The peroxisomal multifunctional protein interacts with cortical microtubules in plant cells
Bibliographic record
Abstract
BACKGROUND: The plant peroxisomal multifunctional protein (MFP) possesses up to four enzymatic activities that are involved in catalyzing different reactions of fatty acid beta-oxidation in the peroxisome matrix. In addition to these peroxisomal activities, in vitro assays revealed that rice MFP possesses microtubule- and RNA-binding activities suggesting that this protein also has important functions in the cytosol. RESULTS: We demonstrate that MFP is an authentic microtubule-binding protein, as it localized to the cortical microtubule array in vivo, in addition to its expected targeting to the peroxisome matrix. MFP does not, however, interact with the three mitotic microtubule arrays. Microtubule co-sedimentation assays of truncated versions of MFP revealed that multiple microtubule-binding domains are present on the MFP polypeptide. This indicates that these regions function together to achieve high-affinity binding of the full-length protein. Real-time imaging of a transiently expressed green fluorescent protein-MFP chimera in living plant cells illustrated that a dynamic, spatial interaction exits between peroxisomes and cortical microtubules as peroxisomes move along actin filaments or oscillate at fixed locations. CONCLUSION: Plant MFP is associated with the cortical microtubule array, in addition to its expected localization in the peroxisome. This observation, coupled with apparent interactions that frequently occur between microtubules and peroxisomes in the cell cortex, supports the hypothesis that MFP is concentrated on microtubules in order to facilitate the regulated import of MFP into 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".