Speakers

Biography

Alessandra Renella is a Ph.D. student in Biology and Applied Sciences at the University of Molise researching the characterization and conservation of agro-biodiversity, particularly autochthonous legume ecotypes. In detail, supervised by Prof. Gabriella Stefania Scippa and Prof. Dalila Trupiano, she is studying the metabolomic profile of autochthonous legume ecotypes to explore their characteristics and support their valorization and, more in general, biodiversity conservation strategies. Ms. Renella obtained a master’s degree from the University of Molise in 2021, during which she worked on the physiological and biochemical responses of autochthonous bean ecotypes to abiotic stress.

Abstract

Autochthonous ecotypes or local varieties are plant genetic resources characterized by high genetic variability, specific adaptation to the environmental conditions of the cultivation area (i.e., tolerance to biotic and abiotic stresses), and the presence of specialized metabolites (terpenoids, flavonoids, alkaloids) and health-promoting compounds. Thus, their diversity is relevant not only in nutritional terms but also due to the content of phytochemicals or secondary metabolites. However, local varieties are gravely threatened by extinction mainly for their replacement by modern genetically uniform varieties. Untargeted metabolomics is a strong approach that allows for investigating a wide range of metabolite classes. It gives crucial data for identifying distinct local varieties, identifying their metabolic fingerprints, and promoting their valorization and conservation. The current study aimed to provide a global view of the metabolite diversity of three autochthonous lentil ecotypes from different villages of Molise region (Italy) - Capracotta, CA; Rionero Sannitico, RS; and Agnone, A - in comparison to one ecotype from Umbria region (Italy) - Castelluccio di Norcia, CS - and three autochthonous bean ecotypes from Molise region (Italy) - Ciliegino bean, CI -, Basilicata region (Italy) - Spanish Ciliegino bean (CI SP). Untargeted metabolomics, performed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) allowed the detection of 344 and 544 differential accumulated metabolites (DAMs) for bean (Be) and lentil (Le) ecotypes, respectively. The annotation of the DAMs, by consulting home-made spectral and MS libraries (e.g., the GNPS Public Spectral Library) and a molecular network approach (MetGem and Cytoscape software), allowed 53% and 54% of them to be assigned to different metabolic categories (10 for Be and 13 for Le). Flavonoids were the most represented metabolic category (66 for Be, 87 for Le), followed by amino acids and derivatives (59 for Be, 61 for Le) and cinnamic acids (22 for Be, 50 for Le). Significant variations in Be and Le metabolite composition were also observed through the combination of univariate and multivariate statistical analyses. According to the PCA scores plot, A, CA, and RS lentil ecotypes were separated from CS by the PC1 (56 % variance).

Similarly, CI and CI SP were separated from SMR by the PC1 (58 % of variance). Two clusters for Be and Le, respectively, were revealed by hierarchical cluster analysis, indicating the distinctiveness of the traditional varieties. A partial least squares-discriminant analysis (PLS-DA) found 15 variables important in the projection (VIP) scores of metabolites belonging to flavonoids, amino acids, and terpenoids metabolic categories for Le and flavonoids for Be. The enrichment analysis is in progress to assess the presence of enriched metabolomic categories, allowing the selection of ecotypes-specific metabolic features. In addition, the antibacterial and antioxidant activity of both legume extracts has been evaluated and analyzed in light of each ecotype's distinct metabolic profiles to understand better the beneficial/ health-promoting effect of some bioactive compounds.