Desialylation: A Novel Platelet Clearance Mechanism and a Potential New Therapeutic Target in Anti-GPIb Antibody Mediated Thrombocytopenia
Bibliographic record
Abstract
Abstract Abstract 265 Background: Immune thrombocytopenia (ITP) is an autoimmune disease characterized by autoantibodies directed at patient's own platelet antigens, primarily glycoprotein (GP)IIbIIIa-integrin (70–80%) and GPIb-complex (20–40%). Current paradigm suggests that clearance of opsonized platelets through the reticuloendothelial system via Fcγ-receptors results in thrombocytopenia and bleeding disorders. However, evidence from others and our group demonstrated that anti-GPIbα, but not anti-GPIIbIIIa, can induce thrombocytopenia via an Fc-independent pathway, which is resistant to intravenous IgG (IVIG) therapy in murine ITP-models (Blood 2006). These observations are consistent with subsequent IVIG studies in human ITP patients. Interestingly, human anti-GPIb-mediated ITP patients seem also resistant to steroid therapy in our recent retrospective study (American Journal of Hematology 2012). This suggests that binding of anti-GPIbα antibodies may induce platelet clearance through a different mechanism which is currently poorly understood. Methods: We developed unique mouse anti-mouse monoclonal antibodies (mAbs) in GPIIIa or GPIba deficient mice. Some of the mAbs have cross-reactivity to both mouse and human GPIIbIIIa and GPIba. Flow cytometry was used to evaluate whether these mAbs were able to induce platelet activation, apoptosis and desialylation. GPIbα is heavily glycosylated and the role of desialylation and exposure of underlying galactose and β-N-acetyl-D-glucosamine (βGN) residues on GPIbα in platelet clearance was assessed using the sialidase neuraminidase (NA) and it's inhibitor N-acetyl-2,3-dehydro-2-deoxy neuraminic acid (DANA). Desialyation effects on platelet activation and apoptosis was measured by flow cytometry. We also repeated these experiments with human platelets and plasma from human ITP-patients. We also investigated the effects of anti-GPIbα antibodies on platelet activation, apoptosis and clearance in vivo. Briefly, BALB/c mice were injected with anti-GPIbαor anti-GPIIIa mAbs and 24 hrs later, platelet desialylation, activation and apoptosis were measured by flow cytometry. The effect of desialylation on platelet clearance was assessed with DANA. The possible roles of Ashwell-Morell and MAC-1 receptors in GPIbα-mediated platelet clearance in the liver were examined using immunohistochemistry (anti-CD11b) or blocking of the Ashwell-Morell receptor with asialofetuin. Results and Discussion: We found that anti-GPIbα, but not anti-GPIIbIIIa mAbs, induced significant P-selectin expression and phosphatidylserine (PS)-exposure, and increased inner membrane mitochondrial depolarization (ΔYm). Interestingly, platelets were desialylated in the presence of anti-GPIbα but not anti-GPIIbIIIa mAbs. Moreover, we found that desialylation of GPIbα lies directly upstream of platelet activation and apoptosis, as prior treatment with DANA diminished PS-exposure, and P-selectin expression. Most importantly, incubations of human platelets with ITP-patient plasma showed similar effects. In vivo, we found significant increases in PS-exposure and ΔYm induced by anti-GPIbα, but not by anti-GPIIIa mAbs, independent of IgG subclass. Interestingly, prior injection with DANA rescued platelets numbers in anti-GPIbα, but not in anti-GPIIIa injected mice. A significant role for the Ashwell-Morell and MAC-1 receptors in the clearance of deglycosylated platelets was observed; blocking of the Ashwell-Morell receptors by asialofetuin, decreased platelet clearance in anti-GPIbα, but not anti-GPIIbIIIa antibody injected mice, there was also increased staining for MAC-1 on Kupffer cells, exclusively in the presence of an anti-GPIbα mAb tested. Thus, we demonstrate for the first time that anti-GPIbα antibodies induce GPIbα desialyation, leading to platelet activation and apoptosis. Therefore, we identified novel Fc-independent platelet clearance pathways, more specifically, via Ashwell-Morell and MAC-1 receptors on hepatocytes and liver macrophages. These findings may lead to novel therapeutic regimens including the potential use of sialidase inhibitors as a solution for anti-GPIb-mediated ITP patients previously refractory to both steroid and IVIG therapies. Disclosures: No relevant conflicts of interest to declare.
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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".