Red Blood Cells Homeostatically Bind Mitochondrial DNA through TLR9 to Maintain Quiescence and to Prevent Lung Injury
Bibliographic record
Abstract
RATIONALE: Potentially hazardous CpG-containing cell-free mitochondrial DNA (cf-mtDNA) is routinely released into the circulation and is associated with morbidity and mortality in critically ill patients. How the body avoids inappropriate innate immune activation by cf-mtDNA remains unknown. Because red blood cells (RBCs) modulate innate immune responses by scavenging chemokines, we hypothesized that RBCs may attenuate CpG-induced lung inflammation through direct scavenging of CpG-containing DNA. OBJECTIVES: To determine the mechanisms of CpG-DNA binding to RBCs and the effects of RBC-mediated DNA scavenging on lung inflammation. METHODS: mtDNA on murine RBCs was measured under basal conditions and after systemic inflammation. mtDNA content on human RBCs from healthy control subjects and trauma patients was measured. Toll-like receptor 9 (TLR9) expression on RBCs and TLR9-dependent binding of CpG-DNA to RBCs were determined. A murine model of RBC transfusion after CpG-DNA-induced lung injury was used to investigate the role of RBC-mediated DNA scavenging in mitigating lung injury in vivo. MEASUREMENTS AND MAIN RESULTS: Under basal conditions, RBCs bind CpG-DNA. The plasma-to-RBC mtDNA ratio is low in naive mice and in healthy volunteers but increases after systemic inflammation, demonstrating that the majority of cf-mtDNA is RBC-bound under homeostatic conditions and that the unbound fraction increases during inflammation. RBCs express TLR9 and bind CpG-DNA through TLR9. Loss of TLR9-dependent RBC-mediated CpG-DNA scavenging increased lung injury in vivo. CONCLUSIONS: RBCs homeostatically bind mtDNA, and RBC-mediated DNA scavenging is essential in mitigating lung injury after CpG-DNA. Our data suggest a role for RBCs in regulating lung inflammation during disease states where cf-mtDNA is elevated, such as sepsis and trauma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".