Proteo-genomic identification of drug targets for periodontitis

• Applicant: Dr. Michael Nolde
• Subject Area: Dentistry, Oral Surgery ; Epidemiology and Medical Biometry/Statistics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 541494246

Severe periodontitis is the sixth most common human disease. If it is untreated, it may lead to poor quality of life and tooth loss. It is a chronic inflammatory condition that is initiated by dysbiotic microbiome on the tooth surface and exaggerated by a destructive inflammatory host response. The standard therapy removes the subgingival biofilm to remodel the original eubiotic biofilm and has been shown to effectively decrease clinical inflammation and to slow down disease progression in many patients. However, in about 1 in 4 susceptible patients with persisting risk factors disease progression continues despite subgingival instrumentation and supportive care. Accordingly, adjunctive therapies are being experimentally tested to improve the success rate of periodontal therapy. Such host modulation approaches aim to downregulate the host inflammatory response that propels the disease process. However, drug development and clinical trials continue to be slow and expensive, with high attrition rates for early-stage developmental therapeutics. Genetics-driven drug discovery have substantially reduced failure rates in recent years. Human genetic studies take advantage of naturally occurring genetic variation that mimics the effect of therapeutically perturbing a gene. To fast-track drug development and clinical trials in periodontitis, the proposed study will implement a proteo-genomics analysis pipeline for identifying novel druggable targets. Leveraging proteome-genome-wide studies and genome-wide association studies of periodontitis, we perform Mendelian randomization (MR) of protein quantitative trait loci. We validate positive protein-periodontitis signals from MR using statistical colocalization analysis, extensive sensitivity analyses, and by mining drug target knowledge databases. The study will generate a list of potentially successful targets for further preclinical and clinical development. On a broader scale, it pioneers the use of genomics- driven drug discovery using proteo-genomics and state-of-the-art causal inference methods in dentistry.

• DFG Programme: Research Grants
• Co-Investigator: Professor Dr. Sebastian-Edgar Baumeister