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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".