Dynamic metabolic flux analysis using B-splines to study the effects of temperature shift on CHO cell metabolism

Investigating the metabolic load of monoclonal antibody production conveyed to an inducible CHO cell line using a transfer-rate online monitoring system

Shake flasks are a foundational tool in early process development by allowing high throughput exploration of the design space. However, lack of online data at this scale can hamper rapid decision making. Oxygen transfer rate (OTR) monitoring has been readily applied as an online process characterization tool at the benchtop bioreactor scale. Recent advances in modern sensing technology have allowed OTR monitoring to be available at the shake flask level. It is now possible to multiplex time-of-action (e.g., Induction, temperature shift, pH shift, feeding initiation, point of harvest) characterization studies by relying on careful analysis of OTR profile kinetics. As a result, there is potential to save time and capital expenditures while exploring process intensification studies though accurate and physiologically relevant online data. In this article, we detail the application of OTR monitoring to characterize the impact that recombinant protein production has on an inducible CHO cell line expressing Palivizumab. We then test out time-of-action studies to intensify protein production outcomes. We observe that recombinant protein expression causes a metabolic load that diminishes potential biomass growth. As a result, when compared to a control standard process, delaying induction and temperature shift has the potential to improve viable cell densities (VCD) by 2-fold thus increasing recombinant protein yield by over 30 %. The study also demonstrates that OTR can serve as a useful tool to detect cessation of exponential growth. Consequently, time-of-action points that are characteristic of inducible systems can be formulated accurately and reliably to maximize production performance.

Optimum blue light exposure: a means to increase cell-specific productivity in Chinese hamster ovary cells

Research for biopharmaceutical production processes with mammalian cells steadily aims to enhance the cell-specific productivity as a means for optimizing total productivities of bioreactors. Whereas current technologies such as pH, temperature, and osmolality shift require modifications of the cultivation medium, the use of optogenetic switches in recombinant producer cells might be a promising contact-free alternative. However, the proper application of optogenetically engineered cells requires a detailed understanding of basic cellular responses of cells that do not yet contain the optogenetic switches. The knowhow of ideal light exposure to enable the optimum use of related approaches is missing so far. Consequently, the current study set out to find optimum conditions for IgG1 producing Chinese hamster ovary (CHO) cells which were exposed to blue LED light. Growth characteristics, cell-specific productivity using enzyme-linked immunosorbent assay, as well as cell cycle distribution using flow cytometry were analyzed. Whereas too harsh light exposure causes detrimental growth effects that could be compensated with antioxidants, a surprising boost of cell-specific productivity by 57% occurred at optimum high light doses. The increase coincided with an increased number of cells in the G1 phase of the cell cycle after 72 h of illumination. The results present a promising new approach to boost biopharmaceutical productivity of mammalian cells simply by proper light exposure without any further optogenetic engineering. KEY POINTS: • Blue LED light hinders growth in CHO DP-12 cells • Antioxidants protect to a certain degree from blue light effects • Illumination with blue LED light raises cell-specific productivity.

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.

Sodium acetate increases the productivity of HEK293 cells expressing the ECD-Her1 protein in batch cultures: experimental results and metabolic flux analysis

Human Embryonic Kidney cells (HEK293) are a popular host for recombinant protein expression and production in the biotechnological industry. This has driven within both, the scientific and the engineering communities, the search for strategies to increase their protein productivity. The present work is inserted into this search exploring the impact of adding sodium acetate (NaAc) into a batch culture of HEK293 cells. We monitored, as a function of time, the cell density, many external metabolites, and the supernatant concentration of the heterologous extra-cellular domain ECD-Her1 protein, a protein used to produce a candidate prostate cancer vaccine. We observed that by adding different concentrations of NaAc (0, 4, 6 and 8 mM), the production of ECD-Her1 protein increases consistently with increasing concentration, whereas the carrying capacity of the medium decreases. To understand these results we exploited a combination of experimental and computational techniques. Metabolic Flux Analysis (MFA) was used to infer intracellular metabolic fluxes from the concentration of external metabolites. Moreover, we measured independently the extracellular acidification rate and oxygen consumption rate of the cells. Both approaches support the idea that the addition of NaAc to the culture has a significant impact on the metabolism of the HEK293 cells and that, if properly tuned, enhances the productivity of the heterologous ECD-Her1 protein.

