Skip to main content

Advertisement

SpringerLink
Log in

Identification and evaluation of suitable reference genes for gene expression analysis in rubber tree leaf

  • Original Article
  • Published:
Molecular Biology Reports Aims and scope Submit manuscript

Abstract

Gene expression profiles are increasingly applied to investigate molecular mechanism for which, normalization with suitable reference genes is critical. Previously we have reported several suitable reference genes for laticifer samples from rubber tree, however, little is known in leaf. The main objective of this current study was to identify some stable expression reference genes at various developmental stages of leaf, as well as during abiotic (high and low temperature extremes) and biotic stresses (pathogen stress). Gene expression profilings identified the ubiquitin–proteasome system as excellent potential as reference genes for rubber tree leaf. Among a total of 30 tested genes investigated, 24 new candidate (including 11 genes involved in the ubiquitin–proteasome system), 4 previously identified and 2 specific genes, were further evaluated using quantitative real-time PCR. Our results indicated that the new candidate genes had better expression stability comparing with others. For instance, an ubiquitin conjugating enzyme (RG0099) and three ubiquitin-protein ligases (RG0928, RG2190 and RG0118) expressed stably in all samples, and were confirmed to be suitable reference genes for rubber tree leaf under four different conditions. Finally, we suggest that using more than one reference gene may be appropriate in gene expression studies when employing different software to normalize gene expression data. Our findings have significant implications for the reliability of data obtained from genomics studies in rubber tree and perhaps in other species.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. 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  PubMed  Google Scholar 

  2. Bevitori R, Oliveira MB, Grossi-de-Sa MF, Lanna AC, da Silveira RD, Petrofeza S (2014) Selection of optimized candidate reference genes for qRT-PCR normalization in rice (Oryza sativa L.) during Magnaporthe oryzae infection and drought. Genet Mol Res GMR 13:9795–9805

    Article  CAS  PubMed  Google Scholar 

  3. Bustin SA (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29:23–39

    Article  CAS  PubMed  Google Scholar 

  4. Chao J, Yang S, Chen Y, Tian WM (2016) Evaluation of reference genes for quantitative real-time PCR analysis of the gene expression in laticifers on the basis of latex flow in rubber tree (Hevea brasiliensis Muell. Arg.). Front Plant Sci 7:1149

    Article  PubMed  PubMed Central  Google Scholar 

  5. Chao J, Yang S, Chen Y, Tian WM (2017) Transcript profiling of Hevea brasiliensis during latex flow. Front Plant Sci 8:1904

    Article  PubMed  PubMed Central  Google Scholar 

  6. Chen J, Huang Z, Huang H, Wei S, Liu Y, Jiang C, Zhang J, Zhang C (2017) Selection of relatively exact reference genes for gene expression studies in goosegrass (Eleusine indica) under herbicide stress. Sci Rep 7:46494

    Article  PubMed  PubMed Central  Google Scholar 

  7. Cheng Y, Pang X, Wan H, Ahammed GJ, Yu J, Yao Z, Ruan M, Ye Q, Li Z, Wang R, Yang Y, Zhou G (2017) Identification of optimal reference genes for normalization of qPCR analysis during pepper fruit development. Front Plant Sci 8:1128

    Article  PubMed  PubMed Central  Google Scholar 

  8. Czechowski T, Stitt 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  CAS  PubMed  PubMed Central  Google Scholar 

  9. Dekkers BJ, Willems L, Bassel GW, van Bolderen-Veldkamp RP, Ligterink W, Hilhorst HW, Bentsink L (2012) Identification of reference genes for RT-qPCR expression analysis in Arabidopsis and tomato seeds. Plant Cell Physiol 53:28–37

    Article  CAS  PubMed  Google Scholar 

  10. Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, Rook GA, Zumla A (2005) The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem 344:141–143

    Article  CAS  PubMed  Google Scholar 

  11. Fang Y, Mei H, Zhou B, Xiao X, Yang M, Huang Y, Long X, Hu S, Tang C (2016) De novo transcriptome analysis reveals distinct defense mechanisms by young and mature leaves of Hevea brasiliensis (para rubber tree). Sci Rep 6:33151

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Galeano E, Vasconcelos TS, Ramiro DA, De Martin Vde F, Carrer H (2014) Identification and validation of quantitative real-time reverse transcription PCR reference genes for gene expression analysis in teak (Tectona grandis L.f.). BMC Res Notes 7:464

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gill G (2004) SUMO and ubiquitin in the nucleus: different functions, similar mechanisms? Genes Dev 18:2046–2059

    Article  CAS  PubMed  Google Scholar 

  14. Gu CS, Liu LQ, Xu C, Zhao YH, Zhu XD, Huang SZ (2014) Reference gene selection for quantitative real-time RT-PCR normalization in Iris. lactea var. chinensis roots under cadmium, lead, and salt stress conditions. Sci World J 2014:532713

