Metabolomics analysis and metabolite-agronomic trait associations using kernels of wheat (Triticum aestivum) recombinant inbred lines

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

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

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.

Depicting the genetic and metabolic panorama of chemical diversity in the tea plant

As a frequently consumed beverage worldwide, tea is rich in naturally important bioactive metabolites. Combining genetic, metabolomic and biochemical methodologies, here, we present a comprehensive study to dissect the chemical diversity in tea plant. A total of 2837 metabolites were identified at high-resolution with 1098 of them being structurally annotated and 63 of them were structurally identified. Metabolite-based genome-wide association mapping identified 6199 and 7823 metabolic quantitative trait loci (mQTL) for 971 and 1254 compounds in young leaves (YL) and the third leaves (TL), respectively. The major mQTL (i.e., P < 1.05 × 10-5, and phenotypic variation explained (PVE) > 25%) were further interrogated. Through extensive annotation of the tea metabolome as well as network-based analysis, this study broadens the understanding of tea metabolism and lays a solid foundation for revealing the natural variations in the chemical composition of the tea plant. Interestingly, we found that galloylations, rather than hydroxylations or glycosylations, were the largest class of conversions within the tea metabolome. The prevalence of galloylations in tea is unusual, as hydroxylations and glycosylations are typically the most prominent conversions of plant specialized metabolism. The biosynthetic pathway of flavonoids, which are one of the most featured metabolites in tea plant, was further refined with the identified metabolites. And we demonstrated the further mining and interpretation of our GWAS results by verifying two identified mQTL (including functional candidate genes CsUGTa, CsUGTb, and CsCCoAOMT) and completing the flavonoid biosynthetic pathway of the tea plant.

Constructing the metabolic network of wheat kernels based on structure-guided chemical modification and multi-omics data

Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities, although it often proves to be challenging and labor-intensive, particularly with non-model organisms. In this study, we develop a computational approach that employs reaction models based on structure-guided chemical modification and related compounds to construct a metabolic network in wheat. This construction results in a comprehensive structure-guided network, including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database. Using a combination of gene annotation, reaction classification, structure similarity, and transcriptome and metabolome analysis correlations, a total of 229 potential genes related to these reactions are identified within this network. To validate the network, the functionality of a hydroxycinnamoyltransferase (TraesCS3D01G314900) for the synthesis of polyphenols and a rhamnosyltransferase (TraesCS2D01G078700) for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests, respectively. Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.

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.

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.

Metabolic GWAS-based dissection of genetic basis underlying nutrient quality variation and domestication of cassava storage root

BACKGROUND

Metabolites play critical roles in regulating nutritional qualities of plants, thereby influencing their consumption and human health. However, the genetic basis underlying the metabolite-based nutrient quality and domestication of root and tuber crops remain largely unknown.

RESULTS

We report a comprehensive study combining metabolic and phenotypic genome-wide association studies to dissect the genetic basis of metabolites in the storage root (SR) of cassava. We quantify 2,980 metabolic features in 299 cultivated cassava accessions. We detect 18,218 significant marker-metabolite associations via metabolic genome-wide association mapping and identify 12 candidate genes responsible for the levels of metabolites that are of potential nutritional importance. Me3GT, MeMYB4, and UGT85K4/UGT85K5, which are involved in flavone, anthocyanin, and cyanogenic glucoside metabolism, respectively, are functionally validated through in vitro enzyme assays and in vivo gene silencing analyses. We identify a cluster of cyanogenic glucoside biosynthesis genes, among which CYP79D1, CYP71E7b, and UGT85K5 are highly co-expressed and their allelic combination contributes to low linamarin content. We find MeMYB4 is responsible for variations in cyanidin 3-O-glucoside and delphinidin 3-O-rutinoside contents, thus controlling SR endothelium color. We find human selection affects quercetin 3-O-glucoside content and SR weight per plant. The candidate gene MeFLS1 is subject to selection during cassava domestication, leading to decreased quercetin 3-O-glucoside content and thus increased SR weight per plant.

CONCLUSIONS

These findings reveal the genetic basis of cassava SR metabolome variation, establish a linkage between metabolites and agronomic traits, and offer useful resources for genetically improving the nutrition of cassava and other root crops.

Discovery of isoflavone phytoalexins in wheat reveals an alternative route to isoflavonoid biosynthesis

Isoflavones are a group of phenolic compounds mostly restricted to plants of the legume family, where they mediate important interactions with plant-associated microbes, including in defense from pathogens and in nodulation. Their well-studied health promoting attributes have made them a prime target for metabolic engineering, both for bioproduction of isoflavones as high-value molecules, and in biofortification of food crops. A key gene in their biosynthesis, isoflavone synthase, was identified in legumes over two decades ago, but little is known about formation of isoflavones outside of this family. Here we identify a specialized wheat-specific isoflavone synthase, TaCYP71F53, which catalyzes a different reaction from the leguminous isoflavone synthases, thus revealing an alternative path to isoflavonoid biosynthesis and providing a non-transgenic route for engineering isoflavone production in wheat. TaCYP71F53 forms part of a biosynthetic gene cluster that produces a naringenin-derived O-methylated isoflavone, 5-hydroxy-2',4',7-trimethoxyisoflavone, triticein. Pathogen-induced production and in vitro antimicrobial activity of triticein suggest a defense-related role for this molecule in wheat. Genomic and metabolic analyses of wheat ancestral grasses further show that the triticein gene cluster was introduced into domesticated emmer wheat through natural hybridization ~9000 years ago, and encodes a pathogen-responsive metabolic pathway that is conserved in modern bread wheat varieties.

Prediction of plant secondary metabolic pathways using deep transfer learning

BACKGROUND

Plant secondary metabolites are highly valued for their applications in pharmaceuticals, nutrition, flavors, and aesthetics. It is of great importance to elucidate plant secondary metabolic pathways due to their crucial roles in biological processes during plant growth and development. However, understanding plant biosynthesis and degradation pathways remains a challenge due to the lack of sufficient information in current databases. To address this issue, we proposed a transfer learning approach using a pre-trained hybrid deep learning architecture that combines Graph Transformer and convolutional neural network (GTC) to predict plant metabolic pathways.

RESULTS

GTC provides comprehensive molecular representation by extracting both structural features from the molecular graph and textual information from the SMILES string. GTC is pre-trained on the KEGG datasets to acquire general features, followed by fine-tuning on plant-derived datasets. Four metrics were chosen for model performance evaluation. The results show that GTC outperforms six other models, including three previously reported machine learning models, on the KEGG dataset. GTC yields an accuracy of 96.75%, precision of 85.14%, recall of 83.03%, and F1_score of 84.06%. Furthermore, an ablation study confirms the indispensability of all the components of the hybrid GTC model. Transfer learning is then employed to leverage the shared knowledge acquired from the KEGG metabolic pathways. As a result, the transferred GTC exhibits outstanding accuracy in predicting plant secondary metabolic pathways with an average accuracy of 98.30% in fivefold cross-validation and 97.82% on the final test. In addition, GTC is employed to classify natural products. It achieves a perfect accuracy score of 100.00% for alkaloids, while the lowest accuracy score of 98.42% for shikimates and phenylpropanoids.

