Mapping the Arabidopsis Metabolic Landscape by Untargeted Metabolomics at Different Environmental Conditions

Metabolic fingerprinting reveals roles of Arabidopsis thaliana BGLU1, BGLU3, and BGLU4 in glycosylation of various flavonoids

Flavonoids are specialized metabolites that play important roles in plants, including interactions with the environment. The high structural diversity of this metabolite group is largely due to enzyme-mediated modifications of flavonoid core skeletons. In particular, glycosylation with different sugars is very common. In this study, the functions of the Arabidopsis thaliana glycoside hydrolase family 1-type glycosyltransferase proteins BGLU1, BGLU3, and BGLU4 were investigated, using a reverse genetics approach and untargeted metabolic fingerprinting. We screened for metabolic differences between A. thaliana wild type, loss-of-function mutants, and overexpression lines and partially identified differentially accumulating metabolites, which are putative products and/or substrates of the BGLU enzymes. Our study revealed that the investigated BGLU proteins are glycosyltransferases involved in the glycosylation of already glycosylated flavonoids using different substrates. While BGLU1 appears to be involved in the rhamnosylation of a kaempferol diglycoside in leaves, BGLU3 and BGLU4 are likely involved in the glycosylation of quercetin diglycosides in A. thaliana seeds. In addition, we present evidence that BGLU3 is a multifunctional enzyme that catalyzes other metabolic reactions with more complex substrates. This study deepens our understanding of the metabolic pathways and enzymes that contribute to the high structural diversity of flavonoids.

Glycoside-specific metabolomics reveals the novel mechanism of glycinebetaine-induced cold tolerance by regulating apigenin glycosylation in tea plants

Glycosylation is a key modification that affects secondary metabolites under stress and is influenced by glycinebetaine (GB) to regulate plant stress tolerance. However, the complexity and detection challenges of glycosides hinder our understanding of the regulatory mechanisms of their metabolic interaction with GB during stress. A glycoside-specific metabolomic approach utilizing cone voltage-induced in-source dissociation was developed, achieving precise and high-throughput detection of glycosides in tea plants by narrowing the target ion range by 94.3%. Combined with enzyme activity assays, exogenous spraying, and gene silencing, this approach helps investigate the role of GB-glycosides cascade effect in enhancing cold tolerance of tea plants. Our method demonstrated that silencing betaine aldehyde dehydrogenase (CsBADH1) in tea plants altered 60 glycoside ions while reducing GB content and cold tolerance, indicating that glycosylation affects GB-mediated cold tolerance. By combining glycoside-specific with conventional metabolomics, isorhoifolin, a GB-regulated cold response metabolite was discovered, and its precursor apigenin was found to be a new cold tolerance metabolite that enhanced cold tolerance by scavenging reactive oxygen species. This study reveals a new mechanism by which GB mediated cold tolerance in tea plants through regulating apigenin glycosylation, broadening our understanding of the role of glycosylation in plant cold tolerance.

Metabolomic Analysis Reveals the Diversity of Defense Metabolites in Nine Cereal Crops

Cereal crops are important staple foods, and their defense metabolites hold significant research importance. In this study, we employed LC-MS-based untargeted and widely-targeted metabolomics to profile the leaf metabolome of nine cereal species, including rice, wheat, maize, barley, sorghum, common oat, foxtail millet, broomcorn millet, and adlay. A total of 9869 features were detected, among them, 1131 were annotated, encompassing 18 classes such as flavonoids, lipids, and alkaloids. Results revealed that 531 metabolites were detected in all species, while each cereal crop possessed 4 to 12 unique metabolites. Focusing on defense metabolites, we identified eight benzoxazinoids uniquely present in maize, wheat, and adlay. Hierarchical clustering based on metabolite abundance divided all metabolites into nine clusters, and subsequent pathway enrichment analysis revealed that the stress-related flavonoid biosynthesis pathway was enriched in multiple clusters. Further analysis showed that four downstream compounds of HBOA (2-hydroxy-1,4-benzoxazin-3-one) in the benzoxazinoid biosynthesis pathway were enriched in maize. Wheat uniquely accumulated the 4'-methylated product of tricin, trimethoxytricetin, whereas adlay accumulated the tricin precursor tricetin in the flavonoid biosynthesis pathway. In summary, this study elucidates the metabolic diversity in defense metabolites among various cereal crops, providing valuable background information for the improvement of stress resistance in cereal crops.

A comprehensive study of the physiology and chemistry of tea withering based on untargeted metabolomic, transcriptomic, and biochemical analyses

Withering is an important process for achieving high-quality flavor in tea. In this study, histological, metabolomics, transcriptomics, and biochemical analyses were combined to comprehensively explore the accumulation and molecular regulatory profiles of quality metabolites during tea withering. The results of tissue staining indicated that as the water content decreased, the vitality of the nucleus weakened, cytoplasmic content increased, flavone content decreased, and proteins degraded. Omics analysis showed that the total content of soluble sugars, free amino acids, and terpenoids increased, whereas that of catechins decreased significantly, although the caffeine content barely changed. Biochemical analysis revealed that the translated products of genes CSA010827 and CSA001819 catalyzed the biosynthesis of galactose and flavanol 3-O-glycosides, respectively, thereby increasing the content of soluble sugars and contributing to the astringent taste. Overall, by combining omics with histological and biochemical analyses, we revealed the metabolic profile and possible molecular mechanisms during the withering process of tea.

NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy

Near-infrared spectroscopy (NIRS) has become a popular tool for investigating phenotypic variability in plants. We developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits, including classical functional traits as well as a large variety of commonly measured chemical compounds, based from near-infrared spectroscopy values based on deep learning. It is freely accessible at the following URL: https://shiny.cefe.cnrs.fr/NirsPredict/ . NIRSpredict has three main functionalities. First, it allows users to submit their spectrum values to get the predictions of plant traits from models built with the hosted A. thaliana database. Second, users have access to the database of traits used for model calibration. Data can be filtered and extracted on user's choice and visualized in a global context. Third, a user can submit his own dataset to extend the database and get part of the application development. NIRSpredict provides an easy-to-use and efficient method for trait prediction and an access to a large dataset of A. thaliana trait values. In addition to covering many of functional traits it also allows to predict a large variety of commonly measured chemical compounds. As a reliable way of characterizing plant populations across geographical ranges, NIRSpredict can facilitate the adoption of phenomics in functional and evolutionary ecology.

