Kynurenine, a Tryptophan Metabolite That Increases with Age, Induces Muscle Atrophy and Lipid Peroxidation
Bibliographic record
Abstract
The cellular and molecular mechanisms underlying loss of muscle mass with age (sarcopenia) are not well-understood; however, heterochronic parabiosis experiments show that circulating factors are likely to play a role. Kynurenine (KYN) is a circulating tryptophan metabolite that is known to increase with age and is a ligand of the aryl hydrocarbon receptor (Ahr). Here, we tested the hypothesis that KYN activation of Ahr plays a role in muscle loss with aging. Results indicate that KYN treatment of mouse and human myoblasts increased levels of reactive oxygen species (ROS) 2-fold and KYN treatment in vivo reduced muscle size and strength and increased muscle lipid peroxidation in young mice. PCR array data indicate that muscle fiber size reduction with KYN treatment reduces protein synthesis markers whereas ubiquitin ligase gene expression is not significantly increased. KYN is generated by the enzyme indoleamine 2,3-dioxygenase (IDO), and aged mice treated with the IDO inhibitor 1-methyl-D-tryptophan showed an increase in muscle fiber size and muscle strength. Small-molecule inhibition of Ahr in vitro , and Ahr knockout in vivo , did not prevent KYN-induced increases in ROS, suggesting that KYN can directly increase ROS independent of Ahr activation. Protein analysis identified very long-chain acyl-CoA dehydrogenase as a factor activated by KYN that may increase ROS and lipid peroxidation. Our data suggest that IDO inhibition may represent a novel therapeutic approach for the prevention of sarcopenia and possibly other age-associated conditions associated with KYN accumulation such as bone loss and neurodegeneration.
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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".