Extracellular RNA Sensing Mediates Inflammation and Organ Injury in a Murine Model of Polytrauma
Bibliographic record
Abstract
Severe traumatic injury leads to marked systemic inflammation and multiorgan injury. Endogenous drivers such as extracellular nucleic acid may play a role in mediating innate immune response and the downstream pathogenesis. Here, we explored the role of plasma extracellular RNA (exRNA) and its sensing mechanism in inflammation and organ injury in a murine model of polytrauma. We found that severe polytrauma-bone fracture, muscle crush injury, and bowel ischemia-induced a marked increase in plasma exRNA, systemic inflammation, and multiorgan injury in mice. Plasma RNA profiling with RNA sequencing in mice and humans revealed a dominant presence of miRNAs and marked differential expression of numerous miRNAs after severe trauma. Plasma exRNA isolated from trauma mice induced a dose-dependent cytokine production in macrophages, which was almost abolished in TLR7-deficient cells but unchanged in TLR3-deficient cells. Moreover, RNase or specific miRNA inhibitors against the selected proinflammatory miRNAs (i.e., miR-7a-5p, miR-142, let-7j, miR-802, and miR-146a-5p) abolished or attenuated trauma plasma exRNA-induced cytokine production, respectively. Bioinformatic analyses of a group of miRNAs based on cytokine readouts revealed that high uridine abundance (>40%) is a reliable predictor in miRNA mimic-induced cytokine and complement production. Finally, compared with the wild-type, TLR7-knockout mice had attenuated plasma cytokine storm and reduced lung and hepatic injury after polytrauma. These data suggest that endogenous plasma exRNA of severely injured mice and ex-miRNAs with high uridine abundance prove to be highly proinflammatory. TLR7 sensing of plasma exRNA and ex-miRNAs activates innate immune responses and plays a role in inflammation and organ injury after trauma.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".