Advanced search
Start date
Betweenand

Modeling, analysis, and comparison of multiple sugarcane metabolic networks to unravel mechanisms of the smut disease progression

Grant number: 19/13530-4
Support type:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): October 01, 2019
Effective date (End): September 30, 2020
Field of knowledge:Biological Sciences - Genetics - Molecular Genetics and Genetics of Microorganisms
Principal Investigator:Claudia Barros Monteiro Vitorello
Grantee:Hugo Rody Vianna Silva
Supervisor abroad: Loren H Rieseberg
Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Local de pesquisa : University of British Columbia (UBC), Canada  
Associated to the scholarship:18/04555-0 - RGAs sugarcane expression profile during infection with various pathogens, BP.PD

Abstract

Organisms metabolic networks can be resembled based on graph theory, and its integration with omics data has been demonstrated as important strategy to further gather knowledge about the relationships between genotype and phenotype. Here, we are proposing to establish sugarcane metabolic networks integrated with transcriptomic data to investigate the smut-sugarcane pathosystem. The method developed here will be foundation for the better understanding of the modulation of sugarcane metabolism during interaction with smut, but will also be feasible to apply to another sugarcane pathosystems in the future. We intend to attribute functional metabolic annotation from model organisms to a collection of predicted sugarcane protein/nucleotide sequences. Metabolic networks will be then established based on graph theory using Python3 programming. Reactions or proteins will be placed as nodes in the networks, with connections performed by edges if there is a biochemical interaction between nodes. Transcriptome data from two sugarcane genotypes with divergent degrees of resistance to smut will be used to weight the nodes and edges of the networks and several statistics will be calculated. Comparisons throughout networks alignment are expected to highlight the divergences between sugarcane genotypes immune systems.