Integrative microbiome- and metatranscriptome-based analyses reveal diagnostic biomarkers for peri-implantitis

Abstract

Peri-implantitis is a severe biofilm-associated infection of the tissues around dental implants that increases the risk of implant failure. The prognosis of peri-implantitis treatment is compromised by the resistance of well-organized and mature bacterial biofilms. Thus, early diagnosis of a pathogenic biofilm would enable treatment at a prognostically favourable stage. However, relatively little is known about how the microbial constitution of the biofilm changes during disease development. The aim of this cross-sectional study was therefore to identify peri-implant taxonomic and functional biomarkers that reliably indicate peri-implantitis using paired data from full length 16S rRNA gene amplicon sequencing (full-16S) and metatranscriptomics (RNAseq). Disease signatures were identified using 24 healthy and 24 peri-implantitis-associated biofilm samples from 32 patients. The taxonomic measurements were validated with 68 additional full-16S samples from another 40 patients. Both full-16S and RNAseq revealed significant differences between healthy and peri-implantitis samples, with respect to both the microbiome and functional profiles. A shift from aerotolerant Gram-positive bacteria to anaerobic Gram-negative bacteria was observed in peri-implantitis. Distinct metabolic pathways were expressed in healthy and peri-implantitis samples. Our results, based on paired taxonomic and functional profiles, provide for the first time important insights into the complex peri-implant biofilm ecology related to amino acid metabolism. Integrating taxonomic and functional information improved the predictive ability (AUC = 0.85) of the machine learning models and revealed diagnostic biomarkers with large effect sizes (Cohens d > 0.8). Primary biomarkers included health-associated Streptococcus, Rothia species and enzymes associated with peri-implantitis (urocanate hydratase, tripeptide aminopeptidase, NADH:ubiquinone reductase, phosphoenolpyruvate carboxykinase and polyribonucleotide nucleotidyltransferase - mostly expressed by Fusobacteriia and Bacteroidia). Thus, biofilm profiling at these two molecular levels reveals highly predictive disease biomarkers and provide the basis for developing early diagnostics and individualized therapy approaches for peri-implant diseases.