Transcriptome analysis of skeletal muscle in dermatomyositis, polymyositis, and dysferlinopathy, using a bioinformatics approach

Ha-Neul Jeong; Taek Gyu Lee; Hyung Jun Park; Young Yang; Seung-Hun Oh; Seong-Woong Kang; Young-Chul Choi

doi:10.3389/fneur.2023.1328547

Transcriptome analysis of skeletal muscle in dermatomyositis, polymyositis, and dysferlinopathy, using a bioinformatics approach

Front Neurol. 2023 Dec 6:14:1328547. doi: 10.3389/fneur.2023.1328547. eCollection 2023.

Authors

Ha-Neul Jeong^#¹, Taek Gyu Lee^#², Hyung Jun Park¹, Young Yang³, Seung-Hun Oh⁴, Seong-Woong Kang^{5

6}, Young-Chul Choi^{1

6}

Affiliations

¹ Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea.
² Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, Republic of Korea.
³ Research Institute of Women's Disease, Sookmyumg Women's University, Seoul, Republic of Korea.
⁴ Department of Neurology, CHA Bundang Medical Center, School of Medicine, CHA University, Seongnam-si, Republic of Korea.
⁵ Department of Rehabilitation Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
⁶ Rehabilitation Institute of Neuromuscular Disease, Yonsei University College of Medicine, Seoul, Republic of Korea.

^# Contributed equally.

Abstract

Background: Polymyositis (PM) and dermatomyositis (DM) are two distinct subgroups of idiopathic inflammatory myopathies. Dysferlinopathy, caused by a dysferlin gene mutation, usually presents in late adolescence with muscle weakness, degenerative muscle changes are often accompanied by inflammatory infiltrates, often resulting in a misdiagnosis as polymyositis.

Objective: To identify differential biological pathways and hub genes related to polymyositis, dermatomyositis and dysferlinopathy using bioinformatics analysis for understanding the pathomechanisms and providing guidance for therapy development.

Methods: We analyzed intramuscular ribonucleic acid (RNA) sequencing data from seven dermatomyositis, eight polymyositis, eight dysferlinopathy and five control subjects. Differentially expressed genes (DEGs) were identified by using DESeq2. Enrichment analyses were performed to understand the functions and enriched pathways of DEGs. A protein-protein interaction (PPI) network was constructed, and clarified the gene cluster using the molecular complex detection tool (MCODE) analysis to identify hub genes.

Results: A total of 1,048, 179 and 3,807 DEGs were detected in DM, PM and dysferlinopathy, respectively. Enrichment analyses revealed that upregulated DEGs were involved in type 1 interferon (IFN1) signaling pathway in DM, antigen processing and presentation of peptide antigen in PM, and cellular response to stimuli in dysferlinopathy. The PPI network and MCODE cluster identified 23 genes related to type 1 interferon signaling pathway in DM, 4 genes (PDIA3, HLA-C, B2M, and TAP1) related to MHC class 1 formation and quality control in PM, and 7 genes (HSPA9, RPTOR, MTOR, LAMTOR1, LAMTOR5, ATP6V0D1, and ATP6V0B) related to cellular response to stress in dysferliniopathy.

Conclusion: Overexpression of genes related to the IFN1 signaling pathway and major histocompatibility complex (MHC) class I formation was identified in DM and PM, respectively. In dysferlinopathy, overexpression of HSPA9 and the mTORC1 signaling pathway genes was detected.

Keywords: bioinformatics; dermatomyositis; dysferlinopathy; polymyositis; transcriptome.

Grants and funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.