Hepatocytes as cytotoxic effector cells can induce cell death by CD95 ligand‐mediated pathway†
Bibliographic record
Abstract
The liver plays an increasingly recognized role in the host's immune responses. The direct contribution of hepatocytes as effector cells to local immunity, pathogen containment, and liver disease is not determined. This in vitro study examined whether hepatocytes can eliminate other cells via a CD95 ligand (CD95L or FasL)/CD95 (Fas)-mediated mechanism and whether this cytotoxic activity can be modulated by cytokines such as interferon gamma (IFN-gamma) or tumor necrosis factor alpha (TNF-alpha). We have found that normal woodchuck and human hepatocytes, both cultured and primary freshly isolated, as well as human HepG2 cells, intrinsically transcribe not only CD95 but also CD95L when examined by reverse transcription-polymerase chain reaction (RT-PCR) assays. The functional competence of CD95L, which was detectable in hepatocytes and HepG2 cells by Western blotting, was confirmed in bioassays by induction of apoptosis of CD95-bearing P815 and LS102.9 cell targets and validated by inhibition of the cell killing with CD95 antagonistic antibody or with a general caspase inhibitor. Furthermore, exposure of cultured hepatocytes to IFN-gamma or their stable transfection with IFN-gamma cDNA or TNF-alpha cDNA increased hepatocyte CD95L/CD95-mediated cell killing. In conclusion, hepatocytes express both CD95L and CD95 and they can induce death of other cells by a CD95L-dependent mechanism. IFN-gamma and, to a lesser extent, TNF-alpha can enhance hepatocyte CD95L-mediated cytotoxicity. This suggests that the local cytokine environment may modulate the hepatocyte contribution to liver immunity.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.010 |
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; both teacher heads agree on what is shown here.
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".