Developing diagnostic tools for canine periodontitis: combining molecular techniques and machine learning models

Dental plaque microbes play a key role in the development of periodontal disease. Numerous high-throughput sequencing studies have generated understanding of the bacterial species associated with both canine periodontal health and disease. Opportunities therefore exist to utilise these bacterial biomarkers to improve disease diagnosis in conscious-based veterinary oral health checks. Here, we demonstrate that molecular techniques, specifically quantitative polymerase chain reaction (qPCR) can be utilised for the detection of microbial biomarkers associated with canine periodontal health and disease. Over 40 qPCR assays targeting single microbial species associated with canine periodontal health, gingivitis and early periodontitis were developed and validated. These were used to quantify levels of the respective taxa in canine subgingival plaque samples collected across periodontal health (PD0), gingivitis (PD1) and early periodontitis (PD2). When qPCR outputs were compared to the corresponding high-throughput sequencing data there were strong correlations, including a periodontal health associated taxa, Capnocytophaga sp. COT-339 (rs =0.805), and two periodontal disease associated taxa, Peptostreptococcaceae XI [G-4] sp. COT-019 (rs=0.902) and Clostridiales sp. COT-028 (rs=0.802). The best performing models, from five machine learning approaches applied to the qPCR data for these taxa, estimated 85.7