Genome‐wide analysis of the transcriptional response of murine hybridomas to osmotic shock

Abstract

Hyperosmotic stress has been shown to increase specific antibody productivity in murine hybridoma systems; however, the mechanisms underlying this phenomenon are still poorly understood. To elucidate the mechanisms for this phenomenon as well as other physiological changes that occur in response to hyperosmotic stress, we performed a genome-wide analysis of the transcriptional response of murine hybridoma OKT3 toward hyperosmotic stress using DNA microarrays. GeneChip MOE430A from Affymetrix was used to determine the differences in transcription patterns between OKT3 in hyperosmotic culture (approximately 100 mOsm above control) and control culture. The chip contains 22,690 probe sets for over 14,000 known genes and more than 4,000 ESTs. Signals were normalized using the GC-RMA algorithm and the effectiveness of hyperosmotic stress in altering the expression of each gene was evaluated using one-way ANOVA. 2,793 probe sets on the chip were differentially expressed with a P < 0.05. Among them, 349 probe sets exhibited a two-fold or greater change (with 202 up-regulated and 147 down-regulated) at one or more time points. Within the 215 characterized, differentially expressed genes, many are involved in metabolism/catabolism (19 induced, 12 repressed), cell-cycle regulation (10 induced, 5 repressed) and apoptosis (8 induced, 2 repressed), regulation of transcription (18 induced, 13 repressed) and translation (2 induced, 2 repressed), transport and signaling pathways (24 induced, 12 repressed). Surprisingly, there were very few changes within the stress-response genes. Interestingly, the transcription levels of both the immunoglobulin kappa and lambda light chains showed a significant change in response to hyperosmotic stress, although there is no detectable lambda chain in the immunoglobulin produced in this cell line. Quantitative PCR assays with TaqMan probes were applied to selected genes to validate the results obtained from microarray analysis.

Fucose content of monoclonal antibodies can be controlled by culture medium osmolality for high antibody-dependent cellular cytotoxicity

“…cDNAs were prepared from mRNAs extracted from YB2/0 cells expressing IgG1 B that were sampled on days 6, 9, 11, and 12 during serum-free 1-L fed-batch culture in hypoosmotic (250 mOsm/kg) and hyperosmotic (410 mOsm/kg) media. Microarray analyses indicated that hypoosmotic conditions led to the downregulation of glycolysis gene expression, particularly pyruvate kinase and aldolase (data not shown), which is in agreement with the proteomic and transcriptomic analyses of the effects of osmotic stress on CHO and hybridoma (Shen and Sharfstein 2006;Shen et al 2010). Next, we used semi-quantitative RT-PCR to evaluate gene expression under hypoosmotic versus hyperosmotic conditions and observed three marked responses (Table 3).…”

Functional genome‐wide analysis of antibody producing NS0 cell line cultivated at different temperatures

“…It has been widely reported that different environmental stresses such as osmotic shock (Shen and Sharfstein, 2006;Wu et al, 2004a), oxygen tension (Reuveny et al, 1986), sodium butyrate (Hendrick et al, 2001) and glucose limitation (Korke et al, 2004) enhance the productivity of mammalian cells. Temperature seems to be another significant parameter which can be manipulated to enhance the production of recombinant proteins.…”

Effects of clonal variation on growth, metabolism, and productivity in response to trophic factor stimulation: a study of Chinese hamster ovary cells producing a recombinant monoclonal antibody

“…To achieve that goal, industrial studies focused primarily on factorial experimental design and spent medium analysis (Chun et al 2003;Ganne and Mignot 1991;Kim and Lee 2009;Kontoravdi et al 2005;Liu et al 2001;Sandadi et al 2006). However, with the development of genome-scale models that can address all of the metabolic pathways in cells (Selvarasu et al 2009;Sheikh et al 2005) and the development of microarray technologies that can evaluate gene expression levels (Dorai et al 2007;Griffin et al 2007;Shen and Sharfstein 2006;Swiderek and Al-Rubeai 2007;Shen et al 2010), there has been renewed interest in understanding cellular metabolism and its relationship with protein productivity (Baughman et al 2010a, b).…”