Triple Space-Time Yield in Discontinuous Antibody Biomanufacturing by Combination of Synergetic Process Intensification Strategies

Monoclonal antibodies are the workhorse of the pharmaceutical industry due to their potential to treat a variety of different diseases while providing high specificity and efficiency. As a consequence, a variety of production processes have been established within the biomanufacturing industry. However, the rapidly increasing demand for therapeutic molecules amid the recent COVID-19 pandemic demonstrated that there still is a clear need to establish novel, highly productive, and flexible production processes. Within this work, we designed a novel discontinuous process by combining two intensification strategies, thus increasing inoculation density and media exchange via a fluidized bed centrifuge, to fulfill the need for a flexible and highly productive production process for therapeutic molecules. To establish this new process, firstly, a small-scale experiment was conducted to verify synergies between both intensification strategies, followed by a process transfer towards the proof-of-concept scale. The combination of these two-process intensification measures revealed overall synergies resulting in decreased process duration (-37%) and strongly enhanced product formation (+116%) in comparison to the not-intensified standard operation. This led to an impressive threefold increase in space-time yield, while only negligible differences in product quality could be observed. Overall, this novel process not only increases the ways to react to emergency situations thanks to its flexibility and possible short development times, but also represents a possible alternative to the current established processes due to high increases in productivity, in comparison to standard fed-batch operations.

Rewiring of cortical glucose metabolism fuels human brain cancer growth

The brain avidly consumes glucose to fuel neurophysiology. Cancers of the brain, such as glioblastoma (GBM), lose aspects of normal biology and gain the ability to proliferate and invade healthy tissue. How brain cancers rewire glucose utilization to fuel these processes is poorly understood. Here we perform infusions of 13 C-labeled glucose into patients and mice with brain cancer to define the metabolic fates of glucose-derived carbon in tumor and cortex. By combining these measurements with quantitative metabolic flux analysis, we find that human cortex funnels glucose-derived carbons towards physiologic processes including TCA cycle oxidation and neurotransmitter synthesis. In contrast, brain cancers downregulate these physiologic processes, scavenge alternative carbon sources from the environment, and instead use glucose-derived carbons to produce molecules needed for proliferation and invasion. Targeting this metabolic rewiring in mice through dietary modulation selectively alters GBM metabolism and slows tumor growth.

Significance

This study is the first to directly measure biosynthetic flux in both glioma and cortical tissue in human brain cancer patients. Brain tumors rewire glucose carbon utilization away from oxidation and neurotransmitter production towards biosynthesis to fuel growth. Blocking these metabolic adaptations with dietary interventions slows brain cancer growth with minimal effects on cortical metabolism.

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.

Model-driven insights into the effects of temperature on metabolism

Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.

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.

Interpreting metabolic complexity via isotope-assisted metabolic flux analysis

Isotope-assisted metabolic flux analysis (iMFA) is a powerful method to mathematically determine the metabolic fluxome from experimental isotope labeling data and a metabolic network model. While iMFA was originally developed for industrial biotechnological applications, it is increasingly used to analyze eukaryotic cell metabolism in physiological and pathological states. In this review, we explain how iMFA estimates the intracellular fluxome, including data and network model (inputs), the optimization-based data fitting (process), and the flux map (output). We then describe how iMFA enables analysis of metabolic complexities and discovery of metabolic pathways. Our goal is to expand the use of iMFA in metabolism research, which is essential to maximizing the impact of metabolic experiments and continuing to advance iMFA and biocomputational techniques.