Proteomic and metabolomic insights into the mechanisms of calcium-mediated salt stress tolerance in hemp

Industrial hemp (Cannabis sativa L.) is a multifaced crop that has the potential to be exploited for many industrial applications, and making use of salt lands is considered to be a sustainable development strategy for the hemp industry. However, no elite salt-tolerant hemp varieties have been developed, and therefore supplementing appropriate exogenous substances to saline soil is one possible solution. Calcium-containing compounds are well-known for their salt tolerance enhancing effects, but the underlying molecular mechanisms remain largely unclear. Here, we first assessed the ameliorative effects of calcium amendments on salt-stressed hemp plants and then investigated these mechanisms on hemp using integrative analysis of proteomics and metabolomics. The stress phenotypes could be lessened by Ca2+ treatment. Certain concentrations of Ca2+ maintained relative electrical conductivity and the contents of malondialdehyde and chlorophyll. Ca2+ treatment also generally led to greater accumulations of soluble proteins, soluble carbohydrates and proline, and enhanced the activities of superoxide dismutase and peroxidase. Through functional classification, pathway enrichment, and network analysis, our data reveal that accumulation of dipeptides is a prominent metabolic signature upon exogenous Ca2+ treatment, and that changes in mitochondrial properties may play an important role in enhancing the salt tolerance. Our results outline the complex metabolic alternations involved in calcium-mediated salt stress resistance, and these data and analyses would be useful for future functional studies.

Uncovering fruit flavor and genetic diversity across diploid wild Fragaria species via comparative metabolomics profiling

Wild Fragaria resources exhibit extensive genetic diversity and desirable edible traits, such as high soluble solid content and flavor compounds. However, specific metabolites in different wild strawberry fruits remain unknown. In this study, we characterized 1008 metabolites covering 11 subclasses among 13 wild diploid resources representing eight species, including F. vesca, F. nilgerrensis, F. viridis, F. nubicola, F. pentaphylla, F. mandschurica, F. chinensis, and F. emeiensis. Fifteen potential metabolite biomarkers were identified to distinguish fruit flavors among the 13 diploid wild Fragaria accessions. A total of nine distinct modules were employed to explore key metabolites related to fruit quality through weighted gene co-expression module analysis, with significant enrichment in amino acid biosynthesis pathway. Notably, the identified significantly different key metabolites highlighted the close association of amino acids, sugars, and anthocyanins with flavor formation. These findings offer valuable resources for improving fruit quality through metabolome-assisted breeding.

T and Z, partial seed coat patterning genes in common bean, provide insight into the structure and protein interactions of a plant MBW complex

Flavonoids are secondary metabolites associated with plant seed coat and flower color. These compounds provide health benefits to humans as anti-inflammatory and antioxidant compounds. The expression of the late biosynthetic genes in the flavonoid pathway is controlled by a ternary MBW protein complex consisting of interfacing MYB, beta-helix-loop-helix (bHLH), and WD40 Repeat (WDR) proteins. P, the master regulator gene of the flavonoid expression in common bean (Phaseolus vulgaris L.), was recently determined to encode a bHLH protein. The T and Z genes control the distribution of color in bean seeds and flowers and have historically been considered regulators of the flavonoid gene expression. T and Z candidates were identified using reverse genetics based on genetic mapping, phylogenetic analysis, and mutant analysis. Domain and AlphaFold2 structure analyses determined that T encodes a seven-bladed β-propeller WDR protein, while Z encodes a R2R3 MYB protein. Deletions and SNPs in T and Z mutants, respectively, altered the 3D structure of these proteins. Modeling of the Z MYB/P bHLH/T WDR MBW complex identified interfacing sequence domains and motifs in all three genes that are conserved in dicots. One Z MYB motif is a possible beta-molecular recognition feature (β-MoRF) that only appears in a structured state when Z MYB is modeled as a component of a MBW complex. Complexes containing mutant T and Z proteins changed the interaction of members of the complex in ways that would alter their role in regulating the expression of genes in the flavonoid pathway.

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.

The natural variance of Arabidopsis secondary metabolism on extended darkness

In plants due to their sessile nature, secondary metabolites are important components against different abiotic and biotic stress, such as extended darkness. For this reason, the variation of secondary metabolite content of the Arabidopsis thaliana HapMap natural population following 0-and 6-d darkness treatment were detected and the raw data of different accessions at two timepoints were deposited in the Zenodo database. Moreover, the annotated secondary metabolites of these samples are presented in this data descriptor, which we believe will be a usefully re-usable resource for future integrative analysis with dark-treated transcripts, proteins or other phenotypic data in order to comprehensively illustrate the multiomic landscape of Arabidopsis in response to the stresses exerted by extended darkness.

Genome-wide association studies identify loci controlling specialized seed metabolites in Arabidopsis

Plants synthesize specialized metabolites to facilitate environmental and ecological interactions. During evolution, plants diversified in their potential to synthesize these metabolites. Quantitative differences in metabolite levels of natural Arabidopsis (Arabidopsis thaliana) accessions can be employed to unravel the genetic basis for metabolic traits using genome-wide association studies (GWAS). Here, we performed metabolic GWAS on seeds of a panel of 315 A. thaliana natural accessions, including the reference genotypes C24 and Col-0, for polar and semi-polar seed metabolites using untargeted ultra-performance liquid chromatography-mass spectrometry. As a complementary approach, we performed quantitative trait locus (QTL) mapping of near-isogenic introgression lines between C24 and Col-0 for specific seed specialized metabolites. Besides common QTL between seeds and leaves, GWAS revealed seed-specific QTL for specialized metabolites, indicating differences in the genetic architecture of seeds and leaves. In seeds, aliphatic methylsulfinylalkyl and methylthioalkyl glucosinolates associated with the ALKENYL HYDROXYALKYL PRODUCING loci (GS-ALK and GS-OHP) on chromosome 4 containing alkenyl hydroxyalkyl producing 2 (AOP2) and 3 (AOP3) or with the GS-ELONG locus on chromosome 5 containing methylthioalkyl malate synthase (MAM1) and MAM3. We detected two unknown sulfur-containing compounds that were also mapped to these loci. In GWAS, some of the annotated flavonoids (kaempferol 3-O-rhamnoside-7-O-rhamnoside, quercetin 3-O-rhamnoside-7-O-rhamnoside) were mapped to transparent testa 7 (AT5G07990), encoding a cytochrome P450 75B1 monooxygenase. Three additional mass signals corresponding to quercetin-containing flavonols were mapped to UGT78D2 (AT5G17050). The association of the loci and associating metabolic features were functionally verified in knockdown mutant lines. By performing GWAS and QTL mapping, we were able to leverage variation of natural populations and parental lines to study seed specialized metabolism. The GWAS data set generated here is a high-quality resource that can be investigated in further studies.

