Effects of genetics and environment on the metabolome of commercial maize hybrids: a multisite study

Loss-of-function of triacylglycerol lipases are associated with low flour rancidity in pearl millet [Pennisetum glaucum (L.) R. Br.]

Pearl millet is an important cereal crop of semi-arid regions since it is highly nutritious and climate resilient. However, pearl millet is underutilized commercially due to the rapid onset of hydrolytic rancidity of seed lipids post-milling. We investigated the underlying biochemical and molecular mechanisms of rancidity development in the flour from contrasting inbred lines under accelerated aging conditions. The breakdown of storage lipids (triacylglycerols; TAG) was accompanied by free fatty acid accumulation over the time course for all lines. The high rancidity lines had the highest amount of FFA by day 21, suggesting that TAG lipases may be the cause of rancidity. Additionally, the high rancidity lines manifested substantial amounts of volatile aldehyde compounds, which are characteristic products of lipid oxidation. Lipases with expression in seed post-milling were sequenced from low and high rancidity lines. Polymorphisms were identified in two TAG lipase genes (PgTAGLip1 and PgTAGLip2) from the low rancidity line. Expression in a yeast model system confirmed these mutants were non-functional. We provide a direct mechanism to alleviate rancidity in pearl millet flour by identifying mutations in key TAG lipase genes that are associated with low rancidity. These genetic variations can be exploited through molecular breeding or precision genome technologies to develop elite pearl millet cultivars with improved flour shelf life.

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).

Evaluation of the use of untargeted metabolomics in the safety assessment of genetically modified crops

BACKGROUND

The safety assessment of foods and feeds from genetically modified (GM) crops includes the comparison of key characteristics, such as crop composition, agronomic phenotype and observations from animal feeding studies compared to conventional counterpart varieties that have a history of safe consumption, often including a near isogenic variety. The comparative compositional analysis of GM crops has been based on targeted, validated, quantitative analytical methods for the key food and feed nutrients and antinutrients for each crop, as identified by Organization of Economic Co-operation and Development (OCED). As technologies for untargeted metabolomic methods have evolved, proposals have emerged for their use to complement or replace targeted compositional analytical methods in regulatory risk assessments of GM crops to increase the number of analyzed metabolites.

AIM OF REVIEW

The technical opportunities, challenges and strategies of including untargeted metabolomics analysis in the comparative safety assessment of GM crops are reviewed. The results from metabolomics studies of GM and conventional crops published over the last eight years provide context to enable the discussion of whether metabolomics can materially improve the risk assessment of food and feed from GM crops beyond that possible by the Codex-defined practices used worldwide for more than 25 years.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Published studies to date show that environmental and genetic factors affect plant metabolomics profiles. In contrast, the plant biotechnology process used to make GM crops has little, if any consequence, unless the inserted GM trait is intended to alter food or feed composition. The nutritional value and safety of food and feed from GM crops is well informed by the quantitative, validated compositional methods for list of key analytes defined by crop-specific OECD consensus documents. Untargeted metabolic profiling has yet to provide data that better informs the safety assessment of GM crops than the already rigorous Codex-defined quantitative comparative assessment. Furthermore, technical challenges limit the implementation of untargeted metabolomics for regulatory purposes: no single extraction method or analytical technique captures the complete plant metabolome; a large percentage of metabolites features are unknown, requiring additional research to understand if differences for such unknowns affect food/feed safety; and standardized methods are needed to provide reproducible data over time and laboratories.

Multiplex Design of the Metabolic Network for Production of l-Homoserine in Escherichia coli

In this study, the bottlenecks that sequentially limit l-homoserine biosynthesis were identified and resolved, based on rational and efficient metabolic-engineering strategies, coupled with CRISPR interference (CRISPRi)-based systematic analysis. The metabolomics data largely expanded our understanding of metabolic effects and revealed relevant targets for further modification to achieve better performance. The systematic analysis strategy, as well as metabolomics analysis, can be used to rationally design cell factories for the production of highly valuable chemicals.

