Multiomics data integration and analyses of photosynthetic methabolism and CH4 from cambial zone in seedlings and trees of eucalyptus

• Grant number: 24/18615-6
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: December 01, 2024
• End date: November 30, 2026
• Field of knowledge: Agronomical Sciences - Forestry Resources and Forestry Engineering - Forestry
• Principal Investigator: Carlos Alberto Labate
• Grantee: Felipe Alexsander Rodrigues da Silva
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Associated research grant: 21/01012-9 - Biology of xylogenesis, methane production and emission by eucalypt, AP.TEM

Abstract

Omics technologies have transformed our ability to collect biological information on a large scale, ranging from DNA sequences to metabolites. Although this advance has considerably increased our understanding of the biology of plants, microorganisms and animals, we still face challenges in the integrated analysis of these data. Most research in transcriptomics, proteomics and metabolomics remains descriptive, without fully capturing the complexity of the phenotype resulting from the interaction between all these molecular components and the environment. This activity plan mainly aims to assist in obtaining transcriptomic, proteomic, metabolomic and CH4 emission data in the cambial region under different conditions in eucalyptus plants. In addition, the lead researcher will be mainly involved in the integration of all the data obtained with the aim of understanding and characterizing the functions and responses of photosynthetic and methane metabolism in a holistic manner. For this purpose, samples from plants aged 1 to 3 years and also seedlings up to 6 months old, collected in 2 different seasons (winter and summer), will be used. Transcriptomics, proteomics, metabolomics and methane quantification techniques will be used to obtain samples. Regularized canonical correlation analysis (rCCA) will be the main approach for data correlation and result inference.