4.7 Article

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

期刊

NATURE NEUROSCIENCE
卷 24, 期 2, 页码 186-196

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41593-020-00767-4

关键词

-

资金

  1. NIMH [R01MH094740]
  2. Stanford Schizophrenia Genetics Research Fund
  3. National Heart, Lung, and Blood Institute [U01MH106874, U01MH106876, U01MG106882, U01MH106883, U01MH106884, U01MH106891, U01MH106892, U01MH106893, U01MH108898, T32 HL110952]
  4. Jaswa Innovator Award
  5. NIH [S10RR025518-01]

向作者/读者索取更多资源

Individual somatic retrotransposon insertions in the human brain play a role in brain development and potential neuropsychiatric disorders. Retrotransposition occurs during early embryogenesis and can impact gene expression associated with neuropsychiatric disorders.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据