# Epidemiology of Staphylococcus aureus food isolates: Comparison of conventional methods with whole genome sequencing typing methods

Noémie Vingadassalon, Déborah Merda, Arnaud Felten, Virginie Chesnais, Christos Kourtis, Tom van Nieuwenhuysen, Yacine Nia, Jacques-Antoine Hennekinne, Marina Cavaiuolo

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Noémie Vingadassalon, Déborah Merda, Arnaud Felten, Virginie Chesnais, Christos Kourtis, et al. Epidemiology of Staphylococcus aureus food isolates: Comparison of conventional methods with whole genome sequencing typing methods. Food Microbiology, 2025, 125, pp.104625. 10.1016/j.fm.2024.104625. anses-04694068

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# Epidemiology of *Staphylococcus aureus* food isolates: Comparison of conventional methods with whole genome sequencing typing methods

Noémie Vingadassalon<sup>a,*</sup>, Déborah Merda<sup>b</sup>, Arnaud Felten<sup>c</sup>, Virginie Chesnais<sup>b</sup>, Christos Kourtis<sup>d</sup>, Tom Van Nieuwenhuysen<sup>e</sup>, Yacine Nia<sup>a</sup>, Jacques-Antoine Hennekinne<sup>a</sup>, Marina Cavaiuolo<sup>a</sup>

## ARTICLE INFO

**Keywords:**

*Staphylococcus aureus*
Food poisoning outbreak
PFGE
cgMLST
coreSNP
Enterotoxins genes

## ABSTRACT

A variety of methods exists for typing bacteria. However, guidelines for the application and interpretation of typing tools in epidemiologic investigations of *Staphylococcus aureus* are lacking. This study aimed to identify appropriate typing methods for *S. aureus* population studies and outbreak investigation. We compared pulsed-field gel electrophoresis (PFGE), seven loci multi-locus sequence typing (MLST), core genome MLST (cgMLST), core single nucleotide polymorphism (cSNP), and enterotoxin (se/SE) profiles on 351 *S. aureus* isolates. The discriminatory power, concordance, and congruence of typing results were assessed. cgMLST, cSNP, and PFGE yielded the highest discrimination value, followed by se/SE typing and MLST. The best concordance of results was found between cgMLST and cSNP, while the best congruence was observed for cgMLST and cSNP with all methods, followed by PFGE with MLST. The strengths and weaknesses of each method are highlighted. For population structure, cgMLST and cSNP performed better than PFGE and MLST in terms of resolution of clusters and in phylogenetic inference. Enterotoxin profiles matched with MLST groups, suggesting the use of se/SE typing to predict MLST results. For the retrospective analysis of 31 outbreaks, all methods performed almost equally to discriminate epidemiologically related strains and can be used to unambiguously distinguish outbreak strains.

## 1. Introduction

The coagulase-positive staphylococci (CPS), including *Staphylococcus aureus*, are a leading cause of staphylococcal food poisoning outbreak (SFPO) due to their ability to produce and secrete staphylococcal enterotoxins (SEs) in foods (Authority (EFSA) and European Centre for Disease Prevention and Control (ECDC), 2023; Balaban and Rasooly, 2000; Ostyn et al., 2010).

Up to 33 SEs have been described to date: SEA to SEE, TSST1, SEG to SEIV, SEIW, SEX to SEZ, SEI26, SEI27, SEI29p, and SEI30 to SEI33 (Dicks et al., 2021; Merda et al., 2020). However, only five of them (SEA to SEE) can be detected in food remnants using commercially available immunoassays methods (Nia et al., 2016). Because of the unavailability of antibodies for all SEs, the isolates recovered from the suspected foods

are often characterized by PCR targeting the enterotoxin (se) encoding genes to predict the enterotoxins putatively involved in the outbreak (Denayer et al., 2017; Macori et al., 2016; Romano et al., 2023). In addition, genetic and molecular typing – the practice of identifying bacteria at the strain level – is applied in outbreak settings to detect the epidemiologic linkage between the isolates from the contaminated food and those from the human cases, as well as to study genetic dynamics in the *S. aureus* population (Azarian et al., 2021; Durand et al., 2018; Etter et al., 2020; Lozano et al., 2016).

Several methods are used to type *S. aureus* and vary in their discriminatory power, reproducibility, cost and labor-intensiveness. While Spa-typing (Aires-de-Sousa et al., 2006; Shopsin, 1999) or multiple-locus variable number tandem-repeat analysis (MLVA) have proved their value in typing *S. aureus* (Pourcel et al., 2009; Schouls et al., 2009; Sobral et al., 2012), pulsed-field gel electrophoresis (PFGE) and

<footnote><sup>a</sup> ANSES, Laboratory for Food Safety, Staphylococcus, Bacillus and Clostridium Unit (SBCL), 94706, Maisons-Alfort, France</footnote>

<footnote><sup>b</sup> ANSES, Laboratory for Food Safety, Shared Support Service for Data Analysis (SPAAD), 94706, Maisons-Alfort, France</footnote>

<footnote><sup>c</sup> ANSES, Ploufragan-Plouzané-Niort Laboratory, GVB Unit, 22440, Ploufragan, France</footnote>

<footnote><sup>d</sup> State General Laboratory, Food Microbiology Laboratory, 1082, Nicosia, Cyprus</footnote>

<footnote><sup>e</sup> Sciensano, Foodborne Pathogens, 1050, Ixelles, Belgium</footnote>

<footnote>* Corresponding author.
E-mail address: noemie.vingadassalon@anses.fr (N. Vingadassalon).</footnote>
Abbreviations

AD allelic differences

CC clonal complex

cgMLST core genome multi-locus sequence typing

CPS coagulase-positive staphylococci

cSNP core single nucleotide polymorphism

cSNPT core single nucleotide polymorphisms typing

CT complex type

MLST multi-locus sequence typing

MLVA multiple-locus variable number tandem-repeat analysis

NRL National Reference Laboratory

PFGE pulsed-field gel electrophoresis

SE staphylococcal enterotoxin

P/A se presence/absence of staphylococcal enterotoxin genes

SFPO staphylococcal food poisoning outbreak

ST sequence type

WGS whole genome sequencing

seven loci multi-locus sequence typing (MLST) are the methods mostly commonly used in public health laboratories (Alkharsah et al., 2019; Kumar et al., 2021; Weller, 2000). Previously considered the “gold standard” of bacterial typing, PFGE is a DNA fingerprinting method involving the digestion of chromosomal DNA by a specific restriction endonuclease (Golding et al., 2015; Murchan et al., 2003). Comparison of PFGE patterns of bacterial isolates is used to identify and genetically distinguish related from unrelated strains (Bannerman et al., 1995). However, certain strains are unable to be typed due to methylation-mediated blockage of the restriction site. In addition, this method is labor intensive (Goering, 2010) with problems in reproducibility and comparison of results among laboratories, which constitutes a major drawback.

Following the development of the MLST method on S. aureus in 2000 (Enright et al., 2000), many laboratories transitioned from PFGE to MLST as typing method (Goudarzi et al., 2017; Saunders and Holmes, 2014; Trindade et al., 2003). MLST relies on the sequencing of seven housekeeping loci and discriminates isolates according to the unique combination of polymorphisms observed for each of the loci. Compared to PFGE, MLST provides standardized, portable, easily understandable genotypes, is less labor intensive, and is easily transferable between laboratories. However, it does have lower resolution as it targets a limited portion of the genome.

Nowadays, typing methods based on whole genome sequencing (WGS), such as core genome (cg) MLST and single nucleotide polymorphism (SNP), are more and more commonly used for surveillance, monitoring, and investigations of outbreaks (“The European Union One Health 2019 Zoonoses Report | EFSA,” 2021; Uelze et al., 2020). cgMLST upscales the MLST concept to include 1861 loci for *Staphylococcus aureus* (Leopold et al., 2020), while SNP analysis discriminates isolates based on the profile of mutations compared with a reference genome (Gardner and Hall, 2013; Leggett and MacLean, 2014; Strauß et al., 2016). By giving access to the whole genetic content of a strain, these approaches can assess the genetic relatedness of bacterial isolates with higher accuracy and resolution. However, even though they have revolutionized typing and characterization, WGS requires molecular and bioinformatics competences for both wet- and dry-lab parts, respectively and remains expensive and not affordable for many laboratories.

