Altered microRNA expression profile in amyotrophic lateral sclerosis: a role in the regulation of NFL mRNA levels
Bibliographic record
Abstract
BACKGROUND: Amyotrophic Lateral Sclerosis (ALS) is a progressive, adult onset, fatal neurodegenerative disease of motor neurons. There is emerging evidence that alterations in RNA metabolism may be critical in the pathogenesis of ALS. MicroRNAs (miRNAs) are small non-coding RNAs that are key determinants of mRNA stability. Considering that miRNAs are increasingly being recognized as having a role in a variety of neurodegenerative diseases, we decided to characterize the miRNA expression profile in spinal cord (SC) tissue in sporadic ALS (sALS) and controls. Furthermore, we performed functional analysis to identify a group of dysregulated miRNAs that could be responsible for the selective suppression of low molecular weight neurofilament (NFL) mRNA observed in ALS. RESULTS: Using TaqMan arrays we analyzed 664 miRNAs and found that a large number of miRNAs are differentially expressed in ventral lumbar SC in sALS compared to controls. We observed that the majority of dysregulated miRNAs are down-regulated in sALS SC tissues. Ingenuity Pathway Analysis (IPA) showed that dysregulated miRNAs are linked with nervous system function and cell death. We used two prediction algorithms to develop a panel of miRNAs that have recognition elements within the human NFL mRNA 3'UTR, and then we performed functional analysis for these miRNAs. Our results demonstrate that three miRNAs that are dysregulated in sALS (miR-146a*, miR-524-5p and miR-582-3p) are capable of interacting with NFL mRNA 3'UTR in a manner that is consistent with the suppressed steady state mRNA levels observed in spinal motor neurons in ALS. CONCLUSIONS: The miRNA expression profile is broadly altered in the SC in sALS. Amongst these is a group of dysregulated miRNAs directly regulate the NFL mRNA 3'UTR, suggesting a role in the selective suppression of NFL mRNA in the ALS spinal motor neuron neurofilamentous aggregate formation.
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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".