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A new metabonomic strategy for analysing the growth process of the poplar tree.

Identifieur interne : 004119 ( Main/Exploration ); précédent : 004118; suivant : 004120

A new metabonomic strategy for analysing the growth process of the poplar tree.

Auteurs : Susanne Wiklund [Suède] ; Marlene Karlsson ; Henrik Antti ; Dan Johnels ; Michael Sjöström ; Gunnar Wingsle ; Ulf Edlund

Source :

RBID : pubmed:17129317

Abstract

High-resolution, magic angle spinning, proton nuclear magnetic resonance (1H HR/MAS NMR) spectroscopy and multivariate data analysis using batch processing (BP) were applied to the analysis of two different genotypes of poplar tree (Populus tremula L. x tremuloides Michx.) containing an antisense construct of PttMYB76 and control (wild-type). A gene encoding a MYB transcription factor, with unknown function, PttMYB76, was selected from a cambial expressed sequence tag (EST) library of poplar tree (Populus tremula L. x tremuloides Michx.) for metabonomic characterization. The PttMYB76 gene is believed to affect different paths in the phenyl propanoid synthetic pathway. This pathway leads to the formation of S- and G-lignin, flavonoids and sinapate esters. Milled poplar samples collected at the internodes of the tree were analysed using 1H HR/MAS NMR spectroscopy. The application of multivariate BP of the NMR results revealed a growth-related gradient in the plant internode direction, as well as the discrimination between the trees with down-regulated PttMYB76 expression and wild-type populations. This paper focuses on the potential of a new analytical multivariate approach for analysing time-related plant metabonomic data. The techniques used could, with the aid of suitable model compounds, be of high relevance to the detection and understanding of the different lignification processes within the two types of poplar tree. Additionally, the findings highlight the importance of applying robust and organized multivariate data analysis approaches to facilitate the modelling and interpretation of complex biological data sets.

DOI: 10.1111/j.1467-7652.2005.00129.x
PubMed: 17129317


Affiliations:


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