Discovering new genes for alfalfa (Medicago sativa) growth and biomass resilience in combined salinity and Phoma medicaginis infection through GWAS.

Salinity and Phoma medicaginis infection represent significant challenges for alfalfa cultivation in South Africa, Europe, Australia, and, particularly, Tunisia. These constraints have a severe impact on both yield and quality. The primary aim of this study was to establish the genetic basis of traits associated with biomass and growth of 129 Medicago sativa genotypes through genome-wide association studies (GWAS) under combined salt and P. medicaginis infection stresses. The results of the analysis of variance (ANOVA) indicated that the variation in these traits could be primarily attributed to genotype effects. Among the test genotypes, the length of the main stem, the number of ramifications, the number of chlorotic leaves, and the aerial fresh weight exhibited the most significant variation. The broad-sense heritability (H²) was relatively high for most of the assessed traits, primarily due to genetic factors. Cluster analysis, applied to morpho-physiological traits under the combined stresses, revealed three major groups of accessions. Subsequently, a GWAS analysis was conducted to validate significant associations between 54,866 SNP-filtered single-nucleotide polymorphisms (SNPs) and seven traits. The study identified 27 SNPs that were significantly associated with the following traits: number of healthy leaves (two SNPs), number of chlorotic leaves (five SNPs), number of infected necrotic leaves (three SNPs), aerial fresh weight (six SNPs), aerial dry weight (nine SNPs), number of ramifications (one SNP), and length of the main stem (one SNP). Some of these markers are related to the ionic transporters, cell membrane rigidity (related to salinity tolerance), and the NBS_LRR gene family (associated with disease resistance). These findings underscore the potential for selecting alfalfa genotypes with tolerance to the combined constraints of salinity and P. medicaginis infection.

The Effect of Genome Parametrization and SNP Marker Subsetting on Genomic Selection in Autotetraploid Alfalfa.

BACKGROUND: Alfalfa, the most economically important forage legume worldwide, features modest genetic progress due to long selection cycles and the extent of the non-additive genetic variance associated with its autotetraploid genome. METHODS: To improve the efficiency of genomic selection in alfalfa, we explored the effects of genome parametrization (as tetraploid and diploid dosages, plus allele ratios) and SNP marker subsetting (all available SNPs, only genic regions, and only non-genic regions) on genomic regressions, together with various levels of filtering on reading depth and missing rates. We used genotyping by sequencing-generated data and focused on traits of different genetic complexity, i.e., dry biomass yield in moisture-favorable (FE) and drought stress (SE) environments, leaf size, and the onset of flowering, which were assessed in 143 genotyped plants from a genetically broad European reference population and their phenotyped half-sib progenies. RESULTS: On average, the allele ratio improved the predictive ability compared with other genome parametrizations (+7.9% vs. tetraploid dosage, +12.6% vs. diploid dosage), while using all the SNPs offered an advantage compared with any specific SNP subsetting (+3.7% vs. genic regions, +7.6% vs. non-genic regions). However, when focusing on specific traits, different combinations of genome parametrization and subsetting achieved better performances. We also released Legpipe2, an SNP calling pipeline tailored for reduced representation (GBS, RAD) in medium-sized genotyping experiments.

White Lupin Drought Tolerance: Genetic Variation, Trait Genetic Architecture, and Genome-Enabled Prediction.

White lupin is a high-protein crop requiring drought tolerance improvement. This study focused on a genetically-broad population of 138 lines to investigate the phenotypic variation and genotype × environment interaction (GEI) for grain yield and other traits across drought-prone and moisture-favourable managed environments, the trait genetic architecture and relevant genomic regions by a GWAS using 9828 mapped SNP markers, and the predictive ability of genomic selection (GS) models. Water treatments across two late cropping months implied max. available soil water content of 60-80% for favourable conditions and from wilting point to 15% for severe drought. Line yield responses across environments featured a genetic correlation of 0.84. Relatively better line yield under drought was associated with an increased harvest index. Two significant QTLs emerged for yield in each condition that differed across conditions. Line yield under stress displayed an inverse linear relationship with the onset of flowering, confirmed genomically by a common major QTL. An adjusted grain yield computed as deviation from phenology-predicted yield acted as an indicator of intrinsic drought tolerance. On the whole, the yield in both conditions and the adjusted yield were polygenic, heritable, and exploitable by GS with a high predictive ability (0.62-0.78). Our results can support selection for climatically different drought-prone regions.

