Journal of Pediatric Perspectives

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

Evaluation of Mutations in SARS-CoV-2 N and S Genes on the Proteins Stability, Immunogenicity, and Pathogenicity in Iranian Patients from Golestan Province

Document Type : original article

Authors

1 Department of Microbiology, School of Medicine, Golestan University of Medical Sciences, Gorgan, Iran.

2 Department of Biotechnology, Golestan University of Medical Sciences, Gorgan, Iran.

3 Senior Scientist, Enterprise-TTM, University of Pittsburgh Medical Center, Pittsburgh, PA.

Abstract

Background: Natural selection such as mutations is known as a constant process for viral fitness and selective adaptation. Understanding the effects of each mutation, especially on structural proteins in the viral life cycle, is important in tracking the viruses behavior. Here, we evaluated the effects of mutations in SARS-CoV-2 nucleoprotein (N) and spike (S) genes on the protein stability, immunogenicity, and pathogenicity in Iranian COVID-19 patients from Golestan province.
Methods: In this study, 8 SARS-CoV-2 RNA samples were enrolled from referral hospitals in Golestan province. These samples were confirmed using a real-time RT-PCR assay targeting the SARS-CoV-2 nucleoprotein (N) and ORF1ab genes (Pishtazteb, Iran). Next-generation sequencing (NGS) was done on samples and subsequent sequences were retrieved from Global Initiative on Sharing All Influenza Data (GISAID) EpiCoV database. Structural analysis was performed between wild type (Wuhan accession number: NC_045512.2) and mutant N and S proteins to evaluate their stability, immunogenicity, and pathogenicity via bioinformatics servers such as Dynamut, Prodigy, IEDB, and software’s (Mega XI and Pymol II.V.II visualizer).
Results: Amino acid codon changes in N and S proteins show that mutations could alter the translation efficiency. Normal Mode Analysis (NMA) by Dynamut server shows that stability and flexibility are changed by the mutations of these proteins. Immunogenicity analysis indicates the potential effects of some mutations such as P681H, Q675R, L699I, and D3L on immune escape. Interaction complex binding energy and affinity are higher in the mutant type compared to the Wuhan wild type, indicating higher pathogenicity.
Conclusion: The results indicate that there are some important mutations in N and S genes that affect the virus behavior in the infectivity. Regarding the sample size limitation and various mutations in SARS-CoV-2 variants, other studies using whole-genome sequencing with larger sample sizes will be required. Therefore, continuous monitoring of the SARS-CoV-2 genome seems important.

