Metabolome-Scale Genome-Wide Association Studies Reveal Chemical Diversity and Genetic Control of Maize Specialized Metabolites

Dissecting genetic regulation of metabolic coordination

Understanding genetic regulation of metabolism is critical for gaining insights into the causes of metabolic diseases. Traditional metabolome-based genome-wide association studies (mGWAS) focus on static associations between single nucleotide polymorphisms (SNPs) and metabolite levels, overlooking the changing relationships caused by genotypes within the metabolic network. Notably, some metabolites exhibit changes in correlation patterns with other metabolites under certain physiological conditions while maintaining their overall abundance level. In this manuscript, we develop Metabolic Differential-coordination GWAS (mdGWAS), an innovative framework that detects SNPs associated with the changing correlation patterns between metabolites and metabolic pathways. This approach transcends and complements conventional mean-based analyses by identifying latent regulatory factors that govern the system-level metabolic coordination. Through comprehensive simulation studies, mdGWAS demonstrated robust performance in detecting SNP-metabolite-metabolite associations. Applying mdGWAS to genotyping and mass spectrometry (MS)-based metabolomics data of the METabolic Syndrome In Men (METSIM) Study revealed novel SNPs and genes potentially involved in the regulation of the coordination between metabolic pathways.

Catechol acetylglucose: a newly identified benzoxazinoid-regulated defensive metabolite in maize

An enormous diversity of specialized metabolites is produced in the plant kingdom, with each individual plant synthesizing thousands of these compounds. Previous research showed that benzoxazinoids, the most abundant class of specialized metabolites in maize, also function as signaling molecules by regulating the production callose as a defense response. We searched for additional benzoxazinoid-regulated specialized metabolites, characterized them, examined whether they too function in herbivore protection, and determined how Spodoptera frugiperda (fall armyworm), a prominent maize pest, copes with these metabolites. We identified catechol acetylglucose (CAG) as a benzoxazinoid-regulated metabolite that is produced from salicylic acid via catechol and catechol glucoside. Genome-wide association studies of CAG abundance identified a gene encoding a predicted acetyltransferase. Knockout of this gene resulted in maize plants that lack CAG and over-accumulate catechol glucoside. Upon tissue disruption, maize plants accumulate catechol, which inhibits S. frugiperda growth. Analysis of caterpillar frass showed that S. frugiperda detoxifies catechol by glycosylation, and the efficiency of catechol glycosylation was correlated with S. frugiperda growth on a catechol-containing diet. Thus, the success of S. frugiperda as an agricultural pest may depend partly on its ability to detoxify catechol, which is produced as a defensive metabolite by maize.

Metabolomics-Assisted Breeding in Oil Palm: Potential and Current Perspectives

Oil palm is presently the most important oil-producing crop worldwide in terms of oil production and consumption. However, oil palm cultivation faces important challenges such as adverse climatic conditions, expensive fertilization requirements, and fungal pathogens, including Ganoderma. Intense efforts in oil palm breeding are devoted to improving both oil production yield and resistance to environmental cues. Metabolomics can be of interest because it provides many quantitative traits and metabolic signatures that can be selected for to optimize oil palm performance. Here, we briefly review how metabolomics can help oil palm breeding, and to do so, we give examples of recent metabolomics analyses and provide a roadmap to use metabolomics-assisted breeding.

Molecular mechanisms underlying gene regulatory variation of maize metabolic traits

Variation in gene expression levels is pervasive among individuals and races or varieties, and has substantial agronomic consequences, for example, by contributing to hybrid vigor. Gene expression level variation results from mutations in regulatory sequences (cis) and/or transcription factor (TF) activity (trans), but the mechanisms underlying cis- and/or trans-regulatory variation of complex phenotypes remain largely unknown. Here, we investigated gene expression variation mechanisms underlying the differential accumulation of the insecticidal compounds maysin and chlorogenic acid in silks of widely used maize (Zea mays) inbreds, B73 and A632. By combining transcriptomics and cistromics, we identified 1,338 silk direct targets of the maize R2R3-MYB TF Pericarp color1 (P1), consistent with it being a regulator of maysin and chlorogenic acid biosynthesis. Among these P1 targets, 464 showed allele-specific expression (ASE) between B73 and A632 silks. Allelic DNA-affinity purification sequencing identified 34 examples in which P1 allelic specific binding (ASB) correlated with cis-expression variation. From previous yeast one-hybrid studies, we identified 9 TFs potentially implicated in the control of P1 targets, with ASB to 83 out of 464 ASE genes (cis) and differential expression of 4 out of 9 TFs between B73 and A632 silks (trans). These results provide a molecular framework for understanding universal mechanisms underlying natural variation of gene expression levels, and how the regulation of metabolic diversity is established.

Integrating QTL mapping and transcriptomics to decipher the genetic architecture of sterol metabolism in Brassica napus L

Sterols are secondary metabolites commonly found in rapeseed that play crucial physiological roles in plants and also benefit human health. Consequently, unraveling the genetic basis of sterol synthesis in rapeseed is highly important. In this study, 21 individual sterols as well as total sterol (TS) content were detected in a double haploid (DH) population of Brassica napus, and a total of 24 quantitative trait loci (QTL) and 157 mQTL were identified that were associated with TS and different individual sterols. Time-series transcriptomic analysis showed that the differentially expressed genes (DEGs) involved in sterol and lipid biosynthesis pathways were enriched. Additionally, a regulatory network between sterol-related DEGs and transcription factors (TFs) was established using coexpression analysis. Some candidate genes were identified with the integration of transcriptomic analysis and QTL mapping, and the key candidate gene BnSQS1.C03 was selected for further functional analysis. BnSQS1.C03 demonstrated squalene synthase activity in vitro and increased the TS by 3.8% when overexpressed in Arabidopsis. The present results provide new insights into sterol regulatory pathways and a valuable genetic basis for breeding rapeseed varieties with high sterol content in the future.

Application of GWAS and mGWAS in Livestock and Poultry Breeding

In recent years, genome-wide association studies (GWAS) and metabolome genome-wide association studies (mGWAS) have emerged as crucial methods for investigating complex traits in animals and plants. These have played pivotal roles in research on livestock and poultry breeding, facilitating a deeper understanding of genetic diversity, the relationship between genes, and genetic bases in livestock and poultry. This article provides a review of the applications of GWAS and mGWAS in animal genetic breeding, aiming to offer reference and inspiration for relevant researchers, promote innovation in animal genetic improvement and breeding methods, and contribute to the sustainable development of animal husbandry.

Prioritizing Maize Metabolic Gene Regulators through Multi-Omic Network Integration

Elucidating gene regulatory networks is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait loci (eQTL) to construct TF-target networks. In total, we analyzed ∼4.6M interactions to generate four distinct types of TF-target networks: co-expression, PDI, trans -eQTL, and cis -eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.

GRAPHICAL ABSTRACT

Dynamic relationships among pathways producing hydrocarbons and fatty acids of maize silk cuticular waxes

The hydrophobic cuticle is the first line of defense between aerial portions of plants and the external environment. On maize (Zea mays L.) silks, the cuticular cutin matrix is infused with cuticular waxes, consisting of a homologous series of very long-chain fatty acids (VLCFAs), aldehydes, and hydrocarbons. Together with VLC fatty-acyl-CoAs (VLCFA-CoAs), these metabolites serve as precursors, intermediates, and end-products of the cuticular wax biosynthetic pathway. To deconvolute the potentially confounding impacts of the change in silk microenvironment and silk development on this pathway, we profiled cuticular waxes on the silks of the inbreds B73 and Mo17, and their reciprocal hybrids. Multivariate interrogation of these metabolite abundance data demonstrates that VLCFA-CoAs and total free VLCFAs are positively correlated with the cuticular wax metabolome, and this metabolome is primarily affected by changes in the silk microenvironment and plant genotype. Moreover, the genotype effect on the pathway explains the increased accumulation of cuticular hydrocarbons with a concomitant reduction in cuticular VLCFA accumulation on B73 silks, suggesting that the conversion of VLCFA-CoAs to hydrocarbons is more effective in B73 than Mo17. Statistical modeling of the ratios between cuticular hydrocarbons and cuticular VLCFAs reveals a significant role of precursor chain length in determining this ratio. This study establishes the complexity of the product-precursor relationships within the silk cuticular wax-producing network by dissecting both the impact of genotype and the allocation of VLCFA-CoA precursors to different biological processes and demonstrates that longer chain VLCFA-CoAs are preferentially utilized for hydrocarbon biosynthesis.

Genetic variation in ZmKW1 contributes to kernel weight and size in dent corn and popcorn

Kernel weight is a critical factor that essentially affects maize (Zea mays) yield. In natural inbred lines, popcorn kernels exhibit overtly smaller sizes compared to dent corn kernels, and kernel weight, which is controlled by multiple genetic loci, varies widely. Here, we characterized a major quantitative trait locus on chromosome 1, responsible for controlling kernel weight (qKW1) and size. The qKW1 locus encodes a protein containing a seven in absentia domain with E3 ubiquitin ligase activity, expressed prominently from the top to the middle region of the endosperm. The presence and function of qKW1 were confirmed through ZmKW1 gene editing, where the mutations in ZmKW1 within dent corn significantly increased kernel weight, consistent with alterations in kernel size, while overexpression of ZmKW1 had the opposite effect. ZmKW1 acts as a negative regulator of kernel weight and size by reducing both the number and size of the endosperm cells and impacting endosperm filling. Notably, the popcorn allele qKW1N and the dent corn allele qKW1D encode identical proteins; however, the differences in promoter activity arise due to the insertion of an Indel-1346 sequence in the qKW1N promoter, resulting in higher expression levels compared to qKW1D, thus contributing to the variation in kernel weight and size between popcorn and dent corn kernels. Linkage disequilibrium analysis of the 2.8 kb promoter region of ZmKW1 in a dataset comprising 111 maize association panels identified two distinct haplotypes. Our results provide insight into the mechanisms underlying kernel development and yield regulation in dent corn and popcorn, with a specific focus on the role of the ubiquitination system.

Recent advances in plant translational genomics for crop improvement

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

Utilizing transcriptomics and metabolomics to unravel key genes and metabolites of maize seedlings in response to drought stress

BACKGROUND

Drought stress can substantially restrict maize growth and productivity, and global warming and an increasing frequency of extreme weather events are likely to result in more yield losses in the future. Therefore, unraveling the molecular mechanism underlying the response to drought stress is essential for breeding drought-resilient crops.

