Recognition-dependent Signaling Events in Response to Apoptotic Targets Inhibit Epithelial Cell Viability by Multiple Mechanisms
Bibliographic record
Abstract
Apoptosis allows for the removal of damaged, aged, and/or excess cells without harm to surrounding tissue. To accomplish this, cells undergoing apoptosis acquire new activities that enable them to modulate the fate and function of nearby cells. We have shown that receptor-mediated recognition of apoptotic versus necrotic target cells by viable kidney proximal tubular epithelial cells (PTEC) modulates the activity of several signaling pathways critically involved in regulation of proliferation and survival. Generally, apoptotic and necrotic targets have opposite effects with apoptotic targets inhibiting and necrotic targets stimulating the activity of these pathways. Here we explore the consequences of these signaling differences. We show that recognition of apoptotic targets induces a profound decrease in PTEC viability through increased responder cell death and decreased proliferation. In contrast, necrotic targets promote viability through decreased death and increased proliferation. Both target types mediate their effects through a network of Akt-dependent and -independent events. Apoptotic targets modulate Akt-dependent viability in part through a reduction in cellular β-catenin and decreased inactivation of Bad. In contrast, Akt-independent modulation of viability occurs through activation of caspase-8, suggesting that death receptor-dependent pathways are involved. Apoptotic targets also activate p38, which partially protects responders from target-induced death. The response of epithelial cells varies depending on their tissue origin. Some cell lines, like PTEC, demonstrate decreased viability, whereas others (e.g. breast-derived) show increased viability. By acting as sentinels of environmental change, apoptotic targets allow neighboring cells, especially non-migratory epithelial cells, to monitor and potentially 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 | 0.002 | 0.001 |
| 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".