Periodontal inflammation: Integrating genes and dysbiosis

• Published in: Periodontology 2000 2020-02, Vol.82 (1), p.129-142
• Main Authors: Zhang, Shaoping, Yu, Ning, Arce, Roger M.
• Format: Article
• Language: English
• Subjects: Animals; Dentistry; Dysbiosis; Genome-Wide Association Study; genome‐wide association study (GWAS); Humans; Inflammation; Mice; microbiome; Periodontitis; Proteomics; Review; transcriptome
• ISSN: 0906-6713; 1600-0757
• DOI: 10.1111/prd.12267

Biofilm bacteria co‐evolve and reach a symbiosis with the host on the gingival surface. The disruption of the homeostatic relationship between plaque bacteria and the host can initiate and promote periodontal disease progression. Recent advances in sequencing technologies allow researchers to profile disease‐associated microbial communities and quantify microbial metabolic activities and host transcriptional responses. In addition to confirming the findings from previous studies, new putative pathogens and novel genes that have not previously been associated with periodontitis, emerge. For example, multiple studies have reported that Synergistetes bacteria are associated with periodontitis. Genes involved in epithelial barrier defense were downregulated in periodontitis, while excessive expression of interleukin‐17 was associated with a hyperinflammatory response in periodontitis and with a unique microbial community. Bioinformatics‐enabled gene ontology pathway analyses provide a panoramic view of the bacterial and host activities as they shift from periodontal health to disease. Additionally, host innate factors, such as genetic variants identified by either a candidate‐gene approach or genome‐wide association analyses, have an impact on subgingival bacterial colonization. Transgenic mice carrying candidate genetic variants, or with the deletion of candidate genes mimicking the deleterious loss‐of‐function variant effect, provide experimental evidence validating the biologic relevance of the novel markers associated with the microbial phenotype identified through a statistical approach. Further refinement in bioinformatics, data management approaches, or statistical tools, are required to gain insight into host‐microbe interactions by harmonizing the multidimensional “big” data at the genomic, transcriptional, and proteomic levels.