OPEN Network Analysis Reveals Functional SUBJECT AREAS: Cross-links between Disease and CANCER GENETICS DATA INTEGRATION Inflammation Genes GENETIC INTERACTION Yunpeng Zhang1*, Huihui Fan1*, Juan Xu1, Yun Xiao1, Yanjun Xu1, Yixue Li1,2 & Xia Li1 BIOINFORMATICS 1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, Chin, 2Shanghai Received Center for Bioinformation Technology, Shanghai, People’s Republic of China. 11 July 2013 Accepted Connections between inflammation and diseases are suggested important in understanding the genetic 18 November 2013 mechanisms of diseases. However, studies on the functional cross-links between inflammation and disease genes are still in their early stages. We integrated the protein–protein interaction (PPI), inflammation genes, Published and gene–disease associations to construct a disease-inflammation network (DIN). We found that nodes, 5 December 2013 which are both inflammation and disease genes (namely inter-genes), are topologically important in the DIN structure. Via mapping inter-genes to PPI, we classified diseases into two categories, which are significantly different in Intimacy measuring the contribution of inflammation genes to the connections between disease pairs. Furthermore, we constructed a cross-talking subpathways network. As indicated, the Correspondence and cross-subpathway analysis shows great performance in capturing higher-level relationship among requests for materials inflammation and disease processes. Collectively, The network-based analysis provides us a rather should be addressed to promising insight into the intricate relationship between inflammation and disease genes. Y.X.L. ([email protected]) or X.L. (lixia@hrbmu. ne of the major tasks for contemporary biology and medicine is to decipher the underlying mechanisms edu.cn) of human complex diseases. Inflammation has been proposed as the seventh hallmark of cancer1, which O significantly contributes to different development stages of various diseases. Researches on the links between inflammation and disease genes are thus helpful to understand the complex nature of human genetic * These authors diseases. However, few systematic studies on the functional links have been reported. contributed equally to During the past decades, great efforts have been dedicated to identifying disease-related genes, proteins, and this work. metabolites, which directly or indirectly connect to each other through computational or experimentally vali- dated interactions. In an early study on inflammatory diseases, such as rheumatoid arthritis and inflammatory bowel disease, Heller et al.2 identified disease-related genes by using cDNA microarrays. Later, with the advent of sequencing, Jones et al.3 implicated PALB2 as a susceptibility gene of pancreatic cancer with the use of exomic sequencing. An early systemic study of human disease genes completed by Wu et al.4, integrated human protein– protein interactions and known gene-phenotype associations to systematically predict disease genes related to various phenotypes. Turner et al.5 and Furney et al.6 also contributed to the studies of human disease genes. However, few studies have focused on the relationships among genes corresponding to different disease pheno- types. Goh et al.7 constructed a disease phenome network (human disease network, HDN) and a disease genome network (disease gene network, DGN), which indicated a common genetic origin of many diseases through disease-gene association pairs. Recently, inflammation, as an important factor in the initiation and progression of various diseases, has been generally accepted. Donoso et al.8 explored the role of inflammation in age-related macular degeneration. In another study on colorectal cancer, Itzkowitz et al.9 emphasized the important roles of inflammation. Nevertheless, none of the studies have systematically characterized the functional links between inflammation and disease genes at a network level. In this study, we focused on inflammation, controlled by both environmental and genetic factors and which is one of the most pivotal factors in inducing various diseases, to study the functional cross-links between inflam- mation and disease genes, as well as the mechanisms of their action. We integrated the human PPI network, inflammation genes, and gene–disease associations to construct a disease-inflammation network (DIN) and then dissected the topological characteristics of the network. In order to describe the relationships between inflam- mation and disease genes from a systems perspective, we classified diseases into type I diseases, which are significantly associated with inflammation genes, and type II diseases, which are not. Subsequently, we defined Intimacy as the contribution