The French Agency for Food, Environmental and Occupational Health & Safety (ANSES) was appointed as the European Union Reference Laboratory (EURL) for CPS, according to Commission Regulation (EC) No 776/2006 of May 23, 2006. One of the tasks of the EURL CPS is to provide its network of 33 National Reference Laboratories (NRLs) with details and guidance on analytical methods for CPS, including

S. aureus analysis. In this framework, the harmonization of typing methods is important to improve the consistency of results and facilitate their sharing across the network, both for investigating SFPOs and for studying the diversity of circulating S. aureus strains. However, each NRL has a different and unique combination of equipment, resources and trained staff, making the capability and capacity in the application of typing methods across the network unbalanced. An important question is how we can reconcile harmonization needs in typing with NRL laboratory capacities. To answer this question, we compared conventional methods, i.e., PFGE and seven loci MLST, to WGS methods, i.e., cgMLST and core SNP, by statistically assessing the power of discrimination of each method, as well as the congruence and the concordance of results obtained by all methods for typing S. aureus isolates. The *se* gene profile was also included, since the combined evaluation of *se* gene profiles and genetic background increases the discrimination of S. aureus strains (Holtfreter et al., 2007; Varshney et al., 2009). The performance of the various methods was evaluated for linking genetically related isolates collected in the context of SFPOs in order to resolve the outbreak and to delineate the genetic structure of S. aureus. Our results provide a better understanding of the advantages and limitations of the available typing methods for S. aureus, and we hope that this will help public health laboratories when choosing appropriate approaches for epidemiologic studies.

## 2. Methods

### 2.1. *Strain collection and growth conditions*

The collection of 351 S. aureus isolates used in this study (Table 1; Supplementary Table 1) was acquired between 1980 and 2020 by ANSES under their national and European reference laboratory mandates. A total of 311 isolates derived from 181 SFPOs occurred in France, one SFPO in Belgium, and one SFPO in Cyprus. A further 13 isolates were sampled from own-check laboratory tests by food producers in France. In 23% of the SFPOs (41 cases) up to 16 different food samples were analyzed, and in 17% of SFPOs (31 cases) three to 16 isolates were recovered. For six SFPOs reported as “strong evidence” outbreaks (Denayer et al., 2017; Kérouanton et al., 2007; Ostyn et al., 2010; Roussel et al., 2015), both human (15) and food isolates (7) were collected from the same epidemic (Supplementary Table 5). To have a wider representation of the genetic diversity of S. aureus, 23 reference strains (usually used as control in PFGE, PCR on *se* and antibiotic-resistance genes) were also included. In all, 120 isolates were

<table><caption>**Table 1**

Composition of the 351 S. aureus isolates used in this study.</caption><thead><tr><th>Context</th><th>Number of isolates</th></tr></thead><tbody><tr><td>SFPO</td><td>313</td></tr><tr><td>Reference strain</td><td>23</td></tr><tr><td>Own check</td><td>15</td></tr><tr><td><strong>Origin</strong></td><td></td></tr><tr><td>Cooked dish</td><td>120</td></tr><tr><td>Milk and dairy products</td><td>103</td></tr><tr><td>Meat and meat products</td><td>41</td></tr><tr><td>Pastry</td><td>26</td></tr><tr><td>Fish and seafood</td><td>9</td></tr><tr><td>Food environment sampling</td><td>9</td></tr><tr><td>Vegetables</td><td>5</td></tr><tr><td>Delicatessen</td><td>5</td></tr><tr><td>Human</td><td>30</td></tr><tr><td>Unknown</td><td>3</td></tr><tr><td><strong>SFPOs (n=183)</strong></td><td></td></tr><tr><td>One food sample (n = 142)</td><td>172</td></tr><tr><td>≥Three isolates (n = 4)</td><td>15</td></tr><tr><td>≥Two food samples (n = 41)</td><td>141</td></tr><tr><td>≥Three isolates (n = 27)</td><td>102</td></tr></tbody></table>
previously characterized by PFGE (Kérouanton et al., 2007; Ostyn et al., 2010; Roussel et al., 2015) and 57 by WGS (Merda et al., 2020). All isolates were cultured on non-selective plate count agar (PCA)-milk medium and incubated for 18–24 h at 37 °C. A single isolated colony was cultured in brain heart infusion (BHI) rich medium for 16–18 h at 37 °C to reach an OD600 nm of ~1.5–2 before DNA extraction.

## 2.2. Pulsed-field gel electrophoresis, family profiles and pulsotype assignment

PFGE was performed as described by Roussel et al. (2015). The migration profiles were analyzed with BioNumerics software (version 7.6 Applied Maths, Kortrijk, Belgium). DNA bands were normalized using as standard reference, the profile of the *S. aureus* NCTC8325 strain (NCBI RefSeq GCF 900475245). Profile comparison dendrograms were built using the Dice similarity coefficient and the unweighted pair group method with arithmetic mean (UPGMA). A pulsotype (PT) number, specific to the number and size of bands that compose the profile, was attributed to each isolate. If at least one band differed from the profiles of the in-house molecular typing database, the PTs were considered different (23). PTs with 70% pattern similarity were grouped into Families. For outbreak investigation, the genetic relatedness among isolates was defined as follows: (i) isolates with identical PT were classified as “clones”; (ii) isolates with PT having one to three bands of difference (a single genetic event) were classified as closely related, and (iii) isolates with PT differing by more than four bands (two genetic events) were classified as “different strains”. PFGE gels are shown in Supplementary Fig. 2.

## 2.3. Genomic DNA extraction

DNA was extracted using a magnetic-particle-based KingFisher™ Cellular and Tissue DNA Kit on a KingFisher Duo Prime automated system (Thermo Fisher Scientific™, Waltham, MA, USA) or using a Wizard® Genomic DNA Purification Kit (Promega, Madison, WI, USA), according to manufacturer's instructions for Gram-positive bacteria. After centrifugation of 2 mL of inoculated BHI, the bacterial pellet was resuspended in 480 µL of 50 mM EDTA, 105 µL of lysozyme (10 mg/mL) and 15 µL of lysostaphin (10 mg/mL) and incubated at 37 °C for 60 min in a dry bath. A proteinase K digestion step, including in kits, was performed for 30 min at 65 °C, followed by a thermal lysis step (30 min 80 °C) with shaking. RNA was removed with RNase A (Promega) (4 mg/mL) for 5 min at 37 °C. DNA purity was determined with a Nano-Drop1000 spectrophotometer (Thermo Fisher Scientific) and quantified using a Qubit fluorometer (Invitrogen, Waltham, MA, USA) with a Qubit dsDNA BR Assay kit. DNA integrity was evaluated by electrophoresis on 0.8% agarose gel with a mix of Seakeam GTG agarose and Nusieve agarose (Lonza, Basel, Switzerland).

## 2.4. Read sequencing, processing and treatment

DNA libraries were prepared with a NextEra XT® Library Prep kit (Illumina, San Diego, CA, USA) and sequenced in “pair-end” mode (2 × 150 bp) using an Illumina® NextSeq 500 sequencer.

Raw sequencing data were analyzed using an in-house pipeline. Reads were processed with fastp v0.22.0 (Chen et al., 2018), Kraken2 v2.1.2 (Wood and Salzberg, 2014), and ConFindr v0.7.4 (Low et al., 2019) to check for sequencing quality (at least 80% of high quality reads with Phred score >30), interspecies contamination (<2% of reads not corresponding to *S. aureus*) and intra-species contamination (absence of multiple alleles of *S. aureus* ribosomal-protein genes), respectively. For each dataset, the closest reference genome was identified using Mash v2.2.2 (Ondov et al., 2016) by estimating the Jaccard index against the NCBI collection of bacterial genomes. Read assembly was achieved using the Shovill pipeline v1.1.0 (https://github.com/tseemann/shovill) with SPAdes (Bankevich et al., 2012) as assembler. The quality of assemblies

was verified using QUAST v5.0.2 https://github.com/ablab/quast. Assembled genomes were annotated with Prokka v1.14.6 (Seemann, 2014). The assemblies contained from 11 to 77 contigs and the average genome size was 2.75 Mb, ranging from 2.6 Mb to 2.9 Mb, in line with the expected size of the *S. aureus* genome (on average 2.7 Mb). Assembly statistics are listed in Supplementary Table 2. Sequencing data were deposited in the European Nucleotide Archive (ENA) under accession number PRJEB61006 and PRJEB36867. Accession numbers for individual genomes (Illumina reads) are listed in Supplementary Table 2.

