Differential TLR7-mediated cytokine expression by R848 in M-CSF- versus GM-CSF-derived macrophages after LCMV infection
Bibliographic record
Abstract
Granulocyte-macrophage colony-stimulating factor (GM-CSF) and macrophage colony-stimulating factor (M-CSF) play an important role in macrophage (MФ) development by influencing their differentiation and polarization. Our goal was to explore the difference between M-CSF- and GM-CSF-derived bone marrow MФ responsiveness to TLR7-mediated signalling pathways that influence cytokine production early after infection in a model of acute virus infection. To do so, we examined cytokine production and TLR7-mediated signalling at 1 h post-lymphocytic choriomeningitis virus (LCMV) Armstrong (ARM) infection. We found that R848-induced cytokine expression was enhanced in these cells, with GM-CSF cells exhibiting higher proinflammatory cytokine expression and M-CSF cells exhibiting higher anti-inflammatory cytokine expression. However, R848-mediated signalling molecule activation was diminished in LCMV-infected M-CSF and GM-CSF macrophages. Interestingly, we observed that TLR7 expression was maintained during LCMV infection of M-CSF and GM-CSF cells. Moreover, TLR7 expression was significantly higher in M-CSF cells compared to GM-CSF cells. Taken together, our data demonstrate that although LCMV restrains early TLR7-mediated signalling, it primes differentiated MФ to enhance expression of their respective cytokine profiles and maintains levels of TLR7 expression early after infection.
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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 | 0.001 |
| 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".