Metabolomics-based identification of apoptosis-inducing metabolites in recombinant fed-batch CHO culture media

Molecular biomarkers identification and applications in CHO bioprocessing

Biomarkers are valuable tools in clinical research where they allow to predict susceptibility to diseases, or response to specific treatments. Likewise, biomarkers can be extremely useful in the biomanufacturing of therapeutic proteins. Indeed, constraints such as short timelines and the need to find hyper-productive cells could benefit from a data-driven approach during cell line and process development. Many companies still rely on large screening capacities to develop productive cell lines, but as they reach a limit of production, there is a need to go from empirical to rationale procedures. Similarly, during bioprocessing runs, substrate consumption and metabolism wastes are commonly monitored. None of them possess the ability to predict the culture behavior in the bioreactor. Big data driven approaches are being adapted to the study of industrial mammalian cell lines, enabled by the publication of Chinese hamster and CHO genome assemblies which allowed the use of next-generation sequencing with these cells, as well as continuous proteome and metabolome annotation. However, if these different -omics technologies contributed to the characterization of CHO cells, there is a significant effort remaining to apply this knowledge to biomanufacturing methods. The correlation of a complex phenotype such as high productivity or rapid growth to the presence or expression level of a specific biomarker could save time and effort in the screening of manufacturing cell lines or culture conditions. In this review we will first discuss the different biological molecules that can be identified and quantified in cells, their detection techniques, and associated challenges. We will then review how these markers are used during the different steps of cell line and bioprocess development, and the inherent limitations of this strategy.

Apoptosis, a Metabolic "Head-to-Head" between Tumor and T Cells: Implications for Immunotherapy

Induction of apoptosis represents a promising therapeutic approach to drive tumor cells to death. However, this poses challenges due to the intricate nature of cancer biology and the mechanisms employed by cancer cells to survive and escape immune surveillance. Furthermore, molecules released from apoptotic cells and phagocytes in the tumor microenvironment (TME) can facilitate cancer progression and immune evasion. Apoptosis is also a pivotal mechanism in modulating the strength and duration of anti-tumor T-cell responses. Combined strategies including molecular targeting of apoptosis, promoting immunogenic cell death, modulating immunosuppressive cells, and affecting energy pathways can potentially overcome resistance and enhance therapeutic outcomes. Thus, an effective approach for targeting apoptosis within the TME should delicately balance the selective induction of apoptosis in tumor cells, while safeguarding survival, metabolic changes, and functionality of T cells targeting crucial molecular pathways involved in T-cell apoptosis regulation. Enhancing the persistence and effectiveness of T cells may bolster a more resilient and enduring anti-tumor immune response, ultimately advancing therapeutic outcomes in cancer treatment. This review delves into the pivotal topics of this multifaceted issue and suggests drugs and druggable targets for possible combined therapies.

Deep hybrid modeling of a HEK293 process: Combining long short-term memory networks with first principles equations

The combination of physical equations with deep learning is becoming a promising methodology for bioprocess digitalization. In this paper, we investigate for the first time the combination of long short-term memory (LSTM) networks with first principles equations in a hybrid workflow to describe human embryonic kidney 293 (HEK293) culture dynamics. Experimental data of 27 extracellular state variables in 20 fed-batch HEK293 cultures were collected in a parallel high throughput 250 mL cultivation system in an industrial process development setting. The adaptive moment estimation method with stochastic regularization and cross-validation were employed for deep learning. A total of 784 hybrid models with varying deep neural network architectures, depths, layers sizes and node activation functions were compared. In most scenarios, hybrid LSTM models outperformed classical hybrid Feedforward Neural Network (FFNN) models in terms of training and testing error. Hybrid LSTM models revealed to be less sensitive to data resampling than FFNN hybrid models. As disadvantages, Hybrid LSTM models are in general more complex (higher number of parameters) and have a higher computation cost than FFNN hybrid models. The hybrid model with the highest prediction accuracy consisted in a LSTM network with seven internal states connected in series with dynamic material balance equations. This hybrid model correctly predicted the dynamics of the 27 state variables (R2 = 0.93 in the test data set), including biomass, key substrates, amino acids and metabolic by-products for around 10 cultivation days.

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.

Amino acid degradation pathway inhibitory by-products trigger apoptosis in CHO cells

Chinese hamster ovary (CHO) cells are widely used to produce complex biopharmaceuticals. Improving their productivity is necessary to fulfill the growing demand for such products. One way to enhance productivity is by cultivating cells at high densities, but inhibitory by-products, such as metabolite derivatives from amino acid degradation, can hinder achieving high cell densities. This research examines the impact of these inhibitory by-products on high-density cultures. We cultured X1 and X2 CHO cell lines in a small-scale semi-perfusion system and introduced a mix of inhibitory by-products on day 10. The X1 and X2 cell lines were chosen for their varied responses to the by-products; X2 was susceptible, while X1 survived. Proteomics revealed that the X2 cell line presented changes in the proteins linked to apoptosis regulation, cell building block synthesis, cell growth, DNA repair, and energy metabolism. We later used the AB cell line, an apoptosis-resistant cell line, to validate the results. AB behaved similar to X1 under stress. We confirmed the activation of apoptosis in X2 using a caspase assay. This research provides insights into the mechanisms of cell death triggered by inhibitory by-products and can guide the optimization of CHO cell culture for biopharmaceutical manufacturing.

NMR as powerful technology for non-invasively monitoring cell health and expansion during bioprocessing

Over the last decades, the success of advanced cell therapies and the increasing production volumes of vaccines, proteins or viral vectors have raised the need of robust cell-based manufacturing processes for ensuring product quality and satisfying GMP requirements. The cultivation process of cells needs to be highly controlled for improved productivity, reduced variability and optimized bioprocesses. Cell cultures can be easily monitored using different technologies, which could deliver direct or indirect assessment of the cells' viability. Among these techniques, Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful technology which permits the evaluation and the identification of key endogenous metabolites. NMR can provide information on the cell metabolic pathways, on the bioprocesses and is also capable to quickly test for impurities. In this study, NMR was successfully used as a technology for monitoring cell viability and expansion in different supports for cell growth (including bioreactors), in order to predict the bioprocess output and for the early identification of key metabolites linked to cell starvation. This investigation will allow the timely control of culture conditions and favour the optimization of the bioprocesses. This article is protected by copyright. All rights reserved.

Metabolic Profiling of CHO Cells during the Production of Biotherapeutics

As indicated by an ever-increasing number of FDA approvals, biotherapeutics constitute powerful tools for the treatment of various diseases, with monoclonal antibodies (mAbs) accounting for more than 50% of newly approved drugs between 2014 and 2018 (Walsh, 2018). The pharmaceutical industry has made great progress in developing reliable and efficient bioproduction processes to meet the demand for recombinant mAbs. Mammalian cell lines are preferred for the production of functional, complex recombinant proteins including mAbs, with Chinese hamster ovary (CHO) cells being used in most instances. Despite significant advances in cell growth control for biologics manufacturing, cellular responses to environmental changes need to be understood in order to further improve productivity. Metabolomics offers a promising approach for developing suitable strategies to unlock the full potential of cellular production. This review summarizes key findings on catabolism and anabolism for each phase of cell growth (exponential growth, the stationary phase and decline) with a focus on the principal metabolic pathways (glycolysis, the pentose phosphate pathway and the tricarboxylic acid cycle) and the families of biomolecules that impact these circuities (nucleotides, amino acids, lipids and energy-rich metabolites).

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.

Generation and Characterization of Stable Redox-Reporter Mammalian Cell Lines of Biotechnological Relevance

Cellular functions such as DNA replication and protein translation are influenced by changes in the intracellular redox milieu. Exogenous (i.e., nutrients, deterioration of media components, xenobiotics) and endogenous factors (i.e., metabolism, growth) may alter the redox homeostasis of cells. Thus, monitoring redox changes in real time and in situ is deemed essential for optimizing the production of recombinant proteins. Recently, different redox-sensitive variants of green fluorescent proteins (e.g., rxYFP, roGFP2, and rxmRuby2) have been engineered and proved suitable to detect, in a non-invasive manner, perturbations in the pool of reduced and oxidized glutathione, the major low molecular mass thiol in mammals. In this study, we validate the use of cytosolic rxYFP on two cell lines widely used in biomanufacturing processes, namely, CHO-K1 cells expressing the human granulocyte macrophage colony-stimulating factor (hGM-CSF) and HEK-293. Flow cytometry was selected as the read-out technique for rxYFP signal given its high-throughput and statistical robustness. Growth kinetics and cellular metabolism (glucose consumption, lactate and ammonia production) of the redox reporter cells were comparable to those of the parental cell lines. The hGM-CSF production was not affected by the expression of the biosensor. The redox reporter cell lines showed a sensitive and reversible response to different redox stimuli (reducing and oxidant reagents). Under batch culture conditions, a significant and progressive oxidation of the biosensor occurred when CHO-K1-hGM-CSF cells entered the late-log phase. Medium replenishment restored, albeit partially, the intracellular redox homeostasis. Our study highlights the utility of genetically encoded redox biosensors to guide metabolic engineering or intervention strategies aimed at optimizing cell viability, growth, and productivity.

