Dataset Information

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

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.

SUBMITTER: Zhu X

PROVIDER: S-EPMC8806165 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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

Zhu Xiaowei X Zhou Bo B Pattni Reenal R Gleason Kelly K Tan Chunfeng C Kalinowski Agnieszka A Sloan Steven S Fiston-Lavier Anna-Sophie AS Mariani Jessica J Petrov Dmitri D Barres Ben A BA Duncan Laramie L Abyzov Alexej A Vogel Hannes H Moran John V JV Vaccarino Flora M FM Tamminga Carol A CA Levinson Douglas F DF Urban Alexander E AE

Nature neuroscience 20210111 2

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 inserti ...[more]

PMID: 33432196

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Dataset Information

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

Publications

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

Similar Datasets

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