Algorithms to infer metabolic flux ratios from fluxomics data

In silico cell simulation approaches based in the use of genome-scale metabolic models (GSMMs) and constraint-based methods such as Flux Balance Analysis are gaining importance, but methods to integrate these approaches with omics data are still greatly needed. In this work, the focus relies on fluxomics data that provide valuable information on the intracellular fluxes, although in many cases in an indirect, incomplete and noisy way. The proposed framework enables the integration of fluxomics data, in the form of 13C labeling distribution for metabolite fragments, with GSMMs enriched with carbon atom transition maps. The algorithms implemented allow to infer labeling distributions for fragments/metabolites not measured and to build expressions for the relevant flux ratios that can be then used to enrich constraint-based methods for flux determination. This approach does not require any assumptions on the metabolic network and reaction reversibility, allowing to compute ratios originating from coupled joint points of the network. Also, when enough data do not exist, the system tries to infer ratio bounds from the measurements

Metabolic footprint analysis of recombinant Escherichia coli strains during fed-batch fermentations

Metabolic footprinting has become a valuable analytical approach for the characterization of phenotypes and the distinction of specific metabolic states resulting from environmental and/or genetic alterations. The metabolic impact of heterologous protein production in Escherichia coli cells is of particular interest, since there are numerous cellular stresses triggered during this process that limit the overall productivity. Because the knowledge on the metabolic responses in recombinant bioprocesses is still scarce, metabolic footprinting can provide relevant information on the intrinsic metabolic adjustments. Thus, the metabolic footprints generated by Escherichia coli W3110 and the ΔrelA mutant strain during recombinant fed-batch fermentations at different experimental conditions, were measured and interpreted. The IPTG-induction of the heterologous protein expression resulted in the rapid accumulation of inhibitors of the glyoxylate shunt in the culture broth, suggesting the clearance of this anaplerotic route to replenish the TCA intermediaries withdrawn for the additional formation of heterologous protein. Nutritional shifts were also critical in the recombinant cellular metabolism, indicating that cells employ diverse strategies to counteract imbalances in the cellular metabolism, including the secretion of certain metabolites that are, most likely, used as a metabolic relief to survival processes.The authors thank to Raphael Aggio for assisting in the automatic refinement and correction of the GC-MS data. This work was supported in part by the research project Bridging Systems and Synthetic Biology for the development of Improved Microbial Cell Factories (MIT-Pt/BS-BB/0082/2008) and HeliSysBio-Molecular Systems Biology Helicobacter pylori (FCT PTDC/EBB-EBI/104235/2008), both financed by the Portuguese Fundacao para a Ciencia e Tecnologia. Sonia Carneiro was also supported by a PhD grant from the same institution (ref. SFRH/BD/22863/2005)

Applying a metabolic footprinting approach to characterize the impact of the recombinant protein production in Escherichia coli

In this study metabolic footprinting was applied to evaluate the metabolic consequences of protein overproduction at slow growth conditions (μ = 0.1 h-1). The extracellular metabolites detected by gas chromatography-mass spectrometry characterized the metabolic footprints before and after the induction of the recombinant protein production (i.e. pre- and post-induction phases). Metabolic footprinting enabled the discrimination between the two growth phases and ex-posed significant metabolic alterations in the extracellular milieu during the re-combinant processes.This work is partly funded by the Portuguese FCT (Fundacao para a Ciencia e Tecnologia) funded MIT-Portugal Program in Bioengineering (MIT-Pt/BSBB/0082/2008). The work of Sonia Carneiro is supported by a PhD grant from FCT (ref. SFRH/BD/22863/2005)

Influence of the RelA activity on E. coli metabolism by metabolite profiling of glucose-limited chemostat cultures

