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Metabolomics studies of ALS: a multivariate search for clues about a devastating disease
Umeå University, Faculty of Medicine, Pharmacology and Clinical Neuroscience, Neurology.
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 [en]
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: urn:nbn:se:umu:diva-26894ISBN: 978-91-7264-885-2 (print)OAI: oai:DiVA.org:umu-26894DiVA: diva2:274654
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
List of papers
1. Predictive metabolomics for detection, interpretation and validation of metabolite patterns in human cerebrospinal fluid
Open this publication in new window or tab >>Predictive metabolomics for detection, interpretation and validation of metabolite patterns in human cerebrospinal fluid
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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
cerebrospinal fluid, metabolomics, predictive metabolomics, GC-MS, chemometrics
Identifiers
urn:nbn:se:umu:diva-26871 (URN)
Available from: 2009-10-30 Created: 2009-10-30 Last updated: 2009-11-02Bibliographically approved
2. Optimization of procedures for collecting and storing of CSF for studying the metabolome in ALS
Open this publication in new window or tab >>Optimization of procedures for collecting and storing of CSF for studying the metabolome in ALS
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2009 (English)In: Amyotrophic Lateral Sclerosis, ISSN 1748-2968, Vol. 10, no 4, 229-236 p.Article in journal (Refereed) Published
Abstract [en]

There is a need for biomarkers for early diagnosis, development and evaluation of treatment efficacy in amyotrophic lateral sclerosis (ALS). We aimed to investigate if pre-analytical factors induce artefacts in metabolomic data of cerebrospinal fluid (CSF) from patients with ALS. CSF from 16 patients was studied using a statistical experimental design protocol with the following parameters: storage temperature (-80 degrees C/ - 20 degrees C), type of collection tube (polypropylene/polystyrene), and time delay from collecting to freezing (0, 10, 30, 90, 150 min). Gas chromatography-mass spectrometry was used to analyse CSF from 12 of the patients while CSF from one patient was analysed with nuclear magnetic resonance spectroscopy. The extent of CO(2) evaporization from CSF collected in tubes of different sizes at different temperatures and with/without lid were studied in three addtional patients. We found that alterations in storage temperature affect the metabolite composition of CSF more than any other studied pre-analytical parameter. CO(2) evaporization may induce artefacts in the metabolome by increasing the pH. In conclusion, minimization of evaluated artefacts can be obtained by collecting the CSF directly into tubes with tightly sealed lids in N(2)(l) and after freezing transfer of the tubes to -80 degrees C to minimize evaporation of CO(2).

Keyword
Cerebrospinal fluid, metabolomics, amyotrophic lateral sclerosis, chemometrics, CO2 evaporization
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:umu:diva-22625 (URN)10.1080/17482960902871009 (DOI)19412814 (PubMedID)
Available from: 2009-05-14 Created: 2009-05-14 Last updated: 2012-03-19Bibliographically approved
3. Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data: a potential tool for multi-parametric diagnosis
Open this publication in new window or tab >>Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data: a potential tool for multi-parametric diagnosis
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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
Keyword
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:nbn:se:umu:diva-11772 (URN)10.1021/pr0600071 (DOI)16739992 (PubMedID)
Available from: 2007-12-06 Created: 2007-12-06 Last updated: 2013-03-19
4. Studies of the human cerebrospinal fluid metabolome reveal alterations associated with amyotrophic lateral sclerosis and subtypes of the disease
Open this publication in new window or tab >>Studies of the human cerebrospinal fluid metabolome reveal alterations associated with amyotrophic lateral sclerosis and subtypes of the disease
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(English)Article in journal (Other academic) Submitted
Abstract [en]

Background: The composition of the metabolome in the cerebrospinal fluid of patients with amyotrophic lateral sclerosis is unknown. Previous studies of single metabolites have shown conflicting results.

Methods: Using GC-TOFMS and multivariate statistical modeling, we studied the metabolome signature of ~120 compounds in the cerebrospinal fluid of ALS patients stratified according to hereditary disposition and clinical subtypes of the disease.

Findings: Sporadic ALS has a heterogeneous metabolite signature in the CSF, in some patients being almost identical to controls. Familial ALS without SOD1 gene mutation is less heterogeneous than sporadic ALS. The metabolome of the CSF of the 17 ALS patients with a SOD1 gene mutation appeared as a separate homogeneous group. Analysis of single metabolites revealed that glutamate, pyroglutamate and glutamine were all reduced, in particular in patients with a familial disposition.

Interpretation: There are significant differences in the metabolite profile and composition among patients with familial ALS, sporadic ALS and patients carrying a mutation in the SOD1 gene suggesting that the neurodegenerative process in different subtypes of ALS may be different. Patients with a genetic predisposition to ALS have a more distinct signature than patients with a sporadic disease.

Identifiers
urn:nbn:se:umu:diva-26872 (URN)
Available from: 2009-10-30 Created: 2009-10-30 Last updated: 2009-10-30Bibliographically approved
5. ALS patients with mutations in the SOD1 gene have an unique metabolomic profile in the cerebrospinal fluid compared with ALS patients without mutations
Open this publication in new window or tab >>ALS patients with mutations in the SOD1 gene have an unique metabolomic profile in the cerebrospinal fluid compared with ALS patients without mutations
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2012 (English)In: Molecular Genetics and Metabolism, ISSN 1096-7192, E-ISSN 1096-7206, Vol. 105, no 3, 472-478 p.Article in journal (Refereed) Published
Abstract [en]

A specific biochemical marker for early diagnosing and for monitoring disease progression in amyotrophic lateral sclerosis (ALS) will have important clinical applications. ALS is a heterogeneous syndrome with multiple subtypes with ill-defined borders. A minority of patients carries mutations in the Cu/Zn-superoxide dismutase (SOD1) gene but the disease mechanism remains unknown for all types of ALS. Using a GC-TOFMS platform we studied the cerebrospinal fluid (CSF) metabolome in 16 ALS patients with six different mutations in the SOD1 gene and compared with ALS-patients without such mutations. OPLS-DA was used for classification modeling. We find that patients with a SOD1 mutation have a distinct metabolic profile in the CSF. In particular, the eight patients homozygous for the D90A SOD1 mutation showed a distinctively different signature when modeled against ALS patients with other SOD1 mutations and sporadic and familial ALS patients without a SOD1 gene mutation. This was found irrespective of medication with riluzole and survival time. Among the metabolites that contributed most to the CSF signature were arginine, lysine, ornithine, serine, threonine and pyroglutamic acid, all found to be reduced in patients carrying a D90A SOD1 mutation. ALS-patients with a SOD1 gene mutation appear as a distinct metabolic entity in the CSF, in particular in patients with the D90A mutation, the most frequently identified cause of ALS. The findings suggest that metabolomic profiling using GC-TOFMS and multivariate data analysis may be a future tool for diagnosing and monitoring disease progression, and may cast light on the disease mechanisms in ALS.

Place, publisher, year, edition, pages
Elsevier, 2012
Keyword
Amyotrophic lateral sclerosis (ALS), D90A SOD1, metabolome, metabolomics, biomarker, chemometrics
National Category
Genetics
Identifiers
urn:nbn:se:umu:diva-26873 (URN)10.1016/j.ymgme.2011.11.201 (DOI)
Note
Available online 9 December 2011Available from: 2009-10-30 Created: 2009-10-30 Last updated: 2012-04-27Bibliographically approved

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