## 2.5. *se* genes and *SE* protein typing

For *se* genes, typing assembled genomes were screened for the presence of the 33 *se* genes as referenced in (Dicks et al., 2021; Merda et al., 2020; Vrieling et al., 2020) using BLASTn from BLAST+(v2.7.1) at a cut-off value of 80% for base identity and coverage. For *SE* typing, the NAuRA tool was used to identify in-frame *SE* and annotate protein variants against an in-house database of *SE* protein sequences (Merda et al., 2020). *SE* variants were annotated with number between brackets.

## 2.6. Multi-locus sequence typing, sequence type and clonal complex assignment

MLST was performed on assembled genomes using the MLST program (https://github.com/tseemann/mlst) with default parameters, and against the PubMLST (https://pubmlst.org) *S. aureus* scheme of seven housekeeping genes (*arcC*, *aroE*, *glpF*, *gmk*, *pta*, *tpi*, and *yqiL*) (Enright et al., 2000) allowing ≥50% identity to matching MLST alleles. A sequence type (ST) was deduced from the unique combination of alleles at each of these loci. Clonal complexes (CCs) were defined as a group of STs that shared at least four of the seven loci, according to PubMLST nomenclature. When there was no close ST to assign a CC, and there was only one ST in the CC, the CC was named with the number of the only ST present.

## 2.7. Single nucleotide polymorphism and core genome MLST analyses

Core SNP (cSNP) analysis was performed with Snippy using default parameters https://github.com/tseemann/snippy. The NCBI *S. aureus* genome NZ,CP026962.1 was used as a reference based on its closest mash value to the majority of the isolates. The resulting 160,531 bp cSNP alignment was corrected for high densities of base substitutions (re-conjugations) using Gubbins (Croucher et al., 2015)(https://github.com/nickjcroucher/gubbins) to generate a corrected alignment of 132, 581 bp.

cgMLST analysis was performed with chewBACCA with default parameters (Silva et al., 2018) using the *S. aureus* cgMLST scheme of 1861 core genes (Leopold et al., 2020) https://www.cgmlst.org/ncs/schem/a/141106/) extracted from Ridom SeqSphere+ (version, 8.2.0 (2021–12).

A 153,1786 bp concatenated cgMLST target gene sequence was generated using an in-house python script and filtered with Gubbins to a 93,540 bp corrected alignment. cgMLST and cSNP phylogenetic trees were obtained with iQtree v1.6.9 (Chernomor et al., 2016) from Gubbins corrected alignments using the maximum likelihood method and a number of bootstraps set to 1000. Comparison of cSNP with cgMLST tree topologies and calculation of a co-phenetic coefficient of correlation “cor_cophenetic” were obtained using the “phytools” v0.7-90 (Revell, 2011) and “dendextend” v1.17.1 (Galili, 2015) packages, respectively in R studio, PBC v1.4.1717.

To create cSNP and cgMLST clusters, the cSNP distance matrix was produced using snp-dists v0.8.2 on the Galaxy interface https://useg.alaxy.eu/, and the cgMLST distance matrix was generated with grape-tree (Zhou et al., 2018) on 1783 core loci present in a threshold proportion of 90% of the 351 genomes.

Clusters were created from the two distance matrices using the
"cluster" V2.1.2 R package in R studio, PBC (Version April 1, 1717). Since every isolate has a unique allelic or SNP profile, it was not practical to assign to every combination of genomic alleles or SNP differences a unique identifier in the same way as is done for MLST ST or PFGE PT subtyping. In the same way as MLST CC or PFGE Families, isolates were grouped into cSNP profile type (cSNPT) based on a 15 core SNPs cut-off (Coll et al., 2020) and into cgMLST complex type (CT) at a threshold of 24 allelic differences (AD) (Szarvas et al., 2021). When available, we used the CT nomenclature from the Ridom Seqsphere cgMLST database. When no nomenclature was available, CTs numbers were attributed arbitrarily in a sequential manner. cgMLST and SNP clusters were both used for outbreak investigation and population structure.

## 2.8. Statistical analysis

The Simpson's index of diversity (ID) with a confidence interval (CI) (Carriço et al., 2006; Hunter and Gaston, 1988) was used to test the discriminatory power. The Adjusted Rand Index (ARI) (Carriço et al., 2006) was calculated to evaluate concordance between two typing methods. The adjusted Wallace coefficient (AW)(Severiano et al., 2020) was used to evaluate the bi-directional congruence between two typing methods. The ID, ARI, and AW coefficients were calculated using the Online tool for quantitative assessment of classification agreement http://www.comparingpartitions.info.

# 3. Results

## 3.1. Discriminatory power and measure of congruence between typing methods

351 S. aureus isolates (Table 1; Supplementary Table 1) were typed with PFGE and their sequenced genomes were submitted to in silico seven loci MLST, se/SE analysis, cgMLST, and cSNP (Table 2; Supplementary Table 2). For MLST and PFGE, two outputs were used: CC and Families, frequently used to describe populations; and ST and PT, more often applied for subtyping and outbreak analyses. For enterotoxin typing, we defined *se* profiles based on the presence/absence (P/A) of *se* genes and SE profiles based on the P/A of in-frame *se* genes and protein sequence variants. For cgMLST and cSNP, a single output was used: complex types (CT) and cSNP profile types (cSNP-T) defined by single-linkage clustering threshold of 24 allelic mismatches and 15 cSNP difference, respectively.

MLST and PFGE resulted in 26 CCs and 27 Families, respectively each with 13–19 isolates on average, which were further discriminated into 59 ST and 195 PT, respectively with on average ~6 isolates per ST and ~2 isolates per PT. cgMLST and cSNP defined 252 CTs and 255 cSNPTs, ~80% of which were composed of a single isolate (singletons). Concerning enterotoxin typing, 61 distinct *se* profiles were identified each, with ~6 isolates on average. The classification based on SE diversity with 1–33 different variants identified per gene (Supplementary Fig. 1) further identified 124 types with on average ~3 isolates per group.

To identify to what extent the isolates could be typed and grouped in

the same way by the six methods, we compared the indices of discriminatory power (Simpson index (ID) of each method as well as the strength (ARI) and directionality (Wallace coefficient - AW) of their congruence.

Both cgMLST and cSNP had the highest ID (0.996), followed by PFGE PT (0.991), SE protein (0.979) and *se* gene profile (0.951), while MLST ST (0.926), MLST CC (0.885) and PFGE Family (0.860) had the lowest values (Table 3). The two WGS-based methods displayed the highest ARI values between them (0.977), indicating near perfect agreement in the attribution of results (Table 4). Identical ARI values (0.585) were observed for cSNP-PFGE PT and cgMLST-PFGE PT, respectively which indicated a concordance close to 60% between the three most discriminating methods. The *se/SE* showed low concordance values ranging from 0.269 to 0.466 with MLST ST and CC, but still higher than when compared with WGS and PFGE PT or Family, indicating that these less discriminating methods agreed more with each other at the level of interpretation of the results.

As for the AW coefficients shown in (Fig. 1; Table 4), the cgMLST and cSNP displayed values ranging from 0.986 to 1 with PFGE PT and Family, while the reverse comparison showed values below 0.430, indicating that clusters defined by genomics methods could not be predicted in the same way by the two PFGE outputs. Similarly, the AW values of PFGE PT/cgMLST/cSNP with MLST ST and CC ranged from 0.908 to 1, and were much higher than the AW calculated from the opposite comparisons (values < 0.103), clearly showing that MLST ST typing assignments and CC groups could be predicted by PFGE, cgMLST or cSNP, but not the contrary. Finally, the AW of *se/SE* typing methods with PFGE PT/cgMLST/cSNP were the lowest (ranging from 0.079 to 0.400), while a higher correlation from 60% to 85% was observed with MLST ST and PFGE F. However, this was not true for the reverse relationships. We concluded that *se/SE* typing could be better predicted by cgMLST and cSNP methods and that it could infer MLST type assignments and groups more efficiently than the other methods.

## 3.2. Phylogenetic relationships and comparison of clusters by the different methods

To characterize and compare the population structure of S. aureus as defined by the six typing methods, we studied the relationship between MLST (CC) and PFGE (Families) typing groups with the occurrence of *se* genes/SE variants and with the cgMLST and cSNP clusters and phylogenies.

