Integrative Computational Identifications of the Signaling Pathway Network Related to TNF-alpha Stimulus in Vascular Endothelial Cells

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1 Integratve Computatonal Identfcatons of the Sgnalng Pathway Network Related to -alpha Stmulus n Vascular Endothelal Cells Jn Gu, Shao L, Yang Chen, Yanda L MOE Key Laboratory of Bonformatcs and Bonformatcs Dvson TNLIST / Department of Automaton, Tsnghua Unversty Bejng , Chna Emal: gujn_00@mals.tsnghua.edu.cn, {shaol, yc, daulyd}@tsnghua.edu.cn Abstract-Integratng multple datasets becomes essental for systematcally understandng complex bologcal processes. Here, we presented a new method to dentfy and annotate the downstream s related to -alpha () stmulus n vascular endothelal cells (VECs). It s found that the downstream s of can both cause cell prolferaton and cell death, whch may provde an explanaton for the ambguous roles of n angogeness. By calculatng the dstances n proten-proten nteracton (PPI) network between dfferent sgnalng s, many possble cross-talks are predcted, whch suggests complex downstream responses for stmulus. Keywords-Systems bology; ; -alpha (); vascular endothela cells (VECs) I. INTRODUCTION Understandng the complex responses of cells to envronmental stmul s a promsng task for systems bology. Here, we present a new ntegratve computatonal approach to dentfy the -level responses to alpha (herenafter referred as ) stmulus n vascular endothelal cells., manly secreted by macrophages, s an mportant cytokne, whch s nvolved n a wde spectrum of bologcal and physologcal processes. One of the -nvolved processes s angogeness, a process nvolvng the growth of new blood vessels from pre-exstng vessels and essental for many tumor growth [1-4]. Vascular endothelal cells (VECs), such as human umblcal ven endothelal cells (HUVECs) and human dermal mcrovascular endothelal cells (HDMVECs), are usually used as n vtro model of angogeness. stmulus can affect the prolferaton and mgraton of VECs, whch are essental steps for angogeness. But whether promotes or nhbts angogeness remans ambguous [5-7]. Accordng to current knowledge, bnds to ts receptors, R1 and BR, and stmulates downstream sgnalng s, such as TGF-beta sgnalng, NF-kB sgnalng and many other s [7, 8]. Prevous studes manly focused on sngle gene or but thought lttle of the possble cross-talks between multple s. Based on gene expresson and proten-proten nteracton (PPI) data, an ntegratve computatonal method was developed to analyze the s related to stmulus n a systematcal vew. One common computatonal strategy for systematcally analyzng a specfc bologcal process s to fnd the neghbor genes of a few essental genes n proten-proten nteracton network (PPI network). For examples, Pujana et al. constructed a breast cancer gene network based on three genes and successfully found a new breast cancer related gene HMMR [9]; Bromberg et al. constructed a sub-network related to CB1R-nduced neurte outgrowth by analyzng 23 actvated transcrpton factors n PPI network [10, 11]. For angogeness, Abdollah et al. constructed an angogenc swtch network: they frst screened a set of ant-expressed genes by stmulatng HUVECs usng pro-angogenc and ant-angogenc factors; then the genes drectly nteracted wth the ant-expressed ones were dentfed by lterature mnng; fnally, the angogenc network was establshed based on all those genes and ther nteractons [12]. In the frst place, our method s smlar to above approaches, the dfferentally expressed genes detected by mcroarray and ther drectly nteracted genes n PPI network were dentfed. Whle n the followng steps, our method dffers: 1) we manly studed the responses n level rather than sngle genes; 2) a typcal pro-angogenc process nduced by vascular endothelal growth factor A () was used as reference to analyze the dentfed s. II. METHODS AND MATERIALS A. Identfcatons of the seed genes affected by and n vascular endothelal cells (VECs) Under envronmental stmul, the transcrpton of downstream genes wll be actvated or repressed. The dfferentally expressed genes provde mportant nformaton of the nduced process. Two mcroarray datasets, GSE2638 and GSE2639 (all the mcroarray datasets can be found n NCBI GEO database wth the gven IDs), were used to dentfy the deferentally expressed genes n VECs after stmulus by SAM package wth default parameters [13]. To reduce the nose n mcroarray data, addtonal evdences were used to flter the deferentally expressed genes [14]: the gene dfferentally expressed at least n one mcroarray dataset and also supported by addtonal evdences (at least three evdences n total) were left for the followng analyss.

