Impact of genetics and environment on the metabolite composition of maize grain

Conservation Agriculture Affects Grain and Nutrient Yields of Maize (Zea Mays L.) and Can Impact Food and Nutrition Security in Sub-Saharan Africa

Maize is a major staple and plays an essential role in food and nutrition security in Sub-Saharan Africa (SSA). Conservation agriculture (CA), a climate-smart agriculture practise based on minimum soil disturbance, crop residue retention, and crop diversification, has been widely advocated but without extensive research on the impact it may have on maize nutrient composition, and food and nutrition security. This study assessed the grain yield, macro- and micronutrient mineral content, and nutrient yield of eight maize varieties grown in Malawi, and how these are affected by CA practises over two seasons. The minerals were analysed by inductively coupled plasma (ICP) coupled to optical emission spectroscopy (OES) and to mass spectroscopy (MS). Grain yield and Se content differed among the varieties, while C, N, Fe, K, Mg, Mn, P, and Zn were similar. The local variety Kanjerenjere showed lowest grain and nutrient yields. The open-pollinated varieties (OPVs) concentrated more minerals than the F1 hybrids, but the latter showed higher yields for both grain and nutrients. Typical consumption of the eight maize varieties could fully meet the protein and Mg dietary reference intake (DRIs) of Malawian children (1–3 years), as well as Mg and Mn needs of adult women (19–50 years), but their contribution to dietary requirements was low for Fe (39–41%) and K (13–21%). The trials showed that CA increased grain yield (1.2- to 1.8-fold) and Se content (1.1- to 1.7-fold), but that it had no effect on C, K, Mg, P, and Zn, and that N (1.1- to 1.2-fold), Mn (1.1- to 1.8-fold), and Fe (1.3- to 3.4-fold) were reduced. The high increase in grain yield under CA treatments resulted in increased yields of protein and Se, no effect on the yields of K, Mg, Mn, P, Zn, and reduced Fe yield. Conservation agriculture could contribute in reducing the risk of Se deficiency in Malawian women and children but exacerbates the risk of Fe deficiency. A combination of strategies will be needed to mitigate some of the foreseen effects of climate change on agriculture, and food and nutrition security, and improve nutrient intake.

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).

Location and Variety but Not Phosphate Starter Fertilization Influence the Profiles of Fatty Acids, Carotenoids, and Tocochromanols in Kernels of Modern Corn (Zea mays L.) Hybrids Cultivated in Germany

Phosphate is a limiting plant nutrient and essential for corn growth and development. Thus, the impact of phosphate fertilization, location, and the variety of modern corn (Zea mays L.) hybrids on the profiles of fatty acids, carotenoids, and tocochromanols (vitamin E) was assessed in corn grains. Eight different corn hybrids were grown with (52.9 kg of phosphorus per ha) or without starter fertilizer at three experimental sites in Germany. Location (p < 0.05) and genetics (p < 0.001) but not phosphate fertilization significantly altered the concentrations of individual saturated and unsaturated fatty acids, carotenoids, and tocochromanols. Significant (p < 0.05) interaction effects on the concentrations were mainly observed between the variety and the location. In conclusion, the choice of the corn variety had a more significant impact on the biosynthesis of fatty acids, carotenoids, and tocochromanols than the location or phosphate application on phosphate-sufficient soils.

Widely Targeted Metabolomics Analysis Reveals Key Quality-Related Metabolites in Kernels of Sweet Corn

Sweet corn (Zea mays convar. saccharata var. rugosa) is a major economic vegetable crop. Different sweet corn cultivars vary largely in flavor, texture, and nutrition. The present study performed widely targeted metabolomics analysis based on the HPLC-MS/MS technology to analyze the metabolic profiles in three sweet corn cultivars widely grown in China. A total of 568 metabolites in the three sweet corn cultivars were detected, of which 262 differential metabolites significantly changed among cultivars. Carbohydrates, organic acids, and amino acids were the majority detected primary metabolites. Organic acids were mainly concentrated on shikimate, benzoic acids, and quinic acid with aromatic groups. And the essential amino acids for the human body, methionine and threonine, were highly accumulated in the high-quality cultivar. In addition, phenylpropanoids and alkaloids were the most enriched secondary metabolites while terpenes were low-detected in sweet corn kernels. We found that the flavonoids exist in both free form and glycosylated form in sweet corn kernels. PCA and HCA revealed clear separations among the three sweet corn cultivars, suggesting distinctive metabolome profiles among three cultivars. The differential metabolites were mapped into flavonoid biosynthesis, phenylpropanoid biosynthesis, biosynthesis of amino acids, and other pathways according to the KEGG classification. Furthermore, we identified skimmin, N',N^″-diferuloylspermidine, and 3-hydroxyanthranilic acid as the key quality-related metabolites related to grain quality traits in sweet corn. The results suggested variations of metabolic composition among the three cultivars, providing the reference quality-related metabolites for sweet corn breeding.

