The severity of human peri-implantitis lesions correlates with the level of submucosal microbial dysbiosis.

AIM: To cross-sectionally analyse the submucosal microbiome of peri-implantitis (PI) lesions at different severity levels. MATERIALS AND METHODS: Microbial signatures of 45 submucosal plaque samples from untreated PI lesions obtained from 30 non-smoking, systemically healthy subjects were assessed by 16s sequencing. Linear mixed models were used to identify taxa with differential abundance by probing depth, after correction for age, gender, and multiple samples per subject. Network analyses were performed to identify groups of taxa with mutual occurrence or exclusion. Subsequently, the effects of peri-implant probing depth on submucosal microbial dysbiosis were calculated using the microbial dysbiosis index. RESULTS: In total, we identified 337 different taxa in the submucosal microbiome of PI. Total abundance of 12 taxa correlated significantly with increasing probing depth; a significant relationship with lower probing depth was found for 16 taxa. Network analysis identified two mutually exclusive complexes associated with shallow pockets and deeper pockets, respectively. Deeper peri-implant pockets were associated with significantly increased dysbiosis. CONCLUSION: Increases in peri-implant pocket depth are associated with substantial changes in the submucosal microbiome and increasing levels of dysbiosis.

Genome-Wide Analysis of Periodontal and Peri-Implant Cells and Tissues.

Omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ or tissue sample, are powerful means of generating comprehensive genome-level data sets on complex diseases. We have systematically assessed the transcriptome, miRNome and methylome of gingival tissues from subjects with different diagnostic entities of periodontal disease, and studied the transcriptome of primary cells ex vivo, or in vitro after infection with periodontal pathogens. Our data further our understanding of the pathobiology of periodontal diseases and indicate that the gingival -omes translate into discernible phenotypic characteristics and possibly support an alternative, "molecular" classification of periodontitis.Here, we outline the laboratory steps required for the processing of periodontal cells and tissues for -omics analyses using current microarrays or next-generation sequencing technology.