Physiological State Dictates the Proteasomal-Mediated Purging of Misfolded Protein Fragments
Bibliographic record
Abstract
A pivotal feature that underlies the development of neurodegeneration is the accumulation of protein aggregates. In response, eukaryotic cells have evolved sophisticated quality control mechanisms to identify, repair and/or eliminate the misfolded abnormal proteins. Chaperones identify any otherwise abnormal conformations in proteins and often help them to regain their correct conformation. However, if repair is not an option, the abnormal protein is selectively degraded to prevent its oligomerization into toxic multimeric complexes. Autophagiclysosomal system and the ubiquitin-proteasome system mediate the targeted degradation of the aberrant protein fragments. Despite the increasing understanding of the molecular counteracting responses toward the accumulation of dysfunctional misfolded proteins, the molecular links between the upstream physiological inputs and the clearance of abnormal misfolded proteins is relatively poorly understood. Recent work has demonstrated that certain physiological states such as vigorous exercise and fasting may enhance the ability of mammalian cells to clear misfolded, toxic and aberrant protein fragments. These findings unveil a novel mechanism that activates the cells' protein-disposal machinery, facilitating the adaptation process of cellular proteome to fluctuations in cellular demands and alterations of environmental cues. Herein, we briefly discuss the molecular interconnection between certain physiological cues and proteasomal degradation pathway in the context of these interesting findings and highlight some of the future prospects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".