Beyond pathways: Accelerated flavonoids candidate identification and novel exploration of enzymatic properties using combined mapping populations of wheat

Although forward-genetics-metabolomics methods such as mGWAS and mQTL have proven effective in providing myriad loci affecting metabolite contents, they are somehow constrained by their respective constitutional flaws such as the hidden population structure for GWAS and insufficient recombinant rate for QTL. Here, the combination of mGWAS and mQTL was performed, conveying an improved statistical power to investigate the flavonoid pathways in common wheat. A total of 941 and 289 loci were, respectively, generated from mGWAS and mQTL, within which 13 of them were co-mapped using both approaches. Subsequently, the mGWAS or mQTL outputs alone and their combination were, respectively, utilized to delineate the metabolic routes. Using this approach, we identified two MYB transcription factor encoding genes and five structural genes, and the flavonoid pathway in wheat was accordingly updated. Moreover, we have discovered some rare-activity-exhibiting flavonoid glycosyl- and methyl-transferases, which may possess unique biological significance, and harnessing these novel catalytic capabilities provides potentially new breeding directions. Collectively, we propose our survey illustrates that the forward-genetics-metabolomics approaches including multiple populations with high density markers could be more frequently applied for delineating metabolic pathways in common wheat, which will ultimately contribute to metabolomics-assisted wheat crop improvement.

Chemical-tag-based semi-annotated metabolomics facilitates gene identification and specialized metabolic pathway elucidation in wheat

The importance of metabolite modification and species-specific metabolic pathways has long been recognized. However, linking the chemical structure of metabolites to gene function in order to explore the genetic and biochemical basis of metabolism has not yet been reported in wheat (Triticum aestivum). Here, we profiled metabolic fragment enrichment in wheat leaves and consequently applied chemical-tag-based semi-annotated metabolomics in a genome-wide association study in accessions of wheat. The studies revealed that all 1,483 quantified metabolites have at least one known functional group whose modification is tailored in an enzyme-catalyzed manner and eventually allows efficient candidate gene mining. A Triticeae crop-specific flavonoid pathway and its underlying metabolic gene cluster were elucidated in further functional studies. Additionally, upon overexpressing the major effect gene of the cluster TraesCS2B01G460000 (TaOMT24), the pathway was reconstructed in rice (Oryza sativa), which lacks this pathway. The reported workflow represents an efficient and unbiased approach for gene mining using forward genetics in hexaploid wheat. The resultant candidate gene list contains vast molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets and will ultimately aid in achieving wheat crop improvement.

PMhub 1.0: a comprehensive plant metabolome database

The Plant Metabolome Hub (PMhub), available at https://pmhub.org.cn, is a valuable resource designed to provide scientists with comprehensive information on plant metabolites. It offers extensive details about their reference spectra, genetic foundations, chemical reactions, metabolic pathways and biological functions. The PMhub contains chemical data for 188 837 plant metabolites gathered from various sources, with 1 467 041 standard/in-silico high-resolution tandem mass-spectrometry (HRMS/MS) spectra corresponding to these metabolites. Beyond its extensive literature-derived data, PMhub also boasts a sizable collection of experimental metabolites; it contains 144 366 detected features in 10 typical plant species, with 16 423 successfully annotated by using standard/in-silico HRMS/MS data, this collection is further supplemented with thousands of features gathered from reference metabolites. For each metabolite, the PMhub enables the reconstructed of a simulated network based on structural similarities and existing metabolic pathways. Unlike previous plant-specific metabolome databases, PMhub not only contains a vast amount of metabolic data but also assembles the corresponding genomic and/or transcriptomic information, incorporating multiple methods for the comprehensive genetic analysis of metabolites. To validate the practicality, we verified a synthetic pathway for N-p-coumaroyltyramine by in vitro enzymatic activity experiments. In summary, the robust functionality provided by the PMhub will make it an indispensable tool for studying plant metabolomics.