5-Aza-Cytidine Enhances Terminal Polyadenylation Site Usage for Full-Length Transcripts in Cells
Bibliographic record
Abstract
Abstract As an inhibitor of DNA methyltransferases (DNMTs) and an anti-cancer drug, 5-aza-cytidine (5-azaC)’s many effects on gene expression remains unclear. Here, we show that 5-azaC treatment of cultured GH 3 pituitary tumour cells increases relative usage of genomic terminal exons (GTEs) across the transcriptome. This effect is largely achieved by shifting mRNA polyadenylation from proximal poly(A) sites to GTEs, which harbour a more optimal consensus motif of poly(A) signals. Consistent with this shift, 5-azaC upregulates the mRNA anti-termination factors Scaf4 and Scaf8 while downregulating the early termination enhancer E2f2. In MOLM-13 leukaemia cells, 5-azaC similarly promotes the production of full-length transcripts and regulates alternative polyadenylation factors, some of which in the same direction as observed in GH 3 cells. Moreover, PCF11, a factor known to promote proximal poly(A) site usage, is upregulated in both cell lines, suggesting a homeostatic response by these cells to counteract transcript lengthening during 5-azaC treatment. Together, these findings uncover a previously unrecognized effect of 5-azaC on gene expression: directional promotion of terminal polyadenylation site usage, driving a transcriptome-wide switch from shortened to full-length mRNAs in tumour or cancer cells and consequently altering the alternative usage of multiple 3′ exons.
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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".