A (1)H HR-MAS NMR-Based Metabolomic Study for Metabolic Characterization of Rice Grain from Various Oryza sativa L. Cultivars

Olive Fruit Phenolic Profiling Using High Resolution-Magic Angle Spinning (HR-MAS) Solid-State NMR Spectroscopy

High Resolution-Magic Angle Spinning (HR-MAS) solid-state NMR spectroscopy is finding increasing application in the analysis of solid foods, bypassing the need for complicated solvent extraction procedures. In the present protocol, we report a simple analytical approach based on HR-MAS NMR spectroscopy for the phenolic profiling of olive fruits, flesh, or skin. This approach allows the facile characterization of phenolic compounds in olive fruits cultivated for extra-virgin olive oil production as a function of maturation and variety, in addition to processing technology for table olives.

Molecular Profiling of Peanut under Raw, Roasting, and Autoclaving Conditions Using High-Resolution Magic Angle Spinning and Solution 1H NMR Spectroscopy

Higher rates of peanut allergy have been observed in countries that commonly roast peanuts prior to consumption. Despite the importance of understanding the role of thermal processing in allergy and on peanut composition, studies toward generating signatures that identify molecular contents following processing are scant. Here, we identified spectral signatures to track changes and differences in the molecular composition of peanuts under raw, roasted, and high-pressure and high-temperature autoclaved conditions. We analyzed both the solid flesh of the seed and solutions derived from soaking peanuts using High-Resolution Magic Angle Spinning (HR-MAS) and solution 1H Nuclear Magnetic Resonance (NMR) spectroscopy, respectively. The NMR spectra of intact peanuts revealed triglycerides as the dominant species, assigned on the basis of multiplets at 4.1 and 4.3 ppm, and corresponding defatted flours revealed the presence of sugars. Sucrose assigned based on a doublet at 5.4 ppm (anomeric proton), and triglycerides were the most abundant small molecules observed, with little variation between conditions. Soaked peanut solutions were devoid of lipids, and their resulting spectra matched the profiles of defatted peanuts. Spectral signatures resulting from autoclaving differed strikingly between those from raw and roasted peanuts, with considerable line-broadening in regions corresponding to proteins and amino-acid side chains, from 0.5 to 2.0 ppm and 6.5 to 8.5 ppm. Taken together, by using complementary NMR methods to obtain a fingerprint of the molecular components in peanuts, we demonstrated that autoclaving led to a distinct composition, likely resulting from the hydrolytic cleavage of proteins, the most important molecule of the allergic reaction.

A comparative metabolomics study of polyphenols in highland barley (Hordeum vulgare L.) grains with different colors

Highland barley (HB) grains are gaining increasing popularity owing to their high nutritional merits. However, only limited information is available on the metabolic profiles of HB grains polyphenols, especially the difference of polyphenols in different colors of HB. In this study, we determined the metabolic profiles of black, blue, and white HB grains via an ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS)-based metabolomics. A total of 402 metabolites were identified, among which 198, 62, and 189 metabolites displayed different accumulation patterns in the three comparison groups (WHB vs. BKHB, WHB vs. BEHB, BEHB vs. BKHB), respectively. In particular, flavonoids and phenolic acids contents displayed considerable differences among the three HB cultivars. The phenolics content of black HB was relatively high. Additionally, "Flavonoid biosynthesis" and "flavone and flavonol biosynthesis" were the significantly enriched pathways. In conclusion, this study provides comprehensive insights into the adequate utilization and development of novel HB-based functional foods.