RESULTS

In this study, we subjected the 3-leaf-period plants of two maize inbred lines, a drought-tolerant line (si287) and a drought-sensitive line (X178), to drought stress for seven days while growing in a chamber. Subsequently, we measured physiological traits and analyzed transcriptomic and metabolic profiles of two inbred lines. Our KEGG analysis of genes and metabolites revealed significant differences in pathways related to glycolysis/gluconeogenesis, flavonoid biosynthesis, starch and sucrose metabolism, and biosynthesis of amino acids. Additionally, our joint analysis identified proline, tryptophan and phenylalanine are crucial amino acids for maize response to drought stress. Furthermore, we concentrated on tryptophan (Trp), which was found to enhance tolerance via IAA-ABA signaling, as well as SA and nicotinamide adenine dinucleotide (NAD) consequent reactive oxygen species (ROS) scavenging. We identified three hub genes in tryptophan biosynthesis, indole-3-acetaldehyde oxidase (ZmAO1, 542,228), catalase 1 (ZmCAT1, 542,369), and flavin-containing monooxygenase 6 (ZmYUC6, 103,629,142), High expression of these genes plays a significant role in regulating drought tolerance. Two metabolites related to tryptophan biosynthesis, quinolinic acid, and kynurenine improved maize tolerance to drought stress by scavenging reactive oxygen species.

CONCLUSIONS

This study illuminates the mechanisms underlying the response of maize seedlings to drought stress. Especially, it identifies novel candidate genes and metabolites, enriching our understanding of the role of tryptophan in drought stress. The identification of distinct resistance mechanisms in maize inbred lines will facilitate the exploration of maize germplasm and the breeding of drought-resilient hybrids.

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

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

Cell type-specific proteomics uncovers a RAF15-SnRK2.6/OST1 kinase cascade in guard cells

Multicellular organisms such as plants contain various cell types with specialized functions. Analyzing the characteristics of each cell type reveals specific cell functions and enhances our understanding of organization and function at the organismal level. Guard cells (GCs) are specialized epidermal cells that regulate the movement of the stomata and gaseous exchange, and provide a model genetic system for analyzing cell fate, signaling, and function. Several proteomics analyses of GC are available, but these are limited in depth. Here we used enzymatic isolation and flow cytometry to enrich GC and mesophyll cell protoplasts and perform in-depth proteomics in these two major cell types in Arabidopsis leaves. We identified approximately 3,000 proteins not previously found in the GC proteome and more than 600 proteins that may be specific to GC. The depth of our proteomics enabled us to uncover a guard cell-specific kinase cascade whereby Raf15 and Snf1-related kinase2.6 (SnRK2.6)/OST1(open stomata 1) mediate abscisic acid (ABA)-induced stomatal closure. RAF15 directly phosphorylated SnRK2.6/OST1 at the conserved Ser175 residue in its activation loop and was sufficient to reactivate the inactive form of SnRK2.6/OST1. ABA-triggered SnRK2.6/OST1 activation and stomatal closure was impaired in raf15 mutants. We also showed enrichment of enzymes and flavone metabolism in GC, and consistent, dramatic accumulation of flavone metabolites. Our study answers the long-standing question of how ABA activates SnRK2.6/OST1 in GCs and represents a resource potentially providing further insights into the molecular basis of GC and mesophyll cell development, metabolism, structure, and function.

Plant biochemical genetics in the multiomics era

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

Approach strategies and application of metabolomics to biotechnology in plants

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

Expanding our coverage: Strategies to detect a greater range of metabolites

Whilst the study of metabolites can arguably be traced back several hundred years it began in earnest in the 20th century with studies based on single metabolites or simple metabolic pathways. The advent of metabolomics and in particular the adoption of high-resolution mass spectrometry now means we can faithfully annotate and quantify in excess of 1000 plant metabolites. Whilst this is an impressive leap it falls well short of the estimated number of metabolites in the plant kingdom. This, whilst considerable and important insights have been achieved using commonly utilized approaches, there is a need to improve the coverage of the metabolome. Here, we review three largely complementary strategies (i) methods based on using chemical libraries (ii) methods based on molecular networking and (iii) approaches that link metabolomics and genetic variance. It is our contention that using all three approaches in tandem represents the best approach to tackle this challenge.

Elucidating the patterns of pleiotropy and its biological relevance in maize

Pleiotropy-when a single gene controls two or more seemingly unrelated traits-has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low.

Watermelon domestication was shaped by stepwise selection and regulation of the metabolome

Although crop domestication has greatly aided human civilization, the sequential domestication and regulation of most quality traits remain poorly understood. Here, we report the stepwise selection and regulation of major fruit quality traits that occurred during watermelon evolution. The levels of fruit cucurbitacins and flavonoids were negatively selected during speciation, whereas sugar and carotenoid contents were positively selected during domestication. Interestingly, fruit malic acid and citric acid showed the opposite selection trends during the improvement. We identified a novel gene cluster (CGC1, cucurbitacin gene cluster on chromosome 1) containing both regulatory and structural genes involved in cucurbitacin biosynthesis, which revealed a cascade of transcriptional regulation operating mechanisms. In the CGC1, an allele caused a single nucleotide change in ClERF1 binding sites (GCC-box) in the promoter of ClBh1, which resulted in reduced expression of ClBh1 and inhibition of cucurbitacin synthesis in cultivated watermelon. Functional analysis revealed that a rare insertion of 244 amino acids, which arose in C. amarus and became fixed in sweet watermelon, in ClOSC (oxidosqualene cyclase) was critical for the negative selection of cucurbitacins during watermelon evolution. This research provides an important resource for metabolomics-assisted breeding in watermelon and for exploring metabolic pathway regulation mechanisms.

