- Research article
- Open Access
Preliminary comparative genomics revealed pathogenic potential and international spread of Staphylococcus argenteus
BMC Genomics volume 18, Article number: 808 (2017)
Staphylococcus argenteus and S. schweitzeri, were recently proposed as novel species within S. aureus complex (SAC). S. argenteus has been reported in many countries and can threaten human health. S. schweitzeri has not been associated with human infections, but has been isolated from non-human primates. Questions regarding the evolution of pathogenicity of these two species will remain elusive until an exploratory evolutionary framework is established.
We present genomic comparison analysis among members of SAC based on a pan-genome definition, which included 15 S. argenteus genomes (five newly sequenced), six S. schweitzeri genomes and 30 divergent S. aureus genomes. The three species had divergent core genomes and rare interspecific recombination was observed among the core genes. However, some subtypes of staphylococcal cassette chromosome mec (SCCmec) elements and prophages were present in different species. Of 111 tested virulence genes of S. aureus, 85 and 86 homologous genes were found in S. argenteus and S. schweitzeri, respectively. There was no difference in virulence gene content among the three species, but the sequence of most core virulence genes was divergent. Analysis of the agr locus and the genes in the capsular polysaccharides biosynthetic operon revealed that they both diverged before the speciation of SAC members. Furthermore, the widespread geographic distribution of S. argenteus, sequence type 2250, showed ambiguous biogeographical structure among geographically isolated populations, demonstrating an international spread of this pathogen.
S. argenteus has spread among several countries, and invasive infections and persistent carriage may be not limited to currently reported regions. S. argenteus probably had undergone a recent host adaption and can cause human infections with a similar pathogenic potential.
Staphylococcus aureus is a bacterial species often associated with primate hosts, and specific sequence types (ST) can be found frequently living on different domestic animals . In humans, S. aureus is frequently isolated from nasal membranes and skin as residents or transients . Because of its association with clinically significant infections and foodborne diseases [3, 4], S. aureus has been extensively investigated, leading to massive datasets on its species diversity. After the genetically divergent ST 75 was first reported in Australia in 2002 , many other isolates genetically related to clonal complex 75 (CC 75) were described in Belgium , Cambodia , China , Fiji [9, 10], France , French Guiana , New Zealand , Thailand [13, 14], Trinidad & Tobago , and the UK . Genomic data tracking showed that this lineage also appeared in the United States . Meanwhile, another S. aureus lineage has recently been recovered from nonhuman primates [18, 19] and bats  in Africa. Recently, these two genetically divergent lineages have received formal taxonomic classification and were recognized as S. argenteus and S. schweitzeri, respectively, two novel species within the S. aureus complex (SAC) .
S. argenteus cannot be distinguished from S. aureus using routine diagnostic microbiology identification methods [7, 8, 10, 12, 13], and PCR amplification of the gene nucA, which is used as a standard confirmatory marker for S. aureus, may be positive in S. argenteus . However, considerable difficulties were observed in amplification of some multilocus sequence typing (MLST) gene loci from S. argenteus using standard MLST primers used for typing S. aureus [7, 21]. These difficulties may result in S. argenteus isolates being excluded from or misidentified as S. aureus. Data on clinical features of S. argenteus infection are limited, but these studies indicated that S. argenteus was associated with skin and soft tissue infections, nosocomial infections, invasive staphylococcal sepsis, and even death [13, 14, 22]. Genes encoding the Panton-Valentine leukocidin (PVL) cytotoxin and staphylococcal enterotoxin B (SEB) were detected in S. argenteus isolates [11, 12, 14]. Community-acquired methicillin-resistant “S. aureus” (CA-MRSA) isolates, which were subsequently characterized as S. argenteus, were predominant in remote aboriginal communities of Australia . Therefore, there is no doubt that S. argenteus is a threat to human health.
The emergence of S. argenteus and S. schweitzeri results in the need to determine whether they should be distinguished from S. aureus in routine practice. Chantratita et al. suggested that this might be necessary if the infection is associated with different clinical manifestations, and/or requires different antimicrobial regimens . According to this suggestion, S. schweitzeri is clearly separated from the other two species though one isolate was obtained from human (ST 1822, cause no infection) , but S. argenteus is difficult to make a judgment based on current available data. In this study, we sequenced the genomes of five S. argenteus isolates, and performed a genomic comparison among SAC, so as to clarify their evolutionary relationships, to evaluate their pathogenic potential, and to identify genomic differences. This study is expected to establish an exploratory evolutionary framework regarding the evolution of pathogenicity of SAC.
Genomic features of SAC species
The sequences of 51 SAC genomes were used in this study and shown in Fig. 1. The draft genome sequences of five S. argenteus strains previously identified in our laboratory  were sequenced in this study. Thirty S. aureus genomes, which represent a genetically diverse collection of strains of several different STs (Additional file 1: Figure S1), were selected from the complete genome sequences available in the NCBI genome database. Ten S. argenteus and six S. schweitzeri genomes (complete or draft) were also downloaded from NCBI.
For S. aureus, the genome sizes varied from 2.74 Mb (strain ST398/S0385) to 2.99 Mb (TW20), the GC contents varied from 32.75% (MN8) to 32.96% (LGA251), and the number of coding sequences (CDS) per genome varied from 2399 (RKI4) to 3009 (TW20) (Fig. 1). For S. argenteus, the genome sizes varied from 2.71 Mb (M051_MSHR) to 2.87 Mb (SJTU F21285), the GC contents varied from 32.20% (M051_MSHR) to 32.42% (SJTU F21285), and the number of CDSs per genome varied from 2490 (M051_MSHR) to 2662 (SH3) (Fig. 1). For S. schweitzeri, the genome sizes varied from 2.67 Mb (FSCB1B) to 2.81 Mb (FSA096), the GC contents varied from 32.21% (FSA096) to 32.28% (FSCB5), and the number of CDSs per genome varied from 2434 (FSCB1B) to 2587 (FSA096) (Fig. 1).
The genome size, GC content and number of CDSs were significantly different between S. aureus and the two new species (p < 0.01), while no significant differences were observed between S. argenteus and S. schweitzeri (P > 0.01; Additional file 2: Figure S2). The number of genomes used in this study was limited, particularly for S. argenteus and S. schweitzeri. Furthermore, some genome sizes and CDS numbers were estimated from draft genome assemblies and remained incomplete. Nevertheless, the difference in GC content, which is less affected by the quality of genome assembly, suggests an apparent divergence between S. aureus and the other two members of the SAC.
Pan-genome of SAC species
To facilitate a genomic comparison and achieve a sound comparison at the whole genome scale, the pan-genome of SAC was defined. A pan-genome was previously described to include two distinct components, the core and variable genomes, which represent the essence and the diversity of the population, respectively . The core genome consists of genes that are common to all strains while the variable genome is composed of genes absent or present at least in one strain, due to either gene loss or acquisition . Here we expanded the classification of the SAC pan-genome into four genomic components: 1) core genes (CR), present in all strains; 2) core variable genes (CV), present in all strains with at least one pseudogene; 3) variable genes (VR), present in at least two strains and absent at least in one strain; and 4) unique genes (UQ), present in only one strain. The pan-genome of the 51 SAC genomes was grouped into 4249 homologous gene families, including 1671 CRs, 328 CVs, 1634 VRs, and 616 UQs (Additional file 3: Table S1; Additional file 4: Figure S3). The pan-genome of the 30 S. aureus genomes was grouped into 3966 homologous gene families, including 1752 CRs, 330 CVs, 1329 VRs, and 555 UQs. The sizes of CRs, CVs, VRs and UQ were also calculated for S. argenteus and S. schweitzeri (Additional file 5: Figure S4). The category analysis of the Cluster of Orthologous Groups (COGs)  for the pan-genome of the SAC and each species showed similar profiles.
