Advances in Proteomics Approaches for Food Authentication

Introduction

Food authentication is a process that verifies if a food is in compliance with its label description. This may include the origin (species, geographical or genetic), production method (conventional, organic, traditional procedures, free range), or processing technologies (irradiation, freezing, microwave heating). The declaration of specific quality attributes in high-value products is of particular interest since these products are often the target of fraudulent labeling (Aung and Chang, 2014). Due to the globalization of food markets and the resulting increase in variability and availability of food products from other countries, consumers are increasingly interested in knowing the geographical origin along with the assumed quality of the products they eat and drink. The quality assurance and the methods used to authenticate foodstuffs are of great interest both from commercial and legal points of view (Drivelos and Georgiou, 2012). Due to the increasing demand for food, there is a higher risk that adulteration can occur throughout the food chain.

Food products can be deliberately substituted, partially or entirely, with similar, lower quality, and cheaper counterparts or unintentional errors can cause inadvertent mislabeling of products (Moore et al., 2012). Examples of common frauds are replacing key ingredients with cheaper alternatives, mislabeling of the animal species used in a food product, incorrectly labeling of ingredient proportions, selling aquaculture fish as wild-caught, and labeling ordinary foods as organic (Ortea et al., 2016). The non-declared introduction of food ingredients, such as toxic or allergenic products may be harmful to consumer's health, thus representing a potential public health risk (Spink and Moyer, 2011). For all these reasons, the authentication of food labeling claims must be guaranteed, and therefore, accurate and reliable analytical methods are needed in order to verify that the components used in a food product are of the nature or quality demanded by the purchaser and compatible with the declaration of the seller.

The most common type of food fraud, reported in 95 per cent of publications, is substitution (Stamatis et al., 2015) of an original ingredient with a similar cheaper one, difficult to recognize by the consumer and difficult to detect by routine analytical techniques. In animal-origin food industry, important food frauds occur in meat, honey, milk, and dairy products, fish and seafood (Cubero-Leon et al., 2014). Identification of the species' origin is also important to consumers due to the economic loss arising on the grounds of fraudulent substitution, as well as for health- related (food allergies) and religious reasons (Asensio et al., 2008). This chapter discusses the various proteomics tools used in detection of food adulterants in food technology.

Proteomics Tool for Food Technology Research

Proteomics is emerging as a powerful tool for food-technology research (Ortea et al., 2012; Carrera et al., 2013) because it helps to address the following major challenges faced by food analysts and researchers:

  • • the development of simple and fast methodologies for routine use;
  • • the analysis of complex or highly processed food matrices; and
  • • the quantification of trace levels of analytes with a high degree of selectivity.

Proteomics is defined as a large-scale analysis of proteins in a particular biological system at a certain time (Pandey and Mann, 2000). Proteomics is a new promising approach to identify protein in food matrix and to study protein-protein interactions in both raw and processed foods, as well as interactions between proteins and other food components (Kvasnicka,

  • 2003). It provides sensitive information on changes in protein structure occurring in specific amino acid residues during processing events and helps to predict the quality and stability of the food product. Moreover, peptide- based methods can overcome one of the major drawbacks of DNA methods, that is, the degradation of DNA in highly processed samples (Buckley et al., 2009), since (i) marker peptides can be quite stable against processing, (ii) modifications in amino acidic sequence due to food processing (non- enzymatic PTMs) can be monitored, and (iii) heat-stable proteins can be selected as targets. Depending on the general objective, most proteomic studies can be divided in three different areas.
  • 2.1 Qualitative Proteomics (Protein Characterization and Identification)

Qualitative proteomics involves protein characterization and identification by using mass spectrometry (MS). Proteome studies involve separation of proteins by various techniques followed by MS analysis. MS experimental data is then compared with calculated mass values obtained from a sequence database using a search engine, such as Mascot (Perkins et al., 1999).

Qualitative proteomics also involves the study of protein post- translational modifications (PTMs) by peptide mass fingerprinting (PMF) (Pappin et al., 1993) and peptide fragmentation fingerprinting (PFF) (Eng et al., 1994). PTMs play a crucial role since they affect protein activity and stability. PTMs can occur due to a wide range of biological signals, as well as due to food-processing methods, including cooking and preservation treatments. More than 300 different types of PTMs are known, although only a few of them are being extensively investigated, such as phosphorylation, acetylation, glycosylation, or oxidation (Zhao and Jensen, 2009). During food processing and storage, non-biological (environmental or process-induced) PTMs, termed non-enzymatic PTMs (nePTMs), such as carbonylation, thiol oxidation, aromatic hydroxylation, Maillard glycation, condensation, elimination of side chains, and peptide backbone breakdown, occur regularly (Clerens et al., 2012). In PMF, an unknown protein is isolated by two-dimensional gel electrophoresis (2-DE) followed by enzymatic digestion into peptides and then subjected to MS (Pappin et al., 1993). Another approach, i.e., PFF uses tandem MS (MS/MS) to produce fragment-ion data from one or more peptides from the protein to identify the protein unambiguously (Eng et al., 1994).

