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The Analysis of unintended open reading frame ORF-130 expression in maize event MZIR098 by LC-MS as part of the allergenicity risk assessment of genetically modified crops


  • Ivan Birukou Syngenta Crop Protection, LLC
  • Kathryn Bailey
  • James Sugar
  • Scott McClain
  • Scott Young



parallel reaction monitoring, open reading frame, risk assessment, genetically modified (GM) crops, LC-MS/MS


Rigorous safety assessments are required for genetically modified (GM) crops to support their use as food or feed.  These assessments include bioinformatics analysis of the amino acid sequences of all potential open reading frames (ORFs) encoded in the exogenous DNA introduced into the plant through genetic modification.  Sequence similarities of potential DNA products to known or potential toxins and allergens trigger subsequent assessments of potential hazards for the consumer or the environment.  The bioinformatic analysis of Syngenta maize event MZIR098 insert DNA revealed a total of 415 ORFs.  One putative ORF (ORF-130) triggered further risk assessment based on its sequence similarities with known allergens, including peanut allergen Ara h 1, wheat glutenin, and others. We examined the potential expression of ORF-130 at a protein level to exclude the possibility of exposure during consumption of MZIR098 maize.  Specifically, we developed and validated a liquid chromatography approach coupled to a tandem mass spectrometry (LC-MS/MS) method, called parallel reaction monitoring (PRM). This measure of expression was quantitative and targeted against the peptides unique to ORF-130.  The linear range of the method was 25-5000 amol ORF-130/µl.  Both, limit of detection (LOD) and lower limit of quantitation (LLOQ) of the method, were 12 μg ORF-130 per gram of dry MZIR098 maize leaf.  The method was accurate and specific to ORF-130.  PRM analysis of MZIR098 extracts did not detect putative ORF-130 above the limits of detection/quantitation.  Furthermore, the broad proteomic data dependent acquisition (DDA) analysis of the MZIR098 extracts demonstrated no evidence of ORF-130 corroborating the PRM analysis.  Our results confirm that ORF-130 is not characterized as a functional gene in Syngenta maize event MZIR098 insert DNA and does not express gene products at detectable levels.


Abendroth, L. J., Elmore, R. W., Boyer, M. J., & Marlay, S. K. (2011). Corn growth and development. Iowa State University Extension, PMR 1009, 49.

Aebersold, R., & Mann, M. (2003). Mass spectrometry-based proteomics. Nature, 422(6928), 198-207.

Aebersold, R., & Mann, M. (2016). Mass-spectrometric exploration of proteome structure and function. Nature, 537(7620), 347-355.

Armbruster, D. A., & Pry, T. (2008). Limit of blank, limit of detection and limit of quantitation. Clin Biochem Rev, 29 Suppl 1, S49-52.

Bekker-Jensen, D. B., Martinez-Val, A., Steigerwald, S., Ruther, P., Fort, K. L., Arrey, T. N., Harder, A., Makarov, A., & Olsen, J. V. (2020). A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients. Mol Cell Proteomics, 19(4), 716-729.

Bourmaud, A., Gallien, S., & Domon, B. (2016). Parallel reaction monitoring using quadrupole-Orbitrap mass spectrometer: Principle and applications. Proteomics, 16(15-16), 2146-2159.

Codex. (2009). Foods derived from modern biotechnology.

COMPARE. (2017). Comprehensive Protein Database International Life Sciences Institute; Health and Environmental Sciences Institute Protein Allergenicity Technical Committee

Cox, J., & Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol, 26(12), 1367-1372.

Delaney, B., Goodman, R. E., & Ladics, G. S. (2018). Food and Feed Safety of Genetically Engineered Food Crops. Toxicol Sci, 162(2), 361-371.

DeSilva, B., Smith, W., Weiner, R., Kelley, M., Smolec, J., Lee, B., Khan, M., Tacey, R., Hill, H., & Celniker, A. (2003). Recommendations for the bioanalytical method validation of ligand-binding assays to support pharmacokinetic assessments of macromolecules. Pharm Res, 20(11), 1885-1900.

EFSA. (2010). Scientific Opinion on the assessment of allergenicity of GM plants and microorganisms and derived food and feed. EFSA Journal, 8(7), 1700.

FDA, U. (2018). Bioanalytical method validation. Guidance for industry. In. Washington, DC: US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Veterinary Medicine (CVM).

Gessulat, S., Schmidt, T., Zolg, D. P., Samaras, P., Schnatbaum, K., Zerweck, J., Knaute, T., Rechenberger, J., Delanghe, B., Huhmer, A., Reimer, U., Ehrlich, H. C., Aiche, S., Kuster, B., & Wilhelm, M. (2019). Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat Methods, 16(6), 509-518.

Grothaus, G. D., Bandla, M., Currier, T., Giroux, R., Jenkins, G. R., Lipp, M., Shan, G., Stave, J. W., & Pantella, V. (2006). Immunoassay as an analytical tool in agricultural biotechnology. J AOAC Int, 89(4), 913-928.

