Mitochondrial DNA via recipient TLR9 acts as a potent first hit in murine transfusion-related acute lung injury
Bibliographic record
Abstract
ABSTRACT: Transfusion-related acute lung injury (TRALI) is a leading cause of transfusion-related mortality. Though the precise mechanism is not fully understood, a 2-hit model is widely accepted, involving both a predisposing patient condition and the transfusion itself. Mitochondrial damage-associated molecular patterns (mtDAMPs), such as mitochondrial DNA (mtDNA) and N-formylated peptides (NFPs), are elevated in patients who have experienced trauma, and stored blood products, and have been implicated in adverse transfusion outcomes, prompting us to investigate whether mtDAMPs could serve as a priming "first hit" in TRALI. Using a murine model, we found that injection of purified mitochondria followed by a monoclonal anti-major histocompatibility complex class I antibody (34-1-2s) induced significantly greater lung injury compared with the isotype control. This was evidenced by increased pulmonary edema, elevated plasma macrophage inflammatory protein 2, enhanced neutrophil lung infiltration, hypothermia, and respiratory distress. Similar effects were observed using a Toll-like receptor 9 (TLR9) agonist (oligonucleotide 2395), purified mtDNA, and a synthetic NFP (WKYMVm), agonist of formyl peptide receptor (FPR). Notably, a TLR9 antagonist blocked the mtDAMP-induced TRALI response, whereas 2 FPR antagonists did not, underscoring a key role for mtDNA and TLR9 signaling in disease priming. These findings suggest that mtDAMPs, particularly mtDNA, present in both transfusion products and recipient plasma, may predispose patients to antibody-mediated TRALI. Targeting mtDAMPs or their receptors may offer a novel therapeutic strategy to mitigate TRALI risk.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".