The inhibitory receptor Siglec-E controls antigen-presenting cell activation and T cell–mediated transplant rejection
Bibliographic record
Abstract
After transplantation, inflammation and tissue injury release danger signals that activate myeloid cells, driving adaptive immune responses and acute rejection. Current immunosuppressants primarily target T cells but inadequately control innate immunity. Regulatory signals controlling innate responses in transplantation remain elusive. The sialic acid-binding immunoglobulin-like lectin-E (Siglec-E, or SigE) binds sialylated ligands to suppress inflammation. In mouse heart transplants, SigE is up-regulated in graft-infiltrating myeloid cells, including dendritic cells (DCs). SigE deficiency in recipients, but not donors, accelerates acute rejection by enhancing DC activation, nuclear factor κB (NF-κB) signaling, and tumor necrosis factor-α (TNF-α) production, thereby boosting alloreactive T cell responses. Conversely, SigE overexpression on DCs reduces activation by danger signals and their T cell allostimulatory capacity. The human homologs Siglecs-7 and -9 were up-regulated in rejecting allograft biopsies, and their higher expression correlated with improved allograft survival. Thus, SigE/7/9 is a crucial inhibitory receptor controlling antigen-presenting cell activation and T cell-mediated transplant rejection, offering therapeutic potential.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".