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Monitoring of multiple sclerosis immunotherapy

From single candidates to biomarker networks

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Abstract

Applying microarray technology to identify new diagnostic and prognostic markers in peripheral blood cells (PBC) after therapeutic intervention opens great perspectives regarding patient subclassification. Three recombinant products of the pleiotropic agent interferon beta (rIFN-β) are available for disease modifying therapy of relapsing remitting multiple sclerosis (RRMS), a complex inflammatory autoimmune disease that targets the central nervous system. They differ according to formulation, route of administration and dosage regimens. The currently, only partially understood mechanism of action of injected rIFN-β into human organisms needs provision with accessory key molecules; in addition, the significance of established clinical IFN-β response criteria that distinguish responding from non-responding patients remain unclear.

With respect to these major questions, we discuss promising candidates on the gene transcription level, attained from scientific MS literature that included a longitudinal aspect. Reviewed studies were in part carried out with distinct gene interrogating platforms (GeneArrays; RT-PCR), settings (in vitro; ex vivo), and study designs (drug formulations and regimen; inclusion criteria and clinical endpoints), hampering meaningful meta-analysis. Nevertheless, PBC from therapy-naïve MS patients, rIFN-β treated MS patients, and healthy controls served to characterize facets of both the disease and its treatment. Hence, the field of MS transcriptomics in immunomodulatory therapy is (by far) not adequately understood and should be embedded into systems biology disciplines, yielding multi-layer analyses that deliver timely identification of MS subjects who will profit from applied rIFN-β therapy.

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References

  1. Achiron A, Gurevich M, MagalashviliD, Kishner I, Dolev M, Mandel M(2004) Understanding autoimmunemechanisms in multiple sclerosis usinggene expression microarrays: treatmenteffect and cytokine-related pathways.Clin Dev Immunol 11:299–305

    Article  PubMed  CAS  Google Scholar 

  2. Annibali V, Di Giavanni S, Cannoni S,Giugni E, Bomprezzi R, Mattei C,Elkahloun A, Coccia EM, Alfo M, OrziF, Ristori G, Salvetti M (2007) Geneexpression profiles reveal homeostaticdynamics during interferon-beta therapyin multiple sclerosis. Autoimmunity40:16–22

    Article  PubMed  CAS  Google Scholar 

  3. Baranzini SE, Mousavi P, Rio J, CaillierSJ, Stillman A, Villoslada P, Wyatt MM,Comabella M, Greller LD, Somogyi R,Montalban X, Oksenberg JR (2005)Transcription-based prediction ofresponse to IFNbeta using supervisedcomputational methods. PLoS Biol 3:e2

    Article  PubMed  Google Scholar 

  4. Baranzini SE (2006) Systems-basedmedicine approaches to understandand treat complex diseases. The exampleof multiple sclerosis. Autoimmunity39:651–662

    Article  PubMed  Google Scholar 

  5. Bauch A, Superti-Furga G (2006)Charting protein complexes, signalingpathways, and networks in theimmune system. Immunol Rev 210:187–207

    Article  PubMed  CAS  Google Scholar 

  6. Beckmann JS, Estivill X, AntonarakisSE (2007) Copy number variants andgenetic traits: closer to the resolutionof phenotypic to genotypic variability.Nat Rev Genet 8:639–646

    Article  PubMed  CAS  Google Scholar 

  7. Boehm U, Klamp T, Groot M, HowardJC (1997) Cellular responses to interferon-gamma. Annu Rev Immunol15:749–795

    Article  PubMed  CAS  Google Scholar 

  8. Borden EC, Sen GC, Uze G, SilvermanRH, Ransohoff RM, Foster GR, StarkGR (2007) Interferons at age 50: past,current and future impact on biomedicine.Nat Rev Drug Discov 6:975–990

    Article  PubMed  CAS  Google Scholar 

  9. Calvano SE, Xiao WZ, Richards DR,Felciano RM, Baker HV, Cho RJ, ChenRO, Brownstein BH, Cobb JP, TschoekeSK, Miller-Graziano C, Moldawer LL,Mindrinos MN, Davis RW, TompkinsRG, Lowry SF (2005) A network-basedanalysis of systemic inflammation inhumans. Nature 437:1032–1037

    Article  PubMed  CAS  Google Scholar 

  10. Comabella M, Balashov K, Issazadeh S,Smith D, Weiner HL, Khoury SJ (1998)Elevated interleukin-12 in progressivemultiple sclerosis correlates withdisease activity and is normalized bypulse cyclophosphamide therapy.J Clin Invest 102:671–678

    Article  PubMed  CAS  Google Scholar 

  11. Comabella M, Imitola J, Weiner HL,Khoury SJ (2002) Interferon-treatmentalters peripheral blood monocyteschemokine production in MS patients.J Neuroimmunol 126:205–212

    Article  PubMed  CAS  Google Scholar 

  12. Comabella M, Martin R (2007) Genomicsin multiple sclerosis – currentstate and future directions. J Neuroimmunol187:1–8

    Article  PubMed  CAS  Google Scholar 

  13. Der SD, Zhou A, Williams BR, SilvermanRH (1998) Identification of genesdifferentially regulated by interferonalpha, beta, or gamma using oligonucleotidearrays. Proc Natl Acad Sci USA95:15623–15628

    Article  PubMed  CAS  Google Scholar 

  14. de Veer MJ, Holko M, Frevel M, WalkerE, Der S, Paranjape JM, Silverman RH,Williams BR (2001) Functionalclassification of interferon-stimulatedgenes identified using microarrays.J Leukocyte Biol 69:912–920

    PubMed  CAS  Google Scholar 

  15. Efroni S, Harel D, Cohen IR (2003)Toward rigorous comprehension ofbiological complexity: modeling,execution, and visualization of thymicT-cell maturation. Genome Res 13:2485–2497

    Article  PubMed  CAS  Google Scholar 

  16. Eisen MB, Spellman PT, Brown PO,Botstein D (1998) Cluster analysis anddisplay of genome-wide expressionpatterns. Proc Natl Acad Sci USA 95:14863–14868

    Article  PubMed  CAS  Google Scholar 

  17. Fernald GH, Knott S, Pachner A,Caillier SJ, Narayan K, Oksenberg JR,Mousavi P, Baranzini SE (2007)Genome-wide network analysis revealsthe global properties of IFN-betaimmediate transcriptional effects inhumans. J Immunol 178:5076–5085

    PubMed  CAS  Google Scholar 

  18. Floris S, Ruuls SR, Wierinckx A, vander Pol SM, Dopp E, van der Meide PH,Dijkstra CD, De Vries HE (2002) Interferon-directly influences monocyteinfiltration into the central nervoussystem. J Neuroimmunol 127:69–79

    Article  PubMed  CAS  Google Scholar 

  19. Galboiz Y, Shapiro S, Lahat N, Miller A(2002) Modulation of monocytesmatrix metalloproteinase-2, MT1-MMP and TIMP-2 by interferon-gammaand -beta: implications tomultiple sclerosis. J Neuroimmunol131:191–200

    Article  PubMed  CAS  Google Scholar 

  20. GO Consortium (2000) Gene ontology:tool for the unification of biology. NatGenetics 25:25–29

    Google Scholar 

  21. Goertsches R, Hecker M, Koczan D,Thiesen HJ, Zettl UK (2008) Comparisonof 2 year whole genome RNAexpression profiles derived fromperipheral blood reveals evidentpharmacological differences due todistinct Interferon-beta therapies.Neurology 70:A381

    Google Scholar 

  22. Goertsches R, Hecker M, Koczan D,Thiesen HJ, Zettl UK (2008) Time-dependentRNA signatures of peripheralblood cells from Interferon betatreated MS patients disclose consistentdifferential gene expression. Neurology70:A379

    Google Scholar 

  23. Goodin DS, Hurwitz B, Noronha A(2007) Neutralizing antibodies tointerferon beta-1b are not associatedwith disease worsening in multiplesclerosis. J Int Med Res 35:173–187

    PubMed  CAS  Google Scholar 

  24. Hartrich L, Weinstock-Guttman B, HallD, Badgett D, Baier M, Patrick K,Feichter J, Hong J, Ramanathan M(2003) Dynamics of immune celltrafficking in interferon-treated multiplesclerosis patients. J Neuroimmunol139:84–92

    Article  PubMed  CAS  Google Scholar 

  25. Hecker M, Goertsches R, Koczan D,Kekow J, Thiesen HJ, Guthke R (2008)Integrative modeling of gene regulatoryinteractions relevant in antirheumatictherapy. The 9th InternationalConference on Systems Biology(ICSB):A139

  26. Hilpert J, Beekman JM, Schwenke S,Kowal K, Bauer D, Lampe J, SandbrinkR, Heubach JF, Stürzebecher S, ReischlJ (2008) Biological response genesafter single dose administration ofinterferon beta-1b to healthy malevolunteers. J Neuroimmunol 199:115–125

    Article  PubMed  CAS  Google Scholar 

  27. Hoops S, Sahle S, Gauges R, Lee C,Pahle J, Simus N, Singhal M, Xu L,Mendes P, Kummer U (2006) COPASI –a COmplex PAthway SImulator. Bioinformatics22:3067–3074

    Article  PubMed  CAS  Google Scholar 

  28. Hong J, Zang YC, Hutton G, Rivera VM,Zhang JZ (2004) Gene expression profilingof relevant biomarkers for treatmentevaluation in multiple sclerosis.J Neuroimmunol 152:126–139

    Article  PubMed  CAS  Google Scholar 

  29. Hwang D, Rust AG, Ramsey S, Smith JJ,Leslie DM, Weston AD, de Atauri P,Aitchison JD, Hood L, Siegel AF,Bolouri H (2005) A data integrationmethodology for systems biology. ProcNatl Acad Sci USA 102:17296–17301

    Article  CAS  Google Scholar 

  30. Iglesias AH, Camelo S, Hwang D,Villanueva R, Stephanopoulos G,Dangond F (2004) Microarray detectionof E2F pathway activation andother targets in multiple sclerosisperipheral blood mononuclear cells.J Neuroimmunol 150:163–177

    Article  PubMed  CAS  Google Scholar 

  31. Kantor AB, Deng J, Waubant E, Lin H,Becker CH, Lacy JR, Perrone AM,Bennett D, Goelz SE (2007) Identificationof short-term pharmacodynamiceffects of interferon-beta-1a in multiplesclerosis subjects with broad-basedphenotypic profiling. J Neuroimmunol188:103–116

    Article  PubMed  CAS  Google Scholar 

  32. Koike F, Satoh J, Miyake S, Yamamoto T,Kawai M, Kikuchi S, Nomura K,Yokoyama K, Ota K, Kanda T, FukazawaT, Yamamura T (2003) Microarrayanalysis identifies interferonbeta-regulated genes in multiple sclerosis.J Neuroimmunol 139:109–118

    Article  PubMed  CAS  Google Scholar 

  33. Lamb J, Crawford ED, Peck D, ModellJW, Blat IC, Wrobel MJ, Lerner J, BrunetJP, Subramanian A, Ross KN, Reich M,Hieronymus H, Wei G, Armstrong SA,Haggarty SJ, Clemons PA, Wei R, CarrSA, Lander ES, Golub TR (2006) TheConnectivity Map: using gene-expressionsignatures to connect small molecules,genes, and disease. Science 313:1929–1935

    Article  PubMed  CAS  Google Scholar 

  34. Limmroth V, Malessa R, Zettl UK,Koehler J, Japp G, Haller P, Elias W,Obhof W, Viehöver A, Meier U, BrosigA, Hasford J, Putzki N, Kalski G,Wernsdörfer C, QUASIMS Study Group(2007) Quality Assessment in MultipleSclerosis Therapy (QUASIMS): a comparisonof interferon beta therapies forrelapsing-remitting multiple sclerosis.J Neurol 254:67–77

    Article  PubMed  CAS  Google Scholar 

  35. O’Doherty C, Villoslada P, VandenbroeckK (2007) Pharmacogenomics ofType I interferon therapy: a survey ofresponse-modifying genes. CytokineGrowth Factor Rev 18:211–222

    Article  Google Scholar 

  36. Oksenberg JR, Baranzini SE, Sawcer S,Hauser SL (2008) The genetics ofmultiple sclerosis: SNPs to pathways topathogenesis. Nat Rev Genet 9:516–526

    Article  PubMed  CAS  Google Scholar 

  37. Palacios R, Goni J, Martinez-Forero I,Iranzo J, Sepulcre J, Melero I, VillosladaP (2007) A network analysis of thehuman T-cell activation gene networkidentifies JAGGED1 as a therapeutictarget for autoimmune diseases. PLoSONE 2:e1222

    Google Scholar 

  38. Pathway Architect 2.0.1 (Iobion; http://www.iobion.com)

  39. Prat E, Martin R (2002) The immunopathogenesisof multiple sclerosis.J Rehabil Res Dev 39:187–199

    PubMed  Google Scholar 

  40. Quintana FJ, Farez MF, Weiner HL(2008) Systems biology approaches forthe study of multiple sclerosis. J CellMol Med 12:1087–1093

    Article  CAS  Google Scholar 

  41. Rani MR, Shrock J, Appachi S, RudickRA, Williams BR, Ransohoff RM (2007)Novel interferon-beta-induced geneexpression in peripheral blood cells.J Leukocyte Biol 82:1353–1360

    Article  PubMed  CAS  Google Scholar 

  42. Reder AT, Velichko S, Yamaguchi KD,Hamamcioglu K, Ku K, Beekman J,Wagner TC, Perez HD, Salamon H,Croze E (2008) IFN-beta1b inducestransient and variable gene expressionin relapsing-remitting multiple sclerosispatients independent of neutralizingantibodies or changes in IFNreceptor RNA expression. J InterferonCytokine Res 28:317–331

    Article  CAS  Google Scholar 

  43. Río J, Nos C, Tintoré M, Téllez N, GalánI, Pelayo R, Comabella M, Montalban X(2006) Defining the Response to Interferon-β in Relapsing-Remitting MultipleSclerosis Patients. Ann Neurol 59:344–352

    Article  PubMed  Google Scholar 

  44. Rudick RA, Lee JC, Simon J, RansohoffRM, Fisher E (2004) Defining InterferonResponse Status in Multiple SclerosisPatients. Ann Neurol 56:548–555

    Article  PubMed  CAS  Google Scholar 

  45. Satoh J, Nakanishi M, Koike F, OnoueH, Aranami T, Yamamoto T, Kawai M,Kikuchi S, Nomura K, Yokoyama K, OtaK, Saito T, Ohta M, Miyake S, Kanda T,Fukazawa T, Yamamura T (2006) T cellgene expression profiling identifiesdistinct subgroups of Japanese multiplesclerosis patients. J Neuroimmunol174:108–118

    Article  PubMed  CAS  Google Scholar 

  46. Satoh J, Nanri Y, Tabunoki H, YamamuraT (2006) Microarray analysisidentifies a set of CXCR3 and CCR2ligand chemokines as early IFNbeta-responsivegenes in peripheral bloodlymphocytes in vitro: an implicationfor IFNbeta-related adverse effects inmultiple sclerosis. BMC Neurol 19:6–18

    Google Scholar 

  47. Shannon P, Markiel A, Ozier O, BaligaNS, Wang JT, Ramage D, Amin N,Schwikowski B, Ideker T (2003)Cytoscape: a software environment forintegrated models of biomolecularinteraction networks. Genome Res13:2498–2504

