Open access

Wnt Signaling Network in Homo Sapiens

Written By

Bahar Nalbantoglu, Saliha Durmuş Tekir and Kutlu Ö. Ülgen

Submitted: 11 February 2011 Published: 25 January 2012

DOI: 10.5772/26792

Chapter metrics overview

3,061 Chapter Downloads

View Full Metrics

1. Introduction

Signaling is a part of system communication among living cells by processing biological information that governs basic cellular activities and coordinates cell actions. The signals are transmitted by means of signaling molecules, and this process ends up with altering of gene transcription in the nucleus resulting in many cellular processes such as differentiation and proliferation. Systems biology research helps us understand the underlying structure of cell signaling networks and how changes in these networks affect the transmission and flow of information. Here we thoroughly investigate Wntsignaling, a major signaling system known to have role in regulating processes such as embryonic development and growth, cell differentiation, proliferation, migration and polarity.

There are two major types of Wntsignaling (Figure 1) classified as canonical (Wnt/β-catenin) and non-canonical (planar cell polarity (PCP) and the Wnt-calcium (Wnt/Ca2+)) pathways (Katoh, 2005, Nusse, 2005, DasGupta, 2005). In canonical Wntsignaling, the

Figure 1.

Regulation of β-catenin stability by Wnt signaling.

absence of the Wnt ligand, the signal transduction is at non-operating state (off), the protein complex that involves APC and AXIN proteins facilitates the phosphorylation of β-catenin by glycogen synthase kinase (GSK3β). Then β-catenin binds to a protein called β−TrCP (beta-transducin repeat containing protein) that mediates the ubiquitination of CTNNB1 and the proteins tagged with ubiquitin are degraded by the proteosome that results in β-catenin destruction by the proteosomal degradation. When Wnt proteins bind to Frizzled receptors, this receptor activation works counter to destruction complex (APC-AXIN-GSK3β) by preventing β-catenin phosphorylation via Dishvelled protein. Then β–catenin accumulates and enters into the nucleus where it binds to a DNA binding protein of the TCF/LEF family and then activates new gene expression programs (Cadigan, 2008).

The illustration (Figure 1) depicts a cell in the absence (A) and in the presence (B) of Wnt protein. In unstimulated cells, β-catenin not complexed with the cadherin adhesion complex is phosphorylated by CKI and GSK3β, leading to β-TrCP dependent ubiquitination and proteosomal degradation. The presence of Wnt, on the hand, promotes LRP (lipoprotein receptor-related protein), cell-surface coreceptor, and Fz association, leading to recruitment of Dvl to the complex, followed by GSK3β and CKI casein kinase phosphorylation of LRP. This stabilizes recruitment of AXIN to the receptor, which in turn may disrupt the activity of the destruction complex, and hence allows accumulation of β-catenin and nuclear translocation (Cadigan, 2008). An overview of types of Wntsignaling in different species is illustrated in Figure 2.

Figure 2.

An overview of Wnt signaling pathways:(a)Wnt/ β-catenin (b) Wnt/calcium (c) Wnt/Planar cell polarity in different species (KEGG: Kyoto Encyclopedia of Genes and Genomes;

The number of non-canonical pathways is unclear (Veemanet al., 2003,Eisenmann, 2005, Pandur, 2005). A Wnt signalling pathway that increases intracellular Ca2+ levels has been proposed as Wnt/Ca2+ pathway in vertebrates (Kuhlet al., 2000). There is also a Wnt/JNK (Wnt/c-Jun N terminal kinase) pathway in vertebrates that controls cell polarity (Veemanet al., 2003, Pandur, 2005). A new Wnt signalling pathway called Wnt-5A/Ror2 signalling has been proposed in X. leavis(Schambony and Wedlich, 2007).In flies, the Wnt/PCP pathway (Figure 2c), which controls planar cell polarity, is the best-known non-canonical Wnt signalling pathway so far (Veemanet al., 2003, Eisenmann, 2005). Processes such as P2/EMS signalling, T cell polarity, Z1/Z4 polarity and B cell polarity utilize non-canonical Wnt signalling in C. elegans. Furthermore, a non-canonical pathway, a PCP like pathway, has been proposed to control the polarity of B cells (Wu and Herman, 2006). Recently a novel non-canonical Wnt pathway, which is also β-catenin independent, in vertebrate and fly has been proposed to act in neuronal specification (Hingwinget al., 2009).

Wnt-planar cell polarity (PCP) and Wnt-calcium(Ca2+) pathways that are independent of β-catenin are considered the two most well-recognized non-canonical pathways in Wntsignaling. The activation of kinase C (PKC), Jun kinase (JNK), RHOA and nemo-like kinase (NLK) mediates the Wnt-planar cell polarity (PCP) pathway which is related to tissue polarity control and cell movement. The Wnt/PCP signaling pathway is mostly characterized in D. Melanogaster. Frizzled, Van Gogh, Stan/Flamingo, Dishevelled, Prickle and Diego proteins are the major proteins of DrosophiliaWnt/PCP signaling. Diego activates the pathway with binding to Dishevelled whereas Prickle prevents this association and negatively regulates the pathway. The tissue polarity is established by asymmetrical localization of FZD-Dsh-Diego-Stan complex and Vang-Prickle complex. The main proteins of the human Wnt/PCP pathway are mostly the homologs of theDrosophilia proteins. VANGL1, VANGL2 (Van Gogh homologs), DVL1, DVL2, DVL3 (Dishevelled homologs), PRICKLE1, PRICKLE2 (Prickle homologs) and ANKRD6 (Diego homolog) are the core proteins having essential roles in human Wnt/PCP signaling cascade. In Wnt/PCP signaling, Wnt ligands (WNT5A, WNT5B and WNT11) first bind to Frizzled receptors (FZD3, FZD6). Then, the association of Prickle, Vangl and Dishevelled proteins result in RHOA activation following the binding of Dishevelled proteins to Daam proteins which are implicated in actin-cytoskeleton re-organization. Moreover, by Dvl-dependent manner, the JNK cascade is activated by the association of dishevelled proteins with MAPK3. On the other hand, NLK signaling cascade is activated by Dvl-independent manner in which the Ca2+ release by FZD proteins activates MAP3K7 (TAK1). NLK protein is known to be an activator of Wnt/PCP signaling as well as the inhibitor of canonical Wnt pathway (Katoh, 2005, Katoh 2003, Wu and Maniatis, 1999).

Calcium has been implicated as an important messenger in Wnt pathway and recent studies showed that higher frequencies of calcium transients were associated with faster rates of outgrowth (Veemanet al., 2003, Pandur 2005, Cadigan, 2008). WNT5a also activates the Wnt/Ca2+signaling. The binding of Wnts to Frizzled receptors leads to an activation of heterotrimeric G-proteins and subsequent activation of phospholipase C by the G-protein beta/gamma dimer. This enzyme cleaves phosphatidylinositol-4,5-bisphosphate (PIP2) into inositol-1,4,5-trisphosphate (IP3) and diacylglycerol (DAG). IP3 is released from the membrane, and binds to the IP3 receptor which subsequently releases calcium ions from intracellular stores. This locally restricted calcium release subsequently activates calcium-sensitive proteins like protein kinase C (PKC), calcium-calmodulin dependent kinase II

(CamKII), and/or calcineurin (CaCN). IP3 is then degraded to inositol by means of specific phosphatases and recycled to PIP2 (Kuhl et al., 2000, Kohn and Moon, 2005, Kuhl, 2004, Slusarskia et al., 1997). Activated CaCN dephosphorylates NF-AT (nuclear factor of activated T-cells), which then translocates to nucleus to regulate gene expression. Studies performed for Xenopus showed that this signaling is active in the development of ventral cell fate which inhibits Wnt/β-catenin pathway (Saneyoshi et al., 2002, Veeman et al., 2003, Pandur, 2005). In addition to that, recent studies reported that there is a crosstalk between canonical and non-canonical Wntsignaling through GSK3b since it can phosphorylate NF-AT (Beals et al., 2003, Ohteki et al., 2000). The Wnt/PCP and Wnt/Calcium signaling pathways have overlaps since both of them are mediated by calcium release, and Dishevelled proteins play essential roles in activation of these cascades.

The deregulations and mutations in this signaling pathway cause several human diseases including lung, breast, colon and colorectal cancers. Recent works have demonstrated that Wnt-activated excessive β-catenin accumulation in nucleus plays an important role in tumour formation. On the other hand, one of the β-catenin independent signalling pathways; the Wnt/Ca2+ signalling pathway, is proposed to antagonize Wnt/β-catenin signalling pathway. Therefore, Wnt/Ca2+ cascade may have the ability to act as a tumour suppressor. However, still much more work has to be done on this subject as the mechanism of this antagonism is not well known. (Wang and Malbon, 2003, Giles et al., 2003, Veemanet al., 2003, Kohn and Moon, 2005, Slusarski and Pelegri, 2007, Maieseet al., 2008).

