A quantitative atlas of histone modification signatures from human cancer cells

BACKGROUND

An integral component of cancer biology is the understanding of molecular properties uniquely distinguishing one cancer type from another. One class of such properties is histone post-translational modifications (PTMs). Many histone PTMs are linked to the same diverse nuclear functions implicated in cancer development, including transcriptional activation and epigenetic regulation, which are often indirectly assayed with standard genomic technologies. Thus, there is a need for a comprehensive and quantitative profiling of cancer lines focused on their chromatin modification states.

RESULTS

To complement genomic expression profiles of cancer lines, we report the proteomic classification of 24 different lines, the majority of which are cancer cells, by quantifying the abundances of a large panel of single and combinatorial histone H3 and H4 PTMs, and histone variants. Concurrent to the proteomic analysis, we performed transcriptomic analysis on histone modifying enzyme abundances as a proxy for quantifying their activity levels. While the transcriptomic and proteomic results were generally consistent in terms of predicting histone PTM abundance from enzyme abundances, several PTMs were regulated independently of the modifying enzyme expression. In addition, combinatorial PTMs containing H3K27 methylation were especially enriched in breast cell lines. Knockdown of the predominant H3K27 methyltransferase, enhancer of zeste 2 (EZH2), in a mouse mammary xenograft model significantly reduced tumor burden in these animals and demonstrated the predictive utility of proteomic techniques.

CONCLUSIONS

Our proteomic and genomic characterizations of the histone modification states provide a resource for future investigations of the epigenetic and non-epigenetic determinants for classifying and analyzing cancer cells.

Quantitative dynamics of the link between cellular metabolism and histone acetylation

Acetylation on the tails of histones plays an important role in controlling transcription initiation. Although the steady-state abundances of histone acetyl groups have been reported, the rate at which histones are acetylated and deacetylated on a residue-specific basis has not been quantitatively established. We added [(13)C]glucose to human cells and monitored the dynamic incorporation of (13)C-labeled acetyl groups onto specific histone lysines with quantitative mass spectrometry. We determined the turnover of acetylation to be generally slower than phosphorylation, but fast relative to methylation, and that the rate varied depending on the histone, the residue modified, and also the neighboring modifications. Cells were also treated with a deacetylase inhibitor to determine the rate due to histone acetyltransferase activity alone and in the absence of deacetylase activity. Introduction of (13)C-labeled glucose also resulted in the incorporation of (13)C into alanine, which allowed us to partition histones into existing and newly synthesized protein categories. Newly synthesized histones were slower to accumulate histone modifications, especially modifications associated with silent chromatin. Finally, we applied our new approaches to find that quiescent fibroblasts exhibited lower levels of labeled acetyl accumulation compared with proliferating fibroblasts. This suggests that acetylation rates can be modulated in cells in different biological states and that these changes can be detected with the approach presented here. The methods we describe can be broadly applied to defining the turnover of histone acetylation in other cell states such as during cellular reprogramming and to quantify non-histone protein acetylation dynamics.

PILOT_PROTEIN: identification of unmodified and modified proteins via high-resolution mass spectrometry and mixed-integer linear optimization

A novel protein identification framework, PILOT_PROTEIN, has been developed to construct a comprehensive list of all unmodified proteins that are present in a living sample. It uses the peptide identification results from the PILOT_SEQUEL algorithm to initially determine all unmodified proteins within the sample. Using a rigorous biclustering approach that groups incorrect peptide sequences with other homologous sequences, the number of false positives reported is minimized. A sequence tag procedure is then incorporated along with the untargeted PTM identification algorithm, PILOT_PTM, to determine a list of all modification types and sites for each protein. The unmodified protein identification algorithm, PILOT_PROTEIN, is compared to the methods SEQUEST, InsPecT, X!Tandem, VEMS, and ProteinProspector using both prepared protein samples and a more complex chromatin digest. The algorithm demonstrates superior protein identification accuracy with a lower false positive rate. All materials are freely available to the scientific community at http://pumpd.princeton.edu.

A chemical probe selectively inhibits G9a and GLP methyltransferase activity in cells

Protein lysine methyltransferases G9a and GLP modulate the transcriptional repression of a variety of genes via dimethylation of Lys9 on histone H3 (H3K9me2) as well as dimethylation of non-histone targets. Here we report the discovery of UNC0638, an inhibitor of G9a and GLP with excellent potency and selectivity over a wide range of epigenetic and non-epigenetic targets. UNC0638 treatment of a variety of cell lines resulted in lower global H3K9me2 levels, equivalent to levels observed for small hairpin RNA knockdown of G9a and GLP with the functional potency of UNC0638 being well separated from its toxicity. UNC0638 markedly reduced the clonogenicity of MCF7 cells, reduced the abundance of H3K9me2 marks at promoters of known G9a-regulated endogenous genes and disproportionately affected several genomic loci encoding microRNAs. In mouse embryonic stem cells, UNC0638 reactivated G9a-silenced genes and a retroviral reporter gene in a concentration-dependent manner without promoting differentiation.

A Mixed-Integer Optimization Framework for De Novo Peptide Identification

A novel methodology for the de novo identification of peptides by mixed-integer optimization and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. Several problems are examined to illustrate the proposed method's ability to address (1) residue-dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum.