Chemical inhibitors of hexokinase-2 enzyme reduce lactate accumulation, alter glycosylation processing, and produce altered glycoforms in CHO cell cultures.

Chinese hamster ovary (CHO) cells, predominant hosts for recombinant biotherapeutics production, generate lactate as a major glycolysis by-product. High lactate levels adversely impact cell growth and productivity. The goal of this study was to reduce lactate in CHO cell cultures by adding chemical inhibitors to hexokinase-2 (HK2), the enzyme catalyzing the conversion of glucose to glucose 6-phosphate, and examine their impact on lactate accumulation, cell growth, protein titers, and N-glycosylation. Five inhibitors of HK2 enzyme at different concentrations were evaluated, of which 2-deoxy- d-glucose (2DG) and 5-thio- d-glucose (5TG) successfully reduced lactate accumulation with only limited impacts on CHO cell growth. Individual 2DG and 5TG supplementation led to a 35%-45% decrease in peak lactate, while their combined supplementation resulted in a 60% decrease in peak lactate. Inhibitor supplementation led to at least 50% decrease in moles of lactate produced per mol of glucose consumed. Recombinant EPO-Fc titers peaked earlier relative to the end of culture duration in supplemented cultures leading to at least 11% and as high as 32% increase in final EPO-Fc titers. Asparagine, pyruvate, and serine consumption rates also increased in the exponential growth phase in 2DG and 5TG treated cultures, thus, rewiring central carbon metabolism due to low glycolytic fluxes. N-glycan analysis of EPO-Fc revealed an increase in high mannose glycans from 5% in control cultures to 25% and 37% in 2DG and 5TG-supplemented cultures, respectively. Inhibitor supplementation also led to a decrease in bi-, tri-, and tetra-antennary structures and up to 50% lower EPO-Fc sialylation. Interestingly, addition of 2DG led to the incorporation of 2-deoxy-hexose (2DH) on EPO-Fc N-glycans and addition of 5TG resulted in the first-ever observed N-glycan incorporation of 5-thio-hexose (5TH). Six percent to 23% of N-glycans included 5TH moieties, most likely 5-thio-mannose and/or 5-thio-galactose and/or possibly 5-thio-N-acetylglucosamine, and 14%-33% of N-glycans included 2DH moieties, most likely 2-deoxy-mannose and/or 2-deoxy-galactose, for cultures treated with different concentrations of 5TG and 2DG, respectively. Our study is the first to evaluate the impact of these glucose analogs on CHO cell growth, protein production, cell metabolism, N-glycosylation processing, and formation of alternative glycoforms.

An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells.

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.

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.

Measuring the Composition and Stable-Isotope Labeling of Algal Biomass Carbohydrates via Gas Chromatography/Mass Spectrometry.

We have developed a method to measure carbohydrate composition and stable-isotope labeling in algal biomass using gas chromatography/mass spectrometry (GC/MS). The method consists of two-stage hydrochloric acid hydrolysis, followed by chemical derivatization of the released monomer sugars and quantification by GC/MS. Fully (13)C-labeled sugars are used as internal standards for composition analysis. This convenient, reliable, and accurate single-platform workflow offers advantages over existing methods and opens new opportunities to study carbohydrate metabolism of algae under autotrophic, mixotrophic, and heterotrophic conditions using metabolic flux analysis and isotopic tracers such as (2)H2O and (13)C-glucose.