Primary metabolite contents are correlated with seed protein and oil traits in near-isogenic lines of soybean

Role of particle size-dependent copper bioaccumulation-mediated oxidative stress on Glycine max (L.) yield parameters with soil-applied copper oxide nanoparticles

Increased impetus on the application of nano-fertilizers to improve sustainable food production warrants understanding of nanophytotoxicity and its underlying mechanisms before its application could be fully realized. In this study, we evaluated the potential particle size-dependent effects of soil-applied copper oxide nanoparticles (nCuO) on crop yield and quality attributes (photosynthetic pigments, seed yield and nutrient quality, seed protein, and seed oil), including root and seed Cu bioaccumulation and a suite of oxidative stress biomarkers, in soybean (Glycine max L.) grown in field environment. We synthesized three distinct sized (25 nm = S [small], 50 nm = M [medium], and 250 nm = L [large]) nCuO with same surface charge and compared with soluble Cu2+ ions (CuCl2) and water-only controls. Results showed particle size-dependent effects of nCuO on the photosynthetic pigments (Chla and Chlb), seed yield, potassium and phosphorus accumulation in seed, and protein and oil yields, with nCuO-S showing higher inhibitory effects. Further, increased root and seed Cu bioaccumulation led to concomitant increase in oxidative stress (H2O2, MDA), and as a response, several antioxidants (SOD, CAT, POX, and APX) increased proportionally, with nCuO treatments including Cu2+ ion treatment. These results are corroborated with TEM ultrastructure analysis showing altered seed oil bodies and protein storage vacuoles with nCuO-S treatment compared to control. Taken together, we propose particle size-dependent Cu bioaccumulation-mediated oxidative stress as a mechanism of nCuO toxicity. Future research investigating the potential fate of varied size nCuO, with a focus on speciation at the soil-root interface, within the root, and edible parts such as seed, will guide health risk assessment of nCuO.

Differential gene expression provides leads to environmentally regulated soybean seed protein content

Soybean is an important global source of plant-based protein. A persistent trend has been observed over the past two decades that soybeans grown in western Canada have lower seed protein content than soybeans grown in eastern Canada. In this study, 10 soybean genotypes ranging in average seed protein content were grown in an eastern location (control) and three western locations (experimental) in Canada. Seed protein and oil contents were measured for all lines in each location. RNA-sequencing and differential gene expression analysis were used to identify differentially expressed genes that may account for relatively low protein content in western-grown soybeans. Differentially expressed genes were enriched for ontologies and pathways that included amino acid biosynthesis, circadian rhythm, starch metabolism, and lipid biosynthesis. Gene ontology, pathway mapping, and quantitative trait locus (QTL) mapping collectively provide a close inspection of mechanisms influencing nitrogen assimilation and amino acid biosynthesis between soybeans grown in the East and West. It was found that western-grown soybeans had persistent upregulation of asparaginase (an asparagine hydrolase) and persistent downregulation of asparagine synthetase across 30 individual differential expression datasets. This specific difference in asparagine metabolism between growing environments is almost certainly related to the observed differences in seed protein content because of the positive correlation between seed protein content at maturity and free asparagine in the developing seed. These results provided pointed information on seed protein-related genes influenced by environment. This information is valuable for breeding programs and genetic engineering of geographically optimized soybeans.

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.

Addressing global food insecurity: Soil-applied zinc oxide nanoparticles promote yield attributes and seed nutrient quality in Glycine max L

