Korean Journal of Poultry Science (한국가금학회지)

The Korean Society of Poultry Science (한국가금학회)
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Reconstruction of Metabolic Pathway for the Chicken Genome

닭 특이 대사 경로 재확립

  • Kim, Woon-Su (Department of Animal Biosystem Science, Chungnam National University) ;
  • Lee, Se-Young (Department of Animal Biosystem Science, Chungnam National University) ;
  • Park, Hye-Sun (Department of Animal Biosystem Science, Chungnam National University) ;
  • Baik, Woon-Kee (National Science Museum) ;
  • Lee, Jun-Heon (Department of Animal Science and Biotechnology, Chungnam National University) ;
  • Seo, Seong-Won (Department of Animal Biosystem Science, Chungnam National University)
  • 김운수 (충남대학교 동물바이오시스템과학과) ;
  • 이세영 (충남대학교 동물바이오시스템과학과) ;
  • 박혜선 (충남대학교 동물바이오시스템과학과) ;
  • 백운기 (국립중앙과학관) ;
  • 이준헌 (충남대학교 동물자원생명과학과) ;
  • 서성원 (충남대학교 동물바이오시스템과학과)
  • Received : 2010.09.07
  • Accepted : 2010.09.20
  • Published : 2010.09.30
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Abstract

Chicken is an important livestock as a valuable biomedical model as well as food for human, and there is a strong rationale for improving our understanding on metabolism and physiology of this organism. The first draft of chicken genome assembly was released in 2004, which enables elaboration on the linkage between genetic and metabolic traits of chicken. The objectives of this study were thus to reconstruct metabolic pathway of the chicken genome and to construct a chicken specific pathway genome database (PGDB). We developed a comprehensive genome database for chicken by integrating all the known annotations for chicken genes and proteins using a pipeline written in Perl. Based on the comprehensive genome annotations, metabolic pathways of the chicken genome were reconstructed using the PathoLogic algorithm in Pathway Tools software. We identified a total of 212 metabolic pathways, 2,709 enzymes, 71 transporters, 1,698 enzymatic reactions, 8 transport reactions, and 1,360 compounds in the current chicken genome build, Gallus_gallus-2.1. Comparative metabolic analysis with the human, mouse and cattle genomes revealed that core metabolic pathways are highly conserved in the chicken genome. It was indicated the quality of assembly and annotations of the chicken genome need to be improved and more researches are required for improving our understanding on function of genes and metabolic pathways of avian species. We conclude that the chicken PGDB is useful for studies on avian and chicken metabolism and provides a platform for comparative genomic and metabolic analysis of animal biology and biomedicine.

닭의 대사 생리에 대한 연구는 산업적 가치 및 생물학, 의학적으로도 매우 중요하다. 닭의 유전체 염기서열 분석 결과는 2004년에 처음 발표되었고, 이러한 유전체 정보를 바탕으로 유전형과 표현형의 상관관계를 분석하는 연구가 필요하다. 따라서 본 연구는 닭 유전체 정보를 바탕으로 대사 경로를 재확립하고, 닭 특이 대사 경로 유전체 데이터베이스를 구축하였다. 이를 위해 Perl 언어를 기반으로 개발된 자동 파이프라인(pipeline)을 이용하여 여러 생물정보 데이터베이스에 산재해 있는 닭 유전체에 관한 정보를 통합한 닭 특이 통합 데이터베이스를 구축하였다. 또한, 구축된 닭 특이 통합 데이터베이스를기반으로PathoLogic 알고리즘을구현한Pathway Tools 소프트웨어를 이용하여 닭 특이 대사 경로를 재확립하였다. 결과적으로, 닭 유전체 Gallus_gallus-2.1에서 2,709개의 효소, 71개의 운반체(transporter)와 1,698개의 효소 반응, 8개의 운반 반응(transport reaction)이 도출되었다. 이를 통해 총 212개의 대사 경로가 재확립되었고, 1,360개의 화합물(compound)이 닭 특이 대사 데이터베이스에 포함되었다. 다른 종(사람, 생쥐, 소)과의 비교 분석을 통해 중요한 대사 경로가 닭 유전체에 보존되어 있음을 보였다. 또한, 닭 유전체의 assembly와 annotation의 질을 높이는 노력과 닭 및 조류에서 유전자 기능 및 대사 경로에 대한 연구가 필요한 것으로 나타났다. 결론적으로, 본 연구에서 재확립된 닭의 대사 경로 및 데이터베이스는 닭 및 조류의 대사 연구뿐만 아니라 포유동물 및 미생물과의 비교 생물학적 접근을 통한 의학 및 생물학적 연구에 활용될 것으로 기대된다.

