Constraint-based modeling has been applied to analyze metabolism of numerous organisms via flux balance analysis and genome-scale metabolic models, including mammalian cells such as the Chinese hamster ovary (CHO) cells—the principal cell factory platform for therapeutic protein production. Unfortunately, the application of genome-scale model methodologies using the conventional biomass objective function is challenged by the presence of overly-restrictive constraints, including essential amino acid exchange fluxes that can lead to improper predictions of growth rates and intracellular flux distributions. In this study, these constraints are found to be reliably predicted by an “essential nutrient minimization” approach. After modifying these constraints with the predicted minimal uptake values, a series of unconventional objective functions are applied to minimize each individual non-essential nutrient uptake rate, revealing useful insights about metabolic exchange rates and flows across different cell lines and culture conditions. This unconventional uptake-rate objective functions (UOFs) approach is able to distinguish metabolic differences between three distinct CHO cell lines (CHO-K1, -DG44, and -S) not directly observed using the conventional biomass growth maximization solutions. Further, a comparison of model predictions with experimental data from literature correctly correlates with the specific CHO-DG44-derived cell line used experimentally, and the corresponding dual prices provide fruitful information concerning coupling relationships between nutrients. The UOFs approach is likely to be particularly suited for mammalian cells and other complex organisms which contain multiple distinct essential nutrient inputs, and may offer enhanced applicability for characterizing cell metabolism and physiology as well as media optimization and biomanufacturing control.
- Award ID(s):
- 1624718
- NSF-PAR ID:
- 10175160
- Date Published:
- Journal Name:
- Processes
- Volume:
- 8
- Issue:
- 3
- ISSN:
- 2227-9717
- Page Range / eLocation ID:
- 331
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
Hallam, Steven J. (Ed.)ABSTRACT Nitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis ( i Nmo686) and used flux balance analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and [ 13 C]bicarbonate and [ 13 C]formate tracer experiments coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings corroborate that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO 2 fixation, and we also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO 2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira ’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes. IMPORTANCE Nitrospira spp. are globally abundant nitrifying bacteria in soil and aquatic ecosystems and in wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of Nitrospira moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13 C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite-oxidizing bacteria.more » « less
-
more » « less
Abstract The Chinese hamster ovary (CHO) cell lines that are used to produce commercial quantities of therapeutic proteins commonly exhibit a decrease in productivity over time in culture, a phenomenon termed production instability. Random integration of the transgenes encoding the protein of interest into locations in the CHO genome that are vulnerable to genetic and epigenetic instability often causes production instability through copy number loss and silencing of expression. Several recent publications have shown that these cell line development challenges can be overcome by using site‐specific integration (SSI) technology to insert the transgenes at genomic loci, often called “hotspots,” that are transcriptionally permissive and have enhanced stability relative to the rest of the genome. However, extensive characterization of the CHO epigenome is needed to identify hotspots that maintain their desirable epigenetic properties in an industrial bioprocess environment and maximize transcription from a single integrated transgene copy. To this end, the epigenomes and transcriptomes of two distantly related cell lines, an industrially relevant monoclonal antibody‐producing cell line and its parental CHO‐K1 host, were characterized using high throughput chromosome conformation capture and RNAseq to analyze changes in the epigenome that occur during cell line development and associated changes in system‐wide gene expression. In total, 10.9% of the CHO genome contained transcriptionally permissive three‐dimensional chromatin structures with enhanced genetic and epigenetic stability relative to the rest of the genome. These safe harbor regions also showed good agreement with published CHO epigenome data, demonstrating that this method was suitable for finding genomic regions with epigenetic markers of active and stable gene expression. These regions significantly reduce the genomic search space when looking for CHO hotspots with widespread applicability and can guide future studies with the goal of maximizing the potential of SSI technology in industrial production CHO cell lines.
-
Optimization-based models have been used to predict cellular behavior for over 25 years. The constraints in these models are derived from genome annotations, measured macromolecular composition of cells, and by measuring the cell's growth rate and metabolism in different conditions. The cellular goal (the optimization problem that the cell is trying to solve) can be challenging to derive experimentally for many organisms, including human or mammalian cells, which have complex metabolic capabilities and are not well understood. Existing approaches to learning goals from data include (a) estimating a linear objective function, or (b) estimating linear constraints that model complex biochemical reactions and constrain the cell's operation. The latter approach is important because often the known reactions are not enough to explain observations; therefore, there is a need to extend automatically the model complexity by learning new reactions. However, this leads to nonconvex optimization problems, and existing tools cannot scale to realistically large metabolic models. Hence, constraint estimation is still used sparingly despite its benefits for modeling cell metabolism, which is important for developing novel antimicrobials against pathogens, discovering cancer drug targets, and producing value-added chemicals. Here, we develop the first approach to estimating constraint reactions from data that can scale to realistically large metabolic models. Previous tools were used on problems having less than 75 reactions and 60 metabolites, which limits real-life-size applications. We perform extensive experiments using 75 large-scale metabolic network models for different organisms (including bacteria, yeasts, and mammals) and show that our algorithm can recover cellular constraint reactions. The recovered constraints enable accurate prediction of metabolic states in hundreds of growth environments not seen in training data, and we recover useful cellular goals even when some measurements are missing.more » « less
-
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.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/pr8030331
