Verónica S. Martínez

Flux balance analysis of CHO cells before and after a metabolic switch from lactate production to consumption

Mammalian cell cultures typically exhibit an energy inefficient phenotype characterized by the consumption of large quantities of glucose and the concomitant production of large quantities of lactate. Under certain conditions, mammalian cells can switch to a more energy efficient state during which lactate is consumed. Using a metabolic model derived from a mouse genome scale model we performed flux balance analysis of Chinese hamster ovary cells before and after a metabolic switch from lactate production (in the presence of glucose) to lactate consumption (after glucose depletion). Despite a residual degree of freedom after accounting for measurements, the calculated flux ranges and associated errors were narrow enough to enable investigation of metabolic changes across the metabolic switch. Surprisingly, the fluxes through the lower part of the TCA cycle from oxoglutarate to malate were very similar (around 60 µmol/gDW/h) for both phases. A detailed analysis of the energy metabolism showed that cells consuming lactate have an energy efficiency (total ATP produced per total C-mol substrate consumed) six times greater than lactate producing cells.

Recon 2.2: from reconstruction to model of human metabolism

IntroductionThe human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.ObjectivesWe report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.MethodsRecon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.ResultsRecon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.ConclusionThrough these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).

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.

Microbial iron management mechanisms in extremely acidic environments: comparative genomics evidence for diversity and versatility

BackgroundIron is an essential nutrient but can be toxic at high intracellular concentrations and organisms have evolved tightly regulated mechanisms for iron uptake and homeostasis. Information on iron management mechanisms is available for organisms living at circumneutral pH. However, very little is known about how acidophilic bacteria, especially those used for industrial copper bioleaching, cope with environmental iron loads that can be 1018 times the concentration found in pH neutral environments. This study was motivated by the need to fill this lacuna in knowledge. An understanding of how microorganisms thrive in acidic ecosystems with high iron loads requires a comprehensive investigation of the strategies to acquire iron and to coordinate this acquisition with utilization, storage and oxidation of iron through metal responsive regulation. In silico prediction of iron management genes and Fur regulation was carried out for three Acidithiobacilli: Acidithiobacillus ferrooxidans (iron and sulfur oxidizer) A. thiooxidans and A. caldus (sulfur oxidizers) that can live between pH 1 and pH 5 and for three strict iron oxidizers of the Leptospirillum genus that live at pH 1 or below.ResultsAcidithiobacilli have predicted FeoB-like Fe(II) and Nramp-like Fe(II)-Mn(II) transporters. They also have 14 different TonB dependent ferri-siderophore transporters of diverse siderophore affinity, although they do not produce classical siderophores. Instead they have predicted novel mechanisms for dicitrate synthesis and possibly also for phosphate-chelation mediated iron uptake. It is hypothesized that the unexpectedly large number and diversity of Fe(III)-uptake systems confers versatility to this group of acidophiles, especially in higher pH environments (pH 4–5) where soluble iron may not be abundant. In contrast, Leptospirilla have only a FtrI-Fet3P-like permease and three TonB dependent ferri-dicitrate siderophore systems. This paucity of iron uptake systems could reflect their obligatory occupation of extremely low pH environments where high concentrations of soluble iron may always be available and were oxidized sulfur species might not compromise iron speciation dynamics. Presence of bacterioferritin in the Acidithiobacilli, polyphosphate accumulation functions and variants of FieF-like diffusion facilitators in both Acidithiobacilli and Leptospirilla, indicate that they may remove or store iron under conditions of variable availability. In addition, the Fe(II)-oxidizing capacity of both A. ferrooxidans and Leptospirilla could itself be a way to evade iron stress imposed by readily available Fe(II) ions at low pH. Fur regulatory sites have been predicted for a number of gene clusters including iron related and non-iron related functions in both the Acidithiobacilli and Leptospirilla, laying the foundation for the future discovery of iron regulated and iron-phosphate coordinated regulatory control circuits.ConclusionIn silico analyses of the genomes of acidophilic bacteria are beginning to tease apart the mechanisms that mediate iron uptake and homeostasis in low pH environments. Initial models pinpoint significant differences in abundance and diversity of iron management mechanisms between Leptospirilla and Acidithiobacilli, and begin to reveal how these two groups respond to iron cycling and iron fluctuations in naturally acidic environments and in industrial operations. Niche partitions and ecological successions between acidophilic microorganisms may be partially explained by these observed differences. Models derived from these analyses pave the way for improved hypothesis testing and well directed experimental investigation. In addition, aspects of these models should challenge investigators to evaluate alternative iron management strategies in non-acidophilic model organisms.

Reducing Recon 2 for steady-state flux analysis of HEK cell culture.

A representative stoichiometric model is essential to perform metabolic flux analysis (MFA) using experimentally measured consumption (or production) rates as constraints. For Human Embryonic Kidney (HEK) cell culture, there is the opportunity to use an extremely well-curated and annotated human genome-scale model Recon 2 for MFA. Performing MFA using Recon 2 without any modification would have implied that cells have access to all functionality encoded by the genome, which is not realistic. The majority of intracellular fluxes are poorly determined as only extracellular exchange rates are measured. This is compounded by the fact that there is no suitable metabolic objective function to suppress non-specific fluxes. We devised a heuristic to systematically reduce Recon 2 to emphasize flux through core metabolic reactions. This implies that cells would engage these dominant metabolic pathways to grow, and any significant changes in gross metabolic phenotypes would have invoked changes in these pathways. The reduced metabolic model becomes a functionalized version of Recon 2 used for identifying significant metabolic changes in cells by flux analysis.

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).

Toward Synthetic Biology Strategies for Adipic Acid Production: An in Silico Tool for Combined Thermodynamics and Stoichiometric Analysis of Metabolic Networks

Adipic acid, a nylon-6,6 precursor, has recently gained popularity in synthetic biology. Here, 16 different production routes to adipic acid were evaluated using a novel tool for network-embedded thermodynamic analysis of elementary flux modes. The tool distinguishes between thermodynamically feasible and infeasible modes under determined metabolite concentrations, allowing the thermodynamic feasibility of theoretical yields to be assessed. Further, patterns that always caused infeasible flux distributions were identified, which will aid the development of tailored strain design. A review of cellular efflux mechanisms revealed that significant accumulation of extracellular product is only possible if coupled with ATP hydrolysis. A stoichiometric analysis demonstrated that the maximum theoretical product carbon yield heavily depends on the metabolic route, ranging from 32 to 99% on glucose and/or palmitate in Escherichia coli and Saccharomyces cerevisiae metabolic models. Equally important, metabolite concentrations appeared to be thermodynamically restricted in several pathways. Consequently, the number of thermodynamically feasible flux distributions was reduced, in some cases even rendering whole pathways infeasible, highlighting the importance of pathway choice. Only routes based on the shikimate pathway were thermodynamically favorable over a large concentration and pH range. The low pH capability of S. cerevisiae shifted the thermodynamic equilibrium of some pathways toward product formation. One identified infeasible-pattern revealed that the reversibility of the mitochondrial malate dehydrogenase contradicted the current state of knowledge, which imposes a major restriction on the metabolism of S. cerevisiae. Finally, the evaluation of industrially relevant constraints revealed that two shikimate pathway-based routes in E. coli were the most robust.

Quantification of Microbial Phenotypes

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.