Biomedical subjects

Dieter Beule

Publications and source records attributed to Dieter Beule.

3 recordsLinked to original sources

PubMed2006

Global analysis of host tissue gene expression in the invasive front of colorectal liver metastases.

Host cell reactions are a crucial determinant for tumor invasion. We analyzed on a genomewide scale gene expression differences between microdissected tissues taken from unaffected liver tissue of a human colorectal tumor (LS174) growing in the livers of nude mice and tissue from the host part of the invasive front. Due to the low degree of interspecies cross-hybridization of 15% as determined on Affymetrix microarrays, our xenograft model allowed for the distinction of genes of murine versus human origin even if the respective tissues could not be isolated separately. Using the gene ontology (GO) classification, we were able to determine patterns of up- and downregulated genes in the liver part of the invasive front. We observed a pronounced overrepresentation, e.g., of the GO terms "extracellular matrix," "cell communication," "response to biotic stimulus," "structural molecule activity" and "cell growth," indicating a very pronounced host cell response to tumor invasion. On the single gene level, hepatic stellate cell (HSC) activation markers were overrepresented in the liver part of the invasion front. Immunohistochemistry and qPCR confirmed an activation of HSC as well as an increased number of HSC in the invasive front as compared to the noninvaded liver tissue. In summary, our data demonstrate the feasibility of an interspecies differential gene expression approach on a genomewide scale.

Adenocarcinoma↗

PubMed2005

Biological profiling of gene groups utilizing Gene Ontology.

Increasingly used high throughput experimental techniques, like DNA or protein microarrays give as a result groups of interesting, e.g. differentially regulated genes which require further biological interpretation. With the systematic functional annotation provided by the Gene Ontology the information required to automate the interpretation task is now accessible. However, the determination of statistical significance of a biological process within these groups is still an open question. In answering this question, multiple testing issues must be taken into account to avoid misleading results. Here we present a statistical framework that tests whether functions, processes or locations described in the Gene Ontology are significantly enriched within a group of interesting genes when compared to a reference group. First we define an exact analytical expression for the expected number of false positives that allows us to calculate adjusted p-values to control the false discovery rate. Next, we demonstrate and discuss the capabilities of our approach using publicly available microarray data on cell-cycle regulated genes. Further, we analyze the robustness of our framework with respect to the exact gene group composition and compare the performance with earlier approaches. The software package GOSSIP implements our method and is made freely available at http://gossip.gene-groups.net/.

Binding Sites↗

PubMed2001

Extracting information from cDNA arrays.

High-density DNA arrays allow measurements of gene expression levels (messenger RNA abundance) for thousands of genes simultaneously. We analyze arrays with spotted cDNA used in monitoring of expression profiles. A dilution series of a mouse liver probe is deployed to quantify the reproducibility of expression measurements. Saturation effects limit the accessible signal range at high intensities. Additive noise and outshining from neighboring spots dominate at low intensities. For repeated measurements on the same filter and filter-to-filter comparisons correlation coefficients of 0.98 are found. Next we consider the clustering of gene expression time series from stimulated human fibroblasts which aims at finding co-regulated genes. We analyze how preprocessing, the distance measure, and the clustering algorithm affect the resulting clusters. Finally we discuss algorithms for the identification of transcription factor binding sites from clusters of co-regulated genes. (c) 2001 American Institute of Physics.

Journal Article↗