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

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.

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.

Metabolomics analysis identifies metabolites associated with systemic acquired resistance in Arabidopsis

Background

Systemic acquired resistance (SAR) is a type of plant defense response that provides a long-lasting resistance to broad-spectrum pathogens in uninfected distal tissues following an initial localized infection. However, little information is available at present on the biological basis of SAR at the molecular level, especially in uninfected distal leaves.

Methods

In the present work, we used two SAR-inducing pathogens, avirulent Pseudomonas syringae pv. maculicola ES4326 harboring avrRpm1 (Psm avrRpm1) and virulent P. syringae pv. maculicola ES4326 (Psm ES4326), to induce SAR in Arabidopsis ecotype Col-0. A metabolomics approach based on ultra-high-performance liquid chromatography (UPLC) coupled with mass spectrometry (MS) was used to identify SAR-related metabolites in infected local leaves, and in uninfected distal leaves.

Results

Differentially accumulated metabolites were distinguished by statistical analyses. The results showed that both the primary metabolism and the secondary metabolism were significantly altered in infected local leaves and in uninfected distal leaves, including phenolic compounds, amino acids, nucleotides, organic acids, and many other metabolites.

Conclusions

The content of amino acids and phenolic compounds increased in uninfected distal leaves, suggesting their contribution to the establishment of SAR. In addition, 2′-hydroxy-4, 4′, 6′-trimethoxychalcone, phenylalanine, and p-coumaric acid were identified as potential components which may play important roles both in basic resistance and in SAR. This work provides a reference for understanding of the metabolic mechanism associated with SAR in plants, which will be useful for further investigation of the molecular basis of the systemic immunity.

Liquid Chromatography-Mass Spectrometry (LC-MS)-Based Analysis for Lipophilic Compound Profiling in Plants

Lipids are fascinating due to their chemical diversity, which is especially vast in the plant kingdom thanks to the high plasticity of the plant biosynthetic machinery. Lipidomic studies aim to simultaneously analyze a large number of lipid compounds of diverse classes in a given sample. The method presented here uses liquid chromatography-mass spectrometry (LC-MS)-based lipidomic profiling in a relatively fast, robust, and high-throughput manner for high-coverage quantification and annotation of lipophilic compounds. Protocols cover sample preparation, LC-MS-based measurement, and data extraction and annotation. An extensive lipid library for triacylglycerols, galactolipids, and phospholipids is provided. The extended profiling described here could be used in a range of applications and is suitable for integration with other omic datasets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Sample preparation and metabolite extraction Basic Protocol 2: Liquid chromatography-mass spectrometry (LC-MS) analysis Basic Protocol 3: Data extraction, annotation, and quantification.

Three-dimensional genetic networks among seed oil-related traits, metabolites and genes reveal the genetic foundations of oil synthesis in soybean

Although the biochemical and genetic basis of lipid metabolism is clear in Arabidopsis, there is limited information concerning the relevant genes in Glycine max (soybean). To address this issue, we constructed three-dimensional genetic networks using six seed oil-related traits, 52 lipid metabolism-related metabolites and 54 294 SNPs in 286 soybean accessions in total. As a result, 284 and 279 candidate genes were found to be significantly associated with seed oil-related traits and metabolites by phenotypic and metabolic genome-wide association studies and multi-omics analyses, respectively. Using minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) analyses, six seed oil-related traits were found to be significantly related to 31 metabolites. Among the above candidate genes, 36 genes were found to be associated with oil synthesis (27 genes), amino acid synthesis (four genes) and the tricarboxylic acid (TCA) cycle (five genes), and four genes (GmFATB1a, GmPDAT, GmPLDα1 and GmDAGAT1) are already known to be related to oil synthesis. Using this information, 133 three-dimensional genetic networks were constructed, 24 of which are known, e.g. pyruvate-GmPDAT-GmFATA2-oil content. Using these networks, GmPDAT, GmAGT and GmACP4 reveal the genetic relationships between pyruvate and the three major nutrients, and GmPDAT, GmZF351 and GmPgs1 reveal the genetic relationships between amino acids and seed oil content. In addition, GmCds1, along with average temperature in July and the rainfall from June to September, influence seed oil content across years. This study provides a new approach for the construction of three-dimensional genetic networks and reveals new information for soybean seed oil improvement and the identification of gene function.

