Analysis of acetylation stoichiometry suggests that SIRT 3 repairs nonenzymatic acetylation lesions - Brian T. Weinert; Tarek Moustafa; Vytautas Iešmantavičius; Rudolf Zechner; Chunaram Choudhary (2015)
Article10 September 2015Open Access Analysis of acetylation stoichiometry suggests that SIRT3 repairs nonenzymatic acetylation lesions Brian T Weinert Corresponding Author Brian T Weinert The NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Tarek Moustafa Tarek Moustafa Division of Gastroenterology and Hepatology, Medical University Graz, Graz, Austria Search for more papers by this author Vytautas Iesmantavicius Vytautas Iesmantavicius The NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Rudolf Zechner Rudolf Zechner Institute of Molecular Biosciences, University of Graz, Graz, Austria Search for more papers by this author Chunaram Choudhary Corresponding Author Chunaram Choudhary The NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Brian T Weinert Corresponding Author Brian T Weinert The NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Tarek Moustafa Tarek Moustafa Division of Gastroenterology and Hepatology, Medical University Graz, Graz, Austria Search for more papers by this author Vytautas Iesmantavicius Vytautas Iesmantavicius The NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Rudolf Zechner Rudolf Zechner Institute of Molecular Biosciences, University of Graz, Graz, Austria Search for more papers by this author Chunaram Choudhary Corresponding Author Chunaram Choudhary The NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Author Information Brian T Weinert 1, Tarek Moustafa2, Vytautas Iesmantavicius1, Rudolf Zechner3 and Chunaram Choudhary 1 1The NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark 2Division of Gastroenterology and Hepatology, Medical University Graz, Graz, Austria 3Institute of Molecular Biosciences, University of Graz, Graz, Austria *Corresponding author. Tel: +45 40607184; E-mail: [email protected] *Corresponding author. Tel: +45 35325020; E-mail: [email protected] The EMBO Journal (2015)34:2620-2632https://doi.org/10.15252/embj.201591271 See also: DB Lombard et al (November 2015) PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Acetylation is frequently detected on mitochondrial enzymes, and the sirtuin deacetylase SIRT3 is thought to regulate metabolism by deacetylating mitochondrial proteins. However, the stoichiometry of acetylation has not been studied and is important for understanding whether SIRT3 regulates or suppresses acetylation. Using quantitative mass spectrometry, we measured acetylation stoichiometry in mouse liver tissue and found that SIRT3 suppressed acetylation to a very low stoichiometry at its target sites. By examining acetylation changes in the liver, heart, brain, and brown adipose tissue of fasted mice, we found that SIRT3-targeted sites were mostly unaffected by fasting, a dietary manipulation that is thought to regulate metabolism through SIRT3-dependent deacetylation. Globally increased mitochondrial acetylation in fasted liver tissue, higher stoichiometry at mitochondrial acetylation sites, and greater sensitivity of SIRT3-targeted sites to chemical acetylation in vitro and fasting-induced acetylation in vivo, suggest a nonenzymatic mechanism of acetylation. Our data indicate that most mitochondrial acetylation occurs as a low-level nonenzymatic protein lesion and that SIRT3 functions as a protein repair factor that removes acetylation lesions from lysine residues. Synopsis Proteomics analyses show that mitochondrial lysine acetylation occurs as a low-level non-enzymatic protein lesion, and SIRT3 deacetylase functions as a protein repair factor removing acetylation lesions. Most protein lysine acetylation occurs at a very low stoichiometry. SIRT3 suppresses acetylation at targeted sites, maintaining low stoichiometry acetylation. SIRT3-targeted sites are more sensitive to non-enzymatic acetylation. SIRT3-targeted sites are mostly unaffected by dietary manipulations in wild-type mice. Introduction Lysine acetylation is a reversible protein posttranslational modification (PTM) that regulates protein