Identification and evaluation of reference genes for normalization in quantitative real-time PCR analysis in the premodel tree Betula luminifera

Abstract

Betula luminifera is a commercial tree species that is emerging as a new model system for tree genomics research. A draft genomic sequence is expected to be publicly available in the near future, which means that an explosion of gene expression studies awaits. Thus, the work of selecting appropriate reference genes for qPCR normalization in different tissues or under various experimental conditions is extremely valuable. In this study, ten candidate genes were analyzed in B. luminifera subjected to different abiotic stresses and at various flowering stages. The expression stability of these genes was evaluated using three distinct algorithms implemented using geNorm, NormFinder and BestKeeper. The best-ranked reference genes varied across different sample sets, though RPL39, MDH and EF1a were determined as the most stable by the three programs among all tested samples. RPL39 and EF1a should be appropriate for normalization in N-starved roots, while the combination of RPL39 and MDH should be appropriate for N-starved stems and EF1a and MDH should be appropriate in N-starved leaves. In PEG-treated (osmotic) roots, MDH was the most suitable, whereas EF1a was suitable for PEG-treated stems and leaves. TUA was also stably expressed levels in PEG-treated plants. The combination of RPL39 and TUB should be appropriate for heat-stressed leaves and flowering stage. For reference gene validation, the expression levels of SOD and NFYA-3 were investigated. This work will be beneficial to future studies on gene expression under different abiotic stress conditions and flowering status in B. luminifera.

Keywords

Reference genes / Gene expression / Betula luminifera / Abiotic stresses / Premodel tree

References

[1] Alscher RG, Erturk N, Heath LS. Role of superoxide dismutases (SODs) in controlling oxidative stress in plants. J Exp Bot, 2002, 53: 1331-1341.

[2] Amil-Ruiz F, Garrido-Gala J, Blanco-Portales R, Folta KM, Munoz-Blanco J, Caballero JL. Identification and validation of reference genes for transcript normalization in strawberry (Fragaria × ananassa) defense responses. PLoS One, 2013 8 8 e70603

[3] 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.

[4] Bigelow SW, Canham CD. Nutrient limitation of juvenile trees in a northern hardwood forest: calcium and nitrate are preeminent. For Ecol Manag, 2007, 243: 310-319.

[5] Brunner AM, Yakovlev IA, Strauss SH. Validating internal controls for quantitative plant gene expression studies. BMC Plant Biol, 2004, 4: 14.

[6] Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem, 2009, 55: 611-622.

[7] Chandna R, Augustine R, Bisht NC. Evaluation of Candidate Reference Genes for Gene Expression Normalization in Brassica juncea using real time quantitative RT-PCR. PLoS One, 2012 7 5 e36918

[8] Chen L, Zhong HY, Kuang JF, Li JG, Lu WJ, Chen JY. Validation of reference genes for RT-qPCR studies of gene expression in banana fruit under different experimental conditions. Planta, 2011, 234: 377-390.

[9] Cruz F, Kalaoun S, Nobile P, Colombo C, Almeida J, Barros LMG, Romano E, Grossi-de-Sa MF, Vaslin M, Alves-Ferreira M. Evaluation of coffee reference genes for relative expression studies by quantitative real-time RT-PCR. Mol Breed, 2009, 23: 607-616.

[10] 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: 5-17.

[11] de Carvalho K, Bespalhok JC, dos Santos TB, de Souza SGH, Vieira LGE, Pereira LFP, Domingues DS. Nitrogen starvation, salt and heat stress in coffee (Coffea arabica L.): identification and validation of new genes for qPCR normalization. Mol Biotechnol, 2013, 53: 315-325.

[12] Ding JY, Jia JW, Yang LT, Wen HB, Zhang CM, Liu WX, Zhang DB. Validation of a rice specific gene, sucrose phosphate synthase, used as the endogenous reference gene for qualitative and real-time quantitative PCR detection of transgenes. J Agric Food Chem, 2004, 52: 3372-3377.

[13] Guenin S, Mauriat M, Pelloux J, Van Wuytswinkel O, Bellini C, Gutierrez L. Normalization of qRT-PCR data: the necessity of adopting a systematic, experimental conditions-specific, validation of references. J Exp Bot, 2009, 60: 487-493.

