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Research Paper|Volume 16, Issue 8|pp 7267—7276

Biological significance of METTL5 in atherosclerosis: comprehensive analysis of single-cell and bulk RNA sequencing data

Jianjin Wu1, Lei Wang2, Shuaishuai Xi3, Chao Ma3, Fukang Zou1, Guanyu Fang1, Fangbing Liu1, Xiaokai Wang4, Lefeng Qu1
  • 1Department of Vascular and Endovascular Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai, China
  • 2Department of Vascular Surgery, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China
  • 3Department of Vascular Surgery, Weifang Yidu Central Hospital, Weifang, Shandong, China
  • 4Department of Interventional and Vascular Surgery, The First People’s Hospital of Xuzhou, Xuzhou, Jiangsu, China
* Equal contribution
Received: September 18, 2023Accepted: March 27, 2024Published: April 24, 2024
https://doi.org/10.18632/aging.205755

Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: N6-methyladenosine (m6A) methylation is involved in the pathogenesis of atherosclerosis (AS). Limited studies have examined the role of the m6A methyltransferase METTL5 in AS pathogenesis.

Methods: This study subjected the AS dataset to differential analysis and weighted gene co-expression network analysis to identify m6A methylation-associated differentially expressed genes (DEGs). Next, the m6A methylation-related DEGs were subjected to consensus clustering to categorize AS samples into distinct m6A subtypes. Single-cell RNA sequencing (scRNA-seq) analysis was performed to investigate the proportions of each cell type in AS and adjacent healthy tissues and the expression levels of key m6A regulators. The mRNA expression levels of METTL5 in AS and healthy tissues were determined using quantitative real-time polymerase chain reaction (qRT-PCR) analysis.

Results: AS samples were classified into two subtypes based on a five-m6A regulator-based model. scRNA-seq analysis revealed that the proportions of T cells, monocytes, and macrophages in AS tissues were significantly higher than those in healthy tissues. Additionally, the levels of m6A methylation were significantly different between AS and healthy tissues. METTL5 expression was upregulated in macrophages, smooth muscle cells (SMCs), and endothelial cells (ECs). qRT-PCR analysis demonstrated that the METTL5 mRNA level in AS tissues was downregulated when compared with that in healthy tissues.

Conclusions: METTL5 is a potential diagnostic marker for AS subtypes. Macrophages, SMCs, and ECs, which exhibit METTL5 upregulation, may modulate AS progression by regulating m6A methylation levels.