A synthetic non-histone substrate to study substrate targeting by the Gcn5 HAT and sirtuin HDACs
Bibliographic record
Abstract
Gcn5 and sirtuins are highly conserved histone acetyltransferase (HAT) and histone deacetylase (HDAC) enzymes that were first characterized as regulators of gene expression. Although histone tails are important substrates of these enzymes, they also target many nonhistone proteins that function in diverse biological processes. However, the mechanisms used by these enzymes to choose their nonhistone substrates are unknown. Previously, we used SILAC-based MS to identify novel nonhistone substrates of Gcn5 and sirtuins in yeast and found a shared target consensus sequence. Here, we use a synthetic biology approach to demonstrate that this consensus sequence can direct acetylation and deacetylation targeting by these enzymes in vivo . Remarkably, fusion of the sequence to a nonsubstrate confers de novo acetylation that is regulated by both Gcn5 and sirtuins. We exploit this synthetic fusion substrate as a tool to define subunits of the Gcn5-containing SAGA and ADA complexes required for nonhistone protein acetylation. In particular, we find a key role for the Ada2 and Ada3 subunits in regulating acetylations on our fusion substrate. In contrast, other subunits tested were largely dispensable, including those required for SAGA stability. In an extended analysis, defects in proteome-wide acetylation observed in ada3 Δ mutants mirror those in ada2 Δ mutants. Altogether, our work argues that nonhistone protein acetylation by Gcn5 is determined in part by specific amino acids surrounding target lysines but that even optimal sequences require both Ada2 and Ada3 for robust acetylation. The synthetic fusion substrate we describe can serve as a tool to further dissect the regulation of both Gcn5 and sirtuin activities in vivo .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".