The rate of glycolysis quantitatively mediates specific histone acetylation sites
© Cluntun et al. 2015
Received: 20 June 2015
Accepted: 28 August 2015
Published: 23 September 2015
Glucose metabolism links metabolic status to protein acetylation. However, it remains poorly understood to what extent do features of glucose metabolism contribute to protein acetylation and whether the process can be dynamically and quantitatively regulated by differing rates of glycolysis.
Here, we show that titratable rates of glycolysis with corresponding changes in the levels of glycolytic intermediates result in a graded remodeling of a bulk of the metabolome and resulted in gradual changes in total histone acetylation levels. Dynamic histone acetylation levels were found and most strongly correlated with acetyl coenzyme A (ac-CoA) levels and inversely associated with the ratio of ac-CoA to free CoA. A multiplexed stable isotopic labeling by amino acids in cell culture (SILAC)-based proteomics approach revealed that the levels of half of identified histone acetylation sites as well as other lysine acylation modifications are tuned by the rate of glycolysis demonstrating that glycolytic rate affects specific acylation sites.
We demonstrate that histone acylation is directly sensed by glucose flux in a titratable, dose-dependent manner that is modulated by glycolytic flux and that a possible function of the Warburg Effect, a metabolic state observed in cancers with enhanced glucose metabolism, is to confer specific signaling effects on cells.
KeywordsAcetyl coenzyme A (acetyl-CoA) Histone acetylation Warburg effect Glycolysis Cancer
The metabolism of carbohydrates through glycolysis affects numerous cellular processes through its effects on biosynthetic, energy, and reactive oxygen species metabolism. Glucose metabolism is altered in pathological conditions such as cancer and autoimmunity [1, 2]. In these cases, cells metabolize their glucose, in oxygenated conditions, at higher rates and ferment the glucose to produce lactate that together is referred to as the Warburg effect or aerobic glycolysis . There are many hypotheses for the biological function of the Warburg effect [4, 5]. It has been proposed that since glycolysis to lactate occurs at a faster rate than that of oxidative phosphorylation, glycolysis provides a means to generate ATP more rapidly [6, 7]. It has also been proposed that the Warburg effect is an adaptation to support the biosynthetic requirements of cancer cells [8, 9]. Each of these proposals however is not without difficulties. For example, estimates have found that over 90 % of the carbon from glucose is secreted as lactate and alanine, leaving little room for biosynthesis . Also, the rapid ATP produced by glycolysis can also be obtained by other mechanisms such as creatine kinase and adenylate kinase [11, 12].
Additionally, direct biochemical signaling functions of glucose metabolism are also possible [13, 14]. When the rate of glycolytic flux changes, it is possible that the levels of metabolites that serve as intermediates in glycolysis and associated pathways are consequentially altered. Changes in metabolite levels, if they are used as substrates or cofactors for enzymes that carry out posttranslational modifications (PTMs), could allow for metabolism to confer an active role in cell physiology thus conferring signaling functions [4, 15–17]. For example, if these PTMs affect the conformation of chromatin, they can potentially alter the expression of thousands of genes.
Numerous studies have shown that changes in glucose metabolism can regulate histone acetylation [18–25]. There are multiple molecular links from glucose metabolism to protein acetylation status including intracellular pH . pH is mediated by lactate, the product of aerobic glycolysis. Acetyl coenzyme A (ac-CoA), a product of glycolysis is the substrate used by acetyltransferases to modify transfer of the ac-CoA moiety to histones. In addition, the free coenzyme A (CoA) product can inhibit these enzymes . Also, the redox status mediated by NAD+ and NADH can affect the activity of certain deacetylases . Furthermore, ketone bodies, which are produced during fatty acid oxidation, can also inhibit histone deacetylases [28, 29]. Despite these intriguing biochemical links, it remains unknown how these phenomena conspire together and how they are quantitatively mediated. Their specificity to directly defined acetylation sites is also poorly understood. Also, multiple factors can contribute to the regulation of histone acetylation from altered glucose metabolism and pinpointing the affecting factors in metabolism that regulates histone acetylation. Moreover, recently discovered histone modifications such as histone propionylation, butyrylation, and 2-hydroxyisobutyrylation have each been identified as unique histone modifications important for gene expression [30–33]. Much like histone acetylation, these modifications use their corresponding ac-CoA metabolite species as substrates for the PTM. How metabolic flux emanating from glucose affects the specific pathways that supply these cofactors for these modifications remains unknown. Furthermore, it remains unclear how coordinated changes in metabolite concentrations at the systems level conspire in the face of altered glucose metabolism to affect biologically important histone acetylation.
