de Jesus Vivianne Cruz, Khan Mohd Wasif, Mittermuller Betty-Anne, Duan Kangmin, Hu Pingzhao, Schroth Robert J, Chelikani Prashen
Manitoba Chemosensory Biology Research Group, Department of Oral Biology, University of Manitoba, Winnipeg, MB, Canada.
Children's Hospital Research Institute of Manitoba (CHRIM), Winnipeg, MB, Canada.
Front Microbiol. 2021 Jun 25;12:683685. doi: 10.3389/fmicb.2021.683685. eCollection 2021.
The human oral cavity harbors one of the most diverse microbial communities with different oral microenvironments allowing the colonization of unique microbial species. This study aimed to determine which of two commonly used sampling sites (dental plaque vs. oral swab) would provide a better prediction model for caries-free vs. severe early childhood caries (S-ECC) using next generation sequencing and machine learning (ML). In this cross-sectional study, a total of 80 children (40 S-ECC and 40 caries-free) < 72 months of age were recruited. Supragingival plaque and oral swab samples were used for the amplicon sequencing of the V4-16S rRNA and ITS1 rRNA genes. The results showed significant differences in alpha and beta diversity between dental plaque and oral swab bacterial and fungal microbiomes. Differential abundance analyses showed that, among others, the cariogenic species was enriched in the dental plaque, compared to oral swabs, of children with S-ECC. The fungal species and were more abundant in the oral swab samples of children with S-ECC compared to caries-free controls. They were also among the top 20 most important features for the classification of S-ECC vs. caries-free in oral swabs and for the classification of dental plaque vs. oral swab in the S-ECC group. ML approaches revealed the possibility of classifying samples according to both caries status and sampling sites. The tested site of sample collection did not change the predictability of the disease. However, the species considered to be important for the classification of disease in each sampling site were slightly different. Being able to determine the origin of the samples could be very useful during the design of oral microbiome studies. This study provides important insights into the differences between the dental plaque and oral swab bacteriome and mycobiome of children with S-ECC and those caries-free.
人类口腔中存在着最多样化的微生物群落之一，不同的口腔微环境使得独特的微生物物种得以定植。本研究旨在确定两种常用的采样部位（牙菌斑与口腔拭子）中哪一种能利用下一代测序和机器学习（ML）为无龋与重度幼儿早期龋（S-ECC）提供更好的预测模型。在这项横断面研究中，共招募了80名年龄小于72个月的儿童（40名S-ECC患儿和40名无龋儿童）。龈上菌斑和口腔拭子样本用于V4-16S rRNA和ITS1 rRNA基因的扩增子测序。结果显示，牙菌斑和口腔拭子的细菌和真菌微生物群在α和β多样性上存在显著差异。差异丰度分析表明，与口腔拭子相比，S-ECC患儿的牙菌斑中致龋菌等物种富集。与无龋对照组相比，S-ECC患儿的口腔拭子样本中真菌物种和更为丰富。它们也是口腔拭子中S-ECC与无龋分类以及S-ECC组中牙菌斑与口腔拭子分类的前20个最重要特征之一。ML方法揭示了根据龋病状态和采样部位对样本进行分类的可能性。测试的样本采集部位并未改变疾病的可预测性。然而，在每个采样部位被认为对疾病分类重要的物种略有不同。在口腔微生物组研究设计过程中，能够确定样本来源可能非常有用。本研究为S-ECC患儿和无龋患儿的牙菌斑与口腔拭子细菌群落和真菌群落之间的差异提供了重要见解。
