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Niche specialization and spread of Staphylococcus capitis involved in neonatal sepsis

Nature Microbiology volume 5, pages 735–745 (2020)Cite this article

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Abstract

The multidrug-resistant Staphylococcus capitis NRCS-A clone is responsible for sepsis in preterm infants in neonatal intensive care units (NICUs) worldwide. Here, to retrace the spread of this clone and to identify drivers of its specific success, we investigated a representative collection of 250 S. capitis isolates from adults and newborns. Bayesian analyses confirmed the spread of the NRCS-A clone and enabled us to date its emergence in the late 1960s and its expansion during the 1980s, coinciding with the establishment of NICUs and the increasing use of vancomycin in these units, respectively. This dynamic was accompanied by the acquisition of mutations in antimicrobial resistance- and bacteriocin-encoding genes. Furthermore, combined statistical tools and a genome-wide association study convergently point to vancomycin resistance as a major driver of NRCS-A success. We also identified another S. capitis subclade (alpha clade) that emerged independently, showing parallel evolution towards NICU specialization and non-susceptibility to vancomycin, indicating convergent evolution in NICU-associated pathogens. These findings illustrate how the broad use of antibiotics can repeatedly lead initially commensal drug-susceptible bacteria to evolve into multidrug-resistant clones that are able to successfully spread worldwide and become pathogenic for highly vulnerable patients.

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Fig. 1: Phylogenetic inference of the global S. capitis strain collection and expansion of the multidrug-resistant NRCS-A clone.
Fig. 2: Demographic and temporal evolution parameters of the NRCS-A strain population obtained from Bayesian inferences.
Fig. 3: Distribution of SCCmec cassettes and antibiotic phenotypic resistance profiles of the S. capitis isolates.
Fig. 4: GWAS scatter plot.
Fig. 5: Associations of antibiotic resistance profiles with epidemic success and neonatal infection in S. capitis isolates.

Data availability

The datasets supporting the results of this article are available from the Sequence Read Archive under accession no. PRJNA493527. Additional data on the 250 strains are available in Supplementary Table 1.

References

  1. Howson, C. P., Kinney, M. V., McDougall, L. & Lawn, J. E., Born too soon preterm birth action group. Born too soon: preterm birth matters. Reprod. Health 10, S1 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Liu, L. et al. Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the sustainable development goals. Lancet 388, 3027–3035 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Boghossian, N. S. et al. Late-onset sepsis in very low birth weight infants from singleton and multiple-gestation births. J. Pediatr. 162, 1120–1124 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Stoll, B. J. et al. Late-onset sepsis in very low birth weight neonates: the experience of the NICHD neonatal research network. Pediatrics 110, 285–291 (2002).

    Article  PubMed  Google Scholar 

  5. Cohen-Wolkowiez, M. et al. Early and late onset sepsis in late preterm infants. Pediatr. Infect. Dis. J. 28, 1052–1056 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Rasigade, J. P. et al. Methicillin-resistant Staphylococcus capitis with reduced vancomycin susceptibility causes late-onset sepsis in intensive care neonates. PLoS ONE 7, e31548 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Butin, M. et al. Wide geographical dissemination of the multiresistant Staphylococcus capitis NRCS-A clone in neonatal intensive-care units. Clin. Microbiol. Infect. 22, 46–52 (2016).

    Article  PubMed  Google Scholar 

  8. Butin, M., Martins-Simoes, P., Rasigade, J. P., Picaud, J. C. & Laurent, F. Worldwide endemicity of a multidrug-resistant Staphylococcus capitis clone involved in neonatal sepsis. Emerg. Infect. Dis. 23, 538–539 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Carter, G. P. et al. Genomic analysis of multi-resistant Staphylococcus capitis associated with neonatal sepsis. Antimicrob. Agents Chemother. 62, e00898–18 (2018).

    PubMed  PubMed Central  Google Scholar 

  10. Ben Said, M. et al. Late-onset sepsis due to Staphylococcus capitis ‘neonatalis’ in low-birthweight infants: a new entity? J. Hosp. Infect. 94, 95–98 (2016).

