Network Analysis Reveals Functional Cross-Links Between Disease And

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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 validated 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 phenotypes. 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 inflammation 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 inflammation 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

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Figure 1 | The disease and inflammation network (DIN). The network is constructed by mapping disease genes and inflammation genes to the PPI network and then generating the maximal connected component as the DIN. Inflammation genes are colored in grey and disease genes are colored according to their classes. Those genes that are both inflammation genes and disease genes are drawn with black border. MD denotes genes involved in multiple disease classes.

SCIENTIFIC REPORTS | 3 : 3426 | DOI: 10.1038/srep03426 2 www.nature.com/scientificreports

A HPRD Inflammation

Inflammation genes only

Inter-genes; namely both inflammation and disease genes Disease genes only

Disease

3 B 10 C

p<0.01 p<0.01

102

101 Degree Number of genes 101

0 0 10 10 100 101 102 Inter-genes Disease genes only Inflammation genes only Degree DE0 1 10 10

0 10-1 10

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Clustering coefficient 10 10 Topological coefficient

-3 -2 10 10 0 1 2 100 101 102 10 10 10 Degree Degree

Figure 2 | Topological analysis of the DIN. The number of overlapping genes among the nodes of the PPI network, disease genes, and inflammation genes are shown in (A). Black arrow indicates the three categories of inter-genes, inflammation genes only and disease genes only in the DIN. (B) Degree distribution for all the nodes in the DIN is plotted on the x-axis, and the numbers of genes are plotted on the y-axis. (C) Degree distribution for three categories of nodes in the DIN. Significance tests were based on the Wilcoxon’s Rank-Sum Test and p values for comparisons between inter-genes and disease genes only, and between inter-genes and inflammation genes only are both less than 0.01. Clustering coefficients (D) and topological coefficients (E) for all the nodes in the DIN are plotted on the y-axis, and the corresponding degrees are plotted on the x-axis.

PPI network. As indicated, the general degree distribution is signifi- (p 5 2.4687e-75, Wilcoxon’s Rank-Sum Test, Supplementary Figure 1). cantly higher when comparing inflammation genes with non-disease Moreover, in the DIN, the shortest paths show a similar tendency just genes (p 5 6.8293e-07, Wilcoxon’s Rank-Sum Test, Supplementary as the degree distributions for the three kinds of nodes, with nodes Figure 1), as well as comparing disease genes with non-disease genes that are inter-genes having significantly shorter length of paths.

SCIENTIFIC REPORTS | 3 : 3426 | DOI: 10.1038/srep03426 3 www.nature.com/scientificreports

