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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 鍾嘉綾(Chia-Lin Chung) | |
dc.contributor.author | Chi-Ming Huang | en |
dc.contributor.author | 黃啓銘 | zh_TW |
dc.date.accessioned | 2021-06-16T08:11:10Z | - |
dc.date.available | 2024-07-30 | |
dc.date.copyright | 2014-07-22 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-02-25 | |
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S., Atkins, S. D., Emberlin, J., and Fitt, B. D. L. 2008. PCR to predict risk of airborne disease. Trends in Microbiology 16(8):380-387. 127. Wong, M.-H., Henderson, J., and Drenth, A. 2013. Identification and differentiation of Phyllosticta species causing freckle disease of banana using high resolution melting (HRM) analysis. Plant Pathology 62(1285-1293). 128. Wu, L., Damicone, J. P., Duthie, J. A., and Melouk, H. A. 1999. Effects of temperature and wetness duration on infection of peanut cultivars by Cercospora arachidicola. Phytopathology 89:653-659. 129. Yang, Y. Z., Cin, C. S., and Chang, D. C. 1982. Field studies on the epidemiology of rice blast in Taichung area. Bulletin of Taichung District Agricultural Improvement Station 6:28-40. 130. Yoshida, K., Saitoh, H., Fujisawa, S., Kanzaki, H., Matsumura, H., Tosa, Y., Chuma, I., Takano, Y., Win, J., Kamoun, S., and Terauchi, R. 2009. Association genetics reveals three novel avirulence genes from the rice blast fungal pathogen Magnaporthe oryzae. The Plant Cell 21(5):1573-1591. 131. Yoshino, R. 1971. Ecological studies on the infection in rice blast epidemics. I. Infection rates and hyphal growth in epidermal cells. Proc. Assoc. Plant Prot. Hokuriku 19:14-17. 132. Zhang, N., Zhao, S., and Shen, Q. 2011. A six-gene phylogeny reveals the evolution of mode of infection in the rice blast fungus and allied species. Mycologia 103(6):1267-1276. 133. Zhou, G., Whong, W. Z., Ong, T., and Chen, B. 2000. Development of a fungus-specific PCR assay for detecting low-level fungi in an indoor environment. Molecular and Cellular Probes 14(6):339-348. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58316 | - |
dc.description.abstract | 由病原真菌Magnaporthe oryzae所造成之稻熱病,為水稻在全球各產區的最重要的病害之一。臺灣於1970年代已有稻熱病預測模式之相關研究,但目前稻熱病之監測預警仍仰賴植保人員按時赴田間視察發病狀況,再依結果發布警報通知農民加強防治。本研究著眼於開發不同於傳統目視調查法之新監測技術,透過發展便於採集田間稻熱病菌Magnaporthe oryzae分生孢子與定量之技術,配合定期收集田間發病情形及氣象因子建立稻熱病預測模式。本研究已完成旋風式孢子收集器、空飄孢子核酸萃取技術的開發,並以稻熱病菌Magnaporthe infection structure specific protein (mif23) gene為模版,設計出適用於即時定量PCR (quantitative real-time PCR, qPCR) 且能專一性增幅稻熱病菌核酸之引子對。本研究開發之SYBR Green qPCR定量法,最低可偵測到4 copy numbers之稻熱病菌DNA,但能精確及穩定偵測的極限則約10 copy numbers;若以TaqMan系統進行定量,其精確偵測之極限約為4 copy numbers。為發展預測系統,本研究自2012年第一期作起,於農試所嘉義分所、嘉義溪口農場、以及全臺共七個農業改良場選定之田區等處設置監測點,收集包含每日田間稻熱病菌孢子、每日氣象數據與每週發病程度等資料 (部分監測點並未收集到完整之資料)。初步分析發現,本技術確實可在田間發現病徵前偵測到孢子,且田間孢子數量在發病度提升時大幅增加,至抽穗期間則下降。另一方面,本研究同時模擬孢子實際收集狀況,期望了解最適合保存樣本之方式,結果發現樣本應避免UVB照射,收取後先以CTAB緩衝液懸浮,再保存於室溫或4℃冷藏,但應儘速在兩週內完成核酸萃取,以避免樣本降解。