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Background <p>Alzheimer’s disease (AD) is a complex neurodegenerative disorder with a largely unexplored epigenetic landscape.</p> Objective <p>This study employs an innovative approach that integrates multi-omics analysis and explainable machine learning to explore the epigenetic regulatory mechanisms underlying the epigenetic signature of <i>PRRT1</i> implicated in AD.</p> Methods <p>Through comprehensive DNA methylation and transcriptomic profiling, we identified distinct epigenetic signatures associated with gene <i>PRRT1</i> expression in AD patient samples compared to healthy controls. Utilizing interpretable machine learning models and ELMAR analysis, we dissected the complex relationships between these epigenetic signatures and gene expression patterns, revealing novel regulatory elements and pathways. Finally, the epigenetic mechanisms of these genes were investigated experimentally.</p> Results <p>This study identified ten epigenetic signatures, constructed an interpretable AD diagnostic model, and utilized various bioinformatics methods to create an epigenomic map. Subsequently, the ELMAR R package was used to integrate multi-omics data and identify the upstream transcription factor MAZ for <i>PRRT1</i>. Finally, experiments confirmed the interaction between MAZ and <i>PRRT1</i>, which mediated apoptosis and autophagy in AD.</p> Conclusion <p>This study adopts a strategy that integrates bioinformatics analysis with molecular experiments, providing new insights into the epigenetic regulatory mechanisms of <i>PRRT1</i> in AD and demonstrating the importance of explainable machine learning in elucidating complex disease mechanisms.</p>

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Uncovering the epigenetic regulatory clues of PRRT1 in Alzheimer’s disease: a strategy integrating multi-omics analysis with explainable machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月7日

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  • Fang Wang,
  • Ying Liang,
  • Qin-Wen Wang

摘要

Background

Alzheimer’s disease (AD) is a complex neurodegenerative disorder with a largely unexplored epigenetic landscape.

Objective

This study employs an innovative approach that integrates multi-omics analysis and explainable machine learning to explore the epigenetic regulatory mechanisms underlying the epigenetic signature of PRRT1 implicated in AD.

Methods

Through comprehensive DNA methylation and transcriptomic profiling, we identified distinct epigenetic signatures associated with gene PRRT1 expression in AD patient samples compared to healthy controls. Utilizing interpretable machine learning models and ELMAR analysis, we dissected the complex relationships between these epigenetic signatures and gene expression patterns, revealing novel regulatory elements and pathways. Finally, the epigenetic mechanisms of these genes were investigated experimentally.

Results

This study identified ten epigenetic signatures, constructed an interpretable AD diagnostic model, and utilized various bioinformatics methods to create an epigenomic map. Subsequently, the ELMAR R package was used to integrate multi-omics data and identify the upstream transcription factor MAZ for PRRT1. Finally, experiments confirmed the interaction between MAZ and PRRT1, which mediated apoptosis and autophagy in AD.

Conclusion

This study adopts a strategy that integrates bioinformatics analysis with molecular experiments, providing new insights into the epigenetic regulatory mechanisms of PRRT1 in AD and demonstrating the importance of explainable machine learning in elucidating complex disease mechanisms.