Renal Tubular Epithelial Cell Self-Injury Through Fas/Fas Ligand Interaction Promotes Renal Allograft Injury
Bibliographic record
Abstract
Tubular epithelial cells (TECs) coexpress Fas and Fas ligand (FasL), which could influence renal allograft injury. While TECs can resist apoptosis by Fas antibody, TEC apoptosis by contact with adjacent TECs has not been studied. Fas expression increased in TECs with cytokine treatment (IFN-gamma, TNF-alpha) while abundant FasL levels were not altered. Apoptosis (Annexin-V, DNA fragmentation) occurred in cytokine-treated TECs monolayers from C3H-HeJ mice by 24 h, but was absent in similarly treated TECs from Fas-deficient (lpr) or FasL-mutant (gld) mice, suggesting that 'self injury' occurred through Fas/FasL. Membrane labeling of TECs in cocultures confirmed that FasL-bearing TECs induced apoptosis when in contact with Fas-bearing TECs. Culturing TECs with allogeneic C57BL/6 (H-2b) splenocytes resulted in apoptosis and elimination of C3H-HeJ TECs by 48 h, with enhanced survival and reduced apoptosis using lpr or gld TECs. In a renal allograft model, survival of C57BL/6 recipients was greater (p < 0.05) and renal function improved (p < 0.001) using C3H-lpr or C3H-gld (H-2 k) donor kidneys compared with C3H-HeJ kidneys. These data demonstrate for the first time that cytokine-activated TECs can injure TECs through expression of functional FasL and Fas. We suggest that inhibition of TEC-TEC 'self injury' may be a novel strategy to augment renal allograft survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".