Inhibition of Excessive Monoamine Oxidase A/B Activity Protects Against Stress-induced Neuronal Death in Huntington Disease
Bibliographic record
Abstract
Monoamine oxidases (MAO) are important components of the homeostatic machinery that maintains the levels of monoamine neurotransmitters, including dopamine, in balance. Given the imbalance in dopamine levels observed in Huntington disease (HD), the aim of this study was to examine MAO activity in a mouse striatal cell model of HD and in human neural cells differentiated from control and HD patient-derived induced pluripotent stem cell (hiPSC) lines. We show that mouse striatal neural cells expressing mutant huntingtin (HTT) exhibit increased MAO expression and activity. We demonstrate using luciferase promoter assays that the increased MAO expression reflects enhanced epigenetic activation in striatal neural cells expressing mutant HTT. Using cellular stress paradigms, we further demonstrate that the increase in MAO activity in mutant striatal neural cells is accompanied by enhanced susceptibility to oxidative stress and impaired viability. Treatment of mutant striatal neural cells with MAO inhibitors ameliorated oxidative stress and improved cellular viability. Finally, we demonstrate that human HD neural cells exhibit increased MAO-A and MAO-B expression and activity. Altogether, this study demonstrates abnormal MAO expression and activity and suggests a potential use for MAO inhibitors in HD.
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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 | 0.000 |
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".