Biomedical subjects

Thomas Karopka

Publications and source records attributed to Thomas Karopka.

6 recordsLinked to original sources

PubMed2006

Inhibitory effects of interferon-gamma on activation of rat pancreatic stellate cells are mediated by STAT1 and involve down-regulation of CTGF expression.

Pancreatic stellate cells (PSCs) are the main source of extracellular matrix proteins in pancreatic fibrosis, a pathological feature of chronic pancreatitis and pancreatic cancer. Interferon-gamma (IFN-gamma) is an antifibrotic cytokine, but how precisely it exerts its effects on PSCs is largely unknown. Here, we have focussed on the role of STAT1 as well as target genes of IFN-gamma signalling. Our data indicate that IFN-gamma regulates the expression of two autocrine mediators of PSC activation, connective tissue growth factor and endothelin-1, in a transforming growth factor-beta1-antagonistic manner. STAT1 overexpression under the control of a tetracycline-dependent promoter revealed a close correlation between STAT1 expression and activation, the biological effects of IFN-gamma (growth inhibition, induction of apoptosis), and target gene expression. Our data further support the hypothesis that IFN-gamma interferes with stellate cell activation in the pancreas and suggest activated STAT1 as an inductor of a quiescent PSC phenotype.

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PubMed2006

3-Hydroxy-3-methylglutaryl coenzyme A reductase inhibitor Atorvastatin mediated effects depend on the activation status of target cells in PLP-EAE.

The effect of Atorvastatin on transcriptional activity in murine experimental autoimmune encephalomyelitis (EAE) induced by PLP peptide 139-151 was analyzed by DNA microarray technique in lymph nodes and spinal cord at onset (10 days), height (20 days) and first remission (30 days) of disease. Fourteen genes were selectively influenced by Atorvastatin in EAE mice. They are mainly related to immune cell functions and regulation of cell-to-cell interaction. Interestingly, seven genes were also differentially regulated in CFA-injected control mice. But qualitative and quantitative differences to EAE mice argue for a dependency of statin effects on the activation status of target cells. Differential regulation of the newly detected candidate genes of statin effects COX-1 and HSP-105 and the previously known statin-responsive genes ICAM-1 and CD86 was confirmed by Western blot and immunohistochemistry. Flow cytometric analysis of lymph node cells revealed that the effect of Atorvastatin treatment in non-immunized healthy animals resembled the effect of immunization with PLP peptide regarding changes of T helper cells, activated B cells and macrophages. In EAE mice, these effects were partially reversed by Atorvastatin treatment. Monitoring of expression of the newly identified candidate genes and patterns of lymphocyte subpopulations might predict the responsiveness of multiple sclerosis patients to statin treatment.

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PubMed2006

The Autoimmune Disease Database: a dynamically compiled literature-derived database.

BACKGROUND: Autoimmune diseases are disorders caused by an immune response directed against the body's own organs, tissues and cells. In practice more than 80 clinically distinct diseases, among them systemic lupus erythematosus and rheumatoid arthritis, are classified as autoimmune diseases. Although their etiology is unclear these diseases share certain similarities at the molecular level i.e. susceptibility regions on the chromosomes or the involvement of common genes. To gain an overview of these related diseases it is not feasible to do a literary review but it requires methods of automated analyses of the more than 500,000 Medline documents related to autoimmune disorders. RESULTS: In this paper we present the first version of the Autoimmune Disease Database which to our knowledge is the first comprehensive literature-based database covering all known or suspected autoimmune diseases. This dynamically compiled database allows researchers to link autoimmune diseases to the candidate genes or proteins through the use of named entity recognition which identifies genes/proteins in the corresponding Medline abstracts. The Autoimmune Disease Database covers 103 autoimmune disease concepts. This list was expanded to include synonyms and spelling variants yielding a list of over 1,200 disease names. The current version of the database provides links to 541,690 abstracts and over 5,000 unique genes/proteins. CONCLUSION: The Autoimmune Disease Database provides the researcher with a tool to navigate potential gene-disease relationships in Medline abstracts in the context of autoimmune diseases.

Autoimmune Diseases↗

PubMed2005

Representation of individual gene expression in completely pooled mRNA samples.

Designing microarray experiments, scientists are often confronted with the question of pooling due to financial constraints, but discussion of the validity of pooling tends toward a sub-pooling recommendation. Since complete pooling protocols can be considered part of sub-pooling designs, gene expression data from three complete pooling experiments were analyzed. Data from complete pooled versus individual mRNA samples of rat brain tissue were compared to answer the question whether the pooled sample represents individual samples in small-sized experiments. Our analytic approach provided clear results concerning the Affymetrix MAS 5.0 signal and detection call parameters. Despite a strong similarity of arrays within experimental groups, the individual signals were evidently not appropriately represented in the pooled sample, with slightly more than half of all the genes considered. Our analysis reveals problems in cases of small complete pooling designs with less than six subjects pooled.

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PubMed2004

Automatic construction of gene relation networks using text mining and gene expression data.

Microarray gene expression analysis is a powerful high-throughput technique that enables researchers to monitor the expression of thousands of genes simultaneously. Using this methodology huge amounts of data are produced which have to be analysed. Clustering algorithms are used to group genes together based on a predefined distance measure. However, clustering algorithms do not necessarily group the genes in a biological meaningful way. Additional information is needed to improve the identification of disease relevant genes. The primary objective of our project is to support the analysis of microarray gene expression data by construction of gene relation networks (GRNs). Required information can not be found in a structured representation like a database. In contrast, a large number of relations are described in biomedical literature. The main outcome of this project is the implementation of a software system that provides clinicians and researchers with a tool that supports the analysis of microarray gene expression data by mapping known relationships from the biomedical literature to local gene expression experiments.

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PubMedDate not supplied

Peroxisome proliferator-activated receptor gamma overexpression inhibits pro-fibrogenic activities of immortalised rat pancreatic stellate cells.

Pancreatic stellate cells (PSCs) play a key role in the development of pancreatic fibrosis, a constant feature of chronic pancreatitis and pancreatic cancer. In response to pro-fibrogenic mediators, PSCs undergo an activation process that involves proliferation, enhanced production of extracellular matrix proteins and a phenotypic transition towards myofibroblasts. Ligands of the peroxisome proliferator-activated receptor gamma (PPARgamma), such as thiazolidinediones, are potent inhibitors of stellate cell activation and fibrogenesis in pancreas and liver. The effects of PPARgamma ligands, however, are at least in part mediated through PPARgamma-independent pathways. Here, we have chosen a different approach to study regulatory functions of PPARgamma in PSCs. Using immortalised rat PSCs, we have established a model of tetracycline (tet)-regulated PPARgamma overexpression. Induction of PPARgamma expression strongly inhibited proliferation and enhanced the rate of apoptotic cell death. Furthermore, PPARgamma-overexpressing cells synthesised less collagen than controls. To monitor effects of PPARgamma on PSC gene expression, we employed Affymetrix microarray technology. Using stringent selection criteria, we identified 21 up- and 19 down-regulated genes in PPARgamma-overexpressing cells. Most of the corresponding gene products are either involved in lipid metabolism, play a role in signal transduction, or are secreted molecules that regulate cell growth and differentiation. In conclusion, our data suggest an active role of PPARgamma in the induction of a quiescent PSC phenotype. PPARgamma-regulated genes in PSCs may serve as novel targets for the development of antifibrotic therapies.

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