The Korean journal of internal medicine

  • 제30권2호
  • /
  • Pages.148-160
  • /
  • 2015
  • /
  • 1226-3303(pISSN)
  • /
  • 2005-6648(eISSN)
대한내과학회 (The Korean Association of Internal Medicine)
권호 표지
DOI QR코드

DOI QR Code

Applications of systems approaches in the study of rheumatic diseases

  • Kim, Ki-Jo (Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Lee, Saseong (POSTECH-CATHOLIC BioMedical Engineering Institute, The Catholic University of Korea) ;
  • Kim, Wan-Uk (POSTECH-CATHOLIC BioMedical Engineering Institute, The Catholic University of Korea)
  • 투고 : 2014.10.30
  • 심사 : 2014.12.23
  • 발행 : 2015.03.01
⟨ 이전 논문 다음 논문 ⟩

초록

The complex interaction of molecules within a biological system constitutes a functional module. These modules are then acted upon by both internal and external factors, such as genetic and environmental stresses, which under certain conditions can manifest as complex disease phenotypes. Recent advances in high-throughput biological analyses, in combination with improved computational methods for data enrichment, functional annotation, and network visualization, have enabled a much deeper understanding of the mechanisms underlying important biological processes by identifying functional modules that are temporally and spatially perturbed in the context of disease development. Systems biology approaches such as these have produced compelling observations that would be impossible to replicate using classical methodologies, with greater insights expected as both the technology and methods improve in the coming years. Here, we examine the use of systems biology and network analysis in the study of a wide range of rheumatic diseases to better understand the underlying molecular and clinical features.

키워드

과제정보

연구 과제 주관 기관 : Ministry for Health, Welfare and Family Affairs, National Research Foundation of Korea (NRF)

