Nuclear Translocation Uncovers the Amyloid Peptide Aβ42 as a Regulator of Gene Transcription*
Bibliographic record
Abstract
Although soluble species of the amyloid-β peptide Aβ42 correlate with disease symptoms in Alzheimer disease, little is known about the biological activities of amyloid-β (Aβ). Here, we show that Aβ peptides varying in lengths from 38 to 43 amino acids are internalized by cultured neuroblastoma cells and can be found in the nucleus. By three independent methods, we demonstrate direct detection of nuclear Aβ42 as follows: (i) biochemical analysis of nuclear fractions; (ii) detection of biotin-labeled Aβ in living cells by confocal laser scanning microscopy; and (iii) transmission electron microscopy of Aβ in cultured cells, as well as brain tissue of wild-type and transgenic APPPS1 mice (overexpression of amyloid precursor protein and presenilin 1 with Swedish and L166P mutations, respectively). Also, this study details a novel role for Aβ42 in nuclear signaling, distinct from the amyloid precursor protein intracellular domain. Chromatin immunoprecipitation showed that Aβ42 specifically interacts as a repressor of gene transcription with LRP1 and KAI1 promoters. By quantitative RT-PCR, we confirmed that mRNA levels of the examined candidate genes were exclusively decreased by the potentially neurotoxic Aβ42 wild-type peptide. Shorter peptides (Aβ38 or Aβ40) and other longer peptides (nontoxic Aβ42 G33A substitution or Aβ43) did not affect mRNA levels. Overall, our data indicate that the nuclear translocation of Aβ42 impacts gene regulation, and deleterious effects of Aβ42 in Alzheimer disease pathogenesis may be influenced by altering the expression profiles of disease-modifying genes.
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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".