An Early Pandemic Analysis of SARS-CoV-2 Population Structure and Dynamics in Arizona

Abstract

In December of 2019, a novel coronavirus, SARS-CoV-2, emerged in the city of Wuhan, China, causing severe morbidity and mortality. Since then, the virus has swept across the globe, causing millions of confirmed infections and hundreds of thousands of deaths. To better understand the nature of the pandemic and the introduction and spread of the virus in Arizona, we sequenced viral genomes from clinical samples tested at the TGen North Clinical Laboratory, the Arizona Department of Health Services, and those collected as part of community surveillance projects at Arizona State University and the University of Arizona. Phylogenetic analysis of 84 genomes from across Arizona revealed a minimum of 11 distinct introductions inferred to have occurred during February and March. We show that >80% of our sequences descend from strains that were initially circulating widely in Europe but have since dominated the outbreak in the United States. In addition, we show that the first reported case of community transmission in Arizona descended from the Washington state outbreak that was discovered in late February. Notably, none of the observed transmission clusters are epidemiologically linked to the original travel-related case in the state, suggesting successful early isolation and quarantine. Finally, we use molecular clock analyses to demonstrate a lack of identifiable, widespread cryptic transmission in Arizona prior to the middle of February 2020.IMPORTANCE As the COVID-19 pandemic swept across the United States, there was great differential impact on local and regional communities. One of the earliest and hardest hit regions was in New York, while at the same time Arizona (for example) had low incidence. That situation has changed dramatically, with Arizona now having the highest rate of disease increase in the country. Understanding the roots of the pandemic during the initial months is essential as the pandemic continues and reaches new heights. Genomic analysis and phylogenetic modeling of SARS-COV-2 in Arizona can help to reconstruct population composition and predict the earliest undetected introductions. This foundational work represents the basis for future analysis and understanding as the pandemic continues.

Keywords: Arizona; COVID-19; genome analysis; molecular clock; phylogenetic analysis.

FIG 1

Bayesian maximum clade credibility time-calibrated phylogeny inferred from 376 SARS-CoV-2 genomes, including 84 from Arizona and 292 representatives from around the world. Tips are colored by origin of sequence, and major lineages assigned by Pangolin (https://github.com/cov-lineages/pangolin) with more than two sequence representatives in Arizona are indicated by vertical bars. B.1.X is a well-supported sublineage of B.1 that has not been named by Pangolin. All nodes with posterior probabilities >0.9 are colored black. The tree was visualized with a custom Python script that utilized the software package BALTIC (https://github.com/evogytis/baltic).

FIG 2

Posterior density estimates of TMRCAs for Arizona genomes that belong to seven major lineages/sublineages. Posterior density estimates were parsed from 12,001 trees sampled from four independent MCMC chains, following burn-in removal. Hatch marks indicate regions outside the 95% HPD. The samples included in each lineage can be seen in Fig. 1.

FIG 3

Sequence database representation through time for each of the six major named lineages or sublineages observed in Arizona. Stacked bars are colored according to location. To estimate A.1, B.1, and A.3, nested sublineages were collapsed to calculate the frequencies for the broader clades. Lineages were assigned using Pangolin (37) for all sequences uploaded to GISAID as of 16 April 2020.

FIG 4

Abundance over time (A) and cycle threshold values (B) for viruses in Arizona with or without the D614G substitution. Both panels were generated using the 79 Arizona genomes we report here. Plots of abundance over time were generated using a window size of 1 week and a step size of 2 days.

FIG 5

Nonsynonymous mutations in Arizona isolates. (A) Diagram showing the SARS-CoV-2 genome and annotated open reading frames. The genome positions of nonsynonymous mutations in Arizona SARS-CoV-2 isolates are indicated in orange. (B) Nonsynonymous mutations of Arizona isolates in nsp’s involved in the SARS-CoV-2 RNA synthesis complex. Mutations (indicated in orange) are labeled by amino acid position within the protein, reference amino acid, amino acid change, and number of Arizona isolates with the mutation. (C) ORF10 alignment showing a 2-nucleotide insertion and subsequent early truncation in two Arizona SARS-CoV-2 isolates. GenBank and GISAID accession numbers: SARS-CoV-2 AZ-TG271866 (EPI_ISL_427271), SARS-CoV-2 AZ-TG271868 (EPI_ISL_427272), SARS-CoV-2 AZ1 (MN997409.1, EPI_ISL_406223), Bat-RaTG13 (MN996532.1), Pangolin (EPI_ISL_410721), and SARS-CoV (NC_004718.3).