Oxidative Stress and Transcriptional Regulation in Alzheimer Disease
Bibliographic record
Abstract
Alzheimer disease (AD) is defined by progressive impairments in memory and cognition and by the presence of extracellular neuritic plaques and intracellular neurofibrillary tangles. However, oxidative stress and impaired mitochondrial function always accompany AD. Mitochondria are a major site of production of free radicals [ie, reactive oxygen species (ROS)] and primary targets of ROS. ROS are cytotoxic, and evidence of ROS-induced damage to cell membranes, proteins, and DNA in AD is overwhelming. Nevertheless, therapies based on antioxidants have been disappointing. Thus, alternative strategies are necessary. ROS also act as signaling molecules including for transcription. Thus, chronic exposure to ROS in AD could activate cascades of genes. Although initially protective, prolonged activation may be damaging. Thus, therapeutic approaches based on modulation of these gene cascades may lead to effective therapies. Genes involved in several pathways including antioxidant defense, detoxification, inflammation, etc, are induced in response to oxidative stress and in AD. However, genes that are associated with energy metabolism, which is necessary for normal brain function, are mostly down-regulated. Redox-sensitive transcription factors such as activator protein-1, nuclear factor-kappaB, specificity protein-1, and hypoxia-inducible factor are important in redox-dependent gene regulation. Peroxisome proliferators-activated receptor-gamma coactivator (PGC-1alpha) is a coactivator of several transcription factors and is a potent stimulator of mitochondrial biogenesis and respiration. Down-regulated expression of PGC-1alpha has been implicated in Huntington disease and in several Huntington disease animal models. PGC-1alpha role in regulation of ROS metabolism makes it a potential candidate player between ROS, mitochondria, and neurodegenerative diseases. This review summarizes the current progress on how oxidative stress regulates the expression of genes that might contribute to AD pathophysiology and the implications of the transcriptional modifications for AD. Finally, potential therapeutic strategies based on the updated understandings of redox state-dependent gene regulation in AD are proposed to overcome the lack of efficacy of antioxidant therapies.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".