Expanding the Coverage of Metabolic Landscape in Cultivated Rice with Integrated Computational Approaches

Genome-scale metabolomics analysis is increasingly used for pathway and function discovery in post-genomics era. The great potential offered by developed mass spectrometry (MS)-based technology has been hindered by the obstacle that 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, structural motif search combined with neutral loss scanning, and metabolite association network were incorporated into our strategy. An untargeted metabolomics analysis was performed on 150 rice cultivars using Ultra Performance Liquid Chromatography (UPLC)-Quadrupole (Q)-Orbitrap mass spectrometer. 1939 of 4491 metabolite features in MS/MS spectral tag (MS2T) library were annotated, representing an extension of annotation coverage by an order of magnitude on 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 a great template in the exploration of phytochemical diversity for more plant species.