, Volume 24, Issue 7–8, pp 1714–1721 | Cite as

Identification of suitable reference genes in mangrove Aegiceras corniculatum under abiotic stresses



Gene expression studies could provide insight into the physiological mechanisms and strategies used by plants under stress conditions. Selection of suitable internal control gene(s) is essential to accurately assess gene expression levels. For the mangrove plant, Aegiceras corniculatum, reliable reference genes to normalize real-time quantitative PCR data have not been previously investigated. In this study, the expression stabilities of five candidate reference genes [glyceraldehydes-3-phosphate dehydrogenase (GAPDH), 18SrRNA, β-Actin, 60S ribosomal protein L2, and elongation factor-1-A] were determined in leaves of A. corniculatum treated by cold, drought, salt, heavy metals, and pyrene and in different tissues of A. corniculatum under normal condition. Two software programs (geNorm and NormFinder) were employed to analyze and rank the tested genes. Results showed that GAPDH was the most suitable reference gene in A. corniculatum and the combination of two or three genes was recommended for greater accuracy. To assess the value of these tested genes as internal controls, the relative quantifications of CuZnSOD gene were also conducted. Results showed that the relative expression levels of CuZnSOD gene varied depending on the internal reference genes used, which highlights the importance of the choice of suitable internal controls in gene expression studies. Furthermore, the results also confirmed that GAPDH was a suitable reference gene for qPCR normalization in A. corniculatum under abiotic stresses. Identification of A. corniculatum reference gens in a wide range of experimental samples will provide a useful reference in future gene expression studies in this species, particularly involving similar stresses.


Mangrove plants Aegiceras corniculatum Quantitative real-time PCR Reference gene Normalization 



This research was supported by the National Natural Science Foundation of China (Nos. 41430966 and 41176101), the Projects of Guangzhou Science and Technology (No. 15020024), the key projects in the National Science and Technology Pillar Program in the Eleventh Five-year Plan Period (No. 2012BAC07B0402), and the projects of the Knowledge Innovation Program of the Chinese Academy of Sciences (No. KSCX2-SW-132).

