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Reference gene selection for quantitative RT-PCR in Miscanthus sacchariflorus under abiotic stress conditions

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Abstract

Background

Reference genes are necessary for quantitative real-time PCR (qRT-PCR) analysis and their stability can directly influence the accuracy of gene expression result. Miscanthus sacchariflorus, a perennial C4 grass that serves as promising biofuel plant for temperate climates, has not been explored for the identification of stable reference genes yet.

Materials and methods

Nine potential reference genes (ACT, EF1a, FBOX, GAPDH, PP2A, SAND, TIP41, TUB and UBC) of M. sacchariflorus under different abiotic (salinity, drought and cadmium) stresses, as well as in two tissues (roots and leaves) were evaluated. The expression stability of these genes were analyzed by four commonly used software programs (geNorm, NormFinder, BestKeeper, ΔCt method and RefFinder).

Results

Our results found that FBOX and SAND are the most stable genes among all tested samples. FBOX and EF1a are suitable for gene expression normalization of cadmium-treated samples and salinity-treated leaves. FBOX and PP2A are appropriate reference genes for salt-stressed roots and PEG-treated leaves. The traditional reference gene ACT and GAPDH exhibited the most variable pattern, which would not be recommended for qRT-PCR analysis under different abiotic stresses. Furthermore, the expression levels of PIP2, NHX1 and MT2a under drought, salt and cadmium treatment were detected with above reference genes.

Conclusions

This work identified the appropriate reference genes for qRT-PCR in M. sacchariflorus and FBOX was recommended to be effective internal control for gene expression normalization in M. sacchariflorus in response to different abiotic stresses.

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References

  1. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:34

    Article  Google Scholar 

  2. Czechowski TStitt M, Altmann T, Udvardi MK, Scheible WR (2005) Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol 139:5–17

    Article  Google Scholar 

  3. Nicot N, Hausman JF, Hoffmann L, Evers D (2005) Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress. J Exp Bot 56:2907–2914

    Article  CAS  Google Scholar 

  4. Li W, Qian YQ, Han L, Liu JX, Sun ZY (2014) Identification of suitable reference genes in buffalo grass for accurate transcript normalization under various abiotic stress conditions. Gene 547:55–62

    Article  CAS  Google Scholar 

  5. Saraiva KC, Fernandes de Melo D, Morais V, Vasconcelos I, Costa J (2014) Selection of suitable soybean EF1α genes as internal controls for real-time PCR analyses of tissues during plant development and under stress conditions. Plant Cell Rep 33:1453–1465

    Article  CAS  Google Scholar 

  6. Gao MM, Liu YP, Ma X, Shuai Q, Gai JY, Li Y (2017) Evaluation of reference genes for normalization of gene expression using quantitative RT-PCR under aluminum, cadmium, and heat stresses in soybean. PLoS ONE 12:e0168965

    Article  Google Scholar 

  7. Zhang Y, Han X, Chen S, Zheng L, He X, Liu M, Qiao G, Wang Y, Zhuo R (2017) Selection of suitable reference genes for quantitative real-time PCR gene expression analysis in Salix matsudana under different abiotic stresses. Sci Rep 7:40290

    Article  CAS  Google Scholar 

  8. Niu K, Shi Y, Ma H (2017) Selection of candidate reference genes for gene expression analysis in Kentucky Bluegrass (Poa pratensis L.) under abiotic stress. Front Plant Sci 8:193

    PubMed  PubMed Central  Google Scholar 

  9. Wang HL, Chen J, Tian Q, Wang S, Xia X, Yin W (2014) Identification and validation of reference genes for Populus euphratica gene expression analysis during abiotic stresses by quantitative real-time PCR. Physiol Plant 152:529–545

    Article  CAS  Google Scholar 

  10. Yang Q, Yin J, Li G, Qi L, Yang F, Wang R (2014) Reference gene selection for qRT-PCR in Caragana korshinskii Kom. under different stress conditions. Mol Biol Rep 41:2325–2334

