Elementary mode analysis: a useful metabolic pathway analysis tool for reprograming microbial metabolic pathways

In Silico Prediction of Metabolic Fluxes in Cancer Cells with Altered S-adenosylmethionine Decarboxylase Activity

This paper investigates the redistribution of metabolic fluxes in the cell with altered activity of S-adenosylmethionine decarboxylase (SAMdc, EC: 4.1.1.50), the key enzyme of the polyamine cycle and the common target for antitumor therapy. To address these goals, a stoichiometric metabolic model was developed that includes five metabolic pathways: polyamine, methionine, methionine salvage cycles, folic acid cycle, and the pathway of glutathione and taurine synthesis. The model is based on 51 reactions involving 57 metabolites, 31 of which are internal metabolites. All calculations were performed using the method of Flux Balance Analysis. The outcome indicates that the inactivation of SAMdc results in a significant increase in fluxes through the methionine, the taurine and glutathione synthesis, and the folate cycles. Therefore, when using therapeutic agents inactivating SAMdc, it is necessary to consider the possibility of cellular tumor metabolism reprogramming. S-adenosylmethionine affects serine methylation and activates serine-dependent de novo ATP synthesis. Methionine-depleted cell becomes methionine-dependent, searching for new sources of methionine. Inactivation of SAMdc enhances the transformation of S-adenosylmethionine to homocysteine and then to methionine. It also intensifies the transsulfuration process activating the synthesis of glutathione and taurine.

lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites

In the post-genomic era, Genome-scale metabolic networks (GEMs) have emerged as invaluable tools to understand metabolic capabilities of organisms. Different parts of these metabolic networks are defined as subsystems/pathways, which are sets of functional roles to implement a specific biological process or structural complex, such as glycolysis and TCA cycle. Subsystem/pathway definition is also employed to delineate the biosynthetic routes that produce biomass building blocks. In databases, such as MetaCyc and SEED, these representations are composed of linear routes from precursors to target biomass building blocks. However, this approach cannot capture the nested, complex nature of GEMs. Here we implemented an algorithm, lumpGEM, which generates biosynthetic subnetworks composed of reactions that can synthesize a target metabolite from a set of defined core precursor metabolites. lumpGEM captures balanced subnetworks, which account for the fate of all metabolites along the synthesis routes, thus encapsulating reactions from various subsystems/pathways to balance these metabolites in the metabolic network. Moreover, lumpGEM collapses these subnetworks into elementally balanced lumped reactions that specify the cost of all precursor metabolites and cofactors. It also generates alternative subnetworks and lumped reactions for the same metabolite, accounting for the flexibility of organisms. lumpGEM is applicable to any GEM and any target metabolite defined in the network. Lumped reactions generated by lumpGEM can be also used to generate properly balanced reduced core metabolic models.

Stoichiometric models have been used in the area of metabolic engineering and systems biology for many decades. The early examples of these models include simplified ad hoc built metabolic pathways, and biomass compositions. The development of genome scale models (GEMs) brought a standard to metabolic network modeling. However, the vast amount of detailed biochemistry in GEMs makes it necessary to develop methods to manage the complexity in them. In this study, we developed lumpGEM, a tool that can systematically identify subnetworks from metabolic networks that can perform certain tasks, such as biosynthesis of a biomass building block and any other target metabolite. By generating alternative subnetworks, lumpGEM also accounts for the redundancy in metabolic networks. We applied lumpGEM on latest E. coli GEM iJO1366 and identified subnetworks/lumped reactions for every biomass building block defined in its biomass formulation. We also compared the results from lumpGEM with existing knowledge in the literature. The lumped reactions generated by lumpGEM can be used to generate consistently reduced metabolic network models.

Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome

BACKGROUND

Clostridium thermocellum is a gram-positive thermophile that can directly convert lignocellulosic material into biofuels. The metabolism of C. thermocellum contains many branches and redundancies which limit biofuel production, and typical genetic techniques are time-consuming. Further, the genome sequence of a genetically tractable strain C. thermocellum DSM 1313 has been recently sequenced and annotated. Therefore, developing a comprehensive, predictive, genome-scale metabolic model of DSM 1313 is desired for elucidating its complex phenotypes and facilitating model-guided metabolic engineering.

RESULTS

We constructed a genome-scale metabolic model iAT601 for DSM 1313 using the KEGG database as a scaffold and an extensive literature review and bioinformatic analysis for model refinement. Next, we used several sets of experimental data to train the model, e.g., estimation of the ATP requirement for growth-associated maintenance (13.5 mmol ATP/g DCW/h) and cellulosome synthesis (57 mmol ATP/g cellulosome/h). Using our tuned model, we investigated the effect of cellodextrin lengths on cell yields, and could predict in silico experimentally observed differences in cell yield based on which cellodextrin species is assimilated. We further employed our tuned model to analyze the experimentally observed differences in fermentation profiles (i.e., the ethanol to acetate ratio) between cellobiose- and cellulose-grown cultures and infer regulatory mechanisms to explain the phenotypic differences. Finally, we used the model to design over 250 genetic modification strategies with the potential to optimize ethanol production, 6155 for hydrogen production, and 28 for isobutanol production.

CONCLUSIONS

Our developed genome-scale model iAT601 is capable of accurately predicting complex cellular phenotypes under a variety of conditions and serves as a high-quality platform for model-guided strain design and metabolic engineering to produce industrial biofuels and chemicals of interest.

Systematic development of biomass overproducing Scheffersomyces stipitis for high-cell-density fermentations

The development of economically feasible bio-based process requires efficient cell factories capable of producing the desired product at high titer under high-cell-density fermentation. Herein we present a combinatorial approach based on systems metabolic engineering and metabolic evolution for the development of efficient biomass-producing strain. Systems metabolic engineering guided by flux balance analysis (FBA) was first employed to rationally design mutant strains of Scheffersomyces stipitis with high biomass yield. By experimentally implementing these mutations, the biomass yield was improved by 30% in GPD1, 25% in TKL1, 30% in CIT1, and 44% in ZWF1 overexpressed mutants compared to wild-type. These designed mutants were further fine-tuned through metabolic evolution resulting in the maximal biomass yield of 0.49 g-cdw/g-glucose, which matches well with predicted yield phenotype. The constructed mutants are beneficial for biotechnology applications dealing with high cell titer cultivations. This work demonstrates a solid confirmation of systems metabolic engineering in combination with metabolic evolution approach for efficient strain development, which could assist in rapid optimization of cell factory for an economically viable and sustainable bio-based process.

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.

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.