1887

Microbial Genomics logo

Volume 7, Issue 2

Comparative genomics of from prosthetic-joint infections and nares highlights genetic traits associated with antimicrobial resistance, not virulence Open Access

Abstract

There is increased awareness of the worldwide spread of specific epidemic multidrug-resistant (MDR) lineages of the human commensal . Here, using bioinformatic analyses accounting for population structure, we determined genomic traits (genes, SNPs and mers) that distinguish causing prosthetic-joint infections (PJIs) from commensal isolates from nares, by analysing whole-genome sequencing data from from PJIs prospectively collected over 10 years in Sweden, and contemporary from the nares of patients scheduled for arthroplasty surgery. Previously suggested virulence determinants and the presence of genes and mutations linked to antimicrobial resistance (AMR) were also investigated. Publicly available sequences were used for international extrapolation and validation of findings. Our data show that causing PJIs differed from nasal isolates not by virulence but by traits associated with resistance to compounds used in prevention of PJIs: β-lactams, aminoglycosides and chlorhexidine. Almost a quarter of the PJI isolates did not belong to any of the previously described major nosocomial lineages, but the AMR-related traits were also over-represented in these isolates, as well as in international isolates originating from PJIs. Genes previously associated with virulence in were over-represented in individual lineages, but failed to reach statistical significance when adjusted for population structure. Our findings suggest that the current strategies for prevention of PJIs select for nosocomial MDR lineages that have arisen from horizontal gene transfer of AMR-related traits into multiple genetic backgrounds.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): antimicrobial resistance , GWAS , prosthetic-joint infection , SCCmec and Staphylococcus epidermidis
Funding
This study was supported by the:
  • Regionala forskningsrådet Uppsala-Örebro (Award RFR-228551)
    • Principle Award Recipient: BoSöderquist
  • Region Östergötland
    • Principle Award Recipient: ÅsaNilsdotter-Augustinsson
  • Region Västmanland
    • Principle Award Recipient: EmeliMånsson
  • Region Örebro län (Award OLL-767591, OLL-502241, OLL-502241)
    • Principle Award Recipient: BoSöderquist
© 2021 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000504
2021-01-13
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/mgen/7/2/mgen000504.html?itemId=/content/journal/mgen/10.1099/mgen.0.000504&mimeType=html&fmt=ahah

