Biomedical subjects

Jochen Förster

Publications and source records attributed to Jochen Förster.

10 recordsLinked to original sources

PubMed2006

Characterization of a cyanobacterial-like uptake [NiFe] hydrogenase: EPR and FTIR spectroscopic studies of the enzyme from Acidithiobacillus ferrooxidans.

Electron paramagnetic resonance (EPR) and Fourier transform IR studies on the soluble hydrogenase from Acidithiobacillus ferrooxidans are presented. In addition, detailed sequence analyses of the two subunits of the enzyme have been performed. They show that the enzyme belongs to a group of uptake [NiFe] hydrogenases typical for Cyanobacteria. The sequences have also a close relationship to those of the H(2)-sensor proteins, but clearly differ from those of standard [NiFe] hydrogenases. It is concluded that the structure of the catalytic centre is similar, but not identical, to that of known [NiFe] hydrogenases. The active site in the majority of oxidized enzyme molecules, 97% in cells and more than 50% in the purified enzyme, is EPR-silent. Upon contact with H(2) these sites remain EPR-silent and show only a limited IR response. Oxidized enzyme molecules with an EPR-detectable active site show a Ni(r)*-like EPR signal which is light-sensitive at cryogenic temperatures. This is a novelty in the field of [NiFe] hydrogenases. Reaction with H(2) converts these active sites to the well-known Ni(a)-C* state. Illumination below 160 K transforms this state into the Ni(a)-L* state. The reversal, in the dark at 200 K, proceeds via an intermediate Ni EPR signal only observed with the H(2)-sensor protein from Ralstonia eutropha. The EPR-silent active sites in as-isolated and H(2)-treated enzyme are also light-sensitive as observed by IR spectra at cryogenic temperatures. The possible origin of the light sensitivity is discussed. This study represents the first spectral characterization of an enzyme of the group of cyanobacterial uptake hydrogenases.

Acidithiobacillus↗

PubMed2005

Evolutionary programming as a platform for in silico metabolic engineering.

BACKGROUND: Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. RESULTS: In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed. CONCLUSION: We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems.

Algorithms↗

PubMed2005

In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production.

In silico genome-scale cell models are promising tools for accelerating the design of cells with improved and desired properties. We demonstrated this by using a genome-scale reconstructed metabolic network of Saccharomyces cerevisiae to score a number of strategies for metabolic engineering of the redox metabolism that will lead to decreased glycerol and increased ethanol yields on glucose under anaerobic conditions. The best-scored strategies were predicted to completely eliminate formation of glycerol and increase ethanol yield with 10%. We successfully pursued one of the best strategies by expressing a non-phosphorylating, NADP(+)-dependent glyceraldehyde-3-phosphate dehydrogenase in S. cerevisiae. The resulting strain had a 40% lower glycerol yield on glucose while the ethanol yield increased with 3% without affecting the maximum specific growth rate. Similarly, expression of GAPN in a strain harbouring xylose reductase and xylitol dehydrogenase led to an improvement in ethanol yield by up to 25% on xylose/glucose mixtures.

Computer Simulation↗

PubMed2005

Modeling Lactococcus lactis using a genome-scale flux model.

BACKGROUND: Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) were used as modeling frameworks. RESULTS: The metabolic network was reconstructed using the annotated genome sequence from L. lactis ssp. lactis IL1403 together with physiological and biochemical information. The established network comprised a total of 621 reactions and 509 metabolites, representing the overall metabolism of L. lactis. Experimental data reported in the literature was used to fit the model to phenotypic observations. Regulatory constraints had to be included to simulate certain metabolic features, such as the shift from homo to heterolactic fermentation. A minimal medium for in silico growth was identified, indicating the requirement of four amino acids in addition to a sugar. Remarkably, de novo biosynthesis of four other amino acids was observed even when all amino acids were supplied, which is in good agreement with experimental observations. Additionally, enhanced metabolic engineering strategies for improved diacetyl producing strains were designed. CONCLUSION: The L. lactis metabolic network can now be used for a better understanding of lactococcal metabolic capabilities and potential, for the design of enhanced metabolic engineering strategies and for integration with other types of 'omic' data, to assist in finding new information on cellular organization and function.

Bacteria, Anaerobic↗

PubMed2004

Integration of gene expression data into genome-scale metabolic models.

A framework for integration of transcriptome data into stoichiometric metabolic models to obtain improved flux predictions is presented. The key idea is to exploit the regulatory information in the expression data to give additional constraints on the metabolic fluxes in the model. Measurements of gene expression from chemostat and batch cultures of Saccharomyces cerevisiae were combined with a recently developed genome-scale model, and the computed metabolic flux distributions were compared to experimental values from carbon labeling experiments and metabolic network analysis. The integration of expression data resulted in improved predictions of metabolic behavior in batch cultures, enabling quantitative predictions of exchange fluxes as well as qualitative estimations of changes in intracellular fluxes. A critical discussion of correlation between gene expression and metabolic fluxes is given.

Databases, Genetic↗

PubMed2003

Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae.