l-Homoserine, which is one of the few amino acids that is not produced on a large scale by microbial fermentation, plays a significant role in the synthesis of a series of valuable chemicals. In this study, systematic metabolic engineering was applied to target Escherichia coli W3110 for the production of l-homoserine. Initially, a basic l-homoserine producer was engineered through the strategies of overexpressing thrA (encoding homoserine dehydrogenase), removing the degradative and competitive pathways by knocking out metA (encoding homoserine O-succinyltransferase) and thrB (encoding homoserine kinase), reinforcing the transport system, and redirecting the carbon flux by deleting iclR (encoding the isocitrate lyase regulator). The resulting strain constructed by these strategies yielded 3.21 g/liter of l-homoserine in batch cultures. Moreover, based on CRISPR-Cas9/dCas9 (nuclease-dead Cas9)-mediated gene repression for 50 genes, the iterative genetic modifications of biosynthesis pathways improved the l-homoserine yield in a stepwise manner. The rational integration of glucose uptake and recovery of l-glutamate increased l-homoserine production to 7.25 g/liter in shake flask cultivation. Furthermore, the intracellular metabolic analysis further provided targets for strain modification by introducing the anaplerotic route afforded by pyruvate carboxylase to oxaloacetate formation, which resulted in accumulating 8.54 g/liter l-homoserine (0.33 g/g glucose, 62.4% of the maximum theoretical yield) in shake flask cultivation. Finally, a rationally designed strain gave 37.57 g/liter l-homoserine under fed-batch fermentation, with a yield of 0.31 g/g glucose.

IMPORTANCE In this study, the bottlenecks that sequentially limit l-homoserine biosynthesis were identified and resolved, based on rational and efficient metabolic-engineering strategies, coupled with CRISPR interference (CRISPRi)-based systematic analysis. The metabolomics data largely expanded our understanding of metabolic effects and revealed relevant targets for further modification to achieve better performance. The systematic analysis strategy, as well as metabolomics analysis, can be used to rationally design cell factories for the production of highly valuable chemicals.

Plant breeding involving genetic engineering does not result in unacceptable unintended effects in rice relative to conventional cross-breeding

Advancements in -omics techniques provide powerful tools to assess the potential effects in composition of a plant at the RNA, protein and metabolite levels. These technologies can thus be deployed to assess whether genetic engineering (GE) causes changes in plants that go beyond the changes introduced by conventional plant breeding. Here, we compare the extent of transcriptome and metabolome modification occurring in leaves of four GE rice lines expressing Bacillus thuringiensis genes developed by GE and seven rice lines developed by conventional cross-breeding. The results showed that both types of crop breeding methods can bring changes at transcriptomic and metabolic levels, but the differences were comparable between the two methods, and were less than those between conventional non-GE lines were. Metabolome profiling analysis found several new metabolites in GE rice lines when compared with the closest non-GE parental lines, but these compounds were also found in several of the conventionally bred rice lines. Functional analyses suggest that the differentially expressed genes and metabolites caused by both GE and conventional cross-breeding do not involve detrimental metabolic pathways. The study successfully employed RNA-sequencing and high-performance liquid chromatography mass spectrometry technology to assess the unintended changes in new rice varieties, and the results suggest that GE does not cause unintended effects that go beyond conventional cross-breeding in rice.

Evaluating maize phenotypic variance, heritability, and yield relationships at multiple biological scales across agronomically relevant environments

A challenge to improve an integrative phenotype, like yield, is the interaction between the broad range of possible molecular and physiological traits that contribute to yield and the multitude of potential environmental conditions in which they are expressed. This study collected data on 31 phenotypic traits, 83 annotated metabolites, and nearly 22,000 transcripts from a set of 57 diverse, commercially relevant maize hybrids across three years in central U.S. Corn Belt environments. Although variability in characteristics created a complex picture of how traits interact produce yield, phenotypic traits and gene expression were more consistent across environments, while metabolite levels showed low repeatability. Phenology traits, such as green leaf number and grain moisture and whole plant nitrogen content showed the most consistent correlation with yield. A machine learning predictive analysis of phenotypic traits revealed that ear traits, phenology, and root traits were most important to predicting yield. Analysis suggested little correlation between biomass traits and yield, suggesting there is more of a sink limitation to yield under the conditions studied here. This work suggests that continued improvement of maize yields requires a strong understanding of baseline variation of plant characteristics across commercially-relevant germplasm to drive strategies for consistently improving yield.

