An apoplastic fluid extraction method for the characterization of grapevine leaves proteome and metabolome from a single sample

• Main Author: Figueiredo, J.
• Publication Date: 2021
• Other Authors: Cavaco, A.R., Guerra-Guimarães, L., Leclerq, C., Renaut, J., Cunha, J., Eiras-Dias, J., Cordeiro, C., Matos, A.R., Silva, M.S., Figueiredo, A.
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10400.5/23295
• topic: grapevine leaves; proteome; metabolome

The analysis of complex biological systems keeps challenging researchers. The main goal of systems biology is to decipher interactions within cells, by integrating datasets from large scale analytical approaches including transcriptomics, proteomics and metabolomics andmore specialized ‘OMICS’ such as epigenomics and lipidomics. Studying different cellular compartments allows a broader understanding of cell dynamics. Plant apoplast, the cellular compartment external to the plasma membrane including the cell wall, is particularly demanding to analyze. Despite our knowledge on apoplast involvement on several processes from cell growth to stress responses, its dynamics is still poorly known due to the lack of efficient extraction processes adequate to each plant system.Analyzing woody plants such as grapevine raises even more challenges. Grapevine is among the most important fruit crops worldwide and awider characterization of its apoplast is essential for a deeper understanding of its physiology and cellular mechanisms. Here, we describe, for the first time, a vacuum-infiltrationcentrifugationmethod that allows a simultaneous extraction of grapevine apoplastic proteins and metabolites from leaves on a single sample, compatible with high-throughput mass spectrometry analyses. The extracted apoplast from two grapevine cultivars, Vitis vinifera cv ‘Trincadeira’ and ‘Regent’, was directly used for proteomics and metabolomics analysis. The proteome was analyzed by nanoLC-MS/MS and more than 700 common proteinswere identified, with highly diverse biological functions. The metabolome profile through FT-ICR-MS allowed the identification of 514 unique putative compounds revealing a broad spectrum of molecular classes