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  • 1. Bengtsson, Anders A.
    et al.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wuttge, Dirk M.
    Sturfelt, Gunnar
    Theander, Elke
    Donten, Magdalena
    Moritz, Thomas
    Sennbro, Carl-Johan
    Torell, Frida
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lood, Christian
    Surowiec, Izabella
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Rännar, Stefan
    Lundstedt, Torbjörn
    Metabolic Profiling of Systemic Lupus Erythematosus and Comparison with Primary Sjögren’s Syndrome and Systemic Sclerosis2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 7, article id e0159384Article in journal (Refereed)
    Abstract [en]

    Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease which can affect most organ systems including skin, joints and the kidney. Clinically, SLE is a heterogeneous disease and shares features of several other rheumatic diseases, in particular primary Sjögrens syndrome (pSS) and systemic sclerosis (SSc), why it is difficult to diag- nose The pathogenesis of SLE is not completely understood, partly due to the heterogeneity of the disease. This study demonstrates that metabolomics can be used as a tool for improved diagnosis of SLE compared to other similar autoimmune diseases. We observed differences in metabolic profiles with a classification specificity above 67% in the comparison of SLE with pSS, SSc and a matched group of healthy individuals. Selected metabolites were also significantly different between studied diseases. Biochemical pathway analysis was conducted to gain understanding of underlying pathways involved in the SLE pathogenesis. We found an increased oxidative activity in SLE, supported by increased xanthine oxidase activity and an increased turnover in the urea cycle. The most discriminatory metabolite observed was tryptophan, with decreased levels in SLE patients compared to control groups. Changes of tryptophan levels were related to changes in the activity of the aromatic amino acid decarboxylase (AADC) and/or to activation of the kynurenine pathway. 

  • 2. Dhillon, Sundeep S.
    et al.
    Torell, Frida
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Accelerator Lab (ACL), Karlsruhe Institute of Technology, Karlsruhe, Germany; AcureOmics, Umeå, Sweden.
    Donten, Magdalena
    Lundstedt-Enkel, Katrin
    Bennett, Kate
    Raennar, Stefan
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. AcureOmics, Umeå, Sweden.
    Lundstedt, Torbjorn
    Metabolic profiling of zebrafish embryo development from blastula period to early larval stages2019In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 5, article id e0213661Article in journal (Refereed)
    Abstract [en]

    The zebrafish embryo is a popular model for drug screening, disease modelling and molecular genetics. In this study, samples were obtained from zebrafish at different developmental stages. The stages that were chosen were 3/4, 4/5, 24, 48, 72 and 96 hours post fertilization (hpf). Each sample included fifty embryos. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). Principle component analysis (PCA) was applied to get an overview of the data and orthogonal projection to latent structure discriminant analysis (OPLS-DA) was utilised to discriminate between the developmental stages. In this way, changes in metabolite profiles during vertebrate development could be identified. Using a GC-TOF-MS metabolomics approach it was found that nucleotides and metabolic fuel (glucose) were elevated at early stages of embryogenesis, whereas at later stages amino acids and intermediates in the Krebs cycle were abundant. This agrees with zebrafish developmental biology, as organs such as the liver and pancreas develop at later stages. Thus, metabolomics of zebrafish embryos offers a unique opportunity to investigate large scale changes in metabolic processes during important developmental stages in vertebrate development. In terms of stability of the metabolic profile and viability of the embryos, it was concluded at 72 hpf was a suitable time point for the use of zebrafish as a model system in numerous scientific applications.

  • 3.
    Surowiec, Izabella
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Erik
    Sartorius Stedim Data Analytics AB, Umeå, Sweden.
    Torell, Frida
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Idborg, Helena
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Gunnarsson, Iva
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Svenungsson, Elisabet
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Jakobsson, Per-Johan
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Sartorius Stedim Data Analytics AB, Umeå, Sweden.
    Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics2017In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, no 10, article id 114Article in journal (Refereed)
    Abstract [en]

    Introduction Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

    Objectives We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

    Methods Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOFMS). For each batch OPLS-DA (R) was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

    Results A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

    Conclusion Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.

