Keynote presentation for Network Biology SIG 2013 by Esti Yeger-Lotem, Senior Lecturer in Clinical Biochemistry at The National Institute for Biotechnology in the Negev, Israel

1. Decoding the Tissue-Specificity
of Hereditary Diseases
by using Tissue Interactomes
Esti Yeger-Lotem

2. 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

3. 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

4. 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

5. 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

6. 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)
RPKM(RNA-seq)

7. 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

8. 66
tissues
78
tissues
Su et al HPA RNA-Seq
16
tissues
MINTBIOGRID DIP INTACT
16 tissue expressomes Global human interactome
Integrating expression & interactions
11,225 proteins (52% of proteins),
67,439 interactions

9. 66
tissues
78
tissues
HPA RNA-Seq
16
tissues
MINTBIOGRID DIP INTACT
16 tissue expressomes Global human interactome
Integrating expression & interactions
PPI in tissue if both
proteins are expressed
GNF

10. 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
Percentageoftotalset
Number of expressing tissues
GNF HPA RNA-seq Combined
Enriched for
basic cellular
processes
(translation
elongation, ..)
1. Most genes are globally expressed or
tissue specific

11. 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

12. 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

13. 4. PPI degree and expression levels
are correlated across all tissues
Gene2
Gene3
Gene4
Gene1
Gene1
Gene1
Gene2
Gene6
Gene4
Gene3
Gene8
Gene9
Gene10
Gene1Gene1
0
5
10
15
20
1 2 3 4 5 6 7 8 9 10
Degree
RPKM percentile
Adipose
Spearman r= 0.98
• Previously shown in yeast
von Mering et al, Nature 2002

14. 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

15. Layout
• Tissue interactomes
– extensive up-to-date resource
• Decoding the tissue-specificity
of hereditary diseases
• Our open web-tool
Familial Parkinson disease:
SNCA aberration

16. 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 16
Number of expressing tissues
Percentageoftotal
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 16
Number of expressing tissues
P
• Hereditary diseases - causal genes associations: OMIM, COSMIC
• Disease-tissue associations: Lage et al, PNAS 2008
Barshir et al, in revision

17. 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
MediannumberofTS-PPIof
diseasegenes
Tissue-specific PPIs
21% of the genes, p<10-4
Barshir et al, in revision

18. 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

19. 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

20. Layout
• Tissue interactomes
– extensive up-to-date resource
• Decoding the tissue-specificity
of hereditary diseases
• Our open web-tool
Familial Parkinson disease:
SNCA aberration

21. 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

22. 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

23. The ResponseNet web-server
http://netbio.bgu.ac.il/respnet
Basha et al, Nucleic Acids
Research 2013
Mutations
Diff. exp. genes
Human tissue interactomes

24. Identifying context-sensitive pathways
http://netbio.bgu.ac.il/ContextNet

25. 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