As for conventional methods, when counting the number of MLST CC observed among isolates of a particular PFGE Family and *vice versa*, the data from both typing techniques correlated well with each other (Fig. 2; Supplementary Table 3) with AW coefficients of 0.778 and 0.632, respectively. For the 317 isolates typed in 12 CCs as shown in Fig. 2, those belonging to the same CC were divided into two to seven PFGE Families. For each of these CCs, on average, 90% of the isolates were predominantly typed into a single PFGE Family, such as CC15 (Family 3: 10/10), CC30 (Family 21: 8/28), and CC97 (Family 27: 21/28). CC5 (49 isolates) was divided into PFGE Family 1 (12 isolates/104) and Family 17 (37 isolates/39). Only 21 isolates typed into 4 distinct CCs belonged to a single PFGE Family, such as CC45 (Family 4) and CC504 (Family

<caption>**Table 2**</caption>

**Genetic diversity attributed by different typing methods.** Statistics of six typing methods derived from typing S. aureus. Three isolates were not assigned to any PFGE Family because they had 70% similarity with no other strain profile in our database. The MLST CCs of two isolates and the STs of eight isolates could not be assigned because the allele of one locus could not be identified.

<table><thead><tr><th rowspan="2"></th><th colspan="2">PFGE</th><th colspan="2">MLST</th><th colspan="2">cgMLST</th><th rowspan="2">cSNP</th><th rowspan="2">P/A <i>se gene</i></th><th rowspan="2">SEprotein</th></tr><tr><th>F</th><th>PT</th><th>CC</th><th>ST</th><th>CT</th><th>cSNPT</th></tr></thead><tbody><tr><td>Number of groups or types</td><td>27</td><td>195</td><td>26</td><td>59</td><td>252</td><td>255</td><td>61</td><td>124</td></tr><tr><td>Number of groups or types with only one isolate</td><td>6</td><td>138</td><td>6</td><td>29</td><td>203</td><td>203</td><td>20</td><td>66</td></tr><tr><td>Largest group/type size (number of isolates)</td><td>104</td><td>11</td><td>76</td><td>67</td><td>6</td><td>4</td><td>42</td><td>34</td></tr><tr><td>Mean number of isolates per group/type (±SD)</td><td>19 ± 22.12</td><td>2 ± 1.96</td><td>13 ± 19.58</td><td>6 ± 11.04</td><td>1 ± 1.20</td><td>1 ± 1.18</td><td>6 ± 8.28</td><td>3 ± 4</td></tr></tbody></table>
<caption>**Table 3**</caption>

**Simpson's Index of diversity and 95% confidence interval (95% CI) for six typing methods.** Distinct type indicates the number of different types observed in the collection. Unique type represents the number of types observed only once in the collection.

<table><thead><tr><th>Method</th><th>Number of isolates</th><th>Simpson's ID and confidence interval (95% CI)</th><th>Total number of groups or types</th><th>Total number of unique groups or types</th></tr></thead><tbody><tr><td>MLST CC</td><td>349</td><td>0.885 (0.869-0.901)</td><td>26</td><td>5</td></tr><tr><td>MLST ST</td><td>343</td><td>0.926 (0.911-0.940)</td><td>59</td><td>29</td></tr><tr><td>PFGE F</td><td>348</td><td>0.860 (0.837-0.884)</td><td>27</td><td>7</td></tr><tr><td>PFGE PT</td><td>351</td><td>0.991 (0.989-0.994)</td><td>195</td><td>138</td></tr><tr><td>cgMLST CT</td><td>351</td><td>0.996 (0.994-0.998)</td><td>252</td><td>203</td></tr><tr><td>cSNP-T</td><td>351</td><td>0.996 (0.994-0.998)</td><td>255</td><td>203</td></tr><tr><td>P/A <i>se genes</i></td><td>351</td><td>0.951 (0.943-0.960)</td><td>61</td><td>20</td></tr><tr><td>SE protein</td><td>351</td><td>0.979 (0.973-0.985)</td><td>124</td><td>66</td></tr></tbody></table>

11). Similarly, 123 isolates from the same PFGE Family belonged to the same CCs, with only PFGE Family 1 (104 isolates) corresponding to 9 CCs and Family 2 (7 isolates) equally distributed between CC700 (3) and CC5841 (4) (not shown in the figure). Seven PFGE Families corresponded to up to 8 CCs, but only one CC was the majority for an average of 92% of isolates, e.g. Family 1 (72/104 CC1) and Family 20 (23/24 CC389).

A frequent association of enterotoxin gene types and variants with specific CC and/or PFGE Families was observed (Fig. 2), as expected from the high AW values calculated when comparing *se/SE* with these methods (Fig. 1; Table 4). Importantly, most isolates (229) presented a similar *se* gene content per Family/CC. In all, 73% of CC1 isolates belonging to the PFGE Family 1 harbored the *sea*, *seh*, *sek*, and *seq* genes. For CC97, 19/22 isolates did not contain any other *se* gene than *sex* and *sew*, except 11 of them which coded the *sez* gene, which is very rarely observed in the panel (24/351 isolates). All CC5, CC389, and CC30 isolates typed as PFGE Family 17, 20, and 21, respectively and contained the *egc* cluster. Lastly, 78% of CC8 isolates corresponding to PFGE Family 16 coded *sea*, *sew*, and *sex*.

Interestingly, for 17 SE, 2 to 5 variants per toxin were found within the same CC, e.g., 2 to 3 SEA variants were found in CC5, CC8, and CC30 (Supplementary Table 4). For 18 SE, the same variant was present in 2-4 CCs, e.g., SEC (3) was found in CC12, CC45, CC88, and CC389. For 18 SE, specific variants correlated with a unique CC, e.g., SEC(4) and SEL(4) were only found in CC8 isolates, TSST1(3) was observed in CC30 isolates, and SEP(3) was only present in CC7 isolates. Specific variants of SEIW and SEX also corresponded to the same CC, e.g., SEX(5) for CC8 (52), SEX(9) and SEX(8) for CC5 (49 isolates) or SEX(10) for CC389 (25 isolates) and SELW(5) and (6) for CC1 (68 isolates) or SEIW(3) for CC8 (56 isolates). Similarly, a unique combination of SE variants from the *egc* cluster, SEG(4), SEI(4), SEM(5), SEN(4), SEO(4) and SEIU(1), was only associated with CC30 (28 isolates regardless of the year and context of isolation). Some 208 isolates typed 11 CCs harbored *se* genes with no predicted reading frame. This was the case for *sew* in all isolates from CC5, CC7, CC15, CC88, CC97, and CC504, *sex* in all isolates typed as CC15, CC45, and CC88, *seu* in isolates from CC5 and *sel* in CC12 isolates.

As for WGS methods, 249 cSNP-Ts were in accordance with 246 cgMLST-CTs (Supplementary Table 2). Isolates clustered into two distinct cSNP-Ts (cSNP-T 113 and cSNP-T 115) and belonged to a single cgMLST-CT 12492. Isolates belonging to two distinct cgMLST-CT (CT, 19942 and CT 12943) actually clustered into five cSNP-Ts (113,114,115,118, and 121). As expected from the higher resolution of the WGS methods, more than one distinct cgMLST-CT and cSNP-T were grouped in the same CC or Family.

The phylogenetic analysis from recombination-filtered genomic sequences using cgMLST and cSNP led to an equivalent reconstruction (Fig. 3) with a cophenetic value of 0.996 and isolates from distinct cgMLST CTs or cSNP-Ts were often found on the same branches of the tree (not shown). Interestingly, the phylogenetic relationships were equally inferred by both phylogenies for isolates grouped into 11 PFGE Families and 10 CCs (Fig. 3). However, some discrepancies were also observed. Isolates belonging to PFGE Family 1 typed as CC5, CC7, and CC12 were distributed into two distinct and separate clades in both

phylogenies. These results showed the congruence of conventional methods in population structure since most isolates from the same CCs/Families grouped together regardless of the genomic methodology used. However, these methods cannot represent the true genetic distance of bacterial strains, as opposed to WGS-based phylogenies.

### 3.3. Assessment of the congruence among typing methods for the retrospective investigation of SFPOs

In order to assess which typing methods are most valuable for S. aureus outbreak investigations, we examined the concordance of the six methods in attributing the isolates to similar or related strains.

