Dental implants are installed in an increasing number of patients. Mucositis and peri-implantitis are common microbial–biofilm-associated diseases affecting the tissues that surround the dental implant and are a major medical and socioeconomic burden. By metagenomic sequencing of the plaque microbiome in different peri-implant health and disease conditions (113 samples from 72 individuals), we found microbial signatures for peri-implantitis and mucositis and defined the peri-implantitis-related complex (PiRC) composed by the 7 most discriminative bacteria. The peri-implantitis microbiome is site specific as contralateral healthy sites resembled more the microbiome of healthy implants, while mucositis was specifically enriched for Fusobacterium nucleatum acting as a keystone colonizer. Microbiome-based machine learning showed high diagnostic and prognostic power for peri-implant diseases and strain-level profiling identified a previously uncharacterized subspecies of F. nucleatum to be particularly associated with disease. Altogether, we associated the plaque microbiome with peri-implant diseases and identified microbial signatures of disease severity.

越来越多的患者安装了牙种植体。粘膜炎和种植体周围炎是常见的微生物-生物膜相关疾病，会影响种植牙周围的组织，是主要的医学和社会经济负担。通过在不同的种植体周围健康和疾病状况下对菌斑微生物组进行宏基因组测序（来自72个个体的113个样本），我们发现了种植体周围炎和粘膜炎的微生物特征，并定义了由7种成分组成的种植体周围炎相关复合物（PiRC）最有区别的细菌。种植体周围微生物群是特定部位的，因为对侧健康部位更像是健康植入物的微生物群，而粘膜炎则特别富集了核梭状芽胞杆菌。充当基石殖民者。基于微生物组的机器学习显示出对植入物周围疾病的高诊断和预后能力，并且菌株水平的分析鉴定出先前未被鉴定的F. nucleatum亚种与疾病特别相关。总之，我们将斑块微生物组与种植体周围疾病相关联，并确定了疾病严重程度的微生物特征。