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  • 1.
    Björkblom, Benny
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wibom, Carl
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Mörén, Lina
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Andersson, Ulrika
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Johannesen, Tom Borge
    langseth, Hilde
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Melin, Beatrice
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Metabolomic screening of pre-diagnostic serum samples identifies association between alpha- and gamma-tocopherols and glioblastoma risk2016In: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 7, no 24, p. 37043-37053Article in journal (Refereed)
    Abstract [en]

    Glioblastoma is associated with poor prognosis with a median survival of one year. High doses of ionizing radiation is the only established exogenous risk factor. To explore new potential biological risk factors for glioblastoma, we investigated alterations in metabolite concentrations in pre-diagnosed serum samples from glioblastoma patients diagnosed up to 22 years after sample collection, and undiseased controls. The study points out a latent biomarker for future glioblastoma consisting of nine metabolites (gamma-tocopherol, alpha-tocopherol, erythritol, erythronic acid, myo-inositol, cystine, 2-keto-L-gluconic acid, hypoxanthine and xanthine) involved in antioxidant metabolism. We detected significantly higher serum concentrations of alpha-tocopherol (p=0.0018) and gamma-tocopherol (p=0.0009) in future glioblastoma cases. Compared to their matched controls, the cases showed a significant average fold increase of alpha- and gamma-tocopherol levels: 1.2 for alpha-T (p=0.018) and 1.6 for gamma-T (p=0.003). These tocopherol levels were associated with a glioblastoma odds ratio of 1.7 (alpha-T, 95% CI: 1.0-3.0) and 2.1 (gamma-T, 95% CI: 1.2-3.8). Our exploratory metabolomics study detected elevated serum levels of a panel of molecules with antioxidant properties as well as oxidative stress generated compounds. Additional studies are necessary to confirm the association between the observed serum metabolite pattern and future glioblastoma development.

  • 2.
    Bruce, Stephen J
    et al.
    Umeå Plant Science Center, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, Sweden.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Cloarec, Olivier
    Technologie Servier, 45000 Orleans, France.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Marklund, Stefan L
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Moritz, Thomas
    Umeå Plant Science Center, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, Sweden.
    Evaluation of a protocol for metabolic profiling studies on human blood plasma by combined ultra-performance liquid chromatography/mass spectrometry: From extraction to data analysis2009In: Analytical Biochemistry, ISSN 0003-2697, E-ISSN 1096-0309, Vol. 372, no 2, p. 237-249Article in journal (Refereed)
    Abstract [en]

    The investigation presented here describes a protocol designed to perform high-throughput metabolic profiling analysis on human blood plasma by ultra-performance liquid chromatography/mass spectrometry (UPLC/MS). To address whether a previous extraction protocol for gas chromatography (GC)/MS-based metabolic profiling of plasma could be used for UPLC/MS-based analysis, the original protocol was compared with similar methods for extraction of low-molecular-weight compounds from plasma via protein precipitation. Differences between extraction methods could be observed, but the previously published extraction method was considered the best. UPLC columns with three different stationary phases (C8, C18, and phenyl) were used in identical experimental runs consisting of a total of 60 injections of extracted male and female plasma samples. The C8 column was determined to be the best for metabolic profiling analysis on plasma. The acquired UPLC/MS data of extracted male and female plasma samples was subjected to principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS–DA). Furthermore, a strategy for compound identification was applied here, demonstrating the strength of high-mass-accuracy time-of-flight (TOF)/MS analysis in metabolic profiling.

  • 3.
    Chorell, Elin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Thysell, Elin
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eklund, Caroline
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences.
    Silfver, Anders
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences.
    Carlsson, Inga-Britt
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Lundgren, Krister
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Svensson, Michael
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Sports Medicine.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    A Multivariate Screening Strategy for Investigating Metabolic Effects of Strenuous Physical Exercise in Human Serum2007In: Journal of Proteome Research, ISSN 1535-3893, Vol. 6, no 6, p. 2113-2120Article in journal (Refereed)
    Abstract [en]

    A novel hypothesis-free multivariate screening methodology for the study of human exercise metabolism in blood serum is presented. Serum gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) data was processed using hierarchical multivariate curve resolution (H-MCR), and orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to model the systematic variation related to the acute effect of strenuous exercise. Potential metabolic biomarkers were identified using data base comparisons. Extensive validation was carried out including predictive H-MCR, 7-fold full cross-validation, and predictions for the OPLS-DA model, variable permutation for highlighting interesting metabolites, and pairwise t tests for examining the significance of metabolites. The concentration changes of potential biomarkers were verified in the raw GC/TOFMS data. In total, 420 potential metabolites were resolved in the serum samples. On the basis of the relative concentrations of the 420 resolved metabolites, a valid multivariate model for the difference between pre- and post-exercise subjects was obtained. A total of 34 metabolites were highlighted as potential biomarkers, all statistically significant (p < 8.1E-05). As an example, two potential markers were identified as glycerol and asparagine. The concentration changes for these two metabolites were also verified in the raw GC/TOFMS data.The strategy was shown to facilitate interpretation and validation of metabolic interactions in human serum as well as revealing the identity of potential markers for known or novel mechanisms of human exercise physiology. The multivariate way of addressing metabolism studies can help to increase the understanding of the integrative biology behind, as well as unravel new mechanistic explanations in relation to, exercise physiology.

  • 4.
    Chorell, Elin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Thysell, Elin
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lindberg, Johan
    Schuppe-Koistinen, Ina
    Moritz, Thomas
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Statistical multivariate metabolite profiling for aiding biomarker pattern detection and mechanistic interpretations in GC/MS based metabolomics2006In: Metabolomics, Vol. 2, no 4, p. 257-68Article in journal (Refereed)
    Abstract [en]

    A strategy for robust and reliable mechanistic statistical modelling of metabolic responses in relation to drug induced toxicity is presented. The suggested approach addresses two cases commonly occurring within metabonomic toxicology studies, namely; 1) A pre-defined hypothesis about the biological mechanism exists and 2) No such hypothesis exists. GC/MS data from a liver toxicity study consisting of rat urine from control rats and rats exposed to a proprietary AstraZeneca compound were resolved by means of hierarchical multivariate curve resolution (H-MCR) generating 287 resolved chromatographic profiles with corresponding mass spectra. Filtering according to significance in relation to drug exposure rendered in 210 compound profiles, which were subjected to further statistical analysis following correction to account for the control variation over time. These dose related metabolite traces were then used as new observations in the subsequent analyses. For case 1, a multivariate approach, named Target Batch Analysis, based on OPLS regression was applied to correlate all metabolite traces to one or more key metabolites involved in the pre-defined hypothesis. For case 2, principal component analysis (PCA) was combined with hierarchical cluster analysis (HCA) to create a robust and interpretable framework for unbiased mechanistic screening. Both the Target Batch Analysis and the unbiased approach were cross-verified using the other method to ensure that the results did match in terms of detected metabolite traces. This was also the case, implying that this is a working concept for clustering of metabolites in relation to their toxicity induced dynamic profiles regardless if there is a pre-existing hypothesis or not. For each of the methods the detected metabolites were subjected to identification by means of data base comparison as well as verification in the raw data. The proposed strategy should be seen as a general approach for facilitating mechanistic modelling and interpretations in metabolomic studies.

