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  • 1.
    Eliasson, Mattias
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Rännar, Stefan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Madsen, Rasmus
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Donten, Magdalena A
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Marsden-Edwards, Emma
    Waters Corp, Milford, MA 01757 USA .
    Moritz, Thomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC).
    Shockcor, John P
    Waters Corp, Milford, MA 01757 USA .
    Johansson, Erik
    Umetr AB, Umeå, Sweden.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Strategy for optimizing LC-MS data processing in Metabolomics: A design of experiments approach2012Inngår i: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 84, nr 15, s. 6869-6876Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline.

  • 2.
    Lundstedt, Torbjörn
    et al.
    AcureOmics AB.
    Moritz, Tomas
    Madsen, Rasmus
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Lundstedt-Enkel, Katrin
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Endogenous metabolic profiling as a tool in drug discovery2010Inngår i: Abstract book for the 7th annual global conference on Neuroprotection and Neuroregeneration 2010, Stockholm, Sweden, Ingenta Connect , 2010Konferansepaper (Annet vitenskapelig)
  • 3.
    Madsen, Rasmus
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Banday, Viqar Showkat
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Moritz, Thomas
    Umeå Plant Science Center, Department of Forest Genetics and Plant Physiology, Swedish University of Agriculture Sciences, Umeå, Sweden.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Lejon, Kristina
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi, Immunologi/immunkemi.
    Altered metabolic signature in Pre-Diabetic NOD Mice2012Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, nr 4, s. e35445-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Altered metabolism proceeding seroconversion in children progressing to Type 1 diabetes has previously been demonstrated. We tested the hypothesis that non-obese diabetic (NOD) mice show a similarly altered metabolic profile compared to C57BL/6 mice. Blood samples from NOD and C57BL/6 female mice was collected at 0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13 and 15 weeks and the metabolite content was analyzed using GC-MS. Based on the data of 89 identified metabolites OPLS-DA analysis was employed to determine the most discriminative metabolites. In silico analysis of potential involved metabolic enzymes was performed using the dbSNP data base. Already at 0 weeks NOD mice displayed a unique metabolic signature compared to C57BL/6. A shift in the metabolism was observed for both strains the first weeks of life, a pattern that stabilized after 5 weeks of age. Multivariate analysis revealed the most discriminative metabolites, which included inosine and glutamic acid. In silico analysis of the genes in the involved metabolic pathways revealed several SNPs in either regulatory or coding regions, some in previously defined insulin dependent diabetes (Idd) regions. Our result shows that NOD mice display an altered metabolic profile that is partly resembling the previously observation made in children progressing to Type 1 diabetes. The level of glutamic acid was one of the most discriminative metabolites in addition to several metabolites in the TCA cycle and nucleic acid components. The in silico analysis indicated that the genes responsible for this reside within previously defined Idd regions.

    Fulltekst (pdf)
    fulltext
  • 4.
    Madsen, Rasmus Kirkegaard
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Metabolic variation in autoimmune diseases2012Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    The human being and other animals contain immensely complex biochemical processes that govern their function on a cellular level. It is estimated that several thousand small molecules (metabolites) are produced by various biochemical pathways in humans. Pathological processes can introduce perturbations in these biochemical pathways which can lead to changes in the amounts of some metabolites.Developments in analytical chemistry have made it possible measure a large number metabolites in a single blood sample, which gives a metabolic profile. In this thesis I have worked on establishing and understanding metabolic profiles from patients with rheumatoid arthritis (RA) and from animal models of the autoimmune diseases diabetes mellitus type 1 (T1D) and RA.Using multivariate statistical methods it is possible to identify differences between metabolic profiles of different groups. As an example we identified differences between patients with RA and healthy volunteers. This can be used to elucidate the biochemical processes that are active in a given pathological condition.Metabolite concentrations are affected by a many other things than the presence or absence of a disease. Both genomic and environmental factors are known to influence metabolic profiles. A main focus of my work has therefore been on finding strategies for ensuring that the results obtained when comparing metabolic profiles were valid and relevant. This strategy has included repetition of experiments and repeated measurement of individuals’ metabolic profiles in order to understand the sources of variation.Finding the most stable and reproducible metabolic effects has allowed us to better understand the biochemical processes seen in the metabolic profiles. This makes it possible to relate the metabolic profile differences to pathological processes and to genes and proteins involved in these.The hope is that metabolic profiling in the future can be an important tool for finding biomarkers useful for disease diagnosis, for identifying new targets for drug design and for mapping functional changes of genomic mutations. This has the potential to revolutionize our understanding of disease pathology and thus improving health care.