Volatilome-Genome-Wide Association Study on Wholemeal Maize Flour

Odor and aroma, resulting from the perception of volatiles by the olfactory receptors, are important in consumer food acceptance. To develop more efficient molecular breeding tools to improve the odor/aroma on maize (Zea mays L.), a staple food crop, increasing the knowledge on the genetic basis of maize volatilome is needed. In this work, we conducted a genome-wide association study on a unique germplasm collection to identify genomic regions controlling maize wholemeal flour's volatilome. We identified 64 regions on the maize genome and candidate genes controlling the levels of 15 volatiles, mainly aldehydes. As an example, the Zm00001d033623 gene was within a region associated with 2-octenal (E) and 2-nonenal (E), two byproducts of linoleic acid oxidation. This gene codes for linoleate 9S-lipoxygenase, an enzyme responsible for oxidizing linoleic acid. This knowledge can now support the development of molecular tools to increase the selection efficacy/efficiency of these volatiles within maize breeding programs.

Integrative Analyses of Widely Targeted Metabolic Profiling and Transcriptome Data Reveals Molecular Insight into Metabolomic Variations during Apple (Malus domestica) Fruit Development and Ripening

The apple is a favorite fruit for human diet and is one of the most important commercial fruit crops around the world. Investigating metabolic variations during fruit development can provide a better understanding on the formation of fruit quality. The present study applied a widely targeted LC-MS-based metabolomics approach with large-scale detection, identification and quantification to investigate the widespread metabolic changes during “Pinova” apple development and ripening. A total of 462 primary and secondary metabolites were simultaneously detected, and their changes along with the four fruit-development stages were further investigated. The results indicated that most of the sugars presented increasing accumulation levels while organic acid, including Tricarboxylic acid cycle (TCA) intermediates, showed a distinct decreasing trend across the four fruit-development stages. A total of 207 secondary metabolites consisted of 104 flavonoids and 103 other secondary metabolites. Many flavonoids maintained relatively high levels in the early fruit stage and then rapidly decreased their levels at the following developmental stages. Further correlation analyses of each metabolite–metabolite pair highlighted the cross talk between the primary and secondary metabolisms across fruit development and ripening, indicating the significant negative correlations between sugars and secondary metabolites. Moreover, transcriptome analysis provided the molecular basis for metabolic variations during fruit development. The results showed that most differentially expressed genes (DEGs) involved in the TCA cycle were upregulated from the early fruit stage to the preripening stage. The extensive downregulation of controlling genes involved in the flavonoid pathway is probably responsible for the rapid decrease of flavonoid content at the early fruit stage. These data provide a global view of the apple metabolome and a comprehensive analysis on metabolomic variations during fruit development, providing a broader and better understanding on the molecular and metabolic basis of important fruit quality traits in commercial apples.

Exploiting Natural Variation in Tomato to Define Pathway Structure and Metabolic Regulation of Fruit Polyphenolics in the Lycopersicum Complex

