From Seeds to Fibrils and Back: Fragmentation as an Overlooked Step in the Propagation of Prions and Prion-Like Proteins
Bibliographic record
Abstract
Many devastating neurodegenerative diseases are driven by the misfolding of normal proteins into a pathogenic abnormal conformation. Examples of such protein misfolding diseases include Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, and prion diseases. The misfolded proteins involved in these diseases form self-templating oligomeric assemblies that recruit further correctly folded protein and induce their conversion. Over time, this leads to the formation of high molecular and mostly fibrillar aggregates that are increasingly inefficient at converting normal protein. Evidence from a multitude of in vitro models suggests that fibrils are fragmented to form new seeds, which can convert further normal protein and also spread to neighboring cells as observed in vivo. While fragmentation and seed generation were suggested as crucial steps in aggregate formation decades ago, the biological pathways involved remain largely unknown. Here, we show that mechanisms of aggregate clearance-namely the mammalian Hsp70-Hsp40-Hsp110 tri-chaperone system, macro-autophagy, and the proteasome system-may not only be protective, but also play a role in fragmentation. We further review the challenges that exist in determining the precise contribution of these mechanisms to protein misfolding diseases and suggest future directions to resolve these issues.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".