Genetic control of abiotic stress-related specialized metabolites in sunflower

BACKGROUND

Abiotic stresses in plants include all the environmental conditions that significantly reduce yields, like drought and heat. One of the most significant effects they exert at the cellular level is the accumulation of reactive oxygen species, which cause extensive damage. Plants possess two mechanisms to counter these molecules, i.e. detoxifying enzymes and non-enzymatic antioxidants, which include many classes of specialized metabolites. Sunflower, the fourth global oilseed, is considered moderately drought resistant. Abiotic stress tolerance in this crop has been studied using many approaches, but the control of specialized metabolites in this context remains poorly understood. Here, we performed the first genome-wide association study using abiotic stress-related specialized metabolites as molecular phenotypes in sunflower. After analyzing leaf specialized metabolites of 450 hybrids using liquid chromatography-mass spectrometry, we selected a subset of these compounds based on their association with previously known abiotic stress-related quantitative trait loci. Eventually, we characterized these molecules and their associated genes.

RESULTS

We putatively annotated 30 compounds which co-localized with abiotic stress-related quantitative trait loci and which were associated to seven most likely candidate genes. A large proportion of these compounds were potential antioxidants, which was in agreement with the role of specialized metabolites in abiotic stresses. The seven associated most likely candidate genes, instead, mainly belonged to cytochromes P450 and glycosyltransferases, two large superfamilies which catalyze greatly diverse reactions and create a wide variety of chemical modifications. This was consistent with the high plasticity of specialized metabolism in plants.

CONCLUSIONS

This is the first characterization of the genetic control of abiotic stress-related specialized metabolites in sunflower. By providing hints concerning the importance of antioxidant molecules in this biological context, and by highlighting some of the potential molecular mechanisms underlying their biosynthesis, it could pave the way for novel applications in breeding. Although further analyses will be required to better understand this topic, studying how antioxidants contribute to the tolerance to abiotic stresses in sunflower appears as a promising area of research.

The R2R3-MYB transcription factor EVER controls the emission of petunia floral volatiles by regulating epicuticular wax biosynthesis in the petal epidermis

The epidermal cells of petunia (Petunia × hybrida) flowers are the main site of volatile emission. However, the mechanisms underlying the release of volatiles into the environment are still being explored. Here, using cell-layer-specific transcriptomic analysis, reverse genetics by virus-induced gene silencing and clustered regularly interspaced short palindromic repeat (CRISPR), and metabolomics, we identified EPIDERMIS VOLATILE EMISSION REGULATOR (EVER)-a petal adaxial epidermis-specific MYB activator that affects the emission of volatiles. To generate ever knockout lines, we developed a viral-based CRISPR/Cas9 system for efficient gene editing in plants. These knockout lines, together with transient-suppression assays, revealed EVER's involvement in the repression of low-vapor-pressure volatiles. Internal pools and annotated scent-related genes involved in volatile production and emission were not affected by EVER. RNA-Seq analyses of petals of ever knockout lines and EVER-overexpressing flowers revealed enrichment in wax-related biosynthesis genes. Liquid chromatography/gas chromatography-MS analyses of petal epicuticular waxes revealed substantial reductions in wax loads in ever petals, particularly of monomers of fatty acids and wax esters. These results implicate EVER in the emission of volatiles by fine-tuning the composition of petal epicuticular waxes. We reveal a petunia MYB regulator that interlinks epicuticular wax composition and volatile emission, thus unraveling a regulatory layer in the scent-emission machinery in petunia flowers.

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.

Exploratory optimisation of a LC-HRMS based analytical method for untargeted metabolomic screening of Cannabis Sativa L. through Data Mining

BACKGROUND

Recent increase in public acceptance of cannabis as a natural medical alternative for certain neurological pathologies has led to its approval in different regions of the world. However, due to its previous illegal background, little research has been conducted around its biochemical insights. Therefore, in the current framework, metabolomics may be a suitable approach for deepening the knowledge around this plant species. Nevertheless, experimental methods in metabolomics must be carefully handled, as slight modifications can lead to metabolomic coverage loss. Hence, the main objective of this work was to optimise an analytical method for appropriate untargeted metabolomic screening of cannabis.

RESULTS

We present an empirically optimised experimental procedure through which the broadest metabolomic coverage was obtained, in which extraction solvents for metabolite isolation, chromatographic columns for LC-qOrbitrap analysis and plant-representative biological tissues were compared. By exploratory means, it was determined that the solvent combination composed of CHCl3:H2O:CH3OH (2:1:1, v/v) provided the highest number of features from diverse chemical classes, as it was a two-phase extractant. In addition, a reverse phase 2.6 μm C18 100 Å (150 × 3 mm) chromatographic column was determined as the appropriate choice for adequate separation and further detection of the diverse metabolite classes. Apart from that, overall chromatographic peak quality provided by each column was observed and the need for batch correction methods through quality control (QC) samples was confirmed. At last, leaf and flower tissues resulted to provide complementary metabolic information of the plant, to the detriment of stem tissue, which resulted to be negligible.

SIGNIFICANCE

It was concluded that the optimised experimental procedure could significantly ease the path for future research works related to cannabis metabolomics by LC-HRMS means, as the work was based on previous plant metabolomics literature. Furthermore, it is crucial to highlight that an optimal analytical method can vary depending on the main objective of the research, as changes in the experimental factors can lead to different outcomes, regardless of whether the results are better or worse.