    Article  PubMed  Google Scholar 

  11. Lee, J. Y. H. et al. Global spread of three multidrug-resistant lineages of Staphylococcus epidermidis. Nat. Microbiol. 3, 1175–1185 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Didelot, X. & Wilson, D. J. ClonalFrameML: efficient inference of recombination in whole bacterial genomes. PLoS Comput. Biol. 11, e1004041 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Stegger, M. et al. Origin and evolution of European community-acquired methicillin-resistant Staphylococcus aureus. mBio 5, e01044–14 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Nubel, U. et al. A timescale for evolution, population expansion, and spatial spread of an emerging clone of methicillin-resistant Staphylococcus aureus. PLoS Pathog. 6, e1000855 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Simoes, P. M. et al. Single-molecule sequencing (PacBio) of the Staphylococcus capitis NRCS-A clone reveals the basis of multidrug resistance and adaptation to the neonatal intensive care unit environment. Front. Microbiol. 7, 1991 (2016).

    PubMed  PubMed Central  Google Scholar 

  16. Martins Simoes, P. et al. Characterization of a novel composite staphylococcal cassette chromosome mec (SCCmec-SCCcad/ars/cop) in the neonatal sepsis-associated Staphylococcus capitis pulsotype NRCS-A. Antimicrob. Agents Chemother. 57, 6354–6357 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Duforet-Frebourg, N., Bazin, E. & Blum, M. G. B. Genome scans for detecting footprints of local adaptation using a Bayesian factor model. Mol. Biol. Evol. 31, 1–13 (2014).

    Article  CAS  Google Scholar 

  18. Merker, M. et al. Evolutionary history and global spread of the Mycobacterium tuberculosis Beijing lineage. Nat. Genet. 47, 242–249 (2015).

    Article  CAS  PubMed  Google Scholar 

  19. Beabout, K. et al. The ribosomal S10 protein is a general target for decreased tigecycline susceptibility. Antimicrob. Agents Chemother. 59, 5561–5566 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kuroda, M., Kuwahara-Arai, K. & Hiramatsu, K. Identification of the up- and down-regulated genes in vancomycin-resistant Staphylococcus aureus strains Mu3 and Mu50 by cDNA differential hybridization method. Biochem. Biophys. Res. Commun. 269, 485–490 (2000).

    Article  CAS  PubMed  Google Scholar 

  21. Grayczyk, J. P., Harvey, C. J., Laczkovich, I. & Alonzo, F. 3rd A lipoylated metabolic protein released by Staphylococcus aureus suppresses macrophage activation. Cell Host Microbe 22, 678–687 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zorzoli, A., Grayczyk, J. P. & Alonzo, F. III. Staphylococcus aureus tissue infection during sepsis is supported by differential use of bacterial or host-derived lipoic acid. PLoS Pathog. 12, e1005933 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Hu, Q., Peng, H. & Rao, X. Molecular events for promotion of vancomycin resistance in vancomycin intermediate Staphylococcus aureus. Front. Microbiol. 7, 1601 (2016).

    PubMed  PubMed Central  Google Scholar 

  24. Krzyzaniak, N., Pawlowska, I. & Bajorek, B. Review of drug utilization patterns in NICUs worldwide. J. Clin. Pharm. Ther. 41, 612–620 (2016).

    Article  CAS  PubMed  Google Scholar 

  25. Rasigade, J.-P. et al. Strain-specific estimation of epidemic success provides insights into the transmission dynamics of tuberculosis. Sci. Rep. 7, 45326 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lamichhane-Khadka, R. et al. sarA inactivation reduces vancomycin-intermediate and ciprofloxacin resistance expression by Staphylococcus aureus. Int. J. Antimicrob. Agents 34, 136–141 (2009).