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  16. Gutierrez L, Mauriat M, Guenin S, Pelloux J, Lefebvre JF, Louvet R, Rusterucci C, Moritz T, Guerineau F, Bellini C, Van Wuytswinkel O (2008) 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 6:609–618

    Article  CAS  PubMed  Google Scholar 

  17. Hu Y, Fu H, Qiao H, Sun S (2018) Validation and evaluation of reference genes for quantitative real-time PCR in Macrobrachium nipponense. Int Mol Sci. https://doi.org/10.3390/ijms19082258

    Article  Google Scholar 

  18. Huang Y, Fang Y, Long X, Liu L, Wang J, Zhu J, Ma Y, Qin Y, Qi J, Hu X, Tang C (2018) Characterization of the rubber tree metallothionein family reveals a role in mitigating the effects of reactive oxygen species associated with physiological stress. Tree Physiol 38:911–924

    Article  CAS  PubMed  Google Scholar 

  19. Huggett J, Dheda K, Bustin S, Zumla A (2005) Real-time RT-PCR normalisation; strategies and considerations. Genes Immun 6:279–284

    Article  CAS  PubMed  Google Scholar 

  20. Ji Y, Tu P, Wang K, Gao F, Yang W, Zhu Y, Li S (2014) Defining reference genes for quantitative real-time PCR analysis of anther development in rice. Acta Biochim Biophys Sin 46:305–312

    Article  CAS  PubMed  Google Scholar 

  21. Joseph JT, Poolakkalody NJ, Shah JM (2018) Plant reference genes for development and stress response studies. J Biosci 43:173–187

    Article  CAS  PubMed  Google Scholar 

  22. Kanakachari M, Solanke AU, Prabhakaran N, Ahmad I, Dhandapani G, Jayabalan N, Kumar PA (2016) Evaluation of suitable reference genes for normalization of qPCR gene expression studies in brinjal (Solanum melongena L.) during fruit developmental stages. Appl Biochem Biotechnol 178:433–450

    Article  CAS  PubMed  Google Scholar 

  23. Kudo T, Sasaki Y, Terashima S, Matsuda-Imai N, Takano T, Saito M, Kanno M, Ozaki S, Suwabe K, Suzuki G, Watanabe M, Matsuoka M, Takayama S, Yano K (2016) Identification of reference genes for quantitative expression analysis using large-scale RNA-seq data of Arabidopsis thaliana and model crop plants. Genes Genet Systems 91:111–125

    Article  CAS  Google Scholar 

  24. Li H, Qin Y, Xiao X, Tang C (2011) Screening of valid reference genes for real-time RT-PCR data normalization in Hevea brasiliensis and expression validation of a sucrose transporter gene HbSUT3. Plant Sci 181:132–139

    Article  CAS  PubMed  Google Scholar 

  25. Lieberei R (2007) South American leaf blight of the rubber tree (Hevea spp.): new steps in plant domestication using physiological features and molecular markers. Ann Bot 100:1125–1142

    Article  PubMed  PubMed Central  Google Scholar 

  26. Lin F, Jiang L, Liu Y, Lv Y, Dai H, Zhao H (2014) Genome-wide identification of housekeeping genes in maize. Plant Mol Biol 86:543–554

    Article  CAS  PubMed  Google Scholar 

  27. Lin Y, Zhang C, Lan H, Gao S, Liu H, Liu J, Cao M, Pan G, Rong T, Zhang S (2014) Validation of potential reference genes for qPCR in maize across abiotic stresses, hormone treatments, and tissue types. PLoS ONE 9:e95445

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Liu J, Huang S, Niu X, Chen D, Chen Q, Tian L, Xiao F, Liu Y (2018) Genome-wide identification and validation of new reference genes for transcript normalization in developmental and post-harvested fruits of Actinidia chinensis. Gene 645:1–6

    Article  CAS  PubMed  Google Scholar 

  29. Liu JP, Hu J, Liu YH, Yang CP, Zhuang YF, Guo XL, Li YJ, Zhang L (2018) Transcriptome analysis of Hevea brasiliensis in response to exogenous methyl jasmonate provides novel insights into regulation of jasmonate-elicited rubber biosynthesis. Physiol Mol Biol Plants 24:349–358

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Liu S, Lan J, Zhou B, Qin Y, Zhou Y, Xiao X, Yang J, Gou J, Qi J, Huang Y, Tang C (2015) HbNIN2, a cytosolic alkaline/neutral-invertase, is responsible for sucrose catabolism in rubber-producing laticifers of Hevea brasiliensis (para rubber tree). New Phytol 206:709–725

