Legume Breeding for the Agroecological Transition of Global Agri-Food Systems: A European Perspective

Metabolic profiling of pea (Pisum sativum) cultivars in changing environments: Implications for nutritional quality in animal feed

Field pea seeds have long been recognized as valuable feed ingredients for animal diets, due to their high-quality protein and starch digestibility. However, the chemical composition of pea cultivars can vary across different growing locations, consequently impacting their nutrient profiles. This study employs untargeted metabolomics in conjunction with the quantification of fatty acids and amino acids to explore the influence of three different growing locations in Spain (namely Andalusia, Aragon and Asturias), on the nutritional characteristics of seeds of various pea cultivars. Significant interactions between cultivar and environment were observed, with 121 metabolites distinguishing pea profiles. Lipids, lipid-like molecules, phenylpropanoids, polyketides, carbohydrates, and amino acids were the most affected metabolites. Fatty acid profiles varied across locations, with higher C16:0, C18:0, and 18:1 n-9 concentration in Aragón, while C18:2 n-6 predominated in Asturias and C18:3 n-3 in Andalusia. Amino acid content was also location-dependent, with higher levels in Asturias. These findings underscore the impact of environmental factors on pea metabolite profiles and emphasize the importance of selecting pea cultivars based on specific locations and animal requirements. Enhanced collaboration between research and industry is crucial for optimizing pea cultivation for animal feed production.

Novel SNP markers for flowering and seed quality traits in faba bean (Vicia faba L.): characterization and GWAS of a diversity panel

Faba bean (Vicia faba L.) is a legume crop grown in diverse climates worldwide. It has a high potential for increased cultivation to meet the need for more plant-based proteins in human diets, a prerequisite for a more sustainable food production system. Characterization of diversity panels of crops can identify variation in and genetic markers for target traits of interest for plant breeding. In this work, we collected a diversity panel of 220 accessions of faba bean from around the world consisting of gene bank material and commercially available cultivars. The aims of this study were to quantify the phenotypic diversity in target traits to analyze the impact of breeding on these traits, and to identify genetic markers associated with traits through a genome-wide association study (GWAS). Characterization under field conditions at Nordic latitude across two years revealed a large genotypic variation and high broad-sense heritability for eleven agronomic and seed quality traits. Pairwise correlations showed that seed yield was positively correlated to plant height, number of seeds per plant, and days to maturity. Further, susceptibility to bean weevil damage was significantly higher for early flowering accessions and accessions with larger seeds. In this study, no yield penalty was found for higher seed protein content, but protein content was negatively correlated to starch content. Our results showed that while breeding advances in faba bean germplasm have resulted in increased yields and number of seeds per plant, they have also led to a selection pressure towards delayed onset of flowering and maturity. DArTseq genotyping identified 6,606 single nucleotide polymorphisms (SNPs) by alignment to the faba bean reference genome. These SNPs were used in a GWAS, revealing 51 novel SNP markers significantly associated with ten of the assessed traits. Three markers for days to flowering were found in predicted genes encoding proteins for which homologs in other plant species regulate flowering. Altogether, this work enriches the growing pool of phenotypic and genotypic data on faba bean as a valuable resource for developing efficient breeding strategies to expand crop cultivation.

Genomic prediction and allele mining of agronomic and morphological traits in pea (Pisum sativum) germplasm collections

Well-performing genomic prediction (GP) models for polygenic traits and molecular marker sets for oligogenic traits could be useful for identifying promising genetic resources in germplasm collections, setting core collections, and establishing molecular variety distinction. This study aimed at (i) defining GP models and key marker sets for predicting 15 agronomic or morphological traits in germplasm collections, (ii) verifying the GP model usefulness also for selection in breeding programs, (iii) investigating the consistency between molecular and phenotypic diversity patterns, and (iv) identifying genomic regions associated with to the target traits. The study was based on phenotyping data and over 41,000 genotyping-by-sequencing-generated SNP markers of 220 landraces or old cultivars belonging to a world germplasm collection and 11 modern cultivars. Non-metric multi-dimensional scaling (NMDS) and an analysis of population genetic structure indicated a high level of genetic differentiation of material from Western Asia, a major West-East diversity gradient, and quite limited genetic diversity of the improved germplasm. Mantel's test revealed a low correlation (r = 0.12) between phenotypic and molecular diversity, which increased (r = 0.45) when considering only the molecular diversity relative to significant SNPs from genome-wide association analyses. These analyses identified, inter alia, several areas of chromosome 6 involved in a largely pleiotropic control of vegetative or reproductive organ pigmentation. We found various significant SNPs for grain and straw yield under severe drought and onset of flowering, and one SNP on chromosome 5 for grain protein content. GP models displayed moderately high predictive ability (0.43 to 0.61) for protein content, grain and straw yield, and onset of flowering, and high predictive ability (0.76) for individual seed weight, based on intra-population, intra-environment cross-validations. The inter-population, inter-environment assessment of the models trained on the germplasm collection for breeding material of three recombinant inbred line (RIL) populations, which was challenged by much narrower diversity of the material, over eight-fold less available markers and quite different test environments, led to an overall loss of predictive ability of about 40% for seed weight, 50% for protein content and straw yield, and 60% for onset of flowering, and no prediction for grain yield. Within-RIL population predictive ability differed among populations.