2 TABLE I. THE MULTIPLE DATASETS USED TO IDENTIFY SEED GENES Source Descrpton Cut-off Relaton Mcroarray GSE2638 SAM default Mcroarray GSE2639 SAM default Mcroarray GSE837 SAM default Mcroarray GSE3299 SAM default PubMed PubMed GO Pathway Pathway dbest Abstracts wth key words ( AND (angogeness OR nflammaton) ) Abstracts wth key words ( AND (angogeness OR nflammaton) ) Genes n angogeness (GO: wth all chld terms, 68 genes) and response to nflammaton (GO: wth all chld terms, 180 genes) Genes n the s contanng Genes n the s contanng Expressed sequence tags n endothelal cells # abstract 5 hts # abstract 5 hts 1 ht 1 ht 1 ht 10 hts The other four evdences are 1) lterature nformaton from PubMed; 2) GO annotatons; 3) s; 4) expressed sequences tags (ESTs). Please see table 1 for detal. The dentfed genes were called seed genes n ths study. Fnally, 70 seeds genes were dentfed for stmulus n VECs. Wth the same procedure, another two mcroarray datasets were used for stmulus, GSE3299 and GSE837, and 78 seed genes were dentfed. B. Valdatons of the seed genes by another tme-course mcroarray dataset To valdate the seed genes, another ndependent tmecourse mcroarray dataset GSE9055 (HUVEC wth 10ng/ml treatment, 0~8h, 25 tme ponts) was used. Usng the clusterng software CLUSTER and the tree vewng software TreeVew [15], two tght clusters, ncludng 25 and 15 genes wth Pearson Correlaton > 0.9 were dentfed and vsualzed (Fg. 1). The two clusters were sgnfcantly co-expressed than background (p-value 4.74E-30), whch supports that the seed genes are closely related to -nduced process. C. Identfcatons of the brdge genes n proten-proten nteracton (PPI) networks Comparng the lsts of the 70 seed genes and the 78 seed genes, only 8 genes are overlapped (Fsher s exact test p-value > 0.05). It seems that the two processes are ndependent. The dfferentally expressed seed genes may not contan all nformaton about the two processes, because many genes are actvated by post-transcrptonal modfcatons, such as STAT3, a member of sgnal transducer and actvator of transcrpton (STAT) gene famly, s regulated by proten Fgure 1. Two co-expressed clusters (correlaton > 0.9) of seed genes n tme-course mcroarray GSE9055, whch contans 25 tme ponts at the frst 8 hours after 10ng/ml treatment of HUVECs. phosphorylaton [16]. The genes nvolved n the same process are more lkely to nteract wth each other than unrelated genes. So searchng for the neghbors of seed genes n PPI network can cover an addtonal set of downstream genes, whch may not have sgnfcant changes n mrna expresson level [9-12]. The PPI network was constructed accordng to annotated PPIs n HPRD [17-20]. Only the genes wthn the bggest connected sub-network, ncludng 9,028 nodes and 34,803 edges, were left for the followng analyss. Ffty-nne of the 70 seed genes and 68 of the 78 seed genes can be mapped on to the network. Then the genes drected nteracted wth the seeds (DIGs) were dentfed n the network. To avod bas, the eght overlapped genes were removed from seeds, so n the followng analyss, there are only 70 seed genes. We found 570 genes drectly nteracted wth seed genes () and 521 genes drectly nteracted wth seed genes (-DIGs). One hundred and thrty-fve genes were overlapped (brdge genes, BGs) between the two sets of DIGs (Fsher s exact test p-value, 8.09E-12), whch ndcates that -nduced process s ndeed closely related to nduced process.