Clearly, the pan-genomes for each group were all open (i.e., not complete), due to the limited number of genomes included in the analyses. The accumulation curves and pan-genome sizes largely depend on sampling size and diversity . In this study, S. aureus was better sampled than S. argenteus and S. schweitzeri in both the number of strains and strain diversity. For example, of the fifteen S. argenteus genomes examined, nine were ST2250 demonstrating little genome diversity (see below). Therefore, a brief comparison of the pan-genome is shown in Additional files 6: Figure S5, and the details will not be discussed here. However, it is worth noting that the 1999 common gene families (CR plus CV) in the SAC pan-genome were approximately three quarters of the size of an individual SAC genome, and the 2578 sharing gene families (genes present in at least one genome of each species, including CR, CV and partial VR) were approximately equivalent to the CDS number of an individual SAC species genome. This is indicative of high level of sharing of homologous gene families between the species and strains of the SAC. It is predictable that S. aureus, S. argenteus and S. schweitzeri share many biological characteristics, many of which have been demonstrated in previous studies as a common phenotypes .
Core genome of the SAC species
Similar metabolic pathways and phenotypes between species may be the result of frequent genetic exchanges, such as horizontal gene transfer (HGT) mediated by bacteriophage [28, 29], or limited evolutionary time for divergence from a common ancestor. HGT mediated by mobilome mainly contributes to the VRs and UQs, respectively as follows (Additional file 4: Figure S3; Additional file 5: Figure S4, code X): SAC, VRs and UQs accounting for 62.7% and 34.7% of the genes assigned to X class, respectively; S. aureus, 63.5% and 33.6%; S. argenteus, 61.5% and 32.3%; and S. schweitzeri, 64.8% and 27.8%. Also, it is believed that distinct species are separated by apparent genetic distances that can act as barriers to recombination [30,31,32]. To better understand the genetic distances, the average nucleotide identities based on BLAST (bANI) were calculated using the core genes shared among SAC species (Fig. 2). Most of the intraspecific sequence identity values were greater than 95%, which was consistent with the species definition of an ANI cutoff of 95–96% . The interspecific values from different groups formed three peaks similar to normal distributions and exhibited apparently differences: the peak of S. argenteus versus S. schweitzeri was near 95% while those of S. argenteus versus S. aureus and S. schweitzeri versus S. aureus were 86–89%. The core genome of S. schweitzeri (90.92 ± 4.77%) was closer related to that of S. aureus than S. argenteus (89.79 ± 4.63%, Fig. 2). Notably, in pairwise comparisons between species, bANI values greater than 95% were a small but notable part of the total. To determine whether these identity values (> 95%) were generated from highly conserved house-keeping genes (evolving very slowly), the pairwise distance among alleles of each gene family was tested. It was found that the number of gene families with an average interspecific identity greater than 95% was 188 between S. aureus and S. argenteus, 307 between S. aureus and S. schweitzeri, and 698 between S. argenteus and S. schweitzeri (161 in common). Meanwhile, 34 gene families between S. aureus and S. argenteus, 33 between S. aureus and S. schweitzeri, and 80 between S. argenteus and S. schweitzeri (28 in common) had the same amino acid sequence. It indicated that the bANI values greater than 95% were mainly due to slowly evolving genes, but horizontal gene transfer between species may also be present.
The species of SAC have identical 16S rRNA gene while MLST and other house-keeping genes can be used to distinguish them from each other [16, 21]. Nevertheless, the MLST loci are just a small part of the genome. Here, an upgraded MLST method  was used to compare 1375 single copy core genes of SAC and S. simiae (used as an outgroup), which was previously considered as the closest taxa to S. aureus , were used to infer phylogeny. The Maximum Likelihood (ML) algorithm was used and a well-supported topology was obtained (Fig. 1). S. argenteus and S. schweitzeri clustered together (3.05% in distance based on amino acid sequence) and then clustered with S. aureus (4.42% in distance) in the core-genome tree, with perfect supporting bootstrap values (100%). S. simiae grouped far away from SAC with 21.34% in distance. This suggests that SAC members separated from each other much later than from other staphylococci, resulting in species divergence from a shared global core genome of a common ancestor.
The program Structure version 2.3.4 , which implements a model-based clustering method, was used to infer the SAC population structure using genotype data of the 1596 single copy core genes. Structure Harvester was used for collating results generated by the program Structure . The ΔK values, an ad hoc quantity related to the second order rate of change of the log probability of data with respect to the number of clusters, were calculated by Structure Harvester to detecting the number of K populations that best fit the data. A higher ΔK means a better fitness of the K value. The highest ΔK value (= 5840) emerged when K = 2 (Fig. 3). It indicated that staphylococcal strains investigated here fall into two distinct populations corresponding to S. aureus and S. argenteus, respectively (Fig. 3a). Six individuals of S. schweitzeri seemed to be hybrids between S. aureus (24–25%) and S. argenteus (75–76%). Five S. aureus strains, JKD6159, CA-347, H-EMRSA-15, JS395 and ST398/S0385, appeared to be hybrids among the S. aureus (red) and the S. argenteus (green) populations, with all individuals showing < 3.2% ancestry from S. argenteus (green) population. None of S. argenteus strains was found to be a hybrid. We also observed a higher ΔK value (= 579) when K = 3, and the three distinct populations corresponded to S. aureus, S. argenteus and S. schweitzeri, respectively (Fig. 3a). None of S. schweitzeri strains was found to be a hybrid, but four strains of S. argenteus were hybrids showing < 1.5% ancestry from S. aureus (red) and S. schweitzeri (blue) populations. And five strains of S. aureus were hybrids showing < 2.3% ancestry from S. argenteus (green) and S. schweitzeri (blue) populations. These results suggested that S. argenteus had an independent population structure with rare recombination occurred between the core genomes of S. argenteus and the other two species during the speciation. Notably, S. schweitzeri was found to be hybrids when K = 2, and it was independent population when K = 3, which implied this species might be the descendant of an ancient hybrid between S. aureus and S. argenteus. Nevertheless, it was not further discussed in this study, considering limited genomes and knowledge on this species.
SAC species-specific genes
The species-specific genes (present or absent in all strains of one species) among SAC species were identified and shown in Table 1. We arbitrarily defined one gene is acquired in one species if the gene (including related pseudogene) is not found in the other species, and one gene is lost in one species if the gene is found in the other species. It shoud be a gene loss event if a pseudogene is found in one species and the related gene is present in the other species. Based on this definition, S. aureus acquired 24 unique genes while S. argenteus and S. schweitzeri acquired six and zero, respectively. S. aureus lost four genes while S. argenteus and S. schweitzeri lost 16 and ten, respectively. Some newly acquired genes were not required for growth under some conditions and became pseudogenes in some strains, for example, a gene encoding a protein N-acetyltransferase was acquired by but became pseudogenes in some strains (gene family 2832 in Table 1). Meanwhile, some genes were inherited from the common ancestor but were kept as pseudogenes in all strains of one species (Table 1). These pairwise species-specific genes and single species-specific genes were reannotated in COG, UniPro database, and KEGG Automatic Annotation Server [27, 38, 39], but most of their functions were unknown, especially those of S. aureus (Table 1). S. aureus and S. schweitzeri are different from S. argenteus in that S. argenteus lacks the crtOPQMN operon encoding the staphyloxanthin pathway (resulting in yellow colonies) [8, 16, 22]. Our data also indicated that white-colony forming S. aureus isolates might have resulted from inactivation of crtM, crtN, and crtO (Table 1) or a non-functional sigB operon . Some species-specific genes are also prevalent in coagulase-negative staphylococci (CoNS) with low identities (< 90%), indicating that they might be inherited from the common staphylococcal ancestor, with some species of SAC losing them during speciation. The other species-specific genes, which were absent or present in a limited number of CoNS, probably were gained by HGT. Interestingly, all S. aureus-specific genes, most encoding small proteins (< 80 aa) with unknown functions, were absent in other staphylococci.
