ATG14 and RB1CC1 play essential roles in maintaining muscle homeostasis
Bibliographic record
Abstract
Defects in macroautophagy/autophagy are implicated in the pathogenesis of neuromuscular and heart diseases. To precisely define the roles of autophagy-related genes in skeletal and cardiac muscles, we generated muscle-specific rb1cc1- and atg14-conditional knockout (cKO) mice by using Ckm/Ckmm2-Cre and compared their phenotypes to those of ulk1 ulk2-conditional double-knockout (cDKO) mice. atg14-cKO mice developed hypertrophic cardiomyopathy, which was associated with abnormal accumulation of autophagic cargoes in the heart and early mortality. Skeletal muscles of both atg14-cKO and rb1cc1-cKO mice showed features of autophagic vacuolar myopathy with ubiquitin+ SQSTM1+ deposits, but only those of rb1cc1-cKO mice showed TARDBP/TDP-43+ pathology and other features of the inclusion body myopathy–like disease we previously described in ulk1 ulk2-cDKO mice. Herein, we highlight tissue-specific differences between skeletal and cardiac muscles in their reliance on core autophagy proteins and unique roles for ULK1-ULK2 and RB1CC1 among these proteins in the development of TARDBP+ pathology. ABBREVIATIONS:AVM: autophagic vacuolar myopathy; cDKO: conditional double knockout; cKO: conditional knockout; H&E: hematoxylin and eosin; IBM: inclusion body myopathy; mtDNA: mitochondrial DNA; PFA: paraformaldehyde; RNP: ribonucleoprotein; TBST: Tris-buffered saline with 0.2% Triton X-100
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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) | 0.001 | 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".