Assessment of possible allergenicity of hypothetical ORFs in common food crops using current bioinformatic guidelines and its implications for the safety assessment of GM crops

Assessment of the potential allergenicity of genetically-engineered food crops

An extensive safety assessment process exists for genetically-engineered (GE) crops. The assessment includes an evaluation of the introduced protein as well as the crop containing the protein with the goal of demonstrating the GE crop is "as-safe-as" non-GE crops in the food supply. One of the evaluations for GE crops is to assess the expressed protein for allergenic potential. Currently, no single factor is recognized as a predictor for protein allergenicity. Therefore, a weight-of-the-evidence approach, which accounts for a variety of factors and approaches for an overall assessment of allergenic potential, is conducted. This assessment includes an evaluation of the history of exposure and safety of the gene(s) source; protein structure (e.g. amino acid sequence identity to human allergens); stability of the protein to pepsin digestion in vitro; heat stability of the protein; glycosylation status; and when appropriate, specific IgE binding studies with sera from relevant clinically allergic subjects. Since GE crops were first commercialized over 20 years ago, there is no proof that the introduced novel protein(s) in any commercialized GE food crop has caused food allergy.

Current overview of allergens of plant pathogenesis related protein families

Pathogenesis related (PR) proteins are one of the major sources of plant derived allergens. These proteins are induced by the plants as a defense response system in stress conditions like microbial and insect infections, wounding, exposure to harsh chemicals, and atmospheric conditions. However, some plant tissues that are more exposed to environmental conditions like UV irradiation and insect or fungal attacks express these proteins constitutively. These proteins are mostly resistant to proteases and most of them show considerable stability at low pH. Many of these plant pathogenesis related proteins are found to act as food allergens, latex allergens, and pollen allergens. Proteins having similar amino acid sequences among the members of PR proteins may be responsible for cross-reactivity among allergens from diverse plants. This review analyzes the different pathogenesis related protein families that have been reported as allergens. Proteins of these families have been characterized in regard to their biological functions, amino acid sequence, and cross-reactivity. The three-dimensional structures of some of these allergens have also been evaluated to elucidate the antigenic determinants of these molecules and to explain the cross-reactivity among the various allergens.

Comparative assessment of multiple criteria for the in silico prediction of cross-reactivity of proteins to known allergens

Genetically modified crops are becoming important components of a sustainable food supply and must be brought to market efficiently while also safeguarding the public from cross-reactivity of novel proteins to known allergens. Bioinformatic assessments can help to identify proteins warranting further experimental checks for cross-reactivity. This study is a large-scale in silico evaluation of assessment criteria, including searches for: alignments between a query and an allergen having ≥ 35% identity over a length ≥ 80; any sequence (of some minimum length) found in both a query and an allergen; any alignment between a query and an allergen with an E-value below some threshold. The criteria and an allergen database (AllergenOnline) are used to assess 27,243 Viridiplantae proteins for potential allergenicity. (A protein is classed as a "real allergen" if it exceeds a test-specific level of identity to an AllergenOnline entry; assessment of real allergens in the query set is against a reduced database from which the identifying allergen has been removed.) Each criterion's ability to minimize false positives without increasing false negative levels of current methods is determined. At best, the data show a reduction in false positives to ∼6% (from ∼10% under current methods) without any increase in false negatives.