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Predictive metabolomics for detection, interpretation and validation of metabolite patterns in human cerebrospinal fluid
Umeå University, Faculty of Medicine, Pharmacology and Clinical Neuroscience, Neurology.
Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre.
Umeå University, Faculty of Medicine, Medical Biosciences, Clinical chemistry.
Umeå University, Faculty of Medicine, Pharmacology and Clinical Neuroscience, Neurology.
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(English)Article in journal (Other academic) Submitted
Abstract [en]

We here present our predictive metabolomics approach for screening and comparing metabolomics data from human cerebrospinal fluid (CSF) generated by gas chromatography-time of flight mass spectrometry (GC-TOFMS). The approach is based on a combination of hierarchical multivariate curve resolution (HMCR) and manual integration of the GC–TOFMS data for quantification and identification of metabolites in multiple CSF samples. Chemometric data analysis, orthogonal partial least squares (OPLS), for multiple CSF sample comparisons. We show how the predictive feature of both HMCR and OPLS can be used for biomarker detection and verification as well as for diagnostic modelling. To exemplify the capability of the method we have used human CSF from two test subjects aliquoted into 44 tubes stored at either -80 °C or -20 °C as a model system. A total of 170 potential metabolites were resolved from the GC-TOFMS data using HMCR. OPLS modelling revealed a clear separation of the samples according to storage temperature, with a prediction accuracy of 100% using a test set.

Keyword [en]
cerebrospinal fluid, metabolomics, predictive metabolomics, GC-MS, chemometrics
URN: urn:nbn:se:umu:diva-26871OAI: diva2:274604
Available from: 2009-10-30 Created: 2009-10-30 Last updated: 2009-11-02Bibliographically approved
In thesis
1. 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.
Umeå University medical dissertations, ISSN 0346-6612 ; 1303
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
Research subject
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)
Available from: 2009-10-30 Created: 2009-10-30 Last updated: 2009-10-30Bibliographically approved

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