CONCLUSIONS

The proposed GTC effectively captures molecular features, and achieves high performance in classifying KEGG metabolic pathways and predicting plant secondary metabolic pathways via transfer learning. Furthermore, GTC demonstrates its generalization ability by accurately classifying natural products. A user-friendly executable program has been developed, which only requires the input of the SMILES string of the query compound in a graphical interface.

Approach strategies and application of metabolomics to biotechnology in plants

Metabolomics refers to the technology for the comprehensive analysis of metabolites and low-molecular-weight compounds in a biological system, such as cells or tissues. Metabolites play an important role in biological phenomena through their direct involvement in the regulation of physiological mechanisms, such as maintaining cell homeostasis or signal transmission through protein-protein interactions. The current review aims provide a framework for how the integrated analysis of metabolites, their functional actions and inherent biological information can be used to understand biological phenomena related to the regulation of metabolites and how this information can be applied to safety assessments of crops created using biotechnology. Advancement in technology and analytical instrumentation have led new ways to examine the convergence between biology and chemistry, which has yielded a deeper understanding of complex biological phenomena. Metabolomics can be utilized and applied to safety assessments of biotechnology products through a systematic approach using metabolite-level data processing algorithms, statistical techniques, and database development. The integration of metabolomics data with sequencing data is a key step towards improving additional phenotypical evidence to elucidate the degree of environmental affects for variants found in genome associated with metabolic processes. Moreover, information analysis technology such as big data, machine learning, and IT investment must be introduced to establish a system for data extraction, selection, and metabolomic data analysis for the interpretation of biological implications of biotechnology innovations. This review outlines the integrity of metabolomics assessments in determining the consequences of genetic engineering and biotechnology in plants.

Uncovering mechanisms governing stem growth in peanut (Arachis hypogaea L.) with varying plant heights through integrated transcriptome and metabolomics analyses

The mechanisms responsible for stem growth in peanut (Arachis hypogaea L.) cultivars with varying plant heights remain unclear, despite the significant impact of plant height on peanut yield. Therefore, this study aimed to investigate the underlying mechanisms of peanut stem growth using phenotypic, physiological, transcriptomic, and metabolomic analyses. The findings revealed that the tallest cultivar, HY33, exhibited the highest rate of stem growth and accumulated the most stem dry matter, followed by the intermediate cultivar, SH108, while the dwarf cultivar, Df216, displayed the lowest values. Furthermore, SH108 exhibited a higher harvest index, as well as superior pod and kernel yields compared to both HY33 and Df216. Transcriptome and metabolome analyses identified differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs) associated with phenylpropanoid and flavonoid biosynthesis. Notably, downregulated DEGs in Df216/HY33 and Df216/SH108 included phenylalanine ammonia-lyase (PAL), caffeoyl-CoA O-methyltransferase (COMT), and ferulate-5-hydroxylase (F5H), while downregulated DEMs included p-coumaryl alcohol, chlorogenic acid, and L-epicatechin. Compared to HY33, the reduced activities of PAL, COMT, and F5H resulted in a decreased stem lignin content in Df216. Additionally, downregulated DEGs involved in gibberellin (GA) and brassinosteroid (BR) biosynthesis were identified in Df216/HY33, which contributed to the lowest levels of GA1, GA3, and BR contents in Df216. The results suggest that the dwarf phenotype arises from impaired GA and BR biosynthesis and signaling, resulting in a slower stem growth rate and reduced lignin accumulation.

Genomic basis of selective breeding from the closest wild relative of large-fruited tomato

The long and intricate domestication history of the tomato (Solanum lycopersicum) includes selection sweeps that have not been fully explored, and these sweeps show significant evolutionary trajectories of domestication traits. Using three distinct selection strategies, we represented comprehensive selected sweeps from 53 Solanum pimpinellifolium (PIM) and 166 S. lycopersicum (BIG) accessions, which are defined as pseudo-domestication in this study. We identified 390 potential selection sweeps, some of which had a significant impact on fruit-related traits and were crucial to the pseudo-domestication process. During tomato pseudo-domestication, we discovered a minor-effect allele of the SlLEA gene related to fruit weight (FW), as well as the major haplotypes of fw2.2/cell number regulator (CNR), fw3.2/SlKLUH, and fw11.3/cell size regulator (CSR) in cultivars. Furthermore, 18 loci were found to be significantly associated with FW and six fruit-related agronomic traits in genome-wide association studies. By examining population differentiation, we identified the causative variation underlying the divergence of fruit flavonoids across the large-fruited tomatoes and validated BRI1-EMS-SUPPRESSOR 1.2 (SlBES1.2), a gene that may affect flavonoid content by modulating the MYB12 expression profile. Our results provide new research routes for the genetic basis of fruit traits and excellent genomic resources for tomato genomics-assisted breeding.

QT-GWAS: A novel method for unveiling biosynthetic loci affecting qualitative metabolic traits

Although the plant kingdom provides an enormous diversity of metabolites with potentially beneficial applications for humankind, a large fraction of these metabolites and their biosynthetic pathways remain unknown. Resolving metabolite structures and their biosynthetic pathways is key to gaining biological understanding and to allow metabolic engineering. In order to retrieve novel biosynthetic genes involved in specialized metabolism, we developed a novel untargeted method designated as qualitative trait GWAS (QT-GWAS) that subjects qualitative metabolic traits to a genome-wide association study, while the conventional metabolite GWAS (mGWAS) mainly considers the quantitative variation of metabolites. As a proof of the validity of QT-GWAS, 23 and 15 of the retrieved associations identified in Arabidopsis thaliana by QT-GWAS and mGWAS, respectively, were supported by previous research. Furthermore, seven gene-metabolite associations retrieved by QT-GWAS were confirmed in this study through reverse genetics combined with metabolomics and/or in vitro enzyme assays. As such, we established that CYTOCHROME P450 706A5 (CYP706A5) is involved in the biosynthesis of chroman derivatives, UDP-GLYCOSYLTRANSFERASE 76C3 (UGT76C3) is able to hexosylate guanine in vitro and in planta, and SULFOTRANSFERASE 202B1 (SULT202B1) catalyzes the sulfation of neolignans in vitro. Collectively, our study demonstrates that the untargeted QT-GWAS method can retrieve valid gene-metabolite associations at the level of enzyme-encoding genes, even new associations that cannot be found by the conventional mGWAS, providing a new approach for dissecting qualitative metabolic traits.

