Restoration of DNA repair mitigates genome instability and increases productivity of Chinese hamster ovary cells.

Chinese hamster ovary (CHO) cells are the primary host for manufacturing of therapeutic proteins. However, productivity loss is a major problem and is associated with genome instability, as chromosomal aberrations reduce transgene copy number and decrease protein expression. We analyzed whole-genome sequencing data from 11 CHO cell lines and found deleterious single-nucleotide variants in DNA repair genes. Comparison with primary Chinese hamster cells confirmed DNA repair to be compromised in CHO. Correction of key DNA repair genes by single-nucleotide variant reversal or expression of intact complementary DNAs successfully improved DNA repair and mitigated karyotypic instability. Moreover, overexpression of intact copies of LIG4 and XRCC6 in a CHO cell line expressing secreted alkaline phosphatase mitigated transgene copy loss and improved protein titer retention. These results show that correction of DNA repair genes yields improvements in genome stability in CHO, and provide new opportunities for cell line development for sustainable protein expression.

Genome-wide screens uncover KDM2B as a modifier of protein binding to heparan sulfate.

Heparan sulfate (HS) proteoglycans bind extracellular proteins that participate in cell signaling, attachment and endocytosis. These interactions depend on the arrangement of sulfated sugars in the HS chains generated by well-characterized biosynthetic enzymes; however, the regulation of these enzymes is largely unknown. We conducted genome-wide CRISPR-Cas9 screens with a small-molecule ligand that binds to HS. Screening of A375 melanoma cells uncovered additional genes and pathways impacting HS formation. The top hit was the epigenetic factor KDM2B, a histone demethylase. KDM2B inactivation suppressed multiple HS sulfotransferases and upregulated the sulfatase SULF1. These changes differentially affected the interaction of HS-binding proteins. KDM2B-deficient cells displayed decreased growth rates, which was rescued by SULF1 inactivation. In addition, KDM2B deficiency altered the expression of many extracellular matrix genes. Thus, KDM2B controls proliferation of A375 cells through the regulation of HS structure and serves as a master regulator of the extracellular matrix.

A metabolic CRISPR-Cas9 screen in Chinese hamster ovary cells identifies glutamine-sensitive genes.

Media and feed optimization have fueled many-fold improvements in mammalian biopharmaceutical production, but genome editing offers an emerging avenue for further enhancing cell metabolism and bioproduction. However, the complexity of metabolism, involving thousands of genes, makes it unclear which engineering strategies will result in desired traits. Here we present a comprehensive pooled CRISPR screen for CHO cell metabolism, including ~16,000 gRNAs against ~2500 metabolic enzymes and regulators. Using this screen, we identified a glutamine response network in CHO cells. Glutamine is particularly important since it is often over-fed to drive increased TCA cycle flux, but toxic ammonia may accumulate. With the screen we found one orphan glutamine-responsive gene with no clear connection to our network. Knockout of this novel and poorly characterized lipase, Abhd11, substantially increased growth in glutamine-free media by altering the regulation of the TCA cycle. Thus, the screen provides an invaluable targeted platform to comprehensively study genes involved in any metabolic trait, and elucidate novel regulators of metabolism.

ZNF263 is a transcriptional regulator of heparin and heparan sulfate biosynthesis.

Heparin is the most widely prescribed biopharmaceutical in production globally. Its potent anticoagulant activity and safety makes it the drug of choice for preventing deep vein thrombosis and pulmonary embolism. In 2008, adulterated material was introduced into the heparin supply chain, resulting in several hundred deaths and demonstrating the need for alternate sources of heparin. Heparin is a fractionated form of heparan sulfate derived from animal sources, predominantly from connective tissue mast cells in pig mucosa. While the enzymes involved in heparin biosynthesis are identical to those for heparan sulfate, the factors regulating these enzymes are not understood. Examination of the promoter regions of all genes involved in heparin/heparan sulfate assembly uncovered a transcription factor-binding motif for ZNF263, a C2H2 zinc finger protein. CRISPR-mediated targeting and siRNA knockdown of ZNF263 in mammalian cell lines and human primary cells led to dramatically increased expression levels of HS3ST1, a key enzyme involved in imparting anticoagulant activity to heparin, and HS3ST3A1, another glucosaminyl 3-O-sulfotransferase expressed in cells. Enhanced 3-O-sulfation increased binding to antithrombin, which enhanced Factor Xa inhibition, and binding of neuropilin-1. Analysis of transcriptomics data showed distinctively low expression of ZNF263 in mast cells compared with other (non-heparin-producing) immune cells. These findings demonstrate a novel regulatory factor in heparan sulfate modification that could further advance the possibility of bioengineering anticoagulant heparin in cultured cells.

PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens.

Large-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise. To make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots. PinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions and test datasets.

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

Biosimilar drugs must closely resemble the pharmacological attributes of innovator products to ensure safety and efficacy to obtain regulatory approval. Glycosylation is one critical quality attribute that must be matched, but it is inherently difficult to control due to the complexity of its biogenesis. This usually implies that costly and time-consuming experimentation is required for clone identification and optimization of biosimilar glycosylation. Here, a computational method that utilizes a Markov model of glycosylation to predict optimal glycoengineering strategies to obtain a specific glycosylation profile with desired properties is described. The approach uses a genetic algorithm to find the required quantities to perturb glycosylation reaction rates that lead to the best possible match with a given glycosylation profile. Furthermore, the approach can be used to identify cell lines and clones that will require minimal intervention while achieving a glycoprofile that is most similar to the desired profile. Thus, this approach can facilitate biosimilar design by providing computational glycoengineering guidelines that can be generated with a minimal time and cost.

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.

Modulating carbohydrate-protein interactions through glycoengineering of monoclonal antibodies to impact cancer physiology.

Diverse glycans on proteins impact cell and organism physiology, along with drug activity. Since many protein-based biotherapeutics are glycosylated and these glycans have biological activity, there is a desire to engineer glycosylation for recombinant protein-based biotherapeutics. Engineered glycosylation can impact the recombinant protein efficacy and also influence many cell pathways by first changing glycan-protein interactions and consequently modulating disease physiologies. However, its complexity is enormous. Recent advances in glycoengineering now make it easier to modulate protein-glycan interactions. Here, we discuss how engineered glycans contribute to therapeutic monoclonal antibodies (mAbs) in the treatment of cancers, how these glycoengineered therapeutic mAbs affect the transformed phenotypes and downstream cell pathways. Furthermore, we suggest how systems biology can help in the next generation mAb glycoengineering process by aiding in data analysis and guiding engineering efforts to tailor mAb glycan and ultimately drug efficacy, safety and affordability.

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.

Systems glycobiology for glycoengineering.

Glycosylation serves essential functions on many proteins produced in biopharmaceutical manufacturing, making it mandatory to thoroughly consider its biogenesis during the production process. Glycoengineering efforts involve the rational design of glycosylation through adjustments in culturing conditions or genetic modifications. Computational models have been developed to aid this process, aiming to offer cheaper and faster alternatives to costly screening strategies. Recently, these models have been successfully utilized to predict glycosylation of products of industrial relevance. Furthermore, systems-level analyses of glycan diversity are elucidating deeper insights into the mechanisms underlying glycosylation. As computational models of glycosylation continue to be expanded, refined, and leveraged for detailed analysis of glycomics data, they will become invaluable resources for cell line development and glycoengineering.