Species-specific 'specialized' genomic region provides the new insights into the functional genomics characterizing metabolic polymorphisms in plants

Functional genomics approaches with comparative omics analyses of wild-accessions and cultivars/wild species, as well as comparative genomic analyses in plant species focusing on gene clusters, have successfully detected key metabolic polymorphisms in plant specialized metabolism. In recent decades, (i) intra-species specific metabolic polymorphisms, (ii) new functionalization of tandem duplicated genes, and (iii) metabolic gene clusters were found as the main factors creating metabolic diversity of specialized metabolites in plants. However, given findings aware us that the identification of genes in plant specialized metabolism requires strategic approaches depending on the target metabolic pathways. The increasing availability of plant genome sequences and transcriptome data has facilitated inter-specific comparative analyses, including genomic analysis and gene co-expression network analysis. Here, we introduce functional genomics approaches with the integration of inter-/intra-species comparative metabolomics, their key roles in providing genomic signatures of metabolic evolution, and discuss future prospects of functional genomics on plant specialized metabolism.

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.

Hello darkness, my old friend: 3-KETOACYL-COENZYME A SYNTHASE4 is a branch point in the regulation of triacylglycerol synthesis in Arabidopsis thaliana

Plant lipids are important as alternative sources of carbon and energy when sugars or starch are limited. Here, we applied combined heat and darkness or extended darkness to a panel of ∼300 Arabidopsis (Arabidopsis thaliana) accessions to study lipid remodeling under carbon starvation. Natural allelic variation at 3-KETOACYL-COENZYME A SYNTHASE4 (KCS4), a gene encoding an enzyme involved in very long chain fatty acid (VLCFA) synthesis, underlies the differential accumulation of polyunsaturated triacylglycerols (puTAGs) under stress. Ectopic expression of KCS4 in yeast and plants proved that KCS4 is a functional enzyme localized in the endoplasmic reticulum with specificity for C22 and C24 saturated acyl-CoA. Allelic mutants and transient overexpression in planta revealed the differential role of KCS4 alleles in VLCFA synthesis and leaf wax coverage, puTAG accumulation, and biomass. Moreover, the region harboring KCS4 is under high selective pressure and allelic variation at KCS4 correlates with environmental parameters from the locales of Arabidopsis accessions. Our results provide evidence that KCS4 plays a decisive role in the subsequent fate of fatty acids released from chloroplast membrane lipids under carbon starvation. This work sheds light on both plant response mechanisms and the evolutionary events shaping the lipidome under carbon starvation.

Limitations and advantages of using metabolite-based genome-wide association studies: focus on fruit quality traits

In the last decades, linkage mapping has help in the location of metabolite quantitative trait loci (QTL) in many species; however, this approach shows some limitations. Recently, thanks to the most recent advanced in high-throughput genotyping technologies like next-generation sequencing, metabolite genome-wide association study (mGWAS) has been proposed a powerful tool to identify the genetic variants in polygenic agrinomic traits. Fruit flavor is a complex interaction of aroma volatiles and taste being sugar and acid ratio key parameter for flavor acceptance. Here, we review recent progress of mGWAS in pinpoint gene polymorphisms related to flavor-related metabolites in fruits. Despite clear successes in discovering novel genes or regions associated with metabolite accumulation affecting sensory attributes in fruits, GWAS incurs in several limitations summarized in this review. In addition, in our own work, we performed mGWAS on 194 Citrus grandis accessions to investigate the genetic control of individual primary and lipid metabolites in ripe fruit. We have identified a total of 667 associations for 14 primary metabolites including amino acids, sugars, and organic acids, and 768 associations corresponding to 47 lipids. Furthermore, candidate genes related to important metabolites related to fruit quality such as sugars, organic acids and lipids were discovered.

Integrative Analysis of Metabolome and Transcriptome Reveals Molecular Insight into Metabolomic Variations during Hawthorn Fruit Development

Hawthorn (Crataegus pinnatifida var. major), a cultivated fruit tree, is native and unique to China. Its fruits have high nutritional, health, and medicinal values. However, the development and ripening process of hawthorns is accompanied by dramatic changes in flavor, aroma, and bioactive phytochemicals, which are the fundamental factors that contribute to the potential health benefits and establishment of fruit quality. Therefore, an exploration of the dynamic changes in metabolites and their regulatory networks during the development of hawthorn fruits can elucidate the formation mechanisms of active substances in hawthorn fruits. In this study, we used a broad targeted metabolomics approach to identify and analyze the dynamics of metabolites in hawthorn fruits at five developmental stages. The results revealed 998 primary and secondary metabolites that were classified into 15 categories. The accumulation levels of most sugars increased during fruit development and then accelerated at the fruit ripening stage. The accumulation levels of a few organic acids (e.g., citric acid, isocitric acid, and quinic acid) continuously increased. Many organic acids exhibited significant decreasing trends. Among the 561 secondary metabolites detected, 189 were phenolic acids and 199 were flavonoids. The levels of many flavonoids were significantly reduced at later stages of fruit development; in contrast, the levels of two anthocyanins significantly increased during fruit ripening. Correlation analysis revealed that there is a certain correlation within and between primary as well as secondary metabolites during fruit development. Furthermore, the integration of metabolomic and transcriptomic data in this study revealed that changes in the expression of some differentially expressed genes (DEGs) were associated with the accumulation of metabolites such as sugars, organic acids, and flavonoids, e.g., the upregulated expression levels of CS (citrate synthase) genes were consistent with the continued accumulation of citric acid. Overall, this study demonstrates the metabolic changes that occur during the development of hawthorn fruit, explores the molecular mechanisms that underlie metabolite changes during fruit development, and lays a strong theoretical foundation for the improvement of hawthorn fruit quality and the development of functional components.

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.

Glycoside-specific metabolomics combined with precursor isotopic labeling for characterizing plant glycosyltransferases

Glycosylation by uridine diphosphate-dependent glycosyltransferases (UGTs) in plants contributes to the complexity and diversity of secondary metabolites. UGTs are generally promiscuous in their use of acceptors, making it challenging to reveal the function of UGTs in vivo. Here, we described an approach that combined glycoside-specific metabolomics and precursor isotopic labeling analysis to characterize UGTs in Arabidopsis. We revisited the UGT72E cluster, which has been reported to catalyze the glycosylation of monolignols. Glycoside-specific metabolomics analysis reduced the number of differentially accumulated metabolites in the ugt72e1e2e3 mutant by at least 90% compared with that from traditional untargeted metabolomics analysis. In addition to the two previously reported monolignol glycosides, a total of 62 glycosides showed reduced accumulation in the ugt72e1e2e3 mutant, 22 of which were phenylalanine-derived glycosides, including 5-OH coniferyl alcohol-derived and lignan-derived glycosides, as confirmed by isotopic tracing of [¹³C₆]-phenylalanine precursor. Our method revealed that UGT72Es could use coumarins as substrates, and genetic evidence showed that UGT72Es endowed plants with enhanced tolerance to low iron availability under alkaline conditions. Using the newly developed method, the function of UGT78D2 was also evaluated. These case studies suggest that this method can substantially contribute to the characterization of UGTs and efficiently investigate glycosylation processes, the complexity of which have been highly underestimated.

