Advertisement

Annals of Forest Science

, Volume 66, Issue 5, pp 509–509 | Cite as

Generation of Expressed Sequence Tags and development of microsatellite markers for Castanopsis sieboldii var. sieboldii (Fagaceae)

  • Saneyoshi UenoEmail author
  • Kyoko Aoki
  • Yoshihiko Tsumura
Original Article

Abstract

  • Castanopsis sieboldii var. sieboldii is an evergreen broadleaved canopy tree that grows on Honshu, Shikoku and Kyushu Islands in Japan. A closely-related, hybridizing, congener species, C. cuspidata var. cuspidata, has different leaf epidermis structure and different seed nuts morphology compared to C. sieboldii var. sieboldii. Furthermore, the habitats in which the two species grow often indicate different water requirements.

  • • Analysis of genetic diversity, using transcribed sequences, will provide a sound basis for the management of this species, allowing successful reforestation, incorporating phylogeographic conservation. In order to obtain transcripts sequences for the species and to develop transcript-based markers, we constructed and analyzed a cDNA library, surveyed microsatellite sequences within it and designed PCR primers for the microsatellites.

  • • The cDNA library was constructed using tissue from the inner bark of C. sieboldii var. sieboldii; 3 354 Expressed Sequence Tags (ESTs) were identified. We constructed 2 417 putative unigenes and assigned putative functions to 1 856 of them. Three hundred and fourteen microsatellites were found within the putative unigenes and 16 EST-SSR (Simple Sequence Repeat) markers were developed. The EST-SSR markers developpe in this study should facilitate future analysis of the genetic diversity of this and related species.

Keywords

broadleaved tree gene ontology Castanopsis cuspidata var. cuspidata Castanopsis sieboldii var. lutchuensis Castanopsis sieboldii var. carlesii 

Generation de marqueurs de séquences exprimées et développement de marqueurs microsatellites pour Castanopsis sieboldii var. sieboldii (Fagaceae)

Résumé

  • Castanopsis sieboldii var. sieboldii est un feuillu sempervirent qui pousse sur les îles japonaises de Honshu, Shikoku et Kyushu. C. cuspidata var. cuspidata une espèce congénère, étroitement liée, hybridée, a une structure de l’épiderme des feuilles différente et une morphologie différente des noix semences par rapport à C. sieboldii var. sieboldii. En outre, les habitats dans lesquels les deux espèces poussent indiquent souvent des besoins en eau différents.

  • • L’analyse de la diversité génétique, en utilisant des séquences transcrites, fournira une base solide pour la gestion de cette espèce, ce qui permettra le succès des reboisements, en intégrant une conservation phylogéographique. Afin d’obtenir les transcriptions des séquences de l’espèce et de mettre au point des marqueurs à base de transcription, nous avons construit et analysé une banque d’ADNc, étudié des séquences microsatellites et conçu des amorces PCR pour les microsatellites.

  • • La banque d’ADNc a été construite en utilisant les tissus de l’écorce interne de C. sieboldii var. sieboldii, 3 354 marqueurs de séquences exprimées (EST) ont été identifiés. Nous avons construit 2 417 unigènes putatifs et assigné des fonctions putatives à 1 856 d’entre eux. Trois cents quatorze microsatellites ont été trouvés à l’intérieur des unigènes putatifs et 16 EST-SSR (Simple Sequence Repeat) marqueurs ont été développés. Les marqueurs EST-SSR développés dans cette étude devraient faciliter l’analyse future de la diversité génétique de cette espèce et des espèces apparentées.

Mots-clés

arbre feuillu sempervirent Castanopsis cuspidata var. cuspidata Castanopsis sieboldii var. lutchuensis Castanopsis sieboldii var. carlesii 