Metabolite profiling of E. coli W3110 and the isogenic ∆relA mutant cells was used to characterize the RelA-dependent stringent control of metabolism under different growth conditions. Metabolic profiles were obtained by gas chromatography–mass spectrometry (GC-MS) analysis and revealed significant differences between E. coli strains grown at different conditions. Major differences between the two strains were assessed in the levels of amino acids and fatty acids and their precursor metabolites, especially when growing at the lower dilution rates, demonstrating differences in their metabolic behavior. Despite the fatty acid biosynthesis being the most affected due to the lack of the RelA activity, other metabolic pathways involving succinate, lactate and threonine were also affected. Overall, metabolite profiles indicate that under nutrient-limiting conditions the RelA-dependent stringent response may be elicited and promotes key changes in the E. coli metabolism.The authors thank to Raphael Aggio for assisting in the automatic refinement and correction of the GC-MS data, Katie Smart for performing acetate analyses and Clark Ehlers for his support with the bioreactor set up. This work was supported by the Portuguese FCT (Fundacao para a Ciencia e Tecnologia) funded MIT-Portugal Program in Bioengineering (MIT-Pt/BS-BB/0082/2008) and by ERDF-European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within the project FCOMP-01-0124-FEDER-009707 (HeliSysBio. molecular Systems Biology in Helicobacter pylori). The work was also supported by a PhD grant from FCT (ref. SFRH/BD/22863/2005)

Genome-scale metabolic network of the central carbon metabolism of Enterococcus faecalis

The profound advance in experimental high throughput techniques (generally referred to as omics techniques) has enabled the analysis of a large number of components within a living cell. The vast amount of data obtained from the different omics (genomics, proteomics, fluxomics, metabolomics, transcriptomics) demands the use of bioinformatics tools. These methods comprise the development of comparative tools and maintenance of databases for the analysis of genomics data, in addition to the construction of models for the analysis and integration of data in a system-wide approach. Enterococcus faecalis is a gram-positive bacterium that is getting more attention due to its two-face behavior. This natural inhabitant of the mammalian gastrointestinal tract is also an opportunist pathogen responsible for urinary tract infections, nosocomial infections, bacteremia and infective endocarditis. Besides, its intrinsic physiological properties such as inherent antibiotic resistance and exceptional ability to adapt to harsh conditions provide this organism with an enormous advantage in the infection processes. Here, we propose to reconstruct the genome scale metabolic network of the central carbon metabolism of Enterococcus faecalis using genome sequencing information available on different databases as well as proteomics and metabolomics data. The first metabolic model generated for this bacterium will allow correlating metabolite levels and fluxes which enables identification of key control points in its metabolism. As it has been previously shown for other organisms, the metabolic network reconstruction may serve as a valuable tool to predict the phenotypic behaviour under various genetic and environmental conditions.Supported by a PhD grant from the FCT (Portuguese Science Foundation): SFRH/BD/47016/2008 and funding from HRC (Health Research Council of New Zealand)

Genome scale metabolic network reconstruction of pathogen – Enterococcus faecalis

Enterococcus faecalis is a Gram-positive bacterium that is getting more attention due to its “two-face” behavior. This natural inhabitant of the gastrointestinal mammalian tract is also an opportunist pathogen responsible for urinary tract infections, nosocomial infections, bacteremia and infective endocarditis (1). Since the metabolic reconstruction of Haemophilus influenzae was published in 1999 (2), many other researchers have focused their attention into the possibilities that the new era of genome-scale metabolic models could bring to the scientific scene, both in prokaryotic and eukaryotic organisms

Metabolic network reconstruction of the central carbon metabolism of Enterococcus faecalis

