Food and Chemical Toxicology 147 (2021) 111888

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Food and Chemical Toxicology
journal homepage: www.elsevier.com/locate/foodchemtox

Evaluating potential risks of food allergy of novel food sources based oncomparison of proteins predicted from genomes and compared to www.
AllergenOnline.org☆
Mohamed Abdelmoteleb a, b, Chi Zhang c, Brian Furey d, Mark Kozubal d, Hywel Griffiths e,
Marion Champeaud e, Richard E. Goodman a, *a

Food Allergy Research and Resource Program, Dept. of Food Science & Technology, University of of Nebraska, 1901 North 21St Street, Lincoln, NE 68588-6207, USA
Department of Botany, Faculty of Science, Mansoura University, Mansoura, 35516, EgyptcSchool of Biological Sciences, University of Nebraska, Lincoln, NE, 68588, USAd
The Fynder Group, Inc. DBA Nature’s Fynd, 815 W Pershing Rd Unit 4, Chicago, IL, 60609, USAeFermentalg, 4 rue Rivière, 33500 Libourne, Franceb

A R T I C L E I N F O

A B S T R A C T

Keywords:
Chlorella
Galdieria
Fusarium
Bioinformatics
Proteins
Allergy
Cross-reactivity
GenomePotential proteins from three novel food sources (Chlorella variabilis, Galdieria sulphuraria, and Fusarium strain
flavolapis) were predicted from genomic sequences and were evaluated for potential risks of allergic crossreactivity by comparing the predicted amino acid sequences against the allergens in the www.AllergenOnline.
org (AOL) database. The preliminary analysis used CODEX Alimentarius limits of >35% identity over 80
amino acids to evaluate the predicted proteins which include many evolutionarily conserved proteins. Regulators
might expect clinical serum IgE tests based on identity matches above the criteria if the proteins were introduced
in genetically engineered crops. Some regulators have the same expectations for proteins in novel foods. To
address the inequality of extensively conserved sequences, we compared the predicted proteins from curated
genomes of 23 highly diverse allergenic species from animals, plants and arthropods as well as humans to AOL
sequences and compiled identities. Identity matches greater than CODEX limits (>35% ID over 80 AA) arecommon for many proteins that are conserved through extensive evolution but are not predictive of publishedallergy risks based on observed taxonomic cross-reactivity. Therefore, we recommend changes in the allergen
databases or methods of identifying matches for risk evaluation of new food sources. Our results provide critical
data for redefining allergens in AOL or for providing guidance on more predictive sequence identity matches forrisk assessment of possible risks of food allergy.

1. Introduction
Allergic reactions to food can pose a serious risk to the health andwellbeing of consumers. Allergen management of commercial packaged
foods is through appropriate labeling to warn allergic consumers of thespecific contents so they can avoid foods that would put them at risk.
The United States, the European Union, and many other countries
require labeling of all ingredients, and certainly those viewed as major
allergenic sources. Regulations also exist in many countries for

managing and identifying potential cross-contact between allergens forfoods which do not contain allergens in their ingredient lists. The US
recognizes eight major sources as priority allergenic sources (peanut,
tree nuts, milk, eggs, crustacean shellfish, finned fish, soybeans andwheat), the European Union recognizes 14 allergenic sources that
require labeling in packaged foods with a reduced number of tree nuts,
but adding barley, rye and oats to cereals for gluten, mustard, sesame
seeds, lupin, molluscan shell fish and the preservative sulfites including
sulfur dioxide (Taylor and Hefle, 2006; Muraro et al., 2014).

Abbreviations: AA, amino acid; AOL, AllergenOnline; BLAST, FASTA sequence search tool; NCBI, National Center for Biotechnology Information.
Grant support: This research was funded primarily by the Goodman laboratory at the Food Allergy Research and Resource Program (FARRP) at the University of
Nebraska with partial funding from Fermentalg and Nature’s Fynd.
* Corresponding author.
E-mail addresses: dmotelb87@gmail.com (M. Abdelmoteleb), zhang.chi@unl.edu (C. Zhang), bfurey@naturesfynd.com (B. Furey), mkozubal@naturesfynd.com(M. Kozubal), hgriffiths@fermentalg.com (H. Griffiths), mchampeaud@fermentalg.com (M. Champeaud), rgoodman2@unl.edu (R.E. Goodman).
☆

https://doi.org/10.1016/j.fct.2020.111888
Received 16 September 2020; Received in revised form 23 November 2020; Accepted 25 November 2020
Available online 1 December 2020
0278-6915/© 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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Food and Chemical Toxicology 147 (2021) 111888

The history of human exposure to the relevant food source and re­cords related to the developed food (novel food source, or gene/protein
donor) including the published history of allergy is important in judging
potential hazards and risks.
Understanding food allergy risks requires knowledge of the proteinsin various foods that commonly or less-commonly cause food allergy as
well as the mechanisms of the allergic response. For example, peanut(Arachis hypogaea) is a source of severe allergic reactions in many
countries. The dominant allergens in peanut are the most abundant seedstorage proteins: Ara h 1, vicilin; Ara h 2 and Ara h 6, 2S albumins andAra h 3, a legumin-like protein (Porterfield et al., 2009; Palladino andBreitender, 2018; Cabanillas et al., 2018). Of these, two are highly sol­uble proteins that are not rapidly digested at acidic pH by pepsin and so
are readily available for immediate reactivity in the mouth or the in­testinal tract when consumed. Two are less soluble in water yet arepresent in sufficient quantities that they still present significant risk.
Twelve other peanut proteins are recognized as allergens, though clearlyless potent clinically than Ara h 1, Ara h 2, Ara h 3 and Ara h 6. These
proteins have been reported to be bound by IgE from some allergic
subjects and in some cases when presented at unnatural high abundancein basophil assays, they may stimulate histamine release. The proteinsthat are low in abundance in the natural food have not been identified as
major allergens except possibly the peanut oleosins (Schwager et al.,2017). The majority of proteins from Peanut, however, are not recog­
nized as allergens.
Risks of allergy are dose dependent so identification of a protein as
an allergen does not mean it represents a significant risk of food allergy
unless it is common and abundant. Risks of allergy also vary markedly
among people allergic to the same source (Westerhout et al., 2019).
Protein homologues of the dominant peanut allergens are found in otherlegumes and tree nuts and are the major allergens for most people withclinical allergy to those sources (Cabanillas et al., 2018). Proteins thatcause cross-reactions can usually be grouped into protein families,
although there are many non-allergic proteins within any of the iden­tified biochemical protein groups. For example, the important muscle
allergen tropomyosin from crustaceans is highly conserved. The
sequence homology between allergenic crustaceans, mollusks and in­sects such as mealworm is over 60% identity by BLASTP or FASTA andthere is IgE cross-reactivity from the proteins of these organisms using
sera from many shrimp allergic subjects. However, homologues in birds
and mammals including humans are more than 52% identical to shrimp
tropomyosin and while some in vitro IgE cross-reactivity is observed forsome subjects’ sera, there is little evidence of shared allergy (Faber et al.,2017; Ruethers et al., 2018).
Novel food ingredient sources are being developed to meet thegrowing demand for dietary proteins in industrialized countries due tothe increasing human population, concerns for animal welfare, andenvironmental impacts of traditional sources of protein (Bleakley andHayes, 2017; Frigerio et al., 2020). Many diverse food sources have been
consumed in some geographic regions with a history of safe use,although the use and safety or risk are rarely well documented in lessindustrially developed regions. Some potential food sources are truly
novel, with no history of safe human consumption including microbial
sources such as specific microalgae, fungi or yeasts as whole foods or
ingredients. Since there are no validated methods for predicting de novo
sensitization, the allergenicity assessment for these truly novel foods isfocused on immediate risks to consumers due to the presence of existingIgE that could arise either from unexpected exposure to an allergen towhich they are already allergic, or to a likely cross-reactive protein. A
sound risk assessment process will have the primary focus on judging
knowledge of history of allergy to the source, and similarity of theproteins of the source to known allergens.
The safety assessment of genetically engineered (GE) organisms has
served as a model for assessing allergenicity risk of some new foods in the
United States (US). Hazard identification and risk assessment steps for GE
organisms were broadly discussed in the early 1990’s (Federal Register

