TWIST1 methylation by SETD6 selectively antagonizes LINC-PINT expression in glioma
Bibliographic record
Abstract
Gliomas are one of the most common and lethal brain tumors among adults. One process that contributes to glioma progression and recurrence is the epithelial to mesenchymal transition (EMT). EMT is regulated by a set of defined transcription factors which tightly regulate this process, among them is the basic helix-loop-helix family member, TWIST1. Here we show that TWIST1 is methylated on lysine-33 at chromatin by SETD6, a methyltransferase with expression levels correlating with poor survival in glioma patients. RNA-seq analysis in U251 glioma cells suggested that both SETD6 and TWIST1 regulate cell adhesion and migration processes. We further show that TWIST1 methylation attenuates the expression of the long-non-coding RNA, LINC-PINT, thereby promoting EMT in glioma. Mechanistically, TWIST1 methylation represses the transcription of LINC-PINT by increasing the occupancy of EZH2 and the catalysis of the repressive H3K27me3 mark at the LINC-PINT locus. Under un-methylated conditions, TWIST1 dissociates from the LINC-PINT locus, allowing the expression of LINC-PINT which leads to increased cell adhesion and decreased cell migration. Together, our findings unravel a new mechanistic dimension for selective expression of LINC-PINT mediated by TWIST1 methylation.
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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) | 0.002 | 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".