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Hinnerk Boriss

Publications and source records attributed to Hinnerk Boriss.

3 recordsLinked to original sources

Differentially expressed cortical genes contribute to perivascular deposition in transgenic mice with inducible neuron-specific expression of TGF-beta1.

In the brain the expression of transforming growth factor beta1 (TGF-beta1) is involved both in neuroprotective and neurodegenerative processes. Recently, we have established a transgenic mouse model with inducible neuron-specific expression of TGF-beta1 based on the tetracycline-regulated gene expression system. A long-term expression of TGF-beta1 results in persisting perivascular thioflavin-positive depositions, which did not disappear even though the transgene synthesis was repressed completely by administration of doxycycline. Formation and composition of these depositions are hardly elucidated. The aim of this study was to identify TGF-beta1 responding genes potentially participating in forming these depositions. To address this problem we have compared the cortical mRNA expression pattern of TGF-beta1 expressing mice with mice impeded to express the transgenic protein using oligonucleotide microarray analysis. Differential gene expression was further characterized by quantitative real-time reverse transcription-polymerase chain reaction including animals, where the long-lasting TGF-beta1 expression was repressed. While no change of amyloid precursor protein RNA expression level was detected, various genes strongly involved in calcium homeostasis, tissue mineralization or vascular calcification were identified differentially expressed. It is suggested, that these genes might contribute to the perivascular depositions in the TGF-beta1 expressing mice.

Animals↗

Meta-analysis of microarray data on pancreatic cancer defines a set of commonly dysregulated genes.

Pancreatic ductal adenocarcinoma is the eighth most common cancer with the lowest overall 5-year relative survival rate of any tumor type today. Expression profiling using microarrays has been widely used to identify genes associated with pancreatic cancer development. To extract maximum value from the available gene expression data, we applied a meta-analysis to search for commonly differentially expressed genes in pancreatic ductal adenocarcinoma. We obtained data sets from four different gene expression studies on pancreatic cancer. We selected a consensus set of 2984 genes measured in all four studies and applied a meta-analysis approach to evaluate the combined data. Of the genes identified as differentially expressed, several were validated using RT-PCR and immunohistochemistry. Additionally, we used a class discovery algorithm to identify a gene expression signature. Our meta-analysis revealed that the pancreatic cancer gene expression data sets shared a significant number of up- and downregulated genes, independent of the technology used. This interstudy crossvalidation approach generated a set of 568 genes that were consistently and significantly dysregulated in pancreatic cancer. Of these, 364 (64.1%) were upregulated and 204 (35.9%) were downregulated in pancreatic cancer. Only 127 (22%) were described in the published individual analyses. Functional annotation of the genes revealed that genes presumably associated with the cell adhesion-mediated drug resistance pathway are frequently overexpressed in pancreatic cancer. Meta-analysis is an important tool for the identification and validation of differentially expressed genes. These could represent good candidates for novel diagnostic and therapeutic approaches to pancreatic cancer.

Adenocarcinoma↗

Expression profiling: toward an application in sepsis diagnostics.

Sepsis is a common and serious health problem whereby improvements in diagnosis are crucial in increasing survival rates. To test whether profiling transcription is applicable to sepsis diagnosis, we analyzed whole blood using a microarray containing probes for 340 genes relevant to inflammation. The patient's gene expression pattern was highly homogenous, resulting in 69% of differentially expressed genes. With a positive predictive value of 98%, a list of 50 differentially expressed genes was compiled, and randomly chosen transcripts were confirmed by PCR. Here, we present the first evidence that microarrays can identify typical gene expression profiles in the blood of patients with severe sepsis. Regardless of the heterogeneity of the patients, we observed a striking correlation between the conventional diagnostic classification and our approach. The unity of responses suggests that the principle of this multiparameter approach can be adapted to early stage sepsis diagnosis.

APACHE↗