Integrated multi-omics and serological profiling identifies five immunodominant allergens from the high-abundance protein repertoire of Haliotis discus hannai

Integrated multi-omics and serological profiling identifies five immunodominant allergens from the high-abundance protein repertoire of Haliotis discus hannai | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrated multi-omics and serological profiling identifies five immunodominant allergens from the high-abundance protein repertoire of Haliotis discus hannai Shuai Zhi, Xin-yu Han, Tian-bin Chen, Jia-han Que, Qiong Xiao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9243755/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background: The rising consumption of Haliotis discus hannai (HDH) has brought its associated food allergy into focus. However, studies on its allergens and their role in patient reactions remain limited. This study aimed to construct a high-abundance allergen profile for HDH by integrating multi-omics data and to identify its immunodominant allergens through immunobinding assays with volunteers’ sera. Methods: We first constructed a predicted proteome for HDH using in-house generated transcriptome data along with public genome resources. High-throughput proteome and quantitative expression data were employed to establish a high-abundance allergen profile. Immunobinding assays, incorporating Western-blot analyses with volunteer-derived sera, were utilized to delineate allergens characterized by high immunoreactivity. Their tertiary structures and B-cell epitopes were subsequently analyzed using multiple bioinformatics tools. Results: We predicted 53,245 protein-coding genes from a high-quality assembled genome and transcriptome, of which 33,109 were functionally annotated. Proteome sequencing identified 3,916 reliably expressed proteins. Cross-referencing with two authoritative allergen databases revealed 37 high-abundance allergens in HDH. Sequence comparisons indicated a high similarity for homologous allergens between HDH and other aquatic mollusks, crustaceans, and terrestrial arthropods, indicating a potential widespread cross-reactivity. Dot-blot and Western-blot assays using sera from 90 volunteers identified six protein bands with significant IgE-binding activity. Based on molecular weight and prior allergen profiling, the six bands were hypothesized to be a polymer, paramyosin (PM), arginine kinase (AK), tropomyosin (TM), triosephosphate isomerase (TIM), and fructose-bisphosphate aldolase (FBA), respectively. Subsequent immunobinding assays identified PM, AK, TM, TIM, and FBA as the immunodominant allergens, as they each exhibited relatively high seropositivity rate in volunteers’ sera. Treatment with denaturants (SDS and β-me) revealed that PM and TM are primarily linear allergens, whereas AK is predominantly conformational, explaining the higher detection rate in Dot-blot versus Western-blot. Homology modeling showed that PM and TM possess relatively simple structures dominated by α-helices, while AK, TIM, and FBA have more complex tertiary structures containing significant proportions of β-sheets and random coil. B-cell epitope prediction indicated that PM and TM harbor mainly linear epitopes, whereas AK, TIM, and FBA possess additional conformational epitopes. Conclusions: This study provided the first comprehensive allergen profile for HDH by integrating multi-omics data and immunoassays, and further identified its immunodominant allergens. Through bioinformatics predictions and denaturant experiments, we elucidated the structural conformations and B-cell epitopes of immunodominant allergens. These findings offer crucial data for the precise identification of abalone allergens, supporting the development of accurate diagnostic strategies for affected individuals. Biological sciences/Biochemistry Biological sciences/Biological techniques Biological sciences/Biotechnology Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Biological sciences/Molecular biology Haliotis discus hannai Muli-omics sequencing data Allergenome IgE-binding activity Spatial conformation of allergens B-cell epitopes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction In recent years, the global incidence of food allergy has shown a continuous upward trend, emerging as a significant food safety and public health concern for governments and the public worldwide [ 1 ] . Seafood allergy is one of the most prevalent food allergy types in China, primarily triggered by crustaceans, mollusks, and fish [ 2 ] . A study from Jiangxi Province in China reported a food allergy prevalence of approximately 5.4%, with nearly 40% of these patients allergic to shrimp. Allergy to molluscan shellfish accounted for about 20.8%, ranking second, while fish allergy was reported in 7.5% of patients [ 3 ] . Another study from Taiwan region indicated a local food allergy prevalence of around 6.9%, with molluscan shellfish allergy constituting approximately 18.4% of all allergic individuals [ 4 ] . These epidemiological findings underscore that molluscan shellfish allergy represents a considerable proportion of the global, and particularly the Chinese, allergic population, imposing substantial pressure and burden on affected families and society at large [ 5 ] . Food allergy caused by molluscan shellfish, especially abalone allergy, is increasingly becoming one of the predominant allergic conditions in China [ 6 ] . The Haliotis discus hannai (HDH), as a major economic molluscan species in China, is highly favored by consumers for its rich nutritional value and succulent meat. Common consumption methods include boiling and steaming, and it is also processed into canned or dried products. In 2024, the total production of crustacean and molluscan aquatic products in China exceeded 28.03 million tons, with molluscan products alone reaching 18.19 million tons. As a relatively prized economic species, the HDH production has surged alongside rising living standards and increasing demand for high-quality food among Chinese consumers. Statistics data from the China Fishery Statistical Yearbook reveal that China's mariculture output of abalone skyrocketed from ~ 128,000 tons in 2015 to 252,000 tons in 2024, reflecting a robust growth trend in demand. With the rapid increase in both the production and consumption of HDH in China, the associated food allergy problem has become increasingly prominent [ 7 ] . There is a pressing need to investigate the primary causes of allergic reactions to HDH consumption, which is crucial for potentially reducing the frequency of such reactions among Chinese consumers. Molluscan shellfish allergy could induce IgE-mediated type I hypersensitivity reactions in patients, often persisting throughout their lifetime [ 8 ] . Typical clinical manifestations include facial and lip swelling, chest tightness, shortness of breath, numbness in the mouth, tongue and limbs, and skin itching. In severe cases, these may progress to collapse, shock, and even life-threatening conditions [ 9 ] . These symptoms significantly impair the physical health and quality of life of allergic individuals, which could lead to long-term psychological distress, including anxiety and depression, for both patients and their families [ 10 ] . Consequently, there is an urgent need to systematically identify the immunodominant allergens responsible for allergic reactions to the HDH, thereby providing a reliable scientific basis of allergen data for clinical detection. Although the incidence of HDH-induced food allergies has been increasing annually, research in this area remains notably limited. Existing studies on HDH allergy have primarily concentrated on tropomyosin (TM), a major allergen in aquatic foods, while a systematic elucidation of the complete allergen profile of HDH is conspicuously absent [ 11 ] . Our research team conducted an in-depth comparative investigation of TM allergens from three mollusks (HDH, Alectryonella plicatula , and Mimachlamys nobilis ), revealing their high sequence similarity and potent immunoreactivity [ 11 ] . Subsequent work identified multiple conserved B-cell epitopes among these three homologous TMs, which underlie the strong cross-reactivity observed across these species [ 12 ] . Furthermore, research on the North Sea brown shrimp allergen demonstrated that its TM reacted with approximately 68% of serum samples from allergic patients, underscoring its substantial contribution to clinical allergic presentations [ 13 ] . However, this finding also indicated that TM alone is insufficient to fully account for all food allergy reactions triggered by this species. Consequently, there is a pressing need to systematically delineate the allergen profile of HDH, identifying individual allergens and assessing their sensitization potential. This effort would provide reliable, specific allergen data essential for improving diagnostic accuracy for HDH allergy. The identification of high-abundance allergens is also crucial for advancing the development of clinically convenient and efficient detection methods for abalone allergens. Such advancements would enhance early diagnosis rates, reduce misdiagnoses stemming from unidentified allergens, and ultimately mitigate the burden abalone allergy imposes on both patients and healthcare systems. To date, multi-omics studies, encompassing transcriptome and proteome, on abalone species have primarily focused on aquaculture-related traits, nutritional value, muscle mass, and developmental stages [ 14 – 16 ] . A significant gap exists in employing integrated multi-omics strategies to systematically construct a comprehensive allergen profile. For instance, Huang et al. utilized iTRAQ-based proteome to identify 125 differentially expressed proteins associated with muscle growth in HDH, which were enriched in pathways significantly related to muscle development, such as apoptosis and thyroid hormone signaling, thereby enhancing the understanding of its molecular growth mechanisms [ 14 ] . However, sole reliance on proteomic analysis for allergen identification presents several inherent challenges. While conventional proteomics enables the quantification of protein abundance, it often falls short in resolving complete sequence coverage, which constrains the development of highly specific clinical detection methods. Furthermore, although proteome data could reveal the expression levels of target proteins in a given sample, these measurements do not necessarily reflect the abundance or stability of the same proteins within complex crude protein extracts. Consequently, such proteins may not be consistently detectable in functional validation assays (e.g., immunoblotting) that rely on these extracts as the antigen source [ 17 ] . Additionally, employing transcriptome in isolation for allergen research has also inherent limitations. Transcript levels do not fully correlate with actual protein expression due to the intricate post-transcriptional regulation of translation, which is modulated by various epigenetic modifications such as methylation and acetylation, ultimately influencing final protein expression and function [ 18 ] . Systematic analyses comparing transcriptomes and proteomes in mammals have revealed a significant correlation for only approximately half of all genes [ 18 ] . Therefore, establishing an integrated multi-omics analytical framework for HDH is of paramount importance for the comprehensive and accurate prediction of allergens, along with their molecular characteristics and functions. This approach will provide reliable data on allergen sequences and expression levels, forming a solid foundation for in-depth investigations into the molecular mechanisms of allergic responses. In this study, we integrated multi-omics big data from HDH, including genome, transcriptome, and proteome datasets. Our approach not only involved the systematic analysis and identification of the coding and amino acid sequences for each protein but also determined their actual expression levels by integrating transcriptional and translational data. By cross-referencing all identified proteins against authoritative allergen databases, we have first constructed a high-abundance allergen profile for HDH. This profile provides specific allergen data to support clinical testing for patients with aquatic food allergies. Furthermore, we conducted an in-depth comparative analysis of the sequences of the identified allergens with their homologous counterparts from other species to infer the potential for cross-reactivity. Subsequently, we employed multiple wet-lab methodologies, including SDS-PAGE and Western blotting, to validate these high-abundance allergens and identify those capable of immunobinding with our existing panel of antibodies. We also collected positive serum samples from volunteers with HDH allergy, confirmed by specific IgE testing assays. Using these samples, we investigated the reaction rates of high-abundance allergens with the positive sera, analyzed the linear and conformational B-cell epitopes of five high-abundance putative allergens, and thereby revealed the potential molecular mechanisms underlying the immunobinding reactions elicited by HDH allergens. 2. Materials and Methods De novo transcriptome and proteome of HDH, and publicly available genome assembly To generate an unbiased atlas of gene expression and protein abundance in the HDH, three clinically healthy samples were procured from Fujian Zhongxin Yongfeng Industrial limited company. Immediately after arrival, animals were anaesthetized on ice and the foot muscle was excised. Every individual yielded four technical replicates for RNA sequencing (n = 12) and three for mass-spectrometry-based proteome analyses (n = 9). Briefly, tissue fragments were rinsed in ice-cold sterile phosphate-buffered saline, freed from adherent shell and viscera, cut into 50-100 mg aliquots, snap-frozen in liquid nitrogen and stored at -80℃ until further processing. Total RNA was extracted using TRIzol reagent followed by DNase I treatment; integrity was verified on an Agilent 2100 Bioanalyzer (RIN ≥ 8.0). Poly(A)-enriched libraries were constructed and sequenced on an Illumina NovaSeq xplus platform (150 bp paired-end reads, ~6 Gb per sample). For proteome profiling, proteins were extracted and subjected to reduction and alkylation to disrupt disulfide bonds and stabilize cysteine residues. Protein concentration and integrity were assessed using the Bradford protein assay. Subsequently, the protein samples were digested with trypsin, and the resulting peptides were desalted using a C18 column. After pretreatment, peptides were separated on a Thermo Scientific Vanquish Neo UHPLC system with a programmed gradient. Eluted peptides were then analyzed on a Thermo Scientific Orbitrap Astral mass spectrometer, which was operated in a high-resolution data-independent acquisition (DIA) mode to obtain comprehensive proteome data. RNA-seq reads and mass spectra were processed by Novogene Bioinformatics Technology Co., Ltd. (Beijing, China) following ISO 17025 standards. The reference genome of HDH (GenBank id: GCA_044707095.1) was retrieved from the NCBI database and used for read alignment, gene model prediction and downstream functional inference. De novo reconstruction of HDHtranscriptome To generate a high-confidence reference for gene prediction and allergen annotation, we first filtered the in-house RNA-seq reads using stringent quality control criteria. Adapters, nucleotides with Phred scores below 5 and fragments shorter than 36 bp were removed using Trim Galore v0.6.10 [19] , yielding a robust cleansed data set. Clean reads were then assembled de novo with Trinity v2.15.1 [20] , producing a comprehensive catalogue of putative transcripts. Assembly fidelity was interrogated through two independent metrics. First, alignment rates were derived by mapping the quality filtered reads back to the reconstructed transcriptome with Bowtie2 v2.3.5.1 [21] , thereby quantifying sequence level coverage. Second, completeness of assembled transcriptome was assessed with BUSCO v5.5.0 against the Mollusca lineage data set [22] , evaluating the proportion of evolutionarily conserved single copy orthologues present in the assembly. Together, these assessments ensure that the resultant assembly is both representative and accurate, providing a reliable template for downstream functional annotation and allergen discovery. Genome preprocessing of HDH The raw genome data of HDH were preprocessed using a stringent multi-stage pipeline designed to improve gene prediction accuracy in subsequent analyses. First, the scaffold-level assembly was processed with the Funannotate program (v1.8.17) [23] , whereby duplicated contigs were purged, scaffolds were length ranked, and FASTA headers were normalized to ensure compatibility with downstream tools. Next, a de novo repeat library was constructed with RepeatModeler (v2.0.6) to capture lineage-specific transposable elements, and the resulting consensus sequences were subsequently deployed in RepeatMasker (v4.1.9) for soft masking, thereby preserving exonic information while attenuating low complexity regions [24, 25] . This disciplined preprocessing strategy markedly mitigated the impact of repetitive regions and delivered a high confidence assembly that underpins all ensuing gene prediction, functional annotation, and comparative genome analyses. Assembly of a proteome atlas and absolute quantification for HDH With a scaffold-scale genome and a fully curated transcriptome in hand, we generated a high-confidence proteome map for HDH through a consensus annotation pipeline that couples proteogenome evidence with advanced gene-prediction algorithms. First, Funannotate program (v1.8.17) was run to integrate homology-based and ab initio evidence for gene prediction [23] . Protein homology evidence was aligned to the genome using DIAMOND and refined with Exonerate [26, 27] , and ab initio gene predictions were generated by multiple predictors including Augustus, Augustus-HiQ, GeneMark-ES, GlimmerHMM, and SNAP [28-31] . Transcriptome-based gene models were additionally derived using PASA [32] . EVidenceModeler was used to weight and reconcile all evidence into a single, non-redundant set of protein‑coding gene models [33] . The resulting consensus gene set was then refined with PASA, incorporating transcript alignments to add untranslated regions (UTRs) and improve structural annotation based on expressed sequence evidence [32] . Completeness of proteome was further benchmarked using the Mollusca BUSCO lineage set [22] . Following the prediction of all protein-coding genes, we performed comprehensive functional annotation primarily utilizing the DIAMOND (for Uniprotdb, merops), HMMER3 (for pfam, CAZymes), InterProScan (version 5.73-104.0), the EggNOG-mapper program, and the SignalP program to assign Uniprot annotations, Pfam domains, GO terms, COG and secretion signals, respectively [26, 34-39] . These tools collectively establish a complementary annotation framework that spans from elucidating micro-scale structural domains (InterProScan) to inferring macro-scale functional classifications (EggNOG), and further to predicting subcellular localization (SignalP). This integrated pipeline represents a widely adopted and standard workflow in functional genome analysis. To ensure the reliability of the annotations, only those genes that received consistent functional predictions from at least one of these programs were considered to encode proteins with potential biological functions. For quantitative proteome, the raw DIA-MS acquisitions were searched against this in silico atlas using DIA-NN (v2.0) with a spectral library-free strategy [40] . Stringent filtering retained only peptides and proteins achieving ≥99% posterior probability and ≤1% global FDR, culminating in a high-resolution abundance matrix of proteins across all muscular samples. Prediction and prioritization of putative allergens in HDH To establish a high-confidence allergen repertoire for HDH, we implemented three complementary strategies integrating the inferred proteome derived from genome and transcriptome predictions, transcriptome expression levels, and proteome detection-based abundance profiles. i. First, the newly assembled proteome, derived from integrated genome and transcriptome predictions, was systematically interrogated against two authoritative allergen repositories, the WHO/IUIS Allergen Nomenclature Database and AllergenOnline, using the BLASTP program (v.2.14) [41] . To maximize reliability and minimize false-positive identifications from the extensive predicted proteome, stringent filtering criteria were applied, retaining only sequences with an E-value 60% as primary candidates. ii. Transcriptome support for these candidates was rigorously evaluated using Salmon (version 1.10.2) [42] . Putative allergens were required to demonstrate robust transcriptional activity, specifically an average Transcripts Per Million (TPM) value exceeding 10 across individual samples, to qualify for further analysis. iii. Candidate validation was further extended to the proteome expression level. Proteins were required to be consistently detected in more than 50% of mass spectrometry runs, a criterion designed to eliminate mere transcriptional noise and confirm bona fide translation. Furthermore, to distinguish highly abundant allergens, a global mean expression value was calculated from all confirmed candidates; only those exhibiting expression levels above this mean threshold were classified as high-abundance allergens. To assess the sequence similarity and potential cross-reactivity of the prioritized allergens within Mollusca, pairwise alignments were generated using EMBOSS Needle, while multiple sequence alignments were conducted with Clustal Omega [43, 44] . Notably, our multiple sequence alignments were primarily conducted using protein sequences sourced from the UniProt database, encompassing both abalone ( Haliotis genus) and other peer-reviewed mollusk sequences. A maximum likelihood phylogeny was inferred in the MEGA12 program under the Jones-Taylor-Thornton (JTT) model with 1,000 bootstrap replicates to quantify branch support [45] . Preparation of total protein extracts from HDH and positive sera from volunteers We prepared total protein extracts (designated HDHpro) from fresh abductor muscle tissue of HDH using an extraction buffer composed of 10 mM PBS (pH 7.5) containing 0.5 M NaCl, following a method adapted from our previous study [46] . Positive and negative serum samples from volunteers with HDH allergy were provided by the First Affiliated Hospital of Fujian Medical University (Fuzhou, Fujian Province, China). Serum samples were stored at -80℃ until further analysis. These serum samples were screened according to criteria previously established in our study [11] . Sera samples were classified as abalone-positive when immunoreactive IgE levels exceeded twice those of healthy controls [47] . All participants provided written informed consent. The present study was conducted in accordance with the guidelines of the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (approval no.: [2022]075). Western blotting and serological profiling of high-abundance putative allergens Western-blot assays were performed following a protocol modified from Li et al. [48] . The primary antibodies consisted of diluted serum from allergic volunteers and animal-derived antibodies prepared in our laboratory. Specifically, volunteer serum was diluted at 1:4, while rabbit anti-HDH paramyosin (PM), rabbit anti-oyster arginine kinase (AK), and rabbit anti-HDH tropomyosin (TM) antibodies were diluted at 1:5×10⁵, 1:1×10⁶, and 1:5×10⁶, respectively. The corresponding secondary antibodies were HRP-conjugated goat anti-human IgG and HRP-conjugated goat anti-rabbit IgG, both diluted at 1:1×10 4 . Densitometric analysis of protein band intensities were performed using ImageJ software (version 1.38). Subsequently, we investigated the effects of chemical denaturants on HDHpro. The experimental procedure, adapted from our previous study [49] , consisted of incubating HDHpro with various concentrations of sodium dodecyl sulfate (SDS) or β-mercaptoethanol (β-Me). All mixtures were incubated at 4℃ for 16 hours. Following denaturant treatment, the IgG-binding activity of HDHpro was analyzed using a dot-blot assay. Untreated HDHpro and bovine serum albumin (BSA) served as positive and negative controls, respectively. Secondary and tertiary structures of high-abundance allergens To elucidate the protein structures of the high-abundance allergens, we first predicted their secondary structures using the PSIPRED online server [50] . PSIPRED is a neural network-based method capable of predicting fundamental secondary structure elements, such as α-helices, β-sheets, and random coils, directly from amino acid sequences. To further explore the three-dimensional folding conformations of the putative allergens, we employed homology modeling methods. The tertiary structures of the high-abundance allergens AK, TM, fructose-bisphosphate aldolase (FBA), and triosephosphate isomerase (TIM) were modeled using SWISS-MODEL, a tool widely recognized for its high accuracy in allergen homology modeling [51, 52] . This process utilized known homologous protein structures as templates. In contrast, the 3D model for PM was predicted using AlphaFold3 [53] , which employs deep learning and multiple sequence alignment algorithms to infer atomic-level spatial structures directly from amino acid sequences. B-cell epitopes and their spatial localization in high-abundance allergens For TM, the B-cell epitopes analyzed in this study were primarily based on results reported in one of our previous studies [12] . For the other high-abundance allergens, we predicted linear B-cell epitopes for PM, AK, FBA, and TIM using the online servers ABCPred, BcePred, and Bepipred [54-57] . Conformational B-cell epitopes for AK, FBA, and TIM were predicted using the online tools DiscoTope, SEPPA, and CBTOPE [58-61] . All B-cell epitope predictions were conducted on December 3 rd , 2025. Within the constructed 3D models of allergens, we further used PyMOL software to locate and annotate both linear and conformational B-cell epitopes (https://pymol.org/) [62] . 