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

CropMetabolome: a comprehensive metabolome database for major crops cross eight categories

Chemical compositions of crops are of great agronomical importance, as crops serve as resources for nutrition, energy, and medicines for human and livestock. For crop metabolomics research, the lack of crop reference metabolome and high-quality reference compound mass spectra, as well as utilities for metabolic profiling, has hindered the discovery and functional study of phytochemicals in crops. To meet these challenging needs, we have developed the Crop Metabolome database (abbreviated as CropMetabolome) that is dedicated to the construction of crop reference metabolome, repository, and dissemination of crop metabolomic data, and profiling and analytic tools for metabolomics research. CropMetabolome contains a metabolomics database for more than 50 crops (belonging to eight categories) that integrated self-generated raw mass spectral data and public-source datasets. The reference metabolome for 59 crop species was constructed, which have functions that parallel those of reference genome in genomic studies. CropMetabolome also contains 'Standard compound mass spectral library', 'Flavonoids library', 'Pesticide library', and a set of related analytical tools that enable metabolic profiling based on a reference metabolome (CropRefMetaBlast), annotation and identification of new metabolites (CompoundLibBlast), deducing the structure of novel flavonoid derivatives (FlavoDiscover), and detecting possible residual pesticides in crop samples (PesticiDiscover). In addition, CropMetabolome is a repository to share and disseminate metabolomics data and a platform to promote collaborations to develop reference metabolome for more crop species. CropMetabolome is a comprehensive platform that offers important functions in crop metabolomics research and contributes to improve crop breeding, nutrition, and safety. CropMetabolome is freely available at https://www.cropmetabolome.com/.

RefMetaPlant: a reference metabolome database for plants across five major phyla

Plants are unique with tremendous chemical diversity and metabolic complexity, which is highlighted by estimates that green plants collectively produce metabolites numbering in the millions. Plant metabolites play crucial roles in all aspects of plant biology, like growth, development, stress responses, etc. However, the lack of a reference metabolome for plants, and paucity of high-quality standard compound spectral libraries and related analytical tools, have hindered the discovery and functional study of phytochemicals in plants. Here, by leveraging an advanced LC-MS platform, we generated untargeted mass spectral data from >150 plant species collected across the five major phyla. Using a self-developed computation protocol, we constructed reference metabolome for 153 plant species. A 'Reference Metabolome Database for Plants' (RefMetaPlant) was built to encompass the reference metabolome, integrated standard compound mass spectral libraries for annotation, and related query and analytical tools like 'LC-MS/MS Query', 'RefMetaBlast' and 'CompoundLibBlast' for searches and profiling of plant metabolome and metabolite identification. Analogous to a reference genome in genomic research, RefMetaPlant provides a powerful platform to support plant genome-scale metabolite analysis to promote knowledge/data sharing and collaboration in the field of metabolomics. RefMetaPlant is freely available at https://www.biosino.org/RefMetaDB/.

Metabolomic Analysis Reveals Domestication-Driven Reshaping of Polyphenolic Antioxidants in Soybean Seeds

Crop domestication has resulted in nutrient losses, so evaluating the reshaping of phytonutrients is crucial for improving nutrition. Soybean is an ideal model due to its abundant phytonutrients and wild relatives. In order to unravel the domestication consequence of phytonutrients, comparative and association analyses of metabolomes and antioxidant activities were performed on seeds of six wild (Glycine soja (Sieb. and Zucc.)) and six cultivated soybeans (Glycine max (L.) Merr.). Through ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), we observed a greater metabolic diversity in wild soybeans, which also displayed higher antioxidant activities. (-)-Epicatechin, a potent antioxidant, displayed a 1750-fold greater abundance in wild soybeans than in cultivated soybeans. Multiple polyphenols in the catechin biosynthesis pathway were significantly higher in wild soybeans, including phlorizin, taxifolin, quercetin 3-O-galactoside, cyanidin 3-O-glucoside, (+)-catechin, (-)-epiafzelechin, catechin-glucoside, and three proanthocyanidins. They showed significant positive correlations with each other and antioxidant activities, indicating their cooperative contribution to the high antioxidant activities of wild soybeans. Additionally, natural acylation related to functional properties was characterized in a diverse range of polyphenols. Our study reveals the comprehensive reprogramming of polyphenolic antioxidants during domestication, providing valuable insights for metabolism-assisted fortification of crop nutrition.

Plant Metabolic Network 15: A resource of genome-wide metabolism databases for 126 plants and algae

To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we generated genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants (https://plantcyc.org). Of these, 104 have not been reported before. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell type-specific metabolic pathways. This work shows the quality and quantity of our resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.