Combined in silico modeling and metabolomics analysis to characterize fed-batch CHO cell culture

The increasing demand for recombinant therapeutic proteins highlights the need to constantly improve the efficiency and yield of these biopharmaceutical products from mammalian cells, which is fully achievable only through proper understanding of cellular functioning. Towards this end, the current study exploited a combined metabolomics and in silico modeling approach to gain a deeper insight into the cellular mechanisms of Chinese hamster ovary (CHO) fed-batch cultures. Initially, extracellular and intracellular metabolite profiling analysis shortlisted key metabolites associated with cell growth limitation within the energy, glutathione, and glycerophospholipid pathways that have distinct changes at the exponential-stationary transition phase of the cultures. In addition, biomass compositional analysis newly revealed different amino acid content in the CHO cells from other mammalian cells, indicating the significance of accurate protein composition data in metabolite balancing across required nutrient assimilation, metabolic utilization, and cell growth. Subsequent in silico modeling of CHO cells characterized internal metabolic behaviors attaining physiological changes during growth and non-growth phases, thereby allowing us to explore relevant pathways to growth limitation and identify major growth-limiting factors including the oxidative stress and depletion of lipid metabolites. Such key information on growth-related mechanisms derived from the current approach can potentially guide the development of new strategies to enhance CHO culture performance.     

Metabolic analysis of antibody producing CHO cells in fed‐batch production

Chinese hamster ovary (CHO) cells are commonly used for industrial production of recombinant proteins in fed batch or alternative production systems. Cells progress through multiple metabolic stages during fed‐batch antibody (mAb) production, including an exponential growth phase accompanied by lactate production, a low growth, or stationary phase when specific mAb production increases, and a decline when cell viability declines. Although media composition and cell lineage have been shown to impact growth and productivity, little is known about the metabolic changes at a molecular level. Better understanding of cellular metabolism will aid in identifying targets for genetic and metabolic engineering to optimize bioprocess and cell engineering. We studied a high expressing recombinant CHO cell line, designated high performer (HP), in fed‐batch productions using stable isotope tracers and biochemical methods to determine changes in central metabolism that accompany growth and mAb production. We also compared and contrasted results from HP to a high lactate producing cell line that exhibits poor growth and productivity, designated low performer (LP), to determine intrinsic metabolic profiles linked to their respective phenotypes. Our results reveal alternative metabolic and regulatory pathways for lactate and TCA metabolite production to those reported in the literature. The distribution of key media components into glycolysis, TCA cycle, lactate production, and biosynthetic pathways was shown to shift dramatically between exponential growth and stationary (production) phases. We determined that glutamine is both utilized more efficiently than glucose for anaplerotic replenishment and contributes more significantly to lactate production during the exponential phase. Cells shifted to glucose utilization in the TCA cycle as growth rate decreased. The magnitude of this metabolic switch is important for attaining high viable cell mass and antibody titers. We also found that phosphoenolpyruvate carboxykinase (PEPCK1) and pyruvate kinase (PK) are subject to differential regulation during exponential and stationary phases. The concomitant shifts in enzyme expression and metabolite utilization profiles shed light on the regulatory links between cell metabolism, media metabolites, and cell growth. Biotechnol. Bioeng. 2013; 110: 1735–1747. © 2013 Wiley Periodicals, Inc.     

Genome-scale modeling and in silico analysis of ethanologenic bacteria Zymomonas mobilis

Bioethanol has been recognized as a potential alternative energy source. Among various ethanol-producing microbes, Zymomonas mobilis has acquired special attention due to its higher ethanol yield and tolerance. However, cellular metabolism in Z. mobilis remains unclear, hindering its practical application for bioethanol production. To elucidate such physiological characteristics, we reconstructed and validated a genome-scale metabolic network (iZM363) of Z. mobilis ATCC31821 (ZM4) based on its annotated genome and biochemical information. The phenotypic behaviors and metabolic states predicted by our genome-scale model were highly consistent with the experimental observations of Z. mobilis ZM4 strain growing on glucose as well as NMR-measured intracellular fluxes of an engineered strain utilizing glucose, fructose, and xylose. Subsequent comparative analysis with Escherichia coli and Saccharomyces cerevisiae as well as gene essentiality and flux coupling analyses have also confirmed the functional role of pdc and adh genes in the ethanologenic activity of Z. mobilis, thus leading to better understanding of this natural ethanol producer. In future, the current model could be employed to identify potential cell engineering targets, thereby enhancing the productivity of ethanol in Z. mobilis.     

Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling

Genome‐scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype–phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task‐driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type‐specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty‐two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.     

Genome-scale analysis of Mannheimia succiniciproducens metabolism

Mannheimia succiniciproducens MBEL55E isolated from bovine rumen is a capnophilic gram-negative bacterium that efficiently produces succinic acid, an industrially important four carbon dicarboxylic acid. In order to design a metabolically engineered strain which is capable of producing succinic acid with high yield and productivity, it is essential to optimize the whole metabolism at the systems level. Consequently, in silico modeling and simulation of the genome-scale metabolic network was employed for genome-scale analysis and efficient design of metabolic engineering experiments. The genome-scale metabolic network of M. succiniciproducens consisting of 686 reactions and 519 metabolites was constructed based on reannotation and validation experiments. With the reconstructed model, the network structure and key metabolic characteristics allowing highly efficient production of succinic acid were deciphered; these include strong PEP carboxylation, branched TCA cycle, relative weak pyruvate formation, the lack of glyoxylate shunt, and non-PTS for glucose uptake. Constraints-based flux analyses were then carried out under various environmental and genetic conditions to validate the genome-scale metabolic model and to decipher the altered metabolic characteristics. Predictions based on constraints-based flux analysis were mostly in excellent agreement with the experimental data. In silico knockout studies allowed prediction of new metabolic engineering strategies for the enhanced production of succinic acid. This genome-scale in silico model can serve as a platform for the systematic prediction of physiological responses of M. succiniciproducens to various environmental and genetic perturbations and consequently for designing rational strategies for strain improvement.     

A genome‐scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism

Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health‐promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome‐scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome‐scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses.     

Dynamic flux responses in riboflavin overproducing Bacillus subtilis to increasing glucose limitation in fed‐batch culture

How do intracellular fluxes respond to dynamically increasing glucose limitation when the physiology changes from strong overflow metabolism near to exclusively maintenance metabolism? Here we investigate this question in a typical industrial, glucose‐limited fed‐batch cultivation with a riboflavin overproducing Bacillus subtilis strain. To resolve dynamic flux changes, a novel approach to ¹³C flux analysis was developed that is based on recording ¹³C labeling patterns in free intracellular amino acids. Fluxes are then estimated with stationary flux ratio and iterative isotopomer balancing methods, for which a decomposition of the process into quasi‐steady states and estimation of isotopic steady state ¹³C labeling patterns was necessary. By this approach, we achieve a temporal resolution of 30–60 min that allows us to resolve the slow metabolic transients that typically occur in such cultivations. In the late process phase we found, most prominently, almost exclusive respiratory metabolism, significantly increased pentose phosphate pathway contribution and a strongly decreased futile cycle through the PEP carboxykinase. As a consequence, higher catabolic NADPH formation occurred than was necessary to satisfy the anabolic demands, suggesting a transhydrogenase‐like mechanism to close the balance of reducing equivalents. Biotechnol. Bioeng. 2010. 105: 795–804. © 2009 Wiley Periodicals, Inc.     

Segmented linear modeling of CHO fed‐batch culture and its application to large scale production

We describe a systematic approach to model CHO metabolism during biopharmaceutical production across a wide range of cell culture conditions. To this end, we applied the metabolic steady state concept. We analyzed and modeled the production rates of metabolites as a function of the specific growth rate. First, the total number of metabolic steady state phases and the location of the breakpoints were determined by recursive partitioning. For this, the smoothed derivative of the metabolic rates with respect to the growth rate were used followed by hierarchical clustering of the obtained partition. We then applied a piecewise regression to the metabolic rates with the previously determined number of phases. This allowed identifying the growth rates at which the cells underwent a metabolic shift. The resulting model with piecewise linear relationships between metabolic rates and the growth rate did well describe cellular metabolism in the fed‐batch cultures. Using the model structure and parameter values from a small‐scale cell culture (2 L) training dataset, it was possible to predict metabolic rates of new fed‐batch cultures just using the experimental specific growth rates. Such prediction was successful both at the laboratory scale with 2 L bioreactors but also at the production scale of 2000 L. This type of modeling provides a flexible framework to set a solid foundation for metabolic flux analysis and mechanistic type of modeling. Biotechnol. Bioeng. 2017;114: 785–797. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.     