    Article  CAS  PubMed  Google Scholar 

  27. Schaaff, F., Reipert, A. & Bierbaum, G. An elevated mutation frequency favors development of vancomycin resistance in Staphylococcus aureus. Antimicrob. Agents Chemother. 46, 3540–3548 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jacqz-Aigrain, E., Zhao, W., Sharland, M. & van den Anker, J. N. Use of antibacterial agents in the neonate: 50 years of experience with vancomycin administration. Semin. Fetal Neonatal Med. 18, 28–34 (2013).

    Article  PubMed  Google Scholar 

  29. Levine, D. P. Vancomycin: a history. Clin. Infect. Dis. 42, S5–S12 (2006).

    Article  CAS  PubMed  Google Scholar 

  30. Mukhopadhyay, S., Sengupta, S. & Puopolo, K. M. Challenges and opportunities for antibiotic stewardship among preterm infants. Arch. Dis. Child Fetal Neonatal Ed. 104, F327–F332 (2019).

    Article  PubMed  Google Scholar 

  31. Cailes, B. et al. Antimicrobial resistance in UK neonatal units: neonIN infection surveillance network. Arch. Dis. Child Fetal Neonatal Ed. 103, F474–F478 (2018).

    Article  PubMed  Google Scholar 

  32. Butin, M. et al. Adaptation to vancomycin pressure of multiresistant Staphylococcus capitis NRCS-A involved in neonatal sepsis. J. Antimicrob. Chemother. 70, 3027–3031 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Williamson, D. A. et al. High usage of topical fusidic acid and rapid clonal expansion of fusidic acid-resistant Staphylococcus aureus: a cautionary tale. Clin. Infect. Dis. 59, 1451–1454 (2014).

    Article  CAS  PubMed  Google Scholar 

  34. Millette, M. et al. Capacity of human nisin- and pediocin-producing lactic acid bacteria to reduce intestinal colonization by vancomycin-resistant enterococci. Appl. Environ. Microbiol. 74, 1997–2003 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Butin, M. et al. Vancomycin treatment is a risk factor for vancomycin-nonsusceptible Staphylococcus capitis sepsis in preterm neonates. Clin. Microbiol. Infect. 23, 839–844 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Brown, S., Santa Maria, J. P. Jr & Walker, S. Wall teichoic acids of gram-positive bacteria. Annu. Rev. Microbiol. 67, 313–336 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. Nasser, R. M. et al. Outbreak of Burkholderia cepacia bacteremia traced to contaminated hospital water used for dilution of an alcohol skin antiseptic. Infect. Control Hosp. Epidemiol. 25, 231–239 (2004).

    Article  PubMed  Google Scholar 

  38. Ory, J. et al. Successful implementation of infection control measure in a neonatal intensive care unit to combat the spread of pathogenic multidrug resistant Staphylococcus capitis. Antimicrob. Resist. Infect. Control 8, 57 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 13, e1005595 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Inouye, M. et al. SRST2: Rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 6, 90 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Kaya, H. et al. SCCmecFinder, a web-based tool for typing of staphylococcal cassette chromosome mec in Staphylococcus aureus using whole-genome sequence data. mSphere 3, e00612–17 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Couvin, D. et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 46, W246–W251 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Schmidt, H. A., Strimmer, K., Vingron, M. & von Haeseler, A. TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing. Bioinformatics 18, 502–504 (2002).

    Article  CAS  PubMed  Google Scholar 

  47. Guindon, S. et al. New algorithms and mehtods to estimate maximum-likelihood phylogenies: asessing the performance of PhyML 2.0. Syst. Biol. 59, 307–321 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nuc. Acids Res. 44, W242–W245 (2016).

    Article  CAS  Google Scholar 

  49. Rambaut, A., Lam, T. T., Max Carvalho, L. & Pybus, O. G. Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol. 2, vew007 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Anderson, M. J. & Robinson, J. Permutation tests for linear models. Aust. NZ J. Stat. 43, 75–88 (2001).

    Article  Google Scholar 

  51. Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Huson, D. H. & Bryant, D. Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267 (2006).