Docket No. 92N-0139, Vol 57, No. 104, May 29, 1992) and (Metcalfe
et al., 1996). A primary health related concern has always been whether a
new gene in a GE organism encodes an allergen or a potentiallycross-reactive protein that would act as an allergen for those who are
already allergic. Advisory groups were convened by the Food an Agri­cultural Organization (FAO) and World Health Organization (WHO)panels in 1996 and 2000. In 2001 the FAO/WHO held a meeting andrecommended untested steps including looking for peptide matches of 6
contiguous amino acids and targeted serum IgE binding studies(FAO/WHO, 2001). In 1996 only a few hundred allergenic protein aminoacid (AA) sequences were known in publications. The AA sequences of the
new protein in the GE crops were compared to allergens in private da­tabases of the developer, or in the NCBI Protein non-redundant (nr)database using keyword search limits. Searches were accomplished by
FASTA in small databases or BLASTP in NCBI (Pearson, WR, 2000;
Pearson WR, 2014). Alignments that might represent an allergen weresearched for identity matches of eight contiguous amino acids to any
segment of any allergen. If matched, serum IgE binding tests would be
conducted focusing on those with allergies to the source of the new pro­tein. However, in practical terms, developers often abandon those as
potential products.
Evaluation of the short segment amino acid comparisons (6–8 aminoacid matches) were later shown to be non-predictive (Hileman et al.,2002). The CODEX Alimentarius meeting in 2001 as published in 2003
and reaffirmed in 2009 (CODEX, CAC/GL 44 in 2003 and reviewed in2009 (CODEX Alimentarius Commission, 2009)) considered thosecriteria and other information and the consensus was that a FASTA
search looking for minimum identity matches of >35% over 80 aminoacids was a more predictive test (Goodman et al., 2008).
It has been suggested that the current CODEX guideline of >35%
identity over at least 80 amino acids threshold be considered inconjunction with E-scores (expectation scores) generated from the FASTA
algorithm to make a more informed decision as to whether a protein has
the potential to cause allergenic cross-reactivity (Thomas et al., 2005;
Ladics et al., 2007; Silvanovich et al., 2009; Cressman et al., 2009). The
E-score reflects the measure of relatedness among protein sequences andcan help separate the potential random occurrence of aligned sequencesfrom those alignments that may share structurally relevant similarities. A
small E-score (e.g., less than 1e-7) reflects a likely functional similarityand may suggest a biologically relevant similarity for allergy or potential
cross-reactivity, while large E-scores (>1.0) are typically associated withalignments that do not represent a biologically relevant similarity(Pearson 2000, 2014, 2016; Henikoff and Henikoff 1992, 1996).
However, this guidance should be viewed as highly conservative andprecautionary based on historical experiences of cross-reactivity andclinical co-reactivity. Clinically important IgE cross reactivity is com­
mon for proteins sharing >70% AA identity over nearly their fulllengths, yet cross-reactivity is extremely rare for proteins sharing lessthan 50% identity (Aalberse, 2000). Other aspects of protein structure
and IgE binding are important to consider cross-reactivity (Aalberse
et al., 2001).
The AllergenOnline.org (AOL) database at the Food Allergy
Research and Resource Program (FARRP) at the University of Nebraska
was started in 2004–2005. It is a public, peer-reviewed database ofallergens based on protein AA sequences in the NCBI Protein database
following evaluation of published evidence in peer-reviewed literature(Goodman et al., 2005, 2016). The AOL database includes proteinsfrom studies of airway, contact, food, venom and salivary allergensources with IgE binding. When provided in publications, evidence ofhistamine release and clinical reactivity adds confidence to calling theproteins an allergen. The AllergenOnline.org database has been
updated annually by adding newly published allergens every year from2006 through 2020 by a review process with a panel of allergen expertsthat include researchers and clinicians (Goodman et al., 2016). It has
been used for evaluating risks of food allergy for many GE crops andcan be used for evaluating new foods.
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AOL uses FASTA comparison with the criteria of matches being>35% identity over 80 amino acids as was set by the CODEX Allerge­
nicity guideline in 2003. But since some proteins or alignments might
be of less than 80AA if fragments of allergens were transferred into
other species, or if these sections contain high identity segments that
could cause severe cross-reactivity AOL also adjusts the calculation
with normalization of alignments less than 80 AA. As an example an Nterminal segment of 77AA of Ara h 2 includes two or three IgE binding
epitopes and if transferred to a non-peanut food could cause severeclinical reactions in some peanut allergic consumers (Dreskin et al.,2019). As described online (www.allergenonline.org) in a support pagefor sequence searches, the number of AA identity matches of anyalignment less than 80 AA is recalculated by dividing by 0.80 tonormalize to an 80 AA length. The minimum identity match to consider
as possibly cross-reactivity is 29 identical AA in any FASTA alignment
which is calculated as 36.25%. This modified FASTA search provides amore reliable evaluation of potential risks than either a strict FASTA
search eliminating sequences shorter than 80 AA or a short (8 AA)alignment.
For truly new foods it is now possible to use modern techniques ofproteomics and genomics to predict all potential proteins from newsources. Evaluating all proven proteins of a whole organism for potential
risks of food allergy would not be efficient or effective if that required
identification of each individual protein in the food with tests of possible
IgE binding, or clinical reactivity. Therefore, evaluation of potential
risks of food allergy from an organism such as an alga, fungus or new
plant that does not have a history of human consumption requires new
evaluation steps. Some regulators and scientific advisors have recom­
mended using predicted proteins from the whole organism’s genome ortranscriptome for comparison to allergen databases using the CODEX
guidelines to predict risks of food allergy. Importantly, the CODEX
guideline was not intended to evaluate the full-proteome or predicted
protein dataset of a whole organism as the criteria of >35% identity over
80 has not been validated for whole proteome comparisons.
The end-result of the bioinformatics comparison of proteins withallergens is a decision about the need for specific serum testing and if so,
the specific allergic population that should be used to collect serum
samples (Goodman et al., 2005). But, since appropriate serum testing is
not trivial, correct interpretation of bioinformatics findings are impor­tant. Many genes and their expressed proteins, including many genesthat encode “minor” allergens are highly conserved across species and so
it is highly probable that these will trigger a match using the CODEX
guidelines. Predictions of protein sequences from genomic and tran­
scriptomic evaluations therefore require quality checks to understandrelevance before deciding on the need for clinical testing and criticalevaluation of the criteria used for decision making is required (Siruguri
et al., 2015).
Based on our years of use and development of AllergenOnline.org, itappears that the CODEX guidelines are far too conservative to judge
proteins that match evolutionarily conserved allergens, especially whenapplied to whole genomes. We have therefore performed this study inpart to understand the extent of over-predictions. We have evaluatedprotein sequence identity matches between three diverse species (agreen alga Chlorella sp., a red alga Galdieria sulpharuraia and a Fusarium
strain flavolapis) searching the AllergenOnline.org (AOL) database andthe NCBI Protein database to consider matches to likely allergens.

animals, plants and arthropods to all AOL sequences, compiled identities
to understand how common high identity matches are, and evaluatedpatterns of identity across protein types.
Third, to critically evaluate the limits of the CODEX guidelines whenused as a whole genome analysis, using all types of proteins. The overall
goal being to determine what in addition to the CODEX criteria isreasonable for risk assessment of whole foods.

1.1. There are three objectives in this study

The predicted proteins for the genome of Chlorella variabilis NC64A
were downloaded from the NCBI genome library (https://www.ncbi.
nlm.nih.gov/genome/?term=Chlorella+variabilis+ %5Borgn%5D).
For Galdieria sulphuraria, the company Fermentalg provided the DNA
sequences which were identified using Illumina sequencing (2x150 bp
reads). The sequencing quality was checked using FastQC (Andrews
2010) and cleaned using PRINSEQ (prinseq.sourceforge.net) by trim­
ming off low quality bases. Two assemblers were used, SPAdes with 21,
33, 55 and 77 k-mer values (Bankevich et al., 2012), and Trinity using 25

1.2. Tests of three species based on genomic predictions of proteins
We chose to use a green alga Chlorella variabilis, a red alga Galdieria
sulphuraria, and a newly identified Fusarium strain flavolapis fungus astest organisms. These organisms are being developed as single-cell foodprotein resources. Chlorella is a genus of single-celled green algae whichcontains high concentrations of protein (51%–60% of dry matter),amino acids, vitamins, dietary fiber, and a variety of antioxidants,
bioactive materials, and chlorophylls. Green algae have a history ofsustainable production and consumption. (Klamcyzynska and Mooney,2017). Chlorella vulgaris and Chlorella pyrenoidosa are not considerednovel in the EU since they have been historically consumed by humans(Regulation EC No. 258/97). In the US they are recognized as GRAS by
the FDA as algae commonly consumed in foods in many countries (Wells
et al., 2017). Recently the genome of Chlorella variabilis, NC64A wascompleted and was used here as a model genome (Blanc et al., 2010).
The unicellular red algae, Galdieria sulphuraria, was isolated by de­velopers from extreme environments (from pH 0 to 4, and up to 56 ◦ C)
and has been proposed as an edible alga with a high content of protein
and other dietary important nutrients. This alga can be grown via
fermentation and is being developed for use in food products (Schon­knecht et al., 2013), but has not yet been consumed by humans.
A single species of Fusarium is already used in several food productswith the brand name, Quorn. Quorn is produced and marketed as ahuman food by Marlow Foods, Ltd. Quorn foods contain mycoprotein
which is derived from Fusarium venenatum, which is grown by fermen­
tation (Finnigan et al., 2019). Products of Quorn have been consumed asa non-meat protein source in the United Kingdom for 30 years and since
2002 in the US. There are a few case reports of food allergy to Quorn(Katona and Kaminski, 2002; Hoff et al., 2003a). Some of those may be
due to inhalation allergy to proteins of Fusarium sp. (Weber and Levetin,
2014). Some consumers of Quorn have experienced transient GI symp­
toms without IgE antibody production. A very small number haveexperienced possible IgE mediated food allergic reactions including one
reported fatal reaction (Tee et al., 1993; Hoff et al., 2003a, 2003b; Yehet al., 2016; Jacobson and DePorter, 2018). To put this in perspective,
many common food sources have caused at least one fatal food allergic
reaction and as long as packaged food is labeled clearly, consumers withallergies can avoid consumption of foods that may cause allergic re­actions if they are properly labeled (Ramsey et al., 2019; Gowland andWalker, 2015). Other strains or species of Fusarium with different
compositions are now under development as possible food sources
including, Fusarium strain flavolapis, the strain we are using here forwhich the developers have performed whole genome sequencing.
2. Methods
2.1. Preparation of protein sequences of the three targeted genomes

First, to evaluate identities of all possible proteins from the genomesof three species intended for food use based on comparison of the pre­dicted proteins against allergens in the AllergenOnline.org databaseusing the CODEX guidelines of >35% identity over 80 AA.
Second, to address the inequality of extensively conserved se­quences, we compared the predicted proteins from the genomes of 23
highly diverse allergenic and non-allergenic species; including human,3

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Food and Chemical Toxicology 147 (2021) 111888

k-mer (https://github.com/trinityrnaseq/trinityrnaseq/wiki). Post as­
sembly polishing was performed using Pilon (Walker et al., 2014). The
quality of assembly was checked using Quast (Gurevich et al., 2013).
The percentage of mapping was evaluated using BWA mapper (Li andDurbin, 2009). Genes were predicted using the Galdieria model from
AUGUSTUS (Stanke and Morgenstern, 2005). Sequences to exclude
included tRNA sequences which were predicted using tRNAscan-SE(Lowe and Eddy, 1997) and rRNA which were predicted using barrnap(https://github.com/tseemann/barrnap). Functional annotation was
conducted by a combination of AUGUSTUS software and BLASTP
comparison for the predicted proteins against the published Galdieria
sulphuraria genome (https://www.ncbi.nlm.nih.-gov/genome/?term=Galdieria+sulphuraria) from the NCBI library. Sequences werecompiled into FASTA format files for comparison to the AllergenOnline.
org database. The compiled sequences were also compared to the pub­lished Galdieria sulphuraria genomic sequences filed by Schonknecht
et al., as described in 2013 as ASM34128v1 using alignment tools inorder to check for potential sources of inaccuracy.
Nature’s Fynd provided the genomic sequences for Fusarium strain
flavolapis, which they are developing for use as a food ingredient. They
performed genomic sequencing using Pacbio (for long-reads) andIllumina (2x250 bp reads) for short, high quality reads of this cultured
species. These sequences were compiled and evaluated for accuracy
and completeness using FASTQC. Sequences were compiled using as­
semblers MaSuRCA with 22 k-mer value (Zimin et al., 2013) and
SPAdes (Bankevich et al., 2012) used K-mers of 21, 33, 55, 77, 99 and127. Post assembly polishing used Pilon (Walker et al., 2014). Pacbio
reads were mapped using Minimap2 (Li 2016), and Illumina reads were
mapped using Bowtie2 (Langmead and Salzberg, 2012). Genes werepredicted using the Fusarium model from AUGUSTUS (King et al.,2015), mitochondrial genes were predicted using Prodigal (Hyatt et al.,2010), tRNA were predicted using tRNA scan-SE (Lowe and Chan,
2016) and rRNA were predicted using Barrnap software (https://gith
ub.com/tseeman/barrnap/). Functional annotation was done using
the ERGO software package of IgenBio (Wilder et al., 2016). The
overall sequence completeness was further evaluated by comparison to
the genomes of strains of Fusarium sp. which had been previously
characterized to provide a framework for understanding completeness(Niehaus et al., 2016).
To provide reasonable comparisons, the predicted proteins for the ge­
nomes of 23 species representing foods of diverse allergenic risks andincluded those of human, other animals and plants. The sequences were
downloaded from public databases including the NCBI genome library(https://www.ncbi.nlm.nih.gov/genome), EnsemblPlants (http://plants.
ensembl.org/index .html), and Phytozome V. 12, the Plant Genomics
Resource (https://phytozome.jgi.doe.gov/pz/portal.html#) as summa­
rized in Table 1. For species without published genomes as of October
2018, we downloaded the predicted protein sequences from the NCBI
protein library. All protein sequences were downloaded on October 2018.
The bioinformatics pipeline was completed using our lab cluster on the
Holland Computer Center server at the University of Nebraska.