Genetic mapping of maize metabolites using high-throughput mass profiling

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

Functional diversity and metabolic engineering of plant-specialized metabolites

Plants are talented biochemists that produce a broad diversity of small molecules. These so-called specialized metabolites (SMs) play critical roles in the adaptive evolution of plants to defend against biotic and abiotic stresses, attract pollinators, and modulate soil microbiota for their own benefits. Many plant SMs have been used as nutrition and flavor compounds in our daily food, as well as drugs for treatment of human diseases. Current multi-omics tools have significantly accelerated the process of biosynthetic pathway elucidation in plants through correlation analyses, genetic mapping, and de novo biosynthetic gene cluster predictions. Understanding the biosynthesis of plant SMs has enabled reconstitution of naturally occurring specialized metabolic pathways in microbial hosts, providing a sustainable supply of these high-value molecules. In this review, we illustrate the general functions of several typical plant SMs in natural ecosystems and for human societies. We then provide an overview of current methods elucidating the biosynthetic pathways of plant SMs, and synthetic biology strategies that optimize the efficiency of heterologous biosynthetic pathways in microbial hosts. Moving forward, dissection of the functions and application of plant SMs by using current multidiscipline approaches would be greatly benefit to the scientific community and human societies.

Targeted and Untargeted Metabolomic Analyses Reveal Organ Specificity of Specialized Metabolites in the Model Grass Brachypodium distachyon

Brachypodium distachyon, because of its fully sequenced genome, is frequently used as a model grass species. However, its metabolome, which constitutes an indispensable element of complex biological systems, remains poorly characterized. In this study, we conducted comprehensive, liquid chromatography-mass spectrometry (LC-MS)-based metabolomic examination of roots, leaves and spikes of Brachypodium Bd21 and Bd3-1 lines. Our pathway enrichment analysis emphasised the accumulation of specialized metabolites representing the flavonoid biosynthetic pathway in parallel with processes related to nucleotide, sugar and amino acid metabolism. Similarities in metabolite profiles between both lines were relatively high in roots and leaves while spikes showed higher metabolic variance within both accessions. In roots, differences between Bd21 and Bd3-1 lines were manifested primarily in diterpenoid metabolism, while differences within spikes and leaves concerned nucleotide metabolism and nitrogen management. Additionally, sulphate-containing metabolites differentiated Bd21 and Bd3-1 lines in spikes. Structural analysis based on MS fragmentation spectra enabled identification of 93 specialized metabolites. Among them phenylpropanoids and flavonoids derivatives were mainly determined. As compared with closely related barley and wheat species, metabolic profile of Brachypodium is characterized with presence of threonate derivatives of hydroxycinnamic acids.

The Musa troglodytarum L. genome provides insights into the mechanism of non-climacteric behaviour and enrichment of carotenoids

BACKGROUND

Karat (Musa troglodytarum L.) is an autotriploid Fe'i banana of the Australimusa section. Karat was domesticated independently in the Pacific region, and karat fruit are characterized by a pink sap, a deep yellow-orange flesh colour, and an abundance of β-carotene. Karat fruit showed non-climacteric behaviour, with an approximately 215-day bunch filling time. These features make karat a valuable genetic resource for studying the mechanisms underlying fruit development and ripening and carotenoid biosynthesis.

RESULTS

Here, we report the genome of M. troglodytarum, which has a total length of 603 Mb and contains 37,577 predicted protein-coding genes. After divergence from the most recent common ancestors, M. troglodytarum (T genome) has experienced fusion of ancestral chromosomes 8 and 9 and multiple translocations and inversions, unlike the high synteny with few rearrangements found among M. schizocarpa (S genome), M. acuminata (A genome) and M. balbisiana (B genome). Genome microsynteny analysis showed that the triplication of MtSSUIIs due to chromosome rearrangement may lead to the accumulation of carotenoids and ABA in the fruit. The expression of duplicated MtCCD4s is repressed during ripening, leading to the accumulation of α-carotene, β-carotene and phytoene. Due to a long terminal repeat (LTR)-like fragment insertion upstream of MtERF11, karat cannot produce large amounts of ethylene but can produce ABA during ripening. These lead to non-climacteric behaviour and prolonged shelf-life, which contributes to an enrichment of carotenoids and riboflavin.

CONCLUSIONS

The high-quality genome of M. troglodytarum revealed the genomic basis of non-climacteric behaviour and enrichment of carotenoids, riboflavin, flavonoids and free galactose and provides valuable resources for further research on banana domestication and breeding and the improvement of nutritional and bioactive qualities.

Natural variation in root exudation of GABA and DIMBOA impacts the maize root endosphere and rhizosphere microbiomes

Root exudates are important for shaping root-associated microbiomes. However, studies on a wider range of metabolites in exudates are required for a comprehensive understanding about their influence on microbial communities. We identified maize inbred lines that differ in exudate concentrations of 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) and γ-aminobutyric acid (GABA) using a semi-hydroponic system. These lines were grown in the field to determine the changes in microbial diversity and gene expression due to varying concentrations of DIMBOA and GABA in exudates using 16S rRNA amplicon sequencing and metatranscriptomics. Results showed individual and interaction effects of DIMBOA and GABA on the rhizosphere and root endosphere β-diversity, most strongly at the V10 growth stage. The main bacterial families affected by both compounds were Ktedonobacteraceae and Xanthomonadaceae. Higher concentrations of DIMBOA in exudates affected the rhizosphere metatranscriptome, enriching for metabolic pathways associated with plant disease. This study validated the use of natural variation within plant species as a powerful approach for understanding the role of root exudates on microbiome selection. We also showed that a semi-hydroponic system can be used to identify maize genotypes that differ in GABA and DIMBOA exudate concentrations under field conditions. The impact of GABA exudation on root-associated microbiomes is shown for the first time.

