Dataset Information

A wheat cytochrome P450 enhances both resistance to deoxynivalenol and grain yield.

ABSTRACT: The mycotoxin deoxynivalenol (DON) serves as a plant disease virulence factor for the fungi Fusarium graminearum and F. culmorum during the development of Fusarium head blight (FHB) disease on wheat. A wheat cytochrome P450 gene from the subfamily CYP72A, TaCYP72A, was cloned from wheat cultivar CM82036. TaCYP72A was located on chromosome 3A with homeologs present on 3B and 3D of the wheat genome. Using gene expression studies, we showed that TaCYP72A variants were activated in wheat spikelets as an early response to F. graminearum, and this activation was in response to the mycotoxic Fusarium virulence factor deoxynivalenol (DON). Virus induced gene silencing (VIGS) studies in wheat heads revealed that this gene family contributes to DON resistance. VIGS resulted in more DON-induced discoloration of spikelets, as compared to mock VIGS treatment. In addition to positively affecting DON resistance, TaCYP72A also had a positive effect on grain number. VIGS of TaCYP72A genes reduced grain number by more than 59%. Thus, we provide evidence that TaCYP72A contributes to host resistance to DON and conclude that this gene family warrants further assessment as positive contributors to both biotic stress resistance and grain development in wheat.

SUBMITTER: Gunupuru LR

PROVIDER: S-EPMC6185721 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A wheat cytochrome P450 enhances both resistance to deoxynivalenol and grain yield.

Gunupuru Lokanadha R LR Arunachalam Chanemougasoundharam C Malla Keshav B KB Kahla Amal A Perochon Alexandre A Jia Jianguang J Thapa Ganesh G Doohan Fiona M FM

PloS one 20181012 10

The mycotoxin deoxynivalenol (DON) serves as a plant disease virulence factor for the fungi Fusarium graminearum and F. culmorum during the development of Fusarium head blight (FHB) disease on wheat. A wheat cytochrome P450 gene from the subfamily CYP72A, TaCYP72A, was cloned from wheat cultivar CM82036. TaCYP72A was located on chromosome 3A with homeologs present on 3B and 3D of the wheat genome. Using gene expression studies, we showed that TaCYP72A variants were activated in wheat spikelets a ...[more]

PMID: 30312356

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Project description:Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.

Dataset Information

A wheat cytochrome P450 enhances both resistance to deoxynivalenol and grain yield.

Publications

A wheat cytochrome P450 enhances both resistance to deoxynivalenol and grain yield.

Similar Datasets

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