Meuric Vincent, Le Gall-David Sandrine, Boyer Emile, Acuña-Amador Luis, Martin Bénédicte, Fong Shao Bing, Barloy-Hubler Frederique, Bonnaure-Mallet Martine
CHU Rennes, Pôle Odontologie, Rennes, France
Université de Rennes 1, EA 1254, INSERM 1241, Equipe de Microbiologie, Rennes, France.
Appl Environ Microbiol. 2017 Jun 30;83(14). doi: 10.1128/AEM.00462-17. Print 2017 Jul 15.
Periodontitis is driven by disproportionate host inflammatory immune responses induced by an imbalance in the composition of oral bacteria; this instigates microbial dysbiosis, along with failed resolution of the chronic destructive inflammation. The objectives of this study were to identify microbial signatures for health and chronic periodontitis at the genus level and to propose a model of dysbiosis, including the calculation of bacterial ratios. Published sequencing data obtained from several different studies (196 subgingival samples from patients with chronic periodontitis and 422 subgingival samples from healthy subjects) were pooled and subjected to a new microbiota analysis using the same Visualization and Analysis of Microbial Population Structures (VAMPS) pipeline, to identify microbiota specific to health and disease. Microbiota were visualized using CoNet and Cytoscape. Dysbiosis ratios, defined as the percentage of genera associated with disease relative to the percentage of genera associated with health, were calculated to distinguish disease from health. Correlations between the proposed dysbiosis ratio and the periodontal pocket depth were tested with a different set of data obtained from a recent study, to confirm the relevance of the ratio as a potential indicator of dysbiosis. Beta diversity showed significant clustering of periodontitis-associated microbiota, at the genus level, according to the clinical status and independent of the methods used. Specific genera (, , , , and ) were highly prevalent (>95%) in health, while other genera (, , , and ) were associated with chronic periodontitis. The calculation of dysbiosis ratios based on the relative abundance of the genera found in health versus periodontitis was tested. Nonperiodontitis samples were significantly identifiable by low ratios, compared to chronic periodontitis samples. When applied to a subgingival sample set with well-defined clinical data, the method showed a strong correlation between the dysbiosis ratio, as well as a simplified ratio (, , and to and ), and pocket depth. Microbial analysis of chronic periodontitis can be correlated with the pocket depth through specific signatures for microbial dysbiosis. Defining microbiota typical of oral health or chronic periodontitis is difficult. The evaluation of periodontal disease is currently based on probing of the periodontal pocket. However, the status of pockets "on the mend" or sulci at risk of periodontitis cannot be addressed solely through pocket depth measurements or current microbiological tests available for practitioners. Thus, a more specific microbiological measure of dysbiosis could help in future diagnoses of periodontitis. In this work, data from different studies were pooled, to improve the accuracy of the results. However, analysis of multiple species from different studies intensified the bacterial network and complicated the search for reproducible microbial signatures. Despite the use of different methods in each study, investigation of the microbiota at the genus level showed that some genera were prevalent (up to 95% of the samples) in health or disease, allowing the calculation of bacterial ratios (i.e., dysbiosis ratios). The correlation between the proposed ratios and the periodontal pocket depth was tested, which confirmed the link between dysbiosis ratios and the severity of the disease. The results of this work are promising, but longitudinal studies will be required to improve the ratios and to define the microbial signatures of the disease, which will allow monitoring of periodontal pocket recovery and, conceivably, determination of the potential risk of periodontitis among healthy patients.
牙周炎是由口腔细菌组成失衡引发的宿主炎症免疫反应失调所致;这会引发微生物群落失调,同时慢性破坏性炎症无法得到缓解。本研究的目的是在属水平上确定健康和慢性牙周炎的微生物特征,并提出一种失调模型,包括计算细菌比例。将从几项不同研究中获得的已发表测序数据(196份慢性牙周炎患者的龈下样本和422份健康受试者的龈下样本)汇总,并使用相同的微生物种群结构可视化与分析(VAMPS)流程进行新的微生物群分析,以识别健康和疾病特有的微生物群。使用CoNet和Cytoscape对微生物群进行可视化。计算失调比例,即与疾病相关的属的百分比相对于与健康相关的属的百分比,以区分疾病与健康。使用从最近一项研究中获得的另一组数据测试所提出的失调比例与牙周袋深度之间的相关性,以确认该比例作为失调潜在指标的相关性。β多样性显示,在属水平上,根据临床状态且独立于所使用的方法,牙周炎相关微生物群存在显著聚类。特定的属(、、、、和)在健康状态下高度普遍(>95%),而其他属(、、、和)与慢性牙周炎相关。测试了基于健康与牙周炎中发现的属的相对丰度计算失调比例。与慢性牙周炎样本相比,非牙周炎样本通过低比例可显著识别。当应用于具有明确临床数据的龈下样本集时,该方法显示失调比例以及简化比例(、、和至和)与袋深度之间存在强相关性。慢性牙周炎的微生物分析可通过微生物失调的特定特征与袋深度相关联。定义口腔健康或慢性牙周炎典型的微生物群很困难。目前牙周疾病的评估基于对牙周袋的探测。然而,“正在愈合”的袋或有牙周炎风险的龈沟的状态不能仅通过袋深度测量或从业者可用的当前微生物学测试来解决。因此,一种更具体的微生物失调测量方法可能有助于未来牙周炎的诊断。在这项工作中,汇总了不同研究的数据,以提高结果的准确性。然而,对来自不同研究的多个物种的分析强化了细菌网络,并使寻找可重复的微生物特征变得复杂。尽管每项研究使用了不同的方法,但在属水平上对微生物群的研究表明,一些属在健康或疾病中普遍存在(高达95%的样本),从而能够计算细菌比例(即失调比例)。测试了所提出的比例与牙周袋深度之间的相关性,这证实了失调比例与疾病严重程度之间的联系。这项工作的结果很有前景,但需要进行纵向研究以改进比例并定义疾病的微生物特征,这将允许监测牙周袋的恢复情况,并可以想象,确定健康患者中牙周炎的潜在风险。