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Biomedical subjects

Andreas Wilke

Publications and source records attributed to Andreas Wilke.

6 recordsLinked to original sources
PubMed2006

Familial inflammatory dilated cardiomyopathy.

BACKGROUND: Systematic family screening has recently identified dilated cardiomyopathy as an inherited disorder in up to 30% of cases. Mutations in genes encoding proteins responsible for myocardial architecture have been identified, but additional pathophysiological mechanisms including inflammatory reactions have been proposed. AIMS: Identification and characterization of familial DCM, where at least one affected family member fulfils the criteria for inflammatory DCM may lead to a better understanding of the aetiology and pathogenesis of (inflammatory) DCM. METHODS AND RESULTS: Ten families were examined. In six families, clinical characteristics and mode of inheritance were compatible with pure fDCM, fDCM with conduction defect and autosomal recessive fDCM. In four families, (auto-)immune features were diagnosed in affected and non-affected family members. CONCLUSIONS: Familial DCM with an inflammatory component was identified as a specific subgroup of familial DCM. In most cases, the inflammatory process seems to modify, i.e. aggravate, the "classic, cytoskeletopathic" familial DCM, but in some, especially when taking clinical and genetic aspects into account, inflammatory (auto-)immune features can be addressed as the leading pathogenetic principle. Further elucidation of these families may provide a better insight into pathophysiologic processes and may aid in the development of specific therapeutic strategies.

Adult↗
PubMed2006

Domain specificity in experimental measures and participant recruitment: an application to risk-taking behavior.

We challenge the prevailing notion that risk taking is a stable trait, such that individuals show consistent risk-taking/aversive behavior across domains. We subscribe to an alternative approach that appreciates the domain-specific nature of risk taking. More important, we recognize heterogeneity of risk profiles among experimental samples and introduce a new methodology that takes this heterogeneity into account. Rather than using a convenient subject pool (i.e., university students), as is typically done, we specifically targeted relevant subsamples to provide further validation of the domain-specific nature of risk taking. Our research shows that individuals who exhibit high levels of risk-taking behavior in one content area (e.g., bungee jumpers taking recreational risks) can exhibit moderate levels in other risky domains (e.g., financial). Furthermore, our results indicate that risk taking among targeted subsamples can be explained within a cost-benefit framework and is largely mediated by the perceived benefit of the activity, and to a lesser extent by the perceived risk.

Adult↗
PubMed2005

Comprehensive analysis of the extracellular proteins from Xanthomonas campestris pv. campestris B100.

The extracellular proteome of Xanthomonas campestris pv. campestris (Xcc) cultivated in minimal medium was isolated from the cell-free culture supernatant and separated by two-dimensional gel electrophoresis. This technique resolved 97 clearly visible protein spots, which were excised, digested with trypsin and identified on the basis of their peptide mass fingerprints generated by matrix assisted laser desorption/ionisation-time of flight-mass spectrometry. Using this approach 87 different proteins could be distinguished. The Signal P software predicted putative signal peptides for 53% of the extracellular proteins. These proteins are probably transported over the inner membrane and are localized in the periplasm, the outer membrane or secreted into the extracellular space. Among the secreted proteins are 11 degradative enzymes, which are involved in pathogenesis of Xcc. The proteins without obvious secretion signals are known to serve functions in the cytosol. How the cytosolic proteins are delivered to the extracellular space remains unclear.

Bacterial Proteins↗
PubMed2003

Building a BRIDGE for the integration of heterogeneous data from functional genomics into a platform for systems biology.

The flood of data acquired from the increasing number of publicly available genomes has led to new demands for bioinformatics software. With the growing amount of information resulting from high throughput experiments new questions arise that often focus on the comparison of genes, genomes, and their expression profiles. Inferring new knowledge by combining different kinds of "post-genomics" data obviously necessitates the development of new approaches that allow the integration of variable data sources into a flexible framework. In this paper, we describe our concept for the integration of heterogeneous data into a platform for systems biology. We have implemented a Bioinformatics Resource for the Integration of heterogeneous Data from Genomic Explorations (BRIDGE) and illustrate the usability of our approach as a platform for systems biology for two sample applications.

Algorithms↗
PubMed2003

Bioinformatics support for high-throughput proteomics.

In the "post-genome" era, mass spectrometry (MS) has become an important method for the analysis of proteome data. The rapid advancement of this technique in combination with other methods used in proteomics results in an increasing number of high-throughput projects. This leads to an increasing amount of data that needs to be archived and analyzed. To cope with the need for automated data conversion, storage, and analysis in the field of proteomics, the open source system ProDB was developed. The system handles data conversion from different mass spectrometer software, automates data analysis, and allows the annotation of MS spectra (e.g. assign gene names, store data on protein modifications). The system is based on an extensible relational database to store the mass spectra together with the experimental setup. It also provides a graphical user interface (GUI) for managing the experimental steps which led to the MS data. Furthermore, it allows the integration of genome and proteome data. Data from an ongoing experiment was used to compare manual and automated analysis. First tests showed that the automation resulted in a significant saving of time. Furthermore, the quality and interpretability of the results was improved in all cases.

Algorithms↗