Untargeted proteomics reveals upregulation of stress response pathways during CHO-based monoclonal antibody manufacturing process leading to disulfide bond reduction

Abstract

Monoclonal antibody (mAb) interchain disulfide bond reduction can cause a loss of function and negatively impact the therapeutic's efficacy and safety. Disulfide bond reduction has been observed at various stages during the manufacturing process, including processing of the harvested material. The factors and mechanisms driving this phenomenon are not fully understood. In this study, we examined the host cell proteome as a potential factor affecting the susceptibility of a mAb to disulfide bond reduction in the harvested cell culture fluid (HCCF). We used untargeted liquid-chromatography-mass spectrometry-based proteomics experiments in conjunction with a semi-automated protein identification workflow to systematically compare Chinese hamster ovary (CHO) cell protein abundances between bioreactor conditions that result in reduction-susceptible and reduction-free HCCF. Although the growth profiles and antibody titers of these two bioreactor conditions were indistinguishable, we observed broad differences in host cell protein (HCP) expression. We found significant differences in the abundance of glycolytic enzymes, key protein reductases, and antioxidant defense enzymes. Multivariate analysis of the proteomics data determined that upregulation of stress-inducible endoplasmic reticulum (ER) and other chaperone proteins is a discriminatory characteristic of reduction-susceptible HCP profiles. Overall, these results suggest that stress response pathways activated during bioreactor culture increase the reduction-susceptibility of HCCF. Consequently, these pathways could be valuable targets for optimizing culture conditions to improve protein quality.

Keywords: CHO cell culture; Monoclonal antibody; cellular stress response; disulfide bond reduction; heat shock proteins; proteomics.

Figure 1.

Abundance of lower glycolysis enzymes (a-d) and reductases (e, f). Error bars show SEM (N = 2). Asterisk (*) indicates significant difference (p < .05) in the time profile of protein abundance

Figure 2.

(a, b) Scatter plots of first two principal component scores (PC1 and PC2) for data set 1 (a) and data set 2 (b). Values in parentheses show percent variance explained by the corresponding principal component axes. Circles and squares show control and rolled samples, respectively. (c, d) First latent variable (LV1) scores from PLS-DA of data set 1 (c) and data set 2 (d). Panels (e) and (f) show the corresponding LV1 loadings determined from PLS-DA for all peptides in the respective data sets

Figure 3.

Mean abundance profiles (normalized as described in Section ‘Untargeted Proteomics Data Processing and Annotation’) of significant discriminatory proteins grouped into five clusters. Each line represents one of 85 proteins shown in Table S2. Clusters were obtained using k-means clustering. Optimality of cluster number was determined using the Calinski-Harabasz criterion. The numbers of proteins in each cluster are shown in parentheses next to cluster number

Figure 4.

Significant interactions between proteins in clusters 4 and 5. Functional protein interaction network analysis was performed on the basis of interaction data in the STRING database (version 11.0). Cytoscape was used to visualize the resulting connected graph. Dark and light nodes are proteins from cluster 4 and 5, respectively. A line between two nodes indicates that the pair has at least one known or predicted interaction. A thicker line indicates that there is higher confidence in the interaction, e.g., due to experimental evidence for protein-protein binding

Figure 5.

Oxidative stress response and protein reduction pathways in cells from the reduction-susceptible bioreactor condition. Blue arrows indicate proteins detected at greater (up arrow) or lower (down arrow) abundance in cells from the rolled condition compared to control. Some signaling pathway components (e.g., TRX) that we were unable to quantify are included in the figure to connect the detected proteins according to known pathways of oxidative stress signaling and ER stress response. The proteins were grouped into subsystems (numbers in circles) based on their pathway membership (Supplementary Table S2) and cellular compartment. The subsystems and their interactions are discussed Section ‘ER Stress and Oxidative Stress are potential Drivers for Increased Abundance of Reductases in the Reduction-Susceptible HCCF’. BiP: immunoglobin protein; CAM: calmodulin; CAMK: CAM-dependent protein kinase III; CAT: catalase; CNX: calnexin; CRT: calreticulin; EFF2: Elongation factor 2; ERO1: ER oxidoreductin 1; GPx: glutathione peroxidase; GSH: glutathione; GSSG: glutathione disulfide; GSR: Glutathione-disulfide reductase; GST: glutathione s-transferase; HSPs: heat-shock proteins; NADPH: nicotinamide adenine dinucleotide phosphate; PDI: protein disulfide isomerase; TRX: thioredoxin; TRXR: thioredoxin reductase; UPR: unfolded protein response

Figure 6.

Data processing workflow for untargeted LC-MS proteomics of CHO cell extracts