Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis

Genome Medicine

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. Alexopoulos^1,10  Wolfgang Faigle^1,11  Roland Martin^1,11 

[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