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Identification of Key Metabolites Involved in Quantitative Growth of Pinus koraiensis trees (II)

잣나무 생장과 관련이 있는 주요 대사물질 인자(II)

  • Lee, Wi Young (Division of Forest Biotechnology, Korea Forest Research Institute) ;
  • Park, Eung-Jun (Division of Forest Biotechnology, Korea Forest Research Institute) ;
  • Kim, Hyun-Tae (Division of Forest Biotechnology, Korea Forest Research Institute) ;
  • Han, Sang Urk (Division of Forest Tree Improvement, Korea Forest Research Institute)
  • 이위영 (국립산림과학원 산림생명공학과) ;
  • 박응준 (국립산림과학원 산림생명공학과) ;
  • 김현태 (국립산림과학원 산림생명공학과) ;
  • 한상억 (국립산림과학원 임목육종과)
  • Received : 2014.03.13
  • Accepted : 2014.05.07
  • Published : 2014.06.30

Abstract

A metabolomic study using GC/MS analysis was conducted to identify key metabolic components regulating the growth of open-pollinated Pinus koraiensis families, which were grown for 29 years at three different locations. Among 110 individual metabolites identified, the contents of 62 metabolites were higher in the superior than in the inferior families (p<0.05), together with 22 metabolites, such as phosphoric acid, alanine, glycine, malic acid, and sucrose, being accumulated 1.5-fold higher in the superior families. In addition, 15 metabolites including alanine, malic acid, sucrose, d-turanose, and succinic acid showed positive correlation with the growth (p<0.01). Furthermore, the metabolites, of which contents were correlated with the growth but not significantly changed at different locations, were acetic acid, succinic acid, butanoic acid, glutamic acid, and inositol. Therefore we suggest that several metabolites selected in this study may be used as metabolic markers for quantitative growth trait in P. koraiensis.

잣나무의 주요생장관련 대사물질 인자를 구명하기 위하여 3개 지역에 풍매차대 가계로 조성한 29년생 잣나무의 대사물질을 GC/MS을 이용하여 대사체분석을 실시하였다. 분리한 110종의 대사 물질 중 62종이 우수가계와 저조가계 간에 유의적 차이(p<0.05)가 있었으며 모두 상대적으로 우수가계에서 대사물질 함량을 높게 함유하고 있는 것으로 나타났다. Phosphoric acid, alanine, glycine, malic acid, sucrose, d-turanose, succinic acid 등 22종의 물질은 생장 저조 가계에 비해 생장 우수 가계에서 1.5배 이상 높게 함유되어 있었다. 한편 생장특성과 대사물질간의 상관관계를 분석한 결과 생장 우수가계와 생장 저조가계 그룹 간에 유의적 차이가 있었던 alanine, malic acid, sucrose, d-turanose, succinic acid 등 15종에서 고도의 정의 상관관계(p<0.01)가 있었다. 또한 생장과 유의적 상관관계(p<0.05)가 있으면서 지역간에 변이가 적어 환경에 영향을 적게 받고 있는 대사물질로는 acetic acid, succinic acid, butanoic acid, glutamic acid 및 inositol로 분석되었다. 이들 물질은 잣나무 생장 우수개체에 유의적으로 높게 함유하고 있는 대사물질 인자로 추정되었다.

