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Reliable Selection and Holistic Stability Evaluation of Reference Genes for Rice Under 22 Different Experimental Conditions

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Abstract

Stable and uniform expression of reference genes across samples plays a key role in accurate normalization of gene expression by reverse-transcription quantitative polymerase chain reaction (RT-qPCR). For rice study, there is still a lack of validation and recommendation of appropriate reference genes with high stability depending on experimental conditions. Eleven candidate reference genes potentially owning high stability were evaluated by geNorm and NormFinder for their expression stability in 22 various experimental conditions. Best combinations of multiple reference genes were recommended depending on experimental conditions, and the holistic stability of reference genes was also evaluated. Reference genes would become more variable and thus needed to be critically selected in experimental groups of tissues, heat, 6-benzylamino purine, and drought, but they were comparatively stable under cold, wound, and ultraviolet-B stresses. Triosephosphate isomerase (TI), profilin-2 (Profilin-2), ubiquitin-conjugating enzyme E2 (UBC), endothelial differentiation factor (Edf), and ADP-ribosylation factor (ARF) were stable in most of our experimental conditions. No universal reference gene showed good stability in all experimental conditions. To get accurate expression result, suitable combination of multiple reference genes for a specific experimental condition would be a better choice. This study provided an application guideline to select stable reference genes for rice gene expression study.

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Abbreviations

RT-qPCR:

Reverse-transcription quantitative polymerase chain reaction

UBQ5 :

Ubiquitin5

EF-1α :

Eukaryotic elongation factor-1 alpha

UBC :

Ubiquitin-conjugating enzyme E2

ARF :

ADP-ribosylation factor

TI :

Triosephosphate isomerase

RTP :

Retrotransposon protein

Ppcti :

Peptidyl-prolyl cis-trans isomerase

Edf :

Endothelial differentiation factor

PtfS :

Protein translation factor SUI1

UV:

Ultraviolet

6BA:

6-Benzylamino purine

IAA:

Indole-3-acetic acid

ABA:

Abscisic acid

GA:

Gibberellic acid

SA:

Salicylic acid

H40:

Heat of 40 °C

Cold4:

Cold of 4 °C

Sub:

Submergence

PEG:

Polyethylene glycol

Dr:

Drought

UV-B:

Ultraviolet-B

RSLP:

Root, stem, leaf, and panicle

Tm:

Melting temperature

V:

Pairwise variation

SD:

Standard deviation

References

  1. Huang, J., Zhao, X., Weng, X., Wang, L., & Xie, W. (2012). The rice B-box zinc finger gene family: genomic identification, characterization, expression profiling and diurnal analysis. PloS One, 7, e48242.

    Article  CAS  Google Scholar 

  2. Qin, Y., Shen, X., Wang, N., & Ding, X. (2015). Characterization of a novel cyclase-like gene family involved in controlling stress tolerance in rice. Journal of Plant Physiology, 181, 30–41.

    Article  CAS  Google Scholar 

  3. Gachon, C., Mingam, A., & Charrier, B. (2004). Real-time PCR: what relevance to plant studies? Journal of Experimental Botany, 55, 1445–1454.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. 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 Biology, 3 RESEARCH0034.

  6. Nolan, T., Hands, R. E., & Bustin, S. A. (2006). Quantification of mRNA using real-time RT-PCR. Nature Protocols, 1, 1559–1582.

    Article  CAS  Google Scholar 

  7. Bustin, S. A. (2002). Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. Journal of Molecular Endocrinology, 29, 23–39.

    Article  CAS  Google Scholar 

  8. Radonic, A., Thulke, S., Mackay, I. M., Landt, O., Siegert, W., & Nitsche, A. (2004). Guideline to reference gene selection for quantitative real-time PCR. Biochemical and Biophysical Research Communications, 313, 856–862.

    Article  CAS  Google Scholar 

  9. Thomas, C., Meyer, D., Wolff, M., Himber, C., Alioua, M., & Steinmetz, A. (2003). Molecular characterization and spatial expression of the sunflower ABP1 gene. Plant Molecular Biology, 52, 1025–1036.

