Global turnover of histone post-translational modifications and variants in human cells
© Zee et al; licensee BioMed Central Ltd. 2010
Received: 27 September 2010
Accepted: 6 December 2010
Published: 6 December 2010
Post-translational modifications (PTMs) on the N-terminal tails of histones and histone variants regulate distinct transcriptional states and nuclear events. Whereas the functional effects of specific PTMs are the current subject of intense investigation, most studies characterize histone PTMs/variants in a non-temporal fashion and very few studies have reported kinetic information about these histone forms. Previous studies have used radiolabeling, fluorescence microscopy and chromatin immunoprecipitation to determine rates of histone turnover, and have found interesting correlations between increased turnover and increased gene expression. Therefore, histone turnover is an understudied yet potentially important parameter that may contribute to epigenetic regulation. Understanding turnover in the context of histone modifications and sequence variants could provide valuable additional insight into the function of histone replacement.
In this study, we measured the metabolic rate of labeled isotope incorporation into the histone proteins of HeLa cells by combining stable isotope labeling of amino acids in cell culture (SILAC) pulse experiments with quantitative mass spectrometry-based proteomics. In general, we found that most core histones have similar turnover rates, with the exception of the H2A variants, which exhibit a wider range of rates, potentially consistent with their epigenetic function. In addition, acetylated histones have a significantly faster turnover compared with general histone protein and methylated histones, although these rates vary considerably, depending on the site and overall degree of methylation. Histones containing transcriptionally active marks have been consistently found to have faster turnover rates than histones containing silent marks. Interestingly, the presence of both active and silent marks on the same peptide resulted in a slower turnover rate than either mark alone on that same peptide. Lastly, we observed little difference in the turnover between nearly all modified forms of the H3.1, H3.2 and H3.3 variants, with the notable exception that H3.2K36me2 has a faster turnover than this mark on the other H3 variants.
Quantitative proteomics provides complementary insight to previous work aimed at quantitatively measuring histone turnover, and our results suggest that turnover rates are dependent upon site-specific post-translational modifications and sequence variants.
In eukaryotes, stable genetic storage is accomplished through the local organization of DNA around histone proteins to form the chromatin fiber. Histones have been long recognized as the structural scaffolds of chromatin, but more recent research has suggested that they possess a broader role. The epigenetic influence of histones is mediated primarily by post-translational modifications (PTMs) and also by selective deposition of histone variants, which in combination influence gene transcription and other processes such as DNA damage and replication . In particular, histone PTMs such as trimethylation of lysine 4 on histone H3 (H3K4me3) recruit or displace other proteins that regulate transcription, such as the chromatin remodeler nucleosome remodeling factor (NURF) . Although the underlying mechanism through which histone variants influence gene expression is unclear, certain histone variants have been shown to be linked with specialized genomic roles. For instance, replication-independent H3.3 variant deposition occurs at the transcriptional start sites in various organisms . This specificity probably involves recognition by variant-specific remodeling complexes and chaperones, as is the case for Mis16 and Mis18 interaction with the centromere-specific H3 variant centromere protein (CENP)-A .
Implicit to the current theories of histone epigenetic regulation is that nucleosome occupancy over specific genomic regions is intimately linked to transcription . The biological consequences of histone turnover were first explored with 14C-and 3H-radiolabeling, and among the findings was that specific histone pools were observed to turnover both dependently and independently of DNA replication [6, 7]. It is now known that the majority of histone synthesis is synchronized with S-phase, and that H3.1 and H3.3 are deposited in a replication-dependent and-independent manner, respectively [8, 9]. Expression of the histone genes, which are often clustered within chromosomes, is further regulated at the level of messenger (m)RNA expression, pre-mRNA processing and mRNA stability . Offsetting histone synthesis and deposition is histone degradation and eviction; for instance Saccharomyces cerevisiae SWR1 and human SRCAP (sucrose non-fermentation (SNF)2 C-AMP response element binding protein binding protein (CBP) activator protein replaces H2A with H2A.Z in an ATP-dependent manner . Excess production of histones is known to result in defects in mitotic chromosome segregation [12, 13]. Thus, both histone synthesis/deposition and histone degradation/eviction must occur at approximately equal rates to maintain steady state DNA-bound histone levels and nucleosomal and genomic stability. Yet the absolute value of the rates for each process (synthesis/deposition and degradation/eviction) can differ significantly depending upon the enzyme and substrate. We describe the absolute values of these rates as turnover. In contrast to the relatively slow turnover of histones, which are known to have half-lives in the order of days as determined by radiolabeling studies, the rapid modification of histones after synthesis and incorporation into chromatin is known to be a rapid process .