The clustering coefficient of each node in the network is a measure define the significance of the Intimacy for the pair of diseases of the tendency of nodes in a network to form clusters or groups12.In (Figure 5A). Figure 2D, we found that, with an increase in the node degree, the As indicated by the results, Intimacy, among IRD tended to be clustering coefficient decreases. In addition to clustering coefficient, higher than that in NIRD, which suggests the potential functions of the topological coefficient is also computed13, which is used to mea- inflammation genes in bridging the connection between each pair of sure the extent to which a node shares links with the others in a diseases. Biologically, the computation of Intimacy is designed to network. As shown in Figure 2E, with an increase in the node degree, take the direction of disease conversion between each pair of diseases the topological coefficient increases, which was consistent with pre- into consideration. Therefore, in order to measure the general level of vious researches on biological network structures14–16. Additionally, Intimacy between each disease pair, we ranked all disease pairs based in consideration of the accuracy of topological analysis, we also on the sum of Intimacy for each disease pair with direction informa- compared mean values of degree (Supplementary Figure 2), cluster- tion (i.e. sum of Intimacy from disease A to B and that from disease B ing coefficient (Supplementary Figure 3), and topological coefficient to A; Supplementary Table 1). The most associated disease classes (Supplementary Figure 4) of all nodes in the DIN with random bridged by inflammation genes are ‘‘immune’’ and ‘‘infection’’, distribution, separately. 1000 random DINs were extracted from which has already been supported by literature19–21. Therefore, 1000 random PPI networks, using edge permutation with degree of Intimacy defined by inflammation genes, which are also part of each node in the original PPI unchanged. As shown, all these topo- immune response and infection, contributing to the connections logical parameters were significantly higher as comparing with the between immune and infection is unsurprisingly the most relevant random distributions. one. Others ranking in the top 5 includes disease pair of cardiovascu- To further confirm the reality of the DIN, the number of nodes and lar and metabolic (Supplementary Figure 5)22,23, cardiovascular and edges in the real DIN were also compared with the random distri- immune (Supplementary Figure 6)24,25, and aging and cancer bution. As shown in Figure 3, the average number of nodes and edges (Supplementary Figure 7)26,27, whose connections have already been across 1000 random DINs were significantly smaller than that of the shown in relation with the bridgeness of inflammation genes. Disease real DIN. pair of normal variation and metabolic was suggested by us to be newly involved. As supported by the literature28,29, metabolic system Characterization of the functional cross-links between disease and is one of the most fundamental requirements for survival, whose inflammation genes at a network level. To explore the functional proper function is mutually dependent on immune response. cross-links between disease and inflammation genes, we further Inflammation could cause disequilibrium of the mutual dependence mapped 121 inter-genes to the human PPI network and then of metabolic and immune systems and then lead to chronic disorders calculated the significance of the overlap between inter-genes and of homeostasis. Beta-glucuronidase belonging to the disease class of the PPI network through hypergeometric distribution. With 121 normal mutation, which is generally known to be associated with inter-genes mapped to the human PPI network, a significant inflammation in the exudates from gingival30. We therefore inferred overlap was observed with a p-value of 3.6713e-32 (Figure 4A, the disease pair of normal variation and metabolic could be bridged hypergeometric distribution). Subsequently, we focused on the 121 by inflammation genes in proper conditions. overlapping genes, which formed a new network with the maximal To further explain in detail the Intimacy bridged by inflammation connected component containing 32 genes and 49 relationships genes, we focused on two specific disease pairs. The maximal con- (Figure 4B). As shown by the result, inflammation genes tend to be nected component of the genes of each given pair of diseases was associated with multiple disease genes, which indicates an important generated after mapping these genes to the human PPI network and role of inflammation genes in contributing to genetic diseases in the then defined as the gene module of this pair of disease classes. We network. We also examined the distribution of 121 genes into 18 took the gene modules of two pairs of diseases as examples: the pair of disease classes according to the GAD classification system by infection and immunity (Figure 5B) and the pair of infection and excluding the classes ‘‘unknown’’ and ‘‘other’’ (Figure 4C). We cancer (Figure 5C). Generally, genes with higher degree, such as showed that the class ‘‘immune’’ overlaps with most inflammation NFKB1 (Figure 5B), RXRA (Figure 5B), RELA (Figure 5C) and genes, which suggests that alterations in the focal inflammatory CCR5 (Figure 5C), tend to be hubs in the gene modules, and are microenvironment are more associated with immunity. Further- believed to have much more impact on the global structure of the more, some inter-genes are shared by different disease classes, module networks. The transcription factor NFKB1 (Figure 5B) is the termed as multiple diseases (MD), as illustrated in Figure 4B, most abundant form of NF-kappa-B, which is complexed with which shows the multiple functions of inflammation in various the product of the gene RELA (Figure 5C). NF-kappa-B31,32 is a genetic diseases (Figure 4C). transcription factor that is activated by various intra- and extracel- To show the statistical significance of the overlap between inflam- lular stimuli, such as cytokines, oxidant free radicals, ultraviolet irra- mation and disease genes, the p-value and fold-enrichment ratios diation, and bacterial or viral products. The expression of genes (FER) were calculated (Figure 4D). Only four classes, namely regulated by Rel/NFKB members is involved in immunity and apop- chemo-dependency, developmental, normal variation, and psycho- totic and oncogenic processes; thus, NFKB133, as an inflammation logical disease, were not significant (p . 0.001). We termed these gene, is important in linking infection and immunity. Because NF- diseases as non-inflammation-related diseases (NIRD). On the con- KB plays a well-known function in the regulation of inflammation, trary, those diseases with a significant overlap with inflammation we thus reasoned that inflammation bridges infection and immunity. were termed as inflammation-related diseases (IRD). In addition to Epidemiologic studies34,35 have shown that chronic inflammation the most highly associated class ‘‘immune’’, the disease class cardio- predisposes individuals to various types of cancer. Furthermore, it vascular, infection, and cancer were also in relation to inflammation, is estimated that the underlying infections that could cause chronic which has been supported by literature17,18. In order to measure the inflammation are linked to approximately 15% of all deaths from contribution of inflammation genes to the connections between one cancer worldwide36. RELA, as an important inflammatory factor, is disease to another, we further examined the Intimacy of each pair of involved in linking infection and cancer through the mediator of diseases in the two disease categories of IRD and NIRD (details in inflammation37–39. We thus conclude that the inflammation genes Methods). For each pair of diseases, we constructed 1000 random NFKB1 and RELA are important in the link between infection and pseudo-inflammation gene sets, and then computed the random immunity, as well as between infection and cancer. Additionally, Intimacy values to construct a random distribution. By comparing disease gene RXRA (Figure 5B) and CCR5 (Figure 5C) have been the real Intimacy with the random distribution of Intimacy, we could partially validated to be linked to the process of immune40,41 and