3. Results Protein coding gene annotation of HDH At the transcriptome level, twelve specimens from foot muscle tissue of HDH were collected (three biological replicates × four technical replicates) and subjected to paired-end RNA sequencing, yielding ~ 84Gb of raw data (Table 1 ). Pre-processing removed only ~ 2% of bases, indicating a high overall quality. De novo assembly with Trinity produced a reference transcriptome that achieved 94% completeness by BUSCO and > 95% read mapping with Bowtie2, providing a robust template for downstream gene prediction and annotation (Table 2 ). Table 1 Statistics of RNA sequencing data quality and preprocessing results for Haliotis discus hannai. Sample ID Raw reads Raw bases (gb) Clean reads Clean bases (gb) Cleaned rate (%) Q20 a (%) Q30 b (%) GC c (%) mRNA1_1 23,310,917 6.99 22,759,356 6.83 97.71 99.25 97.64 45.8 mRNA1_2 23,791,383 7.14 23,360,196 7.01 98.18 99.24 97.63 45.83 mRNA1_3 22,661,920 6.8 22,224,819 6.67 98.09 99.29 97.79 45.14 mRNA1_4 23,584,848 7.08 22,981,729 6.89 97.32 99.26 97.68 47.08 mRNA2_1 23,554,061 7.07 22,931,374 6.88 97.31 99.26 97.66 45.93 mRNA2_2 23,163,882 6.95 22,733,845 6.82 98.13 99.28 97.76 44.47 mRNA2_3 22,713,430 6.81 22,361,572 6.71 98.53 99.27 97.74 45.41 mRNA2_4 26,335,473 7.9 25,957,492 7.79 98.61 99.26 97.71 44.38 mRNA3_1 23,621,905 7.09 23,238,709 6.97 98.31 99.3 97.8 46.86 mRNA3_2 23,695,180 7.11 23,306,251 6.99 98.31 99.32 97.85 46.17 mRNA3_3 22,980,068 6.89 22,562,152 6.77 98.26 99.25 97.62 47.37 mRNA3_4 23,891,853 7.17 23,468,059 7.04 98.19 99.29 97.78 47.04 a Q20: Percentage of bases with a phred quality score ≥ 20; b Q30: Percentage of bases with a phred quality score ≥ 30; c GC (%): Percentage of GC content. Table 2 Basic information of Genome & Transcriptome assembling for Haliotis discus hannai. Haliotis discus hannai Genome Transcriptome Assembly statistics Total length (Mb) 1,883.59 NA Longest scaffold (Mb) 26.19 NA No. scaffolds 22,772 NA N50 (kbp) 3,153.27 NA L50 163 NA N90 (kbp) 544.23 NA L90 686 NA No. Ns per 100kbp 6,908.73 NA GC (%) 37.71 42.12 Repeat (%) 37.56 NA Total BUSCOs a (%) 89.6 94 S.C (%) 83.2 9.4 D.C (%) 6.4 84.6 F (%) 2.7 1.7 M (%) 7.8 4.3 Annotation statistics Number of protein-coding genes 53,245 NA Total BUSCOs (%) 72.9 NA S.C (%) 63.0 NA D.C (%) 9.9 NA F (%) 5.9 NA M (%) 21.2 NA a Total BUSCOs: BUSCO completeness score; S.C: Percentages of Complete and Single-copy BUSCOs; D.C: Percentages of Complete and Duplicated BUSCOs; F: Percentages of Fragmented BUSCOs; M: Percentages of Missing BUSCOs. To ensure concordance, a draft genome of HDH (~ 1.9Gb) was obtained from the NCBI database and generated from the same tissue source using a hybrid assembly strategy (PacBio plus Illumina) at 58× coverage and delivered to scaffold level, thereby preserving upstream and downstream regulatory sequences. The BUSCO assessment returned 89.6% completeness (Table 2 ), attesting to high contiguity and accuracy. Repetitive elements were catalogued de novo with RepeatModeler and masked with RepeatMasker, soft-masking 37.56% of the assembly (Supplementary Table 1, Table S1 ) and minimizing false positives during gene prediction. Integrating ab initio algorithms, transcriptome evidence and homology-based searches, we delineated 53,245 protein-coding loci, of which 33,109 received successfully functional annotations from at least two widely-adopted programs ( Table S2 ). For proteome validation, nine samples (three biological replicates × three technical replicates) were analyzed by data-independent acquisition (DIA) on an Orbitrap Astral platform. Spectra were searched against the in silico protein atlas using DIA-NN, resulting in the confident quantification of 4,273 proteins. After filtering for presence in > 50% of specimens, 3,916 proteins were retained for subsequent analyses ( Table S3 ). Mapping and prediction of putative allergens in HDH All 33,109 predicted functional proteins were queried against the WHO/IUIS Allergen Nomenclature and AllergenOnline repositories using an e-value threshold of 1×10⁻⁷ and ≥ 60% pairwise identity. Candidates were further filtered for transcriptional support (average TPM > 10), yielding 291 high-confidence matches that correspond to 57 putative allergens ( Table S4 ). Integration of these candidate proteins with the quantitative data from the DIA proteome (comprising 3,916 proteins) further refined the list to 37 high-abundance functional proteins that were empirically detected at the peptide level (Table 3 ). Based on established protein nomenclature and primary biological functions, the 37 putative allergens were categorized into 23 distinct protein groups (Table 3 ). To ensure the reproducibility of the analytical workflow and its corresponding results, we have publicly released the complete pipeline on the GitHub repository, which integrates genome, transcriptome, and proteome data to establish a reliable allergen repertoire and identify high-abundance putative allergens in HDH. This open-source analytical framework is designated as MOHADA (Multi-Omics for High-abundance Allergen Discovery in Abalone) and is accessible at https://github.com/strao1986/MOHADA . Table 3 List of 37 predicted allergens from Haliotis discus hannai according to similarity search against WHO/IUIS Nomenclature and AllergenOnline database. Group No. Protein Species WHO/IUIS id Accession ID Identity (%) E-value a AvgTPM b AvgProtExp c Length d MW (KDa) e pI f 1 Tropomyosin Haliotis laevigata x Haliotis rubra Hal l 1 APG42675 99.65 < 1E-300 15,186.97 32.28 284 32.9 4.57 2 Paramyosin Haliotis discus discus Unassigned BAJ61596 99.65 < 1E-300 24,762 33.74 860 99.6 5.31 3 Arginine kinase Amphioctopus fangsiao Unassigned AEK65120 67.93 4.59E-176 8,709.92 31.44 355 39.5 5.74 Amphioctopus fangsiao Unassigned AEK65120 65.22 9.58E-171 54.42 24.31 380 42.4 6.06 Fenneropenaeus chinensis Unassigned AAS98890 71.7 4.09E-22 82.71 27.59 722 81.7 6.05 4 Fructose-bisphosphate aldolase Charybdis feriata Cha f 10 WXT82172 66.49 7.45E-83 1,738.55 28.38 186 19.8 5.62 Thunnus albacares Thu a 3 P86979 72.97 3.66E-11 1,506.22 29.99 370 40.4 8.43 5 Triosephosphate-isomerase Dermatophagoides farinae Der f 25 L7UZA7 71.02 2.14E-128 378 27.77 250 26.9 6.02 6 Enolase Ambrosia artemisiifolia Amb a 12 A0A1B2H9Q1 87.5 9.11E-18 203.84 28.49 366 39.8 6.62 7 Filamin c Scylla paramamosain Scy p 9 QFI57017 61.18 < 1E-300 1,272.6 30.03 948 101.7 6.25 8 Glycogen phosphorylase-like protein Penaeus monodon Pen m 14 URW11955 80.46 < 1E-300 109.37 27.32 544 62.4 6.03 9 Mitochondrial malate dehydrogenase Paralithodes camtschaticus Para c 11 WYC14267 65.68 2.44E-159 121.65 24.86 339 35.7 7.75 10 Glyceraldehyde-3-phosphate-dehydrogenase Periplaneta americana Per a 13 A0A2R3WIG9 74.47 < 1E-300 1,408.21 29.7 333 35.8 6.51 11 Alpha actinin Dermatophagoides farinae Unassigned L7UZ85 76.7 < 1E-300 458.9 29.2 889 103.5 5.34 12 Alpha-tubulin Periplaneta americana Per a 17 UZC36340 95.1 < 1E-300 596.68 27.49 450 50.2 4.97 Dermatophagoides pteronyssinus Der p 33 QAT18644 90.11 < 1E-300 427.73 26.68 453 50.8 4.99 Periplaneta americana Per a 17 UZC36340 94.5 9.58E-64 143.73 24.18 158 17.3 8.24 13 Cyclophilin, peptidyl-prolyl cis-trans isomerase Periplaneta americana Per a 18 UZC36341 75.76 7.62E-95 589.16 26.86 165 18.1 8.74 Dermatophagoides farinae Der f 29 A1KXG2 73.78 4.75E-90 1,089.71 25.67 164 17.6 8.3 Malassezia sympodialis Mala s 6 O93970 61.87 4.18E-56 98.36 24.47 203 21.8 5.01 Solanum lycopersicum Sola l 5 P21568 65.7 2.43E-79 139.14 21.49 172 18.9 8.29 14 Heat shock protein 70 kda (hsp70) Penicillium citrinum Pen c 19 Q92260 79.16 < 1E-300 432.34 27.18 655 71.6 5.19 Dermatophagoides pteronyssinus Der p 28 QAT18639 72.92 < 1E-300 29.28 26.78 633 69.6 5.46 Dermatophagoides pteronyssinus Der p 28 QAT18639 75.57 < 1E-300 48.98 26.48 636 69.6 5.21 Dermatophagoides pteronyssinus Der p 28 QAT18639 74.5 < 1E-300 578.43 25.95 635 69.8 5.68 Heat shock cognate protein-70 Aedes aegypti Aed a 8 Q1HR69 81 < 1E-300 27.06 26.53 669 73.4 4.96 15 L3 ribosomal protein Aspergillus fumigatus Asp f 23 Q8NKF4 64.14 < 1E-300 1,234.49 22.85 400 46.0 10.38 60s acidic ribosomal protein p2 Fusarium culmorum Fus c 1 Q8TFM9 60.36 9.44E-23 4,535.88 21.19 111 11.3 4.57 16 Cu-zn superoxide dismutase Aspergillus fumigatus Asp f 35 UYL70859 66.89 2.86E-69 145.05 24.7 154 15.7 5.74 17 Peroxiredoxin-6 (prx6) Periplaneta americana Per a 20 UZC36343 68.95 3.87E-113 39.7 21.38 218 24.0 7.79 Peroxiredoxin 1 Dermatophagoides farinae Der f 43 A0AAU8LNY5 70.85 5.00E-107 42.66 22.17 199 22.3 6.91 18 Ferritin Blomia tropicalis Blo t 30 A0A9Q0MF70 62.16 3.70E-49 2,702.2 22.45 117 13.4 4.81 19 Cytochrome c Periplaneta americana Per a 15 UZC36338 81.13 1.30E-63 333.38 22.87 109 12.0 9.66 20 Lysosomal aspartic protease Aedes aegypti Aed a 11 Q03168 65.62 < 1E-300 88.63 21.31 386 41.5 8.03 21 Porin 3 Aedes aegypti Aed a 6 Q1HR57 64.54 2.06E-142 472.4 24.96 282 30.5 7.02 22 Thioredoxin Malassezia sympodialis Mala s 13 Q1RQI9 85 6.03E-09 127.39 21.83 75 8.2 6.76 23 Nascent polypeptide-associated complex alpha subunit Homo sapiens Hom s 2 Q13765 76.43 1.87E-63 112.32 22.62 924 102.9 6.61 a E-value: Expect value; The expected number of chance hits with a score at least this good when searching the same database with a random query of the same length. b Average TPM: Average Transcripts Per Million; Normalized average gene expression across samples, scaled to transcripts per million. c AvgProtExp: Average Protein Expression; Normalized average protein abundance across samples. d length: the predicted allergen length. e MW(KDa): The theoretical molecular weight (1000 Dalton). f pI: The theoretical isoelectric point. A systematic evaluation of their allergenic potential revealed that the majority of HDH putative allergens exhibited substantial sequence similarity to allergens from aquatic mollusks and crustaceans, including other abalone species, octopus, shrimp, and crab, suggesting a high likelihood of cross-reactivity between HDH and these related taxa (Table 3 ). Furthermore, 16 putative allergens (43.2%) showed significant sequence matches to published allergens from Der f, Der p, Per a, and Aed a, suggesting the possibility of broader cross-reactivity between this aquatic food source and terrestrial arthropods. Additionally, a proportion of allergens demonstrated sequence similarity to plant- and fungus-derived allergens, hinting at potential, albeit less characterized, cross-reactive relationships with plant and fungal food sources. Beyond confirming the two previously documented allergens (PM and TM) in Haliotis genus, our integrated multi-omics approach successfully identified 21 novel allergen categories in HDH. These newly identified allergens principally include arginine kinase (AK), fructose-bisphosphate aldolase (FBA), triosephosphate isomerase (TIM), filamin c, enolase, α-actinin, and cyclophilin, among others (Table 3 ). Based on the results of quantitative proteome analysis, TM, PM, three AK isoforms, two FBA isoforms, and TIM exhibited consistently high expression levels across all specimens, substantially surpassing the global mean expression of proteins (global avgProtExp = 18.72, Table 3 ). Manual curation against the high-quality genome and transcriptome allowed unambiguous assignment of the TM locus and revealed the complete TM gene repertoire within the ancestral gastropod lineage. Transcript and protein quantification both identified TM as the second most abundant polypeptide in foot muscle (identifier FUN_084428-T1; avgTPM = 15,186.97; avgProtExp = 32.28, Table 3 ). Comparative sequence analysis demonstrated 99.6% identity and similarity between the predicted HDH TM and the validated abalone allergen from Haliotis laevigata x Haliotis rubra (Table 4 ), differing by only a single amino-acid residue. Phylogenetic reconstruction of molluscan tropomyosin sequences retrieved from UniProt, including five congeneric species ( H. asinina, H. laevigata x H. rubra, H. diversicolor, H. rufescens and H. discus discus ) resolved a monophyletic clade with pairwise identities exceeding 94.72% (Fig. 1 A and Fig. S1 A ), underscoring the remarkable conservation of molluscan TMs. Table 4 Pairwise sequence alignments between predicted proteins from Haliotis discus hannai and allergens/proteins/predicted proteins from Haliotis genus in the Uniprot database. Predicted proteins Target proteins Species Aligned Protein type Accession ID/Seq ID Predicted protein length Aligned protein length Pairwise Identity (%) Similarity (%) Gaps (%) FUN_084428-T1 Tropomyosin Haliotis laevigata x Haliotis rubra Allergen (Hal l 1) APG42675.1 284 284 99.6 99.6 0 FUN_013191-T1 Paramyosin Haliotis discus discus Allergen (Unassigned) BAJ61596.1 860 860 99.7 99.8 0 FUN_074973-T1 Arginine kinase Haliotis madaka Uniprot protein P51544 380 358 77.9 94.5 5.8 FUN_074974-T1 Arginine kinase Haliotis madaka Uniprot protein P51544 355 358 98.9 99.2 0.8 FUN_074973-T1 Arginine kinase HDH Predicted Allergen FUN_074974-T1 380 355 78.2 84.5 6.6 FUN_001862-T1 Fructose-bisphosphate aldolase HDH Predicted Allergen FUN_035972-T1 370 186 40.5 41.6 55.6 FUN_035972-T1 Fructose-bisphosphate aldolase Charybdis feriata Allergen (Cha f 10) WXT82172 186 363 34.2 40 49.6 FUN_073831-T1 Triosephosphate isomerase Haliotis rufescens Uniprot protein Q45Y86 250 210 81.6 83.2 16 Paramyosin, encoded by the identifier FUN_013191-T1, emerged as the most highly expressed transcript (avgTPM = 24,762) and protein (avgProtExp = 33.74) in our sequencing results (Table 3 ). Phylogenetic inference placed the assembled PM in an evolutionarily stable cluster with scallop and oyster orthologs. Although this protein has not been officially designated as a major allergen in the HDH by authoritative databases such as the IUIS Allergen Nomenclature and Allergen Online, the predicted sequence is identical to an unreviewed HDH entry in the UniProt database (Fig. 1 B and Fig. S1 B ). Pairwise alignment with the congeneric H. discus discus PM revealed 99.7% identity and 99.8% similarity (Table 4 ). For AK, three predicted isoforms exhibited significant sequence similarity to known crustacean and molluscan allergens. The longest isoform (FUN_009085-T1; 722 amino acids) possesses an internal 65.7% sequence repeat and exceeds the canonical length (348–358 residues) of validated molluscan AKs, suggesting a tandem duplication event. Consequently, this isoform was excluded from downstream analyses. The remaining two isoforms (FUN_074974-T1, AK1; FUN_074973-T1, AK2) share 67.93% and 65.22% identity with Amphioctopus fangsiao AK allergen (Table 3 ), and 98.9% and 77.9% identity with the Japanese abalone H. madaka ortholog, respectively (Table 4 ). Both isoforms cluster within the abalone-specific clade and exhibit > 60% identity to other molluscan sequences (Fig. 1 C and Fig. S1 C ). Notably, the AK2 isoform is inferred to be a tandem paralog of AK1 isoform (78.2% identity, 84.5% similarity, Table 4 ). Similar to the immunodominant allergens described above, triosephosphate isomerase (TIM) from HDH was also identified through integrated annotation, with its expression maintained at consistently high levels in both transcriptome and proteome analyses (FUN_073831-T1, avgTPM = 378; avgProtExp = 27.77, Table 3 ). Allergen sequence alignment revealed that the TIM of HDH shared the highest sequence identity (71.02%, Table 3 ) with the TIM protein from the house dust mite Dermatophagoides farinae (Der f 25). A global sequence alignment between the TIM of HDH and the only Haliotis TIM available in UniProt (from Haliotis rufescens , UniProt ID: Q45Y86) showed a sequence identity of 81.6% and a similarity of 83.2%, indicating a high degree of sequence conservation while acknowledging the presence of sequence divergence (Table 4 ). Beyond the canonical myofibrillar allergens, two FBA isoforms (FBA1 and FBA2) displayed substantial conservation. BLAST identity values of the two isoforms against database allergens were 66.49% and 72.97%, respectively (Table 3 ). Phylogenetic analysis of the FBA isoforms resolved a single major branch that includes one predicted HDH sequences with orthologs from H. gigantea, H. diversicolor, H. madaka, H. discus discus and H. rufescens ( Fig. S2 ), corroborating their orthology and potential allergenic relevance. Western blotting and serological profiling of high-abundance putative allergens Serum samples were collected from 86 abalone-allergic and four non-allergic control volunteers (Table 5 ). Initial screening of positive serum samples by ELISA revealed significantly higher IgE reactivity to total abalone protein in allergic volunteer sera, with optical density values exceeding twice those of healthy controls. Detailed volunteer serological data are presented in Table 5 . Subsequent Dot-blot analysis, using BSA as a negative control, confirmed specific IgE binding between allergic volunteer sera and HDHpro (Fig. 2 ). Further resolution by Western blot assays identified six major protein bands in HDHpro with pronounced IgE-binding activity, corresponding to molecular weights of > 130 kDa, ~ 99 kDa, ~ 44 kDa, ~ 36 kDa, ~ 27 kDa, and ~ 19 kDa (Fig. 3 ). The number of sera showing positive reactivity to these bands was 30, 41, 31, 45, 26, and 15, respectively (Fig. 4 A). Notably, the number of positive sera detected by Western blot was lower than that identified by Dot-blot (68 vs. 86). Table 5 Serological Profiles of Total HDH Protein in 90 Allergic and Non-Allergic Volunteers. HDHpro: Total protein extracts of HDH. No. Sex Age (years) sIgE of HDHpro No. Sex Age (years) sIgE of HDHpro No. Sex Age (years) sIgE of HDHpro 1 female 18 0.20 31 female 18 0.19 61 male 18 0.19 2 male 18 0.21 32 male 18 0.20 62 male 18 0.19 3 female 18 0.20 33 male 20 0.21 63 female 18 0.16 4 male 19 0.19 34 female 17 0.20 64 male 18 0.17 5 female 18 0.17 35 female 17 0.19 65 male 21 0.23 6 male 19 0.23 36 male 18 0.17 66 male 19 0.18 7 female 17 0.18 37 female 18 0.23 67 male 20 0.17 8 female 17 0.17 38 male 19 0.18 68 male 18 0.19 9 male 19 0.19 39 female 19 0.17 69 male 18 0.19 10 female 18 0.19 40 male 19 0.19 70 male 19 0.19 11 male 19 0.19 41 female 18 0.19 71 male 18 0.20 12 female 18 0.20 42 male 18 0.19 72 female 22 0.21 13 male 19 0.21 43 male 18 0.16 73 female 21 0.20 14 female 25 0.20 44 female 20 0.17 74 female 25 0.20 15 female 20 0.20 45 male 18 0.23 75 female 18 0.20 16 female 18 0.20 46 male 18 0.18 76 female 18 0.17 17 female 18 0.17 47 male 18 0.17 77 female 18 0.17 18 male 19 0.17 48 male 18 0.19 78 male 18 0.17 19 female 18 0.17 49 male 18 0.19 79 male 18 0.19 20 female 18 0.19 50 male 18 0.19 80 female 18 0.23 21 male 19 0.19 51 male 19 0.20 81 female 18 0.18 22 male 18 0.19 52 male 18 0.21 82 male 18 0.20 23 female 22 0.19 53 male 19 0.20 83 female 18 0.20 24 female 23 0.19 54 male 18 0.20 84 female 19 0.17 25 female 27 0.20 55 male 18 0.20 85 male 19 0.17 26 female 18 0.21 56 female 18 0.17 86 female 18 0.17 27 female 18 0.20 57 female 18 0.17 87 female 18 0.07 28 female 19 0.24 58 male 18 0.17 88 female 20 0.08 29 male 18 0.18 59 male 18 0.19 89 male 19 0.08 30 female 19 0.16 60 male 19 0.23 90 male 18 0.07 Densitometric quantification of band intensities further demonstrated that the IgE-binding intensities of the > 130 kDa, ~ 99 kDa, ~ 44 kDa, and ~ 36 kDa bands were significantly higher than those of the ~ 27 kDa and ~ 19 kDa bands ( P < 0.0192, Fig. 4 B). Based on their molecular weights and cross-referenced with the predicted allergen repertoire (Table 3 ), the ~ 99 kDa, ~ 44 kDa, ~ 36 kDa, ~ 27 kDa, and ~ 19 kDa bands were hypothesized to correspond to PM, AK, TM, TIM, and FBA, respectively. SDS-PAGE analysis of HDHpro resolved 15 distinct protein bands (Fig. 5 A). Immunoblot validation using specific IgG polyclonal antibodies confirmed these assignments: the ~ 99 kDa band showed specific reactivity with rabbit anti-HDH PM polyclonal antibody (Fig. 5 B), the ~ 44 kDa band with rabbit anti- Alectryonella plicatula AK polyclonal antibody (Fig. 5 C), and the ~ 36 kDa band with rabbit anti-HDH TM polyclonal antibody (Fig. 5 D). Comparative modeling and spatial conformational landscape of multiple candidate allergens in HDH To elucidate the discrepancy wherein fewer serum samples tested positive in Western-blot compared to Dot-blot assays, we conducted two subsequent experimental series. First, we treated HDHpro with two distinct protein denaturants (sodium dodecyl sulfate (SDS) and β-mercaptoethanol (β-me)), and evaluated their impact on the immunoreactivity of HDH allergens using specific IgG polyclonal antibodies. As shown in Fig. 6 , SDS treatment did not markedly reduce the IgG-binding activity of HDHpro to antibodies against all the three putative allergens (Fig. 6 A–C). In contrast, treatment with β-me significantly attenuated IgG reactivity to AK (Fig. 6 B), while the binding activities to PM and TM remained largely unaffected (Fig. 6 A and 6 C). To interpret these findings, we further predicted B-cell epitopes closely associated with immunoreactivity. For the five immunodominant allergens, we initially modeled their three-dimensional structures using the SWISS-MODEL and AlphaFold3 programs based on seven putative protein sequences. The optimal models revealed that PM and TM adopt relatively simple, linear-dominated conformations (Fig. 7 A and 7 B), whereas