Stefano Pavan

Traditional Brassica crops are valuable sources of bioactive compounds and contribute to the preservation of agricultural biodiversity. This study compared the phytochemical composition of two Apulian Brassica landraces, ‘Cavolo riccio’ (Brassica oleracea var. acephala) and ‘Cima di rapa di febbraio’ (Brassica rapa subsp. sylvestris), and evaluated the effects of boiling and steaming on their nutritional quality. Samples were analyzed in raw, boiled, and steamed forms for dry matter, polyphenols, flavonoids, phytosterols, photosynthetic pigments, anthocyanins, and glucosinolates. The two landraces showed complementary phytochemical profiles. ‘Cavolo riccio’ contained higher levels of dry matter, polyphenols, chlorophylls, carotenoids, and anthocyanins, whereas ‘Cima di rapa di febbraio’ was characterized by a substantially higher glucosinolate content. Phytosterol concentrations were similar in both landraces, averaging about 7 mg 100 g⁻¹ FW. Cooking significantly affected most bioactive compounds. Boiling caused the greatest reductions in dry matter, phenolic compounds, pigments, and glucosinolates, whereas steaming generally ensured higher retention. Total polyphenols decreased by 59% after boiling, while total glucosinolate content declined from 95.6 to 25.9 μmol kg⁻¹ FW. In contrast, steamed samples largely preserved glucosinolates and polyphenols, although reductions in flavonoids, carotenoids, and anthocyanins were still observed. Phytosterols were unaffected by thermal processing. These findings highlight the nutritional complementarity of the two Brassica landraces and identify steaming as the most suitable cooking method for preserving their nutraceutical value while supporting the valorization of local vegetable biodiversity.

Fusarium wilt, caused by Fusarium oxysporum f. sp. melonis (FOM), is a main disease of melon (Cucumis melo L.). FOM 1.2 is the most widespread and detrimental variant of FOM, causing substantial economic losses under severe disease conditions. Current information suggests that resistance to race 1.2 (FOM 1.2) is controlled by multiple recessive genes and is strongly influenced by the environment. Therefore, identifying genetic polymorphisms within diverse melon populations is essential to elucidate the loci and putative candidate genes associated with resistance. The objective of this investigation was to identify single-nucleotide polymorphism (SNP) and structural variant (SV) markers associated with FOM 1.2 resistance utilizing a panel of 160 genotypes through a genome-wide association study (GWAS). Phenotypic evaluation was performed two weeks after sowing, at the first-true-leaf stage, on 2400 individual plants inoculated by the root dip method with a concentration of about 10⁶ spores/mL. Biochemical and disease-related traits, including area under disease progress curve (AUDPC), disease severity index (DSI), standardized AUDPC (SAUDPC), latent period (LP), catalase, peroxidase activity, and ascorbate peroxidase activity were measured 35 days after inoculation. PCA identified eighty-three individual melon plants with a broad range of disease-response variation. Genotyping-by-sequencing (GBS) was conducted on these plants, resulting in the identification of 737,435 SNPs and 75,133 SVs. Evaluation of the population structure outlined four genetic groups, including one associated with germplasm highly resistant to FOM 1.2. We used SNP data to describe linkage disequilibrium (LD), which was estimated to decay at 14 kb, on average. A GWAS was performed using the Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK) method, which revealed nine SNPs significantly associated with several disease indices, namely ascorbate peroxidase activity, AUDPC, catalase, peroxidase activity, rAUDPC, and SAUDPC. Also, eight SVs were associated with AUDPC and relative area under disease progress curve (rAUDPC), including translocation and deletion types. In addition, GWAS using the fixed and random model circulating probability unification (FarmCPU) method unveiled thirteen SVs associated with rAUDPC, peroxidase activity and ascorbate peroxidase activity, including translocation and inversion types. According to the performed models of GWAS, several significant SNPs and SVs, associated with putative candidate genes, including multidrug resistance-associated protein 6 (MRP6), LOB domain-containing protein 15 (LBD15), phosphomannomutase, and NADH-ubiquinone oxidoreductase B8 subunit, which may be involved in FOM 1.2 resistance. However, these findings represent a preliminary genome-wide survey and require further validation using high-coverage or long-read sequencing approaches. The results provide remarkable insights into the genetic control of FOM 1.2 resistance and valuable information for the implementation of the putative molecular markers identified in this study in melon breeding programs.