References

  1. Allona I., Quinn M., Shoop E., Swope K., St Cyr S., Carlis J., Riedl J., Retzel E., Campbell M.M., Sederoff R., and Whetten R.W., 1998. Analysis of xylem formation in pine by cDNA sequencing. Proc. Natl. Acad. Sci. USA 95: 9693–9698.PubMedCrossRefGoogle Scholar
  2. Altschul S.F., Gish W., Miller W., Myers E.W., and Lipman D.J., 1990. Basic local alignment search tool. J. Mol. Biol. 215: 403–410.PubMedGoogle Scholar
  3. Apweiler R., Bairoch A., Wu C.H., Barker W.C., Boeckmann B., Ferro S., Gasteiger E., Huang H., Lopez R., Magrane M., Martin M.J., Natale D.A., O’Donovan C., Redaschi N., and Yeh L.S., 2004. UniProt: the Universal Protein knowledgebase. Nucleic Acids Res. 32: D115-D119.PubMedCrossRefGoogle Scholar
  4. Bhalerao R., Keskitalo J., Sterky F., Erlandsson R., Bjorkbacka H., Birve S.J., Karlsson J., Gardestrom P., Gustafsson P., Lundeberg J., and Jansson S., 2003. Gene expression in autumn leaves. Plant Physiol. 131: 430–442.PubMedCrossRefGoogle Scholar
  5. Bommer U.A. and Thiele B.J., 2004. The translationally controlled tumour protein (TCTP). Int. J. Biochem. Cell Biol. 36: 379–385.PubMedCrossRefGoogle Scholar
  6. Cans C., Passer B.J., Shalak V., Nancy-Portebois V., Crible V., Amzallag N., Allanic D., Tufino R., Argentini M., Moras D., Fiucci G., Goud B., Mirande M., Amson R., and Telerman A., 2003. Translationally controlled tumor protein acts as a guanine nucleotide dissociation inhibitor on the translation elongation factor eEF1A. Proc. Natl. Acad. Sci. USA 100: 13892–13897.PubMedCrossRefGoogle Scholar
  7. Chagne D., Chaumeil P., Ramboer A., Collada C., Guevara A., Cervera M.T., Vendramin G.G., Garcia V., Frigerio J.M., Echt C., Richardson T., and Plomion C., 2004. Cross-species transferability and mapping of genomic and cDNA SSRs in pines. Theor. Appl. Genet. 109: 1204–1214.PubMedCrossRefGoogle Scholar
  8. Chang S., Puryear J., and Cairney J., 1993. A simple and efficient method for isolating RNA from pine trees. Plant Mol. Biol. Rep. 11: 113–116.CrossRefGoogle Scholar
  9. Cho C., Lee H., Chung E., Kim K., Heo J., Kim J., Chung J., Ma Y., Fukui K., Lee D., Kim D., Chung Y., and Lee J., 2007. Molecular characterization of the soybean L-asparaginase gene induced by low temperature stress. Mol. Cells 23: 280–286.PubMedGoogle Scholar
  10. Delectis florae reipublicae popularis sinicae agendae academiae sinicae edita, 1998. Flora: reipublicae popularis sinicae (in Chinese) Science Press, Beijing, Vol. 22, 66–67.Google Scholar
  11. Dieringer D. and Schlotterer C., 2003. Microsatellite analyzer (MSA): a platform independent analysis tool for large microsatellite data sets. Mol. Ecol. Notes 3: 167–169.CrossRefGoogle Scholar
  12. Eveno E., Collada C., Guevara M.A., Leger V., Soto A., Diaz L., Leger P., Gonzalez-Martinez S.C., Cervera M.T., Plomion C., and Garnier-Gere P.H., 2008. Contrasting patterns of selection at Pinus pinaster Ait. Drought stress candidate genes as revealed by genetic differentiation analyses. Mol. Biol. Evol. 25: 417–437.PubMedCrossRefGoogle Scholar
  13. Ewing B. and Green P., 1998. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8: 186–194.PubMedGoogle Scholar
  14. Ewing B., Hillier L., Wendl M.C., and Green P., 1998. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 8: 175–185.PubMedGoogle Scholar
  15. Ewing R.M., Ben Kahla A., Poirot O., Lopez F., Audic S., and Claverie J.M. (1999) Large-scale statistical analyses of rice ESTs reveal correlated patterns of gene expression. Genome Res. 9: 950–959PubMedCrossRefGoogle Scholar
