Open Access

Inferring pleiotropy by network analysis: linked diseases in the human PPI network

BMC Systems Biology20115:179

DOI: 10.1186/1752-0509-5-179

Received: 5 July 2011

Accepted: 31 October 2011

Published: 31 October 2011

Abstract

Background

Earlier, we identified proteins connecting different disease proteins in the human protein-protein interaction network and quantified their mediator role. An analysis of the networks of these mediators shows that proteins connecting heart disease and diabetes largely overlap with the ones connecting heart disease and obesity.

Results

We quantified their overlap, and based on the identified topological patterns, we inferred the structural disease-relatedness of several proteins. Literature data provide a functional look of them, well supporting our findings. For example, the inferred structurally important role of the PDZ domain-containing protein GIPC1 in diabetes is supported despite the lack of this information in the Online Mendelian Inheritance in Man database. Several key mediator proteins identified here clearly has pleiotropic effects, supported by ample evidence for their general but always of only secondary importance.

Conclusions

We suggest that studying central nodes in mediator networks may contribute to better understanding and quantifying pleiotropy. Network analysis provides potentially useful tools here, as well as helps in improving databases.

Background

The systems perspective on complex biological systems emphasizes that individual genes act in genetic networks and individual proteins play their roles in protein-protein interaction (PPI) networks [1]. There is increasing interest in these networks, as their analysis helps to understand the relationship between the components (i.e. genes, proteins) and how these are positioned in the whole system. Well-connected hubs seem to be of high functional importance [2, 3]. Consequently, studies on diseases based on PPI networks had the starting point by analysing the centrality of disease proteins. Genes associated with a particular phenotype or function are not randomly positioned in the PPI network, but tend to exhibit high connectivity; they may cluster together and can occur in central network locations [4, 5].

Beyond focusing on the number of neighbours of graph nodes (their degree), wider neighbourhoods, indirect effects and larger subsets of nodes can also be analyzed by the rich arsenal of network analytical tools. This non-local information may help, for example, to quantify the structural relationships between different sets of proteins. In an earlier paper [6], we have determined proteins that mediate indirect effects between sets of proteins causing five diseases in the human PPI network. Their mediator role was quantified and they were ranked according to structural importance. Their functional role may be of high interest, as proteins involved in certain pairs of diseases have no direct interactions among them [6]. These findings motivated an appealing problem: „which proteins connect diseases in the human PPI network?".

To be connected to diverse regions of the PPI network may lend a functionally pleiotropic character to a protein in a classical, genetic sense: it has been demonstrated that high connectivity correlates well with pleiotropic effects [7, 8]. The most central mediators are especially important in connecting apparently distant nodes in the human PPI network. Specific network positions may render strange but characteristic behaviour (expression pattern) to different proteins [9, 10]. Instead of being exceptional, these epistatic effects may be of primary importance in physiology [11] and in better understanding animal development and adaptation.

In this paper, (1) we compare two interaction networks of mediators (mediating indirect effects between heart disease and obesity, and between heart disease and diabetes), (2) we analyse the structure of these two networks and their aggregated total network, (3) we study the overlap between the two mediator networks, and (4) we infer biological functions for some proteins and provide supporting literature data. All in all, we illustrate that network analysis is an excellent tool for identifying pleiotropy and epistasis from complex networks extracted from multiple databases.

Results

Network analysis

We obtained 9 proteins involved in heart diseases (H), as well as 44 and 20 involved in diabetes (D) and obesity (O), respectively. The HD network contains N = 2142 nodes and L = 3537 links, while the HO network contains N = 1746 nodes and L = 2567 links and the total network contains N = 2221 nodes and L = 3686 links. Figure 1 provides a schematic illustration for how the networks had been constructed (see Methods). Figure 2 shows the relationships between mediator proteins in the HD (Figure 2a) and the HO (Figure 2b) networks. The HD network (Figure 3a) contains 25 HD mediators and their 2117 neighbours and the HO network (Figure 3b) contains 12 HO mediators and their 1734 neighbours. In the „total" network (Figure 4), 9 shared mediators appear, so it contains only 28 mediator proteins. In this total network, 1667 nodes are present in both the HD and the HO network, 475 only in the HD and 79 only in the HO network.
https://static-content.springer.com/image/art%3A10.1186%2F1752-0509-5-179/MediaObjects/12918_2011_Article_789_Fig1_HTML.jpg
Figure 1

The HD and HO mediator networks and their subnetworks. Red, blue and yellow proteins are involved in three diseases (H: heart diseases, D: diabetes, O: obesity). Pink proteins mediate indirect effects between the red and the blue ones, while orange proteins mediate between the red and the yellow ones. Black proteins mediate between both pairs. White proteins are the non-mediator neighbours of the mediator proteins. We analyzed five networks: the HD mediator network (pink and black nodes with their white neighbours), the HO mediator network (orange and black nodes with their white neighbours), the total mediator network (pink, orange and black nodes with their white neighbours), the subnetwork of interactions among HD mediators (pink and black nodes) and the subnetwork of interactions among HO mediators (orange and black nodes).

