Advanced proteomics and systems biology applied to study food allergy

Allergic to bureaucracy? Regulatory allergenicity assessments of novel food: Motivations, challenges, compromises, and possibilities

New sources of proteins are essential to meet the demands of the growing world population and evolving food trends. Assessing the allergenicity of proteins in novel food (NF) poses a significant food safety regulatory challenge. The Codex Alimentarius Commission presented an allergenicity assessment protocol for genetically modified (GM) foods, which can also be adapted for NF. Since no single laboratory test can adequately predict the allergenic potential of NF, the protocol follows a weight-of-evidence approach, evaluated by experts, as part of a risk management process. Regulatory bodies worldwide have adopted this safety protocol, which, among other things, promotes global harmonization. This review unravels the reliability and various motivations, terms, concepts, and approaches of allergenicity assessments, aiming to enhance understanding among manufacturers and the public. Health Canada, Food Safety Commission JAPAN, and Food Standards Australia New Zealand were surveyed, focusing on the European Food Safety Authority and the US Food Safety Administration for examples of scientific opinions regarding allergenicity assessments for novel and GM foods, from 2019 to 2023. According to our findings, current regulatory allergenicity assessments for NF approval primarily rely on literature reviews. Only a few of the NF assessments proactively presented additional tests. We recommend conducting bioinformatic analyses on NF when a panel of experts deems that there is insufficient prior scientific research.

Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework

Plant-allergenic proteins (PAPs) have the potential to induce allergic reactions in certain individuals. While these proteins are generally innocuous for the majority of people, they can elicit an immune response in those with particular sensitivities. Thus, screening and prioritizing the allergenic potential of plant proteins is indispensable for the development of diagnostic tools, therapeutic interventions or medications to treat allergic reactions. However, investigating the allergenic potential of plant proteins based on experimental methods is costly and labour-intensive. Therefore, we develop StackPAP, a three-layer stacking ensemble framework for accurate large-scale identification of PAPs. In StackPAP, at the first layer, we conducted a comprehensive analysis of an extensive set of feature descriptors. Subsequently, we selected and fused five potential sequence-based feature descriptors, including amphiphilic pseudo-amino acid composition, dipeptide deviation from expected mean, amino acid composition, pseudo amino acid composition and dipeptide composition. Additionally, we applied an efficient genetic algorithm (GA-SAR) to determine informative feature sets. In the second layer, 12 powerful machine learning (ML) methods, in combination with all the informative feature sets, were employed to construct a pool of base classifiers. Finally, 13 potential base classifiers were selected using the GA-SAR method and combined to develop the final meta-classifier. Our experimental results revealed the promising prediction performance of StackPAP, with an accuracy, Matthew's correlation coefficient and AUC of 0.984, 0.969 and 0.993, respectively, as judged by the independent test dataset. In conclusion, both cross-validation and independent test results indicated the superior performance of StackPAP compared with several ML-based classifiers. To accelerate the identification of the allergenicity of plant proteins, we developed a user-friendly web server for StackPAP (https://pmlabqsar.pythonanywhere.com/StackPAP). We anticipate that StackPAP will be an efficient and useful tool for rapidly screening PAPs from a vast number of plant proteins.Communicated by Ramaswamy H. Sarma.

Cross-reactivity analysis of milk proteins from different goat breeds with cow's milk allergens using a proteomic approach

INTRODUCTION

Goat's milk thought to be a good substitute for cow's milk protein allergic (CMPA) individuals. However, there is growing evidence that their proteins have cross-reactivities with cow's milk allergens. This study aimed to profile and compare milk proteins from different goat breeds that have cross-reactivity to cow's milk allergens.

METHODOLOGY

Proteomics was used to compare protein extracts of skim milk from Saanen, Jamnapari, and Toggenburg. Cow's milk was used as a control. IgE-immunoblotting and mass spectrometry were used to compare and identify proteins that cross-reacted with serum IgE from CMPA patients (n = 10).

RESULTS

The analysis of IgE-reactive proteins revealed that the protein spots identified with high confidence were proteins homologous to common cow's milk allergens such as α-S1-casein (αS1-CN), β-casein (β-CN), κ-casein (κ-CN), and beta-lactoglobulin (β-LG). Jamnapari's milk proteins were found to cross-react with four major milk allergens: α-S1-CN, β-CN, κ-CN, and β-LG. Saanen goat's milk proteins, on the other hand, cross-reacted with two major milk allergens, α-S1-CN and β-LG, whereas Toggenburg goat's milk proteins only react with one of the major milk allergens, κ-CN.

CONCLUSION

These findings may help in the development of hypoallergenic goat milk through cross-breeding strategies of goat breeds with lower allergenic milk protein contents.

