α1β1 integrin and interleukin‐7 receptor up‐regulate the expression of RANKL in human T cells and enhance their osteoclastogenic function
Bibliographic record
Abstract
Activated T cells, through the production of the receptor activator of NF-kappaB ligand (RANKL) cytokine, have been implicated in the osteoclast development and bone loss that are associated with autoimmune diseases such as rheumatoid arthritis. However, the cellular pathways that regulate the expression of RANKL and the induction of osteoclasts are still unclear. In this study, we show that, in human effector CD4(+) T cells, activation of alpha1beta1 integrin and interleukin (IL)-7 receptor (IL-7R) up-regulates the expression and production of RANKL but has no effect on the production of interferon-gamma, an inhibitor of T-cell-mediated osteoclastogenesis. Thus, both alpha1beta1 integrin and IL-7R enhance the ability of these cells to induce the formation of osteoclasts from human monocytes. Furthermore, we found that simultaneous activation of effector CD4(+) T cells via alpha1beta1 integrin and IL-7R synergistically increases the production of RANKL and enhances their osteoclastogenic function. We also show that, although alpha1beta1 integrin does not protect human effector CD4(+) T cells from IL-2-withdrawal-induced apoptosis, it does enhance the pro-survival effect of IL-7, further emphasizing the importance of the alpha1beta1/IL-7R synergistic effect. Together our results identify a new function of alpha1beta1 integrin in T cells and suggest that activation of effector CD4(+) T cells through alpha1beta1 integrin and IL-7R is an important regulatory pathway in T-cell-dependent osteoclastogenesis. Further understanding of the mechanisms by which IL-7R and alpha1beta1 integrin promote T-cell-mediated osteoclastogenesis will lead to new insights into the regulatory pathways of T-cell-dependent bone resorption associated with autoimmune diseases.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".