Skip to main content


  • Open Access

Non-contiguous finished genome sequence of Staphylococcus capitis CR01 (pulsetype NRCS-A)

  • 1, 2,
  • 2, 3,
  • 1,
  • 3,
  • 4,
  • 1,
  • 2, 3 and
  • 2, 3, 5Email author
Standards in Genomic Sciences20149:9031118

  • Published:


Staphylococcus capitis is a coagulase-negative staphylococcus (CoNS) commonly found in the human microflora. Recently, a clonal population of Staphylococcus capitis (denominated NRCS-A) was found to be a major cause of late-onset sepsis (LOS) in several neonatal intensive care units in France. Here, we report the complete genome sequence and annotation of the prototype Staphylococcus capitis NCRS-A strain CR01. The 2,504,472 bp long genome (1 chromosome and no plasmids) exhibits a G+C content of 32.81%, and contains 2,468 protein-coding and 59 tRNA genes and 4 rRNA genes.


  • Staphylococcus capitis (NCRS-A)
  • draft-genome
  • methicillin resistance
  • late-onset sepsis


A frequent cause of low-weight newborns mortality and morbidity in Neonatal Intensive Care Units (NICUs) are late-onset sepsis (LOS), that are defined as sepsis occurring after 3 days of age. The most frequently encountered pathogens are coagulase-negative staphylococci (CoNS) and within those Staphylococcus epidermidis has been shown to be the most prevalent [1,2]. However, a few studies have reported the emergence of Staphylococcus capitis as a main CoNS- and LOS-causative pathogen in NICU settings [24]. A study in French NICUs [2] has demonstrated the spread of a single clonal population of methicillin-resistant S. capitis (pulsotype NRCS-A) associated to reduced susceptibility to vancomycin, the first line of antibiotics used in cases of LOS. Moreover, this clone has also been recently identified in NICUs in Belgium, United Kingdom and Australia, which suggests a worldwide distribution. In contrast, in adult bacteremia, S. capitis are rarely found and when detected, it presents a bigger diversity in terms of genotypes as well as antimicrobial susceptibility profiles than neonates bacteremia.

In order to elucidate the molecular mechanisms behind the wide spreading of the S. capitis NRCS-A clone in NICUs throughout the world, we sequenced a prototype strain (CR01).

Classification and information

A strain belonging to the clonal population of Staphylococcus capitis NCRS-A pulsetype (Table 1) was isolated from the blood culture of a preterm infant with LOS, hospitalized in the NICU of the Northern Hospital Group Center (Hospices Civils de Lyon, Lyon, France) and suffering of LOS.
Table 1.

Classification and general features of Staphylococcus capitis strain CR01, pulsetype-NRCS-A according the MIGS recommendation [5].




Evidence codea


Current classification

Domain Bacteria

TAS [6]


Phylum Firmicutes

TAS [7,8]


Class Bacilli

TAS [9,10]


Order Bacillales

TAS [11,12]


Family Staphylococcaceae

TAS [13,14]


Genus Staphylococcus

TAS [11,15,16]


Species Staphylococcus capitis

TAS [17]


Strain CR01, pulsetype-NRCS-A

TAS [2,18]


Gram stain


TAS [19]


Cell shape


TAS [17]




TAS [19]




TAS [19]


Temperature range




Optimum temperature


TAS [17]


Carbon source

Carbohydrates (glucose, sacharose, fructose, manitol, mannose)

TAS [17]


Energy source


TAS [17]


Terminal electron receptor


TAS [17]



Skin of humans

TAS [17]




TAS [17]



Facultative anaerobes

TAS [17]


Biotic relationship


TAS [17]



Opportunistic pathogen (Nosocomial bacteremia in premature neonates)

TAS [2]


Geographic location

NICU Lyon, France

TAS [2]


Sample collection time




Latitude - Longitude

45° 45′ 35″ N 4° 50′ 32″ E






Not applicable




162 m


aEvidence codes - IDA: Inferred from Direct Assay; TAS: Traceable Author Statement (i.e., a direct report exists in the literature); NAS: Non-traceable Author Statement (i.e., not directly observed for the living, isolated sample, but based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from the Gene Ontology project [20].

