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dc.contributor.authorKotelnikova, Ekaterina
dc.contributor.authorKiani, Narsis A.
dc.contributor.authorMessinis, Dimitris
dc.contributor.authorPertsovskaya, Inna
dc.contributor.authorPliaka, Vicky
dc.contributor.authorBernardo-Faura, Marti
dc.contributor.authorRinas, Melanie
dc.contributor.authorVila, Gemma
dc.contributor.authorZubizarreta, Irati
dc.contributor.authorPulido-Valdeolivas, Irene
dc.contributor.authorSakellaropoulos, Theodore
dc.contributor.authorFaigle, Wolfgang
dc.contributor.authorSilberberg, Gilad
dc.contributor.authorMasso, Mar
dc.contributor.authorStridh, Pernilla
dc.contributor.authorBehrens, Janina
dc.contributor.authorOlsson, Tomas
dc.contributor.authorMartin, Roland
dc.contributor.authorPaul, Friedemann
dc.contributor.authorAlexopoulos, Leonidas G.
dc.contributor.authorSaez-Rodriguez, Julio
dc.contributor.authorTegner, Jesper
dc.contributor.authorVilloslada, Pablo
dc.date.accessioned2019-08-08T08:25:38Z
dc.date.available2019-08-08T08:25:38Z
dc.date.issued2019-04-19
dc.identifier.citationKotelnikova, E., Kiani, N. A., Messinis, D., Pertsovskaya, I., Pliaka, V., Bernardo-Faura, M., … Villoslada, P. (2019). MAPK pathway and B cells overactivation in multiple sclerosis revealed by phosphoproteomics and genomic analysis. Proceedings of the National Academy of Sciences, 201818347. doi:10.1073/pnas.1818347116
dc.identifier.doi10.1073/pnas.1818347116
dc.identifier.urihttp://hdl.handle.net/10754/656425
dc.description.abstractDysregulation of signaling pathways in multiple sclerosis (MS) can be analyzed by phosphoproteomics in peripheral blood mono-nuclear cells (PBMCs). We performed in vitro kinetic assays on PBMCs in 195 MS patients and 60 matched controls and quantified the phosphorylation of 17 kinases using xMAP assays. Phosphopro-tein levels were tested for association with genetic susceptibility by typing 112 single-nucleotide polymorphisms (SNPs) associated with MS susceptibility. We found increased phosphorylation of MP2K1 in MS patients relative to the controls. Moreover, we identified one SNP located in the PHDGH gene and another on IRF8 gene that were associated with MP2K1 phosphorylation levels, providing a first clue on how this MS risk gene may act. The analyses in patients treated with disease-modifying drugs identified the phosphorylation of each receptor’s downstream kinases. Finally, using flow cytometry, we detected in MS patients increased STAT1, STAT3, TF65, and HSPB1 phosphorylation in CD19+ cells. These findings indicate the activation of cell survival and proliferation (MAPK), and proinflammatory (STAT) pathways in the immune cells of MS patients, primarily in B cells. The changes in the activation of these kinases suggest that these pathways may represent therapeutic targets for modulation by kinase inhibitors.
dc.description.sponsorshipWe thank Mark Sefton for editorial assistance. This study was supported by the European Commission (CombiMS project) under Grant Agreement 305397 (FP7/2007-2013); Sys4MS project (Horizon2020: Eracosysmed: ID-43); the Instituto de Salud Carlos III (Fondo Europeo de Desarrollo Regional funds “Otra forma de hacer Europa” Grant AC15-00052); and Centres de Recerca de Catalunya Programme/Generalitat de Catalunya.
dc.publisherProceedings of the National Academy of Sciences
dc.relation.urlhttp://www.pnas.org/lookup/doi/10.1073/pnas.1818347116
dc.rightsArchived with thanks to Proceedings of the National Academy of Sciences of the United States of America
dc.subjectmultiple sclerosis
dc.subjectphosphoproteomics
dc.subjectsignaling pathways
dc.subjectB cells
dc.subjectautoimmunity
dc.titleMAPK pathway and B cells overactivation in multiple sclerosis revealed by phosphoproteomics and genomic analysis
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.identifier.journalProceedings of the National Academy of Sciences of the United States of America
dc.rights.embargodate2019-10-19
dc.eprint.versionPost-print
dc.contributor.institutionCenter of Neuroimmunology, Institut d’Investigacions Biomèdiques August Pi Sunyer, Barcelona, 08036, Spain
dc.contributor.institutionKharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, 127051, Russia
dc.contributor.institutionClarivate Analytics, Barcelona, 08025, Spain
dc.contributor.institutionUnit of Computational Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm, SE-171 76, Sweden
dc.contributor.institutionCenter for Molecular Medicine, Karolinska Institutet, Stockholm, SE-171 77, Sweden
dc.contributor.institutionProtatOnce, Athens, 15343, Greece
dc.contributor.institutionDepartment of Mechanical Engineering, National Technical University of Athens, Athens, 15780, Greece
dc.contributor.institutionEuropean Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, CB10 1SD, United Kingdom
dc.contributor.institutionJoint Research Centre for Computational Biomedicine, Rheinisch-Westfälische Technische Hochschule - Aachen University Hospital, Aachen, 52074, Germany
dc.contributor.institutionDepartment of Neurology, University of Zurich, Zurich, 8091, Switzerland
dc.contributor.institutionBionure Farma SL, Barcelona, 08028, Spain
dc.contributor.institutionDepartment of Neurology, Karolinska University Hospital, Stockholm, SE-171 77, Sweden
dc.contributor.institutionNeuroCure Clinical Research Center, Charité University Universitätsmedizin Berlin, Berlin, 13125, Germany
dc.contributor.institutionDepartment of Neurology, Charité University Universitätsmedizin Berlin, Berlin, 13125, Germany
dc.contributor.institutionExperimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine, Berlin, 13125, Germany
dc.contributor.institutionInstitute of Computational Biomedicine BioQuant, Faculty of Medicine, Heidelberg University, Heidelberg, 69120, Germany
dc.contributor.institutionHeidelberg University Hospital, Heidelberg University, Heidelberg, 69120, Germany
dc.contributor.institutionScience for Life Laboratory, Solna, SE-171 65, Sweden
kaust.personTegner, Jesper
kaust.personTegner, Jesper
dc.relation.issupplementedbygithub:saezlab/combiMS
refterms.dateFOA2019-10-19T00:00:00Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: saezlab/combiMS: combiMS code for Prediction of combination therapy based on perturbation modeling of the multiple sclerosis signaling network. Code started by Marti at EBI on Feb 2013. Publication Date: 2016-10-26. github: <a href="https://github.com/saezlab/combiMS" >saezlab/combiMS</a> Handle: <a href="http://hdl.handle.net/10754/666983" >10754/666983</a></a></li></ul>


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