ATF4 regulates neuronal death in models of Parkinson’s Disease
Bibliographic record
Abstract
Abstract Parkinson’s Disease (PD) is characterized by the loss of dopaminergic neurons in the substantia nigra resulting in severe motor impairments. However, the mechanisms underlying this neuronal loss remain largely unknown. Activating Transcription Factor 4 (ATF4), a key mediator of the Integrated Stress Response (ISR), is a transcription factor that during prolonged activation can induce the expression of pro-apoptotic target genes. Oxidative stress and ER stress have been implicated in PD and these factors are known to activate the ISR. In this study, we have determined, that both PD neurotoxins (MPP+ and 6-OHDA) and α-synuclein aggregation induced by pre-formed human alpha-synuclein fibrils (PFFs) cause sustained upregulation of ATF4 expression in mouse primary cortical and mesencephalic neurons. Furthermore, we demonstrate that PD neurotoxins induce the expression of the pro-apoptotic factors Chop, Trb3 and Puma in an ATF4-dependent manner. Importantly, using neurons derived from ATF4 +/+ and ATF4 -/- mice, we demonstrate that ATF4 promotes neuronal apoptosis and dopaminergic cell loss in cellular models of PD. Finally, we demonstrate that the eIF2α kinase inhibitor C16 suppresses MPP+ and 6-OHDA induced ATF4 activation and protects against PD neurotoxin induced neuronal death. Taken together these results indicate that ATF4 is a key regulator of dopaminergic cell death induced by PD neurotoxins and pathogenic α-synuclein aggregates and highlight the ISR as a potential therapeutic target in PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".