참고문헌

  1. Craig J. Complex diseases: research and applications. Nat Educ 2008;1:184.
  2. Chuang HY, Hofree M, Ideker T. A decade of systems biology. Annu Rev Cell Dev Biol 2010;26:721-744. https://doi.org/10.1146/annurev-cellbio-100109-104122
  3. Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet 2011;12:56-68. https://doi.org/10.1038/nrg2918
  4. Ku CS, Loy EY, Pawitan Y, Chia KS. The pursuit of genome-wide association studies: where are we now? J Hum Genet 2010;55:195-206. https://doi.org/10.1038/jhg.2010.19
  5. Ormond KE, Wheeler MT, Hudgins L, et al. Challenges in the clinical application of whole-genome sequencing. Lancet 2010;375:1749-1751. https://doi.org/10.1016/S0140-6736(10)60599-5
  6. Wang J, Zhang Y, Marian C, Ressom HW. Identification of aberrant pathways and network activities from high-throughput data. Brief Bioinform 2012;13:406-419. https://doi.org/10.1093/bib/bbs001
  7. Altman RB. Translational bioinformatics: linking the molecular world to the clinical world. Clin Pharmacol Ther 2012;91:994-1000. https://doi.org/10.1038/clpt.2012.49
  8. Likic VA, McConville MJ, Lithgow T, Bacic A. Systems biology: the next frontier for bioinformatics. Adv Bioinformatics 2010;2010:268925.
  9. Hartwell LH, Hopfield JJ, Leibler S, Murray AW. From molecular to modular cell biology. Nature 1999;402(6761 Suppl):C47-C52. https://doi.org/10.1038/35011540
  10. Koutsogiannouli E, Papavassiliou AG, Papanikolaou NA. Complexity in cancer biology: is systems biology the answer? Cancer Med 2013;2:164-177. https://doi.org/10.1002/cam4.62
  11. Furlong LI. Human diseases through the lens of network biology. Trends Genet 2013;29:150-159. https://doi.org/10.1016/j.tig.2012.11.004
  12. Sarkar IN, Butte AJ, Lussier YA, Tarczy-Hornoch P, Ohno-Machado L. Translational bioinformatics: linking knowledge across biological and clinical realms. J Am Med Inform Assoc 2011;18:354-357. https://doi.org/10.1136/amiajnl-2011-000245
  13. MacLellan WR, Wang Y, Lusis AJ. Systems-based approaches to cardiovascular disease. Nat Rev Cardiol 2012;9:172-184. https://doi.org/10.1038/nrcardio.2011.208
  14. You S, Cho CS, Lee I, Hood L, Hwang D, Kim WU. A systems approach to rheumatoid arthritis. PLoS One 2012;7:e51508. https://doi.org/10.1371/journal.pone.0051508
  15. You S, Yoo SA, Choi S, et al. Identification of key regu lators for the migration and invasion of rheumatoid synoviocytes through a systems approach. Proc Natl Acad Sci U S A 2014;111:550-555. https://doi.org/10.1073/pnas.1311239111
  16. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL. The human disease network. Proc Natl Acad Sci U S A 2007;104:8685-8690. https://doi.org/10.1073/pnas.0701361104
  17. Yildirim MA, Goh KI, Cusick ME, Barabasi AL, Vidal M. Drug-target network. Nat Biotechnol 2007;25:1119-1126. https://doi.org/10.1038/nbt1338
  18. Bauer-Mehren A, Bundschus M, Rautschka M, Mayer MA, Sanz F, Furlong LI. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases. PLoS One 2011;6:e20284. https://doi.org/10.1371/journal.pone.0020284
  19. Suthram S, Dudley JT, Chiang AP, Chen R, Hastie TJ, Butte AJ. Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Comput Biol 2010;6:e1000662. https://doi.org/10.1371/journal.pcbi.1000662
  20. Emig D, Ivliev A, Pustovalova O, et al. Drug target prediction and repositioning using an integrated network-based approach. PLoS One 2013;8:e60618. https://doi.org/10.1371/journal.pone.0060618
  21. Hurle MR, Yang L, Xie Q, Rajpal DK, Sanseau P, Agarwal P. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther 2013;93:335-341. https://doi.org/10.1038/clpt.2013.1
  22. Stumpf MP, Thorne T, de Silva E, et al. Estimating the size of the human interactome. Proc Natl Acad Sci U S A 2008;105:6959-6964. https://doi.org/10.1073/pnas.0708078105
  23. Venkatesan K, Rual JF, Vazquez A, et al. An empirical framework for binary interactome mapping. Nat Methods 2009;6:83-90. https://doi.org/10.1038/nmeth.1280
  24. Schwanhausser B, Busse D, Li N, et al. Global quantification of mammalian gene expression control. Nature 2011;473:337-342. https://doi.org/10.1038/nature10098
  25. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 2004;431:931-945. https://doi.org/10.1038/nature03001
  26. Jensen ON. Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry. Curr Opin Chem Biol 2004;8:33-41. https://doi.org/10.1016/j.cbpa.2003.12.009