AK, FBA, and TIM exhibit more complex tertiary structures (Fig. 7 C– 7 G). Secondary structure predictions via the PSIPRED program corroborated these observations (Table 6 ). PM and TM were predominantly α-helical (~ 99% of residues), with minimal random coil. In contrast, TIM secondary structure comprised similar proportions of α-helix and random coil (differing by only ~ 1.6%), alongside approximately 16% β-sheet. The two AK isoforms (AK1 and AK2) shared comparable secondary structure profiles, with α-helix content below 40% and β-sheet plus random coil exceeding 60%. The two FBA isoforms (FBA1 and FBA2) also displayed similar patterns, with α-helix near 40–47% and the combined β-sheet and random coil close to 60%. Table 6 Secondary Structure Distribution of Putative Allergens in HDH. Allergen name length Helix a Sheet b Coil c Helix (%) Sheet (%) Coil (%) PM 860 852 0 8 99.1 0.0 0.9 TM 284 281 0 3 98.9 0.0 1.1 TIM 250 107 40 103 42.8 16.0 41.2 AK1 355 132 42 181 37.2 11.8 51.0 AK2 380 147 37 196 38.7 9.7 51.6 FBA1 186 87 15 84 46.8 8.1 45.2 FBA2 370 161 45 164 43.5 12.2 44.3 a Helix: α-helical content (residue count). b Sheet: β-sheet content (residue count). c Coil: Random coil content (residue count). Subsequent in silico epitope prediction identified multiple linear B-cell epitopes for PM, AK, FBA, and TIM (Table 7 and Table S5 ), as well as conformational epitopes for AK, FBA, and TIM (Table 7 and Table S6 ). Using PyMOL, predicted linear epitopes for PM, AK, FBA, and TIM, along with previously identified TM epitopes from one of our previous studies, were mapped onto their respective structural models (Fig. 7 A–G). Conformational epitopes for AK, FBA, and TIM were similarly visualized (Fig. 8 A–E). These analyses exhibited that B-cell epitopes in PM and TM are primarily linear, whereas those in two AK isoforms, two FBA isoforms, and TIM are predominantly conformational. Taken together, these experimental series suggested that β-me, used in Western-blot sample preparation, disrupts the conformational epitopes of allergens such as AK, thereby significantly reducing their immunobinding activity and contributing to the observed reduction in positive sera compared to the native condition–preserving Dot-blot assay. Table 7 Linear and Conformational B-Cell Epitopes of High-Abundance Putative Allergens. Allergens Amino Acid Residue Regions Harboring Predicted B-Cell Epitopes Linear B-Cell Epitopes PM 21 ~ 32; 41 ~ 53; 70 ~ 77; 86 ~ 94; 109 ~ 124; 130 ~ 134; 136 ~ 141; 144 ~ 147; 155; 158; 159; 162 ~ 171; 230 ~ 248; 265 ~ 278; 306; 308; 321 ~ 338; 361 ~ 373; 411 ~ 429; 443; 465 ~ 477; 493 ~ 514; 529; 531; 540; 542; 543; 545; 546; 609; 619 ~ 629; 646; 648; 650 ~ 653; 665 ~ 680; 692 ~ 704; 725 ~ 729; 748 ~ 752; 754; 755; 765; 770 ~ 782; 797; 812; 815; 816; 830 ~ 834 AK1 13 ~ 16; 18 ~ 28; 30 ~ 41; 63 ~ 69; 81 ~ 99; 106 ~ 109; 121 ~ 136; 141 ~ 149; 165 ~ 179; 189; 192 ~ 199; 285; 305 ~ 324 AK2 2 ~ 25; 34 ~ 48; 55 ~ 65; 87 ~ 94; 105 ~ 123; 131 ~ 138; 145 ~ 173; 186 ~ 199; 216 ~ 225; 277 ~ 284; 309 ~ 316; 327 ~ 338; 341 ~ 348 FBA1 21 ~ 23; 62 ~ 76; 95 ~ 104; 114 ~ 118; 138 ~ 141; 144 ~ 159; 164 ~ 176 FBA2 11 ~ 20; 35 ~ 45; 53 ~ 64; 89 ~ 97; 129 ~ 134; 140 ~ 148; 160 ~ 167; 243 ~ 257; 276 ~ 285; 295 ~ 299 TIM 14 ~ 19; 32 ~ 37; 39; 41 ~ 50; 76 ~ 80; 93 ~ 101; 103; 104; 108 ~ 110; 129 ~ 142; 170 ~ 184; 210 ~ 216; 220 ~ 228; 236 ~ 238 Conformational B-Cell Epitopes AK1 pCB1: 19 ~ 21-58-78-79; pCB2: 81–84 ~ 87–89 ~ 92-259-260-268-320 ~ 327; pCB3: 96–101 ~ 104–234 ~ 237-330-331; pCB4: 107 ~ 116–281; pCB5: 300 ~ 305–316 ~ 318. AK2 pCB1: 35 ~ 41-43-46-79 ~ 82; pCB2: 327 ~ 329–333 ~ 335-337-340 ~ 342; pCB3: 184 ~ 187-228-229; pCB4: 117-118-280-283 ~ 287; pCB5: 321 ~ 325–345 ~ 347–358 ~ 361–363. FBA1 pCB1: 7 ~ 20-62-64; pCB2: 10 ~ 12–34 ~ 36-39-40; pCB3: 100–102 ~ 111–161 ~ 175; pCB4: 118 ~ 126–128 ~ 159. FBA2 pCB1: 36–45 ~ 47–307 ~ 309–311 ~ 315; pCB2: 137-140-141-144; pCB3: 317-321-323-327; pCB4: 61 ~ 63–65 ~ 67–91 ~ 94. TIM pCB1: 1 ~ 15-38-40 ~ 42; pCB2: 46 ~ 51–53 ~ 60–62 ~ 64–85 ~ 90; pCB3: 66 ~ 71–73 ~ 75–92 ~ 94-115-116-120-121; pCB4: 175 ~ 180–183 ~ 188–210 ~ 213–217 ~ 223. AK: arginine kinase; FBA: Fructose-bisphosphate aldolase; TIM: triosephosphate-isomerase; pCB: predicted conformational B-cell epitopes. 4. Discussion By integrating genome, transcriptome, and proteome datasets, this study first established a reproducible analytical framework that integrates multi-omics data to systematically construct a comprehensive proteome profile for HDH. This profile was subsequently interrogated against two authoritative allergen repositories and filtered using transcriptome and proteome expression levels. This integrated approach culminated in the establishment of a putative allergen repertoire for HDH, comprising 37 potential high-abundance allergens. Based on established nomenclature and biological functions, these allergens were categorized into 23 distinct groups. Currently, few allergens have been definitively characterized from the edible muscular tissue of abalone species, with only TM formally named in major databases [ 46 ] . Our research team previously not only confirmed this allergen but also systematically compared its cross-reactivity across three mollusk species and elucidated the underlying molecular mechanisms [ 11 , 46 ] . Another recognized allergen PM has been identified as a potential allergen in HDH [ 63 ] , though it lacks formal nomenclature. While these focused studies on individual allergens are valuable, they are insufficient for simultaneously identifying other prevalent allergens in abalone, leading to potential false negatives in clinical diagnostics and an incomplete explanation for all allergic cases. Our systematic identification of high-abundance potential allergens in HDH has significantly expanded its known allergen spectrum. Beyond confirming the two previously reported major allergens, we identified 21 novel groups of high-abundance allergens. Experimental validations via SDS-PAGE and Western-blot detected five of these high-abundance allergens, thereby providing a robust data foundation for improving clinical detection of abalone allergy. Integrated Multi-Omics and Experimental Validation Systematically Identify High-Abundance Putative Allergens in HDH This study systematically deciphered the allergen repertoire of HDH through an integrated strategy of multi-omics big data analysis and wet-lab experimentation, such as SDS-PAGE and Western-blot assays. A total of 23 putative allergen groups were identified, encompassing two previously reported groups [ 46 , 63 ] and 21 newly discovered groups. Transcriptome and proteome quantification revealed that the vast majority of these allergens are expressed at high levels in both omics layers, indicating they constitute major proteins within the muscular tissue. To date, only PM and TM have been identified as potential allergens in Haliotis genus, with only TM formally recorded and named in authoritative databases [ 46 ] . This scarcity of data has resulted in limited research on the cross-reactivity between HDH and other species. Our group previously conducted an in-depth comparison of the TM allergen from HDH with homologs from Alectryonella plicatula and Mimachlamys nobilis , revealing not only a high sequence similarity but also multiple conserved B-cell epitopes capable of eliciting strong cross-reactivity [ 12 ] . However, comparative sequence analysis and cross-reactivity studies for other high-abundance allergens remain entirely unexplored. This study, through comparison with authoritative allergen databases, discovered that allergens from HDH exhibited significant similarity to those derived from aquatic mollusks and crustaceans. This finding suggests a high likelihood of cross-reactivity among these species, which may trigger severe allergic reactions in patients. This conclusion is supported by previous studies noting the high sequence conservation and broad cross-reactivity between major allergens from mollusks and crustaceans [ 64 , 65 ] . It is important to note that the cross-reactivity discussed above are currently inferred from sequence similarities, which requires further confirmations through serological inhibition experiments. Subsequent Western-blot analysis demonstrated pronounced IgE-binding activity between sera from abalone-allergic volunteers and six major protein bands in crude muscular tissue extracts. Correlating these bands with their molecular weights and the predicted allergen repertoire led to the provisional identification of five bands as PM, AK, TM, TIM, and FBA. The identities of the first three (PM, AK, TM) were conclusively verified using existing specific IgG polyclonal antibodies from our laboratory. Multiple sequence alignment further revealed high sequence identity between these allergens and their homologs from other common crustaceans and mollusks [ 64 , 66 , 67 ] , suggesting a strong potential to elicit allergic reactions. These findings not only confirmed the reliability of our multi-omics pipeline for predicting immunodominant allergens but also demonstrated that HDH possesses a diverse array of allergens with varying molecular weights and significant immunoreactivity. Notably, PM, AK, and TM exhibited significantly higher IgE-binding activity compared to others, identifying them as likely immunodominant allergens. The bands at ~ 27 kDa and ~ 19 kDa showed lower IgE reactivity and, lacking direct verification by specific antibodies, require confirmation through subsequent experiments such as Western-blot with additional antibodies, mass spectrometry, or recombinant protein studies. Furthermore, the > 130 kDa high-molecular-weight band, while showing strong IgE binding, remains to be definitively characterized. It may represent polymeric forms of known allergens, glycosylated variants, or a novel, unidentified allergen, providing a compelling direction for future research. Our results underscored that relying solely on SDS-PAGE of crude extracts for protein identification is unreliable, as this technique has limited resolution and may miss low-abundance or co-migrating proteins [ 68 , 69 ] . Therefore, accurate and reliable identification of potential allergens necessitates integrating SDS-PAGE results with prior molecular weight predictions and multi-sequence alignment data from bioinformatics analyses. Impact of Linear and Conformational Epitopes of High-Abundance Allergens on Immunobinding Activity In this study, discrepant positive serum detection rates were observed between Dot-blot and Western-blot analyses (86 vs. 68). This discrepancy suggests varying capacities of different detection methods to present allergen epitopes. The Dot-blot method, which may better preserve native conformational structures, is capable of detecting both linear and conformational epitopes. In contrast, Western-blot is performed under denaturing conditions with SDS and β-me, primarily exposing linear epitopes, potentially leading to the omission of serum samples containing IgE antibodies targeting conformational epitopes [ 70 ] . Subsequent experiments with two denaturants confirmed this hypothesis: SDS treatment did not significantly affect the binding activity of PM, AK, or TM to polyclonal antibodies, whereas β-me treatment markedly reduced the binding activity of AK, with minimal impact on PM and TM. Given that β-me disrupts protein tertiary structure by reducing disulfide bonds, this finding strongly suggests that the immunodominant epitopes of AK are critically dependent on its three-dimensional conformation stabilized by disulfide bonds. Our finding is consistent with reports on the conformational epitopes of homologous AK in other crustacean foods [ 71 , 72 ] . To elucidate this phenomenon at the molecular level, we modeled the three-dimensional structures of the five immunodominant allergens and predicted their B-cell epitopes. The findings revealed that PM and TM are predominantly α-helical with relatively simple tertiary structures, and their B-cell epitopes are primarily linear. This explains why their antibody-binding activities were preserved under denaturing conditions (β-me treatment). Conversely, AK, TIM, and FBA possess more complex spatial structures rich in β-sheets and random coils, and their predicted B-cell epitopes are predominantly conformational. This aligns perfectly with the experimental result that AK’s binding activity is dependent on disulfide bonds (β-me sensitive) and partially explains the inconsistent positivity rates between Western-blot (linear epitope-based) and Dot-blot (conformation-preserving) assays: IgE antibodies in some patient sera may primarily target these conformational epitopes. Collectively, these findings demonstrated that the five immunodominant allergens could be categorized into two groups based on the nature of their immunodominant epitopes: one represented by PM and TM, whose immunorecognition relies mainly on linear epitopes stable under denaturation; the other represented by AK, TIM, and FBA, whose immunorecognition highly depends on spatial conformational epitopes stabilized by factors such as disulfide bonds and is sensitive to reductive denaturation. This elucidation of the structure-immunoactivity relationship not only provides a molecular basis for understanding methodological discrepancies between assays but also offers crucial theoretical support for developing targeted allergen reduction processing technologies (e.g., reductive treatment for conformational allergens) and more precise diagnostic strategies (requiring coverage of both linear and conformational epitopes) [ 73 , 74 ] . Future research should further validate the clinical relevance of these conformational epitopes using patient’s sera and explore their stability during thermal processing or digestion. Limitations Several limitations in this study warrant consideration. First, while the use of genome data to identify multiple protein sequences enables the simultaneous discovery of numerous potential allergens in HDH, it does not guarantee the authentic expression of each protein. To address this, our systematic analysis of potential allergen sequences was integrated with quantitative transcriptome and proteome data. Furthermore, wet-lab methodologies, including SDS-PAGE and Western-blot, were employed to confirm the genuine presence of these putative allergens and to delineate their abundance levels, thereby enhancing the reliability of our findings. Second, although SDS-PAGE analysis of crude protein extracts from abalone muscular tissue provides a general overview of protein composition, it offers limited resolution for distinguishing proteins with similar molecular weights. In such instances, complementary approaches are necessary. As implemented in this study, Western-blot analysis could provide further confirmation, and more definitive identification can be achieved via mass spectrometry, albeit with greater demands on time and resources [ 75 ] . Finally, our integrative multi-omics analysis underscores the insufficiency of relying solely on transcriptome quantification to determine protein or potential allergen expression levels [ 18 ] . Protein expression is subject to post-translational modifications, such as glycosylation or acetylation, which could substantially alter final abundance. Therefore, we incorporated proteome sequencing and quantitative analysis, a strategy that delivers accurate protein quantification and provides a robust foundation for subsequent experimental validation. Conclusions By integrating sequence identification and matching from genome, transcriptome, and proteome data, along with quantitative expression analyses from the latter two platforms, this study systematically deciphered the high-abundance allergen repertoire of HDH, a species of high consumption and frequent allergic incidence. We identified a total of 23 groups of potential high-abundance allergens, substantially addressing the prior paucity of known allergens for this species. Utilizing positive serum samples from 86 abalone-allergic volunteers, we confirmed five allergens exhibiting significant IgE-binding activity. These allergens provide reliable targets for clinical diagnostics, promising to improve detection rates for HDH allergy, furnish a more accurate basis for assessing its population prevalence, and aid in more precise abalone-specific detection assays. Notably, we observed considerable divergence in the secondary structures and B-cell epitope conformations among these five allergens. This conformational dichotomy (linear vs. spatial) critically influences their detectability in Western-blot assays and elucidates the molecular basis for the observed discrepancy between Western-blot positivity and the total number of positive sera. Furthermore, our systematic comparison revealed significant sequence similarity between putative HDH allergens and homologs from other abalone species, crustaceans, and terrestrial arthropods. This strongly indicates a broad potential for cross-reactivity among these groups, offering valuable data to guide dietary management and cross-allergy prevention for susceptible individuals. Declarations Code availability To enhance the reproducibility of the analytical workflow and its outcomes, we have curated and openly deposited the source codes for the proposed ‘Multi-Omics integration framework for High-abundance Allergen Discovery in Abalone (MOHADA)’ in the GitHub repository (Link: https://github.com/strao1986/MOHADA). Author Contributions S.T. Rao and G.M. Liu overall designed and supervised the whole study. S.T. Rao, S. Zhi and X.Y. Han obtained foot tissue samples of HDH and performed the preprocessing steps. S. Zhi and J. H. Que obtained omics data of HDH and conducted full analyses. X.Y. Han, T. B. Chen and Y. N. Xie collected serum samples and performed immunobinding assays. X.Y. Han, Q. Xiao and S. Y. Yang predicted the epitopes of allergens and further located them in the 3D structures. S.T. Rao, G.M. Liu and Stephen K.W. Tsui contributed to drafting and reviewing of manuscript. The corresponding authors attest that all listed authors met the authorship criteria and that no others meeting the criteria were omitted. All listed authors read and approved the final version of the manuscript. Funding This study was supported by the Young Scientific Innovation Project of the Natural Science Foundation of Fujian Province (2025J08196), and the Research start-up funds for high-level talents from Fujian Medical University (XRCZX2021009). The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication. Acknowledgements We gratefully acknowledge the support from several public databases for openly sharing their datasets. We extend our sincere gratitude to the NCBI database for its systematic management and open sharing of the genome data for the Haliotis discus hannai . Furthermore, the allergen sequence information and other data stored in authoritative databases, including the WHO/IUIS Allergen Nomenclature, AllergenOnline, and UniProt, proved invaluable for advancing our research findings. These publicly available resources served as the cornerstone for our analytical framework in this study. Competing Interests The authors have no conflict of interest to declare. Data Availability All data supporting the findings of this study are available within this paper and its supplementary materials. 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Journal of Allergy and Clinical Immunology, 2024. 153 (3): p. 560–571. Pang, L., R. Li, C. Chen, et al., Combined processing technologies: Promising approaches for reducing Allergenicity of food allergens. Food Chemistry, 2025. 463 : p. 141559. Lin, F., W.C. Soko, J. Xie, et al., On-Chip Discovery of Allergens from the Exudate of Large Yellow Croaker (Larimichthys Crocea) Muscle Food by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry. Journal of Agricultural and Food Chemistry, 2023. 71 (36): p. 13546–13553. Additional Declarations No competing interests reported. Supplementary Files 4.Supplementary.Tables1620260327.xlsx Supplementary Tables Table S1. Repeat contents in the assembling genome of Haliotis discus hannai . Table S2. Annotation results of the whole-genome predicted protein-coding genes in Haliotis discus hannai . Table S3. Quantification results of the predicted HDH protein repertoire. Table S4. BLAST results between the predicted HDH protein repertoire and the two authoritative allergen databases: WHO/IUIS Nomenclature and AllergenOnline. Table S5. Linear B-cell epitopes of four putative allergens predicted by multiple bioinformatics programs. Table S6. Conformational B-cell epitopes of three putative allergens predicted by multiple bioinformatics programs. 5.Supplementary.Fig12.20260327.docx FulluneditedblotsforFig.2.pdf FulluneditedblotsforFig.3.pdf FulluneditedblotsforFig.5.pdf FulluneditedblotsforFig.6.pdf GraphicalAbstract.jpg Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 23 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 27 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9243755","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":623661027,"identity":"0ecafa1a-dca9-4260-a2ef-2c0be97d3cb1","order_by":0,"name":"Shuai Zhi","email":"","orcid":"","institution":"FuJian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Zhi","suffix":""},{"id":623661028,"identity":"5ca145a7-eb1b-4ad6-ba92-f9a0597d3b79","order_by":1,"name":"Xin-yu Han","email":"","orcid":"","institution":"Xiamen Ocean Vocational College","correspondingAuthor":false,"prefix":"","firstName":"Xin-yu","middleName":"","lastName":"Han","suffix":""},{"id":623661029,"identity":"b1ca1b50-ae9e-4a9e-ae0e-7ef4baeb591c","order_by":2,"name":"Tian-bin Chen","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tian-bin","middleName":"","lastName":"Chen","suffix":""},{"id":623661030,"identity":"97e9ea7d-0b1b-47a8-bfb8-8fd67b43ae6c","order_by":3,"name":"Jia-han Que","email":"","orcid":"","institution":"FuJian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jia-han","middleName":"","lastName":"Que","suffix":""},{"id":623661031,"identity":"bee56049-4292-4c2d-a436-dd873f54b356","order_by":4,"name":"Qiong Xiao","email":"","orcid":"","institution":"Jimei University","correspondingAuthor":false,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Xiao","suffix":""},{"id":623661032,"identity":"785e2806-db11-45f6-b628-f370ac22e623","order_by":5,"name":"Sheng-yan Yang","email":"","orcid":"","institution":"Jimei University","correspondingAuthor":false,"prefix":"","firstName":"Sheng-yan","middleName":"","lastName":"Yang","suffix":""},{"id":623661033,"identity":"9c141438-be29-4497-a367-ef40572ff507","order_by":6,"name":"Yong-neng Xie","email":"","orcid":"","institution":"Jimei University","correspondingAuthor":false,"prefix":"","firstName":"Yong-neng","middleName":"","lastName":"Xie","suffix":""},{"id":623661034,"identity":"c0671c53-4f77-4743-947c-5050a02e163a","order_by":7,"name":"Stephen Kwok-wing Tsui","email":"","orcid":"","institution":"The Chinese University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"Kwok-wing","lastName":"Tsui","suffix":""},{"id":623661035,"identity":"e779740b-af22-429b-bbed-caf165a587bc","order_by":8,"name":"Guang-ming Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYFAC5oYDDBVsYKYEkVoYgVrOkKqFgbGNgQQt8mEHGw/zzuOLNjjAfPA2D4NdHkEthrcTGw7zbmPL3XCALdmahyG5mLCW2XAtPGbSPAwHEhuI0zIHpIX/G3Fa5KVBWhrAtrARp8UAqOXgnGNsuTMPsxlbzjFIJsKW2cmHP7ypOZbbd7z54Y03FXZE2HIATB0DpgIwl5B6kC0QQ2uIUDoKRsEoGAUjFgAAi/E+Cn+wbaoAAAAASUVORK5CYII=","orcid":"","institution":"Xiamen Ocean Vocational College","correspondingAuthor":true,"prefix":"","firstName":"Guang-ming","middleName":"","lastName":"Liu","suffix":""},{"id":623661036,"identity":"9277860b-16a6-462e-8860-26f00abdbf41","order_by":9,"name":"Shi-tao Rao","email":"","orcid":"","institution":"FuJian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shi-tao","middleName":"","lastName":"Rao","suffix":""}],"badges":[],"createdAt":"2026-03-27 10:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9243755/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9243755/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107484460,"identity":"d207f688-ee2f-4cee-b406-9e0334782765","added_by":"auto","created_at":"2026-04-22 02:32:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5802354,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple sequence alignment analysis for three high-abundance predicted allergens of \u003cem\u003eHaliotis discus hannai\u003c/em\u003e(HDH) with other Mollusca species. (A) Multiple sequence alignment results (shown as identity matrix) of amino acid sequence of HDH tropomyosin (TM) with other Molluscs. (B) Multiple sequence alignment results of amino acid sequence of HDH paramyosin (PM) with other Molluscs. (C) Multiple sequence alignment results of amino acid sequence of HDH arginine kinase (AK) with other Molluscs.