While the structures of plant primary metabolic pathways are generally well defined and highly conserved across species, those defining specialized metabolism are less well characterized and more highly variable across species. In this study, we investigated polyphenolic metabolism in the lycopersicum complex by characterizing the underlying biosynthetic and decorative reactions that constitute the metabolic network of polyphenols across eight different species of tomato. For this purpose, GC-MS- and LC-MS-based metabolomics of different tissues of Solanum lycopersicum and wild tomato species were carried out, in concert with the evaluation of cross-hybridized microarray data for MapMan-based transcriptomic analysis, and publicly available RNA-sequencing data for annotation of biosynthetic genes. The combined data were used to compile species-specific metabolic networks of polyphenolic metabolism, allowing the establishment of an entire pan-species biosynthetic framework as well as annotation of the functions of decoration enzymes involved in the formation of metabolic diversity of the flavonoid pathway. The combined results are discussed in the context of the current understanding of tomato flavonol biosynthesis as well as a global view of metabolic shifts during fruit ripening. Our results provide an example as to how large-scale biology approaches can be used for the definition and refinement of large specialized metabolism pathways.

Specialized phenolic compounds in seeds: structures, functions, and regulations

Plants produce a huge diversity of specialized metabolites (SM) throughout their life cycle that play important physiological and ecological functions. SM can protect plants and seeds against diseases, predators, and abiotic stresses, or support their interactions with beneficial or symbiotic organisms. They also have strong impacts on human nutrition and health. Despite this importance, the biosynthesis and biological functions of most of the SM remain elusive and their diversity and/or quantity have been reduced in most crops during domestication. Seeds present a large number of SM that are important for their physiological, agronomic, nutritional or industrial qualities and hence, provide interesting models for both studying biosynthesis and producing large amounts of specialized metabolites. For instance, phenolics are abundant and widely distributed in seeds. More specifically, flavonoid pathway has been instrumental for understanding environmental or developmental regulations of specialized metabolic pathways, at the molecular and cellular levels. Here, we summarize current knowledge on seed phenolics as model, and discuss how recent progresses in omics approaches could help to further characterize their diversity, regulations, and the underlying molecular mechanisms involved.

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

Plants produce numerous metabolites that are important for their development and growth. However, the genetic architecture of the wheat metabolome has not been well studied. Here, utilizing a high-density genetic map, we conducted a comprehensive metabolome study via widely targeted LC-MS/MS to analyze the wheat kernel metabolism. We further combined agronomic traits and dissected the genetic relationship between metabolites and agronomic traits. In total, 1260 metabolic features were detected. Using linkage analysis, 1005 metabolic quantitative trait loci (mQTLs) were found distributed unevenly across the genome. Twenty-four candidate genes were found to modulate the levels of different metabolites, of which two were functionally annotated by in vitro analysis to be involved in the synthesis and modification of flavonoids. Combining the correlation analysis of metabolite-agronomic traits with the co-localization of methylation quantitative trait locus (mQTL) and phenotypic QTL (pQTL), genetic relationships between the metabolites and agronomic traits were uncovered. For example, a candidate was identified using correlation and co-localization analysis that may manage auxin accumulation, thereby affecting number of grains per spike (NGPS). Furthermore, metabolomics data were used to predict the performance of wheat agronomic traits, with metabolites being found that provide strong predictive power for NGPS and plant height. This study used metabolomics and association analysis to better understand the genetic basis of the wheat metabolism which will ultimately assist in wheat breeding.

SNPeffect: identifying functional roles of SNPs using metabolic networks

Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.

Gene and genome duplications in the evolution of chemodiversity: perspectives from studies of Lamiaceae

Plants are reservoirs of extreme chemical diversity, yet biosynthetic pathways remain underexplored in the majority of taxa. Access to improved, inexpensive genomic and computational technologies has recently enhanced our understanding of plant specialized metabolism at the biochemical and evolutionary levels including the elucidation of pathways leading to key metabolites. Furthermore, these approaches have provided insights into the mechanisms of chemical evolution, including neofunctionalization and subfunctionalization, structural variation, and modulation of gene expression. The broader utilization of genomic tools across the plant tree of life, and an expansion of genomic resources from multiple accessions within species or populations, will improve our overall understanding of chemodiversity. These data and knowledge will also lead to greater insight into the selective pressures contributing to and maintaining this diversity, which in turn will enable the development of more accurate predictive models of specialized metabolism in plants.