3 TABLE II. EASE MODIFIED FISHER S EXACT TEST TABLE BGs NOT BGs Total Genes n ( # BGs n # n By calculus Pathway ) - 1 Genes NOT n Pathway By calculus By calculus By calculus Total # BGs By calculus # EASE modfed Fsher s exact test table for dentfyng sgnfcantly enrched s n brdge genes (BGs) aganst the genes drectly nteracted wth seed genes (). The four numbers n bold font should be provded to calculate the p-value. D. Two-Stage Pathway Enrchment Analyss In the frst stage, enrchments were smply calculated n seed genes,, -DIGs and BGs usng DAVID web-based tool [21, 22]. Sgnfcantly enrched s wth EASE p-value < were dentfed [23]. Several annotatons are avalable n DAVID. In ths study, we manly used sgnalng s and another two essental s, cell cycle and apoptoss. In the second stage, also usng EASE modfed Fsher s exact test, the s wth sgnfcant hgher enrchment n BGs aganst were dentfed (p-value < 0.05). These s wth sgnfcant enrchment ncrease are predcted as pro-angogenc, because these s are sgnfcantly overlapped wth the downstream s n the typcal pro-angogenc process nduced by. The Fsher s exact test table was shown n table 2. E. Analyss of cross-talks between s In bologcal systems, multple s cooperate or cross-talk wth each other nstead of workng separately. But n most databases, s are annotated sngly. One strategy to predct the possble cross-talks s to embed ndvdual s nto a genome-wde PPI network [24]. For example, Lu et al. dentfed a set of cross-talks smply by searchng the neghbors and the PPIs of the genes n dfferent s [24]. Makng a step forward, L et al. proposed a method by examnng the sgnfcance of drect nteractons between dfferent s n PPI network based on Fsher s exact test [25]. In our study, dstance was ntroduced to predct the possble cross-talks between two s. Denote the - th and the genes n the as: p and { g 1, g2, L, gn }, and the shortest path length n PPI network between two genes as: ls( g 1j, g ), 1 2j The dstance between any par of s can be calculated as the average of the shortest path lengths between all pars of genes n the two s: n n l( p, p { } 1 ) = (, ), 2 ls g 1j g 1 2j n. j 1 1= 1 n j 2 2= 1 To estmate the sgnfcance of the dstance, we randomly shuffled the genes n s to generate background datasets. Each tme, 1) two dfferent s are randomly selected, and then 2) two dfferent genes, one gene for one, are randomly selected. To reduce bas, the two genes are requred to have equal degree n PPI network, because the genes wth hgher degree (means more neghbor genes) are more lkely to have shorter shortest path length than other genes. Fnally, 3) swap the two genes between the selected s. A background dataset s generated by repeatng shufflng 1000,000 tmes. Accordng to the shufflng procedure, 1,000 background datasets were generated. By comparng the dstance of any par of s n orgnal dataset and the background datasets, p-value was calculated as: p #{ k lk l,1 k 1000} 1000 =. The pars wth p-value < are predcted as cross-talks. III. RESUTLS A. Identfcatons of downstream s related to stmulus n vascular endothelal cells (VECs) Prevous expermental studes show that s an mportant cytokne nvolvng n angogeness (see revew n [6]), but ts role n angogeness remans ambguous. Identfyng ts downstream genes and s wll help understand the complex process of angogeness. By combnng multple evdences, ncludng mcroarray expresson profles, PubMed abstracts, GO / annotatons and ESTs (Tab. 1), 70 seed genes related to stmulus n VECs were dentfed. These genes were valdated by another ndependent tme-course mcroarray dataset. Usng DAVID web-based tool, Toll-lke receptor sgnalng (p-value 1.32E-08) and apoptoss (p-value 1.60E-09) were dentfed as sgnfcantly enrched n the 70 seed genes. Toll-lke receptor sgnalng s an mportant n pro-nflammatory reacton and closedrelated to stmulus [26, 27]. And years before, apoptoss n endothelal cells has been reported after stmulus [28, 29]. So the computatonal results are consstent wth the reports n lteratures. The actvty changes cannot be detected on mrna expresson level for many genes due to complex posttranscrptonal regulatons. But now, there s stll no hghthroughput method to profle proten actvtes. The genes nvolved n the same process are more lkely to nteract wth each other n PPI network (see two examples n [30, 31]). So we extended the seed genes to ther drect neghbors n PPI network to cover a broader set of downstream genes. A set of 570 genes drectly nteracted wth seeds () were dentfed n PPI network. Then these were also mapped to s by DAVID enrchment analyss. Except the above two s, other sx s were also sgnfcantly enrched (p-value < 0.001): ErbB sgnalng, MAPK sgnalng, TGF-beta sgnalng, sgnalng and cell cycle (Tab. 3).