To test the hypothesis that glycolysis rate alters the levels of metabolites that in turn regulate histone acetylation, we considered a system where we can quantitatively tune the rate of glycolysis and as a result affect the concentrations of metabolites in glycolysis. We then carried out a metabolomics study that considered how differences in metabolite concentrations quantitatively affect histone acetylation. In measuring each potential contribution to histone acetylation, we found several factors that contribute to histone acetylation. Together, these results provide a quantitative model for the contributions of glycolysis to altered histone acetylation and acylation and provide evidence for the direct sensing of glucose metabolism by histone acetylation. Since histone acetylation controls and in part determines the expression of thousands of genes, it is tempting to speculate that this mechanism may provide a general link from metabolism to the chromatin state in cells.
Cell culture and reagents
HCT116 colorectal cancer cell lines were cultured in full media composed of RPMI 1640, 10 % fetal bovine serum (FBS), 100 U/mL penicillin, and 100 mg/mL streptomycin. All cell lines were obtained as gifts from Lewis Cantley’s laboratory. Cells were cultured in a 37 °C, 5 % CO2 atmosphere. For 2-deoxy-D-glucose (2DG) titration experiments, either 2DG (Sigma) was added to the media at the respective concentrations or 0.01 % DMSO (cellgro) for the vehicle (Veh). At the start of each experiment, cells were seeded at a density of 2.2 × 106 cells for 10-cm plates for protein collection or 3 × 105 cells/well in a 6-well plate for metabolite collection and allowed to adhere and grow to 80 % confluence. Cells were then washed with PBS and allowed to incubate in the respective treatments for 6 h. Histones and metabolites were extracted as described below.
Samples were homogenized in Triton extraction buffer (TEB, 0.5 % Triton X 100, 2 mM phenylmethylsulfonyl fluoride (PMSF), 0.02 % NaN3 in PBS) and centrifuged at 2000 rpm for 10 min at 4 °C. Pellets were resuspended in 0.2 N HCl, and histones were acid extracted overnight at 4 °C. Histones were then precipitated in 100 % trichloroacetic acid (Sigma-Aldrich), washed with cold acetone, and allowed to air dry. Pellets were either stored at −20 or dissolved in ddH2O. Histone extracts were loaded onto 12 % SDS-PAGE gels and transferred to polyvinylidene difluoride (PVDF) membranes. Membranes were blocked in 5 % dry milk in TBST and incubated with anti-acetyl-H3K27 (Abcam) 1:2000, anti-acetyl H3 (Millipore) 1:2000, or anti-H3 (Millipore) 1:10000. Horseradish peroxidase-conjugated anti-rabbit (Rockland), 1:10,000, was used as secondary antibodies. Chemiluminescent signals were detected with Clarity Western ECL Detection Kit (Bio-Rad) and imaged using a ChemiDoc MP System (Bio-Rad).
The procedures for cultured cells are described in previous studies [34, 35]. Briefly, for culture from adherent cells, the media were quickly aspirated and the cells were washed with cold PBS on dry ice. Then, 1 mL of extraction solvent (80 % methanol/water) cooled to −80 °C was added immediately to each well, and the dishes were transferred to −80 °C for 15 min. The plates were removed, and the cells were scraped into the extraction solvent on dry ice. All metabolite extracts were centrifuged at 20,000g at 4 °C for 10 min. Finally, each solvent in each sample was evaporated in a speed vacuum for metabolomics analysis. For polar metabolite analysis, the cell extract was dissolved in 15 μL water and 15 μL methanol/acetonitrile (1:1 v/v) (liquid chromatography-mass spectrometry (LC-MS) optima grade, Thermo Scientific). For CoA metabolite analysis, the cell extract was dissolved in 50 mM ammonium acetate (pH 6.8). The samples were briefly centrifuged, and the supernatants were transferred to liquid chromatography (LC) vials. The injection volume for polar metabolite analysis was 5μL and for coA analysis it was 8μL.
