Kupffer cells and activation of endothelial TLR4 coordinate neutrophil adhesion within liver sinusoids during endotoxemia
Bibliographic record
Abstract
A key pathological feature of the systemic inflammatory response of sepsis/endotoxemia is the accumulation of neutrophils within the microvasculature of organs such as the liver, where they cause tissue damage and vascular dysfunction. There is emerging evidence that the vascular endothelium is critical to the orchestration of inflammatory responses to blood-borne microbes and microbial products in sepsis/endotoxemia. In this study, we aimed to understand the role of endothelium, and specifically endothelial TLR4 activation, in the regulation of neutrophil recruitment to the liver during endotoxemia. Intravital microscopy of bone marrow chimeric mice revealed that TLR4 expression by non-bone marrow-derived cells was required for neutrophil recruitment to the liver during endotoxemia. Furthermore, LPS-induced neutrophil adhesion in liver sinusoids was equivalent between wild-type mice and transgenic mice that express TLR4 only on endothelium (tlr4(-/-)Tie2(tlr4)), revealing that activation of endothelial TLR4 alone was sufficient to initiate neutrophil adhesion. Neutrophil arrest within sinusoids of endotoxemic mice requires adhesive interactions between neutrophil CD44 and endothelial hyaluronan. Intravital immunofluorescence imaging demonstrated that stimulation of endothelial TLR4 alone was sufficient to induce the deposition of serum-derived hyaluronan-associated protein (SHAP) within sinusoids, which was required for CD44/hyaluronan-dependent neutrophil adhesion. In addition to endothelial TLR4 activation, Kupffer cells contribute to neutrophil recruitment via a distinct CD44/HA/SHAP-independent mechanism. This study sheds new light on the control of innate immune activation within the liver vasculature during endotoxemia, revealing a key role for endothelial cells as sentinels in the detection of intravascular infections and coordination of neutrophil recruitment to the liver.
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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) | 0.000 | 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".