NetBioSIG2013-Talk Vuk Janjic

Outline

Background

MethodsDataConstructing the networksGraphletsK-core decomposition

The Core DiseasomeTopological uniquenessFunctional annotationDrug targetsComputing the Core Diseasome

Key cardio-vascular disease genes

G-protein coupled receptors

Imperial College London Vuk Janjić [email protected]

Outline

Background






Background

I A LOT of system-level biological data due to advances inbiotechnology

I We’re looking for a “core subnetwork” of the humanprotein-protein interaction (PPI) network in which genes (theirprotein products) involved in a multitude of diseases reside

I No a priori knowledge of genes’ involvement in disease and byusing k-core decomposition

I Other studies have used a similar approach, but with adifferent goal in mind

Imperial College London Vuk Janjić [email protected] 1/17

Background






Background






Background






Outline

Background






Data

# of nodes # of edges ReferenceProtein-protein 11,100 56,708 HPRD, BioGRID

Genetic 274 281 BioGRIDDisease-gene 561 / 4,004 4,029 Disease Ontology

(diseases/genes)

Table: Interaction data

Janjić V. & Pržulj N., Molecular BioSystems, 8, 2614-2625 (2012).


Constructing the networks

Table: Basic network properties for our four networks

H-ALL H-SIM REST CORENumber of nodes 11,100 1,706 8,227 88Number of edges 56,807 8,655 24,730 865

Clustering coefficient 0.125 0.173 0.102 0.462Diameter 13 9 16 3

Radius 7 5 8 2Avg. degree 10.23 10.14 4.53 19.65

Avg. path length 3.69 3.48 4.53 1.87







Avg. path length 3.69 3.48 4.53 1.87







Avg. path length 3.69 3.48 4.53 1.87



2-node

graphlet

4-node graphlets3-node graphlets

5-node graphlets

G0 G1 G2 G3 G4 G5 G6 G7 G8

0

1

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34

5 6

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G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19

G20 G21 G22 G23 G24 G25 G26 G27 G28 G29

15

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25

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29

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34 36

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4143

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51 54

5557

58

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61

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6567

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72

Figure: Graphlets with automorphism orbits.

Pržulj N., Bioinformatics, 23, e177-e183 (2007).







Avg. path length 3.69 3.48 4.53 1.87







Avg. path length 3.69 3.48 4.53 1.87







Avg. path length 3.69 3.48 4.53 1.87







Avg. path length 3.69 3.48 4.53 1.87







Avg. path length 3.69 3.48 4.53 1.87



3-core

2-core

1-core

Figure: A three-level deep k-core decomposition of a network.







Avg. path length 3.69 3.48 4.5 1.87







Avg. path length 3.69 3.48 4.5 1.87







Avg. path length 3.69 3.48 4.5 1.87


Outline

Background






Topological uniqueness

Maxim um EC = 10.52%

Algorithm executions 1-4,000

Edg

e c

orr

ectn

ess (

%)

13

12

11

10

9

8

7

6


Functional annotation

I Statistics performed using:I hypergeometric testI H-ALL as the background modelI Benjamini-Hochberg False Discovery Rate correction for

multiple hypothesis testing

I Enriched Molecular Function Gene Ontology (GO) termsI enzyme binding, transcription factor binding, transcription

regulator activity, DNA binding, promoter bindingI Enriched Biological Process GO terms (mostly regulatory)

I positive regulation of macromolecule metabolic process,positive regulation of cellular biosynthetic process, response toorganic substance, regulation of cell proliferation, positiveregulation of gene expression




multiple hypothesis testingI Enriched Molecular Function Gene Ontology (GO) terms

I enzyme binding, transcription factor binding, transcriptionregulator activity, DNA binding, promoter binding

I Enriched Biological Process GO terms (mostly regulatory)I positive regulation of macromolecule metabolic process,

positive regulation of cellular biosynthetic process, response toorganic substance, regulation of cell proliferation, positiveregulation of gene expression




multiple hypothesis testingI Enriched Molecular Function Gene Ontology (GO) terms

I enzyme binding, transcription factor binding, transcriptionregulator activity, DNA binding, promoter binding

I Enriched Biological Process GO terms (mostly regulatory)I positive regulation of macromolecule metabolic process,

positive regulation of cellular biosynthetic process, response toorganic substance, regulation of cell proliferation, positiveregulation of gene expression



Table: Regulation of cell death and apoptosis enrichment.

regulation of cell death regulation of apoptosis(GO:10941) (GO:42981)

H-ALL 8.9% 8.8%H-SIM 19.9% (p = 8.59× 10−60) 19.8% (p = 1.13× 10−59)REST no enrichment no enrichmentCORE 32.1% (p = 6.93× 10−10) 29.8% (p = 1.1× 10−8)



I Top 1% hubs contain only 9 (out of 185) apoptosis annotatedproteins

I These 9 are evenly split between H-SIM and REST (5 are inH-SIM and 4 in REST)

I Cell death has no annotated proteins in the top 1% of hubs.













