نبذة مختصرة : With a targeted proteomic approach, we could quantify 90% of the enzymes involved in carbon and amino-acid metabolism in yeast, including complete isoenzyme families, throughout a set of different metabolic states. The data, interpreted through flux balance modeling, indicate that S. cerevisiae expresses enzymes, not necessarily used in a particular metabolic condition. For many isoenzymes our data suggest functional diversification, which might explain their parallel presence in the S. cerevisiae genome.
The metabolic network in the yeast Saccharomyces cerevisiae has been very well characterized in terms of components and topology. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. In this study, we exploited quantitative proteomic assays based on selected reaction monitoring (SRM) mass spectrometry to comprehensively analyze the set of enzymes involved in carbon and amino-acid metabolism in yeast (Figure 1), throughout a set of different metabolic states. To elucidate how this metabolic network of proteins adapts to environmental challenges, we chose five nutritional conditions resulting in maximal difference in magnitude and direction of metabolic fluxes. We could reproducibly detect and quantify across the different conditions, 90% of the targeted metabolic proteome, including complete families of isoenzymes, sharing up to 99.5% sequence identity and multi-subunit enzyme complexes. This yielded an information-rich proteomic data set that represents a nutritionally perturbed biological system with high coverage of its components. Interpreted through flux balance modeling, the data indicate that S. cerevisiae expresses—at least at a basal level—more proteins than are actually necessary for sustaining a given metabolic condition. One potential explanation for the presence of non-necessary proteins is that these enzymes could realize immediate basal metabolic fluxes upon a change to new environmental conditions. Next, we asked whether our data set could contribute to unravel the function of isoenzymes in the metabolic set. Previously proposed roles for isoenzymes include redundancy to buffer against mutations, a means to gene dosage or facilitation of evolutionary innovation and functional diversification. To address the role of isoenzymes in central metabolism, we used hierarchical clustering to analyze the abundance patterns of the metabolic proteins and their relationship to different functional classes and metabolic pathways. Interestingly, while subunits of the same protein complex preferably cluster in proximate branches, members of the same isoenzyme family often clustered in distant branches (Figure 5). The data therefore suggested functional diversification within most isoenzyme families and allowed to propose different functions of divergent isoenzymes. We expect that the comprehensive data set presented in this study will be an ideal blueprint for further developing models of yeast metabolism and for follow-up studies on the function of target metabolic proteins.
Decades of biochemical research have identified most of the enzymes that catalyze metabolic reactions in the yeast Saccharomyces cerevisiae. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. As an important stepping stone toward such understanding, we exploit the power of proteomics assays based on selected reaction monitoring (SRM) mass spectrometry to quantify abundance changes of the 228 proteins that constitute the central carbon and amino-acid metabolic network in the yeast Saccharomyces cerevisiae, at five different metabolic steady states. Overall, 90% of the targeted proteins, including families of isoenzymes, were consistently detected and quantified in each sample, generating a proteomic data set that represents a nutritionally perturbed biological system at high reproducibility. The data set is near comprehensive because we detect 95–99% of all proteins that are required under a given condition. Interpreted through flux balance modeling, the data indicate that S. cerevisiae retains proteins not necessarily used in a particular environment. Further, the data suggest differential functionality for several metabolic isoenzymes.
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