Guan, S., Moran, M. F., & Ma, B. (2019). Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning. Mol Cell Proteomics, 18(10), 2099-2107.

Henderson, C. M., Shulman, N. J., MacLean, B., MacCoss, M. J., & Hoofnagle, A. N. (2018). Skyline Performs as Well as Vendor Software in the Quantitative Analysis of Serum 25-Hydroxy Vitamin D and Vitamin D Binding Globulin. Clin Chem, 64(2), 408-410.

Kettenbach, A. N., Rush, J., & Gerber, S. A. (2011). Absolute quantification of protein and post-translational modification abundance with stable isotope-labeled synthetic peptides. Nat Protoc, 6(2), 175-186.

Kobayashi, Y., Dokiya, Y., Kumazawa, Y., & Sugita, M. (2002). Non-AUG translation initiation of mRNA encoding plastid-targeted phage-type RNA polymerase in Nicotiana sylvestris. Biochem Biophys Res Commun, 299(1), 57-61.

Ladics, G. S., Cressman, R. F., Herouet-Guicheney, C., Herman, R. A., Privalle, L., Song, P., Ward, J. M., & McClain, S. (2011). Bioinformatics and the allergy assessment of agricultural biotechnology products: industry practices and recommendations. Regul Toxicol Pharmacol, 60(1), 46-53.

Lange, V., Picotti, P., Domon, B., & Aebersold, R. (2008). Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol, 4, 222.

Liebler, D. C., & Zimmerman, L. J. (2013). Targeted quantitation of proteins by mass spectrometry. Biochemistry, 52(22), 3797-3806.

Michalski, A., Damoc, E., Hauschild, J. P., Lange, O., Wieghaus, A., Makarov, A., Nagaraj, N., Cox, J., Mann, M., & Horning, S. (2011). Mass spectrometry-based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer. Mol Cell Proteomics, 10(9), M111 011015.

Olsen, J. V., Macek, B., Lange, O., Makarov, A., Horning, S., & Mann, M. (2007). Higher-energy C-trap dissociation for peptide modification analysis. Nat Methods, 4(9), 709-712.

Peterson, A. C., Russell, J. D., Bailey, D. J., Westphall, M. S., & Coon, J. J. (2012). Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol Cell Proteomics, 11(11), 1475-1488.

Picotti, P., & Aebersold, R. (2012). Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions. Nature Methods, 9(6), 555-566.

Pitt, J. J. (2009). Principles and applications of liquid chromatography-mass spectrometry in clinical biochemistry. Clin Biochem Rev, 30(1), 19-34.

Privalle, L. S., Chen, J., Clapper, G., Hunst, P., Spiegelhalter, F., & Zhong, C. X. (2012). Development of an agricultural biotechnology crop product: testing from discovery to commercialization. J Agric Food Chem, 60(41), 10179-10187.

Rauniyar, N. (2015). Parallel Reaction Monitoring: A Targeted Experiment Performed Using High Resolution and High Mass Accuracy Mass Spectrometry. Int J Mol Sci, 16(12), 28566-28581.

Riechmann, J. L., Ito, T., & Meyerowitz, E. M. (1999). Non-AUG initiation of AGAMOUS mRNA translation in Arabidopsis thaliana. Mol Cell Biol, 19(12), 8505-8512.

Searle, B. C., Swearingen, K. E., Barnes, C. A., Schmidt, T., Gessulat, S., Küster, B., & Wilhelm, M. (2020). Generating high quality libraries for DIA MS with empirically corrected peptide predictions. Nature Communications, 11(1), 1548.

Taylor, S. C., Berkelman, T., Yadav, G., & Hammond, M. (2013). A defined methodology for reliable quantification of Western blot data. Mol Biotechnol, 55(3), 217-226.

Taylor, S. C., & Posch, A. (2014). The design of a quantitative western blot experiment. Biomed Res Int, 2014, 361590.

Tiwary, S., Levy, R., Gutenbrunner, P., Salinas Soto, F., Palaniappan, K. K., Deming, L., Berndl, M., Brant, A., Cimermancic, P., & Cox, J. (2019). High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis. Nat Methods, 16(6), 519-525.

Vidova, V., & Spacil, Z. (2017). A review on mass spectrometry-based quantitative proteomics: Targeted and data independent acquisition. Anal Chim Acta, 964, 7-23.

Walters, F. S., Young, S., & Graser, G. (2020). Meeting technical challenges for protein characterization and surrogate equivalence studies that resulted from insecticidal protein co-expression in maize event MZIR098. Transgenic Res, 29(1), 109-124.

Wootton, J. C. (1994). Non-globular domains in protein sequences: automated segmentation using complexity measures. Comput Chem, 18(3), 269-285.

Wootton, J. C., & Federhen, S. (1996). Analysis of compositionally biased regions in sequence databases. Methods Enzymol, 266, 554-571.

Zhang, S., Garcia-D'Angeli, A., Brennan, J. P., & Huo, Q. (2014). Predicting detection limits of enzyme-linked immunosorbent assay (ELISA) and bioanalytical techniques in general. Analyst, 139(2), 439-445.







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