Integrating Long-Read 16S rRNA Sequencing Pipelines to Investigate Microbiome Responses to Drought Stress in Common Bean

• Grant number: 25/17075-0
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: December 15, 2025
• End date: April 14, 2026
• Field of knowledge: Biological Sciences - Microbiology
• Principal Investigator: Lucas William Mendes
• Grantee: Felipe Stolf Brasil Piovesan
• Supervisor: Rodrigo Gouvea Taketani
• Host Institution: Centro de Energia Nuclear na Agricultura (CENA). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Institution abroad: Rothamsted Research, England
• Associated to the scholarship: 25/03647-2 - EFFECT OF MICROBIOME TRANSPLANTATION ON THE PHENOTYPE OF Phaseolus vulgaris L. CULTIVARS SUSCEPTIBLE TO WATER STRESS, BP.IC

Abstract

This research internship has two complementary aims: (i) to acquire hands-on training in full-length 16S rRNA gene sequencing using Oxford Nanopore's PromethION platform and the associated bioinformatics pipelines for long-read data processing, and (ii) to apply advanced metataxonomic analyses to datasets generated in Brazil from common bean (Phaseolus vulgaris L.) grown under three drought stress conditions. In the first phase, the student will learn sample preparation, library construction, sequencing, and downstream analysis using the PromethION platform with PRO-114M flowcells. This training, carried out within ongoing projects at Rothamsted, will provide practical experience with real experimental datasets and ensure proficiency in state-of-the-art long-read metataxonomic workflows. The expertise gained will be transferred to the Brazilian research group, enabling the future implementation of long-read sequencing technologies and strengthening the lab's capacity for cutting-edge microbiome research. In the second phase, the student will process and analyze metataxonomic data from rhizosphere and bulk soil samples collected under drought stress. This will include ASV inference and taxonomic classification using DADA2 with SILVA and UNITE databases, diversity and dissimilarity analyses with phyloseq and vegan, and differential abundance testing using DESeq2, ALDEx2, and MaAsLin2. Functional inference will be performed with PICRUSt2 to link microbial shifts with metabolic pathways, integrating these results with soil physicochemical and plant physiological data. The expected outcome is the identification of microbial signatures and functional traits associated with drought resilience, supporting the development of bio-based strategies for sustainable agriculture. (AU)