Intra-host Variation and Evolutionary Dynamics of SARS-CoV-2 Population in COVID-19 Patients

  1. Yanqun Wang
  2. Daxi Wang
  3. Lu Zhang
  4. Wanying Sun
  5. Zhaoyong Zhang
  6. Weijun Chen
  7. Airu Zhu
  8. Yongbo Huang
  9. Fei Xiao
  10. Jinxiu Yao
  11. Mian Gan
  12. Fang Li
  13. Ling luo
  14. Xiaofang Huang
  15. Yanjun Zhang
  16. Sook-san Wong
  17. Xinyi Cheng
  18. Jingkai Ji
  19. Zhihua Ou
  20. Minfeng Xiao
  21. Min Li
  22. Jiandong Li
  23. Peidi Ren
  24. Ziqing Deng
  25. Huanzi Zhong
  26. Huanming Yang
  27. Jian Wang
  28. Xun Xu
  29. Tie Song
  30. Chris Ka Pun Mok
  31. Malik Peiris
  32. Nanshan Zhong
  33. Jingxian Zhao
  34. Yimin Li
  35. Junhua Li
  36. Jincun Zhao

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Abstract

As of middle May 2020, the causative agent of COVID-19, SARS-CoV-2, has infected over 4 million people with more than 300 thousand death as official reports 1,2 . The key to understanding the biology and virus-host interactions of SARS-CoV-2 requires the knowledge of mutation and evolution of this virus at both inter- and intra-host levels. However, despite quite a few polymorphic sites identified among SARS-CoV-2 populations, intra-host variant spectra and their evolutionary dynamics remain mostly unknown. Here, using deep sequencing data, we achieved and characterized consensus genomes and intra-host genomic variants from 32 serial samples collected from eight patients with COVID-19. The 32 consensus genomes revealed the coexistence of different genotypes within the same patient. We further identified 40 intra-host single nucleotide variants (iSNVs). Most (30/40) iSNVs presented in single patient, while ten iSNVs were found in at least two patients or identical to consensus variants. Comparison of allele frequencies of the iSNVs revealed genetic divergence between intra-host populations of the respiratory tract (RT) and gastrointestinal tract (GIT), mostly driven by bottleneck events among intra-host transmissions. Nonetheless, we observed a maintained viral genetic diversity within GIT, showing an increased population with accumulated mutations developed in the tissue-specific environments. The iSNVs identified here not only show spatial divergence of intra-host viral populations, but also provide new insights into the complex virus-host interactions.

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    SciScore for 10.1101/2020.05.20.103549: (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
    Full-length consensus genomes were generated from reads mapped to the reference genome (GISAID accession: EPI_ISL_402119) using Pilon (v. 1.23)16.
    Pilon
    suggested: (Pilon , RRID:SCR_014731)
    The collected coronaviridae-like reads were also de novo assembled using SPAdes (v. 3.14.0) with default settings17 with a maximum of 100-fold coverage of read data.
    SPAdes
    suggested: (SPAdes, RRID:SCR_000131)
    Nucleotide differences between the consensus sequences and the reference genome were summarized into artificial Variant Call Format (VCF) files, which were annotated using SnpEff (v.2.0.5)18 with default settings.
    SnpEff
    suggested: (SnpEff, RRID:SCR_005191)
    The assembled SARS-CoV-2 and selected representative genomes were aligned using MAFFT with default settings.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    A maximum likelihood (ML) tree was inferred using the software IQ-TREE (v.1.6.12)19, with the best fit nucleotide substitution model selected by ModelFinder from the same software.
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    The linkage disequilibrium among the identified consensus variants were estimated using VCFtools (v.0.1.16).
    VCFtools
    suggested: (VCFtools, RRID:SCR_001235)
    First, paired-end metatranscriptomic reads were mapped to the reference genome (GISAID accession: EPI_ISL_402119) using BWA aln (v.0.7.16) with default parameters22.
    BWA
    suggested: (BWA, RRID:SCR_010910)
    Duplicated reads were marked using Picard MarkDuplicates (v. 2.10.10) (http://broadinstitute.github.io/picard) with default settings.
    Picard
    suggested: (Picard, RRID:SCR_006525)
    A heatmap was generated to visualize the AAFs for all samples using the pheatmap package in R (v.3.6.1).
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    Statistics of iSNVs: The distribution of iSNVs among genetic components and patients were summarized and visualized using the Python package matplotlib (v.3.2.1)
    Python
    suggested: (IPython, RRID:SCR_001658)
    matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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.

    About SciScore

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  2. Version published to 10.1101/2020.05.20.103549 on bioRxiv