Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia

机器学习揭示了人类神经元和神经胶质细胞中体细胞 L1 插入的双侧分布

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作者:Xiaowei Zhu, Bo Zhou, Reenal Pattni, Kelly Gleason, Chunfeng Tan, Agnieszka Kalinowski, Steven Sloan, Anna-Sophie Fiston-Lavier, Jessica Mariani, Dmitri Petrov, Ben A Barres, Laramie Duncan, Alexej Abyzov, Hannes Vogel; Brain Somatic Mosaicism Network; John V Moran, Flora M Vaccarino, Carol A Tammin

Abstract

Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesized to have relevance to brain development and neuropsychiatric disease. However, the detection of individual somatic mobile element insertions presents a difficult signal-to-noise problem. Using a machine-learning method (RetroSom) and deep whole-genome sequencing, we analyzed L1 and Alu retrotransposition in sorted neurons and glia from human brains. We characterized two brain-specific L1 insertions in neurons and glia from a donor with schizophrenia. There was anatomical distribution of the L1 insertions in neurons and glia across both hemispheres, indicating retrotransposition occurred during early embryogenesis. Both insertions were within the introns of genes (CNNM2 and FRMD4A) inside genomic loci associated with neuropsychiatric disorders. Proof-of-principle experiments revealed these L1 insertions significantly reduced gene expression. These results demonstrate that RetroSom has broad applications for studies of brain development and may provide insight into the possible pathological effects of somatic retrotransposition.

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