    Article  CAS  Google Scholar 

  11. Chi C, Shen YQ, Yin LH, Ke XW, Han D, Zuo YH (2016) Selection and validation of reference genes for gene expression analysis in Vigna angularis using quantitative real-time RT-PCR. PLoS ONE 11:e0168479

    Article  Google Scholar 

  12. Liu Y, Liu J, Xu L, Lai H, Chen Y, Yang Z, Huang B (2017) Identification and validation of reference genes for seashore paspalum response to abiotic stresses. Int J Mol Sci 18:1322

    Article  Google Scholar 

  13. Demidenko NV, Logacheva MD, Penin AA (2011) Selection and validation of reference genes for quantitative real-time PCR in buckwheat (Fagopyrum esculentum) based on transcriptome sequence data. PLoS ONE 6:e19434

    Article  CAS  Google Scholar 

  14. Marum L, Miguel A, Ricardo CP, Miguel C (2012) Reference gene selection for quantitative real-time PCR normalization in Quercus suber. PLoS ONE 7:e35113

    Article  CAS  Google Scholar 

  15. Zhu J, Zhang L, Li W, Han S, Yang W, Qi L (2013) Reference gene selection for quantitative real-time PCR normalization in Caragana intermedia under different abiotic stress conditions. PLoS ONE 8:e53196

    Article  CAS  Google Scholar 

  16. Atkinson CJ (2009) Establishing perennial grass energy crops in the UK: a review of current propagation options for Miscanthus. Biomass Bioenergy 33:752–759

    Article  Google Scholar 

  17. Lalitha S (2005) Primer Premier 5. Biotech Softw Internet Rep 1:270–272

    Article  Google Scholar 

  18. Ruijter JM, Ramakers C, Hoogaars WM, Karlen Y, Bakker O, Van den Hoff MJ, Moorman AF (2009) Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res 37:e45

    Article  CAS  Google Scholar 

  19. Andersen CL, Jensen JL, Orntoft TF (2004) 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 64:5245–5250

    Article  CAS  Google Scholar 

  20. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP (2004) Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper—Excel-based tool using pair-wise correlations. Biotechnol Lett 26:509–515

    Article  CAS  Google Scholar 

  21. Silver N, Best S, Jiang J, Thein SL (2006) Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol 7:33

    Article  Google Scholar 

  22. Xie F, Xiao P, Chen D, Xu L, Zhang B (2012) miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs. Plant Mol Biol 80:75–84

    Article  CAS  Google Scholar 

  23. Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29:e45

    Article  CAS  Google Scholar 

  24. Chen Y, Chen C, Tan Z, Liu J, Zhuang L, Yang Z, Huang B (2016) Functional identification and characterization of genes cloned from halophyte seashore paspalum conferring salinity and cadmium tolerance. Front Plant Sci 7:102

    PubMed  PubMed Central  Google Scholar 

  25. Chen Y, Jiang J, Chang Q, Gu C, Song A, Chen S, Dong B, Chen F (2014) Cold acclimation induces freezing tolerance via antioxidative enzymes, proline metabolism and gene expression changes in two Chrysanthemum species. Mol Biol Rep 41:815–822

    Article  CAS  Google Scholar 

  26. Chen Y, Jiang J, Song A, Chen S, Shan H, Luo H, Gu C, Sun J, Zhu L, Fang W et al (2013) Ambient temperature enhanced freezing tolerance of Chrysanthemum dichrum CdICE1 Arabidopsis via miR398. BMC Biol 11:121

    Article  Google Scholar 

  27. Gimeno J, Eattock N, Van Deynze A, Blumwald E (2014) Selection and validation of reference genes for gene expression analysis in switchgrass (Panicum virgatum) using quantitative real-time RT-PCR. PLoS ONE 9:e91474

    Article  Google Scholar 

  28. Chen Y, Tan Z, Hu B, Yang Z, Xu B, Zhuang L, Huang B (2015) Selection and validation of reference genes for target gene analysis with quantitative RT-PCR in leaves and roots of bermudagrass under four different abiotic stresses. Physiol Plant 155:138–148