    Article  CAS  PubMed  Google Scholar 

  53. Didelot, X., Lawson, D., Darling, A. & Falush, D. Inference of homologous recombination in bacteria using whole genome sequences. Genetics 186, 1435–1449 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Joseph, S. J., Didelot, X., Gandhi, K., Dean, D. & Read, T. D. Interplay of recombination and selection in the genomes of Chlamydia trachomatis. Biol. Direct 6, 28 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article  CAS  PubMed  Google Scholar 

  57. Andrews, J. M. Determination of minimum inhibitory concentrations. J. Antimicrob. Chemother. 48, 5–16 (2001).

    Article  CAS  PubMed  Google Scholar 

  58. Satola, S. W., Farley, M. M., Anderson, K. F. & Patel, J. B. Comparison of detection methods for heteroresistant vancomycin-intermediate Staphylococcus aureus, with the population analysis profile method as the reference method. J. Clin. Microbiol. 49, 177–183 (2011).

    Article  PubMed  Google Scholar 

  59. Barbier, M. et al. Changing patterns of human migrations shaped the global population structure of Mycobacterium tuberculosis in France. Sci. Rep. 8, 5855 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Jaillard, M. et al. A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events. PLoS Genet. 14, e1007758 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Vallenet, D. et al. MaGe: a microbial genome annotation system supported by synteny results. Nuc. Acids Res. 34, 53–65 (2006).

    Article  CAS  Google Scholar 

  62. Maali, Y. et al. Understanding the virulence of Staphylococcus pseudintermedius: a major role of pore-forming toxins. Front. Cell Infect. Microbiol. 8, 221 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Loftus, R. W., Dexter, F., Robinson, A. D. M. & Horswill, A. R. Desiccation tolerance is associated with Staphylococcus aureus hypertransmissibility, resistance and infection development in the operating room. J. Hosp. Infect. 100, 299–308 (2018).

    Article  CAS  PubMed  Google Scholar 

  64. Karauzum, H. et al. Comparison of adhesion and virulence of two predominant hospital-acquired methicillin-resistant Staphylococcus aureus clones and clonal methicillin-susceptible S. aureus isolates. Infect. Immun. 76, 5133–5138 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Streker, K., Freiberg, C., Labischinski, H., Hacker, J. & Ohlsen, K. Staphylococcus aureus NfrA (SA0367) is a flavin mononucleotide-dependent NADPH oxidase involved in oxidative stress response. J. Bacteriol. 187, 2249–2256 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Loo, C., Mitrakul, K., Voss, I., Hughes, C. & Ganeshkumar, N. Involvement of an inducible fructose phosphotransferase operon in Streptococcus gordonii biofilm formation. J. Bacteriol. 185, 6241–6254 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Valour, F. et al. Staphylococcus epidermidis in orthopedic device infections: the role of bacterial internalization in human osteoblasts and biofilm formation. PLoS ONE 8, e67240 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Tissieres, P. et al. Innate immune deficiency of extremely premature neonates can be reversed by interferon-γ. PLoS ONE 7, e32863 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank M. Stegger and his team for insightful exchanges during the manuscript drafting and C. Allix-Béguec, C. Gaudin, M. Mairey and S. Duthoy for their help in genome sequencing. This project was supported by the European Society of Clinical Microbiology and Infectious Diseases study group (Project P307-14), the Fondation pour la Recherche Médicale (project ING20160435683) and the European Union Patho-Ngen-Trace (project FP7-278864).

