Decoding the Tissue-Specificity

of Hereditary Diseases by using Tissue Interactomes

Esti Yeger-Lotem

Layout

• Human tissue interactomes – extensive up-to-date resource

• Decoding the tissue-specificity

of hereditary diseases

• Our open web-tool

Familial Parkinson disease: SNCA aberration

P1 P2

P3

From a global human interactome to tissue interactomes

• Known protein-protein interactions (PPIs) - however no tissue context!

• Use tissue expression data – Filter interactome per tissue – Most studies relied on GNF: the microarray study of

Su et al, PNAS 2004, (e.g., Lehner 2008)

• New large-scale data emerging (e.g., Sandberg 2009, Albrecht 2011) – RNA-Seq data &

protein large-scale data available!

P1

P2

P3

66 tissues

78 tissues

GNF HPA RNA-Seq 16

tissues

16 tissue expressomes

Integrating tissue expression data

• Protein=gene, no splice-variants • Used stringent cutoffs for

expression

Tissue GNF HPA RNA- seq

Adipose 2,533 N/A 10,269

Adrenal 2,498 7,235 10,822

Brain 4,335 7,692 10,925

Breast N/A 6,526 10,698

Colon 2,807 7,244 10,519

Heart 3,345 6,189 9,827

Kidney 2,025 7,672 10,945

Liver 2,531 6,202 8,842

Lung 3,010 7,465 11,063

Lymph Node 2,441 6,183 10,973

Ovary 1,567 5,111 11,165

Prostate 3,075 6,508 11,250

Skeletal Muscle 1,751 5,805 8,851

Testis 3,176 7,744 12,567

Thyroid 3,360 6,982 10,938

White Blood Cells 5,750 N/A 9,466

Median 2,807 6,754 10,873

66 tissues

78 tissues

GNF HPA RNA-Seq 16

tissues

16 tissue expressomes

Integrating tissue expression data

• ~70% overlap between RNA-seq & GNF or HPA • Single resource not enough

66 tissues

78 tissues

GNF HPA RNA-Seq 16

tissues

16 tissue expressomes

Integrating tissue expression data

• Matching tissues correlated significantly (best match)

1

10

100

1000

10000

100000

100 1000 10000 100000

Gene expression level (GNF)

RP

KM

(R

NA

-se

q)

66 tissues

78 tissues

GNF HPA RNA-Seq 16

tissues

16 tissue expressomes

Integrating tissue expression data

Tissue Com-bined

GNF HPA RNA- seq

Adipose 10,859 2,533 N/A 10,269

Adrenal 13,592 2,498 7,235 10,822

Brain 14,000 4,335 7,692 10,925

Breast 12,669 N/A 6,526 10,698

Colon 13,312 2,807 7,244 10,519

Heart 12,766 3,345 6,189 9,827

Kidney 13,662 2,025 7,672 10,945

Liver 11,958 2,531 6,202 8,842

Lung 13,853 3,010 7,465 11,063

Lymph Node 13,185 2,441 6,183 10,973

Ovary 12,918 1,567 5,111 11,165

Prostate 13,586 3,075 6,508 11,250

Skeletal Muscle 11,736 1,751 5,805 8,851

Testis 14,819 3,176 7,744 12,567

Thyroid 13,518 3,360 6,982 10,938

White Blood Cells 10,844 5,750 N/A 9,466

Median 13,248 2,807 6,754 10,873

Tissue expressed gene: detected in ≥ 1 sample

66 tissues

78 tissues

Su et al HPA RNA-Seq 16

tissues

MINT BIOGRID DIP INTACT

16 tissue expressomes Global human interactome

Integrating expression & interactions

11,225 proteins (52% of proteins), 67,439 interactions

66 tissues

78 tissues

HPA RNA-Seq 16

tissues

MINT BIOGRID DIP INTACT

16 tissue expressomes Global human interactome

Integrating expression & interactions

PPI in tissue if both proteins are expressed

GNF

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Perc

enta

ge o

f tot

al se

t

Number of expressing tissues

GNF HPA RNA-seq CombinedEnriched for basic cellular processes (translation elongation, ..)

