RegulatING chromatin regulators: post-translational modification of the ING family of epigenetic regulators
Bibliographic record
Abstract
The five human ING genes encode at least 15 splicing isoforms, most of which affect cell growth, differentiation and apoptosis through their ability to alter gene expression by epigenetic mechanisms. Since their discovery in 1996, ING proteins have been classified as type II tumour suppressors on the basis of reports describing their down-regulation and mislocalization in a variety of cancer types. In addition to their regulation by transcriptional mechanisms, understanding the range of PTMs (post-translational modifications) of INGs is important in understanding how ING functions are fine-tuned in the physiological setting and how they add to the repertoire of activities affected by the INGs. In the present paper we review the different PTMs that have been reported to occur on INGs. We discuss the PTMs that modulate ING function under normal conditions and in response to a variety of stresses. We also describe the ING PTMs that have been identified by several unbiased MS-based PTM enrichment techniques and subsequent proteomic analysis. Among the ING PTMs identified to date, a subset has been characterized for their biological significance and have been shown to affect processes including subcellular localization, interaction with enzymatic complexes and ING protein half-life. The present review aims to highlight the emerging role of PTMs in regulating ING function and to suggest additional pathways and functions where PTMs may effect ING function.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".