Prediction of Human Disease-Related Gene Clusters by Clustering Analysis

Transcription

1 Int. J. Bol. Sc. 2011, 7 61 Research Paper Internatonal Journal of Bologcal Scences 2011; 7(1):61-73 Ivysprng Internatonal Publsher. All rghts reserved Predcton of Human Dsease-Related Gene Clusters by Clusterng Analyss Peng Gang Sun 1, Ln Gao 1 and Shan Han 2 1. School of Computer Scence and Technology, Xdan Unversty, X an, , Chna 2. Faculty of Scence, Unversty of Copenhagen, Copenhagen, 1307K, Denmark Correspondng author: psun@mal.xdan.edu.cn or lgao@mal.xdan.edu.cn Receved: ; Accepted: ; Publshed: Abstract Snce genes assocated wth smlar dseases/dsorders show an ncreased tendency for ther proten products to nteract wth each other through proten proten nteractons (PPI), clusterng analyss obvously as an effcent technque can be easly used to predct human dsease-related gene clusters/subnetworks. Frstly, we used clusterng algorthms, Markov cluster algorthm (MCL), Molecular complex detecton (MCODE) and Clque percolaton method (CPM) to decompose human PPI network nto dense clusters as the canddates of dsease-related clusters, and then a log lkelhood model that ntegrates multple bologcal evdences was proposed to score these dense clusters. Fnally, we dentfed dsease-related clusters usng these dense clusters f they had hgher scores. The effcency was evaluated by a leave-one-out cross valdaton procedure. Our method acheved a success rate wth 98.59% and recovered the hdden dsease-related clusters n 34.04% cases when removed one known dsease gene and all ts gene-dsease assocatons. We found that the clusters decomposed by CPM outperformed MCL and MCODE as the canddates of dsease-related clusters wth well-supported bologcal sgnfcance n bologcal process, molecular functon and cellular component of Gene Ontology (GO) and expresson of human tssues. We also found that most of the dsease-related clusters conssted of tssue-specfc genes that were hghly expressed only n one or several tssues, and a few of those were composed of housekeepng genes (mantenance genes) that were ubqutously expressed n most of all the tssues. Key words: Dsease-related gene cluster, Clusterng analyss, PPI network, Gene expresson data Introducton Wth the ncrease n avalablty of human proten nteracton data and gene expresson data, the focus of bonformatcs development has shfted from understandng networks encoded by model speces to understandng the networks underlyng human dsease [1]. Predctng human dsease-related clusters/subnetworks usng a bomolecular network s crtcal to gan an understandng of dsease mechansms, and s also essental for the development of new dagnostcs and therapeutcs. Subnetworks are of great mportance because they not only provde concrete hypotheses as to the molecular complexes, sgnalng pathways, but also offer mechanstc hypotheses about the causes of dsease [2]. Integratng known dsease genes wth physcal or bomolecular networks and gene expresson data to dentfy dsease-related subnetworks can help us explan many genetc and envronmental factors nfluencng a dsease n the context of a smaller number of dscrete subnetworks as well as the causes or effects of the dsease phenotype. In recent years, many studes had shown the utlty of these networks n extractng dsease-related clusters/subnetworks [2] and nferrng dsease-causng genes [2, 7-11]. Qu et al. [3] proposed a method to detect dsease-related gene modules or dysfunctonal pathways based on global characterstcs of nteractome coupled wth gene expresson data. The modules or pathways were nferred based on gene s actve score functon whch was defned based on the kernel trck. They appled the proposed method to two cancer related problems,.e. breast cancer and prostate cancer, and successfully

2 Int. J. Bol. Sc. 2011, 7 62 dentfed actve modules or dysfunctonal pathways related to these two types of cancers wth lterature confrmed evdences. Karn et al. [4] presented an approach to causal gene predcton that was based on ntegratng PPI network data wth gene expresson data under a condton of nterest. They appled a set-cover-lke heurstc to dentfy a small set of genes that best cover the dsease-related genes and predcted possble genes that were nvolved n myasthena gravs. Calvano et al. [5] assembled an endotoxn nflammatory response network by ntegratng functonal nteractons curated from the lterature wth gene expresson nformaton. The response network enabled the dentfcaton of new endotoxn-responsve modules. Ghazalpour et al. [6] constructed a gene coexpresson network usng mcroarray profles gathered from the lvers of a panel of mce, and plenty of subnetworks