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

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess.

Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ

Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While genome-scale metabolic models (GEMs) show promise in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigate shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical therapeutic targets and biomarkers that can potentially contribute to the development of therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments.

Elucidating uptake and metabolic fate of dipeptides in CHO cell cultures using 13C labeling experiments and kinetic modeling

The rapidly growing market of biologics including monoclonal antibodies has stimulated the need to improve biomanufacturing processes including mammalian host systems such as Chinese Hamster Ovary (CHO) cells. Cell culture media formulations continue to be enhanced to enable intensified cell culture processes and optimize cell culture performance. Amino acids, major components of cell culture media, are consumed in large amounts by CHO cells. Due to their low solubility and poor stability, certain amino acids including tyrosine, leucine, and phenylalanine can pose major challenges leading to suboptimal bioprocess performance. Dipeptides have the potential to replace amino acids in culture media. However, very little is known about the cleavage, uptake, and utilization kinetics of dipeptides in CHO cell cultures. In this study, replacing amino acids, including leucine and tyrosine by their respective dipeptides including but not limited to Ala-Leu and Gly-Tyr, supported similar cell growth, antibody production, and lactate profiles. Using 13C labeling techniques and spent media studies, dipeptides were shown to undergo both intracellular and extracellular cleavage in cultures. Extracellular cleavage increased with the culture duration, indicating cleavage by host cell proteins that are likely secreted and accumulate in cell culture over time. A kinetic model was built and for the first time, integrated with 13C labeling experiments to estimate dipeptide utilization rates, in CHO cell cultures. Dipeptides with alanine at the N-terminus had a higher utilization rate than dipeptides with alanine at the C-terminus and dipeptides with glycine instead of alanine at N-terminus. Simultaneous supplementation of more than one dipeptide in culture led to reduction in individual dipeptide utilization rates indicating that dipeptides compete for the same cleavage enzymes, transporters, or both. Dipeptide utilization rates in culture and cleavage rates in cell-free experiments appeared to follow Michaelis-Menten kinetics, reaching a maximum at higher dipeptide concentrations. Dipeptide utilization behavior was found to be similar in cell-free and cell culture environments, paving the way for future testing approaches for dipeptides in cell-free environments prior to use in large-scale bioreactors. Thus, this study provides a deeper understanding of the fate of dipeptides in CHO cell cultures through an integration of cell culture, 13C labeling, and kinetic modeling approaches providing insights in how to best use dipeptides in media formulations for robust and optimal mammalian cell culture performance.

Harnessing metabolic plasticity in CHO cells for enhanced perfusion cultivation

Chinese Hamster Ovary (CHO) cells have rapidly become a cornerstone in biopharmaceutical production. Recently, a reinvigoration of perfusion culture mode in CHO cell cultivation has been observed. However, most cell lines currently in use have been engineered and adapted for fed-batch culture methods, and may not perform optimally under perfusion conditions. To improve the cell's resilience and viability during perfusion culture, we cultured a triple knockout CHO cell line, deficient in three apoptosis related genes BAX, BAK, and BOK in a perfusion system. After 20 days of culture, the cells exhibited a halt in cell proliferation. Interestingly, following this phase of growth arrest, the cells entered a second growth phase. During this phase, the cell numbers nearly doubled, but cell specific productivity decreased. We performed a proteomics investigation, elucidating a distinct correlation between growth arrest and cell cycle arrest and showing an upregulation of the central carbon metabolism and oxidative phosphorylation. The upregulation was partially reverted during the second growth phase, likely caused by intragenerational adaptations to stresses encountered. A phase-dependent response to oxidative stress was noted, indicating glutathione has only a secondary role during cell cycle arrest. Our data provides evidence of metabolic regulation under high cell density culturing conditions and demonstrates that cell growth arrest can be overcome. The acquired insights have the potential to not only enhance our understanding of cellular metabolism but also contribute to the development of superior cell lines for perfusion cultivation.

Deciphering Metabolic Pathways in High-Seeding-Density Fed-Batch Processes for Monoclonal Antibody Production: A Computational Modeling Perspective

Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high-seeding-density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in Chinese hamster ovary (CHO) cells, focused on identifying significant metabolites and their interactions. We observed high glycolytic fluxes, the depletion of asparagine, and a shift from lactate production to consumption. Using a metabolic network and flux analysis, we compared the standard fed-batch (STD FB) with HSD cultivations, exploring supplementary lactate and cysteine, and a bolus medium enriched with amino acids. We reconstructed a metabolic network and kinetic models based on the observations and explored the effects of different feeding strategies on CHO cell metabolism. Our findings revealed that the addition of a bolus medium (BM) containing asparagine improved final titers. However, increasing the asparagine concentration in the feed further prevented the lactate shift, indicating a need to find a balance between increased asparagine to counteract limitations and lower asparagine to preserve the shift in lactate metabolism.

Driving towards digital biomanufacturing by CHO genome-scale models

Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms.