Investigating Wntsignaling is therefore attractive for researchers to identify the suitable drug targets for therapeutic intervention in cancer treatment (Cadigan and Liu, 2006, Cong et al., 2004, Widelitz, 2005 , Nusse, 2005). This signaling pathway has been studied and analyzed using different species such as worm (Caenorhabditiselegans), fly (Drosophila melanogaster), frog (Xenopusleavis) etc. in order to enucleate the governing mechanisms through essential components (DasGupta, 2005, Kuhl et al., 2000, Kohn and Moon, 2005). However, the whole Wnt mechanism is not well understood in human due to the restrictions on experiments with human, and more attention should be devoted to this organism.

In this study, the reconstruction of Wntsignaling network was computationally performed for Homo Sapiens via integration of interactome data and Gene Ontology annotations. The graph theoretical analysis was then performed for analyzing the topological properties of the network proteins. Moreover, the linear paths in which the signal is transferred from ligands (input) to transcription factors (output) were identified in order to follow signal transmittal as well as to identify the specific proteins of canonical and non-canonical Wnt pathways. Furthermore, the crosstalk analysis was applied in order to detect the significant bridging proteins in all these pathways. Finally, the proteins which might be targets of drugs against diseases involving Wnt pathways are indicated and these proteins are compared with the ones reported in literature in terms of topological properties and their roles in canonical and non-canonical Wnt pathways.

This work is aimed to give an insight on the role of Wnt signalling in maintaining the homeostasis as well as reacting to cellular stress. Understanding the molecular basis underlying the ability of Wnt proteins to perform antagonistic or similar signalling activities would eventually lead us to new ideas about how to suppress cancer cells in human metabolism.


2. Materials and methods

2.1. Network reconstruction by GO annotations

The reconstruction of Wntsignaling network in human was performed by integrating Gene Ontology (GO) annotations and PPI data. First of all, the core proteins that are known to have roles in Wntsignaling in Homo Sapienswere obtained from literature. Then an annotation collection table was prepared by using GO annotations (biological process, cellular component and molecular function) of the core proteins. All the GO annotations of all human proteins were downloaded from GO website ( by October 2010. Next, the human proteins whose all three GO terms match with those in the annotation collection table were included into the network. Thereafter, the physical protein-protein interaction data among these human proteins were extracted from MINT (Ceolet al., 2009) HPRD (Prasad et al., 2009) and BioGRID (Stark et al., 2010) by January 2011. Finally, the interaction partners of the proteins that passed the GO annotation filter were obtained and these protein pairs were used to reconstruct the network (Figure 3).

Figure 3.

The schematic representation of the algorithm used for the network reconstruction.

2.2. Graph theoretical analysis

Graph theoretical analysis enables better understanding of the structure of the complex networks and the distribution of the components via topological properties. The topology of the reconstructed network was determined by Network Analyzer Plug-in of Cytoscape (ver. 2.7.0). In the network analysis, the proteins are nodes whereas the interactions between the nodes are edges. The input of the calculations is the binary protein interactions and the output is the topological parameters such as degree distribution of the nodes, number of highly connected nodes (hubs), network diameter, mean path length, clustering coefficient, number of shortest paths and average shortest path length.

The network diameter is the maximum node eccentricity. The eccentricity here can be defined as the maximum non-infinite length of a shortest path from a node to another node in the network. The average shortest path length (or the characteristic path length) is the expected distance between two connected nodes. The betweenness centrality is another topological aspect and it is defined as the number of shortest paths going through a node. The proteins with high betweenness centrality values are called as bottleneck proteins. Another parameter, clustering coefficient of a node N can be defined as the ratio of the number of edges between the neighbors of N to the maximum number of edges that could possibly exist between the neighbors of N. The network clustering coefficient is the average of the clustering coefficients for all nodes in the network. This value is always between 0 and 1.

2.3. Module detection and analysis

In order to get an insight on the cellular role of the network, a Cytoscape plug-in, MCODE (1.31) was used to identify these highly interconnected regions in the present protein-protein interaction network. After the detection of the modules, the statistically overrepresented Gene Ontology functions of the proteins in the modules are obtained by the Biological Networks Gene Ontology tool (BINGO 2.44). BiNGO combines the relevant GO annotations and relates them upwards through the GO hierarchy. The output of this analysis gives the most significant GO annotations with the lowest P-value (Maereet al., 2005).

2.4. Network decomposition analysis

In network decomposition analysis, the protein interaction networks are decomposed into linear paths in which ligands are inputs and transcription factors are outputs (Tekir et al., 2008). This analysis enables us to investigate the network communication route via linear paths. The linear paths of the reconstructed network were found by the NetSearch program (Steffen et al., 2002). The participation frequencies of the proteins were calculated at a specified length for determining the essentiality of the proteins in the network. Next, crosstalk analysis, in which the network crosstalk value of a node is obtained by the difference in degree of the node in all considered networks and the maximum degree of this node in any individual pathway, was performed to detect the components that connect the pathways in signal transduction. A high path crosstalk value implies that a node is more important in the combined network than it was in the individual pathways (Zielinski et al., 2009).


3. Results

In the present study, the protein-protein interaction sub-networks of Wntsignaling in Homo sapiens were reconstructed using the system biology approach in which many types of biological data and analysis techniques are integrated. Also, the topological analyses of these sub-networks were performed.

3.1. Reconstruction of Wnt signaling networks

In the first step, the proteins that are known to be involved in human Wntsignaling were identified through literature search. These selected Wnt proteins that belong to both canonical and non-canonical Wnt pathways in human were taken as the core proteins of the sub-networks to be constructed (Tables 1 and 2). The algorithm explained in section 2.1 was followed during the reconstruction process.

For canonical Wnt pathway 68 core proteins were identified. For PCP and Ca++ pathways these numbers were 33 and 32, respectively. For canonical pathway, 10592 physical protein interactions, which were obtained from MINT, BioGRID and HPRD databases, were accepted to the network. For PCP network 5928 protein-protein interactions and for Ca++ network 6080 protein-protein interactions were accepted to these sub-networks.

After removing the isolated smaller parts a network of 3251 nodes and 9304 edges was obtained for Wnt/β-catenin pathway; 1952 nodes and 5001 edges were obtainedfor PCP pathway; 2112 nodes and 5293 edges were obtainedfor Wnt/calcium pathway.

Figure 4.

Venn Schema Representation of Common Nodes.

Finally, the sub-networks were integrated and the whole Wnt network was obtained with 3489 nodes and 10092 edges. The common proteins of each sub-network were identified (Figure 4) and among them the proteins such as dishvelled (DVL1, DVL2, DVL3), AXIN (AXIN 1, AXIN 2), casein kinase (CSNK1A1, CSNK1D, CSNK1E, CSNK2A1, CSNK2B), β-catenin (CTNB1), frizzled (FZD1, FZD2, FZD4, FZD7, FZD8, FZD9), adenomatous polyposis coli protein (APC), glycogen synthase kinase-3 beta (GSK3β), mitogen-activated protein kinase (MAP3K2, MAP3K4, MAP3K7, MAP3K8), nuclear factor of activated T-cells (NFATC1, NFATC2), Smad family (SMAD 1, SMAD 2, SMAD 3, SMAD 4, SMAD 5, SMAD 6, SMAD 7, SMAD 9), transcription factor 7-like 2 (TCF7L2), cellular tumor antigen p53 (TP53), protein kinase C inhibitor protein 1 (YWHAZ) are found to be related to diseases like cancer, Alzheimer’s, diabetes andosteoarthritis (Table 3).

General Name(Canonical) Uniprot ID
Wnt1 P04628 SENP2 Q9HC62
Wnt2 P09544 DKK1 O94907
Wnt2B NKD2
Wnt3 P56703 NKD1 Q969G9
Wnt3A P56704 CXXC4 Q9H2H0
Wnt4 P56705 SKP1 P63208
Wnt7A O00755 CUL1 Q13616
Wnt10B O00744 NLK Q9UBE8
FZD2 Q14332 SMAD4 Q13485
FZD4 Q9ULV1 SMAD3 P84022
FZD5 Q13467 CTBP1 Q13363
FZD7 O75084 CTBP2 P56545
LRP5 O75197 MAP3K7 O43318
LRP6 O75581 LEF1 Q9UJU2
DVL1 O14640 TCF7 P36402
DVL2 O14641 TCF7L1 Q9HCS4
DVL3 Q92997 BTRC Q9Y297
FRAT2 O75474 EP300 Q09472
GSK3B P49841 FBXW11 Q9UKB1
AXIN1 O15169 PSEN1 P49768
APC2 O95996 PORCN Q9H237
APC P25054 CER1 O95813
PPP2CA P67775 SFRP1 Q8N474
CSNK1E P49674 SOX17 Q9H6I2
CSNK2A2 P19784 CHD8
CSNK2B P67870 TBL1X O60907
Wnt1 P04628 SENP2 Q9HC62

Table 1.