  • 5.
    Figueira, Joao
    et al.
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Nordin Adolfsson, Annelie
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    Adolfsson, Rolf
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    Nyberg, Lars
    Umeå University, Faculty of Medicine, Department of Radiation Sciences. Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB).
    Öhman, Anders
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    NMR analysis of the human saliva metabolome distinguishes dementia patients from matched controls2016In: Molecular Biosystems, ISSN 1742-206X, E-ISSN 1742-2051, Vol. 12, no 8, p. 2562-2571Article in journal (Refereed)
    Abstract [en]

    Saliva is a biofluid that is sensitive to metabolic changes and is straightforward to collect in a non-invasive manner, but it is seldom used for metabolite analysis when studying neurodegenerative disorders. We present a procedure for both an untargeted and targeted analysis of the saliva metabolome in which nuclear magnetic resonance (NMR) spectroscopy is used in combination with multivariate data analysis. The applicability of this approach is demonstrated on saliva samples selected from the 25 year prospective Betula study, including samples from dementia subjects with either Alzheimer's disease (AD) or vascular dementia at the time of sampling or who developed it by the next sampling/assessment occasion five years later, and age-, gender-, and education-matched control individuals without dementia. Statistically significant multivariate models were obtained that separated patients with dementia from controls and revealed seven discriminatory metabolites. Dementia patients showed significantly increased concentrations of acetic acid (fold change (fc) = 1.25, p = 2 x 10(-5)), histamine (fc = 1.26, p = 0.019), and propionate (fc = 1.35, p = 0.002), while significantly decreased levels were observed for dimethyl sulfone (fc = 0.81, p = 0.005), glycerol (fc = 0.79, p = 0.04), taurine (fc = 0.70, p = 0.007), and succinate (fc = 0.62, p = 0.008). Histamine, succinate, and taurine are known to be important in AD, and acetic acid and glycerol are involved in related pathways. Dimethyl sulfone and propionate originate from the diet and bacterial flora and might reflect poorer periodontal status in the dementia patients. For these seven metabolites, a weak but statistically significant pre-diagnostic value was observed. Taken together, we present a robust and general NMR analysis approach for studying the saliva metabolome that has potential use for screening and early detection of dementia.

  • 6.
    Franklin, Oskar
    et al.
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Billing, Ola
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
    Lundberg, Erik
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
    Öhlund, Daniel
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
    Nyström, Hanna
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
    Lundin, Christina
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Sund, Malin
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
    Plasma micro-RNA alterations appear late in pancreatic cancer2018In: Annals of Surgery, ISSN 0003-4932, E-ISSN 1528-1140, Vol. 267, no 4, p. 775-781Article in journal (Refereed)
    Abstract [en]

    Objectives: The aim of this research was to study whether plasma microRNAs (miRNA) can be used for early detection of pancreatic cancer (PC) by analyzing prediagnostic plasma samples collected before a PC diagnosis. Background: PC has a poor prognosis due to late presenting symptoms and early metastasis. Circulating miRNAs are altered in PC at diagnosis but have not been evaluated in a prediagnostic setting. Methods: We first performed an initial screen using a panel of 372 miRNAs in a retrospective case-control cohort that included early-stage PC patients and healthy controls. Significantly altered miRNAs at diagnosis were then measured in an early detection case-control cohort wherein plasma samples in the cases are collected before a PC diagnosis. Carbohydrate antigen 19–9 (Ca 19–9) levels were measured in all samples for comparison. Results: Our initial screen, including 23 stage I-II PC cases and 22 controls, revealed 15 candidate miRNAs that were differentially expressed in plasma samples at PC diagnosis. We combined all 15 miRNAs into a multivariate statistical model, which outperformed Ca 19–9 in receiver-operating characteristics analysis. However, none of the candidate miRNAs, individually or in combination, were significantly altered in prediagnostic plasma samples from 67 future PC patients compared with 132 matched controls. In comparison, Ca 19–9 levels were significantly higher in the cases at <5 years before diagnosis. Conclusion: Plasma miRNAs are altered in PC patients at diagnosis, but the candidate miRNAs found in this study appear late in the course of the disease and cannot be used for early detection of the disease.

  • 7.
    Gullberg, Jonas
    et al.
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Nordström, Anders
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry2004In: Analytical Biochemistry, ISSN 0003-2697, E-ISSN 1096-0309, Vol. 331, no 2, p. 283-295Article in journal (Refereed)
    Abstract [en]

    The usual aim in metabolomic studies is to quantify the entire metabolome of each of a series of biological samples. To do this for complex biological matrices, e.g., plant tissues, efficient and reproducible extraction protocols must be developed. However, derivatization protocols must also be developed if GC/MS (one of the mostly widely used analytical methods for metabolomics) is involved. The aim of this study was to investigate how different chemical and physical factors (extraction solvent, derivatization reagents, and temperature) affect the extraction and derivatization of the metabolome from leaves of the plant Arabidopsis thaliana. Using design of experiment procedures, variation was systematically introduced, and the effects of this variation were analyzed using regression models. The results show that this approach allows a reliable protocol for metabolomic analysis of Arabidopsis to be determined with a relatively limited number of experiments. Following two different investigations an extraction and derivatization protocol was chosen. Further, the reproducibility of the analysis of 66 endogenous compounds was investigated, and it was shown that both hydrophilic and lipophilic compounds were detected with high reproducibility.