Mobile genetic elements (MGE), agr and the capsular polysaccharide gene cluster
Temperate bacteriophages of the major Siphoviridae family play an important role in the pathogenicity of S. aureus by mediating the HGT of virulence factors . The detection of genes encoding these prophages’ integrases, integrase groups Sa1–3, indicated the presence of the prophages in the genomes of S. argenteus and S. schweitzeri (Fig. 1), which take many virulence genes along with them . Methicillin-resistant S. aureus (MRSA) strains are of particular importance because they are a leading cause of nosocomial infections worldwide . Methicillin resistance in MRSA is due to an acquisition of the staphylococcal cassette chromosome mec (SCCmec) element. Evidence for the presence of SCCmec in S. argenteus has been reported previously [6, 23] and is further substantiated in this study (Additional file 3: Table S1; mecA, gene family 2509; ccrA, family 2525; ccrB/C, family 2583). The clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins (CRISPR-Cas) modules are adaptive immunity systems that are present in many archaea and bacteria , but are not very common in S. aureus (Fig. 1). Some strains of S. argenteus and S. aureus harbor a characteristic cas10/csm1 (gene family 2896 in Additional file 3: Table S1), and a subtype of CRISPR-Cas system III was present in them according to previous classification , especially strains of ST2250. Related genes of S. argenteus CRISPR-Cas system are a somewhat divergent from those of S. aureus strain JS395 (nucleotide sequence similarity 93.8%). However, further comparison with other S. aureus strains in NCBI showed that some S. aureus strains, such as M1169 (JEKD00000000) possessed a CRISPR-cas system with high similarity to that of S. argenteus (> 99%), suggesting that CRISPR loci can be mobilized and can transfer between different but closely related species.
The staphylococcal agr locus encodes a quorum sensing system (QS) that controls the expression of virulence and other accessory genes by a classical two-component signaling module, and it is distinct among staphylococcal species . However, S. argenteus harbors S. aureus arg type I and IV while S. schweitzeri type I, II, and IV (Fig. 1). Further analysis suggested that agrA was highly conserved within species and had no common sequence differences among agr types. The agrB gene may suffer ancient HGT among species, especially that of S. schweitzeri agrI and ‘agrIII’ (maybe novel type), while the agrCD genes cluster primarily by arg type and secondarily by species (Additional file 7: Figure S6). The agrD alignment is shown in Fig. 4. It looks like that the evolution of agr locus among SAC species was polyphyletic. Based on this conjecture, the same agr types in different SAC species would have evolved independently. Gene agrD produces a ribosomal propeptide of which the middle section encodes the seven to nine residue autoinducing peptide (AIP) used as a QS signal molecule . We found two possible novel types of agr most closely related to type III agr (Fig. 4). These novel agr types were present in three strains of S. argenteus (SJTU F20124, H115100079 and M051_MSHR) and one strain of S. schweitzeri (FSA090) (Fig. 1).
Like many other bacterial pathogens, S. aureus produces capsular polysaccharide (CP) that enhances its resistance to clearance by host innate immune defenses, with strains of serotypes 5 and 8 being the most among clinical isolates . We compared the CP gene clusters among SAC members (Additional file 8: Figure S7). For most of the genes common region of the CP gene cluster (capA-P, see reference 4 for a review), S. aureus, S. argenteus and S. schweitzeri were all phylogenetically distinct from each other. The exceptions were for the capM and capN genes where the phylogenetic trees showed that S. aureus and S. schweitzeri clustered together and HGT is likely to have occurred. In the serotype specific gene region (capHIJK), S. aureus was clearly divided into two groups consisting of strains of serotype 5 and 8, respectively. Strain S. schweitzeri FSA090 clustered with 14 S. argenteus strains in a group more closely related to S. aureus strains of serotype 5. Similarly, one strain S. argenteus, H115100079, clustered with five S. schweitzeri strains in a group most closely related to S. aureus strains of serotype 8. These results suggest that S. argenteus and S. schweitzeri may express their own serotype(s) that could have been derived from the ancestors of S. aureus serotype 5 and 8, respectively.
Virulence gene distribution
A hallmark of S. aureus infections is their frequent recurrence, which results from the manipulation of host immune responses by dozens of virulence genes . Several virulence genes have been reported in S. argenteus and S. schweizeri, such as nuc m , pvl, sak, scn, seb, sec, selk, selq, and tsst [6, 11, 14, 18, 46], but the presence of many other potential virulence genes have not been tested for yet. Here, we performed a systematic investigation of genome sequences for the virulence gene distribution among SAC. The results indicated that (Additional file 9: Table S2): of 111 virulence genes previously reported in S. aureus, only five were not found in the genome sequences of any strain examined in this study, 92 (82.9%) were present in at least two SAC species, 85 (76.6%) in S. argenteus, and 86 (77.5%) in S. schweizeri. Of the 19 (17.1%) genes absent in S. argenteus or S. schweizeri, 11 encode enterotoxins or related superantigen-like proteins, and four encode leukocidins related proteins. Besides, of the 71 (64.0%) genes that were present in at least two species and at least three strains per species, 56 (50.5%) showed a significant difference in nucleotide sequence identity in a pairwise comparison of genes between species (p < 0.01). It is notable that nine genes (clfA-B, fnbA-B, sdrC-E, spa and coa) have repeat regions of uneven numbers in some strains, of which a subtyping method targeting spa gene (coding Staphylococcus Protein A) is widely used to characterize S. aureus isolates . This could result in inaccurate identification of the genetic divergence based on these genes because of difficulties to align homologous nucleotide loci and a rapid change in the number of repeats within these regions. The principal component analysis (PCA) was employed to evaluate the differences in gene content and overall divergence of virulence genes among SAC members. The results suggested that the virulence gene content of each SAC species did not differ from each other while the divergence in core virulence gene is noteworthy (Fig. 5). In fact, most virulence genes were divergent at nucleotide sequence level among S. aureus, S. argenteus and S. schweitzeri (please find the ML trees in Additional file 9: Table S2). However, HGT may occur in some non-MGE-associated virulence genes, such as nucA of S. aureus JS395.
Overall, the S. argenteus and S. schweitzeri pan-genome harbor all the virulence genes expressing the essential functions required for the pathogenicity in S. aureus. For examples, icaA-D encodes polysaccharide biosynthesis, which is critical to biofilm elaboration ; esaA-C, essA-C, and esxAB encodes the ESAT-6 system for secretion of exoenzymes or exotoxins into the extracellular milieu ; isdA-G and srtB code for heme uptake . The genomic islands νSaα and νSaβ are also present in these two species, which carry many virulence determine genes . Most of the virulence genes absent in the S. argenteus and S. schweitzeri pan-genome code for enterotoxins and other exotoxins (Additional file 9: Table S2), which are usually located in MGE and easily acquired or lost.
