Dynamics of BMP signaling and distribution during zebrafish dorsal-ventral patterning

  1. Autumn P Pomreinke
  2. Gary H Soh
  3. Katherine W Rogers
  4. Jennifer K Bergmann
  5. Alexander J Bläßle
  6. Patrick Müller  Is a corresponding author
  1. Friedrich Miescher Laboratory of the Max Planck Society, Germany

Abstract

During vertebrate embryogenesis, dorsal-ventral patterning is controlled by the BMP/Chordin activator/inhibitor system. BMP induces ventral fates, whereas Chordin inhibits BMP signaling on the dorsal side. Several theories can explain how the distributions of BMP and Chordin are regulated to achieve patterning, but the assumptions regarding activator/inhibitor diffusion and stability differ between models. Notably, “shuttling” models in which the BMP distribution is modulated by a Chordin-mediated increase in BMP diffusivity have gained recent prominence. Here, we directly test five major models by measuring the biophysical properties of fluorescently tagged BMP2b and Chordin in zebrafish embryos. We found that BMP2b and Chordin diffuse and rapidly form extracellular protein gradients, Chordin does not modulate the diffusivity or distribution of BMP2b, and Chordin is not required to establish peak levels of BMP signaling. Our findings challenge current self-regulating reaction-diffusion and shuttling models and provide support for a graded source-sink mechanism underlying zebrafish dorsal-ventral patterning.

Article and author information

Author details

  1. Autumn P Pomreinke

    Systems Biology of Development Group, Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  2. Gary H Soh

    Systems Biology of Development Group, Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Katherine W Rogers

    Systems Biology of Development Group, Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5700-2662
  4. Jennifer K Bergmann

    Systems Biology of Development Group, Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  5. Alexander J Bläßle

    Systems Biology of Development Group, Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  6. Patrick Müller

    Systems Biology of Development Group, Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
    For correspondence
    pmueller@tuebingen.mpg.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0702-6209

Funding

Max-Planck-Gesellschaft

  • Patrick Müller

Human Frontier Science Program (Career Development Award CDA00031/2013-C)

  • Patrick Müller

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

Reviewing Editor

  1. Deborah Yelon, University of California, San Diego, United States

Publication history

  1. Received: February 8, 2017
  2. Accepted: August 30, 2017
  3. Accepted Manuscript published: August 31, 2017 (version 1)
  4. Version of Record published: October 26, 2017 (version 2)

Copyright

© 2017, Pomreinke 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

  • 3,426
    Page views
  • 540
    Downloads
  • 36
    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)

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

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

  1. Autumn P Pomreinke
  2. Gary H Soh
  3. Katherine W Rogers
  4. Jennifer K Bergmann
  5. Alexander J Bläßle
  6. Patrick Müller
(2017)
Dynamics of BMP signaling and distribution during zebrafish dorsal-ventral patterning
eLife 6:e25861.
https://doi.org/10.7554/eLife.25861

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Maryam H Mofrad et al.
    Tools and Resources

    Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and clinical intracranial electroencephalogram (iEEG) recordings in the human. Within each recording type, we find widespread spindles occur much more frequently than previously reported. We then analyzed the spatiotemporal patterns of these large-scale, multi-area spindles and, in the EEG recordings, how spindle patterns change following a visual memory task. Our results reveal a potential role for widespread, multi-area spindles in consolidation of memories in networks widely distributed across primate cortex.

    1. Computational and Systems Biology
    2. Stem Cells and Regenerative Medicine
    Genki N Kanda et al.
    Research Article

    Induced differentiation is one of the most experience- and skill-dependent experimental processes in regenerative medicine, and establishing optimal conditions often takes years. We developed a robotic AI system with a batch Bayesian optimization algorithm that autonomously induces the differentiation of induced pluripotent stem cell-derived retinal pigment epithelial (iPSC-RPE) cells. From 200 million possible parameter combinations, the system performed cell culture in 143 different conditions in 111 days, resulting in 88% better iPSC-RPE production than that obtained by the pre-optimized culture in terms of the pigmentation scores. Our work demonstrates that the use of autonomous robotic AI systems drastically accelerates systematic and unbiased exploration of experimental search space, suggesting immense use in medicine and research.