Switchgrass Metabolomics Reveals Striking Genotypic and Developmental Differences in Specialized Metabolic Phenotypes

Switchgrass (Panicum virgatum L.) is a bioenergy crop that grows productively on lands not suitable for food production and is an excellent target for low-pesticide input biomass production. We hypothesize that resistance to insect pests and microbial pathogens is influenced by low-molecular-weight compounds known as specialized metabolites. We employed untargeted liquid chromatography-mass spectrometry, quantitative gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance spectroscopy to identify differences in switchgrass ecotype metabolomes. This analysis revealed striking differences between upland and lowland switchgrass metabolomes as well as distinct developmental profiles. Terpenoid- and polyphenol-derived specialized metabolites were identified, including steroidal saponins, di- and sesqui-terpenoids, and flavonoids. The saponins are particularly abundant in switchgrass extracts and have diverse aglycone cores and sugar moieties. We report seven structurally distinct steroidal saponin classes with unique steroidal cores and glycosylated at one or two positions. Quantitative GC-MS revealed differences in total saponin concentrations in the leaf blade, leaf sheath, stem, rhizome, and root (2.3 ± 0.10, 0.5 ± 0.01, 2.5 ± 0.5, 3.0 ± 0.7, and 0.3 ± 0.01 μg/mg of dw, respectively). The quantitative data also demonstrated that saponin concentrations are higher in roots of lowland (ranging from 3.0 to 6.6 μg/mg of dw) than in upland (from 0.9 to 1.9 μg/mg of dw) ecotype plants, suggesting ecotypic-specific biosynthesis and/or biological functions. These results enable future testing of these specialized metabolites on biotic and abiotic stress tolerance and can provide information on the development of low-input bioenergy crops.

Switchgrass Metabolomics Reveals Striking Genotypic and Developmental Differences in Specialized Metabolic Phenotypes

Switchgrass (Panicum virgatum L.) is a bioenergy crop that grows productively on lands not suitable for food production and is an excellent target for low-pesticide input biomass production. We hypothesize that resistance to insect pests and microbial pathogens is influenced by low-molecular-weight compounds known as specialized metabolites. We employed untargeted liquid chromatography-mass spectrometry, quantitative gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance spectroscopy to identify differences in switchgrass ecotype metabolomes. This analysis revealed striking differences between upland and lowland switchgrass metabolomes as well as distinct developmental profiles. Terpenoid- and polyphenol-derived specialized metabolites were identified, including steroidal saponins, di- and sesqui-terpenoids, and flavonoids. The saponins are particularly abundant in switchgrass extracts and have diverse aglycone cores and sugar moieties. We report seven structurally distinct steroidal saponin classes with unique steroidal cores and glycosylated at one or two positions. Quantitative GC-MS revealed differences in total saponin concentrations in the leaf blade, leaf sheath, stem, rhizome, and root (2.3 ± 0.10, 0.5 ± 0.01, 2.5 ± 0.5, 3.0 ± 0.7, and 0.3 ± 0.01 μg/mg of dw, respectively). The quantitative data also demonstrated that saponin concentrations are higher in roots of lowland (ranging from 3.0 to 6.6 μg/mg of dw) than in upland (from 0.9 to 1.9 μg/mg of dw) ecotype plants, suggesting ecotypic-specific biosynthesis and/or biological functions. These results enable future testing of these specialized metabolites on biotic and abiotic stress tolerance and can provide information on the development of low-input bioenergy crops.

Phenolic sucrose esters: evolution, regulation, biosynthesis, and biological functions

Phenolic sucrose esters (PSEs) are a diverse group of specialized metabolites that are present in several angiosperm lineages. Phylogenetic reconstruction and structural variation suggest that these metabolites may have evolved independently in monocots and dicots. Constitutive variation in PSE abundance across plant organs and developmental stages is correlated with transcriptional regulation of the upstream phenylpropanoid pathway, whereas pathogen induction is regulated by stress-related phytohormones such as ethylene. Shared structural features of PSEs indicate that their biosynthesis may involve one or more hydroxycinnamoyl transferases and BAHD acetyltransferases, which could be identified by correlative analyses of multi-omics datasets. Elucidation of the core biosynthetic pathway of PSEs will be essential for more detailed studies of the biological function of these compounds and their potential medicinal and agricultural applications.

Using interdisciplinary, phylogeny-guided approaches to understand the evolution of plant metabolism

To cope with relentless environmental pressures, plants produce an arsenal of structurally diverse chemicals, often called specialized metabolites. These lineage-specific compounds are derived from the simple building blocks made by ubiquitous core metabolic pathways. Although the structures of many specialized metabolites are known, the underlying metabolic pathways and the evolutionary events that have shaped the plant chemical diversity landscape are only beginning to be understood. However, with the advent of multi-omics data sets and the relative ease of studying pathways in previously intractable non-model species, plant specialized metabolic pathways are now being systematically identified. These large datasets also provide a foundation for comparative, phylogeny-guided studies of plant metabolism. Comparisons of metabolic traits and features like chemical abundances, enzyme activities, or gene sequences from phylogenetically diverse plants provide insights into how metabolic pathways evolved. This review highlights the power of studying evolution through the lens of comparative biochemistry, particularly how placing metabolism into a phylogenetic context can help a researcher identify the metabolic innovations enabling the evolution of structurally diverse plant metabolites.

Advances in Metabolomics-Driven Diagnostic Breeding and Crop Improvement

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

Generalizable approaches for genomic prediction of metabolites in plants

Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.

Computational Metabolomics Tools Reveal Metabolic Reconfigurations Underlying the Effects of Biostimulant Seaweed Extracts on Maize Plants under Drought Stress Conditions

Drought is one of the major abiotic stresses causing severe damage and losses in economically important crops worldwide. Drought decreases the plant water status, leading to a disruptive metabolic reprogramming that negatively affects plant growth and yield. Seaweed extract-based biostimulants show potential as a sustainable strategy for improved crop health and stress resilience. However, cellular, biochemical, and molecular mechanisms governing the agronomically observed benefits of the seaweed extracts on plants are still poorly understood. In this study, a liquid chromatography–mass spectrometry-based untargeted metabolomics approach combined with computational metabolomics strategies was applied to unravel the molecular ‘stamps’ that define the effects of seaweed extracts on greenhouse-grown maize (Zea mays) under drought conditions. We applied mass spectral networking, substructure discovery, chemometrics, and metabolic pathway analyses to mine and interpret the generated mass spectral data. The results showed that the application of seaweed extracts induced alterations in the different pathways of primary and secondary metabolism, such as phenylpropanoid, flavonoid biosynthesis, fatty acid metabolism, and amino acids pathways. These metabolic changes involved increasing levels of phenylalanine, tryptophan, coumaroylquinic acid, and linolenic acid metabolites. These metabolic alterations are known to define some of the various biochemical and physiological events that lead to enhanced drought resistance traits. The latter include root growth, alleviation of oxidative stress, improved water, and nutrient uptake. Moreover, this study demonstrates the use of molecular networking in annotating maize metabolome. Furthermore, the results reveal that seaweed extract-based biostimulants induced a remodeling of maize metabolism, subsequently readjusting the plant towards stress alleviation, for example, by increasing the plant height and diameter through foliar application. Such insights add to ongoing efforts in elucidating the modes of action of biostimulants, such as seaweed extracts. Altogether, our study contributes to the fundamental scientific knowledge that is necessary for the development of a biostimulants industry aiming for a sustainable food security.

Genome-wide mediation analysis: an empirical study to connect phenotype with genotype via intermediate transcriptomic data in maize

Mapping genotype to phenotype is an essential topic in genetics and genomics research. As the Omics data become increasingly available, 2-variable methods have been widely applied to associate genotype with the phenotype (genome-wide association study), gene expression with the phenotype (transcriptome-wide association study), and genotype with gene expression. However, signals detected by these 2-variable association methods suffer from low mapping resolution or inexplicit causality between genotype and phenotype, making it challenging to interpret and validate the molecular mechanisms of the underlying genomic variations and the candidate genes. Under the context of genetics research, we hypothesized a causal chain from genotype to phenotype partially mediated by intermediate molecular processes, i.e. gene expression. To test this hypothesis, we applied the high-dimensional mediation analysis, a class of causal inference method with an assumed causal chain from the exposure to the mediator to the outcome, and implemented it with a maize association panel (N = 280 lines). Using 40 publicly available agronomy traits, 66 newly generated metabolite traits, and published RNA-seq data from 7 different tissues, our empirical study detected 736 unique mediating genes. Noticeably, 83/736 (11%) genes were identified in mediating more than 1 trait, suggesting the prevalence of pleiotropic mediating effects. We demonstrated that several identified mediating genes are consistent with their known functions. In addition, our results provided explicit hypotheses for functional validation and suggested that the mediation analysis is a powerful tool to integrate Omics data to connect genotype to phenotype.

Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.)

Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats (Avena sativa L.), seed metabolites are of interest for their antioxidant properties, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic, and phenotypic analyses for three types of seed specialized metabolites—avenanthramides, avenacins, and avenacosides—we found reduced heritable genetic variation in modern germplasm compared with diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes. In contrast, avenacoside abundance decreases with seed size and plant breeding intensity. In addition, these different specialized metabolites do not share large-effect loci. Overall, we show that increased seed size associated with intensive plant breeding has uneven effects on the oat seed metabolome, but variation also exists independently of seed size to use in plant breeding. This work broadly contributes to our understanding of how plant breeding has influenced plant traits and tradeoffs between traits (like growth and defense) and the genetic bases of these shifts.

Preparation and Curation of Omics Data for Genome-Wide Association Studies

With the development of large-scale molecular phenotyping platforms, genome-wide association studies have greatly developed, being no longer limited to the analysis of classical agronomic traits, such as yield or flowering time, but also embracing the dissection of the genetic basis of molecular traits. Data generated by omics platforms, however, pose some technical and statistical challenges to the classical methodology and assumptions of an association study. Although genotyping data are subject to strict filtering procedures, and several advanced statistical approaches are now available to adjust for population structure, less attention has been instead devoted to the preparation of omics data prior to GWAS. In the present chapter, we briefly present the methods to acquire profiling data from transcripts, proteins, and small molecules, and discuss the tools and possibilities to clean, normalize, and remove the unwanted variation from large datasets of molecular phenotypic traits prior to their use in GWAS.

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

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

Integrated Transcriptomics and Widely Targeted Metabolomics Analyses Provide Insights Into Flavonoid Biosynthesis in the Rhizomes of Golden Buckwheat (Fagopyrum cymosum)

Golden buckwheat (Fagopyrum cymosum) is used in Traditional Chinese Medicine. It has received attention because of the high value of its various medicinal and nutritional metabolites, especially flavonoids (catechin and epicatechin). However, the metabolites and their encoding genes in golden buckwheat have not yet been identified in the global landscape. This study performed transcriptomics and widely targeted metabolomics analyses for the first time on rhizomes of golden buckwheat. As a result, 10,191 differentially expressed genes (DEGs) and 297 differentially regulated metabolites (DRMs) were identified, among which the flavonoid biosynthesis pathway was enriched in both transcriptome and metabolome. The integration analyses of the transcriptome and the metabolome revealed a network related to catechin, in which four metabolites and 14 genes interacted with each other. Subsequently, an SG5 R2R3-MYB transcription factor, named FcMYB1, was identified as a transcriptional activator in catechin biosynthesis, as it was positively correlated to eight flavonoid biosynthesis genes in their expression patterns and was directly bound to the promoters of FcLAR2 and FcF3'H1 by yeast one hybrid analysis. Finally, a flavonoid biosynthesis pathway was proposed in the rhizomes of golden buckwheat, including 13 metabolites, 11 genes encoding 9 enzymes, and 1 MYB transcription factor. The expression of 12 DEGs were validated by qRT-PCR, resulting in a good agreement with the Pearson R ranging from 0.83 to 1. The study provided a comprehensive flavonoid biosynthesis and regulatory network of golden buckwheat.

