中国神经再生研究(英文版) ›› 2025, Vol. 20 ›› Issue (3): 646-659.doi: 10.4103/NRR.NRR-D-23-01568

• 综述:退行性病与再生 • 上一篇    下一篇

基因组重复元件在神经退行性疾病中的作用

  

  • 出版日期:2025-03-15 发布日期:2024-06-25

Toward understanding the role of genomic repeat elements in neurodegenerative diseases

Zhengyu An1, #, Aidi Jiang1, #, Jingqi Chen1, 2, 3, 4, *   

  1. 1 Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; 2 MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; 3 MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China; 4 Zhangjiang Fudan International Innovation Center, Shanghai, China
  • Online:2025-03-15 Published:2024-06-25
  • Contact: Jingqi Chen, PhD, jingqichen@fudan.edu.cn.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China, No. 61932008 and Natural Science Foundation of Shanghai, No. 21ZR1403200 (both to JC).

摘要:

神经退行性疾病发病的分子机制复杂。随着高覆盖率测序技术的发展,研究人员开始注意到,以前在寻找疾病元凶时被忽视的基因组重复区域,是多种神经退行性疾病的积极诱因。文章(1)介绍了通过全基因组关联研究和靶向测序发现的重复元件变异与多种退行性疾病的关联。随着长时间测序技术及其相关工具的发展,疾病相关重复元件变异的鉴定工作已经并将进一步得到推动。(2)总结了重复元素变异在脑退化中的分子机制方面的最新发现,如导致转录沉默或 RNA 介导的毒性功能增益。(3)介绍利用深度学习语言模型等前沿计算模型进行室内预测如何增强和加快对重复元件变异的功能影响的理解。(4)讨论推进当前研究成果的未来方向,以便更好地理解神经退行性疾病并最终实现临床应用。

https://orcid.org/0000-0002-2454-0058 (Jingqi Chen)

Abstract: Neurodegenerative diseases cause great medical and economic burdens for both patients and society; however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.

Key words: Alzheimer’s disease, ataxia, deep learning, long-read sequencing, neurodegeneration, neurodegenerative diseases, Parkinson’s disease, repeat element, structural variant