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联合多元统计和通量平衡分析揭示了中国仓鼠卵巢细胞培养物生长和生产力的培养基瓶颈。

Combined multivariate statistical and flux balance analyses uncover media bottlenecks to the growth and productivity of Chinese hamster ovary cell cultures.

机构信息

Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.

School of Chemical Engineering, Sungkyunkwan University, Suwon, Korea.

出版信息

Biotechnol Bioeng. 2022 Jul;119(7):1740-1754. doi: 10.1002/bit.28104. Epub 2022 Apr 25.

DOI:10.1002/bit.28104
PMID:35435243
Abstract

Chinese hamster ovary (CHO) cells are widely used for producing recombinant proteins. To enhance their productivity and product quality, media reformulation has been a key strategy, albeit with several technical challenges, due to the myriad of complex molecular mechanisms underlying media effects on culture performance. Thus, it is imperative to characterize metabolic bottlenecks under various media conditions systematically. To do so, we combined partial least square regression (PLS-R) with the flux balance analysis of a genome-scale metabolic model to elucidate the physiological states and metabolic behaviors of human alpha-1 antitrypsin producing CHO-DG44 cells grown in one commercial and another two in-house media under development. At the onset, PLS-R was used to identify metabolite exchanges that were correlated to specific growth and productivity. Then, by comparing metabolic states described by resultant flux distributions under two of the media conditions, we found suboptimal level of four nutrients and two metabolic wastes, which plausibly hindered cellular growth and productivity; mechanistically, lactate and ammonia recycling were modulated by glutamine and asparagine metabolisms in the media conditions, and also by hitherto unsuspected folate and choline supplements. Our work demonstrated how multivariate statistical analysis can be synergistically combined with metabolic modeling to uncover the mechanistic elements underlying differing media performance. It thus paved the way for the systematic identification of nutrient targets for medium reformulation to enhance recombinant protein production in CHO cells.

摘要

中国仓鼠卵巢(CHO)细胞广泛用于生产重组蛋白。为了提高它们的生产力和产品质量,培养基的重新配方是一项关键策略,尽管存在一些技术挑战,因为培养基对培养性能的影响涉及到许多复杂的分子机制。因此,系统地表征各种培养基条件下的代谢瓶颈至关重要。为此,我们将偏最小二乘回归(PLS-R)与基于基因组规模代谢模型的通量平衡分析相结合,阐明了在一种商业培养基和两种正在开发的内部培养基中生长的人α-1 抗胰蛋白酶产生 CHO-DG44 细胞的生理状态和代谢行为。首先,PLS-R 用于识别与特定生长和生产力相关的代谢物交换。然后,通过比较两种培养基条件下的通量分布描述的代谢状态,我们发现有四种营养物质和两种代谢废物的水平不理想,这可能阻碍了细胞的生长和生产力;从机制上讲,谷氨酰胺和天冬酰胺代谢调节了培养基条件下的乳酸和氨的再循环,也调节了先前未被怀疑的叶酸和胆碱补充。我们的工作展示了多元统计分析如何与代谢建模协同结合,以揭示不同培养基性能背后的机制因素。因此,为系统地确定营养物质目标以改进 CHO 细胞中的重组蛋白生产铺平了道路。

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