Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map
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 ty...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/11/8/785 |
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| author | Quentin Miagoux Vidisha Singh Dereck de Mézquita Valerie Chaudru Mohamed Elati Elisabeth Petit-Teixeira Anna Niarakis |
| author_facet | Quentin Miagoux Vidisha Singh Dereck de Mézquita Valerie Chaudru Mohamed Elati Elisabeth Petit-Teixeira Anna Niarakis |
| author_sort | Quentin Miagoux |
| collection | DOAJ |
| description | 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 <i>in silico</i> 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. |
| first_indexed | 2024-03-10T08:41:30Z |
| format | Article |
| id | doaj.art-eadaea12768b445da03b3292ced8c994 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-10T08:41:30Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-eadaea12768b445da03b3292ced8c9942023-11-22T08:19:20ZengMDPI AGJournal of Personalized Medicine2075-44262021-08-0111878510.3390/jpm11080785Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease MapQuentin Miagoux0Vidisha Singh1Dereck de Mézquita2Valerie Chaudru3Mohamed Elati4Elisabeth Petit-Teixeira5Anna Niarakis6Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, FranceUniversité Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, FranceUniversité Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, FranceUniversité Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, FranceCANTHER, University of Lille, CNRS UMR 1277, Inserm U9020, 59045 Lille, FranceUniversité Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, FranceUniversité Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, FranceRheumatoid 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 <i>in silico</i> 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.https://www.mdpi.com/2075-4426/11/8/785network inferenceintegrative biologyrheumatoid arthritissignaling cascadesgene regulationtranscription factors |
| spellingShingle | Quentin Miagoux Vidisha Singh Dereck de Mézquita Valerie Chaudru Mohamed Elati Elisabeth Petit-Teixeira Anna Niarakis Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map Journal of Personalized Medicine network inference integrative biology rheumatoid arthritis signaling cascades gene regulation transcription factors |
| title | Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map |
| title_full | Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map |
| title_fullStr | Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map |
| title_full_unstemmed | Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map |
| title_short | Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map |
| title_sort | inference of an integrative executable network for rheumatoid arthritis combining data driven machine learning approaches and a state of the art mechanistic disease map |
| topic | network inference integrative biology rheumatoid arthritis signaling cascades gene regulation transcription factors |
| url | https://www.mdpi.com/2075-4426/11/8/785 |
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