More recent studies exploring histone turnover have mostly relied on tagging histones with green fluorescent protein, Myc or other epitopes to allow fluorescence recovery after photobleaching, chromatin immunoprecipitation or other techniques to measure turnover. One notable finding includes the existence of at least two pools of H1 with distinct DNA exchange rates in 3T3 cells . Other studies involving the mapping of histone turnover to the genome have shown increased histone turnover on promoters relative to coding regions in S. cerevisiae, and on binding sites for trithorax group proteins relative to binding sites for polycomb group proteins in Drosophila melanogaster S2 cells [17, 18]. These studies suggest that increased turnover within a particular genomic region disrupts the local chromatin environment and renders genes accessible to transcription factors, subsequently leading to gene activation. The results from D. melanogaster also point to an intriguing correlation between increased histone turnover and binding of the origin recognition complex, raising questions about the connection between DNA replication and chromatin .
A valuable complement to these ongoing investigations is the study of histone turnover when distinct site-specific PTMs and the specific histone variants are simultaneously considered. To obtain a quantitative measure of global histone turnover as a function of modification status and type of sequence variant, we designed a time course experiment using stable isotope labeling with amino acids in cell culture (SILAC) in conjunction with high-resolution mass spectrometry (MS). MS enables precise quantification of both histone post-translational modification sites, and allows sequence variants to be identified, thus we believe these attributes qualify MS as a useful technique for studying histone biology in general . Furthermore, because we studied endogenous histones, there are no tags to interfere with higher-order chromatin structure and our measurements accurately capture global in vivo global histone turnover. In this study, we report that turnover rates of histone proteins vary widely depending upon the modification status and sequence variant. Our approach also produced important quantitative information, thus providing a useful and complementary platform for understanding chromatin biology.
Results and discussion
Histone post-translational modification and variant-specific turnover.1
Turnover, per day5
Turnover, per day5
0.6230 ± 0.0001
1.9213 ± 0.0001
0.6400 ± 0.0001
1.1391 ± 0.0000
0.6638 ± 0.0001
1.6913 ± 0.0000
0.4863 ± 0.0000
0.8207 ± 0.0000
0.6378 ± 0.0000
1.0892 ± 0.0000
2.4335 ± 0.0014
0.5148 ± 0.0000
0.6806 ± 0.0001
0.7540 ± 0.0000
0.8793 ± 0.0000
0.6210 ± 0.0001
1.1446 ± 0.0001
0.4537 ± 0.0000
0.6785 ± 0.0001
0.3681 ± 0.0000
0.4526 ± 0.0000
0.4547 ± 0.0000
0.3841 ± 0.0000
0.6495 ± 0.0000
1.1335 ± 0.0001
0.7773 ± 0.0000
0.7967 ± 0.0001
0.9819 ± 0.0000
0.6620 ± 0.0000
1.0423 ± 0.0000
0.4652 ± 0.0000
1.0056 ± 0.0000
1.3393 ± 0.0001
2.2672 ± 0.0005
0.9205 ± 0.0000
1.3340 ± 0.0002
0.6454 ± 0.0000
0.5177 ± 0.0000
0.7721 ± 0.0000
0.3307 ± 0.0000
0.6182 ± 0.0001
0.8016 ± 0.0001
0.7222 ± 0.0000
0.7135 ± 0.0000
0.6311 ± 0.0000
0.6448 ± 0.0000
0.7215 ± 0.0001
0.6293 ± 0.0000
We also examined peptides from several H2A and H2B variants. Similar to the aforementioned unmodified peptides for H1.4, H3 and H4, we never observed forms of the H2A or H2B peptides to be modified and thus reasoned that they also represented bulk turnover of their respective protein (Figure 3, Figure 4). However, some of the tryptic peptides are not unique to a particular variant, as many variants are largely homologous (Table 1). For example, we could only link the peptide NDEELNKLLGR, which is found in H2B types 1-C, 3 and 1-B/E, to the average turnover associated with all the homologous histones. We observed that the H2B variants containing the peptide sequence LAHYNKR or PEPAK had turnover rates similar to H3 and H4 (see Additional file 6). By contrast, a