Synthetic hormone-responsive transcription factors can monitor and re-program plant development
Abstract
Developmental programs sculpt plant morphology to meet environmental challenges, and these same programs have been manipulated to increase agricultural productivity1,2. Hormones coordinate these programs, creating chemical circuitry3 that has been represented in mathematical models4,5; however, model-guided engineering of plant morphology has been limited by a lack of tools6,7. Here, we introduce a novel set of synthetic and modular hormone activated Cas9-based repressors (HACRs) in Arabidopsis thaliana that respond to three hormones: auxin, gibberellins and jasmonates. We demonstrate that HACRs are sensitive to both exogenous hormone treatments and local differences in endogenous hormone levels associated with development. We further show that this capability can be leveraged to reprogram development in an agriculturally relevant manner by changing how the hormonal circuitry regulates target genes. By deploying a HACR to re-parameterize the auxin-induced expression of the auxin transporter PIN-FORMED1 (PIN1), we decreased shoot branching and phyllotactic noise, as predicted by existing models4,5.
Data availability
Links to all plasmid sequences are in manuscript and data analysis code is available on github.
Article and author information
Author details
Funding
National Science Foundation (MCB-1411949)
- Eric Klavins
National Institutes of Health (R01-GM107084)
- Jennifer L Nemhauser
Howard Hughes Medical Institute (Faculty Scholars Program)
- Jennifer L Nemhauser
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Dominique C Bergmann, Stanford University/HHMI, United States
Publication history
- Received: January 2, 2018
- Accepted: April 30, 2018
- Accepted Manuscript published: May 1, 2018 (version 1)
- Version of Record published: May 30, 2018 (version 2)
Copyright
© 2018, Khakhar 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.
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Further reading
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- Plant Biology
Plant genomes encode hundreds of secreted peptides; however, relatively few have been characterised. We report here an uncharacterised, stress-induced family of plant signalling peptides, which we call CTNIPs. Based on the role of the common co-receptor BRASSINOSTEROID INSENSITIVE 1-ASSOCIATED KINASE 1 (BAK1) in CTNIP-induced responses, we identified in Arabidopsis thaliana the orphan receptor kinase HAESA-LIKE 3 (HSL3) as the CTNIP receptor via a proteomics approach. CTNIP-binding, ligand-triggered complex formation with BAK1, and induced downstream responses all involve HSL3. Notably, the HSL3-CTNIP signalling module is evolutionarily conserved amongst most extant angiosperms. The identification of this novel signalling module will further shed light on the diverse functions played by plant signalling peptides and will provide insights into receptor-ligand co-evolution.
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- Developmental Biology
- Plant Biology
Positional information is a central concept in developmental biology. In developing organs, positional information can be idealized as a local coordinate system that arises from morphogen gradients controlled by organizers at key locations. This offers a plausible mechanism for the integration of the molecular networks operating in individual cells into the spatially coordinated multicellular responses necessary for the organization of emergent forms. Understanding how positional cues guide morphogenesis requires the quantification of gene expression and growth dynamics in the context of their underlying coordinate systems. Here, we present recent advances in the MorphoGraphX software (Barbier de Reuille et al., 2015) that implement a generalized framework to annotate developing organs with local coordinate systems. These coordinate systems introduce an organ-centric spatial context to microscopy data, allowing gene expression and growth to be quantified and compared in the context of the positional information thought to control them.