Mass Spectroscopy as an Analytical Tool to Harness the Production of Secondary Plant Metabolites: The Way Forward for Drug Discovery

The molecular map of diverse biological molecules linked with structure, function, signaling, and regulation within a cell can be elucidated using an analytically demanding omic approach. The latest trend of using "metabolomics" technologies has explained the natural phenomenon of opening a new avenue to understand and enhance bioactive compounds' production. Examination of sequenced plant genomes has revealed that a considerable portion of these encodes genes of secondary metabolism. In addition to genetic and molecular tools developed in the current era, the ever-increasing knowledge about plant metabolism's biochemistry has initiated an approach for wisely designed, more productive genetic engineering of plant secondary metabolism for improved defense systems and enhanced biosynthesis of beneficial metabolites. Secondary plant metabolites are natural products synthesized by plants that are not directly involved with their average growth and development but play a vital role in plant defense mechanisms. Plant secondary metabolites are classified into four major classes: terpenoids, phenolic compounds, alkaloids, and sulfur-containing compounds. More than 200,000 secondary metabolites are synthesized by plants having a unique and complex structure. Secondary plant metabolites are well characterized and quantified by omics approaches and therefore used by humans in different sectors such as agriculture, pharmaceuticals, chemical industries, and biofuel. The aim is to establish metabolomics as a comprehensive and dynamic model of diverse biological molecules for biomarkers and drug discovery. In this chapter, we aim to illustrate the role of metabolomic technology, precisely liquid chromatography-mass spectrometry, capillary electrophoresis mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance spectroscopy, specifically as a research tool in the production and identification of novel bioactive compounds for drug discovery and to obtain a unified insight of secondary metabolism in plants.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

HR-MAS NMR Applications in Plant Metabolomics

Metabolomics is used to reduce the complexity of plants and to understand the underlying pathways of the plant phenotype. The metabolic profile of plants can be obtained by mass spectrometry or liquid-state NMR. The extraction of metabolites from the sample is necessary for both techniques to obtain the metabolic profile. This extraction step can be eliminated by making use of high-resolution magic angle spinning (HR-MAS) NMR. In this review, an HR-MAS NMR-based workflow is described in more detail, including used pulse sequences in metabolomics. The pre-processing steps of one-dimensional HR-MAS NMR spectra are presented, including spectral alignment, baseline correction, bucketing, normalisation and scaling procedures. We also highlight some of the models which can be used to perform multivariate analysis on the HR-MAS NMR spectra. Finally, applications of HR-MAS NMR in plant metabolomics are described and show that HR-MAS NMR is a powerful tool for plant metabolomics studies.

Cytosine methylations in the promoter regions of genes involved in the cellular oxidation equilibrium pathways affect rice heat tolerance

Background

High temperatures, particularly at night, decrease rice yield and quality. As high nighttime temperatures (HNTs) become increasingly frequent due to climate change, it is imperative to develop rice crops that tolerate HNTs. DNA methylation may represent a potential avenue for HNT-tolerant rice strain development, as this mechanism regulates gene activity and cellular phenotype in response to adverse environmental conditions without changing the nucleotide sequence.

Results

After HNT exposure, the methylation patterns of cytosines in the CHH context differed noticeably between two coisogenic rice strains with significantly different levels in heat tolerance. Methylation differences between strains were primarily observed on successive cytosines in the promoter or downstream regions of transcription factors and transposon elements. In contrast to the heat-sensitive rice strain, the regions 358–359 bp and 2–60 bp downstream of two basal transcriptional factors (TFIID subunit 11 and mediator of RNA polymerase II transcription subunit 31, respectively) were fully demethylated in the heat-tolerant strain after HNT exposure. In the heat-tolerant strain, HNTs reversed the methylation patterns of successive cytosines in the promoter regions of various genes involved in abscisic acid (ABA)-related reactive oxygen species (ROS) equilibrium pathways, including the pentatricopeptide repeat domain gene PPR (LOC_Os07g28900) and the homeobox domain gene homeobox (LOC_Os01g19694). Indeed, PRR expression was inhibited in heat-sensitive rice strains, and the methylation rates of the cytosines in the promoter region of PRR were greater in heat-sensitive strains as compared to heat-tolerant strains.