Alfalfa genomic selection for different stress-prone growing regions.

Alfalfa (Medicago sativa L.) selection for stress-prone regions has high priority for sustainable crop-livestock systems. This study assessed the genomic selection (GS) ability to predict alfalfa breeding values for drought-prone agricultural sites of Algeria, Morocco, and Argentina; managed-stress (MS) environments of Italy featuring moderate or intense drought; and one Tunisian site irrigated with moderately saline water. Additional aims were to investigate genotype × environment interaction (GEI) patterns and the effect on GS predictions of three single-nucleotide polymorphism (SNP) calling procedures, 12 statistical models that exclude or incorporate GEI, and allele dosage information. Our study included 127 genotypes from a Mediterranean reference population originated from three geographically contrasting populations, genotyped via genotyping-by-sequencing and phenotyped based on multi-year biomass dry matter yield of their dense-planted half-sib progenies. The GEI was very large, as shown by 27-fold greater additive genetic variance × environment interaction relative to the additive genetic variance and low genetic correlation for progeny yield responses across environments. The predictive ability of GS (using at least 37,969 SNP markers) exceeded 0.20 for moderate MS (representing Italian stress-prone sites) and the sites of Algeria and Argentina while being quite low for the Tunisian site and intense MS. Predictions of GS were complicated by rapid linkage disequilibrium decay. The weighted GBLUP model, GEI incorporation into GS models, and SNP calling based on a mock reference genome exhibited a predictive ability advantage for some environments. Our results support the specific breeding for each target region and suggest a positive role for GS in most regions when considering the challenges associated with phenotypic selection.

Pea Breeding for Intercropping With Cereals: Variation for Competitive Ability and Associated Traits, and Assessment of Phenotypic and Genomic Selection Strategies.

Mixed stand (MS) cropping of pea with small-grain cereals can produce more productive and environment-friendly grain crops relative to pure stand (PS) crops but may require selection to alleviate the pea competitive disadvantage. This study aimed to assess the pea variation for competitive ability and its associated traits and the efficiency of four phenotypic or genomic selection strategies. A set of 138 semi-leafless, semi-dwarf pea lines belonging to six recombinant inbred line populations and six parent lines were genotyped using genotyping-by-sequencing and grown in PS and in MS simultaneously with one barley and one bread wheat cultivar in two autumn-sown trials in Northern Italy. Cereal companions were selected in a preliminary study that highlighted the paucity of cultivars with sufficient earliness for association. Pea was severely outcompeted in both years albeit with variation for pea proportion ranging from nearly complete suppression (<3%) to values approaching a balanced mixture. Greater pea proportion in MS was associated with greater total yield of the mixture (r ≥ 0.46). The genetic correlation for pea yield across MS and PS conditions slightly exceeded 0.40 in both years. Later onset of flowering and taller plant height at flowering onset displayed a definite correlation with pea yield in MS (r ≥ 0.46) but not in PS, whereas tolerance to ascochyta blight exhibited the opposite pattern. Comparisons of phenotypic selection strategies within or across populations based on predicted or actual yield gains for independent years indicated an efficiency of 52-64% for indirect selection based on pea yield in PS relative to pea yield selection in MS. The efficiency of an indirect selection index including onset of flowering, plant height, and grain yield in PS was comparable to that of pea yield selection in MS. A genome-wide association study based on 5,909 SNP markers revealed the substantial diversity of genomic areas associated with pea yield in MS and PS. Genomic selection for pea yield in MS displayed an efficiency close to that of phenotypic selection for pea yield in MS, and nearly two-fold greater efficiency when also taking into account its shorter selection cycle and smaller evaluation cost.

