The effects of microRNAs on regulatory T cells in multiple sclerosis (HUM1P.319)
Bibliographic record
Abstract
Abstract Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the CNS. The pathophysiology that propagates MS is hypothesized to be an autoimmune reaction mediated by pro-inflammatory T cells activated against myelin. Regulatory T cells (Tregs), protectors against such autoimmune responses, are dysfunctional in MS patients. We investigated which microRNAs, modulators of cellular and disease processes, are regulators of MS pathogenesis and the extent to which these miRNAs regulate genes critical for Treg development. To this end, miRNA profiling studies performed on naïve CD4+ T cells have identified 85 differentially expressed miRNAs in MS patients compared to healthy donors. Of the miRNAs, 23 were predicted to target genes of the transforming growth factor beta (TGFβ) signaling pathway. Given the importance of TGFβ in regards to Treg development and function, we hypothesized that dysregulated miRNAs in the naïve CD4+ T cells of MS patients target the TGFβ-signaling pathway, resulting in defective Tregs and enhanced susceptibility to developing MS. Our data indicate that 1) expression of TGFβ-associated genes is decreased in MS patients, 2) several of the identified miRNAs directly target genes of the TGFβ-signaling pathway, and 3) overexpression of TGFβ-targeting miRNAs causes a decrease in Treg development. Taken together, our data have allowed us to highlight specific miRNAs as being potentially highly relevant in the development of defective Tregs in MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".