Transcriptomics Integrated With Metabolomics Reveal the Effects of Ultraviolet-B Radiation on Flavonoid Biosynthesis in Antarctic Moss

Bryophytes are the dominant vegetation in the Antarctic continent. They have suffered more unpleasant ultraviolet radiation due to the Antarctic ozone layer destruction. However, it remains unclear about the molecular mechanism of Antarctic moss acclimation to UV-B light. Here, the transcriptomics and metabolomics approaches were conducted to uncover transcriptional and metabolic profiling of the Antarctic moss Leptobryum pyriforme under UV-B radiation. Totally, 67,290 unigenes with N₅₀ length of 2,055 bp were assembled. Of them, 1,594 unigenes were significantly up-regulated and 3353 unigenes were markedly down-regulated under UV-B radiation. These differentially expressed genes (DEGs) involved in UV-B signaling, flavonoid biosynthesis, ROS scavenging, and DNA repair. In addition, a total of 531 metabolites were detected, while flavonoids and anthocyanins accounted for 10.36% of the total compounds. There were 49 upregulated metabolites and 41 downregulated metabolites under UV-B radiation. Flavonoids were the most significantly changed metabolites. qPCR analysis showed that UVR8-COP1-HY5 signaling pathway genes and photolyase genes (i.e., LpUVR3, LpPHR1, and LpDPL) were significantly up-regulated under UV-B light. In addition, the expression levels of JA signaling pathway-related genes (i.e., OPR and JAZ) and flavonoid biosynthesis-related genes were also significantly increased under UV-B radiation. The integrative data analysis showed that UVR8-mediated signaling, jasmonate signaling, flavonoid biosynthesis pathway and DNA repair system might contribute to L. pyriforme acclimating to UV-B radiation. Therefore, these findings present a novel knowledge for understanding the adaption of Antarctic moss to polar environments and provide a foundation for assessing the impact of global climate change on Antarctic land plants.

Omics Technologies to Enhance Plant Based Functional Foods: An Overview

Functional foods are natural products of plants that have health benefits beyond necessary nutrition. Functional foods are abundant in fruits, vegetables, spices, beverages and some are found in cereals, millets, pulses and oilseeds. Efforts to identify functional foods in our diet and their beneficial aspects are limited to few crops. Advances in sequencing and availability of different omics technologies have given opportunity to utilize these tools to enhance the functional components of the foods, thus ensuring the nutritional security. Integrated omics approaches including genomics, transcriptomics, proteomics, metabolomics coupled with artificial intelligence and machine learning approaches can be used to improve the crops. This review provides insights into omics studies that are carried out to find the active components and crop improvement by enhancing the functional compounds in different plants including cereals, millets, pulses, oilseeds, fruits, vegetables, spices, beverages and medicinal plants. There is a need to characterize functional foods that are being used in traditional medicines, as well as utilization of this knowledge to improve the staple foods in order to tackle malnutrition and hunger more effectively.

Metabolite Diversity and Metabolic Genome-Wide Marker Association Studies (Mgwas) for Health Benefiting Nutritional Traits in Pearl Millet Grains

As efforts are made to increase food security, millets are gaining increasing importance due to their excellent nutritional credentials. Among the millets, pearl millet is the predominant species possessing several health benefiting nutritional traits in its grain that are helpful in mitigating chronic illnesses such as type−2 diabetes and obesity. In this paper, we conducted metabolomic fingerprinting of 197 pearl millet inbred lines drawn randomly from within the world collection of pearl millet germplasm and report the extent of genetic variation for health benefitting metabolites in these genotypes. Metabolites were extracted from seeds and assessed using flow infusion high-resolution mass spectrometry (FIE-HRMS). Metabolite features (m/z), whose levels significantly differed among the germplasm inbred lines, were identified by ANOVA corrected for FDR and subjected to functional pathway analysis. A number of health-benefiting metabolites linked to dietary starch, antioxidants, vitamins, and lipid metabolism-related compounds were identified. Metabolic genome-wide association analysis (mGWAS) performed using the 396 m/z as phenotypic traits and the 76 K SNP as genotypic variants identified a total of 897 SNPs associated with health benefiting nutritional metabolite at the -log p-value ≤ 4.0. From these associations, 738 probable candidate genes were predicted to have an important role in starch, antioxidants, vitamins, and lipid metabolism. The mGWAS analysis focused on genes involved in starch branching (α-amylase, β-amylase), vitamin-K reductase, UDP-glucuronosyl, and UDP-glucosyl transferase (UGTs), L-ascorbate oxidase, and isoflavone 2′-monooxygenase genes, which are known to be linked to increases in human health benefiting metabolites. We demonstrate how metabolomic, genomic, and statistical approaches can be utilized to pinpoint genetic variations and their functions linked to key nutritional properties in pearl millet, which in turn can be bred into millets and other cereals crops using plant breeding methods.

