Literature DB >> 16564577

T cell gene expression profiling identifies distinct subgroups of Japanese multiple sclerosis patients.

Jun-ichi Satoh1, Megumi Nakanishi, Fumiko Koike, Hiroyuki Onoue, Toshimasa Aranami, Toshiyuki Yamamoto, Mitsuru Kawai, Seiji Kikuchi, Kyouichi Nomura, Kazumasa Yokoyama, Kohei Ota, Toshiro Saito, Masayuki Ohta, Sachiko Miyake, Takashi Kanda, Toshiyuki Fukazawa, Takashi Yamamura.   

Abstract

To clarify the molecular background underlying the heterogeneity of multiple sclerosis (MS), we characterized the gene expression profile of peripheral blood CD3+ T cells isolated from MS and healthy control (CN) subjects by using a cDNA microarray. Among 1258 cDNAs on the array, 286 genes were expressed differentially between 72 untreated Japanese MS patients and 22 age- and sex-matched CN subjects. When this set was used as a discriminator for hierarchical clustering analysis, it identified four distinct subgroups of MS patients and five gene clusters differentially expressed among the subgroups. One of these gene clusters was overexpressed in MS versus CN, and particularly enhanced in the clinically most active subgroup of MS. After 46 of the MS patients were treated with interferon-beta (IFNbeta-1b) for two years, IFNbeta responders were clustered in two of the four MS subgroups. Furthermore, the IFNbeta responders differed from nonresponders in the kinetics of IFN-responsive genes at 3 and 6 months after starting IFNbeta treatment. These results suggest that T-cell gene expression profiling is valuable to identify distinct subgroups of MS associated with differential disease activity and therapeutic response to IFNbeta.

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Year:  2006        PMID: 16564577     DOI: 10.1016/j.jneuroim.2006.02.004

Source DB:  PubMed          Journal:  J Neuroimmunol        ISSN: 0165-5728            Impact factor:   3.478


  18 in total

1.  Multiple sclerosis-linked and interferon-beta-regulated gene expression in plasmacytoid dendritic cells.

Authors:  Latt Latt Aung; Andrew Brooks; Steven A Greenberg; Michael L Rosenberg; Suhayl Dhib-Jalbut; Konstantin E Balashov
Journal:  J Neuroimmunol       Date:  2012-06-09       Impact factor: 3.478

2.  Abrogation of T cell quiescence characterizes patients at high risk for multiple sclerosis after the initial neurological event.

Authors:  Jean-Christophe Corvol; Daniel Pelletier; Roland G Henry; Stacy J Caillier; Joanne Wang; Derek Pappas; Simona Casazza; Darin T Okuda; Stephen L Hauser; Jorge R Oksenberg; Sergio E Baranzini
Journal:  Proc Natl Acad Sci U S A       Date:  2008-08-08       Impact factor: 11.205

3.  Orphan nuclear receptor NR4A2 expressed in T cells from multiple sclerosis mediates production of inflammatory cytokines.

Authors:  Yoshimitsu Doi; Shinji Oki; Tomoko Ozawa; Hirohiko Hohjoh; Sachiko Miyake; Takashi Yamamura
Journal:  Proc Natl Acad Sci U S A       Date:  2008-06-11       Impact factor: 11.205

4.  Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20.

Authors: 
Journal:  Nat Genet       Date:  2009-06-14       Impact factor: 38.330

5.  Beta-lactam antibiotics modulate T-cell functions and gene expression via covalent binding to cellular albumin.

Authors:  Felix Mor; Irun R Cohen
Journal:  Proc Natl Acad Sci U S A       Date:  2013-02-04       Impact factor: 11.205

6.  Learning from nature: pregnancy changes the expression of inflammation-related genes in patients with multiple sclerosis.

Authors:  Francesca Gilli; Raija L P Lindberg; Paola Valentino; Fabiana Marnetto; Simona Malucchi; Arianna Sala; Marco Capobianco; Alessia di Sapio; Francesca Sperli; Ludwig Kappos; Raffaele A Calogero; Antonio Bertolotto
Journal:  PLoS One       Date:  2010-01-29       Impact factor: 3.240

7.  Gene expression studies in multiple sclerosis.

Authors:  Lotti Tajouri; Francesca Fernandez; Lyn R Griffiths
Journal:  Curr Genomics       Date:  2007-05       Impact factor: 2.236

Review 8.  Monitoring of multiple sclerosis immunotherapy: from single candidates to biomarker networks.

Authors:  Robert H Goertsches; Michael Hecker; Uwe K Zettl
Journal:  J Neurol       Date:  2008-12       Impact factor: 4.849

9.  Systematic review of genome-wide expression studies in multiple sclerosis.

Authors:  A K Kemppinen; J Kaprio; A Palotie; J Saarela
Journal:  BMJ Open       Date:  2011-07-18       Impact factor: 2.692

10.  Constrained mixture estimation for analysis and robust classification of clinical time series.

Authors:  Ivan G Costa; Alexander Schönhuth; Christoph Hafemeister; Alexander Schliep
Journal:  Bioinformatics       Date:  2009-06-15       Impact factor: 6.937

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