In vitro reproducible model of subgingival microbiome for high throughput screening for microbiome modulators

项目总结 牙周炎仍然是一个全球性的健康问题：再加上无牙畸形和严重的牙齿脱落，它 构成了全球第六大最流行的长期疾病，占1100万年的患者 残疾和生产力损失1170亿美元。目前，牙周炎被视为一种免疫性疾病。 牙周组织破坏，由低丰度病原体在不平衡的牙龈下策划 微生物群落--所谓的微生物生态失调假说。这就需要有选择地瞄准 病原体或/和刺激共生体生长以逆转龈下微生物失调(或促进 常生菌)是预防和辅助治疗牙周炎的一种很有前途的策略。是这样的 微生物群的调节可以通过使用益生菌和益生菌等制剂来实现。值得注意的是，当一个 在文献中已经描述了许多体外牙科生物膜/微生物组模型，还没有一个 开发的目的是探索微生物组调节器。这款为期两年的R03只有一个目标：开发 一种健壮、高通量、可重复性的、专门为测试而优化的体外龈下微生物组模型 微生物组调节剂。该模型将包括一个从 牙周炎相关的龈下样本，以及从健康- 相关的龈下菌斑样本。生长条件将进行微调，以最大限度地提高相似性 在体外微生物群和原始样本之间。我们还将探索复制的可能性 通过传代或使用冷冻的股票产生的微生物群，消除了获得更多患者的需要 样本。此外，一种新的龈下微生物生态失调指数(SMDI)作为衡量牙周炎患者生态失调的指标 发展微生物菌群。微生物群和原始样本的微生物组成将 使用16S rRNA测序与我们基于BLASTN的物种级别分类相结合进行评估 分配算法。将使用距离矩阵、主成分分析来比较微生物群 和相似性指数。这里描述的模型将为科学界提供一个重要的工具 筛选大量候选调节剂并定量评价其对牙周组织的影响 微生物组，然后才能考虑在动物身上进行进一步的测试，最终在人类身上。

Project summary Periodontitis continues to be a global health problem: combined with edentulism and severe tooth loss, it constitutes the 6th most prevalent long-term disease worldwide, accounting for 11 million years lived with disability and lost productivity of 117 billion USD. Currently, periodontitis is viewed as an immunological destruction of the periodontium, orchestrated by low abundance pathogens in an unbalanced subgingival microbial community--the so called microbial dysbiosis hypothesis. This entails that selectively targeting pathogens or/and stimulating growth of commensals to reverse subgingival microbial dysbiosis (or promote normobiosis) represents a promising strategy for prevention and adjunctive treatment of periodontitis. Such microbiome modulation can be achieved by using agents like prebiotics and probiotics. Remarkably, while a number of in vitro dental biofilm/microbiome models has been described in the literature, none has been developed for the purpose of exploring microbiome modulators. This two-year R03 has a single aim: to develop a robust, high- throughput, reproducible in vitro subgingival microbiome model specifically optimized for testing of microbiome modulators. The model will include a dysbiotic (experimental) microbiome grown from periodontitis-associated subgingival samples, and a normobiotic (reference) microbiome grown from health- associated subgingival plaques samples. The growth conditions will be fine-tuned to maximize similarity between the in vitro microbiomes and the original samples. We will also explore the possibility of reproducing the generated microbiomes by passaging or using frozen stocks, eliminating the need to obtain more patient samples. In addition, a novel subgingival microbial dysbiosis index (SMDI) as a measure of dysbiosis in the microbiomes will be developed. The microbial composition of the microbiomes as well as original samples will be assessed using 16S rRNA sequencing coupled with our BLASTN-based, species-level taxonomy assignment algorithm. Microbiomes will be compared using distance matrices, principle component analysis and a similarity index. The model characterized here will provide the scientific community with an important tool to screen large numbers of candidate modulators and quantitatively assess their effects on subgingival microbiome, before they can be considered for further testing in animals, and eventually, humans.