VirulenceFinder for Enterococcus faecium and Enterococcus lactis: an enhanced database for detection of putative virulence markers by using whole-genome sequencing data

ABSTRACT

Enterococcus faecium (Efm) is a leading cause of hospital-associated (HA) infections, often enriched in putative virulence markers (PVMs). Recently, the Efm clade B was assigned as Enterococcus lactis (Elts), which usually lack HA-Efm infection markers. Available databases for extracting PVM are incomplete and/or present an intermix of genes from Efm and Enterococcus faecalis, with distinct virulence profiles. In this study, we constructed a new database containing 27 PVMs [acm, scm, sgrA, ecbA, fnm, sagA, hylEfm, ptsD, orf1481, fms15, fms21-fms20 (pili gene cluster 1, PGC-1), fms14-fms17-fms13 (PGC-2), empA-empB-empC (PGC-3), fms11-fms19-fms16 (PGC-4), ccpA, bepA, gls20-glsB1, and gls33-glsB] from nine reference genomes (seven Efm + two Elts). The database was validated against these reference genomes and further evaluated using a collection of well-characterized Efm (n = 43) and Elts (n = 7) control strains, by assessing PVM presence/absence and its variants together with a genomic phylogeny constructed as single-nucleotide polymorphisms. We found a high concordance between the phylogeny and in silico findings of the PVM, with Elts clustering separately and mostly carrying Elts-specific PVM gene variants. Based on our validation results, we recommend using the database with raw reads instead of assemblies to avoid missing gene variants. This newly constructed database of 27 PVMs will enable a more comprehensive characterization of Efm and Elts based on WGS data. The developed database exhibits scalability and boasts a range of applications in public health, including diagnostics, outbreak investigations, and epidemiological studies. It can be further used in risk assessment for distinguishing between safe and unsafe enterococci.

IMPORTANCE

The newly constructed database, consisting of 27 putative virulence markers, is highly scalable and serves as a valuable resource for the comprehensive characterization of these closely related species using WGS data. It holds significant potential for various public health applications, including hospital outbreak investigations, surveillance, and risk assessment for probiotics and feed additives.

INTRODUCTION

Enterococcus faecium (Efm) is one of the most common causes of hospital-associated (HA) infections and is among the most difficult-to-treat organisms (1–3). Traditionally, phylogenetic analysis of Efm has supported the species split into two major clades: clade A, which includes most isolates associated with hospitalized humans, and clade B, which includes isolates from community-associated (CA) individuals. However, recent research has reassigned clade B as Enterococcus lactis (Elts) species, which generally does not contain the markers present in Efm isolates of clade A (4). This implies that the population structure of Efm is currently exclusively represented by what is referred to as clade A. Clade A further subdivides into A1, primarily comprising clinical isolates, and A2, predominantly consisting of animal-associated isolates. Notably, the classification of animal isolates has been a subject of recent controversy, as these isolates have demonstrated the tendency to form multiple distinct clusters (5). Nonetheless, human A2 carriage is primarily observed in the gut of individuals from the community rather than hospital patients (5–7). When compared to CA Efm isolates or Elts, HA-Efm are clearly associated with increased presence, differential expression, or functional forms of genes encoding a variety of putative or confirmed virulence determinants, enabling them to thrive in the harsh environment of hospitals (8). HA-Efm are often enriched (>25 genes) in a plethora of putative virulence markers (PVMs) including many surface proteins, pili, and secreted virulence factors relevant to the adhesion to host tissues, biofilm production, and pathogenesis (8–10). Whole-genome sequencing (WGS) has provided a better understanding of the core and accessory genomes of commensal and outbreak Efm isolates and unraveled several novel PVM in recent years (11–16). Nevertheless, they are often arbitrarily selected to be tested in epidemiological studies, thus precluding harmonized correlations to infer a pathogenic potential. Moreover, currently available web tools to extract PVM from WGS data [VirulenceFinder, Center for Genomic Epidemiology (CGE), www.genomicepidemiology.org, and virulence factor database (VFDB), http://www.mgc.ac.cn/VFs/] are incomplete and intermix genes from Efm and Enterococcus faecalis that have clear distinct virulence profiles. Finally, the nomenclature of several virulence markers is often confusing (especially for surface genes), and merely assessing the presence/absence of specific genes may not always be sufficient (e.g., pili genes) (9, 11).

