0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessAbscisic acid (ABA) is a key phytohormone that controls plant growth and stress responses. It is sensed by the pyrabactin resistance 1 (PYR1)/PYR1-like (PYL)/regulatory components of the ABA receptor (RCAR) family of proteins. Here, we utilized CRISPR/Cas9 technology to edit group I (PYL1-PYL6 and PYL12) and group II (PYL7-PYL11 and PYL13) PYL genes in rice. Characterization of the combinatorial mutants suggested that genes in group I have more important roles in stomatal movement, seed dormancy, and growth regulation than those in group II. Among all of the single pyl mutants, only pyl1 and pyl12 exhibited significant defects in seed dormancy. Interestingly, high-order group I mutants, but not any group II mutants, displayed enhanced growth. Among group I mutants, pyl1/4/6 exhibited the best growth and improved grain productivity in natural paddy field conditions, while maintaining nearly normal seed dormancy. Our results suggest that a subfamily of rice PYLs has evolved to have particularly important roles in regulating plant growth and reveal a genetic strategy to improve rice productivity.
Chunbo Miao, Lihong Xiao, Kai Hua, Changsong Zou, Yang Zhao, Ray A. Bressan, Jian Kang Zhu (2018). Mutations in a subfamily of abscisic acid receptor genes promote rice growth and productivity. , 115(23), DOI: https://doi.org/10.1073/pnas.1804774115.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2018
Authors
7
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1073/pnas.1804774115
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access