Author information

Author notes

  1. These authors contributed equally: Marine Butin, Frédéric Laurent.

Authors and Affiliations

  1. Institut Systématique Evolution Biodiversité (ISYEB), Muséum national d’Histoire naturelle, CNRS, Sorbonne Université, Université des Antilles, EPHE, Paris, France

    Thierry Wirth, Jean-Philippe Rasigade & Maxime Barbier

  2. PSL University, EPHE, Paris, France

    Thierry Wirth

  3. Institut des Agents Infectieux, Département de Bactériologie, Centre National de Référence des Staphylocoques, Hospices Civils de Lyon, Lyon, France

    Marine Bergot, Jean-Philippe Rasigade, Patricia Martins-Simoes & Frédéric Laurent

  4. Centre International de recherche en Infectiologie, INSERM U1111 - CNRS UMR5308 - ENS Lyon - Université Lyon 1, Lyon, France

    Jean-Philippe Rasigade, Patricia Martins-Simoes, Marine Butin, Frédéric Laurent, Francois Vandenesch & Francois Vandenesch

  5. Staphylococcus Reference Section, National Infection Service, Public Health England, London, UK

    Bruno Pichon, Rachel Pike & Angela Kearns

  6. Laboratoire Biométrie et Biologie Evolutive, CNRS UMR5558, Université Lyon 1, Lyon, France

    Laurent Jacob

  7. Institut de Biologie de la cellule (I2BC-UMR9198), CNRS, CEA, Univ. Paris Sud, Université Paris Saclay, Gif-sur-Yvette, France

    Pierre Tissieres

  8. Service de Réanimation Néonatale, Hôpitaux Universitaires Paris Sud APHP, Le Kremlin-Bicêtre, Paris, France

    Pierre Tissieres

  9. Service de Réanimation Néonatale, Hôpital Croix Rousse, Hospices Civils de Lyon, Lyon, France

    Jean-Charles Picaud

  10. Université de Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 8204 - CIIL - Centre d’Infection et d’Immunité de Lille, Lille, France

    Philip Supply

  11. Service de Réanimation Néonatale, Hôpital Femme Mère Enfant, Hospices Civils de Lyon, Lyon, France

    Marine Butin & Olivier Claris

  12. Institute of Medical Biochemistry and Laboratory Diagnostics, General University Hospital, Prague, Czech Republic

    Vaclava Adamkova

  13. Tan Tock Seng Hospital, Singapore, Singapore

    Timothy Barkham

  14. University Hospital Münster, Münster, Germany

    Karsten Becker

  15. Irish Meningitis and Sepsis Reference Laboratory, Temple Street Children’s University Hospital, Dublin, Ireland

    Desiree Bennett

  16. Vanderbilt University School of Medicine, Nashville, USA

    Clarence Buddy Creech

  17. Instituto de Tecnologia Quimica e Biologica, Oeiras, Portugal

    Herminia De Lencastre

  18. School of Applied Sciences, RMIT University, Bundoora, Australia

    Margaret Deighton

  19. Hôpital Erasme – ULB, Bruxelles, Belgique

    Olivier Denis

  20. University of New Castle, Callaghan, Australia

    John Ferguson

  21. Chang Gung Children’s Hospital, Taoyuan, Taiwan

    Yhu-Chering Huang

  22. University Hospital of North Norway, Tromsø, Norway

    Claus Klingenberg

  23. Oslo University Hospital Rikshospitalet, Oslo, Norway

    Andre Ingebretsen

  24. CHU Sainte-Justine, Montréal, Canada

    Celine Laferrière

  25. Rio de Janeiro Federal University, Rio de Janeiro, Brazil

    Katia Regina Netto dos Santos

  26. Laboratory of Bacteriology and the Genome Research Lab, Geneva University Hospital, Geneva, Switzerland

    Jacques Schrenzel

  27. University of Patras, Patras, Greece

    Iris Spiliopoulou

  28. University of Catania, Catania, Italy

    Stefania Stefani

  29. Seoul National University Hospital, Seoul, Korea

    Kim TaekSoo

  30. Helsinki University Central Hospital laboratory HUSLAB, Helsinki, Finland

    Eveliina Tarkka

  31. Medical Microbiology and Infection Prevention, University Medical Center, Groningen, Netherlands

    Alex Friedrich

  32. Medical Microbiology & Infection Control, Amsterdam, Netherlands

    Christina Vandenbroucke-Grauls

  33. University of Otago, Otago, New Zealand

    James Ussher

  34. Children Healthcare of Atlanta, Atlanta, USA

    Lars Westblade

  35. Institute for Infection and Immunity, St George’s, University of London, London, United Kingdom

    Jodi Lindsay

  36. Statens Serum Institut, Microbiology and Infection Control, Reference Laboratory for Antimicrobial Resistance and Staphylococci, Copenhagen, Denmark

