Genetic dissection of metabolite variation in Arabidopsis seeds: evidence for mQTL hotspots and a master regulatory locus of seed metabolism

Functional characterization of plant specific Indeterminate Domain (IDD) transcription factors in tomato (Solanum lycopersicum L.)

Plant-specific transcription factors (TFs) are responsible for regulating the genes involved in the development of plant-specific organs and response systems for adaptation to terrestrial environments. This includes the development of efficient water transport systems, efficient reproductive organs, and the ability to withstand the effects of terrestrial factors, such as UV radiation, temperature fluctuations, and soil-related stress factors, and evolutionary advantages over land predators. In rice and Arabidopsis, INDETERMINATE DOMAIN (IDD) TFs are plant-specific TFs with crucial functions, such as development, reproduction, and stress response. However, in tomatoes, IDD TFs remain uncharacterized. Here, we examined the presence, distribution, structure, characteristics, and expression patterns of SlIDDs. Database searches, multiple alignments, and motif alignments suggested that 24 TFs were related to Arabidopsis IDDs. 18 IDDs had two characteristic C2H2 domains and two C2HC domains in their coding regions. Expression analyses suggest that some IDDs exhibit multi-stress responsive properties and can respond to specific stress conditions, while others can respond to multiple stress conditions in shoots and roots, either in a tissue-specific or universal manner. Moreover, co-expression database analyses suggested potential interaction partners within IDD family and other proteins. This study functionally characterized SlIDDs, which can be studied using molecular and bioinformatics methods for crop improvement.

Chemical-tag-based semi-annotated metabolomics facilitates gene identification and specialized metabolic pathway elucidation in wheat

The importance of metabolite modification and species-specific metabolic pathways has long been recognized. However, linking the chemical structure of metabolites to gene function in order to explore the genetic and biochemical basis of metabolism has not yet been reported in wheat (Triticum aestivum). Here, we profiled metabolic fragment enrichment in wheat leaves and consequently applied chemical-tag-based semi-annotated metabolomics in a genome-wide association study in accessions of wheat. The studies revealed that all 1,483 quantified metabolites have at least one known functional group whose modification is tailored in an enzyme-catalyzed manner and eventually allows efficient candidate gene mining. A Triticeae crop-specific flavonoid pathway and its underlying metabolic gene cluster were elucidated in further functional studies. Additionally, upon overexpressing the major effect gene of the cluster TraesCS2B01G460000 (TaOMT24), the pathway was reconstructed in rice (Oryza sativa), which lacks this pathway. The reported workflow represents an efficient and unbiased approach for gene mining using forward genetics in hexaploid wheat. The resultant candidate gene list contains vast molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets and will ultimately aid in achieving wheat crop improvement.

Metabolite-based genome-wide association studies enable the dissection of the genetic bases of flavonoids, betaine and spermidine in wolfberry (Lycium)

Wolfberry is a plant with medicinal and food values. However, its bioactive ingredients and the corresponding genetic bases have not been determined. Here, we de novo generated a chromosome-level genome assembly for wolfberry, yielding a genome sequence of ~1.77 Gb with contig N50 of 50.55 Mb and 39 224 predicted gene models. A variation map, using 307 re-sequenced accessions, was called based on this genome assembly. Furthermore, the fruit metabolome of these accessions was profiled using 563 annotated metabolites, which separated Lycium barbarum L. and non-L. barbarum L. The flavonoids, coumarins, alkaloids and nicotinic acid contents were higher in the former than in the latter. A metabolite-based genome-wide association study mapped 156 164 significant single nucleotide polymorphisms corresponding to 340 metabolites. This included 19 219 unique lead single nucleotide polymorphisms in 1517 significant association loci, of which three metabolites, flavonoids, betaine and spermidine, were highlighted. Two candidate genes, LbUGT (evm.TU.chr07.2692) and LbCHS (evm.TU.chr07.2738), with non-synonymous mutations, were associated with the flavonoids content. LbCHS is a structural gene that interacts with a nearby MYB transcription factor (evm.TU.chr07.2726) both in L. barbarum and L. ruthenicum. Thus, these three genes might be involved in the biosynthesis/metabolism of flavonoids. LbSSADH (evm.TU.chr09.627) was identified as possibly participating in betaine biosynthesis/metabolism. Four lycibarbarspermidines (E-G and O) were identified, and only the lycibarbarspermidines O content was higher in L. barbarum varieties than in non-L. barbarum varieties. The evm.TU.chr07.2680 gene associated with lycibarbarspermidines O was annotated as an acetyl-CoA-benzylalcohol acetyltransferase, suggesting that it is a candidate gene for spermidine biosynthesis. These results provide novel insights into the specific metabolite profile of non-L. barbarum L. and the genetic bases of flavonoids, betaine and spermidine biosynthesis/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.

Natural variation of respiration-related traits in plants

Plant respiration is one of the greatest global metabolic fluxes, but rates of respiration vary massively both within different cell types as well as between different individuals and different species. Whilst this is well known, few studies have detailed population-level variation of respiration until recently. The last 20 years have seen a renaissance in studies of natural variance. In this review, we describe how experimental breeding populations and collections of large populations of accessions can be used to determine the genetic architecture of plant traits. We further detail how these approaches have been used to study the rate of respiration per se as well as traits that are intimately associated with respiration. The review highlights specific breakthroughs in these areas but also concludes that the approach should be more widely adopted in the study of respiration per se as opposed to the more frequently studied respiration-related traits.

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

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

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.

Comparative analysis of wild-type accessions reveals novel determinants of Arabidopsis seed longevity

Understanding the genetic factors involved in seed longevity is of paramount importance in agricultural and ecological contexts. The polygenic nature of this trait suggests that many of them remain undiscovered. Here, we exploited the contrasting seed longevity found amongst Arabidopsis thaliana accessions to further understand this phenomenon. Concentrations of glutathione were higher in longer-lived than shorter-lived accessions, supporting that redox poise plays a prominent role in seed longevity. However, high seed permeability, normally associated with shorter longevity, is also present in long-lived accessions. Dry seed transcriptome analysis indicated that the contribution to longevity of stored messenger RNA (mRNAs) is complex, including mainly accession-specific mechanisms. The detrimental effect on longevity caused by other factors may be counterbalanced by higher levels of specific mRNAs stored in dry seeds, for instance those of heat-shock proteins. Indeed, loss-of-function mutant analysis demonstrated that heat-shock factors HSF1A and 1B contributed to longevity. Furthermore, mutants of the stress-granule zinc-finger protein TZF9 or the spliceosome subunits MOS4 or MAC3A/MAC3B, extended seed longevity, positioning RNA as a novel player in the regulation of seed viability. mRNAs of proteins with putative relevance to longevity were also abundant in shorter-lived accessions, reinforcing the idea that resistance to ageing is determined by multiple factors.

Research Progress and Trends in Metabolomics of Fruit Trees

Metabolomics is an indispensable part of modern systems biotechnology, applied in the diseases' diagnosis, pharmacological mechanism, and quality monitoring of crops, vegetables, fruits, etc. Metabolomics of fruit trees has developed rapidly in recent years, and many important research results have been achieved in combination with transcriptomics, genomics, proteomics, quantitative trait locus (QTL), and genome-wide association study (GWAS). These research results mainly focus on the mechanism of fruit quality formation, metabolite markers of special quality or physiological period, the mechanism of fruit tree's response to biotic/abiotic stress and environment, and the genetics mechanism of fruit trait. According to different experimental purposes, different metabolomic strategies could be selected, such as targeted metabolomics, non-targeted metabolomics, pseudo-targeted metabolomics, and widely targeted metabolomics. This article presents metabolomics strategies, key techniques in metabolomics, main applications in fruit trees, and prospects for the future. With the improvement of instruments, analysis platforms, and metabolite databases and decrease in the cost of the experiment, metabolomics will prompt the fruit tree research to achieve more breakthrough results.

