Why plants make puzzle cells, and how their shape emerges
- Max Planck Institute for Plant Breeding Research, Germany
- Cornell University, United States
- Université de Lyon, France
- Stockholm University, Sweden
- University of Calgary, Canada
Abstract
The shape and function of plant cells are often highly interdependent. The puzzle-shaped cells that appear in the epidermis of many plants are a striking example of a complex cell shape, however their functional benefit has remained elusive. We propose that these intricate forms provide an effective strategy to reduce mechanical stress in the cell wall of the epidermis. When tissue-level growth is isotropic, we hypothesize that lobes emerge at the cellular level to prevent formation of large isodiametric cells that would bulge under the stress produced by turgor pressure. Data from various plant organs and species support the relationship between lobes and growth isotropy, which we test with mutants where growth direction is perturbed. Using simulation models we show that a mechanism actively regulating cellular stress plausibly reproduces the development of epidermal cell shape. Together, our results suggest that mechanical stress is a key driver of cell-shape morphogenesis.
Article and author information
Author details
Adrienne HK Roeder
Funding
Swiss National Science Foundation (SystemsX.ch iPhD grant 2010/073)
- Richard S Smith
Bundesministerium für Bildung und Forschung (031A492)
- Richard S Smith
Human Frontier Science Program (RGP0008/2013)
- Chun-Biu Li
- Olivier Hamant
- Adrienne HK Roeder
- Richard S Smith
European Commission (Marie Skłodowska-Curie individual fellowship (Horizon 2020 703886))
- Adam Runions
Natural Science and Engineering Research Council of Canada (Discovery Grant RGPIN-2014-05325)
- Przemyslaw Prusinkiewicz
European Research Council (ERC-2013-CoG-615739 'MechanoDevo')
- Olivier Hamant
Max Planck Society (Core grant and open-access funding)
- Miltos Tsiantis
- Richard S Smith
Bundesministerium für Bildung und Forschung (031A494)
- Richard S Smith
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Sheila McCormick, University of California-Berkeley, United States
Version history
- Received: October 13, 2017
- Accepted: January 31, 2018
- Accepted Manuscript published: February 27, 2018 (version 1)
- Version of Record published: March 7, 2018 (version 2)
Copyright
© 2018, Sapala 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
-
- 14,977
- views
-
- 1,663
- downloads
-
- 176
- 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)
Why plants make puzzle cells, and how their shape emerges
eLife 7:e32794.
https://doi.org/10.7554/eLife.32794
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.
-
- Computational and Systems Biology
Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.
