Amino-Terminal β-Amyloid Antibody Blocks β-Amyloid-Mediated Inhibition of the High-Affinity Choline Transporter CHT
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a common age-related neurodegenerative disorder that is characterized by progressive cognitive decline. The deficits in cognition and attentional processing that are observed clinically in AD are linked to impaired function of cholinergic neurons that release the neurotransmitter acetylcholine (ACh). The high-affinity choline transporter (CHT) is present at the presynaptic cholinergic nerve terminal and is responsible for the reuptake of choline produced by hydrolysis of ACh following its release. Disruption of CHT function leads to decreased choline uptake and ACh synthesis, leading to impaired cholinergic neurotransmission. We report here that cell-derived β-amyloid peptides (Aβ) decrease choline uptake activity and cell surface CHT protein levels in SH-SY5Y neural cells. Moreover, we make the novel observation that the amount of CHT protein localizing to early endosomes and lysosomes is decreased significantly in cells that have been treated with cell culture medium that contains Aβ peptides released from neural cells. The Aβ-mediated loss of CHT proteins from lysosomes is prevented by blocking lysosomal degradation of CHT with the lysosome inhibitor bafilomycin A1 (BafA1). BafA1 also attenuated the Aβ-mediated decrease in CHT cell surface expression. Interestingly, however, lysosome inhibition did not block the effect of Aβ on CHT activity. Importantly, neutralising Aβ using an anti-Aβ antibody directed at the N-terminal amino acids 1-16 of Aβ, but not by an antibody directed at the mid-region amino acids 22-35 of Aβ, attenuates the effect of Aβ on CHT activity and trafficking. This indicates that a specific N-terminal Aβ epitope, or specific conformation of soluble Aβ, may impair CHT activity. Therefore, Aβ immunotherapy may be a more effective therapeutic strategy for slowing the progression of cognitive decline in AD than therapies designed to promote CHT cell surface levels.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".