Currently, the CGE provides the publicly available, user-friendly web tool VirulenceFinder, which enables the identification of 26 virulence genes in WGS data of enterococci. This database includes only the most important gene markers, 5 from Efm and 22 from E. faecalis, known in 2016, when the database was constructed. Nevertheless, it does not account for the significant differences between species, gene sequences, and the vast body of new knowledge generated through the analysis of WGS data in PVM described afterward in Efm species in particular.

One of the most comprehensive studies on this topic analyzed the distribution of 33 PVMs in a set of 328 diverse Efm isolates (clinical, healthy humans, food-producing animals, foodstuffs, wild birds, and aquatic environments) obtained during 1986–2015, by PCR-based assays and sequencing (9). Freitas et al. described that 21 out of the 33 PVMs were strain-/clade-specific. Building on these findings, we selected these 21 genes and included six additional PVMs that were described after that project, making a total of 27 genes that have been experimentally tested in Efm (or Elts) to create a novel and robust database that should be updated along the growing knowledge of new PVM. Given the genomic proximity between Efm and Elts in comparison to other enterococcal species, we aimed to shape a user-friendly web tool for the detection and extraction of virulence genes from genomes representing both species.

MATERIALS AND METHODS

Selection of virulence markers and construction of Efm-Elts virulence gene database

Based on the results from Freitas et al. (9), 21 out of the 33 genes that were significantly more associated with human infections and epidemic scenarios (but not exclusive) were selected for the database. In addition to the 21 genes selected from the study by Freitas et al. (9), we have incorporated an additional six genes [ccpA (15), bepA (16), gls20-glsB1, and gls33-glsB (17)] that were rarely investigated at the time of the first project. In total, our database includes 27 PVMs that have been experimentally tested in Efm or Elts and have been shown to be differentially distributed between clades or hosts. The genes are involved in different cellular functions and correspond to those (i) encoding cell wall-anchored proteins involved in surface adhesion to the extracellular matrix (acm, scm, fnm, sgrA, ecbA, fms15, and four pili gene clusters); (ii) with a role in carbohydrate metabolism and cell growth (orf1481, ccpA); (iii) encoding phosphotransferase systems (PTSs) important in carbohydrate transport (ptsD, bepA); (iv) that are secreted extracellularly with putative roles in cell growth, biofilm formation (sagA), and colonization loads (hyl_Efm); and (v) encoding general stress proteins important in bile salt tolerance and general intestinal adaptation (gls-like). Despite its relevance in the pathogenesis of Efm (18), the esp gene was not included in our database (see Results and Discussion for explanation). A description of the 27 PVMs is listed in Table 1. A detailed list of the genes, including reference genomes, position in references, and locus tag are listed in Table S1.

TABLE 1.

