TLR7 Ligation Inhibits TLR8 Responsiveness in IL-27-Primed Human THP-1 Monocytes and Macrophages
Bibliographic record
Abstract
Regulation of proinflammatory cytokine expression is critical in the face of single-stranded RNA (ssRNA) virus infections. Many viruses, including coronavirus and influenza virus, wreak havoc on the control of cytokine expression, leading to the formation of detrimental cytokine storms. Understanding the regulation and interplay between inflammatory cytokines is critical to the identification of targets involved in controlling the induction of cytokine expression. In this study, we focused on how the antiviral cytokine interleukin-27 (IL-27) regulates signal transduction downstream of Toll-like receptor 7 (TLR7) and TLR8 ligation, which recognize endosomal single-stranded RNA. Given that IL-27 alters bacterial-sensing TLR expression on myeloid cells and can inhibit replication of single-stranded RNA viruses, we investigated whether IL-27 affects expression and function of TLR7 and TLR8. Analysis of IL-27-treated THP-1 monocytic cells and THP-1-derived macrophages revealed changes in mRNA and protein expression of TLR7 and TLR8. Although treatment with IL-27 enhanced TLR7 expression, only TLR8-mediated cytokine secretion was amplified. Furthermore, we demonstrated that imiquimod, a TLR7 agonist, inhibited cytokine and chemokine production induced by a TLR8 agonist, TL8-506. Delineating the immunomodulatory role of IL-27 on TLR7 and TLR8 responses provides insight into how myeloid cell TLR-mediated responses are regulated during virus infection.
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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) | 0.002 | 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".