Geographic characteristics of S. argenteus ST2250
S. argenteus ST2250 was the most frequent lineage isolated, and strains of this ST have a broad geographic distribution [6, 8, 11, 13,14,15,16,17]. The widespread distribution of S. argenteus ST2250 may have occurred long ago or may have occurred only recently. Signatures from core housekeeping genes and variable genes suggest that geographic barriers promote divergence among microbial populations in the environment [26, 51]. However, increased mobility of human populations may break these geographical barriers for pathogenic bacteria . To clarify the question as to why S. argenteus ST2250 was well distributed, the core and variable genes were further analyzed. The core genome of ST2250 contained 2348 single copy gene families, of which the concatenated alignment had a length of 2.07 M bp and 1177 single nucleotide polymorphisms (SNP). However, more than a half of the SNPs (653 bp) were due to indel variation located in the tandem repeat regions of three genes, including a hypothetic serine protease gene (gene family 1742 in Additional file 3: Table S1), a serine-rich adhesin for platelets gene (sraP, gene family 2098), and a hypothetic membrane anchored protein gene (gene family 2132). Due to the high rate of mutation, these loci were excluded from the analysis. The remaining 524 SNPs were used to infer maximum likelihood phylogenies (Fig. 6a). This phylogenetic tree matches the core gene nucleotide phylogeny (Fig. 1) very well with significant bootstrap support, but does not resolve a distinct population in each geographical region. Strains SH3, SJTU F21164 and F21224 were isolated from China (CN) and clustered with LBSA043 from Australia (AU) and 1299_SAUR from America (US). The presence/absence of gene content matrix based on variable genes also showed similar clustering (Fig. 6b). Notably, the number of SNPs was very small compared with S. aureus ST239 lineage, which were also distributed intercontinentally and from which 4310 SNPs were found among the isolates from different countries . By comparison, only 382 VR and UQ gene families were identified in the S. argenteus pan-genome, of which strain LSBA043 was present in the least gene families (43) and strain SH3 in the most (167). All 382 gene families were annotated by ACLAME web site  and homologues of 247 were reported to be located in MGE. These results demonstrated that isolates of S. argenteus ST2250 lineage were very closely related and might have spread internationally by human hosts.
Isolates of S. argenteus and S. schweitzeri were originally classified as parts of lineages of S. aureus because they share many phenotype and genotype properties [8, 16]. However, when using some molecular subtyping methods, considerable difficulties were observed, such as PCR amplification of aroE and glpf in MLST , and nucA identification of S. schweitzeri . Few large-scale virulence gene investigations on S. argenteus and S. schweitzeri have been reported, and studies relying on PCR amplification of virulence targets should be regarded with skepticism because many S. argenteus and S. schweitzeri virulence genes are significantly divergent from those in S. aureus (Additional file 9: Table S2). Nonetheless, there is a great deal of genetic relatedness among SAC species, and homologous genes have been leveraged to develop many broadly recognized descriptive indexes among SAC, such as MLST, SCCmec, spa, agr, and prophage typing. Obviously, S. aureus, S. argenteus and S. schweitzeri are more closely related to each other than they are to other Staphylococcus species, of which these descriptive indexes are very distinct, for example, agr and prophage types of SAC never reported to be present in other staphylococci [29, 43]. Therefore, as previously suggested , the term SAC can be used to describe the closely related group of species including S. aureus, S. argenteus and S. schweitzeri.
Taking SAC as a starting point, many descriptions should be re-examined, for example, MRSA could refer to any SAC isolate harboring a SCCmec element. There is also a need to unify other descriptive indexes as well as diagnostic and typing methods, especially virulence gene detection and molecular subtyping. For example, we recently reported redesigned aroE and glpf primers for MLST of SAC and proposed a simple PCR method for identification and differentiation of SAC isolates . The gene family list generated in the current study will also accelerate the development of uniform methods for the detection of many virulence genes (Additional file 3: Table S1; Additional file 9: Table S2). However, some virulence genes (especially core genes) may evolve quickly, with several very divergent subtypes occurring among or within SAC species. For example, alleles of S. schweitzeri nuc2 formed two distant clades, one closely related to S. aureus and the other closely to S. argenteus. Furthermore, divergent subtypes appear in many virulence genes of S. aureus and S. schweitzeri (note the high intraspecific average distance in Additional file 9: Table S2). The nuc M recently identified in S. schweitzeri  is a divergent homolog of the S. aureus nucA, and S. argenteus harbors an additional nucA variant. Thus, the current nomenclature of nuc M does not properly reflect its relationships to nucA (ortholog) and nuc2 (paralog). Therefore, much effort needs to be devoted to properly name genes among species of SAC.
We demonstrated here that, S. schweitzeri and S. argenteus have a similar distance to S. aureus, and that they are much closer to each other than to S. aureus (Fig. 2; ML trees in Additional file 9: Table S2). The relationships of these two species to S. aureus were reflected by whole genome ANI and DNA–DNA hybridization analysis in previous study . Therefore, the most recent common ancestor of all three was earlier than that of S. schweitzeri and S. argenteus. However, this relationship is in contrast to host association for SAC infection. That is, S. aureus and S. argenteus are commonly associated with human disease, while S. schweitzeri rarely. S. argenteus and S. schweitzeri have lower GC content compared to S. aureus, which may be the result of mutation and selection involving multiple factors, such as the environment, symbiotic lifestyle and aerobiosis . Additionally, S. argenteus shows a low occurrence in many regions where it has been reported [6, 8, 13]. It seems unlikely that S. argenteus emerged as a human pathogen and can escape clinical monitoring until the last decade. It is more likely that S. argenteus may have originally been a different ecotype from S. aureus and a host adaption occurred to allow it to infect humans, which is also implied by a very recent report . Nonethelessly, we can not deny another possibility that S. argenteus have been previously mistaken as S. aureus and failed to spread and draw enough attention.
Although MRSA of S. argenteus was predominant in community-acquired isolates in some regions , S. argenteus showed resistances to fewer antimicrobials than is typical in S. aureus [12, 14]. Thus it seems that there is currently no need to modify therapeutic regimes for S. aureus and S. argenteus infections. Nevertheless, genes responsible for antimicrobial resistance are often located in MGEs and easily acquired, enhancement of S. argenteus antimicrobial resistance may be just a matter of time and the rapid international spread of S. aureus , and S. argenteus (this study) may aggravate the situation. In this study, it was suggested that S. argenteus harbors all the core virulence genes of S. aureus (Additional file 9: Table S2), but the expression and regulations of most of these virulence factors have not yet been characterized. In addition, many species-specific genes were recognized that presently have unknown functions (Table 1). The population structure analysis (Fig. 3) suggested distinct evolution background of the core genome between S. aureus and S. argenteus. These differences may result in different mechanisms of virulence to S. argenteus and S. aureus and different invasiveness between S. schweitzeri and S. aureus, which will impact the development of approaches to targeted drug design and therapeutic regimens in the post-antibiotic era . For example, it was recently shown that diapophytoene desaturase (CrtN, the second enzyme of the staphyloxanthin biosynthesis pathway) is a potential target for drug development against S. aureus infections , but, to date, this drug targets are absent in all S. argenteus isolates. The future development of targeted drug therapies and diagnostic tests that distinguish these two species should allow for improved patient outcomes.
S. argenteus was found to possess most of the virulence genes of S. aureus (Fig. 5; Additional file 9: Table S2), which implied a pathogenic potential similar to S. aureus at a genomic level, and the international spread of S. argenteus ST2250, most likely anthropogenically, may worsen the situation. Furthermore, species-specific genes among SAC members recognized in this study may be responsible for the different ecotypes. Further investigation on the function of these unique gene products will help determine their contribution to speciation and ecotype. It is fascinating that the diversity of the agr locus indicates a polyphyletic relationship among SAC, suggesting that each species of SAC was derived from more than one common ancestor . However, the number of available genome sequences for S. argenteus and S. schweitzeri are still very limited and their diversity is unclear, so it was not discussed in this study.
We proposed to use the term SAC to cover S. aureus, S. argenteus and S. schweitzeri to indicate their close relationships. Considering difficulties in subtyping and virulence gene detecting using the methods designed for S. aureus, much effort needs to be devoted to developing universal and robust methods among SAC species. It is found in this study that S. argenteus harbored most virulence genes of S. aureus and had spread internationally, which suggested that S. argenteus may have a similar pathogenic potential as S. aureus. However, genomic divergence was also observed, especially regions of virulence genes, which draw necessary to distinguish S. argenteus from S. aureus in routine practice. Data from this study and previous ones draws a hypothesis that S. argenteus should have originally been a different ecotype from S. aureus and a host adaption occurred to allow it to infect humans. Finally, the clinical importance of S. argenteus underscores the need for broader genomic epidemiological investigations. Such studies would be expected to provide clarification on the origin of S. argenteus and the evolution of its infectivity and pathogenicity. Additional microbiological studies are also needed to determine the species environmental niche and further elaborate mechanisms of pathogenicity.
