Investigating the microbial basis of early childhood caries via metagenomics and metatranscriptomics analyses

通过宏基因组学和微生物学研究儿童早期龋病的微生物基础 元转录组学分析 摘要 组学数据的可用性和规模不断增加，彻底改变了我们理解 健康和疾病背后的复杂生物过程。这种生物学上的见解 与精准医学的概念相一致，并有可能改善诊断， 预防和治疗。在口腔健康领域，多个组学数据层（例如，基因组学， 宏基因组学、转录组学、代谢组学），旨在捕获 不可观察的生物学，越来越多地被收集在口腔健康研究中。然而，方法 对于从这些多个数据层中获得的信息， 仍然难以捉摸这一提案的重点，幼儿龋齿（ECC），是最常见的 慢性儿童疾病幼儿保育中心的定义是6岁以下儿童的蛀牙， 它仍然是一个临床和牙科公共卫生问题，并赋予实质性和多层次的 人类和经济影响。精确口腔保健的出现，基于一种新的， 微生物知情的理解ECC，有望阐明机制方面 的疾病过程，并揭示新的方法来预防它。为此，我们将分析 现有的临床（即，ECC病例状态）和匹配的宏基因组学（全基因组测序 鸟枪; WGS）和元转录组学（RNA-seq.）数据来自以下人群的龈上菌斑样本 170名学龄前儿童，主要是3岁和4岁，参加了以社区为基础的口腔健康 在NC学习这项研究的目的是鉴定ECC相关细菌， 通过宏基因组学和元转录组学分析， 单独和联合。除了独特的特性（例如，匹配的WGS和RNA-seq. 来自每个参与者中相同生物膜样品的数据）、数据集的质量和大小、 建议的新奇通过测试、开发和传播适当的 统计方法和优化的分析管道。七个模型将通过以下方式进行评估： 严格的模拟，处理过分散，零通货膨胀，超过2 表型组和批次效应，并将在真实的研究数据之前进行优化 应用程序.完成后，我们预计这项研究将提供新的见解， ECC的微生物基础综合数据分析框架将提供机会， 适应更多的代谢组学数据，因为他们变得可用，以进一步增加 潜在的机械洞察力。

Investigating the microbial basis of early childhood caries via metagenomics and metatranscriptomics analyses Abstract The increasing availability and scale of omics data have revolutionized our ability to understand complex biological processes underlying health and disease. Such biologically-informed insights are aligned with the notion of precision medicine and have the potential to improve diagnoses, prevention and treatment. In the oral health domain, multiple omics data layers (e.g., genomics, metagenomics, transcriptomics, metabolomics), intended to capture aspects of otherwise unobservable biology, are increasingly being collected in oral health studies. However, methods for powerful and informative integration of information gained from these multiple data layers remains elusive. The focus of this proposal, early childhood caries (ECC), is the most common chronic childhood disease. ECC is defined as dental decay among children under the age of 6— it persists as a clinical and dental public health problem, and confers substantial and multi-level human and economic impacts. The advent of precision oral health care, based upon a new, microbially-informed understanding of ECC, is expected to shed light onto mechanistic aspects of the disease processes and reveal new ways to prevent it. To this end, we will analyze existing clinical (i.e., ECC case status) and matched metagenomics (whole genome sequencing shotgun; WGS) and metatranscriptomics (RNA-seq.) data from supragingival plaque samples of 170 preschool-age children, mainly ages 3 and 4, enrolled in a community-based oral health study in NC. The goal of the proposed study is to identify ECC-associated bacteria, bacterial genes and pathways via metagenomics and metatranscriptomics analyses, conducted separately and jointly. Aside from the unique characteristics (e.g., matched WGS and RNA-seq. data from the same biofilm sample in each participant), quality and size of the dataset, the proposal's novelty is amplified by the testing, development and dissemination of appropriate statistical methods and optimized analytical pipelines. Seven models will be evaluated via rigorous simulations, accounting for the handling of over-dispersion, zero-inflation, more than 2 phenotype groups and batch effects, and will be optimized prior to the real study data application. Upon completion, we anticipate that the study will provide novel insights into the microbial basis of ECC. The integrative data analysis framework will offer opportunities to accommodate additional metabolomics data as they become available, to further increase the potential for mechanistic insights.