Orchestrating epigenetics: a comprehensive review of the methyltransferase SETD6
Bibliographic record
Abstract
Transcription is regulated by an intricate and extensive network of regulatory factors that impinge upon target genes. This process involves crosstalk between a plethora of factors that include chromatin structure, transcription factors and posttranslational modifications (PTMs). Among PTMs, lysine methylation has emerged as a key transcription regulatory PTM that occurs on histone and non-histone proteins, and several enzymatic regulators of lysine methylation are attractive targets for disease intervention. SET domain-containing protein 6 (SETD6) is a mono-methyltransferase that promotes the methylation of multiple transcription factors and other proteins involved in the regulation of gene expression programs. Many of these SETD6 substrates, such as the canonical SETD6 substrate RELA, are linked to cellular pathways that are highly relevant to human health and disease. Furthermore, SETD6 regulates numerous cancerous phenotypes and guards cancer cells from apoptosis. In the past 15 years, our knowledge of SETD6 substrate methylation and the biological roles of this enzyme has grown immensely. Here we provide a comprehensive overview of SETD6 that will enhance our understanding of this enzyme's role in chromatin and in selective transcriptional control, the contextual biological roles of this enzyme, and the molecular mechanisms and pathways in which SETD6 is involved, and we highlight the major trends in the SETD6 field.
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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 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".