Validation of expression stability of reference genes in response to herbicide stress in wild oat (Avena ludoviciana)

Ali Akbarabadi; Ahmad Ismaili; Danial Kahrizi; Farhad Nazarian Firouzabadi

doi:10.14715/cmb/2018.64.4.19

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Vol. 64 No. 4: Issue 4

Issue Published : April 2, 2018

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Validation of expression stability of reference genes in response to herbicide stress in wild oat (Avena ludoviciana)

https://doi.org/10.14715/cmb/2018.64.4.19

Ali Akbarabadi

Department of Agronomy and Plant Breeding, Faculty of Agriculture, Lorestan University, Khorramabad, Iran

Ahmad Ismaili

Department of Agronomy and Plant Breeding, Faculty of Agriculture, Lorestan University, Khorramabad, Iran

Danial Kahrizi

Department of Agronomy and Plant Breeding, Razi University, Kermanshah, Iran

Farhad Nazarian Firouzabadi

Department of Agronomy and Plant Breeding, Faculty of Agriculture, Lorestan University, Khorramabad, Iran

Corresponding Author(s) : Ahmad Ismaili

ismaili.a@lu.ac.ir

Cellular and Molecular Biology, Vol. 64 No. 4: Issue 4
Article Published : March 31, 2018

Abstract

Weeds are serious problem in crop production and wild oat is a grass weed of economic and agronomic significance. We need to extend our basic knowledge of weeds especially in molecular genetics and gene expression. For study of gene expression by semi-quantitative and quantitative PCR, it is recommended that normalization of reference genes be carried out in order to select the most stable reference gene for a precise gene expression study. The purpose of this research was evaluation of four reference genes in response to treated and untreated (control) by herbicide in two tissues (stem and leaf) of non-target site resistance wild oat (A. ludoviciana). Four candidate reference genes including Actin, Ef1Î± (elongation factor 1 alpha), GAPDH (glyceraldehyde 3-phosphate dehydrogenase) and TBP (TATA-box-binding protein) were used to determine stable reference gene exposed to the herbicide using the statistical methods of NormFinder, BestKeeper and delta-Ct. NormFinder indicated that TBP and Actin genes are the best combination of two genes for normalizing calculations (with a combined gene stability value of 0.012) for qPCR analysis under herbicide stress in different tissues of non-target site resistance wild oat. Based on the statistical results, the Ef1Î± gene was identified as the unstable reference gene. Totally, according to results of this study, TBP gene is the most stable reference gene and therefore, this gene can be used as a reference gene for future studies of quantitative PCR analysis of herbicide stress-responsive gene expression in wild oat and potentially in other grass weed species.

Keywords

Gene stability Herbicide resistance Tissue Real-time PCR Wild oat.

Akbarabadi, A., Ismaili, A., Kahrizi, D., & Nazarian Firouzabadi, F. (2018). Validation of expression stability of reference genes in response to herbicide stress in wild oat (Avena ludoviciana). Cellular and Molecular Biology, 64(4), 113–118. https://doi.org/10.14715/cmb/2018.64.4.19

