Dynamics of SARS-CoV-2 mutations reveals regional-specificity and similar trends of N501 and high-frequency mutation N501Y in different levels of control measures

  1. Santiago Justo Arevalo
  2. Daniela Zapata Sifuentes
  3. César J. Huallpa
  4. Gianfranco Landa Bianchi
  5. Adriana Castillo Chávez
  6. Romina Garavito-Salini Casas
  7. Carmen Sofia Uribe Calampa
  8. Guillermo Uceda-Campos
  9. Roberto Pineda Chavarría

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Abstract

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This disease has spread globally, causing more than 161.5 million cases and 3.3 million deaths to date. Surveillance and monitoring of new mutations in the virus’ genome are crucial to our understanding of the adaptation of SARS-CoV-2. Moreover, how the temporal dynamics of these mutations is influenced by control measures and non-pharmaceutical interventions (NPIs) is poorly understood. Using 1,058,020 SARS-CoV-2 from sequenced COVID-19 cases from 98 countries (totaling 714 country-month combinations), we perform a normalization by COVID-19 cases to calculate the relative frequency of SARS-CoV-2 mutations and explore their dynamics over time. We found 115 mutations estimated to be present in more than 3% of global COVID-19 cases and determined three types of mutation dynamics: high-frequency, medium-frequency, and low-frequency. Classification of mutations based on temporal dynamics enable us to examine viral adaptation and evaluate the effects of implemented control measures in virus evolution during the pandemic. We showed that medium-frequency mutations are characterized by high prevalence in specific regions and/or in constant competition with other mutations in several regions. Finally, taking N501Y mutation as representative of high-frequency mutations, we showed that level of control measure stringency negatively correlates with the effective reproduction number of SARS-CoV-2 with high-frequency or not-high-frequency and both follows similar trends in different levels of stringency.

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  1. Version published to 10.1038/s41598-021-97267-7
  2. Version published to 10.1101/2021.06.01.446571v3 on bioRxiv
  3. Version published to 10.1101/2021.06.01.446571v2 on bioRxiv
  4. ScreenIT

    SciScore for 10.1101/2021.06.01.446571: (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
    Data manipulation was done using R and python scripts.
    python
    suggested: (IPython, RRID:SCR_001658)
    Correlations of the relative frequency change of HF mutations with the level of international travel controls and the relative number of daily flight departures: Analysis of Spearman correlations by region-month of the relative frequency change of HF mutations and the relative level of international travel controls or the relative number of daily flight departures were performed using R [38] and the packages ggplot2 [39] and ggpubr [40].
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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: We detected the following sentences addressing limitations in the study:
    Limitations of the study: International travel controls obtained from OxCGRT are based on a semiquantitative criterion that embraces just 5 levels. Due to the variations in sociocultural and economic factors among different countries, this criterion does not have enough resolution to reveal small differences between countries on international movement controls policies. However, it allows us to have a general global vision at a regional level. Unfortunately, to our knowledge, there are few publicly available databases where we can obtain detailed data about international movement. The number of flight departures may not be the best indicator of how many people traveled, but it gave us an approximate. Although the methodology of normalization by cases alleviates the differences in the number of genomes sequenced by country, confidence in the calculation of relative frequencies of mutations is still low in regions with a low number of genomes sequenced. For example, a mutation with 0.5 relative frequency that comes from a sample of 15 genomes will have a confidence interval between 0.25 and 0.75; on the other hand, a sample of 150 genomes will generate a confidence interval between 0.58 and 0.42. Moreover, the number of cases is still subjected to bias due to for instance, the difference in the number of tests that each country performs.

    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

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  5. Version published to 10.1101/2021.06.01.446571v1 on bioRxiv