1H NMR, GC−EI-TOFMS, and Data Set Correlation for Fruit Metabolomics: Application to Spatial Metabolite Analysis in Melon

Spatio-temporal dynamics of the metabolome of climacteric fruit during ripening and post-harvest storage

Fruit quality traits are determined to a large extent by their metabolome. The metabolite content of climacteric fruit changes drastically during ripening and post-harvest storage, and has been investigated extensively. However, the spatial distribution of metabolites and how it changes in time has received much less attention as fruit are usually considered as homogenous plant organs. Yet, spatio-temporal changes of starch, which is hydrolyzed during ripening, has been used for a long time as a ripening index. As vascular transport of water, and hence convective transport of metabolites, slows down in mature fruit and even stalls after detachment, spatio-temporal changes in their concentration are probably affected by diffusive transport of gaseous molecules that act as substrate (O2), inhibitor (CO2), or regulator (ethylene and NO) of the metabolic pathways that are active during climacteric ripening. In this review, we discuss such spatio-temporal changes of the metabolome and how they are affected by transport of metabolic gases and gaseous hormones. As there are currently no techniques available to measure the metabolite distribution repeatedly by non-destructive means, we introduce reaction-diffusion models as an in silico tool to compute it. We show how the different components of such a model can be integrated and used to better understand the role of spatio-temporal changes of the metabolome in ripening and post-harvest storage of climacteric fruit that is detached from the plant, and discuss future research needs.

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

Metabolomic NMR analysis and organoleptic perceptions of pomegranate wines: Influence of cultivar and yeast on the product characteristics

Pomegranate (Punica granatum L.) fruits are a historical agricultural product of the Mediterranean basin that became increasingly popular in the latest years for being rich in antioxidants and other micronutrients, and are extensively commercialized as fruits, juice, jams and, in some Eastern countries, as a fermented alcoholic beverage. In this work, four different pomegranate wines specifically designed using combinations of two cultivars (Jolly Red and Smith) and two yeast starters with markedly different characteristics (Saccharomyces cerevisiae Clos and Saccharomyces cerevisiae ex-bayanus EC1118) were analyzed. The chemical characterization of the wines together with the originating unfermented juices was performed by ¹H NMR spectroscopy metabolomic analysis. The full spectra were used for unsupervised and supervised statistical multivariate analysis (MVA), namely Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and sparse PCA (SPCA). The MVA of the wines showed a clear discrimination between the cultivars, and a smaller, yet significant, discrimination between the yeasts used. In particular, a higher content of citrate and gallate was observed for the Smith cv. and, on the contrary, a statistically significant higher content of fructose, malate, glycerol, 2,3 butanediol, trigonelline, aromatic amino acids and 4-hydrophenylacetate was observed in Jolly Red pomegranate wines samples. Significant interaction among the pomegranate cultivar and the fermenting yeast was also observed. Sensorial analysis was performed by a panel of testing experts. MVA of tasting data showed that the cultivar significantly affected the organoleptic parameters considered, while the yeast had a minor impact. Correlation analysis between NMR-detected metabolites and organoleptic descriptors identified several potential sensorially-active molecules as those significantly impacting the characteristics of the pomegranate wines.

Evaluation of ORAC, IR and NMR metabolomics for predicting ripening stage and variety in melon (Cucumis melo L.)

A combination of FTIR- and NMR-based metabolomics approaches coupled to multivariate data analysis techniques was used for the first time to characterize and explore the metabolic changes along the ripening of two Spanish melon landraces (Jimbee N and Jimbee XL). Furthermore, the fatty acids profile and the antioxidant capacity were investigated by GC-FID and ORAC method, respectively. Despite FTIR was able to identify changes in the sugars content between fruits of different maturity stages, it failed to discriminate between both varieties, being exceeded by NMR in both situations. Increased fruit maturation led to an increase of sucrose, fatty acids and β-carotene, accompanied by a decrease of glucose, fructose, citrate, amino acids, and polyphenols (which were positively correlated with fruits antioxidant activity).

Characterization of five Indonesian mangoes using gas chromatography-mass spectrometry-based metabolic profiling and sensory evaluation

