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dc.contributor.authorMoreno-Torres, Irene
dc.contributor.authorGonzález-García, Coral
dc.contributor.authorMarconi, Marco
dc.contributor.authorGarcía-Grande, Aranzazu
dc.contributor.authorRodriguez-Esparragoza, Luis 
dc.contributor.authorElvira, Víctor
dc.contributor.authorRamil, Elvira
dc.contributor.authorCampos-Ruíz, Lucía
dc.contributor.authorGarcía-Hernández, Ruth
dc.contributor.authorAl-Shahrour , Fatima 
dc.contributor.authorFustero-Torre, Coral
dc.contributor.authorSánchez-Sanz, Alicia
dc.contributor.authorGarcía-Merino, Antonio
dc.contributor.authorSánchez López, Antonio José
dc.date.accessioned2018-11-15T11:36:57Z
dc.date.available2018-11-15T11:36:57Z
dc.date.issued2018
dc.identifier.citationFront Immunol. 2018; 9:1693.es_ES
dc.identifier.issn1664-3224es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/6601
dc.description.abstractBackground: Fingolimod is a functional sphingosine-1-phosphate antagonist approved for the treatment of multiple sclerosis (MS). Fingolimod affects lymphocyte subpopulations and regulates gene expression in the lymphocyte transcriptome. Translational studies are necessary to identify cellular and molecular biomarkers that might be used to predict the clinical response to the drug. In MS patients, we aimed to clarify the differential effects of fingolimod on T, B, and natural killer (NK) cell subsets and to identify differentially expressed genes in responders and non-responders (NRs) to treatment. Materials and methods: Samples were obtained from relapsing-remitting multiple sclerosis patients before and 6 months after starting fingolimod. Forty-eight lymphocyte subpopulations were measured by flow cytometry based on surface and intracellular marker analysis. Transcriptome sequencing by next-generation technologies was used to define the gene expression profiling in lymphocytes at the same time points. NEDA-3 (no evidence of disease activity) and NEDA-4 scores were measured for all patients at 1 and 2 years after beginning fingolimod treatment to investigate an association with cellular and molecular characteristics. Results: Fingolimod affects practically all lymphocyte subpopulations and exerts a strong effect on genetic transcription switching toward an anti-inflammatory and antioxidant response. Fingolimod induces a differential effect in lymphocyte subpopulations after 6 months of treatment in responder and NR patients. Patients who achieved a good response to the drug compared to NR patients exhibited higher percentages of NK bright cells and plasmablasts, higher levels of FOXP3, glucose phosphate isomerase, lower levels of FCRL1, and lower Expanded Disability Status Scale at baseline. The combination of these possible markers enabled us to build a probabilistic linear model to predict the clinical response to fingolimod. Conclusion: MS patients responsive to fingolimod exhibit a recognizable distribution of lymphocyte subpopulations and a different pretreatment gene expression signature that might be useful as a biomarker.es_ES
dc.description.sponsorshipThis work was mostly supported by grants from Novartis (PI110/13 JGM-INM2014-01) and Fondo de Investigación Sanitaria FIS PI12/02672 and PI15/02099 integrated in the Plan Nacional de I+D+I (2008–2011 and 2013–2016, respectively), supported by the ISCIII—subdirección General de Evaluación and co-financed by the Fondo Europeo de Desarrollo Regional (FEDER). The funding sources had no involvement in the study design, data collection, analysis, interpretation, preparation of the manuscript, and the decision to submit the article for publication.es_ES
dc.language.isoenges_ES
dc.publisherFrontiers Mediaes_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectRNA-seqes_ES
dc.subjectbiomarkerses_ES
dc.subjectfingolimodes_ES
dc.subjectlymphocyte subpopulationses_ES
dc.subjectmultiple sclerosises_ES
dc.subjecttranscriptomees_ES
dc.titleImmunophenotype and Transcriptome Profile of Patients With Multiple Sclerosis Treated With Fingolimod: Setting Up a Model for Prediction of Response in a 2-Year Translational Studyes_ES
dc.typeArtículoes_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID30090102es_ES
dc.format.volume9es_ES
dc.format.page1693es_ES
dc.identifier.doi10.3389/fimmu.2018.01693es_ES
dc.contributor.funderNovartis Farmaceutica S.A.es_ES
dc.contributor.funderInstituto de Salud Carlos III - ISCIIIes_ES
dc.contributor.funderEuropean Regional Development Fund (ERDF/FEDER)
dc.description.peerreviewed
dc.relation.publisherversionhttps://doi.org/10.3389/fimmu.2018.01693.es_ES
dc.identifier.journalFrontiers in immunologyes_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Laboratorio Traslacional para la Imagen y Terapia Cardiovascular
dc.repisalud.institucionCNIOes_ES
dc.repisalud.institucionCNIC
dc.repisalud.orgCNIOCNIO::Unidades técnicas::Unidad de Bioinformáticaes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PI110/13 JGM-INM2014-01es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PI12/02672es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/PI15/02099es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Atribución 4.0 Internacional
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