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

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

Effects of light, anoxia and salinity on the expression of dihydroxyacid dehydratase in maize

Dihydroxyacid dehydratase (EC 4.2.1.9) participates in metabolism of branched chain amino acids, in CoA biosynthesis and in the conversion of hydroxycitric acid that accumulates in several plants. In maize (Zea mays L.), this enzyme is encoded by the two genes (Dhad1 and Dhad2), having different patterns of their expression during germination. We have demonstrated the inhibition of Dhad1 expression by light and the opposite effect of light on Dhad2. These effects were phytochrome-dependent and involved methylation/demethylation of promoters. Incubation of maize plants in a nitrogen atmosphere resulted in Dhad1 activation peaking at 12 h, which coincided with the decrease in promoter methylation. The gene Dhad2 was activated only during the first 6 h of anoxia, with no correlation with the level of promoter methylation. Salt stress (150 mM NaCl) caused the activation of expression of Dhad2 while the expression of Dhad1 was inhibited in the first hour and then after 12 h incubation with NaCl. We conclude that the expression of two genes encoding dihydroxyacid dehydratase reveals the opposite or different patterns of regulation by light, anoxia and salinity. The mechanisms underlying these modifications involve promoter methylation and result in corresponding changes in the enzymatic activity of the conversion of hydroxycitrate to 2-oxoglutarate.

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.

CsMYB96 enhances citrus fruit resistance against fungal pathogen by activating salicylic acid biosynthesis and facilitating defense metabolite accumulation

Citrus fruit are generally confronted with various fungal diseases that cause fruit deterioration and economic loss. Salicylic acid (SA), a plant hormone, is an important signal molecule required for stimulating the disease resistance of plants. However, there has been limited information about the molecular mechanism of SA biosynthesis involving biotic stress response in citrus fruit. In the present study, an R2R3 MYB transcription factor (CsMYB96) was identified to mediate SA signaling in response to fungal diseases. The transient overexpression assay revealed that CsMYB96 contributed to the strong tolerance of citrus fruit to Penicillium italicum along with an increase in SA content; meanwhile, CsMYB96 conferred resistance to Botrytis cinerea in Arabidopsis plants. Further metabolomic profiling of stable transgenic Arabidopsis revealed that CsMYB96 participated in the regulation of various metabolism pathways and enhanced the accumulation of phenolic acids. RNA-seq analysis confirmed that overexpression of CsMYB96 activated the expression of genes involved in plant-pathogen interaction, phenylpropanoid biosynthesis, and SA signaling. Besides, CsMBY96 directly activated the transcription of calmodulin binding protein 60g (CsCBP60g), a predominant transcription factor required for the activation of SA signaling. In summary, our results reveal that CsMYB96 promotes SA biosynthesis and the accumulation of defense metabolites to enhance the fungal pathogen resistance of citrus fruit and Arabidopsis and provide new insights into the regulation of disease response.

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

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

The utility of metabolomics as a tool to inform maize biology

With the rise of high-throughput omics tools and the importance of maize and its products as food and bioethanol, maize metabolism has been extensively explored. Modern maize is still rich in genetic and phenotypic variation, yielding a wide range of structurally and functionally diverse metabolites. The maize metabolome is also incredibly dynamic in terms of topology and subcellular compartmentalization. In this review, we examine a broad range of studies that cover recent developments in maize metabolism. Particular attention is given to current methodologies and to the use of metabolomics as a tool to define biosynthetic pathways and address biological questions. We also touch upon the use of metabolomics to understand maize natural variation and evolution, with a special focus on research that has used metabolite-based genome-wide association studies (mGWASs).

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.

Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance

BACKGROUND

Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown.

RESULTS

Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A, which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding.

CONCLUSION

Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.

Getting back to the grass roots: harnessing specialized metabolites for improved crop stress resilience

Roots remain an understudied site of complex and important biological interactions mediating plant productivity. In grain and bioenergy crops, grass root specialized metabolites (GRSM) are central to key interactions, yet our basic knowledge of the chemical language remains fragmentary. Continued improvements in plant genome assembly and metabolomics are enabling large-scale advances in the discovery of specialized metabolic pathways as a means of regulating root-biotic interactions. Metabolomics, transcript coexpression analyses, forward genetic studies, gene synthesis and heterologous expression assays drive efficient pathway discoveries. Functional genetic variants identified through genome wide analyses, targeted CRISPR/Cas9 approaches, and both native and non-native overexpression studies critically inform novel strategies for bioengineering metabolic pathways to improve plant traits.

Metabolomics-driven gene mining and genetic improvement of tolerance to salt-induced osmotic stress in maize

The farmland of the world's main corn-producing area is increasingly affected by salt stress. Therefore, the breeding of salt-tolerant cultivars is necessary for the long-term sustainability of global corn production. Previous studies have shown that natural maize varieties display a large diversity of salt tolerance, yet the genetic variants underlying such diversity remain poorly discovered and applied, especially those mediating the tolerance to salt-induced osmotic stress (SIOS). Here we report a metabolomics-driven understanding and genetic improvement of maize SIOS tolerance. Using a LC-MS-based untargeted metabolomics approach, we profiled the metabolomes of 266 maize inbred lines under control and salt conditions, and then identified 37 metabolite biomarkers of SIOS tolerance (METO1-37). Follow-up metabolic GWAS (mGWAS) and genotype-to-phenotype modeling identified 10 candidate genes significantly associating with the SIOS tolerance and METO abundances. Furthermore, we validated that a citrate synthase, a glucosyltransferase and a cytochrome P450 underlie the genotype-METO-SIOS tolerance associations, and showed that their favorable alleles additively improve the SIOS tolerance of elite maize inbred lines. Our study provides a novel insight into the natural variation of maize SIOS tolerance, which boosts the genetic improvement of maize salt tolerance, and demonstrates a metabolomics-based approach for mining crop genes associated with this complex agronomic trait.