Analysis of metabolites and metabolic pathways in three maize (Zea mays L.) varieties from the same origin using GC-MS

Metabolites of the Jinyu 88, Huanong 18, and Demeiya 9 maize varieties from the same origin were isolated and identified by GC–MS, and the specific metabolites and metabolic mechanisms of these three varieties of maize were preliminarily analysed and discussed. The metabolites were extracted with 80% methanol and derived with N,O-bis(trimethylsilyl)trifluoroacetamide. A total of 59 metabolites were identified. The specific metabolites of these three varieties of maize were identified. Four possible unknown-structure metabolites were hypothesized. The results showed that the specific metabolites of JY88 were only involved in fatty acid metabolism. The specific metabolites of HN18 were determined to be involved in fatty acid metabolism, glucose metabolism, and phytosterol metabolism. The specific metabolites of DM9 were observed to participate in glucose metabolism and fatty acid metabolism. The disease resistance of HN18 was higher than that of DM9, and its grain bulk density was higher than that of DM9. JY88 was determined to be significantly different from the other two varieties, and its appearance and disease resistance were worse than those of the other two varieties. The variety with the highest nutritional value was determined to be HN18, and the variety with the lowest nutritional value was JY88. This finding indicated that different maize varieties from the same origin had different metabolites and different metabolic mechanisms, which caused the three maize varieties to exhibit different characteristics and qualities.

Roles of metabolic regulation in developing Quercus variabilis acorns at contrasting geologically-derived phosphorus sites in subtropical China

BACKGROUND

Phosphorus (P) -rich soils develop in phosphorite residing areas while P-deficient soils are ubiquitous in subtropical regions. Little has been reported that how metabolites participate in the seed development and the processes involved in their coping with contrasting-nutrient environments.

RESULTS

Here we quantified the metabolites of Quercus variabilis acorns in the early (July), middle (August), late (September) development stages, and determined element (C, H, O, N, P, K, Ca, Mg, S, Fe, Al, Mn, Na, Zn, and Cu) concentrations of acorns in the late stage, at geologically-derived contrasting-P sites in subtropical China. The primary metabolic pathways included sugar metabolism, the TCA cycle, and amino acid metabolism. Most metabolites (especially C- and N-containing metabolites) increased and then decreased from July to September. Acorns between the two sites were significantly discriminated at the three stages, respectively, by metabolites (predominantly sugars and organic acids). Concentrations of P, orthophosphoric acid and most sugars were higher; erythrose was lower in late-stage acorns at P-rich sites than those at P-deficient sites. No significant differences existed in the size and dry mass of individual acorns between oak populations at the two sites.

CONCLUSIONS

Oak acorns at the two sites formed distinct metabolic phenotypes related to their distinct geologically-derived soil conditions, and the late-stage acorns tended to increase P-use-efficiency in the material synthesis process at P-deficient sites, relative to those at P-rich sites.

The Application of Metabolomics for the Study of Cereal Corn (Zea mays L.)

Corn (Zea mays L.) is an important cereal crop indigenous to the Americas, where its genetic biodiversity is still preserved, especially among native populations from Mesoamerica and South America. The use of metabolomics in corn has mainly focused on understanding the potential differences of corn metabolomes under different biotic and abiotic stresses or to evaluate the influence of genetic and environmental factors. The increase of diet-linked non-communicable diseases has increased the interest to optimize the content of bioactive secondary metabolites in current corn breeding programs to produce novel functional foods. This review provides perspectives on the role of metabolomics in the characterization of health-relevant metabolites in corn biodiversity and emphasizes the integration of metabolomics in breeding strategies targeting the enrichment of phenolic bioactive metabolites such as anthocyanins in corn kernels.

Metabolomic approach to study the impact of flour type and fermentation process on volatile profile of bakery products

Metabolomic approaches applied to fermented foods are at the state of the science and represent a robust and reliable approach to identify, quantify and characterise the biochemical profiles of raw materials and transformed products. The outcomes so far obtained are cornerstones to understand mainly nutritional and sensorial inherent features. Formulations of new bakery products with increased nutritional values is trending the market, but sensorial attributes still need to be improved to reach a wider audience. The present work describes the application of gas chromatography-mass spectrometry (GC-MS) and electronic nose analyses, to investigate over the volatilome of different bakery products, obtained from mature and immature grains (KAMUT® khorasan and durum wheat) and transformed by a sourdough made of Lactobacillus spp. and Saccharomyces cerevisiae. From the recipient results has emerged that the sensors used can distinguish the KAMUT® khorasan doughs fermented industrially at the fully ripe stage, the same doughs at the milky stage and KAMUT® khorasan sourdough at the fully ripe stage. Electronic nose allowed discriminating between different types of flours and GC-MS indicated the volatilome of sourdough KAMUT® khorasan case as the most promising. Thus, the combination of different independent variables in the bread process to improve the sensorial quality of the product, when is backed by metabolomics, represents an effective approach to study, characterise and exploit the sensorial quality of breads.

Characterization of GMO or glyphosate effects on the composition of maize grain and maize-based diet for rat feeding

INTRODUCTION

In addition to classical targeted biochemical analyses, metabolomic analyses seem pertinent to reveal expected as well as unexpected compositional differences between plant genetically modified organisms (GMO) and non-GMO samples. Data previously published in the existing literature led to divergent conclusions on the effect of maize transgenes on grain compositional changes and feeding effects. Therefore, a new study examining field-grown harvested products and feeds derived from them remains useful.

OBJECTIVES

Our aim was to use a metabolomics approach to characterize grain and grain-based diet compositional changes for two GMO events, one involving Bacillus thuringiensis toxin to provide insect resistance and the other one conferring herbicide tolerance by detoxification of glyphosate. We also investigated the potential compositional modifications induced by the use of a glyphosate-based herbicide on the transgenic line conferring glyphosate tolerance.

RESULTS

The majority of statistically significant differences in grain composition, evidenced by the use of 1H-NMR profiling of polar extracts and LC-ESI-QTOF-MS profiling of semi-polar extracts, could be attributed to the combined effect of genotype and environment. In comparison, transgene and glyphosate effects remained limited in grain for the compound families studied. Some but not all compositional changes observed in grain were also detected in grain-based diets formulated for rats.