Table 1
Sources for predicted protein sequences from 23 genomes of diverse species.
Species

SourceHuman (Homo sapiens)
Baker’s yeast(Saccharomyces
cerevisiae)
Candida albicans SC5314

ftp://ftp.ncbi.nih.gov/genomes/H sapiens/protein/
http://downloads.yeastgenome.org/sequence/S288C.
reference/orfprotein/

Cod (Gadus morhua)
Chicken (Gallus gallus)
Bovine (Bos taurus)
Drosophila melanogaster
Salmon (Salmo salar)
Papaya (Carica papaya)
Soybeans (Glycine max)
Apple (Malus domestica)
Rice (Oryza sativa)
Peanut (Arachis hypogaea)
Peach (Prunus persica)
Beans (Phaseolus vulgaris)
Potato (Solanum tuberosum)
Wheat (Triticum aestivum)
Maize (Zea mays)
Arabidopsis thaliana
Almond (Prunus dulcis)a
Pecan (Carya illinoinensis)a
Pistachio (Pistacia vera)aEnglish Walnut (Juglans
regia)ahttp://www.candidagenome.org/download/sequenc
e/C.albicans.SC5314/Assembly22/current/
ftp://ftp.ensembl.org/pub/release-86/fasta/gadus.
morhua/pep/
ftp://ftp.ensembl.org/pub/release-86/fasta/gallus.ga
llus/pep/
ftp://ftp.ensembl.org/pub/release-86/fasta/bos.ta
urus/pep/
ftp://ftp.flybase.net/genomes/Drosophila.mel
anogaster/dmel.r6.09.FB2016.01/fasta/
ftp://ftp.ncbi.nih.gov/genomes/Salmo.salar/protein/
ftp://ftp.ncbi.nih.gov/genomes/Carica.papaya/protein/ftp://ftp.ncbi.nih.gov/genomes/Glycine.max/protein/
ftp://ftp.ncbi.nih.gov/genomes/Malus.domestica/
protein/
ftp://ftp.ncbi.nih.gov/genomes/Oryza.sativa.Japo
nica.Group/protein/
ftp://ftp.ncbi.nih.gov/genomes/Arachis.hypogaea/protein/
ftp://ftp.ncbi.nih.gov/genomes/Prunus persica/protein/https://www.ncbi.nlm.nih.gov/genome/?term=Ph
aseolus+vulgaris+%5Borgn%5D
ftp://ftp.ncbi.nih.gov/genomes/Solanum.tuberosum/protein/https://phytozome.jgi.doe.gov/pz/portal.html#!
info?alias=Org.Taestivum.er
ftp://ftp.ncbi.nih.gov/genomes/Zea.mays/protein/https://www.ncbi.nlm.nih.gov/genome/?term=arabidopsis++thaliana+%5Borgn%5D
https://www.ncbi.nlm.nih.gov/protein/?
term=prunus+dulcis
https://www.ncbi.nlm.nih.gov/protein/?term=carya+illinoinensis
https://www.ncbi.nlm.nih.gov/protein/?term=pistac
ia+vera+%5Borgn%5D
ftp://ftp.ncbi.nih.gov/genomes/Juglans.regia/protein/Species without complete published genomes before October 2018.

were compiled in an Excel worksheet with a record of the highest
match identity. The resulted matches were evaluated to identify matchesof >35% identity over 80 or more amino acid segments.
2.3. BLASTP comparison of predicted protein sequences within the NCBI
non-redundant protein sequences database that includes annotated protein
sequences from GenBank, RefSeq and TPA as well as SwissProt, PIR, PRF
and PDB2.2. FASTA comparison for the predicted protein sequences of the
genomes were compared to Allergenonline.org version 16 and 18B

Predicted protein sequences of Chlorella variabilis, Galdieria sp. andFusarium strain flavolapis. as well as the 23 other species used in thisstudy were used to search the general protein database using the current
version of BLASTP in 2018 and early in 2019. The website is https://bla
st.ncbi.nlm.nih.gov/BLAST.cgi. The current version of BLASTP outputs
changed markedly in July 2019, removing the ability to use keyword
limits in BLASTP searches to restrict matches to particular categories ofsequences based on keywords. In addition, the output of BLASTP haschanged and we used the Traditional Results for historical comparisons.
Searches without keyword limits allows the highest identity matches tobe viewed for evaluation of the common conservation of the protein
sequences. The previous selection criteria using keyword limits such as“allergy” or “allergen” were removed. Those changes speed the searches
but eliminates useful screening decisions. We also used BLASTP searches

Predicted protein sequences from the proposed three novel foodspecies and 23 diverse species were compared to allergens in versions 16
and 18B of www.AllergenOnline.org by overall FASTA 35. FASTA
version 35 was installed on the Holland Computing Center server toallow batch searches that mimic the individual protein searches avail­able on our AllergenOnline.org website, however based on the best
identity matches over 80 AA long. Different E-score thresholds (10, 1,
0.001, 1e-7, 1e-30, 1e-50, 1e-75, 1e-100) were used to check the sig­
nificance of matches on the private HCC searches. The same scoring
matrix was used (BLOSUM 50) as on the public AllergenOnline.org
database. The sequence matches to proteins in AllergenOnline.org
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Food and Chemical Toxicology 147 (2021) 111888

of species targets from the 23 species and of the matched allergens fromout AllergenOnline.org to provide guidance on the relevance of lowidentity matches including >35% identity over 80 amino acids.

expected based on numbers of allergenic proteins in commonly aller­
genic sources. Experiences in clinical research demonstrates that eventhe most commonly allergenic species such as peanut, produce only 4 to
6 commonly allergenic unique proteins (Ara h 1, Ara h 2, Ara h 3, Ara h 6
and possibly Ara h 8 and Ara h 9) and a total of <20 total allergens.
Many other commonly allergenic species, such as shrimp list fewer than
10 allergenic proteins that elicit symptoms from human exposure by the
airway, contact or ingestion allergens (www.allergen.org). A few sour­ces of airway allergy such as the common house dust mite (HDM) Der­
matophagoides farina and the evolutionarily related Dermatophagoides
pteronyssinus have nearly 40 different proteins that may be bound by IgEof people with inhalation allergies. However only three proteins from
HDM (Der f 1, Der f 2 and Der f 23, or Der p 1, Der p 2 and Der p 23) areconsidered major allergens and four others (Der f 4, Der f 5, Der f 7 andDer f 21) are considered mid-level allergens (Thomas, 2015). The other
HDM proteins are unlikely to be clinically important because of lowlevel expression, high instability, and unlikely inhalation exposure.
Interestingly many of the allergens that have been identified arecommonly conserved proteins that share high identity scores acrossrelatively unrelated taxa such as profilins, heat shock proteins andbeta-expansins. There are rare to fairly common reports of allergy tosome of these species, while only a few clear reports of allergy arecommon for many species. Our intent in testing 23 species including
human proteins was to identify an E-score limit that might be valuable
for risk assessment and also to test percent identity scores that might bemore predictive than the CODEX limit of >35% identity over 80 AA andto consider the relevance of >35% identity.

3. Results
3.1. Prediction of Galdieria sulphuraria. And Fusarium strain flavolapis
proteins based on genomic DNA sequences
For Galdieria sulphuraria, the number of reads after checking quality
and trimming are 26.4 Mbp. Assembly metrics are: 1998 contigs, largest
contig 294001B, N50 54420B, N75 16958B, L50 66, L75 164, and GC%
40.27 for SPAdes; and for Trinity are 2890 contigs, largest contig
154130B, N50 24717B, N75 11797B, L50 292, L75 677, and GC% 40.30.
The reads were mapped at 99.67% for SPAdes, and 99.59% for Trinity.
The number of predicted proteins for Galdieria sulphuraria was 5701
from SPAdes and 11976 from Trinity.
For Fusarium strain flavolapis, the quality of the sequences included
340k reads after trimming and correcting from Pacbio, and 56.5 M readpairs from Illumina. Assembled sequences included 89 contigs, with thelargest contig being 4.9 MB, N50 for 3.2 MB, N75 for 2.3 MB and L50 6,
L75 10 and 0 Ns with a GC content of 48.3%. Pacbio reads mapped at
99.95% using Minimap2 software. Illumina reads mapped at 99.81%
using Bowtie2 software. The number of predicted proteins were 14239.
3.2. Comparison of all possible proteins from the genome of the threenovel foods against allergens in AOL
The total number of unique matches to allergens for predicted pro­teins from the three potential food species that scored over a range of Escores with results >35% identity limit over 80 AA of CODEX guidelines
are shown in Table 2. The normal default E score for FASTA or for BLAST
is 10, but smaller E score numbers restrict the output to provide more
stringent alignments. The purpose of these comparisons was to evaluate
whether the CODEX criteria are reasonable for risk assessment of thethree proteins using >35% identity over 80 AA as the criteria to benchmark a need for serum IgE tests or other additional evaluations. As
shown in Table 2, the three species of interest have not been consumed(widely) by humans and are thus not known to cause allergies, yet theyshow very high numbers of matches greater than 35% identity over 80
AA at 1e-07 to allergens in AOL, with E score settings much smaller thanthe default of BLASTP. More realistic numbers of alignments, meaning
identities between species that have been reported as possibly being
cross-reactive were found when the E-score was set to 1e-100.
For comparison, we tested all predicted proteins from the genomes of
23 species ranging from humans to fungi, fish, mammals and many
species of plants to evaluate the number of possible risky proteins. Thesematches are summarized in Table 3 for comparison to the three species
of interest. Matches following CODEX guidelines are intended to identify
proteins that may be sufficiently similar to an allergen to suspectpossible IgE cross-reactivity and the possibility of triggering a clinicallyimportant allergic reaction. As shown in Table 3, a significant number ofmatches >35% identity to multiple allergens was found for proteinsfrom all 23 species with E scores of 10 or even 1. Even using an E-score of
1e-100, the number of any match unique proteins seems far higher than

