Classifying Rheumatoid Arthritis gene network signatures for identifying key regulatory molecules and their altered pathways by adopting network biology approach

Abstract

Rheumatoid Arthritis (RA) is an auto-immune inflammatory disease which abnormally affects synovial joints of the body. Synovial joint consists of synovial fluid lined by the synovial membrane which includes two prominent cell types, i.e., Synovial fibroblast (SF) and Macrophages (MΦ). These two cell types are highly reported as key players in RA. In the present research, RA expression datasets related to SF and MΦ were collected from Gene Expression Omnibus (GEO) to identify the signature molecule associated with RA pathway. These RA datasets which were analyzed at expression level and correlated based on Pearson Correlation coefficient (r-value) for the construction of the biological interactome. These networks were further explored to identify functional crosstalk between signature molecules in RA. Network centrality parameters and functional characterization were taken into consideration based on power law to predict the key regulatory molecules in the pathogenesis of RA. Groups of highly interconnected signature molecules were further validated by using the integrated approach of The International Mouse Phenotyping Consortium, KEGG and Database of Essential Genes. The research analysis accounted for 9 proteins to be significant in the pathogenesis of RA at three leading levels of function i.e., associated with joint like N-Acetyl Glucosamine metabolism, Fibroblast initiation for pannus formation and Th17 initiation for bone degradation. This type of methodology has enabled to explore novel biomarkers against this multifactorial disease, due to which significant combination of therapies will concentrate on multiple pathways associated with RA rather that single protein.