Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Feb 7:14:1090717.
doi: 10.3389/fphar.2023.1090717. eCollection 2023.

Variant-specific deleterious mutations in the SARS-CoV-2 genome reveal immune responses and potentials for prophylactic vaccine development

Affiliations

Variant-specific deleterious mutations in the SARS-CoV-2 genome reveal immune responses and potentials for prophylactic vaccine development

Md Aminul Islam et al. Front Pharmacol. .

Abstract

Introduction: Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has had a disastrous effect worldwide during the previous three years due to widespread infections with SARS-CoV-2 and its emerging variations. More than 674 million confirmed cases and over 6.7 million deaths have been attributed to successive waves of SARS-CoV-2 infections as of 29th January 2023. Similar to other RNA viruses, SARS-CoV-2 is more susceptible to genetic evolution and spontaneous mutations over time, resulting in the continual emergence of variants with distinct characteristics. Spontaneous mutations of SARS-CoV-2 variants increase its transmissibility, virulence, and disease severity and diminish the efficacy of therapeutics and vaccines, resulting in vaccine-breakthrough infections and re-infection, leading to high mortality and morbidity rates. Materials and methods: In this study, we evaluated 10,531 whole genome sequences of all reported variants globally through a computational approach to assess the spread and emergence of the mutations in the SARS-CoV-2 genome. The available data sources of NextCladeCLI 2.3.0 (https://clades.nextstrain.org/) and NextStrain (https://nextstrain.org/) were searched for tracking SARS-CoV-2 mutations, analysed using the PROVEAN, Polyphen-2, and Predict SNP mutational analysis tools and validated by Machine Learning models. Result: Compared to the Wuhan-Hu-1 reference strain NC 045512.2, genome-wide annotations showed 16,954 mutations in the SARS-CoV-2 genome. We determined that the Omicron variant had 6,307 mutations (retrieved sequence:1947), including 67.8% unique mutations, more than any other variant evaluated in this study. The spike protein of the Omicron variant harboured 876 mutations, including 443 deleterious mutations. Among these deleterious mutations, 187 were common and 256 were unique non-synonymous mutations. In contrast, after analysing 1,884 sequences of the Delta variant, we discovered 4,468 mutations, of which 66% were unique, and not previously reported in other variants. Mutations affecting spike proteins are mostly found in RBD regions for Omicron, whereas most of the Delta variant mutations drawn to focus on amino acid regions ranging from 911 to 924 in the context of epitope prediction (B cell & T cell) and mutational stability impact analysis protruding that Omicron is more transmissible. Discussion: The pathogenesis of the Omicron variant could be prevented if the deleterious and persistent unique immunosuppressive mutations can be targeted for vaccination or small-molecule inhibitor designing. Thus, our findings will help researchers monitor and track the continuously evolving nature of SARS-CoV-2 strains, the associated genetic variants, and their implications for developing effective control and prophylaxis strategies.

