Pharmacological Rescue with SR8278, a Circadian Nuclear Receptor REV-ERBα Antagonist as a Therapy for Mood Disorders in Parkinson's Disease
Bibliographic record
Abstract
Parkinson's disease is a neurodegenerative disease characterized by progressive dopaminergic neuronal loss. Motor deficits experienced by patients with Parkinson's disease are well documented, but non-motor symptoms, including mood disorders associated with circadian disturbances, are also frequent features. One common phenomenon is "sundowning syndrome," which is characterized by the occurrence of neuropsychiatric symptoms at a specific time (dusk), causing severe quality of life challenges. This study aimed to elucidate the underlying mechanisms of sundowning syndrome in Parkinson's disease and their molecular links with the circadian clock. We demonstrated that 6-hydroxydopamine (6-OHDA)-lesioned mice, as Parkinson's disease mouse model, exhibit increased depression- and anxiety-like behaviors only at dawn (the equivalent of dusk in human). Administration of REV-ERBα antagonist, SR8278, exerted antidepressant and anxiolytic effects in a circadian time-dependent manner in 6-OHDA-lesioned mice and restored the circadian rhythm of mood-related behaviors. 6-OHDA-lesion altered DAergic-specific Rev-erbα and Nurr1 transcription, and atypical binding activities of REV-ERBα and NURR1, which are upstream nuclear receptors for the discrete tyrosine hydroxylase promoter region. SR8278 treatment restored the binding activities of REV-ERBα and NURR1 to the tyrosine hydroxylase promoter and the induction of enrichment of the R/N motif, recognized by REV-ERBα and NURR1, as revealed by ATAC-sequencing; therefore, tyrosine hydroxylase expression was elevated in the ventral tegmental area of 6-OHDA-injected mice, especially at dawn. These results indicate that REV-ERBα is a potential therapeutic target, and its antagonist, SR8278, is a potential drug for mood disorders related to circadian disturbances, namely sundowning syndrome, in Parkinson's disease.
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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".