The profound advance in experimental high throughput techniques (generally referred to as “omics techniques”) has enabled the analysis of a large number of components within a living cell. The vast amount of data obtained from the different “omics” (genomics, proteomics, fluxomics, metabolomics, transcriptomics) demands the use of bioinformatics tools. These methods comprise the development of comparative tools and maintenance of databases for the analysis of genomics data, in addition to the construction of models for the analysis and integration of data in a system-wide approach. Enterococcus faecalis is a Gram-positive bacterium that is getting more attention due to its “two-face” behavior. This natural inhabitant of the mammalian gastrointestinal tract is also an opportunist pathogen responsible for urinary tract infections, nosocomial infections, bacteremia and infective endocarditis. Besides, its intrinsic physiological properties such as inherent antibiotic resistance and exceptional ability to adapt to harsh conditions provide this organism with an enormous advantage in the infection processes. Here, we propose to reconstruct the genome scale metabolic network of the central carbon metabolism of Enterococcus faecalis using genome sequencing information available on different databases as well as proteomics and metabolomics data. The first metabolic model generated for this bacterium will allow correlating metabolite levels and fluxes which enables identification of key control points in its metabolism. As it has been previously shown for other organisms, the metabolic network reconstruction may serve as a valuable tool to predict the phenotypic behaviour under various genetic and environmental conditions

Flexible and user friendly tools for the incorporation of fluxomics data into metabolic models

The measurement of fluxes and the understanding of their control are at the core of Metabolic Engineering (ME). In this context, this work presents two integrated open- source software tools that allow to perform tasks of metabolic flux analysis (MFA). Both are platform independent, written in Java, and interact with the OptFlux framework [1], which also facilitates their communication (Figure 1). OptFlux is a modular open-source software that incorporates tools for strain optimization, i.e., the identification of ME targets. It also provides tools to use stoichiometric metabolic models for phenotype simulation of both wild-type and mutant organisms, using methods such as the well known Flux Balance Analysis (FBA). Graphical user interfaces are made available for every operation and to check the results that are obtained. Moreover, a network visualization system is offered, where simulation results can be added to overlap the network graph. The developed tools exploit OptFluxâ??s capabilities in terms of model interaction, simulation methods and visualization features. The first proposed software, named MetabolIc NEtwork Ratio AnaLysis (MiNeRAl) (Figure 1, bottom), aims at analyzing labeling experiments to infer flux constraints that for stoichiometric models. From a set of measurements of a 13C-labelling experiment, mass isotopomer distribution vectors (MDV) are calculated. If aminoacids are measured, the measured fragments, coupled with a carbon transition map provided by the user, are used to determine their precursors, and the corresponding MDVs are calculated. Based on the set of MDVs, the software uses the carbon transitions to determine the flux ratios that produce a given metabolite through the different pathways. These ratios are probabilistic equations that translate how the 13C-labeling pattern is distributed throughout the metabolic network [2]. Since the calculation of the flux ratios is independent of the flux distribution, this software can be used independently of other flux calculation processes, and the ratios can be further exploited to reduce the degrees of freedom of systems obtained in other MFA approaches [3,4]. The main differentiating characteristics of this tool are, besides being usr-friendly, the fact that it is generic for any type of metabolite fragmentation originating from GC-MS techniques and metabolic network topology. Furthermore, the software is also able to investigate what flux ratio constraints are possible to be inferred for a certain experiment beforehand. On the other hand, the second software application here described, jMFA (Figure 1, top), is focused on using different types of experimental flux data to constrain metabolic models and improve their predictions with a variety of tools. It allows users to define constraints associated with measured fluxes and/ or flux ratios, together with environmental conditions (e.g. media) and reaction/ gene knockouts. The application identifies the set of applicable methods based on the constraints defined from user inputs, allowing to select the desired approach, encompassing algebraic and constraint- based simulation methods (such as Flux Balance Analysis and its variants). Anytime a set of constraints is selected, the software calculates the degrees of freedom of the configured system, and updates the admissible methods depending on whether the system is underdetermined, determined or overdetermined, as shown in Figure 1. A method to perform robustness analysis is also implemented. The integration of jMFA within the OptFlux framework allows the use of different model formats and the integration with complementary methods for phenotype simulation and visualization of the results. Moreover, the flux ratio constraints can be obtained from previous calculations in MiNeRAl, or manually defined by the user. The first option provides a straightforward way to integrate both applications in a ME workflow