https://static-content.springer.com/image/art%3A10.1186%2F1752-0509-5-179/MediaObjects/12918_2011_Article_789_Fig2_HTML.jpg
Figure 2

Subgraphs of the HD (a) and HO (b) networks, showing the interactions only between HD and HO mediators, respectively.

https://static-content.springer.com/image/art%3A10.1186%2F1752-0509-5-179/MediaObjects/12918_2011_Article_789_Fig3_HTML.jpg
Figure 3

The HD (a) and HO (b) networks: pink and orange nodes are the HD and HO mediators, respectively, while the white nodes are their non-mediator neighbours.

https://static-content.springer.com/image/art%3A10.1186%2F1752-0509-5-179/MediaObjects/12918_2011_Article_789_Fig4_HTML.jpg
Figure 4

The total network: black nodes are the HD or HO mediators, while the white nodes are their non-mediator neighbours.

The distributions of individual structural indices are very similar for all of the three analyzed networks. Additional file 1 shows all values of the six network indices for all nodes in the three networks. Figure 5 shows these distributions only for the total network. We can observe that almost all indices follow a strongly left-skewed distribution where only a few nodes are extremely important. While degree (D), topological importance (TI) and betweenness centrality (BC) have really only one or a few hubs, topological overlap (TO) indicates several key nodes. Closeness centrality (CC) has a unimodal, normal-like distribution.
https://static-content.springer.com/image/art%3A10.1186%2F1752-0509-5-179/MediaObjects/12918_2011_Article_789_Fig5_HTML.jpg
Figure 5

The distributions of nodal index values in the total network.

For each network, there seem to be strong and positive rank correlation between all centrality indices but not for the overlap indices (TO30.01 and TO30.005). TO indices correlate positively and weakly with other centrality indices whereas they correlate negatively and weakly with CC (see Table 1). D best correlates with TI 3 . The TO measure offers different, complementary information than the centrality indices.
Table 1

Correlations between indices of the real networks.

HD

nCC

nBC

TI3

TO30.01

TO30.005

nD

0.713

0.816

0.862

0.216

0.309

nCC

 

0.59

0.516

-0.092

-0.051

nBC

  

0.73

0.249

0.324

TI3

   

0.265

0.265

TI30.01

    

0.717

HO

nCC

nBC

TI3

TO30.01

TO30.005

nD

0.737

0.72

0.82

0.069

0.187

nCC

 

0.579

0.377

-0.225

-0.138

nBC

  

0.625

0.181

0.249

TI3

   

0.262

0.302

TI30.01

    

0.836

total

nCC

nBC

TI3

TO30.01

TO30.005

nD

0.704

0.819

0.862

0.169

0.27

nCC

 

0.585

0.47

-0.186

-0.132

nBC

  

0.732

0.223

0.307

TI3

   

0.272

0.29

TI30.01

    

0.775

The Spearman rank correlation coefficients between each pair of centrality indices for the HD and HO networks as well as the total network.

Table 2 summarizes the results of the randomization test (note that only the means are shown in the table, for simplicity). The observed rank correlation coefficients are all significantly lower than those for the random networks (with 95% confidence interval). This suggests that there are stronger rank correlations between different centrality indices in the random networks, in comparison to the results obtained from the HD, HO and total networks. One possible explanation for this discrepancy is that, beyond the mathematical properties, real networks are structured also by biological constraints. Thus, different centrality indices can capture different aspects of network topology, therefore correlation between different indices are weaker for real networks. This provides more support on using various network indices to capture different topological properties embedded in real networks.
Table 2

Correlations between indices of the randomized networks.

HD

nCC

nBC

TI3

TO30.01

TO30.005

nD

0.82

0.96

0.935

0.929

0.93

nCC

 

0.878

0.621

0.836

0.887

nBC

  

0.87

0.918

0.927

TI3

   

0.79

0.773

TI30.01

    

0.942

HO

nCC

nBC

TI3

TO30.01

TO30.005

nD

0.796

0.954

0.926

0.919

0.91

nCC

 

0.853

0.571

0.857

0.894

nBC

  

0.849

0.913

0.908

TI3

   

0.749

0.723

TI30.01

    

0.95

total

nCC

nBC

TI3

TO30.01

TO30.005

nD

0.839

0.962

0.936

0.931

0.933

nCC

 

0.893

0.647

0.864

0.901

nBC

  

0.874

0.921

0.931

TI3

   

0.792

0.779

TI30.01

    

0.945

The mean Spearman rank correlation coefficients between each pair of centrality indices obtained from 1000 random networks of the same size as the HD, HD and the total network.

