@article{KittelSchneiderKenisScheketal.2012,
  author    = {Kittel-Schneider, Sarah and Kenis, Gunter and Schek, Julia and van den Hove, Daniel and Prickaerts, Jos and Lesch, Klaus-Peter and Steinbusch, Harry and Reif, Andreas},
  title     = {Expression of monoamine transporters, nitric oxide synthase 3, and neurotrophin genes in antidepressant-stimulated astrocytes},
  series = {Frontiers in Psychiatry},
  volume    = {3},
  journal   = {Frontiers in Psychiatry},
  doi       = {10.3389/fpsyt.2012.00033},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-123627},
  pages     = {33},
  year      = {2012},
  abstract  = {Background: There is increasing evidence that glial cells play a role in the pathomechanisms of mood disorders and the mode of action of antidepressant drugs. Methods: To examine whether there is a direct effect on the expression of different genes encoding proteins that have been implicated in the pathophysiology of affective disorders, primary astrocyte cell cultures from rats were treated with two different antidepressant drugs, imipramine and escitalopram, and the RNA expression of brain-derived neurotrophic factor (Bdnf), serotonin transporter (5Htt), dopamine transporter (Dat), and endothelial nitric oxide synthase (Nos3) was examined. Results: Stimulation of astroglial cell culture with imipramine, a tricyclic antidepressant, led to a significant increase of the Bdnf RNA level whereas treatment with escitalopram did not. In contrast, 5Htt was not differentially expressed after antidepressant treatment. Finally, neither Dat nor Nos3 RNA expression was detected in cultured astrocytes. Conclusion: These data provide further evidence for a role of astroglial cells in the molecular mechanisms of action of antidepressants.},
  language  = {en}
}
@phdthesis{Vainshtein2010,
  author    = {Vainshtein, Yevhen},
  title     = {Applying microarray-based techniques to study gene expression patterns: a bio-computational approach},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-51967},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2010},
  abstract  = {The regulation and maintenance of iron homeostasis is critical to human health. As a constituent of hemoglobin, iron is essential for oxygen transport and significant iron deficiency leads to anemia. Eukaryotic cells require iron for survival and proliferation. Iron is part of hemoproteins, iron-sulfur (Fe-S) proteins, and other proteins with functional groups that require iron as a cofactor. At the cellular level, iron uptake, utilization, storage, and export are regulated at different molecular levels (transcriptional, mRNA stability, translational, and posttranslational). Iron regulatory proteins (IRPs) 1 and 2 post-transcriptionally control mammalian iron homeostasis by binding to iron-responsive elements (IREs), conserved RNA stem-loop structures located in the 5'- or 3'- untranslated regions of genes involved in iron metabolism (e.g. FTH1, FTL, and TFRC). To identify novel IRE-containing mRNAs, we integrated biochemical, biocomputational, and microarray-based experimental approaches. Gene expression studies greatly contribute to our understanding of complex relationships in gene regulatory networks. However, the complexity of array design, production and manipulations are limiting factors, affecting data quality. The use of customized DNA microarrays improves overall data quality in many situations, however, only if for these specifically designed microarrays analysis tools are available. Methods In this project response to the iron treatment was examined under different conditions using bioinformatical methods. This would improve our understanding of an iron regulatory network. For these purposes we used microarray gene expression data. To identify novel IRE-containing mRNAs biochemical, biocomputational, and microarray-based experimental approaches were integrated. IRP/IRE messenger ribonucleoproteins were immunoselected and their mRNA composition was analysed using an IronChip microarray enriched for genes predicted computationally to contain IRE-like motifs. Analysis of IronChip microarray data requires specialized tool which can use all advantages of a customized microarray platform. Novel decision-tree based algorithm was implemented using Perl in IronChip Evaluation Package (ICEP). Results IRE-like motifs were identified from genomic nucleic acid databases by an algorithm combining primary nucleic acid sequence and RNA structural criteria. Depending on the choice of constraining criteria, such computational screens tend to generate a large number of false positives. To refine the search and reduce the number of false positive hits, additional constraints were introduced. The refined screen yielded 15 IRE-like motifs. A second approach made use of a reported list of 230 IRE-like sequences obtained from screening UTR databases. We selected 6 out of these 230 entries based on the ability of the lower IRE stem to form at least 6 out of 7 bp. Corresponding ESTs were spotted onto the human or mouse versions of the IronChip and the results were analysed using ICEP. Our data show that the immunoselection/microarray strategy is a feasible approach for screening bioinformatically predicted IRE genes and the detection of novel IRE-containing mRNAs. In addition, we identified a novel IRE-containing gene CDC14A (Sanchez M, et al. 2006). The IronChip Evaluation Package (ICEP) is a collection of Perl utilities and an easy to use data evaluation pipeline for the analysis of microarray data with a focus on data quality of custom-designed microarrays. The package has been developed for the statistical and bioinformatical analysis of the custom cDNA microarray IronChip, but can be easily adapted for other cDNA or oligonucleotide-based designed microarray platforms. ICEP uses decision tree-based algorithms to assign quality flags and performs robust analysis based on chip design properties regarding multiple repetitions, ratio cut-off, background and negative controls (Vainshtein Y, et al., 2010).},
  subject      = {Microarray},
  language  = {en}
}