Ziomara P. Gerdtzen

Comparative Transcriptome Analysis to Unveil Genes Affecting Recombinant Protein Productivity in Mammalian Cells

Low temperature culture (33°C) has been shown to enhance the specific productivity of recombinant antibodies in Chinese hamster ovary (CHO) cells but did not affect antibody productivity in hybridoma (MAK) cells. We probed the transcriptional response of both cells undergoing temperature shift using cDNA microarrays. Among the orthologous gene probes, common trends in the expression changes between CHO and MAK are not prominent. Instead, many transcriptional changes were specific to only one cell line. Notably, oxidative phosphorylation and ribosomal genes were downregulated in MAK but not in CHO. Conversely, several protein trafficking genes and cytoskeleton elements were upregulated in CHO but remained unchanged in MAK. Interestingly, at 33°C, immunoglobulin heavy and light chain showed no significant changes in CHO, but the immunoglobulin light chain was downregulated in MAK. Overall, a clear distinction in the transcriptional response to low temperature was seen in the two cell lines. To further elucidate the set of genes responsible for increased antibody productivity, the expression data of low temperature cultures was compared to that of butyrate treatment which increased specific antibody productivity in both cell lines. Genes which are commonly differentially expressed under conditions that increased productivity are likely to reflect functional classes that are important in the productivity changes. This comparative transcriptome analysis suggests that vesicle trafficking, endocytosis and cytoskeletal elements are involved in increased specific antibody productivity. Biotechnol. Bioeng. 2009;102: 246–263.

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

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.

Mathematical modeling of the dynamic storage of iron in ferritin

BackgroundIron is essential for the maintenance of basic cellular processes. In the regulation of its cellular levels, ferritin acts as the main intracellular iron storage protein. In this work we present a mathematical model for the dynamics of iron storage in ferritin during the process of intestinal iron absorption. A set of differential equations were established considering kinetic expressions for the main reactions and mass balances for ferritin, iron and a discrete population of ferritin species defined by their respective iron content.ResultsSimulation results showing the evolution of ferritin iron content following a pulse of iron were compared with experimental data for ferritin iron distribution obtained with purified ferritin incubated in vitro with different iron levels. Distinctive features observed experimentally were successfully captured by the model, namely the distribution pattern of iron into ferritin protein nanocages with different iron content and the role of ferritin as a controller of the cytosolic labile iron pool (cLIP). Ferritin stabilizes the cLIP for a wide range of total intracellular iron concentrations, but the model predicts an exponential increment of the cLIP at an iron content > 2,500 Fe/ferritin protein cage, when the storage capacity of ferritin is exceeded.ConclusionsThe results presented support the role of ferritin as an iron buffer in a cellular system. Moreover, the model predicts desirable characteristics for a buffer protein such as effective removal of excess iron, which keeps intracellular cLIP levels approximately constant even when large perturbations are introduced, and a freely available source of iron under iron starvation. In addition, the simulated dynamics of the iron removal process are extremely fast, with ferritin acting as a first defense against dangerous iron fluctuations and providing the time required by the cell to activate slower transcriptional regulation mechanisms and adapt to iron stress conditions. In summary, the model captures the complexity of the iron-ferritin equilibrium, and can be used for further theoretical exploration of the role of ferritin in the regulation of intracellular labile iron levels and, in particular, as a relevant regulator of transepithelial iron transport during the process of intestinal iron absorption.

Viral vectors for the treatment of alcoholism: Use of metabolic flux analysis for cell cultivation and vector production

The HEK293 cell line has been used for the production of adenovirus vectors to be used in the potential treatment of alcoholism using a gene therapy strategy. Culture optimization and scale-up has been achieved by first adapting the cells to serum-free media and secondly by growing them in suspension. Adenovirus production after infection was increased, resulting in higher specific glucose consumption and lactate accumulation rates compared to the growth phase. We applied media design tools and Metabolic Flux Analysis (MFA) to compare the metabolic states of cells during growth and adenovirus production and to optimize culture media according to the metabolic demand of the cells in terms of glucose and glutamine concentrations. This allowed obtaining a higher maximum cell concentration and increased adenovirus production by minimizing the production of metabolites that can have an inhibitory effect on cell growth. We have proposed a stoichiometric equation for adenovirus synthesis. MFA results allowed determination of how these changes in composition affected the way cells distribute their nutrient resources during cell growth and virus production. Virus purification was successfully achieved using chromatography and Aqueous Two-Phase Systems (ATPS).

Comparative Metabolic Analysis of CHO Cell Clones Obtained through Cell Engineering, for IgG Productivity, Growth and Cell Longevity

Cell engineering has been used to improve animal cells’ central carbon metabolism. Due to the central carbon metabolism’s inefficiency and limiting input of carbons into the TCA cycle, key reactions belonging to these pathways have been targeted to improve cultures’ performance. Previous works have shown the positive effects of overexpressing PYC2, MDH II and fructose transporter. Since each of these modifications was performed in different cell lines and culture conditions, no comparisons between these modifications can be made. In this work we aim at contrasting the effect of each of the modifications by comparing pools of transfected IgG producing CHO cells cultivated in batch cultures. Results of the culture performance of engineered clones indicate that even though all studied clones had a more efficient metabolism, not all of them showed the expected improvement on cell proliferation and/or specific productivity. CHO cells overexpressing PYC2 were able to improve their exponential growth rate but IgG synthesis was decreased, MDH II overexpression lead to a reduction in cell growth and protein production, and cells transfected with the fructose transporter gene were able to increase cell density and reach the same volumetric protein production as parental CHO cells in glucose. We propose that a redox unbalance caused by the new metabolic flux distribution could affect IgG assembly and protein secretion. In addition to reaction dynamics, thermodynamic aspects of metabolism are also discussed to further understand the effect of these modifications over central carbon metabolism.