of inflammation genes to the connections between one disease and another, which SCIENTIFIC REPORTS | 3 : 3426 | DOI: 10.1038/srep03426 1 www.nature.com/scientificreports tends to be higher in type I diseases. Finally, we showed that inflam- first examined the overlap among disease genes, inflammation genes, mation genes make great contributions to connections between most and PPI nodes (Figure 2A). As shown, we could classify the nodes in genetic diseases, especially in the associations between infection and the DIN as disease genes only, inflammation genes only, or both immunity, as well as between infection and cancer, at a network level disease and inflammation genes (henceforth, inter-genes). Subsequ- and a subpathway network level. ently, we determined the topological characteristics of the DIN, such as the degree, clustering, and topological coefficient. The degree Results distribution followed p(k) / k20.9536 using all the nodes in the DIN Construction of the DIN . The local inflammatory microenviron- (Figure 2B), which showed that the DIN is a scale-free biological ment, which is essential for the initiation and progression of various network. In a scale-free network11,mostofthenodeshaveonlya diseases, can be depicted by representative inflammation genes. To few interactions, whereas a few nodes with a large number of interpret the functional links between genetic diseases and inflam- interactions are tended to be hubs. We further examined the degree matory microenvironment, we used disease and inflammation genes distributions of three kinds of nodes in the DIN. As shown, the to construct a disease-inflammation network (DIN), integrating PPI general degree distribution of nodes that are inter-genes was information and gene–disease associations. There are 2831 disease- significantly greater than that of nodes that are disease genes only, related genes curated by the GAD and 231 inflammation genes generated from the GO database10. We mapped all these genes to with a p-value of 0.005 (Wilcoxon’s Rank-Sum Test, Figure 2C). the PPI network of the HPRD and then extracted the maximal Similarly, it was also significantly higher, when comparing the connected component as the DIN (Figure 1). degree distribution of inter-genes with inflammation genes only (p As shown in Figure 1, the DIN contained 1867 nodes with 6252 5 0.0012, Wilcoxon’s Rank-Sum Test, Figure 2C). While those nodes interactions. Finally, 1815 disease genes and 160 inflammation genes in the DIN that are inflammation genes only had a median degree of were included. 3, which is definitely equal to that of nodes that are disease genes only (p 5 0.0628, Wilcoxon’s Rank-Sum Test, Figure 2C). Besides, in order Dissection of the DIN. To delicately depict the functional cross- to properly control the analysis of inflammation genes and disease links between disease and inflammation genes in the network, we genes, we again compared them with non-disease genes in the original CALCA HCRTR22 PRXP HBE1 AMY1Y1A CALCRLCCRL TNFT FSFF44 HBA2 ABCC8 RDH5 LAAMCAMC2HCRTT APOC2 PLAA HHBGBGG2 CXCLC 6 HCRTR1HC 1 CCCCR7CCR7 DRD4 CXCXCLCL3 CCL2CC 24 LAMAMA2 HRH4HRH44 CHRM2CHHR 2 CYP2C9C 9 GNRHRR P2RYY122 CXCR3CCXXCRC 3 VIP AKAP1AKAP110 PTPRPRT APOC1 SLC2C22AA4 JPHJJPPH2 ACTN3CYCYP17AGCKAK1R ABCBCCCC 2 KEL PIGRR CCCL1CCCL1CLCL119 RLBPR B 1 GCGCGR SLCSLLCCO1ACO A2 AVP CCYPP2E1P2 1 AVPAVVVPR11B ADCDCCYAPPTGPGGDGDR1 R CYYPCYPCCY1YYPAP2P222C19 MMTUS1MTTUS11 AADRARA2RRAA2B EDNREDNE NNRRA APOL1 FKBPF P1B PODXDXL EDN33 KIR2DK 2DDLDL4 TFR2T R22 APOA F DDRP2CETPCETCEETPETT2P CHRM1C LGALSS2 KCNJ1KCCN 1 SHHS KLRCK 2 NPR2 ADAM1AMAM1M 0 NPHSS2 PRRCPCPLOPOXSPPIN PCSKPCSCSC K9 GJB6 BPI P2RY1P2RRYY1 CCL2L2L20 GCCK NPPCN PCP SCG3 HFEF GLGLS ABCCABBCCBCC9 IHH CCL223 LAMB GNGT1 OXTROXOOXTXXT CACNCACNA1CAAC 1C KIR3DL1 PVR CACNA1A11A FCACAR KCNQ4 GP1BB LOXLXL1 HBAA1 ADORA2AADDORRARA2A2A SLC2C A2 CD1B SAAA2 RGS10S110 SCN4SC 4A CACCNA1CN 1S SOD3 ACACE AOXA 1 KIR3DSKIR33DDS1 SCARB1 PDLD IIMM5 CNNP CD1A NPFFRNNP FFR22 TBXABXA2R HTR3A SESEMAEMEMAMA4F CCHCRCCHCC CCRR1 PORPPOOOR KLRDLRDD1 GHRH VAPAPB SNAPAP29 SLCLLC1CC11A1 HLA-G ACE2 XK LCL T NPR3NP 3 CRRH NPPN B RERECE K AVPAVVPPR1R1A DRDDDRRD5RDD DDKKK1 CHCHGH A ADORA1 HTHHTR1FTR F RYRRYR22 LDHLDHHA MUUTETEPSEDESSMDMMBCCRHCRRHR8HHR2HRR22 MPOO KISSK 1R SNTBSSNN 1 CACACNA1AC AA11D cancer CRCRHRHBP MMATN3 GPR50GGPGPR5PRPR50 SLCLLC6CC66A2CCDDD1E GHRHRR CPLCPCPLXPLXX1 NMB PTGIRPPTGI TATAPP2 NPHS1PHHSHS11 GABGABRA4 KIR2K R2DS1 UC4UUCC4 SLC11A6 KIR2DR2DDSS4 SNCSNCC FFHHL1 KIRK R3DR33DL3D 2 CRHR1CCRHRHR1 HHLA-- KIRKKIR2D2DLD 1 aging TRTRDMT1R 1 CD2424 GJBGJBB2 KIR2DR DS2 GHSR GGP9 TNFSF1F 5 T SNAPA 25 SLC6A4 PYYY DTNDTTNT A GNBGNGNBN 1 TAPBP KIR2DL2 RNASE1 FZD3Z TNXT B UMPS CPC OXTOOXT cardiovascular NPY1R CD1DCCDD1DDD11D1D NPY2R NPY5R