Trogocytosis drives red blood cell antigen loss in association with antibody-mediated immune suppression
Bibliographic record
Abstract
ABSTRACT: Red blood cell (RBC) alloimmunization to paternal antigens during pregnancy can cause hemolytic disease of the fetus and newborn (HDFN). This severe and potentially fatal neonatal disorder can be prevented by the administration of polyclonal anti-D through a mechanism referred to as antibody-mediated immune suppression (AMIS). Although anti-D prophylaxis effectively prevents HDFN, a lack of mechanistic clarity has hampered its replacement with recombinant agents. The major theories behind AMIS induction in the hematologic literature have classically centered around RBC clearance; however, antigen modulation/loss has recently been proposed as a potential mechanism of AMIS. To explore the primary mechanisms of AMIS, we studied the ability of 11 different antibodies to induce AMIS, RBC clearance, antigen loss, and RBC membrane loss in the HOD (hen egg lysozyme-ovalbumin-human Duffy) murine model. Antibodies targeting different portions of the HOD molecule could induce AMIS independent of their ability to clear RBCs; however, all antibodies capable of inducing a strong AMIS effect also caused significant in vivo loss of the HOD antigen in conjunction with RBC membrane loss. In vitro studies of AMIS-inducing antibodies demonstrated simultaneous RBC antigen and membrane loss, which was mediated by macrophages. Confocal live-cell microscopy revealed that AMIS-inducing antibodies triggered RBC membrane transfer to macrophages, consistent with trogocytosis. Furthermore, anti-D itself can induce trogocytosis even at low concentrations, when phagocytosis is minimal or absent. In view of these findings, we propose trogocytosis as a mechanism of AMIS induction.
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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 | 0.001 | 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".