Strains and genomes
The five S. argenteus strains were isolated from China and characterized previously . The genomes were sequenced using a MiSeq 300PE sequencer (Illumina, San Diego, CA, USA) at GENEWIZ (Suzhou, China). Velvet version 1.2.10 , SSPACE version 3.0 , and GapFiller version 1–10  software packages were used for genome assembly. Annotation was performed by the NCBI Prokaryotic Genome Annotation Pipeline (https://www.ncbi.nlm.nih.gov/genome/annotation_prok/). An additional 46 genomes, including ten of S. argenteus, six of S. schweitzeri, and 30 of S. aureus, were obtained from the NCBI public database (ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/bacteria/Staphylococcus_aureus/) and Whole Genome Shotgun (WGS) sequencing projects databases (http://www.ncbi.nlm.nih.gov/Traces/wgs/) in May 2016. The genomic data was generated from the genome sequences and related files from NCBI. The STs were assigned by the public MLST database of S. aureus (http://saureus.mlst.net/), using the seven homologous fragments from the genome sequences.
Assignment of homologous gene families
The deduced amino acid sequences of all CDSs from the 51 genomes were adjusted to a prescribed format and were grouped into homologous gene families using OrthoMCL version 2.0.9  based on sequence similarity. The BLAST reciprocal best hit algorithm  was employed with a 70% match cutoff and 1e-5 e-value cutoff, and Markov Cluster Algorithms (MCL)  were applied with an inflation index of 1.5.
To account for pseudogenes and cases where genes were missed in the gene-calling step, BLAST (blast + package, version 2.2.29)  was used to align all genes of one family (assigned by OrthoMCL) against the genome sequence of the strains in which the family was not previously identified. If an alignment with at least 90% nucleotide sequence identity covering at least 90% of the sequence length was detected in the genome, the gene/pseudogene was considered present and the related location was recorded in the complete homologous gene families list using a different font (Additional file 3: Table S1). This produced a more robust pan-genome outcome and accommodated the differences in the gene prediction. For example, 137 and 140 genes were newly detected in genomes of strains S. aureus DAR4 and RKI4, respectively. The sizes of the four components of the pan-genome (CR, CV, VR and UQ) were simulated as has been done in previous studies  using Perl scripts. For example, pan-genome analysis of SAC was conducted starting from one single genome to 51 genomes. Genomes were added 1000 times in a randomized order without replacement for each fixed number of genomes, and the gene reservoir was accumulated. The functional category of each homologous gene family was determined by using the BLAST program locally or on the web server against the Cluster of Orthologous Groups (COGs), UniProt, KEGG Automatic Annotation Server (KAAS), and ACLAME database [27, 38, 39, 53], with 50% identity cutoff and 1e-5 e-value.
Phylogenetic analysis and bANI
To determine the phylogenetic relationships of SAC members based on genomic data, both supermatrix and gene content methods were applied to infer phylogenetic trees. For the supermatrix approach, 1375 single-copy orthologous genes shared by all 51 SAC strains and S. simiae CCM 7213 (AEUN00000000.1) were selected from the homologous gene families. For each orthologous family, protein sequences were aligned using Clustal Omega version 1.2.1  and the resulting alignments of individual proteins were concatenated to infer the phylogeny using the maximum likelihood algorithm (ML) in RAxML version 8.1.2  under the substitution matrix JTTDCMUT which was selected by the Perl script in the software package. The gene content matrix was parsed for the gene content method using a phyletic pattern indicating the presence (1) or absence (0) of the respective non-core genes of all strains. The Neighbor-Joining distance between pairwise genomes was calculated based on the gene content matrix to reconstruct the gene content dendrogram using package Splits Tree version 4.13.1 .
The SNPs among S. argenteus ST2250 strains were detected, extracted and concatenated from nucleotide sequence alignments of 2348 orthologous gene families using Perl scripts. The maximum likelihood algorithm (ML) in RAxML version 8.1.2  was used to infer the phylogeny under the substitution matrix GTR. The Neighbor-Joining phylogenetic trees of 16 genes of capsular polysaccharide (CP) were constructed based on amino acid sequence using the Poisson correction method in MEGA6 . The bANI tests were simulated as done in previous studies . Briefly, the core genes’ nucleotide sequences of each of the genomes were extracted and used in BLAST against the other whole genome sequences, and all the identity values were grouped into six groups, including three intraspecific groups and three interspecific groups. Then the data of each group were counted and presented as percentages in a histogram.
To investigate the population structure of the SAC and the relationships among species groups, the Markov chain Monte Carlo (MCMC) based program Structure version 2.3.4  was used to cluster individuals into populations. Initially, we treated core genes as MLST sequence data from Extended FASTA Format into the Structure Format using xmfa2struct (available from http://www.xavierdidelot.xtreemhost.com/clonalframe.htm). The admixture ancestry model with the assumption of correlated allele frequencies among populations was used. We ran the simulation five times under a burn-in period of 10,000 and a run length of 20,000 MCMC, without prior population information. K values from one to seven were tested to identify the best K value, represented by the highest value of K and ΔK . Results of the five independent runs were averaged for each K value was used to determine the most likely model, i.e., the one with the highest likelihood, and they were subsequently plotted using Distruct version 1.1 . The identification of the best K was evaluated following the ΔK-method through local program Structure Harvester .
Species-specific genes and virulence genes
Species-specific genes were inferred from the homologous gene families list (Additional file 3: Table S1) and reanalyzed against the missing species genome using BLAST algorithm to confirm that they were lost or became pseudogenes. The 111 virulence genes were identified and collected from a virulence factor database (VRprofile, http://bioinfo-mml.sjtu.edu.cn/VRprofile/)  and a recent review of the literature . Reference sequences of these genes were used with BLAST algorithm to find the homologous gene families presented in Additional file 3: Table S1. Some virulence genes, which have a paralogous relationship and may be assigned to one gene family by OrthoMcl software (Additional file 9: Table S2), were separated for subsequent analysis. The pairwise k-tuple distance among each of these virulence genes was calculated and outputted from the Clustal Omega (version 1.2.1) created alignment . The distance matrix was then parsed using Perl script. Principal component analysis (PCA) were performed using software Past software version 3.13 (http://folk.uio.no/ohammer/past/), and other statistical analyses were performed using the R package (version 3.1.1, http://www.R-project.org).
Average nucleotide identities based on BLAST
Community-acquired methicillin-resistant S. aureus
Cluster of Orthologous Groups
Clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins
Core variable genes
Horizontal gene transfer
KEGG Automatic Annotation Server
Markov Cluster Algorithms
Markov chain Monte Carlo
Mobile genetic elements
Multilocus sequence typing
Methicillin-resistant S. aureus
Principal component analysis
Quorum sensing system
S. aureus complex
- SCCmec :
Staphylococcal cassette chromosome mec element
Single nucleotide polymorphism
Sequence Read Archive
United States of America
Whole Genome Shotgun
Schleifer KH, Bell JA. Staphylococcus Rosenbach 1884, 18AL (nom. Cons. Opin. 17 Jud. Comm. 1958, 153.) In: De VP, editor. Bergey's manual of systematic bacteriology second edition, vol. Three, 2009. second ed. Heidelberg: Springer. p. 392–421.
Wertheim HFL, Melles DC, Vos MC, van Leeuwen W, van Belkum A, Verbrugh HA, Nouwen JL. The role of nasal carriage in Staphylococcus Aureus infections. Lancet Infect Dis. 2005;5(12):751–62.
DeLeo FR, Otto M, Kreiswirth BN, Chambers HF. Community-associated meticillin-resistant Staphylococcus aureus. Lancet. 2010;375(9725):1557–68.
