
Protein engineering expands the effector recognition profile of a rice NLR immune receptor
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Abstract
Plant NLR receptors detect pathogen effectors and initiate an immune response. Since their discovery, NLRs have been the focus of protein engineering to improve disease resistance. However, this has proven challenging, in part due to their narrow response specificity. Previously, we revealed the structural basis of pathogen recognition by the integrated HMA of the rice NLR Pikp (Maqbool, Saitoh et al. 2015). Here, we used structure-guided engineering to expand the response profile of Pikp to variants of the rice blast pathogen effector AVR-Pik. A mutation located within an effector binding interface of the integrated Pikp-HMA domain increased the binding affinity for AVR-Pik variants in vitro and in vivo. This translates to an expanded cell death response to AVR-Pik variants previously unrecognized by Pikp in planta. Structures of the engineered Pikp-HMA in complex with AVR-Pik variants revealed the mechanism of expanded recognition. These results provide a proof-of-concept that protein engineering can improve the utility of plant NLR receptors where direct interaction between effectors and NLRs is established, particularly via integrated domains.
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Funding
Biotechnology and Biological Sciences Research Council (BB/J004553)
- Sophien Kamoun
- Mark J Banfield
Biotechnology and Biological Sciences Research Council (BB/P012574)
- Sophien Kamoun
- Mark J Banfield
Biotechnology and Biological Sciences Research Council (BB/M02198X)
- Marina Franceschetti
- Sophien Kamoun
- Mark J Banfield
H2020 European Research Council (743165)
- Sophien Kamoun
- Mark J Banfield
John Innes Foundation
- Juan Carlos De la Concepcion
- Marina Franceschetti
- Mark J Banfield
Gatsby Charitable Foundation
- Sophien Kamoun
Japan Society for the Promotion of Science (15H05779)
- Ryohei Terauchi
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Thorsten Nürnberger, University of Tübingen, Germany
Publication history
- Received: April 17, 2019
- Accepted: September 17, 2019
- Accepted Manuscript published: September 19, 2019 (version 1)
- Version of Record published: September 30, 2019 (version 2)
Copyright
© 2019, De la Concepcion 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
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- Computational and Systems Biology
- Plant Biology