    Article  CAS  Google Scholar 

  10. Jain, M., Nijhawan, A., Tyagi, A. K., & Khurana, J. P. (2006). Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochemical and Biophysical Research Communications, 345, 646–651.

    Article  CAS  Google Scholar 

  11. Wan, H., Zhao, Z., Qian, C., Sui, Y., Malik, A. A., & Chen, J. (2010). Selection of appropriate reference genes for gene expression studies by quantitative real-time polymerase chain reaction in cucumber. Analytical Biochemistry, 399, 257–261.

    Article  CAS  Google Scholar 

  12. Gutierrez, L., Mauriat, M., Guenin, S., Pelloux, J., Lefebvre, J. F., Louvet, R., Rusterucci, C., Moritz, T., Guerineau, F., Bellini, C., et al. (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 Biotechnology Journal, 6, 609–618.

    Article  CAS  Google Scholar 

  13. Gutierrez, L., Mauriat, M., Pelloux, J., Bellini, C., & Van Wuytswinkel, O. (2008). Towards a systematic validation of references in real-time rt-PCR. The Plant Cell, 20, 1734–1735.

    Article  CAS  Google Scholar 

  14. Glare, E. M., Divjak, M., Bailey, M. J., & Walters, E. H. (2002). beta-Actin and GAPDH housekeeping gene expression in asthmatic airways is variable and not suitable for normalising mRNA levels. Thorax, 57, 765–770.

    Article  CAS  Google Scholar 

  15. Galli, V., da Silva Messias, R., Dos Anjos, E.S.S.D., & Rombaldi, C.V. (2013). Selection of reliable reference genes for quantitative real-time polymerase chain reaction studies in maize grains. Plant Cell Reports.

  16. Borges, A., Tsai, S. M., & Caldas, D. G. (2012). Validation of reference genes for RT-qPCR normalization in common bean during biotic and abiotic stresses. Plant Cell Reports, 31, 827–838.

    Article  CAS  Google Scholar 

  17. Kanakachari, M., Solanke, A.U., Prabhakaran, N., Ahmad, I., Dhandapani, G., Jayabalan, N., & Kumar, P.A. (2015) Evaluation of Suitable Reference Genes for Normalization of qPCR Gene Expression Studies in Brinjal (Solanum melongena L.) During Fruit Developmental Stages. Applied Biochemistry and Biotechnology.

  18. Li, Q.-F., Sun, S. S. M., Yuan, D.-Y., Yu, H.-X., Gu, M.-H., & Liu, Q.-Q. (2009). Validation of candidate reference genes for the accurate normalization of real-time quantitative RT-PCR data in rice during seed development. Plant Molecular Biology Reporter, 28, 49–57.

    Article  Google Scholar 

  19. Jain, M. (2009). Genome-wide identification of novel internal control genes for normalization of gene expression during various stages of development in rice. Plant Science, 176, 702–706.

    Article  CAS  Google Scholar 

  20. Wang, L., Xie, W., Chen, Y., Tang, W., Yang, J., Ye, R., Liu, L., Lin, Y., Xu, C., Xiao, J., et al. (2010). A dynamic gene expression atlas covering the entire life cycle of rice. The Plant Journal: for Cell and Molecular Biology, 61, 752–766.

    Article  CAS  Google Scholar 

  21. Narsai, R., Ivanova, A., Ng, S., & Whelan, J. (2010). Defining reference genes in Oryza sativa using organ, development, biotic and abiotic transcriptome datasets. BMC Plant Biology, 10, 56.

    Article  Google Scholar 

  22. Tricarico, C., Pinzani, P., Bianchi, S., Paglierani, M., Distante, V., Pazzagli, M., Bustin, S. A., & Orlando, C. (2002). Quantitative real-time reverse transcription polymerase chain reaction: normalization to rRNA or single housekeeping genes is inappropriate for human tissue biopsies. Analytical Biochemistry, 309, 293–300.

