High-intensity interval training remodels the proteome and acetylome of human skeletal muscle.

Exercise is an effective strategy in the prevention and treatment of metabolic diseases. Alterations in the skeletal muscle proteome, including post-translational modifications, regulate its metabolic adaptations to exercise. Here, we examined the effect of high-intensity interval training (HIIT) on the proteome and acetylome of human skeletal muscle, revealing the response of 3168 proteins and 1263 lysine acetyl-sites on 464 acetylated proteins. We identified global protein adaptations to exercise training involved in metabolism, excitation-contraction coupling, and myofibrillar calcium sensitivity. Furthermore, HIIT increased the acetylation of mitochondrial proteins, particularly those of complex V. We also highlight the regulation of exercise-responsive histone acetyl-sites. These data demonstrate the plasticity of the skeletal muscle proteome and acetylome, providing insight into the regulation of contractile, metabolic and transcriptional processes within skeletal muscle. Herein, we provide a substantial hypothesis-generating resource to stimulate further mechanistic research investigating how exercise improves metabolic health.

Introduction

Exercise training is the most effective means to improve cardiovascular fitness and metabolic health, both being predominant determinants of life expectancy and all-cause mortality (Mandsager et al., 2018). Alterations to the proteome of skeletal muscle underpin its adaptations to exercise training, including glucose and fatty acid metabolism, insulin sensitivity, mitochondrial respiration, immune function, and excitation-contraction coupling (Deshmukh et al., 2021; Egan and Zierath, 2013; Gonzalez-Franquesa et al., 2021; Holloway et al., 2009; Hostrup and Bangsbo, 2017; Robinson et al., 2017; Ubaida-Mohien et al., 2019). Furthermore, proteome-wide post-translational modifications play an important role in regulating metabolism via modulating signaling, protein stability and enzyme activity (Baeza et al., 2016; Cohen, 2000), and are sensitive to exercise stimuli (Hoffman et al., 2015; Overmyer et al., 2015; Parker et al., 2020).

The application of mass-spectrometry-based proteomics has vastly expanded the catalog of post-translationally modified proteins, and in doing so has revealed novel insights into both histone and non-histone acetylation (Choudhary et al., 2014; Hansen et al., 2019; Narita et al., 2019; Svinkina et al., 2015). Nevertheless, the understanding of the acetylome in human skeletal muscle is still incomplete and remains to be studied in response to exercise training. Numerous cellular processes are regulated by protein acetylation, including transcription, metabolism, apoptosis, growth and muscle contraction among others (Narita et al., 2019). Lysine acetylation is an evolutionarily conserved post-translational modification, whereby lysine acetyltransferases catalyze the transfer of an acetyl group from acetyl-CoA to the ε-amino acid side chain of lysine and deacetylases remove acetyl groups from lysine residues. Alternatively, acetyl groups can be non-enzymatically transferred to lysine from acetyl-CoA (Hansen et al., 2019; James et al., 2018; Pougovkina et al., 2014; Weinert et al., 2015). In human vastus lateralis muscle biopsies from male athletes, 941 acetylated proteins containing 2811 lysine acetylation sites have been identified (Lundby et al., 2012). Mitochondria were the cellular component of human skeletal muscle exhibiting the greatest proportion of acetylated proteins (Lundby et al., 2012). Furthermore, the sensitivity of the acetylome to physiological stimuli has been demonstrated in rodents in response to acute fasting and chronic caloric restriction in liver (Hebert et al., 2013; Pougovkina et al., 2014; Weinert et al., 2015) as well as acute exercise (Overmyer et al., 2015) and high-fat diet (Williams et al., 2020) in skeletal muscle (Overmyer et al., 2015).

Continual advances in mass spectrometry-based proteomic technologies and approaches are increasing the depth and coverage of proteomic analyses. Nevertheless, proteome and proteome-wide post-translational modification analyses of skeletal muscle tissue remain highly challenging due to a wide dynamic range of protein abundances (Deshmukh et al., 2015). Single-shot data-dependent acquisition (DDA) analyses are often limited in their protein identifications and are characterized by relatively poor data completeness (i.e. high number of missing values; Tabb et al., 2010). However, the relatively recent development of data-independent acquisition (DIA) is helping to vastly reduce the proportion of missing data while simultaneously improving proteome depth in single-shot analyses of complex tissues (Ludwig et al., 2018). Nonetheless, DDA in combination with peptide-level fractionation remains a useful approach to overcome sample complexity, particularly when performing post-translational modification proteomics.

In this study, untrained men undertook 5 weeks of high-intensity interval training (HIIT) and vastus lateralis muscle biopsies were collected before and after HIIT for proteomic and lysine-acetylome analyses. We investigated the adaptation to HIIT in a deep human skeletal muscle proteome and undertook the first investigation of the acetylomic adaptations to exercise training within human skeletal muscle. HIIT remodeled skeletal muscle favoring mitochondrial biogenesis, but induced changes in protein abundance indicative of reduced calcium sensitivity. Furthermore, HIIT increased acetylation, particularly of the mitochondria and enzymes of the tricarboxylic acid (TCA) cycle.

Results and discussion

Physiological adaptations to HIIT

Eight untrained men (23–38 years of age; Figure 1—figure supplement 1) completed a HIIT regimen (Figure 1A) that consisted of 5 weeks of supervised cycling, performed as 4–5×4 min intervals at a target heart rate of >90% max interspersed by 2 min of active recovery, undertaken three times weekly. Participants were healthy, non-smokers and occasionally active, but otherwise untrained. Characteristics of the participants are presented in Figure 1B. Participants experienced a 14% and 17% improvement in maximal oxygen consumption (V̇O2max) and incremental peak power output, respectively, during HIIT (Figure 1B and C; p<0.001), which compares favorably to typical HIIT-induced adaptations (Wen et al., 2019). In addition, participants gained an average of 1.0±0.3 kg lean mass during HIIT training, without changes in fat mass (Figure 1B). The rate of fat oxidation during submaximal cycling (50–150 W) increased following HIIT (Figure 1D), while no apparent changes were observed for carbohydrate oxidation (Figure 1E).

Figure 1—figure supplement 1.

CONSORT flow diagram of enrolled participants.

Figure 1.

Physiological adaptations to HIIT.

(A) Experimental overview. (B). Subject characteristics. (C). Five weeks of HIIT increased maximal oxygen uptake (V̇O2max) and incremental peak power output (iPPO). (D) HIIT increased whole-body fat oxidation during submaximal exercise (50–150 W) without altering (E). carbohydrate oxidation. (F). HIIT increased mitochondrial respiration in skeletal muscle (LN: leak respiration, FAO: fatty acid oxidation, CID: submaximal CI respiration, PD: submaximal CI +II respiration, P: oxidative phosphorylation capacity, E: electron transport system capacity, ECII: succinate-supported electron transport system capacity). (G). HIIT increased skeletal muscle citrate synthase (CS) activity. (H). Analytical workflow. Summary statistics are mean ± SEM (n=8). * p<0.05, ** p<0.01, *** p<0.001.

(A) Quantified proteins from each sample. (B). Representative Pearson correlation between biological replicates (Pre 4 v Pre 5). (C). Cellular compartment (GOCC) coverage of the proteome. (D). Principal component analysis of the proteome. P: participant.

HIIT increases mitochondrial respiratory capacity of skeletal muscle

Resting muscle biopsies were collected from the vastus lateralis before and three days after the final training session. Mitochondrial respirometry analyses were immediately performed on the freshly excised muscle samples using a substrate-uncoupler-inhibitor titration protocol (Pesta et al., 2011) on a high-resolution O2K respirometer (Oroboros, Innsbruck, Austria). In line with previous findings (Christensen et al., 2016; Dohlmann et al., 2018; Granata et al., 2016; Jacobs et al., 2013; Larsen et al., 2015; Meinild Lundby et al., 2018), HIIT increased mitochondrial respiration across a range of respiratory states, increasing CI +II coupled respiration by 30% (p<0.001) and electron transport system capacity by 15% (p=0.049) (Figure 1F). Furthermore, HIIT induced a robust increase in citrate synthase activity by 42% (p=0.002) (Figure 1G), a validated marker of mitochondrial volume in human skeletal muscle (Larsen et al., 2012).

Elevated mitochondrial respiration is underpinned by increased abundance of mitochondrial proteins following HIIT

Skeletal muscle biopsy samples were prepared for liquid-chromatography tandem mass spectrometry and measured using single-shot DIA (Ludwig et al., 2018; Figure 1H). We obtained extensive proteome coverage identifying 3343 proteins (Supplementary file 1). After applying a filter of four valid values out of the eight participants in at least one time point, 3168 proteins were quantified within skeletal muscle (Supplementary file 2). Due to the nature of DIA, few missing values required imputation in each sample (Figure 1—figure supplement 2A; mean proteins quantified per sample: 2853±33). Good correlation (median correlation: 0.88) between biological replicates (participants at the same time point) was apparent (Figure 1—figure supplement 2B). Furthermore, the proteome contained good coverage across multiple cellular compartments (Figure 1—figure supplement 2C). Principal component analysis demonstrated inter-sample variation; however, samples did cluster by time point (Figure 1—figure supplement 2D). Overall, our DIA proteome analysis displayed extensive coverage, demonstrated by the quantification of low abundant proteins such as transcription factors (e.g. CREB1 and TFEB) and myokines (e.g. IL18; Supplementary file 1).

Figure 1—figure supplement 2.

Proteome quality control.

(A) Quantified proteins from each sample. (B). Representative Pearson correlation between biological replicates (Pre 4 v Pre 5). (C). Cellular compartment (GOCC) coverage of the proteome. (D). Principal component analysis of the proteome. P: participant.

HIIT regulated 126 proteins (permutation-based FDR <0.05). Of these, 102 proteins were upregulated, while 24 were downregulated (Figure 2A, Supplementary file 2). These included the upregulation of classical endurance exercise training-responsive mitochondrial proteins (e.g. cytochrome c oxidase subunit 5b; COX5B & NADH dehydrogenase 1 alpha subcomplex subunit 7; NDUFA7), as well as proteins involved in NAD+ metabolism (e.g. nicotinamide phosphoribosyltransferase; NAMPT), branched-chain amino acid metabolism (branched-chain alpha-ketoacid dehydrogenase kinase; BCKDK) and ubiquinone biosynthesis (5-demethoxyubiquinone hydroxylase; COQ7), the latter of which we have recently described in exercise-trained mouse skeletal muscle (Gonzalez-Franquesa et al., 2021; Figure 2A). We also identified novel exercise-training regulated proteins, including, but not limited to, glutaminyl-tRNA synthase (QARS) and rab GDP dissociation inhibitor alpha (GDI1). QARS, which catalyzes the glutamate aminoacylation of tRNA and thus plays a central role in translation (Deutscher, 1984), was amongst the most significantly upregulated proteins within the proteome. In addition, GDI1, which inhibits insulin-stimulated glucose uptake by preventing the dissociation of GDP from Rab10 (Chen et al., 2009), was lowered, providing a novel potential mechanism for the insulin-sensitizing effects of exercise training.

Figure 2.

HIIT increases mitochondrial proteins and reduces a subset of contractile fiber associated proteins.

(A) Volcano plot displaying 102 upregulated and 24 downregulated proteins following HIIT (FDR <0.05). (B). Fisher’s exact tests identified the enrichment of mitochondrial terms within the differentially regulated proteins (enrichment analysis FDR <0.02). (C). Hierarchical clustering and enrichment analysis (enrichment analysis FDR <0.02) on differentially regulated proteins identified that mitochondrial terms are enriched within the upregulated proteins, while cytoplasm (UniProt Keyword) is enriched amongst the downregulated proteins. (D). Summed total protein abundances for different organelles (UniProt Keyword) shows upregulation of the mitochondrial protein content. (E–G). Summed total protein abundances display upregulation of electron transport chain complexes (E), mitochondrially encoded proteins (F) and proteins with a transit peptide (G). Summary statistics are mean ± SEM (n=8). * p<0.05, ** p<0.01, *** p<0.001.

Immunoblotting analyses confirmed the upregulation of electron transport chain proteins. Representative images confirming equal loading are displayed in Figure 5—figure supplement 2. Summary statistics are mean ± SEM (n=8). * p<0.05, ** p<0.01, *** p<0.001.

Figure 5—figure supplement 2.

Representative images of equal loading for immunoblot analyses.

(A) Coomassie stain for n=8 immunoblots of human skeletal muscle (Figure 5E (SIRT1-6), Figure 7E, Figure 2—figure supplement 1, and Figure 3—figure supplement 1). (B). Stain-free image for n=7 immunoblots of human skeletal muscle (Figure 5E (GCN5L1), and Figure 5—figure supplement 1A). (C). Stain-free image for EP300 KD immunoblots (Figure 7F).

After filtering for differentially regulated proteins, enrichment analysis using Fisher’s exact test indicated the predominant regulation of mitochondrial proteins (e.g. GOCC: mitochondrial part and mitochondrial inner membrane) (Figure 2B, Supplementary file 3). Hierarchical clustering analysis on z-scored differentially regulated proteins identified that the mitochondrial terms were enriched within the upregulated proteins (Figure 2C). Summed protein abundances also demonstrated mitochondrial biogenesis (Figure 2D), including upregulated protein content of electron transport chain complexes (Figure 2E and Figure 2—figure supplement 1), mitochondrially encoded proteins (Figure 2F) and proteins containing a mitochondria-targeting transit peptide (Figure 2G). Thus, the increase in the proteome mirrors the functional increase in mitochondrial respiratory capacity of skeletal muscle after HIIT (Figure 1F).

Figure 2—figure supplement 1.

HIIT increases electron transport chain subunits.

Immunoblotting analyses confirmed the upregulation of electron transport chain proteins. Representative images confirming equal loading are displayed in Figure 5—figure supplement 2. Summary statistics are mean ± SEM (n=8). * p<0.05, ** p<0.01, *** p<0.001.

