CD154 inhibits death of T cells via a Cis interaction with the α5β1 integrin
Bibliographic record
Abstract
CD154 plays a major role in the pathogenesis of several autoimmune and inflammatory diseases. In addition to CD40, soluble CD154 (sCD154) binds to other receptors namely αIIbβ3, αMβ2, α5β1 and αvβ3 integrins. We have previously reported that binding of sCD154 to α5β1 integrin expressed on several human T cell lines is capable of inhibiting Fas-induced cell death. In the current study, we show that such effect of the sCD154/α5β1 interaction is not restricted to the cell death response induced by Fas but could also be exhibited toward other death signals such as TRAIL and TNF- α. We also demonstrate that sCD154 is capable of inhibiting Fas-mediated death of human activated T cells, more importantly of CD4+ than CD8+ T ones. Our data also show that membrane-bound CD154 and α5β1 integrin expressed on the surface of distinct cells failed to influence cell death responses. However, when membrane-bound CD154 and α5β1 are expressed on the surface of same cell, their interaction was capable of down regulating cell death. CD154 was shown to co-localize with the α5β1 integrin on the surface of these cells. These data strongly suggest a cis-type of interaction between CD154 and α5β1 when both are expressed on the same cell surface, rather than a trans-interaction which usually implicates the ligand and its receptor each expressed on the surface of a distinct cell. Taken together, these findings add to the list of roles through which CD154 is contributing to the pathogenesis of autoimmune-inflammatory diseases, i.e. by protecting T cells from death and enhancing their survival.
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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) | 0.001 | 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".