Requirement for Microtubules and Dynein Motors in the Earliest Stages of Peroxisome Biogenesis
Bibliographic record
Abstract
Our aim was to determine the role of microtubules in the biogenesis of peroxisomes. Fusion experiments between human PEX16- and PEX1-mutant cells in the presence of nocodazol implied that microtubules were not required for import of proteins into the peroxisomal matrix after cell fusion complementation. We further studied the importance of microtubules in the early stages of peroxisome biogenesis following the microinjection complementation of PEX16-mutant cells. In the absence of nocodazol, nuclear microinjection of plasmids expressing EGFP-SKL and Pex16p in PEX16-mutant cells resulted in the accumulation of EGFP-SKL into newly formed peroxisomes. However, pretreatment of the cells with nocodazol, prior to microinjection, resulted in the inhibition of complementation of the PEX16 mutant and the cytosolic location of the EGFP-SKL. In addition, coexpression of a dominant-negative CC1 subunit of the dynein/dynactin motor complex resulted in the inability to complement PEX16-mutant cells. Both of these treatments resulted in the cytosolic localization of expressed Pex16p. Our results demonstrate that the formation of peroxisomes via the preperoxisomal compartment is dependent upon microtubules and minus-end-directed motor proteins and that the inhibition described above occurs at a step that precedes the association of Pex16p with the vesicles that would otherwise become the peroxisomes.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".