臺灣博碩士論文加值系統

A型流感病毒的遺傳物質由8段RNA所組成，其中以HA (Hemagglutinin)和NA (Neuraminidase)具有最高的氨基酸多樣性，同時也是宿主所辨認最重要的兩個抗原。目前的流感序列資料庫以8個片段作為第一階的分類依據，形成各自獨立的子資料庫系統，例如HA資料庫以及NA資料庫，彼此之間無法連動，也因此沒有跨片段的演化分析工具。從基因體的角度出發，我們認為新一代的流感病毒資料庫應該是以各個病毒名稱做為分類依據，將8個病毒蛋白質片段依序排列在名稱之後，因此著手開發自動化整理序列的工具，重新架構整個流感病毒資料庫結構。首先從資料庫下載各片段的序列資料，並且將這些片段序列快速且精準地排列成矩陣，接著利用我們所開發的軟體檢驗及轉換其矩陣格式，依照病毒株名稱將各個片段的矩陣依序排列串聯在一起，彙整到我們基於Microsoft® Office Excel®所開發工具中，並得以利用Excel®的試算功能進行全基因且多面向的分析。接著以NA上的第276個胺基酸位置為例，依照過往的文獻在這個點位上分為對克流感敏感的H (Histidin)型以及抗藥的Y (Tyrosin)型，系統可以自動比對出在整體4428個胺基酸點位中，兩型之間有481個位置的consensus是不同的，而在這些不同的點位中，再分別藉由自動繪圖排序點位與各型的相關性，得到分布在各個片段上共36個與抗藥性最相關的點位。然而相較於以往回朔型資料分析僅能推論相關性，我們所開發的系統可以依照序列的年份進行時間軸分析，在36個點位中更進一步找出22個點位，這些點位大多出現在NA或HA上，並且他們的時間軸分布數據顯示在歷史上他們比抗藥性NA_276Y大爆發的2007~2008年更早出現，之後也偕同隨著NA_276Y爆發與消失，因此這22個點位很可能是造成NA_276Y爆發的前期突變點位 (permissive mutations)，而不是隨著NA_276Y爆發而產生的對應突變位置。我們完成了一系列自動快速整理病毒序列且能夠針對於多個片段之間進行交互分析比對的工具，進而探討其病毒抗原變異及影響宿主免疫上的意義，希望可以透過此新的分析方式突破過去無法針對片段間演化分析之窠臼，為流感病毒與宿主間之免疫研究提供嶄新的思維與面向。

The entire genome of influenza A virus is composed of eight RNA segments which are separately recognized as different sequences with individual identification numbers on database. Current studies are therefore limited by such database architecture which allows mutation or polymorphism identification within only one segment at a time with no further extension potential to inter-segment evolution tracking. In order to perform whole genome analysis, we re-organized existing Influenza database by align all the protein sequences under the name of viral strain and consider the entire coding as a single item in the system. Consequently, virus can be grouped at given position of amino acid character and the rest proteome are also sorted into the same group so that further comprehensive analyses on other segments can be achieved. Moreover, multi-layer sub-grouping including date and area of isolation and host species can be performed as well in our Excel-based system. We have also developed an automated analysis algorism to quickly identify every polymorphic position on entire genome and map the individual correlation almost instantly on dynamic graph, with a time-lapse analysis capability to further dissect simple correlation or potential cause and effect relationships between the two variables. To the best of our knowledge, this is the first tool that allows genome-wide, cross-segment, time-lapse analysis tool to identify potential permissive mutations that confers Influenza Oseltamivir resistance which potentially will facilitate the investigation of novel hypothesis systematically and efficiently.

