跳到主要內容

臺灣博碩士論文加值系統

(44.201.99.222) 您好!臺灣時間:2022/11/30 18:37
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:邱沛甄
研究生(外文):Pei-Chen Chiu
論文名稱:直系同源基因序列分析應用於細菌菌種分類和巨觀基因體學研究
論文名稱(外文):Orthologous sequence analysis for bacterial classification and metagenomics study
指導教授:林奇宏林奇宏引用關係
指導教授(外文):Chi-Hung Lin
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:微生物及免疫學研究所
學門:生命科學學門
學類:微生物學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:43
中文關鍵詞:直系同源核醣體16S RNA單套基因細菌分類序列分析巨觀基因體學
外文關鍵詞:orthologous16S rRNAsingle copy genebacterial classificationsequence analysismetagenomics
相關次數:
  • 被引用被引用:0
  • 點閱點閱:292
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
微生物鑑定的技術至今已發展數百年,提供完整的分類系統做細菌鑑定和病原菌偵測等應用,而直系同源基因,在演化上負責保留一些重要的基因資訊及功能,在序列上具有高保留性,我們可以透過直系同源基因的序列比對,來得知物種間親源遠近,核醣體16S RNA(16S rRNA)基因對於原核細胞轉譯(Translation)功能是必須的,在演化過程變異速度較慢,是目前最廣泛使用於菌種鑑定的直系同源基因,然而在許多研究中發現到,核醣體16S RNA在大部分的菌種中都是以多套基因組(Multiple-copies)方式存在,這會造成我們在定量微生物樣本的困難,而在不同基因拷貝(Gene copy)上序列也不盡相同,不僅如此,因為核醣體16S RNA的序列保留性太高,用來分類鑑定的解析度不足以分辨屬或種以下的層級,而有研究開始找能替代核醣體16S RNA的基因,結果顯示大部分的具有管家基因(Housekeeping gene)特色的標誌基因(Marker gene),雖然多為單套(Single-copy),但序列的變異度過高,不易取代目前核醣體16S RNA基因能廣泛應用的特性。
隨著現今快速發展的定序技術,建構出的資料庫越來越完善,我們能收集到更完整的直系同源基因資訊,從KEGG Orthology資料庫取得的所有同源序列基因組,統計分析出基因覆蓋率(Coverage)高且單套的基因組,篩選出151同源序列基因組透過實驗室軟體(hcMSA)進行多序列比對分析,序列比對分析結果能顯示基因組序列的保留區(Conserved region)和變異區(Variable region),使用者可應用引子(Primer)設計功能透過序列保留區域設計出實驗所需的引子,再利用序列變異區設計出鑑定圖譜,在此篇研究中,我們建立一個直系同源序列比對分析系統(OSVAS),系統提供151個同源序列基因組統計分析數據和序列比對分析結果,引子和鑑定圖譜的設計可依實驗所需進行調整,同源序列比對分析系統提供一個方法能快速的篩選並評估適合的單套直系同源基因,可以用來提升微生物分類鑑定的準確性。
Microbial identification has developed over hundreds years, and established a taxonomy system for bacterial identification and pathogen detection. As development of molecular biology techniques, the diversity of orthologous gene sequences has become an important clue for microbial identification. The universality of the ribosomal RNA (rRNA) genes makes them an ideal target for taxonomy classification and bacterial phylogenetic studies. The 16S rRNA gene has become the most common taxonomic marker and is the gold standard for the current classification of prokaryote. It is standard practice to obtaining a 16S rRNA gene sequence for characterizing novel isolates. However, it is known that 16S rRNA genes present multiple copies in most of the bacterial and archaeal genomes. Some studies have shown that the resolution of 16S rRNA sequence is not enough at the species and/or genus level. The ambiguous association reduces the accuracy of classification by using 16S rRNA sequence only.
With the advancement of next generation sequencing technique, increasing number of fully sequenced genomes create a new opportunity for microbial identification. We collected all available orthologous gene information from KEGG OC (Ortholog clusters) database. All of the orthologous gene clusters were collected and analyzed. The top-ranked 151 orthologous gene clusters were selected with high coverage and single copy as our candidates. The sequences of 151 orthologous gene clusters have been analyzed by lab developed highly conserved multiple sequences alignment (hcMSA). Results of sequence alignment revealed conserved and variable region that will further be used to design board-range primer and identification pattern. In this study, we constructed an orthologous sequence variation analysis system (OSVAS). OSVAS present a software platform to analysis orthologous genes for specific target. Combining top-ranked 151 orthologous gene clusters information and the sequence alignment results with powerful multiple sequence alignment tool. OSVAS provide a way to select and validate the alternative single-copy orthologous genes to improve the accuracy of microbiome population estimation.
Contents

