Expression and functional profiling of neprilysin, insulin‐degrading enzyme, and endothelin‐converting enzyme in prospectively studied elderly and Alzheimer’s brain
Bibliographic record
Abstract
The brain steady state level of β-amyloid (Aβ) is determined by the balance between its production and removal, the latter through egress across blood and CSF barriers as well as Aβ degradation. The major Aβ-degrading enzymes are neprilysin (NEP), insulin-degrading enzyme (IDE), and endothelin-converting enzyme (ECE-1). Although evidence suggests that NEP is down-regulated in Alzheimer's disease (AD), the role of IDE and ECE in the Aβ accumulation in aging and dementia remains less certain. In this study, we examined mRNA and protein expression, as well as biological activity of NEP, IDE, and ECE-1 in human frontal cortex by real-time RT-PCR for mRNA, immunoblotting for protein, and highly sensitive and specific fluorescence assays for activity. The relationships between Aβ-degrading enzymes and pathologic measures and clinical features were also assessed. The results showed that NEP mRNA, protein level, and activity were decreased in AD compared with normal controls with no cognitive impairment (NCI). In contrast, IDE activity was unchanged, but there was higher expression of IDE mRNA, indicating a possible compensatory reaction because of deficits in activity. ECE-1 expression in AD brain showed no significant difference compared with age-matched controls. Correlation analyses suggested that NEP expression was correlated with Aβ accumulation and clinical diagnosis, being lower in AD than in no cognitive impairment. In contrast, neither IDE nor ECE-1 correlated with Aβ or clinical diagnosis. These findings provide additional support for NEP as the major protease involved in Aβ degradation and suggest its possible therapeutic targeting in AD.
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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 | 0.001 | 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".