In total, we studied 31 SFPOs (Table 5; Supplementary Table 5), for which at least three isolates were recovered from the examined samples. In all, 25 SFPOs (upper panel, Table 5) were defined as “food isolate SFPOs” since only food isolates were recovered during the investigation. Additionally, 6 SFPOs were defined as “strong evidence SFPOs” (lower panel, Table 5), based on previous studies (Denayer et al., 2017; Kérouanton et al., 2007; Ostyn et al., 2010; Roussel et al., 2015) and on the availability of isolates from both human cases and foods.

In total for 27 SFPOs, the six methods provided the same results in attributing outbreak isolates to the same number of strains, in agreement with the measures of congruence between the tested methods.

In 21 out of the 25 “food isolate SFPOs”, the isolates were clustered in the same way by all methods, and more than one strain type was identified in 11 SFPOs. For SFPO Nos. 42, 66, 87, 159, 172, 202, and 205 all methods performed equally to explore strain-level diversity within the same food sample.

However, for four SFPOs, the six methods identified a different number of strains. In SFPO No. 194, WGS methods, MLST and *se/SE* typing attributed the five isolates to two distinct strains, while PFGE typed them to three distinct PTs (263, 265, and 182). Even though the isolate with PT 263 belonged to the same Family as the isolates typed PT 265, their DNA pattern differed by 4 bands, which was below the PFGE strain relatedness threshold used in this study.

In SFPO No. 92, MLST attributed the five isolates to two distinct strains, compared with the three strains identified by the WGS methods, PFGE and *se/SE* typing.

The cases of SFPO Nos. 158 and 160 were emblematic. For SFPO No.158, among the four isolates examined, the cgMLST, MLST, *se/SE* typing, and PFGE distinguished only two strains while cSNP discriminated four distinct strains. In particular, the cSNP analysis identified 16 and 18 SNPs of difference between two isolates, just one and three SNPs more than the cut-off (Supplementary Table 7), respectively. In SFPO No. 160, cSNP attributed the five isolates to five different strains, cgMLST to four strains at the threshold limit for cgMLST (Supplementary Table 6), while *se/SE* typing, PFGE, and MLST discriminated only three strains. The discrepancy observed between WGS approaches and conventional methods relies on the higher discrimination power provided by WGS approaches. However, the divergence between cSNP and cgMLST could depend on the thresholds established in the literature, which may not yet be optimal for food isolates.

Finally, among the six “strong evidence” SFPOs, the perfect
<table><caption>**Table 4** Adjusted Rand index and Adjusted Wallace coefficient (95% CI) for six typing methods.</caption><thead><tr><th></th><th>MLST CC</th><th>MLST ST</th><th>PFGE F</th><th>PFGE PT</th><th>cgMLST CT</th><th>cSNP-T</th><th>P/A se</th><th>MLST CC</th><th>MLST ST</th><th>PFGE F</th><th>PFGE PT</th><th>cgMLST CT</th><th>cSNP-T</th><th>P/A se</th><th>SE</th></tr></thead><tbody><tr><td>MLST CC</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.621<br>(0.546-0.696)</td><td>0.778<br>(0.710-0.847)</td><td>0.065<br>(0.044-0.087)</td><td>0.033<br>(0.016-0.050)</td><td>0.032<br>(0.015-0.048)</td><td>0.258<br>(0.206-0.311)</td><td>0.157<br>(0.116-0.199)</td></tr><tr><td>MLST ST</td><td>0.766</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1<br>(1,000-1,000)</td><td>0.935<br>(0.889-0.981)</td><td>0.103<br>(0.072-0.134)</td><td>0.053<br>(0.028-0.078)</td><td>0.051<br>(0.026-0.076)</td><td>0.379<br>(0.313-0.445)</td><td>0.233<br>(0.177-0.290)</td><td></td></tr><tr><td>PFGE F</td><td>0.698</td><td>0.627</td><td></td><td></td><td></td><td></td><td></td><td>0.632<br>(0.548-0.715)</td><td>0.471<br>(0.389-0.553)</td><td>0.057<br>(0.038-0.076)</td><td>0.026<br>(0.012-0.041)</td><td>0.025<br>(0.011-0.040)</td><td>0.194<br>(0.148-0.241)</td><td>0.120<br>(0.086-0.154)</td><td></td></tr><tr><td>PFGE PT</td><td>0.122</td><td>0.185</td><td>0.108</td><td></td><td></td><td></td><td></td><td>0.930<br>(0.885-0.974)</td><td>0.908<br>(0.864-0.953)</td><td>1<br>(1,000-1,000)</td><td>0.430<br>(0.346-0.513)</td><td>0.424<br>(0.341-0.507)</td><td>0.778<br>(0.723-0.833)</td><td>0.587<br>(0.508-0.666)</td><td></td></tr><tr><td>cgMLST CT</td><td>0.064</td><td>0.101</td><td>0.051</td><td>0.585</td><td></td><td></td><td></td><td>1<br>(1,000-1,000)</td><td>1<br>(1,000-1,000)</td><td>0.986<br>(0.978-0.994)</td><td>0.919<br>(0.893-0.945)</td><td>0.960<br>(0.944-0.975)</td><td>0.987<br>(0.980-0.995)</td><td>0.967<br>(0.956-0.979)</td><td></td></tr><tr><td>cSNP-T</td><td>0.061</td><td>0.097</td><td>0.049</td><td>0.585</td><td>0.977</td><td></td><td></td><td>1<br>(1,000-1,000)</td><td>1<br>(1,000-1,000)</td><td>1<br>(1,000-1,000)</td><td>0.941<br>(0.818-0.964)</td><td>0.996<br>(0.992-1.000)</td><td>0.987<br>(0.979-0.994)</td><td>0.966<br>(0.954-0.978)</td><td></td></tr><tr><td>P/A se</td><td>0.372</td><td>0.466</td><td>0.295</td><td>0.238</td><td>0.147</td><td>0.149</td><td></td><td>0.663<br>(0.633-0.694)</td><td>0.605<br>(0.549-0.661)</td><td>0.615<br>(0.569-0.661)</td><td>0.141<br>(0.100-0.181)</td><td>0.083<br>(0.048-0.118)</td><td>0.178<br>(0.046-0.115)</td><td>0.915<br>(0.844-0.985)</td><td></td></tr><tr><td>SE</td><td>0.269</td><td>0.366</td><td>0.210</td><td>0.341</td><td>0.297</td><td>0.301</td><td>0.561</td><td>0.915<br>(0.836-0.994)</td><td>0.843<br>(0.754-0.931)</td><td>0.857<br>(0.766-0.948)</td><td>0.240<br>(0.117-0.313)</td><td>0.176<br>(0.111-0.253)</td><td>0.080<br>(0.080-1.000)</td><td>0.404<br>(0.335-0.474)</td><td></td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Strength of concordance between typing methods as determined by the AW coefficient. Circles include the typing assignments and arrows of different widths correspond to different AW values.</figcaption></figure>

congruence of the six methods in typing isolates to similar strains confirmed previous findings on the epidemiologic linkage between food and human cases (Supplementary Table 5).

## 4. Discussion

The present study aimed to identify the strengths and weaknesses of six typing techniques and to determine which method or methods would be best suited for accurately discriminating *S. aureus* strains for epidemiologic applications. Since no definitions and guidelines on the performance of typing methods are available for *S. aureus*, this study represents the first and most extensive evaluation of multiple approaches for population-based studies and outbreak investigations. Compared to previous studies (Babenko, 2016; Peacock et al., 2002), we used a larger and diverse number of isolates (351) that included mostly food isolates from various contexts in France (outbreak, own-check) and differing in year and enterotoxin gene content. Moreover, since the detection of enterotoxin genes in isolates is often used to predict the toxins potentially involved in SFPOs, we included for the first time *se* gene content and SE sequence diversity as new methods for typing *S. aureus*.

