Long-term genome-wide blood RNA expression profiles yield novel molecular response candidates for IFN-beta-1b treatment in relapsing remitting MS.

AIMS In multiple sclerosis patients, treatment with recombinant IFN-beta (rIFN-beta) is partially efficient in reducing clinical exacerbations. However, its molecular mechanism of action is still under scrutiny. MATERIALS & METHODS We used DNA microarrays (Affymetrix, CA, USA) and peripheral mononuclear blood cells from 25 relapsing remitting multiple sclerosis patients to analyze the longitudinal transcriptional profile within 2 years of rIFN-beta administration. Sets of differentially expressed genes were attained by applying a combination of independent criteria, thereby providing efficient data curation and gene filtering that accounted for technical and biological noise. Gene ontology term-association analysis and scientific literature text mining were used to explore evidence of gene interaction. RESULTS Post-therapy initiation, we identified 42 (day 2), 175 (month 1), 103 (month 12) and 108 (month 24) differentially expressed genes. Increased expression of established IFN-beta marker genes, as well as differential expression of circulating IFN-beta-responsive candidate genes, were observed. MS4A1 (CD20), a known target of B-cell depletion therapy, was significantly downregulated after one month. CMPK2, FCER1A, and FFAR2 appeared as hitherto unrecognized multiple sclerosis treatment-related differentially expressed genes that were consistently modulated over time. Overall, 84 interactions between 54 genes were attained, of which two major gene networks were identified at an earlier stage of therapy: the first (n = 15 genes) consisted of mostly known IFN-beta-activated genes, whereas the second (n = 12) mainly contained downregulated genes that to date have not been associated with IFN-beta effects in multiple sclerosis array research. CONCLUSION We achieved both a broadening of the knowledge of IFN-beta mechanism-of-action-related constituents and the identification of time-dependent interactions between IFN-beta regulated genes.

[1]  J. Sepulcre,et al.  A Network Analysis of the Human T-Cell Activation Gene Network Identifies Jagged1 as a Therapeutic Target for Autoimmune Diseases , 2007, PloS one.

[2]  P. Rieckmann,et al.  Basic and escalating immunomodulatory treatments in multiple sclerosis: Current therapeutic recommendations , 2008, Journal of Neurology.

[3]  M. Ramanathan,et al.  Genomic effects of once-weekly, intramuscular interferon-β1a treatment after the first dose and on chronic dosing: Relationships to 5-year clinical outcomes in multiple sclerosis patients , 2008, Journal of Neuroimmunology.

[4]  Doron Lancet,et al.  Novel definition files for human GeneChips based on GeneAnnot , 2007, BMC Bioinformatics.

[5]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[6]  A. Bertolotto,et al.  Biological markers of interferon-beta therapy: comparison among interferon-stimulated genes MxA, TRAIL and XAF-1 , 2006, Multiple sclerosis.

[7]  S. Hauser,et al.  The Neurobiology of Multiple Sclerosis: Genes, Inflammation, and Neurodegeneration , 2006, Neuron.

[8]  Michael Hecker,et al.  Integrative modeling of transcriptional regulation in response to antirheumatic therapy , 2009, BMC Bioinformatics.

[9]  H. Salamon,et al.  IFN-beta1b induces transient and variable gene expression in relapsing-remitting multiple sclerosis patients independent of neutralizing antibodies or changes in IFN receptor RNA expression. , 2008, Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research.

[10]  L. Staudt,et al.  Complex immunomodulatory effects of interferon‐β in multiple sclerosis include the upregulation of T helper 1‐associated marker genes , 2001, Annals of neurology.

[11]  V. Rivera,et al.  Gene expression profiling of relevant biomarkers for treatment evaluation in multiple sclerosis , 2004, Journal of Neuroimmunology.

[12]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[13]  B. Williams,et al.  Identification of genes differentially regulated by interferon α, β, or γ using oligonucleotide arrays , 1998 .

[14]  Murali Ramanathan,et al.  Genomic Effects of IFN-β in Multiple Sclerosis Patients 1 , 2003, The Journal of Immunology.

[15]  J. Satoh,et al.  Microarray analysis identifies interferon β-regulated genes in multiple sclerosis , 2003, Journal of Neuroimmunology.

[16]  Kazumasa Yokoyama,et al.  T cell gene expression profiling identifies distinct subgroups of Japanese multiple sclerosis patients , 2006, Journal of Neuroimmunology.

[17]  M. Krumbholz,et al.  Interferon-beta increases BAFF levels in multiple sclerosis: implications for B cell autoimmunity. , 2008, Brain : a journal of neurology.

[18]  H. Weiner,et al.  Systems biology approaches for the study of multiple sclerosis , 2008, Journal of cellular and molecular medicine.

[19]  Graham R. Foster,et al.  Interferons at age 50: past, current and future impact on biomedicine , 2007, Nature Reviews Drug Discovery.

[20]  Tsippi Iny Stein,et al.  GeneAnnot: comprehensive two-way linking between oligonucleotide array probesets and GeneCards genes. , 2004, Bioinformatics.

[21]  A. Bertolotto,et al.  Biological responsiveness to first injections of interferon-beta in patients with multiple sclerosis , 2005, Journal of Neuroimmunology.

[22]  M. Hecker,et al.  Monitoring of multiple sclerosis immunotherapy , 2008, Journal of Neurology.