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References

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Beckie, H.J., and Tardif, F.J. (2012). Herbicide cross resistance in weeds. Crop Protection 35, 15-28.
Bhatia, P., Taylor, W.R., Greenberg, A.H., and Wright, J.A. (1994). Comparison of glyceraldehyde-3-phosphate dehydrogenase and 28S-ribosomal RNA gene expression as RNA loading controls for northern blot analysis of cell lines of varying malignant potential. Analytical Biochemistry 216, 223-226.
Brunner, A.M., Yakovlev, I.A., and Strauss, S.H. (2004). Validating internal controls for quantitative plant gene expression studies. BMC Plant Biology 4, 14.
Bustin, S. (2002). Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. Journal of Molecular Endocrinology 29, 23-39.
Bustin, S.A., Beaulieu, J.F., Huggett, J., Jaggi, R., Kibenge, F.S., Olsvik, P.A., Penning, L.C., and Toegel, S. (2010). MIQE precis: Practical implementation of minimum standard guidelines for fluorescence-based quantitative real-time PCR experiments. BMC Molecular Biology 11, 74.
Carroll, S.P., Jí¸rgensen, P.S., Kinnison, M.T., Bergstrom, C.T., Denison, R.F., Gluckman, P., Smith, T.B., Strauss, S.Y., and Tabashnik, B.E. (2014). Applying evolutionary biology to address global challenges. Science 346, 1245993.
Delye, C. (2013). Unravelling the genetic bases of non"target"site"based resistance (NTSR) to herbicides: a major challenge for weed science in the forthcoming decade. Pest Management Science 69, 176-187.
Délye, C., Zhang, X.Q., Michel, S., Matéjicek, A., and Powles, S.B. (2005). Molecular bases for sensitivity to acetyl-coenzyme A carboxylase inhibitors in black-grass. Plant Physiology 137, 794-806.
Dombrowski, J.E., and Martin, R.C. (2009). Evaluation of reference genes for quantitative RT-PCR in Lolium temulentum under abiotic stress. Plant Science 176, 390-396.
Duhoux, A., Carrère, S., Gouzy, J., Bonin, L., and Délye, C. (2015). RNA-Seq analysis of rye-grass transcriptomic response to an herbicide inhibiting acetolactate-synthase identifies transcripts linked to non-target-site-based resistance. Plant Molecular Biology 87, 473-487.
Duhoux, A., and Délye, C. (2013). Reference genes to study herbicide stress response in Lolium sp.: up-regulation of P450 genes in plants resistant to acetolactate-synthase inhibitors. PloS one 8, e63576.
Faccioli, P., Ciceri, G.P., Provero, P., Stanca, A.M., Morcia, C., and Terzi, V. (2007). A combined strategy of "in silico” transcriptome analysis and web search engine optimization allows an agile identification of reference genes suitable for normalization in gene expression studies. Plant Molecular Biology 63, 679-688.
Gaines, T.A., Lorentz, L., Figge, A., Herrmann, J., Maiwald, F., Ott, M.C., Han, H., Busi, R., Yu, Q., and Powles, S.B. (2014). RNA"Seq transcriptome analysis to identify genes involved in metabolism"based diclofop resistance in Lolium rigidum. The Plant Journal 78, 865-876.
Heap, I. (2014). Herbicide resistant weeds. In Integrated Pest Management (Springer), pp. 281-301.
Hong, S.Y., Seo, P.J., Yang, M.S., Xiang, F., and Park, C.M. (2008). Exploring valid reference genes for gene expression studies in Brachypodium distachyon by real-time PCR. BMC Plant Biology 8, 112.
Iwakami, S., Endo, M., Saika, H., Okuno, J., Nakamura, N., Yokoyama, M., Watanabe, H., Toki, S., Uchino, A., and Inamura, T. (2014). Cytochrome P450 CYP81A12 and CYP81A21 are associated with resistance to two acetolactate synthase inhibitors in Echinochloa phyllopogon. Plant Physiology 165, 618-629.
Jain, M., Nijhawan, A., Tyagi, A.K., and 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.
Jarošová, J., and Kundu, J.K. (2010). Validation of reference genes as internal control for studying viral infections in cereals by quantitative real-time RT-PCR. BMC Plant Biology 10, 146.
Jiang, Q., Wang, F., Li, M.Y., Ma, J., Tan, G.F., and Xiong, A.S. (2014). Selection of suitable reference genes for qPCR normalization under abiotic stresses in Oenanthe javanica (BI.) DC. PloS one 9, e92262.