Argudin MA, Mendoza MC, Rodicio MR. Food poisoning and Staphylococcus aureus Enterotoxins. Toxins. 2010;2(7):1751–U1342.
Okuma K, Iwakawa K, Turnidge JD, Grubb WB, Bell JM, O'Brien FG, Coombs GW, Pearman JW, Tenover FC, Kapi M, et al. Dissemination of new methicillin-resistant Staphylococcus aureus clones in the community. J Clin Microbiol. 2002;40(11):4289–94.
Argudin MA, Dodemont M, Vandendriessche S, Rottiers S, Tribes C, Roisin S, de Mendonca R, Nonhoff C, Deplano A, Denis O. Low occurrence of the new species Staphylococcus argenteus in a Staphylococcus aureus collection of human isolates from Belgium. Eur J Clin Microbiol Infect Dis. 2016;35(6):1017–22.
Ruimy R, Armand-Lefevre L, Barbier F, Ruppe E, Cocojaru R, Mesli Y, Maiga A, Benkalfat M, Benchouk S, Hassaine H, et al. Comparisons between geographically diverse samples of carried Staphylococcus aureus. J Bacteriol. 2009;191(18):5577–83.
Zhang DF, Xu X, Song Q, Bai Y, Zhang Y, Song M, Shi C, Shi X. Identification of Staphylococcus argenteus in eastern China based on a nonribosomal peptide synthetase (NRPS) gene. Future Microbiol. 2016;11:1113–21.
Ritchie SR, Thomas MG, Rainey PB. The genetic structure of Staphylococcus aureus populations from the Southwest Pacific. PLoS One. 2014;9(7):e100300.
Jenney A, Holt D, Ritika R, Southwell P, Pravin S, Buadromo E, Carapetis J, Tong S, Steer A. The clinical and molecular epidemiology of Staphylococcus aureus infections in Fiji. BMC Infect Dis. 2014;14:160.
Dupieux C, Blonde R, Bouchiat C, Meugnier H, Bes M, Laurent S, Vandenesch F, Laurent F, Tristan A. Community-acquired infections due to Staphylococcus argenteus lineage isolates harbouring the Panton-valentine leucocidin, France, 2014. Eur Secur. 2015;20(23):6–8.
Ruimy R, Angebault C, Djossou F, Dupont C, Epelboin L, Jarraud S, Armand Lefevre L, Bes M, Lixandru BE, Bertine M, et al. Are host genetics the predominant determinant of persistent nasal Staphylococcus aureus carriage in humans? J Infect Dis. 2010;202(6):924–34.
Thaipadungpanit J, Amornchai P, Nickerson EK, Wongsuvan G, Wuthiekanun V, Limmathurotsakul D, Peacock SJ. Clinical and molecular epidemiology of Staphylococcus argenteus infections in Thailand. J Clin Microbiol. 2015;53(3):1005–8.
Chantratita N, Wikraiphat C, Tandhavanant S, Wongsuvan G, Ariyaprasert P, Suntornsut P, Thaipadungpanit J, Teerawattanasook N, Jutrakul Y, Srisurat N, et al. Comparison of community-onset Staphylococcus argenteus and Staphylococcus aureus sepsis in Thailand: a prospective multicentre observational study. Clin Microbiol Infect. 2016;22(5):458. e11-e19
Monecke S, Stieber B, Roberts R, Akpaka PE, Slickers P, Ehricht R. Population structure of Staphylococcus aureus from Trinidad & Tobago. PLoS One.2014;9(2): e89120.
Tong SY, Schaumburg F, Ellington MJ, Corander J, Pichon B, Leendertz F, Bentley SD, Parkhill J, Holt DC, Peters G, et al. Novel staphylococcal species that form part of a Staphylococcus aureus-Related Complex: the non-pigmented Staphylococcus argenteus sp. nov. and the non-human primate-associated Staphylococcus schweitzeri sp. nov. Int J Syst Evol Microbiol. 2015;65:15–22.
Roach DJ, Burton JN, Lee C, Stackhouse B, Butler-Wu SM, Cookson BT, Shendure J, Salipante SJ. A year of infection in the intensive care unit: prospective whole genome sequencing of bacterial clinical isolates reveals cryptic transmissions and novel microbiota. PLoS Genet. 2015;11(7):e1005413.
Schaumburg F, Pauly M, Anoh E, Mossoun A, Wiersma L, Schubert G, Flammen A, Alabi AS, Muyembe-Tamfum JJ, Grobusch MP, et al. Staphylococcus aureus Complex from animals and humans in three remote African regions. Clin Microbiol Infect. 2015;21(4):345. e341-348
Schaumburg F, Alabi AS, Kock R, Mellmann A, Kremsner PG, Boesch C, Becker K, Leendertz FH, Peters G. Highly divergent Staphylococcus Aureus isolates from African non-human primates. Environ Microbiol Rep. 2012;4(1):141–6.
Akobi B, Aboderin O, Sasaki T, Shittu A. Characterization of Staphylococcus aureus isolates from faecal samples of the straw-Coloured fruit bat (Eidolon helvum) in Obafemi Awolowo university (OAU), Nigeria. BMC Microbiol. 2012;12:279.
Ng JWS, Holt DC, Lilliebridge RA, Stephens AJ, Huygens F, Tong SYC, Currie BJ, Giffard PM. Phylogenetically distinct Staphylococcus aureus lineage prevalent among indigenous communities in northern Australia. J Clin Microbiol. 2009;47(7):2295–300.
Tong SYC, Sharma-Kuinkel BK, Thaden JT, Whitney AR, Yang SJ, Mishra NN, Rude T, Lilliebridge RA, Selim MA, Ahn SH, et al. Virulence of endemic nonpigmented northern Australian Staphylococcus aureus clone (Clonal complex 75, S-argenteus) is not augmented by Staphyloxanthin. J Infect Dis. 2013;208(3):520–7.
McDonald M, Dougall A, Holt D, Huygens F, Oppedisano F, Giffard PM, Inman-Bamber J, Stephens AJ, Towers R, Carapetis JR, et al. Use of a single-nucleotide polymorphism genotyping system to demonstrate the unique epidemiology of methicillin-resistant Staphylococcus aureus in remote aboriginal communities. J Clin Microbiol. 2006;44(10):3720–7.
Ngoa UA, Schaumburg F, Adegnika AA, Kosters K, Moller T, Fernandes JF, Alabi A, Issifou S, Becker K, Grobusch MP, et al. Epidemiology and population structure of Staphylococcus Aureus in various population groups from a rural and semi urban area in Gabon, Central Africa. Acta Trop. 2012;124(1):42–7.
Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. The microbial pan-genome. Curr Opin Genet Dev. 2005;15(6):589–94.
Reno ML, Held NL, Fields CJ, Burke PV, Whitaker RJ: Biogeography of the Sulfolobus islandicus pangenome. P Natl Acad Sci USA 2009, 106(44):18873-18873.
Galperin MY, Makarova KS, Wolf YI, Koonin EV. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res. 2015;43:D261–9.
Thomas CM, Nielsen KM. Mechanisms of, and barriers to, horizontal gene transfer between bacteria. Nat Rev Microbiol. 2005;3(9):711–21.
Goerke C, Pantucek R, Holtfreter S, Schulte B, Zink M, Grumann D, Broker BM, Doskar J, Wolz C. Diversity of prophages in dominant Staphylococcus aureus clonal lineages. J Bacteriol. 2009;191(11):3462–8.
Fraser C, Alm EJ, Polz MF, Spratt BG, Hanage WP. The bacterial species challenge: making sense of genetic and ecological diversity. Science. 2009;323(5915):741–6.
Achtman M, Wagner M. Microbial diversity and the genetic nature of microbial species. Nat Rev Microbiol. 2008;6(6):431–40.
Fraser C, Hanage WP, Spratt BG. Recombination and the nature of bacterial speciation. Science. 2007;315(5811):476–80.