Advancements in CHO metabolomics: techniques, current state and evolving methodologies

Background: Investigating the metabolic behaviour of different cellular phenotypes, i.e., good/bad grower and/or producer, in production culture is important to identify the key metabolite(s)/pathway(s) that regulate cell growth and/or recombinant protein production to improve the overall yield. Currently, LC-MS, GC-MS and NMR are the most used and advanced technologies for investigating the metabolome. Although contributed significantly in the domain, each technique has its own biasness towards specific metabolites or class of metabolites due to various reasons including variability in the concept of working, sample preparation, metabolite-extraction methods, metabolite identification tools, and databases. As a result, the application of appropriate analytical technique(s) is very critical. Purpose and scope: This review provides a state-of-the-art technological insights and overview of metabolic mechanisms involved in regulation of cell growth and/or recombinant protein production for improving yield from CHO cultures. Summary and conclusion: In this review, the advancements in CHO metabolomics over the last 10 years are traced based on a bibliometric analysis of previous publications and discussed. With the technical advancement in the domain of LC-MS, GC-MS and NMR, metabolites of glycolytic and nucleotide biosynthesis pathway (glucose, fructose, pyruvate and phenylalanine, threonine, tryptophan, arginine, valine, asparagine, and serine, etc.) were observed to be upregulated in exponential-phase thereby potentially associated with cell growth regulation, whereas metabolites/intermediates of TCA, oxidative phosphorylation (aspartate, glutamate, succinate, malate, fumarate and citrate), intracellular NAD+/NADH ratio, and glutathione metabolic pathways were observed to be upregulated in stationary-phase and hence potentially associated with increased cell-specific productivity in CHO bioprocess. Moreover, each of technique has its own bias towards metabolite identification, indicating their complementarity, along with a number of critical gaps in the CHO metabolomics pipeline and hence first time discussed here to identify their potential remedies. This knowledge may help in future study designs to improve the metabolomic coverage facilitating identification of the metabolites/pathways which might get missed otherwise and explore the full potential of metabolomics for improving the CHO bioprocess performances.

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Flux Balance Analysis of Mammalian Cell Systems

Flux balance analysis (FBA) is a computational methodology to model and analyze the metabolic behavior of cells. In this chapter, we break down the key steps for formulating an FBA model and other FBA-derived methodologies in the context of mammalian cell biology, including strain design, developing cell line-specific models, and conducting flux sampling. We provide annotated COBRApy code for each step to show how it would work in practice.

Inferring secretory and metabolic pathway activity from omic data with secCellFie

Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation

In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.

From observational to actionable: rethinking omics in biologics production

As the era of omics continues to expand with increasing ubiquity and success in both academia and industry, omics-based experiments are becoming commonplace in industrial biotechnology, including efforts to develop novel solutions in bioprocess optimization and cell line development. Omic technologies provide particularly valuable 'observational' insights for discovery science, especially in academic research and industrial R&D; however, biomanufacturing requires a different paradigm to unlock 'actionable' insights from omics. Here, we argue the value of omic experiments in biotechnology can be maximized with deliberate selection of omic approaches and forethought about analysis techniques. We describe important considerations when designing and implementing omic-based experiments and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in mammalian-based biologics production.

CHOmpact: A reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability

Metabolic modeling has emerged as a key tool for the characterization of biopharmaceutical cell culture processes. Metabolic models have also been instrumental in identifying genetic engineering targets and developing feeding strategies that optimize the growth and productivity of Chinese hamster ovary (CHO) cells. Despite their success, metabolic models of CHO cells still present considerable challenges. Genome-scale metabolic models (GeMs) of CHO cells are very large (>6000 reactions) and are difficult to constrain to yield physiologically consistent flux distributions. The large scale of GeMs also makes the interpretation of their outputs difficult. To address these challenges, we have developed CHOmpact, a reduced metabolic network that encompasses 101 metabolites linked through 144 reactions. Our compact reaction network allows us to deploy robust, nonlinear optimization and ensure that the computed flux distributions are physiologically consistent. Furthermore, our CHOmpact model delivers enhanced interpretability of simulation results and has allowed us to identify the mechanisms governing shifts in the anaplerotic consumption of asparagine and glutamate as well as an important mechanism of ammonia detoxification within mitochondria. CHOmpact, thus, addresses key challenges of large-scale metabolic models and will serve as a platform to develop dynamic metabolic models for the control and optimization of biopharmaceutical cell culture processes.

Critical challenges and advances in recombinant adeno-associated virus (rAAV) biomanufacturing

Gene therapy is a promising therapeutic approach for genetic and acquired diseases nowadays. Among DNA delivery vectors, recombinant adeno-associated virus (rAAV) is one of the most effective and safest vectors used in commercial drugs and clinical trials. However, the current yield of rAAV biomanufacturing lags behind the necessary dosages for clinical and commercial use, which embodies a concentrated reflection of low productivity of rAAV from host cells, difficult scalability of the rAAV-producing bioprocess, and high levels of impurities materialized during production. Those issues directly impact the price of gene therapy medicine in the market, limiting most patients' access to gene therapy. In this context, the current practices and several critical challenges associated with rAAV gene therapy bioprocesses are reviewed, followed by a discussion of recent advances in rAAV-mediated gene therapy and other therapeutic biological fields that could improve biomanufacturing if these advances are integrated effectively into the current systems. This review aims to provide the current state-of-the-art technology and perspectives to enhance the productivity of rAAV while reducing impurities during production of rAAV.