Gene content of the E. faecium and E. lactis virulence database^a

Gene	Description	Other designations	Variants in the database	Reference
Surface-exposed cell wall-anchored proteins (E. faecium surface proteins)
acm	Adhesin of collagen from E. faecium	fms8	Efm-HV, Efm-CV, Elts-V	(19)
scm	Second collagen adhesin of E. faecium	fms10, orf418	Efm-HV, Efm-CV, Elts-V	(11)
fnm	Fibronectin-binding protein of E. faecium	fbpA	Efm-HV, Efm-CV, Elts-V	(9)
sgrA	Serine-glutamate repeat containing protein A	fms2, orf2351	Efm-HV	(12)
ecbA	E. faecium collagen binding protein A	fms18, orf2430	Efm-HV, Efm-CV	(11)
fms15	E. faecium surface protein 15	orf2514/orf2515	Efm-HV	(11)
PGC-1 cluster [fms21(pilA)-fms20]
fms21	E. faecium surface protein 21	pilA, orf1904	Efm-HV, Efm-CV, Elts-V	(11)
fms20	E. faecium surface protein 20	orf1901	Efm-HV, Efm-CV, Elts-V	(11)
PGC-2 cluster (fms14-fms17-fms13)
fms14	E. faecium surface protein 14	orf2010	Efm-HV, Efm-CV, Elts-V	(11)
fms17	E. faecium surface protein 17	orf2009	Efm-HV, Efm-CV, Elts-V	(11)
fms13	E. faecium surface protein 13	orf2008	Efm-V, Elts-V	(11)
PGC-3 cluster (empABC)
empA	Endocarditis and biofilm-associated pili A	ebpAfm, fms1, orf2571	Efm-HV, Elts-V	(11)
empB	Endocarditis and biofilm-associated pili B	ebpBfm, fms5, orf2570	Efm-HV, Efm-CV, Elts-V	(11)
empC	Endocarditis and biofilm-associated pili C	ebpCfm, fms9, orf2569	Efm-HV, Efm-CV, Elts-V	(11)
PGC-4 cluster (fms11-fms19-fms16)
fms11	E. faecium surface protein 11	orf903	Efm-HV, Elts-V	(11)
fms19	E. faecium surface protein 19	orf905	Efm-HV, Elts-V	(11)
fms16	E. faecium surface protein 16	orf907	Efm-HV, Elts-V	(11)
Carbohydrate metabolism and cell growth
orf1481	Sugar binding protein encoded by a genomic island		Efm-HV	(20)
ccpA	Carbon catabolite control protein A		Efm-HV, Efm-CV, Elts-V	(15)
Phosphotransferase systems
ptsD	Enzyme IID subunit of a phosphotransferase system		Efm-HV	(14)
bepA	Biofilm and endocarditis-associated permease A/PTS transporter subunit EIIC	fruA	Efm-HV, Efm-CV	(16)
Biofilm and colonization
hylEfm	Glycosyl hydrolase		Efm-HV	(21)
sagA	Secreted antigen A		Efm-HV, Efm-CV, Elts-V	(20)
General stress proteins
glsB1	General stress protein GlsB1 (gls20-glsB1 cluster)		Efm-V, Elts-V	(17)
gls20	General stress protein GlsB20 (gls20-glsB1 cluster)		Efm-V, Elts-V	(17)
glsB	General stress protein GlsB (gls33-glsB cluster)		Efm-V, Elts-V	(17)
gls33	General stress protein Gls33 (gls33-glsB cluster)		Efm-V, Elts-V	(17)

^a Efm-HV, E. faecium hospital variant; Efm-CV, E. faecium community variant; Efm-V, E. faecium variant; Elts-V, E. lactis variant.

The Efm-Elts database was constructed as a FASTA database, comprising a total of 27 PVMs. Each sequence represents a specific gene variant (if existing) representing the Efm hospital and Efm community clades or Efm in general, together with the Elts clade. The different variants were assigned based on their comparison with the virulence gene variants previously described by Qin et al. (22) as hospital and community variants extracted from HA and CA genomes, respectively. Some of these genomes were included in this study as HA and CA reference genomes, with some of the latter actually corresponding to Elts. Accordingly, Efm hospital gene variants (designated as Efm-HV here, with an extended description as “Entfm-hosp” in the database) were extracted from one of the four clinical Efm genomes: Efm_DO, E1162, E1636, or TX0082. The Efm community gene variants (designated Efm-CV here, with an extended description as “Entfm-com” in the database) were extracted from one of the three community Efm genomes: C59, VE40C2, or E1071. If the Efm hospital variant and community variant were identical, the variant was designated as an Efm gene variant (Efm-V here and as “Entfm” in the database). The Elts gene variants (Elts-V here and as “Entls” in the database) were extracted from one of the two Elts genomes: TX1330 or 1,141,733 [previously considered as community Efm by Qin et al. (22)]. The complete list of genomes with accession numbers is listed in Table S2. For each gene, the coding sequence derived from gene prediction was extracted from the NCBI nucleotide database.

