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Proposed virulence-associated genes of Streptococcus suis isolates from the United States serve as predictors of pathogenicity

Porcine Health Management volume 7, Article number: 22 (2021) Cite this article

Abstract

Background

There is limited information on the distribution of virulence-associated genes (VAGs) in U.S. Streptococcus suis isolates, resulting in little understanding of the pathogenic potential of these isolates. This lack also reduces our understanding of the epidemiology associated with S. suis in the United States and thus affects the efficiency of control and prevention strategies. In this study we applied whole genome sequencing (WGS)-based approaches for the characterization of S. suis and identification of VAGs.

Results

Of 208 S. suis isolates classified as pathogenic, possibly opportunistic, and commensal pathotypes, the genotype based on the classical VAGs (epf, mrp, and sly encoding the extracellular protein factor, muramidase-release protein, and suilysin, respectively) was identified in 9% (epf+/mrp+/sly+) of the pathogenic pathotype. Using the chi-square test and LASSO regression model, the VAGs ofs (encoding the serum opacity factor) and srtF (encoding sortase F) were selected out of 71 published VAGs as having a significant association with pathotype, and both genes were found in 95% of the pathogenic pathotype. The ofs+/srtF+ genotype was also present in 74% of ‘pathogenic’ isolates from a separate validation set of isolates.

Pan-genome clustering resulted in the differentiation of a group of isolates from five swine production companies into clusters corresponding to clonal complex (CC) and virulence-associated (VA) genotypes. The same CC-VA genotype patterns were identified in multiple production companies, suggesting a lack of association between production company, CC, or VA genotype.

Conclusions

The proposed ofs and srtF genes were stronger predictors for differentiating pathogenic and commensal S. suis isolates compared to the classical VAGs in two sets of U.S. isolates. Pan-genome analysis in combination with metadata (serotype, ST/CC, VA genotype) was illustrated to be a valuable subtyping tool to describe the genetic diversity of S. suis.

Background

The severe clinical presentation associated with Streptococcus suis infection is of increasing concern in the U.S. swine industry. The heterogeneity of S. suis can be described by serotyping and multi-locus sequence typing (MLST), and currently 29 true serotypes (1–19, 21, 23–25, 27–31, and 1/2) and 1551 registered sequence type (ST) profiles (as of December 2020) exist [1,2,3,4,5]. The numerous S. suis serotypes and STs limit our attempts to understand the epidemiology of the disease in an effort to prevent and manage the various clinical manifestations. Further, S. suis has zoonotic potential, and many of the effective antibiotics available for treatment are of high or critically important status per U.S. Food and Drug Administration’s Guidance for Industry #152 [6]. Also, serotype variations make it difficult to compare isolates within and across geographically distinct pig populations. The development of effective universal vaccines is hindered by the number of different virulent serotypes and the lack of knowledge of serotype- or ST-specific virulence markers and associated clinical disease.

Historically, systematic characterization of S. suis isolates occurred more extensively in other countries compared to the United States. For instance, serotypes 2, 3, and 1/2 have been well-characterized in Canadian swine populations [7,8,9]. In addition, virulence-associated genes (VAGs) (epf, mrp, and sly) and STs indicative of virulence potential were identified, mostly for serotype 2 [9,10,11,12]. Experimental studies illustrating the virulence potential of Canadian serotype 2 strains (ST1, ST25, and ST28) suggest the virulence potential of ST28 strains is low at best in Canada, but a very different clinical presentation was being observed on U.S. swine farms with ST28 [11, 13, 14]. In the past 4 to 5 years, S. suis infections on U.S. swine farms appeared to be more persistent and severe [15, 16]. However, whether this is a result of new circulating strains, an increase in virulence, or some other cause is not well understood, reinforcing the importance of the characterization of S. suis to monitor changes in strains within a herd.

U.S. swine practitioners utilize herd vaccination strategies as a means of controlling S. suis disease. However, selecting representative isolates and properly timing the administration of vaccines still remain a challenge [17]. As a result of the diversity of S. suis, limited commercial vaccines are available, and many practitioners develop and maintain farm-specific autogenous vaccines. In addition, clear criteria for identifying pathogenic strains that cause primary disease are lacking, making isolate selection for vaccines more difficult [18]. Isolates are commonly selected based on criteria such as serotype and isolation from systemic tissues [19, 20]. However, due to the diversity within and between serotypes, cross-protection between, and even within, different serotypes is difficult to attain [21,22,23,24]. Moreover, the presence of virulence markers is critical for selecting isolates for autogenous vaccines. Over 100 putative and confirmed virulence factors and markers (not crucial or critical for virulence) for S. suis have been described in the literature, but few have been verified in experimental models [18, 25]. These include Eurasian serotype 2 virulence markers extracellular protein factor (epf gene), muramidase-released protein (mrp gene), and suilysin (sly gene), which have been investigated in STs 1, 25, and 28 strains from North America [11, 26, 27].

The application of genomic approaches to identify associations between VAGs and disease manifestation can lead to a better understanding of S. suis pathogenesis. However, a comparative genomic study investigating the current distribution of S. suis VAGs in U.S. isolates is lacking. Recently, we reported associations of various pathotypes with subtypes, including serotype and ST, of S. suis [28]. In this current study, a genomic approach was utilized to identify associations between VAGs and pathotype of U.S. isolates while evaluating the likelihood of classical Eurasian serotype 2 VAGs and newly proposed VAGs to identify pathogenic strains. In addition, pan-genome genetic relationships, along with their VAG profiles (virulence-associated genotypes), were investigated for isolates within and between swine production companies. Finally, we applied the genomic approach for identifying associations between VAGs and pathogenicity classification to a validation set of S. suis isolates to determine whether our approach was robust enough to identify pathogenic strains isolated from other swine production companies.

Materials and methods

Source of isolates and collection of epidemiological data

A training set of 208 S. suis isolates were used in this study. These isolates, previously described by Estrada et al. (2019), were classified into three pathotypes (pathogenic, possibly opportunistic, and commensal) based on clinical information and site of isolation. “Pathogenic” isolates were obtained from systemic tissues such as the brain/meninges and heart. “Possibly opportunistic” isolates were predominantly from lung samples from pigs without signs of neurological or systemic disease. “Commensal” isolates were from laryngeal, tonsil, or nasal samples retrieved from farms with no current control methods for S. suis disease.

Furthermore, epidemiological data, such as swine production company and site, were collected for the training set of isolates. The swine production companies coded as A, D, E, K, and M are all large operations that range in size from 70,000 to 340,000 sows and with headquarters in the United States (A and D = MN, E = MO, K = KS, and M = IL).

VAG profiling

VAG profiling was performed on the training set using a custom database of previously published VAGs of S. suis (Additional file 1) [28]. Illumina sequencing reads were mapped to reference DNA sequences (≥ 60% coverage and ≥ 90% sequence identity) using the SRST2 (Short Read Sequence Typing for Bacterial Pathogens) program [29]. The construction of a presence and absence heatmap (Euclidian distances and UPGMA clustering) was performed with R software [30].

Statistical analysis

Associations between published S. suis VAGs and pathotype, as previously defined by Estrada et al. [28], were investigated. Published VAGs present in a majority of isolates (> 90%, 188/208) were removed. VAGs present in < 50% of the pathogenic pathotype (< 70/139) were also removed. Remaining VAGs were tested by chi-square, comparing the three pathotypes and the status (presence/absence) of individual genes. Genes lacking a significant (chi-square p-value < 0.05) association with pathotype were removed from the analysis. The remaining genes were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model [31].

