Contribution of Untargeted Metabolomics for Future Assessment of Biotech Crops

Metabolomics-driven investigation of plant defense response against pest and pathogen attack

The advancement of metabolomics has assisted in the identification of various bewildering characteristics of the biological system. Metabolomics is a standard approach, facilitating crucial aspects of system biology with absolute quantification of metabolites using minimum samples, based on liquid/gas chromatography, mass spectrometry and nuclear magnetic resonance. The metabolome profiling has narrowed the wide gaps of missing information and has enhanced the understanding of a wide spectrum of plant-environment interactions by highlighting the complex pathways regulating biochemical reactions and cellular physiology under a particular set of conditions. This high throughput technique also plays a prominent role in combined analyses of plant metabolomics and other omics datasets. Plant metabolomics has opened a wide paradigm of opportunities for developing stress-tolerant plants, ensuring better food quality and quantity. However, despite advantageous methods and databases, the technique has a few limitations, such as ineffective 3D capturing of metabolites, low comprehensiveness, and lack of cell-based sampling. In the future, an expansion of plant-pathogen and plant-pest response towards the metabolite architecture is necessary to understand the intricacies of plant defence against invaders, elucidation of metabolic pathway operational during defence and developing a direct correlation between metabolites and biotic stresses. Our aim is to provide an overview of metabolomics and its utilities for the identification of biomarkers or key metabolites associated with biotic stress, devising improved diagnostic methods to efficiently assess pest and pathogen attack and generating improved crop varieties with the help of combined application of analytical and molecular tools.

Stressing the importance of plant specialized metabolites: omics-based approaches for discovering specialized metabolism in plant stress responses

Plants produce a diverse range of specialized metabolites that play pivotal roles in mediating environmental interactions and stress adaptation. These unique chemical compounds also hold significant agricultural, medicinal, and industrial values. Despite the expanding knowledge of their functions in plant stress interactions, understanding the intricate biosynthetic pathways of these natural products remains challenging due to gene and pathway redundancy, multifunctionality of proteins, and the activity of enzymes with broad substrate specificity. In the past decade, substantial progress in genomics, transcriptomics, metabolomics, and proteomics has made the exploration of plant specialized metabolism more feasible than ever before. Notably, recent advances in integrative multi-omics and computational approaches, along with other technologies, are accelerating the discovery of plant specialized metabolism. In this review, we present a summary of the recent progress in the discovery of plant stress-related specialized metabolites. Emphasis is placed on the application of advanced omics-based approaches and other techniques in studying plant stress-related specialized metabolism. Additionally, we discuss the high-throughput methods for gene functional characterization. These advances hold great promise for harnessing the potential of specialized metabolites to enhance plant stress resilience in the future.

The overshadowed role of electron ionization-mass spectrometry in analytical biotechnology

Target and untargeted analysis of several compounds are crucial methods in important areas such as omics sciences. Gas chromatography coupled to mass spectrometry (GC-MS) is widely used for volatile and thermally stable compounds. In this case, the electron ionization technique (EI) is preferable as it produces highly fragmented and reproducible spectra comparable to spectral libraries. However, only a fraction of target compounds is analyzable by GC without chemical derivatization. Therefore, liquid chromatography (LC) coupled with MS is the most used technique. Contrary to EI, electrospray ionization does not produce reproducible spectra. That is why researchers have been working on interfaces between LC and EI-MS to bridge the gap between those techniques. This short review will discuss advancements, applications, and perspectives on biotechnological analysis.

Molecular and genetic insights into secondary metabolic regulation underlying insect-pest resistance in legumes

