The effect of miRNAs targeting TGFβ-Signaling in Multiple Sclerosis
Bibliographic record
Abstract
Abstract Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS) that is mediated by a dysregulated immune system. It can lead to severe neurological issues and is one of the major causes for disability in young adults. The cause of the disease is unknown, the immune and neurodegenerative mechanisms underlying the pathophysiology of the disease are poorly understood. To this date there is no cure and current therapies are not able to completely slow down disease progression. In a large miRNA profiling study on naïve and effector/memory CD4 T cell in untreated MS patients, our lab found a number of miRNAs differently expressed. One of the main pathways affected by this dysregulation of miRNAs is the TGFβ signaling pathway. This impairment limits the differentiation of regulatory T cells. To study the effect of TGFβ-targeting miRNAs in a murine model of MS, neonatal mice were injected with miRNAs that were found to be upregulated in MS patients at day 0 and 21 of life and experimental autoimmune encephalomyelitis (EAE) was induced using conditions under which the controls would only get a mild form of the disease. Mice, injected with these miRNAs had an earlier onset of EAE and disease severity was significantly increased compared to control mice. Not only did an injection of TGFβ-targeting miRNAs caused mice to have reduced numbers of Tregs in the spleen and thymus compared to control mice, but these Tregs also showed a dramatic loss of TCR diversity in most Vβ genes as TCR Vβ deep sequencing of sorted Tregs revealed. Our findings contribute to the understanding of Treg dysfunction in MS patients and determining candidate markers for disease susceptibility and treatment efficacy. Supported by grants from NIH (R01 AI152435-01)
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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 | 0.001 |
| 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".