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Selection of appropriate reference genes for quantitative real-time reverse transcription PCR in Betula platyphylla under salt and osmotic stress conditions


Autoři: Ziyi Li aff001;  Huijun Lu aff002;  Zihang He aff002;  Chao Wang aff002;  Yucheng Wang aff001;  Xiaoyu Ji aff001
Působiště autorů: College of Forestry, Shenyang Agricultural University, Shenyang, China aff001;  State Key Laboratory of Tree Genetics and Breeding (Northeast Forestry University), Harbin, China aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0225926

Souhrn

Selecting appropriate reference genes is vital to normalize gene expression analysis in birch (Betula platyphylla) under different abiotic stress conditions using quantitative real-time reverse transcription PCR (qRT-PCR). In this study, 11 candidate birch reference genes (ACT, TUA, TUB, TEF, 18S rRNA, EF1α, GAPDH, UBC, YLS8, SAND, and CDPK) were selected to evaluate the stability of their expression in different tissues and under different abiotic stress conditions. Three statistical algorithms (GeNorm, NormFinder, and BestKeeper) were used to analyze the stability of the 11 candidate reference genes to identify the most appropriate one. The results indicated that EF-1α was the most stable reference gene in different birch tissues, ACT was the most stable reference gene for normal conditions, ACT and TEF were the most stable reference genes for salt stress treatment, TUB was the most stable reference gene for osmotic stress treatment, and ACT was the most appropriate choice in all samples of birch. In conclusion, the most appropriate reference genes varied among different experimental conditions. However, in this study, ACT was the optimum reference gene in all experimental groups, except in the different tissues group. GAPDH was the least stable candidate reference gene in all experimental conditions. In addition, three stress-induced genes (BpGRAS1, BpGRAS16, and BpGRAS19) were chosen to verify the stability of the selected reference genes in different tissues and under salt stress. This study laid the foundation for the selection of appropriate reference gene(s) for future gene expression pattern studies in birch.

Klíčová slova:

Birches – Gene amplification – Gene expression – Leaves – Osmotic shock – Plant resistance to abiotic stress – Polymerase chain reaction – Ribosomal RNA


Zdroje

1. Morandi A, Zhaxybayeva O, Gogarten JP, Graf J. Evolutionary and diagnostic implications of intragenomic heterogeneity in the 16S rRNA gene in Aeromonas strains. J Bacteriol. 2005;187(18):6561–6564. doi: 10.1128/JB.187.18.6561-6564.2005 16159790

2. Garrido-Maestu A, Chapela MJ, Vieites JM, Cabado AG. Lolb gene, a valid alternative for qPCR detection of Vibrio cholerae in food and environmental samples. Food Microbiology. 2015;46(46):535–540.

3. Godhe A, Otta SK, Rehnstam-Holm AS, Karunasagar I. Polymerase chain reaction in detection of Gymnodinium mikimotoi and Alexandrium minutum in field samples from southwest India. Mar Biotechnol (NY). 2001;3(2):152–162. doi: 10.1007/s101260000052 14961378

4. Steeples LR, Guiver M, Jones NP. Real-time PCR using the 529 bp repeat element for the diagnosis of atypical ocular toxoplasmosis. Br J Ophthalmo. 2016;100(2): 200–203.

5. Zhu J, Zhang L, Li W, Han S, Yang W, Qi L. Reference gene selection for quantitative real-time PCR normalization in Caragana intermedia under different abiotic stress conditions. PLoS ONE. 2013;8:e53196. doi: 10.1371/journal.pone.0053196 23301042

6. Dean JD, Goodwin PH, Hsiang T. Comparison of relative RT-PCR and northern blot analyses to measure expression of β-1,3-glucanase in nicotiana benthamiana, infected with colltotrichum destructivum. Plant Molecular Biology Reporter. 2002;20(4):347–356.