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 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 64:5245–5250CrossRefGoogle Scholar
  2. Barber RD, Harmer DW, Coleman RA, Clark BJ (2005) GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol Genomics 21:389CrossRefGoogle Scholar
  3. 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 10:1CrossRefGoogle Scholar
  4. Basyuni M, Kinjo Y, Baba S, Shinzato N, Iwasaki H, Siregar EBM, Oku H (2010) Isolation of salt stress tolerance genes from roots of mangrove plant, Rhizophora stylosa Griff., using PCR-based suppression subtractive hybridization. Plant Mol Bio Rep 29:533–543CrossRefGoogle Scholar
  5. Burchett M, Clarke C, Field C, Pulkownik A (2006) Growth and respiration in two mangrove species at a range of salinities. Physiol Plant 75:299–303CrossRefGoogle Scholar
  6. Carvalho K, de Campos MKF, Pereira LFP, Vieira LGE (2010) Reference gene selection for real-time quantitative polymerase chain reaction normalization in “Swingle” citrumelo under drought stress. Anal Biochem 402:197–199CrossRefGoogle Scholar
  7. Czechowski T (2005) Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol 139:5–17CrossRefGoogle Scholar
  8. Fu X, Huang Y, Deng S, Zhou R, Yang G, Ni X, Li W, Shi S (2005) Construction of a SSH library of Aegiceras corniculatum under salt stress and expression analysis of four transcripts. Plant Sci 169:147–154CrossRefGoogle Scholar
  9. Gilman EL, Ellison J, Duke NC, Field C (2008) Threats to mangroves from climate change and adaptation options: a review. Aquat Bot 89:237–250CrossRefGoogle Scholar
  10. Huang G-Y, Wang Y-S (2009) Expression analysis of type 2 metallothionein gene in mangrove species (Bruguiera gymnorrhiza) under heavy metal stress. Chemoshpere 77:1026–1029CrossRefGoogle Scholar
  11. Huang GY, Wang YS (2010) Expression and characterization analysis of type 2 metallothionein from grey mangrove species (Avicennia marina) in response to metal stress. Aquat Toxicol 99:86–92CrossRefGoogle Scholar
  12. Jithesh MN, Prashanth SR, Sivaprakash KR, Parida A (2006) Monitoring expression profiles of antioxidant genes to salinity, iron, oxidative, light and hyperosmotic stresses in the highly salt tolerant grey mangrove, Avicennia marina (Forsk.) Vierh. by mRNA analysis. Plant Cell Rep 25:865–876CrossRefGoogle Scholar
  13. Marchand C, Lallier-Vergès E, Baltzer F, Albéric P, Cossa D, Baillif P (2006) Heavy metals distribution in mangrove sediments along the mobile coastline of French Guiana. Mar Chem 98:1–17CrossRefGoogle Scholar
  14. 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 3:343–357Google Scholar
  15. Nicot N, Hausman JF, Hoffman L, Evers D (2005) Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress. J Exp Bot 56:2907–2914CrossRefGoogle Scholar
  16. Peng YL, Wang YS, Cheng H, Sun CC, Wu P, Wang LY, Fei J (2013) Characterization and expression analysis of three CBF/DREB 1 transcriptional factor genes from mangrove Avicennia marina. Aquat Toxicol 140–141:68–76CrossRefGoogle Scholar
  17. Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29:2004–2007CrossRefGoogle Scholar
  18. Qi J, Yu S, Zhang F, Shen X, Zhao X, Yu Y, Zhang D (2010) Reference gene selection for real-time quantitative polymerase chain reaction of mRNA transcript levels in Chinese cabbage (Brassica rapa L. ssp. pekinensis). Plant Mol Biol Rep 28:597–604CrossRefGoogle Scholar
  19. Remans T, Smeets K, Opdenakker K, Mathijsen D, Vangronsveld J, Cuypers A (2008) Normalization of real-time RT-PCR gene expression measurements in Arabidopsis thaliana exposed to increased metal concentrations. Planta 227:1343–1349CrossRefGoogle Scholar
  20. Selvey S, Thompson E, Matthaei K, Lea RA, Irving MG, Griffiths L (2001) Beta-actin: an unsuitable internal control for RT-PCR. Mol Cell Probe 15:307CrossRefGoogle Scholar
  21. Tam N, Wong Y, Wong M (2009) Novel technology in pollutant removal at source and bioremediation. Ocean Coast Manag 52:368–373CrossRefGoogle Scholar
  22. Tomlinson PB (1994) The botany of mangroves. Cambridge University Press, CambridgeGoogle Scholar
  23. Udvardi MK, Czechowski T, Scheible WR (2008) Eleven golden rules of quantitative RT-PCR. Plant Cell Online 20:1736CrossRefGoogle Scholar
  24. 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:research0034CrossRefGoogle Scholar
  25. Xu M, Zhang B, Su X, Zhang S, Huang M (2011) Reference gene selection for quantitative real-time polymerase chain reaction in Populus. Anal Biochem 408:337–339CrossRefGoogle Scholar
  26. Zhang F-Q, Wang Y-S, Lou Z-P, Dong J-D (2007) Effect of heavy metal stress on antioxidative enzymes and lipid peroxidation in leaves and roots of two mangrove plant seedlings (Kandelia candel and Bruguiera gymnorrhiza). Chemosphere 67:40–50Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.State Key Laboratory of Tropical Oceanography, South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhouChina
  2. 2.Daya Bay Marine Biology Research StationChinese Academy of SciencesShenzhenChina
  3. 3.Laboratory of Environmental Toxicology, Department of Ecology & BiodiversityThe University of Hong KongHong Kong SARChina

Personalised recommendations