Metabolic GWAS-based dissection of genetic bases underlying the diversity of plant metabolism

Genomic and metabolomic insights into the selection and differentiation of bioactive compounds in citrus

Bioactive compounds play an increasingly prominent role in breeding functional and nutritive fruit crops such as citrus. However, the genomic and metabolic bases for the selection and differentiation underlying bioactive compound variations in citrus remain poorly understood. In this study, we constructed a species-level variation atlas of genomes and metabolomes using 299 citrus accessions. A total of 19 829 significant SNPs were targeted to 653 annotated metabolites, among which multiple significant signals were identified for secondary metabolites, especially flavonoids. Significant differential accumulation of bioactive compounds in the phenylpropane pathway, mainly flavonoids and coumarins, was unveiled across ancestral citrus species during differentiation, which is likely associated with the divergent haplotype distribution and/or expression profiles of relevant genes, including p-coumaroyl coenzyme A 2'-hydroxylases, flavone synthases, cytochrome P450 enzymes, prenyltransferases, and uridine diphosphate glycosyltransferases. Moreover, we systematically evaluated the beneficial bioactivities such as the antioxidant and anticancer capacities of 219 citrus varieties, and identified robust associations between distinct bioactivities and specific metabolites. Collectively, these findings provide citrus breeding options for enrichment of beneficial flavonoids and avoidance of potential risk of coumarins. Our study will accelerate the application of genomic and metabolic engineering strategies in developing modern healthy citrus cultivars.

Preparation, pungency and bioactivity transduction of piperine from black pepper (Piper nigrum L.): A comprehensive review

Piperine, derived from black pepper (Piper nigrum L.), is responsible for the pungent sensation. The diverse bioactivities of piperine underscores its promising potential as a functional food ingredient. This review presents a comprehensive overview of the research progress in extraction, synthesis, pungency transduction mechanism and bioactivities of piperine. Piperine can be extracted through various methods, such as traditional, modern, and innovative extraction techniques. Its synthesis mainly included both chemical and biosynthetic approaches. It exhibits a diverse range of bioactivities, including anticancer, anticonvulsant, antidepressant, anti-inflammatory, antioxidant, immunomodulatory, anti-obesity, neuroprotective, antidiabetic, hepatoprotective, and cardiovascular protective activities. Piperine can bind to TRPV1 receptor to elicit pungent sensation. Overall, the present review can provide a theoretical reference for advancing the potential application of piperine in the field of food science.

Dissecting the genetic basis of UV-B responsive metabolites in rice

BACKGROUND

UV-B, an important environmental factor, has been shown to affect the yield and quality of rice (Oryza sativa) worldwide. However, the molecular mechanisms underlying the response to UV-B stress remain elusive in rice.

RESULTS

We perform comprehensive metabolic profiling of leaves from 160 diverse rice accessions under UV-B and normal light conditions using a widely targeted metabolomics approach. Our results reveal substantial differences in metabolite accumulation between the two major rice subspecies indica and japonica, especially after UV-B treatment, implying the possible role and mechanism of metabolome changes in subspecies differentiation and the stress response. We next conduct a transcriptome analysis from four representative rice varieties under UV-B stress, revealing genes from amino acid and flavonoid pathways involved in the UV-B response. We further perform a metabolite-based genome-wide association study (mGWAS), which reveals 3307 distinct loci under UV-B stress. Identification and functional validation of candidate genes show that OsMYB44 regulates tryptamine accumulation to mediate UV-B tolerance, while OsUVR8 interacts with OsMYB110 to promote flavonoid accumulation and UV-B tolerance in a coordinated manner. Additionally, haplotype analysis suggests that natural variation of OsUVR8groupA contributes to UV-B resistance in rice.

CONCLUSIONS

Our study reveals the complex biochemical and genetic foundations that govern the metabolite dynamics underlying the response, tolerance, and adaptive strategies of rice to UV-B stress. These findings provide new insights into the biochemical and genetic basis of the metabolome underlying the crop response, tolerance, and adaptation to UV-B stress.

Genome-wide association identifies a BAHD acyltransferase activity that assembles an ester of glucuronosylglycerol and phenylacetic acid

Genome-wide association studies (GWAS) are an effective approach to identify new specialized metabolites and the genes involved in their biosynthesis and regulation. In this study, GWAS of Arabidopsis thaliana soluble leaf and stem metabolites identified alleles of an uncharacterized BAHD-family acyltransferase (AT5G57840) associated with natural variation in three structurally related metabolites. These metabolites were esters of glucuronosylglycerol, with one metabolite containing phenylacetic acid as the acyl component of the ester. Knockout and overexpression of AT5G57840 in Arabidopsis and heterologous overexpression in Nicotiana benthamiana and Escherichia coli demonstrated that it is capable of utilizing phenylacetyl-CoA as an acyl donor and glucuronosylglycerol as an acyl acceptor. We, thus, named the protein Glucuronosylglycerol Ester Synthase (GGES). Additionally, phenylacetyl glucuronosylglycerol increased in Arabidopsis CYP79A2 mutants that overproduce phenylacetic acid and was lost in knockout mutants of UDP-sulfoquinovosyl: diacylglycerol sulfoquinovosyl transferase, an enzyme required for glucuronosylglycerol biosynthesis and associated with glycerolipid metabolism under phosphate-starvation stress. GGES is a member of a well-supported clade of BAHD family acyltransferases that arose by duplication and neofunctionalized during the evolution of the Brassicales within a larger clade that includes HCT as well as enzymes that synthesize other plant-specialized metabolites. Together, this work extends our understanding of the catalytic diversity of BAHD acyltransferases and uncovers a pathway that involves contributions from both phenylalanine and lipid metabolism.

Dynamic metabolite QTL analyses provide novel biochemical insights into kernel development and nutritional quality improvement in common wheat

Despite recent advances in crop metabolomics, the genetic control and molecular basis of the wheat kernel metabolome at different developmental stages remain largely unknown. Here, we performed widely targeted metabolite profiling of kernels from three developmental stages (grain-filling kernels [FKs], mature kernels [MKs], and germinating kernels [GKs]) using a population of 159 recombinant inbred lines. We detected 625 annotated metabolites and mapped 3173, 3143, and 2644 metabolite quantitative trait loci (mQTLs) in FKs, MKs, and GKs, respectively. Only 52 mQTLs were mapped at all three stages, indicating the high stage specificity of the wheat kernel metabolome. Four candidate genes were functionally validated by in vitro enzymatic reactions and/or transgenic approaches in wheat, three of which mediated the tricin metabolic pathway. Metabolite flux efficiencies within the tricin pathway were evaluated, and superior candidate haplotypes were identified, comprehensively delineating the tricin metabolism pathway in wheat. Finally, additional wheat metabolic pathways were re-constructed by updating them to incorporate the 177 candidate genes identified in this study. Our work provides new information on variations in the wheat kernel metabolome and important molecular resources for improvement of wheat nutritional quality.

