Cooperation between toll‐like receptor 2 and 4 in the brain of mice challenged with cell wall components derived from gram‐negative and gram‐positive bacteria
Bibliographic record
Abstract
In this study we investigated whether induction of toll-like receptor 2 (TLR2) amplifies the effect of a cell wall component derived from gram-positive bacteria, namely peptidoglycan (PGN). Mice received a first systemic lipopolysaccharide (LPS) injection to pre-induce TLR2 in various regions of the brain, and 6 h later, a second administration of either LPS or PGN. The data show a robust transcriptional activation of TLR2, TNF-alpha and monocyte chemotactic protein-1 (MCP-1) in microglial cells of mice challenged twice with LPS, whereas PGN essentially abolished this response. TLR4 plays a critical role in this process, because C3H/HeJ mice no longer responded to LPS but exhibited a normal reaction to PGN. Conversely, a robust signal for genes encoding innate immune proteins was found in the brain of TLR2-deficient mice challenged with LPS. However, the second LPS bolus failed to trigger TNF-alpha and IL-12 in TLR2-deficient mice, while the same treatment caused a strong induction of these genes in the cerebral tissue of wild-type littermates. The present data provide evidence that cooperation exists between TLR4 and TLR2. While TLR4 is absolutely necessary to engage the innate immune response in the brain, TLR2 participates in the regulation of genes encoding TNF-alpha and IL-12 during severe endotoxemia. Such collaboration between TLR4 and TLR2 may be determinant for the transfer from the innate to the adaptive immunity within the CNS of infected animals.
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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 | 0.001 |
| 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".