1. Oct 12, 2007 Research Review Day 1 Josh Stuart, Ph.D. Biomolecular Engineering UCSC Research Review Day 2007 Biological Discovery From Genetic Network Perturbations Reverse-Engineering by Knocking Down

2. Oct 12, 2007 Research Review Day 2 “software” of life

3. Oct 12, 2007 Research Review Day 3 Genomes to function ? Genome Hair Neuron

4. Oct 12, 2007 Research Review Day 4 Genomes to function Genome Neuron Hair Gene switched “on” Transcriptome Interactome Genes signaling

5. Oct 12, 2007 Research Review Day 5 Function from Genetic Knock-downs Genome sequence provides complete parts lists Allows targeting of specific genes –Cloning –RNA interference High-throughput technologies allow monitoring genome-wide responses to knock-down Phenotype gives clues about gene function

6. Oct 12, 2007 Research Review Day 6 What’s a knock-down?

7. Oct 12, 2007 Research Review Day 7 Genetic Knock-Downs RNA interference RNAi A. Fire et. al 1998

8. Oct 12, 2007 Research Review Day 8 Two Examples Infer disease pathways Predict genetic interactions

9. Oct 12, 2007 Research Review Day 9 Reverse engineering by knocking-down Infer disease pathways Predict genetic interactions

10. Oct 12, 2007 Research Review Day 10 Knock-downs to Understand Cancer Invasiveness Lee Lab, GWUMC

11. Oct 12, 2007 Research Review Day 11 Knock-downs to Understand Cancer Invasiveness 1.Identify knock-downs that reverse cancer invasion 2.Genome-wide expression under knock- downs 3.Infer invasiveness network 4.Predict new genes involved in process Go to step 1.

12. Oct 12, 2007 Research Review Day 12 up-regulated in knock-down down-regulated in knock-down Sensitive, genome-wide phenotypes: DNA Microarrays knock-downnormal cells

13. Oct 12, 2007 Research Review Day 13 DRY WET Iterative invasiveness network strategy

14. Oct 12, 2007 Research Review Day 14 Infer Network from Secondary Effects Single Phenotype Network Genes Perturbed by RNAi or Knockout Effect Genes Measured by microarray

15. Oct 12, 2007 Research Review Day 15 Expression under e e e e A B ∆A ∆B e Expression under

16. Oct 12, 2007 Research Review Day 16 Predictions for Colon Cancer Invasiveness Identified a putative signaling network Expanded the list of candidates in the network Testing candidates for loss-of-invasiveness phenotype conserved role in cell-migration

17. Oct 12, 2007 Research Review Day 17 V. cholera Networks Yildiz Lab, UCSC

18. Oct 12, 2007 Research Review Day 18 New Biofilm genes

19. Oct 12, 2007 Research Review Day 19 Two Examples Infer disease pathways Predict genetic interactions

20. Oct 12, 2007 Research Review Day 20 Function from catastrophe Most genes are nonessential Genes knocked down together give phenotype Can we infer function from knock-down combos?

21. Oct 12, 2007 Research Review Day 21 Gene Network Discovery build networks from all interactions discover function from a gene’s links understand bigger picture of gene regulation

22. Oct 12, 2007 Research Review Day 22 Understanding Gene Function through Modifier Screens Wild Type A B C X A B / R C X Wild Type A B C X D E F Phenotype Roy Lab, Univ Toronto B --- R Within Pathway Link B --- F Cross Pathway Link

23. Oct 12, 2007 Research Review Day 23 Understanding Gene Function through Modifier Screens: Synthetic Genetic Array (SGA) analysis in S. cerevisiae arrayed library of ~4800 viable gene deletions gene ‘x’ deleted Tong et al. (2001) systematic generation of double mutants

24. Oct 12, 2007 Research Review Day 24 Understanding Gene Function through Modifier Screens: Synthetic Genetic Array (SGA) analysis in S. cerevisiae Tong et al. (2001) gene ‘x’ deleted systematic generation of double mutants

