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Cytotechnology. 2014 Oct;66(5):853-60. doi: 10.1007/s10616-013-9637-4. Epub 2013 Aug 30.

Identification of the pivotal role of glutamate in enhancing insect cell growth using factor analysis.

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1
Department of Life Science, Institute of Molecular Biology, National Chung Cheng University, Chia-Yi, 62102, Taiwan, bioccc@ccu.edu.tw.

Abstract

Although the insect cell/baculovirus system is an important expression platform for recombinant protein production, our understanding of insect cell metabolism with respect to enhancing cell growth capability and productivity is still limited. Moreover, different host insect cell lines may have different growth characteristics associated with diverse product yields, which further hampers the elucidation of insect cell metabolism. To address this issue, the growth behaviors and utilization profiles of common metabolites among five cultured insect cell lines (derived from two insect hosts, Spodoptera frugiperda and Spodoptera exigua) were investigated in an attempt to establish a metabolic framework that can interpret the different cell growth behaviors. To analyze the complicated metabolic dataset, factor analysis was introduced to differentiate the crucial metabolic variations among these cells. Factor analysis was used to decompose the metabolic data to obtain the underlying factors with biological meaning that identify glutamate (a metabolic intermediate involved in glutaminolysis) as a key metabolite for insect cell growth. Notably, glutamate was consumed in significant amounts by fast-growing insect cell lines, but it was produced by slow-growing lines. A comparative experiment using cells grown in culture media with and without glutamine (the starting metabolite in glutaminolysis) was conducted to further confirm the pivotal role of glutamate. The factor analysis strategy allowed us to elucidate the underlying structure and inter-correlation between insect cell growth and metabolite utilization to provide some insights into insect cell growth and metabolism, and this strategy can be further extended to large-scale metabolomic analysis.

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