica and salamae genomes. The tree is shown with branch lengths. Polyphyletic serovars are shaded in different colors. Complete genomes are highlighted with circles and contigs with triangles. Labels contain the serovar and the MLST profile of each strain.

References

1. Alikhan NF, Zhou Z, Sergeant MJ, Achtman M. A genomic overview of the population structure of Salmonella. PLoS Genet. (2018) 14:e1007261. doi: 10.1371/journal.pgen.1007261

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Mughini-Gras L, Kooh P, Fravalo P, Augustin JC, Guillier L, David J, et al. Critical Orientation in the Jungle of Currently Available Methods and Types of Data for Source Attribution of Foodborne Diseases. Front Microbiol. (2019) 10:2578. doi: 10.3389/fmicb.2019.02578

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Nadon C, Van Walle I, Gerner-Smidt P, Campos J, Chinen I, Concepcion-Acevedo J, et al. PulseNet International: vision for the implementation of whole genome sequencing (WGS) for global food-borne disease surveillance. Euro Surveill. (2017) 22:30544. doi: 10.2807/1560-7917.ES.2017.22.23.30544

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Sevellec Y, Granier SA, Le Hello S, Weill FX, Guillier L, Mistou MY, et al. Source attribution study of sporadic Salmonella derby cases in France. Front Microbiol. (2020) 11:889. doi: 10.3389/fmicb.2020.00889

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Cherchame E, Guillier L, Lailler R, Vignaud ML, Jourdan-Da Silva N, Le Hello S, et al. Salmonella enterica subsp. enterica Welikade: guideline for phylogenetic analysis of serovars rarely involved in foodborne outbreaks. BMC Genomics. (2022) 23:217. doi: 10.1186/s12864-022-08439-2

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Cherchame E, Ilango G, Cadel-Six S. Retrieving good-quality Salmonella genomes from the genbank database using a python tool, SalmoDEST. Bioinform Biol Insights. (2022) 16:11779322221080264. doi: 10.1177/11779322221080264

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Bush SJ, Foster D, Eyre DW, Clark EL, De Maio N, Shaw LP, et al. Genomic diversity affects the accuracy of bacterial single-nucleotide polymorphism-calling pipelines. Gigascience. (2020) 9:giaa007. doi: 10.1093/gigascience/giaa007

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Achtman M, Zhou Z, Alikhan NF, Tyne W, Parkhill J, Cormican M, et al. Genomic diversity of Salmonella enterica-The UoWUCC 10K genomes project. Wellcome Open Res. (2020) 5:223. doi: 10.12688/wellcomeopenres.16291.1

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Zhou Z, Alikhan NF, Mohamed K, Fan Y, Agama Study G, Achtman M. The EnteroBase user's guide, with case studies on Salmonella transmissions, Yersinia pestis phylogeny, and Escherichia core genomic diversity. Genome Res. (2020) 30:138–52. doi: 10.1101/gr.251678.119

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Anses. Inventaire des Salmonella d'origine non humaine. Available online at: https://www.anses.fr/fr/content/inventaire-des-salmonella-dorigine-non-humaine (accessed August 26, 2022).

Google Scholar

11. CDC Centers for Disease Control Prevention. National Surveillance of Bacterial Foodborne Illnesses (Enteric Diseases). Available online at: https://www.cdc.gov/nationalsurveillance/salmonella-surveillance.html (accessed August 26, 2022).

Google Scholar

12. Grimont P, Weill F-X. Antigenic Formulae of the Salmonella serovars. 9th ed. Paris: WHO Collaborating Center for Reference and Research on Salmonella, Institut Pasteur (2007).

Google Scholar

13. Sevellec Y, Vignaud ML, Granier SA, Lailler R, Feurer C, Le Hello S, et al. Polyphyletic nature of Salmonella enterica serotype derby and lineage-specific host-association revealed by genome-wide analysis. Front Microbiol. (2018) 9:891. doi: 10.3389/fmicb.2018.00891

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Timme RE, Pettengill JB, Allard MW, Strain E, Barrangou R, Wehnes C, et al. Phylogenetic diversity of the enteric pathogen Salmonella enterica subsp. enterica inferred from genome-wide reference-free SNP characters. Genome Biol Evol. (2013) 5:2109–23. doi: 10.1093/gbe/evt159

PubMed Abstract | CrossRef Full Text | Google Scholar

15. ECDC. European Centre for Disease Prevention and Control. Available online at: https://www.ecdc.europa.eu/en/search?s=salmonella&sort_by=search_api_relevance&sort_order=DESC&page=2 (accessed August 26, 2022).

Google Scholar

16. European, Food Safety Authority. Tables, Figures and Country Datasets complementing the European Union Summary Report on Zoonoses and Food-borne Outbreaks 2016. Available online at: https://zenodo.org/record/1044742#.YjsspurMLIW (accessed August 26, 2022).

Google Scholar

17. United United States Depertment of Agriculture Food Safety Inspection Service. Serotypes Profile of Salmonella Isolates from Meat and Poultry Products January 1998 through December 2014. Available online at: https://www.fsis.usda.gov/sites/default/files/media_file/2020-10/Salmonella-Serotype-Annual-2014.pdf (accessed August 26, 2022).

Google Scholar

18. U.S. Department of Agriculture, Food Safety and Inspection Service. Quarterly Sampling Reports on Salmonella and Campylobacter. Available online at: https://www.fsis.usda.gov/science-data/data-sets-visualizations/microbiology/microbiological-testing-program-rte-meat-and-7 (accessed August 26, 2022).