function. The role of acetylation in regulating chromatin structure and gene transcription is well studied with both lysine acetyltransferases and deacetylases taking part (Shahbazian & Grunstein, 2007). Mass spectrometry (MS), combined with acetylated peptide enrichment, has enabled the identification of thousands of acetylation sites on diverse proteins (Kim et al, 2006; Choudhary et al, 2009; Lundby et al, 2012), implicating acetylation in regulating various cellular processes. Metabolic proteins, particularly in mitochondria, were found to be frequently acetylated, and a multitude of studies have investigated the role of acetylation in regulating metabolism (Wang et al, 2010; Zhao et al, 2010; Newman et al, 2012). While thousands of acetylation sites have been identified on mitochondrial proteins, few acetyltransferases have been implicated in their regulation (Scott et al, 2012; Fan et al, 2014), and recent studies suggest most mitochondrial acetylation occurs non-enzymatically through exposure to acetyl-CoA (Wagner & Payne, 2013; Pougovkina et al, 2014; Weinert et al, 2014). The mitochondrial sirtuin deacetylase SIRT3 regulates acetylation at hundreds of sites (Sol et al, 2012; Hebert et al, 2013; Rardin et al, 2013b), and SIRT3 levels are increased by dietary manipulations such as calorie restriction and fasting (Shi et al, 2005; Palacios et al, 2009; Hirschey et al, 2010; Tao et al, 2010; Newman et al, 2012). Several studies have suggested that calorie restriction regulates metabolism through SIRT3-mediated deacetylation of mitochondrial enzymes (Hirschey et al, 2010; Qiu et al, 2010; Shimazu et al, 2010; Someya et al, 2010; Hallows et al, 2011; Hebert et al, 2013). However, it is unclear whether SIRT3 is activated to regulate protein function in response to metabolic changes or whether SIRT3 constitutively deacetylates proteins to preserve their normal function. Notably, acetylation typically inhibits the catalytic activity of mitochondrial enzymes and deacetylation by SIRT3 rescues their activity (Lin et al, 2012; Ghanta et al, 2013). A recent review suggested that nonenzymatic acetylation may represent a type of "carbon stress" which SIRT3 counteracts to ensure metabolic fidelity in mitochondria (Wagner & Hirschey, 2014). If this were the case, we would expect that SIRT3 suppresses acetylation to very low stoichiometry under both basal conditions and the conditions that cause increased acetylation of mitochondrial proteins, such as calorie restriction and fasting. In this study, we used quantitative MS to estimate acetylation stoichiometry in mouse liver tissue with a particular focus on SIRT3-targeted sites. We used a knockout mouse model for adipose triglyceride lipase (ATGL) to study the dependence of mitochondrial acetylation dynamics on fatty acid oxidation in the liver, heart, brain, and brown adipose tissues of fed and fasted mice. Our data indicate that most acetylation occurs at very low stoichiometry (< 0.1%), that SIRT3 suppresses acetylation to background levels, and that SIRT3-targeted sites are only modestly affected by dietary manipulations such as fasting and calorie restriction, suggesting that a major role of SIRT3 is to remove acetylation lesions on mitochondrial proteins. Results Method used to estimate acetylation stoichiometry We previously showed that partial chemical acetylation could be used to estimate acetylation stoichiometry (Weinert et al, 2014). In this study, we partially acetylated denatured proteins, measured the relative increase in acetylated peptide abundance and the median decrease in all corresponding unmodified peptides (CPs) (Fig 1A). The median degree (%) of partial chemical acetylation is equal to 1 minus the median CP ratio; thus, for a fixed degree of chemical acetylation (5%), the CP ratios will be constant (0.95) while the acetylated peptide ratios will depend on the initial stoichiometry of acetylation (Fig 1B). Acetylation stoichiometry was calculated based on the relative increase in acetylation (acetylated peptide ratio) and the median decrease in CPs (CP ratio) using a previously described formula (Olsen et al, 2010) (Fig 