Dynamic changes in the subgingival microbiome and their potential for diagnosis and prognosis of periodontitis.
Dynamic changes in the subgingival microbiome and their potential for diagnosis and prognosis of periodontitis.
复制标题
作者：
Shi B;Chang M;Martin J;Mitreva M;Lux R;Klokkevold P;Sodergren E;Weinstock GM;Haake SK;Li H
人体微生物组影响并反映宿主的健康或疾病状态。牙周炎是一种影响大约一半美国成年人的疾病，它与单个牙齿部位牙龈下微生物群的改变有关。虽然可以治疗，但这种疾病可能会复发，并可能在没有症状的情况下发展。如果没有预后标志物，则需要随访检查以评估复发和疾病进展，并确定是否需要额外治疗。为了更好地识别和预测疾病进展，我们的目的是确定龈下微生物组是否可以作为诊断和预后指标。使用宏基因组霰弹枪测序，我们描述了牙周炎患者在同一牙齿部位治疗前后龈下微生物组的动态变化。在分类学组成水平上，牙周炎相关微生物从患病状态下的高度相关显著转变为治疗后的低相关，表明致病微生物之间的协调相互作用对疾病的发病至关重要。在功能水平上，我们确定了两种状态中相对丰富的显著改变的疾病相关途径。此外，使用龈下微生物组谱，我们能够以81.1%的准确率将样本分类到临床状态。对采样部位的随访临床检查支持微生物组谱对疾病进展的预测能力。我们的研究揭示了牙龈下微生物组在牙周炎中的动态变化，并提出了监测牙龈下微生物组作为疾病诊断和预后指标的潜在临床应用。牙周炎是一种常见的口腔疾病。虽然可以治疗，但这种疾病可能会在没有明显症状的情况下复发。目前的临床检查参数对疾病诊断有用，但不能充分预测治疗后单个牙齿部位的结果。牙龈下微生物群与牙周炎之间的联系先前已被确定；然而，微生物组是否可以作为诊断和预后指标仍有待研究。在这项研究中，我们首次使用大规模宏基因组分析表征了治疗前后单个牙齿部位的龈下微生物组。我们的纵向研究揭示了微生物群在分类组成、龈下微生物共发生和功能组成方面的变化。利用微生物组谱，我们能够以高精度对龈下菌斑样本的临床状态进行分类。对采样部位的后续临床检查表明，牙龈下微生物组概况显示出诊断和预后工具发展的希望。
The human microbiome influences and reflects the health or disease state of the host. Periodontitis, a disease affecting about half of American adults, is associated with alterations in the subgingival microbiome of individual tooth sites. Although it can be treated, the disease can reoccur and may progress without symptoms. Without prognostic markers, follow-up examinations are required to assess reoccurrence and disease progression and to determine the need for additional treatments. To better identify and predict the disease progression, we aim to determine whether the subgingival microbiome can serve as a diagnosis and prognosis indicator. Using metagenomic shotgun sequencing, we characterized the dynamic changes in the subgingival microbiome in periodontitis patients before and after treatment at the same tooth sites. At the taxonomic composition level, the periodontitis-associated microorganisms were significantly shifted from highly correlated in the diseased state to poorly correlated after treatment, suggesting that coordinated interactions among the pathogenic microorganisms are essential to disease pathogenesis. At the functional level, we identified disease-associated pathways that were significantly altered in relative abundance in the two states. Furthermore, using the subgingival microbiome profile, we were able to classify the samples to their clinical states with an accuracy of 81.1%. Follow-up clinical examination of the sampled sites supported the predictive power of the microbiome profile on disease progression. Our study revealed the dynamic changes in the subgingival microbiome contributing to periodontitis and suggested potential clinical applications of monitoring the subgingival microbiome as an indicator in disease diagnosis and prognosis. Periodontitis is a common oral disease. Although it can be treated, the disease may reoccur without obvious symptoms. Current clinical examination parameters are useful in disease diagnosis but cannot adequately predict the outcome of individual tooth sites after treatment. A link between the subgingival microbiota and periodontitis was identified previously; however, it remains to be investigated whether the microbiome can serve as a diagnostic and prognostic indicator. In this study, for the first time, we characterized the subgingival microbiome of individual tooth sites before and after treatment using a large-scale metagenomic analysis. Our longitudinal study revealed changes in the microbiota in taxonomic composition, cooccurrence of subgingival microorganisms, and functional composition. Using the microbiome profiles, we were able to classify the clinical states of subgingival plaque samples with a high accuracy. Follow-up clinical examination of sampled sites indicates that the subgingival microbiome profile shows promise for the development of diagnostic and prognostic tools.