A community genome-scale model of Chinese Hamster ovary cell metabolism identifies differences in the efficiency of resource utilization for various bioprocesses

Genome-scale models of metabolism have successfully been employed in many microbial and eukaryotic metabolic engineering efforts by guiding pathway engineering and media optimization. They have also been used to explore the genotype-phenotype relationship in mammalian cells. The publication of the genomic sequence for Chinese hamster ovary (CHO) cells has allowed generation of genome-scale metabolic models (GeMs) for this organism. Here we have developed a high-quality community CHO GeM via careful reconciliation and manual curation of three independently developed CHO GeMs. This metabolic model, consisting of over 4000 metabolites and 6000 reactions, is capable of integrating proteomic, transcriptomic, and metabolomic data and can accurately simulate experimentally measured growth rates. Integration of transcriptomic and proteomic data from CHO-K1 and CHO-S shed light on the enzymatic basis for various amino acid auxotrophies characteristic of the cell lines. We show that experimental arginine and cysteine auxotrophies are recapitulated by model predictions (via reaction inactivation) while the characteristic proline auxotrophy is not, due to detectable levels of expression in biosynthetic pathways for this amino acid. We additionally used the model to assess the metabolic limitations on recombinant protein producing lines subject to different cell line and process modifications and found that some alterations result in specific productivities up to 20-fold lower than computational predictions of metabolically feasible production rates. The results indicate a possible secretory bottleneck and implicate engineering the secretory pathway as a lucrative target to pursue in future CHO cell line engineering

From observational to actionable: Rethinking omic studies in biopharmaceutical protein production

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CRISPR-CAS9 knockout library for CHO

Traditionally, screening of large CHO cell population have been utilized to identify clones with desired phenotypic properties such as product quality, e.g. specific glyco forms, and population characteristics, e.g. ability to grow in high cell densities. This has largely depended on the genomic variety naturally present in a large cell population or occasionally utilizing random mutagenesis to increase this variety. The ability to precisely create genomic variety in mammalian cells have improved dramatically over the past decade and in the past few years the price has dropped substantially due to the CRISPR/Cas9 technology. E.g. knocking out a gene using CRISPR/Cas9 is a simple, fast and cheap process (1). However, rational identification of which genes to target can be quite difficult and the cellular processes underlying many desired traits are simply unknown or only poorly/partially understood. To both improve our understanding of phenotypes of interest and identify targets to modify, we have created a lentiviral guideRNA library against CHO genes. Using this guideRNA library we subject cells to various phenotype selection assays, harvest genomic DNA from the selected cells and perform targeted next generation sequencing to identify the guideRNA sequences which led to the improved phenotype. As an example: Using a toxic fucose binding lectin, one can potentially identify all genes required for fucosylation in one experiment, simply by identifying the guideRNA present in the surviving population

Engineering CHO cells for the production of Hard-To-Produce proteins

Over the past decades, the CHO cell has become increasingly popular as the favorite host cell line for the production of protein based therapeutic drugs. In comparison with the popularity of the CHO cells and the frequent use of these cells to produce a large part of the bestselling blockbuster drugs, less intensive efforts have been done to understand the machinery used by the CHO cells during growth and production. The main approach has (broadly speaking) been to approach the CHO cell as a “black box” where one could insert the gene of interest, perform a number of amplifying steps, like gene amplification, selection for stable clones, intense screening for stably expressing high producers, and massive efforts to optimize a specific bioprocess for the selected cell line(s). Since 2013, the Novo Nordisk Foundation Center for Biosustainablity at the Technical University of Denmark has embarked on a large CHO program to open up the “black box”, to get a deeper understanding of the available machinery inside the protein producing “cell factory” that is CHO cells. We are using this understanding to engineer new CHO cell lines having significantly improved features for the production of therapeutic proteins. We are not only doing this by improving the titer, quality, downstream processing and speed of development for already well-known proteins (e.g. Ab), but also for the production of therapeutic proteins that cannot be produced in CHO cells today, due low titer, wrong post translational modifications, and/or low activity. By combining the competences embedded in the CHO program, we are able to exploit the combination of genome scale modelling, high throughput protein expression, deep understanding of both the glycosylation machinery as well as the secretory and metabolic pathways involved in the expression of secreted proteins. This knowledge is being used as input to a high throughput CHO cell line engineering pipeline, able to engineer up to 10 cell lines and 25 gene targets in parallel. This has resulted in a large number of new CHO cell lines enabling the production of proteins with specific tailor-made glycoprofiles, higher quality, less degradation, improved bioprocess, higher viable cell density and better cell viability. We have made a cell lines where we have removed a number of naturally expressed host cell proteins (HCP) from CHO, which has resulted in higher titer and higher VCD, cell lines showing increased resistance to viral infections, cell lines displaying homogenous glycoprofiles, reduced degradation, and drastically changed cell lines that does not produce lactate. These features are currently being combined to engineer CHO cells able to produce proteins that have not been possible to produce with adequate product quality and titer using CHO cells to date

Enhancing CHO cell productivity through a novel dual selection system using Aspg and Gs in glutamine free medium

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Genome-wide CRISPR-Cas9 screen identifies hyperosmotic stress responsive genes in Chinese hamster ovary cells

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Recon 2.2: from reconstruction to model of human metabolism.

IntroductionThe human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.ObjectivesWe report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.MethodsRecon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.ResultsRecon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.ConclusionThrough these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001)