Tuftsin: A Natural Molecule Against SARS-CoV-2 Infection

  1. Jiahao Huang
  2. Jing Wang
  3. Yan Li
  4. Ziyuan Wang
  5. Ming Chu
  6. Yuedan Wang

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Abstract

Coronavirus disease 2019 (COVID-19) continuously progresses despite the application of a variety of vaccines. Therefore, it is still imperative to find effective ways for treating COVID-19. Recent studies indicate that NRP1, an important receptor of the natural peptide tuftsin (released from IgG), facilitates SARS-CoV-2 infection. Here, we found 91 overlapping genes between tuftsin targets and COVID-19-associated genes. We have demonstrated that tuftsin could also target ACE2 and exert some immune-related functions. Molecular docking results revealed that tustin could combine with ACE2 and NRP1 in stable structures, and their interacted regions cover the binding surfaces of S1-protein with the two receptors. Using surface plasmon resonance (SPR) analysis, we confirmed that tuftsin can bind ACE2 and NRP1 directly. Importantly, using SPR-based competition assay we have shown here that tuftsin effectively prevented the binding of SARS-CoV-2 S1-protein to ACE2. Collectively, these data suggest that tuftsin is an attractive therapeutic candidate against COVID-19 and can be considered for translational as well as clinical studies.

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  1. Version published to 10.3389/fmolb.2022.859162
  2. ScreenIT

    SciScore for 10.1101/2022.01.10.475746: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Compound profiling and disease-related gene identification: The structure of tuftsin was found in PubChem (https://pubchem.ncbi.nlm.nih.gov/).
    PubChem
    suggested: (PubChem, RRID:SCR_004284)
    https://pubchem.ncbi.nlm.nih.gov/
    suggested: (PubChem BioAssay, RRID:SCR_010734)
    Afterward, the target proteins corresponding to tuftsin screened from the Pharmmapper database and PubMed database were standardized in UniProt (http://www.uniprot.org/).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    http://www.uniprot.org/
    suggested: (Universal Protein Resource, RRID:SCR_002380)
    Finally, Cytoscape 3.8.2 was used to determine the drug-target network.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    COVID-19-related genes were mined from the GeneCards database.
    GeneCards
    suggested: (GeneCards, RRID:SCR_002773)
    The crossover genes were filtered with R software using the Venn Diagram package.
    Diagram
    suggested: (DIAGRAM, RRID:SCR_015675)
    The STRING 11.5 database (http://string-db.org/) was used to analyse the intersecting protein–protein interactions (PPIs), and the common targets were counted with R software.
    STRING
    suggested: (STRING, RRID:SCR_005223)
    Enrichment analysis: The proteins with overlapping expression patterns were evaluated by bioinformatics annotation with R software using the Bioconductor package, including a panther classification system (http://www.pantherdb.org/), a gene ontology (GO) annotation database website (http://www.geneontology.org), and Kyoto Encyclopedia of Genes and Genomes
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    (KEGG) pathway enrichment analysis (http://www.genome.jp/kegg/).
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Statistical analysis: The results were analysed using Student’s t test with SPSS software and R 4.1.0.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2022.01.10.475746v1 on bioRxiv