Metabolomics-assisted breeding: a viable option for crop improvement?

The genetic architecture of the pepper metabolome and the biosynthesis of its signature capsianoside metabolites

Capsicum (pepper) is among the most economically important species worldwide, and its fruits accumulate specialized metabolites with essential roles in plant environmental interaction and human health benefits as well as in conferring their unique taste. However, the genetics underlying differences in metabolite presence/absence and/or accumulation remain largely unknown. In this study, we carried out a genome-wide association study as well as generating and characterizing a novel backcross inbred line mapping population to determine the genetic architecture of the pepper metabolome. This genetic analysis provided over 1,000 metabolic quantitative trait loci (mQTL) for over 250 annotated metabolites. We identified 92 candidate genes involved in various mQTLs. Among the identified loci, we described and validated a gene cluster of eleven UDP-glycosyltransferases (UGTs) involved in monomeric capsianoside biosynthesis. We additionally constructed the gene-by-gene-based biosynthetic pathway of pepper capsianoside biosynthesis, including both core and decorative reactions. Given that one of these decorative pathways, namely the glycosylation of acyclic diterpenoid glycosides, contributes to plant resistance, these data provide new insights and breeding resources for pepper. They additionally provide a blueprint for the better understanding of the biosynthesis of species-specific natural compounds in general.

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Accelerating wheat improvement through trait characterization: advances and perspectives

Wheat (Triticum spp.) is a primary dietary staple food for humanity. Many wheat genetic resources with variable genomes have a record of domestication history and are widespread throughout the world. To develop elite wheat varieties, agronomical and stress-responsive trait characterization is foremost for evaluating existing germplasm to promote breeding. However, genomic complexity is one of the primary impediments to trait mining and characterization. Multiple reference genomes and cutting-edge technologies like haplotype mapping, genomic selection, precise gene editing tools, high-throughput phenotyping platforms, high-efficiency genetic transformation systems, and speed-breeding facilities are transforming wheat functional genomics research to understand the genomic diversity of polyploidy. This review focuses on the research achievements in wheat genomics, the available omics approaches, and bioinformatic resources developed in the past decades. Advances in genomics and system biology approaches are highlighted to circumvent bottlenecks in genomic and phenotypic selection, as well as gene transfer. In addition, we propose conducting precise functional genomic studies and developing sustainable breeding strategies for wheat. These developments in understanding wheat traits have speed up the creation of high-yielding, stress-resistant, and nutritionally enhanced wheat varieties, which will help in addressing global food security and agricultural sustainability in the era of climate change.

Integrating physiological and multi-omics methods to elucidate heat stress tolerance for sustainable rice production

Heat stress presents unique challenges compared to other environmental stressors, as predicting crop responses and understanding the mechanisms for heat tolerance are complex tasks. The escalating impact of devastating climate changes heightens the frequency and intensity of heat stresses, posing a noteworthy threat to global agricultural productivity, especially in rice-dependent regions of the developing world. Humidity has been demonstrated to negatively affect rice yields worldwide. Plants have evolved intricate biochemical adaptations, involving intricate interactions among genes, proteins, and metabolites, to counter diverse external signals and ensure their survival. Modern-omics technologies, encompassing transcriptomics, metabolomics, and proteomics, have revolutionized our comprehension of the intricate biochemical and cellular shifts that occur in stressed agricultural plants. Integrating these multi-omics approaches offers a comprehensive view of cellular responses to heat stress and other challenges, surpassing the insights gained from multi-omics analyses. This integration becomes vital in developing heat-tolerant crop varieties, which is crucial in the face of increasingly unpredictable weather patterns. To expedite the development of heat-resistant rice varieties, aiming at sustainability in terms of food production and food security globally, this review consolidates the latest peer-reviewed research highlighting the application of multi-omics strategies.

OsLSC6 Regulates Leaf Sheath Color and Cold Tolerance in Rice Revealed by Metabolite Genome Wide Association Study

Plant metabolites including anthocyanins play an important role in the growth of plants, as well as in regulating biotic and abiotic stress responses to the environment. Here we report comprehensive profiling of 3315 metabolites and a further metabolic-based genome-wide association study (mGWAS) based on 292,485 SNPs obtained from 311 rice accessions, including 160 wild and 151 cultivars. We identified hundreds of common variants affecting a large number of secondary metabolites with large effects at high throughput. Finally, we identified a novel gene namely OsLSC6 (Oryza sativa leaf sheath color 6), which encoded a UDP 3-O-glucosyltransferase and involved in the anthocyanin biosynthesis of Cyanidin-3-Galc (sd1825) responsible for leaf sheath color, and resulted in significant different accumulation of sd1825 between wild (purple) and cultivars (green). The results of knockout transgenic experiments showed that OsLSC6 regulated the biosynthesis and accumulation of sd1825, controlled the purple leaf sheath. Our further research revealed that OsLSC6 also confers resistance to cold stress during the seedling stage in rice. And we identified that a SNP in OsLSC6 was responsible for the leaf sheath color and chilling tolerance, supporting the importance of OsLSC6 in plant adaption. Our study could not only demonstrate that OsLSC6 is a vital regulator during anthocyanin biosynthesis and abiotic stress responses, but also provide a powerful complementary tool based on metabolites-to-genes analysis by mGWAS for functional gene identification andpromising candidate in future rice breeding and improvement.

Dynamic metabolite QTL analyses provide novel biochemical insights into kernel development and nutritional quality improvement in common wheat

Despite recent advances in crop metabolomics, the genetic control and molecular basis of the wheat kernel metabolome at different developmental stages remain largely unknown. Here, we performed widely targeted metabolite profiling of kernels from three developmental stages (grain-filling kernels [FKs], mature kernels [MKs], and germinating kernels [GKs]) using a population of 159 recombinant inbred lines. We detected 625 annotated metabolites and mapped 3173, 3143, and 2644 metabolite quantitative trait loci (mQTLs) in FKs, MKs, and GKs, respectively. Only 52 mQTLs were mapped at all three stages, indicating the high stage specificity of the wheat kernel metabolome. Four candidate genes were functionally validated by in vitro enzymatic reactions and/or transgenic approaches in wheat, three of which mediated the tricin metabolic pathway. Metabolite flux efficiencies within the tricin pathway were evaluated, and superior candidate haplotypes were identified, comprehensively delineating the tricin metabolism pathway in wheat. Finally, additional wheat metabolic pathways were re-constructed by updating them to incorporate the 177 candidate genes identified in this study. Our work provides new information on variations in the wheat kernel metabolome and important molecular resources for improvement of wheat nutritional quality.

Decoding chronic rhinosinusitis: A metabolomics-based approach

BACKGROUND

Chronic rhinosinusitis (CRS) is a common and intractable disease in otorhinolaryngology, laying a heavy burden on healthcare systems. The worldwide researchers are making efforts to find solutions to this disease. Metabolomics has recently gained more and more traction, and might become a promising tool to unravel the complexity of CRS. This paper provides an overview of current studies on the metabolomics of various CRS subtypes.

METHODS

We conducted a comprehensive literature search in PubMed, Web of Science, EMBASE, Google Scholar, and Cochrane Library, up to May 25, 2023. Search strategies incorporated key terms such as "chronic rhinosinusitis" and "metabolomics" with relevant synonyms and MeSH terms. Titles and abstracts of 86 screened articles were assessed for relevance to CRS and metabolomics. Methodological robustness, data reliability, and relevance were considered for shortlisted articles.

RESULTS

After the refined process, a total of 26 articles were included in this study and sorted out by research themes, methodology and pivotal discoveries. These included studies identified the metabolic pathways and markers related to the pathophysiology in each subtype of CRS.

CONCLUSIONS

Metabolomics helps to shed light on the complexity of CRS. The mentioned findings highlight the importance of specific metabolic pathways and markers in understanding the pathophysiology of CRS. Despite that, challenges and future directions in metabolomics research for CRS would be worth being further explored.

