High-Quality RNA Extraction and Evaluation of Reference Genes for qPCR Assay of Pinus sylvestris L. Trunk Tissues

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

Scots pine (Pinus sylvestris L.) is a species of tree with heartwood (HW), which is forming during aging of sapwood (SW). Due to clear-cut border between SW and HW P. sylvestris should be used as a model woody plant for studying patterns of HW formation. Currently, molecular genetic methods are used to study the processes of trunk tissues formation in woody plants often. A feature of trunk tissues of coniferous trees is a high content of secondary metabolites, a low content of nucleic acids, and potential partial degradation of RNA. In this work we discuss the choice of most successful method for extraction a high-quality RNA for real-time PCR (RT-PCR) in P. sylvestris trunk tissues along the radial vector “conductive phloem/cambial zone – differentiating xylem – exterior part of SW (1–2 annual rings) – interior part of SW (1–2 annual rings afore transition zone (TZ)) – TZ (2 annual rings afore HW)” for reproducible RT-PCR data. The expression stability of six potential reference genes (Actin1, α-Tubulin, β-Tubulin, Ef1a, GAPDH, UBQ) was assessed in all describe tissues. Differences in expression levels of target genes are shown by data normalization using reference genes with different stability of expression.

About the authors

Yu. L. Moshchenskaya

Forest Research Institute of the Karelian Research Centre of the Russian Academy of Sciences

Author for correspondence.
Email: moshchenskaya@krc.karelia.ru
Russian, Petrozavodsk

N. A. Galibina

Forest Research Institute of the Karelian Research Centre of the Russian Academy of Sciences

Email: moshchenskaya@krc.karelia.ru
Russian, Petrozavodsk

M. A. Korzhenevskiy

Forest Research Institute of the Karelian Research Centre of the Russian Academy of Sciences

Email: moshchenskaya@krc.karelia.ru
Russian, Petrozavodsk

T. V. Tarelkina

Forest Research Institute of the Karelian Research Centre of the Russian Academy of Sciences

Email: moshchenskaya@krc.karelia.ru
Russian, Petrozavodsk

K. M. Nikerova

Forest Research Institute of the Karelian Research Centre of the Russian Academy of Sciences

Email: moshchenskaya@krc.karelia.ru
Russian, Petrozavodsk

O. V. Chirva

Forest Research Institute of the Karelian Research Centre of the Russian Academy of Sciences