Keywords

References

  1. Dinant, S. and Lemoine, R. 2010. The phloem pathway: new issues and old debates. Comptes Rendus biologie. 333: 307-319. https://doi.org/10.1016/j.crvi.2010.01.006
  2. Fernie, A.R. and Schauer, N. 2009. Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genetic 25: 39-48. https://doi.org/10.1016/j.tig.2008.10.010
  3. Fernie1, A.R. and Schauer, N. 2008. Metabolomics-assisted breeding: a viable option for crop improvement? Trends in Genetics 25: 39-48.
  4. Greenwood, M.S. and Volkaert, H.A. 1992. Morphophysiological traits as markers for the early selection of conifer genetic families. Canadian Journal of Forest Research 22: 1001-1008. https://doi.org/10.1139/x92-134
  5. Han, S.U. and Lee, J.S. 1995. Estimate of early selection effiency for height growth using age-age correlation in Pinus koraiensis. Journal of Korean Forest Society 84(3): 356-360.
  6. Kerwin, R.E., Jimenez-Gomez, J.M., Fulop, D., Harmer, S.L., Maloof, J.N., and Kliebenstein, D.J. 2011. Network quantitative trait loci mapping of circadian clock outputs identies metabolic pathway-to-clock linkages in arabidopsis. Plant Cell 23: 471-485. https://doi.org/10.1105/tpc.110.082065
  7. Kim, D.E. and Chon, S.K. 1990. Trens in genetic parameters with age and site for early implications of genetic improvement in Korean white pine. Journal of Korean Forestry Society 79(1): 56-70.
  8. Korn, M., Gartner, T., Erban, A., Kopka, J., Selbig, J., and Hincha, D.K. 2010. Predicting arabidopsis freezing tolerance and heterosis in freezing tolerance from metabolite composition. Molecular Plant 3(1): 224-235. https://doi.org/10.1093/mp/ssp105
  9. Kozlowski, T.T., Kramer, P.J., and Pallardy, S.G. 1991. The physiological ecology of woody plants. Academic Press. pp. 13-15.
  10. Lee, W.Y. and Han, S.U. 2012. Identification of key metabolites in the regulation of metabolism and clonal growth of Populus davidiana Dode. Korean Journal of Breeding Science 44(3): 318-327.
  11. Lee, W.Y, Park, E.J., and Han, S.U. 2010. Correlation of growth performance with total nitrogen, carbon and nitrogen isotope compositions in the xylem of Pinus koraiensis. Journal of Korean Forest Society 99(3): 353-358.
  12. Lee, W.Y, Park, E.J., and Han, S.U. 2012. Identification of Key Metabolites Involved in Quantitative Growth of Pinus koraiensis. Journal of Korean Forest Society 101(4): 640-647.
  13. Lisec, J., Meyer, R.C., Steinfath, M., Redestig, H., Becher, M., Witucka-Wall, H., Fiehn, O., Torjek, O., Selbig, J., Altmann, T., and Willmitzer, L. 2008. Identication of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations. Plant Journal 53: 960-972.
  14. Meyer, R.C., Steinfath, M., Lisec, J., Becher, M., Witucka-Wall, H., Torjek, O., Fiehn, O., Eckardt, A., Willmitzer L., Selbig, J., and Altmann, T. 2007. The metabolic signature related to high plant growth rate in Arabidopsis thaliana. Proceeding of National Academic Science USA 104: 4759-4764. https://doi.org/10.1073/pnas.0609709104
  15. Nicholson, J.K. and Lindon, J.C. 2008. Metabonomics. Nature 455: 1054-1056. https://doi.org/10.1038/4551054a
  16. Okazaki, Y. and Saito, K. 2012. Recent advances of metabolomics in plant biotechnology. Plant Biotechnology Report 6: 1-15. https://doi.org/10.1007/s11816-011-0191-2
  17. Ossipov, V., Ossipova, S., Bykov, V., Oksanen, E., Koricheva, J. and Haukioja, E. 2008. Application of metabolomics to genotype and phenotype discrimination of birch trees grown in a long-term open-field experiment. Metabolomics 4: 39-51. https://doi.org/10.1007/s11306-007-0097-8
  18. Robinson, A.R., Gheneim, R., Kozak, R.A., Ellis, D.D., and Mansfield, S.D. 2005. The potential of metabolite profiling as a selection tool for genotype discrimination in Populus. Journal of Experimental Botany 56: 2807-2819.0-647. https://doi.org/10.1093/jxb/eri273
  19. Sinha, A.K., Hofmann, M.G.,, Romer, U., Kockenberger, W., Elling, L., and Roitsch, T. 2002. Metabolizable and nonmetabolizable sugars activate different signal transduction pathways in tomato. Plant Physiol 128: 1480-1489. https://doi.org/10.1104/pp.010771
  20. Schauer, N., Semel, Y., Roessner, U., Gur, A., Balbo, I., Carrari, F., Pleban, T., Perez-Melis, A., Bruedigam, C., Kopka, J., Willmitzer, L., Zamir, D., and Fernie, A.R. 2006. Comprehensive metabolic proling and phenotyping of interspecic introgression lines for tomato improvement. Nature Biotechnology 24: 447-454. https://doi.org/10.1038/nbt1192
  21. Stevenson, J.M., Perera, I.Y., Heilmann, I., Persson, S., and Boss, W.F. 2000. Inositol signaling and plant growth. Trends in Plant Science Reviews 5: 252-258. https://doi.org/10.1016/S1360-1385(00)01652-6
  22. Thissen, U., Coulier, L., Overkamp, K.M., Jetten, J., Van der Werf, B.J.C., Van de Ven, T., and Van der Werf, M.J. 2011. A proper metabolomics strategy supports efficient food quality improvement: A case study on tomato sensory properties. Food Quality and Preference 22: 499-506. https://doi.org/10.1016/j.foodqual.2010.12.001
  23. Viant, M.R. 2008. Recent developments in environmental metabolomics. Molecular BioSystems 4: 980-986. https://doi.org/10.1039/b805354e
  24. Yi, J.S., Song, J.H., and Han, S.U. 2007. Estimate of early selection using age-age correlation by stem analysis in Pinus koraiensis. Korea White Pine 2: 51-61.