  • 4.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Accelerator Lab (ACL), Karlsruhe Institute of Technology, Karlsruhe 76344, Germany.
    Bennet, Kate
    Cereghini, Silvia
    Fabre, Mélanie
    Rännar, Stefan
    Lundstedt-Enkel, Katrin
    Moritz, Thomas
    Haumaitre, Cécile
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lundstedt, Torbjörn
    Metabolic Profiling of Multiorgan Samples: Evaluation of MODY5/RCAD Mutant Mice2018In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 17, no 7, p. 2293-2306Article in journal (Refereed)
    Abstract [en]

    In the present study, we performed a metabolomics analysis to evaluate a MODY5/RCAD mouse mutant line as a potential model for HNF1B-associated diseases. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) of gut, kidney, liver, muscle, pancreas, and plasma samples uncovered the tissue specific metabolite distribution. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) was used to identify the differences between MODY5/RCAD and wild-type mice in each of the tissues. The differences included, for example, increased levels of amino acids in the kidneys and reduced levels of fatty acids in the muscles of the MODY5/RCAD mice. Interestingly, campesterol was found in higher concentrations in the MODY5/RCAD mice, with a four-fold and three-fold increase in kidneys and pancreas, respectively. As expected, the MODY5/RCAD mice displayed signs of impaired renal function in addition to disturbed liver lipid metabolism, with increased lipid and fatty acid accumulation in the liver. From a metabolomics perspective, the MODY5/RCAD model was proven to display a metabolic pattern similar to what would be suspected in HNF1B-associated diseases. These findings were in line with the presumed outcome of the mutation based on the different anatomy and function of the tissues as well as the effect of the mutation on development.

  • 5.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Bennett, Kate
    Cereghini, Silvia
    Raennar, Stefan
    Lundstedt-Enkel, Katrin
    Moritz, Thomas
    Haumaitre, Cecile
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lundstedt, Torbjoern
    Multi-Organ Contribution to the Metabolic Plasma Profile Using Hierarchical Modelling2015In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 6, article id e0129260Article in journal (Refereed)
    Abstract [en]

    Hierarchical modelling was applied in order to identify the organs that contribute to the levels of metabolites in plasma. Plasma and organ samples from gut, kidney, liver, muscle and pancreas were obtained from mice. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS) at the Swedish Metabolomics centre, Umea University, Sweden. The multivariate analysis was performed by means of principal component analysis (PCA) and orthogonal projections to latent structures (OPLS). The main goal of this study was to investigate how each organ contributes to the metabolic plasma profile. This was performed using hierarchical modelling. Each organ was found to have a unique metabolic profile. The hierarchical modelling showed that the gut, kidney and liver demonstrated the greatest contribution to the metabolic pattern of plasma. For example, we found that metabolites were absorbed in the gut and transported to the plasma. The kidneys excrete branched chain amino acids (BCAAs) and fatty acids are transported in the plasma to the muscles and liver. Lactic acid was also found to be transported from the pancreas to plasma. The results indicated that hierarchical modelling can be utilized to identify the organ contribution of unknown metabolites to the metabolic profile of plasma.

  • 6.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Bennett, Kate
    AcureOmics AB, Umeå, Sweden.
    Cereghini, Silvia
    Paris, France.
    Rännar, Stefan
    AcureOmics AB, Umeå, Sweden.
    Lundstedt-Enkel, Katrin
    AcureOmics AB, Umeå, Sweden; Uppsala, Sweden.
    Moritz, Thomas
    AcureOmics AB, Umeå, Sweden.
    Haumaitre, Cecile
    Paris, France.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lundstedt, Torbjörn
    AcureOmics AB, Umeå, Sweden.
    Tissue sample stability: thawing effect on multi-organ samples2016In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 12, no 2, article id 19Article in journal (Refereed)
    Abstract [en]

    Correct handling of samples is essential in metabolomic studies. Improper handling and prolonged storage of samples has unwanted effects on the metabolite levels. The aim of this study was to identify the effects that thawing has on different organ samples. Organ samples from gut, kidney, liver, muscle and pancreas were analyzed for a number of endogenous metabolites in an untargeted metabolomics approach, using gas chromatography time of flight mass spectrometry at the Swedish Metabolomics Centre, Umeå University, Sweden. Multivariate data analysis was performed by means of principal component analysis and orthogonal projection to latent structures discriminant analysis. The results showed that the metabolic changes caused by thawing were almost identical for all organs. As expected, there was a marked increase in overall metabolite levels after thawing, caused by increased protein and cell degradation. Cholesterol was one of the eight metabolites found to be decreased in the thawed samples in all organ groups. The results also indicated that the muscles are less susceptible to oxidation compared to the rest of the organ samples.

