Determining the Impact of Genotype × Environment on Oat Protein Isolate Composition Using HPLC and LC-MS Techniques

The effect of genotype and environment on oat protein composition was analyzed through size exclusion-high-performance liquid chromatography (SE-HPLC) and liquid chromatography-mass spectrometry (LC-MS) to characterize oat protein isolate (OPI) extracted from three genotypes grown at three locations in the Canadian Prairies. SE-HPLC identified four fractions in OPI, including polymeric globulins, avenins, glutelins, and albumins, and smaller proteins. The protein composition was dependent on the environment, rather than the genotype. The proteins identified through LC-MS were grouped into eight categories, including globulins, prolamins/avenins, glutelins, enzymes/albumins, enzyme inhibitors, heat shock proteins, grain softness proteins, and allergenic proteins. Three main globulin protein types were also identified, including the P14812|SSG2-12S seed storage globulin, the Q6UJY8_TRITU-globulin, and the M7ZQM3_TRIUA-Globulin-1 S. Principal component analysis indicated that samples from Manitoba showed a positive association with the M7ZQM3_TRIUA-Globulin-1 S allele and Q6UJY8_TRITU-globulin, while samples from Alberta and Saskatchewan had a negative association with them. The results show that the influence of G × E on oat protein fractions and their relative composition is crucial to understanding genotypes' behavior in response to different environments.

Mass spectrometry-based protein and peptide profiling for food frauds, traceability and authenticity assessment

The ever-growing use of mass spectrometry (MS) methodologies in food authentication and traceability originates from their unrivalled specificity, accuracy and sensitivity. Such features are crucial for setting up analytical strategies for detecting food frauds and adulterations by monitoring selected components within food matrices. Among MS approaches, protein and peptide profiling has become increasingly consolidated. This review explores the current knowledge on recent MS techniques using protein and peptide biomarkers for assessing food traceability and authenticity, with a specific focus on their use for unmasking potential frauds and adulterations. We provide a survey of the current state-of-the-art instrumentation including the most reliable and sensitive acquisition modes highlighting advantages and limitations. Finally, we summarize the recent applications of MS to protein/peptide analyses in food matrices and examine their potential in ensuring the quality of agro-food products.

Mapping Coeliac Toxic Motifs in the Prolamin Seed Storage Proteins of Barley, Rye, and Oats Using a Curated Sequence Database

Wheat gluten, and related prolamin proteins in rye, barley and oats cause the immune-mediated gluten intolerance syndrome, coeliac disease. Foods labelled as gluten-free which can be safely consumed by coeliac patients, must not contain gluten above a level of 20 mg/Kg. Current immunoassay methods for detection of gluten can give conflicting results and may underestimate levels of gluten in foods. Mass spectrometry methods have great potential as an orthogonal method, but require curated protein sequence databases to support method development. The GluPro database has been updated to include avenin-like sequences from bread wheat (n = 685; GluPro v1.1) and genes from the sequenced wheat genome (n = 699; GluPro v 1.2) and Triticum turgidum ssp durum (n = 210; GluPro v 2.1). Companion databases have been developed for prolamin sequences from barley (n = 64; GluPro v 3.0), rye (n = 41; GluPro v 4.0), and oats (n = 27; GluPro v 5.0) and combined to provide a complete cereal prolamin database, GluPro v 6.1 comprising 1,041 sequences. Analysis of the coeliac toxic motifs in the curated sequences showed that they were absent from the minor avenin-like proteins in bread and durum wheat and barley, unlike the related avenin proteins from oats. A comparison of prolamin proteins from the different cereal species also showed α- and γ-gliadins in bread and durum wheat, and the sulphur poor prolamins in all cereals had the highest density of coeliac toxic motifs. Analysis of ion-mobility mass spectrometry data for bread wheat (cvs Chinese Spring and Hereward) showed an increased number of identifications when using the GluPro v1.0, 1.1 and 1.2 databases compared to the limited number of verified sequences bread wheat sequences in reviewed UniProt. This family of databases will provide a basis for proteomic profiling of gluten proteins from all the gluten containing cereals and support identification of specific peptide markers for use in development of new methods for gluten quantitation based on coeliac toxic motifs found in all relevant cereal species.

Assessing the Utility of Multiplexed Liquid Chromatography-Mass Spectrometry for Gluten Detection in Australian Breakfast Food Products

Coeliac disease (CD) is an autoimmune disorder triggered by the ingestion of gluten that is associated with gastrointestinal issues, including diarrhea, abdominal pain, and malabsorption. Gluten is a general name for a class of cereal storage proteins of wheat, barley, and rye that are notably resistant to gastrointestinal digestion. After ingestion, immunogenic peptides are subsequently recognized by T cells in the gastrointestinal tract. The only treatment for CD is a life-long gluten-free diet. As such, it is critical to detect gluten in diverse food types, including those where one would not expect to find gluten. The utility of liquid chromatography-mass spectrometry (LC-MS) using cereal-specific peptide markers to detect gluten in heavily processed food types was assessed. A range of breakfast products, including breakfast cereals, breakfast bars, milk-based breakfast drinks, powdered drinks, and a savory spread, were tested. No gluten was detected by LC-MS in the food products labeled gluten-free, yet enzyme-linked immunosorbent assay (ELISA) measurement revealed inconsistencies in barley-containing products. In products containing wheat, rye, barley, and oats as labeled ingredients, gluten proteins were readily detected using discovery proteomics. Panels comprising ten cereal-specific peptide markers were analyzed by targeted proteomics, providing evidence that LC-MS could detect and differentiate gluten in complex matrices, including baked goods and milk-based products.