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.

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.

The genetics underlying metabolic signatures in a brown rice diversity panel and their vital role in human nutrition

Brown rice (Oryza sativa) possesses various nutritionally dense bioactive phytochemicals exhibiting a wide range of antioxidant, anti-cancer, and anti-diabetic properties known to promote various human health benefits. However, despite the wide claims made about the importance of brown rice for human nutrition the underlying metabolic diversity has not been systematically explored. Non-targeted metabolite profiling of developing and mature seeds of a diverse genetic panel of 320 rice cultivars allowed quantification of 117 metabolites. The metabolite genome-wide association study (mGWAS) detected genetic variants influencing diverse metabolic targets in developing and mature seeds. We further interlinked genetic variants on chromosome 7 (6.06-6.43 Mb region) with complex epistatic genetic interactions impacting multi-dimensional nutritional targets, including complex carbohydrate starch quality, the glycemic index, antioxidant catechin, and rice grain color. Through this nutrigenomics approach rare gene bank accessions possessing genetic variants in bHLH and IPT5 genes were identified through haplotype enrichment. These variants were associated with a low glycemic index, higher catechin levels, elevated total flavonoid contents, and heightened antioxidant activity in the whole grain with elevated anti-cancer properties being confirmed in cancer cell lines. This multi-disciplinary nutrigenomics approach thus allowed us to discover the genetic basis of human health-conferring diversity in the metabolome of brown rice.

Temporal dynamics of QTL effects on vegetative growth in Arabidopsis thaliana

We assessed early vegetative growth in a population of 382 accessions of Arabidopsis thaliana using automated non-invasive high-throughput phenotyping. All accessions were imaged daily from 7 d to 18 d after sowing in three independent experiments and genotyped using the Affymetrix 250k SNP array. Projected leaf area (PLA) was derived from image analysis and used to calculate relative growth rates (RGRs). In addition, initial seed size was determined. The generated datasets were used jointly for a genome-wide association study that identified 238 marker-trait associations (MTAs) individually explaining up to 8% of the total phenotypic variation. Co-localization of MTAs occurred at 33 genomic positions. At 21 of these positions, sequential co-localization of MTAs for 2-9 consecutive days was observed. The detected MTAs for PLA and RGR could be grouped according to their temporal expression patterns, emphasizing that temporal variation of MTA action can be observed even during the vegetative growth phase, a period of continuous formation and enlargement of seemingly similar rosette leaves. This indicates that causal genes may be differentially expressed in successive periods. Analyses of the temporal dynamics of biological processes are needed to gain important insight into the molecular mechanisms of growth-controlling processes in plants.

PANOMICS meets germplasm

Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.

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.

PANOMICS meets germplasm

Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.

Evolutionary Metabolomics Identifies Substantial Metabolic Divergence between Maize and Its Wild Ancestor, Teosinte

Maize (Zea mays subsp mays) was domesticated from its wild ancestor, teosinte (Zea mays subsp parviglumis). Maize's distinct morphology and adaptation to diverse environments required coordinated changes in various metabolic pathways. However, how the metabolome was reshaped since domestication remains poorly understood. Here, we report a comprehensive assessment of divergence in the seedling metabolome between maize and teosinte. In total, 461 metabolites exhibited significant divergence due to selection. Interestingly, teosinte and tropical and temperate maize, representing major stages of maize evolution, targeted distinct sets of metabolites. Alkaloids, terpenoids, and lipids were specifically targeted in the divergence between teosinte and tropical maize, while benzoxazinoids were specifically targeted in the divergence between tropical and temperate maize. To identify genetic factors controlling metabolic divergence, we assayed the seedling metabolome of a large maize-by-teosinte cross population. We show that the recent metabolic divergence between tropical and temperate maize tended to have simpler genetic architecture than the divergence between teosinte and tropical maize. Through integrating transcriptome data, we identified candidate genes contributing to metabolic divergence, many of which were under selection at the nucleotide and transcript levels. Through overexpression or mutant analysis, we verified the roles of Flavanone 3-hydroxylase1, Purple aleurone1, and maize terpene synthase1 in the divergence of their related biosynthesis pathways. Our findings not only provide important insights into domestication-associated changes in the metabolism but also highlight the power of combining omics data for trait dissection.