Kong, Q., Yuan, J., Niu, P., Xie, J., Jiang, W., Huang, Y., and Bie, Z. (2014). Screening suitable reference genes for normalization in reverse transcription quantitative real-time PCR analysis in melon. PLoS One 9, e87197.
Lee, J.M., Roche, J.R., Donaghy, D.J., Thrush, A., and Sathish, P. (2010). Validation of reference genes for quantitative RT-PCR studies of gene expression in perennial ryegrass (Lolium perenne L.). BMC Molecular Biology 11, 8.
Livak, K.J., and Schmittgen, T.D. (2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2âˆ’ Î”Î”CT method. Methods 25, 402-408.
Long, X.Y., Wang, J.R., Ouellet, T., Rocheleau, H., Wei, Y.M., Pu, Z.E., Jiang, Q.T., Lan, X.J., and Zheng, Y.L. (2010). Genome-wide identification and evaluation of novel internal control genes for Q-PCR based transcript normalization in wheat. Plant Molecular Biology 74, 307-311.
Lí¸vdal, T., and Lillo, C. (2009). Reference gene selection for quantitative real-time PCR normalization in tomato subjected to nitrogen, cold, and light stress. Analytical Biochemistry 387, 238-242.
Neve, P., Busi, R., Renton, M., and Vila"Aiub, M.M. (2014). Expanding the eco"evolutionary context of herbicide resistance research. Pest Management science 70, 1385-1393.
Ovesna, J., Kućera, L., Vaculová, K., Å trymplová, K., Svobodova, I., and Milella, L. (2012). Validation of the Î²-amy1 transcription profiling assay and selection of reference genes suited for a RT-qPCR assay in developing barley caryopsis. PloS one 7, e41886.
Petit, C., Pernin, F., Heydel, J.M., and Délye, C. (2012). Validation of a set of reference genes to study response to herbicide stress in grasses. BMC Research Notes 5, 18.
Pfaffl, M.W. (2001). A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Research 29, e45-e45.
Pfaffl, M.W., Tichopad, A., Prgomet, C., and Neuvians, T.P. (2004). Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise correlations. Biotechnology Letters 26, 509-515.
Piper, T. (1990). Field trials on diclofop-methyl tolerant wild oats (Avena fatua). Paper presented at: Proceedings of the 9th Australian Weeds Conference.
Powles, S.B., and Yu, Q. (2010). Evolution in action: plants resistant to herbicides. Annual Review of Plant Biology 61, 317-347.
Silveira, í‰.D., Alves-Ferreira, M., Guimarí£es, L.A., da Silva, F.R., and de Campos Carneiro, V.T. (2009). Selection of reference genes for quantitative real-time PCR expression studies in the apomictic and sexual grass Brachiaria brizantha. BMC Plant Biology 9, 84.
Silver, N., Best, S., Jiang, J., and Thein, S.L. (2006). Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Molecular Biology 7, 33.
Stürzenbaum, S.R., and Kille, P. (2001). Control genes in quantitative molecular biological techniques: the variability of invariance. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 130, 281-289.
Thellin, O., Zorzi, W., Lakaye, B., De Borman, B., Coumans, B., Hennen, G., Grisar, T., Igout, A., and Heinen, E. (1999). Housekeeping genes as internal standards: use and limits. Journal of Biotechnology 75, 291-295.
Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., and Speleman, F. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology 3, Research 0034. 0031.
WrzesiÅ„ska, B., Kierzek, R., and Obrć™palska"Stć™plowska, A. (2016). Evaluation of six commonly used reference genes for gene expression studies in herbicide"resistant Avena fatua biotypes. Weed Research 56, 284-292.
Xu, H., Li, J., Wu, R., Su, W., Wu, X., Wang, L., and Dong, L. (2017). Identification of reference genes for studying herbicide resistance mechanisms in Japanese foxtail (Alopecurus japonicus). Weed Science 65, 557-566.
Yang, Q., Yin, J., Li, G., Qi, L., Yang, F., Wang, R., and Li, G. (2014). Reference gene selection for qRT-PCR in Caragana korshinskii Kom. under different stress conditions. Molecular Biology Reports 41, 2325-2334.
Zhang, J., and Snyder, S.H. (1992). Nitric oxide stimulates auto-ADP-ribosylation of glyceraldehyde-3-phosphate dehydrogenase. Proceedings of the National Academy of Sciences 89, 9382-9385.

