Bioprospecting of the Amazonian biodiversity in the Tembé community and its biotechnological potential in the context of sustainable development and environmental preservation

• Grant number: 24/23121-2
• Support Opportunities: Regular Research Grants
• Start date: April 01, 2025
• End date: March 31, 2028
• Field of knowledge: Biological Sciences - Microbiology - Biology and Physiology of Microorganisms
• Principal Investigator: Renata Cristina Picão
• Grantee: Renata Cristina Picão
• Host Institution: Centro de Ciências Biológicas e da Saúde (CCBS). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Associated researchers: Gilmar Perbiche Neves

Abstract

Bacteria, fungi, viruses, and small animals play fundamental roles in maintaining ecosystem balance. These microscopic organisms can also present biotechnological potential for applications in bioremediation, the production of antimicrobials, and other molecules for various sectors, in addition to indicating possible environmental impacts that may compromise the health of the system. This proposal aims to describe, in a transdisciplinary manner, the diversity of microorganisms in representative samples from the Amazon ecosystem, including river waters, forest and agricultural soils, and fish consumed by the Tembé indigenous community. The Uraim River, which flows near the village, is impacted by urbanized areas upstream, and thus the microbiota assessment will be conducted by measuring anthropogenic impact through various physicochemical and biological analyses of water and sediment samples from the river, fish, and soils, as well as georeferencing to record land use indices. Traditional knowledge will be leveraged to guide the selection of sampling sites, aiming to assess the diversity of environments and prospect microorganisms with biotechnological potential. The approach will involve techniques such as metagenomics, culturomics for viruses, bacteria, and fungi, phenotypic, genotypic, and structural characterization of identified organisms, and integration of results with computational approaches based on machine learning. This comprehensive study will enable us to prospect and propose solutions to stimulate sustainable practices for the maintenance or recovery of natural capital, promote the bioeconomy, and identify potential indicators of environmental impacts in the region. (AU)