Abstract 44: Organ-specific Stochastic Phenotype Switching is Required for Endothelial Health
Bibliographic record
Abstract
Among unicellular organisms, stochastic phenotype switching is a documented strategy for survival. These populations "hedge their bets": while the majority of their cells are adapted to their present environment, a minority remains poised to thrive under drastically different conditions. Bet hedging has also been described in metazoan cells, primarily in vitro. However, its role in tissue homeostasis has yet to be established. Here, we show that von Willebrand factor (vWF) is expressed in a spatially heterogeneous manner in a small fraction of capillary endothelial cells in the heart, skeletal muscle, lung and brain. Moreover, these mosaic patterns are dynamic, in that vWF expression stochastically toggles ON/OFF over time. By contrast, expression of vWF in the aorta and liver is static in time. In cultured primary endothelial cells, biological noise resulted in mosaic vWF heterogeneity through a promoter-level DNA methylation switch. Finally, vWF-/- mice demonstrated extensive endothelial cell damage in capillaries of the heart and impaired cardiac function, but not kidney or aorta. Taken together, these findings suggest that dynamic mosaicism of vWF expression is functionally relevant and that bet hedging represents a previously unrecognized strategy for adaptive, organ-specific homeostasis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".