Staphylococcus epidermidis pan-genome sequence analysis reveals diversity of skin commensal and hospital infection-associated isolates
- Sean Conlan†1,
- Lilia A Mijares†1, 2, 3,
- NISC Comparative Sequencing Program4,
- Jesse Becker4,
- Robert W Blakesley4,
- Gerard G Bouffard4,
- Shelise Brooks4,
- Holly Coleman4,
- Jyoti Gupta4,
- Natalie Gurson4,
- Morgan Park4,
- Brian Schmidt4,
- Pamela J Thomas4,
- Michael Otto5,
- Heidi H Kong6,
- Patrick R Murray2 and
- Julia A Segre1Email author
© Conlan et al.; licensee BioMed Central Ltd. 2012
Received: 28 March 2012
Accepted: 25 July 2012
Published: 25 July 2012
While Staphylococcus epidermidis is commonly isolated from healthy human skin, it is also the most frequent cause of nosocomial infections on indwelling medical devices. Despite its importance, few genome sequences existed and the most frequent hospital-associated lineage, ST2, had not been fully sequenced.
We cultivated 71 commensal S. epidermidis isolates from 15 skin sites and compared them with 28 nosocomial isolates from venous catheters and blood cultures. We produced 21 commensal and 9 nosocomial draft genomes, and annotated and compared their gene content, phylogenetic relatedness and biochemical functions. The commensal strains had an open pan-genome with 80% core genes and 20% variable genes. The variable genome was characterized by an overabundance of transposable elements, transcription factors and transporters. Biochemical diversity, as assayed by antibiotic resistance and in vitro biofilm formation, demonstrated the varied phenotypic consequences of this genomic diversity. The nosocomial isolates exhibited both large-scale rearrangements and single-nucleotide variation. We showed that S. epidermidis genomes separate into two phylogenetic groups, one consisting only of commensals. The formate dehydrogenase gene, present only in commensals, is a discriminatory marker between the two groups.
Commensal skin S. epidermidis have an open pan-genome and show considerable diversity between isolates, even when derived from a single individual or body site. For ST2, the most common nosocomial lineage, we detect variation between three independent isolates sequenced. Finally, phylogenetic analyses revealed a previously unrecognized group of S. epidermidis strains characterized by reduced virulence and formate dehydrogenase, which we propose as a clinical molecular marker.
Staphylococcus epidermidis is a common human skin commensal, cultured from virtually every body surface of healthy individuals. The beneficial role of S. epidermidis is demonstrated by its ability to inhibit colonization by the pathogenic Staphylococcus aureus . While S. epidermidis is a commensal on the skin, if it breaches the skin surface and enters the bloodstream, it is considered a pathogen. S. aureus and coagulase negative Staphylococcus, including S. epidermidis, comprise 30% of hospital-acquired infections , associated with an estimated $2 billion annually in treatment costs . S. epidermidis forms biofilms on medical devices, such as contact lenses, catheters, and prosthetic heart valves. The detachment of bacterial cells from biofilms on these devices can lead to bacteremia, with increased morbidity and potential mortality . In clinical settings, Staphylococcal species are frequently resistant to antibiotics, particularly to penicillinase-resistant penicillins (for example, methicillin, oxacillin, nafcillin), constraining treatment options. S. epidermidis is also suspected to be a source of genetic diversity for S. aureus to acquire genes enabling better adherence to skin cells . Methicillin-resistant S. aureus (MRSA) is considered a re-emerging pathogen, showing increased drug resistance and causing an estimated 18,650 deaths per year in the US . The contrasting roles of S. epidermidis in both health and disease make it an important and central player in the human microbiome.
Hospital patients are typically monitored for sepsis from a blood sample. However, commensal staphylococci from the skin can contaminate venipuncture cultures, leading to false positives. This complicates the decision of whether to treat with antibiotics, remove the medical device, or wait-and-see, any of which can extend the patient's hospital stay . One approach to predicting the invasiveness of a strain is the use of marker genes. For instance, the ica operon, contributing to biofilm formation, has been proposed as a marker for invasiveness . The IS256 insertion sequence has also been shown to be associated with biofilm formation and resistance to aminoglycosides  and has been proposed as a marker for invasive strains . Despite the statistical significance of these markers, Rohde and colleagues  have shown that existing marker genes are not sufficient for discriminating invasive strains in a clinical setting.