1. Most genes are globally expressed or tissue specific

0

5000

10000

15000

20000

25000

30000

2. A common core network dominates all

tissue interactomes

> 50% of proteins & PPIs in each tissue appear in all tissues - 26,370 interactions, 4,989 proteins

Genes PPIs

3. Tissue hub proteins: persistent regulators

• 451 tissue hubs: Hubs = proteins with top number of interactions (5%, > 45 interactions)

• Highly enriched for regulatory processes - transcription regulation (42%, p<10-15) - protein kinase cascade (12%, p<10-8) - also relative to core proteins

• Much of the regulatory components are similar across tissues

Number of PPIs 30 45 150

Hubs

Tissues

4. PPI degree and expression levels are correlated across all tissues

Gene2

Gene3

Gene4

Gene1

Gene1

Gene1

Gene2

Gene6

Gene4

Gene3

Gene8

Gene9

Gene10

Gene1 Gene1

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10

Deg

ree

RPKM percentile

Adipose

Spearman r= 0.98 • Previously shown in yeast

von Mering et al, Nature 2002

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Adipose

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Adrenal

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Brain

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Breast

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Heart

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Kidney

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Liver

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Colon

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Lymph Node

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Lung

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Ovary

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Prostate

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Skeletal Muscle

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Testis

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Thyroid

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

WBC

4. PPI degree and expression levels are correlated across all tissues

Layout

• Tissue interactomes – extensive up-to-date resource

• Decoding the tissue-specificity

of hereditary diseases

• Our open web-tool

Familial Parkinson disease: SNCA aberration

Familial Parkinson disease: SNCA aberration

SNCA expression

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Number of expressing tissues

Perc

enta

ge o

f tot

al 342 hereditary diseases

266 causal disease genes

The enigmatic tissue-specific manifestation of hereditary diseases

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Number of expressing tissues

P

• Hereditary diseases - causal genes associations: OMIM, COSMIC • Disease-tissue associations: Lage et al, PNAS 2008

Barshir et al, in revision

0

10

20

30

40

50

60

disease tissues non disease tissues

Factors governing tissue-specificity (TS)

Disease tissues Other expressing tissues

63% of the genes, p<10-4

Expression level (RPKM)

0

0.5

1

1.5

2

2.5

Disease tissues Other expressing tissues

Med

ian

num

ber o

f TS-

PPI o

f di

seas

e ge

nes

Tissue-specific PPIs

21% of the genes, p<10-4

Barshir et al, in revision

TS-PPIs illuminate disease-related mechanisms

Hereditary breast cancer predisposition BRCA1 network in breast

Familial lung adenocarcinoma EGFR network in lung

Muscular dystrophy DAG1 network in muscle

14-16 tissues

4-13 tissues

1-3 tissues

Protein expressed in:

~90% PPIs filtered out

Barshir et al, in revision

Factors distribution across hereditary diseases

TS-PPIs 15%

TS-PPIs + elevated

expression 12%

Elevated expression:

33%

Unknown 33%

Disease genes tissue-

specific: 7%

Barshir et al, in revision

Layout

• Tissue interactomes – extensive up-to-date resource

• Decoding the tissue-specificity

of hereditary diseases

• Our open web-tool

Familial Parkinson disease: SNCA aberration

Barshir et al, NAR 2013

TissueNet: an open database

14-16 tissues

4-13 tissues

1-3 tissues

Protein expressed in:

http://netbio.bgu.ac.il/tissuenet

Disease/Stimulus

Differentially expressed genes

Genetic screening (mutations)

Known protein-DNA interactions

Known protein-protein interactions

Interactome (~60,000 edges)

Identifying signaling pathways

Identify regulatory pathways connecting

screening data

ResponseNet

Yeger-Lotem et al, Nature Genetics 2009

The ResponseNet web-server http://netbio.bgu.ac.il/respnet

Basha et al, Nucleic Acids Research 2013

Mutations

Diff. exp. genes

Human tissue interactomes

Identifying context-sensitive pathways

http://netbio.bgu.ac.il/ContextNet

Thanks!

Marie Curie International Reintegration Grant

TissueNet Galila Agam Haim Belmaker Assaf Rudich Vered Chalifa-Caspi Inbar plaschkes

My lab @ BGU Ruth Barshir Omer Basha Alex Lan Ilan Smoly Shoval Tirman Amir Eluk Omer Schwartz

ContextNet Michal Ziv-Ukelson

ResponseNet Ernest Fraenkel Susan Lindquist