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. 2014 Aug 1;289(31):21326-38.
doi: 10.1074/jbc.M114.581843. Epub 2014 Jun 10.

Stoichiometry of site-specific lysine acetylation in an entire proteome

Josue Baeza  1 , James A Dowell  2 , Michael J Smallegan  2 , Jing Fan  2 , Daniel Amador-Noguez  3 , Zia Khan  4 , John M Denu  5
Affiliations

Stoichiometry of site-specific lysine acetylation in an entire proteome

Josue Baeza et al. J Biol Chem. .

Abstract

Acetylation of lysine ϵ-amino groups influences many cellular processes and has been mapped to thousands of sites across many organisms. Stoichiometric information of acetylation is essential to accurately interpret biological significance. Here, we developed and employed a novel method for directly quantifying stoichiometry of site-specific acetylation in the entire proteome of Escherichia coli. By coupling isotopic labeling and a novel pairing algorithm, our approach performs an in silico enrichment of acetyl peptides, circumventing the need for immunoenrichment. We investigated the function of the sole NAD(+)-dependent protein deacetylase, CobB, on both site-specific and global acetylation. We quantified 2206 peptides from 899 proteins and observed a wide distribution of acetyl stoichiometry, ranging from less than 1% up to 98%. Bioinformatic analysis revealed that metabolic enzymes, which either utilize or generate acetyl-CoA, and proteins involved in transcriptional and translational processes displayed the highest degree of acetylation. Loss of CobB led to increased global acetylation at low stoichiometry sites and induced site-specific changes at high stoichiometry sites, and biochemical analysis revealed altered acetyl-CoA metabolism. Thus, this study demonstrates that sirtuin deacetylase deficiency leads to both site-specific and global changes in protein acetylation stoichiometry, affecting central metabolism.

Keywords: Acetyl-CoA Synthetase; Acetylation; Escherichia coli (E. coli); Mass Spectrometry (MS); Metabolism; Proteomics; Stoichiometry.

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Figures

FIGURE 1.
FIGURE 1.
Diagram of method for determining direct acetylation stoichiometry. Extracted protein was denatured, chemically acetylated using isotopic acetic anhydride followed by trypsin digestion. Peptides were analyzed by LC-MS/MS and quantified to determine site-specific stoichiometry.
FIGURE 2.
FIGURE 2.
Acetic anhydride comprehensively modifies lysine residues on a wide range of protein sizes and to a high degree. Chemical acetylation by acetic anhydride was assessed qualitatively and quantitatively. A, Western blotting analysis with α-acetyl-lysine antibody with an unmodified and modified E. coli sample shows chemical labeling across a full range of proteins in a complex mixture. B, degree of acetylation was determined using bovine serum albumin. The indicated peptides were quantified in the unmodified and modified samples. The abundance of each peptide correlates to the degree of chemical acetylation by acetic anhydride.
FIGURE 3.
FIGURE 3.
Diagram of LC-MS/MS data analysis algorithm used for identifying isotopic pairs for stoichiometry determination. The pairing algorithm was carried out using XICs. The peaks here are for illustration purposes only. A, we created two separate MS2 peptide search databases as follows: 1) a “light” database with native acetylation as a fixed modification for each lysine; and 2) a “heavy” database where every lysine is modified by the isotopic acetic anhydride. The algorithm used two iterations as follows: light-to-heavy pairing when the native acetyl peptide was the most highly abundant species (B) and a heavy-to-light pairing when chemically labeled peptide was the most highly abundant species (C). In each respective iteration for an XIC with an associated MS2 spectrum, we determined the best matching peptide based on MS2 search score using the light or heavy database. We used the expected isotopic shift to find the corresponding MS1 peak. D, for low stoichiometry sites, the algorithm is able to perform in silico enrichment. The algorithm uses the MS2 spectrum and MS1 signal of the heavy acetyl peptide as a proxy for the native acetyl peptide. Using this proxy information, the algorithm identifies a low level signal from the native acetyl peptide.
FIGURE 4.
FIGURE 4.
Acetylation stoichiometry is detected and quantified across a full range of values. Light and Δ5-acetic anhydride-labeled BSA protein digests were mixed at different ratios corresponding to 1–99% acetylation followed by MS analysis and quantitation. A, MS/MS spectrum for the acetylated BSA peptide, ALK(Ac)AWSVAR, is shown. (Product ions shown are charge 1+.) B–D, MS1 spectrum showing light and heavy forms of the peptide in A (charge 2+) corresponding to 10, 50, 90% acetylation, respectively, with a mass shift of ∼2.5 m/z. E, scatterplot comparing percent volume input (light/total) to the corrected measured stoichiometry of the BSA peptide, ALK(Ac)AWSVAR. Stoichiometry values were corrected due to the isotopic purity of ∼96%. F, linear regression analysis of all the acetyl peptides quantified shows the best fit line and high R2 value.
FIGURE 5.
FIGURE 5.
Site-specific and global acetylation stoichiometry in BL21 (DE3) WT and ΔCobB strains. A, distribution of acetylation stoichiometry obtained in WT and ΔCobB strains and B, a pie chart showing the distribution of stoichiometry across the entire E. coli proteome. C, acetylated peptides of all biological conditions from the Δ5-acetic anhydride labeling were compared. Left of diagonal shows the scatterplot of acetyl peptides from each biological condition. Right of diagonal is the Spearman's correlation coefficient. Correlation within biological replicates is high (0.811–0.849 for WT and 0.813–0.845 for ΔCobB), whereas the correlation decreased across biological conditions (0.5632–0.6208). Number in parentheses represents the total number of peptides analyzed in scatterplot. D, histogram of log2 fold change between ΔCobB and WT acetyl peptides. The plot shows a normal distribution and slight shift toward the right.
FIGURE 6.
FIGURE 6.
Dynamics of acetylation stoichiometry in WT and ΔCobB strains. Stoichiometry line plot demonstrates peptide level stoichiometry in WT and ΔCobB and the magnitude change between strains. Peptides are shown that have a higher stoichiometry in WT (black lines) and ΔCobB (red lines).
FIGURE 7.
FIGURE 7.
Bioinformatic and network analysis of acetylation stoichiometry. KEGG pathway analysis (A) and gene ontology (B) of biological process for acetylated proteins in both WT and ΔCobB using an acetylation stoichiometry greater than 7% (average stoichiometry). Pathways and biological processes are significantly enriched (p < 0.05) above an unmodified, trypsin-digested E. coli proteome (background proteome used for DAVID analysis). C, pie chart of acetylation sites detected per protein. Network analysis of metabolic pathways (D) and transcriptional and translational biological processes (E). F, legend for D and E. Shapes represent the number of acetylation sites detected per protein. The size of the shape is proportional to the highest acetyl peptide stoichiometry of a protein in either WT or ΔCobB. Proteins are colored according to pathway or biological process.
FIGURE 8.
FIGURE 8.
Acetylation of enzymes in metabolic pathways and metabolite analysis. A, enzymes of pyruvate metabolism and the TCA cycle are shown. Filled circles represent a peptide identified with an acetyl stoichiometry greater than 7% (including single and double lysine-containing peptides). The color of each circle represents the degree of acetylation according to heat map legend. Enzymes with the highest degree of acetylation include PykA, Pta, AccA, AccC, Acs, GltA, AdhE, and Msa. Enzymes with the highest number of acetyl-lysine peptides include PpsA, Pta, AdhE, AcnA, AcnB, and SucA. B, acetyl phosphate is significantly increased in the ΔCobB strain (p = 0.01).
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