The LASSO regression model reduces coefficients to zero and gradually eliminates genes that have no or low correlation with the target variable. The LASSO model was used to determine VAGs that may serve as the ‘best’ predictors of pathogenicity, in this case using the pathogenic pathotype as the indicator of pathogenicity. The analysis was performed using the R package glmnet and the best lambda penalty value to determine the fewest number of predictor genes [32]. Due to variation in the number of predictor VAGs in each run, we ran 100 iterations of the LASSO model to determine the most relevant predictor genes. Predictor VAGs reported in all 100 iterations were considered relevant candidate VAGs.

Genome assembly and pan-genome analysis

Genome assembly was performed on Illumina sequencing data from the training set [28]: SRA accession numbers SRR9123061-SRR9123268. Genome assemblies were generated using MEGAHIT de-novo assembler (k-mer range of 25–225) and polished using Pilon [33, 34]. QUAST was used to evaluate the genome assemblies [35]. Only contigs that were 500 bp or larger were kept for annotation by Prokka to predict coding sequences [36]. The pan-genome was annotated using Roary with a 90% BLASTp identity cut-off to define clusters of genes and allowing paralog clustering [37, 38]. The FastTree program was used to generate an approximately-maximum-likelihood phylogenetic tree based on the binary presence and absence of core and accessory genes. Percent similarity was calculated as the percentage of shared genes in the pan-genome.

Selection and whole genome sequencing of validation set

Thirty-two isolates obtained from a single swine production company from 2017 to 2019 were classified as either ‘pathogenic’ or of ‘unknown-pathogenicity’ based on tissue source (Additional file 2). Isolates classified as ‘pathogenic’ were obtained from the brain (n = 19). The isolates of ‘unknown-pathogenicity’ were isolated from non-systemic tissues (no neurological signs) (n = 13). The S. suis isolates were sequenced and the sequencing reads were processed using a similar method as described for the training set [28]. Isolates were confirmed as S. suis if they possessed the S. suis-specific recombination/repair protein (recN) sequence (Streptococcus suis 05HAS68, Accession CP002007).

Serotype, MLST, VAG profile, and pan-genome analysis of validation set

The serotyping of the validation set of S. suis isolates was verified using a S. suis serotyping pipeline described by Athey et al. (2016) to differentiate serotypes 2 and 1/2 and serotypes 1 and 14 [39]. In-silico MLST analysis was performed using the SRST2 program, and the ST allele sequences and profiles obtained from the S. suis MLST database [5]. Novel STs were further grouped into major clonal complexes (CCs) as previously described [28]. Data on presence or absence of the classical VAGs (epf, mrp, and sly) was obtained for each of the 32 isolates as described above for the training set. Similar genome assembly and pan-genome analysis as described for the training set were performed on the 32 isolates. The number of gene clusters identified for the training and validation sets may differ due to gene duplication, pseudogenes, gene acquisition/loss, and other genomic variations, as well as differences in the number of genomes included in the pan-genome analysis [40, 41].

Results

VAG profiling

Distribution of the epf, mrp, and sly genes

In our previous study of 208 S. suis isolates (referred to as the training set), 139, 47, and 22 were classified as the pathogenic, possibly opportunistic, and commensal pathotype, respectively [28]. The training set was also characterized by determination of serotype, MLST, and CC. In the current study, the distribution of the epf, mrp, and sly genes was bioinformatically determined for the 208 isolates in the training set. These classical VAGs epf, mrp, and sly were identified in 20 (14.4%), 127 (91.4%), and 77 (55.4%) isolates of the pathogenic pathotype, respectively (Table 1). The epf gene was predominantly present in serotypes 1, 2, and 14 and CC1 isolates while mrp and sly were distributed among a diverse set of subtypes. The epf, mrp, and sly genes were identified in 0 (0%), 6 (27.3%), and 4 (18.2%) isolates of the commensal pathotype, respectively (Table 2). We further investigated genotype combinations of the epf, mrp, and sly genes and their distributions in STs 1, 25, and 28 (Table 2). The predominant genotype in the pathogenic pathotype was epf−/mrp+/sly- (41.0%, 57/139) followed by epf−/mrp+/sly+ (36.0%, 50/139). A majority of the ST28 (94%) and both ST25 isolates in the training set possessed the epf−/mrp+/sly- genotype. The epf+/mrp+/sly+ genotype was identified in only 20 of the 139 isolates classified as the pathogenic pathotype. All 17 ST1 isolates possessed the epf+/mrp+/sly+ genotype. In summary, a majority of isolates, even those of the pathogenic pathotype, lacked the three classical VAGs, but all the isolates containing the three VAGs were classified as pathogenic or ST1.

Table 1 Classical VAGs identified in the pathogenic pathotype of S. suis isolates (n = 139)
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Table 2 Classical VAGs epf, mrp, and sly genotypes identified in the pathotypes of S. suis isolates (n = 208)
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Determining predictors of pathotype by VAG profiling

Given the limited distribution of classical VAGs (epf and sly) among isolates in the pathogenic pathotype, the classical VAGs are not appropriate indicators of pathogenicity for non-serotype 2 S. suis isolates from the United States. Thus, a total of 71 previously published S. suis VAGs (including epf, mrp, and sly) were investigated for the presence of alternative genes that may be indicators of pathogenic strains. Thirty-two (45%) VAGs were present in all genomes regardless of pathotype and were clearly not indicators of the pathogenic pathotype (Table 3 & Additional file 3). Five VAGs were absent in all of the isolates in the commensal pathotype. SalK and salR, which encode the SalK/SalR two-component signal transduction system [42], were not detected in any of the isolates despite mapping to different reference sequences.

Table 3 Distribution of 71 VAGs for the pathotypes of S. suis isolates (n = 208)
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Clustering analysis was used to determine if relationships between the presence of previously published VAGs and pathotype existed. The analysis of 71 VAGs identified three clusters (Cluster I-III), two of which associated with pathotype (Fig. 1 & Additional file 4). Cluster I consisted of isolates of all three pathotypes. Cluster II predominantly consisted of isolates from the pathogenic pathotype and lacked isolates from the commensal pathotype. Cluster III contained the majority of isolates from the commensal pathotype (73%). Isolates in the pathogenic cluster (Cluster II) were predominantly characterized as serotype 1/2 CC28. Serotype 1/14 CC1 isolates formed a subcluster of Cluster I which lacked isolates from the commensal pathotype. Clustering analysis also illustrated multiple candidate published VAGs for discriminating between pathotypes, specifically VAGs present in the two pathogenic clusters and absent in the commensal cluster.

Fig. 1
figure 1

Virulence-associated gene (VAG) profiling of 208 S. suis isolates. Heatmap illustrating the presence and absence of 71 previously published VAGs in 208 isolates. Isolates are annotated (right) by pathotype (pathogenic, possibly opportunistic, commensal). Clustering of the 208 S. suis isolates by VAGs illustrated three clusters (Clusters I-III), two of which (Clusters II and III) suggest associations between VAGs and pathotype. Serotype and clonal complex (CC) distributions in each of the three clades are also denoted

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We then performed statistical analyses to test for associations between VAGs and pathotype. Of the 71 published VAGs detected in the genomes, 16 were tested by chi-square and 14 were considered significant (chi-square p < 0.05) (Table 3 & Additional file 5). The classical VAGs mrp and sly were considered significant by chi-square. The 14 VAGs that were significant by chi-square were further analyzed by the LASSO model. The sly gene was in the top ten VAGs identified by LASSO, but mrp was not [data not shown]. The LASSO model identified four other candidate VAGs associated with the pathogenic pathotype (Table 4). The VAGs ofs and srtF were present in over 95% (≥ 132/139) of isolates in the pathogenic pathotype and thus, the presence of both genes was tested as predictors of the pathogenic pathotype. Ninety-five percent (132/139) of the pathogenic pathotype contained both genes while only 23% (5/22) of the commensal pathotype contained both genes.

