Identifying metabolic features and engineering targets for productivity improvement in CHO cells by integrated transcriptomics and genome-scale metabolic model
Identifying metabolic features and engineering targets for productivity improvement in CHO cells by integrated transcriptomics and genome-scale metabolic model
复制标题
通过整合转录组学和基因组尺度代谢模型鉴定CHO细胞的代谢特征和提高生产力的工程目标
DOI：
10.1016/j.bej.2020.107624
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发表时间：
2020-07-15
影响因子：
3.9
通讯作者：
Yoon, Seongkyu
中科院分区：
文献类型：
--
作者：
Huang, Zhuangrong;Yoon, Seongkyu
在这项研究中，我们提出了一个综合的系统生物学方法来阐明细胞代谢的关键特征，在中国仓鼠卵巢（CHO）细胞生产单克隆抗体（mAb）。在高和低生产者在分批条件下的细胞代谢进行了询问动态内和细胞之间。首先，使用转录组学分析来研究与mAb生产率增加相关的细胞内代谢途径的时程变化。第二，在每个生长阶段寻求高和低生产者之间的差异调节途径。在高产菌株的生长后期发现了几条上调的途径，包括柠檬酸循环、氧化磷酸化和磷酸戊糖途径。通过基因组规模CHO模型估计的细胞内通量分布进一步分析这些活性。我们的研究结果表明，这些关键途径被确定为高mAb生产的特征，不仅是高产细胞系，而且是mAb生产细胞培养物中的动态现象。这项研究表明，整合转录组学和通量分析的方法可以更好地了解与mAb生产率相关的细胞代谢。反过来，这允许识别代谢瓶颈和用于细胞系开发和工艺优化的潜在工程目标。
In this study, we presented an integrated systems biology approach to elucidate the key characteristics of cellular metabolism in Chinese hamster ovary (CHO) cells producing monoclonal antibodies (mAb). The cellular metabolism in high and low producers under batch conditions was interrogated dynamically both within and among cells. First, transcriptomics analysis was used to study the time-course change in the metabolic pathway within cells that was correlated with mAb productivity increase. Second, differentially regulated pathways between high and low producers were sought at each growth phase. Several up-regulated pathways were identified in the high producer at the late growth phase, including citrate cycle, oxidative phosphorylation, and pentose phosphate pathway. These activities were further analyzed by intracellular flux distributions estimated through a genome-scale CHO model. Our results revealed that these key pathways are identified to be characteristics of high mAb production, not only for the high-producing cell line but also a dynamic phenomenon in mAb-producing cell cultures. This study showed that the approach of integrating transcriptomics and flux analysis leads to a better understanding of cellular metabolism related to mAb productivity. In turn, this allows for the identification of metabolic bottlenecks and potential engineering targets for cell line development and process optimization.