RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
Analyst. 2017 Nov 6;142(22):4161-4172. doi: 10.1039/c7an01019b.
NMR offers tremendous advantages in the analyses of molecular complexity, such as crude bio-fluids, bio-extracts, and intact cells and tissues. Here we introduce recent applications of NMR approaches, as well as next generation sequencing (NGS), for the evaluation of human and environmental health (i.e., maintenance of a homeostatic state) based on metabolic and microbial profiling and data science. We describe useful databases and web tools that are used to support these studies by facilitating the characterization of metabolites from complex NMR spectra. Because the NMR spectra of metabolic mixtures can produce numerical matrix data (e.g., chemical shift versus intensity) with high reproducibility and inter-institution convertibility, advanced data science approaches, such as multivariate analysis and machine learning, are desirable; therefore, we also introduce informatics techniques derived from heterogeneously measured data, such as environmental microbiota, for the extraction of submerged information using data science approaches. We summarize recent studies of microbiomes that are based on these techniques and show that, particularly in human studies, NMR-based metabolic characterization of non-invasive samples, such as feces, can provide a large quantity of beneficial information regarding human health and disease.
NMR 在分析分子复杂性方面具有巨大的优势，例如粗生物流体、生物提取物以及完整的细胞和组织。在这里，我们介绍了基于代谢组学和微生物组学以及数据科学的 NMR 方法以及下一代测序 (NGS) 在评估人类和环境健康（即维持动态平衡状态）方面的最新应用。我们描述了有用的数据库和网络工具，这些工具通过促进从复杂 NMR 光谱中鉴定代谢物来支持这些研究。由于代谢混合物的 NMR 光谱可以产生具有高重现性和机构间可转换性的数值矩阵数据（例如，化学位移与强度），因此需要先进的数据科学方法，例如多元分析和机器学习；因此，我们还介绍了源自异质测量数据（例如环境微生物组）的信息学技术，用于使用数据科学方法提取淹没信息。我们总结了基于这些技术的微生物组的最新研究，并表明，特别是在人类研究中，基于 NMR 的非侵入性样本（如粪便）的代谢特征可以提供大量关于人类健康和疾病的有益信息。
