A role for Mitochondrial Rho GTPase 1 (MIRO1) in motility and membrane dynamics of peroxisomes
Bibliographic record
Abstract
Peroxisomes are dynamic organelles which fulfil essential roles in lipid and ROS metabolism. Peroxisome movement and positioning allows interaction with other organelles and is crucial for their cellular function. In mammalian cells, such movement is microtubule-dependent and mediated by kinesin and dynein motors. The mechanisms of motor recruitment to peroxisomes are largely unknown, as well as the role this plays in peroxisome membrane dynamics and proliferation. Here, using a combination of microscopy, live-cell imaging analysis and mathematical modelling, we identify a role for Mitochondrial Rho GTPase 1 (MIRO1) as an adaptor for microtubule-dependent peroxisome motility in mammalian cells. We show that MIRO1 is targeted to peroxisomes and alters their distribution and motility. Using a peroxisome-targeted MIRO1 fusion protein, we demonstrate that MIRO1-mediated pulling forces contribute to peroxisome membrane elongation and proliferation in cellular models of peroxisome disease. Our findings reveal a molecular mechanism for establishing peroxisome-motor protein associations in mammalian cells and provide new insights into peroxisome membrane dynamics in health and disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".