De Novo Gene Transcription of Connexin Mediates Cytoplasmic Fluid Exchange and Flocking Transitions in Physiological and Cancerous Epithelial Systems
Bibliographic record
Abstract
The initial invasion of tumors requires a transition from a solid, jammed state to a fluid-like, flocking, unjammed state that enables collective migration. Here, we show that de novo gene transcription is essential for the emergence of flocking in epithelial tissues and identify connexins (Cx) as key mediators of this transition. Using quiescent HaCaT keratinocytes, tumorigenic A431 epidermoid carcinoma cells, primary bronchial epithelial explants, and vocal fold carcinoma (VFC) cells, we find that flocking induction depends on transcriptional programs activated downstream of epidermal growth factor (EGF). EGF stimulation upregulates Cx26 and Cx31 and enhances gap-junctional intercellular communication (GJIC), which is necessary-though not sufficient-to generate the large-scale cell-volume fluctuations and density heterogeneity that accompany unjamming. Sustained signaling through extracellular signal-regulated kinase 1/2 (ERK1/2) and AKT serine/threonine kinase (AKT) downstream of the EGF receptor (EGFR) is required for connexin induction, linking mechanical state transitions to extracellular cues. Pharmacological inhibition and CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats-CRISPR associated protein 9) knockout of connexins block unjamming and collective motility. VFC cells display constitutively elevated connexins and persistent flocking that is highly sensitive to connexin inhibition. Consistently, high Cx26 expression correlates with reduced survival across carcinomas. These findings reveal a transcriptionally controlled, connexin-dependent mechanism that enables tissue fluidization and collective invasion.
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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".