Computational Modeling of the Metabolic Reprogramming in Rheumatoid Arthritis Synovial Fibroblasts and Cancer Associated Fibroblasts

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease affecting 1% of the population. Its pathogenesis mainly revolves around synovial hyperplasia, consisting of fibroblast-like synoviocytes (RASFs) accumulation. After acquiring an aggressive phenotype, RASFs reduce contact inhibition, resist to apoptosis, migrate and invade periarticular tissues (e.g. bone and cartilage). Similarly in the Cancer field, the importance of the stromal microenvironment has been recognized for years. It is widely accepted that cancer develops as a result of genetic and epigenetic alterations in clonal cells, but that growth, survival, and metastasis are regulated by stromal cells, also known as Cancer Associated Fibroblasts (CAFs). Thus, proliferating and aggressive fibroblasts not only seem to be a key feature in RA but also in cancer.

An increasing number of studies have shown obvious parallels between CAFs and RASFs, not considering them anymore as passive responders but key disease effectors. A critical property lies in CAFs’ and RAFs’ altered glucose metabolism: they both are prone to metabolic reprogramming leading to a glycolytic switch. This common feature could prove to be critical and identifying the molecular links between metabolic reprogramming and fibroblasts activation may represent a serious lead in the development of complementary therapies for these pathologies.

To study similarities in RASFs and CAFs glucose metabolism regulation, detailed molecular maps will be used as knowledge bases about the associated phenotypes. Regarding RA, a state-of-the-art molecular map has been published (Singh et al., 2020) illustrating pathways involved and allowing to extract specific fibroblast information. Similarly, a molecular interaction map for CAFs is also available (Kuperstein et al., 2015). After both being greatly enriched with metabolic information (focusing on the glycolytic switch), these maps will represent templates for various stages of data visualization and bioinformatic analysis. Both up-to-date molecular maps will also be converted into cell-specific executable Boolean models of intracellular networks, using the CaSQ tool (Aghamiri et al., 2020). By conducting several in-silico perturbations, fibroblasts’ response to different conditions will be studied, allowing to decipher the input/output relationships at a cellular level.

Based on the bioinformatic and dynamic analysis, the identification of a regulatory mechanism (common or not) explaining fibroblasts’ metabolic reprogramming in disease-specific conditions and the phenotypic changes leading to the pathogenic transformation in RASFs and CAFs is expected. This will lead to a better understanding of the mechanisms underlying RA and Cancer pathogenesis at a global systemic level, potentially leading to the identification of new therapeutic targets.