Coordination of Tissue Cell Polarity by Auxin Transport and Signaling
Bibliographic record
Abstract
Abstract Coordination of polarity between cells in tissues is key to multicellular organism development. In animals, coordination of this tissue cell polarity often requires direct cell-cell interactions and cell movements, which are precluded in plants by a wall that separates cells and holds them in place; yet plants coordinate the polarity of hundreds of cells during the formation of the veins in their leaves. Overwhelming experimental evidence suggests that the plant signaling molecule auxin coordinates tissue cell polarity to induce vein formation, but how auxin does so is unclear. The prevailing hypothesis proposes that GNOM, a regulator of vesicle formation during protein trafficking, positions auxin transporters of the PIN-FORMED family to the correct side of the plasma membrane. The resulting cell-to-cell, polar transport of auxin would coordinate tissue cell polarity and would induce vein formation. Here we tested this hypothesis by means of a combination of cellular imaging, molecular genetic analysis, and chemical induction and inhibition. Contrary to predictions of the hypothesis, we find that auxin-induced vein formation occurs in the absence of PIN-FORMED proteins or any known intercellular auxin transporter, that the residual auxin-transport-independent vein-patterning activity relies on auxin signaling, and that a GNOM -dependent signal that coordinates tissue cell polarity to induce vein formation acts upstream of both auxin transport and signaling. Our results reveal synergism between auxin transport and signaling, and their unsuspected control by GNOM , in the coordination of tissue cell polarity during vein patterning, one of the most spectacular and informative expressions of tissue cell polarization in plants.
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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 | 0.000 |
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".