Insect pests pose a major threat to agricultural production, resulting in significant economic losses for countries. A high infestation of insects in any given area can severely reduce crop yield and quality. This review examines the existing resources for managing insect pests and highlights alternative eco-friendly techniques to enhance insect pest resistance in legumes. Recently, the application of plant secondary metabolites has gained popularity in controlling insect attacks. Plant secondary metabolites encompass a wide range of compounds such as alkaloids, flavonoids, and terpenoids, which are often synthesized through intricate biosynthetic pathways. Classical methods of metabolic engineering involve manipulating key enzymes and regulatory genes to enhance or redirect the production of secondary metabolites in plants. Additionally, the role of genetic approaches, such as quantitative trait loci mapping, genome-wide association (GWAS) mapping, and metabolome-based GWAS in insect pest management is discussed, also, the role of precision breeding, such as genome editing technologies and RNA interference for identifying pest resistance and manipulating the genome to develop insect-resistant cultivars are explored, highlighting the positive contribution of plant secondary metabolites engineering-based resistance against insect pests. It is suggested that by understanding the genes responsible for beneficial metabolite compositions, future research might hold immense potential to shed more light on the molecular regulation of secondary metabolite biosynthesis, leading to advancements in insect-resistant traits in crop plants. In the future, the utilization of metabolic engineering and biotechnological methods may serve as an alternative means of producing biologically active, economically valuable, and medically significant compounds found in plant secondary metabolites, thereby addressing the challenge of limited availability.

NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches

Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encoding insecticidal proteins (mainly Cry proteins) with an aim to protect it against insect attack. Several studies have been reported on the impact of these genes on cotton production and fiber quality. However, the metabolites responsible for conferring resistance in genetically modified cotton need to be explored. The current work aims to unveil the key metabolites responsible for insect resistance in Bt cotton and also compare the conventional multivariate analysis methods with deep learning approaches to perform clustering analysis. We aim to unveil the marker compounds which are responsible for inducing insect resistance in cotton plants. For this purpose, we employed 1H-NMR spectroscopy to perform metabolite profiling of Bt and non-Bt cotton varieties, and a total of 42 different metabolites were identified in cotton plants. In cluster analysis, deep learning approaches (linear discriminant analysis (LDA) and neural networks) showed better separation among cotton varieties compared to conventional methods (principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLSDA)). The key metabolites responsible for inter-class separation were terpinolene, α-ketoglutaric acid, aspartic acid, stigmasterol, fructose, maltose, arabinose, xylulose, cinnamic acid, malic acid, valine, nonanoic acid, citrulline, and shikimic acid. The metabolites which regulated differently with the level of significance p < 0.001 amongst different cotton varieties belonged to the tricarboxylic acid cycle (TCA), Shikimic acid, and phenylpropanoid pathways. Our analyses underscore a biosignature of metabolites that might involve in inducing insect resistance in Bt cotton. Moreover, novel evidence from our study could be used in the metabolic engineering of these biological pathways to improve the resilience of Bt cotton against insect/pest attacks. Lastly, our findings are also in complete support of employing deep machine learning algorithms as a useful tool in metabolomics studies.

Comparative metabolomics provides novel insights into correlation between dominant habitat factors and constituents of Stellaria Radix (Stellaria dichotoma L. var. lanceolata Bge.)

Stellaria dichotoma L. var. lanceolata Bge. (SDL) is the original plant of the traditional Chinese medicine Yinchaihu (Stellaria Radix). It is mainly distributed in the arid desert areas of northwest China, which is the genuine medicinal material and characteristic cultivated crop in Ningxia. This study aims to analyze the effects of different origins on SDL metabolites and quality, as well as to screen the dominant habitat factors affecting SDL in different origins. In this study, metabolites of SDL from nine different production areas were analyzed by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS) based metabolomics. And field investigations were conducted to record thirteen habitat-related indicators. Results showed that 1586 metabolites were identified in different origins, which were classified as thirteen categories including lipids, organic acids and organic heterocyclic compounds derivatives. Multivariate statistical analysis showed that the metabonomic spectra of SDL from different origins had various characteristics. What's more, co-expression network correlation analysis revealed that three metabolites modules (MEturquoise, MEbrown and MEblue) were more closely with the habitat factors and 104 hub metabolites were further screened out as the habitat-induced metabolite indicators. Besides, soil texture, soil pH value and soil total salt content were found as the dominant habitat factors which affect SDL metabolites. In conclusion, the study showed different habitat factors had various effects on SDL's quality and established relationship between them, which provide reference for revealing SDL's genuineness formation mechanism and guiding industrial crops practical production by habitat factors selection.

