Double negative <scp>T</scp>reg cells promote nonmyeloablative bone marrow chimerism by inducing <scp>T</scp>‐cell clonal deletion and suppressing <scp>NK</scp> cell function
Bibliographic record
Abstract
The establishment of immune tolerance and prevention of chronic rejection remain major goals in clinical transplantation. In bone marrow (BM) transplantation, T cells and NK cells play important roles for graft rejection. In addition, graft-versus-host-disease (GVHD) remains a major obstacle for BM transplantation. In this study, we aimed to establish mixed chimerism in an irradiation-free condition. Our data indicate that adoptive transfer of donor-derived T-cell receptor (TCR) αβ(+) CD3(+) CD4(-) CD8(-) NK1.1(-) (double negative, DN) Treg cells prior to C57BL/6 to BALB/c BM transplantation, in combination with cyclophosphamide, induced a stable-mixed chimerism and acceptance of C57BL/6 skin allografts but rejection of third-party C3H (H-2k) skin grafts. Adoptive transfer of CD4(+) and CD8(+) T cells, but not DN Treg cells, induced GVHD in this regimen. The recipient T-cell alloreactive responsiveness was reduced in the DN Treg cell-treated group and clonal deletions of TCRVβ2, 7, 8.1/2, and 8.3 were observed in both CD4(+) and CD8(+) T cells. Furthermore, DN Treg-cell treatment suppressed NK cell-mediated BM rejection in a perforin-dependent manner. Taken together, our results suggest that adoptive transfer of DN Treg cells can control both adoptive and innate immunities and promote stable-mixed chimerism and donor-specific tolerance in the irradiation-free regimen.
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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) | 0.003 | 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".