Dissecting N-Glycosylation Dynamics in Chinese Hamster Ovary Cells Fed-batch Cultures using Time Course Omics Analyses

Sumit, Madhuresh; Dolatshahi, Sepideh; Chu, An-Hsiang Adam; Cote, Kaffa; Scarcelli, John J.; Marshall, Jeffrey; Cornell, Richard J.; Weiss, Ron; Lauffenburger, Douglas A.; Mulukutla, Bhanu Chandra; Figueroa, Bruno

doi:10.1016/j.isci.2019.01.006

Cited by 51 publications

(55 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combining gene expression analysis with standard measurements taken throughout the culture like VCD, titer, and glycan distribution provides physiological insight into media supplement effects. While we performed targeted analysis on key genes, a broader‐omics approach would provide additional information about the interplay between the transcriptome, metabolome, and substrate availability as well as identify bottlenecks that affect culture growth, protein production, and glycan distribution (e.g., see Sumit et al, 2019). The main limitation of these approaches is the need for in‐depth experimental probing after the identification of potentially important factors.…”

Section: Discussionmentioning

confidence: 99%

Media supplementation for targeted manipulation of monoclonal antibody galactosylation and fucosylation

Wells

Song

Greer

et al. 2020

Biotech & Bioengineering

View full text Add to dashboard Cite

Monoclonal antibodies are critically important biologics as the largest class of molecules used to treat cancers, rheumatoid arthritis, and other chronic diseases. Antibody glycosylation is a critical quality attribute that has ramifications for patient safety and physiological efficacy-one that can be modified by such factors as media formulation and process conditions during production. Using a design-ofexperiments approach, we examined the effect of 2-F-peracetyl fucose (2FP), uridine, and galactose on cell growth and metabolism, titer, and gene expression of key glycosylation-related proteins, and report how the glycoform distribution changed from Days 4 to 7 in a batch process used for IgG1 production from Chinese hamster ovary cells. We observed major glycosylation changes upon supplement addition, where the addition of 2FP decreased antibody fucosylation by up to 48%, galactose addition increased galactosylation by up to 21%, and uridine addition decreased fucosylation and increased galactosylation by 6% and 2%, respectively. Despite having major effects on glycosylation, neither galactose nor 2FP significantly affected cell culture growth, metabolism, or titer. Uridine improved peak cell densities by 23% but also reduced titer by ∼30%. The supplements caused significant changes in gene expression by Day 4 of the cultures where 2FP addition significantly reduced fucosyltransferase 8 and nucleotide sugar transporter gene expression (by ∼2-fold), and uridine addition significantly increased expression of UDP-GlcNAcT (SLC35A3) and B4GALT1-6 genes (by 1.5-3-fold). These gene expression data alongside glycosylation, metabolic, and growth data improve our understanding of the cellular mechanisms affected by media supplementation and suggest approaches for modifying antibody glycosylation in antibody production processes. K E Y W O R D S antibody, Chinese hamster ovary, design of experiments 1 | INTRODUCTION Monoclonal antibodies (mAbs) have been very effective in treating numerous types of diseases and have become a driving force behind the growth of the biotherapeutic industry (Kunert & Reinhart, 2016). More than 86 mAbs and mAb derivatives have been approved by the US Food and Drug Administration (FDA) with ∼90 more currently in clinical trials (Kaplon & Reichert, 2019). In 2019, for example, of the 20 FDA-approved biologics, nine of them are antibodies, antibody fragments, or antibody conjugates (Morrison, 2020).

show abstract

Section: Discussionmentioning

confidence: 99%

Media supplementation for targeted manipulation of monoclonal antibody galactosylation and fucosylation

Wells

Song

Greer

et al. 2020

Biotech & Bioengineering

View full text Add to dashboard Cite

show abstract

“…The kinetic model used in this study has already been tested with experimental metabolic data of another process (Erklavec Zajec et al, 2021). To upgrade the model by the inclusion of transcriptomics, data from the work of Sumit et al (2019), containing extensive time‐course data of the transcriptomics, metabolomics and glycomics of CHO cells for two bioprocesses (CC and HD) in six time points, was included. The data is freely available in the Supporting information; Supplementary Excel data, Sheet “mean_RPKM” presents mean RPKM (reads per kilobase per million) values for 16,850 genes, Supplementary Excel data, Sheet “extra_Metabolites” contains concentrations of 314 extracellular metabolites, and Supplementary Excel data, Sheet “Aa&glycans,” contains data for nine glycans, expressed as the percentage of individual glycoform attached to mAb.…”

