Influence of Vitamins A and D on the Expression of MicroRNA27-3p Isoforms and GATA3 in Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
Vitamins A, D, and microRNAs contribute to T cell differentiation into TH2 phenotypes. We investigated the molecular mechanisms and effects of vitamin A and D on the expression of GATA3 and miR-27-3p isoforms in experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. EAE was induced in C57BL/6 mice by immunization with myelin oligodendrocyte glycoprotein, mixed with Complete Freund's Adjuvant, together with injection of pertussis toxin. Treatments began one day before immunization with (200 μg and 100 ng of vitamin A and vitamin D per mouse, respectively, and vitamin A+D (100 μg+50 ng) per mouse. Expression levels of GATA3 and miR‑27‑3p isoforms were measured in the CNS and splenocytes by real-time RT-PCR. The expression level of GATA3 in the mice spinal cords and splenocytes was increased in the vitamin A and A+D-treated EAE mice at 24 h and 48 h after restimulation by 10 µg and 40 µg of myelin oligodendrocyte glycoprotein. Vitamins A and D and their combination upregulated the miR-27-3p isoforms compared with EAE mice with no treatments. We also demonstrated that miR-273p isoform expression was altered in splenocytes of vitamin-treated EAE mice. The results showed a positive correlation between splenocyte GATA3 levels and miR-27-3p isoform expression. The protective impacts of vitamins A and D in EAE mice may be mediated by the upregulation of GATA3. However, it is not specified whether suppression of GATA3-targeting miRNAs of the miR-27-3p family is involved in this effect. These results do not rule out the possibility that miR-27-3p isoforms might have beneficial effects by targeting other transcripts, such as GluA2 and NR2B.
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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".