[23]  J. Satoh,et al.  Aberrant transcriptional regulatory network in T cells of multiple sclerosis , 2007, Neuroscience Letters.

[24]  A. Compston,et al.  Recommended diagnostic criteria for multiple sclerosis: Guidelines from the international panel on the diagnosis of multiple sclerosis , 2001, Annals of neurology.

[25]  L. Staudt,et al.  Expression profiling identifies responder and non-responder phenotypes to interferon-beta in multiple sclerosis. , 2003, Brain : a journal of neurology.

[26]  G. Stark,et al.  How cells respond to interferons. , 1998, Annual review of biochemistry.

[27]  F. Sellebjerg,et al.  Absence of MxA induction by interferon β in patients with MS reflects complete loss of bioactivity , 2009, Neurology.

[28]  K. Vandenbroeck,et al.  Pharmacogenomics of the response to IFN-beta in multiple sclerosis: ramifications from the first genome-wide screen. , 2008, Pharmacogenomics.

[29]  X. Montalban,et al.  A type I interferon signature in monocytes is associated with poor response to interferon-beta in multiple sclerosis. , 2009, Brain : a journal of neurology.

[30]  S. Akira,et al.  Small anti-viral compounds activate immune cells via the TLR7 MyD88–dependent signaling pathway , 2002, Nature Immunology.

[31]  A. Lutterotti,et al.  Differing immunogenic potentials of interferon beta preparations in multiple sclerosis patients , 2006, Multiple sclerosis.

[32]  J. Reischl,et al.  Biological response genes after single dose administration of interferon β-1b to healthy male volunteers , 2008, Journal of Neuroimmunology.

[33]  L. O’Neill,et al.  Targeting signal transduction as a strategy to treat inflammatory diseases , 2006, Nature Reviews Drug Discovery.

[34]  L. Steinman,et al.  Systems biology and its application to the understanding of neurological diseases , 2009, Annals of neurology.

[35]  B. Williams,et al.  Functional classification of interferon‐stimulated genes identified using microarrays , 2001, Journal of leukocyte biology.

[36]  H. Hartung,et al.  Toward the development of rational therapies in multiple sclerosis: what is on the horizon? , 2007, Annals of neurology.

[37]  B. Williams,et al.  Novel interferon‐β‐induced gene expression in peripheral blood cells , 2007, Journal of leukocyte biology.

[38]  Takashi Yamamura,et al.  Microarray analysis identifies a set of CXCR3 and CCR2 ligand chemokines as early IFNβ-responsive genes in peripheral blood lymphocytes in vitro: an implication for IFNβ-related adverse effects in multiple sclerosis , 2006, BMC neurology.

[39]  E. Coccia,et al.  Gene expression profiles reveal homeostatic dynamics during interferon-β therapy in multiple sclerosis , 2007, Autoimmunity.

[40]  A. Achiron,et al.  Understanding Autoimmune Mechanisms in Multiple Sclerosis Using Gene Expression Microarrays: Treatment Effect and Cytokine-related Pathways , 2004, Clinical & developmental immunology.

[41]  P. Villoslada,et al.  Pharmacogenomics of Type I interferon therapy: a survey of response-modifying genes. , 2007, Cytokine & growth factor reviews.

[42]  W. A. LaFramboise,et al.  Gene expression changes in peripheral blood mononuclear cells from multiple sclerosis patients undergoing β-interferon therapy , 2007, Journal of the Neurological Sciences.

[43]  Robert Gentleman,et al.  Using GOstats to test gene lists for GO term association , 2007, Bioinform..

[44]  Jerry Li,et al.  Within the fold: assessing differential expression measures and reproducibility in microarray assays , 2002, Genome Biology.

[45]  Steven Jupe,et al.  A family of fatty acid binding receptors. , 2005, DNA and cell biology.

[46]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[47]  Giulio Superti-Furga,et al.  Charting protein complexes, signaling pathways, and networks in the immune system , 2006, Immunological reviews.

[48]  A. Svejgaard,et al.  Identification of new sensitive biomarkers for the in vivo response to interferon‐β treatment in multiple sclerosis using DNA‐array evaluation , 2009, European journal of neurology.

[49]  R. Strieter,et al.  CXCL10 impairs beta cell function and viability in diabetes through TLR4 signaling. , 2009, Cell metabolism.

[50]  Hamid Bolouri,et al.  A data integration methodology for systems biology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[51]  L. Greller,et al.  Transcription-Based Prediction of Response to IFNβ Using Supervised Computational Methods , 2004, PLoS biology.

[52]  Takafumi Hara,et al.  Free fatty acid receptors and drug discovery. , 2008, Biological & pharmaceutical bulletin.

[53]  Crispin J. Miller,et al.  Cell Culture , 2010, Cell.

[54]  C. Polman,et al.  Pharmacogenomics of Interferon-ß Therapy in Multiple Sclerosis: Baseline IFN Signature Determines Pharmacological Differences between Patients , 2008, PloS one.

[55]  George Stephanopoulos,et al.  Microarray detection of E2F pathway activation and other targets in multiple sclerosis peripheral blood mononuclear cells , 2004, Journal of Neuroimmunology.

[56]  Parvin Mousavi,et al.  Genome-Wide Network Analysis Reveals the Global Properties of IFN-β Immediate Transcriptional Effects in Humans12 , 2007, The Journal of Immunology.