HIV and HIV Tat inhibit LPS-induced IL-23 production in human macrophages by distinct intracellular signaling pathways
Bibliographic record
Abstract
Abstract Monocyte-derived macrophages (MDMs) from HIV-infected patients and MDMs infected in vitro with HIV manifest inhibition of IL-12. Whether HIV infection or HIV accessory proteins such as tat, impact production of IL-23, a member of IL-12 family cytokines, in macrophages remains unknown. Our results show that in-vitro HIV infection, intracellular HIV-tat and HIV tat peptides inhibit LPS-induced IL-23 production in MDMs suggesting impairment of TLR-4 signalling. To study the mechanism governing HIV- and HIV-tat-mediated inhibition of LPS-induced IL-23 production, we first established that p38 mitogen-activated protein kinase (MAPK), the phosphoinositide-3-kinase (PI3K), and SRC homology region 2 domain-containing tyrosine phosphatase-1 (SHP-1) positively regulated whereas c-Jun N-terminal kinase (JNK) MAPK negatively regulated LPS-induced IL-23 production in MDMs. HIV-Tat downregulated TNF-receptor associated factor (TRAF)-6 and inhibitor of apoptosis-1 (cIAP-1) and caused decreased phosphorylation of downstream PI3K, and p38 MAPKs. In contrast, HIV-tat-mediated inhibition of JNK MAPK negatively regulated IL-23 production. However, SHP-1 and Src kinases were not inhibited by HIV-Tat and hence were not implicated in tat-mediated inhibition of LPS-induced IL-23 production. In contrast to HIV-Tat, in vitro HIV infection of MDM inhibited LPS-induced IL-23 production via inhibition of p38 MAPK activation. Overall, HIV and HIV-Tat regulate LPS-induced IL-23 production in human macrophages via distinct mechanisms: HIV-Tat through the inhibition of cIAP-1-TRAF-6, and subsequent inhibition of PI3K and p38 and JNK MAPKs whereas HIV through the inhibition of p38 MAPK activation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".