    Anders Rhod Larsen

  37. Institute of Global Health, Epidemiology & Biostatistics, Ruprecht Karls University, Heidelberg, Germany

    Philipp Zanger

  38. Institut für Med. Mikrobiologie Universitätsklinikum Münster, Münster, Germany

    Barbara C. Kahl

  39. Hospital Universitari Germans Trias i Pujol, Microbiology, Badalona, Spain

    Cristina Prat Aymerich

Authors
  1. Thierry Wirth
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  2. Marine Bergot
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  3. Jean-Philippe Rasigade
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  4. Bruno Pichon
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  5. Maxime Barbier
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  6. Patricia Martins-Simoes
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  7. Laurent Jacob
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  8. Rachel Pike
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  9. Pierre Tissieres
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  10. Jean-Charles Picaud
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  11. Angela Kearns
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  12. Philip Supply
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  13. Marine Butin
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  14. Frédéric Laurent
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Consortia

the International Consortium for Staphylococcus capitis neonatal sepsis

  • Vaclava Adamkova
  • , Timothy Barkham
  • , Karsten Becker
  • , Desiree Bennett
  • , Olivier Claris
  • , Clarence Buddy Creech
  • , Herminia De Lencastre
  • , Margaret Deighton
  • , Olivier Denis
  • , John Ferguson
  • , Yhu-Chering Huang
  • , Claus Klingenberg
  • , Andre Ingebretsen
  • , Celine Laferrière
  • , Katia Regina Netto dos Santos
  • , Jacques Schrenzel
  • , Iris Spiliopoulou
  • , Stefania Stefani
  • , Kim TaekSoo
  • , Eveliina Tarkka
  • , Alex Friedrich
  • , Christina Vandenbroucke-Grauls
  • , James Ussher
  • , Francois Vandenesch
  •  & Lars Westblade

the ESGS Study Group of ESCMID

  • Jodi Lindsay
  • , Francois Vandenesch
  • , Anders Rhod Larsen
  • , Philipp Zanger
  • , Barbara C. Kahl
  •  & Cristina Prat Aymerich

Contributions

M.Butin, T.W., J.-C.P. and F.L. conceived the project. M.Butin and F.L. established and analysed clinical and reference isolate datasets. B.P., A.K. and R.P. performed DNA extractions. P.S. performed DNA sequencing. B.P., A.K. and R.P. performed antimicrobial susceptibility testing. P.T. performed phagocytosis assays. M.Butin performed all additional phenotypic assays. T.W., M.Barbier, P.M.-S. and M.Bergot analysed genomic data. J.-P.R. participated in genomic analyses and performed THD analysis. M.Bergot and L.J. performed GWAS analysis. T.W., M.Butin, P.S. and F.L. drafted the manuscript. All authors reviewed and contributed to the final manuscript.

Corresponding authors

Correspondence to Thierry Wirth or Marine Butin.

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Competing interests

The authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 CLONALFRAMEML analysis of recombination in S. capitis.

Analysis was based on 55 genomes: all non-NRCS-A strains were included, however the clone NRCS-A was undersampled to avoid a statistical bias in favor of mutational changes. Dark blue horizontal bars indicate recombination events detected by the analysis.

Extended Data Fig. 2 NRCS-A host types and genetic structure.

a, NRCS-A isolates within an MSTREE based on the whole genome sequencing data. Each strain is represented by a circle or a fraction of a circle, colors correspond to different host types. Numbers indicate the mutational steps between the strains. b, Same data as above but represented in an MDS plot. c, Within NRCS-A diversity as assessed by mean pairwise SNP distances (N=197). d, Graphical chart representing the fraction of strains obtained from newborns in the basal, Proto-outbreak 1 and 2 and Outbreak strains.

