Glycoengineered recombinant alpha1-antitrypsin results in comparable in vitro and in vivo activities to human plasma-derived protein.

Alpha-1-antitrypsin (A1AT) is a multifunctional, clinically important, high value therapeutic glycoprotein that can be used for the treatment of many diseases such as alpha-1-antitrypsin deficiency, diabetes, graft-versus-host-disease, cystic fibrosis and various viral infections. Currently, the only FDA-approved treatment for A1AT disorders is intravenous augmentation therapy with human plasma-derived A1AT. In addition to its limited supply, this approach poses a risk of infection transmission, since it uses therapeutic A1AT harvested from donors. To address these issues, we sought to generate recombinant human A1AT (rhA1AT) that is chemically and biologically indistinguishable from its plasma-derived counterpart using glycoengineered Chinese Hamster Ovary (geCHO-L) cells. By deleting nine key genes that are part of the CHO glycosylation machinery and expressing the human ST6GAL1 and A1AT genes, we obtained stable, high producing geCHO-L lines that produced rhA1AT having an identical glycoprofile to plasma-derived A1AT (pdA1AT). Additionally, the rhA1AT demonstrated in vitro activity and in vivo half-life comparable to commercial pdA1AT. Thus, we anticipate that this platform will help produce human-like recombinant plasma proteins, thereby providing a more sustainable and reliable source of therapeutics that are cost-effective and better-controlled with regard to purity, clinical safety and quality.

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling.

Glycosylation affects many vital functions of organisms. Therefore, its surveillance is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, conventional glycan structure analysis faces challenges with throughput and cost. Lectins offer an alternative approach for analyzing glycans, but they only provide glycan epitopes and not full glycan structure information. To overcome these limitations, we developed LeGenD, a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns. We trained the LeGenD model using 309 glycoprofiles from 10 recombinant proteins, produced in 30 glycoengineered CHO cell lines. Our approach accurately reconstructed experimentally-measured N-glycoprofiles of bovine Fetuin B and IgG from human sera. Explanatory AI analysis with SHapley Additive exPlanations (SHAP) helped identify the critical lectins for glycoprofile predictions. Our LeGenD approach thus presents an alternative approach for N-glycan analysis.

CHOGlycoNET: Comprehensive glycosylation reaction network for CHO cells.

Chinese hamster ovary (CHO) cells are extensively used for the production of glycoprotein therapeutics proteins, for which N-linked glycans are a critical quality attribute due to their influence on activity and immunogenicity. Manipulation of protein glycosylation is commonly achieved through cell or process engineering, which are often guided by mathematical models. However, each study considers a unique glycosylation reaction network that is tailored around the cell line and product at hand. Herein, we use 200 glycan datasets for both recombinantly produced and native proteins from different CHO cell lines to reconstruct a comprehensive reaction network, CHOGlycoNET, based on the individual minimal reaction networks describing each dataset. CHOGlycoNET is used to investigate the distribution of mannosidase and glycosyltransferase enzymes in the Golgi apparatus and identify key network reactions using machine learning and dimensionality reduction techniques. CHOGlycoNET can be used for accelerating glycomodel development and predicting the effect of glycoengineering strategies. Finally, CHOGlycoNET is wrapped in a SBML file to be used as a standalone model or in combination with CHO cell genome scale models.

Highly Selective Lysine Acylation in Proteins Using a Lys-His Tag Sequence.

Chemical modification of proteins has numerous applications, but it has been challenging to achieve the required high degree of selectivity on lysine amino groups. Recently, we described the highly selective acylation of proteins with an N-terminal Gly-His6 segment. This tag promoted acylation of the N-terminal Nα -amine resulting in stable conjugates. Herein, we report the peptide sequences Hisn -Lys-Hism , which we term Lys-His tags. In combination with simple acylating agents, they facilitate the acylation of the designated Lys Nϵ -amine under mild conditions and with high selectivity over native Lys residues. We show that the Lys-His tags, which are 7 to 10 amino acids in length and still act as conventional His tags, can be inserted in proteins at the C-terminus or in loops, thus providing high flexibility regarding the site of modification. Finally, the selective and efficient acylation of the therapeutic antibody Rituximab, pure or mixed with other proteins, demonstrates the scope of the Lys-His tag acylation method.

CHOmics: A web-based tool for multi-omics data analysis and interactive visualization in CHO cell lines.

Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. While high-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an interactive visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and interactively. The tool was also designed with the flexibility to accommodate other types of omics data and thereby enabling multi-omics comparison and visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, visualization and management with expectations to promote the broader use of omics in CHO cell research.

A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering.

Glycosylated biopharmaceuticals are important in the global pharmaceutical market. Despite the importance of their glycan structures, our limited knowledge of the glycosylation machinery still hinders controllability of this critical quality attribute. To facilitate discovery of glycosyltransferase specificity and predict glycoengineering efforts, here we extend the approach to model N-linked protein glycosylation as a Markov process. Our model leverages putative glycosyltransferase (GT) specificity to define the biosynthetic pathways for all measured glycans, and the Markov chain modelling is used to learn glycosyltransferase isoform activities and predict glycosylation following glycosyltransferase knock-in/knockout. We apply our methodology to four different glycoengineered therapeutics (i.e., Rituximab, erythropoietin, Enbrel, and alpha-1 antitrypsin) produced in CHO cells. Our model accurately predicted N-linked glycosylation following glycoengineering and further quantified the impact of glycosyltransferase mutations on reactions catalyzed by other glycosyltransferases. By applying these learned GT-GT interaction rules identified from single glycosyltransferase mutants, our model further predicts the outcome of multi-gene glycosyltransferase mutations on the diverse biotherapeutics. Thus, this modeling approach enables rational glycoengineering and the elucidation of relationships between glycosyltransferases, thereby facilitating biopharmaceutical research and aiding the broader study of glycosylation to elucidate the genetic basis of complex changes in glycosylation.

