Characterization of endothelin-converting enzyme 1 as a key enzyme in the multienzyme Aβ degradation pathway
Bibliographic record
Abstract
Altered β-amyloid (Aβ) homeostasis is a critical event triggering the shift from healthy aging to Alzheimer disease (AD) through the overproduction and impaired clearance of Aβ peptides. The Aβ-degrading enzymes (ADEs) are a collective group of proteases that normally promote clearance to counteract Aβ-induced neurodegeneration. We previously discovered that the beta-site amyloid precursor protein cleaving enzyme 1 is an atypical ADE that produces the nontoxic fragment Aβ34 by recognizing 40- or 42-residue-long Aβ peptides as substrates in vitro and in vivo. Here, we examined other known ADEs for their potential roles in degrading Aβ34, Aβ40, and Aβ42. By genetic, cellular, and pharmacological approaches, we identified and characterized endothelin-converting enzyme 1 (ECE1) in a human neuroblastoma cell line, human brain vascular pericytes, and primary rat cortical cultures as a major enzyme degrading Aβ34 but not Aβ40 or Aβ42. Notably, we found that ECE1 cleaves Aβ34, an indicator of amyloid clearance, to a unique and unusually stable Aβ20-34 fragment that has the potential to serve as a measurable biomarker. Biocomputational analyses from non-AD controls and individuals with AD pathology showed that the highest messenger ribonucleic acid (mRNA) levels of ECE1 expression were found in pericytes (i.e., cells within the brain microvasculature that are known to produce Aβ34) compared to other cell types. Given Aβ34 is an indicator for prodromal AD, we postulate that our collective findings (i.e., generation of Aβ34 and Aβ20-34 intermediates within the "amyloidolytic" degradation pathway) will generate a set of biomarkers to detect amyloid clearance activity in vivo.
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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) | 0.000 | 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".