Indonesia is one of the world's leading mango producers and grows many cultivars. However, only a few cultivars have been commercialized, perhaps because of limited information on consumer preferences that meet the market demands. Here, non-targeted gas chromatography-mass spectrometry (GC-MS)-based metabolome analysis was used to characterize five Indonesian mango cultivars considering their taste characteristics. A total of 95 components (47 annotated and 48 unknown metabolites) were identified. Cultivars with a higher general impression score (Arumanis 143 and Gedong) in principal component analysis (PCA) accumulated more sugars and sweetening components, such as glycine and lyxose. Meanwhile, cultivars with a lower general impression score in PCA (Lalijiwo and Cengkir Indramayu) accumulated more aspartic acid, isocitric acid, and citric acid, which increase sourness; methionine, which is a precursor of sulfur-containing volatile aroma components; and phenylalanine, which contributes to bitterness. Furthermore, orthogonal projection to latent structures discriminant analysis revealed that nicotinic acid, glutamic acid, aspartic acid, glycine, and ribose characterized higher or lower general impression cultivars. In addition, metabolic profiling of eight mango cultivars, including five Indonesian and three overseas cultivars, suggested that taste was more influential than differences in cultivars, production areas, and cultivation conditions by its hydrophilic primary metabolomics. These findings will serve as fundamental data for future mango industry development considering the association between the unique taste of each cultivar and its metabolites as well as the consumer preferences for Indonesian mango.

Assessing the impact of nitrogen supplementation in oats across multiple growth locations and years with targeted phenotyping and high-resolution metabolite profiling approaches

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The response to nitrogen of 4 winter oat varieties in three field trials was analysed.

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A novel high-resolution method was developed to profile metabolite changes.

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Conditions that enhance yield do not necessarily result in higher nutritional value.

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Choice of variety is of equally high importance as the nitrogen levels applied.

Oats (Avena sativa L.) are a healthy food, being high in dietary fibre (e.g. β-glucans), antioxidants, minerals, and vitamins. Understanding the effect of variety and crop management on nutritional quality is important. The response of four oat varieties to increased nitrogen levels was investigated across multiple locations and years with respect to yield, grain quality and metabolites (assessed via GC- and LC- MS). A novel high-resolution UHPLC-PDA-MS/MS method was developed, providing improved metabolite enrichment, resolution, and identification. The combined phenotyping approach revealed that, amino acid levels were increased by nitrogen supplementation, as were total protein and nitrogen containing lipid levels, whereas health-beneficial avenanthramides were decreased. Although nitrogen addition significantly increased grain yield and β-glucan content, supporting increasing the total nitrogen levels recommended within agricultural guidelines, oat varietal choice as well as negative impacts upon health beneficial secondary metabolites and the environmental burdens associated with nitrogen fertilisation, require further consideration.

Aloe vera Flowers, a Byproduct with Great Potential and Wide Application, Depending on Maturity Stage

Flowers of Aloe vera are a byproduct providing a valuable source of bioactive compounds with different functions for health benefits. The characterization in amino acids, organic acids, sugars, trigonelline, volatiles compounds, fatty acids, total phenolic, carotenoids, vitamin C content, and antioxidant capacity of Aloe flowers (Aloe barbadensis Miller) has been studied at three maturity stages (I: immature; II: mature; III: mature, with flowers buds opened). Immature flowers presented the highest content in phenyl alanine, tyrosine, citric acid, trigonelline, carotenoids, retinol activity equivalent, vitamin C, and total phenolic and antioxidant capacity. As the flower develops, the content of these compounds decreases. Aloe vera flowers presented an important content in fatty acids, and the principal concentration was identified in polyunsaturated unsaturated fatty acids (PUFAs) as α-linolenic acid, and linoleic acid, with a ratio close to one. The main saturated fatty acid was palmitic acid, followed by stearic acid. Maturity stage III showed the lowest fatty acid content. The bioactive compounds found in Aloe vera flowers have potential applications in the cosmetic, pharmaceutical, nutraceutical, and food industries. Depending on the compound of interest, it could be worthwhile harvesting flowers at maturity stage I, thereby reducing the energy consumption of flowers from the plant and thus favoring plant development. This is an example of a circular economy for Aloe vera producers, generating economic and business opportunities and thus providing environmental and social benefits.

Metabolite Changes during Postharvest Storage: Effects on Fruit Quality Traits

Metabolic changes occurring in ripe or senescent fruits during postharvest storage lead to a general deterioration in quality attributes, including decreased flavor and ‘off-aroma’ compound generation. As a consequence, measures to reduce economic losses have to be taken by the fruit industry and have mostly consisted of storage at cold temperatures and the use of controlled atmospheres or ripening inhibitors. However, the biochemical pathways and molecular mechanisms underlying fruit senescence in commercial storage conditions are still poorly understood. In this sense, metabolomic platforms, enabling the profiling of key metabolites responsible for organoleptic and health-promoting traits, such as volatiles, sugars, acids, polyphenols and carotenoids, can be a powerful tool for further understanding the biochemical basis of postharvest physiology and have the potential to play a critical role in the identification of the pathways affected by fruit senescence. Here, we provide an overview of the metabolic changes during postharvest storage, with special attention to key metabolites related to fruit quality. The potential use of metabolomic approaches to yield metabolic markers useful for chemical phenotyping or even storage and marketing decisions is highlighted.