Chemical inhibitors of hexokinase-2 enzyme reduce lactate accumulation, alter glycosylation processing, and produce altered glycoforms in CHO cell cultures

Chinese hamster ovary (CHO) cells, predominant hosts for recombinant biotherapeutics production, generate lactate as a major glycolysis by-product. High lactate levels adversely impact cell growth and productivity. The goal of this study was to reduce lactate in CHO cell cultures by adding chemical inhibitors to hexokinase-2 (HK2), the enzyme catalyzing the conversion of glucose to glucose 6-phosphate, and examine their impact on lactate accumulation, cell growth, protein titers, and N-glycosylation. Five inhibitors of HK2 enzyme at different concentrations were evaluated, of which 2-deoxy- d-glucose (2DG) and 5-thio- d-glucose (5TG) successfully reduced lactate accumulation with only limited impacts on CHO cell growth. Individual 2DG and 5TG supplementation led to a 35%-45% decrease in peak lactate, while their combined supplementation resulted in a 60% decrease in peak lactate. Inhibitor supplementation led to at least 50% decrease in moles of lactate produced per mol of glucose consumed. Recombinant EPO-Fc titers peaked earlier relative to the end of culture duration in supplemented cultures leading to at least 11% and as high as 32% increase in final EPO-Fc titers. Asparagine, pyruvate, and serine consumption rates also increased in the exponential growth phase in 2DG and 5TG treated cultures, thus, rewiring central carbon metabolism due to low glycolytic fluxes. N-glycan analysis of EPO-Fc revealed an increase in high mannose glycans from 5% in control cultures to 25% and 37% in 2DG and 5TG-supplemented cultures, respectively. Inhibitor supplementation also led to a decrease in bi-, tri-, and tetra-antennary structures and up to 50% lower EPO-Fc sialylation. Interestingly, addition of 2DG led to the incorporation of 2-deoxy-hexose (2DH) on EPO-Fc N-glycans and addition of 5TG resulted in the first-ever observed N-glycan incorporation of 5-thio-hexose (5TH). Six percent to 23% of N-glycans included 5TH moieties, most likely 5-thio-mannose and/or 5-thio-galactose and/or possibly 5-thio-N-acetylglucosamine, and 14%-33% of N-glycans included 2DH moieties, most likely 2-deoxy-mannose and/or 2-deoxy-galactose, for cultures treated with different concentrations of 5TG and 2DG, respectively. Our study is the first to evaluate the impact of these glucose analogs on CHO cell growth, protein production, cell metabolism, N-glycosylation processing, and formation of alternative glycoforms.

How reliable are Chinese hamster ovary (CHO) cell genome-scale metabolic models?

Genome-scale metabolic models (GEMs) possess the power to revolutionize bioprocess and cell line engineering workflows thanks to their ability to predict and understand whole-cell metabolism in silico. Despite this potential, it is currently unclear how accurately GEMs can capture both intracellular metabolic states and extracellular phenotypes. Here, we investigate this knowledge gap to determine the reliability of current Chinese hamster ovary (CHO) cell metabolic models. We introduce a new GEM, iCHO2441, and create CHO-S and CHO-K1 specific GEMs. These are compared against iCHO1766, iCHO2048, and iCHO2291. Model predictions are assessed via comparison with experimentally measured growth rates, gene essentialities, amino acid auxotrophies, and 13 C intracellular reaction rates. Our results highlight that all CHO cell models are able to capture extracellular phenotypes and intracellular fluxes, with the updated GEM outperforming the original CHO cell GEM. Cell line-specific models were able to better capture extracellular phenotypes but failed to improve intracellular reaction rate predictions in this case. Ultimately, this work provides an updated CHO cell GEM to the community and lays a foundation for the development and assessment of next-generation flux analysis techniques, highlighting areas for model improvements.

Systems biology and metabolic modeling for cultivated meat: A promising approach for cell culture media optimization and cost reduction

The cultivated meat industry, also known as cell-based meat, cultured meat, lab-grown meat, or meat alternatives, is a growing field that aims to generate animal tissues ex-vivo in a cost-effective manner that achieves price parity with traditional agricultural products. However, cell culture media costs account for 55%-90% of production costs. To address this issue, efforts are aimed at optimizing media composition. Systems biology-driven approaches have been successfully used to improve the biomass and productivity of multiple bioproduction platforms, like Chinese hamster ovary cells, by accelerating the development of cell line-specific media and reducing research and development and production costs related to cell media and its optimization. In this review, we summarize systems biology modeling approaches, methods for cell culture media and bioprocess optimization, and metabolic studies done in animals of interest to the cultivated meat industry. More importantly, we identify current gaps in knowledge that prevent the identification of metabolic bottlenecks. These include the lack of genome-scale metabolic models for some species (pigs and ducks), a lack of accurate biomass composition studies for different growth conditions, and 13 C-metabolic flux analysis (MFA) studies for many of the species of interest for the cultivated meat industry (only shrimp and duck cells have been subjected to 13 C-MFA). We also highlight the importance of characterizing the metabolic requirements of cells at the organism, breed, and cell line-specific levels, and we outline future steps that this nascent field needs to take to achieve price parity and production efficiency similar to those of other bioproduction platforms. Practical Application: Our article summarizes systems biology techniques for cell culture media design and bioprocess optimization, which may be used to significantly reduce cell-based meat production costs. We also present the results of experimental studies done on some of the species of interest to the cultivated meat industry and highlight why modeling approaches are required for multiple species, cell-types, and cell lines.

