Altered Expression of Mitoferrin and Frataxin, Larger Labile Iron Pool and Greater Mitochondrial DNA Damage in the Skeletal Muscle of Older Adults
Bibliographic record
Abstract
Mitochondrial dysfunction and iron (Fe) dyshomeostasis are invoked among the mechanisms contributing to muscle aging, possibly via a detrimental mitochondrial–iron feed-forward loop. We quantified the labile Fe pool, Fe isotopes, and the expression of mitochondrial Fe handling proteins in muscle biopsies obtained from young and older adults. The expression of key proteins of mitochondrial quality control (MQC) and the abundance of the mitochondrial DNA common deletion (mtDNA4977) were also assessed. An inverse association was found between total Fe and the heavier Fe isotope (56Fe), indicating an increase in labile Fe abundance in cells with greater Fe content. The highest levels of labile Fe were detected in old participants with a Short Physical Performance Battery (SPPB) score ≤ 7 (low-functioning, LF). Protein levels of mitoferrin and frataxin were, respectively, higher and lower in the LF group relative to young participants and older adults with SPPB scores ≥ 11 (high-functioning, HF). The mtDNA4977 relative abundance was greater in old than in young participants, regardless of SPPB category. Higher protein levels of Pink1 were detected in LF participants compared with young and HF groups. Finally, the ratio between lipidated and non-lipidated microtubule-associated protein 1A/1B-light chain 3 (i.e., LC3B II/I), as well as p62 protein expression was lower in old participants regardless of SPPB scores. Our findings indicate that cellular and mitochondrial Fe homeostasis is perturbed in the aged muscle (especially in LF older adults), as reflected by altered levels of mitoferrin and frataxin, which, together with MQC derangements, might contribute to loss of mtDNA stability.
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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 | 0.001 | 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".