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Toward understanding the role of genomic repeat elements in neurodegenerative diseases.
An, Zhengyu; Jiang, Aidi; Chen, Jingqi.
Afiliação
  • An Z; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
  • Jiang A; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
  • Chen J; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Neural Regen Res ; 20(3): 646-659, 2025 Mar 01.
Article em En | MEDLINE | ID: mdl-38886931
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.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2025 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2025 Tipo de documento: Article