Temperature measurement of aqueous solution in miniature sample chamber in microscopic system based on near-infrared spectrum

Measurement of the sample temperature in biophysics research is challenging, as the samples are commonly placed in a miniature sample chamber under a microscope. In this study, we proposed a method to measure the temperature of an aqueous solution in miniature sample chambers in a microscopic system. Existing studies show that the absorption coefficient spectrum of water shifts with temperature, especially in the near-infrared (NIR) band. We measured the absorption spectra of water with different temperatures and analyzed them, to build a mathematical model relating the temperature and the spectrum. A setup for temperature measurement in a microscopic system was designed and implemented by coupling a spectrometer and a light source to a microscope. The temperature could be calculated by the spectral data and the mathematical model while simultaneously observing the micro-image of the sample. A series of liquid samples at different temperatures were tested using the setup, and the root mean square error of the calculated temperature is less than 0.5 °C. The results demonstrate that the method based on the NIR spectrum can be used for noncontact and quick measurement of the liquid sample temperature in a microscopic system.

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

Quantitative metabolic fluxes regulated by trans-omic networks

Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.

Stress‐induced increase of monoclonal antibody production in CHO cells

Monoclonal antibodies (mAbs) are of great interest to the biopharmaceutical industry due to their widely used application as human therapeutic and diagnostic agents. As such, mAb require to exhibit human‐like glycolization patterns. Therefore, recombinant Chinese hamster ovary (CHO) cells are the favored production organisms; many relevant biopharmaceuticals are already produced by this cell type. To optimize the mAb yield in CHO DG44 cells a corelation between stress‐induced cell size expansion and increased specific productivity was investigated. CO₂ and macronutrient supply of the cells during a 12‐day fed‐batch cultivation process were tested as stress factors. Shake flasks (500 mL) and a small‐scale bioreactor system (15 mL) were used for the cultivation experiments and compared in terms of their effect on cell diameter, integral viable cell concentration (IVCC), and cell‐specific productivity. The achieved stress‐induced increase in cell‐specific productivity of up to 94.94.9%–134.4% correlates to a cell diameter shift of up to 7.34 μm. The highest final product titer of 4 g/L was reached by glucose oversupply during the batch phase of the process.

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.

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.

A new strategy for dynamic metabolic flux estimation by integrating transient metabolome data into genome-scale metabolic models

Metabolic flux analysis (MFA) is a powerful tool for studying microbial cell physiology. Isotope-based MFA is the accepted standard for studying metabolic fluxes under steady-state conditions. However, its application under dynamic extracellular conditions is limited due to lack of proper techniques, such as rapid sampling and quenching, high cost and laborious execution. Here, we propose a new strategy to tackle this through incorporating dynamic metabolite abundance data into genome-scale metabolic models (GEM). First, a dummy extracellular pool concept is proposed for each dynamically changing metabolite, which represents a "sink" or "source", with corresponding dummy reactions coded into the GEM model. The dynamic model (expressed as differential equations) is then transformed into a quasi-steady-state model (expressed as linear equations), which can be easily solved by constraining the GEM model with the dynamic metabolite quantification data. For this, common linear-programming optimization algorithms were utilized to estimate the dynamic fluxes. Dynamic high-accuracy metabolite abundance data were obtained through the Isotope Dilution Mass Spectrometry (IDMS) method and high-speed sampling-quenching, and it was demonstrated that the newly proposed strategy could be successfully applied to obtain intracellular dynamic fluxes of Aspergillus niger under regimes of single and periodic extracellular glucose pulses. The applicability of the new method was also tested on dynamic fluxes estimation in a glucose pulse-response study of Saccharomyces cerevisiae. The proposed method provides a powerful tool to investigate cell physiology under dynamic conditions, especially relevant for bioprocess scale-up to industrial-scale bioreactors.