I Could the Core Diseasome be capturing genes causal todiseases for which we generally have no effective cure,including cancer, hematologic diseases, neurodegenerativediseases, progression of viral and HIV infection?


Driver genes

I Genetic interactions are increasingly starting to show that avery small number of genetic changes may trigger diseaseonset. These mutations are usually called driver mutations.

Ashworth A. et al., Cell, 145, 30–38, (2011).


Driver genes

I We verify that CORE genes:I are enriched in genetic interactions (GIs)

I 22 of them participate in 21 GIs within CORE (p = 10−16)I 32 of them participate in 100 GIs total (including 59 genes

outside of core)

I capture 15 driver genes (both known and predicted).


Driver genes

I We verify that CORE genes:I are enriched in genetic interactions (GIs)

I 22 of them participate in 21 GIs within CORE (p = 10−16)I 32 of them participate in 100 GIs total (including 59 genes

outside of core)I capture 15 driver genes (both known and predicted).


Driver genes

SIN3A

NCOR1

HDAC5

PMLGATA1

CTBP1

SUMO1

RUNX1

SMARCB1SMARCC1

UBE2I

ETS1

DAXX

RUNX2

SMARCA4

CEBPA MYOD1

SMAD3

RARA

HDAC4

NCOR2SP1

EP300

HDAC3

RXRA

MYC

TP73

CEBPB

KAT2BJUN

CREBBPBRCA1

SMARCA2

SMAD4

POLR2A

STUB1SMAD2

NCOA2

CCND1

CDKN1A

HIF1A

MDM2

PARP1

CSNK2A1

RELA

CAV1

ABL1

HSPA8

HSPA4

UBC

HSP90AA1

PAK1

EGFR

RAF1

MST1R

ERBB2

KHDRBS1

CASP3

CHUK

CTNNB1

ESR1

FOXO1RB1AKT1

AR ESR2

PTPN11LYN

PTK2B CRK

CRKL

KIT

CBL

PTPN6

PLCG1

LCKJAK2

PIK3R1INSR

BCR

EPOR

IRS1

SHC1

PTPN1

IGF1R

PTK2

BCAR1

PXN


Drug targets

I Amongst the 22 genes participating in genetic interactionswithin CORE, there are 11 drug targets linked to 116 distinctdrugs (p = 8.64× 10−5)

MDM2MDM2

JUNJUN

RB1RB1

ARAR

SMAD2SMAD2

NCOA2NCOA2

KAT2BKAT2BCCND1CCND1

ESR1ESR1

CTNNB1CTNNB1

CREBBPCREBBP

I Out of these 11 drug targets, 3 aretargeted by 23 or more drugs:ESR1 is targeted by 61 differentdrugs, AR by 40, and NCOA2 by23. (the p-value of any targetbeing hit by more than 22 drugs is0.0017)

I 2 known driver genes in CORE aredrug targets: RB1 and CTNNB1


Drug targets


MDM2MDM2

JUNJUN

RB1RB1

ARAR

SMAD2SMAD2

NCOA2NCOA2


ESR1ESR1

CTNNB1CTNNB1

CREBBPCREBBP




Drug targets


MDM2MDM2

JUNJUN

RB1RB1

ARAR

SMAD2SMAD2

NCOA2NCOA2


ESR1ESR1

CTNNB1CTNNB1

CREBBPCREBBP




Computing the Core Diseasome

Breast cancer

Prostate cancer

Leukemia

Yersinia infection

Adenovirus infection

Rheumatoid arthritis

Embryoma

Alzheimer's disease

Systemic scleroderma

Lymphoma

Parkinson disease

Melanoma

Colon cancer

Brain tumor

Eating disorder

CoreDiseasome

(88 genes)

H-ALL Core(17 genes)

H-SIM Core(12 genes)

