Vitamin D [1,25(OH)2D3] Differentially Regulates Human Innate Cytokine Responses to Bacterial versus Viral Pattern Recognition Receptor Stimuli
Bibliographic record
Abstract
Vitamin D plays multiple roles in regulation of protective and maladaptive immunity. Although epidemiologic studies link poor in vivo 25(OH)D status to increased viral respiratory infections, we poorly understand how vitamin D affects viral pattern recognition receptor (PRR)-driven cytokine production. In this study, we hypothesized that the biologically active metabolite of vitamin D, 1,25(OH)2D3, inhibits human proinflammatory and anti-inflammatory innate cytokine responses stimulated by representative bacterial or viral PRR ligands. Fresh PBMCs or CD14(+) monocytes were stimulated with TLR4, TLR7/8-selective ligands, or respiratory syncytial virus (RSV) ± 1,25(OH)2D3. Proinflammatory and anti-inflammatory responses resulting from TLR4 stimulation were inhibited ∼50% in the presence of 1,25(OH)2D3. Conversely, its usage at physiologic through pharmacologic concentrations inhibited neither proinflammatory nor anti-inflammatory responses evoked by viral PRR ligands or infectious RSV. This differential responsiveness was attributed to the finding that TLR7/8, but not TLR4, stimulation markedly inhibited vitamin D receptor mRNA and protein expression, selectively reducing the sensitivity of viral PRR responses to modulation. 1,25(OH)2D3 also enhanced expression of IkBa, a potent negative regulator of NF-κB and cytokine production, in TLR4-stimulated monocytes while not doing so upon TLR7/8 stimulation. Thus, 1,25(OH)2D3 inhibits both proinflammatory and a broad panel of anti-inflammatory responses elicited by TLR4 stimulation, arguing that the common view of it as an anti-inflammatory immune response modifier is an oversimplification. In viral responses, it consistently fails to modify TLR7/8- or RSV-stimulated innate cytokine production, even at supraphysiologic concentrations. Collectively, the data call into question the rationale for increasingly widespread self-medication with vitamin D supplements.
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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) | 0.001 | 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".