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Mapping signaling networks through genetic interaction analysis by RNAi

Mapping signaling networks through genetic interaction analysis by RNAi. Thomas Sandmann Department of Signaling and Functional Genomics, German Cancer Research Institute (DKFZ) Heidelberg. Genetic interaction maps in yeast. “DNA replication and repair subcluster ”, Collins et al, 2007.

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Mapping signaling networks through genetic interaction analysis by RNAi

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  1. Mapping signaling networks through genetic interaction analysis by RNAi Thomas Sandmann Department of Signaling and Functional Genomics, German Cancer Research Institute (DKFZ) Heidelberg

  2. Genetic interaction maps in yeast “DNA replication and repair subcluster”, Collins et al, 2007

  3. RNAi Specificity Efficiency Reproducibility

  4. Mapping genetic interactions using RNAi Pilot study: 96 Drosophila signaling proteins + controls Expression verified in S2 cells (RNAseq) • Specificity: • 2 independent long dsRNAs / gene • Latest design criteria • Efficiency: • qPCR validation of all reagents • Reproducibility: • 8 technical + 2 biological replicates NextRNAi, Horn, Sandmann, Boutros, Genome Biol, 2010

  5. RNAi2 Horn, Sandmann, Fischer et al, submitted

  6. RNAi2

  7. RNAi2

  8. RNAi2

  9. Predicting co-RNAi phenotypes main effect of dsRNAj baseline error term interaction term measurement (growth, cells, …) main effect of dsRNAi

  10. RNAi2 > 70.000 assays Reproducibility: Features : R=0.95 (cell number) Interactions: R=0.62 (cell number)

  11. Double RNAi effects

  12. Genetic interaction matrix: cell number

  13. Genetic interaction matrix: nuclear area Nuclear area

  14. Phenotype-specific interactions

  15. Phenotype-specific interactions

  16. Phenotype-specific interactions

  17. Phenotype-specific interactions

  18. Phenotype-specific interactions

  19. Observing different features revealsnon-redundant sets of interactions FDR < 0.05 Significant enrichment of annotated Drosophila genetic interacions and orthologous human protein-protein interactions

  20. Genetic interaction matrix: cell number

  21. Identifying pathway components Training and classification using sparse Linear Discriminant Analysis (LDA) Distance to the apexes: predicted class probabilities Circle diameter: probability of assignment to any of the three classes

  22. Attenuation of basal Ras/MAPK activity HEK293T cells, starved O/N in in serum-free medium Drosophila S2 cells, starved O/N in serum-free medium

  23. Cka binds to known Ras/MAPKregulators Co-IPs from Drosophila S2 cells

  24. Genetic interactions in vivo

  25. Conclusions Quantitative, combinatorial RNAi yields informative genetic interaction profiles in vitro. Analysis of different phenotypes reveals non-redundant sets of interactions. Cka is a novel, conserved regulator of Ras/MAPK signaling. High-throughput mapping of genetic interactions in metazoan cells

  26. Outlook: Interaction network of chromatin biology 1400 gene, annotated or uncharacterized 2 dsRNAs / gene High-resolution fluorescence microscopy (HOECHST, Tubulin, phospho-His3) ctrl dsRNA Rho1 dsRNA Dynein light chain dsRNA

  27. Acknowledgements Thomas Horn Bernd Fischer ElinAxelson Michael Boutros Wolfgang Huber

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