  • 8. Hoffman, Daniel E.
    et al.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry. SweTree Technologies AB, Umeå, Sweden.
    Bylesjö, Max
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Almac Diagnostics Ltd, Craigavon, UK.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eriksson, Maria E.
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC). Umeå University, Faculty of Science and Technology, Department of Plant Physiology.
    Moritz, Thomas
    Changes in diurnal patterns within the Populus transcriptome and metabolome in response to photoperiod variation2010In: Plant, Cell and Environment, ISSN 0140-7791, E-ISSN 1365-3040, Vol. 33, no 8, p. 1298-1313Article in journal (Refereed)
    Abstract [en]

    Changes in seasonal photoperiod provides an important environmental signal that affects the timing of winter dormancy in perennial, deciduous, temperate tree species, such as hybrid aspen (Populus tremula x Populus tremuloides). In this species, growth cessation, cold acclimation and dormancy are induced in the autumn by the detection of day-length shortening that occurs at a given critical day length. Important components in the detection of such day-length changes are photoreceptors and the circadian clock, and many plant responses at both the gene regulation and metabolite levels are expected to be diurnal. To directly examine this expectation and study components in these events, here we report transcriptomic and metabolomic responses to a change in photoperiod from long to short days in hybrid aspen. We found about 16% of genes represented on the arrays to be diurnally regulated, as assessed by our pre-defined criteria. Furthermore, several of these genes were involved in circadian-associated processes, including photosynthesis and primary and secondary metabolism. Metabolites affected by the change in photoperiod were mostly involved in carbon metabolism. Taken together, we have thus established a molecular catalog of events that precede a response to winter.

  • 9.
    Jiye, A
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Granström, Micael
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Marklund, Stefan
    Johansson, Annika
    Stenlund, Hans
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jiye, A
    Guangji, Wang
    Dynamic modification of blood erythrocytes metabolism based on GC/TOF-MS analysis2006Other (Other (popular science, discussion, etc.))
  • 10.
    Jiye, A
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Gullberg, Jonas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Johansson, Annika I.
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Marklund, Stefan L.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Extraction and GC/MS analysis of the human blood plasma metabolome2005In: ANALYTICAL CHEMISTRY, ISSN 0003-2700, Vol. 77, no 24, p. 8086-94Article in journal (Refereed)
    Abstract [en]

    Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis. In the investigation, we developed an extraction and derivatization protocol, using experimental design theory (design of experiment), for analyzing the human blood plasma metabolome by GC/MS. The protocol was optimized by evaluating the data for more than 500 resolved peaks using multivariate statistical tools including principal component analysis and partial least-squares projections to latent structures (PLS). The performance of five organic solvents (methanol, ethanol, acetonitrile, acetone, chloroform), singly and in combination, was investigated to optimize the LMC extraction. PLS analysis demonstrated that methanol extraction was particularly efficient and highly reproducible. The extraction and derivatization conditions were also optimized. Quantitative data for 32 endogenous compounds showed good precision and linearity. In addition, the determined amounts of eight selected compounds agreed well with analyses by independent methods in accredited laboratories, and most of the compounds could be detected at absolute levels of similar to 0.1 pmol injected, corresponding to plasma concentrations between 0.1 and 1 mu M. The results suggest that the method could be usefully integrated into metabolomic studies for various purposes, e.g., for identifying biological markers related to diseases.

  • 11.
    Jonsson, Pär
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Bruce, Stephen J
    Moritz, Thomas
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Plumb, Robert
    Granger, Jennifer
    Maibaum, Elaine
    Nicholson, Jeremy K
    Holmes, Elaine
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets2005In: The Analyst, ISSN 0003-2654, E-ISSN 1364-5528, Vol. 130, no 5, p. 701-707Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    LC/MS is an analytical technique that, due to its high sensitivity, has become increasingly popular for the generation of metabolic signatures in biological samples and for the building of metabolic data bases. However, to be able to create robust and interpretable ( transparent) multivariate models for the comparison of many samples, the data must fulfil certain specific criteria: (i) that each sample is characterized by the same number of variables, (ii) that each of these variables is represented across all observations, and (iii) that a variable in one sample has the same biological meaning or represents the same metabolite in all other samples. In addition, the obtained models must have the ability to make predictions of, e. g. related and independent samples characterized accordingly to the model samples. This method involves the construction of a representative data set, including automatic peak detection, alignment, setting of retention time windows, summing in the chromatographic dimension and data compression by means of alternating regression, where the relevant metabolic variation is retained for further modelling using multivariate analysis. This approach has the advantage of allowing the comparison of large numbers of samples based on their LC/MS metabolic profiles, but also of creating a means for the interpretation of the investigated biological system. This includes finding relevant systematic patterns among samples, identifying influential variables, verifying the findings in the raw data, and finally using the models for predictions. The presented strategy was here applied to a population study using urine samples from two cohorts, Shanxi (People's Republic of China) and Honolulu ( USA). The results showed that the evaluation of the extracted information data using partial least square discriminant analysis (PLS-DA) provided a robust, predictive and transparent model for the metabolic differences between the two populations. The presented findings suggest that this is a general approach for data handling, analysis, and evaluation of large metabolic LC/MS data sets.

  • 12.
    Jonsson, Pär
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Gullberg, Jonas
    Nordström, Anders
    Kusano, Miyako
    Kowalczyk, Mauriusz
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS2004In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 76, no 6, p. 1738-1745Article in journal (Refereed)
    Abstract [en]

    In metabolomics, the purpose is to identify and quantify all the metabolites in a biological system. Combined gas chromatography and mass spectrometry (GC/MS) is one of the most commonly used techniques in metabolomics together with 1H NMR, and it has been shown that more than 300 compounds can be distinguished with GC/MS after deconvolution of overlapping peaks. To avoid having to deconvolute all analyzed samples prior to multivariate analysis of the data, we have developed a strategy for rapid comparison of nonprocessed MS data files. The method includes baseline correction, alignment, time window determinations, alternating regression, PLS-DA, and identification of retention time windows in the chromatograms that explain the differences between the samples. Use of alternating regression also gives interpretable loadings, which retain the information provided by m/z values that vary between the samples in each retention time window. The method has been applied to plant extracts derived from leaves of different developmental stages and plants subjected to small changes in day length. The data show that the new method can detect differences between the samples and that it gives results comparable to those obtained when deconvolution is applied prior to the multivariate analysis. We suggest that this method can be used for rapid comparison of large sets of GC/MS data, thereby applying time-consuming deconvolution only to parts of the chromatograms that contribute to explain the differences between the samples.

