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

Wiklund, Susanne; Karlsson, Marlene; Antti, Henrik; Johnels, Dan; Sjöstróm, Michael; Wingsle, Gunnar; Edlund, Ulf

doi:10.1111/j.1467-7652.2005.00129.x

Cited by 17 publications

(13 citation statements)

References 18 publications

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“…However, if one considers each animal or plant in a study as a batch then the demands for BP are fulfilled. Recent work has shown that using this approach can be useful in both toxicology (Antti et al 2002) and plant growth studies (Wiklund et al 2005). BP was undertaken using the normal metabolic variation from control samples over time as the "good batch" as well as to map the dynamic response of the pathogen treated plants in relation to the control in both the compatible and incompatible interactions.…”

Section: Metabolic Dynamics-pca Analysis Of the Aqueous Phasementioning

confidence: 99%

Using metabolic profiling to assess plant-pathogen interactions: an example using rice (Oryza sativa) and the blast pathogen Magnaporthe grisea

et al. 2010

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A metabolomics based approach has been used to study the infection of the Hwacheong rice cultivar (Oryza sativa L. cv. Hwacheong) with compatible (KJ201) and incompatible (KJ401) strains of the rice blast fungal pathogen Magnaporthe grisea. The metabolic response of the rice plants to each strain was assessed 0, 6, 12, 24, 36, and 48 h post inoculation. Nuclear Magnetic Resonance (NMR) spectroscopy and Gas and Liquid Chromatography Tandem Mass spectrometry (GC/LC-MS/MS) were used to study both aqueous and organic phase metabolites, collectively resulting in the identification of 93 compounds. Clear metabolic profiles were observed at each time point but there were no significant differences in the metabolic response elicited by each pathogen strain until 24 h post inoculation. The largest change was found to be in alanine, which was~30% (±9%) higher in the leaves from the compatible, compared to the resistant, plants. Together with several other metabolites (malate, Eur J Plant Pathol (glutamine, proline, cinnamate and an unknown sugar) alanine exhibited a good correlation between time of fungal penetration into the leaf and the divergence of metabolite profiles in each interaction. The results indicate both that a wide range of metabolites can be identified in rice leaves and that metabolomics has potential for the study of biochemical changes in plant-pathogen interactions.

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Section: Metabolic Dynamics-pca Analysis Of the Aqueous Phasementioning

confidence: 99%

Using metabolic profiling to assess plant-pathogen interactions: an example using rice (Oryza sativa) and the blast pathogen Magnaporthe grisea

et al. 2010

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“…3,11,27,[31][32][33][34] Various approaches to the modeling of 3D X arrays that arise in batch data are presented.…”

Section: Discussionmentioning

confidence: 99%

Batch Process Modeling and MSPC

Wold

Kettaneh-Wold²,

MacGregor

et al. 2009

Comprehensive Chemometrics

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“…The PttMYB76 gene, selected from a cambial expressed sequence tag library of poplar tree, is believed to affect different paths in the phenyl propanoid synthetic pathway that leads to the formation of S-and Glignin, flavonoids and sinapate esters. Based on batch processing-based multivariate data analysis, Wiklund et al [29] had been able to measure development-specific divergence of transgenic trees in metabolic composition from normal wild types.…”

Section: Integration Of Functional Mapping and Omic Profilesmentioning

confidence: 99%

“…This technique can quantify a wide range of metabolities simultaneously. Wiklund et al [29] used 1 H-NMR spectroscopy to study the composition of biofluids at different internodes of stem growth in poplar. In their study, two different genotypes of poplar tree, one being an antisense construct of PttMYB76 and the other being wide-type control, were used.…”

Section: Integration Of Functional Mapping and Omic Profilesmentioning

confidence: 99%

Network Models for Dissecting Plant Development by Functional Mapping

Yap

et al. 2009

CBIO

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Understanding the genetic machinery of plant growth and development is of fundamental importance in agriculture and biology. Recently, a novel statistical framework, coined functional mapping, has been developed to study the genetic architecture of the dynamic pattern of phenotypic development at different levels of organization. By integrating mathematical aspects of cellular and biological processes, functional mapping provides a quantitative platform in which a seemingly unlimited number of hypotheses about the interplay between genes and development can be asked and tested. However, plant development involves a series of multi-hierarchical, sequential pathways from DNA to mRNA to proteins to metabolites and finally to high-order phenotypes, and thus it is unlikely that the control mechanisms of plant development can be understood using genetic knowledge alone. Here, we describe a network biology approach for functional mapping of phenotypic formation and progression through their underlying biochemical pathways. The integration of functional mapping with information-rich spectroscopic data sets including transcriptome, proteome, and metabolome can be used to model and predict physiological variation and plant development, and will pave the way for future genetic studies capable of addressing the complex nature of growth and development.

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

Abstract: SummaryHigh-resolution, magic angle spinning, proton nuclear magnetic resonance (

Cited by 17 publications

References 18 publications

Using metabolic profiling to assess plant-pathogen interactions: an example using rice (Oryza sativa) and the blast pathogen Magnaporthe grisea

Using metabolic profiling to assess plant-pathogen interactions: an example using rice (Oryza sativa) and the blast pathogen Magnaporthe grisea

Batch Process Modeling and MSPC

Network Models for Dissecting Plant Development by Functional Mapping

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