Evolutionary Analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Reveals Genomic Divergence with Implications for Universal Vaccine Efficacy
Nanda Kumar Yellapu,
Shachi Patel,
Bo Zhang,
Richard Meier,
Lisa Neums,
Dong Pei,
Qing Xia,
Duncan Rotich,
Rosalyn C. Zimmermann,
Emily Nissen,
Shelby Bell-Glenn,
Whitney Shae,
Jinxiang Hu,
Prabhakar Chalise,
Lynn Chollet-Hinton,
Devin C. Koestler,
Jeffery A. Thompson
Affiliations
Nanda Kumar Yellapu
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Shachi Patel
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Bo Zhang
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Richard Meier
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Lisa Neums
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Dong Pei
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Qing Xia
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Duncan Rotich
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Rosalyn C. Zimmermann
Department of Cancer Biology, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Emily Nissen
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Shelby Bell-Glenn
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Whitney Shae
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Jinxiang Hu
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Prabhakar Chalise
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Lynn Chollet-Hinton
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Devin C. Koestler
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Jeffery A. Thompson
Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA
Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the pressing contemporary public health challenges. Investigations into the genomic structure of SARS-CoV-2 may inform ongoing vaccine development efforts and/or provide insights into vaccine efficacy to fight against COVID-19. Evolutionary analysis of 540 genomes spanning 20 different countries/territories was conducted and revealed an increase in the genomic divergence across successive generations. The ancestor of the phylogeny was found to be the isolate from the 2019/2020 Wuhan outbreak. Its transmission was outlined across 20 countries/territories as per genomic similarity. Our results demonstrate faster evolving variations in the genomic structure of SARS-CoV-2 when compared to the isolates from early stages of the pandemic. Genomic alterations were predominantly located and mapped onto the reported vaccine candidates of structural genes, which are the main targets for vaccine candidates. S protein showed 34, N protein 25, E protein 2, and M protein 3 amino acid variations in 246 genomes among 540. Among identified mutations, 23 in S protein, 1 in E, 2 from M, and 7 from N protein were mapped with the reported vaccine candidates explaining the possible implications on universal vaccines. Hence, potential target regions for vaccines would be ideally chosen from the structural regions of the genome that lack high variation. The increasing variations in the genome of SARS-CoV-2 together with our observations in structural genes have important implications for the efficacy of a successful universal vaccine against SARS-CoV-2.