Cell cycle oscillations in a polarity network facilitate state switching by morphogenetic cues
Bibliographic record
Abstract
Abstract The proper establishment of cell form, fate, and function during morphogenesis requires precise coordination between cell polarity and developmental cues. To achieve this, cells must establish polarity domains that are stable yet sensitive to guiding cues. Here we show that C. elegans germline blastomeres resolve this trade-off by creating a time-varying polarization landscape. Specifically, coupling the PAR polarity network to the cell-cycle kinase CDK-1 ensures that newborn cells operate in a low-feedback regime that lowers barriers to polarity state switching, allowing spatial cues to induce and orient PAR protein asymmetries. As CDK-1 activity rises at mitotic entry, increasing molecular feedback reinforces cue-induced asymmetries to yield robust and stable patterning of PAR domains. Consistent with this model, optogenetic and chemical perturbations show that low-CDK/low-feedback regimes destabilize PAR domains but are required for both de novo polarization and the reorientation of polarity in response to inductive cues. We propose that mitotic oscillations in cell polarity circuits dynamically optimize the polarization landscape to enable coordination of polarity with morphogenesis. Such temporal control of developmental networks is likely a general mechanism to balance robustness of cellular states with sensitivity to signal-induced state switching. Abstract Figure
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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) | 0.005 | 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".