Peak antibody production is associated with increased oxidative metabolism in an industrially relevant fed‐batch CHO cell culture

Cell metabolism can vary considerably over the course of a typical fed‐batch antibody production process. However, the intracellular pathway alterations associated with various phases of growth and antibody production have yet to be fully elucidated using industrially relevant production hosts. Therefore, we performed ¹³C labeling experiments and metabolic flux analysis (MFA) to characterize CHO cell metabolism during four separate phases of a fed‐batch culture designed to closely represent industrial process conditions. First, we found that peak specific growth rate was associated with high lactate production and minimal TCA cycling. Conversely, we found that lactate metabolism switched from net production to net consumption as the culture transitioned from peak growth to peak antibody production. During the peak antibody production phase, energy was primarily generated through oxidative phosphorylation, which was also associated with elevated oxidative pentose phosphate pathway (oxPPP) activity. Interestingly, as TCA cycling and antibody production reached their peaks, specific growth rate continued to diminish as the culture entered stationary phase. However, TCA cycling and oxPPP activity remained high even as viable cell density began to decline. Overall, we found that a highly oxidative state of metabolism corresponded with peak antibody production, whereas peak cell growth was characterized by a highly glycolytic metabolic state. Biotechnol. Bioeng. 2013; 110: 2013–2024. © 2013 Wiley Periodicals, Inc.     

Genome-scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture

A dynamic flux balance model based on a genome-scale metabolic network reconstruction is developed for in silico analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture. Metabolic engineering strategies previously identified for their enhanced steady-state biomass and/or ethanol yields are evaluated for fed-batch performance in glucose and glucose/xylose media. Dynamic analysis is shown to provide a single quantitative measure of fed-batch ethanol productivity that explicitly handles the possible tradeoff between the biomass and ethanol yields. Productivity optimization conducted to rank achievable fed-batch performance demonstrates that the genetic manipulation strategy and the fed-batch operating policy should be considered simultaneously. A library of candidate gene insertions is assembled and directly screened for their achievable ethanol productivity in fed-batch culture. A number of novel gene insertions with ethanol productivities identical to the best metabolic engineering strategies reported in previous studies are identified, thereby providing additional targets for experimental evaluation. The top performing gene insertions were substrate dependent, with the highest ranked insertions for glucose media yielding suboptimal performance in glucose/xylose media. The analysis results suggest that enhancements in biomass yield are most beneficial for the enhancement of fed-batch ethanol productivity by recombinant xylose utilizing yeast strains. We conclude that steady-state flux balance analysis is not sufficient to predict fed-batch performance and that the media, genetic manipulations, and fed-batch operating policy should be considered simultaneously to achieve optimal metabolite productivity.     

Microbial laboratory evolution in the era of genome‐scale science

Laboratory evolution studies provide fundamental biological insight through direct observation of the evolution process. They not only enable testing of evolutionary theory and principles, but also have applications to metabolic engineering and human health. Genome‐scale tools are revolutionizing studies of laboratory evolution by providing complete determination of the genetic basis of adaptation and the changes in the organism's gene expression state. Here, we review studies centered on four central themes of laboratory evolution studies: (1) the genetic basis of adaptation; (2) the importance of mutations to genes that encode regulatory hubs; (3) the view of adaptive evolution as an optimization process; and (4) the dynamics with which laboratory populations evolve.     

Dissecting the energy metabolism in Mycoplasma pneumoniae through genome‐scale metabolic modeling

Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint‐based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time‐dependent changes, albeit using a static model. By performing an in silico knock‐out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms.     

A comprehensive genome‐scale reconstruction of Escherichia coli metabolism—2011

The initial genome‐scale reconstruction of the metabolic network of Escherichia coli K‐12 MG1655 was assembled in 2000. It has been updated and periodically released since then based on new and curated genomic and biochemical knowledge. An update has now been built, named iJO1366, which accounts for 1366 genes, 2251 metabolic reactions, and 1136 unique metabolites. iJO1366 was (1) updated in part using a new experimental screen of 1075 gene knockout strains, illuminating cases where alternative pathways and isozymes are yet to be discovered, (2) compared with its predecessor and to experimental data sets to confirm that it continues to make accurate phenotypic predictions of growth on different substrates and for gene knockout strains, and (3) mapped to the genomes of all available sequenced E. coli strains, including pathogens, leading to the identification of hundreds of unannotated genes in these organisms. Like its predecessors, the iJO1366 reconstruction is expected to be widely deployed for studying the systems biology of E. coli and for metabolic engineering applications.