  27. Altelaar AF, Munoz J, Heck AJ. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat Rev Genet 2013;14:35-48. https://doi.org/10.1038/nrg3356
  28. Chiche L, Jourde-Chiche N, Pascual V, Chaussabel D. Current perspectives on systems immunology approaches to rheumatic diseases. Arthritis Rheum 2013;65:1407-1417. https://doi.org/10.1002/art.37909
  29. Sirota M, Butte AJ. The role of bioinformatics in studying rheumatic and autoimmune disorders. Nat Rev Rheumatol 2011;7:489-494. https://doi.org/10.1038/nrrheum.2011.87
  30. Altman RB. Introduction to translational bioinformatics collection. PLoS Comput Biol 2012;8:e1002796. https://doi.org/10.1371/journal.pcbi.1002796
  31. Lee DM, Weinblatt ME. Rheumatoid arthritis. Lancet 2001;358:903-911. https://doi.org/10.1016/S0140-6736(01)06075-5
  32. Moelants EA, Mortier A, Van Damme J, Proost P. Regulation of TNF-alpha with a focus on rheumatoid arthritis. Immunol Cell Biol 2013;91:393-401. https://doi.org/10.1038/icb.2013.15
  33. Rubbert-Roth A, Finckh A. Treatment options in patients with rheumatoid arthritis failing initial TNF inhibitor therapy: a critical review. Arthritis Res Ther 2009;11 Suppl 1:S1. https://doi.org/10.1186/ar2666
  34. Tanaka Y. Next stage of RA treatment: is TNF inhibitor-free remission a possible treatment goal? Ann Rheum Dis 2013;72 Suppl 2:ii124-ii127. https://doi.org/10.1136/annrheumdis-2012-202350
  35. Lipsky PE, van der Heijde DM, St Clair EW, et al. Infliximab and methotrexate in the treatment of rheumatoid arthritis: anti-tumor necrosis factor trial in rheumatoid arthritis with Concomitant Therapy Study Group. N Engl J Med 2000;343:1594-1602. https://doi.org/10.1056/NEJM200011303432202
  36. Weinblatt ME, Keystone EC, Furst DE, et al. Adalimumab, a fully human anti-tumor necrosis factor alpha monoclonal antibody, for the treatment of rheumatoid arthritis in patients taking concomitant methotrexate: the ARMADA trial. Arthritis Rheum 2003;48:35-45. https://doi.org/10.1002/art.10697
  37. Weinblatt ME, Kremer JM, Bankhurst AD, et al. A trial of etanercept, a recombinant tumor necrosis factor receptor:Fc fusion protein, in patients with rheumatoid arthritis receiving methotrexate. N Engl J Med 1999;340:253-259. https://doi.org/10.1056/NEJM199901283400401
  38. Finckh A, Simard JF, Gabay C, Guerne PA; SCQM physicians. Evidence for differential acquired drug resistance to anti-tumour necrosis factor agents in rheumatoid arthritis. Ann Rheum Dis 2006;65:746-752. https://doi.org/10.1136/ard.2005.045062
  39. Prince FH, Bykerk VP, Shadick NA, et al. Sustained rheumatoid arthritis remission is uncommon in clinical practice. Arthritis Res Ther 2012;14:R68. https://doi.org/10.1186/ar3785
  40. Klarenbeek NB, van der Kooij SM, Guler-Yuksel M, et al. Discontinuing treatment in patients with rheumatoid arthritis in sustained clinical remission: exploratory analyses from the BeSt study. Ann Rheum Dis 2011;70:315-319. https://doi.org/10.1136/ard.2010.136556
  41. Aguilar-Lozano L, Castillo-Ortiz JD, Vargas-Serafin C, et al. Sustained clinical remission and rate of relapse after tocilizumab withdrawal in patients with rheumatoid arthritis. J Rheumatol 2013;40:1069-1073. https://doi.org/10.3899/jrheum.121427
  42. Toonen EJ, Barrera P, Radstake TR, et al. Gene expression profiling in rheumatoid arthritis: current concepts and future directions. Ann Rheum Dis 2008;67:1663-1669. https://doi.org/10.1136/ard.2007.076588
  43. Viatte S, Plant D, Raychaudhuri S. Genetics and epigenetics of rheumatoid arthritis. Nat Rev Rheumatol 2013;9:141-153. https://doi.org/10.1038/nrrheum.2012.237
  44. Nakaoka H, Cui T, Tajima A, et al. A systems genetics approach provides a bridge from discovered genetic variants to biological pathways in rheumatoid arthritis. PLoS One 2011;6:e25389. https://doi.org/10.1371/journal.pone.0025389
  45. Sakaguchi N, Takahashi T, Hata H, et al. Altered thymic T-cell selection due to a mutation of the ZAP-70 gene causes autoimmune arthritis in mice. Nature 2003;426:454-460. https://doi.org/10.1038/nature02119
  46. Xing H, McDonagh PD, Bienkowska J, et al. Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis. PLoS Comput Biol 2011;7:e1001105. https://doi.org/10.1371/journal.pcbi.1001105
  47. Schiff M. Abatacept treatment for rheumatoid arthritis. Rheumatology (Oxford) 2011;50:437-449. https://doi.org/10.1093/rheumatology/keq287
