Aβ treatment and P301L tau expression in an Alzheimer's disease tissue culture model act synergistically to promote aberrant cell cycle re‐entry
Bibliographic record
Abstract
Microarrays enable the observation of gene expression in experimental models of Alzheimer's disease (AD), with implications for the human pathology. Histopathologically, AD is characterized by Abeta-containing plaques and tau-containing neurofibrillary tangles. Here, we used a human SH-SY5Y neuroblastoma cell system to assess the role of P301L mutant human tau expression, and treatment with or without Abeta on gene regulation. We found that Abeta and P301L tau expression independently affect the regulation of genes controlling cell proliferation and synaptic elements. Moreover, Abeta and P301L tau act synergistically on cell cycle and DNA damage genes, yet influence specific genes within these categories. By using neuronally differentiated P301L tau cells, we can show that Abeta treatment induces an early upregulation of cell cycle control and synaptic genes. At the protein level, by using Kinetworks multi-immunoblotting and BrdU labelling, we found that although P301L tau and Abeta both affected levels of cell cycle proteins, their effects were distinct, in particular concerning DNA damage proteins. Moreover, DNA synthesis was observed only when SH-SY5Y cells overexpressed human wild-type or P301L tau and were incubated with Abeta. Thus, our study shows that Abeta treatment and human tau overexpression in an AD cell culture model act synergistically to promote aberrant cell cycle re-entry, supporting the mitosis failure hypothesis in 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".