Back

Loss of OsARF18 confers glufosinate ammonium herbicide resistance in rice

Xia, J.-Q.; He, D.-Y.; Zhao, P.-X.; Xiang, C.

2023-06-07 plant biology
10.1101/2023.06.05.543806 bioRxiv
Show abstract

Weed is one of the major biotic stresses that causes severe loss of crop yield. Herbicide is one of the most cost-effective ways to control weeds. Thus, the development of herbicide-resistant crops is critical for the application of herbicides. To isolate new glufosinate ammonium resistance loci, we screened a rice ethyl methyl sulfonate-mutagenized library and obtained the glufosinate ammonium-resistant mutant gar1-1. GAR1 encodes auxin response factor 18 (OsARF18). A G-to-A substitution in the coding region of OsARF18 results in loss of function of OsARF18 and thereby enhances glufosinate ammonium resistance of gar1-1, which was confirmed by three additional CRISPR/Cas9-edited gar1 alleles. GLUTAMINE SYNTHETASE 1;1 (OsGS1;1) and GLUTAMINE SYNTHETASE 1;2 (OsGS1;2) were upregulated in gar1-1 upon glufosinate ammonium treatment, directly contributing to increased GS activity that enhances glufosinate ammonium herbicide resistance. We further show that OsARF18 suppresses OsGS1;1 and OsGS1;2 expression. Comparative transcriptomic analyses reveal a huge shift in the gene expression profile involved in stress tolerance and growth. A large number of detoxification-related genes are enriched in gar1-1, which may also contribute to enhanced herbicide resistance. Moreover, stress tolerance-related genes are upregulated and growth-related genes are downregulated in gar1-1, consistent with the improved tolerance to salt and osmotic stress of gar1 mutants. Taken together, our study demonstrates that OsARF18 is a negative regulator of glufosinate ammonium resistance as well as salt and osmotic stress tolerance, suggesting a role in balancing the stress response and growth.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.