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| Item Type: | Article |
|---|---|
| Title: | Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis |
| Creators Name: | Bernardo-Faura, M., Rinas, M., Wirbel, J., Pertsovskaya, I., Pliaka, V., Messinis, D.E., Vila, G., Sakellaropoulos, T., Faigle, W., Stridh, P., Behrens, J.R., Olsson, T., Martin, R., Paul, F., Alexopoulos, L.G., Villoslada, P. and Saez-Rodriguez, J. |
| Abstract: | BACKGROUND: Multiple 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. METHODS: Here, 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. RESULTS: Analysis 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. CONCLUSIONS. Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases. |
| Keywords: | Signaling Networks, Pathways, Network Modeling, Logic Modeling, Kinases, Treatment, Personalized Medicine, Combination Therapy, Multiple Sclerosis, Immunotherapy, Phosphoproteomics, xMAP Assay |
| Source: | Genome Medicine |
| ISSN: | 1756-994X |
| Publisher: | BioMed Central |
| Volume: | 13 |
| Number: | 1 |
| Page Range: | 117 |
| Date: | 16 July 2021 |
| Official Publication: | https://doi.org/10.1186/s13073-021-00925-8 |
| PubMed: | View item in PubMed |
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