1. Plant Biology
Download icon

FRET kinase sensor development reveals SnRK2/OST1 activation by ABA but not by MeJA and high CO2 during stomatal closure

  1. Li Zhang
  2. Yohei Takahashi  Is a corresponding author
  3. Po-Kai Hsu
  4. Kollist Hannes
  5. Ebe Merilo
  6. Patrick J Krysan
  7. Julian I Schroeder  Is a corresponding author
  1. University of California, San Diego, United States
  2. University of Tartu, Estonia
  3. University of Wisconsin-Madison, United States
Research Article
  • Cited 0
  • Views 1,943
  • Annotations
Cite this article as: eLife 2020;9:e56351 doi: 10.7554/eLife.56351

Abstract

Sucrose-non-fermenting-1-related protein kinase-2s (SnRK2s) are critical for plant abiotic stress responses, including abscisic acid (ABA) signaling. Here, we develop a genetically encoded reporter for SnRK2 kinase activity. This sensor, named SNACS, shows an increase in the ratio of yellow to cyan fluorescence emission by OST1/SnRK2.6-mediated phosphorylation of a defined serine residue in SNACS. ABA rapidly increases FRET efficiency in N. benthamiana leaf cells and Arabidopsis guard cells. Interestingly, protein kinase inhibition decreases FRET efficiency in guard cells, providing direct experimental evidence that basal SnRK2 activity prevails in guard cells. Moreover, in contrast to ABA, the stomatal closing stimuli, elevated CO2 and MeJA, did not increase SNACS FRET ratios. These findings and gas exchange analyses of quintuple/sextuple ABA receptor mutants show that stomatal CO2 signaling requires basal ABA and SnRK2 signaling, but not SnRK2 activation. A recent model that CO2 signaling is mediated by PYL4/PYL5 ABA-receptors could not be supported here in two independent labs. We report a potent approach for real-time live-cell investigations of stress signaling.

Article and author information

Author details

  1. Li Zhang

    Division of Biological Sciences, Cell and Developmental Biology Section, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

  2. Yohei Takahashi

    Division of Biological Sciences, Cell and Developmental Biology Section, University of California, San Diego, La Jolla, United States
    For correspondence
    ytakahashi@UCSD.EDU
    Competing interests
    The authors declare that no competing interests exist.

  3. Po-Kai Hsu

    Division of Biological Sciences, Cell and Developmental Biology Section, University of California, San Diego, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Kollist Hannes

    Institute of Technology, University of Tartu, Tartu, Estonia
    Competing interests
    The authors declare that no competing interests exist.

  5. Ebe Merilo

    Institute of Technology, University of Tartu, Tartu, Estonia
    Competing interests
    The authors declare that no competing interests exist.

  6. Patrick J Krysan

    Horticulture Department, University of Wisconsin-Madison, Madison, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Julian I Schroeder

    Division of Biological Sciences, Cell and Developmental Biology Section, University of California, San Diego, La Jolla, United States
    For correspondence
    jischroeder@ucsd.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3283-5972

Funding

National Science Foundation (MCB-1900567)

  • Julian I Schroeder

National Institutes of Health (GM060396)

  • Julian I Schroeder

China Scholarship Council

  • Li Zhang

Japan Society for the Promotion of Science

  • Yohei Takahashi

Eesti Teadusagentuur (PUT1133)

  • Kollist Hannes

Eesti Teadusagentuur (PRG719)

  • Kollist Hannes

Eesti Teadusagentuur (PRG433)

  • Kollist Hannes

European Regional Development Fund

  • Ebe Merilo

National Science Foundation (MCB‐1137950)

  • Po-Kai Hsu

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

Reviewing Editor

  1. Dominique C Bergmann, Stanford University, United States

Publication history

  1. Received: February 25, 2020
  2. Accepted: May 20, 2020
  3. Accepted Manuscript published: May 28, 2020 (version 1)
  4. Version of Record published: June 11, 2020 (version 2)

Copyright

© 2020, Zhang 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

  • 1,943
    Page views
  • 485
    Downloads
  • 0
    Citations

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

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)

Categories and tags

Research organism

Further reading

    1. Plant Biology

    Accurate and versatile 3D segmentation of plant tissues at cellular resolution

    Adrian Wolny et al.
    Tools and Resources

    Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on 1xed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface (https://github.com/hci-unihd/plant-seg).

    1. Biochemistry and Chemical Biology
    2. Plant Biology

    Evolution of a plant gene cluster in Solanaceae and emergence of metabolic diversity

    Pengxiang Fan et al.
    Research Article Updated

    Plants produce phylogenetically and spatially restricted, as well as structurally diverse specialized metabolites via multistep metabolic pathways. Hallmarks of specialized metabolic evolution include enzymatic promiscuity and recruitment of primary metabolic enzymes and examples of genomic clustering of pathway genes. Solanaceae glandular trichomes produce defensive acylsugars, with sidechains that vary in length across the family. We describe a tomato gene cluster on chromosome 7 involved in medium chain acylsugar accumulation due to trichome specific acyl-CoA synthetase and enoyl-CoA hydratase genes. This cluster co-localizes with a tomato steroidal alkaloid gene cluster and is syntenic to a chromosome 12 region containing another acylsugar pathway gene. We reconstructed the evolutionary events leading to this gene cluster and found that its phylogenetic distribution correlates with medium chain acylsugar accumulation across the Solanaceae. This work reveals insights into the dynamics behind gene cluster evolution and cell-type specific metabolite diversity.

    1. Plant Biology

    Lack of evidence for associative learning in pea plants

    Kasey Markel
    Short Report

    Gagliano et al. (Learning by association in plants, 2016) reported associative learning in pea plants. Associative learning has long been considered a behavior performed only by animals, making this claim particularly newsworthy and interesting. In the experiment, plants were trained in Y-shaped mazes for 3 days with fans and lights attached at the top of the maze. Training consisted of wind consistently preceding light from either the same or the opposite arm of the maze. When plant growth forced a decision between the two arms of the maze, fans alone were able to influence growth direction, whereas the growth direction of untrained plants was not affected by fans. However, a replication of their protocol failed to demonstrate the same result, calling for further verification and study before mainstream acceptance of this paradigm-shifting phenomenon. This replication attempt used a larger sample size and fully blinded analysis.