CHO cell productivity improvement by genome-scale modeling and pathway analysis: Application to feed supplements

Huang, Zhuangrong; Xu, Jinying; Yongky, Andrew; Morris, Caitlin; Polanco, Ashli; Reily, Michael D.; Borys, Michael; Li, Zheng Jian; Yoon, Seongkyu

doi:10.1016/j.bej.2020.107638

Cited by 36 publications

(39 citation statements)

References 64 publications

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“…Each sample was run four times with different combinations of chromatography methods and ionization modes (positive and negative). The chromatographic separation was performed with a binary pump HPLC system (1260 Infinity, Agilent, Santa Clara, CA, USA) using either a HILIC column (Phenomenex Luna NH2, Torrance, CA, USA) or a reverse-phase column (Phenomenex Synergi Hydro-RP, Torrance, CA, USA) as described previously [19]. The gradient methods and mobile phases are described in Supplementary Methods.…”

Section: Untargeted Lc-msmentioning

confidence: 99%

“…Dean and Reddy compared metabolic fluxes between a low-and a high-productivity cell line using isotope labeling experiments, identifying differences in glycolysis, the TCA cycle, and lactate metabolism; Templeton et al found similar differences among multiple low-and high-productivity cell lines [17,18]. Recently, Huang et al used targeted metabolomics data in conjunction with a genome scale modeling approach to optimize media for enhanced qP [19]. Qian et al also used targeted metabolomics to connect a decrease in qP over time to elevated lipid oxidation in unstable cell lines [20].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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

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Section: Untargeted Lc-msmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…[ 2,37 ] Since medium components, nutritional supplements, and culture additives directly affect cellular metabolism, [ 38 ] CBM can be a suitable approach to analyze the effect of medium components on cell growth and productivity. CBM‐based methods have been extensively used to analyze and optimize the culture medium and develop appropriate feeding strategies to overproduce a wide range of products such as recombinant proteins, [ 39–43 ] biofuels, [ 44–46 ] lipids, [ 47,48 ] chemicals, [ 49–51 ] and foods. [ 23 ] Swayambhu et al.…”

Section: Cbm: a Mechanistic‐driven Approachmentioning

confidence: 99%

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

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Recent noteworthy advances in developing high‐performing microbial and mammalian strains have enabled the sustainable production of bio‐economically valuable substances such as bio‐compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass‐production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge‐ and data‐driven modeling methods have received much attention. Constraint‐based modeling (CBM) is a knowledge‐driven mathematical approach that has been widely used in fermentation analysis and optimization due to its ability to predict the cellular phenotype from genotype through high‐throughput means. On the other hand, machine learning (ML) is a data‐driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint‐based models in a reciprocal manner when one is used as a pre‐step of another. As a result, a more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

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“…Since medium components, nutritional supplements, and culture additives directly affect cellular metabolism [53], CBM can be a suitable approach to analyze the effect of medium components on cell growth and productivity. CBM-based methods have been extensively used to analyze and optimize the culture medium and develop appropriate feeding strategies to overproduce a wide range of products such as recombinant proteins [54][55][56][57][58], biofuels [59][60][61], lipids [62,63], chemicals [64][65][66], and foods [36]. For a more detailed example, Swayambhu et al used FBA and its derivatives to identify gene deletion/overexpression targets and culture medium components to enhance the production of siderophore compounds in recombinant Escherichia coli .…”

Section: Instances Of Cbm Applications In Fermentation Optimizationmentioning

confidence: 99%

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

Isp

Lewis

et al. 2021

Preprint

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Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable models are produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

show abstract

CHO cell productivity improvement by genome-scale modeling and pathway analysis: Application to feed supplements

Cited by 36 publications

References 64 publications

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

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

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

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