The Peroxisome-Mitochondria Connection: How and Why?
Bibliographic record
Abstract
Over the past decades, peroxisomes have emerged as key regulators in overall cellular lipid and reactive oxygen species metabolism. In mammals, these organelles have also been recognized as important hubs in redox-, lipid-, inflammatory-, and innate immune-signaling networks. To exert these activities, peroxisomes must interact both functionally and physically with other cell organelles. This review provides a comprehensive look of what is currently known about the interconnectivity between peroxisomes and mitochondria within mammalian cells. We first outline how peroxisomal and mitochondrial abundance are controlled by common sets of cis- and trans-acting factors. Next, we discuss how peroxisomes and mitochondria may communicate with each other at the molecular level. In addition, we reflect on how these organelles cooperate in various metabolic and signaling pathways. Finally, we address why peroxisomes and mitochondria have to maintain a healthy relationship and why defects in one organelle may cause dysfunction in the other. Gaining a better insight into these issues is pivotal to understanding how these organelles function in their environment, both in health and disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".