TLR Agonists That Induce IFN-β Abrogate Resident Macrophage Suppression of T Cells
Bibliographic record
Abstract
Resident tissue macrophages (Mφs) continually survey the microenvironment, ingesting Ags and presenting them on their surface for recognition by T cells. Because these Ags can be either host cell- or pathogen-derived, Mφs must be able to distinguish whether a particular Ag should provoke an immune response or be tolerated. However, the mechanisms that determine whether Mφs promote or inhibit T cell activation are not well understood. To investigate this, we first determined the mechanism by which murine resident peritoneal Mφs suppress in vitro T cell proliferation in the absence of pathogens and then explored the effects of different pathogen-derived molecules on Mφ immunosuppression. Our results suggest that, in response to IFN-γ, which is secreted by TCR-activated T cells, resident peritoneal Mφs acquire immunosuppressive properties that are mediated by NO. However, pretreatment of Mφs with LPS or dsRNA, but not CpG or peptidoglycan, eliminates their suppressive properties, in part via the induction of autocrine-acting IFN-β. These results suggest TLR agonists that activate TRIF, and consequently induce IFN-β, but not those that exclusively signal through MyD88, abrogate the immunosuppressive properties of Mφs, and thus promote T cell expansion and elimination of invading microorganisms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".