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

Abstract

We have employed an inverse engineering strategy based on quantitative proteome analysis to identify changes in intracellular protein abundance that correlate with increased specific recombinant monoclonal antibody production (qMab) by engineered murine myeloma (NS0) cells. Four homogeneous NS0 cell lines differing in qMab were isolated from a pool of primary transfectants. The proteome of each stably transfected cell line was analyzed at mid-exponential growth phase by two-dimensional gel electrophoresis (2D-PAGE) and individual protein spot volume data derived from digitized gel images were compared statistically. To identify changes in protein abundance associated with qMab datasets were screened for proteins that exhibited either a linear correlation with cell line qMab or a conserved change in abundance specific only to the cell line with highest qMab. Several proteins with altered abundance were identified by mass spectrometry. Proteins exhibiting a significant increase in abundance with increasing qMab included molecular chaperones known to interact directly with nascent immunoglobulins during their folding and assembly (e.g., BiP, endoplasmin, protein disulfide isomerase). 2D-PAGE analysis showed that in all cell lines Mab light chain was more abundant than heavy chain, indicating that this is a likely prerequisite for efficient Mab production. In summary, these data reveal both the adaptive responses and molecular mechanisms enabling mammalian cells in culture to achieve high-level recombinant monoclonal antibody production.

Dynamics of unfolded protein response in recombinant CHO cells

“…Factors such as nutritional status may also play a role and are discussed later. Note that, other studies have also observed a positive correlation of mRNA levels of chaperones GRP78, GRP94 and CRT and XBP1 S with that of secreted antibody concentration in production clones (Smales et al 2004;Kober et al 2012).…”

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

“…Overall, the focus on fold changes distracts attention from changes in gene expression that are regulated at a more physiologic scale such as for instance transcription factors that have an effect on the cell upon slight changes in their expression [30]: if within a pathway each individual gene is upregulated 1.4-fold, none of these will show up in the list of differentially expressed genes when using a threshold of 1.5, however, the overall flux through the pathway will increase significantly. [7,66,69,70] Down regulation of growth [7,32,66,68] In case of antibodies: enhanced [4,69,71,[76][77][78]] assembly and secretion due to a surplus of light chain over heavy chain These challenges in identifying relevant genes and understanding their biological roles are further compounded by the fact that many genes have functional assignments in the GO or KEGG annotation databases that reflect the context in which they were first discovered, which may be completely irrelevant to cells grown in suspension in a protein free medium (such as organism development, tissue specific functions and similar). Moreover, pleiotropy, the phenomenon where a single gene may have multiple functions, which may not all be known [31], can make the interpretation of results difficult.…”

Host cell proteins in biologics development: Identification, quantitation and risk assessment

“…A number of studies comparing the HCP proteomes from different production cell lines using the same platform have been reported. These studies showed that the HCP proteomes are very similar among the cell lines evaluated, providing the basis for the platform-based approach for HCP evaluation (Champion et al, 2001;Krawitz et al, 2006;Smales et al, 2004). The second more practical reason, to take the platform approach is the high attrition rate of early drug candidates.…”

“…Many studies have been carried out in the application of proteomics for HCP analysis (Dyk et al, 2003;Hayduk et al, 2004;Krawitz et al, 2006;Smales et al, 2004). Krawitz et al (2006) demonstrated using 2D gels that it is possible to establish the comparability of HCPs from different biologics expressed in the same cell line, providing the basis for the platform-based HCP approach.…”