Restoration of Peroxisomal Catalase Import in a Model of Human Cellular Aging
Bibliographic record
Abstract
Peroxisomes play an important role in human cellular metabolism by housing enzymes involved in a number of essential biochemical pathways. Many of these enzymes are oxidases that transfer hydrogen atoms to molecular oxygen forming hydrogen peroxide. The organelle also contains catalase, which readily decomposes the hydrogen peroxide, a potentially damaging oxidant. Previous work has demonstrated that aging compromises peroxisomal protein import with catalase being particularly affected. The resultant imbalance in the relative ratio of oxidases to catalase was seen as a potential contributor to cellular oxidative stress and aging. Here we report that altering the peroxisomal targeting signal of catalase to the more effective serine-lysine-leucine (SKL) sequence results in a catalase molecule that more strongly interacts with its receptor and is more efficiently imported in both in vitro and in vivo assays. Furthermore, catalase-SKL monomers expressed in cells interact with endogenous catalase subunits resulting in altered trafficking of the latter molecules. A dramatic reduction in cellular hydrogen peroxide levels accompanies this increased peroxisomal import of catalase. Finally, we show that catalase-SKL stably expressed in cells by retroviral-mediated transduction repolarizes mitochondria and reduces the number of senescent cells in a population. These results demonstrate the utility of a catalase-SKL therapy for the restoration of a normal oxidative state in aging cells.
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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 | 0.001 |
| 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".