n the network were extracted to be enrched for genes n loc wth strong assocatons to a physologcal trat, yeldng a matrx of module/trat assocatons. Lage et al. [12] devsed a phenotype smlarty score and used t to look for proten complexes whose genes were assocated wth smlar phenotypes. Smlarly, Fraser et al. [13] showed that dentfyng human proten complexes contanng known dsease genes was an effcent method for large-scale dsease gene dscovery. In contrast to the above studes, Goh et al. [14] bult a network of human dsease/ human gene assocatons, whch was a bpartte graph consstng of two dsjont sets of nodes. One set corresponded to all known genetc dsorders, whereas the other set corresponded to all known dsease genes n the human genome. A dsorder and a gene were then connected by a lnk f mutatons n that gene were mplcated n that dsorder. They found that dsease genes causng smlar dseases exhbted an ncreased tendency for ther proten products to nteract wth one another, and tend to be coexpressed n specfc tssues [2]. Combnng these network-based dsease studes, the overrdng concluson s that genes assocated wth a partcular dsease tend to exhbt hgh connectvty and cluster together [2, 14, 15, 16]. Thus, the hypothess s that dsease genes wthn such dense clusters n a bomolecular network that more lkely nteract wth one another than wth others often cause smlar dseases and s becomng an ncreasngly sgnfcant factor for huntng human dsease-related gene clusters/subnetworks. In ths paper, we tackled the predcton problem by clusterng analyss ntegratng PPI networks and gene expresson data, and supermposng a set of known dsease genes on human PPI network n a dfferent way. Frstly, we used clusterng algorthms, Markov cluster algorthm (MCL) [22, 23], Molecular complex detecton (MCODE) [21] and Clque percolaton method (CPM) [24] to decompose human PPI network nto dense clusters, and then a log lkelhood model that ntegrates multple bologcal evdences was proposed to score these dense clusters. Fnally, we dentfed dsease-related clusters usng these dense clusters f they had hgher scores. The effcency was evaluated by a leave-one-out cross valdaton procedure. In addton, we also gave a comparson of the clusters decomposed by MCL, MCODE and CPM as the canddates of dsease-related clusters. Materals and Methods Bologcal Data The dsease genes data was obtaned from Goh et al. [14], and they collected the data from the Onlne Mendelan Inhertance n Man (OMIM) [17] whch contans 1284 dsorders and 1777 dsease genes. Further, they classfed each dsorder nto 22 prmary dsease/dsorder classes manually based on the physologcal system affected by the dsorder. The human proten-proten nteracton (PPI) data was also ganed from Goh et al. [14], and they combned two hgh qualty systematc yeast two-hybrd experments [18, 19] wth PPIs obtaned from lterature by manual curaton [18]. The ntegrated set of PPIs contans non-self-nteractng, non-redundant nteractons between 7533 genes. The used gene expresson mcroarray data was from Ge et al. [20], whch s avalable for 36 normal human tssues. A gene s consdered to be expressed f the P-value assocated wth ts transcrpt abundance s less than the threshold, P-value<0.02. A gene s consdered as housekeepng gene (mantenances gene) f t s expressed, and confdently detectable (P-value<0.01) n most human tssues [20]. Clusterng Algorthms Three classc clusterng algorthms used to decompose the human PPI networks nto dense clusters are shown n the followng: Molecular complex detecton (MCODE) proposed by Bader and Hogue [21] was an effectve approach for detectng densely-connected regons n large PPI networks. MCODE made use of local graph densty to fnd proten complex. PPI networks were transformed to weghted graphs n whch vertces were protens and edges represented proten nteractons. The algorthm operated n three stages: vertex weghtng, complex predcton and optmal post-processng. Frst t assgned a weght to each vertex, correspondng to ts local neghborhood densty. Then, startng from the top weghted vertex (seed