Biological results

We now examine more closely the rank order of the top nodes in each network. The degree ranks for the three networks are almost identical (see Tables 3, 4 and 5). The most central nodes are P62993 (Growth factor receptor-bound protein 2), P63104 (14-3-3 protein zeta/delta) and P06241 (Tyrosine-protein kinase Fyn). The 9 shared proteins rank in the same order in HD and HO and there is no change in rank order also in the total network. In the HO network, the 12 mediators lead the ranking, and then come their neighbours. However, in the HD rank (and also in the total network), there is one non-mediator protein in the top 26 of the rank (among the 25 HD mediators); this is P00533 (Epidermal growth factor receptor) in the 23rd position.
Table 3

Centrality ranks for the HD network.

 

nD

 

nCC

 

nBC

 

TI3

 

TO30.01

 

TO30.005

P62993

31.11

P62993

46.54

P62993

37.39

P62993

277.53

P62993

8066

P12931

20642

P63104

19.80

P12931

44.47

P63104

26.06

P63104

204.08

P63104

7107

P62993

19760

P06241

16.21

P22681

44.31

P12931

13.57

P06241

117.74

P12931

6893

P06241

19595

P12931

14.67

P00533

43.76

P17252

13.52

P12931

100.73

P06241

5541

P63104

18434

P17252

10.32

P06241

42.78

P49407

13.33

P49407

100.05

P17252

4025

P49407

16818

P49407

10.32

Q13813

42.67

P06241

12.97

P62736

95.47

P28482

2338

P62736

16177

P62736

9.81

P00519

42.01

P62736

11.69

P17252

90.13

P07948

2104

P17252

15588

P28482

8.97

P21333

41.75

P02768

10.94

P02768

86.69

P62736

1875

P28482

13598

P27361

7.43

P63104

41.74

P28482

9.30

P28482

66.61

P27361

1800

P22681

13337

P02768

7.19

P17252

41.69

P22681

5.71

P27361

52.43

P49407

1661

P07948

12088

P22681

5.42

P07355

41.48

P27361

5.35

Q03135

36.93

P22681

1634

P27361

9436

P07948

5.37

P61978

41.45

Q03135

4.67

P22681

36.86

P51681

1280

Q03135

9271

Q03135

4.58

P28482

41.39

P07948

3.38

P07948

35.10

P05129

955

P41240

7227

P41240

2.80

Q02156

40.82

P09471

2.25

Q99962

18.32

P09471

925

P05129

3574

P05129

2.43

P29353

40.77

Q99962

2.08

P05106

18.32

O15303

875

P05106

3532

P05106

2.38

P07900

40.70

P05106

2.02

P09471

18.02

O15492

875

P98082

1867

Q99962

2.01

P62988

40.68

P41240

1.44

P05129

17.42

O15539

875

P51681

1828

P09471

1.54

O14939

40.64

P00533

0.97

P41240

15.68

O15552

875

Q99962

1730

P13500

1.03

P43405

40.52

Q9UBS5

0.89

P48745

6.64

O43566

875

P18031

1574

O14788

0.79

Q06124

40.51

P18545

0.84

P13500

6.11

O43665

875

P00533

1521

P80098

0.79

Q07889

40.51

P13500

0.84

P80098

5.16

O76081

875

O15492

1504

P48745

0.61

P06396

40.29

P05129

0.82

P54646

5.08

P04899

875

P04004

1471

P00533

0.51

P56945

40.28

P06396

0.80

O14788

4.81

P08913

875

P49757

1471

P80075

0.51

P06213

40.19

P48745

0.76

P80075

2.53

P16473

875

P16284

1440

P54646

0.47

O43707

40.16

P80098

0.69

Q99616

2.42

P18545

875

P29353

1440

Q99616

0.47

Q05655

40.06

P61981

0.69

P00533

2.14

P18825

875

Q05397

1440

P29353

0.42

P49407

40.01

P11532

0.67

P29353

1.58

P30542

875

Q99704

1440

P06213

0.37

Q15746

40.00

O14788

0.64

P17302

1.51

P32302

875

P31751

1308

P17302

0.37

P23528

39.93

Q92616

0.62

P43405

1.44

P34998

875

P02751

1297

P43405

0.37

Q00839

39.93

P02751

0.59

P06213

1.43

P35372

875

P06756

1297

The rank of the most central 30 nodes in the HD network, based on the six importance indices analyzed.

Table 4

Centrality ranks in the HO network.

