Biological networks for sugarcane metabolism under different cultivation conditions (Data reuse from omics and generation of new knowledge)

• Grant number: 25/10321-6
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: July 01, 2025
• End date: June 30, 2027
• Field of knowledge: Agronomical Sciences - Agronomy - Plant Health
• Principal Investigator: Claudia Barros Monteiro Vitorello
• Grantee: Jorge Mario Muñoz Pérez
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Associated research grant: 24/00985-1 - Smart B100 Advanced Research Center (CCD-SB100), AP.CCD

Abstract

This postdoctoral project is part of the research initiative approved by FAPESP (CCD-SB100, sMART BOLETIM 100), which aims to deepen the understanding of transcriptional modulation in sugarcane under different cultivation conditions by integrating molecular, environmental, and agronomic data. The proposal addresses a central challenge in agriculture: the interaction between plant genotype, the microbiome, and the physicochemical and biological characteristics of the soil, which directly influence agricultural performance and productivity. This approach is supported by recent studies highlighting the importance of such integration for more sustainable and efficient agricultural practices (Lehmann et al., 2020).The main goal of this work plan is the strategic reuse of data available in public repositories and specialized scientific literature, integrating them with data already compiled in Boletim 100. Computational tools and biological network models will be employed to explore and generate new knowledge from omics data, such as transcriptomes and metagenomes. Specifically, the objectives are: (1) to identify relevant experiments based on quality criteria and alignment with the SB100 project context; (2) to reuse data in in silico experiments using metabolic networks built upon graph theory and databases such as KEGG, STRING, and BIOGRID; (3) to incorporate into the biological network attributes derived from metabolic reconstruction of microbiomes from agricultural soils; and (4) to combine these networks with comparative gene expression analyses to identify patterns associated with cultivation conditions.The methodological strategy includes data retrieval and selection from repositories such as NCBI's BioProjects. These datasets will enable the analysis of molecular responses in sugarcane and related crops under various environmental stresses, plant-pathogen interactions, and edaphoclimatic variations. The proposed computational approach will support the detection of functional network-based patterns and help identify current knowledge gaps that may guide future experimental efforts.The expected outcome is not only to enhance the interpretive power of Boletim 100 data but also to support hypothesis generation regarding the relationships among sugarcane genotypes, soil microbiomes, and the specific physicochemical conditions of different regions. By integrating data from metataxonomics, metabolomics, and transcriptomics, the biological network published by Rody et al. (2021) can be expanded with new functional layers related to soil quality and plant health. Compliance with the scope and guidelines of SB100 will be ensured through rigorous curation of the data used and the methodologies applied.This work plan embraces an interdisciplinary approach that combines bioinformatics, systems biology, and agronomy, with the potential to support crop improvement programs and more precise management practices adapted to real-world cultivation conditions. The expected results include the identification of relevant molecular patterns, novel genotype-environment associations, and valuable contributions to translational science applied to agriculture. (AU)