Engulfing to Adapt: Efferocytosis by Epithelial Cells Triggers Cell State Transitions During Tissue Remodeling
Bibliographic record
Abstract
Abstract Hormone deprivation treatments are a leading approach in prostate cancer treatment. Following deprivation, the organ regresses from a fully differentiated secretory organ to one less than 50% of the original size, that has been reprogrammed to a state primed for regeneration. Despite widespread use of hormone treatments, the events linking cellular apoptotic responses following prostate hormone deprivation and cell state reprogramming are not fully understood. We report that the absence of hormones creates a microenvironment rich in apoptotic cells that are engulfed by neighboring prostate epithelial cells. Mechanistically, we found that epithelial cells alter their metabolism and gene expression during efferocytosis. Efferocytic epithelial cells adopt a more glycolytic metabolism, resulting in elevated levels of lactate production. This correlates with increased histone Lactylation over gene regulatory elements for genes regulating autophagy and catabolism. To further demonstrate that efferocytosis is necessary to remodel tissue during regression, we expressed a mutant of the receptor protein MFGE8, MFGE8-D89E to “mask” PtdSer in the prostate epithelium and reduce efferocytosis. This effectively prevented the cell and lumen size changes hormone-deprived prostates usually go through. Our findings suggest new perspectives on how epithelial cell efferocytosis can impact tissue remodeling and altered cellular states.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".