Recent advances in plant translational genomics for crop improvement

The growing population, climate change, and limited agricultural resources put enormous pressure on agricultural systems. A plateau in crop yields is occurring and extreme weather events and urbanization threaten the livelihood of farmers. It is imperative that immediate attention is paid to addressing the increasing food demand, ensuring resilience against emerging threats, and meeting the demand for more nutritious, safer food. Under uncertain conditions, it is essential to expand genetic diversity and discover novel crop varieties or variations to develop higher and more stable yields. Genomics plays a significant role in developing abundant and nutrient-dense food crops. An alternative to traditional breeding approach, translational genomics is able to improve breeding programs in a more efficient and precise manner by translating genomic concepts into practical tools. Crop breeding based on genomics offers potential solutions to overcome the limitations of conventional breeding methods, including improved crop varieties that provide more nutritional value and are protected from biotic and abiotic stresses. Genetic markers, such as SNPs and ESTs, contribute to the discovery of QTLs controlling agronomic traits and stress tolerance. In order to meet the growing demand for food, there is a need to incorporate QTLs into breeding programs using marker-assisted selection/breeding and transgenic technologies. This chapter primarily focuses on the recent advances that are made in translational genomics for crop improvement and various omics techniques including transcriptomics, metagenomics, pangenomics, single cell omics etc. Numerous genome editing techniques including CRISPR Cas technology and their applications in crop improvement had been discussed.

Changes in root metabolites and soil microbial community structures in rhizospheres of sugarcanes under different propagation methods

Root metabolites and soil microbial community structure in the rhizosphere play critical roles in crop growth. Here, we assessed the efficiency of conventional and tissue culture propagation methods in modulating the soil health and microbiota in the rhizosphere of sugarcane (Saccharum officinarum L.) plants. The seeding canes were obtained using newly planted and two-year ratooned canes propagated by conventional (CSN and CSR) or tissue culture (TCN and TCR) methods. Changes in soil fertility, root metabolites and soil microbial community structure in the rhizosphere of sugarcane plants obtained using these canes were assessed. The activities of soil β-glucosidase and aminopeptidase, soil microbial biomass nitrogen, and abundances of soil beneficial microbes, both at phyla and genera levels, were significantly higher in the rhizosphere of sugarcane plants in TCN and TCR treatments than those in that of plants in CSN and CSR treatments. Furthermore, flavonoid and flavonol biosynthesis and alanine, aspartate and glutamate metabolism were significantly upregulated in the roots of TCR and TCN plants compared with those in the roots of CSN and CSR plants. These results suggest that the tissue culture propagation method is a sustainable method for sugarcane cultivation to improve soil fertility and health in sugarcane rhizosphere.

Volatile metabolomics reveals the characteristics of the unique flavor substances in oats

Oats is a cereal well known for its high nutritional value and unique flavor. This study investigated the metabolomics data from oats, wheat, and barley using broadly targeted GC-MS metabonomic techniques. A total of 437 volatile organic compounds (VOCs) were identified, of which 414 were shared metabolites, with three metabolites unique to oats. Three hundred and seven differentially accumulated metabolites (DAMs) were screened from all the comparison groups, of which 27 metabolites were shared by oats and barley, and 121 shared by oats and wheat. Terpenoids and esters were the key metabolites determining the differences in flavor. A KEGG analysis indicated that the alpha-linolenic acid and phenylalanine pathways were the most significant metabolic pathways. The 42 DAMs found may be the main substances leading to the flavor differences between the different varieties. Overall, this study reveals the main reasons for the unique flavor of oats through metabolomic evidence.

Differences in the metabolites of brown and milled rice grains of semiwaxy and conventional japonica varieties

In this study, we analyzed the differences in metabolites between semiwaxy japonica rice (Yangnongxiang 28 [YNX28]) and conventional japonica rice (Hongyang 5 [HY5]) before and after brown rice milling. The metabolites of brown and milled rice grains from the two rice varieties were analyzed by LC-MS-based nontargeted metabolomics. A total of 266 differentially abundant metabolites (DMs) were tentatively identified in brown rice grains of YNX28 (YNX28B) compared with milled rice grains of YNX28 (YNX28H), and these included 248 upregulated and 12 downregulated DMs. A total of 273 (234 upregulated and 39 downregulated) DMs were tentatively identified in brown rice grains of HY5 (HY5B) compared with milled rice grains of this variety (HY5H). Kyoto Encyclopedia of Genes and Genomes pathway involved and enrichment analyses revealed that 53 and 7 metabolite pathways were enriched and significantly enriched (p < 0.05), respectively, in the DMs identified in YNX28B compared with YNX28H, and the main enriched pathways were related to starch and sucrose metabolism, glycerol phospholipid metabolism, arginine and proline metabolism, and glycine, serine and threonine metabolism. Forty-six metabolite pathways were enriched in DMs identified in HY5B compared with HY5H, and these included 16 pathways that were significantly enriched (p < 0.05); in addition, the main enriched pathways were related to starch and sucrose metabolism, glycerol phospholipid metabolism, arginine and proline metabolism, and glycine, serine and threonine metabolism. This study provides a theoretical reference for further on the changes in metabolites during rice processing and provides a basis for improving the nutritional quality in rice. PRACTICAL APPLICATION: Original data were obtained regarding the changes of different metabolites in semiwaxy japonica rice and conventional japonica rice before and after processing. The purpose of this study was to investigate the difference of metabolite loss in two rice varieties before and after processing. This paper reports on the differences of metabolites between the two types of japonica rice before and after processing, as well as the changes of key metabolites before and after processing, it also provides important theoretical basis for developing new rice varieties with good nutritional quality.

Integrating omics databases for enhanced crop breeding

Crop plant breeding involves selecting and developing new plant varieties with desirable traits such as increased yield, improved disease resistance, and enhanced nutritional value. With the development of high-throughput technologies, such as genomics, transcriptomics, and metabolomics, crop breeding has entered a new era. However, to effectively use these technologies, integration of multi-omics data from different databases is required. Integration of omics data provides a comprehensive understanding of the biological processes underlying plant traits and their interactions. This review highlights the importance of integrating omics databases in crop plant breeding, discusses available omics data and databases, describes integration challenges, and highlights recent developments and potential benefits. Taken together, the integration of omics databases is a critical step towards enhancing crop plant breeding and improving global food security.

Plant Metabolomics: Current Initiatives and Future Prospects

Plant metabolomics is a rapidly advancing field of plant sciences and systems biology. It involves comprehensive analyses of small molecules (metabolites) in plant tissues and cells. These metabolites include a wide range of compounds, such as sugars, amino acids, organic acids, secondary metabolites (e.g., alkaloids and flavonoids), lipids, and more. Metabolomics allows an understanding of the functional roles of specific metabolites in plants' physiology, development, and responses to biotic and abiotic stresses. It can lead to the identification of metabolites linked with specific traits or functions. Plant metabolic networks and pathways can be better understood with the help of metabolomics. Researchers can determine how plants react to environmental cues or genetic modifications by examining how metabolite profiles change under various crop stages. Metabolomics plays a major role in crop improvement and biotechnology. Integrating metabolomics data with other omics data (genomics, transcriptomics, and proteomics) provides a more comprehensive perspective of plant biology. This systems biology approach enables researchers to understand the complex interactions within organisms.