  48. Keystone E, Burmester GR, Furie R, et al. Improvement in patient-reported outcomes in a rituximab trial in patients with severe rheumatoid arthritis refractory to anti-tumor necrosis factor therapy. Arthritis Rheum 2008;59:785-793. https://doi.org/10.1002/art.23715
  49. Yoon HJ, You S, Yoo SA, et al. NF-AT5 is a critical regulator of inf lammatory arthritis. Arthritis Rheum 2011;63:1843-1852. https://doi.org/10.1002/art.30229
  50. Wu G, Zhu L, Dent JE, Nardini C. A comprehensive molecular interaction map for rheumatoid arthritis. PLoS One 2010;5:e10137. https://doi.org/10.1371/journal.pone.0010137
  51. Liu Z, Davidson A. Taming lupus-a new understanding of pathogenesis is leading to clinical advances. Nat Med 2012;18:871-882. https://doi.org/10.1038/nm.2752
  52. Baechler EC, Batliwalla FM, Karypis G, et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci U S A 2003;100:2610-2615. https://doi.org/10.1073/pnas.0337679100
  53. Bennett L, Palucka AK, Arce E, et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med 2003;197:711-723. https://doi.org/10.1084/jem.20021553
  54. Feng X, Wu H, Grossman JM, et al. Association of increased interferon-inducible gene expression with disease activity and lupus nephritis in patients with systemic lupus erythematosus. Arthritis Rheum 2006;54:2951-2962. https://doi.org/10.1002/art.22044
  55. Kirou KA, Lee C, George S, Louca K, Peterson MG, Crow MK. Activation of the interferon-alpha pathway identifies a subgroup of systemic lupus erythematosus patients with distinct serologic features and active disease. Arthritis Rheum 2005;52:1491-1503. https://doi.org/10.1002/art.21031
  56. Landolt-Marticorena C, Bonventi G, Lubovich A, et al. Lack of association between the interferon-alpha signature and longitudinal changes in disease activity in systemic lupus erythematosus. Ann Rheum Dis 2009;68:1440-1446. https://doi.org/10.1136/ard.2008.093146
  57. Nikpour M, Dempsey AA, Urowitz MB, Gladman DD, Barnes DA. Association of a gene expression prof ile from whole blood with disease activity in systemic lupus erythaematosus. Ann Rheum Dis 2008;67:1069-1075. https://doi.org/10.1136/ard.2007.074765
  58. Petri M, Singh S, Tesfasyone H, et al. Longitudinal expression of type I interferon responsive genes in systemic lupus erythematosus. Lupus 2009;18:980-989. https://doi.org/10.1177/0961203309105529
  59. Chaussabel D, Quinn C, Shen J, et al. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity 2008;29:150-164. https://doi.org/10.1016/j.immuni.2008.05.012
  60. Chiche L, Jourde-Chiche N, Whalen E, et al. Modular transcriptional repertoire analyses of adults with systemic lupus erythematosus reveal distinct type I and type II interferon signatures. Arthritis Rheumatol 2014;66:1583-1595. https://doi.org/10.1002/art.38628
  61. Siddani BR, Pochineni LP, Palanisamy M. Candidate gene identification for systemic lupus erythematosus using network centrality measures and gene ontology. PLoS One 2013;8:e81766. https://doi.org/10.1371/journal.pone.0081766
  62. Pflegerl P, Vesely P, Hantusch B, et al. Epidermal loss of JunB leads to a SLE phenotype due to hyper IL-6 signaling. Proc Natl Acad Sci U S A 2009;106:20423-20428. https://doi.org/10.1073/pnas.0910371106
  63. Jeffries MA, Dozmorov M, Tang Y, Merrill JT, Wren JD, Sawalha AH. Genome-wide DNA methylation patterns in CD4+ T cells from patients with systemic lupus erythematosus. Epigenetics 2011;6:593-601. https://doi.org/10.4161/epi.6.5.15374
  64. Romzova M, Hohenadel D, Kolostova K, et al. NFkappaB and its inhibitor IkappaB in relation to type 2 diabetes and its microvascular and atherosclerotic complications. Hum Immunol 2006;67:706-713. https://doi.org/10.1016/j.humimm.2006.05.006
  65. Jacob CO, Eisenstein M, Dinauer MC, et al. Lupus-associated causal mutation in neutrophil cytosolic factor 2 (NCF2) brings unique insights to the structure and function of NADPH oxidase. Proc Natl Acad Sci U S A 2012;109:E59-E67. https://doi.org/10.1073/pnas.1118675109
  66. Ding Y, Chen M, Liu Z, et al. atBioNet: an integrated network analysis tool for genomics and biomarker discovery. BMC Genomics 2012;13:325. https://doi.org/10.1186/1471-2164-13-325
  67. Salvador JM, Hollander MC, Nguyen AT, et al. Mice lacking the p53-effector gene Gadd45a develop a lupus-like syndrome. Immunity 2002;16:499-508. https://doi.org/10.1016/S1074-7613(02)00302-3
