Comprehensive site- and structure-specific characterization of N-glycosylation in model plant Arabidopsis using mass-spectrometry-based N-glycoproteomics

Dual-functionalized two-dimensional metal-organic framework composite with highly hydrophilicity for effective enrichment of glycopeptides

Protein glycosylation research is currently focused on the development of various functionalized materials that can effectively enrich the levels of glycopeptides in samples. However, most of these materials possess limited glycopeptide-specific recognition sites because of large steric hindrance, unsuitable mass transfer kinetics, and relatively low surface areas. Herein, a highly hydrophilic two-dimensional (2-D) metal-organic framework (MOF) nanosheet modified with glutathione (GSH) and l-cysteine (l-Cys) (denoted as Zr-Fc MOF@Au@GC) has been synthesized for efficient glycopeptide enrichment. Using this composite material, 39 and 44 glycopeptides from horseradish peroxidase (HRP) and human serum immunoglobulin G (IgG) digests were detected, respectively, which represents a higher efficiency for glycopeptide enrichment from model glycoprotein digests than has been previously reported. The material Zr-Fc MOF@Au@GC exhibited ultra-high sensitivity (0.1 fmol/µL), excellent selectivity (weight ratio of HRP tryptic digest to bovine serum albumin (BSA) tryptic digest = 1:2000), good binding capacity (200 mg/g), satisfactory reusability, and long-term storage capacity. In addition, 655 glycopeptides corresponding to 366 glycoproteins were identified from human serum samples. To the best of our knowledge, this is the largest number of glycoproteins detected in human serum samples to date. These results indicated that Zr-Fc MOF@Au@GC has the potential to be used for the enrichment of glycopeptides in biological samples and the analysis of protein glycosylation.

Rice-produced classical swine fever virus glycoprotein E2 with herringbone-dimer design to enhance immune responses

Pestiviruses, including classical swine fever virus, remain a concern for global animal health and are responsible for major economic losses of livestock worldwide. Despite high levels of vaccination, currently available commercial vaccines are limited by safety concerns, moderate efficacy, and required high doses. The development of new vaccines is therefore essential. Vaccine efforts should focus on optimizing antigen presentation to enhance immune responses. Here, we describe a simple herringbone-dimer strategy for efficient vaccine design, using the classical swine fever virus E2 expressed in a rice endosperm as an example. The expression of rE2 protein was identified, with the rE2 antigen accumulating to 480 mg/kg. Immunological assays in mice, rabbits, and pigs showed high antigenicity of rE2. Two immunizations with 284 ng of the rE2 vaccine or one shot with 5.12 μg provided effective protection in pigs without interference from pre-existing antibodies. Crystal structure and small-angle X-ray scattering results confirmed the stable herringbone dimeric conformation, which had two fully exposed duplex receptor binding domains. Our results demonstrated that rice endosperm is a promising platform for precise vaccine design, and this strategy can be universally applied to other Flaviviridae virus vaccines.

Identification of animal species of origin in meat based on glycopeptide analysis by UPLC-QTOF-MS

Adulteration of meat and meat products causes a concerning threat for consumers. It is necessary to develop novel robust and sensitive methods which can authenticate the origin of meat species to compensate for the drawbacks of existing methods. In the present study, the sarcoplasmic proteins of six meat species, namely, pork, beef, mutton, chicken, duck and turkey, were analyzed by one-dimensional gel electrophoresis. It was found that enolase could be used as a potential biomarker protein to distinguish between livestock and poultry meats. The glycosylation sites and glycans of enolase were analyzed by UPLC-QTOF-MS and a total of 41 glycopeptides were identified, indicating that the enolase N-glycopeptide profiles of different meats were species-specific. The identification models of livestock meat, poultry and mixed animal were established based on the glycopeptide contents, and the explanation degree of the three models was higher than 90%. The model prediction performance and feasibility results showed that the average prediction accuracy of the three models was 75.43%, with the animal-derived meat identification model showing superiority in identifying more closely related species. The obtained results indicated that the developed strategy was promising for application in animal-derived meat species monitoring and the quality supervision of animal-derived food.

Critical considerations in N-glycoproteomics

N-Glycoproteomics, the system-wide study of glycans asparagine-linked to protein carriers, holds a unique and still largely untapped potential to provide deep insights into the complexity and dynamics of the heterogeneous N-glycoproteome. Despite the advent of innovative analytical and informatics tools aiding the analysis, N-glycoproteomics remains challenging and consequently largely restricted to specialised laboratories. Aiming to stimulate discussions of method harmonisation, data standardisation and reporting guidelines to make N-glycoproteomics more reproducible and accessible to the community, we here discuss critical considerations related to the design and execution of N-glycoproteomics experiments and highlight good practices in N-glycopeptide data collection, analysis, interpretation and sharing. Giving the rapid maturation and, expectedly, a wide-spread implementation of N-glycoproteomics capabilities across the community in future years, this piece aims to point out common pitfalls, to encourage good data sharing and documentation practices, and to highlight practical solutions and strategies to enhance the insight into the N-glycoproteome.