Report on the potential allergenicity of genetically modified organisms and their products

Lack, Gideon; Chapman, Martin D.; Kalsheker, Noor; King, Vickie L.; Robinson, Clive; Venables, K M

doi:10.1046/j.1365-2222.2002.01464.x

Cited by 38 publications

(11 citation statements)

References 105 publications

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“…At present, the most important food allergens are peanuts, tree nuts, soy, milk, fish, shellfish, wheat, and egg. However, with the rapid introduction of novel foods, including foods and food products derived from genetically-modified crops, there has been a growing interest in approaches available to assess potential allergenicity of novel gene products (Taylor and Hefle, 2001;Lack et al, 2002;Hollingworth et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Allergic reactions compared between BN and Wistar rats after oral exposure to ovalbumin

Sun¹,

Zhou²,

Pu³

et al. 2012

Journal of Immunotoxicology

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Section: Introductionmentioning

confidence: 99%

Allergic reactions compared between BN and Wistar rats after oral exposure to ovalbumin

Sun¹,

Zhou²,

Pu³

et al. 2012

Journal of Immunotoxicology

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“…The inadvertent introduction of allergenic properties by transgene proteins can emanate either from sources with a known offending capacity or from organisms devoid of a history of safe consumption. Moreover, transgenes can in principle also alter the host’s expression of endogenous allergens [8]. …”

Section: Introductionmentioning

confidence: 99%

Statistical Evaluation of Local Alignment Features Predicting Allergenicity Using Supervised Classification Algorithms

Soeria-Atmadja

Zorzet

Gustafsson

et al. 2004

Int Arch Allergy Immunol

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Background: Recently, two promising alignment-based features predicting food allergenicity using the k nearest neighbor (kNN) classifier were reported. These features are the alignment score and alignment length of the best local alignment obtained in a database of known allergen sequences. Methods: In the work reported here a much more comprehensive statistical evaluation of the potential of these features was performed, this time for the prediction of allergenicity in general. The evaluation consisted of the following four key components. (1) A new high quality database consisting of 318 carefully selected, non-redundant allergens and 1,007 sequences carefully selected to be non-allergens. (2) Three different supervised algorithms: the kNN classifier, the Bayesian linear Gaussian classifier, and the Bayesian quadratic Gaussian classifier. (3) A large set of local alignment procedures defined using the FASTA3 alignment program by means of a wide range of different parameter settings. (4) Novel performance curves, alternative to conventional receiver-operating characteristic curves, to display not only average behaviors but also statistical variations due to small data sets. Results: The linear Gaussian classifier proved most useful among the tested supervised machine learning algorithms, closely followed by the quadratic Gaussian equivalent and kNN. The overall best classification results were obtained with a novel feature vector consisting of the combined alignment scores derived from local alignment procedures using different substitution matrices. Conclusions: The models reported here should be useful as a part of an integrated assessment scheme for potential protein allergenicity and for future comparisons with alternative bioinformatic approaches.

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“…In such cases the assessment of allergenicity becomes more difficult to obtain because of the absence of valid methods and models. [13][14][15][16] …”

mentioning

confidence: 99%

Lack of detectable allergenicity of transgenic maize and soya samples

Batista

Nunes

Carmo

et al. 2005

Journal of Allergy and Clinical Immunology

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Background: The safety issues regarding foods derived from genetically modified (GM) plants are central to their acceptance into the food supply. The potential allergenicity of proteins newly introduced in GM foods is a major safety concern. Objective: We sought to monitor, in potentially sensitive human populations, the allergenicity effects of 5 GM materials obtained from sources with no allergenic potential and already under commercialization in the European Union. Methods: We have performed skin prick tests with protein extracts prepared from transgenic maize (MON810, Bt11, T25, Bt176) and soya (Roundup Ready) samples and from nontransgenic control samples in 2 sensitive groups: children with food and inhalant allergy and individuals with asthmarhinitis. We have also tested IgE immunoblot reactivity of sera from patients with food allergy to soya (Roundup Ready) and maize (MON810, Bt11, Bt176) samples, as well as to the pure transgenic proteins (CryIA[b] and CP4 5-enolpyruvylshikimate-3-phosphate synthase). Results: None of the individuals undergoing tests reacted differentially to the transgenic and nontransgenic samples under study. None of the volunteers tested presented detectable IgE antibodies against pure transgenic proteins. Conclusion: The transgenic products under testing seem to be safe in terms of allergenic potential. We propose postmarket testing as an important screening strategy for putative allergic sensitization to proteins introduced in transgenic plants. (J Allergy Clin Immunol 2005;116:403-10.)

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Report on the potential allergenicity of genetically modified organisms and their products

Cited by 38 publications

References 105 publications

Allergic reactions compared between BN and Wistar rats after oral exposure to ovalbumin

Allergic reactions compared between BN and Wistar rats after oral exposure to ovalbumin

Statistical Evaluation of Local Alignment Features Predicting Allergenicity Using Supervised Classification Algorithms

Lack of detectable allergenicity of transgenic maize and soya samples

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