1C). Figure 1. Estimating acetylation stoichiometry using partial chemical acetylation Model illustrating the reciprocal relationship between acetylated peptides and their corresponding unmodified peptides (CPs) after 5% chemical acetylation. The diagram illustrates the relative amounts of unmodified and acetylated peptides occurring at an individual acetylation site; as acetylation increases, the amount of CPs is proportionally decreased. The table shows the relationship between acetylation stoichiometry, acetylated peptide ratio, and CP ratio after 5% chemical acetylation at the indicated initial acetylation stoichiometry. Equation used to calculate initial acetylation stoichiometry (I) based on acetylated peptide ratios (A) at individual sites measured in panel (D), and median CP ratios (C) measured in panel (E). The equation is derived from Olsen et al (2010). Acetyl phosphate (AcP) causes a concentration-dependent increase in acetylation. The box plots show the distributions of acetylated peptide ratios (AcP/control) quantified by SILAC or TMT. The box portion of the plot shows the interquartile range (IQR) and the line indicates the median value, whisker ends extend to 1.5 times the length of the IQR (unless the data range is smaller, in which case the whisker ends extend to the minimum or maximum value), outliers are not shown. Data is from a single experimental replicate that was performed independently for each quantitative method (SILAC or TMT). AcP causes a concentration-dependent, significant reduction in median CP abundance. The column graph shows median CP ratios based on an internally controlled comparison (see Materials and Methods). Ratios are for all CPs, unless indicated; sites where acetylation was mapped in this study (Ac mapped), sites on mitochondrial proteins (mitochondrial), or SIRT3-targeted sites [defined as having > 2-fold (S3 > 2), > 4-fold (S3 > 4), or > 8-fold (S3 > 8) increased acetylation in liver tissue from SIRT3 knockout (KO) mice (Hebert et al, 2013)]. Statistical significance was determined by Wilcoxon's test. Download figure Download PowerPoint Quantification of acetylated peptide and corresponding peptide ratios We partially acetylated liver proteins in vitro under denaturing conditions using three concentrations of acetyl phosphate (AcP). The relative increase in acetylation at individual sites (acetylated peptide ratios) was quantified by MS using two different methods, stable isotope labeling by amino acids (SILAC) (Ong et al, 2002) and tandem mass tags (TMT) (Thompson et al, 2003) (Datasets EV1 and EV2). AcP caused increased acetylation relative to untreated control samples in a concentration-dependent manner at nearly all quantified acetylation sites (Fig 1D). Because of the low level of partial chemical acetylation, we were unable to accurately measure the reduction in CP abundance for individual peptides, as this reduction was less than the resolution of SILAC or TMT quantification in our experiments. In order to overcome this limitation, we devised a method to examine decreased CP abundance at a population level using an internally controlled comparison based on thousands of peptide measurements (see Materials and Methods). We validated this approach by showing that median CP ratios were reduced by chemical acetylation in a concentration- and pH-dependent manner and that the degree of acetylation predicted from the median CP ratios was correlated with the number of acetylation sites detected in these samples (Appendix Fig S1). Partial chemical acetylation caused a significant reduction in median CP ratios as detected by both quantitative methods (Fig 1E). The median CP ratio at 100 mM AcP was 0.96 (TMT quantification) or 0.95 (SILAC quantification), indicating 4% or 5% chemical acetylation, respectively. Since TMT quantification can be affected by parent ion interference, which buffers ratio changes toward the median (Ting et al, 2011), we used the median CP ratio based on SILAC quantification for calculating acetylation stoichiometry. We calculated acetylation stoichiometry from samples treated with 100 mM AcP using the equation shown in Fig 