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Metabolic responses of CHO cells to limitation of key amino acids

天冬酰胺 生物化学 中国仓鼠卵巢细胞 天冬酰胺合成酶 谷氨酰胺 丝氨酸 氨基酸 新陈代谢 生物 细胞内 代谢途径 丙氨酸 化学 受体
作者
Tiago M. Duarte,Nuno Carinhas,Laura Calvo‐Barreiro,Manuel J.T. Carrondo,Paula M. Alves,Ana P. Teixeira
出处
期刊:Biotechnology and Bioengineering [Wiley]
卷期号:111 (10): 2095-2106 被引量:88
标识
DOI:10.1002/bit.25266
摘要

Chinese hamster ovary (CHO) cells are the predominant host for production of therapeutic glycoproteins. In particular, the glutamine-synthetase (GS) expression system has been widely used in the biopharmaceutical industry for efficient selection of high-yielding clones. However, much remains unclear on how metabolic wiring affects culture performance. For instance, asparagine and serine have been observed to be the largest nitrogen sources taken up by GS-CHO cells, but their roles in biosynthesis and energy generation are poorly understood. In this work, a comprehensive profiling of extracellular metabolites coupled with an analysis of intracellular label distributions after 1-(13) C-pyruvate supplementation were used to trace metabolic rearrangements in different scenarios of asparagine and serine availability. The absence of asparagine in the medium caused growth arrest, and was associated with a dramatic increase in pyruvate uptake, a higher ratio of pyruvate carboxylation to dehydrogenation and an inability for de novo asparagine synthesis. The release of ammonia and amino acids such as aspartate, glutamate, and alanine were deeply impacted. This confirms asparagine to be essential for these GS-CHO cells as the main source of intracellular nitrogen as well as having an important anaplerotic role in TCA cycle activity. In turn, serine unavailability also negatively affected culture growth while triggering its de novo synthesis, confirmed by label incorporation coming from pyruvate, and reduced glycine and formate secretion congruent with its role as a precursor in the metabolism of one-carbon units. Overall, these results unfold important insights into GS-CHO cells metabolism that lay a clearer basis for fine-tuning bioprocess optimization.
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