P4‐093: MiRNA regulation of APP, BACE1 and Nicastrin and the effect of 3'UTR polymorphisms
Bibliographic record
Abstract
It is becoming increasingly acknowledged that polymorphisms in microRNA (miRNA) target sites (PolymiRTS) may influence neurological disorders, including Parkinson's disease and frontotemporal dementia. A number of PolymiRTS in the 3' untranslated region (3'UTR) of AD related genes such APP, BACE1 and nicastrin have been identified, some of which are found exclusively in patients suffering from Alzheimer's disease (AD). Given recent findings, we hypothesize that PolymiRTS could contribute significantly to risk for AD by affecting miRNA binding and increasing the expression of genes like APP involved in the amyloid cascade. Using various bioinformatics logarithms, we established a detailed list of potential miRNA binding sites in the 3'UTRs of APP, BACE1 and Nicastrin. The corresponding miRNAs were tested through luciferase reporter assays as well as by Western blotting for their potential to alter APP, BACE1 and Nicastrin expression. In order to further establish whether the 3'UTR PolymiRTS affect the function of the identified miRNAs, mutagenesis of the 3'UTR of these genes and subsequent luciferase reporter assays were performed. We have identified novel miRNAs that regulate APP, BACE1 and Nicastrin expression. Our results suggest that certain polymorphisms influence miRNA binding and therefore expression. These data could help to focus future association studies aimed at identifying novel risk factors for AD. The identification of novel miRNAs involved in the physiological regulation of APP may provide novel targets for potential future diagnostics and therapy purposes.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".