3 Int. J. Bol. Sc. 2011, 7 63 vertex), t recursvely moved outward, ncludng n the cluster vertces whose weght was above a gven threshold. Ths threshold corresponded to a user-defned percentage of the weght of the seed vertex. The results showed that MCODE effectvely found densely-connected regons of a molecular nteracton network solely based on connectvty data. Many of these regons corresponded to the known molecular complexes. Markov cluster algorthm (MCL) [22, 23] was a fast and scalable unsupervsed clusterng algorthm. It was desgned to meet the challenge of fndng cluster structure n smple and weghted graphs. The MCL algorthm smulated random walks wthn a graph by the alternaton of expanson and nflaton operatons. Expanson referred to takng the power of a stochastc matrx usng the normal matrx product. Inflaton corresponded to takng the Hadamard power of a matrx, followed by a scalng step, so that the resultng matrx was agan stochastc. Eventually, teratng expanson and nflaton resulted n the separaton of the graph nto dfferent segments. A novel network clusterng method, Clque Percolaton Method (CPM) was proposed to reveal the overlappng modules n PPI networks [24]. In CPM, a module was defned as a unon of all k-clques (complete subgraph of sze k) that can be reached from each other through a seres of adjacent k-clques (where adjacency means sharng k-1 nodes). Ths method performed well n detectng overlappng functonal modules/proten complexes. Evaluatng Crtera Dsease Related Coeffcent (DsRC) s used to evaluate the degree of the cover between the clusters decomposed from human PPI network, and the classes of dsease assocated genes. C D DsRC( C) Max( ) (1) where, C s the set of genes of a cluster; D s the set of genes that causng dsease,. C and D denote the number of genes n C and D respectvely. C D DsRC(C) equals the maxmal that represents the best cover, and C s assgned to the correspondng dsease class. Snce dsease assocated genes whch more lkely nteract wth each other often lead to smlar dsease/dsorder, a group of genes assocated wth the same dsease/ dsorder should share smlar cellular and functonal characterstcs, as annotated n Gene Ontology (GO) [14, 27]. To nvestgate ts valdty, we ntroduced the Bologcal Process Related Coeffcent (BPRC), Molecular Functon Related Coeffcent (MFRC) and Cellular Component Related Coeffcent (CCRC) of a dsease-related cluster, defned as the maxmum fracton of genes among those belongng to a dsease-related cluster that had same GO annotaton n bologcal process, molecular functon and cellular component respectvely. Usng these crtera, we measured the consstency of each dsease-related cluster separately wth each branch of GO, bologcal process, molecular functon, and cellular component. BPRC, MFRC and CCRC are used to score the consstency of genes wthn dsease-related clusters n GO annotatons respectvely. BP t j BPRC( C) Max( ) (2) MF t j MFRC( C) Max( ) (3) CC t j CCRC( C) Max( ) (4) where, t j BP denotes the number of genes have same GO annotaton, j n bologcal process. t j MF and t j CC are smlar to t j BP. Dsease genes encodng protens that nteract hghly wth each other tend to be coexpressed n the same human tssues. To measure ths, we ntroduced the Tssue-Related Coeffcent (TRC) of a dsease-related cluster, defned as the maxmum fracton of genes among those belongng to a dsease-related cluster that were coexpressed n a specfc tssue [14, 20]. TRC quantfes whether genes that are n a dsease-related cluster tend to be coexpressed n smlar human tssues. TRC( C) Max( ) (5) where, n t denotes the number of genes, that are coexpressed n the tssue, t. If all the genes are coexpressed together n at least one tssue, the maxmal value s 1; the mnmum value s 1, when all are coexpressed n dfferent tssues [14]. Our Method The nput to a dsease-related cluster predcton problem conssts of a human PPI network, the classes of known dsease genes based on physologcal system affected, and gene expresson mcroarray data. The goal s to dentfy dsease-related clusters. n t

4 Int. J. Bol. Sc. 2011, 7 64 Snce genes assocated wth smlar dseases/dsorders show an ncreased tendency for ther proten products to nteract wth each other through PPIs, we decomposed the human PPI network nto dense clusters by clusterng algorthms (MCL, MCODE and CPM) as the canddates of dsease-related clusters. In order to extract dsease-related clusters from these canddate clusters and evaluate the statstcal sgnfcance of the dsease-related clusters n multple bologcal evdences, we gave a log lkelhood model that was smlar to that recently proposed by Sharan et al. [28, 29] to measure the ft of the canddate cluster to a dsease-related cluster. L( C) Max( w F( p )) (6) p T where 0, 0 p (log log ) /, 1 p 0.5 ; the p 1 p F( p ) 1 (log log ) /, 0.5 p p 1 p 1, p 1 genes wthn a canddate cluster nteract wth a hgh probablty α, and ths cluster may be suggested as a dsease-related cluster that s not random; β,γ are the tunng parameters that are used for normalzaton; w =1/ T ; T={DsRC(C);BPRC(C);MFRC(C);CCRC(C);TRC(C)}. Ths model ntegrates multple bologcal evdences n T to score the statstcal sgnfcance of a dsease-related cluster. For each canddate cluster C, we calculated the