Evidence map›Paper›PMID 39104618›Full record

ArticleBioImpacts : BI2024

Interaction of toll-like receptors and ACE-2 with different variants of SARS-CoV-2: A computational analysis.

Azadeh Zahmatkesh, Elham Salmasi, Reza Gholizadeh

Abstract read
In one paragraph

Article in BioImpacts : BI, 2024. 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

3 authors.

Azadeh ZahmatkeshDepartment of Anaerobic Bacterial Vaccines Research and Production, Razi Vaccine and ‎Serum Research Institute, Agricultural Research, Education and Extension Organization, ‎Karaj, Iran.ORCID https://orcid.org/0000-0001-6960-8557
Elham SalmasiDepartment of Biomedical Engineering, School of Medicine, Tsinghua ‎University, Beijing, PR China.
Reza GholizadehDepartment of Catalysis and Chemical Reaction Engineering, ‎National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Computational studies were performed to investigate the unknown status of endosomal and cell surface receptors in SARS-CoV-2 infection. The interactions between Toll-like receptors (TLRs)- 4/7/8/9 or ACE2 receptor and different SARS-CoV-2 variants were investigated. Methods: The RNA motifs for TLR7, TLR8 and a CpG motif for TLR9 were analyzed in different variants. Molecular docking and molecular dynamics (MD) simulations were performed to investigate receptor-ligand interactions. Results: The number of motifs recognized by TLR7/8/9 in the Alpha, Delta and Iranian variants was lower than in the wild type (WT). Docking analysis revealed that the Alpha, Delta and some Iranian spike variants had a higher affinity for ACE2 and TLR4 than the WT, which may account for their higher transmission rate. The MD simulation also showed differences in stability and structure size between the variants and the WT, indicating potential variations in viral load. Conclusion: It appears that Alpha and some Iranian isolates are the variants of concern due to their higher transmissibility and rapid spread. The Delta mutant is also a variant of concern, not only because of its closer interaction with ACE2, but also with TLR4. Our results emphasize the importance of ACE2 and TLR4, rather than endosomal TLRs, in mediating the effects of different viral mutations and suggest their potential therapeutic applications.

Indexed as

Alpha variantDelta variantMotifTLR4TLR7/8/9Transmission

Identifiers

PMID39104618
PMCPMC11298020

What Socratic holds

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LicenceCC BY-NC
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Registered trials

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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 Socratic graph.