1887

Abstract

Treatment failure of methicillin-resistant (MRSA) infections remains problematic in clinical practice because therapeutic options are limited. Penicillin plus potassium clavulanate combination (PENC) was shown to have potential for treating some MRSA infections. We investigated the susceptibility of MRSA isolates and constructed a drug susceptibility prediction model for the phenotype of the PENC. We determined the minimum inhibitory concentration of PENC for MRSA (=284) in a teaching hospital (SRRSH-MRSA). PENC susceptibility genotypes were analysed using a published genotyping scheme based on the sequence. expression in MRSA isolates was analysed by qPCR. We established a random forest model for predicting PENC-susceptible phenotypes using core genome allelic profiles from cgMLST analysis. We identified S2-R isolates with susceptible genotypes but PENC-resistant phenotypes; these isolates expressed at higher levels than did S2 MRSA (2.61 vs 0.98, <0.05), indicating the limitation of using a single factor for predicting drug susceptibility. Using the data of selected UK-sourced MRSA (=74) and MRSA collected in a previous national survey (NA-MRSA, =471) as a training set, we built a model with accuracies of 0.94 and 0.93 for SRRSH-MRSA and UK-sourced MRSA (=287, NAM-MRSA) validation sets. The AUROC of this model for SRRSH-MRSA and NAM-MRSA was 0.96 and 0.97. Although the source of the training set data affects the scope of application of the prediction model, our data demonstrated the power of the machine learning approach in predicting susceptibility from cgMLST results.

Funding
This study was supported by the:
  • National Science and Technology Planning Project (Award 2018ZX10714002)
    • Principle Award Recipient: YanJiang
  • Natural Science Foundation of Zhejiang Province (Award LQ20H190005)
    • Principle Award Recipient: LuSun
  • National Natural Science Foundation of China (Award 81971977)
    • Principle Award Recipient: YanChen
  • 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.0.000610
2021-09-23
2021-10-24
Loading full text...

Full text loading...

/deliver/fulltext/mgen/7/9/mgen000610.html?itemId=/content/journal/mgen/10.1099/mgen.0.000610&mimeType=html&fmt=ahah

