Functional proteomic analysis of GS-NS0 murine myeloma cell lines with varying recombinant monoclonal antibody production rate

Abstract

We previously compared changes in individual protein abundance between the proteomes of GS-NS0 cell lines with varying rates of cell-specific recombinant monoclonal antibody production (qMab). Here we extend analyses of our proteomic dataset to statistically determine if particular cell lines have distinct functional capabilities that facilitate production of secreted recombinant Mab. We categorized 79 proteins identified by mass spectrometry according to their biological function or location in the cell and statistically compared the relative abundance of proteins in each category between GS-NS0 cell lines with varying qMab. We found that the relative abundance of proteins in ER chaperone, non-ER chaperone, cytoskeletal, cell signaling, metabolic, and mitochondrial categories were significantly increased with qMab. As the GS-NS0 cell line with highest qMab also had an increased intracellular abundance of unassembled Mab heavy chain (HC), we tested the hypothesis that the increased ER chaperone content was caused by induction of an unfolded protein response (UPR) signaling pathway. Immunoblot analyses revealed that spliced X-box binding protein 1 (XBP1), a marker for UPR induction, was not detectable in the GS-NS0 cells with elevated qMab, although it was induced by chemical inhibitors of protein folding. These data suggest that qMab is functionally related to the abundance of specific categories of proteins that together facilitate recombinant protein production. We infer that individual cells within parental populations are more functionally equipped for high-level recombinant protein production than others and that this bias could be used to select cells that are more likely to achieve high qMab.

Microarray profiling of preselected CHO host cell subclones identifies gene expression patterns associated with in‐creased production capacity

“…These studies were initially driven by the hope of finding the rate limiting step for protein production and identify the one gene or the few genes that would either increase cellular productivity if appropriately engineered, or that would allow the efficient identification of high producing cell lines. However, by now it has been acknowledged that it is not a single step or even pathway that controls productivity but the coordinated interplay and cooperation of multiple steps, none of which must be allowed to become limiting, and all of which have to be expressed and active in a balanced way [4,7,8]. In fact, Clarke et al 2011 found a set of 287 genes for the prediction of cellular productivity in the model they established.…”

Mammalian cells as biopharmaceutical production hosts in the age of omics

“…The same proteomics data were analyzed in a follow-on study using more elaborate statistical analysis [32]. A total of 79 proteins were identified by MALDI-TOF MS or MALDI MS/MS, which were grouped according to the following functional classes: ER chaperones, non-ER chaperones, redox control, transport, protein synthesis, protein degradation, cell signaling, metabolism, nucleic acid synthesis and processing and cytoskeletal organization [32].…”

Using cell engineering and omic tools for the improvement of cell culture processes

“…This complex forms the primary means of capturing the secreted target antibody product which can then be fluorescently labeled for sorting by flow cytometry. This method significantly speeds up the selection process (Carroll and Al-Rubeai 2004), where the critical path activity of commercial cell lines can be minimized to 31 weeks compared to a conventional 40 (Racher 2006). …”

“…The complexity of this relationship was demonstrated in several studies on the proteome maps of NS0 clones varying in their specific recombinant antibody production rates where the identification of a total of 79 proteins and their categorization into groups suggested that high producer cells are more functionally equipped in some cellular areas, together facilitating improved recombinant protein production instead of only single genes (Alete et al 2005;Dinnis et al 2006;Smales et al 2004). In a study that looked at the proteomic comparison of different productivities of recombinant CHO clones selected under high MTX concentrations, it has been suggested that actin capping protein, one of 21 differentially expressed proteins, might play an important role in heterologous protein secretion as well (Hayduk and Lee 2005).…”