Towards dynamic metabolic flux analysis in CHO cell cultures

A robust scale-down model development and process characterization for monoclonal antibody biomanufacturing using multivariate data analysis

Quality by Design (QbD) principles are extensively applied in biopharmaceutical manufacturing processes to ensure the consistent production of high-quality biotherapeutic products through achieving a deeper understanding of critical process parameters (CPPs), critical quality attributes (CQAs), and their interrelationships as well as establishing appropriate process control strategies. To do so, herein, we involve utilizing advanced multivariate data analysis (MVDA) in the context of scale-down model (SDM) development and validation as an ingenious approach for enhancing process efficiency and achieving greater regulatory compliance in the biomanufacturing of biologics. First, MVDA was applied to develop and evaluate several SDMs under various production conditions, including changes in scale-dependent parameters. This allowed the establishment of a practical SDM that closely approximated the process performance of manufacturing-scale batches. Furthermore, this approach enabled the identification not only of potential CPPs but also specific performance attributes such as ammonia, that had a significant impact on the CQAs. Moreover, it was deduced that the N-1 seed culture represents a critical process step influencing both quality and performance attributes in the upstream process from these approaches. This deduction was subsequently confirmed through experimental validation. Our findings offer valuable insights into streamlining the development of upstream biologics, particularly in terms of process characterization, thereby suggesting strategies for time and cost savings.

Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning

The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigated label-free multimodal nonlinear optical microscopy for non-perturbative profiling of biopharmaceutical cell lines based on their intrinsic molecular contrast. Employing simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy with fluorescence lifetime imaging microscopy (FLIM), we characterized Chinese hamster ovary (CHO) cell lines at early passages (0-2). A machine learning (ML)-assisted analysis pipeline leveraged high-dimensional information to classify single cells into their respective lines. Remarkably, the monoclonal cell line classifiers achieved balanced accuracies exceeding 96.8% as early as passage 2. Correlation features and FLIM modality played pivotal roles in early classification. This integrated optical bioimaging and machine learning approach presents a promising solution to expedite cell line selection process while ensuring identification of high-performing biopharmaceutical cell lines. The techniques have potential for broader single-cell characterization applications in stem cell research, immunology, cancer biology and beyond.

Comprehensive stable-isotope tracing of glucose and amino acids identifies metabolic by-products and their sources in CHO cell culture

Mammalian cell culture processes are widely utilized for biotherapeutics production, disease diagnostics, and biosensors, and hence, should be optimized to support robust cell growth and viability. However, toxic by-products accumulate in cultures due to inefficiencies in metabolic activities and nutrient utilization. In this study, we applied comprehensive 13C stable-isotope tracing of amino acids and glucose to two Immunoglobulin G (IgG) producing Chinese Hamster Ovary (CHO) cell lines to identify secreted by-products and trace their origins. CHO cells were cultured in media formulations missing a single amino acid or glucose supplemented with a 13C-tracer of the missing substrate, followed by gas chromatography-mass spectrometry (GC-MS) analysis to track labeled carbon flows and identify by-products. We tracked the sources of all secreted by-products and verified the identity of 45 by-products, majority of which were derived from glucose, leucine, isoleucine, valine, tyrosine, tryptophan, methionine, and phenylalanine. In addition to by-products identified previously, we identified several metabolites including 2-hydroxyisovaleric acid, 2-aminobutyric acid, L-alloisoleucine, ketoisoleucine, 2-hydroxy-3-methylvaleric acid, desmeninol, and 2-aminobutyric acid. When added to CHO cell cultures at different concentrations, certain metabolites inhibited cell growth while others including 2-hydroxy acids, surprisingly, reduced lactate accumulation. In vitro enzymatic analysis indicated that 2-hydroxy acids were metabolized by lactate dehydrogenase suggesting a possible mechanism for lowered lactate accumulation, e.g., competitive substrate inhibition. The 13C-labeling assisted metabolomics pipeline developed and the metabolites identified will serve as a springboard to reduce undesirable by-products accumulation and alleviate inefficient substrate utilization in mammalian cultures used for biomanufacturing and other applications through altered media formulations and pathway engineering strategies.

