Shotgun proteomics approaches for authentication, biological analyses, and allergen detection in feed and food-grade insect species

Increasing protein identifications in bottom-up proteomics of T. castaneum - Exploiting synergies of protein biochemistry and bioinformatics

Depending on the respective research question, LC-MS/MS based bottom-up proteomics poses challenges from the initial biological sample all the way to data evaluation. The focus of this study was to investigate the influence of sample preparation techniques and data analysis parameters on protein identification in Tribolium castaneum by applying free software proteomics platform Max Quant. Multidimensional protein extraction strategies in combination with electrophoretic or chromatographic off-line protein pre-fractionation were applied to enhance the spectrum of isolated proteins from T. castaneum and reduce the effect of co-elution and ion suppression effects during nano-LC-MS/MS measurements of peptides. For comprehensive data analysis, MaxQuant was used for protein identification and R for data evaluation. A wide range of parameters were evaluated to gain reproducible, reliable, and significant protein identifications. A simple phosphate buffer, pH 8, containing protease and phosphatase inhibitor cocktail and application of gentle extraction conditions were used as a first extraction step for T.castaneum proteins. Furthermore, a two-dimensional extraction procedure in combination with electrophoretic pre-fractionation of extracted proteins and subsequent in-gel digest resulted in almost 100% increase of identified proteins when compared to chromatographic fractionation as well as one-pot-analysis. The additionally identified proteins could be assigned to new molecular functions or cell compartments, emphasizing the positive effect of extended sample preparation in bottom-up proteomics. Besides the number of peptides during post-processing, MaxQuant's Match between Runs exhibited a crucial effect on the number of identified proteins. A maximum relative standard deviation of 2% must be considered for the data analysis. Our work with Tribolium castaneum larvae demonstrates that sometimes - depending on matrix and research question - more complex and time-consuming sample preparation can be advantageous for isolation and identification of additional proteins in bottom-up proteomics.

Allergenicity of alternative proteins: research hotspots, new findings, evaluation strategies, regulatory status, and future trends: a bibliometric analysis

As the world population rises, the demand for protein increases, leading to a widening gap in protein supply. There is an unprecedented interest in the development of alternative proteins, but their allergenicity has raised consumer concerns. This review aims to highlight and correlate the current research status of allergenicity studies on alternative proteins based on previously published studies. Current research keywords, hotspots and trends in alternative protein sensitization were analyzed using a mixed-method approach that combined bibliometric analysis and literature review. According to the bibliometric analysis, current research is primarily focused on food science, agriculture, and immunology. There are significant variations in the type and amount of allergens found in alternative proteins. A significant amount of research has been focused on studying plant-based proteins and the cross-reactivity of insect proteins. The allergenicity of alternative proteins has not been studied extensively or in depth. The allergenicity of other alternative proteins and the underlying mechanisms warrant further study. In addition, the lack of a standardized allergy assessment strategy calls for additional efforts by international organizations and collaborations among different countries. This review provides new research and regulatory perspectives for the safe utilization of alternative proteins in human food systems.

Unveiling the potential of proteomics in addressing food and feed safety challenges

The food and feed sector in Europe is rapidly evolving to address contemporary challenges, striving for fairer, safer, greener and more sustainable food systems. This includes the exploration of new protein sources for human consumption and animal feed such as protein derived from insects, algae or novel plant-derived proteins, and the re-evaluation of existing sources like processed animal protein (PAP). To generate reliable data on the diverse array of emerging protein sources for future food and feed safety assessments, a growing demand for the development and implementation of advanced analytical techniques exists. New approach methodologies (NAMs) including, mass spectrometry (MS)-based proteomics methods have been emerging as valuable techniques which potentially can be implemented in regulatory laboratory settings to complement conventional approaches in this realm. These MS-driven strategies have already proven their utility in diverse applications, including the detection of prohibited substances in feed, identification of allergens, differentiation of fish species in complex mixtures for fraud detection and the verification of novel foods and alternative protein sources. This EU-FORA programme was focused on three core objectives namely: (i) the training of the fellow in utilising MS-based proteomics for food and feed safety analyses, (ii) the involvement of the fellow in the development of standardised operating procedures (SOP) for targeted and non-targeted proteomic MS-based workflows for species and tissues specific PAP identification in a national reference laboratory (NRL) and (iii) the transfer and implementation of MS-based approaches and standardised protocols for PAP analysis at the fellow's home institution. Altogether, this programme facilitates the broadening and diversification of use of MS-based proteomic methodologies for reinforcing their significance within the domains of food and feed safety research and regulatory science applications.

