Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has been facilitated by the construction of MS(2) spectral tag (MS2T) library from the total scan ESI MS/MS data, and the development of widely targeted metabolomics method using MS/MS data gathered from authentic standards. In this report, a novel strategy called stepwise multiple ion monitoring-enhanced product ions (stepwise MIM-EPI) was developed to construct the MS2T library, in which stepwise MIM was used as survey scans to trigger the acquisition of EPI. A total number of 698 (almost) non-redundant metabolites with MS(2) spectra were obtained, of which 135 metabolites were identified/annotated. Integrating the data gathered from our MS2T library and other available multiple reaction monitoring (MRM) information, a widely targeted metabolomics method was developed to quantify 277 metabolites, including some phytohormones. Evaluation of the dehydration responses and natural variations of these metabolites in rice leaf not only suggested the coordinated regulation of abscisic acid (ABA) with metabolites such as serotonin derivative(s), polyamine conjugates under drought stress, but also revealed some C-glycosylated flavones as the potential markers for the discrimination of indica and japonica rice subspecies. The new MS2T library construction and widely targeted metabolomics strategy could be used as a tool for rice functional genomics.

Plant metabolites are important to world food security in terms of maintaining sustainable yield and providing food with enriched phytonutrients. Here we report comprehensive profiling of 840 metabolites and a further metabolic genome-wide association study based on ∼6.4 million SNPs obtained from 529 diverse accessions of Oryza sativa. We identified hundreds of common variants influencing numerous secondary metabolites with large effects at high resolution. We observed substantial heterogeneity in the natural variation of metabolites and their underlying genetic architectures among different subspecies of rice. Data mining identified 36 candidate genes modulating levels of metabolites that are of potential physiological and nutritional importance. As a proof of concept, we functionally identified or annotated five candidate genes influencing metabolic traits. Our study provides insights into the genetic and biochemical bases of rice metabolome variation and can be used as a powerful complementary tool to classical phenotypic trait mapping for rice improvement.

The plant metabolome is characterized by extensive diversity and is often regarded as a bridge between genome and phenome. Here we report metabolic and phenotypic genome-wide studies (mGWAS and pGWAS) in rice grain that, in addition to previous metabolic GWAS in rice leaf and maize kernel, show both distinct and overlapping aspects of genetic control of metabolism within and between species. We identify new candidate genes potentially influencing important metabolic and/or morphological traits. We show that the differential genetic architecture of rice metabolism between different tissues is in part determined by tissue specific expression. Using parallel mGWAS and pGWAS we identify new candidate genes potentially responsible for variation in traits such as grain colour and size, and provide evidence of metabotype-phenotype linkage. Our study demonstrates a powerful strategy for interactive functional genomics and metabolomics in plants, especially the cloning of minor QTLs for complex phenotypic traits.

Plant metabolites are crucial for both plant life and human nutrition. Despite recent advance in metabolomics, the genetic control of plant metabolome remains largely unknown. Here, we performed a genetic analysis of the rice metabolome that provided over 2,800 highly resolved metabolic quantitative trait loci for 900 metabolites. Distinct and overlapping accumulation patterns of metabolites were observed and complex genetic regulation of metabolism was revealed in two different tissues. We associated 24 candidate genes to various metabolic quantitative trait loci by data mining, including ones regulating important morphological traits and biological processes. The corresponding pathways were reconstructed by updating in vivo functions of previously identified and newly assigned genes. This study demonstrated a powerful tool and provided a vast amount of high-quality data for understanding the plasticity of plant metabolome, which may help bridge the gap between the genome and phenome.Oryza sativa | recombinant inbred line | metabolic profiling | gene function P lants are highly enriched in specific metabolites with extensive quantitative and qualitative variation both among and within different plant species (1, 2). Understanding the genes involved in metabolism and dissection of the metabolic pathway are essential to improve plant adaptation to environmental stresses, to improve food quality, and to increase crop yield. Recent advance in metabolomics together with transcriptomics and gene/metabolite coexpression networks analyses has proven to be powerful in functional gene elucidation, although it is specified for transcriptionally regulated genes with limited throughput so far.A large number of metabolic quantitative trait loci (mQTLs) have been identified based on linkage maps using low-density markers, such as restriction fragment length polymorphism and simple sequence repeat markers (3-5). However, the underlying genes remain elusive because of the relatively low resolution of the genetic maps associated with these markers. In addition to the species-specific accumulation of the metabolites in plant, tissue-specific accumulation of metabolites, especially secondary metabolites, are of special importance for the survival and adaptation of plant species. Although the genetics of tissue-specific regulation gene expression across the tissues as revealed by expression quantitative trait locus analysis is one of the major topics in animal and plant, the genetics of tissue-specific metabolome is somewhat overlooked (5, 6). Despite the advance in metabolomics (2), the genetic control of the plant metabolome is still largely unknown.Moreover, the elucidation of pathways for the biosyntheses of important metabolites such as amino acids, lysophosphatidylcholines (LPCs), and flavonoids, and the variation and the genetics control of them are vital for both basic research and breeding application. For example, flavonoids are one of the main groups of secondary metabolites that play important roles in a number of biolo...

