IL-27: An Antiviral Cytokine or Cell Traffic Controller? 9282
Bibliographic record
Abstract
Abstract Description Recent research has supported IL-27 as a cytokine of interest in the antiviral response, as it inhibits a wide range of viral infections, including that of IAV. IL-27 has also been shown to influence the function of TLRs. However, the exact mechanisms behind IL-27 function and its modulation of TLR expression and activation is not well understood. We hypothesize that IL-27 promotes antiviral responses by modifying TLR responses. A549 lung epithelial cells were pre-treated with IL-27 (150 ng/mL) for 24 h and then infected with A/New York/18/2009 (MOI=1) for 1 h. Following, infection the media was replaced, and the cells were collected at 0.5, 4, 8, 12 and 24 h post-infection (hpi). IL-27 pre-treatment reduced IAV infection in A549 cells and led to an associated increase of TLR8-positive endosome-like structures, appearing as early as 0.5 hpi. However, there was no change in overall TLR8 expression levels within cells, suggesting IL-27 modulates its localization rather than its expression. Interestingly, IL-27 treatment induced an increase in expression of UCN93B1, a chaperone protein which promotes TLR8 localization and function within endosomes, starting as early as 0.5 hpi. When assessing TLR7 localization and expression, we did not observe any differences due to IL-27. Taken together, these results suggest that IL-27 may influence TLR8 function through modulating its cellular localization. Funding Sources KG and CCC are funded by National Science Research Council Grants, and SKS is funded by a Canadian Graduate Scholarship-Doctoral grant. Topic Categories Innate Immune Responses and Host Defense: Cellular Mechanisms (INC)
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".