Sequence data, whole-genome sequencing, and sequence analysis

Paired-end sequence data were either retrieved from the European Nucleotide Archive (https://www.ebi.ac.uk/ena/) or sequenced at Statens Serum Institut (according to Table S3). Genomic DNA was extracted (DNeasy blood and tissue kit; Qiagen, Copenhagen, Denmark) with subsequent library construction (Nextera kit; Illumina, Little Chesterford, UK) and whole-genome sequencing (NextSeq 550; Illumina, Little Chesterford, UK) according to the manufacturer’s instructions, to obtain paired-end reads 2 × 150 bp in length. Raw sequencing data were analyzed using the Bifrost pipeline v. 2.0.8 at Statens Serum Institut (https://github.com/ssi-dk/bifrost) with accepted average coverage of 30× or higher (avg_coverage ≥30). WGS data were either used as raw data or were de novo assembled using SKESA v. 2.2 (23), generated with Bifrost. The complete list of control and test genomes is listed in Table S3 with detailed information and accession numbers.

MLST was inferred with the Bifrost pipeline, using the pubMLST database (24). The genomes were further subtyped using the Efm cgMLST scheme by de Been et al. in SeqSphere+ v.8.0.1 (Ridom, Münster, Germany), with the suggested threshold of ≤20 allele differences (25). New complex types were registered on the cgMLST Nomenclature Server (https://www.cgmlst.org/ncs).

Validation of the database and method

To evaluate the genes in the database and validate the method for predicting the presence of the genes, a data set with raw reads and SKESA assemblies of the nine reference genomes was constructed (control samples in Table S3). VirulenceFinder 2.0 at the CGE was designed to use KMA mapping on raw reads and blastn on assemblies (https://cge.food.dtu.dk/services/VirulenceFinder/). Thus, the database was tested with KMA mapping of both raw reads and assemblies against the Efm-Elts virulence database, using MyKMAfinder 1.0 (https://bitbucket.org/genomicepidemiology/mykmafinder/src/master/) (26), and blastn on assemblies only, by using MyDbFinder 2.0 (https://bitbucket.org/genomicepidemiology/mydbfinder/src/master/). For validation of the method, the PVM from the corresponding reference should be present at identity (%ID) and coverage (%Cov) of 100%.

Evaluation of the database

Forty-three Efm (17 HA and 26 CA) and seven Elts (all CA) isolates presenting different virulence profiles, as obtained by PCR in the study of Freitas et al. (9), were selected for whole-genome sequencing, as described here. These isolates were obtained during 1996–2015 from different sources including hospitalized patients, farm animals, foodstuffs, and the environment (test genomes in Table S3). The 50 test genomes were analyzed for the presence of PVM by mapping the reads against the database, using MyKMAfinder 1.0. A hit with %ID > 95% and %Cov > 80% was considered as the presence of the PVM.

To visualize the presence of PVM in contract to the phylogeny, a phylogenetic analysis was performed on the 50 test genomes, together with the nine control genomes (all listed in Table S3). Single Nucleotide Polymorphisms (SNPs) were identified using the Northern Arizona SNP Pipeline v. 1.2 (27). Briefly, Efm_DO with plasmids (CP003583.1, CP003584.1, CP003585.1, and CP003586.1) was used as a reference strain. Duplicate regions of the reference were identified by aligning the reference against itself using NUCmer (28), followed by mapping the raw reads from the test and control genomes against the reference using the Burrows–Wheeler Aligner (29). GATK was used for the identification of SNPs (30).