Cells were first cultured in standard RPMI 1640, 10 % FBS, 100 U/mL penicillin, and 100 mg/mL streptomycin media to 70 % confluence. They were then washed with PBS and treated with corresponding 2DG in fresh media as described above and incubated for 6 h. Subsequently, the cells were washed with PBS and corresponding media conditions where glucose is replaced with U-13C6-D-glucose (Cambridge Isotope Laboratories, Inc.) was considered. Metabolites were then extracted at the indicated time points as described in the text from the spent media.
Liquid chromatography for metabolite analysis
For polar metabolites, an Xbridge amide column (100 × 2.1 mm i.d., 3.5 μm; Waters) is employed, whereas for nonpolar ac-CoA metabolites, a LUNA C18 column (100 × 2.0 mm; phenomenex) is employed on a Dionex (Ultimate 3000 UHPLC) for compound separation at room temperature. For the polar method, mobile phase A is 20 mM ammonium acetate and 15 mM ammonium hydroxide in water with 3 % acetonitrile, pH 9.0, and mobile phase B is acetonitrile. The linear is as gradient as follows: 0 min, 85 % B; 1.5 min, 85 % B, 5.5 min, 35 % B; and 10 min, 35 % B, 10.5 min, 35 % B, 14.5 min, 35 % B, 15 min, 85 % B, and 20 min, 85 % B. The flow rate was 0.15 ml/min, from 0 to 10 min and 15 to 20 min, and 0.3 ml/min from 10.5 to 14.5 min. For the coA method, mobile phase A is water with 5 mM ammonium acetate, and mobile phase B: methanol. Linear gradient is: 0 min, 2 % B; 1.5 min, 2 % B; 3 min, 15 % B; 5.5 min, 95 % B; 14.5 min, 95 % B; 15 min, 2 % B, 20 min, 2 % B. All solvents are LC-MS grade and purchased from Fisher Scientific.
The Q Exactive MS (Thermo Scientific) is equipped with a heated electrospray ionization probe (HESI), and the relevant parameters are as listed: evaporation temperature, 120 °C; sheath gas, 30; auxiliary gas, 10; sweep gas, 3; and spray voltage, 3.6 kV for positive mode and 2.5 kV for negative mode. Capillary temperature was set at 320 °C, and S-lens was 55. A full scan range from 60 to 900 (m/z) was used. The resolution was set at 70,000. The maximum injection time was 200 ms. Automated gain control (AGC) was targeted at 3,000,000 ions.
Metabolomics and data analysis
Raw data collected from LC-Q Exactive MS is processed on Sieve 2.0 (Thermo Scientific). Peak alignment and detection are performed according to the protocol described by Thermo Scientific. For a targeted metabolite analysis, the method “peak alignment and frame extraction” is applied. An input file of theoretical m/z and detected retention time of 263 known metabolites is used for targeted metabolites analysis with data collected in positive mode, while a separate input file of 197 metabolites is used for negative mode. M/Z width is set at 10 ppm. The output file including detected m/z and relative intensity in different samples is obtained after data processing. Quantitation and statistics were calculated and visualized with Microsoft Excel, Gene-E, and MetaboAnalyst online software.
Heavy lysine (13C6 15N2-L-lysine:2HCl), medium lysine (4,4,5,5-D4-L-lysine:2HCl) and RPMI 1640 Media for stable isotopic labeling by amino acids in cell culture (SILAC) (minus L-lysine and L-arginine) were purchased from Cambridge Isotope Laboratories, Inc. Light lysine (L-lysine HCl) and arginine (L-arginine HCl) were purchased from Amresco.
Cells were inoculated into a 6-well plates and allowed to grow for at least six doublings with respect to SILAC media. Media was replenished every 3 days. The cells were then expanded to 15-cm plates till 80 % confluence. The cells were then treated with corresponding 2DG treatments (100uM, 5 mM, Veh) for 6 h. (5 mM, heavy lysine; 100 μM, medium lysine, Veh, light lysine).
Histone protein extraction and in-solution digestion
The “heavy”-, “medium,”- and “light”-labeled cells were washed twice with pre-chilled PBS and the core histone proteins were prepared using an acid extraction procedure previously described , respectively. Each acid-extracted histone protein solution was clarified by centrifugation for 10 min at 20,000×g. The protein concentrations of the supernatants were measured, and equal amounts of proteins from the three pools of cells were combined. Then trypsin (Promega Corp., Madison, WI) was added into the protein mixtures at a trypsin-to-protein ratio of 1:50 (w/w) for digestion at 37 °C for 16 h.
