1. Neuroscience
Download icon

Downregulation of ribosome biogenesis during early forebrain development

Research Article
  • Cited 21
  • Views 3,791
  • Annotations
Cite this article as: eLife 2018;7:e36998 doi: 10.7554/eLife.36998

Abstract

Forebrain precursor cells are dynamic during early brain development, yet the underlying molecular changes remain elusive. We observed major differences in transcriptional signatures of precursor cells from mouse forebrain at embryonic days E8.5 vs. E10.5 (before vs. after neural tube closure). Genes encoding protein biosynthetic machinery were strongly downregulated at E10.5. This was matched by decreases in ribosome biogenesis and protein synthesis, together with age-related changes in proteomic content of the adjacent fluids. Notably, c-MYC expression and mTOR pathway signaling were also decreased at E10.5, providing a potential driver for the effects on ribosome biogenesis and protein synthesis. Interference with c-MYC at E8.5 prematurely decreased ribosome biogenesis, while persistent c-MYC expression in cortical progenitors increased transcription of protein biosynthetic machinery and enhanced ribosome biogenesis, as well as enhanced progenitor proliferation leading to subsequent macrocephaly. These findings indicate large, coordinated changes in molecular machinery of forebrain precursors during early brain development.

Article and author information

Author details

  1. Kevin F Chau

    Department of Pathology, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Morgan L Shannon

    Department of Pathology, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Ryann M Fame

    Department of Pathology, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8244-2624
  4. Erin Fonseca

    Department of Pathology, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Hillary Mullan

    Department of Pathology, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Matthew B Johnson

    Division of Genetics, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6909-5712
  7. Anoop K Sendamarai

    Department of Pathology, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Mark W Springel

    Department of Pathology, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Benoit Laurent

    Division of Newborn Medicine and Epigenetics Program, Department of Medicine, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Maria K Lehtinen

    Department of Pathology, Boston Children's Hospital, Boston, United States
    For correspondence
    maria.lehtinen@childrens.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7243-2967

Funding

National Science Foundation (Graduate Research Fellowship)

  • Kevin F Chau

National Institutes of Health (R01 NS088566)

  • Maria K Lehtinen

Pediatric Hydrocephalus Foundation (Research grant)

  • Maria K Lehtinen

Simons Foundation (SFARI Pilot Grant)

  • Maria K Lehtinen

New York Stem Cell Foundation (Robertson Investigator)

  • Maria K Lehtinen

National Institutes of Health (NIH T32 HL110852)

  • Kevin F Chau

National Institutes of Health (NIH T32 HL110852)

  • Ryann M Fame

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 animal experimentation was carried out under protocols approved by the IACUC of Boston Children's Hospital (protocol number 17-10-3547R).

Reviewing Editor

  1. Marianne Bronner, California Institute of Technology, United States

Publication history

  1. Received: March 28, 2018
  2. Accepted: May 9, 2018
  3. Accepted Manuscript published: May 10, 2018 (version 1)
  4. Version of Record published: June 1, 2018 (version 2)

Copyright

© 2018, Chau 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.

Metrics

  • 3,791
    Page views
  • 564
    Downloads
  • 21
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Shivesh Chaudhary et al.
    Research Article

    Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers' experiences. Here we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in C. elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems.

    1. Neuroscience
    2. Structural Biology and Molecular Biophysics
    Carlos A Z Bassetto Jnr et al.
    Research Article

    In Shaker K+ channels, the S4-S5 linker couples the voltage sensor (VSD) and pore domain (PD). Another coupling mechanism is revealed using two W434F-containing channels: L361R:W434F and L366H:W434F. In L361R:W434F, W434F affects the L361R VSD seen as a shallower Q-V curve that crosses the G-V. In L366H:W434F, L366H relieves the W434F effect converting a non-conductive channel in a conductive one. We report a chain of residues connecting the VSD (S4) to the selectivity filter (SF) in the PD of an adjacent subunit as the molecular basis for voltage-sensor selectivity filter gate (VS-SF) coupling. Single alanine substitutions in this region (L409A, S411A, S412A or F433A) are enough to disrupt the VS-SF coupling, shown by the absence of Q-V and G-V crossing in L361R:W434F mutant and by the lack of ionic conduction in the L366H:W434F mutant. This residue chain defines a new coupling between the VSD and the PD in voltage-gated channels.