    Article  CAS  Google Scholar 

  29. Chen Y, Hu B, Tan Z, Liu J, Yang Z, Li Z, Huang B (2015) Selection of reference genes for quantitative real-time PCR normalization in creeping bentgrass involved in four abiotic stresses. Plant Cell Rep 34:1825–1834

    Article  CAS  Google Scholar 

  30. Yang Z, Chen Y, Hu B, Tan Z, Huang B (2015) Identification and validation of reference genes for quantification of target gene expression with quantitative real-time PCR for tall fescue under four abiotic stresses. PLoS ONE 10:e0119569

    Article  Google Scholar 

  31. Lin L, Han X, Chen Y, Wu Q, Wang Y (2013) Identification of appropriate reference genes for normalizing transcript expression by quantitative real-time PCR in Litsea cubeba. Mol Genet Genomics 288:727–737

    Article  CAS  Google Scholar 

  32. He YH, Yan HL, Hua WP, Huang YY, Wang ZZ (2016) Selection and validation of reference genes for quantitative real-time PCR in gentiana macrophylla. Front Plant Sci 7:945

    PubMed  PubMed Central  Google Scholar 

  33. Kundu A, Patel A, Pal A (2013) Defining reference genes for qPCR normalization to study biotic and abiotic stress responses in Vigna mungo. Plant Cell Rep 32:1647–1658

    Article  CAS  Google Scholar 

  34. Ma SH, Niu HW, Liu CJ, Zhang J, Hou CY, Wang DM (2013) Expression stabilities of candidate reference genes for RT-qPCR under different stress conditions in soybean. PLoS ONE 8:e75271

    Article  CAS  Google Scholar 

  35. Gu C, Chen S, Liu Z, Shan H, Luo H, Guan Z, Chen F (2011) Reference gene selection for quantitative real-time PCR in Chrysanthemum subjected to biotic and abiotic stress. Mol Biotechnol 49:192–7

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  37. Reid KE, Olsson N, Schlosser J, Peng F, Lund ST (2006) An optimized grapevine RNA isolation procedure and statistical determination of reference genes for real-time RT-PCR during berry development. BMC Plant Biol 6:27

    Article  Google Scholar 

  38. Gu CS, Liu LQ, Zhao YH, Deng YM, Zhu XD, Huang SZ (2014) Overexpression of Iris. lactea var. chinensis metallothionein llMT2a enhances cadmium tolerance in Arabidopsis thaliana. Ecotoxicology and Environmental Safety 105: 22-28

  39. Wu GQ, Xi JJ, Wang Q, Bao AK, Ma Q, Zhang JL, Wang SM (2011) The ZxNHX gene encoding tonoplast Na(+)/H(+) antiporter from the xerophyte Zygophyllum xanthoxylum plays important roles in response to salt and drought. J Plant Physiol 168:758–767

    Article  CAS  Google Scholar 

Download references

Funding

This work was supported by the the Program of National Natural Science Foundation of China (31771870, 31201262), Natural Science Foundation of Jiangsu Province (BK2012790) and Public Science and Technology Research Funds Projects of Ocean (201505023).

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Contributions

JZ, JC, JL and JxL conceived the study and designed the experiments. JZ, JC and LL performed the experiments. JjL and DL analyzed the data with suggestions by JL and JxL. JW provided in material. JZ and JL wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jun Liu or Jianxiu Liu.

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The authors have no conflict of interest.

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The present research work did not involve human participants and/or animals.

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Zong, J., Chen, J., Li, L. et al. Reference gene selection for quantitative RT-PCR in Miscanthus sacchariflorus under abiotic stress conditions. Mol Biol Rep 49, 907–915 (2022). https://doi.org/10.1007/s11033-021-06902-z

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  • DOI: https://doi.org/10.1007/s11033-021-06902-z

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