Alex Thomas1,2, Andreas Dräger1, Nathan Lewis1,2

1University of California, San Diego, 2Novo Nordisk Foundation Center for Biosustainability

1. Data Processing

2. Data Mapping

3. Visualization

• Evaluate the modelling bounds

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56 76 96 116 136 156 176

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CHO-S Growth Curve

• Quality assess metabolomics

• Sample reaction fluxes• Calculate flux data to constrainboundary fluxes

• Cluster with stoichiometricconnectivity constraints

• Visualize with BioNetView

Model end of exponential phase

Top SVD Dimensions

Component 2 Component 1

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Time course progression

Glucose

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CHO CellMetabolic Model

Discussion:• Using a preliminary GEM for CHO-S metabolism, there are key shifts seen in late-stage exponential phase.• Large decreases are seen in linked amino acid metabolism after the 72 hour time point, suggesting many anabolic processes slow down during late-exponential phase.• The production of biomass, linked to growth rate, peaks at 108 hours, de-spite decreases in amino acid metabolism, which suggests other amino acid demands play a significant role during exponential growth.

Conclusion:• The BioNetView pipeline can be used to map high throughput data onto GEMs and statistically assess biochemical shifts in pathways.• The pipeline has been applied to CHO-cell batch growth experiments to assess the genome-scale metabolic state of the late-exponential phase, and has suggested biochemical pathways to experiment with.• This pipeline can enable quality assurance and assess inefficiencies see throughout the culture process, potentially motivating improvements in CHO cell bioprocessing.

Future:• We anticipate new pathways to be seen in shifts as we run this analysis with the upcoming, larger community CHO GEM.References:1Lewis, Nathan E., Harish Nagarajan, and Bernhard O. Palsson. "Constraining the metabolic genotype–phenotype rela-tionship using a phylogeny of in silico methods." Nature Reviews Microbiology 10.4 (2012): 291-305.2Mo, Monica L., Bernhard Ø. Palsson, and Markus J. Herrgård. "Connecting extracellular metabolomic measurements to intracellular flux states in yeast." BMC systems biology 3.1 (2009): 37.3Schellenberger, Jan, and Bernhard Ø. Palsson. "Use of randomized sampling for analysis of metabolic networks." Journal of Biological Chemistry 284.9 (2009): 5457-5461.

Acknowledgements:Hooman Hefzi for providing the preliminary CHO GEM. The Center for Biosus-tainability, at the Denmark Technical University for generating the metabolo-mics and cell line measurements.

Abstract:• CHO cells are the primary hosts for production of many human recombinant proteins. However, our incomplete understanding of CHO cell metabolism has limited our ability to rationally enhance transgene expression and limit the se-cretion of toxic byproducts.• Therefore, there is a need for rigorous assessment of the molecular mecha-nisms that affect production objectives and eventually introduces enough metabolic inefficiencies to decrease bioproduct yield.• We present an analysis pipeline that characterizes this diversity by using time course exo-metabolomics and genome-scale, mass-balance models of CHO cell metabolism to visualize shifts in reaction pathways, such as glycolytic and oxi-dative metabolism versus anabolic pathways of growth and protein produc-tion, in order to comprehensively characterize CHO metabolism during batch culture for the first time.

Background:• Protein producing CHO cells have a fundamental trade-off between anabolic processes, such as producing cell growth precursors, and protein production.• Genome-scale models(GEMs) have been usedto map high-throughputdata onto a genome-scale perspectiveperspectives of cell meta-bolism1.• Extracellular samplingof the metabolites in themedia can provide input/output constraints forGEMs2.• Sampled flux distributions of reactions can be used to assess the metabolic capacity of the cell at different phases of the batch run3.

Methods:

Cell Growth

Protein Production

Using Time Course Metabolomics to Elucidate Genome-Scale Pathway Utilization for CHO-S Cell Lines in Batch Culture

Cholesterol

Results:• From 72 hours to 120 hours of batch culture, we assess the transition of CHO-S cell metabolism from late-stage exponential into stationary phase.• Yellow clusters are significant; red is high flux and blue is low flux• Linked pathways with decreasing metabolic flux entering stationary phase:

• There are non-significant decreasing shifts seen in vitamin A, folate, pyru-vate metabolism and amino acid exchange.• Pathways with increasing metabolic flux entering stationary phase.

• There are non-significant increasing shifts seen in glycolysis, diacylglycerol synthesis, and glycine metabolism.

Tyrosine andphenylalanine metabolism

Lysine

IMP Biosynthesis

Tryptophan

HistidineTetrahydrobiopterin

Tyrosine andphenylalaninemetabolism

LysineTryptophanIMP BiosynthesisHistidineTetrahydrobiopterinmetabolism

BrancedChain Amino AcidMetabolism

BrancedChain Amino AcidMetabolism

MalateIsocitrate

Glutathione

GlutamateCysteine

Steroid

BiomassSuccinate

Cholesterolmetabolism

Malate, Isocitrate synthesis

GlutamateCysteineGlutathioneSteroidmetabolism

Biomass

Succinate

Cholesterol

Triacylglycerol