Consumed globally, oilseeds serve as a major source of proteins and oils in human and animal nutrition, supporting global food security. Zinc (Zn) is an essential micronutrient critical for oil and protein synthesis in plants. In this study, we synthesized three distinct sized zinc oxide nanoparticles (nZnO: 38 nm = S [small], 59 nm = M [medium], and > 500 nm = L [large], and assessed the potential effects of varied particle sizes and concentrations (0, 50, 100, 200, and 500 mg/kg-soil) on seed yield attributes, nutrient quality and oil and protein yield in soybean (Glycine max L.) grown for a full lifecycle of 120 days, and compared with soluble Zn²⁺ ions (ZnCl₂) and water-only controls. We observed particle size- and concentration-dependent influence of nZnO on photosynthetic pigments, pod formation, potassium and phosphorus accumulation in seed, and protein and oil yields. Overall, soybean showed significant stimulatory responses to nZnO-S for most of the parameters tested compared to nZnO-M, nZnO-L, and Zn²⁺ ions treatments up to 200 mg/kg, suggesting the potential for small size nZnO to improve seed quality and production in soybean. At 500 mg/kg, however, for all endpoints (except for carotenoids and seed formation) toxicity was observed with all Zn compounds. Further, TEM analysis of seed ultrastructure indicated potential alterations in seed oil bodies and protein storage vacuoles at a toxic concentration (500 mg/kg) of nZnO-S compared to control. These findings suggest 200 mg/kg as an optimal dose for the smallest size nZnO-S (38 nm) to significantly improve seed yield, nutrient quality, and oil and protein yield, paving a path for addressing global food insecurity using small sized nZnO as a novel nano-fertilizer to promote crop yield and nutrient quality, in soil-grown soybean.

Metabolomics-centered mining of plant metabolic diversity and function: Past decade and future perspectives

Plants are natural experts in organic synthesis, being able to generate large numbers of specific metabolites with widely varying structures that help them adapt to variable survival challenges. Metabolomics is a research discipline that integrates the capabilities of several types of research including analytical chemistry, statistics, and biochemistry. Its ongoing development provides strategies for gaining a systematic understanding of quantitative changes in the levels of metabolites. Metabolomics is usually performed by targeting either a specific cell, a specific tissue, or the entire organism. Considerable advances in science and technology over the last three decades have propelled us into the era of multi-omics, in which metabolomics, despite at an earlier developmental stage than genomics, transcriptomics, and proteomics, offers the distinct advantage of studying the cellular entities that have the greatest influence on end phenotype. Here, we summarize the state of the art of metabolite detection and identification, and illustrate these techniques with four case study applications: (i) comparing metabolite composition within and between species, (ii) assessing spatio-temporal metabolic changes during plant development, (iii) mining characteristic metabolites of plants in different ecological environments and upon exposure to various stresses, and (iv) assessing the performance of metabolomics as a means of functional gene identification , metabolic pathway elucidation, and metabolomics-assisted breeding through analyzing plant populations with diverse genetic variations. In addition, we highlight the prominent contributions of joint analyses of plant metabolomics and other omics datasets, including those from genomics, transcriptomics, proteomics, epigenomics, phenomics, microbiomes, and ion-omics studies. Finally, we discuss future directions and challenges exploiting metabolomics-centered approaches in understanding plant metabolic diversity.

4D genetic networks reveal the genetic basis of metabolites and seed oil-related traits in 398 soybean RILs

Background

The yield and quality of soybean oil are determined by seed oil-related traits, and metabolites/lipids act as bridges between genes and traits. Although there are many studies on the mode of inheritance of metabolites or traits, studies on multi-dimensional genetic network (MDGN) are limited.

Results

In this study, six seed oil-related traits, 59 metabolites, and 107 lipids in 398 recombinant inbred lines, along with their candidate genes and miRNAs, were used to construct an MDGN in soybean. Around 175 quantitative trait loci (QTLs), 36 QTL-by-environment interactions, and 302 metabolic QTL clusters, 70 and 181 candidate genes, including 46 and 70 known homologs, were previously reported to be associated with the traits and metabolites, respectively. Gene regulatory networks were constructed using co-expression, protein–protein interaction, and transcription factor binding site and miRNA target predictions between candidate genes and 26 key miRNAs. Using modern statistical methods, 463 metabolite–lipid, 62 trait–metabolite, and 89 trait–lipid associations were found to be significant. Integrating these associations into the above networks, an MDGN was constructed, and 128 sub-networks were extracted. Among these sub-networks, the gene–trait or gene–metabolite relationships in 38 sub-networks were in agreement with previous studies, e.g., oleic acid (trait)–GmSEI–GmDGAT1a–triacylglycerol (16:0/18:2/18:3), gene and metabolite in each of 64 sub-networks were predicted to be in the same pathway, e.g., oleic acid (trait)–GmPHS–d-glucose, and others were new, e.g., triacylglycerol (16:0/18:1/18:2)–GmbZIP123–GmHD-ZIPIII-10–miR166s–oil content.

