The genetics underlying phenotype-genotype relationships during development and disease is often complex with many genes contributing to a particular phenotypic outcome. While forward genetic screens have uncovered many mutations that are limiting at a particular stage or tissue, the majority of genes in most genomes remain genetically untouched. Recent studies in model organisms, in particular in yeast, have provided evidence for the existence of pervasive genetic interactions with large effects on many phenotypes. Such genetic factors have been rather difficult to identify in classical loss-of-function screens due to buffering and other compensatory mechanisms, therefore requiring novel methodological approaches. The systematic analysis of synthetic genetic interactions also revealed how biological systems achieve a high level of complexity with a limited repertoire of components. Large-scale studies yeast have taken advantage of deletion strains to construct matrices of quantitative interaction profiles and infer gene function. However, comparable approaches in higher organisms have been difficult to implement in a robust manner. We here propose to create the first systematic synthetic genetic interaction map of a metazoan cell. Based on methods that we recently pioneered, we will quantitatively measure genetic interactions between genes on a genome scale using Drosophila cells as a model. We will utilize the map to globally dissect the interaction between processes with a focus on signaling networks. Furthermore, we will follow-up on selected interactions by in-depth characterization. Synthetic genetic interactions will be further analysed in vivo in tissues to complement the cell-based approaches. The project will advance new experimental and computational approaches applicable also to human cells and provide fundamental insights into the cellular circuitries that govern many processes in a metazoan cell.
Start date: 01.08.2012 End date: 31.07.2017 EU Contribution: 2.5 Mio Euro Total costs: 2.5 Mio Euro Funding Scheme: ERC Advanced Grant 2011
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