Revisiting SARS-CoV-2: Evolution, polymorphism and compatibility with human tRNA pool in a lineage over a month
Abstract
Introduction: The present study presents a comparative analysis of SARS-CoV-2‟s survival capacity in its human host in an area within a month. Materials and Methods: Codon usage bias study has been carried using Emboss package and evolutionary study has been carried using pn/ps packages in python and R for statistical analysis. Results: The virus has overlapping genes exhibiting a high codon usage bias and optimization with human Lung housekeeping genes. Viral ORFs have near values of minimum folding energies and codon adaptation index with mRNAs of the human Lung housekeeping genes. Then too, viruses showed a greater expression capacity. Polymorphism is in the virus for ORF1ab, surface glycoprotein and nucleocapsid phosphoprotein ORFs. Non-synonymous mutations have shown non-polar substitutions. Out of the twelve mutations nine are for a higher t-RNA copy number. Synonymous mutation simulation mimicking evolution revealed fitter newer strains. Conclusion: Through this study we have explained the inherent codon adaptation of SARS-CoV-2 with human tRNA pool and how the virus shows polymorphism in-order to keep up with its infectious capacity. Hence, giving an insight into viral rapid adaptability.
References
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INDIAN JOURNAL OF APPLIED MICROBIOLOGY Vol. 25 No. 2 July – September 2023
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Natl Acad Sci U S A 86, 7706-7710.
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53. B. Weaver, R. Koopman,2014,“An SPSS Macro to Compute Confidence Intervals for Pearson‟s Correlation”,The Quantitative Methods for Psychology 10, 29-39.
2. D. Wang et al., 2020,”Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China”, JAMA.
3. R. Lu et al.,2015, “Complete Genome Sequence of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) from the First Imported MERS-CoV Case in China”, Genome Announc 3.
4. P. A. Rota et al., 2003, “Characterization of a novel coronavirus associated with severe acute respiratory syndrome” ,Science 300, 1394-1399.
5. F. Wu et al., 2020, “A new coronavirus associated with human respiratory disease in China”,. Nature 579, 265-269.
6. 6. A. A. T. Naqvi et al.,2020, “Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies”,Structural genomics approach. Biochim Biophys Acta Mol Basis Dis 1866,165878.
7. T. R. Tong,2009, “Drug targets in severe acute respiratory syndrome (SARS) virus and other coronavirus infections”,Infect Disord Drug Targets 9, 223-245.
8. Y. Z. Zhang, E. C. Holmes,2020, “A Genomic Perspective on the Origin and Emergence of SARS-CoV- 2”, Cell 181, 223-227.
M. Hoffmann et al.,2020, “SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked
by a Clinically Proven Protease Inhibitor”,Cell 181, 271-280.e278.
10. A. Nasir, P. Forterre, K. M. Kim, G. Caetano-Anollés,2014, “The distribution and impact of viral lineages in domains of life”, Front Microbiol 5, 194.
11. E. Trotta,2013, “Selection on codon bias in yeast: a transcriptional hypothesis”,Nucleic Acids Res 41, 9382-9395.
12. R. Hershberg,D. A. Petrov,2008, “Selection on codon bias”,Annu Rev Genet 42, 287-299.
13. Y. Mao, H. Liu, Y. Liu, S. Tao,2014, “Deciphering the rules by which dynamics of mRNA secondary structure affect translation efficiency in Saccharomyces cerevisiae”,Nucleic Acids Res 42, 4813-4822.
14. P. M. Sharp, L. R. Emery, K. Zeng,2010, “Forces that influence the evolution of codon bias”,Philosophical Transactions Of The Royal Society B, 1203–1212.
15. M. BULMER,1991,“THE SELECTION-MUTATION-DRIFT THEORY OF SYNONYMOUS CODON USAGE”,Genetics 129, 897-907.
16. T. Ikemura,1985, “Codon usage and tRNA content in unicellular and multicellular organisms”, Mol Biol Evol 2, 13-34.
17. S. K. Behura, D. W. Severson,2013, “Codon usage bias: causative factors, quantification methods and genome-wide patterns: with emphasis on insect genomes.”,Biol Rev Camb Philos Soc 88, 49-61.
18. T. Ikemura,1982, “Correlation between the abundance of yeast transfer RNAs and the occurrence of the respective codons in protein genes. Differences in synonymous codon choice patterns of yeast and Escherichia coli with reference to the abundance of isoaccepting transfer RNAs”,J Mol Biol 158, 573- 597.
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20. P. P. Chan, T. M. Lowe,2016, “GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes”,Nucleic Acids Res 44, D184-189.
21. M. dos Reis, R. Savva, L. Wernisch,2004, “Solving the riddle of codon usage preferences: a test for translational selection”,Nucleic Acids Res 32, 5036-5044.
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23. M. P. Victor, D. Acharya, S. Chakraborty, T. C. Ghosh,2020, “The combined influence of codon composition and tRNA copy number regulates translational efficiency by influencing synonymous nucleotide substitution”,Gene 745, 144640.
