Biomedical subjects

Sabina Bijlsma

Publications and source records attributed to Sabina Bijlsma.

6 recordsLinked to original sources

PubMed2006

Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation.

A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.

Chromatography, Liquid↗

PubMed2005

Fusion of mass spectrometry-based metabolomics data.

A general method is presented for combining mass spectrometry-based metabolomics data. Such data are becoming more and more abundant, and proper tools for fusing these types of data sets are needed. Fusion of metabolomics data leads to a comprehensive view on the metabolome of an organism or biological system. The ideas presented draw upon established techniques in data analysis. Hence, they are also widely applicable to other types of X-omics data provided there is a proper pretreatment of the data. These issues are discussed using a real-life metabolomics data set from a microbial fermentation process.

Databases as Topic↗

PubMed2004

Quantitative structure activity relationship studies on the flavonoid mediated inhibition of multidrug resistance proteins 1 and 2.

In the present study, the effects of a large series of flavonoids on multidrug resistance proteins (MRPs) were studied in MRP1 and MRP2 transfected MDCKII cells. The results were used to define the structural requirements of flavonoids necessary for potent inhibition of MRP1- and MRP2-mediated calcein transport in a cellular model. Several of the methoxylated flavonoids are among the best MRP1 inhibitors (IC(50) values, ranging between 2.7 and 14.3 microM) followed by robinetin, myricetin and quercetin (IC(50) values ranging between 13.6 and 21.8 microM). Regarding inhibition of MRP2 activity especially robinetin and myricetin appeared to be good inhibitors (IC(50) values of 15.0 and 22.2 microM, respectively). Kinetic characterization revealed that the two transporters differ marginally in the apparent K(m) for the substrate calcein. For one flavonoid, robinetin, the kinetics of inhibition were studied in more detail and revealed competitive inhibition with respect to calcein, with apparent inhibition constants of 5.0 microM for MRP1 and 8.5 microM for MRP2. For inhibition of MRP1, a quantitative structure activity relationship (QSAR) was obtained that indicates three structural characteristics to be of major importance for MRP1 inhibition by flavonoids: the total number of methoxylated moieties, the total number of hydroxyl groups and the dihedral angle between the B- and C-ring. Regarding MRP2 mediated calcein efflux inhibition, only the presence of a flavonol B-ring pyrogallol group seems to be an important structural characteristic. Overall, this study provides insight in the structural characteristics involved in MRP inhibition and explores the differences between inhibitors of these two transporters, MRP1 and MRP2. Ultimately, MRP2 displays higher selectivity for flavonoid type inhibition than MRP1.

Animals↗

PubMed2004

A combination of proteomics, principal component analysis and transcriptomics is a powerful tool for the identification of biomarkers for macrophage maturation in the U937 cell line.

The monocyte-like human histiocytic lymphoma cell line U937 can be induced by phorbol 12-myristate 13-acetate (PMA) to undergo differentiation into a macrophage-like phenotype. We have used two-dimensional gel electrophoresis (2-DE), oligonucleotide microarrays and principal component analysis (PCA) to characterize the U937 cell line as a model system for the differentiation of monocytes into macrophages. A total of 226 differentially expressed proteins were found, of which 41 were selected by PCA for identification using matrix-assisted laser desorption/ionization tandem mass spectrometry. Based on the PCA results, three marker proteins were selected for confirmation of differential expression using Western blot and quantitative real time-PCR. The selected marker proteins were: gamma interferon inducible lysosomal thiol reductase, cathepsin D and adipocyte-fatty acid binding protein. All three proved to be good differentiation markers for macrophage maturation of U937 cells as well as peripheral blood-derived macrophages. The transcriptomics data revealed a large number of additional putative differentiation markers in U937 macrophages, many of which are known to be expressed in peripheral blood-derived macrophages. These include osteospontin, matrix metalloproteinase 9, and HC-gp39. Our results show that the characteristics of U937 macrophages resemble those of inflammatory (exudate) macrophages, exemplified by the down-regulation of 5' nucleotidase and the up-regulation of leucine aminopeptidase mRNAs. In conclusion, using the powerful combination of transcriptomics, 2-DE and PCA, our results show that U937 cells differentiated by PMA treatment are an excellent model system for monocyte derived macrophage generation from blood.

Cell Differentiation↗

PubMed2004

Characterization of anti-inflammatory compounds using transcriptomics, proteomics, and metabolomics in combination with multivariate data analysis.

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.

Adrenergic beta-2 Receptor Agonists↗

PubMedDate not supplied

In search of secreted protein biomarkers for the anti-inflammatory effect of beta2-adrenergic receptor agonists: application of DIGE technology in combination with multivariate and univariate data analysis tools.

Two-dimensional difference gel electrophoresis (DIGE) in combination with univariate (Student's t-test) and multivariate data analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to study the anti-inflammatory effects of the beta(2)-adrenergic receptor (beta(2)-AR) agonist zilpaterol. U937 macrophages were exposed to the endotoxin lipopolysaccharide (LPS) to induce an inflammatory reaction, which was inhibited by the addition of zilpaterol (LZ). This inhibition was counteracted by addition of the beta(2)-AR antagonist propranolol (LZP). The extracellular proteome of the U937 cells induced by the three treatments were examined by DIGE. PCA was used as an explorative tool to investigate the clustering of the proteome dataset. Using this tool, the dataset obtained from cells treated with LPS and LZP were separated from those obtained from LZ treated cells. PLS-DA, a multivariate data analysis tool that also takes correlations between protein spots and class assignment into account, correctly classified the different extracellular proteomes and showed that many proteins were differentially expressed between the proteome of inflamed cells (LPS and LZP) and cells in which the inflammatory response was inhibited (LZ). The Student's t-test revealed 8 potential protein biomarkers, each of which was expressed at a similar level in the LPS and LZP treated cells, but differently expressed in the LZ treated cells. Two of the identified proteins, macrophage inflammatory protein-1beta (MIP-1beta) and macrophage inflammatory protein-1alpha (MIP-1alpha) are known secreted proteins. The inhibition of MIP-1beta by zilpaterol and the involvement of the beta(2)-AR and cAMP were confirmed using a specific immunoassay.

Adrenergic Agonists↗