TLR-4 Agonist Induces IFN-γ Production Selectively in Proinflammatory Human M1 Macrophages through the PI3K-mTOR– and JNK-MAPK–Activated p70S6K Pathway
Bibliographic record
Abstract
IFN-γ, a proinflammatory cytokine produced primarily by T cells and NK cells, activates macrophages and engages mechanisms to control pathogens. Although there is evidence of IFN-γ production by murine macrophages, IFN-γ production by normal human macrophages and their subsets remains unknown. Herein, we show that human M1 macrophages generated by IFN-γ and IL-12- and IL-18-stimulated monocyte-derived macrophages (M0) produce significant levels of IFN-γ. Further stimulation of IL-12/IL-18-primed macrophages or M1 macrophages with agonists for TLR-2, TLR-3, or TLR-4 significantly enhanced IFN-γ production in contrast to the similarly stimulated M0, M2a, M2b, and M2c macrophages. Similarly, M1 macrophages generated from COVID-19-infected patients' macrophages produced IFN-γ that was enhanced following LPS stimulation. The inhibition of M1 differentiation by Jak inhibitors reversed LPS-induced IFN-γ production, suggesting that differentiation with IFN-γ plays a key role in IFN-γ induction. We subsequently investigated the signaling pathway(s) responsible for TLR-4-induced IFN-γ production in M1 macrophages. Our results show that TLR-4-induced IFN-γ production is regulated by the ribosomal protein S6 kinase (p70S6K) through the activation of PI3K, the mammalian target of rapamycin complex 1/2 (mTORC1/2), and the JNK MAPK pathways. These results suggest that M1-derived IFN-γ may play a key role in inflammation that may be augmented following bacterial/viral infections. Moreover, blocking the mTORC1/2, PI3K, and JNK MAPKs in macrophages may be of potential translational significance in preventing macrophage-mediated inflammatory diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".