Predicting the Potential Ankylosing Spondylitis-Related Genes Utilizing Bioinformatics Approaches

Rheumatol Int (2015) 35:973–979 Rheumatology DOI 10.1007/s00296-014-3178-9 INTERNATIONAL

ORIGINAL ARTICLE - GENES AND DISEASE

Predicting the potential ankylosing spondylitis-related genes utilizing bioinformatics approaches

Hao Zhao · Dan Wang · Deyu Fu · Luan Xue

Received: 25 August 2014 / Accepted: 11 November 2014 / Published online: 29 November 2014 © Springer-Verlag Berlin Heidelberg 2014

Abstract Given that ankylosing spondylitis (AS) occurs down-regulated genes. These DEGs were significantly in approximately 5 out of 1,000 adults of European descent enriched in phosphorylation (p 1.21E 05) and positive = − and the unclear pathogenesis, the aim of the research was regulation of gene expression (p 1.25E 03). Further- = − to further predict the molecular mechanism of this dis- more, one module was screened out from the up-regulated ease. The Affymetrix chip data GSE25101 were available network, which contained 39 nodes and 205 edges. More- from Gene Expression Omnibus database. First of all, dif- over, the nodes in the module were significantly enriched ferentially expressed genes (DEGs) were identified by in ribosomal protein (RPL17, ribosomal protein L17 and Limma package in R. Moreover, DAVID was used to per- MRPL22, mitochondrial ribosomal protein L22) and pro- form gene set enrichment analysis of DEGs. In addition, teasome (PSMA6, proteasome subunit, alpha type 6, miRanda, miRDB, miRWalk, RNA22 and TargetScan were PSMA4)-related domains. Our findings that might explore applied to predict microRNA-target associations. Mean- the potential pathogenesis of AS and RPL17, MRPL22, while, STRING 9.0 was utilized to collect protein–protein PSMA6 and PSMA4 have the potential to be the biomark- interactions (PPIs) with confidence score >0.4. Then, the ers for the disease. PPI networks for up- and down-regulated genes were constructed, and the clustering analysis was undergone using Keywords Ankylosing spondylitis · Differentially ClusterONE. Finally, protein-domain enrichment analy- expressed gene · MicroRNA · Protein–protein interaction sis of modules was conducted using DAVID. Total 145 network · Functional analysis DEGs were identified, including 103 up-regulated and 42

Introduction H. Zhao (*) Department of Arthritis Emergency, Guanghua Integrative Medicine Hospital, Changning District, Shanghai, China Ankylosing spondylitis (AS) has been found to be a com- e-mail: zhh‑[email protected] mon inflammatory rheumatic disease predominantly of the axial skeleton, causing severe inflammatory back H. Zhao pain, inducing structural and functional impairments Institute of Arthritis Research, Shanghai Academy of Chinese Medical Sciences, 540 Xinhua Road, Shanghai 200052, China and decreasing patients’ quality of life [1]. The disease is characterized by the inflammation of the spine and sacro- D. Wang · L. Xue iliac joints, which will then causing pain and stiffness and Department of Rheumatology, Yueyang Hospital of Integrated ultimately new bone formation and leading to progressive Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China joint ankylosis [2]. However, the disease status, including terms of disease activity, disease progression and prognosis D. Fu are difficult to define in AS3 [ ]. Currently, the underlying Department of Cardiovascular Medicine, Yueyang Hospital molecular mechanism of the disease is still unclear. There- of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, fore, there is an urgent need to predict the pathogenesis of Shanghai 200437, China AS.

