Abstract
Methylation of specific histone residues is capable of both gene activation and silencing. Despite vast work on the function of methylation, most studies either present a static snapshot of methylation or fail to assign kinetic information to specific residues. Using liquid chromatography-tandem mass spectrometry on a high-resolution mass spectrometer and heavy methyl-SILAC labeling, we studied site-specific histone lysine and arginine methylation dynamics. The detection of labeled intermediates within a methylation state revealed that mono-, di-, and trimethylated residues generally have progressively slower rates of formation. Furthermore, methylations associated with active genes have faster rates than methylations associated with silent genes. Finally, the presence of both an active and silencing mark on the same peptide results in a slower rate of methylation than the presence of either mark alone. Here we show that quantitative proteomic approaches such as this can determine the dynamics of multiple methylated residues, an understudied portion of histone biology.
Highlights
These features suggest that histone lysine and arginine methylations are dynamically regulated processes
The importance of understanding the dynamics of histone methylation is illustrated with past studies demonstrating that the shift from H3K4me2 and H3K36me2/3 to H3K4me3 and H3K79me2 defined the temporal transition from RNA polymerase II binding to release for transcript elongation [16]
Using heavy methyl SILAC to quantify histone methylation dynamics, we report that different lysine and arginine residues, and different methylation states within the same residue, have different rates of formation
Summary
Histone Methylation Dynamics Studied by MS acetic acid) with a flow rate of ϳ200 nl/min on an Agilent 1200 binary. The labeled media was supplemented with 5% dialyzed fetal abundance of H3K36me1:0 would be determined with respect bovine serum (Invitrogen), penicillin, streptomycin, and 1% to H3K36me1:0 and me1:1. Kinetics Modeling—To quantify relative distributions, we applied another differential equation (supplemental Fig. S2), to determine the half-maximal times of the formation of the fully labeled species and used a Wilcoxon rank sum and KruskalWallis test to compare half-maximal times between active and silencing marks. Relative distributions of the labeled methylation states and 13CD3-methionine incorporated protein from each replicate were fitted with MATLAB (7.8.0) (supplemental data and Fig. S1). In the entire parameter space determined, we averaged only the sets of parameters that did not produce a RMSD greater than 125% of the minimum RMSD
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