Identification of core T cell network based on immunome interactome

BMC Systems Biology

Mauno Vihinen¹  Csaba Ortutay¹  Gabriel N Teku² 

[1] BioMediTech, University of Tampere, Tampere, Finland;Department of Experimental Medical Science, Lund University, Lund, Sweden

关键词: PPI;    Signaling;    TPPIN;    T cell;    Filtering;    Network;    Protein-protein interaction;   

Others  :  1141451
DOI  :  10.1186/1752-0509-8-17

 received in 2013-07-05, accepted in 2014-02-05,  发布年份 2014

【 摘 要 】

Background

Data-driven studies on the dynamics of reconstructed protein-protein interaction (PPI) networks facilitate investigation and identification of proteins important for particular processes or diseases and reduces time and costs of experimental verification. Modeling the dynamics of very large PPI networks is computationally costly.

Results

To circumvent this problem, we created a link-weighted human immunome interactome and performed filtering. We reconstructed the immunome interactome and weighed the links using jackknife gene expression correlation of integrated, time course gene expression data. Statistical significance of the links was computed using the Global Statistical Significance (GloSS) filtering algorithm. P-values from GloSS were computed for the integrated, time course gene expression data. We filtered the immunome interactome to identify core components of the T cell PPI network (TPPIN). The interconnectedness of the major pathways for T cell survival and response, including the T cell receptor, MAPK and JAK-STAT pathways, are maintained in the TPPIN network. The obtained TPPIN network is supported both by Gene Ontology term enrichment analysis along with study of essential genes enrichment.

Conclusions

By integrating gene expression data to the immunome interactome and using a weighted network filtering method, we identified the T cell PPI immune response network. This network reveals the most central and crucial network in T cells. The approach is general and applicable to any dataset that contains sufficient information.