1 3 974 Rheumatol Int (2015) 35:973–979

MicroRNAs (miRNAs) are endogenous 22-nucleotide was used to conduct the multiple correction, and false dis- RNAs, some of which are discovered playing important covery rate (FDR) was obtained. Finally, FDR < 0.05 and regulatory roles in animals by targeting the messages of |log2FC| > 0.5 were set as thresholds to identify DEGs protein-coding genes for translational repression [4]. The between the two kinds of samples. alterations of miRNAs are involved in the initiation and progression of human cancer. MicroRNA-146a (miR-146a) Functional enrichment analysis and its target IL-1R-associated kinase (IRAK1) have been detected playing a role in psoriatic arthritis susceptibility In the present study, database for annotation, visualiza- [5]. Additionally, human leukocyte antigen (HLA)-B27 is tion and integrated discovery (DAVID) [14] was applied to discovered playing an important role in AS, and the evi- conduct gene ontology (GO) analysis of DEGs. GO terms dence came from linkage and association studies both in are significantly overrepresented in a set of genes from humans and in transgenic animal models [6, 7]. HLA mark- three aspects, including cellular component (CC), molecu- ers and linkage disequilibrium blocks near HLA-DPA1 lar function (MF) and biological process (BP) [15]. In our and HLA-DPB1 are statistically associated with AS [8]. work, the significant GO terms with p < 0.05 and the num- Additionally, endoplasmic reticulum aminopeptidase 1 ber of DEGs > 2 were selected for further analysis. (ERAP1), interactived with HLA-B27, is also important in the pathogenesis of AS [9]. Although several factors have Predicting miRNAs for DEGs been found related to the disease, the molecular mechanism has not been fully described. Thus, in the present study, we Total five miRNA-target prediction tools, including utilized several informatics approaches to further investi- miRanda [16], miRDB [17], miRWalk [18], RNA22 [19] gate the mechanism of AS. and TargetScan [20] were used to predict miRNAs that reg- In the current study, original chip data were downloaded ulate the identified DEGs. miRNAs, predicted by more than and then the differentially expressed genes (DEGs) were three times, were screened out for further analysis. identified between AS and normal controls. By analyzing the gene expression alterations and miRNA-target associa- Constructing protein–protein interaction network tions, we predicted the roles of genes in AS progression. Furthermore, the modules in PPI networks were ana- As a database of predicted functional associations between lyzed and the significantly enriched GO terms and protein proteins, Search Tool for the Retrieval of Interacting Genes domains were screened out. Based on these results, several (STRING) [21] was used in the current research. Func- genes were detected playing important roles in AS initia- tional links between proteins are usually inferred from tion and progression. genomic associations between the genes that encode them: groups of genes that are required for the same function tend to show similar species coverage and are often located in Materials and methods close proximity on the genome. In the present study, the protein–protein interactions (PPIs) with confidence scores Affymetrix chip data more than 0.4 were selected for PPI network construc- tion. Moreover, the PPIs network was visualized using The Gene Expression Omnibus (GEO) database at National Cytoscape [22], which is a popular bioinformatics package Center for Biotechnology Information (NCBI) is currently for biological network visualization and data integration. the largest fully public gene expression resource. This database includes 214,268 samples and 4,500 platforms Clustering analysis for protein–protein interaction network [10]. The microarray dataset GSE25101 [11] were available from GEO, which included 16 AS patients with active Clustering analysis is a method for detecting potentially disease and 16 gender- and age-matched healthy controls. overlapping protein complexes from PPI data. After The platform was GPL6947 Illumina HumanHT-12 V3.0 the PPI network was constructed, ClusterONE [23] in expression beadchip, containing 49576 probes. Cytoscape was utilized to perform clustering analysis for PPI network with minimum size 5 and minimum = Identifying DEGs density 0.05. Finally, modules with p < 1.0E 5 were = − selected for analysis. Then, DAVID was applied to conduct Given that the chip data were normalized, we utilized protein-domain analysis to the modules based on InterPro Limma package [12] in R (V.3.0.1) to screen out DEGs database [24], and the remarkable domains with p < 0.05 between AS and normal controls. Bayesian methods [13] were selected.

