Rapid factor depletion highlights intricacies of nucleoplasmic RNA degradation
Bibliographic record
Abstract
Turnover of nucleoplasmic transcripts by the mammalian multi-subunit RNA exosome is mediated by two adaptors: the Nuclear EXosome Targeting (NEXT) complex and the Poly(A) tail eXosome Targeting (PAXT) connection. Functional analyses of NEXT and PAXT have largely utilized long-term factor depletion strategies, facilitating the appearance of indirect phenotypes. Here, we rapidly deplete NEXT, PAXT and core exosome components, uncovering the direct consequences of their acute losses. Generally, proteome changes are sparse and largely dominated by co-depletion of other exosome and adaptor subunits, reflecting possible subcomplex compositions. While parallel high-resolution 3' end sequencing of newly synthesized RNA confirms previously established factor specificities, it concomitantly demonstrates an inflation of long-term depletion datasets by secondary effects. Most strikingly, a general intron degradation phenotype, observed in long-term NEXT depletion samples, is undetectable upon short-term depletion, which instead emphasizes NEXT targeting of snoRNA-hosting introns. Further analysis of these introns uncovers an unusual mode of core exosome-independent RNA decay. Our study highlights the accumulation of RNAs as an indirect result of long-term decay factor depletion, which we speculate is, at least partly, due to the exhaustion of alternative RNA decay pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".