Immunoprecipation of post-translational modifications
To enrich the peptides with specific PTMs (lysine acetylation, lysine propionylation, lysine butyrylation, and lysine 2-hydroxyisobutyrylation), 200 μg of tryptic peptides were incubated with PTM-specific antibody-immobilized beads (containing 20 μg of antibody, PTM Biolabs, Chicago, IL) in NETN buffer (50 mM Tris pH 8.0, 100 mM NaCl, 1 mM EDTA, 0.5 % NP40) at 4 °C for 6 h with gentle rotation. The beads were carefully washed three times with NETN buffer, twice with ETN buffer (50 mM Tris pH 8.0, 100 mM NaCl, 1 mM EDTA), and once with ddH2O. After immunoaffinity purification (IP), 24.22 % (on average) of the total identified peptides are modified. The bound peptides were eluted from the beads with 0.1 % TFA in water and vacuum-dried. The resulting peptides were dissolved in 0.5 % formic acid-water solution and desalted with Ziptip C18 (EMD Millipore Corp., Darmstadt, Germany) according to the manufacturer’s instructions.
Analysis of tryptic peptides
The desalted samples were dissolved in 2.5 μL HPLC A buffer containing 0.1 % formic acid and 99.9 % water (v/v). The solution was directly loaded onto a homemade capillary column (10 cm length with 75 μm inner diameter) packed with Jupiter C12 resin (4 μm particle size, 90 Å pore size, Phenomenex Inc., Torrance, CA) and connected to a NanoLC-1D plus HPLC system (Eksigent Technologies, LLC., Dublin, CA). Peptides were eluted with a gradient of 5 to 90 % HPLC buffer B (0.1 % formic acid in acetonitrile, v/v) in buffer A (0.1 % formic acid in water, v/v) at a flow rate of 300 nL/min over 76 min. The eluted peptides were ionized and introduced into an LTQ-Orbitrap Velos mass spectrometer (Thermo Fisher Scientific Inc., Waltham, MA) using a nanospray source. Full MS scans were acquired in the Orbitrap mass analyzer over the range m/z 300–1800 with a mass resolution of 60,000 at m/z 400. The 20 most intense peaks with charge of +2–+4 were isolated for MS/MS analysis.
SILAC-based quantification analysis
Protein and PTM site identification and quantification were performed with MaxQuant version 188.8.131.52 software [37, 38] against Uniprot human reference protein database concatenated with reverse decoy database and protein sequences of common contaminants. Trypsin was specified as cleavage enzyme with two and four maximum missed cleavages for protein and PTM site quantification, respectively. Maximum numbers of labeled amino acids were set as 3 and 5 for protein and PTM quantification, respectively. Several variable modifications were selected: oxidation (M), acetylation (protein N-term), acetylation (K), tri-methylation (K), di-methylation (K,R), monomethylation (K,R), and specific PTM at lysine residue corresponding to each immunoaffinity purification. False discovery rate (FDR) thresholds for protein, peptide, and modification site were specified at 0.01. Minimum peptide length was set at 7 for protein quantification and 5 for PTM site quantification. Identified PTM sites with Andromeda score less than 40 or site localization probability less than 0.75 as well as site identifications from reverse or contaminant protein sequences were removed. In addition, C-terminal modified peptides were also removed unless it is located at the peptide C-terminal residue of the corresponding protein. Protein ratios were determined based on a minimum of two peptide ratios using razor and unique peptides. The final PTM site ratios were normalized by their protein ratios.
Development of a system for titratable rates of glucose metabolism
The levels of cofactors and substrates that mediate protein acetylation are affected by glycolysis
We observed a dose-dependent gradual decrease of intracellular levels of ac-CoA (Fig. 3b) in response to 2DG treatment. We also observed a graded increase in the ac-CoA/CoA ratio (Fig. 3c). A smaller decrease in overall NAD+ levels was also observed (Fig. 3d). The Pyr/Lac ratio, which is a surrogate for the cytosolic NAD+/NADH ratio, decreased with 2DG treatment; however, this decrease was constant with all treatments (Fig. 3e). Both ac-acetate, a ketone body, (Fig. 3f) and β-OHB (Fig. 3g) were unaffected by 2DG treatment. Together, these results indicate that metabolites known to mediate protein acetylation via lysine acetyl transferases (KATs) are sensitive to glycolytic flux, whereas those known to be important for HDACs appear to be regulated by other fluxes.