Multi-omics identification of a key glycosyl hydrolase gene FtGH1 involved in rutin hydrolysis in Tartary buckwheat (Fagopyrum tataricum)

Rutin, a flavonoid rich in buckwheat, is important for human health and plant resistance to external stresses. The hydrolysis of rutin to quercetin underlies the bitter taste of Tartary buckwheat. In order to identify rutin hydrolysis genes, a 200 genotypes mini-core Tartary buckwheat germplasm resource was re-sequenced with 30-fold coverage depth. By combining the content of the intermediate metabolites of rutin metabolism with genome resequencing data, metabolite genome-wide association analyses (GWAS) eventually identified a glycosyl hydrolase gene FtGH1, which could hydrolyse rutin to quercetin. This function was validated both in Tartary buckwheat overexpression hairy roots and in vitro enzyme activity assays. Mutation of the two key active sites, which were determined by molecular docking and experimentally verified via overexpression in hairy roots and transient expression in tobacco leaves, exhibited abnormal subcellular localization, suggesting functional changes. Sequence analysis revealed that mutation of the FtGH1 promoter in accessions of two haplotypes might be necessary for enzymatic activity. Co-expression analysis and GWAS revealed that FtbHLH165 not only repressed FtGH1 expression, but also increased seed length. This work reveals a potential mechanism behind rutin metabolism, which should provide both theoretical support in the study of flavonoid metabolism and in the molecular breeding of Tartary buckwheat.

New Insights into the Genetic Basis of Lysine Accumulation in Rice Revealed by Multi-Model GWAS

Lysine is an essential amino acid that cannot be synthesized in humans. Rice is a global staple food for humans but has a rather low lysine content. Identification of the quantitative trait nucleotides (QTNs) and genes underlying lysine content is crucial to increase lysine accumulation. In this study, five grain and three leaf lysine content datasets and 4,630,367 single nucleotide polymorphisms (SNPs) of 387 rice accessions were used to perform a genome-wide association study (GWAS) by ten statistical models. A total of 248 and 71 common QTNs associated with grain/leaf lysine content were identified. The accuracy of genomic selection/prediction RR-BLUP models was up to 0.85, and the significant correlation between the number of favorable alleles per accession and lysine content was up to 0.71, which validated the reliability and additive effects of these QTNs. Several key genes were uncovered for fine-tuning lysine accumulation. Additionally, 20 and 30 QTN-by-environment interactions (QEIs) were detected in grains/leaves. The QEI-sf0111954416 candidate gene LOC_Os01g21380 putatively accounted for gene-by-environment interaction was identified in grains. These findings suggested the application of multi-model GWAS facilitates a better understanding of lysine accumulation in rice. The identified QTNs and genes hold the potential for lysine-rich rice with a normal phenotype.

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.

Recent advances in plant translational genomics for crop improvement

The growing population, climate change, and limited agricultural resources put enormous pressure on agricultural systems. A plateau in crop yields is occurring and extreme weather events and urbanization threaten the livelihood of farmers. It is imperative that immediate attention is paid to addressing the increasing food demand, ensuring resilience against emerging threats, and meeting the demand for more nutritious, safer food. Under uncertain conditions, it is essential to expand genetic diversity and discover novel crop varieties or variations to develop higher and more stable yields. Genomics plays a significant role in developing abundant and nutrient-dense food crops. An alternative to traditional breeding approach, translational genomics is able to improve breeding programs in a more efficient and precise manner by translating genomic concepts into practical tools. Crop breeding based on genomics offers potential solutions to overcome the limitations of conventional breeding methods, including improved crop varieties that provide more nutritional value and are protected from biotic and abiotic stresses. Genetic markers, such as SNPs and ESTs, contribute to the discovery of QTLs controlling agronomic traits and stress tolerance. In order to meet the growing demand for food, there is a need to incorporate QTLs into breeding programs using marker-assisted selection/breeding and transgenic technologies. This chapter primarily focuses on the recent advances that are made in translational genomics for crop improvement and various omics techniques including transcriptomics, metagenomics, pangenomics, single cell omics etc. Numerous genome editing techniques including CRISPR Cas technology and their applications in crop improvement had been discussed.

Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize

Genome-wide association studies (GWAS) have emerged as a powerful tool for unraveling intricate genotype-phenotype association across various species. Maize (Zea mays L.), renowned for its extensive genetic diversity and rapid linkage disequilibrium (LD), stands as an exemplary candidate for GWAS. In maize, GWAS has made significant advancements by pinpointing numerous genetic loci and potential genes associated with complex traits, including responses to both abiotic and biotic stress. These discoveries hold the promise of enhancing adaptability and yield through effective breeding strategies. Nevertheless, the impact of environmental stress on crop growth and yield is evident in various agronomic traits. Therefore, understanding the complex genetic basis of these traits becomes paramount. This review delves into current and future prospectives aimed at yield, quality, and environmental stress resilience in maize and also addresses the challenges encountered during genomic selection and molecular breeding, all facilitated by the utilization of GWAS. Furthermore, the integration of omics, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics has enriched our understanding of intricate traits in maize, thereby enhancing environmental stress tolerance and boosting maize production. Collectively, these insights not only advance our understanding of the genetic mechanism regulating complex traits but also propel the utilization of marker-assisted selection in maize molecular breeding programs, where GWAS plays a pivotal role. Therefore, GWAS provides robust support for delving into the genetic mechanism underlying complex traits in maize and enhancing breeding strategies.

Common and specific genetic basis of metabolite-mediated drought responses in rice

Plants orchestrate drought responses at metabolic level but the genetic basis remains elusive in rice. In this study, 233 drought-responsive metabolites (DRMs) were quantified in a large rice population comprised of 510 diverse accessions at the reproductive stage. Large metabolic variations in drought responses were detected, and little correlation of metabolic levels between drought and normal conditions were observed. Interestingly, most of these DRMs could predict drought resistance in high accuracy. Genome-wide association study revealed 2522 significant association signals for 233 DRMs, and 98% (2471/2522) of the signals were co-localized with the association loci for drought-related phenotypic traits in the same population or the linkage-mapped QTLs for drought resistance in other populations. Totally, 10 candidate genes were efficiently identified for nine DRMs, seven of which harbored cis-eQTLs under drought condition. Based on comparative GWAS of common DRMs in rice and maize, representing irrigated and upland crops, we have identified three pairs of homologous genes associated with three DRMs between the two crops. Among the homologous genes, a transferase gene responsible for metabolic variation of N-feruloylputrescine was confirmed to confer enhanced drought resistance in rice. Our study provides not only genetic architecture of metabolic responses to drought stress in rice but also metabolic data resources to reveal the common and specific metabolite-mediated drought responses in different crops.

Plant Sample Collection and Shipment for Multi-omic Analyses and Phytosanitary Evaluation

Plant sample preparation for analyses is a fundamental step in high-throughput omics strategies. Especially for plant metabolomics, quenching of hydrolytic enzymes able to affect metabolite concentrations is crucial for the accuracy of results. Given that DNA is usually less labile than metabolites, most sampling and shipment procedures able to preserve the metabolome are also suitable for preventing the degradation of plant DNA or of DNA of pathogens in the plant tissue. In this article, we describe all the steps of sample collection, shipment (including the phytosanitary issues of moving plant samples), and processing for combined genomics and metabolomics from a single sample, as well as the protocols used in our laboratories for downstream approaches for crop plants, allowing collection of multi-omic datasets in large experimental setups. The protocols have been adjusted to apply to both freeze-dried and fresh-frozen material to allow the processing of crop plant samples that will require long-distance transport. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Preparation of freeze-dried leaf disks for multiplexed PCR or DArT-Seq genotyping Basic Protocol 2: Medium-throughput preparation of pathogen-free nucleic acids for most genotyping-resequencing applications or pathogen detection Alternate Protocol: Low-throughput extraction of high-quality DNA for resequencing using commercial kits Support Protocol: DNA quality control Basic Protocol 3: Preparation of freeze-dried plant material for metabolomics Basic Protocol 4: Preparation of fresh-frozen plant material for metabolomics Basic Protocol 5: Preparation and shipment of metabolite extracts for metabolomic analyses Basic Protocol 6: Sample shipping and long-term storage.

