Beyond the Oral Microbiome: Developing a Bioinformatic Toolkit for Investigating the Oral Ecosystem and Disease Development

Aims: The oral microbiome (OM) is an extremely diverse environment, and our knowledge of its composition is based on the hundreds of oral- and amplicon sequencing-related studies conducted over the last decade. However, shotgun metagenomic sequencing (SMS) reads whole genomes, which can provide a more representative snapshot of the OM. This project aims to utilise existing databases and generate a new clinical tonsillitis and health-based dataset to develop a reproducible bioinformatic toolkit to investigate the roles of OM members and their metagenomic profiles in health and disease. Methods : A bank of OM/metagenome and caries, periodontitis, and endodontitis related datasets has been curated. A comparative dataset through a meta-analysis of these preexisting studies provides a basis for a subsequent bioinformatic pipeline developed to study the oral and tonsil SMS data of tonsillitis patients and healthy participants. Metagenomic sequences will undergo taxonomic profiling, gene annotation, functional prediction, and comparative analyses. Unique tonsillar isolates (UI) from tonsillitis and control subjects will be identified by 16S-23S rRNA sequencing. Participants were surveyed on their demographics, regular oral care practices, medical histories, and lifestyle choices. Machine learning algorithms will be trained with the microbiome and metagenome data to predict patterns and correlations within all datasets. Results: The clinical study recruited 50 tonsillitis patients and 31 healthy controls for oral rinse and tonsil swab microbiome and metagenomic investigation. Streptococcus species were the most prevalent UIs identified across both the tonsillitis and health cohorts. Streptococcus pyogenes has only been isolated from the disease group (so far), and Neisseria species was most prevalent amongst health UIs. Whole genome sequencing of a UI subset matched the taxonomic profiles found by amplicon sequencing, validating the amplicon-based protocol. Conclusions: Upon analyses of the pre-existing and new microbiome and metagenome data, associations between taxonomical and genetic factors on oral disease, non-oral conditions, and health will be better understood. The curated datasets and pipelines will be applied to an online educational user-interface, where researchers can upload their own sequences for analysis and meta-analysis.