The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

Key Challenges in Designing CHO Chassis Platforms

Following the success of and the high demand for recombinant protein-based therapeutics during the last 25 years, the pharmaceutical industry has invested significantly in the development of novel treatments based on biologics. Mammalian cells are the major production systems for these complex biopharmaceuticals, with Chinese hamster ovary (CHO) cell lines as the most important players. Over the years, various engineering strategies and modeling approaches have been used to improve microbial production platforms, such as bacteria and yeasts, as well as to create pre-optimized chassis host strains. However, the complexity of mammalian cells curtailed the optimization of these host cells by metabolic engineering. Most of the improvements of titer and productivity were achieved by media optimization and large-scale screening of producer clones. The advances made in recent years now open the door to again consider the potential application of systems biology approaches and metabolic engineering also to CHO. The availability of a reference genome sequence, genome-scale metabolic models and the growing number of various “omics” datasets can help overcome the complexity of CHO cells and support design strategies to boost their production performance. Modular design approaches applied to engineer industrially relevant cell lines have evolved to reduce the time and effort needed for the generation of new producer cells and to allow the achievement of desired product titers and quality. Nevertheless, important steps to enable the design of a chassis platform similar to those in use in the microbial world are still missing. In this review, we highlight the importance of mammalian cellular platforms for the production of biopharmaceuticals and compare them to microbial platforms, with an emphasis on describing novel approaches and discussing still open questions that need to be resolved to reach the objective of designing enhanced modular chassis CHO cell lines.

Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli

Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement.

Dynamic Modeling of CHO Cell Metabolism Using the Hybrid Cybernetic Approach With a Novel Elementary Mode Analysis Strategy

Chinese hamster ovary (CHO) cell culture has a major importance on the production of biopharmaceuticals, including recombinant therapeutic proteins such as monoclonal antibodies (MAb). Mathematical modeling of biological systems can successfully assess metabolism complexity while providing logical and systematic methods for relevant genetic target and culture parameter identification toward cell growth and productivity improvements. Most modeling approaches on CHO cells have been performed under stationary constraints, and only a few dynamic models have been presented on simplified reaction sets, due to substantial overparameterization problems. The hybrid cybernetic modeling (HCM) approach has been recently used to describe the dynamic behavior by incorporating regulation between different metabolic states by elementary mode participation control, with sets of equations evaluated by objective functions. However, as metabolic networks evaluated are constructed toward a genomic scale, and cell compartmentalization is considered, identification of the active set becomes more difficult as EM number exponentially grows. Thus, the development of robust approaches for EM active set selection and analysis with smaller computational requirements is required to impulse the use of cybernetic modeling on larger up to genome-scale networks. In this report, a novel elementary mode selection strategy, based on a polar representation of the convex solution space is presented and coupled to a cybernetic approach to model the dynamic physiologic and metabolic behavior of CHO-S cell cultures. The proposed Polar Space Yield Analysis (PSYA) was compared to other reported elementary mode selection approaches derived from Common Metabolic Objective Analysis (CMOA) used in Flux Balance Analysis (FBA), Yield Space Analysis (YSA), and Lumped Yield Space Analysis (LYSA). For this purpose, exponential growth phase dynamic metabolic models were calculated using kinetic rate equations based on previously modeled growth parameters. Finally, complete culture dynamic metabolic flux models were constructed using the HCM approach with selected elementary mode sets. The yield space elementary mode- and the polar space elementary mode- hybrid cybernetic models presented the best fits and performances. Also, a flux reaction perturbation prediction approach based on the polar yield solution space resulted useful for metabolic network flux distribution capability analysis and identification of potential genetic modifications targets.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

Dynamic 13C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin

Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient 13C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation.

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster ovary cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology.

Forecasting and control of lactate bifurcation in Chinese hamster ovary cell culture processes

Biomanufacturing exhibits inherent variability that can lead to variation in performance attributes and batch failure. To help ensure process consistency and product quality the development of predictive models and integrated control strategies is a promising approach. In this study, a feedback controller was developed to limit excessive lactate production, a widespread metabolic phenomenon that is negatively associated with culture performance and product quality. The controller was developed by applying machine learning strategies to historical process development data, resulting in a forecast model that could identify whether a run would result in lactate consumption or accumulation. In addition, this exercise identified a correlation between increased amino acid consumption and low observed lactate production leading to the mechanistic hypothesis that there is a deficiency in the link between glycolysis and the tricarboxylic acid cycle. Using the correlative process parameters to build mechanistic insight and applying this to predictive models of lactate concentration, a dynamic model predictive controller (MPC) for lactate was designed. This MPC was implemented experimentally on a process known to exhibit high lactate accumulation and successfully drove the cell cultures towards a lactate consuming state. In addition, an increase in specific titer productivity was observed when compared with non-MPC controlled reactors.