Plant Networks as Traits and Hypotheses: Moving Beyond Description

Biology relies on the central thesis that the genes in an organism encode molecular mechanisms that combine with stimuli and raw materials from the environment to create a final phenotypic expression representative of the genomic programming. While conceptually simple, the genotype-to-phenotype linkage in a eukaryotic organism relies on the interactions of thousands of genes and an environment with a potentially unknowable level of complexity. Modern biology has moved to the use of networks in systems biology to try to simplify this complexity to decode how an organism's genome works. Previously, biological networks were basic ways to organize, simplify, and analyze data. However, recent advances are allowing networks to move beyond description and become phenotypes or hypotheses in their own right. This review discusses these efforts, like mapping responses across biological scales, including relationships among cellular entities, and the direct use of networks as traits or hypotheses.

Regulatory actors and alternative routes for Arabidopsis seed germination are revealed using a pathway-based analysis of transcriptomic datasets

Regulation of seed germination by dormancy relies on a complex network of transcriptional and post-transcriptional modifications during seed imbibition that controls seed adaptive responses to environmental cues. High-throughput technologies have brought significant progress in the understanding of this phenomenon and have led to identify major regulators of seed germination, mostly by studying the behaviour of highly differentially expressed genes. However, the actual models of transcriptome analysis cannot catch additive effects of small variations of gene expression in individual signalling or metabolic pathways, which are also likely to control germination. Therefore, the comprehension of the molecular mechanism regulating germination is still incomplete and to gain knowledge about this process we have developed a pathway-based analysis of transcriptomic Arabidopsis datasets, to identify regulatory actors of seed germination. The method allowed quantifying the level of deregulation of a wide range of pathways in dormant versus non-dormant seeds. Clustering pathway deregulation scores of germinating and dormant seed samples permitted the identification of mechanisms involved in seed germination such as RNA transport or vitamin B6 metabolism, for example. Using this method, which was validated by metabolomics analysis, we also demonstrated that Col and Cvi seeds follow different metabolic routes for completing germination, demonstrating the genetic plasticity of this process. We finally provided an extensive basis of analysed transcriptomic datasets that will allow further identification of mechanisms controlling seed germination.

Genetic Variation for Seed Metabolite Levels in Brachypodium distachyon

Metabolite composition and concentrations in seed grains are important traits of cereals. To identify the variation in the seed metabolotypes of a model grass, namely Brachypodium distachyon, we applied a widely targeted metabolome analysis to forty inbred lines of B. distachyon and examined the accumulation patterns of 183 compounds in the seeds. By comparing the metabolotypes with the population structure of these lines, we found signature metabolites that represent different accumulation patterns for each of the three B. distachyon subpopulations. Moreover, we found that thirty-seven metabolites exhibited significant differences in their accumulation between the lines Bd21 and Bd3-1. Using a recombinant inbred line (RIL) population from a cross between Bd3-1 and Bd21, we identified the quantitative trait loci (QTLs) linked with this variation in the accumulation of thirteen metabolites. Our metabolite QTL analysis illustrated that different genetic factors may presumably regulate the accumulation of 4-pyridoxate and pyridoxamine in vitamin B6 metabolism. Moreover, we found two QTLs on chromosomes 1 and 4 that affect the accumulation of an anthocyanin, chrysanthemin. These QTLs genetically interacted to regulate the accumulation of this compound. This study demonstrates the potential for metabolite QTL mapping in B. distachyon and provides new insights into the genetic dissection of metabolomic traits in temperate grasses.

