Ubr1-induced selective endo-phagy/autophagy protects against the endosomal and Ca <sup>2+</sup> -induced proteostasis disease stress
Bibliographic record
Abstract
Abstract The defence mechanisms against endo-lysosomal homeostasis stress remain incompletely understood. Here, we identify Ubr1 as a protein quality control (QC) ubiquitin ligase that counteracts proteostasis stress by enhancing cargo selective autophagy for lysosomal degradation. Astrocyte regulatory cluster membrane protein MLC1 mutations increased intracellular Ca 2+ and caused endosomal compartment stress by fusion and enlargement. Endosomal protein QC pathway using ubiquitin QC ligases CHIP and Ubr1 with ESCRT-machinery was able to target only a fraction of MLC1-mutants for lysosomal degradation. As a consequence of the endosomal stress, we found an alternative QC route dependent on Ubr1, SQSTM1/p62 and arginylation to bypass MLC1-mutants to endosomal autophagy (endo-phagy). Significantly, this unfolded a general biological endo-lysosomal QC pathway for arginylated Ubr1-SQSTM1/p62 autophagy targets during Ca 2+ -assault. Conversely, the loss of Ubr1 with the absence of arginylation elicited endosomal compartment stress. These findings underscore the critical housekeeping role of Ubr1-dependent endo-phagy/autophagy in constitutive and provoked endo-lysosomal proteostasis stress, and link Ubr1 to Ca 2+ -homeostasis and proteins implicated in various diseases including cancers and brain disorders.
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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 | 0.001 |
| 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".