Regulation of Epidermal Growth Factor Receptor Trafficking by Lysine Deacetylase HDAC6
Bibliographic record
Abstract
Binding of epidermal growth factor (EGF) to its receptor leads to receptor dimerization, assembly of protein complexes, and activation of signaling networks that control key cellular responses. Despite their fundamental role in cell biology, little is known about protein complexes associated with the EGF receptor (EGFR) before growth factor stimulation. We used a modified membrane yeast two-hybrid system together with bioinformatics to identify 87 candidate proteins interacting with the ligand-unoccupied EGFR. Among them was histone deacetylase 6 (HDAC6), a cytoplasmic lysine deacetylase, which we found negatively regulated EGFR endocytosis and degradation by controlling the acetylation status of alpha-tubulin and, subsequently, receptor trafficking along microtubules. A negative feedback loop consisting of EGFR-mediated phosphorylation of HDAC6 Tyr(570) resulted in reduced deacetylase activity and increased acetylation of alpha-tubulin. This study illustrates the complexity of the EGFR-associated interactome and identifies protein acetylation as a previously unknown regulator of receptor endocytosis and degradation.
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 | 0.000 | 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".