Table 4 LASSO results for the four candidate VAGs in the pathotypes of S. suis isolates (n = 208)
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Diversity of U.S. S. suis by pan-genome analysis

Relatedness of 208 S. suis isolates by pan-genome analysis

Pan-genome analysis of 208 S. suis genomes generated a pan-genome of 8373 gene clusters and illustrated multiple clusters that corresponded to the five MLST CCs (CC1, CC28, CC94, CC104, and CC750) (Fig. 2) [28]. Isolates from at least sixteen swine production companies (A-P) (≥ 2 isolates each) were identified in the data set, with A (n = 13), D (n = 16), E (n = 18), K (n = 21), and M (n = 16) representing the five production companies with the most isolates in this study (predominant production companies). The most predominant CCs (CC1, CC28, and CC94) were identified in multiple production companies. CCs 1, 28, and 94 were identified in 12, 11, and 12 of the 16 production companies, respectively.

Fig. 2
figure 2

Relatedness of 208 S. suis isolates by pan-genome analysis. Genetic relationships between isolates are based on the presence and absence of 8373 gene clusters among 208 S. suis genomes. The phylogenetic tree is colored-coded (branches) and labeled (right) by CC; multiple STs did not form a CC or formed a CC without a primary founder. Isolates from at least sixteen swine production companies (A-P) (≥ 2 isolates each) were identified in the data set. Misc. refers to miscellaneous production companies (single isolates each). Isolates belonging to the five predominant production companies (A, D, E, K, and M) are color-coded by their respective production company. * strains in the commensal pathotype (n = 22)

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Relatedness of isolates within the five predominant production companies

The genetic relationships between pathogenic and possibly opportunistic isolates within a production company were investigated using pan-genome analysis for each of the five predominant production companies A, D, E, K, and M (Fig. 3). None of the isolates from these production companies were classified as the commensal pathotype [28]. In addition, we explored associations between pan-genome clusters and genotypes of the classical (epf, mrp, and sly) and proposed pathogenic (ofs and srtF genes) VAGs. The isolates demonstrated various genotypes of classical VAGs, and a majority (96.4%) possessed the proposed ofs+/srtF+ genotype for predicting pathogenic strains.

Fig. 3
figure 3

Pan-genome analysis of isolates from the five predominant production companies. The predominant production companies are presented as A, D, E, K, and M. Color-coding of isolate names by production company and color-coding of phylogenetic tree branches by CC follow the same color schemes as Fig. 2. The percent similarity of isolates within a cluster is defined as the percentage of shared genes from a total of 8373 genes. The presence of the classical VAGs epf, mrp, and sly is represented in green and the proposed VAGs ofs and srtF in orange

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Due to the diversity of isolates within each production company, we investigated pan-genome clusters and genotypes for each predominant production company. The isolates originating from production companies A, E, and M were placed into two, one, and three clusters, respectively, and multiple singletons each and demonstrated an overall 83.1–99.9%, 89.3–100% and 85.3–99.9% similarity, respectively. A majority of the isolates originating from A and E (A = 54%, E = 72%), and many from M (50%) possessed the classical VAG mrp but lacked the epf and sly genes. The isolates originating from production companies D and K were placed into three clusters and multiple singletons each and demonstrated an overall 89.4–100% and 86.6.-99.9% similarity, respectively. Multiple isolates from D and K (D = 44%, K = 48%) possessed mrp and sly but lacked the epf gene. A few isolates from each production company (n = 1 from companies D, E, K, and M, n = 2 from company A) possessed all three classical VAGs epf, mrp, and sly. A majority of isolates in all five production companies (A = 92.3%, D = 100%, E = 100%, K = 95.2%, M = 93.8%) possessed the proposed ofs+/srtF+ genotype, indicating the presence of both ofs and srtF genes are a better predictor of pathogenicity than the presence of the epf, mrp, and sly genes.

Relatedness of commensal isolates

We further investigated the genetic relationships between the 22 isolates of the commensal pathotype. An 82.6–99.9% similarity was observed, with isolates forming two large clusters and multiple sub-clusters (Fig. 4). Thirteen isolates lacked a CC, while one, three, and five isolates were assigned to CC1, CC94, and CC750, respectively. The CC1 and CC94 isolates possessed more VAGs than the other commensal isolates with all possessing mrp and sly, and both ofs and srtF, while a majority of commensal isolates (77.3%) lacked the classical and proposed pathogenic VAGs.

Fig. 4
figure 4

Pan-genome analysis of the 22 commensal isolates. Color-coding of phylogenetic tree branches by CC follows the same color scheme as Fig. 2. The presence of the classical VAGs epf, mrp, and sly is represented in green and the proposed VAGs ofs and srtF in orange

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Characterization of the validation set of S. suis isolates

A distinct validation set of 32 S. suis isolates was obtained from a single production company to perform pan-genome analysis and further test the novel proposed pathogenic genotype (ofs+/srtF+). These isolates were classified as either ‘pathogenic’ or of ‘unknown-pathogenicity.’ The pan-genome consisted of 7078 gene clusters among the 32 genomes, and these pan-genome clusters associated with the ‘pathogenic’ and ‘unknown-pathogenicity’ classifications, as well as with virulence-associated genotypes (Fig. 5). Clusters c-f corresponded to the ‘pathogenic’ classification. Only the isolates in clusters e and f possessed the classical VAGs epf, mrp, and sly. Moreover, all the isolates in these two clusters possessed the proposed pathogenic ofs+/srtF+ genotype. A majority of the isolates in cluster d (67%) possessed the ofs+/srtF+ genotype.

Fig. 5
figure 5

Pan-genome analysis of the validation set of 32 S. suis isolates. Isolates are color coded by classification, ‘pathogenic’ (red) or ‘unknown-pathogenicity’ (blue). The phylogenetic tree branches are colored-coded by CC. The percent similarity of isolates within a cluster is defined as the percentage of shared genes from a total of 7078 genes. The presence of the classical VAGs epf, mrp, and sly is represented in green and the proposed VAGs ofs and srtF in orange

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Cluster a and singletons g-n corresponded to the ‘unknown-pathogenicity’ classification (Fig. 5). A majority (86%) of the isolates in cluster a possessed the classical VAGs mrp and sly, but three isolates (43%) also possessed the proposed pathogenic genotype. A majority (75%) of the singletons g-n lacked both the classical and proposed VAGs. Two isolates possessed the proposed pathogenic genotype. The diversity and lack of VAGs in clusters g-n is similar to the diversity seen among the commensal pathotype, suggesting these isolates are commensal strains.

Discussion

In this study, 71 published S. suis VAGs (including the classical VAGs epf, mrp, sly) were evaluated to identify pathogenic isolates associated with systemic and neurological disease from the United States. Notably, VAGs ofs and srtF demonstrated stronger associations with the pathogenic pathotype than the other 69 VAGs, suggesting novel published VAGs associated with pathogenicity. A genotyping scheme consisting of these two genes (ofs+/srtF+ genotype) identified pathogenic isolates in a validation set of S. suis isolates, demonstrating its potential application for predicting pathogenicity in other swine production companies. The genetic diversity of isolates within and between swine production companies was evaluated by pan-genome analysis, and important associations were observed among pan-genome clusters, CCs, and virulence-associated (VA) genotypes.

