Ubiquilin-1 Protects Cells from Oxidative Stress and Ischemic Stroke Caused Tissue Injury in Mice
Bibliographic record
Abstract
Ubiquilin-1 (Ubqln1 or Ubqln), a ubiquitin-like protein, mediates degradation of misfolded proteins and has been implicated in a number of pathological and physiological conditions. To better understand its function in vivo, we recently generated transgenic (Tg) mice that globally overexpress mouse Ubqln in a variety of tissues and ubqln conditional knock-out mice. The Tg mice were viable and did not show any developmental or behavioral abnormalities compared with their wild-type (WT) littermates. When subjected to oxidative stress or ischemia/reperfusion, however, ubqln Tg mice but not the WT littermates showed increased tolerance to these insults. Following ischemic stroke, ubqln Tg mice recovered motor function more rapidly than did the WT mice. In contrast, KO of ubqln exacerbated neuronal damage after stroke. In addition, KO of ubqln also caused accumulation of ubiquitinated proteins. When ubqln KO mice were crossed with a ubiquitin-proteasome system function reporter mouse, the accumulation of a proteasome surrogate substrate was observed. These results suggest that Ubqln protects mice from oxidative stress and ischemic stroke-caused neuronal injury through facilitating removal of damaged proteins. Thus, enhanced removal of unwanted proteins is a potential therapeutic strategy for treating stroke-caused neuronal injury.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".