Plant Growth Regulation

, Volume 88, Issue 1, pp 1–7 | Cite as

Effect on metabolome of the grains of transgenic rice containing insecticidal cry and glyphosate tolerance epsps genes

  • Cheng Peng
  • Lin Ding
  • Chaoyang Hu
  • Xiaoyun Chen
  • Xiaofu Wang
  • Xiaoli Xu
  • Yueying LiEmail author
  • Junfeng XuEmail author
Original Paper


Genetically modified organisms (GMOs) need to be evaluated for safety before their release. Metabolome techniques provide effective methods to evaluate unintended effects. In this study, the grain metabolome of six transgenic rice lines containing secticidal cry and glyphosate tolerance epsps genes were tested by using an non-targeted metabolite profiling, and 161 and 138 metabolites were identified in grains at grain-filling stage and mature stage, respectively. The metabolic profiles of genetically modified (GM) lines and non-GM lines were all significantly different from each other at grain-filling stage. Although the levels of many metabolites were significantly changed in each transgenic line, only seven of them were simultaneously changed in all the six lines compared with those in non-GM lines at grain-filling stage. The number of significantly changed metabolites was much less at mature stage than that at grain-filling stage. Besides, none of these metabolites was simultaneously changed in all the six lines, suggesting that the metabolites of GMOs are different at different stages of GMO development. This study provides useful information about the metabolic variation between GMO and non-GMO in two development stages of rice grains. These findings suggest that non-targeted metabolite profiling can strengthen the assessment of risk based on metabolites.


Metabolite profiling Genetically modified organisms (GMOs) Secticidal cry gene Glyphosate tolerance epsps gene Correlation analysis 



This work was supported by the National Natural Science Foundation of China (Grant Nos. 31501280, 31671753) and the Natural Science Foundation of Zhejiang Province (Grant No. LQ15C130002).

Author contributions

CP and JX designed the research. LD and XW carried out the field tests. CP and XC performed sample preparation. CH and CP carried out the metabolic profiling, the annotation of the metabolites and data analysis. CP, CH and JX wrote the manuscript. XC supervised the research.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

10725_2019_482_MOESM1_ESM.xlsx (486 kb)
Supplementary material 1 (XLSX 486 KB)
10725_2019_482_MOESM2_ESM.docx (101 kb)
Supplementary material 2 (DOCX 100 KB)


  1. Aarab S, Arakrak A, Ollero FJ, Megias M, Gomes DF, Ribeiro RA, Hungria M (2016) Draft genome sequence of pseudomonas fluorescens strain ET76, isolated from rice Rhizosphere in Northwestern Morocco. Genome Announc. Google Scholar
  2. Azevedo RA, Lancien M, Lea PJ (2006) The aspartic acid metabolic pathway, an exciting and essential pathway in plants. Amino Acids 30:143–162CrossRefGoogle Scholar
  3. Barros E, Lezar S, Anttonen MJ, van Dijk JP, Rohlig RM, Kok EJ, Engel KH (2010) Comparison of two GM maize varieties with a near-isogenic non-GM variety using transcriptomics, proteomics and metabolomics Plant. Biotechnol J 8:436–451. Google Scholar
  4. Caldovic L, Tuchman M (2003) N-acetylglutamate and its changing role through evolution. Biochem J 372:279–290CrossRefGoogle Scholar
  5. Catchpole GS et al (2005) Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc Natl Acad Sci USA 102:14458–14462. CrossRefGoogle Scholar
  6. Coll A et al (2009) Gene expression profiles of MON810 and comparable non-GM maize varieties cultured in the field are more similar than are those of conventional lines. Transgenic Res 18:801–808. CrossRefGoogle Scholar
  7. Coll A, Nadal A, Collado R, Capellades G, Kubista M, Messeguer J, Pla M (2010) Natural variation explains most transcriptomic changes among maize plants of MON810 and comparable non-GM varieties subjected to two N-fertilization farming practices. Plant Mol Biol 73:349–362. CrossRefGoogle Scholar
  8. Dong W et al (2008) GMDD: a database of GMO detection methods. BMC Bioinform 9:260. CrossRefGoogle Scholar
  9. Farag KM, Palta JP (2010) Use of lysophosphatidylethanolamine, a natural lipid, to retard tomato leaf and fruit senescence. Physiol Plant 87:515–521CrossRefGoogle Scholar
  10. Gamir J, Pastor V, Cerezo M, Flors V (2012) Identification of indole-3-carboxylic acid as mediator of priming against Plectosphaerella cucumerina. Plant Physiol Biochem 61:169–179CrossRefGoogle Scholar
  11. Hong JH, Sungkee H, Gukhoon C (2008) Influence of lysophosphatidylethanolamine on reactive oxygen species, ethylene biosynthesis, and auxin action in plant tissue. Kor J Hortic Sci Technol 26:209–214Google Scholar
  12. Horai H et al (2010) MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom 45:703–714. CrossRefGoogle Scholar
  13. Hu C et al (2016) Identification of conserved and diverse metabolic shifts during rice grain. Dev Sci Rep 6:20942. CrossRefGoogle Scholar
  14. Hu C, Zhao H, Wang W, Xu M, Shi J, Nie X, Yang G (2018) Identification of conserved and diverse metabolic shift of the stylar, intermediate and peduncular segments of cucumber fruit during development. Int J Mol Sci Google Scholar
  15. Kalamaki MS et al (2009) Over-expression of a tomatoN-acetyl-L-glutamate synthase gene (SlNAGS1) in Arabidopsis thaliana results in high ornithine levels and increased tolerance in salt and drought stresses. J Exp Bot 60:1859–1871CrossRefGoogle Scholar
  16. Ladics GS et al (2015) Genetic basis and detection of unintended effects in genetically modified crop plants. Transgenic Res 24:587–603. CrossRefGoogle Scholar
  17. Montero M, Coll A, Nadal A, Messeguer J, Pla M (2011) Only half the transcriptomic differences between resistant genetically modified and conventional rice are associated with the transgene. Plant Biotechnol J 9:693–702. CrossRefGoogle Scholar
  18. Negre F et al (2003) Regulation of methylbenzoate emission after pollination in snapdragon and petunia flowers. Plant Cell 15:2992CrossRefGoogle Scholar
  19. Ozgen M, Palta JP (1999) Use of lysophosphatidylethanolamine (LPE), a natural lipid, to accelerate ripening and enhance shelf life of cranberry fruit. Hortscience 34:538Google Scholar
  20. Özgen M, SerçE S, AkçA Y, Hong JH (2015) Lysophosphatidylethanolamine (LPE) improves fruit size, color, quality and phytochemical contents of sweet cherry cv. ‘0900 Ziraat’. Wonye kwahak kisulchi = Korean J Hortic Sci Technol 33:196–201CrossRefGoogle Scholar
  21. Paxton J (1980) A new working definition of the term “Phytoalexin”. Plant Dis 64:734Google Scholar
  22. Peng C, Chen X, Wang X, Xu X, Wei W, Wang C, Xu J (2018) Comparative analysis of miRNA expression profiles in transgenic and non-transgenic rice using miRNA. Seq Sci Rep 8:338. CrossRefGoogle Scholar
  23. Ricroch AE, Berge JB, Kuntz M (2011) Evaluation of genetically engineered crops using transcriptomic, proteomic, and metabolomic profiling techniques. Plant Physiol 155:1752–1761. CrossRefGoogle Scholar
  24. Schnell J et al (2015) A comparative analysis of insertional effects in genetically engineered plants: considerations for pre-market assessments. Transgenic Res 24:1–17. CrossRefGoogle Scholar
  25. Ubaidillah M et al (2016) Roles of plant hormones and anti-apoptosis genes during drought stress in rice (Oryza sativa L.). Biotech 6:247Google Scholar
  26. Wang XJ, Zhang X, Yang JT, Wang ZX (2018) Effect on transcriptome and metabolome of stacked transgenic maize containing insecticidal cry and glyphosate tolerance epsps genes. Plant J 93:1007–1016. CrossRefGoogle Scholar
  27. Yamamura C et al (2016) Diterpenoid phytoalexin factor, a bHLH transcription factor, plays a central role in the biosynthesis of diterpenoid phytoalexins in rice. Plant J 84:1100–1113CrossRefGoogle Scholar
  28. Zhou J et al (2009) Metabolic profiling of transgenic rice with cryIAc and sck genes: an evaluation of unintended effects at metabolic level by using GC-FID and GC-MS. J Chromatogr 877:725–732. Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Cheng Peng
    • 1
  • Lin Ding
    • 1
    • 2
  • Chaoyang Hu
    • 3
  • Xiaoyun Chen
    • 1
  • Xiaofu Wang
    • 1
  • Xiaoli Xu
    • 1
  • Yueying Li
    • 2
    Email author
  • Junfeng Xu
    • 1
    Email author
  1. 1.State Key Laboratory Breeding Base for Zhejiang Sustainable Pest and Disease Control, Institute of Quality and Standard for Agro-productsZhejiang Academy of Agricultural SciencesHangzhouChina
  2. 2.College of Chemistry and Life ScienceShenyang Normal UniversityShenyangChina
  3. 3.Key Laboratory of Marine Biotechnology of Zhejiang Province, School of Marine SciencesNingbo UniversityNingboChina

Personalised recommendations