Species identification of the bacterial isolates and antimicrobial susceptibility testing (AST) were performed, respectively, using Vitek MS (bioMérieux, Marcy l’Etoile), 16S rDNA sequencing, the automated BD Phoenix system (Becton Dickinson, Sparks, MD) and with Shimadzu-MALDI-TOF MS system (Shimadzu Corporation), as implemented on [21].

The strain was identified as being a Staphylococcus capitis by VITEK MS with 99.9% and at 93.7% by the MALDI-TOF MS, using the Shimadzu Launchpad software program and the SARAMIS database application (AnagnosTec GmbH) for automatic measurement and identification (Figure 1). Based on the information provided by the manufacture, when the score is ≥70%, identification is considered of high confidence.
Figure 1.
Figure 1.

Reference mass spectrum from Staphylococcus capitis strain (CR01).

The antimicrobial susceptibility test (AST) results were analyzed according to the recommendations of the French Microbiology Society [22]. The S. capitis bacteremia was considered positive based on a single positive blood culture [2,23]. The S. capitis NCRS-A isolate CR01, as all isolates from this clone, is resistant to penicillin, methicillin, gentamicin, rifampicin, hetero-resistant to vancomycin and sensitive to fusidic acid and fluoroquinolones.

Table 1, Figure 2 and Figure 3 show detailed information concerning general features of Staphylococcus capitis strain (CR01) and position within the genus Staphylococcus.
Figure 2.
Figure 2.

Transmission electron microscopy of Staphylococcus capitis strain (CR01) using a JEOL 1400. The scale bar represents 200 nm.

Figure 3.
Figure 3.

16S rRNA Phylogenetic tree highlighting the position of Staphylococcus capitis strain CR01 (indicated by the yellow circle) relative to other type strains within the genus Staphylococcus. All 16S rRNA sequences were obtained from the RDP database using as filtering criteria: sequences with more than 1200 nt and classified as “good” quality sequences. The tree uses sequences aligned with the MUSCLE software, with the default parameters as implemented on Seaview version 4 [24], and a tree was inferred based on 1285 sites using the distance model of observed divergence, as implemented in the BioNJ algorithm.

The 16S rRNA sequences were aligned using the MUSCLE software, with the default parameters as implemented on Seaview version 4 [24], and a tree was inferred based on 1285 sites using the distance model of observed divergence, as implemented in the BioNJ algorithm, and a bootstrapping process repeated 500 times.

The final tree was rooted using the 16S rRNA sequence of Macrococcus equipercicus Type strain that belongs to a closely-related sister genus.

Genome sequencing information

The genome sequence of S. capitis strain CR01 was determined by high-throughput sequencing performed on a Genome Sequencer FLX + system (454 Life Sciences/Roche) using FLX Titanium reagents according to the manufacturer’s protocols and instructions, with approximately 47-fold coverage of the genome. This platform provides longer read lengths than other sequencing platforms to obtain raw sequences. De novo assemblies were performed using the Roche Newbler (v 2.7) software package.

Genome project history

Table 2 presents the project information and its association with MIGS version 2.0 compliance [5].
Table 2.

Project information





Finishing quality

Non-contiguous finished


Libraries used

454 pyrosequence rapid library


Sequencing platforms

454 GS FLX+


Fold coverage

47.0 × pyrosequence



Newbler Assembler 2.7




Growth conditions and DNA isolation

The sample was prepared for sequencing by growing S. capitis CR01, aerobically at 37°C in Blood Agar for 24–48 hours. Genomic DNA was extracted using the PureLinkTM genomic DNA kit (InvitrogenTM) according to the manufacturer’s recommended protocol. The quantity of DNA obtained was determined using a NanoVue TM Plus (HVD Life Sciences), and 1 µg of DNA was used for sequencing of whole-genome of this strain.

