Dataset Information

Characterization of metabolic responses, genetic variations, and microsatellite instability in ammonia-stressed CHO cells grown in fed-batch cultures.

ABSTRACT:

Background

As bioprocess intensification has increased over the last 30?years, yields from mammalian cell processes have increased from 10's of milligrams to over 10's of grams per liter. Most of these gains in productivity can be attributed to increasing cell densities within bioreactors. As such, strategies have been developed to minimize accumulation of metabolic wastes, such as lactate and ammonia. Unfortunately, neither cell growth nor biopharmaceutical production can occur without some waste metabolite accumulation. Inevitably, metabolic waste accumulation leads to decline and termination of the culture. While it is understood that the accumulation of these unwanted compounds imparts a suboptimal culture environment, little is known about the genotoxic properties of these compounds that may lead to global genome instability. In this study, we examined the effects of high and moderate extracellular ammonia on the physiology and genomic integrity of Chinese hamster ovary (CHO) cells.

Results

Through whole genome sequencing, we discovered 2394 variant sites within functional genes comprised of both single nucleotide polymorphisms and insertion/deletion mutations as a result of ammonia stress with high or moderate impact on functional genes. Furthermore, several of these de novo mutations were found in genes whose functions are to maintain genome stability, such as Tp53, Tnfsf11, Brca1, as well as Nfkb1. Furthermore, we characterized microsatellite content of the cultures using the CriGri-PICR Chinese hamster genome assembly and discovered an abundance of microsatellite loci that are not replicated faithfully in the ammonia-stressed cultures. Unfaithful replication of these loci is a signature of microsatellite instability. With rigorous filtering, we found 124 candidate microsatellite loci that may be suitable for further investigation to determine whether these loci may be reliable biomarkers to predict genome instability in CHO cultures.

Conclusion

This study advances our knowledge with regards to the effects of ammonia accumulation on CHO cell culture performance by identifying ammonia-sensitive genes linked to genome stability and lays the foundation for the development of a new diagnostic tool for assessing genome stability.

SUBMITTER: Chitwood DG

PROVIDER: S-EPMC7791692 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Characterization of metabolic responses, genetic variations, and microsatellite instability in ammonia-stressed CHO cells grown in fed-batch cultures.

Chitwood Dylan G DG Wang Qinghua Q Elliott Kathryn K Bullock Aiyana A Jordana Dwon D Li Zhigang Z Wu Cathy C Harcum Sarah W SW Saski Christopher A CA

BMC biotechnology 20210108 1

<h4>Background</h4>As bioprocess intensification has increased over the last 30 years, yields from mammalian cell processes have increased from 10's of milligrams to over 10's of grams per liter. Most of these gains in productivity can be attributed to increasing cell densities within bioreactors. As such, strategies have been developed to minimize accumulation of metabolic wastes, such as lactate and ammonia. Unfortunately, neither cell growth nor biopharmaceutical production can occur without ...[more]

PMID: 33419422

Similar Datasets

Project description:Although process intensification by continuous operation has been successfully applied in the chemical industry, the biopharmaceutical industry primarily uses fed-batch, rather than continuous or perfusion methods, to produce stable monoclonal antibodies (mAbs) from Chinese hamster ovary (CHO) cells. Conventional fed-batch bioreactors may start with an inoculation viable cell density (VCD) of ~0.5 × 106 cells/mL. Increasing the inoculation VCD in the fed-batch production bioreactor (referred to as N stage bioreactor) to 2-10 × 106 cells/mL by introducing perfusion operation or process intensification at the seed step (N-1 step) prior to the production bioreactor has recently been used because it increases manufacturing output by shortening cell culture production duration. In this study, we report that increasing the inoculation VCD significantly improved the final titer in fed-batch production within the same 14-day duration for 3 mAbs produced by 3 CHO GS cell lines. We also report that other non-perfusion methods at the N-1 step using either fed batch or batch mode with enriched culture medium can similarly achieve high N-1 final VCD of 22-34 × 106 cells/mL. These non-perfusion N-1 seeds supported inoculation of subsequent production fed-batch production bioreactors at increased inoculation VCD of 3-6 × 106 cells/mL, where these achieved titer and product quality attributes comparable to those inoculated using the perfusion N-1 seeds demonstrated in both 5-L bioreactors, as well as scaled up to 500-L and 1000-L N-stage bioreactors. To operate the N-1 step using batch mode, enrichment of the basal medium was critical at both the N-1 and subsequent intensified fed-batch production steps. The non-perfusion N-1 methodologies reported here are much simpler alternatives in operation for process development, process characterization, and large-scale commercial manufacturing compared to perfusion N-1 seeds that require perfusion equipment, as well as preparation and storage vessels to accommodate large volumes of perfusion media. Although only 3 stable mAbs produced by CHO cell cultures are used in this study, the basic principles of the non-perfusion N-1 seed strategies for shortening seed train and production culture duration or improving titer should be applicable to other protein production by different mammalian cells and other hosts at any scale biologics facilities.

Project description:Introduction: Hybrid modeling combining First-Principles with machine learning is becoming a pivotal methodology for Biopharma 4.0 enactment. Chinese Hamster Ovary (CHO) cells, being the workhorse for industrial glycoproteins production, have been the object of several hybrid modeling studies. Most previous studies pursued a shallow hybrid modeling approach based on three-layered Feedforward Neural Networks (FFNNs) combined with macroscopic material balance equations. Only recently, the hybrid modeling field is incorporating deep learning into its framework with significant gains in descriptive and predictive power. Methods: This study compares, for the first time, deep and shallow hybrid modeling in a CHO process development context. Data of 24 fed-batch cultivations of a CHO-K1 cell line expressing a target glycoprotein, comprising 30 measured state variables over time, were used to compare both methodologies. Hybrid models with varying FFNN depths (3-5 layers) were systematically compared using two training methodologies. The classical training is based on the Levenberg-Marquardt algorithm, indirect sensitivity equations and cross-validation. The deep learning is based on the Adaptive Moment Estimation Method (ADAM), stochastic regularization and semidirect sensitivity equations. Results and conclusion: The results point to a systematic generalization improvement of deep hybrid models over shallow hybrid models. Overall, the training and testing errors decreased by 14.0% and 23.6% respectively when applying the deep methodology. The Central Processing Unit (CPU) time for training the deep hybrid model increased by 31.6% mainly due to the higher FFNN complexity. The final deep hybrid model is shown to predict the dynamics of the 30 state variables within the error bounds in every test experiment. Notably, the deep hybrid model could predict the metabolic shifts in key metabolites (e.g., lactate, ammonium, glutamine and glutamate) in the test experiments. We expect deep hybrid modeling to accelerate the deployment of high-fidelity digital twins in the biopharma sector in the near future.

Dataset Information

Characterization of metabolic responses, genetic variations, and microsatellite instability in ammonia-stressed CHO cells grown in fed-batch cultures.

Background

Results

Conclusion

Publications

Characterization of metabolic responses, genetic variations, and microsatellite instability in ammonia-stressed CHO cells grown in fed-batch cultures.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets