Systems biotechnology of animal cells: the road to prediction

Autophagy Enhances Longevity of Induced Pluripotent Stem Cell-Derived Endothelium via mTOR-Independent ULK1 Kinase

Stem cells are enabling an improved understanding of the peripheral arterial disease, and patient-specific stem cell-derived endothelial cells (ECs) present major advantages as a therapeutic modality. However, applications of patient-specific induced pluripotent stem cell (iPSC)-derived ECs are limited by rapid loss of mature cellular function in culture. We hypothesized that changes in autophagy impact the phenotype and cellular proliferation of iPSC-ECs. Endothelial cells were differentiated from distinct induced pluripotent stem cell lines in 2D culture and purified for CD144 positive cells. Autophagy, mitochondrial morphology, and proliferation were characterized during differentiation and over serial passages in culture. We found that autophagy activity was stimulated during differentiation but stagnated in mature iPSC-ECs. Mitochondria remodeled through mitophagy during differentiation and demonstrated increasing membrane potential and mass through serial passages; however, these plateaued, coinciding with decreased proliferation. To evaluate for oxidative damage, iPSC-ECs were alternatively grown under hypoxic culture conditions; however, hypoxia only transiently improved the proliferation. Stimulating mTOR-independent ULK1-mediated autophagy with a plant derivative AMP kinase activator Rg2 significantly improved proliferative capacity of iPSC-ECs over multiple passages. Therefore, autophagy, a known mediator of longevity, played an active role in remodeling mitochondria during maturation from pluripotency to a terminally differentiated state. Autophagy failed to compensate for increasing mitochondrial mass over serial passages, which correlated with loss of proliferation in iPSC-ECs. Stimulating ULK1-kinase-driven autophagy conferred improved proliferation and longevity over multiple passages in culture. This represents a novel approach to overcoming a major barrier limiting the use of iPSC-ECs for clinical and research applications.

A Fully Integrated Online Platform For Real Time Monitoring Of Multiple Product Quality Attributes In Biopharmaceutical Processes For Monoclonal Antibody Therapeutics

In response to FDA's call for Quality by Design (QbD) in biopharmaceutical product development, the biopharmaceutical industry has been developing highly sensitive and specific technologies in the monitoring and controlling of product quality attributes for bioprocesses. We previously published the successful application of an off-line multi-attribute method (MAM) to monitor more than 20 critical quality attributes (CQA) with superior sensitivity for the upstream process. To further remove the hurdles of laborious process sampling and sample preparation associated with the offline method, we present here a fully integrated MAM based online platform for automated real time online process monitoring. This integrated system includes Modular Automated Sampling Technology (MAST) based aseptic sampling, multi-function Sequential Injection Analysis (SIA) sample preparation, UHPLC separation and high-resolution mass spectrometry (HRMS) analysis. Continuous automated daily monitoring of a 17-day cell culture process was successfully demonstrated for a model monoclonal antibody (mAb) molecule with similar specificity and sensitivity as we reported earlier. To the best of our knowledge, this is the first report of an end-to-end automated online MAM system, which would allow the MAM to be applied to routine bioprocess monitoring, potentially replacing multiple conventional low resolution and low sensitivity off-line methods. The online HPLC or HPLC/MS platform could be easily adapted to support other processing steps such as downstream purification with minimal software re-configuration.

Dynamic multiscale metabolic network modeling of Chinese hamster ovary cell metabolism integrating N-linked glycosylation in industrial biopharmaceutical manufacturing

Experimental and modeling work, described in this article, is focused on the metabolic pathway of Chinese hamster ovary (CHO) cells, which are the preferred expression system for monoclonal antibody protein production. CHO cells are one of the primary hosts for monoclonal antibodies production, which have extensive applications in multiple fields like biochemistry, biology and medicine. Here, an approach to explain cellular metabolism with in silico modeling of a microkinetic reaction network is presented and validated with unique experimental results. Experimental data of 25 different fed-batch bioprocesses included the variation of multiple process parameters, such as pH, agitation speed, oxygen and CO₂ content, and dissolved oxygen. A total of 151 metabolites were involved in our proposed metabolic network, which consisted of 132 chemical reactions that describe the reaction pathways, and include 25 reactions describing N-glycosylation and additional reactions for the accumulation of the produced glycoforms. Additional eight reactions are considered for accumulation of the N-glycosylation products in the extracellular environment and one reaction to correlate cell degradation. The following pathways were considered: glycolysis, pentose phosphate pathway, nucleotide synthesis, tricarboxylic acid cycle, lipid synthesis, protein synthesis, biomass production, anaplerotic reactions, and membrane transport. With the applied modeling procedure, different operational scenarios and fed-batch techniques can be tested.

Systematically gap-filling the genome-scale metabolic model of CHO cells

OBJECTIVE

Chinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells.