Pea Grain Protein Content Across Italian Environments: Genetic Relationship With Grain Yield, and Opportunities for Genome-Enabled Selection for Protein Yield.

Wider pea (Pisum sativum L.) cultivation has great interest for European agriculture, owing to its favorable environmental impact and provision of high-protein feedstuff. This work aimed to investigate the extent of genotype × environment interaction (GEI), genetically based trade-offs and polygenic control for crude protein content and grain yield of pea targeted to Italian environments, and to assess the efficiency of genomic selection (GS) as an alternative to phenotypic selection (PS) to increase protein yield per unit area. Some 306 genotypes belonging to three connected recombinant inbred line (RIL) populations derived from paired crosses between elite cultivars were genotyped through genotyping-by-sequencing and phenotyped for grain yield and protein content on a dry matter basis in three autumn-sown environments of northern or central Italy. Line variation for mean protein content ranged from 21.7 to 26.6%. Purely genetic effects, compared with GEI effects, were over two-fold larger for protein content, and over 2-fold smaller for grain and protein yield per unit area. Grain yield and protein content exhibited no inverse genetic correlation. A genome-wide association study revealed a definite polygenic control not only for grain yield but also for protein content, with small amounts of trait variation accounted for by individual loci. On average, the GS predictive ability for individual RIL populations based on the rrBLUP model (which was selected out of four tested models) using by turns two environments for selection and one for validation was moderately high for protein content (0.53) and moderate for grain yield (0.40) and protein yield (0.41). These values were about halved for inter-environment, inter-population predictions using one RIL population for model construction to predict data of the other populations. The comparison between GS and PS for protein yield based on predicted gains per unit time and similar evaluation costs indicated an advantage of GS for model construction including the target RIL population and, in case of multi-year PS, even for model training based on data of a non-target population. In conclusion, protein content is less challenging than grain yield for phenotypic or genome-enabled improvement, and GS is promising for the simultaneous improvement of both traits.

Development and Proof-of-Concept Application of Genome-Enabled Selection for Pea Grain Yield under Severe Terminal Drought.

Terminal drought is the main stress limiting pea (Pisum sativum L.) grain yield in Mediterranean environments. This study aimed to investigate genotype × environment (GE) interaction patterns, define a genomic selection (GS) model for yield under severe drought based on single nucleotide polymorphism (SNP) markers from genotyping-by-sequencing, and compare GS with phenotypic selection (PS) and marker-assisted selection (MAS). Some 288 lines belonging to three connected RIL populations were evaluated in a managed-stress (MS) environment of Northern Italy, Marchouch (Morocco), and Alger (Algeria). Intra-environment, cross-environment, and cross-population predictive ability were assessed by Ridge Regression best linear unbiased prediction (rrBLUP) and Bayesian Lasso models. GE interaction was particularly large across moderate-stress and severe-stress environments. In proof-of-concept experiments performed in a MS environment, GS models constructed from MS environment and Marchouch data applied to independent material separated top-performing lines from mid- and bottom-performing ones, and produced actual yield gains similar to PS. The latter result would imply somewhat greater GS efficiency when considering same selection costs, in partial agreement with predicted efficiency results. GS, which exploited drought escape and intrinsic drought tolerance, exhibited 18% greater selection efficiency than MAS (albeit with non-significant difference between selections) and moderate to high cross-population predictive ability. GS can be cost-efficient to raise yields under severe drought.

Pea genomic selection for Italian environments.

