The ability of natural tolerance to be applied to allogeneic tissue: determinants and limits
Bibliographic record
Abstract
BACKGROUND: Transplant rejection has been considered to occur primarily because donor antigens are not present during the development of the recipient's immune system to induce tolerance. Thus, transplantation prior to recipient immune system development (pre-immunocompetence transplants) should induce natural tolerance to the donor. Surprisingly, tolerance was often not the outcome in such 'natural tolerance models'. We explored the ability of natural tolerance to prevent immune responses to alloantigens, and the reasons for the disparate outcomes of pre-immunocompetence transplants. RESULTS: We found that internal transplants mismatched for a single minor-H antigen and 'healed-in' before immune system development were not ignored but instead induced natural tolerance. In contrast, multiple minor-H or MHC mismatched transplants did not consistently induce natural tolerance unless they carried chimerism generating passenger lymphocytes. To determine whether the systemic nature of passenger lymphocytes was required for their tolerizing capacity, we generated a model of localized vs. systemic donor lymphocytes. We identified the peritoneal cavity as a site that protects allogeneic lymphocytes from killing by NK cells, and found that systemic chimerism, but not chimerism restricted to the peritoneum, was capable of generating natural tolerance. CONCLUSION: These data provide an explanation for the variable results with pre-immunocompetence transplants and suggest that natural tolerance to transplants is governed by the systemic vs. localized nature of donor antigen, the site of transplantation, and the antigenic disparity. Furthermore, in the absence of systemic lymphocyte chimerism the capacity to establish natural tolerance to allogeneic tissue appears strikingly limited. REVIEWERS: This article was reviewed by Matthias von Herrath, Irun Cohen, and Wei-Ping Min (nominated by David Scott).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".