  16. Finn R.D., Tate J., Mistry J., Coggill P.C., Sammut S.J., Hotz H.R., Ceric G., Forslund K., Eddy S.R., Sonnhammer E.L., and Bateman A., 2008. The Pfam protein families database. Nucleic Acids Res. 36: D281-D288.PubMedCrossRefGoogle Scholar
  17. Fukuoka H., Nunome T., Minamiyama Y., Kono I., Namiki N., and Kojima A., 2005. Read2Marker: a data processing tool for microsatellite marker development from a large data set. Biotechniques 39: 472, 474, 476.PubMedCrossRefGoogle Scholar
  18. Girke T., Lauricha J., Tran H., Keegstra K., and Raikhel N., 2004. The cell wall navigator database. A systems-based approach to organism-unrestricted mining of protein families involved in cell wall metabolism. Plant Physiol. 136: 3003–3008; discussion 3001.PubMedCrossRefGoogle Scholar
  19. Grant M. and Bevan M.W., 1994. Asparaginase gene expression is regulated in a complex spatial and temporal pattern in nitrogen-sink tissues. Plant J. 5: 695–704.CrossRefGoogle Scholar
  20. Green P., Documentation for phrap and cross_match. 1999. [online] Available from http://bozeman.mbt.washington.edu/phrap.docs/phrap.html[accessed 7 March 2007].Google Scholar
  21. Guillaumie S., San-Clemente H., Deswarte C., Martinez Y., Lapierre C., Murigneux A., Barriere Y., Pichon M., and Goffner D., 2007. MAIZEWALL. Database and developmental gene expression profiling of cell wall biosynthesis and assembly in maize. Plant Physiol. 143: 339–363.PubMedCrossRefGoogle Scholar
  22. Iseli C., Jongeneel C.V., and Bucher P., 1999. ESTScan: a program for detecting, evaluating, and reconstructing potential coding regions in EST sequences. Proc. Int. Conf. Intell. Syst. Mol. Biol. 138–148.Google Scholar
  23. Kado T., Yoshimaru H., Tsumura Y., and Tachida H., 2003. DNA variation in a conifer, Cryptomeria japonica (Cupressaceae sensu lato). Genetics 164: 1547–1559.PubMedGoogle Scholar
  24. Kane N.C. and Rieseberg L.H., 2007. Selective sweeps reveal candidate genes for adaptation to drought and salt tolerance in common sunflower, Helianthus annuus. Genetics 175: 1823–1834.PubMedCrossRefGoogle Scholar
  25. Kantety R.V., La Rota M., Matthews D.E., and Sorrells M.E., 2002. Data mining for simple sequence repeats in expressed sequence tags from barley, maize, rice, sorghum and wheat. Plant Mol. Biol. 48: 501–510.PubMedCrossRefGoogle Scholar
  26. Kobayashi S. and Hiroki S., 2003. Patterns of occurrence of hybrids of Castanopsis cuspidata and C. sieboldii in the IBP Minamata Special Research Area, Kumamoto Prefecture, Japan. J. Phytogeogr. Taxon. 51: 63–67.Google Scholar
  27. Kobayashi Y. and Sugawa T., 1959. Identification of wood of some Castanopsis species in Japan (in Japanese with English abstract). Bull. Gov. For. Exp. Stn. 118: 139–178.Google Scholar
  28. Kondo H., Tahira T., Hayashi H., Oshima K., and Hayashi K., 2000. Microsatellite genotyping of post-PCR fluorescently labeled markers. Biotechniques 29: 868–872.PubMedGoogle Scholar
  29. Kumpatla S.P. and Mukhopadhyay S., 2005. Mining and survey of simple sequence repeats in expressed sequence tags of dicotyledonous species. Genome 48: 985–998.PubMedCrossRefGoogle Scholar
  30. Manos P.S. and Stanford A.M., 2001. The historical biogeography of Fagaceae: Tracking the tertiary history of temperate and subtropical forests of the Northern Hemisphere. Int. J. Plant Sci. 162: S77-S93.CrossRefGoogle Scholar
  31. Metzgar D., Bytof J., and Wills C., 2000. Selection against frameshift mutations limits microsatellite expansion in coding DNA. Genome Res. 10: 72–80.PubMedGoogle Scholar
