PERM1—An Emerging Transcriptional Regulator of Mitochondrial Biogenesis: A Systematic Review
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: This systematic review aims to explore the role of PERM1 across different organisms, tissues, and cellular functions, with a particular focus on its involvement in regulating skeletal muscle mitochondrial biogenesis. METHODS: This systematic review follows The PRISMA 2020 Statement. We used the Covidence systematic review software for abstract/title screening, full-text review, and data extraction. The review included studies that examined PERM1 expression or activity in skeletal muscle, heart, and adipose tissue and/or cells, from mice, rats, and humans, and involved exercise or disease models. Risk of bias was assessed using the Cochrane Collaboration tool, and the data were extracted and synthesized qualitatively, with bioinformatic analyses performed using the MetaMEx database. RESULTS: Twenty-one studies were included in our data extraction process, where 10 studies involved humans, 21 involved mice, four involved rats, and 11 involved cells. CONCLUSIONS: PERM1 in skeletal muscle increases with endurance exercise, affecting muscle function and oxidative metabolism, but its role in humans is not well understood. In cardiac tissue, PERM1 is vital for function and mitochondrial biogenesis purposes, but decreases with disease and pressure overload. Our review synthesizes the current understanding of PERM1's function, raises awareness of its role in mitochondrial regulation, and identifies key areas for future research in the field.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".