Application of (1)h NMR profiling to assess seed metabolomic diversity. A case study on a soybean era population

Comparative transcriptome analysis of vegetable soybean grain discloses genes essential for grain quality

BACKGROUND

Vegetable soybean is an important vegetable crop in world. Seed size and soluble sugar content are considered crucial indicators of quality in vegetable soybean, and there is a lack of clarity on the molecular basis of grain quality in vegetable soybean.

RESULTS

In this context, we performed a comprehensive comparative transcriptome analysis of seeds between a high-sucrose content and large-grain variety (Zhenong 6, ZN6) and a low-sucrose content and small-grain variety (Williams 82, W82) at three developmental stages, i.e. stage R5 (Beginning Seed), stage R6 (Full Seed), and stage R7 (Beginning Maturity). The transcriptome analysis showed that 17,107 and 13,571 differentially expressed genes (DEGs) were identified in ZN6 at R6 (vs. R5) and R7 (vs. R6), respectively, whereas 16,203 and 16,032 were detected in W82. Gene expression pattern and DEGs functional enrichment proposed genotype-specific biological processes during seed development. The genes participating in soluble sugar biosynthesis such as FKGP were overexpressed in ZN6, whereas those responsible for lipid and protein metabolism such as ALDH3 were more enhanced in W82, exhibiting different dry material accumulation between two genotypes. Furthermore, hormone-associated transcriptional factors involved in seed size regulation such as BEH4 were overrepresented in ZN6, exhibiting different seed size regulation processes between two genotypes.

CONCLUSIONS

Herein, we not only discovered the differential expression of genes encoding metabolic enzymes involved in seed composition, but also identified a type of hormone-associated transcriptional factors overexpressed in ZN6, which may regulate seed size and soluble content. This study provides new insights into the underlying causes of differences in the soybean metabolites and appearance, and suggests that genetic data can be used to improve its appearance and textural quality.

Influence of Silver Nanoparticles on the Metabolites of Two Transgenic Soybean Varieties: An NMR-Based Metabolomics Approach

This study investigated the effect of AgNPs and AgNO3, at concentrations equivalent, on the production of primary and secondary metabolites on transgenic soybean plants through an NMR-based metabolomics. The plants were cultivated in a germination chamber following three different treatments: T0 (addition of water), T1 (addition of AgNPs), and T2 (addition of AgNO3). Physiological characteristics, anatomical analyses through microscopic structures, and metabolic profile studies were carried out to establish the effect of abiotic stress on these parameters in soybean plants. Analysis of the 1H NMR spectra revealed the presence of amino acids, organic acids, sugars, and polyphenols. The metabolic profiles of plants with AgNP and AgNO3 were qualitatively similar to the metabolic profile of the control group, suggesting that the application of silver does not affect secondary metabolites. From the PCA, it was possible to differentiate the three treatments applied, mainly based on the content of fatty acids, pinitol, choline, and betaine.

Integrative analysis of metabolome and transcriptome reveals the improvements of seed quality in vegetable soybean (Glycine max (L.) Merr.)

Vegetable soybean is derived from grain soybean. Seeds of vegetable soybean are bigger, sweeter, and have smoother texture and better flavor than those of grain soybean. To better understand the improvements of seed quality in vegetable soybean, comparative metabolome and transcriptome analyses were performed in the developing seeds between grain (Williams 82) and vegetable (Jiaoda 133) soybeans. A total of 299 differential metabolites were identified between two genotypes, with an increase in free amino acids, carbohydrates, sterols, and flavonoids and a decrease in fatty acid in vegetable soybean. Thousands of differentially expressed genes (DEGs) were identified by transcriptome analysis. DEGs were used for weighted gene co-expression network analysis (WGCNA), yielding 16 co-expression modules. The expression patterns of DEGs within these modules were distinct between two genotypes. Functional enrichment analysis revealed that metabolic pathways, including alanine, aspartate and glutamate metabolism, fatty acid degradation, starch and sucrose metabolism, sucrose transport, and flavonoid biosynthesis, were up-regulated, whereas photosynthesis, arginine biosynthesis, arginine and proline metabolism, glycolysis/gluconeogenesis, and fatty acid biosynthesis were down-regulated in vegetable soybean. Reasonably, the alterations of metabolic pathways corresponding to DEGs partly explained the formation of differential metabolites. These findings provide a better understanding of seed development and breeding improvements of vegetable soybean.

