Analysis of metabolic flux distributions in relation to the extracellular environment in Avian cells

Continuous cell lines that proliferate in chemically defined and simple media have been highly regarded as suitable alternatives for vaccine production. One such cell line is the AG1.CR.pIX avian cell line developed by PROBIOGEN. This cell line can be cultivated in a fully scalable suspension culture and adapted to grow in chemically defined, calf serum free, medium [1]–[5]. The medium composition and cultivation strategy are important factors for reaching high virus titers. In this project, a series of computational methods was used to simulate the cell’s response to different environments. The study is based on the metabolic model of the central metabolism proposed in [1]. In a first step, Metabolic Flux Analysis (MFA) was used along with measured uptake and secretion fluxes to estimate intracellular flux values. The network and data were found to be consistent. In a second step, Flux Balance Analysis (FBA) was performed to access the cell’s biological objective. The objective that resulted in the best predicted results fit to the experimental data was the minimization of oxidative phosphorylation. Employing this objective, in the next step Flux Variability Analysis (FVA) was used to characterize the flux solution space. Furthermore, various scenarios, where a reaction deletion (elimination of the compound from the media) was simulated, were performed and the flux solution space for each scenario was calculated. Growth restrictions caused by essential and non-essential amino acids were accurately predicted. Fluxes related to the essential amino acids uptake and catabolism, the lipid synthesis and ATP production via TCA were found to be essential to exponential growth. Finally, the data gathered during the previous steps were analyzed using principal component analysis (PCA), in order to assess potential changes in the physiological state of the cell. Three metabolic states were found, which correspond to zero, partial and maximum biomass growth rate. Elimination of non-essential amino acids or pyruvate from the media showed no impact on the cell’s assumed normal metabolic state.

Algorithms and computational tools for metabolic flux analysis

PhD thesis in Biomedical Engineering

Metabolic flux analysis of neural cell metabolism in primary cultures

Dissertation presented to obtain the Ph.D degree in Biochemistry, Neuroscience

Metabolic flux analysis for directing metabolism of Streptomyces olindensis from growth to anti-tumor drug (cosmomycin) production

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES, Brazil

Constraint-based modelling of mixed microbial populations: Application to polyhydroxyalkanoates production

Dissertação para obtenção do Grau de Doutor em Engenharia Química e Bioquímica

Development of process control strategies exploiting knowledge from systems biology: application to MDCK suspension cells

Madine Darby Canine Kidney (MDCK) cell lines have been extensively evaluated for their potential as host cells for influenza vaccine production. Recent studies allowed the cultivation of these cells in a fully defined medium and in suspension. However, reaching high cell densities in animal cell cultures still remains a challenge. To address this shortcoming, a combined methodology allied with knowledge from systems biology was reported to study the impact of the cell environment on the flux distribution. An optimization of the medium composition was proposed for both a batch and a continuous system in order to reach higher cell densities. To obtain insight into the metabolic activity of these cells, a detailed metabolic model previously developed by Wahl A. et. al was used. The experimental data of four cultivations of MDCK suspension cells, grown under different conditions and used in this work came from the Max Planck Institute, Magdeburg, Germany. Classical metabolic flux analysis (MFA) was used to estimate the intracellular flux distribution of each cultivation and then combined with partial least squares (PLS) method to establish a link between the estimated metabolic state and the cell environment. The validation of the MFA model was made and its consistency checked. The resulted PLS model explained almost 70% of the variance present in the flux distribution. The medium optimization for the continuous system and for the batch system resulted in higher biomass growth rates than the ones obtained experimentally, 0.034 h-1 and 0.030 h-1, respectively, thus reducing in almost 10 hours the duplication time. Additionally, the optimal medium obtained for the continuous system almost did not consider pyruvate. Overall the proposed methodology seems to be effective and both proposed medium optimizations seem to be promising to reach high cell densities.

Análise de fluxos metabólicos para otimização da síntese do antibiótico cosmomicina por Streptomyces olindensis ICB20

Metabolic flux analysis (MFA) is a powerful tool for analyzing cellular metabolism. In order to control the growth conditions of a specific organism, it is important to have a complete understanding of its MFA. This would allowed us to improve the processes for obtaining products of interest to human and also to understand how to manipulate the genome of a cell, allowing optimization process for genetic engineering. Streptomyces olindensis ICB20 is a promising producer of the antibiotic cosmomycin, a powerful antitumor drug. Several Brazilian researchers groups have been developing studies in order to optimize cosmomycin production in bioreactors. However, to the best of our knowledge, nothing has been done on metabolic fluxes analysis field. Therefore, the aim of this work is to identify several factors that can affect the metabolism of Streptomyces olindensis ICB20, through the metabolic flux analysis. As a result, the production of the secondary metabolite, cosmomycin, can be increased. To achieve this goal, a metabolic model was developed which simulates a distribution of internal cellular fluxes based on the knowledge of metabolic pathways, its interconnections, as well as the constraints of microorganism under study. The validity of the proposed model was verified by comparing the computational data obtained by the model with the experimental data obtained from the literature. Based on the analysis of intracellular fluxes, obtained by the model, an optimal culture medium was proposed. In addition, some key points of the metabolism of Streptomyces olindensis were identified, aiming to direct its metabolism to a greater cosmomycin production. In this sense it was found that by increasing the concentration of yeast extract, the culture medium could be optimized. Furthermore, the inhibition of the biosynthesis of fatty acids was found to be a interesting strategy for genetic manipulation. Based on the metabolic model, one of the optimized medium conditions was experimentally tested in order to demonstrate in vitro what was obtained in silico. It was found that by increasing the concentration of yeast extract in the culture medium would induce to an increase of the cosmomycin production

Metabolic Flux and Compartmentation Analysis in the Brain In vivo.

Through significant developments and progresses in the last two decades, in vivo localized nuclear magnetic resonance spectroscopy (MRS) became a method of choice to probe brain metabolic pathways in a non-invasive way. Beside the measurement of the total concentration of more than 20 metabolites, (1)H MRS can be used to quantify the dynamics of substrate transport across the blood-brain barrier by varying the plasma substrate level. On the other hand, (13)C MRS with the infusion of (13)C-enriched substrates enables the characterization of brain oxidative metabolism and neurotransmission by incorporation of (13)C in the different carbon positions of amino acid neurotransmitters. The quantitative determination of the biochemical reactions involved in these processes requires the use of appropriate metabolic models, whose level of details is strongly related to the amount of data accessible with in vivo MRS. In the present work, we present the different steps involved in the elaboration of a mathematical model of a given brain metabolic process and its application to the experimental data in order to extract quantitative brain metabolic rates. We review the recent advances in the localized measurement of brain glucose transport and compartmentalized brain energy metabolism, and how these reveal mechanistic details on glial support to glutamatergic and GABAergic neurons.

Metabolic analysis of poly(3-hydroxybutyrate) production by recombinant Escherichia coli

Poly(3-hydroxybutyrate) (PHB) production by fermentation was examined under both restricted- and ample-oxygen supply conditions in a single fed-batch fermentation. Recombinant Escherichia coli transformed with the PHB production plasmid pSYL107 was grown to reach high cell density (227 g/l dry cell weight) with a high PHB content (78% of dry cell weight), using a glucose-based minimal medium. A simple flux model containing 12 fluxes was developed and applied to the fermentation data. A superior closure (95%) of the carbon mass balance was achieved. When the data were put into use, the results demonstrated a surprisingly large excretion of formate and lactate. Even though periods of severe oxygen limitation coincided with rapid acetate and lactate excretion, PHB productivity and carbon utilization efficiency were not significantly impaired. These results are very positive in reducing oxygen demand in an industrial PHA fermentation without sacrificing its PHA productivity, thereby reducing overall production costs.

Metabolic and kinetic analysis of poly(3-hydroxybutyrate) production by recombinant Escherichia coli

A quantitatively repeatable protocol was developed for poly(3-hydroxybutyrate) (PHB) production by Escherichia coli XL1-Blue (pSYL107). Two constant-glucose fed-batch fermentations of duration 25 h were carried out in a 5-L bioreactor, with the measured oxygen volumetric mass-transfer coefficient (k(L)a) held constant at 1.1 min(-1). All major consumption and production rates were quantified. The intracellular concentration profiles of acetyl-CoA (300 to 600 mug.g RCM-1) and 3-hydroxy-butyryl-CoA (20 to 40 mug.g RCM-1) were measured, which is the first time this has been performed for E. coli during PHB production. The kinetics of PHB production were examined and likely ranges were established for polyhydroxyalkanoate (PHA) enzyme activity and the concentration of pathway metabolites. These measured and estimated values are quite similar to the available literature estimates for the native PHB producer Ralstonia eutropha. Metabolic control analysis performed on the PHB metabolic pathway showed that the PHB flux was highly sensitive to acetyl-CoA/CoA ratio (response coefficient 0.8), total acetyl-CoA + CoA concentration (response coefficient 0.7), and pH (response coefficient -1.25). It was less sensitive (response coefficient 0.25) to NADPH/NADP ratio. NADP(H) concentration (NADPH + NADP) had a negligible effect. No single enzyme had a dominant flux control coefficient under the experimental conditions examined (0.6, 0.25, and 0.15 for 3-ketoacyl-CoA reductase, PHA synthase, and 3-ketothiolase, respectively). In conjunction with metabolic flux analysis, kinetic analysis was used to provide a metabolic explanation for the observed fermentation profile. In particular, the rapid onset of PHB production was shown to be caused by oxygen limitation, which initiated a cascade of secondary metabolic events, including cessation of TCA cycle flux and an increase in acetyl-CoA/CoA ratio. (C) 2001 John Wiley & Sons. Inc. Biotechnol Bioeng 74: 70-80, 2001.

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

A new approach for potential drug target discovery through in silico metabolic pathway analysis using Trypanosoma cruzi genome information

The current drug options for the treatment of chronic Chagas disease have not been sufficient and high hopes have been placed on the use of genomic data from the human parasite Trypanosoma cruzi to identify new drug targets and develop appropriate treatments for both acute and chronic Chagas disease. However, the lack of a complete assembly of the genomic sequence and the presence of many predicted proteins with unknown or unsure functions has hampered our complete view of the parasite's metabolic pathways. Moreover, pinpointing new drug targets has proven to be more complex than anticipated and has revealed large holes in our understanding of metabolic pathways and their integrated regulation, not only for this parasite, but for many other similar pathogens. Using an in silicocomparative study on pathway annotation and searching for analogous and specific enzymes, we have been able to predict a considerable number of additional enzymatic functions in T. cruzi. Here we focus on the energetic pathways, such as glycolysis, the pentose phosphate shunt, the Krebs cycle and lipid metabolism. We point out many enzymes that are analogous to those of the human host, which could be potential new therapeutic targets.