【 授权许可】

   
2014 Teku et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Csermely P, Korcsmaros T, Kiss HJ, London G, Nussinov R: Structure and dynamics of molecular networks: A novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013, 138(3):333-408.
• [2]Karlebach G, Shamir R: Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 2008, 9(10):770-780.
• [3]Kim JR, Kim J, Kwon YK, Lee HY, Heslop-Harrison P, Cho KH: Reduction of complex signaling networks to a representative kernel. Sci Signal 2011, 4:175. ra35
• [4]Newman ME: Finding community structure in networks using the eigenvectors of matrices. Phys Rev E Stat Nonlin Soft Matter Phys 2006, 74(3 Pt 2):036104.
• [5]Kobayashi K, Ehrlich SD, Albertini A, Amati G, Andersen KK, Arnaud M, Asai K, Ashikaga S, Aymerich S, Bessieres P, Boland F, Brignell SC, Bron S, Bunai K, Chapuis J, Christiansen LC, Danchin A, Debarbouille M, Dervyn E, Deuerling E, Devine K, Devine SK, Dreesen O, Errington J, Fillinger S, Foster SJ, Fujita Y, Galizzi A, Gardan R, Eschevins C, Fukushima T, Haga K, Harwood CR, Hecker M, Hosoya D, Hullo MF, Kakeshita H, Karamata D, Kasahara Y, Kawamura F, Koga K, Koski P, Kuwana R, Imamura D, Ishimaru M, Ishikawa S, Ishio I, Le Coq D, Masson A, Mauel C, Meima R, Mellado RP, Moir A, Moriya S, Nagakawa E, Nanamiya H, Nakai S, Nygaard P, Ogura M, Ohanan T, O'Reilly M, O'Rourke M, Pragai Z, Pooley HM, Rapoport G, Rawlins JP, Rivas LA, Rivolta C, Sadaie A, Sadaie Y, Sarvas M, Sato T, Saxild HH, Scanlan E, Schumann W, Seegers JF, Sekiguchi J, Sekowska A, Seror SJ, Simon M, Stragier P, Studer R, Takamatsu H, Tanaka T, Takeuchi M, Thomaides HB, Vagner V, van Dijl JM, Watabe K, Wipat A, Yamamoto H, Yamamoto M, Yamamoto Y, Yamane K, Yata K, Yoshida K, Yoshikawa H, Zuber U, Ogasawara N: Essential Bacillus subtilis genes. Proc Natl Acad Sci U S A 2003, 100(8):4678-4683.
• [6]Commichau FM, Pietack N, Stulke J: Essential genes in Bacillus subtilis: a re-evaluation after ten years. Mol Biosyst 2013, 9(6):1068-1075.
• [7]Song C, Havlin S, Makse HA: Self-similarity of complex networks. Nature 2005, 433(7024):392-395.
• [8]Itzkovitz S, Levitt R, Kashtan N, Milo R, Itzkovitz M, Alon U: Coarse-graining and self-dissimilarity of complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 2005, 71(1 Pt 2):016127.
• [9]Santoni D, Pedicini M, Castiglione F: Implementation of a regulatory gene network to simulate the TH1/2 differentiation in an agent-based model of hypersensitivity reactions. Bioinformatics 2008, 24(11):1374-1380.
• [10]Serrano MA, Boguna M, Vespignani A: Extracting the multiscale backbone of complex weighted networks. Proc Natl Acad Sci U S A 2009, 106(16):6483-6488.
• [11]Grady D, Thiemann C, Brockmann D: Robust classification of salient links in complex networks. Nat Commun 2012, 3:864.
• [12]Tumminello M, Aste T, Di Matteo T, Mantegna RN: A tool for filtering information in complex systems. Proc Natl Acad Sci U S A 2005, 102(30):10421-10426.
• [13]Radicchi F, Ramasco JJ, Fortunato S: Information filtering in complex weighted networks. Phys Rev E Stat Nonlin Soft Matter Phys 2011, 83(4 Pt 2):046101.
• [14]Klebanov LB, Yakovlev AY: A nitty-gritty aspect of correlation and network inference from gene expression data. Biol Direct 2008, 3:35. BioMed Central Full Text
• [15]Ortutay C, Vihinen M: Immunome knowledge base (IKB): an integrated service for immunome research. BMC Immunol 2009, 10:3. BioMed Central Full Text
• [16]Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012, 40(1):D109-D114.
• [17]Razick S, Magklaras G, Donaldson IM: iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinforma 2008, 9:405.
• [18]Stojmirovic A, Yu YK: ppiTrim: constructing non-redundant and up-to-date interactomes. Database (Oxford) 2011 2011. bar036
• [19]Sayers EW, Barrett T, Benson DA, Bolton E, Bryant SH, Canese K, Chetvernin V, Church DM, Dicuccio M, Federhen S, Feolo M, Fingerman IM, Geer LY, Helmberg W, Kapustin Y, Krasnov S, Landsman D, Lipman DJ, Lu Z, Madden TL, Madej T, Maglott DR, Marchler-Bauer A, Miller V, Karsch-Mizrachi I, Ostell J, Panchenko A, Phan L, Pruitt KD, Schuler GD, Sequeira E, Sherry ST, Shumway M, Sirotkin K, Slotta D, Souvorov A, Starchenko G, Tatusova TA, Wagner L, Wang Y, Wilbur WJ, Yaschenko E, Ye J: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2012, 40(1):D13-D25.
• [20]Parkinson H, Sarkans U, Kolesnikov N, Abeygunawardena N, Burdett T, Dylag M, Emam I, Farne A, Hastings E, Holloway E, Kurbatova N, Lukk M, Malone J, Mani R, Pilicheva E, Rustici G, Sharma A, Williams E, Adamusiak T, Brandizi M, Sklyar N, Brazma A: ArrayExpress update-an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res 2011, 39(Database issue):D1002-D1004.
• [21]Wippler J, Kouns WC, Schlaeger EJ, Kuhn H, Hadvary P, Steiner B: The integrin αIIb-β3, platelet glycoprotein IIb-IIIa, can form a functionally active heterodimer complex without the cysteine-rich repeats of the β3 subunit. J Biol Chem 1994, 269(12):8754-8761.
• [22]Nakayama T, Yamashita M: The TCR-mediated signaling pathways that control the direction of helper T cell differentiation. Semin Immunol 2010, 22(5):303-309.
• [23]Takino J, Yamagishi S, Takeuchi M: Cancer malignancy is enhanced by glyceraldehyde-derived advanced glycation end-products. J Oncol 2010, 2010:739852.