7. Gare EM, Divjak M, Bailey MJ, Walters EH. Beta-Actin and GAPDH housekeeping gene expression in asthmatic airways is variable and not suitable for normalising mRNA levels. Thorax. 2002;57(9):765–770. doi: 10.1136/thorax.57.9.765 12200519

8. Migocka M, Papierniak A. Identification of suitable reference genes for studying gene expression in cucumber plants subjected to abiotic stress and growth regulators. Mol. Breed. 2011;28:343–357.

9. Reddy PS, Reddy DS, Sharma KK, Bhatnagar-Mathur P, Vadez V. Cloning and validation of reference genes for normalization of gene expression studies in pearl millet by quantitative real-time PCR. Plant Gene. 2015;1:35–42.

10. Lv S, Jiang P, Chen X, Fan P, Wang X, Li Y. Multiple compartmentalization of sodium conferred salt tolerance in Salicornia europaea. Plant Physiol Biochem. 2011;51:47–52. doi: 10.1016/j.plaphy.2011.10.015 22153239

11. Ma J, Zhang M, Xiao X, You J, Wang J, Wang T, et al. Global transcriptome profiling of Salicornia europaea L. shoots under NaCl treatment. PLoS ONE. 2013;8:e65877. doi: 10.1371/journal.pone.0065877 23825526

12. Xiao X, Ma J, Wang J, Wu X, Li P, Yao Y. Validation of suitable reference genes for gene expression analysis in the halophyte Salicornia europaea by real-time quantitative PCR. Front Plant Sci.2014;5:788. doi: 10.3389/fpls.2014.00788 25653658

13. Kiarash JG, Dayton WH, Amirmahani F, Mehdi MM, Zaboli M, Nazari M.Selection and validation of reference genes for normalization of qRT-PCR gene expression in wheat (Triticum durum L.) under drought and salt stresses. J Genet. 2018;97(5): 1433–1444. 30555091

14. Tang X, Wang H, Shao C, Shao H. Reference Gene Selection for qPCR Normalization of Kosteletzkya virginica under Salt Stress. Biomed Res Int. 2015: 823806. doi: 10.1155/2015/823806 26581422

15. Wan H, Zhao Z, Qian C, Sui Y, Malik AA, Chen J. Selection of appropriate reference genes for gene expression studies by quantitative real-time polymerase chain reaction in cucumber. Anal Biochem. 2010;399(2):257–261. doi: 10.1016/j.ab.2009.12.008 20005862

16. Zhu X, Li X, Chen W, Chen J, Lu W, Chen L, et al. Evaluation of new reference genes in papaya for accurate transcript normalization under different experimental conditions. PLoS One. 2012;7(8):e44405. doi: 10.1371/journal.pone.0044405 22952972

17. Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR. Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol.2005;139(1):5–17. doi: 10.1104/pp.105.063743 16166256

18. Wan Q, Chen S, Shan Z, Yang Z, Chen L, Zhang C, et al. Stability evaluation of reference genes for gene expression analysis by RT-qPCR in soybean under different conditions. PLoS One.2017;12(12):e0189405. doi: 10.1371/journal.pone.0189405 29236756

19. Shukla P, Reddy RA, Ponnuvel KM, Rohela GK, Shabnam AA, Ghosh MK, et al. Selection of suitable reference genes for quantitative real-time PCR gene expression analysis in Mulberry (Morus alba L.) under different abiotic stresses. Mol Biol Rep. 2019;6(2):1809–1817.

20. Zhang K, Li M, Cao S, Sun Y, Long R, Kang J, et al. Selection and alidation of reference genes for target gene analysis with quantitative real-time PCR in the leaves and roots of Carex rigescens under abiotic stress. Ecotoxicol Environ Saf. 2019;168:127–137. doi: 10.1016/j.ecoenv.2018.10.049 30384160

21. Kang D, Guo Y, Ren C, Zhao F, Feng Y, Han X, et al. Population structure and spatial pattern of main tree species in secondary Betula platyphylla forest in Ziwuling Mountains, China. Sci Rep. 2014;4:6873. doi: 10.1038/srep06873 25362993