Combined nature and human selections reshaped peach fruit metabolome

Background

Plant metabolites reshaped by nature and human beings are crucial for both their lives and human health. However, which metabolites respond most strongly to selection pressure at different evolutionary stages and what roles they undertake on perennial fruit crops such as peach remain unclear.

Results

Here, we report 18,052 significant locus-trait associations, 12,691 expression-metabolite correlations, and 294,676 expression quantitative trait loci (eQTLs) for peach. Our results indicate that amino acids accumulated in landraces may be involved in the environmental adaptation of peaches by responding to low temperature and drought. Moreover, the contents of flavonoids, the major nutrients in fruits, have kept decreasing accompanied by the reduced bitter flavor during both domestication and improvement stages. However, citric acid, under the selection of breeders’ and consumers’ preference for flavor, shows significantly different levels between eastern and western varieties. This correlates with differences in activity against cancer cells in vitro in fruit from these two regions. Based on the identified key genes regulating flavonoid and acid contents, we propose that more precise and targeted breeding technologies should be designed to improve peach varieties with rich functional contents because of the linkage of genes related to bitterness and acid taste, antioxidant and potential anti-cancer activity that are all located at the top of chromosome 5.

Conclusions

This study provides powerful data for future improvement of peach flavor, nutrition, and resistance in future and expands our understanding of the effects of natural and artificial selection on metabolites.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02719-6.

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.

Metabolomics: a systems biology approach for enhancing heat stress tolerance in plants

Comprehensive metabolomic investigations provide a large set of stress-related metabolites and metabolic pathways, advancing crops under heat stress conditions. Metabolomics-assisted breeding, including mQTL and mGWAS boosted our understanding of improving numerous quantitative traits under heat stress. During the past decade, metabolomics has emerged as a fascinating scientific field that includes documentation, evaluation of metabolites, and chemical methods for cell monitoring programs in numerous plant species. A comprehensive metabolome profiling allowed the investigator to handle the comprehensive data groups of metabolites and the equivalent metabolic pathways in an extraordinary manner. Metabolomics, together with transcriptomics, plays an influential role in discovering connections between stress and genes/metabolite, phenotyping, and biomarkers documentation. Further, it helps to decode several metabolic systems connected with heat stress (HS) tolerance in plants. Heat stress is a critical environmental factor that is globally affecting the growth and productivity of plants. Thus, there is an urgent need to exploit modern breeding and biotechnological tools like metabolomics to develop cultivars with improved HS tolerance. Several studies have reported that amino acids, carbohydrates, nitrogen metabolisms, etc. and metabolites involved in the biosynthesis and catalyzing actions play a game-changing role in HS response and help plants to cope with the HS. The use of metabolomics-assisted breeding (MAB) allows a well-organized transmission of higher yield and HS tolerance at the metabolome level with specific properties. Progressive metabolomics systematic techniques have accelerated metabolic profiling. Nonetheless, continuous developments in bioinformatics, statistical tools, and databases are allowing us to produce ever-progressing, comprehensive insights into the biochemical configuration of plants and by what means this is inclined by genetic and environmental cues. Currently, assimilating metabolomics with post-genomic platforms has allowed a significant division of genetic-phenotypic connotation in several plant species. This review highlights the potential of a state-of-the-art plant metabolomics approach for the improvement of crops under HS. The development of plants with specific properties using integrated omics (metabolomics and transcriptomics) and MAB can provide new directions for future research to enhance HS tolerance in plants to achieve a goal of "zero hunger".