Stressing the importance of plant specialized metabolites: omics-based approaches for discovering specialized metabolism in plant stress responses

Plants produce a diverse range of specialized metabolites that play pivotal roles in mediating environmental interactions and stress adaptation. These unique chemical compounds also hold significant agricultural, medicinal, and industrial values. Despite the expanding knowledge of their functions in plant stress interactions, understanding the intricate biosynthetic pathways of these natural products remains challenging due to gene and pathway redundancy, multifunctionality of proteins, and the activity of enzymes with broad substrate specificity. In the past decade, substantial progress in genomics, transcriptomics, metabolomics, and proteomics has made the exploration of plant specialized metabolism more feasible than ever before. Notably, recent advances in integrative multi-omics and computational approaches, along with other technologies, are accelerating the discovery of plant specialized metabolism. In this review, we present a summary of the recent progress in the discovery of plant stress-related specialized metabolites. Emphasis is placed on the application of advanced omics-based approaches and other techniques in studying plant stress-related specialized metabolism. Additionally, we discuss the high-throughput methods for gene functional characterization. These advances hold great promise for harnessing the potential of specialized metabolites to enhance plant stress resilience in the future.

Elucidation of the melitidin biosynthesis pathway in pummelo

Specialized plant metabolism is a rich resource of compounds for drug discovery. The acylated flavonoid glycoside melitidin is being developed as an anti-cholesterol statin drug candidate, but its biosynthetic route in plants has not yet been fully characterized. Here, we describe the gene discovery and functional characterization of a new flavonoid gene cluster (UDP-glucuronosyltransferases (CgUGTs), 1,2 rhamnosyltransferase (Cg1,2RhaT), acyltransferases (CgATs)) that is responsible for melitidin biosynthesis in pummelo (Citrus grandis (L.) Osbeck). Population variation analysis indicated that the tailoring of acyltransferases, specific for bitter substrates, mainly determine the natural abundance of melitidin. Moreover, 3-hydroxy-3-methylglutaryl-CoA reductase enzyme inhibition assays showed that the product from this metabolic gene cluster, melitidin, may be an effective anti-cholesterol statin drug candidate. Co-expression of these clustered genes in Nicotiana benthamiana resulted in the formation of melitidin, demonstrating the potential for metabolic engineering of melitidin in a heterologous plant system. This study establishes a biosynthetic pathway for melitidin, which provides genetic resources for the breeding and genetic improvement of pummelo aimed at fortifying the content of biologically active metabolites.

Genome-wide association study unveils ascorbate regulation by PAS/LOV PROTEIN during high light acclimation

Varying light conditions elicit metabolic responses as part of acclimation with changes in ascorbate levels being an important component. Here, we adopted a genome-wide association-based approach to characterize the response in ascorbate levels on high light (HL) acclimation in a panel of 315 Arabidopsis (Arabidopsis thaliana) accessions. These studies revealed statistically significant SNPs for total and reduced ascorbate under HL conditions at a locus in chromosome 2. Ascorbate levels under HL and the region upstream and within PAS/LOV PROTEIN (PLP) were strongly associated. Intriguingly, subcellular localization analyses revealed that the PLPA and PLPB splice variants co-localized with VITAMIN C DEFECTIVE2 (VTC2) and VTC5 in both the cytosol and nucleus. Yeast 2-hybrid and bimolecular fluorescence complementation analyses revealed that PLPA and PLPB interact with VTC2 and that blue light diminishes this interaction. Furthermore, PLPB knockout mutants were characterized by 1.5- to 1.7-fold elevations in their ascorbate levels, whereas knockout mutants of the cry2 cryptochromes displayed 1.2- to 1.3-fold elevations compared to WT. Our results collectively indicate that PLP plays a critical role in the elevation of ascorbate levels, which is a signature response of HL acclimation. The results strongly suggest that this is achieved via the release of the inhibitory effect of PLP on VTC2 upon blue light illumination, as the VTC2-PLPB interaction is stronger under darkness. The conditional importance of the cryptochrome receptors under different environmental conditions suggests a complex hierarchy underpinning the environmental control of ascorbate levels. However, the data we present here clearly demonstrate that PLP dominates during HL acclimation.

Genome-Wide Association Studies Using 3VmrMLM Model Provide New Insights into Branched-Chain Amino Acid Contents in Rice Grains

Rice (Oryza sativa L.) is a globally important food source providing carbohydrates, amino acids, and dietary fiber for humans and livestock. The branched-chain amino acid (BCAA) level is a complex trait related to the nutrient quality of rice. However, the genetic mechanism underlying the BCAA (valine, leucine, and isoleucine) accumulation in rice grains remains largely unclear. In this study, the grain BCAA contents and 239,055 SNPs of a diverse panel containing 422 rice accessions were adopted to perform a genome-wide association study (GWAS) using a recently proposed 3VmrMLM model. A total of 357 BCAA-content-associated main-effect quantitative trait nucleotides (QTNs) were identified from 15 datasets (12 BCAA content datasets and 3 BLUP datasets of BCAA). Furthermore, the allelic variation of two novel candidate genes, LOC_Os01g52530 and LOC_Os06g15420, responsible for the isoleucine (Ile) content alteration were identified. To reveal the genetic basis of the potential interactions between the gene and environmental factor, 53 QTN-by-environment interactions (QEIs) were detected using the 3VmrMLM model. The LOC_Os03g24460, LOC_Os01g55590, and LOC_Os12g31820 were considered as the candidate genes potentially contributing to the valine (Val), leucine (Leu), and isoleucine (Ile) accumulations, respectively. Additionally, 10 QTN-by-QTN interactions (QQIs) were detected using the 3VmrMLM model, which were putative gene-by-gene interactions related to the Leu and Ile contents. Taken together, these findings suggest that the implementation of the 3VmrMLM model in a GWAS may provide new insights into the deeper understanding of BCAA accumulation in rice grains. The identified QTNs/QEIs/QQIs serve as potential targets for the genetic improvement of rice with high BCAA levels.

Plant biochemical genetics in the multiomics era

Our understanding of plant biology has been revolutionized by modern genetics and biochemistry. However, biochemical genetics can be traced back to the foundation of Mendelian genetics; indeed, one of Mendel's milestone discoveries of seven characteristics of pea plants later came to be ascribed to a mutation in a starch branching enzyme. Here, we review both current and historical strategies for the elucidation of plant metabolic pathways and the genes that encode their component enzymes and regulators. We use this historical review to discuss a range of classical genetic phenomena including epistasis, canalization, and heterosis as viewed through the lens of contemporary high-throughput data obtained via the array of approaches currently adopted in multiomics studies.

Population analysis reveals the roles of DNA methylation in tomato domestication and metabolic diversity

DNA methylation is an important epigenetic marker, yet its diversity and consequences in tomato breeding at the population level are largely unknown. We performed whole-genome bisulfite sequencing (WGBS), RNA sequencing, and metabolic profiling on a population comprising wild tomatoes, landraces, and cultivars. A total of 8,375 differentially methylated regions (DMRs) were identified, with methylation levels progressively decreasing from domestication to improvement. We found that over 20% of DMRs overlapped with selective sweeps. Moreover, more than 80% of DMRs in tomato were not significantly associated with single-nucleotide polymorphisms (SNPs), and DMRs had strong linkages with adjacent SNPs. We additionally profiled 339 metabolites from 364 diverse accessions and further performed a metabolic association study based on SNPs and DMRs. We detected 971 and 711 large-effect loci via SNP and DMR markers, respectively. Combined with multi-omics, we identified 13 candidate genes and updated the polyphenol biosynthetic pathway. Our results showed that DNA methylation variants could complement SNP profiling of metabolite diversity. Our study thus provides a DNA methylome map across diverse accessions and suggests that DNA methylation variation can be the genetic basis of metabolic diversity in plants.

