Short Vita: 10/2008- today: Postdoc in the Computational Diagnostics group of the Institute for Functional Genomics, Regensburg University
04/2008-09/2008: Trainee/Pre-Postdoc at the Dep. of Oncology and Dep. of Biostatistics, Johns Hopkins University and Medical School, Baltimore, USA
2004 - 2008: PhD student in the department of Bioinformatics, University of Wuerzburg. Title of thesis: "DNA microarrays: Applications and novel approaches for analysis and interpretation."
2004: Diploma in Biology. Title of diploma thesis: "Vergleichende Analyse der Genexpressionsprofile von Arabidopsis thaliana Tumoren und Kontrollgewebe" English title: "Comparative Analysis of Gene Expression in Tumor and Reference Tissue of Arabidopsis thaliana"
2001- 2004: Graduate studies in Biology at the Julius- Maximilians- University in Wuerzburg, Germany
1999- 2001: Undergraduate studies in Biology at the Georg-August University in Goettingen, Germany
Abstract: Tardigrades have unique stress-adaptations that allow them to survive extremes of cold, heat, radiation and vacuum.
To study this, encoded protein clusters and pathways from an ongoing transcriptome study on the tardigrade Milnesium tardigradum were analyzed using bioinformatics tools and compared to expressed sequence tags (ESTs) from Hypsibius dujardini, revealing major pathways involved in resistance against extreme environmental conditions.
ESTs are available on the Tardigrade Workbench along with software and databank updates. Our analysis reveals that RNA stability motifs for M. tardigradum are different from typical motifs known from higher animals. M. tardigradum and H. dujardini protein clusters and conserved domains imply metabolic storage pathways for glycogen, glycolipids and specific secondary metabolism as well as stress response pathways (including heat shock proteins, bmh2, and specific repair pathways). Redox-, DNA-, stress- and protein protection pathways complement specific repair capabilities to achieve the strong robustness of M. tardigradum.
These pathways are partly conserved in other animals and their manipulation could boost stress adaptation even in human cells. However, the unique combination of resistance and repair pathways make tardigrades and M. tardigradum in particular so highly stress resistant.
Abstract: Agrobacterium tumefaciens causes crown gall disease by transferring and integrating bacterial DNA (T-DNA) into the plant genome. To examine the physiological changes and adaptations during Agrobacterium-induced tumor development, we compared the profiles of salicylic acid (SA), ethylene (ET), jasmonic acid (JA), and auxin (indole-3-acetic acid [IAA]) with changes in the Arabidopsis thaliana transcriptome. Our data indicate that host responses were much stronger toward the oncogenic strain C58 than to the disarmed strain GV3101 and that auxin acts as a key modulator of the Arabidopsis–Agrobacterium interaction. At initiation of infection, elevated levels of IAA and ET were associated with the induction of host genes involved in IAA, but not ET signaling. After T-DNA integration, SA as well as IAA and ET accumulated, but JA did not. This did not correlate with SA-controlled pathogenesis-related gene expression in the host, although high SA levels in mutant plants prevented tumor development, while low levels promoted it. Our data are consistent with a scenario in which ET and later on SA control virulence of agrobacteria, whereas ET and auxin stimulate neovascularization during tumor formation. We suggest that crosstalk among IAA, ET, and SA balances pathogen defense launched by the host and tumor growth initiated by agrobacteria.
Abstract: Background:
Tardigrades represent an animal phylum with extraordinary resistance to environmental stress.
Results:
To gain insights into their stress-specific adaptation potential, major clusters of related and similar proteins are identified, as well as specific functional clusters delineated comparing all tardigrades and individual species (Milnesium tardigradum, Hypsibius dujardini, Echiniscus testudo, Tulinus stephaniae, Richtersius coronifer) and functional elements in tardigrade mRNAs are analysed. We find that 39.3% of the total sequences clustered in 58 clusters of more than 20 proteins. Among these are ten tardigrade specific as well as a number of stress-specific protein clusters. Tardigrade-specific functional adaptations include strong protein, DNA- and redox protection, maintenance and protein recycling. Specific regulatory elements regulate tardigrade mRNA stability such as lox P DICE elements whereas 14 other RNA elements of higher eukaryotes are not found. Further features of tardigrade specific adaption are rapidly identified by sequence and/or pattern search on the web-tool tardigrade analyzer http://waterbear.bioapps.biozentrum.uni-wuerzburg.de webcite. The work-bench offers nucleotide pattern analysis for promotor and regulatory element detection (tardigrade specific; nrdb) as well as rapid COG search for function assignments including species-specific repositories of all analysed data.
Conclusion:
Different protein clusters and regulatory elements implicated in tardigrade stress adaptations are analysed including unpublished tardigrade sequences.
Abstract: DNA microarrays are a popular technique for the detection of microorganisms. Several approaches using specific oligomers targeting one or a few marker genes for each species have been proposed. Data analysis is usually limited to call a species present when its oligomer exceeds a certain intensity threshold. While this strategy works reasonably well for distantly related species, it does not work well for very closely related species: Cross-hybridization of nontarget DNA prevents a simple identification based on signal intensity. The majority of species of the same genus has a sequence similarity of over 90%. For biodiversity studies down to the species level, it is therefore important to increase the detection power of closely related species. We propose a simple, cost-effective and robust approach for biodiversity studies using DNA microarray technology and demonstrate it on scenedesmacean green algae. The internal transcribed spacer 2 (ITS2) rDNA sequence was chosen as marker because it is suitable to distinguish all eukaryotic species even though parts of it are virtually identical in closely related species. We show that by modelling hybridization behaviour with a matrix algebra approach, we are able to identify closely related species that cannot be distinguished with a threshold on signal intensity. Thus this proof-of-concept study shows that by adding a simple and robust data analysis step to the evaluation of DNA microarrays, species detection can be significantly improved for closely related species with a high sequence similarity.
Abstract: BACKGROUND: Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. RESULTS: We have developed GEPAT, Genome Expression Pathway Analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at http://gepat.sourceforge.net. CONCLUSION: GEPAT is a modular, scalable and professional-grade software integrating analysis and interpretation of microarray gene expression data. An installation available for academic users can be found at http://gepat.bioapps.biozentrum.uni-wuerzburg.de.
Abstract: Transformation of plant cells with T-DNA of virulent agrobacteria is one of the most extreme triggers of developmental changes in higher plants. For rapid growth and development of resulting tumors, specific changes in the gene expression profile and metabolic adaptations are required. Increased transport and metabolic fluxes are critical preconditions for growth and tumor development. A functional genomics approach, using the Affymetrix whole genome microarray (approximately 22,800 genes), was applied to measure changes in gene expression. The solute pattern of Arabidopsis thaliana tumors and uninfected plant tissues was compared with the respective gene expression profile. Increased levels of anions, sugars, and amino acids were correlated with changes in the gene expression of specific enzymes and solute transporters. The expression profile of genes pivotal for energy metabolism, such as those involved in photosynthesis, mitochondrial electron transport, and fermentation, suggested that tumors produce C and N compounds heterotrophically and gain energy mainly anaerobically. Thus, understanding of gene-to-metabolite networks in plant tumors promotes the identification of mechanisms that control tumor development.