24. J. F. Peden,1999,University of Nottingham.
25. J. Timmons,l. Leshane,2019, “Predict the minimum free energy structure of nucleic acids”, (M.I.T, 2019), pp.
26. C. K. Chang, M. H. Hou, C. F. Chang, C. D. Hsiao, T. H. Huang,2014,“The SARS coronavirus nucleocapsid protein--forms and functions”,Antiviral Res 103, 39-50.
INDIAN JOURNAL OF APPLIED MICROBIOLOGY Vol. 25 No. 2 July – September 2023
48 MANISH PRAKASH VICTOR et al
27. H. J. Dyson, P. E. Wright, H. A. Scheraga,2006,“The role of hydrophobic interactions in initiation and
propagation of protein folding,Proc Natl Acad Sci U S A 103, 13057-13061.
28. G. Huguet et al.,2014, “Heterogeneous pattern of selective pressure for PRRT2 in human populations, but no association with autism spectrum disorders”,PLoS One 9, e88600.
29. T. Tanaka, M. Nei,1989, “Positive darwinian selection observed at the variable-region genes of immunoglobulins”,Mol Biol Evol 6, 447-459.
30. .Z. L. Fuller et al.,2015, “Genome-wide analysis of signatures of selection in populations of African honey bees (Apis mellifera) using new web-based tools”,BMC Genomics 16, 518.
31. E. Eisenberg,2013, “E. Y. Levanon, Human housekeeping genes, revisited”,Trends Genet 29, 569-574.
32. R. Yin, F. Tian, B. Frankenberger, M. H. de Angelis, T. Stoeger,2010, “Selection and evaluation of stable housekeeping genes for gene expression normalization in carbon nanoparticle-induced acute pulmonary inflammation in mice”,Biochem Biophys Res Commun 399, 531-536
33. B. Alberts et al.,2013, “Essential Cell Biology”,CRC Press.
34. F. Wright,1990, “The 'effective number of codons' used in a gene”,Gene 87, 23-29.
35. T. Ikemura,1981, “Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes: a proposal for a synonymous codon choice that is optimal for the E. coli translational system”,J Mol Biol 151, 389-409.
36. M. Gouy, C. Gautier,1982,“Codon usage in bacteria: correlation with gene expressivity”, Nucleic Acids Res 10, 7055-7074.
37. M. P. Victor, D. Acharya, T. Begum, T. C. Ghosh,2019,“The optimization of mRNA expression level by its intrinsic properties-Insights from codon usage pattern and structural stability of mRNA”,Genomics 111, 1292-1297.
38. R. Sanjuán, P. Domingo-Calap,2016, “Mechanisms of viral mutation”,Cell Mol Life Sci 73, 4433- 4448.
39. N. T. Ingolia, S. Ghaemmaghami, J. R. Newman, J. S. Weissman,2009, “Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling”,Science 324, 218-223.
40. C. Park, X. Chen, J. R. Yang, J. Zhang,2013, “Differential requirements for mRNA folding partially explain why highly expressed proteins evolve slowly”,Proc Natl Acad Sci U S A110, E678-686.
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42. J. Timmons, l. Leshane,2019,Predict the minimum free energy structure of nucleic acids,MITpp.
43. R. Nussinov, A. B. Jacobson, Fast algorithm for predicting the secondary structure of single-stranded
RNA. Proc Natl Acad Sci U S A 77, 6309-6313 (1980).
44. M. Zuker, P. Stiegler,1981,“Optimal computer folding of large RNA sequences using thermodynamics
and auxiliary information”,Nucleic Acids Res 9, 133-148.
45. J. A. Jaeger, D. H. Turner, M. Zuker,1989,“Improved predictions of secondary structures for RNA”,Proc
Natl Acad Sci U S A 86, 7706-7710.
46. J. SantaLucia, D. Hicks,2004,“The thermodynamics of DNA structural motifs”,Annu Rev Biophys
Biomol Struct 33, 415-440.
47. D. H. Turner, D. H. Mathews,2010,“NNDB: the nearest neighbor parameter database for predicting
stability of nucleic acid secondary structure”, Nucleic Acids Res 38, D280-282.
48. M. Ward, A. Datta, M. Wise, D. H. Mathews,2017,“Advanced multi-loop algorithms for RNA secondary
structure prediction reveal that the simplest model is best”,Nucleic Acids Res 45, 8541-8550.
49. J. M. Bland, D. G. Altman,2009,“Analysis of continuous data from small samples”,BMJ 338, a3166.
50. A. Hackshaw,2008,“Small studies: strengths and limitations”, Eur Respir J 32, 1141-1143.
51. L. Lin,2018,“Bias caused by sampling error in meta-analysis with small sample sizes”,PLoS One 13, e0204056.
52. C. J. Baker, D. L. Kasper, M. S. Edwards, G. Schiffman,1980,“Influence of preimmunization antibody levels on the specificity of the immune response to related polysaccharide antigens”,N Engl J Med 303, 173-178.
53. B. Weaver, R. Koopman,2014,“An SPSS Macro to Compute Confidence Intervals for Pearson‟s Correlation”,The Quantitative Methods for Psychology 10, 29-39.
Published
2023-09-13
Section
Articles