[14] Gutierrez L, Mauriat M, Guenin S, Pelloux J, Lefebvre JF, Louvet R, Rusterucci C, Moritz T, Guerineau F, Bellini C, Van Wuytswinkel O. The lack of a systematic validation of reference genes: a serious pitfall undervalued in reverse transcription-polymerase chain reaction (RT-PCR) analysis in plants. Plant Biotechnol J, 2008, 6: 609-618.

[15] Hong SM, Bahn SC, Lyu A, Jung HS, Ahn JH. Identification and testing of superior reference genes for a starting pool of transcript normalization in Arabidopsis. Plant Cell Physiol, 2010, 51: 1694-1706.

[16] Huang HH, Jiang C, Tong ZK, Cheng LJ, Zhu MY, Lin EP. Eight distinct cellulose synthase catalytic subunit genes from Betula luminifera are associated with primary and secondary cell wall biosynthesis. Cellulose, 2014, 21: 2183-2198.

[17] Jian B, Liu B, Bi YR, Hou WS, Wu CX, Han TF. Validation of internal control for gene expression study in soybean by quantitative real-time PCR. BMC Mol Biol, 2008, 9: 1.

[18] Kong QS, Yuan JX, Niu PH, Xie JJ, Jiang W, Huang Y, Bie ZL. Screening suitable reference genes for normalization in reverse transcription quantitative real-time PCR analysis in melon. PLoS One, 2014 9 1 e87197

[19] Lovdal T, Lillo C. Reference gene selection for quantitative real-time PCR normalization in tomato subjected to nitrogen, cold, and light stress. Anal Biochem, 2009, 387: 238-242.

[20] Mittler R, Blumwald E. Genetic engineering for modern agriculture: challenges and perspectives. Annu Rev Plant Biol, 2010, 61: 443-462.

[21] Obrero A, Die JV, Roman B, Gomez P, Nadal S, Gonzalez-Verdejo CI. Selection of reference genes for gene expression studies in Zucchini (Cucurbita pepo) using qPCR. J Agric Food Chem, 2011, 59: 5402-5411.

[22] Paolacci AR, Tanzarella OA, Porceddu E, Ciaffi M. Identification and validation of reference genes for quantitative RT-PCR normalization in wheat. BMC Mol Biol, 2009, 10: 1.

[23] Park SC, Kim YH, Ji CY, Park S, Jeong JC, Lee HS, Kwak SS. Stable internal reference genes for the normalization of real-time PCR in different sweetpotato cultivars subjected to abiotic stress conditions. PLoS One, 2012 7 12 e51502

[24] Pfaffl MW. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res, 2001, 29: e45.

[25] 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.

[26] Podevin N, Krauss A, Henry I, Swennen R, Remy S. Selection and validation of reference genes for quantitative RT-PCR expression studies of the non-model crop Musa. Mol Breed, 2012, 30: 1237-1252.

[27] Silveira ED, Alves-Ferreira M, Guimaraes LA, da Silva FR, Carneiro VT. Selection of reference genes for quantitative real-time PCR expression studies in the apomictic and sexual grass Brachiaria brizantha. BMC Plant Biol, 2009, 9: 84.

[28] Udvardi MK, Czechowski T, Scheible WR. Eleven golden rules of quantitative RT-PCR. Plant Cell, 2008, 20: 1736-1737.

[29] Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol, 2002 3 7 1

[30] Wan HJ, Zhao ZG, Qian CT, Sui YH, Malik AA, Chen JF. Selection of appropriate reference genes for gene expression studies by quantitative real-time polymerase chain reaction in cucumber. Anal Biochem, 2010, 399: 257-261.

[31] Yin H, Chen QM, Yi MF. Effects of short-term heat stress on oxidative damage and responses of antioxidant system in Lilium longiflorum. Plant Growth Regul, 2008, 54: 45-54.

[32] Zhao M, Ding H, Zhu JK, Zhang F, Li WX. Involvement of miR169 in the nitrogen-starvation responses in Arabidopsis. New Phytol, 2011, 190: 906-915.

[33] Zhong HY, Chen JW, Li CQ, Chen L, Wu JY, Chen JY, Lu WJ, Li JG. Selection of reliable reference genes for expression studies by reverse transcription quantitative real-time PCR in litchi under different experimental conditions. Plant Cell Rep, 2011, 30: 641-653.

[34] 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.