HIIT regulates proteins involved in skeletal muscle excitation-contraction coupling

We next investigated whether HIIT influenced the fiber-type proportions of skeletal muscle. While HIIT did not change skeletal muscle fiber-type composition in terms of myosin heavy chain isoforms (Figure 3A) and myosin light chain proteins (Figure 3B), a coordinated regulation of proteins modulating myosin light chain phosphorylation was identified (Figure 3C). Abundance of myosin light chain kinase 2 (MYLK2) decreased concomitantly with a reduction in smoothelin-like protein 1 (SMTNL1), an inhibitor of the myosin phosphatase complex (Wooldridge et al., 2008) and an increase in myosin phosphatase-targeting subunit 1 (PPP1R12A), the myosin-targeting regulatory subunit of protein phosphatase 1 (Alessi et al., 1992; Figure 3C). Reduced MYLK2 abundance was confirmed by immunoblotting (Figure 3—figure supplement 1A). Furthermore, we have previously detected these adaptations in slow- and fast-twitch muscle fibers following moderate-intensity continuous cycling training (Deshmukh et al., 2021). Given that phosphorylation of myosin light chains promotes myofibrillar calcium sensitivity (Davis et al., 2001), the reduction in both MYLK2 and SMTNL1 alongside an increase in PPP1R12A indicates an adaptation towards reduced myosin phosphorylation and lowered calcium sensitivity following exercise training. Indeed, myofibrillar calcium sensitivity decreases following high-intensity sprint training in untrained males, particularly at a low pH representing physiological exercising conditions (Lynch et al., 1994). Although the functional importance of such adaptations remains elusive, knockdown of Smtnl1 augments exercise training-induced improvements in endurance performance, while sedentary Smtnl1-/- mice display a fiber-type switch mimicking endurance exercise training (Wooldridge et al., 2008). While we did not observe any concomitant myosin fiber-type switching (Figure 3A), the changes observed in proteins regulating myosin phosphorylation following HIIT could result in a right-shift of the Ca2+-force relationship, meaning that a higher myoplasmic calcium concentration would be required for a given force production. This decline in myofibrillar calcium sensitivity may, however, support faster off-kinetics of calcium from troponin-C to be re-sequestered in the sarcoplasmic reticulum. Therefore, the changes observed in proteins regulating myosin phosphorylation following HIIT provide an intriguing mechanism for a HIIT-induced reduction in calcium sensitivity and warrants further attention.

Figure 3.

HIIT regulates proteins involved in skeletal muscle calcium sensitivity and handling HIIT did not alter the abundance of myosin heavy chain (MYH1: MyHC2x, MYH2: MyHC2a, MYH4: MyHC2x, MYH7: MyHCβ; A) or light chain (B) isoforms.

(C). HIIT regulates proteins controlling myosin phosphorylation. (D). HIIT reduces abundance of subunits of the dihydropyridine receptor. (E). Summed total protein abundances display downregulation of the dihydropyridine receptor. (F). HIIT does not alter the abundance of ryanodine receptor 1. Summary statistics are mean ± SEM (n=8). ^ FDR <0.05. * p<0.05, ** p<0.01, *** p<0.001.

(A) Immunoblotting analysis confirms the downregulation of MYLK2. Representative images confirming equal loading are displayed in Figure 5—figure supplement 2. (B). No change in the abundance of sarcoplasmic/endoplasmic reticulum calcium ATPases 1–3 (SERCAs). Summary statistics are mean ± SEM (n=8). * p<0.05, ** p<0.01, *** p<0.001.

Figure 3—figure supplement 1.

HIIT decreases MYLK2 abundance.

(A) Immunoblotting analysis confirms the downregulation of MYLK2. Representative images confirming equal loading are displayed in Figure 5—figure supplement 2. (B). No change in the abundance of sarcoplasmic/endoplasmic reticulum calcium ATPases 1–3 (SERCAs). Summary statistics are mean ± SEM (n=8). * p<0.05, ** p<0.01, *** p<0.001.

Providing a further link to the altered calcium handling following HIIT (Lynch et al., 1994), we identified a downregulation of L-type calcium channel subunits, otherwise known as the dihydropyridine receptor (DHPR) (Figure 3D and E). DHPR controls the coupling of membrane depolarization and sarcoplasmic reticulum Ca2+-release in skeletal muscle via its interaction with ryanodine receptor 1 (RYR1) (Flucher and Franzini-Armstrong, 1996). Conversely to DHPR, HIIT did not influence the abundance of RYR1 (Figure 3F) or the sarcoplasmic/endoplasmic reticulum calcium ATPases 1–3 (ATP2A1-3 (SERCAs); Figure 3—figure supplement 1B). The observation that DHPR abundance declined following HIIT points to a ‘slowing’ of muscle fibers. Indeed, a period of training can induce rapid changes in the abundance of proteins with importance for calcium handling, irrespective of changes in fiber type distribution (Hostrup et al., 2019; Hostrup et al., 2018; Majerczak et al., 2008; Munkvik et al., 2010; Ortenblad et al., 2000). Fast-twitch fibers have greater sarcoplasmic reticulum volume, faster calcium release and re-uptake kinetics, and a greater content of DHPR than slow-twitch fibers (Banas et al., 2011; Deshmukh et al., 2021; Murgia et al., 2017). Thus, the HIIT-induced reduction of DHPR abundance may represent a slowing of muscle fibers independently of their myosin heavy chain abundance.

Lysine acetylomics of skeletal muscle identifies the predominant acetylation of mitochondrial and acetyl-coA metabolic proteins

Skeletal muscle biopsy samples were prepared for lysine acetylomics using a PTMscan Acetyl-Lysine Motif kit (Cell Signaling Technology) (Svinkina et al., 2015) and measured via liquid-chromatography tandem mass spectrometry using DDA. Due to limited sample availability from one participant, lysine acetylomics was performed for 7 of the participants. We identified a total of 1990 acetyl-sites on 664 proteins (Supplementary file 4). While this is fewer than the 2,11 acetyl-sites on 941 proteins identified in the largest human skeletal muscle acetylome to date (Lundby et al., 2012), we identified 1073 acetyl-sites that were not identified in the analysis of Lundby et al (Figure 4—figure supplement 1A). The difference in acetyl-sites identified between the two studies likely reflects a combination of exercise-induced acetylation revealing novel-acetyl sites, and the stochastic nature of data-dependent acquisition, the different LC-MS/MS instruments employed, and the use of updated FASTA files. Nonetheless, we substantially extend the human skeletal muscle acetylome. After applying a filter of 4 valid values out of the seven participants in at least one time point we quantified 1263 acetylated sites on 464 proteins within skeletal muscle (Figures 1G and 4A, Supplementary file 5). Six and 42 acetyl-sites were quantified exclusively in the pre- and post-HIIT samples, respectively. The mean number of acetyl-sites quantified in each sample was 976 (Figure 4—figure supplement 1B). Median correlation between biological replicates (between participants at the same time point) was 0.76 (Figure 4—figure supplement 1C), indicating greater variation in the human acetylome than the proteome, as would be expected (Lundby et al., 2012). The acetylome also displayed extensive coverage across multiple cellular compartments (Figure 4—figure supplement 1D), which largely reflected the abundance distribution of the proteome (Figure 1—figure supplement 2C), albeit with a slight relative increase in the mitochondrial identifications. Principal component analysis identified variation in the acetylome between samples (Figure 4—figure supplement 1E). Although there was no clear separation of samples by time point, a leftward shift is apparent on component 1 from pre to post samples for almost all participants (six out of seven). There was considerable overlap with proteins quantified in the proteome, with 421 proteins quantified in both (Figure 4A). Of the quantified acetylated sites (1263), the majority (1232) were located on these 421 proteins (Figure 4A). Single acetyl-sites were quantified on the majority of proteins, with a trend for decreasing frequency with additional acetyl-sites (Figure 4B). However, several proteins were quantified with a large number of acetylation sites (e.g. ≥15 acetyl-sites) (Figure 4B), examples of which include the contractile proteins titin (TTN) and slow-twitch myosin beta (MYH7).

Figure 4—figure supplement 1.

Acetylome quality control.

(A) Overlap between identified acetyl-site with those identified in human skeletal muscle by Lundby et al., 2012. (B). Quantified acetyl-sites from each sample. (C). Representative Pearson correlation between biological replicates (Pre 4 v Pre 5). (D). Cellular compartment (GOCC) coverage of the acetylome. (E). Principal component analysis of the acetylome. P: participant.

Figure 4.

The human skeletal muscle acetylome displays higher stoichiometry on mitochondrial proteins and lower stoichiometry on contractile proteins.

(A) We quantified 1263 acetyl-sites corresponding to 464 proteins (n=7). Of these, 421 proteins were also quantified in the proteome with 1232 of the quantified acetyl-sites located on these 421 proteins. (B). Distribution of acetyl-sites on proteins within the pre-HIIT acetylome. (C). Top 10 highest intensity acetylated proteins (acetyl-peptide intensities were summed for each protein) within the pre-HIIT acetylome. (D). Cumulative protein abundance and rank within the proteome (the top 10 highest intensity acetyl-proteins are highlighted). (E). One-dimensional enrichment analysis of acetylated protein intensity identified mitochondrial proteins, particularly those involved in carboxylic acid metabolism and monovalent inorganic cation transport (e.g. complex V) as having systematically high acetylation intensities (enrichment analysis FDR <0.02). (F). Top 10 acetyl-sites with highest abundance-corrected intensities (ACIs). (G). One-dimensional enrichment analysis of acetyl-site ACIs identified mitochondrial and carboxylic acid catabolic proteins as higher stoichiometry (positive enrichment factor), while contractile fiber cytosolic and plasma membrane proteins were enriched as lower stoichiometry negative enrichment factor; (enrichment analysis FDR <0.02; enrichment performed on leading protein IDs). (H). Histogram depicting the ACI distribution of the total acetylome (blue), the mitochondrial acetyl-sites (green) and the contractile fiber acetyl-sites (yellow). Mitochondrial proteins were distributed at higher ACI values and contractile fiber proteins at lower ACI values than the total acetylome.

(A) Overlap between identified acetyl-site with those identified in human skeletal muscle by Lundby et al., 2012. (B). Quantified acetyl-sites from each sample. (C). Representative Pearson correlation between biological replicates (Pre 4 v Pre 5). (D). Cellular compartment (GOCC) coverage of the acetylome. (E). Principal component analysis of the acetylome. P: participant.

After summing the median intensities of acetyl peptides from each protein in the pre-HIIT samples, we ranked the abundance of acetylated proteins within skeletal muscle (Supplementary file 6) and highlighted the top 10 proteins with the highest acetyl-intensity (Figure 4C). Histone H4 (HIST1H4A) had the highest acetylation intensity, making up approximately 10% of the total acetylome intensity, supporting the regulatory role of histone acetylation in modulating transcription (Sterner and Berger, 2000; Strahl and Allis, 2000). The majority of the remaining top acetylated proteins were metabolic enzymes, including the TCA cycle proteins malate dehydrogenase (MDH2) and fumarate hydratase (FH), as well as subunits of complex V (ATP5H and ATP5O) within the electron transport chain. Superoxide dismutase (SOD2), a canonical acetylated enzyme involved in mitochondrial reactive oxygen species handling (St Clair et al., 1992; Tao et al., 2009), also displayed high acetylation intensity. We further compared the protein abundance rank for these high-intensity acetylated proteins within the proteome (Figure 4D). These proteins did not appear to be the highest intensity acetylated proteins simply due to their relative protein abundance. While they all fell within the top 318 proteins in the proteome, they did not make up the most abundant proteins, except for creatine kinase M-type (CKM), which is extremely abundant in skeletal muscle (Figure 4D). To identify systematic trends in protein acetylation, we performed a one-dimensional enrichment analysis (Cox and Mann, 2012) on the summed protein acetylation intensities, which ranked the proteins by acetylation intensity and identifies Gene Ontology (GO) annotations that are systematically over-represented with higher intensities (positive enrichment factor) and lower intensities (negative enrichment factor). This identified mitochondrial terms (e.g. GOCC: mitochondrial part and mitochondrial matrix) as enriched for high acetylation intensity (Figure 4E and Supplementary file 7). In particular, proteins of carboxylic acid metabolic processes (e.g. TCA cycle) and monovalent inorganic cation transport (e.g. electron transport chain complex V proteins) display systematically high acetylation intensity (Figure 4E and Supplementary file 7). Furthermore, the Uniprot keyword term ‘muscle protein’ was also enriched for high acetylation intensity, a term that mainly encompasses the contractile machinery of skeletal muscle (e.g. myosins).

In order to examine acetylation stoichiometry within skeletal muscle, we estimated the relative stoichiometries of acetyl-sites using abundance-corrected intensities (ACI) (Supplementary file 8). ACIs were calculated by dividing acetyl-peptide intensities by the intensities of their corresponding protein, with ACI values correlating with stoichiometry (Hansen et al., 2019). After ranking acetyl-site ACI for the pre-HIIT samples, we highlighted the top 10 acetyl-sites with the highest ACI (Figure 4F). Trifunctional enzyme subunit alpha (HADHA), FH, HIST1H4A and 3- ketoacyl-CoA thiolase (ACAA2), which were identified as among the highest intensity acetylated proteins (Figure 4C), all contain high stoichiometry acetyl-sites (Figure 4F). However, nicotinamide nucleotide transhydrogenase (NNT) K70 had the highest ACI within skeletal muscle (Figure 4F). NNT is an inner mitochondrial membrane protein, which uses the proton motive force to maintain high levels of NADPH (Hoek and Rydström, 1988) and in doing so can regulate metabolism (Freeman et al., 2006; Ho et al., 2017). NNT is also highly acetylated in cardiac muscle (Foster et al., 2013), although the effect of acetylation on NNT activity remains to be determined. Nonetheless, NNT can regulate acetylation via NADPH-mediated regulation of histone deacetylase 1 (HDAC1) activity (Ho et al., 2017), thus acetylation of NNT may represent a feedback mechanism controlling cellular acetylation and metabolism.

