Project description:Nutrient sensors allow cells to adapt their metabolisms to match nutrient availability by regulating metabolic pathway expression. Many such sensors are cytosolic receptors that measure intracellular nutrient concentrations. One might expect that inducing the metabolic pathway that degrades a nutrient would reduce intracellular nutrient levels, destabilizing induction. However, in the galactose-responsive (GAL) pathway of Saccharomyces cerevisiae, we find that induction is stabilized by flux sensing. Previously proposed mechanisms for flux sensing postulate the existence of metabolites whose concentrations correlate with flux. The GAL pathway flux sensor uses a different principle: the galactokinase Gal1p both performs the first step in GAL metabolism and reports on flux by signalling to the GAL repressor, Gal80p. Both Gal1p catalysis and Gal1p signalling depend on the concentration of the Gal1p-GAL complex and are therefore directly correlated. Given the simplicity of this mechanism, flux sensing is probably a general feature throughout metabolic regulation.
Project description:Algorithms that constrain metabolic network models with enzyme levels to predict metabolic activity assume that changes in enzyme levels are indicative of flux variations. However, metabolic flux can also be regulated by other mechanisms such as allostery and mass action. To systematically explore the relationship between fluctuations in enzyme expression and flux, we combine available yeast proteomic and fluxomic data to reveal that flux changes can be best predicted from changes in enzyme levels of pathways, rather than the whole network or only cognate reactions. We implement this principle in an 'enhanced flux potential analysis' (eFPA) algorithm that integrates enzyme expression data with metabolic network architecture to predict relative flux levels of reactions including those regulated by other mechanisms. Applied to human data, eFPA consistently predicts tissue metabolic function using either proteomic or transcriptomic data. Additionally, eFPA efficiently handles data sparsity and noisiness, generating robust flux predictions with single-cell gene expression data. Our approach outperforms alternatives by striking an optimal balance, evaluating enzyme expression at pathway level, rather than either single-reaction or whole-network levels
Project description:Cellular metabolic fluxes are determined by enzyme activities and metabolite abundances. Biochemical approaches reveal the impact of specific substrates or regulators on enzyme kinetics but do not capture the extent to which metabolite and enzyme concentrations vary across physiological states and, therefore, how cellular reactions are regulated. We measured enzyme and metabolite concentrations and metabolic fluxes across 25 steady-state yeast cultures. We then assessed the extent to which flux can be explained by a Michaelis-Menten relationship between enzyme, substrate, product, and potential regulator concentrations. This revealed three previously unrecognized instances of cross-pathway regulation, which we biochemically verified. One of these involved inhibition of pyruvate kinase by citrate, which accumulated and thereby curtailed glycolytic outflow in nitrogen-limited yeast. Overall, substrate concentrations were the strongest driver of the net rates of cellular metabolic reactions, with metabolite concentrations collectively having more than double the physiological impact of enzymes.
Project description:MotivationFlux balance analysis and its variants are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations.ResultsDistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes.Availability and implementationThe code is freely available on github.com/opencobra/COBRA.jl. The documentation can be found at opencobra.github.io/COBRA.jl.Contactronan.mt.fleming@gmail.com.Supplementary informationSupplementary data are available at Bioinformatics online.
Project description:BackgroundIn systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to predict flux distributions, because recombinant microbes significantly change the distribution of gene expressions. The current problem is how to integrate such heterogeneous data to build a network-based model.ResultsTo link enzyme activity data to flux distributions of metabolic networks, we have proposed Enzyme Control Flux (ECF), a novel model that integrates enzyme activity into elementary mode analysis (EMA). ECF presents the power-law formula describing how changes in enzyme activities between wild-type and a mutant are related to changes in the elementary mode coefficients (EMCs). To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of Escherichia coli and Bacillus subtilis wild-type. The ECF model effectively uses an enzyme activity profile to estimate the flux distribution of the mutants and the increase in the number of incorporated enzyme activities decreases the model error of ECF.ConclusionThe ECF model is a non-mechanistic and static model to link an enzyme activity profile to a metabolic flux distribution by introducing the power-law formula into EMA, suggesting that the change in an enzyme profile rather reflects the change in the flux distribution. The ECF model is highly applicable to the central metabolism in knockout mutants of E. coli and B. subtilis.
