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Protease resistance of food proteins: a mixed picture for predicting allergenicity but a useful tool for assessing exposure.

ABSTRACT: Susceptibility to pepsin digestion of candidate transgene products is regarded an important parameter in the weight-of-evidence approach for allergenicity risk assessment of genetically modified crops. It has been argued that protocols used for this assessment should better reflect physiological conditions encountered in representative food consumption scenarios.To evaluate whether inclusion of more physiological conditions, such as sub-optimal and lower pepsin concentrations, in combination with pancreatin digestion, improved the performance of digestibility protocols used in characterization of protein stability.Four pairs of established allergens and their related non/weakly-allergenic counterparts (seed albumins, muscle tropomyosins, plant lipid transfer proteins [LTP] and collagens) plus fish parvalbumin, were subjected to nine combinations of pH (1.2-2.5-4.0) and pepsin-to-protein ratio (PPR: 10-1-0.1 U/µg) for pepsin digestion, followed by pancreatin digestion in the presence of bile salts. Digestion was monitored by SDS-PAGE in conjunction with Coomassie staining and immunoblotting using rabbit antisera and human IgE.At pH 4.0 and at PPR 0.1 most proteins, both allergen and non-allergen, were highly resistant to pepsin. Under conditions known to favor pepsin proteolysis, the established major allergens Ara h 2, Pru p 3 and Pen a 1 were highly resistant to proteolysis, while the allergen Cyp c 1 was not. However, this resistance to pepsin digestion only made Ara h 2 and to a lesser extent Pen a 1 and Pru p 3 stand out compared to their non-allergenic counterparts. Largely irrespective of preceding pepsin digestion conditions, pancreatin digestion was very effective for all tested proteins, allergens and non-allergens, except for Cyp c 1 and bovine collagen.Sub-optimal pH, low pepsin-to protein ratio, and sequential pepsin and pancreatin digestion protocols do not improve the predictive value in distinguish allergens from non-allergens. Digestion conditions facilitating such distinction differ per protein pair.

SUBMITTER: Akkerdaas J

PROVIDER: S-EPMC6085708 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Protease resistance of food proteins: a mixed picture for predicting allergenicity but a useful tool for assessing exposure.

Akkerdaas Jaap J Totis Muriel M Barnett Brian B Bell Erin E Davis Tom T Edrington Thomas T Glenn Kevin K Graser Gerson G Herman Rod R Knulst Andre A Ladics Gregory G McClain Scott S Poulsen Lars K LK Ranjan Rakesh R Rascle Jean-Baptiste JB Serrano Hector H Speijer Dave D Wang Rong R Pereira Mouriès Lucilia L Capt Annabelle A van Ree Ronald R

Clinical and translational allergy 20180810

<h4>Background</h4>Susceptibility to pepsin digestion of candidate transgene products is regarded an important parameter in the weight-of-evidence approach for allergenicity risk assessment of genetically modified crops. It has been argued that protocols used for this assessment should better reflect physiological conditions encountered in representative food consumption scenarios.<h4>Aim</h4>To evaluate whether inclusion of more physiological conditions, such as sub-optimal and lower pepsin con ...[more]

PMID: 30116520

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Project description:The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at http://lightning.med.monash.edu.au/PROSPER/.

Project description:BackgroundAnoikis-related long non-coding RNAs (ARLs) play a critical role in tumor metastasis and progression, suggesting that they may serve as risk markers for cancer. This study aimed to investigate the prognostic value of ARLs in patients with lung adenocarcinoma (LUAD).MethodsClinical data, RNA sequencing (RNA-seq) data, and mutation data from the LUAD project were obtained from The Cancer Genome Atlas (TCGA) database. The Molecular Signatures Database (MSigDB) and the GeneCard database were used to collect an anoikis-related gene (ARG) set. Pearson correlation analysis was performed to identify ARLs. LASSO and Cox regression were then used to establish a prognostic risk signature for ARLs. The median risk score served as the basis for categorizing patients into high and low-risk groups. Kaplan-Meier analysis was utilized to compare the prognosis between these two groups. The study also examined the associations between risk scores and prognosis, clinicopathological characteristics, immune status, tumor mutation burden (TMB), and chemotherapeutic agents. LncRNA expression was assessed using quantitative real-time PCR (qRT-PCR).ResultsA total of 480 RNA expression profiles, 501 ARGs, and 2698 ARLs were obtained from the database. A prognostic ARL signature for LUAD was established, consisting of 9 lncRNAs. Patients in the low-risk group exhibited significantly better prognosis compared to those in the high-risk group (P < 0.001). The 9 lncRNAs from the ARL signature were identified as independent prognostic factors (P < 0.001). The signature demonstrated high accuracy in predicting LUAD prognosis, with area under the curve values exceeding 0.7. The risk scores for ARLs showed strong negative correlations with stroma score (P = 5.9E-07, R = -0.23), immune score (P = 9.7E-09, R = -0.26), and microenvironment score (P = 8E-11, R = -0.29). Additionally, the low-risk group exhibited significantly higher TMB compared to the high-risk group (P = 4.6E-05). High-risk status was significantly associated with lower half-maximal inhibitory concentrations for most chemotherapeutic drugs.ConclusionThis newly constructed signature based on nine ARLs is a useful instrument for the risk stratification of LUAD patients. The signature has potential clinical significance for predicting the prognosis of LUAD patients and guiding personalized immunotherapy.

Dataset Information

Protease resistance of food proteins: a mixed picture for predicting allergenicity but a useful tool for assessing exposure.

Publications

Protease resistance of food proteins: a mixed picture for predicting allergenicity but a useful tool for assessing exposure.

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