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'.

Combined nature and human selections reshaped peach fruit metabolome

Background

Plant metabolites reshaped by nature and human beings are crucial for both their lives and human health. However, which metabolites respond most strongly to selection pressure at different evolutionary stages and what roles they undertake on perennial fruit crops such as peach remain unclear.

Results

Here, we report 18,052 significant locus-trait associations, 12,691 expression-metabolite correlations, and 294,676 expression quantitative trait loci (eQTLs) for peach. Our results indicate that amino acids accumulated in landraces may be involved in the environmental adaptation of peaches by responding to low temperature and drought. Moreover, the contents of flavonoids, the major nutrients in fruits, have kept decreasing accompanied by the reduced bitter flavor during both domestication and improvement stages. However, citric acid, under the selection of breeders’ and consumers’ preference for flavor, shows significantly different levels between eastern and western varieties. This correlates with differences in activity against cancer cells in vitro in fruit from these two regions. Based on the identified key genes regulating flavonoid and acid contents, we propose that more precise and targeted breeding technologies should be designed to improve peach varieties with rich functional contents because of the linkage of genes related to bitterness and acid taste, antioxidant and potential anti-cancer activity that are all located at the top of chromosome 5.

Conclusions

This study provides powerful data for future improvement of peach flavor, nutrition, and resistance in future and expands our understanding of the effects of natural and artificial selection on metabolites.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02719-6.

Plant growth-promoting and non-promoting rhizobacteria from avocado trees differentially emit volatiles that influence growth of Arabidopsis thaliana

Microbial volatile organic compounds (mVOCs) play important roles in inter- and intra-kingdom interactions, and they are also important as signal molecules in physiological processes acting either as plant growth-promoting or negatively modulating plant development. We investigated the effects of mVOCs emitted by PGPR vs non-PGPR from avocado trees (Persea americana) on growth of Arabidopsis thaliana seedlings. Chemical diversity of mVOCs was determined by SPME-GC-MS; selected compounds were screened in dose-response experiments in A. thaliana transgenic lines. We found that plant growth parameters were affected depending on inoculum concentration. Twenty-six compounds were identified in PGPR and non-PGPR with eight of them not previously reported. The VOCs signatures were differential between those groups. 4-methyl-2-pentanone, 1-nonanol, 2-phenyl-2-propanol and ethyl isovalerate modified primary root architecture influencing the expression of auxin- and JA-responsive genes, and cell division. Lateral root formation was regulated by 4-methyl-2-pentanone, 3-methyl-1-butanol, 1-nonanol and ethyl isovalerate suggesting a participation via JA signalling. Our study revealed the differential emission of volatiles by PGPR vs non-PGPR from avocado trees and provides a general view about the mechanisms by which those volatiles influence plant growth and development. Rhizobacteria strains and mVOCs here reported are promising for improvement the growth and productivity of avocado crop.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.

An efficient LC-MS method for isomer separation and detection of sugars, phosphorylated sugars, and organic acids

Assessing central carbon metabolism in plants can be challenging due to the dynamic range in pool sizes, with low levels of important phosphorylated sugars relative to more abundant sugars and organic acids. Here, we report a sensitive liquid chromatography-mass spectrometry method for analysing central metabolites on a hybrid column, where both anion-exchange and hydrophilic interaction chromatography (HILIC) ligands are embedded in the stationary phase. The liquid chromatography method was developed for enhanced selectivity of 27 central metabolites in a single run with sensitivity at femtomole levels observed for most phosphorylated sugars. The method resolved phosphorylated hexose, pentose, and triose isomers that are otherwise challenging. Compared with a standard HILIC approach, these metabolites had improved peak areas using our approach due to ion enhancement or low ion suppression in the biological sample matrix. The approach was applied to investigate metabolism in high lipid-producing tobacco leaves that exhibited increased levels of acetyl-CoA, a precursor for oil biosynthesis. The application of the method to isotopologue detection and quantification was considered through evaluating 13C-labeled seeds from Camelina sativa. The method provides a means to analyse intermediates more comprehensively in central metabolism of plant tissues.

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.

2',3'-cAMP treatment mimics the stress molecular response in Arabidopsis thaliana

The role of the RNA degradation product 2',3'-cyclic adenosine monophosphate (2',3'-cAMP) is poorly understood. Recent studies have identified 2',3'-cAMP in plant material and determined its role in stress signaling. The level of 2',3'-cAMP increases upon wounding, in the dark, and under heat, and 2',3'-cAMP binding to an RNA-binding protein, Rbp47b, promotes stress granule (SG) assembly. To gain further mechanistic insights into the function of 2',3'-cAMP, we used a multi-omics approach by combining transcriptomics, metabolomics, and proteomics to dissect the response of Arabidopsis (Arabidopsis thaliana) to 2',3'-cAMP treatment. We demonstrated that 2',3'-cAMP is metabolized into adenosine, suggesting that the well-known cyclic nucleotide-adenosine pathway of human cells might also exist in plants. Transcriptomics analysis revealed only minor overlap between 2',3'-cAMP- and adenosine-treated plants, suggesting that these molecules act through independent mechanisms. Treatment with 2',3'-cAMP changed the levels of hundreds of transcripts, proteins, and metabolites, many previously associated with plant stress responses, including protein and RNA degradation products, glucosinolates, chaperones, and SG components. Finally, we demonstrated that 2',3'-cAMP treatment influences the movement of processing bodies, confirming the role of 2',3'-cAMP in the formation and motility of membraneless organelles.

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".

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.