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/d5519742fbe8b08df27a9154.png"},{"id":107482551,"identity":"87101730-a0cf-4e3c-9778-299d3d99ad62","added_by":"auto","created_at":"2026-04-22 02:23:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15067041,"visible":true,"origin":"","legend":"\u003cp\u003eIgE-binding activity of \u003cem\u003eHaliotis discus hannai\u003c/em\u003e total protein extracts (HDHpro) against sera from 90 allergic and non-allergic volunteers. Negative control: bovine serum albumin (BSA); Lanes 1–86: sera samples from abalone-allergic volunteers; Lanes 87–90: sera samples from healthy volunteers.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/a4a419f45c70f9c5c4121003.png"},{"id":107482368,"identity":"7547b2cb-d605-4fd4-9e0f-1610f8696fe5","added_by":"auto","created_at":"2026-04-22 02:23:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10709724,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of IgE-binding activity of high-abundance putative allergens from \u003cem\u003eH. discus hannai\u003c/em\u003e at distinct molecular weights by Western-blot assays. M: protein marker; NC: negative control.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/650582005a2029063563144c.png"},{"id":107255676,"identity":"f6c5ef2f-9f06-474c-81f9-9f40720068c5","added_by":"auto","created_at":"2026-04-19 12:11:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10429350,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency and densitometric analysis of IgE-binding activity of high-abundance allergens from \u003cem\u003eH. discus hannai\u003c/em\u003e at different molecular weights. (A) Number of sera samples exhibiting IgE-binding reactivity to allergens of varying molecular weights; (B) Densitometric quantification of IgE-binding intensity for predicted allergens at different molecular weights. ns: not significant (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05); *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ****: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/bf579a17f5bec7ecda289cc2.png"},{"id":107255678,"identity":"b79427c6-46ff-445c-9ff6-d53c2c5d23f8","added_by":"auto","created_at":"2026-04-19 12:11:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7931925,"visible":true,"origin":"","legend":"\u003cp\u003eSDS-PAGE analysis of \u003cem\u003eH. discus hannai\u003c/em\u003e total protein extracts (HDHpro) and its specific IgG-binding activity. (A) SDS-PAGE profile of HDHpro; (B–D) Specific IgG-binding activity of HDHpro probed with rabbit anti-HDH paramyosin (PM) polyclonal antibody, rabbit anti-\u003cem\u003eAlectryonella plicatula\u003c/em\u003e arginine kinase (AK) polyclonal antibody, and rabbit anti-HDH tropomyosin (TM) polyclonal antibody, respectively.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/c16312bae1a291ddb06995ca.png"},{"id":107255684,"identity":"82265e91-9dc0-4cb8-958e-a7c493d38c61","added_by":"auto","created_at":"2026-04-19 12:11:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6654451,"visible":true,"origin":"","legend":"\u003cp\u003eImmunobinding activity of \u003cem\u003eH. discus hannai\u003c/em\u003e total protein extracts (HDHpro) following treatment with two different denaturants. A–C: Binding activity of HDHpro after denaturant treatment probed with (A) rabbit anti-HDH PM polyclonal antibody, (B) rabbit anti-\u003cem\u003eA. plicatula\u003c/em\u003e AK polyclonal antibody, and (C) rabbit anti-HDH TM polyclonal antibody, respectively.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/e0e192d41dca829333c6382d.png"},{"id":107255681,"identity":"9f7a2ba9-d5a0-4c87-94c7-5566bbcf3816","added_by":"auto","created_at":"2026-04-19 12:11:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":7356767,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of linear B-cell epitopes mapped onto the three-dimensional structures of five high-abundance putative allergens from \u003cem\u003eH. discus hannai\u003c/em\u003e. (A) Spatial distribution of 22 predicted linear B-cell epitopes on paramyosin (PM); (B) Spatial distribution of 11 previously identified linear B-cell epitopes on tropomyosin (TM); (C) Spatial distribution of 9 predicted linear B-cell epitopes on arginine kinase isoform 1 (AK1); (D) Spatial distribution of 13 predicted linear B-cell epitopes on arginine kinase isoform 2 (AK2); (E) Spatial distribution of 5 predicted linear B-cell epitopes on fructose-bisphosphate aldolase isoform 1 (FBA1); (F) Spatial distribution of 10 predicted linear B-cell epitopes on fructose-bisphosphate aldolase isoform 2 (FBA2); (G) Spatial distribution of 9 predicted linear B-cell epitopes on triosephosphate isomerase (TIM).\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/8634f46c2876c04b2364ae4d.png"},{"id":107482414,"identity":"9c38bb1c-dd1f-454c-ae0b-699ef3862336","added_by":"auto","created_at":"2026-04-22 02:23:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9259369,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of conformational B-cell epitopes mapped onto the 3D structures of three high-abundance putative allergens from \u003cem\u003eH. discus hannai\u003c/em\u003e. A-E: Spatial distribution of predicted conformational B-cell epitopes on (A) AK isoform 1 (AK1), (B) AK isoform 2 (AK2), (C) FBA isoform 1 (FBA1), (D) FBA isoform 2 (FBA2), (E) TIM, respectively.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/434cc9f348ff5f76f09166c5.png"},{"id":107704942,"identity":"8523af0d-7a04-4030-8e5a-1346cc2c6561","added_by":"auto","created_at":"2026-04-24 09:04:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":74447176,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/5462ec50-7d04-477b-bcf7-a671f44df921.pdf"},{"id":107255671,"identity":"9d0a3d17-15fe-421a-b878-a349eac6278d","added_by":"auto","created_at":"2026-04-19 12:11:01","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6520405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Tables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1.\u003c/strong\u003e Repeat contents in the assembling genome of \u003cem\u003eHaliotis discus hannai\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2\u003c/strong\u003e. Annotation results of the whole-genome predicted protein-coding genes in \u003cem\u003eHaliotis discus hannai\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3\u003c/strong\u003e. Quantification results of the predicted HDH protein repertoire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4\u003c/strong\u003e. BLAST results between the predicted HDH protein repertoire and the two authoritative allergen databases: WHO/IUIS Nomenclature and AllergenOnline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S5\u003c/strong\u003e. Linear B-cell epitopes of four putative allergens predicted by multiple bioinformatics programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S6\u003c/strong\u003e. Conformational B-cell epitopes of three putative allergens predicted by multiple bioinformatics programs.\u003c/p\u003e","description":"","filename":"4.Supplementary.Tables1620260327.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/55ad67cc767cbb5a32307237.xlsx"},{"id":107255674,"identity":"e3ed6ca3-ad72-4036-9470-fa14d4604a48","added_by":"auto","created_at":"2026-04-19 12:11:01","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1427363,"visible":true,"origin":"","legend":"","description":"","filename":"5.Supplementary.Fig12.20260327.docx","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/7b32104064a5d6dbc6e1bf67.docx"},{"id":107483243,"identity":"38ad0c7b-8914-4937-884b-fe9f513894df","added_by":"auto","created_at":"2026-04-22 02:27:00","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":172844,"visible":true,"origin":"","legend":"","description":"","filename":"FulluneditedblotsforFig.2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/adc6884272220b0a10a9f454.pdf"},{"id":107482367,"identity":"15cba812-913e-4f69-97fd-b1ff1a47cc9a","added_by":"auto","created_at":"2026-04-22 02:23:21","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1090004,"visible":true,"origin":"","legend":"","description":"","filename":"FulluneditedblotsforFig.3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/9129ea39a569221d671fcad8.pdf"},{"id":107255680,"identity":"fcf54bfa-b78e-4d70-be03-6105aa8699ca","added_by":"auto","created_at":"2026-04-19 12:11:01","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":78555,"visible":true,"origin":"","legend":"","description":"","filename":"FulluneditedblotsforFig.5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/ebdb22a46b86e40fcc163429.pdf"},{"id":107255685,"identity":"f0fd873e-f8e9-48c9-96b5-a056859bd8aa","added_by":"auto","created_at":"2026-04-19 12:11:02","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":110528,"visible":true,"origin":"","legend":"","description":"","filename":"FulluneditedblotsforFig.6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/d24cc8097b63ce3b29647a7f.pdf"},{"id":107255683,"identity":"4d2a3b6d-9170-4aaa-a973-fae400220114","added_by":"auto","created_at":"2026-04-19 12:11:01","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":3256663,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9243755/v1/1da49239dc735d9af0e9b722.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated multi-omics and serological profiling identifies five immunodominant allergens from the high-abundance protein repertoire of Haliotis discus hannai","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn recent years, the global incidence of food allergy has shown a continuous upward trend, emerging as a significant food safety and public health concern for governments and the public worldwide \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Seafood allergy is one of the most prevalent food allergy types in China, primarily triggered by crustaceans, mollusks, and fish \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. A study from Jiangxi Province in China reported a food allergy prevalence of approximately 5.4%, with nearly 40% of these patients allergic to shrimp. Allergy to molluscan shellfish accounted for about 20.8%, ranking second, while fish allergy was reported in 7.5% of patients \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Another study from Taiwan region indicated a local food allergy prevalence of around 6.9%, with molluscan shellfish allergy constituting approximately 18.4% of all allergic individuals \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. These epidemiological findings underscore that molluscan shellfish allergy represents a considerable proportion of the global, and particularly the Chinese, allergic population, imposing substantial pressure and burden on affected families and society at large \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFood allergy caused by molluscan shellfish, especially abalone allergy, is increasingly becoming one of the predominant allergic conditions in China \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The \u003cem\u003eHaliotis discus hannai\u003c/em\u003e (HDH), as a major economic molluscan species in China, is highly favored by consumers for its rich nutritional value and succulent meat. Common consumption methods include boiling and steaming, and it is also processed into canned or dried products. In 2024, the total production of crustacean and molluscan aquatic products in China exceeded 28.03\u0026nbsp;million tons, with molluscan products alone reaching 18.19\u0026nbsp;million tons. As a relatively prized economic species, the HDH production has surged alongside rising living standards and increasing demand for high-quality food among Chinese consumers. Statistics data from the China Fishery Statistical Yearbook reveal that China's mariculture output of abalone skyrocketed from ~\u0026thinsp;128,000 tons in 2015 to 252,000 tons in 2024, reflecting a robust growth trend in demand. With the rapid increase in both the production and consumption of HDH in China, the associated food allergy problem has become increasingly prominent \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. There is a pressing need to investigate the primary causes of allergic reactions to HDH consumption, which is crucial for potentially reducing the frequency of such reactions among Chinese consumers.\u003c/p\u003e \u003cp\u003eMolluscan shellfish allergy could induce IgE-mediated type I hypersensitivity reactions in patients, often persisting throughout their lifetime \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Typical clinical manifestations include facial and lip swelling, chest tightness, shortness of breath, numbness in the mouth, tongue and limbs, and skin itching. In severe cases, these may progress to collapse, shock, and even life-threatening conditions \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. These symptoms significantly impair the physical health and quality of life of allergic individuals, which could lead to long-term psychological distress, including anxiety and depression, for both patients and their families \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Consequently, there is an urgent need to systematically identify the immunodominant allergens responsible for allergic reactions to the HDH, thereby providing a reliable scientific basis of allergen data for clinical detection.\u003c/p\u003e \u003cp\u003eAlthough the incidence of HDH-induced food allergies has been increasing annually, research in this area remains notably limited. Existing studies on HDH allergy have primarily concentrated on tropomyosin (TM), a major allergen in aquatic foods, while a systematic elucidation of the complete allergen profile of HDH is conspicuously absent \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Our research team conducted an in-depth comparative investigation of TM allergens from three mollusks (HDH, \u003cem\u003eAlectryonella plicatula\u003c/em\u003e, and \u003cem\u003eMimachlamys nobilis\u003c/em\u003e), revealing their high sequence similarity and potent immunoreactivity \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Subsequent work identified multiple conserved B-cell epitopes among these three homologous TMs, which underlie the strong cross-reactivity observed across these species \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Furthermore, research on the North Sea brown shrimp allergen demonstrated that its TM reacted with approximately 68% of serum samples from allergic patients, underscoring its substantial contribution to clinical allergic presentations \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. However, this finding also indicated that TM alone is insufficient to fully account for all food allergy reactions triggered by this species. Consequently, there is a pressing need to systematically delineate the allergen profile of HDH, identifying individual allergens and assessing their sensitization potential. This effort would provide reliable, specific allergen data essential for improving diagnostic accuracy for HDH allergy. The identification of high-abundance allergens is also crucial for advancing the development of clinically convenient and efficient detection methods for abalone allergens. Such advancements would enhance early diagnosis rates, reduce misdiagnoses stemming from unidentified allergens, and ultimately mitigate the burden abalone allergy imposes on both patients and healthcare systems.\u003c/p\u003e \u003cp\u003eTo date, multi-omics studies, encompassing transcriptome and proteome, on abalone species have primarily focused on aquaculture-related traits, nutritional value, muscle mass, and developmental stages \u003csup\u003e[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. A significant gap exists in employing integrated multi-omics strategies to systematically construct a comprehensive allergen profile. For instance, Huang \u003cem\u003eet al.\u003c/em\u003e utilized iTRAQ-based proteome to identify 125 differentially expressed proteins associated with muscle growth in HDH, which were enriched in pathways significantly related to muscle development, such as apoptosis and thyroid hormone signaling, thereby enhancing the understanding of its molecular growth mechanisms \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, sole reliance on proteomic analysis for allergen identification presents several inherent challenges. While conventional proteomics enables the quantification of protein abundance, it often falls short in resolving complete sequence coverage, which constrains the development of highly specific clinical detection methods. Furthermore, although proteome data could reveal the expression levels of target proteins in a given sample, these measurements do not necessarily reflect the abundance or stability of the same proteins within complex crude protein extracts. Consequently, such proteins may not be consistently detectable in functional validation assays (e.g., immunoblotting) that rely on these extracts as the antigen source \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Additionally, employing transcriptome in isolation for allergen research has also inherent limitations. Transcript levels do not fully correlate with actual protein expression due to the intricate post-transcriptional regulation of translation, which is modulated by various epigenetic modifications such as methylation and acetylation, ultimately influencing final protein expression and function \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Systematic analyses comparing transcriptomes and proteomes in mammals have revealed a significant correlation for only approximately half of all genes \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Therefore, establishing an integrated multi-omics analytical framework for HDH is of paramount importance for the comprehensive and accurate prediction of allergens, along with their molecular characteristics and functions. This approach will provide reliable data on allergen sequences and expression levels, forming a solid foundation for in-depth investigations into the molecular mechanisms of allergic responses.\u003c/p\u003e \u003cp\u003eIn this study, we integrated multi-omics big data from HDH, including genome, transcriptome, and proteome datasets. Our approach not only involved the systematic analysis and identification of the coding and amino acid sequences for each protein but also determined their actual expression levels by integrating transcriptional and translational data. By cross-referencing all identified proteins against authoritative allergen databases, we have first constructed a high-abundance allergen profile for HDH. This profile provides specific allergen data to support clinical testing for patients with aquatic food allergies. Furthermore, we conducted an in-depth comparative analysis of the sequences of the identified allergens with their homologous counterparts from other species to infer the potential for cross-reactivity.\u003c/p\u003e \u003cp\u003eSubsequently, we employed multiple wet-lab methodologies, including SDS-PAGE and Western blotting, to validate these high-abundance allergens and identify those capable of immunobinding with our existing panel of antibodies. We also collected positive serum samples from volunteers with HDH allergy, confirmed by specific IgE testing assays. Using these samples, we investigated the reaction rates of high-abundance allergens with the positive sera, analyzed the linear and conformational B-cell epitopes of five high-abundance putative allergens, and thereby revealed the potential molecular mechanisms underlying the immunobinding reactions elicited by HDH allergens.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDe novo\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;transcriptome and proteome of HDH, and publicly available genome assembly\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo generate an unbiased atlas of gene expression and protein abundance in the HDH, three clinically healthy samples were procured from Fujian Zhongxin Yongfeng Industrial limited company. Immediately after arrival, animals were anaesthetized on ice and the foot muscle was excised. Every individual yielded four technical replicates for RNA sequencing (n = 12) and three for mass-spectrometry-based proteome analyses (n = 9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBriefly, tissue fragments were rinsed in ice-cold sterile phosphate-buffered saline, freed from adherent shell and viscera, cut into 50-100 mg aliquots, snap-frozen in liquid nitrogen and stored at -80℃ until further processing. Total RNA was extracted using TRIzol reagent followed by DNase I treatment; integrity was verified on an Agilent 2100 Bioanalyzer (RIN ≥ 8.0). Poly(A)-enriched libraries were constructed and sequenced on an Illumina NovaSeq xplus platform (150 bp paired-end reads, ~6 Gb per sample).\u0026nbsp;For proteome profiling, proteins were extracted and subjected to reduction and alkylation to disrupt disulfide bonds and stabilize cysteine residues. Protein concentration and integrity were assessed using the Bradford protein assay. Subsequently, the protein samples were digested with trypsin, and the resulting peptides were desalted using a C18 column. After pretreatment, peptides were separated on a Thermo Scientific Vanquish Neo UHPLC system with a programmed gradient. Eluted peptides were then analyzed on a Thermo Scientific Orbitrap Astral mass spectrometer, which was operated in a high-resolution data-independent acquisition (DIA) mode to obtain comprehensive proteome data.\u0026nbsp;RNA-seq reads and mass spectra were processed by Novogene Bioinformatics Technology Co., Ltd. (Beijing, China) following ISO 17025 standards. The reference genome of HDH (GenBank id: GCA_044707095.1) was retrieved from the NCBI database and used for read alignment, gene model prediction and downstream functional inference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDe novo\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;reconstruction of HDHtranscriptome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo generate a high-confidence reference for gene prediction and allergen annotation, we first filtered the in-house RNA-seq reads using stringent quality control criteria. Adapters, nucleotides with Phred scores below 5 and fragments shorter than 36 bp were removed using Trim Galore v0.6.10 \u003csup\u003e[19]\u003c/sup\u003e, yielding a robust cleansed data set. Clean reads were then assembled \u003cem\u003ede novo\u003c/em\u003e with Trinity v2.15.1 \u003csup\u003e[20]\u003c/sup\u003e, producing a comprehensive catalogue of putative transcripts.