Mimicking CHO large-scale effects in the single multicompartment bioreactor: A new approach to access scale-up behavior

During the scale-up of biopharmaceutical production processes, insufficiently predictable performance losses may occur alongside gradients and heterogeneities. To overcome such performance losses, tools are required to explain, predict, and ultimately prohibit inconsistencies between laboratory and commercial scale. In this work, we performed CHO fed-batch cultivations in the single multicompartment bioreactor (SMCB), a new scale-down reactor system that offers new access to study large-scale heterogeneities in mammalian cell cultures. At volumetric power inputs of 20.4-1.5 W m-3, large-scale characteristics like long mixing times and dissolved oxygen (DO) heterogeneities were mimicked in the SMCB. Compared to a reference bioreactor (REFB) set-up, the conditions in the SMCB provoked an increase in lactate accumulation of up to 87%, an increased glucose uptake, and reduced viable cell concentrations in the stationary phase. All are characteristic for large-scale performance. The unique possibility to distinguish between the effects of changing power inputs and observed heterogeneities provided new insights into the potential reasons for altered product quality attributes. Apparently, the degree of galactosylation in the evaluated glycan patterns changed primarily due to the different power inputs rather than the provoked heterogeneities. The SMCB system could serve as a potent tool to provide new insights into scale-up behavior and to predict cell line-specific drawbacks at an early stage of process development.

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.

An Investigation into the Metabolic Differences between Conventional and High Seeding Density Fed-Batch Cell Cultures by Applying a Segmented Modeling Approach

The conventional fed-batch process characterized by a low titer currently challenges pharmaceutical development. Process optimization by applying a perfusion process in the pre-stage and subsequent production phase at a high seeding density (HSD) can meet this challenge. In this study, we employed a simplified approach based on measured experiments, namely segmented modeling, to systematically analyze an HSD fed-batch process compared to a standard process. A comparison indicated that the metabolic phases of HSD processes are not only shifted in time, but metabolite trends show an altered metabolism. In an extended study, we integrated the intracellular fluxes determined by a metabolic flux analysis into the segmented modeling approach. Compared to using only extracellular rates, similar phases are identified, and this highlights the reliability of phase identification modeling using extracellular rates only. Furthermore, the segmented linear regression approach is used to create a model that describes cellular behavior and that can be used to predict potential improvements in the feeding strategy and in harvest viability. Here, overfeeding was eliminated and a significantly higher titer was achieved. This work provides insights into the overall metabolic changes in the HSD process and paves the way towards the optimization of the feeding regime.

A case study: Correlation of the nutrient composition in Chinese Hamster Ovary cultures with cell growth, antibody titre and quality attributes using multivariate analyses for guiding medium and feed optimization in early upstream process development

In this case-study, we demonstrate an approach for identifying correlations between nutrients/metabolites in the spent medium of CHO cell cultures and cell growth, mAb titre and critical quality attributes, using multivariate analyses, which can aid in selection of targets for medium and feed optimization. An extensive LC-MS-based method was used to analyse the spent medium composition. Partial least squares (PLS) model was used to identify correlations between nutrient composition and cell growth and mAb titre and orthogonal projections to latent structures (OPLS) model was used to determine the effect of the changing nutrient composition during the culture on critical quality attributes. The PLS model revealed that the initial concentrations of several amino acids as well as pyruvic acid and pyridoxine, governed the early cell growth, while the concentrations of TCA cycle intermediates and several vitamins highly influenced the stationary phase, in which mAb production was maximum. For the first time, with the help of the OPLS model, we were able to draw correlations between nutrients/metabolites during the culture and critical quality attributes, for example, optimizing the supply of certain amino acids and vitamins could reduce impurities while simultaneously increasing desirable glycoforms. The unique correlations obtained from such an exploratory analysis, utilizing conditions that are commonly adopted in early process development, present opportunities for optimizing the compositions of the growth media and the feed media for enhancing cell growth, mAb production and quality, thereby proving to be a useful preliminary step in bioprocess optimization.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10616-022-00561-z.