    Fulltekst (pdf)
    Metabolic Variation in Autoimmune Diseases
  • 5.
    Madsen, Rasmus Kirkegaard
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Kelkka, Tiina
    MediCity Research Laboratory, University of Turku, Turku, Finland.
    Raposo, Bruno
    Karolinska Institutet, Stockholm, Sweden.
    Pizzolla, Angela
    Karolinska Institutet, Stockholm, Sweden.
    Linusson Jonsson, Anna
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Moritz, Thomas
    Umeå Plant Science Center, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, Sweden.
    Torbjörn, Lundstedt
    AcureOmics AB, Umeå, Sweden.
    Rikard, Holmdahl
    MediCity Research Laboratory, University of Turku, Turku, Finland.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Physiological metabolic differences between Ncf1 mutant and wild type miceManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    The Ncf1 gene is a major determinant of disease severity in experimental animal models of Rheumatoid Arthritis. The Ncf1 codes a protein that is important for regulating the activity of the NADPH oxidase (NOX2) complex. This complex produces reactive oxygen species (ROS) important both for killing off pathogens but also for regulating the immune response.Using metabolic profiling techniques we have found that mutation of the Ncf1 gene leads to alteration of the metabolic profile even without induction of inflammation, thus demonstrating a physiological role for the gene. Transgenic expression of Ncf1 in macrophages restored the metabolic profile so it was very similar to that seen in wild type animals. This indicates that macrophages have an immune regulatory role even outside inflammation.The metabolic differences between genotypes were subtle so the experiments were repeated to ensure validity of the results. The most stable metabolic effect across studies was an increase in free fatty acids in animals with functional NOX2 oxidation. This is likely due to production of immune regulatory compounds in the pathways initiated by phospholipase A2.

  • 6.
    Madsen, Rasmus Kirkegaard
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Lundstedt, Torbjörn
    Department of Medicinal Chemistry, BMC, Uppsala University, SE-75123 Uppsala, Sweden.
    Gabrielsson, Jon
    AcureOmics, Tvistevägen 48, SE-90736 Umeå, Sweden.
    Sennbro, Carl-Johan
    Active Biotech Research, Scheelevägen 22, SE-22007 Lund, Sweden.
    Alenius, Gerd-Marie
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Reumatologi.
    Moritz, Thomas
    Umeå Plant Science Center, Swedish University of Agricultural Sciences, SE-90183 Umeå, Sweden.
    Rantapää-Dahlqvist, Solbritt
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Reumatologi.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Diagnostic properties of metabolic perturbations in rheumatoid arthritis2011Inngår i: Arthritis Research & Therapy, ISSN 1478-6354, E-ISSN 1478-6362, Vol. 13, nr 1, s. R19-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    INTRODUCTION: The aim of the study was to assess the feasibility of diagnosing early rheumatoid arthritis (RA) by measuring selected metabolic biomarkers. METHODS: We compared the metabolic profile of patients with RA with those of healthy controls and patients with psoriatic arthritis (PsoA). The metabolites were measured using two different chromatography-mass spectrometry platforms, thereby giving a broad overview of serum metabolites. The metabolic profiles of patient and control groups were compared using multivariate statistical analysis. The findings were validated in a follow-up study of RA patients and healthy volunteers. RESULTS: RA patients were diagnosed with a sensitivity of 93 % and a specificity of 70 % in a validation study using detection of 52 metabolites. Patients with RA or PsoA could be distinguished with a sensitivity of 90 % and a specificity of 94 %. Glyceric acid, D-ribofuranoise and hypoxanthine were increased in RA patients, whereas histidine, threonic acid, methionine, cholesterol, asparagine and threonine were all decreased when compared with healthy controls. CONCLUSIONS: Metabolite profiling (metabolomics) is a potentially useful technique for diagnosing RA. The predictive value was irrespective of the presence of antibodies against cyclic citrullinated peptides (ACPA).

  • 7.
    Madsen, Rasmus Kirkegaard
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Rantapää-Dahlqvist, Solbritt
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Reumatologi.
    Lundstedt, Torbjörn
    AcureOmics AB, Tvistevägen 48, 907 36 Umeå, Sweden.
    Moritz, Thomas
    Umeå Plant Science Center, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183 Umeå, Sweden.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Metabolic responses to change in disease activity during tumor necrosis factor inhibition in patients with rheumatoid arthritis2012Inngår i: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 11, nr 7, s. 3796-3804Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Assessment of disease activity in patients with rheumatoid arthritis (RA) is of importance in the evaluation of treatment. The most important measure of disease activity is the Disease Activity Score counted in 28 joints (DAS28). In this study, we evaluated whether metabolic profiling could complement current measures of disease activity. Fifty-six patients, in two separate studies, were followed for two years after commencing anti-TNF therapy. DAS28 was assessed, and metabolic profiles were recorded at defined time points. Correlations between metabolic profile and DAS28 scores were analyzed using multivariate statistics. The metabolic responses to lowering DAS28 scores varied in different patients but could predict DAS28 scores at the individual and subgroup level models. The erythrocyte sedimentation rate (ESR) component in DAS28 was most correlated to the metabolite data, pointing to inflammation as the primary effect driving metabolic profile changes. Patients with RA had differing metabolic response to changes in DAS28 following anti-TNF therapy. This suggests that discovery of new metabolic biomarkers for disease activity will derive from studies at the individual and subgroup level. Increased inflammation, measured as ESR, was the main common effect seen in metabolic profiles from periods associated with high DAS28.