Keywords

References
  1. Hardenbrook NJ, Zhang P. A structural view of the SARS-CoV-2 virus and its assembly. Current Opinion in Virology. 2022; 52:123-34.
  2. Rochman ND, Wolf YI, Faure G, Mutz P, Zhang F, Koonin EV. Ongoing global and regional adaptive evolution of SARS-CoV-2. Proceedings of the National Academy of Sciences. 2021; 118(29):e2104241118.
  3. Li Q, Wu J, Nie J, Zhang L, Hao H, Liu S, et al. The Impact of Mutations in SARS-CoV-2 Spike on Viral Infectivity and Antigenicity. Cell. 2020; 182(5):1284-94.e9.
  4. Nguyen TT, Pathirana PN, Nguyen T, Nguyen QVH, Bhatti A, Nguyen DC, et al. Genomic mutations and changes in protein secondary structure and solvent accessibility of SARS-CoV-2 (COVID-19 virus). Scientific Reports. 2021; 11(1):3487.
  5. Hirabara SM, Serdan TDA, Gorjao R, Masi LN, Pithon-Curi TC, Covas DT, et al. SARS-COV-2 Variants: Differences and Potential of Immune Evasion. Frontiers in cellular and infection microbiology. 2021; 11:781429.
  6. Altmann DM, Boyton RJ, Beale R. Immunity to SARS-CoV-2 variants of concern. Science (New York, NY). 2021; 371(6534):1103-4.
  7. Mistry P, Barmania F, Mellet J, Peta K, Strydom A, Viljoen IM, et al. SARS-CoV-2 Variants, Vaccines, and Host Immunity. Frontiers in immunology. 2021; 12:809244.
  8. Focosi D, Maggi F. Neutralizing antibody escape of SARS-CoV-2 spike protein: Risk assessment for antibody-based Covid-19 therapeutics and vaccines. Reviews in medical virology. 2021; 31(6):e2231.
  9. Tomaszewski T, DeVries RS, Dong M, Bhatia G, Norsworthy MD, Zheng X, et al. New Pathways of Mutational Change in SARS-CoV-2 Proteomes Involve Regions of Intrinsic Disorder Important for Virus Replication and Release. bioRxiv. 2020:2020.07.31.231472.
  10. Woo J, Lee EY, Lee M, Kim T, Cho YE. An in vivo cell-based assay for investigating the specific interaction between the SARS-CoV N-protein and its viral RNA packaging sequence. Biochemical and biophysical research communications. 2019; 520(3):499-506.
  11. Oliveira SC, de Magalhães MTQ, Homan EJ. Immunoinformatics Analysis of SARS-CoV-2 Nucleocapsid Protein and Identification of COVID-19 Vaccine Targets. Frontiers in immunology. 2020; 11:587615.
  12. Mohammad T, Choudhury A, Habib I, Asrani P, Mathur Y, Umair M, et al. Genomic Variations in the Structural Proteins of SARS-CoV-2 and Their Deleterious Impact on Pathogenesis: A Comparative Genomics Approach. Front Cell Infect Microbiol. 2021; 11:765039.
  13. Rodriguez-Rivas J, Croce G, Muscat M, Weight M. Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes. Proceedings of the National Academy of Sciences. 2022; 119(4):e2113118119.
  14. Das JK, Thakuri B, MohanKumar K, Roy S, Sljoka A, Sun G-Q, et al. Mutation-Induced Long-Range Allosteric Interactions in the Spike Protein Determine the Infectivity of SARS-CoV-2 Emerging Variants. ACS Omega. 2021; 6(46):31305-20.
  15. Fung TS, Liu DX. Post-translational modifications of coronavirus proteins: roles and function. Future virology. 2018; 13(6):405-30.
  16. Mohammad T, Choudhury A, Habib I, Asrani P, Mathur Y, Umair M, et al. Genomic Variations in the Structural Proteins of SARS-CoV-2 and Their Deleterious Impact on Pathogenesis: A Comparative Genomics Approach. Front Cell Infect Microbiol. 2021; 11:765039-.
  17. Al-Zyoud W, Haddad H. Dynamic’s prediction of emerging notable spike protein mutations in SARS-CoV-2 implies a need for updated vaccines. Biochimie. 2021; 191:91-103.
  18. Chambers JP, Yu J, Valdes JJ, Arulanandam BP. SARS-CoV-2, Early Entry Events. J Pathog. 2020; 2020:9238696-.
  19. Giron CC, Laaksonen A, Barroso da Silva FL. Up State of the SARS-COV-2 Spike Homotrimer Favors an Increased Virulence for New Variants. Front Med Technol. 2021; 3:694347-.
  20. Mori T, Jung J, Kobayashi C, Dokainish HM, Re S, Sugita Y. Elucidation of interactions regulating conformational stability and dynamics of SARS-CoV-2 S-protein. Biophysical Journal. 2021; 120(6):1060-71.
  21. Quaglia F, Salladini E, Carraro M, Minervini G, Tosatto SCE, Le Mercier P. SARS-CoV-2 variants preferentially emerge at intrinsically disordered protein sites helping immune evasion. The FEBS journal. 2022.
  22. Tenchov R, Zhou QA. Intrinsically Disordered Proteins: Perspective on COVID-19 Infection and Drug Discovery. ACS Infectious Diseases. 2022; 8(3):422-32.
  23. Bai Z, Cao Y, Liu W, Li J. The SARS-CoV-2 Nucleocapsid Protein and Its Role in Viral Structure, Biological Functions, and a Potential Target for Drug or Vaccine Mitigation. Viruses. 2021; 13(6):1115.
  24. Azad GK. Identification and molecular characterization of mutations in nucleocapsid phosphoprotein of SARS-CoV-2. PeerJ. 2021; 9:e10666.