Muramidase-released protein has been associated with enhanced survival of S. suis in human blood and an increase in blood-brain barrier permeability in mice while suilysin plays a role in the inflammatory response although neither of which have been described as being critical as virulence factors [43,44,45,46,47,48]. The epf gene was identified in only 14% and the sly gene was identified in 55% of isolates in the pathogenic pathotype. The mrp gene was identified in 91% of the pathogenic pathotype and 27% of the commensal pathotype suggesting the classical VAG mrp continues to be an adequate identifier of pathogenic strains. The epf+/mrp+/sly+ genotype is correlated with S. suis clinical disease caused by European and Asian ST1 strains belonging to serotypes 1, 2, 9, and 14 [27, 49, 50]. The ST1 isolates in the training set had the epf+/mrp+/sly+ genotype, and the ST25 and ST28 isolates had the epf−/mrp+/sly- genotype, confirming the use of the classical VAGs for identifying virulent ST1 strains but the limited use for identifying ST25 and ST28 strains in North America [11].

Various subtyping methods, including serotyping and MSLT, have been used for evaluating the genetic diversity of S. suis isolates and identifying patterns specific to clinical isolates. Pulsed-field gel electrophoresis (PFGE) has been used for evaluating the genetic diversity of S. suis serotype 2, 1/2, 3, 7, and 9 strains [51, 52]. Although PFGE has high discriminatory power, typing a large number of isolates is time consuming and labor intensive. Unique randomly amplified polymorphic DNA patterns have been recovered from S. suis isolates from diseased pigs and correlated with the production of virulence markers [53, 54]. However, these analyses were mainly focused on serotype 2 strains. Multiplex PCR assays were developed for the differentiation of isolates into serotypes and detection of multiple VAGs [55,56,57]. A limitation of multiplex PCR assays is the number of targets that can successfully be tested in a single assay [58]. In this study, we utilized pan-genome analysis in conjunction with serotyping, MLST, and VAG profiling as a subtyping tool for S. suis. Whole genome sequencing (WGS)-based approaches, such as comparative genome hybridization, minimum core genome sequence typing, pan-genome and Bayesian analysis of population structure, and genome-wide association studies, have been used in combination with phenotypic methods for the identification and classification of S. suis strains into groups of differing levels of virulence [21, 22, 59,60,61,62]. WGS-based approaches have multiple advantages to molecular subtyping techniques, such as the ability to characterize the entire genome, a higher discriminatory power capable of discriminating closely related strains, the ability to perform in silico (via computer simulation) analyses, and access to a vast number of bioinformatics tools for the analysis of whole genomes [63, 64].

Novel published VAGs for the identification of pathogenic S. suis isolates were selected using a chi-square test and a LASSO regression model testing associations between published VAGs and pathotype. As a result, the two genes ofs and srtF were selected as the ‘best’ indicators of pathogenicity for isolates in our study. The ofs gene encodes a serum opacity factor and was associated with virulence attenuation in an experimental pig model [65]. The srtF gene encodes a class C sortase and is part of the srtF pilus gene cluster composed of four genes, srtF, sipF, sfp1, and sfp2 [66]. SrtF gene mutants of S. suis serotype 2 ST1 strain P1/7 caused attenuation of virulence in an intranasal caesarean-derived colostrum-deprived (CDCD) pig model [67]. However, the presence of the pilus gene cluster does not guarantee pilus protein expression [11]. Our research identifies the genes as markers for pathogenicity and not the expression of proteins. The percentage of isolates containing the ofs+/srtF+ genotype that were classified as pathogenic increased from 79 to 96% (132/137) when excluding the possibly opportunistic pathotype (isolates possibly associated with respiratory disease) from the analysis. The proposed pathogenic genotype for predicting pathogenicity was further tested in a validation set consisting of 32 S. suis isolates to evaluate the likelihood of these two genes identifying pathogenic strains in other swine production companies. The ofs+/srtF+ genotype was observed in 73.7% (14/19) of the ‘pathogenic’ isolates, together indicating a ≥ 74% probability that an isolate will be classified as pathogenic given the proposed genotype. The proposed ofs+/srtF+ genotype, in complement to the classical VAGs for ST1, identifies pathogenic strains in the United States. A potential application of this research is the development of a diagnostic PCR test targeting these two proposed VAGs.

Nineteen of 139 isolates in the pathogenic pathotype lacked the ofs+/srtF+ genotype suggesting the possibility of misclassification of these isolates based on tissue source (systemic versus non-systemic). In addition to pathogen-specific traits, environmental and management conditions and host traits contribute to the development of S. suis disease. These factors include temperature fluctuations, overcrowding, concurrent infections, and host immunity and genetics [20, 68, 69]. The ofs+/srtF+ genotype was identified in five isolates in the commensal pathotype but four of these isolates were characterized as CC1 or CC94, which are generally pathogenic subtypes [70, 71]. The five commensal isolates are present in Cluster I (Fig. 1), which represents a cluster containing all three pathotypes, indicating these isolates share similar VAGs with pathogenic isolates. Virulent strains have been previously isolated from the nasal cavities and tonsils of clinically healthy pigs, so characterization by tissue source can be misleading [72, 73].

Pan-genome analysis in combination with metadata (serotype, ST/CC, VA genotype) was used in this study as a subtyping tool to describe the genetic diversity of S. suis isolates within a production company and between companies for epidemiological purposes. The differentiation of S. suis may provide information on the origin of isolates (geographical location, year, source, etc.) or aid in the identification and tracking of strains over time [74,75,76]. Isolates from the pathogenic pathotype in this study formed distinct clusters with correlation to CC and VA genotypes, which is consistent with previous studies [54, 77]. The same CC-VA genotype patterns were identified in multiple production companies, suggesting a lack of association between production company, CC, or VA genotype. These observed patterns may be widespread as opposed to originating from a common source of infection as previously suggested [78,79,80]. Furthermore, the high genetic similarity and identical CC and VAG genotypes within a pan-genome cluster (such as in cluster A in production company E) are indicative of a clone, providing useful information for the identification and tracking of clones over time [81,82,83]. Thus, the use of WGS to complement metadata (e.g. epidemiological, clinical and demographical data) provides a valuable tool for subtyping S. suis as part of epidemiological studies [84, 85]. Further, pan-genome analysis of U.S. S. suis isolates may be used to identify candidate VAGs not yet identified or characterized.

The differentiation of S. suis isolates is also crucial for the development of autogenous vaccines [86]. Different strains have been recovered from diseased pigs from the same herd and selecting the strain or strains associated with disease is challenging [52, 87,88,89]. For the validation set, multiple CC-VA genotype patterns were found among the ‘pathogenic’ clusters, indicating multiple clones were present in this production company. This diversity of isolates is supported by the identification of five serotypes (1, 1/2, 2, 14, and 7) in the validation set, all of which are generally pathogenic subtypes [24, 90, 91]. Despite the diversity of clinical strains in the same herd, previous reports indicate a specific strain is the predominant cause of disease and the primary candidate for an autogenous vaccine [87,88,89, 92]. CC1 was predominantly identified in this production company, and these CC1/ST1 isolates (cluster e and f) demonstrated similar gene content (99% similarity) and genotypes but had different serotypes (serotypes 1/14 vs serotype 2). These results suggest two sub-populations with differences in virulence potential and the need for multiple isolates in a vaccine [93, 94]. On the other hand, the CC28 isolates (cluster d) demonstrated similar gene content (92–99% similarity), serotypes, and genotypes, suggesting similar virulence potential, and the selection of a single isolate for vaccine [11, 13]. As these isolates came from the same production company, all three isolates may by recommended for vaccine development. In addition to the genetic diversity of S. suis isolates, historical background of a production company should be considered while selecting isolates. Historical factors such as prior on-farm identification of S. suis, historic and current sources of replacement animals, and other confounding disease factors can further support the inclusion of multiple isolates in vaccine development.

