PROTEOME-SCALE MOLECULAR NETWORKS MECHANISTICALLY LINK ALPHA-SYNUCLEIN TO DIVERSE GENETIC RISK FACTORS FOR PARKINSONISM (P1.004)
Bibliographic record
Abstract
It is unclear how diverse genetic risk factors for neurodegenerative diseases relate to the misfolding of specific proteins that characterize their neuropathology. a-synuclein (a-syn) is a small lipid-binding protein that misfolds in diverse neurodegenerative diseases known as synucleinopathies. These include Parkinson’s disease and multiple system atrophy. Currently, there are no therapies targeting a-syn-induced cellular pathologies. We have recently developed a suite of experimental and computational approaches to model and target a-syn toxicity in cellular systems, ranging from simple yeast cells to complex patient-derived stem-cell models (Science, 2013a,b). We have now employed unbiased proteome-scale screens to assemble molecular networks comprised of genetic and physical interactors of a-syn (Cell Systems 2016a,b in press). These approaches have linked a-syn proteotoxicity to diverse genetic risk factors for parkinsonism through specific molecular pathways. We have been able to predict convergent pathologies in pluripotent stem cell-derived neurons from patients with diverse forms of parkinsonism, and identified small-molecules capable of reversing the toxicity in these models. Thus, proteome-scale cellular screens combined with computational network approaches and stem-cell models offer promising approaches to stratify patients and target treatments according to molecular mechanisms.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".