Oct 12, 2007Research Review Day
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Josh Stuart, Ph.D.Biomolecular Engineering
UCSC Research Review Day 2007
Biological Discovery FromBiological Discovery FromGenetic Network PerturbationsGenetic Network Perturbations
Reverse-EngineeringReverse-Engineeringby Knocking Downby Knocking Down
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“software” of life
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Genomes to function
Genome
Neuron
Hair
Gene switched “on”
Transcriptome Interactome
Genes signaling
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Function fromGenetic 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
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Two Examples
• Infer disease pathways
• Predict genetic interactions
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Reverse engineering by knocking-down
• Infer disease pathways
• Predict genetic interactions
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11Knock-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.
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up-regulatedin knock-down
down-regulatedin knock-down
Sensitive, genome-wide phenotypes: DNA Microarrays
knock-down
normal cells
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Infer Networkfrom Secondary Effects
Single Phenotype
Network GenesPerturbed by
RNAi or Knockout
Effect GenesMeasured by microarray
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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 rolein cell-migration
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Two Examples
• Infer disease pathways
• Predict genetic interactions
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Function from catastrophe• Most genes are nonessential• Genes knocked down together give phenotype• Can we infer function from knock-down combos?
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Gene Network Discovery
• build networks from all interactions
• discover function from a gene’s links
• understand bigger picture of gene regulation
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22Understanding Gene Function through Modifier ScreensUnderstanding Gene Function through Modifier Screens
Wild Type
A
B
C
X
A
B / R
C
XWild Type
A
B
C
X
D
E
F
Wild TypePhenotype Phenotype
Roy Lab, Univ Toronto
B --- RWithin Pathway Link
B --- FCross Pathway Link
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23Understanding Gene Function through Modifier Screens:Understanding Gene Function through Modifier Screens:Synthetic Genetic Array (SGA) analysis in Synthetic Genetic Array (SGA) analysis in S. cerevisiaeS. cerevisiae
arrayed library of ~4800 viable gene deletions
gene ‘x’deleted
Tong et al. (2001)
systematic generationof double mutants
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24Understanding Gene Function through Modifier Screens:Understanding Gene Function through Modifier Screens:Synthetic Genetic Array (SGA) analysis in Synthetic Genetic Array (SGA) analysis in S. cerevisiaeS. cerevisiae
Tong et al. (2001)
gene ‘x’deleted
systematic generationof double mutants
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25Synthetic Genetic Array (SGA) analysis in Metazoans?Synthetic Genetic Array (SGA) analysis in Metazoans?
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26Synthetic Genetic Array (SGA) analysis in Metazoans?Synthetic Genetic Array (SGA) analysis in Metazoans?
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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 The Nematode Worm Caenorhabditis elegansCaenorhabditis elegans
RNA interference (RNAi)
Roy Lab, Univ. Toronto
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30The SGI Network: 1246 Interactions among 461 GenesThe 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
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++
Co-expression Physical Interactions
Phenotype
Previously Identified NetworksPreviously Identified Networks
Genetic Interactions
Creating a Superimposed NetworkCreating a Superimposed Network
SGI NetworkSGI Network
==
Superimposed NetworkSuperimposed Network
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Superimposed NetworkSuperimposed Network
SGICo-expressionWorm PhenotypeProtein-proteinWorm GeneticSGI Gene“The bar-1 Subnetwork”
Mining the Superimposed for Multiply-supported SubnetworksMining the Superimposed for Multiply-supported Subnetworks
verified interaction
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N2; Ø(RNAi) (DIC) N2; T20B12.7(RNAi) (DIC)
N2; Ø(RNAi) (Nile Red) N2; T20B12.7(Nile Red)
Genes in the Genes in the bar-1 bar-1 Subnetwork have a Shared PhenotypeSubnetwork have a Shared Phenotype
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N2; Ø(R
NAi) F2
bar-1
; Ø(R
NAi) F2
N2; Y48
E1B.5
(RNAi)
F1
N2; mrp
-5(R
NAi) F1
N2; F29
C12.4
(RNAi)
F1
N2; ZC39
5.10
(RNAi)
F2
N2; lin-
2(RNAi)
F2
N2; B04
32.3(
RNAi) F1
N2; T20
B12.7(
RNAi) F2
N2; efl-
1(RNAi)
F2
N2; lin-
39(R
NAi) F2
N2; C27F
2.10
(RNAi)
F2
N2; lin-
35(R
NAi) F2
N2; ogt
-1(R
NAi) F2
N2; prx
-5(R
NAi) F1
N2; T09
A5.5(
RNAi) F1
N2; ubc
-18(R
NAi) F1
N2; lin-
23(R
NAi) F1
N2; F54
C9.6(
RNAi) F1
N2; exo
-3(R
NAi) F1
N2; lin-
7(RNAi)
F2
N2; T01
E8.6(
RNAi) F1
0
0.2
0.4
0.6
0.8
1
1.2
Genotype
Nor
mal
ized
N2
Val
ues
Genes in the Genes in the bar-1 bar-1 Subnetwork have a Shared PhenotypeSubnetwork have a Shared Phenotype
75% of genes in subnetwork have altered fat levels.
