Refine
Document Type
- Article (2)
- Doctoral Thesis (2)
Has Fulltext
- yes (4)
Is part of the Bibliography
- no (4)
Keywords
- microarray (4) (remove)
Institute
Publisher
- MDPI (2)
Diese Arbeit beschäftigt sich mit der Analyse und Modellierung des Microarrayexperiments. Hierfür wird das gesamte Experiment in fünf Teilprozesse zerlegt, die Reverse Transkription, die Hybridisierung, das Waschen, die Fluoreszenz und die Detektion. Jeder Teilprozess wurde separat modelliert und analysiert. Anschließend wurde die Teilprozesse im Gesamtmodell vereint und dieses für verschiedene Parametersituationen simuliert. Diese Arbeit ermöglicht eine mathematische Handhabung des Microarrayexperiments und deckt seine Abhängigkeit von den einzelnen Schritten des Experiments auf. Dies kann benutzt werden, um Normalisierung und Analyse zu verbessern.
Rhinoviruses (RV) account for a significant number of asthma exacerbations, and RV species C may be associated with a severe course in vulnerable patient groups. Despite important evidence on the role of RV reported by clinicians and life scientists, there are still unanswered questions regarding their influence on asthma exacerbation in young patients. Thus, we measured the RVspecies-specific IgG titers in our German pediatric exacerbation cohort using a microarray-based technology. For this approach, human sera of patients with exacerbated asthma and wheeze, as well as healthy control subjects (n = 136) were included, and correlation analyses were performed. Concordantly with previously published results, we observed significantly higher cumulative levels of RV species A-specific IgG (p = 0.011) and RV-C-specific IgG (p = 0.051) in exacerbated asthma group compared to age-matched controls. Moreover, atopic wheezers had increased RV-specific IgG levels for species A (p = 0.0011) and species C (p = 0.0009) compared to non-atopic wheezers. Hypothesizing that bacterial infection positively correlates with immune memory against RV, we included nasopharyngeal swab results in our analyses and detected limited correlations. Interestingly, the eosinophil blood titer positively correlated with RV-specific IgG levels. With these observations, we add important observations to the existing data regarding exacerbation in pediatric and adolescent medicine. We propose that scientists and clinicians should pay more attention to the relevance of RV species in susceptible pediatric patients.
Approaches to the Analysis of Proteomics and Transcriptomics Data based on Statistical Methodology
(2014)
Recent developments in genomics and molecular biology led to the generation of an enormous amount of complex data of different origin. This is demonstrated by a number of published results from microarray experiments in Gene Expression Omnibus. The number was growing in exponential pace over the last decade. The challenge of interpreting these vast amounts of data from different technologies led to the development of new methods in the fields of computational biology and bioinformatics. Researchers often want to represent biological phenomena in the most detailed and comprehensive way. However, due to the technological limitations and other factors like limited resources this is not always possible. On one hand, more detailed and comprehensive research generates data of high complexity that is very often difficult to approach analytically, however, giving bioinformatics a chance to draw more precise and deeper conclusions. On the other hand, for low-complexity tasks the data distribution is known and we can fit a mathematical model. Then, to infer from this mathematical model, researchers can use well-known and standard methodologies. In return for using standard methodologies, the biological questions we are answering might not be unveiling the whole complexity of the biological meaning. Nowadays it is a standard that a biological study involves generation of large amounts of data that needs to be analyzed with a statistical inference. Sometimes data challenge researchers with low complexity task that can be performed with standard and popular methodologies as in Proteomic analysis of mouse oocytes reveals 28 candidate factors of the "reprogrammome". There, we established a protocol for proteomics data that involves preprocessing of the raw data and conducting Gene Ontology overrepresentation analysis utilizing hypergeometric distribution. In cases, where the data complexity is high and there are no published frameworks a researcher could follow, randomization can be an approach to exploit. In two studies by The mouse oocyte proteome escapes maternal aging and CellFateScout - a bioinformatics tool for elucidating small molecule signaling pathways that drive cells in a specific direction we showed how randomization can be performed for distinct complex tasks. In The mouse oocyte proteome escapes maternal aging we constructed a random sample of semantic similarity score between oocyte transcriptome and random transcriptome subset of oocyte proteome size. Therefore, we could calculate whether the proteome is representative of the trancriptome. Further, we established a novel framework for Gene Ontology overrepresentation that involves randomization testing. Every Gene Ontology term is tested whether randomly reassigning all gene labels of belonging to or not belonging to this term will decrease the overall expression level in this term. In CellFateScout - a bioinformatics tool for elucidating small molecule signaling pathways that drive cells in a specific direction we validated CellFateScout against other well-known bioinformatics tools. We stated the question whether our plugin is able to predict small molecule effects better in terms of expression signatures. For this, we constructed a protocol that uses randomization testing. We assess here if the small molecule effect described as a (set of) active signaling pathways, as detected by our plugin or other bioinformatics tools, is significantly closer to known small molecule targets than a random path.
Molecular Mechanisms of Bortezomib Action: Novel Evidence for the miRNA−mRNA Interaction Involvement
(2020)
Bortezomib is an anti-tumor agent, which inhibits 26S proteasome degrading ubiquitinated
proteins. While apoptotic transcription-associated activation in response to bortezomib has been
suggested, mechanisms related to its influence on post-transcriptional gene silencing mediated
regulation by non-coding RNAs remain not fully elucidated. In the present study, we examined
changes in global gene and miRNA expression and analyzed the identified miRNA–mRNA interactions
after bortezomib exposure in human neuroblastoma cells to define pathways affected by this agent in
this type of cells. Cell viability assays were performed to assess cytotoxicity of bortezomib. Global gene
and miRNA expression profiles of neuroblastoma cells after 24-h incubation with bortezomib were
determined using genome-wide RNA and miRNA microarray technology. Obtained results were
then confirmed by qRT-PCR and Western blot. Further bioinformatical analysis was performed
to identify affected biological processes and pathways. In total, 719 genes and 28 miRNAs were
downregulated, and 319 genes and 61 miRNAs were upregulated in neuroblastoma cells treated with
bortezomib. Possible interactions between dysregulated miRNA/mRNA, which could be linked to
bortezomib-induced neurotoxicity, affect neurogenesis, cellular calcium transport, and neuron death.
Bortezomib might exert toxic effects on neuroblastoma cells and regulate miRNA–mRNA interactions
influencing vital cellular functions. Further studies on the role of specific miRNA–mRNA interactions
are needed to elucidate mechanisms of bortezomib action.