UNVEILING HIDDEN CHEMICAL BRAZILIAN BIODIVERSITY: A METABOLOMIC APPROACH TO DISCOVER NOVEL NATURAL PRODUCTS

• Grant number: 25/06762-7
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: September 01, 2025
• End date: August 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Chemistry - Organic Chemistry
• Principal Investigator: Vanderlan da Silva Bolzani
• Grantee: Marcelino Santos do Rosario
• Supervisor: Tomas Pluskal
• Host Institution: Instituto de Química (IQ). Universidade Estadual Paulista (UNESP). Campus de Araraquara. Araraquara , SP, Brazil
• Institution abroad: Institute Of Organic Chemistry And Biochemistry Of The Czech Academy Of Sciences, Czech Republic
• Associated to the scholarship: 24/02602-2 - Metabolomics, a cutting-edge methodology for micromolecular identification and chemosystematic studies in Bignoniaceae, BP.DR

Abstract

Traditionally, phytochemistry have relied on bioactivity- and/or chemical signature-guided fractionation approaches to isolate and identify individual constituents from complex secondary metabolites mixtures. Currently, the studies on natural products have changed away from traditional activity-guided approaches to bioanalytical methods. The advances on high-resolution mass spectrometry have provided valuable molecular insights into the several level of life, highlighting studies on the molecular richness of terrestrial biodiversity. The state-of-art of non-targeted metabolomics aims to identify and quantify all small molecules in different conditions or timepoints in a biological system, is an advanced new tool to understand the biologically functions of natural products in nature. Thus, this project aims to integrate genomics and metabolomics datasets to prioritize the identification of novel, bioactive metabolites and link them to their biosynthetic pathways. It is pioneer research with Brazilian plants, using omics science with advanced bioinformatics tools to discovery of novel natural products, and thus, provides new data to the 375 species of Angiospermae. The methodology will systematically annotate and map the chemical diversity of Bignoniaceae plant species. By molecular networking, machine learning-based compound classification, and structural annotation algorithms, the objective is to streamline the discovery process and enhance the annotation and identification of all metabolites in the studied taxa. The experimental study will be focus on (1) sample preparation, (2) mass spectrometry analysis, (3) compound annotation, prioritization, isolation, and (4) multi-omics studies. These methodologies have proven highly effective for the analysis of small molecules.