Comparative Analysis of Secondary Metabolites Produced by Ascochyta fabae under In Vitro Conditions and Their Phytotoxicity on the Primary Host, Vicia faba, and Related Legume Crops

Ascochyta blight, caused by Ascochyta fabae, poses a significant threat to faba bean and other legumes worldwide. Necrotic lesions on stems, leaves, and pods characterize the disease. Given the economic impact of this pathogen and the potential involvement of secondary metabolites in symptom development, a study was conducted to investigate the fungus's ability to produce bioactive metabolites that might contribute to its pathogenicity. For this investigation, the fungus was cultured in three substrates (Czapek-Dox, PDB, and rice). The produced metabolites were analyzed by NMR and LC-HRMS methods, resulting in the dereplication of seven metabolites, which varied with the cultural substrates. Ascochlorin, ascofuranol, and (R)-mevalonolactone were isolated from the Czapek-Dox extract; ascosalipyrone, benzoic acid, and tyrosol from the PDB extract; and ascosalitoxin and ascosalipyrone from the rice extract. The phytotoxicity of the pure metabolites was assessed at different concentrations on their primary hosts and related legumes. The fungal exudates displayed varying degrees of phytotoxicity, with the Czapek-Dox medium's exudate exhibiting the highest activity across almost all legumes tested. The species belonging to the genus Vicia spp. were the most susceptible, with faba bean being susceptible to all metabolites, at least at the highest concentration tested, as expected. In particular, ascosalitoxin and benzoic acid were the most phytotoxic in the tested condition and, as a consequence, expected to play an important role on necrosis's appearance.

Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics

This study presents a tentative analysis of the lipid composition of 47 legume samples, encompassing species such as Phaseolus spp., Vicia spp., Pisum spp., and Lathyrus spp. Lipid extraction and GC/MS (gas chromatography with mass spectrometric detection) analysis were conducted, followed by multivariate statistical methods for data interpretation. Hierarchical Cluster Analysis (HCA) revealed two major clusters, distinguishing beans and snap beans (Phaseolus spp.) from faba beans (Vicia faba), peas (Pisum sativum), and grass peas (Lathyrus sativus). Principal Component Analysis (PCA) yielded 2D and 3D score plots, effectively discriminating legume species. Linear Discriminant Analysis (LDA) achieved a 100% accurate classification of the training set and a 90% accuracy of the test set. The lipid-based fingerprinting elucidated compounds crucial for discrimination. Both PCA and LDA biplots highlighted squalene and fatty acid methyl esters (FAMEs) of 9,12,15-octadecatrienoic acid (C18:3) and 5,11,14,17-eicosatetraenoic acid (C20:4) as influential in the clustering of beans and snap beans. Unique compounds, including 13-docosenoic acid (C22:1) and γ-tocopherol, O-methyl-, characterized grass pea samples. Faba bean samples were discriminated by FAMEs of heneicosanoic acid (C21:0) and oxiraneoctanoic acid, 3-octyl- (C18-ox). However, C18-ox was also found in pea samples, but in significantly lower amounts. This research demonstrates the efficacy of lipid analysis coupled with multivariate statistics for accurate differentiation and classification of legumes, according to their botanical origins.