While marker-based assays are important tools for epidemiological studies, a deeper understanding of S. epidermidis is required for foundational studies on population structure . Standard microbiological typing methods such as pulsed field gel electrophoresis and multi-locus sequence typing are used to track the spread of strains and measure clonality within patient populations , hospitals  and geographic areas , but are inherently low resolution methods. Microarrays have been used for genome-wide analyses and have successfully identified putative virulence determinants in S. epidermidis strains . New technologies, including optical mapping, which produce ordered restriction enzyme maps, will provide increased physical resolution of strains . Recently, high-throughput sequencing and SNP analysis were used to perform a detailed study of the spread and evolution of MRSA clones , demonstrating the power of direct sequencing to study bacterial population dynamics. To date, genome-wide studies on S. epidermidis were hampered by the fact that only two complete reference genomes existed, ATCC12228  and RP62A . Based on the limited number of reference genomes, it was unclear how much sequence diversity existed among S. epidermidis strains.
The pan-genome , or collection of genes found among members of a species, is a useful framework for describing genomic diversity within a taxon. For example, Tettelin and colleagues  characterized the pan-genome of Streptococcus agalactiae, an important pathogen for newborn infants. Based on the analysis of publicly available genomes as well as genomes generated specifically for their study, they found that approximately 20% of an individual S. agalactiae genome is made up of genes that are only partially shared with other strains, including unique genes. Furthermore, they found that sequencing additional genomes was predicted to increase the size of the pan-genome, suggesting that S. agalactiae has an open genome. This type of analysis has been used to characterize a number of bacterial species  as well as genera  and has been used to estimate the pan-genome size of all bacteria .
To explore the genomic variability of commensal and nosocomial S. epidermidis isolates, we produced draft genome sequences for 30 isolates selected from our extensive strain collection of commensals and pathogens at the NIH Clinical Center. Comparative genomics was used to characterize the pan-genome and deduce the phylogenetic relationship between strains.
Results and discussion
Genomic variability of commensal S. epidermidis
The function of the genes within the variable genome was investigated by assigning all gene clusters to clusters of orthologous groups (COG) categories . Three of the 20 COG categories were significantly enriched in the variable genome by Fisher's exact test (P < 0.05): (1) replication, recombination and repair; (2) transcriptional regulators; and (3) defense mechanisms (Figure 2b). Enrichment of these classes was driven by diversity in mobile genetic elements (recombinase and integrase genes), transcriptional regulators, and ABC-type multidrug transporters, respectively. In addition, 40% of the genes in each genome were not assigned a COG function, reflecting novel gene clusters as well as limitations in COG classification.
Genomic variability of nosocomial S. epidermidis
Phylogenetic relationship of S. epidermidis commensal and nosocomial genomes
Strikingly, the 35 S. epidermidis isolates formed two distinct groups, called A and B (Figure 4) with excellent bootstrap support. All of the nosocomial isolates mapped to the upper group A in the phylogenetic tree. In contrast, the commensals were distributed between groups A and B. The 24 commensal isolates fall into 22 different sequence types, including many new sequence types (12/22), made up of new combinations of alleles as well as novel alleles. When overlayed on the phylogenetic tree, isolates with new sequence types were disproportionately found in the group B (7/8 isolates) compared to group A (7/27 isolates) (Figure 4; P = 0.01). Furthermore, eBurst  analysis shows that the majority of group A commensal isolates belong to the CC2 lineage while none of the sequence types in group B are connected to the CC2 lineage.
The fdh gene is a marker of commensal strains
Commensal (n = 71)
Nosocomiala (n = 46)
Discriminating commensal and nosocomial isolates
Group A isolates were a mix of nosocomial and commensal isolates, raising the question of whether differences existed in gene content between the 9 nosocomial and 14 commensal group A strains. While no gene clusters discriminated perfectly, 21 clusters were enriched in genes from nosocomial group A genomes, including nine in the SCCmec cassette, the IS256 element and the aacA antibiotic resistance gene. In addition to genes encoding hypothetical proteins, three orthologous gene clusters (RP62A genes SERP0245 to SERP0247) were found in nine nosocomials and only 3 of 14 commensal isolates. These genes encode a putative transport system and transcriptional regulator, possibly associated with antibiotic resistance. As such, while the nosocomial and commensal isolates clustered together phylogenetically, there appeared to be gene content adaptations to hospital life.