1 3 Rheumatol Int (2015) 35:973–979 975

Table 1 Top 10 terms for up- and down-regulated genes with p < 0.05, respectively DEGs Term ID Description Count p value

Up genes GO:0006119 Oxidative phosphorylation 7 1.21E 05 − GO:0006414 Translational elongation 7 1.44E 05 − GO:0022900 Electron transport chain 7 2.88E 05 − GO:0006412 Translation 10 5.53E 05 − GO:0042773 ATP synthesis coupled electron transport 5 2.08E 04 − GO:0042775 Mitochondrial ATP synthesis coupled electron transport 5 2.08E 04 − GO:0006091 Generation of precursor metabolites and energy 9 2.25E 04 − GO:0022904 Respiratory electron transport chain 5 3.49E 04 − GO:0045333 Cellular respiration 5 1.67E 03 − GO:0015980 Energy derivation by oxidation of organic compounds 5 6.89E 03 − Down genes GO:0010628 Positive regulation of gene expression 7 1.25E 03 − GO:0010604 Positive regulation of macromolecule metabolic process 8 1.82E 03 − GO:0007155 Cell adhesion 7 3.21E 03 − GO:0022610 Biological adhesion 7 3.23E 03 − GO:0006357 Regulation of transcription from RNA polymerase II promoter 7 3.87E 03 − GO:0006968 Cellular defense response 3 7.51E 03 − GO:0045935 Positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process 6 9.72E 03 − GO:0051173 Positive regulation of nitrogen compound metabolic process 6 1.11E 02 − GO:0045860 Positive regulation of protein kinase activity 4 1.18E 02 − GO:0033674 Positive regulation of kinase activity 4 1.29E 02 − Count represents the DEGs enriched in the corresponding term DEGs differentially expressed genes

Results selected with p < 0.05 and number of enriched DEGs > 2. Top 10 terms for DEGs were listed in Table 1. The up-reg- Identification of DEGs ulated genes were discovered significantly enriched in oxidative phosphorylation (p 1.21E 05) and translational = − Gene expression microarrays data were well normalized and elongation (p 1.44E 05). Meanwhile, down-regulated = − were adequated for further analysis. Based on the microar- genes were remarkably disturbed in positive regulation of ray data GSE25101, total 145 DEGs with FDR < 0.05 and gene expression (p 1.25E 03) and positive regulation = − |log FC| > 0.5 were identified between AS patients and nor- of macromolecule metabolic process (p 1.82E 05). The 2 = − mal controls. Moreover, compared with healthy controls, a DEGs enriched in these terms included RPL17 (ribosomal total of 103 and 42 genes were up-regulated and down-regu- protein L17), RPS17 (ribosomal protein S17), NDUFS5 lated in AS samples, respectively. GPR56 (G protein-coupled (NADH dehydrogenase (ubiquinone) Fe–S protein 5, receptor 56) (log FC 0.810) and CCDC72 (coiled-coil 15 kDa), IL2RB (interleukin 2 receptor, beta) and EP300 2 = − domain containing 72) (log FC 1.142) were the most sig- (E1A binding protein p300) and so on. 2 = nificantly down or up-regulated genes, respectively. Addition- ally, other genes such as CX3CR1 (C-X3-C motif chemokine DEGs‑related miRNAs screening and protein–protein receptor 1), IL2RB (interleukin 2 receptor, beta) were down- interaction network regulated genes; meanwhile, TNFSF10 (tumor necrosis fac- tor superfamily, member 10), IL17RC (interleukin 17 recep- Based on the five miRNA prediction tools, the miRNAs for tor C), IL17RD (interleukin 17 receptor D) and COMMD6 up- and down-regulated genes were predicted. Finally, 144 up- (COMM domain containing 6) were up-regulated genes. regulated miRNA-target relationships were obtained, which included 13 targets genes and 118 miRNAs. Meanwhile, total Functional enrichment analysis 406 down-regulated associations were collected containing 14 targets and 289 miRNAs. Interestingly, 5 up-regulated and 15 Using DAVID, GO analysis was performed to the up- and down-regulated miRNA-target relationships with the miRNA down-regulated genes and the significant BP terms were occurred in total five approaches were listed in Table 2.