Publishing Efm-Elts VirulenceFinder on CGE

The Efm-Elts virulence database was published on the VirulenceFinder 2.0 web tool (https://cge.food.dtu.dk/services/VirulenceFinder/) as “Enterococcus faecium & Enterococcus lactis,” enabling a simple and user-friendly tool for the detection of PVM from WGS data. Sequence data can be submitted as either assemblies or raw reads. The tool makes it possible to select the threshold for %ID, describing the threshold of %ID between the input and the best match in the virulence database. Further, it is possible to select a minimum length for the hits to be presented in the output. The output consists of the best-matching PVM from the virulence database and the submitted genome. If the input genome is raw reads, KMA mapping is used to identify the best-matching PVM, whereas blastn is used for assembled genomes. The output contains information on the virulence gene, %ID, length of query, and database gene (template length). If the input data are an assembled genome, information on the contig and position in the contig are provided.

RESULTS AND DISCUSSION

Construction and validation of the Efm-Elts virulence database

The Efm-Elts virulence database was constructed of 27 PVMs, with 22 assigned Efm hospital gene variants, 13 Efm community gene variants, 5 Efm gene variants (same variant represented in both hospital and community Efm), and 20 Elts gene variants. To evaluate the method and the gene correctness in the database, raw reads and assemblies of the nine control genomes, used for the construction of the database, were tested against the database with two different methods: KMA mapping of raw reads and assemblies against the database and blast of the assemblies against the database (Table 2). From the KMA mapping of the assemblies against the reference PVM, the method correctly assigned the variants in 55 of 65 cases (84.62%), whereas all 65 variants were correctly assigned from the reads (100%). However, for ccpA, the Efm community gene variant control C59 was reported with an additional hit to the Efm hospital gene variant. By assessing the results for C59 (data not shown), the hit to the Efm hospital gene variant showed %ID = 97.45% and %Cov = 99.50% with a depth of 18.22×, compared to correct the Efm community gene variant with %ID = 100% and %Cov = 100% with a depth of 231.29×. Additionally, when assessing the other hits for C59, the depth was a minimum of 114×, indicating that the extra ccpA hit could be due to sequencing errors.

TABLE 2.

Concordance of PVM in the database and reference genomes

	Total gene variant	No. of control genomes with the virulence gene variant found by
		KMA mapping		blastn
		Assembly	Reads	Assembly
Efm hospital variant	22	20	22	21
Efm community variant	13	7	13	13
Efm varianta	5	10	10	10
Elts variant	20	18	20	19

^a For the five genes fms13, glsB1, gls20, glsB, and gls33, the Efm hospital gene variant and community gene variant were 100% identical; thus, only one variant designated Efm variant was included in the database. The two references Efm_DO (hospital variant) and C59 (community variant) were evaluated against the genes.

By using blastn of the assemblies against the database, the magnitude of errors decreased, and a total of 63 of 65 were correctly assigned (96.92%) for assemblies, indicating that using the current VirulenceFinder tool at CGE would be suitable for our current database, but to ensure the most accurate results in assigning the correct variants of PVMs, it is recommended to use raw reads instead of assembled genomes.

By comparing the newly constructed database with the old version, it becomes evident that the updated database represents a substantial enhancement. The original database comprised a total of 26 genes, with only 5 featuring Efm variants (acm, efaAfm, fsrB, hylEfm, and espfm), while 22 genes were associated with E. faecalis species. In contrast, in the new database, all 27 genes have been described as Efm variants (either Efm, Efm-HA, or Efm-CA variants). Notably, the foundation for this enhancement was laid in the study by Freitas et al. (9), where three of the five Efm genes in the original database (acm, hylEfm, and espfm) were thoroughly investigated.

The esp (Enterococcus surface protein) gene is known for its crucial role in biofilm formation and correlation with diseases. Esp possesses a highly repetitive structure (31). When searching for esp from Efm in the UniProt database (http://www.unipro.org) on 20 February 2023, we obtained 17 results for the gene, with lengths ranging from 112 to 1,975 amino acids. Given the considerable diversity in both size and structure of this gene, it proves challenging to select representatives for the various variants. Based on this, we decided not to include esp in this current version of the database.

The efaAfm gene has been shown to be widely distributed among Efm strains, appearing almost as species-specific, and its role is still not totally understood (32), and for these reasons, it was not included in the previous study by Freitas et al. (9). Based on the limited knowledge of this gene, it is currently not incorporated into the new database. However, as our understanding is growing and new insights emerge, there remains the possibility of incorporating the gene into future iterations of the database.

