Evaluation of global sequence comparison and one-to-one FASTA local alignment in regulatory allergenicity assessment of transgenic proteins in food crops

Predicting the allergenic risk of Phosphite-NAD+-Oxidoreductase and purple acid phosphatase 17 proteins in genetically modified canola using bioinformatic approaches

Recent advancements in the generation of high-throughput multi-omics data have provided a vast array of candidate genes for the genetic engineering of plants. However, as part of their safety assessment, newly expressed proteins in genetically modified crops must be evaluated for potential cross-reactivity with known allergens. In this study, we developed transgenic canola plants expressing the Arabidopsis thaliana PAP17 gene and a novel selectable marker composed of the ptxD gene from Pseudomonas stutzeri. To evaluate the potential allergenic cross-reactivity of the AtPAP17 and PTXD proteins expressed in transgenic canola, we applied a comprehensive approach utilizing sequence-based, motif-based, and 3D structure-based analyses. Our results demonstrate that the risk of conferring cross-reactivity with known allergens is negligible, indicating that the expression of these proteins in transgenic canola poses a low allergenic risk.

The COMPARE Database: A Public Resource for Allergen Identification, Adapted for Continuous Improvement

Motivation: The availability of databases identifying allergenic proteins via a transparent and consensus-based scientific approach is of prime importance to support the safety review of genetically-modified foods and feeds, and public safety in general. Over recent years, screening for potential new allergens sequences has become more complex due to the exponential increase of genomic sequence information. To address these challenges, an international collaborative scientific group coordinated by the Health and Environmental Sciences Institute (HESI), was tasked to develop a contemporary, adaptable, high-throughput process to build the COMprehensive Protein Allergen REsource (COMPARE) database, a publicly accessible allergen sequence data resource along with bioinformatics analytical tools following guidelines of FAO/WHO and CODEX Alimentarius Commission.

Results: The COMPARE process is novel in that it involves the identification of candidate sequences via automated keyword-based sorting algorithm and manual curation of the annotated sequence entries retrieved from public protein sequence databases on a yearly basis; its process is meant for continuous improvement, with updates being transparently documented with each version; as a complementary approach, a yearly key-word based search of literature databases is added to identify new allergen sequences that were not (yet) submitted to protein databases; in addition, comments from the independent peer-review panel are posted on the website to increase transparency of decision making; finally, sequence comparison capabilities associated with the COMPARE database was developed to evaluate the potential allergenicity of proteins, based on internationally recognized guidelines, FAO/WHO and CODEX Alimentarius Commission

Evidence-based regulations for bioinformatic prediction of allergen cross-reactivity are needed

The bioinformatic criteria adopted by regulatory agencies to predict the potential cross reactivity between newly expressed proteins in genetically engineered crops and known allergens involves amino acid identity thresholds and was formulated nearly two decades ago based on the opinion of allergy experts. Over the subsequent years, empirical evidence has been developed indicating that better bioinformatic tools based on amino acid similarity are available to detect real allergen cross-reactive risk while substantially reducing false-positive detections. Although the formulation of safety regulations, in the absence of empirical evidence, may require reliance on expert opinion, such expert opinion should not trump empirical evidence once it becomes available. The failure of regulation to maintain consistency with the best available scientific evidence diminishes its value and creates arbitrary barriers to the use of beneficial technologies by society.

Immunoglobulin E and immunoglobulin G cross-reactive allergens and epitopes between cow milk αS1-casein and soybean proteins

Some infants allergic to cow milk-based formula are also sensitive to soybean-based formula. This paper aimed to explore the association of IgE and IgG cross-reactivity between αS1-casein in cow milk (CM) and soybean proteins. The IgE and IgG cross-reactive allergens and epitopes were identified using sera from infants allergic to CM or mice monoclonal antibodies. The AA sequence alignment was performed using bioinformatics software. Finally, the digestion and heating stability of the cross-reactive allergen were explored by sodium dodecyl sulfate (SDS)-PAGE and Western blotting. The results showed that the IgE and IgG cross-reactive allergen was α subunit of β-conglycinin named Gly m Bd 60K. The IgE and IgG epitopes were the sequences at AA 319-341 and AA 164-182. No intact Gly m Bd 60K allergen could be observed after 2 min in simulated gastric fluid by SDS-PAGE. Heating did not change IgE and IgG cross-reactivity by Western blotting. Therefore, the existence of cross-reactivity between CM αS1-casein and soybean proteins possibly contributes to the frequently observed cosensitization for these allergens in cow milk-allergic patients. The same IgE- and IgG-binding epitopes of cross-reactive allergens may provide important information for elucidation of the association between IgG and IgE antibody generation.