1 3 976 Rheumatol Int (2015) 35:973–979

Table 2 Significant miRNA-target gene with occurrence 5 regulated by more miRNAs; meanwhile, AAK1 (AP2-asso- = Up genes Down genes ciated kinase 1) and BCL11B (B cell CLL/lymphoma 11B) were the top two nodes in the down-regulated network. Target gene MicroRNA Target gene MicroRNA

CISD2 hsa-miR-134 HNRNPR hsa-miR-335 Clustering and functional analyses CLEC4D hsa-miR-106a HNRNPR hsa-miR-433 CLEC4D hsa-miR-20a EP300 hsa-miR-212 One module from the up-regulated PPI network with density 0.277, quality 0.940 and p 0.00 was screened CLEC4D hsa-miR-106b EP300 hsa-miR-132 = = = AAK1 hsa-miR-203 out and shown in Fig. 2. The module contained 39 nodes AAK1 hsa-miR-34b and 205 edges. Then, DAVID was utilized to perform pro- AAK1 hsa-miR-381 tein-domain analysis to the 39 nodes. Finally, the genes AAK1 hsa-miR-448 were discovered significantly enriched in four domains AAK1 hsa-miR-498 which were related with ribosomal protein and protea- GOLGA8A hsa-miR-182 some (Table 3). RPL17 and MRPL22 mainly enriched in CECR1 hsa-miR-495 ribosomal protein-related domains; meanwhile, PSMA6 BCL11B hsa-miR-363 and PSMA4 were detected to have relationship with BCL11B hsa-miR-20b proteasome. BCL11B hsa-miR-519d CDC25B hsa-miR-214 Discussion

AS is a common inflammatory rheumatic disease that Furthermore, STRING was used, and 250 up-regulated affects the predominantly axial skeleton and causes char- and 3 down-regulated PPIs with score > 0.4 were identi- acteristic inflammatory back pain, which can decrease fied. Then, the miRNA-target relationships and PPIs were the quality of life [25]. In our present research, we aimed combined and visualized (Fig. 1). Total 187 nodes and to explore the potential molecular mechanism of AS by 378 edges were obtained as the up-regulated PPI network using several informatics approaches. In details, the DEGs shown in Fig. 1a. Meanwhile, 307 nodes and 409 edges between AS patients and normal controls were selected and were in the down-regulated PPI network (Fig. 1b). In the their functions were predicted. Then, the networks were up-regulated network, CLEC4D (C-type lectin domain constructed and nodes in the clustered modules were ana- family 4, member D), CISD2 (CDGSH iron sulfur domain lyzed. In a word, the pathogenesis of AS may be associated 2) and AMD1 (adenosylmethionine decarboxylase 1) were with the expression alterations of these genes.

Fig. 1 Network for miRNA-target associations and PPIs. a, b Network with up- and down-regulated genes, respectively. The red and blue dots are up and down-regulated genes. In addition, the yellow triangle nodes are miRNAs

1 3 Rheumatol Int (2015) 35:973–979 977

Fig. 2 Clustered module in up-regulated network with 39 nodes and 205 edges

Table 3 Result of protein- Category Term Description p value Genes domain enrichment analysis for modules (p < 0.05) INTERPRO IPR018260 Ribosomal protein L22/L17, conserved site 0.004437 RPL17, MRPL22 INTERPRO IPR001063 Ribosomal protein L22/L17 0.004437 RPL17, MRPL22 INTERPRO IPR000426 Proteasome, alpha subunit, conserved site 0.017634 PSMA6, PSMA4 INTERPRO IPR001353 Proteasome, subunit alpha/beta 0.041388 PSMA6, PSMA4

Firstly, a total of 145 DEGs (103 up- and 42 down-regu- could play important roles in cell metabolism and the lated genes) between AS patients and normal controls were changes of them may induce many diseases. For example, screened out. The expression alterations of these genes oxidative phosphorylation dysfunction is linked closely may play roles in the AS progression. Interestingly, several with several human diseases [29]. Besides, there is an TNF and interleukin (IL)-related factors were detected as increase of oxidative metabolism of the phagocyte sys- DEGs, such as TNFSF10, IL2RB, IL17RB and IL17RD. tem in AS [30]. In addition, some DEGs may have more It has been found that the TNF-related apoptosis inducing important roles than the others. RPL17 is an inhibitor of ligand is expressed in patients with AS [26]. Besides, Cic- vascular smooth muscle growth and carotid intima forma- cia et al. [27] have reported that blocking IL-10 is sufficient tion [31], and RPS17 is found to be mutated in Diamond– to induce Th17 polarization on lamina propria mononuclear Blackfan anemia [32]. The DEGs, including NDUFS5, cells isolated from AS patients. Then, Shaw et al. [28] have IL2RB and EP300 were also enriched in these terms. In discovered that IL17 secreting CD4 T cells implicate in the particular, EP300 is a miRNA regulated metastasis sup- pathogenesis of AS. Our results were consistent with the pressor gene in ductal adenocarcinomas of the pancreas previous reports. [33]. Therefore, the alteration of these gene expressions Subsequently, GO analysis showed that biological pro- may induce AS by affecting these biological processes cesses that enriched by up- and down-regulated DEGs mentioned above.