CONCLUSION

Only part of the data previously published in the existing literature on maize grains of plants with the same GMO events could be reproduced in our experiment. All spectra have been deposited in a repository freely accessible to the public. Our grain and diet characterization opened the way for an in depth study of the effects of these diets on rat health.

Deciphering drought-induced metabolic responses and regulation in developing maize kernels

Drought stress conditions decrease maize growth and yield, and aggravate preharvest aflatoxin contamination. While several studies have been performed on mature kernels responding to drought stress, the metabolic profiles of developing kernels are not as well characterized, particularly in germplasm with contrasting resistance to both drought and mycotoxin contamination. Here, following screening for drought tolerance, a drought-sensitive line, B73, and a drought-tolerant line, Lo964, were selected and stressed beginning at 14 days after pollination. Developing kernels were sampled 7 and 14 days after drought induction (DAI) from both stressed and irrigated plants. Comparative biochemical and metabolomic analyses profiled 409 differentially accumulated metabolites. Multivariate statistics and pathway analyses showed that drought stress induced an accumulation of simple sugars and polyunsaturated fatty acids and a decrease in amines, polyamines and dipeptides in B73. Conversely, sphingolipid, sterol, phenylpropanoid and dipeptide metabolites accumulated in Lo964 under drought stress. Drought stress also resulted in the greater accumulation of reactive oxygen species (ROS) and aflatoxin in kernels of B73 in comparison with Lo964 implying a correlation in their production. Overall, field drought treatments disordered a cascade of normal metabolic programming during development of maize kernels and subsequently caused oxidative stress. The glutathione and urea cycles along with the metabolism of carbohydrates and lipids for osmoprotection, membrane maintenance and antioxidant protection were central among the drought stress responses observed in developing kernels. These results also provide novel targets to enhance host drought tolerance and disease resistance through the use of biotechnologies such as transgenics and genome editing.

UPLC-QTOF-MS metabolomics analysis revealed the contributions of metabolites to the pathogenesis of Rhizoctonia solani strain AG-1-IA

To explore the pathogenesis of Rhizoctonia solani and its phytotoxin phenylacetic acid (PAA) on maize leaves and sheaths, treated leaf and sheath tissues were analyzed and interpreted by ultra-performance liquid chromatography-mass spectrometry combined with chemometrics. The PAA treatment had similar effects to those of R. solani on maize leaves regarding the metabolism of traumatin, phytosphingosine, vitexin 2'' O-beta-D-glucoside, rutin and DIBOA-glucoside, which were up-regulated, while the synthesis of OPC-8:0 and 12-OPDA, precursors for the synthesis of jasmonic acid, a plant defense signaling molecule, was down-regulated under both treatments. However, there were also discrepancies in the influences exhibited by R. solani and PAA as the metabolic concentration of zeaxanthin diglucoside in the R. solani infected leaf group decreased. Conversely, in the PAA-treated leaf group, the synthesis of zeaxanthin diglucoside was enhanced. Moreover, although the synthesis of 12 metabolites were suppressed in both the R. solani- and PAA-treated leaf tissues, the inhibitory effect of R. solani was stronger than that of PAA. An increased expression of quercitrin and quercetin 3-O-glucoside was observed in maize sheaths treated by R. solani, while their concentrations were not changed significantly in the PAA-treated sheaths. Furthermore, a significant decrease in the concentration of L-Glutamate, which plays important roles in plant resistance to necrotrophic pathogens, only occurred in the R. solani-treated sheath tissues. The differentiated metabolite levels may be the partial reason of why maize sheaths were more susceptible to R. solani than leaves and may explain the underlying mechanisms of R. solani pathogenesis.

Development, Validation, and Interlaboratory Evaluation of a Quantitative Multiplexing Method To Assess Levels of Ten Endogenous Allergens in Soybean Seed and Its Application to Field Trials Spanning Three Growing Seasons

As part of the regulatory approval process in Europe, comparison of endogenous soybean allergen levels between genetically engineered (GE) and non-GE plants has been requested. A quantitative multiplex analytical method using tandem mass spectrometry was developed and validated to measure 10 potential soybean allergens from soybean seed. The analytical method was implemented at six laboratories to demonstrate the robustness of the method and further applied to three soybean field studies across multiple growing seasons (including 21 non-GE soybean varieties) to assess the natural variation of allergen levels. The results show environmental factors contribute more than genetic factors to the large variation in allergen abundance (2- to 50-fold between environmental replicates) as well as a large contribution of Gly m 5 and Gly m 6 to the total allergen profile, calling into question the scientific rational for measurement of endogenous allergen levels between GE and non-GE varieties in the safety assessment.

Metabolic characterization of human aqueous humor in relation to high myopia

High myopia is the common eye disorder worldwide, which may contribute to increase the risk of serious disorders including glaucoma and cataract. Although various studies including genomics, transcriptomics, and proteomics have been implicated to identify potential biomarkers (genes or proteins) for predicting high myopia and to reveal the underlying mechanism, the comprehensive metabolomics in relation to high myopia is very limited. In this study, we identified 242 metabolites in aqueous humor (AH) from a set of 40 cataract patients (including 20 with high myopia and 20 for controls), using a non-targeted metabolomic technology, gas chromatography coupled to time-of-flight mass spectrometer (GC/TOF MS). Further statistical analysis showed that 29 metabolites were significantly changed (Variable important for the projection, VIP ≥ 1 and p ≤ 0.05), between those two groups, while only 2 decreased metabolites were included. Moreover, for the first time, metabolite-metabolite correlations for AH were analyzed, which may dissect key regulatory elements or pathways involved in metabolism of amino acids, carbohydrates, and lipids. Accordingly, metabolic network was constructed based on those 29 changed metabolites in patients with high myopia. More than half of the changed metabolites were highly and positively associated, suggesting important roles of pathways involved in the metabolism of these metabolites in relation to high myopia. Altogether, this work not only provided potential biomarkers for the diagnosis and monitoring of high myopia formation, but also provided new insights into the underlying mechanisms.