S. epidermidis biochemical properties: biofilm formation and antibiotic resistance
Antibiotic resistance is common among S. epidermidis isolates and often limits treatment options. Isolates from medical devices were largely resistant to methicillin (21/28). Among the commensal isolates examined in this study, methicillin resistance was confined to isolates from HV10 (9/32). Commensal isolates were less likely to be resistant to trimethoprim-sulfamethoxazole (4/32) or clindamycin (2/32) than nosocomial isolates, which were resistant to each drug in >50% of isolates tested. First-line antibiotic therapy for catheter-related bloodstream infections is vancomycin. None of the isolates tested were resistant to linezolid, quinupristin-dalfopristin or vancomycin, regardless of isolation source.
A defining feature of methicillin-resistant S. epidermidis (MRSE) is the presence of the SCCmec cassette; a heterogenous sequence defined by combinations of mec gene complexes, cassette recombinases and accessory genes. In agreement with the results of Li and colleagues , the ST2 nosocomial isolates have a type III mec cassette. In fact, all of the nosocomial isolates with an SCCmec cassette (8/9) were either type III or type IV, as found in previous studies of CC2 strains . Both MRSE and methicillin-sensitive S. epidermidis (MSSE) isolates were cultured from HV10 across multiple skin sites. Comparing MRSE (9/15) and MSSE (6/15) isolates from HV10, the ability to form biofilms did not correlate with resistance to methicillin, a finding that was not surprising given that biofilm-formation alone confers a degree of antibiotic resistance. Of the sequenced MRSE commensal isolate genomes, NIHLM061 and NIHLM053/057 had type IV and V cassettes, respectively. NIHLM049 had a mecA gene and partial mecR1 gene but no detectable cassette recombinase genes. These multiple unique SCCmec cassettes demonstrated the independent acquisition, selection and persistence of multiple MSSE and MRSE isolates within a single individual.
S. epidermidis plasmids and CRISPR
Mechanisms of resistance to other antibiotics were generally explained by the presence of common resistance genes. A number of these genes are likely carried on plasmids, including pUB110 (bleomycin/kanamycin), pSE-12228-01 (tetracycline), and pKH19 (erythromycin). Some resistance phenotypes could not be immediately accounted for by the gene catalog and may be explained by novel genes . For instance, tetracycline resistance is explained by the presence of the pSE12228-01 plasmid in two isolates but three other isolates (NIHLM053/057/061) had no obvious tetracycline resistance genes, suggesting an unidentified gene product may be responsible. For the 30 genomes sequenced in this study, nucleotide homology searches produced strong evidence of 13 different plasmids (free or integrated) in 17 isolates.
In addition, we detected five strains with clustered, regularly interspaced, short, palindromic repeat (CRISPR) loci, which confer sequence-specific immunity to bacteriophage and restrict the spread of conjugative plasmids . CRISPR regions in these five strains contained two to three loci with three to ten spacer regions each. Approximately 90% of the unique spacers identified in this study did not match targets in the public sequence database, pointing to a large pool of unsequenced staphylococcal phage and plasmids, similar to recently described streptococcal CRISPR diversity . One exception was a nine spacer CRISPR in NIH06004, with five spacers matching plasmid borne proteins, including pre, traE and rep.
This study presents the largest analysis of S. epidermidis genome sequences to date, and includes both commensal and nosocomial isolates to examine the full pan-genome. Our analysis demonstrates that S. epidermidis has a relatively compact genome with a fixed size of approximately 2.5 Mb, and yet as much as 20% of this genome is in flux, exchanging with a large pool of genes. These values are in agreement with what was reported by Tettelin and colleagues  for the S. agalactiae pan-genome and led us to similar conclusions about the importance of sequencing many isolates from a species. Recently, it was shown for Salmonella enterica that core gene variation could be used to construct high-resolution phylogenetic trees, similar to what is shown in Figure 4, that reveal important details not seen by traditional multi-locus sequence typing (MLST) . In addition, they identified a subset of core genes with high sequence variability that appear to be under selection. We also find a small number of core genes with higher than average variability (as measured by average entropy across aligned columns). The etiology of this variability is unclear, but, interestingly, many of the core genes were syntenic gene clusters, including SERP2041-2043 and ureFGD (gene names from the RP62A genome; NC_002976). In a detailed study of the pan-genome of the gut microbe Methanobrevibacter smithii, Hansen and colleagues  showed that principal components analysis of orthologous genes was able to cluster strains by family of origin. We did not observe a clustering by individual, but this may be related to the ecological heterogeneity of the skin environment (moist, oily, dry) compared to the gut.
Analysis of the phylogenetic tree based on the 35 genomes in this study showed that S. epidermidis strains clustered into two distinct groups A and B, differentiated by the fdh gene (group B). Group B represents a lineage with reduced virulence, as nosocomial genomes are found only within group A. Moreover, in a larger replication set of 117 S. epidermidis isolates, the fdh gene had discriminatory power as largely absent in nosocomials. While clinical markers have traditionally focused on what 'is' present in nosocomials versus commensals, the fdh gene has the potential to identify possible contaminants in the blood culture from the venipuncture procedure.
Within group A, nosocomial and commensal strains are intermingled phylogenetically, but nosocomials are still enriched in markers traditionally associated with antibiotic resistance. The observation that isolates exist on a gradient of pathogenicity is further supported by principal components analysis of gene content that shows separation based on isolation source (hospital infections versus healthy volunteers). However, strains from both healthy individuals and hospital-associated infections can have nearly identical genomes (for ecample, NIHLM020 from a healthy volunteer's axilla and NIH05003 from a leukemia patient's catheter) indicating that opportunity and environment clearly contribute to hospital-acquired infections. Furthermore, a single individual can carry many S. epidermidis strains with differing antibiotic resistance profiles, capacities to form biofilms and overall gene compositions. These data, combined with the difficulty assigning isolates as strictly commensal or nosocomial, highlights the challenges associated with identifying the genetic determinants of virulence.
The ST2 lineage is the most common hospital-acquired strain. However, it is unclear why this particular lineage is dominant. The three ST2 strains sequenced in this study, the first sequences of this dominant sequence type, are similar but not nearly as identical as MRSA USA300 genomes. The only genes we found specifically associated with ST2 strains were part of the type III SCCmec element, which suggested additional roles for this cassette in S. epidermidis virulence. For instance, type III cassettes carry a phenol soluble modulin psm-mec that has been shown to affect the virulence of S. aureus . While the acquisition of antibiotic resistance genes and other defense mechanisms may predispose a strain to a pathogenic lifestyle, it may also be a consequence of exposure to a hospital environment.
Metagenomics is one of the ultimate goals of the HMP. While the initial metagenomic sequencing projects relied upon alignment to a limited set of 'reference' genomes, here we clearly show that S. epidermidis should be viewed as a pan-genome to empower full metagenomic studies. While complicating a one-to-one assignment of genes to a species, this pan-genomic understanding opens the window to future studies, such as defining the composition of S. epidermidis strains on catheter-derived biofilms and their roles in establishment of infection. This representative catalog of commensal and pathogenic staphylococci from the skin will also empower quantitative assessments of changes in staphylococcal burden during disease exacerbation, such as chronic relapsing atopic dermatitis or MRSA infection. Finally, these results pose the evolutionary question of how and why ST2, the most common nosocomial strain, has been optimized for a pathogenic lifestyle, given the large number of gene combinations available in the S. epidermidis pan-genome.