Metabolomics and health: from nutritional crops and plant-based pharmaceuticals to profiling of human biofluids

During the past decade metabolomics has emerged as one of the fastest developing branches of “-omics” technologies. Metabolomics involves documentation, identification, and quantification of metabolites through modern analytical platforms in various biological systems. Advanced analytical tools, such as gas chromatography–mass spectrometry (GC/MS), liquid chromatography–mass spectroscopy (LC/MS), and non-destructive nuclear magnetic resonance (NMR) spectroscopy, have facilitated metabolite profiling of complex biological matrices. Metabolomics, along with transcriptomics, has an influential role in discovering connections between genetic regulation, metabolite phenotyping and biomarkers identification. Comprehensive metabolite profiling allows integration of the summarized data towards manipulation of biosynthetic pathways, determination of nutritional quality markers, improvement in crop yield, selection of desired metabolites/genes, and their heritability in modern breeding. Along with that, metabolomics is invaluable in predicting the biological activity of medicinal plants, assisting the bioactivity-guided fractionation process and bioactive leads discovery, as well as serving as a tool for quality control and authentication of commercial plant-derived natural products. Metabolomic analysis of human biofluids is implemented in clinical practice to discriminate between physiological and pathological state in humans, to aid early disease biomarker discovery and predict individual response to drug therapy. Thus, metabolomics could be utilized to preserve human health by improving the nutritional quality of crops and accelerating plant-derived bioactive leads discovery through disease diagnostics, or through increasing the therapeutic efficacy of drugs via more personalized approach. Here, we attempt to explore the potential value of metabolite profiling comprising the above-mentioned applications of metabolomics in crop improvement, medicinal plants utilization, and, in the prognosis, diagnosis and management of complex diseases.

Metabolomics as a Tool to Study Underused Soy Parts: In Search of Bioactive Compounds

The valorization of agri-food by-products is essential from both economic and sustainability perspectives. The large quantity of such materials causes problems for the environment; however, they can also generate new valuable ingredients and products which promote beneficial effects on human health. It is estimated that soybean production, the major oilseed crop worldwide, will leave about 597 million metric tons of branches, leaves, pods, and roots on the ground post-harvesting in 2020/21. An alternative for the use of soy-related by-products arises from the several bioactive compounds found in this plant. Metabolomics studies have already identified isoflavonoids, saponins, and organic and fatty acids, among other metabolites, in all soy organs. The present review aims to show the application of metabolomics for identifying high-added-value compounds in underused parts of the soy plant, listing the main bioactive metabolites identified up to now, as well as the factors affecting their production.

Discrimination of the Geographical Origin of Soybeans Using NMR-Based Metabolomics

With the increase in soybean trade between countries, the intentional mislabeling of the origin of soybeans has become a serious problem worldwide. In this study, metabolic profiling of soybeans from the Republic of Korea and China was performed by nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to predict the geographical origin of soybeans. The optimal orthogonal partial least squares-discriminant analysis (OPLS-DA) model was obtained using total area normalization and unit variance (UV) scaling, without applying the variable influences on projection (VIP) cut-off value, resulting in 96.9% sensitivity, 94.4% specificity, and 95.6% accuracy in the leave-one-out cross validation (LOO-CV) test for discriminating between Korean and Chinese soybeans. Soybeans from the northeastern, middle, and southern regions of China were successfully differentiated by standardized area normalization and UV scaling with a VIP cut-off value of 1.0, resulting in 100% sensitivity, 91.7%–100% specificity, and 94.4%–100% accuracy in a LOO-CV test. The methods employed in this study can be used to obtain essential information for the authentication of soybean samples from diverse geographical locations in future studies.

Exposure of livestock to GM feeds: Detectability and measurement

This review explores the possibilities to determine livestock consumption of genetically modified (GM) feeds/ingredients including detection of genetically modified organism (GMO)-related DNA or proteins in animal samples, and the documentary system that is in place for GM feeds under EU legislation. The presence and level of GMO-related DNA and proteins can generally be readily measured in feeds, using established analytical methods such as polymerase chain reaction and immuno-assays, respectively. Various technical challenges remain, such as the simultaneous detection of multiple GMOs and the identification of unauthorized GMOs for which incomplete data on the inserted DNA may exist. Given that transfer of specific GMO-related DNA or protein from consumed feed to the animal had seldom been observed, this cannot serve as an indicator of the individual animal's prior exposure to GM feeds. To explore whether common practices, information exchange and the specific GM feed traceability system in the EU would allow to record GM feed consumption, the dairy chain in Catalonia, where GM maize is widely grown, was taken as an example. It was thus found that this system would neither enable determination of an animal's consumption of specific GM crops, nor would it allow for quantitation of the exposure.

Identification of Urinary Food Intake Biomarkers for Milk, Cheese, and Soy-Based Drink by Untargeted GC-MS and NMR in Healthy Humans

The measurement of food intake biomarkers (FIBs) in biofluids represents an objective tool for dietary assessment. FIBs of milk and cheese still need more investigation due to the absence of candidate markers. Thus, an acute intervention study has been performed to sensitively and specifically identify candidate FIBs. Eleven healthy male and female volunteers participated in the randomized, controlled crossover study that tested a single intake of milk and cheese as test products, and soy-based drink as a control. Urine samples were collected at baseline and up to 24 h at distinct time intervals (0-1, 1-2, 2-4, 4-6, 6-12, and 12-24 h) and were analyzed using an untargeted multiplatform approach (GC-MS and ¹H NMR). Lactose, galactose, and galactonate were identified exclusively after milk intake while for other metabolites (allantoin, hippurate, galactitol, and galactono-1,5-lactone) a significant increase has been observed. Urinary 3-phenyllactic acid was the only compound specifically reflecting cheese intake although alanine, proline, and pyroglutamic acid were found at significantly higher levels after cheese consumption. In addition, several novel candidate markers for soy drink were identified, such as pinitol and trigonelline. Together, these candidate FIBs of dairy intake could serve as a basis for future validation studies under free-living conditions.