L(C) of the cluster related to a specfc dsease, and assgned t to the correspondng dsease that receved the maxmal value. A group of genes wth a hgher score s more sgnfcant correspondng to a dsease-related cluster than the one wth a smaller score. Eq. 6 can be smplfed n the followng: L( C) Max( w F( p )) ; p T Max( w F( p )) ; p T w Max( F( p )) ; p T w Max( F( p )) ; p T w F( Max( p )) ; p T w F( p ); p T Here, the α was set to 0.9 [28, 29], and β,γ=2; DsRC 0.5 whch kept 50% genes out of the canddate clusters were known dsease genes nvolvng n specfc dsease. We fnally fltered these canddate clusters wth L(C) 0.5 to ensure the statstcal sgnfcance of dsease-related clusters n multple bologcal evdences. Results Dsease-Related Clusters Detecton The bologcal data nvolvng n dsease genes data, human PPI data and gene expresson data used by our method for dsease-related clusters detecton have been dscussed beforehand. The three classc clusterng algorthms: MCODE (Parameters: Include Loops: false, Degree Cutoff: 2, Node Score Cutoff: 0.2, Harcut: true, Fluff: false, K-Core: 2, Max. Depth from Seed: 100), MCL (Expand: 2.0, Inflaton: 2.0) and CPM (3-clques) can be found n the above secton. Snce our method for dsease-related clusters detecton made use of the dense clusters decomposed by these clusterng algorthms from the human PPI network, n ths secton, we evaluated our method s performance based on the canddate clusters from each of these clusterng algorthms respectvely, and by the way, compared these clusterng algorthms performance. Table 1 The results for detectng dsease-related clusters based on the log lkelhood model. Methods No. of clusters No. of dsease-related clusters cluster sze L(C) 0.5 Max L(C) Mn L(C) Avg. L(C) 3 CPM MCODE MCL In the table, our method detected 47 (47/350=13.43%) dsease-related clusters from 350 canddate clusters of CPM wth L(C) 0.5. Smlarly, one (1/49=2.04%) dsease-related cluster from 49 canddate clusters of MCODE, and 44 (44/1021=4.31%) dsease-related clusters from 1021 canddate clusters of MCL were dscovered respectvely. The L(C) = 1.0 means that the dsease-related clusters acheve perfect support n multple bologcal evdences (DsRC, BPRC, MFRC, CCRC, TRC=1.0, smultaneously). Fg. 1 showed the L(C) of each dsease-related cluster n an ascendng order. From the fgure, we

5 Int. J. Bol. Sc. 2011, 7 65 found that most of the dsease-related clusters obtaned from the canddate clusters of CPM ganed hgher L(C) than MCL, t was smlar to the mean value of L(C). Snce only one dsease-related cluster was acqured from the canddate clusters decomposed by MCODE, we only dscussed MCL and MCODE n ths secton. Leave-One-Out Cross Valdaton To evaluate the performance of our method, we employed a leave-one-out cross valdaton procedure [29]. In each cross valdaton tral, we selected k known dsease genes that assocated wth dsease-related clusters (128 known dsease genes are assocated wth 47 dsease-related clusters of CPM; 130 known dsease genes are assocated wth 44 dsease-related clusters of MCL, these k known dsease genes are unformly dstrbuted n the detected dsease-related clusters) wth equprobablty and removed all the gene-dsease assocatons nvolvng the genes from the data, and our method was evaluated by ts success n dentfyng the dsease-related clusters that had been hdden. Gven that the dsease-related clusters detected above were the putatve dsease-related clusters. A dsease-related cluster was correctly dentfed f t was assgned to a same dsease wth the above secton. Here, we valdated our method to use the dsease-related clusters data detected from the canddate clusters of CPM and MCL respectvely. We evaluated our method s performance n terms of precson versus recall when consderng varous values of k (k= 15,,1). Precson s the fracton of true dsease-related clusters that are correctly detected n the correspondng tral of the cross valdaton procedure. Recall s the fracton of trals n whch the hdden dsease-related clusters were recovered. The results were depcted n Fg. 2. For k=1, n usng dsease-related clusters dentfed from canddate clusters of CPM, our method acheved a success rate wth 98.59% and recovered the hdden dsease-related clusters n 34.04% cases when removed one known dsease gene and all ts gene-dsease assocatons. Smlarly, n usng dsease-related clusters dentfed from canddate clusters of MCL: Precson = 98.45% and Recall = 31.81%. For 1 k 15, we found that the hgher the value of k, the lower the value of Precson and Recall. Fg.1 The L(C) of these dsease-related clusters n an ascendng order. The black lne n the purple pane denotes the mean value of the L(C).