Genome-wide association of the metabolic shifts underpinning dark-induced senescence in Arabidopsis

Dark-induced senescence provokes profound metabolic shifts to recycle nutrients and to guarantee plant survival. To date, research on these processes has largely focused on characterizing mutants deficient in individual pathways. Here, we adopted a time-resolved genome-wide association-based approach to characterize dark-induced senescence by evaluating the photochemical efficiency and content of primary and lipid metabolites at the beginning, or after 3 or 6 days in darkness. We discovered six patterns of metabolic shifts and identified 215 associations with 81 candidate genes being involved in this process. Among these associations, we validated the roles of four genes associated with glycine, galactinol, threonine, and ornithine levels. We also demonstrated the function of threonine and galactinol catabolism during dark-induced senescence. Intriguingly, we determined that the association between tyrosine contents and TYROSINE AMINOTRANSFERASE 1 influences enzyme activity of the encoded protein and transcriptional activity of the gene under normal and dark conditions, respectively. Moreover, the single-nucleotide polymorphisms affecting the expression of THREONINE ALDOLASE 1 and the amino acid transporter gene AVT1B, respectively, only underlie the variation in threonine and glycine levels in the dark. Taken together, these results allow us to present a very detailed model of the metabolic aspects of dark-induced senescence, as well as the process itself.

Plant Microbiome-Based Genome-Wide Association Studies

Plants form intimate associations with microorganisms, and these associations are directly impacted by the host genotype. However, identifying specific host genetic pathways that influence these microbial interactions has proved challenging. Genome-wide association-based approaches that use features of microbiome composition as a quantitative trait represent a novel and underutilized strategy to identify such pathways. Several recent studies have demonstrated the potential utility of plant microbiome-based genome-wide association studies (GWAS). In this chapter, we describe the process of implementing GWAS using the plant microbiome as the primary quantitative trait, considering experimental design, sample harvest, and processing, but with an emphasis on data filtering, data normalization, and statistical analyses.

Genome wide association study reveals plant loci controlling heritability of the rhizosphere microbiome

Host genetics has recently been shown to be a driver of plant microbiome composition. However, identifying the underlying genetic loci controlling microbial selection remains challenging. Genome-wide association studies (GWAS) represent a potentially powerful, unbiased method to identify microbes sensitive to the host genotype and to connect them with the genetic loci that influence their colonization. Here, we conducted a population-level microbiome analysis of the rhizospheres of 200 sorghum genotypes. Using 16S rRNA amplicon sequencing, we identify rhizosphere-associated bacteria exhibiting heritable associations with plant genotype, and identify significant overlap between these lineages and heritable taxa recently identified in maize. Furthermore, we demonstrate that GWAS can identify host loci that correlate with the abundance of specific subsets of the rhizosphere microbiome. Finally, we demonstrate that these results can be used to predict rhizosphere microbiome structure for an independent panel of sorghum genotypes based solely on knowledge of host genotypic information.

Untargeted metabotyping to study phenylpropanoid diversity in crop plants

Plant genebanks constitute a key resource for breeding to ensure crop yield under changing environmental conditions. Because of their roles in a range of stress responses, phenylpropanoids are promising targets. Phenylpropanoids comprise a wide array of metabolites; however, studies regarding their diversity and the underlying genes are still limited for cereals. The assessment of barley diversity via genotyping-by-sequencing is in rapid progress. Exploring these resources by integrating genetic association studies to in-depth metabolomic profiling provides a valuable opportunity to study barley phenylpropanoid metabolism; but poses a challenge by demanding large-scale approaches. Here, we report an LC-PDA-MS workflow for barley high-throughput metabotyping. Without prior construction of a species-specific library, this method produced phenylpropanoid-enriched metabotypes with which the abundance of putative metabolic features was assessed across hundreds of samples in a single-processed data matrix. The robustness of the analytical performance was tested using a standard mix and extracts from two selected cultivars: Scarlett and Barke. The large-scale analysis of barley extracts showed (1) that barley flag leaf profiles were dominated by glycosylation derivatives of isovitexin, isoorientin, and isoscoparin; (2) proved the workflow's capability to discriminate within genotypes; (3) highlighted the role of glycosylation in barley phenylpropanoid diversity. Using the barley S42IL mapping population, the workflow proved useful for metabolic quantitative trait loci purposes. The protocol can be readily applied not only to explore the barley phenylpropanoid diversity represented in genebanks but also to study species whose profiles differ from those of cereals: the crop Helianthus annuus (sunflower) and the model plant Arabidopsis thaliana.

Genomic basis underlying the metabolome-mediated drought adaptation of maize

Background

Drought is a major environmental disaster that causes crop yield loss worldwide. Metabolites are involved in various environmental stress responses of plants. However, the genetic control of metabolomes underlying crop environmental stress adaptation remains elusive.

Results

Here, we perform non-targeted metabolic profiling of leaves for 385 maize natural inbred lines grown under well-watered as well as drought-stressed conditions. A total of 3890 metabolites are identified and 1035 of these are differentially produced between well-watered and drought-stressed conditions, representing effective indicators of maize drought response and tolerance. Genetic dissections reveal the associations between these metabolites and thousands of single-nucleotide polymorphisms (SNPs), which represented 3415 metabolite quantitative trait loci (mQTLs) and 2589 candidate genes. 78.6% of mQTLs (2684/3415) are novel drought-responsive QTLs. The regulatory variants that control the expression of the candidate genes are revealed by expression QTL (eQTL) analysis of the transcriptomes of leaves from 197 maize natural inbred lines. Integrated metabolic and transcriptomic assays identify dozens of environment-specific hub genes and their gene-metabolite regulatory networks. Comprehensive genetic and molecular studies reveal the roles and mechanisms of two hub genes, Bx12 and ZmGLK44, in regulating maize metabolite biosynthesis and drought tolerance.

Conclusion

Our studies reveal the first population-level metabolomes in crop drought response and uncover the natural variations and genetic control of these metabolomes underlying crop drought adaptation, demonstrating that multi-omics is a powerful strategy to dissect the genetic mechanisms of crop complex traits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-021-02481-1.

Genome-wide association studies: assessing trait characteristics in model and crop plants

GWAS involves testing genetic variants across the genomes of many individuals of a population to identify genotype–phenotype association. It was initially developed and has proven highly successful in human disease genetics. In plants genome-wide association studies (GWAS) initially focused on single feature polymorphism and recombination and linkage disequilibrium but has now been embraced by a plethora of different disciplines with several thousand studies being published in model and crop species within the last decade or so. Here we will provide a comprehensive review of these studies providing cases studies on biotic resistance, abiotic tolerance, yield associated traits, and metabolic composition. We also detail current strategies of candidate gene validation as well as the functional study of haplotypes. Furthermore, we provide a critical evaluation of the GWAS strategy and its alternatives as well as future perspectives that are emerging with the emergence of pan-genomic datasets.

