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Torell, F., Eketjäll, S., Idborg, H., Jakobsson, P.-J., Gunnarsson, I., Svenungsson, E. & Trygg, J. (2019). Cytokine Profiles in Autoantibody Defined Subgroups of Systemic Lupus Erythematosus. Journal of Proteome Research, 18(3), 1208-1217
Open this publication in new window or tab >>Cytokine Profiles in Autoantibody Defined Subgroups of Systemic Lupus Erythematosus
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2019 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 18, no 3, p. 1208-1217Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2019
Keywords
cytokine, HCA, multivariate data analysis, OPLS-DA, subgrouping, systemic lupus erythematosus
National Category
Rheumatology and Autoimmunity
Identifiers
urn:nbn:se:umu:diva-157770 (URN)10.1021/acs.jproteome.8b00811 (DOI)000460491800035 ()30742448 (PubMedID)2-s2.0-85062355413 (Scopus ID)
Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-04-03Bibliographically approved
Dhillon, S. S., Torell, F., Donten, M., Lundstedt-Enkel, K., Bennett, K., Raennar, S., . . . Lundstedt, T. (2019). Metabolic profiling of zebrafish embryo development from blastula period to early larval stages. PLoS ONE, 14(5), Article ID e0213661.
Open this publication in new window or tab >>Metabolic profiling of zebrafish embryo development from blastula period to early larval stages
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2019 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 5, article id e0213661Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
San Francisco: Public Library of Science, 2019
National Category
Developmental Biology
Identifiers
urn:nbn:se:umu:diva-159600 (URN)10.1371/journal.pone.0213661 (DOI)000467843000002 ()31086370 (PubMedID)
Available from: 2019-06-17 Created: 2019-06-17 Last updated: 2019-06-17Bibliographically approved
Torell, F., Bennet, K., Cereghini, S., Fabre, M., Rännar, S., Lundstedt-Enkel, K., . . . Lundstedt, T. (2018). Metabolic Profiling of Multiorgan Samples: Evaluation of MODY5/RCAD Mutant Mice. Journal of Proteome Research, 17(7), 2293-2306
Open this publication in new window or tab >>Metabolic Profiling of Multiorgan Samples: Evaluation of MODY5/RCAD Mutant Mice
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2018 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 17, no 7, p. 2293-2306Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2018
Keywords
HNF1B-associated diseases, metabolomics, OPLS-DA, multiorgan samples, MODY5, RCAD, mouse model
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:umu:diva-150378 (URN)10.1021/acs.jproteome.7b00821 (DOI)000438469900004 ()29873499 (PubMedID)2-s2.0-85048373012 (Scopus ID)
Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2018-08-08Bibliographically approved
Surowiec, I., Johansson, E., Torell, F., Idborg, H., Gunnarsson, I., Svenungsson, E., . . . Trygg, J. (2017). Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics. Metabolomics, 13(10), Article ID 114.
Open this publication in new window or tab >>Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics
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2017 (English)In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, no 10, article id 114Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
SPRINGER, 2017
Keywords
OPLS, Metabolomics, Multi-batch analysis, Representative sample selection
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:umu:diva-140022 (URN)10.1007/s11306-017-1248-1 (DOI)000410911200007 ()28890672 (PubMedID)
Note

Open Access, link to the Creative Commons license: https://creativecommons.org/licenses/by/4.0/