Genome sequencing and assembly

The isolated DNA of S. capitis CR01, was used to create 454-shotgun libraries following the GS Rapid library protocol (Roche 454, Roche). The resulting 454 DNA libraries were sequenced using a whole-genome shotgun strategy by GS FLX Titanium sequencing kit XL+ [25] (202,108 reads totaling 2.5 Mb, X48 fold coverage of the genome). Genome sequences were processed by Roche’s sequencing software according to the manufacturer’s instructions (454 Life Science). The resulting shotgun reads were assembled de novo using the Roche Newbler assembly software 2.7 (454 Life Science) and 26 large contigs (Contig00001 to Contig00026) were obtained. The N50 was 176239 bp.

Genome annotation

An automatic syntactic and functional annotation of the draft genome was performed using the MicroScope platform pipeline [26,27]. The syntactic analysis combines a set of programs including AMIGene [28], tRNAscan-SE [29], RNAmmer [30], Rfam scan [31] and Prodigal software [32] to predict genomic objects that are mainly CDSs and RNA genes. More than 20 bioinformatics methods are then used for functional and relational analyses: homology search in the generalist databank UniProt [33] and in more specialized databases as COG [34], InterPro [35], PRIAM profiles for enzymatic classification [36], prediction of protein localization using TMHMM [37], SignalP [38] and PsortB [39] tools.

Genome properties

The genome includes one circular chromosome of 2,504,472 bp (32.81% GC content). A total of 2,565 genes were predicted with 2,453 being protein-coding genes, 59 tRNA-enconding genes, 4 rRNA-encoding genes (including 2 copies of 5S rRNA, 1 copy of both the large and the small-subunits, respectively, 23S and 16S rRNA) and 34 other RNA related ORFs. No plasmid was detected.

Of the 2,453 protein-coding genes, 1,892 genes (76.7%) were assigned to a putative function with the remaining annotated as hypothetical proteins. The predicted coding density in S. capitis strain CR01 was 86%.

Table 3 and 4 and Figure 4 detailed description of the properties and the statistics of Staphylococcus capitis strain CR01 genome. The distribution of the genes into COGs functional categories is presented in Table 4.
Figure 4.
Figure 4.

Graphical circular map of the chromosome. From outside to the center: Genes on the forward strand (colored by COG categories), genes on the reverse strand colored by COG categories), RNA genes (tRNAs green, rRNAs blue), GC content, and GC skew

Table 3.

Nucleotide content and gene count levels of the genome



% of totala

Genome size (bp)



DNA G+C content (bp)



DNA coding region (bp)



Number of Scaffolds



Total genesb



RNA genes



tRNA-enconding genes



rRNA-encoding genes



Protein-coding genes (CDS)



Genes assigned to COGs



Genes of unknown function



Genes with transmembrane helicesc



CRISPR repeats



a) The total is based on either the size of the genome in base pairs or the total number of protein coding genes in the annotated genome.

b) Total number of genes includes CDS, RNA genes and pseudogenes.

c) Detection of transmembrane helices was performed using TMHMM v. 2.0 [40]

Table 4.

Number of genes associated with general COG functional categories.
















Replication, recombination and repair




Cell cycle control, mitosis and meiosis




Defense mechanisms




Signal transduction mechanisms




Cell wall/membrane biogenesis




Cell motility




Extracellular structures




Intracellular trafficking and secretion




Posttranslational modification, protein turnover, chaperones




Energy production and conversion




Carbohydrate transport and metabolism




Amino acid transport and metabolism




Nucleotide transport and metabolism




Coenzyme transport and metabolism




Lipid transport and metabolism




Inorganic ion transport and metabolism




Secondary metabolites biosynthesis, transport and catabolism




General function prediction only




Function unknown




Not in COGs

a) The total is based on the total number of protein coding genes in the annotated genome.


