RNA-seq analysis of Rhizoctonia solani AG-4HGI strain BJ-1H infected by a new viral strain of Rhizoctonia solani partitivirus 2 reveals a potential mechanism for hypovirulence

Rhizoctonia solani partitivirus 2 (RsPV2), in the genus Alphapartitivirus, confers hypovirulence on Rhizoctonia solani AG-1-IA, the causal agent of rice sheath blight. In this study, a new strain of RsPV2 obtained from R. solani AG-4HGI strain BJ-1H, the causal agent of black scurf on potato, was identified and designated as Rhizoctonia solani partitivirus 2 strain BJ-1H (RsPV2-BJ). An RNA sequencing analysis of strain BJ-1H and the virus RsPV2-BJ-free strain BJ-1H-VF derived from strain BJ-1H was conducted to investigate the potential molecular mechanism of hypovirulence induced by RsPV2-BJ. In total, 14319 unigenes were obtained, and 1341 unigenes were identified as differentially expressed genes (DEGs), with 570 DEGs being down-regulated and 771 being up-regulated. Notably, several up-regulated DEGs were annotated to cell-wall-degrading enzymes, including β-1,3-glucanases. Strain BJ-1H exhibited increased expression of β-1,3-glucanase following RsPV2-BJ infection, suggesting that cell wall autolysis activity in R. solani AG-4HGI strain BJ-1H might be promoted by RsPV2-BJ, inducing hypovirulence in its host fungus R. solani AG-4HGI. To the best of our knowledge, this is the first report on the potential mechanism of hypovirulence induced by a mycovirus in R. solani.

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.

Bismuth Vanadium Oxide Can Promote Growth and Activity in Arabidopsis thaliana

The excellent properties of nanomaterials have been confirmed in many fields, but their effects on plants are still unclear. In this study, different concentrations of bismuth vanadate (BV) were added to the growth medium to analyze the growth of seedlings, including taproots, lateral roots, leaf stomata, root activity, and superoxide anion O2.- generation. Gene expression levels related to root growth were determined by quantitative PCR in Arabidopsis thaliana. The results showed that BV promoted the growth of taproots and the development of lateral roots, enhanced the length of the extension zone in roots, increased the number and size of leaf stomata and root activity, reduced the accumulation of ROS in seedlings, and changed the expression levels of genes related to polyamines or hormones. At the same time, we investigated the antibacterial activity of BV against a variety of common pathogens causing crop diseases. The results showed that BV could effectively inhibit the growth of Fusarium wilt of cotton and rice sheath blight. These results provide a new prospect for the development of nanomaterial-assisted plants, which is expected to become one of the ways to solve the problem of controlling and promoting the development of plants. At the same time, it also provides a reference for the study of the effect of BV on plants.

Gene Expression Analysis of Resistant and Susceptible Rice Cultivars to Sheath Blight after Inoculation with Rhizoctonia Solani

Background: Rice sheath blight, caused by Rhizoctonia solani Kühn (teleomorph: Thanatephorus cucumeris), is one of the most severe diseases in rice (Oryza sativa L.) worldwide. Studies on resistance genes and resistance mechanisms of rice sheath blight has mainly focused on indica rice. Rice sheath blight is a growing threat to rice production with the increasing planting area of japonica rice in Northeast China, and it is therefore essential to explore the mechanism of sheath blight resistance in this rice subspecies.Results: In this study, RNA-seq technology was used to analyse the gene expression changes of leaf sheath inoculation with R. solani at 12, 24, 36, 48, and 72 h after inoculation of the resistant cultivar ‘Shennong 9819’ and susceptible cultivar ‘Yueguang’. A total of 2275 differentially expressed genes (DEGs) were identified in our study. Gene Ontology (GO) annotation and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis of these DEGs showed that the metabolic pathways of the two rice cultivars were similar and approximately covered most of the biological processes after inoculation with R. solani. Conclusion: The main differences between the resistant and susceptible cultivars were the activity of DEGs in response to R. solani and the enrichment of metabolic pathways involved in these genes. Upon experiencing external stress, the genes in the leaf sheath of Shennong 9819 showed a fluctuating response, whereas the genes in the leaf sheath of Yueguang had a higher activity. The phenylalanine metabolism and plant hormone signal transduction play important roles in disease resistance. Their response in the leaf sheath of Shennong 9819 was faster than that of Yueguang, and they were highly enriched at 12 h after inoculation.