RESULTS

We expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased.

CONCLUSIONS

The present CHO model is an important step towards more complete metabolic models of CHO cells.

High-Dimensional Design-Of-Experiments Extracts Small-Molecule-Only Induction Conditions for Dorsal Pancreatic Endoderm from Pluripotency

The derivation of endoderm and descendant organs, such as pancreas, liver, and intestine, impacts disease modeling and regenerative medicine. Use of TGF-β signaling agonism is a common method for induction of definitive endoderm from pluripotency. By using a data-driven, High-Dimensional Design of Experiments (HD-DoE)-based methodology to address multifactorial problems in directed differentiation, we found instead that optimal conditions demanded BMP antagonism and retinoid input leading to induction of dorsal foregut endoderm (DFE). We demonstrate that pancreatic identity can be rapidly, and robustly, induced from DFE and that such cells are of dorsal pancreatic identity. The DFE population was highly competent to differentiate into both stomach organoids and pancreatic tissue types and able to generate fetal-type β cells through two subsequent differentiation steps using only small molecules. This alternative, rapid, and low-cost basis for generating pancreatic insulin-producing cells may have impact for the development of cell-based therapies for diabetes.

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Method development for addressing multifactorial problems in directed differentiation

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Generation of endodermal populations without the use of TGF-β agonism

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Small-molecule-based pancreatic differentiation protocol

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.

The emerging role of systems biology for engineering protein production in CHO cells

To meet the ever-growing demand for effective, safe, and affordable protein therapeutics, decades of intense efforts have aimed to maximize the quantity and quality of recombinant proteins produced in CHO cells. Bioprocessing innovations and cell engineering efforts have improved product titer; however, uncharacterized cellular processes and gene regulatory mechanisms still hinder cell growth, specific productivity, and protein quality. Herein, we summarize recent advances in systems biology and data-driven approaches aiming to unravel how molecular pathways, cellular processes, and extrinsic factors (e.g. media supplementation) influence recombinant protein production. In particular, as the available omics data for CHO cells continue to grow, predictive models and screens will be increasingly used to unravel the biological drivers of protein production, which can be used with emerging genome editing technologies to rationally engineer cells to further control the quantity, quality and affordability of many biologic drugs.

Application of 13C flux analysis to identify high-productivity CHO metabolic phenotypes

Industrial bioprocesses place high demands on the energy metabolism of host cells to meet biosynthetic requirements for maximal protein expression. Identifying metabolic phenotypes that promote high expression is therefore a major goal of the biotech industry. We conducted a series of ¹³C flux analysis studies to examine the metabolic response to IgG expression during early stationary phase of CHO cell cultures grown in 3L fed-batch bioreactors. We examined eight clones expressing four different IgGs and compared with three non-expressing host-cell controls. Some clones were genetically manipulated to be apoptosis-resistant by expressing Bcl-2Δ, which correlated with increased IgG production and elevated glucose metabolism. The metabolic phenotypes of the non-expressing, IgG-expressing, and Bcl-2Δ/IgG-expressing clones were fully segregated by hierarchical clustering analysis. Lactate consumption and citric acid cycle fluxes were most strongly associated with specific IgG productivity. These studies indicate that enhanced oxidative metabolism is a characteristic of high-producing CHO cell lines.

A control strategy to investigate the relationship between specific productivity and high-mannose glycoforms in CHO cells

The integration of physiological knowledge into process control strategies is a cornerstone for the improvement of biopharmaceutical cell culture technologies. The present contribution investigates the applicability of specific productivity as a physiological control parameter in a cell culture process producing a monoclonal antibody (mAb) in CHO cells. In order to characterize cell physiology, the on-line oxygen uptake rate (OUR) was monitored and the time-resolved specific productivity was calculated as physiological parameters. This characterization enabled to identify the tight link between the deprivation of tyrosine and the decrease in cell respiration and in specific productivity. Subsequently, this link was used to control specific productivity by applying different feeding profiles. The maintenance of specific productivity at various levels enabled to identify a correlation between the rate of product formation and the relative abundance of high-mannose glycoforms. An increase in high mannose content was assumed to be the result of high specific productivity. Furthermore, the high mannose content as a function of cultivation pH and specific productivity was investigated in a design of experiment approach. This study demonstrated how physiological parameters could be used to understand interactions between process parameters, physiological parameters, and product quality attributes.

