13C metabolic flux analysis in cell line and bioprocess development

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producer clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation

In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.

Overexpression of peroxisome proliferator-activated receptor γ co-activator-1⍺ (PGC-1⍺) in Chinese hamster ovary cells increases oxidative metabolism and IgG productivity

Chinese hamster ovary (CHO) cells are used extensively to produce protein therapeutics, such as monoclonal antibodies (mAbs), in the biopharmaceutical industry. MAbs are large proteins that are energetically demanding to synthesize and secrete; therefore, high-producing CHO cell lines that are engineered for maximum metabolic efficiency are needed to meet increasing demands for mAb production. Previous studies have identified that high-producing cell lines possess a distinct metabolic phenotype when compared to low-producing cell lines. In particular, it was found that high mAb production is correlated to lactate consumption and elevated TCA cycle flux. We hypothesized that enhancing flux through the mitochondrial TCA cycle and oxidative phosphorylation would lead to increased mAb productivities and final titers. To test this hypothesis, we overexpressed peroxisome proliferator-activated receptor γ co-activator-1⍺ (PGC-1⍺), a gene that promotes mitochondrial metabolism, in an IgG-producing parental CHO cell line. Stable cell pools overexpressing PGC-1⍺ exhibited increased oxygen consumption, indicating increased mitochondrial metabolism, as well as increased mAb specific productivity compared to the parental line. We also performed 13C metabolic flux analysis (MFA) to quantify how PGC-1⍺ overexpression alters intracellular metabolic fluxes, revealing not only increased TCA cycle flux, but global upregulation of cellular metabolic activity. This study demonstrates the potential of rationally engineering the metabolism of industrial cell lines to improve overall mAb productivity and to increase the abundance of high-producing clones in stable cell pools.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Effects of Oleic Acid Addition Methods on the Metabolic Flux Distribution of Ganoderic Acids R, S and T's Biosynthesis

The effects of oleic acid addition methods on the metabolic flux distribution of ganoderic acids R, S and T's biosynthesis from Ganoderma lucidum were investigated. The results showed that adding filter-sterilized oleic acid in the process of submerged fermentation and static culture is of benefit to the synthesis of ganoderic acids R, S and T. The metabolic fluxes were increased by 97.48%, 78.42% and 43.39%, respectively. The content of ganoderic acids R, S and T were 3.11 times, 5.19 times and 1.44 times higher, respectively, than they were in the control group, which was without additional oleic acid. Ganoderic acids R, S and T's synthesis pathways (GAP), tricarboxylic acid cycles (TCA), pentose phosphate pathways (PP) and glycolysis pathways (EMP) were all enhanced in the process. Therefore, additional oleic acid can strengthen the overall metabolic flux distribution of G. lucidum in a submerged fermentation-static culture and it can reduce the accumulation of the by-product mycosterol. This study has laid an important foundation for improving the production of triterpenes in the submerged fermentation of G. lucidum.