LC-HRMS based Targeted Metabolomics for High-throughput and Quantitative Analysis of 21 Growth Inhibition Related Metabolites in CHO Cell Fed-Batch Cultures

Chinese hamster ovary (CHO) cells have been widely used in the biopharmaceutical industry for production of therapeutic proteins. CHO cells in fed-batch cultures produce various amino acid-derived intermediate metabolites. These small molecule metabolic byproducts have proven to be critical to cell growth, culture performance, and more interestingly antibody drug productivity. Herein, we developed a liquid chromatography - high resolution mass spectrometry (LC-HRMS) based targeted metabolomics approach for comprehensive quantification of total 21 growth inhibition related metabolites generated from 14 different amino acids in CHO cell fed-batch cultures. High throughput derivatization procedures, matrix-matched calibration curves, stable isotope-labeled internal standards, and accurate mass Full MS scan were utilized in order to achieve our goal for wide scope of metabolite screening as well as validity and reliability of metabolite quantification. We further present a novel analytical strategy for extending the assay's dynamic range by utilizing naturally occurring isotope M+1 ion as a quantification analogue in the circumstances where the principal M ion is beyond its calibration range. The integrated method was qualified for selectivity, sensitivity, linearity, accuracy, precision, isotope analysis and other analytical aspects to demonstrate assay robustness. We then applied this metabolomics approach to characterize metabolites of interest in a CHO cell based monoclonal antibody (mAb) production process with fed-batch bioreactor culture mode. Absolute quantification combined with multivariate statistical analysis illustrated that our target analytes derived from amino acids, especially from branched-chain amino acids, closely correlated with cell viability and significantly differentiated cellular stages in production process.

A Metabolomics Approach to Increasing Chinese Hamster Ovary (CHO) Cell Productivity

Much progress has been made in improving the viable cell density of bioreactor cultures in monoclonal antibody production from Chinese hamster ovary (CHO) cells; however, specific productivity (qP) has not been increased to the same degree. In this work, we analyzed a library of 24 antibody-expressing CHO cell clones to identify metabolites that positively associate with qP and could be used for clone selection or medium supplementation. An initial library of 12 clones, each producing one of two antibodies, was analyzed using untargeted LC-MS experiments. Metabolic model-based annotation followed by correlation analysis detected 73 metabolites that significantly correlated with growth, qP, or both. Of these, metabolites in the alanine, aspartate, and glutamate metabolism pathway, and the TCA cycle showed the strongest association with qP. To evaluate whether these metabolites could be used as indicators to identify clones with potential for high productivity, we performed targeted LC-MS experiments on a second library of 12 clones expressing a third antibody. These experiments found that aspartate and cystine were positively correlated with qP, confirming the results from untargeted analysis. To investigate whether qP correlated metabolites reflected endogenous metabolic activity beneficial for productivity, several of these metabolites were tested as medium additives during cell culture. Medium supplementation with citrate improved qP by up to 490% and more than doubled the titer. Together, these studies demonstrate the potential for using metabolomics to discover novel metabolite additives that yield higher volumetric productivity in biologics production processes.

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.

Identification of novel inhibitory metabolites and impact verification on growth and protein synthesis in mammalian cells