Google Scholar

19. NRC. Rapport d'activité annuel 2017 - Année d'exercice 2016. Centre National de Référence des Escherichia coli Shigella et Salmonella, Unité de Recherche et d'Expertise des Bactéries Pathogènes Entériques, Laboratoire associé Service de Microbiologie Hôpital Robert Debré - Paris. Available online at: https://www.pasteur.fr/fr/sante-publique/CNR/les-cnr/escherichia-coli-shigella-salmonella/rapports-d-activite (accessed August 26, 2022).

Google Scholar

20. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. (2004) 5:R80. doi: 10.1186/gb-2004-5-10-r80

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Zhang S, den Bakker HC, Li S, Chen J, Dinsmore BA, Lane C, et al. SeqSero2: rapid and improved Salmonella serotype determination using whole-genome sequencing data. Appl Environ Microbiol. (2019) 85:e01746-19. doi: 10.1128/AEM.01746-19

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Cadel-Six S, Cherchame E, Douarre PE, Tang Y, Felten A, Barbet P, et al. The spatiotemporal dynamics and microevolution events that favored the success of the highly clonal multidrug-resistant monophasic Salmonella Typhimurium circulating in Europe. Front Microbiol. (2021) 12:651124. doi: 10.3389/fmicb.2021.651124

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Felten A, Durimel K. ARtWORK. Available online at: https://github.com/afelten-Anses/ARtWORK (accessed August 26, 2022).

Google Scholar

24. Seemann T. mlst. Available online at: https://github.com/tseemann/mlst (accessed August 26, 2022).

Google Scholar

25. Achtman M, Wain J, Weill FX, Nair S, Zhou Z, Sangal V, et al. Multilocus sequence typing as a replacement for serotyping in Salmonella enterica. PLoS Pathog. (2012) 8:e1002776. doi: 10.1371/journal.ppat.1002776

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Radomski N, Cadel-Six S, Cherchame E, Felten A, Barbet P, Palma F, et al. A simple and robust statistical method to define genetic relatedness of samples related to outbreaks at the genomic scale - application to retrospective Salmonella foodborne outbreak investigations. Front Microbiol. (2019) 10:2413. doi: 10.3389/fmicb.2019.02413

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. (2016) 17:132. doi: 10.1186/s13059-016-0997-x

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Sukumaran J, Holder MT. DendroPy: a Python library for phylogenetic computing. Bioinformatics. (2010) 26:1569–71. doi: 10.1093/bioinformatics/btq228

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Yu G, Lam TT, Zhu H, Guan Y. Two methods for mapping and visualizing associated data on phylogeny using Ggtree. Mol Biol Evol. (2018) 35:3041–3. doi: 10.1093/molbev/msy194

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Yu G, Smith DK, Zhu H, Guan Y, Lam TTY, McInerny G. GGTREE: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Method Ecol Evol. (2016) 8:28–36. doi: 10.1111/2041-210X.12628

CrossRef Full Text | Google Scholar

31. Criscuolo A, Issenhuth-Jeanjean S, Didelot X, Thorell K, Hale J, Parkhill J, et al. The speciation and hybridization history of the genus Salmonella. Microb Genom. (2019) 5:e000284. doi: 10.1099/mgen.0.000284

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. The microbial pan-genome. Curr Opin Genet Dev. (2005) 15:589–94. doi: 10.1016/j.gde.2005.09.006

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MT, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. (2015) 31:3691–3. doi: 10.1093/bioinformatics/btv421

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Sangal V, Harbottle H, Mazzoni CJ, Helmuth R, Guerra B, Didelot X, et al. Evolution and population structure of Salmonella enterica serovar Newport. J Bacteriol. (2010) 192:6465–76. doi: 10.1128/JB.00969-10

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Zheng J, Luo Y, Reed E, Bell R, Brown EW, Hoffmann M. Whole-genome comparative analysis of Salmonella enterica Serovar newport strains reveals lineage-specific divergence. Genome Biol Evol. (2017) 9:1047–50. doi: 10.1093/gbe/evx065

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Pettengill JB, Pightling AW, Baugher JD, Rand H, Strain E. Real-time pathogen detection in the era of whole-genome sequencing and big data: comparison of k-mer and site-based methods for inferring the genetic distances among tens of thousands of Salmonella samples. PLoS ONE. (2016) 11:e0166162. doi: 10.1371/journal.pone.0166162

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Sevellec Y, Granier SA, Radomski N, Felten A, Le Hello S, Feurer C, et al. Complete Genome Sequence of Salmonella enterica subsp. enterica Serotype Derby, Associated with the Pork Sector in France. Microbiol Resour Announc. (2018) 7:e01027-18. doi: 10.1128/MRA.01027-18

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Chattaway MA, Langridge GC, Wain J. Salmonella nomenclature in the genomic era: a time for change. Sci Rep. (2021) 11:7494. doi: 10.1038/s41598-021-86243-w

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Larsen MV, Cosentino S, Rasmussen S, Friis C, Hasman H, Marvig RL, et al. Multilocus sequence typing of total-genome-sequenced bacteria. J Clin Microbiol. (2012) 50:1355–61. doi: 10.1128/JCM.06094-11

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Bale J, Meunier D, Weill FX, dePinna E, Peters T, Nair S. Characterization of new Salmonella serovars by whole-genome sequencing and traditional typing techniques. J Med Microbiol. (2016) 65:1074–8. doi: 10.1099/jmm.0.000325

PubMed Abstract | CrossRef Full Text | Google Scholar