Metabolomics of the sugar accumulation in sugar cane (Saccharum L.)

Abstract

The research project aims to establish an overview of the genetic factors that control the accumulation of sucrose in cane sugar, combining data on gene expression, proteins and metabolites, focusing on the concept of systems biology. The first part will be to characterize the metabolome of leaves, roots, immature and mature internodes at different developmental stages of sugarcane (cultivar SP80-3280). This plant material was chosen as a model plant because its genomewill be completely sequenced by SucEST. Moreover, this cultivar also has good agronomic properties such as high brix, high productivity, higher fiber content, rust resistance and other major diseases of sugarcane. Changes in the metabolome in different plant tissues at different stages of development, will be compared with particular interest during the accumulation of sucrose. The database's primary metabolome will be built using the mass spectrometer from Leco Pegasus 4D (GC /GC-TOF-MS), available in the laboratory. In these first steps will also be developed computational tools to improve the analysis and annotation of the data obtained by mass spectrometry. This step is carried out in partnership with the group of Prof. Ricardo N. Zorzetto Vêncio (Department of Computing and Mathematics - FFCLRP / USP). Once the metabolites profile and bioinformatics platforms are established, a new set of experiments will be conducted in collaboration with the research group of Prof. Glaucia Mendes Souza (USP-SP), involving analysis of transcriptomics, proteomics and metabolomics - as the project theme (Signaling cane and Regulatory Networks - FAPESP / BIOEN - PRONEX 2008/52146-0). The long term goal is to connect the networks of metabolites of information with data from transcriptome and proteomics in order to identify functions of new genes and phenotypes related to sucrose accumulation. All information generated in this project will be integrated in server SucEST-FUN bioinformatics, enabling users to crosslink the data with transcriptomics, proteomics and metabolomics, molecular markers (such as QTL and E-QTLs) or any other service available on the server SucEST-FUN . (AU)