A review on matcha: Chemical composition, health benefits, with insights on its quality control by applying chemometrics and multi-omics

This review discussed the origin, manufacturing process, chemical composition, factors affecting quality and health benefits of matcha (Camellia sinensis), and the application of chemometrics and multi-omics in the science of matcha. The discussion primarily distinguishes between matcha and regular green tea with processing and compositional factors, and demonstrates beneficial health effects of consuming matcha. Preferred Reporting Items for Systematic Reviews and Meta-Analyses was adopted to search for relevant information in this review. Boolean operators were incorporated to explore related sources in various databases. Notably, climate, cultivar, maturity of tea leaves, grinding process and brewing temperature impact on the overall quality of matcha. Besides, sufficient shading prior to harvesting significantly increases the contents of theanine and chlorophyll in the tea leaves. Furthermore, the ground whole tea leaf powder delivers matcha with the greatest benefits to the consumers. The health promoting benefits of matcha are mainly contributed by its micro-nutrients and the antioxidative phytochemicals, specifically epigallocatechin-gallate, theanine and caffeine. Collectively, the chemical composition of matcha affected its quality and health benefits significantly. To this end, more studies are required to elucidate the biological mechanisms of these compounds for human health. Chemometrics and multi-omics technologies are useful to fill up the research gaps identified in this review.

Metabolic-GWAS provides insights into genetic architecture of seed metabolome in buckwheat

BACKGROUND

Buckwheat (Fagopyrum spp.), belonging to the Polygonaceae family, is an ancient pseudo-cereal with high nutritional and nutraceutical properties. Buckwheat proteins are gluten-free and show balanced amino acid and micronutrient profiles, with higher content of health-promoting bioactive flavonoids that make it a golden crop of the future. Plant metabolome is increasingly gaining importance as a crucial component to understand the connection between plant physiology and environment and as a potential link between the genome and phenome. However, the genetic architecture governing the metabolome and thus, the phenome is not well understood. Here, we aim to obtain a deeper insight into the genetic architecture of seed metabolome in buckwheat by integrating high throughput metabolomics and genotyping-by-sequencing applying an array of bioinformatics tools for data analysis.

RESULTS

High throughput metabolomic analysis identified 24 metabolites in seed endosperm of 130 diverse buckwheat genotypes. The genotyping-by-sequencing (GBS) of these genotypes revealed 3,728,028 SNPs. The Genome Association and Prediction Integrated Tool (GAPIT) assisted in the identification of 27 SNPs/QTLs linked to 18 metabolites. Candidate genes were identified near 100 Kb of QTLs, providing insights into several metabolic and biosynthetic pathways.

CONCLUSIONS

We established the metabolome inventory of 130 germplasm lines of buckwheat, identified QTLs through marker trait association and positions of potential candidate genes. This will pave the way for future dissection of complex economic traits in buckwheat.

Validation of a metabolite-GWAS network for Populus trichocarpa family 1 UDP-glycosyltransferases

Metabolite genome-wide association studies (mGWASs) are increasingly used to discover the genetic basis of target phenotypes in plants such as Populus trichocarpa, a biofuel feedstock and model woody plant species. Despite their growing importance in plant genetics and metabolomics, few mGWASs are experimentally validated. Here, we present a functional genomics workflow for validating mGWAS-predicted enzyme-substrate relationships. We focus on uridine diphosphate-glycosyltransferases (UGTs), a large family of enzymes that catalyze sugar transfer to a variety of plant secondary metabolites involved in defense, signaling, and lignification. Glycosylation influences physiological roles, localization within cells and tissues, and metabolic fates of these metabolites. UGTs have substantially expanded in P. trichocarpa, presenting a challenge for large-scale characterization. Using a high-throughput assay, we produced substrate acceptance profiles for 40 previously uncharacterized candidate enzymes. Assays confirmed 10 of 13 leaf mGWAS associations, and a focused metabolite screen demonstrated varying levels of substrate specificity among UGTs. A substrate binding model case study of UGT-23 rationalized observed enzyme activities and mGWAS associations, including glycosylation of trichocarpinene to produce trichocarpin, a major higher-order salicylate in P. trichocarpa. We identified UGTs putatively involved in lignan, flavonoid, salicylate, and phytohormone metabolism, with potential implications for cell wall biosynthesis, nitrogen uptake, and biotic and abiotic stress response that determine sustainable biomass crop production. Our results provide new support for in silico analyses and evidence-based guidance for in vivo functional characterization.

INDETERMINATE1 autonomously regulates phosphate homeostasis upstream of the miR399-ZmPHO2 signaling module in maize

The macronutrient phosphorus is essential for plant growth and development. Plants have evolved multiple strategies to increase the efficiency of phosphate (Pi) acquisition to protect themselves from Pi starvation. However, the crosstalk between Pi homeostasis and plant development remains to be explored. Here, we report that overexpressing microRNA399 (miR399) in maize (Zea mays) is associated with premature senescence after pollination. Knockout of ZmPHO2 (Phosphate 2), a miR399 target, resulted in a similar premature senescence phenotype. Strikingly, we discovered that INDETERMINATE1 (ID1), a floral transition regulator, inhibits the transcription of ZmMIR399 genes by directly binding to their promoters, alleviating the repression of ZmPHO2 by miR399 and ultimately contributing to the maintenance of Pi homeostasis in maize. Unlike ZmMIR399 genes, whose expression is induced by Pi deficiency, ID1 expression was independent of the external inorganic orthophosphate status, indicating that ID1 is an autonomous regulator of Pi homeostasis. Furthermore, we show that ZmPHO2 was under selection during maize domestication and cultivation, resulting in a more sensitive response to Pi starvation in temperate maize than in tropical maize. Our study reveals a direct functional link between Pi-deprivation sensing by the miR399-ZmPHO2 regulatory module and plant developmental regulation by ID1.

Genetic resources and breeding of maize for Striga resistance: a review

The potential yield of maize (Zea mays L.) and other major crops is curtailed by several biotic, abiotic, and socio-economic constraints. Parasitic weeds, Striga spp., are major constraints to cereal and legume crop production in sub-Saharan Africa (SSA). Yield losses reaching 100% are reported in maize under severe Striga infestation. Breeding for Striga resistance has been shown to be the most economical, feasible, and sustainable approach for resource-poor farmers and for being environmentally friendly. Knowledge of the genetic and genomic resources and components of Striga resistance is vital to guide genetic analysis and precision breeding of maize varieties with desirable product profiles under Striga infestation. This review aims to present the genetic and genomic resources, research progress, and opportunities in the genetic analysis of Striga resistance and yield components in maize for breeding. The paper outlines the vital genetic resources of maize for Striga resistance, including landraces, wild relatives, mutants, and synthetic varieties, followed by breeding technologies and genomic resources. Integrating conventional breeding, mutation breeding, and genomic-assisted breeding [i.e., marker-assisted selection, quantitative trait loci (QTL) analysis, next-generation sequencing, and genome editing] will enhance genetic gains in Striga resistance breeding programs. This review may guide new variety designs for Striga-resistance and desirable product profiles in maize.