Linked via

57 intermediary genes

and 175 edges

Linked via

57 intermediary genes

and 175 edges

BCL2

BCL3

CARM1

CASP8

E2F1

GNB2L1

HSF1

IKBKB

AHR

ARNT

CSK

GAB2

HIPK2

MEN1

NG1

JAK1

KAT5

KRT18

MAPK14

MDM4

RBBP4

SMARCE1

TSC2

MYB

NCK1

NEDD9

PIAS3

SKI

SOS1

BCL2

BCL3

CARM1

CASP8

E2F1

GNB2L1

HSF1

IKBKB

AHR

ARNT

CSK

GAB2

HIPK2

MEN1

NG1

JAK1

KAT5

KRT18

MAPK14

MDM4

RBBP4

SMARCE1

TSC2

MYB

NCK1

NEDD9

PIAS3

SKI

SOS1


Outline

Background







I Cardio-vascular disease (CVD)

I Interlogous Interaction Database (I2D), Jurisica Lab @Toronto

I around 15,000 nodes and 173,000 interactions (60,000predicted interactions)

I Study identifies 10 “Key CVD proteins” via clustering methodsI All 10 “key” CVD proteins captured by k-core decomp. of:

I the whole PPI network (p = 10−11)I induced CVD network (p = 10−10)



I Cardio-vascular disease (CVD)I Interlogous Interaction Database (I2D), Jurisica Lab @

TorontoI around 15,000 nodes and 173,000 interactions (60,000

predicted interactions)








I Study identifies 10 “Key CVD proteins” via clustering methods

I All 10 “key” CVD proteins captured by k-core decomp. of:I the whole PPI network (p = 10−11)I induced CVD network (p = 10−10)










SMARCC1

ETS1

SMARCB1

RUNX1

SMARCA4

RUNX2

NCOR1

SMAD3

CTBP1

HDAC5

KAT2B

GATA1

UBE2I

SIN3A

JUN

MYOD1

PML

SUMO1

ESR2

EP300HDAC4

NCOR2

RXRA

CREBBP

CHUK

DAXX

ESR1

RB1

CASP3

CSNK2A1

SMAD4

MYC

SMAD2

HSP90AA1

HSPA8

ABL1

UBC

CAV1

SMARCA2

LCK

EGFR

KHDRBS1

RAF1

MST1R

PTPN6

PAK1

ERBB2

STUB1

JAK2

POLR2A

TP73

HSPA4

CEBPA

BRCA1

CEBPB

AR

AKT1

CTNNB1

PARP1

NCOA2CCND1RELA

HIF1A

HDAC3

RARA

MDM2

CDKN1A

FOXO1

SP1

KIT

CBL

CRKL

PLCG1EPOR

PXN

BCAR1

PTK2

IGF1R

PTPN1

IRS1

SHC1INSR

LYN

PTK2BCRK

PTPN11

PIK3R1

BCR

8 Key CVD genes

8 validated CVD gene predictions

2 non-valdated CVD gene predictions

11 drug targets

5 driver gene

Sarajlic, A. et al., PLoS One, in press (2013)


Outline

Background







I New unpublished interaction network of human G-proteincoupled receptors (GPCRs) from Štagljar Lab (U-of-T)

I The whole GPCR network is basically a signal transduction“backbone” of the human PPI network — it’s wiring allows itto quickly reach all parts of the interactome

I The “core” of this GPCR network has 68 interactions between25 proteins

I Its “core” proteins primarily expressed in brain, and involved ina range of personality and behavioural disorders:

I attention deficit hyperactivity disorder, weight gain, bipolardisorder, antipsychotic agent-induced weight gain, attentiondeficit disorder / conduct disorder / oppositional defiantdisorder, schizophrenia, weight loss, obesity, mood disorders,tardive dyskinesia, and personality traits.























We’ve seen that. . . (i.e., take-home messages)

I A sub-network of the human PPI network exist, such that it’stopology is unique within that context and it captures diseasegenes, driver genes and their drug targets

I ...and it can be obtained purely computationallyI Usability of the “core” approach in identifying therapeutically

relevant regions of the interactome in two case studies —Cardiovascular disease and G-protein coupled receptors




I ...and it can be obtained purely computationally

I Usability of the “core” approach in identifying therapeuticallyrelevant regions of the interactome in two case studies —Cardiovascular disease and G-protein coupled receptors




I ...and it can be obtained purely computationallyI Usability of the “core” approach in identifying therapeutically

relevant regions of the interactome in two case studies —Cardiovascular disease and G-protein coupled receptors


Questions...

Health & Medicine

NetBioSIG2013-Talk Vuk Janjic