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

Plants have served as sources providing humans with metabolites for food and nutrition, biomaterials for living, and treatment for pain and disease. Plants produce a huge array of metabolites, with an immense diversity at both the population and individual levels. Dissection of the genetic bases for metabolic diversity has attracted increasing research attention. The concept of genome-wide association study (GWAS) was extended to studies on the diversity of plant metabolome that benefitted from the development of mass-spectrometry-based analytical systems and genome sequencing technologies. Metabolic genome-wide association study (mGWAS) is one of the most powerful tools for global identification of genetic determinants for diversity of plant metabolism. Recently, mGWAS has been performed for various species with continuous improvements, providing deeper insights into the genetic bases of metabolic diversity. In this review, we discuss fully the achievements to date and remaining challenges that are associated with both mGWAS and mGWAS-based multi-dimensional analysis. We begin with a summary of GWAS and its development based on statistical methods and populations. As variation in targeted traits is essential for GWAS, we review metabolic diversity and its rise at both the population and individual levels. Subsequently, the application of mGWAS for plants and its corresponding achievements are fully discussed. We address the current knowledge on mGWAS-based multi-dimensional analysis and emerging insights into the diversity of metabolism.

The Diversity of Nutritional Metabolites: Origin, Dissection, and Application in Crop Breeding

The chemical diversity of plants is very high, and plant-based foods provide almost all the nutrients necessary for human health, either directly or indirectly. With advancements in plant metabolomics studies, the concept of nutritional metabolites has been expanded and updated. Because the concentration of many nutrients is usually low in plant-based foods, especially those from crops, metabolome-assisted breeding techniques using molecular markers associated with the synthesis of nutritional metabolites have been developed and used to improve nutritional quality of crops. Here, we review the origins of the diversity of nutrient metabolites from a genomic perspective and the role of gene duplication and divergence. In addition, we systematically review recent advances in the metabolomic and genetic basis of metabolite production in major crops. With the development of genome sequencing and metabolic detection technologies, multi-omic integrative analysis of genomes, transcriptomes, and metabolomes has greatly facilitated the deciphering of the genetic basis of metabolic pathways and the diversity of nutrient metabolites. Finally, we summarize the application of nutrient diversity in crop breeding and discuss the future development of a viable alternative to metabolome-assisted breeding techniques that can be used to improve crop nutrient quality.

Exploring the Diversity of Plant Metabolism

Plants produce a huge array of metabolites, far more than those produced by most other organisms. Unraveling this diversity and its underlying genetic variation has attracted increasing research attention. Post-genomic profiling platforms have enabled the marriage and mining of the enormous amount of phenotypic and genetic diversity. We review here achievements to date and challenges remaining that are associated with plant metabolic research using multi-omic strategies. We focus mainly on strategies adopted in investigating the diversity of plant metabolism and its underlying features. Recent advances in linking metabotypes with phenotypic and genotypic traits are also discussed. Taken together, we conclude that exploring the diversity of metabolism could provide new insights into plant evolution and domestication.

Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses

Phenotypic complexity is caused by the contributions of environmental factors and multiple genetic loci, interacting or acting independently. Studies of yeast and Arabidopsis often find that the majority of natural variation across phenotypes is attributable to independent additive quantitative trait loci (QTL). Detected loci in these organisms explain most of the estimated heritable variation. By contrast, many heritable components underlying phenotypic variation in metazoan models remain undetected. Before the relative impacts of additive and interactive variance components on metazoan phenotypic variation can be dissected, high replication and precise phenotypic measurements are required to obtain sufficient statistical power to detect loci contributing to this missing heritability. Here, we used a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a high-throughput fitness assay to detect loci underlying responses to 16 different toxins, including heavy metals, chemotherapeutic drugs, pesticides, and neuropharmaceuticals. Using linkage mapping, we identified 82 QTL that underlie variation in responses to these toxins, and predicted the relative contributions of additive loci and genetic interactions across various growth parameters. Additionally, we identified three genomic regions that impact responses to multiple classes of toxins. These QTL hotspots could represent common factors impacting toxin responses. We went further to generate near-isogenic lines and chromosome substitution strains, and then experimentally validated these QTL hotspots, implicating additive and interactive loci that underlie toxin-response variation.

Metabolome Analysis of Multi-Connected Biparental Chromosome Segment Substitution Line Populations

Metabolomic analysis coupled with advanced genetic populations represents a powerful tool with which to investigate the plant metabolome. However, genetic analyses of the rice (Oryza sativa) metabolome have been conducted mainly using natural accessions or a single biparental population. Here, the flag leaves from three interconnected chromosome segment substitution line populations with a common recurrent genetic background were used to dissect rice metabolic diversity. We effectively used multiple interconnected biparental populations, constructed by introducing genomic segments into Zhenshan 97 from ACC10 (A/Z), Minghui 63 (M/Z), and Nipponbare (N/Z), to map metabolic quantitative trait loci (mQTL). A total of 1,587 mQTL were generated, of which 684, 479, and 722 were obtained from the A/Z, M/Z, and N/Z chromosome segment substitution line populations, respectively, and we designated 99 candidate genes for 367 mQTL. In addition, 1,001 mQTL were generated specifically from joint linkage analysis with 25 candidate genes assigned. Several of these candidates were validated, such as LOC_Os07g01020 for the in vivo content of pyridoxine and its derivative and LOC_Os04g25980 for cis-zeatin glucosyltransferase activity. We propose a novel biosynthetic pathway for O-methylapigenin C-pentoside and demonstrated that LOC_Os04g11970 encodes a component of this pathway through fine-mapping. We postulate that the methylated apigenin may confer plant disease resistance. This study demonstrates the power of using multiple interconnected populations to generate a large number of veritable mQTL. The combined results are discussed in the context of functional metabolomics and the possible features of assigned candidates underlying respective metabolites.

Extracting genotype information of Arabidopsis thaliana recombinant inbred lines from transcript profiles established with high-density oligonucleotide arrays

Polymorphic probes identified via a sequence-based approach are suitable to infer the genotypes of recombinant inbred lines from hybridisation intensities of GeneChip ^® transcript profiling experiments. The sequences of the probes of the ATH1 GeneChip^® exactly match transcript sequences of the Arabidopsis thaliana reference genome Col-0, whereas nucleotide differences and/or insertions/deletions may be observed for transcripts of other A. thaliana accessions. Individual probes of the GeneChip^® that show sequence polymorphisms between different A. thaliana accessions may serve as single-feature polymorphism (SFP) markers, provided that the sequence changes cause differences in hybridisation intensity for the accessions of interest. A sequence-based approach identified features on the high-density oligonucleotide array that showed sequence polymorphisms between A. thaliana accessions Col-0 and C24. Hybridisation intensities of polymorphic probes were extracted from genome-wide transcript profiles of Col-0/C24 and C24/Col-0 recombinant inbred lines and assessed after standardisation via sliding window analyses to identify SFP markers. The genotypes of the recombinant inbred lines were determined with the SFP markers and the resulting data were integrated with information, which had been established previously with single nucleotide polymorphism and insertion/deletion markers, to enrich the linkage map of the Col-0/C24 and C24/Col-0 recombinant inbred populations. Congruence between the molecular marker map and the sequence maps of the A. thaliana Col-0 chromosomes proved the reliability of the genotype information which was deduced from the transcript profiles of the Col-0/C24 and C24/Col-0 recombinant inbred lines.