Conclusions

After HNT exposure, cytosines in the CHH context were more likely than cytosines in other contexts to be methylated differently between the heat-sensitive and heat-tolerant rice strains. Methylation in the promoter regions of the genes associated with ABA-related oxidation and ROS scavenging improved heat tolerance in rice. Our results help to clarify the molecular mechanisms underlying rice heat tolerance.

The magic angle view to food: magic-angle spinning (MAS) NMR spectroscopy in food science

Nuclear Magnetic Resonance (NMR) spectroscopy has been used in food science and nutritional studies for decades and is one of the major analytical platforms in metabolomics. Many foods are solid or at least semi-solid, which denotes that the molecular motions are restricted as opposed to in pure liquids. While the majority of NMR spectroscopy is performed on liquid samples and a solid material gives rise to constraints in terms of many chemical analyses, the magic angle thrillingly enables the application of NMR spectroscopy also on semi-solid and solid materials. This paper attempts to review how magic-angle spinning (MAS) NMR is used from 'farm-to-fork' in food science.

High Nighttime Temperature Induces Antioxidant Molecule Perturbations in Heat-Sensitive and Heat-Tolerant Coisogenic Rice ( Oryza sativa) Strains

Global warming-associated increases in temperature, particularly at nighttime, are detrimental to rice yield and quality. Metabolomic profiling was used to examine and compare the short-term extreme high nighttime temperature-induced molecular perturbations in rice ( Oryza sativa) coisogenic strains with contrasting heat-tolerances at the first stage of seed ripening. Compared to the heat-sensitive strain, antioxidant molecules were higher in abundance in the heat-tolerant strain, whereas the abundances of molecules involved in photosynthesis, nucleotide catabolism, and the S-adenosylmethionine (SAM) cycle varied only slightly. Thus, we proposed that the high abundance of antioxidant molecules in the heat-tolerant strain alleviated cellular oxidative stress, which protected photosynthesis, nucleotide catabolism, and the SAM cycle, leading to good grain filling.

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.

Growth and Metabolic Responses of Rice (Oryza sativa L.) Cultivated in Phosphorus-Deficient Soil Amended with TiO2 Nanoparticles

Plants have the natural ability to withstand stress conditions through metabolic adjustments. The present study aimed at investigating the effects of titanium dioxide nanoparticles (TiO₂ NPs) application (0, 25, 50, 150, 250, 500, and 750 mg kg^-1) in phosphorus-deficient soil in terms of growth responses, P contents, and metabolic alterations in rice. TiO₂ NPs application increased shoot length up to 14.5%. Phosphorus contents in rice roots, shoots, and grains were increased by 2.6-, 2.4-, and 1.3-fold, respectively, at 750 mg kg^-1 of TiO₂ NPs. Gas chromatography-mass spectrometry (GC-MS)-based metabolomics revealed increased levels of amino acids, palmitic acid, and glycerol content in grains resulting from plants grown in 750 mg kg^-1 TiO₂ NPs-treated soil. Furthermore, no translocation of TiO₂ NPs from the treated soil to rice grains was detected by inductively coupled plasma-optical emission spectrometry (ICP-OES), which suggests no risk of TiO₂ NPs intake via grain consumption. The observed data indicates the strong relationship among NPs application, P contents, and metabolic alterations.

Metabolomics, a Powerful Tool for Agricultural Research

Metabolomics, which is based mainly on nuclear magnetic resonance (NMR), gas-chromatography (GC) or liquid-chromatography (LC) coupled to mass spectrometry (MS) analytical technologies to systematically acquire the qualitative and quantitative information of low-molecular-mass endogenous metabolites, provides a direct snapshot of the physiological condition in biological samples. As complements to transcriptomics and proteomics, it has played pivotal roles in agricultural and food science research. In this review, we discuss the capacities of NMR, GC/LC-MS in the acquisition of plant metabolome, and address the potential promise and diverse applications of metabolomics, particularly lipidomics, to investigate the responses of Arabidopsis thaliana, a primary plant model for agricultural research, to environmental stressors including heat, freezing, drought, and salinity.