口試委員會審定書 i
碩士論文考試審定書 ii
碩士論文授權書 iii
誌謝 iv
中文摘要 v
Abstract vi
第一章　緒論 1
1.1 H1N1背景知識 1
1.2 傳統病毒資料庫建置方式 3
1.3 Oseltamivir及抗藥性H1N1背景知識 4
1.4 Oseltamivir抗藥性H1N1 permissive mutations過去研究 5
第二章　研究目標 7
2.1 長期目標 7
2.2 確切目標 7
第三章　研究材料與方法 8
3.1 硬體設備 8
3.2 軟體與系統之開發建置 8
3.2.1 使用之軟體架構 8
3.2.2 開發使用之程式語言 9
3.2.3 本篇所開發之系統 10
3.3 資料收集與整理 11
3.4 序列multiple alignment和轉換 12
3.5 建立以序列命名資料庫及cross-segment arrangement 13
3.6 篩選分群與計算consensus和統計各胺基酸型筆數及比例 14
3.7 時間軸分析並統計任意兩點位共出現比例及筆數 14
第四章　結果與討論 15
4.1 建立H1N1資料庫並計算NA_276Y consensus點位 15
4.2 NA_276Y consensus點位分群排序 15
4.2.1 傳統NA_276Y分群排序方式 15
4.2.2 各片段差異突變點位分群以觀察NA_276Y排序方式 17
4.3 統計與NA_276Y相關演化點位於各年代分布並尋找permissive mutations 17
4.3.1 統計與NA_276Y相關演化點位 18
4.3.2 尋找與NA_276Y共演化點位及permissive mutations點位 19
參考文獻 21
圖 1 系統開發建置流程圖 25
圖 2 流感病毒序列不同ORF混和技術圖 26
圖 3 過去傳統建立資料庫方法 27
圖 4 以病毒標準命名名稱建立資料庫方法 28
圖 5 系統cross-segment arrangement排列畫面 30
圖 6 透過模擬矩陣來驗證系統匯入資料正確性畫面 31
圖 7 資料可於系統中任意篩選、交集、排序和計算 32
圖 8 系統中可統計分群後胺基酸筆數及比例分布 33
圖 9 傳統排序方式下NA_276Y子群突變點位consensus出現機率值 34
圖 10 傳統排序NA_276Y子群consensus出現機率值(以NA_234M為閥值) 36
圖 11 各片段差異突變點位分群以觀察NA_276Y排序之出現機率值 37
圖 12 各片段差異突變點位分群以觀察NA_276Y排序出現機率值(234M為閥值) 42
圖 13 NA_276Y相關演化點位胺基酸型分布(1995~2015年) 43
圖 14 NA_276Y相關演化點位出現機率值累積風花圖(2006~2009年) 46
圖 15 NA_276Y相關共演化點位與交集Strain筆數統計圖 47
圖 16 permissive NA_276Y mutations點位與交集Strain筆數統計圖 48
圖 17 NA_276Y相關共演化點位 50
圖 18 permissive NA_276Y mutations點位 51
表 1 NA_276Y及H兩子群各點位consensus差異及所占點位比例及筆數 52
表 2 結合時間軸分析工具尋找相關permissive NA_276Y mutations點位 53

1.Key Facts about Influenza (Flu). Available from: http://www.cdc.gov/flu/keyfacts.htm.
2.A revision of the system of nomenclature for influenza viruses: a WHO Memorandum. Bulletin of the World Health Organization, 1980. 58(4): p. 585-591.
3.Hause, B.M., et al., Characterization of a novel influenza virus in cattle and Swine: proposal for a new genus in the Orthomyxoviridae family. MBio, 2014. 5(2): p. e00031-14.
4.Bouvier, N.M. and P. Palese, THE BIOLOGY OF INFLUENZA VIRUSES. Vaccine, 2008. 26(Suppl 4): p. D49-D53.
5.Wagner, R., M. Matrosovich, and H.D. Klenk, Functional balance between haemagglutinin and neuraminidase in influenza virus infections. Rev Med Virol, 2002. 12(3): p. 159-66.
6.Tong, S., et al., A distinct lineage of influenza A virus from bats. Proc Natl Acad Sci U S A, 2012. 109(11): p. 4269-74.
7.Tong, S., et al., New World Bats Harbor Diverse Influenza A Viruses. PLoS Pathogens, 2013. 9(10): p. e1003657.
8.THE VIRUS OF INFLUENZA. The Lancet, 1933. 222(5732): p. 83.
9.Smith, W., C.H. Andrewes, and P.P. Laidlaw, Originally published as Volume 2, Issue 5732A VIRUS OBTAINED FROM INFLUENZA PATIENTS. The Lancet, 1933. 222(5732): p. 66-68.
10.Jeffery, T. and M.M. David, 1918 Influenza: the Mother of All Pandemics. Emerging Infectious Disease journal, 2006. 12(1): p. 15.
11.Kobasa, D., et al., Aberrant innate immune response in lethal infection of macaques with the 1918 influenza virus. Nature, 2007. 445(7125): p. 319-23.
12.Belshe, R.B., The Origins of Pandemic Influenza — Lessons from the 1918 Virus. New England Journal of Medicine, 2005. 353(21): p. 2209-2211.
13.McCullers, J.A., et al., Recipients of vaccine against the 1976 "swine flu" have enhanced neutralization responses to the 2009 novel H1N1 influenza virus. Clin Infect Dis, 2010. 50(11): p. 1487-92.
14.Hurt, A.C., et al., Oseltamivir resistance and the H274Y neuraminidase mutation in seasonal, pandemic and highly pathogenic influenza viruses. Drugs, 2009. 69(18): p. 2523-31.
15.Bloom, J.D., L.I. Gong, and D. Baltimore, Permissive secondary mutations enable the evolution of influenza oseltamivir resistance. Science, 2010. 328(5983): p. 1272-5.
16.Dawood, F.S., et al., Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: a modelling study. The Lancet Infectious Diseases, 2012. 12(9): p. 687-695.
17.Moore, C., et al., Evidence of person-to-person transmission of oseltamivir-resistant pandemic influenza A(H1N1) 2009 virus in a hematology unit. J Infect Dis, 2011. 203(1): p. 18-24.
18.Okomo-Adhiambo, M., et al., Oseltamivir-resistant influenza A(H1N1)pdm09 viruses, United States, 2013-14. Emerg Infect Dis, 2015. 21(1): p. 136-41.
