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Sequence Structure Analysis and Prediction Platform Establishment in Rice Bacterial Blight Resistance Genes
|關鍵字:||Xa gene;水稻白葉枯病抗性基因;phylogenetic;leucine-rich repeat;gene prediction;resistance-gene analogues;親緣關係;多白胺酸重複;基因預測;抗性基因類似物||出版社:||農藝學系所||引用:||Adhikari TB, A Shrestha, RC Basnyat, TW Mew (1999) Use of partial host resistance in the management of bacterial blight of rice. Plant Disease 83:896-901. Altschul SF, TL Madden, AA Schäffer, J Zhang, Z Zhang, W Miller, DJ Lipman (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl. Acids Res. 25:3389-3402. Arumuganathan K, ED Earle (1991) Nuclear DNA content of some important plant species. Plant Molecular Biology Rep. 9:208-218. Ashfield Y, LE Ong, K Nobuta, CM Schneider, RW Innes (2004) Convergent evolution of disease resistance gene specificity in two flowering plant families. Plant Cell 16:309-318. Bai J, LA Pennill, J Ning, SW Lee, J Ramalingam, CR Webb, B Zhao, Q Sun, JC Nelson, JE Leach, SH Hulbert (2003) Diversity in nucleotide binding site-leucine-rich repeat genes in cereals. 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黃單孢桿菌(Xanthomonas oryzae pv. oryzae, Xoo)所引起的白葉枯病，是世界水稻生產中最嚴重的細菌性病害。目前已知32個水稻白葉枯病抗性基因(Xa)，而利用基因圖譜選殖方法已完成定序的基因僅有Xa1、Xa21、Xa26、Xa27等4個。為利於Xa基因的序列結構剖析與預測，本研究利用生物資訊手段研發一套適用於發掘新的Xa基因之分析策略。首先探究這4個基因在不同分子序列型態的親緣關係，而以相鄰結合法(neighbor joining method, NJ)與最大概度法(maximum likelihood method, ML)兩種親緣分析方法所獲結果一致，且其所獲親緣結構較能解釋現有Xa基因之間的關係，即可區分為Xa1、Xa21、Xa26及Xa27等4個親緣群別，且Xa21基因家族可再細分為兩個子群。接著根據Xa基因所具有的多白胺酸重複(leucine-rich repeat, LRR)保守結構特性，在合併所有Xa基因之LRR motif的序列後，利用Clustal W軟體提供之多序列排比方法，建構出一個具有Xa基因專一性且符合植物類LRR motif的Xa模板。進而評估並找出適用於Xa基因且具有高準確度的基因預測軟體，結果以GeneMark.hmm軟體在核苷酸與外顯子兩個層次上的預測準確度最高，同時也利用Bioperl程式模組建置一個基因視覺化分析平台，可用來快速檢視及比較不同軟體之預測效果。最後以Xa模板為查詢序列，到含有水稻基因組序列的NCBI蛋白質資料庫、NCBI水稻資料庫以及下載自TIGR之水稻虛擬分子序列所建立之本地資料庫，以BLAST方法進行相似序列搜尋，結果共搜尋到13條與Xa基因或抗病性有關之抗性基因類似物的蛋白質序列。經兩兩排比結果，這些序列與已知Xa基因之序列的一致性很低，顯示這13條序列極有可能是Xa候選基因或是其相關之蛋白質產物的序列。因此，本研究所建立的分析策略及平台，能有效且快速在不同序列資料庫中偵測到與查詢模板有關的序列，促進水稻抗病基因之發現。
Bacterial blight, caused by Xanthomonas oryzae pv. Oryzae (Xoo), is the most destructive disease of rice worldwide. Currently, there have been 32 genes of rice conferring host resistance against Xoo, but only 4 among them, i.e., Xa1, Xa21, Xa26 and Xa27 have been sequenced via map-based gene cloning. To facilitate the sequence structure analysis and gene prediction for resistance to Xoo in rice, an analytical strategy of finding Xa genes based on bioinformatics was designed and implemented in this study. Firstly, the molecular phylogeny of the sequence patterns of the current 4 Xa genes was analyzed. Phylogenetic analysis using neighbor joining and maximum likelihood methods consistently supported a distinct phylogenetic relationship among the 4 Xa genes, that is, a set of 4 conserved clades was identified. The family of Xa21 genes was further also divided into 2 sub-groups. The second step was to construct a Xa templet based on structurally conserved leucine-rich repeat (LRR) motifs in Xa genes, using multiple sequence alignment of Clustal W for all the LRR sequence fragments of Xa genes. This templet was specific to Xa genes and had the characteristics of plant-LRR motif. The third step was to evaluate and select a gene prediction program that can be applied to find the most probable candidate Xa genes. GeneMark.hmm gave the most accurate in nucleotide and exon prediction of Xa genes. Using Bioperl modules, a gene modeling visualization platform was also designed to enable a clear comparative display of the sequence structure predicted from various prediction programs. Finally search of Xa gene analogues was performed by using our Xa templet as a query to blast the NCBI protein database, NCBI rice database and our local database of rice pseudomolecules from TIGR. A total of 13 protein sequences related to Xa, or resistance-gene analogues was obtained. Pairwise sequence identities between the analogue sequences and the known Xa-gene sequences were low in the alignment. It implies that these analogue sequences might be the most probable candidate Xa genes or the protein product of candidate genes. The results showed that our analytical strategy and platform could practically and rapidly detect the query-templet related sequences from the databases, thus accelerates the gene finding of rice resistant to disease.
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