PROJECT SUMMARY/ABSTRACT The overall goal of the present application is to define dysbiosis and identify microbial changes that are predictive of an ecological disruption that ultimately leads to periodontal destruction and to provide mechanistic insights into why this occurs. The major strength of our approach is our existing biobank derived from U01- DE021127 “Biomarkers of Periodontal Disease Progression (BPDP) [R. Teles (PI), F. Teles (Co-I) et al.]. Periodontitis is the most common cause of tooth loss among US adults. Further, it increases the risk for systemic conditions. Proper patient management is critical to minimize tooth loss, allow resource allocation and limit the potential systemic sequelae of these infections. By exploring our biobank, we have a unique opportunity to capitalize on existing longitudinal clinical data as well as subgingival plaque and gingival crevicular fluid samples from periodontitis patients who experienced disease progression over a 12-month period of monitoring, in the absence of treatment. This approach will reduce costs of conducting prospective, longitudinal trials. Results from our study will provide mechanistic insight into periodontal destruction based on human clinical data. Such data does not currently exist. Aim 1 will determine longitudinal dysbiotic microbial changes that culminate in periodontitis progression versus microbial signatures that reflect periodontal stability. Aim 2 will utilize computational approaches and predictive models to gain mechanistic insight into clinically relevant dysbiotic changes. Strengths of this proposal include: 1) readily available, curated and validated samples coupled with periodontal data from hundreds of periodontitis patients; 2) utilization of pairs of samples with and without periodontitis progression, collected from the same subject, 3) Analysis of microbial, immunological and metabolomic signatures from the same periodontal site, 4) a multidisciplinary study team that includes a PI involved in the original study (U01-DE021127); and experts in microbiology, immunology, host microbial interactions and computational biology tools, 5) utilization of state-of-the-art platforms, including whole genomic sequencing, metabolomics and immunoassasys; 6) application of bioinformatics tools, multi-omics integration and predictive models that have been established in other fields to provide mechanistic insight in bacterial and host changes that lead to dysbiosis. This project supports the NIDCR’s commitment to facilitate the translation of science into clinical practice. Our contribution to the field will be the generation of models for mechanistic validation that will affect strategies for the prevention and treatment of periodontitis based on an understanding of dysbiosis in the periodontium that is lacking.