A metabolic perspective of selection for fruit quality related to apple domestication and improvement

BACKGROUND

Apple is an economically important fruit crop. Changes in metabolism accompanying human-guided evolution can be revealed using a multiomics approach. We perform genome-wide metabolic analysis of apple fruits collected from 292 wild and cultivated accessions representing various consumption types.

RESULTS

We find decreased amounts of certain metabolites, including tannins, organic acids, phenolic acids, and flavonoids as the wild accessions transition to cultivated apples, while lysolipids increase in the "Golden Delicious" to "Ralls Janet" pedigree, suggesting better storage. We identify a total of 222,877 significant single-nucleotide polymorphisms that are associated with 2205 apple metabolites. Investigation of a region from 2.84 to 5.01 Mb on chromosome 16 containing co-mapping regions for tannins, organic acids, phenolic acids, and flavonoids indicates the importance of these metabolites for fruit quality and nutrition during breeding. The tannin and acidity-related genes Myb9-like and PH4 are mapped closely to fruit weight locus fw1 from 3.41 to 3.76 Mb on chromosome 15, a region under selection during domestication. Lysophosphatidylethanolamine (LPE) 18:1, which is suppressed by fatty acid desaturase-2 (FAD2), is positively correlated to fruit firmness. We find the fruit weight is negatively correlated with salicylic acid and abscisic acid levels. Further functional assays demonstrate regulation of these hormone levels by NAC-like activated by Apetala3/Pistillata (NAP) and ATP binding cassette G25 (ABCG25), respectively.

CONCLUSIONS

This study provides a metabolic perspective for selection on fruit quality during domestication and improvement, which is a valuable resource for investigating mechanisms controlling apple metabolite content and quality.

Multi-omics integration to explore the molecular insight into the volatile organic compounds in watermelon

A range of volatile organic compounds played an important role in the formation of watermelon fruit aroma, while due to the low content and difficulty in detection, it is often neglected in watermelon breeding programs, resulting in a decline in fruit flavor. VOCs in the flesh of 194 watermelon accessions and seven cultivars at four developmental stages were determined by SPME-GC-MS. Ten metabolites with significant differences in the natural population and positive accumulation during fruit development are considered to be the key metabolite related to watermelon fruit aroma. And the link between metabolite and, flesh color and sugar content by correlation analysis was established. The results of the genome-wide association study showed that (5E)-6,10-dimethylundeca-5,9-dien-2-one, and 1-(4-methylphenyl) ethanone were colocalized with watermelon flesh color on chromosome 4, which may be regulated by LCYB and CCD. (E)-4-(2,6,6-trimethylcyclohexen-1-yl)but-3-en-2-one is the VOC produced by the cleavage of carotenoids, which has a positive correlation with the sugar content of the fruit, and the candidate gene Cla97C05G092490 on chromosome 5 may interact with PSY to influence the accumulation of this metabolite. In addition, Cla97C02G049790 (enol reductase), Cla97C03G051490 (omega-3 fatty acid desaturase gene), LOX, and ADH may play important roles in the synthesis of fatty acids and their derived VOCs. Taken together, our findings provide molecular insights into the accumulation and natural variation of VOCs in watermelon, and give data support for breeding watermelon cultivars with better flavor.

The SlDOG1 Affect Biosynthesis of Steroidal Glycoalkaloids by Regulating GAME Expression in Tomato

Steroidal alkaloids (SAs) and steroidal glycoalkaloids (SGAs) are common constituents of plant species belonging to the Solanaceae family. However, the molecular mechanism regulating the formation of SAs and SGAs remains unknown. Here, genome-wide association mapping was used to elucidate SA and SGA regulation in tomatoes: a SlGAME5-like glycosyltransferase (Solyc10g085240) and the transcription factor SlDOG1 (Solyc10g085210) were significantly associated with steroidal alkaloid composition. In this study, it was found that rSlGAME5-like can catalyze a variety of substrates for glycosidation and can catalyze SA and flavonol pathways to form O-glucoside and O-galactoside in vitro. The overexpression of SlGAME5-like promoted α-tomatine, hydroxytomatine, and flavonol glycoside accumulation in tomatoes. Furthermore, assessments of natural variation combined with functional analyses identified SlDOG1 as a major determinant of tomato SGA content, which also promoted SA and SGA accumulation via the regulation of GAME gene expression. This study provides new insights into the regulatory mechanisms underlying SGA production in tomatoes.

Natural variations of OsAUX5, a target gene of OsWRKY78, control the neutral essential amino acid content in rice grains

Grain essential amino acid (EAA) levels contribute to rice nutritional quality. However, the molecular mechanisms underlying EAA accumulation and natural variation in rice grains remain unclear. Here we report the identification of a previously unrecognized auxin influx carrier subfamily gene, OsAUX5, which encodes an amino acid transporter that functions in uptake of multiple amino acids. We identified an elite haplotype of Pro::OsAUX5^Hap2 that enhances grain EAA accumulation without an apparent negative effect on agronomic traits. Natural variations of OsAUX5 occur in the cis elements of its promoter, which are differentially activated because of the different binding affinity between OsWRKY78 and the W-box, contributing to grain EAA variation among rice varieties. The two distinct haplotypes were shown to have originated from different Oryza rufipogon progenitors, which contributed to the divergence between japonica and indica. Introduction of the indica-type Pro::OsAUX5^Hap2 genotype into japonica could significantly increase EAA levels, indicating that indica-type Pro::OsAUX5^Hap2 can be utilized to increase grain EAAs of japonica varieties. Collectively, our study uncovers an WRKY78-OsAUX5-based regulatory mechanism controlling grain EAA accumulation and provides a potential target for breeding EAA-rich rice.

LettuceGDB: The community database for lettuce genetics and omics

As a globally popular leafy vegetable and a representative plant of the Asteraceae family, lettuce has great economic and academic significance. In the last decade, high-throughput sequencing, phenotyping, and other multi-omics data in lettuce have accumulated on a large scale, thus increasing the demand for an integrative lettuce database. Here, we report the establishment of a comprehensive lettuce database, LettuceGDB (https://www.lettucegdb.com/). As an omics data hub, the current LettuceGDB includes two reference genomes with detailed annotations; re-sequencing data from over 1000 lettuce varieties; a collection of more than 1300 worldwide germplasms and millions of accompanying phenotypic records obtained with manual and cutting-edge phenomics technologies; re-analyses of 256 RNA sequencing datasets; a complete miRNAome; extensive metabolite information for representative varieties and wild relatives; epigenetic data on the genome-wide chromatin accessibility landscape; and various lettuce research papers published in the last decade. Five hierarchically accessible functions (Genome, Genotype, Germplasm, Phenotype, and O-Omics) have been developed with a user-friendly interface to enable convenient data access. Eight built-in tools (Assembly Converter, Search Gene, BLAST, JBrowse, Primer Design, Gene Annotation, Tissue Expression, Literature, and Data) are available for data downloading and browsing, functional gene exploration, and experimental practice. A community forum is also available for information sharing, and a summary of current research progress on different aspects of lettuce is included. We believe that LettuceGDB can be a comprehensive functional database amenable to data mining and database-driven exploration, useful for both scientific research and lettuce breeding.