Proteomic Profile of Glyphosate-Resistant Soybean under Combined Herbicide and Drought Stress Conditions

While some genetically modified (GM) plants have been targeted to confer tolerance to abiotic stressors, transgenes are impacted by abiotic stressors, causing adverse effects on plant physiology and yield. However, routine safety analyses do not assess the response of GM plants under different environmental stress conditions. In the context of climate change, the combination of abiotic stressors is a reality in agroecosystems. Therefore, the aim of this study was to analyze the metabolic cost by assessing the proteomic profiles of GM soybean varieties under glyphosate spraying and water deficit conditions compared to their non-transgenic conventional counterparts. We found evidence of cumulative adverse effects that resulted in the reduction of enzymes involved in carbohydrate metabolism, along with the expression of amino acids and nitrogen metabolic enzymes. Ribosomal metabolism was significantly enriched, particularly the protein families associated with ribosomal complexes L5 and L18. The interaction network map showed that the affected module representing the ribosome pathway interacts strongly with other important proteins, such as the chloro-plastic gamma ATP synthase subunit. Combined, these findings provide clear evidence for increasing the metabolic costs of GM soybean plants in response to the accumulation of stress factors. First, alterations in the ribosome pathway indicate that the GM plant itself carries a metabolic burden associated with the biosynthesis of proteins as effects of genetic transformation. GM plants also showed an imbalance in energy demand and production under controlled conditions, which was increased under drought conditions. Identifying the consequences of altered metabolism related to the interaction between plant transgene stress responses allows us to understand the possible effects on the ecology and evolution of plants in the medium and long term and the potential interactions with other organisms when these organisms are released in the environment.

Soybean Metabolomics Based in Mass Spectrometry: Decoding the Plant's Signaling and Defense Responses under Biotic Stress

Metabolomics is an omics technology that is extremely valuable to analyze all small-molecule metabolites in organisms. Recent advances in analytical instrumentation, such as mass spectrometry combined with data processing tools, chemometrics, and spectral data libraries, allow plant metabolomics studies to play a fundamental role in the agriculture field and food security. Few studies are found in the literature using the metabolomics approach in soybean plants on biotic stress. In this review, we provide a new perspective highlighting the potential of metabolomics-based mass spectrometry for soybean in response to biotic stress. Furthermore, we highlight the response and adaptation mechanisms of soybean on biotic stress about primary and secondary metabolism. Consequently, we provide subsidies for further studies of the resistance and improvement of the crop.

Transparency in risk-disproportionate regulation of modern crop-breeding techniques

Despite over 25 years of safe deployment of genetically engineered crops, the number, complexity, and scope of regulatory studies required for global approvals continue to increase devoid of adequate scientific justification. Recently, there have been calls to further expand the scope of study and data requirements to improve public acceptance. However, increased regulation can actually generate consumer distrust due to the misperception that risks are high. We believe risk-disproportionate regulation as a means to advocate for acceptance of technology is counterproductive, even though some regulatory authorities believe it part of their mandate. To help avoid public distrust, the concept of regulatory transparency to demystify regulatory decision-making should be extended to clearly justifying specific regulatory requirements as: 1) risk-driven (i.e., proportionately addressing increased risk compared with traditional breeding), or 2) advocacy-driven (i.e., primarily addressing consumer concerns and acceptance). Such transparency in the motivation for requiring risk-disproportionate studies would: 1) lessen over-prescriptive regulation, 2) save public and private resources, 3) make beneficial products and technologies available to society sooner, 4) reduce needless animal sacrifice, 5) improve regulatory decision-making regarding safety, and 6) lessen public distrust that is generated by risk-disproportionate regulation.

Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional "omics" data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.

Compositional equivalence assessment of insect-resistant genetically modified rice using multiple statistical analyses

The safety of transgenic Bt rice containing bacteria-derived mCry1Ac gene from Bacillus thuringiensis (Bt) was assessed by conducting field trials at two locations for two consecutive years in South Korea, using the near-isogenic line comparator rice cultivar ('Ilmi', non-Bt rice) and four commercial cultivars as references. Compositional analyses included measurement of proximates, minerals, amino acids, fatty acids, vitamins, and antinutrients. Significant differences between Bt rice and non-Bt rice were detected; however, all differences were within the reference range. The statistical analyses, including analysis of % variability, analysis of similarities (ANOISM), similarity percentage (SIMPER) analysis, and permutational multivariate analysis of variance (PERMANOVA) were performed to study factors contributing to compositional variability. The multivariate analyses revealed that environmental factors more influenced rice components' variability than by genetic factors. This approach was shown to be a powerful method to provide meaningful evaluations between Bt rice and its comparators. In this study, Bt rice was proved to be compositionally equivalent to conventional rice varieties through multiple statistical methods.