Systems biology approach in the formulation of chemically defined media for recombinant protein overproduction

The cell culture medium is an intricate mixture of components which has a tremendous effect on cell growth and recombinant protein production. Regular cell culture medium includes various components, and the decision about which component should be included in the formulation and its optimum amount is an underlying issue in biotechnology industries. Applying conventional techniques to design an optimal medium for the production of a recombinant protein requires meticulous and immense research. Moreover, since the medium formulation for the production of one protein could not be the best choice for another protein, hence, the most suitable media should be determined for each recombinant cell line. Accordingly, medium formulation becomes a laborious, time-consuming, and costly process in biomanufacturing of recombinant protein, and finding alternative strategies for medium development seems to be crucial. In silico modeling is an attractive concept to be adapted for medium formulation due to its high potential to supersede laboratory examinations. By emerging the high-throughput datasets, scientists can disclose the knowledge about the effect of medium components on cell growth and metabolism, and via applying this information through systems biology approach, medium formulation optimization could be accomplished in silico with no need of significant amount of experimentation. This review demonstrates some of the applications of systems biology as a powerful tool for medium development and illustrates the effect of medium optimization with system-level analysis on the production of recombinant proteins in different host cells.

An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells

Constraint-based modeling has been applied to analyze metabolism of numerous organisms via flux balance analysis and genome-scale metabolic models, including mammalian cells such as the Chinese hamster ovary (CHO) cells-the principal cell factory platform for therapeutic protein production. Unfortunately, the application of genome-scale model methodologies using the conventional biomass objective function is challenged by the presence of overly-restrictive constraints, including essential amino acid exchange fluxes that can lead to improper predictions of growth rates and intracellular flux distributions. In this study, these constraints are found to be reliably predicted by an "essential nutrient minimization" approach. After modifying these constraints with the predicted minimal uptake values, a series of unconventional objective functions are applied to minimize each individual non-essential nutrient uptake rate, revealing useful insights about metabolic exchange rates and flows across different cell lines and culture conditions. This unconventional uptake-rate objective functions (UOFs) approach is able to distinguish metabolic differences between three distinct CHO cell lines (CHO-K1, -DG44, and -S) not directly observed using the conventional biomass growth maximization solutions. Further, a comparison of model predictions with experimental data from literature correctly correlates with the specific CHO-DG44-derived cell line used experimentally, and the corresponding dual prices provide fruitful information concerning coupling relationships between nutrients. The UOFs approach is likely to be particularly suited for mammalian cells and other complex organisms which contain multiple distinct essential nutrient inputs, and may offer enhanced applicability for characterizing cell metabolism and physiology as well as media optimization and biomanufacturing control.

Analysis of the immunoglobulin G (IgG) secretion efficiency in recombinant Chinese hamster ovary (CHO) cells by using Citrine-fusion IgG

Biopharmaceuticals represented by immunoglobulin G (IgG) are produced by the cultivation of recombinant animal cells, especially Chinese hamster ovary (CHO) cells. It is thought that the intracellular secretion process of IgG is a bottleneck in the production of biopharmaceuticals. Many studies on the regulation of endogenous secretory protein expression levels have shown improved productivity. However, these strategies have not universally improved the productivity of various proteins. A more rational and efficient establishment of high producer cells is required based on an understanding of the secretory processes in IgG producing CHO cells. In this study, a CHO cell line producing humanized IgG1, which was genetically fused with fluorescent proteins, was established to directly analyze intracellular secretion. The relationship between the amount of intracellular and secreted IgG was analyzed at the single cell level by an automated single-cell analysis and isolation system equipped with dual color fluorescent filters. The amounts of intracellular and secreted IgG showed a weak positive correlation. The amount of secreted IgG analyzed by the system showed a weak negative linear correlation with the specific growth of isolated clones. An immunofluorescent microscopy study showed that the established clones could be used to analyze the intracellular secretion bottleneck. This is the first study to report the use of fluorescent protein fusion IgG as a tool to analyze the secretion of recombinant CHO cells.

