Vitamins A and D Enhance the Expression of Ror-γ-Targeting miRNAs in a Mouse Model of Multiple Sclerosis
Bibliographic record
Abstract
Abstract Background: Multiple Sclerosis (MS) is an autoimmune disease of the central nervous system. Autoreactive T cells including cells with a Th17 phenotype are critical players in MS pathogenesis. In this study, we investigated the effects of VitA/D on miRNAs expression involved in Th17 development neuroinflammation using (EAE). Methods: EAE was induced in C57BL/6 mice and received IP injections of vitamins A, D or their combination starting one day before the immunization and continued every other day for 30 days. Animals were scored for 30 days. Percentages of Th17 cells were measured in splenocytes following in vitro re-stimulation with MOG using intracellular staining and flow cytometry. Expression of miR-98-5p and Let-7a-5p, two miRNAs that are known to target Ror-t and Ror-t was measured in MOG-stimulated splenocytes as well as in spinal cord tissues using real-time RT-PCR. Results: Treated mice showed decreased frequency of Th17 cells in their spleens following in vitro re-stimulation with antigen, also lower expression of IL17 and Ror-t in their in CNS and splenocytes. Vitamin A and vitamin D-treated splenocytes showed significant upregulation of miR-98-5p in 24 hour and 48 hours time-points and Let-7a-5p expression was induced at 48-hour post-treatment in MOG-treated cells, which showed a strong negative correlation with splenocyte Ror-t levels. Conclusion: Our data suggest that treatment with vitamins A and D can decreased differentiation of Th17 phenotype. This is likely due to upregulation of Ror-t-targeting miRNAs, miR-98-5p and Let-7a-5p following treatment. These findings point to a potential protective role for miRNAs in the context of autoimmune neuroinflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".