microRNA-133a as an indicator of disease progression and treatment response in X-linked myotubular myopathy
Bibliographic record
Abstract
X-linked myotubular myopathy (XLMTM) is a rare pediatric neuromuscular disease caused by loss-of-function variants in myotubularin (MTM1 ). With novel therapies entering clinical trials, the discovery of robust biomarkers that reflect disease severity and therapeutic efficacy is critically required. Using high-throughput and directed approaches, we identified a decrease in miR-133a expression as a marker of XLMTM disease in skeletal muscle and plasma of a mouse model of XLMTM ( Mtm1 KO). miR-133a is a muscle-enriched non-coding RNA (myomiR) involved in muscle development and function and is implicated in the regulation of the XLMTM modifier gene DNM2 . miR-133a has emerged as both a treatment-effect biomarker and therapeutic candidate in other neuromuscular diseases. We demonstrate that miR-133a expression negatively correlates with disease severity in Mtm1 KO mice and is upregulated in response to treatments that improve DNM2 expression and/or significantly rescue XLMTM. Moreover, we show that miR-133a expression in treated Mtm1 KO mice positively correlates with treatment response and was shown to have high discrimination accuracy for XLMTM by linear discriminant analysis (79%–90%) and receiver operating characteristic curve analysis (AUC >0.80). These results support miR-133a as a robust, circulating biomarker that reflects disease severity and treatment response in XLMTM.
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 | 0.000 | 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 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".