Mapping the human interactome: a update

1. Mapping the human interactome:a update

2. the genomic revolution in numbers

3. from gene sequence to protein function

4. large-scale protein interaction mapping yeast two-hybrid binary protein interactions transient AP/MS protein complexes stable different network topology different interactome subspace interrogated >> complementary similar high quality from Yu et al. High-quality binary protein interaction map of the yeast interactome network. Science 2008

5. MAPPIT validation of Y2H protein network maps

6. yeast two-hybrid

7. other two-hybrid methods

8. other two-hybrid methods

9. MAPPIT • operates in mammalian cells • ligand-inducible > extra level of control • simple readout > automation

10. MAPPIT validation of Y2H protein network maps >> CCSB-YI1: 1.809 interactions between 1.278 proteins (estimated interactome size 18.000 +/- 4.500)

11. MAPPIT validation of Y2H protein network maps WI-2007: 1.816 interactions between 1.496 proteins (estimated interactome size 115.600 +- 26.400)

12. MAPPIT validation of Y2H protein network maps ~700 full length (bait) x ~700 fragments (prey) 40 fragments per ORF >> 755 interactions between 522 proteins (only 92 previously identified by Y2H !)

13. MAPPIT validation of Y2H protein network maps • framework for large-scale Y2H human interactome mapping • -validation of available HT-YTH interactome maps: • (Vidal & Wanker groups) • >> high quality (> literature curated) • estimation of interactome size: • ~130.000 interactions

14. MAPPIT validation of Y2H protein network maps • framework for large-scale Y2H human interactome mapping • -validation of available HT-YTH interactome maps: • (Vidal & Wanker groups) • >> high quality (> literature curated) • estimation of interactome size: • ~130.000 interactions • -standardized confidence scoring method

15. empirical confidence score from Braun et al. An experimentally derived confidence score for binary protein-protein interactions. Nature Methods 2009

16. empirical confidence score from Braun et al. An experimentally derived confidence score for binary protein-protein interactions. Nature Methods 2009

17. mapping the human interactome • 3 year NIH grant • Y2H: 16.000 x 16.000 full lenght human ORFs (~ 50% of total matrix of 22.000 x 22.000) • interaction toolkit re-test: ~25-30.000 interactions (~10.000/year; ~20% of the map)

18. what did we learn ?

19. benchmarking binary interaction mapping methods >> MAPPIT performance is similar to that of the other tested methods from Braun et al. An experimentally derived confidence score for binary protein-protein interactions. Nature Methods 2009

20. benchmarking binary interaction mapping methods >> the interaction mapping methods are highly complementary from Braun et al. An experimentally derived confidence score for binary protein-protein interactions. Nature Methods 2009

21. the ORFeome collection • 15.483 full length human ORFs • derived from Mammalian Gene Collection (MGC) • cloned in Gateway vectors from http://horfdb.dfci.harvard.edu/

22. MAPPIT for large-scale interactome analysis ? • high quality assay • access to a large collection of easily transferred cDNAs • different and complementary network subspace probed > screening for novel interactions

23. towards an efficient screening format: reverse transfection

24. ArrayMAPPIT screening prey (+reporter) plasmid human ORFeome collection transfection reagent reverse transfection mix -/+ ligand MAPPIT prey collection MAPPIT bait cell line luciferase read-out MAPPIT prey array (stable for months !)

25. current screening setup • prey collection: 2.000 human ORF preys (GO annotation “signal transduction”) • assay format: 96well > 384well • automation: • Tecan EVO150 (DNA preps) • Tecan EVO200/Perkin-Elmer Envision (array production array + assay read-out)

26. screening for interaction partners of E3 ligase complex adaptors “Specificity module”: SCF – Skp1 + F-boxprotein ECS – ElonginB/C + SOCS-boxprotein

27. SKP1 screen • 10-fold cut-off >> 5 hits: 3 known (blue), 3 novel (green); all F-box proteins • no other known Skp1 interaction partners in the array

