Assessment of transient expression strategies to sialylate recombinant proteins in N. benthamiana

Production of therapeutic glycoproteins in glycoengineered plant: old farm for new crops

Plant-based expression systems have emerged as promising avenues for the production of recombinant N-linked glycoproteins. This review offers insights into the evolution and progress of plant glycoengineering. It delves into the distinctive features of plant-derived N-glycans, the diverse range of plant hosts employed for glycoprotein synthesis, and the advancements in glycoengineering strategies aimed at generating glycoproteins with N-glycan structures akin to those produced in mammalian cell lines. Furthermore, alternative strategies for augmenting glycoengineering efforts and the current spectrum of applications for plant-produced N-glycan recombinant proteins are examined, underscoring their potential significance in biopharmaceutical manufacturing.

Efficient Expression of Functionally Active Aflibercept with Designed N-glycans

Aflibercept is a therapeutic recombinant fusion protein comprising extracellular domains of human vascular endothelial growth factor receptors (VEGFRs) and IgG1-Fc. It is a highly glycosylated protein with five N-glycosylation sites that might impact it structurally and/or functionally. Aflibercept is produced in mammalian cells and exhibits large glycan heterogeneity, which hampers glycan-associated investigations. Here, we report the expression of aflibercept in a plant-based system with targeted N-glycosylation profiles. Nicotiana benthamiana-based glycoengineering resulted in the production of aflibercept variants carrying designed carbohydrates, namely, N-glycans with terminal GlcNAc and sialic acid residues, herein referred to as AFLIGnGn and AFLISia, respectively. Both variants were transiently expressed in unusually high amounts (2 g/kg fresh leaf material) in leaves and properly assembled to dimers. Mass spectrometric site-specific glycosylation analyses of purified aflibercept showed the presence of two to four glycoforms in a consistent manner. We also demonstrate incomplete occupancy of some glycosites. Both AFLIGnGn and AFLISia displayed similar binding potency to VEGF165, with a tendency of lower binding to variants with increased sialylation. Collectively, we show the expression of functionally active aflibercept in significant amounts with controlled glycosylation. The results provide the basis for further studies in order to generate optimized products in the best-case scenario.

Precise Therapy Using the Selective Endogenous Encapsidation for Cellular Delivery Vector System

Interindividual variability in drug response is a major problem in the prescription of pharmacological treatments. The therapeutic effect of drugs can be influenced by human genes. Pharmacogenomic guidelines for individualization of treatment have been validated and used for conventional dosage forms. However, drugs can often target non-specific areas and produce both desired and undesired pharmacological effects. The use of nanoparticles, liposomes, or other available forms for drug formulation could help to overcome the latter problem. Virus-like particles based on retroviruses could be a potential envelope for safe and efficient drug formulations. Human endogenous retroviruses would make it possible to overcome the host immune response and deliver drugs to the desired target. PEG10 is a promising candidate that can bind to mRNA because it is secreted like an enveloped virus-like extracellular vesicle. PEG10 is a retrotransposon-derived gene that has been domesticated. Therefore, formulations with PEG10 may have a lower immunogenicity. The use of existing knowledge can lead to the development of suitable drug formulations for the precise treatment of individual diseases.

A genome-edited N. benthamiana line for industrial-scale production of recombinant glycoproteins with targeted N-glycosylation

Control over glycosylation is an important quality parameter in recombinant protein production. Here, we demonstrate the generation of a marker-free genome edited Nicotiana benthamiana N-glycosylation mutant (NbXF-KO) carrying inactivated β1,2-xylosyltransferase and α1,3-fucosyltransferase genes. The knockout of seven genes and their stable inheritance was confirmed by DNA sequencing. Mass spectrometric analyses showed the synthesis of N-glycans devoid of plant-specific β1,2-xylose and core α 1,3-fucose on endogenous proteins and a series of recombinantly expressed glycoproteins with different complexities. Further transient glycan engineering towards more diverse human-type N-glycans resulted in the production of recombinant proteins decorated with β1,4-galactosylated and α2,6-sialylated structures, respectively. Notably, a monoclonal antibody expressed in the NbXF-KO displayed glycosylation-dependent activities. Collectively, the engineered plants grow normally and are well suited for upscaling, thereby meeting industrial and regulatory requirements for the production of high-quality therapeutic proteins.

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.