Integration of omics approaches to understand oil/protein content during seed development in oilseed crops

Oilseed crops, especially soybean (Glycine max) and canola/rapeseed (Brassica napus), produce seeds that are rich in both proteins and oils and that are major sources of energy and nutrition worldwide. Most of the nutritional content in the seed is accumulated in the embryo during the seed filling stages of seed development. Understanding the metabolic pathways that are active during seed filling and how they are regulated are essential prerequisites to crop improvement. In this review, we summarize various omics studies of soybean and canola/rapeseed during seed filling, with emphasis on oil and protein traits, to gain a systems-level understanding of seed development. Currently, most (80-85%) of the soybean and rapeseed reference genomes have been sequenced (950 and 850 megabases, respectively). Parallel to these efforts, extensive omics datasets from different seed filling stages have become available. Transcriptome and proteome studies have detected preponderance of starch metabolism and glycolysis enzymes to be the possible cause of higher oil in B. napus compared to other crops. Small RNAome studies performed during the seed filling stages have revealed miRNA-mediated regulation of transcription factors, with the suggestion that this interaction could be responsible for transitioning the seeds from embryogenesis to maturation. In addition, progress made in dissecting the regulation of de novo fatty acid synthesis and protein storage pathways is described. Advances in high-throughput omics and comprehensive tissue-specific analyses make this an exciting time to attempt knowledge-driven investigation of complex regulatory pathways.

Natural Variability of Allergen Levels in Conventional Soybeans: Assessing Variation across North and South America from Five Production Years

Soybean (Glycine max L. Merrill) is one of eight major allergenic foods with endogenous proteins identified as allergens. To better understand the natural variability of five soybean allergens (Gly m 4, Gly m 5, Gly m 6, Gly m Bd 28k, and Gly m Bd 30k), validated enzyme-linked immunosorbent assays (ELISAs) were developed. These ELISAs measured allergens in 604 soybean samples collected from locations in North and South America over five growing seasons (2009-2013/2014) and including 37 conventional varieties. Levels of these five allergens varied 5-19-fold. Multivariate statistical analyses and pairwise comparisons show that environmental factors have a larger effect on allergen levels than genetic factors. Therefore, from year to year, consumers are exposed to highly variable levels of allergens in soy-based foods, bringing into question whether quantitative comparison of endogenous allergen levels of new genetically modified soybean adds meaningful information to their overall safety risk assessment.

Fortune telling: metabolic markers of plant performance

BACKGROUND

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

AIM OF REVIEW

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

KEY MESSAGE

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

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.

Holistic Analysis Enhances the Description of Metabolic Complexity in Dietary Natural Products

In the field of food and nutrition, complex natural products (NPs) are typically obtained from cells/tissues of diverse organisms such as plants, mushrooms, and animals. Among them, edible fruits, grains, and vegetables represent most of the human diet. Because of an important dietary dependence, the comprehensive metabolomic analysis of dietary NPs, performed holistically via the assessment of as many metabolites as possible, constitutes a fundamental building block for understanding the human diet. Both mass spectrometry (MS) and nuclear magnetic resonance (NMR) are important complementary analytic techniques, covering a wide range of metabolites at different concentrations. Particularly, 1-dimensional 1H-NMR offers an unbiased overview of all metabolites present in a sample without prior knowledge of its composition, thereby leading to an untargeted analysis. In the past decade, NMR-based metabolomics in plant and food analyses has evolved considerably. The scope of the present review, covering literature of the past 5 y, is to address the relevance of 1H-NMR–based metabolomics in food plant studies, including a comparison with MS-based techniques. Major applications of NMR-based metabolomics for the quality control of dietary NPs and assessment of their nutritional values are presented.

NMR Spectroscopy Identifies Metabolites Translocated from Powdery Mildew Resistant Rootstocks to Susceptible Watermelon Scions

Powdery mildew (PM) disease causes significant loss in watermelon. Due to the unavailability of a commercial watermelon variety that is resistant to PM, grafting susceptible cultivars on wild resistant rootstocks is being explored as a short-term management strategy to combat this disease. Nuclear magnetic resonance-based metabolic profiles of susceptible and resistant rootstocks of watermelon and their corresponding susceptible scions (Mickey Lee) were compared to screen for potential metabolites related to PM resistance using multivariate principal component analysis. Significant score plot differences between the susceptible and resistant groups were revealed through Mahalanobis distance analysis. Significantly different spectral buckets and their corresponding metabolites (including choline, fumarate, 5-hydroxyindole-3-acetate, and melatonin) have been identified quantitatively using multivariate loading plots and verified by volcano plot analyses. The data suggest that these metabolites were translocated from the powdery mildew resistant rootstocks to their corresponding powdery mildew susceptible scions and can be related to PM disease resistance.