Biochemical and metabolic signatures are fundamental to drought adaptation in PGPR Enterobacter bugandensis WRS7

Drought alone causes more annual loss in crop yield than the sum of all other environmental stresses. There is growing interest in harnessing the potential of stress-resilient PGPR in conferring plant resistance and enhancing crop productivity in drought-affected agroecosystems. A detailed understanding of the complex physiological and biochemical responses will open up the avenues to stress adaptation mechanisms of PGPR communities under drought. It will pave the way for rhizosphere engineering through metabolically engineered PGPR. Therefore, to reveal the physiological and metabolic networks in response to drought-mediated osmotic stress, we performed biochemical analyses and applied untargeted metabolomics to investigate the stress adaptation mechanisms of a PGPR Enterobacter bugendensis WRS7 (Eb WRS7). Drought caused oxidative stress and resulted in slower growth rates in Eb WRS7. However, Eb WRS7 could tolerate drought stress and did not show changes in cell morphology under stress conditions. Overproduction of ROS caused lipid peroxidation (increment in MDA) and eventually activated antioxidant systems and cell signalling cascades, which led to the accumulation of ions (Na+, K+, and Ca2+), osmolytes (proline, exopolysaccharides, betaine, and trehalose), and modulated lipid dynamics of the plasma membranes for osmosensing and osmoregulation, suggesting an osmotic stress adaption mechanism in PGPR Eb WRS7. Finally, GC-MS-based metabolite profiling and deregulated metabolic responses highlighted the role of osmolytes, ions, and intracellular metabolites in regulating Eb WRS7 metabolism. Our results suggest that understanding the role of metabolites and metabolic pathways can be exploited for future metabolic engineering of PGPR and developing bio inoculants for plant growth promotion under drought-affected agroecosystems.

Comprehensive investigation of cucumber heat shock proteins under abiotic stress conditions: A multi-omics survey

Heat-shock proteins (Hsps) are a family of proteins essential in preserving the vitality and functionality of proteins under stress conditions. Cucumber (Cucumis sativus) is a widely grown plant with high nutritional value and is used as a model organism in many studies. This study employed a genomics, transcriptomics, and metabolomics approach to investigate cucumbers' Hsps against abiotic stress conditions. Bioinformatics methods were used to identify six Hsp families in the cucumber genome and to characterize family members. Transcriptomics data from the Sequence Read Archive (SRA) database was also conducted to select CsHsp genes for further study. Real-time PCR was used to evaluate gene expression levels under different stress conditions, revealing that CssHsp-08 was a vital gene for resistance to stress conditions; including drought, salinity, cold, heat stresses, and ABA application. Gas Chromatography-Mass Spectrometry (GC-MS) analysis of plant extracts revealed that amino acids accumulate in leaves under high temperatures and roots under drought, while sucrose accumulates in both tissues under applied most stress factors. The study provides valuable insights into the structure, organization, evolution, and expression profiles of the Hsp family and contributes to a better understanding of plant stress mechanisms. These findings have important implications for developing crops that can withstand environmental stress conditions better.

Microbiability of milk composition and genetic control of microbiota effects in sheep

Recently, high-dimensional omics data are becoming available in larger quantities, and models have been developed that integrate them with genomics to understand in finer detail the relationship between genotype and phenotype, and thus improve the performance of genetic evaluations. Our objectives are to quantify the effect of the inclusion of microbiome data in the genetic evaluation for dairy traits in sheep, through the estimation of the heritability, microbiability, and how the microbiome effect on dairy traits decomposes into genetic and nongenetic parts. In this study we analyzed milk and rumen samples of 795 Lacaune dairy ewes. We included, as phenotype, dairy traits and milk fatty acids and proteins composition; as omics measurements, 16S rRNA rumen bacterial abundances; and as genotyping, 54K SNP chip for all ewes. Two nested genomic models were used: a first model to predict the individual contributions of the genetic and microbial abundances to phenotypes, and a second model to predict the additive genetic effect of the microbial community. In addition, microbiome-wide association studies for all dairy traits were applied using the 2,059 rumen bacterial abundances, and the genetic correlations between microbiome principal components and dairy traits were estimated. Results showed that in general the inclusion of both genetic and microbiome effect did not improve the fit of the model compared with the model with the genetic effect only. In addition, for all dairy traits the total heritability was equal to the direct heritability after fitting microbiota effects, due to a microbiability being almost zero for most dairy traits and heritability of the microbial community was very close to zero. Microbiome-wide association studies did not show operational taxonomic units with major effect for any of the dairy traits evaluated, and the genetic correlations between the first 5 principal components and dairy traits were low to moderate. So far, we can conclude that, using a substantial data set of 795 Lacaune dairy ewes, rumen bacterial abundances do not provide improved genetic evaluation for dairy traits in sheep.

Metabolomic analysis reveals key metabolites and metabolic pathways in Suaeda salsa under salt and drought stress

Suaeda salsa is an important salt- and drought-tolerant plant with important ecological restoration roles. However, little is known about its underlying molecular regulatory mechanisms. Therefore, understanding the response mechanisms of plants to salt and drought stress is of great importance. In this study, metabolomics analysis was performed to evaluate the effects of salt and drought stress on S. salsa . The experiment consisted of three treatments: (1) control (CK); (2) salt stress (Ps); and (3) drought stress (Pd). The results showed that compared with the control group, S. salsa showed significant differences in phenotypes under salt and drought stress conditions. First, a total of 207 and 292 differential metabolites were identified in the Ps/CK and Pd/CK groups, respectively. Second, some soluble sugars and amino acids, such as raffinose, maltopentoses, D -altro-beptulose, D -proline, valine-proline, proline, tryptophan and glycine-L -leucine, showed increased activity under salt and drought stress conditions, suggesting that these metabolites may be responsible for salt and drought resistance in S. salsa . Third, the flavonoid biosynthetic and phenylalanine metabolic pathways were significantly enriched under both salt and drought stress conditions, indicating that these two metabolic pathways play important roles in salt and drought stress resistance in S. salsa . The findings of this study provide new insights into the salt and drought tolerance mechanisms of S. salsa .

Widely untargeted metabolomic profiling unearths metabolites and pathways involved in leaf senescence and N remobilization in spring-cultivated wheat under different N regimes

Progression of leaf senescence consists of both degenerative and nutrient recycling processes in crops including wheat. However, the levels of metabolites in flag leaves in spring-cultivated wheat, as well as biosynthetic pathways involved under different nitrogen fertilization regimes, are largely unknown. Therefore, the present study employed a widely untargeted metabolomic profiling strategy to identify metabolites and biosynthetic pathways that could be used in a wheat improvement program aimed at manipulating the rate and onset of senescence by handling spring wheat (Dingxi 38) flag leaves sampled from no-, low-, and high-nitrogen (N) conditions (designated Groups 1, 2, and 3, respectively) across three sampling times: anthesis, grain filling, and end grain filling stages. Through ultrahigh-performance liquid chromatography-tandem mass spectrometry, a total of 826 metabolites comprising 107 flavonoids, 51 phenol lipids, 37 fatty acyls, 37 organooxygen compounds, 31 steroids and steroid derivatives, 18 phenols, and several unknown compounds were detected. Upon the application of the stringent screening criteria for differentially accumulated metabolites (DAMs), 28 and 23 metabolites were differentially accumulated in Group 1_vs_Group 2 and Group 1_vs_Group 3, respectively. From these, 1-O-Caffeoylglucose, Rhoifolin, Eurycomalactone;Ingenol, 4-Methoxyphenyl beta-D-glucopyranoside, and Baldrinal were detected as core conserved DAMs among the three groups with all accumulated higher in Group 1 than in the other two groups. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that tropane, piperidine, and pyridine alkaloid biosynthesis; acarbose and validamycin biosynthesis; lysine degradation; and biosynthesis of alkaloids derived from ornithine, lysine, and nicotinic acid pathways were the most significantly (p < 0.05) enriched in Group 1_vs_Group 2, while flavone and flavonol as well as anthocyanins biosynthetic pathways were the most significantly (p < 0.05) enriched in Group 1_vs_Group 3. The results from this study provide a foundation for the manipulation of the onset and rate of leaf senescence and N remobilization in wheat.

Major viral diseases in grain legumes: designing disease resistant legumes from plant breeding and OMICS integration

Grain legumes play a crucial role in human nutrition and as a staple crop for low-income farmers in developing and underdeveloped nations, contributing to overall food security and agroecosystem services. Viral diseases are major biotic stresses that severely challenge global grain legume production. In this review, we discuss how exploring naturally resistant grain legume genotypes within germplasm, landraces, and crop wild relatives could be used as promising, economically viable, and eco-environmentally friendly solution to reduce yield losses. Studies based on Mendelian and classical genetics have enhanced our understanding of key genetic determinants that govern resistance to various viral diseases in grain legumes. Recent advances in molecular marker technology and genomic resources have enabled us to identify genomic regions controlling viral disease resistance in various grain legumes using techniques such as QTL mapping, genome-wide association studies, whole-genome resequencing, pangenome and 'omics' approaches. These comprehensive genomic resources have expedited the adoption of genomics-assisted breeding for developing virus-resistant grain legumes. Concurrently, progress in functional genomics, especially transcriptomics, has helped unravel underlying candidate gene(s) and their roles in viral disease resistance in legumes. This review also examines the progress in genetic engineering-based strategies, including RNA interference, and the potential of synthetic biology techniques, such as synthetic promoters and synthetic transcription factors, for creating viral-resistant grain legumes. It also elaborates on the prospects and limitations of cutting-edge breeding technologies and emerging biotechnological tools (e.g., genomic selection, rapid generation advances, and CRISPR/Cas9-based genome editing tool) in developing virus-disease-resistant grain legumes to ensure global food security.

Characterization of the Key Compounds of Bell Pepper by Spectrophotometry and Gas Chromatography on the Effects of Induced Stress on the Concentration of Secondary Metabolite

Sweet peppers are consumed worldwide, and traditional uses have sparked interest in their applications as dietary antioxidants, which can be enhanced in plants using elicitors. These are endowed with phytochemicals with potential health benefits such as antioxidants, bioavailability, and bioaccessibility. The trend in metabolomics shows us chemical fingerprints linking metabolomics, innovative analytical form, and bioinformatics tools. The objective was to evaluate the impact of multiple stress interactions, elicitor concentrations, and electrical conductivity on the concentration of secondary metabolites to relate their response to metabolic pathways through the foliar application of a cocktail of said elicitors in pepper crops under greenhouse conditions. The extracts were analyzed by spectrophotometry and gas chromatography, and it was shown that the PCA analysis identified phenolic compounds and low molecular weight metabolites, confirming this as a metabolomic fingerprint in the hierarchical analysis. These compounds were also integrated by simultaneous gene and metabolite simulants to obtain effect information on different metabolic pathways. Showing changes in metabolite levels at T6 (36 mM H₂O₂ and 3.6 dS/m) and T7 (0.1 mM SA and 3.6 dS/m) but showing statistically significant changes at T5 (3.6 dS/m) and T8 (0.1 mM SA, 36 mM H₂O₂, and 3.6 dS/m) compared to T1 (32 dS/m) or control. Six pathways changed significantly (p < 0.05) in stress-induced treatments: aminoacyl t-RNA and valine-leucine-isoleucine biosynthesis, and alanine-aspartate-glutamate metabolism, glycoxylate-dicarboxylate cycle, arginine-proline, and citrate. This research provided a complete profile for the characterization of metabolomic fingerprint of bell pepper under multiple stress conditions.

Multi-omics assisted breeding for biotic stress resistance in soybean

Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker-trait associations involved in regulating biotic stress responses in soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We suggest using 'integrated omics' to precisely understand how soybean responds to different biotic stresses. We also discuss the potential challenges of integrating multi-omics for the functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.

A 21-bp InDel in the promoter of STP1 selected during tomato improvement accounts for soluble solid content in fruits

Domestication and improvement are important processes that generate the variation in genome and phonotypes underlying crop improvement. Unfortunately, during selection for certain attributes, other valuable traits may be inadvertently discarded. One example is the decline in fruit soluble solids content (SSC) during tomato breeding. Several genetic loci for SSC have been identified, but few reports on the underlying mechanisms are available. In this study we performed a genome-wide association study (GWAS) for SSC of the red-ripe fruits in a population consisting of 481 tomato accessions with large natural variations and found a new quantitative trait locus, STP1, encoding a sugar transporter protein. The causal variation of STP1, a 21-bp InDel located in the promoter region 1124 bp upstream of the start codon, alters its expression. STP1 ^Insertion accessions with an 21-bp insertion have higher SSC than STP1 ^Deletion accessions with the 21-bp deletion. Knockout of STP1 in TS-23 with high SSC using CRISPR/Cas9 greatly decreased SSC in fruits. In vivo and in vitro assays demonstrated that ZAT10-LIKE, a zinc finger protein transcription factor (ZFP TF), can specifically bind to the promoter of STP1 ^Insertion to enhance STP1 expression, but not to the promoter of STP1 ^Deletion , leading to lower fruit SSC in modern tomatoes. Diversity analysis revealed that STP1 was selected during tomato improvement. Taking these results together, we identified a naturally occurring causal variation underlying SSC in tomato, and a new role for ZFP TFs in regulating sugar transporters. The findings enrich our understanding of tomato evolution and domestication, and provide a genetic basis for genome design for improving fruit taste.

Watermelon domestication was shaped by stepwise selection and regulation of the metabolome

Although crop domestication has greatly aided human civilization, the sequential domestication and regulation of most quality traits remain poorly understood. Here, we report the stepwise selection and regulation of major fruit quality traits that occurred during watermelon evolution. The levels of fruit cucurbitacins and flavonoids were negatively selected during speciation, whereas sugar and carotenoid contents were positively selected during domestication. Interestingly, fruit malic acid and citric acid showed the opposite selection trends during the improvement. We identified a novel gene cluster (CGC1, cucurbitacin gene cluster on chromosome 1) containing both regulatory and structural genes involved in cucurbitacin biosynthesis, which revealed a cascade of transcriptional regulation operating mechanisms. In the CGC1, an allele caused a single nucleotide change in ClERF1 binding sites (GCC-box) in the promoter of ClBh1, which resulted in reduced expression of ClBh1 and inhibition of cucurbitacin synthesis in cultivated watermelon. Functional analysis revealed that a rare insertion of 244 amino acids, which arose in C. amarus and became fixed in sweet watermelon, in ClOSC (oxidosqualene cyclase) was critical for the negative selection of cucurbitacins during watermelon evolution. This research provides an important resource for metabolomics-assisted breeding in watermelon and for exploring metabolic pathway regulation mechanisms.

First assessment of Iranian pomegranate germplasm using targeted metabolites and morphological traits to develop the core collection and modeling of the current and future spatial distribution under climate change conditions

Pomegranate has been considered a medicinal plant due to its rich nutrients and bioactive compounds. Since environmental conditions affect the amount and composition of metabolites, selecting suitable locations for cultivation would be vital to achieve optimal production. In this study, data on the diversity of targeted metabolites and morphological traits of 152 Iranian pomegranate genotypes were collected and combined in order to establish the first core collection. The multivariate analyses were conducted including principal component analysis (PCA), and cluster analysis. In addition, the current and future geographical distribution of pomegranate in Iran was predicted to identify suitable locations using the MaxEnt model. The results showed high diversity in the studied morphological and metabolic traits. The PCA results indicated that FFS, NFT, JA, and AA are the most important traits in discriminating the studied genotypes. A constructed core collection using maximization strategy consisted of 20 genotypes and accounted for 13.16% of the entire collection. Shannon-Weaver diversity index of a core collection was similar or greater than the entire collection. Evaluation of the core collection using four parameters of MD, VD, CR, and VR also indicated the maintenance of the genetic diversity of the original set. According to the MaxEnt model, altitude, average temperature of coldest quarter, and isothertmality were the key factors for the distribution of pomegranate. The most suitable areas for pomegranate cultivation were also determined which were located in arid and semi-arid regions of Iran. The geographic distribution of pomegranate in the future showed that the main provinces of pomegranate cultivation would be less affected by climatic conditions by the middle of the century. The results of this study provide valuable information for selection of elite genotypes to develop the breeding programs to obtain the cultivars with the highest levels of metabolic compounds for pharmaceutical purposes, as well as identification of the most suitable agro-ecological zones for orchard establishment.