  • 13.
    Jonsson, Pär
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Annika I.
    Gullberg, Jonas
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jiye, A
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Grung, Björn
    Marklund, Stefan
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses2005In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 77, no 17, p. 5635-5642Article in journal (Refereed)
    Abstract [en]

    In metabolomics, the objective is to identify differences in metabolite profiles between samples. A widely used tool in metabolomics investigations is gas chromatography-mass spectrometry (GC/MS). More than 400 compounds can be detected in a single analysis, if overlapping GC/ MS peaks are deconvoluted. However, the deconvolution process is time-consuming and difficult to automate, and additional processing is needed in order to compare samples. Therefore, there is a need to improve and automate the data processing strategy for data generated in GC/MS-based metabolomics; if not, the processing step will be a major bottleneck for high-throughput analyses. Here we describe a new semiautomated strategy using a hierarchical multivariate curve resolution approach that processes all samples simultaneously. The presented strategy generates (after appropriate treatment, e.g., multivariate analysis) tables of all the detected metabolites that differ in relative concentrations between samples. The processing of 70 samples took similar time to that of the GC/TOFMS analyses of the samples. The strategy has been validated using two different sets of samples: a complex mixture of standard compounds and Arabidopsis samples.

    KeyWords Plus: CHROMATOGRAPHY MASS-SPECTROMETRY; PRINCIPAL COMPONENT ANALYSIS; SYSTEMS BIOLOGY; ARABIDOPSIS-THALIANA; CHEMOMETRIC ANALYSIS; 2-WAY DATA; MS; REGRESSION; RESOLUTION; ALIGNMENT

  • 14.
    Jonsson, Pär
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wallbäcks, Lars
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Strategies for implementation and validation of on-line models for multivariate monitoring and control of wood chip properties2004In: Journal of Chemometrics, Vol. 18, no 3-4, p. 203-7Article in journal (Refereed)
    Abstract [en]

    Here we present an approach for on-line control and monitoring of pulpwood chip properties based on near infrared (NIR) spectroscopy and multivariate data analysis. In addition, this paper suggests how to deal with large multivariate data sets in order to extract information which can be used as a basis for changes in raw material or process conditions in the drive towards more optimal intermediate or end product properties within the pulp and paper industry. The pulpwood chips used as raw material in a pulp and paper making process were characterized at- and on-line using NIR spectroscopic measurements. Collected NIR spectra were used in multivariate calibration models for prediction of the moisture content as well as the between- and within-species variation in the studied raw material. Statistical experimental design was used to form a calibration data set including most of the variation occurring in a real on-line situation. NIR spectra for all designed samples were measured at-line and the estimated calibration models were used for carrying out predictions on-line. Predictions of the moisture content (% dry weight) as well as the percentage contents of pine and sawmill chips in the raw material were carried out using partial least squares projections to latent structures (PLS) methodology. NIR spectra were collected subsequently on-line once every minute, and, to reduce the problem with noise in the time series predictions, the measured signals were filtered using a moving average of 100 predicted values. This provided smoother predictions more suitable for process monitoring and control. To validate the quality of the predictions, wood chips from the studied process were sampled and analysed in the laboratory before being subjected to predictions in the on-line model. Comparison of the filtered on-line predictions with the results obtained from the laboratory measurements indicated that moisture and pine chip contents could be well predicted by the on-line model, while predictions of sawmill chip content showed less promising results.

  • 15.
    Jonsson, Pär
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Sjövik-Johansson, Elin
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wuolikainen, Anna
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lindberg, Johan
    Schuppe-Koistinen, Ina
    Kusano, Miyako
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data: a potential tool for multi-parametric diagnosis2006In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 5, no 6, p. 1407-1414Article in journal (Refereed)
    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.

  • 16.
    Jonsson, Pär
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Stenlund, Hans
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Verheij, Elwin R
    Lindberg, Johan
    Schuppe-Koistinen, Ina
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    A strategy for modelling dynamic responses in metabolic samples characterized by GC/MS2006In: Metabolomics, Vol. 2, no 3, p. 135-143Article in journal (Refereed)
    Abstract [sv]

    A multivariate strategy for studying the metabolic response over time in urinary GC/MS data is presented and exemplified by a study of drug-induced liver toxicity in the rat. The strategy includes the generation of representative data through hierarchical multivariate curve resolution (H-MCR), highlighting the importance of obtaining resolved metabolite profiles for quantification and identification of exogenous (drug related) and endogenous compounds (potential biomarkers) and for allowing reliable comparisons of multiple samples through multivariate projections. Batch modelling was used to monitor and characterize the normal (control) metabolic variation over time as well as to map the dynamic response of the drug treated animals in relation to the control. In this way treatment related metabolic responses over time could be detected and classified as being drug related or being potential biomarkers. In summary the proposed strategy uses the relatively high sensitivity and reproducibility of GC/MS in combination with efficient multivariate curve resolution and data analysis to discover individual markers of drug metabolism and drug toxicity. The presented results imply that the strategy can be of great value in drug toxicity studies for classifying metabolic markers in relation to their dynamic responses as well as for biomarker identification.

  • 17.
    Jonsson, Pär
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Stenlund, Hans
    Moritz, Thomas
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Verheij, Elwin R
    Lindberg, Johan
    Schuppe-Koistinen, Ina
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Modeling of time dependent toxicological responses in urinary GC/MS dataArticle in journal (Refereed)
  • 18.
    Jonsson, Pär
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wuolikainen, Anna
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Thysell, Elin
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Chorell, Elin
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Stattin, Pär
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Urology and Andrology.
    Wikström, Pernilla
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Constrained randomization and multivariate effect projections improve information extraction and biomarker pattern discovery in metabolomics studies involving dependent samples2015In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 11, no 6, p. 1667-1678Article in journal (Refereed)
    Abstract [en]

    Analytical drift is a major source of bias in mass spectrometry based metabolomics confounding interpretation and biomarker detection. So far, standard protocols for sample and data analysis have not been able to fully resolve this. We present a combined approach for minimizing the influence of analytical drift on multivariate comparisons of matched or dependent samples in mass spectrometry based metabolomics studies. The approach is building on a randomization procedure for sample run order, constrained to independent randomizations between and within dependent sample pairs (e.g. pre/post intervention). This is followed by a novel multivariate statistical analysis strategy allowing paired or dependent analyses of individual effects named OPLS-effect projections (OPLS-EP). We show, using simulated data that OPLS-EP gives improved interpretation over existing methods and that constrained randomization of sample run order in combination with an appropriate dependent statistical test increase the accuracy and sensitivity and decrease the false omission rate in biomarker detection. We verify these findings and prove the strength of the suggested approach in a clinical data set consisting of LC/MS data of blood plasma samples from patients before and after radical prostatectomy. Here OPLS-EP compared to traditional (independent) OPLS-discriminant analysis (OPLS-DA) on constrained randomized data gives a less complex model (3 versus 5 components) as well a higher predictive ability (Q2 = 0.80 versus Q2 = 0.55). We explain this by showing that paired statistical analysis detects 37 unique significant metabolites that were masked for the independent test due to bias, including analytical drift and inter-individual variation.

