Microarray and proteomics expression profiling identifies several candidates, including the valosin‐containing protein (VCP), involved in regulating high cellular growth rate in production CHO cell lines

Citations Over TimeTop 10% of 2010 papers

Abstract

A high rate of cell growth (micro) leading to rapid accumulation of viable biomass is a desirable phenotype during scale up operations and the early stages of production cultures. In order to identify genes and proteins that contribute to higher growth rates in Chinese hamster ovary (CHO) cells, a combined approach using microarray and proteomic expression profiling analysis was carried out on two matched pairs of CHO production cell lines that displayed either fast or slow growth rates. Statistical analysis of the microarray and proteomic data separately resulted in the identification of 118 gene transcripts and 58 proteins that were differentially expressed between the fast- and slow-growing cells. Overlap comparison of both datasets identified a priority list of 21 candidates associated with a high growth rate phenotype in CHO. Functional analysis (by siRNA) of five of these candidates identified the valosin-containing protein (VCP) as having a substantial impact on CHO cell growth and viability. Knockdown of HSPB1 and ENO1 also had an effect on cell growth (negative and positive, respectively). Further functional validation in CHO using both gene knockdown (siRNA) and overexpression (cDNA) confirmed that altered VCP expression impacted CHO cell proliferation, indicating that VCP and other genes and proteins identified here may play an important role in the regulation of CHO cell growth during log phase culture and are potential candidates for CHO cell line engineering strategies.

Related Papers

• → Differential gene expression profiles in peripheral blood in Northeast Chinese Han people with acute myocardial infarction(2018)22 cited
• → Comparative study of microarray and experimental data on Schwann cells in peripheral nerve degeneration and regeneration: big data analysis(2019)14 cited
• → The promise and perils of microarray analysis(2006)8 cited
• → A standardized fold change method for microarray differential expression analysis used to reveal genes involved in acute rejection in murine allograft models(2017)3 cited
• Preparation PRV,PPV and JEV detection microarray(2007)