Tenofovir Selectively Regulates Production of Inflammatory Cytokines and Shifts the IL-12/IL-10 Balance in Human Primary Cells
Bibliographic record
Abstract
OBJECTIVES: In this study, we aimed to investigate the possible immune modulatory effects of HIV nucleoside reverse transcriptase inhibitors during secondary infections and inflammation, focusing on inflammatory cytokine responses and the interleukin (IL)-12/IL-10 balance. METHODS: We investigated the in vitro effect of tenofovir and zidovudine (AZT) on production of proinflammatory cytokines in monocytes and human peripheral blood mononuclear cells (PBMCs). Stimulation panels included Toll-Like receptor (TLR) ligands; the inflammation mediator tumor necrosis factor-α; and the pathogens cytomegalovirus, Neisseria meningitides, Escherichia coli, and Streptococcus pneumoniae. Cytokine levels were measured using enzyme-linked immunosorbent assay and luminex technology. RNA levels were assessed using real-time polymerase chain reaction. Activity of mitogen-activated protein kinase and NF-κB signaling was evaluated using flow cytometry and multispectral imaging cytometry, respectively. RESULTS: Tenofovir decreased and AZT increased both IL-8 and CCL3 production from monocytes after stimulation with TLR ligands, tumor necrosis factor-α, or live pathogens. Similarly, tenofovir decreased CCL3 levels in human PBMCs. Furthermore, tenofovir strongly decreased induction of IL-10 but increased levels of IL-12. AZT did not affect IL-12 or IL-10 levels. The observed drug-induced changes in cytokine production were independent from transcriptional regulation through the mitogen-activated protein kinase and nuclear factor kappa B pathways. CONCLUSIONS: Our data suggest divergent effects of tenofovir and AZT on proinflammatory responses in monocytes (CCL3 and IL-8) and PBMCs (CCL3). Moreover, tenofovir shifts the IL-10/IL-12 balance after cell stimulation with TLR ligands or infection with live bacteria, thus suggesting that the choice of nucleoside reverse transcriptase inhibitor affects overall inflammation and early immune responses against secondary pathogens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".