BACKGROUND: A thorough verification of the ability of genomic selection (GS) to predict estimated breeding values for pea (Pisum sativum L.) grain yield is pending. Prediction for different environments (inter-environment prediction) has key importance when breeding for target environments featuring high genotype × environment interaction (GEI). The interest of GS would increase if it could display acceptable prediction accuracies in different environments also for germplasm that was not used in model training (inter-population prediction). RESULTS: Some 306 genotypes belonging to three connected RIL populations derived from paired crosses between elite cultivars were genotyped through genotyping-by-sequencing and phenotyped for grain yield, onset of flowering, lodging susceptibility, seed weight and winter plant survival in three autumn-sown environments of northern or central Italy. The large GEI for grain yield and its pattern (implying larger variation across years than sites mainly due to year-to-year variability for low winter temperatures) encouraged the breeding for wide adaptation. Wider within-population than between-population variation was observed for nearly all traits, supporting GS application to many lines of relatively few elite RIL populations. Bayesian Lasso without structure imputation and 1% maximum genotype missing rate (including 6058 polymorphic SNP markers) was selected for GS modelling after assessing different GS models and data configurations. On average, inter-environment predictive ability using intra-population predictions reached 0.30 for yield, 0.65 for onset of flowering, 0.64 for seed weight, and 0.28 for lodging susceptibility. Using inter-population instead of intra-population predictions reduced the inter-environment predictive ability to 0.19 for grain yield, 0.40 for onset of flowering, 0.28 for seed weight, and 0.22 for lodging susceptibility. A comparison of GS vs phenotypic selection (PS) based on predicted genetic gains per unit time for same selection costs suggested greater efficiency of GS for all traits under various selection scenarios. For yield, the advantage in predicted efficiency of GS over PS was at least 80% using intra-population predictions and 20% using inter-population predictions. A genome-wide association study confirmed the highly polygenic control of most traits. CONCLUSIONS: Genome-enabled predictions can increase the efficiency of pea line selection for wide adaptation to Italian environments relative to phenotypic selection.

Phenolic Content and Antioxidant Activity in Trifolium Germplasm from Different Environments.

Phenolics are important mediators in plant-environment interactions. The presence and concentration of phenolic compounds and their antioxidant activity were evaluated in leaves and flowers of a set of Trifolium species originating from contrasting environments encompassing lowland and mountain sites. The current germplasm proved a great reservoir of phenolic compounds, with different chemical structure and, possibly, diversified biological activity. Germplasm groups with specific phenolic composition were observed. In some cases, different patterns bore a taxonomic meaning. Lowland germplasm showed higher concentration of total phenolics in leaves than mountain accessions (50.30 vs. 34.19 mg/g dry matter (DM)), while the latter had higher concentration in flowers (114.16 vs. 57.44 mg/g DM). Outstanding concentration of isoflavones was observed in leaves of lowland germplasm (24.19 mg/g DM), and of both proanthocyanidins and flavonoids in flowers of mountain germplasm (53.81 and 56.62 mg/g DM, respectively). The pattern of phenolic composition in lowland and mountain germplasm was suggestive of different adaptive strategies. Three assays of antioxidant activity were tested, which were characterised by rather different reactivity towards phenolic composition. The scavenging activity was higher for leaf extracts of lowland germplasm, and for flower extracts of mountain germplasm. Besides identifying germplasm of interest, this study also suggested possible links between environmental factors and concentration and composition of phenolic compounds.

GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal Drought.

