Mapping of signaling networks through synthetic genetic interaction analysis by RNAi

Abstract

The analysis of synthetic genetic interaction networks can reveal how biological systems achieve a high level of complexity with a limited repertoire of components. Studies in yeast and bacteria have taken advantage of collections of deletion strains to construct matrices of quantitative interaction profiles and infer gene function. Yet comparable approaches in higher organisms have been difficult to implement in a robust manner. Here we report a method to identify genetic interactions in tissue culture cells through RNAi. By performing more than 70,000 pairwise perturbations of signaling factors, we identified >600 interactions affecting different quantitative phenotypes of Drosophila melanogaster cells. Computational analysis of this interaction matrix allowed us to reconstruct signaling pathways and identify a conserved regulator of Ras-MAPK signaling. Large-scale genetic interaction mapping by RNAi is a versatile, scalable approach for revealing gene function and the connectivity of cellular networks.

Widespread genetic epistasis among cancer genes

“…To calculate the GI scores, we adopted the regression framework, comparing the observed and predicted phenotypes after double knockdowns in a single linear model, similar to the p score method 11,12 (see Methods section). However, since the three phenotypes in our assay are correlated ( Supplementary Fig.…”

“…Our results strongly indicate that epistatic effects of somatic mutations are widespread, revealing a highly connected and complicated genetic architecture associated with breast cancer. Our GI mapping strategy is similar to those developed in flies and mammalian systems [11][12][13] , using combinatorial RNAi and high-content imaging. In all of these studies, epistasis is commonly defined as deviation from the expected phenotype of combining two alleles.…”

“…For example, additive models examine the difference from the sum of the two single-gene phenotypes, whereas multiplicative models examine the fold change over the product of the two singlegene phenotypes (see Methods section). Here, we adopted a regression framework, following the definition in classical quantitative genetics 50 and similar to previous approaches 11,12 . In this case, we used SUR to provide a unified framework for both additive and multiplicative models.…”

“…For the combinatorial RNAi screen, a templatequery design was used similar to the one described in Horn et al 11 A quantity of 100 nM each of the 132 template siRNAs stocks were arrayed twice in a 384-well plate (Beckman), and aliquoted to 63 plates. Each plate also contained 16 empty wells, five positive controls (PLK2) and eight negative controls (non-targeted cells), all randomly arrayed on the plate.…”

“…Pairwise GIs for thousands of genes have been systematically mapped in yeast by using a large collection of deletion strains [6][7][8][9][10] . In higher organisms, combinatorial RNA interference (RNAi) methods have been used in cell culture to generate GI maps for genes involved in kinase signalling, chromatin regulation and ricin susceptibility [11][12][13][14] . Such analyses of large-scale GI maps have successfully identified functionally connected pathways and genetic networks, allowed functional prediction for uncharacterized genes and revealed changes in the interaction landscape upon stimulus [11][12][13][14] .…”