A simulation approach for COVID-19 pandemic assessment based on vaccine logistics, SARS-CoV-2 variants, and spread rate
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ABSTRACT:
SUBMITTER: Erkayman B
PROVIDER: S-EPMC9895289 | biostudies-literature | 2023 Feb
REPOSITORIES: biostudies-literature
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Project description:The recent global surge in COVID-19 infections has been fueled by new SARS-CoV-2 variants, namely Alpha, Beta, Gamma, Delta, etc. The molecular mechanism underlying such surge is elusive due to 4,653 non-degenerate mutations on the spike protein, which is the target of most COVID-19 vaccines. The understanding of the molecular mechanism of transmission and evolution is a prerequisite to foresee the trend of emerging vaccine-breakthrough variants and the design of mutation-proof vaccines and monoclonal antibodies. We integrate the genotyping of 1,489,884 SARS-CoV-2 genomes isolates, 130 human antibodies, tens of thousands of mutational data points, topological data analysis, and deep learning to reveal SARS-CoV-2 evolution mechanism and forecast emerging vaccine-escape variants. We show that infectivity-strengthening and antibody-disruptive co-mutations on the S protein RBD can quantitatively explain the infectivity and virulence of all prevailing variants. We demonstrate that Lambda is as infectious as Delta but is more vaccine-resistant. We analyze emerging vaccine-breakthrough co-mutations in 20 countries, including the United Kingdom, the United States, Denmark, Brazil, and Germany, etc. We envision that natural selection through infectivity will continue to be the main mechanism for viral evolution among unvaccinated populations, while antibody disruptive co-mutations will fuel the future growth of vaccine-breakthrough variants among fully vaccinated populations. Finally, we have identified the co-mutations that have the great likelihood of becoming dominant: [A411S, L452R, T478K], [L452R, T478K, N501Y], [V401L, L452R, T478K], [K417N, L452R, T478K], [L452R, T478K, E484K, N501Y], and [P384L, K417N, E484K, N501Y]. We predict they, particularly the last four, will break through existing vaccines. We foresee an urgent need to develop new vaccines that target these co-mutations.
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Project description:SARS-CoV-2 is a single-stranded RNA betacoronavirus with a high mutation rate. The rapidly emerging SARS-CoV-2 variants could increase transmissibility and diminish vaccine protection. However, whether coinfection with multiple SARS-CoV-2 variants exists remains controversial. This study collected 12,986 and 4,113 SARS-CoV-2 genomes from the GISAID database on May 11, 2020 (GISAID20May11), and Apr 1, 2021 (GISAID21Apr1), respectively. With single-nucleotide variant (SNV) and network clique analyses, we constructed single-nucleotide polymorphism (SNP) coexistence networks and discovered maximal SNP cliques of sizes 16 and 34 in the GISAID20May11 and GISAID21Apr1 datasets, respectively. Simulating the transmission routes and SNV accumulations, we discovered a linear relationship between the size of the maximal clique and the number of coinfected variants. We deduced that the COVID-19 cases in GISAID20May11 and GISAID21Apr1 were coinfections with 3.20 and 3.42 variants on average, respectively. Additionally, we performed Nanopore sequencing on 42 COVID-19 patients and discovered recurrent heterozygous SNPs in twenty of the patients, including loci 8,782 and 28,144, which were crucial for SARS-CoV-2 lineage divergence. In conclusion, our findings reported SARS-CoV-2 variants coinfection in COVID-19 patients and demonstrated the increasing number of coinfected variants.
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