接著以2012-2013年農試所嘉義分所、嘉義溪口農場監測點之資料,配合預測日前1-14天或7-14天之累積氣象資料,初步建立預測能力良好之模式,包含可針對臺稉9號、臺南11號或臺中192號等單一品種進行預測之模式,以及可運用於多種品種之模式。模式建立過程中發現,孢子數並未如預期為一顯著因子,其原因除了氣象因子 (如相對濕度及降雨時數等) 對於稻熱病發展趨勢之影響力可能較大之外,推測亦與孢子收集及病害調查所採取之方式、範圍、頻率等有關。最後,為能將空飄孢子收集技術擴大運用於病原菌生理小種之判別,針對稻熱病菌pex31 (Avr-Pik/kp/km) 基因,開發出能鑑別A、D、A+D及C type等不同型態對偶基因之高解析度解離分析 (High Resolution Melt, HRM) 新技術,其正確鑑別之極限約為25顆空飄孢子。本研究所開發之空飄孢子收集、定量及Avirulence基因型判別技術,未來若配合適當之引子對,將可應用於其他病害之偵測或監測。初步建立之稻熱病監測預測模式,則仍須透過多年度、不同地點之氣象及發病度等資料之持續累積與驗證,才能改進其預測能力並使之逐步符合病害預警之實務需求。 | zh_TW |
dc.description.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. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T08:11:10Z (GMT). No. of bitstreams: 1 ntu-103-R00633009-1.pdf: 5485282 bytes, checksum: 4988738cdfe4411c2f0d0792e9d5f6be (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 口試委員會審定書.…………...……………......................................................……..#
誌謝 I 摘要 III ABSTRACT V 目錄 VII 表目錄 IX 圖目錄 XI 補充表目錄 XIV 補充圖目錄 XV 第 1 章 研究動機 1 第 2 章 文獻回顧 2 2.1 水稻稻熱病 2 2.2 水稻稻熱病菌之生活史 4 2.2.1 產孢 4 2.2.2 孢子釋放與散佈 5 2.2.3 孢子發芽與壓器 (appressorium) 產生 5 2.2.4 病原菌感染、纏據 (colonization) 以及病斑產生 6 2.2.5 小結 6 2.3 空飄粒子採樣技術 8 2.3.1 被動採樣 8 2.3.2 主動採樣 – Inertial sampler 8 2.3.3 主動採樣 – Non-inertial sampler 10 2.4 空飄孢子qPCR定量 11 2.5 稻熱病之監測與預測模式 13 2.5.1 臺灣 13 2.5.2 國外地區 13 2.5.3 小結 18 2.6 稻熱病菌之病原多型性avr gene 19 第 3 章 材料與方法 21 3.1 培養基製備 21 3.2 真菌孢子之核酸萃取 21 3.3 真菌菌絲核酸萃取 22 3.4 稻熱病菌qPCR專一性引子對設計 22 3.5 稻熱病菌qPCR定量技術 24 3.6 田間空飄孢子採樣技術之建立 25 3.7 監測點之佈建及定期採樣 27 3.8 樣本保存法測試 28 3.9 稻熱病預測模式建立與印證 29 3.10 應用孢子收集與高解析度解離分析 (High Resolution Melt, HRM) 技術判別田間稻熱病菌之生理小種 30 第 4 章 結果 32 4.1 以稻熱病菌mif23基因開發SYBR Green及TaqMan qPCR技術 32 4.2 田間稻熱病菌孢子量及葉稻熱病發病程度之動態變化 33 4.3 空飄孢子樣本之保存方法建立 34 4.4 以不同形態之資料建立稻熱病預測模式 36 4.4.1 比較以不同累計天數氣象資料所建立之模式 36 4.4.2 將發病級數轉換為百分比之模式 36 4.4.3 比較以不同氣象資料所建立之模式 37 4.4.4 比較以不同田區資料所建立之模式 38 4.4.5 同時預測多種水稻品種之模式建立 39 4.4.6 小結 40 4.5 以HRM技術分辨田間稻熱病菌之pex31 (Avr-Pik/kp/km) 對偶基因型 40 第 5 章 討論 41 5.1 新開發之孢子收集器之效能 41 5.2 空飄孢子樣本之最佳保存方式 41 5.3 新開發之稻熱病菌qPCR定量法之優勢 43 5.4 田間稻熱病菌孢子量消長之可能原因 44 5.5 稻熱病預測模式之建立 45 5.6 新開發之HRM技術對pex31 (Avr-Pik/kp/km) 不同對偶基因型之鑑別力 49 5.7 孢子收集技術應用於稻熱病監測防治之未來展望 51 參考文獻 52 Tables 68 Figures 98 Supplemental Table 127 Supplemental Figures 129 | |
dc.language.iso | zh-TW | |
dc.title | 結合即時定量PCR及孢子收集技術發展稻熱病監測預測模式 | zh_TW |
dc.title | Applying quantitative real-time PCR and spore trapping techniques for the development of a rice blast monitoring and forecasting model | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張瑞璋,沈偉強,鄧文玲,蔣國司,劉力瑜 | |
dc.subject.keyword | 稻熱病,Magnaporthe oryzae,即時定量聚合?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, | zh_TW |
dc.subject.keyword | Rice blast,Magnaporthe oryzae,Quantitative real-time PCR (qPCR),Epidemiology,Spore trap,Forecasting model,High resolution melt (HRM) analysis,Avirulence gene, | en |
dc.relation.page | 131 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2014-02-26 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 植物病理與微生物學研究所 | zh_TW |
顯示於系所單位: | 植物病理與微生物學系 |
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