Comparative Metabolomics and Molecular Phylogenetics of Melon (Cucumis melo, Cucurbitaceae) Biodiversity

The broad variability of Cucumis melo (melon, Cucurbitaceae) presents a challenge to conventional classification and organization within the species. To shed further light on the infraspecific relationships within C. melo, we compared genotypic and metabolomic similarities among 44 accessions representative of most of the cultivar-groups. Genotyping-by-sequencing (GBS) provided over 20,000 single-nucleotide polymorphisms (SNPs). Metabolomics data of the mature fruit flesh and rind provided over 80,000 metabolomic and elemental features via an orchestra of six complementary metabolomic platforms. These technologies probed polar, semi-polar, and non-polar metabolite fractions as well as a set of mineral elements and included both flavor- and taste-relevant volatile and non-volatile metabolites. Together these results enabled an estimate of "metabolomic/elemental distance" and its correlation with the genetic GBS distance of melon accessions. This study indicates that extensive and non-targeted metabolomics/elemental characterization produced classifications that strongly, but not completely, reflect the current and extensive genetic classification. Certain melon Groups, such as Inodorous, clustered in parallel with the genetic classifications while other genome to metabolome/element associations proved less clear. We suggest that the combined genomic, metabolic, and element data reflect the extensive sexual compatibility among melon accessions and the breeding history that has, for example, targeted metabolic quality traits, such as taste and flavor.

Global metabolite profiles of rice brown planthopper-resistant traits reveal potential secondary metabolites for both constitutive and inducible defenses

INTRODUCTION

Brown planthopper (BPH) is a phloem feeding insect that causes annual disease outbreaks, called hopper burn in many countries throughout Asia, resulting in severe damage to rice production. Currently, mechanistic understanding of BPH resistance in rice plant is limited, which has caused slow progression on developing effective rice varieties as well as effective farming practices against BPH infestation.

OBJECTIVE

To reveal rice metabolic responses during 8 days of BPH attack, this study examined polar metabolome extracts of BPH-susceptible (KD) and its BPH-resistant isogenic line (IL308) rice leaves.

METHODS

Ultra high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QToF-MS) was combined with multi-block PCA to analyze potential metabolites in response to BPH attack.

RESULTS

This multivariate statistical model revealed different metabolic response patterns between the BPH-susceptible and BPH-resistant varieties during BPH infestation. The metabolite responses of the resistant IL308 variety occurred on Day 1, which was significantly earlier than those of the susceptible KD variety which showed an induced response by Days 4 and 8. BPH infestation caused metabolic perturbations in purine, phenylpropanoid, flavonoid, and terpenoid pathways. While found in both susceptible and resistant rice varieties, schaftoside (1.8 fold), iso-schaftoside (1.7 fold), rhoifolin (3.4 fold) and apigenin 6-C-α-L-arabinoside-8-C-β-L-arabinoside levels (1.6 fold) were significantly increased in the resistant variety by Day 1 post-infestation. 20-hydroxyecdysone acetate (2.5 fold) and dicaffeoylquinic acid (4.7 fold) levels were considerably higher in the resistant rice variety than those in the susceptible variety, both before and after infestation, suggesting that these secondary metabolites play important roles in inducible and constitutive defenses against the BPH infestation.

CONCLUSIONS

These potential secondary metabolites will be useful as metabolite markers and/or bioactive compounds for effective and durable approaches to address the BPH problem.

NMR-based metabolic profiling and comparison of Japanese persimmon cultivars

Persimmons are a traditional, autumnal, and healthy fruit commonly consumed in Japan and East Asia based on the saying, "a persimmon a day keeps the doctor away." The differences in metabolites among five major Japanese persimmon cultivars were investigated using a nuclear magnetic resonance (NMR)-based metabolomics approach. By using a broadband water suppression enhanced through T₁ effects (WET) method for the sensitive detection of minor metabolites, better discrimination among cultivars and more informative details regarding their metabolic differences have been achieved compared to those achieved in conventional ¹H NMR sequences. Among the nonastringent cultivars analyzed, the Taishu cultivar has the highest abundance of amino acids. The Matsumotowase-Fuyu cultivar contains ethyl-β-glycosides as characteristic components, which may relate to fruit softening. Citric acid concentration is higher in Maekawa Jiro than in other nonastringent cultivars. Among the two astringent cultivars analyzed, ethanol was significantly higher in Hiratanenashi than in Yotsumizo, which indicates different reactivity during deastringency treatments. The present study proposes an efficient and relatively quantitative metabolomics approach based on broadband WET NMR spectra.

