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      The metabolic analysis of psoriasis identifies the associated metabolites while providing computational models for the monitoring of the disease

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          Abstract

          The majority of studies on psoriasis have focused on explaining the genetic background and its associations with the immune system’s response. The aim of this study was to identify the low-molecular weight compounds contributing to the metabolomic profile of psoriasis and to provide computational models that help with the classification and monitoring of the severity of the disease. We compared the results from targeted and untargeted analyses of patients’ serums with plaque psoriasis to controls. The main differences were found in the concentrations of acylcarnitines, phosphatidylcholines, amino acids, urea, phytol, and 1,11-undecanedicarboxylic acid. The data from the targeted analysis were used to build classification models for psoriasis. The results from this study provide an overview of the metabolomic serum profile of psoriasis along with promising statistical models for the monitoring of the disease.

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          The online version of this article (doi:10.1007/s00403-017-1760-1) contains supplementary material, which is available to authorized users.

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          Most cited references 56

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          KEGG: kyoto encyclopedia of genes and genomes.

           M Kanehisa (2000)
          KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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            XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.

            Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
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              Induction of decision trees

               J. R. Quinlan (1986)
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                Author and article information

                Contributors
                +372 737 4414 , aigar.ottas@ut.ee
                Journal
                Arch Dermatol Res
                Arch. Dermatol. Res
                Archives of Dermatological Research
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0340-3696
                1432-069X
                10 July 2017
                10 July 2017
                2017
                : 309
                : 7
                : 519-528
                Affiliations
                [1 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, Department of Biochemistry, Institute of Biomedicine and Translational Medicine, , University of Tartu, ; Ravila 14b, 50411 Tartu, Estonia
                [2 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, Faculty of Science and Technology, Institute of Computer Science, , University of Tartu, ; Tartu, Estonia
                [3 ]GRID grid.436973.c, Quretec OÜ, ; Tartu, Estonia
                [4 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, University of Tartu, ; Tartu, Estonia
                [5 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, Department of Dermatology, , University of Tartu, ; Tartu, Estonia
                [6 ]ISNI 0000 0001 0585 7044, GRID grid.412269.a, Clinic of Dermatology, , Tartu University Hospital, ; Tartu, Estonia
                [7 ]ISNI 0000 0001 0943 7661, GRID grid.10939.32, Centre of Excellence for Genomics and Translational Medicine, , University of Tartu, ; Tartu, Estonia
                Article
                1760
                10.1007/s00403-017-1760-1
                5577063
                28695330
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                Funding
                Funded by: Eesti Teadusagentuur (EE)
                Award ID: PUT177, IUT20-42
                Award ID: PUT1465
                Award Recipient :
                Funded by: Tartu Ülikool (EE), European Union
                Award ID: SP1GVARENG
                Award Recipient :
                Categories
                Original Paper
                Custom metadata
                © Springer-Verlag GmbH Germany 2017

                Dermatology

                computational model, metabolomics, psoriasis, targeted analysis, untargeted analysis

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