Core proteins of canonical Wnt signaling pathway

General Name (PCP) Uniprot ID General Name (Ca++)-Uniprot ID
Wnt5A P41221 Wnt5A P41221
Wnt5B Q9H1J7 Wnt11 O96014
Wnt11 O96014 Wnt1 P04628
FZD2 Q14332 PLCB2 Q00722
FZD6 O60353 PLCB3 Q01970
MAGI3 PLCB4 Q15147
ROR2 CAMK2B Q13554
PTK7 CAMK2D Q13557
DVL1 O14640 PPP3R1 P63098
DVL2 O14641 PPP3R2 Q96LZ3
DVL3 Q92997 CHP2 O43745
NKD1 Q969G9 PRKCG P05129
NKD2 NFAT5 O94916
ROCK1 Q13464 FZD2 Q14332
ROCK2 O75116 FZD3 Q9NPG1
RAC1 P63000 FZD4 Q9ULV1
RAC2 P15153 FZD6 O60353
MAPK9 P45984
MAPK10 P53779
Wnt5A P41221 Wnt5A P41221

Table 2.

Core proteins of non-canonical Wnt signaling pathway

1.1.1 Protein 1.1.2 Disease 1.1.3 References
β-catenin Carcinogenesis, hepatocellular carcinomas Wilms’ tumors Klaus and Birchmeier,2008;Maiti et al.,2000
DVL Lung cancer Yang et al.,2010
FZDs Gastric cancer,colorectal cancer& carcinogenesis Kirikoshi, Sekihara and M. Katoh,2001;Ueno et al,2008
APC Colorectal cancer, carcinogenesis Klaus andBirchmeier, 2008; Ueno et al, 2008
KC1AL Alzheimer Disease Li,Yin and Kuret,2004
YWHAZ Breast cancer, Obesity, Diabetes Peng, Wang and Shan, 2009
sFRP(s) colon cancer, mesothelioma, bladder cancer Tan and Kelsey, 2009; Paul and Dey, 2008; Gehrke, Gandhirajan and Kreuzer, 2009.
GSK-3β colorectal cancer Ge and Wang, 2010
Smad3 Osteoarthritis Valdes et al, 2010

Table 3.

The common proteins found to be related to diseases.

3.2. Graph theoretical analysis

In order to gain insight on the characteristics of canonical and noncanonical pathways of Wntsignaling, the mean degree (average number of interactions per protein), clustering coefficients (normalized number of interactions between neighbors of each protein), mean path lengths, network diameters (longest path between any two nodes), power-law distribution exponents (γ), and centrality values were estimated using Network Analyzer. The degree distribution of each sub-network have scale-free topology and approximates a power law model (𝑃(𝑘)≅𝑘−𝛾) with few nodes having high degree (hub proteins) and the others having low degree (Table 4). The network diameter value indicates the speed of signal flow. The diameters are 14, 13, and 15 for Wntβ-catenin, PCP and calcium signaling networks, respectively. The network diameter of the whole Wntsignaling network in which these three sub-networks are integrated, is found to be 15. The network diameter and the shortest path length distribution indicate small-world properties of the analyzed network. In addition to that, the average (mean) connectivity values are 5.72, 5.12 and 5.01 for β-catenin, PCP and calcium pathways. The topological properties of the present networks are consistent with many networks reported in literature (Table 4).

The hubs of the canonical pathway are obtained as KC1AL (Casein kinase I isoform alpha-like), YWHAZ (Protein kinase C inhibitor protein 1) and TBL1XR1 (F-box-like/WD repeat-containing protein). Casein kinase-1-alpha forms β-catenin destruction complex when connected to the proteins of APC, β-catenin and glycogen synthase kinase-3-beta (GSK3-β) (Faux et al., 2008). KC1AL has interactions with the core proteins, AXIN1, AXIN2, CSNK1A1, CSNK1D and CSNK1E (String database). TBL1XR1, also a core protein of canonical Wntsignaling, is involved in signal transduction and cytoskeletal assembly and plays an essential role in transcription activation mediated by nuclear receptors and has effects on cytotypic differentiation. Besides, low levels of TBL1XR1 gene expression cause

Model Number of Nodes Number of Interactions Power Law exponent(γ) Mean Path Length Network Diameter Reference
(H. Sapiens)
3251 9304 1.78 4.46 14 Present work
(H. Sapiens)
1952 5001 1.80 4.61 13 Present work
(H. Sapiens)
2112 5293 1.68 4.56 15 Present work
Wnt (whole)
(H. Sapiens)
3489 10092 1.75 4.40 15 Present work
Wnt/β-catenin (D.melanogaster) 656 1253 1.78 4.80 13 Toku et al., 2010
Hedgehog (D.melanogaster) 568 975 1.75 4.80 14 Toku et al., 2010
(Oda et., 2005)
329 1795 1.86 4.70 11 Tekir et al., 2009
(S. cerevisiae)
1388 4640 1.76 6.81 9 Arga et al., 2007
2638 4030 - 4.80 14 Wu et al., 2005
(H. Sapiens)
3097 11064 1.68 4.10 13 Özbayraktar, 2011
Insulin_glucose transporting
(H. Sapiens)
498 2887 1.53 2.9 5 Tekir et al., 2010
(H. Sapiens)
1826 10078 1.49 3.57 11 Tiveci et al., 2011

Table 4.

Graph theoretical properties of the protein interaction networks.The hubs of the Wnt/Ca2+ pathway are PRKCB (Protein kinase C beta type), PRKCA (Protein kinase C alpha type) and also YWHAZ (Protein kinase C inhibitor protein 1). Protein kinase C (PKC) is a family of serine- and threonine-specific protein kinases that can be activated by calcium and second messenger diacylglycerol. PKC family members phosphorylate a wide variety of protein targets and are known to be involved in diverse cellular signaling pathways. PRKCA also binds to RHOA which is another core protein in Wnt/PCP signaling. PRKCB, calcium-activated and phospholipid-dependent serine/threonine-protein kinase, is involved in various processes such as regulation of the B-cell receptor (BCR) signalosome, apoptosis and transcription regulation and it has an interation with the core protein, dishevelled 2 (DVL2) and the common hub protein YWHAZ. These hub proteins were also detected as the bottleneck proteins of the networks, due to their high betweenness centrality values. The topological properties of the hubs are listed in Table 5.

breast cancer (Kadota et al., 2009). YWHAZ (14-3-3 protein zeta/delta /Protein kinase C inhibitor protein 1), which is a member of highly conserved 14.3.3 proteins that are involved in many vital cellular processes such as metabolism, protein trafficking, signal transduction, apoptosis and cell cycle regulation, is a key component in both canonical and non-canonical Wntsignaling. In addition to its interaction with canonical pathway core protein of CSNK1A1, YWHAZ also has interactions with core proteins of NFATC2, NFATC4 and MAPK8 of non-canonical Wntsignaling. YWHAZ protein is the common hub and also a bottleneck protein in all reconstructed Wntsignaling sub-networks. YWHAZ contributes to chemotherapy resistance and recurrence of breast cancer (Ralhan et al., 2008).

Model Uniprot
ID (Name)
Betweenness Centrality Closeness Centrality Clustering Coefficient Degree Average Shortest Path Length
Wnt/Canonical Q8N752 (KC1AL) 0.168 0.356 0.0060 241 2.817
0.124 0.350 0.0071 189 2.855
0.052 0.289 0.0071 107 3.464
Wnt/PCP P63104
0.182 0.351 0.0094 133 2.850
Wnt/Ca2+ P17252
0.160 0.353 0.0122 129 2.830
0.136 0.343 0.0099 125 2.917
0.135 0.334 0.0074 149 2.997

Table 5.

Topological properties of bottleneck proteins in human Wnt signaling.


3.3 Module detection and analysis

Scale-free networks are known to be composed of clustered regions and in biological networks these clustered regions correspond to molecular complexes named as modules (Bader and Houge, 2003). The canonical pathway was clustered into 75 complexes. Many of the proteins in the modules have roles in binding, catalytic activity and transcriptional regulation. The modules with significant molecular functions directly related to Wntsignaling were then detected by GO enrichment analysis. Some examples are as follows: The proteins in one module of Wnt/β-catenin (canonical) pathway were enriched in Wnt protein binding. NADH dehydrogenase (ubiquinone) activity was dominant in another module. In Wnt/Planar Cell Polarity (PCP) sub-network, a module showed potassium channel activity. The proteins of a module in Wnt/Ca2+subnetwork were enriched in calcium ion binding.

The information obtained by module analysis such as finding of proteins behaving functionally similar in modules enabled us to confirm the present Wntsignaling network reconstructed using an integrated approach of interactomics and GO annotations.

3.4. Network decomposition analysis

The linear paths in the reconstructed Wntsignaling network as a whole and those in each canonical and noncanonicalWnt pathway were determined via NetSearch algorithm (Steffen et al. 2002) in order to examine the signal transmittal steps. In this algorithm, the membrane (ligand) proteins were set as input whereas the transcription factors were set as output components (Table 6) of Wntsignaling network in Homo Sapiens.

In the Wntsignaling network as a whole, the shortest path length is found to be 4, which includes 5 proteins connected by 4 linear interactions for two linear paths from Wnt3A to LEF1 (Table 7). The path length is increased in order to cover all the proteins in the network. However, a maximum number of 12 steps that has 1 086 956 linear paths in which only 59 (50%) of 118 core proteins and 1244 (34%) of 3676 proteins are covered, is achieved due to computer capacity. The linear paths were found to reach to LEF1 (Q9UJU2) in canonical subnetwork and NFATC1 (O95644), NFATC2 (Q13469), NFATC3 (Q12968) in noncanonicalsubnetwork.