In the scenario of ongoing climate changes, the selection of plant genotypes with high salt tolerance is emerging as the most sustainable strategy to safeguard crop yield and quality and make productive use of salinized soils. Glassworts are annual and perennial halophytes found in inner and coastal wastelands, indistinctly consumed as high-nutritional green vegetables. Traditional taxonomic classification based on morphological traits can be very challenging in glasswort, due to phenotypic plasticity, reduced plant morphology, and inbreeding. In this work, we used DNA-based molecular tools to overcome such constraints and assess inter-generic and inter-specific genetic diversity in a collection of ecotypes from different Apulian areas. A fast and reliable Allele-Specific PCR assay was optimized to enable molecular detection of annual and perennial genera. Species-level classification was obtained through a similarity- and phylogeny-based approach relying on matK and rbcL DNA barcoding. Combined DNA tools identified perennial samples as Sarcocornia fruticosa and Arthrocaulon macrostachyum, along with annual Salicornia europaea, and phylogenetic trees unveiled genetic distances between glassworts, which clustered according to life cycle. The relationship between genotypes and nutritional profiles was finally investigated, suggesting that environmental factors may play a predominant role over taxonomic relatedness in shaping interspecific differences in nutrient composition of the analyzed samples.

Predicting phenotypes from genomic data can significantly advance agriculture. Genomic selection, which uses genome-wide DNA markers to identify individuals with high genetic value, enhances the accuracy of breeding programs. While linear models are routinely used for genomic selection (GS), machine learning (ML) models offer complementary potential. In this study, robust ML-based models were developed to predict five phenotypic traits—three related to flowering time and two to leaf number—in Arabidopsis thaliana, a model plant with a fully sequenced genome. Using explainable artificial intelligence (XAI), specifically SHapley Additive exPlanations (SHAP) values, we identified SNPs that contributed most to trait prediction. Many of these SNPs were located in or near genes known to regulate flowering and stem elongation, such as DOG1 and VIN3, supporting the biological plausibility of the model. SHAP also enabled local interpretability at the single-plant level, revealing the genotypic basis of individual predictions. Our results indicate that integrating ML with XAI improves model interpretability and provides predictive performance comparable to traditional methods. This approach confirms known genotype–phenotype relationships and highlights new candidate loci, paving the way for functional validation. The proposed methodology offers promising applications in precision breeding and translation of insights from Arabidopsis to crop species.

Background: Orobanchaceae are parasitic weeds causing substantial yield losses in many crops, including pea ( Pisum sativum L.). Within host species, genotypes that display enhanced resistance to Orobanchaceae often exude low levels of strigolactones (SLs) from their roots, in line with evidence that SLs stimulate the germination of Orobanchaceae. However, to which extent genetically determined low SL exudation contributes to field resistance to Orobanchaceae was poorly investigated. Here, we studied the relation between SL exudation and field response to Orobanche crenata Forsk. ( Oc ) in pea. Results: The screening of a germplasm panel identified three novel sources of field resistance to Oc and revealed an association between field resistance and low SL exudation. Although the SL-deficient mutants rms1 and rms5 were more resistant than their wild-type backgrounds, they still suffered substantial Oc parasitization. Genetic analysis and RNA-seq of recombinant inbred lines uncovered both SL-dependent and SL-independent mechanisms contributing to the near-complete resistance to Oc previously reported in the breeding line ROR12, and identified candidate genes possibly underlying resistance loci. Conclusions: Besides identifying novel sources of resistance to Oc , our study indicates that reduced or absent SL exudation alone is not sufficient to confer complete field resistance to Oc in pea. This suggests the necessity of exploring SL-independent resistance mechanisms for breeding purposes. Further investigations are needed to clarify whether a similar scenario applies to other crops affected by Orobanchaceae, and to characterize genes causally related to Oc resistance.

