Recognition of Apoptotic Cells by Epithelial Cells
Bibliographic record
Abstract
During apoptosis, cells acquire new activities that enable them to modulate the fate and function of interacting phagocytes, particularly macrophages (m). Although the best known of these activities is anti-inflammatory, apoptotic targets also influence m survival and proliferation by modulating proximal signaling events, such as MAPK modules and Akt. We asked whether modulation of these same signaling events extends to epithelial cells, a minimally phagocytic cell type. We used BU.MPT cells, a mouse kidney epithelial cell line, as our primary model, but we also evaluated several epithelial cell lines of distinct tissue origins. Like m, mouse kidney epithelial cells recognized apoptotic and necrotic targets through distinct non-competing receptors, albeit with lower binding capacity and markedly reduced phagocytosis. Also, modulation of inflammatory activity and MAPK-dependent signaling by apoptotic and necrotic targets was indistinguishable in kidney epithelial cells and m. In contrast, modulation of Akt-dependent signaling differed dramatically between kidney epithelial cells and m. In kidney epithelial cells, modulation of Akt was linked to target cell recognition, independently of phagocytosis, whereas in m, modulation was linked to phagocytosis. Moreover, recognition of apoptotic and necrotic targets by kidney epithelial cells elicited opposite responses; apoptotic targets inhibited whereas necrotic targets stimulated Akt activity. These data confirm that nonprofessional phagocytes recognize and respond to dying cells, albeit in a manner partially distinct from m. By acting as sentinels of environmental change, apoptotic and necrotic targets may permit neighboring viable cells, especially non-migratory epithelial cells, to monitor and adapt to local stresses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".