SCIENTIFIC REPORTS | 3 : 3426 | DOI: 10.1038/srep03426 4 www.nature.com/scientificreports

0.10

0.05 Density

DIN

0 1740 1760 1780 1800 1820 1840 1860 1880 B number of nodes 0.10

0.08

0.06 Density 0.04

DIN 0.02

0 4800 5000 5200 5400 5600 5800 6000 6200 6400 number of edges

Figure 3 | The further analysis of the DIN. Density plot of the random number of nodes (A) and edges (B) in 1000 random DINs, with the real values of the number of nodes and edges in the DIN indicated by a red downward arrow. infection, respectively42,43. Nevertheless, further researches on these structure data. Using the iSubpathwayMiner software package, two and some other genes are needed to understand the cellular and disease class-related and inflammation-related subpathways were molecular mechanisms mediated by them underlying those complex generated according to 15149 unique gene-disease associations diseases. involving 18 disease classes and 2831 disease genes. Any two subpathways that are significantly enriched for common genes were Further dissection of the functional cross-links based on disease- connected by an edge in the final subpathway–subpathway net- and inflammation-related subpathways. To further assess the func- work if the gene overlapping between them was significant (p , tional cross-links between disease and inflammation genes, we 0.01, hypergeometric distribution). We thus constructed a subpath- constructed a subpathway–subpathway network based on disease- way–subpathway cross-talk network (Figure 6A) with 202 subpath- related subpathways, inflammation-related subpathways, and pathway way nodes and 716 edges.

SCIENTIFIC REPORTS | 3 : 3426 | DOI: 10.1038/srep03426 5 www.nature.com/scientificreports

CXXCXCLCLL1 CXXCCLL6 B CXXCXCLCLL22

CCXXCXCRR1R CCXXCRR2R cancer C55 aging cardiovascular IL88 C33 chemdependency CC33ARR1R developmental CCCLL4L CCXCRXCRR4R hematological CCCLL2L immune CCCCLCL1171 OORMM1M CC4AA CCCRR55 infection CCCCLCL11616 CCCLL8L kidney BMMMPRR1BR CCCRR4R CCCCLCL1111 CCCRCCR1R metabolic BBCLL6 BBMPP2P CCCLL3L3 neurological BBMPP6P CCCRR3R CCCLL7L HHDDACC4C TTGFBGFBB1B normal variation CCCCLCL2262 CXXXCLL10L CCEBPEBPPBP CCCCLCL2L224 CCIITTA other pharmacogenomic IIL1AL1AA RELAA TTLRR5R TTLRR1R1 psychological NNFKBB11 reproduction ILLL1RA1RAAPA RXRAA ITITTGBB2B unknown TOOOLLLIPLIL KKNGG1G1 FOSS TTLR1LR101 vision IIL1BL1BB CCD114 MD KKLLKBB1B inflammation ILLL233R3R ILILL233A CDDD400LG0LGCCD4404 CCYBBBBB CCYBBA TNNFFRRSSF4 TNNNFSSF4S pharmacogenomic (17) CCMAA1A SEERRPIINNA3 MMSMS4AS4AA2A AKT11 C normal variation (5) reproduction (13) neurological (23) vision (11) A psychological (13) metabolic (40) aging (10) kidney (20) 9028 genes in PPI network cancer (34)

189121 2168 Overlap infection (44) cardiovascular (48) Inflammation Disease chemdependency (7) developmental (6) hematological (9) immune (82)

D infection FER=8.44 kidney FER=7.24 immune FER=6.25 aging FER=5.49 cardiovascular FER=4.31 vision FER=3.86 hematological FER=3.74 reproduction FER=3.39 pharmacogenomic FER=3.30 metabolic FER=3.29 normal variation cancer FER=2.66 neurological FER=2.50 chemdependency developmental psychological 0 5 101520253035404550

Figure 4 | Analysis for the importance of inter-genes at the level of network. (A) The number of the overlapping genes between inflammation and disease in human PPI network. (B) A subnetwork was generated by mapping inter-genes to the human PPI network. (C) Distribution of inter-genes into different disease classes. (D) Fold enrichment ratios (FERs) were computed according to the overlapping between inflammation genes and genes from different disease classes.