1C, the acetylated peptide ratios for individual sites (Datasets EV1 and EV2), and a median CP ratio of 0.95. SIRT3-targeted acetylation sites (defined as having > 2-fold (S3 > 2), > 4-fold (S3 > 4), or > 8-fold (S3 > 8) increased acetylation in liver tissue from SIRT3 knockout (KO) mice (Hebert et al, 2013)) had lower median CP ratios (Fig 1E), suggesting a slightly greater degree of chemical acetylation at these sites. Therefore, to more accurately estimate stoichiometry at SIRT3-targeted sites, we used the median CP ratios shown in Fig 1E to calculate stoichiometry at these sites (0.94 for S3 > 2, 0.93 for S3 > 4, and 0.92 for S3 > 8). We consider these stoichiometry calculations to be estimates, since the CP ratios for individual sites were not determined and the degree of chemical acetylation at individual sites may be variable. In addition, we cannot account for additional modifications at these positions, the presence of which would cause us to overestimate acetylation stoichiometry. Validating estimated acetylation stoichiometry In order to directly validate our stoichiometry estimates, we devised a spike-in absolute quantification (AQUA) approach using heavy-isotope-labeled peptide standards (AQUA peptides) (Gerber et al, 2003). We spiked in acetylated AQUA peptides at 0.01%, 0.1%, and 1% stoichiometry and used affinity enrichment to enable the detection of both native and AQUA peptides (Fig 2A and B, and Dataset EV3). Acetylation stoichiometry determined by AQUA was significantly correlated with our stoichiometry estimates based on partial chemical acetylation (Fig 2C). To further validate our stoichiometry estimates at a larger number of sites, we used comprehensive (100%) chemical acetylation to essentially generate hundreds of acetylated AQUA peptides. Comprehensively acetylated proteins were mixed with unlabeled liver proteins to 0.01%, 0.1%, and 1% stoichiometry, prior to affinity enrichment of acetylated peptides and SILAC quantification by MS (Dataset EV4, Materials and Methods). Stoichiometry estimates based on 100% chemical acetylation were highly significantly correlated with our estimates based on 5% chemical acetylation, were similar in magnitude, and showed low variability (Fig 2D). Together, these data provide confidence in the accuracy of our stoichiometry estimates based on partial chemical acetylation. Figure 2. Validating estimated acetylation stoichiometry The mass spectra show AQUA quantification of an endogenous acetylation site (MDH2 K301) using three dilutions (1%, 0.1%, and 0.01%) of AQUA peptide. Table comparing acetylation stoichiometry determined using AQUA peptides (AQUA stoichiometry) to estimates based on partial (5%) chemical acetylation using SILAC (SILAC stoichiometry) and TMT (TMT stoichiometry) quantification. AQUA stoichiometry is the median of two independent experimental replicates; SIRT3-targeted sites are indicated by * (see Appendix Table S1 for references). The scatterplot shows the correlation between AQUA stoichiometry and stoichiometry estimates based on partial chemical acetylation using either SILAC (black) or TMT (red) quantification. Spearman's correlation (Corr.) and P-value (p) by two-tailed test are shown. The scatterplot shows the correlation between estimated stoichiometry based on partial (5%) chemical acetylation and comprehensive (100%) chemical acetylation using SILAC quantification. Spearman's correlation (Corr.), P-value (p) by two-tailed test, and number (n) of estimates analyzed are shown. Acetylation levels are comparable in mice with different ages. The box plots show the distributions of acetylation site ratios comparing 4-month-old mice to 1-month-, 8-month-, and 16-month-old mice. Acetylation levels were compared using SILAC liver tissue as an internal standard for comparison; the number (n) of sites analyzed is shown. The box portion of the plot indicates the middle 50% of the distribution, inner hatch marks denote 9-91%, whisker ends 2-98%, and outliers are not shown. Acetylation levels are comparable in liver tissue from different mice. The box plots show acetylated peptide ratio distributions comparing SILAC liver