Biomedical subjects

Reinhard Guthke

Publications and source records attributed to Reinhard Guthke.

6 recordsLinked to original sources

PubMed2006

Convergence of the proteomic pattern in cancer.

MOTIVATION: On the histological level the differentiation of normal epithelial tissues is well known. The phenomenon of dedifferentiation, which occurs as cells develop towards malignancy is also well described. To identify an epithelial tumor-specific proteomic profile as well as to measure the proximities between we used data from tumor tissue and adjacent normal tissue microdissected from head and neck and colon cancer samples which were analyzed using ProteinChip technology and performed a bioinformatic meta-analysis on the resulting four complex datasets. RESULTS: All four groups could be identified based on their proteomic signatures and the tumor tissues were found to be more similar to one another than to the normal epithelial tissue from which they progressed. This study shows at the proteomic level that changes in the histological features of tumors as compared to the tissues from which they arise are reflected in the convergence of proteomic pattern during the development to cancer.

Cell Differentiation↗

PubMed2006

Rheumatoid arthritis, a complex multifactorial disease: on the way toward individualized medicine.

With the availability of the human genome sequence and those of related species like chimpanzee, mouse, and rat, data driven research for tackling the molecular grounds of rheumatoid arthritis (RA), a multifactorial polygenic disease, can be considered a realistic challenge to the scientific community. A comprehensive research strategy is presented enabling the integration of multiple research efforts on studying autoimmunity by so called systems biology approaches. An integrative scientific concept is discussed of how to unravel molecular mechanisms of complex diseases by making use of state-of-the-art methodologies in functional and comparative genomics. A continuous interchange of data-driven and hypothesis-driven research is adjoined to determine the nature of rheumatic diseases with autoimmune background. Instead of studying single genes and proteins, RNA and protein microarray profiles are currently obtained in numerous research projects producing read-outs termed gene signatures rather than DNA and/or protein markers. A comprehensive study of the RNA, protein, and metabolite regimes is undertaken that eventually will lead to a "holistic" view of how all respective molecules, pathways and cells themselves interact with each other. Some of the above mentioned research aspects have already been studied by the authors, hopefully leading to new diagnostics and therapeutics in the future.

Animals↗

PubMed2005

Global epithelial cell transcriptional responses reveal Streptococcus pyogenes Fas regulator activity association with bacterial aggressiveness.

The bacterial human pathogen Streptococcus pyogenes (group A streptococci, GAS) is able to adhere to, internalize into and cross-talk on multiple levels with its host cells. To gain insight into the Fas function in pathogenesis we used Affymetrix human genome DNA-arrays to measure temporal and global transcriptional responses of HEp-2 cells infected with M49 S. pyogenes wild-type bacteria and DeltafasX, an isogenic S. pyogenes two-component-signal-transduction system mutant. A modified stringent statistical analysis method identified a total of 86 HEp-2 cell genes as differentially transcribed upon infection over the investigated time course. Increased expression of genes encoding proteins involved in GAS host cell adherence and internalization (fibronectin, integrin-alpha5) was found as a common response. In contrast to earlier reports investigating other GAS serotype strains, Ras superfamily and RhoA pathways are exploited by M49 GAS, suggesting serotype specific interactions with the host cell cytoskeleton. Despite transcriptional induction, secreted IL-8 levels of deltafasX mutant infected cells were below those of non-infected cells, indicating an absence of Fas expression could be important for GAS tissue colonization and long-term intracellular persistence. Oppositely, activity of the S. pyogenes Fas-system apparently promotes high adherence and internalization rates, massive cytokine gene transcription and cytokine release, host cell apoptosis via a caspase-2 activation pathway, and cytotoxicity. Thus, the S. pyogenes Fas two-component signal transduction system could be involved in local tissue destruction and general bacterial aggressiveness towards host cells.

Apoptosis↗

PubMed2004

Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

MOTIVATION: The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. RESULTS: The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. CONTACT: Reinhard.Guthke@hki-jena.de.

Algorithms↗

PubMed2002

Knowledge-based assessment of gene expression data from chemiluminescence detection.

The first problem in gene expression profiling to be solved is choosing the appropriate gene array, detection procedure, image analysis and data generation depending on the organism of interest, equipment and budget. The next one is how to deduce biologically meaningful data. We assessed gene expression data from chemiluminescent detection and empirically found criteria for the reliable identification of biologically meaningful expression ratios. Current statistical assessments are often applied unreflectedly concerning problems occurring in practice. So interesting results are considered to be irrelevant. This requires a laborious data check. We suggest automation. Our empirically found criteria were transformed into and validated by a knowledge-based system. This system is adaptable to all other methods of expression profiling. We compared the experience-based and new knowledge-based assessment of the expression data from our chemiluminescent and additionally radioactive detection of several experiments with published data to evaluate our entire procedure. With our adaptation of chemiluminescence detection to commercially available Escherichia coli gene arrays we present a useful alternative to common procedures in gene expression monitoring. Moreover, with our consideration of plasmid-harbouring E. coli strains we provide the opportunity to monitor gene expression during processes requiring any plasmids (e.g. recombinant protein expression).

Artificial Intelligence↗

PubMedDate not supplied

Gene expression profiling of peripheral blood mononuclear leukocytes from psoriasis patients identifies new immune regulatory molecules.

In the present report gene expression profiling of peripheral blood mononuclear cells (PBMC) from psoriasis patients suffering from severe generalized disease was performed comparing diseased stage with cured stage. By this means, 18 genes were identified which showed differential expression. The most significant differences were found for IL-8, annexin A3, cycloxygenase-2 (COX-2), cell cycle regulator G0S2, and pre-B cell enhancing factor (PBEF), all of which showed upregulation in the diseased stage. Microarray data were confirmed by real-time RT-PCR. Further analyses using support vector machines identified three pairs of genes (IL-8 - CDKN1C/p57, cyclooxygenase-2 - NR1D2, and desmocollin-2 - CDKN1C/p57) which allowed an accuracy of disease stage prediction of 86%, based on gene expression patterns. Taken together, this is the first large-scale gene expression study of psoriasis PBMC identifying candidate genes that might contribute to psoriasis immunopathogenesis. The genes identified in the present report and the molecular mechanisms underlying their regulation might serve as future targets for therapeutic intervention in psoriasis.

Gene Expression Profiling↗