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Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data: a potential tool for multi-parametric diagnosis
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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2006 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 5, no 6, 1407-1414 p.Article in journal (Refereed) Published
Abstract [en]

A method for predictive metabolite profiling based on resolution of GC-MS data followed by multivariate data analysis is presented and applied to three different biofluid data sets (rat urine, aspen leaf extracts, and human blood plasma). Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e.g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample (approximately 15 min in total). In addition, the presented results show that the suggested approach works for describing metabolic changes in different biofluids, indicating that this is a general approach for high-throughput predictive metabolite profiling, which could have important applications in areas such as plant functional genomics, drug toxicity, treatment efficacy and early disease diagnosis.

Place, publisher, year, edition, pages
American Chemical Society , 2006. Vol. 5, no 6, 1407-1414 p.
Keyword [en]
Animals, Blood Proteins/*analysis, Data Interpretation; Statistical, Gas Chromatography-Mass Spectrometry, Humans, Laboratory Techniques and Procedures, Male, Multivariate Analysis, Plant Leaves/*chemistry, Proteome/*analysis, Rats, Urine/chemistry
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:umu:diva-11772DOI: 10.1021/pr0600071PubMedID: 16739992OAI: oai:DiVA.org:umu-11772DiVA: diva2:151443
Available from: 2007-12-06 Created: 2007-12-06 Last updated: 2017-12-14
In thesis
1. Multivariate processing and modelling of hyphenated metabolite data
Open this publication in new window or tab >>Multivariate processing and modelling of hyphenated metabolite data
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

One trend in the ‘omics’ sciences is the generation of increasing amounts of data, describing complex biological samples. To cope with this and facilitate progress towards reliable diagnostic tools, it is crucial to develop methods for extracting representative and predictive information. In global metabolite analysis (metabolomics and metabonomics) NMR, GC/MS and LC/MS are the main platforms for data generation. Multivariate projection methods (e.g. PCA, PLS and O-PLS) have been recognized as efficient tools for data analysis within subjects such as biology and chemistry due to their ability to provide interpretable models based on many, correlated variables. In global metabolite analysis, these methods have been successfully applied in areas such as toxicology, disease diagnosis and plant functional genomics.

This thesis describes the development of processing methods for the unbiased extraction of representative and predictive information from metabolic GC/MS and LC/MS data characterizing biofluids, e.g. plant extracts, urine and blood plasma. In order to allow the multivariate projections to detect and highlight differences between samples, one requirement of the processing methods is that they must extract a common set of descriptors from all samples and still retain the metabolically relevant information in the data. In Papers I and II this was done by applying a hierarchical multivariate compression approach to both GC/MS and LC/MS data. In the study described in Paper III a hierarchical multivariate curve resolution strategy (H-MCR) was developed for simultaneously resolving multiple GC/MS samples into pure profiles. In Paper IV the H-MCR method was applied to a drug toxicity study in rats, where the method’s potential for biomarker detection and identification was exemplified. Finally, the H-MCR method was extended, as described in Paper V, allowing independent samples to be processed and predicted using a model based on an existing set of representative samples. The fact that these processing methods proved to be valid for predicting the properties of new independent samples indicates that it is now possible for global metabolite analysis to be extended beyond isolated studies. In addition, the results facilitate high through-put analysis, because predicting the nature of samples is rapid compared to the actual processing. In summary this research highlights the possibilities for using global metabolite analysis in diagnosis.

Place, publisher, year, edition, pages
Umeå: Kemi, 2005. 66 p.
Keyword
Chemometrics, Curve Resolution, GC/MS, LC/MS, Metabolomics, Metabonomics, Multivariate Analysis and Multivariate Curve Resolution.
National Category
Organic Chemistry
Identifiers
urn:nbn:se:umu:diva-663 (URN)91-7305-922-7 (ISBN)
Public defence
2006-01-27, KB3B1, KBC, Umeå Univeristet, Umeå, 10:00 (English)
Opponent
Supervisors
Available from: 2005-12-22 Created: 2005-12-22 Last updated: 2009-12-03Bibliographically approved
2. Metabolomics studies of ALS: a multivariate search for clues about a devastating disease
Open this publication in new window or tab >>Metabolomics studies of ALS: a multivariate search for clues about a devastating disease
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Amyotrophic lateral sclerosis (ALS), also known as Charcot’s disease, motor neuron disease (MND) and Lou Gehrig’s disease, is a deadly, adult-onset neurodegenerative disorder characterized by progressive loss of upper and lower motor neurons, resulting in evolving paresis of the linked muscles. ALS is defined by classical features of the disease, but may present as a wide spectrum of phenotypes. About 10% of all ALS cases have been reported as familial, of which about 20% have been associated with mutations in the gene encoding for CuZn superoxide dismutase (SOD1). The remaining cases are regarded as sporadic. Research has advanced our understanding of the disease, but the cause is still unknown, no reliable diagnostic test exists, no cure has been found and the current therapies are unsatisfactory. Riluzole (Rilutek®) is the only registered drug for the treatment of ALS. The drug has shown only a modest effect in prolonging life and the mechanism of action of riluzole is not yet fully understood. ALS is diagnosed by excluding diseases with similar symptoms. At an early stage, there are numerous possible diseases that may present with similar symptoms, thereby making the diagnostic procedure cumbersome, extensive and time consuming with a significant risk of misdiagnosis. Biomarkers that can be developed into diagnostic test of ALS are therefore needed. The high number of unsuccessful attempts at finding a single diseasespecific marker, in combination with the complexity of the disease, indicates that a pattern of several markers is perhaps more likely to provide a diagnostic signature for ALS. Metabolomics, in combination with chemometrics, can be a useful tool with which to study human disease. Metabolomics can screen for small molecules in biofluids such as cerebrospinal fluid (CSF) and chemometrics can provide structure and tools in order to handle the types of data generated from metabolomics. In this thesis, ALS has been studied using a combination of metabolomics and chemometrics. Collection and storage of CSF in relation to metabolite stability have been extensively evaluated. Protocols for metabolomics on CSF samples have been proposed, used and evaluated. In addition, a new feature of data processing allowing new samples to be predicted into existing models has been tested, evaluated and used for metabolomics on blood and CSF. A panel of potential biomarkers has been generated for ALS and subtypes of ALS. An overall decrease in metabolite concentration was found for subjects with ALS compared to their matched controls. Glutamic acid was one of the metabolites found to be decreased in patients with ALS. A larger metabolic heterogeneity was detected among SALS cases compared to FALS. This was also reflected in models of SALS and FALS against their respective matched controls, where no significant difference from control was found for SALS while the FALS samples significantly differed from their matched controls. Significant deviating metabolic patterns were also found between ALS subjects carrying different mutations in the gene encoding SOD1.

Place, publisher, year, edition, pages
Umeå: Umeå university, 2009. 72 p.
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1303
Keyword
Amyotrophic lateral sclerosis (ALS), motor neuron disease, Lou Gehrig’s disease, human disease, CSF, biomarkers, metabolomics, metabonomics, chemometrics, design of experiments, multivariate analysis.
National Category
Neurology
Research subject
Neurology
Identifiers
urn:nbn:se:umu:diva-26894 (URN)978-91-7264-885-2 (ISBN)
Public defence
2009-11-20, KB3B1 (Stora hörsalen), KBC, Linnaeus väg 6, SE-901 87, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2009-10-30 Created: 2009-10-30 Last updated: 2009-10-30Bibliographically approved
3. 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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