Melatonin regulates the transcription of βAPP‐cleaving secretases mediated through melatonin receptors in human neuroblastoma SH‐SY5Y cells
Bibliographic record
Abstract
Melatonin is involved in the control of various physiological functions, such as sleep, cell growth and free radical scavenging. The ability of melatonin to behave as an antioxidant, together with the fact that the Alzheimer-related amyloid β-peptide (Aβ) triggers oxidative stress through hydroxyl radical-induced cell death, suggests that melatonin could reduce Alzheimer's pathology. Although the exact etiology of Alzheimer's disease (AD) remains to be established, excess Aβ is believed to be the primary contributor to the dysfunction and degeneration of neurons that occurs in AD. Aβ peptides are produced via the sequential cleavage of β-secretase β-site APP-cleaving enzyme 1 (BACE1) and γ-secretase (PS1/PS2), while α-secretase (ADAM10) prevents the production of Aβ peptides. We hypothesized that melatonin could inhibit BACE1 and PS1/PS2 and enhance ADAM10 expression. Using the human neuronal SH-SY5Y cell line, we found that melatonin inhibited BACE1 and PS1 and activated ADAM10 mRNA level and protein expression in a concentration-dependent manner and mediated via melatonin G protein-coupled receptors. Melatonin inhibits BACE1 and PS1 protein expressions through the attenuation of nuclear factor-κB phosphorylation (pNF-κB). Moreover, melatonin reduced BACE1 promoter transactivation and consequently downregulated β-secretase catalytic activity. The present data show that melatonin is not only a potential regulator of β/γ-secretase but also an activator of α-secretase expression through the activation of protein kinase C, thereby favoring the nonamyloidogenic pathway over the amyloidogenic pathway. Altogether, our findings suggest that melatonin may be a potential therapeutic agent for reducing the risk of AD in humans.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".