Inhibitory and Stimulatory Effects of IL-32 on HIV-1 Infection
Bibliographic record
Abstract
Abstract The proinflammatory cytokine IL-32 is elevated in the plasma and tissues of HIV-1–infected individuals. However, its significance in HIV-1 infection remains unclear because IL-32 inhibits and stimulates viral production in monocyte-derived macrophages (MDMs) and CD4+ T cells, respectively. In this study, we initially found that the inhibitory effect on human MDMs depends on SAMHD1, a dNTP triphosphohydrolase that inhibits viral reverse transcription. IL-32 increased the unphosphorylated active form of SAMHD1, which was consistent with the reduced expression of the upstream cyclin-dependent kinases. Indeed, IL-32 lost its anti–HIV-1 activity in MDMs when SAMHD1 was depleted. These results explain why IL-32 inhibits HIV-1 in MDMs but not CD4+ T cells, because SAMHD1 restricts HIV-1 in noncycling MDMs but not in cycling CD4+ T cells. Another unique feature of IL-32 is the induction of the immunosuppressive molecule IDO1, which is beneficial for HIV-1 infection. In this study, we found that IL-32 also upregulates other immunosuppressive molecules, including PD-L1, in MDMs. Moreover, IL-32 promoted the motility of MDMs, which potentially facilitates intercellular HIV-1 transmission. Our findings indicate that IL-32 has both the direct inhibitory effect on HIV-1 production in MDMs and the indirect stimulatory effects through phenotypic modulation of MDMs, and they suggest that the stimulatory effects may outweigh the inhibitory effect because the window for IL-32 to inhibit HIV-1 is relatively confined to SAMHD1-mediated reverse transcription suppression in the viral life cycle.
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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) | 0.002 | 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".