6 Int. J. Bol. Sc. 2011, 7 66 Fg. 2 The leave-one-out cross valdaton for dsease-related clusters detecton. The fgure shows recall versus precson when consderng varous values of k. Statstcal Analyss Table 2 showed the results of the dsease-related clusters wth dfferent crtera. In the table, the dsease-related clusters detected from the canddate clusters of CPM obtaned better performance than MCL n DsRC (0.715>0.696), BPRC (0.895>0.805), MFRC (0.697>0.630), CCRC (0.770>0.733) and TRC (0.839>0.771). In these crtera, the mnmal average value was n MFRC, whch showed better enrchment n multple bologcal evdences. Table 2 The Comparson of dsease-related clusters detecton. Methods No. of dsease-related Avg. clusters L(C) 0.5 DsRC BPRC MFRC CCRC TRC CPM MCL Fg. 3 presented the results of the dsease-related clusters at each dsease class. In Fg. 3A, 11 dsease-related clusters out of 47 assocated wth dsease class: Immunologcal were detected that was more than other dsease classes n CPM. Smlarly, In Fg. 3B, 5 dsease-related clusters out of 44 related to dsease class: Multple was n MCL. From Fg. 3C and Fg. 3D, n most of the dsease classes, we found that the average value of each crteron was above 0.6 whch denoted the hgher homogenety of the genes wthn these dsease-related clusters n bologcal process, molecular functon and cellular component of GO, and expresson n the same human tssues. The dstrbuton of dsease-related clusters was showed n Fg. 4. From Fg. 4A and Fg. 4B, we found that a common feature that most of dsease-related clusters were dstrbuted n DsRC, BPRC, MFRC, CCRC, TRC [0.6, 0.8), and =1.0, and a few of those were n DsRC, BPRC, MFRC, CCRC, TRC [0.8, 0.1). In partcular, almost 50% of dsease-related clusters were n BPRC, MFRC, CCRC, TRC =1.0, whch showed that the genes wthn these dsease-related clusters won perfect bologcal sgnfcance n bologcal process, molecular functon and cellular component, and expresson n the same human tssues. It was n contrast to L(C), most of dsease-related clusters were concentrated n L(C) [0.6, 0.8) and [0.8, 0.1), and a few of those were n L(C) =1.0. Snce L(C) was an ntegrated evaluatng crteron of DsRC, BPRC, MFRC, CCRC, TRC, t had a dfferent dstrbuton.