  32. Moriguchi Y., Iwata H., Ujino-Ihara T., Yoshimura K., Taira H., and Tsumura Y., 2003. Development and characterization of microsatellite markers for Cryptomeria japonica D. Don. Theor. Appl. Genet. 106: 751–758.Google Scholar
  33. Murray M.G. and Thompson W.F., 1980. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 8: 4321–4325.PubMedCrossRefGoogle Scholar
  34. Nanjo T., Futamura N., Nishiguchi M., Igasaki T., Shinozaki K., and Shinohara K., 2004. Characterization of full-length enriched expressed sequence tags of stress-treated poplar leaves. Plant Cell Physiol. 45: 1738–1748.PubMedCrossRefGoogle Scholar
  35. Navabpour S., Morris K., Allen R., Harrison E., S A.H.-M., and Buchanan-Wollaston V., 2003. Expression of senescence-enhanced genes in response to oxidative stress. J. Exp. Bot. 54: 2285–2292.PubMedCrossRefGoogle Scholar
  36. Nei M. and Kumar S., 2000. Molecular evolution and phylogenetics, Oxford University Press, New York, 333 p.Google Scholar
  37. Parkinson J., Anthony A., Wasmuth J., Schmid R., Hedley A., and Blaxter M., 2004. PartiGene—constructing partial genomes. Bioinformatics 20: 1398–1404.PubMedCrossRefGoogle Scholar
  38. Parkinson J., Guiliano D.B., and Blaxter M., 2002. Making sense of EST sequences by CLOBBing them. BMC Bioinformatics 3: 31.PubMedCrossRefGoogle Scholar
  39. Pashley C.H., Ellis J.R., McCauley D.E., and Burke J.M., 2006. EST databases as a source for molecular markers: lessons from Helianthus. J. Hered. 97: 381–388.PubMedCrossRefGoogle Scholar
  40. Pavy N., Paule C., Parsons L., Crow J.A., Morency M.J., Cooke J., Johnson J.E., Noumen E., Guillet-Claude C., Butterfield Y., Barber S., Yang G., Liu J., Stott J., Kirkpatrick R., Siddiqui A., Holt R., Marra M., Seguin A., Retzel E., Bousquet J., and MacKay J., 2005. Generation, annotation, analysis and database integration of 16,500 white spruce EST clusters. BMC Genomics 6: 144.PubMedCrossRefGoogle Scholar
  41. Petit R.J., El Mousadik A., and Pons O., 1998. Identifying populations for conservation on the basis of genetic markers. Conserv. Biol. 12: 844–855.CrossRefGoogle Scholar
  42. Raes J., Rohde A., Christensen J.H., Van de Peer Y., and Boerjan W., 2003. Genome-wide characterization of the lignification toolbox in Arabidopsis. Plant Physiol. 133: 1051–1071.PubMedCrossRefGoogle Scholar
  43. Rozen S. and Skaletsky H.J., 2000. Primer3 on the WWW for general users and for biologist programmers. In: Krawetz S.A. and Misener S. (Eds.), Bioinformatics methods and protocols: Methods in molecular biology, Humana Press, Totowa, pp. 365–386.Google Scholar
  44. Rungis D., Berube Y., Zhang J., Ralph S., Ritland C.E., Ellis B.E., Douglas C., Bohlmann J., and Ritland K., 2004. Robust simple sequence repeat markers for spruce (Picea spp.) from expressed sequence tags. Theor. Appl. Genet. 109: 1283–1294.PubMedCrossRefGoogle Scholar
  45. Stephenson P., Collins B.A., Reid P.D., and Rubinstein B., 1996. Localization of ubiquitin to differentiating vascular tissues. Am. J. Bot. 83: 140–147.CrossRefGoogle Scholar
  46. Sterky F., Regan S., Karlsson J., Hertzberg M., Rohde A., Holmberg A., Amini B., Bhalerao R., Larsson M., Villarroel R., Van Montagu M., Sandberg G., Olsson O., Teeri T.T., Boerjan W., Gustafsson P., Uhlen M., Sundberg B., and Lundeberg J., 1998. Gene discovery in the wood-forming tissues of poplar: analysis of 5, 692 expressed sequence tags. Proc. Natl. Acad. Sci. USA 95: 13330–13335.PubMedCrossRefGoogle Scholar
