Advertisement

3 Biotech

, 8:383 | Cite as

Genes encoding membrane proteins showed stable expression in rice under aerobic condition: novel set of reference genes for expression studies

  • Amol S. Phule
  • Kalyani M. Barbadikar
  • M. S. Madhav
  • P. Senguttuvel
  • M. B. B. Prasad Babu
  • P. Ananda Kumar
Original Article

Abstract

A set of reference genes expressing stably under aerobic and anaerobic conditions in rice is essential to execute omics studies relating to aerobic adaptations. Stability of expression of ten rice reference genes, viz. Actin, eEF-1a, eIF-5C, Exp1, Exp2, Memp, SKP1A, TF-SUI1, TPH, and UBQ5 was validated across six experimental sets in shoot and root tissues at seedling, tillering, and panicle initiation stages. Comprehensively, Memp (Membrane protein), TPH (Tumor protein homolog), and Exp1 (Expressed protein) were revealed as the most stable set with acceptable M and V value according to the gold standards of qRT-PCR using various algorithms/tools. The identified set of reference genes was validated using root trait genes, which showed concurrence with the functional expression patterns in the aerobic and anaerobic adapted cultivars. The Memp (Membrane protein), TPH (Tumor protein homolog), and Exp1 (Expressed protein) genes are the most stable reference genes across the root and shoot at various developmental stages under aerobic and anaerobic conditions in rice. This is the first study for accurate and reliable relative gene expression analysis in rice grown in aerobic and anaerobic conditions.

Keywords

Aerobic Anaerobic Gene expression qRT-PCR Reference gene Rice 

Abbreviations

qRT-PCR

Quantitative real-time polymerase chain reaction

CT

Cycle threshold

CV

Coefficient of variation

DAG

Days after germination

Dro1

Deeper rooting 1

Exp1

Expressed protein

Memp

Membrane protein

PI stage

Panicle initiation stage

SD

Standard deviation

SV

Stability value

TPH

Tumor protein homolog

Notes

Acknowledgements

The authors are thankful to Director, ICAR-IIRR (Institute Research Council Project Codes ABR/CI/BT/12 and ABR/CI/BT/15) and Director, Institute of Biotechnology, PJTSAU for providing necessary facilities for research. Amol S. Phule acknowledges the University Grant Commission (UGC), New Delhi, India for providing National Fellowship for furnishing the doctoral programme.

Author contributions

PAK, MSM, and KMB conceived, planned, and designed study. ASP, KMB, PS, and MBB conducted experiments. KMB and ASP analyzed the data and wrote the manuscript. PAK and MSM critically edited manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

13205_2018_1406_MOESM1_ESM.docx (658 kb)
Supplementary material 1 (DOCX 657 KB)

