Metabolic fluxes may be regulated ''hierarchically,'' e.g., by changes of gene expression that adjust enzyme capacities (Vmax) and/or ''metabolically'' by interactions of enzymes with substrates, products, or allosteric effectors. In the present study, a method is developed to dissect the hierarchical regulation into contributions by transcription, translation, protein degradation, and posttranslational modification. The method was applied to the regulation of fluxes through individual glycolytic enzymes when the yeast Saccharomyces cerevisiae was confronted with the absence of oxygen and the presence of benzoic acid depleting its ATP. Metabolic regulation largely contributed to the Ϸ10-fold change in flux through the glycolytic enzymes. This contribution varied from 50 to 80%, depending on the glycolytic step and the cultivation condition tested. Within the 50 -20% hierarchical regulation of fluxes, transcription played a minor role, whereas regulation of protein synthesis or degradation was the most important. These also contributed to 75-100% of the regulation of protein levels.gene-expression cascade ͉ glycolysis ͉ posttranscriptional regulation ͉ regulation analysis ͉ systems biology T he 1990s have witnessed a revolution in molecular cell biology. Nucleotide sequences of complete genomes were elucidated, and new techniques enabled genome-wide analysis of mRNA and protein concentrations and accurate estimates of metabolic flux distributions (1). The central dogma of molecular biology is that DNA encodes mRNA and mRNA encodes proteins, which in turn fulfill the many functions in the cell. Therefore, a strong correlation was anticipated among mRNA concentrations, protein concentrations, and metabolic fluxes. However, subsequent gene-expression studies led to the paradoxical conclusion that correlations between mRNA levels and protein levels (2, 3), between mRNA and in vivo fluxes (4, 5), and between enzyme activities and fluxes (6, 7) were far from perfect.There are several explanations for the lack of correlation between the different levels of gene expression. Clearly defined and strictly controlled cultivation methods are required to obtain highquality datasets (8, 9). Furthermore, there should be a time delay between changes at the mRNA level and the corresponding changes of protein concentrations and enzyme activities. However, even in steady-state chemostat cultures, in which the cells grow in a constant environment for prolonged periods of time, mRNA levels, protein concentrations/activities, and fluxes correlated poorly (4, 6, 10). A remaining explanation might be that much of the regulation of gene expression is posttranscriptional. Indeed, regulatory mechanisms that affect translation, protein degradation, posttranslational modification of proteins, and enzymes directly have been documented extensively. High-throughput measurements of translation rates and protein turnover in Saccharomyces cerevisiae showed that these varied significantly between proteins and conditions (11-13). Posttranslational mo...

An important question is to what extent metabolic fluxes are regulated by gene expression or by metabolic regulation. There are two distinct aspects to this question: (i) the local regulation of the fluxes through the individual steps in the pathway and (ii) the influence of such local regulation on the pathway's flux. We developed regulation analysis so as to address the former aspect for all steps in a pathway. We demonstrate the method for the issue of how Saccharomyces cerevisiae regulates the fluxes through its individual glycolytic and fermentative enzymes when confronted with nutrient starvation. Regulation was dissected quantitatively into (i) changes in maximum enzyme activity (V max, called hierarchical regulation) and (ii) changes in the interaction of the enzyme with the rest of metabolism (called metabolic regulation). Within a single pathway, the regulation of the fluxes through individual steps varied from fully hierarchical to exclusively metabolic. Existing paradigms of flux regulation (such as single-and multisite modulation and exclusively metabolic regulation) were tested for a complete pathway and falsified for a major pathway in an important model organism. We propose a subtler mechanism of flux regulation, with different roles for different enzymes, i.e., ''leader,'' ''follower,'' or ''conservative,'' the latter attempting to hold back the change in flux. This study makes this subtlety, so typical for biological systems, tractable experimentally and invites reformulation of the questions concerning the drives and constraints governing metabolic flux regulation.gene expression and metabolic regulation ͉ glycolysis ͉ regulation analysis ͉ metabolic control analysis T he flux through a metabolic pathway is determined by the activities of its enzymes and by their interactions with other enzymes. Metabolic-flux changes have often been observed in response to environmental or genetic changes. In the yeast Saccharomyces cerevisiae, for example, changes in glycolytic flux have frequently been found to be accompanied by a myriad of changes in glycolytic enzyme activities (e.g., 1, 2, this work) or amounts (3), which varied in magnitude and direction. The complexity of interactions between enzymes translates into a vast possibility space of combinations of enzyme-activity modulations leading to the same flux change. We wondered how the cell actually regulates its fluxes.Among the proposed mechanisms for metabolic-flux changes, the two clearest hypotheses are (i) modulation of single ratelimiting enzymes and (ii) multisite modulation, i.e., simultaneous and proportional modulation of all enzymes in the pathway, thus causing a change in flux while leaving metabolite concentrations unchanged (4). Although single rate-limiting enzymes exist, control of flux is quite often distributed over several enzymes (5). In the latter case, modulation of a single enzyme is likely to be an ineffective mechanism for changing a pathway's flux. Indeed, attempts to correlate flux changes with changes in single enzyme ac...

A novel method dissecting the regulation of a cellular function into direct metabolic regulation and hierarchical (e.g., gene-expression) regulation is applied to yeast starved for nitrogen or carbon. Upon nitrogen starvation glucose influx is down-regulated hierarchically. Upon carbon starvation it is down-regulated both metabolically and hierarchically. The method is expounded in terms of its implications for diverse types of regulation. It is also fine-tuned for cases where isoenzymes catalyze the flux through a single metabolic step.

Salmonella enterica sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of S. Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.

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The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of biotechnological processes that use complex, largely uncharacterized media, such as biofuel production.

Cells adapt to changes in their environment by the concerted action of many different regulatory mechanisms. Examples of such mechanisms are feedback inhibition by intermediates of metabolism, covalent modification of enzymes and changes in the abundance of mRNAs and proteins. These mechanisms act in parallel at different levels in the cellular hierarchy while regulating a single process. Existing hierarchical regulation analysis determines the relative importance of these mechanisms when the cell regulates a transition from one steady-state to another. Here, the analysis is extended to the regulation of time-dependent phenomena, for which two methods are introduced and illustrated with a kinetic model incorporating transcription and translation of metabolic enzymes.

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