Population structure studies aim at grouping strains of different types and from various geographic origins and years in order to identify the spread of a particular strain and the flow from one group to another over time and space. In these cases, a valuable bacterial typing technique must have a high discriminatory power and be able to elucidate true genetic relationships. With an ID of 0.99, both cgMLST and SNP methods led to a near equivalent structuration at the level of cluster resolution, which resulted in an identical and higher number of groups compared to the less discriminating MLST CC and PFGE Families approaches. Moreover, a near perfect congruence and concordance of results was observed between the two WGS methods, and only in 6 cases
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** PFGE-based dendrogram, MLST typing and presence of *se* genes showing the relationships of *S. aureus* isolates. Distinct colored PFGE Families were assigned by ≥ 70% pattern similarity. Only Families and MLST CCs represented by more than 4 isolates are shown. The number of isolates per Family and CC is indicated in parentheses. The heatmap shows the presence (in black) or absence (in white) of the *se* genes. * indicates genes belonging to the *ecg* cluster.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Population structure of *S. aureus*. cgMLST (on the left) and cSNP (on the right) mid-point rooted co-phylogenetic trees. Connections between isolates are colored according to the MSLT CC annotation, and PFGE Families are indicated on the branches. CCs and PFGE Families represented by more than 4 isolates are shown. CCs that are poorly represented are colored in gray.</figcaption></figure>
<caption>**Table 5**</caption>

**Concordance of methods in the investigation of 31 SFPOs.** For each SFPO, the number of isolates of food and human origin is shown. The fifth column indicates the total number of isolates per SFPO. For each method and output, the number of strain profiles assigned is indicated. SFPOs for which all methods yielded the same results are highlighted in bold. Underlined SFPOs are those for which a deviation was observed for at least one method. The framed boxes indicate the observed differences. * means that one of the PFGE PTs differs by no more than one genetic event from the other, and that these isolates can be considered related according to (24). After interpretation, the number of isolates defined by PFGE corresponds to the number of different Families observed.

<table><thead><tr><th rowspan="2">SFPO No.</th><th rowspan="2">Number of samples</th><th rowspan="2">Number of food isolates (per food sample)</th><th rowspan="2">Number of human isolates (per human sample)</th><th rowspan="2">Total number of isolates</th><th colspan="8">Number of strain types</th></tr><tr><th>PFGE F</th><th>PFGE PT</th><th>MLST CC</th><th>MLST ST</th><th>P/A se SE</th><th>cgMLST CT&lt;24</th><th>cSNP-T&lt;15</th></tr></thead><tbody><tr><td colspan="11" style="text-align: center; font-weight: bold;">25 food isolate SFPOs</td></tr><tr><td>23</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>55</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1*</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>61</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>64</td><td>1</td><td>3</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>65</td><td>3</td><td>3</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>102</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>145</td><td>4</td><td>1</td><td>-</td><td>4</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>153</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>166</td><td>4</td><td>1</td><td>-</td><td>4</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>182</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>188</td><td>2</td><td>1(1),1(2)</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>206</td><td>16</td><td>1</td><td>-</td><td>16</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>208</td><td>4</td><td>1</td><td>-</td><td>4</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>220</td><td>4</td><td>1</td><td>-</td><td>4</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>42</td><td>1</td><td>4</td><td>-</td><td>4</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td></tr><tr><td>66</td><td>4</td><td>1</td><td>-</td><td>4</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td></tr><tr><td>87</td><td>1</td><td>5</td><td>-</td><td>5</td><td>4</td><td>4</td><td>4</td><td>4</td><td>4</td><td>4</td><td>4</td></tr><tr><td>92</td><td>5</td><td>1</td><td>-</td><td>5</td><td>2</td><td>3</td><td>2</td><td>2</td><td>3</td><td>3</td><td>3</td></tr><tr><td>158</td><td>2</td><td>2(2)</td><td>-</td><td>4</td><td>2</td><td>2*</td><td>2</td><td>2</td><td>2</td><td>2</td><td>4</td></tr><tr><td>159</td><td>3</td><td>2(2)</td><td>-</td><td>4</td><td>3</td><td>4</td><td>3</td><td>4</td><td>4</td><td>4</td><td>4</td></tr><tr><td>160</td><td>4</td><td>2(2)</td><td>-</td><td>5</td><td>3</td><td>3*</td><td>3</td><td>3</td><td>3</td><td>4</td><td>5</td></tr><tr><td>172</td><td>3</td><td>2(1),1(2)</td><td>-</td><td>4</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td></tr><tr><td>194</td><td>4</td><td>3(1),1(2)</td><td>-</td><td>5</td><td>2</td><td>3</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td></tr><tr><td>202</td><td>2</td><td>2(2)</td><td>-</td><td>4</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td></tr><tr><td>205</td><td>3</td><td>1</td><td>-</td><td>3</td><td>2</td><td>2</td><td>1</td><td>2</td><td>2</td><td>2</td><td>2</td></tr><tr><td colspan="11" style="text-align: center; font-weight: bold;">6 epidemiologically "strong" SFPOs</td></tr><tr><td>3</td><td>3</td><td>1</td><td>2</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>8</td><td>3</td><td>1</td><td>2</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>20</td><td>3</td><td>1</td><td>2</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>27</td><td>3</td><td>1</td><td>2</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td></tr><tr><td>142</td><td>2</td><td>1</td><td>4</td><td>6</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>162</td><td>4</td><td>2(1)</td><td>1(2)</td><td>4</td><td>1</td><td>1*</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr></tbody></table>

did the cgMLST CT not directly correlated with cSNP-T. This can be explained by the fact that in the cgMLST, multiple SNPs of the core genes are collapsed into allele numbers and lead to underestimation of the SNP distance. By contrast, allele differences resulting from insertions and deletions might overestimate the SNP distance. Similarly, the MLST and PFGE clustered most of the isolates into the same groups (ARI = 70%), as previously shown for other pathogens (Félix et al., 2018; Gona et al., 2020).

It should be pointed out that the clusters obtained by all approaches were not directly measuring the DNA sequence. Importantly, for isolates grouped in the same PFGE Family, similar or identical patterns do not necessarily mean that DNA fragments of the same size have the same sequence. MLST grouping in CCs is based on number assignments with no representation of the gene sequence. Concerning cgMLST or cSNP, the clusters are instead built based on the number of alleles or SNP differences. Here, further assessment of phylogeny by WGS methods
provided a more robust interpretation and understanding of genetic
distances. The cSNP and cgMLST trees were highly congruent, as pre-
viously observed (Gona et al., 2020; Henri et al., 2017), despite notable
differences in topology. Given that both phylogenies were gener-
ated with the same method (maximum likelihood), the reason behind
the observed differences was the intrinsic specificities and resolution of
the two techniques. The cSNP analysis inferred phylogeny based on
variants detected throughout the core genome of the isolates being
compared to a common reference, including coding sequences and
intergenic regions. On the other hand, cgMLST analyzed the allelic dif-
ference only from the coding sequence of a fixed set of S. aureus core
genes. Hence, the tree derived from the cSNPs provided a more robust
phylogenetic inference of the isolates than the tree derived from cgMLST
concatenated alleles. Both WGS phylogenies validated the relationship
of the isolates as defined by CCs and Families, except for isolates
grouped in CC5 and PFGE Family 1. This indicated moderate robustness
of the conventional methods for population structure studies, but
highlighted that both PFGE and MLST can lead to false clustering of
phylogenetically distant isolates. Therefore, cgMLST or cSNP phylog-
enies should be the methods of choice for the analysis of the S. aureus
population structure, in support of clustering by typing methods. Future
work will be carried out, the development and application of statistical
methods, such as discriminant principal component analysis (DAPC)
(Jombart et al., 2010), to phylogenetic data could help in defining
genetically structured populations more accurately.