tissue (SILAC) to liver tissue from an independent source (WT), and to the 4-month-old mice used to estimate acetylation stoichiometry in this study. Box plots are described in (E), the number of acetylated peptide SILAC ratios quantified are shown in parentheses. Download figure Download PowerPoint Stoichiometry estimates based on SILAC and TMT quantification were poorly, yet significantly correlated (Appendix Fig S2A). Similarly, estimates based on TMT quantification were poorly correlated with estimates based on 100% chemical acetylation (Appendix Fig S2B). For this reason, all subsequent analyses of acetylation stoichiometry are based on SILAC quantification (unless otherwise noted). Although less accurate for individual sites, the estimates based on TMT quantification were comparable to SILAC for different classes of acetylation sites (Appendix Fig S2C) and are presented in Dataset EV2 as an additional resource. Acetylation levels in the four-month-old mouse liver tissue used to estimate stoichiometry in this study were comparable to acetylation levels in liver tissues from one-, eight-, and sixteen-month-old animals (Fig 2E). Acetylation levels were also similar in SILAC liver tissue, liver tissue from an independent source (wild-type (WT) fed mice, see Fig 4), and liver tissue from four-month-old mice (Fig 2F). These mice were raised in independent animal facilities and their liver tissue harvested by different individuals. Therefore, our stoichiometry estimates appear to be generally applicable to diverse mouse liver tissues. Stoichiometry of acetylation Our measurements indicate that the vast majority of acetylation sites are modified at a very low stoichiometry. We calculated a median stoichiometry of acetylation that was ~0.05%. Mitochondrial sites had a significantly higher median stoichiometry (~0.11%) than non-mitochondrial sites (~0.04%) (Fig 3A), an observation that is consistent with our previous analysis in yeast cells (Weinert et al, 2014), and with the existence of separate mitochondrial and non-mitochondrial acetyl-CoA pools (Wellen et al, 2009). In yeast, high stoichiometry acetylation sites were associated with nuclear proteins involved in chromatin organization and transcription (Weinert et al, 2014). In this study, we estimated acetylation stoichiometry on a known regulatory site in the nucleus, structural maintenance of chromosomes protein 3 (SMC3) K106, which was 4% acetylated (Rolef Ben-Shahar et al, 2008; Zhang et al, 2008). However, most of the high stoichiometry sites we detected were on mitochondrial proteins, perhaps because the abundance of mitochondrial proteins in liver tissue limited the dynamic range of our analysis. Of the 4,322 sites with stoichiometry estimates, 344 occurred on proteins associated with the UniProt keyword "nucleus" while 1,532 occurred on proteins associated with the keyword "mitochondrion" (Dataset EV1). Although we detected known regulatory acetylation sites on histone N-terminal domains, these sites occurred on multiply acetylated peptides for which we were unable to accurately estimate stoichiometry and were therefore excluded from our analysis. Figure 3. Acetylation stoichiometry The box plots show the distributions of stoichiometry estimates based on partial (5%) chemical acetylation and SILAC quantification. The median stoichiometry of acetylation is shown above each box plot, and the number of sites analyzed is shown in parenthesis. Proteins were classified by association with the UniProt keywords "nuclear" (nuclear), "cytoplasmic" (cyto.), or "mitochondrion" (mito.). SIRT3-targeted sites were classified based on the degree of increased acetylation in SIRT3 KO liver tissue (Hebert et al, 2013); untargeted sites (S3 < 2) and sites with > 2-fold (S3 > 2), > 4-fold (S3 > 4), or > 8-fold (S3 > 8) increased acetylation are shown. SIRT3-targeted sites were additionally classified based on the degree of increased acetylation in SIRT3 KO liver tissue (Rardin et al, 2013b); sites with > 2-fold (S3 > 2), > 4-fold (S3 > 4), or > 8-fold (S3 > 8) increased acetylation are shown. Significance was determined by Wilcoxon's test (*P < 