 

nD

 

nCC

 

nBC

 

TI3

 

TO30.01

 

TO30.005

P62993

38.17

P62993

50.40

P62993

47.09

P62993

307.09

P62993

6637

P62993

14457

P63104

24.30

P12931

46.24

P63104

32.23

P63104

215.63

P63104

6333

P63104

14457

P06241

19.89

P21333

45.68

P17252

17.01

P06241

134.44

P12931

5744

P49407

14244

P12931

17.99

P07355

45.61

P12931

16.91

P12931

117.55

P06241

5056

P17252

12792

P17252

12.67

Q13813

45.61

P49407

16.71

P49407

108.09

P17252

3089

P12931

12396

P49407

12.67

P22681

45.42

P06241

16.12

P17252

106.48

P28482

2667

P06241

10742

P28482

11.00

P00533

45.40

P28482

13.75

P28482

88.40

P49407

1797

P28482

10633

P41240

3.44

P61978

44.96

Q5JY77

4.04

Q5JY77

26.59

P41240

1035

P41240

4498

Q5JY77

2.69

Q07889

44.56

O14908

3.32

O14908

22.70

P07550

244

O14908

3414

O14908

2.58

Q13322

44.56

P41240

2.03

P41240

19.48

Q14232

242

Q5JY77

3363

Q14232

0.97

P06241

44.20

P07550

1.51

Q14232

8.44

P08913

215

P04629

3281

P54646

0.57

P11142

44.16

Q14232

0.94

P54646

5.41

P14866

215

P08588

3281

P00533

0.34

P07900

44.01

P08588

0.73

P07550

1.40

P18089

215

P98164

3281

P07550

0.34

P62988

44.00

P54646

0.69

P07900

1.37

P18825

215

P07550

2835

P07900

0.34

P63104

43.46

P21333

0.59

P00533

1.32

P81605

215

P08069

1976

P22681

0.34

P29353

43.38

P07900

0.57

P61978

1.28

O14908

208

O00222

1872

P29353

0.34

P35568

43.32

P07355

0.51

P22681

1.28

Q92793

189

O15534

1872

P61978

0.34

P00519

43.29

Q13813

0.51

P29353

1.25

P23508

186

O43193

1872

O14939

0.29

P56945

43.29

P61978

0.51

P21333

1.19

P05198

185

O43504

1872

P04049

0.29

P28482

42.94

Q9UQ35

0.48

P07355

1.18

P13667

180

O60518

1872

P06213

0.29

O43707

42.74

P00533

0.47

Q13813

1.18

P20042

180

O60925

1872

P07355

0.29

P11274

42.74

O43707

0.46

P04049

1.12

P49703

180

O75665

1872

P21333

0.29

P02545

42.47

P11274

0.46

Q02156

1.12

P49770

180

O95295

1872

P49023

0.29

P05783

42.47

P22681

0.46

Q05513

1.12

P52565

180

P08173

1872

P98082

0.29

Q16658

42.47

P04629

0.46

Q07889

1.11

Q13144

180

P08912

1872

Q02156

0.29

P17252

42.45

P23458

0.41

Q13322

1.11

Q9BYD3

180

P11229

1872

Q05513

0.29

P23458

42.45

P11142

0.39

P06213

1.06

Q9NR50

180

P20309

1872

Q07889

0.29

P04049

42.41

Q7KZI7

0.39

P08588

1.05

Q5JY77

168

P21452

1872

Q13322

0.29

Q02156

42.41

P30556

0.39

P49023

1.03

O00418

165

P25025

1872

Q13813

0.29

Q05513

42.41

Q07889

0.37

O14939

1.02

O00763

165

P25103

1872

The rank of the most central 30 nodes in the HO network, based on the six importance indices analyzed.

Table 5

Centrality ranks in the total network.

 

nD

 

nCC

 

nBC

 

TI3

 

TO30.01

 

