HIV and HIV-Tat inhibit LPS-induced IL-27 production in human macrophages by distinct intracellular signaling pathways
Bibliographic record
Abstract
Monocyte-derived Mϕs (MDMs) from HIV-infected patients and MDM infected in vitro with HIV exhibit a reduced ability to secrete various cytokines, including IL-12. Recently, IL-27, an IL-12 family cytokine, was shown to inhibit HIV replication in Mϕ. Whether HIV infection or HIV accessory protein(s) impact IL-27 production in Mϕs remains unknown. Herein, we show that in vitro HIV infection, as well as intracellular HIV-Tat (Tat) and Tat peptides, inhibit LPS-induced IL-27 production in human MDMs, suggesting impairment of the TLR4 signaling pathway. To understand the signaling pathways governing HIV or Tat-mediated inhibition of LPS-induced IL-27 production, we first demonstrated that p38 MAPK, PI3K, Src-homology region 2 domain-containing tyrosine phosphatase 1 (SHP-1), and Src kinases regulate LPS-induced IL-27 production in MDMs. Tat caused down-regulation of TNFR-associated factor (TRAF)-6 and inhibitor of apoptosis 1 (cIAP-1) and subsequently decreased phosphorylation of downstream PI3K and p38 MAPKs, which were implicated in LPS-induced IL-27 production. Whereas SHP-1 and Src kinases regulated LPS-induced IL-27 production, Tat did not inhibit these kinases, suggesting that they were not involved in Tat-mediated inhibition of LPS-induced IL-27 production. In contrast to Tat, in vitro HIV infection of MDM inhibited LPS-induced IL-27 production via inhibition of p38 MAPK activation. Overall, HIV and Tat inhibit LPS-induced IL-27 production in human macrophages via distinct mechanisms: Tat through the inhibition of cIAP-1-TRAF-6 and subsequent inhibition of PI3K and p38 MAPKs, whereas HIV through the inhibition of p38 MAPK activation.
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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 | 0.000 |
| 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".