NAD+ supplementation improves mAb productivity in CHO cells via a glucose metabolic shift

Aerobic glycolysis and its by-product lactate accumulation are usually associated with adverse culture phenotypes such as poor cell viability and productivity. Due to the lack of knowledge on underlying mechanisms and accompanying biological processes, the regulation of aerobic glycolysis has been an ongoing challenge in culture process development for therapeutic protein productivity. Nicotinamide adenine dinucleotide (NAD⁺ ), a coenzyme and co-substrate in energy metabolism, promotes the conversion of inefficient glycolysis into an efficient oxidative phosphorylation (OXPHOS) pathway. However, the effect of NAD⁺ on Chinese hamster ovary (CHO) cells for biopharmaceutical production has not been reported yet. In this work, we aimed to elucidate the influence of NAD⁺ on cell culture performance by examining metabolic shifts and mAb productivity. The supplementation of NAD⁺ increased the intracellular concentration of NAD⁺ and promoted SIRT3 expression. Antibody titer and the specific productivity in the growth phase were improved by up to 1.82- and 1.88-fold, respectively, with marginal restrictions on cell growth. NAD⁺ significantly reduced the accumulation of reactive oxygen species (ROS) and the lactate yield from glucose, determined by lactate accumulation versus glucose consumption (Y_LAC/GLC ). In contrast, OXPHOS capacity and amino acid consumption rate increased substantially. Collectively, these results suggest that NAD⁺ contributes to improving therapeutic protein productivity in bioprocessing via inducing an energy metabolic shift.

Metabolic Analysis of the Asparagine and Glutamine Dynamics in an Industrial CHO Fed-Batch Process

Chinese hamster ovary (CHO) cell lines are grown in cultures with varying asparagine and glutamine concentrations, but further study is needed to characterize the interplay between these amino acids. By following ¹³C‐glucose, ¹³C‐glutamine, and ¹³C‐asparagine tracers using metabolic flux analysis (MFA), CHO cell metabolism was characterized in an industrially relevant fed‐batch process under glutamine supplemented and low glutamine conditions during early and late exponential growth. For both conditions MFA revealed glucose as the primary carbon source to the tricarboxylic acid (TCA) cycle followed by glutamine and asparagine as secondary sources. Early exponential phase CHO cells prefer glutamine over asparagine to support the TCA cycle under the glutamine supplemented condition, while asparagine was critical for TCA activity for the low glutamine condition. Overall TCA fluxes were similar for both conditions due to the trade‐offs associated with reliance on glutamine and/or asparagine. However, glutamine supplementation increased fluxes to alanine, lactate and enrichment of glutathione, N‐acetyl‐glucosamine and pyrimidine‐containing‐molecules. The late exponential phase exhibited reduced central carbon metabolism dominated by glucose, while lactate reincorporation and aspartate uptake were preferred over glutamine and asparagine. These ¹³C studies demonstrate that metabolic flux is process time dependent and can be modulated by varying feed composition.

Compartment-specific metabolome labeling enables the identification of subcellular fluxes that may serve as promising metabolic engineering targets in CHO cells

¹³C labeling data are used to calculate quantitative intracellular flux patterns reflecting in vivo conditions. Given that approaches for compartment-specific metabolomics exist, the benefits they offer compared to conventional non-compartmented ¹³C flux studies remain to be determined. Using compartment-specific labeling information of IgG1-producing Chinese hamster ovary cells, this study investigated differences of flux patterns exploiting and ignoring metabolic labeling data of cytosol and mitochondria. Although cellular analysis provided good estimates for the majority of intracellular fluxes, half of the mitochondrial transporters, and NADH and ATP balances, severe differences were found for some reactions. Accurate flux estimations of almost all iso-enzymes heavily depended on the sub-cellular labeling information. Furthermore, key discrepancies were found for the mitochondrial carriers v_AGC1 (Aspartate/Glutamate antiporter), v_DIC (Malate/H⁺ symporter), and v_OGC (α-ketoglutarate/malate antiporter). Special emphasis is given to the flux of cytosolic malic enzyme (v_ME): it could not be estimated without the compartment-specific malate labeling information. Interesting enough, cytosolic malic enzyme is an important metabolic engineering target for improving cell-specific IgG1 productivity. Hence, compartment-specific ¹³C labeling analysis serves as prerequisite for related metabolic engineering studies.

Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes

The calculation of temporally varying upstream process outcomes is a challenging task. Over the last years, several parametric, semi-parametric as well as non-parametric approaches were developed to provide reliable estimates for key process parameters. We present generic and product-specific recurrent neural network (RNN) models for the computation and study of growth and metabolite-related upstream process parameters as well as their temporal evolution. Our approach can be used for the control and study of single product-specific large-scale manufacturing runs as well as generic small-scale evaluations for combined processes and products at development stage. The computational results for the product titer as well as various major upstream outcomes in addition to relevant process parameters show a high degree of accuracy when compared to experimental data and, accordingly, a reasonable predictive capability of the RNN models. The calculated values for the root-mean squared errors of prediction are significantly smaller than the experimental standard deviation for the considered process run ensembles, which highlights the broad applicability of our approach. As a specific benefit for platform processes, the generic RNN model is also used to simulate process outcomes for different temperatures in good agreement with experimental results. The high level of accuracy and the straightforward usage of the approach without sophisticated parameterization and recalibration procedures highlight the benefits of the RNN models, which can be regarded as promising alternatives to existing parametric and semi-parametric methods.