  • 19. Kusano, Miyako
    et al.
    Fukushima, Atsushi
    Arita, Masanori
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    Kobayashi, Makoto
    Hayashi, Naomi
    Tohge, Takayuki
    Saito, Kazuki
    Unbiased characterization of genotype-dependent metabolic regulations by metabolomic approach in Arabidopsis thaliana2007In: BMC Systems Biology, ISSN 1752-0509, Vol. 1, no 53, p. 1-17Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Metabolites are not only the catalytic products of enzymatic reactions but also the active regulators or the ultimate phenotype of metabolic homeostasis in highly complex cellular processes. The modes of regulation at the metabolome level can be revealed by metabolic networks. We investigated the metabolic network between wild-type and 2 mutant (methionine-over accumulation 1 [mto1] and transparent testa4 [tt4]) plants regarding the alteration of metabolite accumulation in Arabidopsis thaliana. RESULTS: In the GC-TOF/MS analysis, we acquired quantitative information regarding over 170 metabolites, which has been analyzed by a novel score (ZMC, z-score of metabolite correlation) describing a characteristic metabolite in terms of correlation. Although the 2 mutants revealed no apparent morphological abnormalities, the overall correlation values in mto1 were much lower than those of the wild-type and tt4 plants, indicating the loss of overall network stability due to the uncontrolled accumulation of methionine. In the tt4 mutant, a new correlation between malate and sinapate was observed although the levels of malate, sinapate, and sinapoylmalate remain unchanged, suggesting an adaptive reconfiguration of the network. Gene-expression correlations presumably responsible for these metabolic networks were determined using the metabolite correlations as clues. CONCLUSION: Two Arabidopsis mutants, mto1 and tt4, exhibited the following changes in entire metabolome networks: the overall loss of metabolic stability (mto1) or the generation of a metabolic network of a backup pathway for the lost physiological functions (tt4). The expansion of metabolite correlation to gene-expression correlation provides detailed insights into the systemic understanding of the plant cellular process regarding metabolome and transcriptome.

  • 20. Kusano, Miyako
    et al.
    Fukushima, Atsushi
    Kobayashi, Makoto
    Hayashi, Naomi
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    Ebana, Kaworu
    Saito, Kazuki
    Application of a metabolomic method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice2007In: Journal of chromatography. B, ISSN 1570-0232, E-ISSN 1873-376X, Vol. 855, no 1, p. 71-79Article in journal (Refereed)
    Abstract [en]

    We have developed a comprehensive method combining analytical techniques of one-dimensional (I D) and two-dimensional (GC x GC) gas chromatography-time-of-flight (TOF)-mass spectrometry. This method was applied to the metabolic phenotyping of natural variants in rice for the 68 world rice core collection (WRC) and two other varieties. Ten metabolites were selected as metabolite representatives, and the selected ion current of each metabolite peak obtained from both techniques were statistically compared. Our method of combining I D- and GC x GC-TOF/MS is useful for the metabolic phenotyping of natural variants in rice for further studies in breeding programs.

  • 21. Kusano, Miyako
    et al.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Fukushima, Atsushi
    Gullberg, Jonas
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    Metabolite signature during short-day induced growth cessation in populus2011In: Frontiers in Plant Physiology, Vol. 2, no 29Article in journal (Refereed)
    Abstract [en]

    The photoperiod is an important environmental signal for plants, and influences a wide range of physiological processes. For woody species in northern latitudes, cessation of growth is induced by short photoperiods. In many plant species, short photoperiods stop elongational growth after a few weeks. It is known that plant daylength detection is mediated by Phytochrome A (PHYA) in the woody hybrid aspen species. However, the mechanism of dormancy involving primary metabolism remains unclear. We studied changes in metabolite profiles in hybrid aspen leaves (young, middle, and mature leaves) during short-day-induced growth cessation, using a combination of gas chromatography–time-of-flight mass spectrometry, and multivariate projection methods. Our results indicate that the metabolite profiles in mature source leaves rapidly change when the photoperiod changes. In contrast, the differences in young sink leaves grown under long and short-day conditions are less distinct. We found short daylength induced growth cessation in aspen was associated with rapid changes in the distribution and levels of diverse primary metabolites. In addition, we conducted metabolite profiling of leaves of PHYA overexpressor (PHYAOX) and those of the control to find the discriminative metabolites between PHYAOX and the control under the short-day conditions. The metabolite changes observed in PHYAOX leaves, together with those in the source leaves, identified possible candidates for the metabolite signature (e.g., 2-oxo-glutarate, spermidine, putrescine, 4-amino-butyrate, and tryptophan) during short-day-induced growth cessation in aspen leaves.

