The importance of the thioredoxin system in muscle mitochondrial reactive oxygen species metabolism (1159.10)
Bibliographic record
Abstract
Mitochondria are widely recognized as a potential source of reactive oxygen species (ROS); however, mitochondria also possess a strong capacity for ROS consumption that is often underappreciated. In skeletal muscle the glutathione and thioredoxin based peroxidase systems are likely the major H2O2 consumption pathways. Here we demonstrate the thioredoxin‐based pathway is the major H2O2 consumer in isolated rat skeletal muscle mitochondria. Unlike 1‐chloro‐2,4‐dinitrobenzene, the thioredoxin reductase inhibitor auranofin does not elevate ROS production in disrupted membranes that are devoid of the capacity to consume H2O2. Inhibition of thioredoxin reductase with auranofin leads to a marked increase in apparent ROS production but no change in mitochondrial bioenergetic characteristics (oxygen consumption, membrane potential, %NAD(P)H). Moreover, auranofin also inhibits the capacity for H2O2 consumption by isolated mitochondria and does not appear to act through the inhibition of the glutathione reduction system. We conclude that the apparent increase is H2O2 release by treatment with auranofin is due to impaired matrix level thioredoxin‐dependent H2O2 consumption and not reflective of an activation of ROS production. Grant Funding Source : Supported by Canada Research Chairs (CRC), CFI, Manitoba Research and Innovation Fund and NSERC
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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".