中文摘要 i
Abstract ii
Table of Contents iv
List of Figures vi
List of Tables vii
Abbreviations viii
Introduction 1
Orthologous Sequence diversity are wildly used in microbial classification 1
Microbial Taxonomy based on small subunit ribosomal RNA 2
Alternative orthologous genes for microbial classification 3
Design of orthologous based microbial classification system 4
A global orthologous gene sequence analysis of all bactiera 5
Materials and Methods 7
Materials 7
Methods 8
Instrument 10
System and Software 11
Results 12
Orthologous gene database survey and data collection 12
Frequency and copy number analysis of orthologous genes among 3198 completed sequenced microbial genomes 12
Sequence analysis of the candidate orthologous genes 13
Design universal primer for metagenomics analysis 14
Construction of Orthologous sequence variation analysis system 14
Application on clinical diagnosis 15
Discussion 18
References 32
Appendices 35

List of Figures

Figure 1 Sequence alignment result which record relative difference of each test sequence were packed into freq. file. 19
Figure 2 Primer design interface of OSAnalyzer 20
Figure 3 Sequence alignment results compared between COG and KEGG OC database within the same gene cluster 21
Figure 4 Frequency and copy number distribution of each KO IDs.. 22
Figure 5 Comparison of Sequence alignment results between top-ranked KO IDs K02933 and 16S rRNA gene were shown in bar chart. 22
Figure 6 Universal primer design and validation of K02887 23
Figure 7 Workflow of Orthologous Sequence Variation Analysis System (OSVAS) 24
Figure 8 In-silico primer design for common septic bacteria 25
Figure 9 PCR amplify lab stocked 19 isolates with designed primer F438453(+) and R688711(-). 26