Available from: 2017-09-29 Created: 2017-09-29 Last updated: 2018-06-09Bibliographically approved
Torell, F., Bennett, K., Rännar, S., Lundstedt-Enkel, K., Lundstedt, T. & Trygg, J. (2017). The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples. Metabolomics, 13(6), Article ID 66.
Open this publication in new window or tab >>The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples
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2017 (English)In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, no 6, article id 66Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Mouse, Metabolomics, Plasma, Multi-organ, Freeze-thaw cycle, OPLS-DA
National Category
Biochemistry and Molecular Biology Endocrinology and Diabetes
Identifiers
urn:nbn:se:umu:diva-135201 (URN)10.1007/s11306-017-1196-9 (DOI)000401711400001 ()28473743 (PubMedID)
Available from: 2017-05-22 Created: 2017-05-22 Last updated: 2018-06-09Bibliographically approved
Bengtsson, A. A., Trygg, J., Wuttge, D. M., Sturfelt, G., Theander, E., Donten, M., . . . Lundstedt, T. (2016). Metabolic Profiling of Systemic Lupus Erythematosus and Comparison with Primary Sjögren’s Syndrome and Systemic Sclerosis. PLoS ONE, 11(7), Article ID e0159384.
Open this publication in new window or tab >>Metabolic Profiling of Systemic Lupus Erythematosus and Comparison with Primary Sjögren’s Syndrome and Systemic Sclerosis
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2016 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 7, article id e0159384Article in journal (Refereed) Published
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. 

Keywords
Systemic lupus erythematosus, Metabolites, Metabolomics, Biomarkers, Drug metabolism, Oxidative stress, Complement system, Rheumatology
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:umu:diva-124383 (URN)10.1371/journal.pone.0159384 (DOI)000380797500069 ()
Funder
Swedish Research Council, 2011-6044
Available from: 2016-08-08 Created: 2016-08-08 Last updated: 2018-06-07Bibliographically approved
Torell, F., Bennett, K., Cereghini, S., Rännar, S., Lundstedt-Enkel, K., Moritz, T., . . . Lundstedt, T. (2016). Tissue sample stability: thawing effect on multi-organ samples. Metabolomics, 12(2), Article ID 19.
Open this publication in new window or tab >>Tissue sample stability: thawing effect on multi-organ samples
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2016 (English)In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 12, no 2, article id 19Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2016
Keywords
Thawing effect, Metabolomics, OPLS, Multivariate analysis, Multi-organ
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:umu:diva-117461 (URN)10.1007/s11306-015-0933-1 (DOI)000369343900010 ()
Note

Electronic supplementary material The online version of this article (doi:10.1007/s11306-015-0933-1) contains supplementary material, which is available to authorized users.

This research was supported by the Swedish Research Council Grant No. 2011-6044 (to JT), the Biology of Liver and Pancreatic Development and Disease (BOLD) Marie Curie Initial Training Network (MCITN) within EU’s FP7 programme (to TL, JT, KB, FT, SC, CH, TM) and the CNRS and Universite´ Pierre et Marie Curie (to SC, CH), the Institut National de la Sante´ et de la Recherche Me´dicale, INSERM (to SC), the Socie´te´ Francophone du Diabe`te and Emergence UPMC (to CH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest: JT, TM and TL are shareholders of AcureOmics AB. No financing has been received from this company.

Available from: 2016-03-01 Created: 2016-03-01 Last updated: 2018-06-07Bibliographically approved
Torell, F., Bennett, K., Cereghini, S., Raennar, S., Lundstedt-Enkel, K., Moritz, T., . . . Lundstedt, T. (2015). Multi-Organ Contribution to the Metabolic Plasma Profile Using Hierarchical Modelling. PLoS ONE, 10(6), Article ID e0129260.
Open this publication in new window or tab >>Multi-Organ Contribution to the Metabolic Plasma Profile Using Hierarchical Modelling
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2015 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 6, article id e0129260Article in journal (Refereed) Published
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.

National Category
Organic Chemistry
Identifiers
urn:nbn:se:umu:diva-106564 (URN)10.1371/journal.pone.0129260 (DOI)000356567500041 ()26086868 (PubMedID)
Funder
Swedish Research Council, 2011-6044
Available from: 2015-07-20 Created: 2015-07-20 Last updated: 2018-06-07Bibliographically approved
Torell, F., Skotare, T. & Trygg, J.Multi-Tissue Metabolomics Integration Utilising Hierarchical Modelling and Data Integration Methods.
Open this publication in new window or tab >>Multi-Tissue Metabolomics Integration Utilising Hierarchical Modelling and Data Integration Methods
(English)Manuscript (preprint) (Other academic)
National Category
Analytical Chemistry
Identifiers
urn:nbn:se:umu:diva-158329 (URN)
Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-04-25
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6294-7844

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