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Reliable Profile Detection in Comparative Metabolomics
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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2007 (English)In: Omics, ISSN 1536-2310, E-ISSN 1557-8100, Vol. 11, no 2, 209-224 p.Article in journal (Refereed) Published
Abstract [en]

A strategy for processing of metabolomic GC/MS data is presented. By considering the relationship between quantity and quality of detected profiles, representative data suitable for multiple sample comparisons and metabolite identification was generated. Design of experiments (DOE) and multivariate analysis was used to relate the changes in settings of the hierarchical multivariate curve resolution (H-MCR) method to quantitative and qualitative characteristics of the output data. These characteristics included number of resolved profiles, chromatographic quality in terms of reproducibility between analytical replicates, and spectral quality defined by purity and number of spectra containing structural information. The strategy was exemplified in two datasets: one containing 119 common metabolites, 18 of which were varied according to a DOE protocol; and one consisting of rat urine samples from control rats and rats exposed to a liver toxin. It was shown that the performance of the data processing could be optimized to produce metabolite data of high quality that allowed reliable sample comparisons and metabolite identification. This is a general approach applicable to any type of data processing where the important processing parameters are known and relevant output data characteristics can be defined. The results imply that this type of data quality optimization should be carried out as an integral step of data processing to ensure high quality data for further modeling and biological evaluation. Within metabolomics, this degree of optimization will be of high importance to generate models and extract biomarkers or biomarker patterns of biological or clinical relevance.

Place, publisher, year, edition, pages
Mary Ann Liebert , 2007. Vol. 11, no 2, 209-224 p.
National Category
Other Medical Sciences
URN: urn:nbn:se:umu:diva-16147DOI: 10.1089/omi.2007.0006OAI: diva2:155820
Available from: 2007-09-28 Created: 2007-09-28 Last updated: 2013-03-19Bibliographically approved
In thesis
1. Multivariate profiling of metabolites in human disease: Method evaluation and application to prostate cancer
Open this publication in new window or tab >>Multivariate profiling of metabolites in human disease: Method evaluation and application to prostate cancer
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

There is an ever increasing need of new technologies for identification of molecular markers for early diagnosis of fatal diseases to allow efficient treatment. In addition, there is great value in finding patterns of metabolites, proteins or genes altered in relation to specific disease conditions to gain a deeper understanding of the underlying mechanisms of disease development. If successful, scientific achievements in this field could apart from early diagnosis lead to development of new drugs, treatments or preventions for many serious diseases.  Metabolites are low molecular weight compounds involved in the chemical reactions taking place in the cells of living organisms to uphold life, i.e. metabolism. The research field of metabolomics investigates the relationship between metabolite alterations and biochemical mechanisms, e.g. disease processes. To understand these associations hundreds of metabolites present in a sample are quantified using sensitive bioanalytical techniques. In this way a unique chemical fingerprint is obtained for each sample, providing an instant picture of the current state of the studied system. This fingerprint or picture can then be utilized for the discovery of biomarkers or biomarker patterns of biological and clinical relevance.

In this thesis the focus is set on evaluation and application of strategies for studying metabolic alterations in human tissues associated with disease. A chemometric methodology for processing and modeling of gas chromatography-mass spectrometry (GC-MS) based metabolomics data, is designed for developing predictive systems for generation of representative data, validation and result verification, diagnosis and screening of large sample sets.

The developed strategies were specifically applied for identification of metabolite markers and metabolic pathways associated with prostate cancer disease progression. The long-term goal was to detect new sensitive diagnostic/prognostic markers, which ultimately could be used to differentiate between indolent and aggressive tumors at diagnosis and thus aid in the development of personalized treatments. Our main finding so far is the detection of high levels of cholesterol in prostate cancer bone metastases. This in combination with previously presented results suggests cholesterol as a potentially interesting therapeutic target for advanced prostate cancer. Furthermore we detected metabolic alterations in plasma associated with metastasis development. These results were further explored in prospective samples attempting to verify some of the identified metabolites as potential prognostic markers.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet, 2012. 43 p.
metabolite profiling, metabolomics, predictive metabolomics, mass spectrometry, GC-MS, biomarkers, chemometrics, design of experiments, multivariate data analysis, prostate cancer, bone metastases, plasma
National Category
Other Medical Sciences not elsewhere specified
Research subject
biological chemistry
urn:nbn:se:umu:diva-50968 (URN)978-91-7459-344-0 (ISBN)
Public defence
2012-01-27, KBC-huset, KB3B1, Umeå universitet, Umeå, 10:00 (Swedish)
Available from: 2012-01-04 Created: 2012-01-02 Last updated: 2012-01-11Bibliographically approved

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Thysell, ElinPohjanen, ElinJonsson, PärAntti, Henrik
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