1. Developmental Biology
  2. Neuroscience
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Dlk1-Dio3 locus-derived LncRNAs perpetuate postmitotic motor neuron cell fate and subtype identity

  1. Ya-Ping Yen
  2. Wen-Fu Hsieh
  3. Ya-Yin Tsai
  4. Ya-Lin Lu
  5. Ee Shan Liau
  6. Ho-Chiang Hsu
  7. Yen-Chung Chen
  8. Ting-Chun Liu
  9. Mien Chang
  10. Joye Li
  11. Shau-Ping Lin  Is a corresponding author
  12. Jui-Hung Hung  Is a corresponding author
  13. Jun-An Chen  Is a corresponding author
  1. Academia Sinica, Taiwan, Republic of China
  2. National Chiao Tung University, Taiwan, Republic of China
  3. National Taiwan University, Taiwan, Republic of China
Research Article
  • Cited 21
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Cite this article as: eLife 2018;7:e38080 doi: 10.7554/eLife.38080

Abstract

The mammalian imprinted Dlk1-Dio3 locus produces multiple long non-coding RNAs (lncRNAs) from the maternally inherited allele, including Meg3 (i.e., Gtl2) in the mammalian genome. Although this locus has well-characterized functions in stem cell and tumor contexts, its role during neural development is unknown. By profiling cell types at each stage of embryonic stem cell derived motor neurons (ESC~MNs) that recapitulate spinal cord development, we uncovered that lncRNAs expressed from the Dlk1-Dio3 locus are predominantly and gradually enriched in rostral motor neurons (MNs). Mechanistically, Meg3 and other Dlk1-Dio3 locus-derived lncRNAs facilitate Ezh2/Jarid2 interactions. Loss of these lncRNAs compromises the H3K27me3 landscape, leading to aberrant expression of progenitor and caudal Hox genes in postmitotic MNs. Our data thus illustrate that these lncRNAs in the Dlk1-Dio3 locus, particularly Meg3, play a critical role in maintaining postmitotic MN cell fate by repressing progenitor genes and they shape MN subtype identity by regulating Hox genes.

Data availability

All microarray, RNA-seq, ChIP-seq data have been deposited in GEO under accession codes GSE114283, GSE114285 and GSE114228.

The following data sets were generated
The following previously published data sets were used
    1. Mazzoni et al
    (2013) Isl1/2 in iNIL3-induced motor neurons (Day 4)
    Publicly available at the NCBI Gene Expression Omnibus (accession no: GSM782848).
    1. Narendra et al
    (2015) H3K4me3
    Publicly available at the NCBI Gene Expression Omnibus (accession no: GSM1468401).
    1. Rhee et al
    (2016) H3K27ac_day6
    Publicly available at the NCBI Gene Expression Omnibus (accession no: GSM2098385).
    1. Rhee et al
    (2016) ATAC_seq_day6
    Publicly available at the NCBI Gene Expression Omnibus (accession no: GSM2098391).
    1. Mahony et al
    (2011) RAR_Day2+8hrsRA
    Publicly available at the NCBI Gene Expression Omnibus (accession no: GSM482750).
    1. Mahony et al
    (2011) Pol2-S5P_Day2+8h
    Publicly available at the NCBI Gene Expression Omnibus (accession no: GSM981593).
    1. Li et al
    (2017) ES-WT
    Publicly available at the NCBI Gene Expression Omnibus (accession no: GSM2420680).
    1. Li et al
    (2017) AK4-WT
    Publicly available at the NCBI Gene Expression Omnibus (accession no: GSM2420683).
    1. Li et al
    (2017) AK7-WT
    Publicly available at the NCBI Gene Expression Omnibus (accession no: GSM2420684).

Article and author information

Author details

  1. Ya-Ping Yen

    Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan, Republic of China
    Competing interests
    The authors declare that no competing interests exist.
  2. Wen-Fu Hsieh

    Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan, Republic of China
    Competing interests
    The authors declare that no competing interests exist.
  3. Ya-Yin Tsai

    Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan, Republic of China
    Competing interests
    The authors declare that no competing interests exist.
  4. Ya-Lin Lu

    Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan, Republic of China
    Competing interests
    The authors declare that no competing interests exist.
  5. Ee Shan Liau

    Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan, Republic of China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4115-5573
  6. Ho-Chiang Hsu

    Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan, Republic of China
    Competing interests
    The authors declare that no competing interests exist.
  7. Yen-Chung Chen

    Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan, Republic of China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8529-1251
  8. Ting-Chun Liu

    Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan, Republic of China
    Competing interests
    The authors declare that no competing interests exist.
  9. Mien Chang

    Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan, Republic of China
    Competing interests
    The authors declare that no competing interests exist.
  10. Joye Li

    Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan, Republic of China
    Competing interests
    The authors declare that no competing interests exist.
  11. Shau-Ping Lin

    Institute of Biotechnology, College of Bio-Resources and Agriculture, National Taiwan University, Taipei, Taiwan, Republic of China
    For correspondence
    shaupinglin@ntu.edu.tw
    Competing interests
    The authors declare that no competing interests exist.
  12. Jui-Hung Hung

    Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan, Republic of China
    For correspondence
    juihunghung@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2208-9213
  13. Jun-An Chen

    Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan, Republic of China
    For correspondence
    jachen@imb.sinica.edu.tw
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9870-3203

Funding

Ministry of Science and Technology, Taiwan (RO1)

  • Jun-An Chen

National Health Research Institutes (CDG)

  • Jun-An Chen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All of the live animals were kept in an SPF animal facility, approved and overseen by IACUC (12-07-389 ) Academia Sinica.

Reviewing Editor

  1. Alejandro Sánchez Alvarado, Stowers Institute for Medical Research, United States

Publication history

  1. Received: May 3, 2018
  2. Accepted: October 11, 2018
  3. Accepted Manuscript published: October 12, 2018 (version 1)
  4. Version of Record published: November 7, 2018 (version 2)
  5. Version of Record updated: February 5, 2020 (version 3)

Copyright

© 2018, Yen et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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Further reading

    1. Developmental Biology
    2. Physics of Living Systems
    Yonghyun Song, Changbong Hyeon
    Research Article Updated

    Spatial boundaries formed during animal development originate from the pre-patterning of tissues by signaling molecules, called morphogens. The accuracy of boundary location is limited by the fluctuations of morphogen concentration that thresholds the expression level of target gene. Producing more morphogen molecules, which gives rise to smaller relative fluctuations, would better serve to shape more precise target boundaries; however, it incurs more thermodynamic cost. In the classical diffusion-depletion model of morphogen profile formation, the morphogen molecules synthesized from a local source display an exponentially decaying concentration profile with a characteristic length λ. Our theory suggests that in order to attain a precise profile with the minimal cost, λ should be roughly half the distance to the target boundary position from the source. Remarkably, we find that the profiles of morphogens that pattern the Drosophila embryo and wing imaginal disk are formed with nearly optimal λ. Our finding underscores the cost-effectiveness of precise morphogen profile formation in Drosophila development.