Prospects of microgreens as budding living functional food: Breeding and biofortification through OMICS and other approaches for nutritional security

Nutrient deficiency has resulted in impaired growth and development of the population globally. Microgreens are considered immature greens (required light for photosynthesis and growing medium) and developed from the seeds of vegetables, legumes, herbs, and cereals. These are considered "living superfood/functional food" due to the presence of chlorophyll, beta carotene, lutein, and minerals like magnesium (Mg), Potassium (K), Phosphorus (P), and Calcium (Ca). Microgreens are rich at the nutritional level and contain several phytoactive compounds (carotenoids, phenols, glucosinolates, polysterols) that are helpful for human health on Earth and in space due to their anti-microbial, anti-inflammatory, antioxidant, and anti-carcinogenic properties. Microgreens can be used as plant-based nutritive vegetarian foods that will be fruitful as a nourishing constituent in the food industryfor garnish purposes, complement flavor, texture, and color to salads, soups, flat-breads, pizzas, and sandwiches (substitute to lettuce in tacos, sandwich, burger). Good handling practices may enhance microgreens'stability, storage, and shelf-life under appropriate conditions, including light, temperature, nutrients, humidity, and substrate. Moreover, the substrate may be a nutritive liquid solution (hydroponic system) or solid medium (coco peat, coconut fiber, coir dust and husks, sand, vermicompost, sugarcane filter cake, etc.) based on a variety of microgreens. However integrated multiomics approaches alongwith nutriomics and foodomics may be explored and utilized to identify and breed most potential microgreen genotypes, biofortify including increasing the nutritional content (macro-elements:K, Ca and Mg; oligo-elements: Fe and Zn and antioxidant activity) and microgreens related other traits viz., fast growth, good nutritional values, high germination percentage, and appropriate shelf-life through the implementation of integrated approaches includes genomics, transcriptomics, sequencing-based approaches, molecular breeding, machine learning, nanoparticles, and seed priming strategiesetc.

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.

Genetic mapping of maize metabolites using high-throughput mass profiling

Plant metabolites are the basis of human nutrition and have biological relevance in ecology. Farmers selected plants with favorable characteristics since prehistoric times and improved the cultivars, but without knowledge of underlying mechanisms. Understanding the genetic basis of metabolite production can facilitate the successful breeding of plants with augmented nutritional value. To identify genetic factors related to the metabolic composition in maize, we generated mass profiles of 198 recombinant inbred lines (RILs) and their parents (B73 and Mo17) using direct-injection electrospray ionization mass spectrometry (DLI-ESI MS). Mass profiling allowed the correct clustering of samples according to genotype. We quantified 71 mass features from grains and 236 mass features from leaf extracts. For the corresponding ions, we identified tissue-specific metabolic 'Quantitative Trait Loci' (mQTLs) distributed across the maize genome. These genetic regions could regulate multiple metabolite biosynthesis pathways. Our findings demonstrate that DLI-ESI MS has sufficient analytical resolution to map mQTLs. These identified genetic loci will be helpful in metabolite-focused maize breeding. Mass profiling is a powerful tool for detecting mQTLs in maize and enables the high-throughput screening of loci responsible for metabolite biosynthesis.

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence

Recent developments allowed generating multiple high-quality 'omics' data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.

Metabolome genome-wide association study provides biochemical and genetic insights into natural variation of primary metabolites in sesame

Plants' primary metabolites are of great importance from the survival and nutritional perspectives. However, the genetic bases underlying the profiles of primary metabolites in oilseed crops remain largely unclear. As one of the main oilseed crops, sesame (Sesamum indicum L.) is a potential model plant for investigating oil metabolism in plants. Therefore, the objective of this study is to disclose the genetic variants associated with variation in the content of primary metabolites in sesame. We performed a comprehensive metabolomics analysis of primary metabolites in 412 diverse sesame accessions using gas chromatography-mass spectrometry and identified a total of 45 metabolites, including fatty acids, monoacylglycerols (MAGs), and amino acids. Genome-wide association study unveiled 433 significant single-nucleotide polymorphism loci associated with variation in primary metabolite contents in sesame. By integrating diverse genomic analyses, we identified 10 key candidate causative genes of variation in MAG, fatty acid, asparagine, and sucrose contents. Among them, SiDSEL was significantly associated with multiple traits. SiCAC3 and SiKASI were strongly associated with variation in oleic acid and linoleic acid contents. Overexpression of SiCAC3, SiKASI, SiLTPI.25, and SiLTPI.26 in transgenic Arabidopsis and Saccharomyces cerevisiae revealed that SiCAC3 is a potential target gene for improvement of unsaturated fatty acid levels in crops. Furthermore, we found that it may be possible to breed several quality traits in sesame simultaneously. Our results provide valuable genetic resources for improving sesame seed quality and our understanding of oilseed crops' primary metabolism.

Combined analysis of transcriptome and metabolome reveals the molecular mechanism and candidate genes of Haloxylon drought tolerance

Haloxylon ammodendron and Haloxylon persicum, as typical desert plants, show strong drought tolerance and environmental adaptability. They are ideal model plants for studying the molecular mechanisms of drought tolerance. Transcriptomic and metabolomic analyses were performed to reveal the response mechanisms of H. ammodendron and H. persicum to a drought environment at the levels of transcription and physiological metabolism. The results showed that the morphological structures of H. ammodendron and H. persicum showed adaptability to drought stress. Under drought conditions, the peroxidase activity, abscisic acid content, auxin content, and gibberellin content of H. ammodendron increased, while the contents of proline and malondialdehyde decreased. The amino acid content of H. persicum was increased, while the contents of proline, malondialdehyde, auxin, and gibberellin were decreased. Under drought conditions, 12,233 and 17,953 differentially expressed genes (DEGs) were identified in H. ammodendron and H. persicum , respectively, including members of multiple transcription factor families such as FAR1, AP2/ERF, C2H2, bHLH, MYB, C2C2, and WRKY that were significantly up-regulated under drought stress. In the positive ion mode, 296 and 452 differential metabolites (DEMs) were identified in H. ammodendron and H. persicum, respectively; in the negative ion mode, 252 and 354 DEMs were identified, primarily in carbohydrate and lipid metabolism. A combined transcriptome and metabolome analysis showed that drought stress promoted the glycolysis/gluconeogenesis pathways of H. ammodendron and H. persicum and increased the expression of amino acid synthesis pathways, consistent with the physiological results. In addition, transcriptome and metabolome were jointly used to analyze the expression changes of the genes/metabolites of H. ammodendron and H. persicum that were associated with drought tolerance but were regulated differently in the two plants. This study identified drought-tolerance genes and metabolites in H. ammodendron and H. persicum and has provided new ideas for studying the drought stress response of Haloxylon.

Targeted approaches to improve tomato fruit taste

Tomato (Solanum lycopersicum) is the most valuable fruit and horticultural crop species worldwide. Compared with the fruits of their progenitors, those of modern tomato cultivars are, however, often described as having unsatisfactory taste or lacking flavor. The flavor of a tomato fruit arises from a complex mix of tastes and volatile metabolites, including sugars, acids, amino acids, and various volatiles. However, considerable differences in fruit flavor occur among tomato varieties, resulting in mixed consumer experiences. While tomato breeding has traditionally been driven by the desire for continual increases in yield and the introduction of traits that provide a long shelf-life, consumers are prepared to pay a reasonable premium for taste. Therefore, it is necessary to characterize preferences of tomato flavor and to define its underlying genetic basis. Here, we review recent conceptual and technological advances that have rendered this more feasible, including multi-omics-based QTL and association analyses, along with the use of trained testing panels, and machine learning approaches. This review proposes how the comprehensive datasets compiled to date could allow a precise rational design of tomato germplasm resources with improved organoleptic quality for the future.