Systematic development of temperature shift strategies for Chinese hamster ovary cells based on short duration cultures and kinetic modeling

Temperature shift (TS) to a hypothermic condition has been widely used during protein production processes that use Chinese hamster ovary (CHO) cells. The effect of temperature on cell growth, metabolites, protein titer and quality depends on cell line, product, and other bioreactor conditions. Due to the large numbers of experiments, which typically last 2-3 weeks each, limited systematic TS studies have been reported with multiple shift temperatures and steps at different times. Here, we systematically studied the effect of temperature on cell culture performance for the production of two monoclonal antibodies by industrial GS and DG44 CHO cell lines. Three 2-8 day short-duration methods were developed and validated for researching the effect of many different temperatures on CHO cell culture and quality attributes. We found that minor temperature differences (1-1.5 °C) affected cell culture performance. The kinetic parameters extracted from the short duration data were subsequently used to compute and predict cell culture performance in extended duration of 10-14 days with multiple TS conditions for both CHO cell lines. These short-duration culture methods with kinetic modeling tools may be used for effective TS optimization to achieve the best profiles for cell growth, metabolites, titer and quality attributes. Although only three short-duration methods were developed with two CHO cell lines, similar short-duration methods with kinetic modeling may be applied for different hosts, including both microbial and other mammalian cells.

Transcriptome Analysis of CHO Cell Size Increase During a Fed-Batch Process

In a Chinese Hamster Ovary (CHO) cell fed-batch process, arrest of cell proliferation and an almost threefold increase in cell size occurred, which is associated with an increase in cell-specific productivity. In this study, transcriptome analysis is performed to identify the molecular mechanisms associated with this. Cell cycle analysis reveals that the cells are arrested mainly in the G₀ /G₁ phase. The cell cycle arrest is associated with significant up-regulation of cyclin-dependent kinases inhibitors (CDKNs) and down-regulation of cyclin-dependent kinases (CDKs) and cyclins. During the cell size increase phase, the gene expression of the upstream pathways of mechanistic target of rapamycin (mTOR), which is related to the extracellular growth factor, cytokine, and amino acid conditions, shows a strongly synchronized pattern to promote the mTOR activity. The downstream genes of mTOR also show a synchronized pattern to stimulate protein translation and lipid synthesis. The results demonstrate that cell cycle inhibition and stimulated mTOR activity at the transcriptome level are related to CHO cell size increase. The cell size increase is related to the extracellular nutrient conditions through a number of cascade pathways, indicating that by rational design of media and feeds, CHO cell size can be manipulated during culture processes, which may further improve cell growth and specific productivity.

Mass Spectrometry-Based Method to Study Inhibitor-Induced Metabolic Redirection in the Central Metabolism of Cancer Cells

