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. 2021 Aug;19(8):1553-1566.
doi: 10.1111/pbi.13569. Epub 2021 Mar 18.

Identification of rice (Oryza sativa L.) genes involved in sheath blight resistance via a genome-wide association study

Aijun Wang  1   2   3 , Xinyue Shu  1   2   3 , Xin Jing  4 , Chengzhi Jiao  4 , Lei Chen  2 , Jinfeng Zhang  2 , Li Ma  1   2   3 , Yuqi Jiang  1   2   3 , Naoki Yamamoto  1   2   3 , Shuangcheng Li  1   2 , Qiming Deng  2 , Shiquan Wang  2 , Jun Zhu  2 , Yueyang Liang  2 , Ting Zou  1   2 , Huainian Liu  2 , Lingxia Wang  2 , Yubi Huang  1 , Ping Li  1   2   3 , Aiping Zheng  1   2   3
Affiliations

Identification of rice (Oryza sativa L.) genes involved in sheath blight resistance via a genome-wide association study

Aijun Wang et al. Plant Biotechnol J. 2021 Aug.

Abstract

Rice sheath blight (RSB) is an economically significant disease affecting rice yield worldwide. Genetic resistance to RSB is associated with multiple minor genes, with each providing a minor phenotypic effect, but the underlying dominant resistance genes remain unknown. A genome-wide association study (GWAS) of 259 diverse rice varieties, with genotypes based on a single nucleotide polymorphism (SNP) and haplotype, was conducted to assess their sheath blight reactions at three developmental stages (seedlings, tillering and booting). A total of 653 genes were correlated with sheath blight resistance, of which the disease resistance protein RPM1 (OsRSR1) and protein kinase domain-containing protein (OsRLCK5) were validated by overexpression and knockdown assays. We further found that the coiled-coil (CC) domain of OsRSR1 (OsRSR1-CC) and full-length OsRLCK5 interacted with serine hydroxymethyltransferase 1 (OsSHM1) and glutaredoxin (OsGRX20), respectively. It was found that OsSHM1, which has a role in the reactive oxygen species (ROS) burst, and OsGRX20 enhanced the antioxidation ability of plants. A regulation model of the new RSB resistance though the glutathione (GSH)-ascorbic acid (AsA) antioxidant system was therefore revealed. These results enhance our understanding of RSB resistance mechanisms and provide better gene resources for the breeding of disease resistance in rice.