References

  1. Andersen C, Jensen J, Orntoft T (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.  https://doi.org/10.1158/0008 CrossRefPubMedGoogle Scholar
  2. Bennett J, Hondred D, Register JC (2015) Keeping qRT-PCR rigorous and biologically relevant. Plant Cell Rep 34(1):1–3.  https://doi.org/10.1007/s00299-014-1692-6 CrossRefPubMedGoogle Scholar
  3. Bevitori R, Oliveir MB, Grossi-de-Sá 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 13(4):9795–9805.  https://doi.org/10.4238/2014.November.27.7 CrossRefPubMedGoogle Scholar
  4. Bu Y (2011) Research progress of ammonium transporter in rice plants in plants. Genomics 2(3):19–23.  https://doi.org/10.5376/gab.2011.02.0003 CrossRefGoogle Scholar
  5. Bustin SA (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29:23–39CrossRefPubMedGoogle Scholar
  6. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Shipley GL (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55(4):611–622.  https://doi.org/10.1373/clinchem.2008.112797 CrossRefPubMedGoogle Scholar
  7. Bustin SA, Beaulieu J, Huggett J, Jaggi R, Kibenge FSB, Olsvik PA, Toegel S (2010) MIQE précis: practical implementation of minimum standard guidelines for fluorescence-based quantitative real-time PCR experiments. BMC Mol Biol 11:74CrossRefPubMedPubMedCentralGoogle Scholar
  8. Chomczynski P, Sacchi N (1987) Single-step method of RNA isolation by acid guanidinium thiocyanate–phenol–chloroform extraction. Anal Biochem 162(1):156–159.  https://doi.org/10.1016/0003-2697(87)90021-2 CrossRefPubMedGoogle Scholar
  9. Dixit S, Grondin A, Lee C, Henry A, Olds T (2015) Understanding rice adaptation to varying agro-ecosystems: trait interactions and quantitative trait loci. BMC Genet.  https://doi.org/10.1186/s12863-015-0249-1 CrossRefPubMedPubMedCentralGoogle Scholar
  10. Fan X, Tang Z, Tan Y, Zhang Y, Luo B, Yang M, Xu G (2016) Overexpression of a pH-sensitive nitrate transporter in rice increases crop yields. Proc Natl Acad Sci.  https://doi.org/10.1073/pnas.1525184113 CrossRefPubMedGoogle Scholar
  11. Ginzinger DG (2002) Gene quantification using real-time quantitative PCR: an emerging technology hits the mainstream. Exp Hematol 30:503–512CrossRefPubMedGoogle Scholar
  12. Hellemans J, Mortier G, Paepe A, De Speleman F, Vandesompele J (2007) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8:R19.  https://doi.org/10.1186/gb-2007-8-2-r19 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 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 Biophys Res Commun 345(2):646–651.  https://doi.org/10.1016/j.bbrc.2006.04.140 CrossRefPubMedGoogle Scholar
  14. Kato Y, Okami M (2010) Root growth dynamics and stomatal behaviour of rice (Oryza sativa L.) grown under aerobic and flooded conditions. Field Crops Res 117(1):9–17.  https://doi.org/10.1016/j.fcr.2009.12.003 CrossRefGoogle Scholar
  15. Kim BR, Nam HY, Kim SU, Kim SI, Chang YJ (2003) Normalization of reverse transcription quantitative-PCR with housekeeping genes in rice. Biotechnol Lett 25(21):1869–1872.  https://doi.org/10.1023/A:1026298032009 CrossRefPubMedGoogle Scholar
  16. Li QF, Sun SSM, Yuan DY, Yu HX, Gu MH, Liu QQ (2009) Validation of candidate reference genes for the accurate normalization of real-time quantitative RT-PCR data in rice during seed development. Plant Mol Biol Rep 28(1):49–57.  https://doi.org/10.1007/s11105-009-0124-1 CrossRefGoogle Scholar
  17. Li Y, Ouyang J, Wang YY, Hu R, Xia K, Duan J, Zhang M (2015) Disruption of the rice nitrate transporter OsNPF2.2 hinders root-to-shoot nitrate transport and vascular development. Sci Rep 5:9635.  https://doi.org/10.1038/srep09635 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Mai CD, Phung NTP, To HTM, Gonin M, Hoang GT, Nguyen KL, Do VN, Courtois B, Gantet P (2014) Genes controlling root development in rice. Rice 7:1–11.  https://doi.org/10.1186/s12284-014-0030-5 CrossRefGoogle Scholar
  19. Maksup S, Supaibulwatana K, Selvaraj G (2013) High-quality reference genes for quantifying the transcriptional responses of Oryza sativa L. (ssp. indica and japonica) to abiotic stress conditions. Chi Sci Bull 58(16):1919–1930.  https://doi.org/10.1007/s11434-013-5726-1 CrossRefGoogle Scholar
  20. Moraes GP, Benitez LC, do Amaral MN, Vighi IL, Auler PA, da Maia LC, Braga EJB (2015) Evaluation of reference genes for RT-qPCR studies in the leaves of rice seedlings under salt stress. Genet Mol Res 14(1):2384–2398.  https://doi.org/10.4238/2015.March.27.24 CrossRefPubMedGoogle 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 Biol 10:56.  https://doi.org/10.1186/1471-2229-10-56 CrossRefPubMedPubMedCentralGoogle Scholar