SCIENTIFIC REPORTS | 3 : 3426 | DOI: 10.1038/srep03426 6 www.nature.com/scientificreports

A chemodependency 3 developmental normal variation 2.5 psychological neurological cancer 2 metabolic pharmacogenomic 1.5 reproduction -logP hematological

vision 1 cardiovascular aging 0.5 immune kidney

infection 0

kidney aging vision cancer infection immune metabolic neurological reproduction psychological cardiovascularhematological developmental LEPFCGR2B normal variation B FCGR2A pharmacogenomic C CXCL5 C7 chemodependency FCGR1A CRP FGF2 C4A C6 PTX3 IL1A C1QA ADRA1A C4B CASP1 CXCL2 IL1RN CR1 C5 infection CFH C3AR1 ADRB2 CXCR2 infection C2 IL1R1 TBP CXCR1 CFBMASP1 NR1I2 immnue CD46 MMP7 cancer LRP1 VDR TGFBR1 IL1B PPARG MMP13 IL8 GC MD CR2 THRB IL12A MMP9SPP1 MD C3 TGFB2 F2 TGFB1 MMP2 RXRA MMP3 CTSG PPARA TGFBR2 NFKBIATNFSF11 IGFBP3 TGFBR3 CCL7 AGT PARP1 CCL3 CCL4 MMP1 THBS1 CMA1 F5 IL2RA EDN1 CYBA CCR3 NCF1 AR CCL8 NCF4 CYBB EDN1 IRF2 CCL16 ACE CCL2 MMP8 NCF2 SERPINA3 SOCS3 CCL5 KITLG NFKB1 ESR1 NOS1 CCL11 IRF1 CCL3L1 BDKRB2 NOS3 CCR1 CMA1 ETS1 STAT6 AGTR1 STAT5A PPIA FCER1A NR3C1 S100A8 AGT MS4A2 IL23A CCR5 TLR9 PTPN6 PTPN11 HSPD1 IL12RB1 SMAD3 ARL6IP5 ITGAL PTPRCCXCR4 CD55 CXCL12 JAK1 TLR10 CREB1 PLCG1 ACVR2A TLR1 MMP1 IL23R TLR3 TLR6 CEBPB JAK2 SOCS1 SELE CAT EGFR AHR CD4 XRCC5 LBP CD14 TERT STAT1 EIF2AK2 RUNX1 XRCC6 TLR2 TIRAP CD4 BMPR1A PTK2 TLR5 DPP4 BMP6 PPP2R1B JAK1 STAT5A SELL SIGIRRPTGS2 TLR4 STAT3 SELPLG NCOA3 SELP PPP1R13L CCL2 CD24 TAF1 SELL HSPA8 VCAM1 TP53 IL1RL1 SELE CXCR4 IL4R MIF TOLLIP LCK CXCL10 CD40 IL13 STAT1 CCR5 PGR CD44 PPARD PTPN11 ELANE IRAK1 CCL11 IL6 MMP14 CXCR3 TRAF6 IL1RAP CCL16 CHEK1 CAV1 MS4A1 IL18R1 MMP3 ZBTB16 IRF3 CD40LG CCL3L1 RELA CXCL12PTPRC CCR1 MMP7 IGKC CCL8 MMP13 CCL3 CD40 IL1R1 F2 TP53BP2 CD40LG SPP1 IRF4 BCL6 IGHG1 CCL26 CCR3 PTGS2 IL1R2 CCL7 PRKCA PIK3R1 CCL5 CCL4 TRAF5 IL4R A2M IGFBP5 CTSG TP53 IL1RN IL1A CCR4 CCL24 NOTCH1 TP53BP1 IL1B MYC MIF PML ADRB2 HCK MMP2 BTRC EP300 CCL17 MEN1 IL13 CTGF ITGAV VCAM1 NR3C1 AR TP73 NME1 IFNG ABL1 CASP1 IRF1 COL2A1TGFB1SPARC C3 IL13RA1IL13RA2 IL8 TNF NFKB1 STAT6 SMAD3 NLRC4 CXCR1 HRAS DAXX CSF1 IL2RA WT1 CXCL2 CXCR2 TNFRSF1B PARP1 NOD1 CASP9 CDK2 E2F1 MDM2 TGFBR3 MMP9 TNFRSF1A CFLAR ESR1BRCA1 SMAD2 IL12A LTBP4 TXN GC CR2CR1 TGFB2 DCN GSK3B NLRP1 NOD2 CTNNB1 NFKBIA LRP1

Figure 5 | Intimacy heatmap of disease pairs and examples indicating the functional cross-links between inflammation genes and disease genes. (A) Examination of the Intimacy bridged by inflammation genes using all disease pairs from different disease classes. Diseases are ordered by their FER values (fold enrichment ratios), and minus 10-based logarithm p-value is showed as the right-side color bar. (B , C) Examples show the functional roles of inflammation genes in bridging the connections between disease pairs. Gene modules were generated from disease pair of infection and immunity, and disease pair of infection and cancer, separately.

