Abstract
Chinese hamster ovary (CHO) cells are the primary host used for biopharmaceutical protein production. The engineering of CHO cells to produce higher amounts of biopharmaceuticals has been highly dependent on empirical approaches, but recent high-throughput “omics” methods are changing the situation in a rational manner. Omics data analyses using gene expression or metabolite profiling make it possible to identify key genes and metabolites in antibody production. Systematic omics approaches using different types of time-series data are expected to further enhance understanding of cellular behaviours and molecular networks for rational design of CHO cells. This study developed a systematic method for obtaining and analysing time-dependent intracellular and extracellular metabolite profiles, RNA-seq data (enzymatic mRNA levels) and cell counts from CHO cell cultures to capture an overall view of the CHO central metabolic pathway (CMP). We then calculated correlation coefficients among all the profiles and visualised the whole CMP by heatmap analysis and metabolic pathway mapping, to classify genes and metabolites together. This approach provides an efficient platform to identify key genes and metabolites in CHO cell culture.
Highlights
High demand for mammalian-derived biopharmaceuticals continues to stimulate the development of cell lines and bioprocess conditions
The differences in the maximum number of cells and the slopes of the growth curves were dependent on the amount of lactate added, so we conclude there was a definite causal relationship between cell growth and lactate addition. This tendency held true for glucose consumption and lactate accumulation; the time-dependent changes in the concentrations of glucose and lactate were largest in the case of the control, and the shapes of the curves correlated strongly with the amount of lactate added. These results suggest that the effect of lactate addition on cell growth arose from glucose and lactate metabolism
We focused on the central metabolic pathway (CMP), which includes glycolysis, the TCA cycle, the pentose phosphate pathway, and amino acid metabolism related to these pathways, for subsequent measurements
Summary
High demand for mammalian-derived biopharmaceuticals continues to stimulate the development of cell lines and bioprocess conditions. Recent developments in omics technologies have resulted in understanding host cell culture state and rational improvement of industrial mammalian cell lines by regulating growth, death and other cellular pathways through manipulation of media, feeding strategies, and other process parameters[2]. Software tools including Paintomics[13], INMEX14, and MultiAlign[15] were developed for transcriptomic, metabolomic, and liquid chromatography mass spectrometry (LC-MS) proteomic data analysis. INMEX is a web-based tool designed for analysis of multiple data sets from gene expression and metabolomic experiments[14]. In addition to the integrated methods mentioned above[13,14,15], systematic omics approaches producing time-series data are required to fill gaps in knowledge and to provide an overall view of CHO cells
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