Materials and methods
S. epidermidis isolates
Commensal isolates were obtained from swabs of healthy volunteers as described previously , classified by 16S rRNA gene sequencing and verified with ribosomal protein signature provided by MALDI-TOF mass spectrometry. The DiversiLab Staphylococcus kit for DNA fingerprinting (Bacterial Barcodes Inc., Athens, GA, USA) was used to fingerprint 71 isolates; detection and cluster analysis of fingerprint patterns was performed with DiversiLab software version 3.3. Of 71 isolates, 21 were selected for further characterization based on observed Rep-PCR profiles. Isolates were grown on sheep blood agar and passed three times to confirm homogeneity of the strain. The strains were maintained as frozen glycerol stocks at -80°C. Medical device-associated strains were collected between 2004 and 2010 at the NIH Department of Laboratory Medicine. Genomic DNA was prepared using MoBio Laboratories UltraClean Microbial DNA kit (Mo Bio Laboratories, Inc., Carlsbad, CA, USA) according to manufacturer instructions. DNA was quantified prior to sequencing using the Quant-iT dsDNA BR assay (Invitrogen, Grand Island, NY, USA). All strains sequenced in this study are available from BEI Resources under BEI numbers HM-896 to HM-925.
Functional assays: antibiotic susceptibility and biofilm formation
MicroScan (Dade Behring, CA, USA) dried MIC/Combo Gram Positive panels were used to evaluate biochemical properties and antibiotic sensitivities based on manufacturer's protocol. Biofilm assays were performed according to Vuong et al. . Briefly, S. epidermidis isolates from a frozen stock were used to inoculate 1 ml of trypticase soy broth, grown overnight and then a 1:100 dilution was prepared in 0.5% glucose trypticase soy broth. Wells of a microtiter polystyrene plate (BD Falcon, Franklin Lakes, NJ, USA) were inoculated in triplicate. After incubation for 24 h at 37°C, the plates were washed twice with double distilled water and stained with 0.1% safranin for 1 minute. Excess safranin was removed by blotting and air-drying. Absorbance was read at 490 nm using a SpectraMax 384 (Molecular Devices, Sunnyvale, CA, USA) with SpectraMaxPro software.
MLST was carried out using sub-sequences for seven housekeeping genes  extracted from the draft genomes. Any allele that did not match an existing allele in the S. epidermidis MLST database  was verified by manual examination of the sequence assembly for coverage and quality and Sanger sequencing. New sequence types and alleles were submitted for inclusion in the S. epidermidis MLST database. PCR assays for icaA, mecA and IS256 were performed as described by Rhode et al. . The fdh gene was detected by PCR by using the following primers: forward, 5'-ATA ATG GTG ATA TTC ATT CG; reverse, 5'-CCG TAT TAG TAA AAG TTC CA. Universal primers against the 16S gene were used as a positive control. SCCmec cassette types were determined by comparing all contigs to a database of SCCmec protein sequences derived from the following GenBank accessions: AB033763, D86934, AB037671, AB063172, AB121219, AF411935, FJ390057. While SCCmec cassettes cannot be definitively subtyped from draft genomes due to fragmentation by multiple transposable elements, a preliminary classification was made based on mec gene complex, ccr gene complex and characteristic joining regions. Types were assigned based on the type descriptions from the International Working Group on the Classification of Staphylococcal Cassette Chromosome Elements (IWG-SCC) .
Genome sequencing and annotation
Isolates were sequenced on a Roche 454FLX Ti instrument (Roche Diagnostics GmbH, Mannheim, Germany) by the NIH Intramural Sequencing Center. All isolates were initially sequenced using unidirectional fragment reads; 12 genomes were supplemented with paired-end reads (3 kb insert size). Genomes were assembled using the Roche gsAssembler v2.3 (091027_1459). All genome assemblies exceeded the provisional assembly metrics set forth by the HMP . Specifically, >90% of the genome included in the contigs, >90% of bases have >5× read coverage, >5 kb contig N50, >20 kb scaffold N50 for genomes with paired ends, >5 kb average contig length, >90% of core genes found. Optical maps of selected strains were generated by OpGen (Gaithersburg, MD, USA) and used to identify large-scale changes in genome architecture. Glimmer v.3.02  was used to predict protein-coding genes. Orthologous protein clusters were generated using OrthoMCL . Analysis using TBLASTN indicated that relatively few genes were absent due to errors in gene calling or pseudogene formation. Unclustered genes were filtered as described in Lefébure and Stanhope . Briefly, genes were retained as singletons only if they were at least 50 amino acids long and did not have homology to other proteins by BLAST (E-value <1e-10). Final annotation of each genome was generated using the National Center for Biotechnology Information's (NCBI's) Prokaryotic Genomes Automatic Annotation Pipeline (PGAAP) and further processed by the HMP annotation working group pipeline, which ensures uniform gene product naming. Assemblies and raw sequencing data (SRA) are available from GenBank under BioProject 62343.