Advanced Proteomic and Bioinformatic Tools for Predictive Analysis of Allergens in Novel Foods

In recent years, novel food is becoming an emerging trend increasingly more demanding in developed countries. Food proteins from vegetables (pulses, legumes, cereals), fungi, bacteria and insects are being researched to introduce them in meat alternatives, beverages, baked products and others. One of the most complex challenges for introducing novel foods on the market is to ensure food safety. New alimentary scenarios drive the detection of novel allergens that need to be identified and quantified with the aim of appropriate labelling. Allergenic reactions are mostly caused by proteins of great abundance in foods, most frequently of small molecular mass, glycosylated, water-soluble and with high stability to proteolysis. The most relevant plant and animal food allergens, such as lipid transfer proteins, profilins, seed storage proteins, lactoglobulins, caseins, tropomyosins and parvalbumins from fruits, vegetables, nuts, milk, eggs, shellfish and fish, have been investigated. New methods for massive screening in search of potential allergens must be developed, particularly concerning protein databases and other online tools. Moreover, several bioinformatic tools based on sequence alignment, motif identification or 3-D structure predictions should be implemented as well. Finally, targeted proteomics will become a powerful technology for the quantification of these hazardous proteins. The ultimate objective is to build an effective and resilient surveillance network with this cutting-edge technology.

Unpacking the Proteome and Metaproteome of the Black Soldier Fly Larvae: Efficacy and Complementarity of Multiple Protein Extraction Protocols

The larvae of the black soldier fly (BSF), Hermetia illucens (Diptera: Stratiomyidae), have demonstrated the ability to efficiently bioconvert organic waste into a sustainable source of food and feed, but fundamental biology remains to be discovered to exploit their full biodegradative potential. Herein, LC-MS/MS was used to assess the efficiency of eight differing extraction protocols to build foundational knowledge regarding the proteome landscape of both the BSF larvae body and gut. Each protocol yielded complementary information to improve BSF proteome coverage. Protocol 8 (liquid nitrogen, defatting, and urea/thiourea/chaps) was better than all other protocols for the protein extraction from larvae gut samples, and the exclusion of defatting steps yielded the highest number of proteins for the larval body samples. Protocol-specific functional annotation using protein level information has shown that the selection of extraction buffer can affect protein detection and their associated functional classes within the measured BSF larval gut proteome. A targeted LC-MRM-MS experiment was performed on the selected enzyme subclasses to assess the influence of protocol composition using peptide abundance measurements. Metaproteome analysis of the BSF larvae gut has uncovered the prevalence of two bacterial phyla: actinobacteria and proteobacteria. We envisage that using complementary extraction protocols and investigating the proteome from the BSF body and gut separately will expand the fundamental knowledge of the BSF proteome and thereby provide translational opportunities for future research to enhance their efficiency for waste degradation and contribution to the circular economy.