Input Protein (Uniprot_ID) Protein Name Output Protein
Protein Name
P04628 Wnt1 O94916 NFAT5
P09544 Wnt2 O95644 NFATC1
Q93097 Wnt2B Q13469 NFATC2
P56703 Wnt3 Q12968 NFATC3
P56704 Wnt3A Q14934 NFATC4
P56705 Wnt4 Q9UJU2 LEF1
P41221 Wnt5A P36402 TCF7
Q9H1J7 Wnt5B Q9HCS4 TF7L1
O00755 Wnt7A
O00744 Wnt10B
O96014 Wnt11

Table 6.

Input and output proteins of the linear paths.

Path Length Input Protein Output
4 P56704
(Wnt 3A)
(Wnt 3A)

Table 7.

The linear paths at path length of 5.

3.4.1. Canonical vs non-canonical Wnt pathways

Network decomposition analysis was performed for canonical and non-canonical Wnt pathways separately. A maximum number of 12 steps that has 815627 linear paths, in which only 33 of 68 core proteins (42%) and 1115 of 3251 proteins (32%) are participated, can be obtained for canonical pathway. The number of linear paths at 12 steps is found to be 29082 for non-canonical pathway, in which 546 of 2547 nodes and only one core protein of 60 core proteins are covered. It has 1098373 linear paths at 14 steps, and 27 of 60 core proteins (48%) and 817 of 2547 proteins (34%) are covered. This result seems to be logical since the diameter of the non-canonical pathway is found to be larger than that of canonical pathway, which implies that the signal transfer is slower in non-canonical pathway. A minimum number of 4 steps (5 proteins) was necessary to reach the end transcription factor in canonical pathway whereas the signal has to pass at least 7 proteins in case of non-canonical pathways such as PCP or Wnt/Ca2+ signaling. In general, the information flow preferring short routes is faster in canonical pathways.

3.4.2. Participation of proteins in linear paths

For identification of the significant proteins in the whole Wnt network, the percentages of each protein contributing to linear paths were calculated (Table 8) and the proteins having participation percentages higher than 20 are discussed below. T cell specific transcription factor 1-alpha (LEF1) has the highest percentage since it is one of the output proteins. WNT7A and WNT1 are the input proteins. These three proteins (WNT7A, WNT1 and LEF1)

Protein Name Recommended Name Canonical/
Participation in linear paths (%) Degree
Q9UJU2 LEF1 T cell-specific transcription factor 1-alpha Canonical 56.19 17
O00755 WNT7A Protein Wnt-7A Canonical 51.91 2
O00144 FZD9 Frizzled-9 Canonical/PCP/Ca2+ 51.91 4
Q99750 MDFI MyoDfamilyinhibitor Canonical/PCP/Ca2+ 50.94 50
P04628 WNT1 Proto-oncogene Wnt-1 Canonical/Ca2+ 47.20 10
Q9HD26 GOPC Golgi-associated PDZ andcoiled-coil motif-containing protein Canonical/PCP 46.89 18
Q9H461 FZD8 Frizzled-8 Canonical/PCP/Ca2+ 46.87 4
P33992 MCM5 DNA replicationlicensingfactor MCM5 Canonical/Ca2+ 42.94 6
Q14566 MCM6 DNA replicationlicensingfactor MCM6 Canonical/Ca2+ 38.83 28
Q15797 SMAD1 SMAD familymember 1 Canonical/PCP/Ca2+ 29.23 60
P28070 PSB4 Proteasomesubunit beta type-4 Canonical 28.75 19

Table 8.

Proteins with the highest participation percentages in Wnt signaling pathway.

are also the core proteins of the canonical Wntsignaling sub-network and they bind to essential proteins, which are common to many paths in the network. Frizzled 9 (FZD9), which is a receptor for Wnt proteins, is common to all three sub-networks of Wntsignaling. It leads to the activation of dishevelled proteins, inhibition of GSK-3β kinase, nuclear accumulation of β-catenin and activation of Wnt target genes. It was hypothesized that FZD9 may be involved in transduction and intercellular transmission of polarity information during tissue morphogenesis and/or in differentiated tissues ( Another protein common to all three Wnt sub-networks is MyoD family inhibitor protein (MDFI), which regulates the transcriptional activity of TCF7L1/TCF3 by direct interaction to it, and it prevents TCF7L1/TCF3 from binding to DNA. The DNA replication licensing factor proteins (MCM5 and MCM6) have interaction with each other and MCM5 also binds to MDFI and β-catenin, which is an essential protein for Wntsignaling pathway. Besides that, SMAD1-OAZ1-PSMB4 complex mediates the degradation of the CREBBP/EP300 repressor SNIP1.

When the proteins with low participation percentages in linear paths are evaluated according to the criteria of low betweenness and high closeness centrality values, four proteins (LRSAM1, MLTK, MARK1 and miyosin 9) seem to be important for consideration as putative drug targets (either by activation or inhibition) and need further examination (Table 9).

Name Protein
Name Protein _ID Name Protein _ID Name
Input Protein O00755 Wnt7A P04628 Wnt1 P04628 Wnt1 P04628 Wnt1
O00144 FZD9 Q9H461 FZD8 Q9H461 FZD8 Q9H461 FZD8
Q12906 ILF3 P13569 CFTR P13569 CFTR P13569 CFTR
Q8N752 KC1AL P08670 VIME P08670 VIME P08670 VIME
Q9UQM7 CAMK2A O43353 RIPK2 Q12873 CHD3 O43353 RIPK2
Q13554 CAMK2B P05771 PRKCB Q14974 IMB1 P05771 PRKCB
P48443 RXRG Q9P0L2 MARK1 Q00722 PLCB2 Q9NYL2 ZAK
Q6UWE0 LRSAM1 P31947 SFN Q96QT4 TRPM7 P31947 SFN
Q99816 TS101 P63104 YWHAZ P35579 MHY9 P63104 YWHAZ
Q13464 ROCK1 P30291 WEE1 P19838 NFKB1 P30291 WEE1
Q15796 SMAD2 P84022 SMAD3 P17252 PRKCA P84022 SMAD3
Output Protein Q9UJU2 LEF1 Q9UJU2 LEF1 O95644 NFAC1 Q9UJU2 LEF1
12 12 12 12

Table 9.

Linear paths of lowest participant proteins.

LRSAM1 (leucine rich repeat and sterile alpha motif containing1), also called RIFLE and TAL (TSG101-associated ligase), is an E3 type ubiquitin ligase. TSG101 itself is a tumor suppressor gene, which has a role in maturation of human immunodeficiency virus, and LRSAM1 is implicated in its metabolism directly by polyubiquitination (Guernsey et al., 2010). The functional disruption of TSG101 led both to cellular transformation and to tumors that metastasized spontaneously in nude mice (Li and Cohen, 1996). In addition to that, although genomic alterations in TSG101 are rare in human cancer, functional inactivation of the gene enhances metastatic growth of murine fibroblasts (Li and Cohen 1996). Another protein is ZAK (MLTK - Q9NYL2) which inhibits human lung cancer cell growth via ERK and JNK activation in an AP-1-dependent manner (Yang et al., 2010). Also, overexpression of ZAK results in apoptosis (OMIM).

Another protein is serine/threonine-protein kinase MARK1. Cellular studies showed that overexpression of MARK1 resulted in shorter dendrite length and decreased transport speed. MARK1 overexpression in individuals with autism may underlie subtle changes in synaptic plasticity linked to dendritic trafficking (Maussion et al., 2008; OMIM). The last protein is miyosin9. Fechtner syndrome, which is an autosomal dominant disorder characterized by the triad of thrombocytopenia, giant platelets, and Dohle body-like inclusions in peripheral blood leukocytes, with the additional features of nephritis, hearing loss, and eye abnormalities, mostly cataracts, is caused by heterozygous mutation in the gene encoding nonmuscle myosin heavy chain-9 (MYH9; 160775) on chromosome 22q11 (Peterson et al., 1985; OMIM). ZAK and MARK1 both bind to SFN which has interaction with YWHAZ. YWHAZ is found to be hub and bottleneck protein in these reconstructed canonical and noncanonicalWnt pathways due to its high degree and betweenness centrality value, respectively. YWHAZ also has a low participation percentage of 0.95 in linear paths. YWHAZ is found to be a key mediator protein in various diseases involving various types of cancers, heart diseases, obesity, diabetes and autism (Nguyen and Jordá, 2010). Key mediators are proteins that bind to significant proteins (mostly hubs) and so they can be chosen as the drug targets.

3.4.3. Specific proteins in linear paths

The proteins in the linear paths ending at transcription factors specific to canonical and noncanonical pathways were further examined in detail. The proteins, which participate in the linear paths leading to one transcription factor only, are called specific proteins of that particular pathway.