7 Int. J. Bol. Sc. 2011, 7 67 Fg. 3 The results of dsease-related clusters at each dsease class.

8 Int. J. Bol. Sc. 2011, 7 68 Fg. 4 The dstrbuton of dsease-related clusters at each evaluatng crteron. Tssue-specfc genes are only coexpressed n one or several organ, whch s n contrast to housekeepng genes (mantenance genes) that are ubqutously coexpressed n almost tssues [25, 26]. Mantenance genes play key roles n varous cellular process, tssue-specfc genes are related to the functonng of dfferent organs. Knowng how genes are expressed n normal tssues not only s of fundamental mportance for functonal genomcs, but also mght contrbute to the study of complex dseases [4, 20, 25, 26]. In the results, most of the dsease-related clusters conssted of tssue-specfc genes, and a few of those were composed of housekeepng genes (mantenance genes). For example, 9 (9/47=19.15%) dsease-related clusters out of 47 conssted of housekeepng genes n CPM, and 5 (5/44=11.36%) dsease-related clusters out of 44 were composed of housekeepng genes n MCL. Fg. 5 showed two dsease-related consstng of tssue-specfc genes and ther correspondng dseases. In Fg. 5B, ths dsease-related cluster caused Cancer wth DsRC=1, BPRC=1, MFRC=1, CCRC=1 and TRC=1, whch acqured perfect bologcal sgnfcances. Fg. 6 showed a dsease-related cluster that was composed of housekeepng genes (mantenance genes) (see Fg. 6A). From Fg. 6B and Fg. 6C, we found that the genes wthn ths dsease-related cluster were coexpressed n most of human tssues wth P-value <

9 Int. J. Bol. Sc. 2011, 7 69 Fg. 5 Two dsease-related clusters consstng of tssue-specfc genes, the red nodes denote genes, and the lnks between them represent nteractons, the emerald green nodes are dsease IDs, and the lnks between them shows they share at least one common dsease genes. The lnk between a gene and a dsease ID represents that the gene leads to ths dsease. The table n the bottom shows the detals about dseases, one gene may lead to multple dseases.

10 Int. J. Bol. Sc. 2011, 7 70 Fg. 6 One dsease-related cluster consstng of housekeepng genes (mantenance genes). (A) the dsease-related cluster and ther correspondng dsease; (B) the expresson levels of the genes n the dsease-related cluster; (C) the detected P-value of each gene n human tssues. Note that the mantenances genes should be confdently detectable (P-value<0.01) n most tssues.

11 Int. J. Bol. Sc. 2011, 7 71 Analyss of Dsease-Related Clusters In the results of our experments, we found a dsease-related cluster (PEX1, PEX6, PEX26) nvolvng n dsease class, Multple was detected from both the canddate clusters of CPM and MCL wth L(C) =1.0. The PEX1 leads to Zellweger_syndrome (OMIM ID: ), Refsum dsease (OMIM ID: ), Adrenoleukodystrophy (OMIM ID: ). Smlarly, the PEX6 results n Peroxsomal_bogeness_dsorder, and the PEX26 causes Zellweger_syndrome, Refsum dsease, Adrenoleukodystrophy. The protens wthn ths cluster are also enrched wth GO terms: proten mport nto peroxsome matrx (P-value = 1.38e-09), proten targetng to peroxsome (P-value = 4.19e-09), peroxsomal transport (P-value = 5.23e-09) of bologcal process, and proten C-termnus bndng (P-value = 5.01e-06), proten complex bndng (P-value = 2.42e-05) of molecular functon, and peroxsomal membrane (P-value = 1.11e-07), mcrobody membrane (P-value = 1.11e-07), mcrobody part (P-value = 2.13e-07) of cellular component. The dsease-related clusters (CEBPA, CTNNB1, TCF1) (see Fg. 5B) assocated wth dsease class, Cancer was obtaned from the canddate clusters of CPM wth L(C) =1.0. The CEBPA s a causal gene of Leukema, acute myelod (OMIM ID: ), the CTNNB1 causes Colorectal cancer, Hepatoblastoma, Hepatocellular carcnoma (OMIM ID: ), Ovaran carcnoma, Plomatrcoma (OMIM ID: ), and the TCF1 s assocated wth Dabetes melltus, nsuln-dependent (OMIM ID: ), Dabetes melltus, nonnsuln-dependent (OMIM ID: ), Hepatc adenoma (OMIM ID: ), MODY, type III (OMIM ID: ). Ths cluster (CEBPA, CTNNB1, TCF1) s also abounded n GO terms: lver development (P-value = ) of bologcal process, and specfc RNA