Food Allergens pp 175-192 | Cite as

Predicting Potential Allergenicity of New proteins Introduced by Biotechnology

  • Tanja Ćirković VeličkovićEmail author
  • Marija Gavrović-Jankulović
Part of the Food Microbiology and Food Safety book series (FMFS)


The potential allergenicity of newly introduced proteins in genetically engineered foods has become an important safety evaluation issue. Food allergy is an important and common health issue, and therefore there is a need to characterize the sensitizing potential of novel food proteins. Approaches currently used include consideration of structural similarity to, or amino acid sequence homology with, known allergens using bioinformatics tools; immunologic cross-reactivity with known allergens; and the measurement of resistance to proteolytic digestion by pepsin in a simulated gastric fluid. Although these methods provide information that contributes to safety assessment, they do not provide a direct evaluation of the ability of a novel protein to cause allergic sensitization. For this reason, considerable interest exists in the design and evaluation of suitable animal models that may provide a more holistic assessment of allergenic potential. An appropriate animal model should produce sensitization and/or elicitation of allergic symptoms at a physiologically relevant dose, via the relevant route of exposure in a standard mouse strain. So far, developed mouse models of food allergy mostly use adjuvants (such as cholera toxin and staphylococcal enterotoxin B) and the oral route of exposure. None of the currently studied models has been widely accepted and validated. More work is needed on identification of appropriate end points, particularly those that reflect anaphylactic activity. Before validation can be considered, decisions have to be made regarding which mouse strains and adjuvants to include, as well as the doses of test materials. Appropriate test substances that represent a range from highly allergenic to poorly allergenic need to be selected. The data also indicate that the food matrix can influence responses to individual proteins and, therefore, the food matrix should be taken into account when developing models for predicting the allergenic potential of new proteins introduced by biotechnology.


Genetically Modify Food Allergy Food Allergen Genetically Modify Crop Oral Tolerance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Allergen-representative peptides