List of Tables

Table 1 Top 10 KO IDs ranking by the frequency from high to low. 28
Table 2 Functional category of highly multiple-copies genes 29
Table 3 18 most common nosocomial pathogens (bacteria) 30
Table 4 19 lab stocked common bacteria list with 9 gram negative bacteria isolates and 10 gram positive bacteria isolates 30
Table 5 Designed primer sequence of K02886 within 266 fetching sequences 31
Supplementary table 1 Top-ranked 151 KO IDs with over 90% frequency and single-copy count 41
Supplementary table 2 Total 266 KEGG organism code according to 18 species 43
1. Jensen, R. A. Orthologs and paralogs - we need to get it right. Genome Biology 2, interactions1002 1001-1003 (2001).
2. Gerlt, J. A. &; Babbitt, P. C. Can sequence determine function? Genome Biology 1, REVIEWS0005, doi:10.1186/gb-2000-1-5-reviews0005 (2000).
3. Gabaldon, T. &; Koonin, E. V. Functional and evolutionary implications of gene orthology. Nature reviews. Genetics 14, 360-366, doi:10.1038/nrg3456 (2013).
4. Fitch, W. M. Homology a personal view on some of the problems. Trends in genetics : TIG 16, 227-231 (2000).
5. Tatusov, R. L., Koonin, E. V. &; Lipman, D. J. A genomic perspective on protein families. Science (New York, N.Y.) 278, 631-637 (1997).
6. Zuckerkandl, E. &; Pauling, L. Molecules as documents of evolutionary history. Journal of theoretical biology 8, 357-366 (1965).
7. Relman, D. A. Detection and identification of previously unrecognized microbial pathogens. Emerging infectious diseases 4, 382-389, doi:10.3201/eid0403.980310 (1998).
8. Woese, C. R., Kandler, O. &; Wheelis, M. L. Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya. Proceedings of the National Academy of Sciences of the United States of America 87, 4576-4579 (1990).
9. Woese, C. R. &; Fox, G. E. Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proceedings of the National Academy of Sciences of the United States of America 74, 5088-5090 (1977).
10. Acinas, S., Marcelino, L., Klepac-Ceraj, V. &; Polz, M. Divergence and redundancy of 16S rRNA sequences in genomes with multiple rrn operons. Journal of Bacteriology 186, 2629 - 2635 (2004).
11. Gray, M. W., Burger, G. &; Lang, B. F. Mitochondrial evolution. Science (New York, N.Y.) 283, 1476-1481 (1999).
12. 12 Woese, C. R. Bacterial evolution. Microbiological Reviews 51, 221-271 (1987).
13. Daubin, V., Moran, N. A. &; Ochman, H. Phylogenetics and the cohesion of bacterial genomes. Science (New York, N.Y.) 301, 829-832, doi:10.1126/science.1086568 (2003).
14. Pereira, F. et al. Identification of species by multiplex analysis of variable-length sequences. Nucleic Acids Research 38, e203 (2010).
15. Kolbert, C. P. &; Persing, D. H. Ribosomal DNA sequencing as a tool for identification of bacterial pathogens. Current opinion in microbiology 2, 299-305, doi:10.1016/s1369-5274(99)80052-6 (1999).
16. Acinas, S. G., Marcelino, L. A., Klepac-Ceraj, V. &; Polz, M. F. Divergence and Redundancy of 16S rRNA Sequences in Genomes with Multiple rrn Operons. Journal of Bacteriology 186, 2629-2635 (2004).
17. James, G. in PCR for Clinical Microbiology (eds Margret Schuller et al.) Ch. 28, 209-214 (Springer Netherlands, 2010).
18. Clarridge, J. E. Impact of 16S rRNA Gene Sequence Analysis for Identification of Bacteria on Clinical Microbiology and Infectious Diseases. Clinical Microbiology Reviews 17, 840-862 (2004).
19. Schmidt, T. in Bacterial Genomes (eds FransJ de Bruijn, JamesR Lupski, &; GeorgeM Weinstock) Ch. 21, 221-229 (Springer US, 1998).
20. Klappenbach, J. A., Saxman, P. R., Cole, J. R. &; Schmidt, T. M. rrndb: the Ribosomal RNA Operon. Nucleic Acids Research 29, 181-184 (2001).
21. Frisli, T., Haverkamp, T. H., Jakobsen, K. S., Stenseth, N. C. &; Rudi, K. Estimation of metagenome size and structure in an experimental soil microbiota from low coverage next-generation sequence data. Journal of applied microbiology 114, 141-151, doi:10.1111/jam.12035 (2013).
22. Conville, P. S. &; Witebsky, F. G. Analysis of multiple differing copies of the 16S rRNA gene in five clinical isolates and three type strains of Nocardia species and implications for species assignment. Journal of Clinical Microbiology 45, 1146-1151, doi:10.1128/jcm.02482-06 (2007).
23. 23 STACKEBRANDT, E. &; GOEBEL, B. M. Taxonomic Note: A Place for DNA-DNA Reassociation and 16S rRNA Sequence Analysis in the Present Species Definition in Bacteriology. International Journal of Systematic and Evolutionary Microbiology 44, 846-849, doi:doi:10.1099/00207713-44-4-846 (1994).
24. Janda, J. M. &; Abbott, S. L. 16S rRNA Gene Sequencing for Bacterial Identification in the Diagnostic Laboratory: Pluses, Perils, and Pitfalls. Journal of Clinical Microbiology 45, 2761-2764 (2007).
25. Fox, G. E., Wisotzkey, J. D. &; Jurtshuk, P., Jr. How close is close: 16S rRNA sequence identity may not be sufficient to guarantee species identity. International journal of systematic bacteriology 42, 166-170 (1992).
26. Achenbach, L. A., Carey, J. &; Madigan, M. T. Photosynthetic and phylogenetic primers for detection of anoxygenic phototrophs in natural environments. Applied and Environmental Microbiology 67, 2922-2926, doi:10.1128/aem.67.7.2922-2926.2001 (2001).
27. Walsh, D. A., Bapteste, E., Kamekura, M. &; Doolittle, W. F. Evolution of the RNA polymerase B' subunit gene (rpoB') in Halobacteriales: a complementary molecular marker to the SSU rRNA gene. Molecular biology and evolution 21, 2340-2351, doi:10.1093/molbev/msh248 (2004).
28. Case, R. J. et al. Use of 16S rRNA and rpoB genes as molecular markers for microbial ecology studies. Applied and Environmental Microbiology 73, 278-288, doi:10.1128/aem.01177-06 (2007).
29. Vos, M., Quince, C., Pijl, A. S., de Hollander, M. &; Kowalchuk, G. A. A Comparison of rpoB and 16S rRNA as Markers in Pyrosequencing Studies of Bacterial Diversity. PloS one 7 (2012).
30. Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266-267 (2010).
31. Nakaya, A. et al. KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters. Nucleic Acids Research 41, D353-357 (2013).
32. Ide, S., Miyazaki, T., Maki, H. &; Kobayashi, T. Abundance of Ribosomal RNA Gene Copies Maintains Genome Integrity. Science (New York, N.Y.) 327, 693-696, doi:10.1126/science.1179044 (2010).
33. Rastogi, R., Wu, M., DasGupta, I. &; Fox, G. Visualization of ribosomal RNA operon copy number distribution. BMC Microbiology 9, 208 (2009).
34. Withers, M., Wernisch, L. &; dos Reis, M. Archaeology and evolution of transfer RNA genes in the Escherichia coli genome. RNA (New York, N.Y.) 12, 933-942, doi:10.1261/rna.2272306 (2006).
35. Davidson, A. L., Laghaeian, S. S. &; Mannering, D. E. The maltose transport system of Escherichia coli displays positive cooperativity in ATP hydrolysis. The Journal of biological chemistry 271, 4858-4863 (1996).
36. Cole, J. R. et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Research 42, D633-642, doi:10.1093/nar/gkt1244 (2014).
37. Regueiro, B. J. et al. Automated Extraction Improves Multiplex Molecular Detection of Infection in Septic Patients. PloS one 5 (2010).
38. Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. &; Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Research 43, D593-598, doi:10.1093/nar/gku1201 (2015).
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top