4 TABLE III. ENRICHED PATHWAYS IN DIFFERENT SETS OF GENES. Pathway p-value a Foreground Background Apoptoss Toll-lke receptor sgnalng Toll-lke receptor sgnalng 1.60E E E-14 seed genes seed genes Cell Cycle 2.82E-12 ErbB sgnalng MAPK sgnalng 5.03E E-10 Apoptoss 5.46E-08 TGF-beta sgnalng Jak-STAT sgnalng sgnalng ErbB sgnalng sgnalng 3.79E E E E-04 BGs 2.09E-02 BGs a. The p-values for enrchments are calculated usng EASE modfed Fsher s exact test. B. Comparng to the seed genes n the -nduced proangogenc process s the one of key growth factors whch greatly promotes endothelal cells mgraton and prolferaton, and nduces angogeness. Comparng to -nduced process would provde mportant nformaton to understand the stmulus n VECs. Usng the same method, 78 seed genes were dentfed. But only eght of them are overlapped wth seed genes (Fsher s exact test p-value > 0.05). It seems that the two processes are rrelevant. Then we tred to analyze the relatonshps between the two sets of genes n PPI network. It s found that they are close-related n the network: 1) the shortest paths from the seed genes to the seed genes are sgnfcantly shorter than the other genes (average shortest path length: 1.98 for seeds, 2.41 for background genes, t-test p- value 9.08E-06); 2) n the 521 -DIGs (already excludng the genes nteracted wth the eght overlapped genes), 135 are overlapped wth (p-value 8.09E- 12). These results suggest that n VECs the -nduced process s hghly related to -nduced process or proangogenc process. C. Predctons of the s hghly related to proangogenc process In PPI network, 135 brdge genes (BGs) were dentfed to be both drectly nteracted wth and seed genes. These genes are more lkely to be nvolved n proangogenc process than the other. The enrchment was compared n BGs aganst usng EASE modfed Fsher s exact test (Tab. 2). Fgure 2. The network of -nduced process n vascular endothelal cells. The s n crcle are enrched s n seed genes or/and. The s outsde crcle are enrched s only n seed genes or/and -DIGs. Two s, ErbB sgnalng (p-value 5.47E- 04) and sgnalng (p-value 2.09E-02) were predcted as pro-angogenc, because they have sgnfcant enrchment ncrease n BGs than n all. Accordng to annotaton, the major role of the two s s to promote cell prolferaton and dfferentaton. Addtonally, the enrchments of MAPK sgnalng (p-value 0.150) and cell cycle (p-value 0.183) are also slghtly ncreased. Except the four ncreased s, the other s are more related to apoptoss or cell death. For example, actvaton of TGF-beta sgnalng wll cause cell death of endothelal cells [32, 33]. These results ndcate that the nduced downstream s have both roles n promotng cell prolferaton (pro-angogenc) and promotng cell death (ant-angogenc). The dfferent dynamc changes of the actvtes n these s may cause dfferent effects on angogeness. D. Network of the s related to stmulus Accordng to prevous bologcal results and above computatonal analyss, t s suggested that sngle s not enough to explan the complex process n VECs after stmulus. Put all the related nto a unfed network wll beneft future studes. By analyzng the dstance n PPI network, possble crosstalks between dfferent s were dentfed. Many s are close-related n PPI network (Fg. 2), whch suggestng complex nteractons between them. MAPK sgnalng and ErbB sgnalng are hub nodes n the network, both of whch connect wth seven dfferent s. Whle TGF-beta sgnalng does not connect wth any other. Based on ths network, other types of s, such as human dseases s, can also be easly added.

5 IV. DISCUSSIONS Identfcatons of the downstream s are very useful for understandng a specfc bologcal or physologcal process. Here, we presented a new method to dentfy and analyze the downstream s manly based on mcroarray data, genome-wde proten-proten nteracton (PPI) data and annotatons. Ths method was appled on an mportant bologcal process, stmulus n vascular endothelal cells (VECs), and successfully dentfed several downstream sgnalng s and the possble cross-talks between them. By comparng to a typcal proangogenc process nduced by, the roles of the dentfed s n angogeness were also predcted. In ths study, we manly focus on s nstead of sngle gene. Genome-wde data, such as mcroarray or yeast two hybrd (for proflng proten-proten nteractons), contan many noses. By mappng sngle genes to s can reduce the noses and provde clearer bologcal nterpretatons of the computatonal results. 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