TLR-2, TLR-3 and TLR-4 agonists induce synthesis of IFN-γ in human M1 but not in M2a, M2b and M2c macrophages through the activation of S6K1-S6 pathwayγ
Bibliographic record
Abstract
Abstract IFN-γ, a pro-inflammatory cytokine produced primarily by T cells and NK cells, activates macrophages and engage mechanisms to control pathogens. Although murine macrophages have been shown to produce IFN-γ, very little is known about IFN-γ production by normal human macrophages and their subsets. Herein, we show that priming of human monocyte-derived macrophages (MDM-M0) with IL-12 and IL-18 or IFN-γ alone leads to IFN-γ production in M1 macrophages that is significantly enhanced transcriptionally as well as at the protein level following stimulation with Toll-like receptors (TLR)-2, TLR-3 or TLR-4 agonists in contrast to the M0, M2a, M2b and M2c macrophages. The macrophage derived IFN-γ is biologically active and can activate macrophages in an autocrine manner. To determine the signalling pathway responsible for TLR-4-induced IFN-γ production in M1 macrophages, our results show that TLR-4-induced IFN-γ production is regulated by mTORC-1/2, PI3K, p38 and JNK MAPKs and S6Kinase-S6 pathway. The S6K1-S6 pathway plays a crucial role in TLR-induced IFN-γ production in M1 macrophages as IFN-γ-induced S6 phsophorylation (Ser 235/236) was inhibited by the inhibitors specific for PI3K, p38-MAPK and JNK MAPK, and mTORC-1/2 suggesting that S6K1-S6 pathway plays a critical role in TLR-4 induced IFN-γ production in human M1 macrophages.
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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) | 0.003 | 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".