Basic local alignment search tool


Detection based on automated selection of allergen-representative peptides


DNA Data Bank of Japan


European molecular biology laboratory


Enzyme-linked immunosorbent assay


Food and agriculture organization


Genetically modified


Hidden Markov model


Motif-based sequence analysis


National center for biotechnology information


Polyacrylamide gel electrophoresis


Propensity distance


Quantitative structure-activity relationship


Sodium dodecyl sulfate


Simulated gastric fluid


Staphylococcal enterotoxin B


Structural database of allergenic proteins


World Health Organization


  1. 1.
    Miller SA, Artuso A, Avery D, Beachy RN, Day PR, Fennema OR, Hardy R, Keeling PL, Klaenhammer TR, McGloughlin M et al: Benefits and concerns associated with recombinant DNA biotechnology-derived foods. Food Technol 2000, 54(10):61.Google Scholar
  2. 2.
    Metcalfe DD: Genetically modified crops and allergenicity. Nat Immunol 2005, 6(9):857–860.CrossRefGoogle Scholar
  3. 3.
    Harlander SK: The evolution of modern agriculture and its future with biotechnology. J Am Coll Nutr 2002, 21(3 Suppl):161S–165S.CrossRefGoogle Scholar
  4. 4.
    Nordlee JA, Taylor SL, Townsend JA, Thomas LA, Bush RK: Identification of a Brazil-nut allergen in transgenic soybeans. N Engl J Med 1996, 334(11):688–692.CrossRefGoogle Scholar
  5. 5.
    European Food Safety Authority (EFSA). Guidance for risk assessment of food and feed from genetically modified plants. EFSA Journal 2011, 9(5):2150Google Scholar
  6. 6.
    Codex Alimentarius Commission. Alinorm 03/34: Joint FAO/WHO Food Standard Programme CAC, Twenty-Fifth Session, Rome, 30 June–5 July, 2003: Appendix III, Guideline for the conduct of food safety assessment of foods derived from recombinant-DNA plants and Appendix IV, Annex on the assessment of possible allergenicity. 2003:47–60.Google Scholar
  7. 7.
    Codex Alimentarius Commission: Foods Derived From Modern Biotechnology. FAO/WHO, Rome 2009:1–85.Google Scholar
  8. 8.
    Goodman RE, Vieths S, Sampson HA, Hill D, Ebisawa M, Taylor SL, van Ree R: Allergenicity assessment of genetically modified crops–what makes sense? Nature Biotechol 2008, 26(1):73–81.CrossRefGoogle Scholar
  9. 9.
    Metcalfe DD, Astwood JD, Townsend R, Sampson HA, Taylor SL, Fuchs RL: Assessment of the allergenic potential of foods derived from genetically engineered crop plants. Crit Rev Food Sci Nutr 1996, 36 Suppl:S165–186.Google Scholar
  10. 10.
    FAO/WHO: Evaluation of allergenicity of genetically modified foods. Report of a joint FAO/WHO expert consultation on allergenicity of foods derived from biotechnology (Food and Agriculture Organization of the United Nations (FAO), Rome, 2001.Google Scholar
  11. 11.
    van Ree R: Carbohydrate epitopes and their relevance for the diagnosis and treatment of allergic diseases. Int Arch Allergy Immunol 2002, 129(3):189–197.CrossRefGoogle Scholar
  12. 12.
    Altmann F: The role of protein glycosylation in allergy. Int Arch Allergy Immunol 2007, 142(2):99–115.CrossRefGoogle Scholar
  13. 13.
    Asero R, Mistrello G, Roncarolo D, de Vries SC, Gautier MF, Ciurana CL, Verbeek E, Mohammadi T, Knul-Brettlova V, Akkerdaas JH et al: Lipid transfer protein: a pan-allergen in plant-derived foods that is highly resistant to pepsin digestion. Int Arch Allergy Immunol 2000, 122(1):20–32.CrossRefGoogle Scholar
  14. 14.
    Fu TT, Abbott UR, Hatzos C: Digestibility of food allergens and nonallergenic proteins in simulated gastric fluid and simulated intestinal fluid—A comparative study. J Agric Food Chem 2002, 50(24):7154–7160.CrossRefGoogle Scholar
  15. 15.
    Thomas K, Aalbers M, Bannon GA, Bartels M, Dearman RJ, Esdaile DJ, Fu TJ, Glatt CM, Hadfield N, Hatzos C et al: A multi-laboratory evaluation of a common in vitro pepsin digestion assay protocol used in assessing the safety of novel proteins. Regul Toxicol Pharmacol 2004, 39(2):87–98.CrossRefGoogle Scholar
  16. 16.
    EFSA Panel on Genetically Modified Organisms (GMO): Scientific Opinion on the assessment of allergenicity of GM plants and microorganisms and derived food and feed. EFSA Journal 2010, 8(7):168.Google Scholar
  17. 17.