Metabolomic analysis of tomato seed germination

INTRODUCTION

Seed germination is inherently related to seed metabolism, which changes throughout its maturation, desiccation and germination processes. The metabolite content of a seed and its ability to germinate are determined by underlying genetic architecture and environmental effects during development.

OBJECTIVE

This study aimed to assess an integrative approach to explore genetics modulating seed metabolism in different developmental stages and the link between seed metabolic- and germination traits.

METHODS

We have utilized gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) metabolite profiling to characterize tomato seeds during dry and imbibed stages. We describe, for the first time in tomato, the use of a so-called generalized genetical genomics (GGG) model to study the interaction between genetics, environment and seed metabolism using 100 tomato recombinant inbred lines (RILs) derived from a cross between Solanum lycopersicum and Solanum pimpinellifolium.

RESULTS

QTLs were found for over two-thirds of the metabolites within several QTL hotspots. The transition from dry to 6 h imbibed seeds was associated with programmed metabolic switches. Significant correlations varied among individual metabolites and the obtained clusters were significantly enriched for metabolites involved in specific biochemical pathways.

CONCLUSIONS

Extensive genetic variation in metabolite abundance was uncovered. Numerous identified genetic regions that coordinate groups of metabolites were detected and these will contain plausible candidate genes. The combined analysis of germination phenotypes and metabolite profiles provides a strong indication for the hypothesis that metabolic composition is related to germination phenotypes and thus to seed performance.

Environmental and genetic effects on tomato seed metabolic balance and its association with germination vigor

BACKGROUND

The metabolite content of a seed and its ability to germinate are determined by genetic makeup and environmental effects during development. The interaction between genetics, environment and seed metabolism and germination was studied in 72 tomato homozygous introgression lines (IL) derived from Solanum pennelli and S. esculentum M82 cultivar. Plants were grown in the field under saline and fresh water irrigation during two consecutive seasons, and collected seeds were subjected to morphological analysis, gas chromatograph-mass spectrometry (GC-MS) metabolic profiling and germination tests.

RESULTS

Seed weight was under tight genetic regulation, but it was not related to germination vigor. Salinity significantly reduced seed number but had little influence on seed metabolites, affecting only 1% of the statistical comparisons. The metabolites negatively correlated to germination were simple sugars and most amino acids, while positive correlations were found for several organic acids and the N metabolites urea and dopamine. Germination tests identified putative loci for improved germination as compared to M82 and in response to salinity, which were also characterized by defined metabolic changes in the seed.

CONCLUSIONS

An integrative analysis of the metabolite and germination data revealed metabolite levels unambiguously associated with germination percentage and rate, mostly conserved in the different tested seed development environments. Such consistent relations suggest the potential for developing a method of germination vigor prediction by metabolic profiling, as well as add to our understanding of the importance of primary metabolic processes in germination.

Genetic variability of the phloem sap metabolite content of maize (Zea mays L.) during the kernel-filling period

Using a metabolomic approach, we have quantified the metabolite composition of the phloem sap exudate of seventeen European and American lines of maize that had been previously classified into five main groups on the basis of molecular marker polymorphisms. In addition to sucrose, glutamate and aspartate, which are abundant in the phloem sap of many plant species, large quantities of aconitate and alanine were also found in the phloem sap exudates of maize. Genetic variability of the phloem sap composition was observed in the different maize lines, although there was no obvious relationship between the phloem sap composition and the five previously classified groups. However, following hierarchical clustering analysis there was a clear relationship between two of the subclusters of lines defined on the basis of the composition of the phloem sap exudate and the earliness of silking date. A comparison between the metabolite contents of the ear leaves and the phloem sap exudates of each genotype, revealed that the relative content of most of the carbon- and nitrogen-containing metabolites was similar. Correlation studies performed between the metabolite content of the phloem sap exudates and yield-related traits also revealed that for some carbohydrates such as arabitol and sucrose there was a negative or positive correlation with kernel yield and kernel weight respectively. A posititive correlation was also found between kernel number and soluble histidine.

Characterization of factors underlying the metabolic shifts in developing kernels of colored maize

Elucidation of the metabolic pathways determining pigmentation and their underlying regulatory mechanisms in maize kernels is of high importance in attempts to improve the nutritional composition of our food. In this study, we compared dynamics in the transcriptome and metabolome between colored SW93 and white SW48 by integrating RNA-Seq and non-targeted metabolomics. Our data revealed that expression of enzyme coding genes and levels of primary metabolites decreased gradually from 11 to 21 DAP, corresponding well with the physiological change of developing maize kernels from differentiation through reserve accumulation to maturation, which was cultivar independent. A remarkable up-regulation of anthocyanin and phlobaphene pathway distinguished SW93 from SW48, in which anthocyanin regulating transcriptional factors (R1 and C1), enzyme encoding genes involved in both pathways and corresponding metabolic intermediates were up-regulated concurrently in SW93 but not in SW48. The shift from the shikimate pathway of primary metabolism to the flavonoid pathway of secondary metabolism, however, appears to be under posttranscriptional regulation. This study revealed the link between primary metabolism and kernel coloration, which facilitate further study to explore fundamental questions regarding the evolution of seed metabolic capabilities as well as their potential applications in maize improvement regarding both staple and functional foods.

