Hormonal vitamin D up-regulates tissue-specific PD-L1 and PD-L2 surface glycoprotein expression in humans but not mice
Bibliographic record
Abstract
PD-L1 (programmed death ligand 1) and PD-L2 are cell-surface glycoproteins that interact with programmed death 1 (PD-1) on T cells to attenuate inflammation. PD-1 signaling has attracted intense interest for its role in a pathophysiological context: suppression of anti-tumor immunity. Similarly, vitamin D signaling has been increasingly investigated for its non-classical actions in stimulation of innate immunity and suppression of inflammatory responses. Here, we show that hormonal 1,25-dihydroxyvitamin D (1,25D) is a direct transcriptional inducer of the human genes encoding PD-L1 and PD-L2 through the vitamin D receptor, a ligand-regulated transcription factor. 1,25D stimulated transcription of the gene encoding PD-L1 in epithelial and myeloid cells, whereas the gene encoding the more tissue-restricted PD-L2 was regulated only in myeloid cells. We identified and characterized vitamin D response elements (VDREs) located in both genes and showed that 1,25D treatment induces cell-surface expression of PD-L1 in epithelial and myeloid cells. In co-culture experiments with primary human T cells, epithelial cells pretreated with 1,25D suppressed activation of CD4 + and CD8 + cells and inhibited inflammatory cytokine production in a manner that was abrogated by anti-PD-L1 blocking antibody. Consistent with previous observations of species-specific regulation of immunity by vitamin D, the VDREs are present in primate genes, but neither the VDREs nor the regulation by 1,25D is present in mice. These findings reinforce the physiological role of 1,25D in controlling inflammatory immune responses but may represent a double-edged sword, as they suggest that elevated vitamin D signaling in humans could suppress anti-tumor immunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 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".