broader range of turnover values was found for the H2A variants, with some being notably faster than H1.4. This wide range of turnover values observed for the H2A variants suggests that different variants may serve different purposes in chromatin assembly. For instance, the human H2A variants that contain the sequence ATIAGGGVIPHIHK, which include H2AZ, have the fastest turnover (see Additional file 6). Intriguingly, H2AZ is known to localize specifically to transcriptional start sites, and the increased turnover is consistent with previous work showing that histones over promoters have a faster turnover than histones over the gene coding region [16, 25]. It is important to note that H2AZ localization over promoters does not indicate that H2AZ is associated with gene activation, but is currently believed to bind to and prime silent promoters for subsequent activation . Despite the differences in turnover values between the core histones, all the core peptides have a turnover rate in the order of ln(2) = 0.6931/day, which is approximately the expected rate if half of peptide population become labeled after each day. Thus, with few exceptions, bulk H1.4, H2A, H2B, H3 and H4 peptides generally turnover at a rate indistinguishable from the rate predicted from HeLa division approximately every 24 hours. This is consistent with previous work showing that most newly synthesized histones are deposited onto newly replicated DNA during S phase, and that the different histone families are synthesized in equal stoichiometry with each other [12, 27].
To place our bulk histone turnover values in the context of previous work, our finding that specific H2A variants have faster turnover than the other core histones is consistent with previous radiolabeling work with tritiated amino acids in Friend murine erythroleukemia cells , Another radiolabeling study that administered tritiated water to mice and examined liver histones found that histone turnover generally occurs on the same time scale as cellular proliferation, which is in excess of 100 days for these cell types [29, 30]. Despite the heterogeneity of cell types with vastly different proliferation rates in adult tissue, the whole-animal work is similar to our findings in HeLa cells; namely, that most bulk histones turn over with the cell cycle.
Relative turnover of H3 and H4 modified peptides
How acetylation and methylation structurally affect the nucleosome itself is not entirely clear. Some notable examples of a direct structural effect of histone PTMs and nucleosomal structure are the electrostatic interaction between the H4 tail with respect to H2A/H2B on the adjacent nucleosome , the electrostatic interaction between H3K56ac and the DNA backbone , and the general destabilization of the nucleosome bound to positively supercoiled DNA by the hyperacetylated histones H3 and H4 . The acetyl moiety removes the positive charge from lysines due to resonance effects of the carbonyl group, whereas the methyl moiety stabilizes charge by raising the acid dissociation constant (pKa) of the remaining acidic protons. We believe that our PTM-specific data supports a general model in which changes to higher-order chromatin structure via charge stabilization or removal respectively impedes or facilitates, subsequent chromatin remodeling. ATP-dependent chromatin remodeling generally proceeds via three pathways: nucleosome sliding along the DNA, nucleosome conformational change, and nucleosome eviction from the DNA [34, 35]. Because we measured turnover by quantifying the appearance of isotopically labeled histones after a pulse, our turnover measurements may reflect the activity of remodelers and chaperones responsible for histone eviction and replacement. To a lesser extent, our turnover measurements may also reflect how quickly the histones become modified into a different peptide; for instance, an unmodified peptide that becomes quickly acetylated would probably have a faster turnover than another peptide that is less rapidly modified.
