A Nutrient-Based Cellular Model to Characterize Acetylation-Dependent Protein-Protein Interactions
Bibliographic record
Abstract
Cellular homeostasis requires the orderly expression of thousands of transcripts. Gene expression is regulated by numerous proteins that recognize post-translational modifications-in particular, the acetylation of lysine residues (Kac) on histones. In addition to affecting the general condensation state of the chromatin, acetylated histones act as anchor points for bromodomain (BRD)-containing adapter proteins. BRDs are the primary Kac reader domains in humans, and proteins containing them act as chromatin scaffolds that organize large networks of interactions to regulate transcription. To characterize BRD-dependent interaction networks, we established cell lines in which histone acetylation is dependent on acetate supplementation. To do this, we used genome editing to knock out ATP citrate lyase (ACLY), the enzyme responsible for converting citrate to oxaloacetate and acetyl-CoA in the cytoplasm and nucleus. In our cellular model, removing acetate from the culture medium resulted in the rapid catabolism of acetylated histones to restore the nucleocytoplasmic acetyl-CoA pool. Here we report the use of our new model in functional proteomics studies to characterize BRD-dependent interaction networks on the chromatin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".