An integrated modular framework for modeling the effect of ammonium on the sialylation process of monoclonal antibodies produced by CHO cells

BACKGROUND

Monoclonal antibodies (mABs) have emerged as one of the most important therapeutic recombinant proteins in the pharmaceutical industry. Their immunogenicity and therapeutic efficacy are influenced by post-translational modifications, specifically the glycosylation process. Bioprocess conditions can influence the intracellular process of glycosylation. Among all the process conditions that have been recognized to affect the mAB glycoforms, the detailed mechanism underlying how ammonium could perturb glycosylation remains to be fully understood. It was shown that ammonium induces heterogeneity in protein glycosylation by altering the sialic acid content of glycoproteins. Hence, understanding this mechanism would aid pharmaceutical manufacturers to ensure consistent protein glycosylation.

METHODS

Three different mechanisms have been proposed to explain how ammonium influences the sialylation process. In the first, the inhibition of CMP-sialic acid transporter, which transports CMP-sialic acid (sialylation substrate) into the Golgi, by an increase in UDP-GlcNAc content that is brought about by the augmented incorporation of ammonium into glucosamine formation. In the second, ammonia diffuses into the Golgi and raises its pH, thereby decreasing the sialyltransferase enzyme activity. In the third, the reduction of sialyltransferase enzyme expression level in the presence of ammonium. We employed these mechanisms in a novel integrated modular platform to link dynamic alteration in mAB sialylation process with extracellular ammonium concentration to elucidate how ammonium alters the sialic acid content of glycoproteins.

RESULTS

Our results show that the sialylation reaction rate is insensitive to the first mechanism. At low ammonium concentration, the second mechanism is the controlling mechanism in mAB sialylation and by increasing the ammonium level (< 8 mM) the third mechanism becomes the controlling mechanism. At higher ammonium concentrations (> 8 mM) the second mechanism becomes predominant again.

CONCLUSION

The presented model in this study provides a connection between extracellular ammonium and the monoclonal antibody sialylation process. This computational tool could help scientists to develop and formulate cell culture media. The model illustrated here can assist the researchers to select culture media that ensure consistent mAB sialylation.

Longitudinal monitoring of cell metabolism in biopharmaceutical production using label-free fluorescence lifetime imaging microscopy

Chinese hamster ovary (CHO) cells are routinely used in the biopharmaceutical industry for production of therapeutic monoclonal antibodies (mAbs). Although multiple offline and time-consuming measurements of spent media composition and cell viability assays are used to monitor the status of culture in biopharmaceutical manufacturing, the day-to-day changes in the cellular microenvironment need further in-depth characterization. In this study, two-photon fluorescence lifetime imaging microscopy (2P-FLIM) was used as a tool to directly probe into the health of CHO cells from a bioreactor, exploiting the autofluorescence of intracellular nicotinamide adenine dinucleotide phosphate (NAD(P)H), an enzymatic cofactor that determines the redox state of the cells. A custom-built multimodal microscope with two-photon FLIM capability was utilized to monitor changes in NAD(P)H fluorescence for longitudinal characterization of a changing environment during cell culture processes. Three different cell lines were cultured in 0.5 L shake flasks and 3 L bioreactors. The resulting FLIM data revealed differences in the fluorescence lifetime parameters, which were an indicator of alterations in metabolic activity. In addition, a simple principal component analysis (PCA) of these optical parameters was able to identify differences in metabolic progression of two cell lines cultured in bioreactors. Improved understanding of cell health during antibody production processes can result in better streamlining of process development, thereby improving product titer and verification of scale-up. To our knowledge, this is the first study to use FLIM as a label-free measure of cellular metabolism in a biopharmaceutically relevant and clinically important CHO cell line.

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

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.

