Predominance of Th2 polarization by Vitamin D through a STAT6-dependent mechanism
Bibliographic record
Abstract
BACKGROUND: Vitamin D has several reported immunomodulatory properties including the reduced generation of pro-inflammatory CD4+ T helper 1 (Th1) cells and the increase in levels of the anti-inflammatory Th2 subset. Less clear has been the impact of vitamin D on the pro-inflammatory Th17 subset, and whether and how vitamin D may preferentially drive the polarization of one of the T helper subsets. METHODS: Using human peripheral blood-derived mononuclear cells and mouse splenocytes and lymph node cells in culture, we examined whether and how vitamin D preferentially skews T cells towards the Th1, Th2 or Th17 subsets. Mice afflicted with the multiple sclerosis-like condition, experimental autoimmune encephalomyelitis (EAE), were examined in vivo for the relevance of the tissue culture-derived results. RESULTS: We report that the biologically active form of vitamin D, 1,25-dihydroxyvitamin D3 {1,25(OH)2D3}, consistently generates human and murine Th2 cells in culture, frequently leaving unchanged the levels of Th1/Th17 cytokines. As a result, the ratio of Th2 to Th1 and Th17 is increased by 1,25(OH)2D3. The upregulation of Th2 to Th1 or Th17 subsets by 1,25(OH)2D3 is enabled by an increase of the GATA-3 transcription factor, which itself is promoted upstream by an elevation of the STAT6 transcription factor. In mice, the alleviation of EAE severity by 1,25(OH)2D3 is accompanied by elevation of levels of GATA-3 and STAT6. Significantly, the efficacy of 1,25(OH)2D3 in ameliorating EAE is completely lost in mice genetically deficient for STAT6, which was accompanied by the inability of 1,25(OH)2D3 to raise GATA-3 in STAT6 null lymphocytes. CONCLUSIONS: These results of vitamin D promoting a Th2 shift through upstream GATA-3 and STAT6 transcription factors shed mechanistic understanding on the utility of vitamin D in MS.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".