Joint analysis of genetic and physical interactions in S. cerevisiae

Joint analysis of genetic Joint analysis of genetic

and physical interactions and physical interactions

in in

S cerevisiaeS cerevisiae

Igor UlitskyIgor UlitskyRon Shamir labRon Shamir lab

School of Computer ScienceSchool of Computer ScienceTel Aviv UniversityTel Aviv University

Genetic interactions (GI)Genetic interactions (GI)

ldquoldquoAggravatingrdquo interactionsAggravatingrdquo interactions

The observed phenotype is The observed phenotype is worseworse than what we would ldquoexpectrdquothan what we would ldquoexpectrdquo

Synthetic lethalitySynthetic lethality joint deletion of joint deletion of two nonessential genes two nonessential genes lethal lethal phenotypephenotype

Synthetic sicknessSynthetic sickness joint deletion of joint deletion of two nonessential genes two nonessential genes slow slow growthgrowth

GI availabilityGI availability

Systematically mapped by SGA and Systematically mapped by SGA and dSLAMdSLAM

Available for asymp200 gene queries and Available for asymp200 gene queries and asymp4500 targets in asymp4500 targets in S cerevisiaeS cerevisiae

GI network 13632 interactions GI network 13632 interactions 2682 genes2682 genes

Physical interactions (PI)Physical interactions (PI)

Protein-protein interactionsProtein-protein interactions Y2HY2H TAPTAP

Protein-DNA interactionsProtein-DNA interactions ChipChip22 experiments experiments

PI network 68172 interactions 6184 PI network 68172 interactions 6184 proteinsproteins

GI Analysis SpectraGI Analysis Spectra

Genetically interacting homologs tend to Genetically interacting homologs tend to exhibit ldquocompensationrdquo on the expression exhibit ldquocompensationrdquo on the expression levellevel R Kafri R Kafri et alet al 2005 2005

Genetically interacting proteins frequently Genetically interacting proteins frequently have a similar foldhave a similar fold O Dror O Dror et alet al 2007 2007

Genetic interactions can be used to Genetic interactions can be used to delineate regulatory pathwaysdelineate regulatory pathways RP Onge RP Onge et alet al 2007 2007

Joint analysis of GI and PIJoint analysis of GI and PI

Motivation Motivation Identifying pathwaysIdentifying pathways Connect pathways with phenotypesConnect pathways with phenotypes Understand system features (robustness Understand system features (robustness

essentialityhellip)essentialityhellip)

Initial analysisInitial analysis Proteins close in the GI network slightly more Proteins close in the GI network slightly more

likely to physically interact (Tong et al 2004) likely to physically interact (Tong et al 2004) PI hubs likely to be GI hubs (Ozier et al PI hubs likely to be GI hubs (Ozier et al

2003)2003)

BPMsBPMs

Kelley and Ideker 2005 Kelley and Ideker 2005 modeling ldquoexplanationsrdquo for GIsmodeling ldquoexplanationsrdquo for GIs

Within-pathway model Between-pathway model

GI

PI

Kelley amp Ideker conclusionsKelley amp Ideker conclusions

40 of the GIs can be explained by physical 40 of the GIs can be explained by physical modelsmodels

Between-pathway models explain x 35 times Between-pathway models explain x 35 times more GIs than within-pathway modelsmore GIs than within-pathway models

Models capable of predicting protein functionsModels capable of predicting protein functions

BPM rationaleBPM rationale

Between pathway Between pathway models suggest models suggest redundancyredundancyAlternative pathsAlternative paths Independent KOs Independent KOs

little effectlittle effect Joint KOs in both Joint KOs in both

severe effectsevere effect

Viable phenotypeViable phenotype

Lethal phenotype

BPM HuntingBPM Hunting

Look for pairs of pathways with GI Look for pairs of pathways with GI evidence for bufferingevidence for buffering

A pathway ndash a A pathway ndash a connectedconnected subnetwork in subnetwork in of PIsof PIs

Model scoring based on the density of GIsModel scoring based on the density of GIs

Log-likelihood scores accounting for GI Log-likelihood scores accounting for GI degreesdegrees

1 2

1 2

( )1 2 BPM1 2

1 2 null ( )