Background/Objectives: Unhealthy nutrition and lifestyles contribute to the development of chronic diseases such as cardiovascular disease, type 2 diabetes, and colorectal cancer. The Western diet can impair gastrointestinal motility and function. The underlying mechanisms that lead to changes in the mucus barrier and mucin profiles in response to these dietary patterns are still being studied. In mice, dietary fiber intake can improve the intestinal mucosal barrier function, enhance the differentiation process of goblet cells, and increase acidic mucin production. Our study aimed to investigate the effects of a high-fat diet (HFD) on colonic mucin expression and to assess whether chickpea accessions, known for their nutritional benefits, can mitigate these adverse effects. Methods: We investigated the effects of an HFD and an HFD associated with two accessions of chickpeas (HFD + MG_13; HFD + PI358934) on the mucin expression in murine colons of mice by conventional histochemistry and lectin-histochemistry, combined with chemical treatment and enzymatic digestion and immunohistochemistry. We evaluated possible alterations of Muc2, the main mucin secreted by the mucous cells of the colon. Results: HFD significantly reduced the expression of the mucin Muc2 and altered its composition in the colon. Compared to the CTRL group, distal and proximal measurements for HFD + PI, respectively, showed reductions of 78% and 36%; for the distal colon, a reduction of 34% was also observed for both the HFD and HFD + MG_13 diets. Changes in mucin glycosylation, including sialylation and sulfation, as well as residues such as N-acetylglucosamine, GalNAc, Mannose, and Galactose, were observed, suggesting a beneficial influence of chickpeas on mucosal integrity. In HFD + MG_13 these effects were reduced and resulted similar to the control. Conclusions: HFD reduces Muc2 expression in the colon and alters mucin composition: chickpea accessions, particularly MG_13, partially restore Muc2 levels and mucin oligosaccharide profiles, suggesting protective effects on the intestinal mucosal barrier.

The species Cucumis melo L. includes two neglected and underutilized vegetable crops, cucumber melon (C. melo subsp. melo var. chate) and snake melon (C. melo subsp. melo var. flexuosus). In particular, cucumber melon was highly popular in Mediterranean civilizations during Antiquity and the Middle Ages, whereas today its cultivation is mostly confined to the Salento area of southern Italy. Here, we describe the collection and characterization of thirteen cucumber melon and two snake melon populations from Salento. Whole-genome resequencing of DNA pools was performed to investigate genetic diversity within and among populations. The cucumber melon population UBGCMC111, most widely cultivated and marketed, exhibited the lowest heterozygosity, possibly reflecting more intense selection by farmers. Hierarchical clustering revealed genetic divergence of UBGCMC111 and UBGCMC053, the latter originating from a unique area of Salento with linguistic and cultural ties to Greek heritage. Despite some unique patterns of variation, snake melons clustered together with cucumber melons, suggesting overall genetic similarity. A total of 1,307 alleles were fixed and private to different populations under study, potentially valuable for their traceability. Some of them were associated with genes possibly underlying deeply grooved and pale green pepo phenotypes of the populations UBGCMC111 and UBGCMC124, respectively. Replicated field trials enabled germplasm characterization and the selection of agronomically superior populations. Overall, this study safeguards valuable C. melo genetic diversity from further genetic erosion. Additionally, it provides genomic and phenotypic data laying a foundation for integrating unexplored genetic resources into mainstream agrifood systems and breeding programs.

Background: Advances in DNA sequencing revolutionized plant genomics and significantly contributed to the study of genetic diversity. However, predicting phenotypes from genomic data remains a challenge, particularly in the context of plant breeding. Despite significant progress, accurately predicting phenotypes from high-dimensional genomic data remains a challenge, particularly in identifying the key genetic factors influencing these predictions. This study aims to bridge this gap by integrating explainable artificial intelligence (XAI) techniques with advanced machine learning models. This approach is intended to enhance both the predictive accuracy and interpretability of genotype-to-phenotype models, thereby improving their reliability and supporting more informed breeding decisions.Results: This study compares several ML methods for genotype-to-phenotype prediction, using data available from an almond germplasm collection. After preprocessing and feature selection, regression models are employed to predict almond shelling fraction. Best predictions were obtained by the Random Forest method (correlation = 0.727 ± 0.020, an R² = 0.511 ± 0.025, and an RMSE = 7.746 ± 0.199). Notably, the application of the SHAP (SHapley Additive exPlanations) values algorithm to explain the results highlighted several genomic regions associated with the trait, including one, having the highest feature importance, located in a gene potentially involved in seed development.Conclusions: Employing explainable artificial intelligence algorithms enhances model interpretability, identifying genetic polymorphisms associated with the shelling percentage. These findings underscore XAI’s efficacy in predicting phenotypic traits from genomic data, highlighting its significance in optimizing crop production for sustainable agriculture.