Richter M, Rossello-Mora R. Shifting the genomic gold standard for the prokaryotic species definition. P Natl Acad Sci USA. 2009;106(45):19126–31.
Maiden MCJ, van Rensburg MJJ, Bray JE, Earle SG, Ford SA, Jolley KA, McCarthy ND. MLST revisited: the gene-by-gene approach to bacterial genomics. Nat Rev Microbiol. 2013;11(10):728–36.
Pantucek R, Sedlacek I, Petras P, Koukalova D, Svec P, Stetina V, Vancanneyt M, Chrastinova L, Vokurkova J, Ruzickova V, et al. Staphylococcus simiae sp. nov., isolated from south American squirrel monkeys. Int J Syst Evol Microbiol. 2005;55:1953–8.
Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14(8):2611–20.
Earl DA, Vonholdt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour. 2012;4(2):359–61.
UniProt C. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43:D204–12.
Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007;35:W182–5.
Kullik I, Giachino P, Fuchs T. Deletion of the alternative sigma factor sigmaB in Staphylococcus aureus reveals its function as a global regulator of virulence genes. J Bacteriol. 1998;180(18):4814–20.
International Working Group on the Classification of Staphylococcal Cassette Chromosome E. Classification of staphylococcal cassette chromosome mec (SCCmec): guidelines for reporting novel SCCmec elements. Antimicrob Agents Ch. 2009;53(12):4961–7.
Makarova KS, Wolf YI, Alkhnbashi OS, Costa F, Shah SA, Saunders SJ, Barrangou R, Brouns SJ, Charpentier E, Haft DH, et al. An updated evolutionary classification of CRISPR-Cas systems. Nat Reviews Microbiol. 2015;13(11):722–36.
Thoendel M, Kavanaugh JS, Flack CE, Horswill AR: Peptide signaling in the staphylococci. Chem Rev 2011, 111(1):117-151.
O'Riordan K, Lee JC. Staphylococcus aureus capsular polysaccharides. Clin Microbiol Rev. 2004;17(1):218–34.
Thammavongsa V, Kim HK, Missiakas D, Schneewind O. Staphylococcal manipulation of host immune responses. Nat Rev Microbiol. 2015;13(9):529–43.
Schaumburg F, Pauly M, Schubert G, Shittu A, Tong S, Leendertz F, Peters G, Becker K. Characterization of a novel thermostable nuclease homolog (NucM) in a highly divergent Staphylococcus aureus clade. J Clin Microbiol. 2014;52(11):4036–8.
Hallin M, Friedrich AW, Struelens MJ. Spa typing for epidemiological surveillance of Staphylococcus Aureus. Methods Mol Biol. 2009;551:189–202.
Gotz F. Staphylococcus and biofilms. Mol Microbiol. 2002;43(6):1367–78.
Burts ML, Williams WA, DeBord K, Missiakas DM. EsxA and EsxB are secreted by an ESAT-6-like system that is required for the pathogenesis of Staphylococcus aureus infections. Proc Natl Acad Sci U S A. 2005;102(4):1169–74.
Skaar EP, Schneewind O. Iron-regulated surface determinants (Isd) of Staphylococcus aureus: stealing iron from heme. Microbes Infect. 2004;6(4):390–7.
Whitaker RJ, Grogan DW, Taylor JW. Geographic barriers isolate endemic populations of hyperthermophilic archaea. Science. 2003;301(5635):976–8.
Harris SR, Feil EJ, Holden MT, Quail MA, Nickerson EK, Chantratita N, Gardete S, Tavares A, Day N, Lindsay JA, et al. Evolution of MRSA during hospital transmission and intercontinental spread. Science. 2010;327(5964):469–74.
Leplae R, Lima-Mendez G, Toussaint A. ACLAME: a CLAssification of mobile genetic elements, update 2010. Nucleic Acids Res. 2010;38:D57–61.
Hildebrand F, Meyer A, Eyre-Walker A. Evidence of selection upon genomic GC-content in bacteria. PLoS Genet. 2010:6(9).
Moradigaravand D, Jamrozy D, Mostowy R, Anderson A, Nickerson EK, Thaipadungpanit J, Wuthiekanun V, Limmathurotsakul D, Tandhavanant S, Wikraiphat C, et al. Evolution of the Staphylococcus argenteus ST2250 clone in northeastern Thailand is linked with the Acquisition of Livestock-Associated Staphylococcal Genes. MBio. 2017;8(4):e00802–17.
Rasko DA, Sperandio V. Anti-virulence strategies to combat bacteria-mediated disease. Nat Rev Drug Discov. 2010;9(2):117–28.
Chen F, Di H, Wang Y, Cao Q, Xu B, Zhang X, Yang N, Liu G, Yang CG, Xu Y, et al. Small-molecule targeting of a diapophytoene desaturase inhibits S. aureus virulence. Nat Chem Biol. 2016;12(3):174–9.
Rossello-Mora R, Amann R. The species concept for prokaryotes. FEMS Microbiol Rev. 2001;25(1):39–67.
Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18(5):821–9.
Boetzer M, Henkel CV, Jansen HJ, Butler D, Pirovano W. Scaffolding pre-assembled contigs using SSPACE. Bioinformatics. 2011;27(4):578–9.
Nadalin F, Vezzi F, Policriti A. GapFiller: a de novo assembly approach to fill the gap within paired reads. BMC bioinformatics. 2012;13 Suppl 14:S8.
Li L, Stoeckert CJ Jr, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13(9):2178–89.
Moreno-Hagelsieb G, Latimer K. Choosing BLAST options for better detection of orthologs as reciprocal best hits. Bioinformatics. 2008;24(3):319–24.
Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002;30(7):1575–84.
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC bioinformatics. 2009;10:421.
Touchon M, Hoede C, Tenaillon O, Barbe V, Baeriswyl S, Bidet P, Bingen E, Bonacorsi S, Bouchier C, Bouvet O, et al. Organised genome dynamics in the Escherichia coli species results in highly diverse adaptive paths. PLoS Genet. 2009;5(1):e1000344.
Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Soding J, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal omega. Mol Syst Biol. 2011;7:539.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312–3.
Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol. 2006;23(2):254–67.
Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30(12):2725–9.
Rosenberg NA. DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes. 2004;4(1):137–8.
Li J, Tai C, Deng Z, Zhong W, He Y, Ou HY. VRprofile: gene-cluster-detection-based profiling of virulence and antibiotic resistance traits encoded within genome sequences of pathogenic bacteria. Brief Bioinform. 2017; 10.1093/bib/bbw141.
We would like to thank Xiangyu Deng and Hong-Yu Ou for their valuable comments on the manuscript. We would also like to thank Xiao-Yang Gao and Ji-Cheng Yao for their helps on bioinformatics analysis.
This study was supported by the National Key R&D Program of China (grant number 2016YFD0401102) and the National Natural Science Foundation of China (grant number 31671943). The funding bodies played no role in the design of the study and collection, analysis, interpretation of the data, or in the writing of the manuscript.
Availability of data and materials
Most of the data supporting the conclusions of this article are included within the article and its Additional files. The genome datasets of five S. argenteus strains generated during the current study are available in the GenBank and NCBI Sequence Read Archive (SRA) repository, under accession numbers: SJTU F20124, LWAN00000000 and SRP072812; F20419, LWAO00000000 and SRP072817; F21164, LWAP00000000 and SRP072819; F21224, LWAQ00000000 and SRP072820; F21285, LWAR00000000 and SRP072821. All the phylogenetic data generated in this study were deposited in TreeBASE database under the accession URL: http://purl.org/phylo/treebase/phylows/study/TB2:S21534. Other datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Ethics approval and consent to participate
Not applicable. No ethics approval is required.
Consent for publication
The authors declare that they have no competing interests.
