Towards rational glyco-engineering in CHO: from data to predictive models

Metabolic modelling strives to develop modelling approaches that are robust and highly predictive. To achieve this, various modelling designs, including hybrid models, and parameter estimation methods that define the type and number of parameters used in the model, are adapted. Accurate input data play an important role so that the selection of experimental methods that provide input data of the required precision with low measurement errors is crucial. For the biopharmaceutically relevant protein glycosylation, the most prominent available models are kinetic models which are able to capture the dynamic nature of protein N-glycosylation. In this review we focus on how to choose the most suitable model for a specific research question, as well as on parameters and considerations to take into account before planning relevant experiments.

Error propagation in constraint-based modeling of Chinese hamster ovary cells

Chinese hamster ovary (CHO) cells are the most popular mammalian cell factories for the production of glycosylated biopharmaceuticals. To further increase titer and productivity and ensure product quality, rational system-level engineering strategies based on constraint-based metabolic modeling, such as flux balance analysis (FBA), have gained strong interest. However, the quality of FBA predictions depends on the accuracy of the experimental input data, especially on the exchange rates of extracellular metabolites. Yet, it is not standard practice to devote sufficient attention to the accurate determination of these rates. In this work, we investigated to what degree the sampling frequency during a batch culture and the measurement errors of metabolite concentrations influence the accuracy of the calculated exchange rates and further, how this error then propagates into FBA predictions of growth rates. We determined that accurate measurements of essential amino acids with low uptake rates are crucial for the accuracy of FBA predictions, followed by a sufficient number of analyzed time points. We observed that the measured difference in growth rates of two cell lines can only be reliably predicted when both high measurement accuracy and sampling frequency are ensured.

Robust Analytical Methods for the Accurate Quantification of the Total Biomass Composition of Mammalian Cells

Biomass composition is an important input for genome-scale metabolic models and has a big impact on their predictive capabilities. However, researchers often rely on generic data for biomass composition, e.g. collected from similar organisms. This leads to inaccurate predictions, because biomass composition varies between different cell lines, conditions, and growth phases. In this chapter we present protocols for the determination of the biomass composition of Chinese Hamster Ovary (CHO) cells. These methods can easily be adapted to other types of mammalian cells. The protocols include the quantification of cell dry mass and of the main biomass components, namely protein, lipid, DNA, RNA, and carbohydrates. Cell dry mass is determined gravimetrically by weighing a defined number of cells. Amino acid composition and protein content are measured by gas chromatography mass spectrometry. Lipids are quantified by shotgun mass spectrometry, which provides quantities for the different lipid classes and also the distribution of fatty acids. RNA is purified and then quantified spectrophotometrically. The methods for DNA and carbohydrates are simple fluorometric and colorimetric assays adapted to a 96-well plate format. To ensure quantitative results, internal standards or spike-in controls are used in all methods, e.g. to account for possible matrix effects or loss of material. Finally, the last section provides a guide on how to convert the measured data into biomass equations, which can then be integrated into a metabolic model.

Designing Optimized Production Hosts by Metabolic Modeling

Many of the complex and expensive production steps in the chemical industry are readily available in living cells. In order to overcome the metabolic limits of these cells, the optimal genetic intervention strategies can be computed by the use of metabolic modeling. Elementary flux mode analysis (EFMA) is an ideal tool for this task, as it does not require defining a cellular objective function. We present two EFMA-based methods to optimize production hosts: (1) the standard approach that can only be used for small and medium scale metabolic networks and (2) the advanced dual system approach that can be utilized to directly compute intervention strategies in a genome-scale metabolic model.

CHOmine: an integrated data warehouse for CHO systems biology and modeling

The last decade has seen a surge in published genome-scale information for Chinese hamster ovary (CHO) cells, which are the main production vehicles for therapeutic proteins. While a single access point is available at www.CHOgenome.org, the primary data is distributed over several databases at different institutions. Currently research is frequently hampered by a plethora of gene names and IDs that vary between published draft genomes and databases making systems biology analyses cumbersome and elaborate. Here we present CHOmine, an integrative data warehouse connecting data from various databases and links to other ones. Furthermore, we introduce CHOmodel, a web based resource that provides access to recently published CHO cell line specific metabolic reconstructions. Both resources allow to query CHO relevant data, find interconnections between different types of data and thus provides a simple, standardized entry point to the world of CHO systems biology.

Database URL:http://www.chogenome.org

Quantitative analysis of proteome and lipidome dynamics reveals functional regulation of global lipid metabolism

Elucidating how and to what extent lipid metabolism is remodeled under changing conditions is essential for understanding cellular physiology. Here, we analyzed proteome and lipidome dynamics to investigate how regulation of lipid metabolism at the global scale supports remodeling of cellular architecture and processes during physiological adaptations in yeast. Our results reveal that activation of cardiolipin synthesis and remodeling supports mitochondrial biogenesis in the transition from fermentative to respiratory metabolism, that down-regulation of de novo sterol synthesis machinery prompts differential turnover of lipid droplet-associated triacylglycerols and sterol esters during respiratory growth, that sphingolipid metabolism is regulated in a previously unrecognized growth stage-specific manner, and that endogenous synthesis of unsaturated fatty acids constitutes an in vivo upstream activator of peroxisomal biogenesis, via the heterodimeric Oaf1/Pip2 transcription factor. Our work demonstrates the pivotal role of lipid metabolism in adaptive processes and provides a resource to investigate its regulation at the cellular level.

