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Prediction of combination therapies based on topological modeling of the immune signaling network in Multiple Sclerosis

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Item Type:Preprint
Title:Prediction of combination therapies based on topological modeling of the immune signaling network in Multiple Sclerosis
Creators Name:Bernardo-Faura, M. and Rinas, M. and Wirbel, J. and Pertsovskaya, I. and Pliaka, V. and Messinis, D.E and Vila, G. and Sakellaropoulos, T. and Faigle, W. and Stridh, P. and Behrens, J.R. and Olsson, T. and Martin, R. and Paul, F. and Alexopoulos, L.G. and Villoslada, P. and Saez-Rodriguez, J.
Abstract:Signal transduction deregulation is a hallmark of many complex diseases, including Multiple Sclerosis (MS). Here, we performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 MS patients and matched healthy controls. 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. Analysis of the models uncovered features of healthy-, disease- and drug-specific signaling networks. 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. We predicted several combinations with approved MS drugs. Specifically, TAK1 kinase, involved in TGF-β, Toll-like receptor, B-cell receptor and response to inflammation pathways were 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. Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases. Significance statement: Multiple Sclerosis (MS) is a major health problem, leading to significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood. Further, current treatments only ameliorate the disease and may produce severe side effects. Here, we applied a network-based modeling approach based on phosphoproteomic data upon perturbation with ligands and drugs of healthy donors and MS patients to create donor-specific models. The models uncover the differential activation in signaling wiring between healthy donors, untreated patients and those under different treatments. Further, based in the patient-specific networks, a new approach identifies drug combinations to revert signaling to a healthy-like state. One sentence summary: A new approach to predict combination therapies based on modeling signaling networks using phosphoproteomics from Multiple Sclerosis patients identifies deregulated pathways and new drug combinations.
Keywords:Signaling Networks, Pathways, Network Modeling, Logic Modeling, Kinases, Treatment, Personalized Medicine, Combination Therapy, Multiple Sclerosis, Immunotherapy, Phosphoproteomics, xMAP Assay
Source:bioRxiv
Publisher:Cold Spring Harbor Laboratory Press (U.S.A.)
Article Number:541458
Date:20 March 2019
Official Publication:https://doi.org/10.1101/541458

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