Skip to main content
Log in

Selection of suitable soybean EF1α genes as internal controls for real-time PCR analyses of tissues during plant development and under stress conditions

  • Original Paper
  • Published:
Plant Cell Reports Aims and scope Submit manuscript

Abstract

Key Message

The EF1α genes were stable in the large majority of soybean tissues during development and in specific tissues/conditions under stress.

Abstract

Quantitative real-time PCR (qPCR) analysis strongly depends on transcript normalization using stable reference genes. Reference genes are generally encoded by multigene families and are used in qPCR normalization; however, little effort has been made to verify the stability of different gene members within a family. Here, the expression stability of members of the soybean EF1α gene family (named EF1α 1a1, 1a2, 1b, 2a, 2b and 3) was evaluated in different tissues during plant development and stress exposure (SA and PEG). Four genes (UKN1, SKIP 16, EF1β and MTP) already established as stably expressed were also used in the comparative analysis. GeNorm analyses revealed different combinations of reference genes as stable in soybean tissues during development. The EF1α genes were the most stable in cotyledons (EF1α 3 and EF1α 1b), epicotyls (EF1α 1a2, EF1α 2b and EF1α 1a1), hypocotyls (EF1α 1a1 and EF1β), pods (EF1α 2a and EF1α 2b) and roots (EF1α 2a and UKN1) and less stable in tissues such as trifoliate and unifoliate leaves and germinating seeds. Under stress conditions, no suitable combination including only EF1α genes was found; however, some genes were relatively stable in leaves (EF1α 1a2) and roots (EF1α 1a1) treated with SA as well as in roots treated with PEG (EF1α 2b). EF1α 2a was the most stably expressed EF1α gene in all soybean tissues under stress. Taken together, our data provide guidelines for the selection of EF1α genes for use as reference genes in qPCR expression analyses during plant development and under stress conditions.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

ABA:

Abscisic acid

ADP-RF :

ADP-ribosylation factor

C t :

Cycle threshold

EF1α :

Elongation factor 1α

EF1β :

Elongation factor 1β

ETIF :

Eukaryotic translation initiation factor

GA:

Gibberellin

MTP :

Metalloprotease, Insulin degrading enzyme

NAA:

Naphthylacetic acid

PEG:

Polyethylene glycol

SA:

Salicylic acid

SKIP :

16 SKP1/Ask-Interacting Protein 16

t m :

Melting temperature

UKN1 :

Hypothetical protein

UTR:

Untranslated region

References

  • Altschul SF, Madden TL, Schäffer AA, Zhange J, Zhange Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl Acid Res 25:3389–3402

    Article  CAS  Google Scholar 

  • Andersen CL, Jensen JL, Ørntoft 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 

  • Barsalobres-Cavallari CF, Severino FE, Maluf MP, Maia IG (2009) Identification of suitable internal control genes for expression studies in Coffea arabica under different experimental conditions. BMC Mol Biol 10:1

    PubMed Central  PubMed  Google Scholar 

  • Battaglia M, Covarrubias AA (2013) Late embryogenesis abundant (LEA) proteins in legumes. Front Plant Sci 4:190

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    PubMed Central  PubMed  Google Scholar 

  • Bustin SA, Nolan T (2004) Pitfalls of quantitative real-time reverse transcription polymerase chain reaction. J Biomol Tech 15:155–166

    PubMed Central  PubMed  Google Scholar 

  • Bustin SA, Benes V, Nolan T, Pfaffl MW (2005) Quantitative real-time RT-PCR- a perspective. J Mol Endocrinol 34:597–601

    CAS  PubMed  Google Scholar 

  • Cavalcanti JH, Oliveira GM, Saraiva KD, Torquato JP, Maia IG, Fernandes de Melo D, Costa JH (2013) Identification of duplicated and stress-inducible Aox2b gene co-expressed with Aox1 in species of the Medicago genus reveals a regulation linked to gene rearrangement in leguminous genomes. J Plant Physiol 170:1609–1619

    CAS  PubMed  Google Scholar 

  • Cordoba EM, Die JV, Gonzáles-Verdejo CL, Nadal S, Román B (2011) Selection of reference genes in Hedysarum coronarium under various stresses and stages of development. Anal Biochem 409:236–243

    CAS  PubMed  Google Scholar 

  • Costa JH, Mota EF, Cambursano MV, Lauxmann MA, Oliveira LMN, Lima MGS, Orellano EG et al (2010) Stress-induced co-expression of two alternative oxidase (VuAox1 and 2b) genes in Vigna unguiculata. J Plant Physiol 167:561–570