Evolution of risk assessment strategies for food and feed uses of stacked GM events

Data requirements are not harmonized globally for the regulation of food and feed derived from stacked genetically modified (GM) events, produced by combining individual GM events through conventional breeding. The data required by some regulatory agencies have increased despite the absence of substantiated adverse effects to animals or humans from the consumption of GM crops. Data from studies conducted over a 15-year period for several stacked GM event maize (Zea mays L.) products (Bt11 ×  GA21, Bt11 ×  MIR604, MIR604 ×  GA21, Bt11 ×  MIR604 ×  GA21, Bt11 ×  MIR162 ×  GA21 and Bt11 ×  MIR604 ×  MIR162 ×  GA21), together with their component single events, are presented. These data provide evidence that no substantial changes in composition, protein expression or insert stability have occurred after combining the single events through conventional breeding. An alternative food and feed risk assessment strategy for stacked GM events is suggested based on a problem formulation approach that utilizes (i) the outcome of the single event risk assessments, and (ii) the potential for interactions in the stack, based on an understanding of the mode of action of the transgenes and their products.

Evolutionary Metabolomics Reveals Domestication-Associated Changes in Tetraploid Wheat Kernels

Domestication and breeding have influenced the genetic structure of plant populations due to selection for adaptation from natural habitats to agro-ecosystems. Here, we investigate the effects of selection on the contents of 51 primary kernel metabolites and their relationships in three Triticum turgidum L. subspecies (i.e., wild emmer, emmer, durum wheat) that represent the major steps of tetraploid wheat domestication. We present a methodological pipeline to identify the signature of selection for molecular phenotypic traits (e.g., metabolites and transcripts). Following the approach, we show that a reduction in unsaturated fatty acids was associated with selection during domestication of emmer (primary domestication). We also show that changes in the amino acid content due to selection mark the domestication of durum wheat (secondary domestication). These effects were found to be partially independent of the associations that unsaturated fatty acids and amino acids have with other domestication-related kernel traits. Changes in contents of metabolites were also highlighted by alterations in the metabolic correlation networks, indicating wide metabolic restructuring due to domestication. Finally, evidence is provided that wild and exotic germplasm can have a relevant role for improvement of wheat quality and nutritional traits.

Variability in nutrient composition of cereal grains from different origins

Nutrient composition of individual feed ingredient in mixed feed is important for accurate formulation of animal feeds. However, each feed ingredient can be different depending on its origin. Therefore, this study was conducted to investigate the variability in nutrient compositions of corn, wheat, and barley grains from different origins. Cereal grains used in this study were from 5 countries for 432 corn samples, 5 countries for 65 wheat samples, and 3 countries for 60 barley samples. They were imported to Korea between 2006 and 2015. These grain samples were subjected to analysis for moisture, crude protein (CP), ether extract (EE), crude fiber (CF), ash, calcium (Ca), phosphorus (P), and gross energy (GE). The concentrations of moisture, CP, EE, CF, ash, Ca, P, and GE of corn differed (P < 0.05) among countries. GE in corn samples ranged from 3836 kcal/kg (Ukraine) to 3995 kcal/kg (Brazil). There were also differences (P < 0.05) in moisture, CP, ash, and P of wheat and in moisture, CF, Ca, P, and GE in barley from different countries. GE values in wheat ranged from 3957 kcal/kg (Brazil) to 4058 kcal/kg (United States) and GE values in barley samples ranged from 3894 kcal/kg (India) to 4059 kcal/kg (Australia). The most different nutrient depending on origins was Ca. The coefficient of variation was 65.7 % for corn, 57.4 % for wheat, and 28.8 % for barley. In conclusion, nutrients and energy contents in corn, wheat, and barley from various origins investigated in the present study were different. Therefore, it is important to consider these variations when formulating animal feeds.

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.

Metabolic changes in transgenic maize mature seeds over-expressing the Aspergillus niger phyA2

Non-targeted metabolomics analysis revealed only intended metabolic changes in transgenic maize over-expressing the Aspergillus niger phyA2. Genetically modified (GM) crops account for a large proportion of modern agriculture worldwide, raising increasingly the public concerns of safety. Generally, according to substantial equivalence principle, if a GM crop is demonstrated to be equivalently safe to its conventional species, it is supposed to be safe. In this study, taking the advantage of an established non-target metabolomic profiling platform based on the combination of UPLC-MS/MS with GC-MS, we compared the mature seed metabolic changes in transgenic maize over-expressing the Aspergillus niger phyA2 with its non-transgenic counterpart and other 14 conventional maize lines. In total, levels of nine out of identified 210 metabolites were significantly changed in transgenic maize as compared with its non-transgenic counterpart, and the number of significantly altered metabolites was reduced to only four when the natural variations were taken into consideration. Notably, those four metabolites were all associated with targeted engineering pathway. Our results indicated that although both intended and non-intended metabolic changes occurred in the mature seeds of this GM maize event, only intended metabolic pathway was found to be out of the range of the natural metabolic variation in the metabolome of the transgenic maize. Therefore, only when natural metabolic variation was taken into account, could non-targeted metabolomics provide reliable objective compositional substantial equivalence analysis on GM crops.

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.

Effect of genetic and climatic variability on the metabolic profiles of black gram (Vigna mungo L.) seeds and sprouts

BACKGROUND

Black gram is becoming increasingly of interest for consumers worldwide. The metabolomics have been conducted to reflect the life history of each individual plant. The metabolic pattern of black gram seeds and sprouts was profiled to investigate genetic and climatic influences on a broad range of chemical constituents.

