The type II transmembrane serine protease matriptase cleaves the amyloid precursor protein and reduces its processing to β-amyloid peptide
Bibliographic record
Abstract
Recent studies have reported that many proteases, besides the canonical α-, β-, and γ-secretases, cleave the amyloid precursor protein (APP) and modulate β-amyloid (Aβ) peptide production. Moreover, specific APP isoforms contain Kunitz protease-inhibitory domains, which regulate the proteolytic activity of serine proteases. This prompted us to investigate the role of matriptase, a member of the type II transmembrane serine protease family, in APP processing. Using quantitative RT-PCR, we detected matriptase mRNA in several regions of the human brain with an enrichment in neurons. RNA sequencing data of human dorsolateral prefrontal cortex revealed relatively high levels of matriptase RNA in young individuals, whereas lower levels were detected in older individuals. We further demonstrate that matriptase and APP directly interact with each other and that matriptase cleaves APP at a specific arginine residue (Arg-102) both in vitro and in cells. Site-directed (Arg-to-Ala) mutagenesis of this cleavage site abolished matriptase-mediated APP processing. Moreover, we observed that a soluble, shed matriptase form cleaves endogenous APP in SH-SY5Y cells and that this cleavage significantly reduces APP processing to Aβ40. In summary, this study identifies matriptase as an APP-cleaving enzyme, an activity that could have important consequences for the abundance of Aβ and in Alzheimer's disease pathology.
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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".