Keywords: COVID-19; SARS-CoV-2; deleterious mutation; delta variant; immune response; omicron variant; unique mutation; vaccine designing.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Schematic diagram representing several stages starting from genomic data collection to analysis.
FIGURE 2
FIGURE 2
An overview of the scenario of coronavirus. (A) Phylogenetic tree of SARS-CoV-2 depicting variant emergence from January 2020 to January 2022. (B) A world map depicting the frequency of variants occurring across the world. (C) The total number of mutations occurring across the whole genome of SARSCoV-2. The length of the bar determines the diversity of mutations at a specific position on the genome. (D) The total number of mutations along specific types like common, unique, neutral, and deleterious across all the variants of SARS-CoV-2. (Analysed by Nextstrain: https://nextstrain.org).
FIGURE 3
FIGURE 3
Comparison of common and unique deleterious mutation patterns in different variants. (A) Frequency of top common deleterious mutations of different proteins in twelve variants. (B) Deleterious mutation score of common mutations. (C) Frequency of top unique deleterious mutations of different proteins in twelve variants. (D) Deleterious mutation score of unique mutations.
FIGURE 4
FIGURE 4
Comparative view of spike mutations within (A) Omicron and (B) Delta variants (Image source: Modified from COG-UK Mutation Explorer: http://sars2.cvr.gla.ac.uk/cog-uk).
FIGURE 5
FIGURE 5
Comparison of the B cell epitope for spike (S) protein of SARS-CoV-2 Delta variant. (A) B cell epitope prediction score from 100 to 400 amino acids (aa) of the S protein. (B) B cell epitope prediction score from 400 to 650 aa of the S protein. (C) B cell epitope prediction score from 720 to 900 aa of S protein. (D) B cell epitope prediction score from 900 to 1050 aa of the S protein. (E) B cell epitope prediction score from 1150 to 1280 aa of the S protein.
FIGURE 6
FIGURE 6
Comparison of the B cell epitope for spike (S) protein of SARS-CoV-2 omicron variant. (A) B cell epitope prediction score from 50 to 380 amino acids (aa) of the S protein. (B) B cell epitope prediction score from 400 to 570 aa of the S protein. (C) B cell epitope prediction score from 600–780 to 900 aa of S protein. (D) B cell epitope prediction score from 780 to 1000 aa of the S protein.
FIGURE 7
FIGURE 7
Most significant two mutations of spike proteins of delta variant with a comparative overview of molecular interaction (A) V915S-chain A-absence of 9 hydrophobic bonds, 2 polar bonds, and 1 Vander Waals bond in the mutein. Chain B- the absence of 9 hydrophobic bonds, 1 polar bond, and 1 Vander Waals bond in the mutein. Chain C- absence of 9 hydrophobic bonds, 1 polar bond, and 1 Vander Waals bond in the mutein. (B) L916S-chain A-the absence of 12 hydrophobic bonds in the mutein. Chain B- absence of 17 hydrophobic bonds, increase of 3 polar bonds and 1 hydrogen bond, decrease of clash in the mutein. Chain C- the absence of around 10 hydrophobic bonds, 1 carbonyl bond, and 1 clash in the mutein. Clashes are defined as unfavorable interactions where atoms are too close together.
FIGURE 8
FIGURE 8
Most significant five mutations of spike proteins of the omicron variant with a comparative overview of molecular interaction. A wild variant of G339D has 1 clash and 2 polar bonds whereas the mutein has an additional 3 polar, 1 hydrophobic and 1 van der waals bonds. Accordingly, wild variant of K417N has 6 polar, 1 ionic and 5 hydrophobic bond whereas the mutein has an additional 2 polar and 1 van der waals bond but lacks ionic and hydrophobic bonds. Wild variant of S477N has 2 polar and 1 van der waals bond whereas the mutein lacks van der waals bond but has an additional 1 clash. Wild variant of Q493R has 6 polar, 1 van der waals, and 3 hydrophobic bond whereas the mutein lacks van der waals bond and 2 polar bonds but has an additional 1 hydrogen bond. Finally, Y505H has 3 polar and 1 clash which results in additional 1 polar, 1 hydrogen, 1 van der waals, and 1 hydrophobic bonds with the lackings of the clash. Clashes are defined as unfavorable interactions where atoms are too close together.

References

    1. Adzhubei I., Jordan D. M., Sunyaev S. R. (2013). Predicting functional effect of human missense mutations using PolyPhen‐2. Curr. Protoc. Hum. Genet. 76 (1), Unit7.20. 10.1002/0471142905.hg0720s76 - DOI - PMC - PubMed
    1. Ahmed F., Aminul Islam M., Kumar M., Hossain M., Bhattacharya P., Tahmidul Islam M., et al. (2020). “First detection of SARS-CoV-2 genetic material in the vicinity of COVID-19 isolation centre through wastewater surveillance in Bangladesh,”. MedRxiv. - PMC - PubMed
    1. Ahmed F., Islam M. A., Kumar M., Hossain M., Bhattacharya P., Islam M. T., et al. (2021). First detection of SARS-CoV-2 genetic material in the vicinity of COVID-19 isolation Centre in Bangladesh: Variation along the sewer network. Sci. Total Environ. 776, 145724. 10.1016/j.scitotenv.2021.145724 - DOI - PMC - PubMed
    1. Aksamentov I., Roemer C., Hodcroft E., Neher R. (2021). Nextclade: Clade assignment, mutation calling and quality control for viral genomes. J. Open Source Softw. 6 (67), 3773. 10.21105/joss.03773 - DOI
    1. Aleem A., Akbar Samad A. B., Slenker A. K. (2022). Emerging variants of SARS-CoV-2 and novel therapeutics against coronavirus (COVID-19). reasure Island: StatPearls. - PubMed