Regarding the fsrB gene, present in the old database, it is a component of the fsr locus, which includes three regulatory genes, fsrA, fsrB, and fsrC, located upstream of the gelE-sprE operon. These genes are mostly detected in E. faecalis (33); however, the old database reports a single detection found in an Efm genome, although this specific Efm genome is not publicly available (34). Thus, we decided not to include fsrB (or the fsr operon and gelE-sprE operon) in this current version of the database.

Phylogenetic analysis and evaluation of the database

To evaluate the performance of the database and the distribution of gene variants, we constructed a phylogenetic tree based on SNP analysis and compared it to the results obtained from the Efm-Elts virulence database. From the SNP analysis, 119,276 SNPs were identified between the 59 genomes (50 test genomes and 9 control genomes, Table S3), based on 61.81% of the reference genome, the Efm_DO with plasmids. The phylogenetic analysis revealed two distinct clades that clearly separate the Elts and Efm genomes (Fig. 1).

Fig 1.

Phylogenetic relationship of 9 E. lactis and 50 E. faecium genomes. The complete genome of Efm_DO was used as a reference, with the SNP analysis identifying 119,276 SNPs. The best hit from the KMA mapping against the virulence database is indicated by color. The bar at the bottom of the figure illustrates if a given gene variant was present in the virulence database.

Assessing the CA clade (generally designated as Efm A2) of the Efm genomes, most isolates were associated with animals, food, and healthy humans, whereas the HA clade (generally designated as Efm A1) is mainly hospital-associated (patients). No community gene variants of sgrA, fms15, empA, fms11, fms19, fms16, hylEfm, orf1481, and ptsD were found among the community reference genomes, thus not included in the database.

Overall, it was notorious from Fig. 1 that HA-Efm strains mostly cluster apart and are greatly enriched in Efm hospital gene variants (purple, Efm-HV) with a few exceptions for fms21-fms20, empB, and ccpA genes. CA-Efm strains from animal, food, and environmental sources contain the greatest admixture of Efm community gene variants (green, Efm-CV), hospital gene variants (purple, Efm-HV), and also Elts gene variants (pink, Elts-V). As previously observed (9, 10), HA-Efm are more enriched in PVM, even though some of the hospital gene variants can be also found among community strains. Elts genomes were greatly associated with unique Elts gene variants (Elts-V in pink), but an intermix of Elts gene variants with Efm-HV and/or Efm-CV occurred in several cases (acm, fms21, fms14, fms17, empB, empC, fms16, orf1481, bepA, and glsB).

Our results showed the clustering of the 27 PVMs included in the database into five groups of genes according to their distribution in the enterococcal genomes analyzed: PVM-1, gene group represented only by Efm hospital gene variants (Efm-HV); PVM-2, gene group represented by Efm hospital gene variants (Efm-HV), Efm community gene variants (Efm-CV), and Elts gene variants (Elts-V); PVM-3, gene group represented by a single gene variant in Efm and Elts; PVM-4, gene group represented by only an Efm hospital gene variant (Efm-HV) and a community gene variant in Efm (Efm-CV); and PVM-5, gene group represented by Efm hospital gene variants (Efm-HV) and Elts gene variants (Elts-V).

PVM-1 is a gene group represented only by an Efm hospital gene variant (Efm-HV): sgrA, fms15, hyl_Efm, orf1481, and ptsD. Based on the reference genomes included in the study, we were not able to identify an Efm-CV or Elts-V in this set of genes. Thus, the variants always corresponded to hospital gene variants, even in the rare positive community Efm and Elts genomes (the exception was fms15 quite commonly found among Efm CA clade). This observation suggests that these genes are significantly linked to HA-Efm isolates but can also exchange with community Efm strains (e.g., sgrA) and even Elts (e.g., orf1481). As suggested by van Hal et al. (5), the HA-Efm clade represents a genetic continuum between the community Efm and Elts and an ongoing source of genetic determinants for clinical strains. Such miscellaneous networks from CA, including strains from various sources, may be highly dynamic with virulence genes being interchangeable within mobile genetic elements among different clades and also between Efm and Elts species.

