Reassessing the Roles of PIN Proteins and Anticlinal Microtubules during Pavement Cell Morphogenesis
Bibliographic record
Abstract
The leaf epidermis is a biomechanical shell that influences the size and shape of the organ. Its morphogenesis is a multiscale process in which nanometer-scale cytoskeletal protein complexes, individual cells, and groups of cells pattern growth and define macroscopic leaf traits. Interdigitated growth of neighboring cells is an evolutionarily conserved developmental strategy. Understanding how signaling pathways and cytoskeletal proteins pattern cell walls during this form of tissue morphogenesis is an important research challenge. The cellular and molecular control of a lobed cell morphology is currently thought to involve PIN-FORMED (PIN)-type plasma membrane efflux carriers that generate subcellular auxin gradients. Auxin gradients were proposed to function across cell boundaries to encode stable offset patterns of cortical microtubules and actin filaments between adjacent cells. Many models suggest that long-lived microtubules along the anticlinal cell wall generate local cell wall heterogeneities that restrict local growth and specify the timing and location of lobe formation. Here, we used Arabidopsis (Arabidopsis thaliana) reverse genetics and multivariate long-term time-lapse imaging to test current cell shape control models. We found that neither PIN proteins nor long-lived microtubules along the anticlinal wall predict the patterns of lobe formation. In fields of lobing cells, anticlinal microtubules are not correlated with cell shape and are unstable at the time scales of cell expansion. Our analyses indicate that anticlinal microtubules have multiple functions in pavement cells and that lobe initiation is likely controlled by complex interactions among cell geometry, cell wall stress patterns, and transient microtubule networks that span the anticlinal and periclinal walls.
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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 | 0.001 |
| 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".