  • 7.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Karlsruhe Institute of Technology, Karlsruhe, Germany.
    Bennett, Kate
    Rännar, Stefan
    Lundstedt-Enkel, Katrin
    Lundstedt, Torbjörn
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples2017In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, no 6, article id 66Article in journal (Refereed)
    Abstract [en]

    Introduction: Post-collection handling, storage and transportation can affect the quality of blood samples. Pre-analytical biases can easily be introduced and can jeopardize accurate profiling of the plasma metabolome. Consequently, a mouse study must be carefully planned in order to avoid any kind of bias that can be introduced, in order not to compromise the outcome of the study. The storage and shipment of the samples should be made in such a way that the freeze–thaw cycles are kept to a minimum. In order to keep the latent effects on the stability of the blood metabolome to a minimum it is essential to study the effect that the post-collection and pre-analytical error have on the metabolome. Objectives: The aim of this study was to investigate the effects of thawing on the metabolic profiles of different sample types. Methods: In the present study, a metabolomics approach was utilized to obtain a thawing profile of plasma samples obtained on three different days of experiment. The plasma samples were collected from the tail on day 1 and 3, while retro-orbital sampling was used on day 5. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS). Results: The thawed plasma samples were found to be characterized by higher levels of amino acids, fatty acids, glycerol metabolites and purine and pyrimidine metabolites as a result of protein degradation, cell degradation and increased phospholipase activity. The consensus profile was thereafter compared to the previously published study comparing thawing profiles of tissue samples from gut, kidney, liver, muscle and pancreas. Conclusions: The comparison between thawed organ samples and thawed plasma samples indicate that the organ samples are more sensitive to thawing, however thawing still affected all investigated sample types.

  • 8.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eketjäll, Susanna
    Idborg, Helena
    Jakobsson, Per-Johan
    Gunnarsson, Iva
    Svenungsson, Elisabet
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Corporate Research, Sartorius AG, Göttingen, Germany.
    Cytokine Profiles in Autoantibody Defined Subgroups of Systemic Lupus Erythematosus2019In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 18, no 3, p. 1208-1217Article in journal (Refereed)
    Abstract [en]

    The aim of this study was to evaluate how the cytokine profiles differed between autoantibody based subgroups of systemic lupus erythematosus (SLE). SLE is a systemic autoimmune disease, characterized by periods of flares (active disease) and remission (inactive disease). The disease can affect many organ systems, e.g., skin, joints, kidneys, heart, and the central nervous system (CNS). SLE patients often have an overproduction of cytokines, e.g., interferons, chemokines, and interleukins. The high cytokine levels are part of the systemic inflammation, which can lead to tissue injury. In the present study, SLE patients were divided into five groups based on their autoantibody profiles. We thus defined these five groups: ANA negative, antiphospholipid (aPL) positive, anti-Sm/anti-RNP positive, Sjögren’s syndrome (SS) antigen A and B positive, and patients positive for more than one type of autoantibodies (other SLE). Cytokines were measured using Mesoscale Discovery (MSD) multiplex analysis. On the basis of the cytokine data, ANA negative patients were the most deviating subgroup, with lower levels of interferon (IFN)-γ, tumor necrosis factor (TNF)-α, interleukin (IL)-12/IL-23p40, and interferon gamma-induced protein (IP)-10. Despite low cytokine levels in the ANA negative group, autoantibody profiles did not discriminate between different cytokine patterns.

  • 9.
    Torell, Frida
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Skotare, Tomas
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Multi-Tissue Metabolomics Integration Utilising Hierarchical Modelling and Data Integration MethodsManuscript (preprint) (Other academic)
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