Widespread micro<scp>RNA</scp>dysregulation in multiple system atrophy – disease‐related alteration in miR‐96
Bibliographic record
Abstract
MicroRNA (miRNA) are short sequences of RNA that function as post-transcriptional regulators by binding to target mRNA transcripts resulting in translational repression. A number of recent studies have identified miRNA as being involved in neurodegenerative disorders including Alzheimer's disease, Parkinson's disease and Huntington's disease. However, the role of miRNA in multiple system atrophy (MSA), a progressive neurodegenerative disorder characterized by oligodendroglial accumulation of alpha-synuclein remains unexamined. In this context, this study examined miRNA profiles in MSA cases compared with controls and in transgenic (tg) models of MSA compared with non-tg mice. The results demonstrate a widespread dysregulation of miRNA in MSA cases, which is recapitulated in the murine models. The study employed a cross-disease, cross-species approach to identify miRNA that were either specifically dysregulated in MSA or were commonly dysregulated in neurodegenerative conditions such as Alzheimer's disease, dementia with Lewy bodies, progressive supranuclear palsy and corticobasal degeneration or the tg mouse model equivalents of these disorders. Using this approach we identified a number of miRNA that were commonly dysregulated between disorders and those that were disease-specific. Moreover, we identified miR-96 as being up-regulated in MSA. Consistent with the up-regulation of miR-96, mRNA and protein levels of members of the solute carrier protein family SLC1A1 and SLC6A6, miR-96 target genes, were down-regulated in MSA cases and a tg model of MSA. These results suggest that miR-96 dysregulation may play a role in MSA and its target genes may be involved in the pathogenesis of MSA.
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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".