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

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.

• Authors: Marti Bernardo-Faura, Melanie Rinas, Jakob Wirbel, Inna Pertsovskaya, Vicky Pliaka, Dimitris E Messinis, Gemma Vila, Theodore Sakellaropoulos, Wolfgang Faigle, Pernilla Stridh, Janina R Behrens, Tomas Olsson, Roland Martin, Friedemann Paul, Leonidas G Alexopoulos, Pablo Villoslada, Julio Saez-Rodriguez
• Journal: Genome medicine (Genome Med) Vol. 13 Issue 1 Pg. 117 (07 16 2021) ISSN: 1756-994X [Electronic] England
• PMID: 34271980 (Publication Type: Journal Article, Multicenter Study, Research Support, Non-U.S. Gov't)
• Copyright: © 2021. The Author(s).

Chemical References

• Biomarkers
• Phosphoproteins
• Proteome

Topics

• Adult
• Algorithms
• Biomarkers
• Case-Control Studies
• Combined Modality Therapy (methods)
• Disease Management
• Disease Susceptibility
• Female
• Humans
• Immune System (drug effects, immunology, metabolism)
• Male
• Middle Aged
• Models, Biological
• Molecular Targeted Therapy
• Multiple Sclerosis (diagnosis, etiology, metabolism, therapy)
• Phosphoproteins (metabolism)
• Prognosis
• Proteome
• Proteomics (methods)
• Signal Transduction (drug effects)
• Treatment Outcome