To identify systematic trends in acetyl-site stoichiometry, we performed a one-dimensional enrichment analysis (Cox and Mann, 2012) on acetyl-site ACI using the leading protein ID for relative enrichment (Tyanova et al., 2016). We identified the mitochondria (e.g. GOCC: mitochondrial part and mitochondrial inner membrane) and carboxylic acid catabolic processes to be enriched within higher stoichiometry acetylated proteins (Figure 4G, H and Supplementary file 9). This extends previous analyses by not only showing that mitochondrial proteins make up the majority of acetylated proteins in skeletal muscle (Lundby et al., 2012), but also by identifying mitochondrial proteins as having relatively high stoichiometry acetyl-sites – an observation that is consistent with analyses in HeLa cells, rodent liver and yeast (Hansen et al., 2019; Weinert et al., 2014; Weinert et al., 2015). Enriched for proteins with low stoichiometry acetyl-sites were proteins involved in contraction (e.g. GOCC: contractile fiber part; Figure 4G, H and Supplementary file 9), including the Uniprot keyword term ‘muscle protein’ (Figure 4G), which, conversely, was enriched for high-protein acetylation intensity (Figure 4E). Myosin displayed the greatest enrichment for low acetylation stoichiometry (Figure 4G). Thus, despite contractile proteins displaying high-protein acetylation intensity and making up a substantial number of known acetylated proteins within skeletal muscle (Lundby et al., 2012), they contain acetylation sites of relatively low stoichiometry. The high acetylation intensity of these proteins is therefore likely due to high protein abundance and a large number of acetylation sites per protein. Proteins annotated to the cytosol and plasma membrane also displayed low acetyl-site ACI (Figure 4G). This highlights differences in the inter-organelle acetylation levels of skeletal muscle and can likely be explained by subcellular compartmentalization of acetyl-CoA (Pietrocola et al., 2015).

HIIT induces acetylation of mitochondrial and TCA cycle proteins

HIIT increased the mean number of acetyl-sites quantified per sample by over 100 sites (Pre: 915±44, Post: 1037±51; p=0.015; Figure 4—figure supplement 1B). Using a stringent statistical approach of permutation-based FDR corrected paired t-tests, we identified 20 upregulated acetyl-sites and 1 downregulated site (FDR <0.05; Figure 5A). In addition, by applying a validated, albeit less stringent, significance score (Π-value), which combines the statistical significance (p-value) with the fold change (Xiao et al., 2014), we extended this to identify 257 upregulated and 26 downregulated acetyl-sites following HIIT (Π<0.05; Figure 5A and Supplementary file 5). In general, there was a trend for increased acetylation following HIIT (Figure 5A), which could not be explained by changes in protein content as the fold changes in acetylation generally exceeded those of protein abundance (Figure 5B). Therefore, we did not normalize the acetylome data to protein abundance. The increase in protein acetylation was subsequently validated via immunoblotting (Figure 5—figure supplement 1A). Within the increased sites, acetyl-sites on metabolic enzymes isocitrate dehydrogenase (IDH2), pyruvate dehydrogenase E1 component subunit alpha (PDH1A), succinate CoA ligase subunit alpha (SUCLG1) and ATP synthase coupling factor 6 (ATP5J) were apparent (Figure 5A), while hyperacetylation of SOD2 occurred at K68, 75, 122, and 130 in response to HIIT (Figure 5A, Figure 5—figure supplement 1B). Included within the downregulated sites were acetyl-sites on annexin A6 (ANXA6), laminin (LMNA) and p38 mitogen-activated protein kinase γ (MAPK12) (Figure 5A). Interestingly, Histone H1.5 (HIST1H1B) K48 showed increased acetylation, despite significantly reduced protein abundance (Figure 5B).

Figure 5.

HIIT increases acetylation of mitochondrial and TCA cycle proteins concomitantly with an increase in SIRT3 abundance.

(A) Volcano plot displaying 20 upregulated (filled red circles) and 1 downregulated (filled blue circle) acetyl-sites following HIIT at an FDR <0.05, while 257 acetyl-sites were upregulated (red circles) and 26 downregulated (blue circles) at Π<0.05 (n=7). (B). Scatter plot indicating that HIIT-induced changes in acetyl-site intensity typically exceeded that of the corresponding protein. (C). Fisher’s exact tests identified the enrichment of mitochondrial and TCA cycle terms within the differentially regulated acetyl-proteins (enrichment analysis FDR <0.02; enrichment performed on leading protein IDs). (D). IceLogo motif enrichment (p<0.01) for the upregulated sites displayed a predominance of proximal cysteine residues relative to the acetylated lysine (position 0). (E). Immunoblotting analysis identified the upregulation of the deacetylases SIRT3, SIRT5, and SIRT6, but no change in SIRT1, whilst the mitochondrial acetyltransferase GCN5L1 remained unchanged following HIIT (n=7–8). Representative images confirming equal loading are displayed in Figure 5—figure supplement 2. * p<0.05, ** p<0.01, *** p<0.001.

(A) Immunoblotting analysis confirmed the upregulation of acetylation following HIIT (n=7). (B) Immunoblotting analysis confirmed the upregulation of acetylation on K68 (n=8) and K122 (n=7) of SOD2 following HIIT. Representative images confirming equal loading are displayed in Figure 5—figure supplement 2. * p<0.05, ** p<0.01, *** p<0.001.

(A) Coomassie stain for n=8 immunoblots of human skeletal muscle (Figure 5E (SIRT1-6), Figure 7E, Figure 2—figure supplement 1, and Figure 3—figure supplement 1). (B). Stain-free image for n=7 immunoblots of human skeletal muscle (Figure 5E (GCN5L1), and Figure 5—figure supplement 1A). (C). Stain-free image for EP300 KD immunoblots (Figure 7F).

Figure 5—figure supplement 1.

HIIT increases protein acetylation.

(A) Immunoblotting analysis confirmed the upregulation of acetylation following HIIT (n=7). (B) Immunoblotting analysis confirmed the upregulation of acetylation on K68 (n=8) and K122 (n=7) of SOD2 following HIIT. Representative images confirming equal loading are displayed in Figure 5—figure supplement 2. * p<0.05, ** p<0.01, *** p<0.001.

Figure 7.

HIIT increases acetylation on specific histone acetyl-sites.

HIIT increased acetylation on (A). H1.5 K48, (B). H2B type 2F K16/20 and (C). H3 K23. (D). HIIT did not alter H4 acetylation (n=7). (E) Immunoblotting analysis identified the upregulation of the nuclear-localized acetyltransferase EP300 (n=8, mean of three technical replicates; participant 1 was excluded as an outlier from statistical analysis (pre, post and fold-change values were all >3 median absolute deviations from the respective median), data for participant 1 is shown in the translucent data points). (F) Knockdown of EP300 reduces acetylation of H2B K20 but not H3 K23 in C2C12 myotubes (n=3). Representative images confirming equal loading are displayed in Figure 5—figure supplement 2. Summary statistics are mean ± SEM. † Π<0.05. * p<0.05, ** p<0.01, *** p<0.001.

Normalization of EP300. EP300 displays a similar increase following HIIT without normalization (A) or when normalized to a stain-free blot (B). Data taken from replicate 1 of EP300 immunoblotting. (C) Images of EP300 immunoblot and stain-free blot for replicate 1 of EP300 immunoblotting.

A Fisher’s exact test of HIIT-regulated acetyl-sites (Π<0.05), using leading protein ID for relative enrichment, indicated the predominant regulation of the TCA cycle pathway, mitochondrial proteins (e.g. GOCC: mitochondrial part and mitochondrial inner membrane) and, in particular, electron transport chain complex V proteins (GOCC: mitochondrial proton-transporting ATP synthase complex) (Figure 5C, Supplementary file 10). These GO terms were strikingly similar to the terms enriched for high acetylation intensity and stoichiometry in the pre-HIIT samples (Figure 4E and G), indicating that proteins that are highly acetylated in the basal state are most susceptible to increased acetylation following HIIT. To further explore the regulation of HIIT-induced acetylation in skeletal muscle, we looked for consensus sequences around the acetylated lysine residues using iceLogo (Colaert et al., 2009). We identified a sequence with a predominance of proximal cysteine residues as well as an aspartic acid residue in the +1 position (Figure 5D). The aspartic acid at +1 is a feature of mitochondrial acetylated proteins (Lundby et al., 2012) and its enrichment is likely due to the over-representation of mitochondrial proteins in the upregulated acetyl-sites (Figure 5C). Proximal cysteine residues are associated with promoting non-enzymatic lysine acetylation (Hansen et al., 2019; James et al., 2018) via initial cysteine acetylation followed by transfer of the acetyl group to lysine (James et al., 2018). Indeed, non-enzymatic acetylation increases alongside elevated fatty acid oxidation (Pougovkina et al., 2014), as is apparent in skeletal muscle mitochondria after HIIT (Figure 1E). This likely occurs through changes in acetyl-CoA concentration and/or increased acetyl-CoA flux (Pougovkina et al., 2014). Although acetyl-CoA content within resting skeletal muscle does not change in response to exercise training (Putman et al., 1998), acetyl-CoA levels and TCA cycle flux increase during exercise (Constantin-Teodosiu et al., 1991; Gibala et al., 1998; Howlett et al., 1998; Putman et al., 1998), which may result in an accumulation of lysine acetylation during repeated bouts of exercise. Conversely, acute exercise decreases skeletal muscle acetylation in exercising rats (Overmyer et al., 2015), but whether this is the case for humans remains to be determined. Nonetheless, we identify a robust increase in skeletal muscle acetylation, predominantly of mitochondrial proteins, following 5 weeks of HIIT in humans.

Because HIIT altered the skeletal muscle acetylome, we examined proteins regulating acetylation in skeletal muscle. A subset of sirtuins (SIRT2, SIRT3, and SIRT5), a family of protein deacetylases, were quantified in the proteome. SIRT3, a mitochondrial-localized deacetylase, displayed a small but non-significant trend for increased abundance following exercise training (Supplementary file 2). To further investigate this, we immunoblotted for SIRT1, SIRT3, SIRT5, and SIRT6. Of the cystosolic and nuclear-localized SIRTs, the abundance of SIRT1 did not change, while SIRT6 increased by 27%. The mitochondrial-localized SIRT3 and SIRT5 increased in abundance by 61% and 32%, respectively, following HIIT (Figure 5E). Thus, mitochondrial acetylation increased concomitantly with an increase in the abundance of mitochondrial sirtuins (Figure 5B and E). Conversely, the protein abundance of the mitochondrial acetyltransferase general control of amino acid synthesis 5 like-1 (GCN5L1) was unchanged by exercise training (Figure 5E). Although the deacetylase activity of SIRT5 has been questioned, instead displaying a preference for desuccinylase and demalyonylase activity (Du et al., 2011; Peng et al., 2011), SIRT3 appears to play a major role in suppressing non-enzymatic mitochondrial acetylation (Weinert et al., 2015). Therefore, elevated SIRT3 abundance following exercise training might be interpreted as a mechanism to suppress excess acetylation, either to preserve the activity of mitochondrial enzymes (Hirschey et al., 2010; Tao et al., 2010; Vassilopoulos et al., 2014) or to scavenge acetyl groups for reintegration into acetyl-CoA and energy production.

Given the enrichment of mitochondrial terms and particularly of complex V proteins in the HIIT-regulated acetylated proteins, we filtered for proteins annotated to the different complexes of the electron transport chain using the HUGO database (Braschi et al., 2019) and highlighted the regulation of each acetyl-site on these proteins alongside the changes in protein abundance (Figure 6A). While proteins of complexes I, II, III and IV showed a mixed acetylation response to exercise training, almost every quantified complex V protein had at least one acetyl-site that increased following HIIT (Figure 6A). In fact, ATPase inhibitor (ATPIF1), one of the two complex V proteins that do not show elevated acetylation, is a negative regulator of complex V and is unlikely to be constitutively bound to the ATP synthase complex (Campanella et al., 2008). Why complex V displays elevated acetylation levels both in the basal (Figure 4E) and exercise trained states is unclear (Figures 5C and 6A). However, as a large proportion of ATP synthase extends into the mitochondrial matrix, many subunits are likely to be more exposed to acetyl-CoA than other membrane-embedded electron transport chain complexes. In support, we detected acetylation on all of the subunits of complex V that reside or extend into the mitochondrial matrix (ATP5A1, ATP5B, ATP5C1, ATP5D, ATP5E, ATP5F1, ATP5H, ATP5J, and ATP5O), while we only detect acetylation on two of the membrane-embedded subunits (ATP5L and MT-ATP8), despite detecting a further four membrane-embedded subunits in the proteome (ATP5I, ATP5J2, MT-ATP6, and USMG5). Indeed, the majority of acetylated proteins quantified on each electron transport chain complex (CI-CV) are exposed to either the mitochondrial matrix or the intermembrane space. Similarly, TCA cycle enzymes, which are localized to the mitochondrial matrix, displayed high acetylation intensity and stoichiometry in the basal state and HIIT augmented levels of acetylation at every stage of the cycle (Figure 6B), again likely due to their proximity to acetyl-CoA. Conversely, cytosolic proteins regulating skeletal muscle contraction showed fewer upregulated acetyl-sites with a number of downregulated sites following HIIT (Figure 6C). Thus, proteins in close proximity to acetyl-CoA appear to be more susceptible to acetylation.

Figure 6.

Individual acetyl-site regulation of mitochondrial, TCA cycle and contractile proteins following HIIT.

Regulation of acetyl-sites on (A). electron transport chain complex subunits (annotated by HUGO), (B) TCA cycle proteins (annotated by KEGG) and (C) muscle contraction proteins (annotated by REACTOME).