Cross Cultivation on Homologous/Heterologous Plant-Based Culture Media Empowers Host-Specific and Real Time In Vitro Signature of Plant Microbiota

Alliances of microbiota with plants are masked by the inability of in vitro cultivation of their bulk. Pure cultures piled in international centers originated from dissimilar environments/hosts. Reporting that plant root/leaf-based culture media support the organ-specific growth of microbiota, it was of interest to further investigate if a plant-based medium prepared from homologous (maize) supports specific/adapted microbiota compared to another prepared from heterologous plants (sunflower). The culture-independent community of maize phyllosphere was compared to communities cross-cultivated on plant broth-based media: CFU counts and taxa prevalence (PCR-DGGE; Illumina MiSeq amplicon sequencing). Similar to total maize phyllospheric microbiota, culture-dependent communities were overwhelmed by Proteobacteria (>94.3–98.3%); followed by Firmicutes (>1.3–3.7%), Bacteroidetes (>0.01–1.58%) and Actinobacteria (>0.06–0.34%). Differential in vitro growth on homologous versus heterologous plant-media enriched/restricted various taxa. In contrast, homologous cultivation over represented members of Proteobacteria (ca. > 98.0%), mainly Pseudomonadaceae and Moraxellaceae; heterologous cultivation and R2A enriched Firmicutes (ca. > 3.0%). The present strategy simulates/fingerprints the chemical composition of host plants to expand the culturomics of plant microbiota, advance real-time in vitro cultivation and lab-keeping of compatible plant microbiota, and identify preferential pairing of plant-microbe partners toward future synthetic community (SynComs) research and use in agriculture.

Multi-omics revolution to promote plant breeding efficiency

Crop production is the primary goal of agricultural activities, which is always taken into consideration. However, global agricultural systems are coming under increasing pressure from the rising food demand of the rapidly growing world population and changing climate. To address these issues, improving high-yield and climate-resilient related-traits in crop breeding is an effective strategy. In recent years, advances in omics techniques, including genomics, transcriptomics, proteomics, and metabolomics, paved the way for accelerating plant/crop breeding to cope with the changing climate and enhance food production. Optimized omics and phenotypic plasticity platform integration, exploited by evolving machine learning algorithms will aid in the development of biological interpretations for complex crop traits. The precise and progressive assembly of desire alleles using precise genome editing approaches and enhanced breeding strategies would enable future crops to excel in combating the changing climates. Furthermore, plant breeding and genetic engineering ensures an exclusive approach to developing nutrient sufficient and climate-resilient crops, the productivity of which can sustainably and adequately meet the world's food, nutrition, and energy needs. This review provides an overview of how the integration of omics approaches could be exploited to select crop varieties with desired traits.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.

Genome-wide association studies of five free amino acid levels in rice

Rice (Oryza sativa L.) is one of the important staple foods for human consumption and livestock use. As a complex quality trait, free amino acid (FAA) content in rice is of nutritional importance. To dissect the genetic mechanism of FAA level, five amino acids' (Val, Leu, Ile, Arg, and Trp) content and 4,325,832 high-quality SNPs of 448 rice accessions were used to conduct genome-wide association studies (GWAS) with nine different methods. Of these methods, one single-locus method (GEMMA), seven multi-locus methods (mrMLM, pLARmEB, FASTmrEMMA, pKWmEB, FASTmrMLM, ISIS EM-BLASSO, and FarmCPU), and the recent released 3VmrMLM were adopted for methodological comparison of quantitative trait nucleotide (QTN) detection and identification of stable quantitative trait nucleotide loci (QTLs). As a result, 987 QTNs were identified by eight multi-locus GWAS methods; FASTmrEMMA detected the most QTNs (245), followed by 3VmrMLM (160), and GEMMA detected the least QTNs (0). Among 88 stable QTLs identified by the above methods, 3VmrMLM has some advantages, such as the most common QTNs, the highest LOD score, and the highest proportion of all detected stable QTLs. Around these stable QTLs, candidate genes were found in the GO classification to be involved in the primary metabolic process, biosynthetic process, and catalytic activity, and shown in KEGG analysis to have participated in metabolic pathways, biosynthesis of amino acids, and tryptophan metabolism. Natural variations of candidate genes resulting in the content alteration of five FAAs were identified in this association panel. In addition, 95 QTN-by-environment interactions (QEIs) of five FAA levels were detected by 3VmrMLM only. GO classification showed that the candidate genes got involved in the primary metabolic process, transport, and catalytic activity. Candidate genes of QEIs played important roles in valine, leucine, and isoleucine degradation (QEI_09_03978551 and candidate gene LOC_Os09g07830 in the Leu dataset), tryptophan metabolism (QEI_01_00617184 and candidate gene LOC_Os01g02020 in the Trp dataset), and glutathione metabolism (QEI_12_09153839 and candidate gene LOC_Os12g16200 in the Arg dataset) pathways through KEGG analysis. As an alternative of the multi-locus GWAS method, these findings suggested that the application of 3VmrMLM may provide new insights into better understanding FAA accumulation and facilitate the molecular breeding of rice with high FAA level.

Metabolome genome-wide association study provides biochemical and genetic insights into natural variation of primary metabolites in sesame

Plants' primary metabolites are of great importance from the survival and nutritional perspectives. However, the genetic bases underlying the profiles of primary metabolites in oilseed crops remain largely unclear. As one of the main oilseed crops, sesame (Sesamum indicum L.) is a potential model plant for investigating oil metabolism in plants. Therefore, the objective of this study is to disclose the genetic variants associated with variation in the content of primary metabolites in sesame. We performed a comprehensive metabolomics analysis of primary metabolites in 412 diverse sesame accessions using gas chromatography-mass spectrometry and identified a total of 45 metabolites, including fatty acids, monoacylglycerols (MAGs), and amino acids. Genome-wide association study unveiled 433 significant single-nucleotide polymorphism loci associated with variation in primary metabolite contents in sesame. By integrating diverse genomic analyses, we identified 10 key candidate causative genes of variation in MAG, fatty acid, asparagine, and sucrose contents. Among them, SiDSEL was significantly associated with multiple traits. SiCAC3 and SiKASI were strongly associated with variation in oleic acid and linoleic acid contents. Overexpression of SiCAC3, SiKASI, SiLTPI.25, and SiLTPI.26 in transgenic Arabidopsis and Saccharomyces cerevisiae revealed that SiCAC3 is a potential target gene for improvement of unsaturated fatty acid levels in crops. Furthermore, we found that it may be possible to breed several quality traits in sesame simultaneously. Our results provide valuable genetic resources for improving sesame seed quality and our understanding of oilseed crops' primary metabolism.

Plant metabolic gene clusters in the multi-omics era

Secondary metabolism in plants gives rise to a vast array of small-molecule natural products. The discovery of operon-like gene clusters in plants has provided a new perspective on the evolution of specialized metabolism and the opportunity to rapidly advance the metabolic engineering of natural product production. Here, we review historical aspects of the study of plant metabolic gene clusters as well as general strategies for identifying plant metabolic gene clusters in the multi-omics era. We also emphasize the exploration of their natural variation and evolution, as well as new strategies for the prospecting of plant metabolic gene clusters and a deeper understanding of how their structure influences their function.

Exploring and exploiting genetics and genomics for sweetpotato improvement: Status and perspectives

Sweetpotato (Ipomoea batatas (L.) Lam.) is one of the most important root crops cultivated worldwide. Because of its adaptability, high yield potential, and nutritional value, sweetpotato has become an important food crop, particularly in developing countries. To ensure adequate crop yields to meet increasing demand, it is essential to enhance the tolerance of sweetpotato to environmental stresses and other yield-limiting factors. The highly heterozygous hexaploid genome of I. batatas complicates genetic studies and limits improvement of sweetpotato through traditional breeding. However, application of next-generation sequencing and high-throughput genotyping and phenotyping technologies to sweetpotato genetics and genomics research has provided new tools and resources for crop improvement. In this review, we discuss the genomics resources that are available for sweetpotato, including the current reference genome, databases, and available bioinformatics tools. We systematically review the current state of knowledge on the polyploid genetics of sweetpotato, including studies of its origin and germplasm diversity and the associated mapping of important agricultural traits. We then outline the conventional and molecular breeding approaches that have been applied to sweetpotato. Finally, we discuss future goals for genetic studies of sweetpotato and crop improvement via breeding in combination with state-of-the-art multi-omics approaches such as genomic selection and gene editing. These approaches will advance and accelerate genetic improvement of this important root crop and facilitate its sustainable global production.