TO30.005

P62993

30.23

P62993

46.11

P62993

36.84

P62993

278.83

P62993

8381

P62993

23017

P63104

19.10

P12931

44.07

P63104

25.10

P63104

201.37

P12931

6791

P63104

22032

P06241

15.81

P22681

43.81

P12931

13.34

P06241

118.70

P63104

6489

P12931

21112

P12931

14.28

P00533

43.04

P17252

13.07

P12931

101.69

P06241

5322

P49407

19777

P49407

10.00

P06241

42.16

P49407

12.99

P49407

98.76

P17252

2966

P06241

19449

P17252

9.96

Q13813

42.00

P06241

12.50

P62736

95.29

P28482

2240

P17252

19070

P62736

9.51

P17252

41.64

P62736

11.36

P17252

88.26

P07948

2084

P62736

17788

P28482

8.78

P63104

41.38

P02768

10.53

P02768

86.12

P62736

1818

P22681

13383

P27361

7.03

P00519

41.20

P28482

9.33

P28482

68.56

P49407

1784

P28482

13267

P02768

6.94

P21333

41.12

P22681

5.49

P27361

51.72

P27361

1734

P07948

12041

P22681

5.27

P28482

41.10

P27361

5.11

P22681

36.87

P22681

1628

P27361

8970

P07948

5.18

P07355

40.88

Q03135

4.51

Q03135

36.27

P05129

944

Q03135

8488

Q03135

4.37

P61978

40.67

P07948

3.22

P07948

35.12

P41240

677

P41240

7515

P41240

2.70

Q02156

40.28

Q5JY77

2.91

Q5JY77

24.85

P13500

559

P09471

4708

P05129

2.34

P29353

40.26

O14908

2.60

O14908

22.16

Q03135

552

O14908

4366

P05106

2.30

O14939

40.12

P09471

2.15

Q99962

18.30

P08254

525

Q5JY77

4319

Q5JY77

2.16

P62988

40.12

Q99962

2.01

P05106

18.15

P03956

511

P08588

4194

O14908

2.12

P43405

40.00

P05106

1.91

P09471

18.11

P05106

497

P05129

3488

Q99962

1.94

Q06124

39.99

P41240

1.45

P05129

17.16

P80075

444

P05106

3468

P09471

1.58

O43707

39.91

P00533

0.90

P41240

15.63

P80098

444

P04629

3288

P13500

0.90

P07900

39.86

P07550

0.89

Q14232

8.00

Q99616

438

P98164

3193

O14788

0.86

Q07889

39.82

Q9UBS5

0.83

P48745

6.63

P51681

435

P41143

2733

P80098

0.77

P49407

39.78

P05129

0.80

O14788

5.75

O14788

412

P35372

2721

Q14232

0.77

P06213

39.71

P18545

0.76

P13500

5.57

O00590

400

P07550

2551

P48745

0.59

P56945

39.58

P06396

0.75

P80098

5.25

P39900

400

P49795

2464

P00533

0.50

P06396

39.57

Q14232

0.74

P54646

5.07

P41597

400

P08648

2456

P80075

0.50

Q15746

39.50

P48745

0.73

P80075

2.48

P51677

400

Q08116

2452

P54646

0.45

Q05655

39.39

O14788

0.73

Q99616

2.38

Q9NPB9

400

P41594

2155

Q99616

0.45

P11142

39.31

P13500

0.71

P00533

2.12

P32246

373

P08069

1854

P29353

0.41

P35568

39.24

P61981

0.66

P29353

1.57

Q16570

373

P98082

1810

The rank of the most central 30 nodes in the total network, based on the six importance indices analyzed.

The betweenness ranks correspond quite well to the degree ranks with some exceptions. For example in the HD network, P06241 (Tyrosine-protein kinase Fyn) is three positions lower in betweenness ranking when compared to its degree rank position. In the HD network, instead of one, now five non-mediators are mixed with HD mediators in the top of the list, while some HD mediators such as Q99616 (C-C motif chemokine 13) lose their high degree-based rank completely. In contrast, the degree rank order seems to be consistent with its betweennes counterpart for HO and total networks.

Despite the large overlap between the HD and HO networks, the rank positions of HD and HO mediator proteins are quite different in the two networks. For example, both P17302 (Gap junction alpha-1 protein) and P43405 (Tyrosine-protein kinase SYK) rank high in the HD network but not in the HO network. As it is shown on Figure 2b, O14908 (PDZ domain-containing protein GIPC1) is the only protein among the three exclusive HO mediators that is part of the interaction network of HO mediators.

Additional File 2 shows the extracted GO terms of proteins ranked by different structural indices for the HD network, the HO network and the total network. For example, by considering the top 30 proteins ranked by degree in the HD network, we found that half of them are related to the processes 'intracellular signaling cascade' (GO:0007242) and 'protein amino acid phosphorylation' (GO:0006468), meanwhile in the HO network 16 of them are located in 'plasma membrane' (GO:0005886) and 13 of them are related to process 'cell surface receptor linked signal transduction' (GO:0007166).

The p-values of proteins quantify their average fit to the studied GO-terms (i.e. to what extent they can be characterized by certain functionality). By comparing those p-values to centrality and overlap indices used in this study, we can conclude that the performance of different indices vary strongly. In the total network, only the TO30.01 index correlates significantly with biological function (Table 6). Note that the performance of TO 3 t depends on the t threshold used. Proteins in unique positions are, thus, typically involved in the above-mentioned key functions. The other relatively well-performing index is CC, whereas D and BC correlate with function only once each. TI 3 and TO30.005 do not correlate with functions defined by GO terms. Furthermore, functional roles are best predictable by these structural indices in the HD network and less so for the HO network.
Table 6

Correlations between p-values and centrality.

 

nD

nCC

nBC

TI3

TO30.01

TO30.005

HD/D

0.2568

0.3077

0.2635

0.2577

0.3365

0.2321

HD/TI

0.2467

0.2216

0.2062

0.1677

0.4971

0.3017

HD/TO

0.3957

0.432

0.3993

0.3775

0.3803

0.3386

 

nD

nCC

nBC

TI3

TO30.01

TO30.005

HO/D

0.1501

0.144

0.1767

0.1925

0.1766

0.1487

HO/TI

0.0966

0.0007

0.0919

0.1127

0.1467

0.1254

HO/TO

0.3064

0.4051

0.3396

0.3211

0.3446

0.251

 

nD

nCC

nBC

TI3

TO30.01

TO30.005

total/D

0.2687

0.2605

0.2136

0.1853

0.4245

0.3045

total/TI

0.2687

0.2605

0.2136

0.1853

0.4245

0.3045

total/TO

0.3734

0.3692

0.3599

0.3372

0.4258

0.3092

The Spearman rank correlation coefficients between the p-values of GO terms calculated for the most central nodes according to particular indices in particular networks and the node centrality values of the nodes. Bold numbers mean p < 0.05.

