Understanding the Nutritional Control of Metabolic Flux in S. cerevisiae

1. Understanding the Nutritional Control of Metabolic Flux in S. cerevisiae Sean R Hackett and Josh Rabinowitz Summary Quantifying fluxes Yeast must efficiently balance anabolism with nutrient availability in order to grow optimally in diverse environments. Previous efforts to characterize the physiology underlying this control have focused on looking at patterns of transcriptional and metabolite variability under 25 nutritional conditions differing either in the nature of the limiting nutrient, or how stringent the limitation is. This has revealed major patterns associated with nutrient invariant and nutrient specific limitation, but given us little insight into how the relative flux through reactions is shaped by nutrient availability and how this flux-control is accomplished at a mechanistic level. As an attempt to address this, I will characterize the fluxes across conditions by measuring the rate that nutrients are absorbed and the rates that they are being polymerized to create the macromolecules necessary for growth. From this point, the flux through all reactions can be predicted using flux balance analysis. To compliment this direct measurement of flux, we can use proteomics and metabolomics to evaluate whether predicted fluxes are consistent with measured fluxes and whether modifications of the mechanism are supported. This will allow us to determine how flux through individual enzymes is controlled across nutrient conditions and ultimately how flux is regulated at a systems-level. The space of possible fluxes through the metabolic network can be bounded by determining the rates that molecules are taken-up from the media, and the rates that the monomers that are polymerized to create macromolecules are created. Under steady-state conditions, the concentration of metabolites won’t change over time so flux balance will be satisfied for each metabolite and mass balance of nutrients and macromolecules will exist. With these boundary fluxes constrained, pathway degeneracies can be resolved by incorporating either kinetic information from isotopic tracer methods or through an estimate of flux carried related to predicted absolute flux. For each condition, this information can be integrated using flux balance analysis, in order to find an optimal set of fluxes that conforms to boundary constraints and experimental data While the information necessary to quantify flux in a metabolism-wide manner is still accumulating, analysis of a limited number of reactions is currently possible. For these reactions, the flux will be proportional to the product of the concentration of a macromolecule and the rate which volume is removed from the reaction vessel. Looking at ribonucleotide synthesis, the intracellular concentration of RNA in faster dividing cultures implies a quadratic relationship between dilution rate and flux into ribonucleotides. Background In order for microbes to have prospered, they have evolved regulatory mechanisms that allow them to adjust their metabolic strategy under varying conditions. On their quest for division, two of the largest challenges that they face are how to attain a relatively invariant quantity of macromolecules from diverse nutrient sources, where some resources may be abundant and others scarce; and how to chemically translate increased nutrient availability into faster production of macromolecules. Looking at nutrient conditions that differ in the nature of the limiting nutrient and how much is available, the physiology underlying these central regulatory questions can be investigated. This has previously been done at the transcriptional and metabolomiclevel. Across these conditions there is both nutrient independent and nutrient-specific variation in flux associated with changes in the rate of division. This suggests that the control of flux across these conditions could be accomplished either through hierarchical control of flux, where variation in the flux through a reaction is related to the level or activity of the enzyme or through metabolic regulation where flux is controlled by substrate occupancy. Explaining flux variation Ascertaining the concentration of species is an experimental challenge. What we will actual use is the relative concentration of enzymes (using proteomics) and the absolute concentration of metabolites (we currently only have relative for both) and we will have to make assumptions about the subcellular provenance of species, initially assuming that concentrations are uniform regardless of compartment. For most reactions, published mechanisms are available, as well as binding affinities for a fair number of metabolites, but these affinities are inconsistent and we may be missing some molecules (or covalent modifications) that are necessary to adequately describe flux as a function of species and parameters. To address these concerns we need to compare predicted to true flux across a set of models characterized by the set of species, the nature of their interaction and the kinetic parameters involved in a reaction. The variation in flux through any reaction can be related to its reaction mechanism, where the flux through the reaction is described as a function of kinetic parameters, concentration of metabolites and enzyme abundance. Within this framework, variation in V (found above) will be related to variation in intracellular concentration of substrates and other competitive species, and Vmax related changes in the level of an enyzme or its activity. To describe this relationship exactly, we need precise measurements of the intracellular concentration of all relevant species, to know the kinetic nature of their interaction and to know which species are sufficient to describe the mechanism. Transcriptomics Metabolomics Recap A simplified case Metabolism-wide strategy Scaling the simplified case up to a metabolism-wide strategy will be conceptually similar. Optimization will still be carried out on a per-reaction basis, but the desire to evaluate different classes of reaction mechanisms will require the use of Bayesian statistics and more powerful optimization tools, namely genetic algorithms. The likelihood of each model, characterized by a set of parameters, can be evaluated as the product of the densities of vi evaluated at a mean vi_pred. This likelihood is then combined with the prior probability of the values of parameters to create a bayes factor. These bayes factors can then be viewed as the fitness of a model and this fitness can be optimized through the process of parameter mutation and selection. • There are large differences in patterns of flux across nutrient conditions. These are primarily related to the growth rate, but there likely limiting-nutrient specific differences as well. • These patterns are linked at the level of reaction mechanisms to variation in metabolites and enzymes • Understanding the source of variation driving flux through individual reactions will allow us to study global regulation and flux control. To determine whether modifications to reaction mechanisms should be sought or whether predictions of flux from the abundance of established metabolites and enzymes are consistent with the carried flux, the best linear relationship between v and vpredcan be sought. This can be found by minimizing the coefficient of variation of v/vpredover a set of parameters Θ. For a set of reactions where the flux is proportional to the rate of RNA synthesis, this approach reveals that for some reactions the established mechanisms are pretty good, while for others the predicted flux is inconsistent with the observed flux. Acknowledgements • Josh Rabinowitz • TomerShlomi • John Storey • Keyur Desai • David Botstein • Pat Gibney • Sandy Silverman • Jonathan Goya • David Perlman P50 GM071508 Brauer 2008 Boer 2010