Mammalian cells consume large amount of nutrients during growth and production. However, endogenous metabolic inefficiencies often prevent cells to fully utilize nutrients to support growth and protein production. Instead, significant fraction of fed nutrients is diverted into extracellular accumulation of waste by-products and metabolites, further inhibiting proliferation and protein synthesis. In this study, an LC-MS/MS based metabolomics pipeline was used to screen Chinese hamster ovary (CHO) extracellular metabolites. Six out of eight identified inhibitory metabolites, caused by the inefficient cell metabolism, were not previously studied in CHO cells: aconitic acid, 2-hydroxyisocaproic acid, methylsuccinic acid, cytidine monophosphate, trigonelline, and n-acetyl putrescine. When supplemented back into a fed-batch culture, significant reduction in cellular growth was observed in the presence of each metabolite and all the identified metabolites were shown to impact the glycosylation of a model secreted antibody, with seven of these also reducing CHO cellular productivity (titer) and all eight inhibiting the formation of mono-galactosylated biantennary (G1F) and biantennary galactosylated (G2F) N-glycans. These inhibitory metabolites further impact the metabolism of cells, leading to a significant reduction in CHO cellular growth and specific productivity in fed-batch culture (maximum reductions of 27.2% and 40.6% respectively). In-depth pathway analysis revealed that these metabolites are produced when cells utilize major energy sources such as glucose and select amino acids (tryptophan, arginine, isoleucine, and leucine) for growth, maintenance, and protein production. Furthermore, these novel inhibitory metabolites were observed to accumulate in multiple CHO cell lines (CHO–K1 and CHO-GS) as well as HEK293 cell line. This study provides a robust and holistic methodology to incorporate global metabolomic analysis into cell culture studies for elucidation and structural verification of novel metabolites that participate in key metabolic pathways to growth, production, and post-translational modification in biopharmaceutical production.

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Mammalian metabolic inefficiencies lead to accumulation of waste by-products.

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Untargeted and targeted metabolomics for identification of novel metabolites.

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Identified six CHO metabolic inhibitors negatively impact growth and titer production.

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Inhibitors were shown to accumulate across different mammalian cell lines.

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A holistic methodology incorporating metabolomics analysis into cell culture studies.

Application of metabolic modeling for targeted optimization of high seeding density processes

Process intensification by application of perfusion mode in pre‐stage bioreactors and subsequent inoculation of cell cultures at high seeding densities (HSD) has the potential to meet the increasing requirements of future manufacturing demands. However, process development is currently restrained by a limited understanding of the cell's requirements under these process conditions. The goal of this study was to use extended metabolite analysis and metabolic modeling for targeted optimization of HSD cultivations. The metabolite analysis of HSD N‐stage cultures revealed accumulation of inhibiting metabolites early in the process and flux balance analysis led to the assumption that reactive oxygen species (ROS) were contributing to the fast decrease in cell viability. Based on the metabolic analysis an optimized feeding strategy with lactate and cysteine supplementation was applied, resulting in an increase in antibody titer of up to 47%. Flux balance analysis was further used to elucidate the surprisingly strong synergistic effect of lactate and cysteine, indicating that increased lactate uptake led to reduced ROS formation under these conditions whilst additional cysteine actively reduced ROS via the glutathione pathway. The presented results finally demonstrate the benefit of modeling approaches for process intensification as well as the potential of HSD cultivations for biopharmaceutical manufacturing.

'Omics driven discoveries of gene targets for apoptosis attenuation in CHO cells

Chinese hamster ovary (CHO) cells are widely used in biopharmaceutical production. Improvements to cell lines and bioprocesses are constantly being explored. One of the major limitations of CHO cell culture is that the cells undergo apoptosis, leading to rapid cell death, which impedes reaching high recombinant protein titres. While several genetic engineering strategies have been successfully employed to reduce apoptosis, there is still room to further enhance CHO cell lines performance. 'Omics analysis is a powerful tool to better understand different phenotypes and for the identification of gene targets for engineering. Here, we present a comprehensive review of previous CHO 'omics studies that revealed changes in the expression of apoptosis-related genes. We highlight targets for genetic engineering that have reduced, or have the potential to reduce, apoptosis or to increase cell proliferation in CHO cells, with the final aim of increasing productivity.

Using Metabolomics to Identify Cell Line-Independent Indicators of Growth Inhibition for Chinese Hamster Ovary Cell-based Bioprocesses

Chinese hamster ovary (CHO) cells are widely used for the production of biopharmaceuticals. Efforts to improve productivity through medium design and feeding strategy optimization have focused on preventing the depletion of essential nutrients and managing the accumulation of lactate and ammonia. In addition to ammonia and lactate, many other metabolites accumulate in CHO cell cultures, although their effects remain largely unknown. Elucidating these effects has the potential to further improve the productivity of CHO cell-based bioprocesses. This study used untargeted metabolomics to identify metabolites that accumulate in fed-batch cultures of monoclonal antibody (mAb) producing CHO cells. The metabolomics experiments profiled six cell lines that are derived from two different hosts, produce different mAbs, and exhibit different growth profiles. Comparing the cell lines’ metabolite profiles at different growth stages, we found a strong negative correlation between peak viable cell density (VCD) and a tryptophan metabolite, putatively identified as 5-hydroxyindoleacetaldehyde (5-HIAAld). Amino acid supplementation experiments showed strong growth inhibition of all cell lines by excess tryptophan, which correlated with the accumulation of 5-HIAAld in the culture medium. Prospectively, the approach presented in this study could be used to identify cell line- and host-independent metabolite markers for clone selection and bioprocess development.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

