Dataset Information

Suppression of ZEAXANTHIN EPOXIDASE 1 restricts stripe rust growth in wheat.

ABSTRACT: Reducing losses caused by pathogens is an effective strategy for stabilizing crop yields. Daunting challenges remain in cloning and characterizing genes that inhibit stripe rust, a devastating disease of wheat (Triticum aestivum) caused by Puccinia striiformis f. sp. tritici (Pst). We found that suppression of wheat zeaxanthin epoxidase 1 (ZEP1) increased wheat defense against Pst. We isolated the yellow rust slower 1 (yrs1) mutant of tetraploid wheat in which a premature stop mutation in ZEP1-B underpins the phenotype. Genetic analyses revealed increased H₂O₂ accumulation in zep1 mutants and demonstrated a correlation between ZEP1 dysfunction and slower Pst growth in wheat. Moreover, wheat kinase START 1.1 (WKS1.1, Yr36) bound, phosphorylated, and suppressed the biochemical activity of ZEP1. A rare natural allele in the hexaploid wheat ZEP1-B promoter reduced its transcription and Pst growth. Our study thus identified a novel suppressor of Pst, characterized its mechanism of action, and revealed beneficial variants for wheat disease control. This work opens the door to stacking wheat ZEP1 variants with other known Pst resistance genes in future breeding programs to enhance wheat tolerance to pathogens.

SUBMITTER: Chang CY

PROVIDER: S-EPMC10504589 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Suppression of ZEAXANTHIN EPOXIDASE 1 restricts stripe rust growth in wheat.

Chang Chao-Yan CY Yang Shu-Xian SX Zhang Mei-Qi MQ Guo Yue-Ting YT Li Xiao-Ming XM Yan Yan Y Ding Ci-Hang CH Niu Ke-Xin KX Wang Meng-Lu ML Li Qin-Quan QQ Zhang Junli J Zhang Xuebin X Chen Shisheng S Xie Chaojie C Ni Zhongfu Z Sun Qixin Q Gou Jin-Ying JY

Plant communications 20230425 5

Reducing losses caused by pathogens is an effective strategy for stabilizing crop yields. Daunting challenges remain in cloning and characterizing genes that inhibit stripe rust, a devastating disease of wheat (Triticum aestivum) caused by Puccinia striiformis f. sp. tritici (Pst). We found that suppression of wheat zeaxanthin epoxidase 1 (ZEP1) increased wheat defense against Pst. We isolated the yellow rust slower 1 (yrs1) mutant of tetraploid wheat in which a premature stop mutation in ZEP1-B ...[more]

PMID: 37101397

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Project description:Wheat stripe rust (WSR), a fungal disease capable of inflicting severe crop loss, threatens most of global wheat production. Breeding for genetic resistance is the primary defense against stripe rust infection. Further development of rust-resistant wheat varieties depends on the ability to accurately and rapidly quantify rust resilience. In this study we demonstrate the ability of visible through shortwave infrared reflectance spectroscopy to effectively provide high-throughput classification of wheat stripe rust severity and identify important spectral regions for classification accuracy. Random forest models were developed using both leaf-level and canopy-level hyperspectral reflectance observations collected across a breeding population that was scored for WSR severity using 10 and 5 severity classes, respectively. The models were able to accurately diagnose scored disease severity class across these fine scoring scales between 45-52% of the time, which improved to 79-96% accuracy when allowing scores to be off-by-one. The canopy-level model demonstrated higher accuracy and distinct spectral characteristics relative to the leaf-level models, pointing to the use of this technology for field-scale monitoring. Leaf-level model performance was strong despite clear variation in scoring conducted between wheat growth stages. Two approaches to reduce predictor and model complexity, principal component dimensionality reduction and backward feature elimination, were applied here. Both approaches demonstrated that model classification skill could remain high while simplifying high-dimensional hyperspectral reflectance predictors, with parsimonious models having approximately 10 unique components or wavebands. Through the use of a high-resolution infection severity scoring methodology this study provides one of the most rigorous tests of the use of hyperspectral reflectance observations for WSR classification. We demonstrate that machine learning in combination with a few carefully-selected wavebands can be leveraged for precision remote monitoring and management of WSR to limit crop damage and to aid in the selection of resilient germplasm in breeding programs.

Dataset Information

Suppression of ZEAXANTHIN EPOXIDASE 1 restricts stripe rust growth in wheat.

Publications

Suppression of ZEAXANTHIN EPOXIDASE 1 restricts stripe rust growth in wheat.

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