Integrated metagenomic data analysis demonstrates that a loss of diversity in oral microbiota is associated with periodontitis.
Integrated metagenomic data analysis demonstrates that a loss of diversity in oral microbiota is associated with periodontitis.
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综合宏基因组数据分析表明，口腔微生物群多样性的丧失与牙周炎有关
DOI：
10.1186/s12864-016-3254-5
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发表时间：
2017-01-25
期刊：
影响因子：
4.4
通讯作者：
Xia LC
中科院分区：
文献类型：
--
作者：
Ai D;Huang R;Wen J;Li C;Zhu J;Xia LC
背景牙周炎是一种影响牙齿支持组织（牙周组织）的炎症性疾病。从多个牙周炎研究的宏基因组样本的综合分析是一个强大的方法来检查微生物多样性和宿主oral cavity.MethodsA内的相互作用，共招募了43名受试者参加了两个以前的研究分析人类龈下菌斑样本的微生物群落，使用鸟枪宏基因组测序。我们整合了来自这两项研究的宏基因组序列数据，包括6名健康对照，14个代表稳定性牙周炎的位点，16个代表进展性牙周炎的位点，以及7个未知状态的牙周位点。我们应用系统发育多样性，差异丰度和网络分析，以及聚类，综合数据集比较不同疾病状态之间的微生物群落概况。在地方尺度上，生境或地点的平均物种多样性是受试者牙周炎状况的单一最强预测因子（P< 0.011）。更具体地说，健康受试者的α多样性最高，而具有稳定位点的受试者的α多样性最低。根据这些结果，我们开发了一种基于α多样性逻辑模型的朴素分类器，能够完美地预测牙周状态未知的七名受试者的疾病状态（未用于训练）。系统发育分析发现了9种标记微生物，这些物种能够区分稳定和进展性牙周炎，准确率达到94.4%。最后，我们发现，减少负相关的物种是一个显着的签名疾病progress.ConclusionsOur结果一致显示口腔微生物多样性的损失和牙周炎的进展之间有很强的关联，这表明宏基因组测序和系统发育分析是预测早期牙周炎，导致潜在的治疗干预。我们的研究结果也支持一个关键的病原体介导的多微生物协同和生态失调（PSD）模型来解释牙周炎的病因。除了P.牙龈炎，我们确定了三个额外的关键物种可能介导牙周炎进展的进展的基础上，类似于那些已知的关键病原体的致病特征。
BackgroundPeriodontitis is an inflammatory disease affecting the tissues supporting teeth (periodontium). Integrative analysis of metagenomic samples from multiple periodontitis studies is a powerful way to examine microbiota diversity and interactions within host oral cavity.MethodsA total of 43 subjects were recruited to participate in two previous studies profiling the microbial community of human subgingival plaque samples using shotgun metagenomic sequencing. We integrated metagenomic sequence data from those two studies,including six healthy controls, 14 sites representative of stable periodontitis, 16 sites representative of progressing periodontitis, and seven periodontal sites of unknown status. We applied phylogenetic diversity, differential abundance, and network analyses, as well as clustering, to the integrated dataset to compare microbiological community profiles among the different disease states.ResultsWe found alpha-diversity, i.e., mean species diversity in sites or habitats at a local scale, to be the single strongest predictor of subjects’ periodontitis status (P< 0.011). More specifically, healthy subjects had the highest alpha-diversity, while subjects with stable sites had the lowest alpha-diversity. From these results, we developed an alpha-diversity logistic model-based naive classifier able to perfectly predict the disease status of the seven subjects with unknown periodontal status (not used in training). Phylogenetic profiling resulted in the discovery of nine marker microbes, and these species are able to differentiate between stable and progressing periodontitis, achieving an accuracy of 94.4%. Finally, we found that the reduction of negatively correlated species is a notable signature of disease progression.ConclusionsOur results consistently show a strong association between the loss of oral microbiota diversity and the progression of periodontitis, suggesting that metagenomics sequencing and phylogenetic profiling are predictive of early periodontitis, leading to potential therapeutic intervention. Our results also support a keystone pathogen-mediated polymicrobial synergy and dysbiosis (PSD) model to explain the etiology of periodontitis. Apart fromP. gingivalis, we identified three additional keystone species potentially mediating the progression of periodontitis progression based on pathogenic characteristics similar to those of known keystone pathogens.