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  4. N6-methyladenosine RNA modification regulates embryonic neural stem cell self-renewal through histone modifications

N6-methyladenosine RNA modification regulates embryonic neural stem cell self-renewal through histone modifications

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sword-2019-07-18T13:25:26.original.xml (130 B)
Original SWORD entry document
Author(s)
Wang, Yang
•
Li, Yue
•
Yue, Minghui
•
Wang, Jun
•
Wechsler-Reya, Robert J.
•
Zhang, Zhaolei
•
Ogawa, Yuya
•
Kellis, Manolis
•
Duester, Gregg
•
Zhao, Jing Crystal
Date Issued
January 2018
Journal
Nature Neuroscience
Publisher
Springer Science and Business Media LLC
Citation
Wang, Yang et al. "N6-methyladenosine RNA modification regulates embryonic neural stem cell self-renewal through histone modifications." Nature Neuroscience 21, 2 (January 2018): 195–206 © 2018 The Author(s)
Version
Final published version
Abstract
Internal N 6-methyladenosine (m6A) modification is widespread in messenger RNAs (mRNAs) and is catalyzed by heterodimers of methyltransferase-like protein 3 (Mettl3) and Mettl14. To understand the role of m6A in development, we deleted Mettl14 in embryonic neural stem cells (NSCs) in a mouse model. Phenotypically, NSCs lacking Mettl14 displayed markedly decreased proliferation and premature differentiation, suggesting that m6A modification enhances NSC self-renewal. Decreases in the NSC pool led to a decreased number of late-born neurons during cortical neurogenesis. Mechanistically, we discovered a genome-wide increase in specific histone modifications in Mettl14 knockout versus control NSCs. These changes correlated with altered gene expression and observed cellular phenotypes, suggesting functional significance of altered histone modifications in knockout cells. Finally, we found that m6A regulates histone modification in part by destabilizing transcripts that encode histone-modifying enzymes. Our results suggest an essential role for m6A in development and reveal m6A-regulated histone modifications as a previously unknown mechanism of gene regulation in mammalian cells.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution 4.0 International license
https://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/128892
DOI of Published Version
https://doi.org/10.1038/s41593-017-0057-1
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