Central Metabolism Is Tuned to the Availability of Oxygen in Developing Melon Fruit

Respiration of bulky plant organs such as fleshy fruits depends on oxygen (O₂) availability and often decreases with O₂ concentration to avoid anoxia, but the relationship between O₂ diffusional resistance and metabolic adjustments remains unclear. Melon fruit (Cucumis melo L.) was used to study relationships between O₂ availability and metabolism in fleshy fruits. Enzyme activities, primary metabolites and O₂ partial pressure were quantified from the periphery to the inner fruit mesocarp, at three stages of development. Hypoxia was gradually established during fruit development, but there was no strong oxygen gradient between the outer- and the inner mesocarp. These trends were confirmed by a mathematical modeling approach combining O₂ diffusion equations and O₂ demand estimates of the mesocarp tissue. A multivariate analysis of metabolites, enzyme activities, O₂ demand and concentration reveals that metabolite gradients and enzyme capacities observed in melon fruits reflect continuous metabolic adjustments thus ensuring a timely maturation of the mesocarp. The present results suggest that the metabolic adjustments, especially the tuning of the capacity of cytochrome c oxidase (COX) to O₂-availability that occurs during growth development, contribute to optimizing the O₂-demand and avoiding the establishment of an O₂ gradient within the flesh.

Application of HPLC-PDA-MS metabolite profiling to investigate the effect of growth temperature and day length on blackcurrant fruit

INTRODUCTION

Blackcurrant (Ribes nigrum L.) is an excellent example of a "super fruit" with potential health benefits. Both genotype and cultivation environment are known to affect the chemical composition of blackcurrant, especially ascorbic acid and various phenolic compounds. Environmental conditions, like temperature, solar radiation and precipitation can also have significant impact on fruit chemical composition. The relevance of the study is further accentuated by the predicted and ongoing changes in global climate.

OBJECTIVES

The aim of the present study was to provide new knowledge and a deeper understanding of the effects of post flowering environmental conditions, namely temperature and day length, on fruit quality and chemical composition of blackcurrant using an untargeted high performance liquid chromatography-photo diode array-mass spectrometry (HPLC-PDA-MS) metabolomics approach.

METHODS

A phytotron experiment with cultivation of single-stemmed potted plants of blackcurrant cv. Narve Viking was conducted using constant temperatures of 12, 18 or 24 °C and three different photoperiods (short day, short day with night interruption, and natural summer daylight conditions). Plants were also grown under ambient outdoor conditions. Ripe berries were analysed using an untargeted HPLC-PDA-MS metabolomics approach to detect the presence and concentration of molecules as affected by controlled climatic factors.

RESULTS

The untargeted metabolomics dataset contained a total of 7274 deconvolved retention time-m/z pairs across both electrospray ionisation (ESI) positive and negative polarities, from which 549 metabolites were identified or minimally annotated based upon accurate mass MS. Conventional principal component analysis (PCA) in combination with the Friedman significance test were applied to first identify which metabolites responded to temperature in a linear fashion. Multi-block hierarchical PCA in combination with the Friedman significance test was secondly applied to identify metabolites that were responsive to different day length conditions. Temperature had significant effect on a total of 365 metabolites representing a diverse range of chemical classes. It was observed that ripening of the blackcurrant berries under ambient conditions, compared to controlled conditions, resulted in an increased accumulation of 34 annotated metabolites, mainly anthocyanins and flavonoids. 18 metabolites were found to be regulated differentially under the different daylength conditions. Moreover, based upon the most abundant anthocyanins, a comparison between targeted and untargeted analyses, revealed a close convergence of the two analytical methods. Therefore, the study not just illustrates the value of non-targeted metabolomics approaches with respect to the huge diversity and numbers of significantly changed metabolites detected (and which would be missed by conventional targeted analyses), but also shows the validity of the non-targeted approach with respect to its precision compared to targeted analyses.

CONCLUSIONS

Blackcurrant maturation under controlled ambient conditions revealed a number of insightful relationships between environment and chemical composition of the fruit. A prominent reduction of the most abundant anthocyanins under the highest temperature treatments indicated that blackcurrant berries in general may accumulate lower total anthocyanins in years with extreme hot summer conditions. HPLC-PDA-MS metabolomics is an excellent method for broad analysis of chemical composition of berries rich in phenolic compounds. Moreover, the experiment in controlled phytotron conditions provided additional knowledge concerning plant interactions with the environment.

Integrating Strategies of Herbal Metabolomics, Network Pharmacology, and Experiment Validation to Investigate Frankincense Processing Effects

In-depth research on processing can promote the globalization of processed herbs. The purpose of this study is to propose an improved strategy for processing effect investigation. Frankincense and processed frankincense were used as research subjects. First, high-speed countercurrent chromatography (HSCCC) and preparation high-performance liquid chromatography (PHPLC) techniques were used for major compounds isolation and minor compounds concentration. Processed frankincense was subjected to two stepwise solvent systems, namely, n-hexane:ethanol:water (6:5:1) and n-hexane:methyl-acetate:acetonitrile:water (4:4:3:4), to yield 12 fractions, and 18 compounds were further separated. Second, a comprehensive metabolomic analysis conducted by ultrahigh-performance liquid-chromatography/electrospray-ionization mass spectrometry (UHPLC-Qtof-MS) coupled with multivariate statistics was performed to fully characterize the chemical components and discover the potential biomarkers between frankincense and processed frankincense. In total, 81 metabolites, including the 18 separated compounds, were selected as potential biomarkers between frankincense and processed frankincense among 153 detected compounds for their VIP values of greater than one. The tirucallane-type compounds and components with 9,11-dehydro structures clearly occurred at high levels in the processed frankincense, while lupine-type compounds and those with 11-keto structures were significantly higher in frankincense. Then, a network pharmacology model was constructed to decipher the potential mechanisms of processing. Intestinal absorption properties prediction indicated the possibility of processing-related absorption enhancement. A systematic analysis of the constructed networks showed that the C-T network was constructed with 18 potential biomarkers and 69 targets. TNF and IL-1β were among the top-ranked and were linked by 8 and 7 pathways, which were mainly involved in inflammation. The arachidonic acid metabolism pathway exhibited the highest number of target connections. Finally, the prediction was validated experimentally by an intestinal permeability and efficacy assay. The experiments provided convincing evidence that processed frankincense harbored stronger inhibition effects toward TNF-α-, IL-1β- and arachidonic acid-induced platelet aggregation. The processing procedure leads to changes of the chemical metabolites, which triggers the enhancement of absorption and cure efficiency. The global change of the metabolites, absorption and pharmacological effects of processing were depicted in a systematic manner.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

Answering biological questions by analysis of the strawberry metabolome

BACKGROUND

The qualitative and quantitative analysis of all low molecular weight metabolites within a biological sample, known as the metabolome, provides powerful insights into their roles in biological systems and processes. The study of all the chemical structures, concentrations, and interactions of the thousands of metabolites is called metabolomics. However present state of the art methods and equipment can only analyse a small portion of the numerous, structurally diverse groups of chemical substances found in biological samples, especially with respect to samples of plant origin with their huge diversity of secondary metabolites. Nevertheless, metabolite profiling and fingerprinting techniques have been applied to the analysis of the strawberry metabolome since their early beginnings.

AIM

The application of metabolomics and metabolite profiling approaches within strawberry research was last reviewed in 2011. Here, we aim to summarize the latest results from research of the strawberry metabolome since its last review with a special emphasis on studies that address specific biological questions.

KEY SCIENTIFIC CONCEPTS

Analysis of strawberry, and other fruits, requires a plethora of analytical methods and approaches encompassing the analysis of primary and secondary metabolites, as well as capturing and quantifying volatile compounds that are related to aroma as well as fruit development, function and plant-to-plant communication. The success and longevity of metabolite and volatile profiling approaches in fruit breeding relies upon the ability of the approach to uncover biologically meaningful insights. The key concepts that must be addressed and are reviewed include: gene function analysis and genotype comparison, analysis of environmental effects and plant protection, screening for bioactive compounds for food and non-food uses, fruit development and physiology as well as fruit sensorial quality. In future, the results will facilitate fruit breeding due to the identification of metabolic QTLs and candidate genes for fruit quality and consumer preference.

Metabotyping of 30 maize hybrids under early-sowing conditions reveals potential marker-metabolites for breeding

INTRODUCTION

In Northern Europe, maize early-sowing used to maximize yield may lead to moderate damages of seedlings due to chilling without visual phenotypes. Genetic studies and breeding for chilling tolerance remain necessary, and metabolic markers would be particularly useful in this context.

OBJECTIVES

Using an untargeted metabolomic approach on a collection of maize hybrids, our aim was to identify metabolite signatures and/or metabolites associated with chilling responses at the vegetative stage, to search for metabolites differentiating groups of hybrids based on silage-earliness, and to search for marker-metabolites correlated with aerial biomass.

METHODS

Thirty genetically-diverse maize dent inbred-lines (Zea mays) crossed to a flint inbred-line were sown in a field to assess metabolite profiles upon cold treatment induced by a modification of sowing date, and characterized with climatic measurements and phenotyping.

RESULTS

NMR- and LC-MS-based metabolomic profiling revealed the biological variation of primary and specialized metabolites in young leaves of plants before flowering-stage. The effect of early-sowing on leaf composition was larger than that of genotype, and several metabolites were associated to sowing response. The metabolic distances between genotypes based on leaf compositional data were not related to the genotype admixture groups, and their variability was lower under early-sowing than normal-sowing. Several metabolites or metabolite-features were related to silage-earliness groups in the normal-sowing condition, some of which were confirmed the following year. Correlation networks involving metabolites and aerial biomass suggested marker-metabolites for breeding for chilling tolerance.