  68. Seok J, Warren HS, Cuenca AG, et al. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc Natl Acad Sci U S A 2013;110:3507-3512. https://doi.org/10.1073/pnas.1222878110
  69. Borchers AT, Leibushor N, Naguwa SM, Cheema GS, Shoenfeld Y, Gershwin ME. Lupus nephritis: a critical review. Autoimmun Rev 2012;12:174-194. https://doi.org/10.1016/j.autrev.2012.08.018
  70. Berthier CC, Bethunaickan R, Gonzalez-Rivera T, et al. Cross-species transcriptional network analysis defines shared inflammatory responses in murine and human lupus nephritis. J Immunol 2012;189:988-1001. https://doi.org/10.4049/jimmunol.1103031
  71. Braun J, Sieper J. Ankylosing spondylitis. Lancet 2007;369:1379-1390. https://doi.org/10.1016/S0140-6736(07)60635-7
  72. Zhao J, Chen J, Yang TH, Holme P. Insights into the pathogenesis of axial spondyloarthropathy from network and pathway analysis. BMC Syst Biol 2012;6 Suppl 1:S4.
  73. Tam LS, Gu J, Yu D. Pathogenesis of ankylosing spondylitis. Nat Rev Rheumatol 2010;6:399-405. https://doi.org/10.1038/nrrheum.2010.79
  74. Pimentel-Santos FM, Ligeiro D, Matos M, et al. Whole blood transcriptional profiling in ankylosing spondylitis identifies novel candidate genes that might contribute to the inflammatory and tissue-destructive disease aspects. Arthritis Res Ther 2011;13:R57. https://doi.org/10.1186/ar3309
  75. Delany AM, Hankenson KD. Thrombospondin-2 and SPARC/osteonectin are critical regulators of bone remodeling. J Cell Commun Signal 2009;3:227-238. https://doi.org/10.1007/s12079-009-0076-0
  76. Machado do Reis L, Kessler CB, Adams DJ, Lorenzo J, Jorgetti V, Delany AM. Accentuated osteoclastic response to parathyroid hormone undermines bone mass acquisition in osteonectin-null mice. Bone 2008;43:264-273. https://doi.org/10.1016/j.bone.2008.03.024
  77. Sharma SM, Choi D, Planck SR, et al. Insights in to the pathogenesis of axial spondyloarthropathy based on gene expression profiles. Arthritis Res Ther 2009;11:R168. https://doi.org/10.1186/ar2855
  78. Gu J, Marker-Hermann E, Baeten D, et al. A 588-gene microarray analysis of the peripheral blood mononuclear cells of spondyloarthropathy patients. Rheumatology (Oxford) 2002;41:759-766. https://doi.org/10.1093/rheumatology/41.7.759
  79. Smith JA, Barnes MD, Hong D, DeLay ML, Inman RD, Colbert RA. Gene expression analysis of macrophages derived from ankylosing spondylitis patients reveals interferon-gamma dysregulation. Arthritis Rheum 2008;58:1640-1649. https://doi.org/10.1002/art.23512
  80. Duan R, Leo P, Bradbury L, Brown MA, Thomas G. Gene expression prof iling reveals a downregulation in immune-associated genes in patients with AS. Ann Rheum Dis 2010;69:1724-1729. https://doi.org/10.1136/ard.2009.111690
  81. Gu J, Wei YL, Wei JC, et al. Identification of RGS1 as a candidate biomarker for undifferentiated spondylarthritis by genome-wide expression prof iling and real-time polymerase chain reaction. Arthritis Rheum 2009;60:3269-3279. https://doi.org/10.1002/art.24968
  82. Assassi S, Reveille JD, Arnett FC, et al. Whole-blood gene expression prof iling in ankylosing spondylitis shows upregulation of toll-like receptor 4 and 5. J Rheumatol 2011;38:87-98. https://doi.org/10.3899/jrheum.100469
  83. Haroon N, Tsui FW, O'Shea FD, et al. From gene expression to serum proteins: biomarker discovery in ankylosing spondylitis. Ann Rheum Dis 2010;69:297-300. https://doi.org/10.1136/ard.2008.102277
  84. Laukens D, Peeters H, Cruyssen BV, et al. Altered gut transcriptome in spondyloarthropathy. Ann Rheum Dis 2006;65:1293-1300. https://doi.org/10.1136/ard.2005.047738
  85. Gu J, Rihl M, Marker-Hermann E, et al. Clues to pathogenesis of spondyloarthropathy derived from synovial fluid mononuclear cell gene expression profiles. J Rheumatol 2002;29:2159-2164.
  86. Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science 2008;321:263-266. https://doi.org/10.1126/science.1158140
  87. Chen R, Mias GI, Li-Pook-Than J, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012;148:1293-1307. https://doi.org/10.1016/j.cell.2012.02.009

피인용 문헌

  1. Treatment of connective tissue disease-associated interstitial lung disease: the pulmonologist’s point of view vol.32, pp.4, 2017, https://doi.org/10.3904/kjim.2016.212
  2. MicroRNA-143 and -145 modulate the phenotype of synovial fibroblasts in rheumatoid arthritis vol.49, pp.8, 2015, https://doi.org/10.1038/emm.2017.108