In parallel with the two gene modules (Figure 5B and 5C) gener- as well as inflammation- and immune-related subpathways. Some ated from the PPI network, two disease- and inflammation-related new genes were also included, such as IRAK4, IRAK1, and MYD88. subpathway-based subnetworks extracted from the subpathway Shared by multiple immune-, inflammation-, and infection-related network were much more representative of the bridgeness of disease subpathways, such as path:05145_1, path:04722_1, and path: and inflammation genes. For example, five inflammation-related 04620_5, the inflammation gene IRAK144 is a critical mediator of subpathways (path:04620_3, path:04620_11, path:04722_1, path: innate immunity, which is also important in the immune response 05131_2, and path:04620_9) mediated the connections between corresponding to viral infection45,46. Similarly, another IRAK family infection and immunity (Figure 6B). In agreement with Figure 5B, member, IRAK4, is also in control of the immune response to intra- NFKB1 and RELA also emerged as the essential genes in linking cellular infection, such as Chlamydia pneumonia47. Furthermore, the infection and immunity when searching for the common genes sharing of path:05145_1, path:04620_3, and path:04620_11 by the shared by both inflammation- and infection-related subpathways, inflammation gene MYD88 is also critical in the connection between

SCIENTIFIC REPORTS | 3 : 3426 | DOI: 10.1038/srep03426 7 www.nature.com/scientificreports

Psychological-path:04730_2

A other-path:04610_1infection-path:05140_1 neurological-path:04720_1 neurological-path:04020_2 neurological-path:05140_1 neurological-path:05150_1

Psychological-path:04730_1 infection-path:04610_3 Psychological-path:04920_1 cardiovascular-path:04610_1 immune-path:05140_1 pharmacogenomic-path:00140_16 cancer-path:04012_1 pharmacogenomic-path:04920_1 immune-path:04610_1 infection-path:05145_6 reproduction-path:00140_1 hematological-path:04610_1 cancer-path:04620_4 metabolic-path:04012_1 pharmacogenomic-path:05214_1 pharmacogenomic-path:04510_1 developmental-path:05014_2 cardiovascular-path:04620_6 other-path:04510_1 immune-path:05140_5 pharmacogenomic-path:04010_4 infection-path:04620_5 metabolic-path:05214_2 infection-path:04620_6 cancer-path:05214_1 aging-path:04510_1 pharmacogenomic-path:05212_1 metabolic-path:05145_1 cancer-path:04722_1 infection-path:05160_4 infection-path:05142_1 cancer-path:05140_4 cancer-path:04210_5 metabolic-path:04520_3 metabolic-path:05212_1 infection-path:04012_2 cancer-path:04114_5 infection-path:04620_2 immune-path:05145_1 inflammation-path:04620_3 neurological-path:05200_7 pharmacogenomic-path:04722_1 metabolic-path:05223_5 inflammation-path:04620_11 cancer-path:05218_4 cancer-path:05145_1 immune-path:04062_1 cancer-path:05214_2 metabolic-path:04722_1 inflammation-path:04722_1 metabolic-path:04210_3 aging-path:04930_1 metabolic-path:04114_2 cardiovascular-path:04512_10 immune-path:04210_4 pharmacogenomic-path:04620_2 infection-path:05200_4 infection-path:05145_1 neurological-path:04620_2 infection-path:05212_2 metabolic-path:05214_1 immune-path:04620_2inflammation-path:05131_2 metabolic-path:04150_1pharmacogenomic-path:05200_1 metabolic-path:05200_3 hematological-path:04512_1 immune-path:04010_9 chemdependency-path:00380_6 cancer-path:05200_13metabolic-path:04620_5 reproduction-path:04114_1 metabolic-path:04960_1 metabolic-path:04510_1cancer-path:05200_5 cancer-path:04620_3 developmental-path:05145_5 metabolic-path:05218_7 cancer-path:04920_1 other-path:04930_1pharmacogenomic-path:05214_2 cancer-path:04010_3unknown-path:04930_1 cancer-path:05142_1 neurological-path:00350_1 cardiovascular-path:04920_1 metabolic-path:04115_1 metabolic-path:00350_1 infection-path:05140_2 pharmacogenomic-path:05218_4 cardiovascular-path:03320_1 pharmacogenomic-path:04960_2metabolic-path:05200_7 vision-path:04115_1 cardiovascular-path:04512_12 chemdependency-path:00380_4 inflammation-path:04620_9 cardiovascular-path:04115_1 Psychological-path:00590_1 metabolic-path:03320_1 other-path:04115_1 pharmacogenomic-path:04115_1metabolic-path:05200_16 developmental-path:05200_15 immune-path:04630_2 cancer-path:05214_4 metabolic-path:04630_2 cancer-path:05142_15 reproduction-path:00982_21 infection-path:04650_1 pharmacogenomic-path:04630_1 metabolic-path:05215_3cancer-path:04115_1 other-path:00350_2 Psychological-path:04722_1 metabolic-path:05222_2 neurological-path:04114_4immune-path:04115_1 developmental-path:04115_4 cancer-path:05142_11 other-path:00982_21 vision-path:04110_1 pharmacogenomic-path:00982_23 cancer-path:05200_10 cancer-path:05200_14cancer-path:05200_29 variation-path:00982_2developmental-path:05416_3 metabolic-path:04920_1 pharmacogenomic-path:05221_1 cancer-path:04110_1 hematological-path:00982_19 cancer-path:05215_4 metabolic-path:00982_20 metabolic-path:04350_6 pharmacogenomic-path:05215_3vision-path:04610_1 developmental-path:04210_4 infection-path:04630_1 neurological-path:00982_21 metabolic-path:00052_2 normal cancer-path:05220_10 aging-path:04910_1 metabolic-path:04930_2 cancer-path:05014_2 metabolic-path:04110_1 neurological-path:05215_3cancer-path:05200_31 Psychological-path:00350_1 metabolic-path:05221_1immune-path:04650_1 cancer-path:05222_1 immune-path:03320_2 cancer-path:05221_1 Psychological-path:00982_11 aging-path:05218_2 cancer-path:04310_2 other-path:03320_1 cardiovascular-path:04110_1 other-path:04920_1 pharmacogenomic-path:04110_1 chemdependency-path:00350_1

