Stochastic and reversible aggregation of mRNA with expanded CUG-triplet repeats
Bibliographic record
Abstract
Transcripts containing expanded CNG repeats, which are found in several neuromuscular diseases, are not exported from the nucleus and aggregate as ribonuclear inclusions by an unknown mechanism. Using the MS2-GFP system, which tethers fluorescent proteins to a specific mRNA, we followed the dynamics of single CUG-repeat transcripts and RNA aggregation in living cells. Single transcripts with 145 CUG repeats from the dystrophia myotonica-protein kinase (DMPK) gene had reduced diffusion kinetics compared with transcripts containing only five CUG repeats. Fluorescence recovery after photobleaching (FRAP) experiments showed that CUG-repeat RNAs display a stochastic aggregation behaviour, because individual RNA foci formed at different rates and displayed different recoveries. Spontaneous clustering of CUG-repeat RNAs was also observed, confirming the stochastic aggregation revealed by FRAP. The splicing factor Mbnl1 colocalized with individual CUG-repeat transcripts and its aggregation with RNA foci displayed the same stochastic behaviour as CUG-repeat mRNAs. Moreover, depletion of Mbnl1 by RNAi resulted in decreased aggregation of CUG-repeat transcripts after FRAP, supporting a direct role for Mbnl1 in CUG-rich RNA foci formation. Our data reveal that nuclear CUG-repeat RNA aggregates are labile, constantly forming and disaggregating structures, and that the Mbnl1 splicing factor is directly involved in the aggregation process.
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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) | 0.000 | 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".