Avoiding the Enumeration of Infeasible Elementary Flux Modes by Including Transcriptional Regulatory Rules in the Enumeration Process Saves Computational Costs

Despite the significant progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We introduce a novel approach to speed up the calculation of elementary flux modes by including transcriptional regulatory information into the analysis of metabolic networks. Taking into account gene regulation dramatically reduces the solution space and allows the presented algorithm to constantly eliminate biologically infeasible modes at an early stage of the computation procedure. Thereby, computational costs, such as runtime, memory usage, and disk space, are extremely reduced. Moreover, we show that the application of transcriptional rules identifies non-trivial system-wide effects on metabolism. Using the presented algorithm pushes the size of metabolic networks that can be studied by elementary flux modes to new and much higher limits without the loss of predictive quality. This makes unbiased, system-wide predictions in large scale metabolic networks possible without resorting to any optimization principle.

Predicting genetic engineering targets with Elementary Flux Mode Analysis: a review of four current methods

Elementary flux modes (EFMs) are a well-established tool in metabolic modeling. EFMs are minimal, feasible, steady state pathways through a metabolic network. They are used in various approaches to predict targets for genetic interventions in order to increase production of a molecule of interest via a host cell. Here we give an introduction to the concept of EFMs, present an overview of four methods which use EFMs in order to predict engineering targets and lastly use a toy model and a small-scale metabolic model to demonstrate and compare the capabilities of these methods.

Design of optimally constructed metabolic networks of minimal functionality

Background

Metabolic engineering aims to design microorganisms that will generate a product of interest at high yield. Thus, a variety of in silico modeling strategies has been applied successfully, including the concepts of elementary flux modes (EFMs) and constrained minimal cut sets (cMCSs). The EFMs (minimal, steady state pathways through the system) can be calculated given a metabolic model. cMCSs are sets of reaction deletions in such a network that will allow desired pathways to survive and disable undesired ones (e.g., those with low product secretion or low growth rates). Grouping the modes into desired and undesired categories had to be done manually until now.

Results

Although the optimal solution for a given set of pathways will always be found with the currently available tools, manual selection may lead to a sub-optimal solution with respect to a metabolic engineering target. A small change in the selection of modes can reduce the number of necessary deletions while only slightly reducing production. Based on our recently introduced formulation of cut set calculations using binary linear programming, we suggest an algorithm that does not require manual selection of the desired pathways.

Conclusions

We demonstrated the principle of our algorithm with the help of a small toy network and applied it to a model of E. coli using different design objectives. Furthermore we validated our method by reproducing previously obtained results without requiring manual grouping of modes.

Elementary flux modes in a nutshell: properties, calculation and applications

Elementary flux mode (EFM) analysis allows the unbiased decomposition of a metabolic network into minimal functional units, making it a powerful tool for metabolic engineering. While the use of EFM analysis (EFMA) is still limited by the size of the models it can handle, EFMA has been successfully applied to solve real-world metabolic engineering problems. Here we provide a user-oriented introduction to EFMA, provide examples of recent applications, analyze current research strategies to overcome the computational restrictions and give an overview over current approaches, which aim to identify and calculate only biologically relevant EFMs.

regEfmtool: speeding up elementary flux mode calculation using transcriptional regulatory rules in the form of three-state logic

Despite the considerable progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We present regEfmtool which is an extension to efmtool that utilizes transcriptional regulatory networks for the computation of elementary flux modes. The implemented extension significantly decreases the computational costs for the calculation of elementary flux modes, such as runtime, memory usage and disk space by omitting biologically infeasible solutions. Hence, using the presented regEfmtool pushes the size of metabolic networks that can be studied by elementary flux modes to new limits.

Nutritional requirements of the BY series of Saccharomyces cerevisiae strains for optimum growth

Among the vast variety of Saccharomyces cerevisiae strains, the BY family is particularly important because the widely used deletion collections are based on this background. Here we demonstrate that some standard growth media recipes require substantial modifications to provide optimum growth conditions for auxotrophic BY strains and to avoid growth arrest before glucose is depleted. In addition to the essential supplements that are required to satisfy auxotrophic requirements, we found the four amino acids phenylalanine, glutamic acid, serine, and threonine to be indispensable for optimum growth, despite the fact that BY is 'prototrophic' for these amino acids. Interestingly, other widely used S. cerevisiae strains, such as strains of the CEN.PK family, are less sensitive to lack of the described supplements. Furthermore, we found that the concentration of inositol in yeast nitrogen base is too low to support fast proliferation of yeast cultures until glucose is exhausted. Depletion of inositol during exponential growth induces characteristic changes, namely a decrease in glucose uptake and maximum specific growth rate, increased cell size, reduced viability, and accumulation of lipid storage pools. Thus, several of the existing growth media recipes need to be revised to achieve optimum growth conditions for BY-derived strains.