Amyloid precursor‐like protein 1 (APLP1) exhibits stronger zinc‐dependent neuronal adhesion than amyloid precursor protein and <scp>APLP</scp>2
Bibliographic record
Abstract
The amyloid precursor protein (APP) and its paralogs, amyloid precursor-like protein 1 (APLP1) and APLP2, are metalloproteins with a putative role both in synaptogenesis and in maintaining synapse structure. Here, we studied the effect of zinc on membrane localization, adhesion, and secretase cleavage of APP, APLP1, and APLP2 in cell culture and rat neurons. For this, we employed live-cell microscopy techniques, a microcontact printing adhesion assay and ELISA for protein detection in cell culture supernatants. We report that zinc induces the multimerization of proteins of the amyloid precursor protein family and enriches them at cellular adhesion sites. Thus, zinc facilitates the formation of de novo APP and APLP1 containing adhesion complexes, whereas it does not have such influence on APLP2. Furthermore, zinc-binding prevented cleavage of APP and APLPs by extracellular secretases. In conclusion, the complexation of zinc modulates neuronal functions of APP and APLPs by (i) regulating formation of adhesion complexes, most prominently for APLP1, and (ii) by reducing the concentrations of neurotrophic soluble APP/APLP ectodomains. Earlier studies suggest a function of the amyloid precursor protein (APP) family proteins in neuronal adhesion. We report here that adhesive function of these proteins is tightly regulated by zinc, most prominently for amyloid precursor-like protein 1 (APLP1). Zinc-mediated APLP1 multimerization, which induced formation of new neuronal contacts and decreased APLP1 shedding. This suggests that APLP1 could function as a zinc receptor processing zinc signals to stabilized or new neuronal contacts.
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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".