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Division of Signaling and Functional Genomics

Prof. Dr. Michael Boutros

By silencing genes using RNAi we can analyse the genes’ effect in a quantitative and highthroughput manner. In this microscopy image of human cancer cells, nuclei are shown in red, cell membranes in green, and the cellular scaffolding in blue.
Vergrößerte Ansicht By silencing genes using RNAi we can analyse the genes’ effect in a quantitative and highthroughput manner. In this microscopy image of human cancer cells, nuclei are shown in red, cell membranes in green, and the cellular scaffolding in blue.

Current Research
Cellular signaling systems control many key decisions during development, stem cell maintenance and tumorigenesis. The focus of our group is on the systematic dissection of signaling pathways to identify novel molecular processes and to understand how pathways connect in cellular networks. We use genetic and genomic approaches for this purpose, but also study specific processes at the molecular level to probe underlying mechanisms. We pursue two main lines of research:

Systematic analysis of Wnt signaling in model organisms and tumor cells
Canonical and non-canonical Wnt signaling play key roles during development and tumorigenesis. Aberrant regulation of Wnt signaling has been implicated in cancer, as exemplified by mutations in APC, a negative regulator of Wnt signaling. During the past years, our laboratory has contributed to the molecular understanding of Wnt signaling by identifying novel components and by the characterization of Wnt-ligand specific cargo receptors.

Systems genetics and synthetic lethality
Genetics underlying many phenotypes, including most common diseases, are complex with contributions from multiple loci. The analysis of synthetic genetic interaction networks reveals how biological systems achieve a high level of complexity with a limited repertoire of components. We have established high-throughput methodologies to measure synthetic genetic interaction in model organisms and cancer cells by RNAi.

Future outlook
We are interested in the systematic analysis of synthetic genetic interactions to dissect genotype-phenotype relationships, with a particular focus on combinatorial mapping approaches using RNAi and small molecules. Interactions between genetic variants may be one important explanation for the ‘missing heritability’ in genome­-wide association studies. Extensive interactions between different genetic alleles with large effects on many phenotypes have been documented in many model systems. We will use quantitative phenotyping by high-throughput microscopy to measure synthetic genetic interactions. Genetic interaction data will be used to model cellular networks with a particular focus on oncogenic signaling pathways. We will also develop novel technologies required for miniaturization of cellular assays and high-throughput imaging, for reproducible cell-based RNAi in primary cells types and novel approaches to data integration.

A second area of interest is the in-depth analysis of Wnt and interacting signaling networks in development, stem and tumor cells. We use a spectrum of model systems, from Drosophila to mouse and human cancer cells to identify key components and understand how they are embedded into physiological processes.

For more information, visit us at http://www.dkfz.de/signaling

Selected Publications

Horn, T., Sandmann, T., Fischer, B., Axelsson, E., Huber, W., & Boutros, M. (2011). Mapping of signaling networks through synthetic genetic interaction analysis by RNAi. Nature Methods, 8, 341-6

Fuchs, F., Pau, G., Kranz, D., Sklyar, O., Budjan, C., Steinbrink, S., Horn, T., Pedal, A., Huber, W. & Boutros, M. (2010). Clustering phenotype populations by genome-wide RNAi and multiparametric imaging. Molecular Systems Biology, 6, 370

Boutros M. & Ahringer, J. (2008). The art and design of genetic screens: RNA interference. Nature Reviews Genetics 9:554-566.

Bartscherer, K., Pelte, N., Ingelfinger, D., & Boutros M. (2006). Secretion of Wnt ligands requires Evi, a conserved transmembrane protein. Cell, 125, 523-33

last update: 11/11/2011 back to top