    Article  CAS  Google Scholar 

  23. Chandna, R., Augustine, R., & Bisht, N. C. (2012). Evaluation of candidate reference genes for gene expression normalization in Brassica juncea using real time quantitative RT-PCR. PloS One, 7, e36918.

    Article  CAS  Google Scholar 

  24. Galli, V., Borowski, J. M., Perin, E. C., Messias Rda, S., Labonde, J., Pereira Idos, S., Silva, S. D., & Rombaldi, C. V. (2015). Validation of reference genes for accurate normalization of gene expression for real time-quantitative PCR in strawberry fruits using different cultivars and osmotic stresses. Gene, 554, 205–214.

    Article  CAS  Google Scholar 

  25. 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  Google Scholar 

  26. 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. Journal of Experimental Botany, 60, 487–493.

    Article  CAS  Google Scholar 

  27. Huang, L., Yan, H., Jiang, X., Zhang, Y., Zhang, X., Ji, Y., Zeng, B., Xu, B., Yin, G., Lee, S., et al. (2014). Reference gene selection for quantitative real-time reverse-transcriptase PCR in orchardgrass subjected to various abiotic stresses. Gene, 553, 158–165.

    Article  CAS  Google Scholar 

  28. Chang, E., Shi, S., Liu, J., Cheng, T., Xue, L., Yang, X., Yang, W., Lan, Q., & Jiang, Z. (2012). Selection of reference genes for quantitative gene expression studies in Platycladus orientalis (Cupressaceae) Using real-time PCR. PloS One, 7, e33278.

    Article  CAS  Google Scholar 

  29. Andersen, C. L., Jensen, J. L., & Orntoft, T. F. (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 Research, 64, 5245–5250.

    Article  CAS  Google Scholar 

  30. Song, Y., Wang, L., & Xiong, L. (2009). Comprehensive expression profiling analysis of OsIAA gene family in developmental processes and in response to phytohormone and stress treatments. Planta, 229, 577–591.

    Article  CAS  Google Scholar 

  31. Zhu, M., Wang, L., & Pan, Q. (2004). Identification and characterization of a new blast resistance gene located on rice chromosome 1 through linkage and differential analyses. Phytopathology, 94, 515–519.

    Article  CAS  Google Scholar 

  32. Gruber, H., Heijde, M., Heller, W., Albert, A., Seidlitz, H. K., & Ulm, R. (2010). Negative feedback regulation of UV-B-induced photomorphogenesis and stress acclimation in Arabidopsis. Proceedings of the National Academy of Sciences of the United States of America, 107, 20132–20137.

    Article  CAS  Google Scholar 

  33. Udvardi, M. K., Czechowski, T., & Scheible, W. R. (2008). Eleven golden rules of quantitative RT-PCR. The Plant Cell, 20, 1736–1737.

    Article  CAS  Google Scholar 

  34. Ishitani, R., Sunaga, K., Hirano, A., Saunders, P., Katsube, N., & Chuang, D. M. (1996). Evidence that glyceraldehyde-3-phosphate dehydrogenase is involved in age-induced apoptosis in mature cerebellar neurons in culture. Journal of Neurochemistry, 66, 928–935.

    Article  CAS  Google Scholar 

  35. Singh, R., & Green, M. R. (1993). Sequence-specific binding of transfer RNA by glyceraldehyde-3-phosphate dehydrogenase. Science, 259, 365–368.

    Article  CAS  Google Scholar 

  36. Sikand, K., Singh, J., Ebron, J. S., & Shukla, G. C. (2012). Housekeeping gene selection advisory: glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and beta-actin are targets of miR-644a. PloS One, 7, e47510.