Mammalian Systems Biotechnology Reveals Global Cellular Adaptations in a Recombinant CHO Cell Line

Effective development of host cells for therapeutic protein production is hampered by the poor characterization of cellular transfection. Here, we employed a multi-omics-based systems biotechnology approach to elucidate the genotypic and phenotypic differences between a wild-type and recombinant antibody-producing Chinese hamster ovary (CHO) cell line. At the genomic level, we observed extensive rearrangements in specific targeted loci linked to transgene integration sites. Transcriptional re-wiring of DNA damage repair and cellular metabolism in the antibody producer, via changes in gene copy numbers, was also detected. Subsequent integration of transcriptomic data with a genome-scale metabolic model showed a substantial increase in energy metabolism in the antibody producer. Metabolomics, lipidomics, and glycomics analyses revealed an elevation in long-chain lipid species, potentially associated with protein transport and secretion requirements, and a surprising stability of N-glycosylation profiles between both cell lines. Overall, the proposed knowledge-based systems biotechnology framework can further accelerate mammalian cell-line engineering in a targeted manner.

Quantitative Proteomic Analysis of Cellular Responses to a Designed Amino Acid Feed in a Monoclonal Antibody Producing Chinese Hamster Ovary Cell Line

Background:

Chinese hamster ovary (CHO) cell line is considered as the most common cell line in the biopharmaceutical industry because of its capability in performing efficient post-translational modifications and producing the recombinant proteins, which are similar to natural human proteins. The optimization of the upstream process via different feed strategies has a great impact on the target molecule expression and yield.

Methods:

To determine and understand the molecular events beneath the feed effects on the CHO cell, a label-free quantitative proteomic analysis was applied. The proteome changes followed by the addition of a designed amino acid feed to the monoclonal antibody producing CHO cell line culture medium were investigated.

Results:

The glutathione synthesis, the negative regulation of the programmed cell death, proteasomal catabolic process, and the endosomal transport pathway were up-regulated in the group fed with a designed amino acid feed compared to the control group.

Conclusion:

Our findings could be helpful to identify new targets for metabolic engineering.

Intracellular CHO Cell Metabolite Profiling Reveals Steady-State Dependent Metabolic Fingerprints in Perfusion Culture

Perfusion cell culture processes allow the steady-state culture of mammalian cells at high viable cell density, which is beneficial for overall product yields and homogeneity of product quality in the manufacturing of therapeutic proteins. In this study, the extent of metabolic steady state and the change of the metabolite profile between different steady states of an industrial Chinese hamster ovary (CHO) cell line producing a monoclonal antibody (mAb) was investigated in stirred tank perfusion bioreactors. Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) of daily cell extracts revealed more than a hundred peaks, among which 76 metabolites were identified by tandem MS (MS/MS) and high resolution Fourier transform ion cyclotron resonance (FT-ICR) MS. Nucleotide ratios (Uridine (U)-ratio, nucleotide triphosphate (NTP)-ratio and energy charge (EC)) and multivariate analysis of all features indicated a consistent metabolite profile for a stable culture performed at 40 × 10⁶ cells/mL over 26 days of culture. Conversely, the reactor was operated continuously so as to reach three distinct steady states one after the other at 20, 60, and 40 × 10⁶ cells/mL. In each case, a stable metabolite profile was achieved after an initial transient phase of approximately three days at constant cell density when varying between these set points. Clear clustering according to cell density was observed by principal component analysis, indicating steady-state dependent metabolite profiles. In particular, varying levels of nucleotides, nucleotide sugar, and lipid precursors explained most of the variance between the different cell density set points. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:879-890, 2017.