Terminal drought is the main stress that limits pea ( L.) grain yield in Mediterranean-climate regions. This study provides an unprecedented assessment of the predictive ability of genomic selection (GS) for grain yield under severe terminal drought using genotyping-by-sequencing (GBS) data. Additional aims were to assess the GS predictive ability for different GBS data quality filters and GS models, comparing intrapopulation with interpopulation GS predictive ability and to perform genome-wide association (GWAS) studies. The yield and onset of flowering of 315 lines from three recombinant inbred line (RIL) populations issued by connected crosses between three elite cultivars were assessed under a field rainout shelter. We defined an adjusted yield, which is associated with intrinsic drought tolerance, as the yield deviation from the value expected as a function of onset of flowering (which correlated negatively with grain yield). Total polymorphic markers ranged from approximately 100 (minimum of eight reads per locus, maximum 10% genotype missing data) to over 7500 markers (minimum of four reads, maximum 50% missing rate). Best predictions were provided by Bayesian Lasso (BL) or ridge regression best linear unbiased prediction (rrBLUP), rather than support vector regression (SVR) models, with at least 400-500 markers. Intrapopulation GS predictive ability exceeded 0.5 for yield and onset of flowering in all populations and approached 0.4 for the adjusted yield of a population with high trait variation. Genomic selection was preferable to phenotypic selection in terms of predicted yield gains. Interpopulation GS predictive ability varied largely depending on the pair of populations. GWAS revealed extensive colocalization of markers associated with high yield and early flowering and suggested that they are concentrated in a few genomic regions.

Genotyping-by-Sequencing and Its Exploitation for Forage and Cool-Season Grain Legume Breeding.

Genotyping-by-Sequencing (GBS) may drastically reduce genotyping costs compared with single nucleotide polymorphism (SNP) array platforms. However, it may require optimization for specific crops to maximize the number of available markers. Exploiting GBS-generated markers may require optimization, too (e.g., to cope with missing data). This study aimed (i) to compare elements of GBS protocols on legume species that differ for genome size, ploidy, and breeding system, and (ii) to show successful applications and challenges of GBS data on legume species. Preliminary work on alfalfa and Medicago truncatula suggested the greater interest of ApeKI over PstI:MspI DNA digestion. We compared KAPA and NEB Taq polymerases in combination with primer extensions that were progressively more selective on restriction sites, and found greater number of polymorphic SNP loci in pea, white lupin and diploid alfalfa when adopting KAPA with a non-selective primer. This protocol displayed a slight advantage also for tetraploid alfalfa (where SNP calling requires higher read depth). KAPA offered the further advantage of more uniform amplification than NEB over fragment sizes and GC contents. The number of GBS-generated polymorphic markers exceeded 6,500 in two tetraploid alfalfa reference populations and a world collection of lupin genotypes, and 2,000 in different sets of pea or lupin recombinant inbred lines. The predictive ability of GBS-based genomic selection was influenced by the genotype missing data threshold and imputation, as well as by the genomic selection model, with the best model depending on traits and data sets. We devised a simple method for comparing phenotypic vs. genomic selection in terms of predicted yield gain per year for same evaluation costs, whose application to preliminary data for alfalfa and pea in a hypothetical selection scenario for each crop indicated a distinct advantage of genomic selection.

Genome-Wide Association Mapping and Genomic Selection for Alfalfa (Medicago sativa) Forage Quality Traits.

Genetic progress for forage quality has been poor in alfalfa (Medicago sativa L.), the most-grown forage legume worldwide. This study aimed at exploring opportunities for marker-assisted selection (MAS) and genomic selection of forage quality traits based on breeding values of parent plants. Some 154 genotypes from a broadly-based reference population were genotyped by genotyping-by-sequencing (GBS), and phenotyped for leaf-to-stem ratio, leaf and stem contents of protein, neutral detergent fiber (NDF) and acid detergent lignin (ADL), and leaf and stem NDF digestibility after 24 hours (NDFD), of their dense-planted half-sib progenies in three growing conditions (summer harvest, full irrigation; summer harvest, suspended irrigation; autumn harvest). Trait-marker analyses were performed on progeny values averaged over conditions, owing to modest germplasm × condition interaction. Genomic selection exploited 11,450 polymorphic SNP markers, whereas a subset of 8,494 M. truncatula-aligned markers were used for a genome-wide association study (GWAS). GWAS confirmed the polygenic control of quality traits and, in agreement with phenotypic correlations, indicated substantially different genetic control of a given trait in stems and leaves. It detected several SNPs in different annotated genes that were highly linked to stem protein content. Also, it identified a small genomic region on chromosome 8 with high concentration of annotated genes associated with leaf ADL, including one gene probably involved in the lignin pathway. Three genomic selection models, i.e., Ridge-regression BLUP, Bayes B and Bayesian Lasso, displayed similar prediction accuracy, whereas SVR-lin was less accurate. Accuracy values were moderate (0.3-0.4) for stem NDFD and leaf protein content, modest for leaf ADL and NDFD, and low to very low for the other traits. Along with previous results for the same germplasm set, this study indicates that GBS data can be exploited to improve both quality traits (by genomic selection or MAS) and forage yield.

