Inference of an integrative, executable network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

Abstract

MotivationRheumatoid arthritis (RA) is a multifactorial autoimmune disease that causes chronic inflammation of the joints. RA is considered a complex disease as it 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 approach, we integrate three different biological layers (gene expression, signalling cascades, mutations), obtained by bottom-up and top-down methods to build an integrative, disease-specific network. The goal behind this endeavour is to see if we can unravel mechanisms governing the regulation of key genes identified as mutation carriers in RA and derive patient-specific models to gain more insights into the disease heterogeneity.ResultsIn this work, we combine biological data relevant to Rheumatoid Arthritis, in the form of a global, integrative network. We first make use of 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 prior knowledge encoded in a state-of-the-art, RA-specific molecular map. Lastly, a list of RA specific variants highlights key genes associated with known disease mutations.AvailabilityDatasets used for the analysis are publicly available. All scripts used to generate results and the Shiny app will be freely accessible after peer-reviewed publication.

A large-scale Boolean model of the Rheumatoid Arthritis Fibroblast-Like Synoviocytes predicts drug synergies in the arthritic joint

“…Indeed, in our modelling framework, all results with anti-TNF drugs were inadequate to entirely suppress the inflammation phenotype, suggesting that blocking TNF alone is insufficient to hamper the signal. Our results also follow the findings of (Miagoux et al , 2021), which demonstrated that IL6 suppression was more successful in downregulating key transcription factors leading to inflammation in patients treated with anti-TNF treatment.…”

High-quality Reconstruction of Disease Mechanisms: Planning, Development and Maintenance

“…• creating computational models based on disease maps, for example for rheumatoid arthritis [16,17] and atherosclerosis [15]; • creating causal-interaction networks based on a disease map [68];…”

Modelling rheumatoid arthritis: A hybrid modelling framework to describe pannus formation in a small joint

“…However it has been suggested that accounting for stochasticity within RA may be key in understanding the evolution of the disease[24, 28] and predicting the success of RA treatments [26]. Along with the stochastic models described in Section 1.2.1, a small number of studies have considered probabilistic Boolean network models for RA [65, 104].…”