  47. Sterky F., Bhalerao R.R., Unneberg P., Segerman B., Nilsson P., Brunner A.M., Charbonnel-Campaa L., Lindvall J.J., Tandre K., Strauss S.H., Sundberg B., Gustafsson P., Uhlen M., Bhalerao R.P., Nilsson O., Sandberg G., Karlsson J., Lundeberg J., and Jansson S., 2004. A Populus EST resource for plant functional genomics. Proc. Natl. Acad. Sci. USA 101: 13951–13956.PubMedCrossRefGoogle Scholar
  48. Tamura K., Dudley J., Nei M., and Kumar S., 2007. MEGA4: Molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol. Biol. Evol. 24: 1596–1599.PubMedCrossRefGoogle Scholar
  49. Temnykh S., DeClerck G., Lukashova A., Lipovich L., Cartinhour S., and McCouch S., 2001. Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. Genome Res. 11: 1441–1452.PubMedCrossRefGoogle Scholar
  50. Tsumura Y., Kado T., Takahashi T., Tani N., Ujino-Ihara T., and Iwata H., 2007. Genome scan to detect genetic structure and adaptive genes of natural populations of Cryptomeria japonica. Genetics 176: 2393–2403.PubMedCrossRefGoogle Scholar
  51. Ueno S., Taguchi Y., and Tsumura Y., 2008. Microsatellite markers derived from Quercus mongolica var. crispula (Fagaceae) inner bark expressed sequence tags. Genes Genet. Syst. 83: 179–187.PubMedCrossRefGoogle Scholar
  52. Ueno S., Yoshimaru H., Kawahara T., and Yamamoto S., 2000. Isolation of microsatellite markers in Castanopsis cuspidata var. sieboldii Nakai from an enriched library. Mol. Ecol. 9: 1188–1190.PubMedGoogle Scholar
  53. Ueno S., Yoshimaru H., Kawahara T., and Yamamoto S., 2003. A further six microsatellite markers for Castanopsis cuspidata var. sieboldii Nakai. Conserv. Genet. 4: 813–815.CrossRefGoogle Scholar
  54. Ujino-Ihara T., Yoshimura K., Ugawa Y., Yoshimaru H., Nagasaka K., and Tsumura Y., 2000. Expression analysis of ESTs derived from the inner bark of Cryptomeria japonica. Plant Mol. Biol. 43: 451–457.PubMedCrossRefGoogle Scholar
  55. Yamada H. and Miyaura T., 2003. Geographic occurrence of intermediate type between Castanopsis sieboldii and C. cuspidata (Fagaceae) based on the structure of leaf epidermis. J. Plant Res. 116: 477–482.PubMedCrossRefGoogle Scholar
  56. Yamaguchi-Shinozaki K. and Shinozaki K., 1993. The plant hormone abscisic acid mediates the drought-induced expression but not the seed-specific expression of rd22, a gene responsive to dehydration stress in Arabidopsis thaliana. Mol. Gen. Genet. 238: 17–25.PubMedGoogle Scholar
  57. Yamanaka T., 1966. Problems of Castanopsis cuspidata Schottky (in Japanese with English abstract). Bull. Fac. Educ., Kochi Univ. 18: 65–73.Google Scholar
  58. Yamazaki T. and Mashiba S., 1987a. A taxonomical revision of Castanopsis cuspidata (Thunb.) Schottky and the allies in Japan, Korea and Taiwan (1). J. Jap. Bot. 62: 289–298.Google Scholar
  59. Yamazaki T. and Mashiba S., 1987b. A taxonomical revision of Castanopsis cuspidata (Thunb.) Schottky and the allies in Japan, Korea and Taiwan (2). J. Jap. Bot. 62: 332–339.Google Scholar
  60. Yasodha R., Sumathi R., Chezhian P., Kavitha S., and Ghosh M., 2008. Eucalyptus microsatellites mined in silico: survey and evaluation. J. Genet. 87: 21–25.PubMedCrossRefGoogle Scholar
  61. Zhang L., Yu S., Cao Y., Wang J., Zuo K., Qin J., and Tang K., 2006. Distributional gradient of amino acid repeats in plant proteins. Genome 49: 900–905.PubMedCrossRefGoogle Scholar

Copyright information

© Springer S+B Media B.V. 2009

Authors and Affiliations

  1. 1.Tree Genetics Laboratory, Department of Forest GeneticsForestry and Forest Products Research InstituteIbarakiJapan
  2. 2.Graduate School of Human and Environmental StudiesKyoto UniversityKyotoJapan

Personalised recommendations