Vitamin D treatment of peripheral blood mononuclear cells modulated immune activation and reduced susceptibility to HIV-1 infection of CD4+ T lymphocytes
Bibliographic record
Abstract
INTRODUCTION: Mucosal immune activation, in the context of sexual transmission of HIV-1 infection, is crucial, as the increased presence of activated T cells enhance susceptibility to infection. In this regard, it has been proposed that immunomodulatory compounds capable of modulating immune activation, such as Vitamin D (VitD) may reduce HIV-1 transmission and might be used as a safe and cost-effective strategy for prevention. Considering this, we examined the in vitro effect of the treatment of peripheral blood mononuclear cells (PBMCs) with the active form of VitD, calcitriol, on cellular activation, function and susceptibility of CD4+ T cells to HIV-1 infection. METHODS: We treated PBMCs from healthy HIV unexposed individuals (Co-HC) and frequently exposed, HIV-1 seronegative individuals (HESNs) from Colombia and from healthy non-exposed individuals from Canada (Ca-HC) with calcitriol and performed in vitro HIV-1 infection assays using X4- and R5-tropic HIV-1 strains respectively. In addition, we evaluated the activation and function of T cells and the expression of viral co-receptors, and select antiviral genes following calcitriol treatment. RESULTS: Calcitriol reduced the frequency of infected CD4+ T cells and the number of viral particles per cell, for both, X4- and R5-tropic viruses tested in the Co-HC and the Ca-HC, respectively, but not in HESNs. Furthermore, in the Co-HC, calcitriol reduced the frequency of polyclonally activated T cells expressing the activation markers HLA-DR and CD38, and those HLA-DR+CD38-, whereas increased the subpopulation HLA-DR-CD38+. Calcitriol treatment also decreased production of granzyme, IL-2 and MIP-1β by T cells and increased the transcriptional expression of the inhibitor of NF-kB and the antiviral genes cathelicidin (CAMP) and APOBEC3G in PBMCs from Co-HC. CONCLUSION: Our in vitro findings suggest that VitD treatment could reduce HIV-1 transmission through a specific modulation of the activation levels and function of T cells, and the production of antiviral factors. In conclusion, VitD remains as an interesting potential strategy to prevent HIV-1 transmission that should be further explored.
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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) | 0.003 | 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".