Acetylation dynamics and stoichiometry in Saccharomyces cerevisiae

Abstract

Lysine acetylation is a frequently occurring posttranslational modification; however, little is known about the origin and regulation of most sites. Here we used quantitative mass spectrometry to analyze acetylation dynamics and stoichiometry in Saccharomyces cerevisiae. We found that acetylation accumulated in growth-arrested cells in a manner that depended on acetyl-CoA generation in distinct subcellular compartments. Mitochondrial acetylation levels correlated with acetyl-CoA concentration in vivo and acetyl-CoA acetylated lysine residues nonenzymatically in vitro. We developed a method to estimate acetylation stoichiometry and found that the vast majority of mitochondrial and cytoplasmic acetylation had a very low stoichiometry. However, mitochondrial acetylation occurred at a significantly higher basal level than cytoplasmic acetylation, consistent with the distinct acetylation dynamics and higher acetyl-CoA concentration in mitochondria. High stoichiometry acetylation occurred mostly on histones, proteins present in histone acetyltransferase and deacetylase complexes, and on transcription factors. These data show that a majority of acetylation occurs at very low levels in exponentially growing yeast and is uniformly affected by exposure to acetyl-CoA.

Figure 1. Acetylation is globally increased in stationary phase yeast cells

Box plots showing the distributions of SILAC ratios comparing stationary phase (SP) to exponential phase (EP) yeast for all quantified proteins (proteins), acetylated proteins (acetyl proteins), acetylation sites corrected for protein abundance changes (acetyl sites), and phosphorylation sites (phospho sites). The box portion of the plot indicates the middle 50% of the distribution, inner hatch marks denote 9–91%, and whisker ends 2–98%, outliers are not shown. Acetylation sites occurring on proteins localized exclusively to mitochondria (Mito.), the cytoplasm (Cyto.), or the nucleus (Nuc.) are shown. Statistical significance was calculated using a Wilcoxon test, data is from two biological replicates.

Figure 2. Quantitative analysis of acetylation dynamics in yeast

1. A

   Model showing the formation of acetyl‐CoA from glucose and acetate, key enzymes are shown in red type.

2. B–E

   The figure shows the conditions analyzed in each experiment, the cell type (wild‐type (wt) or indicated mutant strains), the growth state [exponential phase (EP) or growth‐arrested (GA)], the number of acetylation sites analyzed (# sites), the median Log2 and linear SILAC ratios comparing the indicated condition to wild‐type EP cells, and the subcellular localization of the analyzed acetylation sites on proteins localized to mitochondria (Mito.), the cytoplasm (Cyto.), or the nucleus (Nuc.). Cells were growth‐arrested by transferring an exponential phase culture into media lacking lysine and containing the indicated carbon sources, glucose, acetate, or 2‐deoxy‐d‐glucose (2DG). The bar chart shows the median Log2 SILAC ratios comparing the indicated condition to wild‐type EP cells, statistical significance was calculated by Wilcoxon test. Increased acetylation requires glycolysis (B). Data is from two biological replicates. Mitochondrial acetylation in exponentially growing cells requires Pda1 (C). Increased mitochondrial acetylation in growth‐arrested cells is suppressed by loss of Pda1 and enhanced by loss of Cit1 (D). Data from two biological replicates is shown, significant differences are relative to wild‐type cells. Acetate promotes cytoplasmic and nuclear acetylation (E). Data is from two biological replicates.

Figure 3. Acetyl‐CoA concentration in cells and nonenzymatic acetylation by acetyl‐CoA

1. Acetyl‐CoA concentration was determined in the indicated cell types during exponential phase (EP) growth or after the indicated time of growth‐arrest (GA) in the presence of glucose. Data are from two independent biological replicates.

2. The bar graph shows the abundance of the indicated, acetylated (ac) peptides relative to an untreated control sample. Error bars indicate standard deviation of the indicated number (n) of independently quantified peptides. The significance (P) of increased acetylation at 100 μM acetyl‐CoA was calculated by two‐tailed t‐test assuming equal variance.

3. The column graph shows the relative abundance of non‐acetylated peptides. Error bars indicate standard deviation of the indicated number (n) of independently quantified peptides.