Here, we described a new genome sequence of Staphylococcus capitis (strain CR01 belonging to NRCS-A clone) as a first step toward comparing its content with other sequenced Staphylococcus capitis genomes as well as CoNS genomes of species associated with late-onset sepsis. Detailed analyses are in progress to identify virulence factors and mobile genetic elements (MBE), such as the staphylococcal chromosome cassette (SCCmec) [18], potentially related to the high specificity of the NRCS-A clone to the NICU environment.



European Molecular Biology Laboratory


National Center for Biotechnology Information (Bethesda, MD, USA)


Ribosomal Database Project (East Lansing, MI, USA)



This work was supported by a grant from the Fondation pour la Recherche Médicale (FRM, grant ING20111223510) and by the Institut National de la Recherche Médicale (INSERM) and the French Ministry of Health.

Authors’ Affiliations

Department of Biosecurity PCL3, Laboratory of Research and Medical Analysis of the Fraternal of Gendarmerie Royale, Rabat, Morocco
International Centre for Research in Infectious diseases, INSERM U1111, University of Lyon, Lyon, France
Department of Clinical Microbiology, Northern Hospital Group, Hospices Civils de Lyon, Lyon, France
Medical Biotechnology lab. (MedBiotech), Medical and Pharmacy School, University Mohammed V Souissi, Rabat, Morocco
National Reference Center for Staphylococci, Hospices Civils de Lyon, Lyon, France