Global histone acetylation levels are determined by glycolytic flux
Glycolytic flux can affect other histone acylation PTMs
We so far observed that changes in glycolytic flux induce corresponding changes in global acetylation levels and that there exist tight correlations with the levels of key metabolic variables and global histone acetylation levels. We next considered whether these changes occurred nonspecifically across the histone or whether specificity could be achieved through this mechanism as has been suggested.
The molecular links between the metabolic state of cells and the status of protein acetylation have been intriguing [18, 19, 25, 52]. However, the direct quantitative biochemical interactions between metabolic flux, metabolite levels, and levels of protein acetylation have yet to be demonstrated. By considering a system that allows for controllable rates of glycolysis, this study resulted in a systematic analysis of factors in metabolism that contribute, with specificity, to acetylation levels. By considering histone modifications, this allowed for considerations of possible links between the metabolic state and cellular epigenetics. We were able to show that changes in glycolytic flux can have quantitative effects on global histone acetylation levels. This occurred first through changes in glycolytic rate having effects on the levels of metabolites in glycolysis and peripheral metabolism. Of all metabolites considered, ac-CoA and the ratio of ac-CoA to CoA appeared to have the strongest influence on histone acetylation.
Because most KATs have similar binding affinities to both ac-CoA and CoA, it has been suggested that the ac-CoA/CoA ratio may affect KAT activity as CoA can also have an inhibitory effect on some KATs . Our findings demonstrate that decreasing glycolytic flux can decrease both ac-CoA and free CoA pools and that the regulation of these two pools is directly downstream of glycolysis. More importantly, our data suggests that the supply of intracellular ac-CoA has a greater effect on histone acetylation than the inhibitory effect of free CoA.
Recently, it has been shown that the selectivity of p300, a histone acetyltransferase, can be manipulated by intracellular levels of ac-CoA . Furthermore, work in yeast has shown that Gcn5 is important in mediating histone acetylation and is very sensitive to intracellular ac-CoA levels as well . Our findings show that many other residues not known to be catalyzed by these enzymes are also sensitive to the ac-CoA levels suggesting that such a phenomenon is likely pervasive in cells. Interestingly, it has not escaped our attention that modifications located close to or on globular histone domains seem to be more resistant to changes in glycolytic flux, whereas those on histone tails seem to be more sensitive suggesting that spatial proximity to the metabolic milieu may dictate in part those histone marks that are more dynamic. Moreover, we show that other histone acylation modifications are sensitive to glycolytic flux which likely follow similar principles.
Finally, histone Kac, Kpr, Kbu, and Khib each appear to be globally regulated by glycolytic rate and have all been associated with active gene expression. It has been shown that these PTMs utilize corresponding ac-CoA intermediates. However, it is not known that they are all connected to glycolysis. From our data, we can speculate that the decrease in free CoA restricts the ability to form the corresponding ac-CoAs necessary for these PTMs suggesting some possibility of enzymatic activity mediating these reactions. The sensitivity of these PTMs to glycolytic flux is unexpected, and further investigation is required that includes better understanding the chemical reaction mechanisms that mediate these PTMs. Their sensitivity to glycolytic flux may add to the increasing evolutionary benefits that cancer cells gain from the Warburg effect as glycolytic rates are increased in these cells by as much as 200-fold compared to normal cells. An exciting recent study has demonstrated that resistance to targeted therapy can be driven by elevated nutrient levels . As abnormal metabolism is a recurrent theme in cancer, understanding its effect on chromatin biology may lead to newfound insights into tumor cell biology and the interaction between metabolism and gene regulation.
Multiple functions of the Warburg Effect or the enhanced rate of glycolysis observed in tumors and proliferative tissue have been proposed. This current study provides evidence for a lesser-appreciated interpretation of the Warburg effect in that it confers direct signaling functions to cells by altering the levels of multiple key metabolites that serve as cofactors and substrates for reactions involving posttranslational modifications, notably histone acetylation, histone butyrylation, histone propionylation, and histone 2-hydroxyisobutryrylation.
acetyl coenzyme Aac-acetate aceto-acetate
lysine acetyl transferases
global acetylated histone H3
stable isotopic labeling by amino acids in cell culture
- Khib :
- Kbu :
- Kpr :
- Kac :
The authors thank the members of the Locasale lab for their helpful input and discussions. JWL acknowledges support from the National Institutes of Health (R00CA168997 and R01CA193256). AAC has a graduate fellowship from the King Abdullah International Medical Research Center under the Ministry of National Guard Health Affairs.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Pearce EL, Poffenberger MC, Chang CH, Jones RG. Fueling immunity: insights into metabolism and lymphocyte function. Science. 2013;342(6155):1242454.PubMed CentralPubMedView ArticleGoogle Scholar
- Ward PS, Thompson CB. Metabolic reprogramming: a cancer hallmark even warburg did not anticipate. Cancer Cell. 2012;21(3):297–308.PubMed CentralPubMedView ArticleGoogle Scholar
- Koppenol WH, Bounds PL, Dang CV. Otto Warburg's contributions to current concepts of cancer metabolism. Nat Rev Cancer. 2011;11(5):325–37.PubMedView ArticleGoogle Scholar
- Locasale JW, Cantley LC. Metabolic flux and the regulation of mammalian cell growth. Cell Metab. 2011;14(4):443–51.PubMed CentralPubMedView ArticleGoogle Scholar
- Locasale JW. The consequences of enhanced cell-autonomous glucose metabolism. Trends Endocrinol Metab. 2012;23(11):545–51.PubMedView ArticleGoogle Scholar
- Epstein T, Xu L, Gillies RJ, Gatenby RA. Separation of metabolic supply and demand: aerobic glycolysis as a normal physiological response to fluctuating energetic demands in the membrane. Cancer & Metabolism. 2014;2:7.View ArticleGoogle Scholar
- Pfeiffer T, Schuster S, Bonhoeffer S. Cooperation and competition in the evolution of ATP-producing pathways. Science. 2001;292(5516):504–7.PubMedView ArticleGoogle Scholar
- Slavov N, Budnik BA, Schwab D, Airoldi EM, van Oudenaarden A. Constant growth rate can be supported by decreasing energy flux and increasing aerobic glycolysis. Cell Reports. 2014;7(3):705–14.PubMed CentralPubMedView ArticleGoogle Scholar
- DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 2008;7(1):11–20.PubMedView ArticleGoogle Scholar
- DeBerardinis RJ, Mancuso A, Daikhin E, Nissim I, Yudkoff M, Wehrli S, et al. Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc Natl Acad Sci U S A. 2007;104(49):19345–50.PubMed CentralPubMedView ArticleGoogle Scholar
- Dzeja PP, Terzic A. Phosphotransfer networks and cellular energetics. J Exp Biol. 2003;206(Pt 12):2039–47.PubMedView ArticleGoogle Scholar
- Bittl JA, Ingwall JS. Reaction rates of creatine kinase and ATP synthesis in the isolated rat heart. A 31P NMR magnetization transfer study. J Biol Chem. 1985;260(6):3512–7.PubMedGoogle Scholar
- Chang CH, Curtis JD, Maggi Jr LB, Faubert B, Villarino AV, O'Sullivan D, et al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell. 2013;153(6):1239–51.PubMed CentralPubMedView ArticleGoogle Scholar
- Slavov N, Botstein D. Decoupling nutrient signaling from growth rate causes aerobic glycolysis and deregulation of cell size and gene expression. Mol Biol Cell. 2013;24(2):157–68.PubMed CentralPubMedView ArticleGoogle Scholar
