The Pro-Inflammatory Cytokine, Interleukin-6, Enhances the Polarization of Alternatively Activated Macrophages
Bibliographic record
Abstract
Macrophages are important innate immune cells that are associated with two distinct phenotypes: a pro-inflammatory (or classically activated) subset with prototypic macrophage functions such as inflammatory cytokine production and bactericidal activity, and an anti-inflammatory (or alternatively activated (AAM)) subset linked with wound healing and tissue repair processes. In this study, we examined the effect of interlukein-6 on human and murine macrophage polarization. The results indicate that despite being commonly associated with pro-inflammatory functions and being implicated in the pathogenesis/pathophysiology of numerous inflammatory diseases, interleukin-6 can enhance the polarization of AAMs, based on increased expression of hallmark markers: arginase-1, Ym1 and CD206; this effect required the AAM differentiating cytokines, IL-4 and IL-13. Co-treatment of AAMs with IL-6 resulted in spontaneous release of IL-10, suppressed LPS-induced nitric oxide production and inhibited cytokine production by activated CD4+ T cells - immunoregulatory features not observed in the 'parent' IL-4+IL-13-induced AAM. The effect of IL-6 required signal transducer and activator of transcription (STAT)-3, was partially dependent on up-regulation of the IL4Rα chain, and was independent of autocrine IL-10. In the presence of IFNγ, IL-6 promoted the production of IL-1β and TNFα suggesting that this cytokine can enhance the phenotype to which a macrophage has committed. This finding may explain the pleiotrophic nature of IL-6, where it is associated with the perpetuation and enhancement of disease in inflammatory situations, but is also necessary for resolution of inflammation and adequate wound healing to occur in others. Thus, the potential benefit of IL-6 in promoting an AAM, with its' anti-inflammatory and wound healing ability, may need to be considered in immunotherapies aimed at in vivo modulation or inhibition of IL-6.
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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".