MicroRNAs targeting Nicastrin regulate Aβ production and are affected by target site polymorphisms
Bibliographic record
Abstract
Despite the growing number of genome-wide association studies, the involvement of polymorphisms in microRNA target sites (polymiRTS) in Alzheimer's disease (AD) remains poorly investigated. Recently, we have shown that AD-associated single-nucleotide polymorphisms (SNPs) present in the 3' untranslated region (3'UTR) of amyloid precursor protein (APP) could directly affect miRNA function. In theory, loss of microRNA (miRNA) function could lead to risk for AD by increasing APP expression and Aβ peptide production. In this study, we tested the hypothesis that Nicastrin, a γ-secretase subunit involved in Aβ generation, could be regulated by miRNAs, and consequently affected by 3'UTR polymorphisms. Bioinformatic analysis identified 22 putative miRNA binding sites located in or near Nicastrin 3'UTR polymorphisms. From these miRNA candidates, six were previously shown to be expressed in human brain. We identified miR-24, miR-186, and miR-455 as regulators of Nicastrin expression, both in vitro and under physiological conditions in human cells, which resulted in altered Aβ secretion. Using luciferase-based assays, we further demonstrated that rs113810300 and rs141849450 SNPs affected miRNA-mediated repression of Nicastrin. Notably, rs141849450 completely abolished the miR-455-mediated repression of Nicastrin. Finally, the rs141849450 variant was identified in 1 out of 511 AD cases but not in 631 controls. These observations set the stage for future studies exploring the role of miRNAs and 3'UTR polymorphisms in AD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".