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Spectroscopic data and multivariate analysis: tools to study genetic perturbations in poplar trees
Umeå University, Faculty of Science and Technology, Department of Chemistry.
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In our society in the 21st century one of the greatest challenges is to provide raw materials to the industry in a sustainable way. This requires increased use of renewable raw materials such as wood. Wood is widely used in pulp, paper and textile industries and ongoing research efforts aim to extend the use of wood in e.g. liquid biofuels and green chemicals. At Umeå Plant Science Center (UPSC) poplar trees are used as model systems to study wood formation. The objective is to understand the function of the genes underlying the wood forming process. This knowledge could result in improved chemical and physical wood properties suitable for different industrial processes. This will in turn meet the demands for a sustainable development.

This thesis presents tools and strategies to unravel information regarding genetic perturbations in poplar trees by the use of nuclear magnetic resonance (NMR) spectroscopy and multivariate analysis (MVA). Furthermore, gas chromatography/mass spectrometry (GC/MS) is briefly discussed in this context. Multivariate methods to find patterns and trends in NMR data have been used for more than 30 years. In the beginning MVA was applied in pattern recognition studies in order to characterize chemical structures with different ligands and in different solvents. Today, the multivariate methods have developed and the research have changed focus towards the study of biofluids from plant extracts, urine, blood plasma, saliva etc. NMR spectra of biofluids can contain thousands of resonances, originating from hundreds of different compounds. This type of complex data can be hard to summarize and interpret without appropriate tools and require sophisticated strategies for data evaluation. Related fields of research are rapidly growing and are here referred to as metabolomics.

Five different research projects are presented which includes analysis of poplar samples where macromolecules such as pectin and also small molecules such as metabolites were analysed by high resolution magic angle spinning (HR/MAS) NMR spectroscopy, 1H-13C HSQC NMR and GC/MS. The discussion topics include modelling of metabolomic time dependencies in combination with genetic variation, validation of orthogonal projections to latent structures (OPLS) models, selection of putative biomarkers related to genetic modification from OPLS-discriminant analysis (DA) models, measuring one of the main components found in the primary cell-walls of poplar i.e. pectin, the use of Fourier transformed two-dimensional (2D) NMR data in OPLS modelling and model complexity in a PLS model.

Place, publisher, year, edition, pages
Umeå: Kemi , 2007. , 81 p.
Keyword [en]
NMR spectroscopy, Metabolomics, Degree of methylation, S-plot, SUS-plot, Metabonomics, Poplar, MLR, PLS, OPLS, PCA, Pectin, Wood
National Category
Organic Chemistry
Identifiers
URN: urn:nbn:se:umu:diva-1396ISBN: 978-91-7264-380-2 (print)OAI: oai:DiVA.org:umu-1396DiVA: diva2:140881
Public defence
2007-11-09, N360, Johan bures väg, Umeå, 10:00
Opponent
Supervisors
Available from: 2007-10-19 Created: 2007-10-19 Last updated: 2011-04-21Bibliographically approved
List of papers
1. A new metabonomic strategy for analysing the growth process of the poplar tree
Open this publication in new window or tab >>A new metabonomic strategy for analysing the growth process of the poplar tree
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2005 (English)In: Plant Biotechnology Journal, ISSN 1467-7644, E-ISSN 1467-7652, Vol. 3, no 3, 353-362 p.Article in journal (Refereed) Published
Abstract [en]

High-resolution, magic angle spinning, proton nuclear magnetic resonance (H-1 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 H-1 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.

Keyword
batch process, H-1 HR/MAS NMR, partial least squares projections to latent structures, poplar tree, principal components analysis, wood formation
National Category
Chemical Sciences
Identifiers
urn:nbn:se:umu:diva-13245 (URN)10.1111/j.1467-7652.2005.00129.x (DOI)
Available from: 2007-05-07 Created: 2007-05-07 Last updated: 2013-03-14Bibliographically approved
2. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models
Open this publication in new window or tab >>Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models
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2008 (English)In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 80, no 1, 115-22 p.Article in journal (Refereed) Published
Abstract [en]

Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools. The use of chemometrics tools, e.g., principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data. Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes. The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation. Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability. An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of multiple classification models compared to a common reference, e.g., control. The used example is a gas chromatography coupled mass spectroscopy based metabolomics study in plant biology where two different transgenic poplar lines are compared to wild type. By using OPLS, an improved visualization and discrimination of interesting metabolites could be demonstrated.

Place, publisher, year, edition, pages
Columbus, OH: American Chemical Society, 2008
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:umu:diva-17996 (URN)10.1021/ac0713510 (DOI)
Available from: 2008-01-02 Created: 2008-01-02 Last updated: 2013-03-14
3. Pectin methyl esterase inhibits intrusive and symplastic cell growth in developing wood of Populus trees
Open this publication in new window or tab >>Pectin methyl esterase inhibits intrusive and symplastic cell growth in developing wood of Populus trees
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(English)Manuscript (Other academic)
National Category
Chemical Sciences
Identifiers
urn:nbn:se:umu:diva-2651 (URN)
Available from: 2007-10-19 Created: 2007-10-19 Last updated: 2017-10-16Bibliographically approved
4. Visualization and interpretation of OPLS models based on 2D NMR Data
Open this publication in new window or tab >>Visualization and interpretation of OPLS models based on 2D NMR Data
(English)Article in journal (Refereed) Submitted
National Category
Chemical Sciences
Identifiers
urn:nbn:se:umu:diva-2652 (URN)
Available from: 2007-10-19 Created: 2007-10-19 Last updated: 2016-08-15Bibliographically approved
5. A randomization test for PLS component selection
Open this publication in new window or tab >>A randomization test for PLS component selection
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2007 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 21, no 10-11, 427-439 p.Article in journal (Refereed) Published
Abstract [en]

During the last two decades, a number of methods have been developed and evaluated for selecting the optimal number of components in a PLS model. In this paper, a new method is introduced that is based on a randomization test. The advantage of using a randomization test is that in contrast to cross validation (CV), it requires no exclusion of data, thus avoiding problems related to data exclusion, for example in designed experiments. The method is tested using simulated data sets for which the true dimensionality is clearly defined and also compared to regularly used methods for 10 real data sets. The randomization test works as a good statistical selection tool in combination with other selection rules. It also works as an indicator when the data require a pre-treatment.

Place, publisher, year, edition, pages
Chichester: Wiley & Sons, 2007
Keyword
randomization test, permutation test, component selection, factor selection, latent variable selection
National Category
Chemical Sciences
Identifiers
urn:nbn:se:umu:diva-2653 (URN)10.1002/cem.1086 (DOI)
Available from: 2007-10-19 Created: 2007-10-19 Last updated: 2016-08-15Bibliographically approved

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