Identification of potential regulatory long non-coding RNA-associated competing endogenous RNA axes in periplaque regions in multiple sclerosis

Hani Sabaie; Sharareh Khorami Rouz; Ghazal Kouchakali; Samaneh Heydarzadeh; Mohammad Reza Asadi; Mirmohsen Sharifi-Bonab; Bashdar Mahmud Hussen; Mohammad Taheri; Seyed Abdulmajid Ayatollahi; Maryam Rezazadeh

doi:10.3389/fgene.2022.1011350

Identification of potential regulatory long non-coding RNA-associated competing endogenous RNA axes in periplaque regions in multiple sclerosis

Front Genet. 2022 Oct 17:13:1011350. doi: 10.3389/fgene.2022.1011350. eCollection 2022.

Affiliations

¹ Clinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical Sciences, Tabriz, Iran.
² School of Life Sciences, Manipal University, Dubai, United Arab Emirates.
³ Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
⁴ Department of Pharmacognosy, College of Pharmacy, Hawler Medical University, Erbil, Iraq.
⁵ Center of Research and Strategic Studies, Lebanese French University, Erbil, Iraq.
⁶ Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
⁷ Institute of Human Genetics, Jena University Hospital, Jena, Germany.
⁸ Phytochemistry Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Abstract

Slow-burning inflammation at the lesion rim is connected to the expansion of chronic multiple sclerosis (MS) lesions. However, the underlying processes causing expansion are not clearly realized. In this context, the current study used a bioinformatics approach to identify the expression profiles and related lncRNA-associated ceRNA regulatory axes in the periplaque region in MS patients. Expression data (GSE52139) from periplaque regions in the secondary progressive MS spinal cord and controls were downloaded from the Gene Expression Omnibus database (GEO), which has details on mRNAs and lncRNAs. Using the R software's limma package, the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were found. The RNA interactions were also found using the DIANA-LncBase, miRTarBase, and HMDD databases. The Pearson correlation coefficient was used to determine whether there were any positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Finally, lncRNA-associated ceRNA axes were created based on co-expression and connections between DElncRNA, miRNA, and DEmRNA. We used the Enrichr tool to enrich the biological process, molecular function, and pathways for DEmRNAs and DElncRNAs. A network of DEmRNAs' protein-protein interactions was developed, and the top five hub genes were found using Cytoscape and STRING. The current study indicates that 15 DEmRNAs, including FOS, GJA1, NTRK2, CTNND1, and SP3, are connected to the MS ceRNA network. Additionally, four DElncRNAs (such as TUG1, ASB16-AS1, and LINC01094) that regulated the aforementioned mRNAs by sponging 14 MS-related miRNAs (e.g., hsa-miR-145-5p, hsa-miR-200a-3p, hsa-miR-20a-5p, hsa-miR-22-3p, hsa-miR-23a-3p, hsa-miR-27a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p, hsa-miR-34a-5p) were found. In addition, the analysis of pathway enrichment revealed that DEmRNAs were enriched in the pathways for the "MAPK signaling pathway", "Kaposi sarcoma-associated herpesvirus infection", "Human immunodeficiency virus one infection", "Lipid and atherosclerosis", and "Amphetamine addiction". Even though the function of these ceRNA axes needs to be investigated further, this study provides research targets for studying ceRNA-mediated molecular mechanisms related to periplaque demyelination in MS.

Keywords: bioinformatic analysis; competing endogenous RNA; long non-coding RNA; microarray analysis; multiple sclerosis; periplaque.