HDAC-regulated myomiRs control BAF60 variant exchange and direct the functional phenotype of fibro-adipogenic progenitors in dystrophic muscles
Bibliographic record
Abstract
Fibro-adipogenic progenitors (FAPs) are important components of the skeletal muscle regenerative environment. Whether FAPs support muscle regeneration or promote fibro-adipogenic degeneration is emerging as a key determinant in the pathogenesis of muscular diseases, including Duchenne muscular dystrophy (DMD). However, the molecular mechanism that controls FAP lineage commitment and activity is currently unknown. We show here that an HDAC-myomiR-BAF60 variant network regulates the fate of FAPs in dystrophic muscles of mdx mice. Combinatorial analysis of gene expression microarray, genome-wide chromatin remodeling by nuclease accessibility (NA) combined with next-generation sequencing (NA-seq), small RNA sequencing (RNA-seq), and microRNA (miR) high-throughput screening (HTS) against SWI/SNF BAF60 variants revealed that HDAC inhibitors (HDACis) derepress a "latent" myogenic program in FAPs from dystrophic muscles at early stages of disease. Specifically, HDAC inhibition induces two core components of the myogenic transcriptional machinery, MYOD and BAF60C, and up-regulates the myogenic miRs (myomiRs) (miR-1.2, miR-133, and miR-206), which target the alternative BAF60 variants BAF60A and BAF60B, ultimately directing promyogenic differentiation while suppressing the fibro-adipogenic phenotype. In contrast, FAPs from late stage dystrophic muscles are resistant to HDACi-induced chromatin remodeling at myogenic loci and fail to activate the promyogenic phenotype. These results reveal a previously unappreciated disease stage-specific bipotency of mesenchimal cells within the regenerative environment of dystrophic muscles. Resolution of such bipotency by epigenetic intervention with HDACis provides a molecular rationale for the in situ reprogramming of target cells to promote therapeutic regeneration of dystrophic muscles.
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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) | 0.002 | 0.001 |
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".