Modeling heterocyst pattern formation in cyanobacteria

BackgroundTo allow the survival of the population in the absence of nitrogen, some cyanobacteria strains have developed the capability of differentiating into nitrogen fixing cells, forming a characteristic pattern. In this paper, the process by which cyanobacteria differentiates from vegetative cells into heterocysts in the absence of nitrogen and the elements of the gene network involved that allow the formation of such a pattern are investigated.MethodsA simple gene network model, which represents the complexity of the differentiation process, and the role of all variables involved in this cellular process is proposed. Specific characteristics and details of the systems behavior such as transcript profiles for ntcA, hetR and patS between consecutive heterocysts were studied.ResultsThe proposed model is able to capture one of the most distinctive features of this system: a characteristic distance of 10 cells between two heterocysts, with a small standard deviation according to experimental variability. The systems response to knock-out and over-expression of patS and hetR was simulated in order to validate the proposed model against experimental observations. In all cases, simulations show good agreement with reported experimental results.ConclusionA simple evolution mathematical model based on the gene network involved in heterocyst differentiation was proposed. The behavior of the biological system naturally emerges from the network and the model is able to capture the spacing pattern observed in heterocyst differentiation, as well as the effect of external perturbations such as nitrogen deprivation, gene knock-out and over-expression without specific parameter fitting.

Modeling metabolic networks for mammalian cell systems: general considerations, modeling strategies, and available tools.

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.

Effect of the electrostatic potential on the internalization mechanism of cell penetrating peptides derived from TIRAP

In order to develop future therapeutic applications for cell penetrating peptides (CPPs), it is essential to characterize their internalization mechanisms, as they might affect the stability and the accessibility of the carried drug. Several internalization mechanisms have been described in literature, such as endocytosis and transduction. In this work we study the internalization mechanism in HeLa cells of two TIRAP derived peptides: pepTIRAP and pepTIRAPALA, where some of the cationic amino acids were replaced with alanines. Detailed analysis of internalization and the peptides electrostatic potential was carried out, to shed light on the internalization mechanism involved. Molecular modeling studies showed that the main difference identified between pepTIRAP and pepTIRAPALA is the distribution of their electrostatic potential field. The structure of pepTIRAP displays a predominantly positive potential when compared to pepTIRAPALA, which has a more balanced potential distribution. In addition, docking experiments show that interactions between pepTIRAP and negatively charged molecules on the cellular surface such as heparan sulfate are stronger than the ones exhibited by pepTIRAPALA. A mathematical model was proposed to quantify the amount of peptide internalized or non-specifically bound to the membrane. The model indicates a stronger interaction of pepTIRAP with the plasma membrane, compared to pepTIRAPALA. We propose these discrepancies are related to the differences in the electrostatic potential characteristics of each peptide. In the case of pepTIRAP, these interactions lead to the formation of nucleation zones, which are the first stage of the transduction internalization mechanism. These results should be considered for effective design of a cell penetrating peptide.

Engineering CHO cell metabolism for growth in galactose.

Background Chinese hamster ovary (CHO) cells are one of the main hosts for industrial production of therapeutic proteins, owing to well-characterized technologies for gene transfection, amplification, and selection of high-producer clones. This has motivated the search for different strategies for the improvement of their specific productivity being one of the key points for this approaches the reduction of metabolic end-products like lactate and ammonia. The use of different carbon sources has been an alternative solution for this problem, as they are metabolized more slowly than glucose leading to lower production of metabolic end-products [1]. Particularly, it has been observed that cultures in presence of glucose and galactose undergo a metabolic shift in which they are capable of remetabolize lactate. However, the specific growth rate is diminished due to a slower metabolism associated to the incorporation of galactose [2]. In addition, cells are unable to survive with galactose as their unique carbon source [3]. In this work we aim at identifying culture conditions that extend the culture’s viability for tPA producing CHO cells in media with combined glucose and galactose as carbon sources. Furthermore, we propose reducing the production of secondary metabolites by overexpressing galactokinase (GALK1), a bottleneck point in the galactose metabolism.

Engineering CHO cells for improved central carbon and energy metabolism

Background Investigations have shown animal cell cultures’ performance, in terms of cell proliferation and production of recombinant protein, are negatively affected by both lactate’s concentration and its specific production rate. In a previous work, we determined that lactate production was caused by pyruvate accumulation due to its high synthesis rate in the glycolitic pathway and limited consumption in the TCA cycle, which leads to lactate production [1]. In this work, we use the ΔL/ΔHexose ratio in order to characterize the cells metabolic state. This ratio describes the lactate production rate vs. hexose consumption. Low ΔL/ΔHexose ratios indicate efficient metabolic states where carbons consumed are mainly used to support cell growth, protein synthesis or energy metabolism. Cell engineering has been previously used to improve cultures’ performance by changing the expression of genes involved in metabolism and apoptosis, focusing on the modification of only one gene at the time. These works showed that after overexpression of genes such as fructose transporter (Slc2a5) and yeast’s pyruvate carboxylase (PYC) cells are able to achieve higher cell densities and lower lactate production than wild-type cells under the same culture conditions [2-4]. In this work we aim at introducing multiple changes in the cells’ genome in order to obtain an engineered cell line with reduced lactate production and enhanced energy metabolism, which is capable of achieving higher cell densities and with a longer lifespan. We propose to control both, carbon uptake and its use by the TCA cycle. Cells were transfected with the fructose transporter gene (Slc2a5) and pyruvate carboxylase gene (PYC). Metabolic flux redistribution was studied through metabolic flux analysis, comparing engineered cells and wildtype under normal culture conditions.