286 specific proteins were obtained where 262 of them belong to canonical (transcription factor LEF1) and 24 of them belong to non-canonical pathway (transcription factor NFATC). They were then investigated according to their topological properties such as lower betweenness centrality, higher closeness centrality and higher clustering coefficient than the average for drug target identification. As a result, 51 proteins (48 canonical, 3 noncanonical) meet these criteria. Among 51 proteins 4 proteins in canonical pathway seem to be important since they are either related to important diseases or connected to significant proteins in the network. These proteins are Myc proto-oncogene protein (MYC), TGF-beta receptor type-2 (TGFR2), cyclin-dependent kinase inhibitor 3 (CDKN3) and F-box-like/WD repeat-containing protein TBL1X (canonical).

MYC is a protein that participates in the regulation of gene transcription. The mutations and overexpressions seen in MYC resulted in cell proliferation and consequently formation of cancer. The translocations such as t (8:14) are the reasons of the development of Burkitt's lymphoma. Souceket al., 2008 demonstrated that the temporary inhibition of MYC selectively killed lung cancer cells in mouse, making it a potential drug target in cancer (Gearhart et al., 2007, Souceket al., 2008). TGFR2 is the receptor protein of TGF-beta and also known to be involved in tumor suppression. It forms receptor complexes with serine/threonine protein kinases and has role in activation of SMAD transcriptional regulators. The mutations and defects seen in this protein are associated with Lynch sendrome, Loeys-Deitz aortic aneurysm syndrome, Osler-Weber-Rendu syndrome, hereditary non-polyposis colorectal cancer type 6 (HNPCC6) and esophageal cancer (Tanaka et al., 2000, Lu et al., 1998).

TBL1X is a protein that plays an essential role in transcription activation mediated by nuclear receptors. Besides, it is a component of E3 ubiquitin ligase complex containing UBE2D1, SIAH1, CACYBP/SIP, SKP1, APC and TBL1X proteins. It has interactions with essential proteins of Wntsignaling such as APC and β-catenin and it is also a core protein of reconstructed canonical Wntsignaling pathway (Matsuzawa and Reed, 2001). CDKN3 is a member of cyclin-dependent kinases (CDKs) which have roles in regulating cell cycle, transcription, mRNA processing, and differentiation of nerve cells (Gyuriset al., 1993). The overexpression and defects seen in this protein leads to prostate cancer and hepatocellular carcinoma (HCC) (Yehet al., 2003, Lee et al., 2000).

These specific proteins except TBL1X are related to cancer and they are suitable for drug target applications according to their topological properties. Hence, they need more attention with further experimental investigation.

3.4.4. Crosstalk of proteins in Wntsub-networks

Signaling networks are communicating systems and they interact with each other rather than behaving in isolation. If a node has a high network crosstalk value, which is definedas the difference in degree of the node in all considered networks and the maximum degree of this node in any individual pathway, it means that this component is a branch node connecting two or more pathways. The network crosstalk analysis indicated 239 proteins that are found to be common among Wnt sub-networks.

One of the highest crosstalk values belongs to YWHAZ protein (Table 10). This is rational since this protein was obtained as the hub and bottleneck protein of all canonical and non-canonical Wnt pathways. Besides, DVL2 has a significant crosstalk value. Dishevelled proteins also have high participation in the subnetworks since they interact with the core proteins such as frizzled receptors and GSK3β in Wnt/β-catenin sub-network, and with frizzled receptors and DAAM1 in Wnt/PCP sub-network. Smad proteins also have considerable crosstalk value since they have interactions with AXIN, beta-catenin and LEF1 proteins. PRKCA, which was found as hub and core protein in Wnt/calcium sub-network, has a non-zero crosstalk value. AXIN protein is also a significant protein that has participation in β-catenin destruction complex with APC, GSK3β and CKIα. Detecting these connector proteins by network crosstalk analysis is a promoter step for further experimental studies towards cancer treatment. However, further elaboration on the crosstalk mechanism is difficult due to the fact that the reconstructed networks are undirected.

Proteins Network crosstalk values
YWHAZ Hub-Core protein (all sub-networks) 11
DVL2 Core protein (β-catenin and Wnt/ PCP sub-networks) 11
CAMK2A Core protein (Wnt/Ca2+ sub-network) 4
SMAD3-4 Core proteins (β-catenin subnetwork) 4
GSK3B Core protein (β-catenin sub-network) 2
PRKCA Hub-Core protein (Wnt/ Ca2+subnetwork) 2
RAC1 Core protein (Wnt/PCP sub-network) 2
NFATC2 Core protein (Wnt/Ca2+subnetwork) 1
AXIN1 Core protein (β-catenin sub-network) 1

Table 10.

Proteins and network crosstalk values


4. Discussion

4.1. Wnt signaling in maintaining homeostasis and managing cellular stress

Homeostasis, balance of cellular processes, is an important phenomenon since cells are the factories that maintain the intracellular environment and keep the conditions stable. Therefore, it is essential for cells to maintain homeostasis for the organism to remain healthy. Wntsignaling, being related to embryonic development, generation of cell polarity and specification of cell death, is highly effective in maintaining homeostasis in adults (Peifer and Polakis, 2000). In canonical Wnt pathway, for example, the stabilization of β-catenin plays an essential role in cellular homeostasis. In the absence of Wnt ligands, a destruction complex is formed by AXIN, APC, GSK-3β and β-catenin, that results in β-catenin phosphorylation by GSK-3β followed by ubiquitination and degradation that keeps β-catenin level low in cytoplasm. Wnt ligands, on the other hand, enhance the β-catenin accumulation via inhibition of GSK-3β by dishevelled proteins and free β-catenin is transferred into the nucleus where it interacts with transcription factors. Therefore, AXIN, APC and GSK-3β proteins are significant players for homeostasis.

The mutations seen in AXIN result in hepatocellular carcinoma, which implies that, it has a multi-objective position in tumorigenesis and embryonic axis formation. It is also reported that the main role of AXIN, beside controlling β-catenin level, is to down-regulate cell growth and help sustain cellular homeostasis (Zhang et al., 2001). AXIN is known to be is a “switch” protein for JNK and Wntsignaling pathways. It binds to MEKK1 and activates JNK signaling. MEKK1 is related to microtubule cytoskeletal stress and apoptosis. During JNK activation, AXIN-MEKK1-APC-β-catenin complex transduces the cytoskeletal stress signals for apoptosis (Yujiri et al., 1999, Zhang et al., 2001).

4.2. Wnt/Ca2+-Wnt/β-catenin antagonistic mechanism in H. Sapiens

The non-canonical Wnt signalling pathways do not signal through β-catenin and they can antagonize the functions of canonical Wnt pathway (Mc Donald and Silver, 2009). Wnt5a is known to activate non-canonical signalling via cGMP(cylic guanosine-3’5’-monophosphate) that actives protein kinase G. This leads to an increase in the cellular concentration of Ca2+ and this Ca2+ increase triggers activation of calcium sensitive proteins. Wnt5a also inhibits the activation of canonical signalling via activation of NFAT which is mediated by activation of PLC (phospolipase C). PLC increases the calcium level that results in activation of CaCN (calcineurin) which activates NFAT.

Wnt/Ca2+ signalling pathway can inhibit Wnt/β-catenin pathway in two different ways: CACN-NFAT branch and CAMKII-TAK1-NLK branch (Figure 5). CACN-NFAT branch for inhibiting β-catenin function is mediated by PLC activation, which involves the β/γ subunits of heterotrimeric G-proteins leaving its α unit behind. PLC activation generates diacylglycerol (DAG) and inositol-1,4,5-trisphosphate (IP3) which eventually increases Ca+2 concentration in the cell. The calcium increase sets off the CaCN activation that results in dephosphorylation of NFAT (nuclear factor of activated T-cells). NFAT then translocates to nucleus to regulate gene expression. This CaCN-NFAT activated way inhibiting the canonical Wnt signalling pathway is covered in our reconstructed network (Saneyoshi et al., 2002, Veeman et al., 2003, Pandur, 2005). Moreover, the reconstructed network successfully

Figure 5.

General representation of Wnt signaling pathway in vertebrates.

covers CAMKII-TAK1-NLK branch, which is known to inhibit Wnt/β-catenin signalling pathway. As it is mentioned above, the PLC activation results in calcium release. The increase seen in Ca2+ level may trigger activation of another calcium sensitive protein; Ca2+-calmodulin-dependent protein kinase II (CamKII) which further activates TGF-β activated kinase 1 (TAK1). TAK1 then stimulates nemo-like kinase (NLK), which has a role in TCF phosphorylation. Afterwards, the phosphorylation of TCF inhibits TCF/β−catenin complex (Kuhl et al., 2000, Pandur, 2005).

Besides stimulating non-canonical signaling and inhibiting canonical signalling through CamkII mentioned above, Wnt-5a can also inhibit the activation of canonical signalling through ROR2 signalling pathway that stimulates TAK1-NLK pathway in turn. ROR2 receptor also actives the actin binding protein Filamin A and JNK pathway (Mc Donald and Silver, 2009).

