Comprehensive Profiling of Histone Modifications Using a Label-Free Approach and Its Applications in Determining Structure−Function Relationships
Bibliographic record
Abstract
A two-pronged approach using specialized peptide detection and clustering tools was developed to profile changes in histone post-translational modifications (PTMs). The extent and nature of modification was inferred by comparing the mass profiles of intact core histones from nano LC-MS experiments. Histones displaying changes in their intact mass profiles were fractionated, derivatized with propionic anhydride, and digested with trypsin prior to nano LC-MS analyses. Our methodology was validated by comparing the abundance of histone PTMs in wild type and mutant strains of Saccharomyces cerevisiae lacking the histone acetyltransferase Rtt109 and a nucleosome assembly factor known as Asf1. Both Rtt109 and Asf1 were previously found to be essential for acetylation of histone H3 lysine 56 (H3K56ac), a modification that plays an important role in the response to genotoxic agents that interfere with DNA replication. The generation of ion abundance distribution plots enabled a rapid and comprehensive profiling of histone peptides. Our analytical methodology and data mining approach showed that most common histone PTMs were unaffected in mutant yeast cells lacking Rtt109 and Asf1. However, a subpopulation representing 17% of all H3 histones in wild type cells showed an acetylated K56 residue that was significantly reduced in both mutant strains. Our generic strategy for histone PTM profiling can be applied to assess global changes in histone PTMs across sample sets and to establish structure-function relationships.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".