3.3. Identities of all possible proteins from the genome of the three novel
food sources and 23 common species matches to AOL
The results in Table 2 illustrate that the algae (Chlorella variabilis
NC64A) has sequence matches of >35% identity to between 14 and 991
unique proteins in AOL, depending on which E-score limit was used.
Even at the moderate E-score of 1e-7 there were 159 proteins that sug­gest potential cross-reactivity. By comparing all predicted proteins fromthe 23 diverse species including humans (Homo sapiens) in Table 3, we
found similarly high numbers of matches of the predicted proteins toallergens across the species. Pistachio had the lowest number ofmatches, but few total proteins have been predicted from nucleotide
sequences for pistachio or pecan (Table 3). When we compared thehighest scoring aligned proteins of Chlorella variabilis to all proteins in
AOL version 18B as shown in Table 4. The highest scoring matchedallergen was to cyclophilin of Daucus carota, but that protein is highly
conserved to sequences in all 23 species. Heat shock protein 70 of the
Aedes aegypti mosquito is highly conserved as shown by sequence
matches to proteins in 22 species. The lowest scoring matches in Table 4
include a few bona fide allergens with identity matches close to 35%
identity, and with modest E-scores. Those include matches to thio­
redoxin of fungi at 39–40% identity and venom allergen 5 of a wasp at
35.8% identity. Most of the matched allergens are conserved across
many species of the 23 chosen here. Many are house-keeping proteins
including cyclophilins, heat shock proteins, 60S ribosomal protein, tri­
osephosphate isomerase, aldolase, gliadins. However, the percent
identities are not high compared to BLASTP matches to homologues

Table 2
Total number of matches and unique matches to allergens in AOL (>35% sequence identity over 80 AA alignment length) at different E scores in the three novel foodsources.
Species

Subject Hits

10

1

0.001

1e-7

1e-30

1e-50

1e-75

1e-100Chlorella variabilis NC64A

Total
UniqueTotal
UniqueTotal
Unique277988
991
67989
101
192772
508

82613
752
17792
96
65321
466

9043
297
3202
85
13320
326

3201
159
1222
73
5867
232

413
64
170
39
646
125

119
39
97
32
317
95

57
21
50
12
135
44

35
14
32
8
88
30

Galdieria sulphuraria
Fusarium strain flavolapis

5M. Abdelmoteleb et al.

Food and Chemical Toxicology 147 (2021) 111888

Table 3
Total and unique matches to allergens in AOL for predicted proteins from 23 different allergenic and non-allergenic species.
Species

Subject Hits

10

1

0.001

1e-7

1e-30

1e-50

1e-75

1e-100Homo sapiens(Human)Saccharomyces cerevisiae(Baker’s yeast)
Candida albicans SC5314(Yeast)Gadus morhua(Cod)
Bos Taurus(Bovine)Gallus gallus(Chicken)
Drosophila melanogaster(Fruit fly)
Salmo salar(Salmon)
Carica papaya(Papaya)Glycine max(Soybeans)
Malus domestica(Apple)Oryza sativa(Rice)
Arachis hypogaea(Peanut)
Prunus persica(Peach)Phaseolus vulgaris(Beans)Solanum tuberosum(Potato)Triticum aestivum(Wheat)Zea mays(Maize)Arabidopsis thaliana(Mustard)
Prunus dulcis(Almond)
Carya illinoinensis(Pecan)Pistacia vera
(Pistachio)
Juglans regia(English Walnut)

Total
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
UniqueTotal
Unique6200050
14997
71691
225
185065648
339873850
4317301280
1067198
2964
7355143180
21056617039
3302571140
9169393055
7450672867
6120901710
11938504175
422277
1701
4511341548
462504
1880
50687239064
11260073094
692802
2293
13102
54
5086
32
3755
8
6663382592

2460980
13534
24440
214
73070
621
118495806
1623701190
4636882731
3257472959
9316206489
1133071097
3247202951
2635532760
1747661578
4146334033
1574541637
1497401485
1718291822
12953178557
3469212869
240908
2205
4540
54
2303
32
729
8
2351672505

510958
8546
5320
164
18846
482
24766
638
33305
865
112614
1798
85437
2045
240600
4416
30307
991
85635
2612
74541
2432
30203
1255
109245
3529
45557
1416
42236
1346
50277
1626
213159
7331
60378
2208
61433
1911
2619
52
1273
32
285
8
66984
2291

175239
5565
2043
132
7712
327
10932
502
13860
680
41907
1261
35174
1306
93818
2892
16765
877
46849
2208
41863
2037
17632
981
59692
2971
26298
1201
25113
1181
27881
1374
112949
6267
30418
1661
30158
1618
2323
50
796
20
245
8
36964
1933

19346
2556
384
68
599
113
1910
268
2131
356
6624
636
3969
503
11910
1217
5066
501
12620
1250
13796
1039
5038
523
15021
1506
10252
713
7048
701
7858
723
25380
3290
9059
813
8702
834
699
45
440
17
126
7
11699
1006

7860
1538
243
52
292
75
991
182
760
227
3397
423
2413
286
5695
720
2665
363
6459
881
5996
720
2488
328
8476
1076
5115
517
3626
488
4100
512
9739
1904
4833
574
4575
613
392
25
301
15
42
7
6744
723

2516
912
200
40
174
45
354
108
350
125
865
269
1037
168
1318
487
621
175
1760
407
1614
320
648
163
2356
486
1433
264
1005
220
1000
242
2927
799
1156
242
1083
283
26
15
74
13
21
7
1573
343

1817
557
158
32
140
39
248
72
245
71
450
153
566
117
973
320
149
69
523
179
484
146
279
63
739
218
346
111
265
89
294
86
1436
384
528
127
292
112
4
5
52
13
11
6
386
138

from a variety of protein sources and from species that are not likely torepresent risks. For example, BLASTP comparison of triosephosphate
isomerase (EFN53775.1) in Chlorella variabilis to non-redundant protein
database had the top 100 matches to triosephosphate isomerase indiverse species with sequence identity ranged from 69 to 100%. Simi­
larly, Chlorella heat shock protein 70 (EFN57963.1) had matches to heatchock proteins in different species with sequence identities of
77.5–100%. This shows the conservation of these proteins among
diverse allergenic and non-allergenic species.
Similarly, Table 5 shows that Galdieria sulphuraria had matches to 59
weak or putative allergens and 6 very weak matches to food allergens(tropomyosin, vicilin, and convicilin) with E-scores >0.02. Due to highsequence identity of evolutionary homologues, these identity matches
were over predictive for possible risks of allergic cross-reactivity. The
searches were rerun using an E score of 1e-7 that removed proteins thatare clearly unlikely to cause cross reactive. The results are shown in
Table 5. The identified food allergens represent important proteinclasses of allergens, yet the identity matches shown in this study show
very low identities of proteins as with those from Chlorella, meaning they
are unlikely to be significant risks for cross-reactivity. That can bedemonstrated by comparing the matched allergens to the NCBI Protein
database using BLASTP. The results for FASTA comparison of predicted
proteins of the Quorn fungal genome-predicted proteome, anotherspecies of Fusarium, was tested for background evaluation. Quorn has
been used as a food source in the United Kingdom for >30 years. The
results are shown in Supplementary Table 1, that identified 181 matches
to weak or putative allergens and 12 very low identity matches to foodallergens with very low sequence identity over short AA segments.
3.4. summary examples of FASTA comparisons using all predicted
proteins from the 23 studied species
Predicted proteins from the public genomes of all 23 species werecompared to AllergenOnline.org looking for matches of >35% identity,using an E score cutoff of 1e-07. Wheat genome predicted proteinsmatched 312 putative allergens, but only eight major allergens. Soybean
genome predicted proteins matched 243 putative allergens and 32
matches to major allergens (vicilins and conglycinins of soybean, wal­
nut, pecan and pistachio). Genome predicted human proteins matched
206 weak or putative allergens, one matched the major allergen tropo­
myosins from a variety of sources including crustacean allergens andthose of fruit flies (Drosophila sp.), fish (salmon and cod). Anotherhuman protein matched lipid transfer proteins (LTP) with a modest
identity match to LTP from pomegranate (42.3% identity with an E scoreof 3.7e-19). Searching AllergenOnline.org with the pomegranate LTPshows many higher identity matches, often >55% ID with E scores of6

M. Abdelmoteleb et al.

Food and Chemical Toxicology 147 (2021) 111888

Table 4FASTA comparison of predicted proteins of Chlorella variabilis NC64A compared
to AOL V18B (E-score: 1e-07). The amino acid sequences of all proteins pre­dicted from the genome of this species were used to search version 18B of theAllergenOnline.org database to find identity matches with proteins listed asallergens or putative allergens in the database using full-length FASTA searches
to identify matches of >35% identity with different E scores, those from matches
at 1e-7 are shown here.
AllergenOnline Version 18B

gid|1941|cyclophilin [Daucus
carota]gid|1926|cyclophilin[Catharanthus roseus]gid|2708|heat shock cognate
70 [Aedes aegypti]
gid|2591|heat shock-likeprotein [Tyrophagus
putrescentiae]
gid|2291|Der f 33 allergen[Dermatophagoides farinae]
gid|166|triosephosphatisomerase [Triticum
aestivum]gid|2301|glyceraldehyde-3phosphate dehydrogenase[Triticum aestivum]
gid|338|60S ribosomal protein
L3 (Allergen Asp f 23)[Aspergillus fumigatus]
gid|1033|cytochrome_c_[Curvularia_lunata]
gid|863|cyclophilin[Aspergillus fumigatus]
gid|706|Lactoylglutathione
lyase (Methylglyoxalase)(Aldoketomutase)(Glyoxalase I) (Glx I)(Ketone-aldehyde mutase)(S-D-lactoylglutathione
methylglyoxal lyase)(Allergen Ory s ?) (Allergen
Glb33) (PP33) [Oryza
sativa]gid|543|60S acidic ribosomal
protein P2 [Fusarium
culmorum]gid|2076|heat shock protein
70 [Dermatophagoides
farinae]gid|1092|manganesesuperoxide dismutase-likeprotein [Pistacia vera]
gid|848|60S acidic ribosomal
P1 phosphoprotein Pen b 26
[Penicillium
brevicompactum]
gid|648|major allergenic
protein Mal f4 [Malassezia
furfur]gid|2255|putative chitinase[Musa acuminata]
gid|1707|aldolase A [Thunnus
albacares]
gid|587|Chain A, Latex
Profilin Hevb8 [Hevea
brasiliensis]
gid|489|putative nuclear
transport factor 2[Davidiella tassiana]
gid|2592|aldehyde
dehydrogenase-like protein[Tyrophagus putrescentiae]

