The currently recognized principal forms of periodontitis, chronic (CP) and aggressive (AgP), lack an unequivocal, pathobiology-based foundation. We explored whether gingival tissue transcriptomes can serve as the basis for an alternative classification of periodontitis.
Method:
We used whole-genome gene expression data from 241 gingival tissue biopsies obtained from sites with periodontal pathology, in 120 systemically healthy non-smokers with periodontitis, with available data on clinical periodontal status, subgingival microbial profiles and serum IgG antibodies to periodontal microbiota.
Result:
Adjusted model-based clustering of transcriptomic data using finite mixtures generated two distinct clusters of patients that did not align with the current classification of CP and AgP. Distinct expression profiles primarily related to cell proliferation in Cluster #1 and to lymphocyte activation and unfolded protein responses in Cluster #2. Patients in the two clusters did not differ with respect to age, but presented with distinct phenotypes (statistically significantly different whole-mouth clinical measures of extent and severity, subgingival microbial burden by several species, and selected serum antibody responses). Patients in Cluster #2 showed more extensive/severe disease, and were more often male.
Conclusion:
Our work suggests that distinct gene expression signatures in pathological gingival tissues translate into phenotypic differences and can provide a basis for a novel classification.