When mechanistically considering histone turnover in the context of transcription, another non-mutually exclusive pathway for histone turnover emerges. Several models have proposed the displacement of the nucleosome encountered by RNA polymerase onto the upstream DNA strand  or even onto the nascent RNA strand, followed by rebinding onto DNA .We believe that our measurements reflect this nucleosomal event only to a small extent, as we tracked the incorporation of newly synthesized histones. In particular, given the reported high affinity of H2A and H2B for RNA, it is unlikely that our observed turnover of H2A and H2B occurs during the RNA transition state . However, the lack of preferential affinity for either DNA or RNA by the H3/H4 tetramer as reported in the same study may facilitate H3/H4 turnover during this transition state when the tetramer is no longer bound to the H2A/H2B dimer.
A major strength of mass spectrometric analysis is the ability to simultaneously sequence and quantify multiple modifications on the same histone peptide. In the 9-17 peptide on histone H3 (KSTGGKAPR), we observed the presence of both methylation and acetylation on K9 and K14 in HeLa cells. Interestingly, for H3K9me1K14ac1, the turnover is faster than for the exclusively monomethylated H3K9me1 peptide, but slower than for the exclusively monoacetylated H3K9/K14ac1 peptide. For H3K9me2K14ac1, the turnover is slower than for both H3K9me2 and H3K9/K14ac1. These two observations suggest that histone acetylation, generally considered an active mark, is epistatic to active methyl marks (such as H3K9me1) yet antagonistic towards silent methyl marks . The presence of both active and silent marks in the H3K27K36 peptide (KSAPATGGVKKPHR), such as H3K27me2K36me1, is also consistent with this trend. In particular, the H3K27me2K36me1 peptide has a much slower turnover than a peptide containing either the silent mark H3K27me2 or the active mark H3K36me1 (Table 1). Likewise, the H3K27me1K36me2 peptide has a slower turnover than a peptide containing either H3K27me1 or H3K36me2. However, it should be noted that the slower turnover of these doubly modified peptides is not believed to result simply from the total number of methyl groups, because the H3K27me1K36me2 peptide has a faster turnover than the H3K27me2K36me1 peptide.
The presence of antagonistic PTMs may result in a chromatin domain similar to bivalent domains, which contain histones bearing H3K4me3 and H3K27me3 marks, and are believed to poise genes for either activation or silencing . In principle, the conversion of a bivalent domain to either a fully activating or silencing domain can be achieved by histone-modifying enzymes or replacement of the histone molecule with a new unmodified histone that becomes modified. Given that known bivalent domains are bound by polycomb proteins that methylate H3K27, the former mechanism explaining how bivalent domains function in vivo seems more likely . Thus, a slower histone turnover would be expected if the bivalent domains switch epigenetic function through modification changes rather than protein eviction and exchange, consistent with our observations for the H3K9me2K14ac and H3K27me2K36me1 peptides. We believe a similar logic can be applied to binary switch domains; for instance, an effector molecule (that is, heterochromatin protein 1) recognizing methylation on H3K9 would engage in competitive binding against another effector molecular recognizing a phosphorylation on H3S10 .
H3 variant-specific turnover.
The unmodified 27-40 peptide that contains K27 and K36 also has a higher turnover in both H3.2 and H3.3 than inH3.1. We suspect that this is due to the fact that K27 and K36 become immediately methylated in both H3.2 and H3.3 variants respectively, as both PTMs are known to be enriched on the two variants . Consequently, the unmodified peptide in the H3.2 and H3.3 fractions should turn over faster than in H3.1, because it is immediately methylated into a different peptide.
Using mass spectrometry and SILAC, we found that histones are generally stable proteins, with the H2A variants exhibiting the largest range of global turnover rates, and H1.4 turnover being faster than that of H3 and H4. Exploring the relationship between post-translational modifications and turnover, we found that turnover is significantly greater when a histone peptide becomes acetylated than when it is methylated. When classifying the H3 and H4 modified peptides according to epigenetic function, we found that active marks have a significantly faster turnover than silent marks. However, the dual presence of a silencing and activating mark on the same peptide led to vastly distinct turnover rates compared with either mark alone. The various modified forms of the H3 variants (H3.1, H3.2 and H3.3) generally had a similar global turnover, with the notable exception of K36me2. In conclusion, this study offers novel insights into histone turnover by using techniques complementary to those already in standard use by the general chromatin biology community to examine the role of histone turnover in epigenetic regulation.
Cell culture maintenance and SILAC time course
HeLa S3 were maintained between 5-10 × 105 cells/ml throughout the experiment and, before the time course, were grown in minimum essential Joklik modified media (Sigma Aldrich, St. Louis, MO, USA) as previously described . At the start of the time course, cultures were pelleted at 300 g for 3 minutes in a refrigerated centrifuge, decanted, and resuspended in Joklik media depleted of unlabeled lysine (ThermoScientific HyClone, Logan, UT, USA) and supplemented with L-lysine-13C615N2 (Cambridge Isotope Laboratories Inc., Cambridge, MA, USA), 5% fetal bovine serum (ThermoScientific Hyclone), penicillin, streptomycin and 1% Glutamax (Invitrogen, Carlsbad, USA). Before resuspension, flasks were rinsed with Joklik media depleted of lysine. Every 24 hours for 6 days, half of the culture was separated by centrifugation at 600 g, washed twice with Tris-buffered saline, flash-frozen in liquid N2, and stored at -80°C. The culture was replenished with an approximately equal volume of labeled media after sample collection.
Nuclei isolation and histone extraction
Cell pellets were thawed on ice before nuclei isolation and histone extractions as previously described . Briefly, cells were lysed using NP-40 in nuclei isolation buffer with 5 μmol/l microcystin, 0.3 mmol/l 4-(2-aminoethyl) benzenesulfonyl fluoride hydrochloride (AEBSF) and 10 mmol/l sodium butyrate. Histones were isolated from nuclei by extraction with 0.4 N H2SO4, precipitated with trichloroacetic acid, washed in acetone, dried overnight and resuspended in water.
Reversed-phase HPLC separation of bulk histone
Based on the Bradford assay, 125 μg of protein was allocated for one-pot propionic anhydride derivatization. The remaining extract was separated on a C18 column (4.6 mm internal diameter × 250 mm (Vydac, Hesperia, CA, USA) using HPLC (System Gold HPLC; Beckman Coulter, Fullerton, CA, USA) with a gradient of 30-60% B over 100 min, followed by 20 minutes at 100% B (buffer A was 5% acetonitrile in 0.2% trifluoroacetic acid (TFA), buffer B was 90% acetonitrile in 0.188% TFA) and a flow rate of 0.8 ml/min. Fractions spanning a single variant were pooled, and then dried to completion in a vacuum centrifuge.
Histone preparation for MS analysis
The 125 μg bulk extract and HPLC-separated histone H3.1, H3.2, H3.3 and H4 were derivatized and desalted for MS as previously described, with the exception that the reagent was composed of 3:1 isopropanol:propionic anhydride instead of 3:1 methanol:propionic anhydride . For HPLC-purified H1, H2A and H2B, samples were resuspended in 100 mmol/l ammonium bicarbonate (pH 6-7), digested using trypsin for 20 minutes with a 20:1 substrate/enzyme ratio, and subsequently propionylated.
MS and MS/MS analysis
Samples were loaded by an autosampler (AS-2; Eksigent Technologies Inc., CA, USA) onto a 75 μm fused silica capillary column with ESI tip hand packed with 130 mm of C18 reverse phase resin (5 μm particles, 200Å pore size) (Magic C18; Michrom BioResources Inc., Auburn, CA, USA). Samples were resolved on a 110 minute 1-100% buffer B gradient (buffer A = 0.1 mol/l acetic acid, Buffer B = 70% acetonitrile in 0.1 mol/l acetic acid) at a flow rate of 0.070 ml/min controlled by an HPLC pump (1200 series; Agilent, Santa Rosa, CA, USA). The HPLC was coupled to a mass spectrometer (LTQ-Orbitrap; ThermoFisher Scientific, Carlsbad, CA, USA) with a resolution of 30,000 for full MS followed by seven data-dependent MS/MS analyses. Ions selected for MS/MS interrogation were placed on an exclusion list for 30 seconds. Targeted runs were performed on a number of samples to increase the identification of low-abundance modifications.