Plant metabolic gene clusters in the multi-omics era

Secondary metabolism in plants gives rise to a vast array of small-molecule natural products. The discovery of operon-like gene clusters in plants has provided a new perspective on the evolution of specialized metabolism and the opportunity to rapidly advance the metabolic engineering of natural product production. Here, we review historical aspects of the study of plant metabolic gene clusters as well as general strategies for identifying plant metabolic gene clusters in the multi-omics era. We also emphasize the exploration of their natural variation and evolution, as well as new strategies for the prospecting of plant metabolic gene clusters and a deeper understanding of how their structure influences their function.

Comprehensive transcriptomic and metabolomic analysis revealed distinct flavonoid biosynthesis regulation during abnormal pistil development in Japanese apricot

Japanese apricot is an imperative stone fruit plant with numerous processing importance. The failure of reproductive system is the most common cause of fruit loss, through which pistil abortion is the fundamental one. To understand this mechanism, we used a combination of transcriptomic and metabolomic approaches to investigate the biochemical and molecular basis of flavonoid biosynthesis. Due to the regulated expression of flavonoid pathway-related genes in plants, flavonoid biosynthesis is largely regulated at the transcriptional level. A total of 2272 differently expressed genes and 215 differential metabolites were found. The expression of the genes and metabolites encoding flavonoid biosynthesis was lower in abnormal pistils that are in line with the flavonoid quantification from abnormal pistils. Besides, a couple of genes were also detected related to MYB, MADS, NAC and bHLH transcription factors. Remarkably, we found 'hydroxycinnamoyl transferase (LOC103323133)' and flavonoid related metabolite '2-hydroxycinnamic acid' was lower expressed in abnormal pistil, proposing the cause of pistil abortion. Collectively, the present study delivers inclusive transcriptional and metabolic datasets that proposed valuable prospects to unravel the genetic mechanism underlying pistil abortion.

Exploring Chickpea Germplasm Diversity for Broadening the Genetic Base Utilizing Genomic Resourses

Legume crops provide significant nutrition to humans as a source of protein, omega-3 fatty acids as well as specific macro and micronutrients. Additionally, legumes improve the cropping environment by replenishing the soil nitrogen content. Chickpeas are the second most significant staple legume food crop worldwide behind dry bean which contains 17%–24% protein, 41%–51% carbohydrate, and other important essential minerals, vitamins, dietary fiber, folate, β-carotene, anti-oxidants, micronutrients (phosphorus, calcium, magnesium, iron, and zinc) as well as linoleic and oleic unsaturated fatty acids. Despite these advantages, legumes are far behind cereals in terms of genetic improvement mainly due to far less effort, the bottlenecks of the narrow genetic base, and several biotic and abiotic factors in the scenario of changing climatic conditions. Measures are now called for beyond conventional breeding practices to strategically broadening of narrow genetic base utilizing chickpea wild relatives and improvement of cultivars through advanced breeding approaches with a focus on high yield productivity, biotic and abiotic stresses including climate resilience, and enhanced nutritional values. Desirable donors having such multiple traits have been identified using core and mini core collections from the cultivated gene pool and wild relatives of Chickpea. Several methods have been developed to address cross-species fertilization obstacles and to aid in inter-specific hybridization and introgression of the target gene sequences from wild Cicer species. Additionally, recent advances in “Omics” sciences along with high-throughput and precise phenotyping tools have made it easier to identify genes that regulate traits of interest. Next-generation sequencing technologies, whole-genome sequencing, transcriptomics, and differential genes expression profiling along with a plethora of novel techniques like single nucleotide polymorphism exploiting high-density genotyping by sequencing assays, simple sequence repeat markers, diversity array technology platform, and whole-genome re-sequencing technique led to the identification and development of QTLs and high-density trait mapping of the global chickpea germplasm. These altogether have helped in broadening the narrow genetic base of chickpeas.

Non-targeted metabolomics of cooked cowpea (Vigna unguiculata) and pigeon pea (Cajanus cajan) from Ghana using two distinct and complementary analytical platforms

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Cowpea varieties represent diverse staple foods in Sub-Saharan Africa.

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Cowpea metabolite composition is different from pigeon pea and common bean.

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Cowpea metabolites included tonkinelin, pheophytin A, and linoleoyl ethanolamide.

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Pheophytin A was only detected in the cowpea variety Sangyi.

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Pipecolic acid identification was confirmed for all three legumes.

Legumes are global staple foods with multiple human health properties that merit detailed composition analysis in cooked forms. This study analyzed cowpea [Vigna unguiculata] (three varieties: Dagbantuya, Sangyi, and Tukara), pigeon pea [Cajanus cajan], and common bean [Phaseolus vulgaris] using two distinct ultra-performance liquid chromatography mass spectrometry (UPLC-MS) platforms and analytical workflows. Comparisons between cowpea and pigeon pea consumed in Ghana, and common bean (navy bean) from USA, revealed 75 metabolites that differentiated cowpeas. Metabolite fold-change comparisons resulted in 142 metabolites with significantly higher abundance in cowpea, and 154 higher in abundance from pigeon pea. 3-(all-trans-nonaprenyl)benzene-1,2-diol, N-tetracosanoylphytosphingosine, and sitoindoside II are novel identifications in cowpea, with notably higher abundance than other legumes tested. Cowpea variety specific markers were tonkinelin (Dagbantuya), pheophytin A (Sangyi), and linoleoyl ethanolamide (Tukara). This study identified novel cowpea and pigeon pea food metabolites that warrant continued investigation as bioactive food components following consumption in people.

Advances in Metabolomics-Driven Diagnostic Breeding and Crop Improvement

Climate change continues to threaten global crop output by reducing annual productivity. As a result, global food security is now considered as one of the most important challenges facing humanity. To address this challenge, modern crop breeding approaches are required to create plants that can cope with increased abiotic/biotic stress. Metabolomics is rapidly gaining traction in plant breeding by predicting the metabolic marker for plant performance under a stressful environment and has emerged as a powerful tool for guiding crop improvement. The advent of more sensitive, automated, and high-throughput analytical tools combined with advanced bioinformatics and other omics techniques has laid the foundation to broadly characterize the genetic traits for crop improvement. Progress in metabolomics allows scientists to rapidly map specific metabolites to the genes that encode their metabolic pathways and offer plant scientists an excellent opportunity to fully explore and rationally harness the wealth of metabolites that plants biosynthesize. Here, we outline the current application of advanced metabolomics tools integrated with other OMICS techniques that can be used to: dissect the details of plant genotype–metabolite–phenotype interactions facilitating metabolomics-assisted plant breeding for probing the stress-responsive metabolic markers, explore the hidden metabolic networks associated with abiotic/biotic stress resistance, facilitate screening and selection of climate-smart crops at the metabolite level, and enable accurate risk-assessment and characterization of gene edited/transgenic plants to assist the regulatory process. The basic concept behind metabolic editing is to identify specific genes that govern the crucial metabolic pathways followed by the editing of one or more genes associated with those pathways. Thus, metabolomics provides a superb platform for not only rapid assessment and commercialization of future genome-edited crops, but also for accelerated metabolomics-assisted plant breeding. Furthermore, metabolomics can be a useful tool to expedite the crop research if integrated with speed breeding in future.