As a consequence it can be said that Wnt5a exhibits tumor suppressor activity through inhibiting the activation of canonical Wnt signalling. Recent research showed that, in HTC116 and HT-29 colon cancer cell lines, the activation of β-catenin-mediated transcription is reduced by Wnt5a (Macleod et al., 2007, Ying et al., 2008). Additionally, the reconstructed network provides a chance to investigate the antagonism between Wnt/Ca2+ and Wnt/β-catenin signalling pathways. Although the static nature of the network cannot directly explain the interaction characteristics between these pathways, a dynamic model can enlighten the antagonism between Wnt/Ca2+ and Wnt/β-catenin pathways.

4.3. Potential drug targets in the reconstructed Wnt signaling networks

Wntsignaling pathways regulate many cellular processes such as proliferation, migration and differentiation in embryonic development and maintenance of homeostasis in matured tissues. The deregulations and mutations in Wntsignaling pathway are known to result in cancer. Unfortunately, there is no selective inhibitor for the deficiencies in Wntsignaling. That is why targeting key components, such as SFRPs, WIF-1, DKK-1, APC, AXIN2, ICAT, LEF1 and β-catenin, of the Wntsignaling seems to be reasonable in cancer treatment (Aguilera et al., 2007).

The topological parameters such as centrality values or participation percentages in linear paths are important criteria in identification of putative target proteins. Therefore, the nodes that have lower average shortest path length, higher clustering coefficient, higher closeness centrality, lower betweenness centrality and higher participation percentages than the average values are further investigated (Table 11).

In our reconstructed networks, FZD9, WNT7A and LEF 1 proteins are found to be essential due to their high participation percentages in linear path analysis. Albers et al. (2011) show that the Wnt receptor Frizzled-9 (FZD-9) can be a new potential target for the treatment of osteoporosis by promoting bone formation. Also, it is known that the re-expression of WNT7A and signaling through FZD9 are associated with increased differentiation and used in the lung cancer treatment (Winn et al., 2005). Frizzled proteins are the receptors for Wnt ligand, and they are structurally similar to G protein-coupled receptors (GPCRs) which are targets of more than 50% of chemically applicable drugs (Yanaga and Sasaguri, 2007). So targeting frizzled proteins seems to be logical in cancer treatment. In addition to that, β-catenin has a connectivity value of 40 and participation percentage of 4.36%. β-cateninis

Uniprot ID Name Average
Path Length
Closeness Centrality Clustering Coefficient Degree Participation Percentage
P35222 β-catenin 3.42 0.292 40
Q9UJU2 LEF1 3.58 0.279 17
P49841 GSK3b 3.42 0.292 15
P25054 APC 3.66 0.273 4
Q8N474 SFRP1 6.49 0.154 0 4 -
O94907 DKK-1 4.22 0.237 0 2 -
Q9Y2T1 AXIN2 4.63 0.216 3
Q14332 fzd2 4.57 0.219 0 2 -
Q9NSA3 ICAT 4.41 0.227 0 2
Q9Y5W5 WIF-1 6.49 0 0.154 0 1 -
Q6UWE0 LRSAM1 4.18 0.239 0 5
Q9P0L2 MARK1 3.90 0.256 0 2
Q9NYL2 ZAK 3.90 0.256 0 3
P35579 MHY9 3.58 0.280 10
O00144 FZD9 4.80 0.208 0 4
O00755 WNT7A 5.57 0.179 0 2
Q8N752 KC1AL 2.83 0.354 241
P63104 YWHAZ 2.83 0.353 200
Q9BZK7 TBL1XR1 3.46 0.289 107
Q16667 CNKD3 4.31 0 0.232 2
P37173 TGFR2 4.31 0.232 5
P01106 MYC 4.14 0.242 5
Average 4.40 0.232 5.8

Table 11.

The topological values of the target proteins

encoded by an oncogene and has functions in the maintenance of epithelial cell layers by regulating cell growth and adhesion between cells. β-catenin also anchors the actincytoskeleton (Peifer and Polakis, 2000, Zhang et al., 2001).Luu et al. (2004) suggested that targeting β-catenin could be a rational approach in cancer treatment.

In the present Wnt network, there are two essential proteins (AXIN2 and APC) that have higher clustering coefficient values than the average and it is known that essential proteins tend to be more cliquish within the interaction network (Yu et al., 2004, Estrada E., 2006). Ranking proteins according to their centrality measures can additionally be useful in selecting possible drug targets. Consequently, GSK3β and APC can be seen as potential drug targets in Wntsignaling for having higher closeness centrality value than the average. APC is related with colorectal cancer and APC-activating mutations are very common in colorectal cancer (Estrada E., 2006, Garber, 2009, Yanaga and Sasaguri, 2007).

Moreover, the betweenness centrality and bridging centrality (nodes between modules and connecting clusters defined by the ratio of the number of interactions of a neighboring node over the number of remaining edges) are also effective in identifying the drug targets due to their position in communication (Hopkins, 2008, Hwang et al., 2008). In order to prevent side effects and high lethality, the essential nodes with lower betweenness centrality values are chosen as drug targets on the purpose of not affecting the neighbors of the targeted protein. It is seen that APC, DKK1, AXIN2, FZD2, Wnt7A, ICAT and WIF1 are consistent with this fact (Table 11). SFRP1 protein needs special attention since its loss causes breast cancer (Klopocki et al., 2004).

It is further seen that the nodes which have low participation percentages as well as low degrees (LRSAM1, MARK1, ZAK, MHY9), the nodes which are defined as specific proteins (CNKD3, TGFR2 and MYC) and the nodes which are detected as hub proteins (YWHAZ, TBL1XR1, KC1AL) have the quality of conformance since they have lower average shortest path length and higher closeness centrality values than the average. These proteins can also be suggested as potential drug targets and more attention should be given through experimental analysis.

The gene expression data (microarray data) belonging to these proteins are within reach via several database sources. However, due to the disease heterogeneity, the expression level of a gene /protein can be up-regulated as well as down-regulated in cancer and the expression type may also differ according to the cancer type. Hence, it is difficult to obtain a right answer for the expression level of a gene/protein in diseases like cancer.


5. Conclusion

Recently, the evolutionarily conserved signaling pathways which are involved in embryonic development are on the march for many researches since the deregulations seen in the mechanism of these pathways results in several diseases, especially in cancer. Hence, interaction networks have begun to be appreciated because it may be useful to understand the general principles of biological systems by means of systems biology. Wntsignaling is a major signaling pathway which has important roles in embryonic development of many species. Hence, in this study, Wntsignaling pathway is investigated with the aim of getting an insight on the role of Wnt signalling in maintaining homeostasis as well as managing cellular stress, understanding the molecular basis underlying the ability of Wnt proteins to perform antagonistic or similar signalling activities and identifying the suitable drug targets for therapeutic intervention in cancer treatment.

The reconstruction of Wntsignaling network was performed for Homo sapiens via integration of interactome data and Gene Ontology annotations. The reconstruction process was applied to both canonical (Wnt/β-catenin) and non-canonical Wntsignaling pathways (Wnt/planar cell polarity; Wnt/calcium). The reconstructed whole Wntsignaling network contains 3489 nodes and 10092 interactions. AXIN, APC and GSK-3β proteins are found to be significant players for homeostasis. Moreover, AXIN-MEKK1-APC-β-catenin complex is important in transducing the cytoskeletal stress signals leading to apoptosis.

The ligand Wnt5a has dual role; it activates non-canonical signalling and also inhibits the activation of canonical signalling through a calcium dependent mechanism. This antagonism between noncanonicalWnt/Ca2+ and canonical Wnt/β-catenin signalling pathways is successfully covered in our reconstructed network. CNKD3, TGFR2 and MYC, which are the specific proteins in linear paths leading to specific transcription factors in canonical pathway, are proposed as potential drug targets for cancer. The reconstructed large-scale protein-protein interaction network of Wnt signaling in H. sapiens will allow system biologist to see the global picture and guide them in designing experiments. For further research, experimental and clinical studies can be carried out for the validation of the proposed drug targets leading to design novel drugs.



The financial support for this research was provided by the Research Funds of Boğaziçi University and TÜBİTAK through projects 5554D and 110M428, respectively. The scholarship for SalihaDurmuşTekir, sponsored by TÜBİTAK, is gratefully acknowledged.


