Regulation of yeast central metabolism by enzyme phosphorylation

Abstract

As a frequent post-translational modification, protein phosphorylation regulates many cellular processes. Although several hundred phosphorylation sites have been mapped to metabolic enzymes in Saccharomyces cerevisiae, functionality was demonstrated for few of them. Here, we describe a novel approach to identify in vivo functionality of enzyme phosphorylation by combining flux analysis with proteomics and phosphoproteomics. Focusing on the network of 204 enzymes that constitute the yeast central carbon and amino-acid metabolism, we combined protein and phosphoprotein levels to identify 35 enzymes that change their degree of phosphorylation during growth under five conditions. Correlations between previously determined intracellular fluxes and phosphoprotein abundances provided first functional evidence for five novel phosphoregulated enzymes in this network, adding to nine known phosphoenzymes. For the pyruvate dehydrogenase complex E1 α subunit Pda1 and the newly identified phosphoregulated glycerol-3-phosphate dehydrogenase Gpd1 and phosphofructose-1-kinase complex β subunit Pfk2, we then validated functionality of specific phosphosites through absolute peptide quantification by targeted mass spectrometry, metabolomics and physiological flux analysis in mutants with genetically removed phosphosites. These results demonstrate the role of phosphorylation in controlling the metabolic flux realised by these three enzymes.

Figure 1

Workflow to determine changes in protein, phosphoprotein and degree of phosphorylation of metabolic enzymes. (A) Wild-type S. cerevisiae was cultivated under five different nutritional conditions in biological triplicates. Cells were harvested at mid-exponential phase and lysed, followed by protein precipitation and digestion with trypsin. An aliquot of trypsinised crude cell extract was phosphoenriched and phosphopeptides were analysed by shotgun. Another aliquot was used for relative quantification of total protein for 204 pre-selected metabolic enzymes by SRM (Costenoble et al, 2011). (B) Schematic representation of the abundance fold-change measured (total protein and phosphoprotein fold-change) and derived (degree of phosphorylation fold-change) in this study. (C) Four scenarios of how total protein and phosphoprotein changes relate.

Figure 2

Phosphoenzyme fold-changes mapped onto the yeast central carbon and amino-acid metabolic network. The 35 phosphoenyzmes unambiguously quantified are shown. In addition, we detected but could not resolve the phosphoforms of Pyc1/Pyc2 and Tdh1/Tdh2/Tdh3, because their phosphopeptides were shared by all isoenzyme species (Supplementary Table 2). Positive (/negative) fold-changes indicate higher occurrence in ethanol, galactose, anaerobic or YP condition (/glucose). Change in degree of phosphorylation by more than two-fold is marked with a thick border. Proteins marked with an asterisk have phosphosites quantified by more than one phosphopeptide. Yellow boxes highlight the three enzymes that we followed-up.

Figure 3

Correlation analysis between flux and total protein or phosphoprotein abundances. (A) Schematic representation of phosphorylation being a mechanism to determine the pool of catalytically competent enzyme (in yellow). (B) Schematic example of how to identify the pool of catalytically competent enzyme given measurements of enzyme activity, total protein, phosphoprotein, and non-phosphoprotein. If the catalytically competent enzyme is the non-phosphoprotein, a good correlation would be instead found between flux and non-phosphorylated protein pool. (C) Correlation coefficients (R) between flux and total or phosphoprotein abundance fold-changes determined by shotgun phosphoproteomics for enzymes with significant correlation (P<0.10) between flux and phosphorylated protein. The four points correspond to glucose (black), galactose (red), anaerobic (green) and ethanol (yellow). Glucose is always the unit reference point. Vertical bars mark 10% more or less (phospho)protein relative to the glucose condition. (D) Correlations between flux and absolute concentrations of total, phospho and non-phosphoprotein determined directly from crude cell extracts for Pfk2 at site S163 and Pda1 at site S313 using SRM. Conditions and colour scheme are the same as described in (C).

Figure 4

Functional impact of phosphosite removal on metabolic activity. (A) List of followed up proteins and phosphosites. All relevant phosphopeptides detected by shotgun phosphoproteomics are listed, as well as the corresponding phosphorylation sites and the fold-changes between ethanol and glucose, with a negative value indicating higher abundance in glucose. Phosphosite-deficient mutants were constructed through site-directed mutagenesis by replacing serine by alanine. (B) Evidence for increased pyruvate dehydrogenase activity in the Pda1[S313A] mutant. Relative to the reference strain, the point mutant displayed increased secretion of α-ketoglutarate, glutamine and glycerol during growth in glucose. Extracellular compounds are written in uppercase. (C) Impact of Pfk2 phosphosite loss on the in vivo enzyme activity during growth in ethanol. Only the mutant Pfk2[S163A] displayed different intracellular metabolite levels of reaction product and 20% decreased biomass. Due to the difficulty to control ethanol evaporation, the biomass yield on ethanol is given as relative measurements for strains grown and measured on the same day. (D) Impact of Gpd1 phosphosite loss on the in vivo and in vitro enzyme activity during growth in glucose. Only the quadruple point mutant Gpd1[S23/24/25/27A] displayed different intracellular metabolite levels of reaction product, increased glycerol yield and increased glycerol-3-phosphate dehydrogenase activity.