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A Novel Method to Assess Antimicrobial Susceptibility in Commensal Oropharyngeal Neisseria -A Pilot Study.

Jolein Gyonne Elise LaumenAbdellati SaïdChristophe Van DijckDelphine MartinyDe Baetselier IrithManoharan-Basil Santhini SheebaDorien Van den BosscheChris Richard Kenyon
Published in: Antibiotics (Basel, Switzerland) (2022)
Commensal Neisseria provide a reservoir of resistance genes that can be transferred to the pathogens Neisseria gonorrhoeae and N. meningitidis in the human oropharynx. Surveillance programs are thus needed to monitor resistance in oropharyngeal commensal Neisseria, but currently the isolation and antimicrobial susceptibility testing of these commensals is laborious, complex and expensive. In addition, the posterior oropharyngeal/tonsillar swab, which is commonly used to sample oropharyngeal Neisseria , is poorly tolerated by many individuals. We evaluated an alternative non-invasive method to isolate oropharyngeal commensal Neisseria and to detect decreased susceptibility to azithromycin using selective media (LBVT.SNR) with and without azithromycin (2 µg/mL). In this pilot study, we compared paired posterior oropharyngeal/tonsillar swabs and oral rinse-and-gargle samples from 10 participants and demonstrated that a similar Neisseria species diversity and number of colonies were isolated from both sample types. Moreover, the proportion of Neisseria colonies that had a decreased susceptibility to azithromycin was similar in the rinse samples compared to the swabs. This pilot study has produced encouraging data that a simple protocol of oral rinse-and-gargle and culture on plates selective for commensal Neisseria with and without a target antimicrobial can be used as a surveillance tool to monitor antimicrobial susceptibility in commensal oropharyngeal Neisseria. Larger studies are required to validate these findings.
Keyphrases
  • public health
  • randomized controlled trial
  • endothelial cells
  • gene expression
  • machine learning
  • genome wide
  • dna methylation
  • transcription factor
  • electronic health record
  • deep learning