Effect of Vitamin D Supplementation on Mycobacterium tuberculosis-Induced Innate Immune Responses in a Canadian Dené First Nations Cohort
Bibliographic record
Abstract
Canadian First Nations (FN) population experiences a high burden of tuberculosis. Vitamin D is known to enhance the expression of innate immune effectors, including cathelicidin LL-37, for protection against infections. In this study we performed longitudinal analyses to investigate the impact of vitamin D supplementation on macrophage responses to Mycobacterium tuberculosis (Mtb) lipoprotein (TLR2/1L), in Canadian Dené FN participants compared to Caucasian participants. Serum 25(OH)D and LL-37 levels were evaluated by ELISA. Transcriptional responses and protein expression of TLR2/1L-induced LL-37 and other innate immune cytokines were monitored in monocyte-derived macrophages (MDMs) before and after 8 months of vitamin D supplementation. In this study we showed that serum levels of LL-37 decreased after vitamin D supplementation in both Dené and Caucasian participants. There was no difference in TLR2/1L-induced LL-37 expression in MDMs in the two groups, either pre- or post-vitamin D supplementation. However, vitamin D supplementation markedly enhanced TLR2/1L-induced responses in MDMs e.g. IL-6, IL-12 and IL-23 among Caucasians but not in the Dené participants. In contrast, after vitamin D supplementation TLR2/1L-induced responses e.g. IL-1β, IL-8 and IL-12 were significantly reduced in the Dené MDMs. These results indicate that vitamin D supplementation enhanced TLR2/1L-induced innate immune macrophage responses in the Caucasian but not in the Dené participants. We hypothesize that cytokines may be differentially regulated in Canadian FN compared to Caucasians, in particular those that influence Th-1 and Th-17 responses required for the control of Mtb.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".