Identification and semi-quantification of protein allergens in complex mixtures using proteomic and AllerCatPro 2.0 bioinformatic analyses: a proof-of-concept investigation
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Identification and semi-quantification of protein allergens in complex mixtures using proteomic and AllerCatPro 2.0 bioinformatic analyses: a proof-of-concept investigation
Identification and semi-quantification of protein allergens in complex mixtures using proteomic and AllerCatPro 2.0 bioinformatic analyses: a proof-of-concept investigation
Krutz, N. L., Kimber, I., Winget, J., Nguyen, M. N., Limviphuvadh, V., Maurer-Stroh, S., Mahony, C., & Gerberick, G. F. (2024). Identification and semi-quantification of protein allergens in complex mixtures using proteomic and AllerCatPro 2.0 bioinformatic analyses: a proof-of-concept investigation. Journal of Immunotoxicology, 21(1). https://doi.org/10.1080/1547691x.2024.2305452
Abstract:
The demand for botanicals and natural substances in consumer products has increased in recent years.
These substances usually contain proteins and these, in turn, can pose a risk for immunoglobulin E (IgE)-
mediated sensitization and allergy. However, no method has yet been accepted or validated for assessment
of potential allergenic hazards in such materials. In the studies here, a dual proteomic-bioinformatic
approach is proposed to evaluate holistically allergenic hazards in complex mixtures of plants, insects, or
animal proteins. Twelve commercial preparations of source materials (plant products, dust mite extract,
and preparations of animal dander) known to contain allergenic proteins were analyzed by label-free
proteomic analyses to identify and semi-quantify proteins. These were then evaluated by bioinformatics
using AllerCatPro 2.0 (https://allercatpro.bii.a-star.edu.sg/) to predict no, weak, or strong evidence for allergenicity
and similarity to source-specific allergens. In total, 4,586 protein sequences were identified in the
12 source materials combined. Of these, 1,665 sequences were predicted with weak or strong evidence
for allergenic potential. This first-tier approach provided top-level information about the occurrence and
abundance of proteins and potential allergens. With regards to source-specific allergens, 129 allergens
were identified. The sum of the relative abundance of these allergens ranged from 0.8% (lamb’s quarters)
to 63% (olive pollen). It is proposed here that this dual proteomic-bioinformatic approach has the potential
to provide detailed information on the presence and relative abundance of allergens, and can play an
important role in identifying potential allergenic hazards in complex protein mixtures for the purposes of
safety assessments.
License type:
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Funding Info:
This research / project is supported by the National Research Foundation, Singapore and Agency for Science, Technology and Research - Singapore Food Story R&D Programme
Grant Reference no. : W22W3D0003
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) Joint Council Office (JCO) - Career Development Award/Fund
Grant Reference no. : 222D800029
This research / project is supported by the Agency for Science, Technology and Research - Industry Alignment Fund Pre-Positioning (IAF-PP)
Grant Reference no. : H2001a0P14
This research / project is supported by the Procter & Gamble and Agency for Science, Technology and Research - Industry Alignment Fund - Biomedical Research Council
Grant Reference no. : APG2013/096