Decoration of phytochemicals contributes to the majority of metabolic diversity in nature, whereas how this process alters the biological functions of their precursor molecules remains to be investigated. Flavones, an important yet overlooked subclass of flavonoids, are most commonly conjugated with sugar moieties by UDP-dependent glycosyltransferases (UGTs). Here, we report that the natural variation of rice flavones is mainly determined by OsUGT706D1 (flavone 7-O-glucosyltransferase) and OsUGT707A2 (flavone 5-O-glucosyltransferase). UV-B exposure and transgenic evaluation demonstrate that their allelic variation contributes to UV-B tolerance in nature. Biochemical characterization of over 40 flavonoid UGTs reveals their differential evolution in angiosperms. These combined data provide biochemical insight and genetic regulation into flavone biosynthesis and additionally suggest that adoption of the positive alleles of these genes into breeding programs will likely represent a potential strategy aimed at producing stress-tolerant plants.

Phenolamides constitute a diverse class of secondary metabolites that are found ubiquitously in plants and have been implicated to play an important role in a wide range of biological processes, such as plant development and defense. However, spatiotemporal accumulation patterns of phenolamides in rice, one of the most important crops, are not available, and no gene responsible for phenolamide biosynthesis has been identified in this species. In this study, we report the comprehensive metabolic profiling and natural variation analysis of phenolamides in a collection of rice germplasm using a liquid chromatography-mass spectrometry-based targeted metabolomics method. Spatiotemporal controlled accumulations were observed for most phenolamides, together with their differential accumulations between the two major subspecies of rice. Further metabolic genome-wide association study (mGWAS) in rice leaf and in vivo metabolic analysis of the transgenic plants identified Os12g27220 and Os12g27254 as two spermidine hydroxycinnamoyl transferases that might underlie the natural variation of levels of spermidine conjugates in rice. Our work demonstrates that gene-to-metabolite analysis by mGWAS provides a useful tool for functional gene identification and omics-based crop genetic improvement.

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Flavonoids are widely distributed in plants and play important roles in many biological processes. Citrus fruits are rich dietary sources of flavonoids. However, there have been very few reports about the comprehensive metabolic profile and natural diversity of flavonoids in different tissues of various Citrus cultivars. In this study, based on the 7416 metabolic signals detected with non-targeted metabolomics approach, Principal Component Analysis revealed the flavedo has the largest differences from other tissues in metabolite levels; as many as 198 flavonoid signals were then detected in 62 Citrus germplasms from 5 species mainly cultivated worldwide, while 117 flavonoids were identified, including 39 polymethoxylated flavonoids (PMFs), 7 flavones, 10 C-O-glycosylflavonoids, 44 O-glycosylflavonoids, 10 C-glycosylflavonoids and 7 newly annotated O-glycosylpolymethoxylated flavonoids. Tissue-specific accumulations were observed: O-glycosylated flavonoids were abundant in all fruit tissues, while PMFs were accumulated preferentially in the flavedo. Among different species, mandarins had the highest levels of PMFs and O-glycosylpolymethoxylated flavonoids, followed by sweet oranges. Based on the flavonoid profiles, 62 germplasms could be clearly grouped into five distinct clusters via hierarchical clustering analysis, which were perfectly matched with their species, with sweet oranges and mandarins clustering closely and being further away from other three species.