Literature DB >> 18689680

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

Jean-Christophe Corvol1, 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.   

Abstract

Clinically isolated syndrome (CIS) refers to the earliest clinical manifestation of multiple sclerosis (MS). Currently there are no prognostic biological markers that accurately predict conversion of CIS to clinically definite MS (CDMS). Furthermore, the earliest molecular events in MS are still unknown. We used microarrays to study gene expression in naïve CD4(+) T cells from 37 CIS patients at time of diagnosis and after 1 year. Supervised machine-learning methods were used to build predictive models of disease conversion. We identified 975 genes whose expression segregated CIS patients into four distinct subgroups. A subset of 108 genes further discriminated patients in one of these (group 1) from other CIS patients. Remarkably, 92% of patients in group 1 converted to CDMS within 9 months. Consistent down-regulation of TOB1, a critical regulator of cell proliferation, was characteristic of group 1 patients. Decreased TOB1 expression at the RNA and protein levels also was confirmed in experimental autoimmune encephalomyelitis. Finally, a genetic association was observed between TOB1 variation and MS progression in an independent cohort. These results indicate that CIS patients at high risk of conversion have impaired regulation of T cell quiescence, possibly resulting in earlier activation of pathogenic CD4(+) cells.

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Year:  2008        PMID: 18689680      PMCID: PMC2504481          DOI: 10.1073/pnas.0805065105

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  27 in total

1.  Normalized accurate measurement of longitudinal brain change.

Authors:  S M Smith; N De Stefano; M Jenkinson; P M Matthews
Journal:  J Comput Assist Tomogr       Date:  2001 May-Jun       Impact factor: 1.826

2.  Summaries of Affymetrix GeneChip probe level data.

Authors:  Rafael A Irizarry; Benjamin M Bolstad; Francois Collin; Leslie M Cope; Bridget Hobbs; Terence P Speed
Journal:  Nucleic Acids Res       Date:  2003-02-15       Impact factor: 16.971

3.  Increased transcriptional activity of milk-related genes following the active phase of experimental autoimmune encephalomyelitis and multiple sclerosis.

Authors:  David Otaegui; Sara Mostafavi; Claude C A Bernard; Adolfo Lopez de Munain; Parvin Mousavi; Jorge R Oksenberg; Sergio E Baranzini
Journal:  J Immunol       Date:  2007-09-15       Impact factor: 5.422

4.  The influence of the proinflammatory cytokine, osteopontin, on autoimmune demyelinating disease.

Authors:  D Chabas; S E Baranzini; D Mitchell; C C Bernard; S R Rittling; D T Denhardt; R A Sobel; C Lock; M Karpuj; R Pedotti; R Heller; J R Oksenberg; L Steinman
Journal:  Science       Date:  2001-11-23       Impact factor: 47.728

5.  Tob is a negative regulator of activation that is expressed in anergic and quiescent T cells.

Authors:  D Tzachanis; G J Freeman; N Hirano; A A van Puijenbroek; M W Delfs; A Berezovskaya; L M Nadler; V A Boussiotis
Journal:  Nat Immunol       Date:  2001-12       Impact factor: 25.606

6.  Isolated demyelinating syndromes: comparison of CSF oligoclonal bands and different MR imaging criteria to predict conversion to CDMS.

Authors:  M Tintoré; A Rovira; L Brieva; E Grivé; R Jardí; C Borrás; X Montalban
Journal:  Mult Scler       Date:  2001-12       Impact factor: 6.312

7.  Dissociating perceptual and conceptual implicit memory in multiple sclerosis patients.

Authors:  Diana Blum; Andrew P Yonelinas; Tracy Luks; David Newitt; Joonmi Oh; Ying Lu; Sarah Nelson; Donald Goodkin; Daniel Pelletier
Journal:  Brain Cogn       Date:  2002-10       Impact factor: 2.310