    Hagen JB: The origins of bioinformatics. Nat Rev Genet 2000, 1(3):231–236.CrossRefGoogle Scholar
  18. 18.
    Baxevanis AD, Ouellette B (eds.): Bioinformatics: A practical guide to the analysis of genee and proteins: John Wiley & Sons, New Jersey 2005.Google Scholar
  19. 19.
    Ladics GS: Current codex guidelines for assessment of potential protein allergenicity. Food Chem Toxicol 2008, 46 Suppl 10:S20–23.CrossRefGoogle Scholar
  20. 20.
    Ivanciuc O, Garcia T, Torres M, Schein CH, Braun W: Characteristic motifs for families of allergenic proteins. Mol Immunol 2009, 46(4):559–568.CrossRefGoogle Scholar
  21. 21.
    Radauer C, Bublin M, Wagner S, Mari A, Breiteneder H: Allergens are distributed into few protein families and possess a restricted number of biochemical functions. J Allergy Clin Immunol 2008, 121(4):847–852 e847.CrossRefGoogle Scholar
  22. 22.
    Gendel SM: Sequence analysis for assessing potential allergenicity. Ann N Y Acad Sci 2002, 964:87–98.CrossRefGoogle Scholar
  23. 23.
    Stadler MB, Stadler BM: Allergenicity prediction by protein sequence. Faseb J 2003, 17(9):1141–1143.Google Scholar
  24. 24.
    Ladics GS, Selgrade MK: Identifying food proteins with allergenic potential: evolution of approaches to safety assessment and research to provide additional tools. Regul Toxicol Pharmacol 2009, 54(3 Suppl):S2–6.CrossRefGoogle Scholar
  25. 25.
    Bailey TL, Elkan C: Fitting a mixture model by expectation-maximization to discover motifs in biopolymers. In: Second International Conference on Intelligent Systems for Molecular Biology Menlo Park, CA: AAAI Press: 1994; 1994: 28–36.Google Scholar
  26. 26.
    Li KB, Issac P, Krishnan A: Predicting allergenic proteins using wavelet transform. Bioinformatics 2004, 20(16):2572–2578.CrossRefGoogle Scholar
  27. 27.
    Bjorklund AK, Soeria-Atmadja D, Zorzet A, Hammerling U, Gustafsson MG: Supervised identification of allergen-representative peptides for in silico detection of potentially allergenic proteins. Bioinformatics 2005, 21(1):39–50.CrossRefGoogle Scholar
  28. 28.
    Saha S, Raghava GP: AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res 2006, 34(Web Server issue):W202–209.CrossRefGoogle Scholar
  29. 29.
    Bailey TL, Gribskov M: Score distributions for simultaneous matching to multiple motifs. J Comput Biol 1997, 4(1):45–59.CrossRefGoogle Scholar
  30. 30.
    Wold S, Jonsson J, Sjostrom M, Sandberg M, Rannar S: DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Anal Chim Acta 1993, 277(2):239–253.CrossRefGoogle Scholar
  31. 31.
    Nyström A, Andersson PM, Lundstedt T: Multivariate data analysis of topographically modified α-melanotropin analogues using auto and cross auto covariances (ACC). Quant Struct-Act Relat 2000, 19(3):264–269.CrossRefGoogle Scholar
  32. 32.
    Lapinsh M, Gutcaits A, Prusis P, Post C, Lundstedt T, Wikberg JE: Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences. Protein Sci 2002, 11(4):795–805.CrossRefGoogle Scholar
  33. 33.
    Dimitrov I, Flower DR, Doytchinova I: AllerTOP–a server for in silico prediction of allergens. BMC Bioinformatics 2013, 14 Suppl 6:S4.CrossRefGoogle Scholar
  34. 34.
    Gendel SM: Allergen databases and allergen semantics. Regul Toxicol Pharmacol 2009, 54(3 Suppl):S7–10.CrossRefGoogle Scholar
  35. 35.
    King TP, Hoffman D, Lowenstein H, Marsh DG, Platts-Mills TA, Thomas W: Allergen nomenclature. WHO/IUIS Allergen Nomenclature Subcommittee. Int Arch Allergy Immunol 1994, 105(3):224–233.CrossRefGoogle Scholar
  36. 36.
    Mari A, Scala E, Palazzo P, Ridolfi S, Zennaro D, Carabella G: Bioinformatics applied to allergy: allergen databases, from collecting sequence information to data integration. The Allergome platform as a model. Cell Immunol 2006, 244(2):97–100.CrossRefGoogle Scholar
  37. 37.
    Soeria-Atmadja D, Zorzet A, Gustafsson MG, Hammerling U: Statistical evaluation of local alignment features predicting allergenicity using supervised classification algorithms. Int Arch Allergy Immunol 2004, 133(2):101–112.CrossRefGoogle Scholar
  38. 38.
    King TP, Hoffman D, Lowenstein H, Marsh DG, Platts-Mills TAE, Thomas W: Allergen nomenclature. Bulletin of the World Health Organization 1994, 72(5):797–806.Google Scholar
  39. 39.
