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Chemoprospection of species of Aspergillus section Flavi isolated from peanuts, Brazil nuts and sugar cane

Grant number: 21/00956-3
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): March 01, 2021
Effective date (End): March 31, 2022
Field of knowledge:Agronomical Sciences - Food Science and Technology - Food Science
Principal researcher:Marta Hiromi Taniwaki
Grantee:Augusto Leonardo dos Santos
Home Institution: Instituto de Tecnologia de Alimentos (ITAL). Agência Paulista de Tecnologia dos Agronegócios (APTA). Secretaria de Agricultura e Abastecimento (São Paulo - Estado). Campinas , SP, Brazil
Associated research grant:17/50349-0 - Plan of institutional development in research of the Institute of Food Technology - ITAL (PDIp), AP.PDIP

Abstract

The presence of mycotoxins in food leads to the loss of a large amounts of food. Species of Aspergillus are common in tropical and sub tropical countries like Brazil and can grow in foods such as peanuts, nuts, grains and fruits, generating the accumulation of mycotoxins such as aflatoxins, becoming them unsafe for consumption. Thus, understanding the metabolism of toxigenic fungi in food is of great importance for public health. Tools for de-replication, such as the Global Natural Products Social Molecular Networking (GNPS) platform, have become an alternative to organize mass spectra in the form of molecular networks to understand the metabolome of one or more organisms from a large amount of data obtained from liquid chromatography analysis coupled with high-resolution mass spectrometry. Therefore, the profile metabolome of each species of Aspergillus and the changes in the incubations that occurred can be vizualized through the molecular networks obtained by the GNPS platform. In this study, the metabolites of 3 species of A. section Flavi: A. flavus, A. nomius and A. novoparasiticus, isolated from peanuts, brazil nuts and sugar cane, respectively will be analyzed at different temperatures and incubation periods on substrates based on these foods. The data obtained will allow the construction of moleular networks to know the biosynthetic pathways according to the product classes that will be identified following the grouping of mass spectra by the GNPS platform. (AU)

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