A multi-omic resource of wheat seed tissues for nutrient deposition and improvement for human health

As a globally important staple crop, wheat seeds provide us with nutrients and proteins. The trend of healthy dietary has become popular recently, emphasizing the consumption of whole-grain wheat products and the dietary benefits. However, the dynamic changes in nutritional profiles of different wheat seed regions (i.e., the embryo, endosperm and outer layers) during developmental stages and the molecular regulation have not been well studied. Here, we provide this multi-omic resource of wheat seeds and describe the generation, technical assessment and preliminary analyses. This resource includes a time-series RNA-seq dataset of the embryo, endosperm and outer layers of wheat seeds and their corresponding metabolomic dataset, covering the middle and late stages of seed development. Our RNA-seq experiments profile the expression of 63,708 genes, while the metabolomic data includes the abundance of 984 metabolites. We believe that this was the first reported transcriptome and metabolome dataset of wheat seeds that helps understand the molecular regulation of the deposition of beneficial nutrients and hence improvements for nutritional and processing quality traits.

Serum metabolic profile and metabolome genome-wide association study in chicken

BACKGROUND

Chickens provide globally important livestock products. Understanding the genetic and molecular mechanisms underpinning chicken economic traits is crucial for improving their selective breeding. Influenced by a combination of genetic and environmental factors, metabolites are the ultimate expression of physiological processes and can provide key insights into livestock economic traits. However, the serum metabolite profile and genetic architecture of the metabolome in chickens have not been well studied.

RESULTS

Here, comprehensive metabolome detection was performed using non-targeted LC-MS/MS on serum from a chicken advanced intercross line (AIL). In total, 7,191 metabolites were used to construct a chicken serum metabolomics dataset and to comprehensively characterize the serum metabolism of the chicken AIL population. Regulatory loci affecting metabolites were identified in a metabolome genome-wide association study (mGWAS). There were 10,061 significant SNPs associated with 253 metabolites that were widely distributed across the entire chicken genome. Many functional genes affect metabolite synthesis, metabolism, and regulation. We highlight the key roles of TDH and AASS in amino acids, and ABCB1 and CD36 in lipids.

CONCLUSIONS

We constructed a chicken serum metabolite dataset containing 7,191 metabolites to provide a reference for future chicken metabolome characterization work. Meanwhile, we used mGWAS to analyze the genetic basis of chicken metabolic traits and metabolites and to improve chicken breeding.

The pathway of melatonin biosynthesis in common wheat (Triticum aestivum)

Melatonin (Mel) is a multifunctional biomolecule found in both animals and plants. In plants, the biosynthesis of Mel from tryptophan (Trp) has been delineated to comprise of four consecutive reactions. However, while the genes encoding these enzymes in rice are well characterized no systematic evaluation of the overall pathway has, as yet, been published for wheat. In the current study, the relative contents of six Mel-pathway-intermediates including Trp, tryptamine (Trm), serotonin (Ser), 5-methoxy tryptamine (5M-Trm), N-acetyl serotonin (NAS) and Mel, were determined in 24 independent tissues spanning the lifetime of wheat. These studies indicated that Trp was the most abundant among the six metabolites, followed by Trm and Ser. Next, the candidate genes expressing key enzymes involved in the Mel pathway were explored by means of metabolite-based genome-wide association study (mGWAS), wherein two TDC genes, a T5H gene and one SNAT gene were identified as being important for the accumulation of Mel pathway metabolites. Moreover, a 463-bp insertion within the T5H gene was discovered that may be responsible for variation in Ser content. Finally, a ASMT gene was found via sequence alignment against its rice homolog. Validations of these candidate genes were performed by in vitro enzymatic reactions using proteins purified following recombinant expression in Escherichia coli, transient gene expression in tobacco, and transgenic approaches in wheat. Our results thus provide the first comprehensive investigation into the Mel pathway metabolites, and a swift candidate gene identification via forward-genetics strategies, in common wheat.

Integrating multiomics data accelerates elucidation of plant primary and secondary metabolic pathways

Plants are the most important sources of food for humans, as well as supplying many ingredients that are of great importance for human health. Developing an understanding of the functional components of plant metabolism has attracted considerable attention. The rapid development of liquid chromatography and gas chromatography, coupled with mass spectrometry, has allowed the detection and characterization of many thousands of metabolites of plant origin. Nowadays, elucidating the detailed biosynthesis and degradation pathways of these metabolites represents a major bottleneck in our understanding. Recently, the decreased cost of genome and transcriptome sequencing rendered it possible to identify the genes involving in metabolic pathways. Here, we review the recent research which integrates metabolomic with different omics methods, to comprehensively identify structural and regulatory genes of the primary and secondary metabolic pathways. Finally, we discuss other novel methods that can accelerate the process of identification of metabolic pathways and, ultimately, identify metabolite function(s).

Metabolomic Analysis Reveals Patterns of Whole Wheat and Pearling Fraction Flour Quality Response to Nitrogen in Two Wheat Lines with Contrasting Protein Content

Nitrogen (N) application increases wheat yield and protein content and affects the nutritional quality of the grain. Analysis of N use efficiency revealed that N uptake efficiency is a key factor affecting protein content. Two wheat lines with significant differences in protein content were used to investigate the response of differential accumulation of metabolites to N levels and the spatial variation pattern of metabolites related to nutritional quality in wheat grains using widely targeted metabolomics analysis. The results showed that amino acids, nucleic acids, and phytohormones and their derivatives and glycolytic processes are the crucial factors affecting protein content in two wheat lines. Amino acids and derivatives, lipids, and flavonoids are the main contributors to metabolite spatial variation of grains, which were interactively regulated by nitrogen and genotypes. N application significantly increased the relative accumulation of beneficial bioactive substances in the inner layer (P3 to P5), but excessive N application may inhibit this effect and lead to poor nutritional quality.

QTL analysis of important agronomic traits and metabolites in foxtail millet (Setaria italica) by RIL population and widely targeted metabolome

As a bridge between genome and phenotype, metabolome is closely related to plant growth and development. However, the research on the combination of genome, metabolome and multiple agronomic traits in foxtail millet (Setaria italica) is insufficient. Here, based on the linkage analysis of 3,452 metabolites via with high-quality genetic linkage maps, we detected a total of 1,049 metabolic quantitative trait loci (mQTLs) distributed in 11 hotspots, and 28 metabolite-related candidate genes were mined from 14 mQTLs. In addition, 136 single-environment phenotypic QTL (pQTLs) related to 63 phenotypes were identified by linkage analysis, and there were 12 hotspots on these pQTLs. We futher dissected 39 candidate genes related to agronomic traits through metabolite-phenotype correlation and gene function analysis, including Sd1 semidwarf gene, which can affect plant height by regulating GA synthesis. Combined correlation network and QTL analysis, we found that flavonoid-lignin pathway maybe closely related to plant architecture and yield in foxtail millet. For example, the correlation coefficient between apigenin 7-rutinoside and stem diameter reached 0.98, and they were co-located at 41.33-44.15 Mb of chromosome 5, further gene function analysis revealed that 5 flavonoid pathway genes, as well as Sd1, were located in this interval . Therefore, the correlation and co-localization between flavonoid-lignins and plant architecture may be due to the close linkage of their regulatory genes in millet. Besides, we also found that a combination of genomic and metabolomic for BLUP analysis can better predict plant agronomic traits than genomic or metabolomic data, independently. In conclusion, the combined analysis of mQTL and pQTL in millet have linked genetic, metabolic and agronomic traits, and is of great significance for metabolite-related molecular assisted breeding.

