Microbial Complexes in Subgingival Plaque: A Bacterial Meta-Taxonomic Study

To leverage technological advances to propose a more accurate representation of subgingival microbiota diversity and dynamics across health and disease, 25 years after the seminal and still authoritative contribution of Socransky and his colleagues.

Subgingival plaque from 135 patients (41 healthy, 47 gingivitis, 47 periodontitis) was analysed by V3-V4 16S rRNA sequencing. Sequences were probabilistically assigned to unambiguous taxon groups (UTGs) using advanced bioinformatics. Beyond univariate analyses across health groups, samples were re-classified into microbiota patterns using unsupervised clustering. Bacterial community ordination was performed via correlation analysis.

In total, 394 fully characterized UTGs were detected with a confidence level of at least 98% in all samples, with an average of 157 in each sample and 162 representing more than 95% of the bacteriome. Hierarchical clustering identified 10 UTG complexes, with 1 particularly associated with health (Complex 6) and 1 with disease (Complex 10). Complex 10 corresponded to 29 taxa, among which the three species from Socransky's red complex and additional 8 Treponema UTGs, as well as species from the orange complex. Complex 6 comprised species from Socransky's green, yellow and purple complexes. While gingivitis appeared as an intermediate state between health and periodontitis, k-clustering revealed five microbiota patterns related to disease progression, and with specific contributions of bacterial complexes, evidencing alternative pathophysiological routes.

These 10 new complexes offer an entry point to understanding plaque ecology, beyond the complexity of microbial dynamics in the periodontal microenvironments revealed by the variability within these groups and the existence of 'boundary taxa' linking them. These microbial complexes co-exist in specific microenvironments, and shifts in their relative abundance mark the transition from a healthy microbiota to a diseased microbiota, with intermediary patterns in between. We propose to use these patterns as indicators for the classification of periodontal diseases along a microbial risk scale to complement the current staging and grading system.