Longitudinal host-microbiome dynamics of metatranscription identify hallmarks of progression in periodontitis

In periodontitis, the interplay between the host and microbiome generates a self-perpetuating cycle of inflammation of tooth-supporting tissues, potentially leading to tooth loss. Despite increasing knowledge of the phylogenetic compositional changes of the periodontal microbiome, the current understanding of in situ activities of the oral microbiome and the interactions among community members and with the host is still limited. Prior studies on the subgingival plaque metatranscriptome have been cross-sectional, allowing for only a snapshot of a highly variable microbiome, and do not include the transcriptome profiles from the host, a critical element in the progression of the disease.

To identify the host-microbiome interactions in the subgingival milieu that lead to periodontitis progression, we conducted a longitudinal analysis of the host-microbiome metatranscriptome from clinically stable and progressing sites in 15 participants over 1 year. Our research uncovered a distinct timeline of activities of microbial and host responses linked to disease progression, revealing a significant clinical and metabolic change point (the moment in time when the statistical properties of a time series change) at the 6-month mark of the study, with 1722 genes differentially expressed (DE) in the host and 111,705 in the subgingival microbiome. Genes associated with immune response, especially antigen presentation genes, were highly up-regulated in stable sites before the 6-month change point but not in the progressing sites. Activation of cobalamin, porphyrin, and motility in the microbiome contribute to the progression of the disease. Conversely, inhibition of lipopolysaccharide and glycosphingolipid biosynthesis in stable sites coincided with increased immune response. Correlation delay analysis revealed that the positive feedback loop of activities leading to progression consists of immune regulation and response activation in the host that leads to an increase in potassium ion transport and cobalamin biosynthesis in the microbiome, which in turn induces the immune response. Causality analysis identified two clusters of microbiome genes whose progression can accurately predict the outcomes at specific sites with high confidence (AUC = 0.98095 and 0.97619).

A specific timeline of host-microbiome activities characterizes the progression of the disease. The metabolic activities of the dysbiotic microbiome and the host are responsible for the positive feedback loop of reciprocally reinforced interactions leading to progression and tissue destruction. Video Abstract.