    CAS  PubMed  Google Scholar 

  • Davidson RM, Gowda M, Moghe G, Lin H, Vaillancourt B, Shiu SH, Jiang N et al (2012) Comparative transcriptomics of three Poaceae species reveals patterns of gene expression evolution. Plant J 71:492–502

    CAS  PubMed  Google Scholar 

  • Die JV, Roma’n B, Nadal S, González-Verdejo CI (2010) Evaluation of candidate reference genes for expression studies in Pisum sativum under different experimental conditions. Planta 232:145–153

    CAS  PubMed  Google Scholar 

  • Giménez MJ, Pistón F, Atienza SG (2011) Identification of suitable reference genes for normalization of qPCR data in comparative transcriptomics analyses in the Triticeae. Planta 233:163–173

    PubMed  Google Scholar 

  • Gomes J, Rodrigues FA, Oliveira MCN, Farias JRB, Neumaier N, Abdelnoor RV, Guimarães FCM et al (2013) Expression patterns of GmAP2/EREB-like transcription factors involved in soybean responses to water deficit. PLoS One 8:e62294

    Google Scholar 

  • 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 49:192–197

    CAS  PubMed  Google Scholar 

  • Gutierrez L, Mauriat M, Guenin S, Pelloux J, Lefebvre JF, Louvet R, Rusterucci 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 Biotechnol J 6:609–618

    CAS  PubMed  Google Scholar 

  • Hao QN, Zhou XA, Sha AH, Wang C, Zhou R, Chen SL (2011) Identification of genes associated with nitrogen use efficiency by genome-wide transcriptional analysis of two soybean genotypes. BMC Genom 12:525

    CAS  Google Scholar 

  • Hu R, Fan C, Li H, Zhang QF, Fu YF (2009) Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR. BMC Mol Biol 10:93

    PubMed Central  PubMed  Google Scholar 

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

    Google Scholar 

  • Huis R, Hawkins S, Neutelings G (2010) Selection of reference genes for quantitative gene expression normalization in flax (Linum usitatissimum L.). BMC Plant Biol 10:71

    PubMed Central  PubMed  Google Scholar 

  • Ibrahim HM, Hosseini P, Alkharouf NW, Hussein EH, Gamal El-Din Ael K, Aly MA, Matthews BF (2011) Analysis of gene expression in soybean (Glycine max) roots in response to the root knot nematode Meloidogyne incognita using microarrays and KEGG pathways. BMC Genom 12:220

    CAS  Google Scholar 

  • Irsigler AST, Costa MDL, Zhang P, Reis PAB, Dewey RE, Boston RS, Fontes EPB (2007) Expression profiling on soybean leaves reveals integration of ER and osmotic-stress pathways. BMC Genom 8:431

    Google Scholar 

  • Jain M, Nijhawan A, Tyagi AK, Khurana JP (2006) Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochem Bioph Res Co 345:646–651

    CAS  Google Scholar 

  • Jian B, Liu B, Bi Y, Hou W, Wu C, Han T (2008) Validation of internal control for gene expression study in soybean by quantitative real-time PCR. BMC Mol Biol 9:59

    PubMed Central  PubMed  Google Scholar 

  • Kidou S, Ejiri S (1998) Isolation, characterization and mRNA expression of four cDNAs encoding translation elongation factor 1A from rice (Oryza sativa L.). Plant Mol Biol 36:137–148

    CAS  PubMed  Google Scholar 

  • Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F et al (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23:2947–2948

    CAS  PubMed  Google Scholar 

  • Le DT, Aldrich DL, Valliyodan B, Watanabe Y, Ha CV, Nishiyama R, Guttikonda SK et al (2012) Evaluation of candidate reference genes for normalization of quantitative RT-PCR in soybean tissues under various abiotic stress conditions. PLoS One 7:e46487

    CAS  PubMed Central  PubMed  Google Scholar 

  • Lee JM, Roche JR, Donaghy DJ, Thrush A, Sathish P (2010) Validation of reference genes for quantitative RTPCR studies of gene expression in perennial ryegrass (Lolium perenne L.). BMC Mol Biol 11:8

    PubMed Central  PubMed  Google Scholar 

  • Libault M, Thibivilliers S, Bilgin DD, Radwan O, Benitez M, Clough SJ, Stacey G (2008) Identification of four soybean reference genes for gene expression normalization. Plant Genome 1:44–54

    CAS  Google Scholar 

  • Livak KJ, Schmitten TD (2001) Analysis of gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 25:402–408

    CAS  PubMed  Google Scholar 

  • Løvdal 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

    PubMed  Google Scholar 

  • Luo H, Chen S, Wan H, Chen F, Gu C, Liu Z (2010) Candidate reference genes for gene expression studies in water lily. Anal Biochem 404:100–102

