Abstract
Histones are highly posttranslationally modified proteins that regulate gene expression by modulating chromatin structure and function. Acetylation and methylation are the most abundant histone modifications, with methylation occurring on lysine (mono-, di-, and trimethylation) and arginine (mono- and dimethylation) predominately on histones H3 and H4. In addition, arginine dimethylation can occur either symmetrically (SDMA) or asymmetrically (ADMA) conferring different biological functions. Despite the importance of histone methylation on gene regulation, characterization and quantitation of this modification have proven to be quite challenging. Great advances have been made in the analysis of histone modification using both bottom-up and top-down mass spectrometry (MS). However, MS-based analysis of histone posttranslational modifications (PTMs) is still problematic, due both to the basic nature of the histone N-terminal tails and to the combinatorial complexity of the histone PTMs. In this report, we describe a simplified MS-based platform for histone methylation analysis. The strategy uses chemical acetylation with d0-acetic anhydride to collapse all the differently acetylated histone forms into one form, greatly reducing the complexity of the peptide mixture and improving sensitivity for the detection of methylation via summation of all the differently acetylated forms. We have used this strategy for the robust identification and relative quantitation of H4R3 methylation, for which stoichiometry and symmetry status were determined, providing an antibody-independent evidence that H4R3 is a substrate for both Type I and Type II PRMTs. Additionally, this approach permitted the robust detection of H4K5 monomethylation, a very low stoichiometry methylation event (0.02% methylation). In an independent example, we developed an in vitro assay to profile H3K27 methylation and applied it to an EZH2 mutant xenograft model following small-molecule inhibition of the EZH2 methyltransferase. These specific examples highlight the utility of this simplified MS-based approach to quantify histone methylation profiles.