Oral diseases are a global medical and socioeconomic burden. Diseases affecting dental implants (peri-implant mucositis and peri-implantitis) are particularly on the rise, and despite the plaque microbiome plays a crucial role in their etiology, the potential of profiling the microbial communities around implants for supporting implant care has not been exploited yet for clinical applications. The availability of metagenomic tools able to profile human microbiomes at unprecedented resolution developed within the ERC-StG MetaPG project and a pilot study we recently performed on the peri-implant microbiome are now enabling such tantalizing opportunity. In this PoC proposal, we aim to develop a product for dental clinics to provide clinically actionable indications for the prevention and treatment of peri-implant diseases by a combination of high-resolution strain-level metagenomic profiling and predictive machine learning methods. Such approach would overcome available clinical tests that are based on low-resolution molecular techniques that are inadequate at completely characterizing the plaque microbiome and do not offer a reliable support in the daily clinical practice to the dentists. By profiling and analysing 2,000 peri-implant microbiome samples collected from 50 dental clinics in the R&D phase, we aim to develop and validate a market-ready kit to (1) define a reproducible strain-level microbial signature for peri-implant diseases, (2) identify and characterize clinically relevant strain-level features of Fusobacterium nucleatum in mucositis, and (3) successfully support the clinical dental practice. Because inter-patient strain-level diversity of the plaque microbiome and of F. nucleatum in particular explains the variability in peri-implant health, our commercial proposition will provide the first microbiome-based and personalized approach for dental clinics.
Field of science
- /medical and health sciences/clinical medicine/odontology/dental implantology
- /natural sciences/computer and information sciences/artificial intelligence/machine learning
Call for proposal
See other projects for this call
Funding SchemeERC-POC-LS - ERC Proof of Concept Lump Sum Pilot