    King TP, Hoffman D, Lowenstein H, Marsh DG, Platts-Mills TA, Thomas W: Allergen nomenclature. Allergy 1995, 50(9):765–774.CrossRefGoogle Scholar
  40. 40.
    Chapman MD, Pomes A, Breiteneder H, Ferreira F: Nomenclature and structural biology of allergens. J Allergy Clin Immunol 2007, 119(2):414–420.CrossRefGoogle Scholar
  41. 41.
    Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A: UniProtKB/Swiss-Prot. Methods Mol Biol 2007, 406:89–112.Google Scholar
  42. 42.
    Westbrook J, Feng Z, Jain S, Bhat TN, Thanki N, Ravichandran V, Gilliland GL, Bluhm W, Weissig H, Greer DS et al: The Protein Data Bank: unifying the archive. Nucleic Acids Res 2002, 30(1):245–248.CrossRefGoogle Scholar
  43. 43.
    Sayers EW, Barrett T, Benson DA, Bolton E, Bryant SH, Canese K, Chetvernin V, Church DM, Dicuccio M, Federhen S et al: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2012, 40(Database issue):D13–25.CrossRefGoogle Scholar
  44. 44.
    Hileman RE, Silvanovich A, Goodman RE, Rice EA, Holleschak G, Astwood JD, Hefle SL: Bioinformatic methods for allergenicity assessment using a comprehensive allergen database. Int Arch Allergy Immunol 2002, 128(4):280–291.CrossRefGoogle Scholar
  45. 45.
    Ivanciuc O, Schein CH, Braun W: SDAP: database and computational tools for allergenic proteins. Nucleic Acids Res 2003, 31(1):359–362.CrossRefGoogle Scholar
  46. 46.
    Bairoch A, Apweiler R: The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res 2000, 28(1):45–48.CrossRefGoogle Scholar
  47. 47.
    Barker WC, Garavelli JS, Hou Z, Huang H, Ledley RS, McGarvey PB, Mewes HW, Orcutt BC, Pfeiffer F, Tsugita A et al: Protein Information Resource: a community resource for expert annotation of protein data. Nucleic Acids Res 2001, 29(1):29–32.CrossRefGoogle Scholar
  48. 48.
    Wheeler DL, Church DM, Lash AE, Leipe DD, Madden TL, Pontius JU, Schuler GD, Schriml LM, Tatusova TA, Wagner L et al: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2001, 29(1):11–16.CrossRefGoogle Scholar
  49. 49.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank. Nucleic Acids Res 2000, 28(1):235–242.CrossRefGoogle Scholar
  50. 50.
    Fiers MW, Kleter GA, Nijland H, Peijnenburg AA, Nap JP, van Ham RC: Allermatch, a webtool for the prediction of potential allergenicity according to current FAO/WHO Codex alimentarius guidelines. BMC Bioinformatics 2004, 5:133.CrossRefGoogle Scholar
  51. 51.
    Zhang ZH, Koh JL, Zhang GL, Choo KH, Tammi MT, Tong JC: AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins. Bioinformatics 2007, 23(4):504–506.CrossRefGoogle Scholar
  52. 52.
    Thomas K, Herouet-Guicheney C, Ladics G, Bannon G, Cockburn A, Crevel R, Fitzpatrick J, Mills C, Privalle L, Vieths S: Evaluating the effect of food processing on the potential human allergenicity of novel proteins: international workshop report. Food Chem Toxicol 2007, 45(7):1116–1122.CrossRefGoogle Scholar
  53. 53.
    Astwood JD, Leach JN, Fuchs RL: Stability of food allergens to digestion in vitro. Nat Biotechnol 1996, 14(10):1269–1273.CrossRefGoogle Scholar
  54. 54.
    Ofori-Anti AO, Ariyarathna H, Chen L, Lee HL, Pramod SN, Goodman RE: Establishing objective detection limits for the pepsin digestion assay used in the assessment of genetically modified foods. Regul Toxicol Pharmacol 2008, 52(2):94–103.CrossRefGoogle Scholar
  55. 55.
    Moreno FJ: Gastrointestinal digestion of food allergens: effect on their allergenicity. Biomed Pharmacother 2007, 61(1):50–60.CrossRefGoogle Scholar
  56. 56.
    Fu TJ: Digestion stability as a criterion for protein allergenicity assessment. Ann N Y Acad Sci 2002, 964:99–110.CrossRefGoogle Scholar
  57. 57.
    Untersmayr E, Jensen-Jarolim E: The role of protein digestibility and antacids on food allergy outcomes. J Allergy Clin Immunol 2008, 121(6):1301–1308; quiz 1309–1310.CrossRefGoogle Scholar
  58. 58.
    Ladics GS, Knippels LM, Penninks AH, Bannon GA, Goodman RE, Herouet-Guicheney C: Review of animal models designed to predict the potential allergenicity of novel proteins in genetically modified crops. Regul Toxicol Pharmacol 2010, 56(2):212–224.CrossRefGoogle Scholar
  59. 59.
