Melatonin stimulates the nonamyloidogenic processing of <i>β</i><scp>APP</scp> through the positive transcriptional regulation of ADAM10 and ADAM17
Bibliographic record
Abstract
Melatonin controls many physiological functions including regulation of the circadian rhythm and clearance of free radicals and neuroprotection. Importantly, melatonin levels strongly decrease as we age and patients with Alzheimer's disease (AD) display lower melatonin than age-matched controls. Several studies have reported that melatonin can reduce aggregation and toxicity of amyloid-β peptides that are produced from the β-amyloid precursor protein (βAPP). However, whether melatonin can directly regulate the βAPP-cleaving proteases ('secretases') has not been investigated so far. In this study, we establish that melatonin stimulates the α-secretase cleavage of βAPP in cultured neuronal and non-neuronal cells. This effect is fully reversed by ADAM10- and ADAM17-specific inhibitors and requires both plasma membrane-located melatonin receptor activation, and ERK1/2 phosphorylation. Moreover, we demonstrate that melatonin upregulates both ADAM10 and ADAM17 catalytic activities and endogenous protein levels. Importantly, genetic depletion of one or the other protease in mouse embryonic fibroblasts prevents melatonin stimulating constitutive and PKC-regulated sAPPα secretion and ADAM10/ADAM17 catalytic activities. Furthermore, we show that melatonin induces ADAM10 and ADAM17 promoter transactivation, and we identify the targeted promoter regions. Finally, we correlate melatonin-dependent sAPPα production with a protection against staurosporine-induced apoptosis. Altogether, our results provide the first demonstration that melatonin upregulates the nonamyloidogenic ADAM10 and ADAM17 proteases through melatonin receptor activation, ERK phosphorylation and the transactivation of some specific regions of their promoters and further underline the preventive rather than curative nature of melatonin regarding AD treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".