Multiple genetic pathways regulating lifespan extension are neuroprotective in a G2019S LRRK2 nematode model of Parkinson's disease
Bibliographic record
Abstract
Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are the most frequent cause of late-onset, familial Parkinson's disease (PD), and LRRK2 variants are associated with increased risk for sporadic PD. While advanced age represents the strongest risk factor for disease development, it remains unclear how different age-related pathways interact to regulate LRRK2-driven late-onset PD. In this study, we employ a C. elegans model expressing PD-linked G2019S LRRK2 to examine the interplay between age-related pathways and LRRK2-induced dopaminergic neurodegeneration. We find that multiple genetic pathways that regulate lifespan extension can provide robust neuroprotection against mutant LRRK2. However, the level of neuroprotection does not strictly correlate with the magnitude of lifespan extension, suggesting that lifespan can be experimentally dissociated from neuroprotection. Using tissue-specific RNAi, we demonstrate that lifespan-regulating pathways, including insulin/insulin-like growth factor-1 (IGF-1) signaling, target of rapamycin (TOR), and mitochondrial respiration, can be directly manipulated in neurons to mediate neuroprotection. We extend this finding for AGE-1/PI3K, where pan-neuronal versus dopaminergic neuronal restoration of AGE-1 reveals both cell-autonomous and non-cell-autonomous neuroprotective mechanisms downstream of insulin signaling. Our data demonstrate the importance of distinct lifespan-regulating pathways in the pathogenesis of LRRK2-linked PD, and suggest that extended longevity is broadly neuroprotective via the actions of these pathways at least in part within neurons. This study further highlights the complex interplay that occurs between cells and tissues during organismal aging and disease manifestation.
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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) | 0.001 | 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 | 0.001 | 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".