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...

Saccharomyces cerevisiae is unique among yeasts in its ability to grow rapidly in the complete absence of oxygen. S. cerevisiae is therefore an ideal eukaryotic model to study physiological adaptation to anaerobiosis. Recent transcriptome analyses have identified hundreds of genes that are transcriptionally regulated by oxygen availability but the relevance of this cellular response has not been systematically investigated at the key control level of the proteome. Therefore, the proteomic response of S. cerevisiae to anaerobiosis was investigated using metabolic stable-isotope labelling in aerobic and anaerobic glucose-limited chemostat cultures, followed by relative quantification of protein expression. Using independent replicate cultures and stringent statistical filtering, a robust dataset of 474 quantified proteins was generated, of which 249 showed differential expression levels. While some of these changes were consistent with previous transcriptome studies, many of the responses of S. cerevisiae to oxygen availability were, to our knowledge, previously unreported. Comparison of transcriptomes and proteomes from identical cultivations yielded strong evidence for post-transcriptional regulation of key cellular processes, including glycolysis, amino-acyl-tRNA synthesis, purine nucleotide synthesis and amino acid biosynthesis. The use of chemostat cultures provided well-controlled and reproducible culture conditions, which are essential for generating robust datasets at different cellular information levels. Integration of transcriptome and proteome data led to new insights into the physiology of anaerobically growing yeast that would not have been apparent from differential analyses at either the mRNA or protein level alone, thus illustrating the power of multi-level studies in yeast systems biology.