Alzheimer Amyloid Peptide Aβ42 Regulates Gene Expression of Transcription and Growth Factors
Bibliographic record
Abstract
The pathogenesis of Alzheimer's disease (AD) is characterized by the aggregation of amyloid-β (Aβ) peptides leading to deposition of senile plaques and a progressive decline of cognitive functions, which currently remains the main criterion for its diagnosis. Robust biomarkers for AD do not yet exist, although changes in the cerebrospinal fluid levels of tau and Aβ represent promising candidates in addition to brain imaging and genetic risk profiling. Although concentrations of soluble Aβ42 correlate with symptoms of AD, less is known about the biological activities of Aβ peptides which are generated from the amyloid-β protein precursor. An unbiased DNA microarray study showed that Aβ42, at sub-lethal concentrations, specifically increases expression of several genes in neuroblastoma cells, notably the insulin-like growth factor binding proteins 3 and 5 (IGFBP3/5), the transcription regulator inhibitor of DNA binding, and the transcription factor Lim only domain protein 4. Using qRT-PCR, we confirmed that mRNA levels of the identified candidate genes were exclusively increased by the potentially neurotoxic Aβ42 wild-type peptide, as both the less toxic Aβ40 and a non-toxic substitution peptide Aβ42 G33A did not affect mRNA levels. In vivo immunohistochemistry revealed a corresponding increase in both hippocampal and cortical IGFBP5 expression in an AD mouse model. Proteomic analyses of human AD cerebrospinal fluid displayed increased in vivo concentrations of IGFBPs. IGFBPs and transcription factors, as identified here, are modulated by soluble Aβ42 and may represent useful early biomarkers.
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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".