A Requirement for Microglial TLR4 in Leukocyte Recruitment into Brain in Response to Lipopolysaccharide
Bibliographic record
Abstract
To study the mechanisms involved in leukocyte recruitment induced by local bacterial infection within the CNS, we used intravital microscopy to visualize the interaction between leukocytes and the microvasculature in the brain. First, we showed that intracerebroventricular injection of LPS could cause significant rolling and adhesion of leukocytes in the brain postcapillary venules of wild-type mice, while negligible recruitment was observed in TLR4-deficient C57BL/10ScCr mice and CD14 knockout mice, suggesting recruitment is mediated by TLR4/CD14-bearing cells. Moreover, we observed reduced but not complete inhibition of recruitment in MyD88 knockout mice, indicating both MyD88-dependent and -independent pathways are involved. The leukocyte recruitment responses in chimeric mice with TLR4-positive microglia and endothelium, but TLR4-negative leukocytes, were comparable to normal wild-type mice, suggesting either endothelium or microglia play a crucial role in the induction of leukocyte recruitment. LPS injection induced both microglial and endothelial activation in the CNS. Furthermore, minocycline, an effective inhibitor of microglial activation, completely blocked the rolling and adhesion of leukocytes in the brain and blocked TNF-alpha production in response to LPS in vivo. Minocycline did not affect activation of endothelium by LPS in vitro. TNFR p55/p75 double knockout mice also exhibited significant reductions in both rolling and adhesion in response to LPS, indicating TNF-alpha signaling is critical for the leukocyte recruitment. Our results identify a TLR4 detection system within the blood-brain barrier. The microglia play the role of sentinel cells detecting LPS thereby inducing endothelial activation and leading to efficient leukocyte recruitment to the CNS.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".