1 3 978 Rheumatol Int (2015) 35:973–979

Furthermore, the miRNA-target associations and PPIs Conflict of interest The authors have declared that no competing were collected and the corresponding networks were interests exist. constructed. During the last decades, hsa-miR-20a, hsa- miR-300, hsa-miR-222, hsa-miR-185, hsa-miR-155 and hsa-miR-33a [34] have been reported to be related with References AS. Our findings not only were consistent with the reported 1. Braun J, Sieper J (2007) Ankylosing spondylitis. Lancet paper, but also discovered several new associations of 369(9570):1379–1390 miRNAs and proteins. For example, CLEC4D, AAK1 2. Reveille JD, Sims A-M, Danoy P, Evans DM, Leo P, Pointon JJ, and BCL11B which associate with more than two miR- Jin R, Zhou X, Bradbury LA, Appleton LH (2010) Genome-wide NAs may be crucial DEGs in AS development. Besides, association study of ankylosing spondylitis identifies non-MHC susceptibility loci. Nat Genet 42(2):123 CLEC4D has diverse functions including cell adhesion and 3. Machado P, Landewé R, Lie E, Kvien TK, Braun J, Baker D, van plays roles in inflammation and immune response [35], der Heijde D (2011) Ankylosing spondylitis disease activity score while BCL11B involves in the positive regulation of IL2 (ASDAS): defining cut-off values for disease activity states and expression in CD4 T lymphocytes [36]. These DEGs may improvement scores. Ann Rheum Dis 70(1):47–53 + 4. Yekta S, I-h Shih, Bartel DP (2004) MicroRNA-directed cleavage be the biomarkers of AS and play roles in AS progression. of HOXB8 mRNA. Science 304(5670):594–596 Moreover, one module containing 39 nodes was 5. Chatzikyriakidou A, Voulgari P, Georgiou I, Drosos A (2010) The explored from up-regulated network. Then, we discovered role of microRNA-146a (miR-146a) and its target IL-1R-associ- that the nodes in the module mainly enriched in ribosomal ated kinase (IRAK1) in psoriatic arthritis susceptibility. Scand J Immunol 71(5):382–385 protein and proteasome-related domains. Previous studies 6. Thomas GP, Brown MA (2010) Genetics and genomics of anky- found that the upregulation of proteins was found in ubiq- losing spondylitis. Immunol Rev 233(1):162–180 uitin proteasome pathway for AS patients [37]. This result 7. Taurog JD (2009) Animal models of spondyloarthritis. Molecular indicated that the DEGs significantly affect the normal mechanisms of spondyloarthropathies. Springer, Heidelberg, pp 245–254 metabolism of ribosomal protein and proteasome, and then 8. Díaz-Peña R, Aransay AM, Bruges-Armas J, López-Vázquez induce AS. RPL17 and MRPL22 are ribosomal protein- A, Rodríguez-Ezpeleta N, Mendibil I, Sánchez A, Torre-Alonso related genes; meanwhile, PSMA6 (porcine proteasome JC, Bettencourt BF, Mulero J (2011) Fine mapping of a major submit A6) [38] and PSMA4 are proteasome-related genes. histocompatibility