Differential Constitutive and Cytokine-Modulated Expression of Human Toll-like Receptors in Primary Neutrophils, Monocytes, and Macrophages
Bibliographic record
Abstract
Human Toll-like receptors (TLRs) comprise a family of proteins that recognizes pathogen-associated molecular patterns (PAMPs) and initiates host innate immune responses. Neutrophils, monocytes, and macrophages are critical cellular components of the human innate immune system. Proinflammatory cytokines, such as granulocyte colony-stimulating factor (G-CSF), granulocyte-macrophage colony-stimulating factor (GM-CSF), macrophage colony-stimulating factor (M-CSF), and interferon-gamma (IFN-gamma), have been shown to up-regulate microbicidal activity in these effector cells of innate immunity. Currently, the cellular and molecular mechanisms responsible for these effects are not completely understood. We hypothesized that these cytokines may up-regulate TLR expression as a mechanism to facilitate microbial recognition and augment the innate immune response. Using quantitative realtime rt-PCR technology, we examined constitutive expression of TLR2, TLR4, TLR5, and TLR9 mRNA and the effects of G-CSF, GM-CSF, M-CSF, and IFN-gamma on TLR mRNA expression in purified populations of normal human neutrophils, monocytes, and monocyte-derived macrophages. Relative constitutive expression of TLR2, TLR4, and TLR9 was similar in neutrophils and monocytes. Constitutive expression of TLR5 was less in neutrophils compared to monocytes. Constitutive expression of TLR4 was greater and that of TLR9 lower in monocyte-derived macrophages compared to monocytes. Of the cytokines examined, IFN-gamma and GM-CSF caused the greatest effects on TLR expression. IFN- gamma up-regulated TLR2 and TLR4 in neutrophils and monocytes. GM-CSF up-regulated expression of TLR2 and TLR4 in neutrophils and TLR2 in monocytes. TLR5 was down-regulated by inflammatory cytokines in monocytes. These results suggest a potential role for IFN- gamma and/or GM-CSF as therapeutic immunomodulators of the host defense to infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".