Protein interaction networks in neurodegenerative diseases: From physiological function to aggregation
Bibliographic record
Abstract
The accumulation of protein inclusions is linked to many neurodegenerative diseases that typically develop in older individuals, due to a combination of genetic and environmental factors. In rare familial neurodegenerative disorders, genes encoding for aggregation-prone proteins are often mutated. While the underlying mechanism leading to these diseases still remains to be fully elucidated, efforts in the past 20 years revealed a vast network of protein-protein interactions that play a major role in regulating the aggregation of key proteins associated with neurodegeneration. Misfolded proteins that can oligomerize and form insoluble aggregates associate with molecular chaperones and other elements of the proteolytic machineries that are the frontline workers attempting to protect the cells by promoting clearance and preventing aggregation. Proteins that are normally bound to aggregation-prone proteins can become sequestered and mislocalized in protein inclusions, leading to their loss of function. In contrast, mutations, posttranslational modifications, or misfolding of aggregation-prone proteins can lead to gain of function by inducing novel or altered protein interactions, which in turn can impact numerous essential cellular processes and organelles, such as vesicle trafficking and the mitochondria. This review examines our current knowledge of protein-protein interactions involving several key aggregation-prone proteins that are associated with Alzheimer's disease, Parkinson's disease, Huntington's disease, or amyotrophic lateral sclerosis. We aim to provide an overview of the protein interaction networks that play a central role in driving or mitigating inclusion formation, while highlighting some of the key proteomic studies that helped to uncover the extent of these networks.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".