Highest%Seq_id

Align
length

78.8

170

76.8

168

73.4

305

73.3

659

73.2

455

72.6

248

70.7

EscoreTable 4 (continued )AllergenOnline Version 18B

gid|1248|eukaryotic
translation initiation factor[Forcipomyia taiwana]gid|2463|ElF1 superfamilytranscriptions factor[Triticum aestivum]
gid|2262|transaldolase[Penicillium chrysogenum]
gid|1960|aldolase a, fructosebisphosphate 1 [Salmo salar]gid|509|group 15 allergenprotein [Dermatophagoides
farinae]gid|651|allergen [Malassezia
sympodialis]gid|64|Minor allergen Alt a 7(Alt a VII) [Alternaria
alternata]
gid|126|minor allergen betafructofuranosidase
precursor [Lycopersiconesculentum] [Solanum
lycopersicum (Lycopersiconesculentum)]gid|775|RecName: Full =Serine carboxypeptidase 2;
AltName: Full = Serinecarboxypeptidase II;
AltName: Full =Carboxypeptidase D;
AltName: Full = CPDW-II;
Short = CP-WII; Contains:
RecName: Full = Serinecarboxypeptidase 2 chain A;
AltName: Full = Serinecarboxypeptidase II c
[Triticum aestivum]
gid|1542|peroxiredoxin[Triticum aestivum]
gid|1544|troponin C[Tyrophagus putrescentiae]
gid|650|allergen [Malassezia
sympodialis]gid|1338|ragweed homologueof Art v 1 precursor[Ambrosia artemisiifolia]
gid|951|Der f Mal f 6 allergen[Dermatophagoides farinae]
gid|65|aldehyde
dehydrogenase (NAD+)[Alternaria alternata]gid|64|Allergen Alt a 7[Alternaria alternata]gid|2371|seed maturation-likeprotein precursor [Sesamum
indicum]
gid|2551|Par h I precursor[Parthenium hysterophorus]
gid|18|Actinidain proteaselike [Actinidia deliciosa]gid|775|serinecarboxypeptidase II[Triticum aesivum]
gid|647|allergen [Malassezia
sympodialis ATCC 42132]
gid|154|LMM glutenin 3[Triticum aestivum]
gid|1206|Sal k 3 pollen
allergen [Salsola kali]
gid|496|ferritin heavy chainlike protein[Dermatophagoides farinae]# of 23 species
with matches>35% ID over80AA

9.00e75
2.70e544.70e103
6.10e16820

4.20e155
2.10e10523

334

1.40e100

21

67.1

386

2.00e118

22

66

103

64.6

161

62.9

283

1.5e304.10e47
1.00e40

18
22
22

14

18
13

62.4

109

2.50e23

14

59.6

401

1.80e71

10

58.4

202

4.60e54

17

57.6

85

1.20e11

6

57.5

320

2.60e89

20

56.7

261

13

56.4

353

56.1

132

1.50e65
7.70e772.80e3555.4

112

5.90e25

14

54.8

489

1.50e89

20

54.3

129

1.30e4321

19
1Highest%Seq_id

Align
lengthEscore# of 23 species
with matches>35% ID over
80AA

54.3

81

1.90e22

19

51.4

313

3

50.6

350

50

120

2.70e74
9.50e68
9.30e12

50

140

20

50

200

2.60e27
3.20e4249.3

1401.30e41

13

49.2

195

2.20e55

15

49.1

216

4

49

147

48.9

131

48.8

84

1.90e60
8.60e24
4.60e33
2.10e09

48.7

160

20

46.3

480

1.60e27
1.10e107

45.7

138

9

44.5

330

1.00e27
3.10e50

44.4

81

18

43.8

356

43.8

153

2.00e079.40e59
2.70e33

42.7

82

3

42.5

167

42.3

769

42.1

183

3.30e14
6.40e09
6.00e943.90e22

42.1

164

18
21

15

22
16
21

19

15

19
10

17
15
19
8

(continued on next page)7

M. Abdelmoteleb et al.

Food and Chemical Toxicology 147 (2021) 111888

Table 4 (continued )AllergenOnline Version 18B

gid|496|ferritin[Dermatophagoides farinae]
gid|151|Alpha/beta gliadinlike protein product[Triticum aestivum]
gid|150|omega-5 gliadin[Triticum aestivum]
gid|322|beta-xylosidase[Aspergillus niger]
gid|333|Taka-amylase A (TaaG1) precursor [Aspergillus
oryzae]gid|588|prohevein [Hevea
brasiliensis]
gid|1565|collagen alpha-2(I)chain precursor [Bos taurus]
gid|244|Pen c 1; alkaline
serine protease [Penicillium
citrinum]
gid|154|LMW glutenin-likeprotein product [Triticum
aestivum]gid|325|PPIase [Aspergillus
fumigatus]
gid|588|hevein [Hevea
brasiliensis]
gid|63|Protein disulfideisomerase (PDI) (Allergen
Alt a 4) [Alternaria alternata]
gid|322|xylosidase[Aspergillus niger]
gid|357|trypsin [Blomia
tropicalis]gid|850|catalase [Penicillium
citrinum]gid|2027|allergen [Malassezia
sympodialis ATCC 42132]
gid|876|thioredoxin[Aspergillus fumigatus]
gid|243|allergen Pen n 18
[Penicillium chrysogenum]
gid|2709|lysosomal aspartic
protease [Aedes aegypti]
gid|330|manganesesuperoxide dismutase[Aspergillus fumigatus]
gid|150|D-type LMW gluteninsubunit [Triticum aestivum]
gid|2278|thioredoxin h[Triticum aestivum]
gid|2080|glutathionetransferase [Triticum
aestivum]gid|162|27K protein [Triticum
aestivum]gid|785|Bromelain precursor(Allergen Ana c 2) [Ananas
comosus]gid|1776|thioredoxin [Plodia
interpunctella]
gid|150|omega-gliadin, partial[Triticum aestivum]
gid|833|vacuolar serine
protease [Rhodotorulamucilaginosa]
gid|1171|subtilisin precursor[Bacillus licheniformis]
gid|18|actinidin_[Actinidia_deliciosa]gid|160|glutenin [Triticum
aestivum]gid|151|Gliadin-like protein
product [Triticum aestivum]

Table 4 (continued )Highest%Seq_id

Align
length

41.8

134

41.7

396

41.7

132

41.7

103

41.3

121

41.2

131

41.2

250

40.9

Escore4.40e15
1.10e07# of 23 species
with matches>35% ID over
80AA

20

3.00e212.70e222.00e1221

3.00e18
1.70e072.90e3911

235

7.70e07

19

40.8

130

19

40.8

98

40.7

81

5.30e173.40e198.70e10

40.6

256

12

40.5

237

40.4

483

39.7

816

39.6

91

39.1

266

38.9

522

38.9

208

1.20e36
1.00e25
2.60e421.10e68
2.40e14
6.10e36
7.30e71
3.00e29

38.6

176

21

38.4

86

38.4

159

8.00e07
1.20e113.00e15

38.2

186

17

38.2

152

4.00e30
2.70e23

38.1

97

19

37.7

408

37.7

297

3.00e129.90e112.80e33

37.6

282

2

37.5

307

37.4

123

37.1

170

2.50e142.1e285.10e078.10e07

11
1

19
1

11
16

7
21
21
16
2
19
4

AllergenOnline Version 18B

Highest%Seq_id

Align
lengthEscore# of 23 species
with matches>35% ID over80AA

gid|789|art v 2 allergen[Artemisia vulgaris]gid|1175|prepro AprM
[Bacillus sp.]gid|875|calcium-binding
protein [Ambrosia
artemisiifolia]
gid|987|allergen Bla g 6.0301[Blattella germanica]
gid|853|MPA3 allergen[Periplaneta americana]gid|355|cysteine proteaseprecursor [Blomia
tropicalis]gid|152|gamma-gliadin[Triticum aestivum]
gid|793|thioredoxin[Aspergillus fumigatus]
gid|962|putative Cup a 4
allergen [Hesperocyparis
arizonica]
gid|276|Venom allergen 5(Antigen 5) (Ag5) (Allergen
Pol f 5) (Pol f V) [Polistes
fuscatus]
gid|1171|RecName: Full =Subtilisin Carlsberg; Flags:
Precursor [Bacillus
licheniformis]
gid|2576|enamine/imine
deaminase[Dermatophagoides farinae]
gid|151|alpha-type gliadin
precursor protein [Triticum
aestivum]gid|1174|RecName: Full =Subtilisin Savinase;
AltName: Full = Alkaline
protease [Bacillus lentus]
gid|1959|enolase [Salmosalar]
gid|1743|troponin C [Crangon
crangon]gid|2335|chymotrypsin-likeprotein [Blattella germanica]

37.1

140

7

37

146

36.7

139

5.10e09
7.20e124.00e12

36.6

101

12

36.6

243

36.4

129

2.20e08
6.60e09
4.00e12

36.3

204

19

36

86

36

139

1.50e07
1.70e121.90e0935.8

123

3.50e11

1

35.6

264

9.40e09

2

35.5

124

4.80e23

21

35.5

290

1.80e07

14

35.4

164

6.70e20

3

35.3

428

15

35.2

145

35.1

265

7.60e20
3.00e101.30e17

1
16

7
3

14
14

16
7

smaller than 1e-20 to 1.1e-25. LTPs from a variety of sources have ev­
idence of cross-reactive laboratory IgE binding, but there are fewer re­ports of multiple allergic reactions to diverse sources of LTPs. Thissearch identified many proteins that are unlikely to represent major risksof cross-reactivity as the protein sequences are conserved across broadtaxonomic categories with no history of cross-reactivity.

19
15

17

3.5. Evaluation of the limits of CODEX guidelines looking for matches of>35% identity
3.5.1. Identification of known allergens in AllergenOnline.org databaseusing FASTA at specific E-score limits for significanceThe predicted proteins from some allergenic species were comparedto AllergenOnline.org database at different E-scores, and we focused on
the best E-score threshold for identification of known allergens using theofficial WHO/IUIS Allergen Nomenclature in AOL database. Table 6 il­lustrates the identified allergens using FASTA in different allergenic
species at representative E-scores of 1e-7, 1e-30, and 1e-100. All known
allergens of major and minor allergenic sources in the AllergenOnline.org
database were detected using E-scores of 10, 1, 0.001, and 1e-7. However,some potentially important matches to allergens were missed in the
FASTA searches when the E-score was reduced less than 10e-7.