Lipidomics-Assisted GWAS (lGWAS) Approach for Improving High-Temperature Stress Tolerance of Crops

High-temperature stress (HT) over crop productivity is an important environmental factor demanding more attention as recent global warming trends are alarming and pose a potential threat to crop production. According to the Sixth IPCC report, future years will have longer warm seasons and frequent heat waves. Thus, the need arises to develop HT-tolerant genotypes that can be used to breed high-yielding crops. Several physiological, biochemical, and molecular alterations are orchestrated in providing HT tolerance to a genotype. One mechanism to counter HT is overcoming high-temperature-induced membrane superfluidity and structural disorganizations. Several HT lipidomic studies on different genotypes have indicated the potential involvement of membrane lipid remodelling in providing HT tolerance. Advances in high-throughput analytical techniques such as tandem mass spectrometry have paved the way for large-scale identification and quantification of the enormously diverse lipid molecules in a single run. Physiological trait-based breeding has been employed so far to identify and select HT tolerant genotypes but has several disadvantages, such as the genotype-phenotype gap affecting the efficiency of identifying the underlying genetic association. Tolerant genotypes maintain a high photosynthetic rate, stable membranes, and membrane-associated mechanisms. In this context, studying the HT-induced membrane lipid remodelling, resultant of several up-/down-regulations of genes and post-translational modifications, will aid in identifying potential lipid biomarkers for HT tolerance/susceptibility. The identified lipid biomarkers (LIPIDOTYPE) can thus be considered an intermediate phenotype, bridging the gap between genotype–phenotype (genotype–LIPIDOTYPE–phenotype). Recent works integrating metabolomics with quantitative genetic studies such as GWAS (mGWAS) have provided close associations between genotype, metabolites, and stress-tolerant phenotypes. This review has been sculpted to provide a potential workflow that combines MS-based lipidomics and the robust GWAS (lipidomics assisted GWAS-lGWAS) to identify membrane lipid remodelling related genes and associations which can be used to develop HS tolerant genotypes with enhanced membrane thermostability (MTS) and heat stable photosynthesis (HP).

Multi-omics analyses of 398 foxtail millet accessions reveal genomic regions associated with domestication, metabolite traits, and anti-inflammatory effects

Foxtail millet (Setaria italica), which was domesticated from the wild species green foxtail (Setaria viridis), is a rich source of phytonutrients for humans. To evaluate how breeding changed the metabolome of foxtail millet grains, we generated and analyzed the datasets encompassing the genomes, transcriptomes, metabolomes, and anti-inflammatory indices from 398 foxtail millet accessions. We identified hundreds of common variants that influence numerous secondary metabolites. We observed tremendous differences in natural variations of the metabolites and their underlying genetic architectures between distinct sub-groups of foxtail millet. Furthermore, we found that the selection of the gene alleles associated with yellow grains led to altered profiles of metabolites such as carotenoids and endogenous phytohormones. Using CRISPR-mediated genome editing we validated the function of PHYTOENE SYNTHASE 1 (PSY1) gene in affecting millet grain color and quality. Interestingly, our in vitro cell inflammation assays showed that 83 metabolites in millet grains have anti-inflammatory effects. Taken together, our multi-omics study illustrates how the breeding history of foxtail millet has shaped its metabolite profile. The datasets we generated in this study also provide important resources for further understanding how millet grain quality is affected by different metabolites, laying the foundations for future millet genetic research and metabolome-assisted improvement.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Genome-wide association mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity

A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genetic heterogeneity and epistasis, would be expected to increase in severity with the geographical range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French Arabidopsis thaliana population (less than 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than the previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits. This article is part of the theme issue 'Genetic basis of adaptation and speciation: from loci to causative mutations'.

Genome-Wide Association Study With Growth-Related Traits and Secondary Metabolite Contents in Red- and White-Heart Chinese Fir

Chinese fir [Cunninghamia lanceolata (Lamb.) Hook] is an important evergreen coniferous tree species that is widely distributed in many southern provinces of China and has important economic value. The Chinese fir accounts for 1/4 and 1/3 of the total artificial forest area and stock volume, respectively. Red-heart Chinese fir is popular in the market because of its high density and red heartwood. The long-growth cycle hindered the breeding process of Chinese fir, while molecular marker-assisted breeding could accelerate it. However, Chinese fir, a perennial conifer species, has a large genome, which has not yet been published. In this study, the growth-related traits and secondary metabolite contents of red- and white-heart Chinese fir were measured and found to be different between them. There are extremely significant differences among growth-related traits (p < 0.001), but secondary metabolite contents have different correlations due to differences in chemical structure. Moreover, genotype effect analysis of the substantially correlated single nucleotide polymorphisms (SNPs) revealed that most of the loci related to each growth-related traits were different from each other, indicating a type specificity of the genes regulated different growth-related traits. Furthermore, among the loci related to secondary metabolite contents, nine loci associated with multiple metabolite phenotypes such as Marker21022_4, Marker21022_172, Marker24559_31, Marker27425_37, Marker20748_85, Marker18841_115, Marker18841_198, Marker65846_146, and Marker21486_163, suggesting the presence of pleiotropic genes. This study identified the potential SNP markers associated with secondary metabolites in Chinese fir, thus setting the basis for molecular marker-assisted selection.

Integrated metabolomics and transcriptome analysis on flavonoid biosynthesis in flowers of safflower (Carthamus tinctorius L.) during colour-transition

Background

Safflower (Carthamus tinctorius L.), well known for its flower, is widely used as a dye and traditional Chinese medicine. Flavonoids, especially flavonoid glycosides, are the main pigments and active components. However, their biosynthesis is largely unknown. Interestingly, the colour of flowers in safflower changed from yellow to red during flower development, while much of the gene and chemical bases during colour transition are unclear.

Methods

In this research, widely targeted metabolomics and transcriptomics were used to elucidate the changes in flavonoid biosynthesis from the gene and chemical points of view in flowers of safflower during colour transition. The screening of differential metabolites depended on fold change and variable importance in project (VIP) value. Differential expressed genes (DEGs) were screened by DESeq2 method. RT-PCR was used to analyse relative expressions of DEGs.

Results

A total of 212 flavonoid metabolites, including hydroxysafflor yellow A, carthamin and anthocyanins, were detected and showed a large difference. The candidate genes of glycosyltransferases and flavonoid hydroxylase that might participate in flavonoid glycoside biosynthesis were screened. Ten candidate genes were screened. Through integrated metabolomics and transcriptome analysis, a uridine diphosphate glucose glycosyltransferase gene, CtUGT9 showed a significant correlation with flavonoid glycosides in safflower. In addition, expression analysis showed that CtUGT9 was mainly expressed in the middle development of flowers and was significantly upregulated under MeJA treatment. Our results indicated that CtUGT9 might play an important role in flavonoid glycoside biosynthesis during colour-transition in safflower.

