Perturbation of multiprotein complexes in skeletal muscle induces protective proteases in the CNS that degrade pathogenic proteins
Bibliographic record
Abstract
Many cellular functions rely on multiprotein complexes and their stoichiometric assembly. Reducing the levels of individual complex components can perturb this process and induce corrective stress responses. In addition to local outcomes, cellular stress in one tissue can induce long-distance responses in other tissues. Here, we used muscle-targeted RNAi to examine the systemic stress responses induced by muscle-specific genetic perturbation of four distinct multiprotein complexes: the sarcomere, mitochondrial respiratory complex I, proteasome, and VCP (valosin-containing protein) complex. Muscle-specific disruption of these four complexes produced largely overlapping transcriptional adaptations in the central nervous system (CNS), and these responses were centered on the upregulation of many proteases and peptidases. Testing in a retinal model of Huntington's disease demonstrated that several stress-induced proteases limit the accumulation of huntingtin-polyQ aggregates during aging, indicating that these proteases protect from pathogenic proteins. We next examined whether the myokine Amyrel is a possible mediator of this stress-initiated muscle-to-CNS signaling because of its previously reported role in inducing protease expression. Consistent with this model, Amyrel expression was transcriptionally induced in muscle by perturbation of each of the four multiprotein complexes. Moreover, experimental upregulation of Amyrel in muscle reduced the amount of pathogenic huntingtin-polyQ aggregates in the retina. Taken together, these findings indicate that Amyrel and protective proteases improve CNS proteostasis following the perturbation of multiprotein complexes in skeletal muscle. Thus, this study provides insight into a muscle-to-CNS signaling axis that conveys information on the stress status of multiprotein complexes.
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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 | 0.000 |
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".