Stochastic and reversible aggregation of mRNA with expanded CUG-triplet repeats
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.001 |
| 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 it