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

  1. Bernardo-Faura, Marti
  2. Rinas, Melanie
  3. Wirbel, Jakob
  4. Pertsovskaya, Inna
  5. Pliaka, Vicky
  6. Messinis, Dimitris E.
  7. Vila, Gemma
  8. Sakellaropoulos, Theodore
  9. Faigle, Wolfgang
  10. Stridh, Pernilla
  11. Behrens, Janina R.
  12. Olsson, Tomas
  13. Martin, Roland
  14. Paul, Friedemann
  15. Alexopoulos, Leonidas G.
  16. Villoslada, Pablo
  17. Saez-Rodriguez, Julio ORCID logo

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1000 Titel
  • Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis
1000 Autor/in
  1. Bernardo-Faura, Marti |
  2. Rinas, Melanie |
  3. Wirbel, Jakob |
  4. Pertsovskaya, Inna |
  5. Pliaka, Vicky |
  6. Messinis, Dimitris E. |
  7. Vila, Gemma |
  8. Sakellaropoulos, Theodore |
  9. Faigle, Wolfgang |
  10. Stridh, Pernilla |
  11. Behrens, Janina R. |
  12. Olsson, Tomas |
  13. Martin, Roland |
  14. Paul, Friedemann |
  15. Alexopoulos, Leonidas G. |
  16. Villoslada, Pablo |
  17. Saez-Rodriguez, Julio |
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-07-16
1000 Erschienen in
1000 Quellenangabe
  • 13(1):117
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s13073-021-00925-8 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8284018/ |
1000 Publikationsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Sacherschließung
lokal Multiple Sclerosis/therapy [MeSH]
lokal Multiple Sclerosis/diagnosis [MeSH]
lokal Combined Modality Therapy/methods [MeSH]
lokal Phosphoproteomics
lokal Phosphoproteins/metabolism [MeSH]
lokal Models, Biological [MeSH]
lokal Disease Management [MeSH]
lokal Personalized medicine
lokal Immune System/immunology [MeSH]
lokal Male [MeSH]
lokal Signal Transduction/drug effects [MeSH]
lokal Case-Control Studies [MeSH]
lokal Network modeling
lokal Treatment
lokal Proteome [MeSH]
lokal Multiple Sclerosis/etiology [MeSH]
lokal Algorithms [MeSH]
lokal Female [MeSH]
lokal Proteomics/methods [MeSH]
lokal Adult [MeSH]
lokal Humans [MeSH]
lokal Multiple Sclerosis/metabolism [MeSH]
lokal Treatment Outcome [MeSH]
lokal Middle Aged [MeSH]
lokal Disease Susceptibility [MeSH]
lokal Immune System/drug effects [MeSH]
lokal Immune System/metabolism [MeSH]
lokal Molecular Targeted Therapy [MeSH]
lokal Pathways
lokal Kinases
lokal Multiple sclerosis
lokal xMAP assay
lokal Research
lokal Immunotherapy
lokal Prognosis [MeSH]
lokal Signaling networks
lokal Biomarkers [MeSH]
lokal Logic modeling
lokal Combination therapy
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/QmVybmFyZG8tRmF1cmEsIE1hcnRp|https://frl.publisso.de/adhoc/uri/UmluYXMsIE1lbGFuaWU=|https://frl.publisso.de/adhoc/uri/V2lyYmVsLCBKYWtvYg==|https://frl.publisso.de/adhoc/uri/UGVydHNvdnNrYXlhLCBJbm5h|https://frl.publisso.de/adhoc/uri/UGxpYWthLCBWaWNreQ==|https://frl.publisso.de/adhoc/uri/TWVzc2luaXMsIERpbWl0cmlzIEUu|https://frl.publisso.de/adhoc/uri/VmlsYSwgR2VtbWE=|https://frl.publisso.de/adhoc/uri/U2FrZWxsYXJvcG91bG9zLCBUaGVvZG9yZQ==|https://frl.publisso.de/adhoc/uri/RmFpZ2xlLCBXb2xmZ2FuZw==|https://frl.publisso.de/adhoc/uri/U3RyaWRoLCBQZXJuaWxsYQ==|https://frl.publisso.de/adhoc/uri/QmVocmVucywgSmFuaW5hIFIu|https://frl.publisso.de/adhoc/uri/T2xzc29uLCBUb21hcw==|https://frl.publisso.de/adhoc/uri/TWFydGluLCBSb2xhbmQ=|https://frl.publisso.de/adhoc/uri/UGF1bCwgRnJpZWRlbWFubg==|https://frl.publisso.de/adhoc/uri/QWxleG9wb3Vsb3MsIExlb25pZGFzIEcu|https://frl.publisso.de/adhoc/uri/VmlsbG9zbGFkYSwgUGFibG8=|https://orcid.org/0000-0002-8552-8976
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1000 Erstellt am 2023-11-16T17:43:02.110+0100
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