  • 22. Lindon, John C
    et al.
    Nicholson, Jeremy K
    Holmes, Elaine
    Keun, Hector C
    Craig, Andrew
    Pearce, Jake T M
    Bruce, Stephen J
    Hardy, Nigel
    Sansone, Susanna-Assunta
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Daykin, Clare
    Navarange, Mahendra
    Beger, Richard D
    Verheij, Elwin R
    Amberg, Alexander
    Baunsgaard, Dorrit
    Cantor, Glenn H
    Lehman-McKeeman, Lois
    Earll, Mark
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Erik
    Haselden, John N
    Kramer, Kerstin
    Thomas, Craig
    Lindberg, Johann
    Schuppe-Koistinen, Ina
    Wilson, Ian D
    Reily, Michael D
    Robertson, Donald G
    Senn, Hans
    Krotzky, Arno
    Kochhar, Sunil
    Powell, Jonathan
    Ouderaa, Frans van der
    Plumb, Robert
    Schaefer, Hartmut
    Spraul, Manfred
    Summary recommendations for standardization and reporting of metabolic analyses: The Standard Metabolic Reporting Structures (SMRS) working group outlines its vision for an open,community-driven specification for the standardization and reporting of metabolic studies2005In: Nature Biotechnology, Vol. 23, p. 833-8Article in journal (Refereed)
  • 23.
    Mousavi, Malahat
    et al.
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Adolfsson, Rolf
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    Nordin, Annelie
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    Bergdahl, Jan
    Umeå University, Faculty of Social Sciences, Department of Psychology. Institute of Clinical Dentistry, University of Tromsø, Tromsø, Norway.
    Eriksson, Kåre
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Occupational and Environmental Medicine.
    Moritz, Thomas
    Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences.
    Nilsson, Lars-Göran
    Umeå Center for Functional Brain Imaging, Umeå; Aging Research Center, Karolinska Institutet, Stockholm, Sweden.
    Nyberg, Lars
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Umeå University, Faculty of Medicine, Department of Radiation Sciences. Umeå Center for Functional Brain Imaging, Umeå.
    Serum metabolomic biomarkers of dementia2014In: Dementia and geriatric cognitive disorders extra, E-ISSN 1664-5464, Vol. 4, no 2, p. 252-62Article in journal (Refereed)
    Abstract [en]

    Aims: This study compared serum metabolites of demented patients (Alzheimer's disease and vascular dementia) and controls, and explored serum metabolite profiles of nondemented individuals 5 years preceding the diagnosis. Methods: Cognitively healthy participants were followed up for 5-20 years. Cognitive assessment, serum sampling, and diagnosis were completed every 5 years. Multivariate analyses were conducted on the metabolite profiles generated by gas chromatography/time-of-flight mass spectrometry. Results: A significant group separation was found between demented patients and controls, and between incident cases and controls. Metabolites that contributed in both analyses were 3,4-dihydroxybutanoic acid, docosapentaenoic acid, and uric acid. Conclusions: Serum metabolite profiles are altered in demented patients, and detectable up to 5 years preceding the diagnosis. Blood sampling can make an important contribution to the early prediction of conversion to dementia.

  • 24.
    Nordin, Angelica
    et al.
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Akimoto, Chizuru
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience. Division of Neurology, Department of Internal Medicine, Jichi Medical University, 3311-1 Yakushiji Shimotsukeshi, Tochigi 329-0498, Japan.
    Wuolikainen, Anna
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Alstermark, Helena
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Birve, Anna
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Marklund, Stefan L
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Graffmo, Karin S
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Forsberg, Karin
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Brännström, Thomas
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Andersen, Peter M
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Extensive size variability of the GGGGCC expansion in C9orf72 in both neuronal and non-neuronal tissues in 18 patients with ALS or FTD2015In: Human Molecular Genetics, ISSN 0964-6906, E-ISSN 1460-2083, Vol. 24, no 11, p. 3133-3142Article in journal (Refereed)
    Abstract [en]

    A GGGGCC-repeat expansion in C9orf72 is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) among Caucasians. However, little is known about the variability of the GGGGCC expansion in different tissues and whether this correlates with the observed phenotype. Here, we used Southern blotting to estimate the size of hexanucleotide expansions in C9orf72 in neural and non-neural tissues from 18 autopsied ALS and FTD patients with repeat expansion in blood. Digitalization of the Southern blot images allowed comparison of repeat number, smear distribution and expansion band intensity between tissues and between patients. We found marked intra-individual variation of repeat number between tissues, whereas there was less variation within each tissue group. In two patients, the size variation between tissues was extreme, with repeat numbers below 100 in all studied non-neural tissues, whereas expansions in neural tissues were 20-40 times greater and in the same size range observed in neural tissues of the other 16 patients. The expansion pattern in different tissues could not distinguish between diagnostic groups and no correlation was found between expansion size in frontal lobe and occurrence of cognitive impairment. In ALS patients, a less number of repeats in the cerebellum and parietal lobe correlated with earlier age of onset and a larger number of repeats in the parietal lobe correlated with a more rapid progression. In 43 other individuals without repeat expansion in blood, we find that repeat sizes up to 15 are stable, as no size variation between blood, brain and spinal cord was found.

  • 25.
    Näsström, Elin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Anders
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology. Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS).
    Dongol, Sabina
    Oxford University Clinical Research Unit, Patan Academy of Health Sciences, Kathmandu, Nepal.
    Karkey, Abhilasha
    Oxford University Clinical Research Unit, Patan Academy of Health Sciences, Kathmandu, Nepal.
    Basnyat, Buddha
    Oxford University Clinical Research Unit, Patan Academy of Health Sciences, Kathmandu, Nepal.
    Nga, Tran Vu Thieu
    The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University; Clinical Research Unit, Ho Chi Minh City, Vietnam.
    Tan, Trinh Van
    The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University; Clinical Research Unit, Ho Chi Minh City, Vietnam.
    Thwaites, Guy E
    The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University; Clinical Research Unit, Ho Chi Minh City, Vietnam. Centre for Tropical Medicine and Global Health, Oxford University, Oxford, United Kingdom.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Baker, Stephen
    The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University; Clinical Research Unit, Ho Chi Minh City, Vietnam. Centre for Tropical Medicine and Global Health, Oxford University, Oxford, United Kingdom. The Department of Medicine, The University of Cambridge, Cambridge, United Kingdom .
    Metabolite biomarkers of typhoid chronic carriageManuscript (preprint) (Other academic)
  • 26.
    Näsström, Elin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Anders
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology. Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS).
    Dongol, Sabina
    Karkey, Abhilasha
    Basnyat, Buddha
    Thieu, Nga Tran Vu
    Van, Tan Trinh
    Thwaites, Guy E.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Baker, Stephen
    Diagnostic metabolite biomarkers of chronic typhoid carriage2018In: PLoS Neglected Tropical Diseases, ISSN 1935-2727, E-ISSN 1935-2735, Vol. 12, no 1, article id e0006215Article in journal (Refereed)
    Abstract [en]

    Background: Salmonella Typhi and Salmonella Paratyphi A are the agents of enteric (typhoid) fever; both can establish chronic carriage in the gallbladder. Chronic Salmonella carriers are typically asymptomatic, intermittently shedding bacteria in the feces, and contributing to disease transmission. Detecting chronic carriers is of public health relevance in areas where enteric fever is endemic, but there are no routinely used methods for prospectively identifying those carrying Salmonella in their gallbladder.