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

Direct optical detection of cell density and viability of mammalian cells by means of UV/VIS spectroscopy

The critical process parameters cell density and viability during mammalian cell cultivation are assessed by UV/VIS spectroscopy in combination with multivariate data analytical methods. This direct optical detection technique uses a commercial optical probe to acquire spectra in a label-free way without signal enhancement. For the cultivation, an inverse cultivation protocol is applied, which simulates the exponential growth phase by exponentially replacing cells and metabolites of a growing Chinese hamster ovary cell batch with fresh medium. For the simulation of the death phase, a batch of growing cells is progressively replaced by a batch with completely starved cells. Thus, the most important parts of an industrial batch cultivation are easily imitated. The cell viability was determined by the well-established method partial least squares regression (PLS). To further improve process knowledge, the viability has been determined from the spectra based on a multivariate curve resolution (MCR) model. With this approach, the progress of the cultivations can be continuously monitored solely based on an UV/VIS sensor. Thus, the monitoring of critical process parameters is possible inline within a mammalian cell cultivation process, especially the viable cell density. In addition, the beginning of cell death can be detected by this method which allows us to determine the cell viability with acceptable error. The combination of inline UV/VIS spectroscopy with multivariate curve resolution generates additional process knowledge complementary to PLS and is considered a suitable process analytical tool for monitoring industrial cultivation processes.

Screening and Identification of Differentially Expressed and Adipose Growth-Related Protein-Coding Genes During the Deposition of Perirenal Adipose Tissue in Rabbits

BACKGROUND

Rabbit is a good model for genetic and medical studies in other livestock species. The rabbit shows low adipose tissue deposition, and the phenomena indicates that there is some specificity of adipose deposition during the rabbit growth. However, little is known about genes that regulate the growth of adipose tissue in rabbits.

MATERIALS AND METHODS

Deep RNA-seq and comprehensive bioinformatics analyses were used to characterize the genes of rabbit visceral adipose tissue (VAT) at 35, 85 and 120 days after birth. Differentially expressed genes (DEGs) were identified at the three growth stages by DESeq. To explore the function of the candidate genes, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. Six DEGs were randomly selected, and their expression profiles were validated by q-PCR.

RESULTS

A total of 20,303 known transcripts and 99,199 new transcripts from 8 RNA sequencing libraries were identified, and 34 differentially expressed genes (DEGs) were screened. GO enrichment and KEGG pathway analyses revealed that the DEGs were mainly involved in lipid metabolism regulation including acylglycerol metabolic process and mobilization, and decomposition of lipids to generate ATP in adipocytes and fatty acid metabolism, included LOC100342322 and LOC100342572. In addition, 133 protein-coding genes that play a role in adipose growth and development were screened, including acyl-CoA synthetase long-chain family member 5 (ACSL5) and fatty acid-binding protein 2 (FABP2). The validation results of six DEGs by q-PCR showed similar trends with the results of RNA-seq.

CONCLUSION

In summary, this study provides the first report of the coding genes profiles of rabbit adipose tissue during different growth stages. These data allow for the identification of candidate genes for subsequent studies on rabbit genetics and regulation of adipose cells, and provide an animal model for studying obesity in humans.

Application of a genome-scale model in tandem with enzyme assays for identification of metabolic signatures of high and low CHO cell producers

Biopharmaceutical industrial processes are based on high yielding stable recombinant Chinese Hamster Ovary (CHO) cells that express monoclonal antibodies. However, the process and feeding regimes need to be adapted for each new cell line, as they all have a slightly different metabolism and product performance. A main limitation for accelerating process development is that the metabolic pathways underlying this physiological variability are not yet fully understood. This study describes the evolution of intracellular fluxes during the process for 4 industrial cell lines, 2 high producers and 2 low producers (n = 3), all of them producing a different antibody. In order to understand from a metabolic point of view the phenotypic differences observed, and to find potential targets for improving specific productivity of low producers, the analysis was supported by a tailored genome-scale model and was validated with enzymatic assays performed at different days of the process. A total of 59 reactions were examined from different key pathways, namely glycolysis, pentose phosphate pathway, TCA cycle, lipid metabolism, and oxidative phosphorylation. The intracellular fluxes did not show a metabolic correlation between high producers, but the degree of similitude observed between cell lines could be confirmed with additional experimental observations. The whole analysis led to a better understanding of the metabolic requirements for all the cell lines, allowed to the identification of metabolic bottlenecks and suggested targets for further cell line engineering. This study is a successful application of a curated genome-scale model to multiple industrial cell lines, which makes the metabolic model suitable for process platform.

