ABSTRACT Dental implants restore chewing function to at least 100 million individuals and the market continues to grow 15% annually. However, over one million implants fail every year due to peri-implantitis, a disease that is triggered by disruption of the intricate balance between the microbial ecosystem that colonizes the peri-implant sulcus and host immunity. Our ability to prevent and treat peri-implantitis is hampered by a paucity of information on inter-microbial and host-microbiome interactions in this ecosystem. Therefore, there is an urgent need for biological investigations that will improve our understanding of how this ecosystem is colonized and when, why, and how this homeostasis breaks down. Based on our discovery of a robust viral presence in this ecosystem, we hypothesize that that interactions between implant surface, bacterial and viral communities and the host barrier are critical for homeostasis, and that an abandonment of these transactions moves the ecosystem towards chronic inflammatory programming. We propose to test this hypothesis by combining two novel clinical study designs with integrated ‘-omics’ approaches, image analysis and computational bioinformatics and validating them using a unique in vitro model of the titanium-tissue-microbiome interface, the IMiTATE that we have developed. This approach will bridge the gap between clinical outcome-based studies and in vitro investigations or animal models, both of which have limited capabilities to replicate human behavior and physiology. First, we will create a functional atlas of the peri-implant sulcus from its moment of inception to the establishment of a climax community using dual transcriptome-metatranscriptome sequencing, viral metagenomics and transcript-guided imaging. Second, we will pinpoint timelines for breakdown of mutualism by combining a multi-arm, longitudinal clinical study with a network analysis algorithm. Finally, we will validate these discoveries with the IMiTATE. The proposed studies will bring us closer to understanding the etiopathogenesis of peri-implant diseases, provide biologically-validated timelines for assessing risk, and develop indicators or predictors of disease.