15 research outputs found

    A Ligation-PCR Approach for Generating Gene Replacement Constructs in Magnaporthe grisea

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    The conventional approach for generating gene replacement constructs involves several sequence-specific cloning steps and is time-consuming. A ligation-PCR approach was developed to efficiently generate gene replacement constructs. Two vectors useful for this ligation-PCR approach and another vector suitable for improving the efficiency of knockout mutant screens were constructed

    Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks

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    Among all diseases affecting rice production, rice blast disease has the greatest impact. Thus, monitoring and precise prediction of the occurrence of this disease are important; early prediction of the disease would be especially helpful for prevention. Here, we propose an artificial-intelligence-based model for rice blast disease prediction. Historical data on rice blast occurrence in representative areas of rice production in South Korea and historical climatic data are used to develop a region-specific model for three different regions: Cheolwon, Icheon and Milyang. A rice blast incidence is then predicted a year in advance using long-term memory networks (LSTMs). The predictive performance of the proposed LSTM model is evaluated by varying the input variables (i.e., rice blast disease scores, air temperature, relative humidity and sunshine hours). The most widely cultivated rice varieties are also selected and the prediction results for those varieties are analyzed. Application of the LSTM model to the accumulated rice-blast disease score data confirms successful prediction of rice blast incidence. In all regions, the predictions are most accurate when all four input variables are combined. Rice blast fungus prediction using the proposed LSTM model is variety-based; therefore, this model will be more helpful for rice breeders and rice blast researchers than conventional rice blast prediction models

    Bacillus velezensis TSA32-1 as a Promising Agent for Biocontrol of Plant Pathogenic Fungi

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    The use of synthetic fungicides has caused major problems such as soil and water pollution and negatively affects non-target species. Microbial biocontrol agents are needed for crop disease management to reduce agrochemical use. Bacillus and related genera produce secondary metabolites with agricultural applications, such as the pathogen-control agent Bacillus velezensis. We isolated B. velezensis TSA32-1 from soil and identified its characteristics by sequencing its 16S rRNA. B. velezensis TSA32-1 showed enzyme activity and antimicrobial effects against phytopathogenic fungi by inhibiting the growth of Fusarium graminearum, F. fujikuroi, Alternatia alternate, and Diaporthe actinidiae. Additionally, B. velezensis TSA32-1 protected diseases in corn and pepper seeds caused by F. graminearum and Pythium ultimum. The complete genome of B. velezensis TSA32-1 was 4.05 Mb with a G+C content of 46.3 mol % and possessed the bacillaene biosynthesis cluster, a polyketide that inhibits protein biosynthesis. We also detected a surfactin synthesis cluster, known as non-ribosomal peptide synthetases, which biosynthesizes the antibacterial substance lipopeptide. Surfactin, and fengycin family compounds, secondary metabolites known as key factors in biological control, also detected B. velezensis TSA32-1 which shows potential as a biocontrol agent for controlling plant pathogens in agriculture

    Pathotype Classification of Korean Rice Blast Isolates Using Monogenic Lines for Rice Blast Resistance

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    The rice blast fungus is a representative model phytopathogenic fungus in which Gene-for-Gene interaction with host rice is applicable. After 1980, eight differential varieties have been constructed and classified to analyze the race of rice blast isolates in Korea. However, since there is limited information about the genetic background of rice blast resistance genes within the Korean differentials, scientific analysis on the emergence of new race or resistance break down was difficult. Recently, a differential system has been developed using monogenic resistance lines to understand the interactions of pathogen race and rice resistance genes. In this study, a total of 50 isolates were selected from four different races isolated in Korea, and they were inoculated into monogenic lines. As a result, the isolates in the same race classified by the Korean differential system reacted differently in single monogenic lines. This suggests that the isolates categorized as the same race group contains different avirulence genes and furthermore, it is presumed that the Korean differential system is difficult to provide useful information for breeding program. For this reason, introduction of differential system using monogenic resistance lines is required in addition to the current system