An Improved Scatter Search Algorithm for Parameter Estimation in Large-Scale Kinetic Models of Biochemical Systems

Background:

Mathematical models play a central role in facilitating researchers to better understand and comprehensively analyze various processes in biochemical systems. Their usage is beneficial in metabolic engineering as they help predict and improve desired products. However, one of the primary challenges in model building is parameter estimation. It is the process to find nearoptimal values of kinetic parameters which may culminate in the best fit of model prediction to experimental data.

Methods:

This paper proposes an improved scatter search algorithm to address the challenging parameter estimation problem. The improved algorithm is based on hybridization of quasi opposition-based learning in enhanced scatter search (QOBLESS) method. The algorithm is tested using a large-scale metabolic model of Chinese Hamster Ovary (CHO) cells.

Results:

The experimental result shows that the proposed algorithm performs better than other algorithms in terms of convergence speed and the minimum value of the objective function (loglikelihood). The estimated parameters from the experiment produce a better model by means of obtaining a reasonable good fit of model prediction to the experimental data.

Conclusion:

The kinetic parameters’ value obtained from our work was able to result in a reasonable best fit of model prediction to the experimental data, which contributes to a better understanding and produced more accurate model. Based on the results, the QOBLESS method can be used as an efficient parameter estimation method in large-scale kinetic model building.

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.

A guide to 13C metabolic flux analysis for the cancer biologist

Cancer metabolism is significantly altered from normal cellular metabolism allowing cancer cells to adapt to changing microenvironments and maintain high rates of proliferation. In the past decade, stable-isotope tracing and network analysis have become powerful tools for uncovering metabolic pathways that are differentially activated in cancer cells. In particular, ¹³C metabolic flux analysis (¹³C-MFA) has emerged as the primary technique for quantifying intracellular fluxes in cancer cells. In this review, we provide a practical guide for investigators interested in getting started with ¹³C-MFA. We describe best practices in ¹³C-MFA, highlight potential pitfalls and alternative approaches, and conclude with new developments that can further enhance our understanding of cancer metabolism.

Tracing tagged molecules can help researchers understand the altered metabolism of cancer cells. The abilities of cancer cells to multiply rapidly and invade new tissues are supported by metabolic alterations, which can be investigated by feeding tagged molecules to cells and tracing how they are metabolized. These techniques, such as ¹³C metabolic flux analysis (¹³C-MFA), have been perceived as difficult to use, but recent advances are making them more accessible. Maciek Antoniewicz, University of Delaware, Newark, USA, has published a practical guide for researchers wanting to use 13C-MFA. The review includes best practices, pitfalls, alternative approaches, and new developments, especially new user-friendly software that allows researchers without extensive training in mathematics, statistics, or coding to perform 13C-MFA. Broadening access to tools for investigating altered metabolic pathways may spur development of new cancer therapies targeting these pathways.

Glyco-Mapper: A Chinese hamster ovary (CHO) genome-specific glycosylation prediction tool

Glyco-Mapper is a novel systems biology product quality prediction tool created using a new framework termed: Discretized Reaction Network Modeling using Fuzzy Parameters (DReaM-zyP). Within Glyco-Mapper, users fix the nutrient feed composition and the glycosylation reaction fluxes to fit the model glycoform to the reference experimental glycoform, enabling cell-line specific glycoform predictions as a result of cell engineering strategies. Glyco-Mapper accurately predicts glycoforms associated with genetic alterations that result in the appearance or disappearance of one or more glycans with an accuracy, sensitivity, and specificity of 96%, 85%, and 97%, respectively, for publications between 1999 and 2014. The modeled glycoforms span a large range of glycoform engineering strategies, including the altered expression of glycosylation, nucleotide sugar transport, and metabolism genes, as well as an altered nutrient feeding strategy. A glycoprotein-producing CHO cell line reference glycoform was modeled and a novel Glyco-Mapper prediction was experimentally confirmed with an accuracy and specificity of 95% and 98%, respectively. Glyco-Mapper is a product quality prediction tool that provides a streamlined way to design host cell line genomes to achieve specific product quality attributes.

