Sorghum sheath blight caused by Fusarium spp. in Sinaloa, Mexico

Sorghum (Sorghum bicolor) leaf sheath blight was observed for the first time in Sinaloa, Mexico in the summer of 2020. Fungal isolates were obtained from symptomatic tissue in PDA. Fusarium spp. were associated with symptomatic plants in ten sampling sites under field conditions. No root and stalk rot were observed during the sampling period. Analysis of fragments of the EF-1a and RPB2 genes indicated that all isolates belong to the Fusarium fujikuroi species complex (FFSC). Five groups were delineated from this complex: F. thapsinum, F. verticillioides, Fusarium sp. (four isolates), Fusarium sp. (Fus4), and Fusarium sp. (Fus16), which is closely related to Fusarium madaense. The morphological characteristics (colony color and morphometry of conidia) of isolates with sequence similarities to those of F. thapsinum and F. verticillioides were in the expected range for these species. The morphology of isolates Fus7a, Fus7b, Fus11 and Fus17, as well as Fus4 and Fus16, were similar to those of the FFSC, specially to F. andiyazi and F. madaense. Inoculations of sorghum with representative isolates of F. thapsinum, F. verticillioides and the unidentified Fusarium species resulted in reddish brown lesions similar to those observed under field conditions; the original isolates inoculated were reisolated fulfilling the Koch's postulates. Although leaf sheaths on sorghum plants were heavily damaged, root and stalk rot were not observed in the greenhouse inoculations or under field conditions. Future research should focus on determining the identity of the unknown Fusarium spp. in order to design control measures of the disease. This is the first report of Fusarium spp. causing sorghum leaf sheath blight in Mexico.

Validation of Genomic Selection Approach for Predicting Sheath Blight Resistance in Rice (Oryza sativa L.,)

Rice sheath blight (ShB) is one of the most serious fungal diseases caused by Rhizoctonia solani, instigating significant yield losses in many rice-growing regions of the world. Intensive studies indicated that resistance for sheath blight is controlled possibly by polygenes. Because of complex inheritance, it’s very difficult to exploit and tap all the genomic regions conferring resistance using classical approaches of QTL mapping, it’s very important to have a different strategy to harness such resistance mechanism. One promising approach that can potentially provide accurate predictions of the resistance phenotypes is genomic selection (GS). The research was undertaken with an objective to validate genomic selection approach for predicting sheath blight resistance involving 1545 Recombinant inbred lines (RILs) derived from eleven crosses between resistant and susceptible parents (Jasmine 85XTN1, Jasmine 85XSwarnaSub1, Jasmine 85XII32B, Jasmine 85XIR54, TetepXTN1, TetepXSwarna Sub1, TetepXII32B, TetepXIR54, MTU 9992XTN1, MTU 9992XII32B and MTU 9992XIRBB4). Where, Jasmine 85, Tetep & MTU 9992 were resistant parents and TN1, Swarna Sub1, II32B, IR54 & IRBB4 were susceptible parents. During rainy season (2020) the F7 RILs were screened for their reaction to sheath blight in two hot spot locations. The genotyping was done with Illumina platform having 6564 SNP markers. Bayesian B approach was used to train the statistical model for calculation of marker effects and GEBVs. The prediction accuracy of training set (data fit analysis) obtained was 0.70 and random cross validation with different approaches, the prediction accuracy ranged from 0.67 to 0.74. The results are lucrative, all in all, high prediction accuracies observed in this study suggest genomic selection as a very promising breeding strategy for predicting sheath blight resistance in Rice.

Genome Wide Association Studies to Dissect Genetic Factors Conferring Sheath Blight Resistance in Rice (Oryza sativa L.)

Rice Sheath blight (ShB) is one of the most serious fungal diseases caused by Rhizoctonia solani. Breeding for sheath blight resistance has been ineffective exercise so far, mainly because of lack of good number of reliable sources of resistance in rice germplasm. In this context our studies indicated that the lines Tetep, Jasmine 85 and MTU 9992 confer resistant to moderately resistant reaction against the pathogen. The current investigation was carried out to dissect the genetic factors governing resistance to sheath blight through genome wide association study (GWAS) from the mapping populations developed by design where in, each of the resistant parents were crossed to three to four highly susceptible parents to generate eleven populations (Jasmine 85XTN1, Jasmine 85XSwarnaSub1, Jasmine 85XII32B, Jasmine 85XIR54, TetepXTN1, TetepXSwarnaSub1, TetepXII32B, TetepXIR54, MTU 9992XTN1, MTU 9992XII32B and MTU 9992XIRBB4). A total of 1545 Recombinant inbred lines (RILs) derived from eleven crosses were used for the study. During rainy 2020 the F7 RILs were screened for their reaction to Sheath blight in two hot spot locations. The genotyping was done with Illumina platform having 6564 SNP markers. Genome wide association study was done with two models Generalized Linear Model (GLM) and Mixed Linear Model (MLM). Results clearly indicate the superiority of MLM over GLM in correcting the population structure. With MLM model, in Jasmine 85 half-sib populations with 565 RILs analyzed, five QTLs (Quantitative Trait Loci) were detected on Chr1, Chr3, Chr9, Chr10 and Chr11 with –log10 (P-Value) more than 3. In TETEP half-sib populations with 714 RILs examined, seven QTLs were observed on Chr1, Chr2, Chr5, Chr6, Chr7, Chr8, and Chr11 with –log10 (P-Value) more than 4. Whereas in MTU 9992 half-sib populations with 266 RILs studied, three novel QTLs were identified on Chr2, Chr6 and Chr11 with –log10 (P-Value) more than 3. Some of these QTLs were reported by researches earlier. In the current research, some novel QTLs were detected in Jasmine 85 (Chr10) and Tetep (Chr2, Chr5 and Chr6) apart from three new QTLs discovered in MTU 9992. The results facilitated to have better understanding of the genetic basis for sheath blight resistance in rice. Pyramiding all the QTL identified so far into a susceptible varieties is complicated affair as resistance is governed by not only several large effect QTLs but also medium to small effect QTLs as well, hence genomic selection approach could be rewarding for breeding for sheath blight resistance.

