Genomics and Systems Biology of Mammalian Cell Culture

Transcriptome analysis technologies are important systems-biology methods for the investigation and optimization of mammalian cell cultures concerning with regard to growth rates and productivity. For the production of recombinant proteins, knowledge of the expression conditions of the influencing genes is a major issue in the improvement of cell lines by means of genome engineering. This chapter presents two main techniques for transcriptome analysis: microarray technology and next-generation sequencing. Protein-based methods are also briefly outlined. Furthermore, the impact of these technologies on mammalian cell culture improvement is discussed.

In the past decade, DNA microarrays have fundamentally changed the way we study complex biological systems. By measuring the expression levels of thousands of transcripts, the paradigm of studying organisms has shifted from focusing on the local phenomena of a few genes to surveying the whole genome. DNA microarrays are used in a variety of ways, from simple comparisons between two samples to more intricate time-series studies. With the large number of genes being studied, the dimensionality of the problem is inevitably high. The analysis of microarray data thus requires specific approaches. In the case of time-series microarray studies, data analysis is further complicated by the correlation between successive time points in a series.

In this review, we survey the methodologies used in the analysis of static and time-series microarray data, covering data pre-processing, identification of differentially expressed genes, profile pattern recognition, pathway analysis, and network reconstruction. When available, examples of their use in mammalian cell cultures are presented.

Over the past decades, the availability of large amounts of information regarding cellular processes and reaction rates, along with increasing knowledge about the complex mechanisms involved in these processes, has changed the way we approach the understanding of cellular processes. We can no longer rely only on our intuition for interpreting experimental data and evaluating new hypotheses, as the information to analyze is becoming increasingly complex. The paradigm for the analysis of cellular systems has shifted from a focus on individual processes to comprehensive global mathematical descriptions that consider the interactions of metabolic, genomic, and signaling networks. Analysis and simulations are used to test our knowledge by refuting or validating new hypotheses regarding a complex system, which can result in predictive capabilities that lead to better experimental design. Different types of models can be used for this purpose, depending on the type and amount of information available for the specific system. Stoichiometric models are based on the metabolic structure of the system and allow explorations of steady state distributions in the network. Detailed kinetic models provide a description of the dynamics of the system, they involve a large number of reactions with varied kinetic characteristics and require a large number of parameters. Models based on statistical information provide a description of the system without information regarding structure and interactions of the networks involved. The development of detailed models for mammalian cell metabolism has only recently started to grow more strongly, due to the intrinsic complexities of mammalian systems, and the limited availability of experimental information and adequate modeling tools. In this work we review the strategies, tools, current advances, and recent models of mammalian cells, focusing mainly on metabolism, but discussing the methodology applied to other types of networks as well.

Reaction rates or metabolic fluxes reflect the integrated phenotype of genome, transcriptome and proteome interactions, including regulation at all levels of the cellular hierarchy. Different methods have been developed in the past to analyse intracellular fluxes. However, compartmentation of mammalian cells, varying utilisation of multiple substrates, reversibility of metabolite uptake and production, unbalanced growth behaviour and adaptation of cells to changing environment during cultivation are just some reasons that make metabolic flux analysis (MFA) in mammalian cell culture more challenging compared to microorganisms. In this article MFA using the metabolite balancing methodology and the advantages and disadvantages of ¹³C MFA in mammalian cell systems are reviewed. Application examples of MFA in the optimisation of cell culture processes for the production of biopharmaceuticals are presented with a focus on the metabolism of the main industrial workhorse. Another area in which mammalian cell culture plays a key role is in medical and toxicological research. It is shown that MFA can be used to understand pathophysiological mechanisms and can assist in understanding effects of drugs or other compounds on cellular metabolism.

Development of efficient bioprocesses is essential for cost-effective manufacturing of recombinant therapeutic proteins. To achieve further process improvement and process rationalization comprehensive data analysis of both process data and phenotypic cell-level data is essential.

Here, we present a framework for advanced bioprocess data analysis consisting of multivariate data analysis (MVDA), metabolic flux analysis (MFA), and pathway analysis for mapping of large-scale gene expression data sets. This data analysis platform was applied in a process development project with an IgG-producing Chinese hamster ovary (CHO) cell line in which the maximal product titer could be increased from about 5 to 8 g/L.

Principal component analysis (PCA), k-means clustering, and partial least-squares (PLS) models were applied to analyze the macroscopic bioprocess data. MFA and gene expression analysis revealed intracellular information on the characteristics of high-performance cell cultivations. By MVDA, for example, correlations between several essential amino acids and the product concentration were observed. Also, a grouping into rather cell specific productivity-driven and process control-driven processes could be unraveled. By MFA, phenotypic characteristics in glycolysis, glutaminolysis, pentose phosphate pathway, citrate cycle, coupling of amino acid metabolism to citrate cycle, and in the energy yield could be identified. By gene expression analysis 247 deregulated metabolic genes were identified which are involved, inter alia, in amino acid metabolism, transport, and protein synthesis.

Glycosylation is a post-translational modification that is of paramount importance in the production of recombinant pharmaceuticals as most recombinantly produced therapeutics are N- and/or O-glycosylated. Being a cell-system-dependent process, it also varies with expression systems and growth conditions, which result in glycan microheterogeneity and macroheterogeneity. Glycans have an effect on drug stability, serum half-life, and immunogenicity; it is therefore important to analyze and optimize the glycan decoration of pharmaceuticals. This review summarizes the aspects of protein glycosylation that are of interest to biotechnologists, namely, biosynthesis and biological relevance, as well as the tools to optimize and to analyze protein glycosylation.

The complexity and internal organization of mammalian cells as well as the regulation of intracellular transport processes has increasingly moved into the focus of investigation during the past two decades. Advanced staining and microscopy techniques help to shed light onto spatial cellular compartmentation and regulation, increasing the demand for improved modeling techniques. In this chapter, we summarize recent developments in the field of quantitative simulation approaches and frameworks for the description of intracellular transport processes. Special focus is therefore laid on compartmented and spatiotemporally resolved simulation approaches. The processes considered include free and facilitated diffusion of molecules, active transport via the microtubule and actin filament network, vesicle distribution, membrane transport, cell cycle-dependent cell growth and morphology variation, and protein production. Commercially and freely available simulation packages are summarized as well as model data exchange and harmonization issues.

Animal cells can be regarded as factories for the production of relevant proteins. The advances described in this chapter towards the development of cell lines with higher productivity capacities, certain metabolic and proliferation properties, reduced apoptosis and other features must be regarded in an integrative perspective. The systematic application of systems biology approaches in combination with a synthetic arsenal for targeted modification of endogenous networks are proposed to lead towards the achievement of a predictable and technologically advanced cell system with high biotechnological impact.