Regulation of global gene expression and cell proliferation by APP
Bibliographic record
Abstract
Down syndrome (DS), caused by trisomy of chromosome 21, is one of the most common genetic disorders. Patients with DS display growth retardation and inevitably develop characteristic Alzheimer's disease (AD) neuropathology, including neurofibrillary tangles and neuritic plaques. The expression of amyloid precursor protein (APP) is increased in both DS and AD patients. To reveal the function of APP and elucidate the pathogenic role of increased APP expression in DS and AD, we performed gene expression profiling using microarray method in human cells overexpressing APP. A set of genes are significantly altered, which are involved in cell cycle, cell proliferation and p53 signaling. We found that overexpression of APP inhibits cell proliferation. Furthermore, we confirmed that the downregulation of two validated genes, PSMA5 and PSMB7, inhibits cell proliferation, suggesting that the downregulation of PSMA5 and PSMB7 is involved in APP-induced cell proliferation impairment. Taken together, this study suggests that APP regulates global gene expression and increased APP expression inhibits cell proliferation. Our study provides a novel insight that APP overexpression may contribute to the growth impairment in DS patients and promote AD pathogenesis by inhibiting cell proliferation including neural stem cell proliferation and neurogenesis.
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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".