Crosstalk Between Alpha-Synuclein and Other Human and Non-Human Amyloidogenic Proteins: Consequences for Amyloid Formation in Parkinson’s Disease
Bibliographic record
Abstract
It was recently shown (Sampson et al., Elife9, 2020) that an amyloidogenic protein, CsgA, present in E. coli biofilms in the gut can trigger Parkinson's disease in mice. This study emphasizes the possible role of the gut microbiome in modulation (and even initiation) of human neurodegenerative disorders, such as Parkinson's disease. As the CsgA protein was found to accelerate alpha-synuclein (the key amyloidogenic protein in Parkinson's disease) amyloid formation in vitro, this result suggests that also other amyloidogenic proteins from gut bacteria, and even from the diet (such as stable allergenic proteins), may be able to affect human protein conformations and thereby modulate amyloid-related diseases. In this review, we summarize what has been reported in terms of in vitro cross-reactivity studies between alpha-synuclein and other amyloidogenic human and non-human proteins. It becomes clear from the limited data that exist that there is a fine line between acceleration and inhibition, but that cross-reactivity is widespread, and it is more common for other proteins (among the studied cases) to accelerate alpha-synuclein amyloid formation than to block it. It is of high importance to expand investigations of cross-reactivity between amyloidogenic proteins to both reveal underlying mechanisms and links between human diseases, as well as to develop new treatments that may be based on an altered gut microbiome.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".