\u003c/p\u003e\n\u003cp\u003eAssembly fidelity was interrogated through two independent metrics. First, alignment rates were derived by mapping the quality filtered reads back to the reconstructed transcriptome with Bowtie2 v2.3.5.1\u003csup\u003e[21]\u003c/sup\u003e, thereby quantifying sequence level coverage. Second, completeness of assembled transcriptome was assessed with BUSCO v5.5.0 against the Mollusca lineage data set \u003csup\u003e[22]\u003c/sup\u003e, evaluating the proportion of evolutionarily conserved single copy orthologues present in the assembly. Together, these assessments ensure that the resultant assembly is both representative and accurate, providing a reliable template for downstream functional annotation and allergen discovery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome preprocessing of HDH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The raw genome data of HDH were preprocessed using a stringent multi-stage pipeline designed to improve gene prediction accuracy in subsequent analyses. First, the scaffold-level assembly was processed with the Funannotate program (v1.8.17)\u003csup\u003e[23]\u003c/sup\u003e, whereby duplicated contigs were purged, scaffolds were length ranked, and FASTA headers were normalized to ensure compatibility with downstream tools. Next, a \u003cem\u003ede novo\u003c/em\u003e repeat library was constructed with RepeatModeler (v2.0.6) to capture lineage-specific transposable elements, and the resulting consensus sequences were subsequently deployed in RepeatMasker (v4.1.9) for soft masking, thereby preserving exonic information while attenuating low complexity regions\u003csup\u003e[24, 25]\u003c/sup\u003e. This disciplined preprocessing strategy markedly mitigated the impact of repetitive regions and delivered a high confidence assembly that underpins all ensuing gene prediction, functional annotation, and comparative genome analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssembly of a proteome atlas and absolute quantification for HDH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith a scaffold-scale genome and a fully curated transcriptome in hand, we generated a high-confidence proteome map for HDH through a consensus annotation pipeline that couples proteogenome evidence with advanced gene-prediction algorithms. First, Funannotate program (v1.8.17) was run to integrate homology-based and \u003cem\u003eab initio\u003c/em\u003e evidence for gene prediction\u003csup\u003e[23]\u003c/sup\u003e. Protein homology evidence was aligned to the genome using DIAMOND and refined with Exonerate \u003csup\u003e[26, 27]\u003c/sup\u003e, and \u003cem\u003eab initio\u003c/em\u003e gene predictions were generated by multiple predictors including Augustus, Augustus-HiQ, GeneMark-ES, GlimmerHMM, and SNAP \u003csup\u003e[28-31]\u003c/sup\u003e. Transcriptome-based gene models were additionally derived using PASA \u003csup\u003e[32]\u003c/sup\u003e. EVidenceModeler was used to weight and reconcile all evidence into a single, non-redundant set of protein‑coding gene models\u003csup\u003e[33]\u003c/sup\u003e. The resulting consensus gene set was then refined with PASA, incorporating transcript alignments to add untranslated regions (UTRs) and improve structural annotation based on expressed sequence evidence\u003csup\u003e[32]\u003c/sup\u003e. Completeness of proteome was further benchmarked using the Mollusca BUSCO lineage set\u003csup\u003e[22]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFollowing the prediction of all protein-coding genes, we performed comprehensive functional annotation primarily utilizing the DIAMOND (for Uniprotdb, merops), HMMER3 (for pfam, CAZymes), InterProScan (version 5.73-104.0), the EggNOG-mapper program, and the SignalP program to assign Uniprot annotations, Pfam domains, GO terms, COG and secretion signals, respectively \u003csup\u003e[26, 34-39]\u003c/sup\u003e. These tools collectively establish a complementary annotation framework that spans from elucidating micro-scale structural domains (InterProScan) to inferring macro-scale functional classifications (EggNOG), and further to predicting subcellular localization (SignalP). This integrated pipeline represents a widely adopted and standard workflow in functional genome analysis. To ensure the reliability of the annotations, only those genes that received consistent functional predictions from at least one of these programs were considered to encode proteins with potential biological functions.\u003c/p\u003e\n\u003cp\u003eFor quantitative proteome, the raw DIA-MS acquisitions were searched against this \u003cem\u003ein silico\u003c/em\u003e atlas using DIA-NN (v2.0) with a spectral library-free strategy \u003csup\u003e[40]\u003c/sup\u003e. Stringent filtering retained only peptides and proteins achieving ≥99% posterior probability and ≤1% global FDR, culminating in a high-resolution abundance matrix of proteins across all muscular samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction and prioritization of putative allergens in HDH \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo establish a high-confidence allergen repertoire for HDH, we implemented three complementary strategies integrating the inferred proteome derived from genome and transcriptome predictions, transcriptome expression levels, and proteome detection-based abundance profiles.\u003c/p\u003e\n\u003cp\u003ei. First, the newly assembled proteome, derived from integrated genome and transcriptome predictions, was systematically interrogated against two authoritative allergen repositories, the WHO/IUIS Allergen Nomenclature Database and AllergenOnline, using the BLASTP program (v.2.14) \u003csup\u003e[41]\u003c/sup\u003e. To maximize reliability and minimize false-positive identifications from the extensive predicted proteome, stringent filtering criteria were applied, retaining only sequences with an E-value\u0026lt;1×10⁻⁷ and a pairwise identity \u0026gt; 60% as primary candidates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eii. Transcriptome support for these candidates was rigorously evaluated using Salmon (version 1.10.2)\u003csup\u003e[42]\u003c/sup\u003e. Putative allergens were required to demonstrate robust transcriptional activity, specifically an average Transcripts Per Million (TPM) value exceeding 10 across individual samples, to qualify for further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eiii. Candidate validation was further extended to the proteome expression level. Proteins were required to be consistently detected in more than 50% of mass spectrometry runs, a criterion designed to eliminate mere transcriptional noise and confirm bona fide translation. Furthermore, to distinguish highly abundant allergens, a global mean expression value was calculated from all confirmed candidates; only those exhibiting expression levels above this mean threshold were classified as high-abundance allergens.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To assess the sequence similarity and potential cross-reactivity of the prioritized allergens within Mollusca, pairwise alignments were generated using EMBOSS Needle, while multiple sequence alignments were conducted with Clustal Omega \u003csup\u003e[43, 44]\u003c/sup\u003e. Notably, our multiple sequence alignments were primarily conducted using protein sequences sourced from the UniProt database, encompassing both abalone (\u003cem\u003eHaliotis\u003c/em\u003e genus) and other peer-reviewed mollusk sequences. A maximum likelihood phylogeny was inferred in the MEGA12 program under the Jones-Taylor-Thornton (JTT) model with 1,000 bootstrap replicates to quantify branch support\u003csup\u003e[45]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation of total protein extracts from HDH and positive sera from volunteers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe prepared total protein extracts (designated HDHpro) from fresh abductor muscle tissue of HDH using an extraction buffer composed of 10 mM PBS (pH 7.5) containing 0.5 M NaCl, following a method adapted from our previous study \u003csup\u003e[46]\u003c/sup\u003e. Positive and negative serum samples from volunteers with HDH allergy were provided by the First Affiliated Hospital of Fujian Medical University (Fuzhou, Fujian Province, China). Serum samples were stored at -80℃ until further analysis. These serum samples were screened according to criteria previously established in our study \u003csup\u003e[11]\u003c/sup\u003e. Sera samples were classified as abalone-positive when immunoreactive IgE levels exceeded twice those of healthy controls \u003csup\u003e[47]\u003c/sup\u003e. All participants provided written informed consent. The present study was conducted in accordance with the guidelines of the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (approval no.: [2022]075).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blotting and serological profiling of high-abundance putative allergens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWestern-blot assays were performed following a protocol modified from Li \u003cem\u003eet al.\u003c/em\u003e \u003csup\u003e[48]\u003c/sup\u003e. The primary antibodies consisted of diluted serum from allergic volunteers and animal-derived antibodies prepared in our laboratory. Specifically, volunteer serum was diluted at 1:4, while rabbit anti-HDH paramyosin (PM), rabbit anti-oyster arginine kinase (AK), and rabbit anti-HDH tropomyosin (TM) antibodies were diluted at 1:5×10⁵, 1:1×10⁶, and 1:5×10⁶, respectively. The corresponding secondary antibodies were HRP-conjugated goat anti-human IgG and HRP-conjugated goat anti-rabbit IgG, both diluted at 1:1×10\u003csup\u003e4\u003c/sup\u003e. Densitometric analysis of protein band intensities were performed using ImageJ software (version 1.38). Subsequently, we investigated the effects of chemical denaturants on HDHpro. The experimental procedure, adapted from our previous study \u003csup\u003e[49]\u003c/sup\u003e, consisted of incubating HDHpro with various concentrations of sodium dodecyl sulfate (SDS) or β-mercaptoethanol (β-Me). All mixtures were incubated at 4℃ for 16 hours. Following denaturant treatment, the IgG-binding activity of HDHpro was analyzed using a dot-blot assay. Untreated HDHpro and bovine serum albumin (BSA) served as positive and negative controls, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary and tertiary structures of high-abundance allergens\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; To elucidate the protein structures of the high-abundance allergens, we first predicted their secondary structures using the PSIPRED online server \u003csup\u003e[50]\u003c/sup\u003e. PSIPRED is a neural network-based method capable of predicting fundamental secondary structure elements, such as α-helices, β-sheets, and random coils, directly from amino acid sequences. To further explore the three-dimensional folding conformations of the putative allergens, we employed homology modeling methods. The tertiary structures of the high-abundance allergens AK, TM, fructose-bisphosphate aldolase (FBA), and triosephosphate isomerase (TIM) were modeled using SWISS-MODEL, a tool widely recognized for its high accuracy in allergen homology modeling \u003csup\u003e[51, 52]\u003c/sup\u003e. This process utilized known homologous protein structures as templates. In contrast, the 3D model for PM was predicted using AlphaFold3\u003csup\u003e[53]\u003c/sup\u003e, which employs deep learning and multiple sequence alignment algorithms to infer atomic-level spatial structures directly from amino acid sequences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB-cell epitopes and their spatial localization in high-abundance allergens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor TM, the B-cell epitopes analyzed in this study were primarily based on results reported in one of our previous studies \u003csup\u003e[12]\u003c/sup\u003e. For the other high-abundance allergens, we predicted linear B-cell epitopes for PM, AK, FBA, and TIM using the online servers ABCPred, BcePred, and Bepipred \u003csup\u003e[54-57]\u003c/sup\u003e. Conformational B-cell epitopes for AK, FBA, and TIM were predicted using the online tools DiscoTope, SEPPA, and CBTOPE \u003csup\u003e[58-61]\u003c/sup\u003e. All B-cell epitope predictions were conducted on December 3\u003csup\u003erd\u003c/sup\u003e, 2025. Within the constructed 3D models of allergens, we further used PyMOL software to locate and annotate both linear and conformational B-cell epitopes (https://pymol.org/)\u003csup\u003e[62]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein coding gene annotation of HDH\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAt the transcriptome level, twelve specimens from foot muscle tissue of HDH were collected (three biological replicates \u0026times; four technical replicates) and subjected to paired-end RNA sequencing, yielding ~\u0026thinsp;84Gb of raw data (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Pre-processing removed only\u0026thinsp;~\u0026thinsp;2% of bases, indicating a high overall quality. \u003cem\u003eDe novo\u003c/em\u003e assembly with Trinity produced a reference transcriptome that achieved 94% completeness by BUSCO and \u0026gt;\u0026thinsp;95% read mapping with Bowtie2, providing a robust template for downstream gene prediction and annotation (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistics of RNA sequencing data quality and preprocessing results for \u003cem\u003eHaliotis discus hannai.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eRaw reads\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eRaw bases (gb)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eClean reads\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eClean bases (gb)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eCleaned rate (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eQ20 \u003csup\u003ea\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eQ30 \u003csup\u003eb\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eGC \u003csup\u003ec\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA1_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e23,310,917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e22,759,356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e97.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e45.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA1_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e23,791,383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e23,360,196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e98.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e45.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA1_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e22,661,920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e22,224,819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e98.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e45.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA1_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e23,584,848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e22,981,729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e97.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e47.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA2_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e23,554,061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e22,931,374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e6.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e97.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e45.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA2_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e23,163,882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e22,733,845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e6.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e98.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e44.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA2_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e22,713,430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e22,361,572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e6.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e98.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e45.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA2_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e26,335,473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e25,957,492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e7.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e98.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e44.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA3_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e23,621,905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e7.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e23,238,709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e98.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e46.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA3_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e23,695,180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e23,306,251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e98.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e46.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA3_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e22,980,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e22,562,152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e98.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e47.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003emRNA3_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e23,891,853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e23,468,059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e98.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e99.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e97.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e47.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e Q20: Percentage of bases with a phred quality score\u0026thinsp;\u0026ge;\u0026thinsp;20; \u003csup\u003eb\u003c/sup\u003e Q30: Percentage of bases with a phred quality score\u0026thinsp;\u0026ge;\u0026thinsp;30; \u003csup\u003ec\u003c/sup\u003e GC (%): Percentage of GC content.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBasic information of Genome \u0026amp; Transcriptome assembling for \u003cem\u003eHaliotis discus hannai.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eHaliotis discus hannai\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenome\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTranscriptome\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssembly statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal length (Mb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1,883.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLongest scaffold (Mb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e26.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo. scaffolds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e22,772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eN50 (kbp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3,153.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eL50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eN90 (kbp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e544.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eL90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo. Ns per 100kbp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6,908.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e37.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e42.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRepeat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e37.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal BUSCOs \u003csup\u003ea\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e89.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eS.C (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e83.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eD.C (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e84.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eM (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnotation statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNumber of protein-coding genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e53,245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal BUSCOs (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e72.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eS.C (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e63.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eD.C (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eM (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003e Total BUSCOs: BUSCO completeness score; S.C: Percentages of Complete and Single-copy BUSCOs; D.C: Percentages of Complete and Duplicated BUSCOs; F: Percentages of Fragmented BUSCOs; M: Percentages of Missing BUSCOs.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo ensure concordance, a draft genome of HDH (~\u0026thinsp;1.9Gb) was obtained from the NCBI database and generated from the same tissue source using a hybrid assembly strategy (PacBio plus Illumina) at 58\u0026times; coverage and delivered to scaffold level, thereby preserving upstream and downstream regulatory sequences. The BUSCO assessment returned 89.6% completeness (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), attesting to high contiguity and accuracy. Repetitive elements were catalogued \u003cem\u003ede novo\u003c/em\u003e with RepeatModeler and masked with RepeatMasker, soft-masking 37.56% of the assembly (Supplementary Table\u0026nbsp;1, \u003cstrong\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e) and minimizing false positives during gene prediction.\u003c/p\u003e\n\u003cp\u003eIntegrating \u003cem\u003eab initio\u003c/em\u003e algorithms, transcriptome evidence and homology-based searches, we delineated 53,245 protein-coding loci, of which 33,109 received successfully functional annotations from at least two widely-adopted programs (\u003cstrong\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e). For proteome validation, nine samples (three biological replicates \u0026times; three technical replicates) were analyzed by data-independent acquisition (DIA) on an Orbitrap Astral platform. Spectra were searched against the \u003cem\u003ein silico\u003c/em\u003e protein atlas using DIA-NN, resulting in the confident quantification of 4,273 proteins. After filtering for presence in \u0026gt;\u0026thinsp;50% of specimens, 3,916 proteins were retained for subsequent analyses (\u003cstrong\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMapping and prediction of putative allergens in HDH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll 33,109 predicted functional proteins were queried against the WHO/IUIS Allergen Nomenclature and AllergenOnline repositories using an e-value threshold of 1\u0026times;10⁻⁷ and \u0026ge;\u0026thinsp;60% pairwise identity. Candidates were further filtered for transcriptional support (average TPM\u0026thinsp;\u0026gt;\u0026thinsp;10), yielding 291 high-confidence matches that correspond to 57 putative allergens (\u003cstrong\u003eTable \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/strong\u003e). Integration of these candidate proteins with the quantitative data from the DIA proteome (comprising 3,916 proteins) further refined the list to 37 high-abundance functional proteins that were empirically detected at the peptide level (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on established protein nomenclature and primary biological functions, the 37 putative allergens were categorized into 23 distinct protein groups (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). To ensure the reproducibility of the analytical workflow and its corresponding results, we have publicly released the complete pipeline on the GitHub repository, which integrates genome, transcriptome, and proteome data to establish a reliable allergen repertoire and identify high-abundance putative allergens in HDH. This open-source analytical framework is designated as MOHADA (Multi-Omics for High-abundance Allergen Discovery in Abalone) and is accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/strao1986/MOHADA\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eList of 37 predicted allergens from \u003cem\u003eHaliotis discus hannai\u003c/em\u003e according to similarity search against WHO/IUIS Nomenclature and AllergenOnline database.