Scholarlink

Background <p>Diabetic cardiomyopathy (DCM) occurs in the context of coronary artery disease or pressure overload heart disease, characterized by alterations in cardiac structure and function. The mechanisms linking metabolic memory and METTL1-mediated modifications to DCM progression remain unclear.</p> Methods <p>This study integrated multiple public transcriptome datasets to conduct a systematic analysis of gene expression profiles associated with diabetic cardiomyopathy. Potential key characteristic genes were identified through differential expression analysis and machine learning techniques. Their associated biological processes and signaling pathways were assessed using functional enrichment analysis. Additionally, single-cell RNA sequencing data were employed to examine the expression and distribution of key genes across various cardiac cell types, while gene set enrichment analysis (GSEA) was utilized to explore their potential functional networks.</p> Results <p>The integration of three public transcriptome datasets and subsequent differential expression analysis identified 159 genes associated with diabetic cardiomyopathy, of which 133 were upregulated and 26 downregulated. These differentially expressed genes (DEGs) effectively distinguished between DCM and control samples. Machine learning analyses, including LASSO regression and random forest, identified several key candidate genes, with METTL1 showing a significant association with inflammation, fibrosis, and metabolic disorders. In multiple independent datasets, METTL1 expression was markedly elevated in DCM samples and demonstrated substantial diagnostic potential (ROC AUC = 0.826). Functional enrichment analysis revealed that METTL1-related genes predominantly participated in pathways related to white blood cell migration, inflammatory activation, and extracellular matrix remodeling. Single-cell RNA sequencing further indicated that METTL1 was primarily enriched in the fibroblast population. The proportion of METTL1-positive fibroblasts in DCM samples was significantly increased and was associated with inflammatory and fibrosis-related signaling pathways. A comprehensive analysis suggests that METTL1 may play a role in the pathological progression of DCM by regulating fibroblast activation, amplifying inflammation, and contributing to myocardial remodeling.</p> Conclusions <p>This study elucidates the expression characteristics of METTL1 and its potential regulatory functions in diabetic cardiomyopathy. The findings suggest that METTL1 metabolic memory may be influenced by RNA modifications occurring in the context of persistent inflammation and fibrosis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Hyperglycemia-driven metabolic memory signaling destabilizes METTL1 to trigger inflammatory hypertrophy and fibrosis in diabetic cardiomyopathy

  • 期刊论文

  • 发表日期：

    2026年5月6日

查看全文
  • Yurui Yuan,
  • Ang Zhang,
  • Ying Jiang,
  • Dayun Tao,
  • Zhijiang Guo,
  • Hang Zhu,
  • Yu Zeng,
  • Hongshuo Shi

摘要

Background

Diabetic cardiomyopathy (DCM) occurs in the context of coronary artery disease or pressure overload heart disease, characterized by alterations in cardiac structure and function. The mechanisms linking metabolic memory and METTL1-mediated modifications to DCM progression remain unclear.

Methods

This study integrated multiple public transcriptome datasets to conduct a systematic analysis of gene expression profiles associated with diabetic cardiomyopathy. Potential key characteristic genes were identified through differential expression analysis and machine learning techniques. Their associated biological processes and signaling pathways were assessed using functional enrichment analysis. Additionally, single-cell RNA sequencing data were employed to examine the expression and distribution of key genes across various cardiac cell types, while gene set enrichment analysis (GSEA) was utilized to explore their potential functional networks.

Results

The integration of three public transcriptome datasets and subsequent differential expression analysis identified 159 genes associated with diabetic cardiomyopathy, of which 133 were upregulated and 26 downregulated. These differentially expressed genes (DEGs) effectively distinguished between DCM and control samples. Machine learning analyses, including LASSO regression and random forest, identified several key candidate genes, with METTL1 showing a significant association with inflammation, fibrosis, and metabolic disorders. In multiple independent datasets, METTL1 expression was markedly elevated in DCM samples and demonstrated substantial diagnostic potential (ROC AUC = 0.826). Functional enrichment analysis revealed that METTL1-related genes predominantly participated in pathways related to white blood cell migration, inflammatory activation, and extracellular matrix remodeling. Single-cell RNA sequencing further indicated that METTL1 was primarily enriched in the fibroblast population. The proportion of METTL1-positive fibroblasts in DCM samples was significantly increased and was associated with inflammatory and fibrosis-related signaling pathways. A comprehensive analysis suggests that METTL1 may play a role in the pathological progression of DCM by regulating fibroblast activation, amplifying inflammation, and contributing to myocardial remodeling.

Conclusions

This study elucidates the expression characteristics of METTL1 and its potential regulatory functions in diabetic cardiomyopathy. The findings suggest that METTL1 metabolic memory may be influenced by RNA modifications occurring in the context of persistent inflammation and fibrosis.