References

Andersen, C.L., Jensen, J.L., and í˜rntoft, 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.

Beckie, H.J., and Tardif, F.J. (2012). Herbicide cross resistance in weeds. Crop Protection 35, 15-28.

Bhatia, P., Taylor, W.R., Greenberg, A.H., and Wright, J.A. (1994). Comparison of glyceraldehyde-3-phosphate dehydrogenase and 28S-ribosomal RNA gene expression as RNA loading controls for northern blot analysis of cell lines of varying malignant potential. Analytical Biochemistry 216, 223-226.

Brunner, A.M., Yakovlev, I.A., and Strauss, S.H. (2004). Validating internal controls for quantitative plant gene expression studies. BMC Plant Biology 4, 14.

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

Bustin, S.A., Beaulieu, J.F., Huggett, J., Jaggi, R., Kibenge, F.S., Olsvik, P.A., Penning, L.C., and Toegel, S. (2010). MIQE precis: Practical implementation of minimum standard guidelines for fluorescence-based quantitative real-time PCR experiments. BMC Molecular Biology 11, 74.

Carroll, S.P., Jí¸rgensen, P.S., Kinnison, M.T., Bergstrom, C.T., Denison, R.F., Gluckman, P., Smith, T.B., Strauss, S.Y., and Tabashnik, B.E. (2014). Applying evolutionary biology to address global challenges. Science 346, 1245993.

Delye, C. (2013). Unravelling the genetic bases of non"target"site"based resistance (NTSR) to herbicides: a major challenge for weed science in the forthcoming decade. Pest Management Science 69, 176-187.

Délye, C., Zhang, X.Q., Michel, S., Matéjicek, A., and Powles, S.B. (2005). Molecular bases for sensitivity to acetyl-coenzyme A carboxylase inhibitors in black-grass. Plant Physiology 137, 794-806.

Dombrowski, J.E., and Martin, R.C. (2009). Evaluation of reference genes for quantitative RT-PCR in Lolium temulentum under abiotic stress. Plant Science 176, 390-396.

Duhoux, A., Carrère, S., Gouzy, J., Bonin, L., and Délye, C. (2015). RNA-Seq analysis of rye-grass transcriptomic response to an herbicide inhibiting acetolactate-synthase identifies transcripts linked to non-target-site-based resistance. Plant Molecular Biology 87, 473-487.

Duhoux, A., and Délye, C. (2013). Reference genes to study herbicide stress response in Lolium sp.: up-regulation of P450 genes in plants resistant to acetolactate-synthase inhibitors. PloS one 8, e63576.

Faccioli, P., Ciceri, G.P., Provero, P., Stanca, A.M., Morcia, C., and Terzi, V. (2007). A combined strategy of "in silico” transcriptome analysis and web search engine optimization allows an agile identification of reference genes suitable for normalization in gene expression studies. Plant Molecular Biology 63, 679-688.

Gaines, T.A., Lorentz, L., Figge, A., Herrmann, J., Maiwald, F., Ott, M.C., Han, H., Busi, R., Yu, Q., and Powles, S.B. (2014). RNA"Seq transcriptome analysis to identify genes involved in metabolism"based diclofop resistance in Lolium rigidum. The Plant Journal 78, 865-876.

Heap, I. (2014). Herbicide resistant weeds. In Integrated Pest Management (Springer), pp. 281-301.

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

Iwakami, S., Endo, M., Saika, H., Okuno, J., Nakamura, N., Yokoyama, M., Watanabe, H., Toki, S., Uchino, A., and Inamura, T. (2014). Cytochrome P450 CYP81A12 and CYP81A21 are associated with resistance to two acetolactate synthase inhibitors in Echinochloa phyllopogon. Plant Physiology 165, 618-629.

Jain, M., Nijhawan, A., Tyagi, A.K., and 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.

Jarošová, J., and Kundu, J.K. (2010). Validation of reference genes as internal control for studying viral infections in cereals by quantitative real-time RT-PCR. BMC Plant Biology 10, 146.

Jiang, Q., Wang, F., Li, M.Y., Ma, J., Tan, G.F., and Xiong, A.S. (2014). Selection of suitable reference genes for qPCR normalization under abiotic stresses in Oenanthe javanica (BI.) DC. PloS one 9, e92262.

Kong, Q., Yuan, J., Niu, P., Xie, J., Jiang, W., Huang, Y., and Bie, Z. (2014). Screening suitable reference genes for normalization in reverse transcription quantitative real-time PCR analysis in melon. PLoS One 9, e87197.