Conclusion

In this study, the current distribution of published, including classical, VAGs in U.S. isolates was determined, which indicated that classical VAGs are not sufficient to differentiate pathogenic and commensal U.S. strains. Of the 71 published VAGs investigated, the ofs and srtF genes were shown to be stronger predictors of pathogenicity in both a training and a validation set of isolates. Furthermore, a WGS-based approach was used to determine the genetic diversity of isolates demonstrating its use in epidemiological studies and vaccine isolate selection.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the Sequence Read Archive. Illumina sequencing data from the training set [28] were deposited under the accession numbers SRR9123061-SRR9123268. Illumina sequencing data from the validation set were deposited under the accession numbers SRR12964194-SRR12964227. The accession numbers from the validation set can also be found in Additional file 2.

Abbreviations

CC:

Clonal complex

epf :

Extracellular protein factor

LASSO:

Least Absolute Shrinkage and Selection Operator

mrp :

Muramidase-released protein

ofs :

Serum opacity factor

PFGE:

Pulsed-field gel electrophoresis

sly :

Suilysin

SRST2:

Short Read Sequence Typing for Bacterial Pathogens 2

srtF :

Sortase F

MLST or ST:

Multilocus sequence type, sequence type

WGS:

Whole genome sequencing

VAG or VA genotype:

Virulence-associated gene, virulence-associated genotype

References

  1. Higgins R, Gottschalk M, Boudreau M, Lebrun A, Henrichsen J. Description of six new capsular types (29–34) of Streptococcus suis. J Vet Diagn Investig. 1995;7:405–6.

    Article  CAS  Google Scholar 

  2. Hill JE, Gottschalk M, Brousseau R, Harel J, Hemmingsen SM, Goh SH. Biochemical analysis, cpn60 and 16S rDNA sequence data indicate that Streptococcus suis serotypes 32 and 34, isolated from pigs, are Streptococcus orisratti. Vet Microbiol. 2005;107:63–9.

    Article  CAS  PubMed  Google Scholar 

  3. Tien LHT, Nishibori T, Nishitani Y, Nomoto R, Osawa R. Reappraisal of the taxonomy of Streptococcus suis serotypes 20, 22, 26, and 33 based on DNA–DNA homology and sodA and recN phylogenies. Vet Microbiol. 2013;162:842–9.

    Article  CAS  PubMed  Google Scholar 

  4. King SJ, Leigh JA, Heath PJ, Luque I, Tarradas C, Dowson CG, Whatmore AM. Development of a multilocus sequence typing scheme for the pig pathogen Streptococcus suis: identification of virulent clones and potential capsular serotype exchange. J Clin Microbiol. 2002;40:3671–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Jolley KA, Bray JE, Maiden MCJ. Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications. Wellcome Open Res. 2018;3:124.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Food and Drug Administration Center for Veterinary Medicine. Evaluating the safety of antimicrobial new animal drugs with regard to their microbiological effects on bacteria of human health concern. Guid Ind. 2003;152:1–36.

    Google Scholar 

  7. Higgins R, Gottschalk M. Distribution of Streptococcus suis capsular types in 2000. Can Vet J Rev Veterinaire Can. 2001;42:223.

    CAS  Google Scholar 

  8. Messier S, Lacouture S, Gottschalk M. Distribution of Streptococcus suis capsular types from 2001 to 2007. Can Vet J Rev Veterinaire Can. 2008;49:461–2.

    Google Scholar 

  9. Gottschalk M, Lacouture S, Bonifait L, Roy D, Fittipaldi N, Grenier D. Characterization of Streptococcus suis isolates recovered between 2008 and 2011 from diseased pigs in Québec, Canada. Vet Microbiol. 2013;162:819–25.

    Article  CAS  PubMed  Google Scholar 

  10. Gottschalk M, Lebrun A, Wisselink H, Dubreuil JD, Smith H, Vecht U. Production of virulence-related proteins by Canadian strains of Streptococcus suis capsular type 2. Can J Vet Res Rev Can Rech Veterinaire. 1998;62:75–9.

    CAS  Google Scholar 

  11. Fittipaldi N, Xu J, Lacouture S, Tharavichitkul P, Osaki M, Sekizaki T, Takamatsu D, Gottschalk M. Lineage and virulence of Streptococcus suis serotype 2 isolates from North America. Emerg Infect Dis. 2011;17:2239–44.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Fittipaldi N, Fuller TE, Teel JF, Wilson TL, Wolfram TJ, Lowery DE, Gottschalk M. Serotype distribution and production of muramidase-released protein, extracellular factor and suilysin by field strains of Streptococcus suis isolated in the United States. Vet Microbiol. 2009;139:310–7.

  13. Athey TBT, Auger J-P, Teatero S, Dumesnil A, Takamatsu D, Wasserscheid J, Dewar K, Gottschalk M, Fittipaldi N. Complex population structure and virulence differences among serotype 2 Streptococcus suis strains belonging to sequence type 28. PLoS One. 2015;10:e0137760.

  14. Auger J-P, Fittipaldi N, Benoit-Biancamano M-O, Segura M, Gottschalk M. Virulence studies of different sequence types and geographical origins of Streptococcus suis serotype 2 in a mouse model of infection. Pathogens. 2016;5:48–71.

    Article  PubMed Central  CAS  Google Scholar 

  15. Leuwerke B, Wilson K, Schulz K. Control Strategies for Streptococcus suis. Webinar. 2019; https://www.nationalhogfarmer.com/animal-health/strep-suis-control-topic-upcoming-webinar. Accessed 23 Sept 2020.

  16. Pig Health Today. Watch your Strep: keep your guard up for this evolving bacterium. ThePigSite. 2019. https://www.thepigsite.com/news/2019/08/watch-your-strep-keep-your-guard-up-for-this-evolving-bacterium. Accessed 1 Dec 2020.

  17. Baums CG, Brüggemann C, Kock C, Beineke A, Waldmann K-H, Valentin-Weigand P. Immunogenicity of an autogenous Streptococcus suis bacterin in preparturient sows and their piglets in relation to protection after weaning. Clin Vaccine Immunol CVI. 2010;17:1589–97.

    Article  CAS  PubMed  Google Scholar 

  18. Segura M, Fittipaldi N, Calzas C, Gottschalk M. Critical Streptococcus suis virulence factors: are they all really critical? Trends Microbiol. 2017;25:585–99.

    Article  CAS  PubMed  Google Scholar 

  19. Galina L, Collins JE, Pijoan C. Porcine Streptococcus suis in Minnesota. J Vet Diagn Investig. 1992;4:195–6.

    Article  CAS  Google Scholar 

  20. Reams RY, Glickman LT, Harrington DD, Thacker HL, Bowersock TL. Streptococcus suis infection in swine: a retrospective study of 256 cases. Part II. Clinical signs, gross and microscopic lesions, and coexisting microorganisms. J Vet Diagn Investig. 1994;6:326–34.

    Article  CAS  Google Scholar 

  21. de Greeff A, Wisselink HJ, de Bree FM, Schultsz C, Baums CG, Thi HN, Stockhofe-Zurwieden N, Smith HE. Genetic diversity of Streptococcus suis isolates as determined by comparative genome hybridization. BMC Microbiol. 2011;11:161–75.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zhang A, Yang M, Hu P, Wu J, Chen B, Hua Y, Yu J, Chen H, Xiao J, Jin M. Comparative genomic analysis of Streptococcus suis reveals significant genomic diversity among different serotypes. BMC Genomics. 2011;12:523–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Quessy S, Dubreuil JD, Caya M, Létourneau R, Higgins R. Comparison of pig, rabbit and mouse IgG response to Streptococcus suis serotype 2 proteins and active immunization of mice against the infection. Can J Vet Res Rev Can Rech Veterinaire. 1994;58:220–3.