    Methodology/Principal findings: Here we aimed to identify biomarkers of Salmonella carriage using metabolite profiling. We performed metabolite profiling on plasma from Nepali patients undergoing cholecystectomy with confirmed S. Typhi or S. Paratyphi A gallbladder carriage (and non-carriage controls) using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOFMS) and supervised pattern recognition modeling. We were able to significantly discriminate Salmonella carriage samples from non-carriage control samples. We were also able to detect differential signatures between S. Typhi and S. Paratyphi A carriers. We additionally compared carriage metabolite profiles with profiles generated during acute infection; these data revealed substantial heterogeneity between metabolites associated with acute enteric fever and chronic carriage. Lastly, we found that Salmonella carriers could be significantly distinguished from non-carriage controls using only five metabolites, indicating the potential of these metabolites as diagnostic markers for detecting chronic Salmonella carriers.

    Conclusions/Significance: Our novel approach has highlighted the potential of using metabolomics to search for diagnostic markers of chronic Salmonella carriage. We suggest further epidemiological investigations of these potential biomarkers in alternative endemic enteric fever settings.

  • 27. Srivastava, Vaibhav
    et al.
    Obudulu, Ogonna
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University and Swedish University of Agricultural Sciences.
    Bygdell, Joakim
    Löfstedt, Tommy
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University.
    Rydén, Patrik
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University.
    Nilsson, Robert
    Ahnlund, Maria
    Johansson, Annika
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University.
    Freyhult, Eva
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Computational life science cluster (CLiC), Umeå University.
    Qvarnström, Johanna
    Karlsson, Jan
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Melzer, Michael
    Moritz, Thomas
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University.
    Hvidsten, Torgeir R
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC). Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University and Department of Chemistry, Biotechnology; Food Science, Norwegian, University of Life Sciences, Ås Norwegian, Norway.
    Wingsle, Gunnar
    OnPLS integration of transcriptomic, proteomic and metabolomic data shows multi-level oxidative stress responses in the cambium of transgenic hipI- superoxide dismutase Populus plants2013In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 14, article id 893Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Reactive oxygen species (ROS) are involved in the regulation of diverse physiological processes in plants, including various biotic and abiotic stress responses. Thus, oxidative stress tolerance mechanisms in plants are complex, and diverse responses at multiple levels need to be characterized in order to understand them. Here we present system responses to oxidative stress in Populus by integrating data from analyses of the cambial region of wild-type controls and plants expressing high-isoelectric-point superoxide dismutase (hipI-SOD) transcripts in antisense orientation showing a higher production of superoxide. The cambium, a thin cell layer, generates cells that differentiate to form either phloem or xylem and is hypothesized to be a major reason for phenotypic perturbations in the transgenic plants. Data from multiple platforms including transcriptomics (microarray analysis), proteomics (UPLC/QTOF-MS), and metabolomics (GC-TOF/MS, UPLC/MS, and UHPLC-LTQ/MS) were integrated using the most recent development of orthogonal projections to latent structures called OnPLS. OnPLS is a symmetrical multi-block method that does not depend on the order of analysis when more than two blocks are analysed. Significantly affected genes, proteins and metabolites were then visualized in painted pathway diagrams.

    RESULTS: The main categories that appear to be significantly influenced in the transgenic plants were pathways related to redox regulation, carbon metabolism and protein degradation, e.g. the glycolysis and pentose phosphate pathways (PPP). The results provide system-level information on ROS metabolism and responses to oxidative stress, and indicate that some initial responses to oxidative stress may share common pathways.

    CONCLUSION: The proposed data evaluation strategy shows an efficient way of compiling complex, multi-platform datasets to obtain significant biological information.

  • 28.
    Thysell, Elin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Chorell, Elin
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Svensson, Michael B
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Sports Medicine.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Validated and Predictive Processing of Gas Chromatography-Mass Spectrometry Based Metabolomics Data for Large Scale Screening Studies, Diagnostics and Metabolite Pattern Verification2012In: Metabolites, ISSN 2218-1989, Vol. 2, p. 796-817Article in journal (Refereed)
    Abstract [en]

    The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern. The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern.

  • 29.
    Thysell, Elin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Chorell, Elin
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Svensson, Michael B.
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Sports Medicine.
    Moritz, Thomas
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Processing of mass spectrometry based metabolomics data for large scale screening studies and diagnosticsManuscript (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.

  • 30.
    Thysell, Elin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Chorell, Elin
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Svensson, Michael
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Sports Medicine.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Validated and predictive processing of gas chromatography-mass spectra screening studies, diagnostics and metabolite pattern verification2012In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 2, no 4, p. 796-817Article in journal (Refereed)
    Abstract [en]

    The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern.

  • 31.
    Thysell, Elin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Pohjanen, Elin
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lindberg, Johan
    Schuppe-Koistinen, Ina
    Moritz, Thomas
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Reliable Profile Detection in Comparative Metabolomics2007In: Omics, ISSN 1536-2310, E-ISSN 1557-8100, Vol. 11, no 2, p. 209-224Article in journal (Refereed)
    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.

  • 32.
    Trupp, Miles
    et al.
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Öhrfelt, Annika
    Univ Gothenburg, Sahlgrenska Acad, Dept Psychiat & Neurochem, Mölndal, Sweden.
    Zetterberg, Henrik
    Univ Gothenburg, Sahlgrenska Acad, Dept Psychiat & Neurochem, Molndal, Sweden; UCL Inst Neurol, London, England.
    Obudulu, Ogonna
    Swedish Agr Univ, Dept Plant Physiol & Forest Genet, Swedish Metabol Ctr, Umea, Sweden.
    Malm, Linus
    Swedish Agr Univ, Dept Plant Physiol & Forest Genet, Swedish Metabol Ctr, Umea, Sweden.
    Wuolikainen, Anna
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Linder, Jan
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Moritz, Thomas
    Swedish Agr Univ, Dept Plant Physiol & Forest Genet, Swedish Metabol Ctr, Umea, Sweden.
    Blennow, Kaj
    Univ Gothenburg, Sahlgrenska Acad, Dept Psychiat & Neurochem, Molndal, Sweden.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Forsgren, Lars
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Metabolite and peptide levels in plasma and CSF differentiating healthy controls from patients with newly diagnosed Parkinson's disease2014In: Journal of Parkinson's Disease, ISSN 1877-7171, E-ISSN 1877-718X, Vol. 4, no 3, p. 549-560Article in journal (Refereed)
    Abstract [en]