COGs  were assigned to proteins by first aligning each putative protein sequence against a BLAST database of COG sequences, generated from a file downloaded from the NCBI . A protein was annotated as belonging to a given COG based on the criteria described in Tatusov et al. . Specifically, a protein was assigned to a COG if the best hit in at least two of the COG genomes is annotated to the given COG. CRISPR loci were detected using CRISPRfinder  and confirmed by the presence of CRISPR-associated (cas) genes on the same contig. Putative extrachromosomal elements (for example, plasmids) were identified by inspection of paired-end data and BLASTN alignment to the complete plasmid database from NCBI. Antibiotic resistance genes were identified using ARDB .
The phylogenetic tree was generated from four-fold degenerate positions in a subset of core gene sequences. Core gene clusters without paralogs were aligned using Muscle v.3.7  and ambiguous alignments were removed. Four-fold degenerate codon positions were extracted from the resulting alignments, filtered to remove positions with low quality bases and concatenated. A phylogenetic tree was built using PhyML v.2.4.4 . Single nucleotide polymorphisms were identified in draft genomes by aligning them to the ATCC12228 genome using ABACAS  and then filtering the resultant SNP calls to remove misalignments, homopolymer errors and low quality calls .
The significance of core gene abundance in COG categories was examined using Fisher's exact test as implemented in R (v.2.10.0). Principal components analysis was performed using the prcomp function in R. Random forest classification was performed using the randomForest package in R. The P-test , as implemented in Unifrac suite was used to determine if environment labels were significantly associated with the structure of the phylogenetic tree of isolates.
This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under BioProject 62343. All genomic sequence data links are individually deposited under the accession numbers: AKGI000000000, AKGJ00000000, AKGK00000000, AKGL00000000, AKGM00000000 AKGN00000000, AKGO00000000, AKGP00000000, AKGQ00000000, AKGR00000000, AKGS00000000, AKGT00000000, AKGU00000000, AKGV00000000, AKGW00000000, AKGX00000000, AKGY00000000, AKGZ00000000, AKHA00000000, AKHB00000000, AKHC00000000, AKHD00000000, AKHE00000000, AKHF00000000, AKHG00000000, AKHH00000000, AKHI00000000, AKHJ00000000, AKHK00000000, AKHL00000000. The version described in this paper is the first version.
Short Read Archive study numbers are: SRP012973, SRP012974, SRP012975, SRP012977, SRP013026, SRP013029, SRP013030, SRP013031, SRP013032, SRP013033, SRP013034, SRP013035, SRP013036, SRP013037, SRP013038, SRP013041, SRP013042, SRP013043, SRP013044, SRP013045, SRP013046, SRP013047, SRP013048, SRP013049, SRP013050, SRP013107, SRP013108, SRP013149, SRP013165, SRP013189.
clonal complex 2
clustered regularly interspaced short palindromic repeats
clusters of orthologous groups
- fdh :
Human Microbiome Project
matrix-assisted laser desorption/ionization time of flight
multi-locus sequence typing
methicillin resistant S. aureus
methicillin-resistant S. epidermidis
methicillin-sensitive S. epidermidis
National Center for Biotechnology Information
polymerase chain reaction
single nucleotide polymorphism
We thank the volunteers who participated in this study; C Yang for assistance with sample collection and preparation; E Green, L Marraffini, T Scharschmidt, M Fishback, M Feldgarden and members of the Segre laboratory for valuable comments on the manuscript; The Human Microbiome Project Reference Genome catalog for access to draft sequences for BCM-HMP0060, W23144, SK135. This work was supported by the NHGRI, NIH CC and NCI Intramural Research Programs and NIH Common Fund Human Microbiome Project grant 1UH2AR057504-01 and 4UH3 AR057504-02.
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