Inferring secretory and metabolic pathway activity from omic data with secCellFie

Understanding protein secretion has considerable importance in the biotechnology industry and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

ImmCellFie: A user-friendly web-based platform to infer metabolic function from omics data

Understanding cellular metabolism is important across biotechnology and biomedical research and has critical implications in a broad range of normal and pathological conditions. Here, we introduce the user-friendly web-based platform ImmCellFie, which allows the comprehensive analysis of metabolic functions inferred from transcriptomic or proteomic data. We explain how to set up a run using publicly available omics data and how to visualize the results. The ImmCellFie algorithm pushes beyond conventional statistical enrichment and incorporates complex biological mechanisms to quantify cell activity. For complete details on the use and execution of this protocol, please refer to Richelle et al. (2021).1.

Reconstruction of reverse transsulfuration pathway enables cysteine biosynthesis and enhances resilience to oxidative stress in Chinese Hamster Ovary cells

Cysteine is a critically important amino acid necessary for mammalian cell culture, playing key roles in nutrient supply, disulfide bond formation, and as a precursor to antioxidant molecules controlling cellular redox. Unfortunately, its low stability and solubility in solution make it especially problematic as an essential medium component that must be added to Chinese hamster ovary and other mammalian cell cultures. Therefore, CHO cells have been engineered to include the capacity of endogenously synthesizing cysteine by overexpressing multiple enzymes, including cystathionine beta-synthase (CBS), cystathionine gamma-lyase (CTH) and glycine N-methyltransferase (GNMT) to reconstruct the reverse transsulfuration pathway and overcome a key metabolic bottleneck. Some limited cysteine biosynthesis was obtained by overexpressing CBS and CTH for converting homocysteine to cysteine but robust metabolic synthesis from methionine was only possibly after incorporating GNMT which likely represents a key bottleneck step in the cysteine biosynthesis pathway. CHO cells with the reconstructed pathway exhibit the strong capability to proliferate in cysteine-limited and cysteine-free batch and fed-batch cultures at levels comparable to wildtype cells with ample cysteine supplementation, providing a selectable marker for CHO cell engineering. GNMT overexpression led to the accumulation of sarcosine byproduct, but its accumulation did not affect cell growth. Furthermore, pathway reconstruction enhanced CHO cells' reduced and glutathione levels in cysteine-limited conditions compared to unmodified cells, and greatly enhanced survivability and maintenance of redox homeostasis under oxidative stress induced by addition of menadione in cysteine-deficient conditions. Such engineered CHO cell lines can potentially reduce or even eliminate the need to include cysteine in culture medium, which not only reduces the cost of mammalian media but also promises to transform media design by solving the challenges posed by low stability and solubility of cysteine and cystine in future mammalian biomanufacturing processes.

Machine learning and metabolic modelling assisted implementation of a novel process analytical technology in cell and gene therapy manufacturing

Process analytical technology (PAT) has demonstrated huge potential to enable the development of improved biopharmaceutical manufacturing processes by ensuring the reliable provision of quality products. However, the complexities associated with the manufacture of advanced therapy medicinal products have resulted in a slow adoption of PAT tools into industrial bioprocessing operations, particularly in the manufacture of cell and gene therapy products. Here we describe the applicability of a novel refractometry-based PAT system (Ranger system), which was used to monitor the metabolic activity of HEK293T cell cultures during lentiviral vector (LVV) production processes in real time. The PAT system was able to rapidly identify a relationship between bioreactor pH and culture metabolic activity and this was used to devise a pH operating strategy that resulted in a 1.8-fold increase in metabolic activity compared to an unoptimised bioprocess in a minimal number of bioreactor experiments; this was achieved using both pre-programmed and autonomous pH control strategies. The increased metabolic activity of the cultures, achieved via the implementation of the PAT technology, was not associated with increased LVV production. We employed a metabolic modelling strategy to elucidate the relationship between these bioprocess level events and HEK293T cell metabolism. The modelling showed that culturing of HEK293T cells in a low pH (pH 6.40) environment directly impacted the intracellular maintenance of pH and the intracellular availability of oxygen. We provide evidence that the elevated metabolic activity was a response to cope with the stress associated with low pH to maintain the favourable intracellular conditions, rather than being indicative of a superior active state of the HEK293T cell culture resulting in enhanced LVV production. Forecasting strategies were used to construct data models which identified that the novel PAT system not only had a direct relationship with process pH but also with oxygen availability; the interaction and interdependencies between these two parameters had a direct effect on the responses observed at the bioprocess level. We present data which indicate that process control and intervention using this novel refractometry-based PAT system has the potential to facilitate the fine tuning and rapid optimisation of the production environment and enable adaptive process control for enhanced process performance and robustness.