    Article  PubMed  CAS  Google Scholar 

  48. Sharp FR, Xu H, Lit L, Walker W,Apperson M, Gilbert DL, Glauser TA,Wong B, Hershey A, Liu DZ, Pinter J,Zhan X, Liu X, Ran R (2006) The futureof genomic profiling of neurologicaldiseases using blood. Arch Neurol 63:1529–1536

    Article  PubMed  Google Scholar 

  49. Singh MK, Scott TF, LaFramboise WA,Hu FZ, Post JC, Ehrlich GD (2007)Gene expression changes in peripheralblood mononuclear cells from multiplesclerosis patients undergoing beta-interferontherapy. J Neurol Sci 258:52–59

    Article  PubMed  CAS  Google Scholar 

  50. Stark GR, Kerr IM, Williams BR, SilvermanRH, Schreiber RD (1998) Howcells respond to interferons. Annu RevBiochem 67:227–264

    Article  CAS  Google Scholar 

  51. Steinman L (2001) Gene microarraysand experimental demyelinatingdisease: a tool to enhance serendipity.Brain 124:1897–1899

    Article  PubMed  CAS  Google Scholar 

  52. Sturzebecher S, Wandinger KP, RosenwaldA, Sathyamoorthy M, Tzou A,Mattar P, Frank JA, Staudt L, Martin R,McFarland HF (2003) Expression profilingidentifies responder and nonresponderphenotypes to interferonbetain multiple sclerosis. Brain 126:1419–1429

    Article  PubMed  CAS  Google Scholar 

  53. Tomassini V, Paolillo A, Russo P, GiugniE, Prosperini L, Gasperini C, AntonelliG, Bastianello S, Pozzilli C (2006) Predictorsof long-term clinical responseto interferon beta therapy in relapsingmultiple sclerosis. J Neurol 253:287–293

    Article  PubMed  CAS  Google Scholar 

  54. Tompkins SM, Miller SD (2002) Anarray of possibilities for multiplesclerosis. Nat Med 8:451–453

    Article  PubMed  CAS  Google Scholar 

  55. Wandinger KP, Sturzebecher CS,Bielekova B, Detore G, Rosenwald A,Staudt LM, McFarland HF, Martin R(2001) Complex immunomodulatoryeffects of interferon-beta in multiplesclerosis include the upregulation of Thelper 1-associated marker genes. AnnNeurol 50:349–357

    CAS  Google Scholar 

  56. Waubant E, Vukusic S, Gignoux L,Dubief FD, Achiti I, Blanc S, Renoux C,Confavreux C (2003) Clinical characteristicsof responders to interferontherapy for relapsing MS. Neurology61:184–189

    PubMed  CAS  Google Scholar 

  57. Weinstock-Guttman B, Badgett D,Patrick K, Hartrich L, Santos R, Hall D,Baier M, Feichter J, Ramanathan M(2003) Genomic effects of IFN-beta inmultiple sclerosis patients. J Immunol171:2694–2702

    PubMed  CAS  Google Scholar 

  58. Yamaguchi KD, Ruderman DL, CrozeE, Wagner TC, Velichko S, Reder AT,Salamon H (2008) IFN-beta-regulatedgenes show abnormal expression intherapy-naïve relapsing-remitting MSmononuclear cells: gene expressionanalysis employing all reported protein-protein interactions. J Neuroimmunol195:116–120

    Article  PubMed  CAS  Google Scholar 

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Authors and Affiliations

  1. Dept. of Neurology, University of Rostock, Gehlsheimer Strasse 20, 18147, Rostock, Germany

    Robert H. Goertsches & Uwe K. Zettl

  2. Leibniz Institute for Natural Product Research and Infection Biology –, Hans Knöll Institute, 07745, Jena, Germany

    Michael Hecker

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  1. Robert H. Goertsches
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Correspondence to Robert H. Goertsches or Uwe K. Zettl.

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Goertsches, R.H., Hecker, M. & Zettl, U.K. Monitoring of multiple sclerosis immunotherapy. J Neurol 255 (Suppl 6), 48–57 (2008). https://doi.org/10.1007/s00415-008-6010-1

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