Cancer cells often respond to chemotherapeutic inhibitors by redirecting carbon flow in the central metabolism. To understand the metabolic redirections of inhibitor treatment on cancer cells, this study established a 13C-metabolic flux analysis (13C-MFA)-based method to evaluate metabolic redirection in MCF-7 breast cancer cells using mass spectrometry. A metabolic stationary state necessary for accurate 13C-MFA was confirmed during an 8-24 h window using low-dose treatments of various metabolic inhibitors. Further 13C-labeling experiments using [1-13C]glucose and [U-13C]glutamine, combined with gas chromatography-mass spectrometry (GC-MS) analysis of mass isotopomer distributions (MIDs), confirmed that an isotopic stationary state of intracellular metabolites was reached 24 h after treatment with paclitaxel (Taxol), an inhibitor of mitosis used for cancer treatment. Based on these metabolic and isotopic stationary states, metabolic flux distribution in the central metabolism of paclitaxel-treated MCF-7 cells was determined by 13C-MFA. Finally, estimations of the 95% confidence intervals showed that tricarboxylic acid cycle metabolic flux increased after paclitaxel treatment. Conversely, anaerobic glycolysis metabolic flux decreased, revealing metabolic redirections by paclitaxel inhibition. The gap between total regeneration and consumption of ATP in paclitaxel-treated cells was also found to be 1.2 times greater than controls, suggesting ATP demand was increased by paclitaxel treatment, likely due to increased microtubule polymerization. These data confirm that 13C-MFA can be used to investigate inhibitor-induced metabolic redirection in cancer cells. This will contribute to future pharmaceutical developments and understanding variable patient response to treatment.

Metabolic characterization of a CHO cell size increase phase in fed-batch cultures

Normally, the growth profile of a CHO cell fed-batch process can be divided into two main phases based on changes in cell concentration, being an exponential growth phase and a stationary (non-growth) phase. In this study, an additional phase is observed during which the cell division comes to a halt but the cell growth continues in the form of an increase in cell size. The cell size increase (SI) phase occurs between the exponential proliferation phase (also called the number increase or NI phase) and the stationary phase. During the SI phase, the average volume and dry weight per cell increase threefold linearly with time. The average mAb specific productivity per cell increases linearly with the cell volume and therefore is on average two times higher in the SI phase than in the NI phase. The specific essential amino acids consumption rates per cell remain fairly constant between the NI and the SI phase, which agrees with the similar biomass production rate per cell between these two phases. Accumulation of fatty acids and formation of lipid droplets in the cells are observed during the SI phase, indicating that the fatty acids synthesis rate exceeds the demand for the synthesis of membrane lipids. A metabolic comparison between NI and SI phase shows that the cells with a larger size produce more mAb per unit of O2 and nutrient consumed, which can be used for further process optimization.

Computational modelling of genome-scale metabolic networks and its application to CHO cell cultures

Genome-scale metabolic models (GEMs) have become increasingly important in recent years. Currently, GEMs are the most accurate in silico representation of the genotype-phenotype link. They allow us to study complex networks from the systems perspective. Their application may drastically reduce the amount of experimental and clinical work, improve diagnostic tools and increase our understanding of complex biological phenomena. GEMs have also demonstrated high potential for the optimisation of bio-based production of recombinant proteins. Herein, we review the basic concepts, methods, resources and software tools used for the reconstruction and application of GEMs. We overview the evolution of the modelling efforts devoted to the metabolism of Chinese Hamster Ovary (CHO) cells. We present a case study on CHO cell metabolism under different amino acid depletions. This leads us to the identification of the most influential as well as essential amino acids in selected CHO cell lines.

Metabolic network modeling with model organisms

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.

Selection of chemically defined media for CHO cell fed-batch culture processes

Two CHO cell clones derived from the same parental CHOBC® cell line and producing the same monoclonal antibody (BC-G, a low producing clone; BC-P, a high producing clone) were tested in four basal media in all possible combinations with three feeds (=12 conditions) in fed-batch cultures. Higher amino acid feeding did not always lead to higher mAb production. The two clones showed differences in cell physiology, metabolism and optimal medium-feed combinations. During the phase transitions of all cultures, cell metabolism showed a shift represented by lower specific consumption and production rates, except for the specific glucose consumption rate in cultures fed by Actifeed A/B. The BC-P clone fed by Actifeed A/B showed a threefold cell volume increase and an increase of the specific consumption rate of glucose in the stationary phase. Since feeding was based on glucose this resulted in accumulation of amino acids for this feed, while this did not occur for the poorer feed (EFA/B). The same feed also led to an increase of cell size for the BC-G clone, but to a lesser extent.

Quantification of Microbial Phenotypes

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.

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.