Physiological Responses and Metabonomics Analysis of Male and Female Sargassum thunbergii Macroalgae Exposed to Ultraviolet-B Stress

Ultraviolet-B (UV-B) radiation is a major environmental stress that suppresses or activates defense responses in organisms. UV-B radiation affecting growth and development in intertidal species have been researched for a long time, but a series of unknown knowledge remain in the male and female macroalgae comparison. To compare the different responses of male and female Sargassum thunbergii macroalgae under UV-B radiation, PSII photochemical efficiency determination, metabolomic analysis, and main carbon-based metabolites (including soluble sugar, total amino acid, and lipid) content measuring have been performed in our experiments. Results showed that males have significantly superiority performance in the chlorophyll fluorescence parameters of F _v/F _m, Y(II), and Y(NO) either low or high UV-B radiation treatments. Metabolomics analysis revealed that carbon and nitrogen metabolism pathways in male and female S. thunbergii were significant components responding to enhanced UV-B radiation. Based on measuring, female S. thunbergii lipid content expressed higher than males without any stimulation. Additionally, under low UV-B radiation stimulation, females total amino acid content shown significantly higher than control group and their lipid content also significantly higher than males. Under high UV-B radiation, males soluble sugar, total amino acid, and lipid content significantly varied from females, which meant that enhancing UV-B stress might altered mainly carbon-based metabolites flowing directions. The present study elucidated the potential role of enhanced UV-B radiation in regulating macroalgae physiological responses, metabolites changing, and reflecting differences between male and female S. thunbergii, contributing to understanding of brown-macroalgae diecious adopting mechanisms in defending intertidal UV-B stresses.

Leveraging a graft collection to develop metabolome-based trait prediction for the selection of tomato rootstocks with enhanced salt tolerance

Grafting has been demonstrated to significantly enhance the salt tolerance of crops. However, breeding efforts to develop enhanced graft combinations are hindered by knowledge-gaps as to how rootstocks mediate scion-response to salt stress. We grafted the scion of cultivated M82 onto rootstocks of 254 tomato accessions and explored the morphological and metabolic responses of grafts under saline conditions (EC = 20 dS m^-1) as compared to self-grafted M82 (SG-M82). Correlation analysis and Least Absolute Shrinkage and Selection Operator were performed to address the association between morphological diversification and metabolic perturbation. We demonstrate that grafting the same variety onto different rootstocks resulted in scion phenotypic heterogeneity and emphasized the productivity efficiency of M82 irrespective of the rootstock. Spectrophotometric analysis to test lipid oxidation showed largest variability of malondialdehyde (MDA) equivalents across the population, while the least responsive trait was the ratio of fruit fresh weight to total fresh weight (FFW/TFW). Generally, grafts showed greater values for the traits measured than SG-M82, except for branch number and wild race-originated rootstocks; the latter were associated with smaller scion growth parameters. Highly responsive and correlated metabolites were identified across the graft collection including malate, citrate, and aspartate, and their variance was partly related to rootstock origin. A group of six metabolites that consistently characterized exceptional graft response was observed, consisting of sorbose, galactose, sucrose, fructose, myo-inositol, and proline. The correlation analysis and predictive modelling, integrating phenotype- and leaf metabolite data, suggest a potential predictive relation between a set of leaf metabolites and yield-related traits.

Features of Profiles of Biologically Active Compounds of Primary and Secondary Metabolism of Lines from VIR Flax Genetic Collection, Contrasting in Size and Color of Seeds

Flax is one of the oldest oil crops, but only since the end of the twentieth century nutritional use of its whole seeds and flour has been resumed. This crop has been evaluated for its oil fatty acid composition, content of sterols and tocopherols, carbohydrate composition of mucilage, but a comprehensive study has never been carried out, so the aim of the work was to identify differences in the metabolomic profiles of flax lines contrasting in color and size of seeds. The biochemical composition of seeds from 16 lines of the sixth generation of inbreeding was tested using gas chromatography coupled with mass spectrometry. In total, more than 90 compounds related to sugars (78% of the identified substances), free fatty acids (13%), polyatomic alcohols (5%), heterocyclic compounds, free amino acids, phytosterols and organic acids (no more than 2.5% in total) were identified. Statistical analyses revealed six main factors. The first is a factor of sugar content; the second one affects most of organic acids, as well as some free fatty acids, not related to reserve ones, the third factor is related to compounds that play a certain role in the formation of "storage" substances and resistance to stress, the fourth factor is influencing free polar amino acids, some organic and free fatty acids, the fifth one is a factor of phenolic compounds, the sixth factor combined substances not included in the first five groups. Factor analysis made it possible to differentiate all 16 lines, 10 of which occupied a separate position by one or two factors. Interestingly, the first two factors with the highest loads (20 and 15% of the total variability, respectively) showed a separate position of the gc-432 line, which differed from the others, not only by chemical composition, but also by the phenotype of the seeds, while gc-159 differed from the rest ones by the complex of organic acids and other substances taking about 1% of the extracted substances of the seed. Thus, the analysis of metabolomic profiles is promising for a comprehensive assessment of the VIR flax genetic collection, which has wide biochemical diversity.

Metabolomic selection for enhanced fruit flavor

Consumers often regard heirloom fruit varieties grown in the garden as more flavorful than commercial varieties purchased at the grocery store. While plant breeders have historically focused on improving producer-orientated traits such as yield, consumer-oriented traits such as flavor have regularly been neglected. This is, in part, due to the difficulty associated with measuring the sensory perceptions of flavor. Here, we combine fruit chemical and consumer sensory panel information to train machine learning models that can predict how flavorful a fruit will be from its chemistry. By increasing the throughput of flavor evaluations, these models will help plant breeders to integrate flavor earlier in the breeding pipeline and aid in the design of varieties with exceptional flavor profiles.

Although they are staple foods in cuisines globally, many commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of genotypes, previously limited by the low throughput of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation, and the accuracies of 18 different models were assessed. The prediction accuracies were high for most attributes and ranged from 0.87 for sourness intensity in blueberry using XGBoost to 0.46 for overall liking in tomato using linear regression. Further, the best-performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. We found that the variance decomposition of overall liking score estimates that 42% and 56% of the variance was explained by volatile organic compounds in tomato and blueberry, respectively. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.

Changes in Metabolic Profiles of Pea (Pisum sativum L.) as a Result of Repeated Short-Term Soil Drought and Subsequent Re-Watering

The metabolic re-arrangements of peas (Pisum sativum L.) under soil drought and re-watering are still not fully explained. The search for metabolic markers of the stress response is important in breeding programs, to allow for the selection drought-resistant cultivars. During the present study, changes in the polar metabolite content in pea plant shoots were measured under repeated short-term soil drought and subsequent re-watering. A gas chromatograph, equipped with a mass spectrometer (GC-MS), was used for the metabolite profiling of pea plants during their middle stage of vegetation (14-34 days after sowing, DAS). The major changes occurred in the concentration of amino acids and some soluble carbohydrates. Among them, proline, γ-aminobutyric acid (GABA), branched-chain amino acids, hydroxyproline, serine, myo-inositol, and raffinose were accumulated under each soil drought and decreased after re-watering. Besides, the obtained results show that the first drought/re-watering cycle increased the ability of pea plants to restore a metabolic profile similar to the control after the second similar stress. The accumulation of proline seems to be an important part of drought memory in pea plants. However, confirmation of this suggestion requires metabolite profiling studies on a broader spectrum of pea cultivars.

Physiological and Multi-Omics Approaches for Explaining Drought Stress Tolerance and Supporting Sustainable Production of Rice

Drought differs from other natural disasters in several respects, largely because of the complexity of a crop's response to it and also because we have the least understanding of a crop's inductive mechanism for addressing drought tolerance among all abiotic stressors. Overall, the growth and productivity of crops at a global level is now thought to be an issue that is more severe and arises more frequently due to climatic change-induced drought stress. Among the major crops, rice is a frontline staple cereal crop of the developing world and is critical to sustaining populations on a daily basis. Worldwide, studies have reported a reduction in rice productivity over the years as a consequence of drought. Plants are evolutionarily primed to withstand a substantial number of environmental cues by undergoing a wide range of changes at the molecular level, involving gene, protein and metabolite interactions to protect the growing plant. Currently, an in-depth, precise and systemic understanding of fundamental biological and cellular mechanisms activated by crop plants during stress is accomplished by an umbrella of -omics technologies, such as transcriptomics, metabolomics and proteomics. This combination of multi-omics approaches provides a comprehensive understanding of cellular dynamics during drought or other stress conditions in comparison to a single -omics approach. Thus a greater need to utilize information (big-omics data) from various molecular pathways to develop drought-resilient crop varieties for cultivation in ever-changing climatic conditions. This review article is focused on assembling current peer-reviewed published knowledge on the use of multi-omics approaches toward expediting the development of drought-tolerant rice plants for sustainable rice production and realizing global food security.

