MAPK pathway and B cells overactivation in multiple sclerosis revealed by phosphoproteomics and genomic analysis

Significance The alteration of cell communication in the immune system of patients with multiple sclerosis (MS) can be analyzed by phosphoproteomics. We performed in vitro assays on immune cells from MS patients and controls and quantified the phosphorylation of several kinases. We found increased phosphorylation of several MAPK kinases in patients, which were modulated by several genetic markers associated with the disease. By using flow cytometry, we detected several kinases altered by phosphorylation in B 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. Dysregulation of signaling pathways in multiple sclerosis (MS) can be analyzed by phosphoproteomics in peripheral blood mononuclear 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. Phosphoprotein 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.

[1]  S. Reingold,et al.  Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria” , 2005, Annals of neurology.

[2]  Pablo Moscato,et al.  Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20 , 2009, Nature Genetics.

[3]  Gavin Giovannoni,et al.  A ChIP-seq defined genome-wide map of vitamin D receptor binding: associations with disease and evolution. , 2010, Genome research.

[4]  C. Pfueller,et al.  MAPK3 deficiency drives autoimmunity via DC arming , 2010, European journal of immunology.

[5]  Takayuki Itoh,et al.  Cooperative contributions of Interferon regulatory factor 1 (IRF1) and IRF8 to interferon-γ-mediated cytotoxic effects on oligodendroglial progenitor cells , 2011, Journal of Neuroinflammation.

[6]  N. Schork,et al.  Kinase mutations in human disease: interpreting genotype–phenotype relationships , 2010, Nature Reviews Genetics.

[7]  R. Hohlfeld,et al.  Glatiramer Acetate in the Treatment of Multiple Sclerosis , 2011, CNS drugs.

[8]  L. Huber,et al.  Mapping in vivo signal transduction defects by phosphoproteomics. , 2012, Trends in molecular medicine.

[9]  S. Brownell,et al.  The Protective and Therapeutic Function of Small Heat Shock Proteins in Neurological Diseases , 2012, Front. Immun..

[10]  M. Ban,et al.  MANBA, CXCR5, SOX8, RPS6KB1 and ZBTB46 are genetic risk loci for multiple sclerosis. , 2013, Brain : a journal of neurology.

[11]  M. Pirinen,et al.  Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis , 2013, Nature Genetics.

[12]  J. Arthur,et al.  Mitogen-activated protein kinases in innate immunity , 2013, Nature Reviews Immunology.

[13]  Linda,et al.  MANBA , CXCR 5 , SOX 8 , RPS 6 KB 1 and ZBTB 46 are genetic risk loci for multiple sclerosis , 2013 .

[14]  Sandra D'Alfonso,et al.  Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls. , 2013, American journal of human genetics.

[15]  P. Cohen,et al.  Immune diseases caused by mutations in kinases and components of the ubiquitin system , 2014, Nature Immunology.

[16]  S. Eschrich,et al.  Computational methods and opportunities for phosphorylation network medicine. , 2014, Translational cancer research.

[17]  Leonidas G Alexopoulos,et al.  Phosphoproteomics in drug discovery. , 2014, Drug discovery today.

[18]  Jeffrey A. Cohen,et al.  Defining the clinical course of multiple sclerosis: the 2013 revisions. , 2014, Neurology.

[19]  D. Hafler,et al.  Multiple sclerosis—a quiet revolution , 2015, Nature Reviews Neurology.

[20]  S. Amor,et al.  Small heat shock proteins are induced during multiple sclerosis lesion development in white but not grey matter , 2015, Acta Neuropathologica Communications.

[21]  S. Cook,et al.  MEK1 and MEK2 inhibitors and cancer therapy: the long and winding road , 2015, Nature Reviews Cancer.

[22]  Jeffrey A. Cohen,et al.  An ImmunoChip study of multiple sclerosis risk in African Americans. , 2015, Brain : a journal of neurology.

[23]  Ioannis N. Melas,et al.  Signaling networks in MS: A systems-based approach to developing new pharmacological therapies , 2015, Multiple sclerosis.

[24]  J. Grutzendler,et al.  Genetic variants associated with autoimmunity drive NFκB signaling and responses to inflammatory stimuli , 2015, Science Translational Medicine.

[25]  Adam R. Johnson,et al.  Resolving TYK2 locus genotype-to-phenotype differences in autoimmunity , 2016, Science Translational Medicine.

[26]  A. Arrigo.  Mammalian HspB1 (Hsp27) is a molecular sensor linked to the physiology and environment of the cell , 2017, Cell Stress and Chaperones.

[27]  T. Olsson,et al.  Interactions between genetic, lifestyle and environmental risk factors for multiple sclerosis , 2017, Nature Reviews Neurology.

[28]  Yongshuai Jiang,et al.  Integrating genome-wide association studies and gene expression data highlights dysregulated multiple sclerosis risk pathways , 2017, Multiple sclerosis.

[29]  Roland Martin,et al.  Dynamics and heterogeneity of brain damage in multiple sclerosis , 2017, PLoS Comput. Biol..

[30]  A. Traboulsee,et al.  Ocrelizumab versus Interferon Beta‐1a in Relapsing Multiple Sclerosis , 2017, The New England journal of medicine.

[31]  A. Bar-Or,et al.  Reassessing B cell contributions in multiple sclerosis , 2018, Nature Immunology.

[32]  Dimitris E. Messinis,et al.  Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis , 2019, bioRxiv.