Conclusions

This study showed the advantages of MGDN in dissecting the genetic relationships between complex traits and metabolites. Using sub-networks in MGDN, 3D genetic sub-networks including pyruvate/threonine/citric acid revealed genetic relationships between carbohydrates, oil, and protein content, and 4D genetic sub-networks including PLDs revealed the relationships between oil-related traits and phospholipid metabolism likely influenced by the environment. This study will be helpful in soybean quality improvement and molecular biological research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02191-1.

Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and 'Omics' Tools

Grain legumes are a rich source of dietary protein for millions of people globally and thus a key driver for securing global food security. Legume plant-based 'dietary protein' biofortification is an economic strategy for alleviating the menace of rising malnutrition-related problems and hidden hunger. Malnutrition from protein deficiency is predominant in human populations with an insufficient daily intake of animal protein/dietary protein due to economic limitations, especially in developing countries. Therefore, enhancing grain legume protein content will help eradicate protein-related malnutrition problems in low-income and underprivileged countries. Here, we review the exploitable genetic variability for grain protein content in various major grain legumes for improving the protein content of high-yielding, low-protein genotypes. We highlight classical genetics-based inheritance of protein content in various legumes and discuss advances in molecular marker technology that have enabled us to underpin various quantitative trait loci controlling seed protein content (SPC) in biparental-based mapping populations and genome-wide association studies. We also review the progress of functional genomics in deciphering the underlying candidate gene(s) controlling SPC in various grain legumes and the role of proteomics and metabolomics in shedding light on the accumulation of various novel proteins and metabolites in high-protein legume genotypes. Lastly, we detail the scope of genomic selection, high-throughput phenotyping, emerging genome editing tools, and speed breeding protocols for enhancing SPC in grain legumes to achieve legume-based dietary protein security and thus reduce the global hunger risk.

Metagenomic-Metabolomic Mining of Kinema, a Naturally Fermented Soybean Food of the Eastern Himalayas

Kinema is a popular sticky fermented soybean food of the Eastern Himalayan regions of North East India, east Nepal, and south Bhutan. We hypothesized that some dominant bacteria in kinema may contribute to the formation of targeted and non-targeted metabolites for health benefits; hence, we studied the microbiome-metabolite mining of kinema. A total of 1,394,094,912 bp with an average of 464,698,304 ± 120,720,392 bp was generated from kinema metagenome, which resulted in the identification of 47 phyla, 331 families, 709 genera, and 1,560 species. Bacteria (97.78%) were the most abundant domain with the remaining domains of viruses, eukaryote, and archaea. Firmicutes (93.36%) was the most abundant phylum with 280 species of Bacillus, among which Bacillus subtilis was the most dominant species in kinema followed by B. glycinifermentans, B. cereus, B. licheniformis, B. thermoamylovorans, B. coagulans, B. circulans, B. paralicheniformis, and Brevibacillus borstelensis. Predictive metabolic pathways revealed the abundance of genes associated with metabolism (60.66%), resulting in 216 sub-pathways. A total of 361 metabolites were identified by metabolomic analysis (liquid chromatography-mass spectrophotometry, LC-MS). The presence of metabolites, such as chrysin, swainsonine, and 3-hydroxy-L-kynurenine (anticancer activity) and benzimidazole (antimicrobial, anticancer, and anti-HIV activities), and compounds with immunomodulatory effects in kinema supports its therapeutic potential. The correlation between the abundant species of Bacillus and primary and secondary metabolites was constructed with a bivariate result. This study proves that Bacillus spp. contribute to the formation of many targeted and untargeted metabolites in kinema for health-promoting benefits.

