title: Characterization of anti-inflammatory compounds using transcriptomics, proteomics, and metabolomics in combination with multivariate data analysis.
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 (beta2-agonists). In addition to their primary pharmacological target, beta2-agonists posses certain anti-inflammatory properties. We were able to show that zilpaterol, a poorly characterized beta2-agonist, gives rise to an almost identical expression pattern as the beta2-agonists clenbuterol and salbutamol. Furthermore we have identified specific mRNA, protein and lipid markers for the anti-inflammatory compounds investigated in this study.
authors.affiliation: TNO Pharma, Utrechtseweg 48, PO Box 360, 3700AJ Zeist, The Netherlands. Verhoeckx@pharma.tno.nl
authors.forename: Kitty C M
authors.initials: KC
authors.lastname: Verhoeckx
authors.forename: Sabina
authors.initials: S
authors.lastname: Bijlsma
authors.forename: Sonja
authors.initials: S
authors.lastname: Jespersen
authors.forename: Raymond
authors.initials: R
authors.lastname: Ramaker
authors.forename: Elwin R
authors.initials: ER
authors.lastname: Verheij
authors.forename: Renger F
authors.initials: RF
authors.lastname: Witkamp
authors.forename: Jan
authors.initials: J
authors.lastname: van der Greef
authors.forename: Richard J T
authors.initials: RJ
authors.lastname: Rodenburg
mesh_terms: D058666:Adrenergic beta-2 Receptor Agonists; D000318:Adrenergic beta-Agonists / Q000494:pharmacology; D000893:Anti-Inflammatory Agents / Q000494:pharmacology*; D045744:Cell Line, Tumor; D015180:Electrophoresis, Gel, Two-Dimensional; D008401:Gas Chromatography-Mass Spectrometry; D006801:Humans; D008055:Lipids / Q000096:biosynthesis*; D008070:Lipopolysaccharides / Q000494:pharmacology; D008262:Macrophage Activation / Q000187:drug effects / Q000276:immunology; D008264:Macrophages / Q000187:drug effects / Q000276:immunology / Q000378:metabolism; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis; D020543:Proteome / Q000096:biosynthesis*; D012333:RNA, Messenger / Q000096:biosynthesis*
chemical_list: D058666:Adrenergic beta-2 Receptor Agonists; D000318:Adrenergic beta-Agonists; D000893:Anti-Inflammatory Agents; D008055:Lipids; D008070:Lipopolysaccharides; D020543:Proteome; D012333:RNA, Messenger; C440499:lipopolysaccharide, Escherichia coli O111 B4