牙菌斑微生物组预测儿童龋病发生的机制研究

One key goal of human microbiome projects worldwide is to classify and predict host states based on human microbiota, yet few studies have yet reported successful microbiota-based prediction of future disease outcome, especially for disease onset. Early childhood caries (ECC) is a chronic, polymicrobial infection that is usually irreversible, thus preventive intervention of ECC is of particular importance. However current prevention strategies have been very limited. Recently we showed that specific taxa in plaque microbiota (such as certain Prevotella spp.) are able to define and distinguish a sub-healthy stage that we termed as “Pre-ECC-onset Phase” (PEOP) and thus can be employed to predict ECC onset with 81% accuracy in children. In order to probe the mechanistic foundation of such predicting capability of plaque microbiome, firstly, we will longitudinally sample and multi-dimensionally dissect the plaque microbiota, with particularly high temporal resolution during the PEOP phase, in a cohort of 6-year old child cohort. Those specific “early-alarm microbes” will be identified and validated, based on correlative analysis between the cohort-wide, time-series metagenomics datasets and disease outcomes of individual hosts. Secondly, for the “early-alarm microbes” identified above, a mechanistic model that links their 16S sequences, genomes and metabolic activities to those on the microbiota level will be established, via DNA-probe-based Fluorescence In Situ Hybridization (FISH) and the single-cell phenotyping-genotyping technologies consisting of single-cell Raman imaging, Raman-activated cell sorting and the coupled single-cell sequencing. Finally, we will extend identification of the ECC-predicting factors during PEOP to the deeper level of specific genes and pathways, by demonstrating the multi-taxon Insertion Sequencing technique (INSeq) of selected Prevotella species/strains in an already established rat cariogenesis model. In summary, the mechanistic foundation of microbiota-based ECC prediction will be interrogated at single-cell resolution under the context of microbiota, by first defining and then focusing on a specific temporal phase of PEOP. These efforts are expected to provide new ways of developing new tools, not just for caries-etiology research but also for preventive intervention of caries in children.

通过菌群进行疾病的生态预警乃至干预是人体微生物组计划的核心目标之一，但成功范例和机制认知尚非常有限。早期儿童龋病是菌群介导、通常不可逆的慢性感染性疾病，我们前期研究发现牙菌斑中特定物种（如若干Prevotella）能定义“龋发生前期”这一亚健康阶段，从而预测龋病发生。为探讨其缘由与机制，首先，将针对典型儿童队列，聚焦“龋发生前期”，通过高频率时间序列元基因组和龋发生的关联分析，精确甄别“龋发生预警物种”；其次，通过序列特异DNA荧光探针和单细胞分析技术（单细胞拉曼成像、分选和与之耦合的单细胞测序）的联用，建立“预警物种”之16S、基因组、个体功能及与菌群功能间的关联模型；最后，以无菌大鼠龋模型和Prevotella为模式，通过多类插入序列突变库筛选技术，在基因水平深入理解其预警原理。本研究聚焦于菌群分析所定义的亚健康阶段，多尺度探讨菌群原位预警作用机理，为龋病病因和预防贡献新思路和新工具。

早期儿童龋病是菌群介导、通常不可逆的慢性感染性疾病， 我们首先考察了不同DNA提取方法，不同高变区的引物以及不同测序平台在我们口腔菌群微生物组研究中的影响，奠定了我们进一步研究牙菌斑预测儿童龋病微生物组研究的基础（Teng F, et al, Yang F＊. Sci Rep. 2018)；进一步，我们与通过横向纵向复合的研究追踪设计，监测儿童早期龋病发病情况与口腔菌群的关系，重点考察不同人群之间细菌谱系构成差异，构建基于唾液菌属图谱建立的龋病风险评估模型区分健康和龋病者的准确率可高达70%以上 (Fang Yang, et al. Springerplus. 2016; 孙同正等， 杨芳*，华西口腔医学杂志，2018)；最后，在应用单细胞技术完成对口腔菌物质代谢研究方面，课题组已经建立了活体单细胞的不同代谢水平表型测定技术，通过氘水的加入可以检测出代谢特定底物的细菌细胞，在不需生物标识物、不需进行外加标记的情况下，定量表征与测定单个活体微生物细胞代谢水平差异。针对不同疑似参与龋病的生物因子以及代谢特定底物的细菌细胞，从而探索不基于牙医主观判断，基于口腔菌群检测而诊断甚至预测儿童龋病的新方法和技术路线。

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