Changes in Quinoa Seed Fatty Acid Profile Under Heat Stress Field Conditions

The nutritional quality of quinoa is often related to the high protein content of their seeds. However, and despite not being an oilseed crop, the oil composition of quinoa seeds is remarkable due to its profile, which shows a high proportion of polyunsaturated fatty acids (PUFAs), particularly in essential fatty acids such as linoleic (ω-6) and α-linolenic (ω-3). In line with this, this study aimed at evaluating the effect of elevated temperatures on the oil composition of different quinoa cultivars grown in the field in two consecutive years (i.e., 2017 and 2018). In 2017, heat stress episodes resulted in a reduced oil content and lower quality linked to decreased ratios of oleic acid:linoleic acid, larger omega-6 (ω-6) to omega-3 (ω-3) ratios, and lower monounsaturated fatty acid (MUFA) and higher PUFA contents. Furthermore, the correlations found between mineral nutrients such as phosphorous (P) and the contents of oleic and linoleic acids emphasize the possibility of optimizing oil quality by controlling fertilization. Overall, the results presented in this study show how the environmental and genetic factors and their interaction may impact oil quality in quinoa seeds.

Development of soybean experimental lines with enhanced protein and sulfur amino acid content

Soybean is the preferred protein source for both poultry and swine feed. However, this preferred status is being challenged due to competition from alternative feed ingredients. To overcome this, it becomes necessary for breeders to develop soybean cultivars that contain higher protein and better nutritional composition. In this study, we have developed experimental soybean lines that not only contain significantly higher amounts of protein but also improved sulfur amino acid content. This objective was achieved by crossing a O-acetylserine sulfhydrylase (OASS) overexpressing transgenic soybean line with elevated levels of sulfur amino acid content (CS) with a high protein Korean soybean cultivar (Lee 5). Introgression of high protein and overexpression of OASS was monitored in the experimental lines at each successive generation (F2-F6) by measuring protein content and OASS activity. The average protein content of transgenic CS and Lee 5 seeds were 34.8 % and 44.7 %, while in the experimental soybean lines the protein content ranged from 41.3 %-47.7 %, respectively. HPLC and inductively coupled plasma-mass spectrometry analyses revealed that all the experimental lines developed in this study contained significantly higher amounts of sulfur containing amino acids and elemental sulfur in the seeds. The sulfur amino acid (cysteine + methionine) content of the experimental lines ranged from 1.1 % to 1.26 % while the parents Lee 5 and CS had 0.79 % and 1.1 %, respectively. SDS-PAGE and western blot analysis demonstrated that the accumulation of Bowman-Birk protease inhibitor and lunasin, two sulfur amino acid rich peptides, were elevated in experimental soybean lines. High-resolution 2D-gel electrophoresis and Delta2D gel analysis validated that an overall increase in the different subunits of 7S β-conglycinin and 11S glycinin were mainly responsible for the observed increase in the total amount of protein in experimental lines.

Promoting Human Nutrition and Health through Plant Metabolomics: Current Status and Challenges

Simple Summary

This review summarizes the status, applications, and challenges of plant metabolomics in the context of crop breeding, food quality and safety, and human nutrition and health. It also highlights the importance of plant metabolomics in elucidating biochemical and genetic bases of traits associated with nutritive and healthy beneficial foods and other plant products to secure food supply, to ensure food quality, to protect humans from malnutrition and other diseases. Meanwhile, this review calls for comprehensive collaborations to accelerate relevant researches and applications in the context of human nutrition and health.

Abstract

Plant metabolomics plays important roles in both basic and applied studies regarding all aspects of plant development and stress responses. With the improvement of living standards, people need high quality and safe food supplies. Thus, understanding the pathways involved in the biosynthesis of nutritionally and healthily associated metabolites in plants and the responses to plant-derived biohazards in humans is of equal importance to meet people’s needs. For each, metabolomics has a vital role to play, which is discussed in detail in this review. In addition, the core elements of plant metabolomics are highlighted, researches on metabolomics-based crop improvement for nutrition and safety are summarized, metabolomics studies on plant natural products including traditional Chinese medicine (TCM) for health promotion are briefly presented. Challenges are discussed and future perspectives of metabolomics as one of the most important tools to promote human nutrition and health are proposed.