Haploidentical mixed chimerism cures autoimmunity in established type 1 diabetic mice
Bibliographic record
Abstract
Clinical trials are currently testing whether induction of haploidentical mixed chimerism (Haplo-MC) induces organ transplantation tolerance. Whether Haplo-MC can be used to treat established autoimmune diseases remains unknown. Here, we show that established autoimmunity in euthymic and adult-thymectomized NOD (H-2g7) mice was cured by induction of Haplo-MC under a non-myeloablative anti-thymocyte globulin-based conditioning regimen and infusion of CD4+ T cell-depleted hematopoietic graft from H-2b/g7 F1 donors that expressed autoimmune-resistant H-2b or from H-2s/g7 F1 donors that expressed autoimmune-susceptible H-2s. The cure was associated with enhanced thymic negative selection, increased thymic Treg (tTreg) production, and anergy or exhaustion of residual host-type autoreactive T cells in the periphery. The peripheral tolerance was accompanied by expansion of donor- and host-type CD62L-Helios+ tTregs as well as host-type Helios-Nrp1+ peripheral Tregs (pTregs) and PD-L1hi plasmacytoid DCs (pDCs). Depletion of donor- or host-type Tregs led to reduction of host-type PD-L1hi pDCs and recurrence of autoimmunity, whereas PD-L1 deficiency in host-type DCs led to reduction of host-type pDCs and Helios-Nrp1+ pTregs. Thus, induction of Haplo-MC reestablished both central and peripheral tolerance through mechanisms that depend on allo-MHC+ donor-type DCs, PD-L1hi host-type DCs, and the generation and persistence of donor- and host-type tTregs and pTregs.
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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) | 0.001 | 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".