Limited SARS-CoV-2 diversity within hosts and following passage in cell culture

  1. Gage K. Moreno
  2. Katarina M. Braun
  3. Peter J. Halfmann
  4. Trent M. Prall
  5. Kasen K. Riemersma
  6. Amelia K. Haj
  7. Joseph Lalli
  8. Kelsey R. Florek
  9. Yoshihiro Kawaoka
  10. Thomas C. Friedrich
  11. David H. O’Connor

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Abstract

Since the first reports of pneumonia associated with a novel coronavirus (COVID-19) emerged in Wuhan, Hubei province, China, there have been considerable efforts to sequence the causative virus, SARS-CoV-2 (also referred to as hCoV-19) and to make viral genomic information available quickly on shared repositories. As of 30 March 2020, 7,680 consensus sequences have been shared on GISAID, the principal repository for SARS-CoV-2 genetic information. These sequences are primarily consensus sequences from clinical and passaged samples, but few reports have looked at diversity of virus populations within individual hosts or cultures. Understanding such diversity is essential to understanding viral evolutionary dynamics. Here, we characterize within-host viral diversity from a primary isolate and passaged samples, all originally deriving from an individual returning from Wuhan, China, who was diagnosed with COVID-19 and subsequently sampled in Wisconsin, United States. We use a metagenomic approach with Oxford Nanopore Technologies (ONT) GridION in combination with Illumina MiSeq to capture minor within-host frequency variants ≥1%. In a clinical swab obtained from the day of hospital presentation, we identify 15 single nucleotide variants (SNVs) ≥1% frequency, primarily located in the largest gene – ORF1a. While viral diversity is low overall, the dominant genetic signatures are likely secondary to population size changes, with some evidence for mild purifying selection throughout the genome. We see little to no evidence for positive selection or ongoing adaptation of SARS-CoV-2 within cell culture or in the primary isolate evaluated in this study.

Author Summary

Within-host variants are critical for addressing molecular evolution questions, identifying selective pressures imposed by vaccine-induced immunity and antiviral therapeutics, and characterizing interhost dynamics, including the stringency and character of transmission bottlenecks. Here, we sequenced SARS-CoV-2 viruses isolated from a human host and from cell culture on three distinct Vero cell lines using Illumina and ONT technologies. We show that SARS-CoV-2 consensus sequences can remain stable through at least two serial passages on Vero 76 cells, suggesting SARS-CoV-2 can be propagated in cell culture in preparation for in-vitro and in-vivo studies without dramatic alterations of its genotype. However, we emphasize the need to deep-sequence viral stocks prior to use in experiments to characterize sub-consensus diversity that may alter outcomes.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.20.051011: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIACUC: Work with live virus was performed at biosafety level-3 containment at the Influenza Research Institute at the University of Wisconsin – Madison under a recombinant DNA protocol approved by the Institutional Biosafety Committee.
    IRB: Approval to obtain the de-identified clinical sample was reviewed by the Human Subjects Institutional Review Boards at the University of Wisconsin – Madison.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Sample collection and cell culture passage conditions: Three different Vero cell lines were purchased from ATCC; Vero 76 (ATCC: CRL-1587), Vero C1008 (ATCC: CRL-1586), Vero STAT-1 KO (ATCC: CCL-81-VHG), and were grown in Minimum Essential Medium (MEM) supplemented with 10% fetal bovine serum (FBS) and L-glutamine at 37°C with 5% CO2.
    Vero
    suggested: None
    Vero C1008
    suggested: ATCC Cat# CRL-1586, RRID:CVCL_0574)
    Four samples (primary NP swab, p1 Vero 76, p1 Vero E6, and p1 Vero STAT-1 KO) were pooled on a single flowcell to a final concentration of 8pM with a PhiX-derived control library accounting for 1% of total DNA and was loaded onto a 500-cycle v2 flowcell.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    Minor variants from ONT sequences that comprise at least 10% of total sequences in any of the samples were identified using the bbmap callvariants.sh tool (https://jgi.doe.gov/data-and-tools/bbtools/).
    https://jgi.doe.gov/data-and-tools/bbtools/
    suggested: (Bestus Bioinformaticus Tools, RRID:SCR_016968)
    Briefly, read ends were trimmed to achieve an average read quality score of Q30 and a minimum read length of 100 bases using Trimmomatic (http://www.usadellab.org/cms/?
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Paired-end reads were merged and then mapped to the reference sequence (Genbank MT039887.1: 2019-nCoV/USA-WI1/2020) using Bowtie2 (http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml).
    Bowtie2
    suggested: (Bowtie 2, RRID:SCR_016368)
    Single nucleotide variants (SNVs) were called with Varscan2 (http://varscan.sourceforge.net/using-varscan.html) using a frequency threshold of 1%, a minimum coverage of 100 reads, and a base quality threshold of Q30 or higher [29].
    Varscan2
    suggested: (VARSCAN, RRID:SCR_006849)
    VCF files were cleaned for additional analyses and figure-generation using custom Python scripts, which are all available at the GitHub repository accompanying this manuscript.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and 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.

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  2. Version published to 10.1101/2020.04.20.051011v1 on bioRxiv