In-vitro polymicrobial oral biofilm model represents clinical microbial profile and disease progression during implant-related infections

Abstract

Aim: Clinically relevant in-vitro biofilm models are essential and valuable tools for mechanistically dissecting the etiopathogenesis of infectious diseases and test new antimicrobial therapies. Thus, the aim of this study was to develop and test a clinically relevant in-vitro oral polymicrobial biofilm model that mimics implant-related infections in terms of microbial profile. Methods and Results: For this purpose, 24-well plate system was used to model oral biofilms, using three different microbial inoculums to grow in-vitro biofilms: (1) human saliva from periodontally healthy patients; (2) saliva as in inoculum 1 + Porphyromonas gingivalis strain; and (3) supra and subgingival biofilm collected from peri-implant sites of patients diagnosed with peri-implantitis. Biofilms were grown to represent the dynamic transition from an aerobic to anaerobic community profile. Subsequently, biofilms were collected after each phase and evaluated for microbiological composition, microbial counts, biofilm biomass, structure, and susceptibility to chlorhexidine (CHX). Results showed higher live cell count (P < .05) for biofilms developed from patients’ biofilm inoculum, but biomass volume, dry weight, and microbiological composition were similar among groups (P > .05). Interestingly, according to the checkerboard DNA–DNA hybridization results, the biofilm developed from stimulated human saliva exhibited a microbial composition more similar to the clinical subgingival biofilm of patients with peri-implantitis, with proportions of the main pathogens closer to those found in the disease. In addition, biofilm developed using saliva as inoculum was shown to be susceptible to CHX with significant reduction in bacteria compared with biofilms without exposure to CHX (P < .05). Conclusion: The findings suggested that the in-vitro polymicrobial biofilm developed from human saliva as inoculum is a suitable model and clinically relevant tool for mimicking the microbial composition of implant-related infections.