Analysis of chemotypes and their markers in leaves of core collections of Eucommia ulmoides using metabolomics

The leaves of Eucommia ulmoides contain various active compunds and nutritional components, and have successively been included as raw materials in the Chinese Pharmacopoeia, the Health Food Raw Material Catalogue, and the Feed Raw Material Catalogue. Core collections of E. ulmoides had been constructed from the conserved germplasm resources basing on molecular markers and morphological traits, however, the metabolite diversity and variation in this core population were little understood. Metabolite profiles of E. ulmoides leaves of 193 core collections were comprehensively characterized by GC-MS and LC-MS/MS based non-targeted metabolomics in present study. Totally 1,100 metabolites were identified and that belonged to 18 categories, and contained 120 active ingredients for traditional Chinese medicine (TCM) and 85 disease-resistant metabolites. Four leaf chemotypes of the core collections were established by integrated uses of unsupervised self-organizing map (SOM), supervised orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF) statistical methods, 30, 23, 43, and 23 chemomarkers were screened corresponding to the four chemotypes, respectively. The morphological markers for the chemotypes were obtained by weighted gene co-expression network analysis (WGCNA) between the chenomarkers and the morphological traits, with leaf length (LL), chlorophyll reference value (CRV), leaf dentate height (LDH), and leaf thickness (LT) corresponding to chemotypes I, II, III, and IV, respectively. Contents of quercetin-3-O-pentosidine, isoquercitrin were closely correlated to LL, leaf area (LA), and leaf perimeter (LP), suggesting the quercetin derivatives might influence the growth and development of E. ulmoides leaf shape.

Metabolomics-centered mining of plant metabolic diversity and function: Past decade and future perspectives

Plants are natural experts in organic synthesis, being able to generate large numbers of specific metabolites with widely varying structures that help them adapt to variable survival challenges. Metabolomics is a research discipline that integrates the capabilities of several types of research including analytical chemistry, statistics, and biochemistry. Its ongoing development provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites. Metabolomics is usually performed by targeting either a specific cell, a specific tissue, or the entire organism. Considerable advances in science and technology over the last three decades have propelled us into the era of multi-omics, in which metabolomics, despite at an earlier developmental stage than genomics, transcriptomics, and proteomics, offers the distinct advantage of studying the cellular entities that have the greatest influence on end phenotype. Here, we summarize the state of the art of metabolite detection and identification, and illustrate these techniques with four case study applications: (i) comparing metabolite composition within and between species, (ii) assessing spatio-temporal metabolic changes during plant development, (iii) mining characteristic metabolites of plants in different ecological environments and upon exposure to various stresses, and (iv) assessing the performance of metabolomics as a means of functional gene identification , metabolic pathway elucidation, and metabolomics-assisted breeding through analyzing plant populations with diverse genetic variations. In addition, we highlight the prominent contributions of joint analyses of plant metabolomics and other omics datasets, including those from genomics, transcriptomics, proteomics, epigenomics, phenomics, microbiomes, and ion-omics studies. Finally, we discuss future directions and challenges exploiting metabolomics-centered approaches in understanding plant metabolic diversity.

Rewiring of the seed metabolome during Tartary buckwheat domestication

Crop domestication usually leads to the narrowing genetic diversity. However, human selection mainly focuses on visible traits, such as yield and plant morphology, with most metabolic changes being invisible to the naked eye. Buckwheat accumulates abundant bioactive substances, making it a dual-purpose crop with excellent nutritional and medical value. Therefore, examining the wiring of these invisible metabolites during domestication is of major importance. The comprehensive profiling of 200 Tartary buckwheat accessions exhibits 540 metabolites modified as a consequence of human selection. Metabolic genome-wide association study illustrates 384 mGWAS signals for 336 metabolites are under selection. Further analysis showed that an R2R3-MYB transcription factor FtMYB43 positively regulates the synthesis of procyanidin. Glycoside hydrolase gene FtSAGH1 is characterized as responsible for the release of active salicylic acid, the precursor of aspirin and indispensably in plant defence. UDP-glucosyltransferase gene FtUGT74L2 is characterized as involved in the glycosylation of emodin, a major medicinal component specific in Polygonaceae. The lower expression of FtSAGH1 and FtUGT74L2 were associated with the reduction of salicylic acid and soluble EmG owing to domestication. This first large-scale metabolome profiling in Tartary buckwheat will facilitate genetic improvement of medicinal properties and disease resistance in Tartary buckwheat.

Genetic mapping of maize metabolites using high-throughput mass profiling

Plant metabolites are the basis of human nutrition and have biological relevance in ecology. Farmers selected plants with favorable characteristics since prehistoric times and improved the cultivars, but without knowledge of underlying mechanisms. Understanding the genetic basis of metabolite production can facilitate the successful breeding of plants with augmented nutritional value. To identify genetic factors related to the metabolic composition in maize, we generated mass profiles of 198 recombinant inbred lines (RILs) and their parents (B73 and Mo17) using direct-injection electrospray ionization mass spectrometry (DLI-ESI MS). Mass profiling allowed the correct clustering of samples according to genotype. We quantified 71 mass features from grains and 236 mass features from leaf extracts. For the corresponding ions, we identified tissue-specific metabolic 'Quantitative Trait Loci' (mQTLs) distributed across the maize genome. These genetic regions could regulate multiple metabolite biosynthesis pathways. Our findings demonstrate that DLI-ESI MS has sufficient analytical resolution to map mQTLs. These identified genetic loci will be helpful in metabolite-focused maize breeding. Mass profiling is a powerful tool for detecting mQTLs in maize and enables the high-throughput screening of loci responsible for metabolite biosynthesis.

The use of ecological analytical tools as an unconventional approach for untargeted metabolomics data analysis: the case of Cecropia obtusifolia and its adaptive responses to nitrate starvation

Plant metabolomics studies haves revealed new bioactive compounds. However, like other omics disciplines, the generated data are not fully exploited, mainly because the commonly performed analyses focus on elucidating the presence/absence of distinctive metabolites (and/or their precursors) and not on providing a holistic view of metabolomic changes and their participation in organismal adaptation to biotic and abiotic stress conditions. Therefore, spectral libraries generated from Cecropia obtusifolia cell suspension cultures in a previous study were considered as a case study and were reanalyzed herein. These libraries were obtained from a time-course experiment under nitrate starvation conditions using both electrospray ionization modes. The applied methodology included the use of ecological analytical tools in a systematic four-step process, including a population analysis of metabolite α diversity, richness, and evenness (i); a chemometrics analysis to identify discriminant groups (ii); differential metabolic marker identification (iii); and enrichment analyses and annotation of active metabolic pathways enriched by differential metabolites (iv). Our species α diversity results referring to the diversity of metabolites represented by mass-to-charge ratio (m/z) values detected at a specific retention time (rt) (an uncommon way to analyze untargeted metabolomic data) suggest that the metabolome is dynamic and is modulated by abiotic stress. A total of 147 and 371 m/z_rt pairs was identified as differential markers responsive to nitrate starvation in ESI^- and ESI⁺ modes, respectively. Subsequent enrichment analysis showed a high degree of completeness of biosynthetic pathways such as those of brassinosteroids, flavonoids, and phenylpropanoids.