RESULTS

Distinct differences in metabolite profiles among three black gram varieties for both intact seeds and sprouts were observed. The differential impact of climate on metabolite profiles of the variety Chai Nat 80 during both dry and rainy seasons was investigated. Univariate statistical analysis demonstrated that greater maturity due to adequate moisture in the rainy season led to a higher content of nutritionally relevant polar metabolites, whereas the dry season resulted in a high relative amount of storage lipid because of immaturity due to insufficient rain and water supply.

CONCLUSION

The investigation confirmed the potential of metabolite profiling to assist in breeding and farming practices.

Accumulation of 5-hydroxynorvaline in maize (Zea mays) leaves is induced by insect feeding and abiotic stress

Plants produce a wide variety of defensive metabolites to protect themselves against herbivores and pathogens. Non-protein amino acids, which are present in many plant species, can have a defensive function through their mis-incorporation during protein synthesis and/or inhibition of biosynthetic pathways in primary metabolism. 5-Hydroxynorvaline was identified in a targeted search for previously unknown non-protein amino acids in the leaves of maize (Zea mays) inbred line B73. Accumulation of this compound increases during herbivory by aphids (Rhopalosiphum maidis, corn leaf aphid) and caterpillars (Spodoptera exigua, beet armyworm), as well as in response to treatment with the plant signalling molecules methyl jasmonate, salicylic acid and abscisic acid. In contrast, ethylene signalling reduced 5-hydroxynorvaline abundance. Drought stress induced 5-hydroxynorvaline accumulation to a higher level than insect feeding or treatment with defence signalling molecules. In field-grown plants, the 5-hydroxynorvaline concentration was highest in above-ground vegetative tissue, but it was also detectable in roots and dry seeds. When 5-hydroxynorvaline was added to aphid artificial diet at concentrations similar to those found in maize leaves and stems, R. maidis reproduction was reduced, indicating that this maize metabolite may have a defensive function. Among 27 tested maize inbred lines there was a greater than 10-fold range in the accumulation of foliar 5-hydroxynorvaline. Genetic mapping populations derived from a subset of these inbred lines were used to map quantitative trait loci for 5-hydroxynorvaline accumulation to maize chromosomes 5 and 7.

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

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.

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.

Unintended compositional changes in genetically modified (GM) crops: 20 years of research

The compositional equivalency between genetically modified (GM) crops and nontransgenic comparators has been a fundamental component of human health safety assessment for 20 years. During this time, a large amount of information has been amassed on the compositional changes that accompany both the transgenesis process and traditional breeding methods; additionally, the genetic mechanisms behind these changes have been elucidated. After two decades, scientists are encouraged to objectively assess this body of literature and determine if sufficient scientific uncertainty still exists to continue the general requirement for these studies to support the safety assessment of transgenic crops. It is concluded that suspect unintended compositional effects that could be caused by genetic modification have not materialized on the basis of this substantial literature. Hence, compositional equivalence studies uniquely required for GM crops may no longer be justified on the basis of scientific uncertainty.

Safety of GM crops: compositional analysis

The compositional analysis of genetically modified (GM) crops has continued to be an important part of the overall evaluation in the safety assessment program for these materials. The variety and complexity of genetically engineered traits and modes of action that will be used in GM crops in the near future, as well as our expanded knowledge of compositional variability and factors that can affect composition, raise questions about compositional analysis and how it should be applied to evaluate the safety of traits. The International Life Sciences Institute (ILSI), a nonprofit foundation whose mission is to provide science that improves public health and well-being by fostering collaboration among experts from academia, government, and industry, convened a workshop in September 2012 to examine these and related questions, and a series of papers has been assembled to describe the outcomes of that meeting.

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

This study was designed to elucidate the biological variation in expression of many metabolites due to environment, genotype, or both, and to investigate the potential utility of metabolomics to supplement compositional analysis for substantial equivalence assessments of genetically modified (GM) crops. A total of 654 grain and 695 forage samples from 50 genetically diverse non-GM DuPont Pioneer maize hybrids grown at six locations in the U.S. and Canada were analyzed by coupled gas chromatography time-of-flight-mass spectrometry (GC/TOF-MS). A total of 156 and 185 metabolites were measured in grain and forage samples, respectively. Univariate and multivariate statistical analyses were employed extensively to compare and correlate the metabolite profiles. We show that the environment had far more impact on the forage metabolome compared to the grain metabolome, and the environment affected up to 50% of the metabolites compared to less than 2% by the genetic background. The findings from this study demonstrate that the combination of GC/TOF-MS metabolomics and comprehensive multivariate statistical analysis is a powerful approach to identify the sources of natural variation contributed by the environment and genotype.

Metabolite profiles of rice cultivars containing bacterial blight-resistant genes are distinctive from susceptible rice

The metabolic changes of bacterial blight-resistant line C418/Xa23 generated by molecular marker-assisted selection (n= 12), transgenic variety C418-Xa21 generated by using the Agrobacterium-mediated system (n= 12), and progenitor cultivar C418 (n= 12) were monitored using gas chromatography/mass spectrometry. The validation, discrimination, and establishment of correlative relationships between metabolite signals were performed by cluster analysis, principal component analysis, and partial least squares-discriminant analysis. Significant and unintended changes were observed in 154 components in C418/Xa23 and 48 components in C418-Xa21 compared with C418 (P< 0.05, Fold change > 2.0). The most significant decreases detected (P< 0.001) in both C418/Xa23 and C418-Xa21 were in three amino acids: glycine, tyrosine, and alanine, and four identified metabolites: malic acid, ferulic acid, succinic acid, and glycerol. Linoleic acid was increased specifically in C418/Xa23 which was derived from traditional breeding. This line, possessing a distinctive metabolite profile as a positive control, shows more differences vs. the parental than the transgenic line. Only succinic acid that falls outside the boundaries of natural variability between the two non-transgenic varieties C418 and C418/Xa23 should be further investigated with respect to safety or nutritional impact.