Isoflavone Content in Subterranean Clover Germplasm from Sardinia.

Subterranean clover (Trifolium subterraneum) is an important pasture legume, and Sardinia is known as a major centre of diversification of this species. As other legumes, this clover produces biologically active flavonoids including the subclass of isoflavones that are natural phytoestrogens with positive health effects. Present sources of isoflavones for medical/nutraceutical treatments are red clover (Trifolium pratense) and soybean (Glycine max). This study assessed the content and composition of flavonoids in 14 subterranean clover genotypes from Sardinia, grown ex-situ in comparison with two red clover ecotypes, to acquire information on the potential of the species as an alternative source of isoflavones for possible exploitation. Twenty compounds were tentatively identified across the two clovers after HPLC and LC/ESI-MS analyses, including clovamide, four flavonols, and 15 isoflavones. Most compounds were present as glucosides or glucosyl malonates. Subterranean clover extracts mainly comprised of derivatives of the isoflavones genistein, biochanin A, and formononetin. Compared to red clover, subterranean clover had higher content of total isoflavones and lower concentration of total flavonols. The isoflavone concentration in subterranean clover was higher than literature data for soybean or red clover. The existing genotypic variation warrants the possibility of selecting varieties with high isoflavone concentration for nutraceutical or pharmaceutical purposes.

Variation in Herbage Biochemical Composition among Pitch Trefoil (Bituminaria bituminosa) Populations from Elba Island, Italy.

This study assessed the variation in herbage protein and fiber content as well as concentration of furocoumarins, plicatin B, (E)-werneria chromene, and pterocarpans of pitch trefoil (Bituminaria bituminosa) germplasm sampled in situ in both summer and autumn in Elba Island, Italy. Populations were sampled from a range of climatic and edaphic conditions, on light soils with pH > 7.5. Valuable variation occurred for forage quality and chemical composition. The quality features, relevant for a possible forage utilization of the species, were promising for the leaves, which represented about two-thirds of the total aerial biomass in early summer and the whole green regrowth in autumn. The species can also represent a source of psoralen, angelicin, plicatin B, bitucarpin A, and erybraedin C for possible pharmaceutical and/or agrochemical use, with individual populations showing high levels of these compounds.

Accuracy of genomic selection for alfalfa biomass yield in different reference populations.

BACKGROUND: Genomic selection based on genotyping-by-sequencing (GBS) data could accelerate alfalfa yield gains, if it displayed moderate ability to predict parent breeding values. Its interest would be enhanced by predicting ability also for germplasm/reference populations other than those for which it was defined. Predicting accuracy may be influenced by statistical models, SNP calling procedures and missing data imputation strategies. RESULTS: Landrace and variety material from two genetically-contrasting reference populations, i.e., 124 elite genotypes adapted to the Po Valley (sub-continental climate; PV population) and 154 genotypes adapted to Mediterranean-climate environments (Me population), were genotyped by GBS and phenotyped in separate environments for dry matter yield of their dense-planted half-sib progenies. Both populations showed no sub-population genetic structure. Predictive accuracy was higher by joint rather than separate SNP calling for the two data sets, and using random forest imputation of missing data. Highest accuracy was obtained using Support Vector Regression (SVR) for PV, and Ridge Regression BLUP and SVR for Me germplasm. Bayesian methods (Bayes A, Bayes B and Bayesian Lasso) tended to be less accurate. Random Forest Regression was the least accurate model. Accuracy attained about 0.35 for Me in the range of 0.30-0.50 missing data, and 0.32 for PV at 0.50 missing data, using at least 10,000 SNP markers. Cross-population predictions based on a smaller subset of common SNPs implied a relative loss of accuracy of about 25% for Me and 30% for PV. Genome-wide association analyses based on large subsets of M. truncatula-aligned markers revealed many SNPs with modest association with yield, and some genome areas hosting putative QTLs. A comparison of genomic vs. conventional selection for parent breeding value assuming 1-year vs. 5-year selection cycles, respectively, indicated over three-fold greater predicted yield gain per unit time for genomic selection. CONCLUSIONS: Genomic selection for alfalfa yield is promising, based on its moderate prediction accuracy, moderate value of cross-population predictions, and lack of sub-population structure. There is limited scope for searching individual QTLs with overwhelming effect on yield. Some of our results can contribute to better design of genomic selection experiments for alfalfa and other crops with similar mating systems.

