Mass Spectrometric Quantification of Histone Post-translational Modifications by a Hybrid Chemical Labeling Method

Mass spectrometry is a powerful alternative to antibody-based methods for the analysis of histone post-translational modifications (marks). A key development in this approach was the deliberate propionylation of histones to improve sequence coverage across the lysine-rich and hydrophilic tails that bear most modifications. Several marks continue to be problematic however, particularly di- and tri-methylated lysine 4 of histone H3 which we found to be subject to substantial and selective losses during sample preparation and liquid chromatography-mass spectrometry. We developed a new method employing a “one-pot” hybrid chemical derivatization of histones, whereby an initial conversion of free lysines to their propionylated forms under mild aqueous conditions is followed by trypsin digestion and labeling of new peptide N termini with phenyl isocyanate. High resolution mass spectrometry was used to collect qualitative and quantitative data, and a novel web-based software application (Fishtones) was developed for viewing and quantifying histone marks in the resulting data sets. Recoveries of 53 methyl, acetyl, and phosphoryl marks on histone H3.1 were improved by an average of threefold overall, and over 50-fold for H3K4 di- and tri-methyl marks. The power of this workflow for epigenetic research and drug discovery was demonstrated by measuring quantitative changes in H3K4 trimethylation induced by small molecule inhibitors of lysine demethylases and siRNA knockdown of epigenetic modifiers ASH2L and WDR5.The field of Epigenetics has become important in drug discovery as many diseases have been linked to aberrations in chromatin and changes of histone post-translational modifications (PTMs)¹ (1, 2). The core histones (H2A, H2B, H3, and H4 and their variants) undergo a multitude of PTMs. Some, like lysine acetylation, lysine mono-, di-, and trimethlyation, and serine/threonine phosphorylation are well documented, with over 100 distinct, albeit generally low abundance, modifications reported for H3 alone (3). Mass spectrometry provides an alternative to antibody-based methods for detecting and quantifying histone PTMs, as the latter are prone to problems of specificity and epitope occlusion (4, 5). The most commonly applied approach to date is known as “bottom-up” mass spectrometry and involves an initial processing of the histones into smaller peptides (6). A key development in histone PTM analysis was the deliberate chemical modification of histone tail lysines by propionic anhydride, preventing digestion of these Lys- and Arg-rich domains into peptides too short or hydrophilic to be detected in reverse-phase liquid chromatography-mass spectrometry experiments (7–9).Despite this advance, some marks like H3K4 di- and tri-methylation remain problematic; in several examples from the recent literature the H3K4me3 mark is detected either only by means of specifically targeted methods (5), with larger quantitative variation than other marks (10), or not reported among detected marks at all (3, 11–13). Alternative approaches include top-down or middle-down mass spectrometry, in which entire histones, or large segments thereof are analyzed directly (14–16), but these techniques still suffer from relatively poor sensitivity in comparison to bottom-up workflows, and must contend with the full combinatorial complexity of histone PTMs (17).The H3K4me3 mark is of low natural abundance, having a very restricted genomic localization strongly associated with active gene promotors and enhancers (18, 19), and aberrant activities of writers and erasers of that mark are associated with a variety of diseases (1, 2). Difficulties in its quantitation thus hinder the investigation of both fundamental biology and the discovery of lifesaving drugs. We therefore undertook a re-evaluation of the bottom-up histone PTM workflow, streamlining sample preparation and investigating sources of bias or sample loss. Alternatives to the standard propionylation technique were also explored, resulting in a new hybrid chemical modification workflow yielding across-the-board improvements in recovery of peptides from the N-terminal tail of histone H3, and dramatically improved detection of hydrophilic peptides with marks like H3K4me2/me3.     

Methionine biosynthesis in Saccharomyces cerevisiae: Mutations at the regulatory locus ETH2

Summary Homoallelic and heteroallelic diploids involving the eth2-1, eth2-2 and eth2-7 alleles have been studied on the basis of several criteria used for the study of haploid strains: resistance towards ethionine, overproduction of either methionine or/and S-adenosylmethionine, repressibility of methionine biosynthetic enzymes. Complete recessivity of the three alleles over the wild type allele has been observed, when resistance and methionine synthesis are considered. However, with the eth2-2 allele, repressibility corresponds more to a dose effect of the ETH2 allele than to recessivity. The implications of these findings have been discussed. Results obtained for heteroallelic combinations show significant deviations from the expected values. These results have been interpreted as indicating possible interactions between two differently impaired products of gene ETH2. They render likely that the product of this gene is at least an homopolymer.     

SwissPIT: An workflow‐based platform for analyzing tandem‐MS spectra using the Grid

The identification and characterization of peptides from MS/MS data represents a critical aspect of proteomics. It has been the subject of extensive research in bioinformatics resulting in the generation of a fair number of identification software tools. Most often, only one program with a specific and unvarying set of parameters is selected for identifying proteins. Hence, a significant proportion of the experimental spectra do not match the peptide sequences in the screened database due to inappropriate parameters or scoring schemes. The Swiss protein identification toolbox (swissPIT) project provides the scientific community with an expandable multitool platform for automated in‐depth analysis of MS data also able to handle data from high‐throughput experiments. With swissPIT many problems have been solved: The missing standards for input and output formats (A), creation of analysis workflows (B), unified result visualization (C), and simplicity of the user interface (D). Currently, swissPIT supports four different programs implementing two different search strategies to identify MS/MS spectra. Conceived to handle the calculation‐intensive needs of each of the programs, swissPIT uses the distributed resources of a Swiss‐wide computer Grid (http://www.swing‐grid.ch).     

A simple workflow to increase MS2 identification rate by subsequent spectral library search

Searching a spectral library for the identification of protein MS/MS data has proven to be a fast and accurate method, while yielding a high identification rate. We investigated the potential to increase peptide discovery rate, with little increase in computational time, by constructing a workflow based on a sequence search with Phenyx followed by a library search with SpectraST. Searching a consensus library compiled from the search results of the prior Phenyx search increased the number of confidently matched spectra by up to 156%. Additionally matched spectra by SpectraST included noisy spectra, spectra representing missed cleaved peptides as well as spectra from post‐translationally modified peptides.     

OLAV: towards high-throughput tandem mass spectrometry data identification

Mass spectrometry combined with database searching has become the preferred method for identifying proteins in proteomics projects. Proteins are digested by one or several enzymes to obtain peptides, which are analyzed by mass spectrometry. We introduce a new family of scoring schemes, named OLAV, aimed at identifying peptides in a database from their tandem mass spectra. OLAV scoring schemes are based on signal detection theory, and exploit mass spectrometry information more extensively than previously existing schemes. We also introduce a new concept of structural matching that uses pattern detection methods to better separate true from false positives. We show the superiority of OLAV scoring schemes compared to MASCOT, a widely used identification program. We believe that this work introduces a new way of designing scoring schemes that are especially adapted to high-throughput projects such as GeneProt large-scale human plasma project, where it is impractical to check all identifications manually.     

In vitro and in silico processes to identify differentially expressed proteins

We present an integrated proteomics platform designed for performing differential analyses. Since reproducible results are essential for comparative studies, we explain how we improved reproducibility at every step of our laboratory processes, e.g. by taking advantage of the powerful laboratory information management system we developed. The differential capacity of our platform is validated by detecting known markers in a real sample and by a spiking experiment. We introduce an innovative two-dimensional (2-D) plot for displaying identification results combined with chromatographic data. This 2-D plot is very convenient for detecting differential proteins. We also adapt standard multivariate statistical techniques to show that peptide identification scores can be used for reliable and sensitive differential studies. The interest of the protein separation approach we generally apply is justified by numerous statistics, complemented by a comparison with a simple shotgun analysis performed on a small volume sample. By introducing an automatic integration step after mass spectrometry data identification, we are able to search numerous databases systematically, including the human genome and expressed sequence tags. Finally, we explain how rigorous data processing can be combined with the work of human experts to set high quality standards, and hence obtain reliable (false positive < 0.35%) and nonredundant protein identifications.