Data analysis and modeling
Peptide abundance was calculated by manual chromatographic peak integration of full MS scans using Qual Browser software (version 2.0.7; ThermoFisher Scientific Inc.). Peptide sequence and modifications were confirmed by inspection of the MS/MS data. To identify histone H1, H2A and H2B peptides, a database search was performed using the SEQUEST algorithm within the Bioworks Browser (version 3.3; ThermoFisher Scientific). The search was performed against human histone variants for fully enzymatic tryptic digests, allowing for five missed cleavage sites due to the propionyl derivatization, propionylation of unmodified and monomethylated lysines and N-termini (+56.026 Da) and oxidation of methionine (+15.995 Da).
As a labeling convention, we appended each peptide with two numbers, the first referring to the total number of lysines and the second to the number of labeled lysines. For instance, H3K9me1 2:0, H3K9me1 2:1 and H3K9me1 2:2 refer to the same 9-17 monomethylated peptide containing 0, one and two isotopically labeled lysines, respectively. For quantifying the dynamics of histone turnover, we normalized the relative abundances of each labeled state with respect to all labeled states of the same modified peptide to determine the relative distribution of that labeled state. Thus, we normalized H3K9me1 2:0 to the sum of the H3K9me1 2:0, 2:1 and 2:2 peptides. This method of normalization avoids complications arising from variations in ionization efficiencies between peptides with different modification states.
A fundamental requirement of our turnover modeling is that the relative abundance of a post-translationally modified peptide, summed across all its isotopically labeled states, should remain at a steady state. Assuming that 95% (standard score = 1.96) of our observed data can be accounted by a measurement variability of 10%, a commonly cited upper bound, we checked whether the standard deviation of the relative abundances for a particular modified peptide across the time course remained within 0.10/1.96 = 0.051. For instance, if H3K79un, H3K79me1 and H3K79me2 fitted this criterion, we declared that all the modified forms of the peptide were at steady state relative to each other. For peptides that we never observed to be modified, such as the H4 24-35 peptide, we could not make this calculation because we normalized the peptide to itself and we assumed that these unmodified peptides were at steady state.
For each modified peptide, we then fitted a set of differential equations (see Additional file 1 and 3) to the relative abundance distributions for all labeled states using MATLAB (version 7.9.0; Mathworks, Natick, MA, USA) and iterated the program using 100-200 different initial parameter values to determine the set of optimized parameter values that results in the lowest objective or error value. For statistical comparison, we used either the Wilcoxon rank sum test or Kruskal-Wallis test (MATLAB version 7.9.0) for data points that were normally or non-normally distributed, respectively.
We adopted two independent and complementary approaches to assess the quality of the parameter estimates and the fit of the model to the data. Specifically, we examined the squared norm of the residual and computed confidence regions in parameter subspaces to elucidate parameter significance and independence.
In parameter estimation problems, confidence intervals (based on the Student t-test distribution) and/or elliptical confidence regions (based upon a Taylor series expansion around the parameter estimate) are often used to provide a range of values over which the parameter estimates are valid (that is, how much the estimated parameters are allowed to vary while still allowing the model to fit the data well). However, these aforementioned approaches, which are based on linear approximations , are only valid when the parameters vary symmetrically around the optimal estimates, and are not accurate for models with even moderate degrees of non-linearity . To avoid the limitations associated with the inherent assumptions of these methods, we computed confidence regions around the parameter estimates using the F-test method (see Additional file 8) . This F-test approach was applied to every pair of parameters to manually validate that the parameter estimates were indeed significant from zero and to visually assess any degree of nonlinearity in the confidence regions. The parameter estimates were found to be significant, and it was also observed that the confidence region was only slightly nonlinear (see Additional file 8).
We thank all members of the Garcia laboratory for helpful discussions and experimental assistance, and Bo Xu and Ned Wingreen for helpful discussion on the modeling. BAG is supported by an NSF Early Faculty CAREER award, NSF grant (CBET-0941143), Princeton University and a grant supported by award number DP2OD007447 from the Office Of The Director, National Institutes of Health. BMZ is supported by the NSF GRFP. PAD also gratefully acknowledges funding from an NIH F32 NRSA postdoctoral fellowship. RSL acknowledges support from a Sigma Xi research grant.
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