National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland.
J Biotechnol. 2011 Jan 20;151(2):159-65. doi: 10.1016/j.jbiotec.2010.11.016. Epub 2010 Nov 27.
Improving the rate of recombinant protein production in Chinese hamster ovary (CHO) cells is an important consideration in controlling the cost of biopharmaceuticals. We present the first predictive model of productivity in CHO bioprocess culture based on gene expression profiles. The dataset used to construct the model consisted of transcriptomic data from 70 stationary phase, temperature-shifted CHO production cell line samples, for which the cell-specific productivity had been determined. These samples were utilised to investigate gene expression over a range of high to low monoclonal antibody and fc-fusion-producing CHO cell lines. We utilised a supervised regression algorithm, partial least squares (PLS) incorporating jackknife gene selection, to produce a model of cell-specific productivity (Qp) capable of predicting Qp to within 4.44 pg/cell/day root mean squared error in cross model validation (RMSE(CMV)). The final model, consisting of 287 genes, was capable of accurately predicting Qp in a further panel of 10 additional samples which were incorporated as an independent validation. Several of the genes constituting the model are linked with biological processes relevant to protein metabolism.
提高中国仓鼠卵巢（CHO）细胞中重组蛋白的生产效率是控制生物制药成本的一个重要考虑因素。我们提出了第一个基于基因表达谱的 CHO 生物工艺培养生产力的预测模型。用于构建模型的数据集中包含了 70 个处于静止期、温度转换的 CHO 生产细胞系样本的转录组数据，这些样本的细胞特异性生产率已经确定。这些样本被用于研究在一系列高到低的单克隆抗体和 fc 融合产生的 CHO 细胞系中基因表达情况。我们利用有监督回归算法（偏最小二乘法（PLS））结合自举基因选择，产生了一个能够在交叉模型验证（RMSE(CMV)）中将 Qp 预测到 4.44 pg/细胞/天均方根误差内的细胞特异性生产率（Qp）模型。最终的模型由 287 个基因组成，能够准确预测另外 10 个独立验证样本的 Qp。构成该模型的几个基因与与蛋白质代谢相关的生物学过程有关。
