Mass Spectrometric Quantification of Histone Post-translational Modifications by a Hybrid Chemical Labeling Method
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".