A large-scale in silico evaluation of gene deletions in Saccharomyces cerevisiae was conducted using a genome-scale reconstructed metabolic model. The effect of 599 single gene deletions on cell viability was simulated in silico and compared to published experimental results. In 526 cases (87.8%), the in silico results were in agreement with experimental observations when growth on synthetic complete medium was simulated. Viable phenotypes were correctly predicted in 89.4% (496 out of 555) and lethal phenotypes were correctly predicted in 68.2% (30 out of 44) of the cases considered. The in silico evaluation was solely based on the topological properties of the metabolic network which is based on well-established reaction stoichiometry. No interaction or regulatory information was accounted for in the in silico model. False predictions were analyzed on a case-by-case basis for four possible inadequacies of the in silico model: (1) incomplete media composition, (2) substitutable biomass components, (3) incomplete biochemical information, and (4) missing regulation. This analysis eliminated a number of false predictions and suggested a number of experimentally testable hypotheses. A genome-scale in silico model can thus be used to systematically reconcile existing data and fill in our knowledge gaps about an organism.

Computational Biology↗

PubMed2003

Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network.

The metabolic network in the yeast Saccharomyces cerevisiae was reconstructed using currently available genomic, biochemical, and physiological information. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, and transport steps between the compartments and the environment were included. A total of 708 structural open reading frames (ORFs) were accounted for in the reconstructed network, corresponding to 1035 metabolic reactions. Further, 140 reactions were included on the basis of biochemical evidence resulting in a genome-scale reconstructed metabolic network containing 1175 metabolic reactions and 584 metabolites. The number of gene functions included in the reconstructed network corresponds to approximately 16% of all characterized ORFs in S. cerevisiae. Using the reconstructed network, the metabolic capabilities of S. cerevisiae were calculated and compared with Escherichia coli. The reconstructed metabolic network is the first comprehensive network for a eukaryotic organism, and it may be used as the basis for in silico analysis of phenotypic functions.

Amino Acids↗

PubMed2002

A functional genomics approach using metabolomics and in silico pathway analysis.

In the field of functional genomics increasing effort is being undertaken to analyze the function of orphan genes using metabolome data. Improved analytical equipment allows screening simultaneously for a high number of metabolites. Such metabolite profiles are analyzed using multivariate data analysis techniques and changes in the genotype will in many cases lead to different metabolite profiles. Here, a theoretical framework that may be applied to identify the function of orphan genes is presented. The approach is based on a combination of metabolome analysis combined with in silico pathway analysis. Pathway analysis may be carried out using convex analysis and a change in the active pathway structure of deletion mutants expressed in a different metabolite profile may disclose the function or the functional class of an orphan gene. The concept is illustrated using a simplified model for growth of Saccharomyces cerevisiae.

Algorithms↗

PubMed2002

Aerobic glucose metabolism of Saccharomyces kluyveri: growth, metabolite production, and quantification of metabolic fluxes.

The growth and product formation of Saccharomyces kluyveri was characterized in aerobic batch cultivation on glucose. At these conditions it was found that ethyl acetate was a major overflow metabolite in S. kluyveri. During the exponential-growth phase on glucose ethyl acetate was produced at a constant specific rate of 0.12 g ethyl acetate per g dry weight per hour. The aerobic glucose metabolism in S. kluyveri was found to be less fermentative than in S. cerevisiae, as illustrated by the comparably low yield of ethanol on glucose (0.08 +/- 0.02 g/g), and high yield of biomass on glucose (0.29 +/- 0.01 g/g). The glucose metabolism of S. kluyveri was further characterized by the new and powerful techniques of metabolic network analysis. Flux distributions in the central carbon metabolism were estimated for respiro-fermentative growth in aerobic batch cultivation on glucose and respiratory growth in aerobic glucose-limited continuous cultivation. It was found that in S. kluyveri the flux into the pentose phosphate pathway was 18.8 mmole per 100 mmole glucose consumed during respiratory growth in aerobic glucose-limited continuous cultivation. Such a low flux into the pentose phosphate pathway cannot provide the cell with enough NADPH for biomass formation which is why the remaining NADPH will have to be provided by another pathway. During batch cultivation of S. kluyveri the tricarboxylic acid cycle was working as a cycle with a considerable flux, that is in sharp contrast to what has previously been observed in S. cerevisiae at the same growth conditions, where the tricarboxylic acid cycle operates as two branches. This indicates that the respiratory system was not significantly repressed in S. kluyveri during batch cultivation on glucose.

Acetates↗

PubMedDate not supplied

Modeling hybridoma cell metabolism using a generic genome-scale metabolic model of Mus musculus.

The reconstructed cellular metabolic network of Mus musculus, based on annotated genomic data, pathway databases, and currently available biochemical and physiological information, is presented. Although incomplete, it represents the first attempt to collect and characterize the metabolic network of a mammalian cell on the basis of genomic data. The reaction network is generic in nature and attempts to capture the carbon, energy, and nitrogen metabolism of the cell. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, including transport reactions between the compartments and the extracellular medium. The reaction list consists of 872 internal metabolites involved in a total of 1220 reactions, whereof 473 relate to known open reading frames. Initial in silico analysis of the reconstructed model is presented.

Amino Acids↗