  1. Klingenberg C, Rønnestad A, Anderson AS, Abrahamsen TG, Zorman J, Villaruz A, Flægstad T, Otto M. Sollid, Ericson J. Persistent strains of coagulase-negative staphylococci in a neonatal intensive care unit: virulence factors and invasiveness. Clin Microbiol Infect 2007; 13:1100–1111. PubMed ArticlePubMedGoogle Scholar
  2. Rasigade JP, Raulin O, Picaud JC, Tellini C, Bes M, Grando J, Ben Saïd M, Claris O, Etienne J, Tigaud S, Laurent F. Methicillin-ResistantStaphylococcus capitis with Reduced Vancomycin Susceptibility Causes Late-Onset Sepsis in Intensive Care Neonates. PLoS ONE 2012; 7:e31548. PubMed CentralView ArticlePubMedGoogle Scholar
  3. Ng PC, Chow VC, Lee CH, Ling JM, Wong HL, Chang RCY. Persistent Staphylococcus capitis septicemia in a preterm infant. Pediatr Infect Dis J 2006; 25:652–654. PubMed ArticlePubMedGoogle Scholar
  4. de Silva GDI, Kantzanou M, Justice A, Massey RC, Wilkinson AR, Day NPJ, Peacock SJ. The ica Operon and Biofilm Production in Coagulase-Negative Staphylococci Associated with Carriage and Disease in a Neonatal Intensive Care Unit. J Clin Microbiol 2002; 40:382–388. PubMed CentralView ArticlePubMedGoogle Scholar
  5. Field D, Garrity G, Gray T, Morrison N, Selengut J, Sterk P, Tatusova T, Thomson N, Allen MJ, Angiuoli et al. The minimum information about a genome sequence (MIGS) specification. Nat Biotechnol 2008; 26:541–547. PubMed CentralView ArticlePubMedGoogle Scholar
  6. Woese CR, Kandler O, Wheelis ML. Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eukarya. Proc Natl Acad Sci USA 1990; 87:4576–4579. PubMed CentralView ArticlePubMedGoogle Scholar
  7. Gibbons NE, Murray RGE. Proposals Concerning the Higher Taxa of Bacteria. Int J Syst Bacteriol 1978; 28:1–6; ArticleGoogle Scholar
  8. Murray RGE. The Higher Taxa, or, a Place for Everything…? In: Holt JG (ed), Bergey’s Manual of Systematic Bacteriology, First Edition, Volume 1, The Williams and Wilkins Co., Baltimore, 1984, p. 31–34.Google Scholar
  9. List of new names and new combinations previously effectively, but not validly, published. List no. 132. Int J Syst Evol Microbiol 2010; 60:469–472.
  10. Ludwig W, Schleifer KH, Whitman WB. Class I. Bacilli class nov. In: De Vos P, Garrity G, Jones D, Krieg NR, Ludwig W, Rainey FA, Schleifer KH, Whitman WB (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 3, Springer-Verlag, New York, 2009, p. 19–20.Google Scholar
  11. Skerman VBD, McGowan V, Sneath PHA. Approved Lists of Bacterial Names. Int J Syst Bacteriol 1980; 30:225–420. ArticleGoogle Scholar
  12. Prévot AR. In: Hauderoy P, Ehringer G, Guillot G, Magrou. J., Prévot AR, Rosset D, Urbain A (eds), Dictionnaire des Bactéries Pathogènes, Second Edition, Masson et Cie, Paris, 1953, p. 1–692.Google Scholar
  13. List Editor. List of new names and new combinations previously effectively, but not validly, published. List no. 132. Int J Syst Evol Microbiol 2010; 60:469–472.
  14. Schleifer KH, Bell JA. Family VIII. Staphylococcaceae fam. nov. In: De Vos P, Garrity G, Jones D, Krieg NR, Ludwig W, Rainey FA, Schleifer KH, Whitman WB (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 3, Springer-Verlag, New York, 2009, p. 392.Google Scholar
  15. Rosenbach FJ. In: Bergmann JF (ed), Microorganismen bei den Wund-Infections-Krankheiten des Menschen., Wiesbaden, 1884, p. 1–122.Google Scholar
  16. Judicial Commission. Opinion 17. Conservation of the Generic name Staphylococcus Rosenbach, Designation of Staphylococcus aureus Rosenbach as the Nomenclatural Type of the Genus Staphylococcus Rosenbach, and Designation of the Neotype culture of Staphylococcus aureus Rosenbach. Int Bull Bacteriol Nomencl Taxon 1958; 8:153–154.Google Scholar
  17. Kloos WE, Musselwhite MS. Distribution and Persistence of Staphylococcus and Micrococcus Species and Other Aerobic Bacteria on Human Skin. Appl Microbiol 1975; 30:381–385. PubMedPubMed CentralPubMedGoogle Scholar
  18. Martins Simões P, Rasigade JP, Lemriss H, Butin M, Ginevra C, Lemriss S, Goering RV, Ibrahimi A, Picaud JC, Vandenesch FEL, et al. Characterization of a novel staphylococcal chromosome cassette (SCCmec) within a composite SCC island in neonatal sepsis-associated Staphylococcus capitis pulsotype NRCS-A. Antimicrob Agents Chemother 2013; 57:6354. PubMed CentralView ArticlePubMedGoogle Scholar