neurological-path:04115_1other-path:05211_2 cancer-path:04350_6 metabolic-path:04910_1 cancer-path:05210_3 cancer-path:05213_1 pharmacogenomic-path:03320_3metabolic-path:04950_2 other-path:05211_4 metabolic-path:00071_1 neurological-path:03320_1 other-path:05200_15 cancer-path:03320_2

Psychological-path:03320_1 neurological-path:05014_1 developmental-path:00140_2 reproduction-path:00140_3 infection-path:00982_19 metabolic-path:00670_1 developmental-path:00670_1 chemdependency-path:00982_13 metabolic-path:00140_4 metabolic-path:00140_7 developmental-path:00140_1 pharmacogenomic-path:00240_5 cancer-path:00140_12

cancer-path:00982_25 metabolic-path:00140_5 cancer-path:00670_1pharmacogenomic-path:00670_1 reproduction-path:00140_4

B C cancer-path:03320_2 infection-path:05160_4 infection-path:05140_1 cancer-path:00670_1 infection-path:05140_2 infection-path:05160_4 infection-path:00982_19 cancer-path:05214_1 infection-path:05145_6 infection-path:05145_6 cancer-path:00982_25 infection-path:04610_3infection-path:04620_6 immune-path:04620_2 infection-path:04012_2 cancer-path:05142_15 infection-path:04012_2 cancer-path:00140_12 immune-path:04630_2 cancer-path:04114_5 inflammation-path:04620_3 cancer-path:04722_1 infection-path:04650_1 inflammation-path:04620_3 infection-path:05142_1 immune-path:04062_1 infection-path:05200_4 cancer-path:05142_11cancer-path:05218_4 inflammation-path:04620_11 infection-path:05212_2 infection-path:04620_6 cancer-path:05214_2 infection-path:05200_4 cancer-path:05140_4 immune-path:04650_1 inflammation-path:04620_11 cancer-path:04620_4 cancer-path:05214_4 immune-path:05145_1 inflammation-path:04722_1 infection-path:05212_2 infection-path:05142_1 infection-path:04650_1 inflammation-path:05131_2 inflammation-path:05131_2 cancer-path:04115_1 immune-path:04210_4 infection-path:04630_1 cancer-path:04010_3 infection-path:04620_2 inflammation-path:04620_9 cancer-path:05200_5 immune-path:05140_5 infection-path:05140_2 infection-path:05145_1 cancer-path:04110_1cancer-path:05215_4 cancer-path:05200_29 cancer-path:05200_14 immune-path:04010_9 inflammation-path:04722_1 inflammation-path:04620_9 cancer-path:05142_1 infection-path:05140_1 infection-path:04620_2 cancer-path:05210_3 infection-path:05145_1 cancer-path:05220_10 cancer-path:05221_1 immune-path:05140_1 cancer-path:04210_5 cancer-path:04920_1 infection-path:00982_19 cancer-path:05145_1 cancer-path:05200_31 infection-path:04620_5 cancer-path:05200_13 immune-path:04610_1 infection-path:04610_3 cancer-path:05222_1 cancer-path:04012_1 cancer-path:04310_2 infection-path:04630_1 infection-path:04620_5 cancer-path:04620_3 immune-path:03320_2 immune-path:04115_1 cancer-path:05014_2 cancer-path:05200_10 cancer-path:04350_6 cancer-path:05213_1

