Bioinformatic characterization of agroforestry microbiota: evaluating soil health through microorganisms

• Grant number: 24/03870-0
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: July 01, 2024
• End date: September 27, 2024
• Field of knowledge: Agronomical Sciences - Agronomy - Soil Science
• Principal Investigator: Tsai Siu Mui
• Grantee: Rafael Barty Dextro
• Supervisor: Naoaki Ono
• Host Institution: Centro de Energia Nuclear na Agricultura (CENA). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Institution abroad: Nara Institute Of Science And Technology, Japan
• Associated to the scholarship: 23/08974-6 - Evaluation of agroforest connected with the Amazons soil microbiology, BP.PD

Abstract

The study of microbial biodiversity has expanded to include molecular analysis that generate big data on a complex community's diversity. The processing of these analysis can produce very interesting and informative results on ecological and biological aspects of local environmental conditions. However, to process this data, bioinformatics tools are required with an extensive know-how of different algorithms and programming to process, trim and properly evaluate the obtained results. The sequencing of gDNA from soil samples retrieved from agroforestry systems in Tomé-Açu (Pará-Brazil) are useful to determine how the microbiota intertwines with physicochemical soil conditions and overall plant fitness, ultimately affecting the nutritional value of fruits that are essential both economically and as main food items of the local communities, such as açai. This project's aim to characterize the microbial community from samples collected in two agroforestry systems located in Tomé-Açu that have different ages (18 and 25 years). The selected areas are in close vicinity to one another and share similar management practices, including the consortium of two main plant species (Euterpe oleracea - açaí; Theobroma cacao - cocoa). To determine the diversity and functional genes found in the pool of microorganisms present in three soil depths (0-10 cm/ 10-20 cm/ 20-30cm), the sequenced data will be processed in the Data Science research group at Nara Institute of Science and Technology (Japan), which possesses an adequate workflow and knowledge to process this kind and amount of data. The results will be deposited in public databases after the processing is complete, providing a glimpse on the microbial diversity of the Tome-Açu agroforestry systems and how it might affect fruit nutritional value.