Optimizing eukaryotic cell hosts for protein production through systems biotechnology and genome-scale modeling

Eukaryotic cell lines, including Chinese hamster ovary cells, yeast, and insect cells, are invaluable hosts for the production of many recombinant proteins. With the advent of genomic resources, one can now leverage genome-scale computational modeling of cellular pathways to rationally engineer eukaryotic host cells. Genome-scale models of metabolism include all known biochemical reactions occurring in a specific cell. By describing these mathematically and using tools such as flux balance analysis, the models can simulate cell physiology and provide targets for cell engineering that could lead to enhanced cell viability, titer, and productivity. Here we review examples in which metabolic models in eukaryotic cell cultures have been used to rationally select targets for genetic modification, improve cellular metabolic capabilities, design media supplementation, and interpret high-throughput omics data. As more comprehensive models of metabolism and other cellular processes are developed for eukaryotic cell culture, these will enable further exciting developments in cell line engineering, thus accelerating recombinant protein production and biotechnology in the years to come.

A consensus approach for estimating the predictive accuracy of dynamic models in biology

Mathematical models that predict the complex dynamic behaviour of cellular networks are fundamental in systems biology, and provide an important basis for biomedical and biotechnological applications. However, obtaining reliable predictions from large-scale dynamic models is commonly a challenging task due to lack of identifiability. The present work addresses this challenge by presenting a methodology for obtaining high-confidence predictions from dynamic models using time-series data. First, to preserve the complex behaviour of the network while reducing the number of estimated parameters, model parameters are combined in sets of meta-parameters, which are obtained from correlations between biochemical reaction rates and between concentrations of the chemical species. Next, an ensemble of models with different parameterizations is constructed and calibrated. Finally, the ensemble is used for assessing the reliability of model predictions by defining a measure of convergence of model outputs (consensus) that is used as an indicator of confidence. We report results of computational tests carried out on a metabolic model of Chinese Hamster Ovary (CHO) cells, which are used for recombinant protein production. Using noisy simulated data, we find that the aggregated ensemble predictions are on average more accurate than the predictions of individual ensemble models. Furthermore, ensemble predictions with high consensus are statistically more accurate than ensemble predictions with large variance. The procedure provides quantitative estimates of the confidence in model predictions and enables the analysis of sufficiently complex networks as required for practical applications.

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.

Metabolic responses of CHO cells to limitation of key amino acids

Chinese hamster ovary (CHO) cells are the predominant host for production of therapeutic glycoproteins. In particular, the glutamine-synthetase (GS) expression system has been widely used in the biopharmaceutical industry for efficient selection of high-yielding clones. However, much remains unclear on how metabolic wiring affects culture performance. For instance, asparagine and serine have been observed to be the largest nitrogen sources taken up by GS-CHO cells, but their roles in biosynthesis and energy generation are poorly understood. In this work, a comprehensive profiling of extracellular metabolites coupled with an analysis of intracellular label distributions after 1-(13) C-pyruvate supplementation were used to trace metabolic rearrangements in different scenarios of asparagine and serine availability. The absence of asparagine in the medium caused growth arrest, and was associated with a dramatic increase in pyruvate uptake, a higher ratio of pyruvate carboxylation to dehydrogenation and an inability for de novo asparagine synthesis. The release of ammonia and amino acids such as aspartate, glutamate, and alanine were deeply impacted. This confirms asparagine to be essential for these GS-CHO cells as the main source of intracellular nitrogen as well as having an important anaplerotic role in TCA cycle activity. In turn, serine unavailability also negatively affected culture growth while triggering its de novo synthesis, confirmed by label incorporation coming from pyruvate, and reduced glycine and formate secretion congruent with its role as a precursor in the metabolism of one-carbon units. Overall, these results unfold important insights into GS-CHO cells metabolism that lay a clearer basis for fine-tuning bioprocess optimization.

Towards real-time monitoring of therapeutic protein quality in mammalian cell processes

Protein biopharmaceuticals are mainly produced in mammalian cells which can perform human-like post-translational modifications crucial to protein function. Subject to high variability, these critical quality attributes should be monitored and controlled during the manufacturing process. However, the large time requirements for analysis have been a bottleneck. Recent advances towards automated and high-throughput techniques, combined with multivariate data analysis, are increasingly providing relevant process knowledge in near real-time. New or re-designed analytical tools suited for monitoring product quality are starting to fit in this landscape. Moreover, omics technologies are expanding our understanding of how intracellular mechanisms and the extracellular milieu influence protein quality and quantity, reshaping the adoption of Process Analytical Technology (PAT) and Quality by Design (QbD) in the biopharmaceutical industry.

Multivariate PAT solutions for biopharmaceutical cultivation: current progress and limitations

Increasingly elaborate and voluminous datasets are generated by the (bio)pharmaceutical industry and are a major challenge for application of PAT and QbD principles. Multivariate data analysis (MVDA) is required to delineate relevant process information from large multi-factorial and multi-collinear datasets. Here the key role of MVDA for industrial (bio)process data is discussed, with a focus on progress and limitations of MVDA as a PAT solution for biopharmaceutical cultivation processes. MVDA based models were proven useful and should be routinely implemented for bioprocesses. It is concluded that although the highest level of PAT with process control within its design space in real-time during manufacturing is not reached yet, MVDA will be central to reach this ultimate objective for cell cultivations.