Despite widespread HIIT-induced acetylation of mitochondrial proteins, these data should be considered in the context of acetylation stoichiometry, which is generally low (Weinert et al., 2015). Median acetylation stoichiometry in rodent liver has been calculated at only 0.05%, which increases slightly to 0.11% for mitochondrial proteins (Weinert et al., 2015). Although acetylation is tissue-dependent (Lundby et al., 2012), skeletal muscle levels are unlikely to be vastly different. The median log2-fold change for mitochondrial acetyl-sites following HIIT was 0.9 (the corresponding value in the proteome was 0.3), indicating that median post-HIIT stoichiometry would have remained at less than 1%. Thus, the levels of HIIT-induced changes in acetylation may not be large enough to influence gross activity on most metabolic enzymes. Nonetheless, physiological acetylation may be high enough to be regulatory on some proteins. For example, acetylation of mitochondrial malate dehydrogenase (MDH2) negatively correlates with its enzyme activity (Overmyer et al., 2015). Another such enzyme could be pyruvate dehydrogenase (PDH), whose E1 component subunit alpha (PDHA1) is within the top 25 most acetylated proteins (Supplementary file 6), whilst PDHA1 Lys 336 was within the top 5 most upregulated acetyl-sites with exercise training (Figure 5A). Increased acetylation of PDHA1 occurs alongside suppressed enzyme activity in SIRT3 knockout mice (Jing et al., 2013). By catalyzing the reaction between pyruvate and acetyl-CoA, PDH provides the link between glycolysis and the tricarboxylic acid cycle (Constantin-Teodosiu et al., 2012). Thus, exercise-induced hyperacetylation of PDH could play a role in the metabolic switch towards elevated fat oxidation following exercise training. Furthermore, mitochondrial hyperacetylation increases fatty-acid-supported respiration (Williams et al., 2020). These data are consistent with the concomitant increase in acetylation (Figure 5A) and mitochondrial respiratory capacity and fat oxidation (Figure 1F) following HIIT, suggesting that hyperacetylation of mitochondrial proteins following exercise training may support the bioenergetic adaptations to exercise.

HIIT increases histone acetylation

Aside from metabolic enzymes and contractile proteins, histones are regulated by acetylation (Sterner and Berger, 2000; Strahl and Allis, 2000) and may represent a more specifically regulated pool of proteins, owing to the nuclear localization of acetyltransferases (Hendzel et al., 1994). Increases in certain acetylated histone residues are well characterized to augment transcription, with exercise known to acutely regulate H3 acetylation (e.g. H3 K4, H3 K9/14, and H3 K36) (Joseph et al., 2017; Lochmann et al., 2015; McGee et al., 2009; Smith et al., 2008). Here, we identified 18 distinct acetylated peptides from histones, with acetylated peptides from H1.5 (HIST1H1B K48), H2B type 2 F (HIST2H2BF K16/20), and H3.3 (H3F3B K23) being elevated following HIIT (Figure 7A–D). H1.5 K48 was exclusively quantified in post-HIIT samples, however, it was only quantified in four out of seven of the post-HIIT samples, which is our minimum cut-off for valid values. The regulatory roles of these HIIT-regulated histone acetyl-sites are unknown. However, acetylation of the N-terminal of H2B, including K16 and K20, has been linked to transcriptional activation (Chen et al., 2014; Parra et al., 2006), including the expression of NAD-metabolic genes in yeast (Parra et al., 2006). The role of H3 K23 is also obscure, however, its acetylation contributes to the development of cancer through the recruitment of the transcription factor transcription intermediary factor 1-alpha (TRIM24) and the nuclear receptor estrogen receptor alpha (ERS1) (Lv et al., 2017; Tsai et al., 2010), augmenting expression of phosphatidylinositol 4,5-biphosphate 3-kinase catalytic subunit alpha (PIK3CA), a subunit of phosphoionositide-3-kinase (PI3K), and phosphorylation of protein kinase B (AKT1) (Lv et al., 2017). Indeed, TRIM24 promotes the abundance of glycolytic and TCA cycle genes in transforming human mammary epithelial cells (Pathiraja et al., 2015), while ERS1 regulates mitochondrial biogenesis in skeletal muscle (Ribas et al., 2016). Whether H3 K23 regulates TRIM24, ERS1, PI3K, and AKT1 activity and influences metabolism in skeletal muscle remains unclear. Further studies are warranted to understand the transcriptional and metabolic implications of exercise training-induced acetylation on these histone sites.

Histone acetyltransferase p300 (EP300) is a nuclear-localized acetyltransferase targeting numerous histone acetylation sites (Weinert et al., 2018). Therefore, we hypothesized that EP300 may be involved in the regulation of histone acetylation following exercise training. Similarly to the upregulation of acetylation on H2B K16/20 and H3 K23, HIIT increased the protein abundance of EP300 by 23% (participant 1 was excluded as an outlier; Figure 7E). Given the small effect and the presence of a substantial outlier, we performed this particular analysis in triplicate. Additionally, to rule out that any small differences may be due to uneven loading, we normalized the first experimental replicate to a stain-free gel loading image (a loading image was only generated for the first replicate). EP300 displayed a similar increase whether normalized to the stain-free gel or not (29% vs 32% increase for normalized and non-normalized data, respectively; Figure 7—figure supplement 1). Next, to investigate the potential regulatory role of EP300 in the acetylation of these exercise-responsive histone sites in skeletal muscle, we knocked down EP300 in C2C12 myotubes and assessed the acetylation of H2B K20 and H3 K23 via immunoblotting. Knockdown of EP300 reduced acetylation of H2B K20 by 48%, whilst acetylation of H3 K23 was unaffected (Figure 7F). Together, these analyses identify EP300 as an exercise-training responsive acetyltransferase, which regulates the acetylation of H2B K20 in skeletal muscle.

Figure 7—figure supplement 1.

Normalization of EP300.

Normalization of EP300. EP300 displays a similar increase following HIIT without normalization (A) or when normalized to a stain-free blot (B). Data taken from replicate 1 of EP300 immunoblotting. (C) Images of EP300 immunoblot and stain-free blot for replicate 1 of EP300 immunoblotting.

Conclusion

Here, we leveraged advances in DIA mass spectrometry to produce a deep human skeletal muscle proteome and applied this approach to ascertain exercise training adaptations. Furthermore, we provide the first investigation of the human acetylome in response to exercise training. In the proteome, we highlight known and novel exercise-responsive proteins, including a potential mechanism to explain altered skeletal muscle calcium sensitivity following HIIT. In the acetylome, we describe the predominant acetylation of mitochondrial proteins after HIIT, particularly of TCA cycle proteins and subunits of electron transport chain complex V, as well as highlighting the regulation of novel exercise-responsive histone acetyl-sites. Altogether, we provide a substantial hypothesis-generating resource, identifying novel exercise-regulated proteins and acetyl-sites with the aim of stimulating further mechanistic research investigating how exercise improves metabolic health.

Materials and methods

Participants

Participants were enrolled by AKL and MH between August and September 2017. Eight men were assessed for eligibility. All eight men were eligible and gave their informed consent after receiving written and oral information about the study and potential risks associated with the experimental procedures (Figure 1—figure supplement 1). Inclusion criteria were healthy males, 18–40 years old, V̇O2max between 45–55 mL⋅min–1⋅kg–1 or 3000–4000 mL⋅min–1 and BMI between 19–26 kg⋅m–2. Exclusion criteria were abnormal electrocardiogram, chronic disease, ongoing medical treatment, and smoking. Subject characteristics are presented in Figure 1B. The study was approved by the National Committee on Health Research Ethics (H-17004045) and registered at clinicaltrials.gov (NCT03270475). The study complied with the Declaration of Helsinki (2013).

Study outcomes

The pre-determined primary outcome was the change from baseline in the skeletal muscle proteome. Secondary outcomes were changes from baseline in the skeletal muscle acetylome, skeletal muscle mitochondrial respiration and whole-body maximal oxygen consumption (V̇O2max).

Experimental trials

The study consisted of two experimental days separated by five weeks of high-intensity interval training (HIIT). At each experimental day, body composition was measured by dual-energy X-ray absorptiometry (DXA) scans (Lunar iDXA, GE Healthcare, GE Medical systems, Belgium). Then, subjects rested in a supine position for 15 min and two 4 mm incisions separated by 2 cm were made in the above the vastus lateralis muscle under local anesthesia (Xylocaine 20 mg⋅mL–1 without epinephrine, AstraZeneca, London, UK) and a muscle biopsy was taken from each incision using a Bergström needle with suction. Afterwards, V̇O2max was determined by indirect calorimetry using a breath-by-breath gas analyser (Oxycon Pro, CareFusion, San Diego, USA) during an incremental test on a bike ergometer (Monark LC4, Monark Exercise, Vansbro, Sweden). The incremental test consisted of 5 min rest followed by 4 min stages at 50, 100, 150 W, after which the resistance was increased in increments of 30 W⋅min–1 until exhaustion. Time to exhaustion was used to calculate incremental peak power output (iPPO) accounting for time spent at the last increment. Subjects completed the experimental protocol the same time of day before and after the intervention and were instructed to eat a small meal with 500 mL water two hours before arriving at the laboratory. In addition, subjects abstained from alcohol, caffeine, and physical activity for 48 hr before each experimental day and were instructed to maintain their activity level and diet during the study. The experimental days were performed three days before and after the first and last training, respectively. All experimental trials were performed at the Department of Nutrition, Exercise and Sports, University of Copenhagen. Experimental trials were completed in October 2017.

Body composition

At each experimental day, two scans were performed to reduce scan-to-scan variation (Nana et al., 2012). Before the first scanning, subjects rested in a supine position for 10 min to allow fluid distribution reducing intra-scan variation (Berg et al., 1993; Cerniglia et al., 2007). Whole-body and leg composition were calculated using software (enCORE Forma v. 15, GE Healthcare Lunar, Buckinghamshire, UK).

Substrate utilization

Gas exchange measurements during rest and submaximal exercise were used to calculate fat and glucose oxidative as previously described (Jeukendrup and Wallis, 2005) and corrected for protein oxidation (Lee et al., 2013). Fat oxidation and carbohydrate oxidation were calculated as:

where V̇O and V̇CO are in L⋅min–1 and BM (Body mass) is in kg.

Muscle biopsies

Immediately after collection, muscle biopsies were rinsed in ice-cold saline. A small piece (~15 mg) of the muscle was transferred into ice-cold preservation solution (BIOPS) for analysis of mitochondrial respiratory capacity. The remaining muscle tissue was snap-frozen in liquid nitrogen and stored at –80 °C. The frozen muscle samples were freeze-dried for 48 hr and dissected free from connective tissues, fat and blood under a microscope in a humidity-controlled room (~25% humidity) before storage at –80 °C for later analyses.

High intensity interval training

Subjects underwent a five-week high-intensity interval training intervention consisting of three weekly training sessions with 4–5×4 min intervals interspersed by two min active recovery on indoor spinning bikes. The training volume increased from four intervals during the first two weeks to five intervals during the last three weeks. During each interval, subjects were instructed to reach >90% of maximal heart rate (HRmax). All training sessions were supervised and subjects received verbal encouragement during all intervals. The mean HRmax during intervals was 96% ± 1% of HRmax.

Mitochondrial profiling

Mitochondrial respiratory capacity was determined in duplicate by high-resolution respirometry (Oxygraph-2k, Oroboros Instruments, Innsbruck, Austria) at 37 °C using 1–3 mg wet weight muscle fibers per chamber in MiR06. Oxygen concentrations were kept between 200 and 450 µM throughout the experiment. The muscle sample in BIOPS was prepared for high-resolution respirometry as previously described (Pesta and Gnaiger, 2012). In short, muscle fibers were dissected free from connective tissue and fat followed by permeabilization in BIOPS with saponin and washed in mitochondrial respiration buffer (MiR06). A substrate-uncoupler-inhibitor titration protocol was used to assess different respiratory states as previously described (Pesta et al., 2011). Leak respiration (LN) in the absence of adenylates was induced by the addition of malate (2 mM) and octanoylcarnitine (0.2 mM). Fatty acid oxidation (FAO) was determined after titration of ADP (2.5 mM). Submaximal CI and CI +II-linked respiration (CID and PD) was measured in the presence of glutamate (10 mM) and succinate (10 mM), respectively. Oxidative phosphorylation capacity (P) was assessed after another titration of ADP (2.5 mM). The mitochondrial outer membrane integrity was then tested with the addition of cytochrome C (10 µM). The average change in oxygen flux was 3.8% (range: 0.2–9.4%). Respiratory electron transfer-pathway capacity (E) was measured during step-wise (0.5 µM) titration of carbonyl cyanide p-trifluoro-methoxyphenyl hydrazine (FCCP). Succinate-supported electron transfer-pathway capacity (ECII) was assessed by the addition of rotenone (0.5 µM). Residual oxygen consumption was determined after the addition of malonic acid (5 mM), myxothaizol (0.5 µM), and antimycin A (2.5 µM) and subtracted from each respiratory state.

Maximal enzyme activity of citrate synthase was determined from muscle homogenate using fluorometry (Fluoroscan Ascent, Thermo Fisher Scientific, Waltham, USA) at 25 °C as previously described (Lowry, 2012).

Proteomic sample preparation

Human skeletal muscles were lysed in a buffer consisting of 1% Sodium Deoxycholate (SDC), 10 mM Tris (2-carboxyethyl) phosphine (TCEP), 40 mM Chloroacetamide (CAA) and 100 mM of Tris (pH 8.5). The muscles were homogenized with an Ultra Turrax homogenizer (IKA). Lysates were boiled at 95 °C for 10 min and sonicated using a tip sonicator. Lysates were then centrifuged at 16,000 g for 10 min. Proteins were digested using the endoproteinases LysC and trypsin (1:100 w/w) at 37 °C overnight with shaking. Digested peptides were acidified using 1% Trifluoroacetic acid (TFA) and precipitated SDC was removed by centrifugation. Peptides were purified using Sep-pak C18 cartridges (Waters) and were eluted in 50% acetonitrile. Twenty µg of peptides were saved for total proteome analysis while 3 mg peptides were saved for enrichment of acetylated peptides.

