HIV inhibits LPS-induced IL-27 production via HIV tat through the inhibition of TRAF-6, and consequent inhibition of PI3K and p38 and JNK MAPKs in human macrophages (VIR9P.1140)
Bibliographic record
Abstract
Abstract Monocyte-derived macrophages (MDM) from HIV-infected patients and MDM infected in vitro with HIV manifest inhibition of cytokines including IL12. IL27, an IL12 family cytokine, was shown to inhibit HIV replication in macrophages. Whether HIV infection or HIV accessory protein(s) impact IL27 production in macrophages remains unknown. Herein, we show that in vitro HIV infection as well as intracellular HIV and HIV-tat peptides inhibited LPS-induced IL27 production in MDM suggesting that HIV-tat inhibits IL27 production by impairing TLR-4 signalling. To study the mechanism governing HIV-tat-mediated inhibition of LPS-induced IL27 production, we first established that p38 and c-Jun N-terminal kinase (JNK) mitogen-activated protein kinase (MAPK), the phosphoinositide-3-kinase (PI3K), SRC homology region 2 domain-containing tyrosine phosphatase-1 (SHP-1) and Src kinases regulated LPS-induced IL27 production in MDM. HIV-tat caused TNF receptor associated factor (TRAF)-6 inhibition and consequent decreased phosphorylation of downstream PI3K, and p38 and JNK MAPKs implicated in LPS-induced IL27 production. However, SHP-1 and Src kinases were not involved in HIV-Tat-mediated inhibition of LPS-induced IL27 production. In contrast to HIV-tat, in vitro HIV infection of MDM inhibited LPS-induced p38 and JNK activation. Overall, HIV inhibits LPS-induced IL27 production via HIV tat through the inhibition of TRAF-6, and consequent inhibition of PI3K and p38 and JNK MAPKs in macrophages.
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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".