Discussion

Based on its centrality ranks, P63104 (14-3-3 protein zeta/delta) corresponding to gene YWHAZ seems to be the second most important protein in these mediator processes. This is in concert with the literature, stating that P63104 is a chaperon [12] and is richly connected to several kinds of other molecules with mostly weak links [13]. Specifically, it is involved in cell growth and carcinogenesis [14], breast cancer reoccurrence after chemotherapy resistance [15], luteal sensitivity to PGF [16] and, finally, it is part of antiapoptotic (P13K/AKT) and cell proliferation (ERK/MAPK) pathways [17]. Its connecting position has been demonstrated by network analysis, showing its involvement in several HSNs (high-scoring subnetworks [18]). Ogihara et al. [19] suggested that the association with 14-3-3 protein may play a role in the regulation of insulin sensitivity by interrupting the association between the insulin receptor and IRS1. It means that P63104 probably mediate HD and HO through the regulation process of insulin (as insulin is a crucial hormone in human metabolic system). Typically it is not directly responsible for diseases (not assigned to any disease in the OMIM database) but very frequently mentioned as a candidate protein in the background, requiring further investigation [14].

The most important protein, P62993 (Growth factor receptor-bound protein 2) corresponding to gene GRB2 leads in all of the six structural importance ranks. It appears in the mammalian Grb2-Ras signaling pathway with SH2/SH3 domain interactions and several functions in embryogenesis and cancer [20]. Zhang et al. [21] also found that GRB2 is essential for cardiac hypertrophy and fibrosis in response to pressure overload and that different signaling pathways downstream of GRB2 regulate fibrosis, fetal gene induction, and cardiomyocyte growth. Yet, in the subgraph of the HO mediators, P62993 does not seem to occupy a central position but its phenotypic traits are likely to be affected through the links to non-mediators instead of other HO mediators. This kind of structural arrangement is advantageous for information integration, while a strongly connected mediator subnetwork implies functional redundancy.

Among the three exclusive HO (non-HD) mediators, O14908 (PDZ domain-containing protein GIPC1) corresponding to the gene GIPC1 appears in the HO mediator subgraph, while the other two are isolated (Q14232 - Translation initiation factor eIF-2B subunit alpha corresponding to gene EIF2B1; Q5JY77 - G-protein coupled receptor-associated sorting protein 1 corresponding to gene GPRASP1). This may suggest also that O14908 is an HD mediator. Its connection to heart disease is clear but its interaction with diabetes-related proteins is not documented in the OMIM databases (also not for the other two proteins). However, this inferred function is well supported by Klammt et al. [22] reporting on the role of O14908 in diabetes. A possible outcome of network analysis is to suggest potential updates in the databases.

The only protein that ranks higher than HD mediator proteins in the degree-based centrality rank of the HD network is P00533 (Epidermal growth factor receptor), corresponding to the gene EGFR. We could speculate that this protein might also mediate between H and D proteins. In the total PPI network, it is linked to two D proteins (Q9UQF2 - JNK-interacting protein 1; Q9UQQ2 - Signal transduction protein Lnk) but not to H protein. EGFR and its ligands are cell signaling molecules involved in a wide range of cellular functions, including cell proliferation, differentiation, motility, and tissue development [23]. Research on EGFR's pathogenesis have been focused on lung cancer [24] and have not discovered its link to heart diseases. However, Iwamoto and his colleagues observed the role of ErbB signaling in heart functions [25]. Also, it has been shown to be a central protein according to other sophisticated network analysis techniques [26], dominating the clique composition of certain pathways.

Based on our static, structural inference, it is not easy to decide whether a protein is „strongly linked to a disease" or it is a „disease protein". The definitions are very poor here. Is P00533 a H protein (causing heart diseases) or HD protein (mediating between H and D proteins)? The solution is to use inference for generating new hypotheses, improving databases and designing experiments, instead of regarding the inferred findings as results.

Conclusions

Our study focused on only a few diseases but the approach and the methods used can be generalized. It may be interesting to extend this research to other diseases and to study the pleiotropic effects of mediators linking other disease pairs. The mediator proteins analyzed in this study typically have pleiotropic effects. They connect several pathways and influence several phenotypic traits. The reason why their inferred structural roles miss from the OMIM database is exactly that they act in a non-Mendelian way. They are typically not the singular elements of important pathways but weak connectors among several pathways of high importance. This way, their effects can be fundamental. Their understanding needs a multi-locus, systems-based, network view. As individual pathways are linked to networks, our non-Mendelian knowledge on linkage, epistasis and pleiotropy becomes larger. If network analysis makes these epistatic and pleiotropic effects quantifiable and predictable, we are getting closer to better understand delegated complexity [27]. From an application perspective, it would be interesting to see whether a healthy (intact and well-connected) network of mediators could contribute to healthy phenotypes or, in contrary, disconnecting the mediator network could be used to isolate diseases and reduce side-effects of drugs.