Advances in Metabolomics-Driven Diagnostic Breeding and Crop Improvement

Climate change continues to threaten global crop output by reducing annual productivity. As a result, global food security is now considered as one of the most important challenges facing humanity. To address this challenge, modern crop breeding approaches are required to create plants that can cope with increased abiotic/biotic stress. Metabolomics is rapidly gaining traction in plant breeding by predicting the metabolic marker for plant performance under a stressful environment and has emerged as a powerful tool for guiding crop improvement. The advent of more sensitive, automated, and high-throughput analytical tools combined with advanced bioinformatics and other omics techniques has laid the foundation to broadly characterize the genetic traits for crop improvement. Progress in metabolomics allows scientists to rapidly map specific metabolites to the genes that encode their metabolic pathways and offer plant scientists an excellent opportunity to fully explore and rationally harness the wealth of metabolites that plants biosynthesize. Here, we outline the current application of advanced metabolomics tools integrated with other OMICS techniques that can be used to: dissect the details of plant genotype–metabolite–phenotype interactions facilitating metabolomics-assisted plant breeding for probing the stress-responsive metabolic markers, explore the hidden metabolic networks associated with abiotic/biotic stress resistance, facilitate screening and selection of climate-smart crops at the metabolite level, and enable accurate risk-assessment and characterization of gene edited/transgenic plants to assist the regulatory process. The basic concept behind metabolic editing is to identify specific genes that govern the crucial metabolic pathways followed by the editing of one or more genes associated with those pathways. Thus, metabolomics provides a superb platform for not only rapid assessment and commercialization of future genome-edited crops, but also for accelerated metabolomics-assisted plant breeding. Furthermore, metabolomics can be a useful tool to expedite the crop research if integrated with speed breeding in future.

Metabolic Profiles Reveal Changes in the Leaves and Roots of Rapeseed (Brassica napus L.) Seedlings under Nitrogen Deficiency

Rapeseed (Brassica napus L.) is an important oil crop species and plays a crucial role in supplying edible oil worldwide. However, rapeseed production in the field is often severely inhibited due to nitrogen (N) deficiency. Metabolites play key roles in plant growth and resistance to environmental stress, but little is known about the differential synthesis and accumulation of metabolites underlying rapeseed adaptation to N deficiency. Here, we studied the phenotypic response and used LC–electrospray ionization (ESI), ESI–MS/MS, and widely untargeted metabolomic approaches to detect differences in rapeseed under normal N (HN) and N-deficient (LN) conditions. The results showed that N deficiency severely inhibited rapeseed shoot growth and promoted rapeseed root architectural changes under LN conditions. In total, 574 metabolites were detected, and there were 175 and 166 differentially accumulated metabolites in the leaves and roots between the HN and LN conditions, respectively. The significantly differentially accumulated metabolites were involved in four primary metabolic pathways, namely, sucrose, phenylalanine, amino acid, and tricarboxylic acid cycle metabolism. Notably, we found that plant hormones have distinct accumulation patterns in rapeseed and coordinate to play crucial roles in both maintaining growth and protecting against damage from plant disease under HN and LN conditions. Moreover, our results indicated that flavonoid compounds, especially anthocyanins and rutin, may play important roles in increasing root cell resistance to oxidative damage and soil pathogen infections. Overall, this work provides valuable information for understanding the overall metabolite changes in rapeseed under N deficiency conditions, which may be beneficial for improving and producing new varieties of rapeseed capable of high yields under low N conditions.

Computational prediction of plant metabolic pathways

Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. Here, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.

Bringing more players into play: Leveraging stress in genome wide association studies

In order to meet the demand of the burgeoning human population as well as to adapt crops to the enhanced abiotic and biotic stress caused by the global climatic change, breeders focus on identifying valuable genes to improve both crop stress tolerance and crop quality. Recently, with the development of next-generation sequencing methods, millions of high quality single-nucleotide polymorphisms (SNPs) have been made available and genome-wide association studies (GWAS) are widely used in crop improvement studies to identify the associations between genetic variants of genomes and relevant crop agronomic traits. Here, we review classic cases of use of GWAS to identify genetic variants associated with valuable traits such as geographic adaptation, crop quality and metabolites. We discuss the power of stress GWAS to identify further associations including those with genes that are not, or only lowly, expressed during optimal growth conditions. Finally, we emphasize recent demonstrations of the efficiency and accuracy of time-resolved dynamic stress GWAS and GWAS based on genomic gene expression and structural variations, which can be applied to resolve more comprehensively the genetic regulation mechanisms of complex traits.

Metabolomics Study of Different Germplasm Resources for Three Polygonatum Species Using UPLC-Q-TOF-MS/MS

Rhizomes of the Polygonatum species are well-known in traditional Chinese medicine. The 2020 edition of Chinese Pharmacopoeia includes three different species that possess different pharmacological effects. Due to the lack of standardized discriminant compounds there has often been inadvertently incorrect prescriptions given for these medicines, resulting in serious consequences. Therefore, it is critical to accurately distinguish these herbal Polygonatum species. For this study, UPLC-Q-TOF-MS/MS based metabolomics was employed for the first time to discriminate between three Polygonatum species. Partial least squares discriminant analysis (PLS-DA) models were utilized to select the potential candidate discriminant compounds, after which MS/MS fragmentation patterns were used to identify them. Meanwhile, metabolic correlations were identified using the R language package corrplot, and the distribution of various metabolites was analyzed by box plot and the Z-score graph. As a result, we found that adenosine, sucrose, and pyroglutamic acid were suitable for the identification of different Polygonatum species. In conclusion, this study articulates how various herbal Polygonatum species might be more accurately and efficiently distinguished.

Metabolomics: a systems biology approach for enhancing heat stress tolerance in plants

Comprehensive metabolomic investigations provide a large set of stress-related metabolites and metabolic pathways, advancing crops under heat stress conditions. Metabolomics-assisted breeding, including mQTL and mGWAS boosted our understanding of improving numerous quantitative traits under heat stress. During the past decade, metabolomics has emerged as a fascinating scientific field that includes documentation, evaluation of metabolites, and chemical methods for cell monitoring programs in numerous plant species. A comprehensive metabolome profiling allowed the investigator to handle the comprehensive data groups of metabolites and the equivalent metabolic pathways in an extraordinary manner. Metabolomics, together with transcriptomics, plays an influential role in discovering connections between stress and genes/metabolite, phenotyping, and biomarkers documentation. Further, it helps to decode several metabolic systems connected with heat stress (HS) tolerance in plants. Heat stress is a critical environmental factor that is globally affecting the growth and productivity of plants. Thus, there is an urgent need to exploit modern breeding and biotechnological tools like metabolomics to develop cultivars with improved HS tolerance. Several studies have reported that amino acids, carbohydrates, nitrogen metabolisms, etc. and metabolites involved in the biosynthesis and catalyzing actions play a game-changing role in HS response and help plants to cope with the HS. The use of metabolomics-assisted breeding (MAB) allows a well-organized transmission of higher yield and HS tolerance at the metabolome level with specific properties. Progressive metabolomics systematic techniques have accelerated metabolic profiling. Nonetheless, continuous developments in bioinformatics, statistical tools, and databases are allowing us to produce ever-progressing, comprehensive insights into the biochemical configuration of plants and by what means this is inclined by genetic and environmental cues. Currently, assimilating metabolomics with post-genomic platforms has allowed a significant division of genetic-phenotypic connotation in several plant species. This review highlights the potential of a state-of-the-art plant metabolomics approach for the improvement of crops under HS. The development of plants with specific properties using integrated omics (metabolomics and transcriptomics) and MAB can provide new directions for future research to enhance HS tolerance in plants to achieve a goal of "zero hunger".