    Background: Parkinson's disease (PD) is a progressive, multi-focal neurodegenerative disease for which there is no effective disease modifying treatment. A critical requirement for designing successful clinical trials is the development of robust and reproducible biomarkers identifying PD in preclinical stages. Objective: To investigate the potential for a cluster of biomarkers visualized with multiple analytical platforms to provide a clinically useful tool. Methods: Gas Chromatography-Mass Spectrometry (GC-TOFMS) based metabolomics and immunoassay-based protein/peptide analyses on samples from patients with PD diagnosed in Northern Sweden. Low molecular weight compounds from both plasma and cerebrospinal fluid (CSF) from 20 healthy subjects (controls) and 20 PD patients at the time of diagnosis (baseline) were analyzed. Results: In plasma, we found a significant increase in several amino acids and a decrease in C16-C18 saturated and unsaturated fatty acids in patients as compared to control subjects. We also observed an increase in plasma levels of pyroglutamate and 2-oxoisocaproate (ketoleucine) that may be indicative of increased metabolic stress in patients. In CSF, there was a generally lower level of metabolites in PD as compared to controls, with a specific decrease in 3-hydroxyisovaleric acid, tryptophan and creatinine. Multivariate analysis and modeling of metabolites indicates that while the PD samples can be separated from control samples, the list of detected compounds will need to be expanded in order to define a robust predictive model. CSF biomarker immunoassays of candidate peptide/protein biomarkers revealed a significant decrease in the levels of A beta-38 and A beta-42, and an increase in soluble APP alpha in CSF of patients. Furthermore, these peptides showed significant correlations to each other, and positive correlations to the CSF levels of several 5- and 6-carbon sugars. However, combining these metabolites and proteins/peptides into a single model did not significantly improve the statistical analysis. Conclusions: Together, this metabolomics study has detected significant alterations in plasma and CSF levels of a cluster of amino acids, fatty acids and sugars based on clinical diagnosis and levels of known protein and peptide biomarkers.

  • 33.
    Trygg, Johan
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Gullberg, J
    Johansson, A I
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    II.2 Chemometrics in Metabolomics - An Introduction2006In: Plant Metabolomics: Biotechnology in Agriculture and Forestry 57, Springer Verlag , 2006, p. 117-128Chapter in book (Refereed)
  • 34.
    Wu, Junfang
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Wuolikainen, Anna
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trupp, Miles
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Marklund, Stefan L.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Andersen, Peter M.
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Forsgren, Lars
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Öhman, Anders
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    NMR analysis of the CSF and plasma metabolome of rigorously matched amyotrophic lateral sclerosis, Parkinson's disease and control subjects2016In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 12, no 6, article id 101Article in journal (Refereed)
    Abstract [en]

    Introduction: Amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD) are two severe neurodegenerative disorders for which the disease mechanisms are poorly understood and reliable biomarkers are absent.

    Objectives: To identify metabolite biomarkers for ALS and PD, and to gain insights into which metabolic pathways are involved in disease.

    Methods: Nuclear magnetic resonance (NMR) metabolomics was utilized to characterize the metabolite profiles of cerebrospinal fluid (CSF) and plasma from individuals in three age, gender, and sampling-date matched groups, comprising 22 ALS, 22 PD and 28 control subjects.

    Results: Multivariate analysis of NMR data generated robust discriminatory models for separation of ALS from control subjects. ALS patients showed increased concentrations of several metabolites in both CSF and plasma, these are alanine (CSF fold change = 1.22, p = 0.005), creatine (CSF-fc = 1.17, p = 0.001), glucose (CSF-fc = 1.11, p = 0.036), isoleucine (CSF-fc = 1.24, p = 0.002), and valine (CSF-fc = 1.17, p = 0.014). Additional metabolites in CSF (creatinine, dimethylamine and lactic acid) and plasma (acetic acid, glutamic acid, histidine, leucine, pyruvate and tyrosine) were also important for this discrimination. Similarly, panels of CSF-metabolites that discriminate PD from ALS and control subjects were identified.

    Conclusions: The results for the ALS patients suggest an affected creatine/creatinine pathway and an altered branched chain amino acid (BCAA) metabolism, and suggest links to glucose and energy metabolism. Putative metabolic markers specific for ALS (e.g. creatinine and lactic acid) and PD (e.g. 3-hydroxyisovaleric acid and mannose) were identified, while several (e.g. creatine and BCAAs) were shared between ALS and PD, suggesting some overlap in metabolic alterations in these disorders.

  • 35.
    Wuolikainen, Anna
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Ahnlund, Maria
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Marklund, Stefan L.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Moritz, Thomas
    Forsgren, Lars
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Andersen, Peter M.
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Trupp, Miles
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Multi-platform mass spectrometry analysis of the CSF and plasma metabolomes of rigorously matched amyotrophic lateral sclerosis, Parkinson's disease and control subjects2016In: Molecular Biosystems, ISSN 1742-206X, E-ISSN 1742-2051, Vol. 12, no 4, p. 1287-1298Article in journal (Refereed)
    Abstract [en]

    Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) are protein-aggregation diseases that lack clear molecular etiologies. Biomarkers could aid in diagnosis, prognosis, planning of care, drug target identification and stratification of patients into clinical trials. We sought to characterize shared and unique metabolite perturbations between ALS and PD and matched controls selected from patients with other diagnoses, including differential diagnoses to ALS or PD that visited our clinic for a lumbar puncture. Cerebrospinal fluid (CSF) and plasma from rigorously age-, sex- and sampling-date matched patients were analyzed on multiple platforms using gas chromatography (GC) and liquid chromatography (LC)-mass spectrometry (MS). We applied constrained randomization of run orders and orthogonal partial least squares projection to latent structure-effect projections (OPLS-EP) to capitalize upon the study design. The combined platforms identified 144 CSF and 196 plasma metabolites with diverse molecular properties. Creatine was found to be increased and creatinine decreased in CSF of ALS patients compared to matched controls. Glucose was increased in CSF of ALS patients and alpha-hydroxybutyrate was increased in CSF and plasma of ALS patients compared to matched controls. Leucine, isoleucine and ketoleucine were increased in CSF of both ALS and PD. Together, these studies, in conjunction with earlier studies, suggest alterations in energy utilization pathways and have identified and further validated perturbed metabolites to be used in panels of biomarkers for the diagnosis of ALS and PD.

1 - 35 of 35
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