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Revealing regulatory mechanisms through functional interaction mapping

Revealing regulatory mechanisms through functional interaction mapping. Thomas Sandmann German Cancer Research Center, DKFZ, Heidelberg, Germany. Specific results with cheap components. Akt1 motif RegPhos. Pum1 motif Hafner et al, 2010.

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Revealing regulatory mechanisms through functional interaction mapping

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  1. Revealing regulatory mechanisms through functional interaction mapping Thomas Sandmann German Cancer Research Center, DKFZ, Heidelberg, Germany

  2. Specific results with cheap components Akt1 motif RegPhos Pum1 motif Hafner et al, 2010 "Many of the genetic disorders that were described previously as monogenic are in fact the product of defects at a small number of loci.” Badano and Katsanis, 2002 Twist motif Ozdemir et al, 2011 mir-1 seeds TargetRank

  3. Genetic interaction… ‘… refers to an unexpected phenotype not easily explained by combining the effects of individual genetic variants’. Dixon et al., 2009

  4. Predicting co-RNAi phenotypes main effect of dsRNAj baseline error term measurement (growth, cells, …) main effect of dsRNAi interaction term Reproducibility: Features R=0.95 (cell number) Interactions R=0.62 (cell number)

  5. RNAi2 • 93 signal transducers (detected by RNAseq) • 2 biological replicates (overall ~74.000 co-RNAi assays)

  6. Double RNAi effects

  7. Genetic interaction matrix: cell number

  8. Genetic interaction matrix: nuclear area Nuclear area

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

  10. 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

  11. Cka is a novel modulator ofRas/ERK signaling sty qRT-PCR Western blot Co-immunoprecipitation Student’s t-test, *p<0.05, **p<0.01, n = 4 Drosophila S2 cells, starved O/N in serum-free medium Drosophila S2 cells Ckaorthologs perform a conserved function in human cells

  12. Genetic interactions in vivo • Ckaloss-of-function suppresses ElpB1ectopic wing vein phenotype

  13. Genetic interactions in vivo A P wt sev->RasV12 sev->RasV12 / Cka1 sev->RasV12 / Cka4 • Ckaloss-of-function suppresses RasV12 rough eye phenotype

  14. 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.

  15. Outlook I. Chromatin biology II. RNA biology III. Signal transduction

  16. Outlook I: The functional interaction landscape of chromatin biology Modified from modENCODE, Science, 2010 1,400 genes – transcription factors, chromatin modifiers, replication, cell cycle regulators, etc

  17. Interaction matrix 100 dsRNAs … ~1600 384-well plates ~ 300.000 measurements ~ 2800 dsRNAs (template) …

  18. Multi-channel, automated fluorescence microscopy ctrl dsRNA Rho1 dsRNA Dynein light chain dsRNA HOECHST, Tubulin, phospho-Histone-3

  19. Automated image analysis Segmentation of nuclei Segmentation of cell body

  20. Quantitative phenotypes Nuclear area Cell number z-scores z-scores Local cell density Mitotic ratio z-scores z-scores th msk dsor1 pten drk gap1 Fluc pnt kay

  21. Cell cycle stages Interphase Prophase Prometaphase Metaphase Anaphase Telophase Cytokinesis Dead cells Binucleated

  22. Functional interaction analysis • What is the function of uncharacterized chromatin-associated loci ? • What is the molecular basis for the observed interactions ? • ChIP-Seq, RNAseq • Are these interactions important in vivo / in other species ? • Fly genetics toolkit, e.g. mutants, UAS-Gal4 • Analysis of orthologs in human cells, vertebrate models • How conserved are genetic interactions globally ? • Comparison with fission and budding yeast datasets • Can interaction maps act as “biofilters” for the statistical analysis of population genetics data ?

  23. Outlook II: Regulatory RNA and RNA regulators Keene, Nature Reviews Genetics, 2007

  24. Genomics … • Delineate the genetic interaction network of RNA biology • RNA-binding proteins (> 300 putative RBPs in Drosophila) • long ncRNAs (~ 1800 candidates identified by modEncode) • miRNAs (176 Drosophila miRNAs) … genetics and biochemistry • Perturbation of functional pairs in vivo – e.g. miRNA + targets • Characterization of molecular phenotypes (RNAseq) • Identification of physical interactors (e.g PAR-CLIP, RaPID)

  25. Outlook III: Functional network rewiring after oncogene activation COT Hahn and Weinberg, 2002 Johannessen et al, 2010

  26. Differential epistasis analysis Inspired by Bandyopadhyay et al, Science, 2010

  27. Outlook III: Functional network rewiring after oncogene activation 1.) interactions in wt cells 2.) interactions in oncogene expressing cells 3.) differential interaction networks

  28. Interactions Michael Boutros Wolfgang Huber

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