Figure 6 | The cross-talking subpathway network based on disease- and inflammation-related subpathways. The whole subpathway–subpathway cross- talking network contained 202 subpathway nodes and 716 edges. The rectangles in the network correspond to disease- and inflammation-related subpathways. The nodes are colored according to their categories, which contains 18 disease classes obtained from the GAD database (A). (B , C) Examples of subpathway-supathway network showing functional connections bridged by inflammation genes. The network in (B) was generated by extracting inflammation-, immune-, and infection-related subpathways, and the network in (C) was generated through extracting inflammation-, cancer-, and infection-related subpathways.

SCIENTIFIC REPORTS | 3 : 3426 | DOI: 10.1038/srep03426 8 www.nature.com/scientificreports infection and immunity mediated by inflammation. Takeuchi et al.48 associated with an inflammatory microenvironment. This might confirmed that mice with MyD88 deficiency are highly susceptible to thus suggest that path:04620 (Toll-like receptor signaling pathway) Staphylococcus aureus infection and that immune cells can be acti- could be a good exhibitor of cancer progression activated by inflam- vated through the TLR7 MyD88-dependent signaling pathway49. mation and infection. With regard to the connection between infection and cancer mediated by inflammation (Figure 6C), the IL1B and TNF contained Discussion in path: 04620_9 are also implicated in the link between inflam- The identification of human disease-associated genes has long been mation and cancer, in addition to the above-discussed inflammation of central importance in the study of human genetics. With the genes NFKB1, RELA, MyD88, IRAK4, and IRAK1. Gene poly- establishment of the human disease–disease network, a shift has been morphisms in IL1B and TNF could induce cancer risk through an seen from the study of disease genes to the study of associations inflammatory microenvironment in several different popula- between various disease genes. Because the close relationship tions50,51. In addition, the NFKB1 and RELA that reside in the NF- between inflammation and various disease phenotypes has been kB node of path:04620_9 and the NF-kB signaling pathway that widely accepted55, studies of the roles of inflammation in carcinogen- resides upstream of path:04620_9 are important in the tumor-pro- esis have emerged. However, systematic studies on the functional moting processes activated by inflammation and infection52. links remain in their early stages. Interestingly, three subpathways (path:04620_3, path:04620_9, Inflammation contributes to the diverse progression of human and path:04620_11) reside in the same KEGG pathway, path:04620 complex diseases, such as immunity and cancer. To study the func- (Toll-like receptor signaling pathway; Figure 7), which is associated tional cross-links and the underlying mechanisms of their action, we with infection, inflammation, and cancer53,54. Furthermore, two integrated the PPI network, gene–disease information, and inflam- inflammation-related subpathways, path:04620_3 and path:04620_ mation genes to construct a DIN network. By further dissecting 11, which are associated with immunity and infection mediated by topological parameters, such as the shortest paths of the DIN, we MYD88, are located upstream of path:04620. Moreover, the subpath- found that nodes that are inter-genes are important in the mainten- way path:04620_9 is located downstream of path:04620. Collectively, ance of the network structure, which is consistent with a previous path:04620_9 is related to the promotion of tumor, which is tightly study7. In the present study, we confirmed the topological import-

Figure 7 | Detailed information of subpathways (i.e. path:04620_3, path:04620_11, and path:04620_9) in the KEGG (i.e. path:04620). Nodes marked by red asterisk are genes significantly overlapping with the corresponding subpathways.

SCIENTIFIC REPORTS | 3 : 3426 | DOI: 10.1038/srep03426 9 www.nature.com/scientificreports

