Search for bioactive molecules to pest insect control through comparative metabolomic analysis among genotypes of commercial interest

• Grant number: 21/11878-3
• Support type: Regular Research Grants
• Duration: July 01, 2022 - June 30, 2024
• Field of knowledge: Physical Sciences and Mathematics - Chemistry - Organic Chemistry
• Principal researcher: Moacir Rossi Forim
• Grantee: Moacir Rossi Forim
• Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil

Abstract

Despite significant advances in the production processes and agricultural controls, insects are still the main competitors of humans for food. As a result of breeding programs for productivity gains, among other characteristics, most agricultural cultivars currently available do not present efficient resistance mechanisms against insects. Therefore, the comparative metabolomic study between wild genotypes and cultivars can retrieve fundamental information on constitutive and induced resistance mechanisms, associating these results with plant defense genes, proteins, and secondary metabolites, opening a research field for the development of sustainable products and practices for pest control. Accordingly, this project aims metabolomic studies of different soybean genotypes (Glycine max L.), evaluating their resistance mechanisms along with different biotic and abiotic stress factors. This research line will be carried out comparing biological and metabolomic data. The soybean genotypes will be cultivated in a greenhouse under controlled conditions. To evaluate the induced resistance mechanisms, the genotypes will be subjected to stress factors by radiation, water, and herbivory (Spodoptera frugiperda). Biological assays will be performed evaluating antibiosis and antixenosis responses. The metabolomic analysis will be performed by overlaying chromatographic, spectrometric (UHPLC-qToF MS and Headspace-GC-MS/MS), and spectroscopic (NMR) data. The instrumental extraction and optimization methods will be developed using chemometric tools. Combining biological and chemical data with multivariate (un)supervised statistical methodologies can reveal deep insight into complex patterns of botanical defense metabolites, identifying resistant genotypes and bioactive molecules against pest insects. (AU)