Extended Data Fig. 3 Genome scan analysis of NRCS-A strains for detecting SNPs involved in local adaptation.

a, Plot of the first 2 principal components (PC). The 197 NRCS-A strains are represented by points and colorized according to their phylogenic origin (Proto-outbreak 1 and 2 in blue, and Outbreak in red). PC 2 is the one separating the basal proto-outbreak 1 and 2 strains from the outbreak strains. b, Manhattan plot representing the 3,658 SNPs and values obtained after performing Mahalanobis distances. The SNPs are colorized according to the PC to which they correlate most (PC1 = black, PC2 = red, PC3 = green and PC4 = blue).

Extended Data Fig. 4 Specific SNPs in Outbreak and Alpha isolates.

Respectively 32 and 17 SNPs were specifically identified in Outbreak strains among NRCS-A strains (n=197) or in clade Alpha strains among Basal strains (n=53). Those SNPs were identified using PCADAPT.

Extended Data Fig. 5 Tertiary protein structures.

a, Positions on the tertiary protein structure of outbreak specific non-synonymous mutations detected via PCADAPT and involved in antibiotic resistance (tigecycline and vancomycin). b, Positions on the tertiary protein structure of alpha-clone specific non-synonymous mutations for a set of two genes involved in cell wall synthesis. Visualization and predictions were executed by PHYRE2 software (http://www.sbg.bio.ic.ac.uk/phyre2).

Extended Data Fig. 6 Phenotypic and genotypic resistance patterns of S. capitis isolates.

Phenotypic data of S. capitis isolates (n=250) were obtained from agar dilution and biomarkers of antibiotic resistance were detected using GENEFINDER. Comparison between groups of isolates was performed using two-sided Fisher exact test.

Extended Data Fig. 7 Phenotypic assays comparing a subset of representative isolates of each of the four subgroups identified by the phylogeographical analysis (Outbreak, Proto-outbreak 1, Proto-outbreak 2 and ‘other isolates’).

In all 6 graphs, center values represent means. a, Culture supernatants cytotoxicity assay using THP1 cells, adjusted on a positive control (Triton) of 12 representative S. capitis isolates (two independent experiments in triplicate for each strains). b, Survival of strains (n=12) after 24 hours of persistence in desiccation conditions (two independent experiments in triplicate for each strains). c, Comparison of the doubling time of bacterial growth during the exponential phase in standard conditions (BHI) of 24 representative S. capitis isolates (three independent experiments in triplicate for each strains) and d, Under oxidative stress (ethanol-supplemented medium to a final concentration of 6.5%) (n=24 strains, three independent experiments in triplicate for each strains). e, Quantification of biofilm production of 24 representative S. capitis isolates using crystal violet method (expressed as optic densitometry at 590nm) (three independent experiments in triplicate for each strains). f, Phagocytosis index of monocytes and granulocytes from cord blood for a subset of 5 representative isolates of “Outbreak” and “Basal” isolates (four independent experiments). Of note, results of phagocytosis of neutrophils and activated neutrophils are not represented here because they were similar to those with granulocytes.

Extended Data Fig. 8 Genes associated with vancomycin MIC and/or THD success index using DBGWAS.

Here are represented genes with a -log10 (HMP) > 7.5 on either axis, and/or > 5 on both axes, thus considered significant.

Supplementary information

Supplementary Information

Results, including three supplementary figures.

Reporting Summary

Supplementary Table 1

This table includes source data and details about each isolate (identification, origin, phenotypic and genomic characteristics, genes content and THD index).

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Wirth, T., Bergot, M., Rasigade, JP. et al. Niche specialization and spread of Staphylococcus capitis involved in neonatal sepsis. Nat Microbiol 5, 735–745 (2020). https://doi.org/10.1038/s41564-020-0676-2

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