19.Igarashi, M., et al., Predicting the antigenic structure of the pandemic (H1N1) 2009 influenza virus hemagglutinin. PLoS One, 2010. 5(1): p. e8553.
20.Yang, H., et al., Structural stability of influenza A(H1N1)pdm09 virus hemagglutinins. J Virol, 2014. 88(9): p. 4828-38.
21.Brunak, S., et al., Nucleotide Sequence Database Policies. Science, 2002. 298(5597): p. 1333-1333.
22.National Center for Biotechnology Information (NCBI). Available from: http://www.ncbi.nlm.nih.gov/.
23.EMBL-European Bioinformatics Institute (EMBL-EBI). Available from: http://www.ebi.ac.uk/.
24.DNA Data Bank of Japan (DDBJ). Available from: http://www.ddbj.nig.ac.jp/.
25.NCBI Influenza Virus Resource. Available from: http://www.ncbi.nlm.nih.gov/genomes/FLU/FLU.html.
26.Bao, Y., et al., The influenza virus resource at the National Center for Biotechnology Information. J Virol, 2008. 82(2): p. 596-601.
27.Influenza Research Database. Available from: http://www.fludb.org/brc/home.spg?decorator=influenza.
28.Tamiflu via Diels-Alder, Roche, Editor. 2006.
29.Rungrotmongkol, T., et al., Design of oseltamivir analogs inhibiting neuraminidase of avian influenza virus H5N1. Antiviral Res, 2009. 82(1): p. 51-8.
30.Samji, T., Influenza A: Understanding the Viral Life Cycle. The Yale Journal of Biology and Medicine, 2009. 82(4): p. 153-159.
31.Shi, Y., et al., Enabling the 'host jump': structural determinants of receptor-binding specificity in influenza A viruses. Nat Rev Microbiol, 2014. 12(12): p. 822-31.
32.Webster, D., et al., Oseltamivir-resistant pandemic H1N1 influenza. CMAJ : Canadian Medical Association Journal, 2011. 183(7): p. E420-E422.
33.de Jong , M.D., et al., Oseltamivir Resistance during Treatment of Influenza A (H5N1) Infection. New England Journal of Medicine, 2005. 353(25): p. 2667-2672.
34.Baz, M., et al., Effect of the neuraminidase mutation H274Y conferring resistance to oseltamivir on the replicative capacity and virulence of old and recent human influenza A(H1N1) viruses. J Infect Dis, 2010. 201(5): p. 740-5.
35.Marjuki, H., et al., Neuraminidase Mutations Conferring Resistance to Oseltamivir in Influenza A(H7N9) Viruses. J Virol, 2015. 89(10): p. 5419-26.
36.Myers, J.L. and S.E. Hensley, Oseltamivir-resistant influenza viruses get by with a little help from permissive mutations. Expert Rev Anti Infect Ther, 2011. 9(4): p. 385-8.
37.Bloom, J.D., J.S. Nayak, and D. Baltimore, A computational-experimental approach identifies mutations that enhance surface expression of an oseltamivir-resistant influenza neuraminidase. PLoS One, 2011. 6(7): p. e22201.
38.Abed, Y., et al., Impact of potential permissive neuraminidase mutations on viral fitness of the H275Y oseltamivir-resistant influenza A(H1N1)pdm09 virus in vitro, in mice and in ferrets. J Virol, 2014. 88(3): p. 1652-8.
39.Butler, J., et al., Estimating the fitness advantage conferred by permissive neuraminidase mutations in recent oseltamivir-resistant A(H1N1)pdm09 influenza viruses. PLoS Pathog, 2014. 10(4): p. e1004065.
40.Behera, A.K., S. Basu, and S.S. Cherian, Molecular mechanism of the enhanced viral fitness contributed by secondary mutations in the hemagglutinin protein of oseltamivir resistant H1N1 influenza viruses: modeling studies of antibody and receptor binding. Gene, 2015. 557(1): p. 19-27.
41.Rice, P., I. Longden, and A. Bleasby, EMBOSS: The European Molecular Biology Open Software Suite. Trends in Genetics, 2000. 16(6): p. 276-277.
42.Katoh, K., et al., MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Research, 2002. 30(14): p. 3059-3066.
43.Katoh, K. and H. Toh, Recent developments in the MAFFT multiple sequence alignment program. Brief Bioinform, 2008. 9(4): p. 286-98.
44.Katoh, K. and M.C. Frith, Adding unaligned sequences into an existing alignment using MAFFT and LAST. Bioinformatics, 2012. 28(23): p. 3144-6.
45.Katoh, K. and D.M. Standley, MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol, 2013. 30(4): p. 772-80.
46.Katoh, K. and H. Toh, PartTree: an algorithm to build an approximate tree from a large number of unaligned sequences. Bioinformatics, 2007. 23(3): p. 372-4.
47.Dubois, J., O. Terrier, and M. Rosa-Calatrava, Influenza viruses and mRNA splicing: doing more with less. MBio, 2014. 5(3): p. e00070-14.
48.Taylor, W.R., Residual colours: a proposal for aminochromography. Protein Eng, 1997. 10(7): p. 743-6.