Agronomic and Metabolomic Side-Effects of a Divergent Selection for Indol-3-Ylmethylglucosinolate Content in Kale (Brassica oleracea var. acephala)

Brassica oleracea var. acephala (kale) is a cruciferous vegetable widely cultivated for its leaves and flower buds in Europe and a food of global interest as a “superfood”. Brassica crops accumulate phytochemicals called glucosinolates (GSLs) which play an important role in plant defense against biotic stresses. Studies carried out to date suggest that GSLs may have a role in the adaptation of plants to different environments, but direct evidence is lacking. We grew two kale populations divergently selected for high and low indol-3-ylmethylGSL (IM) content (H-IM and L-IM, respectively) in different environments and analyzed agronomic parameters, GSL profiles and metabolomic profile. We found a significant increase in fresh and dry foliar weight in H-IM kale populations compared to L-IM in addition to a greater accumulation of total GSLs, indole GSLs and, specifically, IM and 1-methoxyindol-3-ylmethylGSL (1MeOIM). Metabolomic analysis revealed a significant different concentration of 44 metabolites in H-IM kale populations compared to L-IM. According to tentative peak identification from MS interpretation, 80% were phenolics, including flavonoids (kaempferol, quercetin and anthocyanin derivates, including acyl flavonoids), chlorogenic acids (esters of hydroxycinnamic acids and quinic acid), hydroxycinnamic acids (ferulic acid and p-coumaric acid) and coumarins. H-IM kale populations could be more tolerant to diverse environmental conditions, possibly due to GSLs and the associated metabolites with predicted antioxidant potential.

Contrast optimization of mass spectrometry imaging (MSI) data visualization by threshold intensity quantization (TrIQ)

Mass spectrometry imaging (MSI) enables the unbiased characterization of surfaces with respect to their chemical composition. In biological MSI, zones with differential mass profiles hint towards localized physiological processes, such as the tissue-specific accumulation of secondary metabolites, or diseases, such as cancer. Thus, the efficient discovery of 'regions of interest' (ROI) is of utmost importance in MSI. However, often the discovery of ROIs is hampered by high background noise and artifact signals. Especially in ambient ionization MSI, unmasking biologically relevant information from crude data sets is challenging. Therefore, we implemented a Threshold Intensity Quantization (TrIQ) algorithm for augmenting the contrast in MSI data visualizations. The simple algorithm reduces the impact of extreme values ('outliers') and rescales the dynamic range of mass signals. We provide an R script for post-processing MSI data in the imzML community format (https://bitbucket.org/lababi/msi.r) and implemented the TrIQ in our open-source imaging software RmsiGUI (https://bitbucket.org/lababi/rmsigui/). Applying these programs to different biological MSI data sets demonstrated the universal applicability of TrIQ for improving the contrast in the MSI data visualization. We show that TrIQ improves a subsequent detection of ROIs by sectioning. In addition, the adjustment of the dynamic signal intensity range makes MSI data sets comparable.

Metabolic source isotopic pair labeling and genome-wide association are complementary tools for the identification of metabolite-gene associations in plants

The optimal extraction of information from untargeted metabolomics analyses is a continuing challenge. Here, we describe an approach that combines stable isotope labeling, liquid chromatography- mass spectrometry (LC-MS), and a computational pipeline to automatically identify metabolites produced from a selected metabolic precursor. We identified the subset of the soluble metabolome generated from phenylalanine (Phe) in Arabidopsis thaliana, which we refer to as the Phe-derived metabolome (FDM) In addition to identifying Phe-derived metabolites present in a single wild-type reference accession, the FDM was established in nine enzymatic and regulatory mutants in the phenylpropanoid pathway. To identify genes associated with variation in Phe-derived metabolites in Arabidopsis, MS features collected by untargeted metabolite profiling of an Arabidopsis diversity panel were retrospectively annotated to the FDM and natural genetic variants responsible for differences in accumulation of FDM features were identified by genome-wide association. Large differences in Phe-derived metabolite accumulation and presence/absence variation of abundant metabolites were observed in the nine mutants as well as between accessions from the diversity panel. Many Phe-derived metabolites that accumulated in mutants also accumulated in non-Col-0 accessions and was associated to genes with known or suspected functions in the phenylpropanoid pathway as well as genes with no known functions. Overall, we show that cataloguing a biochemical pathway's products through isotopic labeling across genetic variants can substantially contribute to the identification of metabolites and genes associated with their biosynthesis.

Three-in-One Simultaneous Extraction of Proteins, Metabolites and Lipids for Multi-Omics

Elucidation of complex molecular networks requires integrative analysis of molecular features and changes at different levels of information flow and regulation. Accordingly, high throughput functional genomics tools such as transcriptomics, proteomics, metabolomics, and lipidomics have emerged to provide system-wide investigations. Unfortunately, analysis of different types of biomolecules requires specific sample extraction procedures in combination with specific analytical instrumentation. The most efficient extraction protocols often only cover a restricted type of biomolecules due to their different physicochemical properties. Therefore, several sets/aliquots of samples are needed for extracting different molecules. Here we adapted a biphasic fractionation method to extract proteins, metabolites, and lipids from the same sample (3-in-1) for liquid chromatography-tandem mass spectrometry (LC-MS/MS) multi-omics. To demonstrate utility of the improved method, we used bacteria-primed Arabidopsis leaves to generate multi-omics datasets from the same sample. In total, we were able to analyze 1849 proteins, 1967 metabolites, and 424 lipid species in single samples. The molecules cover a wide range of biological and molecular processes, and allow quantitative analyses of different molecules and pathways. Our results have shown the clear advantages of the multi-omics method, including sample conservation, high reproducibility, and tight correlation between different types of biomolecules.

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.