• [24]Olivieri KC, Mukerji J, Gabuzda D: Nef-mediated enhancement of cellular activation and human immunodeficiency virus type 1 replication in primary T cells is dependent on association with p21-activated kinase 2. Retrovirology 2011, 8:64-4690. 8-64 BioMed Central Full Text
• [25]Karkkainen S, Hiipakka M, Wang JH, Kleino I, Vaha-Jaakkola M, Renkema GH, Liss M, Wagner R, Saksela K: Identification of preferred protein interactions by phage-display of the human Src homology-3 proteome. EMBO Rep 2006, 7(2):186-191.
• [26]Voll RE, Jimi E, Phillips RJ, Barber DF, Rincon M, Hayday AC, Flavell RA, Ghosh S: NF-κB activation by the pre-T cell receptor serves as a selective survival signal in T lymphocyte development. Immunity 2000, 13(5):677-689.
• [27]Macian F: NFAT proteins: key regulators of T-cell development and function. Nat Rev Immunol 2005, 5(6):472-484.
• [28]Albert R, Jeong H, Barabasi AL: Error and attack tolerance of complex networks. Nature 2000, 406(6794):378-382.
• [29]Cox A, Ackert-Bicknell C, Dumont BL, Ding Y, Bell JT, Brockmann GA, Wergedal JE, Bult C, Paigen B, Flint J, Tsaih SW, Churchill GA, Broman KW: A new standard genetic map for the laboratory mouse. Genetics 2009, 182(4):1335-1344.
• [30]Smith-Garvin JE, Koretzky GA, Jordan MS: T cell activation. Annu Rev Immunol 2009, 27:591-619.
• [31]Berg LJ, Finkelstein LD, Lucas JA, Schwartzberg PL: Tec family kinases in T lymphocyte development and function. Annu Rev Immunol 2005, 23:549-600.
• [32]Carpenter G, Ji Q: Phospholipase C-γ as a signal-transducing element. Exp Cell Res 1999, 253(1):15-24.
• [33]Schmitz ML, Bacher S, Dienz O: NF-κB activation pathways induced by T cell costimulation. FASEB J 2003, 17(15):2187-2193.
• [34]Hogan PG, Chen L, Nardone J, Rao A: Transcriptional regulation by calcium, calcineurin, and NFAT. Genes Dev 2003, 17(18):2205-2232.
• [35]Ebinu JO, Bottorff DA, Chan EY, Stang SL, Dunn RJ, Stone JC: RasGRP, a Ras guanyl nucleotide- releasing protein with calcium- and diacylglycerol-binding motifs. Science 1998, 280(5366):1082-1086.
• [36]Tognon CE, Kirk HE, Passmore LA, Whitehead IP, Der CJ, Kay RJ: Regulation of RasGRP via a phorbol ester-responsive C1 domain. Mol Cell Biol 1998, 18(12):6995-7008.
• [37]Thomas G: MAP kinase by any other name smells just as sweet. Cell 1992, 68(1):3-6.
• [38]Karin M, Liu Z, Zandi E: AP-1 function and regulation. Curr Opin Cell Biol 1997, 9(2):240-246.
• [39]Weil R, Israel A: Deciphering the pathway from the TCR to NF-κB. Cell Death Differ 2006, 13(5):826-833.
• [40]Rincon M: MAP-kinase signaling pathways in T cells. Curr Opin Immunol 2001, 13(3):339-345.
• [41]Shuai K, Liu B: Regulation of JAK-STAT signaling in the immune system. Nat Rev Immunol 2003, 3(11):900-911.
• [42]Taminau J, Meganck S, Lazar C, Steenhoff D, Coletta A, Molter C, Duque R, de Schaetzen V, Weiss Solis DY, Bersini H, Nowe A: Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages. BMC Bioinforma 2012, 13:335-2105. 13-335
• [43]Johnson WE, Li C, Rabinovic A: Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007, 8(1):118-127.
• [44]Guo Y, Xiao P, Lei S, Deng F, Xiao GG, Liu Y, Chen X, Li L, Wu S, Chen Y, Jiang H, Tan L, Xie J, Zhu X, Liang S, Deng H: How is mRNA expression predictive for protein expression? A correlation study on human circulating monocytes. Acta Biochim Biophys Sin (Shanghai) 2008, 40(5):426-436.
• [45]Cohen R, Erez K, ben Avraham D, Havlin S: Resilience of the internet to random breakdowns. Phys Rev Lett 2000, 85(21):4626-4628.
• [46]Rui-Sheng W, Reka A: Elementary signaling modes predict the essentiality of signal transduction network components. BMC Syst Biol 2011, 5:44. BioMed Central Full Text
• [47]Mendoza L, Pardo F: A robust model to describe the differentiation of T-helper cells. Theory Biosci 2010, 129(4):283-293.
• [48]Mendoza L: A network model for the control of the differentiation process in Th cells. BioSystems 2006, 84(2):101-114.
• [49]Mendoza L, Xenarios I: A method for the generation of standardized qualitative dynamical systems of regulatory networks. Theor Biol Med Model 2006, 3:13. BioMed Central Full Text
• [50]Csardi G, Nepusz T: The igraph software package for complex network research. Inter J, Complex Systems 2006, 1(1):1695.
• [51]R: A Language and Environment for Statistical Computing http://www.r-project.org/ webcite
• [52]Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011, 27(3):431-432.
• [53]Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004, 5(10):R80. BioMed Central Full Text
• [54]Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003, 4(2):249-264.
• [55]Gautier L, Cope L, Bolstad BM, Irizarry RA: Affy-analysis of affymetrix GeneChip data at the probe level. Bioinformatics 2004, 20(3):307-315.
• [56]Bioconductor task view: annotation data http://www.bioconductor.org/packages/release/BiocViews.html#___AffymetrixChip webcite
• [57]The Gene Ontology Consortium: The Gene Ontology: enhancements for 2011. Nucleic Acids Res 2012, 40(D1):D559-D564.
• [58]Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Stat Methodol 1995, 57(1):289-300.
• [59]Zhang B, Kirov S, Snoddy J: WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res 2005, 33(Web Server issue):W741-W748.
• [60]Yu G, Li F, Qin Y, Bo X, Wu Y, Wang S: GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 2010, 26(7):976-978.
• [61]Durinck S, Spellman PT, Birney E, Huber W: Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature Protocols 2009, 4(8):1184-1191.