Gut Microbiome Proteomics in Food Allergies

Food allergies (FA) have dramatically increased in recent years, particularly in developed countries. It is currently well-established that food tolerance requires the strict maintenance of a specific microbial consortium in the gastrointestinal (GI) tract microbiome as alterations in the gut microbiota can lead to dysbiosis, causing inflammation and pathogenic intestinal conditions that result in the development of FA. Although there is currently not enough knowledge to fully understand how the interactions between gut microbiota, host responses and the environment cause food allergies, recent advances in '-omics' technologies (i.e., proteomics, genomics, metabolomics) and in approaches involving systems biology suggest future headways that would finally allow the scientific understanding of the relationship between gut microbiome and FA. This review summarizes the current knowledge in the field of FA and insights into the future advances that will be achieved by applying proteomic techniques to study the GI tract microbiome in the field of FA and their medical treatment. Metaproteomics, a proteomics experimental approach of great interest in the study of GI tract microbiota, aims to analyze and identify all the proteins in complex environmental microbial communities; with shotgun proteomics, which uses liquid chromatography (LC) for separation and tandem mass spectrometry (MS/MS) for analysis, as it is the most promising technique in this field.

Applied Proteomics in 'One Health'

‘One Health’ summarises the idea that human health and animal health are interdependent and bound to the health of ecosystems. The purpose of proteomics methodologies and studies is to determine proteins present in samples of interest and to quantify changes in protein expression during pathological conditions. The objectives of this paper are to review the application of proteomics technologies within the One Health concept and to appraise their role in the elucidation of diseases and situations relevant to One Health. The paper develops in three sections. Proteomics Applications in Zoonotic Infections part discusses proteomics applications in zoonotic infections and explores the use of proteomics for studying pathogenetic pathways, transmission dynamics, diagnostic biomarkers and novel vaccines in prion, viral, bacterial, protozoan and metazoan zoonotic infections. Proteomics Applications in Antibiotic Resistance part discusses proteomics applications in mechanisms of resistance development and discovery of novel treatments for antibiotic resistance. Proteomics Applications in Food Safety part discusses the detection of allergens, exposure of adulteration, identification of pathogens and toxins, study of product traits and characterisation of proteins in food safety. Sensitive analysis of proteins, including low-abundant ones in complex biological samples, will be achieved in the future, thus enabling implementation of targeted proteomics in clinical settings, shedding light on biomarker research and promoting the One Health concept.

Current Trends in Proteomic Advances for Food Allergen Analysis

Food allergies are a global food challenge. For correct food labelling, the detection and quantification of allergens are necessary. However, novel product formulations and industrial processes produce new scenarios, which require much more technological developments. For this purpose, OMICS technologies, especially proteomics, seemed to be relevant in this context. This review summarises the current knowledge and studies that used proteomics to study food allergens. In the case of the allergenic proteins, a wide variety of isoforms, post-translational modifications and other structural changes during food processing can increase or decrease the allergenicity. Most of the plant-based food allergens are proteins with biological functions involved in storage, structure, and plant defence. The allergenicity of these proteins could be increased by the presence of heavy metals, air pollution, and pesticides. Targeted proteomics like selected/multiple reaction monitoring (SRM/MRM) have been very useful, especially in the case of gluten from wheat, rye and barley, and allergens from lentil, soy, and fruit. Conventional 1D and 2-DE immunoblotting have been further widely used. For animal-based food allergens, the widely used technologies are 1D and 2-DE immunoblotting followed by MALDI-TOF/TOF, and more recently LC-MS/MS, which is becoming useful to assess egg, fish, or milk allergens. The detection and quantification of allergenic proteins using mass spectrometry-based proteomics are promising and would contribute to greater accuracy, therefore improving consumer information.

Proteomic Strategies to Evaluate the Impact of Farming Conditions on Food Quality and Safety in Aquaculture Products

This review presents the primary applications of various proteomic strategies to evaluate the impact of farming conditions on food quality and safety in aquaculture products. Aquaculture is a quickly growing sector that represents 47% of total fish production. Food quality, dietary management, fish welfare, the stress response, food safety, and antibiotic resistance, which are covered by this review, are among the primary topics in which proteomic techniques and strategies are being successfully applied. The review concludes by outlining future directions and potential perspectives.

