Negative Regulation of AβPP Gene Expression by Pur-alpha
Bibliographic record
Abstract
The nucleic acid binding protein, Pur-alpha, is best characterized as a transcription factor with affinity to single stranded G/C rich regions. Pur-alpha exhibits developmental and tissue-specific regulation and plays a critical role in neuronal development and differentiation. Similar to Pur-alpha, the amyloid-beta protein precursor (AbetaPP) is a developmentally regulated protein which promotes neuronal survival. Both the human and mouse AbetaPP promoters contain multiple G/C rich sequences which regulate AbetaPP at the transcriptional and translational levels. Using an in vitro reporter assay, we confirmed that Pur-alpha consensus binding sites within the human AbetaPP promoter down-regulate AbetaPP transcription. Electrophoretic mobility shift and chromatin immunoprecipitation assays (ChIP) showed direct binding of Pur-alpha to the AbetaPP promoter. Down regulation of AbetaPP went beyond the transcriptional level as overexpression of Pur-alpha in glial and fibroblast cell lines decreased basal levels of AbetaPP while siRNA targeting Pur-alpha increased basal levels of AbetaPP. Similar findings were observed in brain tissue and fibroblasts from mice with targeted deletion of Pur-alpha. These data point to a novel mechanism of controlling AbetaPP levels by the transcriptional regulatory protein, Pur-alpha, and suggest that Pur-alpha may be involved in the dysregulation of AbetaPP in Alzheimer's disease.
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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 | 0.001 |
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".