Metabolite Profiling of Mammalian Cell Culture Processes to Evaluate Cellular Viability

Metabolite profiling allows for the identification of metabolites that become limiting during cell culture and/or for finding bottlenecks in metabolic pathways that limit culture growth and proliferation. Here we describe one protocol with two different sampling methodologies for GC-MS-based metabolite profiling. We also highlight an example of the types of datasets that are attainable and how such datasets can be evaluated to identify factors related to cell viability. We also demonstrate, via the same methodology, the accurate quantification of a number of metabolites of interest.

miRNA profiling of high, low and non-producing CHO cells during biphasic fed-batch cultivation reveals process relevant targets for host cell engineering

Fed-batch cultivation of recombinant Chinese hamster ovary (CHO) cell lines is one of the most widely used production modes for commercial manufacturing of recombinant protein therapeutics. Furthermore, fed-batch cultivations are often conducted as biphasic processes where the culture temperature is decreased to maximize volumetric product yields. However, it remains to be elucidated which intracellular regulatory elements actually control the observed pro-productive phenotypes. Recently, several studies have revealed microRNAs (miRNAs) to be important molecular switches of cell phenotypes. In this study, we analyzed miRNA profiles of two different recombinant CHO cell lines (high and low producer), and compared them to a non-producing CHO DG44 host cell line during fed-batch cultivation at 37°C versus a temperature shift to 30°C. Taking advantage of next-generation sequencing combined with cluster, correlation and differential expression analyses, we could identify 89 different miRNAs, which were differentially expressed in the different cell lines and cultivation phases. Functional validation experiments using 19 validated target miRNAs confirmed that these miRNAs indeed induced changes in process relevant phenotypes. Furthermore, computational miRNA target prediction combined with functional clustering identified putative target genes and cellular pathways, which might be regulated by these miRNAs. This study systematically identified novel target miRNAs during different phases and conditions of a biphasic fed-batch production process and functionally evaluated their potential for host cell engineering.

Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity

The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars) were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS) with the concept of stable isotope dilution (SID) for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs) in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2), showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001). In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001), classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001). These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling approach demonstrates high potential for dynamic biomarker identification and the investigation of kinetic mechanisms in disease or pharmacodynamics studies using MS data from longitudinal cohort studies.

Human metabolism is controlled through basic kinetic regulatory mechanisms, where the overall system aims to maintain a state of homeostasis. In response to external perturbations, such as environmental influences, nutrition or physical exercise, circulating metabolites show specific kinetic response patterns, which can be computationally modeled. In this work, we searched for dynamic metabolic biomarker candidates and analyzed specific kinetic mechanisms from longitudinal metabolic concentration data, obtained through a cycle ergometry stress test. In total, 110 metabolites measured from blood samples of 47 individuals were analyzed using tandem mass spectrometry (MS/MS). Dynamic biomarker candidates could be selected based on the amplitudes of changes in metabolite concentrations and the significance of statistical hypothesis testing. We were able to characterize specific kinetic patterns for groups of similarly behaving metabolites. Kinetic shape templates were identified, defining basic kinetic response patterns to physical exercise, such as sustained, early, late and other shape forms. The presented approach contributes to a better understanding of (patho)physiological biochemical mechanisms in human health, disease or during drug therapy, by offering tools for classifying dynamic biomarker candidates and for modeling and characterizing kinetic regulatory mechanisms from longitudinal experimental data.

High-throughput nucleoside phosphate monitoring in mammalian cell fed-batch cultivation using quantitative matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Current methods for monitoring multiple intracellular metabolite levels in parallel are limited in sample throughput capabilities and analyte selectivity. This article presents a novel high-throughput method based on matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry (TOF-MS) for monitoring intracellular metabolite levels in fed-batch processes. The MALDI-TOF-MS method presented here is based on a new microarray sample target and allows the detection of nucleoside phosphates and various other metabolites using stable isotope labeled internal standards. With short sample preparation steps and thus high sample throughput capabilities, the method is suitable for monitoring mammalian cell cultures, such as antibody producing hybridoma cell lines in industrial environments. The method is capable of reducing the runtime of standard LC-UV methods to approximately 1 min per sample (including 10 technical replicates). Its performance is exemplarily demonstrated in an 8-day monitoring experiment of independently controlled fed-batches, containing an antibody producing mouse hybridoma cell culture. The monitoring profiles clearly confirmed differences between cultivation conditions. Hypothermia and hyperosmolarity were studied in four bioreactors, where hypothermia was found to have a positive effect on the longevity of the cell culture, whereas hyperosmolarity lead to an arrest of cell proliferation. The results are in good agreement with HPLC-UV cross validation experiments. Subsequent principal component analysis (PCA) clearly separates the different bioreactor conditions based on the measured mass spectral profiles. This method is not limited to any cell line and can be applied as a process analytical tool in biotechnological processes.