Email: moshchenskaya@krc.karelia.ru
Russian, Petrozavodsk

References

  1. Су М., Цзан В., Яо Н., Хуан М. Выделение высококачественной РНК из различных тканей Populus // Физиология растений. 2009. Т. 56. № 5. С. 791.
  2. Antonova G.F., Stasova V.V. Effects of environmental factors on wood formation in larch (Larix deciduas Ldb.) stems // Trees. 1997. V. 11. P. 462. https://doi.org/10.1007/PL00009687
  3. Bustin S.A., Beaulieu J.F., Huggett J., Jaggi R., Kibenge F.S., Olsvik P.A., Penning L.C., Toegel S. MIQE précis: Practical implementation of minimum standard guidelines for fluorescence-based quantitative real-time PCR experiments // BMC Mol. Biol. 2010. V. 11. P. 74. https://doi.org/10.1186/1471-2199-11-74
  4. Bustin S.A., Benes V., Garson J.A., Hellemans J., Hugget J., Kubista M., Mueller R., Nolan T., Pfaffl M.W., Shipley G.L., Vandesompele J., Wittwer C.T. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments // Clinical Chemistry. 2009. V. 55. P. 611. https://doi.org/10.1373/clinchem.2008.112797
  5. Chan K.L., Ho C.L., Namasivayam P., Napis S. A simple and rapid method for RNA isolation from plant tissues with high phenolic compounds and polysaccharides // Protocol Exchange. 2007. https://doi.org/10.1038/nprot.2007
  6. Chang E., Shi S., Liu J., Cheng T., Xue L., Yang X., Yang W., Lan Q., Jiang Z. Selection of reference genes for quantitative gene expression studies in Platycladus orientalis (Cupressaceae) Using real-time PCR // PLoS One. 2012. V. 7(3): e33278.https://doi.org/10.1371/journal.pone.0033278
  7. Chang S., Puryear J., Cairney J. A simple and efficient method for isolating RNA from pine trees // Plant Mol. Biol. Report. 1993. V. 11. P. 113.
  8. Chen H., Yang Z., Hu Y., Tan J., Jia J., Xu H., Chen X. Reference genes selection for quantitative gene expression studies in Pinus massoniana L. // Trees. 2016. V. 30. P. 685. https://doi.org/10.1371/journal.pone.0205182
  9. Chen Y., Weining S., Daggard G. Preparation of total RNA from a very small wheat embryo suitable for differential display // Ann. Appl. Biol. 2003. V. 143. P. 261. https://doi.org/10.1111/j.1744-7348.2003.tb00293.x
  10. Chi X., Hu R., Yang Q., Zhang X., Pan L., Chen N., Chen M., Yang Z., Wang T., He Y., Yu S. Validation of reference genes for gene expression studies in peanut by quantitative real-time RT-PCR // Mol. Genet. Genomics. 2012. V. 287(2). P. 167. https://doi.org/10.1007/s00438-011-0665-5
  11. de Castro E., Sigrist C.J., Gattiker A., Bulliard V., Langendijk-Genevaux P.S., Gasteiger E., Bairoch A., Hulo N. ScanProsite: detection of PROSITE signature matches and ProRule-associated functional and structural residues in proteins // Nucleic Acids Res. 2006. V. 34. P. 362. https://doi.org/10.1093/nar/gkl124
  12. Fischer U., Kucukoglu M., Helariutta Y., Bhalerao R.P. The dynamics of cambial stem cell activity // Annu. Rev. Plant Biol. 2019. V. 70. P. 293. https://doi.org/10.1146/annurev-arplant-050718-100402
  13. Ghawana S., Paul A., Kumar H., Kumar A., Singh H., Bhardwaj P.K., Rani A., Singh R.S., Raizada J., Singh K., Kumar S. An RNA isolation system for plant tissues rich in secondary metabolites // BMC Res. Notes. 2007. V. 4. P. 85. https://doi.org/10.1186/1756-0500-4-85
  14. Han X., Lu M., Chen Y., Zhan Z., Cui Q., Wang Y. Selection of reliable reference genes for gene expression studies using real-time PCR in tung tree during seed development // PLoS One. 2012. V. 7. P. e43084. https://doi.org/10.1371/journal.pone.0043084
  15. Kumar S., Stecher G., Tamura K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets // Mol. Biol. Evol. 2016. V. 33. P. 1870. https://doi.org/10.1093/molbev/msw054
  16. Lal L., Sahoo R., Gupta R.K., Sharma P., Kumar S. RNA isolation from highphenolic tea leaves and apical buds // Plant Mol. Biol. Rep. 2001. V. 19. P. 181a. https://doi.org/10.1007/BF02772161
  17. Lim K.J., Paasela T., Harju A., Venäläinen M., Paulin L., Auvinen P., Kärkkäinen K., Teeri T.H. Developmental changes in scots pine transcriptome during heartwood formation // Plant Physiol. 2016. V. 172. P. 1403. https://doi.org/10.1104/pp.16.01082
  18. Marchler-Bauer A., Bryant S.H. CD-Search: protein domain annotations on the fly Nucleic // Acids Res. 2004. V. 32. P. 327. https://doi.org/10.1093/nar/gkh454
  19. Meyer-Gauen G., Herbrand H., Pahnke J., Cerff R., Martin W. Gene structure, expression inEscherichia coliand biochemicalproperties of the NAD1-dependent glyceraldehyde-3-phosphatedehydrogenase from Pinus sylvestris chloroplasts // Gene. 1998. V. 209. P. 167. https://doi.org/10.1016/S0378-1119(98)00034-1
  20. Mo J., Xu J., Jin W., Yang L., Yin T., Shi J. Identification of reference genes for quantitative gene expression studies in Pinus massoniana and its introgression hybrid // Forests 2019. V. 10. P. 787. https://doi.org/10.3390/f10090787
  21. Niu X., Zhang G., Xu J., Tao A., Fang P., Su J. Selection of reliable reference genes for quantitative real-time PCR gene expression analysis in Jute (Corchorus capsularis) under stress treatments // Frontiers in Plant Science. 2015. V. 6. P. 848. https://doi.org/10.3389/fpls.2015.00848
  22. Ramakers C., Ruijter J.M., Deprez R.H., Moorman A.F. Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data // Neurosci. Lett. 2003. V. 339. P. 62. https://doi.org/10.1016/S0304-3940(02)01423-4
  23. Saitou N., Nei M. The Neighbor-Joining Method – a new method for reconstructing phylogenetic trees // Mol. Biol. Evol. 1987. V. 4. P. 406. https://doi.org/10.1093/oxfordjournals.molbev.a040454
  24. Svec D., Tichopad A., Novosadova V., Pfaffl M.W., Kubista M. How good is a PCR efficiency estimate: recommendations for precise and robust qPCR efficiency assessments // Biomol. Detect. Quantif. 2015. V. 3. P. 9. https://doi.org/10.1016/j.bdq.2015.01.005
  25. Taylor S., Wakem M., Dijkman G., Alsarraj M., Nguyen M. A practical approach to RT-qPCR–Publishing data that conform to the MIQE guidelines // Methods. 2010. V. 50. P. S1. https://doi.org/10.1016/j.ymeth.2010.01.005
  26. Zhu P., Ma Y., Zhu L., Chen Y., Li R., Ji K. Selection of suitable reference genes in Pinus massoniana Lamb. under different abiotic stresses for qPCR normalization // Forests. 2019. V. 10. P. 632. https://doi.org/10.3390/f10080632
  27. Pfaffl M.W. A new mathematical model for relative quantification in real-time RT-PCR // Nucleic Acids Res. 2001. V. 29. P. E45. https://doi.org/10.1093/nar/29.9.e45

Supplementary files

Supplementary Files
Action
1. JATS XML
2.

Download (384KB)
3.

Download (179KB)
4.

Download (303KB)
5.

Download (104KB)
6.

Download (31KB)
7.

Download (48KB)

Copyright (c) 2023 Ю.Л. Мощенская, Н.А. Галибина, М.А. Корженевский, Т.В. Тарелкина, К.М. Никерова, О.В. Чирва