ance of disease genes in the DIN. In addition, we showed that inter- can define i(gjo,gkp) from the following transformation: genes, as both inflammation and cancer genes, are topologically 1 i(gjo,gkp)~ , ð2Þ important. After mapping inter-genes to the PPI network, we com- 1zd(gjo,gkp) puted the FER values between inter-genes and different disease gene sets and then classified diseases as either IRD or NIRD. Based on the where d(gjo,gkp) is the shortest path length between gjo and gkp. two classifications, we further examined the Intimacy of each pair of Generation of random networks. The PPI network was randomized 1000 times diseases mediated by inflammation genes. As shown by the results, using edge permutation, with the degree corresponding to each node in the original the Intimacy of IRD was found to be higher than that of NIRD, which PPI network kept unchanged. The edge permutation approach has been widely is a good indicator of the functional cross-links between inflam- applied on various kinds of networks to generate randomized networks60. All 1000 random PPI networks were used to construct 1000 random DINs via mapping mation and various disease phenotypes. inflammation and disease genes to those random PPI networks and then extracting We comprehensively examined all the disease pairs via ranking the maximal connected component, separately. them based on the summed Intimacy for each pair (i.e. sum the Intimacy from disease A to B and disease B to A; disease A and Random test and statistical analysis. In order to compute the significance of disease B belong to one disease pair; Supplementary Table 1). In Intimacy bridged by inflammation genes, we constructed 1000 random pseudo- inflammation gene sets. Each pseudo-inflammation gene set contained the same total, we found that there were only one pair of inflammatory dis- number of genes as the real inflammation gene set, and each pseudo-inflammation eases that ranked in the top 5 of the table, together with other three gene had the same degree as the real one. Given a pair of diseases, we computed the pairs of inflammation-related diseases; their connections bridged by real Intimacy bridged by the real inflammation gene set, and then computed a inflammation were all confirmed by other researches. In addition to random distribution of Intimacy values using the 1000 pseudo-inflammation gene sets. Subsequently, we could define the significance of the Intimacy for the pair of those literature-supported pairs, the remaining pair ranking in the diseases, via comparing with the random distribution of Intimacy. top 5 was newly found by us that might be involved in the bridgeness The significance of the overlap between inflammation genes and disease genes by inflammation genes in proper conditions (i.e. disease pair of nor- against nodes of the human PPI network, and the overlap between gene sets from different subpathways, were computed by hypergeometric distribution as follows: mal variation and metabolic). As shown by the examples of gene modules extracted from the PPI network, inflammation was indeed m N{m X found to be important in mediating between infection and immun- k k n{k P(X§kjN,m,n)~1{ , ð3Þ ity, as well as between infection and cancer. Collectively, as suggested i~0 N by the results, our integrated approach could not only recur the n connections bridged by inflammation genes between those well- considering that a set of N elements has two subsets with m and n elements, known inflammatory disease pairs, but also predict new connections, respectively. We calculated the probability of containing at least k overlapping ele- which could therefore help us to study the functional roles of inflam- ments using the formula. mation genes between disease pairs, via their structural importance at the level of network. Construction of subpathway–subpathway network. We used the method that has Additionally, we also constructed a subpathway–subpathway net- been incorporated into the CRAN package iSubpathwayMiner (http://cran.r-project. org/web/packages/iSubpathwayMiner/) to identify the disease-related subpathways. work based on disease- and inflammation-related subpathways to In this method, the subpathway regions were located by lenient distance similarity of characterize the cross-links, as illustrated by the two examples of signature nodes within the pathway structure. Subsequently, we used hypergeometric disease-related subpathways mediated by inflammation-related sub- test to identify disease-related subpathways. pathways. Furthermore, this network-based analysis adds a new layer of complexity to the study of human diseases in that it considers 1. Colotta, F., Allavena, P., Sica, A., Garlanda, C. & Mantovani, A. 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All authors read and approved the final anuscript. acini to inflammation-associated cell death following cerulein pancreatitis. J Clin Invest 117, 1490–1501 (2007). 40. Li, M., Zhang, J., Wu, Y. & Li, J. The regulation of thymic stromal lymphopoietin Additional information in gut immune homeostasis. Dig Dis Sci 56, 2215–2220 (2011). Supplementary information accompanies this paper at http://www.nature.com/ 41. Clement, S., Pascarella, S. & Negro, F. Hepatitis C virus infection: molecular scientificreports pathways to steatosis, insulin resistance and oxidative stress. Viruses 1, 126–143 (2009). Competing financial interests: The authors declare no competing financial interests. 42. Fox, J. G. et al. Concurrent enteric helminth infection modulates inflammation How to cite this article: Zhang, Y.P. et al. Network Analysis Reveals Functional Cross-links and gastric immune responses and reduces helicobacter-induced gastric atrophy. between Disease and Inflammation Genes. Sci. Rep. 3, 3426; DOI:10.1038/srep03426 Nat Med 6, 536–542 (2000). (2013). 43. Choe, H. et al. The beta-chemokine receptors CCR3 and CCR5 facilitate infection by primary HIV-1 isolates. Cell 85, 1135–1148 (1996). This work is licensed under a Creative Commons Attribution- 44. Gottipati, S., Rao, N. L. & Fung-Leung, W. P. IRAK1: a critical signaling mediator NonCommercial-NoDerivs 3.0 Unported license. To view a copy of this license, of innate immunity. Cell Signal 20, 269–276 (2008). visit http://creativecommons.org/licenses/by-nc-nd/3.0

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