Combined Transcriptomics and Metabolomics Analysis Reveals the Molecular Mechanism of Salt Tolerance of Huayouza 62, an Elite Cultivar in Rapeseed (Brassica napus L.)

Soil salinity is one of the most significant abiotic stresses affecting crop yield around the world. To explore the molecular mechanism of salt tolerance in rapeseed (Brassica napus L.), the transcriptome analysis and metabolomics analysis were used to dissect the differentially expressed genes and metabolites in two rapeseed varieties with significant differences in salt tolerance; one is an elite rapeseed cultivar, Huayouza 62. A total of 103 key differentially expressed metabolites (DEMs) and 53 key differentials expressed genes (DEGs) that might be related to salt stress were identified through metabolomics and transcriptomics analysis. GO and KEGG analysis revealed that the DEGs were mainly involved in ion transport, reactive oxygen scavenging, osmotic regulation substance synthesis, and macromolecular protein synthesis. The DEMs were involved in TCA cycle, proline metabolism, inositol metabolism, carbohydrate metabolic processes, and oxidation-reduction processes. In addition, overexpression of BnLTP3, which was one of the key DEGs, could increase tolerance to salt stress in Arabidopsis plants. This study reveals that the regulation mechanism of salt tolerance in rapeseed at the transcriptome and metabolism level and provides abundant data for further in-depth identification of essential salt tolerance genes.

Comparative Metabolite Profiling of Wheat Cultivars (Triticum aestivum) Reveals Signatory Markers for Resistance and Susceptibility to Stripe Rust and Aluminium (Al3+) Toxicity

Plants continuously produce essential metabolites that regulate their growth and development. The enrichment of specific metabolites determines plant interactions with the immediate environment, and some metabolites become critical in defence responses against biotic and abiotic stresses. Here, an untargeted UHPLC-qTOF-MS approach was employed to profile metabolites of wheat cultivars resistant or susceptible to the pathogen Puccinia striiformis f. sp. tritici (Pst) and Aluminium (Al³⁺) toxicity. Multivariate statistical analysis (MVDA) tools, viz. principal component analysis (PCA) and hierarchical cluster analysis (HiCA) were used to qualify the correlation between the identified metabolites and the designated traits. A total of 100 metabolites were identified from primary and secondary metabolisms, including phenolic compounds, such as flavonoid glycosides and hydroxycinnamic acid (HCA) derivatives, fatty acids, amino acids, and organic acids. All metabolites were significantly variable among the five wheat cultivars. The Pst susceptible cultivars demonstrated elevated concentrations of HCAs compared to their resistant counterparts. In contrast, ‘Koonap’ displayed higher levels of flavonoid glycosides, which could point to its resistant phenotype to Pst and Al³⁺ toxicity. The data provides an insight into the metabolomic profiles and thus the genetic background of Pst- and Al³⁺-resistant and susceptible wheat varieties. This study demonstrates the prospects of applied metabolomics for chemotaxonomic classification, phenotyping, and potential use in plant breeding and crop improvement.

Entailing the Next-Generation Sequencing and Metabolome for Sustainable Agriculture by Improving Plant Tolerance

Crop production is a serious challenge to provide food for the 10 billion individuals forecasted to live across the globe in 2050. The scientists' emphasize establishing an equilibrium among diversity and quality of crops by enhancing yield to fulfill the increasing demand for food supply sustainably. The exploitation of genetic resources using genomics and metabolomics strategies can help generate resilient plants against stressors in the future. The innovation of the next-generation sequencing (NGS) strategies laid the foundation to unveil various plants' genetic potential and help us to understand the domestication process to unmask the genetic potential among wild-type plants to utilize for crop improvement. Nowadays, NGS is generating massive genomic resources using wild-type and domesticated plants grown under normal and harsh environments to explore the stress regulatory factors and determine the key metabolites. Improved food nutritional value is also the key to eradicating malnutrition problems around the globe, which could be attained by employing the knowledge gained through NGS and metabolomics to achieve suitability in crop yield. Advanced technologies can further enhance our understanding in defining the strategy to obtain a specific phenotype of a crop. Integration among bioinformatic tools and molecular techniques, such as marker-assisted, QTLs mapping, creation of reference genome, de novo genome assembly, pan- and/or super-pan-genomes, etc., will boost breeding programs. The current article provides sequential progress in NGS technologies, a broad application of NGS, enhancement of genetic manipulation resources, and understanding the crop response to stress by producing plant metabolites. The NGS and metabolomics utilization in generating stress-tolerant plants/crops without deteriorating a natural ecosystem is considered a sustainable way to improve agriculture production. This highlighted knowledge also provides useful research that explores the suitable resources for agriculture sustainability.

New approaches to improve crop tolerance to biotic and abiotic stresses

During the last years, a great effort has been dedicated at the development and employment of diverse approaches for achieving more stress-tolerant and climate-flexible crops and sustainable yield increases to meet the food and energy demands of the future. The ongoing climate change is in fact leading to more frequent extreme events with a negative impact on food production, such as increased temperatures, drought, and soil salinization as well as invasive arthropod pests and diseases. In this review, diverse "green strategies" (e.g., chemical priming, root-associated microorganisms), and advanced technologies (e.g., genome editing, high-throughput phenotyping) are described on the basis of the most recent research evidence. Particularly, attention has been focused on the potential use in a context of sustainable and climate-smart agriculture (the so called "next agriculture generation") to improve plant tolerance and resilience to abiotic and biotic stresses. In addition, the gap between the results obtained in controlled experiments and those from application of these technologies in real field conditions (lab to field step) is also discussed.

High-Throughput Profiling of Metabolic Phenotypes Using High-Resolution GC-MS

Metabolite profiling provides insights into the metabolic signatures, which themselves are considered as phonotypes closely related to the agronomic and phenotypic traits such as yield, nutritional values, stress resistance, and nutrient use efficiency. GC-MS is a sensitive and high-throughput analytical platform and has been proved to be a vital tool for the analysis of primary metabolism to provide an overview of cellular and organismal metabolic status. The potential of GC-MS metabolite profiling as a tool for detecting metabolic changes in plants grown in a high-throughput plant phenotyping platform was explored. In this chapter, we describe an integrated workflow of semi-targeted GC-high-resolution (HR)-time-of-flight (TOF)-MS metabolomics with both the analytical and computational steps, focusing mainly on the sample preparation, GC-HR-TOF-MS analysis part, and data analysis for plant phenotyping efforts.

Understanding the metabolome and metagenome as extended phenotypes: The next frontier in macroalgae domestication and improvement

"Omics" techniques (including genomics, transcriptomics, metabolomics, proteomics, and metagenomics) have been employed with huge success in the improvement of agricultural crops. As marine aquaculture of macroalgae expands globally, biologists are working to domesticate species of macroalgae by applying these techniques tested in agriculture to wild macroalgae species. Metabolomics has revealed metabolites and pathways that influence agriculturally relevant traits in crops, allowing for informed crop crossing schemes and genomic improvement strategies that would be pivotal to inform selection on macroalgae for domestication. Advances in metagenomics have improved understanding of host-symbiont interactions and the potential for microbial organisms to improve crop outcomes. There is much room in the field of macroalgal biology for further research toward improvement of macroalgae cultivars in aquaculture using metabolomic and metagenomic analyses. To this end, this review discusses the application and necessary expansion of the omics tool kit for macroalgae domestication as we move to enhance seaweed farming worldwide.