2.2 Differential/Quantitative Proteomics

Quantitative information at the protein level, such as the relative abundance of a specific protein among different samples or the absolute amount of the protein, is very helpful when determining differences between different conditions (control vs. case). In food proteomes, the relative amount of proteins can change mainly due to foodstuff composition, technological processing of the food, and biological variability of the food components. Quantitative information at the protein level, such as the relative abundance of a specific protein between different samples, or the absolute amount of the protein, can be very helpful for observing the differences between different conditions (e.g., different technological treatments of food products; GM versus non-GM food). Relative quantification can be achieved with different methodologies, which can be classified as gel-based, label-based, and label-free approaches (Panchaud et al., 2008). Gel-based methods consist of the separation of proteins by two-dimensional electrophoresis and the comparison of protein abundance determined as the spot volume between different samples. Each sample being compared can be run on a different gel or, alternatively, up to three samples can be differentially labeled and run on the same gel using the Differential Gel Electrophoresis (DIGE) technology. In the label-based methods, proteins or peptides are previously labeled using a mass tag and relative quantification is then obtained from the MS or MS/MS read-outs. In label-free approaches, the protein amount is generally calculated based on the MS extracted ion current signal of the peptides/proteins during a liquid chromatography (LC) run. Quantitative proteomic methodologies have been greatly improved with the introduction of Selected Reaction Monitoring experiments (SRM), a highly sensitive LC-MS/MS acquisition mode that is commonly used in research to verify and validate candidate biomarker proteins (Gallien et al., 2011).

2.3 Functional Proteomics

Functional proteomics studies the functional interaction between proteins, or between a protein and other molecules and the consequences of these interactions (Kiemer and Cesareni, 2007). The understanding of protein- protein interactions, through network analysis, will be crucial if further improvements in food quality are to be realized. Activity-based proteomics is another area related to functional proteomics, studying the specific activities of the proteins in a sample, such as function and inhibition (Serim et al., 2012). MS imaging, a new imaging mode that allows mapping proteins within a tissue or sample section (Wu et al., 2012; Angel and Caprioli, 2013), has proved to be a relevant tool for functional proteomics, since the location of the different protein isoforms can help to understand their roles.

In proteomics, the most challenging aspects involve the separation of proteins and peptides from the complex and dynamic concentration range within food. The work flow of proteomics methodology includes:

  • 1. Sample collection, handling and storage.
  • 2. Protein separation (ingel or offgel).
  • 3. Protein identification (peptide mass fingerprinting).
  • 4. Protein characterization (amino acid sequencing).
  • 5. Bioinformatics (cross reference of protein).
  • 2.4 Proteomics Workflows

In proteomics, the most challenging aspect involves the separation of proteins and peptides from the complex and dynamic concentration range within food. The first step in aproteomic analysis is protein extraction. To ensure maximum protein recovery and minimum proteolysis, there is a need for optimized protocol for each food sample. The choice of extraction buffer depends upon the sample and the proteins which are to be extracted. Various compounds are added to the extraction buffer for stabilizing and solubilizing the proteins, such as pH regulators (e.g., Tris, Hepes, MOPS), reducing agents (e.g., dithiothreitol, 2-mercaptoethanol), and denaturing compounds (e.g., 8M urea, SDS, CHAPS), and for the elimination of contaminants, such as nucleic acids (e.g., DNases). The use of protease inhibitors, such as PMSF or EDTA, is strongly recommended. Different extraction methods are available that can be used alone or in combination: organic solvents or detergents; enzymatic extraction; liquid nitrogen; mechanical disruption (e.g., use of ULTRATURRAX®, blenders, manual grinding with mortar or pestle, Ballotini beads); sonication, and compression/expansion (Ortea et al., 2016).

Next step includes protein purification that involves procedures for clarifying and concentrating proteins, such as protein precipitation (e.g., ammonium sulfate, ТСА/acetone, chloroform), centrifugation, filtration (e.g., dialysis, ultrafiltration), lyophilization; procedures for enrichment, depletion, or fractionation, for reducing sample complexity, and for increasing the proportion of the target proteins. Chromatography (e.g., RP; SCX; SEC) and PAGE (1-DE or 2-DE) are the most commonly applied fractionation techniques. Off-gel techniques, such as isoelectric focusing, ultracentrifugation, and phase partitioning are also used. For the depletion of high-abundance proteins, or enrichment in the interesting ones, commercial immunoaffinity depletion kits are available, although generally for the removal of albumin and some other human plasma proteins, this reflecting the difficulty faced in food proteomics in comparison to clinical proteomics in terms of method development (Ortea et al., 2016).

 
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