Sustained IL-4 priming of macrophages enhances the inflammatory response to TLR7/8 ligand R848
Bibliographic record
Abstract
Macrophages (Mϕ) are highly plastic, and can acquire a variety of functional phenotypes depending on the presence of different stimuli in their local environment. Mφ stimulated by interleukin (IL)-4 induce an alternative activation state and function as anti-inflammatory cells and promote tissue repair. However, there is overwhelming evidence that IL-4 can play a role in promoting inflammation. In asthma and allergic inflammation, IL-4 mediates proinflammatory responses that lead to tissue damage. Thus the effect of IL-4 on the outcome of the immune responses is greatly influenced by other cofactors and cytokines present in the microenvironment. R848 (resiquimod), a TLR7/8 agonist is a novel vaccine adjuvant, triggering a strong Th1-skewed response but its efficacy as a vaccine adjuvant shows variable results. It is not currently known whether the presence of IL-4 can dampen or enhance immunity in response to TLR7 agonists. In the present study, we sought to investigate the impact of IL-4-induced Mφ polarization on the outcome of R848 stimulation. The activation marker expression and production of cytokines were measured in murine spleen-derived Mφ. Protein expression levels of innate recognition molecules and transcription factors involved, including retinoic-acid inducible gene I, mitochondrial antiviral signaling protein, stimulator of interferon genes (STING), and IFN regulatory factors were evaluated in activated Mφ. These play a crucial role in the control of viral replication and optimal CD8+ T cell priming. We report that sustained priming with IL-4 alone promotes an antiviral response in Mφ, and enhances proinflammatory responses to R848 treatment. This highlights the need for better understanding of IL-4 proinflammatory functions and its potential use as a broad-acting antiviral in combination with R848 may be used in combination with other therapies to target the innate arm of immunity against emerging infections.
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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 | 0.001 |
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".