18
3

18
10
21

8

M. Abdelmoteleb et al.

Food and Chemical Toxicology 147 (2021) 111888

Table 5FASTA comparison of all proteins predicted for the Galdieria sp. genome
compared to AllergenOnline.org version 18B with >35% identity with an Escore of 10 or smaller. The highest percent identity matches are shown withalignment lengths and with the smallest E scores in columns two to four. The
right-hand column shows the number of 23 common species with genome pre­dictions that have an identity score over 35% identity to the allergens shown in
the left column.
AllergenOnline Version 18B

gid|2591|Putative heatshock-like protein[Tyrophagus
putrescentiae]
gid|2291|Putative Der f 33like protein[Dermatophagoides
pteronyssinus]
gid|338|Putative 60S
ribosomal protein L3(Allergen Asp f 23)
gid|2301|Putative
glyceraldehyde-3phosphate dehydrogenase[Triticum aestivum]
gid|863|Putative cyclophilin[Aspergillus fumigatus]
gid|1033|Allergen
cytochrome c [Curvularia
lunata]gid|2708|Putative heatshock cognate 70 [Aedes
aegypti]gid|1959|Allergen enolase[Salmo salar]gid|1941|Putative
cyclophilin [Daucus
carota]gid|166|Putative
triosephosphat-isomerase[Triticum aestivum]
gid|2236|Putativetransaldolase[Cladosporium
cladosporioides]
gid|1707|Allergen aldolase
A [Thunnus albacares]
gid|651|Putative allergen[Malassezia sympodialis]gid|509|Putative 98 kDa
HDM allergen[Dermatophagoides
farinae]gid|1092|Putative
manganese superoxide
dismutase-like protein[Pistacia vera]
gid|62|Putative RecName:
Full = 60S acidic
ribosomal protein P2;
AltName: Full = Minor
allergen Alt a 5; AltName:
Full = Allergen Alt a 6;
AltName: Full = Allergen
Alt a VI; AltName:
Allergen = Alt a 5
gid|1983|Putative 60S
acidic ribosomal
phosphoprotein P1[Penicillium crustosum]
gid|64|Putative Minor
allergen Alt a 7 (Alt a VII)

Highest% Seq_id

AlignlengthE-score653

1.00e207

22

70.8

452

8.20e150

23

69.4

385

5.80e124

22

68.9

315

4.00e92

21

65.5

145

18

63.1

103

5.70e40
1.70e2662.3

657

2.00e172

22

62

437

15

59.2

169

2.10e112
8.00e4159249

1.30e65

14

59

317

2.90e73

6

58.9

358

19

58.8

102

56.3

87

3.00e84
1.90e24
6.40e1255.1

207

8.30e5217

1.30e19

10

115

AllergenOnline Version 18B

gid|1026|Allergen allergen[Malassezia sympodialis
ATCC 42132]
gid|325|Allergen PPIase[Aspergillus fumigatus]
gid|1926|Allergen
cyclophilin [Catharanthus
roseus]gid|1544|Putative troponin
C [Tyrophagus
putrescentiae]
gid|1248|Putative
eukaryotic translation
initiation factor[Forcipomyia taiwana]gid|1206|Allergen Sal k 3
pollen allergen [Salsolakali]
gid|2849|Allergen Chain A,
Beta-amylase
gid|2582|Putative alcohol
dehydrogenase[Curvularia lunata]
gid|951|Allergen Der f Mal f
6 allergen[Dermatophagoides
farinae]gid|496|Allergen ferritin
heavy chain-like protein[Dermatophagoides
pteronyssinus]
gid|63|Putative Protein
disulfide-isomerase (PDI)(Allergen Alt a 4)
gid|65|Putative aldehyde
dehydrogenase (NAD+)[Alternaria alternata]gid|246|Putative elongation
factor 1 beta-like[Penicillium citrinum]
gid|2076|Putative heatshock protein 70[Dermatophagoides
farinae]gid|850|Putative catalase[Penicillium citrinum]
gid|2293|Allergen Der f 31
allergen[Dermatophagoides
farinae]gid|1617|Putative alpha/beta gliadin precursor[Triticum aestivum]
gid|2592|Putative aldehyde
dehydrogenase-likeprotein [Tyrophagus
putrescentiae]
gid|251|Putative
peroxisomal membraneprotein [Penicillium
citrinum]gid|650|Putative allergen[Malassezia sympodialis]gid|160|Allergen highmolecular weight gluteninsubunit 1Ax1 [Triticum
aestivum]gid|2215|Allergen
RecName: Full =Glutathione S-transferase
1; AltName: Full = GST
class-sigma# of species
from the 23
genomes with>35% ID over
80AA

72.3

54.8

Table 5 (continued )

0

20

20
20

52.7

112

2.20e22

16

52

202

15

50

106

9.40e392.00e180Highest% Seq_id

AlignlengthE-score# of species
from the 23
genomes with>35% ID over
80AA

48.9

135

19

47.6

170

2.80e195.80e3245.9

1463.80e27

22

45.2

325

4.10e64

21

45.1

765

8.60e77

15

44

470

0

43.4

339

1.80e59
1.00e6143.4

1432.30e19

20

42.5

179

2.60e25

19

42.4

92

7.50e10

16

42.3

506

5.60e75

19

42.1

235

3.30e36

20

40.4

560

2.60e61

10

39.9

489

21

39.6

144

4.90e71
1.90e1239.1

161

1.10e12

13

38.5

405

3.90e59

20

37.8

172

2.50e16

2

37.5

144

16

36.4

110

7.50e223.30e0736.3

204

1.60e19

4

18

8

11

4

(continued on next page)9

M. Abdelmoteleb et al.

Food and Chemical Toxicology 147 (2021) 111888

vicilins, glycinins, 2S albumins, tropomyosin, and arginine kinase areshown in Table 7. The matches were related to taxonomic relationshipsof the species as well as the protein families, yet the identity matches arebroadly diverse. Lipid transfer proteins, vicillins and glycinins are highly
conserved in beans, soybeans, apple, peach, and papaya. Yet publica­
tions of cross-reactivity for these proteins among the protein families islimited and it appears true clinical cross-reactivity is very limited. Major
allergens in crustacean shellfish e.g. tropomyosin and arginine kinase
are generally cross-reactive between crustaceans and occasionally toinsect proteins, yet identities of >35% identity were commonly found
for those two proteins between human, drosophila, bovine, salmon, andcod and there is no evidence of clinical cross-reactivity for those taxa
compared to crustaceans. Importantly, human proteins are not consid­
ered to be allergenic for humans.

Table 5 (continued )AllergenOnline Version 18B

Highest% Seq_id

AlignlengthE-score# of species
from the 23
genomes with>35% ID over80AA

gid|799|Allergen NADPdependent mannitol
dehydrogenase[Davidiella tassiana]
gid|1577|Allergen Sal k 4.03
allergen [Salsola kali]
gid|2576|Putative enamine/
imine deaminase[Dermatophagoides
farinae]gid|1171|Allergen subtilisin
precursor [Bacillus
licheniformis]
gid|2551|Putative Par h I
precursor [Parthenium
hysterophorus]

36.2

246

4.20e24

5

35.8

148

0

35.7

126

6.10e122.40e1235.4

178

4.20e14

2

35.2

145

9.60e1218

21

3.5.3. Minor allergens and noise of CODEX limits
To consider protein identity matches to minor allergens, predicted
proteins of the 23 species were compared to AOL version 18B by FASTA
using the HCC supercomputer. Those proteins that had a match of >35%
identity to proteins from at least 10 of these species were considered
evolutionarily conserved minor allergens. Most of the minor allergens
represented had sequence identities less than 50% when comparedwithin the protein type. Matches of >35% identity were found to 170
allergens listed in AOL, and those are considered minor also because
they do not have published evidence of causing clinical reactions, only
IgE binding. They all matched at least 10 different species out of the 23

3.5.2. Major allergens with a high risk of cross-reactivity
Proteins predicted from the 23 genomes that included humans weresearched to allergens having a relatively high risk of clinical crossreactivity to major allergens. The distribution of taxa having matches
to clinically important major allergens included lipid transfer proteins,

Table 6
Identification of known allergens listed in the AllergenOnline.org database using full-length FASTA. Predicted proteins of the allergenic species listed in this table werecompared to the AOL database. Matches above CODEX limits to known allergens were found using the official WHO/IUIS Allergen Nomenclature in the AOL database
identified E-scores from 1e-7, 1e-30 and 1e-100. A few allergens were missed between E-scores of 1e-7 and 1e-30.
SpeciesE-score (1e-7)

E-score (1e-30)

E-score (1e-100)

Peanut (Arachis
hypogaea)
Apple (Malus
domestica)
Chicken (Gallus
gallus)Soybeans(Glycine max)
Bovine (Bostaurus)Candida(Candida
albicans)
Cod (Gadus
morhua)Papaya (Carica
papaya)Almond (Prunus
dulcis)Rice (Oryza
sativa)Pecan (Carya
illinoinensis)
Bean (Phasoleus
vulgaris)
Pistacio(Pistacia vera)
Peach (Prunus
persica)
Salmon (Salmosalar)
Potato (Solanum
tuberosum)
Walnut (Juglan
regia)
Wheat (Triticum
aestivum)Ara h 1, Ara h 2, Ara h 3, Ara h 4, Ara h 6, Ara h 7, Ara h 8,
profilin, lipid transfer proteins, oleosin, conarachin, glycinin
Mal d 1, Mal d 2, Mal d 3, Mal d 4

Ara h 1, Ara h 2, Ara h 3, Ara h 4, Ara h 6, Ara h 7, Ara h 8,
profilin, lipid transfer proteins, oleosin, conarachin, glycinin
Mal d 1, Mal d 2, Mal d 3, Mal d 4

Ara h 1, Ara h 3, Ara h 4,
conarachin, glycinin
Mal d 2Gal d 1, Gal d 2, Gal d 3, Gal d 4, Gal d 5, Gal d 7, Gal d 8

Gal d 1, Gal d 2, Gal d 3, Gal d 4, Gal d 5, Gal d 7, Gal d 8,

Gal d 1, Gal d 2, Gal d 3

Gly m 1, Gly m 3, Gly m 5, Gly m 6, Gly m 8, Gly m Bd 28K, Glym Bd 30K, glycine trypsin inhibitorBos d 2, Bos d 3, Bos d 4, Bos d 5, Bos d 6, Bos d 9 or Bos d 10,
Bos d 11, Bos d 12
Cand a 1, Cand a 3, Enolase 1

Gly m 3, Gly m 5, Gly m 6, Gly m 8, Gly m Bd 28K, Gly m Bd
30K, glycine trypsin inhibitorBos d 2, Bos d 3, Bos d 4, Bos d 5, Bos d 6, Bos d 11, Bos d 12

Gly m 5, Gly m 6

Cand a 1, Cand a 3, Enolase 1

Cand a 1, Cand a 3,
Enolase 1

Gad m 1

Gad m 1

Cari p 1.0101

Cari p 1.0101

Cari p 1.0101

Pru du 1.01, Pru du 2, Pru 4, Pru du 6

Pru du 1.01, Pru du 2, Pru 4 Pru du 6

Pru du 2, Pru du 6

Glyoxalase I, Ory s 1, Polcalcin (Ph1p7)

Glyoxalase I

Glyoxalase I

Car i 1, Car i 4

Car i 4Car i 4

Pha v 3

Pha v 3

Pis v 1, Pis v 2 (2.0201), Pis v 3, Pis v 4, Pis v 5

Pis v 2 (2.0201), Pis v 3, Pis v 4, Pis v 5

Pru p 1 and 1.0201, Pru p 2, 2.01A, 2.01B, 2.02, Pru p 3, Pru du
4.02
Sal s 1, Sal s 2, Sal s 3

Pru p 1 and 1.0201, Pru p 2 (2.01A, 2.01B, 2.02), Pru p 3, Pru du
4.02
Sal s 1, Sal s 2, Sal s 3

Sola t 1, Sola t 2, Sola t 3, Sola t 4, profilin

Sola t 1, Sola t 2, Sola t 3, Sola t 4 profilin,

Jug r 1, Jug r 2, Jug r 3

Jug r 2, Jug r 3

Tri a 12, Tri a 14, Tri a 21, Tri a 25, Tri a 26, Tri a 28, Tri a 29,
Tri a 31, Tri a 33, Tri a 34, Tri a 37, Tri a 39, Tri a 42, Tri a 44,
Tri a 45, thaumatin like protein, serine carboxypeptidase, serinecarboxypeptidase, putative 27K protein, chymotrypsin
inhibitor WSCI

Tri a 12, Tri a 14, Tri a 21, Tri a 25, Tri a 26, Tri a 28, Tri a 29,
Tri a 31, Tri a 33, Tri a 34, Tri a 37, Tri a 39, Tri a 42, Tri a 44,
Tri a 45, thaumatin like protein, serine carboxypeptidase,
putative 27K protein, chymotrypsin inhibitor WSCI

10

Bos d 6

Pis v 2 (2.0201), Pis v 3,
Pis v 4, Pis v 5
Sal s 1, Sal s 2, Sal s 3
Sola t 1

Tri a 31, Tri a 33, Tri a 34,
serine carboxypeptidase

M. Abdelmoteleb et al.

Food and Chemical Toxicology 147 (2021) 111888

Table 7
Distribution of matches of proteins predicted from the 23 species genomes to clinically important allergens in AllergenOnline.org. The matches were identified basedon CODEX guidelines of >35% identity over 80 AA and would be considered as possibly cross-reactive yet, human proteins matched in this search are clearly notallergens, demonstrating over-prediction.
LTPs

Vicilins

Glycinins

Tropomyosins

Arginine kinase

2S albumins

PeanutKidney beans
WalnutSoybeans
Apple
Papaya
Rice
Wheat
Peach
PotatoBovineHuman
Corn
Arabidopsis
AlmondPapayaCorn
Drosophila
Pistachio
Soybeans
PeanutAlmondPecan
WalnutPotato
Apple
Peach
Human
SalmonSoybeans
Kidney beans
PeanutSalmonWalnutChicken
Human
PotatoDrosophila
SalmonAtlantic cod
Chicken
Human
BovineHuman
ChickenBovineSalmonAtlantic cod
Drosophila

Pistachio
PotatoSoybeans
WalnutPeanutspecies. Supplementary Table 2 lists these minor allergens and shows thenumber of species matched out of 23. Searches that identify protein
identity matches to proteins in 10 or more diverse species must be
evolutionarily conserved and are unlikely to represent real risks of crossreactivity.

identity matches of >35% are still common for highly conserved pro­teins at 1e-7.
Examples using three predicted proteomes from three novel foods
were assessed against the AOL database and many identity matches wereseen. The comparison of predicted proteins from 23 test species
demonstrated conclusively that the low-level match of >35% identity
over 80 amino acids over-predicts potential risks of allergy. We haveconcluded that Chlorella variabilis, Galdieria sulphuraria and Fusarium
strain flavolapis do not represent a significant risk of food allergy to thegeneral population as matches to similar proteins from many diverse
species are very common.
Alternative strategies of increasing the match criteria above 35%
identity, possibly to 45% identity; decreasing the E-score below 1e-7 orsmaller; may be needed although matches to a few allergens may be
missed at 1e-20 and ranking of allergens in AOL regarding risks of dis­ease could markedly improve this assessment strategy. Other in­vestigators should use similar strategies and risk assessors should
consider the broad questions of whole food safety for novel or new foods
to establish more predictive assessment limits.

4. Conclusion
It is becoming more common to use a whole genome or a proteomebioinformatics approach to identify potential proteins in a wide varietyof species. Some regulatory agencies or risk assessment scientists havesuggested using these predicted proteins against allergen databases to
identify possible risks of allergenicity for food safety. The CODEX
guideline (>35% identity over 80 amino acids to any known allergen)has become a standard for possible risks of cross-reactivity since 2003.
The comparison to www.AllergenOnline.org was made available to the
public in 2005 to assess individual proteins. The database is updated
annually. The interpretation of identity matches over 35% over 80
amino acids or the equivalent is assumed to be a positive identity matchthat would require serum IgE binding tests sera from subjects allergic to
the matched allergen. Since we know that matches at that identity level
can occur by random chance, we tested the use of protein sequencespredicted from genomes, transcriptomes, or proteomes against AOL toestimate the commonality of false positive matches.
We compared the predicted proteins from the genomes of 23 diverse
allergenic and low- or non-allergenic species including plant sources,
fungi, fish, insect and other animal sources as well as human sequences
against the AOL database using standard CODEX criteria as well as fullFASTA alignments to provide identity matches. We used a wide varietyof E score criteria to consider that as a variable as well. A number ofhousekeeping proteins across many species had moderate to highidentities to minor putative allergens in AOL. However, many of these
proteins are highly conserved in most eukaryotes and as a consequence
would be expected to be found in any search using the standard CODEX
criteria. In contrast, major allergens are not highly conserved insequence and structure and were not identified using the search pa­
rameters except in closely related species.
For those highly conserved proteins identified across many species,
there are nonetheless differences in the levels of AA sequence identity
conservation that impact their potential for shared clinical crossreactivity. Moreover, differences in protein abundance and potency
are significantly different between species, affecting the allergenic po­tential of the species.
We have used a wide range of E-score thresholds to test searchmethods. We propose that an E-score threshold of 1e-7 may be needed
for identification of a few important allergens in this type of study, yet

Author contributions
MA performed the initial literature reviews, performed bioinfor­
matics comparisons to AllergenOnline.org, and drafted the manuscript.
CZ oversaw the design of the bioinformatics pipeline and edited themanuscript. MK oversaw generation of the genomic sequences of Fusa­
rium strain flavolapis and BF contributed to allergenicity discussion ofand edits Fusarium strain flavolapis. MC oversaw and provided genomic
data for Galdieria sulphuraria. HG provided overall suggestions andedited the manuscript. REG developed the original concept for the study
and oversaw the completion and provided allergenicity risk review.
Funding
Some funds were provided by Fermentalg and by Nature’s Fynd
Some funding was provided by the AllergenOnline.org sponsors (Uni­lever and NuSeed). The majority was from revolving research accountsof Professor Goodman.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

11M. Abdelmoteleb et al.

Food and Chemical Toxicology 147 (2021) 111888

Acknowledgements

immunological characterization of potential allergens from the mould Fusarium
culmorum. Mol. Immunol. 39, 965–975.
Hoff, M., Trueb, R.M., Ballmer-Weber, B.K., Vieths, S., Wuethrich, B., 2003a.
Immediatetype hypersensitivity reaction to ingestion of mycoprotein (Quorn) in apatient allergic to molds caused by acidic ribosomal protein P2. J. Allergy Clin.
Immunol. 111, 1106–1110.
Hyatt, D., Chen, G.L., Locascio, P.F., Land, M.L., Larimer, F.W., Hauser, L.J., 2010.
Prodigal: prokaryotic gene recognition and translation initiation site identification.
BMC Bioinf. 11, 119.
Jacobson, M.F., DePorter, J., 2018. Self-reported adverse reactions associated withmycoprotein (Quorn-brand) containing foods. Ann. Allergy Asthma Immunol. 120,
626–630.
Katona, S.J., Kaminski, E.R., 2002. Sensitivity to Quorn mycoprotein (Fusarium
venenatum) in a mould allergic patient. J. Clin. Pathol. 55, 87–88.
King, R., Urban, M., Hammond-Kosack, M.C.U., Hassani-Pak, K., Hammond-Kosak, K.E.
H., 2015. The completed genome sequence of the pathogenic ascomycete fungus
Fusarium graminearum. BMC Genom. 16, 544.
Klamczynska, B., Mooney, W.D., 2017. Heterotrophic Microalgae: a Scalable andSustainable Protein Source. Sustainable Protein Sources.
Ladics, G.S., Bannon, G.A., Silvanovich, A., Cressman, R.F., 2007. Comparison of
conventional FASTA identity searches with the 80 amino acid sliding window FASTA
search for the elucidation of potential identities to known allergens. Mol. Nutr. Food
Res. 51, 985–998.
Langmead, B., Salzberg, S.L., 2012. Fast gapped-read alignment with Bowtie 2. Nat.
Methods 9, 357–359.
Li, H., 2016. Minimap and miniasm: fast mapping and de novo assembly for noisy longsequences. Bioinformatics 32, 2103–2110.
Li, H., Durbin, R., 2009. Fast and accurate short read alignment with Burrows–Wheelertransform. Bioinformatics 25, 1754–1760.
Lowe, T.M., Chan, P.P., 2016. tRNAscan-SE On-Line: search and contextural analysis ofTransfer RNA genes. Nucleic Acids Res. 44, W54–W57.
Lowe, T.M., Eddy, S.R., 1997. tRNAscan-SE: a program for improved detection of transfer
RNA genes in genomic sequence. Nucleic Acids Res. 25 (5), 955–964.
Metcalfe, D.D., Astwood, J.D., Townsend, R., Sampson, H.A., Taylor, S.L., Fuchs, R.L.,
1996. Assessment of the allergenic potential of foods derived from geneticallyengineered crop plants. Crit. Rev. Food Sci. Nutr. 36 (Suppl. ment), S165–S186.
Muraro, A., Hoffmann-Sommergruber, K., Holzhauser, T., Poulsen, L.K., Gowland, M.H.,
Akdis, C.A., Mills, E.N.C., et al., 2014. EAACI food allergy and anaphylaxis
guidelines. Protecting consumers with food allergies: Allergy 69, 1464–1472.
Niehaus, E.-M., Munsterkotter, M., Proctor, R.H., Brown, D.W., Sharon, A., Idan, Y.,
Oren-Young, L., et al., 2016. Comparative “Omics” of the Fusarium fujikuroi species
complex highlights differences in genetic potential and metabolite synthesis.
Genome Biol. Evol. 8 (11), 3574–3599.
Palladino, C., Breiteneder, H., 2018. Peanut allergens. Mol. Immunol. 100, 58–70.
Pearson, W.R., 2000. Flexible sequence similarity searching with the FASTA3 program
package. Methods Mol. Biol. 132, 185–219.
Pearson, W.R., 2014. BLAST and FASTA similarity searching for multiple sequencealignment. Methods Mol. Biol. 1079, 75–101.
Pearson, W.R., 2016. Finding protein and nucleotide similarities with FASTA. Curr.
Protoc. Bioinformatics 53, 3.9.1-25.
Porterfield, H.S., Murray, K.S., Schlichting, D.G., Chen, X., Hansen, K.C., Duncan, M.W.,
Dreskin, S.C., 2009. Effector activity of peanut allergens: a critical role for Ara h 2,
Ara h 6, and their variants. Clin. Exp. Allergy 39, 1099–1108.
Ramsey, N.B., Duffey, D., Anagnostou, K., Coleman, N.E., Davis, C.M., 2019.
Epidemiology of anaphylaxis in critically ill children in the United States andCanada. J. Allergy Clin. Immunol. Pract. 7, 2241–2249.
Ruethers, T., Taki, A.C., Johnston, E.B., Nugraha, R., Le, T.T.K., Kalic, T., McLen, T.R.,
Kamath, S.D., Lopata, A.L., 2018. Seafood allergy: a comprehensive review of fishand shellfish allergens. Mol. Immunol. 100, 28–57.
Schonknecht, G., Chen, W.-H., Ternes, C.M., Barbier, G.G., Shrestha, R.P., Stanke, M.,
Brautigam, A., Baker, B.J., Banfield, J.F., Garavito, R.M., et al., 2013. Gene transferfrom Bacteria and Archaea facilitated evolution of an extremophilic eukaryote.
Science 339, 1207–1210.
Schwager, C., Kull, S., Behrends, J., Rockendorf, N., Schocker, F., Frey, A., Homann, A.,
Becker, W.-.M., Jappe, U., 2017. Peanut oleosins associated with severe peanut
allergy—importance of lipophilic allergens for comprehensive allergy diagnostics.
J. Allergy Clin. Immunol. 140, 1331–1338.
Silvanovich, A., Bannon, G., McClain, S., 2009. The use of E-scores to determine thequality of protein alignments. Regul. Toxicol. Pharmacol. 54 (3 Suppl. l), S26–S31.
Siruguri, V., Bharatraj, D.K., Vankudavath, R.N., Mendu, V.V., Gupta, V., Goodman, R.E.,
2015. Evaluation of Bar, Barnase, and Barstar recombinant proteins expressed in
genetically engineered Brassica juncea (Indian mustard) for potential risks of foodallergy using bioinformatics and literature searches. Food Chem. Toxicol. 83,
93–102.
Stanke, M., Morgenstern, B., 2005. AUGUSTUS: a web server for gene prediction in
eukaryotes that allows user-defined constraints. Nucleic Acids Res. 33 (Web Serverissue), W465–W467.
Taylor, S.L., Hefle, S.L., 2006. Food allergen labeling in the USA and Europe. Curr. Opin.
Allergy Clin. Immunol. 6 (3), 186–190.
Tee, R.D., Gordon, D.J., Welch, J.A., Newman Taylor, A.J., 1993. Investigation of
possible adverse allergic reactions to mycoprotein (‘Quorn’). Clin. Exp. Allergy 23,
257–260.
Thomas, K., Bannon, G., Hefle, S., Herouet, C., Holsapple, M., Ladics, G., Macintosh, S.,
Privalle, L., 2005. In silico methods for evaluating human allergenicity to novel
proteins: international bioinformatics workshop meeting report, 23-24 February.
Toxicol. Sci. 88, 307–310.