Figure 4. Method used to assay acetyl‐phosphate sensitivity in yeast lysate

Yeast proteins were metabolically labeled with unlabeled “light” lysine or with “heavy” isotope labeled lysine. Protein lysates were treated with acetyl‐phosphate (AcP) or were mock‐treated by addition of H₂O. Equal amounts of protein were mixed and digested to peptides with trypsin protease. Acetylated (Ac) peptides were immune‐affinity enriched using agarose‐coupled anti‐acetyllysine polyclonal antibody and analyzed by MS. Increased acetylation from AcP treatment causes an increase in the relative abundance of the “light” labeled peptide, enabling quantification of acetylation changes induced by AcP.

Figure 5. Most acetylation sites are modified with a low stoichiometry

1. The majority of yeast acetylation sites are highly sensitive to partial chemical acetylation by AcP. The histogram shows the distribution of SILAC L/H ratios for the indicated samples.

2. AcP caused substantially increased acetylation at a majority of sites. The histogram shows acetylation changes induced by 100 mM AcP in two experimental replicates, only sites that were independently identified in cells without AcP treatment are shown.

3. Acetylation stoichiometry is inversely proportional to AcP‐sensitivity. The scatterplot shows the relationship between AcP‐sensitivity (SILAC ratio L/H 100 mM AcP) and acetylation stoichiometry (Log10 stoichiometry) as determined by AQUA analysis (Table 1). The Spearman's correlation (ρ) and the significance by two‐tailed test (P‐value) are shown.

4. Most acetylation occurs with a low stoichiometry. Absolute acetylation site stoichiometries were estimated based on relative abundance changes (SILAC ratio L/H) after treatment with 100 mM AcP and using an estimate that AcP treatment caused < 1% chemical acetylation.

5. For comparison, previously determined phosphorylation site stoichiometries are shown (Wu et al, 2011).

6. iBAQ‐based abundance corrected acetylated peptide intensity (I/iBAQ‐A) is proportional to AcP sensitivity. The box plots show the distributions of I/iBAQ‐A values for the indicated classes of acetylation sites. AcP‐insensitive sites had a significantly (p) higher distribution of I/iBAQ‐A values compared to either AcP‐sensitive sites or sites without SILAC ratios. Significance was calculated by Wilcoxen test.

7. Sites without SILAC ratios are highly sensitive to AcP. The minimum ratio of increased acetylation was determined by calculating the increased intensity of AcP‐treated “light” peptides relative to an empirically determined detection limit for “heavy” SILAC peptides (see Materials and Methods).

8. Absolute acetylation stoichiometry of sites without SILAC ratios was estimated to be very low. Stoichiometry was estimated by the same method used to estimate stoichiometry of sites with SILAC ratios in (D) using the minimum ratios of increased acetylation shown in (G).

Figure 6. Functional analysis of high stoichiometry acetylation sites

1. AcP‐insensitive acetylation sites occur on proteins associated with nuclear processes. Gene Ontology (GO) term enrichment was performed by comparing proteins with AcP‐insensitive acetylation sites (ratio L/H < 2) to all acetylated proteins. The bar graph shows the percentage of AcP‐insensitive sites occurring on proteins associated with the indicated GO terms. P‐values (P) indicate the statistical significance of GO term enrichment by Fisher's exact test.

2. AcP‐insensitive acetylation sites occur on nuclear proteins. The histogram shows the distribution of SILAC L/H ratios occurring on proteins localized to the indicated subcellular compartments.

3. Acetylated peptides from mitochondrial proteins have a significantly higher median I/iBAQ‐A value compared to acetylated peptides from cytoplasmic proteins. The box plots show the I/iBAQ‐A distributions for the indicated classes of peptides occurring on mitochondrial (Mito.) or cytoplasmic (Cyto.) proteins. Signficance (p) was determined by Wilcoxon test.

4. AcP‐insensitive sites (SILAC Ratio < 2) have a significantly different subcellular distribution. Sites without SILAC ratios (No ratio) have a similar subcellular distribution to AcP‐sensitive sites (SILAC Ratio > 2). The bar graph shows the fraction of sites localized to the indicated subcellular compartments; mitochondria (Mito.), cytoplasm (Cyto.), or nucleus (Nuc.). Significance (P) was calculated by Fisher exact test.

5. Detection of acetylation sites is biased to occur on abundant proteins and this bias is more pronounced for sites with the lowest estimated stoichiometries. The histograms show the distributions of iBAQ protein abundances for observed proteins (n = 3,104). The distributions of the indicated classes of acetylated proteins occurring exclusively in the indicated subcellular compartments is shown in red. The numbers of acetylated proteins are shown in parenthesis and the median Log10 iBAQ abundance for these acetylated proteins is shown.