Insights into Tissue-specific Specialized Metabolism in Tieguanyin Tea Cultivar by Untargeted Metabolomics

Tea plants produce extremely diverse and abundant specialized metabolites, the types and levels of which are developmentally and environmentally regulated. However, little is known about how developmental cues affect the synthesis of many of these molecules. In this study, we conducted a comparative profiling of specialized metabolites from six different tissues in a premium oolong tea cultivar, Tieguanyin, which is gaining worldwide popularity due to its uniquely rich flavors and health benefits. UPLC-QTOF MS combined with multivariate analyses tentatively identified 68 metabolites belonging to 11 metabolite classes, which exhibited sharp variations among tissues. Several metabolite classes, such as flavonoids, alkaloids, and hydroxycinnamic acid amides were detected predominantly in certain plant tissues. In particular, tricoumaroyl spermidine and dicoumaroyl putrescine were discovered as unique tea flower metabolites. This study offers novel insights into tissue-specific specialized metabolism in Tieguanyin, which provides a good reference point to explore gene-metabolite relationships in this cultivar.

Phenotypic and metabolic variation among spring Brassica napus genotypes during heat stress

Heat stress can frequently limit the yield of Brassica napus L. grown in Canada because of the often unavoidable concurrence of high temperatures and flowering. Ten B. napus inbred genotypes, an open-pollinated B. napus commercial cultivar and a B. juncea genotype were grown in a greenhouse and subjected to two temperature regimes in a growth chamber for 14 days during flowering: control 22°C/10°C and high 31°C/14°C (day/night). Floral buds were sampled at the end of the 14-day treatments, and an untargeted metabolomic assessment was completed using gas chromatography–mass spectrometry. Flower duration, number of flowers, number of pods, biomass, number of seeds and seed weight were recorded. Yield was reduced by 55% in the heat treatment during winter and by 41% during the subsequent autumn experimental run. Of the 12 genotypes, five were classified as heat-tolerant and four as heat-susceptible based on the calculated heat susceptibility index across two experiments. In total, 25 metabolic markers were identified that discriminated between the heat-tolerant and -susceptible genotypes exposed to the heat treatment. The variation identified within this set of germplasm has provided evidence that variation exists within B. napus to enable genetic gain for heat tolerance.

Effect of Genetics, Environment, and Phenotype on the Metabolome of Maize Hybrids Using GC/MS and LC/MS

We evaluated the variability of metabolites in various maize hybrids due to the effect of environment, genotype, phenotype as well as the interaction of the first two factors. We analyzed 480 forage and the same number of grain samples from 21 genetically diverse non-GM Pioneer brand maize hybrids, including some with drought tolerance and viral resistance phenotypes, grown at eight North American locations. As complementary platforms, both GC/MS and LC/MS were utilized to detect a wide diversity of metabolites. GC/MS revealed 166 and 137 metabolites in forage and grain samples, respectively, while LC/MS captured 1341 and 635 metabolites in forage and grain samples, respectively. Univariate and multivariate analyses were utilized to investigate the response of the maize metabolome to the environment, genotype, phenotype, and their interaction. Based on combined percentages from GC/MS and LC/MS datasets, the environment affected 36% to 84% of forage metabolites, while less than 7% were affected by genotype. The environment affected 12% to 90% of grain metabolites, whereas less than 27% were affected by genotype. Less than 10% and 11% of the metabolites were affected by phenotype in forage and grain, respectively. Unsupervised PCA and HCA analyses revealed similar trends, i.e., environmental effect was much stronger than genotype or phenotype effects. On the basis of comparisons of disease tolerant and disease susceptible hybrids, neither forage nor grain samples originating from different locations showed obvious phenotype effects. Our findings demonstrate that the combination of GC/MS and LC/MS based metabolite profiling followed by broad statistical analysis is an effective approach to identify the relative impact of environmental, genetic and phenotypic effects on the forage and grain composition of maize hybrids.