Methods

Data

We have analyzed human protein-protein interaction network (PPI) data extracted from the I2D database. I2D (Interologous Interaction Database) is an on-line database of known and predicted mammalian and eukaryotic protein-protein interactions [28]. It is one of the most comprehensive sources of known and predicted eukaryotic PPIs.

We carefully considered the completeness of the PPI network by investigating various human PPI databases. In their database, the Authors have collected data from almost all of the well-known human protein interaction databases including HRPD http://www.hprd.org/, BIND http://bind.ca/, MINT http://mint.bio.uniroma2.it/mint/ and Intact http://www.ebi.ac.uk/intact/, among others. Those databases are built by arrange of methods, some are experimental ones, some are predicted ones, and some are curated from the literature. By using the I2D database, we could thus construct the network integrated from multiple data sources. We investigated other databases not included in the I2D database, particularly the STRING database http://string.embl.de/ and we found that almost all high-scoring interactions in STRING were covered in our data set. Combining data from various sources is supposed to be more comprehensive for analyzing the PPI network than studying each data source separately. To obtain a more reliable set of protein interactions, we excluded all the interactions obtained by homology methods: only experimentally verified ones were included in our analysis. For the disease phenotypes, the clinical Online Mendelian Inheritance in Man database (OMIM, [29]) was investigated. We have checked whether we need to update our database used in Nguyen and Jordán [6] and found that we can use the same data set as the number of updates is negligible.

Analysis

From the human PPI network data, we constructed: (1) a network of proteins mediating indirect effect between heart disease (H) and diabetes (D) proteins (i.e. HD mediators) and their direct neighbours (i.e. HD network); (2) a network of proteins mediating indirect effect between heart disease (H) and obesity (O) proteins (i.e. HO mediators) and their direct neighbours (i.e. HO network); and (3) an aggregated network of the two previous networks (i.e. total network). We considered only two-step mediator proteins, directly connected to two proteins related to different diseases and being otherwise unconnected (so, we do not consider chains of mediators). We have also studied the subnetworks of (1) and (2) without non-mediator neighbours. See Figure 1 for schematically illustrating the relationships between these five networks. Figure 2 shows the subnetworks without non-mediator neighbours (Figure 2a for HD and Figure 2b for HO). Figure 3a shows the HD and Figure 3b shows the HO network. The total network is shown in Figure 4.

Earlier we have determined the identity of these HD and HO mediators and quantified the strength of their mediator effect [6]. Here, we focus on the networks of mediators. Links in these networks are undirected (if protein i is linked to protein j, then j is also linked to i) and unweighted (we have no data for the intensity or strength of the interactions). We have characterized each network by some simple network statistics.

(i) The simplest index that provides the most local information about node i is its degree (D i ). This is the number of other nodes connected directly to node i. We have calculated the normalized degree:
n D i = D i N - 1 ,
(1)

where N is the number of nodes in the network.

(ii) A measure of positional importance quantifies how frequently a node i is on the shortest path between every pair of nodes j and k. This index is called "betweenness centrality" (BC i ) and it is used routinely in network analysis [30]. The normalized betweenness centrality index for a node i (nBC i ) is:
n B C i 2 × j < k g j k ( i ) g j k ( N - 1 ) ( N - 2 ) ,
(2)

where ij and k; g jk is the number of equally shortest paths between nodes j and k, and g jk (i) is the number of these shortest paths to which node i is incident (g jk may equal one). The denominator is twice the number of pairs of nodes without node i. This index thus measures how central a node is in the sense of being incident to many shortest paths in the network.

(iii) "Closeness centrality" (CC i ) is a measure quantifying how short are the minimal paths from a given node i to all others [30]. The normalized index for a node i (nCC i ) is:
n C C i = N - 1 j = 1 N d i j ,
(3)

where ij, and d ij is the length of the shortest path between nodes i and j in the network. This index thus measures how close a node is to others. The larger nCC i is for node i, the more directly its deletion will affect the majority of other nodes.