Clovamide and Flavonoids from Leaves of Trifolium pratense and T. pratense subsp. nivale Grown in Italy.

The phenolic content and composition in leaves of Trifolium pratense (red clover) and T. pratense subsp. nivale (snow clover) grown in Italy were evaluated by means of ultraperformance liquid chromatography (UPLC) coupled with photodiode array and mass spectrometry detectors. Compound identification was based on UV and MS data comparing results with those of reference compounds. Quantitative evaluation of all detected compounds was based on calibration curves obtained with available standards. Several phenolics were identified in both extracts, including clovamide, flavonols and isoflavones as their glycosilated and malonated derivatives. The total phenolic content was higher in red clover (53.7 ± 2.2 mg/g dry weight) than in snow clover (44.4 ± 4.9 mg/g dry weight). Red clover contained higher amounts of clovamide and isoflavones (15.6 ± 0.6 and 24.6 ± 1.6 mg/g dry weight, respectively) than snow clover (8.2 ± 0.1 mg/g and 16.9 ± 0.4 mg/g dry weight, respectively), while flavonols were quantified almost in the same amount in both extracts (13.2 ± 0.6 mg/g and 15.8 ± 0.6 mg/g dry weight in red clover and snow clover, respectively). Red clover was characterized by the presence of quercetin, formononetin and biochanin A derivatives as the most abundant flavonoids, whereas snow clover was characterized by higher amounts of quercetin and prunetin derivatives. This investigation, conducted for the first time on phenolics from T. pratense subsp. nivale, revealed the presence in this plant of several flavonoid derivatives the same as in T. pratense. The higher amount of prunetin in snow clover suggest a possible role of this isoflavone as a chemotaxonomic marker for this subspecies. Moreover, snow clover may represent an interesting new source of natural isoflavones with a different concentration pattern than in red clover.

Triterpenoid glycosides from Medicago sativa as antifungal agents against Pyricularia oryzae.

The antifungal properties of saponin mixtures from alfalfa (Medicago sativa L.) tops and roots, the corresponding mixtures of prosapogenins from tops, and purified saponins and sapogenins against the causal agent of rice blast Pyricularia oryzae isolates are presented. In vitro experiments highlighted a range of activities, depending upon the assayed metabolite. The antifungal effects of the most promising prosapogenin mixture from alfalfa tops were confirmed by means of in planta tests using three different Italian cultivars of rice (Oryza sativa L. ssp. japonica), known to possess high, medium, and low blast resistance. The evidenced antifungal properties of the tested metabolites allowed some considerations on their structure-activity relationship. Results indicate that prosapogenins are active compounds to prevent the fungal attack of P. oryzae on different rice cultivars. Therefore, if properly formulated, these substances could represent a promising and environmentally friendly treatment to control rice blast.

Variation in terpene and linear-chain hydrocarbon content in yarrow (Achillea millefolium L.) germplasm from the Rhaetian Alps, Italy.

