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

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Zhu, Xiaowei
• dc.contributor.author Brain Somatic Mosaicism Network
• dc.contributor.author Urban, Alexander E.
• dc.date.accessioned 2023-04-27T06:09:14Z
• dc.date.available 2023-04-27T06:09:14Z
• dc.date.issued 2021
• dc.description.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.
• dc.format.mimetype application/pdf
• dc.identifier.citation Zhu X, Zhou B, Pattni R, Gleason K, Tan C, Kalinowski A et al. Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia. Nat Neurosci. 2021;24(2):186-96. DOI: 10.1038/s41593-020-00767-4
• dc.identifier.doi http://dx.doi.org/10.1038/s41593-020-00767-4
• dc.identifier.issn 1097-6256
• dc.identifier.uri http://hdl.handle.net/10230/56582
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Nat Neurosci. 2021;24(2):186-96
• dc.rights © Springer Nature Publishing AG Zhu X, Zhou B, Pattni R, Gleason K, Tan C, Kalinowski A et al. Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia. Nat Neurosci. 2021;24(2):186-96. DOI: 10.1038/s41593-020-00767-4 [http://dx.doi.org/10.1038/s41593-020-00767-4]
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Glial development
• dc.subject.keyword Machine learning
• dc.subject.keyword Neurodevelopmental disorders
• dc.subject.keyword Neuronal development
• dc.subject.keyword Schizophrenia
• dc.title Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion