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: interactions between proteins
(estimated interactome size / )

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

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 !)
many interactions that you don’t see in Y2H with full lengths we do see in mappit >> complementary

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:
~ 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:
~ 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: x full lenght human ORFs (~ 50% of total matrix of x )
interaction toolkit re-test: ~ 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
-mappit sees two phosphorylation-dependent interactions (as do PCA, the YFP complementation technique, and remarkably also Y2H); of course this is done in untreated cells and probably other phosphorylation dependent interactions could be seen upon activation of the proper signalling cascade during the assay
>> 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
full length human ORFs
derived from Mammalian Gene Collection (MGC)
cloned in Gateway vectors
from

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
MAPPIT prey collection
-/+ ligand
MAPPIT bait
cell line
luciferase read-out
MAPPIT prey array
(stable for months !) 24

25. current screening setup
prey collection: 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-box protein
ECS – ElonginB/C + SOCS-box protein

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 27

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 28

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

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

31. SKP1 screen 6 known SKP1 interacting proteins
Symbol
Description
Number of clones (fusions)
FBXL8
F-box and leucine-rich repeat protein 8
9 (5)
FBXL15
F-box and leucine-rich repeat protein 15
1 (1)
FBXW5
F-box and WD domain protein 5
12 (5)
FBX044
F-box protein 44
9 (7)
FBXO2
F-box protein 2
CDCA3
cell division cycle associated 3
FBXL6
F-box and leucine-rich repeat protein 6
3 (1)
FBXW9
F-box and WD-40 domain protein 9
5 (3)
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
example proteins binding to receptor tail > some only upon activation of receptor
sh2 domain binding to tyrosine: only after phosphorylation
requires kinase activity

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 36

37. mapping dynamic aspects of protein interactions: GR signalling
cytoplasm
nucleus
NFkB
monomer
dimer
-gr is a nuclear receptor that in its inactive state sits in the cytoplasm, sequestered by chaperones such as hsp90
-Glucocorticoids act through the glucocorticoid receptor which may remain as a monomer and thereby interact with transcription factors to inhibit transcription of cytokine genes (transrepression >> anti-inflammatory) or they can dimerize and thereby interact with glucocorticoid response elements (GRE) to induce transcription of genes (transactivation) for metabolic enzymes.
-p53 and gr have been described to interact and mutually antagonize eachothers transactivational activities
p53

38. MAPPIT can detect these changes in protein interactions

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

40. screening for DEX-dependent GR interactions
GR bait
NRIP1: known to be a dex-dependent interactor
NCOA4: known ligand-dependent interactor of the androgen receptor
PPP5C: known interactors
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
NRIP1: known to be a dex-dependent interactor
NCOA4: known ligand-dependent interactor of the androgen receptor
PPP5C: known interactors
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
NRIP1: known to be a dex-dependent interactor
NCOA4: known ligand-dependent interactor of the androgen receptor
PPP5C: known interactors
Skp1 bait

43. screening for DEX-dependent GR interactions
GR bait
Skp1 bait
+ STAT3 – STAT5A – HGMB2
1 DEX-repressible interaction:
PPP5C (known interactor)
NRIP1: known to be a dex-dependent interactor
NCOA4: known ligand-dependent interactor of the androgen receptor
PPP5C: known interactors

44. screening for DEX-dependent GR interactions

45. ArrayMAPPIT - further development
prey collection: human ORF preys > (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. CRL Jan Tavernier Dominiek Catteeuw Els Pattyn Delphine Lavens Leentje De Ceuninck Isabel Uyttendaele Celia Bovijn Laura Icardi Margarida Maia Sylvie Seeuws Lennart Zabeau Irma Lemmens Anne-Sophie De Smet
Elien Ruyssinck
Viola Gesellchen
Tim Van Acker Frank Peelman Julie Piessevaux Peter Ulrichts Annick Verhee Joris Wauman José Van der Heyden Nele Vanderroost
Dieter Defever
CCSB Marc Vidal & co

47. screening on glass slides
IL5Ra-staining assay on glass slides
cell lines stably expressing endosialin bait
reverse transfection of prey (rPAP-IL5RaDcyt reporter endogenous)
stimulation with leptin
staining with IRDye labelled antibody & scanning with LI-COR Odyssey
>> optimization (sensitivity, diffusion) & down-scaling (microarray)
IL5RaDcyt
SVT prey
p85 prey
RNF41 prey
TRIP13 prey LR

48. screening for interaction partners of RNF41
GR bait
Skp1 bait