TLR Signaling Tailors Innate Immune Responses in Human Microglia and Astrocytes
Bibliographic record
Abstract
The specific signals mediating the activation of microglia and astrocytes as a prelude to, or consequence of, CNS inflammation continue to be defined. We investigated TLRs as novel receptors mediating innate immune responses in human glial cells. We find that microglia express mRNA for TLRs 1-9, whereas astrocytes express robust TLR3, low-level TLR 1, 4, 5, and 9, and rare-to-undetectable TLR 2, 6, 7, 8, and 10 mRNA (quantitative real-time PCR). We focused on TLRs 3 and 4, which can signal through both the MyD88-dependent and -independent pathways, and on the MyD88-restricted TLR2. By flow cytometry, we established that microglia strongly express cell surface TLR2; TLR3 is expressed at higher levels intracellularly. Astrocytes express both cell surface and intracellular TLR3. All three TLRs trigger microglial activation upon ligation. TLR3 signaling induces the strongest proinflammatory polarizing response, characterized by secretion of high levels of IL-12, TNF-alpha, IL-6, CXCL-10, and IL-10, and the expression of IFN-beta. CXCL-10 and IL-10 secretion following TLR4 ligation are comparable to that of TLR3; however, other responses were lower or absent. TLR2-mediated responses are dominated by IL-6 and IL-10 secretion. Astrocytes respond to TLR3 ligation, producing IL-6, CXCL-10, and IFN-beta, implicating these cells as contributors to proinflammatory responses. Initial TLR-mediated glial activation also regulates consequent TLR expression; while TLR2 and TLR3 are subject to positive feedback, TLR4 is down-regulated in microglia. Astrocytes up-regulate all three TLRs following TLR3 ligation. Our data indicate that activation of innate immune responses in the CNS is not homogeneous but rather tailored according to cell type and environmental signal.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".