Acetylated peptide enrichment

The acetyl peptide enrichment was performed on 3 mg of digested peptides using PTMscan Acetyl-Lysine Motif kit (Cell Signaling #13416). The peptides were mixed with 100 µL of 10 x IP buffer (500 mM MOPS, pH 7.2, 100 mM Na-phosphate, 500 mM NaCl, 5% NP-40). The acetonitrile was removed and the volume was reduced to ~1 mL by vacuum centrifugation. The final volume was adjusted with water to a concentration of 3 mg⋅mL–1. The peptides were clarified by centrifugation at 20,000 g for 5 min. Twenty µL of anti-acetylated lysine antibody was washed 3 times in 1 mL IP buffer and then mixed with the peptides. Peptides were enriched overnight at 4 °C, washed 3 times in 1 mL ice-cold (4 °C) IP buffer, 4 times in 1 mL ice-cold IP buffer without NP-40, and once in 1 mL water. All wash buffer was removed via aspiration. Acetylated peptides were eluted with 100 µL 0.15% TFA. The elution procedure was repeated three times. The eluted peptides were de-salted on C18 stage-tips (Rappsilber et al., 2007), vacuum concentrated and resuspended in buffer A* (5% acetonitrile, 0.1% trifluoroacetic acid).

High pH reversed-phase fractionation

To generate a deep proteome library for data-independent acquisition (DIA), 50 µg of peptides were pooled from pre- and post-HIIT samples, separately, and fractionated using high pH reversed-phase chromatography (Kulak et al., 2017). Twenty-four fractions (per pool) were automatically concatenated using a rotor valve shift of 90 s. Approximately 0.3 µg of each fraction were subjected to LC-MS/MS measurements via data-dependent acquisition (DDA). To obtain comprehensive coverage of the acetylome, acetylated peptides from each sample were fractionated into 8 fractions using a rotor valve shift of 90 s. For the first subject (both pre and post samples), all eight fractions were analyzed separately. After observing lower than expected peptide intensities for the first subject, the neighboring two factions were pooled together for each of the remaining samples (total four fractions/subject/timepoint) before LC-MS analysis.

Mass spectrometry

Peptides were measured using LC-MS instrumentation consisting of an Easy nanoflow HPLC system (Thermo Fisher Scientific, Bremen, Germany) coupled via a nanoelectrospray ion source (Thermo Fischer Scientific, Bremen, Germany) to a Q Exactive HF-X mass spectrometer. Purified peptides were separated on a 50 cm C18 column (inner diameter 75 µm, 1.8 µm beads, Dr. Maisch GmbH, Germany). Peptides were loaded onto the column with buffer A (0.5% formic acid) and eluted with a 100 min linear gradient increasing from 2% to 40% buffer B (80% acetonitrile, 0.5% formic acid). After the gradient, the column was washed with 90% buffer B and re-equilibrated with buffer A. Peptides were measured in singleton.

For the deep proteome library generation, mass spectra were acquired from each fraction via DDA with automatic switching between MS and MS/MS using a top 15 method. MS spectra were acquired in the Orbitrap analyzer with a mass range of 300–1750 m/z and 60,000 resolutions at m/z 200 with a target of 3 × 106 ions and a maximum injection time of 25ms. HCD peptide fragments acquired at 27 normalized collision energy were analyzed at 15,000 resolution in the Orbitrap analyzer with a target of 1 × 105 ions and a maximum injection time of 28 ms. The mass spectra for the acetylome experiments were also acquired from each fraction via DDA with a similar MS method used for deep proteome library generation, except the maximum injection time for the fragment ion spectra was set to 250ms. A DIA MS method was used for the total proteome measurements in which one full scan (300–1650 m/z, resolution = 60,000 at 200 m/z) at a target of 3 × 106 ions was first performed, followed by 32 windows with a resolution of 30,000 where precursor ions were fragmented with higher-energy collisional dissociation (stepped collision energy 25%, 27.5%, 30%) and analyzed with an AGC target of 3 × 106 ions and maximum injection time at 54ms in profile mode using positive polarity.

Data processing

Raw MS files from the experiments measured in DDA mode (muscle proteome library & acetylome), were processed using MaxQuant (Cox and Mann, 2008). MS/MS spectra were searched by the Andromeda search engine (integrated into MaxQuant) against the decoy UniProt-human database (downloaded in December 2017) with forward and reverse sequences. In the main Andromeda search, precursor and fragment mass were matched with an initial mass tolerance of 6 ppm and 20 ppm, respectively. The search included variable modifications of methionine oxidation and N-terminal acetylation and fixed modification of carbamidomethyl cysteine. For the raw files from the acetylome experiments, acetylated lysine was also added as a variable modification. Acetylated peptides were filtered for a minimum Andromeda score of 40, as per the default settings for modified peptides. The false discovery rate (FDR) was estimated for peptides and proteins individually using a target-decoy approach allowing a maximum of 1% false identifications from a revered sequence database. Raw files acquired in the DIA mode (total proteome) were processed using Biognosys Spectronaut software version 13 (Bruderer et al., 2015). A single peptide library was generated in Spectronaut using the combined MaxQuant search results for the DDA runs from both of the fractionated muscle samples. The experimental DIA runs were then analyzed in Spectronaut using default settings.

Bioinformatics

Bioinformatic analyses were performed in the Perseus software (Tyanova et al., 2016). Categorical annotations were supplied in the form of Gene Ontology (GO) biological process (BP), molecular function (MF), and cellular component (CC), as well as UniProt Keywords, KEGG and REACTOME pathways. All annotations were extracted from the UniProt database. Quantified proteins were filtered to have at least 4 valid values in at least one time point. Missing data were imputed by drawing random numbers from a Gaussian distribution with a standard deviation of 30% and a downshift of 1.8 standard deviations from the mean. The imputed values have been tuned in order to simulate the distribution of lowly abundant proteins. To identify differentially regulated proteins and acetyl-sites, two-tailed paired t-tests were performed with a permutation-based false discovery correction applied (FDR = 0.05). In the acetylome, an additional analysis was performed to expand the identification of differentially expressed proteins, whereby an a posteriori information fusion scheme combining the biological relevance (fold change) and the statistical significance (p-value), was implemented, as previously described (Xiao et al., 2014). A Π-value significance score cut-off of 0.05 was selected. Alterations to organelles were assessed using summed total protein abundances (Wiśniewski, 2017) and two-tailed paired t-tests (p<0.05). Comparisons between individual proteins measured via immunoblotting were performed using two-tailed paired t-tests (p<0.05). Hierarchical clustering analysis was performed on z-scored differentially regulated proteins using Euclidean distance. Enrichment analyses were performed on differentially regulated proteins and acetyl-sites using Fisher’s exact tests and the application of a Benjamini-Hochberg false discovery correction (FDR = 0.02), as is the default setting in the Perseus software (Tyanova et al., 2016). Motif enrichment was performed on upregulated acetyl-sites using iceLogo (p<0.01) (Colaert et al., 2009). One-dimensional enrichment analyses were performed as previously described (Cox and Mann, 2012). To analyze the relative stoichiometries of acetylated proteins in Figure 4 abundance corrected intensities (ACIs) were calculated for each acetyl-peptide by dividing the acetyl-peptide intensity by the intensity of the corresponding protein in the proteome. Enrichment analyses were performed against a background of all quantified proteins or acetyl-sites, except for hierarchical clustering analyses where clusters were compared within the z-scored differentially regulated matrix. To visualize complexes and functional protein-interactions in Figure 6, protein interaction networks were mapped using the STRING database, using an overall confidence cutoff of 0.7 without limiting the number of interactions (Szklarczyk et al., 2019), and further processed with Cytoscape (https://www.cytoscape.org).

Cell culture

The mouse skeletal muscle cell line C2C12 (CRL1772, ATCC) was seeded into six-well plates at a density of 105 cells per well. Cells were cultured in growth medium (Dulbeccoo’s modified Eagle’s medium containing 25 mM glucose, supplemented with 10% fetal bovine serum (FBS) and 1%. Penicillin-streptomycin) at 37 °C and 5% CO2. Once the cells reached ~90% confluency, differentiation was induced by replacing the FBS with 2% horse serum. On day 4 of differentiation, cells were transfected with 25 mM EP300 siRNA or scramble control siRNA via the TransIT-X2 Dynamic Delivery System (Mirus Bio), following the manufacturer’s instructions. Cells were harvested in 2% SDS on Day 6. Samples were immediately boiled at 95 °C, sonicated in a Diagenode Bioruptor water bath sonicator (15 × 30 s intervals), and centrifuged at 16,000 g for 10 min. The pellet was discarded and the supernatant was taken and used for immunoblotting analyses.

Immunoblotting

For human skeletal muscle immunoblot analyses, a fresh batch of ice-cold homogenization buffer (10% glycerol, 20 mM Na-pyrophosphate, 150 mM NaCl, 50 mM HEPES (pH 7.5), 1% NP-40, 20 mM β-glycerophosphate, 2 mM Na3VO4, 10 mM NaF, 2 mM PMSF, 1 mM EDTA (pH 8), 1 mM EGTA (pH 8), 10 µg⋅mL–1 Aprotinin, 10 µg⋅mL–1 Leupeptin and 3 mM Benzamidine) was prepared and 80 µL added to 1 mg of freeze-dried and dissected muscle tissue. The tissue was homogenized for 1 min at 28.5 Hz in a TissueLyser (Qiagen TissueLyser II, Retsch GmbH, Germany) before samples were rotated end over end for 1 hr at 4 °C and centrifuged at 18,320 g for 20 min at 4 °C. The pellet was discarded and the supernatant was taken and used for immunoblotting analyses. Total protein concentration in the samples was determined with a standard BSA kit (Millipore). A total of 15 µL of the sample was diluted with 60 µL ultrapure water in order to keep the concentration within the linear range of the calibration curve (0.2–2 µg⋅µL–1). The protein concentration of each sample was adjusted by the addition of 6×Laemmli buffer (7 mL 0.5 M Tris-base, 3 mL glycerol, 0.93 g DTT, 1 g SDS and 1.2 mg bromophenol blue) and ultrapure water to reach equal concentrations (1.5 µg total protein⋅µL–1). Human skeletal muscle standard samples (Ctrl) were obtained as a pool of all samples included in the experiment.

Equal amounts of total protein were loaded on 10% TGX Stain-Free, 4–20% TGX Stain-Free, or 16.5% Criterion Tris-Tricine/Peptide gels (Bio-Rad) alongside two protein markers (Precision plus all blue and dual color standards, Bio-Rad), three human skeletal muscle control samples and a four-point standard curve (skeletal muscle samples of increasing volume). After standard SDS-page gel electrophoresis proteins were semi-dry transferred to a PVDF membrane. Membranes were blocked with either 2% skimmed milk or 3% BSA in Tris-buffered saline with 0.1% Tween-20 (TBST) before overnight incubation with primary antibody. Afterwards, the membranes were washed in TBST, incubated for 1 hr in HRP conjugated secondary antibody at room temperature and washed 3×15 min in TBST before the bands were visualized with an enhanced chemiluminescent reaction (Immobilon Forte Western HRP substrate, Millipore) and signals recorded with a digital camera (ChemiDoc MP Imaging System, Bio-Rad). Densitometry quantification of the immunoblotting band intensities was done using Image Lab version 4 (Bio-Rad) and determined as the total band intensity adjusted for the background intensity. The standard curve on each gel was used to confirm that the loaded amount of protein in samples was capable of determining differences between samples by the signal intensity being on the linear part of the standard curve. The variation of the triplicate human standard sample signal loaded was used to evaluate equal transfer of proteins across each gel. To confirm equal loading from the different samples, Coomasie staining and stain-free blot images were taken for representative immunoblots of each batch of prepared lysate (Figure 5—figure supplement 2). Immunoblotting of biological samples was performed in singleton, with the exception of EP300 which was performed in technical triplicates to confirm the presence of a biological outlier (participant 1).

Listed are the primary antibodies used and, where appropriate, their size of migration. Acetyl-lysine: #9441, Cell Signaling Technologies; EP300: #sc32244 (human skeletal muscle); #sc48343 (C2C12s), Santa Cruz Biotechnology, 300 kDa; ac-H2B K20: #ab177430, Abcam, 14 kDa; H2B: #12364, Cell Signaling Technologies, 14 kDa; ac-H3 K23: #14932, Cell Signaling Technologies, 17 kDa; H3: #4499, Cell Signaling Technologies, 17 kDa; MYLK2: PA5-29324, Invitrogen, 65 kDa; OXPHOS antibody cocktail: ab110411, Abcam, 15–55 kDa; SIRT1: 07–131, Millipore, 80 kDa; SIRT3: #5490, Cell Signaling Technologies, 28 kDa; SIRT5: #8782, Cell Signaling Technologies, 30 kDa; SIRT6: #12486, Cell Signaling Technologies, 36–42 kDa; ac-SOD2 K68: #ab13737, Abcam, 24 kDa; ac-SOD2 K122: #ab214675, Abcam, 24 kDa. The anti-GCN5L1 antibody (15 kDa) was a kind gift from Iain Scott, PhD, University of Pittsburgh (Scott et al., 2012; Thapa et al., 2020). HRP conjugated goat anti-rabbit (4010–05, SouthernBiotech) and a goat anti-mouse (P0447, DAKO Denmark) were used as secondary antibodies.