  1. 1. Aguilera O. Muñoz A. Esteller M. Fraga M. F. 2007 “Epigenetic alterations of the Wnt/beta-catenin pathway in human disease”, EndocrMetab Immune Disord Drug Targets, 7 1), 13 21 .
  2. 2. Apweiler R. Bairoch A. Wu C. H. Barker W. C. Boeckmann B. Ferro S. Gasteiger E. Huang H. Lopez R. Magrane M. Martin M. J. Natale D. A. O’Donovan C. Redaschi N. Yeh L. S. 2004 “UniProt: the Universal Protein knowledgebase”, Nucleic Acids Res., ; 32 D115 D119 .
  3. 3. Arga K. Y. Önsan Z. I. Kırdar B. Ülgen K. Ö. Nielsen 2007 “Understanding Signaling in Yeast: Insights From Network Analysis”, Biotechnology and Bioengineering, 97 5 1246 1258 .
  4. 4. Beals C. R. Sheridan C. M. Turck C. W. et al. 1997 “Nuclear export of NF-ATc enhanced by glycogen synthase kinase 3”, Science, 275 1930 1934 .
  5. 5. Bateman A. Coin L. Durbin R. Finn R. D. Hollich V. Griffiths-Jones S. Khanna A. Marshall M. Moxon S. Sonnhammer E. L. Studholme D. J. Yeats C. Eddy S. R. 2004 "The Pfam protein families database", Nucleic Acids Research, 32 D138 D141 .
  6. 6. Cadigan K. M. Liu Y. I. 2006 “Wnt signaling: complexity at the surface”, J. Cell. Sci., 119 395 402 .
  7. 7. Cadigan K. 2008 "Wnt-β-catenin signaling", Curr. Biol., 18 R943-947.
  8. 8. Cong F. Schweizer L.and. Varmus H. 2004 “Wnt signals across the plasma membrane to activate the beta-catenin pathway by forming oligomers containing its receptors, Frizzled and LRP”, Development, 131 5103 5115 .
  9. 9. Das Gupta. R. 2005 “Functional Genomic Analysis of the Wnt-Wingless Signaling Pathway”, Science, 308 826
  10. 10. Eisenmann D. M. 2005 “Wnt signaling”, WormBook, 1 17 .
  11. 11. Estrada E. 2006 “Virtual identification of essential proteins within the protein interaction network of yeast”, Proteomics, 6 35 40 .
  12. 12. Garber K. 2009 “Drugging the Wnt Pathway: Problems And Progress”, Journal of the National Cancer Institute, 101 8 548 550 .
  13. 13. Ge X. Wang X. 2010 “Role of Wnt canonical pathway in hematological malignancies”, Journal of Hematology& Oncology, 3 33
  14. 14. Gearhart J. Pashos E. E. Prasad M. K. 2007 “PluripotencyRedeux-- advances in stem-cell research”, N Engl J Med, 357 15), 1469 1472
  15. 15. Gehrke I. Gandhirajan R. K. and Kreuzer. 2009 “Targeting the Wnt/β- catenin/TCF/LEF1 axis in solid and haematological cancers: Multiplicity of therapeutic options”, Eur J Cancer, 45 16 2759 2767 .
  16. 16. Giles R. H. van Es J. H. Clevers H. 2003 “Caught up in a Wnt storm: Wnt signaling in cancer”, BiochimBiophysActa, 1653 1 24 .
  17. 17. Guernsey D. L. Jiang H. Bedard K. et al. 2010 “ Mutation in the Gene Encoding Ubiquitin Ligase LRSAM1 in Patients with Charcot-Marie-Tooth Disease”, PLoS Genet., 6 8
  18. 18. Gyuris J. Golemis E. Chertkov H. Brent R. 1993 “Cdi1, a human G1 and S phase protein phosphatase that associates with Cdk2”, Cell, 75 791 803 .
  19. 19. Hingwing K. Lee S. Nykilchuk L. Walston T. Hardin J. Hawkins N. 2009 “CWN-1 functions with DSH-2 to regulate C. elegans asymmetric neuroblast division in a β- catenin independent Wnt pathway”, DevBiol, 328 246 256 .
  20. 20. Hopkins A. L. 2008 “Network pharmacology: the next paradigm in drug discovery”, Nature Chemical Biology, 4 682 690 .
  21. 21. Hwang W. C. Zhang A.and. Ramanathan M. 2008 “Identification of information flow modulating drug targets: a novel bridging paradigm for drug discovery”, Clinical Pharmacology & Therapeutics, 84 563 572 .
  22. 22. Kadota M. Sato M. Duncan B. Ooshima A. et al. 2009 “Identification of novel gene amplifications in breast cancer and coexistence of gene amplification with an activating mutation of PIK3CA”, Cancer Res., 69 18 7357 7365 .
  23. 23. Klaus A. Birchmeier W. 2008 “Wntsignaling and its impact on development and cancer”, Nat Rev Cancer, 8 378 398 .
  24. 24. Kanehisa M. Goto S. 2000 “KEGG: kyotoencyclopedia of genes and genomes”, Nucleic Acids Res., 28 1 27 30 .
  25. 25. Katoh M. 2005 “WNT/PCP signaling pathway and human cancer”, Oncol Rep., 14 6 1583 1588 .
  26. 26. Katoh M. Katoh M. 2003 “ Identification and characterization of human PRICKLE1 and PRICKLE2 genes as well as mouse Prickle1 and Prickle2 genes homologous to Drosophila tissue polarity gene prickle”, Int J Mol Med, 11 249 256 .
  27. 27. Klaus A. Birchmeier W. 2008 “Wnt signaling and its impact on development and cancer”, Nat Rev Cancer, 8 378 398 .
  28. 28. Klopocki E. Kristiansen G. Wild P. J. Klaman I. Castanos-Velez E. Singer G. Stöhr R. Simon R. Sauter G. Leibiger H. Essers L. Weber B. Hermann K. Rosenthal A. Hartmann A. Dahl E. 2004 “Loss of SFRP1 is associated with breast cancer progression and poor prognosis in early stage tumors”, Int J Oncol., 25 3), 641 649 .Kohn,
  29. 29. Kohn A. D. Moon R. T. 2005 “Wnt and calcium signaling: beta-cateninindependent pathways”, Cell Calcium, 38 439 446 .
  30. 30. Kirikoshi H. Sekihara H. Katoh M. 2001 “Expression profiles of 10 members of Frizzled gene family in human gastric cancer”, Int J Oncol., 19 4 767 771 .
  31. 31. Kuhl M. 2004 “The Wnt/Calcium Pathway: Biochemical Mediators, Tools and Future requirements”, Frontiers in Bioscience, 9 967 974 .
  32. 32. Kuhl M. Sheldahl L. C. Park M. Miller J. R. Moon R. T. 2000 “The Wnt/Ca2+ pathway: a new vertebrate Wnt signaling pathway takes shape”, Trends Genet, 16 279 283 .
  33. 33. Li G. Yin H. Kuret J. 2004 “Casein Kinase 1 Delta Phosphorylates Tau and Disrupts Its Binding to Microtubules”, Journal of Biological Chemistry, 279 15938 15945 .
  34. 34. Li L. Cohen S. N. 1996 “Tsg101: A novel tumor susceptibility gene isolated by controlled homozygous functional knockout of allelic loci in mammalian cells”, Cell, 85 319 329 .
  35. 35. Lu S.L. Kawabata M. Imamura T. Akiyama Y. Nomizu T. Miyazono K. Yuasa Y. 1998 “HNPCC associated with germline mutation in the TGF-beta type II receptor gene”, Nature Genet,, 19 17 18
  36. 36. Luu H. H. Zhang R. Haydon R. C. et al. 2004 “Wnt/beta-catenin signalling pathway as a novel cancer drug target”, Curr Cancer Drug Targets, 4 653 671 .
  37. 37. Macleod R. J. Hayes M. Pacheo I. 2007 “Wnt5a secretion stimulated by the extracellular calcium-sensing receptor inhibits defective Wnt signalling in colon cancer cells”, Am J PhysiolGastrintest Liver Physiol, 293 G403-411.
  38. 38. Maere S. Heymans K. Kuiper M. 2005 “BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks”, Bioinformatics, 21 3448 3449 .
  39. 39. Maiese K. Li F. Chong Z. Z. Shang Y. C. 2008 “The Wnt signaling pathway: aging gracefully as a protectionist?”,PharmacolTher, 118 58 81 .
  40. 40. Maiti S. Alam R. Amos C. I. Huff V. 2000 “Frequent Association of beta-Catenin and WNT1 Mutations in Wilms Tumors”, Cancer Res, 60 6288 6292 .
  41. 41. Matsuzawa S. I. Reed J. C. 2001 “Siah-1, SIP, and Ebi collaborate in a novel pathway for beta-catenin degradation linked to p53 responses”, Mol Cell., 7 5), 915 926
  42. 42. Maussion G. et al. 2008 “Convergent evidence identifying MAP/microtubule affinity-regulating kinase 1 (MARK1) as a susceptibility gene for autism”, Hum. Mol. Genet., 17 2541 2551 .
  43. 43. Mcdonald S. Silver A. 2009 “Wnt5a: opposing roles in cancer”, British J. Cancer, 101 209 214 .
  44. 44. Nguyen T. P. Jordán F. 2010 “A quantitative approach to study indirect effects among disease proteins in the human protein interaction network”, BMC Syst Biol., 4 103
