The FGL2‐FcγRIIB pathway: A novel mechanism leading to immunosuppression
Bibliographic record
Abstract
Fibrinogen-like protein 2 (FGL2) is a multifunctional protein, which has been implicated in the pathogenesis of allograft and xenograft rejection. Previously, FGL2 was shown to inhibit maturation of BM-derived DC and T-cell proliferation. The mechanism of the immunosuppressive activity of FGL2 remains poorly elucidated. Here, we focus on identification of FGL2-specific receptor(s) and their ability to modulate APC activity and allograft survival. Using flow cytometry and surface plasmon resonance analysis, we show that FGL2 binds specifically to Fc gamma receptor (FcgammaR)IIB and FcgammaRIII receptors, which are expressed on the surface of APC, including B lymphocytes, macrophages and DC. Antibody to FcgammaRIIB and FcgammaRIII, or deficiency of these receptors, abrogated FGL2 binding. FGL2 inhibited the maturation of BMDC from FcgammaRIIB+/+ mice but not from FcgammaRIIB(-/-) mice and induced apoptosis in the FcgammaRIIB+ mouse B-cell line (A20) but not the A20IIA1.6 cell line that does not express FcgammaRIIB. Recombinant FGL2 infused into FcgammaRIIB+/+ (C57BL/6J, H-2b) mice but not FcgammaRIIB(-/-) mice inhibited rejection of fully mismatched BALB/cJ (H-2d) skin allografts. The identification of specific receptor binding has important implications for the pathogenesis of immune-mediated disease and suggests a potential for targeted FGL2 therapy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".