CAS Key Laboratory of Synthetic Biology / National Center for Gene Research, CAS Center for Excellence in Molecular Plant Sciences / Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Lixiahe Agricultural Research Institute of Jiangsu Province, Yangzhou 225007, China.
Genomics Proteomics Bioinformatics. 2022 Aug;20(4):702-714. doi: 10.1016/j.gpb.2020.06.018. Epub 2021 Feb 23.
Genome-scale metabolomics analysis is increasingly used for pathway and function discovery in the post-genomics era. The great potential offered by developed mass spectrometry (MS)-based technologies has been hindered, since only a small portion of detected metabolites were identifiable so far. To address the critical issue of low identification coverage in metabolomics, we adopted a deep metabolomics analysis strategy by integrating advanced algorithms and expanded reference databases. The experimental reference spectra and in silico reference spectra were adopted to facilitate the structural annotation. To further characterize the structure of metabolites, two approaches were incorporated into our strategy, i.e., structural motif search combined with neutral loss scanning and metabolite association network. Untargeted metabolomics analysis was performed on 150 rice cultivars using ultra-performance liquid chromatography coupled with quadrupole-Orbitrap MS. Consequently, a total of 1939 out of 4491 metabolite features in the MS/MS spectral tag (MS2T) library were annotated, representing an extension of annotation coverage by an order of magnitude in rice. The differential accumulation patterns of flavonoids between indica and japonica cultivars were revealed, especially O-sulfated flavonoids. A series of closely-related flavonolignans were characterized, adding further evidence for the crucial role of tricin-oligolignols in lignification. Our study provides an important protocol for exploring phytochemical diversity in other plant species.
在基因组时代,基于基因组规模的代谢组学分析越来越多地用于途径和功能的发现。尽管基于质谱(MS)的技术已经得到了充分的发展,具有很大的潜力,但由于迄今为止只有一小部分检测到的代谢物可以被识别,因此其应用受到了阻碍。为了解决代谢组学中低鉴定覆盖率的关键问题,我们采用了一种深度代谢组学分析策略,整合了先进的算法和扩展的参考数据库。实验参考光谱和计算参考光谱被采用来促进结构注释。为了进一步描述代谢物的结构,我们的策略中包含了两种方法,即结构基元搜索结合中性丢失扫描和代谢物关联网络。使用超高效液相色谱-四极杆-Orbitrap MS 对 150 个水稻品种进行了非靶向代谢组学分析。结果,在 MS/MS 谱标签(MS2T)库中,共注释了 4491 个代谢物特征中的 1939 个,这将水稻中代谢物的注释覆盖率提高了一个数量级。揭示了籼稻和粳稻品种中类黄酮的差异积累模式,特别是 O-硫酸化类黄酮。一系列密切相关的黄酮木脂素被鉴定出来,进一步证明了荞麦醇木脂素在木质化过程中的关键作用。我们的研究为探索其他植物物种中的植物化学多样性提供了一个重要的方案。