Metataxonomic sequencing reveals compositional shifts within the denture-associated microbiome in pneumonia

Abstract

Background Bacterial pneumonia affects a disproportionate number of the elderly in the UK, with substantial morbidity and mortality. Mounting evidence implicates removable dentures as a potential nidus for respiratory pathogens to form a reservoir which could seed colonisation and infection of respiratory tissues in susceptible individuals. However, research evaluating the denture-associated microbiome in patients with an active diagnosis of pneumonia is lacking. The aim of this study was to characterise denture-associated oral bacterial communities by metataxonomic sequencing of 16S rRNA genes. The prevalence of antimicrobial resistance among two representative pathogenic species Staphylococcus aureus and Pseudomonas aeruginosa was also assessed. Finally, the role of salivary cytokines as diagnostic biomarkers was explored. Results There were significant shifts observed in species composition, diversity and richness in the denture-associated microbiome of pneumonia patients. Importantly, the relative abundance of putative respiratory pathogens in the denture-associated microbiota of pneumonia patients was significantly increased compared with respiratorily healthy care home residents. The magnitude of this increase was approximately three-fold in denture-associated bacterial communities compared with other oral sites examined. Antimicrobial resistance was equivocal between microbes isolated from both participant cohorts, highlighting the potential for oral biofilms to protect microbes from systemic antimicrobial therapy. While salivary cytokine profiles did not correlate with pneumonia status, the concentration of IL-6 and IL-8 positively correlated with the relative abundance of putative respiratory pathogens on denture surfaces. Conclusions This is the first study to directly examine compositional shifts in the denture-associated oral microbiome in respiratory infection, providing a basis for disentangling potential causal relationships.