alexa Correlation of heavy and light chain mRNA copy numbers to antibody productivity in mouse myeloma production cell lines.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Proteomics & Bioinformatics

Author(s): Dorai H, Csirke B, Scallon B, Ganguly S

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Abstract Manufacturing cell line development at Centocor involves transfection of antibody genes into host cell lines and isolating primary transfectomas that upon subcloning yield high expressing cell lines for the desired antibody. In an attempt to increase productivity of these cell lines, we set out to identify the rate-limiting step in the process of antibody expression and secretion. For this purpose, 30 antibody expressing cell lines with variable antibody expression levels were analyzed for heavy-chain and light-chain mRNA expression levels. Results suggested that the increase in antibody titer of the subclones (compared to their primary clones) was partly due to an increase in heavy-chain and light-chain mRNA levels; higher expressers were associated with approximately 1.0 x 10(7) and 1.5 x 10(7) copies of heavy-chain and light-chain per 10 nanogram of cDNA, respectively. Generally, the level of light-chain mRNA was higher compared to the level of heavy-chain mRNA in a majority of the cell lines, and the difference in their levels was not due to their differential stability. The data generated from all the cell lines tested in this study suggested that there was a correlation of light-chain and heavy-chain transcript levels to antibody productivity, with the coefficient of correlation being 0.59 for light chain and 0.81 for heavy chain. We conclude that transcription of heavy chain and to a lesser extent light chain could be one of the rate-limiting steps in the antibody expression pathway. Hence, methods that would increase these mRNA levels could be beneficial in the attempt to improve the antibody expression level of production cell lines. This article was published in Hybridoma (Larchmt) and referenced in Journal of Proteomics & Bioinformatics

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