Comparative Transcriptome Analysis of Rice Cultivars Resistant and Susceptible to Rhizoctonia Solani AG1-IA

Background: Rice sheath blight, which is caused by Rhizoctonia solani, is the most destructive disease affecting rice production, but the resistance mechanism to this pathogen has not been fully elucidated. Results: In this study, we selected two rice cultivars based on their resistance to the pathogen and analyzed and compared the transcriptomic profiles of two cultivars, the moderately resistant variety Gangyuan8 and the highly susceptible variety Yanfeng47, at different time points after inoculation. The comparative transcriptome profiling showed that the expression of related genes gradually increased after pathogen inoculation. The number of differentially expressed genes (DEGs) in Yanfeng47 was higher than that in Gangyuan8, and this result revealed that Yanfeng47 was more susceptible to fungal attack. At the early stage (24 and 48 h), the accumulation of resistance genes and a resistance metabolism occurred earlier in Ganguan8 than in Yanfeng47, and the resistance enrichment entries were more abundant in Ganguan8 than in Yanfeng47. Conclusions: Based on the GO and KEGG enrichment analyses at five infection stages, we concluded that phenylalanine metabolism and the jasmonic acid pathway play a crucial role in the resistance of rice to sheath blight. Through a comparative transcriptome analysis, we preliminarily analyzed the molecular mechanism responsible for resistance to sheath blight in rice, and the results lay the foundation for the development of gene mining and functional research on rice resistance to sheath blight.

Genome-Wide Identification, Transcript Profiling and Bioinformatic Analyses of GRAS Transcription Factor Genes in Rice

Our group has previously identified the activation of a GRAS transcription factor (TF) gene in the gain-of-function mutant population developed through activation tagging in rice (in an indica rice variety, BPT 5204) that was screened for water use efficiency. This family of GRAS transcription factors has been well known for their diverse roles in gibberellin signaling, light responses, root development, gametogenesis etc. Recent studies indicated their role in biotic and abiotic responses as well. Although this family of TFs received significant attention, not many genes were identified specifically for their roles in mediating stress tolerance in rice. Only OsGRAS23 (here named as OsGRAS22) was reported to code for a TF that induced drought tolerance in rice. In the present study, we have analyzed the expression patterns of rice GRAS TF genes under abiotic (NaCl and ABA treatments) and biotic (leaf samples infected with pathogens, Xanthomonas oryzae pv. oryzae that causes bacterial leaf blight and Rhizoctonia solani that causes sheath blight) stress conditions. In addition, their expression patterns were also analyzed in 13 different developmental stages. We studied their spatio-temporal regulation and correlated them with the in-silico studies. Fully annotated genomic sequences available in rice database have enabled us to study the protein properties, ligand interactions, domain analysis and presence of cis-regulatory elements through the bioinformatic approach. Most of the genes were induced immediately after the onset of stress particularly in the roots of ABA treated plants. OsGRAS39 was found to be a highly expressive gene under sheath blight infection and both abiotic stress treatments while OsGRAS8, OsSHR1 and OsSLR1 were also responsive. Our earlier activation tagging based functional characterization followed by the genome-wide characterization of the GRAS gene family members in the present study clearly show that they are highly appropriate candidate genes for manipulating stress tolerance in rice and other crop plants.

Recognition of Rice Sheath Blight Based on a Backpropagation Neural Network

Rice sheath blight is one of the main diseases in rice production. The traditional detection method, which needs manual recognition, is usually inefficient and slow. In this study, a recognition method for identifying rice sheath blight based on a backpropagation (BP) neural network is posed. Firstly, the sample image is smoothed by median filtering and histogram equalization, and the edge of the lesion is segmented using a Sobel operator, which largely reduces the background information and significantly improves the image quality. Then, the corresponding feature parameters of the image are extracted based on color and texture features. Finally, a BP neural network is built for training and testing with excellent tunability and easy optimization. The results demonstrate that when the number of hidden layer nodes is set to 90, the recognition accuracy of the BP neural network can reach up to 85.8%. Based on the color and texture features of the rice sheath blight image, the recognition algorithm constructed with a BP neural network has high accuracy and can effectively make up for the deficiency of manual recognition.