  45. 45. Nusse R. 2005 “ Wnt signaling in disease and in development”, Cell Research, 15 28 32 .
  46. 46. Otheki T. et al. 2000 “Negative regulation of T-cell proliferation and interleukin 2production by the serine threonine kinase Gsk-3”, J. Exp. Med., 192 99 104 .
  47. 47. Özbayraktar B. 2011 “From Yeast to Human: Unraveling Sphingolipid Metabolism through Macroscopic and Microscopic Analyses”, PhD Thesis, BoğaziçiÜniversitesi, İstanbul.
  48. 48. Pandur P. 2005 “Recent discoveries in vertebrate non-canonical Wnt signaling: Towards a Wnt signaling network”, Advances in DevBiol, 14 91 106 .
  49. 49. Paul S. Dey A. 2008 “Wnt Signaling and Cancer Development: Therapeutic Implication”, Neoplasma, 55 165 176 .
  50. 50. Peifer M. Polakis P. 2000 Wnt signaling in oncogenesis and embryogenesis-a look outside the nucleus”, Science, 287 1606-1609.
  51. 51. Peng Z. Wang H. Shan C. 2009 “Expression of ubiquitin and cullin-1 and its clinicopathological significance in benign and malignant lesions of the lung”, Zhong Nan Da XueXueBao Yi Xue Ban., 34 3 204 209 .
  52. 52. Peterson L. C. Rao K. V. Crosson J. al. 1985 “ Fechtner syndrome: a variant of Alport’s syndrome with leukocyte inclusions and macrothrombocytopenia”, Blood, 65 397 406 .
  53. 53. Prasad T. S. K. Goel R. Kandasamy K. et al. 2009 “Human Protein Reference Database--2009 update”,Nucleic Acids Res., 37 D767 D772 .
  54. 54. Ralhan R. De Souza L. V. Matta A. Tripathi S. C. Ghanny S. Gupta S. D. Bahadur S. Siu K. W. M. 2008 “Discovery and Verification of Head-and-neck Cancer Biomarkers by Differential Protein Expression Analysis Using iTRAQLabeling, Multidimensional Liquid Chromatography, and Tandem Mass Spectrometry”, Molecular and Cellular Proteomics, 7 1162 1173 .
  55. 55. Saneyoshi T. Kume S. Amasaki Y. Mikoshiba K. 2002 “The Wnt/calcium pathway activates NF-AT and promotes ventral cell fate in Xenopus embryos”, Nature, 417 295 299 .
  56. 56. Schambony A. Wedlich D. 2007 “Wnt-5A/Ror2 regulate expression of XPAPC through an alternative noncanonical signaling pathway”, Dev Cell, 12 779 792 .
  57. 57. Slusarski D. C. Pelegri F. 2007 “Calcium signaling in vertebrate embryonic patterning and morphogenesis”, DevBiol, 307 1 13 .
  58. 58. Slusarskia D. C. Yang-Snyderb J. Busaa W. B. Moon R. T. 1997 “Modulation of Embryonic Intracellular Ca2+ Signaling by Wnt-5A”, Developmental Biology, 182 114 120 .
  59. 59. Soucek L. Whitfield J. Martins C. P. Finch A. J. Murphy D. J. Sodir N. M. Karnezis A. N. Swigart L. B. Nasi S. Evan G. I. 2008 “ModellingMyc inhibition as a cancer therapy”, Nature,455 7213), 679 683 .
  60. 60. Stark C. Breitkreutz B. J. Chatr-Aryamontri A. et al. 2010 “The BioGRID Interaction Database: 2011 update”, Nucleic Acids Res., 39 D698-704.
  61. 61. Steffen M. Petti A. Aach J. D’haeseleer P. Church G. 2002 “Automated modelling of signal transduction networks”, BMC Bioinformatics, 3 34 1471 2105 .
  62. 62. Tan S. G. Kelsey K. T. 2009 “Epigenetic Inactivation of SFRP1 and SFRP2 Genes as Biomarkers of Invasive Bladder Cancer”, Honors Thesis, Brown University.
  63. 63. Tanaka S. Mori M. Mafune K. Ohno S. Sugimachi K. 2000 “A dominant negative mutation of transforming growth factor-beta receptor type II gene in microsatellite stable oesophageal carcinoma”, Brit. J. Cancer, 82 1557 1560
  64. 64. Tekir S. D. Ümit P. Toku A. E. Ülgen K. Ö. 2010 “Reconstruction of Protein-Protein Interaction Network of Insulin Signaling in Homo Sapiens”, Journal of Biomedicine and Biotechnology, 2010 Article ID 690925.
  65. 65. Tekir S. D. Arga K. Y. Ülgen K. Ö. 2009 “Drug targets for tumorigenesis: Insights from structural analysis of EGFR signaling network”, Journal of Biomedical Informatics, 42 228 236 .
  66. 66. Tiveci S. 2011 “CaSignaling in Multiple Species”, PhD Thesis, BogaziçiÜniversitesi, Istanbul.
  67. 67. Toku A. E. 2010 “Investigation of Crosstalk BetweenWnt and Hedgehog Signaling Pathways in fly”, MSc Thesis, BogaziçiÜniversitesi, Istanbul.
  68. 68. Ueno K. Hiura M. Suehiro Y. Hazama S. Hirata H. Oka M. Imai K. Dahiya R. Hinoda Y. 2008, “Frizzled-7 as a Potential Therapeutic Target in Colorectal Cancer”, Neoplasia, Vol. 10, pp. 697–705.
  69. 69. Valdes A. M. Spector T. D. Tamm A. Kisand K. Doherty S. A. Dennison E. M. Mangino M. Tamm A. Kerna I. Hart D. J. Wheeler M. Cooper C. Lories R. J. Arden N. K. Doherty M. 2010 “Genetic variation in the SMAD3 gene is associated with hip and knee osteoarthritis”, Arthritis Rheum., 66 8 2347 2352 .
  70. 70. Veeman M. T. Axelrod J. D. Moon R. T. 2003 “A second canon: Functions and mechanisms of beta-catenin-independent Wnt signaling”, Dev Cell, 5 367 377 .
  71. 71. Wang H. Y. Malbon C. C. 2003 “Wnt signaling, Ca2+, and cyclic GMP: visualizing Frizzled functions”, Science, 300 1529 1530
  72. 72. Widelitz R. 2005 “Wnt signaling through canonical and non-canonical pathways: recent progress”, Growth Factors, 23 111 116 .
  73. 73. Wu M. Herman M. A. 2006 “A novel noncanonicalWnt pathway is involved in the regulation of the asymmetric B cell division in C. Elegans”, DevBiol, 293 316 329 .
  74. 74. Wu Q. Maniatis T. 1999 “ A striking organization of a large family of human neural cadherin-like cell adhesion genes”, Cell, 97 779 790 .
  75. 75. Wu X. R. Zhu Y. Li Y. 2005, “Analyzing Protein Interaction Networks via Random Graph Model”, International Journal of Information technology, 12 1251 1260 .
  76. 76. Yanaga F. T. Sasaguri T. 2007 “The Wnt/β-Catenin Signaling Pathway as a Target in Drug Discovery”, J Pharmacol Sci., 104 293 302 .
  77. 77. Yang Z. Q. Zhao Y. Liu Y. Zhang J. Y. Zhang S. Jiang G. Y. Zhang P. X. Yang L. H. Liu D. Li Q. C. Wang E. H. 2010 “Downregulation of HDPR1 is associated with poor prognosis and affects expression levels of p120-catenin and β-catenin in nonsmall cell lung cancer”, Molecular Carcinogenesis, 49 5 508 519 .
  78. 78. Yeh C. T. Lu S. C. Chen T. C. Peng C. Y. Liaw Y. F. 2003 “Aberrant transcripts of the cyclin-dependent kinase-associated protein phosphatase in hepatocellular carcinoma”, Cancer Res., 60 4697 4700 .
  79. 79. Ying J. Li H. Yu J. Ng K. M. Poon F. F. Wong S. C. Chan A. T. Sung J. J. Tao Q. 2008 “Wnt5a exhibits tumor-suppressive activity through antagonizing the Wnt/beta-catenin signaling, and is frequently methylated in colorectal cancer”, Clin Cancer Res., 14 55 61 .
  80. 80. Yu H. Greenbaum D. Xin L. H. et al. 2004 Genomic analysis of essentiality within protein networks”, Trends Genet, 20 227 231 .
  81. 81. Yujiri T. Fanger G. R. et al. 1999 “MEK kinase 1 (MEKK1) transduces c-Jun NH2-terminal kinase activation in response to changes in the microtubule cytoskeleton”, J. Biol. Chem., 274 12605 12610 .
  82. 82. Zanzoni A. Montecchi-Palazzi L. Quondam M. Ausiello G. Helmer-Citterich M. Cesareni G. 2002 “MINT: a Molecular INTeraction database”, FEBS Lett., 513 1 135 140 .
  83. 83. Zhang Y. et al. 2001 “Differential Molecular Assemblies Underlie the Dual Function of AXIN in Modulating the Wnt and JNK Pathways”, Journal of Biological Chemistry, 276 34 32152 32159 .
  84. 84. Zielinski R. Przytycki P. F. Zheng J. Zhang D. Przytycka T. M. Capala J. 2009 “The crosstalk between EGF, IGF, and Insulin cell signaling pathways-computational and experimental analysis”, BMC Bioinformatics, 3 88

Written By

Bahar Nalbantoglu, Saliha Durmuş Tekir and Kutlu Ö. Ülgen

Submitted: 11 February 2011 Published: 25 January 2012