Statistical analyses

For non-omics analyses, two-tailed paired t-tests were applied to assess the effect of HIIT, except for substrate oxidation analyses whereby a mixed linear model was performed. Independent samples two-tailed t-tests were used to assess the effect of EP300 KD on histone acetylation. Significance was accepted as p<0.05.

The authors have measured the proteome and acetylome of human skeletal muscle before and after high intensity exercise. They found that many of the subunits of Complex V in mitochondria are selectively acetylated after exercise. This study will serve as a very useful resource for people interested in muscle and the acetyl proteome.

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "High-Intensity Interval Training Remodels the Proteome and Acetylome of Human Skeletal Muscle" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by David James as the Senior and Reviewing Editor. The following individual involved in review of your submission has agreed to reveal their identity: Dr. Shinya Kuroda (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Further support for the conclusion that there is non-enzymatic acetylation is required. The effects of only two SIRT enzymes was reported and this needs to be expanded.

2) The highly descriptive nature of the paper was noted and while the data may serve as a useful resource, examples to illustrate the strength of this claim were not provided.

Reviewer #1 (Recommendations for the authors):

1. Although the authors suggest that increase in acetylation of mitochondrial proteins following HIIT would be via non-enzymatic mechanisms, there does not seem to be enough evidence for it. Addition of quantification of acetyl-CoA amounts in samples with enzymatic kit or mass spectrometry would strengthen the authors' conclusion. Otherwise, the claim of acetylation via non-enzymatic mechanisms should be toned down.

2. Figure 5E shows an increase in abundance of SIRT3 following HIIT. However, the increment looks small. I wonder whether such small increase consistently explain increase in acetylation of mitochondrial proteins. This issue would be further discussed. In addition, the abundance of acetyltransferase for mitochondrial proteins would be also discussed.

Reviewer #2 (Recommendations for the authors):

Strengths:

1) Very well-written manuscript with clear attention to detail.

2) The experimental methods employed are robust and the results were analyzed with well-thought-out and appropriate statistical procedures.

Weaknesses:

1) No post-hoc / orthogonal analyses were performed to assess the validity of the acetylome data.

2) The results are highly descriptive. The authors contend that the data contained within the manuscript could serve as "a substantial hypothesis-generating resource to stimulate further mechanistic research investigating how exercise improves metabolic health". While this may indeed be true, the authors have not provided any evidence to support their position. The manuscript would greatly benefit from a set of follow-up experiments which demonstrate how the data can lead to the discovery of new mechanisms.

Reviewer #3 (Recommendations for the authors):

1) The authors note that there are a substantial number of non-overlapping acetyl sites between the current study and that performed by Lundby et al. (2012). This is mentioned in the text, but there is no discussion of why this discrepancy might be apparent? If the same muscle was sampled, what might explain these discrepancies – different study population, different pulldown and mass spectrometry approach, variations in stringency of the analysis? A brief discussion of these (and other) possibilities would be useful for the non-expert.

2) Samples were taken 3 days after the last training bout, implying that the changes in acetylation are not necessarily a readout of the acute alterations in acetylation that might accompany the substantial changes in metabolite flux present during muscle contraction and the immediate recovery period. Some additional discussion around this would be valuable, particularly regarding the biological relevance of the identified acetylation changes at a timepoint that is temporally distinct from the time where many transcriptional and metabolic adaptations are directly induced by the exercise stimulus (i.e. in the immediate 24-48 hr after exercise). Furthermore, some discussion regarding the potential underlying molecular basis for the acetylation changes would benefit the manuscript. For example, are the changes in distinct acetylation sites the result of a newly 'reprogrammed' steady state of acetylation, or could they reflect sites that have become acetylated during exercise and simply have a slow turnover rate (i.e. deacetylation rate)?

3) The assessment of potential deacetylase enzymes was limited to two sirtuin proteins (SIRT1 and SIRT3), and while SIRT3 is the predominant deacetylase enzyme in the mitochondrial compartment, there a many other deacetylase enzymes that might play an important in regulating the acetylation of histone proteins (one of the novel findings of the current study). Assessment of an expanded range of deacetylase proteins could be considered, especially those outside of the mitochondrion.

4) If available, the authors should consider providing images to confirm equal protein loading for the immunoblots.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "High-Intensity Interval Training Remodels the Proteome and Acetylome of Human Skeletal Muscle" for further consideration by eLife. Your revised article has been evaluated by David James (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

As you can see, two of the reviewers remain concerned about the new experiment involving EP300 and would like to see some connectivity to exercise which is currently lacking. If you could provide at least your rationale for choosing this enzyme this might help placate the reviewers but ultimately, I think they are looking for a linker experiment and if you could provide this it would markedly uplift the study.

Reviewer #1 (Recommendations for the authors):

The authors correctly responded to my previous comments, and I am satisfied with their responses.

Reviewer #2 (Recommendations for the authors):

The authors have satisfactorily addressed several of the previous concerns that were raised but there are still a few issues that remain to be resolved.

1) It was previously suggested that the authors should provide some post-hoc / orthogonal analyses to assess the validity of the acetylome data. In response, western blots were performed to show that HIIT led to an increase in acetylation. The western blots provide support for the author's statement that "In general, there was a trend for increased acetylation following HIIT", but it does not provide an orthogonal means of validation for any of the specific sites that were concluded that have experienced a HIIT-induced change in acetylation. Could the authors please provide an orthogonal means of validation for at least one of the sites? Without such information, the reliability of the data remains questionable.

2) Previous comment: The authors contend that the data contained within the manuscript could serve as "a substantial hypothesis-generating resource to stimulate further mechanistic research investigating how exercise improves metabolic health". While this may indeed be true, the authors have not provided any evidence to support their position. The manuscript would greatly benefit from a set of follow-up experiments which demonstrate how the data can lead to the discovery of new mechanisms.

– In response to this comment, the authors performed siRNA-mediated knockdown of EP300 in C2C12 myotubes, and found the acetylation of H2B K20 to be reduced by the loss of EP300. The authors conclude that these experiments identify EP300 as an acetyltransferase regulating the acetylation of H2B K20 in skeletal muscle. The conclusion appears to be valid but it is not at all clear how the data reported in the study (i.e., the hypothesis-generating resource) led them to examine EP300. The authors need to explain how the data from their study led them to examine the EP300.

Reviewer #3 (Recommendations for the authors):

In the revised manuscript, the authors have addressed the majority of the concerns raised by the reviewers. I have a few follow up queries below that require attention.

The authors have expanded their analysis of enzymes that regulate protein acetylation status to potentially link any alterations in these enzymes to the altered acetylation levels observed in the current study. My concern with the additional analyses is the selection/measurement of SIRT5 as a deacetylase enzyme. There is a strong body of research that has shown that SIRT5 possesses very limited deacetylase activity, and instead has greater affinity for other acyl modifications (e.g. succinylation, malonylation) – PMID: 22076378, PMID: 21908771. On this basis it is unlikely that changes in SIRT5 would be directly involved in the exercise-induced changes in acetylation, and the importance of SIRT5 should be considered in this light.

A concern with this study is the highly descriptive nature of the experiments and the lack of verification of the importance of any of the described changes. To extend their findings, the authors have conducted a study in C2C12 myotubes assessing the impact of a specific deacetylase enzyme (EP300) on site-specific histone acetylation. The additional experiment has been performed in a robust manner and the experimental result is clear, namely that EP300 regulates H2B K20 acetylation. What is difficult to reconcile is how this experiment relates to the major findings of the manuscript? Acetylation marks can be potentially impacted by more than one enzyme, and as noted in this manuscript, there is substantial precedent in the literature for non-enzymatic acetylation to be a prominent mechanism regulating overall acetylation status. The authors present evidence that H2B K20 acetylation in human skeletal muscle is increased by exercise, but it is unclear if these changes in acetylation are in any way related to changes in the activity of EP300? Indeed, there is no evidence presented to directly link changes in EP300 abundance or activity with the reported exercise-induced changes in H2B K20 acetylation. As it stands, the new experiment identifies EP300 as a regulator of a specific acetylation site on H2B, but it does not provide any evidence for a causative role for EP300 in exercise-induced acetylation changes, and it also falls short of ascribing any functional relevance to the described changes in histone acetylation. Could the authors please comment.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "High-Intensity Interval Training Remodels the Proteome and Acetylome of Human Skeletal Muscle" for further consideration by eLife. Your revised article has been evaluated by David James (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below. As you can see Rev #3 is now satisfied that you have adequately revised the paper but Rev #2 is still of the opinion you could address two further comments. Could you please address these comments in full and resubmit your revised manuscript as soon as possible.

Reviewer #2 (Recommendations for the authors):

The effort put forth by the authors is appreciated but one of the primary concerns still remains.

1) It was previously suggested that the authors should provide some kind of post-hoc / orthogonal analysis to assess the validity of the acetylome data. In response, western blots were performed to show that HIIT led to an increase in acetylation. The western blots provide support for the author's statement that "In general, there was a trend for increased acetylation following HIIT", but it did not provide an orthogonal means of validation for any of the specific sites that were concluded that have experienced a HIIT-induced change in acetylation. The authors responded to this comment by indicating that there were limitations in the amount of remaining sample that they had, and they decided to prioritize the remaining sample for the "linker experiment". Understandably, there were limitations in the amount of sample that was available for post-hoc analyses, but unfortunately, this doesn't excuse the concerns with the validity of the outcomes. Without any kind of post-hoc / orthogonal validation, one is still left to wonder whether they can trust the site-specific omics data that was obtained.

Reviewer #3 (Recommendations for the authors):

The authors have clearly put a lot of time and effort into addressing my previous comments, and this is noted and appreciated. I have no further comments on the revised manuscript.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "High-Intensity Interval Training Remodels the Proteome and Acetylome of Human Skeletal Muscle" for further consideration by eLife. Your revised article has been evaluated by David James (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

We understand that in view of sample availability it is difficult to comply with all suggestions and comments.

In regards to Point 1 of Reviewer 2. I believe that the new data looking at one site should suffice.

In regards to Point 2, I think it will be important to address this sufficiently in the manuscript and potentially include the new figure for n=1 provided in your comments at least as a supplementary file and also discuss the point in the Results section of the manuscript.

Essential revisions:

1) Further support for the conclusion that there is non-enzymatic acetylation is required. The effects of only two SIRT enzymes was reported and this needs to be expanded.

We have expanded upon this by measuring the effect of exercise training on two further sirtuins including those outside of the mitochondria (SIRT5 and SIRT6). As well as highlighting data for SIRT2, 3 and 5 from the proteome. In addition, we measured the abundance of the mitochondrial-localized acetyltransferase (GCN5L1) (see Figure 5E).

2) The highly descriptive nature of the paper was noted and while the data may serve as a useful resource, examples to illustrate the strength of this claim were not provided.

To test a hypothesis generated by these data, namely that acetylation of histones is likely controlled enzymatically via acetyltransferases such as EP300, we have performed knockdown studies in myotubes to investigate whether EP300 regulates the acetylation of specific acetyl-sites on histones that we found to be regulated by exercise. We identify H2B K20 as a EP300-regulated acetyl-site (see Figure 7E).

Reviewer #1 (Recommendations for the authors):

1. Although the authors suggest that increase in acetylation of mitochondrial proteins following HIIT would be via non-enzymatic mechanisms, there does not seem to be enough evidence for it. Addition of quantification of acetyl-CoA amounts in samples with enzymatic kit or mass spectrometry would strengthen the authors' conclusion. Otherwise, the claim of acetylation via non-enzymatic mechanisms should be toned down.

Unfortunately, due to the scarcity of sample that can be collected during a human skeletal muscle biopsy using the Bergström needle technique, we do not have sufficient sample remaining to measure acetyl-CoA. However, we refer you to lines 360 to 364, where we discuss the available literature regarding the effect of acute and chronic exercise on acetyl-CoA concentration in skeletal muscle. Nonetheless, we accept your concern that our conclusions relating to non-enzymatic mechanisms may be too strong. As such, we have toned this down by removing “likely by non-enzymatic mechanisms” from the abstract (line 31), and removing the sentence “Altogether, the data contained within Figure 5 indicate a predominantly non-enzymatic acetylation of mitochondrial proteins during HIIT.” from the discussion on lines 390-391.

2. Figure 5E shows an increase in abundance of SIRT3 following HIIT. However, the increment looks small. I wonder whether such small increase consistently explain increase in acetylation of mitochondrial proteins. This issue would be further discussed. In addition, the abundance of acetyltransferase for mitochondrial proteins would be also discussed.

SIRT3 protein abundance increases by approximately 60%, as measured via immunoblotting. In the context of exercise training, we interpret this to be a robust increase. We have measured the abundance of the mitochondrial acetyltransferase GCN5L1 via immuoblotting and find that this is unchanged in response to exercise training (see Figure 5E). This is reported on lines 383-385.

Reviewer #2 (Recommendations for the authors):

Strengths:

1) Very well-written manuscript with clear attention to detail.

2) The experimental methods employed are robust and the results were analyzed with well-thought-out and appropriate statistical procedures.

Thank you for your positive responses.

Weaknesses:

1) No post-hoc / orthogonal analyses were performed to assess the validity of the acetylome data.

We have now included an immunoblot for pan-acetylation within skeletal muscle, which confirms the predominant increase in acetylation following exercise training (see Figure 5—figure supplement 1).

2) The results are highly descriptive. The authors contend that the data contained within the manuscript could serve as "a substantial hypothesis-generating resource to stimulate further mechanistic research investigating how exercise improves metabolic health". While this may indeed be true, the authors have not provided any evidence to support their position. The manuscript would greatly benefit from a set of follow-up experiments which demonstrate how the data can lead to the discovery of new mechanisms.

To identify the mechanism by which the exercise-responsive histone acetylation sites may be regulated we investigated the role of the acetyltransferase EP300. Using siRNA-mediated knockdown of EP300 in C2C12 myotubes, we found the acetylation of H2B K20 to be reduced with the loss of EP300 (Figure 7E). These experiments identify EP300 as an acetyltransferase regulating the acetylation of H2B K20 in skeletal muscle. Please see lines 476-481.

