基因组
失调
生物膜
儿童早期龋齿
微生物群
口腔微生物群
背景(考古学)
牙列
人口
疾病
医学
生物
牙科
生物信息学
口腔健康
环境卫生
遗传学
病理
细菌
基因
古生物学
作者
Kimon Divaris,Dmitry Shungin,Adaris Rodríguez-Cortés,Patricia V. Basta,Jeff Roach,Hunyong Cho,Di Wu,Andréa G. Ferreira Zandoná,Jeannie Ginnis,Sivapriya Ramamoorthy,Jason M. Kinchen,Jakub Kwintkiewicz,Natasha Butz,Ana Clara Sarzedas Ribeiro,M. Andrea Azcarate‐Peril
出处
期刊:Methods in molecular biology
日期:2019-01-01
卷期号:: 525-548
被引量:18
标识
DOI:10.1007/978-1-4939-9012-2_40
摘要
Early childhood caries (ECC) is a biofilm-mediated disease. Social, environmental, and behavioral determinants as well as innate susceptibility are major influences on its incidence; however, from a pathogenetic standpoint, the disease is defined and driven by oral dysbiosis. In other words, the disease occurs when the natural equilibrium between the host and its oral microbiome shifts toward states that promote demineralization at the biofilm-tooth surface interface. Thus, a comprehensive understanding of dental caries as a disease requires the characterization of both the composition and the function or metabolic activity of the supragingival biofilm according to well-defined clinical statuses. However, taxonomic and functional information of the supragingival biofilm is rarely available in clinical cohorts, and its collection presents unique challenges among very young children. This paper presents a protocol and pipelines available for the conduct of supragingival biofilm microbiome studies among children in the primary dentition, that has been designed in the context of a large-scale population-based genetic epidemiologic study of ECC. The protocol is being developed for the collection of two supragingival biofilm samples from the maxillary primary dentition, enabling downstream taxonomic (e.g., metagenomics) and functional (e.g., transcriptomics and metabolomics) analyses. The protocol is being implemented in the assembly of a pediatric precision medicine cohort comprising over 6000 participants to date, contributing social, environmental, behavioral, clinical, and biological data informing ECC and other oral health outcomes.
科研通智能强力驱动
Strongly Powered by AbleSci AI