CONCLUSION

After validation in other experiments and larger genotype panels, these marker-metabolites can contribute to breeding.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.

1H-NMR-based metabolomic profiles of different sweet melon (Cucumis melo L.) Salento varieties: Analysis and comparison

Melon (Cucumis melo L.) is a significant source of substances able to provide human health benefits. From the 18th century in the Salento area (Apulia region), the cultivation of melon varieties (C. melo L.) has always been intense. Over the years, the production of this fruit has involved a large number of selected and preserved varieties in the different local districts. Unfortunately, most of the characteristics of locally grown vegetable varieties do not match the food industry requirements. Moreover, the agricultural land abandon leads these varieties to quickly disappear, thus affecting the intraspecific biodiversity. In order to characterize the inter-variety diversity of sweet melon (C. melo L. ssp. melo group inodorus) and the potential differences in the nutritional quality of fruits, a first investigation on the juice of five sweet melon varieties (locally known as "allungato", "scurzune", "egiziano", "minna de monaca", "pinto"), cultivated exclusively in the Salento area, was performed by ¹H-NMR spectroscopy and Multivariate Analysis (MVA). The analysis grouped the samples into clusters according to the different variety. Interestingly, a different sugar (mono and disaccharides) content was observed among the grouped varieties, being sweetness the main characteristic of sweet melon quality and taste. A relative higher accumulation of monosaccharides (α-d and β-d glucose and α/β-d fructose) was found, in particular for the "minna de monaca" with respect to "allungato", "egiziano" and "pinto" varieties. Moreover, a marked high content of polyphenols and aromatic aminoacids as phenylalanine and tyrosine characterize the "allungato", "minna de monaca" and "pinto" varieties. An NMR-based metabolomic approach was used for the first time to describe these local landraces. This method may integrate other actions in order to achieving a reduction in the current rate of erosion of the biodiversity of Apulian horticultural species.

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.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.

The peach HECATE3-like gene FLESHY plays a double role during fruit development

Tight control of cell/tissue identity is essential for a correct and functional organ patterning, an important component of overall fruit development and eventual maturation and ripening. Despite many investigations regarding the molecular determinants of cell identity in fruits of different species, a useful model able to depict the regulatory networks governing this relevant part of fruit development is still missing. Here we described the peach fruit as a system to link the phenotype of a slow ripening (SR) selection to an altered transcriptional regulation of genes involved in determination of mesocarp cell identity providing insight toward molecular regulation of fruit tissue formation. Morpho-anatomical observations and metabolomics analyses performed during fruit development on the reference cultivar Fantasia, compared to SR, revealed that the mesocarp of SR maintained typical immaturity traits (e.g. small cell size, high amino acid contents and reduced sucrose) throughout development, along with a strong alteration of phenylpropanoid contents, resulting in accumulation of phenylalanine and lignin. These findings suggest that the SR mesocarp is phenotypically similar to a lignifying endocarp. To test this hypothesis, the expression of genes putatively involved in determination of drupe tissues identity was assessed. Among these, the peach HEC3-like gene FLESHY showed a strongly altered expression profile consistent with pit hardening and fruit ripening, generated at a post-transcriptional level. A double function for FLESHY in channelling the phenylpropanoid pathway to either lignin or flavour/aroma is suggested, along with its possible role in triggering auxin-ethylene cross talk at the start of ripening.

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

INTRODUCTION

The generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner.

OBJECTIVES

Currently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow.

METHODS

We assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples.

RESULTS

Our studies have shown that once data are filtered, missing values are not correlated with m/z, retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance.

CONCLUSION

The appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets.

Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with no clinical biomarker. The aims of this study were to characterize a metabolic signature of ASD and to evaluate multiplatform analytical methodologies in order to develop predictive tools for diagnosis and disease follow-up. Urine samples were analyzed using (1)H and (1)H-(13)C NMR-based approaches and LC-HRMS-based approaches (ESI+ and ESI- on HILIC and C18 chromatography columns). Data tables obtained from the six analytical modalities on a training set of 46 urine samples (22 autistic children and 24 controls) were processed by multivariate analysis (orthogonal partial least-squares discriminant analysis, OPLS-DA). The predictions from each of these OPLS-DA models were then evaluated using a prediction set of 16 samples (8 autistic children and 8 controls) and receiver operating characteristic curves. Thereafter, a data fusion block-scaling OPLS-DA model was generated from the 6 best models obtained for each modality. This fused OPLS-DA model showed an enhanced performance (R(2)Y(cum) = 0.88, Q(2)(cum) = 0.75) compared to each analytical modality model, as well as a better predictive capacity (AUC = 0.91, p-value = 0.006). Metabolites that are most significantly different between autistic and control children (p < 0.05) are indoxyl sulfate, N-α-acetyl-l-arginine, methyl guanidine, and phenylacetylglutamine. This multimodality approach has the potential to contribute to find robust biomarkers and characterize a metabolic phenotype of the ASD population.

