Clearance of defective muscle stem cells by senolytics reduces the expression of senescence-associated secretory phenotype and restores myogenesis in myotonic dystrophy type 1
Bibliographic record
Abstract
ABSTRACT Muscle weakness and atrophy are clinical hallmarks of myotonic dystrophy type 1 (DM1). Muscle stem cells, which contribute to skeletal muscle growth and repair, are also affected in this disease. However, the molecular mechanisms leading to this defective activity and the impact on the disease severity are still elusive. Here, we explored through an unbiased approach the molecular signature leading to myogenic cell defects in DM1. Single cell RNAseq data revealed the presence of a specific subset of DM1 myogenic cells expressing a senescence signature, characterized by the high expression of genes related to senescence-associated secretory phenotype (SASP). This profile was confirmed using different senescence markers in vitro and in situ . Accumulation of intranuclear RNA foci in senescent cells, suggest that RNA-mediated toxicity contribute to senescence induction. High expression of IL-6, a prominent SASP cytokine, in the serum of DM1 patients was identified as a biomarker correlating with muscle weakness and functional capacity limitations. Drug screening revealed that the BCL-XL inhibitor (A1155463), a senolytic drug, can specifically target senescent DM1 myoblasts to induce their apoptosis and reduce their SASP. Removal of senescent cells re-established the myogenic function of the non-senescent DM1 myoblasts, which displayed improved proliferation and differentiation capacity in vitro ; and enhanced engraftment following transplantation in vivo . Altogether this study presents a well-defined senescent molecular signature in DM1 untangling part of the pathological mechanisms observed in the disease; additionally, we demonstrate the therapeutic potential of targeting these defective cells with senolytics to restore myogenesis.
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 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) | 0.001 | 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".