Refine
Has Fulltext
- yes (2)
Is part of the Bibliography
- yes (2)
Document Type
- Doctoral Thesis (2) (remove)
Keywords
- classification (2) (remove)
Institute
- Theodor-Boveri-Institut für Biowissenschaften (2) (remove)
In this thesis, the development of a phylogenetic DNA microarray, the analysis of several gene expression microarray datasets and new approaches for improved data analysis and interpretation are described. In the first publication, the development and analysis of a phylogenetic microarray is presented. I could show that species detection with phylogenetic DNA microarrays can be significantly improved when the microarray data is analyzed with a linear regression modeling approach. Standard methods have so far relied on pure signal intensities of the array spots and a simple cutoff criterion was applied to call a species present or absent. This procedure is not applicable to very closely related species with high sequence similarity because cross-hybridization of non-target DNA renders species detection impossible based on signal intensities alone. By modeling hybridization and cross-hybridization with linear regression, as I have presented in this thesis, even species with a sequence similarity of 97% in the marker gene can be detected and distinguished from related species. Another advantage of the modeling approach over existing methods is that the model also performs well on mixtures of different species. In principle, also quantitative predictions can be made. To make better use of the large amounts of microarray data stored in public databases, meta-analysis approaches need to be developed. In the second publication, an explorative meta-analysis exemplified on Arabidopsis thaliana gene expression datasets is presented. Integrating datasets studying effects such as the influence of plant hormones, pathogens and different mutations on gene expression levels, clusters of similarly treated datasets could be found. From the clusters of pathogen-treated and indole-3-acetic acid (IAA) treated datasets, representative genes were selected which pointed to functions which had been associated with pathogen attack or IAA effects previously. Additionally, hypotheses about the functions of so far uncharacterized genes could be set up. Thus, this kind of meta-analysis could be used to propose gene functions and their regulation under different conditions. In this work, also primary data analysis of Arabidopsis thaliana datasets is presented. In the third publication, an experiment which was conducted to find out if microwave irradiation has an effect on the gene expression of a plant cell culture is described. During the first steps, the data analysis was carried out blinded and exploratory analysis methods were applied to find out if the irradiation had an effect on gene expression of plant cells. Small but statistically significant changes in a few genes were found and could be experimentally confirmed. From the functions of the regulated genes and a meta-analysis with publicly available microarray data, it could be suspected that the plant cell culture somehow perceived the irradiation as energy, similar to perceiving light rays. The fourth publication describes the functional analysis of another Arabidopsis thaliana gene expression dataset. The gene expression data of the plant tumor dataset pointed to a switch from a mainly aerobic, auxotrophic to an anaerobic and heterotrophic metabolism in the plant tumor. Genes involved in photosynthesis were found to be repressed in tumors; genes of amino acid and lipid metabolism, cell wall and solute transporters were regulated in a way that sustains tumor growth and development. Furthermore, in the fifth publication, GEPAT (Genome Expression Pathway Analysis Tool), a tool for the analysis and integration of microarray data with other data types, is described. It consists of a web application and database which allows comfortable data upload and data analysis. In later chapters of this thesis (publication 6 and publication 7), GEPAT is used to analyze human microarray datasets and to integrate results from gene expression analysis with other datatypes. Gene expression and comparative genomic hybridization data from 71 Mantle Cell Lymphoma (MCL) patients was analyzed and allowed proposing a seven gene predictor which facilitates survival predictions for patients compared to existing predictors. In this study, it was shown that CGH data can be used for survival predictions. For the dataset of Diffuse Large B-cell lymphoma (DLBCL) patients, an improved survival predictor could be found based on the gene expression data. From the genes differentially expressed between long and short surviving MCL patients as well as for regulated genes of DLBCL patients, interaction networks could be set up. They point to differences in regulation for cell cycle and proliferation genes between patients with good and bad prognosis.
Die verfügbaren in vitro Genotoxizitätstests weisen hinsichtlich ihrer Spezifität und ihres Informationsgehalts zum vorliegenden Wirkmechanismus (Mode of Action, MoA) Einschränkungen auf. Um diese Mängel zu überwinden, wurden in dieser Arbeit zwei Ziele verfolgt, die zu der Entwicklung und Etablierung neuer in vitro Methoden zur Prüfung auf Genotoxizität in der Arzneimittelentwicklung beitragen.
1. Etablierung und Bewertung einer neuen in vitro Genotoxizitätsmethode (MultiFlow Methode)
Die MultiFlow Methode basiert auf DNA-schadensassoziierten Proteinantworten von γH2AX (DNA-Doppelstrangbrüche), phosphorylierten H3 (S10) (mitotische Zellen), nukleären Protein p53 (Genotoxizität) und cleaved PARP1 (Apoptose) in TK6-Zellen. Insgesamt wurden 31 Modellsubstanzen mit dem MultiFlow Assay und ergänzend mit dem etablierten Mikrokerntest (MicroFlow MNT), auf ihre Fähigkeit verschiedene MoA-Gruppen (Aneugene/Klastogene/Nicht-Genotoxine) zu differenzieren, untersucht. Die Performance der „neuen“ gegenüber der „alten“ Methode führte zu einer verbesserten Sensitivität von 95% gegenüber 90%, Spezifität von 90% gegenüber 72% und einer MoA-Klassifizierungsrate von 85% gegenüber 45% (Aneugen vs. Klastogen).
2. Identifizierung mechanistischer Biomarker zur Klassifizierung genotoxischer Substanzen
Die Analyse 67 ausgewählter DNA-schadensassoziierter Gene in der QuantiGene Plex Methode zeigte, dass mehrere Gene gleichzeitig zur MoA-Klassifizierung beitragen können. Die Kombination der höchstrangierten Marker BIK, KIF20A, TP53I3, DDB2 und OGG1 ermöglichte die beste Identifizierungsrate der Modellsubstanzen. Das synergetische Modell kategorisierte 16 von 16 Substanzen korrekt in Aneugene, Klastogene und Nicht-Genotoxine. Unter Verwendung der Leave-One-Out-Kreuzvalidierung wurde das Modell evaluiert und erreichte eine Sensitivität, Spezifität und Prädiktivität von 86%, 83% und 85%. Ergebnisse der traditionellen qPCR Methode zeigten, dass Genotoxizität mit TP53I3, Klastogenität mit ATR und RAD17 und oxidativer Stress mit NFE2L2 detektiert werden kann.
Durch die Untersuchungen von posttranslationalen Modifikationen unter Verwendung der High-Content-Imaging-Technologie wurden mechanistische Assoziationen für BubR1 (S670) und pH3 (S28) mit Aneugenität, 53BP1 (S1778) und FANCD2 (S1404) mit Klastogenität, p53 (K373) mit Genotoxizität und Nrf2 (S40) mit oxidativem Stress identifiziert.
Diese Arbeit zeigt, dass (Geno)toxine unterschiedliche Gen- und Proteinveränderungen in TK6-Zellen induzieren, die zur Erfassung mechanistischer Aktivitäten und Einteilung (geno)toxischer MoA-Gruppen (Aneugen/Klastogen/ Reaktive Sauerstoffspezies) eingesetzt werden können und daher eine bessere Risikobewertung von Wirkstoffkandidaten ermöglichen.