A Network Biology Approach to Understanding the Tissue-Specific Roles of Non-Coding RNAs in Arthritis

Ali, Shabana Amanda, Pastrello, Chiara, Kaur, Navdeep, Peffers, Mandy J ORCID: 0000-0001-6979-0440, Ormseth, Michelle J and Jurisica, Igor
(2021) A Network Biology Approach to Understanding the Tissue-Specific Roles of Non-Coding RNAs in Arthritis. FRONTIERS IN ENDOCRINOLOGY, 12. 744747-.

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Discovery of non-coding RNAs continues to provide new insights into some of the key molecular drivers of musculoskeletal diseases. Among these, microRNAs have received widespread attention for their roles in osteoarthritis and rheumatoid arthritis. With evidence to suggest that long non-coding RNAs and circular RNAs function as competing endogenous RNAs to sponge microRNAs, the net effect on gene expression in specific disease contexts can be elusive. Studies to date have focused on elucidating individual long non-coding-microRNA-gene target axes and circular RNA-microRNA-gene target axes, with a paucity of data integrating experimentally validated effects of non-coding RNAs. To address this gap, we curated recent studies reporting non-coding RNA axes in chondrocytes from human osteoarthritis and in fibroblast-like synoviocytes from human rheumatoid arthritis. Using an integrative computational biology approach, we then combined the findings into cell- and disease-specific networks for in-depth interpretation. We highlight some challenges to data integration, including non-existent naming conventions and out-of-date databases for non-coding RNAs, and some successes exemplified by the International Molecular Exchange Consortium for protein interactions. In this perspective article, we suggest that data integration is a useful <i>in silico</i> approach for creating non-coding RNA networks in arthritis and prioritizing interactions for further <i>in vitro</i> and <i>in vivo</i> experimentation in translational research.

Item Type: Article
Uncontrolled Keywords: epigenetics, microRNA, long non-coding RNA, circular RNA, cartilage, synovium, integrative computational biology, network analysis and visualization
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User: Symplectic Admin
Date Deposited: 29 Nov 2021 08:54
Last Modified: 18 Jan 2023 21:23
DOI: 10.3389/fendo.2021.744747
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3144135