Mice that exclusively express TLR4 on endothelial cells can efficiently clear a lethal systemic Gram-negative bacterial infection
Bibliographic record
Abstract
Recognition of LPS by TLR4 on immune sentinel cells such as macrophages is thought to be key to the recruitment of neutrophils to sites of infection with Gram-negative bacteria. To explore whether endothelial TLR4 plays a role in this process, we engineered and imaged mice that expressed TLR4 exclusively on endothelium (known herein as EndotheliumTLR4 mice). Local administration of LPS into tissue induced comparable neutrophil recruitment in EndotheliumTLR4 and wild-type mice. Following systemic LPS or intraperitoneal E. coli administration, most neutrophils were sequestered in the lungs of wild-type mice and did not accumulate at primary sites of infection. In contrast, EndotheliumTLR4 mice showed reduced pulmonary capillary neutrophil sequestration over the first 24 hours; as a result, they mobilized neutrophils to primary sites of infection, cleared bacteria, and resisted a dose of E. coli that killed 50% of wild-type mice in the first 48 hours. In fact, the only defect we detected in EndotheliumTLR4 mice was a failure to accumulate neutrophils in the lungs following intratracheal administration of LPS; this response required TLR4 on bone marrow-derived immune cells. Therefore, endothelial TLR4 functions as the primary intravascular sentinel system for detection of bacteria, whereas bone marrow-derived immune cells are critical for pathogen detection at barrier sites. Nonendothelial TLR4 contributes to failure to accumulate neutrophils at primary infection sites in a disseminated systemic infection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".