Cytoskeleton‐dependent endomembrane organization in plant cells: an emerging role for microtubules
Bibliographic record
Abstract
Movement of secretory organelles is a fascinating yet largely mysterious feature of eukaryotic cells. Microtubule-based endomembrane and organelle motility utilizing the motor proteins dynein and kinesin is commonplace in animal cells. In contrast, it has been long accepted that intracellular motility in plant cells is predominantly driven by myosin motors dragging organelles and endomembrane-bounded cargo along actin filament bundles. Consistent with this, defects in the acto-myosin cytoskeleton compromise plant growth and development. Recent findings, however, challenge the actin-centric view of the motility of critical secretory organelles and distribution of associated protein machinery. In this review, we provide an overview of the current knowledge on actin-mediated organelle movement within the secretory pathway of plant cells, and report on recent and exciting findings that support a critical role of microtubules in plant cell development, in fine-tuning the positioning of Golgi stacks, as well as their involvement in cellulose synthesis and auxin polar transport. These emerging aspects of the biology of microtubules highlight adaptations of an ancestral machinery that plants have specifically evolved to support the functioning of the acto-myosin cytoskeleton, and mark new trends in our global appreciation of the complexity of organelle movement within the plant secretory pathway.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".