Mechanisms behind the puzzle: microtubule–microfilament cross-talk in pavement cell formationThis review is one of a selection of papers published in the Special Issue on Plant Cell Biology.
Bibliographic record
Abstract
Recent studies are revealing plausible mechanisms that help explain how the two major cytoskeletal systems of plant cells interact to co-ordinate morphogenesis in diffusely expanding cells. In this article, we focus on the development of pavement cells typically found in the leaf epidermis, and highlight work that provides insights into the mechanisms that generate their complex morphology. Pavement cells interdigitate with adjacent cells, forming narrow neck regions interspersed with lobe-like projections. Earlier analysis demonstrated that distinct banding of cortical microtubules and associated accumulation of cell wall material was responsible for maintaining the neck regions during expansion. More recently, it has been determined that patches of fine actin microfilaments regulate the formation of lobing regions. This zonation into microtubule-rich bands and actin patches is coordinated by the activity of Rops, small GTPases that control a wide range of signalling pathways including ones that remodel both actin microfilament and microtubule arrays. Moreover, the formation of microtubule bands and actin patches seems to be self-reinforcing. Loss of microtubule polymers by drug treatment or mutation broadens actin patch formation, apparently by enhancing Rop interactions with a positive regulator of actin polymerization. Thus, cross-talk between microtubule and actin microfilament networks is essential for coordinating and reinforcing pavement cell morphogenesis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".