    Article  CAS  Google Scholar 

  37. Wang, Z., Wang, Y., Hong, X., Hu, D., Liu, C., Yang, J., Li, Y., Huang, Y., Feng, Y., Gong, H., et al. (2015). Functional inactivation of UDP-N-acetylglucosamine pyrophosphorylase 1 (UAP1) induces early leaf senescence and defence responses in rice. Journal of Experimental Botany, 66, 973–987.

    Article  Google Scholar 

  38. Czechowski, T. (2005). Genome-wide identification and testing of superior reference genes for transcript normalization in arabidopsis. Plant Physiology, 139, 5–17.

    Article  CAS  Google Scholar 

  39. Hong, S. Y., Seo, P. J., Yang, M. S., Xiang, F., & Park, C. M. (2008). Exploring valid reference genes for gene expression studies in Brachypodium distachyon by real-time PCR. BMC Plant Biology, 8, 112.

    Article  Google Scholar 

  40. Artico, S., Nardeli, S. M., Brilhante, O., Grossi-de-Sa, M. F., & Alves-Ferreira, M. (2010). Identification and evaluation of new reference genes in Gossypium hirsutum for accurate normalization of real-time quantitative RT-PCR data. BMC Plant Biology, 10, 49.

    Article  Google Scholar 

  41. Maroufi, A., Van Bockstaele, E., & De Loose, M. (2010). Validation of reference genes for gene expression analysis in chicory (Cichorium intybus) using quantitative real-time PCR. BMC Molecular Biology, 11, 15.

    Article  Google Scholar 

  42. Zhong, H. Y., Chen, J. W., Li, C. Q., Chen, L., Wu, J. Y., Chen, J. Y., Lu, W. J., & Li, J. G. (2011). Selection of reliable reference genes for expression studies by reverse transcription quantitative real-time PCR in litchi under different experimental conditions. Plant Cell Reports, 30, 641–653.

    Article  CAS  Google Scholar 

  43. Han, X., Lu, M., Chen, Y., Zhan, Z., Cui, Q., & Wang, Y. (2012). Selection of reliable reference genes for gene expression studies using real-time PCR in tung tree during seed development. PloS One, 7, e43084.

    Article  CAS  Google Scholar 

  44. Nicot, N., Hausman, J. F., Hoffmann, L., & Evers, D. (2005). Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress. Journal of Experimental Botany, 56, 2907–2914.

    Article  CAS  Google Scholar 

  45. Remans, T., Smeets, K., Opdenakker, K., Mathijsen, D., Vangronsveld, J., & Cuypers, A. (2008). Normalisation of real-time RT-PCR gene expression measurements in Arabidopsis thaliana exposed to increased metal concentrations. Planta, 227, 1343–1349.

    Article  CAS  Google Scholar 

  46. Barsalobres-Cavallari, C. F., Severino, F. E., Maluf, M. P., & Maia, I. G. (2009). Identification of suitable internal control genes for expression studies in Coffea arabica under different experimental conditions. BMC Molecular Biology, 10, 1.

    Article  Google Scholar 

  47. Hu, R., Fan, C., Li, H., Zhang, Q., & Fu, Y. F. (2009). Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR. BMC Molecular Biology, 10, 93.

    Article  Google Scholar 

  48. Marum, L., Miguel, A., Ricardo, C. P., & Miguel, C. (2012). Reference gene selection for quantitative real-time PCR normalization in Quercus suber. PloS One, 7, e35113.

    Article  CAS  Google Scholar 

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Acknowledgments

We thank Prof. Dr. Zhihong Zhang for providing the rice blast isolate. This work was supported by Key Grant Project of Chinese Ministry of Education (Grant No. 313039), the Specialized Research Fund for the Doctoral Program of Higher Education (20130141110069), and the National Program of Transgenic Variety Development of China (Grant No. 2011ZX08001-001 and 2011ZX08001-004).