Fine mapping identifies NAD-ME1 as a candidate underlying a major locus controlling temporal variation in primary and specialized metabolism in Arabidopsis

Plant metabolism is modulated by a complex interplay between internal signals and external cues. A major goal of all quantitative metabolomic studies is to clone the underlying genes to understand the mechanistic basis of this variation. Using fine-scale genetic mapping, in this work we report the identification and initial characterization of NAD-DEPENDENT MALIC ENZYME 1 (NAD-ME1) as the candidate gene underlying the pleiotropic network Met.II.15 quantitative trait locus controlling variation in plant metabolism and circadian clock outputs in the Bay × Sha Arabidopsis population. Transcript abundance and promoter analysis in NAD-ME1^Bay-0 and NAD-ME1^Sha alleles confirmed allele-specific expression that appears to be due a polymorphism disrupting a putative circadian cis-element binding site. Analysis of transfer DNA insertion lines and heterogeneous inbred families showed that transcript variation of the NAD-ME1 gene led to temporal shifts of tricarboxylic acid cycle intermediates, glucosinolate (GSL) accumulation, and altered regulation of several GSL biosynthesis pathway genes. Untargeted metabolomic analyses revealed complex regulatory networks of NAD-ME1 dependent upon the daytime. The mutant led to shifts in plant primary metabolites, cell wall components, isoprenoids, fatty acids, and plant immunity phytochemicals, among others. Our findings suggest that NAD-ME1 may act as a key gene to coordinate plant primary and secondary metabolism in a time-dependent manner.

Transcriptome-based metabolic profiling of flavonoids in Agave lechuguilla waste biomass

Agave lechuguilla is one of the most abundant species in arid and semiarid regions of Mexico, and is used to extract fiber. However, 85 % of the harvested plant material is discarded. Previous bioprospecting studies of the waste biomass suggest the presence of bioactive compounds, although the extraction process limited metabolite characterization. This work achieved flavonoid profiling of A. lechuguilla in both processed and non-processed leaf tissues using transcriptomic analysis. Functional annotation of the first de novo transcriptome of A. lechuguilla (255.7 Mbp) allowed identifying genes coding for 33 enzymes and 8 transcription factors involved in flavonoid biosynthesis. The flavonoid metabolic pathway was mostly elucidated by HPLC-MS/MS screening of alcoholic extracts. Key genes of flavonoid synthesis were higher expressed in processed leaf tissues than in non-processed leaves, suggesting a high content of flavonoids and glycoside derivatives in the waste biomass. Targeted HPLC-UV-MS analyses confirmed the concentration of isorhamnetin (1251.96 μg), flavanone (291.51 μg), hesperidin (34.23 μg), delphinidin (24.23 μg), quercetin (15.57 μg), kaempferol (13.71 μg), cyanidin (12.32 μg), apigenin (9.70 μg) and catechin (7.91 μg) per gram of dry residue. Transcriptomic and biochemical profiling concur in the potential of lechuguilla by-products with a wide range of applications in agriculture, feed, food, cosmetics, and pharmaceutical industries.

Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional "omics" data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.

Ancestral sequence reconstruction - An underused approach to understand the evolution of gene function in plants?

Whilst substantial research effort has been placed on understanding the interactions of plant proteins with their molecular partners, relatively few studies in plants - by contrast to work in other organisms - address how these interactions evolve. It is thought that ancestral proteins were more promiscuous than modern proteins and that specificity often evolved following gene duplication and subsequent functional refining. However, ancestral protein resurrection studies have found that some modern proteins have evolved de novo from ancestors lacking those functions. Intriguingly, the new interactions evolved as a consequence of just a few mutations and, as such, acquisition of new functions appears to be neither difficult nor rare, however, only a few of them are incorporated into biological processes before they are lost to subsequent mutations. Here, we detail the approach of ancestral sequence reconstruction (ASR), providing a primer to reconstruct the sequence of an ancestral gene. We will present case studies from a range of different eukaryotes before discussing the few instances where ancestral reconstructions have been used in plants. As ASR is used to dig into the remote evolutionary past, we will also present some alternative genetic approaches to investigate molecular evolution on shorter timescales. We argue that the study of plant secondary metabolism is particularly well suited for ancestral reconstruction studies. Indeed, its ancient evolutionary roots and highly diverse landscape provide an ideal context in which to address the focal issue around the emergence of evolutionary novelties and how this affects the chemical diversification of plant metabolism.

Review: Metabolomics as a prediction tool for plants performance under environmental stress

Metabolomics as a diagnosis tool for plant performance has shown good features for breeding and crop improvement. Additionally, due to limitations in land area and the increasing climate changes, breeding projects focusing on abiotic stress tolerance are becoming essential. Nowadays no universal method is available to identify predictive metabolic markers. As a result, research aims must dictate the best method or combination of methods. To this end, we will introduce the key aspects to consider regarding growth scenarios and sampling strategies and discuss major analytical and data treatment approaches that are available to find metabolic markers of plant performance.

Comparative Metabolomic Profiling of Citrullus spp. Fruits Provides Evidence for Metabolomic Divergence during Domestication

Watermelon (Citrullus lanatus) is one of the most nutritional fruits that is widely distributed in the whole world. The nutritional compositions are mainly influenced by the genotype and environment. However, the metabolomics of different domestication status and different flesh colors watermelon types is not fully understood. In this study, we reported an extensive assessment of metabolomic divergence in the fruit flesh among Citrullus sp. and within Citrullus sp. We demonstrate that metabolic profiling was significantly different between the wild and cultivated watermelons, the apigenin 6-C-glucoside, luteolin 6-C-glucoside, chrysoeriol C-hexoside, naringenin C-hexoside, C-pentosyl-chrysoeriol O-hexoside, and sucrose are the main divergent metabolites. Correlation analysis results revealed that flavonoids were present in one tight metabolite cluster. The main divergent metabolites in different flesh-colored cultivated watermelon fruits are p-coumaric acid, 2,3-dihydroflavone, catechin, N-(3-indolylacetyl)-l-alanine, 3,4-dihydroxycinnamic acid, and pelargonidin o-hexoside. A total of 431 differentially accumulated metabolites were identified from pairwise comparative analyses. C. lanatus edible-seed watermelon (cultivars) and C. mucosospermus (wild) have similar fruit metabolic profiles and phenotypic traits, indicating that edible-seed watermelon may be a relative of wild species and a relatively primitive differentiation type of cultivated watermelon. Our data provide extensive knowledge for metabolomics-based watermelon improvement of Citrullus fruits meet their enhanced nutritive properties or upgraded germplasm utility values.