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Characterization of anti-inflammatory compounds using transcriptions, proteomics, and metabolics in combination with multivariate data analysis

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Author: Verhoeckx, K.C.M. · Bijlsma, S. · Jespersen, S. · Ramaker, R. · Verheij, E.R. · Witkamp, R.F. · Greef, J. van der · Rodenburg, R.J.T.
Type:article
Date:2004
Institution: TNO Voeding Centraal Instituut voor Voedingsonderzoek TNO
Source:International Immunopharmacology, 4, 1499-1514
Identifier: 88523
doi: doi:10.1016/j.intimp.2004.07.008
Keywords: Pharmacology Health · 2-D gel electrophoresis · Anti-inflammatory drugs · Metabolomics · Micro-array · Multivariate data analysis · Principal component discriminant analysis · 4 (4 fluorophenyl) 2 (4 methylsulfinylphenyl) 5 (4 pyridyl)imidazole · Antiinflammatory agent · Beta 2 adrenergic receptor stimulating agent · Clenbuterol · Dexamethasone · Formoterol · Lipopolysaccharide · Salbutamol · Unclassified drug · Zilpaterol · Antiinflammatory activity · controlled study · Data analysis · DNA microarray · Drug determination · Gel electrophoresis · Genetic transcription · Human · Human cell · Lipogenesis · Liquid chromatography · Mass spectrometry · Metabolism · Methodology · Molecule · Monocyte · Multivariate analysis · Priority journal · Protein expression · Proteomics · Adrenergic beta-Agonists · Anti-Inflammatory Agents · Cell Line, Tumor · Electrophoresis, Gel, Two-Dimensional · Gas Chromatography-Mass Spectrometry · Humans · Lipids · Lipopolysaccharides · Macrophage Activation · Macrophages · Multivariate Analysis · Oligonucleotide Array Sequence Analysis · Proteome · Receptors, Adrenergic, beta-2 · RNA, Messenger

Abstract

The discovery of new anti-inflammatory drugs is often based on an interaction with a specific target, although other pathways often play a primary or secondary role. Anti-inflammatory drugs can be categorized into classes, based on their mechanism of action. In this article we investigate the possibility to characterize novel anti-inflammatory compounds by three holistic methods. For this purpose, we make use of macrophage-like U937 cells which are stimulated with LPS in the absence or presence of an anti-inflammatory compound. Using micro-arrays, 2-D gel electrophoresis and a LC-MS method for lipids the effects on the transcriptome, proteome and metabolome of the exposed cells is investigated. The expression patterns are subsequently analyzed using in-house developed pattern recognition tools. Using the methods described above, we have examined the effects of six anti-inflammatory compounds. Our results demonstrate that different classes of anti-inflammatory compounds show distinct and characteristic mRNA, protein, and lipid expression patterns, which can be used to categorise known molecules and to discover and classify new leads. The potential of our approach is illustrated by the analysis of several beta (2)-adrenergic agonists (β<sub>2</sub>-agonists). In addition to their primary pharmacological target, β<sub>2</sub>-agonists posses certain anti-inflammatory properties. We were able to show that zilpaterol, a poorly characterized β<sub>2</sub>-agonist, gives rise to an almost identical expression pattern as the β<sub>2</sub>-agonists clenbuterol and salbutamol. Furthermore we have identified specific mRNA, protein and lipid markers for the anti-inflammatory compounds investigated in this study. © 2004 Elsevier B.V. All rights reserved. Chemicals/CAS: 4 (4 fluorophenyl) 2 (4 methylsulfinylphenyl) 5 (4 pyridyl)imidazole, 152121-47-6; clenbuterol, 21898-19-1, 37148-27-9; dexamethasone, 50-02-2; formoterol, 73573-87-2; salbutamol, 18559-94-9; Adrenergic beta-Agonists; Anti-Inflammatory Agents; Lipids; lipopolysaccharide, Escherichia coli 0111 B4; Lipopolysaccharides; Proteome; Receptors, Adrenergic, beta-2; RNA, Messenger