Multiplex secretome engineering enhances recombinant protein production and purity.

Host cell proteins (HCPs) are process-related impurities generated during biotherapeutic protein production. HCPs can be problematic if they pose a significant metabolic demand, degrade product quality, or contaminate the final product. Here, we present an effort to create a "clean" Chinese hamster ovary (CHO) cell by disrupting multiple genes to eliminate HCPs. Using a model of CHO cell protein secretion, we predict that the elimination of unnecessary HCPs could have a non-negligible impact on protein production. We analyze the HCP content of 6-protein, 11-protein, and 14-protein knockout clones. These cell lines exhibit a substantial reduction in total HCP content (40%-70%). We also observe higher productivity and improved growth characteristics in specific clones. The reduced HCP content facilitates purification of a monoclonal antibody. Thus, substantial improvements can be made in protein titer and purity through large-scale HCP deletion, providing an avenue to increased quality and affordability of high-value biopharmaceuticals.

Reprogramming AA catabolism in CHO cells with CRISPR/Cas9 genome editing improves cell growth and reduces byproduct secretion.

Chinese hamster ovary (CHO) cells are the preferred host for producing biopharmaceuticals. Amino acids are biologically important precursors for CHO metabolism; they serve as building blocks for proteogenesis, including synthesis of biomass and recombinant proteins, and are utilized for growth and cellular maintenance. In this work, we studied the physiological impact of disrupting a range of amino acid catabolic pathways in CHO cells. We aimed to reduce secretion of growth inhibiting metabolic by-products derived from amino acid catabolism including lactate and ammonium. To achieve this, we engineered nine genes in seven different amino acid catabolic pathways using the CRISPR-Cas9 genome editing system. For identification of target genes, we used a metabolic network reconstruction of amino acid catabolism to follow transcriptional changes in response to antibody production, which revealed candidate genes for disruption. We found that disruption of single amino acid catabolic genes reduced specific lactate and ammonium secretion while specific growth rate and integral of viable cell density were increased in many cases. Of particular interest were Hpd and Gad2 disruptions, which show unchanged AA uptake rates, while having growth rates increased up to 19%, and integral of viable cell density as much as 50% higher, and up to 26% decrease in specific ammonium production and to a lesser extent (up to 22%) decrease in lactate production. This study demonstrates the broad potential of engineering amino acid catabolism in CHO cells to achieve improved phenotypes for bioprocessing.

Glyco-engineered CHO cell lines producing alpha-1-antitrypsin and C1 esterase inhibitor with fully humanized N-glycosylation profiles.

Recombinant Chinese hamster ovary (CHO) cells are able to provide biopharmaceuticals that are essentially free of human viruses and have N-glycosylation profiles similar, but not identical, to humans. Due to differences in N-glycan moieties, two members of the serpin superfamily, alpha-1-antitrypsin (A1AT) and plasma protease C1 inhibitor (C1INH), are currently derived from human plasma for treating A1AT and C1INH deficiency. Deriving therapeutic proteins from human plasma is generally a cost-intensive process and also harbors a risk of transmitting infectious particles. Recombinantly produced A1AT and C1INH (rhA1AT, rhC1INH) decorated with humanized N-glycans are therefore of clinical and commercial interest. Here, we present engineered CHO cell lines producing rhA1AT or rhC1INH with fully humanized N-glycosylation profiles. This was achieved by combining CRISPR/Cas9-mediated disruption of 10 gene targets with overexpression of human ST6GAL1. We were able to show that the N-linked glyco-structures of rhA1AT and rhC1INH are homogeneous and similar to the structures obtained from plasma-derived A1AT and C1INH, marketed as Prolastin®-C and Cinryze®, respectively. rhA1AT and rhC1INH produced in our glyco-engineered cell line showed no detectable differences to their plasma-purified counterparts on SDS-PAGE and had similar enzymatic in vitro activity. The work presented here shows the potential of expanding the glyco-engineering toolbox for CHO cells to produce a wider variety of glycoproteins with fully humanized N-glycan profiles. We envision replacing plasma-derived A1AT and C1INH with recombinant versions and thereby decreasing our dependence on human donor blood, a limited and possibly unsafe protein source for patients.

A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism.

Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

A Markov chain model for N-linked protein glycosylation--towards a low-parameter tool for model-driven glycoengineering.

Glycosylation is a critical quality attribute of most recombinant biotherapeutics. Consequently, drug development requires careful control of glycoforms to meet bioactivity and biosafety requirements. However, glycoengineering can be extraordinarily difficult given the complex reaction networks underlying glycosylation and the vast number of different glycans that can be synthesized in a host cell. Computational modeling offers an intriguing option to rationally guide glycoengineering, but the high parametric demands of current modeling approaches pose challenges to their application. Here we present a novel low-parameter approach to describe glycosylation using flux-balance and Markov chain modeling. The model recapitulates the biological complexity of glycosylation, but does not require user-provided kinetic information. We use this method to predict and experimentally validate glycoprofiles on EPO, IgG as well as the endogenous secretome following glycosyltransferase knock-out in different Chinese hamster ovary (CHO) cell lines. Our approach offers a flexible and user-friendly platform that can serve as a basis for powerful computational engineering efforts in mammalian cell factories for biopharmaceutical production.