Desialylation is a mechanism of Fc-independent platelet clearance and a therapeutic target in immune thrombocytopenia
Bibliographic record
Abstract
Immune thrombocytopenia (ITP) is a common bleeding disorder caused primarily by autoantibodies against platelet GPIIbIIIa and/or the GPIb complex. Current theory suggests that antibody-mediated platelet destruction occurs in the spleen, via macrophages through Fc-FcγR interactions. However, we and others have demonstrated that anti-GPIbα (but not GPIIbIIIa)-mediated ITP is often refractory to therapies targeting FcγR pathways. Here, we generate mouse anti-mouse monoclonal antibodies (mAbs) that recognize GPIbα and GPIIbIIIa of different species. Utilizing these unique mAbs and human ITP plasma, we find that anti-GPIbα, but not anti-GPIIbIIIa antibodies, induces Fc-independent platelet activation, sialidase neuraminidase-1 translocation and desialylation. This leads to platelet clearance in the liver via hepatocyte Ashwell-Morell receptors, which is fundamentally different from the classical Fc-FcγR-dependent macrophage phagocytosis. Importantly, sialidase inhibitors ameliorate anti-GPIbα-mediated thrombocytopenia in mice. These findings shed light on Fc-independent cytopenias, designating desialylation as a potential diagnostic biomarker and therapeutic target in the treatment of refractory ITP.
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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".