Allergen false-detection using official bioinformatic algorithms

Bioinformatic amino acid sequence searches are used, in part, to assess the potential allergenic risk of newly expressed proteins in genetically engineered crops. Previous work has demonstrated that the searches required by government regulatory agencies falsely implicate many proteins from rarely allergenic crops as an allergenic risk. However, many proteins are found in crops at concentrations that may be insufficient to cause allergy. Here we used a recently developed set of high-abundance non-allergenic proteins to determine the false-positive rates for several algorithms required by regulatory bodies, and also for an alternative 1:1 FASTA approach previously found to be equally sensitive to the official sliding-window method, but far more selective. The current investigation confirms these earlier findings while addressing dietary exposure.

Validation of bioinformatic approaches for predicting allergen cross reactivity

Part of the allergenicity assessment of newly expressed proteins in genetically engineered food crops involves an assessment of potential cross-reactivity with known allergens. Bioinformatic approaches are used to evaluate the amino acid sequence identity or similarity between newly expressed proteins and the sequences of known allergens. To be useful, such approaches must be sensitive to detecting cross-reactive potential, but also capable of excluding low-risk sequences. One difficulty in comparing the effectiveness of different bioinformatic approaches has been the lack of a standardized validation and evaluation method. Here, we propose a standardized method for evaluating the sensitivity of different bioinformatic algorithms using a comprehensive database of known allergen sequences. We combine this with a previously described method for evaluating selectivity using sequences from a crop not known to commonly cause food allergy (e.g. maize) to compare the standard ">35% identity-criterion over sliding-window of ≥80 amino acids" bioinformatic approach with the previously described "one-to-one (1:1) FASTA" similarity approach using an E-value threshold of 1E-9. Results confirm the superiority of the 1:1 FASTA approach for selectively detecting cross-reactive allergens. The validation methods described here can be applied to other algorithms to select even better fit-for-purpose approaches for evaluating cross-reactive risk.

Food and feed safety of DAS-444Ø6-6 herbicide-tolerant soybean

DAS-444Ø6-6 soybean was genetically engineered (GE) to withstand applications of three different herbicides. Tolerance to glufosinate and glyphosate is achieved through expression of the phosphinothricin acetyltransferase (PAT) and double-mutated maize 5-enolpyruvyl shikimate-3-phosphate synthase (2mEPSPS) enzymes, respectively. These proteins are expressed in currently commercialized crops and represent no novel risk. Tolerance to 2,4-dichlorophenoxyacetic acid (2,4-D) is achieved through expression of the aryloxyalkanoate dioxygenase 12 (AAD-12) enzyme, which is novel in crops. The safety of the AAD-12 protein and DAS-444Ø6-6 event was assessed for food and feed safety based on the weight of evidence and found to be as safe as non-GE soybean.

Bioinformatic screening and detection of allergen cross-reactive IgE-binding epitopes

Protein allergens can be related by cross‐reactivity. Allergens that share relevant sequence can cross‐react, those lacking sufficient similarity in their IgE antibody‐binding epitopes do not cross‐react. Cross‐reactivity is based on shared epitopes that is based on shared sequence and higher level structure (charge and shape). Epitopes are important in predicting cross‐reactivity potential and may provide the potential to establish criteria that identify homology among allergens. Selected allergen's IgE‐binding epitope sequences were used to determine how the FASTA algorithm could be used to identify a threshold of significance. A statistical measure (expectation value, E‐value) was used to identify a threshold specific to identifying cross‐reactivity potential. Peanut Ara h 1 and Ara h 2, shrimp tropomyosin Pen a 1, and birch tree pollen allergen, Bet v 1 were sources of known epitopes. Each epitope or set of epitopes was inserted into random amino acid sequence to create hypothetical proteins used as queries to an allergen database. Alignments with allergens were noted for the ability to match the epitope's source allergen as well as any cross‐reactive or other homologous allergens. A FASTA expectation value range (1 × 10⁻⁵–1 × 10⁻⁶) was identified that could act as a threshold to help identify cross‐reactivity potential.

1:1 FASTA update: Using the power of E-values in FASTA to detect potential allergen cross-reactivity

In the context of regulatory assessment of transgenic proteins for potential allergenicity, a previous investigation demonstrated that a 1:1 FASTA comparison using an E-value of 1.0E-09 as a criterion is superior to the conventional FASTA search (using the whole sequence as a query) for >35% identity over 80 amino acids, but with improved specificity. A further study, using groups of known cross-reactive peanut allergens, indicates the sensitivity of this approach is superior to the conventional FASTA search and equivalent to 80-mer sliding window FASTA search recommended by WHO/FAO. Specifically, the 1:1 FASTA approach eliminated the technical issues resulting from lack of identification of short query sequences with high identity to known allergens, or high identity over short amino acid stretches, and different E-value settings when searching for >35% identity over 80aa. Based on the performance of this simple application of existing bioinformatics tools, and its ease of implementation and interpretation in the context of a regulatory assessment, we advocate that adoption of this 1:1 FASTA approach as a supplement to the FAO/WHO/ CODEX criterion (>35% identity over 80aa) formulated 13 years ago. Adoption of this approach eliminates many biologically irrelevant homology hits generated by the FAO/WHO/CODEX criterion and improves the safety assessment of GM crops.