Toll-Like Receptor 2-Independent and MyD88-Dependent Gene Expression in the Mouse Brain
Bibliographic record
Abstract
Toll-like receptors (TLRs) are essential to mount a rapid innate immune reaction to pathogens. Although TLR2 is the key receptor for pathogen-associated molecular patterns from Gram-positive bacteria, a robust transcriptional activation of the gene encoding this receptor takes place in the brain of mice exposed to the TLR4 ligand lipopolysaccharide (LPS). TLR2 gene expression is actually used as a reliable marker of activated microglia in vivo, but its functions remain unknown. The present study investigated the role of this receptor in mediating LPS-induced gene expression in the mouse brain. Immune genes were measured using both in situ hybridization and real time RT-PCR. Despite the robust microglial TLR2 expression, this receptor does not modulate transcriptional activity by TLR4 signaling. TLR2-deficient mice and their wild-type littermates had similar IkappaBalpha mRNA levels and induction of innate immune genes from 6 h to 10 days after LPS injection. In contrast, NF-kappaB activity, cytokine, chemokine, TLR2 and CD14 transcripts were no longer detected in MyD88-deficient mice. Indeed, the hybridization signal for most of the transcripts measured in this study was similar in the brain of MyD88(-/-) mice exposed to either saline or LPS. These data indicate that while TLR2 transcription is dependent on MyD88 signaling in microglia, this innate immune receptor is not involved in the immune response to LPS. On the other hand, MyD88 pathway is essential for the endotoxin to induce expression of immune genes in the central nervous system.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".