ABO-A antibody induction in mice is T cell-dependent, estrogen-independent, and modulated by CD22
Bibliographic record
Abstract
ABO antibodies pose barriers in transplantation but remain poorly studied. We investigated anti-A natural antibodies (nAbs) and induced antibodies (iAbs) in wild-type (WT), CD19KO, and CD22KO mice in the context of major histocompatibility complex-syngeneic or major histocompatibility complex-allogeneic stimulation by ABO-A blood cell membranes (BCM) from A-transgenic mice, or xenogeneic human (Hu-A) BCM. CD19KO mice failed to produce anti-A nAbs and iAbs. Syngeneic A-transgenic-BCM failed to stimulate anti-A iAbs in WT mice, in contrast to allogeneic A-transgenic-BCM and xenogeneic Hu-A-BCM. Hu-A-BCM failed to stimulate anti-A iAbs in CD4-T cell-depleted or CD4KO mice, reversed with CD4-T cell reconstitution. Although anti-A nAbs were absent in estrogen-receptor-α-deficient mice, anti-A iAbs were easily stimulated. Anti-A nAbs were higher in CD22KO than in WT mice, with pubertal females producing higher levels than males. Anti-A iAbs were stimulated in CD22KO mice by syngeneic A-transgenic-BCM or by Hu-A-BCM after CD4T cell depletion. We conclude that anti-A nAbs and iAbs are produced by B1a-cells. In WT mice, stimulation of anti-A iAbs requires exposure to nonself A-antigen together with foreign proteins and is T cell dependent. Without CD22-mediated inhibition, anti-A iAb stimulation does not require foreign protein and is T cell-independent. Anti-A iAbs are estrogen-independent, whereas anti-A nAbs are estrogen-dependent and could be elicited by estrogen in males.
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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 | 0.001 | 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) | 0.002 | 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".