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

通过元基因组学和生物信息学研究儿童早期龋病的微生物基础 元翻译组学分析 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。这种基于生物信息的洞察力 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， 元基因组学、转录组学、代谢组学)，旨在捕捉其他方面 不可观察的生物学，越来越多地被收集在口腔健康研究中。但是，方法 对从这些多个数据层获得的信息进行强大且信息丰富的集成 仍然难以捉摸。这项提议的重点是幼儿龋病(ECC)，这是最常见的 慢性儿童期疾病。ECC的定义是6岁以下儿童的龋齿- 它一直是一个临床和牙科公共卫生问题，具有实质性和多层次的特点。 对人类和经济的影响。精确口腔保健的出现，基于一种新的， 微生物对ECC的了解，预计将有助于阐明机制方面 并揭示了预防疾病的新方法。为此，我们将分析 现有临床(即ECC病例状态)和匹配的元基因组学(全基因组测序 鸟枪；WGS)和后转录组学(RNA-seq.)牙周上菌斑样本的数据 170名学龄前儿童，主要是3岁和4岁，参加以社区为基础的口腔健康 在北卡罗来纳州学习。这项拟议研究的目标是鉴定与ECC相关的细菌、细菌 通过元基因组学和元转录组学分析进行的基因和途径 单独和联合。除了独特的特征(例如，匹配的WGS和RNA-seq. 来自每个参与者的相同生物膜样本的数据)、数据集的质量和大小、 提案的新颖性通过测试、开发和传播适当的 统计方法和优化的分析管道。七款车型将通过以下方式进行评估 严谨的模拟，占处理的过度分散，零膨胀，超过2 表型群体和批次效应，并将在实际研究数据之前进行优化 申请。完成后，我们预期这项研究将提供有关 ECC的微生物基础。综合数据分析框架将提供以下机会 适应额外的代谢组学数据，当它们可用时，以进一步增加 具有机械洞察力的潜力。

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.