Untargeted metabolomics for Achilles heel of engineered nanomaterials' risk assessment

Owing to the superlative properties, engineered nanomaterials (ENM) are being used in food, cosmetics, medicine, and electronics. Therefore, exogenous ENM can be housed into humans through a multitude of exposure routes, leading to compromise of the biomolecules' functionalities through structural deformations, and even at the metabolic level. Consequently, it is of great importance to understand the perturbations introduced at the metabolic level for the timely risk assessment (RA) of ENM. Current technological advancements in metabolomics empower us to visualize the metabolic dysregulations in biological cells, tissues, and living objects, instigated by the ENM. Given the fact, we propose multitiered untargeted metabolomics for the risk assessment of ENM. We propose largely validated experimental design principles that enable the well-organized and authentic identification of metabolic dysregulation connected with a newly engineered nanomaterial. Our scheme could participate in the enhanced transparency of the RA course of rapidly emerging ENM.

Evaluation of the use of untargeted metabolomics in the safety assessment of genetically modified crops

BACKGROUND

The safety assessment of foods and feeds from genetically modified (GM) crops includes the comparison of key characteristics, such as crop composition, agronomic phenotype and observations from animal feeding studies compared to conventional counterpart varieties that have a history of safe consumption, often including a near isogenic variety. The comparative compositional analysis of GM crops has been based on targeted, validated, quantitative analytical methods for the key food and feed nutrients and antinutrients for each crop, as identified by Organization of Economic Co-operation and Development (OCED). As technologies for untargeted metabolomic methods have evolved, proposals have emerged for their use to complement or replace targeted compositional analytical methods in regulatory risk assessments of GM crops to increase the number of analyzed metabolites.

AIM OF REVIEW

The technical opportunities, challenges and strategies of including untargeted metabolomics analysis in the comparative safety assessment of GM crops are reviewed. The results from metabolomics studies of GM and conventional crops published over the last eight years provide context to enable the discussion of whether metabolomics can materially improve the risk assessment of food and feed from GM crops beyond that possible by the Codex-defined practices used worldwide for more than 25 years.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Published studies to date show that environmental and genetic factors affect plant metabolomics profiles. In contrast, the plant biotechnology process used to make GM crops has little, if any consequence, unless the inserted GM trait is intended to alter food or feed composition. The nutritional value and safety of food and feed from GM crops is well informed by the quantitative, validated compositional methods for list of key analytes defined by crop-specific OECD consensus documents. Untargeted metabolic profiling has yet to provide data that better informs the safety assessment of GM crops than the already rigorous Codex-defined quantitative comparative assessment. Furthermore, technical challenges limit the implementation of untargeted metabolomics for regulatory purposes: no single extraction method or analytical technique captures the complete plant metabolome; a large percentage of metabolites features are unknown, requiring additional research to understand if differences for such unknowns affect food/feed safety; and standardized methods are needed to provide reproducible data over time and laboratories.

Obligatory metabolomic profiling of gene-edited crops is risk disproportionate

It has been argued that the application of metabolomics to gene-edited crops would present value in three areas: (i) the detection of gene-edited crops; (ii) the characterization of unexpected changes that might affect safety; and (iii) building on the track record of rigorous government regulation in supporting consumer acceptance of genetically modified organisms (GMOs). Here, we offer a different perspective, relative to each of these areas: (i) metabolomics is unable to differentiate whether a mutation has resulted from gene editing or from traditional breeding techniques; (ii) it is risk-disproportionate to apply metabolomics for regulatory purposes to search for possible compositional differences within crops developed using the least likely technique to generate unexpected compositional changes; and (iii) onerous regulations for genetically engineered crops have only contributed to unwarranted public fears, and repeating this approach for gene-edited crops is unlikely to result in a different outcome. It is also suggested that article proposing the utility of specific analytical techniques to support risk assessment would benefit from the input of scientists with subject matter expertise in risk assessment.