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Phylogenetic distribution of the 30 S. aureus strains used used in this study. This neighbor-joining phylogenetic tree was constructed based on the predicted amino acid sequences of 3103 STs currently available in MLST database (http://saureus.mlst.net/). The colored dots represent the STs of S. aureus strains used in this study. A red dot represents one ST while each green dot represents two closely related STs. (PDF 386 kb)
Comparison of CDS number, genome size and GC content among SAC members. Additional genomic and typing information are shown in Fig. 1. (PDF 127 kb)
Complete list of homologous gene families in S. aureus complex (SAC) genomes. Fifty-one SAC genomes were analyzed. The 4249 homologous gene families were identified and are listed. Several gene loci were found to be missing in gene calling or present as pseudogenes. Notations are as follows: red text, internal stop; bold text, frameshifted; italicized text, incomplete because of assembly; underlined text, multiple problems; italicized and underlined, missing start or stop codon; regular, missing in original gene calling. (XLSX 1666 kb)
Pan-genome features and related COG functional categories of SAC and S. aureus. The S. aureus and SAC pan-genomes were grouped into four categories: UQ, unique genes; VR, variable genes; CV, core-variable genes; CR, core genes. (a & b): The number of genes in each pan-genome category (y-axis) for a given number of genomes (x-axis) was computed and is presented for (a) SAC and (b) S. aureus. The upper and lower edges of the boxes indicate the 25th and 75th percentiles, respectively, and the horizontal black lines indicate the 50th percentile for 1000 computations where the order of genome input was random. Hollow dots represent abnormal values. Regression analysis of the four SAC curves fitted to the following functions: CR, P(N) = −211.1ln(N) + 2539.4, R 2 = 0.9814; CR + CV, P(N) = 2445.6 N -0.051, R 2 = 0.9365; CR + CV + VR, P(N) = 247.53ln(N) + 2733.6, R 2 = 0.9638; CR + CV + VR + UQ, P(N) = 2604.5 N 0.1232, R 2 = 0.9995. Regression analysis of four S. aureus curves fitted functions as follows: CR, P(N) = −246.4ln(N) + 2616.7, R 2 = 0.9932; CR + CV, P(N) = 2571 N -0.062, R 2 = 0.9696; CR + CV + VR, P(N) = 211.25ln(N) + 2755.1, R 2 = 0.9572; CR + CV + VR + UQ, P(N) = 2654.4 N 0.1164, R 2 = 0.9994. (c & d) The number of genes in each pan-genome group (UQ, VR, CV, or CR) was determined for each functional group (Cluster of Orthologous Groups, or COGs) for both (c) SAC and (d) S. aureus. COG codes: C, Energy production and conversion; D, Cell cycle control, cell division; E, Amino acid transport and metabolism; F, Nucleotide transport and metabolism; G, Carbohydrate transport and metabolism; H, Coenzyme transport and metabolism; I, Lipid transport and metabolism; J, Translation, ribosomal structure and biogenesis; K, Transcription; L, Replication, recombination and repair; M, Cell wall/membrane/envelope biogenesis; N, Cell motility; O, Posttranslational modification, protein turnover, chaperones; P, Inorganic ion transport and metabolism; Q, Secondary metabolites biosynthesis, transport and catabolism; R, General function prediction only; S, Function unknown; T, Signal transduction mechanisms; U, Intracellular trafficking, secretion, and vesicular transport; V, Defense mechanisms; W, Extracellular structures; X, Mobilome, prophages, transposons. (PDF 371 kb)
Pan-genome features and related COG functional categories of S. argenteus and S. schweitzeri. The S. argenteus and S. schweitzeri pan-genomes were grouped into four categories: UQ, unique genes; VR, variable genes; CV, core-variable genes; CR, core genes. (a & b) The number of genes in each pan-genome category (y-axis) for a given number of genomes (x-axis) was computed and is presented for (a) S. argenteus and (b) S. schweitzeri. The upper and lower edges of the boxes indicate the 25th and 75th percentiles, respectively, and the horizontal black lines indicate 50th percentile under 1000 computations where the order of genome input was random. Hollow dots represent abnormal values. Regression analysis of the four S. argenteus curves fitted to the following functions: CR, P(N) = 2498.1 N -0.048, R 2 = 0.9883; CR + CV, P(N) = 2499 N -0.039, R 2 = 0.9644; CR + CV + VR, P(N) = 114.97ln(N) + 2540.3, R 2 = 0.9783; CR + CV + VR + UQ, P(N) = 2506.1 N 0.0752, R 2 = 0.9975. Regression analysis of four S. schweitzeri curves fitted functions as follows: CR, P(N) = 2483 N -0.064, R 2 = 0.9757; CR + CV, P(N) = 2490.8 N -0.058, R 2 = 0.9666; CR + CV + VR, P(N) = −11.771 N 2 + 116.17 N + 2379.7, R 2 = 0.9861; CR + CV + VR + UQ, P(N) = 209.05ln(N) + 2509.8, R 2 = 0.9999. . (c & d) The number of genes in each pan-genome group (UQ, VR, CV, or CR) was determined for each functional group (Cluster of Orthologous Groups, or COGs) for both (c) S. argenteus and (d) S. schweitzeri. COG codes are the same as the described in the legend to Additional file 5: Figure S4. (PDF 276 kb)
A Venn Diagram showing the pan-genome categories that are common among and shared between species within the SAC. Sharing gene families contain at least one genome of the species of interest. Core and core-variable gene families are refer to core gene (CR) and core-variable gene (VR) as described in the main text, respectively. (PDF 138 kb)
Phylogenetic relatedness of SAC species based on predicted amino acid sequences of genes for agr signaling pathway. Phylogenetic trees were constructed using the Neighbor-Joining method in order to infer evolutionary history and relatedness for SAC species. The evolutionary distances were computed using the Poisson correction method. Genes from S. aureus, S. argenteus, and S. schweitzeri are represented by red, green, and blue dots, respectively. (PDF 56 kb)
Phylogenetic relatedness of SAC species based on predicted amino acid sequences of genes for synthesis of capsular polysaccharide. Phylogenetic trees were constructed using the Neighbor-Joining method in order to infer evolutionary history and relatedness for SAC species. The evolutionary distances were computed using the Poisson correction method. For each tree the bar indicates 0.005 substitutions per site. Genes from S. aureus, S. argenteus, and S. schweitzeri are represented by red, green, and blue dots, respectively. For the trees based capH-K, S. aureus strains of serotype 5 and serotype 8 are shaded in light or dark gray, respectively. (PDF 262 kb)
Prevalence and relatedness of virulence genes among S. aureus complex (SAC) genomes. The presence/absence and interspecies divergence was analyzed for 111 virulence genes from 51 SAC genomes (30 S. aureus, 15 S. argenteus and 6 S. schweitzeri. Five virulence genes, sed, see, sej, selr and etb, are not listed in the table because they were not detected in any of the 51 SAC genomes examined. a *, these genes have tandem repeat regions, so the interspecies divergence of the nucleotide sequences may not clearly reflect the true evolutionary divergence. b The number of SAC genomes of each species harboring each virulence gene. c The gene family numbers as listed in Additional file 3: Table S1. d NA, not applicable; SA, S. aureus; SG, S. argenteus; SW, S. schweitzeri. e Maximum likelihood phylogenetic trees were contructed based on nucleotide sequence, using substitution matrix GTR and executing 100 rapid bootstrap inferences. The species names were omitted in tip labels. f p.g., pseudogene. (XLSX 1797 kb)
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Zhang, DF., Zhi, XY., Zhang, J. et al. Preliminary comparative genomics revealed pathogenic potential and international spread of Staphylococcus argenteus . BMC Genomics 18, 808 (2017). https://doi.org/10.1186/s12864-017-4149-9
- Staphylococcus aureus
- Staphylococcus argenteus
- Staphylococcus schweitzeri
- Comparative genomics
- Virulence gene
- Capsular polysaccharides
- Biogeographical structure