Mengucci Carlo, Nissen Lorenzo, Picone Gianfranco, Malpuech-Brugère Corinne, Orfila Caroline, Ricciardiello Luigi, Bordoni Alessandra, Capozzi Francesco, Gianotti Andrea
Department of Agricultural and Food Sciences (DISTAL), University of Bologna, 47521 Cesena, Italy.
Interdepartmental Centre for Agri-Food Industrial Research (CIRI Agrifood), University of Bologna, 47521 Cesena, Italy.
Metabolites. 2022 Aug 10;12(8):736. doi: 10.3390/metabo12080736.
The availability of omics data providing information from different layers of complex biological processes that link nutrition to human health would benefit from the development of integrated approaches combining holistically individual omics data, including those associated with the microbiota that impacts the metabolisation and bioavailability of food components. Microbiota must be considered as a set of populations of interconnected consortia, with compensatory capacities to adapt to different nutritional intake. To study the consortium nature of the microbiome, we must rely on specially designed data analysis tools. The purpose of this work is to propose the construction of a general correlation network-based explorative tool, suitable for nutritional clinical trials, by integrating omics data from faecal microbial taxa, stool metabolome (1H NMR spectra) and GC-MS for stool volatilome. The presented approach exploits a descriptive paradigm necessary for a true multiomics integration of data, which is a powerful tool to investigate the complex physiological effects of nutritional interventions.
组学数据能够提供来自复杂生物过程不同层面的信息,这些过程将营养与人类健康联系起来。若能开发出综合方法,全面整合个体组学数据,包括与微生物群相关的数据(微生物群会影响食物成分的代谢和生物利用度),将会从中受益。微生物群必须被视为一组相互关联的共生体群体,具有适应不同营养摄入的补偿能力。为了研究微生物组的共生体性质,我们必须依靠专门设计的数据分析工具。这项工作的目的是通过整合来自粪便微生物分类群、粪便代谢组(1H NMR光谱)和粪便挥发组的GC-MS的组学数据,提出构建一种基于一般相关网络的探索性工具,适用于营养临床试验。所提出的方法利用了数据真正多组学整合所必需的描述性范式,这是研究营养干预复杂生理效应的有力工具。