Deuterium- and Tritium-Labelled Compounds: Applications in the Life Sciences

Hydrogen isotopes are unique tools for identifying and understanding biological and chemical processes. Hydrogen isotope labelling allows for the traceless and direct incorporation of an additional mass or radioactive tag into an organic molecule with almost no changes in its chemical structure, physical properties, or biological activity. Using deuterium-labelled isotopologues to study the unique mass-spectrometric patterns generated from mixtures of biologically relevant molecules drastically simplifies analysis. Such methods are now providing unprecedented levels of insight in a wide and continuously growing range of applications in the life sciences and beyond. Tritium (³ H), in particular, has seen an increase in utilization, especially in pharmaceutical drug discovery. The efforts and costs associated with the synthesis of labelled compounds are more than compensated for by the enhanced molecular sensitivity during analysis and the high reliability of the data obtained. In this Review, advances in the application of hydrogen isotopes in the life sciences are described.

Elevated pCO2 affects the lactate metabolic shift in CHO cell culture processes

The shift from lactate production to consumption in CHO cell metabolism is a key event during cell culture cultivations and is connected to increased culture longevity and final product titers. However, the mechanisms controlling this metabolic shift are not yet fully understood. Variations in lactate metabolism have been mainly reported to be induced by process pH and availability of substrates like glucose and glutamine. The aim of this study was to investigate the effects of elevated pCO₂ concentrations on the lactate metabolic shift phenomena in CHO cell culture processes. In this publication, we show that at elevated pCO₂ in batch and fed-batch cultures, the lactate metabolic shift was absent in comparison to control cultures at lower pCO₂ values. Furthermore, through metabolic flux analysis we found a link between the lactate metabolic shift and the ratio of NADH producing and regenerating intracellular pathways. This ratio was mainly affected by a reduced oxidative capacity of cultures at elevated pCO₂. The presented results are especially interesting for large-scale and perfusion processes where increased pCO₂ concentrations are likely to occur. Our results suggest, that so far unexplained metabolic changes may be connected to increased pCO₂ accumulation in larger scale fermentations. Finally, we propose several mechanisms through which increased pCO₂ might affect the cell metabolism and briefly discuss methods to enable the lactate metabolic shift during cell cultivations.

A mechanistic kinetic description of lactate dehydrogenase elucidating cancer diagnosis and inhibitor evaluation

As a key enzyme for glycolysis, lactate dehydrogenase (LDH) remains as a topic of great interest in cancer study. Though a number of kinetic models have been applied to describe the dynamic behavior of LDH, few can reflect its actual mechanism, making it difficult to explain the observed substrate and competitor inhibitions at wide concentration ranges. A novel mechanistic kinetic model is developed based on the enzymatic processes and the interactive properties of LDH. Better kinetic simulation as well as new enzyme interactivity information and kinetic properties extracted from published articles via the novel model was presented. Case studies were presented to a comprehensive understanding of the effect of temperature, substrate, and inhibitor on LDH kinetic activities for promising application in cancer diagnosis, inhibitor evaluation, and adequate drug dosage prediction.

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.

Identification of active constraints in dynamic flux balance analysis

This study deals with the calibration of dynamic metabolic flux models that are formulated as the maximization of an objective subject to constraints. Two approaches were applied for identifying the constraints from data. In the first approach a minimal active number of limiting constraints is found based on data that are assumed to be bounded within sets whereas, in the second approach, the limiting constraints are found based on parametric sensitivity analysis. The ability of these approaches to finding the active limiting constraints was verified through their application to two case studies: an in-silico (simulated) data-based study describing the growth of E. coli and an experimental data-based study for Bordetella pertussis (B. pertussis). © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:26-36, 2017.

Strategies for Extending Metabolomics Studies with Stable Isotope Labelling and Fluxomics

This is a perspective from the peer session on stable isotope labelling and fluxomics at the Australian & New Zealand Metabolomics Conference (ANZMET) held from 30 March to 1 April 2016 at La Trobe University, Melbourne, Australia. This report summarizes the key points raised in the peer session which focused on the advantages of using stable isotopes in modern metabolomics and the challenges in conducting flux analyses. The session highlighted the utility of stable isotope labelling in generating reference standards for metabolite identification, absolute quantification, and in the measurement of the dynamic activity of metabolic pathways. The advantages and disadvantages of different approaches of fluxomics analyses including flux balance analysis, metabolic flux analysis and kinetic flux profiling were also discussed along with the use of stable isotope labelling in in vivo dynamic metabolomics. A number of crucial technical considerations for designing experiments and analyzing data with stable isotope labelling were discussed which included replication, instrumentation, methods of labelling, tracer dilution and data analysis. This report reflects the current viewpoint on the use of stable isotope labelling in metabolomics experiments, identifying it as a great tool with the potential to improve biological interpretation of metabolomics data in a number of ways.