References

  1. Tong SY, Davis JS, Eichenberger E, Holland TL, Fowler VG. Staphylococcus aureus infections: epidemiology, pathophysiology, clinical manifestations, and management. Clin Microbiol Rev 2015; 28:603–661 [View Article] [PubMed]
    [Google Scholar]
  2. Geriak M, Haddad F, Rizvi K, Rose W, Kullar R et al. Clinical data on daptomycin plus ceftaroline versus standard of care monotherapy in the treatment of methicillin-resistant Staphylococcus aureus bacteremia. Antimicrob Agents Chemother 2019; 63: [View Article] [PubMed]
    [Google Scholar]
  3. Mediavilla JR, Chen L, Mathema B, Kreiswirth BN. Global epidemiology of community-associated methicillin resistant Staphylococcus aureus (CA-MRSA). Curr Opin Microbiol 2012; 15:588–595 [View Article] [PubMed]
    [Google Scholar]
  4. Liu C, Bayer A, Cosgrove SE, Daum RS, Fridkin SK et al. Clinical practice guidelines by the infectious diseases society of america for the treatment of methicillin-resistant Staphylococcus aureus infections in adults and children: executive summary. Clin Infect Dis 2011; 52:285–292 [View Article] [PubMed]
    [Google Scholar]
  5. Monaco M, Pimentel de Araujo F, Cruciani M, Coccia EM, Pantosti A. Worldwide epidemiology and antibiotic resistance of Staphylococcus aureus. Curr Top Microbiol Immunol 2017; 409:21–56 [View Article] [PubMed]
    [Google Scholar]
  6. Harrison EM, Ba X, Coll F, Blane B, Restif O et al. Genomic identification of cryptic susceptibility to penicillins and beta-lactamase inhibitors in methicillin-resistant Staphylococcus aureus. Nat Microbiol 2019; 4:1680–1691 [View Article] [PubMed]
    [Google Scholar]
  7. Nguyen M, Brettin T, Long SW, Musser JM, Olsen RJ et al. Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Sci Rep 2018; 8:421 [View Article] [PubMed]
    [Google Scholar]
  8. Hicks AL, Wheeler N, Sánchez-Busó L, Rakeman JL, Harris SR et al. Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data. PLoS Comput Biol 2019; 15:e1007349 [View Article] [PubMed]
    [Google Scholar]
  9. Nguyen M, Long SW, McDermott PF, Olsen RJ, Olson R et al. Using machine learning to predict antimicrobial mics and associated genomic features for nontyphoidal salmonella. J Clin Microbiol 2019; 57: [View Article] [PubMed]
    [Google Scholar]
  10. Hyun JC, Kavvas ES, Monk JM, Palsson BO. Machine learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens. PLoS Comput Biol 2020; 16:e1007608 [View Article] [PubMed]
    [Google Scholar]
  11. Aytan-Aktug D, Clausen PTLC, Bortolaia V, Aarestrup FM, Lund O. Prediction of acquired antimicrobial resistance for multiple bacterial species using neural networks. mSystems 2020; 5: [View Article] [PubMed]
    [Google Scholar]
  12. Recker M, Laabei M, Toleman MS, Reuter S, Saunderson RB et al. Clonal differences in Staphylococcus aureus bacteraemia-associated mortality. Nat Microbiol 2017; 2:1381–1388 [View Article] [PubMed]
    [Google Scholar]
  13. Chen Y, Sun L, Wu D, Wang H, Ji S et al. Using core-genome multilocus sequence typing to monitor the changing epidemiology of methicillin-resistant Staphylococcus aureus in a teaching hospital. Clin Infect Dis 2018; 67:S241–S248 [View Article]
    [Google Scholar]
  14. CLSI Performance Standards for Antimicrobial Susceptibility Testing Wayne, PA: Clinical and Laboratory Standards Institute; 2019
    [Google Scholar]
  15. Bronner S, Murbach V, Peter JD, Levêque D, Elkhaïli H et al. Ex vivo pharmacodynamics of amoxicillin-clavulanate against beta-lactamase-producing Escherichia coli in a yucatan miniature pig model that mimics human pharmacokinetics. Antimicrob Agents Chemother 2002; 46:3782–3789 [View Article] [PubMed]
    [Google Scholar]
  16. Jünemann S, Sedlazeck FJ, Prior K, Albersmeier A, John U et al. Updating benchtop sequencing performance comparison. Nat Biotechnol 2013; 31:294–296 [View Article] [PubMed]
    [Google Scholar]
  17. Vuong C, Yeh AJ, Cheung GY, Otto M. Investigational drugs to treat methicillin-resistant Staphylococcus aureus. Expert Opin Investig Drugs 2016; 25:73–93 [View Article] [PubMed]
    [Google Scholar]
  18. Chen Y, Hong J, Chen Y, Wang H, Yu Y et al. Characterization of a community-acquired methicillin-resistant sequence type 338 Staphylococcus aureus strain containing a staphylococcal cassette chromosome mec type V(T. Int J Infect Dis 2020; 90:181–187 [View Article] [PubMed]
    [Google Scholar]
  19. Foster TJ. Can β-lactam antibiotics be resurrected to combat MRSA?. Trends Microbiol 2019; 27:26–38 [View Article] [PubMed]
    [Google Scholar]
  20. Rubin JE, Ball KR, Chirino-Trejo M. Antimicrobial susceptibility of Staphylococcus aureus and Staphylococcus pseudintermedius isolated from various animals. Can Vet J 2011; 52:153–157 [PubMed]
    [Google Scholar]
  21. Roch M, Lelong E, Panasenko OO, Sierra R, Renzoni A et al. Thermosensitive PBP2a requires extracellular folding factors PrsA and HtrA1 for Staphylococcus aureus MRSA β-lactam resistance. Commun Biol 2019; 2:417 [View Article] [PubMed]
    [Google Scholar]
  22. Panchal VV, Griffiths C, Mosaei H, Bilyk B, Sutton JAF et al. Evolving MRSA: High-level β-lactam resistance in Staphylococcus aureus is associated with RNA Polymerase alterations and fine tuning of gene expression. PLoS Pathog 2020; 16:e1008672 [View Article] [PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000610
Loading
/content/journal/mgen/10.1099/mgen.0.000610
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Supplementary material 2

EXCEL

Most cited this month 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