Betamethasone induces potent immunosuppression and reduces HIV infection in a PBMC in vitro model
Bibliographic record
Abstract
Genital inflammation is an established risk factor for increased HIV acquisition risk. Certain HIV-exposed seronegative populations, who are naturally resistant to HIV infection, have an immune quiescent phenotype defined by reduced immune activation and inflammatory cytokines at the genital tract. Therefore, the aim of this study was to create an immune quiescent environment using immunomodulatory drugs to mitigate HIV infection. Using an in vitro peripheral blood mononuclear cell (PBMC) model, we found that inflammation was induced using phytohemagglutinin and Toll-like receptor (TLR) agonists Pam3CSK4 (TLR1/2), lipopolysaccharide (LPS) (TLR4) and R848 (TLR7/8). After treatment with anti-inflammatory drugs, ibuprofen (IBF) and betamethasone (BMS), PBMCs were exposed to HIV NL4-3 AD8. Multiplexed ELISA was used to measure 28 cytokines to assess inflammation. Flow cytometry was used to measure immune activation (CD38, HLA-DR and CCR5) and HIV infection (p24 production) of CD4+ T cells. BMS potently suppressed inflammation (soluble cytokines, p<0.05) and immune activation (CD4+ T cells, p<0.05). BMS significantly reduced HIV infection of CD4+ T cells only in the LPS (0.98%) and unstimulated (1.7%) conditions (p<0.02). In contrast, IBF had minimal anti-inflammatory and immunosuppressive but no anti-HIV effects. BMS demonstrated potent anti-inflammatory effects, regardless of stimulation condition. Despite uniform immunosuppression, BMS differentially affected HIV infection according to the stimulation conditions, highlighting the complex nature of these interactions. Together, these data underscore the importance of interrogating inflammatory signaling pathways to identify novel drug targets to mitigate HIV 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 | 0.000 |
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".