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Processing of mass spectrometry based metabolomics data for large scale screening studies and diagnostics
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Sports Medicine.
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

In mass spectrometry based metabolomics predictive data processing and sample classification based on representative sample subsets makes it possible to screen large sample banks or data sets in an efficient fashion regarding both data quality and processing time. This is a requirement for making use of high sensitivity and complexity metabolite data and to turn the metabolomics field into a competitive omics platform for biological interpretation and diagnostics. Predictive metabolomics by means of hierarchical multivariate curve resolution (H-MCR) followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for the processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human blood serum samples collected in a study of strenuous physical exercise. The efficiency of the predictive processing as a high throughput tool for generating high quality data is clearly proven and stated as a main benefit of the method. Extensive model validation schemes by means of cross validation and external predictions verified the robustness of the extracted systematic patterns in the data. Comparisons regarding the extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power concerning longitudinal predictions provided proof for the diagnostic potential of the methodology. Finally, the predictive metabolite pattern was interpreted physiologically as well as verified in the literature, highlighting the biological relevance of the diagnostic pattern. The suggested approach makes it feasible to screen large data or sample sets with retained data quality and interpretation and to do this in a high throughput fashion. The method could be of value for sample bank mining, metabolome-wide association studies, verification of marker patterns and development of diagnostic systems.

National Category
Other Medical Sciences not elsewhere specified
Identifiers
URN: urn:nbn:se:umu:diva-50969OAI: oai:DiVA.org:umu-50969DiVA: diva2:471637
Available from: 2012-01-02 Created: 2012-01-02 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.
Keyword
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
Identifiers
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)
Opponent
Supervisors
Available from: 2012-01-04 Created: 2012-01-02 Last updated: 2012-01-11Bibliographically approved

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Thysell, ElinChorell, ElinSvensson, Michael B.Jonsson, PärAntti, Henrik

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