complex in ankylosing spondylitis: association of the HLA-DPA1 and HLA-DPB1 regions. Arthritis Rheum Proteasome is involved in the antigen presentation [39] and 63(11):3305–3312 is the almost invariable basis of initial antigen degrada- 9. Haroon N, Tsui FW, Uchanska-Ziegler B, Ziegler A, Inman RD tion [40]; in addition, the N-extended peptides of the MHC (2012) Endoplasmic reticulum aminopeptidase 1 (ERAP1) exhib- class I-presented antigen are trimmed by aminopeptidase its functionally significant interaction with HLA-B27 and relates to subtype specificity in ankylosing spondylitis. Ann Rheum Dis [41]. The abnormal expression of DEGs may disturb the 71(4):589–595 ribosomal protein and proteasome domains that may lead 10. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evan- to the occurrence of AS. Thus, the potential mechanism of gelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R AS may be the alteration of ribosomal protein and proteas- (2007) NCBI GEO: mining tens of millions of expression pro- files—database and tools update. Nucleic Acids Res 35(Suppl ome-related genes. 1):D760–D765 11. Pimentel-Santos FM, Ligeiro D, Matos M, Mourão AF, Costa J, Santos H, Barcelos A, Godinho F, Pinto P, Cruz M (2011) Whole blood transcriptional profiling in ankylosing spondylitis identi- Conclusion fies novel candidate genes that might contribute to the inflammatory and tissue-destructive disease aspects. Arthritis Res Ther By using bioinformatics approaches, we predicted the 13(2):R57 potential mechanism of AS. Finally, the expression changes 12. Diboun I, Wernisch L, Orengo C, Koltzenburg M (2006) Microar- ray analysis after RNA amplification can detect pronounced dif- for some genes, such as RPL17, MRPL22, PSMA6 and ferences in gene expression using limma. BMC Genom 7(1):252 PSMA4, were discovered. These alterations may affect 13. Efron B, Tibshirani R (2002) Empirical Bayes methods and false several normal biological processes and proteins, which discovery rates for microarrays. Genet Epidemiol 23(1):70–86 may be the mechanism of AS. Our findings provide new 14. Alvord G, Roayaei J, Stephens R, Baseler MW, Lane HC, Lem- picki RA (2007) The DAVID Gene Functional Classification knowledge for AS and identify several biomarkers for the Tool: a novel biological module-centric algorithm to functionally disease, which could help to identify patients with AS for analyze large gene lists. Genome Biol 8:R183 early intervention. 15. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT (2000) Gene ontology: tool for the unification of biology. Nat Genet Acknowledgments This work was supported by Shanghai 25(1):25–29 Municipal Health and Family Planning Commission (Project No. 16. John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS 2012QL044A). (2004) Human microRNA targets. PLoS Biol 2(11):e363