Transcriptomics as a tool for assessing the scalability of mammalian cell perfusion systems

DNA microarray-based transcriptomics have been used to determine the time course of laboratory and manufacturing-scale perfusion bioreactors in an attempt to characterize cell physiological state at these two bioreactor scales. Given the limited availability of genomic data for baby hamster kidney (BHK) cells, a Chinese hamster ovary (CHO)-based microarray was used following a feasibility assessment of cross-species hybridization. A heat shock experiment was performed using both BHK and CHO cells and resulting DNA microarray data were analyzed using a filtering criteria of perfect match (PM)/single base mismatch (MM) > 1.5 and PM-MM > 50 to exclude probes with low specificity or sensitivity for cross-species hybridizations. For BHK cells, 8910 probe sets (39 %) passed the cutoff criteria, whereas 12,961 probe sets (56 %) passed the cutoff criteria for CHO cells. Yet, the data from BHK cells allowed distinct clustering of heat shock and control samples as well as identification of biologically relevant genes as being differentially expressed, indicating the utility of cross-species hybridization. Subsequently, DNA microarray analysis was performed on time course samples from laboratory- and manufacturing-scale perfusion bioreactors that were operated under the same conditions. A majority of the variability (37 %) was associated with the first principal component (PC-1). Although PC-1 changed monotonically with culture duration, the trends were very similar in both the laboratory and manufacturing-scale bioreactors. Therefore, despite time-related changes to the cell physiological state, transcriptomic fingerprints were similar across the two bioreactor scales at any given instance in culture. Multiple genes were identified with time-course expression profiles that were very highly correlated (> 0.9) with bioprocess variables of interest. Although the current incomplete annotation limits the biological interpretation of these observations, their full potential may be realized in due course when richer genomic data become available. By taking a pragmatic approach of transcriptome fingerprinting, we have demonstrated the utility of systems biology to support the comparability of laboratory and manufacturing-scale perfusion systems. Scale-down model qualification is the first step in process characterization and hence is an integral component of robust regulatory filings. Augmenting the current paradigm, which relies primarily on cell culture and product quality information, with gene expression data can help make a substantially stronger case for similarity. With continued advances in systems biology approaches, we expect them to be seamlessly integrated into bioprocess development, which can translate into more robust and high yielding processes that can ultimately reduce cost of care for patients.

Metabolic flux rewiring in mammalian cell cultures

Continuous cell lines (CCLs) engage in 'wasteful' glucose and glutamine metabolism that leads to accumulation of inhibitory byproducts, primarily lactate and ammonium. Advances in techniques for mapping intracellular carbon fluxes and profiling global changes in enzyme expression have led to a deeper understanding of the molecular drivers underlying these metabolic alterations. However, recent studies have revealed that CCLs are not necessarily entrenched in a glycolytic or glutaminolytic phenotype, but instead can shift their metabolism toward increased oxidative metabolism as nutrients become depleted and/or growth rate slows. Progress to understand dynamic flux regulation in CCLs has enabled the development of novel strategies to force cultures into desirable metabolic phenotypes, by combining fed-batch feeding strategies with direct metabolic engineering of host cells.

Current state and recent advances in biopharmaceutical production in Escherichia coli, yeasts and mammalian cells

Almost all of the 200 or so approved biopharmaceuticals have been produced in one of three host systems: the bacterium Escherichia coli, yeasts (Saccharomyces cerevisiae, Pichia pastoris) and mammalian cells. We describe the most widely used methods for the expression of recombinant proteins in the cytoplasm or periplasm of E. coli, as well as strategies for secreting the product to the growth medium. Recombinant expression in E. coli influences the cell physiology and triggers a stress response, which has to be considered in process development. Increased expression of a functional protein can be achieved by optimizing the gene, plasmid, host cell, and fermentation process. Relevant properties of two yeast expression systems, S. cerevisiae and P. pastoris, are summarized. Optimization of expression in S. cerevisiae has focused mainly on increasing the secretion, which is otherwise limiting. P. pastoris was recently approved as a host for biopharmaceutical production for the first time. It enables high-level protein production and secretion. Additionally, genetic engineering has resulted in its ability to produce recombinant proteins with humanized glycosylation patterns. Several mammalian cell lines of either rodent or human origin are also used in biopharmaceutical production. Optimization of their expression has focused on clonal selection, interference with epigenetic factors and genetic engineering. Systemic optimization approaches are applied to all cell expression systems. They feature parallel high-throughput techniques, such as DNA microarray, next-generation sequencing and proteomics, and enable simultaneous monitoring of multiple parameters. Systemic approaches, together with technological advances such as disposable bioreactors and microbioreactors, are expected to lead to increased quality and quantity of biopharmaceuticals, as well as to reduced product development times.