Fortune telling: metabolic markers of plant performance

BACKGROUND

In the last decade, metabolomics has emerged as a powerful diagnostic and predictive tool in many branches of science. Researchers in microbes, animal, food, medical and plant science have generated a large number of targeted or non-targeted metabolic profiles by using a vast array of analytical methods (GC-MS, LC-MS, 1H-NMR….). Comprehensive analysis of such profiles using adapted statistical methods and modeling has opened up the possibility of using single or combinations of metabolites as markers. Metabolic markers have been proposed as proxy, diagnostic or predictors of key traits in a range of model species and accurate predictions of disease outbreak frequency, developmental stages, food sensory evaluation and crop yield have been obtained.

AIM OF REVIEW

(i) To provide a definition of plant performance and metabolic markers, (ii) to highlight recent key applications involving metabolic markers as tools for monitoring or predicting plant performance, and (iii) to propose a workable and cost-efficient pipeline to generate and use metabolic markers with a special focus on plant breeding.

KEY MESSAGE

Using examples in other models and domains, the review proposes that metabolic markers are tending to complement and possibly replace traditional molecular markers in plant science as efficient estimators of performance.

Effect of the environment on the secondary metabolic profile of Tithonia diversifolia: a model for environmental metabolomics of plants

Tithonia diversifolia is an invasive weed commonly found in tropical ecosystems. In this work, we investigate the influence of different abiotic environmental factors on the plant's metabolite profile by multivariate statistical analyses of spectral data deduced by UHPLC-DAD-ESI-HRMS and NMR methods. Different plant part samples of T. diversifolia which included leaves, stems, roots, and inflorescences were collected from two Brazilian states throughout a 24-month period, along with the corresponding monthly environmental data. A metabolomic approach employing concatenated LC-MS and NMR data was utilised for the first time to study the relationships between environment and plant metabolism. A seasonal pattern was observed for the occurrence of metabolites that included sugars, sesquiterpenes lactones and phenolics in the leaf and stem parts, which can be correlated to the amount of rainfall and changes in temperature. The distribution of the metabolites in the inflorescence and root parts were mainly affected by variation of some soil nutrients such as Ca, Mg, P, K and Cu. We highlight the environment-metabolism relationship for T. diversifolia and the combined analytical approach to obtain reliable data that contributed to a holistic understanding of the influence of abiotic environmental factors on the production of metabolites in various plant parts.

Metabolomic Assessment of Key Maize Resources: GC-MS and NMR Profiling of Grain from B73 Hybrids of the Nested Association Mapping (NAM) Founders and of Geographically Diverse Landraces

The present study expands metabolomic assessments of maize beyond commercial lines to include two sets of hybrids used extensively in the scientific community. One set included hybrids derived from the nested association mapping (NAM) founder lines, a collection of 25 inbreds selected on the basis of genetic diversity and used to investigate the genetic basis of complex plant traits. A second set included 24 hybrids derived from a collection of landraces representative of native diversity from North and South America that may serve as a source of new alleles for improving modern maize hybrids. Metabolomic analysis of grain harvested from these hybrids utilized gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) and (1)H nuclear magnetic resonance spectroscopy ((1)H NMR) techniques. Results highlighted extensive metabolomic variation in grain from both hybrid sets, but also demonstrated that, within each hybrid set, subpopulations could be differentiated in a pattern consistent with the known genetic and compositional variation of these lines. Correlation analysis did not indicate a strong association of the metabolomic data with grain nutrient composition, although some metabolites did show moderately strong correlations with agronomic features such as plant and ear height. Overall, this study provides insights into the extensive metabolomic diversity associated with conventional maize germplasm.

Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait

INTRODUCTION

Past studies on plant metabolomes have highlighted the influence of growing environments and varietal differences in variation of levels of metabolites yet there remains continued interest in evaluating the effect of genetic modification (GM).

OBJECTIVES

Here we test the hypothesis that metabolomics differences in grain from maize hybrids derived from a series of GM (NK603, herbicide tolerance) inbreds and corresponding negative segregants can arise from residual genetic variation associated with backcrossing and that the effect of insertion of the GM trait is negligible.

METHODS

Four NK603-positive and negative segregant inbred males were crossed with two different females (testers). The resultant hybrids, as well as conventional comparator hybrids, were then grown at three replicated field sites in Illinois, Minnesota, and Nebraska during the 2013 season. Metabolomics data acquisition using gas chromatography-time of flight-mass spectrometry (GC-TOF-MS) allowed the measurement of 367 unique metabolite features in harvested grain, of which 153 were identified with small molecule standards. Multivariate analyses of these data included multi-block principal component analysis and ANOVA-simultaneous component analysis. Univariate analyses of all 153 identified metabolites was conducted based on significance testing (α = 0.05), effect size evaluation (assessing magnitudes of differences), and variance component analysis.

RESULTS

Results demonstrated that the largest effects on metabolomic variation were associated with different growing locations and the female tester. They further demonstrated that differences observed between GM and non-GM comparators, even in stringent tests utilizing near-isogenic positive and negative segregants, can simply reflect minor genomic differences associated with conventional back-crossing practices.

CONCLUSION

The effect of GM on metabolomics variation was determined to be negligible and supports that there is no scientific rationale for prioritizing GM as a source of variation.

Sample-directed pseudotargeted method for the metabolic profiling analysis of rice seeds based on liquid chromatography with mass spectrometry

Rice is one of the most important food crops in the world. Metabolite composition in rice seeds varies significantly depending on genetic variety, climatic alternation and agricultural practice. Metabolomics is a powerful tool to reveal the metabolic response of rice to various conditions. In this work, a rice seed sample-directed pseudotargeted metabolomics method was first established and validated based on ultra high performance liquid chromatography with triple quadrupole mass spectrometry in the multiple reaction monitoring mode. A total of 749 and 617 ion pairs in positive and negative modes were achieved, respectively. Among them, about 200 metabolites were identified or tentatively identified. The developed method showed better linearity and repeatability than those of non-targeted metabolomics method. Good intra-day and inter-day precisions, recoveries and wide linear range were also obtained. Furthermore, the method was applied for the investigation of metabolic variation of rice seeds with two wild cultivars and their transgenic lines that were grown in two locations. Principal component analysis indicated that the effects of cultivar and location on metabolic variations were far more than those of gene modification. The nonparametric Mann-Whitney U test revealed that most metabolites were influenced by cultivar, location and gene modifications together.

Genotypic and Environmental Impact on Natural Variation of Nutrient Composition in 50 Non Genetically Modified Commercial Maize Hybrids in North America

This study was designed to assess natural variation in composition and metabolites in 50 genetically diverse non genetically modified maize hybrids grown at six locations in North America. Results showed that levels of compositional components in maize forage were affected by environment more than genotype. Crude protein, all amino acids except lysine, manganese, and β-carotene in maize grain were affected by environment more than genotype; however, most proximates and fibers, all fatty acids, lysine, most minerals, vitamins, and secondary metabolites in maize grain were affected by genotype more than environment. A strong interaction between genotype and environment was seen for some analytes. The results could be used as reference values for future nutrient composition studies of genetically modified crops and to expand conventional compositional data sets. These results may be further used as a genetic basis for improvement of the nutritional value of maize grain by molecular breeding and biotechnology approaches.

Metabolomics of genetically modified crops

Metabolomic-based approaches are increasingly applied to analyse genetically modified organisms (GMOs) making it possible to obtain broader and deeper information on the composition of GMOs compared to that obtained from traditional analytical approaches. The combination in metabolomics of advanced analytical methods and bioinformatics tools provides wide chemical compositional data that contributes to corroborate (or not) the substantial equivalence and occurrence of unintended changes resulting from genetic transformation. This review provides insight into recent progress in metabolomics studies on transgenic crops focusing mainly in papers published in the last decade.

Nitrogen-use efficiency in maize (Zea mays L.): from 'omics' studies to metabolic modelling

In this review, we will present the latest developments in systems biology with particular emphasis on improving nitrogen-use efficiency (NUE) in crops such as maize and demonstrating the application of metabolic models. The review highlights the importance of improving NUE in crops and provides an overview of the transcriptome, proteome, and metabolome datasets available, focusing on a comprehensive understanding of nitrogen regulation. 'Omics' data are hard to interpret in the absence of metabolic flux information within genome-scale models. These models, when integrated with 'omics' data, can serve as a basis for generating predictions that focus and guide further experimental studies. By simulating different nitrogen (N) conditions at a pseudo-steady state, the reactions affecting NUE and additional gene regulations can be determined. Such models thus provide a framework for improving our understanding of the metabolic processes underlying the more efficient use of N-based fertilizers.