  22. Pabuayon IM, Yamamoto N, Trinidad JL, Longkumer T, Raorane ML, Kohli A (2016) Reference genes for accurate gene expression analyses across different tissues, developmental stages and genotypes in rice for drought tolerance. Rice 9(1):32.  https://doi.org/10.1186/s12284-016-0104-7 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Patel DP, Das A, Munda GC, Ghosh PK, Sandhya J, Kumar M (2010) Evaluation of yield and physiological attributes of high-yielding rice varieties under aerobic and flood-irrigated management practices in mid-hills ecosystem. Agric Water Manag 97(9):1269–1276.  https://doi.org/10.1016/j.agwat.2010.02.018 CrossRefGoogle Scholar
  24. Pathak H, Tewari AN, Sankhyan S, Dubey DS, Mina U, Singh VK, Bhatia A (2011) Direct-seeded rice: potential, performance and problems—a review. Curr Adv Agric Sci 3(2):77–88Google Scholar
  25. 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(6):509–515.  https://doi.org/10.1023/B:BILE.0000019559.84305.47 CrossRefPubMedGoogle Scholar
  26. Sandhu N, Jain S, Kumar A, Mehla BS, Jain R (2013) Genetic variation, linkage mapping of QTL and correlation studies for yield, root, and agronomic traits for aerobic adaptation. BMC Genet 14:104CrossRefPubMedPubMedCentralGoogle Scholar
  27. Schmidt GW, Delaney SK (2010) Stable internal reference genes for normalization of real-time RT-PCR in tobacco (Nicotiana tabacum) during development and abiotic stress. Mol Genet Genom 283(3):233–241.  https://doi.org/10.1007/s00438-010-0511-1 CrossRefGoogle Scholar
  28. Singh A, Kumar P, Gautam V, Rengasamy B, Adhikari B, Udayakumar M, Sarkar AK (2016) Root transcriptome of two contrasting indica rice cultivars uncovers regulators of root development and physiological responses. Sci Rep 6:39266.  https://doi.org/10.1038/srep39266 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Udvardi MK, Czechowski T, Scheible WR (2008) Eleven golden rules of quantitative RT-PCR eleven golden rules of quantitative RT-PCR. Plant Cell 20:1736–1737.  https://doi.org/10.1105/tpc.108.061143 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Uga Y, Okuno K, Yano M (2011) Dro1, a major QTL involved in deep rooting of rice under upland field conditions. J Exp Bot 62(8):2485–2494.  https://doi.org/10.1093/jxb/erq429 CrossRefPubMedGoogle Scholar
  31. 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):RESEARCH0034.  https://doi.org/10.1186/gb-2002-3-7-research0034 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Wang L, Xie W, Chen Y, Tang W, Yang J, Ye R, Zhang Q (2010) A dynamic gene expression atlas covering the entire life cycle of rice. Plant J 61(5):752–766.  https://doi.org/10.1111/j.1365-313X.2009.04100.x CrossRefPubMedGoogle Scholar
  33. Wang ZQ, Li GZ, Gong QQ, Li GX, Zheng SJ (2015) OsTCTP, encoding a translationally controlled tumor protein, plays an important role in mercury tolerance in rice. BMC Plant Biol 15:123.  https://doi.org/10.1186/s12870-015-0500-y CrossRefPubMedPubMedCentralGoogle Scholar
  34. Wu W, Cheng S (2014) Root genetic research, an opportunity and challenge to rice improvement. Field Crops Res 165:111–124.  https://doi.org/10.1016/j.fcr.2014.04.013 CrossRefGoogle Scholar
  35. Xie F, Xiao P, Chen D, Xu L, Zhang B (2012) miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs. Plant Mol Biol 80(1):75–84.  https://doi.org/10.1007/s11103-012-9885-2 CrossRefGoogle Scholar
  36. Xu H, Bao JD, Dai JS, Li Y, Zhu Y (2015) Genome-Wide Identification of new reference genes for qRT-PCR normalization under high temperature stress in rice endosperm. PLoS One 10(11):e0142015.  https://doi.org/10.1371/journal.pone.0142015 CrossRefPubMedPubMedCentralGoogle Scholar
  37. Yang H, Liu J, Huang S, Guo T, Deng L, Hua W (2014) Selection and evaluation of novel reference genes for quantitative reverse transcription PCR (qRT-PCR) based on genome and transcriptome data in Brassica napus L. Gene 538(1):113–122CrossRefPubMedGoogle Scholar
  38. Zhang X, Jiang H, Wang H, Cui J, Wang J, Hu J, Guo L, Qian Q, Xue D (2017) Transcriptome analysis of rice seedling roots in response to potassium deficiency. Sci Rep 7:5523CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Amol S. Phule
    • 1
    • 2
  • Kalyani M. Barbadikar
    • 1
  • M. S. Madhav
    • 1
  • P. Senguttuvel
    • 1
  • M. B. B. Prasad Babu
    • 1
  • P. Ananda Kumar
    • 1
  1. 1.Biotechnology DivisionICAR-Indian Institute of Rice ResearchHyderabadIndia
  2. 2.Institute of BiotechnologyProfessor Jayashankar Telangana State Agricultural UniversityHyderabadIndia

Personalised recommendations