A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism

Abstract

Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

Keywords: CHO; Chinese hamster ovary; biotherapeutic protein production; genome-scale model; metabolic network; systems biology.

Figure 1. A multi-step process was used to reconcile a few existing unpublished models and to generate the final community reconstruction and models

(A) The manually-curated human metabolic network reconstructions (Recon 1 (Duarte et al., 2007) and Recon 2 (Quek et al., 2014)) were used to define an initial set of reactions catalyzed in C. griseus and the genes and proteins involved in each reaction (GPRs). Specifically, Recon 1 and Recon 2 were combined and all enzyme-catalyzed reactions that differed between the two were manually curated and reconciled to obtain consistent GPRs. C. griseus homologs were obtained for each human gene to obtain a set of draft GPRs linked specifically to genes in the CHO-K1 genome annotation. (B) The draft CHO GPRs were then compared with the GPRs from three independently-reconstructed and unpublished CHO genome-scale models, thus leveraging the manual curation invested in each input model. By manually verifying all GPRs and adding additional CHO-specific reactions present in the input CHO genome-scale models, we obtained a more comprehensive community reconstruction for C. griseus. To enable computation with this network, orphan reactions from Recon 2 were added, and omics data were used to build a global and cell-line specific models.

Figure 2. Outcomes from the reconciliation process

GPRs were updated and novel reactions were added in the reconstruction process, as shown for example in (A) mitochondrial and peroxisomal beta oxidation. Specifically, the trifunctional enzyme necessary for the 2^nd, 3^rd, and 4^th steps of mitochondrial beta oxidation has been added to oxidation reactions occurring in the mitochondria while the GPRs of peroxisomal beta oxidation reactions have been updated to reflect additional catalytic activity of the SCP2 protein. (B) Additional enzymes necessary for catabolism of various unsaturated fatty acids have been added for both peroxisomal and mitochondrial beta oxidation. (C) The starting models had considerable differences in content, and following reconciliation, 1,571 new reactions were added to the model that had not been included in any of the starting CHO models. UCSD, NUS, and UQ/BOKU indicate the various groups contributing models to the initial reconciliation effort (see STAR Methods for additional details for each model). (D) The reconstruction refers solely to gene-associated content. To convert the reconstruction into a computable model, a global C. griseus model was built by including orphan reactions from Recon 2. These additions enable the activity of known enzymes in C. griseus and serve as hypotheses for enzyme discovery in CHO. After removing unexpressed genes, cell-line specific models were constructed for CHO-K1, -S, and -DG44 cells. (E) The reactions in the global C. griseus model account for pathways in multiple subcellular compartments.

Figure 3. The CHO cell-line models can compute growth rates for various IgG-producing cell lines

All cell lines are grown in serum-free media and are producing IgG. HP and LP refer to high and low producers respectively while NaBu indicates the presence of sodium butyrate addition to the media (Carinhas et al., 2013). Early exp./Late exp. refer to the early and late exponential phase, respectively, (Selvarasu et al., 2012). Two cell lines are from cultures exposed to a temperature shift (Martínez et al., 2015); however the data points used come from the time period prior to the temperature shift. Simulations for the Selvarasu study utilize the CHO-DG44 cell line model. Other simulations use the CHO-K1 cell line model.

Figure 4. The models provide insights into the molecular basis of CHO-specific amino acid auxotrophies

(A) Arginine, (B) Cysteine, (C) Proline, (D) Asparagine. For each reaction, information in the circles indicates whether the enzyme catalyzing the reaction is seen in transcriptomic (upper left quarter-circle) and/or proteomic (lower left quarter-circle) data, as well as its presence or absence in the cell line specific models generated by GIMME (right semi-circle). The square shows whether the enzyme catalyzing the reaction is seen in transcriptomic data from a mix of C. griseus tissues. Data used is available in Supplemental Data S4. Metabolite abbreviations are as follows: CITR-citrulline, ASP-aspartate, ARGSUCC-argininosuccinate, ARG-arginine, SER-serine, HCYS-homocysteine, CYST-cystathionine, ORN-ornithine, GLU-glutamate, GLU5SA-glutamate 5 semialdehyde, PRO-proline, GLN-glutamine, ASN-asparagine.

Figure 5. Resource utilization efficiency in CHO cell lines is greater after cell line engineering

(A) Feasible uptake fluxes were generated for nutrient utilization efficiency analyses. Growth rates and specific productivities were obtained and used to constrain the model. By sampling the constrained model, a set of feasible metabolite uptake rates and secretion rates was calculated that support growth and production at the specified values. (B) The efficacy of resource utilization following common growth-inhibiting treatments in protein-producing CHO cell lines was quantified. Uptake and secretion rates from (A) were used to predict maximum growth (i.e., no protein production) and maximum protein yield (i.e., no growth), as well as yields at various fractions of maximum growth. These were used to predict a range of optimal protein production rates (i.e. making full use of resources) as growth rate decreases (indicated as a region of full resource utilization), thus showing the theoretical maximal protein secretion rates at decreased growth rates. The 5th, 25th, 50th, 75th, and 95th percentile of theoretical maximal protein production rates at each growth rate are indicated by the gradient in color from black to orange. After several cell treatments, the experimentally measured increased protein yield and decreased cell growth rate were compared to the predicted optimal protein secretion rates to assess how successfully each treatment utilized available resources (e.g., amino acids and sugars) for growth and protein production. Boxes span the 25^th and 75^th percentiles, whiskers represent the 5^th and 95^th percentiles, and a red line denotes the median value of overall resource utilization efficiency for each treatment, calculated as the ratio of experimentally measured protein production to the theoretical maximum protein yield (i.e., no growth). (C) The efficiency of diversion of resources toward protein production following common treatments was assessed. Uptake and secretion rates from (A) were used to compute the theoretical maximum specific productivity after cell line or process modifications yielding a range of theoretical optimal q_p values were computed. Experimentally measured production rates were compared to the computational predictions to assess how effectively the cells are able to make use of resources gained from growing slower. Boxes span the 25^th and 75^th percentiles, whiskers represent the 5^th and 95^th percentiles, and a red line denoting the median value.