CD200Fc(Gly)6TGFβ suppresses transplant rejection and MLCs in vitro (49.15)
Bibliographic record
Abstract
Abstract CD200 suppresses graft rejection after engagement of receptors (CD200R), via induction of Treg and production of TGFβ. We asked if a hybrid molecule linking a soluble form of CD200, CD200Fc, to murine TGFβ through a (Gly)6 linker would produce superior suppression to either CD200FC or TGFβ alone, or in combination. MLC cultures used BL/6 responder cells and irradiated BALB/c DCs, in the presence/absence of varying doses of CD200Fc or TGFβ, alone or in combination, or CD200Fc(Gly)6TGFβ. CD200Fc(Gly)6TGFβ produced suppression at 20-100-fold lower concentrations than all other reagents tested alone or together. When used in vivo to suppress rejection of BALB/c skin grafts in C57BL/6 mice, CD200Fc(Gly)6TGFβ also produced immunosuppression at 100-fold lower concentrations. Further studies suggest the mechanism(s) by which this novel molecule produces suppression involves increased numbers of (inducible) Foxp3+ Treg which cause antigen-specific suppression in MLCs in vitro. Using cells with targeted deletion of the main receptor for CD200 (CD200R1), and/or treated with lentiviral particles encoding shRNAs specific for TGFβRII, we showed that suppression depended on responder T cells with a functional TGFβRII, and DCs expressing CD200R1. Augmented inhibition of suppression by CD200Fc(Gly)6TGFβ using anti-CD200R2 in cultures from CD200R1 knockout mice confirmed a role for CD200R2 in suppression. We conclude that CD200Fc(Gly)6TGFβ may have clinical utility in vivo, or ex vivo.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".