NMR methods for metabolomics of mammalian cell culture bioreactors

Metabolomics has become an important tool for measuring pools of small molecules in mammalian cell cultures expressing therapeutic proteins. NMR spectroscopy has played an important role, largely because it requires minimal sample preparation, does not require chromatographic separation, and is quantitative. The concentrations of large numbers of small molecules in the extracellular media or within the cells themselves can be measured directly on the culture supernatant and on the supernatant of the lysed cells, respectively, and correlated with endpoints such as titer, cell viability, or glycosylation patterns. The observed changes can be used to generate hypotheses by which these parameters can be optimized. This chapter focuses on the sample preparation, data acquisition, and analysis to get the most out of NMR metabolomics data from CHO cell cultures but could easily be extended to other in vitro culture systems.

CHO microRNA engineering is growing up: recent successes and future challenges

microRNAs with their ability to regulate complex pathways that control cellular behavior and phenotype have been proposed as potential targets for cell engineering in the context of optimization of biopharmaceutical production cell lines, specifically of Chinese Hamster Ovary cells. However, until recently, research was limited by a lack of genomic sequence information on this industrially important cell line. With the publication of the genomic sequence and other relevant data sets for CHO cells since 2011, the doors have been opened for an improved understanding of CHO cell physiology and for the development of the necessary tools for novel engineering strategies. In the present review we discuss both knowledge on the regulatory mechanisms of microRNAs obtained from other biological models and proof of concepts already performed on CHO cells, thus providing an outlook of potential applications of microRNA engineering in production cell lines.

LC-MS-based metabolic characterization of high monoclonal antibody-producing Chinese hamster ovary cells

The selection of suitable mammalian cell lines with high specific productivities is a crucial aspect of large-scale recombinant protein production. This study utilizes a metabolomics approach to elucidate the key characteristics of Chinese hamster ovary (CHO) cells with high monoclonal antibody productivities (q(mAb)). Liquid chromatography-mass spectrometry (LC-MS)-based intracellular metabolite profiles of eight single cell clones with high and low q(mAb) were obtained at the mid-exponential phase during shake flask batch cultures. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) subsequently revealed key differences between the high and low q(mAb) clones, as indicated by the variable importance for projection (VIP) scores. The mass peaks were further examined for their potential association with q(mAb) across all clones using Pearson's correlation analysis. Lastly, the identities of metabolites with high VIP and correlation scores were confirmed by comparison with standards through LC-MS-MS. A total of seven metabolites were identified-NADH, FAD, reduced and oxidized glutathione, and three activated sugar precursors. These metabolites are involved in key cellular pathways of citric acid cycle, oxidative phosphorylation, glutathione metabolism, and protein glycosylation. To our knowledge, this is the first study to identify metabolites that are associated closely with q(mAb). The results suggest that the high producers had elevated levels of specific metabolites to better regulate their redox status. This is likely to facilitate the generation of energy and activated sugar precursors to meet the demands of producing more glycosylated recombinant monoclonal antibodies.

Dynamic mRNA and miRNA profiling of CHO-K1 suspension cell cultures

In spite of the importance of Chinese hamster ovary (CHO) cells for recombinant protein production, very little is known about the molecular and gene regulatory mechanisms that control cellular phenotypes such as enhanced growth under serum-free conditions or high productivity. Most microarray analyses to this purpose are performed with samples taken during the exponential growth phase. However, the cellular transcriptome is dynamic, changing in response to external and internal stimuli and thus reflecting the current functional capacity of cells as well as their ability to adapt to a changing environment. Therefore, during batch or fed-batch cultivations it can be expected that the transcription pattern of genes will change and that such changes may give indications on the cellular state in terms of viability, growth, and productivity. In the current study we monitored the change in expression patterns of mRNAs and microRNAs (miRNA) during lag, exponential, and stationary phases in CHO-K1 suspension cell cultures. In total, over 1400 mRNAs and more than 100 miRNAs were differentially regulated (p<0.05) relative to the batch culture at the starting point. Functional clustering revealed groups of genes with similar expression patterns, which were subjected to functional pathway analysis. In addition, as miRNAs generally act as negative post-transcriptional regulators of mRNAs, we looked for changes in their expression that were inverse to those of their predicted target mRNAs.