References

  1. Kurtz S, Ong K, Lau E, Mowat F, Halpern M. Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg Am 2007; 89:780–785 [View Article][PubMed]
     [Google Scholar]
  2. Inacio MCS, Graves SE, Pratt NL, Roughead EE, Nemes S. Increase in total joint arthroplasty projected from 2014 to 2046 in Australia: a conservative local model with international implications. Clin Orthop Relat Res 2017; 475:2130–2137 [View Article][PubMed]
     [Google Scholar]
  3. GBD 2016 Disease and Injury Incidence and Prevalence Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017; 390:1211–1259 [View Article][PubMed]
     [Google Scholar]
  4. Niu NN, Collins JE, Thornhill TS, Alcantara Abreu L, Ghazinouri R et al. Pre-operative status and quality of life following total joint replacement in a developing country: a prospective pilot study. Open Orthop J 2011; 5:307–314 [View Article][PubMed]
     [Google Scholar]
  5. Kurtz SM, Lau E, Schmier J, Ong KL, Zhao K et al. Infection burden for hip and knee arthroplasty in the United States. J Arthroplasty 2008; 23:984–991 [View Article][PubMed]
     [Google Scholar]
  6. Wenzel RP. Surgical site infections and the microbiome: an updated perspective. Infect Control Hosp Epidemiol 2019; 40:590–596 [View Article]
     [Google Scholar]
  7. Flurin L, Greenwood-Quaintance KE, Patel R. Microbiology of polymicrobial prosthetic joint infection. Diagn Microbiol Infect Dis 2019; 94:255–259 [View Article][PubMed]
     [Google Scholar]
  8. Becker K, Heilmann C, Peters G. Coagulase-negative staphylococci. Clin Microbiol Rev 2014; 27:870–926 [View Article][PubMed]
     [Google Scholar]
  9. Lee JYH, Monk IR, Gonçalves da Silva A, Seemann T, Chua KYL et al. Global spread of three multidrug-resistant lineages of Staphylococcus epidermidis . Nat Microbiol 2018; 3:1175–1185 [View Article][PubMed]
     [Google Scholar]
  10. Zamudio R, Oggioni MR, Gould IM, Hijazi K. Time for biocide stewardship?. Nat Microbiol 2019; 4:732–733 [View Article][PubMed]
     [Google Scholar]
  11. Kozitskaya S, Cho S-H, Dietrich K, Marre R, Naber K et al. The bacterial insertion sequence element IS256 occurs preferentially in nosocomial Staphylococcus epidermidis isolates: association with biofilm formation and resistance to aminoglycosides. Infect Immun 2004; 72:1210–1215 [View Article][PubMed]
     [Google Scholar]
  12. Schoenfelder SMK, Lange C, Eckart M, Hennig S, Kozytska S et al. Success through diversity - how Staphylococcus epidermidis establishes as a nosocomial pathogen. Int J Med Microbiol 2010; 300:380–386 [View Article][PubMed]
     [Google Scholar]
  13. Heilmann C, Ziebuhr W, Becker K. Are coagulase-negative staphylococci virulent?. Clin Microbiol Infect 2019; 25:1071–1080 [View Article][PubMed]
     [Google Scholar]
  14. Hellmark B, Söderquist B, Unemo M, Nilsdotter-Augustinsson Åsa. Comparison of Staphylococcus epidermidis isolated from prosthetic joint infections and commensal isolates in regard to antibiotic susceptibility, agr type, biofilm production, and epidemiology. Int J Med Microbiol 2013; 303:32–39 [View Article][PubMed]
     [Google Scholar]
  15. Post V, Harris LG, Morgenstern M, Mageiros L, Hitchings MD et al. Comparative genomics study of Staphylococcus epidermidis isolates from orthopedic-device-related infections correlated with patient outcome. J Clin Microbiol 2017; 55:3089–3103 [View Article][PubMed]
     [Google Scholar]
  16. Oh J, Byrd AL, Deming C, Conlan S. NISC Comparative Sequencing Program et al. Biogeography and individuality shape function in the human skin metagenome. Nature 2014; 514:59–64 [View Article][PubMed]
     [Google Scholar]
  17. Moore AJ, Blom AW, Whitehouse MR, Gooberman-Hill R. Deep prosthetic joint infection: a qualitative study of the impact on patients and their experiences of revision surgery. BMJ Open 2015; 5:e009495 [View Article][PubMed]
     [Google Scholar]
  18. Cahill JL, Shadbolt B, Scarvell JM, Smith PN. Quality of life after infection in total joint replacement. J Orthop Surg 2008; 16:58–65 [View Article][PubMed]
     [Google Scholar]
  19. Zmistowski B, Karam JA, Durinka JB, Casper DS, Parvizi J. Periprosthetic joint infection increases the risk of one-year mortality. J Bone Joint Surg Am 2013; 95:2177–2184 [View Article][PubMed]
     [Google Scholar]
  20. Gundtoft PH, Pedersen AB, Varnum C, Overgaard S. Increased mortality after prosthetic joint infection in primary THA. Clin Orthop Relat Res 2017; 475:2623–2631 [View Article][PubMed]
