Suppression of fibrin(ogen)-driven pathologies in disease models through controlled knockdown by lipid nanoparticle delivery of siRNA
Bibliographic record
Abstract
Fibrinogen plays a pathologic role in multiple diseases. It contributes to thrombosis and modifies inflammatory and immune responses, supported by studies in mice expressing fibrinogen variants with altered function or with a germline fibrinogen deficiency. However, therapeutic strategies to safely and effectively tailor plasma fibrinogen concentration are lacking. Here, we developed a strategy to tune fibrinogen expression by administering lipid nanoparticle (LNP)-encapsulated small interfering RNA (siRNA) targeting the fibrinogen α chain (siFga). Three distinct LNP-siFga reagents reduced both hepatic Fga messenger RNA and fibrinogen levels in platelets and plasma, with plasma levels decreased to 42%, 16%, and 4% of normal within 1 week of administration. Using the most potent siFga, circulating fibrinogen was controllably decreased to 32%, 14%, and 5% of baseline with 0.5, 1.0, and 2.0 mg/kg doses, respectively. Whole blood from mice treated with siFga formed clots with significantly decreased clot strength ex vivo, but siFga treatment did not compromise hemostasis following saphenous vein puncture or tail transection. In an endotoxemia model, siFga suppressed the acute phase response and decreased plasma fibrinogen, D-dimer, and proinflammatory cytokine levels. In a sterile peritonitis model, siFga restored normal macrophage migration in plasminogen-deficient mice. Finally, treatment of mice with siFga decreased the metastatic potential of tumor cells in a manner comparable to that observed in fibrinogen-deficient mice. The results indicate that siFga causes robust and controllable depletion of fibrinogen and provides the proof-of-concept that this strategy can modulate the pleiotropic effects of fibrinogen in relevant disease models.
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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) | 0.001 | 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".