28. Elongin C screen 10-fold induction 5-fold induction 3-fold induction • 10-fold cut-off >> 5 hits: 4 known and 1 novel (all SOCS-box proteins) • 5-fold cut-off >> 8 additional hits: 4 known interactors (all SOCS-box proteins) • 3-fold cut-off >> 14 additional hits: 2 known and 1 novel interactor (all SOCS-box proteins) • 6 false negatives

29. SKP1 Elongin C FBXW11 FBXO46 FBXW9 SOCS2 SPSB2 SPSB4 mock mock WB anti-E lysate WB anti-Elongin C WB anti-E IP anti-Flag WB anti-Elongin C WB anti-Flag IP anti-Flag WB anti-Flag Co-IP confirmation

30. hIL5Rα hIL5Rα Y Y CMV 5’LTR CD90 gp130 prey CMV LR-F3 bait rPAP1 hIL5RαΔcyt MAPPIT cDNA library screening MACS enrichment anti-PE magnetobead FACS sort anti-mIgG-PE Y Y retroviral prey cDNA library Y Y anti-hIL5Rα mEcoR

31. SKP1 screen • 6 known SKP1 interacting proteins • 2 novel interaction partners (both F-box proteins)

32. Array versus cDNA library screening cDNA library screening array screening ‘open’: large & diverse prey pool ‘closed’: fixed set of preys labour intensive fast prey identification is tedious position in array determines prey identity

33. MAPPIT for large-scale interactome analysis ? • high quality assay • access to a large collection of easily transferred cDNAs • different and complementary network subspace probed > > screening for novel interactions • mammalian background

34. yeast two-hybrid interaction maps are static • the human interactome is not static but dynamic • many protein-protein interactions are conditional or context-dependent • require post-translational modifications and/or structural alterations • require co-factors, adaptors or regulatory proteins • yeast cell doesn’t provide the normal cellular environment for human proteins • no accessory proteins • no modifications • no context-dependent interactions

35. MAPPIT for large-scale interactome analysis ? • high quality assay • access to a large collection of easily transferred cDNAs • different and complementary network subspace probed > > screening for novel interactions • mammalian background > > mapping protein network dynamics

36. mapping dynamic aspects of protein networks ? treatment B treatment C -/+ ligand treatment A MAPPIT bait cell line

37. p53 mapping dynamic aspects of protein interactions: GR signalling cytoplasm nucleus NFkB monomer dimer

38. MAPPIT can detect these changes in protein interactions

39. screening for DEX-dependent GR interactions - DEX + DEX -/+ ligand GR-bait expressing cells

40. screening for DEX-dependent GR interactions GR bait Skp1 bait

41. screening for DEX-dependent GR interactions + STAT3 – STAT5A – HGMB2 6 stably interacting proteins: STAT3, STAT5A, HGMB2 (known) HBP1, STAT4, SOCS3 GR bait Skp1 bait

42. screening for DEX-dependent GR interactions + STAT3 – STAT5A – HGMB2 6 DEX-inducible interactions: NRIP1 (known interactor) NCOA4 (AR interactor) FASTK, LPXN, SHC4, DOK3 GR bait Skp1 bait

43. screening for DEX-dependent GR interactions + STAT3 – STAT5A – HGMB2 1 DEX-repressible interaction: PPP5C (known interactor) GR bait Skp1 bait

44. screening for DEX-dependent GR interactions

45. ArrayMAPPIT - further development • prey collection: 2.000 human ORF preys > 10.000 (end 09) • assay format: 384well > glass slides (?) • increase assay sensitivity – decrease assay variability • data-management, optimized experimental setup, objective scoring and quality control tracking (StatGent)

46. CCSBMarc Vidal& co CRLJan TavernierDominiek CatteeuwEls PattynDelphine LavensLeentje De CeuninckIsabel UyttendaeleCelia BovijnLaura IcardiMargarida MaiaSylvie SeeuwsLennart ZabeauIrma LemmensAnne-Sophie De Smet Elien Ruyssinck Viola Gesellchen Tim Van AckerFrank PeelmanJulie PiessevauxPeter UlrichtsAnnick VerheeJoris WaumanJosé Van der Heyden Nele Vanderroost Dieter Defever