Untargeted tail acetylation of histones in chromatin: lessons from yeastThis paper is one of a selection of papers published in this Special Issue, entitled CSBMCB’s 51st Annual Meeting – Epigenetics and Chromatin Dynamics, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Dynamic acetylation of lysine residues in the amino-terminal tails of the core histones is functionally important for the regulation of diverse DNA-dependent processes in the nucleus, including replication, transcription, and DNA repair. The targeted and untargeted activities of histone lysine acetylases (KATs) and deacetylases (HDACs) both contribute to the dynamics of chromatin acetylation. While the mechanisms and functional consequences of targeted on histone acetylation are well understood, relatively little is known about untargeted histone acetylation. Here, we review the current understanding of the mechanisms by which untargeted KAT and HDAC activities modulate the acetylation state of nucleosomal histones, focusing on results obtained for H3 and H4 in budding yeast. We also highlight unresolved problems in this area, including the question of how a particular steady-state level of untargeted acetylation is set in the absence of cis-dependent mechanisms that instruct the activity of KATs and HDACs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".