Dataset Information

Transcriptome Analysis of a Cotton Cultivar Provides Insights into the Differentially Expressed Genes Underlying Heightened Resistance to the Devastating Verticillium Wilt.

ABSTRACT: Cotton is an important economic crop worldwide. Verticillium wilt (VW) caused by Verticillium dahliae (V. dahliae) is a serious disease in cotton, resulting in massive yield losses and decline of fiber quality. Breeding resistant cotton cultivars is an efficient but elaborate method to improve the resistance of cotton against V. dahliae infection. However, the functional mechanism of several excellent VW resistant cotton cultivars is poorly understood at present. In our current study, we carried out RNA-seq to discover the differentially expressed genes (DEGs) in the roots of susceptible cotton Gossypium hirsutum cultivar Junmian 1 (J1) and resistant cotton G.hirsutum cultivar Liaomian 38 (L38) upon Vd991 inoculation at two time points compared with the mock inoculated control plants. The potential function of DEGs uniquely expressed in J1 and L38 was also analyzed by GO enrichment and KEGG pathway associations. Most DEGs were assigned to resistance-related functions. In addition, resistance gene analogues (RGAs) were identified and analyzed for their role in the heightened resistance of the L38 cultivar against the devastating Vd991. In summary, we analyzed the regulatory network of genes in the resistant cotton cultivar L38 during V. dahliae infection, providing a novel and comprehensive insight into VW resistance in cotton.

SUBMITTER: Zhu H

PROVIDER: S-EPMC8616101 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Transcriptome Analysis of a Cotton Cultivar Provides Insights into the Differentially Expressed Genes Underlying Heightened Resistance to the Devastating Verticillium Wilt.

Zhu He H Song Jian J Dhar Nikhilesh N Shan Ying Y Ma Xi-Yue XY Wang Xiao-Lei XL Chen Jie-Yin JY Dai Xiao-Feng XF Li Ran R Wang Zi-Sheng ZS

Cells 20211030 11

Cotton is an important economic crop worldwide. Verticillium wilt (VW) caused by Verticillium dahliae (V. dahliae) is a serious disease in cotton, resulting in massive yield losses and decline of fiber quality. Breeding resistant cotton cultivars is an efficient but elaborate method to improve the resistance of cotton against V. dahliae infection. However, the functional mechanism of several excellent VW resistant cotton cultivars is poorly understood at present. In our curr ...[more]

PMID: 34831184

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Project description:BackgroundVerticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method is still manual, which is subjective, inefficient, and labor-intensive, and therefore, this study has proposed a novel method for cotton verticillium wilt identification based on spectral and image feature fusion. The cotton hyper-spectral images have been collected, while the regions of interest (ROI) have been extracted as samples including 499 healthy leaves and 498 diseased leaves, and the average spectral information and RGB image of each sample were obtained. In spectral feature processing, the preprocessing methods including Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), de-trending (DT) and mean normalization (MN) algorithms have been adopted, while the feature band extraction methods have adopted principal component analysis (PCA) and successive projections algorithm (SPA). In RGB image feature processing, the EfficientNet was applied to build classification model and 16 image features have been extracted from the last convolutional layer. And then, the obtained spectral and image features were fused, while the classification model was established by support vector machine (SVM) and back propagation neural network (BPNN). Additionally, the spectral full bands and feature bands were used as comparison for SVM and BPNN classification respectively.ResultThe results showed that the average accuracy of EfficientNet for cotton verticillium wilt identification was 93.00%. By spectral full bands, SG-MSC-BPNN model obtained the better performance with classification accuracy of 93.78%. By feature bands, SG-MN-SPA-BPNN model obtained the better performance with classification accuracy of 93.78%. By spectral and image fused features, SG-MN-SPA-FF-BPNN model obtained the best performance with classification accuracy of 98.99%.ConclusionsThe study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt.

Dataset Information

Transcriptome Analysis of a Cotton Cultivar Provides Insights into the Differentially Expressed Genes Underlying Heightened Resistance to the Devastating Verticillium Wilt.

Publications

Transcriptome Analysis of a Cotton Cultivar Provides Insights into the Differentially Expressed Genes Underlying Heightened Resistance to the Devastating Verticillium Wilt.

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