(iv) Topological importance can also be quantified by general matrix algebra. In an undirected network, we define a n,ij as the effect of j on i when i can be reached from j in n steps. The simplest way of calculating a n,ij is when n = 1 (i.e. the effect of j on i in 1 step):
a 1 , i , j = 1 D i ,
(4)
where D i is the degree of node i (i.e. the number of its direct neighbours). We assume that indirect effects are multiplicative and additive. For instance, we wish to determine the effect of j on i in 2 steps, and there are two such 2-step pathways from j to i: one is through k and the other is through h. The effects of j on i through k is defined as the product of two direct effects (i.e. a1,kj×a1,ik), therefore the term multiplicative. Similarly, the effect of j on i through h equals to a1,hj,1×a1,ih. To determine the 2-step effect of j on i (a2,ij), we simply sum up those two individual 2-step effects:
a 2 , i j = a 1 , k j a 1 , i k + a 1 , h j a 1 , i h ,
(5)
and therefore the term additive. When the effect of step n is considered, we define the effect received by node i from all other nodes in the same network as:
ψ n , i = j = 1 N a n , i j ,
(6)
which is equal to 1 (i.e. each node is affected by the same unit effect.). Furthermore, we define the n-step effect originated from node i as:
σ n , i = j = 1 N a n , j i ,
(7)
which may vary among different nodes (i.e. effects originated from different nodes may be different). Here, we define the topological importance of node i when effects "up to" n step are considered as:
T I i n = m = 1 n σ m , i n = m = 1 n j = 1 N a m , j i n ,
(8)

which is simply the sum of effects originated from node i up to n steps (one plus two plus three...up to n) averaged over by the maximum number of steps considered (i.e. n). This TI n index measures the positional importance of a node by considering how effects originated from such a given node can spread through the whole network to reach all nodes after a pre-defined n step length [31]. Calculations were performed by the CosBiLAB Graph software [32].

(v) Basically every node in a network is connected to each other, but it still matters how strongly they are connected (whether two nodes are neighbors in the network, second neighbors or more distant ones). Thus, it is of interest to study the indirect neighborhood of particular nodes, considering more than only the neighbors but less than the whole network. For a given step length n and a given network, there is an interaction matrix presenting the relative strengths of interactions between each pair of nodes i and j. We note that interaction strength is used here in a totally structural sense, with no dynamical component. If n exceeds 2 or 3, and the network is not very large, then there is non-zero interaction strength between each pair of nodes (everything is connected to everything else). Thus, an effect threshold (t) can be set, determining the "effective range" of the interaction structure of a given graph node i, and nodes within this effective range are defined as strong interactors of i (i.e. effects received from i being greater than t) whereas nodes outside this range are defined as i's weak interactors (effects received from i is less than t). Since the sets of strong interactors of two or more nodes may overlap, it is possible to quantify this overlap (the number of shared strong interactors) in order to measure the positional uniqueness of individual graph nodes. The topological overlap between nodes i and j up to n steps (TO n t, ij ) is the number of strong interactors appearing in both i's and j's effective ranges determined by the threshold t. The sum of all TO-values between node i and others provides the summed topological overlap of node i:
T O t , i n = j = 1 N T O t , i j n ( i j ) .
(9)

For simplicity of representation, we drop the subscript i for all indices. A more detailed description of this index can be found in [33]. Calculations were performed by the CosBiLAB Graph software [32]. Two thresholds have been used, t1 = 0.01 and t2 = 0.005.

Each of the six above mentioned structural indices were determined for every node in the networks. The 30 most central ones are presented for the HD network (Table 3), the HO network (Table 4) and the total network (Table 5). Additional File 2 presents all index values for all nodes in these networks.

Since different network indices provide different rankings, it is a question of how similar these rankings are. Similarity refers to robust importance ranks (irrespective to the index), while dissimilarity refers to the complementary information content of the different indices. For statistical analysis, we calculated the Spearman rank correlation coefficient for each pair of the indices in the three major networks (Table 1).

In order to better understand the ranking of nodal indices, we determined the distribution of each structural index for each network. We present these distributions for the total network in Figure 5. To test the significance of the observed rank correlation coefficients, we have constructed random networks. For each of our observed networks (i.e. HD, HO, total), we calculated the probability of two nodes being linked together:
p = L ( N 2 - N ) 2 .
(10)

We have constructed 1000 random networks with fixed N and a p link probability. For each random network, we calculated the same centrality indices and determined the Spearman rank correlation coefficient for each pair of centrality indices. Since we have 1000 random networks, for each pair of centrality indices we thus have 1000 Spearman rank correlation coefficients. From their distribution, we determined the mean and the 95% confidence intervals. Results are summarized in Table 2.

For the top 30 nodes ranked by a particular index in a particular network, we quantified their biological function by calculating the p-values of GO terms [34]. Specifically, we determined the ratio of the top 30 nodes that can be characterized by a certain GO term and computed the associated p-values (Table 6). Bold numbers mean p < 0.05.

Abbreviations

OMIM: 

Online Mendelian Inheritance in Man

PPI: 

protein-protein interaction

D: 

degree

BC: 

betweenness centrality

CC: 

closeness centrality

TI: 

topological importance

TO: 

topological overlap

GO: 

gene ontology

Declarations

Authors’ Affiliations

(1)
The Microsoft Research - University of Trento, Centre for Computational and Systems Biology
(2)
Institute of Statistical Science, Academia Sinica

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© Nguyen et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.