Reviewer #3 (Recommendations for the authors):

1) The authors note that there are a substantial number of non-overlapping acetyl sites between the current study and that performed by Lundby et al. (2012). This is mentioned in the text, but there is no discussion of why this discrepancy might be apparent? If the same muscle was sampled, what might explain these discrepancies – different study population, different pulldown and mass spectrometry approach, variations in stringency of the analysis? A brief discussion of these (and other) possibilities would be useful for the non-expert.

We have included a brief discussion of this on lines 227-230. We believe that this discrepancy is due to both the induction of acetylation by exercise revealing previously undetectable acetylation sites, stochastic nature of data dependent acquisition methods, differences in LC-MS/MS instruments, as well as updated FASTA files.

2) Samples were taken 3 days after the last training bout, implying that the changes in acetylation are not necessarily a readout of the acute alterations in acetylation that might accompany the substantial changes in metabolite flux present during muscle contraction and the immediate recovery period. Some additional discussion around this would be valuable, particularly regarding the biological relevance of the identified acetylation changes at a timepoint that is temporally distinct from the time where many transcriptional and metabolic adaptations are directly induced by the exercise stimulus (i.e. in the immediate 24-48 hr after exercise). Furthermore, some discussion regarding the potential underlying molecular basis for the acetylation changes would benefit the manuscript. For example, are the changes in distinct acetylation sites the result of a newly 'reprogrammed' steady state of acetylation, or could they reflect sites that have become acetylated during exercise and simply have a slow turnover rate (i.e. deacetylation rate)?

It is our belief that this timepoint is not temporally distinct from changes in metabolism, demonstrated by the increased mitochondrial respiratory capacity in the same muscle biopsies (i.e. also 3 days after the last training bout). We have expanded our discussion of the possible relationship between acetylation of mitochondrial proteins and mitochondrial respiratory capacity and fat oxidation on lines 428-446.

It is our belief that the increased acetylation after exercise training is a steady-state and that it may occur from accumulation of protein copies/sites becoming acetylated during exercise, when acetyl-coA levels are elevated, which are then either not deacetylated or not at a rate that is sufficient to reduce acetylation prior to the next exercise-induced acetylation event. However, this is purely speculative and, indeed, work in rodents may suggest otherwise (see Overmyer et al., 2015), so we are reticent about over-speculation in the discussion. We do discuss these possibilities and how they relate to acetyl-coA fluxes and rates of fatty acid metabolism on lines 356-368.

3) The assessment of potential deacetylase enzymes was limited to two sirtuin proteins (SIRT1 and SIRT3), and while SIRT3 is the predominant deacetylase enzyme in the mitochondrial compartment, there a many other deacetylase enzymes that might play an important in regulating the acetylation of histone proteins (one of the novel findings of the current study). Assessment of an expanded range of deacetylase proteins could be considered, especially those outside of the mitochondrion.

We have extended our examination of deacetylase abundance to include SIRT1, 3, 5 and 6. In addition we have assessed the abundance of the mitochondrial acetyltransferase GCN5L1. Please see Figure 5E and the results/discussion on lines 372-391.

4) If available, the authors should consider providing images to confirm equal protein loading for the immunoblots.

Representative “loading control” images have been provided in Figure 5—figure supplement 2, and reference made between corresponding immunoblots and loading controls in each figure legend.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

As you can see, two of the reviewers remain concerned about the new experiment involving EP300 and would like to see some connectivity to exercise which is currently lacking. If you could provide at least your rationale for choosing this enzyme this might help placate the reviewers but ultimately, I think they are looking for a linker experiment and if you could provide this it would markedly uplift the study.

Reviewer #2 (Recommendations for the authors):

The authors have satisfactorily addressed several of the previous concerns that were raised but there are still a few issues that remain to be resolved.

1) It was previously suggested that the authors should provide some post-hoc / orthogonal analyses to assess the validity of the acetylome data. In response, western blots were performed to show that HIIT led to an increase in acetylation. The western blots provide support for the author's statement that "In general, there was a trend for increased acetylation following HIIT", but it does not provide an orthogonal means of validation for any of the specific sites that were concluded that have experienced a HIIT-induced change in acetylation. Could the authors please provide an orthogonal means of validation for at least one of the sites? Without such information, the reliability of the data remains questionable.

Owing to the scarce amount of material remaining (due to the limited specimen sizes from human skeletal muscle biopsies), and our decision to prioritise providing a “linker experiment” between our exercise acetylome data and the EP300 knockdown experiments (and, in doing so, addressing your comment #2 and reviewer 3’s similar concerns) we were left without sufficient material to perform additional immunoblots for site- and protein-specific acetylation. We hope that you understand the difficulty of prioritising analyses when specimen amount is limited.

2) Previous comment: The authors contend that the data contained within the manuscript could serve as "a substantial hypothesis-generating resource to stimulate further mechanistic research investigating how exercise improves metabolic health". While this may indeed be true, the authors have not provided any evidence to support their position. The manuscript would greatly benefit from a set of follow-up experiments which demonstrate how the data can lead to the discovery of new mechanisms.

– In response to this comment, the authors performed siRNA-mediated knockdown of EP300 in C2C12 myotubes, and found the acetylation of H2B K20 to be reduced by the loss of EP300. The authors conclude that these experiments identify EP300 as an acetyltransferase regulating the acetylation of H2B K20 in skeletal muscle. The conclusion appears to be valid but it is not at all clear how the data reported in the study (i.e., the hypothesis-generating resource) led them to examine EP300. The authors need to explain how the data from their study led them to examine the EP300.

We thank you for this comment. We were led to examine EP300 by our observations of elevated histone acetylation following HIIT, alongside our previous observation that EP300 is an acetyltransferase targeting a wide-range of histone acetyl-sites in MEF and kasumi-1 cells (Weinert et al., 2018), however we neglected to explain this, nor cite this article, which we have now rectified (lines 456-458). In addition, we have now measured EP300 in the human skeletal muscle biopsies before and after HIIT – identifying a upregulation following training (after the exclusion of an outlier). Please see lines 458-460, figure 7E, and lines 877-881 (the legend for Figure 7). Together, we believe that these data provide a solid rationale for investigating EP300 as an acetyltransferase regulating the acetylation of H2B K20 in skeletal muscle.

Reviewer #3 (Recommendations for the authors):

In the revised manuscript, the authors have addressed the majority of the concerns raised by the reviewers. I have a few follow up queries below that require attention.

The authors have expanded their analysis of enzymes that regulate protein acetylation status to potentially link any alterations in these enzymes to the altered acetylation levels observed in the current study. My concern with the additional analyses is the selection/measurement of SIRT5 as a deacetylase enzyme. There is a strong body of research that has shown that SIRT5 possesses very limited deacetylase activity, and instead has greater affinity for other acyl modifications (e.g. succinylation, malonylation) – PMID: 22076378, PMID: 21908771. On this basis it is unlikely that changes in SIRT5 would be directly involved in the exercise-induced changes in acetylation, and the importance of SIRT5 should be considered in this light.

Thank you for highlighting this point. We have modified the text on lines 368-372 to reflect this.

A concern with this study is the highly descriptive nature of the experiments and the lack of verification of the importance of any of the described changes. To extend their findings, the authors have conducted a study in C2C12 myotubes assessing the impact of a specific deacetylase enzyme (EP300) on site-specific histone acetylation. The additional experiment has been performed in a robust manner and the experimental result is clear, namely that EP300 regulates H2B K20 acetylation. What is difficult to reconcile is how this experiment relates to the major findings of the manuscript? Acetylation marks can be potentially impacted by more than one enzyme, and as noted in this manuscript, there is substantial precedent in the literature for non-enzymatic acetylation to be a prominent mechanism regulating overall acetylation status. The authors present evidence that H2B K20 acetylation in human skeletal muscle is increased by exercise, but it is unclear if these changes in acetylation are in any way related to changes in the activity of EP300? Indeed, there is no evidence presented to directly link changes in EP300 abundance or activity with the reported exercise-induced changes in H2B K20 acetylation. As it stands, the new experiment identifies EP300 as a regulator of a specific acetylation site on H2B, but it does not provide any evidence for a causative role for EP300 in exercise-induced acetylation changes, and it also falls short of ascribing any functional relevance to the described changes in histone acetylation. Could the authors please comment.

To address the link between EP300, H2B K20 acetylation, and exercise we have performed several followup experiments. Firstly, we assessed whether EP300 is regulated by HIIT in human skeletal muscle. Via immunoblotting, we were able to identify the upregulation of EP300 following training (after the exclusion of an outlier). Please see lines 458-460, figure 7E, and lines 877-881. This finding is in line with the changes in H2B K20 acetylation following HIIT (figure 7B), and the in vitro regulation of H2B K20 by EP300 in C2C12 myotubes (figure 7F). We believe that, collectively, these data provide evidence for a role of EP300 in regulating H2B K20 in skeletal muscle, and that EP300 and H2B K20 acetylation are coregulated during exercise training.

Nonetheless, we agree with you that this stops short of a truly causal relationship and we have attempted to establish a model in which we could investigate causality. Although mimicking exercise in vitro is challenging, we performed electrical pulse stimulation (EPS) contraction experiments in C2C12 myotubes. We used a 24-h contraction protocol, which we have previously used to study exercise responses (Gonzalez-Franquesa et al., PMID: 34495765), with the long duration of contraction designed to mimic a training response. Unfortunately, this approach did not alter pan-acetylation nor the acetylation of H2B K20 (see Figure below). These observations rendered this model unsuitable for the direct assessment of EP300 in contraction/exercise-induced H2B K20 acetylation. To truly establish a causal link between EP300 and H2B K20 acetylation in response to exercise, we believe that in vivo studies using p300 knockdown models would be required. However, given the substantial compensation/redundancy between p300 and CBP in vivo (PMIDs 26712218, 30888860) and the lethality of skeletal muscle specific double CBP/p300 knockout mice (PMID: 31898871) these experiments would be highly challenging, comprising a whole research project/manuscript in their own right. Thus, these experiments are outside the scope of this investigation.

We hope that you appreciate our efforts in addressing your concern and recognise that by identifying the upregulation of EP300 following HIIT, we have provided an additional link between exercise-induced acetylation of H2B K20 and activation of EP300.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below.

Reviewer #2 (Recommendations for the authors):

The effort put forth by the authors is appreciated but one of the primary concerns still remains.

1) It was previously suggested that the authors should provide some kind of post-hoc / orthogonal analysis to assess the validity of the acetylome data. In response, western blots were performed to show that HIIT led to an increase in acetylation. The western blots provide support for the author's statement that "In general, there was a trend for increased acetylation following HIIT", but it did not provide an orthogonal means of validation for any of the specific sites that were concluded that have experienced a HIIT-induced change in acetylation. The authors responded to this comment by indicating that there were limitations in the amount of remaining sample that they had, and they decided to prioritize the remaining sample for the "linker experiment". Understandably, there were limitations in the amount of sample that was available for post-hoc analyses, but unfortunately, this doesn't excuse the concerns with the validity of the outcomes. Without any kind of post-hoc / orthogonal validation, one is still left to wonder whether they can trust the site-specific omics data that was obtained.

We have now provided additional immunoblots against specific acetyl-sites on SOD2 (K68 and K122; Figure 5 – supplemental figure 1B), which display increases in acetylation that reflects the changes seen in the acetylome (Figure 5A). Additionally, in the absence of further sample, we attempted to strip the membranes and re-probe for total SOD2, however the signal did not persist after stripping. Nonetheless it is our opinion that the acetyl-blots per se are the correct validation for the changes in acetyl-peptide abundances presented in the acetylome.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

We understand that in view of sample availability it is difficult to comply with all suggestions and comments. In regards to Point 1 of Reviewer 2. I believe that the new data looking at one site should suffice. In regards to Point 2, I think it will be important to address this sufficiently in the manuscript and potentially include the new figure for n=1 provided in your comments at least as a supplementary file and also discuss the point in the Results section of the manuscript.

We thank you for your support of this manuscript and your understanding regarding sample availability. To address your remaining concerns, we have included a comparison of the normalized and non-normalized EP300 immunoblot data as Figure7—figure supplement 1, as well as discussing this in text (lines 460-465).

Appendix 1—key resources table

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Antibody	MYLK2 Antibody, rabbit polyclonal	Invitrogen	Cat#PA5-29324RRID: AB_2546800	(1:1000)
Antibody	Total OXPHOS Human WB Antibody Cocktail, mouse monoclonal	Abcam	Cat#ab110411RRID: AB_2756818	(1:1000)
Antibody	Sirt1(Sir2) Antibody, rabbit polyclonal	Millipore Sigma	Cat#07–131RRID: AB_10067921	(1:1000)
Antibody	Sirt3 (D22A3) Antibody, rabbit monoclonal	Cell Signaling Technologies	Cat#5490RRID: AB_10828246	(1:2000)
Antibody	Sirt5 Antibody, rabbit monoclonal	Cell Signaling Technologies	Cat#8782RRID: AB_2716763	(1:1000)
Antibody	Sirt6 Antibody, rabbit monoclonal	Cell Signaling Technologies	Cat#12486RRID: AB_2636969	(1:1000)
Antibody	Acetyl-lysine Antibody, rabbit polyclonal	Cell Signaling Technologies	Cat#9441RRID: AB_331805	(1:1000)
Antibody	Ac-SOD2 K68 Antibody, rabbit monoclonal	Abcam	Cat#ab13737RRID: AB_2784527	(1:1000)
Antibody	Ac-SOD2 K122 Antibody, rabbit monoclonal	Abcam	Cat#ab214675RRID: AB_2892634	(1:1000)
Antibody	P300 Mouse Antibody, mouse monoclonal	Santa Cruz Biotechnologies	Cat#sc48343RRID: AB_628075	(1:200)
Antibody	P300 Mouse Antibody, mouse monoclonal	Santa Cruz Biotechnologies	Cat#sc32244RRID: AB_628076	(1:500)
Antibody	Ac-H2B K20 Antibody, rabbit monoclonal	Abcam	Cat#ab177430	(1:1000)
Antibody	H2B Antibody, rabbit monoclonal	Cell Signaling Technologies	Cat#12364RRID: AB_2714167	(1:3000)
Antibody	Ac-H3 K23 Antibody, rabbit monoclonal	Cell Signaling Technologies	Cat#14932RRID: AB_2798650	(1:1000)
Antibody	H3 Antibody, rabbit monoclonal	Cell Signaling Technologies	Cat#4499RRID: AB_10544537	(1:3000)
Antibody	Goat Anti-Rabbit Ig, Human ads-HRP, goat polyclonal	Southern Biotech	Cat#4010–05RRID: AB_2632593	(1:5000)
Antibody	Goat Anti-Mouse, goat polyclonal	Dako (Agilent)	Cat# P0447RRID: AB_2617137	(1:5000)
Biological sample (human)	Human skeletal muscle (vastus lateralis)	This paper		Not externally available
Cell line (mouse)	C2C12 (ATCC CRL-1772)	ATCC	Cat#1722RRID: CVCL_0188	Authenticated by ATCC – CO1 assay. Tested in-house for mycoplasma – negative.
Chemical compound, drug	Glycerol	Millipore Sigma	Cat# G5516; CAS# 56-81-5
Chemical compound, drug	Sodium pyrophosphate decahydrate	Millipore Sigma	Cat# 221368; CAS# 13472-36-1
Chemical compound, drug	Sodium chloride	Millipore Sigma	Cat# S9888; CAS# 7647-14-5
Chemical compound, drug	HEPES	Millipore Sigma	Cat# H3375; CAS# 7365-45-9
Chemical compound, drug	IGEPAL CA-630, NP-40	Millipore Sigma	Cat# I8896; CAS# 9002-93-1
Chemical compound, drug	β-Glycerophosphate disodium salt hydrate	Millipore Sigma	Cat# G9422; CAS# 154804-51-0
Chemical compound, drug	Sodium orthovanadate	Millipore Sigma	Cat# S6508; CAS# 13721-39-6
Chemical compound, drug	Sodium fluoride	Millipore Sigma	Cat# S1504; CAS# 7681-49-4
Chemical compound, drug	Phenylmethanesulfonyl fluoride	Millipore Sigma	Cat# P7626; CAS# 329-98-6
Chemical compound, drug	EDTA (for electrophoresis)	Millipore Sigma	Cat# E5134; CAS# 6381-92-6
Chemical compound, drug	EGTA	Millipore Sigma	Cat# E4378; CAS# 67-42-5
Chemical compound, drug	Aprotinin from bovine lung	Millipore Sigma	Cat# A1153; CAS# 9087-70-1
Chemical compound, drug	Leupeptin	Millipore Sigma	Cat# L2884; CAS# 103476-89-7
Chemical compound, drug	Benzamidine hydrochloride hydrate	Millipore Sigma	Cat# B6506; CAS# 206752-36-5
Chemical compound, drug	Bovine serum albumin	Millipore Sigma	Cat# A7906; CAS# 9048-46-8
Chemical compound, drug	Trizma base	Millipore Sigma	Cat# T1503; CAS# 77-86-1
Chemical compound, drug	DL-Dithiothreitol	Millipore Sigma	Cat# D0632 and 43819; CAS# 3483-12-3
Chemical compound, drug	Sodium dodecyl sulfate	Millipore Sigma	Cat# L3771; CAS# 151-21-3
Chemical compound, drug	Bromophenol Blue sodium salt	Millipore Sigma	Cat# B8026; CAS# 34725-61-6
Chemical compound, drug	Tween 20	Millipore Sigma	Cat# P9416; CAS# 9005-64-5
Chemical compound, drug	Skimmed milk powder	Easis	Cat# 801,300
Chemical compound, drug	Glycine	Millipore Sigma	Cat# G7126; CAS# 56-40-6
Chemical compound, drug	2-propanol	Millipore Sigma	Cat# 109634; CAS# 67-63-0
Chemical compound, drug	Ethanol 96%	Plum	Cat# 201146; CAS# 64-17-5
Chemical compound, drug	Brilliant Blue R	Millipore Sigma	Cat# B7920; CAS# 6104-59-2
Chemical compound, drug	Acetic acid (glacial) 100%	Millipore Sigma	Cat# 100063; CAS# 64-19-7
Chemical compound, drug	Precision plus all blue	BioRad	Cat# 1610373
Chemical compound, drug	Precision plus dual color	BioRad	Cat# 1610374
Chemical compound, drug	Immobilon Forte Western HRP substrate	Millipore Sigma	Cat# WBLUF 0500
Chemical compound, drug	Bovine Serum Albumin Standard	Thermo Fisher Scientific	Cat# 23,209
Chemical compound, drug	BCA Protein Assay Reagent A	Thermo Fisher Scientific	Cat# 23,223
Chemical compound, drug	BCA Protein Assay Reagent B	Thermo Fisher Scientific	Cat# 23,224
Chemical compound, drug	Potassium phosphate dibasic trihydrate	Millipore Sigma	Cat# P9666; CAS# 16788-57-1
Chemical compound, drug	Potassium dihydrogen phosphate	Millipore Sigma	Cat# 1.04873; CAS# 7778-77-0
Chemical compound, drug	BCA Protein Assay Kit	Thermo Fisher Scientific	Cat# 23,225
Chemical compound, drug	Trizma hydrochloride	Millipore Sigma	Cat# T3253; CAS# 1185-53-1
Chemical compound, drug	EDTA	Millipore Sigma	Cat# E9884; CAS# 60-00-4
Chemical compound, drug	NAD	Millipore Sigma	Cat# NAD100-RO; CAS# 53-84-9
Chemical compound, drug	L-(−)-Malic acid sodium salt	Millipore Sigma	Cat# M1125; CAS# 68303-40-2
Chemical compound, drug	Acetyl-Coenzyme A	Millipore Sigma	Cat# ACOA-RO
Chemical compound, drug	L-Malate Dehydrogenase (L-MDH)	Millipore Sigma	Cat# LMDH-RO
Chemical compound, drug	NADH	Millipore Sigma	Cat# 10107735001
Chemical compound, drug	Sucrose	Millipore Sigma	Cat# S7903; CAS# 57-50-1
Chemical compound, drug	Magnesium chloride hexahydrate	Millipore Sigma	Cat# M2670; CAS# 7791-18-6
Chemical compound, drug	Taurine	Millipore Sigma	Cat# T0625; CAS# 107-35-7
Chemical compound, drug	Potassium Dihydrogen Phosphate	Millipore Sigma	Cat# P9791; CAS# 7778-77-0
Chemical compound, drug	HEPES	Millipore Sigma	Cat# H3375; CAS# 7365-45-9
Chemical compound, drug	Bovine serum albumin (fatty acid free)	Millipore Sigma	Cat# A7030; CAS# 9048-46-8
Chemical compound, drug	Catalase from bovine liver	Millipore Sigma	Cat# C9322; CAS# 9001-05-2
Chemical compound, drug	Lactobionic acid	Millipore Sigma	Cat# 153516; CAS# 96-82-2
Chemical compound, drug	MES potassium salt	Millipore Sigma	Cat# M0895; CAS# 39946-25-3
Chemical compound, drug	ATP	Millipore Sigma	Cat# A3377; CAS# 34369-07-8
Chemical compound, drug	Sodium creatine phosphate dibasic tetrahydrate	Millipore Sigma	Cat# 27920; CAS# 71519-72-7
Chemical compound, drug	Imidazole	Millipore Sigma	Cat# I0250; CAS# 288-32-4
Chemical compound, drug	Potassium hydroxide	Millipore Sigma	Cat# P1767; CAS# 1310-58-3
Chemical compound, drug	Calcium carbonate	Millipore Sigma	Cat# C4830; CAS# 471-34-1
Chemical compound, drug	Saponin from quillaja bark	Millipore Sigma	Cat# S7900; CAS# 8047-15-2
Chemical compound, drug	L-(−)-Malic acid sodium salt	Millipore Sigma	Cat# M1125; CAS# 68303-40-2
Chemical compound, drug	Octanoyl-L-carnitine	Millipore Sigma	Cat# 50892; CAS# 25243-95-2
Chemical compound, drug	ADP	Millipore Sigma	Cat# A5285; CAS# 72696-48-1
Chemical compound, drug	L-glutamic acid monosodium salt monohydrate	Millipore Sigma	Cat# 49621; CAS# 6106-04-3
Chemical compound, drug	Sodium succinate dibasic hexahydrate	Millipore Sigma	Cat# S2378; CAS# 6106-21-4
Chemical compound, drug	Cytochrome c from equine heart	Millipore Sigma	Cat# C2506; CAS# 9007-43-6
Chemical compound, drug	FCCP	Millipore Sigma	Cat# C2920; CAS# 370-86-5
Chemical compound, drug	Rotenone	Millipore Sigma	Cat# R8875; CAS# 83-79-4
Chemical compound, drug	Malonic acid	Millipore Sigma	Cat# M1296; CAS# 141-82-2
Chemical compound, drug	Myxothiazol	Millipore Sigma	Cat# T5580; CAS# 76706-55-3
Chemical compound, drug	Antimycin A from Streptomyces sp.	Millipore Sigma	Cat# A8674; CAS# 1397-94-0
Chemical compound, drug	Sodium deoxycholate	Sigma-Aldrich	Cat# D6750CAS# 302-95-4
Chemical compound, drug	Tris(2-carboxyethyl)phosphine hydrochloride	Sigma-Aldrich	Cat# C4706CAS# 51805-45-9
Chemical compound, drug	2-Chloroacetamide	Sigma-Aldrich	Cat# 22,790CAS# 79-07-2
Chemical compound, drug	Trypsin	Sigma-Aldrich	Cat# T7575
Chemical compound, drug	Lysine	Wako	Cat# 124–06871
Chemical compound, drug	DMEM high glucose	Gibco	Cat# 41965039
Chemical compound, drug	Fetal bovine serum(FBS)	Sigma-Aldrich	Cat# F7524
Chemical compound, drug	Penicillin-Streptomycin, liquid	Gibco	Cat# 15070–063
Chemical compound, drug	Opti-MEM I Reduced Serum Medium	Gibco	Cat# 31985062
Chemical compound, drug	TransIT-X2 Dynamic Delivery System	Mirus Bio	Cat# MIR6003
Sequence-based reagent	EP300 siRNA	Merck Sigma	Cat# SASI_Mm01_00159721
Commercial assay or kit	PTMScan Acetyl-Lysine Motif [Ac-K] Kit	Cell Signaling Technologies	Cat#13,416
Software, algorithm,	Graphpad Prism 8.0	https://www.graphpad.com/	RRID: SCR_002798
Software, algorithm	MaxQuant 1.5.3.30	https://maxquant.org/	RRID: SCR_014485
Software, algorithm	Spectronaut v14	https://biognosys.com/shop/spectronaut
Software, algorithm	Perseus 1.6.10.50	http://www.coxdocs.org/doku.php?id=perseus:start	RRID: SCR_015753
Software, algorithm	R Studio	https://rstudio.com/	RRID: SCR_000432
Software, algorithm	Cytoscape v3.7.2	https://www.cytoscape.org	RRID: SCR_003032
Software, algorithm	iceLogo	https://iomics.ugent.be/icelogoserver/	RRID: SCR_012137
Software, algorithm	Adobe Illustrator 24.3	https://www.adobe.com/products/illustrator	RRID: SCR_010279
Software, algorithm	Bio-Rad Image Studio	https://www.licor.com/bio/products/Software, algorithm/image_studio/	RRID: SCR_014210
Software, algorithm	Oroboros DatLab 6 V. 6.1.0.7	https://www.oroboros.at/index.php/product/datlab/
Other	Stainless Steel Beads 5 mm	Qiagen	Cat# 69989	Lysis reagent
Other	Stuart Rotator Drive STR4	Stuart-equipment	Cat# STR4	Lysis reagent
Other	Hettich universal 320 R centrifuge	Andrea Hettich GmbH, Germany	Cat# 1406	Lysis reagent
Other	Emax precision microplate reader	Molecular Devices	Cat# LR88026	Protein concentration quantification
Other	White 96-Well Immuno Plates	Fischer Scientific	Cat# 10415985	Protein concentration quantification
Other	Fluoroskan, Microplate Fluorometer, one dispenser	Fischer Scientific	Cat# 5200111	Protein concentration quantification
Other	Multiskan FC Microplate Photometer	Fischer Scientific	Cat# 11500695	Protein concentration quantification
Other	PowerPac HC Power Supply	BioRad	Cat# 1645052	Immunoblotting reagent
Other	TE 77 ECL Semi-Dry Transfer Unit	Amersham Biosciences	Cat# 80-6211-86	Immunoblotting reagent
Other	ChemiDoc MP Imaging System	BioRad	Cat# 731BR00119	Immunoblotting reagent
Other	10% Criterion TGX Stain-Free gel	BioRad	Cat# 567–8035	Immunoblotting reagent
Other	16.5% Criterion Tris-Tricine/Peptide gel	BioRad	Cat# 345–0065	Immunoblotting reagent
Other	Immobilon-P PVDF membrane	Millipore Sigma	Cat# IPVH00010	Immunoblotting reagent
Other	Whatman cellulose chromatography papers	Millipore Sigma	Cat# WHA3030917	Immunoblotting reagent
Other	Sep-Pak C18 Cartrige	Waters	Cat# WAT054955	Proteomic sample preparation
Other	Non-treated 96-Well Microplates	Fischer Scientific	Cat# 10252711	Proteomic sample preparation