Metabolomics should be deployed in the identification and characterization of gene-edited crops

Gene-editing techniques are currently revolutionizing biology, allowing far greater precision than previous mutagenic and transgenic approaches. They are becoming applicable to a wide range of plant species and biological processes. Gene editing can rapidly improve a range of crop traits, including disease resistance, abiotic stress tolerance, yield, nutritional quality and additional consumer traits. Unlike transgenic approaches, however, it is not facile to forensically detect gene-editing events at the molecular level, as no foreign DNA exists in the elite line. These limitations in molecular detection approaches are likely to focus more attention on the products generated from the technology than on the process in itself. Rapid advances in sequencing and genome assembly increasingly facilitate genome sequencing as a means of characterizing new varieties generated by gene-editing techniques. Nevertheless, subtle edits such as single base changes or small deletions may be difficult to distinguish from normal variation within a genotype. Given these emerging scenarios, downstream 'omics' technologies reflective of edited affects, such as metabolomics, need to be used in a more prominent manner to fully assess compositional changes in novel foodstuffs. To achieve this goal, metabolomics or 'non-targeted metabolite analysis' needs to make significant advances to deliver greater representation across the metabolome. With the emergence of new edited crop varieties, we advocate: (i) concerted efforts in the advancement of 'omics' technologies, such as metabolomics, and (ii) an effort to redress the use of the technology in the regulatory assessment for metabolically engineered biotech crops.

Metabolomics to Characterize Adaptive and Signaling Responses in Legume Crops under Abiotic Stresses

Legume species are an important source of protein and other nutrients for human and livestock consumption, playing a central role in food security. Besides, legumes benefit agriculture because of their ability to establish symbiotic interactions with nitrogen-fixing bacteria, providing nitrogen for subsequent crops, which is very much appreciated for sustainable agricultural practices. However, like other food crops, legumes are highly vulnerable to climate variations, water stresses being the main constraint that negatively affects both crop quality and productivity. Because of this, the development of strategies to improve the tolerance of such cultivars against water stresses, as well as the study of effective approaches to monitor these improvements, have gained special attention during the last years. Among these strategies, metabolomics has been considered one of the most promising approaches for the detection and/or quantification of primary and secondary stress-responsive metabolites in abiotic stresses. In plant science, many research groups have been using metabolomics to evaluate the success of genetic modifications by the analysis of chemical markers that can be altered in breeding programs. In addition, metabolomics is a powerful tool for the evaluation and selection of wild specimens with desirable traits that can be used in the development of improved new cultivars. Therefore, the aim of the present paper is to review the recent progress made in the field of metabolomics and plant breeding, especially concerning the adaptive responses of legume species to abiotic stresses as well as to point out the key primary and secondary metabolites involved in the adaptation and sensing mechanisms.

Problem Formulation for Off-Target Effects of Externally Applied Double-Stranded RNA-Based Products for Pest Control

Externally applied dsRNA-based biocontrol products may lead to off-target degradation of messenger RNA in target and non-target organisms. For the purposes of regulatory risk assessment of such products, producing a comprehensive catalog of any off-target effects using profiling methods is unnecessary and would be ineffective in supporting decision-making. Instead, problem formulation should derive criteria that indicate acceptable risk and devise a plan to test the hypothesis that the product meets those criteria. The key to effective risk assessment of dsRNA-based biocontrols is determining whether their properties indicate acceptable or unacceptable risk, not whether they arise from on- or off-target effects of dsRNA.

Placenta, Pericarp, and Seeds of Tabasco Chili Pepper Fruits Show a Contrasting Diversity of Bioactive Metabolites

Chili pepper (Capsicum spp.) is one of the most important horticultural crops worldwide, and its unique organoleptic properties and health benefits have been established for centuries. However, there is little knowledge about how metabolites are distributed throughout fruit parts. This work focuses on the use of liquid chromatography coupled with high resolution mass spectrometry (UHPLC-ESI-HRMS) to estimate the global metabolite profiles of the pericarp, placenta, and seeds of Tabasco pepper fruits (Capsicum frutescens L.) at the red mature stage of ripening. Our main results putatively identified 60 differential compounds between these tissues and seeds. Firstly, we found that pericarp has a higher content of glycosides, showing on average a fold change of 5 and a fold change of 14 for terpenoids when compared with other parts of the fruit. While placenta was the richest tissue in capsaicinoid-related compounds, alkaloids, and tocopherols, with a 35, 3, and 7 fold change, respectively. However, the seeds were richer in fatty acids and saponins with fold changes of 86 and 224, respectively. Therefore, our study demonstrates that a non-targeted metabolomic approach may help to improve our understanding of unexplored areas of plant metabolism and also may be the starting point for a detailed analysis in complex plant parts, such as fruits.

