TNF-α impairs platelet function by inhibiting autophagy and disrupting metabolism via syntaxin 17 downregulation
Bibliographic record
Abstract
Platelets play a dual role in hemostasis and inflammation-associated thrombosis and hemorrhage. Although the mechanisms linking inflammation to platelet dysfunction remain poorly understood, our previous work demonstrated that TNF-α alters mitochondrial mass, platelet activation, and autophagy-related pathways in megakaryocytes. Here, we hypothesized that TNF-α impairs platelet function by disrupting autophagy, a process critical for mitochondrial health and cellular metabolism. Using human and murine models of TNF-α-driven diseases, including myeloproliferative neoplasms and rheumatoid arthritis, we found that TNF-α downregulates syntaxin 17 (STX17), a key mediator of autophagosome-lysosome fusion. This disruption inhibited autophagy, leading to the accumulation of dysfunctional mitochondria and reduced mitochondrial respiration. These metabolic alterations compromised platelet-driven clot contraction, a process linked to thrombotic and hemorrhagic complications. Our findings reveal a mechanism by which TNF-α disrupts hemostasis through autophagy inhibition, highlighting TNF-α as a critical regulator of platelet metabolism and function. This study provides potentially new insights into inflammation-associated pathologies and suggests autophagy-targeting strategies as potential therapeutic avenues to restore hemostatic balance.
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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".