Overexpression of Fibrinogen-Like Protein 2 Promotes Tolerance in a Fully Mismatched Murine Model of Heart Transplantation
Bibliographic record
Abstract
Fibrinogen-like protein 2 (FGL2) is an immunomodulatory protein that is expressed by regulatory T cells (Tregs). The objective of this study was to determine if recombinant FGL2 (rFGL2) treatment or constitutive FGL2 overexpression could promote transplant tolerance in mice. Although rFGL2 treatment prevented rejection of fully mismatched cardiac allografts, all grafts were rejected after stopping treatment. Next, we generated FGL2 transgenic mice (fgl2(Tg) ) that ubiquitously overexpressed FGL2. These mice developed normally and had no evidence of the autoimmune glomerulonephritis seen in fgl2(-/-) mice. Immune characterization showed fgl2(Tg) T cells were hypoproliferative to stimulation with alloantigens or anti-CD3 and anti-CD28 stimulation, and fgl2(Tg) Tregs had increased immunosuppressive activity compared with fgl2(+/+) Tregs. To determine if FGL2 overexpression can promote tolerance, we transplanted fully mismatched cardiac allografts into fgl2(Tg) recipients. Fifty percent of cardiac grafts were accepted indefinitely in fgl2(Tg) recipients without any immunosuppression. Tolerant fgl2(Tg) grafts had increased numbers and proportions of Tregs and tolerant fgl2(Tg) mice had reduced proliferation to donor but not third party antigens. These data show that tolerance in fgl2(Tg) recipients involves changes in Treg and T cell activity that contribute to a higher intragraft Treg-to-T cell ratio and acceptance of fully mismatched allografts.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".