1 3 Rheumatol Int (2015) 35:973–979 979

17. Wang X (2008) miRDB: a microRNA target prediction and 31. Smolock EM, Korshunov VA, Glazko G, Qiu X, Gerloff J, Berk functional annotation database with a wiki interface. RNA BC (2012) Ribosomal protein L17, RpL17, is an inhibitor of vas- 14(6):1012–1017 cular smooth muscle growth and carotid intima formation. Circu- 18. Dweep H, Sticht C, Pandey P, Gretz N (2011) miRWalk–data- lation 126(20):2418–2427 base: prediction of possible miRNA binding sites by “walking” 32. Cmejla R, Cmejlova J, Handrkova H, Petrak J, Pospisilova D the genes of three genomes. J Biomed Inform 44(5):839–847 (2007) Ribosomal protein S17 gene (RPS17) is mutated in Dia- 19. Loher P, Rigoutsos I (2012) Interactive exploration of RNA22 mond–Blackfan anemia. Hum Mutat 28(12):1178–1182 microRNA target predictions. Bioinformatics 28(24):3322–3323 33. Mees ST, Mardin WA, Wendel C, Baeumer N, Willscher E, 20. Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, Senninger N, Schleicher C, Colombo-Benkmann M, Haier J often flanked by adenosines, indicates that thousands of human (2010) EP300—A miRNA-regulated metastasis suppressor genes are microRNA targets. Cell 120(1):15–20 gene in ductal adenocarcinomas of the pancreas. Int J Cancer 21. Von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B 126(1):114–124 (2003) STRING: a database of predicted functional associations 34. Yu F, Cui Y, Zhou X, Zhang X, Han J (2011) Osteogenic differ- between proteins. Nucleic Acids Res 31(1):258–261 entiation of human ligament fibroblasts induced by conditioned 22. Smoot ME, Ono K, Ruscheinski J, Wang P-L, Ideker T (2011) medium of osteoclast-like cells. Biosci Trends 5(2):46 Cytoscape 2.8: new features for data integration and network vis- 35. Graham LM, Gupta V, Schafer G, Reid DM, Kimberg M, Den- ualization. Bioinformatics 27(3):431–432 nehy KM, Hornsell WG, Guler R, Campanero-Rhodes MA, Palma 23. Nepusz T, Yu H, Paccanaro A (2012) Detecting overlapping AS (2012) The C-type lectin receptor CLECSF8 (CLEC4D) protein complexes in protein–protein interaction networks. Nat is expressed by myeloid cells and triggers cellular activation Methods 9(5):471–472 through Syk kinase. J Biol Chem 287(31):25964–25974 24. Apweiler R, Attwood TK, Bairoch A, Bateman A, Birney E, 36. Cismasiu VB, Ghanta S, Duque J, Albu DI, Chen H-M, Kas- Biswas M, Bucher P, Cerutti L, Corpet F, Croning MDR (2001) turi R, Avram D (2006) BCL11B participates in the activa- The InterPro database, an integrated documentation resource for tion of IL2 gene expression in CD4 T lymphocytes. Blood protein families, domains and functional sites. Nucleic Acids Res 108(8):2695–2702 + 29(1):37–40 37. Wright C, Edelmann M, Kollnberger S, Kramer H, McGowan S, 25. Anderson JJ, Baron G, Van Der Heijde D, Felson DT, Dougados McHugh K, Taylor S, Kessler B, Bowness P (2009) Ankylosing M (2001) Ankylosing spondylitis assessment group preliminary spondylitis monocytes show upregulation of proteins involved in definition of short-term improvement in ankylosing spondylitis. inflammation and the ubiquitin proteasome pathway. Ann Rheum Arthritis Rheum 44(8):1876–1886 Dis 68(10):1626–1632 26. Zai-Xing Y, Yan L, Hao W, Ye Z, Chang L, Ren-Qian Z (2008) 38. Aldea G, Výprachtický D, Cimrová V (2004) Modification of Preliminary clinical measurement of the expression of TNF- poly (styrene-alt-maleic anhydride) with 1,3,4-oxadiazole units related apoptosis inducing ligand in patients with ankylosing for electroluminescent devices. Macromolecular symposia. spondylitis. J Clin Lab Anal 22(2):138–145 Wiley, Hoboken, pp 523–528 27. Ciccia F, Accardo-Palumbo A, Giardina A, Di Maggio P, Prin- 39. Haroon N, Maksymowych WP, Rahman P, Tsui FW, O’Shea FD, cipato A, Bombardieri M, Rizzo A, Alessandro R, Ferrante A, Inman RD (2012) Radiographic severity of ankylosing spondyli- Principe S (2010) Expansion of intestinal CD4 CD25 high Treg tis is associated with polymorphism of the large multifunctional cells in patients with ankylosing spondylitis: +a putative role for peptidase 2 gene in the spondyloarthritis research consortium of interleukin-10 in preventing intestinal Th17 response. Arthritis Canada cohort. Arthritis Rheum 64(4):1119–1126 Rheum 62(12):3625–3634 40. van Endert P (2011) Post-proteasomal and proteasome-inde- 28. Shaw J, Kollnberger S (2012) New perspectives on the ligands pendent generation of MHC class I ligands. Cell Mol Life Sci and function of the killer cell immunoglobulin-like receptor 68(9):1553–1567 KIR3DL2 in health and disease. Front Immunol 3:339 41. Parkes M, Cortes A, van Heel DA, Brown MA (2013) Genetic 29. DiMauro S, Schon EA (2008) Mitochondrial disorders in the insights into common pathways and complex relationships among nervous system. Annu Rev Neurosci 31:91–123 immune-mediated diseases. Nat Rev Genet 14(9):661–673 30. Wendling D, Didier J, Vuitton D (1991) The phagocyte oxidative metabolism function in ankylosing spondylitis. Rheumatol Int 11(4–5):187–189

1 3