Yeast proteinopathy models: a robust tool for deciphering the basis of neurodegeneration
Bibliographic record
Abstract
Protein quality control or proteostasis is an essential determinant of basic cell health and aging. Eukaryotic cells have evolved a number of proteostatic mechanisms to ensure that proteins retain functional conformation, or are rapidly degraded when proteins misfold or self-aggregate. Disruption of proteostasis is now widely recognized as a key feature of aging related illness, specifically neurodegenerative disease. For example, Alzheimer's disease, Huntington's disease, Parkinson's disease and Amyotrophic Lateral Sclerosis (ALS) each target and afflict distinct neuronal cell subtypes, yet this diverse array of human pathologies share the defining feature of aberrant protein aggregation within the affected cell population. Here, we review the use of budding yeast as a robust proxy to study the intersection between proteostasis and neurodegenerative disease. The humanized yeast model has proven to be an amenable platform to identify both, conserved proteostatic mechanisms across eukaryotic phyla and novel disease specific molecular dysfunction. Moreover, we discuss the intriguing concept that yeast specific proteins may be utilized as bona fide therapeutic agents, to correct proteostasis errors across various forms of neurodegeneration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".