Hypoxia is able to induce proteolysis in differentiated L6 muscle cells
Bibliographic record
Abstract
Introduction Chronic obstructive pulmonary disease is a progressive and irreversible inflammatory disease where peripheral muscle atrophy, present in many subjects, has significant clinical impacts. Factors initiating muscle atrophy are still unknown. Intermittent to chronic hypoxemia is developing as pulmonary status worsen. Indirect evidences connecting hypoxia and muscle proteolysis bring questions deserving to be studied. Hypoxia is able to generate reactive oxygen species (ROS) which can increase the Ubiquitin‐Proteasome system (UPS) activity. We therefore hypothesized that hypoxia, through the production of ROS, would promote muscle atrophy by increasing UPS subunits expression. Methods and results L6 myotubes were exposed to hypoxic (1% O2) or normoxic (21% O2) atmospheres. After 24 hours of hypoxic exposure, we found an up‐regulation of 1.6 and 2.2 times in the mRNA expression level of MuRF‐1 and Atrogin‐1, respectively. In presence of N‐acetyl‐L‐cystein (NAC), we did not observe any increase in MuRF‐1 or Atrogin‐1 mRNAs expression during hypoxia. To confirm the presence of proteolysis, we quantified actin degradation and found a significant accumulation of a 14‐kDa fragment in hypoxic cells. Insulin treatment decreased the 14‐kDa fragment accumulation, suggesting a role for the PI3K/Akt pathway in attenuating proteolysis. Indeed, after 4 hours of hypoxic exposure, decreased Akt phosphorylation was observed. Conclusion Hypoxia has the potential to increase proteolysis and the effect of NAC on MuRF‐1 and Atrogin‐1 mRNA expression suggests that proteolysis is mediated by ROS production. Funding: Local funding
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".