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"12\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroup No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eWHO/IUIS id\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAccession ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eIdentity\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eE-value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eAvgTPM \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eAvgProtExp \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003eLength \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003eMW\u003c/p\u003e\n \u003cp\u003e(KDa) \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003epI \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTropomyosin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHaliotis laevigata x Haliotis rubra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eHal l 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAPG42675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e99.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e15,186.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e32.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eParamyosin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHaliotis discus discus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eUnassigned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eBAJ61596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e99.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e24,762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e33.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e99.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eArginine kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAmphioctopus fangsiao\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eUnassigned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAEK65120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e67.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.59E-176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8,709.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e31.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e39.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAmphioctopus fangsiao\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eUnassigned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAEK65120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e65.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e9.58E-171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e54.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e24.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e42.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e6.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFenneropenaeus chinensis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eUnassigned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAAS98890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e71.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.09E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e82.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e27.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e81.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eFructose-bisphosphate aldolase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCharybdis feriata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eCha f 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eWXT82172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e66.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e7.45E-83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1,738.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e28.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eThunnus albacares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eThu a 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eP86979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e72.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3.66E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1,506.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e29.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTriosephosphate-isomerase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDermatophagoides farinae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eDer f 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eL7UZA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e71.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.14E-128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e27.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEnolase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAmbrosia artemisiifolia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAmb a 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eA0A1B2H9Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e87.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e9.11E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e203.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e28.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e39.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e6.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eFilamin c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eScylla paramamosain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eScy p 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQFI57017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e61.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1,272.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e30.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e101.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGlycogen phosphorylase-like protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePenaeus monodon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePen m 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eURW11955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e80.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e109.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e27.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e6.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMitochondrial malate dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eParalithodes camtschaticus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePara c 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eWYC14267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e65.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.44E-159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e121.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e24.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGlyceraldehyde-3-phosphate-dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePeriplaneta americana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePer a 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eA0A2R3WIG9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e74.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1,408.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e29.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e35.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAlpha actinin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDermatophagoides farinae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eUnassigned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eL7UZ85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e76.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e458.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e103.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eAlpha-tubulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePeriplaneta americana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePer a 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eUZC36340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e95.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e596.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e27.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e50.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDermatophagoides pteronyssinus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eDer p 33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQAT18644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e90.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e427.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e26.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e50.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePeriplaneta americana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePer a 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eUZC36340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e9.58E-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e143.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e24.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eCyclophilin, peptidyl-prolyl cis-trans isomerase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePeriplaneta americana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePer a 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eUZC36341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e75.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e7.62E-95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e589.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e26.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e8.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDermatophagoides farinae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eDer f 29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eA1KXG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e73.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.75E-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1,089.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e25.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMalassezia sympodialis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMala s 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eO93970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e61.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.18E-56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e98.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e24.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSolanum lycopersicum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSola l 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eP21568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e65.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.43E-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e139.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e21.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e8.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eHeat shock protein 70 kda (hsp70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePenicillium citrinum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePen c 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQ92260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e79.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e432.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e27.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e71.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDermatophagoides pteronyssinus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eDer p 28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQAT18639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e72.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e29.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e26.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e69.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDermatophagoides pteronyssinus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eDer p 28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQAT18639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e75.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e48.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e26.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e69.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e5.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDermatophagoides pteronyssinus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eDer p 28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQAT18639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e74.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e578.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e25.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e69.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHeat shock cognate protein-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAedes aegypti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAed a 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQ1HR69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e27.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e26.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eL3 ribosomal protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAspergillus fumigatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAsp f 23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQ8NKF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e64.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1,234.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e22.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e46.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e10.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e60s acidic ribosomal protein p2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFusarium culmorum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eFus c 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQ8TFM9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e60.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e9.44E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e4,535.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e21.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCu-zn superoxide dismutase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAspergillus fumigatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAsp f 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eUYL70859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e66.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.86E-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e145.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePeroxiredoxin-6 (prx6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePeriplaneta americana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePer a 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eUZC36343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e68.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3.87E-113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e39.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e21.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e7.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePeroxiredoxin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eDermatophagoides farinae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eDer f 43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eA0AAU8LNY5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e70.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5.00E-107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e42.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e22.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eFerritin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBlomia tropicalis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eBlo t 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eA0A9Q0MF70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e62.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3.70E-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2,702.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e22.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCytochrome c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePeriplaneta americana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePer a 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eUZC36338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e81.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.30E-63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e333.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e22.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e9.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLysosomal aspartic protease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAedes aegypti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAed a 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQ03168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e65.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1E-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e88.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e21.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003ePorin 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAedes aegypti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAed a 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQ1HR57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e64.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.06E-142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e472.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e24.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eThioredoxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMalassezia sympodialis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMala s 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQ1RQI9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e6.03E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e127.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e21.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNascent polypeptide-associated complex alpha subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eHom s 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQ13765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e76.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.87E-63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e112.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e22.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e102.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\n \u003cp\u003e6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u003csup\u003ea\u003c/sup\u003e E-value: Expect value; The expected number of chance hits with a score at least this good when searching the same database with a random query of the same length. \u003csup\u003eb\u003c/sup\u003e Average TPM: Average Transcripts Per Million; Normalized average gene expression across samples, scaled to transcripts per million. \u003csup\u003ec\u003c/sup\u003e AvgProtExp: Average Protein Expression; Normalized average protein abundance across samples. \u003csup\u003ed\u003c/sup\u003e length: the predicted allergen length. \u003csup\u003ee\u003c/sup\u003e MW(KDa): The theoretical molecular weight (1000 Dalton). \u003csup\u003ef\u003c/sup\u003e pI: The theoretical isoelectric point.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA systematic evaluation of their allergenic potential revealed that the majority of HDH putative allergens exhibited substantial sequence similarity to allergens from aquatic mollusks and crustaceans, including other abalone species, octopus, shrimp, and crab, suggesting a high likelihood of cross-reactivity between HDH and these related taxa (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, 16 putative allergens (43.2%) showed significant sequence matches to published allergens from Der f, Der p, Per a, and Aed a, suggesting the possibility of broader cross-reactivity between this aquatic food source and terrestrial arthropods. Additionally, a proportion of allergens demonstrated sequence similarity to plant- and fungus-derived allergens, hinting at potential, albeit less characterized, cross-reactive relationships with plant and fungal food sources.\u003c/p\u003e\n\u003cp\u003eBeyond confirming the two previously documented allergens (PM and TM) in \u003cem\u003eHaliotis\u003c/em\u003e genus, our integrated multi-omics approach successfully identified 21 novel allergen categories in HDH. These newly identified allergens principally include arginine kinase (AK), fructose-bisphosphate aldolase (FBA), triosephosphate isomerase (TIM), filamin c, enolase, \u0026alpha;-actinin, and cyclophilin, among others (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on the results of quantitative proteome analysis, TM, PM, three AK isoforms, two FBA isoforms, and TIM exhibited consistently high expression levels across all specimens, substantially surpassing the global mean expression of proteins (global avgProtExp\u0026thinsp;=\u0026thinsp;18.72, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eManual curation against the high-quality genome and transcriptome allowed unambiguous assignment of the TM locus and revealed the complete TM gene repertoire within the ancestral gastropod lineage. Transcript and protein quantification both identified TM as the second most abundant polypeptide in foot muscle (identifier FUN_084428-T1; avgTPM\u0026thinsp;=\u0026thinsp;15,186.97; avgProtExp\u0026thinsp;=\u0026thinsp;32.28, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Comparative sequence analysis demonstrated 99.6% identity and similarity between the predicted HDH TM and the validated abalone allergen from \u003cem\u003eHaliotis laevigata x Haliotis rubra\u003c/em\u003e (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), differing by only a single amino-acid residue. Phylogenetic reconstruction of molluscan tropomyosin sequences retrieved from UniProt, including five congeneric species (\u003cem\u003eH. asinina, H. laevigata x H. rubra, H. diversicolor, H. rufescens and H. discus discus\u003c/em\u003e) resolved a monophyletic clade with pairwise identities exceeding 94.72% (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA \u003cstrong\u003eand Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/strong\u003e), underscoring the remarkable conservation of molluscan TMs.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePairwise sequence alignments between predicted proteins from \u003cem\u003eHaliotis discus hannai\u003c/em\u003e and allergens/proteins/predicted proteins from \u003cem\u003eHaliotis\u003c/em\u003e genus in the Uniprot database.