Membrane nanodomains: Dynamic nanobuilding blocks of polarized cell growth
Bibliographic record
Abstract
Cell polarity is intimately linked to numerous biological processes, such as oriented plant cell division, particular asymmetric division, cell differentiation, cell and tissue morphogenesis, and transport of hormones and nutrients. Cell polarity is typically initiated by a polarizing cue that regulates the spatiotemporal dynamic of polarity molecules, leading to the establishment and maintenance of polar domains at the plasma membrane. Despite considerable progress in identifying key polarity regulators in plants, the molecular and cellular mechanisms underlying cell polarity formation have yet to be fully elucidated. Recent work suggests a critical role for membrane protein/lipid nanodomains in polarized morphogenesis in plants. One outstanding question is how the spatiotemporal dynamics of signaling nanodomains are controlled to achieve robust cell polarization. In this review, we first summarize the current state of knowledge on potential regulatory mechanisms of nanodomain dynamics, with a special focus on Rho-like GTPases from plants. We then discuss the pavement cell system as an example of how cells may integrate multiple signals and nanodomain-involved feedback mechanisms to achieve robust polarity. A mechanistic understanding of nanodomains' roles in plant cell polarity is still in the early stages and will remain an exciting area for future investigations.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".