Profiling of spatial metabolite distributions in wheat leaves under normal and nitrate limiting conditions

The control and interaction between nitrogen and carbon assimilatory pathways is essential in both photosynthetic and non-photosynthetic tissue in order to support metabolic processes without compromising growth. Physiological differences between the basal and mature region of wheat (Triticum aestivum) primary leaves confirmed that there was a change from heterotrophic to autotrophic metabolism. Fourier Transform Infrared (FT-IR) spectroscopy confirmed the suitability and phenotypic reproducibility of the leaf growth conditions. Principal Component-Discriminant Function Analysis (PC-DFA) revealed distinct clustering between base, and tip sections of the developing wheat leaf, and from plants grown in the presence or absence of nitrate. Gas Chromatography-Time of Flight/Mass Spectrometry (GC-TOF/MS) combined with multivariate and univariate analyses, and Bayesian network (BN) analysis, distinguished different tissues and confirmed the physiological switch from high rates of respiration to photosynthesis along the leaf. The operation of nitrogen metabolism impacted on the levels and distribution of amino acids, organic acids and carbohydrates within the wheat leaf. In plants grown in the presence of nitrate there was reduced levels of a number of sugar metabolites in the leaf base and an increase in maltose levels, possibly reflecting an increase in starch turnover. The value of using this combined metabolomics analysis for further functional investigations in the future are discussed.

Metabolomic profiling in tomato reveals diel compositional changes in fruit affected by source-sink relationships

A detailed study of the diurnal compositional changes was performed in tomato (Solanum lycopersicum cv. Moneymaker) leaves and fruits. Plants were cultivated in a commercial greenhouse under two growth conditions: control and shaded. Expanding fruits and the closest mature leaves were harvested during two different day/night cycles (cloudy or sunny day). High-throughput robotized biochemical phenotyping of major compounds, as well as proton nuclear magnetic resonance and mass spectrometry metabolomic profiling, were used to measure the contents of about 70 metabolites in the leaves and 60 metabolites in the fruits, in parallel with ecophysiological measurements. Metabolite data were processed using multivariate, univariate, or clustering analyses and correlation networks. The shaded carbon-limited plants adjusted their leaf area, decreased their sink carbon demand and showed subtle compositional modifications. For source leaves, several metabolites varied along a diel cycle, including those directly linked to photosynthesis and photorespiration. These metabolites peaked at midday in both conditions and diel cycles as expected. However, transitory carbon storage was limited in tomato leaves. In fruits, fewer metabolites showed diel fluctuations, which were also of lower amplitude. Several organic acids were among the fluctuating metabolites. Diel patterns observed in leaves and especially in fruits differed between the cloudy and sunny days, and between the two conditions. Relationships between compositional changes in leaves and fruits are in agreement with the fact that several metabolic processes of the fruit appeared linked to its momentary supply of sucrose.

Accurate, fully-automated NMR spectral profiling for metabolomics

Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person’s biofluids, which means such diseases can often be readily detected from a person’s “metabolic profile"—i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person’s metabolic profile. Given a 1D ¹HNMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the “signatures” of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively—with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.

Untargeted NMR-based methodology in the study of fruit metabolites

In this review, fundamental aspects of the untargeted NMR-based methodology applied to fruit characterization are described. The strategy to perform the structure elucidation of fruit metabolites is discussed with some examples of spectral assignments by 2D experiments. Primary ubiquitous metabolites as well as secondary species-specific metabolites, identified in different fruits using an untargeted ¹H-NMR approach, are summarized in a comprehensive way. Crucial aspects regarding the quantitative elaboration of spectral data are also discussed. The usefulness of the NMR-based metabolic profiling was highlighted using some results regarding quality, adulteration, varieties and geographical origin of fruits and fruit-derived products such as juices.