( ) (1 )(1 ( ))( | )( ) log log

( | ) ( ) (1 )(1 ( ))a b V V

genetica b a ba b V V

I a b I a bP V V MS V V

P V V M r I a b r I a b

AlgorithmicsAlgorithmics

High-scoring seedsHigh-scoring seeds Finding heavy bicliques Finding heavy bicliques

with connectivity with connectivity constraintsconstraints

Seed optimizationSeed optimization Greedy search Greedy search

maintaining maintaining connectivityconnectivity

Significance filteringSignificance filtering

Adding ldquopivot proteinsrdquo to Adding ldquopivot proteinsrdquo to BPMsBPMs

Pathway bifurcation Redundant sub-complexes

Pivot proteinsPivot proteins

Physically connected to both pathways Physically connected to both pathways in the BPMin the BPM

Connection significant given the Connection significant given the general network degreesgeneral network degrees

Analysis flowAnalysis flow

13632 genetic interactions

68172 physical interactions

140 models

124 pivot proteins in 40 models

BPM analysis

Pivot extraction

Physiological characteristics

bullEssentialitybullPhenotypesbullProtein AbundancebullCodon adaptation indexbullmRNA half-lifebullPhoshorylation

BPMs as functional modulesBPMs as functional modules

Functional enrichment analysis Functional enrichment analysis (TANGO)(TANGO) 714 enriched for GO ldquobiological 714 enriched for GO ldquobiological

processrdquoprocessrdquo 693 enriched for GO693 enriched for GO ldquocellular ldquocellular

compartmentrdquocompartmentrdquo 463 of known complexes enriched in 463 of known complexes enriched in

at least one BPMat least one BPM

Pivots tend to be Pivots tend to be multifunctionalmultifunctional

No of No of proteinsproteins

PivotsPivots ExpectedExpected SignificanceSignificance

GO complexesGO complexes 206206 2121 435435 pp=127=1271010-9-9

KEGG pathwaysKEGG pathways 7171 66 150150 pp=368=368 1010-3-3

Curated multi-Curated multi-complexed proteins complexed proteins (Krause et al 2006)(Krause et al 2006) 3939 88 055055 pp=749=749 1010-9-9

Pivot proteins are essentialPivot proteins are essential

Q Are proteins active in multiple partially Q Are proteins active in multiple partially redundant pathways more essentialredundant pathways more essential72124 (58) pivots essential (72124 (58) pivots essential (pp=142middot10=142middot10-23-23))Enrichment is not explained by node degrees Enrichment is not explained by node degrees ((pplt10lt10-5-5))Essential pivots closer in function to their BPMs Essential pivots closer in function to their BPMs than nonessential pivotsthan nonessential pivotsPivots significantly retained during evolution Pivots significantly retained during evolution (p=979middot10(p=979middot10-9-9))

SWR1 Ino80

SWR1Ino80 ExampleSWR1Ino80 ExamplePivots

SAGANuclear pore exampleSAGANuclear pore example

Nuclear pore SAGA

Pivots

Physiological properties of Physiological properties of BPMsBPMs

Proteins studiedProteins studied 850 proteins within BPMs850 proteins within BPMs 120 pivot proteins 120 pivot proteins Focus onFocus on mRNA half lifemRNA half life of phosphorylation sites of phosphorylation sites

BPMs are strongly regulatedBPMs are strongly regulatedGenes within BPMsGenes within BPMs ShorterShorter mRNA half-life ( mRNA half-life (pp=19=191010-9-9)) MoreMore phosphorylation sites ( phosphorylation sites (pp=63=631010-9-9))

Both properties may represent regulationBoth properties may represent regulation

Redundant pathways Redundant pathways Strict regulation Strict regulation

Enrichments not explained by degrees Enrichments not explained by degrees essentiality any enriched functionessentiality any enriched function

mRNA half-life experimental data mRNA half-life experimental data phosphosites predictedphosphosites predicted

Properties generally not correlatedProperties generally not correlated

Next generation ndash quantitative GIsNext generation ndash quantitative GIs

Data on Data on quantitativequantitative genetic interactions genetic interactions scores is becoming availablescores is becoming available

These include also These include also alleviatingalleviating interactions interactions

Alleviating interactionsAlleviating interactions

The observed phenotype is The observed phenotype is betterbetter than than what we would ldquoexpectrdquowhat we would ldquoexpectrdquoldquoldquoExpectrdquo ndash multiplicative modelExpectrdquo ndash multiplicative model Mutation A ndash 80 fitness is retainedMutation A ndash 80 fitness is retained Mutation B ndash 60 fitness is retainedMutation B ndash 60 fitness is retained A amp B ndash 48 fitness is retainedA amp B ndash 48 fitness is retained

The first deletion ruins the functionally of The first deletion ruins the functionally of the pathway and the second one does not the pathway and the second one does not have an effecthave an effectAn alleviating interaction is a sign of An alleviating interaction is a sign of participation in the same pathwayparticipation in the same pathway