Project description:Genome-wide identification of transcription factor (TF) binding sites in the genome of the fission yeast Schizosaccharomyces pombe. The ChIP-nexus method was used. TFs included were: Cbf11-TAP and Cbf12-TAP (and their DBM mutants with impaired DNA binding), TAP-Mga2, and Fkh2-TAP (as an irrelevant control TF). IPs from an untagged WT strain were also analyzed. Cbf11-related IPs were performed from exponential cultures, while Cbf12-related IPs were performed from stationary cultures. YES complex medium was used for all cultivations.
Project description:One of the most obvious phenotypes of a cell is its metabolic activity, which is defined by the fluxes in the metabolic network. Although experimental methods to determine intracellular fluxes are well established, only a limited number of fluxes can be resolved. Especially in eukaryotes such as yeast, compartmentalization and the existence of many parallel routes render exact flux analysis impossible using current methods. To gain more insight into the metabolic operation of S. cerevisiae we developed a new computational approach where we characterize the flux solution space by determining elementary flux modes (EFMs) that are subsequently classified as thermodynamically feasible or infeasible on the basis of experimental metabolome data. This allows us to provably rule out the contribution of certain EFMs to the in vivo flux distribution. From the 71 million EFMs in a medium size metabolic network of S. cerevisiae, we classified 54% as thermodynamically feasible. By comparing the thermodynamically feasible and infeasible EFMs, we could identify reaction combinations that span the cytosol and mitochondrion and, as a system, cannot operate under the investigated glucose batch conditions. Besides conclusions on single reactions, we found that thermodynamic constraints prevent the import of redox cofactor equivalents into the mitochondrion due to limits on compartmental cofactor concentrations. Our novel approach of incorporating quantitative metabolite concentrations into the analysis of the space of all stoichiometrically feasible flux distributions allows generating new insights into the system-level operation of the intracellular fluxes without making assumptions on metabolic objectives of the cell.
Project description:Elevated blood branched-chain amino acids (BCAA) are often associated with insulin resistance and type 2 diabetes, which might result from a reduced cellular utilization and/or incomplete BCAA oxidation. White adipose tissue (WAT) has become appreciated as a potential player in whole body BCAA metabolism. We tested if expression of the mitochondrial BCAA oxidation checkpoint, branched-chain ?-ketoacid dehydrogenase (BCKD) complex, is reduced in obese WAT and regulated by metabolic signals. WAT BCKD protein (E1? subunit) was significantly reduced by 35-50% in various obesity models (fa/fa rats, db/db mice, diet-induced obese mice), and BCKD component transcripts significantly lower in subcutaneous (SC) adipocytes from obese vs. lean Pima Indians. Treatment of 3T3-L1 adipocytes or mice with peroxisome proliferator-activated receptor-? agonists increased WAT BCAA catabolism enzyme mRNAs, whereas the nonmetabolizable glucose analog 2-deoxy-d-glucose had the opposite effect. The results support the hypothesis that suboptimal insulin action and/or perturbed metabolic signals in WAT, as would be seen with insulin resistance/type 2 diabetes, could impair WAT BCAA utilization. However, cross-tissue flux studies comparing lean vs. insulin-sensitive or insulin-resistant obese subjects revealed an unexpected negligible uptake of BCAA from human abdominal SC WAT. This suggests that SC WAT may not be an important contributor to blood BCAA phenotypes associated with insulin resistance in the overnight-fasted state. mRNA abundances for BCAA catabolic enzymes were markedly reduced in omental (but not SC) WAT of obese persons with metabolic syndrome compared with weight-matched healthy obese subjects, raising the possibility that visceral WAT contributes to the BCAA metabolic phenotype of metabolically compromised individuals.
Project description:Quantitative knowledge of intracellular fluxes in metabolic networks is invaluable for inferring metabolic system behavior and the design principles of biological systems. However, intracellular reaction rates can not often be calculated directly but have to be estimated; for instance, via 13C-based metabolic flux analysis, a model-based interpretation of stable carbon isotope patterns in intermediates of metabolism. Existing software such as FiatFlux, OpenFLUX or 13CFLUX supports experts in this complex analysis, but requires several steps that have to be carried out manually, hence restricting the use of this software for data interpretation to a rather small number of experiments. In this paper, we present Flux-P as an approach to automate and standardize 13C-based metabolic flux analysis, using the Bio-jETI workflow framework. Exemplarily based on the FiatFlux software, it demonstrates how services can be created that carry out the different analysis steps autonomously and how these can subsequently be assembled into software workflows that perform automated, high-throughput intracellular flux analysis of high quality and reproducibility. Besides significant acceleration and standardization of the data analysis, the agile workflow-based realization supports flexible changes of the analysis workflows on the user level, making it easy to perform custom analyses.