Inhibition of Cytokine Production and Cytokine-Stimulated T-Cell Activation by FK506 (Tacrolimus)1
Bibliographic record
Abstract
Insofar as it exerted its immunosuppressive effect by inhibiting cytokine expression, we assessed the effect of FK506 (Tacrolimus) on cytokine-stimulated T-cell activation. Human T cells, treated with FK506, or controls were stimulated with the mitogens PHA + PMA, Con A, and the "CD3-bypass" stimulation regimen, PMA + ionomycin. T-cell proliferation was quantitated by measuring the uptake of tritiated thymidine, and mRNA expression was assessed by RT-PCR. FK506, in a concentration-dependent fashion, inhibited T-cell proliferation and steady-state mRNA expression of IL-2 and IL-7; half-maximal suppression was obtained at 10(-7) to 5 x 10(-8) M. We tested whether FK506 antiproliferative effect could be overcome with exogenously reconstituted rIL-2 and/or rIL-7. Neither rIL-2 nor rlL-7, individually in conjunction with suboptimal concentrations of PHA or Con A, or in combination without any costimulus, was capable of abrogating FK506 antiproliferative effect, indicating that FK506 also acted by inhibiting cytokine-stimulated T-cell activation. To confirm this, T cells were treated with FK506 and stimulated by rIL-2 and rIL-7, individually in conjunction with suboptimal concentration of PHA and Con A. In addition, T cells were stimulated with rIL-2 and rIL-7 without any costimuli. FK506 inhibited T-cell activation stimulated by rIL-2 and by rIL-7, individually and in combination. This confirms that, in exerting its antiproliferative effect, FK506 acts at two levels, by inhibiting cytokine availability and by suppressing cytokine effect on target cells, and explains the beneficial effect of FK506 in attenuating ongoing immune responses.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".