In *S. aureus* population structure studies, profiling of enterotoxin
genes could be included to identify the virulence determinants that
contribute, for example, to the distribution of bacterial types. Since most
enterotoxin genes are carried on accessory genetic elements in *S. aureus*,
*se* typing could reveal, at a higher resolution, the differences between
closely related isolates. The newly presented *se* and especially SE typing
methods displayed intermediate (AW CI 60%) and high congruence (AW
CI 91%), respectively with the MLST CC method. Interestingly, a pre-
vious study showed a relationship between enterotoxins and MLST CCs
in strains of human and cattle origin, where a specific vSaβ type carrying
the *egc* cluster was always found to be associated with a specific CC
(Schwendimann et al., 2021). Our results extended this observation to
strains of food origin and to all types of *se* genes and protein variants. A
single *se* gene profile predominated in a given MLST group, although
more than one toxin gene pattern was detected. With respect to SE
typing, the same variant was found within isolates grouped by the same
*se* profile, especially for the most frequent genes, e.g., *sew* and *sex*, while
more than one variant was observed for *se* genes located on mobile el-
ements such as *sea* (prophage) or *sed*, *sej*, and *ser* (plasmids). While MLST
resolution is masked by the use of seven slowly evolving housekeeping
genes, the number of profiles obtained from the combination of all the
possible SE variants (up to 12 per SE; Supplementary Fig. 1) and possible
combination of the 33 *se* genes might be higher. Moreover, a wider
sequence diversity of *se* genes might be expected since their evolution
would provide a significant benefit to mimic host cell proteins or in-
crease toxicity. Additionally, the higher discriminatory power of SE
profiles (ID = 0.979) was reflected by the higher number of SE profiles
(124) found among the isolates compared to the number of *se* gene
profiles (61), MLST STs (59) or CCs (26). Overall, our observations
suggest an advantage of *se*/SE typing to predict the results of MLST when
using, for example, methods based on SE detection by mass spectrom-
etry and *se* gene search by PCR. If a combination of *se* genes is specific to
a CC, the detection of their products by mass spectrometry (Lefebvre
et al., 2021) could make it possible to define the CC of the isolate pro-
ducing the toxin. Similarly, PCR of *se* genes may be sufficient to identify
the presumed CC, without going through additional MLST typing.
Future development of *se*/SE typing methods based on the evaluation of
*se* gene polymorphisms could also make it possible to assign a unique
identifier, in the same way as MLST CC and ST. Interestingly,
multi-virulence-locus sequence typing approaches have been developed
for typing food-related *Listeria* and *S. enterica* strains, and *S. aureus*

strains recovered from nasal swabs of patients (Chen et al., 2017; Liu
et al., 2011; Verghese et al., 2012).

We acknowledge that the performances of the methods on the
structuration of our *S. aureus* collection was limited by the number and
origin of strains analyzed. Naturally, comparing more isolates from
different sources, geographic origins, and years of isolation would in-
crease the diversity of the population under study and will help in
determining whether the correspondences observed among the methods
are representative, or whether they are biased by sampling.

For outbreak investigation, typing methods are useful to confirm the epidemiologic relationships between strains from the human cases and a food item, and to identify case clusters and sources. Typing is also required to discriminate if one or more strains are present in the food. It is important to stress that we do not expect isolates from an outbreak strain to be identical. Considering the high rate of recombination and mutation of *S. aureus* (Everitt et al., 2014; Szafrańska et al., 2019), we might expect that genetic changes occur during cultivation of the isolates in the laboratory prior to their characterization and typing. This is why epidemiologic surveillance relies on typing with predefined thresholds to define relatedness among isolates (Bannerman et al., 1995; Coll et al., 2020; Szarvas et al., 2021).

Here, the retrospective analysis of 31 SFPOs proved a high degree of consistency of the six typing methods for the identification of the number of strains present in a given sample, and for the confirmation of the epidemiologic linkage. Overall and except for SFPO No. 160, the *se*/SE profile always corresponded to the results defined by more than one typing method, suggesting the value of this new approach for differentiating strains in the context of outbreak investigation. The MLST method could not discriminate, at the same level as the other methods, the isolates of only SFPO No. 92 due to its lower resolution power, as anticipated.

In two cases (SFPOs Nos. 158 and 160), the two WGS approaches
showed discordant results between each other, and with the other
methods. This was not only due to the intrinsic specificities of the two
most discriminating approaches, but also to the choice of thresholds for
AD and SNP differences that impacted the interpretation of cgMLST and
cSNP results, respectively. In SFPO No. 158, three isolates were different
from each other by 16-22 SNPs, while they had from 17 to 22 cgMLST
ADs, leading to 7 SNPs more and 2 ADs less than the thresholds used.
Similarly, in SFPO No. 160, the four isolates were classified in two
different CTs differing in 23-24 ADs, while they belonged to four distinct
cSNP-Ts because of 24-26 cSNPs of difference, leading to 10 SNPs more
than the cut-off. Indeed, the application of a more relaxed threshold
would have typed the strains in the same way as the other methods for
these two outbreaks. How much deviation from the cSNP and cgMLST
thresholds should be considered acceptable, without causing significant
differences in the interpretation of the results? For bacterial food path-
ogens, these cut-offs could be dynamic and vary depending on food-
associated factors. Both thresholds were defined in the context of
methicillin-resistant S. aureus hospital outbreaks without considering
the pathogen mutation rate, the duration of source contamination, and
the impact of food related factors. Future work will be carried out to
confirm or better refine the current genetic thresholds for cgMLST and
cSNP by incorporating mutation rate and outbreak duration (Duval
et al., 2023; Octavia et al., 2015) in order to consider S. aureus food
isolates as epidemiologically related.

Typing methods differ with respect to the time required, ease of use,
equipment needs, staff training, setup costs, and cost per test. Hence, the
choice of a typing method and interpretation of results rely on the re-
sources of the laboratory. Thanks to the evaluation of typing methods
carried out in this study, we can propose a method or combination of
methods that public health laboratories can choose to apply depending
on their resources and needs with respect to a given epidemiologic
question.

Since PFGE showed high resolution and epidemiologic concordance
and is inexpensive, we can still recommend this method in the context of
outbreaks to characterize and identify related strains. However, standardization of protocols and profile databases (such as PulseNet) will be needed to enhance the reproducibility of PFGE results between laboratories. Since PFGE is technically demanding, labor-intensive, and time-consuming, this method is not indicated when a large number of isolates are to be analyzed. Moreover, it cannot infer real genetic distances among isolates subtyped or clustered into the same Family, and is therefore not appropriate for population structure studies. The MLST method is an unambiguous typing method thanks to its internationally standardized nomenclature; it is also highly reproducible and less laborious than PFGE. However, for the same reasons as for PFGE, typing by the MLST method insufficiently discriminates strains for structural studies. In the context of SFPO investigation, this method can be associated with, if not completely substituted by, *se/SE* typing to confirm or increase its discriminatory power. However, even though several protocols for PCR of *se* genes are available in the literature, additional research will be needed to develop and validate the applicability of *se/SE* profile-based methods for epidemiologic typing.

Thanks to their high precision and discriminatory power, WGS-based methods are the most appropriate approaches for all epidemiologic applications. WGS has the advantage of combining in a single workflow several typing systems (cgMLST, cSNP, *se/SE* profile, and MLST) and phylogeny. As the costs of WGS continue to decline, this method may become a primary typing approach in the future. Several guidance documents for generating and analyzing WGS data (ISO 23418:2022, 2022; Michelacci et al., 2023) are now available and in recent years, WGS pipelines for bacterial genome analysis have been developed. The EFSA One Health WGS analytical pipeline is designed for analyzing paired-end Illumina data for *S. enterica*, *L. monocytogenes*, and *E. coli* (Authority (EFSA) et al., 2022). Bactopia, a complete pipeline for the analysis of bacterial genomes, including *S. aureus*, has been developed (Petit and Read, 2020). Still, for *S. aureus* one of the problems related to the application of WGS for epidemiologic applications is the limited standardization in terms of tools and parameters. A number of WGS tools are, however, available and new ones are constantly being developed. For SNP analysis, several algorithms and parameters for SNPs calling, quality, and filtering can be applied, and different strategies for choosing a valuable reference genome can be employed. Regarding cgMLST, the existing tools differ in allele calling algorithms, and central and decentralized databases like the Chewie–NS systems are available for allocation of allele types for several pathogens, but not yet for *S. aureus* (Deneke et al., 2021). These technical aspects can impede WGS standardization within and between laboratories, limiting the reproducibility and comparability of results, and leading to incorrect epidemiologic interpretations.

Validation of WGS tools on documented datasets could be a way to ensure the robustness of results instead of standardization. In this scenario, even though WGS data would not be directly comparable, the interpretation of results and the conclusions would be similar. Future work will be carried out to harmonize and standardize WGS methods on a collection of more diverse strains. In this respect, the tools, parameters and protocols available need to be benchmarked for the analysis of *S. aureus* genomes. Advancement in this direction has been recently achieved on assembly and cgMLST tools for *S. aureus* (Merda et al., 2024).

## Funding information

This work was supported by the European Reference Laboratory for Coagulase-Positive Staphylococci (EURL CPS) Grant number (SMP-FOOD-2023-EURL-EURC-AG-IBA). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor HaDEA can be held responsible for them.

