Genomic evidence for divergent co-infections of SARS-CoV-2 lineages

  1. Hang-Yu Zhou
  2. Ye-Xiao Cheng
  3. Lin Xu
  4. Jia-Ying Li
  5. Chen-Yue Tao
  6. Cheng-Yang Ji
  7. Na Han
  8. Rong Yang
  9. Yaling Li
  10. Aiping Wu

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Abstract

Recently, patients co-infected by two SARS-CoV-2 lineages have been sporadically reported. Concerns are raised because previous studies have demonstrated co-infection may contribute to the recombination of RNA viruses and cause severe clinic symptoms. In this study, we have estimated the compositional lineage(s), tendentiousness, and frequency of co-infection events in population from a large-scale genomic analysis for SARS-CoV-2 patients. SARS-CoV-2 lineage(s) infected in each sample have been recognized from the assignment of within-host site variations into lineage-defined feature variations by introducing a hypergeometric distribution method. Of all the 29,993 samples, 53 (~0.18%) co-infection events have been identified. Apart from 52 co-infections with two SARS-CoV-2 lineages, one sample with co-infections of three SARS-CoV-2 lineages was firstly identified. As expected, the co-infection events mainly happened in the regions where have co-existed more than two dominant SARS-CoV-2 lineages. However, co-infection of two sub-lineages in Delta lineage were detected as well. Our results provide a useful reference framework for the high throughput detecting of SARS-CoV-2 co-infection events in the Next Generation Sequencing (NGS) data. Although low in average rate, the co-infection events showed an increasing tendency with the increased diversity of SARS-CoV-2. And considering the large base of SARS-CoV-2 infections globally, co-infected patients would be a nonnegligible population. Thus, more clinical research is urgently needed on these patients.

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  1. ScreenIT

    SciScore for 10.1101/2021.09.03.458951: (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
    Sample collection: All the 29,993 SRA runs in Project PRJNA716985 were collected from NCBI (https://www.ncbi.nlm.nih.gov).
    https://www.ncbi.nlm.nih.gov
    suggested: (GENSAT at NCBI - Gene Expression Nervous System Atlas, RRID:SCR_003923)
    A homemade Python script was applied to extract the mutations that shared by >= 75% of all the viruses in one lineage as the 75% feature variations (FV-75).
    Python
    suggested: (IPython, RRID:SCR_001658)

    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.

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

    About SciScore

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  2. Version published to 10.1101/2021.09.03.458951v1 on bioRxiv