Metabolic flux rearrangement in the amino acid metabolism reduces ammonia stress in the α1-antitrypsin producing human AGE1.HN cell line

Priesnitz, Christian; Niklas, Jens; Rose, Thomas; Sandig, Volker; Heinzle, Elmar

doi:10.1016/j.ymben.2012.01.001

Cited by 10 publications

(14 citation statements)

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“…We chose 6 data sets where equal units were given or could be calculated for comparison, thereof three publications on the metabolism of human AGE1.HN cells [9,10,36], one publication on HEK293 cells [7], and one on MDCK cells [13]. The majority of concentration profiles of CR.pIX cultivations are similar to concentration profiles obtained for other transformed cell lines studied in the past.…”

Section: Resultsmentioning

confidence: 99%

“…All rates are given in μmol/gDW/h. a from [9]; b from [36]; c from [10]; d from [7]; e from [13,14]. …”

Section: Resultsmentioning

confidence: 99%

“…For example, the human cell lines HEK293 [7,8] and AGE1.HN [9,10], other mammalian cells like CHO [11], BHK [12], MDCK [13,14] and hybridoma cells [15,16] or insect cell lines like Sf9 [17,18] have been studied because of their relevance for the production of biopharmaceuticals. For all these cell lines, the two main pathways for energy generation and precursor supply were found to be glucose catabolism via glycolysis and glutamine catabolism via the TCA cycle, referred to as glutaminolysis [19-21].…”

Section: Introductionmentioning

confidence: 99%

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The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis

et al. 2014

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BackgroundIn human vaccine manufacturing some pathogens such as Modified Vaccinia Virus Ankara, measles, mumps virus as well as influenza viruses are still produced on primary material derived from embryonated chicken eggs. Processes depending on primary cell culture, however, are difficult to adapt to modern vaccine production. Therefore, we derived previously a continuous suspension cell line, AGE1.CR.pIX, from muscovy duck and established chemically-defined media for virus propagation.ResultsTo better understand vaccine production processes, we developed a stoichiometric model of the central metabolism of AGE1.CR.pIX cells and applied flux variability and metabolic flux analysis. Results were compared to literature dealing with mammalian and insect cell culture metabolism focusing on the question whether cultured avian cells differ in metabolism. Qualitatively, the observed flux distribution of this avian cell line was similar to distributions found for mammalian cell lines (e.g. CHO, MDCK cells). In particular, glucose was catabolized inefficiently and glycolysis and TCA cycle seem to be only weakly connected.ConclusionsA distinguishing feature of the avian cell line is that glutaminolysis plays only a minor role in energy generation and production of precursors, resulting in low extracellular ammonia concentrations. This metabolic flux study is the first for a continuous avian cell line. It provides a basis for further metabolic analyses to exploit the biotechnological potential of avian and vertebrate cell lines and to develop specific optimized cell culture processes, e.g. vaccine production processes.

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Section: Resultsmentioning

confidence: 99%

“…All rates are given in μmol/gDW/h. a from [9]; b from [36]; c from [10]; d from [7]; e from [13,14]. …”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis

et al. 2014

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“…On the basis of these findings it was possible to redesign cultivation in two ways to increase the production of human α 1 ‐antitrypsin. The first was to limit glucose supply by controlled feeding and the second to add quercitin, an inhibitor for glucose uptake, to the culture 82,83. This example shows that a thorough fluxomic analysis directly leads to ideas for the improvement of production processes.…”

Section: Application Areasmentioning

confidence: 99%

Isotope labeling experiments in metabolomics and fluxomics

Klein

Heinzle

2012

WIREs Mechanisms of Disease

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Metabolomics, the study of all the small molecules in and outside a cell and fluxomics, comprising all conversion rates in a cell, are increasingly used in fundamental and applied sciences to unravel structures and activities of cellular networks and their regulation, to investigate mechanisms of diseases and toxicity, and to improve producing strains among other applications. For both fluxomics and metabolomics the application of isotopes became almost indispensable. Their use in these techniques is discussed, focusing primarily on studies applying stable isotopes and using mass spectrometry. This includes the underlying principles, experimental and computational methods used, and examples of application. WIREs Syst Biol Med 2012 doi: 10.1002/wsbm.1167This article is categorized under: Analytical and Computational Methods > Analytical Methods Models of Systems Properties and Processes > Cellular Models Laboratory Methods and Technologies > Metabolomics

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“…A widespread practice is the development of a dynamic model for cell growth and a constraint‐based model for intracellular metabolism. For instance, for the human suspension cell line AGE1.HN, a simple model for cell growth (Rath et al, 2014) and a constraint‐based model to describe intracellular metabolism (Niklas, Priesnitz, Rose, Sandig, & Heinzle, 2012; Priesnitz, Niklas, Rose, Sandig, & Heinzle, 2012) was established. So far, most models also focus only on a small part of intracellular pathways, that is, glycolysis (König, Bulik, & Holzhütter, 2012; Noguchi et al, 2013) and TCA (Bazil, Buzzard, & Rundell, 2010; Nazaret, Heiske, Thurley, & Mazat, 2009).…”

Section: Introductionmentioning

confidence: 99%

A dynamic model linking cell growth to intracellular metabolism and extracellular by‐product accumulation

Ramos

Rath

Genzel

et al. 2020

Biotech & Bioengineering

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Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1‐antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by‐products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α‐ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.

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Metabolic flux rearrangement in the amino acid metabolism reduces ammonia stress in the α1-antitrypsin producing human AGE1.HN cell line

Cited by 10 publications

References 68 publications

The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis

The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis

Isotope labeling experiments in metabolomics and fluxomics

A dynamic model linking cell growth to intracellular metabolism and extracellular by‐product accumulation

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