應用奈米孔定序平台進行蝴蝶蘭褐斑病菌基因體de novo 組裝之研究

Abstract

蝴蝶蘭出口產值達40 億元，是台灣外銷花卉之首；高溫多濕的栽培環境，易發生由Acidovorax avenae subsp. Cattleyae (AAC) 所引起的蝴蝶蘭褐斑病，影響苗株出口造成產業損失。本研究應用奈米孔定序平台對兩個AAC 菌株收集系進行基因體解序，AAC_10001 與AAC_9906R 之長讀序資料量、平均讀序長度、讀序品質分別為1.49 Gbp、18.7 kb、11.9 與1.96 Gbp、17.29 kb、12.3，基因體DNA 具有高度連續性，定序品質佳。對解序資料進行de novo 組裝，AAC_10001 與AAC_9906R 之連續體數量分別為6 個與5 個，最大的連續體長度為5.7 Mb 與5.6 Mb，序列guanine-cytosine (GC) 含量約為67.4% 與67.5%，基因體平均定序深度為230 倍。AAC_10001 與AAC_9906R 基因體結構比對結果顯示，兩者具有12 個主要的局部共線性區塊 (locally collinear blocks; LCB)，其中AAC_10001 菌株LCB 2 有一段132 kb 的基因體島遺傳元件插入，包含一個DNA修復蛋白、一群參與第四型分泌系統的蛋白、一個整合酶與一個轉位酶，可能影響菌株致病性、共生性、代謝特性、環境適應性或對抗生素產生抗性。本研究使用奈米孔定序平台對蝴蝶蘭AAC 菌株進行基因體解序，建立細菌基因體長片段讀序生物資訊分析流程，完成AAC 菌株基因體序列de novo 組裝與初步分析，是國內首例以三代定序技術完成的蝴蝶蘭褐斑病細菌基因體研究，可提供未來準確診斷和長期監測的方向，期望有助於國內蘭園病害防治策略。 In Taiwan, orchids takes the lead in floral exports, with the value reaching NTD 4 billion in 2020. However, the orchid bacterial brown spot disease caused by Acidovorax avenae subsp. cattleyae (AAC) is commonly observed in greenhouses. The combination of warm temperature and high humidity provides the optimum conditions for brown spot disease outbreak, resulting in the low-export quality of orchids. In this study, two AAC strains were sequenced using nanopore-based sequencing technology. Overall, the average read length and read quality of AAC_10001 and AAC_9906R were 1.49 Gb, 18.7 kb, 11.9 and 1.96 Gb, 17.29 kb, 12.3, respectively. The results showed high contiguity of DNA quality. After de novo genome assembly, 6 and 5 contigs were obtained for AAC_10001 and AAC_9906R, in which the largest contig was 5.7 Mb and 5.6 Mb with 67.4% and 67.5% guanine-cytosine (GC) content, and the average sequencing depths for both largest contigs were 230×. Genome structure comparison revealed 12 locally collinear blocks (LCBs) in AAC_10001 and AAC_9906R. We found a putative 132 kb genomic island (GEI) in LCB2 of AAC_10001, harboring a DNA repair protein, a cluster type IV secretory system proteins, an integrase and a transposase. GEI plays a critical role in the bacterial evolution of pathogenicity, symbiosis, metabolism, environmental adaptation, and antibiotics resistance. In this research, we first present a reliable long-read de novo assembly pipeline for AAC genome study by using nanopore sequencing data only. This could provide fundamental knowledge for strain detection, identification or long-term monitoring and give us a perspective disease prevention strategy to adopt.