Keywords

References

  1. Apweiler R, Martin MJ, O'Donovan C, Magrane M, Alam- Faruque Y, Antunes R, Barrell D, Bely B, Bingley M, Binns D, Bower L, Browne P, Chan WM, Dimmer E, Eberhardt R, Fedotov A, Foulger R, Garavelli J, Huntley R, Jacobsen J, Kleen M, Laiho K, Leinonen R, Legge D, Lin Q, Liu WD, Luo J, Orchard S, Patient S, Poggioli D, Pruess M, Corbett M, di Martino G, Donnelly M, van Rensburg P, Bairoch A, Bougueleret L, Xenarios I, Altairac S, Auchincloss A, Argoud-Puy G, Axelsen K, Baratin D, Blatter MC, Boeckmann B, Bolleman J, Bollondi L, Boutet E, Quintaje SB, Breuza L, Bridge A, deCastro E, Ciapina L, Coral D, Coudert E, Cusin I, Delbard G, Doche M, Dornevil D, Roggli PD, Duvaud S, Estreicher A, Famiglietti L, Feuermann M, Gehant S, Farriol-Mathis N, Ferro S, Gasteiger E, Gateau A, Gerritsen V, Gos A, Gruaz-Gumowski N, Hinz U, Hulo C, Hulo N, James J, Jimenez S, Jungo F, Kappler T, Keller G, Lachaize C, Lane-Guermonprez L, Langendijk-Genevaux P, Lara V, Lemercier P, Lieberherr D, Lima TD, Mangold V, Martin X, Masson P, Moinat M, Morgat A, Mottaz A, Paesano S, Pedruzzi I, Pilbout S, Pillet V, Poux S, Pozzato M, Redaschi N, Rivoire C, Roechert B, Schneider M, Sigrist C, Sonesson K, Staehli S, Stanley E, Stutz A, Sundaram S, Tognolli M, Verbregue L, Veuthey AL, Yip LN, Zuletta L, Wu C, Arighi C, Arminski L, Barker W, Chen CM, Chen YX, Hu ZZ, Huang HZ, Mazumder R, McGarvey P, Natale DA, Nchoutmboube J, Petrova N, Subramanian N, Suzek BE, Ugochukwu U, Vasudevan S, Vinayaka CR, Yeh LS, Zhang J 2010 The Universal Protein resource (UniProt) in 2010. Nucleic Acids Res 38:D142-D148. https://doi.org/10.1093/nar/gkp846
  2. Caspi R, Foerster H, Fulcher CA, Hopkinson R, Ingraham J, Kaipa P, Krummenacker M, Paley S, Pick J, Rhee SY, Tissier C, Zhang P, Karp PD 2006 MetaCyc: A multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res 34:D511-D516. https://doi.org/10.1093/nar/gkj128
  3. Choi C, Munch R, Leupold S, Klein J, Siegel I, Thielen B, Benkert B, Kucklick M, Schobert M, Barthelmes J, Ebeling C, Haddad I, Scheer M, Grote A, Hiller K, Bunk B, Schreiber K, Retter I, Schomburg D, Jahn D 2007 SYSTOMONAS - An integrated database for systems biology analysis of Pseudomonas. Nucleic Acids Res 35:D533-D537. https://doi.org/10.1093/nar/gkl823
  4. Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W 2005 BioMart and Bioconductor: A powerful link between biological databases and microarray data analysis. Bioinformatics 21:3439-3440. https://doi.org/10.1093/bioinformatics/bti525
  5. Evsikov AV, Dolan ME, Genrich MP, Patek E, Bult CJ 2009 MouseCyc: A curated biochemical pathways database for the laboratory mouse. Genome Biol 10:R84. https://doi.org/10.1186/gb-2009-10-8-r84
  6. Hillier LW, Miller W, Birney E, Warren W, Hardison RC, Ponting CP, Bork P, Burt DW, Groenen MAM, Delany ME, Dodgson JB, Chinwalla AT, Cliften PF, Clifton SW, Delehaunty KD, Fronick C, Fulton RS, Graves TA, Kremitzki C, Layman D, Magrini V, McPherson JD, Miner TL, Minx P, Nash WE, Nhan MN, Nelson JO, Oddy LG, Pohl CS, Randall-Maher J, Smith SM, Wallis JW, Yang SP, Romanov MN, Rondelli CM, Paton B, Smith J, Morrice D, Daniels L, Tempest HG, Robertson L, Masabanda JS, Griffin DK, Vignal A, Fillon V, Jacobbson L, Kerje S, Andersson L, Crooijmans RPM, Aerts J, van der Poel JJ, Ellegren H, Caldwell RB, Hubbard SJ, Grafham DV, Kierzek AM, McLaren SR, Overton IM, Arakawa H, Beattie KJ, Bezzubov Y, Boardman PE, Bonfield JK, Croning MDR, Davies RM, Francis MD, Humphray SJ, Scott CE, Taylor