Applying quantitative real-time PCR and spore trapping techniques for the development of a rice blast monitoring and forecasting model
Date Issued
2014
Date
2014
Author(s)
Huang, Chi-Ming
Abstract
Rice blast, caused by Magnaporthe oryzae, is one of the most devastating diseases of rice worldwide. In Taiwan, despite the attempt of developing rice blast forecasting model(s) in 1970s, nationwide disease monitoring and notification has long been relying on periodic surveys by trained plant protection personnel. The objective of this study is to first develop an approach which allows the collection and quantification of M. oryzae conidia (airborne inoculum) in the field. A modified blast disease forecasting model, using the amount of conidia along with several weather factors (including temperature, humidity, rainfall, etc.) as parameters, will then be established. We have successfully developed a cyclone-based spore trap and a standard sample processing protocol for extracting DNA from collected airspores. Using quantitative real-time PCR (qPCR) technology and a specific primer pair designed based on a Magnaporthe infection structure specific protein (mif23) gene, the amount of M. oryzae conidia can be easily quantified. While detection limit for the SYBR Green qPCR assay can be as low as 4 copy numbers of M. oryzae gDNA, the limit for reliable and accurate quantification is 10 copy numbers. For the TaqMan assay, the limit for reliable and accurate quantification is 4 copy numbers. Aiming to build a forecasting model, airspore samples, weather data, and disease severity ratings have been periodically collected from ten monitoring stations located at the paddy field and upland field blast nurseries at Chiayi Agricultural Experiment Station, a field site at Chiayi Sikou Farm, and seven field sites chosen by seven District Agricultural Research and Extension Stations in Taiwan (missing data exist for some of the monitoring stations). With our newly-developed spore trap and qPCR technique, M. oryzae spores can be detected before the appearance of leaf blast. It was observed that during the whole season, the amount of spores first increased while the field plants were commonly infected, and it then dropped after the stage of panicle development. In order to improve the handling and storage of airspore samples, we tested the effects of different treatments on the preservation of spore DNA. The optimized way would be: to avoid UV light exposure while sampling, to suspend the sample with CTAB buffer after collection, to store the sample at room temperature or 4℃, and to finish DNA extraction within two weeks. For disease modeling, we developed preliminary rice blast forecasting models for specific rice cultivars (TK9, TN11 and TC192) and multiple cultivars, on the basis of cumulative meteorological data from 1-14 or 7-14 days prior to the prediction day. It was found that the "number of spores" was not considered a significant parameter in most of the models, indicating that weather parameters such as relative humidity and hours of rainfall may be key factors favoring rice blast development. The approaches, sampling ranges and frequencies of the spore trapping and disease ratings may also have some effect on the result. Finally, to make the spore trapping technique applicable for characterization of pathogen physiological races, we developed a high resolution melt (HRM) technique which was proved to be powerful for the detection of the A, D, A+D, and C types of alleles at the pex31 (Avr-pik/kp/km) gene in M. oryzae. The differentiation limit for the HRM analysis is 25 airspores. In the future, with the use of other specific primer pairs, the spore trapping, qPCR, and HRM techniques develop in this study can be widely applied for the monitoring and detection of various airborne diseases. Since the data used for modeling in our study were from the monitoring stations at the Chiayi blast nursery and Sikou Farm, it is important to know that before the forecasting models can be widely applied, more weather data and disease severity data from multiple years, cultivars, and locations are required for model training, validation, and improvement.
Subjects
稻熱病
Magnaporthe oryzae
即時定量聚合酶鏈鎖反應
流行病學
孢子收集器
預測模式
高解析度解離分析
Avirulence基因
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-103-R00633009-1.pdf
Size
23.54 KB
Format
Adobe PDF
Checksum
(MD5):682fe82509379ad2aa55adf5be54c392