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Background <p>N6-methyladenosine (m6A) RNA modification plays a crucial role in various biological events and is implicated in various metabolic-related diseases. However, its role in MASLD remains unclear. This study aims to investigate the impact of METTL3 on MASLD through multi-omics analysis, with a focus on exploring its potential mechanisms of action.</p> Methods <p>An MASLD mouse model was established by feeding C57BL/6J mice a high-fat diet for 12 weeks. A METTL3 stable overexpression AML12 cell model was also constructed via lentiviral transfection. Subsequent transcriptomic and proteomic analyses, as well as integrated analysis between different omics datasets, were conducted.</p> Results <p>METTL3 expression was significantly increased in the MASLD mouse model. Through our transcriptomic and proteomic analyses, we identified 848 genes with significant inconsistencies between the transcriptomic and proteomic datasets. GO/ KEGG enrichment analyses identified terms that may be involved in post-transcriptional modifications, particularly METTL3-mediated m6A modification. Subsequently, through integrated proteomic analysis of the METTL3-overexpressed AML12 cell model and the MASLD mouse model, we selected the top 20 co-upregulated and co-downregulated GO/ KEGG terms as the main biological processes influenced by METTL3 during MASLD. By intersecting with pathways obtained from previous integrated analyses, we identified GO/ KEGG terms affected by METTL3-induced m6A modification. Protein-protein interaction analysis of proteins involved in these pathways highlighted GAPDH and TPI1 as two key hub genes.</p> Conclusions <p>During MASLD, METTL3 regulates the glycolytic pathway through m6A modification, influencing the occurrence and development of the disease via the key hub genes GAPDH and TPI1. These findings expand our understanding of MASLD and provide strong evidence for potential therapeutic targets and drug development.</p>

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M6A methyltransferase METTL3 promotes glucose metabolism hub gene expression and induces metabolic dysfunction-associated steatotic liver disease (MASLD)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月24日

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  • Shuowen Wang,
  • Ziying Xu,
  • Zijun Wang,
  • Xiaoyu Yi,
  • Jianxin Wu

摘要

Background

N6-methyladenosine (m6A) RNA modification plays a crucial role in various biological events and is implicated in various metabolic-related diseases. However, its role in MASLD remains unclear. This study aims to investigate the impact of METTL3 on MASLD through multi-omics analysis, with a focus on exploring its potential mechanisms of action.

Methods

An MASLD mouse model was established by feeding C57BL/6J mice a high-fat diet for 12 weeks. A METTL3 stable overexpression AML12 cell model was also constructed via lentiviral transfection. Subsequent transcriptomic and proteomic analyses, as well as integrated analysis between different omics datasets, were conducted.

Results

METTL3 expression was significantly increased in the MASLD mouse model. Through our transcriptomic and proteomic analyses, we identified 848 genes with significant inconsistencies between the transcriptomic and proteomic datasets. GO/ KEGG enrichment analyses identified terms that may be involved in post-transcriptional modifications, particularly METTL3-mediated m6A modification. Subsequently, through integrated proteomic analysis of the METTL3-overexpressed AML12 cell model and the MASLD mouse model, we selected the top 20 co-upregulated and co-downregulated GO/ KEGG terms as the main biological processes influenced by METTL3 during MASLD. By intersecting with pathways obtained from previous integrated analyses, we identified GO/ KEGG terms affected by METTL3-induced m6A modification. Protein-protein interaction analysis of proteins involved in these pathways highlighted GAPDH and TPI1 as two key hub genes.

Conclusions

During MASLD, METTL3 regulates the glycolytic pathway through m6A modification, influencing the occurrence and development of the disease via the key hub genes GAPDH and TPI1. These findings expand our understanding of MASLD and provide strong evidence for potential therapeutic targets and drug development.