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems

With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis

Flux balance analysis (FBA) is currently the standard method to compute metabolic fluxes in genome-scale networks. Several FBA extensions employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric FBA extension that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric) in the same linear program. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The mechanistic constraints were deduced from a reduced CHO-K1 genome-scale network with 686 metabolites, 788 reactions and 210 degrees of freedom. The non-parametric constraints were obtained by principal component analysis of the flux dataset. The two types of constraints were integrated in the same linear program showing comparable computational cost to standard FBA. The hybrid FBA is shown to significantly improve the specific growth rate prediction under different constraints scenarios. A metabolically efficient cell growth feed targeting minimal byproducts accumulation was designed by hybrid FBA. It is concluded that integrating parametric and nonparametric constraints in the same linear program may be an efficient approach to reduce the solution space and to improve the predictive power of FBA methods when critical mechanistic information is missing.

The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility

The topology of metabolic networks is recognisably modular with modules weakly connected apart from sharing a pool of currency metabolites. Here, we defined modules as sets of reversible reactions isolated from the rest of metabolism by irreversible reactions except for the exchange of currency metabolites. Our approach identifies topologically independent modules under specific conditions associated with different metabolic functions. As case studies, the E.coli iJO1366 and Human Recon 2.2 genome-scale metabolic models were split in 103 and 321 modules respectively, displaying significant correlation patterns in expression data. Finally, we addressed a fundamental question about the metabolic flexibility conferred by reversible reactions: “Of all Directed Topologies (DTs) defined by fixing directions to all reversible reactions, how many are capable of carrying flux through all reactions?”. Enumeration of the DTs for iJO1366 model was performed using an efficient depth-first search algorithm, rejecting infeasible DTs based on mass-imbalanced and loopy flux patterns. We found the direction of 79% of reversible reactions must be defined before all directions in the network can be fixed, granting a high degree of flexibility.

Genome-scale metabolic reconstructions represent all biochemical reactions that an organism can accomplish. These reconstructions are complex and often difficult to study in great detail. A way to overcome this limitation is to focus on specific pathways or subsystems. We present a novel method to identify metabolic modules based on the network topology. The method relies on reaction directions and ignores currency metabolites, which artificially connect distant metabolic reactions. In this way, topologically independent modules are built, where inputs and outputs are controlled by irreversible reactions. The method is automatic and unbiased, and, the result is a set of condition specific modules with defined metabolic functions. As a proof-of-concept we generated biologically relevant modules for the E.coli and Human genome-scale metabolic reconstructions supported by transcriptomic data. Finally, we applied the novel approach to study the network flexibility conferred by reversible reactions. In the case of the E. coli model, we found that the direction of 79% of structurally reversible reactions (those not directionally constrained by surrounding irreversible reactions) must be fixed to determine all the reaction directions in the network. Therefore, reversible reactions operate practically independent of each other.

SALARECON connects the Atlantic salmon genome to growth and feed efficiency

Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds.

Atlantic salmon aquaculture generates billions of euros annually, but faces challenges of sustainability. Salmon are carnivores by nature, and fish oil and fish meal have become scarce resources in fish feed production. Novel, sustainable feedstuffs are being trialed hand in hand with studies of the genetics of growth and feed efficiency. This calls for a mathematical-biological framework to integrate data with understanding of the effects of novel feeds on salmon physiology and its interplay with genetics. We have developed the SALARECON model of the core salmon metabolic reaction network, linking its genome to metabolic fluxes and growth. Computational analyses show good agreement with observed growth, amino acid limitations, and feed efficiencies, illustrating the potential for in silico studies of potential feed mixtures. In particular, in silico screening of possible diets will enable more efficient animal experiments with improved knowledge gain. We have adopted best practices for test-driven development, virtual experiments to assay metabolic capabilities, revision control, and FAIR data and model management. This facilitates fast, collaborative, reliable development of the model for future applications in sustainable production biology.

Recent developments in miRNA based recombinant protein expression in CHO

It is widely accepted that the growing demand for recombinant therapeutic proteins has led to the expansion of the biopharmaceutical industry and the development of strategies to increase recombinant protein production in mammalian cell lines such as SP2/0 HEK and particularly Chinese hamster ovary cells. For a long time now, most investigations have been focused on increasing host cell productivity using genetic manipulating of cellular processes like cell cycle, apoptosis, cell growth, protein secretory and other pathways. In recent decades MicroRNAs beside different genetic engineering tools (e.g., TALEN, ZFN, and Crisper/Cas) have attracted further attention as a tool in the genetic engineering of host cells to increase protein expression levels. Their ability to simultaneously target multiple mRNAs involved in one or more cellular processes made them a favorable tool in this field. Accordingly, this study aimed to review the methods of selecting target miRNA for cell line engineering, miRNA gain- or loss-of-function strategies, examples of laboratory and pilot studies in this field and discussed advantages and disadvantages of this technology.