Multi-omics analysis reveals the molecular changes accompanying heavy-grazing-induced dwarfing of Stipa grandis

Heavy grazing significantly reduces Stipa grandis growth. To enhance our understanding of plant responses to heavy grazing, we conducted transcriptomic, proteomic, and metabolic analyses of the leaves of non-grazed plants (NG) and heavy-grazing-induced dwarf plants (HG) of S. grandis. A total of 101 metabolites, 167 proteins, and 1,268 genes differed in abundance between the HG and NG groups. Analysis of Kyoto Encyclopedia of Genes and Genomes pathways among differentially accumulated metabolites (DAMs) revealed that the most enriched pathways were flavone and flavonol biosynthesis, tryptophan metabolism, and phenylpropanoid biosynthesis. An integrative analysis of differentially expressed genes (DEGs) and proteins, and DAMs in these three pathways was performed. Heavy-grazing-induced dwarfism decreased the accumulation of DAMs enriched in phenylpropanoid biosynthesis, among which four DAMs were associated with lignin biosynthesis. In contrast, all DAMs enriched in flavone and flavonol biosynthesis and tryptophan metabolism showed increased accumulation in HG compared with NG plants. Among the DAMs enriched in tryptophan metabolism, three were involved in tryptophan-dependent IAA biosynthesis. Some of the DEGs and proteins enriched in these pathways showed different expression trends. The results indicated that these pathways play important roles in the regulation of growth and grazing-associated stress adaptions of S. grandis. This study enriches the knowledge of the mechanism of heavy-grazing-induced growth inhibition of S. grandis and provides valuable information for restoration of the productivity in degraded grassland.

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.

Mass Spectrometry-Based Nontargeted and Targeted Analytical Approaches in Fingerprinting and Metabolomics of Food and Agricultural Research

Mass spectrometry (MS)-based techniques have been extensively applied in food and agricultural research. This review aims to address the advances and applications of MS-based analytical strategies in nontargeted and targeted analysis and summarizes the recent publications of MS-based techniques, including flow injection MS fingerprinting, chromatography-tandem MS metabolomics, direct analysis using ambient mass spectrometry, as well as development in MS data deconvolution software packages and databases for metabolomic studies. Various nontargeted and targeted approaches are employed in marker compounds identification, material adulteration detection, and the analysis of specific classes of secondary metabolites. In the newly emerged applications, the recent advances in computer tools for the fast deconvolution of MS data in targeted secondary metabolite analysis are highlighted.

4D genetic networks reveal the genetic basis of metabolites and seed oil-related traits in 398 soybean RILs

Background

The yield and quality of soybean oil are determined by seed oil-related traits, and metabolites/lipids act as bridges between genes and traits. Although there are many studies on the mode of inheritance of metabolites or traits, studies on multi-dimensional genetic network (MDGN) are limited.

Results

In this study, six seed oil-related traits, 59 metabolites, and 107 lipids in 398 recombinant inbred lines, along with their candidate genes and miRNAs, were used to construct an MDGN in soybean. Around 175 quantitative trait loci (QTLs), 36 QTL-by-environment interactions, and 302 metabolic QTL clusters, 70 and 181 candidate genes, including 46 and 70 known homologs, were previously reported to be associated with the traits and metabolites, respectively. Gene regulatory networks were constructed using co-expression, protein–protein interaction, and transcription factor binding site and miRNA target predictions between candidate genes and 26 key miRNAs. Using modern statistical methods, 463 metabolite–lipid, 62 trait–metabolite, and 89 trait–lipid associations were found to be significant. Integrating these associations into the above networks, an MDGN was constructed, and 128 sub-networks were extracted. Among these sub-networks, the gene–trait or gene–metabolite relationships in 38 sub-networks were in agreement with previous studies, e.g., oleic acid (trait)–GmSEI–GmDGAT1a–triacylglycerol (16:0/18:2/18:3), gene and metabolite in each of 64 sub-networks were predicted to be in the same pathway, e.g., oleic acid (trait)–GmPHS–d-glucose, and others were new, e.g., triacylglycerol (16:0/18:1/18:2)–GmbZIP123–GmHD-ZIPIII-10–miR166s–oil content.

Conclusions

This study showed the advantages of MGDN in dissecting the genetic relationships between complex traits and metabolites. Using sub-networks in MGDN, 3D genetic sub-networks including pyruvate/threonine/citric acid revealed genetic relationships between carbohydrates, oil, and protein content, and 4D genetic sub-networks including PLDs revealed the relationships between oil-related traits and phospholipid metabolism likely influenced by the environment. This study will be helpful in soybean quality improvement and molecular biological research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02191-1.

Elucidation of the Flavor Aspects and Flavor-Associated Genomic Regions in Bottle Gourd (Lagenaria siceraria) by Metabolomic Analysis and QTL-seq

Bottle gourd (Lagenaria siceraria) is a commercially important cucurbitaceous vegetable with health-promoting properties whose collections and cultivars differ considerably in their flavor aspects. However, the metabolomic profile related to flavor has not yet been elucidated. In the present study, a comprehensive metabolite analysis revealed the metabolite profile of the strong-flavor collection “J120” and weak-flavor collection “G32”. The major differentially expressed metabolites included carboxylic acids, their derivatives, and organooxygen compounds, which are involved in amino acid biosynthesis and metabolism. QTL-seq was used to identify candidate genomic regions controlling flavor in a MAGIC population comprising 377 elite lines. Three significant genomic regions were identified, and candidate genes likely associated with flavor were screened. Our study provides useful information for understanding the metabolic causes of flavor variation among bottle gourd collections and cultivars. Furthermore, the identified candidate genomic regions may facilitate rational breeding programs to improve bottle gourd quality.

Metabolomics and Chemoinformatics in Agricultural Biotechnology Research: Complementary Probes in Unravelling New Metabolites for Crop Improvement

Simple Summary

The world is facing an overarching threat to food security, particularly in developing nations. The issue is further exacerbated by the apparent impacts of biotic and abiotic stresses driving down crop yields and productivity. Conventional strategies to improve yields and sustain productivity have been employed, including plant breeding for favourable and resilient agronomic traits. However, the efficacy and success rates of these methods are declining, partly due to the rapid changes in climate variability and the emergence of new and resistant phytopathogens. Additionally, the process of creating new and improved transgenic varieties of crops is long and can be expensive. Thus, new and innovative technologies are required for crop improvement. This review explores recent advances in the science of metabolomics and chemoinformatics, which have presented an avenue for rapid and robust analysis; moreover, it explores the elucidation of the complex plant metabolome, providing the opportunity to decipher the reactionary mechanisms of plants to the surrounding environment through their metabolic activity. As such, specific metabolites can, thus, be selected as biomarkers for crop improvement based on their functional characteristics under varying environmental conditions (growth, development, and defence). This new knowledge can enhance breeding practices through rapid and robust metabolic engineering techniques for sustainable agriculture.