polymerase II transcrpton factor actvty (P-value = ) of molecular functon, and transcrpton factor complex (P-value = ) of cellular component. A dsease-related cluster (NCF2, CYBA, CYBB) nvolvng n dsease class, Immunologcal was ganed from the canddate clusters of MCL wth L(C) =1.0. The NCF2 s a causal gene of Chronc granulomatous dsease due to defcency of NCF-2, (OMIM ID: ), the CYBA causes Chronc granulomatous dsease, autosomal, due to defcency of CYBA (OMIM ID: ), and the CYBB s assocated wth Chronc granulomatous dsease, X-lnked (OMIM ID: ). The cluster s also enrched wth GO terms: superoxde anon generaton (P-value = 6.44e-09), respratory burst (P-value = 1.31e-08), superoxde metabolc process (P-value = 3.77e-08) of bologcal process, and superoxde-generatng NADPH oxdase actvty (P-value = 5.15e-06), electron carrer actvty (P-value = 6.63e-06) of molecular functon, and NADPH oxdase complex (P-value = 1.27e-09) of cellular component. In Fg. 6A, the dsease-related cluster consstng of housekeepng genes (EIF2B2, EIF2B3, EIF2B4, EIF2B5, EIF2S2) s assocated wth Leukoencephalopathy wth vanshng whte matte and Ovaroleukodystrophy (OMIM ID: ). The GO annotaton ndcates that ths cluster takes part n bologcal process: olgodendrocyte development (P-value = 8.40e-11), olgodendrocyte dfferentaton (P-value = 2.41e-09), glal cell development (P-value = 3.40e-09), has same molecular functon n translaton ntaton factor actvty (P-value = 6.84e-13), translaton factor actvty, nuclec acd bndng (P-value = 1.54e-11), RNA bndng (P-value = 6.93e-07), and corresponds to eukaryotc translaton ntaton factor 2B complex (P-value = 5.28e-14). Two dsease-related clusters nvolvng n BRCA1 (breast cancer 1, early onset) and BRCA2 (breast cancer 2, early onset) were dscovered. The BRCA1 s a causal gene of Breast-ovaran cancer, Ovaran cancer, Papllary serous carcnoma of the pertoneum, and the BRCA2 s assocated wth Breast cancer, male, susceptblty to (OMIM ID: ), Fancon anema, complementaton group D1 (OMIM ID: ), Pancreatc cancer (OMIM ID: ). We also found a dsease-related cluster nvolvng n RAD51A and RAD54L that are assocated wth several Breast Cancer varants (RAD51A, Breast cancer, susceptblty to, OMIM ID: ; RAD54L, Breast cancer, nvasve ntraductal), and two dsease-related clusters, (BUB1B, BUBR1, BUB1) and (MLH1, PMS1, MLH3, MLH3, PMS2), whch are assocated wth several Colorectal Cancer varants. Dscusson Human dsease-related gene clusters/subnetworks are of great mportance because they not only provde concrete hypotheses as to the molecular complexes, sgnalng pathways, but also offer mechanstc hypotheses about the causes of dsease [2]. Wth the development of bologcal experment methods, proten nteractons and gene expresson data are becomng more and more complete, whch offer valuable bologcal materals for dsease-related clusters analyss. The used clusterng algorthms such as CPM, MCODE and MCL were ntally proposed to dentfy functonal modules or proten complexes groups of genes wthn whch connectons are dense whle between whch they are sparse, t s consstent wth the characterstc of dsease-related gene clusters of Goh et al. [2, 14, 15, 16] that dsease genes causng smlar

12 Int. J. Bol. Sc. 2011, 7 72 dseases exhbt an ncreased tendency for ther proten products to nteract wth each other. We used CPM, MCODE and MCL to decompose human PPI network nto dense clusters as the canddates of dsease-related clusters. It s analogous to Lage et al. [12] and Fraser et al. [13] who looked for proten complexes whose genes were assocated wth smlar phenotypes and dscovered large-scale dsease genes. In prevous studes, many methods used PPI networks to uncover novel dsease-causng genes [2]. Lm et al. [7] bult a PPI network around 23 protens nvolved n nherted ataxas usng Y2H screens, and used ths network n uncoverng novel ataxa-causng genes and genetc modfers for ataxa. Pujana et al. [8] constructed a breast cancer-related network startng wth four known breast cancer-assocated genes for predctng new genes assocated wth breast cancer. Ot