    CAS  Google Scholar 

  24. Segura M, Aragon V, Brockmeier SL, Gebhart C, de Greeff A, Kerdsin A, O’Dea MA, Okura M, Saléry M, Schultsz C, Valentin-Weigand P, Weinert LA, Wells JM, Gottschalk M. Update on Streptococcus suis research and prevention in the era of antimicrobial restriction: 4th International Workshop on S. suis. Pathogens. 2020;9:374.

  25. Fittipaldi N, Segura M, Grenier D, Gottschalk M. Virulence factors involved in the pathogenesis of the infection caused by the swine pathogen and zoonotic agent Streptococcus suis. Future Microbiol. 2012;7:259–79.

    Article  CAS  PubMed  Google Scholar 

  26. Gottschalk M, Segura M. The pathogenesis of the meningitis caused by Streptococcus suis: the unresolved questions. Vet Microbiol. 2000;76:259–72.

    Article  CAS  PubMed  Google Scholar 

  27. Goyette-Desjardins G, Auger J-P, Xu J, Segura M, Gottschalk M. Streptococcus suis, an important pig pathogen and emerging zoonotic agent-an update on the worldwide distribution based on serotyping and sequence typing. Emerg Microbes Infect. 2014;3:e45.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Estrada AA, Gottschalk M, Rossow S, Rendahl A, Gebhart C, Marthaler DG. Serotype and genotype (multilocus sequence type) of Streptococcus suis isolates from the United States serve as predictors of pathotype. J Clin Microbiol. 2019;57:e00377–19.

  29. Inouye M, Dashnow H, Raven L-A, Schultz MB, Pope BJ, Tomita T, Zobel J, Holt KE. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 2014;6:90–105.

    Article  PubMed  PubMed Central  Google Scholar 

  30. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2017. https://www.R-project.org/

    Google Scholar 

  31. Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B Methodol. 1996;58:267–88.

    Google Scholar 

  32. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Li D, Luo R, Liu C-M, Leung C-M, Ting H-F, Sadakane K, Yamashita H, Lam T-W. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods San Diego Calif. 2016;102:3–11.

    Article  CAS  Google Scholar 

  34. Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A, Sakthikumar S, Cuomo CA, Zeng Q, Wortman J, Young SK, Earl AM. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One. 2014;9:e112963.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinforma Oxf Engl. 2014;30:2068–9.

    Article  CAS  Google Scholar 

  37. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, Fookes M, Falush D, Keane JA, Parkhill J. Roary: rapid large-scale prokaryote pan genome analysis. Bioinforma Oxf Engl. 2015;31:3691–3.

    Article  CAS  Google Scholar 

  38. van Vliet AHM. Use of pan-genome analysis for the identification of lineage-specific genes of Helicobacter pylori. FEMS Microbiol Lett. 2017;364:fnw296.

    Article  PubMed  CAS  Google Scholar 

  39. Athey TBT, Teatero S, Lacouture S, Takamatsu D, Gottschalk M, Fittipaldi N. Determining Streptococcus suis serotype from short-read whole-genome sequencing data. BMC Microbiol. 2016;16:162–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Meng P, Lu C, Zhang Q, Lin J, Chen F. Exploring the genomic diversity and cariogenic differences of Streptococcus mutans strains through pan-genome and comparative genome analysis. Curr Microbiol. 2017;74:1200–9.

    Article  CAS  PubMed  Google Scholar 

  41. Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. The microbial pan-genome. Curr Opin Genet Dev. 2005;15:589–94.

    Article  CAS  PubMed  Google Scholar 

  42. Li M, Wang C, Feng Y, Pan X, Cheng G, Wang J, Ge J, Zheng F, Cao M, Dong Y, Liu D, Wang J, Lin Y, Du H, Gao GF, Wang X, Hu F, Tang J. SalK/SalR, a two-component signal transduction system, is essential for full virulence of highly invasive Streptococcus suis serotype 2. PLoS One. 2008;3:e2080.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Pian Y, Li X, Zheng Y, Wu X, Yuan Y, Jiang Y. Binding of human fibrinogen to MRP enhances Streptococcus suis survival in host blood in a αXβ2 integrin-dependent manner. Sci Rep. 2016;6:26966.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Wang J, Kong D, Zhang S, Jiang H, Zheng Y, Zang Y, Hao H, Jiang Y. Interaction of fibrinogen and muramidase-released protein promotes the development of Streptococcus suis meningitis. Front Microbiol. 2015;6:1001.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Tenenbaum T, Asmat TM, Seitz M, Schroten H, Schwerk C. Biological activities of suilysin: role in Streptococcus suis pathogenesis. Future Microbiol. 2016;11:941–54.

    Article  CAS  PubMed  Google Scholar 

  46. Allen AG, Bolitho S, Lindsay H, Khan S, Bryant C, Norton P, Ward P, Leigh J, Morgan J, Riches H, Eastty S, Maskell D. Generation and characterization of a defined mutant of Streptococcus suis lacking suilysin. Infect Immun. 2001;69:2732–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Lun S, Perez-Casal J, Connor W, Willson PJ. Role of suilysin in pathogenesis of Streptococcus suis capsular serotype 2. Microb Pathog. 2003;34:27–37.

    Article  CAS  PubMed  Google Scholar 

  48. Smith HE, Vecht U, Wisselink HJ, Stockhofe-Zurwieden N, Biermann Y, Smits MA. Mutants of Streptococcus suis types 1 and 2 impaired in expression of muramidase-released protein and extracellular protein induce disease in newborn germfree pigs. Infect Immun. 1996;64:4409–12.

  49. Wisselink HJ, Smith HE, Stockhofe-Zurwieden N, Peperkamp K, Vecht U. Distribution of capsular types and production of muramidase-released protein (MRP) and extracellular factor (EF) of Streptococcus suis strains isolated from diseased pigs in seven European countries. Vet Microbiol. 2000;74:237–48.

    Article  CAS  PubMed  Google Scholar 

  50. Wei Z, Li R, Zhang A, He H, Hua Y, Xia J, Cai X, Chen H, Jin M. Characterization of Streptococcus suis isolates from the diseased pigs in China between 2003 and 2007. Vet Microbiol. 2009;137:196–201.

    Article  CAS  PubMed  Google Scholar 

  51. Berthelot-Hérault F, Marois C, Gottschalk M, Kobisch M. Genetic diversity of Streptococcus suis strains isolated from pigs and humans as revealed by pulsed-field gel electrophoresis. J Clin Microbiol. 2002;40:615–9.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Vela AI, Goyache J, Tarradas C, Luque I, Mateos A, Moreno MA, Borge C, Perea JA, Dominguez L, Fernandez-Garayzabal JF. Analysis of genetic diversity of Streptococcus suis clinical isolates from pigs in Spain by pulsed-field gel electrophoresis. J Clin Microbiol. 2003;41:2498–502.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Chatellier S, Gottschalk M, Higgins R, Brousseau R, Harel J. Relatedness of Streptococcus suis serotype 2 isolates from different geographic origins as evaluated by molecular fingerprinting and phenotyping. J Clin Microbiol. 1999;37:362–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Maneerat K, Yongkiettrakul S, Kramomtong I, Tongtawe P, Tapchaisri P, Luangsuk P, Chaicumpa W, Gottschalk M, Srimanote P. Virulence genes and genetic diversity of Streptococcus suis serotype 2 isolates from Thailand. Transbound Emerg Dis. 2013;60(Suppl 2):69–79.

