An amyloidogenic fragment of the SARS CoV-2 envelope protein promotes serum amyloid A misfolding and fibrillization
Bibliographic record
Abstract
ABSTRACT SARS CoV-2 infection can affect a surprising number of organs in the body and cause symptoms such as abnormal blood coagulation, fibrinolytic disturbances, and neurodegeneration. Our study delves into the intricate pathogenic potential of a SARS-CoV-2 envelope protein peptide, shedding light on its implications for multi-organ effects and amyloid formation. Specifically, we focus on the peptide SK9 or 54 SFYVYSRVK 62 derived from the C-terminus of human SARS coronavirus 2 envelope protein. We demonstrate that SK9 containing peptides readily form classic amyloid structures consistent with predictions of amyloid aggregation algorithms. In vivo , overexpression of proteases such as neutrophil elastase during inflammation can potentially lead to C-terminal peptides containing SK9. We also demonstrate that SK9 can promote the fibrillization of SAA, a protein marker of acute inflammation. Our investigations reveal that the aromatic residues Phe2 and Tyr3 of SK9 play a pivotal role in its amyloidogenic function. We show that the primary sites of SK9-SAA binding lie in the amyloidogenic hotspots of SAA itself. Our results highlight two possible complications of SARS CoV-2 infection in individuals with hyper-inflammation either due to amyloids arising from SK9 containing peptides or SK9-induced AA amyloidosis.
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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 | 0.001 |
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".