Lee, J.M., Roche, J.R., Donaghy, D.J., Thrush, A., and Sathish, P. (2010). Validation of reference genes for quantitative RT-PCR studies of gene expression in perennial ryegrass (Lolium perenne L.). BMC Molecular Biology 11, 8.

Livak, K.J., and Schmittgen, T.D. (2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2âˆ’ Î”Î”CT method. Methods 25, 402-408.

Long, X.Y., Wang, J.R., Ouellet, T., Rocheleau, H., Wei, Y.M., Pu, Z.E., Jiang, Q.T., Lan, X.J., and Zheng, Y.L. (2010). Genome-wide identification and evaluation of novel internal control genes for Q-PCR based transcript normalization in wheat. Plant Molecular Biology 74, 307-311.

Lí¸vdal, T., and Lillo, C. (2009). Reference gene selection for quantitative real-time PCR normalization in tomato subjected to nitrogen, cold, and light stress. Analytical Biochemistry 387, 238-242.

Neve, P., Busi, R., Renton, M., and Vila"Aiub, M.M. (2014). Expanding the eco"evolutionary context of herbicide resistance research. Pest Management science 70, 1385-1393.

Ovesna, J., Kućera, L., Vaculová, K., Å trymplová, K., Svobodova, I., and Milella, L. (2012). Validation of the Î²-amy1 transcription profiling assay and selection of reference genes suited for a RT-qPCR assay in developing barley caryopsis. PloS one 7, e41886.

Petit, C., Pernin, F., Heydel, J.M., and Délye, C. (2012). Validation of a set of reference genes to study response to herbicide stress in grasses. BMC Research Notes 5, 18.

Pfaffl, M.W. (2001). A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Research 29, e45-e45.

Pfaffl, M.W., Tichopad, A., Prgomet, C., and Neuvians, T.P. (2004). Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise correlations. Biotechnology Letters 26, 509-515.

Piper, T. (1990). Field trials on diclofop-methyl tolerant wild oats (Avena fatua). Paper presented at: Proceedings of the 9th Australian Weeds Conference.

Powles, S.B., and Yu, Q. (2010). Evolution in action: plants resistant to herbicides. Annual Review of Plant Biology 61, 317-347.

Silveira, í‰.D., Alves-Ferreira, M., Guimarí£es, L.A., da Silva, F.R., and de Campos Carneiro, V.T. (2009). Selection of reference genes for quantitative real-time PCR expression studies in the apomictic and sexual grass Brachiaria brizantha. BMC Plant Biology 9, 84.

Silver, N., Best, S., Jiang, J., and Thein, S.L. (2006). Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Molecular Biology 7, 33.

Stürzenbaum, S.R., and Kille, P. (2001). Control genes in quantitative molecular biological techniques: the variability of invariance. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 130, 281-289.

Thellin, O., Zorzi, W., Lakaye, B., De Borman, B., Coumans, B., Hennen, G., Grisar, T., Igout, A., and Heinen, E. (1999). Housekeeping genes as internal standards: use and limits. Journal of Biotechnology 75, 291-295.

Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., and Speleman, F. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology 3, Research 0034. 0031.

WrzesiÅ„ska, B., Kierzek, R., and Obrć™palska"Stć™plowska, A. (2016). Evaluation of six commonly used reference genes for gene expression studies in herbicide"resistant Avena fatua biotypes. Weed Research 56, 284-292.

Xu, H., Li, J., Wu, R., Su, W., Wu, X., Wang, L., and Dong, L. (2017). Identification of reference genes for studying herbicide resistance mechanisms in Japanese foxtail (Alopecurus japonicus). Weed Science 65, 557-566.

Yang, Q., Yin, J., Li, G., Qi, L., Yang, F., Wang, R., and Li, G. (2014). Reference gene selection for qRT-PCR in Caragana korshinskii Kom. under different stress conditions. Molecular Biology Reports 41, 2325-2334.

Zhang, J., and Snyder, S.H. (1992). Nitric oxide stimulates auto-ADP-ribosylation of glyceraldehyde-3-phosphate dehydrogenase. Proceedings of the National Academy of Sciences 89, 9382-9385.

Author biographies is not available.