Paired transcriptional analysis of periodontitis and peri-implantitis within same host: A pilot study

Peri-implantitis, a plaque-associated pathological condition, has been on the rise with the increasing prevalence of dental implants. Despite its similarities to periodontitis, peri‑implantitis is difficult to control completely and has high relapse rates. This has sparked interest in exploring the pathogenic differences between the two conditions. This cross-sectional study involved 10 participants to concurrently examine periodontitis and peri‑implantitis within the same patients, thereby minimizing inter-individual variation. Gingival tissue samples were collected from each participant, comprising 10 periodontitis and 10 peri‑implantitis tissues, and RNAs were extracted. Using RNA sequencing and bioinformatics analysis, we investigated complex gene interactions, immune responses, and the role of the extracellular matrix in both conditions. We identified hub genes in each enhanced Protein-Protein Interaction network, providing crucial insights into these diseases’ pathogenesis. Our findings highlighted the potential involvement of activated fibroblasts in the pathogenesis of peri‑implantitis, identifying three marker genes (ACTA2, FAP, and PDGFRβ) overexpressed in peri‑implantitis, thus highlighting their potential as disease-specific biomarkers. Our study uncovered a novel connection between peri‑implantitis and activated fibroblasts, examining specific markers and microbial differences between periodontitis and peri‑implantitis. These insights improve our understanding of peri‑implantitis pathogenesis, encouraging future research for better management and prevention strategies. This study identifies key insights into the pathogenesis of peri‑implantitis compared to periodontitis. These findings promise to advance clinical approaches for better managing and preventing peri‑implantitis, addressing its complexities and high relapse rates effectively. [Display omitted] Using NGS sequencing, we investigated differences in gene expression between individuals, predicted immune cell fractions, and estimated the abundance levels of the microbiome. Our statistical and functional analysis may provide a deep understanding of the functional changes between PT and PI.