- Gut P, Verdin E. The nexus of chromatin regulation and intermediary metabolism. Nature. 2013;502(7472):489–98.PubMedView ArticleGoogle Scholar
- Yun J, Johnson JL, Hanigan CL, Locasale JW. Interactions between epigenetics and metabolism in cancers. Frontiers in Oncology. 2012;2:163.PubMed CentralPubMedView ArticleGoogle Scholar
- Katada S, Imhof A, Sassone-Corsi P. Connecting threads: epigenetics and metabolism. Cell. 2012;148(1-2):24–8.PubMedView ArticleGoogle Scholar
- Lu C, Thompson CB. Metabolic regulation of epigenetics. Cell Metab. 2012;16(1):9–17.PubMed CentralPubMedView ArticleGoogle Scholar
- Cai L, Sutter BM, Li B, Tu BP. Acetyl-CoA induces cell growth and proliferation by promoting the acetylation of histones at growth genes. Mol Cell. 2011;42(4):426–37.PubMed CentralPubMedView ArticleGoogle Scholar
- Evertts AG, Zee BM, Dimaggio PA, Gonzales-Cope M, Coller HA, Garcia BA. Quantitative dynamics of the link between cellular metabolism and histone acetylation. J Biol Chem. 2013;288(17):12142–51.PubMed CentralPubMedView ArticleGoogle Scholar
- Moussaieff A, Rouleau M, Kitsberg D, Cohen M, Levy G, Barasch D, et al. Glycolysis-Mediated Changes in Acetyl-CoA and Histone Acetylation Control the Early Differentiation of Embryonic Stem Cells. Cell Metab. 2015;21(3):392–402.PubMedView ArticleGoogle Scholar
- Liu XS, Little JB, Yuan ZM. Glycolytic metabolism influences global chromatin structure. Oncotarget. 2015;6(6):4214–25.PubMed CentralPubMedView ArticleGoogle Scholar
- Takahashi H, McCaffery JM, Irizarry RA, Boeke JD. Nucleocytosolic acetyl-coenzyme a synthetase is required for histone acetylation and global transcription. Mol Cell. 2006;23(2):207–17.PubMedView ArticleGoogle Scholar
- Donohoe DR, Collins LB, Wali A, Bigler R, Sun W, Bultman SJ. The Warburg effect dictates the mechanism of butyrate-mediated histone acetylation and cell proliferation. Mol Cell. 2012;48(4):612–26.PubMed CentralPubMedView ArticleGoogle Scholar
- Sutendra G, Kinnaird A, Dromparis P, Paulin R, Stenson TH, Haromy A, et al. A nuclear pyruvate dehydrogenase complex is important for the generation of acetyl-CoA and histone acetylation. Cell. 2014;158(1):84–97.PubMedView ArticleGoogle Scholar
- Wellen KE, Hatzivassiliou G, Sachdeva UM, Bui TV, Cross JR, Thompson CB. ATP-citrate lyase links cellular metabolism to histone acetylation. Science. 2009;324(5930):1076–80.PubMed CentralPubMedView ArticleGoogle Scholar
- McBrian MA, Behbahan IS, Ferrari R, Su T, Huang TW, Li K, et al. Histone acetylation regulates intracellular pH. Mol Cell. 2013;49(2):310–21.PubMed CentralPubMedView ArticleGoogle Scholar
- Albaugh BN, Arnold KM, Denu JM. KAT(ching) metabolism by the tail: insight into the links between lysine acetyltransferases and metabolism. Chembiochem. 2011;12(2):290–8.PubMed CentralPubMedView ArticleGoogle Scholar
- Finkel T, Deng CX, Mostoslavsky R. Recent progress in the biology and physiology of sirtuins. Nature. 2009;460(7255):587–91.PubMed CentralPubMedView ArticleGoogle Scholar
- Shimazu T, Hirschey MD, Newman J, He W, Shirakawa K, Le Moan N, et al. Suppression of oxidative stress by beta-hydroxybutyrate, an endogenous histone deacetylase inhibitor. Science. 2013;339(6116):211–4.PubMed CentralPubMedView ArticleGoogle Scholar
- Huang H, Sabari BR, Garcia BA, Allis CD, Zhao Y. SnapShot: histone modifications. Cell. 2014;159(2):458. e451.PubMed CentralPubMedView ArticleGoogle Scholar
- Dai L, Peng C, Montellier E, Lu Z, Chen Y, Ishii H, et al. Lysine 2-hydroxyisobutyrylation is a widely distributed active histone mark. Nat Chem Biol. 2014;10(5):365–70.PubMedView ArticleGoogle Scholar