An experimental dataset on yields of pulses across Europe

Future European agriculture should achieve high productivity while limiting its impact on the environment. Legume-supported crop rotations could contribute to these goals, as they request less nitrogen (N) fertilizer inputs, show high resource use efficiency and support biodiversity. However, legumes grown for their grain (pulses) are not widely cultivated in Europe. To further expand their cultivation, it remains crucial to better understand how different cropping and environmental features affect pulses production in Europe. To address this gap, we collected the grain yields of the most cultivated legumes across European countries, from both published scientific papers and unpublished experiments of the European projects LegValue and Legato. Data were integrated into an open-source, easily updatable dataset, including 5229 yield observations for five major pulses: chickpea (Cicer arietinum L.), faba bean (Vicia faba L.), field pea (Pisum sativum L.), lentil (Lens culinaris Medik.), and soybean (Glycine max (L.) Merr.). These data were collected in 177 field experiments across 21 countries, from 37° N (southern Italy) to 63° N (Finland) of latitude, and from ca. 8° W (western Spain) to 47° E (Turkey), between 1980 and 2020. Our dataset can be used to quantify the effects of the soil, climate, and agronomic factors affecting pulses yields in Europe and could contribute to identifying the most suitable cropping areas in Europe to grow pulses.

Checking the pulse: perspectives on grain legume production

Including more grain legumes in cropping systems is considered a climate-smart solution to increase sustainability, soil fertility, and cropping systems diversification, while reducing nitrogen (N) inputs. However, increasing pulse production in temperate areas for food and feed comes with challenges that should be addressed and require more research for successful implementation.

Engaging Precision Phenotyping to Scrutinize Vegetative Drought Tolerance and Recovery in Chickpea Plant Genetic Resources

Precise and high-throughput phenotyping (HTP) of vegetative drought tolerance in chickpea plant genetic resources (PGR) would enable improved screening for genotypes with low relative loss of biomass formation and reliable physiological performance. It could also provide a basis to further decipher the quantitative trait drought tolerance and recovery and gain a better understanding of the underlying mechanisms. In the context of climate change and novel nutritional trends, legumes and chickpea in particular are becoming increasingly important because of their high protein content and adaptation to low-input conditions. The PGR of legumes represent a valuable source of genetic diversity that can be used for breeding. However, the limited use of germplasm is partly due to a lack of available characterization data. The development of HTP systems offers a perspective for the analysis of dynamic plant traits such as abiotic stress tolerance and can support the identification of suitable genetic resources with a potential breeding value. Sixty chickpea accessions were evaluated on an HTP system under contrasting water regimes to precisely evaluate growth, physiological traits, and recovery under optimal conditions in comparison to drought stress at the vegetative stage. In addition to traits such as Estimated Biovolume (EB), Plant Height (PH), and several color-related traits over more than forty days, photosynthesis was examined by chlorophyll fluorescence measurements on relevant days prior to, during, and after drought stress. With high data quality, a wide phenotypic diversity for adaptation, tolerance, and recovery to drought was recorded in the chickpea PGR panel. In addition to a loss of EB between 72% and 82% after 21 days of drought, photosynthetic capacity decreased by 16-28%. Color-related traits can be used as indicators of different drought stress stages, as they show the progression of stress.

Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case on grain legumes

While the continuing decline in genotyping and sequencing costs has largely benefited plant research, some key species for meeting the challenges of agriculture remain mostly understudied. As a result, heterogeneous datasets for different traits are available for a significant number of these species. As gene structures and functions are to some extent conserved through evolution, comparative genomics can be used to transfer available knowledge from one species to another. However, such a translational research approach is complex due to the multiplicity of data sources and the non-harmonized description of the data. Here, we provide two pipelines, referred to as structural and functional pipelines, to create a framework for a NoSQL graph-database (Neo4j) to integrate and query heterogeneous data from multiple species. We call this framework Orthology-driven knowledge base framework for translational research (Ortho_KB). The structural pipeline builds bridges across species based on orthology. The functional pipeline integrates biological information, including QTL, and RNA-sequencing datasets, and uses the backbone from the structural pipeline to connect orthologs in the database. Queries can be written using the Neo4j Cypher language and can, for instance, lead to identify genes controlling a common trait across species. To explore the possibilities offered by such a framework, we populated Ortho_KB to obtain OrthoLegKB, an instance dedicated to legumes. The proposed model was evaluated by studying the conservation of a flowering-promoting gene. Through a series of queries, we have demonstrated that our knowledge graph base provides an intuitive and powerful platform to support research and development programmes.

Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits

China's and Europe's dependence on imported protein is a threat to the food self-sufficiency of these regions. It could be solved by growing more legumes, including alfalfa that is the highest protein producer under temperate climate. To create productive and high-value varieties, the use of large genetic diversity combined with genomic evaluation could improve current breeding programs. To study alfalfa diversity, we have used a set of 395 alfalfa accessions (i.e. populations), mainly from Europe, North and South America and China, with fall dormancy ranging from 3 to 7 on a scale of 11. Five breeders provided materials (617 accessions) that were compared to the 400 accessions. All accessions were genotyped using Genotyping-by-Sequencing (GBS) to obtain SNP allele frequency. These genomic data were used to describe genetic diversity and identify genetic groups. The accessions were phenotyped for phenology traits (fall dormancy and flowering date) at two locations (Lusignan in France, Novi Sad in Serbia) from 2018 to 2021. The QTL were detected by a Multi-Locus Mixed Model (mlmm). Subsequently, the quality of the genomic prediction for each trait was assessed. Cross-validation was used to assess the quality of prediction by testing GBLUP, Bayesian Ridge Regression (BRR), and Bayesian Lasso methods. A genetic structure with seven groups was found. Most of these groups were related to the geographical origin of the accessions and showed that European and American material is genetically distinct from Chinese material. Several QTL associated with fall dormancy were found and most of these were linked to genes. In our study, the infinitesimal methods showed a higher prediction quality than the Bayesian Lasso, and the genomic prediction achieved high (>0.75) predicting abilities in some cases. Our results are encouraging for alfalfa breeding by showing that it is possible to achieve high genomic prediction quality.

High Inter- and Intra- Diversity of Amino Acid Content and Protein Digestibility Disclosed in Five Cool Season Legume Species with a Growing Market Demand

Legumes have been sought as alternative protein sources to ensure food security and environmental sustainability. Characterizing their protein content and quality, including in underutilized grain legumes, e.g., grass pea, gives value to the legumes' underexplored variability. To fill the gap of knowledge in legumes' protein quality, for the first time, five extensive collections of cool season grain legumes were cropped under the same environmental conditions and further analyzed. Multivariate analysis showed the existent intra- and inter-species variability. The legume species with the highest protein content, grass pea, Lathyrus sativus (LS), was not the one with the overall highest individual amino acids content and in vitro protein digestibility. With these last characteristics lentil, Lens culinaris (LC), was highlighted. The highest average values of arginine (Arg), glutamic acid (Glu), and threonine (Thr) were found in LS and Vicia faba (VF). Cicer arietinum (CA) stood out as the species with the highest values of Thr and methionine (Met). Regarding the in vitro protein digestibility (IVPD), LC, followed by Pisum sativum (PS) and LS, were the legume species with the highest values. Ultimately, this study bought to the fore legume species that are not commonly used in western diets but have high adaptability to the European agricultural systems.

Cowpea Constraints and Breeding in Europe

Cowpea (Vigna unguiculata (L.) Walp.) is a legume with a constant rate of cultivation in Southern European countries. Consumer demand for cowpea worldwide is rising due to its nutritional content, while Europe is constantly attempting to reduce the deficit in the production of pulses and invest in new, healthy food market products. Although the climatic conditions that prevail in Europe are not so harsh in terms of heat and drought as in the tropical climates where cowpea is mainly cultivated, cowpea confronts with a plethora of abiotic and biotic stresses and yield-limiting factors in Southern European countries. In this paper, we summarize the main constraints for cowpea cultivation in Europe and the breeding methods that have been or can be used. A special mention is made of the availability plant genetic resources (PGRs) and their potential for breeding purposes, aiming to promote more sustainable cropping systems as climatic shifts become more frequent and fiercer, and environmental degradation expands worldwide.

Status of Phytotoxins Isolated from Necrotrophic Fungi Causing Diseases on Grain Legumes

Fungal phytotoxins can be defined as secondary metabolites toxic to host plants and are believed to be involved in the symptoms developed of a number of plant diseases by targeting host cellular machineries or interfering with host immune responses. As any crop, legumes can be affected by a number of fungal diseases, causing severe yield losses worldwide. In this review, we report and discuss the isolation, chemical, and biological characterization of fungal phytotoxins produced by the most important necrotrophic fungi involved in legume diseases. Their possible role in plant-pathogen interaction and structure-toxicity relationship studies have also been reported and discussed. Moreover, multidisciplinary studies on other prominent biological activity conducted on reviewed phytotoxins are described. Finally, we explore the challenges in the identification of new fungal metabolites and their possible applications in future experiments.