2e−16). Gene Ontology enrichment analysis of proteins harboring high stoichiometry (> 1%) acetylation sites. The scatterplot shows the significance of GO term enrichment (-Log10 P-value) and the degree of GO term enrichment (fold enrichment) comparing proteins with high stoichiometry (> 1%) sites to proteins with low stoichiometry (< 1%) sites. The size of each point is proportional to the number of proteins associating with the term. Proteins with high stoichiometry (> 1%), SIRT3-targeted acetylation sites. The table lists the estimated stoichiometry based on partial (5%) chemical acetylation using either SILAC or TMT quantification for SIRT3-targeted sites (defined as having > 2-fold increased acetylation in SIRT3 KO liver tissue (Hebert et al, 2013)). The box plots show the distributions of stoichiometry estimates based on SILAC quantification. Acetylation sites were classified as in (A). Stoichiometry was predicted in SIRT3 knockout (KO), calorie-restricted SIRT3 KO mice, and calorie-restricted wild-type (WT) mice using the acetylation changes quantified by Hebert et al (2013). The median predicted stoichiometry of acetylation is shown above each box plot. Data Information: (A, D) The box portion of the plot shows the interquartile range (IQR) and the line indicates the median value, whisker ends extend to 1.5 times the length of the IQR (unless the data range is smaller, in which case the whisker ends extend to the minimum or maximum value), outliers are not shown. Download figure Download PowerPoint High stoichiometry acetylation (> 1% acetylated) was overrepresented on mitochondrial proteins; acyltransferases; lyases; butanoate metabolism; and valine, leucine, and isoleucine degradation (Fig 3B). Overrepresentation of high stoichiometry acetylation sites on mitochondrial proteins is consistent with our observation of significantly higher median stoichiometry in this organelle (Fig 3A). We further observed that high stoichiometry sites were frequently found on enzymes that catalyze reactions involving acetyl groups, such as AACS (K391, 13% acetylated), NAA30 (K235, 8% acetylated), NAA50 (K34, 5% acetylated), ACSM1 (K200, 5% acetylated), KEG1 (K128, 4% acetylated), and GLYAT (K127, 1.5% acetylated). These findings suggest that enzymes that transfer acetyl groups may accumulate higher levels of acetylation due to autocatalysis or higher local concentrations of acetyl metabolites. A recent study similarly found that acetyl-CoA acetyltransferase 1 (ACAT1) catalyzed lysine acetylation of pyruvate dehydrogenase (PDHA1) K321 and pyruvate dehydrogenase phosphatase (PDP1) K202, mediating the Warburg effect in EGF-treated and immortalized cells (Fan et al, 2014). We determined acetylation stoichiometry at PDHA1 K321 by SILAC (0.84% acetylated) and TMT (0.39% acetylated). The low stoichiometry found at this position in liver tissue is consistent with the idea that PDHA1 K321 acetylation is only found after EGF stimulation or in immortalized cell lines (Fan et al, 2014). We also estimated acetylation stoichiometry at more than 20 sites that were previously suggested to regulate metabolic proteins (Appendix Table S1), and found that nearly all of these sites were < 1% acetylated. Stoichiometry at several of these positions was independently determined using the AQUA method (Fig 2B), providing additional confidence in these findings. Our results differ from a previous study that reported high stoichiometry acetylation on enoyl-coenzyme A hydratase (EHHADH) and malate dehydrogenase (MDH2) (Zhao et al, 2010). While the reasons for these differences are not clear, it is plausible that the levels of acetylation may be different in the analyzed samples. In the study from Zhao et al, EHHADH and MDH2 were expressed ectopically in cultured cells, while we estimated acetylation stoichiometry on the endogenously expressed proteins in liver tissue. Acetylation stoichiometry at SIRT3-targeted sites Acetylation stoichiometry at SIRT3-targeted sites (S3 > 2, median 0.08%) was slightly lower than at mitochondrial sites not targeted by SIRT3 (S3 < 2, median 0.11%), while the sites most affected in SIRT3 KO mice (S3 > 