PVM-2 is a gene group presenting Efm hospital gene variants (Efm-HV), Efm community gene variants (Efm-CV), and Elts gene variants (Elts-V): acm, scm, fms21, fms20, fms14, fms17, empB, empC, sagA, fnm, and ccpA. These genes were found in most isolates, but with variable sequences depending on the origin of the genomes. In most of the cases, HA-Efm isolates carried Efm-HV whereas CA-Efm isolates carried Efm-CV and Elts carried Elts-V, highlighting the importance of considering gene sequences and not just the presence of a particular gene in a given strain. Indeed, differential expression between clinical and community genomes has been previously documented for some of these genes like sagA (35). However, there was no consistent clustering of gene variants into the corresponding clade or species in 100% of the cases (expected would be Efm-HV in HA clade, Efm-CV in CA clade, and Elts-V in Elts genomes).

When examining the gene variants within the Elts genomes, we observed that the Elts gene variants were well represented whenever they were present in the database. However, in the case of the gene fms21, the Elts gene variant was only found in the reference genome and was absent in all other Elts genomes. When assessing the present variants of fms21 across all genomes, the Efm community and hospital gene variants are found among all different sources (animals, hospital-associated, and healthy humans), indicating that fms21, the major pilus protein of this cluster, is not highly conserved in one group or the other. In contrast, fms20 [also belonging to pili gene cluster 1 (PGC-1)] mainly hits to the Elts variant across the entire data set, suggesting a hypothetical origin of this gene in particular from Elts species. The most used reference for the community variant, C59, did not carry either of the two genes from PGC-1. Previous studies suggested that the fms21-fms20 gene cluster has probably originated in ancestral and commensal isolates on large transferable plasmids explaining the lack of significant differences in its occurrence between HA and CA clades (36, 37).

PVM-3 is a gene group represented by an Efm gene variant and an Elts gene variant: fms13, glsB-like genes. Within the PGC-2 gene cluster (fms14-fm17-fms13), fms13, which is the major pilus protein from this cluster, was the only gene that was not represented by an Efm hospital and community variant. However, it is important to note that this finding does not necessarily imply the absence of a community variant for this gene. In our current database, all community reference and test genomes carried fms13 genes with at least 95% identity to that of the Efm_DO reference clinical genome. As the database continues to grow and will be updated, the presence of community variants for fms13 might be found and confirmed in an Efm community reference genome.

Assessing the general stress proteins, the four glsB-like genes were present in all genomes. Except for two Elts genomes (that mapped to Efm-V of glsB), all Elts genomes did map to the Elts-V, and all Efm genomes did map to the Efm-V. Thus, at least glsB1, gls20, and gls33 genes could be suggested as markers to differentiate Elts and Efm.

PVM-4 is a gene group represented by only an Efm hospital gene variant (Efm-HV) and an Efm community gene variant (Efm-CV): ecbA and bepA. For both these genes, the lack of intermixing of variants between the HA clade (all with Efm-HV variants) and the Efm CA clade or Elts genomes was notable. Thus, this corresponds to the only cases where the hospital gene variant was found to be confined to isolates in the HA clade, which corroborates previous findings (16). All Elts genomes that were positive for bepA carried variants with 97.82% identity to the Efm-CV, indicating a possible bepA Elts-V. To extract the Elts-V, additional reference genomes (complete and annotated genomes) should be identified in the future to verify the gene and expand the database.

PVM-5 is a gene group represented by an Efm hospital gene variant (Efm-HV) and an Elts gene variant: empA, fms11, fms19, and fms16. With a unique exception for fms16, all Efm (from both HA and CA clades) carried hospital gene variants, and all Elts carried Elts gene variants of these four genes. The exception corresponded to an Elts isolate carrying an Efm hospital gene variant of the fms16 gene (Fig. 1). In this specific case, it is likely that our database will benefit from future analyses of additional genomes. These analyses can help determine the potential existence of a community gene variant for these genes in Efm, especially since none were identified in any of the community reference genomes used in this study.