25. Oct 12, 2007 Research Review Day 25 Synthetic Genetic Array (SGA) analysis in Metazoans?

26. Oct 12, 2007 Research Review Day 26 Synthetic Genetic Array (SGA) analysis in Metazoans?

27. Oct 12, 2007 Research Review Day 27 high degree of biological conservation to other animals small (~1 mm) hermaphroditic three day life cycle the path to many fundamental discoveries (-) control GFP(dsRNA) The Nematode Worm Caenorhabditis elegans RNA interference (RNAi) Roy Lab, Univ. Toronto

28. The Interaction Matrix of ~56,000 Growth Scores

30. Oct 12, 2007 Research Review Day 30 The SGI Network: 1246 Interactions among 461 Genes 1246 synthetic genetic interactions - 842 (68%) between query genes and one of the genes in the signaling set - 421 (34%) between query genes and one of the genes in the LGIII set

31. Oct 12, 2007 Research Review Day 31+ Co-expressionPhysical Interactions Phenotype Previously Identified Networks Genetic Interactions Creating a Superimposed Network SGI Network = Superimposed Network

32. Oct 12, 2007 Research Review Day 32 Superimposed Network SGI Co-expression Worm Phenotype Protein-protein Worm Genetic SGI Gene “The bar-1 Subnetwork” Mining the Superimposed for Multiply-supported Subnetworks verified interaction

33. Oct 12, 2007 Research Review Day 33 N2; Ø(RNAi) (DIC)N2; T20B12.7(RNAi) (DIC) N2; Ø(RNAi) (Nile Red)N2; T20B12.7(Nile Red) Genes in the bar-1 Subnetwork have a Shared Phenotype

34. Oct 12, 2007 Research Review Day 34 Normalized N2 Values Genes in the bar-1 Subnetwork have a Shared Phenotype 75% of genes in subnetwork have altered fat levels.

35. Oct 12, 2007 Research Review Day 35 Analysis of the SGI Network Can we assign function to uncharacterized genes based on their neighborhood within the network? How do genetic interactions contribute to our understanding of the system? Can we assign function to uncharacterized genes based on their neighborhood within the network? How do genetic interactions contribute to our understanding of the system?

36. Oct 12, 2007 Research Review Day 36 Identified 343 subnetworks 47% are enriched for a specific GO category 46 subnetworks are bridged by SGI links 19-fold enriched or SGI Co-expression Worm Phenotype Protein-protein What is the Connectivity of Synthetic Genetic Links within the Superimposed Network?

37. Oct 12, 2007 Research Review Day 37 SGI Interactions Significantly Bridge Subnetworks

38. Oct 12, 2007 Research Review Day 38 Analysis of gene pairs tested for interaction in both worm and yeast worm yeast OR Is the Connectivity of Genetic Networks Conserved?

39. Oct 12, 2007 Research Review Day 39 No evidence for bridging conservation WORM

40. Oct 12, 2007 Research Review Day 40 YEAST No evidence for bridging conservation

41. Oct 12, 2007 Research Review Day 41 Summary Causal order from phenotypes under knock-down Genome-wide interactions reveal gene functions. Pathway coordination may be evolvable.

42. Oct 12, 2007 Research Review Day 42 Current Directions Predict drug targets from knock-down signatures Develop a tool for visualization and search of integrated data

43. Oct 12, 2007 Research Review Day 43 Directions: Predict Drug Targets Redundancy of pathways gives synthetic lethal signature Compare knock-down profiles of gene A with drug X Lokey Lab (UCSC), Davis Lab (Stanford)

44. Oct 12, 2007 Research Review Day 44 probability genes match drug signatures Directions: Predict Drug Targets knock-down sensitivies to drug are specific pathways predicted?

45. Oct 12, 2007 Research Review Day 45 Directions: Predict Drug Targets See poster by Alex Williams

46. Oct 12, 2007 Research Review Day 46 Directions: Interaction Browser Physical interactions New, high- throughput datasets Browser with “tracks” of interactions Public, 0-setup

47. Oct 12, 2007 Research Review Day 47Acknowledgements Matt Weirauch Roy Lab –Alexandra Byrne Lee Lab Yildiz Lab Davis Lab –Bob St. Onge Stuart Lab –Martina Koeva Funding

48. Oct 12, 2007 Research Review Day 48 Questions? Interaction Browser Demo

49. Oct 12, 2007 Research Review Day 49 Supplementary Material

50. Oct 12, 2007 Research Review Day 50 Principle #2 Genes self assemble into modular subcomponents

51. Oct 12, 2007 Research Review Day 51 Principle #3 Coordinated activity is a signature of gene function proliferation transcription ribosome biogenesis ribosomal subunits respiration protein modification secretion fatty acid metab. tissue growth neuronal immune response development / hox genes cell polarity, cell structure Newly evolved

52. Oct 12, 2007 Research Review Day 52

53. Oct 12, 2007 Research Review Day 53 More Projects Predict cancer signaling pathway from knock-down data (w/ Norm Lee at TIGR) Gene isoform networks to capture alternative splicing (w/ Manny Ares) Predict drug targets from synthetic lethal networks (w/ Scott Lokey)

54. Oct 12, 2007 Research Review Day 54 Elucidation is hierarchical, but no reason network should be!

55. Oct 12, 2007 Research Review Day 55 Colon Cancer Networks

56. Oct 12, 2007 Research Review Day 56

57. Oct 12, 2007 Research Review Day 57 Case I

58. Oct 12, 2007 Research Review Day 58 Case II

59. Oct 12, 2007 Research Review Day 59 Case III

60. Oct 12, 2007 Research Review Day 60 Probability Model

61. Oct 12, 2007 Research Review Day 61 Probability Model

62. Oct 12, 2007 Research Review Day 62 Colon cancer network

63. Oct 12, 2007 Research Review Day 63 Network link probabilies correlate with confidence

64. Oct 12, 2007 Research Review Day 64 Network Links a Nodes b Supported Links c Genetically- Supported Links (A) d Genetically- Supported Links (B) e Physically- Supported Links f Co-Exp.- Supported Links g Co-Phen.- Supported Links h Superimposed network75,2837,825929 (7.2)na wSGI1,24646163 (2.0)43 (1.6)53 (1.8)9 (5.6)2 (9.0)4 (5.9)* Lehner34116125 (5.5)13 (10.8)23 (7.3)3 (22.7)1 (17.9)1 (30.3) Fine genetic interactions2,2791,022152 (4.6)na48 (1.7)61 (27.8)23 (36.1)22 (20.2) Transposed SGA7,52742666 (2.3)5 (4.5)5 (3.2)*43 (2.2)14 (3.0)4 (1.3)* Interolog12,7964,339723 (9.9)61 (27.8)110 (4.8)na577 (14.6)42 (3.9) C. elegans protein interaction3,9672,62427 (3.7)7 (10.6)10 (4.2)na13 (3.8)5 (3.4)* Eukaryotic co-expression43,3635,232695 (11.8)23 (36.1)40 (7.2)577 (14.6)na84 (6.1) C. elegans co-phenotype8,862913153 (5.2)22 (20.2)30 (6.1)42 (3.9)84 (6.1)na

65. Oct 12, 2007 Research Review Day 65 Zhong, W. & Sternberg, P. W. Genome-wide prediction of C. elegans genetic interactions. Science 311, 1481-1484 (2006). Combined interactome, gene expression, phenotype, functional annotation data Yeast, fly, and worm Used a training set of 1816 previously reported genetic interactions and 2878 P2P interactions. Assigned each type of evidence a weighted predictive score Gave a prediction score to each possible pair of genes Predicted 18,183 interactions among 2254 genes Validated 12 of 49 novel predicted interaction with let-60 Validated 2 of 6 novel predicted interactions with itr-1