A Coding Basis and Three-in-One Integrated Data Visualization Method 'Ana' for the Rapid Analysis of Multidimensional Omics Dataset

With innovations and advancements in analytical instruments and computer technology, omics studies based on statistical analysis, such as phytochemical omics, oilomics/lipidomics, proteomics, metabolomics, and glycomics, are increasingly popular in the areas of food chemistry and nutrition science. However, a remaining hurdle is the labor-intensive data process because learning coding skills and software operations are usually time-consuming for researchers without coding backgrounds. A MATLAB^® coding basis and three-in-one integrated method, 'Ana', was created for data visualizations and statistical analysis in this work. The program loaded and analyzed an omics dataset from an Excel^® file with 7 samples * 22 compounds as an example, and output six figures for three types of data visualization, including a 3D heatmap, heatmap hierarchical clustering analysis, and principal component analysis (PCA), in 18 s on a personal computer (PC) with a Windows 10 system and in 20 s on a Mac with a MacOS Monterey system. The code is rapid and efficient to print out high-quality figures up to 150 or 300 dpi. The output figures provide enough contrast to differentiate the omics dataset by both color code and bar size adjustments per their higher or lower values, allowing the figures to be qualified for publication and presentation purposes. It provides a rapid analysis method that would liberate researchers from labor-intensive and time-consuming manual or coding basis data analysis. A coding example with proper code annotations and completed user guidance is provided for undergraduate and postgraduate students to learn coding basis statistical data analysis and to help them utilize such techniques for their future research.

compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets

It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.

Authentication of Edible Insects’ Powders by the Combination of DART-HRMS Signatures: The First Application of Ambient Mass Spectrometry to Screening of Novel Food

This feasibility study reports the use of direct analysis in real-time high-resolution mass spectrometry (DART-HRMS) in profiling the powders from edible insects, as well as the potential for the identification of different insect species by classification modeling. The basis of this study is the revolution that has occurred in the field of analytical chemistry, with the improved capability of ambient mass spectrometry to authenticate food matrices. In this study, we applied DART-HRMS, coupled with mid-level data fusion and a learning method, to discriminate between Acheta domesticus (house cricket), Tenebrio molitor (yellow mealworm), Locusta migratoria (migratory locust), and Bombyx mori (silk moth). A distinct metabolic fingerprint was observed for each edible insect species, while the Bombyx mori fingerprint was characterized by highly abundant linolenic acid and quinic acid; palmitic and oleic acids are the statistically predominant fatty acids in black soldier fly (Hermetia illucens). Our chemometrics also revealed that the amino acid proline is a discriminant molecule in Tenebrio molitor, whereas palmitic and linoleic acids are the most informative molecular features of the house cricket (Acheta domesticus). Good separation between the four different insect species was achieved, and cross-validation gave 100% correct identification for all training samples. The performance of the random forest classifier was examined on a test set and produced excellent results, in terms of overall accuracy, sensitivity, and specificity. These results demonstrate the reliability of the DART-HRMS as a screening method in a future quality control scenario to detect complete substitution of insect powders.

Effect of High Hydrostatic Pressure Intensity on Structural Modifications in Mealworm (Tenebrio molitor) Proteins

Processing edible insects into protein extracts may improve consumer acceptability. However, a better understanding of the effects of food processing on the proteins is needed to facilitate their incorporation into food matrices. In this study, soluble proteins from Tenebrio molitor (10% w/v) were pressurized using high hydrostatic pressure (HHP) at 70–600 MPa for 5 min and compared to a non-pressurized control (0.1 MPa). Protein structural modifications were evaluated using turbidity measurement, particle-size distribution, intrinsic fluorescence, surface hydrophobicity, gel electrophoresis coupled with mass spectrometry, and transmission electron microscopy (TEM). The observed decrease in fluorescence intensity, shift in the maximum emission wavelength, and increase in surface hydrophobicity reflected the unfolding of mealworm proteins. The formation of large protein aggregates consisting mainly of hexamerin 2 and ⍺-amylase were confirmed by protein profiles on gel electrophoresis, dynamic light scattering, and TEM analysis. The typical aggregate shape and network observed by TEM after pressurization indicated the potential involvement of myosin and actin in aggregate formation, and these were detected by mass spectrometry. For the first time, the identification of mealworm proteins involved in protein aggregation phenomena under HHP was documented. This work is the first step in understanding the mealworm protein–protein interactions necessary for the development of innovative insect-based ingredients in food formulations.