     [Google Scholar]
  21. Puhto T, Puhto A-P, Vielma M, Syrjälä H. Infection triples the cost of a primary joint arthroplasty. Infect Dis 2019; 51:348–355 [View Article][PubMed]
     [Google Scholar]
  22. Kurtz SM, Lau E, Watson H, Schmier JK, Parvizi J. Economic burden of periprosthetic joint infection in the United States. J Arthroplasty 2012; 27:61–65 [View Article][PubMed]
     [Google Scholar]
  23. Kapadia BH, Berg RA, Daley JA, Fritz J, Bhave A et al. Periprosthetic joint infection. Lancet 2016; 387:386–394 [View Article][PubMed]
     [Google Scholar]
  24. Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014; 15:R46 [View Article][PubMed]
     [Google Scholar]
  25. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 2012; 19:455–477 [View Article][PubMed]
     [Google Scholar]
  26. MacLea KS, Trachtenberg AM. Complete genome sequence of Staphylococcus epidermidis ATCC 12228 chromosome and plasmids, generated by long-read sequencing. Genome Announc 2017; 5:e00954-17 [View Article][PubMed]
     [Google Scholar]
  27. Sahl JW, Lemmer D, Travis J, Schupp JM, Gillece JD et al. NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats. Microb Genom 2016; 2:e000074 [View Article][PubMed]
     [Google Scholar]
  28. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009; 25:1754–1760 [View Article][PubMed]
     [Google Scholar]
  29. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010; 20:1297–1303 [View Article][PubMed]
     [Google Scholar]
  30. Méric G, Mageiros L, Pensar J, Laabei M, Yahara K et al. Disease-associated genotypes of the commensal skin bacterium Staphylococcus epidermidis . Nat Commun 2018; 9:5034 [View Article][PubMed]
     [Google Scholar]
  31. Croucher NJ, Page AJ, Connor TR, Delaney AJ, Keane JA et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res 2015; 43:e15 [View Article][PubMed]
     [Google Scholar]
  32. Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS One 2010; 5:e9490 [View Article][PubMed]
     [Google Scholar]
  33. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014; 30:1312–1313 [View Article][PubMed]
     [Google Scholar]
  34. Letunic I, Bork P. Interactive tree of life (iTOL) v4: recent updates and new developments. Nucleic Acids Res 2019; 47:W256–W259 [View Article][PubMed]
     [Google Scholar]
  35. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article][PubMed]
     [Google Scholar]
  36. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 2015; 31:3691–3693 [View Article][PubMed]
     [Google Scholar]
  37. Bradley P, Gordon NC, Walker TM, Dunn L, Heys S et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis . Nat Commun 2015; 6:10063 [View Article][PubMed]
     [Google Scholar]
  38. Lees JA, Galardini M, Bentley SD, Weiser JN, Corander J. pyseer: a comprehensive tool for microbial pangenome-wide association studies. Bioinformatics 2018; 34:4310–4312 [View Article][PubMed]
     [Google Scholar]
  39. Collins C, Didelot X. A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination. PLoS Comput Biol 2018; 14:e1005958 [View Article][PubMed]
     [Google Scholar]
  40. Otto M. Molecular basis of Staphylococcus epidermidis infections. Semin Immunopathol 2012; 34:201–214 [View Article][PubMed]
     [Google Scholar]
  41. Rohde H, Kalitzky M, Kröger N, Scherpe S, Horstkotte MA et al. Detection of virulence-associated genes not useful for discriminating between invasive and commensal Staphylococcus epidermidis strains from a bone marrow transplant unit. J Clin Microbiol 2004; 42:5614–5619 [View Article][PubMed]
     [Google Scholar]
  42. Söderquist B, Andersson M, Nilsson M, Nilsdotter-Augustinsson Å, Persson L et al. Staphylococcus epidermidis surface protein I (SesI): a marker of the invasive capacity of S. epidermidis?. J Med Microbiol 2009; 58:1395–1397 [View Article][PubMed]
     [Google Scholar]
  43. Qi X, Jin Y, Duan J, Hao Z, Wang S et al. SesI may be associated with the invasiveness of Staphylococcus epidermidis . Front Microbiol 2017; 8:2574 [View Article][PubMed]
     [Google Scholar]
  44. Conlan S, Mijares LA, Becker J, Blakesley RW. NISC Comparative Sequencing Program et al. Staphylococcus epidermidis pan-genome sequence analysis reveals diversity of skin commensal and hospital infection-associated isolates. Genome Biol 2012; 13:R64 [View Article][PubMed]
     [Google Scholar]
