Collagen type I signaling reduces the expression and the function of human receptor activator of nuclear factor ‐κB ligand (RANKL) in T lymphocytes
Bibliographic record
Abstract
The mechanisms by which beta1 integrins modulate T cell functions are still poorly defined. We have previously reported that signaling via the collagen type I (Coll I) receptor, alpha2beta1 integrin, inhibited FasL expression and protected Jurkat T cells from activation-induced cell death (AICD). In this study, we examined whether Coll I signaling in T cells also modulates the expression of the human receptor activator of nuclear factor-kappaB ligand (RANKL), a recently identified TNF family member which has important functions in osteoclastogenesis, cell survival and apoptosis. Our results show that in both Jurkat T cells and human primary T cells, Coll I signaling significantly reduces activation-induced RANKL expression by 50-60%. We also found that RANKL is not involved in AICD but participates in doxorubicin-induced apoptosis of leukemia T cell lines including Jurkat, CEM and HSB-2. In this respect, Coll I protected leukemia T cell lines from doxorubicin-induced apoptosis by inhibiting doxorubicin-induced RANKL expression. Together, our results suggest that by limiting the production of RANKL, Coll I signaling may contribute to the resistance of leukemia T cells to chemotherapy. Our study also emphasizes the importance Coll I signaling may have in the control of RANKL-associated T cell functions.
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".