Evidence map›Paper›PMID 40353409›Full record

ReviewCurrent protein & peptide science2025

Coupling of SARS-CoV-2 to Amyloid Fibrils and Liquid-Liquid Phase Separation.

Hoang Linh Nguyen, Mai Suan Li

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current protein & peptide science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hoang Linh NguyenInstitute of Fundamental and Applied Sciences, Duy Tan University, Ho Chi Minh City, 700000, Vietnam.ORCID 0000-0003-4141-1642
Mai Suan LiInstitute of Physics, Polish Academy of Sciences, Al. Lotnikow 32/46, Warsaw, 02-668, Poland.ORCID 0000-0001-7021-7916

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 is a respiratory disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), but because the receptor protein of this virus can appear not only in the lungs and throat but also in various parts of the host's body, it causes different diseases. Recent observations have suggested that SARS-CoV-2 damages the central nervous system of patients in a manner similar to amyloid-associated neurodegenerative diseases such as Alzheimer's and Parkinson's. Neurodegenerative diseases are believed to be associated with the self-assembly of amyloid proteins and peptides. On the other hand, whole proteins or parts of them encoded by SARS-CoV-2 can form amyloid fibrils, which may play an important role in amyloid-related diseases. Motivated by this evidence, this mini-review discusses experimental and computational studies of SARS-CoV-2 proteins that can form amyloid aggregates. Liquid-Liquid Phase Separation (LLPS) is a dynamic and reversible process leading to the creation of membrane-less organelles within the cytoplasm, which is not bound by a membrane that concentrates specific types of biomolecules. These organelles play pivotal roles in cellular signaling, stress response, and the regulation of biomolecular condensates. Recently, LLPS of the Nucleocapsid (N) protein and SARS-CoV-2 RNA has been disclosed, but many questions about the phase separation mechanism and the formation of the virion core are still unclear. We summarize the results of this phenomenon and suggest potentially intriguing issues for future research.

Indexed as

AmyloidCOVID-19SARS-CoV-2HumansPhase SeparationAmyloidcentral nervous system.liquid-liquid phase separationneurodegenerative diseasesnucleocapsid proteinprotein aggregationSARS-CoV-2

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.