8.  Antimyelin antibodies as a predictor of clinically definite multiple sclerosis after a first demyelinating event.

Authors:  Thomas Berger; Paul Rubner; Franz Schautzer; Robert Egg; Hanno Ulmer; Irmgard Mayringer; Erika Dilitz; Florian Deisenhammer; Markus Reindl
Journal:  N Engl J Med       Date:  2003-07-10       Impact factor: 91.245

9.  Elevated osteopontin levels in active relapsing-remitting multiple sclerosis.

Authors:  Mario H J Vogt; Luba Lopatinskaya; Monique Smits; Chris H Polman; Lex Nagelkerken
Journal:  Ann Neurol       Date:  2003-06       Impact factor: 10.422

10.  A longitudinal study of abnormalities on MRI and disability from multiple sclerosis.

Authors:  Peter A Brex; Olga Ciccarelli; Jonathon I O'Riordan; Michael Sailer; Alan J Thompson; David H Miller
Journal:  N Engl J Med       Date:  2002-01-17       Impact factor: 91.245

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  44 in total

Review 1.  Multiple sclerosis genetics--is the glass half full, or half empty?

Authors:  Jorge R Oksenberg; Sergio E Baranzini
Journal:  Nat Rev Neurol       Date:  2010-07-13       Impact factor: 42.937

Review 2.  The genetics of multiple sclerosis: an up-to-date review.

Authors:  Pierre-Antoine Gourraud; Hanne F Harbo; Stephen L Hauser; Sergio E Baranzini
Journal:  Immunol Rev       Date:  2012-07       Impact factor: 12.988

Review 3.  Neuroimmunotherapies Targeting T Cells: From Pathophysiology to Therapeutic Applications.

Authors:  Stefan Bittner; Heinz Wiendl
Journal:  Neurotherapeutics       Date:  2016-01       Impact factor: 7.620

4.  Identifying patient subtypes in multiple sclerosis and tailoring immunotherapy: challenges for the future.

Authors:  Philip L De Jager
Journal:  Ther Adv Neurol Disord       Date:  2009-11       Impact factor: 6.570

5.  Characterization of Cdk9 T-loop phosphorylation in resting and activated CD4(+) T lymphocytes.

Authors:  Rajesh Ramakrishnan; Eugene C Dow; Andrew P Rice
Journal:  J Leukoc Biol       Date:  2009-09-10       Impact factor: 4.962

Review 6.  A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.

Authors:  I S Stafford; M Kellermann; E Mossotto; R M Beattie; B D MacArthur; S Ennis
Journal:  NPJ Digit Med       Date:  2020-03-09

7.  Increased expression of microRNAs, miR-20a and miR-326 in PBMCs of patients with type 1 diabetes.

Authors:  Zahra Azhir; Fariba Dehghanian; Zohreh Hojati
Journal:  Mol Biol Rep       Date:  2018-09-07       Impact factor: 2.316

8.  Multiple sclerosis-associated CLEC16A controls HLA class II expression via late endosome biogenesis.

Authors:  Marvin M van Luijn; Karim L Kreft; Marlieke L Jongsma; Steven W Mes; Annet F Wierenga-Wolf; Marjan van Meurs; Marie-José Melief; Rik van der Kant; Lennert Janssen; Hans Janssen; Rusung Tan; John J Priatel; Jacques Neefjes; Jon D Laman; Rogier Q Hintzen
Journal:  Brain       Date:  2015-03-29       Impact factor: 13.501

9.  Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types.

Authors:  Noor B Dawany; Aydin Tozeren
Journal:  BMC Bioinformatics       Date:  2010-09-27       Impact factor: 3.169

Review 10.  Tob, a member of the APRO family, regulates immunological quiescence and tumor suppression.

Authors:  Dimitrios Tzachanis; Vassiliki A Boussiotis
Journal:  Cell Cycle       Date:  2009-04-30       Impact factor: 4.534

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