  25. Zhao X, Nicholls JM, Chen YG. Severe acute respiratory syndrome-associated coronavirus nucleocapsid protein interacts with Smad3 and modulates transforming growth factor-beta signaling. The Journal of biological chemistry. 2008; 283(6):3272-80.
  26. Ferreira-Gomes M, Kruglov A, Durek P, Heinrich F, Tizian C, Heinz GA, et al. SARS-CoV-2 in severe COVID-19 induces a TGF-β-dominated chronic immune response that does not target itself. Nature Communications. 2021; 12(1):1961.
  27. Mu J, Fang Y, Yang Q, Shu T, Wang A, Huang M, et al. SARS-CoV-2 N protein antagonizes type I interferon signaling by suppressing phosphorylation and nuclear translocation of STAT1 and STAT2. Cell Discov. 2020; 6:65-.
  28. Chen K, Xiao F, Hu D, Ge W, Tian M, Wang W, et al. SARS-CoV-2 Nucleocapsid Protein Interacts with RIG-I and Represses RIG-Mediated IFN-β Production. Viruses. 2020; 13(1):47.
  29. Rodrigues CH, Pires DE, Ascher DB. DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic acids research. 2018; 46(W1):W350-w5.
  30. Worth CL, Preissner R, Blundell TL. SDM--a server for predicting effects of mutations on protein stability and malfunction. Nucleic acids research. 2011; 39(Web Server issue):W215-W22.
  31. Chen C-W, Lin M-H, Liao C-C, Chang H-P, Chu Y-W. iStable 2.0: Predicting protein thermal stability changes by integrating various characteristic modules. Comput Struct Biotechnol J. 2020; 18:622-30.
  32. Seifi M, Walter MA. Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms. PLOS ONE. 2018; 13(4):e0195971.
  33. Laimer J, Hofer H, Fritz M, Wegenkittl S, Lackner P. MAESTRO - multi agent stability prediction upon point mutations. BMC Bioinformatics. 2015; 16(1):116.
  34. Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, et al. Immune epitope database analysis resource. Nucleic acids research. 2012; 40(Web Server issue):W525-30.
  35. Christoffer C, Bharadwaj V, Luu R, Kihara D. LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction. Frontiers in molecular biosciences. 2021; 8:724947.
  36. Weng G, Wang E, Wang Z, Liu H, Zhu F, Li D, et al. HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic acids research. 2019; 47(W1):W322-w30.
  37. Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, et al. The ClusPro web server for protein-protein docking. Nature protocols. 2017; 12(2):255-78.
  38. Land H, Humble MS. YASARA: A Tool to Obtain Structural Guidance in Biocatalytic Investigations. Methods in molecular biology (Clifton, NJ). 2018; 1685:43-67.
  39. Xue LC, Rodrigues JP, Kastritis PL, Bonvin AM, Vangone A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics (Oxford, England). 2016; 32(23):3676-8.
  40. Ye Q, West AMV, Silletti S, Corbett KD. Architecture and self-assembly of the SARS-CoV-2 nucleocapsid protein. Protein science: a publication of the Protein Society. 2020; 29(9):1890-901.
  41. Johnson BA, Zhou Y, Lokugamage KG, Vu MN, Bopp N, Crocquet-Valdes PA, et al. Nucleocapsid mutations in SARS-CoV-2 augment replication and pathogenesis. bioRxiv. 2021:2021.10.14.464390.
  42. Hernandez-Alias X, Schaefer MH, Serrano L. Translational adaptation of human viruses to the tissues they infect. bioRxiv. 2020:2020.04.06.027557.
  43. Chen C, Boorla VS, Banerjee D, Chowdhury R, Cavener VS, Nissly RH, et al. Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2. Proceedings of the National Academy of Sciences. 2021; 118(42):e2106480118.
  44. Wu H, Xing N, Meng K, Fu B, Xue W, Dong P, et al. Nucleocapsid mutations R203K/G204R increase the infectivity, fitness, and virulence of SARS-CoV-2. Cell Host Microbe. 2021; 29(12):1788-801.e6.
  45. Davies NG, Jarvis CI, van Zandvoort K, Clifford S, Sun FY, Funk S, et al. Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7. Nature. 2021; 593(7858):270-4.
  46. Washington NL, Gangavarapu K, Zeller M, Bolze A, Cirulli ET, Schiabor Barrett KM, et al. Emergence and rapid transmission of SARS-CoV-2 B.1.1.7 in the United States. Cell. 2021; 184(10):2587-94.e7.
  47. Socher E, Conrad M, Heger L, Paulsen F, Sticht H, Zunke F, et al. Mutations in the B.1.1.7 SARS-CoV-2 Spike Protein Reduce Receptor-Binding Affinity and Induce a Flexible Link to the Fusion Peptide. Biomedicines. 2021; 9(5).
  48. Lubinski B, Fernandes MHV, Frazier L, Tang T, Daniel S, Diel DG, et al. Functional evaluation of the P681H mutation on the proteolytic activation of the SARS-CoV-2 variant B.1.1.7 (Alpha) spike. bioRxiv: the preprint server for biology. 2021:2021.04.06.438731.
  49. Mohammad A, Abubaker J, Al-Mulla F. Structural modeling of SARS-CoV-2 alpha variant (B.1.1.7) suggests enhanced furin binding and infectivity. Virus Res. 2021; 303:198522. 
Journal of Pediatric Perspectives
Volume 10, Issue 8
August 2022
Pages 16486-16497
Files
Share
How to cite
Statistics