Vitamin D Decreases Susceptibility of CD4+ T Cells to HIV Infection by Reducing AKT Phosphorylation and Glucose Uptake: A Bioinformatic and In Vitro Approach
Bibliographic record
Abstract
Activated immune cells are highly susceptible to human immunodeficiency virus (HIV) infection. Vitamin D (VitD) induces antimicrobial responses and reduces cellular activation. We investigated VitD effects on HIV-1 replication, glucose uptake, and gene regulation using computational and in vitro approaches. CD4+ T cells from healthy male donors were treated with VitD and infected with HIV-1. After 72 h, p24 protein was measured to assess viral replication. VitD effects on anti- and pro-HIV genes were analyzed by a Boolean network model based on curated databases and the literature. CCR5 and CXCR4 coreceptor expression, AKT phosphorylation, and glucose uptake were evaluated by flow cytometry, and expression of some model-identified genes was quantified by qPCR. VitD reduced p24 by 53.2% (p = 0.0078). Boolean network modeling predicted that VitD upregulates antiviral, migration, and cell-differentiation related genes, while downregulating genes related to cellular activation, proliferation, glucose metabolism, and HIV replication, notably AKT1, CCNT1, SLC2A1, HIF1A, and PFKL. In vitro, VitD reduced AKT phosphorylation by 26.6% (p = 0.0156), transcription of CCNT1 by 22.7% (p = 0.0391), and glucose uptake by 22.8% (p = 0.0039) without affecting classic antiviral genes or coreceptor expression. These findings suggest an anti-HIV effect of VitD, mediated through AKT and glucose metabolism downmodulation, both involved in cell activation and HIV-1 replication.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".