The regulatory T cell effector molecule fibrinogen‐like protein 2 is necessary for the development of rapamycin‐induced tolerance to fully MHC‐mismatched murine cardiac allografts
Bibliographic record
Abstract
Therapies that promote tolerance in solid organ transplantation will improve patient outcomes by eliminating the need for long-term immunosuppression. To investigate mechanisms of rapamycin-induced tolerance, C3H/HeJ mice were heterotopically transplanted with MHC-mismatched hearts from BALB/cJ mice and were monitored for rejection after a short course of rapamycin treatment. Mice that had received rapamycin developed tolerance with indefinite graft survival, whereas untreated mice all rejected their grafts within 9 days. In vitro, splenic mononuclear cells from tolerant mice maintained primary CD4(+) and CD8(+) immune responses to donor antigens consistent with a mechanism that involves active suppression of immune responses. Furthermore, infection with lymphocytic choriomeningitis virus strain WE led to loss of tolerance suggesting that tolerance could be overcome by infection. Rapamycin-induced, donor-specific tolerance was associated with an expansion of regulatory T (Treg) cells in both the spleen and allograft and elevated plasma levels of fibrinogen-like protein 2 (FGL2). Depletion of Treg cells with anti-CD25 (PC61) and treatment with anti-FGL2 antibody both prevented tolerance induction. Tolerant allografts were populated with Treg cells that co-expressed FGL2 and FoxP3, whereas rejecting allografts and syngeneic grafts were nearly devoid of dual-staining cells. We examined the utility of an immunoregulatory gene panel to discriminate between tolerance and rejection. We observed that Treg-associated genes (foxp3, lag3, tgf-β and fgl2) had increased expression and pro-inflammatory genes (ifn-γ and gzmb) had decreased expression in tolerant compared with rejecting allografts. Taken together, these data strongly suggest that Treg cells expressing FGL2 mediate rapamycin-induced tolerance. Furthermore, a gene biomarker panel that includes fgl2 can distinguish between rejecting and tolerant grafts.
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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".