  • 8.
    Madsen, Rasmus
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Lundstedt, Torbjörn
    AcureOmics AB.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Chemometrics in metabolomics - a review in human disease diagnosis2010Inngår i: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 659, nr 1-2, s. 23-33Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.

  • 9.
    Stenlund, Hans
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Madsen, Rasmus
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Vivi, Antonio
    Nuclear Magnetic Resonance Centre of Siena University, Siena, Italy.
    Calderisi, Marco
    Nuclear Magnetic Resonance Centre of Siena University, Siena, Italy.
    Lundstedt, Torbjörn
    AcureOmics AB, Umeå, Sweden.
    Tassini, Maria
    Nuclear Magnetic Resonance Centre of Siena University, Siena, Italy.
    Carmellini, Mario
    Department of Surgery and Bioengineering, University of Siena, Siena, Italy.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Monitoring kidney-transplant patients using metabolomics and dynamic modeling2009Inngår i: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 98, nr 1, s. 45-50Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A kidney transplant provides the only hope for a normal life for patients with end-stage renal disease, i.e., kidney failure. Unfortunately, the lack of available organs leaves some patients on the waiting list for years. In addition, the post-transplant treatment is extremely important for the final outcome of the surgery, since immune responses, drug toxicity and other complications pose a real and present threat to the patient. In this article, we describe a novel strategy for monitoring kidney transplanted patients for immune responses and adverse drug effects in their early recovery. Nineteen patients were followed for two weeks after renal transplantation, two of them experienced problems related to kidney function, both of whom were correctly identified by means of nuclear magnetic resonance spectroscopic analysis of urine samples and multivariate data analysis.

  • 10.
    Åkesson, Lina
    et al.
    Department of Clinical Sciences, Diabetes and Celiac Disease Unit, Lund University, Malmö, Sweden.
    Trygg, Johan
    Umeå universitet.
    Fuller, Jessica M
    Department of Medicine, University of Washington, Seattle, WA, USA.
    Madsen, Rasmus
    Umeå universitet.
    Gabrielsson, Jon
    AcureOmics AB, Umeå, Sweden.
    Bruce, Stephen
    Nestle´ Research Center–Lausanne, Nestec Ltd, CH-1000 Lausanne 26, Vers-chez-les-Blanc, Switzerland.
    Stenlund, Hans
    Tupling, Terry
    Department of Medicine, University of Washington, Seattle, WA, USA.
    Pefley, Ranae
    Department of Medicine, University of Washington, Seattle, WA, USA.
    Lundstedt, Torbjörn
    AcurePharma AB, Uppsala, Sweden.
    Lernmark, Åke
    Department of Clinical Sciences, Diabetes and Celiac Disease Unit, Lund University, Malmö, Sweden.
    Moritz, Thomas
    Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, Sweden.
    Serum metabolite signature predicts the acute onset of diabetes in spontaneously diabetic congenic BB rats2011Inngår i: Metabolomics, ISSN 1573-3882, Vol. 7, nr 4, s. 593-603Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The clinical presentation of type 1 diabetes is preceded by a prodrome of beta cell autoimmunity. We probed the short period of subtle metabolic abnormalities, which precede the acute onset of diabetes in the spontaneously diabetic BB rat, by analyzing the serum metabolite profile detected with combined gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS). We found that the metabolite pattern prior to diabetes included 17 metabolites, which differed between individual diabetes prone (DP) BB rats and their age and sex matched diabetes resistant (DR) littermates. As the metabolite signature at the 40 days of age baseline failed to distinguish DP from DR, there was a brief 10-day period after which the diabetes prediction pattern was observed, that includes fatty acids (e.g. oleamide), phospholipids (e.g. phosphocholines) and amino acids (e.g. isoleucine). It is concluded that distinct changes in the serum metabolite pattern predict type 1 diabetes and precede the appearance of insulitis in spontaneously diabetic BB DP rats. This observation should prove useful to dissect mechanisms of type 1 diabetes.

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