Electrostatic engineering of the interface between heavy and light chains promotes antibody Fab fragment production

Monoclonal antibodies and antibody fragments are used for diverse diagnostic and therapeutic applications. We have investigated the secretory production of Fab fragments from insect cells cotransfected with plasmid vectors carrying heavy- and light-chain genes. In the present study, to promote the formation of the disulfide bond between the heavy and light chains, some positively charged amino acid residues were introduced near the cysteine residue for the disulfide bond at the C-terminus of C_L, while some negatively charged amino acid residues were added near the cysteine residue for the disulfide bond at the C-terminus of C_H1. This electrostatic steering led to an increase in Fab secretions from insect cells.

Catabolism of Branched Chain Amino Acids Contributes Significantly to Synthesis of Odd-Chain and Even-Chain Fatty Acids in 3T3-L1 Adipocytes

The branched chain amino acids (BCAA) valine, leucine and isoleucine have been implicated in a number of diseases including obesity, insulin resistance, and type 2 diabetes mellitus, although the mechanisms are still poorly understood. Adipose tissue plays an important role in BCAA homeostasis by actively metabolizing circulating BCAA. In this work, we have investigated the link between BCAA catabolism and fatty acid synthesis in 3T3-L1 adipocytes using parallel ¹³C-labeling experiments, mass spectrometry and model-based isotopomer data analysis. Specifically, we performed parallel labeling experiments with four fully ¹³C-labeled tracers, [U-¹³C]valine, [U-¹³C]leucine, [U-¹³C]isoleucine and [U-¹³C]glutamine. We measured mass isotopomer distributions of fatty acids and intracellular metabolites by GC-MS and analyzed the data using the isotopomer spectral analysis (ISA) framework. We demonstrate that 3T3-L1 adipocytes accumulate significant amounts of even chain length (C14:0, C16:0 and C18:0) and odd chain length (C15:0 and C17:0) fatty acids under standard cell culture conditions. Using a novel GC-MS method, we demonstrate that propionyl-CoA acts as the primer on fatty acid synthase for the production of odd chain fatty acids. BCAA contributed significantly to the production of all fatty acids. Leucine and isoleucine contributed at least 25% to lipogenic acetyl-CoA pool, and valine and isoleucine contributed 100% to lipogenic propionyl-CoA pool. Our results further suggest that low activity of methylmalonyl-CoA mutase and mass action kinetics of propionyl-CoA on fatty acid synthase result in high rates of odd chain fatty acid synthesis in 3T3-L1 cells. Overall, this work provides important new insights into the connection between BCAA catabolism and fatty acid synthesis in adipocytes and underscores the high capacity of adipocytes for metabolizing BCAA.

A probabilistic framework for the exploration of enzymatic capabilities based on feasible kinetics and control analysis

BACKGROUND

Analysis of limiting steps within enzyme-catalyzed reactions is fundamental to understand their behavior and regulation. Methods capable of unravelling control properties and exploring kinetic capabilities of enzymatic reactions would be particularly useful for protein and metabolic engineering. While single-enzyme control analysis formalism has previously been applied to well-studied enzymatic mechanisms, broader application of this formalism is limited in practice by the limited amount of kinetic data and the difficulty of describing complex allosteric mechanisms.

METHODS

To overcome these limitations, we present here a probabilistic framework enabling control analysis of previously unexplored mechanisms under uncertainty. By combining a thermodynamically consistent parameterization with an efficient Sequential Monte Carlo sampler embedded in a Bayesian setting, this framework yields insights into the capabilities of enzyme-catalyzed reactions with modest kinetic information, provided that the catalytic mechanism and a thermodynamic reference point are defined.

RESULTS

The framework was used to unravel the impact of thermodynamic affinity, substrate saturation levels and effector concentrations on the flux control and response coefficients of a diverse set of enzymatic reactions.

CONCLUSIONS

Our results highlight the importance of the metabolic context in the control analysis of isolated enzymes as well as the use of statistically sound methods for their interpretation.

GENERAL SIGNIFICANCE

This framework significantly expands our current capabilities for unravelling the control properties of general reaction kinetics with limited amount of information. This framework will be useful for both theoreticians and experimentalists in the field.