et al. [9] predcted new dsease assocated genes that fell wthn one of the sgnfcant loc and had a proten nteracton wth a gene already known to cause dsease. In addton, PPI networks were also employed for dsease canddate gene prortzaton. Franke et al. [10] used the known molecular nteractons and the predcted functonal relatons to construct a functonal human gene network that was used to rank the canddate genes on the bass of ther nteractons. Chen et al. [11] descrbed a canddate gene prortzaton method that was entrely based on PPI network analyses and successfully used for dsease canddate gene prortzaton. Here, we can also predct novel dsease-causng genes based on dsease-related gene clusters. Gven that the unknown dsease genes also cause smlar dsease wth the known dsease genes n the dentfed dsease-related clusters, our method predcted 47 new dsease genes (SEPT4, UBB, RASD1, FBLN1, GFRA1, SCGB1A1, CFH, C8A, BF, IFNG, GATA4, NKX2-5, F3, PLAU, PKN1, MAG, IGFALS, IGFBP3, IGFBP5, HSPA5, IL7, DIPA, CYCS, IL13RA1, SPTAN1, ABCD1, ABCD2, C1R, HRG, PTX3, CGA, CGB, EXO1, PCNA, C5 MASP1, CD8B1, RSN, MSH4, ADD1, DLAT, PDK1, PDK2, RAC1, EIF2S2, TRAP1, FDX1, EVPL) from the dsease-related clusters detected from the canddate clusters of CPM, smlarly, 49 new dsease genes (SLC8A1, DMC1, RAD51AP1, ERCC1, BMP1, DST, CGA, CGB7, ZC3H11A, MBD4, CRYZ, KIRREL, KIRREL3, TBX5, BMPR1A, BMPR2, BMP6, PCM1, KIAA0368, EDNRA, CTSG, DAPK3, F2RL3, GABRA4, AFAP, HBZ, RLN2, HRG, C1QB, C1QG, SLC4A7, COL5A3, NTHL1, TFPI2, SHOC2, PLCE1, COL3A1, CHAD, NDUFS6, INSRR, CHRNG, PKD2, EIF2S2, TRAP1, GALNT5, PHYH, DNM1L, DDO, SIRT3) of MCL. 3 new dsease genes (CGA, EIF2S2, TRAP1) were n both of the above two dsease genes sets. Here, we used the dsease-related clusters to predct novel dsease-causng genes, t not only consdered the hgher nteracton wth known dsease genes, but also the hgher consstency n GO annotatons and expresson of human tssues, whch can gve us a belevable predcton. In our paper, the clusterng algorthms, CPM, MCODE and MCL for functonal modules or proten complexes detecton n general were evaluated by analyzng the consstency of genes or protens wthn the functonal modules n functonal annotatons. Here, we evaluated these clusterng algorthms by decomposng human PPI networks nto dense clusters as the canddates of dsease-related clusters. We found that the clusters decomposed by CPM outperformed MCL and MCODE as the canddates of dsease-related clusters wth well-supported bologcal sgnfcance n bologcal process, molecular functon and cellular component of GO and expresson of human tssues (see Table 2). In the leave-one-out cross valdaton procedure, our method obtaned better performance n usng the dsease-related clusters detected from the canddate clusters of CPM than MCL, when removed one known dsease gene and all ts gene-dsease assocatons. Whle for 2 k 15, MCL ganed better results than CPM n Precson (see Fg. 2). It s because the sze of dsease-related clusters of CPM s smaller than MCL, when we removed known dsease genes and all ther gene-dsease assocatons n the dsease-related clusters, more dsease-related clusters DsRC declned quckly below 0.5 n CPM than MCL, consequently, MCL performed better than CPM. In concluson, we ntegrated known dsease genes wth human PPI networks and gene expresson data to dentfy dsease-related clusters, and our method showed better performance. Ths study not only can help us understand dsease mechansms and nfer new dsease-causng genes, but also help us develop new dagnostcs and therapeutcs. In the future work, we wll apply our approach to other speces such yeast or fly for dsease-related clusters detecton. Acknowledgments The authors would lke to gve knd respect and specal thanks to Prof. Yong Gao for proofreadng the manuscrpt and provdng valuable comments. Ths work s supported by the State Key Program of Natonal Natural Scence of Chna (Grant No ) and the Research Fund for Doctoral Program of Hgher Educaton of Chna (Grant No ).

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