A modified data normalization method for GC-MS-based metabolomics to minimize batch variation

The goal of metabolomics data pre-processing is to eliminate systematic variation, such that biologically-related metabolite signatures are detected by statistical pattern recognition. Although several methods have been developed to tackle the issue of batch-to-batch variation, each method has its advantages and disadvantages. In this study, we used a reference sample as a normalization standard for test samples within the same batch, and each metabolite value is expressed as a ratio relative to its counterpart in the reference sample. We then applied this approach to a large multi-batch data set to facilitate intra- and inter-batch data integration. Our results demonstrate that normalization to a single reference standard has the potential to minimize batch-to-batch data variation across a large, multi-batch data set.

Analytical method evaluation and discovery of variation within maize varieties in the context of food safety: transcript profiling and metabolomics

Profiling techniques such as microarrays, proteomics, and metabolomics are used widely to assess the overall effects of genetic background, environmental stimuli, growth stage, or transgene expression in plants. To assess the potential regulatory use of these techniques in agricultural biotechnology, we carried out microarray and metabolomic studies of 3 different tissues from 11 conventional maize varieties. We measured technical variations for both microarrays and metabolomics, compared results from individual plants and corresponding pooled samples, and documented variations detected among different varieties with individual plants or pooled samples. Both microarray and metabolomic technologies are reproducible and can be used to detect plant-to-plant and variety-to-variety differences. A pooling strategy lowered sample variations for both microarray and metabolomics while capturing variety-to-variety variation. However, unknown genomic sequences differing between maize varieties might hinder the application of microarrays. High-throughput metabolomics could be useful as a tool for the characterization of transgenic crops. However, researchers will have to take into consideration the impact on the detection and quantitation of a wide range of metabolites on experimental design as well as validation and interpretation of results.

Effect of environment and genotype on commercial maize hybrids using LC/MS-based metabolomics

We recently applied gas chromatography coupled to time-of-flight mass spectrometry (GC/TOF-MS) and multivariate statistical analysis to measure biological variation of many metabolites due to environment and genotype in forage and grain samples collected from 50 genetically diverse nongenetically modified (non-GM) DuPont Pioneer commercial maize hybrids grown at six North American locations. In the present study, the metabolome coverage was extended using a core subset of these grain and forage samples employing ultra high pressure liquid chromatography (uHPLC) mass spectrometry (LC/MS). A total of 286 and 857 metabolites were detected in grain and forage samples, respectively, using LC/MS. Multivariate statistical analysis was utilized to compare and correlate the metabolite profiles. Environment had a greater effect on the metabolome than genetic background. The results of this study support and extend previously published insights into the environmental and genetic associated perturbations to the metabolome that are not associated with transgenic modification.

Application of nontargeted metabolite profiling to discover novel markers of quality traits in an advanced population of malting barley

The process of breeding superior varieties for the agricultural industry is lengthy and expensive. Plant metabolites may act as markers of quality traits, potentially expediting the appraisal of experimental lines during breeding. Here, we evaluated the utility of metabolites as markers by assessing metabolic variation influenced by genetic and environmental factors in an advanced breeding setting and in relation to the phenotypic distribution of 20 quality traits. Nontargeted liquid chromatography-mass spectrometry metabolite profiling was performed on barley (Hordeum vulgare L.) grain and malt from 72 advanced malting barley lines grown at two distinct but climatically similar locations, with 2-row and 6-row barley as the main genetic factors. 27 420 molecular features were detected, and the metabolite and quality trait profiles were similarly influenced by genotype and environment; however, malt was more influenced by genotype compared with barley. An O2PLS model characterized molecular features and quality traits that covaried, and 1319 features associated with at least one of 20 quality traits. An indiscriminant MS/MS acquisition and novel data analysis method facilitated the identification of metabolites. The analysis described 216 primary and secondary metabolites that correlated with multiple quality traits and included amines, amino acids, alkaloids, polyphenolics and lipids. The mechanisms governing quality trait-metabolite associations were interpreted based on colocalization to genetic markers and their gene annotations. The results of this study support the hypothesis that metabolism and quality traits are co-influenced by relatively narrow genetic and environmental factors and illustrate the utility of grain metabolites as functional markers of quality traits.