Keywords: Rhizoctonia solani AG1-IA; genome-wide association study; glutathione-ascorbic acid antioxidant system; resistance genes; rice sheath blight.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Population structure of 259 rice accessions. (a) Population structure based on different numbers of ancestry kinships (K) set to 2 or 3. The x‐axis indicates the japonica (red), improved indica cultivar (IC, blue), and landrace (Lan, purple) subgroups. The left y‐axis quantifies genetic diversity in each accession. (b) Neighbour‐joining phylogenetic tree of 259 rice accessions based on 2 888 332 high‐quality SNPs; branch colours indicate different subgroups of rice, using the same colours as in (a). (c) Principal component analysis (PCA) plots of the first two components for all 259 rice accessions, using the same colours as in (a). (d) Genome‐wide average linkage disequilibrium (LD) decay rate of 259 rice accessions.
Figure 2
Figure 2
Genome‐wide association study (GWAS) and weighted gene co‐expression network analysis (WGCNA) of rice accessions for identification of rice sheath blight (RSB) resistance candidate genes. (a, b) Distribution of the loci associated with RSB resistance in rice based on single nucleotide polymorphism (SNP)‐GWAS (a) and haplotype (Hap)‐GWAS (b). Incidence of RSB was classified into three stages: seeding (purple); tillering (milky); booting (blue). The associated loci for each trait are indicated by red vertical lines in the chromosome map. Seeding_2017_WJ indicates the GWAS result for RSB resistance at seeding stage using 2017 phenotypic data; Seeding_2018_LS indicates the GWAS result for RSB resistance at seeding stage using 2018 phenotypic data; Seeding indicates the GWAS result for RSB resistance at seeding stage using the best linear unbiased prediction (BLUP) values for two years. The same markers were used in tillering and booting stages. (c, d) Genes with known function for rice disease resistance under candidate genes are shown in significant GWAS association positions, SNP‐GWAS (c), Hap‐GWAS (d). The x‐axis indicates the genomic coordinates, and the y‐axis indicates the association score of each SNP; the score represents a transformed P value, ‐log10P. (e) Module detection for candidates in the GWAS and DEGs data sets. The correlation of different modules is indicated below. We selected one module showing positive correlation with the trait and two modules showing negative correlation with the trait for further study (high expression of resistance genes leads to low RSB incidence thus the negative correlation modules were selected). Each pane corresponds to a module. The module–trait relationships (MTRs) are coloured based on their correlation: red indicates a strong positive correlation and blue indicates a strong negative correlation. (f) Three modules showing the highest correlation with each trait. We selected the module showing clear correlation with the trait as the final candidate module (gene expression was negatively correlated with the resistant phenotype and positively correlated with the susceptible phenotype). Each row corresponds to a module. Every three columns corresponds to a time result. The MTRs are coloured based on their correlation: red indicates a strong positive correlation and blue indicates a strong negative correlation. (g) Venn diagrams showing the number of core candidate genes detected by GWAS and transcriptome analysis.
Figure 3
Figure 3
OsRSR1 regulates RSB resistance. (a) Manhattan plots of loci on chromosome 11 associated with RSB incidence at the tillering stage at Wenjiang in 2017 (Tillering_2017_WJ). Arrowheads indicate significantly associated SNPs located in a nucleotide‐binding site leucine‐rich repeat proteins encoded (R) gene cluster. Horizontal dashed lines indicate the significance threshold (P < 10−6). LD heat map (bottom) reflected that associated SNP localized in a haploid between the red dashed lines. (b) This haploid (named block 56 755) contains the R gene cluster mentioned above, which included four R genes LOC_Os11g12320, LOC_Os11g12330, LOC_Os11g12340 and LOC_Os11g12350. Red rectangles indicate four R genes, respectively. (c) Three haplotypes were identified through sequence analysis of R gene cluster. Blue areas indicated deletion type mapping to reference genomes (hap1); green areas indicated variant type mapping to reference genomes (hap2); yellow areas indicated consistent with reference genomes (hap3). (d) Box plots for RSB resistance, based on the genotypes of sequence analysis of R cluster. The horizontal line in the centre of each box denotes the median. The upper and lower limits of each box represent quartiles; whiskers indicate the range of the data; statistical significance of differences was analysed by the two‐tailed t‐test. Hap1, hap2 and hap3 are shown in red, blue and