  19. Schleifer KH, Bell JA. Family VIII. Staphylococcaceae fam. nov. In: De Vos P, Garrity G, Jones D, Krieg NR, Ludwig W, Rainey FA, Schleifer KH, Whitman WB (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 3, Springer-Verlag, New York, 2009, p. 392.Google Scholar
  20. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000; 25:25–29. PubMed CentralView ArticlePubMedGoogle Scholar
  21. Cherkaoui A, Hibbs J, Emonet S, Tangomo M, Girard M, Francois P, Schrenzel J. Comparison of two matrix-assisted laser desorption ionization-time of flight mass spectrometry methods with conventional phenotypic identification for routine identification of bacteria to the species level. J Clin Microbiol 2010; 48:1169–1175. PubMed CentralView ArticlePubMedGoogle Scholar
  22. French Society for Microbiology. Recommandations du Comite de l’Antibiogramme de la Société Française de Microbiologie. 2009. Available at: http://www.sfmasofr/doc/downloadphp?doc=DiU8C&fic=casfm_2009pdf. Accessed 22 February 2009.
  23. Hall KK, Lyman JA. Updated review of blood culture contamination. Clin Microbiol Rev 2006; 19:788–802. PubMed CentralView ArticlePubMedGoogle Scholar
  24. Gouy M, Guindon S, Gascuel O. SeaView Version 4: A Multiplatform Graphical User Interface for Sequence Alignment and Phylogenetic Tree Building. Mol Biol Evol 2010; 27:221–224. PubMed ArticlePubMedGoogle Scholar
  25. Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, Bult CJ, Tomb JF, Dougherty BA, Merrick JM, et al. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 1995; 269:496–512. PubMed ArticlePubMedGoogle Scholar
  26. Vallenet D, Belda E, Calteau A, Cruveiller S, Engelen S, Lajus A, Le Fèvre F, Longin C, Mornico D, Roche D, et al. MicroScope—an integrated microbial resource for the curation and comparative analysis of genomic and metabolic data. Nucleic Acids Res 2013; 41:D636–D647. PubMed CentralView ArticlePubMedGoogle Scholar
  27. Vallenet D, Labarre L, Rouy Z, Barbe V, Bocs S, Cruveiller S, Lajus A, Pascal G, Scarpelli C, Médigue C. MaGe: a microbial genome annotation system supported by synteny results. Nucleic Acids Res 2006; 34:53–65. PubMed CentralView ArticlePubMedGoogle Scholar
  28. Bocs S, Cruveiller S, Vallenet D, Nuel G, Médigue C. AMIGene: Annotation of MIcrobial Genes. Nucleic Acids Res 2003; 31:3723–3726. PubMed CentralView ArticlePubMedGoogle Scholar
  29. Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 1997; 25:955–964. PubMed CentralView ArticlePubMedGoogle Scholar
  30. Lagesen K, Hallin P, Rødland EA, Staerfeldt HH, Rognes T, Ussery DW. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res 2007; 35:3100–3108. PubMed CentralView ArticlePubMedGoogle Scholar
  31. Gardner PP, Daub J, Tate JG, Nawrocki EP, Kolbe DL, Lindgreen S, Wilkinson AC, Finn RD, Griffiths-Jones S, Eddy SR, Bateman A. Rfam: updates to the RNA families database. Nucleic Acids Res 2009; 37:D136–D140. PubMed CentralView ArticlePubMedGoogle Scholar
  32. Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010; 11:119. PubMed CentralView ArticlePubMedGoogle Scholar
  33. UnitProt Consortium. The Universal Protein Resource (UniProt) 2009. Nucleic Acids Res 2009; 37:D169–D174. PubMed ArticleGoogle Scholar
  34. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 2003; 4:41. PubMed CentralView ArticlePubMedGoogle Scholar
  35. Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Das U, Daugherty L, Duquenne L, et al. InterPro: the integrative protein signature database. Nucleic Acids Res 2009; 37:D211–D215. PubMed CentralView ArticlePubMedGoogle Scholar
  36. Claudel-Renard C, Chevalet C, Faraut T, Kahn D. Enzyme-specific profiles for genome annotation: PRIAM. Nucleic Acids Res 2003; 31:6633–6639. PubMed CentralView ArticlePubMedGoogle Scholar
  37. Sonnhammer EL, von Heijne G, Krogh A. A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int Conf Intell Syst Mol Biol 1998; 6:175–182. PubMedPubMedGoogle Scholar
  38. Bendtsen JD, Nielsen H, von Heijne G, Brunak S. Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 2004; 340:783–795. PubMed ArticlePubMedGoogle Scholar
  39. Gardy JL, Laird MR, Chen F, Rey S, Walsh CJ, Ester M, Brinkman FS. PSORTb v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics 2005; 21:617–623. PubMed ArticlePubMedGoogle Scholar
  40. Moller S, Croning MDR, Apweiler R. Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics 2001; 17:646–653. PubMed ArticlePubMedGoogle Scholar


© The Author(s) 2014