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研究生:吳睿穎
研究生(外文):Rayean Wu
論文名稱:比較Schwarzengrund 血清型沙門氏桿菌CRISPR、全基因體定序與脈衝式電泳、MLST分子分型方法
論文名稱(外文):Evaluation of the Genotyping methods, PFGE, MLST, CRISPR, and Whole Genome Sequence Analysis for Salmonella enterica Serovar Schwarzengrund
指導教授:周崇熙
口試委員:蔡向榮張紹光宣詩玲陳正文
口試日期:2017-01-09
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:獸醫學研究所
學門:獸醫學門
學類:獸醫學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:62
中文關鍵詞:Schwarzengrund血清型沙門氏菌基因分型鑑別力全基因體序列分析
外文關鍵詞:Salmonella Schwarzengrundgenotypingdiscrimination powerwhole genome sequence analysis
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沙門氏菌是造成多起食物中毒事件的革蘭氏陰性菌。台灣近幾年研究家禽肉品發現在市售的肉雞有超過五成分離到的沙門氏菌都隸屬於血清型Schwarzengrund。而在人類臨床案例,在2004-2012年期間S. Schwarzengrund為排名第12之常見血清型。本研究收集了2000-2012期間分離的15株來自雞、鴨、豬、寵物飼料與野鳥等流行病學不相關的菌株,外加一株來自同一次爆發病例的菌株共16株,想評估傳統基因分型工具脈衝式電泳(PFGE)與多基因座序列分析(MLST)的分型能力,但發現鑑定效果均不足。PFGE方面,在使用沙門氏菌最常用的限制酶XbaI,其D-value值僅0.79。而MLST僅產生一種Sequence Type 96 (ST96),則完全無法辨別細菌。因此尋求其他基因分型方法並與傳統分型方式比較。其中CRISPR分析,其鑑定能力優於MLST和使用XbaI-PFGE,能產生8型CRISPR type,其D-value值為0.87,雖不足以單獨運用但推論可以與PFGE併用增加分型能力。而全基因體序列(WGS)分析,可再進一步依據分析策略分為MUMi、ND與SNV親緣關係樹等三種不同分析方式。而SNV、ND、MUMi個別D-value值為0.90、0.94與0.79。三種方法中僅SNV與ND的分析能力高於標準的0.90。MUMi對於核酸序列的差異最為敏感但鑑別力不足,因此,本研究中推論其不適合作為血清型內的分型工具。結論上,目前基因定序工具越來越優化,但不同親緣性分析方法的選擇,將對於親緣關係的決定產生影響。本研究發現CRISPR、SNV與ND分析都可以提供優於XbaI-PFGE的鑑別力,其中以ND分析效果最好。
Salmonella infections are a public health concern. Several species of animals can potentially transmit these pathogens to humans and molecular typing is a useful tool in epidemiological investigation. To evaluate a plausible genotyping method for Salmonella Schwarzengrund, one of the prevalent serotypes in Taiwan, 16 strains with 15 of them being epidemiologically unrelated were genotyped using different methods. Conventional typing methods (XbaI-PFGE and MLST) were found to be inappropriate, with discrimination indexes of 0.79 and 0, respectively. Only PFGE with combined results of multiple restriction enzymes (AvrII + SfiI) can all unrelated strains be differentiated. For alternative typing schemes, clustered regularly interspaced palindromic repeats analysis generated eight types for the 15 strains, with a discrimination index of 0.87, which coordinated well with the XbaI-PFGE phylogenic results and increased the discrimination power. For the whole genome sequence-based analysis, the discrimination indexes of the three approaches utilized (a single nucleotide polymorphism tree, a nucleotide difference tree, and a maximum unique matches index tree) were 0.90, 0.93, and 0.79, respectively. Only the single nucleotide polymorphism tree and the nucleotide difference tree analyses provided sufficient discrimination power (discrimination index>0.90). The maximum unique matches index tree obtained a lower discrimination index value, even though there is no requirement for a reference genome. In conclusion, the clustered regularly interspaced palindromic repeats analysis, the single nucleotide polymorphism tree, and the nucleotide difference tree all performed better than conventional methods, with the nucleotide difference tree being the best approach for S. Schwarzengrund phylogenic analysis.
論文口試審定書 I
致謝 II
中文摘要 III
Abstract IV
Abbreviation List VI
Contents VII
Table List IX
Figure List X
Chapter 1. Introduction 1
Chapter 2. Literature Review 3
2.1. Salmonella Serotype Schwarzengrund 3
2.1.1. Salmonella 3
2.1.2. Salmonella Schwarzengrund 4
2.2. Conventional Typing (PFGE and MLST) 6
2.2.1. Pulsed-field gel electrophoresis 6
2.2.2. Multilocus sequence typing 8
2.3. CRISPR 10
2.4. WGS 13
2.4.1. From Sanger to high-throughput Sequencing 13
2.4.2. Genome Phylogenic Analysis Approaches 16
2.4.3. In silico Analysis 18
2.5. Discriminatory power of genotyping methods 19
Chapter 3. Materials and Methods 20
3.1. Strains Identification 20
3.2. DNA Extraction 22
3.3. Pulse Field Gel Electrophoresis 23
3.4. CRISPR 24
3.5. Whole Genome Sequence 25
Chapter 4. Results 28
4.1. Conventional Typing: PFGE and MLST 28
4.2. CRISPR 29
4.3. Whole Genome Sequence 30
4.3.1. SNV tree 30
4.3.2. Nucleotide difference tree 31
4.3.3. MUMi tree 31
4.3.4. In silico MLST and antimicrobial resistance gene detection 32
Chapter 5. Discussion 33
5.1. Conventional typing 33
5.2. CRISPR 35
5.3. WGS phylogenic analysis 37
5.4. In silico analysis 38
Chapter 6. Conclusions 39
References 40
Tables 46
Figures 56
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