Abstract
Food allergens have been traditionally identified using biomolecular and immunological approaches. However, the techniques used in extracting proteins from the food source to be analyzed may hinder the availability of all proteins when assessing immunological allergenicity. Additionally, depending on the number and pool of patient sera used to detect the IgE antibody-binding allergens, some allergens may not be detected if not all the patients in the pool are sensitized to all the allergens. To overcome these limitations, we describe an additional approach before the in vitro approaches, by analyzing the transcriptome in silico for all putative allergens within the analyzed food source.
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References
Kamath SD, Rahman AMA, Voskamp A et al (2014) Effect of heat processing on antibody reactivity to allergen variants and fragments of black tiger prawn: a comprehensive allergenomic approach. Mol Nutr Food Res 58:1144–1155. https://doi.org/10.1002/mnfr.201300584
Rahman AMA, Helleur RJ, Jeebhay MF et al (2012) Characterization of seafood proteins causing allergic diseases. Allergic diseases–highlights in the clinic, mechanisms, and treatment. InTech, pp 107–140
Nugraha R, Kamath SD, Johnston E et al (2018) Rapid and comprehensive discovery of unreported shellfish allergens using large-scale transcriptomic and proteomic resources. J Allergy Clin Immunol 141:1501–1504.e8. https://doi.org/10.1016/j.jaci.2017.11.028
Nugraha R, Ruethers T, Johnston EB et al (2021) Effects of extraction buffer on the solubility and immunoreactivity of the Pacific oyster allergens. Foods 10. https://doi.org/10.3390/foods10020409
Aalberse RC (2000) Structural biology of allergens. J Allergy Clin Immunol 106:228–238. https://doi.org/10.1067/mai.2000.108434
Goodman RE, Ebisawa M, Ferreira F et al (2016) Allergen online: a peer-reviewed, curated allergen database to assess novel food proteins for potential cross-reactivity. Mol Nutr Food Res 60:1183–1198. https://doi.org/10.1002/mnfr.201500769
Karnaneedi S, Huerlimann R, Johnston EB et al (2021) Novel allergen discovery through comprehensive De Novo transcriptomic analyses of five shrimp species. Int J Mol Sci 22. https://doi.org/10.3390/ijms22010032
(WHO/IUIS) WHOaIUoIS (2019) Allergen nomenclature
Song L, Florea L (2015) Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads. Giga Sci 4:48. https://doi.org/10.1186/s13742-015-0089-y
Grabherr MG, Haas BJ, Yassour M et al (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29:644–652. https://doi.org/10.1038/nbt.1883
Haas BJ, Papanicolaou A, Yassour M et al (2013) De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc 8:1494–1512. https://doi.org/10.1038/nprot.2013.084
Smith-Unna R, Boursnell C, Patro R et al (2016) Trans rate: reference-free quality assessment of de novo transcriptome assemblies. Genome Res 26:1134–1144. https://doi.org/10.1101/gr.196469.115
Simao FA, Waterhouse RM, Ioannidis P et al (2015) BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31:3210–3212. https://doi.org/10.1093/bioinformatics/btv351
(FARRP) (2019) In: Nebraska-Lincoln Uo (ed) Allergen online: home of the FARRP (Food Allergy Research and Resource Program) allergen protein database, 17th edn. University of Nebraska-Lincoln
Hileman RE, Silvanovich A, Goodman RE et al (2002) Bioinformatic methods for allergenicity assessment using a comprehensive allergen database. Int Arch Allergy Immunol 128:280–291. https://doi.org/10.1159/000063861
Ruethers T, Taki AC, Johnston EB et al (2018) Seafood allergy: a comprehensive review of fish and shellfish allergens. Mol Immunol 100:28–57. https://doi.org/10.1016/j.molimm.2018.04.008
Nguyen MN, Krutz NL, Limviphuvadh V et al (2022) AllerCatPro 2.0: a web server for predicting protein allergenicity potential. Nucleic Acids Res 50:W36–W43. https://doi.org/10.1093/nar/gkac446
Maurer-Stroh S, Krutz NL, Kern PS et al (2019) AllerCatPro—prediction of protein allergenicity potential from the protein sequence. Bioinformatics 35:3020–3027. https://doi.org/10.1093/bioinformatics/btz029
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Karnaneedi, S., Limviphuvadh, V., Maurer-Stroh, S., Lopata, A.L. (2024). De Novo Transcriptomic Analyses to Identify and Compare Allergens in Foods. In: Cabanillas, B. (eds) Food Allergens. Methods in Molecular Biology, vol 2717. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3453-0_24
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DOI: https://doi.org/10.1007/978-1-0716-3453-0_24
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