Precision Periodontics at Texas A&M University

Abstract

The treatment of chronic diseases, like periodontal disease, is sophisticated and sometimes costly to both patients and clinicians. As a result, an emerging trend is to provide precise and customized treatment to each patient to optimize the treatment outcomes. Current clinical periodontal decision making seeks to address a variety of disease criteria such as clinical presentation, diagnosis, radiographic appearance, clinician preference, and patient-related factors with individual patient treatment approaches, including non-surgical scaling and root planning, guided tissue regeneration, biological regenerative strategies, extracting diseased teeth, and more. Unfortunately, at present, there is a lack of literature defining biomarkers predicting clinical periodontal treatment outcomes. Gingival tissue was collected from patients with moderate or severe periodontal disease during one of three procedures: (i) scaling and root planing, (ii) open flap debridement with or without osseous recontouring, or (iii) guided tissue regeneration. GCF was collected at baseline (prior to procedure) and at approximately 1 month and 9 months. Gingival index and Plaque index were recorded at 9 months. Treatment outcome was recorded at 9 months as response (probing depths < 5 mm) vs. no response (any probing depths > 5 mm). On a single cell level, extracellular matrix pathways were substantially downregulated in both epithelial and stromal cell populations, and the level of downregulation was proportionate to the severity of the disease. Gene ontology (GO) enrichment comparisons demonstrated an increase in B-cell and T-cell activity with increased periodontal disease severity and elevated levels of complement activation and inflammatory response GOs in B-cells and T-cells. Desmoplakin and junction plakoglobin were downregulated in responders versus non-responders. Together, these data present the first comprehensive analysis of gene expression ontology-level changes in response to periodontal disease states and treatment and presents the identification of possible biomarkers for predicting periodontal treatment outcomes.