A framework for predicting potential host ranges of pathogenic viruses based on receptor ortholog analysis

  1. Guifang Du
  2. Yang Ding
  3. Hao Li
  4. Xuejun Wang
  5. Junting Wang
  6. Yu Sun
  7. Huan Tao
  8. Xin Huang
  9. Kang Xu
  10. Hao Hong
  11. Shuai Jiang
  12. Shengqi Wang
  13. Hebing Chen
  14. Xiaochen Bo

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Abstract

Viral zoonoses are a serious threat to public health and global security, as reflected by the current scenario of the growing number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases. However, as pathogenic viruses are highly diverse, identification of their host ranges remains a major challenge. Here, we present a combined computational and experimental framework, called REceptor ortholog-based POtential virus hoST prediction (REPOST), for the prediction of potential virus hosts. REPOST first selects orthologs from a diverse species by identity and phylogenetic analyses. Secondly, these orthologs is classified preliminarily as permissive or non-permissive type by infection experiments. Then, key residues are identified by comparing permissive and non-permissive orthologs. Finally, potential virus hosts are predicted by a key residue–specific weighted module. We performed REPOST on SARS-CoV-2 by studying angiotensin-converting enzyme 2 orthologs from 287 vertebrate animals. REPOST efficiently narrowed the range of potential virus host species (with 95.74% accuracy).

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  1. ScreenIT

    SciScore for 10.1101/2020.12.07.414292: (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

    Experimental Models: Cell Lines
    SentencesResources
    Pseudovirus infection of BHK21 cells expressing ACE2 orthologs: BHK-21 cells were seeded at 1 × 104 cells per well in 96-well plates 12–18 h before transfection.
    BHK21
    suggested: None
    BHK-21
    suggested: None
    Software and Algorithms
    SentencesResources
    (https://www.ncbi.nlm.nih.gov/).
    https://www.ncbi.nlm.nih.gov/
    suggested: (GENSAT at NCBI - Gene Expression Nervous System Atlas, RRID:SCR_003923)
    ACE2 protein sequence identity, defined as the percentage of identical residues between two sequences, was analyzed using MEGA-X software (version 10.05) (Kumar, Stecher, Li, Knyaz, & Tamura, 2018) and the MUltiple Sequence Comparison by Log-Expectation (MUSCLE) algorithm (Edgar, 2004).
    MEGA-X
    suggested: None
    For MUSCLE alignment, we used default parameters and removed all positions at which the human ortholog was gapped.
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.12.07.414292 on bioRxiv