A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines

Natural products (NPs) and traditional medicines (TMs) are used for treatment of various diseases and also to develop new drugs. However, identification of drug leads within the immense biodiversity of living organisms is a challenging task that requires considerable time, labor, and computational resources as well as the application of modern analytical instruments. LC-MS platforms are widely used for both drug discovery and quality control of TMs and food supplements. Moreover, a large dataset generated during LC-MS analysis contains valuable information that could be extracted and handled by means of various data mining and statistical tools. Novel sophisticated LC-MS based approaches are being introduced every year. Therefore, this review is prepared for the scientists specialized in pharmacognosy and analytical chemistry of NPs as well as working in related areas, in order to navigate them in the world of diverse LC-MS based techniques and strategies currently employed for NP discovery and dereplication, quality control, pattern recognition and sample comparison, and also in targeted and untargeted metabolomic studies. The suggested classification system includes the following LC-MS based procedures: elemental composition determination, isotopic fine structure analysis, mass defect filtering, de novo identification, clustering of the compounds in Molecular Networking (MN), diagnostic fragment ion (or neutral loss) filtering, manual dereplication using MS/MS data, database-assisted peak annotation, annotation of spectral trees, MS fingerprinting, feature extraction, bucketing of LC-MS data, peak profiling, predicted metabolite screening, targeted quantification of biomarkers, quantitative analysis of multi-component system, construction of chemical fingerprints, multi-targeted and untargeted metabolite profiling.

EFSA Genetically Engineered Crop Composition Equivalence Approach: Performance and Consistency

The European Food Safety Authority (EFSA) oversees the safety assessment of genetically engineered (GE) crops in the European Union and has developed a study design and statistical approach for assessing the compositional equivalency between a GE crop and the corresponding non-GE crop on the basis of the results from a small number of concurrently grown reference lines. Confidence limits around the differences in mean analyte composition between the GE variety and the reference lines are compared with equivalence limits on the basis of the variability of the reference lines. Here, we evaluated the performance and consistency of the equivalence conclusions using a non-GE variety that is, by definition, equivalent to the non-GE crop. Using this approach across the same analytes with the same non-GE variety, it was found that equivalence could not be concluded for 19.7, 22.9, 25.4, and 53.5% of the analytes in four separate studies. In addition, equivalency conclusions for the same analyte often differed from study to study. These results call into question the consistency and value of this approach in the risk assessment of GE crops.

Analytical Methods for Detection of Plant Metabolomes Changes in Response to Biotic and Abiotic Stresses

Abiotic and biotic stresses are the main reasons of substantial crop yield losses worldwide. Research devoted to reveal mechanisms of plant reactions during their interactions with the environment are conducted on the level of genome, transcriptome, proteome, and metabolome. Data obtained during these studies would permit to define biochemical and physiological mechanisms of plant resistance or susceptibility to affecting factors/stresses. Metabolomics based on mass spectrometric techniques is an important part of research conducted in the direction of breeding new varieties of crop plants tolerant to the affecting stresses and possessing good agronomical features. Studies of this kind are carried out on model, crop and resurrection plants. Metabolites profiling yields large sets of data and due to this fact numerous advanced statistical and bioinformatic methods permitting to obtain qualitative and quantitative evaluation of the results have been developed. Moreover, advanced integration of metabolomics data with these obtained on other omics levels: genome, transcriptome and proteome should be carried out. Such a holistic approach would bring us closer to understanding biochemical and physiological processes of the cell and whole plant interacting with the environment and further apply these observations in successful breeding of stress tolerant or resistant crop plants.