NMR methodologies in the analysis of blueberries

An NMR analytical protocol based on complementary high and low field measurements is proposed for blueberry characterization. Untargeted NMR metabolite profiling of blueberries aqueous and organic extracts as well as targeted NMR analysis focused on anthocyanins and other phenols are reported. Bligh-Dyer and microwave-assisted extractions were carried out and compared showing a better recovery of lipidic fraction in the case of microwave procedure. Water-soluble metabolites belonging to different classes such as sugars, amino acids, organic acids, and phenolic compounds, as well as metabolites soluble in organic solvent such as triglycerides, sterols, and fatty acids, were identified. Five anthocyanins (malvidin-3-glucoside, malvidin-3-galactoside, delphinidin-3-glucoside, delphinidin-3-galactoside, and petunidin-3-glucoside) and 3-O-α-l-rhamnopyranosyl quercetin were identified in solid phase extract. The water status of fresh and withered blueberries was monitored by portable NMR and fast-field cycling NMR. (1) H depth profiles, T2 transverse relaxation times and dispersion profiles were found to be sensitive to the withering.

Selective induction and subcellular distribution of ACONITASE 3 reveal the importance of cytosolic citrate metabolism during lipid mobilization in Arabidopsis

Arabidopsis thaliana has three genes that encode distinct aconitases (ACO), but little is known about the function of each isoenzyme during plant development. In newly emerged seedlings of Arabidopsis, transcript and protein levels for ACO3 were selectively induced to yield more than 80% of total aconitase activity. Characterization of knockout mutants for each of the three ACOs suggests a major role for only ACO3 in citrate metabolism. The aco3 mutant showed delayed early seedling growth, altered assimilation of [14C]acetate feeding and elevated citrate levels, which were nearly 4-fold greater than in wild-type, aco1 or aco2. However, both ACO1 and ACO2 are active in seedlings as shown by inhibition of aco3 growth by the toxin monofluoroacetate, and altered [14C]acetate assimilation and metabolite levels in aco1 and aco2. Relative levels of fumarate and malate differed between aco2 and aco3, indicating metabolically isolated pools of these metabolites in seedlings. Our inability to enrich ACO protein through mitochondria isolation, and the reduced cytosolic ACO activity of the iron-sulfur centre assembly mutant atm3-1, indicated a cytosolic localization of ACO3 in 3-day-old seedlings. Subsequently, we determined that more than 90% of ACO3 was cytosolic. We conclude that ACO3 is cytosolic in young seedlings and functions in citrate catabolism consistent with the operation of the classic glyoxylate and not direct catabolism of citrate within mitochondria.

Mapping the variation of the carrot metabolome using 1H NMR spectroscopy and consensus PCA

Genetic variation is the most influential factor for carrot (Daucus carota L.) composition. However, difference in metabolite content between carrot varieties has not been described by NMR, although primary metabolites are important for human health and sensory properties. The aim of the present study was to investigate the effect of genotype on carrot metabolite composition using a (1)H NMR-based metabolomics approach. After extraction using aqueous and organic solvents, 25 hydrophilic metabolites, β-carotene, sterols, triacylglycerols, and phospholipids were detected. Multiblock PCA showed that three principal components could be identified for classification of the five carrot varieties using different spectroscopic regions and the results of the two solvent extraction methods as blocks. The varieties were characterized by differences in carbohydrate, amino acid, nucleotide, fatty acid, sterol, and β-carotene contents. (1)H NMR spectroscopy coupled with multiblock data analysis was an efficient and useful tool to map the carrot metabolome and identify genetic differences between varieties.

Metabolomics in melon: a new opportunity for aroma analysis

Cucumis melo fruit is highly valued for its sweet and refreshing flesh, however the flavour and value are also highly influenced by aroma as dictated by volatile organic compounds (VOCs). A simple and robust method of sampling VOCs on polydimethylsiloxane (PDMS) has been developed. Contrasting cultivars of C. melo subspecies melo were investigated at commercial maturity: three cultivars of var. Cantalupensis group Charentais (cv. Cézanne, Escrito, and Dalton) known to exhibit differences in ripening behaviour and shelf-life, as well as one cultivar of var. Cantalupensis group Ha'Ogan (cv. Noy Yisre'el) and one non-climacteric cultivar of var. Inodorus (cv. Tam Dew). The melon cultivar selection was based upon fruits exhibiting clear differences (cv. Noy Yisre'el and Tam Dew) and similarities (cv. Cézanne, Escrito, and Dalton) in flavour. In total, 58 VOCs were detected by thermal desorption (TD)-GC-MS which permitted the discrimination of each cultivar via Principal component analysis (PCA). PCA indicated a reduction in VOCs in the non-climacteric cv. Tam Dew compared to the four Cantalupensis cultivars. Within the group Charentais melons, the differences between the short, mid and long shelf-life cultivars were considerable. ¹H NMR analysis led to the quantification of 12 core amino acids, their levels were 3-10-fold greater in the Charentais melons, although they were reduced in the highly fragrant cv. Cézanne, indicating their role as VOC precursors. This study along with comparisons to more traditional labour intensive solid phase micro-extraction (SPME) GC-MS VOC profiling data has indicated that the high-throughput PDMS method is of great potential for the assessment of melon aroma and quality.