Considering all results, the presence of PVMs like ptsD, orf1481, and sgrA and the hospital variants of the pili gene clusters (PGC-1 to 4) was identified as good markers for hospital-associated Efm strains. However, assessing Fig. 1, hospital gene variants for the PGC-1 and PGC-3 seem to be highly represented among the community strains, whereas PGC-2 community gene variants seem to be more correlated to community sources, and PGC-4 only seems to be present in few community strains. Thus, ptsD, orf1481, and sgrA and the hospital variants of PGC-2 and PGC-4 seem to be good markers for the hospital-associated clade. Looking carefully at Fig. 1, complete pili gene clusters comprising only hospital gene variants are only observed within HA-Efm strains. One exception is for PGC-3 and five cases of PGC-4, for which complete hospital gene variants of the pili gene clusters were found in Efm community-associated isolates. Nevertheless, as previously documented (9), the presence of PGC-4 has been infrequently observed outside the Efm-HA clade. When encountered, it tends to be associated with multidrug-resistant vancomycin-resistant Enterococcus and/or ampicillin-resistant strains found in both human and animal populations. Considering the main differences between Efm and Elts species, among the 27 genes examined, only ptsD, hylEfm, sgrA, ecbA, and fms15 were found exclusively in Efm.

Finally, it is important to emphasize that the outcomes may vary depending on the collection used. We cannot rule out the possibility of a selection bias in the description of our results using this collection in particular. That is why it is crucial to continuously update the database as new results emerge from different projects. New potential virulence markers can be submitted to curators, who will verify the genes and, if relevant, add them to the database.

Conclusion

This study shows that Efm-Elts VirulenceFinder is able to extract putative virulence markers from uploaded WGS data in a reliable and user-friendly manner, which makes it globally accessible to non-bioinformatics users. Our validation analysis revealed that in silico typing using raw read data yields more accurate results than using assembled genomes. However, users of the database must be cautious, when interpreting the results in particular concerning the differentiation between hospital and community variants, since it may need to be updated as more data become available. From a public health perspective, we clearly demonstrate that all Efm clades or Efm and Elts share common putative virulence gene variants. In this study, specific PVMs (ptsD, hylEfm, sgrA, ecbA, and fms15) were not detected in Elts. In addition, ptsD, orf1481, and sgrA and the PGC-2 and PGC-4 (if hospital variants are confirmed) were revealed as good markers for the Efm hospital-associated clade. The newly constructed database of 27 PVMs is easily scalable and provides a valuable resource for a comprehensive characterization of Efm and Elts using WGS data. It can aid in various public health applications, such as hospital outbreak investigations, surveillance, and risk assessment of probiotics (38), and feed additives facilitating the identification and tracking of potentially pathogenic strains.

AFTER EPUB

[This article was published on 8 February 2024 with incorrect information in the funding table. The table was updated in the current version, posted on 14 February 2024.]

DATA AVAILABILITY

Accession numbers for the whole-genome sequences included in this study can be found in Table S3. The new Efm and Elts VirulenceFinder database (virulence_entfm_entls.fsa) can be downloaded from https://bitbucket.org/genomicepidemiology/virulencefinder_db/src/master/.

SUPPLEMENTAL MATERIAL

The following material is available online at https://doi.org/10.1128/spectrum.03724-23.

Supplemental Table S1. spectrum.03724-23-s0001.xlsx.

Gene content of the E. faecium and E. lactis virulence database.

Supplemental Table S2. spectrum.03724-23-s0002.xlsx.

Overview of reference genomes used to construct the Efm-Elts VirulenceFinder database.

Supplemental Table S3. spectrum.03724-23-s0003.xlsx.

Epidemiological background of the 9 Control- and 50 Test- Enterococcus faecium and Enterococcus lactis genomes analyzed in the present study.

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