Combined multivariate statistical and flux balance analyses uncover media bottlenecks to the growth and productivity of CHO cell cultures

Chinese hamster ovary (CHO) cells are widely used for producing recombinant proteins. To enhance their productivity and product quality, media reformulation has been a key strategy, albeit with several technical challenges, due to the myriad of complex molecular mechanisms underlying media effects on culture performance. Thus, it is imperative to characterize metabolic bottlenecks under various media conditions systematically. To do so, we combined partial least square regression (PLS-R) with the flux balance analysis of a genome-scale metabolic model to elucidate the physiological states and metabolic behaviors of human alpha-1 antitrypsin producing CHO-DG44 cells grown in one commercial and another two in-house media under development. At the onset, PLS-R was used to identify metabolite exchanges that were correlated to specific growth and productivity. Then, by comparing metabolic states described by resultant flux distributions under two of the media conditions, we found sub-optimal level of four nutrients and two metabolic wastes, which plausibly hindered cellular growth and productivity; mechanistically, lactate and ammonia recycling were modulated by glutamine and asparagine metabolisms in the media conditions, and also by hitherto unsuspected folate and choline supplements. Our work demonstrated how multivariate statistical analysis can be synergistically combined with metabolic modelling to uncover the mechanistic elements underlying differing media performance. It thus paved the way for the systematic identification of nutrient targets for medium reformulation to enhance recombinant protein production in CHO cells. This article is protected by copyright. All rights reserved.

Reconstruction of a generic genome-scale metabolic network for chicken: Investigating network connectivity and finding potential biomarkers

Chicken is the first sequenced avian that has a crucial role in human life for its meat and egg production. Because of various metabolic disorders, study the metabolism of chicken cell is important. Herein, the first genome-scale metabolic model of a chicken cell named iES1300, consists of 2427 reactions, 2569 metabolites, and 1300 genes, was reconstructed manually based on KEGG, BiGG, CHEBI, UNIPROT, REACTOME, and MetaNetX databases. Interactions of metabolic genes for growth were examined for E. coli, S. cerevisiae, human, and chicken metabolic models. The results indicated robustness to genetic manipulation for iES1300 similar to the results for human. iES1300 was integrated with transcriptomics data using algorithms and Principal Component Analysis was applied to compare context-specific models of the normal, tumor, lean and fat cell lines. It was found that the normal model has notable metabolic flexibility in the utilization of various metabolic pathways, especially in metabolic pathways of the carbohydrate metabolism, compared to the others. It was also concluded that the fat and tumor models have similar growth metabolisms and the lean chicken model has a more active lipid and carbohydrate metabolism.

Biopharmaceutical Manufacturing: Historical Perspectives and Future Directions

This review describes key milestones related to the production of biopharmaceuticals-therapies manufactured using recombinant DNA technology. The market for biopharmaceuticals has grown significantly since the first biopharmaceutical approval in 1982, and the scientific maturity of the technologies used in their manufacturing processes has grown concomitantly. Early processes relied on established unit operations, with research focused on process scale-up and improved culture productivity. In the early 2000s, changes in regulatory frameworks and the introduction of Quality by Design emphasized the importance of developing manufacturing processes to deliver a desired product quality profile. As a result, companies adopted platform processes and focused on understanding the dynamic interplay between product quality and processing conditions. The consistent and reproducible manufacturing processes of today's biopharmaceutical industry have set high standards for product efficacy, quality, and safety, and as the industry continues to evolve in the coming decade, intensified processing capabilities for an expanded range of therapeutic modalities will likely become routine. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Valine feeding reduces ammonia production through rearrangement of metabolic fluxes in central carbon metabolism of CHO cells

Ammonia is a toxic byproduct of CHO cell metabolism, which inhibits cell growth, reduces cell viability, alters glycosylation, and decreases recombinant protein productivity. In an attempt to minimize the ammonium accumulation in cell culture media, different amino acids were added individually to the culture medium before the production phase to alleviate the negative effects of ammonium on cell culture performance. Among all the amino acids examined in this study, valine showed the most positive impact on CHO cell culture performance. When the cultured CHO cells were fed with 5 mM valine, EPO titer was increased by 25% compared to the control medium, and ammonium and lactate production were decreased by 23 and 26%, respectively, relative to the control culture. Moreover, the sialic acid content of the EPO protein in valine-fed culture was higher than in the control culture, most likely because of the lower ammonium concentration. Flux balance analysis (FBA) results demonstrated that the citric acid cycle was enriched by valine feeding. The measurement of TCA cycle activity supported this finding. The analysis revealed that there might be a link between promoting tricarboxylic acid (TCA) cycle metabolism in valine-fed culture and reduction in lactate and ammonia accumulation. Furthermore, in valine-fed culture, FBA outcomes showed that alanine was excreted into the medium as the primary mechanism for reducing ammonium concentration. It was predicted that the elevated TCA cycle metabolism was concurrent with an increment in recombinant protein production. Taken together, our data demonstrate that valine addition could be an effective strategy for mitigating the negative impacts of ammonium and enhancing glycoprotein production in both quality and quantity. KEY POINTS: • Valine feeding can mitigate the negative impacts of ammonia on CHO cell growth. • Valine addition assists the ammonia removal mechanism by enriching the TCA cycle. • Ammonia is removed from the media through alanine excretion in valine-fed culture.

Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing

Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a ‘hybrid ML’ or ‘white box ML’ to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Advances of Glycometabolism Engineering in Chinese Hamster Ovary Cells

As the most widely used mammalian cell line, Chinese hamster ovary (CHO) cells can express various recombinant proteins with a post translational modification pattern similar to that of the proteins from human cells. During industrial production, cells need large amounts of ATP to support growth and protein expression, and since glycometabolism is the main source of ATP for cells, protein production partly depends on the efficiency of glycometabolism. And efficient glycometabolism allows less glucose uptake by cells, reducing production costs, and providing a better mammalian production platform for recombinant protein expression. In the present study, a series of progresses on the comprehensive optimization in CHO cells by glycometabolism strategy were reviewed, including carbohydrate intake, pyruvate metabolism and mitochondrial metabolism. We analyzed the effects of gene regulation in the upstream and downstream of the glucose metabolism pathway on cell’s growth and protein expression. And we also pointed out the latest metabolic studies that are potentially applicable on CHO cells. In the end, we elaborated the application of metabolic models in the study of CHO cell metabolism.

Compartment-specific 13C metabolic flux analysis reveals boosted NADPH availability coinciding with increased cell-specific productivity for IgG1 producing CHO cells after MTA treatment

Increasing cell-specific productivities (CSPs) for the production of heterologous proteins in Chinese hamster ovary (CHO) cells is an omnipresent need in the biopharmaceutical industry. The novel additive 5'-deoxy-5'-(methylthio)adenosine (MTA), a chemical degradation product of S-(5'-adenosyl)-ʟ-methionine (SAM) and intermediate of polyamine biosynthesis, boosts the CSP of IgG1-producing CHO cells by 50%. Compartment-specific 13C flux analysis revealed a fundamental reprogramming of the central metabolism after MTA addition accompanied by cell-cycle arrest and increased cell volumes. Carbon fluxes into the pentose-phosphate pathway increased 22 fold in MTA-treated cells compared to that in non-MTA-treated reference cells. Most likely, cytosolic ATP inhibition of phosphofructokinase mediated the carbon detour. Mitochondrial shuttle activity of the α-ketoglurarate/malate antiporter (OGC) reversed, reducing cytosolic malate transport. In summary, NADPH supply in MTA-treated cells improved three fold compared to that in non-MTA-treated cells, which can be regarded as a major factor for explaining the boosted CSPs.

Perfusion culture of Chinese Hamster Ovary cells for bioprocessing applications

Much of the biopharmaceutical industry's success over the past 30 years has relied on products derived from Chinese Hamster Ovary (CHO) cell lines. During this time, improvements in mammalian cell cultures have come from cell line development and process optimization suited for large-scale fed-batch processes. Originally developed for high cell densities and sensitive products, perfusion processes have a long history. Driven by high volumetric titers and a small footprint, perfusion-based bioprocess research has regained an interest from academia and industry. The recent pandemic has further highlighted the need for such intensified biomanufacturing options. In this review, we outline the technical history of research in this field as it applies to biologics production in CHO cells. We demonstrate a number of emerging trends in the literature and corroborate these with underlying drivers in the commercial space. From these trends, we speculate that the future of perfusion bioprocesses is bright and that the fields of media optimization, continuous processing, and cell line engineering hold the greatest potential. Aligning in its continuous setup with the demands for Industry 4.0, perfusion biomanufacturing is likely to be a hot topic in the years to come.

A genome-scale nutrient minimization forecast algorithm for controlling essential amino acid levels in CHO cell cultures

Mammalian cell culture processes rely heavily on empirical knowledge in which process control remains a challenge due to the limited characterization/understanding of cell metabolism and inability to predict the cell behaviors. This study facilitates control of Chinese hamster ovary (CHO) processes through a forecast-based feeding approach that predicts multiple essential amino acids levels in the culture from easily acquired viable cell density data. Multiple cell growth behavior forecast extrapolation approaches are considered with logistic curve fitting found to be the most effective. Next, the nutrient-minimized CHO genome-scale model is combined with the growth forecast model to generate essential amino acid forecast profiles of multiple CHO batch cultures. Comparison of the forecast with the measurements suggests that this algorithm can accurately predict the concentration of most essential amino acids from cell density measurement with error mitigated by incorporating off-line amino acids concentration measurements. Finally, the forecast algorithm is applied to CHO fed-batch cultures to support amino acid feeding control to control the concentration of essential amino acids below 1-2 mM for lysine, leucine, and valine as a model over a 9-day fed batch culture while maintaining comparable growth behavior to an empirical-based culture. In turn, glycine production was elevated, alanine reduced and lactate production slightly lower in control cultures due to metabolic shifts in branched-chain amino acid degradation. With the advantage of requiring minimal measurement inputs while providing valuable and in-advance information of the system based on growth measurements, this genome model-based amino acid forecast algorithm represent a powerful and cost-effective tool to facilitate enhanced control over CHO and other mammalian cell-based bioprocesses.

Metabolic trends of Chinese hamster ovary cells in biopharmaceutical production under batch and fed-batch conditions

Extensive knowledge of Chinese hamster ovary (CHO) cell metabolism is required to improve process productivity and culture performance in biopharmaceutical manufacturing. However, CHO cells show a dynamic metabolism during culturing in batch and fed-batch bioreactors. CHO cell metabolism is generally described as taking place in three stages: exponential growth phase, stationary phase, and death phase. This review aims to summarize the trends of central metabolism for CHO cells during each stage. Additional insights into how culture conditions are related to phase transitions and force metabolic rewiring are provided. Understanding of CHO cell metabolism lends itself to improving culture qualities by, for example, identifying sources of toxic byproducts and pathways for cellular engineering. In summary, this review describes the changes in CHO cell central metabolism over the course of the culture.

