Monoamine oxidase-A promotes protective autophagy in human SH-SY5Y neuroblastoma cells through Bcl-2 phosphorylation
Bibliographic record
Abstract
. Increased ROS levels may modulate mitochondrial function and mitochondrial dysfunction is implicated in a vast array of disorders. However, the downstream effects of MAO-A mediated ROS production in a neuronal model has not been previously investigated. In this study, using MAO-A overexpressing neuroblastoma cells, we demonstrate that higher levels of MAO-A protein/activity results in increased basal ROS levels with associated increase in protein oxidation. Increased MAO-A levels result in increased Lysine-63 linked ubiquitination of mitochondrial proteins and promotes autophagy through Bcl-2 phosphorylation. Furthermore, ROS generated locally on the mitochondrial outer membrane by MAO-A promotes phosphorylation of dynamin-1-like protein, leading to mitochondrial fragmentation and clearance without complete loss of mitochondrial membrane potential. Cellular ATP levels are maintained following MAO-A overexpression and complex IV activity/protein levels increased, revealing a close relationship between MAO-A levels and mitochondrial function. Finally, the downstream effects of increased MAO-A levels are dependent on the availability of amine substrates and in the presence of exogenous substrate, cell viability is dramatically reduced. This study shows for the first time that MAO-A generated ROS is involved in quality control signalling, and increase in MAO-A protein levels leads to a protective cellular response in order to mediate removal of damaged macromolecules/organelles, but substrate availability may ultimately determine cell fate. The latter is particularly important in conditions such as Parkinson's disease, where a dopamine precursor is used to treat disease symptoms and highlights that the fate of MAO-A containing dopaminergic neurons may depend on both MAO-A levels and catecholamine substrate availability.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".