Krambeck Frederick J, Bennun Sandra V, Andersen Mikael R, Betenbaugh Michael J
Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
ReacTech Inc., Alexandria, Virginia, United States of America.
PLoS One. 2017 May 9;12(5):e0175376. doi: 10.1371/journal.pone.0175376. eCollection 2017.
The Chinese hamster ovary (CHO) cell is the gold standard for manufacturing of glycosylated recombinant proteins for production of biotherapeutics. The similarity of its glycosylation patterns to the human versions enable the products of this cell line favorable pharmacokinetic properties and lower likelihood of causing immunogenic responses. Because glycan structures are the product of the concerted action of intracellular enzymes, it is difficult to predict a priori how the effects of genetic manipulations alter glycan structures of cells and therapeutic properties. For that reason, quantitative models able to predict glycosylation have emerged as promising tools to deal with the complexity of glycosylation processing. For example, an earlier version of the same model used in this study was used by others to successfully predict changes in enzyme activities that could produce a desired change in glycan structure. In this study we utilize an updated version of this model to provide a comprehensive analysis of N-glycosylation in ten Chinese hamster ovary (CHO) cell lines that include a wild type parent and nine mutants of CHO, through interpretation of previously published mass spectrometry data. The updated N-glycosylation mathematical model contains up to 50,605 glycan structures. Adjusting the enzyme activities in this model to match N-glycan mass spectra produces detailed predictions of the glycosylation process, enzyme activity profiles and complete glycosylation profiles of each of the cell lines. These profiles are consistent with biochemical and genetic data reported previously. The model-based results also predict glycosylation features of the cell lines not previously published, indicating more complex changes in glycosylation enzyme activities than just those resulting directly from gene mutations. The model predicts that the CHO cell lines possess regulatory mechanisms that allow them to adjust glycosylation enzyme activities to mitigate side effects of the primary loss or gain of glycosylation function known to exist in these mutant cell lines. Quantitative models of CHO cell glycosylation have the potential for predicting how glycoengineering manipulations might affect glycoform distributions to improve the therapeutic performance of glycoprotein products.
中国仓鼠卵巢(CHO)细胞是生产用于生物治疗的糖基化重组蛋白的金标准。其糖基化模式与人源糖基化模式的相似性使得该细胞系生产的产品具有良好的药代动力学特性,且引发免疫原性反应的可能性较低。由于聚糖结构是细胞内多种酶协同作用的产物,因此很难预先预测基因操作会如何改变细胞的聚糖结构以及治疗特性。基于此,能够预测糖基化的定量模型已成为应对糖基化过程复杂性的有前景的工具。例如,本研究中使用的同一模型的早期版本被其他研究人员用于成功预测酶活性的变化,这些变化能够使聚糖结构产生预期的改变。在本研究中,我们利用该模型的更新版本,通过解读先前发表的质谱数据,对十种中国仓鼠卵巢(CHO)细胞系(包括一个野生型亲本和九个CHO突变体)的N - 糖基化进行全面分析。更新后的N - 糖基化数学模型包含多达50,605种聚糖结构。通过调整该模型中的酶活性以匹配N - 聚糖质谱,可对每个细胞系的糖基化过程、酶活性谱和完整的糖基化谱进行详细预测。这些图谱与先前报道的生化和遗传数据一致。基于模型的结果还预测了这些细胞系此前未发表的糖基化特征,表明糖基化酶活性的变化比仅由基因突变直接导致的变化更为复杂。该模型预测,CHO细胞系拥有调节机制,使其能够调整糖基化酶活性,以减轻这些突变细胞系中已知存在的糖基化功能主要丧失或获得所带来的副作用。CHO细胞糖基化的定量模型具有预测糖基工程操作如何影响糖型分布以改善糖蛋白产品治疗性能的潜力。