Genomic sequences of Galdieria sp. were provided by Fermentalg and
for Fusarium sp. by Nature’s Fynd. The AllergenOnline.org databasesearch routines were developed by John Wise as an employee of the
University of Nebraska. Jean-Jack Riethoven of the Holland Computing
Center at the University of Nebraska provided BLASTP supercomputer
access for much of this work and loaded the NCBI non-redundant Protein
dataset on HCC.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.fct.2020.111888.
References
Aalberse, R.C., Kleine Budde, I., Stapel, S.O., van Ree, R., 2001. Structural aspects ofcross-reactivity and its relation to antibody affinity. Allergy 56 (Suppl. 67), 27–29.
Aalberse, R.C., 2000. Structural biology of allergens. J. Allergy Clin. Immunol. 106,
228–238.
Andrews, S., 2010. FastQC: a quality control tool for high throughput sequence data.
Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc.
Bankevich, A., Nurk, S., Antipov, D., et al., 2012. SPAdes: a new genome assembly
algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19,
455–477.
Blanc, G., Duncan, G., Agarkova, I., Borodovsky, M., Gurnon, J., Kuo, A., Lindquist, E.,
Lucas, S., Pangilianan, J., Polle, J., et al., 2010. The Chlorella variabilis NC64A
genome reveals adaptation to photosymbiosis, coevolution with viruses, and cryptic
sex. Plant Cell 22, 2943–2955.
Bleakley, S., Hayes, M., 2017. Algal proteins: extraction, application, and challenges
concerning production. Foods 6, 33.
Cabanillas, B., Jappe, U., Novak, N., 2018. Allergy to peanut, soybean and other legumes:
recent advances in allergen characterization, stability to processing and IgE crossreactivity. Mol. Nutr. Food Res. 62, 1.
CODEX Alimentarius Commission, 2009. Foods Derived from Modern Biotechnology,
second ed. World Health Organization, Food and Agricultural Organization of theUnited Nations, Rome, Italy.
Cressman, R.F., Ladics, G., 2009. Further evaluation of the utility of “sliding window”
FASTA in predicting cross-reactivity with allergenic proteins. Regul. Toxicol.
Pharmacol. 54 (3 Suppl. l), S20–S25.
Dreskin, S.C., Germinaro, M., Reinhold, D., et al., 2019. IgE binding to linear epitopes ofAra h 2 in peanut allergic preschool children undergoing oral Immunotherapy.
Pediatr. Allergy Immunol. 30, 817–823.
FAO/WHO, 2001. Evaluation of allergenicity of genetically modified foods. In: Report ofa Joint FAO/WHO Expert Consultation on Allergenicity of Foods Derived from
Biotechnology. Food and Agriculture Organization of the United Nations (FAO),
Rome.
Faber, M.A., Pascal, M., El Karbouchi, O., Sabato, V., Hagendorens, M.M., Decuyper, I.I.,
Bridts, C.H., Ebo, D.G., 2017. Shellfish allergens: tropomyosin and beyond. Allergy
72, 842–848.
Finnigan, T.J.A., Wall, B.T., Wilde, P.J., Stephens, F.B., Taylor, S.L., Freedman, M.R.,
2019. Mycoprotein: the future of nutritious nonmeat protein, a symposium review.
Curr. Dev. Nutr. 3, nzz021.
Frigerio, J., Agostinetto, G., Sandionigi, A., Mezzasalma, V., Maria Berterame, N.,
Casiraghi, M., Labra, M., Galimberti, A., 2020. The hidden ‘plant side’ of insect novel
foods: a DNA-based assessment. Food Res. Int. 128, 108751.
Goodman, R.E., Vieths, S., Sampson, H.A., Hill, D., Ebisawa, M., Taylor, S.L., van Ree, R.,
2008. Allergenicity assessment of genetically modified crops—what makes sense?
Nat. Biotechnol. 26 (1), 73–81. https://doi.org/10.1038/nbt1343.
Goodman, R.E., Ebisawa, M., Ferreira, F., Sampson, H.A., van Ree, R., Vieths, S.,
Baumert, J.L., Bohle, B., Lalithambika, S., Wise, J., Taylor, S.L., 2016.
AllergenOnline: a peer-reviewed, curated allergen database to assess novel foodproteins for potential cross-reactivity. Mol. Nutr. Food Res. 60, 1183–1198.
Goodman, R.E., Hefle, S.L., Taylor, S.L., van Ree, R., 2005. Assessing geneticallymodified crops to minimize the risk of increased food allergy: a review. Int. Arch.
Allergy Immunol. 137, 153–166.
Gowland, M.H., Walker, M.J., 2015. Food allergy, a summary of eight cases in the UK
criminal and civil courts: effective last resort for vulnerable consumers? J. Sci. Food
Agric. 95, 1979–1990.
Gurevich, A., Saveliev, V., Vyahhi, N., Tesler, G., 2013. QUAST: quality assessment toolfor genome assemblies. Bioinformatics 29, 1072–1075.
Henikoff, J.G., Henikoff, S., 1996. Blocks database and its applications. Methods
Enzymol. 266, 88–105.
Henikoff, S., Henikoff, J.G., 1992. Amino acid substitution matrices from protein blocks.
Proc. Natl. Acad. Sci. U.S.A. 89, 10915–10919.
Hileman, R.E., Silvanovich, A., Goodman, R.E., Rice, E.A., Holleschak, G., Astwood, J.D.,
Hefle, S.L., 2002. Bioinformatic methods for allergenicity assessment using acomprehensive allergen database. Int. Arch. Allergy Immunol. 128, 280–291.
Hoff, M., Ballmer-Weber, B.K., Niggemann, B., Cistero-Bahima, A., San MiguelMoncin, M., Conti, A., Haustein, D., Vieths, S., 2003b. Molecular cloning and

12

M. Abdelmoteleb et al.

Food and Chemical Toxicology 147 (2021) 111888

Walker, B.J., Abeel, T., Shea, T., Priest, M., Abouelliel, A., Sakthikumar, S., et al., 2014.
Pilon: an integrated tool for comprehensive microbial variant detection and genome
assembly improvement. PloS One 9, e112963.
Weber, R.W., Levetin, E., 2014. Allergen of the month-Fusarium. Ann. Allergy Asthma
Immunol. 112, A11.
Wells, M.L., Potin, P., Craigie, J.S., Raven, J.A., et al., 2017. Algae as nutritional andfunctional food sources: revisiting our understanding. J. Appl. Phycol. 29, 949–982.
Westerhout, J., Baumert, J.L., Blom, W.M., Allen, K.J., et al., 2019. Deriving individual
threshold doses from clinical challenge data for population risk assessment of foodallergens. J. Allergy Clin. Immunol. 144, 1290–1309.

Wilder, H.K., Raffel, S.J., Barbour, A.G., Porcella, S.F., Sturdevant, D.E., Vaisvil, B.,
Kapatral, V., Schmitt, D.P., Schwan, T.G., Lopez, J.E., 2016. Transcriptional profilingthe 150 kb linear megaplasmid of Borrelia turicatae suggests , a role in vector
colonization and initiating mammalian infection. PloS One 11 (2), 1–17.
Yeh, C.C., Tai, H.Y., Chou, H., Wu, K.G., Shen, H.D., 2016. Acuolar serine protease is amajor allergen of Fusarium proliferatum and an IgE-cross reactive pan-fungal
allergen. Allergy Asthma Immunol. Res. 8, 438–444.
Zimin, A.V., Marcais, G., Puiu, D., Roberts, M., Salzberg, S.L., Yorke, J.A., 2013. The
MaSuRCA genome assembler. Bioinformatics 29, 2669–2677.

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