green, respectively. (e) Expression of four genes in the R gene cluster in resistant and susceptible rice lines (Transcriptome data). Each row corresponds to a time result. Each column corresponds to a gene. The expression level is coloured based on their FPKM value: red indicates a high expression and green indicates a low expression. (f) Verification of four R genes expression in resistant and susceptible rice lines at different infection time points by qRT‐PCR. (g) Expression analysis of OsRSR1 in five resistant and five susceptible varieties at 24 h postinoculation (hpi) of Rhizoctonia solani AG1‐IA by qRT–PCR. (h) Identification of overexpression (OE) and RNA interference (RNAi) lines of OsRSR1 by qRT–PCR. (i) Incidence of OsRSR1‐OE plants was decreased compared with WT, and the incidence of OsRSR1‐RNAi plants was increased compared with WT. (j) Average incidence rate (n = 10 sheath) of Teqing, OsRSR1‐RNAi lines, Lemont, and OsRSR1‐OE lines at 4 day post inoculation (dpi) with R. solani AG1‐IA. (k) Expression analysis of OsRSR1 in different tissues of rice by qRT–PCR (leaves, sheath, root and panicle). The rice UBQ gene was used as an internal control. Data are represented as average values with four biological replicates (e–h, and k).
Figure 4
Figure 4
OsRLCK5 regulates RSB resistance. (a) Manhattan plots of loci on chromosome 1 associated with RSB incidence at the seedling, tillering and booting stages at Wenjiang in 2017 (seedling_2017_WJ, tillering_2017_WJ and booting_2017_WJ, respectively). Red markings indicate the strongly associated loci containing the candidate gene OsRLCK5. Horizontal dashed lines indicate the significance threshold (P < 10−6). (b) Local Manhattan plot. The candidate region lies between the red dashed lines. Significant haplotype structures (block 70), P‐values and variant types are indicated on the right side. Dashed line represents the significance threshold (P < 10−6). Red indicated the strongly associated haplotype, which are located within OsRLCK5. (c) Haplotype with three nonsynonymous variations in OsRLCK5 exon. Blue rectangles and black lines indicate exons and introns, respectively. (d) Box plots for RSB resistance, based on the genotypes of two homozygous haplotypes. The horizontal line in the centre of each box denotes the median. The upper and lower limits of each box represent quartiles; whiskers indicate the range of the data. n indicates the number of accessions with the same genotype. (e) Expression analysis of OsRLCK5 in five resistant and five susceptible varieties at 24 hpi by qRT–PCR. (f) Verification of OsRLCK5 expression in resistant and susceptible rice lines at different infection time points by qRT‐PCR. (g) Spatial expression analysis of OsRLCK5 in rice plants. (h) Expression analysis of OsRLCK5 in five resistant and five susceptible varieties at 24h postinoculation (hpi) of R. solani by qRT–PCR. (i) Identification of OsRLCK5‐OE and ‐RNAi lines by qRT–PCR. The rice UBQ gene was used as an internal control. Data are represented as average values with four biological replicates (e–i). (j) Incidence of OsRLCK5‐OE plants was decreased compared with WT, and the incidence of OsRLCK5‐RNAi plants was increased compared with WT. (k) Average incidence rate (n = 10 sheath) of Teqing, OsRLCK5‐RNAi lines, Lemont and OsRLCK5‐OE lines at 4 dpi with R. solani AG1‐IA.
Figure 5
Figure 5
OsRSR1 and OsRLCK5 regulate reactive oxygen species (ROS) burst. (a) Detection of interaction between OsRLCK5 and OsGRX20, and OsRSR1‐CC and OsSHM1 in a yeast two‐hybrid (Y2H) assay, respectively. Photograph shows the growth behaviour of transformants on SD/Leu‐Trp media (SD‐2) and SD/Ade‐Leu‐Trp‐His (SD‐4) plus AbA and X‐α‐gal media. pGBKT7‐p53 and pGADT7‐SV40 large T‐antigen were set as the positive control pair. pGBKT7‐Lam and pGADT7‐SV40 large T‐antigen were set as the negative control pair. (b) Bimolecular fluorescence complementation (BiFC) assay verified the interaction between OsRLCK5 and OsGRX20, and OsRSR1‐CC and OsSHM1 in tobacco leaf epidermis cells, respectively. Scale bars: 50 mm. 35S‐YFP was employed as positive control, and negative control means the pXY104 (nYFP; contains OsRLCK5 or OsRSR1‐CC) were co‐expressed in tobacco leaf epidermis cells with empty pXY106 (nYFP) vector. (c) Changes in ROS levels, activities of POD, SOD and PPO, and OsCATC expression in transgenic and wild type (WT; Teqing and Lemont) leaves at 72 hpi. Values of POD, SOD and PPO activities represent the average of four lines. Data indicate mean ± standard error of mean (SEM) of four technical replicates. Statistically significant differences were analysed by one‐way ANOVA (*P < 0.05; **P < 0.01; ***P < 0.001).
Figure 6
Figure 6
A model for RSR1‐ and RLCK5‐mediated RSB resistance through glutathione (GSH)‐ascorbic acid (AsA) antioxidant system.
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