Production of butyrate and branched-chain amino acid catabolic byproducts by CHO cells in fed-batch culture enhances their specific productivity

Chinese hamster ovary (CHO) cells in fed-batch cultures produce several metabolic byproducts derived from amino acid catabolism, some of which accumulate to growth inhibitory levels. Controlling the accumulation of these byproducts has been shown to significantly enhance cell proliferation. Interestingly, some of these byproducts have physiological roles that go beyond inhibition of cell proliferation. In this study, we show that, in CHO cell fed-batch cultures, branched-chain amino acid (BCAA) catabolism contributes to the formation of butyrate, a novel byproduct that is also a well-established specific productivity enhancer. We further show that other byproducts of BCAA catabolism, namely isovalerate and isobutyrate, which accumulate in CHO cell fed-batch cultures, also enhance specific productivity. Lastly, we show that the rate of production of these BCAA catabolic byproducts is negatively correlated with glucose uptake and lactate production rates. Thus, limiting glucose supply to suppress glucose uptake and lactate production, as in the case of fed-batch cultures employing high-end pH-controlled delivery of glucose (HiPDOG) technology, significantly enhances BCAA catabolic byproduct accumulation, resulting in higher specific productivities.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Rapid intensification of an established CHO cell fed-batch process

Currently, the mammalian biomanufacturing industry explores process intensification (PI) to meet upcoming demands of biotherapeutics while keeping production flexible but, more importantly, as economic as possible. However, intensified processes often require more development time compared with conventional fed‐batches (FBs) preventing their implementation. Hence, rapid and efficient, yet straightforward strategies for PI are needed. In this study we demonstrate such a strategy for the intensification of an N‐stage FB by implementing N‐1 perfusion cell culture and high inoculum cell densities resulting in a robust intensified FB (iFB). Furthermore, we show successful combination of such an iFB with the addition of productivity enhancers, which has not been reported so far. The conventional CHO cell FB process was step‐wise improved and intensified rapidly in multi‐parallel small‐scale bioreactors using N‐1 perfusion. The iFBs were performed in 15 and 250 ml bioreactors and allowed to evaluate the impact on key process indicators (KPI): the space–time yield (STY) was successfully doubled from 0.28 to 0.55 g/L d, while product quality was maintained. This gain was generated by initially increasing the inoculation density, thus shrinking process time, and second supplementation with butyric acid (BA), which reduced cell growth and enhanced cell‐specific productivity from ~25 to 37 pg/(cell d). Potential impacts of PI on cell metabolism were evaluated using flux balance analysis. Initial metabolic differences between the standard and intensified process were observed but disappeared quickly. This shows that PI can be achieved rapidly for new as well as existing processes without introducing sustained changes in cellular and metabolic behavior.

A Chinese hamster transcription start site atlas that enables targeted editing of CHO cells

Chinese hamster ovary (CHO) cells are widely used for producing biopharmaceuticals, and engineering gene expression in CHO is key to improving drug quality and affordability. However, engineering gene expression or activating silent genes requires accurate annotation of the underlying regulatory elements and transcription start sites (TSSs). Unfortunately, most TSSs in the published Chinese hamster genome sequence were computationally predicted and are frequently inaccurate. Here, we use nascent transcription start site sequencing methods to revise TSS annotations for 15 308 Chinese hamster genes and 3034 non-coding RNAs based on experimental data from CHO-K1 cells and 10 hamster tissues. We further capture tens of thousands of putative transcribed enhancer regions with this method. Our revised TSSs improves upon the RefSeq annotation by revealing core sequence features of gene regulation such as the TATA box and the Initiator and, as exemplified by targeting the glycosyltransferase gene Mgat3, facilitate activating silent genes by CRISPRa. Together, we envision our revised annotation and data will provide a rich resource for the CHO community, improve genome engineering efforts and aid comparative and evolutionary studies.

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by parsimonious flux balance analysis (pFBA) using the CHO GSMM and a wide range of ¹³C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function.

There is an increasing demand for protein pharmaceuticals, especially monoclonal antibodies. Chinese Hamster Ovary (CHO) are currently the leading production host due to their ability to perform human-like post-translational modifications. However, it typically takes several months of trial-and-error approaches to develop a high-producer cell line. Metabolic modelling has the potential to make cell line and process development faster and cheaper by predicting targeted modifications to the cell line genome, cultivation medium or bioprocess. In fact, genome-scale metabolic reconstructions of CHO are already available, and ready for use in cell line development. However, in order to successfully use these models, we need to make sure that they are able to accurately predict metabolic phenotypes. Here we use genome-scale metabolic models of CHO to evaluate the models’ ability to correctly predict intracellular flux distributions. We find that a crucial key ingredient for the correct estimation of central carbon fluxes is the non-growth associated maintenance energy (mATP). We estimated mATP computationally and confirmed it experimentally. Adding this single constraint leads to significantly better predictions of intracellular fluxes, especially in glycolysis and the tricarboxylic acid cycle.