    Article  CAS  PubMed  Google Scholar 

  31. Long X, He B, Fang Y, Tang C (2016) Identification and characterization of the glucose-6-phosphate dehydrogenase gene family in the para rubber tree Hevea brasiliensis. Front Plant Sci 7:215

    PubMed  PubMed Central  Google Scholar 

  32. Long X, He B, Gao X, Qin Y, Yang J, Fang Y, Qi J, Tang C (2015) Validation of reference genes for quantitative real-time PCR during latex regeneration in rubber tree. Gene 563:190–195

    Article  CAS  PubMed  Google Scholar 

  33. Long XY, Wang JR, Ouellet T, Rocheleau H, Wei YM, Pu ZE, Jiang QT, Lan XJ, Zheng YL (2010) Genome-wide identification and evaluation of novel internal control genes for Q-PCR based transcript normalization in wheat. Plant Mol Biol 74:307–311

    Article  CAS  PubMed  Google Scholar 

  34. Moraes GP, Benitez LC, do Amaral, M.N., Vighi, I.L., Auler, P.A., da Maia, L.C., Bianchi, V.J., Braga, E.J. (2015) Evaluation of reference genes for RT-qPCR studies in the leaves of rice seedlings under salt stress. Genet Mol Res GMR 14:2384–2398

    Article  CAS  PubMed  Google Scholar 

  35. Niu L, Tao YB, Chen MS, Fu Q, Li C, Dong Y, Wang X, He H, Xu ZF (2015) Selection of reliable reference genes for gene expression studies of a promising oilseed crop, Plukenetia volubilis, by real-time quantitative PCR. Int J Mol Sci 16:12513–12530

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Niu X, Chen M, Huang X, Chen H, Tao A, Xu J, Qi J (2017) Reference gene selection for qRT-PCR normalization analysis in kenaf (Hibiscus cannabinus L.) under abiotic stress and hormonal stimuli. Front Plant Sci 8:771

    Article  PubMed  PubMed Central  Google Scholar 

  37. Pabuayon, I.M., Yamamoto, N., Trinidad, J.L., Longkumer, T., Raorane, M.L., Kohli, A. (2016). Reference genes for accurate gene expression analyses across different tissues, developmental stages and genotypes in rice for drought tolerance. Rice (New York, NY) 9, 32.

  38. 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  PubMed  Google Scholar 

  39. Qin Y, Huang Y, Fang Y, Qi J, Tang C (2014) Molecular characterization and expression analysis of the small GTPase ROP members expressed in laticifers of the rubber tree (Hevea brasiliensis). Plant Physiol Biochem PPB 74:193–204

    Article  CAS  PubMed  Google Scholar 

  40. Sato T, Maekawa S, Yasuda S, Yamaguchi J (2011) Carbon and nitrogen metabolism regulated by the ubiquitin-proteasome system. Plant Signal Behav 6:1465–1468

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 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  CAS  PubMed  PubMed Central  Google Scholar 

  42. Storch TT, Pegoraro C, Finatto T, Quecini V, Rombaldi CV, Girardi CL (2015) Identification of a novel reference gene for apple transcriptional profiling under postharvest conditions. PLoS ONE 10:e0120599

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Tang C, Huang D, Yang J, Liu S, Sakr S, Li H, Zhou Y, Qin Y (2010) The sucrose transporter HbSUT3 plays an active role in sucrose loading to laticifer and rubber productivity in exploited trees of Hevea brasiliensis (para rubber tree). Plant, Cell Environ 33:1708–1720

    Article  CAS  Google Scholar 

  44. Tang C, Xiao X, Li H, Fan Y, Yang J, Qi J, Li H (2013) Comparative analysis of latex transcriptome reveals putative molecular mechanisms underlying super productivity of Hevea brasiliensis. PLoS ONE 8:e75307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Tang C, Yang M, Fang Y, Luo Y, Gao S, Xiao X, An Z, Zhou B, Zhang B, Tan X, Yeang HY (2016) The rubber tree genome reveals new insights into rubber production and species adaptation. Nat Plants 2:16073

    Article  CAS  PubMed  Google Scholar 

  46. Tang F, Chu L, Shu W, He X, Wang L, Lu M (2019) Selection and validation of reference genes for quantitative expression analysis of miRNAs and mRNAs in Poplar. Plant Methods 15:35

    Article  PubMed  PubMed Central  Google Scholar 

  47. Udvardi MK, Czechowski T, Scheible WR (2008) Eleven golden rules of quantitative RT-PCR. Plant Cell 20:1736–1737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 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(7).