Crystal-C: A Computational Tool for Refinement of Open Search Results

Shotgun proteomics using liquid chromatography coupled to mass spectrometry (LC-MS) is commonly used to identify peptides containing post-translational modifications. With the emergence of fast database search tools such as MSFragger, the approach of enlarging precursor mass tolerances during the search (termed "open search") has been increasingly used for comprehensive characterization of post-translational and chemical modifications of protein samples. However, not all mass shifts detected using the open search strategy represent true modifications, as artifacts exist from sources such as unaccounted missed cleavages or peptide co-fragmentation (chimeric MS/MS spectra). Here, we present Crystal-C, a computational tool that detects and removes such artifacts from open search results. Our analysis using Crystal-C shows that, in a typical shotgun proteomics data set, the number of such observations is relatively small. Nevertheless, removing these artifacts helps to simplify the interpretation of the mass shift histograms, which in turn should improve the ability of open search-based tools to detect potentially interesting mass shifts for follow-up investigation.

Characterization of the Jumbo Squid (Dosidicus gigas) Skin By-Product by Shotgun Proteomics and Protein-Based Bioinformatics

Jumbo squid (Dosidicus gigas) is one of the largest cephalopods, and represents an important economic fishery in several regions of the Pacific Ocean, from southern California in the United States to southern Chile. Large and considerable discards of this species, such as skin, have been reported to constitute an important source of potential by-products. In this paper, a shotgun proteomics approach was applied for the first time to the characterization of the jumbo squid (Dosidicus gigas) skin proteome. A total of 1004 different peptides belonging to 219 different proteins were identified. The final proteome compilation was investigated by integrated in-silico studies, including gene ontology (GO) term enrichment, pathways, and networks studies. Potential new valuable bioactive peptides such as antimicrobial, bioactive collagen peptides, antihypertensive and antitumoral peptides were predicted to be present in the jumbo squid skin proteome. The integration of the global proteomics results and the bioinformatics analysis of the jumbo squid skin proteome show a comprehensive knowledge of this fishery discard and provide potential bioactive peptides of this marine by-product.

Current and Emerging Technologies for the Detection of Norovirus from Shellfish

Reports of norovirus infections associated with the consumption of contaminated bivalve molluscan shellfish negatively impact both consumers and commercial shellfish operators. Current virus recovery and PCR detection methods can be expensive and time consuming. Due to the lack of rapid, user-friendly and onsite/infield methods, it has been difficult to establish an effective virus monitoring regime that is able to identify contamination points across the production line (i.e., farm-to-plate) to ensure shellfish quality. The focus of this review is to evaluate current norovirus detection methods and discuss emerging approaches. Recent advances in omics-based detection approaches have the potential to identify novel biomarkers that can be incorporated into rapid detection kits for onsite use. Furthermore, some omics techniques have the potential to simultaneously detect multiple enteric viruses that cause human disease. Other emerging technologies discussed include microfluidic, aptamer and biosensor-based detection methods developed to detect norovirus with high sensitivity from a simple matrix. Many of these approaches have the potential to be developed as user-friendly onsite detection kits with minimal costs. However, more collaborative efforts on research and development will be required to commercialize such products. Once developed, these emerging technologies could provide a way forward that minimizes public health risks associated with shellfish consumption.

Molecular characterization of B-cell epitopes for the major fish allergen, parvalbumin, by shotgun proteomics, protein-based bioinformatics and IgE-reactive approaches

Parvalbumins beta (β-PRVBs) are the main fish allergens. The only proven and effective treatment for this type of hypersensitivity is to consume a diet free of fish. We present the molecular characterization of B-cell epitopes by shotgun proteomics of different β-PRVBs combined with protein-based bioinformatics and IgE-reactive approaches. The final goal of this work is to identify potential peptide vaccine candidates for fish allergy. Purified β-PRVBs from the main fifteen different fish species that cause allergy were analyzed by shotgun proteomics. Identified β-PRVBs peptide sequences and ninety-eight β-PRVB protein sequences from UniProtKB were combined, aligned and analyzed to determine B-cell epitopes using the Kolaskar and Tongaonkar algorithm. The highest rated predicted B-cell peptide epitopes were evaluated by ELISA using the corresponding synthetic peptides and sera from healthy and fish allergic patients. A total of 35 peptides were identified as B-cell epitopes. The top B-cell peptide epitopes (LKLFLQV, ACAHLCK, FAVLVKQ and LFLQNFV) that may induce protective immune responses were selected as potential peptide vaccine candidates. The 3D model of these peptides were located in the surface of the protein. This study provides the global characterization of B-cell epitopes for all β-PRVBs sequences that will facilitate the design of new potential immunotherapies. SIGNIFICANCE: This work provides the global characterization of B-cell epitopes for all β-PRVBs sequences by Shotgun Proteomics combined with Protein-based Bioinformatics and IgE-reactive approaches. This study will increase our understanding of the molecular mechanisms whereby fish allergens elicit allergic reactions and will facilitate the design of new potential peptide vaccine candidates.