Abstract

The United Nations (UN) estimate that the global population will reach 10 billion people by 2050. These projections have placed the agroeconomic industry under immense pressure to meet the growing demand for food and maintain global food security. However, factors associated with climate variability and the emergence of virulent plant pathogens and pests pose a considerable threat to meeting these demands. Advanced crop improvement strategies are required to circumvent the deleterious effects of biotic and abiotic stress and improve yields. Metabolomics is an emerging field in the omics pipeline and systems biology concerned with the quantitative and qualitative analysis of metabolites from a biological specimen under specified conditions. In the past few decades, metabolomics techniques have been extensively used to decipher and describe the metabolic networks associated with plant growth and development and the response and adaptation to biotic and abiotic stress. In recent years, metabolomics technologies, particularly plant metabolomics, have expanded to screening metabolic biomarkers for enhanced performance in yield and stress tolerance for metabolomics-assisted breeding. This review explores the recent advances in the application of metabolomics in agricultural biotechnology for biomarker discovery and the identification of new metabolites for crop improvement. We describe the basic plant metabolomics workflow, the essential analytical techniques, and the power of these combined analytical techniques with chemometrics and chemoinformatics tools. Furthermore, there are mentions of integrated omics systems for metabolomics-assisted breeding and of current applications.

Underlying mechanism of accelerated cell death and multiple disease resistance in a maize lethal leaf spot 1 allele

Multiple disease resistance (MDR) in maize has attracted increasing attention. However, the interplay between cell death and metabolite changes and their contributions to MDR remains elusive in maize. In this study, we identified a mutant named as lesion mimic 30 (les30) that showed 'suicidal' lesion formation in the absence of disease and had enhanced resistance to the fungal pathogen Curvularia lunata. Using map-based cloning, we identified the causal gene encoding pheophorbide a oxidase (PAO), which is known to be involved in chlorophyll degradation and MDR, and is encoded by LETHAL LEAF SPOT1 (LLS1). LLS1 was found to be induced by both biotic and abiotic stresses. Transcriptomics analysis showed that genes involved in defense responses and secondary metabolite biosynthesis were mildly activated in leaves of the les30 mutant without lesions, whilst they were strongly activated in leaves with lesions. In addition, in les30 leaves with lesions, there was overaccumulation of defense-associated phytohormones including jasmonic acid and salicylic acid, and of phytoalexins including phenylpropanoids, lignin, and flavonoids, suggesting that their biosynthesis was activated in a lesion-dependent manner. Taken together, our study implies the existence of an interactive amplification loop of interrupted chlorophyll degradation, cell death, expression of defense-related genes, and metabolite changes that results in suicidal lesion formation and MDR, and this has the potential to be exploited by genetic manipulation to improve maize disease resistance.

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.

Metabolomic spectra for phenotypic prediction of malting quality in spring barley

We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.

Metabolomics profile responses to changing environments in a common bean (Phaseolus vulgaris L.) germplasm collection

Metabolomics is one of the most powerful -omics to assist plant breeding. Despite the recognized genetic diversity in Portuguese common bean germplasm, details on its metabolomics profiles are still missing. Aiming to promote their use and to understand the environment's effect in bean metabolomics profiles, 107 Portuguese common bean accessions, cropped under contrasting environments, were analyzed using spectrophotometric, untargeted and targeted mass spectrometry approaches. Although genotype was the most relevant factor on bean metabolomics profile, a clear genotype × environment interaction was also detected. Multivariate analysis highlighted, on the heat-stress environment, the existence of higher levels of salicylic acid, and lower levels of triterpene saponins. Three clusters were defined within each environment. White accessions presented the lowest content and the colored ones the highest levels of prenol lipids and flavonoids. Sources of interesting metabolomics profiles are now identified for bean breeding, focusing either on local or on broad adaptation.

Metabolomic Analysis Reveals Nutritional Diversity among Three Staple Crops and Three Fruits

More than 2 billion people worldwide are under threat of nutritional deficiency. Thus, an in-depth comprehension of the nutritional composition of staple crops and popular fruits is essential for health. Herein, we performed LC-MS-based non-targeted and targeted metabolome analyses with crops (including wheat, rice, and corn) and fruits (including grape, banana, and mango). We detected a total of 2631 compounds by using non-targeted strategy and identified more than 260 nutrients. Our work discovered species-dependent accumulation of common present nutrients in crops and fruits. Although rice and wheat lack vitamins and amino acids, sweet corn was rich in most amino acids and vitamins. Among the three fruits, mango had more vitamins and amino acids than grape and banana. Grape and banana provided sufficient 5-methyltetrahydrofolate and vitamin B6, respectively. Moreover, rice and grape had a high content of flavonoids. In addition, the three crops contained more lipids than fruits. Furthermore, we also identified species-specific metabolites. The crops yielded 11 specific metabolites, including flavonoids, lipids, and others. Meanwhile, most fruit-specific nutrients were flavonoids. Our work discovered the complementary pattern of essential nutrients in crops and fruits, which provides metabolomic evidence for a healthy diet.

Metabolomic analysis of the grain pearling fractions of six bread wheat varieties

Bread wheat is a staple food crop that is consumed worldwide. In this study, using widely targeted LC-MS/MS, we conducted a high-throughput metabolomic analysis and determined the contents and spatial distribution of metabolites in pearled fractions of the dried kernels of six representative bread wheat varieties cultivated in China. Our aim was to explore the cultivars and pearling fractions with a view toward developing functional food products. We accordingly identified notable differences in the nutrient and bioactive metabolomes, and established that the pearling fractions of each cultivar had distinct metabolic profiles. Flavonoids varied the most amongst the cultivars and were found in higher concentration in the outer layers of the grain, but only at low concentrations in the kernel. Data from this study add further evidence of benefits of whole grain wheat consumption but, specifically, medium-gluten and pigmented wheat offer other nutrient and bioactive benefits whole grain products.

