Alzheimer-specific variants in the 3'UTR of Amyloid precursor protein affect microRNA function
Bibliographic record
Abstract
BACKGROUND: APP expression misregulation can cause genetic Alzheimer's disease (AD). Recent evidences support the hypothesis that polymorphisms located in microRNA (miRNA) target sites could influence the risk of developing neurodegenerative disorders such as Parkinson's disease (PD) and frontotemporal dementia. Recently, a number of single nucleotide polymorphisms (SNPs) located in the 3'UTR of APP have been found in AD patients with family history of dementia. Because miRNAs have previously been implicated in APP expression regulation, we set out to determine whether these polymorphisms could affect miRNA function and therefore APP levels. RESULTS: Bioinformatics analysis identified twelve putative miRNA bindings sites located in or near the APP 3'UTR variants T117C, A454G and A833C. Among those candidates, seven miRNAs, including miR-20a, miR-17, miR-147, miR-655, miR-323-3p, miR-644, and miR-153 could regulate APP expression in vitro and under physiological conditions in cells. Using luciferase-based assays, we could show that the T117C variant inhibited miR-147 binding, whereas the A454G variant increased miR-20a binding, consequently having opposite effects on APP expression. CONCLUSIONS: Taken together, our results provide proof-of-principle that APP 3'UTR polymorphisms could affect AD risk through modulation of APP expression regulation, and set the stage for further association studies in genetic and sporadic AD.
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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 | 0.001 |
| 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".