Extracting BPMs from quantitative Extracting BPMs from quantitative datadata

Probabilistic model for the Probabilistic model for the aggravatingalleviating interaction scoresaggravatingalleviating interaction scores

Detection of pathways and buffering using Detection of pathways and buffering using quantitative dataquantitative data


Next challenge simultaneously extraction Next challenge simultaneously extraction of multiple pathwaysof multiple pathways

The output includesThe output includes Pathway boundariesPathway boundaries Pairs of buffering pathwaysPairs of buffering pathways


SummarySummaryComputational analysis outlines pathways and Computational analysis outlines pathways and buffering pathway pairs - BPMsbuffering pathway pairs - BPMs

BPM pathways tend to be strictly regulatedBPM pathways tend to be strictly regulated

BPM pivots correspond to essential BPM pivots correspond to essential multifunctional proteinsmultifunctional proteins

Quantitative GIs carry the promise of a Quantitative GIs carry the promise of a reacher analysisreacher analysis

Genetic interactions (GI)Genetic interactions (GI)

ldquoldquoAggravatingrdquo interactionsAggravatingrdquo interactions

The observed phenotype is The observed phenotype is worseworse than what we would ldquoexpectrdquothan what we would ldquoexpectrdquo

Synthetic lethalitySynthetic lethality joint deletion of joint deletion of two nonessential genes two nonessential genes lethal lethal phenotypephenotype

Synthetic sicknessSynthetic sickness joint deletion of joint deletion of two nonessential genes two nonessential genes slow slow growthgrowth


















2003)2003)

BPMsBPMs



GI

PI










Lethal phenotype






1 2

1 2

( )1 2 BPM1 2

1 2 null ( )

( ) (1 )(1 ( ))( | )( ) log log

( | ) ( ) (1 )(1 ( ))a b V V


















140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots











































2003)2003)

BPMsBPMs



GI

PI










Lethal phenotype






1 2

1 2

( )1 2 BPM1 2

1 2 null ( )

( ) (1 )(1 ( ))( | )( ) log log

( | ) ( ) (1 )(1 ( ))a b V V


















140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots







































2003)2003)

BPMsBPMs



GI

PI










Lethal phenotype






1 2

1 2

( )1 2 BPM1 2

1 2 null ( )

( ) (1 )(1 ( ))( | )( ) log log

( | ) ( ) (1 )(1 ( ))a b V V


















140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots



































2003)2003)

BPMsBPMs



GI

PI










Lethal phenotype






1 2

1 2

( )1 2 BPM1 2

1 2 null ( )

( ) (1 )(1 ( ))( | )( ) log log

( | ) ( ) (1 )(1 ( ))a b V V


















140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots































2003)2003)

BPMsBPMs



GI

PI










Lethal phenotype






1 2

1 2

( )1 2 BPM1 2

1 2 null ( )

( ) (1 )(1 ( ))( | )( ) log log

( | ) ( ) (1 )(1 ( ))a b V V


















140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots


























BPMsBPMs



GI

PI










Lethal phenotype






1 2

1 2

( )1 2 BPM1 2

1 2 null ( )

( ) (1 )(1 ( ))( | )( ) log log

( | ) ( ) (1 )(1 ( ))a b V V


















140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots



































Lethal phenotype






1 2

1 2

( )1 2 BPM1 2

1 2 null ( )

( ) (1 )(1 ( ))( | )( ) log log

( | ) ( ) (1 )(1 ( ))a b V V


















140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots































Lethal phenotype






1 2

1 2

( )1 2 BPM1 2

1 2 null ( )

( ) (1 )(1 ( ))( | )( ) log log

( | ) ( ) (1 )(1 ( ))a b V V


















140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots































1 2

1 2

( )1 2 BPM1 2

1 2 null ( )

( ) (1 )(1 ( ))( | )( ) log log

( | ) ( ) (1 )(1 ( ))a b V V


















140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots








































140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots


































140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots
































140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots





























140 models


BPM analysis

Pivot extraction
















SWR1 Ino80



Nuclear pore SAGA

Pivots







































SWR1 Ino80



Nuclear pore SAGA

Pivots


































SWR1 Ino80



Nuclear pore SAGA

Pivots




























SWR1 Ino80



Nuclear pore SAGA

Pivots


























SWR1 Ino80



Nuclear pore SAGA

Pivots



























Nuclear pore SAGA

Pivots





































































































































Documents

Joint analysis of genetic and physical interactions in S. cerevisiae