    CAS  PubMed  Google Scholar 

  • Marshall OJ (2004) Perlprimer: cross-platform, graphical primer design for standard, bisulphite and real-time PCR. Bioinformatics 20:2471–2472

    CAS  PubMed  Google Scholar 

  • Matsumoto T, Wu JZ, Kanamori H, Katayose Y, Fujisawa M, Namiki N, Mizuno H et al (2005) The map-based sequence of the rice genome. Nature 436:793–800

    Google Scholar 

  • Migocka M, Papierniak A (2010) Identification of suitable reference genes for studying gene expression in cucumber plants subjected to abiotic stress and growth regulators. Mol Breed 28:343–357

    Google Scholar 

  • Miranda VJ, Coelho RR, Viana AAB, Neto OBO, Carneiro RMDG, Rocha TL, Sá MFG et al (2013) Validation of reference genes aiming accurate normalization of qPCR data in soybean upon nematode parasitism and insect attack. BMC Res Note 6:196

    CAS  Google Scholar 

  • Neto LB, Oliveira RR, Wiebke-Strohm B, Bencke M, Weber RLM, Cabreira C, Abdelnoor RV (2013) Identification of the soybean HyPRP family and specific gene response to Asian soybean rust disease. Genet Mol Biol 36:214–224

    PubMed Central  PubMed  Google Scholar 

  • Nicot N, Hausman J, 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

    CAS  PubMed  Google Scholar 

  • Paolacci AR, Tanzarella OA, Porceddu E, Ciaffi M (2009) Identification and validation of reference genes for quantitative RT-PCR normalization in wheat. BMC Mol Biol 10:11

    PubMed Central  PubMed  Google Scholar 

  • Ransom-Hodgkins WD (2009) The application of expression analysis in elucidating the eukaryotic elongation factor one alpha gene family in Arabidopsis thaliana. Mol Genet Genomics 281:391–405

    CAS  PubMed  Google Scholar 

  • 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

    PubMed Central  PubMed  Google Scholar 

  • Rutledge RG, Stewart D (2008) Critical evaluation of methods used to determine amplification efficiency refutes the exponential character of real-time PCR. BMC Mol Biol 9:96

    PubMed Central  PubMed  Google Scholar 

  • Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, Nelson W, Hyten DL et al (2010) Genome sequence of the palaeopolyploid soybean. Nature 463:178–183

    CAS  PubMed  Google Scholar 

  • The Arabidopsis Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:796–815

    Google Scholar 

  • Upchurch RG, Ramirez ME (2010) Defense-related gene expression in soybean leaves and seeds inoculated with Cercospora kikuchii and Diaporthe phaseolorum var. meridionalis. Physiol Mol Plant P 75:64–70

    CAS  Google Scholar 

  • Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:1–11

    Google Scholar 

  • Villaseñor T, Brom S, Dávalos A, Lozano L, Romero D, Santos GAL (2011) Housekeeping genes essential for pantothenate biosynthesis are plasmid-encoded in Rhizobium etli and Rhizobium leguminosarum. BMC Microbiol 11:66

    PubMed Central  PubMed  Google Scholar 

  • Wang Y, Yu K, Poysa V, Shi C, Zhou Y (2011) Selection of reference genes for normalization of qRT-PCR analysis of differentially expressed genes in soybean exposed to cadmium. Mol Biol Rep 39:1585–1594

    PubMed  Google Scholar 

  • Xu W-L, Wang X-L, Wang H, Li X-B (2007) Molecular characterization and expression analysis of nine cotton GhEF1Α genes encoding translation elongation factor 1A. Gene 389:27–35

    CAS  PubMed  Google Scholar 

  • Zhang D, Du Q, Xu B, Zhang Z, Li B (2010) The actin multigene family in Populus: organization, expression and phylogenetic analysis. Mol Genet Genomic 284:105–119

    CAS  Google Scholar 

Download references

Acknowledgments

This research was supported by CAPES, CNPq and FUNCAP.

Conflict of interest

The authors declare that have no conflict of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to José H. Costa.

Additional information

Communicated by J. Register.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary data:

Supplemental Fig. 1.

Supplemental Fig. 2.

Supplemental Fig. 3.

Supplemental Fig. 4.

Supplemental Table 1.

Supplemental Table 2.

Supplementary material 1 (DOCX 42 kb)

Supplementary material 2 (DOCX 607 kb)

Supplementary material 3 (DOCX 196 kb)

Supplementary material 4 (DOCX 21 kb)

Supplementary material 5 (DOCX 27 kb)

Supplementary material 6 (DOCX 25 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saraiva, K.D.C., Fernandes de Melo, D., Morais, V.D. et al. 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 (2014). https://doi.org/10.1007/s00299-014-1628-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00299-014-1628-1

Keywords

Navigation