A Study of the Role of the Six Family of Transcription Factors in Adult Skeletal Muscle Homeostasis
Bibliographic record
Abstract
Duchenne Muscular Dystrophy (DMD) is characterized by persistent deterioration and regeneration of skeletal muscles. This occurs when ablation of the Dystrophin protein – through deleterious, nonsense, or frameshift mutations in the coding gene – destabilizes the Dystrophin-Associated Glycoprotein Complex (DAPC), resulting in a multitude of signaling defects. Upregulation of a closely related protein, Utrophin A, has been shown to alleviate the DMD phenotype, and slow-twitch oxidative muscle fibers express innately increased sarcolemmal Utrophin levels conferring resistance to the disease. Studies have shown that the Six family of Transcription Factors (TFs) promote the formation and maintenance of fast-twitch muscle, suggesting that antagonizing the Six TFs and causing a fiber type switch may be a therapeutic approach to treating DMD. In this study, partial loss-of function through RNA interference methodologies in adult skeletal muscles were combined with bioinformatic analyses, to elucidate the therapeutic potential of the antagonism of the Six TFs. Knockdown of Six1 is shown to increase Utrophin levels, with a suggested increase in Nuclear factor of activated T-cells (NFAT) activity. This may be partially mediated by Six1 regulation of a known inhibitor of the NFAT pathway, Myoz1, described herein. Six1 knockdown is also shown to modulate thyroid hormone regulated gene expression, mediated through a novel target, MCT10. This thesis elucidates putative mechanisms by which Six TFs may regulate pro-fast-twitch skeletal muscle fiber type by both antagonizing NFAT activity and promoting thyroid hormone signaling.
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) | 0.000 | 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".