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Authors and Affiliations

  1. State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Hubei, 430072, People’s Republic of China

    Zhaohai Wang, Ya Wang, Jing Yang, Keke Hu, Baoguang An, Xiaolong Deng & Yangsheng Li

  2. State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Hubei, 430072, People’s Republic of China

    Yangsheng Li

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  1. Zhaohai Wang
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Fig S1

Specificity of the amplicons for eleven reference genes. (a) The melting curve analysis showed a specific peak for each reference gene. (b) A 4 % agarose gel electrophoresis showed amplicons with expected sizes (JPG 615 kb)

Fig S2

Ct values of 22 tissue samples for eleven candidate reference genes. Cross dot represents the average Ct value of a single sample with three replicates. And the black horizontal line represents the mean Ct value of 22 tissue samples for each gene. The threshold of Ct values was the same for every gene (JPG 133 kb)

Fig S3

Ranking the best combinations of multiple reference genes for hormone groups. Arrow indicated the best combination of multiple reference genes for (a) hormones, (b) 6BA, (c) IAA, (d) GA, (e) SA, (f) ABA. (g) Arrow indicated the optimal number of reference genes (JPG 378 kb)

Fig S4

Ranking the best combinations of multiple reference genes for environmental factor groups. Arrow indicated the best combination of multiple reference genes for (a) environmental factors, (b) cold4, (c) H40, (d) Sub, (e) Dr, (f) NaCl, (g) PEG. (h) Arrow indicated the optimal number of reference genes (JPG 426 kb)

Fig S5

Ranking the best combinations of multiple reference genes for three other groups. Arrow indicated the best combination of multiple reference genes for (a) wound, (b) rice blast, (c) UV-B. (d) Arrow indicated the optimal number of reference genes (JPG 245 kb)

Fig S6

Relative expression of LOC_Os04g08764 in tissue groups and stresses groups. Relative expression of LOC_Os04g08764 were obtained when normalized with the best combination of multiple reference genes by geNorm for each specific group (from Fig. 1 and Fig S3-5). (a) group of 22 tissue samples; (b) five specific tissue groups: leaf, panicle, spikelets, RSLP, and floret and grain; (c) five specific hormone groups: 6BA, IAA, GA, SA and ABA; (d) six specific environmental factors groups: H40, Cold4, NaCl, PEG, Dr and Sub; (e) another three groups: wound, rice blast and UV-B. The ordinate axis presented normalized relative expression value, and the abscissa axis listed samples of each group. Groups were listed in Supplementary Table S1 and details about samples were described in the section of materials and methods. Expression level of the fist sample in each group was normalized to 1. Bar value represented standard deviation (JPG 627 kb)

Fig S7

Evaluation of stability for combinations of multiple reference genes and each single reference gene in rest tissue groups. (a) leaf, (b) panicle, (c) floret and grain. The figure legend can refer to that of Fig. 2 (JPG 458 kb)

Fig S8

Evaluation of stability for combinations of multiple reference genes and each single reference gene in rest hormone groups. (a) IAA, (b) SA, (c) ABA. The figure legend can refer to that of Fig. 2 (JPG 448 kb)

Fig S9

Evaluation of stability for combinations of multiple reference genes and each single reference gene in rest environmental factor groups. (a) Sub, (b) Dr, (c) NaCl, (d) PEG. The figure legend can refer to that of Fig. 2 (JPG 572 kb)

Fig S10

Evaluation of stability for the combination of multiple reference genes and each single reference gene in three other groups. (a) wound, (b) rice blast, (c) UV-B. The figure legend can refer to that of Fig. 2 (JPG 446 kb)

Table S1

Twenty-two groups for stability analysis of reference genes. Details about every sample were described in the section of materials and methods (DOCX 15 kb)

Table S2

Arrangement of eleven candidate reference genes by M values of geNorm and stability values of NormFinder for 19 specific groups. (DOCX 27 kb)

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Wang, Z., Wang, Y., Yang, J. et al. Reliable Selection and Holistic Stability Evaluation of Reference Genes for Rice Under 22 Different Experimental Conditions. Appl Biochem Biotechnol 179, 753–775 (2016). https://doi.org/10.1007/s12010-016-2029-4

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