Molecular signatures of altered energy metabolism and circadian rhythm perturbations in a model of extra-nigral Synucleinopathy
Bibliographic record
Abstract
ABSTRACT A pathological role of alpha-Synuclein (aSyn) aggregation in the central nervous system (CNS) is a recognized feature in Parkinson disease (PD) and related neurodegenerative conditions termed synucleinopathies. In order to characterize the cellular response in CNS to incipient and advanced aSyn pathology, we applied spatial transcriptomics on brain sections derived from a transgenic mouse model (M83 +/+ line, Prnp-SNCA*A53T ) in which aSyn aggregation was induced in a prion-like fashion through hindlimb intramuscular delivery of pre-formed fibrillar (PFF) murine aSyn. Our spatially-resolved transcriptomics (ST) data point to unique perturbations in brain energy metabolism during the progression of aSyn pathology, such that the early stage of aSyn aggregate pathology activates molecular pathways controlling metabolic flux through glycolysis, oxidative phosphorylation and fatty acid metabolism. In contrast, the ST data indicate a profound decline in mitochondrial metabolism in the brains of symptomatic animals with advanced aSyn pathology. The latter stage was also associated with drastic reduction in mRNA translation machinery, along with aberrant expression of molecular drivers involved in RNA splicing and inflammatory response. Intriguingly, our ST data also point to perturbed regulation of circadian rhythm, was corroborated by increased immunodetection of CREB-binding protein (a modulator of core clock machinery) in the brains of symptomatic animals, and transcriptional upregulation of CREBBP in 4 independent PD microarray datasets. Collectively, we anticipate that our findings offer novel opportunities in knowledge translation for mechanism-based drug discovery and biomarkers in neurodegenerative synucleinopathies.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".