    Article  PubMed  Google Scholar 

  55. Silva LMG, Baums CG, Rehm T, Wisselink HJ, Goethe R, Valentin-Weigand P. Virulence-associated gene profiling of Streptococcus suis isolates by PCR. Vet Microbiol. 2006;115:117–27.

    Article  CAS  PubMed  Google Scholar 

  56. Kerdsin A, Akeda Y, Hatrongjit R, Detchawna U, Sekizaki T, Hamada S, Gottschalk M, Oishi K. Streptococcus suis serotyping by a new multiplex PCR. J Med Microbiol. 2014;63:824–30.

    Article  CAS  PubMed  Google Scholar 

  57. Okura M, Lachance C, Osaki M, Sekizaki T, Maruyama F, Nozawa T, Nakagawa I, Hamada S, Rossignol C, Gottschalk M, Takamatsu D. Development of a two-step multiplex PCR assay for typing of capsular polysaccharide synthesis gene clusters of Streptococcus suis. J Clin Microbiol. 2014;52:1714–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Yang S, Rothman RE. PCR-based diagnostics for infectious diseases: uses, limitations, and future applications in acute-care settings. Lancet Infect Dis. 2004;4:337–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Chen C, Zhang W, Zheng H, Lan R, Wang H, Du P, Bai X, Ji S, Meng Q, Jin D, Liu K, Jing H, Ye C, Gao GF, Wang L, Gottschalk M, Xu J. Minimum core genome sequence typing of bacterial pathogens: a unified approach for clinical and public health microbiology. J Clin Microbiol. 2013;51:2582–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Weinert LA, Chaudhuri RR, Wang J, Peters SE, Corander J, Jombart T, Baig A, Howell KJ, Vehkala M, Välimäki N, Harris D, Chieu TTB, Van Vinh CN, Campbell J, Schultsz C, Parkhill J, Bentley SD, Langford PR, Rycroft AN, Wren BW, Farrar J, Baker S, Hoa NT, Holden MTG, Tucker AW, Maskell DJ. Genomic signatures of human and animal disease in the zoonotic pathogen Streptococcus suis. Nat Commun. 2015;6:6740–8.

    Article  CAS  PubMed  Google Scholar 

  61. Willemse N, Howell KJ, Weinert LA, Heuvelink A, Pannekoek Y, Wagenaar JA, Smith HE, van der Ende A, Schultsz C. An emerging zoonotic clone in the Netherlands provides clues to virulence and zoonotic potential of Streptococcus suis. Sci Rep. 2016;6:28984.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Wileman TM, Weinert LA, Howell KJ, Wang J, Peters SE, Williamson SM, Wells JM, Langford PR, Rycroft AN, Wren BW, Maskell DJ, Tucker AW. Pathotyping the zoonotic pathogen Streptococcus suis: novel genetic markers to differentiate invasive disease-associated isolates from non-disease-associated isolates from England and Wales. J Clin Microbiol. 2019;57:e01712-18.

  63. Quainoo S, Coolen JPM, van Hijum SAFT, Huynen MA, Melchers WJG, van Schaik W, Wertheim HFL. Whole-genome sequencing of bacterial pathogens: the future of nosocomial outbreak analysis. Clin Microbiol Rev. 2017;30:1015–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Uelze L, Grützke J, Borowiak M, Hammerl JA, Juraschek K, Deneke C, Tausch SH, Malorny B. Typing methods based on whole genome sequencing data. One Health Outlook. 2020;2:3.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Baums CG, Kaim U, Fulde M, Ramachandran G, Goethe R, Valentin-Weigand P. Identification of a novel virulence determinant with serum opacification activity in Streptococcus suis. Infect Immun. 2006;74:6154–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Fittipaldi N, Takamatsu D, de la Cruz Domínguez-Punaro M, Lecours M-P, Montpetit D, Osaki M, Sekizaki T, Gottschalk M. Mutations in the gene encoding the ancillary pilin subunit of the Streptococcus suis srtF cluster result in pili formed by the major subunit only. PLoS One. 2010;5:e8426.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Faulds-Pain A, Shaw HA, Terra VS, Kellner S, Brockmeier SL, Wren BW. The Streptococcos suis sortases SrtB and SrtF are essential for disease in pigs. Microbiol Read Engl. 2019;165:163–73.

    Article  CAS  Google Scholar 

  68. Dee SA, Carlson AR, Winkelman NL, Corey MM. Effect of management practices on the Streptococcus suis carrier rate in nursery swine. J Am Vet Med Assoc. 1993;203:295–9.

    CAS  PubMed  Google Scholar 

  69. Segura M, Calzas C, Grenier D, Gottschalk M. Initial steps of the pathogenesis of the infection caused by Streptococcus suis: fighting against nonspecific defenses. FEBS Lett. 2016;590:3772–99.

    Article  CAS  PubMed  Google Scholar 

  70. Hatrongjit R, Fittipaldi N, Gottschalk M, Kerdsin A. Tools for molecular epidemiology of Streptococcus suis. Pathogens. 2020;9:81.

  71. Kerdsin A, Akeda Y, Takeuchi D, Dejsirilert S, Gottschalk M, Oishi K. Genotypic diversity of Streptococcus suis strains isolated from humans in Thailand. Eur J Clin Microbiol Infect Dis Off Publ Eur Soc Clin Microbiol. 2018;37:917–25.

  72. Brisebois LM, Charlebois R, Higgins R, Nadeau M. Prevalence of Streptococcus suis in four to eight week old clinically healthy piglets. Can J Vet Res Rev Can Rech Veterinaire. 1990;54:174–7.

    CAS  Google Scholar 

  73. Marois C, Le Devendec L, Gottschalk M, Kobisch M. Detection and molecular typing of Streptococcus suis in tonsils from live pigs in France. Can J Vet Res Rev Can Rech Veterinaire. 2007;71:14–22.

    CAS  Google Scholar 

  74. Beaudoin M, Harel J, Higgins R, Gottschalk M, Frenette M, MacInnes JI. Molecular analysis of isolates of Streptococcus suis capsular type 2 by restriction-endonuclease-digested DNA separated on SDS-PAGE and by hybridization with an rDNA probe. J Gen Microbiol. 1992;138:2639–45.

    Article  CAS  PubMed  Google Scholar 

  75. Doto DS, Moreno LZ, Calderaro FF, Matajira CEC, de Moura Gomes VT, Ferreira TSP, Mesquita RE, Timenetsky J, Gottschalk M, Moreno AM. Genetic diversity of Streptococcus suis serotype 2 isolated from pigs in Brazil. Can J Vet Res Rev Can Rech Veterinaire. 2016;80:106–11.

    Google Scholar 

  76. Dutkiewicz J, Sroka J, Zając V, Wasiński B, Cisak E, Sawczyn A, Kloc A, Wójcik-Fatla A. Streptococcus suis: a re-emerging pathogen associated with occupational exposure to pigs or pork products. Part I - epidemiology. Ann Agric Environ Med AAEM. 2017;24:683–95.

    Article  CAS  PubMed  Google Scholar 

  77. Dong W, Zhu Y, Ma Y, Ma J, Zhang Y, Yuan L, Pan Z, Wu Z, Yao H. Multilocus sequence typing and virulence genotyping of Streptococcus suis serotype 9 isolates revealed high genetic and virulence diversity. FEMS Microbiol Lett. 2017;364. https://doi.org/10.1093/femsle/fnx192.

  78. Mwaniki CG, Robertson ID, Trott DJ, Atyeo RF, Lee BJ, Hampson DJ. Clonal analysis and virulence of Australian isolates of Streptococcus suis type 2. Epidemiol Infect. 1994;113:321–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Morales B, Ruiz Á, Lacouture S, Gottschalk M. Clonal distribution of Streptococcus suis isolated from diseased pigs in the central region of Chile. Can J Vet Res Rev Can Rech Veterinaire. 2015;79:343–6.