Metabolite profiling of maize kernels--genetic modification versus environmental influence

A metabolite profiling approach based on gas chromatography-mass spectrometry (GC-MS) was applied to investigate the metabolite profiles of genetically modified (GM) Bt-maize (DKC78-15B, TXP 138F) and Roundup Ready-maize (DKC78-35R). For the comparative investigation of the impact of genetic modification versus environmental influence on the metabolite profiles, GM maize was grown together with the non-GM near-isogenic comparators under different environmental conditions, including several growing locations and seasons in Germany and South Africa. Analyses of variance (ANOVA) revealed significant differences between GM and non-GM maize grown in Germany and South Africa. For the factor genotype, 4 and 3%, respectively, of the total number of peaks detected by GC-MS showed statistically significant differences (p < 0.01) in peak heights as compared to the respective isogenic lines. However, ANOVA for the factor environment (growing location, season) revealed higher numbers of significant differences (p < 0.01) between the GM and the non-GM maize grown in Germany (42%) and South Africa (10%), respectively. This indicates that the majority of differences observed are related to natural variability rather than to the genetic modifications. In addition, multivariate data assessment by means of principal component analysis revealed that environmental factors, that is, growing locations and seasons, were dominant parameters driving the variability of the maize metabolite profiles.

Impact of carbon dioxide enrichment on the responses of maize leaf transcripts and metabolites to water stress

Maize (Zea mays) was grown in indoor chambers with ambient (38 Pa) and elevated (70 Pa) CO(2) . Drought treatments were imposed 17 days after sowing by withholding nutrient solution. Decreases of soil water content, leaf water potential, net CO(2) assimilation and stomatal conductance as a result of drought were delayed approximately 2 days by CO(2) enrichment. Concentrations of 28 of 33 leaf metabolites were altered by drought. Soluble carbohydrates, aconitate, shikimate, serine, glycine, proline and eight other amino acids increased, and leaf starch, malate, fumarate, 2-oxoglutarate and seven amino acids decreased with drought. Drought-dependent decreases of nitrate, alanine and aspartate were impacted by limiting nitrogen. Transcript levels of 14 stress-related maize genes responded to drought but this was delayed or modified by CO(2) enrichment. Overall, CO(2) enrichment eliminated many early responses of maize metabolites and transcripts to water stress but was less effective when drought was severe. Four metabolite groupings were identified by clustering analysis. These groupings included compounds that decreased with water stress, compounds involved in osmotic adjustment and aromatic compounds that alleviate oxidative stress. Metabolite changes also supported the suggestion that water stress inhibited C(4) photosynthesis and induced photorespiration.

Metabolomics and food processing: from semolina to pasta

The objective of this study was to investigate the metabolite variations during industrial pasta processing (from semolina to dried pasta) for five different commercial products. Up to 76 metabolites were detected. Significant differences were observed between wholemeal and refined pasta samples, with the wholemeal pasta richer in many classes of compounds such as phytosterols, policosanols, unsaturated fatty acids, amino acids, carotenoids, minerals, and so on. Significant differences were also observed between samples of refined pasta apparently similar for the actual parameters used for the assessment of pasta quality. The results indicated that a number of metabolites undergo a transformation during the pasta-making process depending on the processing conditions adopted. The approach used in this work shows the high potential of metabolite profiling for food investigations with regard to process-related transformation, safety, and nutrition.

Mass spectrometry strategies in metabolomics

MS has evolved as a critical component in metabolomics, which seeks to answer biological questions through large-scale qualitative and quantitative analyses of the metabolome. MS-based metabolomics techniques offer an excellent combination of sensitivity and selectivity, and they have become an indispensable platform in biology and metabolomics. In this minireview, various MS technologies used in metabolomics are briefly discussed, and future needs are suggested.

Metabolomics: a second-generation platform for crop and food analysis

The combined factors of financial and food security, a rapidly increasing population and the associated requirement for food generated sustainably in a changing environment have brought food swiftly to the top of most government agendas. The consequence of this is that we need to produce more food at an equivalent or higher quality with lower inputs. These aims are achievable using conventional breeding, but not in the required timelines, and thus state-of-the-art genetic and analytical technologies are coming to the forefront. The concept of metabolomics, underpinned by mainstream (GC–MS, LC–MS, NMR) and specialist (MALDI-TOF-MS) analytical technologies addressing broad chemical (class) targets and dynamic ranges, offers significant potential to add significant value to crop and food science and deliver on future food demands. Metabolomics has now found a home in the food analytical toolbox with raw material quality and safety the major quality areas, although, as we will show, it is translating beyond this into food storage, shelf-life and post-harvest processing.

Are apparent negative effects of feeding GM MON810 maize to Atlantic salmon, Salmo salar, caused by confounding factors?