Entailing the Next-Generation Sequencing and Metabolome for Sustainable Agriculture by Improving Plant Tolerance

Crop production is a serious challenge to provide food for the 10 billion individuals forecasted to live across the globe in 2050. The scientists' emphasize establishing an equilibrium among diversity and quality of crops by enhancing yield to fulfill the increasing demand for food supply sustainably. The exploitation of genetic resources using genomics and metabolomics strategies can help generate resilient plants against stressors in the future. The innovation of the next-generation sequencing (NGS) strategies laid the foundation to unveil various plants' genetic potential and help us to understand the domestication process to unmask the genetic potential among wild-type plants to utilize for crop improvement. Nowadays, NGS is generating massive genomic resources using wild-type and domesticated plants grown under normal and harsh environments to explore the stress regulatory factors and determine the key metabolites. Improved food nutritional value is also the key to eradicating malnutrition problems around the globe, which could be attained by employing the knowledge gained through NGS and metabolomics to achieve suitability in crop yield. Advanced technologies can further enhance our understanding in defining the strategy to obtain a specific phenotype of a crop. Integration among bioinformatic tools and molecular techniques, such as marker-assisted, QTLs mapping, creation of reference genome, de novo genome assembly, pan- and/or super-pan-genomes, etc., will boost breeding programs. The current article provides sequential progress in NGS technologies, a broad application of NGS, enhancement of genetic manipulation resources, and understanding the crop response to stress by producing plant metabolites. The NGS and metabolomics utilization in generating stress-tolerant plants/crops without deteriorating a natural ecosystem is considered a sustainable way to improve agriculture production. This highlighted knowledge also provides useful research that explores the suitable resources for agriculture sustainability.

Nutritional improvement of cereal crops to combat hidden hunger during COVID-19 pandemic: Progress and prospects

COVID-19 has posed a severe challenge on food security by limiting access to food for the marginally placed population. While access to food is a challenge, access to nutritional food is a greater challenge to the population. The present-day foods are not sufficient to meet the nutritional requirements of the human body. In a pandemic condition, providing nutritious food to the population is imperative to ensure the health and well-being of humankind. Exploiting the existing biodiversity of crop species and deploying classical and modern tools to improve the nutritional potential of these species holds the key to addressing the above challenge. Breeding has been a classical tool of crop improvement that relied predominantly on genetic diversity. Collecting and conserving diverse germplasms and characterizing their diversity using molecular markers is essential to preserve diversity and use them in genetic improvement programs. These markers are also valuable for association mapping analyses to identify the genetic determinants of traits-of-interest in crop species. Association mapping identifies the quantitative trait loci (QTL) underlying the trait-of-interest by exploring marker-trait associations, and these QTLs can further be exploited for the genetic improvement of cultivated species through genomics-assisted breeding. Conventional breeding and genomics approaches are also being applied to develop biofortified cereal crops to reduce nutritional deficiencies in consumers. In this context, chapter explains the prerequisites for association mapping, population structure, genetic diversity, different approaches of performing association mapping to dissect nutritional traits, use the information for genomics-assisted breeding for nutrient-rich cereal crops, and application of genomics strategies in crop biofortification. These approaches will ensure food and nutrition security for all amidst the current COVID-19 crisis.

Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples

LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.

Tricin Biosynthesis and Bioengineering

Tricin (3',5'-dimethoxyflavone) is a specialized metabolite which not only confers stress tolerance and involves in defense responses in plants but also represents a promising nutraceutical. Tricin-type metabolites are widely present as soluble tricin O-glycosides and tricin-oligolignols in all grass species examined, but only show patchy occurrences in unrelated lineages in dicots. More strikingly, tricin is a lignin monomer in grasses and several other angiosperm species, representing one of the "non-monolignol" lignin monomers identified in nature. The unique biological functions of tricin especially as a lignin monomer have driven the identification and characterization of tricin biosynthetic enzymes in the past decade. This review summarizes the current understanding of tricin biosynthetic pathway in grasses and tricin-accumulating dicots. The characterized and potential enzymes involved in tricin biosynthesis are highlighted along with discussion on the debatable and uncharacterized steps. Finally, current developments of bioengineering on manipulating tricin biosynthesis toward the generation of functional food as well as modifications of lignin for improving biorefinery applications are summarized.

Alteration of Metabolites Accumulation in Maize Inbreds Leaf Tissue under Long-Term Water Deficit

Simple Summary

As sessile organisms, plants are constantly exposed to diverse environmental stresses of which water deficit is the most significant because it limits plant growth, development, and productivity. In this work, we showed the influence of non-irrigation treatment on changes in maize leaf metabolite content. We argued that the different susceptibility of maize inbred lines to long-term water deficit will result in different patterns of change in metabolite accumulation. We emphasized the need for the careful interpretation of the level and type of accumulated metabolites in order to assess the drought tolerance status of maize inbred lines in terms of improved grain yield exhibited under severe water deficit conditions. Leaf metabolites that have contributed to higher grain yield under the condition of long-term water deficit could be considered as biochemical markers useful in breeding drought-tolerant maize.

Abstract

Plants reconfigure their metabolic pathways to cope with water deficit. The aim of this study was to determine the status of the physiological parameters and the content of phenolic acids in the upper most ear leaf of maize inbred lines contrasting in drought tolerance in terms of improved plant productivity e.g., increased grain yield. The experiment was conducted under irrigation and rain-fed conditions. In drought-tolerant lines, the effect of water deficit was reflected through a chlorophyll and nitrogen balance index increase followed by a flavonols index decrease. The opposite trend was noticed in drought susceptible inbreds, with the exception of the anthocyanins index. Moreover, in comparison to irrigation treatment, opposite trends in the correlations between grain yield and physiological parameters found under water deficit conditions indicated the activation of different metabolic pathways in defense against water deficit stress. Concerning phenolic acid content, water deficit caused the reduction of protocatechuic, caffeic, and sinapic acid in all inbreds evaluated. However, the highly pronounced increase of ferulic and especially cinnamic acid content under water deficit conditions indicated possible crucial role of these secondary metabolites in preventing the harmful effects of water deficit stress, which, in turn, might be useful in maize breeding selection for drought tolerance.

Characterization of two closely related citrus cultivars using UPLC-ESI-MS/MS-based widely targeted metabolomics

Citrus cultivars are widely spread worldwide, and some of them only differ by specific mutations along the genome. It is difficult to distinguish them by traditional morphological identification. To accurately identify such similar cultivars, the subtle differences between them must be detected. In this study, UPLC-ESI-MS/MS-based widely targeted metabolomics analysis was conducted to study the chemical differences between two closely related citrus cultivars, Citrus reticulata 'DHP' and C. reticulata 'BZH'. Totally 352 metabolites including 11 terpenoids, 35 alkaloids, 80 phenolic acids, 25 coumarins, 7 lignans, 184 flavonoids and 10 other compounds were detected and identified; Among them, 15 metabolites are unique to DHP and 16 metabolites are unique to BZH. Hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal signal correction and partial least squares-discriminant analysis (OPLS-DA) can be used to clearly discriminate between DHP and BZH. 93 metabolites including 36 down-regulated and 57 up-regulated are significantly different in DHP and BZH. They are mainly involved in the biosynthesis of flavonoids, flavones, flavonols, and isoflavonoids. In addition, the relative content levels of flavonoids, alkaloids, and terpenoids are much higher in the peel of DHP than that of BZH, the presence of which may correlate with the quality difference of the peels. The results reported herein indicate that metabolite analysis based on UPLC-ESI-MS/MS is an effective means of identifying cultivars with different genotypes, especially those that cannot be distinguished based on traditional identification methods.