Yarrow (Achillea millefolium L.) is a herbaceous species common in the Alpine region of Europe and used in folk medicine since antiquity. Its organs are rich in monoterpenes and sesquiterpenes, two subclasses of plant terpenoids with relevant ecological significance, which were reported as valuable markers for the traceability of mountain dairy products. The variability in chemical composition of yarrow germplasm may be related with its genetic diversity, accounting for possible differences in medical properties, and supporting its use as a specific territorial marker. Aim of this work was to assess the leaf chemical composition of 16 yarrow populations collected at altitudes exceeding 1600 m in three valleys of the Rhaetian Alps, Italy, and jointly evaluated in a lowland site. The most abundant compounds detected generally differed from those of the germplasm from other countries. A trend of valley-specific pattern of composition was evident. However, the variability among individual populations was even more remarkable, regardless of their valley of origin. The concentrations of sesquiterpene hydrocarbons, oxygenated monoterpenes, and oxygenated sesquiterpenes discriminated the populations in multivariate analysis. A few prevailing chemotypes were characterized, which differed from those previously reported in the literature. The geographic isolation from other germplasms, and the local ecotypization, likely originated a chemically distinct gene pool.

Chemical investigation of saponins from twelve annual Medicago species and their bioassay with the brine shrimp Artemia salina.

The saponin and sapogenin composition of the aerial growth of 12 annual Medicago species sampled at full senescence were investigated. Saponins were extracted from the plant material and obtained in a highly pure grade by reverse-phase chromatography, with a yield ranging from 0.38 +/- 0.04% to 1.35 +/- 0.08% dry matter, depending on the species. Sapogenins were then obtained after acid hydrolysis of saponins, and evaluated by GC/FID and GC/MS methods. Different compositions of the aglycone moieties were observed in the 12 Medicago species. Medicagenic acid was the dominant aglycone in M. x blancheana, M. doliata, M. littoralis, M. rotata, M. rugosa, M. scutellata, M. tornata and M. truncatula, bayogenin and hederagenin in M. arabica and M. rigidula, echinocystic acid in M. polymorpha, and soyasapogenol B in M. aculeata. The purified saponin mixtures, characterized by different chemical compositions, were then used in a toxicity test using the brine shrimp Artemia salina. The most active compounds were the saponins from M. arabica and M. rigidula with LD50 values of 10.1 and 4.6 microg/mL, respectively. A structure-activity relationship for the tested saponin mixtures was observed.

Triterpenoid glycosides from the leaves of two cultivars of Medicago polymorpha L.

The saponin composition of leaves from the Medicago polymorpha cultivars 'Santiago' and 'Anglona' belonging to the botanical varieties brevispina and vulgaris, respectively, was investigated by a combination of chromatographic, spectroscopic, and spectrometric techniques. Several compounds were detected and quantitated by HPLC analysis using the external standard method. Twelve triterpene saponins (1-12) were purified by reverse-phase chromatography and their structures elucidated by spectroscopic (1D and 2D NMR, ESI-MS/MS) and chemical methods. They were identified as glycosides of echinocystic acid, hederagenin, caulophyllogenin, bayogenin, and soyasapogenol B. Two of them (2, 10) were previously reported in M. polymorpha; five of them (4, 6, 7, 9, 12) were already identified in other Medicago species; and three of them (1, 8, 11) were found in other plant genera. The two saponins identified as 3-O-α-L-arabinopyranosyl-28-O-[ß-D-glucopyranosyl(1→6)ß-D-glucopyranoside] echinocystic acid (3) and 3-O-α-L-arabinopyranosyl-28-O-ß-D-glucopyranoside echinocystic acid (5) are newly identified natural compounds. The presence of echinocystic acid is reported here for the first time in the genus Medicago. Saponins from the cultivar 'Anglona' were characterized by a higher amount of echinocystic acid glycosydes, whereas saponins from the cultivar 'Santiago' were characterized by a higher amount of hederagenin glycosydes.