4, S3 > 8) had even lower median stoichiometry (0.05%) (Fig 3A). For these analyses, we used SIRT3-targeted sites defined by Hebert et al (2013); however, estimated stoichiometry was similar if we used SIRT3-targeted sites defined in an independent study that used a different quantitative method (Rardin et al, 2013b) (Fig 3A). These data indicate that SIRT3 suppresses acetylation to background levels at targeted sites. While the vast majority (97%) of SIRT3-targeted sites were < 1% acetylated, some SIRT3-targeted sites were acetylated at a high (> 1%) stoichiometry (Fig 3C). SIRT3-deficient animals have reduced metabolic function, which is linked to increased mitochondrial protein acetylation. In order to systematically investigate acetylation stoichiometry in SIRT3-deficient animals, we used previously determined changes in liver protein acetylation in SIRT3 KO, calorie-restricted SIRT3 KO, and calorie-restricted wild-type mice (Hebert et al, 2013) to predict acetylation stoichiometry under these conditions (Fig 3D and Dataset EV1). SIRT3-targeted sites (S3 > 2) were predicted to have a median 4-fold increase in acetylation stoichiometry in SIRT3 KO animals, and this increase was slightly greater (5-fold) in calorie-restricted SIRT3 KO animals. Sites that were most affected by loss of SIRT3 (S3 > 8) were predicted to have the greatest increase in acetylation stoichiometry, ~11-fold in SIRT3 KO animals and ~20-fold in calorie-restricted SIRT3 KO animals (Fig 3D). Glycine N-acyltransferase (GLYAT) K127 is ~57-fold elevated in both fed and calorie-restricted SIRT3 KO animals (Hebert et al, 2013). We estimated that this lysine is ~1.5% acetylated in wild-type animals, suggesting that acetylation stoichiometry could be as high as 87% in the absence of SIRT3. In addition, we predicted that hydroxymethylglutaryl-CoA lyase (HMGCL) K48 is ~16% acetylated and methylmalonyl-CoA epimerase (MCEE) K152 is ~9% acetylated in SIRT3 KO animals. In total, 48 SIRT3-targeted sites were predicted to have high (> 1%) stoichiometry in SIRT3 KO animals and 57 SIRT3-targeted sites were predicted to have high stoichiometry in calorie-restricted SIRT3 KO animals. In contrast, only 13 SIRT3-targeted sites were predicted to have high stoichiometry in calorie-restricted wild-type animals. SIRT3-targeted sites were not substantially altered by calorie restriction in wild-type animals; therefore, the stoichiometry we predicted at these sites was similarly unaltered (Fig 3D, compare WT to WT calorie-restricted). Notably, sites not targeted by SIRT3 were slightly increased (median 2.3-fold) by calorie restriction in wild-type animals, while the median stoichiometry of SIRT3-targeted sites was unchanged. These analyses suggest that acetylation reaches a sufficient stoichiometry in SIRT KO animals to account for the metabolic defects seen in these animals, particularly if multiple proteins and acetylation sites are affected in a common metabolic pathway. Acetylation at SIRT3-targeted sites is mostly unaffected by fasting Given the low stoichiometry of acetylation at SIRT3-targeted sites, understanding how acetylation at these positions is affected by dietary manipulations is important for determining whether SIRT3 regulates mitochondrial proteins by deacetylating SIRT3-targeted sites in response to dietary changes. In order to examine how fasting affects acetylation at SIRT3-targeted sites, we measured acetylation levels after fasting, and after fasting in a genetic mouse model that is deficient in generating fatty acids (FAs). Fasting triggers the mobilization of FAs from adipose tissues to the liver, where they are oxidized to acetyl-CoA in mitochondria. Adipose triglyceride lipase (ATGL) is important for lipolysis in adipose tissues, and ATGL-deficient mice have a reduced capacity to generate FAs for energy homeostasis (Haemmerle et al, 2006; Zimmermann et al, 2009). We used SILAC liver tissue to quantify acetylation levels in fed and fasted, wild-type and ATGL-deficient mouse liver tissue (Dataset EV5). Since we used the same SILAC liver tissue that was used to estimate stoichiometry in our previous experiments, we could dir