Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis.
Genome Med
; 13(1): 117, 2021 07 16.
Article
em En
| MEDLINE
| ID: mdl-34271980
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-ß, 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-ß activated kinase 1 (TAK1) kinase, involved in Transforming growth factor ß-1 proprotein (TGF-ß), 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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Transdução de Sinais
/
Sistema Imunitário
/
Modelos Biológicos
/
Esclerose Múltipla
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
/
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Genome Med
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Reino Unido