Quantitative Analysis of CBP- and P300-Induced Histone Acetylations In Vivo Using Native Chromatin
Bibliographic record
Abstract
In vivo, histone tails are involved in numerous interactions, including those with DNA, adjacent histones, and other, nonhistone proteins. The amino termini are also the substrates for a number of enzymes, including histone acetyltransferases (HATs), histone deacetylases, and histone methyltransferases. Traditional biochemical approaches defining the substrate specificity profiles of HATs have been performed using purified histone tails, recombinant histones, or purified mononucleosomes as substrates. It is clear that the in vivo presentation of the substrate cannot be accurately represented by using these in vitro approaches. Because of the difficulty in translating in vitro results into in vivo situations, we developed a novel single-cell HAT assay that provides quantitative measurements of endogenous HAT activity. The HAT assay is performed under in vivo conditions by using the native chromatin structure as the physiological substrate. The assay combines the spatial resolving power of laser scanning confocal microscopy with simple statistical analyses to characterize CREB binding protein (CBP)- and P300-induced changes in global histone acetylation levels at specific lysine residues. Here we show that CBP and P300 exhibit unique substrate specificity profiles, consistent with the developmental and functional differences between the two HATs.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".