    Atherton KT, Dearman RJ, Kimber I: Protein allergenicity in mice: a potential approach for hazard identification. Ann N Y Acad Sci 2002, 964:163–171.CrossRefGoogle Scholar
  60. 60.
    Selgrade MK, Bowman CC, Ladics GS, Privalle L, Laessig SA: Safety assessment of biotechnology products for potential risk of food allergy: implications of new research. Toxicol Sci 2009, 110(1):31–39.CrossRefGoogle Scholar
  61. 61.
    Li XM, Schofield BH, Huang CK, Kleiner GI, Sampson HA: A murine model of IgE-mediated cow’s milk hypersensitivity. J Allergy Clin Immunol 1999, 103(2 Pt 1):206–214.CrossRefGoogle Scholar
  62. 62.
    Li XM, Serebrisky D, Lee SY, Huang CK, Bardina L, Schofield BH, Stanley JS, Burks AW, Bannon GA, Sampson HA: A murine model of peanut anaphylaxis: T- and B-cell responses to a major peanut allergen mimic human responses. J Allergy Clin Immunol 2000, 106(1 Pt 1):150–158.CrossRefGoogle Scholar
  63. 63.
    Bowman CC, Selgrade MK: Differences in allergenic potential of food extracts following oral exposure in mice reflect differences in digestibility: potential approaches to safety assessment. Toxicol Sci 2008, 102(1):100–109.CrossRefGoogle Scholar
  64. 64.
    Ganeshan K, Neilsen CV, Hadsaitong A, Schleimer RP, Luo X, Bryce PJ: Impairing oral tolerance promotes allergy and anaphylaxis: a new murine food allergy model. J Allergy Clin Immunol 2009, 123(1):231–238 e234.CrossRefGoogle Scholar
  65. 65.
    Birmingham NP, Parvataneni S, Hassan HM, Harkema J, Samineni S, Navuluri L, Kelly CJ, Gangur V: An adjuvant-free mouse model of tree nut allergy using hazelnut as a model tree nut. Int Arch Allergy Immunol 2007, 144(3):203–210.CrossRefGoogle Scholar
  66. 66.
    Gonipeta B, Parvataneni S, Paruchuri P, Gangur V: Long-term characteristics of hazelnut allergy in an adjuvant-free mouse model. Int Arch Allergy Immunol 2010, 152(3):219–225.CrossRefGoogle Scholar
  67. 67.
    Parvataneni S, Gonipeta B, Tempelman RJ, Gangur V: Development of an adjuvant-free cashew nut allergy mouse model. Int Arch Allergy Immunol 2009, 149(4):299–304.CrossRefGoogle Scholar
  68. 68.
    Bowman CC, Selgrade MK: Failure to induce oral tolerance in mice is predictive of dietary allergenic potency among foods with sensitizing capacity. Toxicol Sci 2008, 106(2):435–443.CrossRefGoogle Scholar
  69. 69.
    Thomas K, MacIntosh S, Bannon G, Herouet-Guicheney C, Holsapple M, Ladics G, McClain S, Vieths S, Woolhiser M, Privalle L: Scientific advancement of novel protein allergenicity evaluation: an overview of work from the HESI Protein Allergenicity Technical Committee (2000–2008). Food Chem Toxicol 2009, 47(6):1041–1050.CrossRefGoogle Scholar
  70. 70.
    Mills EN, Mackie AR: The impact of processing on allergenicity of food. Curr Opin Allergy Clin Immunol 2008, 8(3):249–253.CrossRefGoogle Scholar
  71. 71.
    van Wijk F, Nierkens S, Hassing I, Feijen M, Koppelman SJ, de Jong GA, Pieters R, Knippels LM: The effect of the food matrix on in vivo immune responses to purified peanut allergens. Toxicol Sci 2005, 86(2):333–341.CrossRefGoogle Scholar
  72. 72.
    van Esch BC, van Bilsen JH, Jeurink PV, Garssen J, Penninks AH, Smit JJ, Pieters RH, Knippels LM: Interlaboratory evaluation of a cow’s milk allergy mouse model to assess the allergenicity of hydrolysed cow’s milk based infant formulas. Toxicol Lett 2013, 220(1):95–102.CrossRefGoogle Scholar
  73. 73.
    Knippels LM, Houben GF, Spanhaak S, Penninks AH: An oral sensitization model in Brown Norway rats to screen for potential allergenicity of food proteins. Methods 1999, 19(1):78–82.CrossRefGoogle Scholar
  74. 74.
    Knippels LM, Penninks AH: Assessment of protein allergenicity: studies in brown norway rats. Ann N Y Acad Sci 2002, 964:151–161.CrossRefGoogle Scholar
  75. 75.
    Schouten B, van Esch BC, Hofman GA, de Kivit S, Boon L, Knippels LM, Garssen J, Willemsen LE: A potential role for CD25 + regulatory T-cells in the protection against casein allergy by dietary non-digestible carbohydrates. Br J Nutr 2012, 107(1):96–105.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Tanja Ćirković Veličković
    • 1
    Email author
  • Marija Gavrović-Jankulović
    • 1
  1. 1.Department of Biochemistry Faculty of ChemistryUniversity of BelgradeBelgradeSerbia

Personalised recommendations