Section: Methodsmentioning

confidence: 99%

Incorporating RNA‐Seq transcriptomics into glycosylation‐integrating metabolic network modelling kinetics: Multiomic Chinese hamster ovary (CHO) cell bioreactors

Bezjak

Zajec

Baebler

et al. 2021

Biotech & Bioengineering

View full text Add to dashboard Cite

In this work, the kinetic model based on the previously developed metabolic and glycan reaction networks of the ovarian cells of the Chinese hamster ovary (CHO) cell line was improved by the inclusion of transcriptomic data that took into account the values of the RPKM gene (Reads per Kilobase of Exon per Million Reads Mapped). The transcriptomic (RNASeq) data were obtained together with metabolic and glycan data from the literature, and the concentrations with RPKM values were collected at several points in time from two fed‐batch processes. First, the fluxes were determined by regression analysis of the metabolic data, then these fluxes were corrected by using the fold change in gene expression as a measure of enzyme concentrations. Next, the corrected fluxes in the kinetic model were used to calculate the concentration profiles of the metabolites, and literature data were used to evaluate the predicted results of the model. Compared to other studies where the concentration profiles of CHO cell metabolites were described using a kinetic model without consideration of RNA‐Seq data to correct the fluxes, this model is unique. The additional integration of transcriptomic data led to better predictions of metabolic concentrations in the fed‐batch process, which is a significant improvement of the modelling technique used.

show abstract

“…where Vol = 25 is the volume of the Golgi apparatus [21,23], [ 9] = 55 represents the Man9 concentration at the entrance of the Golgi apparatus.…”

Section: Kinetic Model Buildingmentioning

confidence: 99%

“…In the bioreactor culture system, the glycosylation process can be affected by both chemical and physical stimuli for the same cell line [3,7,8]. These stimuli can change the cell metabolism, such as the glycolytic pathway, purine/pyrimidine metabolism or the TCA cycle, which influence the synthesis and function of nucleotide donors, glycosyltransferases and glycosidase, and further affect the biocatalytic reactions of glycosylation [9]. The limitation of glucose, glutamine [10], galactose [11], manganese [12,13], uridine, ManNAc and sialic acid [3] in culture media and feed additions results in a reduction in protein productivity, glycosylation site occupancy, UDP-Gal, UDP-GlcNAc and Neu5Ac synthesis.…”

Section: Introductionmentioning

confidence: 99%

mAb Production Modeling and Design Space Evaluation Including Glycosylation Process

Ierapetritou

2021

Processes

View full text Add to dashboard Cite

Due to high demand, monoclonal antibodies (mAbs) production needs to be efficient, as well as maintaining a high product quality. Quality by design (QbD) via predictive process modeling greatly facilitates process understanding and can be used to adjust process parameters to further improve the unit operations. In this work, mechanistic and dynamic kriging models are developed to capture the protein productivity and glycan fractions under different temperatures and pH levels. The design of experiments is used to generate input and output data for model training. The dynamic kriging model shows good performance in capturing the dynamic profiles of cell cultures and glycosylation using only limited input data. The developed model is further used for feasibility analysis, and successfully identifies the operating design space, maintaining high productivity and guaranteed product quality.

show abstract

Dissecting N-Glycosylation Dynamics in Chinese Hamster Ovary Cells Fed-batch Cultures using Time Course Omics Analyses

Cited by 51 publications

References 52 publications

Media supplementation for targeted manipulation of monoclonal antibody galactosylation and fucosylation

Media supplementation for targeted manipulation of monoclonal antibody galactosylation and fucosylation

Incorporating RNA‐Seq transcriptomics into glycosylation‐integrating metabolic network modelling kinetics: Multiomic Chinese hamster ovary (CHO) cell bioreactors

mAb Production Modeling and Design Space Evaluation Including Glycosylation Process

Contact Info

Product

Resources

About