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePredicted\u003c/p\u003e\n \u003cp\u003eproteins\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTarget\u003c/p\u003e\n \u003cp\u003eproteins\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAligned\u003c/p\u003e\n \u003cp\u003eProtein type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAccession\u003c/p\u003e\n \u003cp\u003eID/Seq ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003ePredicted\u003c/p\u003e\n \u003cp\u003eprotein\u003c/p\u003e\n \u003cp\u003elength\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eAligned\u003c/p\u003e\n \u003cp\u003eprotein\u003c/p\u003e\n \u003cp\u003elength\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003ePairwise\u003c/p\u003e\n \u003cp\u003eIdentity\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eSimilarity\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003eGaps\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFUN_084428-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTropomyosin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHaliotis laevigata x Haliotis rubra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAllergen (Hal l 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAPG42675.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e99.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e99.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFUN_013191-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eParamyosin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHaliotis discus discus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAllergen (Unassigned)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eBAJ61596.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e99.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e99.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFUN_074973-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eArginine kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHaliotis madaka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eUniprot protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eP51544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e77.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFUN_074974-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eArginine kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHaliotis madaka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eUniprot protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eP51544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e98.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e99.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFUN_074973-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eArginine kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePredicted Allergen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFUN_074974-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e78.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e84.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFUN_001862-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eFructose-bisphosphate aldolase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ePredicted Allergen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFUN_035972-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFUN_035972-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eFructose-bisphosphate aldolase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCharybdis feriata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAllergen\u003c/p\u003e\n \u003cp\u003e(Cha f 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eWXT82172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e49.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFUN_073831-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eTriosephosphate isomerase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHaliotis rufescens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eUniprot protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eQ45Y86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e81.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e83.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eParamyosin, encoded by the identifier FUN_013191-T1, emerged as the most highly expressed transcript (avgTPM\u0026thinsp;=\u0026thinsp;24,762) and protein (avgProtExp\u0026thinsp;=\u0026thinsp;33.74) in our sequencing results (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Phylogenetic inference placed the assembled PM in an evolutionarily stable cluster with scallop and oyster orthologs. Although this protein has not been officially designated as a major allergen in the HDH by authoritative databases such as the IUIS Allergen Nomenclature and Allergen Online, the predicted sequence is identical to an unreviewed HDH entry in the UniProt database (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB \u003cstrong\u003eand Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB\u003c/strong\u003e). Pairwise alignment with the congeneric \u003cem\u003eH. discus discus\u003c/em\u003e PM revealed 99.7% identity and 99.8% similarity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFor AK, three predicted isoforms exhibited significant sequence similarity to known crustacean and molluscan allergens. The longest isoform (FUN_009085-T1; 722 amino acids) possesses an internal 65.7% sequence repeat and exceeds the canonical length (348\u0026ndash;358 residues) of validated molluscan AKs, suggesting a tandem duplication event. Consequently, this isoform was excluded from downstream analyses. The remaining two isoforms (FUN_074974-T1, AK1; FUN_074973-T1, AK2) share 67.93% and 65.22% identity with \u003cem\u003eAmphioctopus fangsiao\u003c/em\u003e AK allergen (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and 98.9% and 77.9% identity with the Japanese abalone \u003cem\u003eH. madaka\u003c/em\u003e ortholog, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Both isoforms cluster within the abalone-specific clade and exhibit\u0026thinsp;\u0026gt;\u0026thinsp;60% identity to other molluscan sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC \u003cstrong\u003eand Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC\u003c/strong\u003e). Notably, the AK2 isoform is inferred to be a tandem paralog of AK1 isoform (78.2% identity, 84.5% similarity, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSimilar to the immunodominant allergens described above, triosephosphate isomerase (TIM) from HDH was also identified through integrated annotation, with its expression maintained at consistently high levels in both transcriptome and proteome analyses (FUN_073831-T1, avgTPM\u0026thinsp;=\u0026thinsp;378; avgProtExp\u0026thinsp;=\u0026thinsp;27.77, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Allergen sequence alignment revealed that the TIM of HDH shared the highest sequence identity (71.02%, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) with the TIM protein from the house dust mite \u003cem\u003eDermatophagoides farinae\u003c/em\u003e (Der f 25). A global sequence alignment between the TIM of HDH and the only \u003cem\u003eHaliotis\u003c/em\u003e TIM available in UniProt (from \u003cem\u003eHaliotis rufescens\u003c/em\u003e, UniProt ID: Q45Y86) showed a sequence identity of 81.6% and a similarity of 83.2%, indicating a high degree of sequence conservation while acknowledging the presence of sequence divergence (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eBeyond the canonical myofibrillar allergens, two FBA isoforms (FBA1 and FBA2) displayed substantial conservation. BLAST identity values of the two isoforms against database allergens were 66.49% and 72.97%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Phylogenetic analysis of the FBA isoforms resolved a single major branch that includes one predicted HDH sequences with orthologs from \u003cem\u003eH. gigantea, H. diversicolor, H. madaka, H. discus discus\u003c/em\u003e and \u003cem\u003eH. rufescens\u003c/em\u003e (\u003cstrong\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e), corroborating their orthology and potential allergenic relevance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blotting and serological profiling of high-abundance putative allergens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum samples were collected from 86 abalone-allergic and four non-allergic control volunteers (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Initial screening of positive serum samples by ELISA revealed significantly higher IgE reactivity to total abalone protein in allergic volunteer sera, with optical density values exceeding twice those of healthy controls. Detailed volunteer serological data are presented in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Subsequent Dot-blot analysis, using BSA as a negative control, confirmed specific IgE binding between allergic volunteer sera and HDHpro (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Further resolution by Western blot assays identified six major protein bands in HDHpro with pronounced IgE-binding activity, corresponding to molecular weights of \u0026gt;\u0026thinsp;130 kDa, ~\u0026thinsp;99 kDa, ~\u0026thinsp;44 kDa, ~\u0026thinsp;36 kDa, ~\u0026thinsp;27 kDa, and ~\u0026thinsp;19 kDa (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The number of sera showing positive reactivity to these bands was 30, 41, 31, 45, 26, and 15, respectively (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Notably, the number of positive sera detected by Western blot was lower than that identified by Dot-blot (68 \u003cem\u003evs.\u003c/em\u003e 86).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSerological Profiles of Total HDH Protein in 90 Allergic and Non-Allergic Volunteers. HDHpro: Total protein extracts of HDH.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"14\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003esIgE of HDHpro\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003esIgE of HDHpro\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c13\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c14\"\u003e\n \u003cp\u003esIgE of HDHpro\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDensitometric quantification of band intensities further demonstrated that the IgE-binding intensities of the \u0026gt;\u0026thinsp;130 kDa, ~\u0026thinsp;99 kDa, ~\u0026thinsp;44 kDa, and ~\u0026thinsp;36 kDa bands were significantly higher than those of the ~\u0026thinsp;27 kDa and ~\u0026thinsp;19 kDa bands (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0192, Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Based on their molecular weights and cross-referenced with the predicted allergen repertoire (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the ~\u0026thinsp;99 kDa, ~\u0026thinsp;44 kDa, ~\u0026thinsp;36 kDa, ~\u0026thinsp;27 kDa, and ~\u0026thinsp;19 kDa bands were hypothesized to correspond to PM, AK, TM, TIM, and FBA, respectively. SDS-PAGE analysis of HDHpro resolved 15 distinct protein bands (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Immunoblot validation using specific IgG polyclonal antibodies confirmed these assignments: the ~\u0026thinsp;99 kDa band showed specific reactivity with rabbit anti-HDH PM polyclonal antibody (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), the ~\u0026thinsp;44 kDa band with rabbit anti-\u003cem\u003eAlectryonella plicatula\u003c/em\u003e AK polyclonal antibody (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), and the ~\u0026thinsp;36 kDa band with rabbit anti-HDH TM polyclonal antibody (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparative modeling and spatial conformational landscape of multiple candidate allergens in HDH\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eTo elucidate the discrepancy wherein fewer serum samples tested positive in Western-blot compared to Dot-blot assays, we conducted two subsequent experimental series. First, we treated HDHpro with two distinct protein denaturants (sodium dodecyl sulfate (SDS) and \u0026beta;-mercaptoethanol (\u0026beta;-me)), and evaluated their impact on the immunoreactivity of HDH allergens using specific IgG polyclonal antibodies. As shown in Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, SDS treatment did not markedly reduce the IgG-binding activity of HDHpro to antibodies against all the three putative allergens (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;C). In contrast, treatment with \u0026beta;-me significantly attenuated IgG reactivity to AK (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), while the binding activities to PM and TM remained largely unaffected (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eTo interpret these findings, we further predicted B-cell epitopes closely associated with immunoreactivity. For the five immunodominant allergens, we initially modeled their three-dimensional structures using the SWISS-MODEL and AlphaFold3 programs based on seven putative protein sequences. The optimal models revealed that PM and TM adopt relatively simple, linear-dominated conformations (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), whereas AK, FBA, and TIM exhibit more complex tertiary structures (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Secondary structure predictions \u003cem\u003evia\u003c/em\u003e the PSIPRED program corroborated these observations (Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). PM and TM were predominantly \u0026alpha;-helical (~\u0026thinsp;99% of residues), with minimal random coil. In contrast, TIM secondary structure comprised similar proportions of \u0026alpha;-helix and random coil (differing by only\u0026thinsp;~\u0026thinsp;1.6%), alongside approximately 16% \u0026beta;-sheet. The two AK isoforms (AK1 and AK2) shared comparable secondary structure profiles, with \u0026alpha;-helix content below 40% and \u0026beta;-sheet plus random coil exceeding 60%. The two FBA isoforms (FBA1 and FBA2) also displayed similar patterns, with \u0026alpha;-helix near 40\u0026ndash;47% and the combined \u0026beta;-sheet and random coil close to 60%.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSecondary Structure Distribution of Putative Allergens in HDH.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAllergen\u003c/p\u003e\n \u003cp\u003ename\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003elength\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eHelix \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSheet \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eCoil \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eHelix\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eSheet\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eCoil\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e99.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e98.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e42.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e37.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e51.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e38.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e51.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFBA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e46.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFBA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e44.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e Helix: \u0026alpha;-helical content (residue count). \u003csup\u003eb\u003c/sup\u003e Sheet: \u0026beta;-sheet content (residue count). \u003csup\u003ec\u003c/sup\u003e Coil: Random coil content (residue count).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSubsequent \u003cem\u003ein silico\u003c/em\u003e epitope prediction identified multiple linear B-cell epitopes for PM, AK, FBA, and TIM (Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cstrong\u003eTable \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/strong\u003e), as well as conformational epitopes for AK, FBA, and TIM (Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cstrong\u003eTable \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e\u003c/strong\u003e). Using PyMOL, predicted linear epitopes for PM, AK, FBA, and TIM, along with previously identified TM epitopes from one of our previous studies, were mapped onto their respective structural models (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u0026ndash;G). Conformational epitopes for AK, FBA, and TIM were similarly visualized (Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u0026ndash;E). These analyses exhibited that B-cell epitopes in PM and TM are primarily linear, whereas those in two AK isoforms, two FBA isoforms, and TIM are predominantly conformational. Taken together, these experimental series suggested that \u0026beta;-me, used in Western-blot sample preparation, disrupts the conformational epitopes of allergens such as AK, thereby significantly reducing their immunobinding activity and contributing to the observed reduction in positive sera compared to the native condition\u0026ndash;preserving Dot-blot assay.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLinear and Conformational B-Cell Epitopes of High-Abundance Putative Allergens.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eAllergens\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAmino Acid Residue Regions Harboring Predicted B-Cell Epitopes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eLinear B-Cell Epitopes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003ePM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e21\u0026thinsp;~\u0026thinsp;32; 41\u0026thinsp;~\u0026thinsp;53; 70\u0026thinsp;~\u0026thinsp;77; 86\u0026thinsp;~\u0026thinsp;94; 109\u0026thinsp;~\u0026thinsp;124; 130\u0026thinsp;~\u0026thinsp;134; 136\u0026thinsp;~\u0026thinsp;141; 144\u0026thinsp;~\u0026thinsp;147; 155; 158; 159; 162\u0026thinsp;~\u0026thinsp;171; 230\u0026thinsp;~\u0026thinsp;248; 265\u0026thinsp;~\u0026thinsp;278; 306; 308; 321\u0026thinsp;~\u0026thinsp;338; 361\u0026thinsp;~\u0026thinsp;373; 411\u0026thinsp;~\u0026thinsp;429; 443; 465\u0026thinsp;~\u0026thinsp;477; 493\u0026thinsp;~\u0026thinsp;514; 529; 531; 540; 542; 543; 545; 546; 609; 619\u0026thinsp;~\u0026thinsp;629; 646; 648; 650\u0026thinsp;~\u0026thinsp;653; 665\u0026thinsp;~\u0026thinsp;680; 692\u0026thinsp;~\u0026thinsp;704; 725\u0026thinsp;~\u0026thinsp;729; 748\u0026thinsp;~\u0026thinsp;752; 754; 755; 765; 770\u0026thinsp;~\u0026thinsp;782; 797; 812; 815; 816; 830\u0026thinsp;~\u0026thinsp;834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eAK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13\u0026thinsp;~\u0026thinsp;16; 18\u0026thinsp;~\u0026thinsp;28; 30\u0026thinsp;~\u0026thinsp;41; 63\u0026thinsp;~\u0026thinsp;69; 81\u0026thinsp;~\u0026thinsp;99; 106\u0026thinsp;~\u0026thinsp;109; 121\u0026thinsp;~\u0026thinsp;136; 141\u0026thinsp;~\u0026thinsp;149; 165\u0026thinsp;~\u0026thinsp;179; 189; 192\u0026thinsp;~\u0026thinsp;199; 285; 305\u0026thinsp;~\u0026thinsp;324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eAK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2\u0026thinsp;~\u0026thinsp;25; 34\u0026thinsp;~\u0026thinsp;48; 55\u0026thinsp;~\u0026thinsp;65; 87\u0026thinsp;~\u0026thinsp;94; 105\u0026thinsp;~\u0026thinsp;123; 131\u0026thinsp;~\u0026thinsp;138; 145\u0026thinsp;~\u0026thinsp;173; 186\u0026thinsp;~\u0026thinsp;199; 216\u0026thinsp;~\u0026thinsp;225; 277\u0026thinsp;~\u0026thinsp;284; 309\u0026thinsp;~\u0026thinsp;316; 327\u0026thinsp;~\u0026thinsp;338; 341\u0026thinsp;~\u0026thinsp;348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eFBA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e21\u0026thinsp;~\u0026thinsp;23; 62\u0026thinsp;~\u0026thinsp;76; 95\u0026thinsp;~\u0026thinsp;104; 114\u0026thinsp;~\u0026thinsp;118; 138\u0026thinsp;~\u0026thinsp;141; 144\u0026thinsp;~\u0026thinsp;159; 164\u0026thinsp;~\u0026thinsp;176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eFBA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11\u0026thinsp;~\u0026thinsp;20; 35\u0026thinsp;~\u0026thinsp;45; 53\u0026thinsp;~\u0026thinsp;64; 89\u0026thinsp;~\u0026thinsp;97; 129\u0026thinsp;~\u0026thinsp;134; 140\u0026thinsp;~\u0026thinsp;148; 160\u0026thinsp;~\u0026thinsp;167; 243\u0026thinsp;~\u0026thinsp;257; 276\u0026thinsp;~\u0026thinsp;285; 295\u0026thinsp;~\u0026thinsp;299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eTIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14\u0026thinsp;~\u0026thinsp;19; 32\u0026thinsp;~\u0026thinsp;37; 39; 41\u0026thinsp;~\u0026thinsp;50; 76\u0026thinsp;~\u0026thinsp;80; 93\u0026thinsp;~\u0026thinsp;101; 103; 104; 108\u0026thinsp;~\u0026thinsp;110; 129\u0026thinsp;~\u0026thinsp;142; 170\u0026thinsp;~\u0026thinsp;184; 210\u0026thinsp;~\u0026thinsp;216; 220\u0026thinsp;~\u0026thinsp;228; 236\u0026thinsp;~\u0026thinsp;238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eConformational B-Cell Epitopes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003epCB1: 19\u0026thinsp;~\u0026thinsp;21-58-78-79; pCB2: 81\u0026ndash;84\u0026thinsp;~\u0026thinsp;87\u0026ndash;89\u0026thinsp;~\u0026thinsp;92-259-260-268-320\u0026thinsp;~\u0026thinsp;327;\u003c/p\u003e\n \u003cp\u003epCB3: 96\u0026ndash;101\u0026thinsp;~\u0026thinsp;104\u0026ndash;234\u0026thinsp;~\u0026thinsp;237-330-331; pCB4: 107\u0026thinsp;~\u0026thinsp;116\u0026ndash;281; pCB5: 300\u0026thinsp;~\u0026thinsp;305\u0026ndash;316\u0026thinsp;~\u0026thinsp;318.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003epCB1: 35\u0026thinsp;~\u0026thinsp;41-43-46-79\u0026thinsp;~\u0026thinsp;82; pCB2: 327\u0026thinsp;~\u0026thinsp;329\u0026ndash;333\u0026thinsp;~\u0026thinsp;335-337-340\u0026thinsp;~\u0026thinsp;342; pCB3: 184\u0026thinsp;~\u0026thinsp;187-228-229; pCB4: 117-118-280-283\u0026thinsp;~\u0026thinsp;287; pCB5: 321\u0026thinsp;~\u0026thinsp;325\u0026ndash;345\u0026thinsp;~\u0026thinsp;347\u0026ndash;358\u0026thinsp;~\u0026thinsp;361\u0026ndash;363.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFBA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003epCB1: 7\u0026thinsp;~\u0026thinsp;20-62-64; pCB2: 10\u0026thinsp;~\u0026thinsp;12\u0026ndash;34\u0026thinsp;~\u0026thinsp;36-39-40; pCB3: 100\u0026ndash;102\u0026thinsp;~\u0026thinsp;111\u0026ndash;161\u0026thinsp;~\u0026thinsp;175; pCB4: 118\u0026thinsp;~\u0026thinsp;126\u0026ndash;128\u0026thinsp;~\u0026thinsp;159.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFBA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003epCB1: 36\u0026ndash;45\u0026thinsp;~\u0026thinsp;47\u0026ndash;307\u0026thinsp;~\u0026thinsp;309\u0026ndash;311\u0026thinsp;~\u0026thinsp;315; pCB2: 137-140-141-144;\u003c/p\u003e\n \u003cp\u003epCB3: 317-321-323-327; pCB4: 61\u0026thinsp;~\u0026thinsp;63\u0026ndash;65\u0026thinsp;~\u0026thinsp;67\u0026ndash;91\u0026thinsp;~\u0026thinsp;94.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003epCB1: 1\u0026thinsp;~\u0026thinsp;15-38-40\u0026thinsp;~\u0026thinsp;42; pCB2: 46\u0026thinsp;~\u0026thinsp;51\u0026ndash;53\u0026thinsp;~\u0026thinsp;60\u0026ndash;62\u0026thinsp;~\u0026thinsp;64\u0026ndash;85\u0026thinsp;~\u0026thinsp;90; pCB3: 66\u0026thinsp;~\u0026thinsp;71\u0026ndash;73\u0026thinsp;~\u0026thinsp;75\u0026ndash;92\u0026thinsp;~\u0026thinsp;94-115-116-120-121; pCB4: 175\u0026thinsp;~\u0026thinsp;180\u0026ndash;183\u0026thinsp;~\u0026thinsp;188\u0026ndash;210\u0026thinsp;~\u0026thinsp;213\u0026ndash;217\u0026thinsp;~\u0026thinsp;223.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eAK: arginine kinase; FBA: Fructose-bisphosphate aldolase; TIM: triosephosphate-isomerase; pCB: predicted conformational B-cell epitopes.