Paraspeckle-independent co-transcriptional regulation of nuclear microRNA biogenesis by SFPQ
Bibliographic record
Abstract
MicroRNAs (miRNAs) play crucial roles in physiological functions and disease, but the regulation of their nuclear biogenesis remains poorly understood. Here, BioID on Drosha, the catalytic subunit of the microprocessor complex, reveals its proximity to splicing factor proline- and glutamine (Q)-rich (SFPQ), a multifunctional RNA-binding protein (RBP) involved in forming paraspeckle nuclear condensates. SFPQ depletion impacts both primary and mature miRNA expression, while other paraspeckle proteins (PSPs) or the paraspeckle scaffolding RNA NEAT1 do not, indicating a paraspeckle-independent role. Comprehensive transcriptomic analyses show that SFPQ loss broadly affects RNAs and miRNA host gene (HG) expression, influencing both their transcription and the stability of their products. Notably, SFPQ protects the oncogenic miR-17∼92 polycistron from degradation by the nuclear exosome targeting (NEXT)-exosome complex and is tightly linked with its overexpression across a broad variety of cancers. Our findings reveal a dual role for SFPQ in regulating miRNA HG transcription and stability, as well as its significance in cancers.
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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) | 0.001 | 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".