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Analysis of the SGI NetworkAnalysis 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?
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• Identified 343 subnetworks• 47% are enriched for a specific GO category
• 46 subnetworks are bridged by SGI links• 19-fold enriched
or
SGICo-expressionWorm PhenotypeProtein-protein
What is the Connectivity of Synthetic Genetic Links within What is the Connectivity of Synthetic Genetic Links within the Superimposed Network?the Superimposed Network?
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37SGI Interactions Significantly Bridge SubnetworksSGI Interactions Significantly Bridge Subnetworks
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Analysis of gene pairs tested for interaction in both worm and yeast
Analysis of subnetwork bridging by worm and/or transposed yeast interactions
wormyeast
OR
OR
Is the Connectivity of Genetic Networks Conserved?Is the Connectivity of Genetic Networks Conserved?
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Summary• Causal order from phenotypes under
knock-down
• Genome-wide interactions reveal gene functions.
• Pathway coordination may be evolvable.
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Current Directions
• Predict drug targets from knock-down signatures
• Develop a tool for visualization and search of integrated data
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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)
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probability genesmatch drug signatures
Directions: Predict Drug Targets
knock-downsensitivies
to drug
are specific pathwayspredicted?
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Directions: Interaction Browser
• Physical interactions
• New, high-throughput datasets
• Browser with “tracks” of interactions
• Public, 0-setup
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AcknowledgementsAcknowledgementsMatt Weirauch
• Roy Lab– Alexandra Byrne
• Lee Lab
• Yildiz Lab
• Davis Lab– Bob St. Onge
• Stuart Lab– Martina Koeva
• Funding
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Principle #2
Genes self assemble into modular subcomponents
0
10
20
30
40
50
605 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
105
110
115
Core Size
Per
cen
t o
f C
ore
s
Network
Random
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Principle #3
Coordinated activity is a signature of gene function
proliferation
transcription
ribosomebiogenesis
ribosomalsubunits
respirationprotein modification
secretion
fatty acidmetab.tissue growth
neuronal
immune response
development /hox genes
cell polarity,cell structure
Newly evolved
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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)
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Network
Linksa Nodesb Supported Linksc
Genetically-Supported Links (A)d
Genetically-Supported Links (B)e
Physically-Supported Linksf
Co-Exp.-Supported Linksg
Co-Phen.-Supported Linksh
Superimposed network 75,283 7,825 929 (7.2) na na na na na
wSGI 1,246 461 63 (2.0) 43 (1.6) 53 (1.8) 9 (5.6) 2 (9.0) 4 (5.9)*
Lehner 341 161 25 (5.5) 13 (10.8) 23 (7.3) 3 (22.7) 1 (17.9) 1 (30.3)
Fine genetic interactions 2,279 1,022 152 (4.6) na 48 (1.7) 61 (27.8) 23 (36.1) 22 (20.2)
Transposed SGA 7,527 426 66 (2.3) 5 (4.5) 5 (3.2)* 43 (2.2) 14 (3.0) 4 (1.3)*
Interolog 12,796 4,339 723 (9.9) 61 (27.8) 110 (4.8) na 577 (14.6) 42 (3.9)
C. elegans protein interaction 3,967 2,624 27 (3.7) 7 (10.6) 10 (4.2) na 13 (3.8) 5 (3.4)*
Eukaryotic co-expression 43,363 5,232 695 (11.8) 23 (36.1) 40 (7.2) 577 (14.6) na 84 (6.1)
C. elegans co-phenotype 8,862 913 153 (5.2) 22 (20.2) 30 (6.1) 42 (3.9) 84 (6.1) na
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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