The present study was conducted to follow up on apparent differences in growth, relative organ sizes, cellular stress and immune function in Atlantic salmon fed feed containing GM Bacillus thuringiensis maize compared with feed containing the non-modified parental maize line. Gene expression profiling on the distal intestinal segment and liver was performed by microarray, and selected genes were followed up by quantitative PCR (qPCR). In the liver, qPCR revealed some differentially regulated genes, including up-regulation of gelsolin precursor, down-regulation of ferritin heavy subunit and a tendency towards down-regulation of metallothionein (MT)-B. This, combined with the up-regulation of anti-apoptotic protein NR13 and similar tendencies for ferritin heavy chain and MT-A and -B in the distal intestine, suggests changes in cellular stress/antioxidant status. This corresponds well with and strengthens previous findings in these fish. To exclude possible confounding factors, the maize ingredients were analysed for mycotoxins and metabolites. The GM maize contained 90 μg/kg of deoxynivalenol (DON), while the non-GM maize was below the detection limit. Differences were also observed in the metabolite profiles of the two maize varieties, some of which seemed connected to the mycotoxin level. The effects on salmon observed in the present and previous studies correspond relatively well with the effects of DON as reported in the literature for other production animals, but knowledge regarding effects and harmful dose levels in fish is scarce. Thus, it is difficult to conclude whether the observed effects are caused by the DON level or by some other aspect of the GM maize ingredient.

Rice bran fermented with saccharomyces boulardii generates novel metabolite profiles with bioactivity

Emerging evidence supporting chronic disease fighting properties of rice bran has advanced the development of stabilized rice bran for human use as a functional food and dietary supplement. A global and targeted metabolomic investigation of stabilized rice bran fermented with Saccharomyces boulardii was performed in three rice varieties. Metabolites from S. boulardii-fermented rice bran were detected by gas chromatography-mass spectrometry (GC-MS) and assessed for bioactivity compared to nonfermented rice bran in normal and malignant lymphocytes. Global metabolite profiling revealed significant differences in the metabolome that led to discovery of candidate compounds modulated by S. boulardii fermentation. Fermented rice bran extracts from three rice varieties reduced growth of human B lymphomas compared to each variety's nonfermented control and revealed that fermentation differentially altered bioactive compounds. These data support that integration of global and targeted metabolite analysis can be utilized for assessing health properties of rice bran phytochemicals that are enhanced by yeast fermentation and that differ across rice varieties.

Geographical origin of durum wheat studied by 1H-NMR profiling

(1)H-Nuclear magnetic resonance (NMR) spectroscopy was used to detect metabolic profiles of wheat flour samples of different geographical and botanical origin. The NMR profiles were analyzed by multivariate statistical techniques in order to establish the origin of the samples. A linear model, able to discriminate among three different locations, was built achieving a prediction level of about 80% of correctly assigned samples. The principal classes of compounds responsible for the geographic origin discrimination were individuated in aromatic compounds and amino acids. The statistical modeling also indicated that botanical origin information is very poor in the NMR profiles of the analyzed wheat samples.

The complex genetic architecture of the metabolome

Discovering links between the genotype of an organism and its metabolite levels can increase our understanding of metabolism, its controls, and the indirect effects of metabolism on other quantitative traits. Recent technological advances in both DNA sequencing and metabolite profiling allow the use of broad-spectrum, untargeted metabolite profiling to generate phenotypic data for genome-wide association studies that investigate quantitative genetic control of metabolism within species. We conducted a genome-wide association study of natural variation in plant metabolism using the results of untargeted metabolite analyses performed on a collection of wild Arabidopsis thaliana accessions. Testing 327 metabolites against >200,000 single nucleotide polymorphisms identified numerous genotype–metabolite associations distributed non-randomly within the genome. These clusters of genotype–metabolite associations (hotspots) included regions of the A. thaliana genome previously identified as subject to recent strong positive selection (selective sweeps) and regions showing trans-linkage to these putative sweeps, suggesting that these selective forces have impacted genome-wide control of A. thaliana metabolism. Comparing the metabolic variation detected within this collection of wild accessions to a laboratory-derived population of recombinant inbred lines (derived from two of the accessions used in this study) showed that the higher level of genetic variation present within the wild accessions did not correspond to higher variance in metabolic phenotypes, suggesting that evolutionary constraints limit metabolic variation. While a major goal of genome-wide association studies is to develop catalogues of intraspecific variation, the results of multiple independent experiments performed for this study showed that the genotype–metabolite associations identified are sensitive to environmental fluctuations. Thus, studies of intraspecific variation conducted via genome-wide association will require analyses of genotype by environment interaction. Interestingly, the network structure of metabolite linkages was also sensitive to environmental differences, suggesting that key aspects of network architecture are malleable.

Understanding how genetic variation can control phenotypic variation is a fundamental goal of modern biology. We combined genome-wide association mapping with metabolomics in the plant Arabidopsis thaliana to explore how species-wide genetic variation controls metabolism. We identified numerous naturally-variable genes that may influence plant metabolism, often clustering in “hotspots.” These hotspots were proximal to selective sweeps, regions of the genome showing decreased diversity possibly from a strong selective advantage of specific variants within the region. This suggests that metabolism may be connected to the selective advantage. Interestingly, metabolite variation in wild Arabidopsis is highly constrained despite the significant genetic variation, thus providing the plant un-sampled metabolic space if the environment shifts. The observed structuring of genetic and metabolic variation suggests individual convergence upon similar phenotypes via different genotypes, possibly intra-specific parallel evolution. This phenotypic convergence couples with a pattern of genotype—phenotype association consistent with metabolite variation largely controlled by numerous small effect genetic variants. This supports the supposition that large magnitude variation is likely unstable in a complex and interconnected metabolism. If this pattern proves generally applicable to other species, it could present a significant hurdle to identifying genes controlling metabolic trait variation via genome-wide association studies.