Persistent DNA damage alters the neuronal transcriptome suggesting cell cycle dysregulation and altered mitochondrial function
Bibliographic record
Abstract
Oxidative DNA damage induces changes in the neuronal cell cycle and activates a DNA damage response (DDR) to promote repair, but these processes may be altered under a chronic oxidative environment, leading to the accumulation of unrepaired DNA damage and continued activation of a DDR. Failure to repair DNA damage can lead to apoptosis or senescence, which is characterized by a permanent cell cycle arrest. Increased oxidative stress and accumulation of oxidative DNA damage are features of brain ageing and neurodegeneration, but the effects of persistent DNA damage in neurons are not well characterized. We developed a model of persistent oxidative DNA damage in immortalized post-mitotic neurons in vitro by exposing them to a sublethal concentration of hydrogen peroxide following a 'double stress' protocol and performed a detailed characterization of the neuronal transcriptome using microarray analysis. Persistent DNA damage significantly altered the expression of genes involved in cell cycle regulation, DDR and repair mechanisms, and mitochondrial function, suggesting an active DDR response to replication stress and alterations in mitochondrial electron transport chain. Quantitative polymerase chain reaction (qPCR) and functional validation experiments confirmed hyperactivation of mitochondrial Complex I in response to persistent DNA damage. These changes in response to persistent oxidative DNA damage may lead to further oxidative stress, contributing to neuronal dysfunction and ultimately neurodegeneration.
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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".