Systems biology approach delineates critical pathways associated with disease progression in rheumatoid arthritis

Abstract

Rheumatoid Arthritis (RA) is a chronic systemic autoimmune disease leading to inflammation, cartilage cell death, synoviocyte proliferation, and increased and impaired differentiation of osteoclasts and osteoblasts leading to joint erosions and deformities. Transcriptomics, proteomics, and metabolomics datasets were analyzed to identify the critical pathways that drive the RA pathophysiology. Single nucleotide polymorphisms (SNPs) associated with RA were analyzed for the functional implications, clinical outcomes, and blood parameters later validated by literature. SNPs associated with RA were grouped into pathways that drive the immune response and cytokine production. Further gene set enrichment analysis (GSEA) was performed on gene expression omnibus (GEO) data sets of peripheral blood mononuclear cells (PBMCs), synovial macrophages, and synovial biopsies from RA patients showed enrichment of Th1, Th2, Th17 differentiation, viral and bacterial infections, metabolic signalling and immunological pathways with potential implications for RA. The proteomics data analysis presented pathways with genes involved in immunological signaling and metabolic pathways, including vitamin B12 and folate metabolism. Metabolomics datasets analysis showed significant pathways like amino-acyl tRNA biosynthesis, metabolism of amino acids (arginine, alanine aspartate, glutamate, glutamine, phenylalanine, and tryptophan), and nucleotide metabolism. Furthermore, our commonality analysis of multi-omics datasets identified common pathways with potential implications for joint remodeling in RA. Disease-modifying anti-rheumatic drugs (DMARDs) and biologics treatments were found to modulate many of the pathways that were deregulated in RA. Overall, our analysis identified molecular signatures associated with the observed symptoms, joint erosions, potential biomarkers, and therapeutic targets in RA.

Communicated by Ramaswamy H. Sarma

Keywords:

• Rheumatoid arthritis
• systems analysis
• disease biology
• biomarkers
• therapeutics

Acknowledgments

We thank the administration of Sri Sathya Sai Institute of Higher Learning (SSSIHL), for the and the Department of Biosciences, SSSIHL, Prasanthi Nilayam, Anantapur, India for infrastructure and financial support. We thank University Grants Commission, Basic Scientific Research, New Delhi, India (F.4-1/2006 (BSR)/7-164/2007 (BSR)) and Council of Scientific and Industrial Research, New Delhi, India (CSIR-UGC-NET-2121530422) for the student fellowship. We also thank the funding agencies Department of Biotechnology (DBT) Project, New Delhi, India (BT/PR8226/BRB/10/1224/2013), Department of Science and Technology (DST), New Delhi, India (EMR/2017/005381), Department of Science and Technology (DST) – FIST, New Delhi, India (SR/FST/LSI-616/2014), Department of Biotechnology – Bioinformatics (DBT-BIF) Facility, New Delhi, India (BT/BI/25/063/2012), University Grants Commission (UGC) – special assistance program (SAP), New Delhi, India (UGC-SAP III: F.3-19/2018/DRS-III(SAP-II)) for financial and infrastructure support.

Disclosure statement

The authors declare no conflict of interest.