RG, Tickle C, Brown WRA, Rogers J, Buerstedde JM, Wilson SA, Stubbs L, Ovcharenko I, Gordon L, Lucas S, Miller MM, Inoko H, Shiina T, Kaufman J, Salomonsen J, Skjoedt K, Wong GKS, Wang J, Liu B, Wang J, Yu J, Yang HM, Nefedov M, Koriabine M, deJong PJ, Goodstadt L, Webber C, Dickens NJ, Letunic I, Suyama M, Torrents D, von Mering C, Zdobnov EM, Makova K, Nekrutenko A, Elnitski L, Eswara P, King DC, Yang S, Tyekucheva S, Radakrishnan A, Harris RS, Chiaromonte F, Taylor J, He JB, Rijnkels M, Griffiths-Jones S, Ureta-Vidal A, Hoffman MM, Severin J, Searle SMJ, Law AS, Speed D, Waddington D, Cheng Z, Tuzun E, Eichler E, Bao ZR, Flicek P, Shteynberg DD, Brent MR, Bye JM, Huckle EJ, Chatterji S, Dewey C, Pachter L, Kouranov A, Mourelatos Z, Hatzigeorgiou AG, Paterson AH, Ivarie R, Brandstrom M, Axelsson E, Backstrom N, Berlin S, Webster MT, Pourquie O, Reymond A, Ucla C, Antonarakis SE, Long MY, Emerson JJ, Betran E, Dupanloup I, Kaessmann H, Hinrichs AS, Bejerano G, Furey TS, Harte RA, Raney B, Siepel A, Kent WJ, Haussler D, Eyras E, Castelo R, Abril JF, Castellano S, Camara F, Parra G, Guigo R, Bourque G, Tesler G, Pevzner PA, Smit A, Fulton LA, Mardis ER, Wilson RK 2004 Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432:695-716. https://doi.org/10.1038/nature03154
  7. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M 2006 From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34:D354-D357. https://doi.org/10.1093/nar/gkj102
  8. Karp PD, Ouzounis CA, Moore-Kochlacs C, Goldovsky L, Kaipa P, Ahren D, Tsoka S, Darzentas N, Kunin V, Lopez-Bigas N 2005 Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res 33:6083-6089. https://doi.org/10.1093/nar/gki892
  9. Karp PD, Paley SM, Krummenacker M, Latendresse M, Dale JM, Lee TJ, Kaipa P, Gilham F, Spaulding A, Popescu L, Altman T, Paulsen I, Keseler IM, Caspi R 2010 Pathway Tools version 13.0: Integrated software for pathway/genome informatics and systems biology. Brief Bioinform 11:40-79. https://doi.org/10.1093/bib/bbp043
  10. Kim WS, Lee SY, Seo S 2010 Development of an amalgamated cattle genome database based on Btau_4.0. Page 30 In: Proceedings of the 14th AAAP Animal Science Congress, Asian- Australasian Association of Animal Production Societies, Pingtung, Taiwan, ROC.
  11. Maglott D, Ostell J, Pruitt KD, Tatusova T 2007 Entrez gene: gene-centered information at NCBI. Nucleic Acids Res 35: D26-D31. https://doi.org/10.1093/nar/gkl993
  12. Poolman MG, Bonde BK, Gevorgyan A, Patel HH, Fell DA 2006 Challenges to be faced in the reconstruction of metabolic networks from public databases. IEE Proc Syst Biol 153:379-384. https://doi.org/10.1049/ip-syb:20060012
  13. Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD 2004 Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 6:R2. https://doi.org/10.1186/gb-2004-6-1-r2
  14. Schilling CH, Schuster S, Palsson BO, Heinrich R 1999 Metabolic pathway analysis: Basic concepts and scientific applications in the post-genomic era. Biotechnol Prog 15:296-303. https://doi.org/10.1021/bp990048k
  15. Seo S, Lewin HA 2009 Reconstruction of metabolic pathways for the cattle genome. BMC Syst Biol 3:33. https://doi.org/10.1186/1752-0509-3-33
  16. Seo S, Paek WK, Lee JH 2009 Recent status of chicken genome researches. Korean J Poult Sci 36:111-115. https://doi.org/10.5536/KJPS.2009.36.2.111
  17. Urbanczyk-Wochniak E, Sumner LW 2007 MedicCyc: a biochemical pathway database for Medicago truncatula. Bioinformatics 23:1418-1423. https://doi.org/10.1093/bioinformatics/btm040