Exploring the genic resources underlying metabolites through mGWAS and mQTL in wheat: From large-scale gene identification and pathway elucidation to crop improvement

Common wheat (Triticum aestivum L.) is a leading cereal crop, but has lagged behind with respect to the interpretation of the molecular mechanisms of phenotypes compared with other major cereal crops such as rice and maize. The recently available genome sequence of wheat affords the pre-requisite information for efficiently exploiting the potential molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets. Meanwhile, the successful application of metabolomics as an emergent large-scale profiling methodology in several species has demonstrated this approach to be accessible for reaching the above goals. One such productive avenue is combining metabolomics approaches with genetic designs. However, this trial is not as widespread as that for sequencing technologies, especially when the acquisition, understanding, and application of metabolic approaches in wheat populations remain more difficult and even arguably underutilized. In this review, we briefly introduce the techniques used in the acquisition of metabolomics data and their utility in large-scale identification of functional candidate genes. Considerable progress has been made in delivering improved varieties, suggesting that the inclusion of information concerning these metabolites and genes and metabolic pathways enables a more explicit understanding of phenotypic traits and, as such, this procedure could serve as an -omics-informed roadmap for executing similar improvement strategies in wheat and other species.

Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research

Ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS) variants currently represent the best tools to tackle the challenges of complexity and lack of comprehensive coverage of the metabolome. UHPLC offers flexible and efficient separation coupled with high-sensitivity detection via HRMS, allowing for the detection and identification of a broad range of metabolites. Here we discuss current common strategies for UHPLC-HRMS-based metabolomics, with a focus on expanding metabolome coverage.

Commonly and Specifically Activated Defense Responses in Maize Disease Lesion Mimic Mutants Revealed by Integrated Transcriptomics and Metabolomics Analysis

Disease lesion mimic (Les/les) mutants display disease-like spontaneous lesions in the absence of pathogen infection, implying the constitutive activation of defense responses. However, the genetic and biochemical bases underlying the activated defense responses in those mutants remain largely unknown. Here, we performed integrated transcriptomics and metabolomics analysis on three typical maize Les mutants Les4, Les10, and Les17 with large, medium, and small lesion size, respectively, thereby dissecting the activated defense responses at the transcriptional and metabolomic level. A total of 1,714, 4,887, and 1,625 differentially expressed genes (DEGs) were identified in Les4, Les10, and Les17, respectively. Among them, 570, 3,299, and 447 specific differentially expressed genes (SGs) were identified, implying a specific function of each LES gene. In addition, 480 common differentially expressed genes (CGs) and 42 common differentially accumulated metabolites (CMs) were identified in all Les mutants, suggesting the robust activation of shared signaling pathways. Intriguingly, substantial analysis of the CGs indicated that genes involved in the programmed cell death, defense responses, and phenylpropanoid and terpenoid biosynthesis were most commonly activated. Genes involved in photosynthetic biosynthesis, however, were generally repressed. Consistently, the dominant CMs identified were phenylpropanoids and flavonoids. In particular, lignin, the phenylpropanoid-based polymer, was significantly increased in all three mutants. These data collectively imply that transcriptional activation of defense-related gene expression; increase of phenylpropanoid, lignin, flavonoid, and terpenoid biosynthesis; and inhibition of photosynthesis are generalnatures associated with the lesion formation and constitutively activated defense responses in those mutants. Further studies on the identified SGs and CGs will shed new light on the function of each LES gene as well as the regulatory network of defense responses in maize.

Understanding Omics Driven Plant Improvement and de novo Crop Domestication: Some Examples

In the current era, one of biggest challenges is to shorten the breeding cycle for rapid generation of a new crop variety having high yield capacity, disease resistance, high nutrient content, etc. Advances in the "-omics" technology have revolutionized the discovery of genes and bio-molecules with remarkable precision, resulting in significant development of plant-focused metabolic databases and resources. Metabolomics has been widely used in several model plants and crop species to examine metabolic drift and changes in metabolic composition during various developmental stages and in response to stimuli. Over the last few decades, these efforts have resulted in a significantly improved understanding of the metabolic pathways of plants through identification of several unknown intermediates. This has assisted in developing several new metabolically engineered important crops with desirable agronomic traits, and has facilitated the de novo domestication of new crops for sustainable agriculture and food security. In this review, we discuss how "omics" technologies, particularly metabolomics, has enhanced our understanding of important traits and allowed speedy domestication of novel crop plants.

Mass spectrometry based untargeted metabolomics for plant systems biology

Untargeted metabolomics enables the identification of key changes to standard pathways, but also aids in revealing other important and possibly novel metabolites or pathways for further analysis. Much progress has been made in this field over the past decade and yet plant metabolomics seems to still be an emerging approach because of the high complexity of plant metabolites and the number one challenge of untargeted metabolomics, metabolite identification. This final and critical stage remains the focus of current research. The intention of this review is to give a brief current state of LC–MS based untargeted metabolomics approaches for plant specific samples and to review the emerging solutions in mass spectrometer hardware and computational tools that can help predict a compound's molecular structure to improve the identification rate.

Metabolomics Analyses of Cotyledon and Plumule Showing the Potential Domestic Selection in Lotus Breeding

Lotus (Nelumbo nucifera) seeds are widely consumed as functional food or herbal medicine, of which cotyledon (CL) is the main edible part, and lotus plumule (LP) is commonly utilized in traditional Chinese medicine. However, few studies have been conducted to investigate the chemical components of CL and LP in dry lotus seeds, not to mention the comparison between wild and domesticated varieties. In this study, a widely targeted metabolomics approach based on Ultra Performance Liquid Chromatography-electrospray ionization-Tandem mass spectrometry (UPLC-ESI-MS/MS) was utilized to analyze the metabolites in CL and LP of China Antique (“CA”, a wild variety) and Jianxuan-17 (“JX”, a popular cultivar). A total of 402 metabolites were identified, which included flavonoids (23.08% to 27.84%), amino acids and derivatives (14.18–16.57%), phenolic acids (11.49–12.63%), and lipids (9.14–10.95%). These metabolites were classified into ten clusters based on their organ or cultivar-specific characters. Most of these metabolites were more abundant in LP than in CL for both varieties, except for metabolites belonging to organic acids and lipids. The analysis of differentially accumulated metabolites (DAMs) demonstrated that more than 25% of metabolites detected in our study were DAMs in CL and LP comparing “JX” with “CA”, most of which were less abundant in “JX”, including 35 flavonoids in LP, 23 amino acids and derivatives in CL, 7 alkaloids in CL, and 10 nucleotides and derivatives in LP, whereas all of 11 differentially accumulated lipids in LP were more abundant in “JX”. Together with the fact that the seed yield of “JX” is much higher than that of “CA”, these results indicated that abundant metabolites, especially the functional secondary metabolites (mainly flavonoids and alkaloids), were lost during the process of breeding selection.