Role of Natural Killer Cells in Intravenous Immunoglobulin–Induced Graft-versus-Host Disease Inhibition in NOD/LtSz-scidIL2rg−/− (NSG) Mice
Bibliographic record
Abstract
Although clinical studies have yet to demonstrate clearly the use of intravenous immunoglobulin (IVIG) for prevention of graft-versus-host disease (GVHD), their effective use in a xenogeneic mouse model has been demonstrated. We aimed to determine the mechanism of action by which IVIG contributes to GVHD prevention in a xenogeneic mouse model. NOD/LtSz-scidIL2rg(-/-) (NSG) mice were used for our xenogeneic mouse model of GVHD. Sublethally irradiated NSG mice were injected with human peripheral blood mononuclear cells (huPBMCs) and treated weekly with PBS or 50 mg IVIG. Incidence of GVHD and survival were noted, along with analysis of cell subsets proliferation in the peripheral blood. Weekly IVIG treatment resulted in a robust and consistent proliferation of human natural killer cells that were activated, as demonstrated by their cytotoxicity against K562 target cells. IVIG treatment did not inhibit GVHD when huPBMCs were depleted in natural killer (NK) cells, strongly suggesting that this NK cell expansion was required for the IVIG-mediated prevention of GVHD in our mouse model. Moreover, inhibition of T cell activation by either cyclosporine A (CsA) or monoclonal antihuman CD3 antibodies abolished the IVIG-induced NK cell expansion. In conclusion, IVIG treatment induces NK cell proliferation, which is essential for IVIG-mediated protection of GVHD in our mouse model. Furthermore, activated T cells are mandatory for effective IVIG-induced NK cell proliferation. These results shed light on a new mechanism of action of IVIG and could explain why the efficacy of IVIG in preventing GVHD in a clinical setting, where patients receive CsA, has never been undoubtedly demonstrated.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".