22. Miao L, Qin X, Gao L, Li Q, Li S, He C, et al. Selection of reference genes for quantitative real-time PCR analysis in cucumber (Cucumis sativus L.), pumpkin (Cucurbita moschata Duch.) and cucumber–pumpkin grafted plants. Peer J. 2019;7:e6536. doi: 10.7717/peerj.6536 31024757

23. Ma R, Xu S, Zhao Y, Xia B, Wang R. Selection and Validation of Appropriate Reference Genes for Quantitative Real-Time PCR Analysis of Gene Expression in Lycoris aurea. Front Plant Sci. 2016;7:536. doi: 10.3389/fpls.2016.00536 27200013

24. Pfaffl MW. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001;29:e45. doi: 10.1093/nar/29.9.e45 11328886

25. Vandesompele J, De PK, Pattyn F, Poppe B, Van RN, De PA, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3(7):RESEARCH0034.

26. Andersen CL, Jensen JL, Orntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res.2004;64:5245–5250. doi: 10.1158/0008-5472.CAN-04-0496 15289330

27. Ramakers C, Ruijter JM, Deprez RH, Moorman AF. Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data. Neurosci Lett. 2003;339(1):62–66. doi: 10.1016/s0304-3940(02)01423-4 12618301

28. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper-Excel-Based tool using pair-wise correlations. Biotechnol Lett.2004;26:509–515. doi: 10.1023/b:bile.0000019559.84305.47 15127793

29. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCt method. Methods.2001;25:402–408. doi: 10.1006/meth.2001.1262 11846609

30. Ni X, Qi J, Zhang G, Xu J, Tao A, Fang P, et al. Selection of reliable reference genes for quantitative real-time PCR gene expression analysis in Jute (Corchorus capsularis) under stress treatments. Front Plant Sci.2015;6:848. doi: 10.3389/fpls.2015.00848 26528312

31. Wang JJ, Han S, Yin W, Xia X, Liu C. Comparison of Reliable Reference Genes Following Different Hormone Treatments by Various Algorithms for qRT-PCR Analysis of Metasequoia. International Journal of Molecular Sciences. 2018.20(1):34.

32. Wei Y, Liu Q, Dong H, Zhou Z, Hao Y, Chen X, et al. Selection of Reference Genes for Real-Time Quantitative PCR in Pinus massoniana.Post Nematode Inoculation. PLoS One. 2016;11(1):e0147224. doi: 10.1371/journal.pone.0147224 26800152

33. Vandesompele J, De PK, Pattyn F, Poppe B, Van RN, De PA, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3(7): RESEARCH0034.

34. Andersen CL, Jensen JL, Orntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res.2004;64:5245–5250. doi: 10.1158/0008-5472.CAN-04-0496 15289330

35. Zeng X, Ling H, Chen X, Gu S. Genome-wide identification, phylogeny and function analysis of GRAS gene family in Dendrobium catenatum (Orchidaceae). Gene. 2019;705:5–15. doi: 10.1016/j.gene.2019.04.038 30999026

36. Ma HS, Xia XL, Yin WL. Cloning and analysis of SCL7 gene from Populus euphratica. Beijing Linye Daxue Xuebao (Journal of Beijing Forestry University).2011;33:1–10.

37. Wang Y, Liu Z, Wu Z, Li H, Wang W, Cui X, et al. Genome-wide identification and expression analysis of GRAS family transcription factors in tea plant (Camellia sinensis). Sci Rep. 2018;8(1):3949. doi: 10.1038/s41598-018-22275-z 29500448

38. Hu R, Fan C, Li H, Zhang Q, Fu YF. Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR. BMC Molecular Biology.2009;10:93. doi: 10.1186/1471-2199-10-93 19785741

39. Jain M, Nijhawan A, Tyagi AK, Khurana JP. Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochem Biophys Res Commun. 2006;345(2):646–651. doi: 10.1016/j.bbrc.2006.04.140 16690022

40. Løvdal T, Lillo C. Reference gene selection for quantitative real-time PCR normalization in tomato subjected to nitrogen, cold, and light stress. Analytical Biochemistry.2009;387(2):238–242. doi: 10.1016/j.ab.2009.01.024 19454243


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