Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map
| Autor: | Quentin, Miagoux, Vidisha, Singh, Dereck, de Mézquita, Valerie, Chaudru, Mohamed, Elati, Elisabeth, Petit-Teixeira, Anna, Niarakis |
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| Přispěvatelé: | Laboratoire de recherche européen pour la polyarthrite rhumatoïde (GenHotel), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay, Computational systems biology and optimization (Lifeware), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 (CANTHER), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), Niarakis, Anna |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
rheumatoid arthritis
[SDV.MHEP.RSOA] Life Sciences [q-bio]/Human health and pathology/Rhumatology and musculoskeletal system integrative biology systems biology signaling cascades [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] Article network inference [SDV.MHEP.RSOA]Life Sciences [q-bio]/Human health and pathology/Rhumatology and musculoskeletal system [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics transcription factors Boolean simulations Medicine [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] gene regulation [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM] |
| Zdroj: | Journal of Personalized Medicine Volume 11 Issue 8 Journal of Personalized Medicine, 2021, 11 (8), pp.785. ⟨10.3390/jpm11080785⟩ Journal of Personalized Medicine, MDPI, 2021, 11 (8), pp.785. ⟨10.3390/jpm11080785⟩ Journal of Personalized Medicine, Vol 11, Iss 785, p 785 (2021) |
| ISSN: | 2075-4426 |
| DOI: | 10.3390/jpm11080785 |
| Popis: | International audience; Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations. |
| Databáze: | OpenAIRE |
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