  45. Hellmark B, Berglund C, Nilsdotter-Augustinsson A, Unemo M, Söderquist B. Staphylococcal cassette chromosome mec (SCCmec) and arginine catabolic mobile element (ACME) in Staphylococcus epidermidis isolated from prosthetic joint infections. Eur J Clin Microbiol Infect Dis 2013; 32:691–697 [View Article][PubMed]
     [Google Scholar]
  46. Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S et al. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother 2012; 67:2640–2644 [View Article][PubMed]
     [Google Scholar]
  47. Wi YM, Greenwood-Quaintance KE, Brinkman CL, Lee JYH, Howden BP et al. Rifampicin resistance in Staphylococcus epidermidis: molecular characterisation and fitness cost of rpoB mutations. Int J Antimicrob Agents 2018; 51:670–677 [View Article][PubMed]
     [Google Scholar]
  48. Hellmark B, Unemo M, Nilsdotter-Augustinsson A, Söderquist B. Antibiotic susceptibility among Staphylococcus epidermidis isolated from prosthetic joint infections with special focus on rifampicin and variability of the rpoB gene. Clin Microbiol Infect 2009; 15:238–244 [View Article][PubMed]
     [Google Scholar]
  49. Castanheira M, Watters AA, Mendes RE, Farrell DJ, Jones RN. Occurrence and molecular characterization of fusidic acid resistance mechanisms among Staphylococcus spp. from European countries (2008). J Antimicrob Chemother 2010; 65:1353–1358 [View Article][PubMed]
     [Google Scholar]
  50. Dale GE, Broger C, D'Arcy A, Hartman PG, DeHoogt R et al. A single amino acid substitution in Staphylococcus aureus dihydrofolate reductase determines trimethoprim resistance. J Mol Biol 1997; 266:23–30 [View Article][PubMed]
     [Google Scholar]
  51. Yang S-J, Mishra NN, Rubio A, Bayer AS. Causal role of single nucleotide polymorphisms within the mprF gene of Staphylococcus aureus in daptomycin resistance. Antimicrob Agents Chemother 2013; 57:5658–5664 [View Article][PubMed]
     [Google Scholar]
  52. Jiang J-H, Dexter C, Cameron DR, Monk IR, Baines SL. Evolution of daptomycin resistance in coagulase-negative staphylococci involves mutations of the essential two-component regulator WalKR. Antimicrob Agents Chemother 2019; 63:e01926-18 [View Article][PubMed]
     [Google Scholar]
  53. Yamada M, Yoshida J, Hatou S, Yoshida T, Minagawa Y. Mutations in the quinolone resistance determining region in Staphylococcus epidermidis recovered from conjunctiva and their association with susceptibility to various fluoroquinolones. Br J Ophthalmol 2008; 92:848–851 [View Article][PubMed]
     [Google Scholar]
  54. Trong HN, Prunier A-L, Leclercq R. Hypermutable and fluoroquinolone-resistant clinical isolates of Staphylococcus aureus . Antimicrob Agents Chemother 2005; 49:2098–2101 [View Article][PubMed]
     [Google Scholar]
  55. Hooper DC. Mechanisms of action and resistance of older and newer fluoroquinolones. Clin Infect Dis 2000; 31 (Suppl. 2):S24–S28 [View Article][PubMed]
     [Google Scholar]
  56. Addetia A, Greninger AL, Adler A, Yuan S, Makhsous N. A novel, widespread qacA allele results in reduced chlorhexidine susceptibility in Staphylococcus epidermidis . Antimicrob Agents Chemother 2019; 63:e02607-18 [View Article][PubMed]
     [Google Scholar]
  57. Kaya H, Hasman H, Larsen J, Stegger M, Johannesen TB. SCCmecFinder, a web-based tool for typing of staphylococcal cassette chromosome mec in Staphylococcus aureus using whole-genome sequence data. mSphere 2018; 3:e00612-17
     [Google Scholar]
  58. International Working Group on the Classification of Staphylococcal Cassette Chromosome Elements (IWG-SCC) Classification of staphylococcal cassette chromosome mec (SCCmec): guidelines for reporting novel SCCmec elements. Antimicrob Agents Chemother 2009; 53:4961–4967 [View Article][PubMed]
     [Google Scholar]
  59. Widerström M, Wiström J, Edebro H, Marklund E, Backman M et al. Colonization of patients, healthcare workers, and the environment with healthcare-associated Staphylococcus epidermidis genotypes in an intensive care unit: a prospective observational cohort study. BMC Infect Dis 2016; 16:743 [View Article][PubMed]
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000504
Loading
Comparative genomics of Staphylococcus epidermidis from prosthetic-joint infections and nares highlights genetic traits associated with antimicrobial resistance, not virulence
M Gen 7, 000504 (2021); https://doi.org/10.1099/mgen.0.000504
/content/journal/mgen/10.1099/mgen.0.000504
/content/journal/mgen/10.1099/mgen.0.000504
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Most cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error