Computational modeling and quantitative physiology reveal central parameters for brassinosteroid-regulated early cell physiological processes linked to elongation growth of the Arabidopsis root

Abstract

Brassinosteroids (BR) are key hormonal regulators of plant development. However, whereas the individual components of BR perception and signaling are well characterized experimentally, the question of how they can act and whether they are sufficient to carry out the critical function of cellular elongation remains open. Here, we combined computational modeling with quantitative cell physiology to understand the dynamics of the plasma membrane (PM)-localized BR response pathway during the initiation of cellular responses in the epidermis of the Arabidopsis root tip that are be linked to cell elongation. The model, consisting of ordinary differential equations, comprises the BR induced hyperpolarization of the PM, the acidification of the apoplast and subsequent cell wall swelling. We demonstrate that the competence of the root epidermal cells for the BR response predominantly depends on the amount and activity of H+-ATPases in the PM. The model further predicts that an influx of cations is required to compensate for the shift of positive charges caused by the apoplastic acidification. A potassium channel was subsequently identified and experimentally characterized, fulfilling this function. Thus, we established the landscape of components and parameters for physiological processes potentially linked to cell elongation, a central process in plant development.

Data availability

All data generated and analysed during this study are included in the manuscript and the supporting file (Appendix 1). Raw and metadata are provided for Figures 4, 5, 6, 7 and 8 as well as for Appendix 1 Figures 2, 3, 4 and 6. Figure 1 represents scheme of early BRI1 signaling and Figure 2 the scheme of the used model structure. Predominantly published scRNA-Seq data were used for Figure 3. Modelling codes are available in supporting file (Appendix 1 - model information).

The following previously published data sets were used

Article and author information

Author details

  1. Ruth Großeholz

    BioQuant, Heidelberg University, Heidelberg, Germany
    For correspondence
    ruth.grosseholz@bioquant.uni-heidelberg.de
    Competing interests
    The authors declare that no competing interests exist.
  2. Friederike Wanke

    Center for Molecular Biology of Plants, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Leander Rohr

    Center for Molecular Biology of Plants, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4592-4197
  4. Nina Glöckner

    Center for Molecular Biology of Plants, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  5. Luiselotte Rausch

    Center for Molecular Biology of Plants, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  6. Stefan Scholl

    Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  7. Emanuele Scacchi

    Center for Molecular Biology of Plants, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  8. Amelie-Jette Spazierer

    Center for Molecular Biology of Plants, University of Tübingen, Tübingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  9. Lana Shabala

    Tasmanian Institute for Agriculture, University of Tasmania, Hobard, Australia
    Competing interests
    The authors declare that no competing interests exist.
  10. Sergey Shabala

    Tasmanian Institute for Agriculture, University of Tasmania, Hobart, Australia
    Competing interests
    The authors declare that no competing interests exist.
  11. Karin Schumacher

    Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6484-8105
  12. Ursula Kummer

    Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  13. Klaus Harter

    Center for Molecular Biology of Plants, University of Tübingen, Tübingen, Germany
    For correspondence
    klaus.harter@zmbp.uni-tuebingen.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2150-6970

Funding

Deutsche Forschungsgemeinschaft (CRC 1101)

  • Karin Schumacher
  • Ursula Kummer
  • Klaus Harter

Deutsche Forschungsgemeinschaft ((INST 37/819- 594 1 FUGG,INST 37/965-1 FUGG,INST 37/991-1 FUGG,INST 37/992-1 FUGG)

  • Klaus Harter

Schmeil Stiftung (RG)

  • Ruth Großeholz

Joachim Herz Stiftung (RG)

  • Ruth Großeholz

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

Copyright

© 2022, Großeholz 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

  • 1,294
    views
  • 323
    downloads
  • 12
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Ruth Großeholz
  2. Friederike Wanke
  3. Leander Rohr
  4. Nina Glöckner
  5. Luiselotte Rausch
  6. Stefan Scholl
  7. Emanuele Scacchi
  8. Amelie-Jette Spazierer
  9. Lana Shabala
  10. Sergey Shabala
  11. Karin Schumacher
  12. Ursula Kummer
  13. Klaus Harter
(2022)
Computational modeling and quantitative physiology reveal central parameters for brassinosteroid-regulated early cell physiological processes linked to elongation growth of the Arabidopsis root
eLife 11:e73031.
https://doi.org/10.7554/eLife.73031

Share this article

https://doi.org/10.7554/eLife.73031

Further reading

    1. Computational and Systems Biology
    2. Developmental Biology
    Rosalio Reyes, Arthur D Lander, Marcos Nahmad
    Research Article

    Understanding the principles underlying the design of robust, yet flexible patterning systems is a key problem in developmental biology. In the Drosophila wing, Hedgehog (Hh) signaling determines patterning outputs using dynamical properties of the Hh gradient. In particular, the pattern of collier (col) is established by the steady-state Hh gradient, whereas the pattern of decapentaplegic (dpp), is established by a transient gradient of Hh known as the Hh overshoot. Here we use mathematical modeling to suggest that this dynamical interpretation of the Hh gradient results in specific robustness and precision properties. For instance, the location of the anterior border of col, which is subject to self-enhanced ligand degradation is more robustly specified than that of dpp to changes in morphogen dosage, and we provide experimental evidence of this prediction. However, the anterior border of dpp expression pattern, which is established by the overshoot gradient is much more precise to what would be expected by the steady-state gradient. Therefore, the dynamical interpretation of Hh signaling offers tradeoffs between

    1. Computational and Systems Biology
    2. Neuroscience
    Sebastian Quiroz Monnens, Casper Peters ... Bernhard Englitz
    Research Advance

    Animal behaviour alternates between stochastic exploration and goal-directed actions, which are generated by the underlying neural dynamics. Previously, we demonstrated that the compositional Restricted Boltzmann Machine (cRBM) can decompose whole-brain activity of larval zebrafish data at the neural level into a small number (∼100-200) of assemblies that can account for the stochasticity of the neural activity (van der Plas et al., eLife, 2023). Here, we advance this representation by extending to a combined stochastic-dynamical representation to account for both aspects using the recurrent temporal RBM (RTRBM) and transfer-learning based on the cRBM estimate. We demonstrate that the functional advantage of the RTRBM is captured in the temporal weights on the hidden units, representing neural assemblies, for both simulated and experimental data. Our results show that the temporal expansion outperforms the stochastic-only cRBM in terms of generalization error and achieves a more accurate representation of the moments in time. Lastly, we demonstrate that we can identify the original time-scale of assembly dynamics by estimating multiple RTRBMs at different temporal resolutions. Together, we propose that RTRBMs are a valuable tool for capturing the combined stochastic and time-predictive dynamics of large-scale data sets.