- Tan M, Luo H, Lee S, Jin F, Yang JS, Montellier E, et al. Identification of 67 histone marks and histone lysine crotonylation as a new type of histone modification. Cell. 2011;146(6):1016–28.PubMed CentralPubMedView ArticleGoogle Scholar
- Chen Y, Sprung R, Tang Y, Ball H, Sangras B, Kim SC, et al. Lysine propionylation and butyrylation are novel post-translational modifications in histones. Mol Cell Proteomics. 2007;6(5):812–9.PubMed CentralPubMedView ArticleGoogle Scholar
- Liu X, Ser Z, Cluntun AA, Mentch SJ, Locasale JW. A strategy for sensitive, large scale quantitative metabolomics. J Vis Exp. 2014;(87)
- Liu X, Ser Z, Locasale JW. Development and quantitative evaluation of a high-resolution metabolomics technology. Anal Chem. 2014;86(4):2175–84.PubMed CentralPubMedView ArticleGoogle Scholar
- Shechter D, Dormann HL, Allis CD, Hake SB. Extraction, purification and analysis of histones. Nat Protoc. 2007;2(6):1445–57.PubMedView ArticleGoogle Scholar
- Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26(12):1367–72.PubMedView ArticleGoogle Scholar
- Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011;10(4):1794–805.PubMedView ArticleGoogle Scholar
- Wick AN, Drury DR, Nakada HI, Wolfe JB. Localization of the primary metabolic block produced by 2-deoxyglucose. J Biol Chem. 1957;224(2):963–9.PubMedGoogle Scholar
- Brown J. Effects of 2-deoxyglucose on carbohydrate metablism: review of the literature and studies in the rat. Metabolism. 1962;11:1098–112.PubMedGoogle Scholar
- Sols A, Crane RK. Substrate specificity of brain hexokinase. J Biol Chem. 1954;210(2):581–95.PubMedGoogle Scholar
- Nirenberg MW, Hogg JF. Inhibition of anaerobic glycolysis in Ehrlich ascites tumor cells by 2-deoxy-Dglucose. Cancer Res. 1958;18(5):518–21.PubMedGoogle Scholar
- Chen W, Gueron M. The inhibition of bovine heart hexokinase by 2-deoxy-D-glucose-6-phosphate: characterization by 31P NMR and metabolic implications. Biochimie. 1992;74(9-10):867–73.PubMedView ArticleGoogle Scholar
- Kurtoglu M, Maher JC, Lampidis TJ. Differential toxic mechanisms of 2-deoxy-D-glucose versus 2-fluorodeoxy-D-glucose in hypoxic and normoxic tumor cells. Antioxid Redox Signal. 2007;9(9):1383–90.PubMedView ArticleGoogle Scholar
- Vander Heiden MG. Targeting cancer metabolism: a therapeutic window opens. Nat Rev Drug Discov. 2011;10(9):671–84.PubMedView ArticleGoogle Scholar
- Xi H, Kurtoglu M, Lampidis TJ. The wonders of 2-deoxy-D-glucose. IUBMB Life. 2014;66(2):110–21.PubMedView ArticleGoogle Scholar
- Ser Z, Liu X, Tang NN, Locasale JW. Extraction parameters for metabolomics from cultured cells. Anal Biochem. 2015;475:22–8.PubMedView ArticleGoogle Scholar
- Lee JV, Carrer A, Shah S, Snyder NW, Wei S, Venneti S, et al. Aktdependent metabolic reprogramming regulates tumor cell histone acetylation. Cell Metab. 2014;20(2):306–19.PubMed CentralPubMedView ArticleGoogle Scholar
- Mann M. Functional and quantitative proteomics using SILAC. Nat Rev Mol Cell Biol. 2006;7(12):952–8.PubMedView ArticleGoogle Scholar
- Ong SE, Mann M. A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat Protoc. 2006;1(6):2650–60.PubMedView ArticleGoogle Scholar
- Zheng Y, Thomas PM, Kelleher NL. Measurement of acetylation turnover at distinct lysines in human histones identifies long-lived acetylation sites. Nat Commun. 2013;4:2203.PubMed CentralPubMedGoogle Scholar
- Henry RA, Kuo YM, Bhattacharjee V, Yen TJ, Andrews AJ: Changing the Selectivity of p300 by Acetyl-CoA Modulation of Histone Acetylation. ACS Chem Biol. 2014.
- Masui K, Tanaka K, Ikegami S, Villa GR, Yang H, Yong WH, Cloughesy TF, Yamagata K, Arai N, Cavenee WK, et al: Glucose-dependent acetylation of Rictor promotes targeted cancer therapy resistance. Proc Natl Acad Sci U S A. 2015.