## CRediT authorship contribution statement

**Noémie Vingadassalon:** Writing – original draft, Visualization, Validation, Investigation, Formal analysis, Conceptualization. Déborah Merda: Validation, Methodology, Formal analysis, Conceptualization. **Arnaud Felten:** Software, Methodology. **Virginie Chesnais:** Software, Methodology. **Christos Kourtis:** Resources. **Tom Van Nieuwenhuy sen:** Resources. **Yacine Nia:** Resources, Project administration, Funding acquisition. **Jacques-Antoine Hennekinne:** Resources, Project administration, Funding acquisition. **Marina Cavaiuolo:** Writing – review & editing, Validation, Supervision, Conceptualization.

## Declaration of competing interest

The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consul tancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

## Acknowledgements

We would like to thank Olivier Firmesse for critical evaluation of the study design, Benjamin Felix for support in PFGE analysis, and Laurent Guillier for support in the representation of the cophylogenetic tree. For the purpose of Open Access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.

## Appendix A Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.fm.2024.104625.

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Update

Food Microbiology

Volume 125, Issue, January 2025, Page

DOI: https://doi.org/10.1016/j.fm.2024.104648
Corrigendum

Corrigendum to “Epidemiology of *Staphylococcus aureus* food isolates: Comparison of conventional methods with whole genome sequencing typing methods” [Food Microbiol. volume 125, January 2025, 104625]

Noémie Vingadassalon<sup>a,∗</sup>, Déborah Merda<sup>b</sup>, Arnaud Felten<sup>c</sup>, Virginie Chesnais<sup>b</sup>, Christos Kourtis<sup>d</sup>, Tom Van Nieuwenhuysen<sup>e</sup>, Yacine Nia<sup>a</sup>, Jacques Antoine Hennekinne<sup>a</sup>, Marina Cavaiuolo<sup>a</sup>

The authors regret that in the original publication of this article there
was an inversion between the images in Figure 2 and Figure 3. The
legend is correct but the images are not. Here are the correct corre-
sponding images:

<figure><img src="image_7.png" /></figure>

Check for
updates

<footnote><sup>a</sup> ANSES, Laboratory for Food Safety, Staphylococcus, Bacillus and Clostridium Unit (SBCL), 94706, Maisons-Alfort, France</footnote>

<footnote><sup>b</sup> ANSES, Laboratory for Food Safety, Shared Support Service for Data Analysis (SPAAD), 94706, Maisons-Alfort, France</footnote>

<footnote><sup>c</sup> ANSES, Ploufragan-Plouzané-Niort Laboratory, GVB Unit, 22440, Ploufragan, France</footnote>

<footnote><sup>d</sup> State General Laboratory, Food Microbiology Laboratory, 1082, Nicosia, Cyprus</footnote>

<footnote><sup>e</sup> Sciensano, Foodborne Pathogens, 1050, Ixelles, Belgium</footnote>

<footnote>DOI of original article: https://doi.org/10.1016/j.fm.2024.104625.</footnote>

<footnote>* Corresponding author. ANSES, Laboratory for Food Safety, SBCL Unit, Maisons-Alfort location, F-94701, France.
*E-mail address: noemie.vingadassalon@anses.fr (N. Vingadassalon).</footnote>
<figure><img src="image_8.png" /><figcaption>**Fig. 2.** PFGE-based dendrogram, MLST typing and presence of *se* genes showing the relationships of *S. aureus* isolates. Distinct colored PFGE Families were assigned by ≥ 70% pattern similarity. Only Families and MLST CCs represented by more than 4 isolates are shown. The number of isolates per Family and CC is indicated in parentheses. The heatmap shows the presence (in black) or absence (in white) of the *se* genes. * indicates genes belonging to the *ecg* cluster.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 3.** Population structure of *S. aureus*. cgMLST (on the left) and cSNP (on the right) mid-point rooted co-phylogenetic trees. Connections between isolates are colored according to the MSLT CC annotation, and PFGE Families are indicated on the branches. CCs and PFGE Families represented by more than 4 isolates are shown. CCs that are poorly represented are colored in gray.</figcaption></figure>

The authors regret that in the legend of Table 5 the sentence "*means that one of the PFGE PTs differs by no more than one genetic event from the other, and that these isolates can be considered related according to (24)" should be modified to "*means that one of the PFGE PTs differs by no more than one genetic event from the other, and that these isolates can be considered related according to Bannerman et al., (1995)".
<table><caption>Table 5. Concordance of methods in the investigation of 31 SFPOs. For each SFPO, the number of isolates of food and human origin is shown. The fifth column indicates the total number of isolates per SFPO. For each method and output, the number of strain profiles assigned is indicated. SFPOs for which all methods yielded the same results are highlighted in bold. Underlined SFPOs are those for which a deviation was observed for at least one method. The framed boxes indicate the observed differences. * means that one of the PFGE PTs differs by no more than one genetic event from the other, and that these isolates can be considered related according to (24). After interpretation, the number of isolates defined by PFGE corresponds to the number of different families observed.</caption><thead><tr><th rowspan="2">SFPO No.</th><th rowspan="2">Number of samples</th><th rowspan="2">Number of food isolates (per food sample)</th><th rowspan="2">Number of human isolates (per human sample)</th><th rowspan="2">Total number of isolates</th><th colspan="8">Number of strain types</th></tr><tr><th>PFGE F</th><th>PFGE PT</th><th>MLST CC</th><th>MLST ST</th><th>P/A se SE</th><th>cgMLST CT&lt;24</th><th>cSNP- T&lt;15</th></tr></thead><tbody><tr><td colspan="11">25 food isolate SFPOs</td></tr><tr><td>23</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>55</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1*</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>61</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>64</td><td>1</td><td>3</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>65</td><td>3</td><td>3</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>102</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>145</td><td>4</td><td>1</td><td>-</td><td>4</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>153</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>166</td><td>4</td><td>1</td><td>-</td><td>4</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>182</td><td>3</td><td>1</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>188</td><td>2</td><td>1(1),1(2)</td><td>-</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>206</td><td>16</td><td>1</td><td>-</td><td>16</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>208</td><td>4</td><td>1</td><td>-</td><td>4</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>220</td><td>4</td><td>1</td><td>-</td><td>4</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>42</td><td>1</td><td>4</td><td>-</td><td>4</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td></tr><tr><td>66</td><td>4</td><td>1</td><td>-</td><td>4</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td></tr><tr><td>87</td><td>1</td><td>5</td><td>-</td><td>5</td><td>4</td><td>4</td><td>4</td><td>4</td><td>4</td><td>4</td><td>4</td></tr><tr><td>92</td><td>5</td><td>1</td><td>-</td><td>5</td><td>2</td><td>3</td><td>2</td><td>2</td><td>3</td><td>3</td><td>3</td></tr><tr><td>158</td><td>2</td><td>2(2)</td><td>-</td><td>4</td><td>2</td><td>2*</td><td>2</td><td>2</td><td>2</td><td>2</td><td>4</td></tr><tr><td>159</td><td>3</td><td>2(2)</td><td>-</td><td>4</td><td>3</td><td>4</td><td>3</td><td>4</td><td>4</td><td>4</td><td>4</td></tr><tr><td>160</td><td>4</td><td>2(2)</td><td>-</td><td>5</td><td>3</td><td>3*</td><td>3</td><td>3</td><td>3</td><td>4</td><td>5</td></tr><tr><td>172</td><td>3</td><td>2(1),1(2)</td><td>-</td><td>4</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td></tr><tr><td>194</td><td>4</td><td>3(1),1(2)</td><td>-</td><td>5</td><td>2</td><td>3</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td></tr><tr><td>202</td><td>2</td><td>2(2)</td><td>-</td><td>4</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td></tr><tr><td>205</td><td>3</td><td>1</td><td>-</td><td>3</td><td>2</td><td>2</td><td>1</td><td>2</td><td>2</td><td>2</td><td>2</td></tr><tr><td colspan="11">6 epidemiologically "strong" SFPOs</td></tr><tr><td>3</td><td>3</td><td>1</td><td>2</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>8</td><td>3</td><td>1</td><td>2</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>20</td><td>3</td><td>1</td><td>2</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>27</td><td>3</td><td>1</td><td>2</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td><td>3</td></tr><tr><td>142</td><td>2</td><td>1</td><td>4</td><td>6</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>162</td><td>4</td><td>2(1)</td><td>1(2)</td><td>4</td><td>1</td><td>1*</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr></tbody></table>

The authors would like to apologise for any inconvenience caused.

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