    CAS  Google Scholar 

  80. Denich LC, Farzan A, Friendship R, Arndt E, Gottschalk M, Poljak Z. A case-control study to investigate the serotypes of S. suis isolates by multiplex PCR in nursery pigs in Ontario, Canada. Pathogens. 2020;9:44.

  81. van Belkum A, Tassios PT, Dijkshoorn L, Haeggman S, Cookson B, Fry NK, Fussing V, Green J, Feil E, Gerner-Smidt P, Brisse S, Struelens M, European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Study Group on Epidemiological Markers (ESGEM). Guidelines for the validation and application of typing methods for use in bacterial epidemiology. Clin Microbiol Infect Off Publ Eur Soc Clin Microbiol Infect Dis. 2007;13(Suppl 3):1–46.

    Google Scholar 

  82. Allgaier A, Goethe R, Wisselink HJ, Smith HE, Valentin-Weigand P. Relatedness of Streptococcus suis isolates of various serotypes and clinical backgrounds as evaluated by macrorestriction analysis and expression of potential virulence traits. J Clin Microbiol. 2001;39:445–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Du P, Zheng H, Zhou J, Lan R, Ye C, Jing H, Jin D, Cui Z, Bai X, Liang J, Liu J, Xu L, Zhang W, Chen C, Xu J. Detection of multiple parallel transmission outbreak of Streptococcus suis human infection by use of genome epidemiology, China, 2005. Emerg Infect Dis. 2017;23:204–11.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Rantsiou K, Kathariou S, Winkler A, Skandamis P, Saint-Cyr MJ, Rouzeau-Szynalski K, Amézquita A. Next generation microbiological risk assessment: opportunities of whole genome sequencing (WGS) for foodborne pathogen surveillance, source tracking and risk assessment. Int J Food Microbiol. 2018;287:3–9.

    Article  CAS  PubMed  Google Scholar 

  85. Van Goethem N, Descamps T, Devleesschauwer B, Roosens NHC, Boon NAM, Van Oyen H, Robert A. Status and potential of bacterial genomics for public health practice: a scoping review. Implement Sci IS. 2019;14:79.

    Article  PubMed  Google Scholar 

  86. Rieckmann K, Pendzialek S-M, Vahlenkamp T, Baums CG. A critical review speculating on the protective efficacies of autogenous Streptococcus suis bacterins as used in Europe. Porc Health Manag. 2020;6:12.

    Article  Google Scholar 

  87. Mogollon JD, Pijoan C, Murtaugh MP, Collins JE, Cleary PP. Identification of epidemic strains of Streptococcus suis by genomic fingerprinting. J Clin Microbiol. 1991;29:782–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Reams RY, Harrington DD, Glickman LT, Thacker HL, Bowersock TL. Multiple serotypes and strains of Streptococcus suis in naturally infected swine herds. J Vet Diagn Investig Off Publ Am Assoc Vet Lab Diagn Inc. 1996;8:119–21.

    Article  CAS  Google Scholar 

  89. Martinez G, Harel J, Lacouture S, Gottschalk M. Genetic diversity of Streptococcus suis serotypes 2 and 1/2 isolates recovered from carrier pigs in closed herds. Can J Vet Res Rev Can Rech Veterinaire. 2002;66:240–8.

    CAS  Google Scholar 

  90. Boetner AG, Binder M, Bille-Hansen V. Streptococcus suis infections in Danish pigs and experimental infection with Streptococcus suis serotype 7. Acta Pathol Microbiol Scand Ser B Microbiol. 1987;95B:233–9.

    Google Scholar 

  91. Smith HE, van Bruijnsvoort L, Buijs H, Wisselink HJ, Smits MA. Rapid PCR test for Streptococcus suis serotype 7. FEMS Microbiol Lett. 1999;178:265–70.

    Article  CAS  PubMed  Google Scholar 

  92. Torremorell M, Pijoan C. Prolonged persistence of an epidemic Streptococcus suis strain in a closed pig population. Vet Rec. 1998;143:394–5.

    Article  CAS  PubMed  Google Scholar 

  93. Haesebrouck F, Pasmans F, Chiers K, Maes D, Ducatelle R, Decostere A. Efficacy of vaccines against bacterial diseases in swine: what can we expect? Vet Microbiol. 2004;100:255–68.

    Article  CAS  PubMed  Google Scholar 

  94. Hopkins D, Poljak Z, Farzan A, Friendship R. Field studies evaluating the direct, indirect, total, and overall efficacy of Streptococcus suis autogenous vaccine in nursery pigs. Can Vet J Rev Veterinaire Can. 2019;60:386–90.

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Acknowledgements

The authors would like to thank the bacteriology section at the University of Minnesota Veterinary Diagnostic Lab for their technical assistance.

Funding

The research project was partially funded by the Minnesota State Legislature through the Rapid Agricultural Response Fund, managed by the Minnesota Agricultural Experiment Station (project #00057268). April A. Estrada’s stipend was funded by the USDA National Needs Fellowship Award, 2016–38420-25288.

Author information

Authors and Affiliations

  1. Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, USA

    April A. Estrada, Aaron Rendahl & Connie J. Gebhart

  2. Faculty of Veterinary Medicine, University of Montreal, Saint-Hyacinthe, Quebec, Canada

    Marcelo Gottschalk

  3. Veterinary Diagnostic Laboratory, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN, USA

    Stephanie Rossow & Lacey Marshall-Lund

  4. Veterinary Diagnostic Laboratory, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA

    Douglas G. Marthaler

Authors
  1. April A. Estrada
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  2. Marcelo Gottschalk
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  3. Aaron Rendahl
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  4. Stephanie Rossow
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  5. Lacey Marshall-Lund
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  6. Douglas G. Marthaler
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  7. Connie J. Gebhart
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Contributions

AAE performed the analyses and drafted the manuscript. AAE, DGM, and CJG interpreted the data and designed the figures. CJG was responsible for the selection of the validation set and the collection of metadata for these isolates. MG, DGM, and CJG took part in the supervision of analyses and critical review of the manuscript. LML provided critical laboratory support. AAE and AR performed statistical analyses. SR provided epidemiological data for the training set of isolates. All authors read and approved the final manuscript.

Corresponding author

Correspondence to April A. Estrada.

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Supplementary Information

Additional file 1.

Confirmed and putative VAGs of S. suis used in this study. Name and accession numbers of the 71 previously published VAGs of S. suis used in this study.

Additional file 2.

Characteristics of the 32 S. suis isolates in the validation set. List and identification of the validation set of isolates, including classification, serotype, ST, and SRA accession numbers.

Additional file 3.

Presence-absence data of the 71 VAGs for the training set of S. suis isolates (n = 208). Binary matrix representing the presence (1) and absence (0) of the 71 VAGs.

Additional file 4.

Pathotype, serotype, and ST distribution of clusters I-III identified by VAG profiling. Supporting information for Fig. 1 illustrating the three clusters identified by VAG profiling.

Additional file 5.

Chi-square and Fisher’s exact test results for the 16 VAGs tested. Chi-square and Fisher’s exact test results for evaluation associations between published S. suis VAGs and pathotype.

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Estrada, A.A., Gottschalk, M., Rendahl, A. et al. Proposed virulence-associated genes of Streptococcus suis isolates from the United States serve as predictors of pathogenicity. Porc Health Manag 7, 22 (2021). https://doi.org/10.1186/s40813-021-00201-6

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  • DOI: https://doi.org/10.1186/s40813-021-00201-6

Keywords

Porcine Health Management

ISSN: 2055-5660

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