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBy integrating genome, transcriptome, and proteome datasets, this study first established a reproducible analytical framework that integrates multi-omics data to systematically construct a comprehensive proteome profile for HDH. This profile was subsequently interrogated against two authoritative allergen repositories and filtered using transcriptome and proteome expression levels. This integrated approach culminated in the establishment of a putative allergen repertoire for HDH, comprising 37 potential high-abundance allergens. Based on established nomenclature and biological functions, these allergens were categorized into 23 distinct groups. Currently, few allergens have been definitively characterized from the edible muscular tissue of abalone species, with only TM formally named in major databases \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Our research team previously not only confirmed this allergen but also systematically compared its cross-reactivity across three mollusk species and elucidated the underlying molecular mechanisms \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Another recognized allergen PM has been identified as a potential allergen in HDH \u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e, though it lacks formal nomenclature. While these focused studies on individual allergens are valuable, they are insufficient for simultaneously identifying other prevalent allergens in abalone, leading to potential false negatives in clinical diagnostics and an incomplete explanation for all allergic cases. Our systematic identification of high-abundance potential allergens in HDH has significantly expanded its known allergen spectrum. Beyond confirming the two previously reported major allergens, we identified 21 novel groups of high-abundance allergens. Experimental validations \u003cem\u003evia\u003c/em\u003e SDS-PAGE and Western-blot detected five of these high-abundance allergens, thereby providing a robust data foundation for improving clinical detection of abalone allergy.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIntegrated Multi-Omics and Experimental Validation Systematically Identify High-Abundance Putative Allergens in HDH\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis study systematically deciphered the allergen repertoire of HDH through an integrated strategy of multi-omics big data analysis and wet-lab experimentation, such as SDS-PAGE and Western-blot assays. A total of 23 putative allergen groups were identified, encompassing two previously reported groups \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e and 21 newly discovered groups. Transcriptome and proteome quantification revealed that the vast majority of these allergens are expressed at high levels in both omics layers, indicating they constitute major proteins within the muscular tissue. To date, only PM and TM have been identified as potential allergens in \u003cem\u003eHaliotis\u003c/em\u003e genus, with only TM formally recorded and named in authoritative databases\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. This scarcity of data has resulted in limited research on the cross-reactivity between HDH and other species. Our group previously conducted an in-depth comparison of the TM allergen from HDH with homologs from \u003cem\u003eAlectryonella plicatula\u003c/em\u003e and \u003cem\u003eMimachlamys nobilis\u003c/em\u003e, revealing not only a high sequence similarity but also multiple conserved B-cell epitopes capable of eliciting strong cross-reactivity \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. However, comparative sequence analysis and cross-reactivity studies for other high-abundance allergens remain entirely unexplored. This study, through comparison with authoritative allergen databases, discovered that allergens from HDH exhibited significant similarity to those derived from aquatic mollusks and crustaceans. This finding suggests a high likelihood of cross-reactivity among these species, which may trigger severe allergic reactions in patients. This conclusion is supported by previous studies noting the high sequence conservation and broad cross-reactivity between major allergens from mollusks and crustaceans \u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e. It is important to note that the cross-reactivity discussed above are currently inferred from sequence similarities, which requires further confirmations through serological inhibition experiments.\u003c/p\u003e \u003cp\u003eSubsequent Western-blot analysis demonstrated pronounced IgE-binding activity between sera from abalone-allergic volunteers and six major protein bands in crude muscular tissue extracts. Correlating these bands with their molecular weights and the predicted allergen repertoire led to the provisional identification of five bands as PM, AK, TM, TIM, and FBA. The identities of the first three (PM, AK, TM) were conclusively verified using existing specific IgG polyclonal antibodies from our laboratory. Multiple sequence alignment further revealed high sequence identity between these allergens and their homologs from other common crustaceans and mollusks \u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e, suggesting a strong potential to elicit allergic reactions.\u003c/p\u003e \u003cp\u003eThese findings not only confirmed the reliability of our multi-omics pipeline for predicting immunodominant allergens but also demonstrated that HDH possesses a diverse array of allergens with varying molecular weights and significant immunoreactivity. Notably, PM, AK, and TM exhibited significantly higher IgE-binding activity compared to others, identifying them as likely immunodominant allergens. The bands at ~\u0026thinsp;27 kDa and ~\u0026thinsp;19 kDa showed lower IgE reactivity and, lacking direct verification by specific antibodies, require confirmation through subsequent experiments such as Western-blot with additional antibodies, mass spectrometry, or recombinant protein studies. Furthermore, the \u0026gt;\u0026thinsp;130 kDa high-molecular-weight band, while showing strong IgE binding, remains to be definitively characterized. It may represent polymeric forms of known allergens, glycosylated variants, or a novel, unidentified allergen, providing a compelling direction for future research. Our results underscored that relying solely on SDS-PAGE of crude extracts for protein identification is unreliable, as this technique has limited resolution and may miss low-abundance or co-migrating proteins \u003csup\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/sup\u003e. Therefore, accurate and reliable identification of potential allergens necessitates integrating SDS-PAGE results with prior molecular weight predictions and multi-sequence alignment data from bioinformatics analyses.\u003c/p\u003e \u003cp\u003e \u003cem\u003eImpact of Linear and Conformational Epitopes of High-Abundance Allergens on Immunobinding Activity\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn this study, discrepant positive serum detection rates were observed between Dot-blot and Western-blot analyses (86 \u003cem\u003evs.\u003c/em\u003e 68). This discrepancy suggests varying capacities of different detection methods to present allergen epitopes. The Dot-blot method, which may better preserve native conformational structures, is capable of detecting both linear and conformational epitopes. In contrast, Western-blot is performed under denaturing conditions with SDS and β-me, primarily exposing linear epitopes, potentially leading to the omission of serum samples containing IgE antibodies targeting conformational epitopes \u003csup\u003e[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e. Subsequent experiments with two denaturants confirmed this hypothesis: SDS treatment did not significantly affect the binding activity of PM, AK, or TM to polyclonal antibodies, whereas β-me treatment markedly reduced the binding activity of AK, with minimal impact on PM and TM. Given that β-me disrupts protein tertiary structure by reducing disulfide bonds, this finding strongly suggests that the immunodominant epitopes of AK are critically dependent on its three-dimensional conformation stabilized by disulfide bonds. Our finding is consistent with reports on the conformational epitopes of homologous AK in other crustacean foods \u003csup\u003e[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo elucidate this phenomenon at the molecular level, we modeled the three-dimensional structures of the five immunodominant allergens and predicted their B-cell epitopes. The findings revealed that PM and TM are predominantly α-helical with relatively simple tertiary structures, and their B-cell epitopes are primarily linear. This explains why their antibody-binding activities were preserved under denaturing conditions (β-me treatment). Conversely, AK, TIM, and FBA possess more complex spatial structures rich in β-sheets and random coils, and their predicted B-cell epitopes are predominantly conformational. This aligns perfectly with the experimental result that AK\u0026rsquo;s binding activity is dependent on disulfide bonds (β-me sensitive) and partially explains the inconsistent positivity rates between Western-blot (linear epitope-based) and Dot-blot (conformation-preserving) assays: IgE antibodies in some patient sera may primarily target these conformational epitopes.\u003c/p\u003e \u003cp\u003eCollectively, these findings demonstrated that the five immunodominant allergens could be categorized into two groups based on the nature of their immunodominant epitopes: one represented by PM and TM, whose immunorecognition relies mainly on linear epitopes stable under denaturation; the other represented by AK, TIM, and FBA, whose immunorecognition highly depends on spatial conformational epitopes stabilized by factors such as disulfide bonds and is sensitive to reductive denaturation. This elucidation of the structure-immunoactivity relationship not only provides a molecular basis for understanding methodological discrepancies between assays but also offers crucial theoretical support for developing targeted allergen reduction processing technologies (e.g., reductive treatment for conformational allergens) and more precise diagnostic strategies (requiring coverage of both linear and conformational epitopes) \u003csup\u003e[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/sup\u003e. Future research should further validate the clinical relevance of these conformational epitopes using patient\u0026rsquo;s sera and explore their stability during thermal processing or digestion.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLimitations\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSeveral limitations in this study warrant consideration. First, while the use of genome data to identify multiple protein sequences enables the simultaneous discovery of numerous potential allergens in HDH, it does not guarantee the authentic expression of each protein. To address this, our systematic analysis of potential allergen sequences was integrated with quantitative transcriptome and proteome data. Furthermore, wet-lab methodologies, including SDS-PAGE and Western-blot, were employed to confirm the genuine presence of these putative allergens and to delineate their abundance levels, thereby enhancing the reliability of our findings. Second, although SDS-PAGE analysis of crude protein extracts from abalone muscular tissue provides a general overview of protein composition, it offers limited resolution for distinguishing proteins with similar molecular weights. In such instances, complementary approaches are necessary. As implemented in this study, Western-blot analysis could provide further confirmation, and more definitive identification can be achieved \u003cem\u003evia\u003c/em\u003e mass spectrometry, albeit with greater demands on time and resources \u003csup\u003e[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/sup\u003e. Finally, our integrative multi-omics analysis underscores the insufficiency of relying solely on transcriptome quantification to determine protein or potential allergen expression levels \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Protein expression is subject to post-translational modifications, such as glycosylation or acetylation, which could substantially alter final abundance. Therefore, we incorporated proteome sequencing and quantitative analysis, a strategy that delivers accurate protein quantification and provides a robust foundation for subsequent experimental validation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBy integrating sequence identification and matching from genome, transcriptome, and proteome data, along with quantitative expression analyses from the latter two platforms, this study systematically deciphered the high-abundance allergen repertoire of HDH, a species of high consumption and frequent allergic incidence. We identified a total of 23 groups of potential high-abundance allergens, substantially addressing the prior paucity of known allergens for this species. Utilizing positive serum samples from 86 abalone-allergic volunteers, we confirmed five allergens exhibiting significant IgE-binding activity. These allergens provide reliable targets for clinical diagnostics, promising to improve detection rates for HDH allergy, furnish a more accurate basis for assessing its population prevalence, and aid in more precise abalone-specific detection assays.\u003c/p\u003e \u003cp\u003eNotably, we observed considerable divergence in the secondary structures and B-cell epitope conformations among these five allergens. This conformational dichotomy (linear \u003cem\u003evs.\u003c/em\u003e spatial) critically influences their detectability in Western-blot assays and elucidates the molecular basis for the observed discrepancy between Western-blot positivity and the total number of positive sera. Furthermore, our systematic comparison revealed significant sequence similarity between putative HDH allergens and homologs from other abalone species, crustaceans, and terrestrial arthropods. This strongly indicates a broad potential for cross-reactivity among these groups, offering valuable data to guide dietary management and cross-allergy prevention for susceptible individuals.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo enhance the reproducibility of the analytical workflow and its outcomes, we have curated and openly deposited the source codes for the proposed ‘Multi-Omics integration framework for High-abundance Allergen Discovery in Abalone (MOHADA)’ in the GitHub repository (Link: https://github.com/strao1986/MOHADA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.T. Rao and G.M. Liu overall designed and supervised the whole study. S.T. Rao, S. Zhi and X.Y. Han obtained foot tissue samples of HDH and performed the preprocessing steps. S. Zhi and J. H. Que obtained omics data of HDH and conducted full analyses. X.Y. Han, T. B. Chen and Y. N. Xie collected serum samples and performed immunobinding assays. X.Y. Han, Q. Xiao and S. Y. Yang predicted the epitopes of allergens and further located them in the 3D structures. S.T. Rao, G.M. Liu and Stephen K.W. Tsui contributed to drafting and reviewing of manuscript. The corresponding authors attest that all listed authors met the authorship criteria and that no others meeting the criteria were omitted. All listed authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Young Scientific Innovation Project of the Natural Science Foundation of Fujian Province (2025J08196), and the Research start-up funds for high-level talents from Fujian Medical University (XRCZX2021009). The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the support from several public databases for openly sharing their datasets. We extend our sincere gratitude to the NCBI database for its systematic management and open sharing of the genome data for the \u003cem\u003eHaliotis discus hannai\u003c/em\u003e. Furthermore, the allergen sequence information and other data stored in authoritative databases, including the WHO/IUIS Allergen Nomenclature, AllergenOnline, and UniProt, proved invaluable for advancing our research findings. These publicly available resources served as the cornerstone for our analytical framework in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within this paper and its supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was conducted in accordance with the guidelines of the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (approval no.: [2022]075). Positive and negative serum samples from volunteers with HDH allergy were provided by the First Affiliated Hospital of Fujian Medical University (Fuzhou, Fujian Province, China).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLeung, A.S.Y., G.W.K. Wong, and M.L.K. Tang, \u003cem\u003eFood allergy in the developing world.\u003c/em\u003e Journal of Allergy and Clinical Immunology, 2018. \u003cstrong\u003e141\u003c/strong\u003e(1): p. 76\u0026ndash;78.e71.\u003c/li\u003e\n\u003cli\u003eAllen, K.J. and J.J. Koplin, \u003cem\u003eThe Epidemiology of IgE-Mediated Food Allergy and Anaphylaxis.\u003c/em\u003e Immunology and Allergy Clinics of North America, 2012. \u003cstrong\u003e32\u003c/strong\u003e(1): p. 35\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eFeng, H., J. Zhou, Y. 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Xie, et al., \u003cem\u003eOn-Chip Discovery of Allergens from the Exudate of Large Yellow Croaker (Larimichthys Crocea) Muscle Food by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry.\u003c/em\u003e Journal of Agricultural and Food Chemistry, 2023. \u003cstrong\u003e71\u003c/strong\u003e(36): p. 13546\u0026ndash;13553.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-science-of-food","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjscifood","sideBox":"Learn more about [npj Science of Food](http://www.nature.com/npjscifood/)","snPcode":"41538","submissionUrl":"https://submission.springernature.com/new-submission/41538/3","title":"npj Science of Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Haliotis discus hannai, Muli-omics sequencing data, Allergenome, IgE-binding activity, Spatial conformation of allergens, B-cell epitopes","lastPublishedDoi":"10.21203/rs.3.rs-9243755/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9243755/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe rising consumption of \u003cem\u003eHaliotis discus hannai\u003c/em\u003e (HDH) has brought its associated food allergy into focus. However, studies on its allergens and their role in patient reactions remain limited. This study aimed to construct a high-abundance allergen profile for HDH by integrating multi-omics data and to identify its immunodominant allergens through immunobinding assays with volunteers’ sera.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We first constructed a predicted proteome for HDH using in-house generated transcriptome data along with public genome resources. High-throughput proteome and quantitative expression data were employed to establish a high-abundance allergen profile. Immunobinding assays, incorporating Western-blot analyses with volunteer-derived sera, were utilized to delineate allergens characterized by high immunoreactivity. Their tertiary structures and B-cell epitopes were subsequently analyzed using multiple bioinformatics tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We predicted 53,245 protein-coding genes from a high-quality assembled genome and transcriptome, of which 33,109 were functionally annotated. Proteome sequencing identified 3,916 reliably expressed proteins. Cross-referencing with two authoritative allergen databases revealed 37 high-abundance allergens in HDH. Sequence comparisons indicated a high similarity for homologous allergens between HDH and other aquatic mollusks, crustaceans, and terrestrial arthropods, indicating a potential widespread cross-reactivity.\u003c/p\u003e\n\u003cp\u003eDot-blot and Western-blot assays using sera from 90 volunteers identified six protein bands with significant IgE-binding activity. Based on molecular weight and prior allergen profiling, the six bands were hypothesized to be a polymer, paramyosin (PM), arginine kinase (AK), tropomyosin (TM), triosephosphate isomerase (TIM), and fructose-bisphosphate aldolase (FBA), respectively. Subsequent immunobinding assays identified PM, AK, TM, TIM, and FBA as the immunodominant allergens, as they each exhibited relatively high seropositivity rate in volunteers’ sera. Treatment with denaturants (SDS and β-me) revealed that PM and TM are primarily linear allergens, whereas AK is predominantly conformational, explaining the higher detection rate in Dot-blot versus Western-blot. Homology modeling showed that PM and TM possess relatively simple structures dominated by α-helices, while AK, TIM, and FBA have more complex tertiary structures containing significant proportions of β-sheets and random coil. B-cell epitope prediction indicated that PM and TM harbor mainly linear epitopes, whereas AK, TIM, and FBA possess additional conformational epitopes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study provided the first comprehensive allergen profile for HDH by integrating multi-omics data and immunoassays, and further identified its immunodominant allergens. Through bioinformatics predictions and denaturant experiments, we elucidated the structural conformations and B-cell epitopes of immunodominant allergens. These findings offer crucial data for the precise identification of abalone allergens, supporting the development of accurate diagnostic strategies for affected individuals.\u003c/p\u003e","manuscriptTitle":"Integrated multi-omics and serological profiling identifies five immunodominant allergens from the high-abundance protein repertoire of Haliotis discus hannai","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:10:52","doi":"10.21203/rs.3.rs-9243755/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T13:45:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T08:17:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T12:30:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285905251758740451225926530131263110445","date":"2026-04-15T13:03:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311643332450177415550137279967269723291","date":"2026-04-15T06:57:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132619143897751834847942941988304340380","date":"2026-04-12T17:27:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88262368257699668911730302194071104767","date":"2026-04-09T20:06:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T13:12:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T06:27:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T04:27:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Science of Food","date":"2026-03-27T10:41:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-science-of-food","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjscifood","sideBox":"Learn more about [npj Science of Food](http://www.nature.com/npjscifood/)","snPcode":"41538","submissionUrl":"https://submission.springernature.com/new-submission/41538/3","title":"npj Science of Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f2523a2-f27f-4c64-bdde-6e74ebdcde40","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T13:45:50+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":66374322,"name":"Biological sciences/Biochemistry"},{"id":66374323,"name":"Biological sciences/Biological techniques"},{"id":66374324,"name":"Biological sciences/Biotechnology"},{"id":66374325,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":66374326,"name":"Biological sciences/Immunology"},{"id":66374327,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2026-05-04T13:54:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:10:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9243755","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9243755","identity":"rs-9243755","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}