Genome Medicine | |
Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis | |
Pernilla Stridh1  Tomas Olsson1  Marti Bernardo-Faura2  Jakob Wirbel3  Julio Saez-Rodriguez4  Inna Pertsovskaya5  Pablo Villoslada5  Gemma Vila5  Melanie Rinas6  Friedemann Paul7  Janina R. Behrens7  Dimitris E. Messinis8  Vicky Pliaka9  Theodore Sakellaropoulos9  Leonidas G. Alexopoulos1,10  Wolfgang Faigle1,11  Roland Martin1,11  | |
[1] Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden;European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK;Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain;European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK;Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH-Aachen University, Aachen, Germany;European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK;Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH-Aachen University, Aachen, Germany;Institute for Computational Biomedicine, Heidelberg University Hospital and Faculty of Medicine, Heidelberg University, Bioquant, Heidelberg, Germany;Institut d’ Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain;Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH-Aachen University, Aachen, Germany;NeuroCure Clinical Research Center and Department of Neurology, Charité University Medicine Berlin, Berlin, Germany;ProtATonce Ltd., Athens, Greece;School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece;School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece;ProtATonce Ltd., Athens, Greece;University of Zurich, Zurich, Switzerland; | |
关键词: Signaling networks; Pathways; Network modeling; Logic modeling; Kinases; Treatment; Personalized medicine; Combination therapy; Multiple sclerosis; Immunotherapy; Phosphoproteomics; xMAP assay; | |
DOI : 10.1186/s13073-021-00925-8 | |
来源: Springer | |
【 摘 要 】
BackgroundMultiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects.MethodsHere, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a “healthy-like” status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies.ResultsAnalysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS.ConclusionsOur approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.
【 授权许可】
CC BY
【 预 览 】
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