MicroRNA-142 regulates inflammation and T cell differentiation in an animal model of multiple sclerosis
Bibliographic record
Abstract
MicroRNAs have emerged as an important class of modulators of gene expression. These molecules influence protein synthesis through translational repression or degradation of mRNA transcripts. Herein, we investigated the potential role of miR-142a isoforms, miR-142a-3p and miR-142a-5p, in the context of autoimmune neuroinflammation. The expression levels of two mature isoforms of miR-142 were measured in the brains of patients with multiple sclerosis (MS) and the CNS tissues from mice with experimental autoimmune encephalomyelitis (EAE), an animal model of MS. Expression analyses were also performed in mitogen and antigen-stimulated splenocytes, as well as macrophages and astrocytes using real-time RT-PCR. The role of the mature miRNAs was then investigated in T cell differentiation by transfection of CD4 + T cells, followed by flow cytometric analysis of intracellular cytokines. Luciferase assays using vectors containing the 3′UTR of predicted targets were performed to confirm the interaction of miRNA sequences with transcripts. Expression of targets were then analyzed in activated splenocytes and MS/EAE tissues. Expression of miR-142-5p was significantly increased in the frontal white matter from MS patients compared with white matter from non-MS controls. Likewise, expression levels of miR-142a-5p and miR-142a-3p showed significant upregulation in the spinal cords of EAE mice at days 15 and 25 post disease induction. Splenocytes stimulated with myelin oligodendrocyte glycoprotein (MOG) peptide or anti-CD3/anti-CD28 antibodies showed upregulation of miR-142a-5p and miR-142a-3p isoforms, whereas stimulated bone marrow-derived macrophages and primary astrocytes did not show any significant changes in miRNA expression levels. miR-142a-5p overexpression in activated lymphocytes shifted the pattern of T cell differentiation towards Th1 cells. Luciferase assays revealed SOCS1 and TGFBR1 as direct targets of miR-142a-5p and miR-142a-3p, respectively, and overexpression of miRNA mimic sequences suppressed the expression of these target transcripts in lymphocytes. SOCS1 levels were also diminished in MS white matter and EAE spinal cords. Our findings suggest that increased expression of miR-142 isoforms might be involved in the pathogenesis of autoimmune neuroinflammation by influencing T cell differentiation, and this effect could be mediated by interaction of miR-142 isoforms with SOCS1 and TGFBR-1 transcripts.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".