Gap junction communication regulates luminal-myoepithelial crosstalk and cell differentiation in a bilayered human mammary epithelial cell model
Bibliographic record
Abstract
Abstract Interactions between luminal and myoepithelial cells are essential for proper mammary gland development and function. However, only a few in vitro models allow the study of these interactions and their modulation by extrinsic factors. We developed a layered co-culture system (LCS) that mimics the bilayered architecture of the human breast epithelium, enabling direct contact and bidirectional crosstalk between luminal (MCF-12A) and myoepithelial (MYO1089) cells. We confirmed the formation of adherens and functional gap junctions across the layers. Transcriptomic analysis revealed that co-culture altered gene expression in pathways related to extracellular matrix remodeling, mRNA processing, response to external stimuli, hormonal signaling, receptor activity, and cell cycle regulation. In the LCS, MCF-12A cells exhibited a more luminal-like phenotype, with increased keratin-18 and decreased keratin-14, α-smooth muscle actin (α-SMA), and caldesmon-1 expression compared to 2D monoculture. Conversely, MYO1089 cells showed enhanced expression of myoepithelial markers, including α-SMA, keratin-14, and caldesmon-1. Inhibition of gap junction communication by carbenoxolone disrupted these lineage-specific differentiation patterns. These findings highlight the importance of direct communication in regulating epithelial identity and underscore the LCS as a physiologically relevant model for studying mammary gland biology and external influences that can impact the epithelial differentiation. Highlights A novel bilayered human co-culture system mimics the luminal and myoepithelial architecture of the mammary epithelium Direct luminal-myoepithelial contact enables formation of functional adherens and gap junctions Direct communication via gap junctions is essential for lineage-specific epithelial differentiation Co-culture induces transcriptional shifts related to proliferation, ECM regulation, and hormone response
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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".