Changes in intra-nuclear mobility of mature snRNPs provide a mechanism for splicing defects in Spinal Muscular Atrophy.
Bibliographic record
Abstract
It is becoming increasingly clear that defects in RNA metabolism can lead to disease. Spinal muscular atrophy (SMA), a leading genetic cause of infant mortality, results from insufficient amounts of survival motor neuron (SMN) protein. SMN is required for the biogenesis of small nuclear ribonucleoproteins (snRNPs): essential components of the spliceosome. Splicing abnormalities have been detected in models of SMA but it is unclear how lowered SMN affects the fidelity of pre-mRNA splicing. We have examined the dynamics of mature snRNPs in cells depleted of SMN and demonstrated that SMN depletion increases the mobility of mature snRNPs within the nucleus. To dissect the molecular mechanism by which SMN deficiency affects intranuclear snRNP mobility, we employed a panel of inhibitors of different stages of pre-mRNA processing. This in vivo modelling demonstrates that snRNP mobility is altered directly as a result of impaired snRNP maturation. Current models of nuclear dynamics predict that subnuclear structures, including the spliceosome, form by self-organization mediated by stochastic interactions between their molecular components. Thus, alteration of the intranuclear mobility of snRNPs provides a molecular mechanism for splicing defects in SMA.
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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) | 0.001 | 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".