Development of Either Split Tolerance or Robust Tolerance along with Humoral Tolerance to Donor and Third-Party Alloantigens in Nonmyeloablative Mixed Chimeras
Bibliographic record
Abstract
Hematopoietic chimerism is considered to generate robust allogeneic tolerance; however, tissue rejection by chimeras can occur. This "split tolerance" can result from immunity toward tissue-specific Ags not expressed by hematopoietic cells. Known to occur in chimeric recipients of skin grafts, it has not often been reported for other donor tissues. Because chimerism is viewed as a potential approach to induce islet transplantation tolerance, we generated mixed bone marrow chimerism in the tolerance-resistant NOD mouse and tested for split tolerance. An unusual multilevel split tolerance developed in NOD chimeras, but not chimeric B6 controls. NOD chimeras demonstrated persistent T cell chimerism but rejected other donor hematopoietic cells, including B cells. NOD chimeras also showed partial donor alloreactivity. Furthermore, NOD chimeras were split tolerant to donor skin transplants and even donor islet transplants, unlike control B6 chimeras. Surprisingly, islet rejection was not a result of autoimmunity, since NOD chimeras did not reject syngeneic islets. Split tolerance was linked to non-MHC genes of the NOD genetic background and was manifested recessively in F(1) studies. Also, NOD chimeras but not B6 chimeras could generate serum alloantibodies, although at greatly reduced levels compared with nonchimeric controls. Surprisingly, the alloantibody response was sufficiently cross-reactive that chimerism-induced humoral tolerance extended to third-party cells. These data identify split tolerance, generated by a tolerance-resistant genetic background, as an important new limitation to the chimerism approach. In contrast, the possibility of humoral tolerance to multiple donors is potentially beneficial.
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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) | 0.000 | 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".