Nuclear TARBP2 drives oncogenic dysregulation of RNA splicing and decay
Bibliographic record
Abstract
SUMMARY Post-transcriptional regulation of RNA stability is a key step in gene expression control. We describe a regulatory program, mediated by the double-stranded RNA binding protein TARBP2, that controls RNA stability in the nucleus. TARBP2 binding to pre-mRNAs results in increased intron retention, subsequently leading to targeted degradation of TARBP2-bound transcripts. This is mediated by TARBP2 recruitment of the m 6 A RNA methylation machinery to its target transcripts, where deposition of m 6 A marks influences the recruitment of splicing regulators, inhibiting efficient splicing. Interactions between TARBP2 and the nucleoprotein TPR then promote degradation of these TARBP2-bound transcripts by the nuclear exosome. Additionally, analysis of clinical gene expression datasets revealed a functional role for this TARBP2 pathway in lung cancer. Using xenograft mouse models, we find that TARBP2 impacts tumor growth in the lung, and that this function is dependent on TARBP2-mediated destabilization of ABCA3 and FOXN3. Finally, we establish the transcription factor ZNF143 as an upstream regulator of TARBP2 expression. RESEARCH HIGHLIGHTS The RNA-binding protein TARBP2 controls the stability of its target transcripts in the nucleus Nuclear TARBP2 recruits the methyltransferase complex to deposit m 6 A marks on its target transcripts TARBP2 and m 6 A-mediated interactions with splicing and nuclear RNA surveillance complexes result in target transcript intron retention and decay. Increased TARBP2 expression is associated with lung cancer and promotes lung cancer growth in vivo . The transcription factor ZNF143 drives oncogenic TARBP2 upregulation in lung cancer.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".