    Article  Google Scholar 

  49. Wan H, Yuan W, Ruan M, Ye Q, Wang R, Li Z, Zhou G, Yao Z, Zhao J, Liu S, Yang Y (2011) Identification of reference genes for reverse transcription quantitative real-time PCR normalization in pepper (Capsicum annuum L.). Biochem Biophys Res Commun 416:24–30

    Article  CAS  PubMed  Google Scholar 

  50. Wan Q, Chen S, Shan Z, Yang Z, Chen L, Zhang C, Yuan S, Hao Q, Zhang X, Qiu D, Chen H (2017) Stability evaluation of reference genes for gene expression analysis by RT-qPCR in soybean under different conditions. PLoS ONE 12:e0189405

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wang M, Wang Q, Zhang B (2013) Evaluation and selection of reliable reference genes for gene expression under abiotic stress in cotton (Gossypium hirsutum L.). Gene 530:44–50

    Article  CAS  PubMed  Google Scholar 

  52. Warzybok A, Migocka M (2013) Reliable reference genes for normalization of gene expression in cucumber grown under different nitrogen nutrition. PLoS ONE 8:e72887

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wu D, Dong J, Yao YJ, Zhao WC, Gao X (2015) Identification and evaluation of endogenous control genes for use in quantitative RT-PCR during wheat (Triticum aestivum L.) grain filling. Genet Mol Res GMR 14:10530–10542

    Article  CAS  PubMed  Google Scholar 

  54. Xia W, Mason AS, Xiao Y, Liu Z, Yang Y, Lei X, Wu X, Ma Z, Peng M (2014) Analysis of multiple transcriptomes of the African oil palm (Elaeis guineensis) to identify reference genes for RT-qPCR. J Biotechnol 184:63–73

    Article  CAS  PubMed  Google Scholar 

  55. Xu Y, Zhu X, Gong Y, Xu L, Wang Y, Liu L (2012) Evaluation of reference genes for gene expression studies in radish (Raphanus sativus L.) using quantitative real-time PCR. Biochem Biophys Res Commun 424:398–403

    Article  CAS  PubMed  Google Scholar 

  56. Yamao F (1999) Ubiquitin system: selectivity and timing of protein destruction. J Biochem 125:223–229

    Article  CAS  PubMed  Google Scholar 

  57. Ye J, Jin CF, Li N, Liu MH, Fei ZX, Dong LZ, Li L, Li ZQ (2018) Selection of suitable reference genes for qRT-PCR normalisation under different experimental conditions in Eucommia ulmoides Oliv. Sci Rep 8:15043

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Zhao S, Torres A, Henry RA, Trefely S, Wallace M, Lee JV, Carrer A, Sengupta A, Campbell SL, Kuo YM, Frey AJ, Meurs N, Viola JM, Blair IA, Weljie AM, Metallo CM, Snyder NW, Andrews AJ, Wellen KE (2016) ATP-citrate lyase controls a glucose-to-acetate metabolic switch. Cell Rep 17:1037–1052

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Zhu J, Qi J, Fang Y, Xiao X, Li J, Lan J, Tang C (2018) Characterization of sugar contents and sucrose metabolizing enzymes in developing leaves of Hevea brasiliensis. Front Plant Sci 9:58

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 31770709), the Central Public-interest Scientific Institution Basal Research Fund for Innovative Research Team Program of CATAS (No. 17CXTD-28) and the Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences (Nos. 1630022018019, 1630022017003, 1630022019016). Original idea was conceived by XY Long. JL Lu designed the experimental plan, collected latex samples and extracted RNA samples. JL Lu, YX Qin and YJ Fang executed the experimental work and performed data analysis. XY Long and Nat N. V. Kav wrote the manuscript. All authors read, edited, and approved the final manuscript.

Author information

Author notes

  1. Xiangyu Long and Jilai Lu contributed equally to this work.

Authors and Affiliations

  1. Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou, 571101, Hainan, China

    Xiangyu Long, Jilai Lu, Yunxia Qin & Yongjun Fang

  2. Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, T6G 2P5, Canada

    Nat N. V. Kav

  3. College of Forestry, Hainan University, Haikou, 570228, Hainan, China

    Jilai Lu

Authors
  1. Xiangyu Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jilai Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nat N. V. Kav
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunxia Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yongjun Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangyu Long.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

11033_2020_5288_MOESM1_ESM.xlsx

Supplementary file1 (XLSX 45 kb) Fig. S1 Expression profile of 82 candidate reference genes under develoment stage and temperture stress of leaf. Fig. S2 Cluster of four experiments according to ranking of expression stability.

Supplementary file2 (TIF 2093 kb)

Supplementary file3 (TIF 2605 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Long, X., Lu, J., Kav, N.N.V. et al. Identification and evaluation of suitable reference genes for gene expression analysis in rubber tree leaf. Mol Biol Rep 47, 1921–1933 (2020). https://doi.org/10.1007/s11033-020-05288-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11033-020-05288-8

Keywords

Search

Navigation